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

Hassan Aftab Sheikh1, Alok Singh2, Neetu Kushwaha1

  • 1Smith School of Enterprise and the Environment, University of Oxford, South Parks Road, Oxford, OX1 3QY, UK.

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|July 15, 2025
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Summary
This summary is machine-generated.

We created an open-source farm dataset using NLP to map farm ownership and land use. This data is crucial for sustainable agriculture finance and decarbonisation efforts in the agricultural sector.

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Area of Science:

  • Agricultural Science
  • Environmental Science
  • Data Science

Background:

  • The agriculture sector significantly contributes to greenhouse gas emissions.
  • A lack of detailed farm-level data impedes effective transition finance and decarbonisation.
  • Accurate asset-level information on ownership, land use, and production is missing.

Purpose of the Study:

  • To develop an open-source, farm-level dataset to address data gaps in agriculture.
  • To facilitate transition finance and decarbonisation efforts in the agricultural sector.
  • To support financial instruments for sustainable agriculture.

Main Methods:

  • Utilized natural language processing (NLP) and unsupervised learning techniques.
  • Mapped farm names to spatial polygons to identify ownership and entity information.
  • Extracted key attributes including addresses, land areas, crop types, production output, and geospatial coordinates.

Main Results:

  • Successfully created a comprehensive farm-level dataset for England.
  • Identified 117,116 distinct farming entities with detailed attributes.
  • The dataset provides essential data for financial and environmental assessments.

Conclusions:

  • Emerging farm-level datasets are critical for sustainable agriculture finance.
  • This dataset can enhance the verification of carbon credits and sustainability-linked loans.
  • Improved data quality will bolster risk assessment for climate finance in agriculture.