When it comes to food system sustainability, relying on averages can mask significant differences between individual farms, products and farming systems. Advances in data science and technology now make it possible to measure agricultural emissions accurately, and to replace lagging indicators with leading indicators based on real-time, verifiable farm-level data. With 82% of food-related emissions occurring on farms, such transparency is vital, not only to avoid accusations of greenwashing, but also to drive improvements in performance. Capturing farm-level variation in real-time can tell us where progress is being made, where help is needed, and where policy and investment can have the greatest impact, writes food chain sustainability expert Rob Chester.

 

For decades, measuring greenhouse-gas emissions in agriculture — especially at the product level — was considered impossible. Farms are living ecosystems, not factories, and unlike other sectors of industry, agriculture seemed too complex and dispersed to measure accurately.

 

That view is changing. Advances in science, technology and data analytics are making it possible to quantify emissions across agri-food systems. Agriculture, long overlooked in climate policy, is stepping forward — not only as part of the problem but also as part of the solution.

 

In a recent commentary on the SAI Platform, agricultural economist Dr Saulé Burkitbayeva points to the OECD report Measuring Carbon Footprints of Agri-Food Products: Eight Building Blocks, which she co-authored with Koen Deconinck and Hillena Thoms, as a potential roadmap for credible, scalable carbon-footprint measurement.

 

Importantly, she highlights one crucial insight: averages are not enough.

 

In climate debates, averages tend to dominate — beef versus chicken, meat versus plant-based - but they obscure wide variation within products. Citing a landmark 2018 study by Joseph Poore and Thomas Nemecek, which examined the comparative environmental impact of thousands of food products worldwide, Dr Burkitbayeva notes that beef’s average footprint of 60 kg CO₂-equivalent per 100 g protein hides a vast range from under 20 to over 100. Production methods — feed, grazing, land use and manure management — make the difference. “Not all beef is created equal,” she stresses.

 

Averages, she argues, mislead in three ways.

 

• they lead to blunt policy instruments, and generic advice like “eat less beef” instead of focusing on the potential for high-impact reductions.

 

• they limit producer incentives to improve performance, masking the efforts of those already adopting good practice and innovation.

 

• they risk undermining consumer trust, as different accounting tools can yield widely varying and inconsistent results.

 

For real progress, Dr Burkitbayeva calls for farm-level data and consistent standards that reveal, rather than conceal, the diversity of agriculture’s environmental footprint. Without standardised methods and quality assurance mechanisms, international comparisons become unreliable, and accusations of greenwashing become inevitable, she argues.

 

At Supply Chain In-Sites (SCI), our mission is to turn that call into action. We are developing a new model for real-time assurance — one that moves away from lagging indicators of what has already happened and towards moving indicators that can inform and influence what happens next. Because if the food industry is to make credible progress towards net zero and more sustainable food production, then the accuracy, reliability and transparency of data at the farm level are not optional extras — they are the absolute foundation.

 

Capturing farm-level variation can tell us where progress is being made, where help is needed, and where policy and investment can have the greatest impact.

 

Why the details matter

The food industry is responsible for around 26% of global greenhouse gas emissions, compared to about 2.5% for aviation. What’s more, 82% of those food-related emissions occur on farms, not in supermarkets, factories, or transport chains

 

Yet, of the world’s 20 largest food retailers, only six have set Scope 3 emission reduction targets - the ones that actually cover the farm-level impact.

 

If 82% of the problem lies on farms, why aren’t we putting 82% of our effort there?

 

The reasons are familiar: fear of extra costs, lack of control over fragmented supply chains, limited data from traditional assurance systems, and low consumer willingness to pay more.

 

But these barriers are all symptoms of the same underlying issue — insufficient, unreliable farm-level data.

 

Retailers and suppliers cannot act confidently without knowing what is really happening on the ground. And until we move beyond modelled assumptions to verified data, our sustainability strategies will continue to rest on shaky foundations.

 

The trust gap: fast models vs. slow audits

In the market today, we see a widening trust gap. On one side are data modelling companies, offering fast, scalable insights that look impressive in PowerPoint but often lack ground truth. Their models can be directionally right but are rarely precise — and almost never verified at the individual farm level.

 

On the other side are traditional audit companies. Now, at SCI we proudly audit. We help customers where audits are a part of their programme and are proud to do so. But for this particular issue audits are an ineffective tool. Because they are infrequent (and cost really means that won't change) and retrospective, they are useful for managing some risks but are not best suited to this challenge. Audits tell us what happened in the past, they don't help you intervene on what’s happening today.

 

This leaves retailers and food businesses in a difficult position: balancing speed against accuracy, and data abundance against data quality. The result, too often, is paralysis — or worse, complacency dressed up as progress.

