Changes in Soybean Yields in the Midwestern United States As a Result of Future Changes in Climate, Climate Variability, and Co2 Fertilization

CHANGES IN SOYBEAN YIELDS IN THE MIDWESTERN UNITED STATES AS A RESULT OF FUTURE CHANGES IN CLIMATE, CLIMATE VARIABILITY, AND CO2 FERTILIZATION

JANE SOUTHWORTH 1,R.A.PFEIFER2, M. HABECK 3, J. C. RANDOLPH 1, O. C. DOERING 3, J. J. JOHNSTON 1 and D. G. RAO 4 1School of Public and Environmental Affairs, 1315 E. Tenth Street, Indiana University, Bloomington, IN 47405-1701, U.S.A. E-mail: [email protected] 2Agronomic consultant, New Palestine, IN, U.S.A. 3Department of Agricultural Economics, Purdue University, West Lafayette, IN, U.S.A. 4Central Research Institute for Dryland Agriculture, Hyderabad, India

Abstract. This modeling study addresses the potential impacts of climate change and changing climate variability due to increased atmospheric CO2 concentration on soybean (Glycine max (L.) Merrill) yields in the Midwestern Great Lakes Region. Nine representative farm locations and six future climate scenarios were analyzed using the crop growth model SOYGRO. Under the future climate scenarios earlier planting dates produced soybean yield increases of up to 120% above current levels in the central and northern areas of the study region. In the southern areas, comparatively small increases (0.1 to 20%) and small decreases (–0.1 to –25%) in yield are found. The decreases in yield occurred under the Hadley Center greenhouse gas run (HadCM2-GHG), representing a greater warming, and the doubled climate variability scenario - a more extreme and variable climate. Optimum planting dates become later in the southern regions. CO2 fertilization effects (555 ppmv) are found to be signiﬁcant for soybean, increasing yields around 20% under future climate scenarios. For the study region as a whole the climate changes modeled in this research would have an overall beneﬁcial effect, with mean soybean yield increases of 40% over current levels.

1. Introduction

In recent years concerted international efforts have been undertaken to address the problem of global climate change (Intergovernmental Panel on Climate Change (IPCC) (1995, 1990) although debate continues as to its causes and potential impacts. Consensus has been reached however, recognizing the serious implications of such potential changes in climate to many sectors. Any change in climate will have implications for climate-sensitive systems such as agriculture, forestry, and natural resources. The combined effect of elevated temperatures, increased atmospheric CO2 concentrations, increased probability of extreme events (droughts, ﬂoods), and reduced crop-water availability, is expected to have signiﬁcant impacts on the agricultural sector (Chiotti and Johnston, 1995). With respect to agriculture, changes in solar radiation, temperature, and precipitation will produce changes in crop yields, crop mix, cropping systems, scheduling

Climatic Change 53: 447–475, 2002. © 2002 Kluwer Academic Publishers. Printed in the Netherlands. 448 JANE SOUTHWORTH ET AL. of field operations, pest conditions, and grain moisture content at harvest. Climate change also will have effects on the economics of agriculture, including changes in crop production practices, farm profitability, trade, and regional or national comparative advantage. The impacts of climate change will depend on both the magnitude of the change in climate and how well agriculture can adapt to these changes (Kaiser et al., 1995). Our research addresses such issues for nine agricultural farm locations across the Midwestern Great Lakes region (Figure 1), a five-state area including Indiana, Illinois, Ohio, Michigan, and Wisconsin. This region is one of the most productive and important agricultural regions in the world, with over 50% of the land-use devoted to agriculture (National Agricultural Statistics Service, 1997). While most studies of climate change impacts on agriculture have analyzed effects of mean changes of climatic variables on crop production, impacts of changes in climate variability have been studied much less (Mearns et al., 1997; Mearns, 1995). Yet the consequences of changes in climate variability may be as important as those that arise due to changes in mean climatic variables (Hulme et al., 1999; Carnell and Senior, 1998; Semenov and Barrow, 1997; Liang et al., 1995; Rind, 1991; Mearns et al., 1984). Climate variability, and its potential changes under future climates, is therefore essential to evaluate, specifically for its possible impacts on agriculture (Semenov and Barrow, 1997; Barrow et al., 1996; Semenov et al., 1996; Rind, 1991). Properly validated crop simulation models can be used to model multiple effects and see their interaction in terms of the environmental effects on crop physiological processes and to evaluate the consequences of such influences (Peiris et al., 1996). The major production region for soybeans lies between 25◦ and 45◦ latitude at altitudes of less than 1000 m where temperature ranges are generally favorable to soybean production. Currently, the United States produces over 50% of the world’s soybeans (Wittwer, 1995), making it one of the nation’s most important crops. Any change in soybean yield brought about by climate change could have major economic consequences in many regions of the United States (Jones et al., 1999). Research by Ferris et al. (1998) suggests that the impact of climatic extremes on determinate crops depends on the stage of crop development. In soybeans, pod development and seed filling are the most sensitive phases of crop development. High temperatures at the time of flowering or early seed filling, and water deficit during the reproductive stage, reduces photosynthesis and seed yield of soybean (Ferris et al., 1998). Modeling studies have found specific relationships between temperatures and crop growth or development. Hoogenboom et al. (1995) found the number of days from planting to flowering or physiological maturity was shortest at temperatures between 25◦ and 30 ◦C. This number of days will increase at higher temperatures, due to the decrease in reproductive development rate. In addition, total biomass growth shows a strong cultivar based response to temperature. Jones et al. (1999) reported optimum temperatures for soybean seed yield with decreases at higher CHANGES IN SOYBEAN YIELDS IN THE MIDWESTERN UNITED STATES 449

