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Estimating soil organic carbon changes: is it feasible?
The document discusses the feasibility of estimating soil organic carbon (SOC) changes, emphasizing that success depends on data availability, suitable modeling methods, and acceptable uncertainty levels. Tools like Comet-Farm are highlighted for analyzing conservation impacts on SOC at a farm scale, utilizing regional data on soils, climate, and management practices. The document also addresses challenges in areas with limited data and suggests computational approaches for carbon assessment.
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Estimating soil organic carbon changes: is it feasible?
- 1. ESTIMATING SOIL ORGANICCARBON CHANGES: IS IT FEASIBLE? Eleanor Milne, Mark Easter and Keith Paustian The Natural Resource Ecology Laboratory, Colorado State University (CSU) GSOC17 - Global Symposium on Soil Organic Carbon 21-23 March 2017 - FAO HQ - Rome, Italy
- 2. ESTIMATING SOC CHANGES:IS IT FEASIBLE? It depends on: Data availability for soils, climate, land use and management (historical and current) for the scale you are working at The availability of suitable models/methods for those systems The level of uncertainty you are willing to accept! Where in the world you are working
- 3. MODELS: CENTURY ANDDAYCENT Century (monthly) Daycent (daily) Parton et al. 1987
- 4. EXAMPLE OF WHATCAN BE DONE WHEN YOU HAVE THE DATA AND THE MODELS
- 5. COMET-FarmTM What is it? Web-basedGreenhouse Gas Inventory Tools for Land Use Designed for Conservation Scenario Analysis •Works at the farm scale •Estimate changes in SOC using Daycent (plus other models) •Can consider effects of different conservation practices on SOC
- 6. DATA FOR COMETFARM Soils – SSURGO (web-served) (1:12,000 – 1:63,630) Climate – NARR (NCAR/NOAA) Regional specific land use/Management Practices National Resources Inventory (NRI) USDA/ERS Cropping Practices Survey NRCS manure management CSRA – regional LU and management surveys User input of detailed management for recent (> yr 2000) and projected practices
- 7. Equation Factors, USDA Methods, IPCC COMET-FarmTM How it works Historic Rotations NRI,Cropping Practices Survey, CSRA Climate & Soil NARR & SSURGO Web Interface CSU Server Empirica l Models Outputs Results Specific Location Specific Activities
- 8. COMET-FarmTM & COMET- PlannerTM Whatare they? Web-based Greenhouse Gas Inventory Tools for Land Use Designed for Conservation Scenario Analysis
- 9. Equation Factors, USDA Methods, IPCC COMET-PlannerTM How it works Historic Rotations NRI,Cropping Practices Survey, CSRA Climate & Soil NARR & SSURGO Web Interface CSU Server Empirica l Models Outputs Average Mitigation Effect Generalized Region Conservatio n Practices
- 10. COMET-Farm Team www.comet-farm.com www.comet- planner.com USDA-NRCS AdamChambers (Portland) Greg Johnson (Portland) USDA-ARS Steve DelGrosso Colorado State University USDA-OCS Marlen Eve (DC) Keith Paustian (Team Leader) Shawn Archibeque Allison Brown Kevin Brown Mark Easter Ram Gurung Melannie Hartman Adriane Huber Ben Johnke Ken Killian Stephen Ogle Bill Parton Geoff Pietz Matt Stermer Ben Sutton Amy Swan Crystal Toureene Sobha Velayudhan Steve Williams Justin Ziegler USDA-OCE Carolyn Olson (DC) Marci Baranski (DC)
- 11. Partner institutions: Colorado StateUniversity, USA Joint Research Center – European Commission Spanish National Research Council (CSIC), Spain Institut Français de Recherche pour le Développement (IRD), France University Court of the University of Aberdeen, Scotland COMET-GLOBAL •In progress •Using the same approach for several places across the globe •Develop a globally applicable tool operational at the farm entity •Use Daycent, RothC and Ecosse
- 12. AREAS WHERE DATAIS LIMITED – • Assemble data sets, fill gaps • Parameterise and validate the models • Example – the GEFSOC project
- 13. AREAS WHERE DATAIS LIMITED – GEFSOC Kenya Brazilian Amazon Indogangetic Plains, India Jordan
- 14. Dynamic SOM modelslinked to spatial data bases Spatial Databases Simulation model Spatial Results
