Half of the countries in Africa plant to use CSA as a strategy to respond to climate change in their intended nationally determined contributions (INDCs). With the surge in interest in CSA, there is a clear need for methods and processes to understand investment priorities, costs and risks. P4S teams have worked on integrating Bayesian networks (BN) in analyses of large bankable investments and comprehensive national CSA programs.
Used increasingly in decision science, BN models combine principles from graph theory, probability theory, computer science and statistics to predict outcomes of systems that show high degrees of uncertainty, nonlinearity and feedback between components, especially when data are scarce. The BN models incorporate the costs and risks of implementation of different CSA options, the scale and magnitude of potential impacts, and policy performance (such as macro-economic, social and environmental co-benefits). To date, Bayesian networks have been used to inform the design of national CSAIPs in Ivory Coast and Mali, offering detailed insights into profitability and multiple risks scenarios (such as weather events and political crises) of various CSA investment options.
Photo: Farmer carrying crops from the field. Picture from the village of Bouwéré in Mali. Photo: P. Casier (CGIAR).