Figure 1. Location of nine representative agricultural areas in the Midwestern Great Lakes Region. temperatures. Higher temperatures were found to have direct physiological effects as well as indirect effects through increased evapotranspiration and hence, water stress. Lal et al. (1999) concluded that both maximum and minimum temperatures are important to soybean production. Moisture stress will also impact soybean yields. Varietal differences to moisture stress can be dramatic, and short season cultivars tend to be most affected during an extended period of drought stress during the warmer part of the growing season (Specht et al., 1986). In addition, the stage of growth of the plant during which moisture stress occurs directly influences the impact upon the plant (Shaw and Laing, 1966). Specifically, stress in early growth stages reduces the number of pods 450 JANE SOUTHWORTH ET AL. produced by the plant, while moisture stress in later periods of growth reduces the number of seeds per pod and bean size, moisture stress at either time eventually contributed to lower yields. Significantly different temperature or precipitation regimes in these regions could have dramatic effects on soybean production, even given the soybean plant’s great ability to recover from these stresses. Another important factor for crop production under a changed climate is the fer- tilizing effect of increased atmospheric concentrations of CO2. This was reported over 100 years ago and may have been observed much earlier. Numerous studies have been conducted throughout western and northern Europe and all reported sig- nificant increases in plant growth and productivity (Wittwer, 1995). However, plant species differ in their response to CO2 fertilization. C3 species, such as soybeans and wheat, benefit from increased atmospheric concentrations of CO2. The primary reason is that the increased atmospheric CO2 will reduce photorespiratory loss of carbon in the C3 plant, enhancing plant growth and productivity (Allen et al., 1987). Wittwer (1995) reported that 93% of over 1000 studies of CO2 fertilization effects found an increase in plant productivity, with a mean increase in yield of 52%. Recent studies of climate change impacts on soybean yields in the U.S. corn belt also reported increases as a result of CO2 fertilization (Kaiser et al., 1995; Phillips et al., 1996; Ritchie, 1989). Yield increases ranged from 15 to 83%. Ritchie (1989) reported yield decreases when the fertilization effect of CO2 was not taken into account. Jones et al. (1999) reported a north-south trend in simulated soybean yields under future climate scenarios. Increase in temperatures of up to 2 ◦C had little effect on yields at most northern locations, and decreased yields by 16% in southern locations. When the CO2 fertilization effect was added, all yields increased unless precipitation decreased. Across all locations average increases in yield ranged from about 1% in Mississippi to 35% in Michigan. In addition, Jones et al. (1999) found that the best planting date under future climates was about 17 days later than present. The change in planting date apparently resulted in cooler conditions during the reproductive period of growth under the warmer environment and provided more favorable water availability during the critical growth phase. The later planting date also resulted in the need for an earlier maturing cultivar. For this project, soybean growth was modeled at nine representative farm locations across the five-state study area. Seven climate scenarios were created: the current climate, two future climates based on mean climate changes, and four variability analyses as part of a sensitivity study. Research questions we address in this paper relate to the impacts of possible future climates, under the assumption of increased atmospheric CO2 concentration, on current agricultural practices across the midwestern Great Lakes Region. Specifically, what are: (1) the impacts of mean climate change on soybean yields in the Midwestern United States, (2) the impacts of changing climate variability in conjunction with changes in the mean climate on soybean yields, (3) the impact of CO2 fertilization and changing future climate (both mean and variability) on soybean yields, (4) the spatial variability of yield CHANGES IN SOYBEAN YIELDS IN THE MIDWESTERN UNITED STATES 451

