Impact of Corn-Ethanol on Greenhouse Gas Emissions
Impact of Corn-Ethanol on Greenhouse Gas Emissions Kenneth G. Cassman Nebraska Ethanol Board Meeting University of Nebraska, Lincoln March 6, 2009
Climate Change Occurs Due to GHG Emissions • Anthropogenic greenhouse gas (GHG) emissions – carbon dioxide-CO2, nitrous oxide-N2O, methane-CH4 • Accumulation of GHGs in the atmosphere is unequivocal due to burning of fossil fuels and land clearing for agriculture and urban/industrial development • Climate models predict substantial rise in temperature unless atmospheric GHG levels are substantially reduced • RSB Version Zero for sustainable biofuels: – “Biofuels shall contribute to climate change mitigation by significantly reducing GHG emissions compared to fossil fuels” 6 March 2009
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How to measure GHG emissions from a production system? • Must consider all phases of the production cycle – Direct effects: feedstock production, conversion to biofuel, use of co-products, waste disposal, and any land conversion to produce the biofuel feedstock (direct land use change) – Indirect effects: primarily indirect land use change caused by macroeconomic effects of biofuel production
• RSB Version Zero for sustainable biofuels AND the 2007 Energy Independence and Security Act: – GHG emissions from direct and indirect effects shall be considered
• Life cycle assessment (LCA): – method to estimate environmental impact of a production system; considers all direct inputs (energy, materials and water) and outputs (products, co-products, useful energy, waste) 6 March 2009
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Energy Independence and Security Act of 2007 • Requires life-cycle GHG emissions reductions vs gasoline: – Starch-ethanol (corn): – Cellulosic ethanol: – Advanced biofuels:
-20% -60% -50%
• “`Lifecycle greenhouse gas emissions' means the aggregate quantity of greenhouse gas emissions (including direct emissions and significant indirect emissions such as significant emissions from land use changes), as determined by the Administrator, related to the full fuel lifecycle, including all stages of fuel and feedstock production and distribution, from feedstock generation or extraction through the distribution and delivery and use of the finished fuel…..” 6 March 2009
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EPA’s Proposed Life Cycle Assessment Approach GHG emissions will be estimated using series of models: • Ethanol plants: USDA engineering models (ASPEN) • Nitrogen fertilizer-N2O: DAYCENT, other sources • Land Use Change: Domestic (FASOM) and international (FAPRI) agricultural sector models • Agricultural inputs/outputs: GREET Model • Co-product credits: GREET Model • GREET model will provide the life-cycle modeling framework; it also includes carbon intensities for all petroleum-based and bio-based fuels: corn-ethanol, cellulosic ethanol, biodiesel, etc. • Appropriate life-cycle methods and models will be finalized by the EPA in 2009, and will be subject to public comment 6 March 2009
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Source: Dunham, U.S. EPA, May 8, 2008; other sources
Actual versus Hypothetical LCA • The 2007 EISA, and perhaps the RSB guidelines will require development of GHG emissions estimates for all biofuels—both first generation biofuels (e.g. corn grain, sugarcane ethanol, or biodiesel from soy, canola, oil palm), and second generation biofuels (cellulosic from switchgrass, Miscanthus, poplar, prairie grasses, etc) • GHG emission estimates from first gen biofuels can be validated with direct measurements in commercial-scale facilities • GHG emission estimates for second gen biofuels are highly uncertain and based on a mix of data sources: lab and pilot-scale facilities; uncertain “winning” technologies 6 March 2009
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What Are the Underlying Measurement Challenges for Improving the Accuracy of Default Parameters in Low Carbon Fuel Standards from Corn-Ethanol?
Accurate Recent Data Available •Crop yields (2007-USDA-NASS) •Fertilizer inputs (2005-USDA-ERS-ARMS) •Emission factors (EPA, IPCC) •Biorefinery parameters— recent surveys and from future LCFS assessments 6 March 2009
Data Deficiencies— Older or Missing Data •Fossil fuel inputs for corn production are from 2001 or before— USDA-ERS stopped surveys! •Recognition of regional variability in crop production •Co-product use is regionally variable, not rigorously documented, could lead to large differences in co-product credit
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US Corn-Ethanol Industry has Rapidly Grown during 2001-08, Implementation of More Efficient Biorefineries, Primarily Natural Gas Powered Dry Mills
Nov. 2008 (RFA) 88% Dry mill, 88% Natural gas, by capacity
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Increasing Tar Sands Fraction in USA Petroleum
million barrels per day
25
2007
20
7%
2020
19%
1.4
3.8
15
Tar Sands
10
16.9
5
25% 0
19.3
0.3
79%
44%
50% 1.2 0.7
1.1
Venezuela Canada
Exports to USA
3.2
USA Venezuela Canada
USA
Use
Use
Exports to USA
Projections assume USA consumption and imports are constant from 2007 to 2020 (US Department of Energy), USA imports the same proportion of Canadian production (89%) and the same rate from Venezuela (1.36 mb/d), which increases tar sands production to 50%. 6 March 2009
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Source: Liska and Perrin, submitted
Figure 2. Net energy yield and greenhouse gas emissions reduction* compared to gasoline from different types of corn-ethanol systems used as default scenarios in the BESS model (www.bess.unl.edu). NEY includes ethanol plus co-product energy credit minus energy inputs. 100
GHG Reduction (%)
80 NE-NGW IA-NG 60
NE-CL
HYP-NG
48% - 59% MW-NNG NE-NG MW-NG
40 Farrell et al.
20
NE-Coal
0 10
20
30
40
50
60
Net Energy Yield (GJ ha-1)
*Direct-effect: considered 6 March 2009 Land use change NEnot Ethanol Board Meeting
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Source: Liska et al, Journal of Industrial Ecology 13, 58-74 (2009)
BESS Model Simulation Scenarios Demonstrating the Spectrum of Ethanol Performance: Combination of Cropping Systems and Survey Data Cropping Data
Survey Data
Scenario ID
Crop production region
Biorefinery energy (dry mill)
Co-product type
NEW Survey Data
MW-NG
USA Midwest Avg.
natural gas-MW
mix dry-wet DGS
RFA-22
MW-NNG USA Midwest Avg.
natural gas-MW
mix dry-wet DGS
UNL-8
IA-NG
Iowa Avg.
natural gas-IA
mix dry-wet DGS
IDNR-9
NE-NG
Nebraska Avg.
natural gas-NE
mix dry-wet DGS
NDEQ-9
NE-NGW
Nebraska Avg.
natural gas-NE
Wet DGS
NDEQ-4
NE-CL
Nebraska Avg.
