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<p> Agricultural sustainability in flood-prone and physiographically diverse regions of Northeastern India is constrained by limited knowledge of land and soil suitability. This study addresses a critical research gap by integrating geospatial techniques with soil analysis to assess soil suitability and crop productivity through detailed mapping of physiographic units and associated soil characteristics in the Jiyadhol watershed, Northeast India. Four major landforms, piedmont plain, upper alluvial plain, lower alluvial plain, and flood plain, were delineated using remote sensing data, and 170 surface soil samples were analyzed for key physicochemical properties. Indices, such as the soil productivity index (12.1–61.7), potential productivity index (25.7–77.2), and crop yield index (0.41–2.76), were derived to assess suitability for major rice ecosystems: <i>Sali</i> rice (Aman), <i>Ahu</i> rice (Aus), and <i>Boro</i> (summer rice). The normalized difference vegetation index (NDVI) during the vegetative stage showed a strong positive correlation with organic matter and crop yield. Piedmont plain soils were found to be unsuitable for rice due to poor fertility and erosion, but they hold promise for oilseeds and pulses under improved management. The results highlight an inverse relationship between soil loss rates and productivity indicators, indicating the urgency of erosion control. Moreover, an integrated GIS and field data-based soil suitability map for rice generated in this study serves as a vital decision-support tool for farmers, planners, and policymakers. Overall,&#xa0;this research advances sustainable and climate-resilient agricultural planning in flood-affected, data-scarce regions like Northeastern India&#xa0;by providing a spatially explicit framework for assessing agro-potential.</p> Graphical abstract <p></p>

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Mapping agro-potential through evaluating soil suitability and crop productivity using geospatial techniques

  • 期刊论文

  • 发表日期：

    2025年11月4日

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  • Rituparna Saikia,
  • Dilip Kumar Patgiri,
  • Bipul Deka,
  • Prem Kumar Bharteey,
  • Suman Dutta,
  • Ramani Kanta Thakuria,
  • Marami Dutta,
  • Sanjib Ranjan Borah,
  • Rahul Sadhukhan,
  • Ranabir Chakraborty,
  • Malini Roy Choudhury,
  • Parag Saikia,
  • Deepsikha Gogoi,
  • Sumanta Das

摘要

Agricultural sustainability in flood-prone and physiographically diverse regions of Northeastern India is constrained by limited knowledge of land and soil suitability. This study addresses a critical research gap by integrating geospatial techniques with soil analysis to assess soil suitability and crop productivity through detailed mapping of physiographic units and associated soil characteristics in the Jiyadhol watershed, Northeast India. Four major landforms, piedmont plain, upper alluvial plain, lower alluvial plain, and flood plain, were delineated using remote sensing data, and 170 surface soil samples were analyzed for key physicochemical properties. Indices, such as the soil productivity index (12.1–61.7), potential productivity index (25.7–77.2), and crop yield index (0.41–2.76), were derived to assess suitability for major rice ecosystems: Sali rice (Aman), Ahu rice (Aus), and Boro (summer rice). The normalized difference vegetation index (NDVI) during the vegetative stage showed a strong positive correlation with organic matter and crop yield. Piedmont plain soils were found to be unsuitable for rice due to poor fertility and erosion, but they hold promise for oilseeds and pulses under improved management. The results highlight an inverse relationship between soil loss rates and productivity indicators, indicating the urgency of erosion control. Moreover, an integrated GIS and field data-based soil suitability map for rice generated in this study serves as a vital decision-support tool for farmers, planners, and policymakers. Overall, this research advances sustainable and climate-resilient agricultural planning in flood-affected, data-scarce regions like Northeastern India by providing a spatially explicit framework for assessing agro-potential.

Graphical abstract