Table I Soybean maturity group for early, mid and late maturing soybeans selected for each location

Area Maturity group # Late-maturing Mid-maturing Early-maturing

Eastern Illinois (EIL) IV a III II East-central Indiana (ECI) IV III II North-west Ohio (NOW) III II I South-central Michigan (SCM) III II I Southern Illinois (SIL) V IV III South-west Indiana (SWI) V IV III Eastern Wisconsin (EWI) II I 0 South-west Wisconsin (SWW) III II I Michigan thumb (MTH) III II I a Roman numerals indicate the maturity group at each location. Higher numbers indicate an increased number of days to maturity. changes and implications of such changes on midwestern agriculture, and (5) the possible adaptation strategies for farmers in the Midwest to potential changes in future climate.

2. Methods

2.1. STUDY REGION

The midwestern Great Lakes region (Indiana, Illinois, Ohio, Michigan, and Wis- consin) was divided into nine ecological regions based on climate, soils, land use, and current agricultural practices. Representative farms were created in each area based upon local characteristics and farm endowments (Figure 1). At each location, yields are simulated for three soybean cultivars, which are early, mid, and late maturing. The soybean maturity groups used in each location are given in Table I.

2.2. SOYBEAN CROP MODEL

The Decision Support System for Agrotechnology Transfer (DSSAT) software is a suite of crop models that share a common input-output data format. The DSSAT itself is a shell that allows the user to organize and manipulate data and to run crop models in various ways and analyze their outputs (Thornton et al., 1997; Hoogenboom et al., 1995). DSSAT version 3.5 was used in this analysis. We selected the SOYGRO model (Jones et al., 1988) for this research because: (1) the daily time step of the model allows analysis from different planting dates to 452 JANE SOUTHWORTH ET AL. meet adaptations and farm management requirements, (2) plant growth dependence on both mean daily temperatures and the amplitude of daily temperature values was desired (not just a daily mean temperature growth dependence), (3) the model is physiologically oriented and simulates crop response to major climate variables, including the effects of soil characteristics on water availability, (4) the model can simulate CO2 fertilization adequately (Hoogenboom et al., 1995; Siqueira et al., 1994; Jones et al., 1988), (5) the model is developed with compatible data struc- tures so that the same soil and climate datasets can be used for all cultivars of crops which helps in comparison (Adams et al., 1990), and (6) comprehensive validation has been done across a wide range of different climate and soil conditions, and for different crop hybrids (Semenov et al., 1996; Wolf et al., 1996; Hoogenboom et al., 1995). Phasic development is quantified according to the plant’s physiological age. The input data required to run the SOYGRO model includes daily weather information (maximum and minimum temperatures, rainfall, and solar radiation); soil characterization data (data by soil layer on extractable nitrogen and phosphorous and soil water content); a set of genetic coefficients characterizing the soybean maturity group being grown (Table I); and crop management information, such as emerged plant population, row spacing, seeding depth, and fertilizer and irrigation schedules (Thornton et al., 1997). The soil data were obtained from the U.S. Nat- ural Resources Conservation Service (USDA, 1994) and were selected to represent the dominant local soil at each of the nine representative farm sites. The model apportions the rain received on any day into runoff and infiltration into the soil, using the runoff curve number technique. We assigned a runoff curve number to each soil, based on the soil type, depth and texture as obtained from the STATSGO database (USDA, 1994). We chose not to make nitrogen and phosphorous limiting to crop growth, so these modules are turned off within our model runs. Temperature is one of the main controls on the processes in plant growth and development. SOYGRO includes two different temperature response functions, one for vegetative development and one for reproductive development. The former has a minimum temperature for development of 7 ◦C, an optimum temperature between 30◦ and 35 ◦C, with decreasing development at higher temperatures, until 45 ◦C is reached. The latter has a curve-linear response curve; with a base temperature of 6.4 ◦C, maximum development between 21.1◦ and 26.8 ◦C, and then a sharp decline at higher temperatures until 41 ◦C, above which there is no reproductive development (Hoogenboom et al., 1995). The SOYGRO model (Jones et al., 1988) includes the capability to simulate the direct physiological effects of increased atmospheric CO2 concentrations on plant photosynthesis and water use. The atmospheric CO2 concentration for the future climate scenarios for the years 2050 through 2059 used in this research is 555 ppmv. This is the value modeled in the HadCM2 models for this time period, assuming a 1% increase in CO2 production per year (D. Viner, pers. comm.). The photosynthetic enhancement of CO2 results in a photosynthesis multiplier of 1.21 CHANGES IN SOYBEAN YIELDS IN THE MIDWESTERN UNITED STATES 453 and a transpiration ratio of 0.96 for soybeans (Hoogenboom et al., 1995; Siqueira et al., 1994).

