7 Things You Need to Know About Reporting Land Emissions With Imperfect Data

Land-related emissions account for nearly a quarter of global greenhouse gas output, yet until recently they’ve been bundled invisibly into corporate Scope 3 inventories. The GHG Protocol’s Land Sector and Removals (LSR) standard, published in January 2026 and effective for 2027 inventories, changes that. Companies with agricultural commodities, forest products, or natural fibres in their value chains must now separate land emissions from energy and industry emissions and report them as distinct line items.

In a recent Terrascope webinar, sustainability expert Xinlu Liu walked through a practical framework for getting started when your data is still evolving. She covered disaggregating average emission factors, building bottom-up calculations from primary farm data, and illustrated both with real worked examples, including what it looks like when land use change turns out to be 80% of a commodity’s footprint.

Here are seven key takeaways.

1. LSR Is Mandatory 

If your company reports a corporate GHG inventory using the GHG Protocol and has land sector activities anywhere in its operations or Scope 3 value chain, LSR applies. Agricultural commodities like palm oil, soy, coffee, dairy, and cotton all trigger it, as do forest products and bioenergy. There is no opt-out, and there is no materiality threshold (although the standard references SBTi’s 20% threshold in its FLAG Guidance as a screening tool).

Under the previous guidance, land emissions were largely bundled into Scope 1, 2, and 3 totals. LSR introduces three structural changes. First, land-related emissions must be reported separately from energy and industry (E&I) emissions. Second, land use change, land management, and biogenic product emissions each become distinct line items. Third, carbon removals, while optional, are subject to strict requirements around permanence and reversal accounting if you choose to claim them.

With IFRS S2 climate disclosures now adopted or under consultation in over 30 jurisdictions, LSR is becoming the backbone of regulated climate reporting.

2. Land Use Change Is Often A Key Driver

Before getting into methodology, it’s worth understanding why LSR reporting matters so much in practice. When Terrascope disaggregated the emission factors for a barley grain example during the webinar, 80% of the total footprint came from a single component: carbon dioxide from soil and biomass stock changes. Energy and industry emissions, the part most companies are used to measuring, accounted for a fraction of the total.

That ratio isn’t unusual. When Terrascope worked with TSE Group, a palm oil producer, to measure their full emissions profile, the breakdown told a similar story: land-related emissions made up roughly 80% of the total, largely driven by land use change, with E&I contributing around 20%.

For any company sourcing agricultural commodities at scale, land use change is often a dominant variable, and where your commodities are grown matters more than almost anything else in the supply chain. A farm with no history of deforestation carries near-zero land use change emissions, while one on converted tropical forest carries decades of carbon debt that must be amortised over at least 20 years under LSR.

This is the fundamental reason LSR exists: to make these numbers visible.

3. Data Maturity Determines Your Approach

The webinar was built around a three-tier data maturity model, which maps your current data to the right LSR approach. 

Spend data only: You know the commodity type and dollar amount. You can map spend-based emission factors to activity-based proxies, document your assumptions clearly, and work towards activity data for the next measurement cycle.

Activity-based data (Approach A): You have physical quantities and country of origin. This is where most companies sit today, and where disaggregating average emission factors can deliver an LSR-compliant breakdown without any farm-level data.

Farm-specific data (Approach B): You have access to land use history, input records (fertiliser, energy), and traceability to specific sourcing regions. This produces the most accurate and auditable results.

These approaches are complementary, not binary. Different commodities in your portfolio will sit at different maturity levels, and you can apply Approach A to some and Approach B to others within the same inventory. A hybrid approach, using primary data where you have it and secondary data to fill the gaps, is the realistic path for most companies.

4. Your Emission Factors Already Contain LSR Data

If you’re using activity-based emission factors from databases like ecoinvent, the land emission components (land use change, land management, biogenic CO₂) are already bundled inside those numbers. They’re just not broken out.

The webinar demonstrated this with barley grain. A single ecoinvent emission factor of 1.88 kgCO₂e per kilogram looks like one number. But when you examine it at the unit process level, looking at each individual input and output that contributes to the total, it reveals a tree of emission drivers showing exactly how much comes from land use change, how much from energy and industry, and how much from land management. Terrascope’s data science team calls this an "emissions factor driver tree". It’s what makes Approach A work: the emission factor database already contains the sub-components you need for LSR reporting.

