TrackCarbon (technical specialists in agricultural sustainability data production and lifecycle analysis), and the Sustainable Food Trust (a sustainable farming NGO who established the Global Farm Metric, a holistic sustainability framework for the value chain) are seeking to collaborate to address a fundamental challenge in sustainable agriculture: how to measure whole-farm environmental and social performance without imposing excessive data burdens on farmers.
Current sustainability frameworks often rely on hundreds or thousands of variables to determine the environmental and social impact of farming practices. While comprehensive, this breadth creates practical barriers to adoption, increases reporting fatigue, and obscures the key system drivers of climate, nature and social outcomes. Furthermore, many focus on carbon outcomes alone, hiding full environmental, social and economic trade‑offs and limiting progress towards sustainable agricultural systems for the benefit of mankind.
This project will apply advanced statistical methods and machine learning to determine whether a significantly reduced set of farm-level characteristics can reliably predict core sustainability outcomes, thereby advancing more efficient monitoring technologies for agricultural systems. It will fill a key knowledge gap by providing a statistical basis for indicator selection and reduction. TrackCarbon will take the lead on the project, and provide an economist and a senior data scientist to undertake model development and data collation.
Hypothesis:
A limited and identifiable subset of farm variables can accurately predict key climate, and nature outcomes across diverse agricultural systems. These variables can form the basis of robust, lower-burden monitoring technologies suitable for farms, supply chains and policy contexts that will accelerate the transition to more sustainable systems.
Expected outcomes:
This work represents a critical and timely foundational phase. The findings will directly inform indicator redesign within both GFM and TrackCarbon tools, reducing the breadth of data required from farms. Findings will complement additional qualitative research lead by the Global Farm Metric involving farm-based trials to understand the usefulness of these indicators for farm management. Alongside this, findings will underpin communications that will enable others to adopt the same indicator-set for cross-industry alignment.
Relevance & Justification:
Sustainable agriculture increasingly relies on robust data to guide management, investment and policy decisions. However, existing sustainability assessments often require the collection of extensive and highly granular datasets. While comprehensive, this approach creates inefficiencies: high reporting burdens for farmers, inconsistent data quality, and limited uptake of digital sustainability tools. Measurement becomes technically detailed but operationally impractical, hindering progress towards sustainability.
This project addresses that challenge directly. By applying advanced statistical methods to identify the minimum set of farm-level variables required to predict key climate and nature indicators, the research will optimise how sustainability is measured. Rather than expanding data collection, it seeks to refine and streamline it, improving efficiency without compromising predictive accuracy.
Key benefits:
For the agricultural sector, the impact will be tangible and measurable. Adoption of a reduced, validated indicator set will:
- Lower the time and administrative burden of sustainability reporting.
- Improve consistency and comparability of environmental metrics across farm systems.
- Increase farmer participation in digital monitoring platforms.
- Strengthen confidence in lifecycle assessment and carbon accounting outputs.
- Create a clearer pathway for rewarding positive on-farm outcomes
The project also delivers professional development benefits. The funded Data Scientist will gain applied experience in modelling complex agricultural systems, working at the interface of environmental science and digital tool development. This builds specialist capacity in agricultural data analytics, a field of increasing strategic importance.
Public benefit:
Findings will be widely disseminated by our NGO partner, for the benefit of farmers and their representatives.
Timeframe: 6-8 months
Funding support:
Given that we are a young business with limited resources to extend beyond our current activities, we are seeking philanthropic support to undertake this specific project. This funding will provide contribution towards the resources that are to be provided by TrackCarbon to deliver the project. TrackCarbon will provide the expertise of an Economist and Data Scientist, and well as project management support.
The results will be disseminated widely by our NGO partner for the benefit of farmers, and there is no commercial profit, or revenue to be gained by TrackCarbon from this project.