python cURL JavaScript (browser) JavaScript (NodeJS)
Overview

Overview

Welcome to the Hestia API documentation!

Hestia API

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

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

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

Hestia Community Edition

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

Getting Started

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

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

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

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

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)

Agribalyse (2016)

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

View source on Gitlab

Fuel and Electricity

Fuel and Electricity

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

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

CH4, to air, crop residue burning

Methane emissions to air, from crop residue burning.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

N2O, to air, crop residue burning, direct

Nitrous oxide emissions to air, from crop residue burning.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

NH3, to air, crop residue burning

Ammonia emissions to air, from crop residue burning.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

NOx, to air, crop residue burning

Nitrogen oxides emissions to air, from crop residue burning.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Different lookup files are used depending on the situation:

Usage

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)

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

CH4, to air, natural vegetation burning

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

N2O, to air, natural vegetation burning, direct

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Different lookup files are used depending on the situation:

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Different lookup files are used depending on the situation:

Usage

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

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

Returns

Requirements

Requirements

Usage

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

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Cycle

These models are specific to Cycle.

View source on Gitlab

Above ground crop residue, total

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Usage

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

Animal Properties

Animal Properties

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

Returns

Returns

Requirements

Requirements

Usage

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

Cold carcass weight per head

The average cold carcass weight of the animals per head.

Returns

Returns

Requirements

Requirements

Usage

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

Cold dressed carcass weight per head

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

Returns

Returns

Requirements

Requirements

Usage

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

Completeness Animal feed

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

Returns

Returns

Requirements

Requirements

Usage

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

Completeness Crop Residue

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

Returns

Returns

Requirements

Requirements

Usage

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

Completeness Excreta

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

Returns

Returns

Requirements

Requirements

Usage

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

Completeness Material

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

Returns

Returns

Requirements

Requirements

Usage

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

Completeness Seed

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Completeness Soil Amendments

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

Returns

Returns

Requirements

Requirements

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Crop Residue Management

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

Returns

Returns

Requirements

Requirements

Usage

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

Cycle duration

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

Returns

Returns

Requirements

Requirements

Usage

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)

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

Returns

Requirements

Requirements

Usage

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)

Excreta (kg)

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

Returns

Returns

Requirements

Requirements

Usage

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)

Excreta (kg N)

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

Returns

Returns

Requirements

Requirements

Usage

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)

Excreta (kg VS)

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

Returns

Returns

Requirements

Requirements

Usage

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)

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Usage

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

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

Returns

Requirements

Requirements

Usage

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

Input Value

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

Returns

Returns

Requirements

Requirements

Usage

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

Irrigated

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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('irrigated', Cycle))

View source on Gitlab

Live Animal

Live Animal

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

Returns

Returns

Requirements

Requirements

Usage

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

Pasture grass

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Usage

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

Post Checks

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

View source on Gitlab

Post Checks Cache

Post Checks Cache

This model removes any cached data on the Cycle.

Returns

Returns

Requirements

Requirements

Usage

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

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

Returns

Requirements

Requirements

Usage

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

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

Returns

Requirements

Requirements

Usage

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

Pre Checks

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

View source on Gitlab

Pre Checks Cache Sources

Pre Checks Cache Sources

This model caches the sources of all models.

Returns

Returns

Requirements

Requirements

Usage

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

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

Returns

Requirements

Requirements

Usage

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

Pre Checks Start Date

This model calculates the startDate from the endDate and cycleDuration.

Returns

Returns

Requirements

Requirements

Usage

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

Product Currency

Converts all the currencies to USD using historical rates.

