Assessment of regional variability in crop yields with spatial autocorrelation: Banana farms and policy implications in Martinique

Agricultural research can support farmers and policy makers’ decisions by identifying the causes of spatial variability in crop yield at a regional level. In this paper, we propose a method that combines spatial autocorrelation measures and a farm network survey. This method is intended to describe the causes of spatial variability in crop yields, along with key crop management practices for reaching the best yields and the physical and socio-economic constraints of adopting these practices. This causal and hierarchical analysis of cropping system performance has the advantage of (1) preventing bias in the correlation between variables from the yield gap analysis and (2) formulating spatially targeted policies that are aimed at relaxing adoption constraints at the territorial level. After introducing the method and its different steps, we present the results of the assessment of the spatial variability in banana yields in Martinique (Caribbean). Our study has clearly shown that the planting stage is one of the most important aspects of banana production: allowing a long fallow period, plowing for soil preparation and using seedlings that are produced by tissue culture were associated with the best yields. However, several constraints limit their adoption by farmers at the regional level. The limiting factors were steep slopes, small farm size and low cash flow. We observed no relationship between pesticide use and yields. These study results finally permit the elaboration of spatially targeted policy recommendations to improve crop yields in a sustainable manner. It mainly consists in promoting and facilitating the adoption of good plantation practices for smallholders.