This document discusses modeling food systems as neural networks. It begins by providing context around global food security goals. The authors then define food systems and discuss challenges in modeling them due to their complex, nonlinear nature. They propose using artificial neural networks, which can model these complex systems. Examples of neural networks being applied to agriculture are provided. The authors then describe their model using US county-level food trade and security data. Their neural network achieved better accuracy than other models. Interpretation of the results found some variables had significant impacts on food insecurity. The authors conclude neural networks show promise but need improved interpretation and additional data to better inform policy.

1. Modelling Food Systems
as Neural Networks
John M. Ulimwengu
Senior Research Fellow, Africa Division, IFPRI (j.ulimwengu@cgiar.org)
Ferguson, Nathaniel
Intern, Africa Division, IFPRI (N.Ferguson@cgiar.org)

2. MOTIVATION
 In 2021, convened the UN Food Systems Summit as part of the Decade of
Action to achieve the Sustainable Development Goals (SDGs) by 2030.
 The Summit launched bold new actions to deliver progress on all 17 SDGs,
each of which relies on healthier, more sustainable, and equitable food
systems.
 The next stage is to maintain the momentum and Secure have countries
domesticate and implement outcomes
 Increasing demand for food system evidence: “identify and model” core
characteristics which enable food systems to deliver food and nutrition
security (Allen & Prosperi, 2016).

3. WHAT IS A FOOD SYSTEM?
Source: Tendall et al. (2015)
• Food systems comprise all
activities/actors required
to feed and nourish
people.
• Multiple value chains
contributing to food
security and other
outcomes of importance in
the region;
• Other non-food sectors;
• Drivers.

4. OBJECTIVE
Estimate the link between geo-localized multiple
components of a food system and food security.

5. CHALLENGE OF MODELLING FOOD SYSTEMS
 Food system is a network of heterogeneous components (activities/actors);
 Components work together to produce a common outcome;
 Interactions between components are non-linear relationships;
 Lack of appropriate datasets.

6. HUMAN BRAIN (NEURON) AND SYSTEM MODELLING
Neuron functioning
System functioning

7. ARTIFICIAL NEURAL NETWORKS
 Machine learning model with input nodes, output nodes, bias nodes, and
nonlinear “hidden nodes”
 Mimic the functioning of the human brain, which contains billions of
interconnected neurons.
 Learning algorithms adjusts weights and biases until the model can
accurately predict outputs.
 Reputation as black box, where results are accurate but uninterpretable.

8. EXAMPLES OF ARTIFICIAL NEURAL NETWORKS
APPLICATIONS
 Jean et al. utilize a convolutional neural network to predict household
expenditures from satellite data in five African countries.
 Ganguli et al. use ANNs in a similar manner to predict local food security
from satellite images.
 Jena & Majhi apply several types of ANNs to predict the behavior of maize
farmers in Kenya based on technology adoption, because decisions in
agricultural households are widely non-linear.
 In all cases, neural networks proved to be more accurate than standard
statistical methods.

9. Data
 Lin et al. (2019), uses existing data to estimate food flows between US
counties.
 Outflows: value of food produced in a county and consumed in another
county (in Billion USD).
 Inflows: value of food that flowed in and was consumed in a county.
 Food insecurity: Map the Meal Gap study estimates county-level food
insecurity in 2019.

10. Food categories
SCTG Products
1 Animals and fish (live)
2 Cereal grains (includes seed)
3 Agricultural products (excludes animal feed, cereal grains, and forage products)
4 Animal feed, eggs, honey, and other products of animal origin
5 Meat, poultry, fish, seafood, and their preparations
6 Milled grain products and preparations, and bakery products
7 Other prepared foodstuffs, fats and oils

11. DATA
7 food networks Food security

12. Data Preparation
 Data split into train and test sets (75/25 percent)
 Min-max method used to normalize input data within [0,1].
 Output data already falls in range of activation function.
xnorm = (x-minX))/(maxX - minX)

13. Neural Network Interpretation
 Garson’s algorithm: computes relative importance of each predictor
variable using the connection weights.
 Connection weight approach (Olden et. al, 2004): improves Garson’s by
retaining sign.
 Problem: Neural network converges to one of several local minima, rather
than “global minimum.”
 Randomization method: test network to see if connection weights differ
significantly from random.
 P-value: proportion of “random” parameter values which have a more
extreme value than the corresponding value in the “best” model

14. Randomization Method
 Find “best” neural network parameters.
 Run “best” model 1000 times with random starting weights, and record
variable importance scores/weight products from most predictive model.
 Randomly permute food insecurity, and run 1000 trials with same start
weights.
 P-value for significance of variable importance scores and weight
products: proportion of “random” values more extreme than corresponding
values in model.

15. Model Setup
 Average MSE from 50 trials of 32 neural network parameters, using
different variables, learning algorithm, and # of hidden layers
 Best model parameters will be used to create 1000 neural networks,
selecting the most accurate trial for significance testing.

16. Randomization Results
 After 1000 trials, the most accurate model had MSE of 0.00095, as
opposed to the generalized linear model, which had an MSE of 0.0021.
 Weight-products and variable importance scores tested for significance.
 Only 17 of 60 input-hidden-output weight-products are significant,
including all originating with inflows of Animals and Fish (live).
 10 of 14 variable importance scores are significant.
 Most extreme variable importance score: Animals and Fish (live), with
positive effect on food insecurity.

17. Randomization Results

18. Neural Interpretation Diagram
I: Inflows (SCTG 1-7)
O: Outflows (SCTG 1-7)

19. Discussion
 Inflows+outflows model more accurate than inflows and outflows models,
as well as linear model.
 Largest importances: Outflows of Animals and Fish (live) (SCTG 1), with
positive effect on food insecurity, and outflows of Other prepared
foodstuffs, fats, and oils (SCTG 7), with negative effect.
 Dissonance between significance of connection weights and of variable
importance scores.
 Interactions among variables can be identified as contrasting connection
weights entering the same hidden neuron. Ex: SCTG 1, 6, and 7 in hidden
neuron B.

20. Conclusion
 The ANN predicts food insecurity with accuracy, but results are difficult to
translate into policy objectives.
 Makes little sense to advise a county that reducing or increasing their
inflows/outflows of food categories will have a measurable impact on food
insecurity.
 Apply improved methods of ANN interpretation to food system modelling.
 Include other food system components as variables (county self-
sufficiency ratio, demographic information, infrastructure).
 Validate ANN results with evidence, as more research devoted to food
flows is published.
 Need to invest in comprehensive and geo-localised databases

21. THANKS