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<p>Agroforestry systems in India’s western coastal region exhibit remarkable diversity, yet existing classification schemes often fail to provide practical guidance for stakeholders; farmers, researchers and policymakers in selecting optimal systems for local condition. This study addresses this gap by developing a locally adapted two-stage classification framework&#xa0;for South Gujarat, integrating structural and functional approaches, addressing its distinct agro-ecological condition and socio-economic needs. The first stage, systems are categorized structurally (based on trees-crops-livestock combinations). While, the second stage, they are functionally classified into&#xa0;conventional agroforestry (CAF) and unique agroforestry (UAF) systems, with nine key characteristics (e.g., improved production, market orientation etc.) distinguishing UAF systems like wadi-based agroforestry, that latter modified to specific livelihood and market needs. Field surveys of 252 farmers revealed that UAF systems—such as wadi-based agroforestry system (D) demonstrate enhanced productivity and income stability when supported by institutional linkages particularly for the marginal farmers in hilly regions. While agrosilvopastoral systems (B) enhanced fodder production and livestock rearing in plains. A novel&#xa0;simplified alphanumeric coding system&#xa0;{e.g., B1(iii)} improved usability, with 78% of farmers finding it accessible after training. Results demonstrate the framework’s effectiveness in aligning agroforestry practices with local needs, offering a scalable tool for sustainable land use. The study bridges gaps in prior systems by integrating structural, functional, and socio-economic factors, providing a model adaptable to similar regions and provides a actionable tool for sustainable agroforestry planning.</p>

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A new framework and classification scheme to indicate best-fit agroforestry systems: a locally adapted approach in the western coast of India

  • 期刊论文

  • 发表日期：

    2025年11月4日

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  • Harshavardhan Sarita Krishnarao Deshmukh,
  • Minalkumar B. Tandel,
  • Manmohan J. Dobriyal,
  • Rajesh Gunaga,
  • Ripu Kunwar,
  • Narender Singh Thakur,
  • Santoshkumar A. Huse,
  • Ram Prasad Acharya,
  • Ramesh K. R.,
  • Shailesh S. Harne,
  • Vishal B. Shambharkar,
  • Ram J. Mevada,
  • Varun Saini

摘要

Agroforestry systems in India’s western coastal region exhibit remarkable diversity, yet existing classification schemes often fail to provide practical guidance for stakeholders; farmers, researchers and policymakers in selecting optimal systems for local condition. This study addresses this gap by developing a locally adapted two-stage classification framework for South Gujarat, integrating structural and functional approaches, addressing its distinct agro-ecological condition and socio-economic needs. The first stage, systems are categorized structurally (based on trees-crops-livestock combinations). While, the second stage, they are functionally classified into conventional agroforestry (CAF) and unique agroforestry (UAF) systems, with nine key characteristics (e.g., improved production, market orientation etc.) distinguishing UAF systems like wadi-based agroforestry, that latter modified to specific livelihood and market needs. Field surveys of 252 farmers revealed that UAF systems—such as wadi-based agroforestry system (D) demonstrate enhanced productivity and income stability when supported by institutional linkages particularly for the marginal farmers in hilly regions. While agrosilvopastoral systems (B) enhanced fodder production and livestock rearing in plains. A novel simplified alphanumeric coding system {e.g., B1(iii)} improved usability, with 78% of farmers finding it accessible after training. Results demonstrate the framework’s effectiveness in aligning agroforestry practices with local needs, offering a scalable tool for sustainable land use. The study bridges gaps in prior systems by integrating structural, functional, and socio-economic factors, providing a model adaptable to similar regions and provides a actionable tool for sustainable agroforestry planning.