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<p>Agricultural development is a multidimensional process shaped by diverse physical, environmental, technological, and institutional factors. This study examines the trends in its indicators and their impact across Punjab’s three agroclimatic regions—the northeast, central plain, and southwest. Using secondary data spanning from 1990–91 to 2022–23, the study employs simple linear regression, correlation analysis, principal component analysis (PCA), and random forest regression. A weighted Agricultural Development Index (ADI), derived from PCA, enables a comparative assessment of the regions’ progress over time. The Random Forest model further identifies key contributors to agricultural development, with tubewell density, road density, and tractor density emerging as critical factors, among others. The findings reveal that the central plain region is the most agriculturally developed due to its superior infrastructure and resource utilization, while the northeast and southwest regions have also significantly progressed. However, this growth has led to serious environmental and economic repercussions, including groundwater depletion, rising input costs, market uncertainties, and soil degradation. The study highlights the need for sustainable agricultural practices, such as water-efficient irrigation and crop diversification. Targeted policy interventions and region-specific resource management strategies are crucial to mitigate environmental degradation while ensuring continued agricultural growth across the regions.</p>

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Key indicators of agricultural development across agroclimatic regions in Punjab: a PCA and random forest approach

  • 期刊论文

  • 发表日期：

    2025年9月13日

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  • Simran Kaur,
  • Suman Chauhan

摘要

Agricultural development is a multidimensional process shaped by diverse physical, environmental, technological, and institutional factors. This study examines the trends in its indicators and their impact across Punjab’s three agroclimatic regions—the northeast, central plain, and southwest. Using secondary data spanning from 1990–91 to 2022–23, the study employs simple linear regression, correlation analysis, principal component analysis (PCA), and random forest regression. A weighted Agricultural Development Index (ADI), derived from PCA, enables a comparative assessment of the regions’ progress over time. The Random Forest model further identifies key contributors to agricultural development, with tubewell density, road density, and tractor density emerging as critical factors, among others. The findings reveal that the central plain region is the most agriculturally developed due to its superior infrastructure and resource utilization, while the northeast and southwest regions have also significantly progressed. However, this growth has led to serious environmental and economic repercussions, including groundwater depletion, rising input costs, market uncertainties, and soil degradation. The study highlights the need for sustainable agricultural practices, such as water-efficient irrigation and crop diversification. Targeted policy interventions and region-specific resource management strategies are crucial to mitigate environmental degradation while ensuring continued agricultural growth across the regions.