Section III Climate Change and Agronomic Production © 2005 by Taylor & Francis Group, LLC 10 Climate Change, Agriculture, and Sustainability CYNTHIA ROSENZWEIG AND DANIEL HILLEL CONTENTS 10.1 10.2 10.3 10.4 Climate Change 245 Goals of Impact Studies 247 Agro-Ecosystem Processes 247 What We Have Learned 249 10.4.1 Agriculture Regions Will Experience Change over Time 249 10.4.2 Effects on Agricultural Production Systems Will Be Heterogeneous 251 10.4.3 Agricultural Production in Many Developing Countries Is Especially Vulnerable 251 10.4.4 Long-Term Effects on Agriculture Are Negative 253 10.4.5 Agricultural Systems Can Adapt, but Not Completely 253 243 © 2005 by Taylor & Francis Group, LLC 244 Rosenzweig and Hillel 10.5 Key Interactions 254 10.5.1 El Niño-Southern Oscillation 254 10.5.2 Water Resources 256 10.5.3 Agricultural Pests 256 10.6 Mitigation and Adaptation Responses 258 10.6.1 Mitigation 259 10.6.2 Adaptation 260 10.7 Interactions 262 10.7.1 Research Pathways 263 10.7.1.1 Climate Variability and Change 263 10.7.1.2 Observed Effects of Warming Trends 264 10.7.1.3 Global and Local Scales 265 10.8 Conclusion 266 References 266 The first global climate model experiments projecting the atmospheric responses of increasing carbon dioxide (CO2) and other greenhouse gases were published in the early 1980s Soon after, research began on the agricultural implications of the changing atmospheric composition and its projected climate shifts As the primary land-based human activity most intimately connected with climate and as the very foundation for human nutrition and indeed survival, agriculture naturally became a key focus for early climate change impact studies Through the ensuing two decades, scientists have employed a variety of analytic approaches in a multitude of studies to answer such research questions as: What might be the major effects of climate changes in the 21st century? Are some regions likely to gain, while others lose? What response measures are indicated? How climate change affects agriculture and how agriculture responds to a changing climate will invariably shape the sustainability of this vital sector Research in the area of climate change impacts on agriculture has involved field experiments, regression analyses, and modeling studies The fields concerned have included agronomy, resource economics, and geography Climate change and © 2005 by Taylor & Francis Group, LLC Climate Change, Agriculture, and Sustainability 245 agriculture studies continue, with broad-brush explorations giving way to more detailed studies of biophysical processes and social responses In this chapter, we review some of the main lessons learned from two decades of research on climate change and agriculture, and then delineate several pathways for continuing research that will help to elucidate further the interactions of climate change and agricultural sustainability 10.1 CLIMATE CHANGE Climate change projections are fraught with much uncertainty in regard to both the rate and magnitude of temperature and precipitation alterations in the coming decades This uncertainty derives from a lack of precise knowledge of how climate system processes will change and of how population growth, economic and technological development, and land use will proceed in the coming century (Intergovernmental Panel on Climate Change [IPCC], 2000, 2001) Nevertheless, three points regarding climate change can be made with some certainty (Figure 10.1) First, greenhouse gas concentrations have increased progressively since the beginning of the Industrial Revolution Second, the natural presence of greenhouse gases is known to affect the planetary energy balance, causing the planet to be warmer than it would be otherwise Thus, any increases in greenhouse gases will tend to enhance the natural “greenhouse effect.” Third, the planet has indeed been warming over the last century, especially in the most recent two and a half decades The IPCC has attributed the observed warming over the last century to anthropogenic emissions of greenhouse gases, especially carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) (IPCC, 2001) Thus, anthropogenic emissions of greenhouse gases appear to be altering our planetary energy balance and to be manifested in a large-scale warming of the planet If warming continues at the global scale, the association among greenhouse gas emissions, greenhouse effect, and surface warming will trend toward greater and greater certainty The ultimate significance of the climate change issue is related to its planetary scale © 2005 by Taylor & Francis Group, LLC 246 Rosenzweig and Hillel A SUN Some solar radiation is reflected by the Earth and the atmosphere Solar radiation passes through the clear atmosphere Some of the infrared radiation passes through the atmosphere, and some is absorbed ane re-emitted in all directions by greenhouse gas molecules The effect of this is to warm the Earth's surface and the lower atmosphere ATMOSPHERE EARTH Most radiation is absorbed by the Earth's surface and warms it Infrared radiation is emitted from