Eugene S. Takle 1 and Zaitao Pan 2 Climate Change Impacts on Agriculture 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

 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 – Reanalysis driven –Control (current) climate (HadCM2) –Future (~ ) (HadCM2)

Validation: RegCM2  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) MeanSt. Dev. Diff Obs St. Dev Observed NCEP-Driven: RegCM HIRHAM Control-Driven: RegCM HIRHAM Scenario-Driven RegCM HIRHAM378 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 with Ames weather observations

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) MeanSt. Dev. Observed Yields Simulated by CERES with Observed weather RegCM2/NCEP HIRHAM/NCEP RegCM2/HadCM2 current HIRHAM/HadCM2 current RegCM2/HadCM2 future10, HIRHAM/HadCM2 future

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

= Dry-land crop

Gross Ecosystem Production is Related to Evapotranspiration* GEP = A*ET + B *Law et al., 2002: Agric. For. Meteorol. 113, Plant classA ( gCO 2 /kg H 2 O )B ( gCO 2 ) r 2 Evergreen conifers Deciduous broadleaf Grasslands Crop (wheat,corn, soyb) Corn/soybean (est)0.89 Tundra

Gross Ecosystem Production is Related to Evapotranspiration* GEP = A*ET + B *Law et al., Agric. For. Meteorol. 113, Plant classA ( gCO 2 /kg H 2 O )B ( gCO 2 ) r 2 Evergreen conifers Deciduous broadleaf Grasslands Crop (wheat,corn, soyb) Corn/soybean (est)0.89 Tundra

Corn/Soybean Evergreen Conifer Broadleaf Deciduous

Corn/Soybean Evergreen Conifer 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(w c, w j, w e ) w c - carboxylation/oxygenation (Rubisco) limiting rate w j - PAR (light) limiting rate w e - export limiting rate Photosynthesis in LSM, CLM, NOAH

Rubisco Export PAR Export Rubisco

V max25 is V max at 25C f(N) - sensitivity parameter to vegetation nitrogen content, N, is assumed to be 1 f(T v ) - sensitivity to leaf temperature T v - vegetation temperature (C) f(  ) - sensitivity to soil water content - is soil volumetric water content - quantum efficiency w c 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

Bondville, IL Calibration of Carbon Uptake Model (Meteorological conditions supplied by MM5) Observed Flux Modeled Flux

µmol CO 2 /s/m 2 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 C4 plants (Maize)

µmol CO 2 /s/m 2 Average Simulated CO 2 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:

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