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Relationship between apparent electrical conductivity and soil physical properties in a Malaysian paddy field

机译:马来西亚稻田表观电导率与土壤物理性质的关系

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摘要

Site-specific crop management, well-established in some developed countries, is now being considered in developing countries such as Malaysia. The apparent electrical conductivity (ECa) of the soil can be used as an indirect indicator of a number of soil physical properties and even crop yield. Commercially available ECa sensors can efficiently develop the spatially dense data sets desirable in describing within-field spatial soil variability for precision farming. The main purpose of this study was to generate a variability map of soil ECa within a Malaysian paddy field using a VerisEC sensor. The ECa values were then compared with some soil variables within classes after delineation. Measured parameters were mapped using the kriging technique and their correlation with soil ECa was determined. The study showed that the VerisEC can determine soil spatial variability, and can acquire soil ECa information quickly. Spatial variability of shallow and deep ECa showed the same patterns. Estimation of soil properties based on ECa varied from one soil parameter to another and all could be estimated better by deep ECa. Cross-validation results showed that shallow and deep ECa, and also bulk density, gave more accurate estimates compared with other variables.
机译:在某些发达国家已经建立了完善的针对特定地点的作物管理,现在正在诸如马来西亚等发展中国家中考虑。土壤的表观电导率(EC a )可以用作许多土壤物理性质甚至农作物产量的间接指标。可商购的EC a 传感器可以有效地开发描述描述精准农业的田间空间土壤变异性所需的空间密集数据集。这项研究的主要目的是使用VerisEC传感器生成马来西亚稻田中土壤EC a 的变异图。划定后,将EC a 值与类别内的一些土壤变量进行比较。使用克里格法对测得的参数进行测绘,并确定它们与土壤EC a 的相关性。研究表明,VerisEC可以确定土壤的空间变异性,并且可以快速获取土壤EC a 信息。浅层EC a 和深层EC a 的空间变异性表现出相同的模式。基于EC a 的土壤性质估算从一个土壤参数变化到另一个土壤参数,通过深层EC a 可以更好地估算所有土壤参数。交叉验证的结果表明,与其他变量相比,浅层EC a 和深层EC a 以及堆密度能够给出更准确的估计。

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  • 来源
    《Archives of Agronomy and Soil Science》 |2012年第2期|p.155-168|共14页
  • 作者单位

    a Department of Biological and Agricultural Engineering, Faculty of Engineering, University Putra Malaysia, Serdang, Malaysia b Department of Land Management, Faculty of Agriculture, University Putra Malaysia, Serdang, Malaysia c Smart Farming Technology Laboratory, Institute of Advanced Technology, University Putra Malaysia, Serdang, Malaysia;

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