Eugene S. Takle1 and Zaitao Pan2

Climate Change Impacts on Agriculture

1Iowa State University, Ames, IA USA2St. 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

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)

Climate Change Impacts on Agriculture: Animals

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

Less that 0.5oC bias for daily maximum temperatures

Less than 0.5oC bias for daily minimum temperature

Precipitation:

Growing Season Precipitation at Ames, IA

0

200

400

600

800

79 80 81 82 83 84 85 86 87 88

Year

P (m

m)

Observed

Simulated

Histogram of May-Aug. Daily Precipitation at Ames

01020

3040

2.5 7.5 12.5 17.5 22.5 27.5 32.5 37.5 42.5 47.5

Daily Precipitation (mm)

Even

ts

Observed

Simulated

Validation: HIRHAM

About +1.5oC bias for daily maximum temperatures

About +5oC bias for daily minimum temperature

Precipitation:

Growing Season Precipitation

0

100

200

300

400

500

600

700

80 81 82 83 84 85 86

Year

Prec

ipita

tion

(mm

)

HIRHAM

Observed

RegCM2

Growing Season Precipitation Summary(all values in mm)

Mean St. Dev. Diff Obs St. Dev

Observed 446 114

NCEP-Driven:RegCM2 341 87 -76 122HIRHAM 275 73 -137 151

Control-Driven:RegCM2 441 102HIRHAM 313 77

Scenario-DrivenRegCM2 483 105HIRHAM 378 80

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

Simulated Corn Yields at Ames, IA

0

5000

10000

15000

20000

79 80 81 82 83 84 85 86 87 88

Year

Yie

ld (

kg

/ha

)

RegCM2 driven

Observation driven

Corn Yields at Ames, IA

0

5000

10000

15000

20000

79 80 81 82 83 84 85 86 87 88

Year

Yie

ld (k

g/h

a)

Reported

Simulated

Simulated withAmes weatherobservations

Simulated Corn Yields at Ames, IA - NCEP Driven

0

5000

10000

15000

80 81 82 83 84 85 86

Year

Yie

ld (

kg

/ha

)

RegCM2

HIRHAM

Corn Yields at Ames, IA

0

5000

10000

15000

20000

79 80 81 82 83 84 85 86 87 88

Year

Yie

ld (k

g/h

a)

Reported

Simulated

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)

Mean St. Dev.Observed Yields 8381 1214

Simulated by CERES withObserved weather 8259 4494RegCM2/NCEP 5487 3796HIRHAM/NCEP 3446 2716

RegCM2/HadCM2 current 5002 1777HIRHAM/HadCM2 current 6264 3110

RegCM2/HadCM2 future 10,610 2721HIRHAM/HadCM2 future 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 vegetation-sensitive 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

*Law et al., 2002: Agric. For. Meteorol. 113, 97-120

Plant class A (gCO2/kg H2O) B (gCO2) 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

Gross Ecosystem Production is Related to Evapotranspiration*

GEP = A*ET + B

*Law et al., Agric. For. Meteorol. 113, 97-120

Plant class A (gCO2/kg H2O) B (gCO2) 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

Bondville (40.00,-88.29) -4

-25

-20

-15

-10

-5

0

5

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121

Day

CO

2 f

lux

(um

ol/

day

Wind River, CA, site 2

-25

-20

-15

-10

-5

0

5

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101 106 111 116 121

Day

CO

2 F

lux

(um

ol/s

/m**

2)

FCO2

FCO2 model

Corn/Soybean

Evergreen Conifer

Walker Brra, site 6

-25

-20

-15

-10

-5

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

Day

CO

2 fl

ux

(mm

ol/d

ay)

Broadleaf Deciduous

Bondville (40.00,-88.29) -4

-25

-20

-15

-10

-5

0

5

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121

Day

CO

2 f

lux

(um

ol/

day

Wind River, CA, site 2

-25

-20

-15

-10

-5

0

5

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101 106 111 116 121

Day

CO

2 F

lux

(um

ol/s

/m**

2)

FCO2

FCO2 model

Corn/Soybean

Evergreen Conifer

Walker Brra, site 6

-25

-20

-15

-10

-5

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

Day

CO

2 fl

ux

(mm

ol/d

ay)

Broadleaf Deciduous

Need to fix this

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

Photosynthesis in LSM, CLM, NOAH

Wind River, CA, site 2

0

10

20

30

40

50

60

70

1 11 21 31 41 51 61 71 81 91 10 111

Day

CO

2 F

lux

(u

mo

ld/s

/m**

2)

wj wc we

Bondville, IL, case4

0

10

20

30

40

50

60

70

1 11 21 31 41 51 61 71 81 91 10 111

Day

CO

2 F

lux

(um

ol/s

/m**

2) wj wc we

Rubisco

Export

PAR

PAR

Export

Rubisco

Vmax25 is Vmax at 25Cf(N) - sensitivity parameter to vegetation nitrogen

content, N, is assumed to be 1 f(Tv) - sensitivity to leaf temperatureTv - vegetation temperature (C) f() - sensitivity to soil water content

- is soil volumetric water content

- quantum efficiency

Vmax Vmax 25amax(T v 25) / 10 f (N ) f (Tv ) f ( )

10/)25(max

vTa

1)])16.273(314.8

)16.273(710220000exp(1[)(

Tv

TvTvf

wc is proportional to maximum carboxylation capacity (Vmax), where

Calibration of Carbon Uptake Model(Meteorological conditions supplied by observations)

Bondville, IL

• CERES seasonal LAI

• 50% plants C4

• More representative root distribution

Observed Flux

Modeled Flux

Modeled Flux

Bondville, IL

Calibration of Carbon Uptake Model(Meteorological conditions supplied by MM5)

Observed Flux

Modeled Flux

Modeled Flux

µmol CO2/s/m2

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)

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