Overview

Welcome to the Hestia API documentation!

Hestia API

To access the Hestia API:

1. Create an Account.
2. Retrieve your access token:

curl -X POST "<APIUrl>/users/signin" \
  -H "Content-Type: application/json" \
  --data '{"email":"email@email.com","password":"pwd"}' | jq '.token'

import json
import requests

headers = {'Content-Type': 'application/json'}
body = json.dumps({"email":"email@email.com","password":"pwd"})
token = requests.post('<APIUrl>/users/signin', body, headers=headers).json().get('token')
print(token)

(async () => {
  const url = '<APIUrl>/users/signin';
  const headers = {
    'Content-Type': 'application/json'
  };
  const body = JSON.stringify({ email: 'email@email.com', password: 'pwd' });
  const response = await fetch(url, { method: 'POST', headers, body });
  const { token } = await response.json();
  console.log(token);
})();

const axios = require('axios');

(async () => {
  const url = '<APIUrl>/users/signin';
  const body = { email: 'email@email.com', password: 'pwd' };
  const { data: { token } } = await axios.post(url, body);
  console.log(token);
})();

You can now test the API on the Swagger docs.

Samples

For uploading data, you can view some samples here.

Retrieving your profile

curl -H "X-ACCESS-TOKEN: <your-token>" \
  -H "Content-Type: application/json" \
  "<APIUrl>/users/me"

import requests

headers = {'Content-Type': 'application/json', 'X-ACCESS-TOKEN': '<your-token>'}
profile = requests.get('<APIUrl>/users/me', headers=headers).json()
print(profile)

(async () => {
  const url = '<APIUrl>/users/me';
  const headers = {
    'Content-Type': 'application/json',
    'X-ACCESS-TOKEN': '<your-token>'
  };
  const response = await fetch(url, { headers });
  const profile = await response.json();
  console.log(profile);
})();

const axios = require('axios');

(async () => {
  const url = '<APIUrl>/users/me';
  const headers = {
    'X-ACCESS-TOKEN': '<your-token>'
  };
  const { data } = await axios.get(url, { headers });
  console.log(data);
})();

Downloading a node (example for a Term)

curl "<APIUrl>/terms/sandContent"

import requests

headers = {'Content-Type': 'application/json'}
node = requests.get('<APIUrl>/terms/sandContent', headers=headers).json()
print(node)

# or use our utils library
from hestia_earth.schema import SchemaType
from hestia_earth.utils.api import download_hestia

node = download_hestia('sandContent', SchemaType.TERM)

(async () => {
  const url = '<APIUrl>/terms/sandContent';
  const headers = {
    'Content-Type': 'application/json'
  };
  const response = await fetch(url, { headers });
  const node = await response.json();
  console.log(node);
})();

const axios = require('axios');

(async () => {
  const url = '<APIUrl>/terms/sandContent';
  const { data } = await axios.get(url);
  console.log(data);
})();

Getting related nodes

curl "<APIUrl>/terms/sandContent/sites"

import requests

headers = {'Content-Type': 'application/json'}
node = requests.get('<APIUrl>/terms/sandContent/sites', headers=headers).json()
print(node)

# or use our utils library
from hestia_earth.schema import SchemaType
from hestia_earth.utils.api import find_related

cycles = find_related(SchemaType.TERM, 'sandContent', SchemaType.SITE)

(async () => {
  const url = '<APIUrl>/terms/sandContent/sites';
  const headers = {
    'Content-Type': 'application/json'
  };
  const response = await fetch(url, { headers });
  const node = await response.json();
  console.log(node);
})();

const axios = require('axios');

(async () => {
  const url = '<APIUrl>/terms/sandContent/sites';
  const { data } = await axios.get(url);
  console.log(data);
})();

All of our Nodes are linked together by a Graph Database, which means you can get a list of Site that are linked to a Term for example.

Full Examples

from hestia_earth.schema import SchemaType
from hestia_earth.utils.api import find_node, download_hestia

# this will give you partial information only
cycles = find_node(SchemaType.CYCLE, {
  'emissions.term.name': 'NO3, to groundwater, soil flux'
})

for cycle in cycles:
  # to retrieve the complete data of the cycle
  data = download_hestia(cycle['@id'], SchemaType.CYCLE, data_state='recaclulated')
  inputs = data.get('inputs', [])
  N_filter = [sum(input.get('value')) if input.get('term', {}).get('units') == 'kg N' else 0 for input in inputs]
  total = sum(N_filter)
  print(cycle, 'total nitrogen fertilizer', total)

from hestia_earth.schema import SchemaType
from hestia_earth.utils.api import find_node, download_hestia
from hestia_earth.utils.model import find_term_match

# searching for aggregated GWP100 emissions on "Maize, grain"
impacts = find_node(SchemaType.IMPACTASSESSMENT, {
  'aggregated': 'true',
  'product.term.name': 'Maize, grain'
})
# only download the first impact to test, but there would be many more
data = download_hestia(impacts[0]['@id'], SchemaType.IMPACTASSESSMENT, data_state='aggregated')
# 'gwp100' here refers to the @id and not the name
emission = find_term_match(data['impacts'], 'gwp100')
print(emission['value'])

(async () => {
  const apiUrl = '<APIUrl>';
  const searchUrl = '<APIUrl>/search';
  const headers = {
    'Content-Type': 'application/json'
  };
  const body = JSON.stringify({
    fields: ['@id'],
    limit: 10,
    query: {
      bool: {
        'must': [
          { match: { '@type': 'Cycle' } },
          { match: { 'emissions.term.name': 'NO3, to groundwater, soil flux' } }
        ]
      }
    }
  });
  const { results: cycles } = await (await fetch(searchUrl, { method: 'POST', headers, body })).json();
  cycles.map(async cycle => {
    const data = await (await fetch(`${apiUrl}/cycles/${cycle['@id']}`, { headers })).json();
    const values = (data.inputs || [])
      .filter(input => input.term.units === 'kg N')
      .flatMap(input => input.value);
    const total = values.reduce((a, b) => a + b, 0);
    console.log(cycle, 'total nitrogen fertilizer', total);
  });
})();

(async () => {
  const apiUrl = '<APIUrl>';
  const searchUrl = '<APIUrl>/search';
  const headers = {
    'Content-Type': 'application/json'
  };
  // searching for aggregated GWP100 emissions on "Maize, grain"
  const body = JSON.stringify({
    fields: ['@id'],
    limit: 10,
    query: {
      bool: {
        'must': [
          { match: { '@type': 'ImpactAssessment' } },
          { match: { 'aggregated': true } },
          { match: { 'product.term': 'Maize, grain' } }
        ]
      }
    }
  });
  const { results: impacts } = await (await fetch(searchUrl, { method: 'POST', headers, body })).json();
  impacts.map(async impact => {
    const url = `${apiUrl}/impactassessments/${impact['@id']}?dataState=aggregated`;
    const data = await (await fetch(url, { headers })).json();
    const value = (data.impacts || []).find(impact => impact.term.name === 'GWP100').value;
    console.log(value);
  });
})();

const axios = require('axios');

(async () => {
  const apiUrl = '<APIUrl>';
  const searchUrl = '<APIUrl>/search';
  const body = {
    fields: ['@id'],
    limit: 10,
    query: {
      bool: {
        'must': [
          { match: { '@type': 'Cycle' } },
          { match: { 'emissions.term.name': 'NO3, to groundwater, soil flux' } }
        ]
      }
    }
  };
  const { data: { results: cycles } } = await axios.post(searchUrl, body);
  cycles.map(async cycle => {
    const { data } = await axios.get(`${apiUrl}/cycles/${cycle['@id']}`);
    const values = (data.inputs || [])
      .filter(input => input.term.units === 'kg N')
      .flatMap(input => input.value);
    const total = values.reduce((a, b) => a + b, 0);
    console.log(cycle, 'total nitrogen fertilizer', total);
  });
})();

const axios = require('axios');

(async () => {
  const apiUrl = '<APIUrl>';
  const searchUrl = '<APIUrl>/search';
  // searching for aggregated GWP100 emissions on "Maize, grain"
  const body = {
    fields: ['@id'],
    limit: 10,
    query: {
      bool: {
        'must': [
          { match: { '@type': 'ImpactAssessment' } },
          { match: { 'aggregated': true } },
          { match: { 'product.term': 'Maize, grain' } }
        ]
      }
    }
  };
  const { data: { results: impacts } } = await axios.post(searchUrl, body);
  impacts.map(async impact => {
    const url = `${apiUrl}/impactassessments/${impact['@id']}?dataState=aggregated`;
    const { data } = await axios.get(url);
    const value = (data.impacts || []).find(impact => impact.term.name === 'GWP100').value;
    console.log(value);
  });
})();

Hestia Community Edition

We also provide a self-hosted version of the full Hestia calculations in our Community Edition.

Getting Started

The easiest way to use the Community Edition is to run the Docker images on your computer or your own servers. Please see the instructions here.

If you have any questions or feedback, please create an issue here.

You can also clone the repository and install the Community Edition locally without Docker.

Hestia Calculation Models

The Calculation Models are a set of modules for creating structured data models from LCA observations and evaluating biogeochemical aspects of specific farming cycles.

Usage

1. You will need to use python 3 (we recommend using python 3.6 minimum).
2. Install the library:

pip install hestia_earth.models

1. Set the following environment variables:

API_URL="<APIUrl>"
WEB_URL="<WEBUrl>"

Now you can scroll to the model you are interested in and follow the instructions to run them.

Logging

The models library is shipped with it's own logging which will be displayed in the console by default. If you want to save the logs into a file, please set the LOG_FILENAME environment variable to the path of the file when running the models.

Example with a my_file.py file like:

from hestia_earth.models.pooreNemecek2018 import run

run('no3ToGroundwaterSoilFlux', cycle_data)

You can save the output in the models.log file by running LOG_FILENAME=models.log python my_file.py.

Orchestrator

Hestia has developed a library to run the models in a specific sequence defined in a configuration file called the Hestia Engine Orchestrator.

Whereas when running a single model you would do:

from hestia_earth.models.pooreNemecek2018 import run

run('no3ToGroundwaterInorganicFertiliser', cycle_data)

You can run a sequence of models by doing instead:

from hestia_earth.orchestrator import run

config = {
  "models": [
    {
      "key": "emissions",
      "model": "pooreNemecek2018",
      "value": "no3ToGroundwaterInorganicFertiliser",
      "runStrategy": "add_blank_node_if_missing"
    },
    {
      "key": "emissions",
      "model": "pooreNemecek2018",
      "value": "no3ToGroundwaterOrganicFertiliser",
      "runStrategy": "add_blank_node_if_missing"
    }
  ]
}

run(cycle_data, config)

More information and examples are available in the Hestia Engine Orchestrator repository.

Agribalyse (2016)

These models use data from the Agribalyse dataset to gap fill average values.

View source on Gitlab

Fuel and Electricity

This model calculates fuel and electricity data from the number of hours each machine is operated for using.

Returns

• A list of Inputs with:
  • methodModel with agribalyse2016
  • term of termType = fuel
  • value
  • operation

Requirements

• A Cycle with:
  • Data completeness assessment for electricityFuel: completeness.electricityFuel must be False
  • a list of practices with:
    • term of termType = operation and value > 0

Lookup used

• operation.csv -> fuelUse

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.agribalyse2016 import run

print(run('fuelElectricity', Cycle))

View source on Gitlab

Machinery infrastructure, depreciated amount per Cycle

The quantity of machinery infrastructure, depreciated over its lifetime, divided by the area it operates over, and expressed in kilograms per functional unit per Cycle.

Machinery gradually depreciates over multiple production Cycles until it reaches the end of its life. As a rough rule, the more the machinery is used, the faster it depreciates. Machinery use can be proxied for by the amount of fuel used. From 139 processes in AGRIBALYSE, the ratio of machinery depreciated per unit of fuel consumed (kg machinery kg diesel–1) was established. Recognizing that farms in less developed countries have poorer access to capital and maintain farm machinery for longer, the machinery-to-diesel ratio was doubled in countries with a Human Development Index of less than 0.8.

Returns

• A list of Inputs with:
  • term with machineryInfrastructureDepreciatedAmountPerCycle
  • methodModel with agribalyse2016
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover and a country with:
      • termType = region
  • a list of inputs with:
    • term of termType = fuel and value
  • Data completeness assessment for material: completeness.material must be False

Lookup used

• region.csv -> HDI
• material.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.agribalyse2016 import run

print(run('machineryInfrastructureDepreciatedAmountPerCycle', Cycle))

View source on Gitlab

Akagi et al (2011) and IPCC (2006)

This model calculates the emissions from crop residue burning using the methodology detailed in the IPCC 2006 guidelines (Volume 4, Chapter 2, Section 2.4) and the emissions factors detailed in Akagi et al (2011).

View source on Gitlab

CH4, to air, crop residue burning

Methane emissions to air, from crop residue burning.

Returns

• A list of Emissions with:
  • term with ch4ToAirCropResidueBurning
  • methodModel with akagiEtAl2011AndIpcc2006
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland
  • either:
    • a list of products with:
      • term with aboveGroundCropResidueBurnt or discardedCropBurnt and value
    • Data completeness assessment for cropResidue: completeness.cropResidue must be True

Lookup used

• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.akagiEtAl2011AndIpcc2006 import run

print(run('ch4ToAirCropResidueBurning', Cycle))

View source on Gitlab

N2O, to air, crop residue burning, direct

Nitrous oxide emissions to air, from crop residue burning.

Returns

• A list of Emissions with:
  • term with n2OToAirCropResidueBurningDirect
  • methodModel with akagiEtAl2011AndIpcc2006
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland
  • either:
    • a list of products with:
      • term with aboveGroundCropResidueBurnt or discardedCropBurnt and value
    • Data completeness assessment for cropResidue: completeness.cropResidue must be True

Lookup used

• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.akagiEtAl2011AndIpcc2006 import run

print(run('n2OToAirCropResidueBurningDirect', Cycle))

View source on Gitlab

NH3, to air, crop residue burning

Ammonia emissions to air, from crop residue burning.

Returns

• A list of Emissions with:
  • term with nh3ToAirCropResidueBurning
  • methodModel with akagiEtAl2011AndIpcc2006
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland
  • either:
    • a list of products with:
      • term with aboveGroundCropResidueBurnt or discardedCropBurnt and value
    • Data completeness assessment for cropResidue: completeness.cropResidue must be True

Lookup used

• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.akagiEtAl2011AndIpcc2006 import run

print(run('nh3ToAirCropResidueBurning', Cycle))

View source on Gitlab

NOx, to air, crop residue burning

Nitrogen oxides emissions to air, from crop residue burning.

Returns

• A list of Emissions with:
  • term with noxToAirCropResidueBurning
  • methodModel with akagiEtAl2011AndIpcc2006
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland
  • either:
    • a list of products with:
      • term with aboveGroundCropResidueBurnt or discardedCropBurnt and value
    • Data completeness assessment for cropResidue: completeness.cropResidue must be True

Lookup used

• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.akagiEtAl2011AndIpcc2006 import run

print(run('noxToAirCropResidueBurning', Cycle))

View source on Gitlab

AWARE

This model characterises water use based on the geospatial AWARE model, see Boulay et al (2018).

View source on Gitlab

Scarcity weighted water use

This model calculates the scarcity weighted water use based on the geospatial AWARE model (see UNEP (2016); Boulay et al (2016); Boulay et al (2020); EC-JRC (2017)).

Returns

• A list of Indicators with:
  • term with scarcityWeightedWaterUse
  • methodModel with aware
  • value

Requirements

• A ImpactAssessment with:
  • a site with:
    • either:
      • awareWaterBasinId
      • a country with:
        • =
  • optional:
    • a list of emissionsResourceUses with:
      • term with freshwaterWithdrawalsDuringCycle and value

Lookup used

Different lookup files are used depending on the situation:

• awareWaterBasinId.csv -> using awareWaterBasinId and YR_IRRI (for cropland, glass or high accessible cover, or permanent pasture) or YR_NONIRRI
• region-aware-factors.csv -> using region and YR_IRRI (for cropland, glass or high accessible cover or permanent pasture) or YR_NONIRRI

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.aware import run

print(run('scarcityWeightedWaterUse', ImpactAssessment))

View source on Gitlab

Blonk Consultants (2016)

This model calculates the land transformation and emissions related to land use change, using the Blonk Consultants (2016) direct land use change assessment model.

View source on Gitlab

CH4, to air, natural vegetation burning

Methane emissions to air, from natural vegetation burning during deforestation or other land conversion.

Returns

• A list of Emissions with:
  • term with ch4ToAirNaturalVegetationBurning
  • methodModel with blonkConsultants2016
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture and a country with:
      • termType = region
  • either:
    • if the cycle.functionalUnit = 1 ha, additional properties are required:
      • cycleDuration
      • a list of products with:
        • = True and > 0 and > 0
      • a list of practices with:
        • and with
    • for plantations, additional properties are required:
      • a list of practices with:
        • and with
        • and with
        • and with
        • and with
        • and with
        • and with

Lookup used

• crop.csv -> isPlantation; cropGroupingFaostatArea
• region-crop-cropGroupingFaostatArea-ch4forestBiomassBurning.csv -> use crop grouping above or default to site.siteType
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.blonkConsultants2016 import run

print(run('ch4ToAirNaturalVegetationBurning', Cycle))

View source on Gitlab

CO2, to air, above ground biomass stock change, land use change

Carbon dioxide emissions to air, from above ground biomass stock change, caused by land use change (e.g., a change from forest land to cropland). Stock changes caused by changes in Site management should be recorded separately by using the term CO2, to air, above ground biomass stock change, management change.

Returns

• A list of Emissions with:
  • term with co2ToAirAboveGroundBiomassStockChangeLandUseChange
  • methodModel with blonkConsultants2016
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture and a country with:
      • termType = region
  • either:
    • if the cycle.functionalUnit = 1 ha, additional properties are required:
      • cycleDuration
      • a list of products with:
        • = True and > 0 and > 0
      • a list of practices with:
        • and with
    • for plantations, additional properties are required:
      • a list of practices with:
        • and with
        • and with
        • and with
        • and with
        • and with
        • and with

Lookup used

• crop.csv -> isPlantation; cropGroupingFaostatArea
• region-crop-cropGroupingFaostatArea-co2LandUseChange.csv -> use crop grouping above or default to site.siteType
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.blonkConsultants2016 import run

print(run('co2ToAirAboveGroundBiomassStockChangeLandUseChange', Cycle))

View source on Gitlab

Land transformation, from forest, 20 year average, during Cycle

The amount of land used by this Cycle, that changed use from forest to the current use in the last 20 years, divided by 20.

Returns

• A list of Indicators with:
  • term with landTransformationFromForest20YearAverageDuringCycle
  • methodModel with blonkConsultants2016
  • value

Requirements

• A ImpactAssessment with:
  • a site with:
    • siteType
  • a product with:
    • a term
  • a cycle with:
    • a list of products with:
      • primary = True and value > 0 and economicValueShare > 0 and either:
      • if the cycle.functionalUnit = 1 ha, additional properties are required:
        • a list of with:
      • for plantations, additional properties are required:
        • a list of with:

Lookup used

• crop.csv -> cropGroupingFaostatArea
• region-crop-cropGroupingFaostatArea-landTransformation20YearsAverage.csv -> use crop grouping above or default to site.siteType
• resourceUse.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.blonkConsultants2016 import run

print(run('landTransformationFromForest20YearAverageDuringCycle', ImpactAssessment))

View source on Gitlab

N2O, to air, natural vegetation burning, direct

Direct nitrous oxide emissions to air, from natural vegetation burning during deforestation or other land conversion.

Returns

• A list of Emissions with:
  • term with n2OToAirNaturalVegetationBurningDirect
  • methodModel with blonkConsultants2016
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture and a country with:
      • termType = region
  • either:
    • if the cycle.functionalUnit = 1 ha, additional properties are required:
      • cycleDuration
      • a list of products with:
        • = True and > 0 and > 0
      • a site with:
        • a list of with:
    • for plantations, additional properties are required:
      • a list of practices with:
        • and with
        • and with
        • and with
        • and with
        • and with
        • and with

Lookup used

• crop.csv -> isPlantation; cropGroupingFaostatArea
• region-crop-cropGroupingFaostatArea-n2oforestBiomassBurning.csv -> use crop grouping above or default to site.siteType
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.blonkConsultants2016 import run

print(run('n2OToAirNaturalVegetationBurningDirect', Cycle))

View source on Gitlab

Chaudhary Brooks (2018)

This model calculates the biodiversity impacts related to habitat loss, accounting for different land use intensities, as defined in Chaudhary & Brooks (2018).

View source on Gitlab

Damage to terrestrial ecosystems, land occupation

The fraction of species richness that may be potentially lost in terrestrial ecosystems due to land occupation. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToTerrestrialEcosystemsLandOccupation
  • methodModel with chaudharyBrooks2018
  • value

Requirements

• A ImpactAssessment with:
  • a site with:
    • siteType and either:
      • ecoregion
      • a country with:
        • =
  • a cycle with:
    • a list of products with:
      • primary = True and value > 0 and economicValueShare > 0 and either:
      • if the cycle.functionalUnit = 1 ha, additional properties are required:
        • a list of with:
      • for plantations, additional properties are required:
        • a list of with:

Lookup used

Different lookup files are used depending on the situation:

• ecoregion-siteType-LandOccupationChaudaryBrooks2018CF.csv -> using ecoregion
• region-siteType-LandOccupationChaudaryBrooks2018CF.csv -> using country

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.chaudharyBrooks2018 import run

print(run('damageToTerrestrialEcosystemsLandOccupation', ImpactAssessment))

View source on Gitlab

Damage to terrestrial ecosystems, land transformation

The fraction of species richness that may be potentially lost in terrestrial ecosystems due to land transformation. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToTerrestrialEcosystemsLandTransformation
  • methodModel with chaudharyBrooks2018
  • value

Requirements

• A ImpactAssessment with:
  • a site with:
    • siteType and either:
      • ecoregion
      • a country with:
        • =
  • a cycle with:
    • a list of products with:
      • primary = True and value > 0 and economicValueShare > 0
  • optional:
    • a list of emissionsResourceUses with:
      • value and term with landTransformationFromForest20YearAverageDuringCycle
      • value and term with landTransformationFromOtherNaturalVegetation20YearAverageDuringCycle

Lookup used

Different lookup files are used depending on the situation:

• ecoregion-siteType-LandTransformationChaudaryBrooks2018CF.csv -> using ecoregion
• region-siteType-LandTransformationChaudaryBrooks2018CF.csv -> using country

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.chaudharyBrooks2018 import run

print(run('damageToTerrestrialEcosystemsLandTransformation', ImpactAssessment))

View source on Gitlab

Damage to terrestrial ecosystems, total land use effects

The fraction of species richness that may be potentially lost in terrestrial ecosystems due to land occupation and transformation. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToTerrestrialEcosystemsTotalLandUseEffects
  • methodModel with chaudharyBrooks2018
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • a list of products with:
      • primary = True and value > 0 and economicValueShare > 0
  • a list of impacts with:
    • value and term with damageToTerrestrialEcosystemsLandOccupation
    • value and term with damageToTerrestrialEcosystemsLandTransformation

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.chaudharyBrooks2018 import run

print(run('damageToTerrestrialEcosystemsTotalLandUseEffects', ImpactAssessment))

View source on Gitlab

CML2001 Baseline

These models characterise emissions and resource uses according to the CML2001 Baseline method, see Guinée et al (2002).

View source on Gitlab

Eutrophication potential, excluding fate

The potential of nutrient emissions to cause excessive growth of aquatic plants and algae in aquatic ecosystems. Some algae - particularly phytoplankton - are inedible to much other aquatic life and go uneaten, meaning the phytoplankton die and get broken down by bacteria which use oxygen for respiration. This oxygen demand depltes oxygen in the water leading to hypoxia.

Returns

• A Indicator with:
  • term with eutrophicationPotentialExcludingFate
  • methodModel with cml2001Baseline
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> po4-EqEutrophicationExcludingFateCml2001Baseline

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cml2001Baseline import run

print(run('eutrophicationPotentialExcludingFate', ImpactAssessment))

View source on Gitlab

Terrestrial acidification potential, including fate, average Europe

Changes in soil chemical properties following the deposition of nitrogen and sulphur in acidifying forms, including average fate of the emissions in Europe.

Returns

• A Indicator with:
  • term with terrestrialAcidificationPotentialIncludingFateAverageEurope
  • methodModel with cml2001Baseline
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> so2EqTerrestrialAcidificationIncludingFateAverageEuropeCml2001Baseline

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cml2001Baseline import run

print(run('terrestrialAcidificationPotentialIncludingFateAverageEurope', ImpactAssessment))

View source on Gitlab

CML2001 Non-Baseline

These models characterise emissions and resource uses according to the CML2001 Non-Baseline method, see Guinée et al (2002).

View source on Gitlab

Eutrophication potential, including fate, average Europe

The potential of nutrient emissions to cause excessive growth of aquatic plants and algae in aquatic ecosystems, including an estimated fate of these emissions in Europe. Some algae - particularly phytoplankton - are inedible to much other aquatic life and go uneaten, meaning the phytoplankton die and get broken down by bacteria which use oxygen for respiration. This oxygen demand depltes oxygen in the water leading to hypoxia.

Returns

• A Indicator with:
  • term with eutrophicationPotentialIncludingFateAverageEurope
  • methodModel with cml2001NonBaseline
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> noxeqEutrophicationIncludingFateAverageEuropeCml2001Non-Baseline

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cml2001NonBaseline import run

print(run('eutrophicationPotentialIncludingFateAverageEurope', ImpactAssessment))

View source on Gitlab

Terrestrial acidification potential, excluding fate

Changes in soil chemical properties following the deposition of nitrogen and sulphur in acidifying forms, excluding average fate of the emissions in Europe.

Returns

• A Indicator with:
  • term with terrestrialAcidificationPotentialExcludingFate
  • methodModel with cml2001NonBaseline
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> so2EqTerrestrialAcidificationExcludingFateCml2001Non-Baseline

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cml2001NonBaseline import run

print(run('terrestrialAcidificationPotentialExcludingFate', ImpactAssessment))

View source on Gitlab

Cycle

These models are specific to Cycle.

View source on Gitlab

Above ground crop residue, total

The total amount of above ground crop residue as dry matter. This total is the value prior to crop residue management practices (for example, burning or removal). Properties can be added, such as the nitrogen content. The amount of discarded crop is not included and should be recorded separately.

This model gap-fills the amount of above ground crop residue total if any matching management practice and crop residue products are provided. Examples: - if residueLeftOnField = 50% and aboveGroundCropResidueLeftOnField = 1000kg, the total is 2000kg; - if residueLeftOnField = 100% and aboveGroundCropResidueLeftOnField = 1000kg, the total is 1000kg.

Returns

• A list of Products with:
  • term with aboveGroundCropResidueTotal
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover
  • a list of products with:
    • value and term of termType = cropResidueManagement
    • term of termType = crop or forage
  • a list of practices with:
    • value and term of termType = cropResidue

Lookup used

• cropResidue.csv -> siteTypesAllowed; productTermTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('aboveGroundCropResidueTotal', Cycle))

View source on Gitlab

Animal Input Hestia Aggregated Data

This model adds impactAssessment to Animal inputs based on data which has been aggregated into country level averages. Note: to get more accurate impacts, we recommend setting the input.impactAssessment instead of the region-level averages using this model.

Returns

• A list of Animals with:
  • a list of inputs with:
    • impactAssessment
    • impactAssessmentIsProxy with True

Requirements

• A Cycle with:
  • a list of animals with:
    • a list of inputs with:
      • value and none of:
        • with True
        • with True and optional:
        • a with:
        • a with:

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('animal.input.hestiaAggregatedData', Cycle))

View source on Gitlab

Input Properties

This model handles multiple cases: - when the impactAssessment field is set, the model adds properties to the Input when they are connected to another Cycle that has an indentical Product; - when the dryMatter property is set, the model recalculates the other properties based on the data from feedipedia; - when the min and max are set, the model averages the value.

Returns

• A list of Animals with:
  • a list of inputs with:
    • properties with [object Object]

Requirements

• A Cycle with:
  • a list of animals with:
    • a list of inputs with:
      • either:
        • a list of with:

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('animal.input.properties', Cycle))

View source on Gitlab

Animal Properties

This model handles multiple cases: - when the min and max are set, the model averages the value.

Returns

• A list of Animals with:
  • a list of properties with:
    • value

Requirements

• A Cycle with:
  • a list of animals with:
    • min and max

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('animal.properties', Cycle))

View source on Gitlab

Cold carcass weight per head

The average cold carcass weight of the animals per head.

Returns

• A list of Products with:
  • a list of properties with:
    • term with coldCarcassWeightPerHead
    • value

Requirements

• A Cycle with:
  • a list of products with:
    • value and term of termType = animalProduct and term of units = kg cold carcass weight and a list of properties with:
      • value and term with liveweightPerHead
      • value and term with processingConversionLiveweightToColdCarcassWeight

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('coldCarcassWeightPerHead', Cycle))

View source on Gitlab

Cold dressed carcass weight per head

The cold dressed carcass weight (i.e., excluding offal and slaughter fats) of the animals per head.

Returns

• A list of Products with:
  • a list of properties with:
    • term with coldDressedCarcassWeightPerHead
    • value

Requirements

• A Cycle with:
  • a list of products with:
    • value and term of termType = animalProduct and term of units = kg cold dressed carcass weight and a list of properties with:
      • value and term with liveweightPerHead
      • value and term with processingConversionLiveweightToColdDressedCarcassWeight

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('coldDressedCarcassWeightPerHead', Cycle))

View source on Gitlab

Completeness Animal feed

This model checks if we have the requirements below and updates the Data Completeness value.

Returns

• A Completeness with:
  • animalFeed

Requirements

• A Cycle with:
  • Data completeness assessment for animalFeed: completeness.animalFeed must be False
  • a site with:
    • siteType with cropland or glass or high accessible cover

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('completeness.animalFeed', Cycle))

View source on Gitlab

Completeness Crop Residue

This model checks if we have the requirements below and updates the Data Completeness value.

Returns

• A Completeness with:
  • cropResidue

Requirements

• A Cycle with:
  • Data completeness assessment for cropResidue: completeness.cropResidue must be False
  • a list of products with:
    • value and term with belowGroundCropResidue
    • value and term with aboveGroundCropResidueTotal
  • optional:
    • a list of products with:
      • value and term with aboveGroundCropResidueRemoved
      • value and term with aboveGroundCropResidueIncorporated
      • value and term with aboveGroundCropResidueBurnt
      • value and term with aboveGroundCropResidueLeftOnField

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('completeness.cropResidue', Cycle))

View source on Gitlab

Completeness Excreta

This model checks if we have the requirements below and updates the Data Completeness value.

Returns

• A Completeness with:
  • excreta

Requirements

• A Cycle with:
  • Data completeness assessment for excreta: completeness.excreta must be False
  • a site with:
    • siteType with cropland or glass or high accessible cover

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('completeness.excreta', Cycle))

View source on Gitlab

Completeness Material

This model checks if we have the requirements below and updates the Data Completeness value.

Returns

• A Completeness with:
  • material

Requirements

• A Cycle with:
  • a list of inputs with:
    • value and term with machineryInfrastructureDepreciatedAmountPerCycle
  • a site with:
    • siteType with cropland or glass or high accessible cover

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('completeness.material', Cycle))

View source on Gitlab

Completeness Seed

This model checks if we have the requirements below and updates the Data Completeness value.

Returns

• A Completeness with:
  • seed

Requirements

• A Cycle with:
  • Data completeness assessment for seed: completeness.seed must be False
  • a list of inputs with:
    • value and term with seed or saplings
  • a site with:
    • siteType with cropland or glass or high accessible cover

Lookup used

• crop.csv -> isPlantation

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('completeness.seed', Cycle))

View source on Gitlab

Completeness Soil Amendments

This model checks if we have the requirements below and updates the Data Completeness value.

Returns

• A Completeness with:
  • soilAmendment

Requirements

• A Cycle with:
  • Data completeness assessment for soilAmendment: completeness.soilAmendment must be False
  • endDate
  • a site with:
    • a list of measurements with:
      • value and term with soilPh

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('completeness.soilAmendment', Cycle))

View source on Gitlab

Concentrate feed Properties

This model calculates all of the nutrient content values and dry matter values for a feed blend if we know the crops that went into the blend by taking a weighted average.

Returns

• A list of Products with:
  • a list of properties
    • term with concentrateFeedBlend or concentrateFeedUnspecified or feedMix

Requirements

• A Cycle with:
  • a list of inputs with:
    • value and term of termType = crop or animalProduct
  • a list of products with:
    • primary = True and term with concentrateFeedUnspecified or concentrateFeedBlend or feedMix

Lookup used

• crop-property.csv -> crudeProteinContent
• property.csv -> commonToSupplementInAnimalFeed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('concentrateFeed', Cycle))

View source on Gitlab

Crop Residue Management

This model gap-fills cropResidueManagement practices to 0 when there are existing ones that sum up to 100%.

Returns

• A list of Practices with:
  • value
  • term of termType = cropResidueManagement

Requirements

• A Cycle with:
  • a list of practices with:
    • term of termType = cropResidueManagement and value with 100

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('cropResidueManagement', Cycle))

View source on Gitlab

Cycle duration

This model calculates the cycle duration using the cropping intensity for a single year.

Returns

• a number or None if requirements are not met

Requirements

• A Cycle with:
  • cycleDuration with 365
  • endDate
  • a list of practices with:
    • value and term with croppingIntensity
  • a site with:
    • siteType with cropland

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('cycleDuration', Cycle))

View source on Gitlab

Energy content (lower heating value)

The amount of heat released by combusting a specified quantity in a calorimiter. The combustion process generates water vapor, but the heat in the water vapour is not recovered and accounted for.

Returns

• A list of Inputs with:
  • a list of properties with:
    • term with energyContentLowerHeatingValue
    • value

Requirements

• A Cycle with:
  • a list of inputs with:
    • term of termType = woodFuel or woodPellets and a list of properties with:
      • value and term with dryMatter

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('energyContentLowerHeatingValue', Cycle))

View source on Gitlab

Excreta (kg)

This model calculates the amount of excreta in kg based on the amount of excreta in kg N or kg Vs.

Returns

• A list of Products with:
  • term of termType = excreta
  • term of units = kg
  • value

Requirements

• A Cycle with:
  • a list of products with:
    • term of termType = excreta and term of units = kg N or kg VS

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('excretaKgMass', Cycle))

View source on Gitlab

Excreta (kg N)

This model calculates the amount of excreta in kg N based on the amount of excreta in kg.

Returns

• A list of Products with:
  • term of termType = excreta
  • term of units = kg N
  • value

Requirements

• A Cycle with:
  • a list of products with:
    • term of termType = excreta and term of units = kg

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('excretaKgN', Cycle))

View source on Gitlab

Excreta (kg VS)

This model calculates the amount of excreta in kg VS based on the amount of excreta in kg.

Returns

• A list of Products with:
  • term of termType = excreta
  • term of units = kg VS
  • value

Requirements

• A Cycle with:
  • a list of products with:
    • term of termType = excreta and term of units = kg

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('excretaKgVs', Cycle))

View source on Gitlab

Feed conversion ratio (carbon)

The feed conversion ratio (kg C of feed per kg of liveweight produced), based on the carbon content of the feed.

Returns

• A list of Practices with:
  • term with feedConversionRatioCarbon
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or permanent pasture or animal housing
  • a list of products with:
    • term of termType = animalProduct and a list of properties with:
      • value and term with liveweightPerHead and optional:
      • a list of properties with:
        • and with or or or
    • term of termType = animalProduct or liveAnimal or liveAquaticSpecies
  • either:
    • the following fields:
      • a list of animals with:
        • a list of with:
    • the following fields:
      • a list of inputs with:
        • of = kg and > 0 and with True and a list of with:

Lookup used

• crop-property.csv -> energyContentHigherHeatingValue
• animalManagement.csv -> siteTypesAllowed; productTermTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('feedConversionRatio.feedConversionRatioCarbon', Cycle))

View source on Gitlab

Feed conversion ratio (dry matter)

The feed conversion ratio (kg of feed per kg of liveweight produced), based on the dry matter weight of the feed.

Returns

• A list of Practices with:
  • term with feedConversionRatioDryMatter
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or permanent pasture or animal housing
  • a list of products with:
    • term of termType = animalProduct and a list of properties with:
      • value and term with liveweightPerHead and optional:
      • a list of properties with:
        • and with or or or
    • term of termType = animalProduct or liveAnimal or liveAquaticSpecies
  • either:
    • the following fields:
      • a list of animals with:
        • a list of with:
    • the following fields:
      • a list of inputs with:
        • of = kg and > 0 and with True and optional:

Lookup used

• animalManagement.csv -> siteTypesAllowed; productTermTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('feedConversionRatio.feedConversionRatioDryMatter', Cycle))

View source on Gitlab

Feed conversion ratio (energy)

The feed conversion ratio (MJ of feed per kg of liveweight produced), based on the energy content of the feed.

