1. 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
GROUP
PRISM

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. Biomass Yield Internet Map Server 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%) =
Lowest production resulting from the following functions:
Water Balance
Winter Low Temperature
Soil Properties pH
Salinity
Drainage

7. Monthly Water Balance ModelP
ETa KS
Kcmid
AWC
TAW
Droot Dr

8. Monthly Water Balance Model
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*
* User input

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

10. Final Water Balance Relative Yield
Calculating Final Water Balance Relative Yield Mo J F M A S O N D RY 5 50 90 80 30 70 60 10 GP 1 FP
Growth Period*
N=3*
Floating N-month* max yield
Final RY = N-month max average RY within the Growth Period
* User input

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

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

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

14. Biomass Yield Internet Map Server 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
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 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
Dryland Winter Wheat, National Regr., All Varieties/Management

25. Grant County, Washington
RMA County Average Yield for Dryland Winter Wheat Higher than Modeled
PRISM Precipitation
Dryland Wheat
Cropland
Data Layer
(30 m)
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
Wood County, Ohio
RMA County Average Yield for Dryland Winter Wheat Higher than Modeled
Corn/Soybean (white)
Dryland Wheat (brown)
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
Wood County, Ohio
RMA County Average Yield for Dryland Winter Wheat Higher than Modeled
Corn/Soybean (white)
Dryland Wheat (brown)
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. 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
GROUP
PRISM

31. 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
GROUP
PRISM

32. 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
GROUP
PRISM