NG, closed-loop
Wet DG
NDEQ-4
NE-Coal
Nebraska Avg.
coal
Dry DGS
EPA
HYP-NG
natural gas-NE Progressive cropping (CSP) NE Ethanol Board Meeting
mix dry-wet DGS
NDEQ-9
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Figure 1. Biorefinery thermal energy efficiency in corn-ethanol production comparing previous estimates to more recent survey data from natural gas powered dry mills in the Corn Belt. -1
Thermal Energy Efficiency (MJ L )
16
Energy Input
14 12
Number of Biorefineries in Each Survey
33
22
8
9
9
4
Avg. of Wet & Dry Mills Dry Mill
10 8
27,300 Btu/gal
6 4 2
Fa rr Ar el go le nn ta e lE N B L C AM hr G R is M EE R t (2 i en an T 00 20 ew so 1) 08 n ab B & -v ui le .1 A lt Fu ss .8 Af a e o te lA c r2 (2 ss 00 00 oc 5, 7) #1 U N N (2 eb L 00 #2 ra 6) sk (2 N a 00 eb D Io 6EQ w ra 07 a sk ) # D 3 a N (2 D R EQ 00 #4 6) #3 (2 00 a (W 406 et ) D G 20 06 )
0
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year of operation
Source: Liska et al, 12 Journal of Industrial Ecology, 13, 58-74 (2009)
Table 1. Performance of the evaluated corn-ethanol systems in the Midwest Corn Belt states, Iowa, and Nebraska with selected input values and output metrics for eight default scenarios in the BESS model. MWNG
Simulation Scenarios:
MWNNG
IANG
NENG
NENGW
IA
NE
NE
NECL
NECoal
HYPNG
NE
NE
HYP
Agricultural Energy Inputs by Cropping Region Region
MW*
MW
-1
Energy inputs GJ Mg 1.7 1.7 1.4 2.3 Biorefinery Energy Inputs by Type, According to Survey Data Survey data
‡
1
Energy source
RFA
UNL
NG*
NG
2
4
3
IDNR
NDEQ
NG
NG
2.3 3a
NDEQ NG
1.9
2.3
3a
EPA**
NDEQ
Coal
NG
NDEQ CL
1.8 3
-1
7.69
4.62
6.95
6.85
5.44
5.44
6.10
6.85
-1
ns
2.98
ns
0.76
0
0
4.00
0.76
kWh L
0.185
0.174
0.185
0.185
0.185
0.291
0.230
0.185
-1
L kg
0.419
0.432
0.393
0.408
0.423
0.423
0.419
0.408
Dry DGS
%
35
66
22
32
0
0
100
32
Modified DGS
%
30
31
23
32
0
0
0
32
Wet DGS
%
35
3
55
36
100
100
0
36
0.13
0.13
0.13
0.13
0.13
0.26
0.13
0.13
Thermal energy TE, drying DG Electricity Conversion yield
MJ L
MJ L
-1
-1
Capital Energy MJ L System Performance Metrics -1 Net energy ratio MJ MJ Ethanol-to-petrol. GHG-intensity
GHG-reduction 6Ethanol March yield 2009
MJ MJ
-1
gCO2e MJ
-1
1.61
1.64
1.76
1.50
1.79
2.23
1.29
1.60
12.3
12.5
12.9
10.1
10.9
9.3
10.3
18.8
45.1
45.0
42.0
48.1
37.5
30.6
76.0
43.8
59
67
17
52
4,116
4,116
4,077
51
% -1
L ha
51 54 48 Ethanol Board Meeting 4,010 NE 4,134 4,205 3,970
5,59013
Source: Liska et al, Journal of Industrial Ecology 13, 58-74 (2009)
Table 2. Greenhouse gas emissions inventory of the corn-ethanol lifecycle for a natural gas dry mill biorefinery in Iowa (BESS model, IA-NG). Component
GHG emission category
-1
gCO2e MJ
Mg CO2e*
% of LC
Crop Production Nitrogen fertilizer, N Phosphorus fertilizer, P Potassium fertilizer, K Lime Herbicides Insecticides Seed Gasoline Diesel LPG Natural gas Electricity Depreciable capital N2O emissions** TOTAL
4.26 0.953 0.542 2.82 1.51 0.018 0.193 0.355 1.73 1.24 0 0.348 0.268 14.1 28.3
34,069 7,618 4,337 22,577 12,079 141 1,540 2,837 13,848 9,932 0 2,785 2,144 112,550 226,456
7.46 1.67 0.950 4.95 2.65 0.031 0.337 0.621 3.03 2.18 0 0.610 0.470 24.7 49.6
Natural gas input † NG Input: drying DGS Electricity input Depreciable capital Grain transportation TOTAL
19.7 0 6.53 0.458 2.11 28.8
157,356 0 52,201 3,663 16,851 230,071
34.5 0 11.4 0.802 3.69 50.4
0.216 -2.62 -11.4 -2.64 -16.5 1.40 42.0 42.0 92.0 50.0
1,731 -20,956 -91,501 -21,102 -131,828 11,196 335,895 335,895 735,715 399,819
0.379 -4.59 -20.0 -4.62 -28.9 0 100
Biorefinery
Co-Product Credit
Diesel Urea production Corn production Enteric fermentation-CH4 TOTAL Transportation of Ethanol from Biorefinery LIFE-CYCLE NET GHG EMISSIONS -1 GHG-intensity of ethanol, g CO2e MJ ‡ -1 GHG-intensity of gasoline , g CO2e MJ GHG reduction relative to gasoline, %
54.3%
Source: Liska et al, Journal of Industrial Ecology, 13, 58-74 (2009)
Table 3. Comparison of results from different models for life-cycle GHG emissions from dry-mill corn-ethanol systems (gCO2e MJ-1). Emissions Crop Production Biorefinery Co-Product Credit Denaturant Land Use Change GWI Gasoline GHG reduction, %
GREET
BEACCON
EBAMM
BESS (MW-NNG)
44 43 -17 70 92 24
41 31 -17 6 1 64 92 30
37 64 -25 76 92 17
29 30 -16 45 92 51
BESS (NE-NG)
BESS (NE-NGW)
35 31 -19 48 92 48
34 25 -22 38 92 59
GREET vs.1.8a: available from: http://www.transportation.anl.gov/software/GREET/ BEACCON vs.1.1: available from www.lifecycleassociates.com; largely based on GREET (error in article is corrected above). EBAMM: vs.1.1-1 (Farrell et al. 2006), "Ethanol Today" avg. 2001 ethanol plant, data for wet and dry mills, see Figure 1; BESS model default scenarios. BESS model has a dynamic co-product credit that is dependent on the GHG intensity of crop production and biorefinery ethanol yield.
