Identifying Critical Drivers of Mango, Tomato, and Maize Postharvest Losses (PHL) in Low-Income Countries and Predicting Their Impact

Abstract

Several studies have identified a host of factors to be considered when attempting to reduce food postharvest loss (PHL). However, very few studies have ranked those factors by their importance in driving PHL. This study used the Random Forest model to rank the critical drivers of PHL in maize, mango, and tomato, cultivated in Tanzania, Kenya, and Nigeria, respectively. The study then predicted the maize, mango, and tomato PHLs by changing the levels of the most critical drivers of PHL and the number of farmers at each level. The results indicate that the most critical drivers of PHL consist of pre-harvest and harvest variables in the field, such as the quantity of maize harvested in the maize value chain, the method used to know when to begin mango harvest, and the type of pest that attacks plants in the tomato value chain. Furthermore, changes in the levels of a critical driver and changes in the number of smallholder farmers at a given level both have an impact on PHL, although the impact of the former is much higher than the latter. This study also revealed that the critical drivers of PHL can be categorized as either passive and difficult to manipulate, such as the geographic area within which a smallholder farmer lives, or active and easier to control, such as services provided by the Rockefeller Foundation YieldWise Initiative. Moreover, the affiliation of smallholder farmers to the YieldWise Initiative and a smallholder farmer’s geographic location are ubiquitous critical drivers across all three value chains. Finally, an online dashboard was created to allow users to explore further the relationship between several critical drivers, the PHL of each crop, and a desired number of smallholder farmers.

Keywords

• variable importance
• Random Forest
• predictive model
• dashboard