North Central Feedstock Assessment Team: GIS Applications to Support Sustainable Biofuels Feedstock Production Michael C. Wimberly, Mirela Tulbure, Ross Bell, Yi Liu, Mark Rop, Rajesh Chintala South Dakota State University

The Big Picture Raw Data Field Measurements Environment Crops Environmental Data Climate/Weather Soils Terrain Geographic Features Political boundaries Transportation network Derived Products Crop Type Maps Drought Maps Crop Yield Maps Hazard Maps Information Optimal Location for Refineries Biomass feedstock production under alternative scenarios Environmental impacts under alternative scenarios Sensitivity to drought, disease, climate change… Predictive Models Statistical Analysis Decision Support Systems Simulation Models Key Considerations Spatial Scale Local Regional National Temporal Scale Long-term averages Annual variability

Modeling Feedstock Production 1. Potential Yield = f(climate, soils) 2. Land Cover/Land Use What is the yield if a crop is planted in a particular area? How might these patterns shift with climate change? Where are crops actually planted? Where will land cover/land use change occur? 3. Risk Factors/Yield Stability What is the potential for yield variability as a result of climatic variability, diseases, pests, fire? Actual Yield 4. Dissemination of Geospatial Information

1. Potential Yield Modeling Literature search/data collection Switchgrass as a model species Evaluation of modeling approaches

1. Potential Yield Modeling Approaches for modeling potential yield –Generalized linear models –Generalized additive models –Recursive partitioning –Multivariate adaptive regression splines –Ecological niche modeling (e.g., GARP, HyperNiche) Temperature Yield

1. Potential Yield Modeling Incorporating Climate Change –Historical trends –Future projections –Climate-agriculture as a complex adaptive system

2. Land Cover/Land Use Data Sources –NLCD land cover (30 m) –NASS cropland data layer (30 m) –MODIS crop type (250 m) –NASS county-level statistics

2. Land Cover/Land Use Marginal Lands –High potential for LCLU change –Classification Soils Terrain Hydrology –Overlay with current LCLU

3. Risk/Stability Fire Pests/Disease Yield Stability Climatic Variability

3. Risk/Stability Interannual Variability in July Precipitation

3. Risk/Stability Spatial and temporal yield patterns Associations with climatic variability Implications for feedstock production BU/Acre Annual Corn for Grain Yield for Six SD Counties

4. Dissemination Approaches –Static maps –Web GIS –Digital Globes

4. Dissemination Web Atlas –CMS for multiple formats –Easy to change content

Overview – North Central Team Potential Yield Modeling –Literature review completed (Rajesh) –Preliminary spatial model of switchgrass yield (Mirela) –Preliminary climate change analyses (Mirela) Land Cover/Land Use –Marginal lands mapping (in development) Risk/Stability –Fire study completed (Mirela) –Analysis and mapping of feedstock yield stability (Rajesh) Dissemination –Web Atlas – Beta version to be completed in April 2010 (Yi and Mark)

DOE’s “Billion study” – 36 billion gallons of ethanol production by 2022 with over half produced from plant biomass; The land cover in the central U.S. is likely to change Changes in regional land cover may affect the risk of wildfires to feedstock crops; Spatial and temporal heterogeneity of distribution of fires in the central United States as a function of land use and land cover

Questions 1.Does the density of fire vary across ecoregions and LULC classes in the central U.S.? 2. What is the seasonal pattern of fire occurrence in the central U.S. ?

Methods MODIS 1km active fire detections Daily product (MOD14A1) Active fire = fire burning at time of satellite overpass Each pixel assigned one of the 8 classes: - Missing data - Water - Cloud - Non-fire - Unknown - Fire (low, nominal, or high confidence) Example 8-Day Fire Product: South Central U.S day 97 Tile H10V05 MODIS Terra (~10.30 overpass)MODIS Aqua (~13.30 overpass)

Active fire detections and % observations labeled as cloudy in 2008

Prairie burning

Burning wheat stubble

Conclusions Agricultural dominated ecoregions had higher fire detection density compared to forested ecoregions Fire detection seasonality - a function of LULC in central U.S. states Quantifying contemporary fire pattern is the first step in understanding the risk of wildfires to feedstock crops

1970 – 2008 NASS corn and soybean yield data – county level PRISM tmin, tmax, avgt, and ppt summarized per county (monthly, two-months, three-month averages) Evaluate different empirical modeling approaches of feedstock crop yields Generalized linear model (GLM), generalized additive models (GAMS), recursive partitioning Assess the sensitivity of corn and soybean production to climatic trends

County level trends from : corn yields

County level trends from : soybean yields

Use other trend analysis models Using the climate variables identified in this step, use a climate-envelope approach to model 1970’s corn and soybean yields as a function of climate; Use data for model validation Future modeling efforts will incorporate downscaled GCM data for future climate change scenarios from the Community Climate System Model (CCSM) to predict potential changes in corn and soybean productivity Next steps

Climatic influences on biomass yields of switchgrass, a model bioenergy species Yield Data: 1,345 observation points associated with 37 field trial locations across the U. S. were gathered from 21 reference papers PRISM data (tmin, tmax, ppt): averaged per month, growing season (A-S), and year before harvesting Best models: March tmin and tmax Feb tmin and tmax Annual ppt Next steps: other predictor variables: soil type, management, origin of switchgrass cultivar

FEEDSTOCK YIELD DATA COLLECTION & COMPILATION Grain yield data from till now Millets – corn, sorghum small grains – wheat, barlely, oats oil seeds - sunflower, canola, safflower, and camelina legume – soybean grasses – switchgrass, alfafalfa NE, SD, WY, MT, MN, IA, ND Published research articles, websites, annual reports of research centers, and yield trails conducted by universities

Crop Residue Variability in North Central Region Rajesh Chintala

Determine the mean and variability in crop residue yields (response variable) of North Central Region Study the spatial patterns and variability of climatic, soil and topographic factors (explanatory variables) over a period of time and derive the empirical relationships with residue yield variability Assess the supply of collectable crop residues after meeting the sustainability criteria OBJECTIVES

Study area : North Central Region Residue production: USDA – NASS data Spatial averages of climatic and soil variables: weather parameters - precipitation, air temperature soil variables – SOM, SWC, slope, soil depth, permeability, texture, pH, CEC Available crop residue – using parameters like SCI METHODS

STATECROPS ILWheat, corn, oats, sorghum INWheat, corn IAWheat, corn, oats MNWheat, corn, oats, barley MTWheat, corn, barley NEWheat, corn, oats, sorghum NDWheat, corn, oats, barely SDWheat, corn, oats, barely WIWheat, corn, oats, barely WYCorn, barley

PREDICTION PROFILERS

Spatial and temporal patterns of crop residue stability, variability and dependability Predictive modeling utilizing the derived empirical relationships Helps to determine the sustainable supply of crop residue quantity and its spatial patterns over north central region IA - Dry tons = * corn acres – 1.04* oat acres – 16.3* wheat acres IN - Dry tons = * corn acres * wheat acres SD - Dry tons = * wheat acres – 0.72* oat acres * corn acres *barley MT - Dry tons = *barley acres – 0.80* wheat acres * corn acres WY - Dry tons = * barley acres * corn acres EXPECTED OUTCOME