Methods

This Methodology generates carbon credits by reducing nonpoint source contamination of watersheds, thereby avoiding the increased greenhouse gas emissions from the status-quo infrastructure construction and electricity use associated with industrial drinking water and wastewater management and treatment.

Kulshan Carbon Trust (KCT)and San Juan Island Conservation District have collaborated to create a method for measuring the carbon impact of conservation burns. The protocol focuses on improving pile construction and reducing emissions.

The “CarbonPlus Methodology for Grazing Systems v1.0” is a measurement-based approach coupled with remote sensing data analysis that can be used to monitor the changes in SOC stocks and co-benefits over time, to generate carbon credits.

This methodology was developed in-house by the Regen Science Team, addressing challenges and experiences from several stakeholders, including land stewards, methodology developers, soil scientists, project developers, and other experts in the space of VCMs.

Version 1.0 of the methodology has gone through an exhaustive formal peer review done by three experts in the fields of soil sciences, geostatistics, and GHG accounting. We welcome constructive comments and debates for continued improvements!

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This Environmental Stewardship methodology is designed to support the use of high-density, short-duration rotational targeted sheep grazing in vineyard systems to improve ecosystem functioning through active management of the soil and herbaceous cover in the vineyard understory. As with other Environmental Stewardship methodologies, the environmental benefits are implicit in the practice. Projects being developed under the Methodology for Grazing in Vineyard Systems are expected, based on prior research, to result in positive ecological outcomes. This methodology is new and likely imperfect and will be improved as new projects are created and lessons are learned.

This methodology protocol proposes the use of remotely sensed multispectral imagery and soil sample results to train an Artificial Neural Network (ANN) to monitor changes in Soil Organic Carbon (SOC) stocks, within a project area, through time.