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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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slide1

North Central Feedstock Assessment Team: GIS Applications to Support Sustainable Biofuels Feedstock Production

Michael C. Wimberly, MirelaTulbure, Ross Bell, Yi Liu, Mark Rop, Rajesh Chintala

South Dakota State University

the big picture
The Big Picture

Statistical Analysis

Decision Support Systems

Simulation Models

Predictive Models

  • Information
  • Optimal Location for Refineries
  • Biomass feedstock production under alternative scenarios
  • Environmental impacts under alternative scenarios
  • Sensitivity to drought, disease, climate change…
  • Derived Products
  • Crop Type Maps
  • Drought Maps
  • Crop Yield Maps
  • Hazard Maps
  • Raw Data
  • Field Measurements
    • Environment
    • Crops
  • Environmental Data
    • Climate/Weather
    • Soils
    • Terrain
  • Geographic Features
    • Political boundaries
    • Transportation network
  • Key Considerations
  • Spatial Scale
    • Local
    • Regional
    • National
  • Temporal Scale
    • Long-term averages
    • Annual variability
modeling feedstock production
Modeling Feedstock Production

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

Actual Yield

1 potential yield modeling
1. Potential Yield Modeling
  • Literature search/data collection
  • Switchgrass as a model species
  • Evaluation of modeling approaches
1 potential yield modeling1
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)

Yield

Temperature

1 potential yield modeling2
1. Potential Yield Modeling
  • Incorporating Climate Change
    • Historical trends
    • Future projections
    • Climate-agriculture as a complex adaptive system
2 land cover land use
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 use1
2. Land Cover/Land Use
  • Marginal Lands
    • High potential for LCLU change
    • Classification
      • Soils
      • Terrain
      • Hydrology
    • Overlay with current LCLU
3 risk stability
3. Risk/Stability
  • Fire
  • Pests/Disease
  • Yield Stability
  • Climatic Variability
3 risk stability1
3. Risk/Stability

Interannual Variability in July Precipitation

3 risk stability2
3. Risk/Stability
  • Spatial and temporal yield patterns
  • Associations with climatic variability
  • Implications for feedstock production

Annual Corn for Grain Yield for Six SD Counties

BU/Acre

4 dissemination
4. Dissemination
  • Approaches
    • Static maps
    • Web GIS
    • Digital Globes
4 dissemination1
4. Dissemination
  • Web Atlas
    • CMS for multiple formats
    • Easy to change content
overview north central team
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)
slide15

Spatial and temporal heterogeneity of distribution of fires in the central United States as a function of land use and land cover

  • 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;
slide16

Questions

  • 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
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)

MODIS Terra (~10.30 overpass)

MODIS Aqua (~13.30 overpass)

Example 8-Day Fire Product: South Central U.S.

2006 day 97 Tile H10V05

slide21

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
slide22

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

  • 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)
slide25

Next steps

  • 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
slide26

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
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
objectives
OBJECTIVES
  • 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
slide30

METHODS

  • 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
slide37

EXPECTED OUTCOME

  • 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
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