the Earth's surface Carbon Dioxide Concentrations B parts per million Ice Core Data 370 360 350 340 330 320 310 300 290 Mauna Loa (Hawaii) 1860 1880 1900 1920 1940 1960 1980 2000 C Global Average Temperature C F 57.92 14.4 14.3 57.74 14.2 57.56 14.1 57.38 14.0 57.20 13.9 57.02 13.8 56.84 13.7 56.66 13.6 56.48 year average 13.5 56.30 1860 1880 1900 1920 1940 1960 1980 2000 © 2005 by Taylor & Francis Group, LLC Climate Change, Agriculture, and Sustainability 247 Figure 10.1 (opposite page) The three certainties of global climate change: (A) the greenhouse effect and planetary energy balance; (B) atmospheric concentrations of greenhouse gases, 1860 to present; and (C) mean global surface temperature, 1860 to present (From OSTP 1997 Climate Change: State of Knowledge Office of Science and Technology Policy, Washington, DC.) 10.2 GOALS OF IMPACT STUDIES Many of the climate change studies done to date, particularly the early ones, were undertaken to aid national policymakers to assess the significance of global climate change and its implications for broad regions as well as for whole countries These studies are thus “policy relevant” in the sense that they may contribute to national decisions on whether and how to participate in the U.N Framework Convention on Climate Change (UNFCCC) and the Kyoto Protocol Questions here revolve around how serious the ultimate warming may be; who may be the “winners and losers”; and what the potential is for adaptation in broad-brush terms Recently, attention has been turning to how to respond to global climate change, including more detailed explorations of adaptation strategies and adaptive capacities at finer spatial scales — even down to individual villages Many of these adaptation studies are focused on defining vulnerability and represent a link between the experience of current climate extremes, disaster management, and potential decadal-tocentury warming A further shift in focus involves the potential role of carbon sequestration in climate change mitigation, and to what extent this can reduce the anthropogenic buildup of greenhouse gases in the atmosphere 10.3 AGRO-ECOSYSTEM PROCESSES Determining what the net effect of a changing climate may be on an agro-ecosystem is complicated due to the interactions of several simultaneous biophysical processes In some cases, changes in climate may be beneficial, while in others they may be detrimental (Figure 10.2) On the beneficial side, © 2005 by Taylor & Francis Group, LLC 248 Rosenzweig and Hillel Possible benefits CO2 Carbon dioxide fertilization Longer growing season Increased precipitation Faster growing periods Increased flooding and salinization Possible drawbacks More frequent droughts Pest Heat stress Figure 10.2 Agro-ecosystem processes and a changing climate (Redrawn from Bongaarts, J 1994 Sci Am., 270:36–42.) increasing levels of atmospheric CO2 have been shown to increase photosynthesis rates and to increase stomatal resistance in crops, leading to overall increased water-use efficiency (Kimball et al., 2002) These processes have been called “CO2 fertilization.” Another beneficial impact would be the prolongation of crop growing seasons in areas where they are now limited by cold temperature, that is, at high latitudes and high elevations A further benefit for crops may accrue in some semiarid locations from increased precipitation, since a warmer atmosphere can hold more water vapor However, the location and extent of any such regions of enhanced precipitation is not known precisely, due primarily to the difficulty of simulating the regional-scale hydrological cycle in global climate models A warmer and more variable climate is likely to have negative as well as positive effects on agricultural regions around the world Potential negative effects include more frequent droughts and floods, heat stress, increased outbreaks of diseases and pests, shortening of crop growing periods, and — in coastal regions — increased flooding and salination due to sea-level rise and impeded drainage While the absolute © 2005 by Taylor & Francis Group, LLC Climate Change, Agriculture, and Sustainability 249 magnitude of precipitation change in any one region or decade is not predictable, global climate models project that hydrological regimes are likely to become more intense as well as more variable (IPCC, 2001) Episodes of heat stress are known to be detrimental to crops, especially during critical growth stages, and such episodes are likely to be more frequent and prolonged in the future An important, albeit counterintuitive, negative effect that warming has on crops is the shortening of their growing period (not their overall growing season) Warmer temperatures speed crops through their growing cycle, especially the grain-filling stage Total yield is a product of the rate and duration of grain filling, which is determined by accumulated temperature Since higher temperatures shorten the duration of grain filling, higher temperature tends to exert a negative pressure on the yield of most annual crops Finally, in agricultural regions close to