Returns

• A list of Practices with:
  • term with feedConversionRatioEnergy
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or permanent pasture or animal housing
  • a list of products with:
    • term of termType = animalProduct and a list of properties with:
      • value and term with liveweightPerHead and optional:
      • a list of properties with:
        • and with or or or
    • term of termType = animalProduct or liveAnimal or liveAquaticSpecies
  • either:
    • the following fields:
      • a list of animals with:
        • a list of with:
    • the following fields:
      • a list of inputs with:
        • of = kg and > 0 and with True and a list of with:

Lookup used

• crop-property.csv -> energyContentHigherHeatingValue
• animalManagement.csv -> siteTypesAllowed; productTermTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('feedConversionRatio.feedConversionRatioEnergy', Cycle))

View source on Gitlab

Feed conversion ratio (fed weight)

The feed conversion ratio (kg of feed per kg of liveweight produced), based on the fed weight of the feed.

Returns

• A list of Practices with:
  • term with feedConversionRatioFedWeight
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or permanent pasture or animal housing
  • a list of products with:
    • term of termType = animalProduct and a list of properties with:
      • value and term with liveweightPerHead and optional:
      • a list of properties with:
        • and with or or or
    • term of termType = animalProduct or liveAnimal or liveAquaticSpecies
  • either:
    • the following fields:
      • a list of animals with:
        • a list of with:
    • the following fields:
      • a list of inputs with:
        • of = kg and > 0 and with True and a list of with:

Lookup used

• animalManagement.csv -> siteTypesAllowed; productTermTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('feedConversionRatio.feedConversionRatioFedWeight', Cycle))

View source on Gitlab

Feed conversion ratio (nitrogen)

The feed conversion ratio (kg N of feed per kg of liveweight produced), based on the nitrogen content of the feed.

Returns

• A list of Practices with:
  • term with feedConversionRatioNitrogen
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or permanent pasture or animal housing
  • a list of products with:
    • term of termType = animalProduct and a list of properties with:
      • value and term with liveweightPerHead and optional:
      • a list of properties with:
        • and with or or or
    • term of termType = animalProduct or liveAnimal or liveAquaticSpecies
  • either:
    • the following fields:
      • a list of animals with:
        • a list of with:
    • the following fields:
      • a list of inputs with:
        • of = kg and > 0 and with True and a list of with:

Lookup used

• crop-property.csv -> crudeProteinContent
• animalManagement.csv -> siteTypesAllowed; productTermTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('feedConversionRatio.feedConversionRatioNitrogen', Cycle))

View source on Gitlab

Inorganic Fertiliser

This model calculates the amount of other nutrient(s) supplied by multi-nutrients inorganic fertilisers when only the amount of one of the nutrients is recorded by the user.

Returns

• A list of Inputs with:
  • term of termType = inorganicFertiliser
  • value
  • min
  • max
  • statsDefinition with modelled

Requirements

• A Cycle with:
  • a list of inputs with:
    • term of termType = inorganicFertiliser and value > 0

Lookup used

• inorganicFertiliser.csv -> mustIncludeId; nitrogenContent; nitrogenContent-min; nitrogenContent-max; phosphateContentAsP2O5; phosphateContentAsP2O5-min; phosphateContentAsP2O5-max; potassiumContentAsK2O; potassiumContentAsK2O-min; potassiumContentAsK2O-max

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('inorganicFertiliser', Cycle))

View source on Gitlab

Input Hestia Aggregated Data

This model adds impactAssessment to inputs based on data which has been aggregated into country level averages. Note: to get more accurate impacts, we recommend setting the input.impactAssessment instead of the region-level averages using this model.

Returns

• A list of Inputs with:
  • impactAssessment
  • impactAssessmentIsProxy with True

Requirements

• A Cycle with:
  • a list of inputs with:
    • value and none of:
      • impactAssessment
      • fromCycle with True
      • producedInCycle with True and optional:
      • a country with:
        • =
      • a region with:
        • =
  • optional:
    • a site with:
      • siteType with cropland or pasture and a country with:
        • =
    • a list of inputs with:
      • term with seed and value and none of:
    • a list of products with:
      • value and primary = True

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('input.hestiaAggregatedData', Cycle))

View source on Gitlab

Input Properties

This model handles multiple cases: - when the impactAssessment field is set, the model adds properties to the Input when they are connected to another Cycle that has an indentical Product; - when the dryMatter property is set, the model recalculates the other properties based on the data from feedipedia; - when the min and max are set, the model averages the value.

Returns

• A list of Inputs with:
  • a list of properties

Requirements

• A Cycle with:
  • a list of inputs with:
    • either:
      • impactAssessment
      • a list of properties with:
        • must be set (is linked to an existing Property)

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('input.properties', Cycle))

View source on Gitlab

Input Value

This model calculates the value of the Input by taking an average from the min and max values.

Returns

• A list of Inputs with:
  • value

Requirements

• A Cycle with:
  • a list of inputs with:
    • min and max

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('input.value', Cycle))

View source on Gitlab

Irrigated, type unspecified

More than 25 mm of irrigation per year (250 m3/ha). The area irrigated can be specified as a percentage.

Returns

• A list of Practices with:
  • term with irrigatedTypeUnspecified
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture
  • Data completeness assessment for water: completeness.water
  • functionalUnit with 1 ha
  • none of:
    • a list of practices with:
      • value > 0 and term of termType = waterRegime
  • optional:
    • a list of inputs with:
      • term of termType = water and value

Lookup used

• waterRegime.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('irrigatedTypeUnspecified', Cycle))

View source on Gitlab

Live Animal

This model calculates the amount of live animal produced during a Cycle, based on the amount of animal product.

Returns

• A list of Products with:
  • term of termType = liveAnimal
  • value

Requirements

• A Cycle with:
  • a list of products with:
    • primary = True and value and term of termType = animalProduct and a list of properties with:
      • value and term with coldCarcassWeightPerHead or coldDressedCarcassWeightPerHead or liveweightPerHead or readyToCookWeightPerHead
  • a site with:
    • siteType with animal housing or permanent pasture

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('liveAnimal', Cycle))

View source on Gitlab

Milk Yield

This model gap-fills the practice "Milk yield per animal X (raw/FPCM)" (e.g. Milk yield per cow (raw)) when:

1. The corresponding milk term in the animalProduct glossary (e.g. Milk, cow, raw) is added as a Product of the Cycle;
2. The number of lactating animals is known.

It also adds the properties of the Product to the gap-filled Practice.

Returns

• A list of Practices with:
  • value
  • min
  • max
  • sd
  • statsDefinition with modelled

Requirements

• A Cycle with:
  • cycleDuration
  • a site with:
    • siteType with cropland or permanent pasture or animal housing
  • a list of products with:
    • value > 0 and termType = animalProduct
  • a list of animals with:
    • value > 0 and termType = liveAnimal

Lookup used

• animalProduct.csv -> liveAnimal; milkYieldPracticeId

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('milkYield', Cycle))

View source on Gitlab

Pasture grass

The type of grass grown on the pasture. Data must be recorded in key:value form. Use the key field to record the term describing the plant (e.g. Alfalfa plant) and the value field to record the area it covers, in percentage. If multiple grasses are grown, add the term Pasture grass multiple times, each time with a different key. The values provided should add up to 100%.

Returns

• A Practice with:
  • term with pastureGrass
  • key with genericGrassPlant
  • value with 100

Requirements

• A Cycle with:
  • a site with:
    • siteType with permanent pasture

Lookup used

• landUseManagement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('pastureGrass', Cycle))

View source on Gitlab

Pasture System

This model returns a default Pasture system when none if provided, for any cycle hapenning on permanent pasture. When the slope is above 2.5%, we assume it is a hilly system.

Returns

• A list of Practices with:
  • term of termType = system
  • value with 100

Requirements

• A Cycle with:
  • a list of products with:
    • primary = True and term of termType = liveAnimal
  • a site with:
    • siteType with permanent pasture

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('pastureSystem', Cycle))

View source on Gitlab

Post Checks

List of models to run after any other model on an Cycle.

View source on Gitlab

Post Checks Cache

This model removes any cached data on the Cycle.

Returns

• A Cycle

Requirements

• A Cycle with:

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run(Cycle))

View source on Gitlab

Post Checks Site

This model is run only if the pre model has been run before. This model will restore the cycle.site as a "linked node" (i.e. it will be set with only @type, @id and name keys).

Returns

• A Cycle with:
  • a site

Requirements

• A Cycle with:
  • a site with:
    • @id must be set (is linked to an existing Site)

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run(Cycle))

View source on Gitlab

Practice Value

This model uses the lookup called "defaultValue" on each Practice to gap-fill a default value. Otherwise, it calculates the value of the Practice by taking an average from the min and max values.

Returns

• A list of Practices with:
  • value

Requirements

• A Cycle with:
  • a list of practices with:
    • min and max

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('practice.value', Cycle))

View source on Gitlab

Pre Checks

List of models to run before any other model on an Cycle.

View source on Gitlab

Pre Checks Cache Sources

This model caches the sources of all models.

Returns

• A Cycle

Requirements

• A Cycle with:

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run(Cycle))

View source on Gitlab

Pre Checks Site

Some Cycle models need a full version of the linked Site to run. This model will fetch the complete version of the Site and include it.

Returns

• A Cycle with:
  • a site

Requirements

• A Cycle with:
  • a site with:
    • @id must be set (is linked to an existing Site)

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run(Cycle))

View source on Gitlab

Pre Checks Start Date

This model calculates the startDate from the endDate and cycleDuration.

Returns

• A Cycle with:
  • startDate

Requirements

• A Cycle with:
  • endDate
  • cycleDuration

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run(Cycle))

View source on Gitlab

Product Currency

Converts all the currencies to USD using historical rates.

Returns

• A list of Products with:
  • currency with USD
  • price with in USD
  • revenue with in USD

Requirements

• A Cycle with:
  • endDate
  • a list of products with:
    • price and currency with not in USD

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('product.currency', Cycle))

View source on Gitlab

Product Economic Value Share

This model quantifies the relative economic value share of each marketable Product in a Cycle. Marketable Products are all Products in the Glossary with the exception of crop residue not sold.

It works in the following order: 1. If revenue data are provided for all marketable products, the economicValueShare is directly calculated as the share of revenue of each Product; 2. If the primary product is a crop and it is the only crop Product, economicValueShare is assigned based on a lookup table containing typical global average economic value shares drawn from Poore & Nemecek (2018).

Returns

• A list of Products with:
  • economicValueShare

Requirements

• A Cycle with:
  • a list of products with:
    • value and economicValueShare and revenue and currency
  • optional:
    • Data completeness assessment for product: completeness.product

Lookup used

Depending on the primary product termType:

• crop.csv -> global_economic_value_share
• excreta.csv -> global_economic_value_share

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('product.economicValueShare', Cycle))

View source on Gitlab

Product Price

Sets the price of products to 0 in specific conditions: if the economicValueShare is 0, or for excreta.

Returns

• A list of Products with:
  • price

Requirements

• A Cycle with:
  • a list of products

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('product.price', Cycle))

View source on Gitlab

Product Primary

Determines the primary product which is the product with the highest economicValueShare.

Returns

• A list of Products with:
  • primary = True

Requirements

• A Cycle with:
  • a list of products with:
    • economicValueShare

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('product.primary', Cycle))

View source on Gitlab

Product Properties

This model handles the following case: - when the dryMatter property is set, the model recalculates the other properties based on the data from feedipedia; - when the min and max are set, the model averages the value.

Returns

• A list of Products with:
  • a list of properties

Requirements

• A Cycle with:
  • a list of products with:
    • either:
      • a list of properties with:
        • must be set (is linked to an existing Property)

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('product.properties', Cycle))

View source on Gitlab

Product Revenue

This model calculates the revenue of each product by multiplying the yield with the revenue.

In the case the product value is 0, the revenue will be set to 0.

In the case the product price is 0, the revenue will be set to 0.

Returns

• A list of Products with:
  • revenue
  • currency with defaults to USD if multiple currencies are used

Requirements

• A Cycle with:
  • a list of products with:
    • price and optional:
      • value

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('product.revenue', Cycle))

View source on Gitlab

Product Value

This model calculates the value of the Product by taking an average from the min and max values.

Returns

• A list of Products with:
  • value

Requirements

• A Cycle with:
  • a list of products with:
    • min and max

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('product.value', Cycle))

View source on Gitlab

Ready-to-cook weight per head

The ready-to-cook weight of the animals per head.

Returns

• A list of Products with:
  • a list of properties with:
    • term with readyToCookWeightPerHead
    • value

Requirements

• A Cycle with:
  • a list of products with:
    • value and term of termType = animalProduct and term of units = kg cold carcass weight and a list of properties with:
      • value and term with liveweightPerHead
      • value and term with processingConversionLiveweightToReadyToCookWeight

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('readyToCookWeightPerHead', Cycle))

View source on Gitlab

Residue burnt

The share of above ground crop residue burnt, after multiplication by the combustion factor (which allows for residue burnt but not combusted).

Returns

• A list of Practices with:
  • term with residueBurnt
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture
  • a list of products with:
    • term with aboveGroundCropResidueTotal and value > 0
    • term with aboveGroundCropResidueBurnt and value > 0
    • term of termType = crop or forage
  • Data completeness assessment for cropResidue: completeness.cropResidue must be False

Lookup used

• cropResidueManagement.csv -> siteTypesAllowed; productTermTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('residueBurnt', Cycle))

View source on Gitlab

Residue incorporated

The share of above ground crop residue incorporated (for example, by ploughing the residue into the soil after harvest).

Returns

• A list of Practices with:
  • term with residueIncorporated
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture
  • a list of products with:
    • term with aboveGroundCropResidueTotal and value > 0
    • term with aboveGroundCropResidueIncorporated and value > 0
    • term of termType = crop or forage
  • Data completeness assessment for cropResidue: completeness.cropResidue must be False
  • none of:
    • a list of practices with:
      • term with residueIncorporatedMoreThan30DaysBeforeCultivation or residueIncorporatedLessThan30DaysBeforeCultivation

Lookup used

• cropResidueManagement.csv -> siteTypesAllowed; productTermTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('residueIncorporated', Cycle))

View source on Gitlab

Residue left on field

The share of above ground crop residue left on the field surface.

Returns

• A list of Practices with:
  • term with residueLeftOnField
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture
  • a list of products with:
    • term with aboveGroundCropResidueTotal and value > 0
    • term with aboveGroundCropResidueLeftOnField and value > 0
    • term of termType = crop or forage
  • Data completeness assessment for cropResidue: completeness.cropResidue must be False

Lookup used

• cropResidueManagement.csv -> siteTypesAllowed; productTermTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('residueLeftOnField', Cycle))

View source on Gitlab

Residue removed

The share of above ground crop residue removed.

Returns

• A list of Practices with:
  • term with residueRemoved
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture
  • a list of products with:
    • term with aboveGroundCropResidueTotal and value > 0
    • term with aboveGroundCropResidueRemoved and value > 0
    • term of termType = crop or forage
  • Data completeness assessment for cropResidue: completeness.cropResidue must be False

Lookup used

• cropResidueManagement.csv -> siteTypesAllowed; productTermTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('residueRemoved', Cycle))

View source on Gitlab

Site duration

This model calculates the siteDuration on the Cycle to the same value as cycleDuration when only a single Site is present.

Returns

• a number or None if the Cycle has multiple Sites

Requirements

• A Cycle with:
  • cycleDuration with 365
  • none of:
    • otherSites

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('siteDuration', Cycle))

View source on Gitlab

Transformations

Returns the Emission from every Transformation to be added in the Cycle.

Returns

• A list of Emissions with:
  • value

Requirements

• A Cycle with:
  • optional:
    • a list of transformations with:
      • a list of inputs with:

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.cycle import run

print(run('transformation', Cycle))

View source on Gitlab

Dämmgen (2009)

These models calculate direct and indirect greenhouse gas emissions from the German GHG inventory guidelines, Dämmgen (2009).

View source on Gitlab

NOx, to air, excreta

Nitrogen oxides emissions to air, from animal excreta.

Returns

• A list of Emissions with:
  • term with noxToAirExcreta
  • methodModel with dammgen2009
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or permanent pasture or animal housing or pond or river or stream or lake or sea or ocean
  • a list of inputs with:
    • term of termType = excreta
  • a list of practices with:
    • value and term of termType = excretaManagement

This model works on the following Node type with identical requirements:

• Cycle
• Transformation

Lookup used

• excretaManagement.csv -> EF_NON-N
• emission.csv -> siteTypesAllowed; inputTermTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.dammgen2009 import run

print(run('noxToAirExcreta', Cycle))

View source on Gitlab

de Ruijter et al (2010)

This model calculates the NH3 emissions due to crop residue decomposition using the regression model in de Ruijter et al (2010).

View source on Gitlab

NH3, to air, crop residue decomposition

Ammonia emissions to air, from crop residue decomposition.

Returns

• A list of Emissions with:
  • term with nh3ToAirCropResidueDecomposition
  • methodModel with deRuijterEtAl2010
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover
  • either:
    • a list of products with:
      • value and term with aboveGroundCropResidueTotal or aboveGroundCropResidueLeftOnField or aboveGroundCropResidueIncorporated or discardedCropTotal or discardedCropLeftOnField or discardedCropIncorporated and a list of properties with:
        • and with
    • Data completeness assessment for electricityFuel: completeness.electricityFuel must be True

Lookup used

• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.deRuijterEtAl2010 import run

print(run('nh3ToAirCropResidueDecomposition', Cycle))

View source on Gitlab

ecoinvent v3

This model calculates background emissions related to the production of Inputs from the ecoinvent database, version 3.

Note: to use the ecoinventV3 model locally or in the Hestia Community Edition you need a valid ecoinvent license. Please contact us at community@hestia.earth for instructions to download the required file to run the model.

Pesticide Brand Name

For Input with a Pesticide Brand Name term, you can override the default list of Pesticide Active Ingredient by specifying the list of properties manually.

Returns

• A list of Emissions with:
  • methodModel with ecoinventV3
  • term
  • value
  • methodTier with background
  • inputs
  • operation
  • animals

Requirements

• A Cycle with:
  • a list of inputs with:
    • value > 0 and none of:
      • fromCycle with True
      • producedInCycle with True
  • optional:
    • a list of animals with:
      • a list of inputs with:
        • > 0 and none of:

Lookup used

• electricity.csv -> ecoinventMapping
• fuel.csv -> ecoinventMapping
• inorganicFertiliser.csv -> ecoinventMapping
• material.csv -> ecoinventMapping
• pesticideAI.csv -> ecoinventMapping
• soilAmendment.csv -> ecoinventMapping
• transport.csv -> ecoinventMapping
• veterinaryDrugs.csv -> ecoinventMapping

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ecoinventV3 import run

print(run('all', Cycle))

View source on Gitlab

EMEP-EEA (2019)

These models characterise emissions according to the EMEP/EEA air pollutant emission inventory guidebook 2019.

View source on Gitlab

CO2, to air, fuel combustion

Carbon dioxide emissions to air, from the combustion of fuel.

Returns

• A list of Emissions with:
  • term with co2ToAirFuelCombustion
  • methodModel with emepEea2019
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • either:
    • a list of inputs with:
      • value and term of termType = fuel and optional:
    • Data completeness assessment for electricityFuel: completeness.electricityFuel must be True

This model works on the following Node type with identical requirements:

• Cycle
• Transformation
• Transport

Lookup used

• fuel.csv -> co2ToAirFuelCombustionEmepEea2019
• operation.csv -> co2ToAirFuelCombustionEmepEea2019
• emission.csv -> typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.emepEea2019 import run

print(run('co2ToAirFuelCombustion', Cycle))

View source on Gitlab

N2O, to air, fuel combustion, direct

Nitrous oxide emissions to air, from the combustion of fuel.

Returns

• A list of Emissions with:
  • term with n2OToAirFuelCombustionDirect
  • methodModel with emepEea2019
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • either:
    • a list of inputs with:
      • value and term of termType = fuel and optional:
    • Data completeness assessment for electricityFuel: completeness.electricityFuel must be True

This model works on the following Node type with identical requirements:

• Cycle
• Transformation
• Transport

Lookup used

• fuel.csv -> n2OToAirFuelCombustionEmepEea2019
• operation.csv -> n2OToAirFuelCombustionEmepEea2019
• emission.csv -> typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.emepEea2019 import run

print(run('n2OToAirFuelCombustionDirect', Cycle))

View source on Gitlab

NH3, to air, excreta

Ammonia emissions to air, from animal excreta.

Returns

• A list of Emissions with:
  • term with nh3ToAirExcreta
  • methodModel with emepEea2019
  • value
  • methodTier with tier 2

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or permanent pasture or animal housing or pond or river or stream or lake or sea or ocean
  • a list of inputs with:
    • value and term of termType = excreta and term of units = kg N and a list of properties with:
      • value and term with totalAmmoniacalNitrogenContentAsN
    • term of termType = excreta
  • a list of practices with:
    • value and term of termType = excretaManagement

This model works on the following Node type with identical requirements:

• Cycle
• Transformation

Lookup used

• excretaManagement-excreta-NH3_EF_2019.csv -> product @id
• emission.csv -> siteTypesAllowed; inputTermTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.emepEea2019 import run

print(run('nh3ToAirExcreta', Cycle))

View source on Gitlab

NH3, to air, inorganic fertiliser

Ammonia emissions to air, from inorganic fertiliser volatilization.

Returns

• A list of Emissions with:
  • term with nh3ToAirInorganicFertiliser
  • methodModel with emepEea2019
  • value
  • methodTier with tier 2

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture and a country with:
      • termType = region and a list of measurements with:
      • value and term with soilPh
      • value and term with temperatureAnnual
  • either:
    • a list of inputs with:
      • value and term of termType = inorganicFertiliser or inorganicNitrogenFertiliserUnspecifiedKgN
    • Data completeness assessment for fertiliser: completeness.fertiliser must be True

Lookup used

• inorganicFertiliser.csv -> NH3_emissions_factor_acidic; NH3_emissions_factor_basic
• region-inorganicFertiliser-fertGroupingNitrogen-breakdown.csv
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.emepEea2019 import run

print(run('nh3ToAirInorganicFertiliser', Cycle))

View source on Gitlab

NOx, to air, fuel combustion

Nitrogen oxides emissions to air, from the combustion of fuel.

Returns

• A list of Emissions with:
  • term with noxToAirFuelCombustion
  • methodModel with emepEea2019
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • either:
    • a list of inputs with:
      • value and term of termType = fuel and optional:
    • Data completeness assessment for electricityFuel: completeness.electricityFuel must be True

This model works on the following Node type with identical requirements:

• Cycle
• Transformation
• Transport

Lookup used

• fuel.csv -> noxToAirFuelCombustionEmepEea2019
• operation.csv -> noxToAirFuelCombustionEmepEea2019
• emission.csv -> typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.emepEea2019 import run

print(run('noxToAirFuelCombustion', Cycle))

View source on Gitlab

SO2, to air, fuel combustion

Sulfur dioxide emissions to air, from the combustion of fuel.

Returns

• A list of Emissions with:
  • term with so2ToAirFuelCombustion
  • methodModel with emepEea2019
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • either:
    • a list of inputs with:
      • value and term of termType = fuel and optional:
    • Data completeness assessment for electricityFuel: completeness.electricityFuel must be True

This model works on the following Node type with identical requirements:

• Cycle
• Transformation
• Transport

Lookup used

• fuel.csv -> so2ToAirFuelCombustionEmepEea2019
• operation.csv -> so2ToAirFuelCombustionEmepEea2019
• emission.csv -> typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.emepEea2019 import run

print(run('so2ToAirFuelCombustion', Cycle))

View source on Gitlab

Emission not relevant

The emission is not relevant for this Site, Cycle, or Product.

Returns

• A list of Emissions with:
  • methodModel with emissionNotRelevant
  • value with 0
  • methodTier with not relevant

Requirements

• A Cycle with:
  • a list of emissions

Lookup used

• emission.csv -> inHestiaDefaultSystemBoundary

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.emissionNotRelevant import run

print(run('all', Cycle))

View source on Gitlab

Environmental Footprint v3

This method characterises emissions and resource uses as described in the European Commission's Environmental Footprint v3 guidelines. For chemicals' toxicity in particular, it follows the USEtox model, version 2.1 with slight modifications to build freshwater ecotoxicity characterisations from HC20-EC10eq values (Owsianiak et al (2023) and Sala et al (2022)). The HC20-EC10eq value represents the hazardous concentration of a chemical at which 20% of the species considered are exposed to a concentration above their EC10.

View source on Gitlab

Freshwater ecotoxicity potential (CTUe)

The potential of chemicals to cause toxic effects in freshwater ecosystems, expressed as an estimate of the potentially affected fraction of species (PAF) integrated over time and volume. This unit is also referred to as Comparative Toxic Unit for ecosystems (CTUe).

Returns

• A Indicator with:
  • term with freshwaterEcotoxicityPotentialCtue
  • methodModel with environmentalFootprintV3
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> pafM3DFreshwaterEcotoxicityUsetox2-1Hc20Ec10eq

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.environmentalFootprintV3 import run

print(run('freshwaterEcotoxicityPotentialCtue', ImpactAssessment))

View source on Gitlab

EPA (2014)

These models calculate direct and indirect greenhouse gas emissions using the methodology detailed in the EPA (2014) guidelines.

View source on Gitlab

NO3, to groundwater, excreta

Nitrate leaching to groundwater, from animal excreta.

Returns

• A list of Emissions with:
  • term with no3ToGroundwaterExcreta
  • methodModel with epa2014
  • value
  • methodTier with tier 2

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or permanent pasture or animal housing or pond or river or stream or lake or sea or ocean
  • a list of inputs with:
    • term of termType = excreta
  • a list of practices with:
    • value and term of termType = excretaManagement

This model works on the following Node type with identical requirements:

• Cycle
• Transformation

Lookup used

• excretaManagement.csv -> EF_NO3-N
• emission.csv -> siteTypesAllowed; inputTermTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.epa2014 import run

print(run('no3ToGroundwaterExcreta', Cycle))

View source on Gitlab

FAOSTAT (2018)

These models uses data from the FAOSTAT database (accessed in 2018) to gap fill values like seed based on crop yield.

View source on Gitlab

Cold carcass weight per head

The average cold carcass weight of the animals per head.

Returns

• A list of Products with:
  • a list of properties with:
    • term with coldCarcassWeightPerHead
    • methodModel with faostat2018
    • value

Requirements

• A Cycle with:
  • endDate
  • a list of products with:
    • value and term of termType = animalProduct and term of units = kg cold carcass weight
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-animalProduct-animalProductGroupingFAO-productionQuantity.csv -> production quantity
• region-animalProduct-animalProductGroupingFAO-head.csv -> number of heads

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.faostat2018 import run

print(run('coldCarcassWeightPerHead', Cycle))

View source on Gitlab

Cold dressed carcass weight per head

The cold dressed carcass weight (i.e., excluding offal and slaughter fats) of the animals per head.

Returns

• A list of Products with:
  • a list of properties with:
    • term with coldDressedCarcassWeightPerHead
    • methodModel with faostat2018
    • value

Requirements

• A Cycle with:
  • endDate
  • a list of products with:
    • value and term of termType = animalProduct and term of units = kg cold dressed carcass weight
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-animalProduct-animalProductGroupingFAO-productionQuantity.csv -> production quantity
• region-animalProduct-animalProductGroupingFAO-head.csv -> number of heads

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.faostat2018 import run

print(run('coldDressedCarcassWeightPerHead', Cycle))

View source on Gitlab

Land transformation, from temporary cropland, 100 year average, during Cycle

The amount of land used by this Cycle, that changed use from temporary cropland to the current use in the last 100 years, divided by 100.

Returns

• A list of Indicators with:
  • term with landTransformationFromTemporaryCropland100YearAverageDuringCycle or landTransformationFromPermanentCropland100YearAverageDuringCycle
  • methodModel with faostat2018
  • value

Requirements

• A ImpactAssessment with:
  • a site with:
    • siteType with glass or high accessible cover or permanent pasture or animal housing or pond or agri-food processor or food retailer
  • endDate
  • a country with:
    • termType = region
  • a list of emissionsResourceUses with:
    • value and term with landTransformationFromCropland100YearAverageDuringCycle

Lookup used

• region-faostatCroplandArea.csv
• resourceUse.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.faostat2018 import run

print(run('landTransformationFromCropland100YearAverage', ImpactAssessment))

View source on Gitlab

Land transformation, from temporary cropland, 20 year average, during Cycle

The amount of land used by this Cycle, that changed use from temporary cropland to the current use in the last 20 years, divided by 20.

Returns

• A list of Indicators with:
  • term with landTransformationFromTemporaryCropland20YearAverageDuringCycle or landTransformationFromPermanentCropland20YearAverageDuringCycle
  • methodModel with faostat2018
  • value

Requirements

• A ImpactAssessment with:
  • a site with:
    • siteType with glass or high accessible cover or permanent pasture or animal housing or pond or agri-food processor or food retailer
  • endDate
  • a country with:
    • termType = region
  • a list of emissionsResourceUses with:
    • value and term with landTransformationFromCropland20YearAverageDuringCycle

Lookup used

• region-faostatCroplandArea.csv
• resourceUse.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.faostat2018 import run

print(run('landTransformationFromCropland20YearAverage', ImpactAssessment))

View source on Gitlab

Liveweight per head

The average liveweight of the animals, expressed as kg liveweight per head.

Returns

• A list of Products with:
  • a list of properties with:
    • term with liveweightPerHead
    • methodModel with faostat2018
    • value

Requirements

• A Cycle with:
  • endDate
  • a list of products with:
    • value and term of termType = animalProduct and term of units = kg liveweight
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-animalProduct-animalProductGroupingFAO-productionQuantity.csv -> production quantity
• region-animalProduct-animalProductGroupingFAO-head.csv -> number of heads

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.faostat2018 import run

print(run('liveweightPerHead', Cycle))

View source on Gitlab

Product Price

Calculates the price of crop and liveAnimal using FAOSTAT data.

Returns

• A list of Products with:
  • methodModel with faostat2018
  • price

Requirements

• A Cycle with:
  • a list of products with:
    • term of termType = crop or animalProduct or liveAnimal
  • a site with:
    • a country with:
      • termType = region

Lookup used

Depending on the primary product termType:

• crop.csv -> cropGroupingFaostatProduction
• region-crop-cropGroupingFaostatProduction-price.csv -> use value from above
• liveAnimal.csv -> primaryMeatProductFAO
• animalProduct.csv -> animalProductGroupingFAOEquivalent; animalProductGroupingFAO; liveAnimal
• region-animalProduct-animalProductGroupingFAO-price.csv -> use value from above
• region-animalProduct-animalProductGroupingFAO-averageColdCarcassWeight.csv -> use value from above

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.faostat2018 import run

print(run('product.price', Cycle))

View source on Gitlab

Ready-to-cook weight per head

The ready-to-cook weight of the animals per head.

Returns

• A list of Products with:
  • a list of properties with:
    • term with readyToCookWeightPerHead
    • methodModel with faostat2018
    • value

Requirements

• A Cycle with:
  • endDate
  • a list of products with:
    • value and term of termType = animalProduct and term of units = kg ready-to-cook weight
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-animalProduct-animalProductGroupingFAO-productionQuantity.csv -> production quantity
• region-animalProduct-animalProductGroupingFAO-head.csv -> number of heads

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.faostat2018 import run

print(run('readyToCookWeightPerHead', Cycle))

View source on Gitlab

Seed

The seed of a crop.

Returns

• A list of Inputs with:
  • term with seed
  • methodModel with faostat2018
  • value
  • sd
  • statsDefinition with regions

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture
  • a list of products with:
    • term of termType = crop and value > 0
  • Data completeness assessment for other: completeness.other must be False

Lookup used

• crop.csv -> seedPerKgYield; seedPerKgYield-sd
• seed.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.faostat2018 import run

print(run('seed', Cycle))

View source on Gitlab

Geospatial Database

These models use geospatial datasets to extract values based on locations.

View source on Gitlab

Aware Water Basin ID

This model calculates the the AWARE water basin identifier.

Returns

• The AWARE water basin identifier as a string

Requirements

• A Site with:
  • either:
    • the following fields:
      • latitude
      • longitude
    • the following fields:
      • a boundary
    • the following fields:
      • a region with:
        • =

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('aware', Site))

View source on Gitlab

Clay content

The clay content of the topsoil. Clay is defined as particles of less than 0.002mm in size.

Returns

• A list of Measurements with:
  • term with clayContent
  • value
  • depthUpper with 0
  • depthLower with 30
  • methodClassification with geospatial dataset

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture
  • either:
    • the following fields:
      • latitude
      • longitude
    • the following fields:
      • a boundary
    • the following fields:
      • a region with:
        • =
  • none of:
    • a list of measurements with:
      • term with clayContent or sandContent or siltContent

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('clayContent', Site))

View source on Gitlab

Cropping intensity

A metric of multi-cropping. If calculated from area, it is the maximum monthly growing area divided by the total cropland area.

Returns

• A list of Practices with:
  • term with croppingIntensity
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover and either:
      • the following fields:
        • a
      • the following fields:
        • a
  • a list of products with:
    • primary = True and term of termType = crop

Lookup used

• crop.csv -> isPlantation
• region-landUseManagement.csv -> croppingIntensity
• landUseManagement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('croppingIntensity', Cycle))

View source on Gitlab

Drainage Class

A six level factor describing drainage class based on soil type, texture, soil phase, and terrain slope. Defined in the Harmonized World Soil Database.

Returns

• A list of Measurements with:
  • term with drainageClass
  • value
  • methodClassification with geospatial dataset

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture
  • either:
    • the following fields:
      • latitude
      • longitude
    • the following fields:
      • a boundary
    • the following fields:
      • a region with:
        • =

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('drainageClass', Site))

View source on Gitlab

Eco-Climate Zone

A grouping areas based on their ecology and climate, defined by the JRC.

They approximataely map to the IPCC (2019) Climate Zones. Data are derived from Hiederer et al. (2010) Biofuels: A new methodology to estimate GHG emissions from global land use change, European Commission Joint Research Centre.

Value	Climate Zone
1	Warm Temperate Moist
2	Warm Temperate Dry
3	Cool Temperate Moist
4	Cool Temperate Dry
5	Polar Moist
6	Polar Dry
7	Boreal Moist
8	Boreal Dry
9	Tropical Montane
10	Tropical Wet
11	Tropical Moist
12	Tropical Dry

Returns

• A list of Measurements with:
  • term with ecoClimateZone
  • value
  • description
  • methodClassification with geospatial dataset

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture or animal housing or pond or river or stream or lake or sea or ocean or agri-food processor
  • either:
    • the following fields:
      • latitude
      • longitude
    • the following fields:
      • a boundary
    • the following fields:
      • a region with:
        • =

Lookup used

• region-measurment.csv -> ecoClimateZone
• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('ecoClimateZone', Site))

View source on Gitlab

Ecoregion

Ecoregions represent the original distribution of distinct assemblages of species and communities. There are 867 terrestrial ecoregions as defined by WWF.

Returns

• The WWF Terrestrial Ecoregion name as a string

Requirements

• A Site with:
  • either:
    • the following fields:
      • latitude
      • longitude
    • the following fields:
      • a boundary
    • the following fields:
      • a region with:
        • =

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('ecoregion', Site))

View source on Gitlab

Erodibility

The erodibility is a quantitative estimate of the vulnerability of the soil to erosion.

Returns

• A list of Measurements with:
  • term with erodibility
  • value
  • methodClassification with geospatial dataset

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture
  • either:
    • the following fields:
      • latitude
      • longitude
    • the following fields:
      • a boundary
    • the following fields:
      • a region with:
        • =

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('erodibility', Site))

View source on Gitlab

Heavy winter precipitation

At least one winter month where precipitation exceeds 15 % of the annual average.

Returns

• A list of Measurements with:
  • term with heavyWinterPrecipitation
  • value
  • methodClassification with geospatial dataset

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or permanent pasture
  • either:
    • the following fields:
      • latitude
      • longitude
    • the following fields:
      • a boundary
    • the following fields:
      • a region with:
        • =

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('heavyWinterPrecipitation', Site))

View source on Gitlab

Histosol

Soils having organic material: 1. starting at the soil surface and having a thickness of ≥ 10 cm and directly overlying: a. ice, or b. continuous rock or technic hard material, or c. coarse fragments, the interstices of which are filled with organic material; or 2. starting ≤ 40 cm from the soil surface and having within ≤ 100 cm of the soil surface a combined thickness of either: a. ≥ 60 cm, if ≥ 75% (by volume) of the material consists of moss fibres; or b. ≥ 40 cm in other materials. The area of the Site occupied by histosols can be specified as a percentage.

Returns

• A list of Measurements with:
  • term with histosol
  • value
  • methodClassification with geospatial dataset

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture
  • either:
    • the following fields:
      • latitude
      • longitude
    • the following fields:
      • a boundary
    • the following fields:
      • a region with:
        • =
  • none of:
    • a list of measurements with:
      • value and term of termType = soilType

Lookup used

• soilType.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('histosol', Site))

View source on Gitlab

Long fallow ratio

The time/area under both cultivation and long fallow relative to the time/area under cultivation. If calculated from time based terms, it is "rotationDuration / (rotationDuration - longFallowPeriod)". If calculated from area, it is the total cropland area divided by the area harvested in a year (where total cropland area included areas under long fallow). Long fallow is defined as areas left fallow for more than a year but less than five years.

