A summary of tools and methodologies used to calculate Scope 3 emissions and removals for customers using Regrow's GHGp-Aligned Reporting Feature.
A. Emissions
This is a summary of tools and methodologies used to calculate Scope 3 emissions. For all emissions values, uncertainty is provided using a standard error methodology. Please see section A.4 for more details.
A.1: Methods, data sources, and assumptions used to calculate scope 3 emissions (by scope 3 category):
The following methodology does not comment on allocation methods for scope 3 emissions. It is the responsibility of the reporting company to ensure discussion of allocation is complete according to GHGp reporting requirements. Furthermore, the reporting company should ensure the following methodology is reported under the appropriate Scope 3 category.
Regrow models on-farm emissions using the Denitrification-Decomposition (DNDC) model, which is a biogeochemical model that is based on first principles of soil biogeochemistry and estimates nutrient cycling in the soil, including dynamic soil responses to practice change adoption. The model predicts greenhouse gas emissions, soil carbon sequestration, and other environmental outcomes and feedbacks of crop production, such as crop growth and yield, based on a series of environmental drivers (crop management, weather, and soil data, cultivar etc.).
Calculation of emissions begins with monitoring a continuous series of agricultural practices at the crop field scale that both inform and drive DNDC. For more information on the data sources used to catalog field-level historical events and create crop field boundaries, please refer to section A.2. Earth observation satellite data along with local climate and weather inputs are compiled over each and every field boundary and implemented in Regrow algorithms to identify agricultural practices or events.
The methods to detect these practices or events are built on peer-reviewed remote sensing science publications and further advanced by Regrow’s expertise and technologies (OpTIS - Operational Tillage Information System). These include information on crop types and cycles, cover cropping, and tillage events and intensity.
Each practice or event, and the compounding factor of multiple events, will affect emissions. For example, adoption of multiple practices such as crop rotations, planting consistent cover crops, and limited to no tillage will have positive effects on soil health and be reflected in the estimated emissions. This comprehensive field level history is passed to the DNDC soil biogeochemical model, which quantifies the effects of these practices on critical soil properties and processes, so that Regrow can simulate GHG emissions (measured in tonnes of carbon dioxide equivalent, or CO2e) consisting of the following components: CH4, N20 (indirect/direct), and soil organic carbon (SOC). Components are converted to CO2e by their global warming potential following IPCC 2014 AR5 values.
N2O emissions from DNDC cannot be explicitly traced to a specific nitrogen source (i.e. fertilizer N vs mineralized N). As such, direct and indirect N2O emissions as displayed in the product are inclusive of all nitrogen sources in the soil, not exclusively that from fertilizer application.
A.2: For each scope and scope 3 category, a description of the types and sources of data, including activity data, emission factors and GWP values, used to calculate emissions, and a description of the data quality of reported emissions data
Regrow utilizes a number of third-party sources as inputs to the OpTIS and DNDC models. In addition, OpTIS data are inputs to the DNDC model.
Field events: Regrow’s OpTIS system catalogs a complete history of events for each crop field in an area of interest using a suite of satellite earth observation data provided primarily by the National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA). The data are sourced primarily from the NASA Landsat and ESA Sentinel missions which provide observations from 10m to 30m resolution and 8-10 day intervals. These data provide continuous high resolution observations (approximately 4 to 36 samples per acre) at temporal intervals that maximize data retrieval opportunities. All remote sensing data undergo robust quality assurance and control to properly account for cloud cover and atmospheric effects prior to algorithm implementation. Yearly median plant and harvest dates are estimated at the geohash 3 level using a time series of Landsat and Sentinel normalized difference vegetation index (NDVI) observations, and these dates are used to parameterize the cover crop and residue cover mapping.
Crop field boundaries: Field boundaries are delineated from Regrow’s machine-learning (ML) parcel identification algorithm or open-source datasets, such as the USDA Crop Sequence Boundaries and Common Land Units. These are then further bolstered and refined using Regrow’s ML algorithm which capitalizes on remote sensing time series data. Field identification and boundaries account for factors that may bisect or interfere with detection and monitoring of agronomic practices (such as roads, waterways, or buffer strips) to ensure only data related to field level practices are used in calculations.
Precipitation: OpTIS uses PRISM (PRISM Climate Group) and ERA 5 in Canada to account for soil and crop residue moisture effects.
