Agrecalc: Modelling methodology for carbon footprinting
The Agrecalc team is committed to maintaining a consistent, defensible model of farm emissions based on the latest published reporting standards and scientific research. Our modelling methodology for carbon footpriting is based on greenhouse gas reporting guidelines published by the IPCC for national inventories. We also incorporate more specific national figures from the UK National Greenhouse Gas Inventory.
Our methods are broadly aligned with PAS 2050:2011, a standard for the robust quantification of product carbon footprints. In the interest of transparency, the following sections detail the methodologies we use to quantify farm greenhouse gas emissions. Agrecalc broadly aligns with LCA guidelines defined by ISO 14044 and PAS 2050 standards as well as IPCC guidelines for greenhouse gas reporting for emissions to the farm-gate.
Agrecalc can be used to monitor emissions against FLAG SBTis and is broadly aligned with the current public draft of the GHG Protocol Agricultural Guidance. We cover areas of emissions and sequestration laid out as part of the SBTi FLAG pathway, with the exception of land use change emissions from wetlands, which will be incorporated in 2024.
Agrecalc combines higher IPCC Tier methods to develop own modelling methodology for carbon footprinting
The IPCC methods for greenhouse gas reporting are split into three tiers of increasing complexity and specificity. Tier I reporting standards use default figures published by the IPCC which provide a general estimate of greenhouse gas emissions, but likely miss important sources of variance.
Tier II reporting standards are slightly more specific than Tier I, as they use national research to generate country-specific emission factors. Finally, Tier III reporting uses process-based models to predict emissions with the greatest accuracy and system-specificity. The IPCC published its original guidelines in 2006 and updated these guidelines in 2019. Our model reflects the latest published guidelines, and we aim to quickly assimilate new guidance to keep our model at the forefront of peer-reviewed published science.
To balance model performance and data requirements, our model makes use of higher Tier methods for large emissions sources. Default methods are used where higher Tier methods would increase data requirements beyond what is generally available on a farm, for smaller emission sources, and for emission sources where more research is needed to improve the resolution of emission factors.
