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ALLFED Integrated Food System Model

Testing DOI License: GPL v3

An integrated food supply model for resilient foods in nuclear winter

Run the model

  • You can create a variety of different scenarios with this model. A collection of possible scenarios are already available in the scenarios folder (e.g. run_model_with_resilient_foods.py). Examples of usage can be found in the scripts folder. Resulting pptx can be found in results/large_reports/. If you don't wish to run these results yourself, you can find more pregenerated results in the Zenodo repository (https://zenodo.org/record/7039924).

Running on command line

(See Dependencies section first)

Results from the paper can be rerun using the following commands in the src/scenarios folder

python create_fig_1ab.py
python create_fig_2abcde.py
python create_fig_3abcd.py
python run_model_baseline.py

For the country-by-country no food trade model, run

python run_baseline_by_country_no_trade.py

if you want to recreate the figures and results from the Nature Foods paper.

More Details

See the zenodo repository for more results and reports: https://zenodo.org/record/7039924

  • A scenario is created by creating a new instance of the Scenario class in scenario.py. This class contains a collection of methods that provide your model with the parameter value it needs to run. Here you can also change the parameter values if you want to change the model to your specifications.
  • Once you got all your parameter values ready you create an Instance of the Parameter class from parameter.py. This class allows you to initialize the model with the parameter values you defined.
  • Finally to create an instance of the Optimizer class from optimizer.py and provide it with your parameters. This will run the model itself and optimize it.
  • to see which parameters and scenarios can be set, look at src/scenarios/run_scenarios.py "set_depending_on_option" method

How the model works in general

Flow Chart

Dependency management with Anaconda

The integrated model is written in python 3, ensure you have some version of python3, although it has only been tested with python 3.9 or later. Then, install the required packages using conda or miniconda:

You'll also need to install conda or miniconda or similar.

See https://docs.anaconda.com/anaconda/install/index.html for installation instructions.

Once the program is installed on your device, set up a separate environment for the project (do not use your base environment). This step and the following can be done in two ways:

  • using the GUI or
  • using the Anaconda Prompt. For people new to coding the GUI is more intuitive.
GUI
  1. Open the Anaconda Navigator.
  2. Select the tap "Environments".
  3. Click "Import" and select the "environment.yml" file from the repository and name the new environment. All dependencies will be installed automatically.
Anaconda Prompt
  1. Open Anaconda Prompt.
  2. Type in the following line:
conda env create -f environment.yml

The dependencies will be installed automatically and the environment will be name intmodel.

If you close out the terminal and open it later you will want to activate the environment again using

conda activate intmodel

For both versions: Code from this project will only run smoothly when opened in the new environment and when the working directory is set to the path location of the repository on your machine.

Directories and What They Do

Below we explain all of the folders used for getting data, processing data, running simulations, then displaying and saving results of models.

Files purely for documentation or creating the python environment are not included below.

Directory Structure

allfed-integrated-model ├── data/ │ All the tabulated data used for the repository, as well as intermediary │ processed results from import scripts. Also contains data explaining │ assumptions of the model in the Supplemental_Data.xlsx file.
├── public │ ├── css │ │ ├── **/*.css │ ├── images │ ├── js │ ├── index.html ├── dist (or build ├── node_modules ├── package.json ├── package-lock.json └── .gitignore

  • subdirectories: data/

| All the tabulated data used for the repository, as well as intermediary | processed results from import scripts. Also contains data explaining | assumptions of the model in the Supplemental_Data.xlsx file.

 results/

    Outputs of the model including figures and pptx generated reports.

 scripts/

    Convenient scripts used to run sets of python scripts.

 src/

    All of the code used to model the food system and to run simulations.

data:

subdirectories:

no_food_trade/

files:

no_resilient_food_primary_results.npy
resilient_food_primary_results.npy

    These are used for plotting from src/scenarios/plot_primary_food.py.


Supplemental_Data.xlsx

    This is part of the raw data imported in the case of Nutrition, or 
    used directly for estimating the performance of crop relocation or expected 
    diet availability, as well as crop seasonality. 
    The spreadsheet contains the following tabs: 
        Summary of field margins
        Cost modelling
        Diet affordability
        Crop years
        Production & stocks seasonality
        Crop Model Results
        Crop Model ASRS Relocation Estimate
        Crop Model No Relocation
        Crop Model 2005
        Production 2005
        Production 2005 Raw
        Nutrition

data/no_food_trade:

subdirectories:

processed_data/

    All of the CSV files in this folder are generated by running the 
    scripts/run_all_imports.sh file. They contain data in standard format for each
    country on values of interest for the food system models (for no food trade
    scenarios).

raw_data/

    All data files used for each country in the no food trade scenarios, directly 
    from the data source (before processing to make standard format).
    Processing of these files happens using the import files in 
    /src/import_scripts_no_food_trade/ directory, which can in turn be called all 
    at once using the scripts/run_all_imports.sh script.

files:

computer_readable_combined.csv

    The ultimate result from running all the import scripts in 
    src/import_scripts_no_food_trade, (also accomplished 
    using scripts/run_all_scripts.py). In particular, the 
    src/import_scripts_no_food_trade/import_food_data.py file creates this by 
    merging the csvs into a dataframe then exporting the result to this csv.

data/no_food_trade/raw_data:

files:

FAOSTAT_*_.CSV

    Data files imported directly from FAOSTAT for the year in question.

