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Prediction and mapping of erodibility factors (USLE and WEPP) by magnetic susceptibility in basalt-derived soils in northeastern Sao Paulo state, Brazil

机译:利用磁化率对巴西圣保罗州东北部玄武岩衍生土壤中的易蚀性因子(USLE和WEPP)进行预测和标测

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摘要

Spatial assessment of soil erosion is essential for the adaptation of agricultural practices and monitoring of soil losses. In this sense, this study aims to assess the efficiency of magnetic susceptibility (MS) as a predictor of soil erodibility factors (K for USLE model; K-i and K-r for WEPP model) fora detailed mapping of Oxisols with different iron contents in northeastern SAo Paulo State, Brazil. This study was carried out in an area of 380 hectares under sugarcane cultivation in SAo Paulo State. Soil samples were collected in a sampling grid (150) and in a transect (86) and physical and chemical analyses and calculations of the erodibility factors/parameters K, K-i, and K-r were performed. Pedotransfer functions (PTFs) were calibrated using simple linear regression analysis to predict the factors/parameters K and K-i using MS as a predictor variable. The observed values of MS and the predicted values of the factors/parameters K, K-i, and K-r were submitted to geostatistical analysis for constructing maps. Magnetic susceptibility can be used as a predictor of erodibility factors (K for USLE model; K-i and K-r for WEPP model) for Oxisols with total iron content ranging from 1 to 20% Fe2O3, with a precision of up to 60% and an accuracy of up to 85%. The results can guide future studies on water erosion in a tropical environment using magnetic soil data as an environmental covariate in the modeling process for large areas.
机译:土壤侵蚀的空间评估对于适应农业实践和监测土壤流失至关重要。从这个意义上讲,本研究旨在评估磁化率(MS)作为土壤易蚀性因子(USLE模型的K; WEPP模型的Ki和Kr)的预测指标的效率,以详细绘制圣保罗东北部不同铁含量的油溶胶的图谱。巴西州。这项研究是在圣保罗州甘蔗种植面积380公顷的地区进行的。将土壤样品收集在采样网格(150)和样条线(86)中,并进行物理和化学分析以及可蚀性因子/参数K,K-i和K-r的计算。使用简单的线性回归分析对Pedotransfer函数(PTF)进行校准,以MS作为预测变量来预测因子/参数K和K-i。 MS的观测值和因子/参数K,K-i和K-r的预测值已提交给地统计分析以构建地图。磁化率可以用作总铁含量为1%至20%Fe2O3的Oxisols侵蚀性因子的预测指标(USLE模型为K; WEPP模型为Ki和Kr),精度高达60%,准确度为高达85%。研究结果可以指导未来在大面积建模过程中使用磁性土壤数据作为环境协变量的热带环境水蚀研究。

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