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<p>Agriculture sector is a major contributor to greenhouse gas emissions, yet the lack of asset-level farm data, including ownership, land use, and production, hinders effective transition finance and decarbonisation efforts. To address this gap, we developed an open-source farm-level dataset using natural language processing (NLP) and unsupervised learning, mapping farm names to spatial polygons to fill ownership and entity gaps. In England, this approach identified 117,116 farming entities with essential attributes such as addresses, land areas, crop types, production output, and geospatial coordinates. Such emerging datasets are also critical for financial instruments supporting sustainable agriculture, enabling verification of carbon credits, enhance sustainability-linked loans and improve risk assessment for climate finance.</p>

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A Field-Level Asset Mapping Dataset for England’s Agricultural Sector

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月15日

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  • Hassan Aftab Sheikh,
  • Alok Singh,
  • Neetu Kushwaha,
  • Christophe Christiaen,
  • Nataliya Tkachenko,
  • Juan Sabuco,
  • Ben Caldecott

摘要

Agriculture sector is a major contributor to greenhouse gas emissions, yet the lack of asset-level farm data, including ownership, land use, and production, hinders effective transition finance and decarbonisation efforts. To address this gap, we developed an open-source farm-level dataset using natural language processing (NLP) and unsupervised learning, mapping farm names to spatial polygons to fill ownership and entity gaps. In England, this approach identified 117,116 farming entities with essential attributes such as addresses, land areas, crop types, production output, and geospatial coordinates. Such emerging datasets are also critical for financial instruments supporting sustainable agriculture, enabling verification of carbon credits, enhance sustainability-linked loans and improve risk assessment for climate finance.