Identifying food insecurity situations timely and accurately is a complex challenge. To prevent food crisis and design appropriate interventions, several food security warning and monitoring systems are very active in food-insecure countries. However, the limited types of data selected and the limitations of data processing methods used make it difficult to apprehend food security in all its complexity. In this work, we propose models that aim to predict two key indicators of food security: the food consumption score and the household dietary diversity score. These indicators are time consuming and costly to obtain. We propose using heterogeneous data as explanatory variables that are more convenient to collect. These indicators are calculated using data from the permanent agricultural survey conducted by the Burkinabe government and available since 2009. The proposed models use deep and machine learning methods to obtain an approximation of food security indicators from heterogeneous explanatory data. The explanatory data are rasters (population densities, rainfall estimates, land use, etc.), GPS points (of hospitals, schools, violent events), quantitative economic variables (maize prices, World Bank variables), meteorological and demographic variables. A basic research issue is to perform pre-processing adapted to each type of data and then to find the right methods and spatio-temporal scale to combine them. This work may also be useful in an operational approach, as the methods discussed could be used by food security warning and monitoring systems to complement their methods to obtain estimates of key indicators a few weeks in advance and to react more quickly in case of famine.
Background
The timely and accurate identification of food insecurity situations represents a challenging issue. Household surveys are routinely used in low-income countries and are an essential tool for obtaining key food security indicators that are used by decision makers to determine the targets of food security interventions.
Methodology
This paper investigates the spatial and temporal quality of the food security indicators obtained through household surveys. The empirical case of Burkina Faso is used in this paper, where a large-scale rural household survey has been conducted yearly since 2009. From this data set, three food security indicators (the Food Consumption Score, the Household Dietary Diversity Score and the Coping Strategies Index) are calculated at the regional level for each year during the 2009–2017 period.
Results
Results highlight that observed spatiotemporal variations in these indicators are consistent with the major regional food shocks reported in food warning system reports and are significantly correlated with variations computed from other sources of data, such as satellite images, rainfall and food prices.
Conclusion
These results raise new research questions on food security monitoring systems and on the use of heterogeneous data and multiple food security indicators.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.