The benefits of an MRV tool for VCS Standard VM0042

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

1. Introduction

Framed by labels and standards that are sometimes public and sometimes private, such as Verra's Verified Carbon Standard (VCS), the voluntary carbon market is opening up to farms.

Private companies can use a variety of methodologies for project development. One of the main approaches, particularly for agricultural projects, is VM0042 (Methodology for the Improvement of Agricultural Land Management). This methodology serves as a tangible illustration, describing the accounting rules and a set of principles (leakage, permanence, additionality, etc.) for achieving real and significant reductions in tCO2e (commonly referred to as carbon credits or VCUs in the Verra context).

Although d-MRV* tools(also MMRV or simply MRV), which in this article refer to a complete software platform, 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 importance in project development, using the example of VM0042 of the VCS standard.

*Digital - Measure, Report, Verify

2. VM0042 in brief

Currently receiving the most attention, VM0042 is the international methodology available with the largest number of associated projects, and is probably an excellent option if you are developing regenerative agriculture projects.
This methodology delineates the process of quantifying greenhouse gas (GHG) emission reductions and soil organic carbon (SOC) removals resulting from Improved Farmland Management practices such as reduced tillage and intermediate cropping, compared to a 3-year historical reference period.

Applicable to arable (crop production) and pastoral (livestock grazing) farmland, VM0042 requires extensive data management across different dimensions:

  • Definition of project zones and associated stratification
  • Quantification of GHG emissions and SOC changes over a 3-year historical period (reference scenario)
  • Simulation and estimation of GHG emission reductions and SOC changes in subsequent years, based on the implementation of levers for improved agricultural land management (project scenario).
  • Assessment of leakage, co-benefits and other quality criteria

3. Simplifying and Automating Data Collection

If you're reading this article, chances are you're already familiar with this methodology, or perhaps even actively involved as a project developer.

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

Once the fields have been delimited, the next task is to gather information on crop production. For example, in France, the Registre Parcellaire Graphique (RPG) provides public access to crop data, enabling d-MRV to track crop rotations and contribute transparently to the SOC stock change model. In addition, activities such as fertilization and pest control, typically recorded in FMIS for compliance purposes, can be collected automatically, further simplifying the process.

What's more, the farm machinery itself serves as a veritable goldmine of data for these projects. Machine records offer invaluable information on practices, detailing what was done, when, input quantities, area worked, and even fuel consumption maps in some cases. These data can be sourced directly from agricultural manufacturers' cloud platforms or via specialized hardware devices.

Of course, it's important to note that not all practices are recorded in these systems, and not all farmers use such software. Therefore, providing an intuitive web or mobile interface for data collection remains crucial.

Illustration of some of MyEasyFarm's available API integrations for automatic data collection.

Illustration of some of MyEasyFarm 's available API integrations 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 data sets. 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 smooth process.

The same principle applies to soil information. The use of field boundaries simplifies the retrieval of spatialized soil data, such as SoilGrids, effortlessly facilitating project data requirements.

4. Overcoming the challenges of modeling and quantification

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 can be useful for the project developer to use a measure-and-measure approach.

Such an approach requires a scientific basis for modeling SOC changes. In addition, it requires the provision of the input data needed for modeling: soil data, climatic data, crop rotations and cropping practices, among others.

When it comes to quantifying GHG emissions, choosing the right emission factor and justifying the data used in modeling are crucial for project developers. The use of a digital MRV tool makes it possible to delegate such tasks.

In addition, it is important to note the importance of involving a third party to mitigate conflicts of interest that could potentially influence the results of carbon farming projects.

Thed-MRV tool streamlines operations for project developers, enabling them to concentrate on their core objectives. The tool automates work processes and facilitates scalability. In addition, it is essential to present information graphically, and once again, the MRV tool provides the necessary interface. To illustrate this aspect, an example report from MyEasyCarbon is provided, demonstrating how SOC stock changes are quantified and the difference between a baseline scenario and a project scenario.

Example of a SOC change modeling report within the digital MRV tool MyEasyCarbon with SIMEOS-AMG by AgroTransfert

Example of a SOC change modeling report within the digital MRV tool MyEasyCarbon with SIMEOS-AMG by AgroTransfert.

5. Strengthening collaboration and project management

As a project developer, you probably use a variety of strategies to implement your carbon farming or regenerative agriculture initiatives.

In some cases, you can anticipate the involvement of farmers to access their farms to help with data collection and reporting, as well as to access actionable insights. However, there are scenarios where you prefer a dedicated team to manage project scenarios and quantification modules, with the aim of minimizing disruption to farmers.

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

Illustration of the collaborative features available in a digital MRV tool such as MyEasyCarbon.

Illustration of the collaborative features available in a digital MRV tool such as MyEasyCarbon.

6. Conclusion and illustration with our digital MRV tool MyEasyCarbon

To illustrate the above, here is a block diagram of our MyEasyCarbon digital MRV tool that we offer to project developers:

Farm Management Information Systems (FMIS) are used to automate farm creation, associated field boundaries and obtain 3 years of historical data. This data can then be updated directly via our interface by an advisor or the project developer. Indeed, we have noticed, for example, that soil cultivation practices are often missing from the systems used by farmers, mainly for compliance reasons.

As a concrete illustration, spatial datasets are used to feed the SOC stock change model: depending on the farm location and field boundaries, we retrieve the dataset required for climate and soil information, which is then integrated with soil samples and field activity/management data.

Remote sensing technology, integrated into a dedicated module and transferred from CESBIO, facilitates the quantification of intermediate crop biomass with digital and verifiable data. In addition, remote sensing helps identify various agricultural practices such as crop rotations and intercropping.

The MyEasyCarbon platform offers an intuitive interface that enables project developers to manage their projects efficiently. In addition, it generates easily interpretable reports on quantified GHG emissions and SOC stock changes, which can be effectively communicated to various stakeholders.

Article written by Guillaume, Carbon Project Manager at MyEasyFarm.

Book a meeting to see an example of d-MRV with MyEasyCarbon.

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