Yield: Regrow utilizes primary data, publicly available data sources (e.g. USDA ARMS and NASS surveys, USGS NAWQA, STATCAN for data for Canada) as well as partnerships in key sourcing regions. The scientific model DNDC also models crop growth and yields. The Regrow platform will utilize the data source with the highest level of certainty per its determination.
A.3: For each scope 3 category, the percentage of scope 3 emissions calculated using data obtained from suppliers or other value chain partners
Emissions factors provided in Sustainability Insights do not include data from customer suppliers or other value chain partners. It is the responsibility of the reporting company to ensure these scope 3 emissions data are obtained and accounted for according to GHGp reporting requirements.
A.4: The uncertainty range associated with reported scope 3 emissions, with justification for how reported emissions use conservative assumptions and values
Regrow has taken a standard error approach to calculating uncertainty for emissions and removals values in Sustainability Insights. Within emissions, standard error is provided for each constituent component of GHG emissions (CH4, direct N2O, indirect N2O) for every region and crop of interest. This is a measure of the variability of the estimated emissions that indicates how much the estimated emissions deviate from a true population regional mean. The variability of standard error will increase as supply shed size decreases.
This calculation methodology is a type of scenario uncertainty, as described in Section 16.6.1 of the draft GHGp Land Sector and Removals Guidance (LSRG). It is important to note that SI uncertainty does not provide an estimate of DNDC structural uncertainty that Regrow uses in the MRV product.
B. Removals
This is a summary of tools and methodologies used to calculate removals in the form of soil organic carbon sequestration. For all removals values, uncertainty is provided using a standard error methodology. Please see section B.4 for more details.
B.1: Methods, data sources and assumptions used to calculate scope 3 removals
SOC is the ability of soil to store, or sequester, greenhouse gasses in the form of carbon. Regrow uses DNDC to measure dSOC, which is the difference of soil organic carbon, by taking the difference of soil organic carbon stock values between 2 points in time (i.e. annual difference). The soil organic carbon stock values encompass the total soil carbon in the soil pools down to a specified depth, but does not include any residue (litter) pools.
Please refer to section A.1 for more details on the data and processes employed by DNDC.
B.2: Systems and procedures for long-term monitoring of carbon pools owned/controlled by the relevant entities in the value chain corresponding to reported scope 3 removals
SOC removals provided in Sustainability Insights do not include data from customer suppliers or other value chain partners, and therefore does not provide complete chain of custody information or traceability from farm to end customer. If relevant, it is the responsibility of the reporting company to ensure these scope 3 data are obtained and accounted for according to GHGp reporting requirements.
B.3: Description of the types and sources of data, including activity data and emission factors, used to calculate scope 3 removals, and a description of the data quality of reported removals data.
Regrow uses the biogeochemical model (DNDC) to model both on-field emissions and removals. Please refer to section A.2 for more details on the types and sources of data used in OpTIS and DNDC in Regrow calculations.
B.4: The uncertainty range associated with reported scope 3 removals, with justification for how reported removals use conservative assumptions and values
Regrow has taken a standard error approach to calculating uncertainty for emissions and removals values in Sustainability Insights. Within removals, standard error is provided for dSOC (YoY delta of soil organic carbon sequestration) for every region and crop of interest. This is a measure of the variability of the estimated emissions that indicates how much the estimated emissions deviate from a true population regional mean. The variability of standard error will increase as supply shed size decreases.
This calculation methodology is a type of scenario uncertainty, as described in Section 16.6.1 of the draft GHGp LSRG. It is important to note that SI uncertainty does not provide an estimate of DNDC structural uncertainty that Regrow uses in the MRV product.
C. Land Use & Land Tracking
This is a summary of tools and methodologies used to calculate land occupation. Please note that this does not include any emissions factors associated with land use change.
C.1 Data sources, methods, and assumptions used to quantify selected land tracking metric(s)
Land occupation is provided in the GHGp reporting feature for each crop and region combination configured in a user’s Sustainability Insights instance. This area is calculated using OpTIS, which utilizes satellite earth observation data provided primarily by the National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA). The data are sourced primarily from the NASA Landsat and ESA Sentinel missions which provide observations from 10m to 30m resolution and 8-10 day intervals. These data provide continuous high resolution observations (approximately 4 to 36 samples per acre) at temporal intervals that maximize data retrieval opportunities.
Please note that region and subregion configurations in Sustainability Insights may be an overestimate of the true land area occupied by entities in a customer’s scope 3 agricultural supply chain. It is the responsibility of the reporting company to ensure these land occupation data are scoped and accounted for according to GHGp reporting requirements.