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Source: Liska et al, Journal of Industrial Ecology, 13, 58-74 (2009)
Most Sensitive Input Parameters On GHG Emissions Reductions & Net Energy Yield of Corn-Ethanol
1. Crop yield and nitrogen fertilizer efficiency 2. Biorefinery thermal energy inputs: MJ per liter (e.g. wet vs. dry distillers grains) 3. Conversion yield: liters ethanol per kg grain 4. Biorefinery electricity use
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Figure 4B. Regional variability in corn-ethanol greenhouse gas intensity of corn production (kg CO2e Mg-1 grain), and life cycle GHG reductions of corn-ethanol compared to gasoline (%), assuming a new natural gas biorefinery.
261 48%
235 230
53%
250
54%
301
51%
48%
327 45%
290 49%
275
236 51%
48%
316
226 56%
347 44%
311
274
287
52%
47%
51%
47%
360 42%
365 45%
382 43%
426
kg CO2e per Mg Grain
40%
226 - 249
319 - 341
250 - 272
342 - 364
273 - 295
365 - 387
296 - 318
388 - 410 411- 423
Life-cycle GHG reduction compared to gasoline w/ new natural gas-powered ethanol plant, DDGS
Source: Liska et al, Journal of Industrial Ecology, 13, 58-74 (2009)
Improvements in Life-Cycle Energy Efficiency and Greenhouse Gas Emissions of Corn-Ethanol*
*Journal of Industrial Ecology, 2009: http://www3.interscience.wiley.com/cgi-bin/fulltext/121647166/PDFSTART.
Adam J. Liska1*, Haishun S. Yang1, Virgil R. Bremer2, Terry J. Klopfenstein2, Daniel T. Walters1, Galen E. Erickson2, and Kenneth G. Cassman1,3 Department of Agronomy and Horticulture1, Department of Animal Science2, Nebraska Center for Energy Sciences Research3, University of Nebraska-Lincoln,
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Conclusions from BESS analysis • Corn-ethanol systems are not accurately evaluated as an aggregate, due to differences in biorefinery designs, energy sources, and crop production practices • Based on state records and new surveys, natural gas powered dry mills (88% of the industry) can reduce GHG emissions by 48-59% compared to gasoline on average, which is a 2-3 fold greater reduction than reported in previous studies • Crop production represents 42-51% of life-cycle GHG emissions for typical USA corn-ethanol systems; needs accurate assessment • Co-product credits offset 26-38% of life-cycle GHGs • Accurate GHG analysis is essential for enabling ethanol producers to meet the GHG emissions reduction standards relative to gasoline for RSB, Energy Independence and Security Act of 2007 and state-level LowBoard Carbon 6 thresholds, March 2009 NE Ethanol MeetingFuel Standards 24
Land Use Change Impacts in Biofuels Lifecycle Emissions •
Land use change released 1/5 of global anthropogenic GHG emissions in the 1990’s, and 1/3 since 1750 (IPCC 2007)
•
High commodity prices are likely to drive agriculture expansion abroad (Morton et al. 2006; Naylor et al. 2007)
•
Indirect land use change from biofuels is difficult to estimate using models (Turner et al. 2007)
•
International land use policies are determined by foreign governments, often without enforcement (e.g. Brazil)
•
If indirect effects are assigned to biofuels, future research needs to more accurately quantify its magnitude and uncertainty
•
If indirect effects are assigned to biofuels, then the indirect GHG effects of gasoline need to be estimated as well 6 March 2009
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How to achieve sustainable corn ethanol • Avoid unacceptable rise in crop commodity prices (food vs fuel challenge) – Accelerate the rate of yield gain on existing farmland • Achieve a large net reduction in GHG emissions, including direct and indirect effects, and protect water and soil quality • The path forward: ecological intensification of corn ethanol systems – Accelerating the rate of gain in corn yields on existing farm land while reducing environmental footprint is the key 6 March 2009
NE Ethanol Board Meeting
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Global Cereal Area Trends, 1966-2006 Grain Harvest Area (Ha6)
800 Total Grain Area (Barley+Maize+Millet+Oats+Rice+Sorghum+Wheat)
700
1966 - 1980 y = 639 + 4.44x r2 = 0.84
600
1981 - 2006 y = 724 - 1.83x 2 r = 0.74
500 Maize+Rice+Wheat y = 470 + 1.29x r2 = 0.66 400 1966
1976
1986
Year 6 March 2009
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1996
2006
FSource: FAO data archives 27
Global Cereal Yield Trends, 1966-2006
-1
Grain Yield (kg ha )
5000 Maize Yield y = 2260 + 62.5x r2 = 0.94
4000
3000
2000
Rice Yield y = 2097 + 53.5x r2 = 0.98
Wheat Yield y = 1373 + 40.1x r2 = 0.97
1000 1966
1976
1986
1996
2006
Year THESE RATES OF INCREASE ARE NOT FAST ENOUGH TO MEET 6 EXPETED March 2009 DEMAND! Source: NE Ethanol FAOBoard dataMeeting archives.
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Rate of gain for all cereals is linear, not exponential, which means that the relative rate of gain is decreasing: relative rates of gain in 1966. Global rate of increase in yield of maize, rice, and wheat, 1966-2006. Crop
_Mean yield (kg ha-1)_ 1966 2006
Rate of gain¶ (kg ha-1 yr-1)
Proportional rate of gain (%) 1966 2006
Maize
2260
4759
62.5
2.77
1.31
Rice
2097
4235
53.5
2.55
1.26
Wheat
1373
2976
40.1
2.92
1.35
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Rate of gain for all cereals is linear, not exponential, which means that the relative rate of gain is decreasing: relative rates of gain in 2006. Global rate of increase in yield of maize, rice, and wheat, 1966-2006. Crop
_Mean yield (kg ha-1)_ 1966 2006
Rate of gain¶ (kg ha-1 yr-1)
Proportional rate of gain (%) 1966 2006
Maize
2260
4759
62.5
2.77
1.31
Rice
2097
4235
53.5
2.55
1.26
Wheat
1373
2976
40.1
2.92
1.35
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Potential Ripple Effect: indirect land use change through conversion of tropical rainforest and wetlands to crop land
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Potential Ripple Effect: unsustainable crop production on marginal land by poor farm families without other options
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USA Corn Yield Trends, 1966-20051 (embodies tremendous technological innovation)
GRAIN YIELD (kg ha-1)
12000
Transgenic (Bt) insect resistance Soil testing, balanced NPK fertilization, conservation tillage
10000
8000
Double-X to single-X hybrids
Reduced N fertilizer & irrigation?
