We are seeking experts in GHG accounting, prescribed grazing, GIS & remote sensing, and soil science to participate in the Expert Peer Review and Public Comment processes for CarbonPlus methodology for grazing systems happening in June-August 2022.
Gisel Booman at the ESA symposium 23-27th May in person, or through https://twitter.com/BoomanGisel
Also, get the automated version for satellite imagery processing and full analysis! User guide with links to Jupyter Notebook on Github (Using Google Earth Engine to Predict Soil Or ganic Carbon using Soil Sample Data & Sentinel-2 Imagery)
Grasslands and pastures cover approximately one quarter of the land on Earth. Conventional cattle management has produced degradation and carbon loss on these ecosystems, whereas well managed grasslands can sequester carbon and provide a lot of co- benefits including increases in biodiversity, soil health, water holding capacity, ecosystem´s resilience and many more. More scientifically robust and cost effective methodologies are needed to account for changes in the carbon stocks and cobenefits, to open the door for economic incentives like carbon credits for the land stewards playing a regenerative role on Earth.
The CarbonPlus Grasslands credit is designed to significantly increase the amount of hectares regeneratively grazed worldwide. The methodology relies on remote sensing to substantially reduce the cost of monitoring carbon sequestration while maintaining location-based scientific rigor.
Methodology for GHG & Co-Benefits in Grazing Systems
The Methodology for GHG and Co-Benefits in Grazing Systems provides a holistic assessment of ecological state indicators for grasslands under regenerative grazing practices. Managed grazing, which involves carefully controlling livestock density and intensity of grazing, has been shown to provide a wide range of ecosystem benefits such as enhanced carbon sequestration, improved soil health, and increased water infiltration.
This methodology combines remote sensing data with in-field measurements to provide high quality estimates of soil organic carbon stock and measures additional ecological co-benefits such as animal welfare, ecosystem health, and soil health.
Methodology Process
Soil samples collected at strategic points across the property are tested for soil organic carbon, bulk density, and soil health variables.
Remote sensing data and the lab-tested soil samples are used to estimate soil organic carbon at unsampled locations using statistical models, machine learning, and spatial interpolation.