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<p>Silvopastoral systems (SPS) are a key agroforestry practice that deliberately integrates trees, fodder species, and livestock to enhance farm productivity and ecosystem resilience. However, information on their spatial suitability under current and future climate scenarios in India remains limited. Therefore, the study aims to (i) assess the spatial suitability of silvopastoral systems across India under current and future climate scenarios using an ensemble modelling approach, and (ii) identify states with high potential for silvopastoral expansion. The study uses an ensemble modelling approach based on multiple machine learning models and 100 georeferenced locations. The results show that approximately 41.02 thousand km<sup>2</sup> of land in India is currently highly suitable for SPS, while 257.82 thousand km<sup>2</sup> is moderately suitable. Future projections under SSP245 and SSP585 scenarios for 2050 and 2070 indicate a significant increase in highly suitable areas (104.6–162.8%) and moderately suitable areas (53–122.8%), along with a decline in least suitable areas (17.3–33.8%), suggesting a shift in suitability classes rather than a uniform decline. The spatial suitability of SPS is concentrated in North, West, and Central India, with states such as Uttar Pradesh, Madhya Pradesh, Rajasthan, and Punjab emerging as key regions under future scenarios. Among the environmental variables, isothermality (bio3) was the most influential predictor, followed by NDVI and clay content, while precipitation and topographic factors showed moderate influence. Overall, the results indicate a redistribution of suitability towards central India under changing conditions. These findings provide important guidance for policymakers, stakeholders, and smallholder farmers to identify priority areas for silvopastoral development, support climate-resilient land-use planning, and improve resource allocation under future climate scenarios.</p>

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The extent of suitable areas for silvopastoral systems in India is projected to increase under future climate scenarios

  • 期刊论文

  • 发表日期：

    2026年5月28日

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  • Jintu Kumar Bania,
  • Anisha Dey,
  • Aditi Nath,
  • Jagadish Chander Dagar,
  • Ashesh Kumar Das,
  • Arun Jyoti Nath

摘要

Silvopastoral systems (SPS) are a key agroforestry practice that deliberately integrates trees, fodder species, and livestock to enhance farm productivity and ecosystem resilience. However, information on their spatial suitability under current and future climate scenarios in India remains limited. Therefore, the study aims to (i) assess the spatial suitability of silvopastoral systems across India under current and future climate scenarios using an ensemble modelling approach, and (ii) identify states with high potential for silvopastoral expansion. The study uses an ensemble modelling approach based on multiple machine learning models and 100 georeferenced locations. The results show that approximately 41.02 thousand km2 of land in India is currently highly suitable for SPS, while 257.82 thousand km2 is moderately suitable. Future projections under SSP245 and SSP585 scenarios for 2050 and 2070 indicate a significant increase in highly suitable areas (104.6–162.8%) and moderately suitable areas (53–122.8%), along with a decline in least suitable areas (17.3–33.8%), suggesting a shift in suitability classes rather than a uniform decline. The spatial suitability of SPS is concentrated in North, West, and Central India, with states such as Uttar Pradesh, Madhya Pradesh, Rajasthan, and Punjab emerging as key regions under future scenarios. Among the environmental variables, isothermality (bio3) was the most influential predictor, followed by NDVI and clay content, while precipitation and topographic factors showed moderate influence. Overall, the results indicate a redistribution of suitability towards central India under changing conditions. These findings provide important guidance for policymakers, stakeholders, and smallholder farmers to identify priority areas for silvopastoral development, support climate-resilient land-use planning, and improve resource allocation under future climate scenarios.