2.3. CROP MODEL VALIDATION

The DSSAT models have been well validated across a large range of environmental conditions and crops, and are not specific to any particular location or soil type (Fischer et al., 1995). SOYGRO has been extensively validated at several locations (Dhakhwa et al., 1997; Hoogenboom et al., 1995). Furthermore, because management practices may be varied (e.g., changing planting dates, selecting different cultivars), it is possible to simulate potential farm-level adjustments in response to climate change (Schimmelpfennig et al., 1996; Rosenberg, 1992). Detailed farm-level data were used to ensure that yields produced by the model reflected the actual yields in each representative agricultural area. Experiments conducted during 1981 and 1982 at the Purdue University Agronomy Farm provided data for eight planting dates (Figure 2). Overall agreement between the observed data and simulated results was good, with an R2 of 0.76. The deviation in the observed values and simulated yield is in part due to the model being a simplified reality and as such it will not capture every relevant phenomenon. How- ever, despite this lack of perfect agreement due to some model imperfection the overall agreement between the observed and simulated yields is high, and as such is appropriate for use here. Each representative farm location was also validated using historical yield and climate data from as close an area as possible to ensure the model could replicate past yields for mid-maturing (currently grown) soybean cultivars as well as both late- and early-maturing cultivars that might be used in the future. These analyses thus ensured that each location could mimic reality. Our future yields should therefore indicate changes due to climate change not due to model or location (Siqueira et al., 1994). While this is an imperfect assumption we cannot validate the model for climate conditions that do not exist and we make the assumption that the responses we see under future climate runs are representing changes based on the climate conditions, as the climate is the only data that changes across these runs.

2.4. CURRENT CLIMATE ANALYSIS: VEMAP

The VEMAP dataset includes daily, monthly, and annual climate data for the United States including maximum, minimum, and mean temperature, precipitation, solar radiation, and humidity (Kittel et al., 1996). The VEMAP baseline (30-year historical mean) climate data was used for each of our nine representative agricultural areas in our study region. The weather generator SIMMETEO (as used in DSSAT version 3.5) used these climate data to stochastically generate daily weather data in model runs. This approach of using monthly data to generate daily data allowed us to generate variability scenarios as well. The climate variables 454 JANE SOUTHWORTH ET AL.

Figure 2. Validation of SOYGRO results for a 2-year, 5-planting date simulation of soybean yield at Purdue University Agronomy Farm. distribution patterns reﬂect the current climate variables distributions, as there is no way to know future distributions. Various combinations of planting and harvest date were used under each climate scenario to determine yield sensitivity to these factors.

