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        <datestamp>2026-09-17T05:33:20Z</datestamp>
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          <dc:title>Data Sheet 1_Soil quality indices and critical limits of indicators under long-term rice-based agroecosystems in subtropical India.pdf</dc:title>
          <dc:creator>Uttam Kumar (3715969)</dc:creator>
          <dc:creator>Vinay Bachkaiya (25002067)</dc:creator>
          <dc:creator>Nirmal Kumar (826359)</dc:creator>
          <dc:creator>Vinod Nayak (12151413)</dc:creator>
          <dc:creator>Pragya Pandey (755609)</dc:creator>
          <dc:creator>L. K. Srivastava (25002070)</dc:creator>
          <dc:creator>Sagar Nandulal Ingle (25002073)</dc:creator>
          <dc:creator>Hareesh Kumar (25002076)</dc:creator>
          <dc:creator>V. N. Mishra (25002079)</dc:creator>
          <dc:subject>Soil Science</dc:subject>
          <dc:subject>critical limits</dc:subject>
          <dc:subject>Inceptisols</dc:subject>
          <dc:subject>MDS</dc:subject>
          <dc:subject>rice-based agroecosystems</dc:subject>
          <dc:subject>soil quality index</dc:subject>
          <dc:subject>Vertisols</dc:subject>
          <dc:description>Introduction&lt;p&gt;Rice-based agroecosystems are predominantly practiced across the subtropical region of South Asia and play a vital role in maintaining soil health and ensuring long-term crop productivity. However, detailed assessments of soil quality and the determination of critical limits for key soil quality indicators in rice-based agroecosystems remain limited. Hence, this study was undertaken to evaluate soil quality indices, identify key minimum data set (MDS) indicators, and determine their critical limits for Vertisols and Inceptisols under subtropical conditions in India.&lt;/p&gt;Materials and methods&lt;p&gt;Soil samples from the surface layer (0–15 cm) were obtained from farmers’ fields under four different rice-based agroecosystems: rice-chickpea (RC), rice-wheat (RW), rice-fallow (RF), and rice-lathyrus (RL), across both soil types, and were evaluated for soil chemical, physical, and biological attributes. Principal component analysis (PCA) was employed to select key MDS indicators, which were subsequently scored, weighted, and integrated to calculate the soil quality index (SQI).&lt;/p&gt;Results&lt;p&gt;In Vertisols, the highest SQI was recorded under RC (0.85), followed by RL (0.81), while the lowest was recorded under RW (0.73). In Inceptisols, the highest SQI was recorded under RL (0.78), followed by RF (0.71), while the lowest was recorded under RW (0.64). Overall, Vertisols had a higher SQI (0.79) than Inceptisols (0.71). The lower and upper critical limits were established for the MDS indicators dehydrogenase activity (DHA), bulk density (BD), mean weight diameter (MWD), organic carbon (OC), available phosphorus (AP), and Zn in Vertisols, with corresponding SQI limits of 0.72 and 0.86. In Inceptisols, the critical limits were similarly established for OC, cation exchange capacity (CEC), alkaline phosphatase activity (AlPA), DHA, hydraulic conductivity (HC), and Zn, with corresponding SQI limits of 0.66 and 0.78.&lt;/p&gt;Conclusion&lt;p&gt;The study concluded that the inclusion of legumes sustained better soil quality than intensive RW and RF systems across soil types. The identification of key MDS indicators and definition of their critical limits can support the development of soil- and crop-specific management practices to sustain soil quality and long-term yield sustainability.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-17T05:33:20Z</dc:date>
          <dc:type>Dataset</dc:type>
          <dc:type>Dataset</dc:type>
          <dc:identifier>10.3389/fsoil.2026.1918756.s001</dc:identifier>
          <dc:relation>https://figshare.com/articles/dataset/Data_Sheet_1_Soil_quality_indices_and_critical_limits_of_indicators_under_long-term_rice-based_agroecosystems_in_subtropical_India_pdf/33871876</dc:relation>
          <dc:rights>CC BY 4.0</dc:rights>
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