Assessing Global Land Regeneration
Comparing the UNCCD & Regen Network Approaches
By Dr. Gisel Booman, Alyssa Pace, and Brecht Deiremaeker
Soil is the foundation for our economic and planetary well being. Yet, the most popular practices of modern-day farming are obliterating soil health.
Degenerative agriculture erodes 75 billion tons of topsoil every year, translating to $400 billion in repair costs for farmers and society (Lal, 2001). Worse yet, degenerative practices release greenhouse gases from our soil when they could be sequestered underground with regenerative practices.
Regen Network is working turn this system around. Instead of plummeting into a spiral of economic loss and malnourished farms, our infrastructure positions farmers as stewards of the land by rewarding their land’s change in state.
Our team uses the phrase “change in state” often — but what exactly does it mean? Well, it’s the core of how our Science Team records and monitors data on the quality of our pilot project’s land over time. We are in the process of developing monitoring protocols for such changes in ecosystems with what we call Ecological State Protocols (ESP). We are constantly reviewing our methods and finding new ways to perform monitoring, because as we build our product, we want to produce the most scientifically robust information.
We aren’t alone in our quest of monitoring change in state. The United Nations Convention to Combat Desertification (UNCCD) works primarily to save our global soils from productivity loss and thinning vegetative cover with their Land Degradation Neutrality (LDN) tool. Applied to real-use cases like the Rio Conventions (UNCCD, CBD, and UNFCCC), FAO and GEF, LDN is crucial for reporting mechanisms and processes accounting for status and trends in land degradation, restoration, and carbon stocks — very similar to what Regen Network is working to monitor.
While Regen Network has our own approach, we could see our work bolstering UNCCD’s LDN and other land monitoring tools—anywhere, at any scale of analysis.
How Can Regen Network Contribute?
More Indicators for Targeted Monitoring
UNCCD defines the conceptual framework of LDN as “a state whereby the amount and quality of land resources, necessary to support ecosystem functions and services and enhance food security, remains stable or increases within specified temporal and spatial scales and ecosystems.”
Their Sustainable Development Goals (SDG) indicator for LDN is formulated by the proportion of degraded land over the total land area (in hectares or km2).
This formula for land condition results in a binary classification: degraded or not degraded. The classification is based on the three sub-indicators:
- Land cover change.
- Land productivity.
- Carbon stocks above and below ground.
Says the UNCCD about their LDN SDG-indicators, “The sub-indicators are few in number, complementary and non-additive components of land-based natural capital and sensitive to different degradation factors. The One Out, All Out (1OAO) principle is applied: if one of the sub-indicators is negative (or stable when degraded in the baseline or previous monitoring year) for a particular land unit, then that land unit would be considered as degraded subject to validation by national authorities. This rule is applied as a precautionary measure, because stability or improvements in land condition in any of the three indicators cannot compensate for degradation in the others.”
In other words, the final measure for a given territory is the proportion of land that has been classified as degraded — but not to the extent of how degraded in terms of soil quality or any other quantifiable parameter.
While the LDN is focused on measuring Land Degradation, Regen Network is developing tools for assessing Land Regeneration of ecosystems. Within that framework, we define two main indicator groups that cover 14 factors rather than 3, and are categorized by time needed to assess the changes (measured through remote sensing, IOT, and user-derived information).
The first group, defined as Short Term Assessment Indicators, are land management practices that can be monitored through remote sensing and show changes towards (or against) land regeneration within 1 year.
The second group, Long Term Assessment Indicators, includes those that need longer periods of time (2-5 years) to show noticeable changes in soil parameters and environmental health.
We see potential to build on UNCCD’s framework by developing algorithms that quantify each indicator to obtain spatially explicit, accurate data. This will be achieved in one of two ways:
- Directly, with strong correlations between remote sensing algorithms and ground truth, or
- Indirectly, by relating remote sensing-measurable parameters to empirical values per unit area, considering climate and geographic location of the farm as potential drivers.
For management practice indicators where we cannot directly link quantitative measures of land regeneration, we will provide expert peer-reviewed and scientific-based rankings to qualify changes in management practices according to the degree of contribution to land and soil regeneration. Ultimately, these indicators will be tangible for decision-makers seeking to improve their land management practices.
Facilitating Worldwide Data Calibration and Validation
UNCCD encourages land stewards around the world to check and validate local data results obtained from SDG-indicator remote sensing. Unfortunately, most developing countries find their local databases to have incomplete, outdated, or unreliable data.
Furthermore, UNCCD mainly obtains data for calculating their 3 SDG-indicators from global datasets that have spatial resolutions varying between 250m-1km. These include the European Space Agency’s Climate Change Initiative Land Cover dataset, which provided annual global land cover maps at 300m spatial resolution from 1992 to 2015; the Joint Research Centre’s Land Productivity Dynamics (LPD) dataset which provides consistent global LPD maps at 1km spatial resolution; and the ISRIC’s SoilGrids250m which derives SOC average (ton/ha) to 30 cm.
As we know, contextual reality on the ground can change from meter to meter. The higher our data resolution, the more accurate our conclusions can be. To measure the diverse indicators mentioned above, Regen Network is developing algorithms which account for regional variations in climate, soil and management practices. These algorithms rely on the latest open source satellite imagery, higher spatial resolution imagery from radar and multispectral satellite sensors (10–20 meter pixels), machine learning, and GIS spatial statistics. Our preliminary analysis show very promising results, which you can find in our past blog New Insights Into Till/ No-Till Monitoring Protocol. Complementary data from drones, field sensors and local sampling will also be included in the analysis when necessary in order to assure accuracy beyond 95% for each indicator.
Regen Network incorporates comprehensive data collection from ecosystems around the world, creating a complete database to enable unprecedented, high-accuracy validating indicators.
Great Potential for Collaboration
The Regen Network framework for land regeneration identifies and quantifies a shift in short term and long term assessment indicators. These indicators show a detailed picture of the ecological state and change of state of an agroecosystem. We applaud the UNCCD for working to tackle one of the most pressing problems facing humanity, and invite dialogue to explore how we might co-develop indicators and protocols to increase the impact of participating programs. And please, if there is any way that we have misrepresented the work of the UNCCD, let us know.
Permissioned data and open protocols are a cornerstone of our platform, meaning that farmers own their own data and scientists and academics can comment on the integrity and soundness of the science. Regen Network will only succeed in its aims through collaboration with a diverse set of partner organizations. We look forward to the opportunity to partner with the UNCCD, and invite the best thinking of our broad community to build the most robust and accurate techniques. If you are part of an organization that has gathered field data, have GIS information on your fields, or are interested in collaborating in the creation of these indicators: don’t hesitate to reach out to us!
