Bayesian geo-additive modeling of zonal level crop production in Ethiopia

Abstract

Crop production plays an important role in global food security, economic stability, and sustainable development, so it is important to identify covariates that linearly and nonlinearly affect it to ensure sustainable food security and economic stability. In this study, we have used a Bayesian geo-additive mixed model to analyze the spatially structured agricultural sample survey data of eight years (Meher seasons from 2012/13 to 2019/20) collected annually by the Central Statistics Agency of Ethiopia (the current Ethiopian Statistical Service). The posterior estimates of the linear fixed effects showed that the proportion of farmers preventing soil erosion, the proportion of educated farmers, the percentage of crop damage, and the number of oxen all have a significant negative effect, while the proportion of farmers who practice pure agriculture and the area used have a significant positive effect on log crop production per household in the zone. The posterior estimates of the non-linear fixed effects showed that year, the proportion of female farmers, the proportion of farmers who practice other agriculture, the proportion of farmers who used broadcast sowing, household age, farmer association crop production, and UREA fertilizer used have significant non-linear effects on log crop production. Pure agricultural farming, cluster farming, farmers’ associations, and UREA fertilizer usage are recommended to increase crop production at the zone level. To attain the main objective of this study, we considered only the spatial structure or dependency of the sample survey data.