6000
4000 Expansion of irrigated area, increased N fertilizer rates
2000 1965
1970
1975
1980
Integrated pest management
1985
1990
y = 112.4 kg/ha-yr [1.79 bu/ac-yr] 2 R = 0.80
1995
2000
2005
YEAR 6 March 2009
NE Ethanol Board Meeting
From: Convergence of Energy and Agriculture, www.cast-science.org
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How to achieve sustainable corn ethanol • Avoid unacceptable rise in crop commodity prices (food vs fuel challenge) – Accelerate the rate of yield gain on existing farmland • Achieve a large net reduction in GHG emissions, including direct and indirect effects, and protect water and soil quality • The path forward: ecological intensification of corn ethanol systems by accelerating the rate of gain
in crop yields on existing farm land while reducing environmental footprint is the key 6 March 2009
NE Ethanol Board Meeting
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USA Corn Yield Trends, 1966-20051 (embodies tremendous technological innovation)
GRAIN YIELD (kg ha-1)
12000 Soil testing, balanced NPK fertilization, conservation tillage
10000
8000
?
Transgenic (Bt) insect resistance
Double-X to single-X hybrids
Reduced N fertilizer & irrigation?
6000
4000 Expansion of irrigated area, increased N fertilizer rates
2000 1965
1970
1975
1980
Integrated pest management
1985
1990
y = 112.4 kg/ha-yr [1.79 bu/ac-yr] 2 R = 0.80
1995
2000
2005
YEAR 6 March 2009
NE Ethanol Board Meeting
From: Convergence of Energy and Agriculture, www.cast-science.org
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Nebraska contest-winning and average yield trends No increase in yield potential ceiling since the 1980s, but a large exploitable yield gap still exists
25
20
15
350 300 250
Rainfed contest winners 208 kg/ha/yr
200 10
Irrigated average 114 kg/ha/yr
150 100
5 Rainfed average 89 kg/ha/yr
+38%
+50%
50
0 0 1965 1970 1975 1980 1985 6 March 2009 NE Ethanol Board1990 Meeting 1995 2000 2005
Year
Corn yield (bu/acre)
-1
Maize yield (Mg ha )
Irrigated contest winners
36
From: Cassman et al., 2003
Large exploitable gap between average and record yields. USA contest-winning corn field, 1997, Sterling NE. 310 bu/ac (ethanol yield of 800 gallons/ha): How to close the gap between highest possible yields (called yield potential) and average farm yields in an environmentally sustainable manner?
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Need for Ecological Intensification¶ Development of high-yield crop production
systems that protect soil and environmental quality and conserve natural resources Characteristics of EI systems: ¾ Yields that reach 80-85% of genetic yield potential ¾ 70-80% N fertilizer uptake efficiency (vs 30-40% now) ¾ Improve soil quality (nutrient stocks, SOM) ¾ Integrated pest management (IPM) ¾ Contribute to net reduction in greenhouse gases ¾ Have a large net positive energy balance ¾ In irrigated systems: 90-95% water use efficiency ¶Cassman,
6 March 2009
1999. in Proc. Natl. Acad. Sci (USA):5952-5959 NE Ethanol Board Meeting
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Ecological Intensification Requires • Interdisciplinary, systems research – agronomy, soil science, plant physiology/pathology/entomology, geology/hydrology, meteorology, conventional breeding and molecular genetics, computer science, engineering, animal science, economics and policy.,,,,,
• Substantial funding—equivalent to support levels for genomics per FTE • Production- and landscape-scale research • An appropriate balance among simulation, validation, and measurement 6 March 2009
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High yields and high N fertilizer and water-use efficiency are possible with progressive management. Based on research in production-scale fields in NE. System
Yield* N fertilizer (kg ha-1) (kg ha-1) Advanced Irrigated
N fert. eff¶ (kg/kg N)
Maize-soybean
13,610
153
89.0 (+65%)
Maize-maize
13,110
196
66.9 (+24%)
Rainfed Maize
8,900
128
69.5 (+30%)
USA Maize (ave.)
8,470
157
53.9
*Based on combine harvest yields from the three CSP field sites.
