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

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    Journal Article (1.807Mb)
    Date
    2022
    Author
    Shehu, B.M.
    Garba, I.I.
    Jibrin, J.M.
    Kamara, A.
    Adam, A.M.
    Craufurd, P.
    Aliyu, K.T.
    Rurinda, J.
    Merckx, R.
    Type
    Journal Article
    Review Status
    Peer Review
    Target Audience
    Scientists
    Metadata
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    Abstract/Description
    Developing 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.
    https://dx.doi.org/10.1002/saj2.20472
    Multi standard citation
    Permanent link to this item
    https://hdl.handle.net/20.500.12478/7970
    IITA Authors ORCID
    Alpha Kamarahttps://orcid.org/0000-0002-1844-2574
    kamaluddin tijjanihttps://orcid.org/0000-0003-1613-1147
    Digital Object Identifier (DOI)
    https://dx.doi.org/10.1002/saj2.20472
    Research Themes
    Plant Production and Health
    IITA Subjects
    Agronomy; Food Security; Maize; Plant Breeding; Plant Production; Soil Fertility
    Agrovoc Terms
    Maize; Nutrient Management; Food Security; Soil Fertility; Yields; Nigeria
    Regions
    Africa; West Africa
    Countries
    Nigeria
    Hubs
    Headquarters and Western Africa Hub
    Journals
    Soil Science Society of America Journal
    Collections
    • Journal and Journal Articles4842
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