2.5. FUTURE CLIMATE SCENARIOS

Future climate experiments performed recently at the Hadley Center in England used the new Unified Model. The Unified Model was modified slightly to produce a new, coupled ocean-atmosphere GCM, referred to as HadCM2, which has been used in a series of transient climate change experiments using historic and future greenhouse gas and sulfate aerosol forcing. Transient model experiments are considered more physically realistic and complex than equilibrium scenarios, and allow atmospheric concentrations of CO2 to rise gradually over time (Harrison and Butterfield, 1996). HadCM2 was also one of the climate models used in the U.S. National Assessment (National Assessment Synthesis Team, 2001). HadCM2 has a spatial resolution of 2.5◦ × 3.75◦ latitude by longitude and the representation produces a grid box resolution of 96×73 grid cells, which produces CHANGES IN SOYBEAN YIELDS IN THE MIDWESTERN UNITED STATES 455 a surface spatial resolution of about 417 × 278 km reducing to 295 × 278 km at latitude 45◦. The atmospheric component of HadCM2 has 19 levels and the ocean component 20 levels. The equilibrium sensitivity of HadCM2, that is the global- ◦ mean temperature response to a doubling of effective CO2 concentration, is 2.5 C, somewhat lower than most other GCMs (IPCC, 1990). The greenhouse-gas-only version, HadCM2-GHG, used the combined forcing of all the greenhouse gases as an equivalent CO2 concentration. HadCM2-SUL used the combined equivalent CO2 concentration plus a negative forcing from sulfate aerosols. The addition of the negative forcing effects of sulfate aerosols represents the direct radiative forcing due to anthropogenic sulfate aerosols by means of an increase in clear-sky surface albedo proportional to the local sulfate loading (Carnell and Senior, 1998). Our research used the period of 2050 to 2059 for climate scenarios. The results from HadCM2-GHG and HadCM2-SUL cannot be viewed as a forecast or prediction, but rather as two possible realizations of how the climate system may respond to a given forcing. A comparison of the main three climate datasets (Table II) highlights the differences in projected climate for the study region. A climate variability analysis was also conducted on these two scenarios, thus increasing the number of future climate scenarios to six. Conse- quently, we have examined a range of probable climate changes and a range of their impacts on soybean growth.

2.6. CLIMATE VARIABILITY ANALYSIS

In order to separate crop response to changes in climatic means from crop response to changes in climate variability it is necessary ﬁrst to model the impacts of mean temperature changes on crop growth. Then a time series of climate variables with changed variability can be constructed and added to the scenarios of mean change. Hence, when the analysis is undertaken on future mean and variability changes it is possible to infer what type of climate change caused changes in yield (Mearns, 1995). The variance of maximum and minimum temperature, and precipitation values for each month were altered separately, according to the following algorithm from Mearns (1995):

= + 1/2 − Xt µ δ (Xt µ), (1) and

δ = σ 2/σ 2 , (2) = where Xt new value of climate variable Xt (e.g., monthly mean maximum February temperature for year t); µ = mean of the time series (e.g., the mean of the monthly mean maximum February temperatures for a series of years); δ = ratio of the new to the old variance of the new and old time series; Xt = old value of 456 JANE SOUTHWORTH ET AL. in total monthly

C) precip. (mm) ◦ in min.

C) temp. ( ◦ in max

in total monthly

C) precip. (mm) temp. ( ◦ Table II in min.

C) temp. ( ◦ in max.

C) precip. (mm) temp. ( ◦ C) temp. ( ◦ VEMAPMaximum Minimum Total monthly HadCM2-GHG HadCM2-SUL temp. ( a Where EIL is eastern Illinois, ECI is east-central Indiana, NWO is north-west Ohio, SCM is south-central Michigan, SIL is southern Illinois, SWI Area EILECI 28.3NWO 27.6 26.8SCM 16.1 26.3SIL 14.5SWI 14.2 30.5EWI 82.0 29.8 14.3 79.0SWW 26.4 77.0 26.8 17.3MTH 17.1 79.0 26.4 13.8 +13.3 15.2 85.0 90.0 +11.5 13.1 +9.9 93.0 +11.8 98.0 +11.2 +9.5 74.0 +11.1 +9.5 –4.1 +12.5 +9.3 +8.5 +12.5 +10.6 +12.1 –8.8 +8.6 –8.7 +10.7 +9.8 –7.1 +9.3 +10.4 –16.9 –0.7 +8.3 +9.7 –21.9 +10.3 +6.4 +9.5 –3.7 +6.1 –14.2 +8.2 +7.4 +8.2 +9.3 +6.1 +6.7 +9.1 +8.9 +3.9 –11.4 +8.8 +6.9 +6.4 –17.2 +7.4 –7.9 –2.8 +7.9 –12.9 –6.4 is south-west Indiana, EWI is eastern Wisconsin, SWW is south-west Wisconsin, and MTH is the Michigan thumb. a Comparison of HadCM2-GHG andcompared HadCM2-SUL to climate VEMAP current scenario mean mean monthly monthly climatic conditions maximum for and August minimum temperature and precipitation values, CHANGES IN SOYBEAN YIELDS IN THE MIDWESTERN UNITED STATES 457 climate variable (e.g., the original monthly mean February temperature for year t); σ 2 = new variance; and σ 2 = old variance. To change the time series to have a new variance σ 2, the variance and mean of the original time series was calculated and then a new ratio (δ) was chosen. For this research we halved the variance and then doubled the variance. From the parameters µ, δ, and the original time series, a new time series with variance was calculated using equation one. This algorithm was used to change both maximum and minimum temperatures and the precipitation time series. This simple method, developed by Mearns (1995), was used although more complex methodologies are being developed to allow comparisons of variability techniques. Mearns et al. (1996, 1997) found the results obtained from more computationally ad- vanced statistical techniques were similar to results from more statistically simple methodologies. The approach we used permits the incorporation of changes in both mean and variability of future climate in a computationally simple, consistent, and reproducible manner.

