Climate, agriculture, and hunger: statistical prediction of undernourishment using nonlinear regression and data-mining techniques

An estimated 1 billion people suffer from hunger worldwide, and climate change, urbanization, and globalization have the potential to exacerbate this situation. Improved models for predicting food security are needed to understand these impacts and design interventions. However, food insecurity is the result of complex interactions between physical and socio-economic factors that can overwhelm linear regression models. More sophisticated data-mining approaches could provide an effective way to model these relationships and accurately predict food insecure situations. In this paper, we compare multiple regression and data-mining methods in their ability to predict the percent of a country's population that suffers from undernourishment using widely available predictor variables related to socio-economic settings, agricultural production and trade, and climate conditions. Averaging predictions from multiple models results in the lowest predictive error and provides an accurate method to predict undernourishment levels. Partial dependence plots are used to evaluate covariate influence and demonstrate the relationship between food insecurity and climatic and socio-economic variables. By providing insights into these relationships and a mechanism for predicting undernourishment using readily available data, statistical models like those developed here could be a useful tool for those tasked with understanding and addressing food insecurity.