Soil quality estimation using environmental covariates and predictive models:an example from tropical soils of Nigeria  

作　　者：Isong Abraham Isong Kingsley John Paul Bassey Okon Peter Ikor Ogban Sunday Marcus Afu 

机构地区：[1]Department of Soil Science,Faculty of Agriculture,University of Calabar,Calabar PMB 1115,Nigeria [2]Department of Soil Science and Soil Protection,Faculty of Agrobiology,Food,and Natural Resources,Czech University of Life Sciences,Kamýcká,12916500 Prague,Czech Republic

出　　处：《Ecological Processes》2022年第1期946-967,共22页生态过程（英文）

摘　　要：Background:Information addressing soil quality in developing countries often depends on results from small experimental plots,which are later extrapolated to vast areas of agricultural land.This approach often results in misin-formation to end-users of land for sustainable soil nutrient management.The objective of this study was to estimate the spatial variability of soil quality index(SQI)at regional scale with predictive models using soil–environmental covariates.Methods:A total of 110 composite soil samples(0–30 cm depth)were collected by stratified random sampling schemes at 2–5 km intervals across the Cross River State,Nigeria,and selected soil physical and chemical properties were determined.We employed environmental covariates derived from a digital elevation model(DEM)and Senti-nel-2 imageries for our modelling regime.We measured soil quality using two approaches[total data set(TDS)and minimum data set(MDS)].Two scoring functions were also applied,linear(L)and non-linear(NL),yielding four indices(MDS_L,MDS_NL,TDS_L,and TDS_NL).Eleven soil quality indicators were used as TDS and were further screened for MDS using principal component analysis(PCA).Random forest(RF),support vector regression(SVR),regression kriging(RK),Cubist regression,and geographically weighted regression(GWR)were applied to predict SQI in unsampled locations.Results:The computed SQI via MDS_L was classified into five classes:≤0.38,0.38–0.48,0.48–0.58,0.58–0.68,and≥0.68,representing very low(classⅤ),low(classⅣ),moderate(classⅢ),high(classⅡ)and very high(classⅠ)soil quality,respectively.GWR model was robust in predicting soil quality(R^(2)=0.21,CCC=0.39,RMSE=0.15),while RF was a model with inferior performance(R^(2)=0.02,CCC=0.32,RMSE=0.15).Soil quality was high in the southern region and low in the northern region.High soil quality class(>49%)and moderate soil quality class(>14%)dominate the study area in all predicted models used.Conclusions:Structural stability index,sand content,soil oganic carbon content,and

关 键 词：Decision support system Geospatial technology Predictive model Remote sensing Soil quality 

分 类 号：S15[农业科学—土壤学]