the ocean, sealevel rise and associated saltwater intrusion and flooding can harm crops through impeded soil aeration and salination This is likely to be most serious in countries such as Egypt and Bangladesh, which have major crop-growing areas in lowlying coastal regions 10.4 WHAT WE HAVE LEARNED 10.4.1 Agriculture Regions Will Experience Change over Time Due to all the agro-ecosystem processes described above, it is fairly certain that agricultural regions will experience some changes, and that these changes will evolve continuously through the coming decades Shifts in crop zonation are likely to occur, with some crop types expanding their ranges and others contracting Given the range of projected temperature and precipitation changes from global climate models, and the unknown degree of manifestation of direct CO2 effects on crops growing in farmers’ fields, however, the magnitudes and rates of these changes are uncertain © 2005 by Taylor & Francis Group, LLC 250 Rosenzweig and Hillel Plant growth rate T opt Tmin T max Temperature Figure 10.3 Temperature response curve for biological processes (From Rosenzweig, C., and D Hillel 1998 Climate Change and the Global Harvest: Potential Impacts of the Greenhouse Effect on Agriculture Oxford University Press, New York With permission.) The interactions between beneficial and detrimental agro-ecosystem processes are likely to change over time for several reasons First, as biophysical responses move through their temperature–response curves, responses to change in temperature may shift from positive to neutral, and then to negative (Figure 10.3) Another reason that climate change effects are likely to be transformed over time is the potential for decadal shifts in the hydrological cycle While it is difficult to predict the direction of change in any specific agricultural region, global climate models show increased decadal variability in hydrological regimes Finally, as crop breeding and pest species evolve in the coming decades under changing climate conditions, new agro-ecosystem weeds, insects, and diseases are likely to emerge, and the adjustment to these may be costly © 2005 by Taylor & Francis Group, LLC Climate Change, Agriculture, and Sustainability 251 100 Index, 0=no change 80 60 40 North plains 20 Appalachian Southeast Delta Northeast -20 Lake states Cornbelt South plains Mountain Pacific -40 -60 2030 2090 2030 w/adapt 2090 w/adapt -80 Figure 10.4 Simulated percentage changes in U.S regional agricultural production, with adaptation, under the Canadian Climate Center scenario (From Reilly, J., F Tubiello, B McCarl, et al 2003 Climatic Change, 57:43–69 With permission.) 10.4.2 Effects on Agricultural Production Systems Will Be Heterogeneous Global studies done to date show that negative and positive effects will occur both within countries and across the world In large countries such as the United States, Russia, Brazil, and Australia, agricultural regions will likely be affected quite differently (Figure 10.4) Some regions will experience increases in production and some declines (e.g., Reilly et al., 2003) At the international level, this implies possible shifts in comparative advantage for export crop production This also implies that adaptive responses to climate change will necessarily be complex and varied 10.4.3 Agricultural Production in Many Developing Countries Is Especially Vulnerable Despite general uncertainties about the rate and magnitude of climate change and about consequent hydrological changes, regional and global studies have consistently shown that © 2005 by Taylor & Francis Group, LLC Climate Change and Crop Production 389 convergence and divergence patterns) and temporal changes associated with smaller horizontal scales (temporal variability in wind speed, precipitation frequency, precipitation intensity, and cloudiness) Regional models use results of global models as lateral boundary conditions and simulate (by use of essentially the same procedures as global models) scenario climates that are dynamically consistent with surface and external radiative forcing and lateral boundary conditions provided by the global model Geographic features are represented with more detail in regional models as can be seen in Figure 15.1 Intercomparison studies of results of several regional models driven at lateral boundaries by the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis (NNR) (Kalnay et al., 1996) have helped reveal the strengths and weaknesses of this approach to downscaling global climate information (Takle et al., 1999; Anderson et al., 2003) The agriculturally intensive central United States is unique in the country in that summertime mesoscale convective precipitation (Wallace and Hobbs, 1977) is dependent on nocturnal water vapor flux convergence (Anderson et al., 2003) Neither the NNR (Higgins et al., 1997) nor global climate models (Ghan et al., 1995) capture this essential mechanism Finer grid spacing is needed to resolve the finescale dynamical processes that lead to timing, location, intensity, and amounts of precipitation (Anderson