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 bio-fuel/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
Data Collection
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 – difficult to extrapolate from data alone
“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 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

8. 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

9. 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

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

11. Summer Temperature Constraint Function
Summer Heat Injury
Potential
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. “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

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

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

17. 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

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

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

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

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

22. 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

23. 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

24. RMA County Average Yield for Dryland Winter Wheat Higher than Modeled
Delaware/Maryland
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

25. Modeling considerations
Merges ranges of many local varieties
Based on 30-year average climate, has not yet been run on individual years to get variability statistics
Growers have modified their soils to improve pH and drainage, but we do not know exactly where and when
Growers make economic decisions on how well to care for crop, which is reflected in yield distribution
Liming
Fertilizer/Fungicide/Pesticide application
Grown as rotation crop, grazed, or for silage - not primary cash crop
Model results assume crop is grown every year – fallow is not accounted for
GROUP
PRISM

26. Major crops help validate the model
Many very capable mechanistic models for major crops such as Wheat and Corn
Wheat and Corn have lots of production history
Environmental suitability modeling of crops with lots of history help validate model output

27. Nationwide Bio-Fuel Resource Mapping
Winter Wheat
Rough Draft - Not Yet Reviewed
All Land
Non-Forest Land
Assumes Amended Soils - Liming (pH) and Tiling (Drainage)

28. Nationwide Bio-Fuel Resource Mapping
Corn
Rough Draft - Not Yet Reviewed
All Land
Non-Forest Land
Assumes Amended Soils - Liming (pH) and Tiling (Drainage)

29. Nationwide Bio-Fuel Resource Mapping
Sorghum
Rough Draft - Not Yet Reviewed
All Land
Non-Forest Land
Assumes Amended Soils - Liming (pH) and Tiling (Drainage)

30. Useful for exploring
Allows for “what if” scenarios such as Genetic selection for variables that allow for
Cold tolerance
Warm tolerance
Drought tolerance
Increased yield
Reduced fertility needs
Estimate production potential if environment changes (climate change)

31. Energycane draft map 2011
Original draft map

32. Based on reviewer comments
Energycane draft map 2012
Based on reviewer comments
January minimum temperature
tolerance increased (winter survivability)

33. January minimum temperature constraints turned off
Energycane draft map 2012
Example
January minimum temperature constraints turned off
(winter survivability)

34. (no Precipitation Constraints)
Irrigation
(no Precipitation Constraints)

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

36. A tool for new crops New crops with little production
Allows for exploration of new crop potential and identification of “environmental performance envelope”
Comparison of crop potential for multiple crops
Time series (year to year variability)?

37. 2006 48
25%
50%
75%
2009
2008 10
2007 NA
2005 57

38. Sun Grant Yield Data are Essential
Validate, transform, and improve modeled yields
Use modeled maps to identify locations where field validation may be lacking (e.g., precipitation gradients, temperature gradients)
The more (good) data we have, the better our maps will be
GROUP
PRISM

39. If additional funding were available
Next Step
Finish the current list of biomass preliminary draft maps
If additional funding were available
Develop potential biomass maps for additional feedstocks that were not modeled in the first round
Convert the system from 4k to 800-meter output
Use 30-meter land use land cover mask
Model using a time series capability to generate a distribution of yields not just a 30-yr average
Add Hawaii and subtropical crops (USDA funding)
RMA interest is in the nationally important crops, suitability mapping to help estimate yield for crop insurance underwriting.
GROUP
PRISM