One size does not fit all: Heterogeneous economic impact of integrated pest management practices for mango fruit flies in Kenya—a machine learning approach

Abstract

AbstractMost previous studies evaluating agricultural technology adoption focus on estimating homogeneous average treatment effects across technology adopters. Understanding the heterogeneous effects and drivers of impact heterogeneity should enable interventions to be better targeted to maximise benefits. We apply machine learning using data from a randomised controlled trial to estimate the heterogeneous treatment effect of fruit fly IPM practices (i.e., parasitoids, orchard sanitation, use of food bait, biopesticides, male annihilation technique, and their combinations) in Central Kenya. Results suggest significant heterogeneity in the effect of IPM practices conditioned on household characteristics. The most important covariates explaining differences in treatment effects are wealth, distance to the mango fruit market, age of the household head, labour and experience in mango farming. Results further indicate that those with fewer mango trees benefit more from most IPM practices. Additional analysis across other covariates shows mixed results but generally suggests significant differences between households benefiting the most and those benefiting the least from IPM practices.