Returns

• A list of Practices with:
  • term with longFallowRatio
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover and either:
      • the following fields:
        • a
      • the following fields:
        • a
  • a list of products with:
    • primary = True and term of termType = crop

Lookup used

• crop.csv -> isPlantation
• region-landUseManagement.csv -> longFallowRatio
• landUseManagement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('longFallowRatio', Cycle))

View source on Gitlab

Nutrient loss to aquatic environment

The percentage of nutrients reaching the aquatic environment. Defined in Scherer & Pfister (2015).

Returns

• A list of Measurements with:
  • term with nutrientLossToAquaticEnvironment
  • value
  • methodClassification with geospatial dataset

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture
  • either:
    • the following fields:
      • latitude
      • longitude
    • the following fields:
      • a boundary
    • the following fields:
      • a region with:
        • =

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('nutrientLossToAquaticEnvironment', Site))

View source on Gitlab

Organic carbon (per kg soil)

The concentration of organic carbon in the soil.

Returns

• A list of Measurements with:
  • term with organicCarbonPerKgSoil
  • value
  • depthUpper with 0
  • depthLower with 30
  • methodClassification with geospatial dataset

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture
  • either:
    • the following fields:
      • latitude
      • longitude
    • the following fields:
      • a boundary
    • the following fields:
      • a region with:
        • =

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('organicCarbonPerKgSoil', Site))

View source on Gitlab

Potential evapotranspiration (annual)

The total annual potential evapotranspiration, expressed in mm / year.

Must be associated with at least 1 Cycle that has an endDate after 1958 and before 2021.

Returns

• A list of Measurements with:
  • term with potentialEvapotranspirationAnnual
  • value
  • startDate
  • endDate
  • methodClassification with geospatial dataset

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture or animal housing or pond or river or stream or lake or sea or ocean
  • either:
    • the following fields:
      • latitude
      • longitude
    • the following fields:
      • a boundary
    • the following fields:
      • a region with:
        • =

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('potentialEvapotranspirationAnnual', Site))

View source on Gitlab

Potential evapotranspiration (long-term annual mean)

The long-term average annual potential evapotranspiration, averaged over all years in the recent climate record.

Returns

• A list of Measurements with:
  • term with potentialEvapotranspirationLongTermAnnualMean
  • value
  • startDate
  • endDate
  • methodClassification with geospatial dataset

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture or animal housing or pond or river or stream or lake or sea or ocean
  • either:
    • the following fields:
      • latitude
      • longitude
    • the following fields:
      • a boundary
    • the following fields:
      • a region with:
        • =

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('potentialEvapotranspirationLongTermAnnualMean', Site))

View source on Gitlab

Precipitation (annual)

The total annual precipitation (defined as the sum of rainfall, sleet, snow, and hail, but excluding fog, cloud, and dew), expressed in mm / year.

Must be associated with at least 1 Cycle that has an endDate after 1979-01-01 and before 2020-06-01.

Returns

• A list of Measurements with:
  • term with precipitationAnnual
  • value
  • startDate
  • endDate
  • methodClassification with geospatial dataset

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture or animal housing or pond or river or stream or lake or sea or ocean
  • either:
    • the following fields:
      • latitude
      • longitude
    • the following fields:
      • a boundary
    • the following fields:
      • a region with:
        • =

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('precipitationAnnual', Site))

View source on Gitlab

Precipitation (long-term annual mean)

The long-term average annual precipitation (defined as the sum of rainfall, sleet, snow, and hail, but excluding fog, cloud, and dew) on the Site. A mean of all available years in the recent climate record.

Returns

• A list of Measurements with:
  • term with precipitationLongTermAnnualMean
  • value
  • startDate
  • endDate
  • methodClassification with geospatial dataset

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture or animal housing or pond or river or stream or lake or sea or ocean
  • either:
    • the following fields:
      • latitude
      • longitude
    • the following fields:
      • a boundary
    • the following fields:
      • a region with:
        • =

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('precipitationLongTermAnnualMean', Site))

View source on Gitlab

Region

This model finds the region that contains the coordinates provided.

Returns

• A Term with:
  • termType = region

Requirements

• A Site with:
  • latitude
  • longitude

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('region', Site))

View source on Gitlab

Sand content

The sand content of the topsoil. Sand is defined as particles of 2.0-0.05mm in size.

Returns

• A list of Measurements with:
  • term with sandContent
  • value
  • depthUpper with 0
  • depthLower with 30
  • methodClassification with geospatial dataset

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture
  • either:
    • the following fields:
      • latitude
      • longitude
    • the following fields:
      • a boundary
    • the following fields:
      • a region with:
        • =
  • none of:
    • a list of measurements with:
      • term with clayContent or sandContent or siltContent

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('sandContent', Site))

View source on Gitlab

Silt content

The silt content of the topsoil. Silt is defined as particles of 0.05mm to 0.002mm in size.

Returns

• A list of Measurements with:
  • term with siltContent
  • value
  • depthUpper with 0
  • depthLower with 30
  • methodClassification with geospatial dataset

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture
  • either:
    • the following fields:
      • latitude
      • longitude
    • the following fields:
      • a boundary
    • the following fields:
      • a region with:
        • =

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('siltContent', Site))

View source on Gitlab

Slope

The inclination of the land's surface, expressed as a percentage. It is equal to the elevation change divided by the horizontal distance covered ("the rise divided by the run"), multiplied by 100.

Returns

• A list of Measurements with:
  • term with slope
  • value
  • methodClassification with geospatial dataset

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture
  • either:
    • the following fields:
      • latitude
      • longitude
    • the following fields:
      • a boundary
    • the following fields:
      • a region with:
        • =

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('slope', Site))

View source on Gitlab

Slope length

The distance from the point of origin of overland flow to either of the following (whichever is limiting for the major part of the area under consideration): (a) the point where the slope decreases to the extent that deposition begins, or (b) the point where runoff enters a well-defined channel that may be part of a drainage network or a constructed channel such as a terrace or diversion. See Wischmeier and Smith (1978).

Returns

• A list of Measurements with:
  • term with slopeLength
  • value
  • methodClassification with geospatial dataset

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture
  • either:
    • the following fields:
      • latitude
      • longitude
    • the following fields:
      • a boundary
    • the following fields:
      • a region with:
        • =

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('slopeLength', Site))

View source on Gitlab

Soil pH

The pH of the topsoil (the negative log of the hydrogen ion concentration in moles per litre). Add the depth interval and further information about the measurement using the appropriate schema fields.

Returns

• A list of Measurements with:
  • term with soilPh
  • value
  • depthUpper with 0
  • depthLower with 30
  • methodClassification with geospatial dataset

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture
  • either:
    • the following fields:
      • latitude
      • longitude
    • the following fields:
      • a boundary
    • the following fields:
      • a region with:
        • =

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('soilPh', Site))

View source on Gitlab

Temperature (annual)

The annual air temperature, averaged over each day in a year.

Must be associated with at least 1 Cycle that has an endDate after 1979-01-01 and before 2020-06-01.

Returns

• A list of Measurements with:
  • term with temperatureAnnual
  • value
  • startDate
  • endDate
  • methodClassification with geospatial dataset

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture or animal housing or pond or river or stream or lake or sea or ocean
  • either:
    • the following fields:
      • latitude
      • longitude
    • the following fields:
      • a boundary
    • the following fields:
      • a region with:
        • =

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('temperatureAnnual', Site))

View source on Gitlab

Temperature (long-term annual mean)

The long-term average annual air temperature, averaged over each day in the year, averaged over all years in the recent climate record.

Returns

• A list of Measurements with:
  • term with temperatureLongTermAnnualMean
  • value
  • startDate
  • endDate
  • methodClassification with geospatial dataset

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture or animal housing or pond or river or stream or lake or sea or ocean
  • either:
    • the following fields:
      • latitude
      • longitude
    • the following fields:
      • a boundary
    • the following fields:
      • a region with:
        • =

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('temperatureLongTermAnnualMean', Site))

View source on Gitlab

Total nitrogen (per kg soil)

The concentration of organic and mineral nitrogen in the soil.

Returns

• A list of Measurements with:
  • term with totalNitrogenPerKgSoil
  • value
  • depthUpper with 0
  • depthLower with 50
  • methodClassification with geospatial dataset

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture
  • either:
    • the following fields:
      • latitude
      • longitude
    • the following fields:
      • a boundary
    • the following fields:
      • a region with:
        • =

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('totalNitrogenPerKgSoil', Site))

View source on Gitlab

Total phosphorus (per kg soil)

The concentration of organic and inorganic phosphorous in the soil.

Returns

• A list of Measurements with:
  • term with totalPhosphorusPerKgSoil
  • value
  • depthUpper with 0
  • depthLower with 50
  • methodClassification with geospatial dataset

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture
  • either:
    • the following fields:
      • latitude
      • longitude
    • the following fields:
      • a boundary
    • the following fields:
      • a region with:
        • =

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('totalPhosphorusPerKgSoil', Site))

View source on Gitlab

Water depth

The depth of a water body.

Returns

• A list of Measurements with:
  • term with waterDepth
  • value
  • methodClassification with geospatial dataset

Requirements

• A Site with:
  • a siteType = pond or river or stream or lake or sea or ocean
  • either:
    • the following fields:
      • latitude
      • longitude
    • the following fields:
      • a boundary
    • the following fields:
      • a region with:
        • =

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.geospatialDatabase import run

print(run('waterDepth', Site))

View source on Gitlab

Global Crop Water Model (2008)

This model adds rooting depths based on the Global Crop Water Model, see Siebert and Doll (2008).

View source on Gitlab

Rooting depth

The rooting depth of the crop.

Returns

• A list of Products with:
  • a list of properties with:
    • term with rootingDepth
    • methodModel with globalCropWaterModel2008
    • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture
  • functionalUnit with 1 ha
  • a list of products with:
    • value and term of termType = crop
  • optional:
    • Data completeness assessment for water: completeness.water
    • a list of inputs with:
      • value and term of termType = water

Lookup used

• crop.csv -> Rooting_depth_irrigated_m; Rooting_depth_rainfed_m; Rooting_depth_average_m
• property.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.globalCropWaterModel2008 import run

print(run('rootingDepth', Cycle))

View source on Gitlab

View source on Gitlab

Haversine formula

A formula to determine the great-circle distance between two points on a sphere given their longitudes and latitudes.

View source on Gitlab

Transport Value

This model calculates the distance of the Transport linked to the Inputs of the Cycle by calculating the distance between the country of the Cycle and the country of origin of the Input (which must be different).

Returns

• A list of Transports with:
  • methodModel with haversineFormula
  • distance

Requirements

• A Cycle with:
  • a list of inputs with:
    • a country with:
      • termType = region and a list of transports
  • a site with:
    • a country with:
      • termType = region

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.haversineFormula import run

print(run('transport.distance', Cycle))

View source on Gitlab

HYDE 3.2

This model uses the HYDE database v3.2 to calculate land use and land use change.

View source on Gitlab

Land transformation, from cropland, 100 year average, during Cycle

The amount of land used by this Cycle, that changed use from cropland (temporary and permanent) to the current use in the last 100 years, divided by 100.

Returns

• A list of Indicators with:
  • term with landTransformationFromCropland100YearAverageDuringCycle
  • methodModel with hyde32
  • value

Requirements

• A ImpactAssessment with:
  • a site with:
    • siteType with glass or high accessible cover or permanent pasture or animal housing or pond or agri-food processor or food retailer
  • either:
    • a country with:
      • termType = region
    • a site with:
      • a region with:
        • =
  • endDate
  • a product
  • a cycle with:
    • either:
      • if the cycle.functionalUnit = 1 ha, additional properties are required:
        • a list of with:
        • a list of with:
      • for plantations, additional properties are required:
        • a list of with:

Lookup used

One of (depending on site.siteType):

• region-forest-landTransformation100years.csv -> cropland
• region-permanent_pasture-landTransformation100years.csv -> cropland
• region-other_natural_vegetation-landTransformation100years.csv -> cropland
• resourceUse.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.hyde32 import run

print(run('landTransformationFromCropland100YearAverageDuringCycle', ImpactAssessment))

View source on Gitlab

Land transformation, from cropland, 20 year average, during Cycle

The amount of land used by this Cycle, that changed use from cropland (temporary and permanent) to the current use in the last 20 years, divided by 20.

Returns

• A list of Indicators with:
  • term with landTransformationFromCropland20YearAverageDuringCycle
  • methodModel with hyde32
  • value

Requirements

• A ImpactAssessment with:
  • a site with:
    • siteType with glass or high accessible cover or permanent pasture or animal housing or pond or agri-food processor or food retailer
  • either:
    • a country with:
      • termType = region
    • a site with:
      • a region with:
        • =
  • endDate
  • a product
  • a cycle with:
    • either:
      • if the cycle.functionalUnit = 1 ha, additional properties are required:
        • a list of with:
        • a list of with:
      • for plantations, additional properties are required:
        • a list of with:

Lookup used

One of (depending on site.siteType):

• region-forest-landTransformation20years.csv -> cropland
• region-permanent_pasture-landTransformation20years.csv -> cropland
• region-other_natural_vegetation-landTransformation20years.csv -> cropland
• resourceUse.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.hyde32 import run

print(run('landTransformationFromCropland20YearAverageDuringCycle', ImpactAssessment))

View source on Gitlab

Land transformation, from forest, 100 year average, during Cycle

The amount of land used by this Cycle, that changed use from forest to the current use in the last 100 years, divided by 100.

Returns

• A list of Indicators with:
  • term with landTransformationFromForest100YearAverageDuringCycle
  • methodModel with hyde32
  • value

Requirements

• A ImpactAssessment with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture or animal housing or pond or agri-food processor or food retailer
  • either:
    • a country with:
      • termType = region
    • a site with:
      • a region with:
        • =
  • endDate
  • a product
  • a cycle with:
    • either:
      • if the cycle.functionalUnit = 1 ha, additional properties are required:
        • a list of with:
        • a list of with:
      • for plantations, additional properties are required:
        • a list of with:

Lookup used

One of (depending on site.siteType):

• region-cropland-landTransformation100years.csv -> forest
• region-permanent_pasture-landTransformation100years.csv -> forest
• region-other_natural_vegetation-landTransformation100years.csv -> forest
• resourceUse.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.hyde32 import run

print(run('landTransformationFromForest100YearAverageDuringCycle', ImpactAssessment))

View source on Gitlab

Land transformation, from forest, 20 year average, during Cycle

The amount of land used by this Cycle, that changed use from forest to the current use in the last 20 years, divided by 20.

Returns

• A list of Indicators with:
  • term with landTransformationFromForest20YearAverageDuringCycle
  • methodModel with hyde32
  • value

Requirements

• A ImpactAssessment with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture or animal housing or pond or agri-food processor or food retailer
  • either:
    • a country with:
      • termType = region
    • a site with:
      • a region with:
        • =
  • endDate
  • a product
  • a cycle with:
    • either:
      • if the cycle.functionalUnit = 1 ha, additional properties are required:
        • a list of with:
        • a list of with:
      • for plantations, additional properties are required:
        • a list of with:

Lookup used

One of (depending on site.siteType):

• region-cropland-landTransformation20years.csv -> forest
• region-permanent_pasture-landTransformation20years.csv -> forest
• region-other_natural_vegetation-landTransformation20years.csv -> forest
• resourceUse.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.hyde32 import run

print(run('landTransformationFromForest20YearAverageDuringCycle', ImpactAssessment))

View source on Gitlab

Land transformation, from other natural vegetation, 100 year average, during Cycle

The amount of land used by this Cycle, that changed use from other natural vegetation to the current use in the last 100 years, divided by 100.

Returns

• A list of Indicators with:
  • term with landTransformationFromOtherNaturalVegetation100YearAverageDuringCycle
  • methodModel with hyde32
  • value

Requirements

• A ImpactAssessment with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture or animal housing or pond or agri-food processor or food retailer
  • either:
    • a country with:
      • termType = region
    • a site with:
      • a region with:
        • =
  • endDate
  • a product
  • a cycle with:
    • either:
      • if the cycle.functionalUnit = 1 ha, additional properties are required:
        • a list of with:
        • a list of with:
      • for plantations, additional properties are required:
        • a list of with:

Lookup used

One of (depending on site.siteType):

• region-cropland-landTransformation100years.csv -> other natural vegetation
• region-forest-landTransformation100years.csv -> other natural vegetation
• region-permanent_pasture-landTransformation100years.csv -> other natural vegetation
• resourceUse.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.hyde32 import run

print(run('landTransformationFromOtherNaturalVegetation100YearAverageDuringCycle', ImpactAssessment))

View source on Gitlab

Land transformation, from other natural vegetation, 20 year average, during Cycle

The amount of land used by this Cycle, that changed use from other natural vegetation to the current use in the last 20 years, divided by 20.

Returns

• A list of Indicators with:
  • term with landTransformationFromOtherNaturalVegetation20YearAverageDuringCycle
  • methodModel with hyde32
  • value

Requirements

• A ImpactAssessment with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture or animal housing or pond or agri-food processor or food retailer
  • either:
    • a country with:
      • termType = region
    • a site with:
      • a region with:
        • =
  • endDate
  • a product
  • a cycle with:
    • either:
      • if the cycle.functionalUnit = 1 ha, additional properties are required:
        • a list of with:
        • a list of with:
      • for plantations, additional properties are required:
        • a list of with:

Lookup used

One of (depending on site.siteType):

• region-cropland-landTransformation20years.csv -> other natural vegetation
• region-forest-landTransformation20years.csv -> other natural vegetation
• region-permanent_pasture-landTransformation20years.csv -> other natural vegetation
• resourceUse.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.hyde32 import run

print(run('landTransformationFromOtherNaturalVegetation20YearAverageDuringCycle', ImpactAssessment))

View source on Gitlab

Land transformation, from permanent pasture, 100 year average, during Cycle

The amount of land used by this Cycle, that changed use from permanent pasture to the current use in the last 100 years, divided by 100.

Returns

• A list of Indicators with:
  • term with landTransformationFromPermanentPasture100YearAverageDuringCycle
  • methodModel with hyde32
  • value

Requirements

• A ImpactAssessment with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or animal housing or pond or agri-food processor or food retailer
  • either:
    • a country with:
      • termType = region
    • a site with:
      • a region with:
        • =
  • endDate
  • a product
  • a cycle with:
    • either:
      • if the cycle.functionalUnit = 1 ha, additional properties are required:
        • a list of with:
        • a list of with:
      • for plantations, additional properties are required:
        • a list of with:

Lookup used

One of (depending on site.siteType):

• region-cropland-landTransformation100years.csv -> permanent pasture
• region-forest-landTransformation100years.csv -> permanent pasture
• region-other_natural_vegetation-landTransformation100years.csv -> permanent pasture
• resourceUse.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.hyde32 import run

print(run('landTransformationFromPermanentPasture100YearAverageDuringCycle', ImpactAssessment))

View source on Gitlab

Land transformation, from permanent pasture, 20 year average, during Cycle

The amount of land used by this Cycle, that changed use from permanent pasture to the current use in the last 20 years, divided by 20.

Returns

• A list of Indicators with:
  • term with landTransformationFromPermanentPasture20YearAverageDuringCycle
  • methodModel with hyde32
  • value

Requirements

• A ImpactAssessment with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or animal housing or pond or agri-food processor or food retailer
  • either:
    • a country with:
      • termType = region
    • a site with:
      • a region with:
        • =
  • endDate
  • a product
  • a cycle with:
    • either:
      • if the cycle.functionalUnit = 1 ha, additional properties are required:
        • a list of with:
        • a list of with:
      • for plantations, additional properties are required:
        • a list of with:

Lookup used

One of (depending on site.siteType):

• region-cropland-landTransformation20years.csv -> permanent pasture
• region-forest-landTransformation20years.csv -> permanent pasture
• region-other_natural_vegetation-landTransformation20years.csv -> permanent pasture
• resourceUse.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.hyde32 import run

print(run('landTransformationFromPermanentPasture20YearAverageDuringCycle', ImpactAssessment))

View source on Gitlab

Impact Assessment

These models are specific to Impact Assessment.

View source on Gitlab

Emissions

Creates an Indicator for every Emission contained within the ImpactAssesment.cycle. It does this by dividing the Emission amount by the Product amount, and applying an allocation between co-products.

Returns

• A list of Indicators with:
  • term
  • value

Requirements

• A ImpactAssessment with:
  • a product with:
    • a term
  • a cycle with:
    • a list of products with:
      • primary = True and value > 0 and economicValueShare > 0 and a list of emissions with:
      • value

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.impact_assessment import run

print(run('emissions', ImpactAssessment))

View source on Gitlab

Freshwater withdrawals, during Cycle

Withdrawals of water from freshwater lakes, rivers, and aquifers that occur during the Cycle.

Returns

• A list of Indicators with:
  • term with freshwaterWithdrawalsDuringCycle
  • value

Requirements

• A ImpactAssessment with:
  • a product with:
    • a term
  • a cycle with:
    • a list of products with:
      • primary = True and value > 0 and economicValueShare > 0 and either:
      • Data completeness assessment for water: completeness.water must be True
      • a list of inputs with:
        • of = and > 0

Lookup used

• crop.csv -> cropGroupingFAO
• region.csv -> Conveyancing_Efficiency_Annual_crops; Conveyancing_Efficiency_Permanent_crops; Conveyancing_Efficiency_Perennial_crops

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.impact_assessment import run

print(run('freshwaterWithdrawalsDuringCycle', ImpactAssessment))

View source on Gitlab

Freshwater withdrawals, inputs production

Withdrawals of water from freshwater lakes, rivers, and aquifers related to producing the inputs used by this Cycle.

Returns

• A list of Indicators with:
  • term with freshwaterWithdrawalsInputsProduction
  • value

Requirements

• A ImpactAssessment with:
  • a product
  • a cycle with:
    • a list of products with:
      • primary = True and value > 0 and economicValueShare > 0 and a list of inputs with:
      • a impactAssessment with:
        • a list of with:

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.impact_assessment import run

print(run('freshwaterWithdrawalsInputsProduction', ImpactAssessment))

View source on Gitlab

Irrigated

Detects if the Cycle was irrigated.

Returns

• true if the Cycle was irrigated, false otherwise

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • a list of practices with:
      • value > 0 and term of termType = waterRegime

Lookup used

• waterRegime.csv -> irrigated

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.impact_assessment import run

print(run('irrigated', ImpactAssessment))

View source on Gitlab

Land occupation, inputs production

The amount of land required to produce the inputs used by the Cycle, multiplied by the time (in years) that the land was occupied including fallow periods.

Returns

• A list of Indicators with:
  • term with landOccupationInputsProduction
  • value

Requirements

• A ImpactAssessment with:
  • a product
  • a cycle with:
    • a list of products with:
      • primary = True and value > 0 and economicValueShare > 0

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.impact_assessment import run

print(run('landOccupationInputsProduction', ImpactAssessment))

View source on Gitlab

Land transformation, from cropland, 100 year average, inputs production

The amount of land used to produce the inputs used by this Cycle, that changed use from cropland (temporary and permanent) to the current use in the last 100 years, divided by 100.

Returns

• A list of Indicators with:
  • term with landTransformationFromCropland100YearAverageInputsProduction
  • value

Requirements

• A ImpactAssessment with:
  • a site with:
    • siteType with glass or high accessible cover or permanent pasture or animal housing or pond or agri-food processor or food retailer
  • a product
  • a cycle with:
    • a list of products with:
      • primary = True and value > 0 and economicValueShare > 0 and a list of inputs with:
      • a impactAssessment with:
        • a list of with:

Lookup used

• resourceUse.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.impact_assessment import run

print(run('landTransformationFromCropland100YearAverageInputsProduction', ImpactAssessment))

View source on Gitlab

Land transformation, from cropland, 20 year average, inputs production

The amount of land used to produce the inputs used by this Cycle, that changed use from cropland (temporary and permanent) to the current use in the last 20 years, divided by 20.

Returns

• A list of Indicators with:
  • term with landTransformationFromCropland20YearAverageInputsProduction
  • value

Requirements

• A ImpactAssessment with:
  • a site with:
    • siteType with glass or high accessible cover or permanent pasture or animal housing or pond or agri-food processor or food retailer
  • a product
  • a cycle with:
    • a list of products with:
      • primary = True and value > 0 and economicValueShare > 0 and a list of inputs with:
      • a impactAssessment with:
        • a list of with:

Lookup used

• resourceUse.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.impact_assessment import run

print(run('landTransformationFromCropland20YearAverageInputsProduction', ImpactAssessment))

View source on Gitlab

Land transformation, from forest, 100 year average, inputs production

The amount of land used to produce the inputs used by this Cycle, that changed use from forest to the current use in the last 100 years, divided by 100.

Returns

• A list of Indicators with:
  • term with landTransformationFromForest100YearAverageInputsProduction
  • value

Requirements

• A ImpactAssessment with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture or animal housing or pond or agri-food processor or food retailer
  • a product
  • a cycle with:
    • a list of products with:
      • primary = True and value > 0 and economicValueShare > 0 and a list of inputs with:
      • a impactAssessment with:
        • a list of with:

Lookup used

• resourceUse.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.impact_assessment import run

print(run('landTransformationFromForest100YearAverageInputsProduction', ImpactAssessment))

View source on Gitlab

Land transformation, from forest, 20 year average, inputs production

The amount of land used to produce the inputs used by this Cycle, that changed use from forest to the current use in the last 20 years, divided by 20.

Returns

• A list of Indicators with:
  • term with landTransformationFromForest20YearAverageInputsProduction
  • value

Requirements

• A ImpactAssessment with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture or animal housing or pond or agri-food processor or food retailer
  • a product
  • a cycle with:
    • a list of products with:
      • primary = True and value > 0 and economicValueShare > 0 and a list of inputs with:
      • a impactAssessment with:
        • a list of with:

Lookup used

• resourceUse.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.impact_assessment import run

print(run('landTransformationFromForest20YearAverageInputsProduction', ImpactAssessment))

View source on Gitlab

Land transformation, from other natural vegetation, 100 year average, inputs production

The amount of land used to produce the inputs used by this Cycle, that changed use from other natural vegetation to the current use in the last 100 years, divided by 100.

Returns

• A list of Indicators with:
  • term with landTransformationFromOtherNaturalVegetation100YearAverageInputsProduction
  • value

Requirements

• A ImpactAssessment with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture or animal housing or pond or agri-food processor or food retailer
  • a product
  • a cycle with:
    • a list of products with:
      • primary = True and value > 0 and economicValueShare > 0 and a list of inputs with:
      • a impactAssessment with:
        • a list of with:

Lookup used

• resourceUse.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.impact_assessment import run

print(run('landTransformationFromOtherNaturalVegetation100YearAverageInputsProduction', ImpactAssessment))

View source on Gitlab

Land transformation, from other natural vegetation, 20 year average, inputs production

The amount of land used to produce the inputs used by this Cycle, that changed use from other natural vegetation to the current use in the last 20 years, divided by 20.

Returns

• A list of Indicators with:
  • term with landTransformationFromOtherNaturalVegetation20YearAverageInputsProduction
  • value

Requirements

• A ImpactAssessment with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture or animal housing or pond or agri-food processor or food retailer
  • a product
  • a cycle with:
    • a list of products with:
      • primary = True and value > 0 and economicValueShare > 0 and a list of inputs with:
      • a impactAssessment with:
        • a list of with:

Lookup used

• resourceUse.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.impact_assessment import run

print(run('landTransformationFromOtherNaturalVegetation20YearAverageInputsProduction', ImpactAssessment))

View source on Gitlab

Land transformation, from permanent pasture, 100 year average, inputs production

The amount of land used to produce the inputs used by this Cycle, that changed use from permanent pasture to the current use in the last 100 years, divided by 100.

Returns

• A list of Indicators with:
  • term with landTransformationFromPermanentPasture100YearAverageInputsProduction
  • value

Requirements

• A ImpactAssessment with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or animal housing or pond or agri-food processor or food retailer
  • a product
  • a cycle with:
    • a list of products with:
      • primary = True and value > 0 and economicValueShare > 0 and a list of inputs with:
      • a impactAssessment with:
        • a list of with:

Lookup used

• resourceUse.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.impact_assessment import run

print(run('landTransformationFromPermanentPasture100YearAverageInputsProduction', ImpactAssessment))

View source on Gitlab

Land transformation, from permanent pasture, 20 year average, inputs production

The amount of land used to produce the inputs used by this Cycle, that changed use from permanent pasture to the current use in the last 20 years, divided by 20.

Returns

• A list of Indicators with:
  • term with landTransformationFromPermanentPasture20YearAverageInputsProduction
  • value

Requirements

• A ImpactAssessment with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or animal housing or pond or agri-food processor or food retailer
  • a product
  • a cycle with:
    • a list of products with:
      • primary = True and value > 0 and economicValueShare > 0 and a list of inputs with:
      • a impactAssessment with:
        • a list of with:

Lookup used

• resourceUse.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.impact_assessment import run

print(run('landTransformationFromPermanentPasture20YearAverageInputsProduction', ImpactAssessment))

View source on Gitlab

Organic

Detects if the Cycle has an organic label.

Returns

• true if the Cycle has an organic label, false otherwise

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • a list of practices with:
      • value and term of termType = standardsLabels

Lookup used

• standardLabels.csv -> isOrganic

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.impact_assessment import run

print(run('organic', ImpactAssessment))

View source on Gitlab

Post Checks

List of models to run after any other model on an ImpactAssessment.

View source on Gitlab

Post Checks Cycle

This model is run only if the pre model has been run before. This model will restore the impactAssessment.cycle as a "linked node" (i.e. it will be set with only @type, @id and name keys).

Returns

• A ImpactAssessment with:
  • a cycle

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • @id must be set (is linked to an existing Cycle)

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.impact_assessment import run

print(run(ImpactAssessment))

View source on Gitlab

Post Checks Site

This model is run only if the pre model has been run before. This model will restore the impactAssessment.site as a "linked node" (i.e. it will be set with only @type, @id and name keys).

Returns

• A ImpactAssessment with:
  • a site

Requirements

• A ImpactAssessment with:
  • a site with:
    • @id must be set (is linked to an existing Site)

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.impact_assessment import run

print(run(ImpactAssessment))

View source on Gitlab

Pre Checks

List of models to run before any other model on an ImpactAssessment.

View source on Gitlab

Pre Checks Cycle

Some ImpactAssessment models need a full version of the linked Cycle to run. This model will fetch the complete version of the Cycle and include it.

Returns

• A ImpactAssessment with:
  • a cycle

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • @id must be set (is linked to an existing Cycle)

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.impact_assessment import run

print(run(ImpactAssessment))

View source on Gitlab

Pre Checks Site

Some ImpactAssessment models need a full version of the linked Site to run. This model will fetch the complete version of the Site and include it.

Returns

• A ImpactAssessment with:
  • a site

Requirements

• A ImpactAssessment with:
  • a site with:
    • @id must be set (is linked to an existing Site)

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.impact_assessment import run

print(run(ImpactAssessment))

View source on Gitlab

Product Economic Value Share

Returns the economicValueShare of the Product linked to the Cycle.

Returns

• A Product with:
  • economicValueShare

Requirements

• A ImpactAssessment with:
  • a product with:
    • a term and none of:
      • economicValueShare
  • a cycle with:
    • a list of products with:
      • economicValueShare

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.impact_assessment import run

print(run('product.economicValueShare', ImpactAssessment))

View source on Gitlab

Product Value

Returns the value of the Product linked to the Cycle.

Returns

• A Product with:
  • value

Requirements

• A ImpactAssessment with:
  • a product with:
    • a term and none of:
      • value
  • a cycle with:
    • a list of products with:
      • value

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.impact_assessment import run

print(run('product.value', ImpactAssessment))

View source on Gitlab

IPCC (2006)

These models, described in the IPCC (2006) guidelines, calculate direct and indirect greenhouse gas emissions and provide data for lookup tables.

View source on Gitlab

Above ground crop residue, removed

The amount of above ground crop residue dry matter that was removed as part of the crop residue management practice. The amount of discarded crop is not included and should be recorded separately.

Returns

• A list of Products with:
  • term with aboveGroundCropResidueRemoved
  • methodModel with ipcc2006
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover
  • a list of products with:
    • primary = True and optional:
      • a list of properties with:
        • and with
    • term of termType = crop or forage
  • Data completeness assessment for cropResidue: completeness.cropResidue must be False

Lookup used

• crop.csv -> isAboveGroundCropResidueRemoved
• cropResidue.csv -> siteTypesAllowed; productTermTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2006 import run

print(run('aboveGroundCropResidueRemoved', Cycle))

View source on Gitlab

Above ground crop residue, total

The total amount of above ground crop residue as dry matter. This total is the value prior to crop residue management practices (for example, burning or removal). Properties can be added, such as the nitrogen content. The amount of discarded crop is not included and should be recorded separately.

Returns

• A list of Products with:
  • term with aboveGroundCropResidueTotal
  • methodModel with ipcc2006
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover
  • a list of products with:
    • primary = True and optional:
      • a list of properties with:
        • and with
    • term of termType = crop or forage
  • Data completeness assessment for cropResidue: completeness.cropResidue must be False

Lookup used

• crop.csv -> Crop_residue_intercept; Crop_residue_slope
• cropResidue.csv -> siteTypesAllowed; productTermTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2006 import run

print(run('aboveGroundCropResidueTotal', Cycle))

View source on Gitlab

Below ground crop residue

The total amount of below ground crop residue as dry matter. Properties can be added, such as the nitrogen composition.

Returns

• A list of Products with:
  • term with belowGroundCropResidue
  • methodModel with ipcc2006
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover
  • a list of products with:
    • primary = True and optional:
      • a list of properties with:
        • and with
    • term of termType = crop or forage
  • Data completeness assessment for cropResidue: completeness.cropResidue must be False

Lookup used

• crop.csv -> Crop_residue_intercept; Crop_residue_slope; IPCC_2019_Ratio_BGRes_AGRes
• cropResidue.csv -> siteTypesAllowed; productTermTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2006 import run

print(run('belowGroundCropResidue', Cycle))

View source on Gitlab

CO2, to air, organic soil cultivation

Carbon dioxide emissions to air, from organic soil (histosol) cultivation.

Returns

• A list of Emissions with:
  • term with co2ToAirOrganicSoilCultivation
  • methodModel with ipcc2006
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture and a list of measurements with:
      • value and term with histosol
      • value and term with ecoClimateZone
  • a list of products with:
    • primary = True and value > 0 and economicValueShare > 0
  • optional:
    • cycleDuration

Lookup used

• crop.csv -> isPlantation
• ecoClimateZone.csv -> IPCC_2006_ORGANIC_SOILS_TONNES_CO2-C_HECTARE
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2006 import run

print(run('co2ToAirOrganicSoilCultivation', Cycle))

View source on Gitlab

N2O, to air, crop residue decomposition, direct

Nitrous oxide emissions to air, from crop residue decomposition.

Returns

• A list of Emissions with:
  • term with n2OToAirCropResidueDecompositionDirect
  • methodModel with ipcc2006
  • value
  • min
  • max
  • methodTier with tier 1
  • statsDefinition with modelled

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover
  • Data completeness assessment for cropResidue: completeness.cropResidue must be True
  • a list of products with:
    • value and term of termType = cropResidue and a list of properties with:
      • value and term with nitrogenContent
  • optional:
    • a list of products with:
      • term with riceGrainInHuskFlooded

Lookup used

• cropResidue.csv -> decomposesOnField
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2006 import run

print(run('n2OToAirCropResidueDecompositionDirect', Cycle))

View source on Gitlab

N2O, to air, crop residue decomposition, indirect

Nitrous oxide emissions to air, indirectly created from NOx, NH3, and NO3 emissions, from crop residue decomposition.

Returns

• A list of Emissions with:
  • term with n2OToAirCropResidueDecompositionIndirect
  • methodModel with ipcc2006
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover
  • Data completeness assessment for cropResidue: completeness.cropResidue must be True
  • a list of emissions with:
    • value and term with no3ToGroundwaterCropResidueDecomposition
    • value and term with nh3ToAirCropResidueDecomposition
    • value and term with noxToAirCropResidueDecomposition

Lookup used

• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2006 import run

print(run('n2OToAirCropResidueDecompositionIndirect', Cycle))

View source on Gitlab

N2O, to air, excreta, direct

Nitrous oxide emissions to air, from animal excreta.

Returns

• A list of Emissions with:
  • term with n2OToAirExcretaDirect
  • methodModel with ipcc2006
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or permanent pasture or animal housing or pond or river or stream or lake or sea or ocean
  • a list of inputs with:
    • value and term of termType = excreta and optional:
      • a list of properties with:
        • and with
    • term of termType = excreta
  • Data completeness assessment for excreta: completeness.excreta must be True

This model works on the following Node type with identical requirements:

• Cycle
• Transformation

Lookup used

• emission.csv -> siteTypesAllowed; inputTermTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2006 import run

print(run('n2OToAirExcretaDirect', Cycle))

View source on Gitlab

N2O, to air, excreta, indirect

Nitrous oxide emissions to air, indirectly created from NOx and NH3 emissions, from animal excreta.

