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

Executive summary Midstream food & agriculture teams have become the de-facto translation layer for land emissions data. They sit between slow, uneven supplier inputs and fast-moving, product-specific customer requests, creating persistent bottlenecks as LSR raises expectations for traceability and proof. The bottleneck is structural, not a capability gap. Strong internal lifecycle assessment (LCA) teams are overwhelmed by bespoke rebuilds, boundary reconciliation, and documentation work that cannot be reused across customers or reporting cycles. LSR turns inefficiency into risk. Higher requirements for consistency, explainability, and audit readiness make spreadsheet-driven, request-by-request workflows slower, costlier, and harder to defend. Leaders can scale without adding headcount. Standardizing a customer-ready data pack, triaging requests by use case, and building product carbon footprints on interoperable standards (e.g. PACT) reduces rework, shortens cycle time, and increases reuse across customers. 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.