Abstract
Site-specific nutrient management can reduce soil degradation and crop production risks related to undesirable timing, amount, and type of fertilizer application. This study was conducted to understand the spatial variability of soil properties and delineate spatially homogenous nutrient management zones (MZs) in the maize belt region of Nigeria. Soil samples (n = 3387) were collected across the area using multistage and random sampling techniques, and samples were analyzed for pH, soil organic carbon (SOC), macronutrients (N, P, K, S, Ca and Mg), micronutrients (S, B, Zn, Mn and Fe) content, and effective cation exchange capacity (ECEC). Spatial distribution and variability of these parameters were assessed using geostatistics and ordinary kriging, while principal component analysis (PCA) and multivariate K-means cluster analysis were used to delineate nutrient management zones. Results show that spatial variation of macronutrients (total N, available P, and K) was largely influenced by intrinsic factors, while that of S, Ca, ECEC, and most micronutrients was influenced by both intrinsic and extrinsic factors with moderate to high spatial variability. Four distinct management zones, namely, MZ1, MZ2, MZ3, and MZ4, were identified and delineated in the area. MZ1 and MZ4 have the highest contents of most soil fertility indicators. MZ4 has a higher content of available P, Zn, and pH than MZ1. MZ2 and MZ3, which constitute the larger part of the area, have smaller contents of the soil fertility indicators. The delineated MZs offer a more feasible option for developing and implementing site-specific nutrient management in the maize belt region of Nigeria.
Highlights
Sustainable management of soil is of prime importance for increasing and sustaining crop yields globally and more importantly in sub-Saharan Africa (SSA), where food production is not at pace with the high food demand that resulted from the rapidly growing population [1,2]
In the same vein, increasing demographic pressure has resulted in the cultivation of marginal lands that are prone to fertility decline and other environmental degradations [3]
Other soil data used in this study were obtained from published (95 data points described by Shehu et al [13]) and unpublished data sets (292 points) collected between 2016 and 2018 by the project “Taking Maize Agronomy to Scale in Africa” (TAMASA) in Nigeria
Summary
Sustainable management of soil is of prime importance for increasing and sustaining crop yields globally and more importantly in sub-Saharan Africa (SSA), where food production is not at pace with the high food demand that resulted from the rapidly growing population [1,2]. Spatial delineation of MZs with their specific soil management recommendations, has proved capable of providing sustainable management decision solutions for natural resource management in many crop productions zones [17]. Various qualitative approaches, such as detailed soil survey maps [18,19], yield maps [20], and interpretation of remotely sensed images [21] have been used for delineation of MZs. Various qualitative approaches, such as detailed soil survey maps [18,19], yield maps [20], and interpretation of remotely sensed images [21] have been used for delineation of MZs These approaches were criticized as being either expensive, less precise, laborious, or localized. With our discussion above in mind, this results in ttontehhhvnueeeettrir1rrre-ei9eego7insri0motasunia[ndm3iadez0rbaee]ea.rr-btlWbaaheepniiolnptctfhegliisrocnleaauifgnrotrigiroodtemnhnilsyeaoctofmusifoofseasnirNnlitoaatiinlhbggizroeeaoedurburito.abgvt.TyhheoMeuioonnsovuoimrefrieolrkio-rsnnmvpodoear,w“rttu,ibhalnelilsasddivntrgeeakeer-rses-,iutaapt”hlpbetefspicelriiilrefntiticyciianslaituzaninoetuddrrniteercraoeniorecftrtnohrifmtemeosrbrmpfteiaiolnecilnazonfondemdrrcia.mnmetTsgiaoeoitMnnniod(otZsnhua)setradicobskeooonvnviueloetdltwrohteiphnrlceoeegidsudsitggoohienihnel, sspuaptpiaolrvt atorioalbsilsiutychanads tchoerrQesupaonntditiantgivMe EZvsaclouvaetiroinngotfhteheenFteirretimlitayizoef bTerlotpreicgailoSnooilfsN(QigUerEiaF.TMS)o[r6e]oavnedr, sthitee-sNpuetcriifiecnnt uEtxripeenrttr(eNcoEm) mtoeonld[a5t]iohnavdeecbiseieonn sduepvpeloorpt etodolfsorsumchaiazsethine QthueaNntiigtaetriivaenEmvaaliuzaetiboenlto.fTthhee Fcheratlilleitnygoef rTermopaiicnasl Stooildse(vQeUloEpFTaSn)d[6i]natengdrathtee NthuetrgieenotspEaxtpiaelrts(oNilE)MtoZosl [s5o] hthaavte tbheeenmdoedveellsopceadn fboer mMcgioetmagpnhauffserafnZaesitoiNinihedikncsszrsciiieioteessnsgfiatopinifgiretenoanieatrtlsffgrlgdhliayttaoiiicnhastncorouh.tudeandfAmestolsN,heisirssbpnndeoamipsygopgnimualaeneua(ttmcgd1roeliethiya)endsaop,plosattnentoiissvrnhslaglostsma,igniaevcsrcptsdaayioiaissasesinfntimzmibatnudotiesihbcgdunalaiabeeky,(ltntlite2yhieriteanl)avffietsfioisgn.ampmsdfecarTpaeliesieldaechnaaoyldlinisttdeislanei.l-ttialcelsoisAlpcylnhcya,lrdvasptuauaoilafelsusnnespolrvuteetgdireefecandparrlhbfg(tornraea2,iimoepelurmn)tivsthtrtaeaidyioerilirlssiiemnsnyseizoolossgneifwtatiennhrutlsips,ehnoefd.aeeeamosiyanmlsrt,tsiptapdaoniiaanelnrigniimodceatztimypxgeenaestveeilduoemblnrmeynsttetflraitosooleefitlieconprslod-camnrtsinerasntcfovaenzeamspttsedorlhei,letponoariecilinvfnenpainamzisatnggcraeegamenugrusriesoizroeeasdmtaegiiirnenlntsaiebdefogffnweeotfnterhotlhrhtxttrioziheitscodlnfoeier,gtnfornNysioeenpsoceorgiiiopsignommslimioepfnuntoeorneasbgsidrss,tiameitintrnpabriuecgnavaloygasttgislstiiiciiooohn(oeceo1innsgneyslf), c2o. mMbaitneartiiaolns apnridncMipeatlhcoodms ponent and multivariate cluster analyses
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