The demand for cost-effective information about property-scale natural capital is growing rapidly as farmers respond to opportunities and pressure from their supply chains and financial institutions to provide environmental performance information. We have developed proof-of-concept farm-scale natural capital accounts that measure and report on natural capital and environmental performance at farm-scale. Demand for farm-scale accounts is outstripping current capacity for generating them. This is largely due to the expense and technological constraints associated with classifying on-farm natural capital assets.

This project will address these constraints by harnessing the power of Machine Learning and Artificial Intelligence, state-of-art remote sensed products and our existing database of thousands of field-validated assessment points to expand and improve our approach and methods for classifying farm-scale natural capital assets. This research will underpin the further development and enhancement of
farm-scale natural capital accounts, increasing cost-efficiency and speed of generating accounts. This will broaden the capacity base for generating accounts and increase adoption of accounts for best-practice natural capital accounting and sustainability reporting in agriculture.