2.7. MODEL LIMITATIONS

The SOYGRO model, as with all models, involves a large number of assumptions. Our simulation results do not take into account possible effects of plant diseases, pest damage or weed competition (Hoogenboom et al., 1995). Competitive crop- weed interactions could change, particularly if the crop and weed have different photosynthetic pathways (C3 versus C4), which may be differentially affected by elevated CO2 or climate changes. Nutrients were non-limiting in our model runs, which may not be the case, especially under the elevated atmospheric CO2 concentrations (Phillips et al., 1996). In addition, the model does not simulate extreme soil conditions such as salinity, acidity, compaction, or extreme weather events such as ﬂoods, tornadoes, hail, droughts, and hurricanes (Hoogenboom et al., 1995). Other limitations relate to the simpliﬁed representation of the farms and the use of a single dominant soil type at each location. The climatic tolerance of cultivars was assumed to be unchanged from the base runs through the simulation. Finally, the experimental conditions used to examine increased CO2 effects on photosynthesis may overestimate yields (Rosenzweig et al., 1994). While the assumptions discussed may tend to either overestimate or underestimate simulated yields (Fig- ure 2), our extensive validation and analysis at the farm level acts to increase the validity of the model runs, at least under current climate conditions.

3. Results

3.1. CLIMATE CHANGE SCENARIOS: TEMPERATURES AND PRECIPITATION

A comparison of the climate scenarios provides a background for the soybean yield results. The most notable change in the climate under the HADCM2-GHG 458 JANE SOUTHWORTH ET AL. and HADCM2-SUL scenarios is the increase in average annual temperatures. The number of days during the growing season with temperatures above 35 ◦Cand above 40 ◦C for each of the locations is given in Table II. The increase is dramatic at all sites, particularly under the HadCM2-GHG scenario. Those very high temperatures may cause some stress on the growing soybeans, hasten senescence, and reduce yields, particularly in the southern reaches of the study area. A comparison of climatic conditions for the month of August (Table III) shows that this particular month may be drier and much warmer under the changed climate at each of the locations. However, a full year’s climate data may be more meaningful for comparison. Two sites, one northern (eastern Wisconsin) and one southern (southern Illinois), were chosen for comparison (Figures 3a,b). In every month at these sites, the average monthly minimum temperatures are signiﬁcantly higher under both the HadCM2-GHG and HadCM2-SUL scenarios. The average monthly maximum temperatures also increase, but most signiﬁcantly in the summer months. The patterns at both locations are similar. The precipitation pattern, however, is not the same. At southern Illinois, the late fall to early spring months tend to be wetter than currently, while at the eastern Wisconsin site, this period is drier. Impacts on yields in the northern and southern areas, then, may be expected to be different. Extrapolation of results from research at northern sites to more southern areas and vice versa would not be advised.