Returns

• A list of Emissions with:
  • term with n2OToAirExcretaIndirect
  • methodModel with ipcc2006
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or permanent pasture or animal housing or pond or river or stream or lake or sea or ocean
  • a list of inputs with:
    • term of termType = excreta
  • Data completeness assessment for excreta: completeness.excreta must be True
  • a list of emissions with:
    • value and term with no3ToGroundwaterExcreta
    • value and term with nh3ToAirExcreta
    • value and term with noxToAirExcreta

This model works on the following Node type with identical requirements:

• Cycle
• Transformation

Lookup used

• emission.csv -> siteTypesAllowed; inputTermTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2006 import run

print(run('n2OToAirExcretaIndirect', Cycle))

View source on Gitlab

N2O, to air, inorganic fertiliser, direct

Nitrous oxide emissions to air, from nitrification and denitrification of inorganic fertiliser.

Returns

• A list of Emissions with:
  • term with n2OToAirInorganicFertiliserDirect
  • methodModel with ipcc2006
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture
  • Data completeness assessment for fertiliser: completeness.fertiliser must be True
  • a list of inputs with:
    • value and term of units = kg or kg N and term of termType = inorganicFertiliser and optional:
      • a list of properties with:
        • and with

Lookup used

• crop.csv -> N2ON_FERT
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2006 import run

print(run('n2OToAirInorganicFertiliserDirect', Cycle))

View source on Gitlab

N2O, to air, inorganic fertiliser, indirect

Nitrous oxide emissions to air, indirectly created from NOx, NH3, and NO3 emissions, from inorganic fertiliser.

Returns

• A list of Emissions with:
  • term with n2OToAirInorganicFertiliserIndirect
  • methodModel with ipcc2006
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture
  • Data completeness assessment for fertiliser: completeness.fertiliser must be True
  • a list of inputs with:
    • value and term of units = kg or kg N and term of termType = inorganicFertiliser and optional:
      • a list of properties with:
        • and with
  • a list of emissions with:
    • value and term with no3ToGroundwaterInorganicFertiliser
    • value and term with nh3ToAirInorganicFertiliser
    • value and term with noxToAirInorganicFertiliser

Lookup used

• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2006 import run

print(run('n2OToAirInorganicFertiliserIndirect', Cycle))

View source on Gitlab

N2O, to air, organic fertiliser, direct

Nitrous oxide emissions to air, from nitrification and denitrification of organic fertiliser.

Returns

• A list of Emissions with:
  • term with n2OToAirOrganicFertiliserDirect
  • methodModel with ipcc2006
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture
  • Data completeness assessment for fertiliser: completeness.fertiliser must be True
  • a list of inputs with:
    • value and term of termType = organicFertiliser and optional:
      • a list of properties with:
        • and with

Lookup used

• crop.csv -> N2ON_FERT
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2006 import run

print(run('n2OToAirOrganicFertiliserDirect', Cycle))

View source on Gitlab

N2O, to air, organic fertiliser, indirect

Nitrous oxide emissions to air, indirectly created from NOx, NH3, and NO3 emissions, from organic fertiliser.

Returns

• A list of Emissions with:
  • term with n2OToAirOrganicFertiliserIndirect
  • methodModel with ipcc2006
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture
  • Data completeness assessment for fertiliser: completeness.fertiliser must be True
  • a list of inputs with:
    • value and term of termType = organicFertiliser and optional:
      • a list of properties with:
        • and with
  • a list of emissions with:
    • value and term with no3ToGroundwaterOrganicFertiliser
    • value and term with nh3ToAirOrganicFertiliser
    • value and term with noxToAirOrganicFertiliser

Lookup used

• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2006 import run

print(run('n2OToAirOrganicFertiliserIndirect', Cycle))

View source on Gitlab

N2O, to air, organic soil cultivation, direct

Direct nitrous oxide emissions to air, from organic soil (histosol) cultivation.

Returns

• A list of Emissions with:
  • term with n2OToAirOrganicSoilCultivationDirect
  • methodModel with ipcc2006
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture and a list of measurements with:
      • value and term with histosol
      • value and term with ecoClimateZone
  • a list of products with:
    • primary = True and value > 0 and economicValueShare > 0
  • optional:
    • cycleDuration

Lookup used

• crop.csv -> isPlantation
• ecoClimateZone.csv -> IPCC_2006_ORGANIC_SOILS_KG_N2O-N_HECTARE
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2006 import run

print(run('n2OToAirOrganicSoilCultivationDirect', Cycle))

View source on Gitlab

IPCC (2013) excluding feedbacks

These models, described in the IPCC (2013) guidelines, characterise different greenhouse gases into a global warming or global temperature potential. Conversions from each gas to CO2 equivalents exclude climate carbon feedbacks. Climate carbon feedbacks were not included in the IPCC (2007) or earlier guidelines, and were first provided in the IPCC (2013) guidelines in Table 8.7.

View source on Gitlab

GWP100

The global warming potential of mixed greenhouse gases on the mid-term climate (100 years), expressed as CO2 equivalents.

Returns

• A Indicator with:
  • term with gwp100
  • methodModel with ipcc2013ExcludingFeedbacks
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> co2EqGwp100ExcludingClimate-CarbonFeedbacksIpcc2013

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2013ExcludingFeedbacks import run

print(run('gwp100', ImpactAssessment))

View source on Gitlab

IPCC (2013) including feedbacks

These models, described in the IPCC (2013) guidelines, characterise different greenhouse gases into a global warming or global temperature potential. Conversions from each gas to CO2 equivalents include climate carbon feedbacks. Climate carbon feedbacks were not included in the IPCC (2007) or earlier guidelines, and were first provided in the IPCC (2013) guidelines in Table 8.7.

View source on Gitlab

GWP100

The global warming potential of mixed greenhouse gases on the mid-term climate (100 years), expressed as CO2 equivalents.

Returns

• A Indicator with:
  • term with gwp100
  • methodModel with ipcc2013IncludingFeedbacks
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> co2EqGwp100IncludingClimate-CarbonFeedbacksIpcc2013

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2013IncludingFeedbacks import run

print(run('gwp100', ImpactAssessment))

View source on Gitlab

IPCC (2019)

These models, described in the IPCC (2019) refinement to the IPCC (2006) guidelines, calculate direct and indirect greenhouse gas emissions and provide data for lookup tables.

View source on Gitlab

Above ground crop residue, total

The total amount of above ground crop residue as dry matter. This total is the value prior to crop residue management practices (for example, burning or removal). Properties can be added, such as the nitrogen content. The amount of discarded crop is not included and should be recorded separately.

Returns

• A list of Products with:
  • term with aboveGroundCropResidueTotal
  • methodModel with ipcc2019
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover
  • a list of products with:
    • term of termType = crop or forage and value > 0 and optional:
      • a list of properties with:
        • and with
    • term of termType = crop or forage
  • Data completeness assessment for cropResidue: completeness.cropResidue must be False

Lookup used

• crop.csv -> IPCC_2019_Ratio_AGRes_YieldDM
• forage.csv -> IPCC_2019_Ratio_AGRes_YieldDM
• cropResidue.csv -> siteTypesAllowed; productTermTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('aboveGroundCropResidueTotal', Cycle))

View source on Gitlab

Below ground crop residue

The total amount of below ground crop residue as dry matter. Properties can be added, such as the nitrogen composition.

Returns

• A list of Products with:
  • term with belowGroundCropResidue
  • methodModel with ipcc2019
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover
  • a list of products with:
    • term of termType = crop or forage and value > 0 and optional:
      • a list of properties with:
        • and with
    • term of termType = crop or forage
  • Data completeness assessment for cropResidue: completeness.cropResidue must be False

Lookup used

• crop.csv -> IPCC_2019_Ratio_AGRes_YieldDM; IPCC_2019_Ratio_BGRes_AGRes
• forage.csv -> IPCC_2019_Ratio_AGRes_YieldDM; IPCC_2019_Ratio_BGRes_AGRes
• cropResidue.csv -> siteTypesAllowed; productTermTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('belowGroundCropResidue', Cycle))

View source on Gitlab

Carbon content

The total (organic plus mineral) carbon content of something, as C, expressed as a percentage.

Returns

• A list of Products with:
  • a list of properties with:
    • term with carbonContent
    • methodModel with ipcc2019
    • value
    • term with carbonContent

Requirements

• A Cycle with:
  • a list of products with:
    • term of termType = cropResidue
    • term of termType = crop or forage and value > 0 and optional:
      • a list of properties with:
        • and with

Lookup used

• crop.csv -> IPCC_2019_Ratio_AGRes_YieldDM; IPCC_2019_Ratio_BGRes_AGRes; C_CONTENT_AG_CROP_RESIDUE; C_CONTENT_BG_CROP_RESIDUE
• forage.csv -> IPCC_2019_Ratio_AGRes_YieldDM; IPCC_2019_Ratio_BGRes_AGRes; C_CONTENT_AG_CROP_RESIDUE; C_CONTENT_BG_CROP_RESIDUE

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('carbonContent', Cycle))

View source on Gitlab

CH4, to air, enteric fermentation

Methane emissions to air, from enteric fermentation by ruminants.

Returns

• A list of Emissions with:
  • term with ch4ToAirEntericFermentation
  • methodModel with ipcc2019
  • value
  • sd
  • methodTier with tier 2
  • statsDefinition with modelled

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or permanent pasture or animal housing
  • a list of products with:
    • term of termType = liveAnimal or animalProduct
  • Data completeness assessment for animalFeed: completeness.animalFeed must be True
  • Data completeness assessment for freshForage: completeness.freshForage must be True
  • either:
    • the following fields:
      • a list of animals with:
        • a list of with:
    • the following fields:
      • a list of inputs with:
        • of = kg and > 0 and with True and optional:

Lookup used

• animalProduct.csv -> digestibility; percentageYmMethaneConversionFactorEntericFermentation; percentageYmMethaneConversionFactorEntericFermentation-sd; defaultPercentageYmMethaneConversionFactorEntericFermentation; defaultPercentageYmMethaneConversionFactorEntericFermentation-min; defaultPercentageYmMethaneConversionFactorEntericFermentation-max
• liveAnimal.csv -> digestibility; percentageYmMethaneConversionFactorEntericFermentation; percentageYmMethaneConversionFactorEntericFermentation-sd; defaultPercentageYmMethaneConversionFactorEntericFermentation; defaultPercentageYmMethaneConversionFactorEntericFermentation-min; defaultPercentageYmMethaneConversionFactorEntericFermentation-max
• crop-property.csv -> neutralDetergentFibreContent; energyContentHigherHeatingValue
• emission.csv -> siteTypesAllowed; productTermIdsAllowed; productTermTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('ch4ToAirEntericFermentation', Cycle))

View source on Gitlab

CH4, to air, excreta

Methane emissions to air, from animal excreta.

Returns

• A list of Emissions with:
  • term with ch4ToAirExcreta
  • methodModel with ipcc2019
  • value
  • methodTier with tier 2

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or permanent pasture or animal housing or pond or river or stream or lake or sea or ocean and a country with:
      • termType = region and a list of measurements with:
      • value and term with ecoClimateZone
  • a list of inputs with:
    • term of termType = excreta
  • Data completeness assessment for excreta: completeness.excreta
  • cycleDuration
  • endDate
  • a list of practices with:
    • value and term of termType = excretaManagement

This model works on the following Node type with identical requirements:

• Cycle
• Transformation

Lookup used

• region.csv -> HDI
• region-excreta-excretaManagement-ch4B0.csv -> use input @id
• excretaManagement-ecoClimateZone-CH4conv.csv -> use ecoClimateZone from site measurements
• emission.csv -> siteTypesAllowed; inputTermTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('ch4ToAirExcreta', Cycle))

View source on Gitlab

CH4, to air, flooded rice

Methane emissions to air, from flooded paddy rice fields.

Returns

• A list of Emissions with:
  • term with ch4ToAirFloodedRice
  • methodModel with ipcc2019
  • value
  • min
  • max
  • sd
  • methodTier with tier 1
  • statsDefinition with modelled

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland and a country with:
      • termType = region
  • a list of products with:
    • term with ricePlantFlooded or riceGrainInHuskFlooded
  • a list of practices with:
    • value and term with croppingDuration
  • optional:
    • a list of inputs with:
      • value and term of termType = organicFertiliser
    • a list of products with:
      • value and term with aboveGroundCropResidueIncorporated
    • a list of practices with:
      • value and term of termType = cropResidueManagement
      • value and term of termType = landUseManagement
      • value and term of termType = waterRegime

Lookup used

• landUseManagement.csv -> IPCC_2019_CH4_rice_SFw; IPCC_2019_CH4_rice_SFw-min; IPCC_2019_CH4_rice_SFw-max; IPCC_2019_CH4_rice_SFw-sd; IPCC_2019_CH4_rice_SFp; IPCC_2019_CH4_rice_SFp-min; IPCC_2019_CH4_rice_SFp-max; IPCC_2019_CH4_rice_SFp-sd
• waterRegime.csv -> IPCC_2019_CH4_rice_SFw; IPCC_2019_CH4_rice_SFw-min; IPCC_2019_CH4_rice_SFw-max; IPCC_2019_CH4_rice_SFw-sd; IPCC_2019_CH4_rice_SFp; IPCC_2019_CH4_rice_SFp-min; IPCC_2019_CH4_rice_SFp-max; IPCC_2019_CH4_rice_SFp-sd
• organicFertiliser.csv -> IPCC_2019_CH4_rice_CFOA_kg_fresh_weight; IPCC_2019_CH4_rice_CFOA_kg_dry_weight
• region-ch4ef-IPCC2019.csv -> CH4_ef; CH4_ef_min; CH4_ef_max; CH4_ef_sd
• emission.csv -> siteTypesAllowed; productTermIdsAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('ch4ToAirFloodedRice', Cycle))

View source on Gitlab

CO2, to air, lime hydrolysis

Carbon dioxide emissions to air, from lime hydrolysis (including dolomitic lime).

Returns

• A list of Emissions with:
  • term with co2ToAirLimeHydrolysis
  • methodModel with ipcc2019
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture
  • Data completeness assessment for soilAmendments: completeness.soilAmendments
  • a list of inputs with:
    • value and term of termType = soilAmendment and term of units = kg CaCO3 or kg MgCO3

Lookup used

• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('co2ToAirLimeHydrolysis', Cycle))

View source on Gitlab

CO2, to air, soil carbon stock change, management change

Carbon dioxide emissions to air, from soil carbon stock change, caused by changes in Site management (e.g., changes in tillage practice). Stock changes caused by land use change (e.g., a change from forest land to cropland) should be recorded separately by using the term CO2, to air, soil carbon stock change, land use change.

Returns

• A list of Emissions with:
  • term with co2ToAirSoilCarbonStockChangeManagementChange
  • methodModel with ipcc2019
  • value
  • methodTier
  • depth with 30

Requirements

• A Cycle with:
  • a site with:
    • siteType with forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture and a list of measurements with:
      • value and dates and depthUpper with 0 and depthLower with 30 and term with organicCarbonPerHa
  • functionalUnit with 1 ha
  • endDate
  • optional:
    • startDate

Lookup used

• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('co2ToAirSoilCarbonStockChangeManagementChange', Cycle))

View source on Gitlab

CO2, to air, urea hydrolysis

Carbon dioxide emissions to air, from urea hydrolysis (a corresponding negative emission occurs during fertiliser production).

Returns

• A list of Emissions with:
  • term with co2ToAirUreaHydrolysis
  • methodModel with ipcc2019
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture
  • either:
    • Data completeness assessment for fertiliser: completeness.fertiliser
    • a list of inputs with:
      • value and term of termType = inorganicFertiliser
  • optional:
    • a list of inputs with:
      • value and term with inorganicNitrogenFertiliserUnspecifiedKgN

Lookup used

• inorganicFertiliser.csv -> Urea_UAS_Amm_Bicarb; UAN_Solu; CO2_urea_emissions_factor
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('co2ToAirUreaHydrolysis', Cycle))

View source on Gitlab

Cropping duration

For temporary crops, the period from planting to harvest in days. For perennial crops such as asparagus, the period from planting to removal in days. For transplant rice and other crops with a nursery stage, cropping duration excludes the nursery stage.

Returns

• A list of Practices with:
  • term with croppingDuration
  • methodModel with ipcc2019
  • value
  • min
  • max
  • sd
  • statsDefinition with modelled

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture and a country with:
      • termType = region
  • cycleDuration
  • a list of products with:
    • primary = True and value and term with riceGrainInHuskFlooded or ricePlantFlooded and term of termType = crop

Lookup used

• region-ch4ef-IPCC2019.csv -> Rice_croppingDuration_days; Rice_croppingDuration_days_min; Rice_croppingDuration_days_max; Rice_croppingDuration_days_sd
• landUseManagement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('croppingDuration', Cycle))

View source on Gitlab

Lignin content

The lignin content of something, expressed as a percentage.

Returns

• A list of Products with:
  • a list of properties with:
    • term with ligninContent
    • methodModel with ipcc2019
    • value
    • term with ligninContent

Requirements

• A Cycle with:
  • a list of products with:
    • term of termType = cropResidue
    • term of termType = crop or forage and value > 0 and optional:
      • a list of properties with:
        • and with

Lookup used

• crop.csv -> IPCC_2019_Ratio_AGRes_YieldDM; IPCC_2019_Ratio_BGRes_AGRes; LIGNIN_CONTENT_AG_CROP_RESIDUE; LIGNIN_CONTENT_BG_CROP_RESIDUE
• forage.csv -> IPCC_2019_Ratio_AGRes_YieldDM; IPCC_2019_Ratio_BGRes_AGRes; LIGNIN_CONTENT_AG_CROP_RESIDUE; LIGNIN_CONTENT_BG_CROP_RESIDUE

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('ligninContent', Cycle))

View source on Gitlab

N2O, to air, crop residue decomposition, direct

Nitrous oxide emissions to air, from crop residue decomposition.

Returns

• A list of Emissions with:
  • term with n2OToAirCropResidueDecompositionDirect
  • methodModel with ipcc2019
  • value
  • min
  • max
  • sd
  • methodTier with tier 1
  • statsDefinition with modelled
  • methodModelDescription with Aggregated version or Disaggregated version

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover
  • Data completeness assessment for cropResidue: completeness.cropResidue must be True
  • a list of products with:
    • value and term of termType = cropResidue and a list of properties with:
      • value and term with nitrogenContent
  • optional:
    • endDate
    • a site with:
      • a list of measurements with:
        • and with
    • a list of products with:
      • term with riceGrainInHuskFlooded
    • a list of practices with:
      • term of termType = waterRegime

Lookup used

• cropResidue.csv -> decomposesOnField
• waterRegime.csv -> IPCC_2019_N2O_rice; IPCC_2019_N2O_rice-min; IPCC_2019_N2O_rice-max
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('n2OToAirCropResidueDecompositionDirect', Cycle))

View source on Gitlab

N2O, to air, crop residue decomposition, indirect

Nitrous oxide emissions to air, indirectly created from NOx, NH3, and NO3 emissions, from crop residue decomposition.

Returns

• A list of Emissions with:
  • term with n2OToAirCropResidueDecompositionIndirect
  • methodModel with ipcc2019
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover
  • Data completeness assessment for cropResidue: completeness.cropResidue must be True
  • a list of emissions with:
    • value and term with no3ToGroundwaterCropResidueDecomposition
    • value and term with nh3ToAirCropResidueDecomposition
    • value and term with noxToAirCropResidueDecomposition
  • optional:
    • a site with:
      • a list of measurements with:
        • and with

Lookup used

• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('n2OToAirCropResidueDecompositionIndirect', Cycle))

View source on Gitlab

N2O, to air, excreta, direct

Nitrous oxide emissions to air, from animal excreta.

Returns

• A list of Emissions with:
  • term with n2OToAirExcretaDirect
  • methodModel with ipcc2019
  • value
  • methodTier with tier 2

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or permanent pasture or animal housing or pond or river or stream or lake or sea or ocean
  • a list of inputs with:
    • term of termType = excreta
  • a list of practices with:
    • value and term of termType = excretaManagement

This model works on the following Node type with identical requirements:

• Cycle
• Transformation

Lookup used

• excretaManagement.csv -> EF_N2O-N
• emission.csv -> siteTypesAllowed; inputTermTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('n2OToAirExcretaDirect', Cycle))

View source on Gitlab

N2O, to air, excreta, indirect

Nitrous oxide emissions to air, indirectly created from NOx and NH3 emissions, from animal excreta.

Returns

• A list of Emissions with:
  • term with n2OToAirExcretaIndirect
  • methodModel with ipcc2019
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or permanent pasture or animal housing or pond or river or stream or lake or sea or ocean
  • a list of inputs with:
    • term of termType = excreta
  • Data completeness assessment for excreta: completeness.excreta must be True
  • a list of emissions with:
    • value and term with no3ToGroundwaterExcreta
    • value and term with nh3ToAirExcreta
    • value and term with noxToAirExcreta
  • optional:
    • a site with:
      • a list of measurements with:
        • and with

This model works on the following Node type with identical requirements:

• Cycle
• Transformation

Lookup used

• emission.csv -> siteTypesAllowed; inputTermTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('n2OToAirExcretaIndirect', Cycle))

View source on Gitlab

N2O, to air, inorganic fertiliser, direct

Nitrous oxide emissions to air, from nitrification and denitrification of inorganic fertiliser.

Returns

• A list of Emissions with:
  • term with n2OToAirInorganicFertiliserDirect
  • methodModel with ipcc2019
  • value
  • min
  • max
  • sd
  • methodTier with tier 1
  • statsDefinition with modelled
  • methodModelDescription with Aggregated version or Disaggregated version

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture
  • Data completeness assessment for fertiliser: completeness.fertiliser must be True
  • a list of inputs with:
    • value and term of units = kg or kg N and term of termType = inorganicFertiliser and optional:
      • a list of properties with:
        • and with
  • optional:
    • endDate
    • a site with:
      • a list of measurements with:
        • and with
    • a list of products with:
      • term with riceGrainInHuskFlooded
    • a list of practices with:
      • term of termType = waterRegime

Lookup used

• waterRegime.csv -> IPCC_2019_N2O_rice; IPCC_2019_N2O_rice-min; IPCC_2019_N2O_rice-max
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('n2OToAirInorganicFertiliserDirect', Cycle))

View source on Gitlab

N2O, to air, inorganic fertiliser, indirect

Nitrous oxide emissions to air, indirectly created from NOx, NH3, and NO3 emissions, from inorganic fertiliser.

Returns

• A list of Emissions with:
  • term with n2OToAirInorganicFertiliserIndirect
  • methodModel with ipcc2019
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture
  • Data completeness assessment for fertiliser: completeness.fertiliser must be True
  • a list of emissions with:
    • value and term with no3ToGroundwaterInorganicFertiliser
    • value and term with nh3ToAirInorganicFertiliser
    • value and term with noxToAirInorganicFertiliser
  • optional:
    • a site with:
      • a list of measurements with:
        • and with

Lookup used

• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('n2OToAirInorganicFertiliserIndirect', Cycle))

View source on Gitlab

N2O, to air, organic fertiliser, direct

Nitrous oxide emissions to air, from nitrification and denitrification of organic fertiliser.

Returns

• A list of Emissions with:
  • term with n2OToAirOrganicFertiliserDirect
  • methodModel with ipcc2019
  • value
  • min
  • max
  • sd
  • methodTier with tier 1
  • statsDefinition with modelled
  • methodModelDescription with Aggregated version or Disaggregated version

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture
  • Data completeness assessment for fertiliser: completeness.fertiliser must be True
  • a list of inputs with:
    • value and term of units = kg or kg N and term of termType = organicFertiliser and optional:
      • a list of properties with:
        • and with
  • optional:
    • endDate
    • a site with:
      • a list of measurements with:
        • and with
    • a list of products with:
      • term with riceGrainInHuskFlooded
    • a list of practices with:
      • term of termType = waterRegime

Lookup used

• waterRegime.csv -> IPCC_2019_N2O_rice; IPCC_2019_N2O_rice-min; IPCC_2019_N2O_rice-max
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('n2OToAirOrganicFertiliserDirect', Cycle))

View source on Gitlab

N2O, to air, organic fertiliser, indirect

Nitrous oxide emissions to air, indirectly created from NOx, NH3, and NO3 emissions, from organic fertiliser.

Returns

• A list of Emissions with:
  • term with n2OToAirOrganicFertiliserIndirect
  • methodModel with ipcc2019
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture
  • Data completeness assessment for fertiliser: completeness.fertiliser must be True
  • a list of emissions with:
    • value and term with no3ToGroundwaterOrganicFertiliser
    • value and term with nh3ToAirOrganicFertiliser
    • value and term with noxToAirOrganicFertiliser
  • optional:
    • a site with:
      • a list of measurements with:
        • and with

Lookup used

• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('n2OToAirOrganicFertiliserIndirect', Cycle))

View source on Gitlab

NH3, to air, inorganic fertiliser

Ammonia emissions to air, from inorganic fertiliser volatilization.

Returns

• A list of Emissions with:
  • term with nh3ToAirInorganicFertiliser
  • methodModel with ipcc2019
  • value
  • sd
  • min
  • max
  • methodTier with tier 1
  • statsDefinition with modelled
  • methodModelDescription with Aggregated version or Disaggragated version

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture
  • Data completeness assessment for fertiliser: completeness.fertiliser must be True
  • a list of inputs with:
    • value and term of termType = inorganicFertiliser and optional:
      • a list of properties with:
        • and with

Lookup used

• inorganicFertiliser.csv -> IPCC_2019_FRACGASF_NH3-N; IPCC_2019_FRACGASF_NH3-N-min; IPCC_2019_FRACGASF_NH3-N-max
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('nh3ToAirInorganicFertiliser', Cycle))

View source on Gitlab

NH3, to air, organic fertiliser

Ammonia emissions to air, from organic fertiliser volatilization.

Returns

• A list of Emissions with:
  • term with nh3ToAirOrganicFertiliser
  • methodModel with ipcc2019
  • value
  • sd
  • min
  • max
  • methodTier with tier 1
  • statsDefinition with modelled
  • methodModelDescription with Aggregated version

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture
  • Data completeness assessment for fertiliser: completeness.fertiliser must be True
  • a list of inputs with:
    • value and term of termType = organicFertiliser and optional:
      • a list of properties with:
        • and with

Lookup used

• organicFertiliser.csv -> IPCC_2019_FRACGASM_NH3-N; IPCC_2019_FRACGASM_NH3-N-min; IPCC_2019_FRACGASM_NH3-N-max
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('nh3ToAirOrganicFertiliser', Cycle))

View source on Gitlab

Nitrogen content

The total nitrogen content of something, as N, expressed as a percentage.

Returns

• A list of Products with:
  • a list of properties with:
    • term with nitrogenContent
    • methodModel with ipcc2019
    • value
    • term with nitrogenContent

Requirements

• A Cycle with:
  • a list of products with:
    • term of termType = cropResidue
    • term of termType = crop or forage and value > 0 and optional:
      • a list of properties with:
        • and with

Lookup used

• crop.csv -> IPCC_2019_Ratio_AGRes_YieldDM; IPCC_2019_Ratio_BGRes_AGRes; N_Content_AG_Residue; N_Content_BG_Residue
• forage.csv -> IPCC_2019_Ratio_AGRes_YieldDM; IPCC_2019_Ratio_BGRes_AGRes; N_Content_AG_Residue; N_Content_BG_Residue

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('nitrogenContent', Cycle))

View source on Gitlab

NO3, to groundwater, crop residue decomposition

Nitrate leaching to groundwater, from crop residue decomposition.

Returns

• A list of Emissions with:
  • term with no3ToGroundwaterCropResidueDecomposition
  • methodModel with ipcc2019
  • value
  • sd
  • min
  • max
  • methodTier with tier 1
  • statsDefinition with modelled

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover and a list of measurements with:
      • value and term with ecoClimateZone
  • Data completeness assessment for cropResidue: completeness.cropResidue must be True
  • Data completeness assessment for water: completeness.water must be True
  • a list of products with:
    • value and term of termType = cropResidue and a list of properties with:
      • value and term with nitrogenContent
  • optional:
    • a list of practices with:
      • value and term of termType = waterRegime

Lookup used

• cropResidue.csv -> decomposesOnField
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('no3ToGroundwaterCropResidueDecomposition', Cycle))

View source on Gitlab

NO3, to groundwater, excreta

Nitrate leaching to groundwater, from animal excreta.

Returns

• A list of Emissions with:
  • term with no3ToGroundwaterExcreta
  • methodModel with ipcc2019
  • value
  • sd
  • min
  • max
  • methodTier with tier 1
  • statsDefinition with modelled

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or permanent pasture or animal housing or pond or river or stream or lake or sea or ocean and a list of measurements with:
      • value and term with ecoClimateZone
  • a list of inputs with:
    • value and term of termType = excreta and optional:
      • a list of properties with:
        • and with
    • term of termType = excreta
  • Data completeness assessment for excreta: completeness.excreta must be True
  • Data completeness assessment for water: completeness.water must be True
  • optional:
    • a list of practices with:
      • value and term of termType = waterRegime

This model works on the following Node type with identical requirements:

• Cycle
• Transformation

Lookup used

• emission.csv -> siteTypesAllowed; inputTermTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('no3ToGroundwaterExcreta', Cycle))

View source on Gitlab

NO3, to groundwater, inorganic fertiliser

Nitrate leaching to groundwater, from inorganic fertiliser.

Returns

• A list of Emissions with:
  • term with no3ToGroundwaterInorganicFertiliser
  • methodModel with ipcc2019
  • value
  • sd
  • min
  • max
  • methodTier with tier 1
  • statsDefinition with modelled

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture and a list of measurements with:
      • value and term with ecoClimateZone
  • Data completeness assessment for fertiliser: completeness.fertiliser must be True
  • Data completeness assessment for water: completeness.water must be True
  • a list of inputs with:
    • value and term of units = kg or kg N and term of termType = inorganicFertiliser and optional:
      • a list of properties with:
        • and with
  • optional:
    • a list of practices with:
      • value and term of termType = waterRegime

Lookup used

• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('no3ToGroundwaterInorganicFertiliser', Cycle))

View source on Gitlab

NO3, to groundwater, organic fertiliser

Nitrate leaching to groundwater, from organic fertiliser.

Returns

• A list of Emissions with:
  • term with no3ToGroundwaterOrganicFertiliser
  • methodModel with ipcc2019
  • value
  • sd
  • min
  • max
  • methodTier with tier 1
  • statsDefinition with modelled

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture and a list of measurements with:
      • value and term with ecoClimateZone
  • Data completeness assessment for fertiliser: completeness.fertiliser must be True
  • Data completeness assessment for water: completeness.water must be True
  • a list of inputs with:
    • value and term of termType = organicFertiliser and optional:
      • a list of properties with:
        • and with
  • optional:
    • a list of practices with:
      • value and term of termType = waterRegime

Lookup used

• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('no3ToGroundwaterOrganicFertiliser', Cycle))

View source on Gitlab

NOx, to air, inorganic fertiliser

Nitrogen oxides emissions to air, from inorganic fertiliser nitrification and denitrification.

Returns

• A list of Emissions with:
  • term with noxToAirInorganicFertiliser
  • methodModel with ipcc2019
  • value
  • sd
  • min
  • max
  • methodTier with tier 1
  • statsDefinition with modelled
  • methodModelDescription with Aggregated version or Disaggragated version

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture
  • Data completeness assessment for fertiliser: completeness.fertiliser must be True
  • a list of inputs with:
    • value and term of termType = inorganicFertiliser and optional:
      • a list of properties with:
        • and with

Lookup used

• inorganicFertiliser.csv -> IPCC_2019_FRACGASF_NOx-N; IPCC_2019_FRACGASF_NOx-N-min; IPCC_2019_FRACGASF_NOx-N-max
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('noxToAirInorganicFertiliser', Cycle))

View source on Gitlab

NOx, to air, organic fertiliser

Nitrogen oxides emissions to air, from organic fertiliser nitrification and denitrification.

Returns

• A list of Emissions with:
  • term with noxToAirOrganicFertiliser
  • methodModel with ipcc2019
  • value
  • sd
  • min
  • max
  • methodTier with tier 1
  • statsDefinition with modelled
  • methodModelDescription with Aggregated version

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture
  • Data completeness assessment for fertiliser: completeness.fertiliser must be True
  • a list of inputs with:
    • value and term of termType = organicFertiliser and optional:
      • a list of properties with:
        • and with

Lookup used

• organicFertiliser.csv -> IPCC_2019_FRACGASM_NOx-N; IPCC_2019_FRACGASM_NOx-N-min; IPCC_2019_FRACGASM_NOx-N-max
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('noxToAirOrganicFertiliser', Cycle))

View source on Gitlab

Organic carbon (per ha)

The stock of organic carbon in the soil.

The IPCC model for estimating soil organic carbon stock changes in the 0 - 30cm depth interval due to management changes. This model combines the Tier 1 & Tier 2 methodologies. It first tries to run Tier 2 (only on croplands remaining croplands). If Tier 2 cannot run, it will try to run Tier 1 (for croplands remaining croplands and for grasslands remaining grasslands). Source: IPCC 2019, Vol. 4, Chapter 10.

Returns

• A list of Measurements with:
  • term with organicCarbonPerHa
  • methodModel with ipcc2019
  • value
  • dates
  • depthUpper with 0
  • depthLower with 30
  • methodClassification

Requirements

• A Site with:
  • a siteType = cropland or permanent pasture or forest or other natural vegetation or permanent pasture
  • a list of measurements with:
    • value and term with ecoClimateZone
  • optional:
    • a list of measurements with:
      • value and term of termType = soilType or usdaSoilType
    • a list of managements with:
      • value and startDate and endDate and term of termType = cropResidueManagement and name containing "burnt,removed"
      • value and startDate and endDate and term of termType = landCover
      • value and startDate and endDate and term of termType = landUseManagement
      • value and startDate and endDate and term of termType = tillage
      • value and startDate and endDate and term of termType = waterRegime and name containing "irrigated,deep water"
      • value and startDate and endDate and term with animalManureUsed
      • value and startDate and endDate and term with inorganicNitrogenFertiliserUsed
      • value and startDate and endDate and term with organicFertiliserUsed
      • value and startDate and endDate and term with amendmentIncreasingSoilCarbonUsed
      • value and startDate and endDate and term with shortBareFallow

Lookup used

• crop.csv -> IPCC_LAND_USE_CATEGORY
• ecoClimateZone.csv -> IPCC_2019_SOC_REF_KG_C_HECTARE_SAN; IPCC_2019_SOC_REF_KG_C_HECTARE_WET; IPCC_2019_SOC_REF_KG_C_HECTARE_VOL; IPCC_2019_SOC_REF_KG_C_HECTARE_POD; IPCC_2019_SOC_REF_KG_C_HECTARE_HAC; IPCC_2019_SOC_REF_KG_C_HECTARE_LAC; IPCC_2019_LANDUSE_FACTOR_GRASSLAND; IPCC_2019_LANDUSE_FACTOR_PERENNIAL_CROPS; IPCC_2019_LANDUSE_FACTOR_PADDY_RICE_CULTIVATION; IPCC_2019_LANDUSE_FACTOR_ANNUAL_CROPS_WET; IPCC_2019_LANDUSE_FACTOR_ANNUAL_CROPS; IPCC_2019_LANDUSE_FACTOR_SET_ASIDE; IPCC_2019_GRASSLAND_MANAGEMENT_FACTOR_SEVERELY_DEGRADED; IPCC_2019_GRASSLAND_MANAGEMENT_FACTOR_IMPROVED_GRASSLAND; IPCC_2019_GRASSLAND_MANAGEMENT_FACTOR_HIGH_INTENSITY_GRAZING; IPCC_2019_GRASSLAND_MANAGEMENT_FACTOR_NOMINALLY_MANAGED; IPCC_2019_TILLAGE_MANAGEMENT_FACTOR_FULL_TILLAGE; IPCC_2019_TILLAGE_MANAGEMENT_FACTOR_REDUCED_TILLAGE; IPCC_2019_TILLAGE_MANAGEMENT_FACTOR_NO_TILLAGE; IPCC_2019_GRASSLAND_CARBON_INPUT_FACTOR_HIGH; IPCC_2019_GRASSLAND_CARBON_INPUT_FACTOR_MEDIUM; IPCC_2019_CROPLAND_CARBON_INPUT_FACTOR_HIGH_WITH_MANURE; IPCC_2019_CROPLAND_CARBON_INPUT_FACTOR_HIGH_WITHOUT_MANURE; IPCC_2019_CROPLAND_CARBON_INPUT_FACTOR_MEDIUM; IPCC_2019_CROPLAND_CARBON_INPUT_FACTOR_LOW
• landCover.csv -> IPCC_LAND_USE_CATEGORY; LOW_RESIDUE_PRODUCING_CROP; N_FIXING_CROP
• landUseManagement.csv -> PRACTICE_INCREASING_C_INPUT
• soilType.csv -> IPCC_SOIL_CATEGORY
• tillage.csv -> IPCC_TILLAGE_MANAGEMENT_CATEGORY
• usdaSoilType.csv -> IPCC_SOIL_CATEGORY
• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('organicCarbonPerHa', Site))

View source on Gitlab

Full Grass Consumption

This model estimates the energetic requirements of ruminants and can be used to estimate the amount of grass they graze. Source: IPCC 2019, Vol.4, Chapter 10.

