A Framework for Evaluating the Impact of Food Security Scenarios
This work addresses food security prediction for policymakers and researchers, but it is incremental as it applies existing methods to a new database without major methodological breakthroughs.
The study tackled predicting food security scenario impacts by proposing a two-step approach using scenario definition and evaluation with a VAR model and Monte Carlo simulation, applied to a proprietary database, and demonstrated its utility in a case study with insights for decision-making on food prices and availability.
This study proposes an approach for predicting the impacts of scenarios on food security and demonstrates its application in a case study. The approach involves two main steps: (1) scenario definition, in which the end user specifies the assumptions and impacts of the scenario using a scenario template, and (2) scenario evaluation, in which a Vector Autoregression (VAR) model is used in combination with Monte Carlo simulation to generate predictions for the impacts of the scenario based on the defined assumptions and impacts. The case study is based on a proprietary time series food security database created using data from the Food and Agriculture Organization of the United Nations (FAOSTAT), the World Bank, and the United States Department of Agriculture (USDA). The database contains a wide range of data on various indicators of food security, such as production, trade, consumption, prices, availability, access, and nutritional value. The results show that the proposed approach can be used to predict the potential impacts of scenarios on food security and that the proprietary time series food security database can be used to support this approach. The study provides specific insights on how this approach can inform decision-making processes related to food security such as food prices and availability in the case study region.