et al., 2003) Most, but not all, regional models are able to capture the nocturnal maximum in hourly precipitation in this region (Anderson et al., 2003), which is an indication that nocturnal moisture convergence at the outflow of the low-level jet is being simulated For this reason, we expect that regional climate models can offer better representation of climate factors of critical importance to agriculture compared to lowresolution climate models The recently issued (24 July 2003) Strategic Plan for the CCSP (2003) calls for more regional climate modeling through Objective 16: “Accelerate the development of scientifically based predictive models to provide regional and fine-scale © 2005 by Taylor & Francis Group, LLC 390 Takle and Pan climate and climate impact information relevant for scientific research and decision support applications.” 15.2.3 Statistical Methods The computational demands of regional climate modeling have stimulated the search for more efficient methods of obtaining site-specific scenarios of future climates Statistical downscaling (IPCC, 2001, chapter 10) assumes that regional climate is determined by two factors: the large-scale climatic state and local factors Features of the large-scale climate and known local factors such as elevation are input to a statistical procedure, usually linear regression, that has been adjusted to yield the local climate Many forms of statistical downscaling have been applied, such as simple regression and sophisticated artificial neural networks The procedure requires good input data, both for predictors and the predictands, in order to arrive at an acceptably accurate statistical model Wilby and Wigley (1997) provide a review of statistical downscaling, and give a variety of predictors and predictands for applications to climate The success of statistical downscaling is highly location dependent, but Wilby and Wigley (2000) confirmed results of previous research that showed strong correlation for winter precipitation and circulation predictors and precipitation for regions near oceanic sources of moisture Kidson and Thompson (1998) assert that empirical downscaling methods provide skill comparable to that of dynamical modeling for application to the current climate In a direct comparison of statistical downscaling to regional climate modeling (Wilby et al., 1999), we found advantages for both methods with neither method clearly superior in our hydrological application Statistical procedures tend to have low computational demand, which makes statistical downscaling attractive However, some cautions should be recognized When downscaling future climate, it must be assumed that statistical relations based on observed climate of the past will hold in the future The statistical relations likely are strongly limited © 2005 by Taylor & Francis Group, LLC Climate Change and Crop Production 391 to those regions with sufficient data for calibration and validation The adherence of statistical relations to physical laws may be obscure, limiting insight into climatic processes These features complicate attempts to conduct systematic appraisals of statistical downscaling 15.2.4 Crop Models Most crop models, such as CROPGRO (Hoogenboom et al., 1994), which has evolved from the earlier soybean model SOYGRO, are process-oriented models that consider crop development, crop–soil carbon and nitrogen balances and soil–water balance The hourly leaf-level photosynthesis formulation of CROPGRO allows for responses to changes in weather conditions that can be supplied by a regional climate model on subdaily time scales Within the ensemble of CERES crop models, CERES-Maize is a model for corn developed originally by J.T Ritchie and colleagues (see Jones and Kiniry, 1986), and is based on radiation-use efficiency as modified by nitrogen stress, water stress, plant population, and temperature CROPGRO and CERES-Maize can be used in sequence to simulate multiyear crop production because they use the same balances for soil water and soil nitrogen, and recently have been coupled with the CENTURY soil organic matter model (Parton et al., 1987) The two crop models have been combined into a single model called the Cropping System Model (CSM) (Jones et al., 2003), representing the newest model development in crop modeling We employed the CSM model for the study reported herein The climate model (whether dynamical or statistical) provides the crop model (CERES-Maize for our analysis) with daily totals of shortwave radiation and rainfall, as well as daily maximum and minimum temperatures On the basis of these daily inputs, the crop model calculates daily updated values of plant biomass, evaporation, and transpiration, as well as the remaining water, carbon, and nitrogen in the soil profile At the end of the growing season, the model supplies a total grain yield © 2005 by Taylor & Francis Group, LLC 392 Takle and Pan 15.3 MODELING FUTURE CLIMATES AND CROP GROWTH 15.3.1 Temperature Determining climate information for assessing future crop growth, by whatever methods, requires more than monthly