2024
DOI: 10.3390/agronomy14030477
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Advancing toward Personalized and Precise Phosphorus Prescription Models for Soybean (Glycine max (L.) Merr.) through Machine Learning

Floyd Muyembe Chipatela,
Lotfi Khiari,
Hamza Jouichat
et al.

Abstract: 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… Show more

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