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

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 Yield4. 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/Stability

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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 yields

Corn 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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Available Crop Residues

Total Harvested Acres

INDIANA - CROP RESIDUES

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Total 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