Climate Change Impacts on Agriculture Eugene S Takle

Climate Change Impacts on Agriculture Eugene S. Takle 1 and Zaitao Pan 2 1 Iowa State University, Ames, IA USA 2 St. 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 Reg. CM 2 and HIRHAM regional climate models Had. CM 2 global model for control and future scenario climate CERES Maize (corn) crop model (DSSATv 3) – 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 (Had. CM 2) – Future (~2040 -2050) (Had. CM 2)

Validation: Reg. CM 2 Less that 0. 5 o. C bias for daily maximum temperatures Less than 0. 5 o. C bias for daily minimum temperature Precipitation:

Validation: HIRHAM About +1. 5 o. C bias for daily maximum temperatures About +5 o. C bias for daily minimum temperature Precipitation:

Growing Season Precipitation Summary (all values in mm) Mean St. Dev. Diff Obs St. Dev Observed NCEP-Driven: Reg. CM 2 HIRHAM Control-Driven: Reg. CM 2 HIRHAM Scenario-Driven Reg. CM 2 HIRHAM 446 114 341 275 87 -76 73 -137 441 313 102 77 483 378 105 80 122 151

Validation: Yields Reported Calculated by crop model by using – Observed weather conditions at Ames station – Reg. CM 2 with NCEP/NCAR reanalysis bc – HIRHAM with NCEP/NCAR reanalysis bc

Simulated with Ames weather observations

Yields Calculated by CERES/RCM/Had. CM 2 current climate -> Reg. CM 2 -> CERES Had. CM 2 current climate -> HIRHAM -> CERES Had. CM 2 future scenario climate -> Reg. CM 2 -> CERES Had. CM 2 future scenario climate -> HIRHAM -> CERES

Yield Summary (all in kg/ha) Observed Yields Mean St. Dev. 8381 1214 Simulated by CERES with Observed weather Reg. CM 2/NCEP HIRHAM/NCEP 8259 4494 5487 3796 3446 2716 Reg. CM 2/Had. CM 2 current HIRHAM/Had. CM 2 current 5002 1777 6264 3110 Reg. CM 2/Had. CM 2 future HIRHAM/Had. CM 2 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 of global models

Need Ensembles of global models Ensembles of regional models

Need Ensembles of global models Ensembles of regional models Ensembles of crops

Need Ensembles of global models Ensembles of regional models Ensembles of crops Ensembles of regions

Need 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 (g. CO /kg H O) B (g. CO ) r 2 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 (g. CO /kg H O) B (g. CO ) r 2 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

Evergreen Conifer Broadleaf Deciduous Corn/Soybean

Evergreen Conifer Broadleaf Deciduous Need to fix this Corn/Soybean

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

PAR Export Rubisco

wc is proportional to maximum carboxylation capacity (Vmax), where Vmax 25 is Vmax at 25 C 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 - is soil volumetric water content - quantum efficiency

Calibration of Carbon Uptake Model (Meteorological conditions supplied by observations) Bondville, IL Observed Flux Modeled Flux • CERES seasonal LAI • 50% plants C 4 • More representative root distribution

Calibration of Carbon Uptake Model (Meteorological conditions supplied by MM 5) Bondville, IL Observed Flux Modeled Flux

Average Simulated CO 2 Flux 1 May – 31 August 1999 Default vegetation µmol CO 2/s/m 2

Average Simulated CO 2 Flux 1 May – 31 August 1999 Full accounting for C 4 plants (Maize) µmol CO 2/s/m 2

Average Simulated CO 2 Flux 1 May – 31 August 2001 Full accounting for C 4 plants (Maize) µmol CO 2/s/m 2 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 MM 5 with modified crop characteristics to investigate interactive climate sensitivity to crop development