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

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

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

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?

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

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

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

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

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

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

Summer Temperature Constraint Function Summer Heat Injury Potential Winter Wheat

Soil Constraint Functions Soil pH Soil Salinity Winter Wheat Soil Drainage

Environmental Model Relative Yield Dryland Winter Wheat, Local Varieties

“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

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

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

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

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

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

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

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

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

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

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

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

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

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)

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

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

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)

Energycane draft map 2011 Original draft map

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

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

(no Precipitation Constraints) Irrigation (no Precipitation Constraints)

Environmental Model Relative Yield Switchgrass, Lowland Varieties, VERY PRELIMINARY

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)?

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

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

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