mean values of temperature and precipitation For a changing or variable climate, higher-order statistics, such as variance, extremes, and persistence, may have more significant influences than changes in the mean (Takle and Mearns, 1995; Mearns et al., 1997) For example, a month having a run of several consecutive days of extreme high temperatures (higher autocorrelation of the time series) has much more impact on crops than a month with a day or two of extreme high temperatures scattered throughout the month but with the same monthly mean Takle and Mearns (1995) reported that a 3.5-year simulation with a regional climate model produced a reduction in the standard deviation and increase in the autocorrelation, in addition to an increase in the mean, for a future scenario climate for July daily maximum temperatures in the Midwest Although the simulated climate record was short, and the quality of the climate models was lower than currently available, these results point out the need for examining more than mean values for assessing agricultural impacts Timing of changes also is critical Heat stress during the vulnerable corn-pollination period (July in the U.S Midwest) can have a particularly significant negative impact on yields (Shaw, 1983; Carlson, 1990) Mearns et al (1984) calculated that a 1.7°C rise in mean maximum July temperature for Des Moines, IA, increases the probability of a heat wave (5 or more consecutive days with temperature above 35°C) from 6% to 21% 15.3.2 Precipitation Interannual variability in precipitation is a key factor that accounts for a large measure of interannual variability in crop yields As with temperature, monthly mean values and timing © 2005 by Taylor & Francis Group, LLC Climate Change and Crop Production 393 are important factors controlling impact of precipitation on crop yields Precipitation is a key input variable to the crop model that accounts for a large measure of interannual variability in crop yields We evaluated the capability of a regional climate model (RegCM2) (Giorgi et al., 1993) to simulate precipitation for the growing season at Ames, IA RegCM2 was driven with lateral boundary conditions over a 10-year period (1979–1988) supplied by the NNR (see Pan et al., 2001b, for more details) This 10-year period starts with the first availability of weather satellite imagery and matches the Atmospheric Model Intercomparison Project experiment (Gates, 1992), which allows comparisons of our regional model results with those of global models Results shown in the upper plot of Figure 15.2, indicate that in some years the model is able to simulate the seasonal total quite well, but in some years (notably 1983) the model fails to capture the large seasonal total There is a general tendency for the model to predict lower values than observed in all years Table 15.1 gives a summary of totals for the 10-year observed and simulated periods The temporal distribution of rainfall intensities also influences crop development Numerous light rainfall events that fail to provide moisture to deep roots may be far less beneficial than a single event of the same total amount Our ability to simulate future crop development and yield by use of crop models will depend strongly on our ability to correctly simulate timing and amount of future precipitation To examine the characteristics of model-simulated precipitation events, we plotted the distribution of daily total rainfall amounts during May to August as produced by the regional climate model RegCM2 Results (Figure 15.3) revealed that the model simulates too many low-precipitation events and not enough events in the range most usable by a crop such as corn, which develops a deep root system by the middle of the growing season By use of the same regional model simulations, Kunkel et al (2002) found annual and interannual extremes of precipitation to be reasonably well represented by the regional model © 2005 by Taylor & Francis Group, LLC 394 Takle and Pan P (mm) Growing Season Precipitation at Ames, IA 800 700 600 500 400 300 200 100 Observed Simulated Mean precip: Obs = 449 mm Sim = 343 mm 79 80 81 82 83 84 85 86 87 (Sim/Obs) Ratios: Precip: 76% Yield: 66% 88 Simulated Corn Yield at Ames, IA 20000 Obs precip driven Sim precip driven 15000 Mean yield: 10000 Obs = 8,300 kg Sim = 5,500 kg 5000 79 80 81 82 83 84 85 86 87 88 Figure 15.2 Growing season precipitation as simulated by RegCM2 for 10 years as compared to observed amounts for Ames, IA Table 15.1 Growing Season Precipitation and Yields Observed and Simulated by RegCM2 and CERES-Maize When Supplied with 10-Year Climate Data Sets for 1979–1988 Period and 10Year Climate Data Sets of Contemporary (contmp) and Future Scenario (futscen) Climates as Simulated by HadCM2 Variables Simulation Runs Standard Mean Deviation Precipitation Observed 449 (mm) RegCM2/NNR 343 Yields (kg ha−1) Observed 8,381 CERES/Observed 8,259 weather CERES/RegCM2/NNR 5,487 CERES/RegCM2/Had 5,002 CM2 contmp CERES/RegCM2/Had 10,610 CM2 futscen © 2005 by Taylor & Francis Group, LLC Normalized Standard Deviation 114 89 1,214 