3.2. CONSEQUENCES OF CHANGING MEAN CLIMATE AND CLIMATE VARIABILITY ON SOYBEAN YIELDS

Changes in soybean yields under climate change are presented in Figures 4– 6. These ﬁgures show the change in average maximum yield for the optimum planting date under each scenario. Percent changes in yield were derived by com- paring the future soybean yields to the current maximum yields. Increases in yield were greatest in HadCM2-SUL runs and for the halved and unchanged variability analyses. Decreases in soybean yields occurred in some southern locations for the HadCM2-GHG runs, most signiﬁcantly in the doubled variability analysis. Late-maturing soybean cultivars showed increases in yields, for all future climate scenarios, in all northern and central locations (Figure 4). The increases range from 0.1 to 120%. In all future climate scenarios the largest increases in yield occurred in south-central Michigan and the Michigan thumb. In contrast, the southernmost locations showed slight decreases in yields under some of these same future climate scenarios. These decreases in yield range from –0.1 to –20%. The changes in soybean yield under the HadCM2-GHG scenarios at the four southernmost agricultural sites (southern Illinois, southwest Indiana, eastern Illinois, and east-central Indiana) ranged from +10% to –20%. Under the HadCM2-SUL scenarios, only southern Illinois under the doubled variability run experienced any decreases in yield, and these only of –0.1 to –5%. The HadCM2-GHG scenarios CHANGES IN SOYBEAN YIELDS IN THE MIDWESTERN UNITED STATES 459 C ◦ )) and 40 C under 2.0 > ◦ –44.9 ◦ C, and 40.0 C Max. temp. ◦ ◦ )/doubled variability ( 35 1.0 > –39.9 ◦ C Max. temp. )/normal variability ( )) climate scenarios for the year 2055 ◦ 0.5 2.0 40 > Table III )/doubled variability ( C Max. temp. ◦ 1.0 35 > 0.5 1.0 2.0 0.5 1.0 2.0 0.5 1.0 2.0 0.5 1.0 2.0 )/normal variability ( 0.5 VEMAP: currentMax. temp Max. temp. Max. temp. HadCM2-GHG: 2055 HadCM2-SUL: 2055 a Where EIL is eastern Illinois, ECI is east-central Indiana, NWO is north-west Ohio, SCM is south-central Michigan, SIL is southern Illinois, Area EIL5 0 5 0 0 0 ECI 011 444537 NWO1 0 000 1 656 132828 SCM0 SIL9 203435 220 928095 0 000 1 0SWI4 0 0 151210 140 0 324444 000 0 132EWI0 0 0SWW0 222830 010 244 171MTH0 4 898393 433544 9 7 000 554845 010 606569 000 151622 000 62 0 81719 0 0 69 56 6 9 3 12 18 14 0 0 0 a SWI is south-west Indiana, EWI is eastern Wisconsin, SWW is south-west Wisconsin, and MTH is the Michigan thumb. Number of days in the growing season (1 May–30 September) with maximum daily temperatures 35.0 1 year of VEMAP current climate and future HadCM2-GHG (halved variability ( HadCM2-SUL (halved variability ( 460 JANE SOUTHWORTH ET AL.

Figure 3. Average maximum and minimum temperatures and change in precipitation at (a) southern Illinois and (b) eastern Wisconsin under future climate conditions. CHANGES IN SOYBEAN YIELDS IN THE MIDWESTERN UNITED STATES 461 resulted in reductions in yields in southern areas and less pronounced increases in yield across the central and northern areas of the study region. The doubled variability runs of both scenarios resulted in decreases in yield in the southern locations and smaller increases in yield in the central and northern locations as compared to unchanged or halved variability runs. The greatest gains in yield of late maturing cultivars occurred in the HadCM2-SUL scenarios. Mid-maturing cultivars (Figure 5) under future scenarios produced soybean yields signiﬁcantly higher than under VEMAP conditions across most of the central and northern locations. Under the HadCM2-SUL scenarios southern locations also showed predominantly increasing patterns of soybean yields. Under the HadCM2-SUL scenarios all areas experienced increases in yield ranging from 0.1 to 90%, except for southern Illinois under doubled variability, where yields decreased from –0.1 to –5%. Under the HadCM2-GHG scenarios southern locations (southern Illinois, southwest Indiana, eastern Illinois, and east-central Indiana) and south-western Wisconsin (for doubled and normal variability runs) show minimal increases in yield from 0.1 to 5% or alternatively show yield decreases ranging from –0.1 to –25%. Under this same scenario the central and northern locations (east Wisconsin, south-central Michigan, north-west Ohio, and the Michigan thumb) show yield increases of 0.1 to 60% as compared to yields using VEMAP. The HadCM2-GHG scenario resulted in greater reductions, or smaller increases in yield than did the HadCM2-SUL scenarios. In addition, the doubled variability runs of both scenarios resulted in more extreme yield decreases and much smaller yield increases, as compared to the unchanged or halved variability runs for each scenario. The greatest gains in yield (and also results with no yield decreases any- where) occurred for the HadCM2-SUL scenarios for the unchanged and halved variability scenarios. Early-maturing soybean cultivars (Figure 6) showed the greatest yield increases under the HadCM2-GHG scenarios with only minimal yield decreases in the southern areas under doubled variability. With no change in variability yields at the east-central Indiana site decreased –5.1 to –10%. Results across all other locations for all variability scenarios showed yields increasing from 0.1 to 80%. Under the HadCM2-SUL scenarios all results across all scenarios and locations show yields increasing from 0.1 to 120%. Across locations increases in yields of late-maturing soybean are greater than early-maturing soybean cultivars for HadCM2-SUL scenarios. However, early- maturing cultivars had higher yields than late- and mid-maturing cultivars under more intensive warming, as represented by the HadCM2-GHG scenario. The HadCM2-GHG scenario resulted in greater yield decreases and smaller yield increases than did the HadCM2-SUL scenario. The doubled variability runs of both scenarios resulted in smaller yield increases and for the HadCM2-GHG scenario some yields decreased. The greatest increases in yield occurred in the halved variability HadCM2-SUL scenario, by as much as 120% in south-central Michigan. 462 JANE SOUTHWORTH ET AL. Percent change in mean maximum decadal yield for late-maturing soybean, compared to VEMAP yields for (a) halved variability HadCM2-GHG, Figure 4. (b) HadCM2-GHG, (c)HadCM2-SUL. doubled variability HadCM2-GHG, (d) halved variability HadCM2-SUL, (e) HadCM2-SUL, and (f) doubled variability CHANGES IN SOYBEAN YIELDS IN THE MIDWESTERN UNITED STATES 463 Percent change in mean maximum decadal yield for mid-maturing soybean, compared to VEMAP yields, for (a) halved variability HadCM2-GHG, Figure 5. (b) HadCM2-GHG, (c)HadCM2-SUL. doubled variability HadCM2-GHG, (d) halved variability HadCM2-SUL, (e) HadCM2-SUL, and (f) doubled variability 464 JANE SOUTHWORTH ET AL. Percent change in mean maximum decadal yield for early-maturing soybean, compared to VEMAP yields, for (a) halved variability Figure 6. HadCM2-GHG, (b) HadCM2-GHG,variability (c) HadCM2-SUL. doubled variability HadCM2-GHG, (d) halved variability HadCM2-SUL, (e) HadCM2-SUL, and (f) doubled CHANGES IN SOYBEAN YIELDS IN THE MIDWESTERN UNITED STATES 465

