Modeling intensification decisions in the Kilombero Valley floodplain: A Bayesian belief network approach

The Kilombero Valley floodplain in Tanzania is a major agricultural area. Government initiatives and projects supported by international funding have long sought to boost productivity. Due increasing population pressure, smallholder farmers are forced increase their output. Nevertheless, the level of intensification still lower than what considered necessary production support livelihoods significantly. This article aims better understand farmers’ choices interdependent determinants. We propose novel modeling approach for identifying determinants interrelationships combining Bayesian belief network (BBN), experimental design, multivariate regression trees. Our complements existing lower-dimensional statistical models considering uncertainty providing an easily updatable model structure. BBN constructed calibrated using data from survey 304 farm households. findings show how data-driven can be used identify variables that influence decision choose one technique over another. Furthermore, most important drivers vary widely, depending on options being considered.