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Spatial variability assessment of soil fertility in black soils of central India using geostatistical modeling

机译:基于地统计模型的印度中部黑土土壤肥力空间变异性评估

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Capturing site-specific variability in soil attributes is crucial for precision agriculture. A study was conducted in Kelapur block of Yavatmal district, Maharashtra, covering 83,000 ha area in the basaltic region of central India for investigating the spatial distribution of soil fertility parameters viz., pH, EC, organic matter, available macro (AvN, AvP, AvK and AvS) and cationic micronutrients (Fe, Mn, Zn and Cu). Total 4627 surface samples (0-15 cm depth) were collected using grid sampling method at 325 m interval and analysed for the soil properties. The geo-database was subjected to kriging through best-fit experimental semivariogram based on lowest root mean squared error. Spherical model was found best fit for AvP, AvK and AvS whereas exponential model was best fit for remaining soil parameters. The spatial distribution of maps showed deficiency of AvN, AvP, Zn and Fe whereas AvK was high in most of the study area. Spatial dependence was moderate for all soil fertility parameters (N:S ratio 0.25-0.75) whereas AvP exhibited strong spatial dependency (N:S ratio 0.19). Strong spatial dependence of AvP is mainly regulated by pH and smectitic clay minerals. This study can support site-specific plant nutrient management at cadastral level for precision farming.
机译:捕捉土壤属性的特定地点变异性对于精准农业至关重要。在马哈拉施特拉邦Yavatmal区的Kelapur区块进行了一项研究,覆盖了印度中部玄武岩地区的83,000公顷区域,以调查土壤肥力参数的空间分布,即pH值、EC、有机质、有效宏观(AvN、AvP、AvK和AvS)和阳离子微量营养素(Fe、Mn、Zn和Cu)。采用网格采样法,间隔325 m,共采集表层样品4627个(深度0-15 cm),并分析土壤性质。通过基于最小均方根误差的最佳拟合实验半变异函数对地理数据库进行克里金法处理。球形模型最适合AvP、AvK和AvS,而指数模型最适合其余土壤参数。图谱空间分布呈现AvN、AvP、Zn和Fe的缺失,而AvK在研究区大部分地区均存在较高水平。土壤肥力参数的空间依赖性均为中等(N:S比为0.25-0.75),而AvP表现出较强的空间依赖性(N:S比为0.19)。AvP的空间依赖性较强,主要受pH值和蒙脱石粘土矿物的调控。本研究可支持地籍水平的植物养分管理,用于精准农业。

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