Returns

• A list of Inputs with:
  • methodModel with ipcc2019
  • term of termType = crop or forage
  • value

Requirements

• A Cycle with:
  • Data completeness assessment for animalFeed: completeness.animalFeed must be True
  • Data completeness assessment for freshForage: completeness.freshForage must be False
  • a site with:
    • siteType with permanent pasture
  • a list of practices with:
    • value and term with pastureGrass and a key with:
      • term of termType = landCover
  • a list of animals with:
    • value > 0 and term of termType = liveAnimal and referencePeriod with average and a list of properties with:
      • value and term with liveweightPerHead or weightAtMaturity and optional:
      • a list of properties with:
        • and with or or
      • a list of inputs with:
        • of = kg and > 0 and optional:
      • a list of practices with:
        • and of = and a list of with:
  • optional:
    • a list of inputs with:
      • term of units = kg and value > 0 and isAnimalFeed with True and optional:
        • a list of with:
    • a list of products with:
      • value and term with animalProduct

Lookup used

• animalManagement.csv -> mjKgEvMilkIpcc2019
• animalProduct.csv -> mjKgEvWoolNetEnergyWoolIpcc2019
• liveAnimal.csv -> ipcc2019AnimalTypeGrouping; mjDayKgCfiNetEnergyMaintenanceIpcc2019; ratioCPregnancyNetEnergyPregnancyIpcc2019; ratioCNetEnergyGrowthCattleBuffaloIpcc2019; mjKgABNetEnergyGrowthSheepGoatsIpcc2019
• system-liveAnimal-activityCoefficient-ipcc2019.csv -> using animal term @id
• crop-property.csv -> energyDigestibilityRuminants; energyContentHigherHeatingValue
• crop.csv -> grazedPastureGrassInputId
• forage-property.csv -> energyDigestibilityRuminants; energyContentHigherHeatingValue
• landCover.csv -> grazedPastureGrassInputId

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2019 import run

print(run('pastureGrass', Cycle))

View source on Gitlab

IPCC (2021)

These models, described in the IPCC (2021) guidelines, including the supplementary material, characterise different greenhouse gases into a global warming or global temperature potential.

View source on Gitlab

GWP100

The global warming potential of mixed greenhouse gases on the mid-term climate (100 years), expressed as CO2 equivalents.

Returns

• A Indicator with:
  • term with gwp100
  • methodModel with ipcc2021
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> co2EqGwp100Ipcc2021

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.ipcc2021 import run

print(run('gwp100', ImpactAssessment))

View source on Gitlab

Jarvis and Pain (1994)

This model, described in Jarvis and Pain (1994), estimates the N2 emissions as a ratio of N2O emissions in excreta management systems.

View source on Gitlab

N2, to air, excreta

Nitrogen emissions to air, from animal excreta.

Returns

• A list of Emissions with:
  • term with n2ToAirExcreta
  • methodModel with jarvisAndPain1994
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or permanent pasture or animal housing or pond or river or stream or lake or sea or ocean
  • a list of inputs with:
    • term of termType = excreta
  • a list of emissions with:
    • value and term with n2OToAirExcretaDirect

This model works on the following Node type with identical requirements:

• Cycle
• Transformation

Lookup used

• emission.csv -> siteTypesAllowed; inputTermTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.jarvisAndPain1994 import run

print(run('n2ToAirExcreta', Cycle))

View source on Gitlab

Koble (2014)

This model estimates the amount of crop residue burnt and removed using country average factors for different crop groupings. The data are described in The Global Nitrous Oxide Calculator – GNOC .

View source on Gitlab

Above Ground Crop Residue

This model returns the amounts and destinations of above ground crop residue, working in the following order: 1. Above ground crop residue, removed; 2. Above ground crop residue, incorporated; 3. Above ground crop residue, burnt; 4. Above ground crop residue, left on field.

Returns

• A list of Products with:
  • term with aboveGroundCropResidueLeftOnField or aboveGroundCropResidueBurnt or aboveGroundCropResidueIncorporated or aboveGroundCropResidueRemoved
  • methodModel with koble2014
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover
  • a list of products with:
    • term of termType = crop or forage
  • Data completeness assessment for cropResidue: completeness.cropResidue must be False
  • a list of practices with:
    • term with residueRemoved or residueIncorporated or residueIncorporatedLessThan30DaysBeforeCultivation or residueIncorporatedMoreThan30DaysBeforeCultivation or residueBurnt

Lookup used

• cropResidue.csv -> siteTypesAllowed; productTermTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.koble2014 import run

print(run('aboveGroundCropResidue', Cycle))

View source on Gitlab

Residue

Re-scale all crop residue management Practices to make sure they all add up to 100%. Note: only practices added by Hestia will be recalculated.

Returns

• A list of Practices with:
  • methodModel with koble2014
  • value

Requirements

• A Cycle with:
  • Data completeness assessment for cropResidue: completeness.cropResidue must be False
  • a list of practices with:
    • term with residueBurnt or residueLeftOnField or residueRemoved and added with value and Data completeness assessment for @id: model.@id must be koble2014

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.koble2014 import run

print(run('cropResidueManagement', Cycle))

View source on Gitlab

Residue burnt

The share of above ground crop residue burnt, after multiplication by the combustion factor (which allows for residue burnt but not combusted).

Returns

• A list of Practices with:
  • term with residueBurnt
  • methodModel with koble2014
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture and a country with:
      • termType = region
  • a list of products with:
    • term of termType = crop or forage
  • Data completeness assessment for cropResidue: completeness.cropResidue must be False

Lookup used

• crop.csv -> cropGroupingResidue; Combustion_Factor_crop_residue
• region-crop-cropGroupingResidue-burnt.csv -> using result from cropGroupingResidue
• cropResidueManagement.csv -> siteTypesAllowed; productTermTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.koble2014 import run

print(run('residueBurnt', Cycle))

View source on Gitlab

Residue left on field

The share of above ground crop residue left on the field surface.

Returns

• A list of Practices with:
  • term with residueLeftOnField
  • methodModel with koble2014
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture
  • a list of products with:
    • term of termType = crop or forage
  • Data completeness assessment for cropResidue: completeness.cropResidue must be False

Lookup used

• cropResidueManagement.csv -> siteTypesAllowed; productTermTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.koble2014 import run

print(run('residueLeftOnField', Cycle))

View source on Gitlab

Residue removed

The share of above ground crop residue removed.

Returns

• A list of Practices with:
  • term with residueRemoved
  • methodModel with koble2014
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture and a country with:
      • termType = region
  • a list of products with:
    • term of termType = crop or forage
  • Data completeness assessment for cropResidue: completeness.cropResidue must be False

Lookup used

• crop.csv -> cropGroupingResidue
• region-crop-cropGroupingResidue-removed.csv -> using result from cropGroupingResidue
• cropResidueManagement.csv -> siteTypesAllowed; productTermTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.koble2014 import run

print(run('residueRemoved', Cycle))

View source on Gitlab

LC-Impact (all effects, 100 years)

These models characterise emissions and resource uses according to the methods defined by the LC-Impact working group. All the effects caused by an impact category that are known to damage one or more areas of protection are considered, and the time horizon is 100 years.

View source on Gitlab

Damage to freshwater ecosystems, climate change

The fraction of species richness that may be potentially lost in freshwater ecosystems due to climate change. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToFreshwaterEcosystemsClimateChange
  • methodModel with lcImpactAllEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> pdfYearsAllEffects100YearsClimateChangeDamageToFreshwaterEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffects100Years import run

print(run('damageToFreshwaterEcosystemsClimateChange', ImpactAssessment))

View source on Gitlab

Damage to freshwater ecosystems, freshwater ecotoxicity

The fraction of species richness that may be potentially lost in freshwater ecosystems due to freshwater ecotoxicity. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToFreshwaterEcosystemsFreshwaterEcotoxicity
  • methodModel with lcImpactAllEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> pdfYear100YearsFreshwaterEcotoxicityDamageToFreshwaterEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffects100Years import run

print(run('damageToFreshwaterEcosystemsFreshwaterEcotoxicity', ImpactAssessment))

View source on Gitlab

Damage to freshwater ecosystems, freshwater eutrophication

The fraction of species richness that may be potentially lost in freshwater ecosystems due to freshwater eutrophication. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToFreshwaterEcosystemsFreshwaterEutrophication
  • methodModel with lcImpactAllEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-emission-FreshwaterEutrophicationDamageToFreshwaterEcosystemsLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffects100Years import run

print(run('damageToFreshwaterEcosystemsFreshwaterEutrophication', ImpactAssessment))

View source on Gitlab

Damage to freshwater ecosystems (PDF*year)

The fraction of freshwater species that are commited to become globally extinct over a certain period of time if the pressure continues to happen. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToFreshwaterEcosystemsPdfYear
  • methodModel with lcImpactAllEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> pdfYearsDamageToFreshwaterEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffects100Years import run

print(run('damageToFreshwaterEcosystemsPdfYear', ImpactAssessment))

View source on Gitlab

Damage to freshwater ecosystems, water stress

The fraction of species richness that may be potentially lost in freshwater ecosystems due to water stress. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToFreshwaterEcosystemsWaterStress
  • methodModel with lcImpactAllEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = resourceUse
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-resourceUse-WaterStressDamageToFreshwaterEcosystemsLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffects100Years import run

print(run('damageToFreshwaterEcosystemsWaterStress', ImpactAssessment))

View source on Gitlab

Damage to human health

The disability-adjusted life years lost in the human population.

Returns

• A Indicator with:
  • term with damageToHumanHealth
  • methodModel with lcImpactAllEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> dalyDamageToHumanHealthLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffects100Years import run

print(run('damageToHumanHealth', ImpactAssessment))

View source on Gitlab

Damage to human health, climate change

The disability-adjusted life years lost in the human population due to climate change. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthClimateChange
  • methodModel with lcImpactAllEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> dalyAllEffects100YearsClimateChangeDamageToHumanHealthLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffects100Years import run

print(run('damageToHumanHealthClimateChange', ImpactAssessment))

View source on Gitlab

Damage to human health, human toxicity (cancerogenic)

The disability-adjusted life years lost in the human population due to cancerogenic toxicity. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthHumanToxicityCancerogenic
  • methodModel with lcImpactAllEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> dalyAllEffects100YearsCancerogenicHumanToxicityDamageToHumanHealthLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffects100Years import run

print(run('damageToHumanHealthHumanToxicityCancerogenic', ImpactAssessment))

View source on Gitlab

Damage to human health, human toxicity (non-cancerogenic)

The disability-adjusted life years lost in the human population due to non-cancerogenic toxicity. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthHumanToxicityNonCancerogenic
  • methodModel with lcImpactAllEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> dalyAllEffects100YearsNonCancerogenicHumanToxicityDamageToHumanHealthLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffects100Years import run

print(run('damageToHumanHealthHumanToxicityNonCancerogenic', ImpactAssessment))

View source on Gitlab

Damage to human health, particulate matter formation

The disability-adjusted life years lost in the human population due to particulate matter formation. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthParticulateMatterFormation
  • methodModel with lcImpactAllEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-emission-ParticulateMatterFormationAllEffectsDamageToHumanHealthLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffects100Years import run

print(run('damageToHumanHealthParticulateMatterFormation', ImpactAssessment))

View source on Gitlab

Damage to human health, photochemical ozone formation

The disability-adjusted life years lost in the human population due to photochemical ozone formation. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthPhotochemicalOzoneFormation
  • methodModel with lcImpactAllEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-emission-OzoneFormationDamageToHumanHealthLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffects100Years import run

print(run('damageToHumanHealthPhotochemicalOzoneFormation', ImpactAssessment))

View source on Gitlab

Damage to human health, stratospheric ozone depletion

The disability-adjusted life years lost in the human population due to stratospheric ozone depletion. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthStratosphericOzoneDepletion
  • methodModel with lcImpactAllEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> dalyAllEffects100YearsStratosphericOzoneDepletionDamageToHumanHealthLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffects100Years import run

print(run('damageToHumanHealthStratosphericOzoneDepletion', ImpactAssessment))

View source on Gitlab

Damage to human health, water stress

The disability-adjusted life years lost in the human population due to water stress. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthWaterStress
  • methodModel with lcImpactAllEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = resourceUse
  • a site with:
    • either:
      • awareWaterBasinId
      • a country with:
        • =

Lookup used

Different lookup files are used depending on the situation:

• awareWaterBasinId-resourceUse-WaterStressDamageToHumanHealthLCImpactCF.csv -> using awareWaterBasinId
• region-resourceUse-WaterStressDamageToHumanHealthLCImpactCF.csv -> using region

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffects100Years import run

print(run('damageToHumanHealthWaterStress', ImpactAssessment))

View source on Gitlab

Damage to marine ecosystems, marine ecotoxicity

The fraction of species richness that may be potentially lost in marine ecosystems due to marine ecotoxicity. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToMarineEcosystemsMarineEcotoxicity
  • methodModel with lcImpactAllEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> pdfYear100YearsMarineEcotoxicityDamageToMarineEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffects100Years import run

print(run('damageToMarineEcosystemsMarineEcotoxicity', ImpactAssessment))

View source on Gitlab

Damage to marine ecosystems, marine eutrophication

The fraction of species richness that may be potentially lost in marine ecosystems due to marine eutrophication. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToMarineEcosystemsMarineEutrophication
  • methodModel with lcImpactAllEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-emission-MarineEutrophicationDamageToMarineEcosystemsLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffects100Years import run

print(run('damageToMarineEcosystemsMarineEutrophication', ImpactAssessment))

View source on Gitlab

Damage to marine ecosystems (PDF*year)

The fraction of marine species that are commited to become globally extinct over a certain period of time if the pressure continues to happen. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToMarineEcosystemsPdfYear
  • methodModel with lcImpactAllEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> pdfYearsDamageToMarineEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffects100Years import run

print(run('damageToMarineEcosystemsPdfYear', ImpactAssessment))

View source on Gitlab

Damage to terrestrial ecosystems, climate change

The fraction of species richness that may be potentially lost in terrestrial ecosystems due to climate change. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToTerrestrialEcosystemsClimateChange
  • methodModel with lcImpactAllEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> pdfYearsAllEffects100YearsClimateChangeDamageToTerrestrialEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffects100Years import run

print(run('damageToTerrestrialEcosystemsClimateChange', ImpactAssessment))

View source on Gitlab

Damage to terrestrial ecosystems (PDF*year)

The fraction of terrestrial species that are commited to become globally extinct over a certain period of time if the pressure continues to happen. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToTerrestrialEcosystemsPdfYear
  • methodModel with lcImpactAllEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> pdfYearsDamageToTerrestrialEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffects100Years import run

print(run('damageToTerrestrialEcosystemsPdfYear', ImpactAssessment))

View source on Gitlab

Damage to terrestrial ecosystems, photochemical ozone formation

The fraction of species richness that may be potentially lost in terrestrial ecosystems due to photochemical ozone formation. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToTerrestrialEcosystemsPhotochemicalOzoneFormation
  • methodModel with lcImpactAllEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-emission-OzoneFormationDamageToTerrestrialEcosystemsLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffects100Years import run

print(run('damageToTerrestrialEcosystemsPhotochemicalOzoneFormation', ImpactAssessment))

View source on Gitlab

Damage to terrestrial ecosystems, terrestrial acidification

The fraction of species richness that may be potentially lost in terrestrial ecosystems due to terrestrial acidification. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToTerrestrialEcosystemsTerrestrialAcidification
  • methodModel with lcImpactAllEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-emission-TerrestrialAcidificationDamageToTerrestrialEcosystemsLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffects100Years import run

print(run('damageToTerrestrialEcosystemsTerrestrialAcidification', ImpactAssessment))

View source on Gitlab

Damage to terrestrial ecosystems, terrestrial ecotoxicity

The fraction of species richness that may be potentially lost in terrestrial ecosystems due to terrestrial ecotoxicity. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToTerrestrialEcosystemsTerrestrialEcotoxicity
  • methodModel with lcImpactAllEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> pdfYear100YearsTerrestrialEcotoxicityDamageToTerrestrialEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffects100Years import run

print(run('damageToTerrestrialEcosystemsTerrestrialEcotoxicity', ImpactAssessment))

View source on Gitlab

LC-Impact (all effects, infinite)

These models characterise emissions and resource uses according to the methods defined by the LC-Impact working group. All the effects caused by an impact category that are known to damage one or more areas of protection are considered, and the time horizon is infinite.

View source on Gitlab

Damage to freshwater ecosystems, climate change

The fraction of species richness that may be potentially lost in freshwater ecosystems due to climate change. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToFreshwaterEcosystemsClimateChange
  • methodModel with lcImpactAllEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> pdfYearsAllEffectsInfiniteClimateChangeDamageToFreshwaterEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffectsInfinite import run

print(run('damageToFreshwaterEcosystemsClimateChange', ImpactAssessment))

View source on Gitlab

Damage to freshwater ecosystems, freshwater ecotoxicity

The fraction of species richness that may be potentially lost in freshwater ecosystems due to freshwater ecotoxicity. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToFreshwaterEcosystemsFreshwaterEcotoxicity
  • methodModel with lcImpactAllEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> pdfYearInfiniteTerrestrialEcotoxicityDamageToTerrestrialEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffectsInfinite import run

print(run('damageToFreshwaterEcosystemsFreshwaterEcotoxicity', ImpactAssessment))

View source on Gitlab

Damage to freshwater ecosystems, freshwater eutrophication

The fraction of species richness that may be potentially lost in freshwater ecosystems due to freshwater eutrophication. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToFreshwaterEcosystemsFreshwaterEutrophication
  • methodModel with lcImpactAllEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-emission-FreshwaterEutrophicationDamageToFreshwaterEcosystemsLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffectsInfinite import run

print(run('damageToFreshwaterEcosystemsFreshwaterEutrophication', ImpactAssessment))

View source on Gitlab

Damage to freshwater ecosystems (PDF*year)

The fraction of freshwater species that are commited to become globally extinct over a certain period of time if the pressure continues to happen. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToFreshwaterEcosystemsPdfYear
  • methodModel with lcImpactAllEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> pdfYearsDamageToFreshwaterEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffectsInfinite import run

print(run('damageToFreshwaterEcosystemsPdfYear', ImpactAssessment))

View source on Gitlab

Damage to freshwater ecosystems, water stress

The fraction of species richness that may be potentially lost in freshwater ecosystems due to water stress. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToFreshwaterEcosystemsWaterStress
  • methodModel with lcImpactAllEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = resourceUse
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-resourceUse-WaterStressDamageToFreshwaterEcosystemsLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffectsInfinite import run

print(run('damageToFreshwaterEcosystemsWaterStress', ImpactAssessment))

View source on Gitlab

Damage to human health

The disability-adjusted life years lost in the human population.

Returns

• A Indicator with:
  • term with damageToHumanHealth
  • methodModel with lcImpactAllEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> dalyDamageToHumanHealthLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffectsInfinite import run

print(run('damageToHumanHealth', ImpactAssessment))

View source on Gitlab

Damage to human health, climate change

The disability-adjusted life years lost in the human population due to climate change. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthClimateChange
  • methodModel with lcImpactAllEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> dalyAllEffectsInfiniteClimateChangeDamageToHumanHealthLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffectsInfinite import run

print(run('damageToHumanHealthClimateChange', ImpactAssessment))

View source on Gitlab

Damage to human health, human toxicity (cancerogenic)

The disability-adjusted life years lost in the human population due to cancerogenic toxicity. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthHumanToxicityCancerogenic
  • methodModel with lcImpactAllEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> dalyAllEffectsInfiniteCancerogenicHumanToxicityDamageToHumanHealthLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffectsInfinite import run

print(run('damageToHumanHealthHumanToxicityCancerogenic', ImpactAssessment))

View source on Gitlab

Damage to human health, human toxicity (non-cancerogenic)

The disability-adjusted life years lost in the human population due to non-cancerogenic toxicity. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthHumanToxicityNonCancerogenic
  • methodModel with lcImpactAllEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> dalyAllEffectsInfiniteNonCancerogenicHumanToxicityDamageToHumanHealthLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffectsInfinite import run

print(run('damageToHumanHealthHumanToxicityNonCancerogenic', ImpactAssessment))

View source on Gitlab

Damage to human health, particulate matter formation

The disability-adjusted life years lost in the human population due to particulate matter formation. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthParticulateMatterFormation
  • methodModel with lcImpactAllEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-emission-ParticulateMatterFormationAllEffectsDamageToHumanHealthLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffectsInfinite import run

print(run('damageToHumanHealthParticulateMatterFormation', ImpactAssessment))

View source on Gitlab

Damage to human health, photochemical ozone formation

The disability-adjusted life years lost in the human population due to photochemical ozone formation. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthPhotochemicalOzoneFormation
  • methodModel with lcImpactAllEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-emission-OzoneFormationDamageToHumanHealthLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffectsInfinite import run

print(run('damageToHumanHealthPhotochemicalOzoneFormation', ImpactAssessment))

View source on Gitlab

Damage to human health, stratospheric ozone depletion

The disability-adjusted life years lost in the human population due to stratospheric ozone depletion. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthStratosphericOzoneDepletion
  • methodModel with lcImpactAllEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> dalyAllEffectsInfiniteStratosphericOzoneDepletionDamageToHumanHealthLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffectsInfinite import run

print(run('damageToHumanHealthStratosphericOzoneDepletion', ImpactAssessment))

View source on Gitlab

Damage to human health, water stress

The disability-adjusted life years lost in the human population due to water stress. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthWaterStress
  • methodModel with lcImpactAllEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = resourceUse
  • a site with:
    • either:
      • awareWaterBasinId
      • a country with:
        • =

Lookup used

Different lookup files are used depending on the situation:

• awareWaterBasinId-resourceUse-WaterStressDamageToHumanHealthLCImpactCF.csv -> using awareWaterBasinId
• region-resourceUse-WaterStressDamageToHumanHealthLCImpactCF.csv -> using region

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffectsInfinite import run

print(run('damageToHumanHealthWaterStress', ImpactAssessment))

View source on Gitlab

Damage to marine ecosystems, marine ecotoxicity

The fraction of species richness that may be potentially lost in marine ecosystems due to marine ecotoxicity. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToMarineEcosystemsMarineEcotoxicity
  • methodModel with lcImpactAllEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> pdfYearInfiniteMarineEcotoxicityDamageToMarineEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffectsInfinite import run

print(run('damageToMarineEcosystemsMarineEcotoxicity', ImpactAssessment))

View source on Gitlab

Damage to marine ecosystems, marine eutrophication

The fraction of species richness that may be potentially lost in marine ecosystems due to marine eutrophication. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToMarineEcosystemsMarineEutrophication
  • methodModel with lcImpactAllEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-emission-MarineEutrophicationDamageToMarineEcosystemsLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffectsInfinite import run

print(run('damageToMarineEcosystemsMarineEutrophication', ImpactAssessment))

View source on Gitlab

Damage to marine ecosystems (PDF*year)

The fraction of marine species that are commited to become globally extinct over a certain period of time if the pressure continues to happen. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToMarineEcosystemsPdfYear
  • methodModel with lcImpactAllEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> pdfYearsDamageToMarineEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffectsInfinite import run

print(run('damageToMarineEcosystemsPdfYear', ImpactAssessment))

View source on Gitlab

Damage to terrestrial ecosystems, climate change

The fraction of species richness that may be potentially lost in terrestrial ecosystems due to climate change. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToTerrestrialEcosystemsClimateChange
  • methodModel with lcImpactAllEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> pdfYearsAllEffectsInfiniteClimateChangeDamageToTerrestrialEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffectsInfinite import run

print(run('damageToTerrestrialEcosystemsClimateChange', ImpactAssessment))

View source on Gitlab

Damage to terrestrial ecosystems (PDF*year)

The fraction of terrestrial species that are commited to become globally extinct over a certain period of time if the pressure continues to happen. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToTerrestrialEcosystemsPdfYear
  • methodModel with lcImpactAllEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> pdfYearsDamageToTerrestrialEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffectsInfinite import run

print(run('damageToTerrestrialEcosystemsPdfYear', ImpactAssessment))

View source on Gitlab

Damage to terrestrial ecosystems, photochemical ozone formation

The fraction of species richness that may be potentially lost in terrestrial ecosystems due to photochemical ozone formation. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToTerrestrialEcosystemsPhotochemicalOzoneFormation
  • methodModel with lcImpactAllEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-emission-OzoneFormationDamageToTerrestrialEcosystemsLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffectsInfinite import run

print(run('damageToTerrestrialEcosystemsPhotochemicalOzoneFormation', ImpactAssessment))

View source on Gitlab

Damage to terrestrial ecosystems, terrestrial acidification

The fraction of species richness that may be potentially lost in terrestrial ecosystems due to terrestrial acidification. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToTerrestrialEcosystemsTerrestrialAcidification
  • methodModel with lcImpactAllEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-emission-TerrestrialAcidificationDamageToTerrestrialEcosystemsLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffectsInfinite import run

print(run('damageToTerrestrialEcosystemsTerrestrialAcidification', ImpactAssessment))

View source on Gitlab

Damage to terrestrial ecosystems, terrestrial ecotoxicity

The fraction of species richness that may be potentially lost in terrestrial ecosystems due to terrestrial ecotoxicity. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToTerrestrialEcosystemsTerrestrialEcotoxicity
  • methodModel with lcImpactAllEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> pdfYearInfiniteTerrestrialEcotoxicityDamageToTerrestrialEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactAllEffectsInfinite import run

print(run('damageToTerrestrialEcosystemsTerrestrialEcotoxicity', ImpactAssessment))

View source on Gitlab

LC-Impact (certain effects, 100 years)

These models characterise emissions and resource uses according to the methods defined by the LC-Impact working group. Only the effects caused by an impact category that are known to damage one or more areas of protection with a high level of robustness are considered, and the time horizon is 100 years.

View source on Gitlab

Damage to freshwater ecosystems, freshwater ecotoxicity

The fraction of species richness that may be potentially lost in freshwater ecosystems due to freshwater ecotoxicity. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToFreshwaterEcosystemsFreshwaterEcotoxicity
  • methodModel with lcImpactCertainEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> pdfYear100YearsFreshwaterEcotoxicityDamageToFreshwaterEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffects100Years import run

print(run('damageToFreshwaterEcosystemsFreshwaterEcotoxicity', ImpactAssessment))

View source on Gitlab

Damage to freshwater ecosystems, freshwater eutrophication

The fraction of species richness that may be potentially lost in freshwater ecosystems due to freshwater eutrophication. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToFreshwaterEcosystemsFreshwaterEutrophication
  • methodModel with lcImpactCertainEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-emission-FreshwaterEutrophicationDamageToFreshwaterEcosystemsLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffects100Years import run

print(run('damageToFreshwaterEcosystemsFreshwaterEutrophication', ImpactAssessment))

View source on Gitlab

Damage to freshwater ecosystems (PDF*year)

The fraction of freshwater species that are commited to become globally extinct over a certain period of time if the pressure continues to happen. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToFreshwaterEcosystemsPdfYear
  • methodModel with lcImpactCertainEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> pdfYearsDamageToFreshwaterEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffects100Years import run

print(run('damageToFreshwaterEcosystemsPdfYear', ImpactAssessment))

View source on Gitlab

Damage to freshwater ecosystems, water stress

The fraction of species richness that may be potentially lost in freshwater ecosystems due to water stress. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToFreshwaterEcosystemsWaterStress
  • methodModel with lcImpactCertainEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = resourceUse
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-resourceUse-WaterStressDamageToFreshwaterEcosystemsLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffects100Years import run

print(run('damageToFreshwaterEcosystemsWaterStress', ImpactAssessment))

View source on Gitlab

Damage to human health

The disability-adjusted life years lost in the human population.

Returns

• A Indicator with:
  • term with damageToHumanHealth
  • methodModel with lcImpactCertainEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> dalyDamageToHumanHealthLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffects100Years import run

print(run('damageToHumanHealth', ImpactAssessment))

View source on Gitlab

Damage to human health, climate change

The disability-adjusted life years lost in the human population due to climate change. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthClimateChange
  • methodModel with lcImpactCertainEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> dalyCertainEffects100YearsClimateChangeDamageToHumanHealthLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffects100Years import run

print(run('damageToHumanHealthClimateChange', ImpactAssessment))

View source on Gitlab

Damage to human health, human toxicity (cancerogenic)

The disability-adjusted life years lost in the human population due to cancerogenic toxicity. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthHumanToxicityCancerogenic
  • methodModel with lcImpactCertainEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> dalyCertainEffects100YearsCancerogenicHumanToxicityDamageToHumanHealthLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffects100Years import run

print(run('damageToHumanHealthHumanToxicityCancerogenic', ImpactAssessment))

View source on Gitlab

Damage to human health, human toxicity (non-cancerogenic)

The disability-adjusted life years lost in the human population due to non-cancerogenic toxicity. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthHumanToxicityNonCancerogenic
  • methodModel with lcImpactCertainEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> dalyCertainEffects100YearsNonCancerogenicHumanToxicityDamageToHumanHealthLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffects100Years import run

print(run('damageToHumanHealthHumanToxicityNonCancerogenic', ImpactAssessment))

View source on Gitlab

Damage to human health, particulate matter formation

The disability-adjusted life years lost in the human population due to particulate matter formation. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthParticulateMatterFormation
  • methodModel with lcImpactCertainEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-emission-ParticulateMatterFormationCertainEffectsDamageToHumanHealthLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffects100Years import run

print(run('damageToHumanHealthParticulateMatterFormation', ImpactAssessment))

View source on Gitlab

Damage to human health, photochemical ozone formation

The disability-adjusted life years lost in the human population due to photochemical ozone formation. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthPhotochemicalOzoneFormation
  • methodModel with lcImpactCertainEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-emission-OzoneFormationDamageToHumanHealthLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffects100Years import run

print(run('damageToHumanHealthPhotochemicalOzoneFormation', ImpactAssessment))

View source on Gitlab

Damage to human health, stratospheric ozone depletion

The disability-adjusted life years lost in the human population due to stratospheric ozone depletion. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthStratosphericOzoneDepletion
  • methodModel with lcImpactCertainEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> dalyCertainEffects100YearsStratosphericOzoneDepletionDamageToHumanHealthLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffects100Years import run

print(run('damageToHumanHealthStratosphericOzoneDepletion', ImpactAssessment))

View source on Gitlab

Damage to human health, water stress

The disability-adjusted life years lost in the human population due to water stress. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthWaterStress
  • methodModel with lcImpactCertainEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = resourceUse
  • a site with:
    • either:
      • awareWaterBasinId
      • a country with:
        • =

Lookup used

Different lookup files are used depending on the situation:

• awareWaterBasinId-resourceUse-WaterStressDamageToHumanHealthLCImpactCF.csv -> using awareWaterBasinId
• region-resourceUse-WaterStressDamageToHumanHealthLCImpactCF.csv -> using region

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffects100Years import run

print(run('damageToHumanHealthWaterStress', ImpactAssessment))

View source on Gitlab

Damage to marine ecosystems, marine ecotoxicity

The fraction of species richness that may be potentially lost in marine ecosystems due to marine ecotoxicity. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToMarineEcosystemsMarineEcotoxicity
  • methodModel with lcImpactCertainEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> pdfYear100YearsMarineEcotoxicityDamageToMarineEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffects100Years import run

print(run('damageToMarineEcosystemsMarineEcotoxicity', ImpactAssessment))

View source on Gitlab

Damage to marine ecosystems, marine eutrophication

The fraction of species richness that may be potentially lost in marine ecosystems due to marine eutrophication. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToMarineEcosystemsMarineEutrophication
  • methodModel with lcImpactCertainEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-emission-MarineEutrophicationDamageToMarineEcosystemsLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffects100Years import run

print(run('damageToMarineEcosystemsMarineEutrophication', ImpactAssessment))

View source on Gitlab

Damage to marine ecosystems (PDF*year)

The fraction of marine species that are commited to become globally extinct over a certain period of time if the pressure continues to happen. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToMarineEcosystemsPdfYear
  • methodModel with lcImpactCertainEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> pdfYearsDamageToMarineEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffects100Years import run

print(run('damageToMarineEcosystemsPdfYear', ImpactAssessment))

View source on Gitlab

Damage to terrestrial ecosystems, climate change

The fraction of species richness that may be potentially lost in terrestrial ecosystems due to climate change. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToTerrestrialEcosystemsClimateChange
  • methodModel with lcImpactCertainEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> pdfYearsCertainEffects100YearsClimateChangeDamageToTerrestrialEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffects100Years import run

print(run('damageToTerrestrialEcosystemsClimateChange', ImpactAssessment))

View source on Gitlab

Damage to terrestrial ecosystems (PDF*year)

The fraction of terrestrial species that are commited to become globally extinct over a certain period of time if the pressure continues to happen. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToTerrestrialEcosystemsPdfYear
  • methodModel with lcImpactCertainEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> pdfYearsDamageToTerrestrialEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffects100Years import run

print(run('damageToTerrestrialEcosystemsPdfYear', ImpactAssessment))

View source on Gitlab

Damage to terrestrial ecosystems, photochemical ozone formation

The fraction of species richness that may be potentially lost in terrestrial ecosystems due to photochemical ozone formation. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToTerrestrialEcosystemsPhotochemicalOzoneFormation
  • methodModel with lcImpactCertainEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-emission-OzoneFormationDamageToTerrestrialEcosystemsLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffects100Years import run

print(run('damageToTerrestrialEcosystemsPhotochemicalOzoneFormation', ImpactAssessment))

View source on Gitlab

Damage to terrestrial ecosystems, terrestrial acidification

The fraction of species richness that may be potentially lost in terrestrial ecosystems due to terrestrial acidification. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToTerrestrialEcosystemsTerrestrialAcidification
  • methodModel with lcImpactCertainEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-emission-TerrestrialAcidificationDamageToTerrestrialEcosystemsLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffects100Years import run

print(run('damageToTerrestrialEcosystemsTerrestrialAcidification', ImpactAssessment))

View source on Gitlab

Damage to terrestrial ecosystems, terrestrial ecotoxicity

The fraction of species richness that may be potentially lost in terrestrial ecosystems due to terrestrial ecotoxicity. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToTerrestrialEcosystemsTerrestrialEcotoxicity
  • methodModel with lcImpactCertainEffects100Years
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> pdfYear100YearsTerrestrialEcotoxicityDamageToTerrestrialEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffects100Years import run

print(run('damageToTerrestrialEcosystemsTerrestrialEcotoxicity', ImpactAssessment))

View source on Gitlab

LC-Impact (certain effects, infinite)

These models characterise emissions and resource uses according to the methods defined by the LC-Impact working group. Only the effects caused by an impact category that are known to damage one or more areas of protection with a high level of robustness are considered, and the time horizon is infinite.

View source on Gitlab

Damage to freshwater ecosystems, freshwater ecotoxicity

The fraction of species richness that may be potentially lost in freshwater ecosystems due to freshwater ecotoxicity. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToFreshwaterEcosystemsFreshwaterEcotoxicity
  • methodModel with lcImpactCertainEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> pdfYearInfiniteTerrestrialEcotoxicityDamageToTerrestrialEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffectsInfinite import run

print(run('damageToFreshwaterEcosystemsFreshwaterEcotoxicity', ImpactAssessment))

View source on Gitlab

Damage to freshwater ecosystems, freshwater eutrophication

The fraction of species richness that may be potentially lost in freshwater ecosystems due to freshwater eutrophication. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToFreshwaterEcosystemsFreshwaterEutrophication
  • methodModel with lcImpactCertainEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-emission-FreshwaterEutrophicationDamageToFreshwaterEcosystemsLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffectsInfinite import run

print(run('damageToFreshwaterEcosystemsFreshwaterEutrophication', ImpactAssessment))

View source on Gitlab

Damage to freshwater ecosystems (PDF*year)

The fraction of freshwater species that are commited to become globally extinct over a certain period of time if the pressure continues to happen. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToFreshwaterEcosystemsPdfYear
  • methodModel with lcImpactCertainEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> pdfYearsDamageToFreshwaterEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffectsInfinite import run

print(run('damageToFreshwaterEcosystemsPdfYear', ImpactAssessment))

View source on Gitlab

Damage to freshwater ecosystems, water stress

The fraction of species richness that may be potentially lost in freshwater ecosystems due to water stress. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToFreshwaterEcosystemsWaterStress
  • methodModel with lcImpactCertainEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = resourceUse
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-resourceUse-WaterStressDamageToFreshwaterEcosystemsLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffectsInfinite import run

print(run('damageToFreshwaterEcosystemsWaterStress', ImpactAssessment))

View source on Gitlab

Damage to human health

The disability-adjusted life years lost in the human population.

