vulnerability of us non-irrigated commodity crops to extremes of weather and climate
DESCRIPTION
Vulnerability of US Non-Irrigated Commodity Crops to Extremes of Weather and Climate. Eugene S. Takle Professor Department of Agronomy Department of Geological and Atmospheric Science Director, Climate Science Program Iowa State University Ames, IA 50011. - PowerPoint PPT PresentationTRANSCRIPT
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Vulnerability of US Non-Irrigated Commodity Crops to Extremes of
Weather and Climate
Eugene S. TakleProfessor
Department of AgronomyDepartment of Geological and Atmospheric Science
Director, Climate Science ProgramIowa State University
Ames, IA 50011
US CLIVAR/NCAR ASP Researcher ColloquiumStatistical Assessment of Extreme Weather Phenomena under Climate Change
NCAR Foothills Lab, Boulder, Colorado, USAJune 13-17, 2011
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Provocative thoughtsIf it weren’t for farmers we would all be hunter-
gatherers
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Provocative thoughtsIf it weren’t for farmers we would all be hunter-
gatherers
Farmers allow the rest of us time to sit around and think about statistics and climate change
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Provocative thoughtsIf it weren’t for farmers we would all be hunter-
gatherers
Farmers allow the rest of us time to sit around and think about statistics and climate change
Maybe we should help farmers do their job better (or at least as well as in the past)
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Provocative thoughts cont’d
No civilization that depended on irrigation for its food supply has survived.
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Provocative thoughts cont’d
No civilization that depended on irrigation for its food supply has survived.
It is our self-interest to assess the impact of climate extremes on rainfed agriculture
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Overview Rainfed agriculture; commodity crops of the
Midwest Production and impacts of technology and
weather/climate on trends Climate extremes Climate conditions leading to changes from
“expected yields” Recent climate changes and farmer adaptations Farmer weather-related decision calendar
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Suitability Index for Rainfed Agriculture
IPCC 2007
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Suitability Index for Rainfed Agriculture
IPCC 2007
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US Precipitation climatology
Corn and SoybeanWheat
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Midwest Regional Climate Center
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Midwest Regional Climate Center
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Midwest Regional Climate Center
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Midwest Regional Climate Center
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Midwest Regional Climate Center
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Midwest Regional Climate Center
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Midwest Regional Climate Center
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Midwest Regional Climate Center
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Midwest Regional Climate Center
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Midwest Regional Climate Center
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Commodity CropsCorn (maize)SoybeanWheatRice
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1860 1880 1900 1920 1940 1960 1980 20000
20
40
60
80
100
120
140
160
180
200Iowa Corn Yields
1866-2009
Year
b=0.033 bu/ac/year
b=1.066 bu/ac/year
2 bu/acre/year
Yiel
d, B
ushe
ls pe
r Acr
e
KR Lamkey
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US Corn Yields (Bushels/Acre)
1 bu = 25.5 kg1 acre = 0.405 ha
1 bu/ac= 63 kg/ha1.96 bu/ac/y
KR Lamkey
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1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
2006
2009
65758595
105115125135145155165
Yield Trend
Bush
els p
er a
cre
US Annual Corn yields
Chad Hart
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Excess water
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Factor
PercentCold Winter
0.9Decline in Price
6.6Drought
35.5Excess Moist/Precip/Rain 38.4Flood
2.6Freeze
0.1Hail
7.2Heat
1.2Hot Wind
0.0Mycotoxin (Aflatoxin) 1.0Plant Disease
0.3Winds/Excess Win
5.0Other
1.1Total
100.0
Insured Crop Loss for Corn
in Iowa*
*Milliman, Inc., based on 1995-2006 data from the Risk Management Agency Website (http://www.rma.usda.gov/)
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1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
2006
2009
25
30
35
40
45
50
Yield Trend
US Annual Wheat yields
Bush
els p
er a
cre
Chad Hart
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1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
2006
2009
-20.0%
-15.0%
-10.0%
-5.0%
0.0%
5.0%
10.0%
15.0%
US Wheat Yield Deviation from Expected
Chad Hart
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1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
2006
2009
20
25
30
35
40
45
50
Yield Trend
US Annual Soybean yields
Bush
els p
er a
cre
Chad Hart
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1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
2006
2009
-20.0%
-15.0%
-10.0%
-5.0%
0.0%
5.0%
10.0%
15.0%
20.0%
US Soybean Yield Deviation from Expected
Chad Hart
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Insured Crop Loss for
Soybeans in Iowa*
FactorPercent
Cold Winter 0.6
Decline in Price 4.8
Drought 56.8
Excess Moist/Precip/Rain 20.2Flood
1.4Freeze
0.1Hail
13.0Heat
0.9Hot Wind
0.0Mycotoxin (Aflatoxin)
0.0Plant Disease
1.1Winds/Excess Wind
0.2Other
1.1Total
100.0
*Milliman, Inc., based on 1995-2006 data from the Risk Management Agency Website (http://www.rma.usda.gov/)
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Much of the trend and part of reduced interannual variability is due to technology
But not all…
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1860 1880 1900 1920 1940 1960 1980 20000
20
40
60
80
100
120
140
160
180
200Iowa Corn Yields
1866-2009
Year
b=0.033 bu/ac/year
b=1.066 bu/ac/year
2 bu/acre/year
3 bu/acre/year
Yiel
d, B
ushe
ls pe
r Acr
e
KR Lamkey
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1960 1970 1980 1990 2000 2010 202010,000
15,000
20,000
25,000
30,000
35,000
29,300
f(x) = 299.126145172657 x − 572057.269203665R² = 0.978728283000678
Iowa Corn Plant Density1965 to 2008
Iowa Plant Density
Linear (Iowa Plant Density)
Year
Plan
t Den
sity
KR Lamkey
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Iowa State-Wide Average Data
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Kucharik. 2006. Agron. J. 98:1544Ave planting date one week earlier
C Kucharik
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Des Moines Airport Data
1983: 131988: 10
2009: 02010: 0
1977: 8
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Des Moines Airport Data
1983: 131988: 10
2009: 02010: 0
6 days ≥ 100oF in the last 22 years1977: 8
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Adapted from Folland et al. [2001]
Observed Summer (JJA) Daily Maximum Temperature Changes (K), 1976-2000
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“Warming Hole”: Regional climate model simulations of changes in daily maximum summertime temperatures between 1990s and 2040s
DTmax (JJA) ˚CPan, Z., R. W. Arritt, E. S. Takle, W. J. Gutowski, Jr., C. J. Anderson, and M. Segal,2004: Altered hydrologic feedback in a warming climate introduces a “warming hole”. Geophys. Res. Lett.31, L17109, doi:10.1029/2004GL020528.
