{"title":"Empirical assessment of the physico-chemical determinants of soil spatial variability in Sub-Saharan Africa","authors":"C. Agbangba, E. E. Gongnet, R. G. Kakaï","doi":"10.16929/ajas/2022.1319.270","DOIUrl":null,"url":null,"abstract":"An appropriate understanding of soil properties’ spatial variability could help to perform sustainable soil nutrient management. The study aims to identify the most important soil characteristics driving spatial variability in tropical soil. A total of 5000 sample locations were randomly generated from the Sub-Saharan Africa map and the sample values were obtained from www.soilgrid.org. Various variogram models were tested and the best fitted variogram parameters were used to simulate 10000 replications of each attributes and the spatial dependence indices were computed. Results suggested that soil N, pH and organic carbon are the most driving spatial variability to better control experimental error.","PeriodicalId":332314,"journal":{"name":"African Journal of Applied Statistics","volume":"199 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"African Journal of Applied Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.16929/ajas/2022.1319.270","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
An appropriate understanding of soil properties’ spatial variability could help to perform sustainable soil nutrient management. The study aims to identify the most important soil characteristics driving spatial variability in tropical soil. A total of 5000 sample locations were randomly generated from the Sub-Saharan Africa map and the sample values were obtained from www.soilgrid.org. Various variogram models were tested and the best fitted variogram parameters were used to simulate 10000 replications of each attributes and the spatial dependence indices were computed. Results suggested that soil N, pH and organic carbon are the most driving spatial variability to better control experimental error.
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