Mapping soil nutrients via different covariates combinations:theory and an example from Morocco  

作　　者：Kingsley John Yassine Bouslihim Isong Abraham Isong Lahcen Hssaini Rachid Razouk Ndiye M.Kebonye Prince C.Agyeman Vit Penížek Tereza Zádorová 

机构地区：[1]Department of Soil Science and Soil Protection,Faculty of Agrobiology,Food,and Natural Resources,Czech University of Life Sciences,Kamýcká129,16500 Prague,Czech Republic [2]National Institute of Agricultural Research,Rabat,Morocco [3]Department of Soil Science,University of Calabar,Calabar,Nigeria

出　　处：《Ecological Processes》2022年第1期302-318,共17页生态过程（英文）

基　　金：This study was supported by an internal Ph.D.grant no.SV20-5-21130 of the Faculty of Agrobiology,Food and Natural Resources of the Czech University of Life Sciences Prague(CZU);And also,NutRisk grant:European Regional Development Fund,project Center for the investigation of synthesis and transformation of nutritional substances in the food chain in interaction with potentially harmful substances of anthropogenic origin:comprehensive assessment of soil contamination risks for the quality of agricultural products,number CZ.02.1.01/0.0/0.0/16_019/0000845.

摘　　要：Background:Mapping of soil nutrients using different covariates was carried out in northern Morocco.This study was undertaken in response to the region’s urgent requirement for an updated soil map.It aimed to test various covariates combinations for predicting the variability in soil properties using ordinary kriging and kriging with external drift.Methods:A total of 1819 soil samples were collected at a depth of 0–40 cm using the 1-km grid sampling method.Samples were screened for their pH,soil organic matter(SOM),potassium(K_(2)O),and phosphorus(P_(2)O_(5))using standard laboratory protocols.Terrain attributes(T)computed using a 30-m resolution digital elevation model,bioclimatic data(C),and vegetation indices(V)were used as covariates in the study.Each targeted soil property was modeled using covariates separately and then combined(e.g.,pH~T,pH~C,pH~V,and pH~T+C+V).k=tenfold cross-validation was applied to examine the performance of each employed model.The statistical parameter RMSE was used to determine the accuracy of different models.Results:The pH of the area is slightly above the neutral level with a corresponding 7.82%of SOM,290.34 ppm of K_(2)O,and 100.86 ppm of P_(2)O_(5).This was used for all the selected targeted soil properties.As a result,the studied soil properties showed a linear relationship with the selected covariates.pH,SOM,and K 2O presented a moderate spatial autocorrelation,while P2O5 revealed a strong autocorrelation.The cross-validation result revealed that soil pH(RMSE=0.281)and SOM(RMSE=9.505%)were best predicted by climatic variables.P_(2)O_(5)(RMSE=106.511 ppm)produced the best maps with climate,while K_(2)O(RMSE=209.764 ppm)yielded the best map with terrain attributes.Conclusions:The findings suggest that a combination of too many environmental covariates might not provide the actual variability of a targeted soil property.This demonstrates that specific covariates with close relationships with certain soil properties might perform better than the compilation of different enviro

关 键 词：Soil mapping Environmental variables AGRICULTURE Soil properties Soil management 

分 类 号：S15[农业科学—土壤学]