Scarcity of food is perhaps the most heartbreaking condition on our planet. More than one-quarter of the world’s population has an insecure food supply, and those populations are also in parts of the world expected to be strongly affected by climate change, which will further impact their food security (Wheeler and von Bruan, 2013). Though we understand the facets of food insecurity – biophysical, climatic, economic, and infrastructure – there’s few stakeholder-based modeling tools available that can capture and evaluate the impacts of these dynamics on food security.
Current modeling focuses mostly on food production, which is an important consideration, but food security is about more than just production, and food policy needs to reflect this. For example, drivers such as climate change on food distribution, access of households to buy or obtain food, and the stability or degree of change in food systems over time have been investigated far less than impacts on production. Further, studies that have combined trade models with crop-production models have provided producer prices (von Lampe et al., 2014), but consumer prices, which are a key indicator of food access, also depend on other factors such as transportation and infrastructure, neither of which is modeled in any detail. Nor do current models typically capture seasonal variations in food security. Without these variations, food storage considerations, including food waste in a post-processing step, don’t come into play.
To address this need, we developed the Food Distributed Extendable Complementarity model, or Food-DECO, a Partial Equilibrium food systems model. (Partial Equilibrium, or PE, is designed to consider the microeconomic factors of an economy.) Our model represents stakeholders within the agricultural, transportation, and economic systems associated with food and combines them into a unified whole.
The Food-DECO model advances current state-of-the-art by (1) capturing important food supply chain components, including trade and food distribution that accounts for infrastructure and geography, including consideration of transportation cost and regional price variation; (2) considering food access and food loss and disaggregating consumption by per capita income, age, and gender, which allows us to provide information regarding nutrition (and public health in general) that is more detailed and more accurate; and (3) evaluating the effects of seasonality and system shocks by using a monthly time-step and by explicitly modeling storage capacity. This allows us to consider how food access varies throughout the year as well as across years, and to study the potential buffering effects of storage and food-aid. Such data can inform a realistic model response to shocks like crop failure.
All this matters because food security varies with geography and over time, as do relevant food loss considerations. Food waste also means that not all of the nutrition in the food produced actually gets used. Finally, consumption point estimates are insufficient to measure food security; disaggregation by age, gender, and income is necessary to capture human nutrition appropriately. These considerations are highly relevant for any policy measures seeking to address food security.
In short, our model divides up the area of interest into separate regions, and within each region representative agents, or players, act as an aggregation of the decision-makers in that region. Currently in our model, each region has an agent for crop production, livestock management, storage, and consumption; there also exist distribution players between each pair of regions. Prices are a key part of the model, and among them are shadow prices, which represent the value or cost of constraints. Prices in general, and shadow prices in particular, are useful for dealing with decision overlap, meaning, the producer, storage operator, and consumer whom we are trying to model may not be distinct, especially in a subsistence farming context.
To demonstrate Food-DECO’s capabilities, we applied it over a six-year period to the food system of Ethiopia, which is frequently food insecure. We used four representative crops (cereals, tubers, other vegetables, and pulses) and two animal products (meat and milk), and modeled over five regions representing different political and agro-ecological zones. We then ran the model on a baseline case and tested several intervention strategies against a regional crop failure.
Food-DECO produced results that showed the effects of seasonality and regional distribution networks on human caloric intake while disaggregating those effects by age, gender, and per capita income. We then investigated the effects of a regional crop failure and evaluated the effectiveness of similarly priced interventions. In our experiments, direct food aid and direct cash aid were the most effective policy measures at increasing overall caloric intake, though we recognize that these approaches can have numerous secondary effects that are not currently considered in our model. Improving the capacity of the existing food distribution network between regions in our model actually ended up reducing the nutritional outcomes for the population experiencing the crop failure: food was instead sent in larger quantities to regions that had a high demand for imports. We were able to see this unexpected behavior because we integrated agriculture and transportation modeling in an economically consistent way.
Despite the limitations of this case study, the application presented here demonstrates the use of models like Food-DECO for the formulation of informed food policy. When formulating short-term disaster preparedness or long-term development plans that involve or affect regional food security, it is valuable to be able to evaluate demographically-specific food security outcomes, consider the potentially counterintuitive impacts of trade during a food shock, and evaluate a range of intervention policies in a socio-economic context. Further development of Food-DECO and models like it can transform our current production-focused lens on climate-resilient development to a more complete, and ultimately more effective, approach to managing evolving food systems.
von Lampe, M., Willenbockel, D., Ahammad, H., Blanc, E., Cai, Y., Calvin, K., Fujimori, S., Hasegawa, T., Havlik, P., Heyhoe, E., Kyle, P., Lotze-Campen, H., d’Croz, D.M., Nelson, G.C., Sands, R.D., Schimtz, C., Tabeau, A., Valin, H., van der Mensbrugghe, D., van Meijl, H., 2014. Why do global long-term scenarios for agriculture differ? An overview of the AgMIP global economic model intercomparison. Agric. Econ. 45: 1–18.