PERFORMANCE OF SOYBEAN (Glycine max L.) INFLUENCED BY DIFFERENT RATES AND SOURCES OF PHOSPHORUS FERTILIZER IN SOUTH-WEST NIGERIA

Olaniyan, Amudalat Bolanle; Udo, Enoobong; Afolami, Ayodeji

doi:10.7251/agreng1603046o

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…Phosphorus is especially important to soy cultivation as inadequate phosphorus fertilization can result in yield reductions of 50% [38,44,45,[47][48][49]. e suggested rate of SSP for tropical/subtropical soy production is at least 40-60 kg•P•ha −1 [50,51]. Soybean in the rotation reduces the need for annual applications of phosphorus to the cereal, and biannual applications maximized economic returns to farmers [52].…”

Section: A Practical Overview Of Soy-maize Rotation In Sub-mentioning

confidence: 99%

Soy-Maize Crop Rotations in Sub-Saharan Africa: A Literature Review

Acevedo‐Siaca

Goldsmith

2020

International Journal of Agronomy

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Empirical evidence shows complementarity between maize and soybean as a sustained agricultural system across North and South America as well as Eastern Europe. The potential application to sub-Saharan Africa motivates this literature review. Maize is one of the most important crops on the African subcontinent, accounting for over half of daily caloric intake in some regions. However, continuous cropping of maize has led to extensive degradation of soil and decrease in crop productivity and endangers household food and nutritional security. The cultivation of soybean holds great promise in improving agricultural systems in sub-Saharan Africa. Introducing soy into rotation with maize is a method to diversify diets, better nutritional status, reduce abiotic and biotic stresses, and improve soil fertility, while enhancing crop productivity and generating more income for farmers. However, limited access to extension services and other sources of technical support constrains adoption of the more complex rotation cropping system involving a new crop, soybean. Rotating soybean with maize too challenges farmers as there is not a specific prescription that can guide farmers operating across Africa’s diverse agroecological environments. Finally, soybean is an input-intensive crop requiring significant investment at planting, which may not allow small holders with limited resources and no access to credit.

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Section: A Practical Overview Of Soy-maize Rotation In Sub-mentioning

confidence: 99%

Soy-Maize Crop Rotations in Sub-Saharan Africa: A Literature Review

Acevedo‐Siaca

Goldsmith

2020

International Journal of Agronomy

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Evaluating the Benefits of Implementing Soy-Maize Crop Rotations in Sub-Saharan Africa

Acevedo‐Siaca

Goldsmith²

2020

AJFAND

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

Chipatela,

Khiari,

Jouichat

et al. 2024

Agronomy

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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.

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PERFORMANCE OF SOYBEAN (Glycine max L.) INFLUENCED BY DIFFERENT RATES AND SOURCES OF PHOSPHORUS FERTILIZER IN SOUTH-WEST NIGERIA

Cited by 3 publications

References 2 publications

Soy-Maize Crop Rotations in Sub-Saharan Africa: A Literature Review

Soy-Maize Crop Rotations in Sub-Saharan Africa: A Literature Review

Evaluating the Benefits of Implementing Soy-Maize Crop Rotations in Sub-Saharan Africa

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

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