LSR’s “Messy Middle”: Lessons from the Field for Midstream Food & Ag Companies

The new Land Sector and Removals (LSR) Standard has broad implications for how to calculate and share land emissions and removals data across food and agri value chains. After dozens of customer calls and workshops on this topic, we have a clear view of which issues concentrate upstream, midstream, and downstream - and where key operational bottlenecks lie. 

This article focuses on the midstream: the businesses that buy and aggregate, move, store, process, and transform raw commodities into ingredients and semi-finished products. Lessons from the “messy middle” are relevant for ingredient manufacturers, primary processors, logistics/storage operators, and traders.

My team held 20 customer calls across 11 organisations spanning upstream processors, midstream manufacturers, and downstream food/retail to understand pain points and problem solve the Greenhouse Gas Protocol’s new LSR standard. The pattern for midstream companies was consistent: customer requests for product-specific emissions data arrive faster than supplier data and internal workflows can absorb.

LSR turns land emissions data into an operations problem for midstream companies. They are stuck translating farm-specific data for downstream customers like food retailers to measure progress against Scope 3 supplier engagement and reduction targets. 

The midstream role creates predictable friction

Midstream companies sit at the meeting point of three constraints, and LSR tightens all three at once.

Supplier data arrives slowly

Teams described supplier-specific data collection taking "months" and sometimes "a year." Midstream companies inherit this delay because they consolidate upstream data into downstream-ready outputs. A sustainability lead at a multinational buyer described supplier data collection as "time consuming" and "error prone."

Midstream leaders also see capability gaps widen as requests become more specific. One manufacturer described asking for product-specific LCAs and getting a simple answer: "We're not there yet."

Customer requests vary by use case, and the variation creates rework

Requests differ by boundary, data quality expectations, and assurance needs. Midstream teams rebuild models to satisfy small differences in customer interpretation.

As one industrial food processor put it: "We can technically do the LCAs - the problem is volume. Every request is slightly different, and none of it is reusable."

This is where the pressure becomes commercial. Customers that have public annual disclosures and emissions reductions targets set for their purchased goods (Scope 3 Category 1) want annual footprinting refreshes for the products that they buy. And customers expect product footprints to reduce over time. Midstream suppliers become the pressure point.

Internal LCA functions become the clearinghouse

Many midstream companies have strong LCA capability. The failure mode comes from throughput: teams spend their time translating, documenting, and reconciling assumptions rather than building reusable assets.

A packaging manufacturer described the constraint directly: "We cannot engage actively with 20 suppliers because we simply don't have the resources." Another company described an internal "waiting queue" that stays "quite long."

The result looks like a permanent queue, because each new customer request triggers a bespoke cycle:

• Interpret the request

• Chase upstream data

• Reconcile boundaries and allocation

• Document defensibility for review and assurance

• Repeat for the next customer with a slightly different ask

What changes with the new LSR standard

LSR increases expectations for traceability, consistency, and explainability of land emissions data. That change shifts land emissions from "we have a number" to "we can show our workings and proof."

Midstream teams already struggle with:

• Consistency: definitions and boundaries that hold across customers and reporting cycles
• Explainability: calculation logic that stands up to challenge and assurance as standards evolve
• Reusability: data structures that move across customers without bespoke rework

LSR makes each weakness more expensive. A rebuild-per-request workflow expands cycle time. Rework becomes normal. Audit questions increase because the rationale sits in people's heads or one-off spreadsheets rather than a repeatable documentation pack.

The buyer problem in one sentence

Midstream sustainability leaders need a way to answer more customer requests with the same team size while increasing defensibility of land emissions data.

A midstream manufacturer described their LCA function as: "A translation layer no one planned for, and no one staffed for."

A framework that improves outcomes

The framework below  shortens cycle time, reduces rework loops, and increases reuse of the same data across customers. 

1. Define a customer-ready data pack
   

    

   Midstream teams move faster once they define a standard internal output that satisfies most customer use cases.

   A customer-ready data pack answers key questions up front:

   • What is the product boundary? (PCF scope, reporting period, allocation rules)
   • What land emissions elements are included? (and what evidence supports them)
   • What data quality and primary data share applies?
   • What versioning and change control applies across reporting cycles?

   
   This is where interoperability matters. PACT (WBCSD's Partnership for Carbon Transparency) provides a common approach for exchanging product carbon footprints (PCFs) so that data can work across different customer and vendor systems.

   Terrascope chose early alignment and achieved WBCSD PACT conformance, positioning product-level data exchange as a core operating capability rather than an ad hoc export.

2. Triage requests by customer use case
   
   Midstream teams burn time when every request is treated as bespoke and urgent. Triage works with explicit criteria, for example:
   • Contract-critical requests: revenue at risk, deadlines fixed
   • Regulatory-driven requests: disclosure or assurance requirements with reputational risk on the line
   • Programmatic requests: supplier program monitoring, improvement over time
   • Exploratory requests: benchmarking, early-stage questions
     
     

     The output of triage is a decision: which requests require a full product-level pack, which accept a standard pack, and which accept a phased approach. This turns "we need everything" into "we need these fields, at this level of specificity, on this cadence."

3. Align supplier asks to the pack
   
   Supplier asks improve when they are bounded (defined minimum fields, not an open-ended questionnaire), repeatable (a standardized request), and paired with incentives (commercial terms, longer contracts, or co-investment where needed). As one buyer bluntly said: “if you want data, you have to pay.”
4. Invest in leverage points, not supplier breadth
   
   Supplier-side friction is real and data requests must be ruthlessly prioritised. Midstream companies rarely have the resourcing to engage deeply with dozens of suppliers at once. 
   
   A clean approach gives procurement a commercial basis for prioritization and gives sustainability a defensible rationale, for example:
   • Priority 1: highest materiality + highest customer pressure
   • Priority 2: materiality high, timing flexible
   • Priority 3: baseline approach, improve later

Proof point: Japan Tobacco's processed foods business

Terrascope's work with Japan Tobacco shows what faster and more defensible looks like when the problem is consolidation, inconsistency, and queue-driven work.

By working with Terrascope, Japan Tobacco achieved these key outcomes:

• Scope 3 Category 1 (purchased goods and services) calculations consolidated across ~20 companies in the processed foods business, addressing differences in data formats, assumptions, and boundaries
• Two years of forest, land and agriculture (FLAG) emissions calculations completed in one month, including the base year, aligned with SBTi FLAG guidance
• Double counting and overestimation risks handled through evidence review, input correction, and consolidation into robust emissions figures
• Land-related emissions identified as 60%+ of purchased goods and services (Scope 3.1) for the processed foods business, clarifying priorities such as raw materials and packaging

Shifting from bespoke rebuilds to consolidation and documentation workflows, built on interoperable standards, reduced cycle time.

Summary (so you can act without rereading)

Midstream LSR readiness comes down to two moves:

1. Build a reusable, customer-ready data pack built on the PACT’s interoperable standard

2. Use triage and supplier tiers to protect your team's time 
   

Next step

If you want our expertise customized for your needs, book a call here.  Bring your top customer request patterns, your most material suppliers, and a sample of the data you currently exchange, and we’ll map your solution.