4,494 0.25 0.26 0.14 0.54 3,796 1,777 0.69 0.36 2,721 0.26 Climate Change and Crop Production 395 40 Observed Simulated Events/year 30 20 10 2.5 7.5 12.5 17.5 22.5 27.5 32.5 37.5 42.5 47.5 Daily precipitation (mm) Figure 15.3 Histogram of daily rainfall amounts as simulated by RegCM2 for May to August in 1979–1988 period In Figure 15.4, we plotted just the range of daily totals considered to be most effective in promoting crop development Although the model simulates a large number of events in this range, the distribution is skewed toward lower daily totals with too few at the higher end of the range Observed Events/year Simulated 12.5 17.5 22.5 27.5 32.5 37.5 42.5 47.5 Daily precipitation (mm) Figure 15.4 Histogram of precipitation events per season as simulated by RegCM2 in the range most usable by corn for May to August in 1979–1988 period © 2005 by Taylor & Francis Group, LLC 396 Takle and Pan 15.3.3 Soil Moisture Soil moisture values for the 1981–1988 period simulated by RegCM2 were compared with observations of soil moisture for the state of Illinois as reported by Hollinger and Isard (1994) and Robock et al (2000) Composite monthly mean values for the 8-year period (Figure 15.5) for the top 10-cm layer show a winter–spring recharge of soil moisture and a growing-season drawdown Simulation of the deep soil (figure not shown) reveals a persistent dry bias in the model, whereas the simulation of the top layer follows the observations much more closely, except for an early fall recharge rate that is too low Interannual variability of moisture in the top layer (Figure 15.6) shows minima in three summers (1983, 1984, and 1988) Simulated moisture tends to capture dry and wet years but with lower interannual variability There is no apparent tendency for long-term drift in simulated soil moisture See Pan (2001a) for further details 50 Obs Volume % 40 Sim 30 20 10 10 11 12 Month Figure 15.5 Monthly composites of observed and simulated volumetric soil moisture in top 10 cm for 1981–1988 period averaged over 19 observer sites in Illinois © 2005 by Taylor & Francis Group, LLC Climate Change and Crop Production 397 50 45 40 Volume % 35 30 25 20 15 Obs 10 Sim 13 25 37 49 61 73 85 97 109 Month Figure 15.6 Time series of observed and simulated monthly volumetric soil moisture in top 10 cm averaged over 19 sites in Illinois 15.3.4 Representing Crop Growth We conducted a pilot study for central Iowa where CERESMaize was used to simulate crop yields when supplied with 10-year data sets representing (1) observed weather conditions, (2) a climate produced by a regional climate model driven by the NNR, (3) a contemporary climate produced by a global model downscaled by the regional model, and (4) a future scenario climate (2040–2049) produced by a global model downscaled by the regional model Results of each of these were compared to observed yields in this region The validation data on corn yields for Ames, IA were taken from annual yields for the north-central reporting district of Iowa, and therefore represent a regional average rather than results from a single locale Results in Table 15.1 and shown in the lower panel of Figure 15.2 suggest that the crop model has higher interannual variability (standard deviation) than the observed values, but produces a mean yield close to observed yield The climates © 2005 by Taylor & Francis Group, LLC 398 Takle and Pan produced by the regional model produced mean yields well below observed yields and standard deviations well above observed levels When results of a global model for the contemporary climate are used to drive the regional model, the mean yield is modestly reduced, but the standard deviation is substantially reduced The combination CERES/RegCM2 produces a doubling of maize yields for Ames under the future scenario climate and reduced variance The reason for the large increase in yield is not completely clear, but may be a combination of more soil moisture and higher mean temperatures, but with little or no increase in the daily maximum temperature during critical parts of the growing season in the scenario climate From this pilot study, we conclude that the crop model is very sensitive to biases introduced by a regional climate model The crop model seems to react more strongly than actual crops to interannual variability in precipitation This sensitivity is exacerbated by inability of the climate models to simulate the proper distribution of rainfall intensities The consequences of higher daytime maximum temperatures are amplified by crops (Mearns et al., 1984), so any termperature biases in the climate models will substantially impact yields By these means, the crop model has exposed weaknesses in the climate model for simulating agricultural impacts of climate change Crops are not passive acceptors of externally imposed climate, but rather are being recognized for their role as a coupling mechanism between the soil and atmosphere Sparks et al (2002) suggest that extensive plantings of corn and soybeans in the Midwest are contributing to higher dew-point temperatures, which