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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. Statistical Analysis
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Michael C. Wimberly, MirelaTulbure, Ross Bell, Yi Liu, Mark Rop, Rajesh Chintala
South Dakota State University
Decision Support Systems
What is the yield if a crop is planted in a particular area? How might these patterns shift with climate change?
1. Potential Yield = f(climate, soils)
Where are crops actually planted? Where will land cover/land use change occur?
2. Land Cover/Land Use
What is the potential for yield variability as a result of climatic variability, diseases, pests, fire?
3. Risk Factors/Yield Stability
4. Dissemination of Geospatial Information
Interannual Variability in July Precipitation
Annual Corn for Grain Yield for Six SD Counties
Spatial and temporal heterogeneity of distribution of fires in the central United States as a function of land use and land cover
of the 8 classes:
- Missing data
- Fire (low, nominal,
or high confidence)
MODIS Terra (~10.30 overpass)
MODIS Aqua (~13.30 overpass)
Example 8-Day Fire Product: South Central U.S.
2006 day 97 Tile H10V05
as cloudy in 2008
of feedstock crop yields
Generalized linear model (GLM), generalized additive
models (GAMS), recursive partitioning
Assess the sensitivity of corn and soybean production to
use a climate-envelope approach to model 1970’s corn
and soybean yields as a function of climate;
Use 1980-2008 data for model validation
data for future climate change scenarios from the
Community Climate System Model (CCSM) to predict
a model bioenergy species
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
March tmin and tmax
Feb tmin and tmax
other predictor variables: soil type,
management, origin of switchgrass