1 / 32

Nationwide Biomass Modeling of Bio-energy Feedstocks

This study aims to understand the spatial distribution of current and potential biofuel/bio-energy feedstock resources across the country by developing national geo-referenced grids that describe their productivity patterns.

eantoinette
Download Presentation

Nationwide Biomass Modeling of Bio-energy Feedstocks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. PRISM GROUP Nationwide Biomass Modeling of Bio-energy Feedstocks Chris Daly, Mike Halbleib, Matt Doggett David Hannaway Sun Grant Western Region GIS Center Oregon State University Corvallis, Oregon, USA

  2. Introduction • GIS Program Objective: Gain an understanding of the spatial distribution of current and potential biofuel/bio-energy feedstock resources across the country • Envisioned outcome: • A series of national geo-referenced grids that describe the actual and potential productivity patterns of various feedstocks

  3. Methods to Accomplish This Currently • Collecting production information from field trials and the literature; some regions developing models to make spatial estimates Issues • Data representativeness- Data are taken from relatively few locations under widely varying management practices and in different years, and span small portions of the environmental gradient – very messy, difficult to extrapolate from data alone • Regional consistency - Difficult to coordinate regional results into a national, “wall-to-wall” assessment that is consistent across the country • “Potential” not the same as “existing” - Unclear how potential biomass production of new crops will be estimated nationwide, esp. under future climates

  4. An Environmental Suitability Modeling Framework Two main objectives: • Develop gridded estimates of current and potential feedstock resources across the entire conterminous US, constrained by climate, soil, and land use patterns • Provide a spatial framework for biomass data collection and field trials: What additional data do we need and where?

  5. Percent of Maximum Yield SSURGO Soil Maps Environmental Model PRISM Climate Maps Internet Map Server Biomass Yield Observed Yield Terrain/Land Cover Constraints

  6. Environmental Model “Limiting Factor” Approach Relative Yield (0,100%) = Lowestproduction resulting from the following functions: • Water Balance • Winter Low Temperature • Soil Properties pH Salinity Drainage

  7. T Monthly Water Balance Model P ETa KS Kcmid AWC TAW Droot Dr

  8. Water stress coefficient • KS = (TAW - Dr) • TAW = = total avail. water cont. = AWC Droot • AWC = avail water content (NRCS data) • Droot = rooting depth* • Root zone moisture depletion • Dr = Drm-1 + (Eta(m-1) – Pm-1) • ETa = actual evapotranspiration • P = precipitation • Evapotranspiration • Eta(m-1) = ET0(m-1) KS(m-1)Kcmid • ET0 = Reference evapotranspiration (based on PRISM climate data) Kcmid = Crop coefficient, mid-growth stage* Monthly Water Balance Model * User input

  9. Temperature coefficient • C2 = (max*-Tday)/max*-optimum*) Monthly Water Balance Model * User input Winter Wheat Monthly Relative Yield (water balance) RY = KS (C2L e ((L/R)(1-C2R))) Water stress coefficient Temperaturegrowth curve

  10. Calculating Final Water Balance Relative Yield Final Water Balance Relative Yield GrowthPeriod* N=3* Floating N-month* maxyield Final RY = N-month max average RY within the Growth Period * User input

  11. Winter Temperature Constraint Function High End - Chilling Requirements Low End - Winter survival Winter Wheat

  12. Soil Constraint Functions Soil pH Soil Salinity Winter Wheat Soil Drainage

  13. Dryland Winter Wheat, Local Varieties Environmental Model Relative Yield

  14. Percent of Maximum Yield SSURGO Soil Maps Environmental Model PRISM Climate Maps Internet Map Server Biomass Yield Observed Yield Terrain/Land Cover Constraints

  15. “Usable” Land Cover and Terrain Masks Land Cover Forest, Urban, Tundra omitted Ag, Grass, Shrub, Savanna allowed Terrain Slopes > 7% omitted Local high ridges and peaks omitted

  16. Environmental Model Relative Yield Dryland Winter Wheat, Local Varieties, “Usable” Land

  17. RMA Reported Yield, 2000-2009 Mean Dryland Winter Wheat, All Varieties/Management, County Average

  18. RMA Reported Yield, 2000-2009 Mean Dryland Winter Wheat, All Varieties/Management, “Core” Counties Only • “Core” Counties • 30-30-30 • ≥30 model cells/county • ≥30% of county “usable” • ≥30 RMA reports/county

  19. National Relative Yield vs. RMA Reported Yield Dryland Winter Wheat, All Varieties/Management, “Core” Counties Only

  20. Final Winter Wheat Straw Yield Dryland Winter Wheat, Natl. Regr., All Varieties/Mgmt, “Usable” Land 0.4 Harvest Index

  21. Using PRISM to Transform Relative Yield to Actual Yield For each pixel, PRISM develops a regression between modeled relative yield and RMA county-average yield. The result is a yield map that has been locally transformed from relative yield to actual yield. Observed Yield (Bu/ac) Modeled Relative Yield (% of optimum)

  22. Final Winter Wheat Straw Yield Dryland Winter Wheat, PRISM Regr., All Varieties/Mgmt, “Usable” Land 0.4 Harvest Index

  23. National Relative Yield vs. RMA Reported Yield Dryland Winter Wheat, All Varieties/Management, “Core” Counties Only

  24. Dryland Winter Wheat, National Regr., All Varieties/Management Outlier Counties

  25. RMA County Average Yield for Dryland Winter Wheat Higher than Modeled Dryland Wheat PRISM Precipitation Cropland Data Layer (30 m) Grant County, Washington Irrigated Crops • RMA county average reflects higher precipitation area in northern corner of county • Modeled county average reflects nearly all land in county

  26. RMA County Average Yield for Dryland Winter Wheat Higher than Modeled Corn/Soybean (white) Dryland Wheat (brown) Wood County, Ohio SSURGO Soil Drainage (4 km) Cropland Data Layer (30 m) • Native soils very poorly drained, reduced yields in model • Actual - field modification of soil drainage

  27. RMA County Average Yield for Dryland Winter Wheat Higher than Modeled Corn/Soybean (white) Dryland Wheat (brown) Wood County, Ohio Cropland Data Layer (30 m) SSURGO Soil pH (4 km) • Native soils very acidic, reduced yields in model • Actual - field modification of soil pH

  28. Environmental Model Relative Yield Switchgrass, Lowland Varieties, VERY PRELIMINARY

  29. Switchgrass Modeled Yield Maps Lowland Cultivars (Tulbure et al., In Prep) All Cultivars (Wullschleger et al., 2010)

  30. PRISM GROUP Next Steps • Update environmental input grids and further refine model • Update PRISM and SSURGO datasets • Model on a time series, not just long–term mean data • Soil constraints – perhaps don’t include in model - think of as costs of management (P. Woodbury) • Collaborate with ORNL, GIS regions, and species teams • Share data • Share expertise • Collaborate on final products • KDF connections

  31. PRISM GROUP Next Steps • Develop potential biomass maps for nationally important feedstocks Corn stover Switchgrass Other small gain residue Willow, Poplar Energycane Sorghum Grasses on CRP land Miscanthus

  32. PRISM GROUP Sun Grant Yield Data are Essential • Validate, transform, and improve modeled yields • No RMA data for many crops • Use modeled maps to identify locations where field validation is needed (e.g., Great Plains precipitation gradient), and where data might be suspect • The more (good) data we have, the better our maps will be

More Related