About
Across agricultural supply chains, understanding how farming practices affected soil carbon had become increasingly important for reducing greenhouse gas (GHG) emissions and supporting customer net‑zero commitments. Theproject developed a spatially detailed assessment of soil organic carbon (SOC) was delivered across a large grower base to improve visibility of soil carbon outcomes over time.
The project used Geospatial Intelligence’s C-EST soil carbon modelling to assess how crop types and land management practices influenced soil carbon levels. The assessment produced estimates of past, current, and future soil carbon stocks, helping the client better understand the emissions intensity of its agricultural supply chain and support downstream customer climate reporting.
The Challenge
Assessing soil carbon across large and diverse farming systems presented several challenges.
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LIMITED INSIGHT INTO SOIL CARBON CHANGE
Soil carbon changes slowly and is influenced by many factors, including climate, soil type, crop choice, and farm management. Many growers and supply‑chain partners previously lacked clear, field‑level insight into how these factors affected soil carbon over time.
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MANAGING LARGE VOLUMES OF FARM DATA
Producing consistent soil carbon estimates required combining crop management information with climate and soil data across hundreds of farms. Ensuring data quality and consistency across different regions and farming systems was a key challenge.
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KEEPING PACE WITH EMERGING STANDARDS
Guidance for accounting for land‑based carbon removals was still evolving under the GHG Protocol Land Sector Removals Guidance. The project needed to deliver useful results while remaining flexible enough to align with future reporting and compliance requirements.
Methodology
The project developed a multi‑year, field‑level assessment of soil organic carbon stocks on mineral soils and changes using the C-EST full biogenic (all carbon pools and transfer between them) model that incorporates RothC as the soil carbon module. This approach was selected because it had been widely used to understand how soil carbon responded to both (but separable) environmental conditions and farm management.
Partnership Approach
This project combined complementary capabilities to deliver a scalable and credible soil carbon assessment across a large grower network. Our partner Map of Ag supported the collection, organisation, and validation of farm‑level crop and land management data, helping ensure that information from multiple farming systems could be used consistently across the assessment. Geospatial Intelligence then applied soil carbon modelling within its platform to translate those inputs into field‑level estimates of historical and projected soil carbon change. This partnership approach helped bridge the gap between farm data and supply‑chain reporting, providing the client with a practical way to generate transparent soil carbon insights that could support emissions reporting, supplier engagement, and future sustainability decision‑making.
Data and Inputs
The assessment combined several sources of information, including crop management data provided by Map of Ag in standard GIS formats, climate and soil datasets relevant to each farming region, and agreed crop types and management practices based on outcomes from an earlier proof of concept.
All farm data underwent validation checks before being processed to ensure consistency and reliability.
Outputs
For each field, the project produced estimates of historical soil carbon stocks and changes over approximately 20 years, projected soil carbon trends under a small number of simple future management scenarios, and total soil carbon levels and key soil carbon components.
Example estimates of carbon benefits from tree planting were also included at a high level. All results were delivered in flexible CSV format and supported by clear documentation explaining assumptions and methods.
Outcome
The project delivered a clear, scalable soil carbon baseline across the grower network. This enabled the client to understand how soil carbon levels had changed over time at field scale, assess the greenhouse gas emissions intensity of agricultural production, support customer net‑zero goals with transparent and consistent soil carbon data, and prepare for future alignment with emerging land‑sector carbon accounting standards.
By combining Map of Ag’s farm‑level insights with Geospatial Intelligence’s soil carbon modelling, the project demonstrated how agricultural supply chains could generate practical and credible carbon information to support climate reporting, supplier engagement, and long‑term sustainability planning.