The tagging work to identify and label those components has been done once across ecoinvent and other emission factor databases, applied automatically to customer measurements.

For Terrascope's customers, this means a first-pass LSR-compliant inventory breakdown is achievable using the data they already have, before any new supplier engagement begins.

There are known limitations. ecoinvent’s structure does not allow reliable separation of net biogenic CO₂ within land management (it shares a node with land use change), and not all databases model biogenic product emissions. If those categories are material to you, that’s a strong reason to move towards bottom-up calculations for those commodities.

5. “Bottom-Up” Calculations Are Less Daunting Than They Sound

The standardized, tier-based formulas provided by the Intergovernmental Panel on Climate Change (IPCC) to estimate greenhouse gas emissions can seem intimidating. Xinlu broke biomass calculation into five steps that make it manageable:

1. Collect land use history and yield: what the land was before conversion, what it is now, when the conversion happened, and the output per hectare per year.

2. Calculate above-ground biomass loss using published IPCC coefficients for the relevant climate zone and land type.

3. Estimate below-ground biomass using a standard ratio of root mass to above-ground mass (typically around 20–25% for tropical forests).

4. Convert from carbon mass to CO₂ equivalent using the molecular weight ratio of CO₂ to carbon (every 12 tonnes of carbon becomes 44 tonnes of CO₂).

5. Annualise and attribute per kilogram of output, using linear discounting over the amortisation period.

Here’s the encouraging part: only step one absolutely requires direct data. Steps two through five reference published IPCC coefficients and standard formulas.

With a platform that automates the calculation chain, the actual data ask to your supply chain is smaller than most teams assume.

6. Better Traceability Almost Always Means Lower Numbers

A counterintuitive insight from the webinar: companies that invest in farm-level traceability and bottom-up calculations typically end up with lower emissions than global averages.

As Xinlu explained, global emission factor databases include worst-case origins in their averages. The land use change component is particularly affected: regional averages factor in high-deforestation sourcing that may not reflect your actual supply chain at all. Companies with the traceability to calculate land use change from their own sourced farms usually have lower numbers precisely because that level of supply chain visibility signals active management of deforestation risk.

There’s a second advantage: bottom-up calculations reflect your actual decarbonisation efforts. If a supplier switches to lower-nitrogen fertiliser or adopts improved land management practices, that improvement shows up in a bottom-up calculation, as long as primary data can be collected and used in the formulas. With average emission factors, those improvements stay invisible. The work required to move from Approach A to Approach B is how you make real supply chain improvements visible in your inventory.

7. Supplier Engagement Works Best When You’re Specific About What You Need

Our advice on supplier engagement: ask suppliers for the LSR categories you need upfront, so they know exactly what to provide.

• For Approach A, this means asking suppliers who provide their own emission factors to include an LSR-compliant breakdown rather than a single aggregated number.

• For Approach B, it means requesting specific activity data mapped to each emission source: land use history, fertiliser application rates, lime usage, energy inputs, and yield.

The realistic expectation is that most suppliers will provide some of this data, and you’ll build up the rest over time. That’s exactly where the hybrid approach becomes practical. You might calculate land use change bottom-up using a supplier’s GPS coordinates and land registry data, while using disaggregated database factors for fertiliser emissions until the input records become available. Each measurement cycle, you replace another secondary data point with a primary one.

The Payoff: Removals Are Where the Strategic Value Lives

Everything above gets you to LSR compliance. But there’s a reason to keep building beyond it. Carbon removals, including agroforestry sequestration, reforestation, and soil carbon restoration, are unique to the land sector, and SBTi estimates that roughly one-third of the mitigation needed to meet FLAG targets will come from removal opportunities.

Claiming removals requires the kind of supply chain traceability and farm-level data that Approach B delivers. Average emission factors from databases like ecoinvent will not qualify. That’s the real case for starting where you are and building from there.

What To Do Next 

Watch the full webinar. See the barley driver tree, the coffee bottom-up walkthrough, and live Q&A with Sustainability Expert Xinlu Liu.

Complimentary 30-minute discussion with an LSR expert. Our team will map your data maturity to the right starting point. 

 

 

 

Lia Nicholson
Head of Sustainability,
Terrascope