Soil and management‐related factors contributing to maize yield gaps in western Kenya

Abstract

AbstractThe solution to reducing existing yield gaps on smallholder farms lies in understanding factors limiting yield in areas with agricultural intensification potential. This study applied an integrated analysis approach comprising Classification and Regression Tree (CART), generalized linear mixed model (GLMM), and factor analysis (FA), to explain soil and management‐related factors influencing maize yield gaps, in order to enhance yields. The study was conducted in Mukuyu and Shikomoli in western Kenya, sites with, respectively, high and low agroecological potential regarding soil fertility. Maize yield gaps were quantified by comparing yields on the 90th percentile of farms to yields determined in 189 fields on 70 randomly sampled smallholdings. Soil and management‐related factors were determined at early and late maize development stages.Maize yield on the 90th percentile of farms in Mukuyu and Shikomoli was 5.1 and 4.8 t/ha, respectively, and the average yield gap was 1.8 and 2.6 t/ha, representing 35% and 54% unachieved yield for Mukuyu and Shikomoli, respectively. In FA, soil was revealed to be the main factor influencing maize yield gaps at both sites, rather than management‐related variables. The CART method identified maize density, chlorophyll values, maize height, and depth to compact layer as consistent factors affecting yield at both sites, while GLMM identified soil texture (silt content) as important. According to CART, weed cover at early stages and maize density at late stages were the most limiting factor in maize production in Mukuyu and Shikomoli, respectively. Generalized linear mixed model analysis identified agroecology‐specific factors influencing maize yield gaps as soil‐available phosphorus and zinc, plus weed pressure at early maize stages in Mukuyu, and plus soil cation exchange capacity and exchangeable magnesium in Shikomoli. Through an integrated approach, it was possible to identify both consistent and agroecology‐specific factors limiting crop yields. This can increase the applicability of the findings to smallholder farms.