Returns

Returns

Requirements

Requirements

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Depending on the primary product termType:

Usage

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

Product Price

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

Returns

Returns

Requirements

Requirements

Usage

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

Product Primary

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

Returns

Returns

Requirements

Requirements

Usage

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

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

Returns

Requirements

Requirements

Usage

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

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

Returns

Requirements

Requirements

Usage

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

Product Value

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

Returns

Returns

Requirements

Requirements

Usage

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

Ready-to-cook weight per head

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

Returns

Returns

Requirements

Requirements

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Residue incorporated

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Residue left on field

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Residue removed

The share of above ground crop residue removed.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Site duration

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

Returns

Returns

Requirements

Requirements

Usage

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

Transformations

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

Returns

Returns

Requirements

Requirements

Usage

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)

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

NOx, to air, excreta

Nitrogen oxides emissions to air, from animal excreta.

Returns

Returns

Requirements

Requirements

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

Lookup used

Lookup used

Usage

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)

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

NH3, to air, crop residue decomposition

Ammonia emissions to air, from crop residue decomposition.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

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

CO2, to air, fuel combustion

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

Returns

Returns

Requirements

Requirements

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

Lookup used

Lookup used

Usage

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

N2O, to air, fuel combustion, direct

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

Returns

Returns

Requirements

Requirements

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

Lookup used

Lookup used

Usage

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

NH3, to air, excreta

Ammonia emissions to air, from animal excreta.

Returns

Returns

Requirements

Requirements

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

Lookup used

Lookup used

Usage

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

NH3, to air, inorganic fertiliser

Ammonia emissions to air, from inorganic fertiliser volatilization.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

NOx, to air, fuel combustion

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

Returns

Returns

Requirements

Requirements

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

Lookup used

Lookup used

Usage

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

SO2, to air, fuel combustion

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

Returns

Returns

Requirements

Requirements

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

Lookup used

Lookup used

Usage

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

Emission not relevant

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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)

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

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

NO3, to groundwater, excreta

Nitrate leaching to groundwater, from animal excreta.

Returns

Returns

Requirements

Requirements

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

Lookup used

Lookup used

Usage

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)

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

Cold carcass weight per head

The average cold carcass weight of the animals per head.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Cold dressed carcass weight per head

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Liveweight per head

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Product Price

Calculates the price of crop and liveAnimal using FAOSTAT data.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Depending on the primary product termType:

Usage

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

Ready-to-cook weight per head

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Seed

The seed of a crop.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Geospatial Database

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

View source on Gitlab

Aware Water Basin ID

Aware Water Basin ID

This model calculates the the AWARE water basin identifier.

Returns

Returns

Requirements

Requirements

Usage

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

Clay content

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Ecoregion

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

Returns

Returns

Requirements

Requirements

Usage

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

Erodibility

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Heavy winter precipitation

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Nutrient loss to aquatic environment

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

Organic carbon (per kg soil)

The concentration of organic carbon in the soil.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

Potential evapotranspiration (long-term annual mean)

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Region

This model finds the region that contains the coordinates provided.

Returns

Returns

Requirements

Requirements

Usage

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

Sand content

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Silt content

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Slope

This model calculates the slope.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Slope length

This model calculates the slope length.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

Total nitrogen (per kg soil)

The concentration of organic and mineral nitrogen in the soil.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

Total phosphorus (per kg soil)

The concentration of organic and inorganic phosphorous in the soil.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Water depth

The depth of a water body.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

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

Rooting depth

The rooting depth of the crop.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

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

Returns

Requirements

Requirements

Usage

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

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

One of (depending on site.siteType):

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

One of (depending on site.siteType):

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

One of (depending on site.siteType):

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

One of (depending on site.siteType):

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

One of (depending on site.siteType):

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

One of (depending on site.siteType):

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

One of (depending on site.siteType):

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

One of (depending on site.siteType):

Usage

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

Impact Assessment

These models are specific to Impact Assessment.

