Validating regenerative agriculture outcomes across a global supply chain

Background

A multinational food and beverage corporation sources agricultural commodities from thousands of farms across multiple continents to supply its production facilities. With commitments to achieve net-zero emissions by 2050 and growing pressure to demonstrate sustainable sourcing practices, the company needed to validate that its regenerative agriculture investments were delivering measurable environmental outcomes.‍

Validating regenerative agriculture outcomes across a global supply network

Large-scale food companies face complex challenges when attempting to measure environmental performance across fragmented agricultural supply chains.

• Scale and fragmentation: With thousands of supplier farms spanning multiple countries and climate zones, the company lacked a cost-effective way to measure natural capital outcomes across its entire agricultural footprint.
• Data collection burden: Traditional methods requiring manual on-farm data collection through surveys and in-person visits were time-intensive, expensive, and difficult to standardize across diverse geographies and farming systems.
• Ground truth validation needs: The company needed confidence that remote sensing measurements aligned with on-ground reality, but lacked systematic ways to compare satellite-derived data with field samples across large portfolios.
• Supplier engagement and incentives: Without scalable measurement systems, the company struggled to demonstrate progress to farmers, create data-driven incentive programs, or show which regenerative practices were delivering the strongest outcomes.
• Risk and resilience assessment: Supply chain managers needed to identify which sourcing regions faced heightened risks from soil degradation, water scarcity, and biodiversity loss that could threaten long-term production viability.
• Multiple environmental dimensions: Carbon storage alone was insufficient—stakeholders demanded comprehensive data on soil health, water management, and biodiversity that could support both internal targets and external reporting frameworks.

Platform-based natural capital monitoring across regenerative agriculture programs

The Landbanking Group deployed scalable monitoring across multiple regenerative agriculture initiatives spanning thousands of hectares and hundreds of supplier farms.

• Multi-dimensional environmental assessment: Continuous satellite-based monitoring of soil organic carbon, water holding capacity, soil moisture, protected on-farm habitat, vegetation condition, species presence, and landscape habitat diversity.
• Ground truth comparison framework: Systematic comparison of TLG's remote sensing outputs against the company's existing field sample data and partner measurements to validate accuracy and identify gaps or improvements needed.
• Scalable analysis across diverse farming systems: Standardized methodology applied across arable cropland, pasture, cocoa plantations, and mixed farming operations in Europe, West Africa, and other regions.
• Project-level and farm-level insights: Data aggregated at multiple scales—from individual farm parcels to supplier clusters to regional programs—enabling insights for farmers, project managers, and corporate leadership.
• Historical baselines and trends: Time-series analysis providing 5+ years of retrospective data to establish baselines and track changes over time as regenerative practices were adopted.
• Transparent uncertainty assessment: Clear communication of data confidence levels and limitations, enabling the company to understand where measurements were robust and where additional ground-truthing would add value.

Evidence-based validation of regenerative agriculture investments

The company achieved comprehensive visibility into natural capital outcomes across its regenerative agriculture portfolio.

• Supply chain-wide measurement: Successfully monitored natural capital indicators across thousands of hectares of supplier farmland, replacing fragmented manual data collection with automated, standardized analysis.
• Validated environmental outcomes: Demonstrated that regenerative agriculture programs were delivering measurable improvements in soil health, carbon storage, and biodiversity, providing evidence for internal stakeholders and external reporting.
• Risk identification: Identified sourcing locations with elevated risks from soil degradation, water stress, or declining habitat quality, enabling proactive interventions to protect supply chain resilience.
• Data-driven farmer engagement: Created foundation for outcome-based farmer incentive programs by establishing clear, measurable environmental baselines and tracking individual farm performance over time.
• Cost-effective scaling: Achieved comprehensive environmental monitoring at a fraction of the cost of traditional field sampling, making continuous measurement financially viable across the company's global sourcing footprint.
• Enhanced reporting credibility: Strengthened sustainability reporting and stakeholder communications with science-backed data validated against ground truth measurements and supported by academic research partners.