- A simple agent-based network model that computes chain-reactions due to production anomalies based on dynamic food balance sheets at the country level. The model is written in R.
- Latest published version of the model described in Heslin, A., M.J. Puma, P. Marchand, J.A. Carr, J. Dell'Angelo, P. D'Odorico, J.A. Gephart, M. Kummu, M. Porkka, M.C. Rulli, D. Seekell, S. Suweis, and A. Tavoni, 2020: Simulating the cascading effects of an extreme agricultural production shock: Global implications of a contemporary US Dust Bowl event. Front. Sustain. Food Syst., 20 March 2020, doi:10.3389/fsufs.2020.00026.
- Earlier model version is available at https://github.com/pmarchand1/cereals-network-shocks and described in the paper: Marchand, P., J.A. Carr, J. Dell'Angelo, M. Fader, J.A. Gephard, M. Kummu, N.R. Magliocca, M. Porkka, M.J. Puma, and Z. Ratajczak, 2016: Reserves and trade jointly determine exposure to food supply shocks. Environ. Res. Lett., 11, no. 9, 095009, doi:10.1088/1748-9326/11/9/095009.
- Resolution: Food balances/inventories computed at some specified administrative level (e.g. country, province, etc) for a time interval (e.g., one year) that is long enough for a shock to propagate through the system.
- Runtime: Preprossesing input data takes roughly 5 minutes; cascade simulations roughly a few minutes on desktop computer
To run a simulation, first clone this repository and navigate to the top-level directory with:
git clone git@github.com:mjpuma/FSC-WorldModelers.git
cd FSC-WorldModelers
cd inputs
wget 'http://fenixservices.fao.org/faostat/static/bulkdownloads/Trade_DetailedTradeMatrix_E_All_Data_(Normalized).zip'
wget 'http://fenixservices.fao.org/faostat/static/bulkdownloads/Production_Crops_Livestock_E_All_Data_(Normalized).zip'
wget 'https://apps.fas.usda.gov/psdonline/downloads/psd_grains_pulses_csv.zip'
unzip 'Trade_DetailedTradeMatrix_E_All_Data_(Normalized).zip'
unzip 'Production_Crops_E_All_Data_(Normalized).zip'
unzip 'psd_grains_pulses_csv.zip'
rscript main/Requirements.R
As part of the FSC model, we have a Scenario Library (see below), which includes a set of scenarios that have input data already processed and ancillary files already prepared. If you want to run one of these scenarios, you can skip over this step.
To customize your own simulation, you need to modify "ProcessInputs.R" and create a file with the relevant production fractional-declines list by year by country. See the file "inputs/Scenario1_COVID_WheatDeclineFraction_1Year_195countries.csv"as an example of a 1-year run and "Scenario4_USDustBowl_WheatDeclineFraction_4Years_195countries.csv" as an example of a 4-year run. Thus, you should add a scenario to "Step 2" of main.R.
Also, you need to specify the years for the baseline state of the food system (e.g., yr_range <- 2015:2017) as well as the 1) list of countries included, 2) set of crops commodities that will be aggregate for bilateral trade data, 3) set of crops commodities that will be aggregate for production data, and 4) set of crops commodities that will be aggregate for reserves data.
This files are as specified below:
- country_list <- read.csv("ancillary/country_list195_2012to2016.csv")
- commodities<-read.csv("ancillary/cropcommodity_tradelist.csv")
- commodities_prod<-read.csv("ancillary/cropcommodity_prodlist.csv")
- commodities_reserves<-read.csv("ancillary/cropcommodity_reserveslist.csv")
rscript main/ProcessInputs.R
To run a simulation you need to choose 1) the version of the FSC, 2) select a scenario from the Scenario Library, and 3) specify the fraction of "accessible" existing reserves.
The family of FSC models includes: 1) a static version without trade reallocation and 2) two dyanmic versions.
For the static version, you can run a simulation with the following command:
rscript main/main_static.R 1
The number (integer) following the call to the R script indicates the simulation scenario from the Scenarios Library (see below). In the example above, "1" corresponds to a scenario for a decline in wheat production due to hypothetical COVID-19 and locust disruptions.
For the dynamic versions, you can run simulation with the following command:
rscript main/main.R 0 1 0.5
The first number (integer) following the R script name indicates the dynamic model version (FSCversion):
- 0 -> Run Proportional Trade Allocation (PTA) version;
- 1 -> Run Reserves-based Trade Allocation (RTA) version
The second number (integer) indicates the simulation scenario from the Scenarios Library (see below).
