Spatial Analysis of Soybean Yield Response oo Fertilizer Application in Ghana

Abstract

Soybean is one of the crops grown in Ghana that generate income and serve as a source of protein, animal feed, and food security. However, yields are low, averaging 1.3 Mt/h compared to a potential yield of 3.0 Mt/h, despite an 8 kg/h increase in fertilizer application. The study aimed to analyse how soybean yields in Ghana respond to fertilizer application through spatial methods. We employed a yield modeling approach utilizing data from agricultural trials. To evaluate the variability in observed yields, we used the Multiple Linear Regression-Akaike Information Criterion (MLR-AIC). Additionally, we applied a Random Forest spatial prediction framework to analyze and map the predicted yields. The final and best MLR model achieved one (R=51%), indicating that the model explains about 51% of the variation in the dependent variable. A detailed regression analysis revealed that calcium (Ca), sodium (Na) and minimum temperature (Tmin) were the variables that had a significant negative (<1000 kg/ha) impact on yield. pH, carbon and potassium were the variables with the greatest positive impact on yield (>1000 kg/ha). The predicted soybean yield based on the trained random forest model ranged from 1.0 to 2.2 t/ha. The forecast remained at 1 to 1.8 t/ha in the northern parts and 2.0 to 2.2 t/ha for the southwest. Policy makers in Ghana need to consider highpotassium fertilizers and maintain sound agronomic practices to increase soybean yields.