Advancing toward Personalized and Precise Phosphorus Prescription Models for Soybean (Glycine max (L.) Merr.) through Machine Learning

• Published in: Agronomy (Basel) 2024-03, Vol.14 (3), p.477
• Main Authors: Chipatela, Floyd Muyembe, Khiari, Lotfi, Jouichat, Hamza, Kouera, Ismail, Ismail, Mahmoud
• Format: Article
• Language: English
• Subjects: Agricultural production; Algorithms; Artificial intelligence; Calibration; Diagnostic systems; Extreme values; Farmers; Fertilization; Fertilizers; Machine learning; optimal field-specific rate; Optimization; Phosphorus; Precipitation; predicting response curves; Predictions; prescription of P; Robustness; Soil chemistry; Soil conditions; Soil fertility; Soil pH; Soil testing; Soils; Soybeans; unified phosphorus fertility classification system
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy14030477

The traditional approach of prescribing phosphate fertilizer solely based on soil test P (STP) has faced criticism from scientists and agriculturists pushing farmers to seek phosphate fertilization models that incorporate additional factors. By embracing integrated approaches, farmers can receive more precise recommendations that align with their specific conditions and fertilization techniques. This study aimed to utilize artificial intelligence prediction to replicate soybean response curves to fertilizer by integrating edaphic and climatic factors. Literature data on soybean response to P fertilization were collected, and the Random Forest (RF) algorithm was applied to predict response curves. The predictions utilized seven predictors: P dose, STP, soil pH, texture, % OM, precipitation, and P application methods. These predictions were compared to the traditional STP-based approach. The STP-based P prescription models exhibited extremely low robustness values (R2) of 1.53% and 0.88% for the PBray-1 and POlsen diagnostic systems, respectively. In contrast, implementing the RF algorithm allowed for more accurate prediction of yield gains at various P doses, achieving robustness values of 87.4% for the training set and 60.9% for the testing set. The prediction errors remained below 10% throughout the analysis. Implementing artificial intelligence modeling enabled the study to achieve precise predictions of the optimal P dose and customized fertilization recommendations tailored to farmers’ specific soil conditions, climate, and individual fertilization practices.