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Exploring the potential of mapped soil properties, rhizobium inoculation, and phosphorus supplementation for predicting soybean yield in the savanna areas of Nigeria

Rapid and accurate soybean yield prediction at an on-farm scale is important for ensuring sustainable yield increases and contributing to food security maintenance in Nigeria. We used multiple approaches to assess the benefits of rhizobium (Rh) inoculation and phosphorus (P) fertilization on soybean...

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Autores principales: Jemo, Martin, Devkota, Krishna Prasad, Epule, Terence Epule, Chfadi, Tarik, Moutiq, Rkia, Hafidi, Mohamed, Silatsa, Francis B. T., Jibrin, Jibrin Mohamed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10126304/
https://www.ncbi.nlm.nih.gov/pubmed/37113594
http://dx.doi.org/10.3389/fpls.2023.1120826
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author Jemo, Martin
Devkota, Krishna Prasad
Epule, Terence Epule
Chfadi, Tarik
Moutiq, Rkia
Hafidi, Mohamed
Silatsa, Francis B. T.
Jibrin, Jibrin Mohamed
author_facet Jemo, Martin
Devkota, Krishna Prasad
Epule, Terence Epule
Chfadi, Tarik
Moutiq, Rkia
Hafidi, Mohamed
Silatsa, Francis B. T.
Jibrin, Jibrin Mohamed
author_sort Jemo, Martin
collection PubMed
description Rapid and accurate soybean yield prediction at an on-farm scale is important for ensuring sustainable yield increases and contributing to food security maintenance in Nigeria. We used multiple approaches to assess the benefits of rhizobium (Rh) inoculation and phosphorus (P) fertilization on soybean yield increase and profitability from large-scale conducted trials in the savanna areas of Nigeria [i.e., the Sudan Savanna (SS), Northern Guinea Savanna (NGS), and Southern Guinea Savanna (SGS)]. Soybean yield results from the established trials managed by farmers with four treatments (i.e., the control without inoculation and P fertilizer, Rh inoculation, P fertilizer, and Rh + P combination treatments) were predicted using mapped soil properties and weather variables in ensemble machine-learning techniques, specifically the conditional inference regression random forest (RF) model. Using the IMPACT model, scenario analyses were employed to simulate long-term adoption impacts on national soybean trade and currency. Our study found that yields of the Rh + P combination were consistently higher than the control in the three agroecological zones. Average yield increases were 128%, 111%, and 162% higher in the Rh + P combination compared to the control treatment in the SS, NGS, and SGS agroecological zones, respectively. The NGS agroecological zone showed a higher yield than SS and SGS. The highest training coefficient of determination (R(2) = 0.75) for yield prediction was from the NGS dataset, and the lowest coefficient (R(2) = 0.46) was from the SS samples. The results from the IMPACT model showed a reduction of 10% and 22% for the low (35% adoption scenario) and high (75% adoption scenario) soybean imports from 2029 in Nigeria, respectively. A significant reduction in soybean imports is feasible if the Rh + P inputs are large-scaled implemented at the on-farm field and massively adopted by farmers in Nigeria.
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spelling pubmed-101263042023-04-26 Exploring the potential of mapped soil properties, rhizobium inoculation, and phosphorus supplementation for predicting soybean yield in the savanna areas of Nigeria Jemo, Martin Devkota, Krishna Prasad Epule, Terence Epule Chfadi, Tarik Moutiq, Rkia Hafidi, Mohamed Silatsa, Francis B. T. Jibrin, Jibrin Mohamed Front Plant Sci Plant Science Rapid and accurate soybean yield prediction at an on-farm scale is important for ensuring sustainable yield increases and contributing to food security maintenance in Nigeria. We used multiple approaches to assess the benefits of rhizobium (Rh) inoculation and phosphorus (P) fertilization on soybean yield increase and profitability from large-scale conducted trials in the savanna areas of Nigeria [i.e., the Sudan Savanna (SS), Northern Guinea Savanna (NGS), and Southern Guinea Savanna (SGS)]. Soybean yield results from the established trials managed by farmers with four treatments (i.e., the control without inoculation and P fertilizer, Rh inoculation, P fertilizer, and Rh + P combination treatments) were predicted using mapped soil properties and weather variables in ensemble machine-learning techniques, specifically the conditional inference regression random forest (RF) model. Using the IMPACT model, scenario analyses were employed to simulate long-term adoption impacts on national soybean trade and currency. Our study found that yields of the Rh + P combination were consistently higher than the control in the three agroecological zones. Average yield increases were 128%, 111%, and 162% higher in the Rh + P combination compared to the control treatment in the SS, NGS, and SGS agroecological zones, respectively. The NGS agroecological zone showed a higher yield than SS and SGS. The highest training coefficient of determination (R(2) = 0.75) for yield prediction was from the NGS dataset, and the lowest coefficient (R(2) = 0.46) was from the SS samples. The results from the IMPACT model showed a reduction of 10% and 22% for the low (35% adoption scenario) and high (75% adoption scenario) soybean imports from 2029 in Nigeria, respectively. A significant reduction in soybean imports is feasible if the Rh + P inputs are large-scaled implemented at the on-farm field and massively adopted by farmers in Nigeria. Frontiers Media S.A. 2023-04-11 /pmc/articles/PMC10126304/ /pubmed/37113594 http://dx.doi.org/10.3389/fpls.2023.1120826 Text en Copyright © 2023 Jemo, Devkota, Epule, Chfadi, Moutiq, Hafidi, Silatsa and Jibrin https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Jemo, Martin
Devkota, Krishna Prasad
Epule, Terence Epule
Chfadi, Tarik
Moutiq, Rkia
Hafidi, Mohamed
Silatsa, Francis B. T.
Jibrin, Jibrin Mohamed
Exploring the potential of mapped soil properties, rhizobium inoculation, and phosphorus supplementation for predicting soybean yield in the savanna areas of Nigeria
title Exploring the potential of mapped soil properties, rhizobium inoculation, and phosphorus supplementation for predicting soybean yield in the savanna areas of Nigeria
title_full Exploring the potential of mapped soil properties, rhizobium inoculation, and phosphorus supplementation for predicting soybean yield in the savanna areas of Nigeria
title_fullStr Exploring the potential of mapped soil properties, rhizobium inoculation, and phosphorus supplementation for predicting soybean yield in the savanna areas of Nigeria
title_full_unstemmed Exploring the potential of mapped soil properties, rhizobium inoculation, and phosphorus supplementation for predicting soybean yield in the savanna areas of Nigeria
title_short Exploring the potential of mapped soil properties, rhizobium inoculation, and phosphorus supplementation for predicting soybean yield in the savanna areas of Nigeria
title_sort exploring the potential of mapped soil properties, rhizobium inoculation, and phosphorus supplementation for predicting soybean yield in the savanna areas of nigeria
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10126304/
https://www.ncbi.nlm.nih.gov/pubmed/37113594
http://dx.doi.org/10.3389/fpls.2023.1120826
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