¶N fertilizer efficiency in terms of kg grain produced per kg N fertilizer applied 6 March 2009
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Critical need for production-scale field research facilities to test critical hypotheses and models about energy yield, efficiency, and environmental impact of biofuel energy systems
Miscanthus
Proposed production-scale research fields for 2nd-gen biofuel crops
rainfed corn-soybean (65 ha)
Existing Production-scale research fields instrumented for C-cycle research
Switchgrass
Irrigated continuous corn (60 ha) 6 March 2009
Irrigated cornsoybean (60 ha)
2 km to a 500 head research cattle feedlot for by-product utilization research
NE Ethanol Board Meeting
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Second Gen Biofuels also face ILUC issues • Ecological intensification will be required for development of second-gen cellulosic biofuels based on dedicated perennial grass crops like switchgrass or miscanthus – First phase of investment will go to the more favorable “marginal” crop land due to its higher yield density potential; competition with corn and other crops • High and consistent yields will be critical for economic viability • Farmers will be motivated to raise yields and increase profits, which will favor higher input levels • Indirect land use change issues will surface – Lessons learned from knowledge and approaches to achieve ecological intensification of corn systems (high yields without negative environmental impact) will be critical for developing economically viable and environmentally sound cellulosic crop feedstock production systems 6 March 2009
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Climate Change Occurs Due to GHG Emissions • Anthropogenic greenhouse gas (GHG) emissions – carbon dioxide-CO2, nitrous oxide-N2O, methane-CH4 • Accumulation of GHGs in the atmosphere is unequivocal due to burning of fossil fuels and land clearing for agriculture and urban/industrial development • Climate models predict substantial rise in temperature unless atmospheric GHG levels are substantially reduced • RSB Version Zero for sustainable biofuels: – “Biofuels shall contribute to climate change mitigation by significantly reducing GHG emissions compared to fossil fuels” 6 March 2009
NE Ethanol Board Meeting
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How to measure GHG emissions from a production system? • Must consider all phases of the production cycle – Direct effects: feedstock production, conversion to biofuel, use of co-products, waste disposal, and any land conversion to produce the biofuel feedstock (direct land use change) – Indirect effects: primarily indirect land use change caused by macroeconomic effects of biofuel production
• RSB Version Zero for sustainable biofuels AND the 2007 Energy Independence and Security Act: – GHG emissions from direct and indirect effects shall be considered
• Life cycle assessment (LCA): – method to estimate environmental impact of a production system; considers all direct inputs (energy, materials and water) and outputs (products, co-products, useful energy, waste) 6 March 2009
NE Ethanol Board Meeting
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Energy Independence and Security Act of 2007 • Requires life-cycle GHG emissions reductions vs gasoline: – Starch-ethanol (corn): – Cellulosic ethanol: – Advanced biofuels:
-20% -60% -50%
• “`Lifecycle greenhouse gas emissions' means the aggregate quantity of greenhouse gas emissions (including direct emissions and significant indirect emissions such as significant emissions from land use changes), as determined by the Administrator, related to the full fuel lifecycle, including all stages of fuel and feedstock production and distribution, from feedstock generation or extraction through the distribution and delivery and use of the finished fuel…..” 6 March 2009
NE Ethanol Board Meeting
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EPA’s Proposed Life Cycle Assessment Approach GHG emissions will be estimated using series of models: • Ethanol plants: USDA engineering models (ASPEN) • Nitrogen fertilizer-N2O: DAYCENT, other sources • Land Use Change: Domestic (FASOM) and international (FAPRI) agricultural sector models • Agricultural inputs/outputs: GREET Model • Co-product credits: GREET Model • GREET model will provide the life-cycle modeling framework; it also includes carbon intensities for all petroleum-based and bio-based fuels: corn-ethanol, cellulosic ethanol, biodiesel, etc. • Appropriate life-cycle methods and models will be finalized by the EPA in 2009, and will be subject to public comment 6 March 2009
NE Ethanol Board Meeting
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Source: Dunham, U.S. EPA, May 8, 2008; other sources
Actual versus Hypothetical LCA • The 2007 EISA, and perhaps the RSB guidelines will require development of GHG emissions estimates for all biofuels—both first generation biofuels (e.g. corn grain, sugarcane ethanol, or biodiesel from soy, canola, oil palm), and second generation biofuels (cellulosic from switchgrass, Miscanthus, poplar, prairie grasses, etc) • GHG emission estimates from first gen biofuels can be validated with direct measurements in commercial-scale facilities • GHG emission estimates for second gen biofuels are highly uncertain and based on a mix of data sources: lab and pilot-scale facilities; uncertain “winning” technologies 6 March 2009
NE Ethanol Board Meeting
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What Are the Underlying Measurement Challenges for Improving the Accuracy of Default Parameters in Low Carbon Fuel Standards from Corn-Ethanol?
Accurate Recent Data Available •Crop yields (2007-USDA-NASS) •Fertilizer inputs (2005-USDA-ERS-ARMS) •Emission factors (EPA, IPCC) •Biorefinery parameters— recent surveys and from future LCFS assessments 6 March 2009
Data Deficiencies— Older or Missing Data •Fossil fuel inputs for corn production are from 2001 or before— USDA-ERS stopped surveys! •Recognition of regional variability in crop production •Co-product use is regionally variable, not rigorously documented, could lead to large differences in co-product credit
NE Ethanol Board Meeting
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US Corn-Ethanol Industry has Rapidly Grown during 2001-08, Implementation of More Efficient Biorefineries, Primarily Natural Gas Powered Dry Mills
Nov. 2008 (RFA) 88% Dry mill, 88% Natural gas, by capacity
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NE Ethanol Board Meeting
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Increasing Tar Sands Fraction in USA Petroleum
million barrels per day
25
2007
20
7%
2020
19%
1.4
3.8
15
Tar Sands
10
16.9
5
25% 0
19.3
0.3
79%
44%
50% 1.2 0.7
1.1
Venezuela Canada
Exports to USA
3.2
USA Venezuela Canada
USA
Use
Use
Exports to USA
Projections assume USA consumption and imports are constant from 2007 to 2020 (US Department of Energy), USA imports the same proportion of Canadian production (89%) and the same rate from Venezuela (1.36 mb/d), which increases tar sands production to 50%. 6 March 2009
NE Ethanol Board Meeting
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Source: Liska and Perrin, submitted
Figure 2. Net energy yield and greenhouse gas emissions reduction* compared to gasoline from different types of corn-ethanol systems used as default scenarios in the BESS model (www.bess.unl.edu). NEY includes ethanol plus co-product energy credit minus energy inputs. 100
GHG Reduction (%)
80 NE-NGW IA-NG 60
NE-CL
HYP-NG
48% - 59% MW-NNG NE-NG MW-NG
40 Farrell et al.
20
NE-Coal
0 10
20
30
40
50
60
Net Energy Yield (GJ ha-1)
*Direct-effect: considered 6 March 2009 Land use change NEnot Ethanol Board Meeting
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Source: Liska et al, Journal of Industrial Ecology 13, 58-74 (2009)
BESS Model Simulation Scenarios Demonstrating the Spectrum of Ethanol Performance: Combination of Cropping Systems and Survey Data Cropping Data
Survey Data
Scenario ID
Crop production region
Biorefinery energy (dry mill)
Co-product type
NEW Survey Data
MW-NG
USA Midwest Avg.
natural gas-MW
mix dry-wet DGS
RFA-22
MW-NNG USA Midwest Avg.
natural gas-MW
mix dry-wet DGS
UNL-8
IA-NG
Iowa Avg.
natural gas-IA
mix dry-wet DGS
IDNR-9
NE-NG
Nebraska Avg.
natural gas-NE
mix dry-wet DGS
NDEQ-9
NE-NGW
Nebraska Avg.
natural gas-NE
Wet DGS
NDEQ-4
NE-CL
Nebraska Avg.
