We’re somewhere north of Fargo, baking under a late-July sun and knee-deep in rolling hills of soy that belong to a farmer who’s been on the forefront of soil building practices for as long as they’ve been in the business. Someone’s just asked about their experience with a carbon program. They pause, then proceed to describe, with audible weariness in their voice, the weeks-long ordeal of trying and failing to enter data in the required format; of bumping up against software bugs (no integrated pest management to call upon here…only far-away engineers with an already-long backlog of issues to fix) that foiled their attempts at drawing field boundaries. Eventually, they threw up their hands and told the company, ‘Here, you do it for me.’ The payout received several months later, they said, hardly measured up to the hassle.
As the focus on the climate-fighting potential of agriculture has intensified, there’s been a corresponding uptick in industry conversations about monitoring, reporting, and verification, colloquially known as “MRV,” or sometimes MMRV, the extra “M” meaning “measurement.” MRV underpins any credible claim around GHG emissions, which explains why there’s been a rising wave of investment in companies with satellite imagery, soil probes, and other technologies capable of detecting both the practices and outcomes associated with regenerative ag. Earlier this fall, the Food Lab and consultant Ryan Whisnant explored the components of MRV in a presentation in the Scale Lab’s Accounting Working Group.
Measurement or monitoring describes the process whereby a company tracks the changes in its greenhouse gas inventory, or the results of interventions in a supply shed, often by employing a combination of methods such as surveying (questionnaires for growers on their management practices), in-situ sampling (field data), remote sensing (the use of radar, satellite, or UAV imagery), and modeling.
Reporting refers to the publication of the impact and inventory data described above for external audiences. Transparency here is the name of the game; as no GHG dataset will ever be complete or perfect, every guidance document stresses the importance of spelling out all assumptions, calculation methods, and allocation decisions made.
Finally, verification, frequently completed by a third-party, confirms the accuracy of a company’s reporting and the extent of its compliance with existing protocols.
Laid out in logical and sequential order, MRV sounds straightforward enough. The reality, of course, is anything but simple. The process of collecting, cleaning, and collating the data for frameworks such as Field to Market and forthcoming guidance (WRI Greenhouse Gas Protocol Land Sector and Removals, for one) is proving a perennial struggle for not only farmers like those we visited in the Northern Plains, but also for our company partners. While farmers must contend with a data sharing process that can feel transactional – or worse, extractive – food and beverage brands, as we hear in the Impact Lab, are bombarded on all sides by MRV service providers clamoring for their business.
The recognition of MRV’s importance and the increasingly crowded arena of service providers has generated a great deal of noise, which threatens to discourage or deter companies, especially those just getting their feet wet in climate accounting. The complexity of this picture inspired us to focus on the basics in the Scale Lab, and so, in addition to outlining the building blocks that make up any MRV approach (which are “protocol-agnostic”), we set out to identify the overarching principles behind complete MRV systems. It’s our hope that the triad of considerations we chose – scientific rigor, feature robustness, and farmer centricity – can steer sustainability professionals in a direction that is not only technically sound but human-centered. Because, at the end of the day, MRV is not so much about numbers as it is about people. Assembling a high functioning MRV model is an interpersonal exercise in every way. It necessitates communicating and working closely with IT colleagues to ensure the software technology selected for MRV has a feature set that aligns with the company’s broader information systems needs and long-term roadmap. It requires understanding what technical rigor, or competencies, are required to run and maintain process-based models, and then either upskilling current staff or identifying new hires with the exact tools to do the job. And it means viewing MRV not just as the acquisition of shapefiles for corporate reporting, but as a value-added interaction for the farmer: an opportunity to not only gain but give value to the farmgate, by providing, through MRV, field-level or financial insights the grower may not be able to get anywhere else.
There is ample hype around the promise of digital-MRV, or “D-MRV,” approaches that apply machine learning (ML) and automated workflows to expedite data collection and minimize the risk of manual error. Still, if scaling regenerative agriculture rests on buy-in from all parties involved, then hiding GHG accounting within black box models and cutting out human interactions – essential to building trust among the farmers and suppliers who form the base of the MRV triangle – could hinder scale-up in the long-run, even if on the surface MRV starts to seem faster and more frictionless. When the digital dashboard doesn’t match the on-the-ground reality and frustrations begin to mount for farmers and other users, no number of corrective algorithms carry the weight of a human guide: what one of our company partners calls a “data doula.”
This week, we’re unveiling the Trusted Advisor Partnership (TAP), a blueprint for accelerating the adoption of soil health practices by training the trainers: the independent crop consultants (CCAs) who help farmers advance more confidently along the regenerative ag journey. In many ways, TAP is a microcosm of the human centered MRV design we’ve been investigating in Scale Lab. As a program, it was born not only from our July trip to North Dakota, but from a belief that making regenerative farming, and by extension MRV, mainstream and accessible will hinge as much on the “other” ML: manual labor –that boots-on-the-ground presence that we should see not as inefficient, but rather integral to scaling. “The amount of time that it takes for farmers to enter this data is not sustainable,” one Impact Lab member said with a shake of her head a few weeks back. Perhaps sustainability is not just a number in an inventory or a net-zero commitment, but also a recognition of each other’s time.
