michael c. wimberly, mirela tulbure , ross bell, yi liu, mark rop , rajesh chintala
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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 AnalysisDecision 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)
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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/Stability2000 2001 2002
2003 2004 2005
2006 2007 2008
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Interannual Variability in July Precipitation
3. Risk/Stability
• Spatial and temporal yield patterns• Associations with climatic variability• Implications for feedstock production
BU
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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 ethanolproduction 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 2006-08• 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.2006 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 detectiondensity 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 feedstockcrops
• 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 1970-2008: corn yieldsCorn Yield Trends from 1970 to 2008
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Soybean Yield Trends from 1970 to 2008
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County level trends from 1970-2008: 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 1980-2008 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
SwitchgrassTrial Locations
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 tmaxFeb tmin and tmaxAnnual ppt
Next steps: other predictor variables: soil type,management, origin of switchgrasscultivar
FEEDSTOCK YIELD DATA COLLECTION & COMPILATION
• Grain yield data from 2000 - 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 1970-2008
• 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
STATE CROPS
IL Wheat, corn, oats, sorghumIN Wheat, cornIA Wheat, corn, oatsMN Wheat, corn, oats, barleyMT Wheat, corn, barleyNE Wheat, corn, oats, sorghumND Wheat, corn, oats, barelySD Wheat, corn, oats, barelyWI Wheat, corn, oats, barelyWY Corn, barley
SOUTH DAKOTA - CROP RESIDUES
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Total Available ResiduesAvailable Crop ResiduesTotal Harvested Acres
INDIANA - CROP RESIDUES
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1970 1975 1980 1985 1990 1995 2000 2005
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Total Crop ResiduesAvailable Crop ResiduesTotal Harvested Acres
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 = - 6485 + 3.2 * corn acres – 1.04* oat acres – 16.3* wheat acres
• IN - Dry tons = - 10407 + 3.08 * corn acres + 1.27* wheat acres
• SD - Dry tons = 3954 + 0.91 * wheat acres – 0.72* oat acres + 2.46* corn acres + 1.98 *barley
• MT - Dry tons = - 5003 + 1.80 *barley acres – 0.80* wheat acres + 5.88* corn acres
• WY - Dry tons = -1252 + 1.94 * barley acres + 2.50* corn acres
EXPECTED OUTCOME