Machine learning algorithms to improve phosphorus management - Ologunde, Olanrewaju Hameed (2023)
Optimizing phosphorus (P) management in agriculture is critical for food security and sustainable development. Models can effectively combine multiple inputs and site conditions to improve understanding and predictions of nutrient dynamics. Machine learning (ML) models are becoming relevant in various agricultural sectors, with widely available, capable and cost efficient. In this study ML models were trained and tested to predict maize yield in response to phosphorus management. The effects of P on crop yield were predicted for Sub-Saharan Africa (SSA) using a total of 438 observations sourced from published articles. Specifically, we considered articles that determined the influence of P management on maize grain yield. Easily accessible soil and weather variables that affect the dynamics of phosphorus in soil-plant systems were included for possible improvement in the ML models. These variables include clay content, soil P, soil carbon content and weather variables, with variation in ranks across the region and sub-regions. Four machine models were used; multiple linear regression (MLR), random forest (RF), support vector regression (SVR) and k-nearest neighbors (KNN). Overall, the artificial intelligence models trained and tested in this work were able to predict plant responses to P addition and management in different cropping environments in SSA. The result showed that RF had better prediction performance for whole SSA (R2 = 0.57), East (R2 = 0.69) and South (R2 = 0.73) regions, while KNN had higher performance in the West region (R2 = 0.76). This study provides a basis for implementing a new framework for optimizing phosphorus management in agricultural systems. Keywords: Soil fertility. Phosphorus. Maize production. Sub Sahara Africa.