TY - JOUR
T1 - Exploring the potential of mapped soil properties, rhizobium inoculation, and phosphorus supplementation for predicting soybean yield in the savanna areas of Nigeria
AU - Jemo, Martin
AU - Devkota, Krishna Prasad
AU - Epule, Terence Epule
AU - Chfadi, Tarik
AU - Moutiq, Rkia
AU - Hafidi, Mohamed
AU - Silatsa, Francis B.T.
AU - Jibrin, Jibrin Mohamed
N1 - Funding Information:
The experimental work was conducted with the support of the Notore Limited Group, Nigeria. The project was financially supported partly by a grant from the Bill and Melinda Gates Foundation to support the on-farm demonstrations in Nigeria. Special thanks to extension agents from the Notore group and the collaborative farmers who participated in the on-farm demonstration trials. MJ was financially supported by a grant from OCP AFRICA to the University of Mohammed VI Polytechnic (UM6P) to develop sustainable grain legume production in Africa.
Funding Information:
The experimental work was conducted with the support of the Notore Limited Group, Nigeria. The project was financially supported partly by a grant from the Bill and Melinda Gates Foundation to support the on-farm demonstrations in Nigeria. Special thanks to extension agents from the Notore group and the collaborative farmers who participated in the on-farm demonstration trials. MJ was financially supported by a grant from OCP AFRICA to the University of Mohammed VI Polytechnic (UM6P) to develop sustainable grain legume production in Africa.
Publisher Copyright:
Copyright © 2023 Jemo, Devkota, Epule, Chfadi, Moutiq, Hafidi, Silatsa and Jibrin.
PY - 2023
Y1 - 2023
N2 - 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 (R2 = 0.75) for yield prediction was from the NGS dataset, and the lowest coefficient (R2 = 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.
AB - 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 (R2 = 0.75) for yield prediction was from the NGS dataset, and the lowest coefficient (R2 = 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.
KW - Nigeria savanna agroecologies
KW - bradyrhizobium inoculation
KW - foresight IMPACT model
KW - participatory on-farm experiment
KW - random forest model
UR - http://www.scopus.com/inward/record.url?scp=85153491427&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85153491427&partnerID=8YFLogxK
U2 - 10.3389/fpls.2023.1120826
DO - 10.3389/fpls.2023.1120826
M3 - Article
AN - SCOPUS:85153491427
SN - 1664-462X
VL - 14
JO - Frontiers in Plant Science
JF - Frontiers in Plant Science
M1 - 1120826
ER -