The final number (decimal, 0 to 1) specifies the faction of reserves that countries are will to use to meet demand.
fractional_reserve_access: Parameter specifying fraction [0 to 1] of "accessible" existing reserves
In the example above, we have chosen the following parameters:
FSCversion: 0 -> PTA versioni_scenario: 1 -> A scenario for a decline in wheat production due to hypothetical COVID-19 and locust disruptions.fractional_reserve_access: 0.5 (i.e., half of each nation's reserves are accessible to buffer shock)
The model outputs are in CSV files. There are three configurations for the output. Below we describe the output variables and the three formats.
Type 1) Bilateral Export Matrices, where the row name is the origin country and the column is the destination. The first set of m columns is the first time step, the second set is the next time step and so forth. Units depend on ProcessInputs.R; typically kilocalories or metric tons.
- BilateralExportMatrix_TimeSeries.csv
Type 2) These files have four columns. The first row is the title: iso3, Country Name ("Country.x"), Year, Value. The values for each individual file are production, reserves, or food shortage (demand-supply) as indicated by the file name. These files have units consistent with the Bilateral Export Matrices.
- Production_TimeSeries.csv
- Reserve_TimeSeries.csv
- Shortage_TimeSeries.csv
There are also two files that are unitless (i.e. normalized). The first is the ratio of consumption to initial consumption and the second is the change in reserves relative to initial consumption.
- ConsumptiontoC0_TimeSeries.csv
- ReserveChangetoC0_TimeSeries.csv
Type 3: These files have network metrics computed from BilateralExportMatrix_TimeSeries.csv. One set of files has the values from the start of the simulation and the other set has the file values. The first row is the title.
The set of initial value files are:
- Export_InitialTotalByCountry.csv: units consistent with export matrices.
- Import_InitialTotalByCountry.csv: units consistent with export matrices.
- NumberExportTradePartners_InitialTotalByCountry.csv: number of partners
- NumberImportTradePartners_InitialTotalByCountry.csv: number of partners
And the final value files:
- Export_FinalTotalByCountry.csv
- Import_FinalTotalByCountry.csv
- NumberExportTradePartners_FinalTotalByCountry.csv NumberImportTradePartners_FinalTotalByCountry.csv
This is the main script for running the dynamic FSC from the command line including time loop and reading of processed inputs and saving of outputs.
This is the main script for running the static FSC from the command line. This script simply computes changes in supply by country due to production decline anomalies (as a country list).
Creates trade, production, and reserves matrices for use in the FSC model. Requires existing files in ancillary and inputs directory. Processed inputs are saved and subsequently accessed by main.R or main_static.R in a separate folder called inputs_processed
Commodity list, country list, conversion factors from commodity mass to common units (e.g. kcal, protein, US dollars):
- country_list195_2012to2016.csv = FAO country code, iso3 abbreviation, and full country names
- cropcommodity_tradelist.csv = Commodity list for bilateral trade with kcal conversions
- cropcommodity_prodlist.csv = Commodity list for production with kcal conversions
- cropcommodity_reserveslist.csv = Commodity list for reserves with kcal conversions
Bilateral trade of commodities at country level, production/consumption/storage for all countries, and production declines:
- Production fractional declines list by year by country. List depends on ancillary country list file. anomalies <- read.csv(paste0("inputs/Prod", name_crop, "_DeclineFraction_195countries.csv"))
- Trade data from FAOSTAT, detailed trail matrix, normalized, all data. The trade matrix is available here - http://www.fao.org/faostat/en/#data/TM - on the right side bar under "Bulk Downloads", select "All Data Normalized". trade_dat <- read.csv("Trade_DetailedTradeMatrix_E_All_Data_(Normalized).csv"
- Production data from FAOSTAT, production quantity in tonnes. The crop production data are available at: http://www.fao.org/faostat/en/#data/QC. prod_dat<-read.csv("productiondataFAOSTAT.csv")
- Reserves data from USDA, downloadable dataset - psd grains pulses. The stocks data are available here - https://apps.fas.usda.gov/psdonline/app/index.html#/app/downloads - listed as "Grains" the file is called "psd_grains_pulses_csv.zip". psd <- read.csv("psd_grains_pulses.csv")
- Export Matrix ordered by FAOSTAT country code (increasing). load("inputs_processed/E0.RData")
- Production. load("inputs_processed/P0.Rdata")
- Reserves. (a.k.a. Stocks) load("inputs_processed/R0.RData")
Main iteration loop to implement FSC Model
Functions for the FSC model
We include scenarios separately for the static FSC model (FSC-static) and the dynamic FSC model (FSC-dynamic). Listed below are the scenarios currently included for each.
