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

作者机构：Department of Soil Science and Soil ProtectionFaculty of AgrobiologyFoodand Natural ResourcesCzech University of Life SciencesKamýcká12916500 PragueCzech Republic National Institute of Agricultural ResearchRabatMorocco Department of Soil ScienceUniversity of CalabarCalabarNigeria 

出 版 物：《Ecological Processes》 (生态过程（英文）)

年 卷 期：2022年第11卷第1期

页      面：302-318页

核心收录：

学科分类：09[农学] 0903[农学-农业资源与环境] 090301[农学-土壤学] 

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 

主　　题：Soil mapping Environmental variables Agriculture Soil properties Soil management 

摘      要：Background:Mapping of soil nutrients using different covariates was carried out in northern *** study was undertaken in response to the region’s urgent requirement for an updated soil *** aimed to test various covariates combinations for predicting the variability in soil properties using ordinary kriging and kriging with external ***:A total of 1819 soil samples were collected at a depth of 0–40 cm using the 1-km grid sampling *** were screened for their pH,soil organic matter(SOM),potassium(K_(2)O),and phosphorus(P_(2)O_(5))using standard laboratory *** attributes(T)computed using a 30-m resolution digital elevation model,bioclimatic data(C),and vegetation indices(V)were used as covariates in the *** 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 *** statistical parameter RMSE was used to determine the accuracy of different ***: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 *** a result,the studied soil properties showed a linear relationship with the selected ***,SOM,and K 2O presented a moderate spatial autocorrelation,while P2O5 revealed a strong *** 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 ***:The findings suggest that a combination of too many environmental covariates might not provide the actual variability of a targeted soil *** demonstrates that specific covariates with close relationships with certain soil properties might perform bet