exacerbate heat waves (Kunkel et al., 1996) Kalnay and Cai (2003) compare a reanalysis of upper air data with surface observations to conclude that about half of the observed decrease in diurnal temperature range over the last 50 years is due to changes in land use, primarily agriculture These and other reports suggest a need for more direct coupling of interactive ecosystem models, such as crop models for the central United States, that incorporate biophysical processes into weather and climate models for more accurate simulation of crop impacts on local weather This, in © 2005 by Taylor & Francis Group, LLC Climate Change and Crop Production 399 turn, should lead to more accurate simulation of crop physiology and yield 15.4 SUMMARY A strategy is needed for translating changes and variability of global climate into impacts on agriculture Global models, which are needed to evaluate global consequences of anthropogenic influences such as changes in greenhouse gas concentrations, not supply climate information of sufficient resolution to meet this need Regional climate modeling is conceptually more appealing than statistical downscaling, and captures some fine-scale dynamical processes that are unresolved by global models However, inaccurate information on the timing and magnitude of precipitation events, and biases in temperature produced by the regional models are amplified by a crop model to produce large discrepancies between simulated and observed yields If, as data seem to show, crops are more efficient than native perennial vegetation at recycling moisture to the atmosphere, then the physiological processes represented within crop models are needed within the climate model to more accurately represent the linkage between soil and atmosphere for simulating climates in intensively cultivated areas For areas of intensive agriculture, such as the U.S Midwest, these advances in climate modeling will be particularly beneficial ACKNOWLEDGMENTS We are grateful to our colleagues R.W Arritt, W.J Gutowski, Jr., and C.J Anderson of the regional climate modeling team at Iowa State University, and J.H Christensen of the Danish Meteorological Institute, for their contributions to this research We also acknowledge the research support of the Iowa State University Agronomy Department Endowment Program and the U.S Department of Energy/Biological and Environmental Research (BER) through the Great Plains Regional Center of the National Institute for Global Environmental Change (DE-FC02-03ER63613) © 2005 by Taylor & Francis Group, LLC 400 Takle and Pan REFERENCES Anderson, C.J., R.W Arritt, E.S Takle, et al 2003 Hydrological processes in regional climate model simulations of the central United States flood of June–July 1993 J Hydrometeorol., 4:584–598 Carlson, R.E 1990 Heat stress, plant-available soil moisture, and corn yields in Iowa: a short- and a long-term view J Prod Agric., 3:293–297 Changnon, S.A and S.E Hollinger 2003 Problems in estimating impacts of future climate change on Midwestern corn yields Climatic Change, 58:109–118 Gates, L.W 1992 The Atmospheric Model Intercomparison Project Bull Am Meteorol Soc., 73:1962–1970 Ghan, S.J., X Bian, and L Corsetti 1995 Simulation of the Great Plains low-level jet and associated clouds by general circulation models Monthly Weather Rev., 124:1388–1408 Giorgi, F., M.R Marinucci, G.T Bates, and G De Canio 1993 Development of a second-generation regional climate model (RegCM2), I Boundary-layer and radiative transfer Monthly Weather Rev., 121:2794–2813 Giorgi, F and L.O Mearns 1991 Approaches to the simulation of regional climate change: a review Rev Geophys., 292:191–216 Giorgi, F and L.O Mearns 1999 Introduction to special section: Regional climate modeling revisited J Geophys Res., 104:6335–6552 Higgins, R.W., Y Yao, E.S Yarosh, J.E Janowiak, and K.C Mo 1997 Influence of the Great Plains low-level jet on summer time precipitation and moisture transport over the central United States J Climate, 10:481–507 Hollinger, S.E and S.A Isard 1994 A soil climatology of Illinois J Climate, 7:822–833 Hoogenboom, G., J.W Jones, P.W Wilkens, et al 1994 Crop models In G.Y Tsuji, G Uehara, and S Balas, Eds DSSAT Version 3, Vol University of Hawaii, Honolulu, HI, pp 95–244 © 2005 by Taylor & Francis Group, LLC Climate Change and Crop Production 401 Intergovernmental Panel on Climate Change 2001 Climate Change 2001: The Scientific Basis Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change Houghton, J.T., Y Ding, D.J Griggs, M Noguer, P.J van der Linden, X Dai, K Maskell, and C.A Johnson, Eds Cambridge University Press, London; New York Jones, J.W., G Hoogenboom, C Porter, et al 2003 DSSAT: cropping system model Eur J Agron., 18:235–265 Jones, C.A and J.A Kiniry 1986 CERES-Maize: A Simulation Model of Maize Growth and Development Texas A&M University Press, College Station, TX Kalnay, E., M Kanamitsu, R Kistler, et al 1996 The NCEP/NCAR 40-year reanalysis project Bull Am Meteorol Soc., 77:437–471 Kalnay, E and M Cai 2003 Impact of urbanization and land-use change on climate Nature, 423:528–531 Kidson, J.W and C.S Thompson 1998 A comparison of statistical and model-based downscaling techniques for estimating local climate variations J Climate, 11:735–753 Kunkel, K.E., K Andsager, X.