Returns

• A Indicator with:
  • term with damageToHumanHealth
  • methodModel with lcImpactCertainEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> dalyDamageToHumanHealthLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffectsInfinite import run

print(run('damageToHumanHealth', ImpactAssessment))

View source on Gitlab

Damage to human health, climate change

The disability-adjusted life years lost in the human population due to climate change. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthClimateChange
  • methodModel with lcImpactCertainEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> dalyCertainEffectsInfiniteClimateChangeDamageToHumanHealthLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffectsInfinite import run

print(run('damageToHumanHealthClimateChange', ImpactAssessment))

View source on Gitlab

Damage to human health, human toxicity (cancerogenic)

The disability-adjusted life years lost in the human population due to cancerogenic toxicity. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthHumanToxicityCancerogenic
  • methodModel with lcImpactCertainEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> dalyCertainEffectsInfiniteCancerogenicHumanToxicityDamageToHumanHealthLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffectsInfinite import run

print(run('damageToHumanHealthHumanToxicityCancerogenic', ImpactAssessment))

View source on Gitlab

Damage to human health, human toxicity (non-cancerogenic)

The disability-adjusted life years lost in the human population due to non-cancerogenic toxicity. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthHumanToxicityNonCancerogenic
  • methodModel with lcImpactCertainEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> dalyCertainEffectsInfiniteNonCancerogenicHumanToxicityDamageToHumanHealthLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffectsInfinite import run

print(run('damageToHumanHealthHumanToxicityNonCancerogenic', ImpactAssessment))

View source on Gitlab

Damage to human health, particulate matter formation

The disability-adjusted life years lost in the human population due to particulate matter formation. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthParticulateMatterFormation
  • methodModel with lcImpactCertainEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-emission-ParticulateMatterFormationCertainEffectsDamageToHumanHealthLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffectsInfinite import run

print(run('damageToHumanHealthParticulateMatterFormation', ImpactAssessment))

View source on Gitlab

Damage to human health, photochemical ozone formation

The disability-adjusted life years lost in the human population due to photochemical ozone formation. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthPhotochemicalOzoneFormation
  • methodModel with lcImpactCertainEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-emission-OzoneFormationDamageToHumanHealthLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffectsInfinite import run

print(run('damageToHumanHealthPhotochemicalOzoneFormation', ImpactAssessment))

View source on Gitlab

Damage to human health, stratospheric ozone depletion

The disability-adjusted life years lost in the human population due to stratospheric ozone depletion. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthStratosphericOzoneDepletion
  • methodModel with lcImpactCertainEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> dalyCertainEffectsInfiniteStratosphericOzoneDepletionDamageToHumanHealthLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffectsInfinite import run

print(run('damageToHumanHealthStratosphericOzoneDepletion', ImpactAssessment))

View source on Gitlab

Damage to human health, water stress

The disability-adjusted life years lost in the human population due to water stress. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToHumanHealthWaterStress
  • methodModel with lcImpactCertainEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = resourceUse
  • a site with:
    • either:
      • awareWaterBasinId
      • a country with:
        • =

Lookup used

Different lookup files are used depending on the situation:

• awareWaterBasinId-resourceUse-WaterStressDamageToHumanHealthLCImpactCF.csv -> using awareWaterBasinId
• region-resourceUse-WaterStressDamageToHumanHealthLCImpactCF.csv -> using region

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffectsInfinite import run

print(run('damageToHumanHealthWaterStress', ImpactAssessment))

View source on Gitlab

Damage to marine ecosystems, marine ecotoxicity

The fraction of species richness that may be potentially lost in marine ecosystems due to marine ecotoxicity. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToMarineEcosystemsMarineEcotoxicity
  • methodModel with lcImpactCertainEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> pdfYearInfiniteMarineEcotoxicityDamageToMarineEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffectsInfinite import run

print(run('damageToMarineEcosystemsMarineEcotoxicity', ImpactAssessment))

View source on Gitlab

Damage to marine ecosystems, marine eutrophication

The fraction of species richness that may be potentially lost in marine ecosystems due to marine eutrophication. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToMarineEcosystemsMarineEutrophication
  • methodModel with lcImpactCertainEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-emission-MarineEutrophicationDamageToMarineEcosystemsLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffectsInfinite import run

print(run('damageToMarineEcosystemsMarineEutrophication', ImpactAssessment))

View source on Gitlab

Damage to marine ecosystems (PDF*year)

The fraction of marine species that are commited to become globally extinct over a certain period of time if the pressure continues to happen. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToMarineEcosystemsPdfYear
  • methodModel with lcImpactCertainEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> pdfYearsDamageToMarineEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffectsInfinite import run

print(run('damageToMarineEcosystemsPdfYear', ImpactAssessment))

View source on Gitlab

Damage to terrestrial ecosystems, climate change

The fraction of species richness that may be potentially lost in terrestrial ecosystems due to climate change. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToTerrestrialEcosystemsClimateChange
  • methodModel with lcImpactCertainEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> pdfYearsCertainEffectsInfiniteClimateChangeDamageToTerrestrialEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffectsInfinite import run

print(run('damageToTerrestrialEcosystemsClimateChange', ImpactAssessment))

View source on Gitlab

Damage to terrestrial ecosystems (PDF*year)

The fraction of terrestrial species that are commited to become globally extinct over a certain period of time if the pressure continues to happen. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToTerrestrialEcosystemsPdfYear
  • methodModel with lcImpactCertainEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> pdfYearsDamageToTerrestrialEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffectsInfinite import run

print(run('damageToTerrestrialEcosystemsPdfYear', ImpactAssessment))

View source on Gitlab

Damage to terrestrial ecosystems, photochemical ozone formation

The fraction of species richness that may be potentially lost in terrestrial ecosystems due to photochemical ozone formation. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToTerrestrialEcosystemsPhotochemicalOzoneFormation
  • methodModel with lcImpactCertainEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-emission-OzoneFormationDamageToTerrestrialEcosystemsLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffectsInfinite import run

print(run('damageToTerrestrialEcosystemsPhotochemicalOzoneFormation', ImpactAssessment))

View source on Gitlab

Damage to terrestrial ecosystems, terrestrial acidification

The fraction of species richness that may be potentially lost in terrestrial ecosystems due to terrestrial acidification. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToTerrestrialEcosystemsTerrestrialAcidification
  • methodModel with lcImpactCertainEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a site with:
    • a country with:
      • termType = region

Lookup used

• region-emission-TerrestrialAcidificationDamageToTerrestrialEcosystemsLCImpactCF.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffectsInfinite import run

print(run('damageToTerrestrialEcosystemsTerrestrialAcidification', ImpactAssessment))

View source on Gitlab

Damage to terrestrial ecosystems, terrestrial ecotoxicity

The fraction of species richness that may be potentially lost in terrestrial ecosystems due to terrestrial ecotoxicity. See lc-impact.eu.

Returns

• A Indicator with:
  • term with damageToTerrestrialEcosystemsTerrestrialEcotoxicity
  • methodModel with lcImpactCertainEffectsInfinite
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> pdfYearInfiniteTerrestrialEcotoxicityDamageToTerrestrialEcosystemsLCImpact

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.lcImpactCertainEffectsInfinite import run

print(run('damageToTerrestrialEcosystemsTerrestrialEcotoxicity', ImpactAssessment))

View source on Gitlab

Linked Impact Assessment

A model which takes recalculated data from an Impact Assessment linked to an Input in a Cycle.

Returns

• A list of Emissions with:
  • methodModel with linkedImpactAssessment
  • value
  • methodTier with background
  • inputs
  • operation
  • animals

Requirements

• A Cycle with:
  • a list of inputs with:
    • value > 0 and a impactAssessment with:
      • a list of emissionsResourceUses with:
  • optional:
    • a list of animals with:
      • a list of inputs with:
        • > 0 and a with:

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.linkedImpactAssessment import run

print(run('all', Cycle))

View source on Gitlab

Hestia Engine Models Mocking

When deploying the calculations models on your own server, you might want to bypass any calls to the Hestia API. You can use this mocking functionality in that purpose.

from hestia_earth.models.pooreNemecek2018.aboveGroundCropResidueTotal import run
from hestia_earth.models.mocking import enable_mock

enable_mock()
run(cycle)

View source on Gitlab

Poore Nemecek (2018)

These models implement the gap filling, emissions, and resource use models described in the supporting material of Poore & Nemecek (2018).

View source on Gitlab

Above ground crop residue, total

The total amount of above ground crop residue as dry matter. This total is the value prior to crop residue management practices (for example, burning or removal). Properties can be added, such as the nitrogen content. The amount of discarded crop is not included and should be recorded separately.

Returns

• A list of Products with:
  • term with aboveGroundCropResidueTotal
  • methodModel with pooreNemecek2018
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover
  • a list of products with:
    • term of termType = crop or forage
  • Data completeness assessment for cropResidue: completeness.cropResidue must be False

Lookup used

• crop.csv -> Default_ag_dm_crop_residue
• cropResidue.csv -> siteTypesAllowed; productTermTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.pooreNemecek2018 import run

print(run('aboveGroundCropResidueTotal', Cycle))

View source on Gitlab

Below ground crop residue

The total amount of below ground crop residue as dry matter. Properties can be added, such as the nitrogen composition.

Returns

• A list of Products with:
  • term with belowGroundCropResidue
  • methodModel with pooreNemecek2018
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover
  • a list of products with:
    • term of termType = crop or forage
  • Data completeness assessment for cropResidue: completeness.cropResidue must be False

Lookup used

• crop.csv -> Default_bg_dm_crop_residue
• cropResidue.csv -> siteTypesAllowed; productTermTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.pooreNemecek2018 import run

print(run('belowGroundCropResidue', Cycle))

View source on Gitlab

CH4, to air, aquaculture systems

Methane emissions to air, from the methanogenesis of organic carbon in excreta, unconsumed feed, fertiliser, and net primary production. Reaches the air through diffusion and ebullition.

Returns

• A list of Emissions with:
  • term with ch4ToAirAquacultureSystems
  • methodModel with pooreNemecek2018
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with pond or river or stream or lake or sea or ocean and either:
      • the following fields:
        • a list of with:
      • the following fields:
        • with sea or ocean
        • a list of with:
  • a list of products with:
    • value and term of termType = excreta and term of units = kg VS
    • term of termType = liveAquaticSpecies
  • endDate
  • a list of practices with:
    • value and term with excretionIntoWaterBody
    • value and term with slaughterAge
    • value and term with yieldOfPrimaryAquacultureProductLiveweightPerM2

Lookup used

• emission.csv -> siteTypesAllowed; productTermTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.pooreNemecek2018 import run

print(run('ch4ToAirAquacultureSystems', Cycle))

View source on Gitlab

Excreta (kg N)

This model uses a mass balance to calculate the total amount of excreta (as N) created by animals. The inputs into the mass balance are the total amount of feed and the total amount of net primary production in the water body. The outputs of the mass balance are the weight of the animal and the excreta. The formula is excreta = feed + NPP - animal.

For live aquatic species, if the mass balance fails (i.e. animal feed is not complete, see requirements below), a simplified formula is used: total nitrogen content of the fish * 3.31. See Poore & Nemecek (2018) for further details.

Returns

• A list of Products with:
  • methodModel with pooreNemecek2018
  • value

Requirements

• A Cycle with:
  • Data completeness assessment for animalFeed: completeness.animalFeed
  • Data completeness assessment for product: completeness.product
  • either:
    • the following fields:
      • a list of animals with:
        • a list of with:
    • the following fields:
      • a list of inputs with:
        • of = kg and > 0 and with True and a list of with:
  • a list of products with:
    • value and term of termType = liveAnimal or animalProduct or liveAquaticSpecies and optional:
      • a list of properties with:
        • and with

Lookup used

• crop-property.csv -> nitrogenContent; crudeProteinContent
• animalProduct.csv -> excretaKgNTermId; allowedExcretaKgNTermIds
• liveAnimal.csv -> excretaKgNTermId; allowedExcretaKgNTermIds
• liveAquaticSpecies.csv -> excretaKgNTermId; allowedExcretaKgNTermIds

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.pooreNemecek2018 import run

print(run('excretaKgN', Cycle))

View source on Gitlab

Excreta (kg VS)

This model calculates the Excreta (kg VS) from the products as described in Poore & Nemecek (2018). The model computes it as the balance between the carbon in the inputs plus the carbon produced in the pond minus the carbon contained in the primary product. If the mass balance fails (i.e. animal feed is not complete, see requirements below), the fomula is = total excreta as N / Volatile solids content.

Returns

• A list of Products with:
  • methodModel with pooreNemecek2018
  • value

Requirements

• A Cycle with:
  • either:
    • the following fields:
      • Data completeness assessment for animalFeed: completeness.animalFeed
      • Data completeness assessment for product: completeness.product
      • either:
        • the following fields:
        • the following fields:
      • a list of products with:
        • = True and and a list of with:
      • a list of practices with:
        • and with
        • and with
      • a site with:
        • a list of with:
    • the following fields:
      • a list of products with:
        • = True and and of = or or

Lookup used

• crop-property.csv -> carbonContent; energyContentHigherHeatingValue
• animalProduct.csv -> excretaKgVsTermId; allowedExcretaKgVsTermIds
• liveAnimal.csv -> excretaKgVsTermId; allowedExcretaKgVsTermIds
• liveAquaticSpecies.csv -> excretaKgVsTermId; allowedExcretaKgVsTermIds

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.pooreNemecek2018 import run

print(run('excretaKgVs', Cycle))

View source on Gitlab

Land occupation, during Cycle

The amount of land required to produce the Product during the Cycle, multiplied by the time (in years) that the land was occupied including fallow periods.

Returns

• A list of Indicators with:
  • term with landOccupationDuringCycle
  • methodModel with pooreNemecek2018
  • value

Requirements

• A ImpactAssessment with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture or animal housing or pond or agri-food processor or food retailer
  • a product
  • a cycle with:
    • either:
      • if the cycle.functionalUnit = 1 ha, additional properties are required:
        • a list of with:
        • a list of with:
      • for plantations, additional properties are required:
        • a list of with:

Lookup used

• resourceUse.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.pooreNemecek2018 import run

print(run('landOccupationDuringCycle', ImpactAssessment))

View source on Gitlab

Long fallow period

The period, in days, from the harvest of the previous crop to the planting of the considered crop. The fallow period is considered long if it lasts 365 days or more (up to 5 years). For shorter periods, use the Short fallow period term.

Returns

• A list of Practices with:
  • term with longFallowPeriod
  • methodModel with pooreNemecek2018
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland
  • a list of products with:
    • value and term of termType = crop

Lookup used

• crop.csv -> Plantation_longFallowPeriod
• landUseManagement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.pooreNemecek2018 import run

print(run('longFallowPeriod', Cycle))

View source on Gitlab

N2O, to air, aquaculture systems, direct

Nitrous oxide emissions to air, directly created from the breakdown of excreta and unconsumed feed in aquaculture systems.

Returns

• A list of Emissions with:
  • term with n2OToAirAquacultureSystemsDirect
  • methodModel with pooreNemecek2018
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with pond or river or stream or lake or sea or ocean
  • a list of products with:
    • term of termType = liveAquaticSpecies
  • a list of inputs with:
    • value and term of termType = excreta and term of units = kg N and a list of properties with:
      • value and term with nitrogenContent
      • value and term with totalAmmoniacalNitrogenContentAsN
  • a list of practices with:
    • value and term of termType = excretaManagement

Lookup used

• emission.csv -> siteTypesAllowed; productTermTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.pooreNemecek2018 import run

print(run('n2OToAirAquacultureSystemsDirect', Cycle))

View source on Gitlab

N2, to air, aquaculture systems

Nitrogen emissions to air, created from the breakdown of excreta and unconsumed feed in aquaculture systems.

Returns

• A list of Emissions with:
  • term with n2ToAirAquacultureSystems
  • methodModel with pooreNemecek2018
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with pond or river or stream or lake or sea or ocean
  • a list of products with:
    • term of termType = liveAquaticSpecies
  • a list of inputs with:
    • value and term of termType = excreta and term of units = kg N and a list of properties with:
      • value and term with nitrogenContent
      • value and term with totalAmmoniacalNitrogenContentAsN
  • a list of practices with:
    • value and term of termType = excretaManagement

Lookup used

• emission.csv -> siteTypesAllowed; productTermTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.pooreNemecek2018 import run

print(run('n2ToAirAquacultureSystems', Cycle))

View source on Gitlab

NH3, to air, aquaculture systems

Ammonia emissions to air, created from the breakdown of excreta and unconsumed feed in aquaculture systems.

Returns

• A list of Emissions with:
  • term with nh3ToAirAquacultureSystems
  • methodModel with pooreNemecek2018
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with pond or river or stream or lake or sea or ocean
  • a list of products with:
    • term of termType = liveAquaticSpecies
  • a list of inputs with:
    • value and term of termType = excreta and term of units = kg N and a list of properties with:
      • value and term with nitrogenContent
      • value and term with totalAmmoniacalNitrogenContentAsN
  • a list of practices with:
    • value and term of termType = excretaManagement
  • either:
    • a site with:
      • siteType with pond or sea or ocean
    • a list of practices with:
      • value and term with yieldOfPrimaryAquacultureProductLiveweightPerM2

Lookup used

• emission.csv -> siteTypesAllowed; productTermTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.pooreNemecek2018 import run

print(run('nh3ToAirAquacultureSystems', Cycle))

View source on Gitlab

NO3, to groundwater, crop residue decomposition

Nitrate leaching to groundwater, from crop residue decomposition.

Returns

• A list of Emissions with:
  • term with no3ToGroundwaterCropResidueDecomposition
  • methodModel with pooreNemecek2018
  • value
  • methodTier with tier 2

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover and a list of measurements with:
      • value and term with clayContent
      • value and term with sandContent
      • value and term with precipitationAnnual or precipitationLongTermAnnualMean
  • Data completeness assessment for cropResidue: completeness.cropResidue must be True
  • a list of products with:
    • value and term of termType = cropResidue and a list of properties with:
      • value and term with nitrogenContent

Lookup used

• cropResidue.csv -> decomposesOnField
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.pooreNemecek2018 import run

print(run('no3ToGroundwaterCropResidueDecomposition', Cycle))

View source on Gitlab

NO3, to groundwater, excreta

Nitrate leaching to groundwater, from animal excreta.

Returns

• A list of Emissions with:
  • term with no3ToGroundwaterExcreta
  • methodModel with pooreNemecek2018
  • value
  • methodTier with tier 2

Requirements

• A Cycle with:
  • a site with:
    • siteType with permanent pasture and a list of measurements with:
      • value and term with clayContent
      • value and term with sandContent
      • value and term with precipitationAnnual or precipitationLongTermAnnualMean
  • a list of inputs with:
    • value and term of units = kg or kg N and term of termType = excreta and a list of properties with:
      • value and term with nitrogenContent
    • term of termType = excreta
  • Data completeness assessment for excreta: completeness.excreta must be True
  • a list of products with:
    • value and term of units = kg or kg N and term of termType = excreta and a list of properties with:
      • value and term with nitrogenContent

This model works on the following Node type with identical requirements:

• Cycle
• Transformation

Lookup used

• emission.csv -> siteTypesAllowed; inputTermTypesAllowed; typesAllowed
• emission-model-siteTypesAllowed.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.pooreNemecek2018 import run

print(run('no3ToGroundwaterExcreta', Cycle))

View source on Gitlab

NO3, to groundwater, inorganic fertiliser

Nitrate leaching to groundwater, from inorganic fertiliser.

Returns

• A list of Emissions with:
  • term with no3ToGroundwaterInorganicFertiliser
  • methodModel with pooreNemecek2018
  • value
  • methodTier with tier 2

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture and a list of measurements with:
      • value and term with clayContent
      • value and term with sandContent
      • value and term with precipitationAnnual or precipitationLongTermAnnualMean
  • Data completeness assessment for fertiliser: completeness.fertiliser must be True
  • a list of inputs with:
    • value and term of units = kg or kg N and term of termType = inorganicFertiliser and optional:
      • a list of properties with:
        • and with

Lookup used

• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.pooreNemecek2018 import run

print(run('no3ToGroundwaterInorganicFertiliser', Cycle))

View source on Gitlab

NO3, to groundwater, organic fertiliser

Nitrate leaching to groundwater, from organic fertiliser.

Returns

• A list of Emissions with:
  • term with no3ToGroundwaterOrganicFertiliser
  • methodModel with pooreNemecek2018
  • value
  • methodTier with tier 2

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture and a list of measurements with:
      • value and term with clayContent
      • value and term with sandContent
      • value and term with precipitationAnnual or precipitationLongTermAnnualMean
  • Data completeness assessment for fertiliser: completeness.fertiliser must be True
  • a list of inputs with:
    • value and term of units = kg or kg N and term of termType = organicFertiliser and optional:
      • a list of properties with:
        • and with

Lookup used

• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.pooreNemecek2018 import run

print(run('no3ToGroundwaterOrganicFertiliser', Cycle))

View source on Gitlab

NO3, to groundwater, soil flux

The total amount of nitrate leaching into groundwater from the soil, including from nitrogen added in fertiliser, excreta, and residue, and from natural background processes.

Returns

• A list of Emissions with:
  • term with no3ToGroundwaterSoilFlux
  • methodModel with pooreNemecek2018
  • value
  • methodTier with tier 2

Requirements

• A Cycle with:
  • a site with:
    • siteType with forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture and a list of measurements with:
      • value and term with clayContent
      • value and term with sandContent
      • value and term with precipitationAnnual or precipitationLongTermAnnualMean
  • Data completeness assessment for excreta: completeness.excreta must be True
  • Data completeness assessment for cropResidue: completeness.cropResidue must be True
  • Data completeness assessment for fertiliser: completeness.fertiliser must be True
  • a list of products with:
    • value and term of termType = cropResidue or excreta and a list of properties with:
      • value and term with nitrogenContent
  • a list of inputs with:
    • value and term of units = kg or kg N and term of termType = organicFertiliser or inorganicFertiliser or excreta and optional:
      • a list of properties with:
        • and with

Lookup used

• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.pooreNemecek2018 import run

print(run('no3ToGroundwaterSoilFlux', Cycle))

View source on Gitlab

NOx, to air, aquaculture systems

Nitrogen oxides emissions to air, created from the breakdown of excreta and unconsumed feed in aquaculture systems.

Returns

• A list of Emissions with:
  • term with noxToAirAquacultureSystems
  • methodModel with pooreNemecek2018
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with pond or river or stream or lake or sea or ocean
  • a list of products with:
    • term of termType = liveAquaticSpecies
  • a list of inputs with:
    • value and term of termType = excreta and term of units = kg N and a list of properties with:
      • value and term with nitrogenContent
      • value and term with totalAmmoniacalNitrogenContentAsN
  • a list of practices with:
    • value and term of termType = excretaManagement
  • optional:
    • a site with:
      • siteType with pond or sea or ocean

Lookup used

• emission.csv -> siteTypesAllowed; productTermTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.pooreNemecek2018 import run

print(run('noxToAirAquacultureSystems', Cycle))

View source on Gitlab

Nursery density

The number of seedlings or saplings grown in a plant nursery per ha.

Returns

• A list of Practices with:
  • term with nurseryDensity
  • methodModel with pooreNemecek2018
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland
  • a list of products with:
    • value and term of termType = crop

Lookup used

• crop.csv -> Nursery_density
• landUseManagement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.pooreNemecek2018 import run

print(run('nurseryDensity', Cycle))

View source on Gitlab

Nursery duration

The time from planting seedlings to the sale of marketable trees

Returns

• A list of Practices with:
  • term with nurseryDuration
  • methodModel with pooreNemecek2018
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland
  • a list of products with:
    • value and term of termType = crop

Lookup used

• crop.csv -> Nursery_duration
• landUseManagement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.pooreNemecek2018 import run

print(run('nurseryDuration', Cycle))

View source on Gitlab

Plantation density

The number of trees or vines per ha in a mature plantation.

Returns

• A list of Practices with:
  • term with plantationDensity
  • methodModel with pooreNemecek2018
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland
  • a list of products with:
    • value and term of termType = crop
    • term with genericTreeWood or genericTreeLeaf or citrusFruit or citrusWood or citrusPulpFresh or citrusPulpDry or fruitCropFruit or treeNutInShell or treeNutKernel or treeNutWood or abacaLeafStalk or abacaFibre or achioteSeed or achioteAril or allspiceFruit or almondWood or almondFruit or almondHull or almondInShell or almondKernel or almondShell or appleFruit or apricotFruit or arecaSeed or arazaFruit or asparagusShoot or avocadoFruit or avocadoWood or starAniseFruit or bambooCulm or bambooShoot or bambooLeaf or bambooFibre or bananaFruit or bananaFruitPlantain or bananaPeel or bananaStalk or bananaLeaf or bananaRoot or bayLeaf or bergamotFruit or pepperFruit or pepperSeed or blackWattleSap or blackberryFruit or blueberryFruit or bilberryFruit or dewberryFruit or loganberryFruit or brazilNutFruit or brazilNutInShell or brazilNutShell or brazilNutKernel or breadfruitFruit or carobPod or carobPulp or carobSeed or carobGum or africanLocustBeanPod or africanLocustBeanPodHusk or africanLocustBeanPodPulp or africanLocustBeanSeed or cashewFruit or cashewKernel or cashewHull or cashewInShell or cashewShell or cashewApple or cashewGum or cashewWood or chayoteFruit or sweetCherryFruit or sourCherryFruit or chestnutInHusk or chestnutHusk or chestnutInShell or chestnutShell or chestnutKernel or cinnamonBark or citronFruit or clementineFruit or cloveFlowerBud or cocoaFruit or cocoaSeedWhole or cocoaSeedDehulled or cocoaHull or cocoaPodHusk or coconutFruitInHusk or coconutFruit or coconutYoungFruit or coconutShell or coconutWater or coconutMeat or coconutMeal or coconutCoir or coconutSap or coconutOilCrude or coconutOilRefined or coconutLeaf or coconutMidrib or coconutFattyAcidDistillate or coffeeFruit or coffeePulp or coffeeSeedWhole or coffeeSeedDehulled or coffeeHull or coffeeLeaf or colaSeed or cranberryFruit or currantFruit or custardAppleFruit or dateFruit or moringaLeaf or moringaYoungPod or moringaSeed or figFruit or devilSCottonBark or devilSCottonFibre or gooseberryFruit or grapefruitFruit or grapesFruit or grapesLeaf or grapesPomace or grapesWood or gumArabicGum or guavaFruit or hazelnutInHusk or hazelnutHusk or hazelnutInShell or hazelnutKernel or hazelnutShell or henequenLeaf or henequenFibre or hennaLeaf or jackfruitFruit or jackfruitSeed or jasmineFlower or juniperCone or jojobaSeed or kapokPod or kapokFibre or kiwifruitFruit or lemonFruit or limeFruit or limeLeaf or limequatFruit or tangeloFruit or lindenFlower or pineSeed or litchiFruit or loquatFruit or loquatWood or macadamiaInShell or macadamiaKernel or macadamiaShell or mandarinFruit or mangoFruit or medlarFruit or mulberryFruit or whiteMulberryFruit or whiteMulberryLeafMeal or nutmegSeed or nutmegAril or oilPalmFruit or oilPalmFrond or oilPalmWood or oilPalmShell or oilPalmKernel or oilPalmKernelMeal or oilPalmKernelOilCrude or oilPalmKernelOilRefined or oilPalmMillEffluent or oilPalmMillEffluentDecanted or oilPalmPressFibre or oilPalmOilCrude or oilPalmOilRefined or oilPalmFattyAcidDistillate or oilPalmStockSoap or oliveFruit or oliveOilVirgin or olivePomaceCrude or oliveOilPomace or olivePomaceExhausted or oliveSeed or oliveWood or orangeFruit or bitterOrangeFruit or palmyraFruit or palmyraSap or palmyraFrond or sagoPalmPith or sagoPalmStarch or papayaFruit or peachFruit or peachPalmFruit or pearFruit or pecanFruit or pecanHull or pecanInShell or pecanShell or pecanKernel or persimmonFruit or pineappleFruit or pistachioFruit or pistachioHull or pistachioInShell or pistachioKernel or pistachioShell or plumFruit or pomegranateFruit or pomeloFruit or mesquiteLeaf or mesquitePod or mesquiteYoungPod or mesquitePodHusk or mesquiteSeedWhole or mesquiteSeedDehulled or mesquiteHull or quinceFruit or quinineBark or raspberryFruit or roseFlower or roseCutFlower or rosePetal or roseHip or rubberLatex or sapodillaFruit or sassafrasLeaf or satsumaFruit or sheanutLeaf or sheanutInShell or sheanutShell or sheanutKernel or sheanutFruit or sugarcaneStalk or sugarcaneBagasse or sugarcaneJuice or sugarcaneMolasses or sugarcanePressMud or sugarcaneTop or sweetSorghumGrain or tangerineFruit or tallowSeed or teaLeaf or teaOilCamelliaSeed or tungSeed or tungOil or vanillaPod or vanillaSeed or walnutKernel or walnutInShell or walnutShell or walnutFruit or walnutHull or yerbaMateLeaf or azaroleFruit or caperFlowerBud or caperFruit or carambolaFruit or cherimoyaFruit or durianFruit or elderFruit or elderFlower or feijoaFruit or jambolanFruit or jujubeFruit or kumquatFruit or longanFruit or longkongFruit or mangosteenFruit or passionfruitFruit or pitayaFruit or pricklyPearFruit or rambutanFruit or soursopFruit or starAppleFruit or sweetGranadillaFruit or tamarilloFruit or ugliFruit or gojiBerryFruit or curryLeaf or physalisFruit or tamarindLeaf or tamarindPod or tamarindPodHusk or tamarindSeed or tamarindWood or sichuanPepperFruit or eucalyptusLeaf or eucalyptusWood or silkOakWood or khatLeaf or palmHeart or agatiHay or giantReedHay or lebbekHay or sesbanHay or umbrellaThornHay or winterThornHay or agatiPod or babulYoungPod or babulPod or babulPodHusk or lebbekPod or rainTreePod or riverTamarindPod or sesbanPod or umbrellaThornPod or umbrellaThornYoungPod or winterThornPod or rainTreeSeed or riverTamarindSeed or sesbanSeed or umbrellaThornSeed or winterThornSeed

Lookup used

• crop.csv -> Plantation_density
• landUseManagement.csv -> siteTypesAllowed; productTermIdsAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.pooreNemecek2018 import run

print(run('plantationDensity', Cycle))

View source on Gitlab

Plantation lifespan

The plantation lifespan in days, from its establishment to its removal.

Returns

• A list of Practices with:
  • term with plantationLifespan
  • methodModel with pooreNemecek2018
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland
  • a list of products with:
    • value and term of termType = crop
    • term with genericTreeWood or genericTreeLeaf or citrusFruit or citrusWood or citrusPulpFresh or citrusPulpDry or fruitCropFruit or treeNutInShell or treeNutKernel or treeNutWood or abacaLeafStalk or abacaFibre or achioteSeed or achioteAril or allspiceFruit or almondWood or almondFruit or almondHull or almondInShell or almondKernel or almondShell or appleFruit or apricotFruit or arecaSeed or arazaFruit or asparagusShoot or avocadoFruit or avocadoWood or starAniseFruit or bambooCulm or bambooShoot or bambooLeaf or bambooFibre or bananaFruit or bananaFruitPlantain or bananaPeel or bananaStalk or bananaLeaf or bananaRoot or bayLeaf or bergamotFruit or pepperFruit or pepperSeed or blackWattleSap or blackberryFruit or blueberryFruit or bilberryFruit or dewberryFruit or loganberryFruit or brazilNutFruit or brazilNutInShell or brazilNutShell or brazilNutKernel or breadfruitFruit or carobPod or carobPulp or carobSeed or carobGum or africanLocustBeanPod or africanLocustBeanPodHusk or africanLocustBeanPodPulp or africanLocustBeanSeed or cashewFruit or cashewKernel or cashewHull or cashewInShell or cashewShell or cashewApple or cashewGum or cashewWood or chayoteFruit or sweetCherryFruit or sourCherryFruit or chestnutInHusk or chestnutHusk or chestnutInShell or chestnutShell or chestnutKernel or cinnamonBark or citronFruit or clementineFruit or cloveFlowerBud or cocoaFruit or cocoaSeedWhole or cocoaSeedDehulled or cocoaHull or cocoaPodHusk or coconutFruitInHusk or coconutFruit or coconutYoungFruit or coconutShell or coconutWater or coconutMeat or coconutMeal or coconutCoir or coconutSap or coconutOilCrude or coconutOilRefined or coconutLeaf or coconutMidrib or coconutFattyAcidDistillate or coffeeFruit or coffeePulp or coffeeSeedWhole or coffeeSeedDehulled or coffeeHull or coffeeLeaf or colaSeed or cranberryFruit or currantFruit or custardAppleFruit or dateFruit or moringaLeaf or moringaYoungPod or moringaSeed or figFruit or devilSCottonBark or devilSCottonFibre or gooseberryFruit or grapefruitFruit or grapesFruit or grapesLeaf or grapesPomace or grapesWood or gumArabicGum or guavaFruit or hazelnutInHusk or hazelnutHusk or hazelnutInShell or hazelnutKernel or hazelnutShell or henequenLeaf or henequenFibre or hennaLeaf or jackfruitFruit or jackfruitSeed or jasmineFlower or juniperCone or jojobaSeed or kapokPod or kapokFibre or kiwifruitFruit or lemonFruit or limeFruit or limeLeaf or limequatFruit or tangeloFruit or lindenFlower or pineSeed or litchiFruit or loquatFruit or loquatWood or macadamiaInShell or macadamiaKernel or macadamiaShell or mandarinFruit or mangoFruit or medlarFruit or mulberryFruit or whiteMulberryFruit or whiteMulberryLeafMeal or nutmegSeed or nutmegAril or oilPalmFruit or oilPalmFrond or oilPalmWood or oilPalmShell or oilPalmKernel or oilPalmKernelMeal or oilPalmKernelOilCrude or oilPalmKernelOilRefined or oilPalmMillEffluent or oilPalmMillEffluentDecanted or oilPalmPressFibre or oilPalmOilCrude or oilPalmOilRefined or oilPalmFattyAcidDistillate or oilPalmStockSoap or oliveFruit or oliveOilVirgin or olivePomaceCrude or oliveOilPomace or olivePomaceExhausted or oliveSeed or oliveWood or orangeFruit or bitterOrangeFruit or palmyraFruit or palmyraSap or palmyraFrond or sagoPalmPith or sagoPalmStarch or papayaFruit or peachFruit or peachPalmFruit or pearFruit or pecanFruit or pecanHull or pecanInShell or pecanShell or pecanKernel or persimmonFruit or pineappleFruit or pistachioFruit or pistachioHull or pistachioInShell or pistachioKernel or pistachioShell or plumFruit or pomegranateFruit or pomeloFruit or mesquiteLeaf or mesquitePod or mesquiteYoungPod or mesquitePodHusk or mesquiteSeedWhole or mesquiteSeedDehulled or mesquiteHull or quinceFruit or quinineBark or raspberryFruit or roseFlower or roseCutFlower or rosePetal or roseHip or rubberLatex or sapodillaFruit or sassafrasLeaf or satsumaFruit or sheanutLeaf or sheanutInShell or sheanutShell or sheanutKernel or sheanutFruit or sugarcaneStalk or sugarcaneBagasse or sugarcaneJuice or sugarcaneMolasses or sugarcanePressMud or sugarcaneTop or sweetSorghumGrain or tangerineFruit or tallowSeed or teaLeaf or teaOilCamelliaSeed or tungSeed or tungOil or vanillaPod or vanillaSeed or walnutKernel or walnutInShell or walnutShell or walnutFruit or walnutHull or yerbaMateLeaf or azaroleFruit or caperFlowerBud or caperFruit or carambolaFruit or cherimoyaFruit or durianFruit or elderFruit or elderFlower or feijoaFruit or jambolanFruit or jujubeFruit or kumquatFruit or longanFruit or longkongFruit or mangosteenFruit or passionfruitFruit or pitayaFruit or pricklyPearFruit or rambutanFruit or soursopFruit or starAppleFruit or sweetGranadillaFruit or tamarilloFruit or ugliFruit or gojiBerryFruit or curryLeaf or physalisFruit or tamarindLeaf or tamarindPod or tamarindPodHusk or tamarindSeed or tamarindWood or sichuanPepperFruit or eucalyptusLeaf or eucalyptusWood or silkOakWood or khatLeaf or palmHeart or agatiHay or giantReedHay or lebbekHay or sesbanHay or umbrellaThornHay or winterThornHay or agatiPod or babulYoungPod or babulPod or babulPodHusk or lebbekPod or rainTreePod or riverTamarindPod or sesbanPod or umbrellaThornPod or umbrellaThornYoungPod or winterThornPod or rainTreeSeed or riverTamarindSeed or sesbanSeed or umbrellaThornSeed or winterThornSeed

Lookup used

• crop.csv -> Plantation_lifespan
• landUseManagement.csv -> siteTypesAllowed; productTermIdsAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.pooreNemecek2018 import run

print(run('plantationLifespan', Cycle))

View source on Gitlab

Plantation productive lifespan

The period, in days, during which a plantation is producing marketed products, as defined in FAOSTAT (2011).