Integrated Model With No Food Trade.xlsx

    The spreadsheet is used as a remaining set of data pertaining to no food trade
    scenarios, which is imported in addition to raw FAOSTAT data to create the 
    final computer_readable_code.csv file.
    Contains the following tabs:
        Population
        Food waste
        Outdoor crop Production Seasonality
        Outdoor Crop Production Baseline
        Outdoor Crop Production NW
        Food Stocks
        Seafood - excluding seaweeds
        Grazing
        Seaweed
        Cellulosic Sugar
        Greenhouses
        Methane SCP
        Combined

rutgers_nw_production_raw.csv

    Raw data provided by the Rutgers Toon group from Xia et al publication
    regarding grasses and staple crops (spring wheat, maize, soybean, rice) 
    fractional crop yields to baseline in a 150 tg nuclear winter scenario.

results:

subdirectories:

large_reports/

    Reports generated with the run_model_no_trade_baseline.py, run_model_no_trade_no_resilient_foods.py, and run_model_no_trade_with_resilient_foods.py in src/scenarios/.


Figure_4/

    Diagrammatic flow chart of the integrated model.

files:

*.fig

    Figures produced by running some src/scenarios/ files. The last stage of
    figure generation (plotting itself) always occurs in the
    src/utilities/plotter.py file.

scripts:

files:

    create_all_no_trade_scenario_pptx.sh

        Generates a full set of results in results/large_reports/ by running run_model_no_trade scripts in src/scenarios/, using both the "single" and "multi" arguments.

    run_all_imports.sh

        This script creates all the imported .csv files in the data/no_food_trade/processed_data folder, by running all the import_*_csv.py scripts in src/import_scripts_no_food_trade/. It's required to run this for importing all the food system input data before running simulations if no_food_trade scenarios are being run.

src

subdirectories:

#### food_system/

        Called by the compute_parameters function in src/optimizer/parameters.py, each function represents a different part of the food system. 

#### import_scripts_no_food_trade/

        This is required to be run before other files in src/ in order to import raw data into a usable format for other files in src/. It is a set of scripts that are run to import food system data, typically by the scripts/run_all_imports.sh file. These create the imported .csv files in the data/no_food_trade/processed_data folder. It's required to run this for importing all the food system input data before running simulations for no_food_trade scenarios.

#### optimizer/


        the optimizer folder runs the following sequence in order for every scenario: compute_parameters, run_optimizer, extract_results, interpret_results, validate_results. The parameters.py file also imports all the src/food_system category-specific calculations. The results of parameters.py are sent to the optimizer.py through the constants_out variable.

        Optimization in optimizer uses the PuLP linear optimizer to optimally satisfy the series of constraints used in that model.


#### scenarios/

        Running a script in scenarios is done after completing import of the scripts, if no_food_trade scenarios are being run. Files in scenarios/ are either utility files used to specify the scenarios or provide an intermediary interface to allow other scripts to run the model in a particular way, scripts used to process and run specific scenarios for the nuclear winter with or without global food trade between trading blocs, or scripts which plot the results of these scenario runs. The scenarios are all either involving full food trade or no food trade between trading blocs, as determined by the trading bloc data in no_food_trade/computer_readable_combined.csv and globally-aggregated data stored in scenarios.py.


        Utility files used to specify the scenarios are: scenarios.py
        Utility files used to provide an intermediary interface to allow other scripts to run the model in a particular way are: run_model_no_trade.py, run_scenario.py

        Scripts used to process and run specific scenarios for the nuclear winter with global food trade between trading blocs: run_model_with_resilient_foods.py, run_model_baseline.py, run_model_no_resilient_foods.py, create_figure_3abcd.py

        Scripts which plot the results of these scenario runs:
        plot_primary_food.py

        Scripts used to process and run specific scenarios for the nuclear winter without global food trade between trading blocs: create_figure_1ab.py, create_figure_2abcde.py, run_model_no_trade_baseline.py, run_model_no_trade_no_resilient_foods.py, run_model_no_trade_with_resilient_foods.py, reproduce_xia_et_al_USA.py, run_USA_with_improved_numbers.py
        
        The files which simply plot results are: plot_primary_food.py

        The scenarios are run under a single set of assumptions or multiple sets of assumptions.  use input data stored in data/no_food_trade/computer_readable_combined.csv and use them to ev  The python files in this directory are all the ways that the model can be run.

        Overall, the scripts in the scenarios folder interrelate as follows:

         ( run_model_with_resilient_foods.py,
           run_model_baseline.py,
           run_model_no_resilient_foods.py,
           create_figure_3abcd.py )
               ==calls=for=each=scenario==>
                    run_scenario.py


         ( create_figure_1ab.py,
           create_figure_2abcde.py,
           reproduce_xia_et_al_USA.py,
           run_USA_with_improved_numbers.py,
           run_model_no_trade_with_resilient_foods.py,
           run_model_no_trade_no_resilient_foods.py,
           run_model_no_trade_baseline.py )
              ==call=with=scenarios=defined==>
                  run_model_no_trade.py

         run_model_no_trade.py ==calls=for=each=scenario==> run_scenario.py

        In the case of no food trade:
         scenarios.py <==loads== data/no_food_trade/computer_readable_combined.csv

         run_scenario.py <==loads== scenarios.py


        Next, the run_scenario.py file calls the optimizer as follows in order to produce results for each scenario:

        run_scenario.py 
            ==calls=in=this=order=>
                src/optimizer/parameters.py
                src/optimizer/optimizer.py
                src/optimizer/extract_results.py
                src/optimizer/interpret_results.py
                src/optimizer/validate_results.py

        Finally the results are returned from run_scenario back up the call chain in reverse order.

#### utilities/

        various useful utilities that are called elsewhere in the program, that do not serve any core function for the modelling. Notably, all the plotting code is located in the utilities directory.