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

## TL;DR

This paper introduces a new open-source dataset mapping farm-level assets in England to support sustainable agriculture and climate finance.

## Contribution

The paper presents a novel method using NLP and unsupervised learning to create a detailed farm-level dataset for England.

## Key findings

- The approach identified 117,116 farming entities with attributes like addresses, land areas, and crop types.
- The dataset supports financial instruments such as carbon credit verification and sustainability-linked loans.
- The method fills ownership and entity gaps, enabling better risk assessment for climate finance.

## Abstract

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.