The benefits of a digital MRV tool for VCS Standard VM0042 of Verra

What are the benefits of using a digital MRV tool for Verra's VCS Standards VM0042?

1. Introduction

Framed by standards that are sometimes public, sometimes private, such as Verra’s VCS Standard, the voluntary carbon market (VCM) is opening up to farms.

Private companies can employ various methodologies for projects development. One prominent approach, particularly for agricultural projects, is VM0042 (Methodology for Improved Agricultural Land Management). This methodology serves as a tangible illustration, outlining accounting rules and a set of principles (Leakage, Permanence, Additionality etc.) to achieve real and impactful reductions in tCO2e (commonly referred to as carbon credits or VCUs in the context of Verra).

While d-MRV* tools (also MMRV or simply MRV), refering to comprehensive software platform in this article, are not mandatory for these methodologies (unlike, for example, the Label Bas Carbone Grandes Cultures methodology in France), they do provide real added value. This article aims to explore their significance in project development, using the example of VM0042 of the VCS Standard.

*Digital – Measure, Report, Verify

2. VM0042 in short

Currently receiving the greatest attention, VM0042 is the international available methodology with the most associated projects and probably a great option if you develop Regenerative Agriculture projects.

This methodology delineates the process for quantifying reductions in greenhouse gas (GHG) emissions and soil organic carbon (SOC) removals resulting from Improved Agricultural Land Management practices such as reduced tillage and cover cropping, compared to a 3-year historical baseline period.

Applicable to both arable (crop producing) and pastoral (livestock grazing) agricultural land, VM0042 necessitates extensive data management across various dimensions:

Defining project areas and associated stratification
Quantifying GHG emissions and SOC changes based on a 3-year historic period (baseline scenario)
Simulating and estimating GHG emissions reductions and SOC changes over subsequent years based on the implementation of levers for an improved agricultural land management (project scenario)
Assessing leakage, co-benefits, and other quality criteria

In simple terms, VM0042 requires careful handling of large sets of data about weather conditions, soil attributes, field specifics, and activity data. This involves not only historical data but also forward-looking projections through simulations. Putting VM0042 into practice involves many aspects and needs a strong understanding of scientific modeling. Its complexity lies in detail data management and advanced modeling involved, making it both challenging to deploy and indispensable for agricultural sustainability efforts.

3. Simplifying and Automating Data Collection

If you’ve found your way to this article, chances are you’re already familiar with this methodology or perhaps even actively involved as a project developer.

One of the initial steps in project development involves defining the project area, particularly in agriculture, where it means establishing the boundaries of the fields involved. Here, d-MRV comes into play, streamlining the process by automating the creation of these field boundaries. Often, these boundaries are already mapped out in the farmer’s FMIS (Farm Management Information Systems) and can be seamlessly integrated using APIs. In Europe, farmers also have to declare their fields for CAP (Common Agricultural Policy) subsidies, further simplifying the process as a simple file import can populate the entire farm and its associated fields.

Once the fields are outlined, the next task is gathering crop production information. For instance, in France, the Registre Parcellaire Graphique (RPG) provides public access to crop data, enabling d-MRV to track crop rotations and feed into the SOC Stock changes model seamlessly. Additionally, activities like fertilization and pest control, typically logged in FMIS for compliance purposes, can be automatically collected, streamlining the process further.

Moreover, farm machinery itself serves as a goldmine of data for these projects. Machinery records offer invaluable insights into practices, detailing what was done, when, the input quantities, worked area, and even fuel consumption maps in some cases. This data can be sourced directly from agricultural manufacturers’ cloud platforms or through specialized hardware devices.

Illustration of some of the API integrations available in MyEasyFarm for automatic data collection.

However, additional data is required for these projects, including climate and soil information.
Once again, a digital MRV tool proves invaluable in automating the collection of these datasets. For example, VM0042 requires a data source of at least 50 km. Since a digital MRV tool can manage field boundaries and farm addresses, obtaining the data required to comply with VM0042 standards becomes a seamless process.
The same principle applies to soil information. The use of field boundaries simplifies the retrieval of spatialised soil data, such as SoilGrids, effortlessly facilitating project data requirements.

4. Overcoming Modeling and Quantification challenges

Once all the necessary 3-year historical data has been gathered for the project area, the next step is quantification (modeling).

For a variety of reasons, including the possibility of issuing carbon credits on an annual basis, it may be worthwhile for the project developer to use a measure and re-measure approach.

Such an approach requires a scientific basis for modeling SOC changes. Furthermore, it requires providing the necessary input data for modeling: soil data, climate data, crop rotations, and cropping practices, among others.

When it comes to quantifying GHG emissions, selecting the appropriate emissions factor and justifying the data used in modeling is crucial for project developers. Leveraging a d-MRV allows for the delegation of such tasks.

Additionally, it’s worth noting the importance of having a third-party involved to mitigate conflicts of interest that could potentially impact the outcomes of carbon farming projects.

The d-MRV streamlines operations for project developers, enabling them to focus on their primary objectives. This tool automates work processes and facilitates scalability. Additionally, it’s essential to present information graphically, and once again, the MRV tool offers the necessary interface. To illustrate this aspect, an example of report by MyEasyCarbon is provided, demonstrating how SOC Stock changes are quantified and the difference between a baseline scenario and a project scenario.

Example of SOC Stock changes modeling report inside the MyEasyCarbon d-MRV with SIMEOS-AMG by AgroTransfert

5. Enhancing Collaboration and Project Management

As a project developer, you likely employ various strategies to implement your carbon farming or regenerative agriculture initiatives.

In some instances, you might anticipate farmers’ involvement in accessing their farms to aid in data collection and reporting, as well as to access actionable insights. However, there are scenarios where you prefer a dedicated team to handle project scenarios and quantification modules, aiming to minimize disruptions for farmers.

In such cases, collaborative features become essential. These features enable individual farmers to access their farms independently, while granting you, as the project developer, access to and utilization of data from all supported farms. Additionally, having a global overview of your entire program is imperative. Relying on spreadsheets to aggregate GHG and SOC Stock changes insights across all project strata may not be the most efficient option.

Illustration of the collaborative features available in a d-MRV such as MyEasyCarbon

6. Conclusion and illustration with our d-MRV MyEasyCarbon

To illustrate the above, here’s a block diagram of our MyEasyCarbon d-MRV tool which we offer to project developers:

Farm Management Information System (FMIS) are used to automate farms creation, associated fields boundaries and get 3 years-historical data. These data can then be updated directly via our interface by an advisor or the project developer. Indeed, we noticed for example that soil work practices are often missing in the systems used by the farmers for compliance reasons mainly.

As a concrete illustration, spatial datasets are used to feed the SOC Stock changes model: based on the farm location and the fields boundaries, we retrieve the necessary dataset for climate and soil information, which are then integrated with soil samples and field activities/management data.

Remote sensing technology, integrated into a dedicated module and transfered from CESBIO, facilitates the quantification of cover crop biomass with numerical and verifiable data. Additionally, remote sensing aids in identifying various agricultural practices such as crop rotations and cover cropping.

The MyEasyCarbon platform offers an intuitive interface that empowers project developers to efficiently manage their projects. Moreover, it generates easily interpretable reports on GHG emissions and SOC Stock changes quantifications, which can be effectively communicated to diverse stakeholders.