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3.5oF increase in Dew Point Temperature
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Something is masking summer expression of the underlying warming
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NASA GISS transient model from 1980’s projected little or no warming in summer (Takle and Zhong, 1991)
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Totals above 40” 8 years2 years
Iowa State-Wide Average Data
30.8”
10% increase34.0”
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28.0” 37.0”32% increase
Cedar Rapids Data
Years with more than 40 inches
1
11
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4.2 days 57% increase 6.6 days
Cedar Rapids Data
1.25 inches
2
13Years having more than 8 days
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“One of the clearest trends in the United States observational record is an increasing frequency and intensity of heavy precipitation events… Over the last century there was a 50% increase in the frequency of days with precipitation over 101.6 mm (four inches) in the upper midwestern U.S.; this trend is statistically significant “
Karl, T. R., J. M. Melillo, and T. C. Peterson, (eds.), 2009: Global Climate Change Impacts in the United States. Cambridge University Press, 2009, 196pp.
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21.2 => 25.3 inches (19% increase) 12.1 => 10.5 inches (13% decrease)
Amplification of the Seasonality of PrecipitationSpring
WinterSummer
Fall
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21.2 => 25.3 inches (19% increase) 12.1 => 10.5 inches (13% decrease)
Amplification of the Seasonality of PrecipitationSpring
WinterSummer
Fall
NASA GISS model from 1980’s projected little or no warming in summer (Takle and Zhong, 1991)
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July Cloud Cover, Des Moines
19731974
19751976
19771978
19791980
19811982
19831984
19851986
19871988
19891990
19911992
19931994
19951996
19971998
19992000
20012002
20032004
20052006
20072008
20092010
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Year
Clou
d Co
ver
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JJA Cloud Cover, Des Moines
19731974
19751976
19771978
19791980
19811982
19831984
19851986
19871988
19891990
19911992
19931994
19951996
19971998
19992000
20012002
20032004
20052006
20072008
20092010
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Year
Clou
d Co
ver
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Iowa Agricultural Producers are Adapting to Climate Change:
Longer growing season: plant earlier, plant longer season hybrids, harvest later
Wetter springs: larger machinery enables planting in smaller weather windows
More summer precipitation: higher planting densities for higher yields Wetter springs and summers: more subsurface drainage tile is being
installed, closer spacing, sloped surfaces Fewer extreme heat events: higher planting densities, fewer pollination
failures Higher humidity: more spraying for pathogens favored by moist
conditions. more problems with fall crop dry-down, wider bean heads for faster harvest due to shorter harvest period during the daytime.
Drier autumns: delay harvest to take advantage of natural dry-down conditions HIGHER YIELDS!! Is it genetics or climate? Likely some of each.
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Lobell et al., Science 2008
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Ninth Climate Prediction Applications Science Workshop draft report
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Ninth Climate Prediction Applications Science Workshop draft report
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Issue Met condition Lead timeDecision
Soil compaction rain 4/25-5/15 1-2 daysto do field work
Hail damage hail occurrence 2 moins
Fertilization winter soil T, summer rain 6-9 mo fall/spr appl, amtPest managemnt dew duration, multiple stress 6-9 mo chem purchField work days rain 4/15-5/15 3-6 mo
corn=>soy, mktPollination failure heat 7/15-7/30 6 mocorn=>soy, mktDrought extnde heat &no rain, Jun-Aug 6-9 mocorn=>soy, ins, mktFreeze T< 0oC 3-6 mo
ins, mktGrowing deg days T > 10oC 3-6 mo
mktNighttime temp Tmin consistently > ~20oC 3-6 mo mktRental agreemnts favorable weather 12 mo change ratesAdaptive mngmnt local climate change 1-3 yr change machinery
Prices global weather 6-12 mocornsoy, mkt
Prices global weather 1-3 yrmachinery purchase
Prices global climate 1-3 yrland purchase
Soil erosion heavy precip 1-5 yrconserv. practices
Waterlogged soil increased precip 5-10 yr install subsoil drain tile
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For More Information Contact me directly:
[email protected] Climate Science Program website:
http://climate.engineering.iastate.edu/ Current research on regional climate and climate change at the
ISU Regional Climate Modeling Laboratory: http://rcmlab.agron.iastate.edu/
North American Regional Climate Change Assessment Program:
http://www.narccap.ucar.edu/
Or just Google Eugene Takle
Iowa precipitation analysis and simulation is a collaborative project under funding from the Iowa Flood Center (http://www.iowafloodcenter.org/)
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2008
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