NG, closed-loop
Wet DG
NDEQ-4
NE-Coal
Nebraska Avg.
coal
Dry DGS
EPA
HYP-NG
natural gas-NE Progressive cropping (CSP) NE Ethanol Board Meeting
mix dry-wet DGS
NDEQ-9
6 March 2009
11
Figure 1. Biorefinery thermal energy efficiency in corn-ethanol production comparing previous estimates to more recent survey data from natural gas powered dry mills in the Corn Belt. -1
Thermal Energy Efficiency (MJ L )
16
Energy Input
14 12
Number of Biorefineries in Each Survey
33
22
8
9
9
4
Avg. of Wet & Dry Mills Dry Mill
10 8
27,300 Btu/gal
6 4 2
Fa rr Ar el go le nn ta e lE N B L C AM hr G R is M EE R t (2 i en an T 00 20 ew so 1) 08 n ab B & -v ui le .1 A lt Fu ss .8 Af a e o te lA c r2 (2 ss 00 00 oc 5, 7) #1 U N N (2 eb L 00 #2 ra 6) sk (2 N a 00 eb D Io 6EQ w ra 07 a sk ) # D 3 a N (2 D R EQ 00 #4 6) #3 (2 00 a (W 406 et ) D G 20 06 )
0
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NE Ethanol Board Meeting
year of operation
Source: Liska et al, 12 Journal of Industrial Ecology, 13, 58-74 (2009)
Table 1. Performance of the evaluated corn-ethanol systems in the Midwest Corn Belt states, Iowa, and Nebraska with selected input values and output metrics for eight default scenarios in the BESS model. MWNG
Simulation Scenarios:
MWNNG
IANG
NENG
NENGW
IA
NE
NE
NECL
NECoal
HYPNG
NE
NE
HYP
Agricultural Energy Inputs by Cropping Region Region
MW*
MW
-1
Energy inputs GJ Mg 1.7 1.7 1.4 2.3 Biorefinery Energy Inputs by Type, According to Survey Data Survey data
‡
1
Energy source
RFA
UNL
NG*
NG
2
4
3
IDNR
NDEQ
NG
NG
2.3 3a
NDEQ NG
1.9
2.3
3a
EPA**
NDEQ
Coal
NG
NDEQ CL
1.8 3
-1
7.69
4.62
6.95
6.85
5.44
5.44
6.10
6.85
-1
ns
2.98
ns
0.76
0
0
4.00
0.76
kWh L
0.185
0.174
0.185
0.185
0.185
0.291
0.230
0.185
-1
L kg
0.419
0.432
0.393
0.408
0.423
0.423
0.419
0.408
Dry DGS
%
35
66
22
32
0
0
100
32
Modified DGS
%
30
31
23
32
0
0
0
32
Wet DGS
%
35
3
55
36
100
100
0
36
0.13
0.13
0.13
0.13
0.13
0.26
0.13
0.13
Thermal energy TE, drying DG Electricity Conversion yield
MJ L
MJ L
-1
-1
Capital Energy MJ L System Performance Metrics -1 Net energy ratio MJ MJ Ethanol-to-petrol. GHG-intensity
GHG-reduction 6Ethanol March yield 2009
MJ MJ
-1
gCO2e MJ
-1
1.61
1.64
1.76
1.50
1.79
2.23
1.29
1.60
12.3
12.5
12.9
10.1
10.9
9.3
10.3
18.8
45.1
45.0
42.0
48.1
37.5
30.6
76.0
43.8
59
67
17
52
4,116
4,116
4,077
51
% -1
L ha
51 54 48 Ethanol Board Meeting 4,010 NE 4,134 4,205 3,970
5,59013
Source: Liska et al, Journal of Industrial Ecology 13, 58-74 (2009)
Table 2. Greenhouse gas emissions inventory of the corn-ethanol lifecycle for a natural gas dry mill biorefinery in Iowa (BESS model, IA-NG). Component
GHG emission category
-1
gCO2e MJ
Mg CO2e*
% of LC
Crop Production Nitrogen fertilizer, N Phosphorus fertilizer, P Potassium fertilizer, K Lime Herbicides Insecticides Seed Gasoline Diesel LPG Natural gas Electricity Depreciable capital N2O emissions** TOTAL
4.26 0.953 0.542 2.82 1.51 0.018 0.193 0.355 1.73 1.24 0 0.348 0.268 14.1 28.3
34,069 7,618 4,337 22,577 12,079 141 1,540 2,837 13,848 9,932 0 2,785 2,144 112,550 226,456
7.46 1.67 0.950 4.95 2.65 0.031 0.337 0.621 3.03 2.18 0 0.610 0.470 24.7 49.6
Natural gas input † NG Input: drying DGS Electricity input Depreciable capital Grain transportation TOTAL
19.7 0 6.53 0.458 2.11 28.8
157,356 0 52,201 3,663 16,851 230,071
34.5 0 11.4 0.802 3.69 50.4
0.216 -2.62 -11.4 -2.64 -16.5 1.40 42.0 42.0 92.0 50.0
1,731 -20,956 -91,501 -21,102 -131,828 11,196 335,895 335,895 735,715 399,819
0.379 -4.59 -20.0 -4.62 -28.9 0 100
Biorefinery
Co-Product Credit
Diesel Urea production Corn production Enteric fermentation-CH4 TOTAL Transportation of Ethanol from Biorefinery LIFE-CYCLE NET GHG EMISSIONS -1 GHG-intensity of ethanol, g CO2e MJ ‡ -1 GHG-intensity of gasoline , g CO2e MJ GHG reduction relative to gasoline, %
54.3%
Source: Liska et al, Journal of Industrial Ecology, 13, 58-74 (2009)
Table 3. Comparison of results from different models for life-cycle GHG emissions from dry-mill corn-ethanol systems (gCO2e MJ-1). Emissions Crop Production Biorefinery Co-Product Credit Denaturant Land Use Change GWI Gasoline GHG reduction, %
GREET
BEACCON
EBAMM
BESS (MW-NNG)
44 43 -17 70 92 24
41 31 -17 6 1 64 92 30
37 64 -25 76 92 17
29 30 -16 45 92 51
BESS (NE-NG)
BESS (NE-NGW)
35 31 -19 48 92 48
34 25 -22 38 92 59
GREET vs.1.8a: available from: http://www.transportation.anl.gov/software/GREET/ BEACCON vs.1.1: available from www.lifecycleassociates.com; largely based on GREET (error in article is corrected above). EBAMM: vs.1.1-1 (Farrell et al. 2006), "Ethanol Today" avg. 2001 ethanol plant, data for wet and dry mills, see Figure 1; BESS model default scenarios. BESS model has a dynamic co-product credit that is dependent on the GHG intensity of crop production and biorefinery ethanol yield.