A scenario for a hypothetical COVID-19 + locust disruption to the global wheat trade network (baseline: 2015 to 2017) for the year 2020. This is a one-year simulation.
The production decline fractions are:
Kenya: 0.4916
Saudi Arabia: 0.4483
Yemen: 0.2335
Ukraine: 0.205
Kazakhstan: 0.1818
Iran: 0.1024
Pakistan: 0.0976
Ethiopia: 0.0874
Russia: 0.0768
A scenario for a hypothetical COVID-19 + locust disruption to the global maize trade network (baseline: 2015 to 2017) for the year 2020. This is a one-year simulation.
The production decline fractions are:
Saudi Arabia 0.5281
Somalia 0.4844
Iran 0.2975
Yemen 0.2518
Kenya 0.1521
Brazil 0.1328
Ethiopia 0.1301
Argentina 0.119
Ukraine 0.0878
Pakistan 0.0717
A scenario for a hypothetical COVID-19 + locust disruption to the global rice trade network (baseline: 2015 to 2017) for the year 2020. This is a one-year simulation.
The production decline fractions are:
Kenya 0.3425
Pakistan 0.197
Iran 0.1883
Thailand 0.021
India 0.0192
Viet Nam 0.0012
A scenario for a hypothetical US Dust Bowl analogue event to the global wheat trade network (baseline: 2015 to 2017) for the year 2020. This is a four-year simulation. The production decline fractions are:
USA: 0.33 0.36 0.24 0.24 (for years 1 to 4, respectively)
A scenario for a hypothetical COVID-19 + locust disruption to the global wheat trade network (baseline: 2015 to 2017) for the year 2020. This is a one-year simulation.
The production decline fractions are:
Kenya: 0.4916
Saudi Arabia: 0.4483
Yemen: 0.2335
Ukraine: 0.205
Kazakhstan: 0.1818
Iran: 0.1024
Pakistan: 0.0976
Ethiopia: 0.0874
Russia: 0.0768
A scenario for a hypothetical COVID-19 + locust disruption to the global maize trade network (baseline: 2015 to 2017) for the year 2020. This is a one-year simulation.
The production decline fractions are:
Saudi Arabia 0.5281
Somalia 0.4844
Iran 0.2975
Yemen 0.2518
Kenya 0.1521
Brazil 0.1328
Ethiopia 0.1301
Argentina 0.119
Ukraine 0.0878
Pakistan 0.0717
A scenario for a hypothetical COVID-19 + locust disruption to the global rice trade network (baseline: 2015 to 2017) for the year 2020. This is a one-year simulation.
The production decline fractions are:
Kenya 0.3425
Pakistan 0.197
Iran 0.1883
Thailand 0.021
India 0.0192
Viet Nam 0.0012
The set of scenarios below were developed for three main staple crops -- wheat, rice and maize -- and focus on a combination of:
- Production anomalies in 2020 due to the locust upsurge, based on USDA country level data
- All export restrictions in 2007-2008 based on reported export bans during this time period and include production anomalies in the top 10 producers for the same years, based on USDA country level production.
A scenario for a hypothetical wheat disruption to the global wheat trade network (baseline: 2015 to 2017) for the year 2020. This is a one-year simulation.
- runname <- c('Wheat_breadbasket_Year2008')
- nameinput <- c('Wheat_Avg20152017')
- shock_scenario <- read.csv(paste0("inputs/2021_C2P2_scenarios/Scenario_Wheat_DeclineFraction_breadbasket_Year2008_195countries.csv"))
- No export restrictions.
- runname <- c('Wheat_locust_Year2020')
- nameinput <- c('Wheat_Avg20152017')
- shock_scenario <- read.csv(paste0("inputs/2021_C2P2_scenarios/Scenario_Wheat_DeclineFraction_locust_Year2020_195countries.csv"))
- No export restrictions.
- runname <- c('Wheat_locust_Year2020_breadbasket_Year2008')
- nameinput <- c('Wheat_Avg20152017')
- shock_scenario <- read.csv(paste0("inputs/2021_C2P2_scenarios/Scenario_Wheat_DeclineFraction_locust_Year2020_breadbasket_year2008_195countries.csv"))
- No export restrictions.
