Download Lorem Ipsum

Climate Change Impacts on
Agriculture
Eugene S. Takle1 and Zaitao Pan2
1Iowa
State University, Ames, IA USA
2St. Louis University, St. Louis, MO USA
Third ICTP Workshop on Theory and Use of Regional Climate Models, Trieste, Italy, 29 May - 9 June 2006
Outline
 Overview
of climate change impacts on
agriculture
 Modeling crop yield changes with
climate model output - an example
 Crop characteristics within land-surface
models
Climate Change Impacts on
Agriculture: Crops

Crop yields (winners and losers)
Climate Change Impacts on
Agriculture: Crops


Crop yields (winners and losers)
Pest changes
– Weed germination changes (soil temperature, soil oxygen)
– Pathogens (fungus, insects, diseases)
– Changes in migratory pest patterns
Climate Change Impacts on
Agriculture: Crops


Crop yields (winners and losers)
Pest changes
– Weed germination changes (soil temperature, soil oxygen)
– Pathogens (fungus, insects, diseases)
– Changes in migratory pest patterns

Water issues
–
–
–
–
Water availability for non-irrigated agriculture
Irrigation water availability
Water quality (nitrates, phosphates, sediment)
Soil water management
Climate Change Impacts on
Agriculture: Crops


Crop yields (winners and losers)
Pest changes
– Weed germination changes (soil temperature, soil oxygen)
– Pathogens (fungus, insects, diseases)
– Changes in migratory pest patterns

Water issues
–
–
–
–

Water availability for non-irrigated agriculture
Irrigation water availability
Water quality (nitrates, phosphates, sediment)
Soil water management
Spread of pollen from genetically modified crops
Climate Change Impacts on
Agriculture: Crops


Crop yields (winners and losers)
Pest changes
– Weed germination changes (soil temperature, soil oxygen)
– Pathogens (fungus, insects, diseases)
– Changes in migratory pest patterns

Water issues
–
–
–
–


Water availability for non-irrigated agriculture
Irrigation water availability
Water quality (nitrates, phosphates, sediment)
Soil water management
Spread of pollen from genetically modified crops
Food crops vs. alterantive crops
– Biofuels (ethanol, cellulosic; impact on water demand)
– Bio-based materials
– “Farm-a-ceuticals”
Climate Change Impacts on
Agriculture: Soil





