Case study
Scaling farm-level emissions data across beef supply chains
Company
Major Retailer and Supplier
Industry
Retail and Agriculture
Region
Europe
Solution
Mondra - Net Zero
Objective
The project set out to:
Prove that farm-level emissions data can be integrated at scale across a broad portfolio of beef products.
Improve the transparency and the quality and completeness of supply chain data
Enable collaborative reduction efforts by providing a dynamic, real-time emissions baseline.
Establish a scalable model for future product categories and suppliers.
Process Overview
Supplier Onboarding
Suppliers were invited to join the platform via a guided workflow.
Emissions data were entered directly into the system using a user-friendly wizard.
Supplier Onboarding
Suppliers were invited to join the platform via a guided workflow.
Emissions data were entered directly into the system using a user-friendly wizard.
Emission Factor Submission
Suppliers submitted farm-level carbon emission factors.
All data followed national data inclusion standards and scored via a Data Quality System (DQS).
Product Targeting
Emission factors were linked to specific SKUs.
Sourcing splits were entered to represent real-world supply dynamics.
Integration & Baseline Update
Data was fully integrated into product digital twins.
A new emissions baseline was established and marked with an “Enhanced” label.
Results
140 Products Enhanced with primary emissions data.
Farm Data Integrated at Scale, confirming feasibility across large assortments.
Data Quality Improved, with DQS scores rising from 3.1 to 2.8 on average.
Emission Reductions Quantified, establishing the new dynamic baseline.
For one flagship beef burger SKU, applying four distinct EFs from different supplier types significantly changed the product’s emissions profile and improved its DQS score.
Value Delivered
Move from inactionable secondary data to connected, traceable primary data.
A platform for retailer and suppliers to set informed targets, on transparent and dynamic primary data. A new product lens enables product level strategy such as meat cut strategies and connects every conversation into the realm of volume and margin.
Move from an annual reporting cadence to dynamic baseline change.
As soon as new data was added the baseline and organic change (true reduction) is quantified and tracked in Mondra.
A pathway to scaled claims and on-pack labelling, enabled by real data.
The requirements for labelling and claims continue to tighten. Claims based on 100% secondary data are unlikely to ever be allowed again. Mondra’s digital supply chain twins + ag data wizards delivered a clear pathway to claims for all products in the assortment.
Increasing data sharing through the supply chain.
The balance of effort, data visibility / value and reduced impact on emissions total has meant that classically there has been little to no farm data sharing between Primary Processor and their downstream Manufacturer customers.
Effort is now reduced, the product lens more meaningful. More data will be shared, improved transparency and faster change.
Looking Ahead
The project laid the groundwork for:
Expanding across all product categories and suppliers.
Continuous updates of farm data to reflect real-world changes.
Smarter sourcing tools to bypass data dilution from contingency suppliers.
A move toward verified Scope 3 emissions reductions and on-pack claims.
Conclusion
This case study proves that integrating primary agricultural emissions data at scale is both feasible and valuable. By enhancing digital product twins with farm-gate insights, the entire supply chain can better measure, manage, and reduce its carbon impact — moving from static reporting to dynamic climate action.
