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<p>Emphasizing sustainable development efforts through the inclusion of the equity of future generations is significantly supported by the agricultural sector. <i>However</i>,<i> the sustainable development of agriculture</i> faces <i>various challenges due to natural resource degradation</i>,<i> economic vulnerability</i>, <i>population pressure</i>,<i> and societal inequalities.</i> Thus, this study aims to examine agricultural sustainability in Solapur district from 2010 to 2023. To assess agricultural sustainability, the study has used a composite sustainable agricultural index (CSAI) incorporating the economic efficiency index (EEI), social equity index (SEI), and ecological security index (ESI). <i>Both</i> arithmetic <i>and weighted mean were utilized to construct</i> CSAI. Additionally, the study used machine learning to predict the sustainable agriculture index based on 22 input indicators and evaluate the comparative significance of each indicator in constructing the index value. The findings revealed EEI was 0.369, SEI was 0.449, <i>and</i> ESI was 0.575 in the study area. The district-level CSAI ranged between 0.464 (equal-weight) and 0.466 (weighted), indicating a medium level of sustainable agricultural development in Solapur district. Sensitivity analysis shows that CSAI values remain within a narrow stability range (0.45–0.48) across alternative weighting schemes, confirming that the observed numerical differences are substantively negligible and do not alter sustainability classification. The block-wise analysis of CSAI indicated that Solapur North (0.540 with equal weight and 0.549 with weighted average<i>)</i> and Mohol (0.403 with equal weight and 0.415 with weighted average) were ranked first and last, respectively. Based on the overall value of the CSAI, Solapur district fell into the category of medium SAD. The dataset of 154 observations was split into training (80%) and testing (20%) sets. The model attained a test RMSE of 0.0038 and MAE of 0.0028, with five-fold cross-validation confirming robust and consistent performance. It predicts future CSAI values using current-year indicators. This study suggests promoting sustainable farming techniques like organic farming, agroforestry, and biofertilizer use to enhance sustainable agriculture and improve the living standards of farmers in Solapur district.</p>

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Assessment of agricultural sustainability in Solapur District: A machine learning model for index prediction and indicators assessment

  • 期刊论文

  • 发表日期：

    2026年2月21日

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  • Digvijay R. Patil,
  • Mahadeo S. Deshmukh,
  • Amanuel Ayele Gebre,
  • Parth S. Thorat,
  • Santosh S. Sutar

摘要

Emphasizing sustainable development efforts through the inclusion of the equity of future generations is significantly supported by the agricultural sector. However, the sustainable development of agriculture faces various challenges due to natural resource degradation, economic vulnerability, population pressure, and societal inequalities. Thus, this study aims to examine agricultural sustainability in Solapur district from 2010 to 2023. To assess agricultural sustainability, the study has used a composite sustainable agricultural index (CSAI) incorporating the economic efficiency index (EEI), social equity index (SEI), and ecological security index (ESI). Both arithmetic and weighted mean were utilized to construct CSAI. Additionally, the study used machine learning to predict the sustainable agriculture index based on 22 input indicators and evaluate the comparative significance of each indicator in constructing the index value. The findings revealed EEI was 0.369, SEI was 0.449, and ESI was 0.575 in the study area. The district-level CSAI ranged between 0.464 (equal-weight) and 0.466 (weighted), indicating a medium level of sustainable agricultural development in Solapur district. Sensitivity analysis shows that CSAI values remain within a narrow stability range (0.45–0.48) across alternative weighting schemes, confirming that the observed numerical differences are substantively negligible and do not alter sustainability classification. The block-wise analysis of CSAI indicated that Solapur North (0.540 with equal weight and 0.549 with weighted average) and Mohol (0.403 with equal weight and 0.415 with weighted average) were ranked first and last, respectively. Based on the overall value of the CSAI, Solapur district fell into the category of medium SAD. The dataset of 154 observations was split into training (80%) and testing (20%) sets. The model attained a test RMSE of 0.0038 and MAE of 0.0028, with five-fold cross-validation confirming robust and consistent performance. It predicts future CSAI values using current-year indicators. This study suggests promoting sustainable farming techniques like organic farming, agroforestry, and biofertilizer use to enhance sustainable agriculture and improve the living standards of farmers in Solapur district.