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dc.contributorDevkota, Krishnaen_US
dc.contributorEpule Epule, Terenceen_US
dc.contributorChfadi, Tariken_US
dc.contributorMoutiq, Rkiaen_US
dc.contributorHafidi, Mohameden_US
dc.contributorSilatsa, Francis B Ten_US
dc.contributorJibrin, Jibrin Mohameden_US
dc.creatorJemo, Martinen_US
dc.date.accessioned2023-05-19T14:50:04Z
dc.date.available2023-05-19T14:50:04Z
dc.identifierhttps://www.frontiersin.org/articles/10.3389/fpls.2023.1120826/full#supplementary-materialen_US
dc.identifierhttps://mel.cgiar.org/reporting/download/hash/c3367c4ded6f559e072edb84152de8e5en_US
dc.identifier.citationMartin Jemo, Krishna Devkota, Terence Epule Epule, Tarik Chfadi, Rkia Moutiq, Mohamed Hafidi, Francis B T Silatsa, Jibrin Mohamed Jibrin. (11/4/2023). Exploring the potential of mapped soil properties, rhizobium inoculation, and phosphorus supplementation for predicting soybean yield in the savanna areas of Nigeria. Frontiers in Plant Science, 14.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.11766/68418
dc.description.abstractRapid 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.en_US
dc.formatPDFen_US
dc.languageenen_US
dc.publisherFrontiers Mediaen_US
dc.rightsCC-BY-4.0en_US
dc.sourceFrontiers in Plant Science;14,(2023)en_US
dc.subjectbradyrhizobium inoculationen_US
dc.subjectforesight impact modelen_US
dc.subjectnigeria savanna agroecologiesen_US
dc.subjectparticipatory on-farm experimenten_US
dc.subjectrandom forest modelen_US
dc.titleExploring the potential of mapped soil properties, rhizobium inoculation, and phosphorus supplementation for predicting soybean yield in the savanna areas of Nigeriaen_US
dc.typeJournal Articleen_US
dcterms.available2023-04-11en_US
cg.creator.idDevkota, Krishna: 0000-0002-2179-8395en_US
cg.contributor.centerInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.centerNational Institute of Agronomic Research Morocco - INRA Moroccoen_US
cg.contributor.centerCadi Ayyad University - UCAen_US
cg.contributor.centerMohammed VI Polytechnic University - UM6Pen_US
cg.contributor.centerBayero University - BUKen_US
cg.contributor.crpResilient Agrifood Systems - RAFSen_US
cg.contributor.funderBill & Melinda Gates Foundation - BMGFen_US
cg.contributor.funderMohammed VI Polytechnic University - UM6Pen_US
cg.contributor.project-lead-instituteInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.coverage.regionWestern Africaen_US
cg.coverage.countryNGen_US
cg.contactm.jemo@ocpafrica.comen_US
cg.identifier.doihttps://dx.doi.org/10.3389/fpls.2023.1120826en_US
cg.isijournalISI Journalen_US
dc.identifier.statusOpen accessen_US
mel.impact-factor5.6en_US
cg.issn1664-462Xen_US
cg.subject.sdgSDG 2 - Zero hungeren_US
cg.subject.actionAreaResilient Agrifood Systemsen_US
cg.subject.impactAreaNutrition, health and food securityen_US
cg.journalFrontiers in Plant Scienceen_US
cg.volume14en_US
cg.contributor.initiativeFragility to Resilience in Central and West Asia and North Africaen_US


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