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NE Ethanol Board Meeting
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Source: Liska et al, Journal of Industrial Ecology, 13, 58-74 (2009)
Most Sensitive Input Parameters On GHG Emissions Reductions & Net Energy Yield of Corn-Ethanol
1. Crop yield and nitrogen fertilizer efficiency 2. Biorefinery thermal energy inputs: MJ per liter (e.g. wet vs. dry distillers grains) 3. Conversion yield: liters ethanol per kg grain 4. Biorefinery electricity use
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NE Ethanol Board Meeting
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Figure 4B. Regional variability in corn-ethanol greenhouse gas intensity of corn production (kg CO2e Mg-1 grain), and life cycle GHG reductions of corn-ethanol compared to gasoline (%), assuming a new natural gas biorefinery.
261 48%
235 230
53%
250
54%
301
51%
48%
327 45%
290 49%
275
236 51%
48%
316
226 56%
347 44%
311
274
287
52%
47%
51%
47%
360 42%
365 45%
382 43%
426
kg CO2e per Mg Grain
40%
226 - 249
319 - 341
250 - 272
342 - 364
273 - 295
365 - 387
296 - 318
388 - 410 411- 423
Life-cycle GHG reduction compared to gasoline w/ new natural gas-powered ethanol plant, DDGS
Source: Liska et al, Journal of Industrial Ecology, 13, 58-74 (2009)
Improvements in Life-Cycle Energy Efficiency and Greenhouse Gas Emissions of Corn-Ethanol*
*Journal of Industrial Ecology, 2009: http://www3.interscience.wiley.com/cgi-bin/fulltext/121647166/PDFSTART.
Adam J. Liska1*, Haishun S. Yang1, Virgil R. Bremer2, Terry J. Klopfenstein2, Daniel T. Walters1, Galen E. Erickson2, and Kenneth G. Cassman1,3 Department of Agronomy and Horticulture1, Department of Animal Science2, Nebraska Center for Energy Sciences Research3, University of Nebraska-Lincoln,
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6 March 2009
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Conclusions from BESS analysis • Corn-ethanol systems are not accurately evaluated as an aggregate, due to differences in biorefinery designs, energy sources, and crop production practices • Based on state records and new surveys, natural gas powered dry mills (88% of the industry) can reduce GHG emissions by 48-59% compared to gasoline on average, which is a 2-3 fold greater reduction than reported in previous studies • Crop production represents 42-51% of life-cycle GHG emissions for typical USA corn-ethanol systems; needs accurate assessment • Co-product credits offset 26-38% of life-cycle GHGs • Accurate GHG analysis is essential for enabling ethanol producers to meet the GHG emissions reduction standards relative to gasoline for RSB, Energy Independence and Security Act of 2007 and state-level LowBoard Carbon 6 thresholds, March 2009 NE Ethanol MeetingFuel Standards 24
Land Use Change Impacts in Biofuels Lifecycle Emissions •
Land use change released 1/5 of global anthropogenic GHG emissions in the 1990’s, and 1/3 since 1750 (IPCC 2007)
•
High commodity prices are likely to drive agriculture expansion abroad (Morton et al. 2006; Naylor et al. 2007)
•
Indirect land use change from biofuels is difficult to estimate using models (Turner et al. 2007)
•
International land use policies are determined by foreign governments, often without enforcement (e.g. Brazil)
•
If indirect effects are assigned to biofuels, future research needs to more accurately quantify its magnitude and uncertainty
•
If indirect effects are assigned to biofuels, then the indirect GHG effects of gasoline need to be estimated as well 6 March 2009
NE Ethanol Board Meeting
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How to achieve sustainable corn ethanol • Avoid unacceptable rise in crop commodity prices (food vs fuel challenge) – Accelerate the rate of yield gain on existing farmland • Achieve a large net reduction in GHG emissions, including direct and indirect effects, and protect water and soil quality • The path forward: ecological intensification of corn ethanol systems – Accelerating the rate of gain in corn yields on existing farm land while reducing environmental footprint is the key 6 March 2009
NE Ethanol Board Meeting
26
Global Cereal Area Trends, 1966-2006 Grain Harvest Area (Ha6)
800 Total Grain Area (Barley+Maize+Millet+Oats+Rice+Sorghum+Wheat)
700
1966 - 1980 y = 639 + 4.44x r2 = 0.84
600
1981 - 2006 y = 724 - 1.83x 2 r = 0.74
500 Maize+Rice+Wheat y = 470 + 1.29x r2 = 0.66 400 1966
1976
1986
Year 6 March 2009
NE Ethanol Board Meeting
1996
2006
FSource: FAO data archives 27
Global Cereal Yield Trends, 1966-2006
-1
Grain Yield (kg ha )
5000 Maize Yield y = 2260 + 62.5x r2 = 0.94
4000
3000
2000
Rice Yield y = 2097 + 53.5x r2 = 0.98
Wheat Yield y = 1373 + 40.1x r2 = 0.97
1000 1966
1976
1986
1996
2006
Year THESE RATES OF INCREASE ARE NOT FAST ENOUGH TO MEET 6 EXPETED March 2009 DEMAND! Source: NE Ethanol FAOBoard dataMeeting archives.
