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<p>Site-specific nutrient management in the vegetable growing belts of lower Himalayan region is a crucial component of sustainable crop production, especially since relatively small landholdings and unpredictable water availability is involved. Such precision approaches require holistic datasets that exhibit an accurate outlay of the existing conditions to allow for augmented soil management protocols and reduced risk from nutrient deficiency and land deterioration patterns. Soil nutrient management strategies can be modelled efficiently with spatial clustering and distribution visualization. Thus, this study was conducted in the cauliflower growing areas of Indora block (Kangra district), Himachal Pradesh, India to study the soil attribute status (physical, electro-chemical, chemical) of the region. Spatial autocorrelation assessment (Global Moran’s I) revealed statistically significant distribution of 6 soil properties (pH, EC, OC, porosity, N, S), with S expressing the highest number of significantly clustered sampling sites (47) via Local Indicator of Spatial Association analysis. Principal component analysis revealed 6 principal components with eigen values &gt; 1, accounting for 69.43% of total variance. Spatial variability maps developed via inverse distance weighted technique depicted spatial variation of the soil properties across the study site as affected by pedogenic processes and localized management practices. The study addresses spatial heterogeneity of soil characteristics, delineates management zones and provides a scientific reference for land resource evaluation studies.</p>

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Spatial characterization and geostatistical analysis of soil properties in vegetable cultivated soils of Indora block (Kangra district), Himachal Pradesh, India

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月8日

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  • Arshdeep Singh Atwal,
  • Aarush Lal,
  • Renu Kapoor,
  • Sanjeev Kumar Sandal,
  • Rakesh Sharma,
  • Swapana Sepehya,
  • Priyanka Kumari

摘要

Site-specific nutrient management in the vegetable growing belts of lower Himalayan region is a crucial component of sustainable crop production, especially since relatively small landholdings and unpredictable water availability is involved. Such precision approaches require holistic datasets that exhibit an accurate outlay of the existing conditions to allow for augmented soil management protocols and reduced risk from nutrient deficiency and land deterioration patterns. Soil nutrient management strategies can be modelled efficiently with spatial clustering and distribution visualization. Thus, this study was conducted in the cauliflower growing areas of Indora block (Kangra district), Himachal Pradesh, India to study the soil attribute status (physical, electro-chemical, chemical) of the region. Spatial autocorrelation assessment (Global Moran’s I) revealed statistically significant distribution of 6 soil properties (pH, EC, OC, porosity, N, S), with S expressing the highest number of significantly clustered sampling sites (47) via Local Indicator of Spatial Association analysis. Principal component analysis revealed 6 principal components with eigen values > 1, accounting for 69.43% of total variance. Spatial variability maps developed via inverse distance weighted technique depicted spatial variation of the soil properties across the study site as affected by pedogenic processes and localized management practices. The study addresses spatial heterogeneity of soil characteristics, delineates management zones and provides a scientific reference for land resource evaluation studies.