Erosion changes (more extreme rainfall)
Salinization
Soil carbon changes
Nutrient deposition
Long-range transport of soil pathogens
Climate Change Impacts on
Agriculture: Animals
 Dairy
production (milk)
 Beef production (metabolism)
 Breeding success
 Stresses for confinement feeding operations
 Changes in disease ranges
 Changes in insect ranges
 Fish farming (reduced dissolved oxygen)
Modeling Crop Yield Changes
with Climate Model Output:
An Example
Climate Models and Crop Model
 RegCM2
and HIRHAM regional climate
models
 HadCM2 global model for control and future
scenario climate
 CERES Maize (corn) crop model (DSSATv3)
– Includes crop physiology
– Daily time step
– Uses Tmax, Tmin, precipitation, solar
radiation from the regional model
CERES Maize
 Phenological
development sensitive to
weather
 Extension growth of leaves, stems,
roots
 Biomass accumulation and partitioning
 Soil water balance and water use by
crop
 Soil nitrogen transformation, uptake by
crop, partitioning
Simulation Domain and Period
 Domain
– Continental US
 Time
Period
– 1979-88 Reanalysis driven
– Control (current) climate (HadCM2)
– Future (~2040-2050) (HadCM2)
Validation: RegCM2
that 0.5oC bias for daily maximum
temperatures
 Less than 0.5oC bias for daily minimum
temperature
 Precipitation:
 Less
Growing Season Precipitation at Ames, IA
Observed
P (mm)
800
Simulated
600
400
200
0
79
80
81
82
83
84
Year
85
86
87
88
Histogram of May-Aug. Daily Precipitation at
Ames
Observed
40
30
20
10
0
Events
Simulated
2.5
7.5 12.5 17.5 22.5 27.5 32.5 37.5 42.5 47.5
Daily Precipitation (mm)
Validation: HIRHAM
+1.5oC bias for daily maximum
temperatures
 About +5oC bias for daily minimum
temperature
 Precipitation:
 About
Growing Season Precipitation
Precipitation (mm)
700
600
500
HIRHAM
Observed
RegCM2
400
300
200
100
0
80
81
82
83
Year
84
85
86
Growing Season Precipitation Summary
(all values in mm)
Mean St. Dev. Diff Obs St. Dev
Observed
NCEP-Driven:
RegCM2
HIRHAM
Control-Driven:
RegCM2
HIRHAM
Scenario-Driven
RegCM2
HIRHAM
446
114
341
275
87
73
441
313
102
77
483
378
105
80
-76
-137
122
151
Validation: Yields
 Reported
 Calculated
by crop model by using
– Observed weather conditions at Ames
station
– RegCM2 with NCEP/NCAR reanalysis bc
– HIRHAM with NCEP/NCAR reanalysis bc
Corn Yields at Ames, IA
Reported
Yield (kg/ha)
20000
Simulated
15000
Simulated with
Ames weather
observations
10000
5000
0
79
80
81
82
83
84
85
86
87
88
87
88
Year
Simulated Corn Yields at Ames, IA
RegCM2 driven
Yield (kg/ha)
20000
Observation driven
15000
10000
5000
0
79
80
81
82
83
Year
84
85
86
Corn Yields at Ames, IA
Reported
Yield (kg/ha)
20000
Simulated
15000
10000
5000
0
79
80
81
82
83
84
85
86
87
88
Year
Simulated Corn Yields at Ames, IA - NCEP
Driven
Yield (kg/ha)
RegCM2
HIRHAM
15000
10000
5000
0
80
81
82
83
Year
84
85
86
Yields Calculated by
CERES/RCM/HadCM2
 HadCM2
current climate -> RegCM2 -> CERES
 HadCM2 current climate -> HIRHAM -> CERES
 HadCM2 future scenario climate -> RegCM2 ->
CERES
 HadCM2 future scenario climate -> HIRHAM ->
CERES
Yield Summary
(all in kg/ha)
Observed Yields
Simulated by CERES with
Observed weather
RegCM2/NCEP
HIRHAM/NCEP
Mean St. Dev.
8381 1214
8259 4494
5487 3796
3446 2716
RegCM2/HadCM2 current
HIRHAM/HadCM2 current
5002 1777
6264 3110
RegCM2/HadCM2 future
HIRHAM/HadCM2 future
10,610 2721
6348 1640
Summary

Crop model offers more detailed plant physiology and
dynamic vegetation for regional models

Current versions of crop models show skill with mean
yield but variability is a challenge

