Karen Macours


The Complexity of Multidimensional Learning in Agriculture


Learning has long been recognized as a decisive driver of the technology adoption process. This paper aims at improving our understanding of the complexity of the learning associated to production processes that require optimizing combination of various inputs. Sustainable agriculture in particular, tends to be more knowledge intensive and yet few studies examine how exposure to a multitude of information signals on different inputs translate in dynamic learning and adoption decisions. We implemented a randomized control trial in Kenya where the treated farmers participated in agronomic research trials that gave them an opportunity to conduct side-by-side comparisons of different combinations of inputs recommended in Integrated Soil Fertility Management in their own parcels during three consecutive rainy seasons. Drawing from detailed data collection during six seasons, we take a deep dive into the learning process, differentiating it by initial farmer skills. Farmers react by increasing experimentation and their farming know-how increases rapidly. High skills farmers experiment the most as a response to the treatment and learn faster, but also make new mistakes and endure a loss in profit in the short term. The paper presents a theoretical model with a multidimensionality of input and practice decisions where complementarities and substituabilities are at the heart of what makes the search for a better equilibrium challenging and costly. This framework highlights learning challenges that external interventions would need to address to help farmers make the shift towards sustainable agricultural intensification.

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