Household-level food security under climatic influences and different adaptation scenarios

• Applicant: Professor Dr. Christoph Gornott
• Subject Area: Public Health, Healthcare Research, Social and Occupational Medicine ; Agricultural Economics, Agricultural Policy, Agricultural Sociology
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 409670289

Climate change will increase the pressure on food security in Burkina Faso and Kenya. The strongest negative climate impacts are projected in the tropics, where farmers often do not have enough capacities to adjust their agronomic management in case of adverse weather conditions. At the same time population growth and rapid urbanization will likely cause an increasing food demand and associated expansion of arable land. These trends, if not properly managed, may prevent the establishment of resilient agricultural systems. Furthermore, they foster food insecurity, undernutrition and malnourishment, which place considerable burden on people’s health. In phase I, we developed a semi-empirical crop model to identify climate-related risks for potential harvest losses. Using this model, we were able to predict food availability for staple crops for Burkina Faso. This allowed us to quantify cereal-based food shortages one month before the harvest. This information can support governments in taking anticipatory actions in case of looming food crises. The results indicate that despite a surplus of produced calories from staple crops on national level in Burkina Faso, a high level of food insecurity prevails for large parts of the population. This suggests that not only food availability, but also the other dimensions of food security – namely food access, utilization and stability – strongly impact food insecurity in Burkina Faso. Still, the literature on the impacts of climate change on food security is dominated by a focus on food availability and in particular food production. In phase II, we will therefore analyse all dimensions of food security based on physical and socio-economic data. This will allow us to comprehensively understand the complex underlying causes of food insecurity and related health impacts. In addition to the climatic influences on food availability, we will therefore integrate household survey data from the Health and Demographic Surveillance System (HDSS). For this purpose, we will use a machine learning technique - a Bayesian Belief Network - to quantify (causal) interactions between weather and socio-economic factors that contribute to food (in)security. Using climate change projections, we will then investigate how climate change will affect food security in future. Last, we will evaluate the impact of adaptation options to climate change on food security outcomes. A focus will be on estimating how optimised planting dates influence the food security status of households. This analysis can support the development and prioritization of adaptation options to climate change and potentially contribute to increasing food and nutrition security.

• DFG Programme: Research Units
• Subproject of: FOR 2936: Climate Change and Health in Sub-Saharan Africa