Returns

• A list of Practices with:
  • term with plantationProductiveLifespan
  • methodModel with pooreNemecek2018
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland
  • a list of products with:
    • value and term of termType = crop
    • term with genericTreeWood or genericTreeLeaf or citrusFruit or citrusWood or citrusPulpFresh or citrusPulpDry or fruitCropFruit or treeNutInShell or treeNutKernel or treeNutWood or abacaLeafStalk or abacaFibre or achioteSeed or achioteAril or allspiceFruit or almondWood or almondFruit or almondHull or almondInShell or almondKernel or almondShell or appleFruit or apricotFruit or arecaSeed or arazaFruit or asparagusShoot or avocadoFruit or avocadoWood or starAniseFruit or bambooCulm or bambooShoot or bambooLeaf or bambooFibre or bananaFruit or bananaFruitPlantain or bananaPeel or bananaStalk or bananaLeaf or bananaRoot or bayLeaf or bergamotFruit or pepperFruit or pepperSeed or blackWattleSap or blackberryFruit or blueberryFruit or bilberryFruit or dewberryFruit or loganberryFruit or brazilNutFruit or brazilNutInShell or brazilNutShell or brazilNutKernel or breadfruitFruit or carobPod or carobPulp or carobSeed or carobGum or africanLocustBeanPod or africanLocustBeanPodHusk or africanLocustBeanPodPulp or africanLocustBeanSeed or cashewFruit or cashewKernel or cashewHull or cashewInShell or cashewShell or cashewApple or cashewGum or cashewWood or chayoteFruit or sweetCherryFruit or sourCherryFruit or chestnutInHusk or chestnutHusk or chestnutInShell or chestnutShell or chestnutKernel or cinnamonBark or citronFruit or clementineFruit or cloveFlowerBud or cocoaFruit or cocoaSeedWhole or cocoaSeedDehulled or cocoaHull or cocoaPodHusk or coconutFruitInHusk or coconutFruit or coconutYoungFruit or coconutShell or coconutWater or coconutMeat or coconutMeal or coconutCoir or coconutSap or coconutOilCrude or coconutOilRefined or coconutLeaf or coconutMidrib or coconutFattyAcidDistillate or coffeeFruit or coffeePulp or coffeeSeedWhole or coffeeSeedDehulled or coffeeHull or coffeeLeaf or colaSeed or cranberryFruit or currantFruit or custardAppleFruit or dateFruit or moringaLeaf or moringaYoungPod or moringaSeed or figFruit or devilSCottonBark or devilSCottonFibre or gooseberryFruit or grapefruitFruit or grapesFruit or grapesLeaf or grapesPomace or grapesWood or gumArabicGum or guavaFruit or hazelnutInHusk or hazelnutHusk or hazelnutInShell or hazelnutKernel or hazelnutShell or henequenLeaf or henequenFibre or hennaLeaf or jackfruitFruit or jackfruitSeed or jasmineFlower or juniperCone or jojobaSeed or kapokPod or kapokFibre or kiwifruitFruit or lemonFruit or limeFruit or limeLeaf or limequatFruit or tangeloFruit or lindenFlower or pineSeed or litchiFruit or loquatFruit or loquatWood or macadamiaInShell or macadamiaKernel or macadamiaShell or mandarinFruit or mangoFruit or medlarFruit or mulberryFruit or whiteMulberryFruit or whiteMulberryLeafMeal or nutmegSeed or nutmegAril or oilPalmFruit or oilPalmFrond or oilPalmWood or oilPalmShell or oilPalmKernel or oilPalmKernelMeal or oilPalmKernelOilCrude or oilPalmKernelOilRefined or oilPalmMillEffluent or oilPalmMillEffluentDecanted or oilPalmPressFibre or oilPalmOilCrude or oilPalmOilRefined or oilPalmFattyAcidDistillate or oilPalmStockSoap or oliveFruit or oliveOilVirgin or olivePomaceCrude or oliveOilPomace or olivePomaceExhausted or oliveSeed or oliveWood or orangeFruit or bitterOrangeFruit or palmyraFruit or palmyraSap or palmyraFrond or sagoPalmPith or sagoPalmStarch or papayaFruit or peachFruit or peachPalmFruit or pearFruit or pecanFruit or pecanHull or pecanInShell or pecanShell or pecanKernel or persimmonFruit or pineappleFruit or pistachioFruit or pistachioHull or pistachioInShell or pistachioKernel or pistachioShell or plumFruit or pomegranateFruit or pomeloFruit or mesquiteLeaf or mesquitePod or mesquiteYoungPod or mesquitePodHusk or mesquiteSeedWhole or mesquiteSeedDehulled or mesquiteHull or quinceFruit or quinineBark or raspberryFruit or roseFlower or roseCutFlower or rosePetal or roseHip or rubberLatex or sapodillaFruit or sassafrasLeaf or satsumaFruit or sheanutLeaf or sheanutInShell or sheanutShell or sheanutKernel or sheanutFruit or sugarcaneStalk or sugarcaneBagasse or sugarcaneJuice or sugarcaneMolasses or sugarcanePressMud or sugarcaneTop or sweetSorghumGrain or tangerineFruit or tallowSeed or teaLeaf or teaOilCamelliaSeed or tungSeed or tungOil or vanillaPod or vanillaSeed or walnutKernel or walnutInShell or walnutShell or walnutFruit or walnutHull or yerbaMateLeaf or azaroleFruit or caperFlowerBud or caperFruit or carambolaFruit or cherimoyaFruit or durianFruit or elderFruit or elderFlower or feijoaFruit or jambolanFruit or jujubeFruit or kumquatFruit or longanFruit or longkongFruit or mangosteenFruit or passionfruitFruit or pitayaFruit or pricklyPearFruit or rambutanFruit or soursopFruit or starAppleFruit or sweetGranadillaFruit or tamarilloFruit or ugliFruit or gojiBerryFruit or curryLeaf or physalisFruit or tamarindLeaf or tamarindPod or tamarindPodHusk or tamarindSeed or tamarindWood or sichuanPepperFruit or eucalyptusLeaf or eucalyptusWood or silkOakWood or khatLeaf or palmHeart or agatiHay or giantReedHay or lebbekHay or sesbanHay or umbrellaThornHay or winterThornHay or agatiPod or babulYoungPod or babulPod or babulPodHusk or lebbekPod or rainTreePod or riverTamarindPod or sesbanPod or umbrellaThornPod or umbrellaThornYoungPod or winterThornPod or rainTreeSeed or riverTamarindSeed or sesbanSeed or umbrellaThornSeed or winterThornSeed

Lookup used

• crop.csv -> Plantation_non-productive_lifespan
• landUseManagement.csv -> siteTypesAllowed; productTermIdsAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.pooreNemecek2018 import run

print(run('plantationProductiveLifespan', Cycle))

View source on Gitlab

Rotation duration

The length, in days, of the entire crop or aquaculture rotation, including the long fallow period (for crops).

Returns

• A list of Practices with:
  • term with rotationDuration
  • methodModel with pooreNemecek2018
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture
  • a list of products with:
    • value and term of termType = crop

Lookup used

• crop.csv -> Plantation_lifespan; Plantation_longFallowPeriod
• landUseManagement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.pooreNemecek2018 import run

print(run('rotationDuration', Cycle))

View source on Gitlab

Saplings

Young trees, around 10cm or less in height.

Returns

• A list of Inputs with:
  • term with saplings
  • methodModel with pooreNemecek2018
  • value

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture
  • Data completeness assessment for other: completeness.other must be False
  • a list of products with:
    • value and term of termType = crop
  • a list of practices with:
    • value and term with plantationLifespan

Lookup used

• crop.csv -> Saplings_required
• seed.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.pooreNemecek2018 import run

print(run('saplings', Cycle))

View source on Gitlab

ReCiPe 2016 Egalitarian

These models characterise emissions and resource uses according to the ReCiPe 2016 method, using an egalitarian perspective (see Huijbregts et al (2016)).

View source on Gitlab

Damage to freshwater ecosystems (species*year)

The number of freshwater species that are committed to local extinction over a certain period of time if the pressure continues to happen. See ReCiPe 2016.

Returns

• A Indicator with:
  • term with damageToFreshwaterEcosystemsSpeciesYear
  • methodModel with recipe2016Egalitarian
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> speciesYearEgalitarianDamageToFreshwaterEcosystemsReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Egalitarian import run

print(run('damageToFreshwaterEcosystemsSpeciesYear', ImpactAssessment))

View source on Gitlab

Damage to human health

The disability-adjusted life years lost in the human population.

Returns

• A Indicator with:
  • term with damageToHumanHealth
  • methodModel with recipe2016Egalitarian
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> dalyEgalitarianDamageToHumanHealthReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Egalitarian import run

print(run('damageToHumanHealth', ImpactAssessment))

View source on Gitlab

Damage to marine ecosystems (species*year)

The number of marine species that are committed to local extinction over a certain period of time if the pressure continues to happen. See ReCiPe 2016.

Returns

• A Indicator with:
  • term with damageToMarineEcosystemsSpeciesYear
  • methodModel with recipe2016Egalitarian
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> speciesYearEgalitarianDamageToMarineEcosystemsReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Egalitarian import run

print(run('damageToMarineEcosystemsSpeciesYear', ImpactAssessment))

View source on Gitlab

Damage to resource availability

The surplus costs of future resource production in 2013 US Dollars over an infinitive timeframe, assuming constant annual production and a 3% discount rate. See ReCiPe 2016.

Returns

• A Indicator with:
  • term with damageToResourceAvailability
  • methodModel with recipe2016Egalitarian
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for electricityFuel: completeness.electricityFuel must be True and a list of inputs with:
      • value and term of termType = fuel

Lookup used

• fuel.csv -> usd2013EgalitarianDamageToResourceAvailabilityReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Egalitarian import run

print(run('damageToResourceAvailability', ImpactAssessment))

View source on Gitlab

Damage to terrestrial ecosystems (species*year)

The number of terrestrial species that are committed to local extinction over a certain period of time if the pressure continues to happen. See ReCiPe 2016.

Returns

• A Indicator with:
  • term with damageToTerrestrialEcosystemsSpeciesYear
  • methodModel with recipe2016Egalitarian
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> speciesYearEgalitarianDamageToTerrestrialEcosystemsReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Egalitarian import run

print(run('damageToTerrestrialEcosystemsSpeciesYear', ImpactAssessment))

View source on Gitlab

Ecosystem damage ozone formation

The potential of emissions to contribute to low level smog (summer smog).

Returns

• A Indicator with:
  • term with ecosystemDamageOzoneFormation
  • methodModel with recipe2016Egalitarian
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• emission.csv -> noxEqEgalitarianEcosystemDamageOzoneFormationReCiPe2016
• pesticideAI.csv -> noxEqEgalitarianEcosystemDamageOzoneFormationReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Egalitarian import run

print(run('ecosystemDamageOzoneFormation', ImpactAssessment))

View source on Gitlab

Fossil resource scarcity

This model calculates the fossil resource scarcity.

Returns

• A Indicator with:
  • term with fossilResourceScarcity
  • methodModel with recipe2016Egalitarian
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for electricityFuel: completeness.electricityFuel must be True and a list of inputs with:
      • value and term of termType = fuel

Lookup used

• fuel.csv -> oilEqEgalitarianFossilResourceScarcityReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Egalitarian import run

print(run('fossilResourceScarcity', ImpactAssessment))

View source on Gitlab

Freshwater aquatic ecotoxicity potential (1,4-DCBeq)

This model calculates the freshwater aquatic ecotoxicity potential (1,4-dcbeq).

Returns

• A Indicator with:
  • term with freshwaterAquaticEcotoxicityPotential14Dcbeq
  • methodModel with recipe2016Egalitarian
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> 14DCBeqEgalitarianFreshwaterEcotoxicityReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Egalitarian import run

print(run('freshwaterAquaticEcotoxicityPotential14Dcbeq', ImpactAssessment))

View source on Gitlab

Freshwater eutrophication potential

The potential of nutrient emissions to cause excessive growth of aquatic plants and algae in freshwater ecosystems (e.g. lakes, rivers, streams). Some algae - particularly phytoplankton - are inedible to much other aquatic life and go uneaten, meaning the phytoplankton die and get broken down by bacteria which use oxygen for respiration. This oxygen demand depltes oxygen in the water leading to hypoxia. Freshwater eutrophication is primarily linked to phosphorus as this tends to be the limiting nutrient in these ecosystems.

Returns

• A Indicator with:
  • term with freshwaterEutrophicationPotential
  • methodModel with recipe2016Egalitarian
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> pEqEgalitarianFreshwaterEutrophicationReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Egalitarian import run

print(run('freshwaterEutrophicationPotential', ImpactAssessment))

View source on Gitlab

Human carcinogenic toxicity

The potential of emissions to contribute to the risk of increased incidence of cancer diseases.

Returns

• A Indicator with:
  • term with humanCarcinogenicToxicity
  • methodModel with recipe2016Egalitarian
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> 14DCBeqEgalitarianHumanCarcinogenicToxicityReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Egalitarian import run

print(run('humanCarcinogenicToxicity', ImpactAssessment))

View source on Gitlab

Human damage ozone formation

The potential of emissions to contribute to the increase in tropospheric ozone intake by humans causing health problems.

Returns

• A Indicator with:
  • term with humanDamageOzoneFormation
  • methodModel with recipe2016Egalitarian
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• emission.csv -> noxEqEgalitarianHumanDamageOzoneFormationReCiPe2016
• pesticideAI.csv -> noxEqEgalitarianHumanDamageOzoneFormationReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Egalitarian import run

print(run('humanDamageOzoneFormation', ImpactAssessment))

View source on Gitlab

Human non-carcinogenic toxicity

The potential of emissions to contribute to the risk of increased incidence of non-cancer diseases.

Returns

• A Indicator with:
  • term with humanNonCarcinogenicToxicity
  • methodModel with recipe2016Egalitarian
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> 14DCBeqEgalitarianHumanNoncarcinogenicToxicityReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Egalitarian import run

print(run('humanNonCarcinogenicToxicity', ImpactAssessment))

View source on Gitlab

Marine aquatic ecotoxicity potential (1,4-DCBeq)

This model calculates the marine aquatic ecotoxicity potential (1,4-dcbeq).

Returns

• A Indicator with:
  • term with marineAquaticEcotoxicityPotential14Dcbeq
  • methodModel with recipe2016Egalitarian
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> 14DCBeqEgalitarianMarineEcotoxicityReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Egalitarian import run

print(run('marineAquaticEcotoxicityPotential14Dcbeq', ImpactAssessment))

View source on Gitlab

Marine eutrophication potential

The potential of nutrient emissions to cause excessive growth of aquatic plants and algae in marine ecosystems (e.g. seas, oceans, estuaries). Some algae - particularly phytoplankton - are inedible to much other aquatic life and go uneaten, meaning the phytoplankton die and get broken down by bacteria which use oxygen for respiration. This oxygen demand depltes oxygen in the water leading to hypoxia. Marine eutrophication is primarily linked to nitrogen as this tends to be the limiting nutrient in these ecosystems.

Returns

• A Indicator with:
  • term with marineEutrophicationPotential
  • methodModel with recipe2016Egalitarian
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> nEqEgalitarianMarineEutrophicationReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Egalitarian import run

print(run('marineEutrophicationPotential', ImpactAssessment))

View source on Gitlab

Ozone depletion potential

The potential of emissions to cause thinning of the stratospheric ozone layer.

Returns

• A Indicator with:
  • term with ozoneDepletionPotential
  • methodModel with recipe2016Egalitarian
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> cfc11EqEgalitarianStratosphericOzoneDepletionReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Egalitarian import run

print(run('ozoneDepletionPotential', ImpactAssessment))

View source on Gitlab

Terrestrial acidification potential

Changes in soil chemical properties following the deposition of nitrogen and sulphur in acidifying forms.

Returns

• A Indicator with:
  • term with terrestrialAcidificationPotential
  • methodModel with recipe2016Egalitarian
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> so2EqEgalitarianTerrestrialAcidificationReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Egalitarian import run

print(run('terrestrialAcidificationPotential', ImpactAssessment))

View source on Gitlab

Terrestrial ecotoxicity potential (1,4-DCBeq)

This model calculates the terrestrial ecotoxicity potential (1,4-dcbeq).

Returns

• A Indicator with:
  • term with terrestrialEcotoxicityPotential14Dcbeq
  • methodModel with recipe2016Egalitarian
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> 14DCBeqEgalitarianTerrestrialEcotoxicityReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Egalitarian import run

print(run('terrestrialEcotoxicityPotential14Dcbeq', ImpactAssessment))

View source on Gitlab

ReCiPe 2016 Hierarchist

These models characterise emissions and resource uses according to the ReCiPe 2016 method, using a hierarchist perspective (see Huijbregts et al (2016)).

View source on Gitlab

Damage to freshwater ecosystems (species*year)

The number of freshwater species that are committed to local extinction over a certain period of time if the pressure continues to happen. See ReCiPe 2016.

Returns

• A Indicator with:
  • term with damageToFreshwaterEcosystemsSpeciesYear
  • methodModel with recipe2016Hierarchist
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> speciesYearHierarchistDamageToFreshwaterEcosystemsReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Hierarchist import run

print(run('damageToFreshwaterEcosystemsSpeciesYear', ImpactAssessment))

View source on Gitlab

Damage to human health

The disability-adjusted life years lost in the human population.

Returns

• A Indicator with:
  • term with damageToHumanHealth
  • methodModel with recipe2016Hierarchist
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> dalyHierarchistDamageToHumanHealthReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Hierarchist import run

print(run('damageToHumanHealth', ImpactAssessment))

View source on Gitlab

Damage to marine ecosystems (species*year)

The number of marine species that are committed to local extinction over a certain period of time if the pressure continues to happen. See ReCiPe 2016.

Returns

• A Indicator with:
  • term with damageToMarineEcosystemsSpeciesYear
  • methodModel with recipe2016Hierarchist
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> speciesYearHierarchistDamageToMarineEcosystemsReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Hierarchist import run

print(run('damageToMarineEcosystemsSpeciesYear', ImpactAssessment))

View source on Gitlab

Damage to resource availability

The surplus costs of future resource production in 2013 US Dollars over an infinitive timeframe, assuming constant annual production and a 3% discount rate. See ReCiPe 2016.

Returns

• A Indicator with:
  • term with damageToResourceAvailability
  • methodModel with recipe2016Hierarchist
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for electricityFuel: completeness.electricityFuel must be True and a list of inputs with:
      • value and term of termType = fuel

Lookup used

• fuel.csv -> usd2013HierarchistDamageToResourceAvailabilityReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Hierarchist import run

print(run('damageToResourceAvailability', ImpactAssessment))

View source on Gitlab

Damage to terrestrial ecosystems (species*year)

The number of terrestrial species that are committed to local extinction over a certain period of time if the pressure continues to happen. See ReCiPe 2016.

Returns

• A Indicator with:
  • term with damageToTerrestrialEcosystemsSpeciesYear
  • methodModel with recipe2016Hierarchist
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> speciesYearHierarchistDamageToTerrestrialEcosystemsReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Hierarchist import run

print(run('damageToTerrestrialEcosystemsSpeciesYear', ImpactAssessment))

View source on Gitlab

Ecosystem damage ozone formation

The potential of emissions to contribute to low level smog (summer smog).

Returns

• A Indicator with:
  • term with ecosystemDamageOzoneFormation
  • methodModel with recipe2016Hierarchist
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• emission.csv -> noxEqHierarchistEcosystemDamageOzoneFormationReCiPe2016
• pesticideAI.csv -> noxEqHierarchistEcosystemDamageOzoneFormationReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Hierarchist import run

print(run('ecosystemDamageOzoneFormation', ImpactAssessment))

View source on Gitlab

Fossil resource scarcity

This model calculates the fossil resource scarcity.

Returns

• A Indicator with:
  • term with fossilResourceScarcity
  • methodModel with recipe2016Hierarchist
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for electricityFuel: completeness.electricityFuel must be True and a list of inputs with:
      • value and term of termType = fuel

Lookup used

• fuel.csv -> oilEqHierarchistFossilResourceScarcityReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Hierarchist import run

print(run('fossilResourceScarcity', ImpactAssessment))

View source on Gitlab

Freshwater aquatic ecotoxicity potential (1,4-DCBeq)

This model calculates the freshwater aquatic ecotoxicity potential (1,4-dcbeq).

Returns

• A Indicator with:
  • term with freshwaterAquaticEcotoxicityPotential14Dcbeq
  • methodModel with recipe2016Hierarchist
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> 14DCBeqHierarchistFreshwaterEcotoxicityReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Hierarchist import run

print(run('freshwaterAquaticEcotoxicityPotential14Dcbeq', ImpactAssessment))

View source on Gitlab

Freshwater eutrophication potential

The potential of nutrient emissions to cause excessive growth of aquatic plants and algae in freshwater ecosystems (e.g. lakes, rivers, streams). Some algae - particularly phytoplankton - are inedible to much other aquatic life and go uneaten, meaning the phytoplankton die and get broken down by bacteria which use oxygen for respiration. This oxygen demand depltes oxygen in the water leading to hypoxia. Freshwater eutrophication is primarily linked to phosphorus as this tends to be the limiting nutrient in these ecosystems.

Returns

• A Indicator with:
  • term with freshwaterEutrophicationPotential
  • methodModel with recipe2016Hierarchist
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> pEqHierarchistFreshwaterEutrophicationReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Hierarchist import run

print(run('freshwaterEutrophicationPotential', ImpactAssessment))

View source on Gitlab

Human carcinogenic toxicity

The potential of emissions to contribute to the risk of increased incidence of cancer diseases.

Returns

• A Indicator with:
  • term with humanCarcinogenicToxicity
  • methodModel with recipe2016Hierarchist
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> 14DCBeqHierarchistHumanCarcinogenicToxicityReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Hierarchist import run

print(run('humanCarcinogenicToxicity', ImpactAssessment))

View source on Gitlab

Human damage ozone formation

The potential of emissions to contribute to the increase in tropospheric ozone intake by humans causing health problems.

Returns

• A Indicator with:
  • term with humanDamageOzoneFormation
  • methodModel with recipe2016Hierarchist
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• emission.csv -> noxEqHierarchistHumanDamageOzoneFormationReCiPe2016
• pesticideAI.csv -> noxEqHierarchistHumanDamageOzoneFormationReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Hierarchist import run

print(run('humanDamageOzoneFormation', ImpactAssessment))

View source on Gitlab

Human non-carcinogenic toxicity

The potential of emissions to contribute to the risk of increased incidence of non-cancer diseases.

Returns

• A Indicator with:
  • term with humanNonCarcinogenicToxicity
  • methodModel with recipe2016Hierarchist
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> 14DCBeqHierarchistHumanNoncarcinogenicToxicityReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Hierarchist import run

print(run('humanNonCarcinogenicToxicity', ImpactAssessment))

View source on Gitlab

Marine aquatic ecotoxicity potential (1,4-DCBeq)

This model calculates the marine aquatic ecotoxicity potential (1,4-dcbeq).

Returns

• A Indicator with:
  • term with marineAquaticEcotoxicityPotential14Dcbeq
  • methodModel with recipe2016Hierarchist
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> 14DCBeqHierarchistMarineEcotoxicityReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Hierarchist import run

print(run('marineAquaticEcotoxicityPotential14Dcbeq', ImpactAssessment))

View source on Gitlab

Marine eutrophication potential

The potential of nutrient emissions to cause excessive growth of aquatic plants and algae in marine ecosystems (e.g. seas, oceans, estuaries). Some algae - particularly phytoplankton - are inedible to much other aquatic life and go uneaten, meaning the phytoplankton die and get broken down by bacteria which use oxygen for respiration. This oxygen demand depltes oxygen in the water leading to hypoxia. Marine eutrophication is primarily linked to nitrogen as this tends to be the limiting nutrient in these ecosystems.

Returns

• A Indicator with:
  • term with marineEutrophicationPotential
  • methodModel with recipe2016Hierarchist
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> nEqHierarchistMarineEutrophicationReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Hierarchist import run

print(run('marineEutrophicationPotential', ImpactAssessment))

View source on Gitlab

Ozone depletion potential

The potential of emissions to cause thinning of the stratospheric ozone layer.

Returns

• A Indicator with:
  • term with ozoneDepletionPotential
  • methodModel with recipe2016Hierarchist
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> cfc11EqHierarchistStratosphericOzoneDepletionReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Hierarchist import run

print(run('ozoneDepletionPotential', ImpactAssessment))

View source on Gitlab

Terrestrial acidification potential

Changes in soil chemical properties following the deposition of nitrogen and sulphur in acidifying forms.

Returns

• A Indicator with:
  • term with terrestrialAcidificationPotential
  • methodModel with recipe2016Hierarchist
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> so2EqHierarchistTerrestrialAcidificationReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Hierarchist import run

print(run('terrestrialAcidificationPotential', ImpactAssessment))

View source on Gitlab

Terrestrial ecotoxicity potential (1,4-DCBeq)

This model calculates the terrestrial ecotoxicity potential (1,4-dcbeq).

Returns

• A Indicator with:
  • term with terrestrialEcotoxicityPotential14Dcbeq
  • methodModel with recipe2016Hierarchist
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> 14DCBeqHierarchistTerrestrialEcotoxicityReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Hierarchist import run

print(run('terrestrialEcotoxicityPotential14Dcbeq', ImpactAssessment))

View source on Gitlab

ReCiPe 2016 Individualist

These models characterise emissions and resource uses according to the ReCiPe 2016 method, using an individualist perspective (see Huijbregts et al (2016)).

View source on Gitlab

Damage to freshwater ecosystems (species*year)

The number of freshwater species that are committed to local extinction over a certain period of time if the pressure continues to happen. See ReCiPe 2016.

Returns

• A Indicator with:
  • term with damageToFreshwaterEcosystemsSpeciesYear
  • methodModel with recipe2016Individualist
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> speciesYearIndividualistDamageToFreshwaterEcosystemsReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Individualist import run

print(run('damageToFreshwaterEcosystemsSpeciesYear', ImpactAssessment))

View source on Gitlab

Damage to human health

The disability-adjusted life years lost in the human population.

Returns

• A Indicator with:
  • term with damageToHumanHealth
  • methodModel with recipe2016Individualist
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> dalyIndividualistDamageToHumanHealthReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Individualist import run

print(run('damageToHumanHealth', ImpactAssessment))

View source on Gitlab

Damage to marine ecosystems (species*year)

The number of marine species that are committed to local extinction over a certain period of time if the pressure continues to happen. See ReCiPe 2016.

Returns

• A Indicator with:
  • term with damageToMarineEcosystemsSpeciesYear
  • methodModel with recipe2016Individualist
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> speciesYearIndividualistDamageToMarineEcosystemsReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Individualist import run

print(run('damageToMarineEcosystemsSpeciesYear', ImpactAssessment))

View source on Gitlab

Damage to resource availability

The surplus costs of future resource production in 2013 US Dollars over an infinitive timeframe, assuming constant annual production and a 3% discount rate. See ReCiPe 2016.

Returns

• A Indicator with:
  • term with damageToResourceAvailability
  • methodModel with recipe2016Individualist
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for electricityFuel: completeness.electricityFuel must be True and a list of inputs with:
      • value and term of termType = fuel

Lookup used

• fuel.csv -> usd2013IndividualistDamageToResourceAvailabilityReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Individualist import run

print(run('damageToResourceAvailability', ImpactAssessment))

View source on Gitlab

Damage to terrestrial ecosystems (species*year)

The number of terrestrial species that are committed to local extinction over a certain period of time if the pressure continues to happen. See ReCiPe 2016.

Returns

• A Indicator with:
  • term with damageToTerrestrialEcosystemsSpeciesYear
  • methodModel with recipe2016Individualist
  • value

Requirements

• A ImpactAssessment with:
  • a list of impacts with:
    • value and a methodModel with:
      • @id must be set (is linked to an existing Term)

Lookup used

• characterisedIndicator.csv -> speciesYearIndividualistDamageToTerrestrialEcosystemsReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Individualist import run

print(run('damageToTerrestrialEcosystemsSpeciesYear', ImpactAssessment))

View source on Gitlab

Ecosystem damage ozone formation

The potential of emissions to contribute to low level smog (summer smog).

Returns

• A Indicator with:
  • term with ecosystemDamageOzoneFormation
  • methodModel with recipe2016Individualist
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• emission.csv -> noxEqIndividualistEcosystemDamageOzoneFormationReCiPe2016
• pesticideAI.csv -> noxEqIndividualistEcosystemDamageOzoneFormationReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Individualist import run

print(run('ecosystemDamageOzoneFormation', ImpactAssessment))

View source on Gitlab

Fossil resource scarcity

This model calculates the fossil resource scarcity.

Returns

• A Indicator with:
  • term with fossilResourceScarcity
  • methodModel with recipe2016Individualist
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for electricityFuel: completeness.electricityFuel must be True and a list of inputs with:
      • value and term of termType = fuel

Lookup used

• fuel.csv -> oilEqIndividualistFossilResourceScarcityReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Individualist import run

print(run('fossilResourceScarcity', ImpactAssessment))

View source on Gitlab

Freshwater aquatic ecotoxicity potential (1,4-DCBeq)

This model calculates the freshwater aquatic ecotoxicity potential (1,4-dcbeq).

Returns

• A Indicator with:
  • term with freshwaterAquaticEcotoxicityPotential14Dcbeq
  • methodModel with recipe2016Individualist
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> 14DCBeqIndividualistFreshwaterEcotoxicityReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Individualist import run

print(run('freshwaterAquaticEcotoxicityPotential14Dcbeq', ImpactAssessment))

View source on Gitlab

Freshwater eutrophication potential

The potential of nutrient emissions to cause excessive growth of aquatic plants and algae in freshwater ecosystems (e.g. lakes, rivers, streams). Some algae - particularly phytoplankton - are inedible to much other aquatic life and go uneaten, meaning the phytoplankton die and get broken down by bacteria which use oxygen for respiration. This oxygen demand depltes oxygen in the water leading to hypoxia. Freshwater eutrophication is primarily linked to phosphorus as this tends to be the limiting nutrient in these ecosystems.

Returns

• A Indicator with:
  • term with freshwaterEutrophicationPotential
  • methodModel with recipe2016Individualist
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> pEqIndividualistFreshwaterEutrophicationReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Individualist import run

print(run('freshwaterEutrophicationPotential', ImpactAssessment))

View source on Gitlab

Human carcinogenic toxicity

The potential of emissions to contribute to the risk of increased incidence of cancer diseases.

Returns

• A Indicator with:
  • term with humanCarcinogenicToxicity
  • methodModel with recipe2016Individualist
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> 14DCBeqIndividualistHumanCarcinogenicToxicityReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Individualist import run

print(run('humanCarcinogenicToxicity', ImpactAssessment))

View source on Gitlab

Human damage ozone formation

The potential of emissions to contribute to the increase in tropospheric ozone intake by humans causing health problems.

Returns

• A Indicator with:
  • term with humanDamageOzoneFormation
  • methodModel with recipe2016Individualist
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• emission.csv -> noxEqIndividualistHumanDamageOzoneFormationReCiPe2016
• pesticideAI.csv -> noxEqIndividualistHumanDamageOzoneFormationReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Individualist import run

print(run('humanDamageOzoneFormation', ImpactAssessment))

View source on Gitlab

Human non-carcinogenic toxicity

The potential of emissions to contribute to the risk of increased incidence of non-cancer diseases.

Returns

• A Indicator with:
  • term with humanNonCarcinogenicToxicity
  • methodModel with recipe2016Individualist
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> 14DCBeqIndividualistHumanNoncarcinogenicToxicityReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Individualist import run

print(run('humanNonCarcinogenicToxicity', ImpactAssessment))

View source on Gitlab

Marine aquatic ecotoxicity potential (1,4-DCBeq)

This model calculates the marine aquatic ecotoxicity potential (1,4-dcbeq).

Returns

• A Indicator with:
  • term with marineAquaticEcotoxicityPotential14Dcbeq
  • methodModel with recipe2016Individualist
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> 14DCBeqIndividualistMarineEcotoxicityReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Individualist import run

print(run('marineAquaticEcotoxicityPotential14Dcbeq', ImpactAssessment))

View source on Gitlab

Marine eutrophication potential

The potential of nutrient emissions to cause excessive growth of aquatic plants and algae in marine ecosystems (e.g. seas, oceans, estuaries). Some algae - particularly phytoplankton - are inedible to much other aquatic life and go uneaten, meaning the phytoplankton die and get broken down by bacteria which use oxygen for respiration. This oxygen demand depltes oxygen in the water leading to hypoxia. Marine eutrophication is primarily linked to nitrogen as this tends to be the limiting nutrient in these ecosystems.

Returns

• A Indicator with:
  • term with marineEutrophicationPotential
  • methodModel with recipe2016Individualist
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> nEqIndividualistMarineEutrophicationReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Individualist import run

print(run('marineEutrophicationPotential', ImpactAssessment))

View source on Gitlab

Ozone depletion potential

The potential of emissions to cause thinning of the stratospheric ozone layer.

Returns

• A Indicator with:
  • term with ozoneDepletionPotential
  • methodModel with recipe2016Individualist
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> cfc11EqIndividualistStratosphericOzoneDepletionReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Individualist import run

print(run('ozoneDepletionPotential', ImpactAssessment))

View source on Gitlab

Terrestrial acidification potential

Changes in soil chemical properties following the deposition of nitrogen and sulphur in acidifying forms.

Returns

• A Indicator with:
  • term with terrestrialAcidificationPotential
  • methodModel with recipe2016Individualist
  • value

Requirements

• A ImpactAssessment with:
  • a list of emissionsResourceUses with:
    • value and term of termType = emission

Lookup used

• emission.csv -> so2EqIndividualistTerrestrialAcidificationReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Individualist import run

print(run('terrestrialAcidificationPotential', ImpactAssessment))

View source on Gitlab

Terrestrial ecotoxicity potential (1,4-DCBeq)

This model calculates the terrestrial ecotoxicity potential (1,4-dcbeq).

Returns

• A Indicator with:
  • term with terrestrialEcotoxicityPotential14Dcbeq
  • methodModel with recipe2016Individualist
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> 14DCBeqIndividualistTerrestrialEcotoxicityReCiPe2016

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.recipe2016Individualist import run

print(run('terrestrialEcotoxicityPotential14Dcbeq', ImpactAssessment))

View source on Gitlab

Scherer Pfister (2015)

This model implements the phosphorus emissions models detailed in Scherer & Pfister (2015), many of which were originally developed in the SALCA guidelines.

View source on Gitlab

N, erosion, soil flux

Nitrogen losses from soil erosion.

Returns

• A list of Emissions with:
  • term with nErosionSoilFlux
  • methodModel with schererPfister2015
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture and a country with:
      • termType = region and a list of measurements with:
      • value and term with nutrientLossToAquaticEnvironment
      • value and term with heavyWinterPrecipitation
      • value and term with totalNitrogenPerKgSoil
      • value and term with precipitationAnnual
      • value and term with erodibility
      • value and term with slopeLength
      • value and term with slope
  • endDate
  • a list of inputs with:
    • value and term of termType = water

Lookup used

• region.csv -> P_EF_C1; EF_P_C2; Practice_Factor
• tillage.csv -> C2_FACTORS
• organicFertiliser.csv -> OrganicFertiliserClassification
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.schererPfister2015 import run

print(run('nErosionSoilFlux', Cycle))

View source on Gitlab

P, erosion, soil flux

Phosphorus losses from soil erosion.

Returns

• A list of Emissions with:
  • term with pErosionSoilFlux
  • methodModel with schererPfister2015
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture and a country with:
      • termType = region and a list of measurements with:
      • value and term with nutrientLossToAquaticEnvironment
      • value and term with heavyWinterPrecipitation
      • value and term with totalPhosphorusPerKgSoil
      • value and term with totalNitrogenPerKgSoil
      • value and term with precipitationAnnual
      • value and term with erodibility
      • value and term with slopeLength
      • value and term with slope
  • endDate
  • a list of inputs with:
    • value and term of termType = water

Lookup used

• region.csv -> P_EF_C1; EF_P_C2; Practice_Factor
• tillage.csv -> C2_FACTORS
• organicFertiliser.csv -> OrganicFertiliserClassification
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.schererPfister2015 import run

print(run('pErosionSoilFlux', Cycle))

View source on Gitlab

P, to drainage water, soil flux

The total amount of phosphorus which enters drainage water, including from phosphorus added in fertiliser, excreta, and residue, and from natural processes.

Returns

• A list of Emissions with:
  • term with pToDrainageWaterSoilFlux
  • methodModel with schererPfister2015
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture and a country with:
      • termType = region and a list of measurements with:
      • value and term with drainageClass
  • endDate
  • a list of inputs with:
    • value and term of termType = organicFertiliser

Lookup used

• organicFertiliser.csv -> OrganicFertiliserClassification
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.schererPfister2015 import run

print(run('pToDrainageWaterSoilFlux', Cycle))

View source on Gitlab

P, to groundwater, soil flux

The total amount of phosphorus which leaches from the soil into the groundwater, including from phosphorus added in fertiliser, excreta, and residue, and from background leaching due to natural processes.

Returns

• A list of Emissions with:
  • term with pToGroundwaterSoilFlux
  • methodModel with schererPfister2015
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture and a country with:
      • termType = region and a list of measurements with:
      • value and term with drainageClass
  • endDate
  • a list of inputs with:
    • value and term of termType = organicFertiliser

Lookup used

• organicFertiliser.csv -> OrganicFertiliserClassification
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.schererPfister2015 import run

print(run('pToGroundwaterSoilFlux', Cycle))

View source on Gitlab

P, to surface water, soil flux

The total amount of phosphorus runoff from the soil, including from phosphate added in fertiliser, excreta, and residue, and from background runoff due to natural processes.

