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Compositional nutrient diagnosis (CND) and associated yield predictions in maize: a case study in the northern Guinea savanna of Nigeria

dc.contributor.authorShehu, B.M.
dc.contributor.authorGarba, I.I.
dc.contributor.authorJibrin, J.M.
dc.contributor.authorKamara, A.
dc.contributor.authorAdam, A.M.
dc.contributor.authorCraufurd, P.
dc.contributor.authorAliyu, K.T.
dc.contributor.authorRurinda, J.
dc.contributor.authorMerckx, R.
dc.date.accessioned2022-12-13T14:48:16Z
dc.date.available2022-12-13T14:48:16Z
dc.date.issued2022
dc.identifier.citationShehu, B.M., Garba, I.I., Jibrin, J.M., Kamara, A., Adam, A.M., Craufurd, P., ... & Merckx, R. (2022). Compositional nutrient diagnosis (CND) and associated yield predictions in maize: a case study in the northern Guinea savanna of Nigeria. Soil Science Society of America Journal, 1-19.
dc.identifier.issn0361-5995
dc.identifier.urihttps://hdl.handle.net/20.500.12478/7970
dc.description.abstractDeveloping optimal strategies for nutrient management of soils and crops at a larger scale requires an understanding of nutrient limitations and imbalances. The availability of extensive data (n = 1,781) from 2-yr nutrient omission trials in the most suitable agroecological zone for maize (Zea mays L.) in Nigeria (i.e., the northern Guinea savanna) provides an opportunity to assess nutrient limitations and imbalances using the concept of multi-ratio compositional nutrient diagnosis (CND). We also compared and contrasted the use of linear regression models and bootstrap forest machine learning to predict maize yield based on nutrient concentration in ear leaves. The results showed that 35% of the experimental plots had low yields due to nutrient imbalances (hereafter referred to as low yield imbalanced [LYI]). These experimental plots were dominated by control plots (without any nutrients applied), plots without N fertilization, and plots without P fertilization. Using the control plot as the ultimate indicator of nutrient imbalance, the significantly limiting nutrients in order of decreasing frequency of deficiency were N, P, S, Ca > Cu, and B. Both linear regression and bootstrap forest machine learning models fairly predicted maize grain yield based on nutrient concentration in ear leaves only in the LYI group and when examining all data with an independent validation dataset. These results suggest that nutrient management strategies, especially through the site-specific management approach, should consider S, Ca, Cu, and B in addition to the existing nutrients N, P, and K to improve nutrient balance and maize yield in the study area.
dc.description.sponsorshipBill & Melinda Gates Foundation
dc.format.extent1-19
dc.language.isoen
dc.subjectMaize
dc.subjectNutrient Management
dc.subjectFood Security
dc.subjectSoil Fertility
dc.subjectYields
dc.subjectNigeria
dc.titleCompositional nutrient diagnosis (CND) and associated yield predictions in maize: a case study in the northern Guinea savanna of Nigeria
dc.typeJournal Article
cg.contributor.crpGrain Legumes
cg.contributor.crpMaize
cg.contributor.affiliationBayero University Kano
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.contributor.affiliationInternational Maize and Wheat Improvement Center
cg.contributor.affiliationUniversity of Zimbabwe
cg.contributor.affiliationKatholieke Universiteit Leuven
cg.coverage.regionAfrica
cg.coverage.regionWest Africa
cg.coverage.countryNigeria
cg.coverage.hubHeadquarters and Western Africa Hub
cg.researchthemePlant Production and Health
cg.identifier.bibtexciteidSHEHU:2022
cg.isijournalISI Journal
cg.authorship.typesCGIAR and developing country institute
cg.iitasubjectAgronomy
cg.iitasubjectFood Security
cg.iitasubjectMaize
cg.iitasubjectPlant Breeding
cg.iitasubjectPlant Production
cg.iitasubjectSoil Fertility
cg.journalSoil Science Society of America Journal
cg.notesOpen Access Article; Published online: 17 Aug 2022
cg.accessibilitystatusOpen Access
cg.reviewstatusPeer Review
cg.usagerightslicenseCreative Commons Attribution 4.0 (CC BY 0.0)
cg.targetaudienceScientists
cg.identifier.doihttps://dx.doi.org/10.1002/saj2.20472
cg.iitaauthor.identifierAlpha Kamara: 0000-0002-1844-2574
cg.iitaauthor.identifierkamaluddin tijjani: 0000-0003-1613-1147
cg.futureupdate.requiredNo


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