COMET farm tool

Contact information: help desk or appnrel@colostate.edu ( for any questions or comments or for any training requests.)

 

COMET API Upload Page: (linked)  UI site which you could use to upload xml files directly 

Link to instructions: https://gitlab.com/comet-api/api-docs/-/blob/master/API%20File%20IO%20Instructions.docx

 

GitLab Repository for API Documentation (linked):

 

  • COMET-Farm API File Description - .xlsx: Explains in detail the input and output .xml file structure, including allowed values. : (added to GHG module folder)

https://docs.google.com/spreadsheets/d/1Veu-TWEVtbebjpgZM9nsN785ChE_BcyK/edit?usp=share_link&ouid=108770921204628315366&rtpof=true&sd=true

 

  • 50 requests limits per day : Please contact COMET Technical Support Team at appnrel@colostate.edu if you have large-scale service needs from the API above a limit of 50 model run requests per day. => has an associated cost.

    • Users are allotted 50 model runs per day for free. The model is run for scenario within each defined parcel. (max 11, 1- baseline [required] , max 10 future scenarios per project [1 required]). 

    • For example, if you have 1 field that has with 11 scenarios (baseline + 10 scenarios), that would be a total of 11 model runs and would be within the daily free limit. 

 

  • Generating API Input Data: expects consumer's to write their own scripts to create XML input files

  • API Output Data: API users can only view a COMET-Farm report as an XML output file.

 

  • COMET’s use outside the US:

    • COMET-Farm is unavailable at the international level at this point, even with local soil and climate data.

    • A similar tool created first by the team called the Carbon Benefits Project that is available for international use.

    • This tool utilizes primarily IPCC methods whereas the principle model in COMET-Farm is the Daycent model for soil carbon, but COMET also uses a set of empirical methods and equations for the animal agriculture, forestry, and agroforestry components.

    • Training materials on the Carbon Benefits Project that uses the IPCC inventory Good Practice Guidelines published in 2006: CBP_Training...  (downloads folder as a zip)

 

2006 IPCC Guidelines for National Greenhouse Gas

 

2019 Refinement to the 2006 IPCC Guidelines:

  • to provide an updated and sound scientific basis for supporting the preparation and continuous improvement of national GHG inventories;

  • not to revise the 2006 IPCC Guidelines, but update, supplement and/or elaborate the 2006 IPCC Guidelines where gaps or out-of-date science have been identified.

Methodologies: https://www.ipcc.ch/site/assets/uploads/2018/02/ipcc_wg3_ar5_annex-ii.pdf 

 

Carbon Benefits Project: http://www.carbonbenefitsproject.org 

Access tool: link 

Training: link 

Contact: link 

Quick Guide

  1. The methodology the tools use

  2. Input and output data 

  3. A step-by-step guide to using the tools

  4. Advantages of using the tools

 

  • The tools are free to use and user friendly. 

  • Could not find an API for this tool

  • The CBP modeling tools were developed by Colorado State University and partners under a Global Environment Facility co-financed project implemented by the United Nations Environment Program. 

 

Tools available:

  1. Simple Assessment: quick estimate of C and GHG impact (learn more)

  2. Detailed Assessment: more detailed analysis (learn more)

  3. Socio-economic tools: (learn more)

    1. online Driver-Impact-Response Analysis and the Cost Benefit Analysis.

Glossary and Definitions: https://cbp.nrel.colostate.edu/PimHelp/Index  

 

Modelling and Measurement: (add link)

Within the Modelling Component, ISRIC provided global soil information for carbon stock assessment across the range of world climate zones, soil types and land use. The resulting SOCref data can be used for IPCC Tier I (national scale) level inventory assessments with the CBP tool's  'simple assessment' option in data poor regions; however, the associated uncertainties are very large.

More detailed data, derived from field monitoring and long-term chronosequence studies, are needed at project-level to verify projections of the process-based models; such data sets were compiled and used for model validation by the respective test case partners.