Returns

• A list of Emissions with:
  • term with pToSurfaceWaterSoilFlux
  • methodModel with schererPfister2015
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture and a country with:
      • termType = region and a list of measurements with:
      • value and term with slope
  • endDate
  • a list of inputs with:
    • value and term of termType = organicFertiliser

Lookup used

• organicFertiliser.csv -> OrganicFertiliserClassification
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.schererPfister2015 import run

print(run('pToSurfaceWaterSoilFlux', Cycle))

View source on Gitlab

Schmidt (2007)

These models calculate various emissions, primarily related to palm oil production and processing, according to the models described in Schmidt (2007).

View source on Gitlab

CH4, to air, waste treatment

Methane emissions to air, from the treatment of wastes excluding excreta.

Returns

• A list of Emissions with:
  • term with ch4ToAirWasteTreatment
  • methodModel with schmidt2007
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a list of products with:
    • term with oilPalmFruit or oilPalmFrond or oilPalmWood or oilPalmShell or oilPalmKernel or oilPalmKernelMeal or oilPalmKernelOilCrude or oilPalmKernelOilRefined or oilPalmMillEffluent or oilPalmMillEffluentDecanted or oilPalmPressFibre or oilPalmOilCrude or oilPalmOilRefined or oilPalmFattyAcidDistillate or oilPalmStockSoap
  • either:
    • a list of products with:
      • value and term of termType = waste
    • Data completeness assessment for waste: completeness.waste

This model works on the following Node type with identical requirements:

• Cycle
• Transformation

Lookup used

• waste.csv -> ch4EfSchmidt2007
• emission.csv -> productTermIdsAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.schmidt2007 import run

print(run('ch4ToAirWasteTreatment', Cycle))

View source on Gitlab

Site

These models are specific to Site.

View source on Gitlab

Cation exchange capacity (per kg soil)

The total capacity of a soil to hold exchangeable cations. It influences the soil's ability to hold onto essential nutrients and provides a buffer against soil acidification. Measured in centimoles of charge per kg of soil at neutrality (cmolc/kg) which is equivalent to meq/100g.

Returns

• A list of Measurements with:
  • term with cationExchangeCapacityPerKgSoil
  • value
  • depthUpper
  • depthLower
  • methodClassification with modelled using other measurements

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture
  • a list of measurements with:
    • value and term with clayContent
    • value and term with soilPh
    • value and term with organicCarbonPerKgSoil

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.site import run

print(run('cationExchangeCapacityPerKgSoil', Site))

View source on Gitlab

Flowing Water

This model returns a measurement of fast flowing water or slow flowing water depending on the type of the site.

Returns

• A list of Measurements with:
  • term with slowFlowingWater or fastFlowingWater
  • value with 1
  • methodClassification with modelled using other measurements

Requirements

• A Site with:
  • a siteType = pond or river or stream or lake or sea or ocean

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.site import run

print(run('flowingWater', Site))

View source on Gitlab

Management node with data gap-filled data from cycles.

Returns

• A list of Managements with:
  • term of termType = landCover or waterRegime or tillage or cropResidueManagement or landUseManagement
  • value
  • endDate
  • startDate

Requirements

• A Site with:
  • related to:
    • a list of Cycles with:
      • startDate and endDate and a list of products with:
        • of = or or and a list of with:
        • of = or or or and

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.site import run

print(run('management', Site))

View source on Gitlab

Measurement Value

This model calculates the value of the Measurement by taking an average from the min and max values.

Returns

• A list of Measurements with:
  • value

Requirements

• A Site with:
  • a list of measurements with:
    • min and max

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.site import run

print(run('measurement.value', Site))

View source on Gitlab

Net Primary Production

The quantity of organic compounds produced from atmospheric or aqueous carbon dioxide.

Returns

• A list of Measurements with:
  • term with netPrimaryProduction
  • value
  • methodClassification with modelled using other measurements

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture or pond or lake
  • a list of measurements with:
    • value and term with temperatureAnnual

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.site import run

print(run('netPrimaryProduction', Site))

View source on Gitlab

Organic carbon (per ha)

The stock of organic carbon in the soil.

Returns

• A list of Measurements with:
  • term with organicCarbonPerHa
  • value
  • depthUpper
  • depthLower
  • methodClassification with modelled using other measurements

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture
  • a list of measurements with:
    • value and term with soilBulkDensity and depthUpper and depthLower
    • value and term with organicCarbonPerKgSoil and depthUpper and depthLower

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.site import run

print(run('organicCarbonPerHa', Site))

View source on Gitlab

Organic carbon (per kg soil)

The concentration of organic carbon in the soil.

Returns

• A list of Measurements with:
  • term with organicCarbonPerKgSoil
  • value
  • min
  • max
  • statsDefinition with modelled
  • methodClassification with modelled using other measurements

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture
  • a list of measurements with:
    • value and term with organicMatterPerKgSoil

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.site import run

print(run('organicCarbonPerKgSoil', Site))

View source on Gitlab

Organic carbon (per m3 soil)

The concentration of organic carbon in the soil.

Returns

• A list of Measurements with:
  • term with organicCarbonPerM3Soil
  • value
  • min
  • max
  • statsDefinition with modelled
  • methodClassification with modelled using other measurements

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture
  • a list of measurements with:
    • value and term with organicMatterPerM3Soil

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.site import run

print(run('organicCarbonPerM3Soil', Site))

View source on Gitlab

Organic matter (per kg soil)

The concentration of organic matter in the soil. The term refers to any material produced originally by living organisms (plant or animal) that is returned to the soil and goes through the decomposition process.

Returns

• A list of Measurements with:
  • term with organicMatterPerKgSoil
  • value
  • min
  • max
  • statsDefinition with modelled
  • methodClassification with modelled using other measurements

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture
  • a list of measurements with:
    • value and term with organicCarbonPerKgSoil

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.site import run

print(run('organicMatterPerKgSoil', Site))

View source on Gitlab

Organic matter (per m3 soil)

The concentration of organic matter in the soil. The term refers to any material produced originally by living organisms (plant or animal) that is returned to the soil and goes through the decomposition process.

Returns

• A list of Measurements with:
  • term with organicMatterPerM3Soil
  • value
  • min
  • max
  • statsDefinition with modelled
  • methodClassification with modelled using other measurements

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture
  • a list of measurements with:
    • value and term with organicCarbonPerM3Soil

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.site import run

print(run('organicMatterPerM3Soil', Site))

View source on Gitlab

Post Checks

List of models to run after any other model on an Site.

View source on Gitlab

Post Checks Cache

This model removes any cached data on the Site.

Returns

• A Site

Requirements

• A Site with:

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.site import run

print(run(Site))

View source on Gitlab

Potential evapotranspiration (annual)

The total annual potential evapotranspiration, expressed in mm / year.

Compute Annual value based on Monthly values.

Returns

• A list of Measurements with:
  • term with potentialEvapotranspirationAnnual
  • value
  • startDate
  • endDate
  • methodClassification with modelled using other measurements

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture or animal housing or pond or river or stream or lake or sea or ocean
  • a list of measurements with:
    • Data completeness assessment for id: term.id must be potentialEvapotranspirationMonthly

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.site import run

print(run('potentialEvapotranspirationAnnual', Site))

View source on Gitlab

Potential evapotranspiration (monthly)

The total potential evapotranspiration, expressed in mm / month.

Compute Monthly value based on Daily values.

Returns

• A list of Measurements with:
  • term with potentialEvapotranspirationMonthly
  • value
  • dates
  • methodClassification with modelled using other measurements

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture or animal housing or pond or river or stream or lake or sea or ocean
  • a list of measurements with:
    • Data completeness assessment for id: term.id must be potentialEvapotranspirationDaily

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.site import run

print(run('potentialEvapotranspirationMonthly', Site))

View source on Gitlab

Pre Checks

List of models to run before any other model on an Site.

View source on Gitlab

Pre Checks Cache Geospatial Database

This model caches results from Geospatial Database.

Returns

• A Site

Requirements

• A Site with:
  • either:
    • the following fields:
      • latitude
      • longitude
    • the following fields:
      • a boundary
    • the following fields:
      • a region with:
        • =

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.site import run

print(run(Site))

View source on Gitlab

Pre Checks Cache Sources

This model caches the sources of all models.

Returns

• A Site

Requirements

• A Site with:

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.site import run

print(run(Site))

View source on Gitlab

Pre Checks Cache Years

This model caches the years of all Cycles related to this Site.

Returns

• A Site

Requirements

• A Site with:

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.site import run

print(run(Site))

View source on Gitlab

Precipitation (annual)

The total annual precipitation (defined as the sum of rainfall, sleet, snow, and hail, but excluding fog, cloud, and dew), expressed in mm / year.

Compute Annual value based on Monthly values.

Returns

• A list of Measurements with:
  • term with precipitationAnnual
  • value
  • startDate
  • endDate
  • methodClassification with modelled using other measurements

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture or animal housing or pond or river or stream or lake or sea or ocean
  • a list of measurements with:
    • Data completeness assessment for id: term.id must be precipitationMonthly

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.site import run

print(run('precipitationAnnual', Site))

View source on Gitlab

Precipitation (monthly)

The total monthly precipitation (defined as the sum of rainfall, sleet, snow, and hail, but excluding fog, cloud, and dew), expressed in mm / month.

Compute Monthly value based on Daily values.

Returns

• A list of Measurements with:
  • term with precipitationMonthly
  • value
  • dates
  • methodClassification with modelled using other measurements

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture or animal housing or pond or river or stream or lake or sea or ocean
  • a list of measurements with:
    • Data completeness assessment for id: term.id must be precipitationDaily

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.site import run

print(run('precipitationMonthly', Site))

View source on Gitlab

Rainfall (annual)

The total annual rainfall, expressed in mm / year.

Compute Annual value based on Monthly values.

Returns

• A list of Measurements with:
  • term with rainfallAnnual
  • value
  • startDate
  • endDate
  • methodClassification with modelled using other measurements

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture or animal housing or pond or river or stream or lake or sea or ocean
  • a list of measurements with:
    • Data completeness assessment for id: term.id must be rainfallMonthly

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.site import run

print(run('rainfallAnnual', Site))

View source on Gitlab

Rainfall (monthly)

The total monthly rainfall, expressed in mm / month.

Compute Monthly value based on Daily values.

Returns

• A list of Measurements with:
  • term with rainfallMonthly
  • value
  • dates
  • methodClassification with modelled using other measurements

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture or animal housing or pond or river or stream or lake or sea or ocean
  • a list of measurements with:
    • Data completeness assessment for id: term.id must be rainfallDaily

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.site import run

print(run('rainfallMonthly', Site))

View source on Gitlab

Soil Measurement

This model harmonises matching soil measurements into depth ranges of 0-30 and 0-50 and gap fills missing measurements.

Returns

• A list of Measurements with:
  • value
  • depthUpper
  • depthLower with 30 or 50
  • dates
  • methodClassification with modelled using other measurements

Requirements

• A Site with:
  • a list of measurements with:
    • depthUpper and depthLower

Lookup used

• measurement.csv -> recommendAddingDepth; depthSensitive

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.site import run

print(run('soilMeasurement', Site))

View source on Gitlab

Temperature (annual)

The annual air temperature, averaged over each day in a year.

Compute Annual value based on Monthly values.

Returns

• A list of Measurements with:
  • term with temperatureAnnual
  • value
  • startDate
  • endDate
  • methodClassification with modelled using other measurements

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture or animal housing or pond or river or stream or lake or sea or ocean
  • a list of measurements with:
    • Data completeness assessment for id: term.id must be temperatureMonthly

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.site import run

print(run('temperatureAnnual', Site))

View source on Gitlab

Temperature (monthly)

The average monthly air temperature, averaged over each day in the month.

Compute Monthly value based on Daily values.

Returns

• A list of Measurements with:
  • term with temperatureMonthly
  • value
  • dates
  • methodClassification with modelled using other measurements

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture or animal housing or pond or river or stream or lake or sea or ocean
  • a list of measurements with:
    • Data completeness assessment for id: term.id must be temperatureDaily

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.site import run

print(run('temperatureMonthly', Site))

View source on Gitlab

Total nitrogen (per kg soil)

The concentration of organic and mineral nitrogen in the soil.

Returns

• A list of Measurements with:
  • term with totalNitrogenPerKgSoil
  • value
  • depthUpper with 0
  • depthLower with 50
  • methodClassification with modelled using other measurements

Requirements

• A Site with:
  • a siteType = forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture
  • a list of measurements with:
    • value and term with organicCarbonPerKgSoil

Lookup used

• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.site import run

print(run('totalNitrogenPerKgSoil', Site))

View source on Gitlab

Water depth

The depth of a water body.

Returns

• A list of Measurements with:
  • term with waterDepth
  • value
  • methodClassification with modelled using other measurements

Requirements

• A Site with:
  • a siteType = pond or river or stream or lake or sea or ocean

Lookup used

• crop.csv -> Non_bearing_duration
• measurement.csv -> siteTypesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.site import run

print(run('waterDepth', Site))

View source on Gitlab

Stehfest Bouwman (2006)

This model calculates the direct N2O and NOx emissions due to the use of fertiliser using the regression model detailed in Stehfest & Bouwman (2006). It takes data on factors including soil, climate, and crop type. Here we also extend it to crop residue and animal excreta deposited directly on pasture.

View source on Gitlab

N2O, to air, crop residue decomposition, direct

Nitrous oxide emissions to air, from crop residue decomposition.

Returns

• A list of Emissions with:
  • term with n2OToAirCropResidueDecompositionDirect
  • methodModel with stehfestBouwman2006
  • value
  • methodTier with tier 2

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover and a list of measurements with:
      • value and term with totalNitrogenPerKgSoil
      • value and term with organicCarbonPerKgSoil
      • value and term with ecoClimateZone
      • value and term with clayContent
      • value and term with sandContent
      • value and term with soilPh
  • Data completeness assessment for cropResidue: completeness.cropResidue must be True
  • a list of products with:
    • value and term of termType = cropResidue and a list of properties with:
      • value and term with nitrogenContent

Lookup used

• cropResidue.csv -> decomposesOnField
• crop.csv -> cropGroupingStehfestBouwman
• ecoClimateZone.csv -> STEHFEST_BOUWMAN_2006_N2O-N_FACTOR
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.stehfestBouwman2006 import run

print(run('n2OToAirCropResidueDecompositionDirect', Cycle))

View source on Gitlab

N2O, to air, excreta, direct

Nitrous oxide emissions to air, from animal excreta.

Returns

• A list of Emissions with:
  • term with n2OToAirExcretaDirect
  • methodModel with stehfestBouwman2006
  • value
  • methodTier with tier 2

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland and a list of measurements with:
      • value and term with totalNitrogenPerKgSoil
      • value and term with organicCarbonPerKgSoil
      • value and term with ecoClimateZone
      • value and term with clayContent
      • value and term with sandContent
      • value and term with soilPh
  • a list of inputs with:
    • value and term of units = kg or kg N and term of termType = excreta and a list of properties with:
      • value and term with nitrogenContent
    • term of termType = excreta
  • Data completeness assessment for excreta: completeness.excreta must be True
  • a list of products with:
    • value and term of units = kg or kg N and term of termType = excreta and a list of properties with:
      • value and term with nitrogenContent

This model works on the following Node type with identical requirements:

• Cycle
• Transformation

Lookup used

• crop.csv -> cropGroupingStehfestBouwman
• ecoClimateZone.csv -> STEHFEST_BOUWMAN_2006_N2O-N_FACTOR
• emission.csv -> siteTypesAllowed; inputTermTypesAllowed; typesAllowed
• emission-model-siteTypesAllowed.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.stehfestBouwman2006 import run

print(run('n2OToAirExcretaDirect', Cycle))

View source on Gitlab

N2O, to air, inorganic fertiliser, direct

Nitrous oxide emissions to air, from nitrification and denitrification of inorganic fertiliser.

Returns

• A list of Emissions with:
  • term with n2OToAirInorganicFertiliserDirect
  • methodModel with stehfestBouwman2006
  • value
  • methodTier with tier 2

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture and a list of measurements with:
      • value and term with totalNitrogenPerKgSoil
      • value and term with organicCarbonPerKgSoil
      • value and term with ecoClimateZone
      • value and term with clayContent
      • value and term with sandContent
      • value and term with soilPh
  • Data completeness assessment for fertiliser: completeness.fertiliser must be True
  • a list of inputs with:
    • value and term of units = kg or kg N and term of termType = inorganicFertiliser and optional:
      • a list of properties with:
        • and with

Lookup used

• crop.csv -> cropGroupingStehfestBouwman
• ecoClimateZone.csv -> STEHFEST_BOUWMAN_2006_N2O-N_FACTOR
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.stehfestBouwman2006 import run

print(run('n2OToAirInorganicFertiliserDirect', Cycle))

View source on Gitlab

N2O, to air, organic fertiliser, direct

Nitrous oxide emissions to air, from nitrification and denitrification of organic fertiliser.

Returns

• A list of Emissions with:
  • term with n2OToAirOrganicFertiliserDirect
  • methodModel with stehfestBouwman2006
  • value
  • methodTier with tier 2

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture and a list of measurements with:
      • value and term with totalNitrogenPerKgSoil
      • value and term with organicCarbonPerKgSoil
      • value and term with ecoClimateZone
      • value and term with clayContent
      • value and term with sandContent
      • value and term with soilPh
  • Data completeness assessment for fertiliser: completeness.fertiliser must be True
  • a list of inputs with:
    • value and term of units = kg or kg N and term of termType = organicFertiliser and optional:
      • a list of properties with:
        • and with

Lookup used

• crop.csv -> cropGroupingStehfestBouwman
• ecoClimateZone.csv -> STEHFEST_BOUWMAN_2006_N2O-N_FACTOR
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.stehfestBouwman2006 import run

print(run('n2OToAirOrganicFertiliserDirect', Cycle))

View source on Gitlab

N2O, to air, soil flux

The total amount of nitrous oxide emissions to air, from the soil, including from nitrogen added in fertiliser, excreta, and residue, and from natural background processes.

Returns

• A list of Emissions with:
  • term with n2OToAirSoilFlux
  • methodModel with stehfestBouwman2006
  • value
  • methodTier with tier 2

Requirements

• A Cycle with:
  • a site with:
    • siteType with forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture and a list of measurements with:
      • value and term with totalNitrogenPerKgSoil
      • value and term with organicCarbonPerKgSoil
      • value and term with ecoClimateZone
      • value and term with clayContent
      • value and term with sandContent
      • value and term with soilPh
  • Data completeness assessment for excreta: completeness.excreta must be True
  • Data completeness assessment for cropResidue: completeness.cropResidue must be True
  • Data completeness assessment for fertiliser: completeness.fertiliser must be True
  • a list of inputs with:
    • value and term of units = kg or kg N and term of termType = organicFertiliser or inorganicFertiliser or excreta and optional:
      • a list of properties with:
        • and with
  • a list of products with:
    • value and term of termType = cropResidue or excreta and a list of properties with:
      • value and term with nitrogenContent

Lookup used

• crop.csv -> cropGroupingStehfestBouwman
• ecoClimateZone.csv -> STEHFEST_BOUWMAN_2006_N2O-N_FACTOR
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.stehfestBouwman2006 import run

print(run('n2OToAirSoilFlux', Cycle))

View source on Gitlab

NOx, to air, crop residue decomposition

Nitrogen oxides emissions to air, from crop residue decomposition.

Returns

• A list of Emissions with:
  • term with noxToAirCropResidueDecomposition
  • methodModel with stehfestBouwman2006
  • value
  • methodTier with tier 2

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover and a list of measurements with:
      • value and term with totalNitrogenPerKgSoil
      • value and term with ecoClimateZone
  • Data completeness assessment for cropResidue: completeness.cropResidue must be True
  • a list of products with:
    • value and term of termType = cropResidue and a list of properties with:
      • value and term with nitrogenContent

Lookup used

• cropResidue.csv -> decomposesOnField
• ecoClimateZone.csv -> STEHFEST_BOUWMAN_2006_NOX-N_FACTOR
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.stehfestBouwman2006 import run

print(run('noxToAirCropResidueDecomposition', Cycle))

View source on Gitlab

NOx, to air, excreta

Nitrogen oxides emissions to air, from animal excreta.

Returns

• A list of Emissions with:
  • term with noxToAirExcreta
  • methodModel with stehfestBouwman2006
  • value
  • methodTier with tier 2

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland and a list of measurements with:
      • value and term with totalNitrogenPerKgSoil
      • value and term with ecoClimateZone
  • a list of inputs with:
    • value and term of units = kg or kg N and term of termType = excreta and a list of properties with:
      • value and term with nitrogenContent
    • term of termType = excreta
  • Data completeness assessment for excreta: completeness.excreta must be True
  • a list of products with:
    • value and term of units = kg or kg N and term of termType = excreta and a list of properties with:
      • value and term with nitrogenContent

This model works on the following Node type with identical requirements:

• Cycle
• Transformation

Lookup used

• ecoClimateZone.csv -> STEHFEST_BOUWMAN_2006_NOX-N_FACTOR
• emission.csv -> siteTypesAllowed; inputTermTypesAllowed; typesAllowed
• emission-model-siteTypesAllowed.csv

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.stehfestBouwman2006 import run

print(run('noxToAirExcreta', Cycle))

View source on Gitlab

NOx, to air, inorganic fertiliser

Nitrogen oxides emissions to air, from inorganic fertiliser nitrification and denitrification.

Returns

• A list of Emissions with:
  • term with noxToAirInorganicFertiliser
  • methodModel with stehfestBouwman2006
  • value
  • methodTier with tier 2

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture and a list of measurements with:
      • value and term with totalNitrogenPerKgSoil
      • value and term with ecoClimateZone
  • Data completeness assessment for fertiliser: completeness.fertiliser must be True
  • a list of inputs with:
    • value and term of units = kg or kg N and term of termType = inorganicFertiliser and optional:
      • a list of properties with:
        • and with

Lookup used

• ecoClimateZone.csv -> STEHFEST_BOUWMAN_2006_NOX-N_FACTOR
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.stehfestBouwman2006 import run

print(run('noxToAirInorganicFertiliser', Cycle))

View source on Gitlab

NOx, to air, organic fertiliser

Nitrogen oxides emissions to air, from organic fertiliser nitrification and denitrification.

Returns

• A list of Emissions with:
  • term with noxToAirOrganicFertiliser
  • methodModel with stehfestBouwman2006
  • value
  • methodTier with tier 2

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture and a list of measurements with:
      • value and term with totalNitrogenPerKgSoil
      • value and term with ecoClimateZone
  • Data completeness assessment for fertiliser: completeness.fertiliser must be True
  • a list of inputs with:
    • value and term of units = kg or kg N and term of termType = organicFertiliser and optional:
      • a list of properties with:
        • and with

Lookup used

• ecoClimateZone.csv -> STEHFEST_BOUWMAN_2006_NOX-N_FACTOR
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.stehfestBouwman2006 import run

print(run('noxToAirOrganicFertiliser', Cycle))

View source on Gitlab

NOx, to air, soil flux

The total amount of nitrous oxide emissions to air, from the soil, including from nitrogen added in fertiliser, excreta, and residue, and from natural background processes.

Returns

• A list of Emissions with:
  • term with noxToAirSoilFlux
  • methodModel with stehfestBouwman2006
  • value
  • methodTier with tier 2

Requirements

• A Cycle with:
  • a site with:
    • siteType with forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture and a list of measurements with:
      • value and term with totalNitrogenPerKgSoil
      • value and term with ecoClimateZone
  • Data completeness assessment for excreta: completeness.excreta must be True
  • Data completeness assessment for cropResidue: completeness.cropResidue must be True
  • Data completeness assessment for fertiliser: completeness.fertiliser must be True
  • a list of inputs with:
    • value and term of units = kg or kg N and term of termType = organicFertiliser or inorganicFertiliser or excreta and optional:
      • a list of properties with:
        • and with
  • a list of products with:
    • value and term of termType = cropResidue or excreta and a list of properties with:
      • value and term with nitrogenContent

Lookup used

• ecoClimateZone.csv -> STEHFEST_BOUWMAN_2006_NOX-N_FACTOR
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.stehfestBouwman2006 import run

print(run('noxToAirSoilFlux', Cycle))

View source on Gitlab

Stehfest Bouwman (2006) GIS Implementation

This model calculates the direct N2O and NOx emissions due to the use of fertiliser, by creating a country-average version of the Stehfest & Bouwman (2006) model using GIS software.

View source on Gitlab

NOx, to air, crop residue decomposition

Nitrogen oxides emissions to air, from crop residue decomposition.

Returns

• A list of Emissions with:
  • term with noxToAirCropResidueDecomposition
  • methodModel with stehfestBouwman2006GisImplementation
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover and a country with:
      • termType = region
  • Data completeness assessment for cropResidue: completeness.cropResidue must be True
  • a list of products with:
    • value and term of termType = cropResidue and a list of properties with:
      • value and term with nitrogenContent

Lookup used

• cropResidue.csv -> decomposesOnField
• region.csv -> EF_NOX
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.stehfestBouwman2006GisImplementation import run

print(run('noxToAirCropResidueDecomposition', Cycle))

View source on Gitlab

NOx, to air, excreta

Nitrogen oxides emissions to air, from animal excreta.

Returns

• A list of Emissions with:
  • term with noxToAirExcreta
  • methodModel with stehfestBouwman2006GisImplementation
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or permanent pasture or animal housing or pond or river or stream or lake or sea or ocean and a country with:
      • termType = region
  • a list of inputs with:
    • value and term of units = kg or kg N and term of termType = excreta and a list of properties with:
      • value and term with nitrogenContent
    • term of termType = excreta
  • Data completeness assessment for excreta: completeness.excreta must be True
  • a list of products with:
    • value and term of units = kg or kg N and term of termType = excreta and a list of properties with:
      • value and term with nitrogenContent

This model works on the following Node type with identical requirements:

• Cycle
• Transformation

Lookup used

• region.csv -> EF_NOX
• emission.csv -> siteTypesAllowed; inputTermTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.stehfestBouwman2006GisImplementation import run

print(run('noxToAirExcreta', Cycle))

View source on Gitlab

NOx, to air, inorganic fertiliser

Nitrogen oxides emissions to air, from inorganic fertiliser nitrification and denitrification.

Returns

• A list of Emissions with:
  • term with noxToAirInorganicFertiliser
  • methodModel with stehfestBouwman2006GisImplementation
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture and a country with:
      • termType = region
  • Data completeness assessment for fertiliser: completeness.fertiliser must be True
  • a list of inputs with:
    • value and term of units = kg or kg N and term of termType = inorganicFertiliser and optional:
      • a list of properties with:
        • and with

Lookup used

• region.csv -> EF_NOX
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.stehfestBouwman2006GisImplementation import run

print(run('noxToAirInorganicFertiliser', Cycle))

View source on Gitlab

NOx, to air, organic fertiliser

Nitrogen oxides emissions to air, from organic fertiliser nitrification and denitrification.

Returns

• A list of Emissions with:
  • term with noxToAirOrganicFertiliser
  • methodModel with stehfestBouwman2006GisImplementation
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture and a country with:
      • termType = region
  • Data completeness assessment for fertiliser: completeness.fertiliser must be True
  • a list of inputs with:
    • value and term of units = kg or kg N and term of termType = organicFertiliser and optional:
      • a list of properties with:
        • and with

Lookup used

• region.csv -> EF_NOX
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.stehfestBouwman2006GisImplementation import run

print(run('noxToAirOrganicFertiliser', Cycle))

View source on Gitlab

NOx, to air, soil flux

The total amount of nitrous oxide emissions to air, from the soil, including from nitrogen added in fertiliser, excreta, and residue, and from natural background processes.

Returns

• A list of Emissions with:
  • term with noxToAirSoilFlux
  • methodModel with stehfestBouwman2006GisImplementation
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with forest or other natural vegetation or cropland or glass or high accessible cover or permanent pasture and a country with:
      • termType = region
  • Data completeness assessment for excreta: completeness.excreta must be True
  • Data completeness assessment for cropResidue: completeness.cropResidue must be True
  • Data completeness assessment for fertiliser: completeness.fertiliser must be True
  • a list of inputs with:
    • value and term of units = kg or kg N and term of termType = organicFertiliser or inorganicFertiliser or excreta and optional:
      • a list of properties with:
        • and with
  • a list of products with:
    • value and term of termType = cropResidue or excreta and a list of properties with:
      • value and term with nitrogenContent

Lookup used

• region.csv -> EF_NOX
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.stehfestBouwman2006GisImplementation import run

print(run('noxToAirSoilFlux', Cycle))

View source on Gitlab

Transformation

These models are specific to Transformation.

View source on Gitlab

Input Excreta

Copy Cycle (or previous Transformation) excreta products into the Transformation inputs if they are missing.

Returns

• A list of Transformations with:
  • a list of inputs with:
    • term of termType = excreta
    • value
    • fromCycle with true

Requirements

• A Cycle with:
  • a list of products with:
    • value and term of termType = excreta
  • a list of transformations with:
    • term of termType = excretaManagement and a list of inputs with:
      • value and term of termType = excreta

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.transformation import run

print(run('input.excreta', Cycle))

View source on Gitlab

Input Value

This model calculates the Input max by taking the max of the same Product of the previous Transformation (or Cycle if first Transformation) and applying the share.

Returns

• A list of Transformations with:
  • a list of inputs with:
    • max
    • statsDefinition with modelled

Requirements

• A Cycle with:
  • a list of products with:
    • max
  • a list of transformations with:
    • transformedShare and a list of inputs

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.transformation import run

print(run('input.max', Cycle))

View source on Gitlab

Input Value

This model calculates the Input min by taking the min of the same Product of the previous Transformation (or Cycle if first Transformation) and applying the share.

Returns

• A list of Transformations with:
  • a list of inputs with:
    • min
    • statsDefinition with modelled

Requirements

• A Cycle with:
  • a list of products with:
    • min
  • a list of transformations with:
    • transformedShare and a list of inputs

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.transformation import run

print(run('input.min', Cycle))

View source on Gitlab

Input Properties

This model copies the Product properties of the Cycle to the first transformations inputs.

Returns

• A list of Transformations with:
  • a list of inputs

Requirements

• A Cycle with:
  • a list of products with:
    • value
  • a list of transformations with:
    • a list of inputs and none of:
      • previousTransformationId

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.transformation import run

print(run('input.properties', Cycle))

View source on Gitlab

Input Value

This model calculates the Input sd by taking the sd of the same Product of the previous Transformation (or Cycle if first Transformation) and applying the share.

Returns

• A list of Transformations with:
  • a list of inputs with:
    • sd
    • statsDefinition with modelled

Requirements

• A Cycle with:
  • a list of products with:
    • sd
  • a list of transformations with:
    • transformedShare and a list of inputs

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.transformation import run

print(run('input.sd', Cycle))

View source on Gitlab

Input Value

This model calculates the Input value by taking the value of the same Product of the previous Transformation (or Cycle if first Transformation) and applying the share.

Returns

• A list of Transformations with:
  • a list of inputs with:
    • value
    • statsDefinition with modelled

Requirements

• A Cycle with:
  • a list of products with:
    • value
  • a list of transformations with:
    • transformedShare and a list of inputs

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.transformation import run

print(run('input.value', Cycle))

View source on Gitlab

Product Excreta

This model calculates the value of every Product by taking the value of the Input with the same term. It also adds other variants of the same Product in different units (e.g. kg N, kg VS and kg). Note: this model also substract Emissions for Input with units = kg N.

Returns

• A list of Products with:
  • term of termType = excreta
  • value

Requirements

• A Transformation with:
  • term of termType = excretaManagement
  • a list of inputs with:
    • value and term of termType = excreta
  • a list of products with:
    • value

Lookup used

• emission.csv -> causesExcretaMassLoss

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.transformation import run

print(run('product.excreta', Transformation))

View source on Gitlab

USEtox v2

This model characterises chemicals' toxicity according to the USEtox model, version 2.1. For freshwater ecotoxicity, HC50-EC50 values are used. The HC50-EC50 value represents the hazardous concentration of a chemical at which 50% of the species considered are exposed to a concentration above their EC50.

View source on Gitlab

Freshwater ecotoxicity potential (CTUe)

The potential of chemicals to cause toxic effects in freshwater ecosystems, expressed as an estimate of the potentially affected fraction of species (PAF) integrated over time and volume. This unit is also referred to as Comparative Toxic Unit for ecosystems (CTUe).

Returns

• A Indicator with:
  • term with freshwaterEcotoxicityPotentialCtue
  • methodModel with usetoxV2
  • value

Requirements

• A ImpactAssessment with:
  • a cycle with:
    • Data completeness assessment for pesticideVeterinaryDrug: completeness.pesticideVeterinaryDrug must be True and a list of inputs with:
      • value and term of termType = pesticideAI

Lookup used

• pesticideAI.csv -> pafM3DFreshwaterEcotoxicityUsetox2-1Hc50Ec50eq

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.usetoxV2 import run

print(run('freshwaterEcotoxicityPotentialCtue', ImpactAssessment))

View source on Gitlab

Webb et al (2012) and Sintermann et al (2012)

This model calculates the NH3 emissions due to the addition of organic fertiliser based on a compilation of emissions factors from Webb et al (2012) and Sintermann et al (2012). The methodology for compiling these emissions is detailed in Poore & Nemecek (2018).

View source on Gitlab

NH3, to air, organic fertiliser

Ammonia emissions to air, from organic fertiliser volatilization.

Returns

• A list of Emissions with:
  • term with nh3ToAirOrganicFertiliser
  • methodModel with webbEtAl2012AndSintermannEtAl2012
  • value
  • methodTier with tier 1

Requirements

• A Cycle with:
  • a site with:
    • siteType with cropland or glass or high accessible cover or permanent pasture
  • Data completeness assessment for fertiliser: completeness.fertiliser
  • a list of inputs with:
    • value and term of termType = organicFertiliser and a list of properties with:
      • value and term with totalAmmoniacalNitrogenContentAsN

Lookup used

• organicFertiliser.csv -> OrganicFertiliserClassification
• emission.csv -> siteTypesAllowed; typesAllowed

Usage

1. Install the library: pip install hestia_earth.models
2. Import the library and run the model:

from hestia_earth.models.webbEtAl2012AndSintermannEtAl2012 import run

print(run('nh3ToAirOrganicFertiliser', Cycle))

View source on Gitlab

Validate CSV/JSON files

Hestia provides some functions to validate a CSV / JSON / JSON-LD files formatted following the Hestia format.

Validating a CSV file

To validate a CSV file, you will first need to convert it to JSON. This can be done using the Hestia's utils package:

1. Install NodeJS version 12
2. Install the utils library globally: npm install --global @hestia-earth/schema-convert
3. Drop your CSV files into a specific folder then run:

hestia-convert-to-json folder

You will find in the folder the list of CSV files converted to JSON with the .json extension. These files can be then used for validation described below.

Validating the Terms

When uploading data on the Hestia platform, you will need to use our Glossary of Terms. You can follow these steps to install a package to validate the terms:

1. Install NodeJS version 12
2. Install the utils library globally: npm install --global @hestia-earth/utils
3. Drop your JSON / JSON-LD files into a specific folder then run:

API_URL=<APIUrl> hestia-validate-terms folder

Errors will appear in the console if any have been found.

Validating the Schema

When uploading data on the Hestia platform, you will need to follow our Schema. You can follow these steps to install a package to validate the schema:

1. Install NodeJS version 12
2. Install the schema validation library globally: npm install --global @hestia-earth/schema-validation
3. Drop your JSON / JSON-LD files into a specific folder then run:

hestia-validate-jsonld '' folder

Errors will appear in the console if any have been found.

Validating the Data

One important step when uploading data on the Hestia platform is making sure the data is consistent using the Data Validation package. You can follow these steps to install a package to validate the data:

1. Install Python version 3 minimum
2. Install the data validation library: pip install hestia_earth.validation
3. Drop your JSON / JSON-LD files into a specific folder then run:

API_URL=<APIUrl> VALIDATE_SPATIAL=false VALIDATE_EXISTING_NODES=true hestia-validate-data folder

Errors will appear in the console if any have been found.

Hestia Utils

The utils library contains useful functions to work with Hestia data.

Pivoting Headers by Terms

After downloading data as CSV from the Hestia platform, the format will look like this:

site.@id	site.measurements.0.term.@id	site.measurements.0.value	site.measurements.1.term.@id	site.measurements.1.value	site.measurements.2.term.@id	site.measurements.2.value	site.dataPrivate
xvflr	sandContent	90	siltContent	6	clayContent	4	false
gght	sandContent	90	siltContent	6	clayContent	4	false

It is possible to pivot some data based on the term.@id and move them as columns, such as:

site.@id	site.measurements.sandContent.value	site.measurements.siltContent.value	site.measurements.clayContent.value	site.dataPrivate
xvflr	90	6	4	false
gght	90	6	4	false

Usage

1. Install Python version 3 minimum
2. Install the data validation library: pip install hestia_earth.utils
3. Run:

hestia-pivot-csv source.csv dest.csv

The dest.csv file will be pivoted.