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<p>Sustainable agricultural productivity in subtropical regions, including Central India (Madhya Pradesh), is at risk due to accelerated soil erosion. In the Kudari watershed, Madhya Pradesh, Central India, we applied the Revised Universal Soil Loss Equation (RUSLE) to assess and spatially represent soil erosion. Multi-criteria evaluation (MCE) methodology was employed for the spatial modelling process in GIS environment. The watershed, with a mean annual rainfall of 1000 to 1200&#xa0;mm, is characterized by clay, clay loam, and sandy clay soil. Approximately 29.09% of the watershed is at risk of severe (20–40 t/ha/yr) to extremely severe (&gt; 80 t/ha/yr) erosion rates. Sub-criteria were evaluated using RUSLE for erosion estimation and analytical hierarchy process (AHP) for assigning weighted importance in erosion susceptibility analysis. The normalized eigenvectors were in the range of 0.03 to 0.48. Areas with steep slopes, shallow soil depth, poor vegetative coverage, and light texture soil have a higher eigenvector value. Findings showed that topography is the most dominant factor influencing soil erosion when other influencing factors are considered. Based on mean susceptibility values and ranking, sub-watershed 2 (SW-2) was assigned the highest priority for erosion management. The SW-2 can be made safer from the soil erosion hazards by adopting contour farming, constructing check dams, terrace farming, afforestation, and restricting large scale overgrazing. The study used 4&#xa0;K UHD Google Earth imagery to validate the final soil loss map for accuracy through visual examination of the image characteristics, ensuring consistency of research findings with high-resolution image characteristics. In the current investigation, the AHP, utilizing the area under the curve (AUC) approach, achieved an accuracy rate of 80%. The ordinary least square (OLS) regression model accurately predicted soil erosion with an <i>R</i><sup>2</sup> of 86.94%, focusing on slope length and steepness factor (LS), cover management factor (<i>C</i>), and conservation practice factor (<i>P</i>) and integrating expert judgment and empirical data for a more comprehensive risk assessment. This study demonstrates the potential of GIS-based RUSLE, AHP, and OLS regression to map soil erosion risk zones and to manage soil and water resources for sustainable farming practices.</p>

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Spatial modelling of soil erosion in sub-tropical region of Central India using AHP and geospatial approach

  • 期刊论文

  • 发表日期：

    2025年8月28日

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  • Ashwini Suryawanshi,
  • Gangalakunta P. Obi Reddy,
  • Nirmal Kumar,
  • Benukantha Das,
  • Deepak Mohekar,
  • Roshan R. Wakode,
  • Burhan U. Choudhury,
  • Meesala Siva Shankar Nagaraju

摘要

Sustainable agricultural productivity in subtropical regions, including Central India (Madhya Pradesh), is at risk due to accelerated soil erosion. In the Kudari watershed, Madhya Pradesh, Central India, we applied the Revised Universal Soil Loss Equation (RUSLE) to assess and spatially represent soil erosion. Multi-criteria evaluation (MCE) methodology was employed for the spatial modelling process in GIS environment. The watershed, with a mean annual rainfall of 1000 to 1200 mm, is characterized by clay, clay loam, and sandy clay soil. Approximately 29.09% of the watershed is at risk of severe (20–40 t/ha/yr) to extremely severe (> 80 t/ha/yr) erosion rates. Sub-criteria were evaluated using RUSLE for erosion estimation and analytical hierarchy process (AHP) for assigning weighted importance in erosion susceptibility analysis. The normalized eigenvectors were in the range of 0.03 to 0.48. Areas with steep slopes, shallow soil depth, poor vegetative coverage, and light texture soil have a higher eigenvector value. Findings showed that topography is the most dominant factor influencing soil erosion when other influencing factors are considered. Based on mean susceptibility values and ranking, sub-watershed 2 (SW-2) was assigned the highest priority for erosion management. The SW-2 can be made safer from the soil erosion hazards by adopting contour farming, constructing check dams, terrace farming, afforestation, and restricting large scale overgrazing. The study used 4 K UHD Google Earth imagery to validate the final soil loss map for accuracy through visual examination of the image characteristics, ensuring consistency of research findings with high-resolution image characteristics. In the current investigation, the AHP, utilizing the area under the curve (AUC) approach, achieved an accuracy rate of 80%. The ordinary least square (OLS) regression model accurately predicted soil erosion with an R2 of 86.94%, focusing on slope length and steepness factor (LS), cover management factor (C), and conservation practice factor (P) and integrating expert judgment and empirical data for a more comprehensive risk assessment. This study demonstrates the potential of GIS-based RUSLE, AHP, and OLS regression to map soil erosion risk zones and to manage soil and water resources for sustainable farming practices.