The Complexity of Multidimensional Learning in Agriculture

Abstract

Studies on agricultural technology adoption often focus on one input, practice, or package, which is analytically useful, but may overlook the complexities involved with multidimensional learning needed for a lot of agricultural decisions. In Kenya, we study farmers' dynamic learning (from oneself and others) and adoption decisions over six seasons after randomly inviting them to participate in agronomic research trials, comparing different combinations of inputs during three consecutive seasons. As a response to the trials, adoption increases steadily despite the absence of positive profits multiple seasons after exposure to the trials. Know‐how increases rapidly and faster for high skill farmers who experiment the most, at the cost of making new mistakes. The findings are consistent with a theoretical model with multidimensionality of input and practice decisions and differential learning from one's own experience by skills, where complementarities imply that adoption of an input requires finding how to re‐optimize other dimensions, which adds to the cost of adoption.