Crop model exposes and amplifies vegetationsensitive features of regional climate model
Need Ensembles
 Ensembles
of global models
Need Ensembles
 Ensembles
of global models
 Ensembles of regional models
Need Ensembles
 Ensembles
of global models
 Ensembles of regional models
 Ensembles of crops
Need Ensembles
 Ensembles
of global models
 Ensembles of regional models
 Ensembles of crops
 Ensembles of regions
Need Ensembles
 Ensembles
of global models
 Ensembles of regional models
 Ensembles of crops
 Ensembles of regions
 Ensembles of minds!!
Crop Characteristics within
Land-Surface Models:
Work in Progress
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
2 = Dry-land crop
Gross Ecosystem Production is Related
to Evapotranspiration*
GEP = A*ET + B
Plant class
A (gCO /kg H O) B (gCO )
r2
Evergreen conifers
3.43
2.43
0.58
Deciduous broadleaf
3.42
-0.35
0.78
Grasslands
3.39
-67.9
0.72
Crop (wheat,corn, soyb)
3.06
-31.6
0.50
Corn/soybean
5.40
-120 (est)
0.89
Tundra
1.46
-0.57
0.44
2
*Law et al., 2002: Agric. For. Meteorol. 113, 97-120
2
2
Gross Ecosystem Production is Related
to Evapotranspiration*
GEP = A*ET + B
Plant class
A (gCO /kg H O) B (gCO )
r2
Evergreen conifers
3.43
2.43
0.58
Deciduous broadleaf
3.42
-0.35
0.78
Grasslands
3.39
-67.9
0.72
Crop (wheat,corn, soyb)
3.06
-31.6
0.50
Corn/soybean
5.40
-120 (est)
0.89
Tundra
1.46
-0.57
0.44
2
*Law et al., Agric. For. Meteorol. 113, 97-120
2
2
Wind River, CA, s ite 2
5
CO2 Flux
(umol/s/m**2)
0
-5
1
6
11
16
21
26
31
36
41
46
51
56
61
66
71
76
81
Evergreen Conifer
-20
91
96 101 106 111 116 121
FCO2
-10
-15
86
FCO2 model
-25
Day
Walker Brra, site 6
5
CO2 flux
(mmol/day)
0
-5 1
5
9
13 17
21
25 29
33
37 41
45
49
53 57
61
65 69
73
77 81
85
89 93
97 101 105 109 113 117 121
91
97 103 109 115 121
-10
-15
Broadleaf Deciduous
-20
-25
Day
Bondville (40.00,-88.29) -4
5
CO2 flux
(umol/day
0
-5
1
7
13
19
25
31
37
43
49
55
61
-10
-15
Corn/Soybean
-20
-25
Day
67
73
79
85
Wind River, CA, s ite 2
5
CO2 Flux
(umol/s/m**2)
0
-5
1
6
11
16
21
26
31
36
41
46
51
56
61
66
71
76
81
Evergreen Conifer
-20
91
96 101 106 111 116 121
FCO2
-10
-15
86
FCO2 model
-25
Day
Walker Brra, site 6
5
CO2 flux
(mmol/day)
0
-5 1
5
9
13 17
21
25 29
33
37 41
45
49
53 57
61
65 69
73
77 81
85
89 93
97 101 105 109 113 117 121
-10
-15
Broadleaf Deciduous
-20
-25
Day
Need to fix this
Bondville (40.00,-88.29) -4
5
CO2 flux
(umol/day
0
-5
1
7
13
19
25
31
37
43
49
55
61
-10
-15
Corn/Soybean
-20
-25
Day
67
73
79
85
91
97 103 109 115 121
Photosynthesis in LSM, CLM, NOAH
Leaf photosynthesis (A) is computed as
minimum of three independent limiting
carbon flux rates in the plants:
A=min(wc, wj, we)
wc - carboxylation/oxygenation (Rubisco)
limiting rate
wj - PAR (light) limiting rate
we - export limiting rate
Wind River, CA, site 2
CO2 Flux (umold/s/m**2)
70
60
wj
wc
we
50
PAR
40
30
Export
20
10
0
1
11
21
31
41
51
61
71
81
91
10
111
Rubisco
Day
Bondville, IL, case4
CO2 Flux (umol/s/m**2)
70
60
wj
wc
we
PAR
50
40
30
Export
20
10
0
1
11
21
31
41
51
61
Day
71
81
91
10
111
Rubisco
wc is proportional to maximum
carboxylation capacity (Vmax), where
(T v 25) / 10
max 25 max
Vmax  V
a
f ( N ) f (Tv ) f ( )
Vmax25 is Vmax at 25C
f(N) - sensitivity parameter to vegetation nitrogen
content, N, is assumed to be 1
f(Tv) - sensitivity to leaf temperature
Tv
- vegetation temperature (C)
f() - sensitivity to soil water content
(T v25) / 10
amax
- is soil volumetric water content
 220000  710(Tv  273.16) 1
f (Tv)  [1  exp(
)] - quantum efficiency
8.314(Tv  273.16)
Calibration of Carbon Uptake Model
(Meteorological conditions supplied by observations)
Bondville, IL
Observed Flux
Modeled Flux
Modeled Flux
• CERES seasonal LAI
• 50% plants C4
• More representative
root distribution
Calibration of Carbon Uptake Model
(Meteorological conditions supplied by MM5)
Bondville, IL
Observed Flux
Modeled Flux
Modeled Flux
Average Simulated CO2 Flux 1 May – 31 August 1999
Default vegetation
µmol CO2/s/m2
Average Simulated CO2 Flux 1 May – 31 August 1999
Full accounting for C4 plants (Maize)
µmol CO2/s/m2
Average Simulated CO2 Flux 1 May – 31 August 2001
Full accounting for C4 plants (Maize)
µmol CO2/s/m2
Fan et al., 1998: A large terrestrial carbon sink in North America... Science 282: 442-446.
Future Work
 Evaluate
role of specialized crops in
moisture recycling (fivefold increase in
GEP requires doubling of ET).
 Use MM5 with modified crop
characteristics to investigate interactive
climate sensitivity to crop development