 

The solution lies in combining the best of both worlds: using digital tools and real-time data capture to create verified, high-frequency insights that are both fast and reliable. That’s exactly what we have built at SCI.

 

Real-time assurance in action: Learning from rice

In March 2024, SCI was approached by the CEO of a major Asian food retailer. They wanted to benchmark and verify the climate impact of their rice supply — not through consultancy spreadsheets, but through verifiable, farm-level data. Rice is a high-impact crop, responsible for 22% of agricultural methane and 11% of nitrous oxide emissions worldwide. This retailer wanted to move beyond offsetting and toward insetting — reducing emissions at source within their own supply chain.

 

We started with 30 rice farmers in Thailand, training them in lower-emission “wet and dry” cultivation techniques and optimised fertiliser use. But the real innovation came from how we monitored progress.

 

Each farmer used a specially developed smartphone app to record practices and outcomes, sending over 37,000 images across the growing season. Using AI-enabled machine learning, our platform processed these images in real time, flagging potential compliance issues and allowing our team to provide immediate corrective feedback. Methane measurements were taken to validate the projected emission reductions, while spot audits confirmed that the digital data matched reality.

 

The results were transformative: a 36% reduction in greenhouse gas emissions at the end of the first 4-month harvest, 95% compliance to the process by the farmers, and no loss of yield. Farmers saved money on inputs and got to benchmark with other farmers for the first time. The retailer got credible, verifiable data for its sustainability report. Consumers could scan a QR code on the pack to see the verified low-carbon story. And the Thai government gained a scalable model for emissions reduction in one of its most important crops.

 

That’s what I call a win-win-win-win — for retailers, farmers, government, and consumers alike.

 

Scaling what works

Based on this success, we are now working with the same retailer to scale the verified low-carbon model to 50% of their rice production by 2030, meaning 1,000 farmers will be using the technology. 

 

We have now had two other global retailers ask us to help them tackle some of the other 15 commodities that make up 82% of the food industry impact. We are underway on how we would tackle beef, dairy, coffee and shrimp.

 

The principle remains the same: rigorous assessment of the options to reduce the emissions impact of the commodity; great training for the farmers; technology for real-time interventions and verification and farmer engagement to generate trustworthy, actionable data for all parties in the chain to see benefits.

 

This is not about chasing “carbon-neutral” badges, which too often rely on offsetting schemes of dubious quality. Instead, it’s about focusing effort where it matters most — on the farm. In compliance terms, you wouldn’t outsource food safety to someone else’s offsetting scheme; why should emissions be any different?

 

From lagging to leading indicators

One of the biggest mindset shifts needed in sustainability assurance is from lagging to leading indicators.

 

Traditional assurance schemes look backwards: they measure what happened last season, last year, or last audit cycle. That’s important for accountability, but it’s too slow to drive improvement. We need systems that can detect issues as they emerge — systems that can support decisions in real time to drive performance improvements and prevent non-compliance.

 

This is where digitalisation and farm-level data come into their own. With the right tools, a farmer can now submit data via smartphone that triggers an automated analysis, alerts an advisor, and feeds directly into a retailer’s sustainability dashboard — all in the same day. That’s not a dream scenario; that’s already happening in our projects.

 

Real-time assurance doesn’t just increase accuracy; it changes behaviour. When farmers see immediate feedback and clear recognition for improvement, they engage. When retailers see reliable, verified data from the field, they invest. When consumers can track the data for themselves, trust is restored.

 

The bigger picture: from words to action

Across the food sector, there is a widening gap between what is said and what is done. The latest emissions report published just this week shows emissions continuing to rise when they should now be markedly falling Government told to prepare for 2C warming by 2050 - BBC News . Sustainability reports are filled with ambitious claims, yet many rest on averages, and assumptions. It’s time to close that gap with evidence of real change happening on the ground. 

 

Reliable farm-level data can help bridge the gap between intention and impact, and to move from from glossy CSR statements to measurable progress.

 

At SCI, we have shown that it can be done — quickly, efficiently, and affordably.

 

But this is not just about technology. When we get the data right, everything else follows: smarter policy, fairer markets, better consumer trust, and genuine climate impact. The future of food sustainability depends on it.

 

Because when it comes to the sustainability footprint of our food, the details aren’t just important — they make all the difference.

 

Rob Chester is CEO of Supply Chain In-Sites (SCI), a UK-based consultancy he established after 40 years in international audit and compliance roles with Tesco and Walmart to deliver innovative, agile and data-driven solutions to help businesses along the supply chain enhance food safety, sustainability, and efficiency.