1. Western Region GIS Update: National Suitability Modeling of Biofuel Feedstocks Chris Daly, Mike Halbleib David Hannaway Sun Grant Western Region GIS Center Oregon State University Corvallis, Oregon, USA

2. Introduction  GIS Program Objective:  GIS Program Objective: Gain an understanding of the spatial distribution of current and potential feedstock resources regionally and nationally  A couple of envisioned outcomes: A series of national maps that describe the actual and potential productivity patterns of various feedstocks Maps showing changes in production patterns under historical and future climates

3. How Are We Accomplishing This? Currently Collecting production information from field trials and the literature; some regions developing models to make spatial estimatesIssues Data are taken from scattered locations under various management practices and at varying times Difficult to coordinate these regional results into a national, “wall-to-wall” (full national) assessment that is consistent across the country Unclear how potential biomass production of new crops will be estimated nationwide, both now and under historical and future climates

4. Suitability Modeling Framework At The National Level Two main objectives:   Develop gridded estimates of current and potential feedstock resources across the conterminous US   Provide a context for biomass data collected by the regional centers, which will aid in prioritizing what data to collect and where, assessing data quality, and siting new field trials

5. Suitability Modeling Framework (Cont.) What We are Doing   Modifying and enhancing a simple suitability model that incorporates the important environmental constraints on biomass production, namely climate and soils Expected Result   High-resolution, gridded “first-guess” potential biomass maps for the conterminous US   Accompanying GIS model that relates biomass potential to limiting environmental factors

6. Suitability Modeling Framework (Concl.) How Will this Help?   A starting point for more refined mapping of current and future potential biomass resources with the aid of field data, management and economic analysis, land use/land cover, etc.   Inform data collection efforts on where additional field data would be helpful, and what variables are most needed   Provide a spatial context for quality control of field data   Generate “what if” maps of potential and future resources

7. Current Suitability Mapping System

8. PRISM Spatial Climate Datasets All State of the Science Physiographically sensitive mapping Official Climate Datasets of the USDA 800-m and 4-km resolutions Monthly tmin, tmax, precip, dew pt. 1971-2000 means

9. CONUS 1895-2008 Monthly Climate Time Series Tmin, tmax, precip, dew point 800-m grid resolution 150 Billion grid cells

10. Simple Environmental Model Tall Fescue

11. Simple Environmental Model for Tall Fescue Example Functions Unsui table Soil Drainage Soil SalinitySoil pH Unsuit able Ambient Temperature

12. “Trickle down” hierarchy of tolerance tables (We don’t have to start from scratch)

13. Tall Fescue Suitability – All Climate & Soil Constraints

14. Tall Fescue Suitability – No Soil Constraint (Climate Only)

15. Tall Fescue Suitability – No precip constraint (Irrigated)

16. Tall Fescue Suitability No soils, irrigated, no overwinter constraint (annual)

17. Start of a Switchgrass Suitability Map? Moisture does not come in warm season

18. Next Steps – FY2010 Upgrade to latest PRISM climate grids Move from qualitative suitability categories to biomass yield using continuous equations Move to a monthly water balance model to handle timing of precip Run suitability model with both 1971-2000 mean climate, and 30 years (e.g., 1979-2008) of monthly time series climate data to develop probability distributions of biomass yield Produce suitability maps for tall fescue and switchgrass via the internet map server Use species experts and field data being collected to validate and improve biomass model Determine which field information is most important for suitability mapping

19. Regional Center Interactions   Come to a consensus on a priority list of biomass species to map   Identify a representative variety for each species or subspecies   For each species, agree on what will be considered “maximum possible yield” (e.g., maximum genetically possible, maximum observed yield, etc.)   Define management criteria assumed for initial mapping (e.g., best practices, typical practices)   Overlay field data onto the national modeled map and help to refine and improve the suitability model

20. Regional Center Interactions (concl.)   Use modeled map to identify locations where field validation of estimated yield should be checked and verified   Use modeled map to assess field data quality (areas of strong disagreement may indicate unrepresentative data)   Incorporate mapped biomass estimates into regional center activities (e.g., POLYSYS, economic modeling, processing facility siting, etc.)   Provide GIS data sets that complement or improve gridded biomass estimates