Scholarlink

Abstract <p>This study aimed to assess the spatial variability of soil quality in the Pashupathihal sub-watershed of Dharwad district, Karnataka, using soil mapping units (SMUs), remote sensing, and GIS techniques. A&#xa0;total of 46 SMUs across 12 soil series were evaluated to derive the Soil Quality Index (SQI). Soil samples were analyzed for depth, pH, EC, CaCO<sub>3</sub>, organic carbon (OC), cation exchange capacity (CEC) and base saturation (BS). Statistical tools such as Principal Component Analysis (PCA), cluster analysis, and regression were applied. SQI values fluctuated between 0.20 and 0.76. Soil depth ranged from 35 to 180 cm (mean 163.07 cm), indicating heterogeneity. CaCO<sub>3</sub> varied from 1.37 to 14.45% (mean 6.34%), reflecting moderate calcareousness. pH ranged from 7.01 to 8.96 (mean 8.70) with high BS (82.35–92.57%, mean 90.95%), indicating alkaline, base-rich soils. OC (2.54–6.77 g/kg, mean 3.75 g/kg) was low, while CEC (21.29–56.49 cmol/kg, mean 49.72 cmol/kg) showed strong nutrient retention. EC (0.16–0.43 dS/m, mean 0.27 dS/m) confirmed non-saline conditions, and exchangeable sodium percentage (3.17–10.17%, mean 8.32%) indicated non-sodic soils. Bartlett’s test (χ<sup>2</sup> = 293.807, <i>p</i> &lt; 0.0001) and a Kaiser–Meyer–Olkin score of 0.597 confirmed moderate suitability for factor analysis. PCA revealed three principal components that accounted for 84.82% of the variance. The radar plot highlights variations in soil quality parameters across SMUs, with pronounced radial extensions indicating superior attributes, such as depth or cation exchange capacity. The linear regression graph revealed strong positive correlations, like the relationship between depth and CaCO<sub>3</sub>. However, there were also negative trends, such as organic carbon decreasing with depth. Cluster analysis grouped SMUs into four clusters (C1–C4) based on pH, EC, and OC. C1 required specialized interventions, while C3 had uniform, fertile soils for consistent management. SMUs like Mevundi (127 ha; SQI 0.76) and Channapura Tanda (766 ha; SQI 0.70) demonstrated an elevated SQI; however, Yelisirunj (5 ha; SQI 0.20) showed poor quality, primarily because of erosion and nutrient depletion. The Bettadapura series, encompassing 2464 ha with an SQI of 0.63, covered the most extensive area, reflecting general robust soil health. Findings highlight spatial soil quality patterns and support site-specific land management to improve soil health and productivity.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Assessment of the Soil Quality Index in the Pashupathihal Sub-Watershed through Soil Mapping Units Using Remote Sensing and GIS

  • 期刊论文

  • 发表日期：

    2025年1月1日

查看全文
  • K. N. Jakir Hussain,
  • B. R. Jagadeesh,
  • M. V. Manjunatha,
  • D. B. Basavaraja,
  • R. C. Vijayakumari,
  • S. R. Varsha,
  • L. V. Hublikar

摘要

Abstract

This study aimed to assess the spatial variability of soil quality in the Pashupathihal sub-watershed of Dharwad district, Karnataka, using soil mapping units (SMUs), remote sensing, and GIS techniques. A total of 46 SMUs across 12 soil series were evaluated to derive the Soil Quality Index (SQI). Soil samples were analyzed for depth, pH, EC, CaCO3, organic carbon (OC), cation exchange capacity (CEC) and base saturation (BS). Statistical tools such as Principal Component Analysis (PCA), cluster analysis, and regression were applied. SQI values fluctuated between 0.20 and 0.76. Soil depth ranged from 35 to 180 cm (mean 163.07 cm), indicating heterogeneity. CaCO3 varied from 1.37 to 14.45% (mean 6.34%), reflecting moderate calcareousness. pH ranged from 7.01 to 8.96 (mean 8.70) with high BS (82.35–92.57%, mean 90.95%), indicating alkaline, base-rich soils. OC (2.54–6.77 g/kg, mean 3.75 g/kg) was low, while CEC (21.29–56.49 cmol/kg, mean 49.72 cmol/kg) showed strong nutrient retention. EC (0.16–0.43 dS/m, mean 0.27 dS/m) confirmed non-saline conditions, and exchangeable sodium percentage (3.17–10.17%, mean 8.32%) indicated non-sodic soils. Bartlett’s test (χ2 = 293.807, p < 0.0001) and a Kaiser–Meyer–Olkin score of 0.597 confirmed moderate suitability for factor analysis. PCA revealed three principal components that accounted for 84.82% of the variance. The radar plot highlights variations in soil quality parameters across SMUs, with pronounced radial extensions indicating superior attributes, such as depth or cation exchange capacity. The linear regression graph revealed strong positive correlations, like the relationship between depth and CaCO3. However, there were also negative trends, such as organic carbon decreasing with depth. Cluster analysis grouped SMUs into four clusters (C1–C4) based on pH, EC, and OC. C1 required specialized interventions, while C3 had uniform, fertile soils for consistent management. SMUs like Mevundi (127 ha; SQI 0.76) and Channapura Tanda (766 ha; SQI 0.70) demonstrated an elevated SQI; however, Yelisirunj (5 ha; SQI 0.20) showed poor quality, primarily because of erosion and nutrient depletion. The Bettadapura series, encompassing 2464 ha with an SQI of 0.63, covered the most extensive area, reflecting general robust soil health. Findings highlight spatial soil quality patterns and support site-specific land management to improve soil health and productivity.