AgriFoodPy is a collection of methods for manipulating and modelling agrifood data. It provides modelling for a variety of aspects of the food system, including food consumption paterns, environmental impact and emissions data, population and land use. It also provides an interface to run external models by using xarray as the data container.

In addition to this package, we have also pre-packaged some datasets for use with agrifood. These can be found on the agrifoodpy_data repository https://github.com/FixOurFood/agrifoodpy-data

AgriFoodPy can be installed using pip, by running

pip install agrifoodpy

UK data to test the package is available from the agrifoodpy_data repository which currently can be installed using

pip install git+https://github.com/FixOurFood/agrifoodpy-data.git@importable

Each of the four basic modules on AgriFoodPy (Food, Land, Impact, Population) has its own set of basic array manipulation functionality, a set of modelling methods to extract basic metrics from datasets, and interfaces with external modelling packages and code.

Agrifoodpy employs xarray accesors to provide additional functionality on top of the array manipulation provided by xarray.

Basic usage of the accesors depend on the type of array being manipulated. The following examples uses the food module with the importable UK data mentioned above:

# import the FoodBalanceSheet accessor and FAOSTAT from agrifoodpy_data
from agrifoodpy.food.food import FoodBalanceSheet
from agrifoodpy_data.food import FAOSTAT
import matplotlib.pyplot as plt

# Extract data for the UK (Region=229)
food_uk = FAOSTAT.sel(Region=229)

# Compute the Self-sufficiency ratio using the fbs accessor SSR function
SSR = food_uk.fbs.SSR(per_item=True)

# Plot the results using the fbs accessor plot_years function
SSR.fbs.plot_years()
plt.show()

To use the specific models and interfaces to external code, these need to be imported

# import the FoodBalanceSheet accessor and FAOSTAT from agrifoodpy_data
from agrifoodpy.food.food import FoodBalanceSheet
from agrifoodpy_data.food import FAOSTAT
import agrifoodpy.food.model as food_model
import matplotlib.pyplot as plt

# Extract data for the UK in 2020 (Region=229, Year=2020)
food_uk = FAOSTAT.sel(Region=229, Year=2020)

# Scale consumption of meat to 50%, 
food_uk_scaled = food_model.balanced_scaling(food_uk,
                                            items=2731,
                                            element="food",
                                            origin="production",
                                            scale=0.5,
                                            constant=True)

# Plot bar summary of resultant food quantities
food_uk_scaled.fbs.plot_bars(elements=["production","imports"],
                            inverted_elements=["exports","food"])
plt.show()

In he future, we plan to implement a pipeline manager to automatize certain aspects of the agrifood execution, and to simulate a comprehensive model where all aspects of the food system are considered simultaneously.

Examples demonstrating the functionality of AgriFoodPy can be the found in the package documentation. These include the use of accessors to manipulate data and access to basic models.

AgriFoodPy is an open-source project which aims at improving the transparency of evidence base food system interventions and policy making. As such, we are happy to hear the input and ideas from the community.

If you want to contribute, have a look at the discussions page or open a new issue

For a comprehensive guide, please refer to the contributing guidelines to open a pull request to contribute new functionality