-Z Liang, R.W Arritt, E.S Takle, W.J Gutowski Jr., and Z Pan 2002 Observations and regional climate model simulations of extreme precipitation events and seasonal anomalies: a comparison J Hydrometeorol., 3:322–334 Kunkel, K.E., S.A Changnon, B.C Reinke, and R.W Arritt 1996 The July 1995 heat wave in the Midwest: a climatic perspective and critical weather factors Bull Am Meteorol Soc., 77:1507–1518 Leung, R., L.O Mearns, F Giorgi, and R.L Wilby 2003 Regional climate research needs and opportunities Bull Am Meteorol Soc., 84:89–95 Mearns, L.O., R.W Katz, and S.H Schneider 1984 Extreme hightemperature events: changes in their probabilities with changes in mean temperature J Climate Appl Meteorol., 23:1601–1613 © 2005 by Taylor & Francis Group, LLC 402 Takle and Pan Mearns, L.O., W Easterling, C Hays, and D Marx 2001 Comparison of agricultural impacts of climate change calculated from high and low resolution climate change scenarios Part I The uncertainty due to spatial scale Climatic Change, 51:131–172 Mearns, L.O., C Rosenzweig, and R Goldberg 1997 Mean and variance change in climate scenarios: methods, agricultural applications, and measures of uncertainty Climatic Change, 35:367–396 National Assessment Synthesis Team 2000 Climate Change Impacts in the U.S.: Overview Cambridge University Press, London; New York Pan, Z., R.W Arritt, W.J Gutowski Jr., and E.S Takle 2001a Soil moisture in a regional climate model: simulation and projection Geophys Res Lett., 28:2947–2950 Pan, Z., J.H Christensen, R.A Arritt, W.J Gutowski Jr., E.S Takle, and F Otenio 2001b Evaluation of uncertainties in regional climate change simulations J Geophys Res., 106:17,735–752 Parton, W.J., D.S Schimel, C.V Cole, and D.S Ojima 1987 Analysis of factors controlling soil organic matter levels in Great Plains grasslands Soil Sci Soc Am J., 51 :1173–1179 Robock, A., K.Y Vinnikov, G Srinivasan, J.K Entin, S.E Hollinger, N.A Speranskaya, S Liu, and A Namkhai 2000 The global soil moisture data bank Bull Am Meteorol Soc., 81:1281–1299 Shaw, M.R., E.S Zavaleta, N.R Chiariello, E.E Cleland, H.A Mooney, and C.B Field 2003 Grassland responses to global environmental changes suppressed by elevated CO2 Science, 298:1987–1990 Shaw, R.H 1983 Estimates in yield reduction in corn caused by water and temperature stress In C.D Raper and P.J Kramer, Eds Crop Reactions to Water and Temperature Stresses in Humid, Temperate Climates Westview, Boulder, CO, pp 49–66 Sparks, J., D Changnon, and J Starke 2002 Changes in the frequency of extreme warm-season surface dewpoints in Northeastern Illinois: implications for cooling system design and operation J Appl Meteorol., 41:890–898 © 2005 by Taylor & Francis Group, LLC Climate Change and Crop Production 403 Takle, E.S., W.J Gutowski Jr., R.W Arritt, et al 1999 Project to intercompare regional climate simulations (PIRCS): description and initial results J Geophys Res., 104:19,443–462 Takle, E.S and L.O Mearns 1995 Midwest temperature means, extremes, and variability: analysis of results from climate models In G.R Carmichael, G.E Folk, and J.L Schnoor, Eds., Preparing for Global Change: A Midwestern Perspective Academic Publishing, Amsterdam, pp 135–142 Tsvetsinskaya, E.A., L.O Mearns, and W.E Easterling 2001 Investigating the effect of seasonal plant growth and development in three-dimensional atmospheric simulations Part I Simulation of surface fluxes over the growing season J Climate, 14:692–709 U.S Climate Change Science Program 2003 Strategic Plan for the Climate Change Science Program Final Report, July 2003 Available at: www.climatescience.gov/Library/Stratplan2003/ final/default.htm Wallace, J.M and P.V Hobbs 1977 Atmospheric Science: An Introduction Academic Press, New York Wilby, R.L., L.E Hay, W.J Gutowki Jr., R.W Arritt, E.S Takle, Z Pan, G.H Leavesley, and M Clark 1999 Hydrological responses to dynamically and statistically downscaled general circulation model output Geophys Res Lett., 27:1199–1202 Wilby, R.L and T.M.L Wigley 1997 Downscaling general circulation model output: a review of methods and limitations Prog Phys Geogr., 21:530–548 Wilby, R and T.M.L Wigley 2000 Precipitation predictors for downscaling: observed and general circulation model relationships Int J Climatol., 20:641–661 © 2005 by Taylor & Francis Group, LLC ... Data 37 0 36 0 35 0 34 0 33 0 32 0 31 0 30 0 290 Mauna Loa (Hawaii) 1860 1880 1900 1920 1940 1960 1980 2000 C Global Average Temperature C F 57.92 14.4 14 .3 57.74 14.2 57.56 14.1 57 .38 14.0 57.20 13. 9... crops withstand Change in soil C, T C ha-1yr -1 0.15 0.10 Corn North Platte, NE Irrigation N Fertilization 0.05 0.00 -0 .05 -0 .10 Current climate Climate change, CCCM Climate change, HAD -0 .15 Figure... Francis Group, LLC Assessing the Consequences of Climate Change for Food Security 291 Pinstrup-Andersen, P., and R Pandya-Lorch 1998 Food security and sustainable use of natural resources: a 2020