- 15. Data Needs Parameterisation ModelParameterisation: 1. Any LTE or chronosequence data for model evaluation - Ideally soil carbon + crop yields and/or plant production. - Soil type (sand/silt/clay fraction + bulk density) - Land use history going back 100 yrs if applicable or to the time of land use change from native vegetation. - Native vegetation type - tillage, fertilization, organic matter additions, irrigation amounts and timing. - crops, dates of planting and harvest, extent of residue removal. - timing and extent of ditch and/or tile drainage, if applicable
- 16. Soils Data A soilsmap with - location and extent of soil type, - drainage status, - content of clay, sand and silt, - SOC content, - Bulk Density GEFSOC DATA NEEDSData Needs Model Runs
- 17. Data Needs ModelRuns Climate - Precipitation - Max temperature - Minimum temp - Mean monthly precipitation - Mean monthly max temperature - Mean monthly min temp
- 18. Land Use Land useand land use transitions - Going back 100 or 50 yrs - Or to the point land use was changed from native vegetation Land Management Cropping practices (crop rotations, tillage, residue management, fert inputs etc.), grassland condition/management, forestland management (tree types, wood removal) etc. Data Needs Model Runs
- 19. Figure 14. Managementsequence diagrams for MLRA 52. System abbreviations are as follows: HG = heavy grazing, GH = grassland hay, IASG = irrigated alfalfa- small grain (conventional tillage), IASGN = irrigated alfalfa-small grain (no tillage), RG = rotational grazing, CSG = continuous small grains, DASG = dryland alfalfa- small grain, FSG = fallow-small grain (conventional tillage), FSGO = fallow-small grain-oilseed, FSGM = fallow-small grain (minimum tillage), FSGN = fallow-small grain (no tillage), CRP = Conservation Reserve Program.
- 20. SOC stocks (t Cha-1) 1990 2000 2030 Agricultural expansion SOC stocks (t C ha-1) 19901990 20002000 20302030 Agricultural expansion Estimated SOC changes in a frontier area of the Brazilian Amazon Cerri et al. 2007. Ag Ec Env.
- 21. NON-DYNAMIC APPROACHES Whatif you don’t have data to parameterise or populate models? Situation in many areas outside N. America and Europe Take a computational approach – IPPC method Several calculators available Example The Carbon Benefits Project tools
- 22. •Two tools utilisingthe IPCC approach • Simple Assessment Tier 1 • Detailed Assessment Tier 2 • Aimed at landscape scale assessments • NET GHG assessments includes estimates of SOC stock change • Takes no account of land use history so doesn’t capture long term dynamic changes • BUT simple to use, just needs land use and management info (soils and climate defaults provided)
- 23. Initial Land Use BaselineScenario Project Scenario Project activities: - Reduced grazing, protection of rangelands - Reforestation/Afforestation Carbon Benefit
- 26. ESTIMATING SOC CHANGES:IS IT FEASIBLE? It depends on: Data availability for soils, climate, land use and management (historical and current) for the scale you are working at The availability of suitable models/methods for those systems The level of uncertainty you are willing to accept! Gaps in our understanding of the determinants of C sequestration potential
- 27.
- 28. GRAZING LANDS INSUB-SAHARAN AFRICA P and the role it plays in C sequestration in C4 grasslands Effect of ultraviolet radiation on decomposition Termites- how they affect the amount and distribution of OM and C in soils Shifts between shrublands and grasslands & impact on above and below C stocks Rate of C sequestration and saturation levels Milne et al. 2016 Environmental Development
- 29. THANK-YOU! COMET Farm -http://cometfarm.nrel.colostate.edu/ GEFSOC – Vol 122, Issue 1 Agriculture Ecosystems and Environment Carbon Benefits Project - http://cbp-web1.nrel.colostate.edu/ Sub-Saharan Africa report - www.vivo.colostate.edu/lccrsp/reports/GrazingLandsLivesto ckClimateMitigation_Paper1_Final6Aug2015editedv4a.pdf and Milne et al. 2016 Environmental Development Vol 19, 70- 74