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Rate of gain for all cereals is linear, not exponential, which means that the relative rate of gain is decreasing: relative rates of gain in 1966. Global rate of increase in yield of maize, rice, and wheat, 1966-2006. Crop
_Mean yield (kg ha-1)_ 1966 2006
Rate of gain¶ (kg ha-1 yr-1)
Proportional rate of gain (%) 1966 2006
Maize
2260
4759
62.5
2.77
1.31
Rice
2097
4235
53.5
2.55
1.26
Wheat
1373
2976
40.1
2.92
1.35
6 March 2009
NE Ethanol Board Meeting
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Rate of gain for all cereals is linear, not exponential, which means that the relative rate of gain is decreasing: relative rates of gain in 2006. Global rate of increase in yield of maize, rice, and wheat, 1966-2006. Crop
_Mean yield (kg ha-1)_ 1966 2006
Rate of gain¶ (kg ha-1 yr-1)
Proportional rate of gain (%) 1966 2006
Maize
2260
4759
62.5
2.77
1.31
Rice
2097
4235
53.5
2.55
1.26
Wheat
1373
2976
40.1
2.92
1.35
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Potential Ripple Effect: indirect land use change through conversion of tropical rainforest and wetlands to crop land
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NE Ethanol Board Meeting
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Potential Ripple Effect: unsustainable crop production on marginal land by poor farm families without other options
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USA Corn Yield Trends, 1966-20051 (embodies tremendous technological innovation)
GRAIN YIELD (kg ha-1)
12000
Transgenic (Bt) insect resistance Soil testing, balanced NPK fertilization, conservation tillage
10000
8000
Double-X to single-X hybrids
Reduced N fertilizer & irrigation?
6000
4000 Expansion of irrigated area, increased N fertilizer rates
2000 1965
1970
1975
1980
Integrated pest management
1985
1990
y = 112.4 kg/ha-yr [1.79 bu/ac-yr] 2 R = 0.80
1995
2000
2005
YEAR 6 March 2009
NE Ethanol Board Meeting
From: Convergence of Energy and Agriculture, www.cast-science.org
33
How to achieve sustainable corn ethanol • Avoid unacceptable rise in crop commodity prices (food vs fuel challenge) – Accelerate the rate of yield gain on existing farmland • Achieve a large net reduction in GHG emissions, including direct and indirect effects, and protect water and soil quality • The path forward: ecological intensification of corn ethanol systems by accelerating the rate of gain
in crop yields on existing farm land while reducing environmental footprint is the key 6 March 2009
NE Ethanol Board Meeting
34
USA Corn Yield Trends, 1966-20051 (embodies tremendous technological innovation)
GRAIN YIELD (kg ha-1)
12000 Soil testing, balanced NPK fertilization, conservation tillage
10000
8000
?
Transgenic (Bt) insect resistance
Double-X to single-X hybrids
Reduced N fertilizer & irrigation?
6000
4000 Expansion of irrigated area, increased N fertilizer rates
2000 1965
1970
1975
1980
Integrated pest management
1985
1990
y = 112.4 kg/ha-yr [1.79 bu/ac-yr] 2 R = 0.80
1995
2000
2005
YEAR 6 March 2009
NE Ethanol Board Meeting
From: Convergence of Energy and Agriculture, www.cast-science.org
35
Nebraska contest-winning and average yield trends No increase in yield potential ceiling since the 1980s, but a large exploitable yield gap still exists
25
20
15
350 300 250
Rainfed contest winners 208 kg/ha/yr
200 10
Irrigated average 114 kg/ha/yr
150 100
5 Rainfed average 89 kg/ha/yr
+38%
+50%
50
0 0 1965 1970 1975 1980 1985 6 March 2009 NE Ethanol Board1990 Meeting 1995 2000 2005
Year
Corn yield (bu/acre)
-1
Maize yield (Mg ha )
Irrigated contest winners
36
From: Cassman et al., 2003
Large exploitable gap between average and record yields. USA contest-winning corn field, 1997, Sterling NE. 310 bu/ac (ethanol yield of 800 gallons/ha): How to close the gap between highest possible yields (called yield potential) and average farm yields in an environmentally sustainable manner?
6 March 2009
NE Ethanol Board Meeting
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Need for Ecological Intensification¶ Development of high-yield crop production
systems that protect soil and environmental quality and conserve natural resources Characteristics of EI systems: ¾ Yields that reach 80-85% of genetic yield potential ¾ 70-80% N fertilizer uptake efficiency (vs 30-40% now) ¾ Improve soil quality (nutrient stocks, SOM) ¾ Integrated pest management (IPM) ¾ Contribute to net reduction in greenhouse gases ¾ Have a large net positive energy balance ¾ In irrigated systems: 90-95% water use efficiency ¶Cassman,
6 March 2009
1999. in Proc. Natl. Acad. Sci (USA):5952-5959 NE Ethanol Board Meeting
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Ecological Intensification Requires • Interdisciplinary, systems research – agronomy, soil science, plant physiology/pathology/entomology, geology/hydrology, meteorology, conventional breeding and molecular genetics, computer science, engineering, animal science, economics and policy.,,,,,
• Substantial funding—equivalent to support levels for genomics per FTE • Production- and landscape-scale research • An appropriate balance among simulation, validation, and measurement 6 March 2009
NE Ethanol Board Meeting
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High yields and high N fertilizer and water-use efficiency are possible with progressive management. Based on research in production-scale fields in NE. System
Yield* N fertilizer (kg ha-1) (kg ha-1) Advanced Irrigated
N fert. eff¶ (kg/kg N)
Maize-soybean
13,610
153
89.0 (+65%)
Maize-maize
13,110
196
66.9 (+24%)
Rainfed Maize
8,900
128
69.5 (+30%)
USA Maize (ave.)
8,470
157
53.9
*Based on combine harvest yields from the three CSP field sites.
¶N fertilizer efficiency in terms of kg grain produced per kg N fertilizer applied 6 March 2009
NE Ethanol Board Meeting
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Critical need for production-scale field research facilities to test critical hypotheses and models about energy yield, efficiency, and environmental impact of biofuel energy systems
Miscanthus
Proposed production-scale research fields for 2nd-gen biofuel crops
rainfed corn-soybean (65 ha)
Existing Production-scale research fields instrumented for C-cycle research
Switchgrass
Irrigated continuous corn (60 ha) 6 March 2009
Irrigated cornsoybean (60 ha)
2 km to a 500 head research cattle feedlot for by-product utilization research
NE Ethanol Board Meeting
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Second Gen Biofuels also face ILUC issues • Ecological intensification will be required for development of second-gen cellulosic biofuels based on dedicated perennial grass crops like switchgrass or miscanthus – First phase of investment will go to the more favorable “marginal” crop land due to its higher yield density potential; competition with corn and other crops • High and consistent yields will be critical for economic viability • Farmers will be motivated to raise yields and increase profits, which will favor higher input levels • Indirect land use change issues will surface – Lessons learned from knowledge and approaches to achieve ecological intensification of corn systems (high yields without negative environmental impact) will be critical for developing economically viable and environmentally sound cellulosic crop feedstock production systems 6 March 2009
NE Ethanol Board Meeting
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