{"title":"利用地统计学方法评价红土土壤性质的空间变异性(西孟加拉邦,印度)","authors":"Gouri Sankar Bhunia , Pravat Kumar Shit , Rabindranath Chattopadhyay","doi":"10.1016/j.aasci.2018.06.003","DOIUrl":null,"url":null,"abstract":"<div><p>Degradation of soil due to unsuitable land management practices is a chief impairment of optimum land productivity. The spatial variability of soil properties is needed for agricultural productivity, food safety and environmental modeling. The present study was conducted in lateritic soils of West Bengal, India to understand the spatial variability of soil properties using a geostatistical model. Nitrogen (N), soil pH, electrical conductivity (EC), Phosphorus (P), Potassium (K) and organic carbon (OC) were measured. Surface maps of soil properties were prepared using the semivariogram model through Kriging techniques. A positive correlation was observed between OC and N. The Quantile-quantile plots showed a normal distribution of EC, K, pH, N, and OC. The value for nugget/sill of K, N, and EC were 0.25–0.75 indicating moderate spatial autocorrelation among the variables. Phosphorus (P) was highly concentrated in the eastern part, whereas the agglomeration of higher EC was found in the north east and south west corner of the study site. The cross validation results illustrated the smoothing effect of the spatial prediction. The present study suggests that the geostatistical model can directly reveal the spatial variability of lateritic soils and will help farmers and decision makers for improving soil-water management.</p></div>","PeriodicalId":100092,"journal":{"name":"Annals of Agrarian Science","volume":"16 4","pages":"Pages 436-443"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.aasci.2018.06.003","citationCount":"59","resultStr":"{\"title\":\"Assessment of spatial variability of soil properties using geostatistical approach of lateritic soil (West Bengal, India)\",\"authors\":\"Gouri Sankar Bhunia , Pravat Kumar Shit , Rabindranath Chattopadhyay\",\"doi\":\"10.1016/j.aasci.2018.06.003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Degradation of soil due to unsuitable land management practices is a chief impairment of optimum land productivity. The spatial variability of soil properties is needed for agricultural productivity, food safety and environmental modeling. The present study was conducted in lateritic soils of West Bengal, India to understand the spatial variability of soil properties using a geostatistical model. Nitrogen (N), soil pH, electrical conductivity (EC), Phosphorus (P), Potassium (K) and organic carbon (OC) were measured. Surface maps of soil properties were prepared using the semivariogram model through Kriging techniques. A positive correlation was observed between OC and N. The Quantile-quantile plots showed a normal distribution of EC, K, pH, N, and OC. The value for nugget/sill of K, N, and EC were 0.25–0.75 indicating moderate spatial autocorrelation among the variables. Phosphorus (P) was highly concentrated in the eastern part, whereas the agglomeration of higher EC was found in the north east and south west corner of the study site. The cross validation results illustrated the smoothing effect of the spatial prediction. The present study suggests that the geostatistical model can directly reveal the spatial variability of lateritic soils and will help farmers and decision makers for improving soil-water management.</p></div>\",\"PeriodicalId\":100092,\"journal\":{\"name\":\"Annals of Agrarian Science\",\"volume\":\"16 4\",\"pages\":\"Pages 436-443\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/j.aasci.2018.06.003\",\"citationCount\":\"59\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of Agrarian Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1512188718300125\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Agrarian Science","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1512188718300125","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Assessment of spatial variability of soil properties using geostatistical approach of lateritic soil (West Bengal, India)
Degradation of soil due to unsuitable land management practices is a chief impairment of optimum land productivity. The spatial variability of soil properties is needed for agricultural productivity, food safety and environmental modeling. The present study was conducted in lateritic soils of West Bengal, India to understand the spatial variability of soil properties using a geostatistical model. Nitrogen (N), soil pH, electrical conductivity (EC), Phosphorus (P), Potassium (K) and organic carbon (OC) were measured. Surface maps of soil properties were prepared using the semivariogram model through Kriging techniques. A positive correlation was observed between OC and N. The Quantile-quantile plots showed a normal distribution of EC, K, pH, N, and OC. The value for nugget/sill of K, N, and EC were 0.25–0.75 indicating moderate spatial autocorrelation among the variables. Phosphorus (P) was highly concentrated in the eastern part, whereas the agglomeration of higher EC was found in the north east and south west corner of the study site. The cross validation results illustrated the smoothing effect of the spatial prediction. The present study suggests that the geostatistical model can directly reveal the spatial variability of lateritic soils and will help farmers and decision makers for improving soil-water management.