Estimating soil organic matter: a case study of soil physical properties for environment-related issues in southeast Nigeria

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dc.contributor.authorOfem, Kokei Ikpi
dc.contributor.authorJohn, Kingsley
dc.contributor.authorPawlett, Mark
dc.contributor.authorEyong, Michael Otu
dc.contributor.authorAwaogu, Chukwuebuka Edwin
dc.contributor.authorUmeugokwe, Pascal
dc.contributor.authorAmbrose-Igho, Gare
dc.contributor.authorEzeaku, Peter Ikemefuna
dc.contributor.authorAsadu, Charles Livinus Anija
dc.date.accessioned2021-10-28T18:23:18Z
dc.date.available2021-10-28T18:23:18Z
dc.date.issued2021-10-17
dc.description.abstractThe different deposition periods in sedimentary geological environment have made the build-up and estimation of soil organic matter ambiguous to study. Soil organic matter has received global attention in the ambience of international policy regarding environmental health and safety. This research was to understand the inter-relationship between soil organic matter and bulk density, saturated hydraulic conductivity (Ksat), total, air-filled and capillary porosities for organic matter estimation, via different multiple linear regression functions (i.e., leapbackward, leap forward, leapseq and lmStepAIC), in soils developed over the sedimentary geological environment. Eight mapping units were obtained in Ishibori, Agoi Ibami and Mfamosing via digital elevation model. Two pits were sited within each mapping unit, and 53 soil samples were used for the study. In soils over shale–limestone–sandstone, two pits were sited, six in alluvium, four in sandstone–limestone and four in limestone. Overall correlation between SOM with Ksat (r = 0.626) and BD (r = − 0.588) was significant (p < 0.001). The strongest correlation was obtained for SOM with BD (r = − 0.783) and Ksat (r = 0.790) in soils over limestone. In contrast, soils over shale–limestone and sandstone geological environment gave the weakest relationship (r < 0.6). Linear regression gave a similar prediction output. The best performing was leapbackward (RMSE = 11.50%, R2 = 0.58, MAE = 8.48%), which produced a smaller error when compared with leap forward, leapseq and lmStepAIC functions in organic matter estimation. Therefore, we recommend applying leapback linear regression when estimating soil organic variation with physical soil properties for solving soil–environmental issues towards sustainable crop production in southeast Nigeria.en_UK
dc.identifier.citationOfem KI, John K, Pawlett M, et al., (2021) Estimating soil organic matter: a case study of soil physical properties for environment-related issues in southeast Nigeria. Earth Systems and Environment, Volume 5, Issue 4, December 2021, pp. 899-908en_UK
dc.identifier.issn2509-9426
dc.identifier.urihttps://doi.org/10.1007/s41748-021-00263-0
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/17218
dc.language.isoenen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectAgricultureen_UK
dc.subjectEnvironmenten_UK
dc.subjectMultivariate statisticsen_UK
dc.subjectSoil healthen_UK
dc.subjectHumid tropicsen_UK
dc.titleEstimating soil organic matter: a case study of soil physical properties for environment-related issues in southeast Nigeriaen_UK
dc.typeArticleen_UK

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