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首页> 外文期刊>Annals of Agrarian Science >Assessment of spatial variability of soil properties using geostatistical approach of lateritic soil (West Bengal, India)
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Assessment of spatial variability of soil properties using geostatistical approach of lateritic soil (West Bengal, India)

机译:利用红土土壤的地统计学方法评估土壤性质的空间变异性(印度西孟加拉邦)

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p id="abspara0010"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.
机译:id =“ abspara0010”>由于不适当的土地管理做法而导致的土壤退化是最佳土地生产力的主要损害。农业生产力,食品安全和环境建模需要土壤特性的空间变异性。本研究是在印度西孟加拉邦的红土土壤中进行的,目的是使用地统计模型了解土壤特性的空间变异性。测量氮(N),土壤pH,电导率(EC),磷(P),钾(K)和有机碳(OC)。通过Kriging技术使用半变异函数模型准备了土壤特性的表面图。在OC和N之间观察到正相关。分位数-分位数图显示EC,K,pH,N和OC呈正态分布。 K,N和EC的块金/基石值为0.25-0.75,表明变量之间存在适度的空间自相关。磷(P)高度集中在东部,而在研究地点的东北和西南角发现了较高的EC团聚。交叉验证结果说明了空间预测的平滑效果。本研究表明,地统计学模型可以直接揭示红土土壤的空间变异性,并将有助于农民和决策者改善土壤水管理。

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