- runname <- c('Wheat_ExportRestrictionFraction_Year2008')
- nameinput <- c('Wheat_Avg20152017')
- No production declines.
- export_scenario <- read.csv(paste0("inputs/2021_C2P2_scenarios/Scenario_Wheat_ExportRestrictionFraction_Year2008_195countries.csv"))
- runname <- c('Wheat_ExportRestrictionFraction_Year2008with_RUS_25')
- nameinput <- c('Wheat_Avg20152017')
- No production declines.
- export_scenario <- read.csv(paste0("inputs/2021_C2P2_scenarios/Scenario_Wheat_ExportRestrictionFraction_Year2008_with_RUS_25%_195countries.csv"))
- runname <- c('Wheat_ExportRestrictionFraction_Year2008with_RUS_50')
- nameinput <- c('Wheat_Avg20152017')
- No production declines.
- export_scenario <- read.csv(paste0("inputs/2021_C2P2_scenarios/Scenario_Wheat_ExportRestrictionFraction_Year2008_with_RUS_50%_195countries.csv"))
- runname <- c('Maize_breadbasket_Year2008')
- nameinput <- c('Maize_Avg20152017')
- shock_scenario <- read.csv(paste0("inputs/2021_C2P2_scenarios/Scenario_Maize_DeclineFraction_breadbasket_Year2008_195countries.csv"))
- No export restrictions.
- runname <- c('Maize_locust_Year2020')
- nameinput <- c('Maize_Avg20152017')
- shock_scenario <- read.csv(paste0("inputs/2021_C2P2_scenarios/Scenario_Maize_DeclineFraction_locust_Year2020_195countries.csv"))
- No export restrictions.
- runname <- c('Maize_locust_Year2020_breadbasket_Year2008')
- nameinput <- c('Maize_Avg20152017')
- shock_scenario <- read.csv(paste0("inputs/2021_C2P2_scenarios/Scenario_Maize_DeclineFraction_locust_Year2020_breadbasket_year2008_195countries.csv"))
- No export restrictions.
- runname <- c('Maize_ExportRestrictionFraction_Year2008')
- nameinput <- c('Maize_Avg20152017')
- No production declines.
- export_scenario <- read.csv(paste0("inputs/2021_C2P2_scenarios/Scenario_Maize_ExportRestrictionFraction_Year2008_195countries.csv"))
- runname <- c('Maize_ExportRestrictionFraction_Year2008with_ARG_25')
- nameinput <- c('Maize_Avg20152017')
- No production declines.
- export_scenario <- read.csv(paste0("inputs/2021_C2P2_scenarios/Scenario_Maize_ExportRestrictionFraction_Year2008_with_ARG_25%_195countries.csv"))
- runname <- c('Maize_ExportRestrictionFraction_Year2008with_ARG_50')
- nameinput <- c('Maize_Avg20152017')
- No production declines.
- export_scenario <- read.csv(paste0("inputs/2021_C2P2_scenarios/Scenario_Maize_ExportRestrictionFraction_Year2008_with_ARG_50%_195countries.csv"))
- runname <- c('Rice_breadbasket_Year2008')
- nameinput <- c('Rice_Avg20152017')
- shock_scenario <- read.csv(paste0("inputs/2021_C2P2_scenarios/Scenario_Rice_DeclineFraction_breadbasket_Year2008_195countries.csv"))
- No export restrictions.
- runname <- c('Rice_locust_Year2020')
- nameinput <- c('Rice_Avg20152017')
- shock_scenario <- read.csv(paste0("inputs/2021_C2P2_scenarios/Scenario_Rice_DeclineFraction_locust_Year2020_195countries.csv"))
- No export restrictions.
- runname <- c('Rice_locust_Year2020_breadbasket_Year2008')
- nameinput <- c('Rice_Avg20152017')
- shock_scenario <- read.csv(paste0("inputs/2021_C2P2_scenarios/Scenario_Rice_DeclineFraction_locust_Year2020_breadbasket_year2008_195countries.csv"))
- No export restrictions.
- runname <- c('Rice_ExportRestrictionFraction_Year2008')
- nameinput <- c('Rice_Avg20152017')
- No production declines.
- export_scenario <- read.csv(paste0("inputs/2021_C2P2_scenarios/Scenario_Rice_ExportRestrictionFraction_Year2008_195countries.csv"))