Why farm data is essential to understanding agri-food emissions

The action in tackling agricultural emissions is being led by food supply chains and financiers, well ahead of regulators. Benchmarking and mitigating emissions needs to be based on farm-level data, providing proof for claims and tools for producers to make changes.


In the previous post we saw how agri-food companies are making commitments to reduce the emissions embedded in their agricultural supply chains (their “scope 3 emissions”). Before they can reduce the footprint of their value chains, organisations need to understand the size of those emissions, what drives them, and where the levers are to reduce greenhouse gas emissions. Where do they start?


Beginning with industry averages

Many businesses rightly start to understand the intensity of supply chain emissions by using industry averages and applying these in a weighted fashion to their product ingredients. As a “first cut”, this made sense for prioritisation.

But industry averages are insufficient. They don’t necessarily reflect regional variations, or the emissions of a particular supply portfolio. High level scans provide initial information to inspire future research – as the FAO report, Livestock’s Long Shadow, did in 2006 for example. The numbers in that report have been disputed and some claims have since been withdrawn: an example of why broad averages can’t be relied on for product and brand claims.


Finding the right scale

Increasingly, large organisations seek to set and track against their targets and avoid greenwashing through the Science-based Targets initiative (SBTi). To be approved by SBTi, and/or align with the GHG Protocol for scope 3 emissions accounting, a company’s targets and measurement programme needs to prioritise collecting accurate, primary data where emissions are most material.

More granular, farm-level emissions calculations allow companies to identify and address hot-spots in their supply chains, and to understand the levers for, and scale of, economically achievable reductions.

A common way for food brands and retailers to address their scope 3 emissions is through engagement with their supply chain. What this looks like varies. For some, it is the active selection of ingredient blends through a sustainable sourcing process and tool such as AtSource. Others choose to send their supply partners questionnaires using programmes such as SupplyShift Thesis. This approach likely won’t scale appropriately to farmers and growers, so aligned retailer or brand supply programmes, processors, and farmer-led supply groups seek to use farm-level calculations directly.


Which brings us to farm scale data

For most agri-food supply chains, on-farm emissions are material, whether for crops or livestock. Increasingly this means that calculations must use data of sufficient accuracy and granularity, potentially with some level of verification.

Companies could just ask every farmer to fill in a GHG calculator. There are many of these in each country, and companies may choose the tool that provides the most precise calculations for a farm type and region; they may use a country-level approved tool; or they may choose a consistent calculator across their global supply chain.

The challenge becomes one of data entry and collection. Asking farmers to use calculators removes the ability to verify the accuracy of the data used and creates an additional burden for farmers. They may even need to use multiple calculators for different customers or regulations.

Granularity questions come into play as well. We can simplify the calculators (“look, only three boxes!”) but at the cost of less accurate data. This typically forces farmers to do more manual collation and back-of-the-envelope calculations themselves. I see this frequently with apparently simple calculators that force farmers to either undertake manual data collation, or just guess at what the right input might be.

At the same time, having a sustainability consultant or advisor drive up every farm driveway to collect and validate input data also doesn’t scale from prototype to full value-chain.


Integrate, pre-populate, and verify

We’re seeing agri-food companies who are engaging with farmers to address scope 3 emissions taking a more advanced approach. Farm-level data – inventories, crops, inputs, and activities are collated through a platform such as Map of Ag’s Pure Farming and delivered into a questionnaire or calculator for farmers with most of the information prefilled.

Farmers control the flow of that data through permissions, and then review the summarised results to ensure that their farm is fairly represented. Gaps can be filled (for instance, entering electricity usage). Verification is possible because the original granular data was collected from source.

UK cereal maker Weetabix uses just this process with its dedicated Weetabix Growers Group, as part of the “Weetabix Protocol” quality, food safety, and sustainability programme it uses to engage with its growers. Map of Ag’s Technical Director for Sustainability, Hugh Martineau, describes the process:

“We get as much information as we can from their farm management software – if they don’t use software then we can collect it online. This includes all inputs, such as fertiliser, sprays, and field operations, as well as outputs such as wheat yields and quality.”

Using the Cool Farm Tool, the data is processed into a carbon footprint and Nitrogen use efficiency (NUE) figure. The platform shows Weetabix the aggregated and individual results and growers also have visibility of what their total emissions are, their emissions intensity per tonne of wheat produced, and how much nitrogen is transferred into the harvested product.

“Nitrogen is responsible for 70-80% of all greenhouse gas (GHG) emissions from wheat production,” Mr Martineau explains.

Long Clawson, the Leicestershire maker of the famous Stilton cheese, uses an equivalent process with its milk suppliers.

“Around eighty per cent of our emissions come from our farms so if we don’t measure their footprints we just wouldn’t know where to start” says Kim Kettle, Long Clawson’s Farm Liaison Director.

The analysis that Long Clawson produces using Map of Ag’s technology allows Long Clawson and their suppliers to focus on the areas where farmers can make changes to reduce emissions – recognising there is a biological limit to savings that can be made while delivering the product needed.


Could this work for you?

To learn more about how your value chain could leverage farm data, get in touch with us by emailing info@mapof.ag, or filling in the form below.


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