首页> 外文期刊>International Journal of Agricultural and Statistical Sciences >MAPPING SPATIAL VARIABILITY IN SOIL PROPERTIES AND FERTILITY AT FIELD-SCALE IN BASALTIC TERRAIN FOR SITE-SPECIFIC AGRICULTURAL INPUT MANAGEMENT USING GEOSPATIAL TECHNIQUES
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MAPPING SPATIAL VARIABILITY IN SOIL PROPERTIES AND FERTILITY AT FIELD-SCALE IN BASALTIC TERRAIN FOR SITE-SPECIFIC AGRICULTURAL INPUT MANAGEMENT USING GEOSPATIAL TECHNIQUES

机译:利用地理空间技术映射特定农田输入管理的玄武岩田间土壤特性和田间肥力的空间变异

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摘要

Soil sampling at large number of places is costly and time consuming and warrants to predict the soil properties and nutrient status at unsampled location from sampled locations using spatial dependence of soil properties through interpolation with reasonable accuracy. Soil samples (0 to 20 cm depth) were collected from a regular grid of400 by 400 m from Savli village (basaltic terrain) and analyzed for soil physical properties (particle size, bulk density), hydraulic properties (moisture retention at -33 kPaand -1500 kPa), chemical properties (pH, organic carbon, cation exchange capacity), soil available macronutrients (N, P, K) and micronutrients (Fe, Mn, Cu, Zn). Spatial variability was quantified through semivariograih analysis and the respective surface maps were prepared through ordinary kriging. Sand, pH, OC has displayed strong spatial dependence whereas, silt, clay, bulk density, cation exchange capacity, moisture retention at -33 kPa and -1500 kPa, available N, P, K, Fe, Mn, Cu and Zn showed moderate spatial dependence. Bulk density, organic carbon, and available Zn were spatially correlated for a short range. The kriged maps of soil properties and soil fertility generated may be useful for better management decisions.
机译:在许多地方进行土壤采样既昂贵又费时,并且可以通过合理的插值方法利用土壤属性的空间依赖性,从采样位置预测未采样位置的土壤属性和养分状况。从Savli村(玄武岩地带)400 x 400 m的规则网格中收集土壤样品(0至20 cm深度),并分析土壤的物理性质(粒度,堆积密度),水硬性(-33 kPaand时的保水率- 1500 kPa),化学性质(pH,有机碳,阳离子交换容量),土壤有效的常量营养素(N,P,K)和微量营养素(Fe,Mn,Cu,Zn)。通过半变异分析定量空间变异性,并通过普通克里金法制备各自的表面图。沙,pH,OC表现出强烈的空间依赖性,而淤泥,粘土,堆积密度,阳离子交换容量,-33 kPa和-1500 kPa的水分保持率,有效氮,磷,钾,铁,锰,铜和锌显示适中空间依赖性。堆密度,有机碳和有效锌在短时间内在空间上相关。生成的土壤特性和土壤肥力的克林克图可能有助于更好的管理决策。

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