View source on Gitlab

Emissions

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

Returns

Requirements

Requirements

Usage

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

Freshwater withdrawals, during Cycle

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Freshwater withdrawals, inputs production

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

Returns

Returns

Requirements

Requirements

Usage

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

Irrigated

Detects if the Cycle was irrigated.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Organic

Detects if the Cycle has an organic label.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Post Checks

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

View source on Gitlab

Post Checks Cycle

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

Returns

Requirements

Requirements

Usage

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

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

Returns

Requirements

Requirements

Usage

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

Pre Checks

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

View source on Gitlab

Pre Checks Cycle

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

Returns

Requirements

Requirements

Usage

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

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

Returns

Requirements

Requirements

Usage

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

Product Economic Value Share

Returns the economicValueShare of the Product linked to the Cycle.

Returns

Returns

Requirements

Requirements

Usage

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

Product Value

Returns the value of the Product linked to the Cycle.

Returns

Returns

Requirements

Requirements

Usage

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)

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

CO2, to air, organic soil cultivation

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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, indirect

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

N2O, to air, excreta, direct

Nitrous oxide emissions to air, from animal excreta.

Returns

Returns

Requirements

Requirements

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

Lookup used

Lookup used

Usage

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

N2O, to air, excreta, indirect

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

Returns

Returns

Requirements

Requirements

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

Lookup used

Lookup used

Usage

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

N2O, to air, inorganic fertiliser, direct

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

N2O, to air, inorganic fertiliser, indirect

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

N2O, to air, organic fertiliser, direct

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

N2O, to air, organic fertiliser, indirect

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

N2O, to air, organic soil cultivation, direct

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

GWP100

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

GWP100

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Carbon content

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

CH4, to air, enteric fermentation

Methane emissions to air, from enteric fermentation by ruminants.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

CH4, to air, excreta

Methane emissions to air, from animal excreta.

Returns

Returns

Requirements

Requirements

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

Lookup used

Lookup used

Usage

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

CH4, to air, flooded rice

Methane emissions to air, from flooded paddy rice fields.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

CO2, to air, lime hydrolysis

Carbon dioxide emissions to air, from lime hydrolysis (including dolomitic lime).

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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, urea hydrolysis

CO2, to air, urea hydrolysis

Carbon dioxide emissions to air, from urea hydrolysis (a corresponding negative emission occurs during fertiliser production).

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Lignin content

The lignin content of something, expressed as a percentage.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

N2O, to air, crop residue decomposition, direct

Nitrous oxide emissions to air, from crop residue decomposition.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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, excreta, direct

N2O, to air, excreta, direct

Nitrous oxide emissions to air, from animal excreta.

Returns

Returns

Requirements

Requirements

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

Lookup used

Lookup used

Usage

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, inorganic fertiliser, indirect

N2O, to air, inorganic fertiliser, indirect

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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, indirect

N2O, to air, organic fertiliser, indirect

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Nitrogen content

Nitrogen content

The total nitrogen content of something, as N, expressed as a percentage.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

NO3, to groundwater, crop residue decomposition

Nitrate leaching to groundwater, from crop residue decomposition.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

NO3, to groundwater, excreta

Nitrate leaching to groundwater, from animal excreta.

Returns

Returns

Requirements

Requirements

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

Lookup used

Lookup used

Usage

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

NO3, to groundwater, inorganic fertiliser

Nitrate leaching to groundwater, from inorganic fertiliser.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

NO3, to groundwater, organic fertiliser

Nitrate leaching to groundwater, from organic fertiliser.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Organic carbon (per ha)

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.

Currently, the Tier 2 implementation does not take into account the irrigation of cycles when estimating soil organic carbon stock changes.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

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

GWP100

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

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

N2, to air, excreta

Nitrogen emissions to air, from animal excreta.

Returns

Returns

Requirements

Requirements

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

Lookup used

Lookup used

Usage

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)

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Residue left on field

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

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Residue removed

The share of above ground crop residue removed.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

Damage to human health

The disability-adjusted life years lost in the human population.

Returns

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Different lookup files are used depending on the situation:

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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)

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

Returns

Requirements

Requirements

Lookup used

Lookup used

Usage

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

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

Returns

Requirements

Requirements