Figure 7. Soybean yield by planting date at southern Illinois and eastern Wisconsin sites under HadCM2-GHG and HadCM2-SUL scenarios as percent of maximum VEMAP yield.

3.3. SHIFTS IN FUTURE PLANTING DATES FOR OPTIMUM YIELDS

Figure 7 shows soybean yields for two locations, southern Illinois and eastern Wis- consin. The yields are plotted on separate axes, as a percent of the maximum yield for each site. Under current (VEMAP) conditions, the optimum planting period for mid-maturing soybeans at the southern Illinois site is from approximately the 110th to 160th day of the year (Figure 7). Soybeans planted during this same period under the HadCM2-GHG scenario can be expected to yield only 65% as much. The optimum planting period under the HadCM2-GHG scenario at this site is much later, around days 170–200. Extremely high temperatures and moisture stress during seed ﬁll limits yield. If climate change is more similar to the sulfate scenario, then soybean yields in southern Illinois will be expected to increase slightly over the range of planting dates. Results from eastern Wisconsin (Figure 7) show a very different pattern. The optimum planting period under current conditions is approximately day 116–160. Soybeans planted during this same period under HadCM2-GHG conditions would show an increase in yield of about 15%. The optimum planting period under HadCM2-GHG conditions is extended and earlier, from approximately day 102 to day 150, with soybeans planted during this time period yielding 30% higher 466 JANE SOUTHWORTH ET AL.

Figure 8. Soybean yield by planting date at southern Illinois and eastern Wisconsin sites under HadCM2-GHG and HadCM2-SUL scenarios as percent of southern Illinois maximum VEMAP yield. than the current maximum. Results from the HadCM2-SUL scenario indicate even higher yield increases throughout much of the growing season due to slightly lower temperature increases. The results are presented slightly differently in Figure 8. This chart shows the yields as a percent of the maximum current yields in southern Illinois. Clearly, soybean yields in eastern Wisconsin under current (VEMAP) conditions are less than those in southern Illinois. However, under both future scenarios soybean yields in eastern Wisconsin compare favorably to those in southern Illinois. In fact, under the HadCM2-GHG scenario, yields at eastern Wisconsin are much higher for a large portion of the planting season. Patterns of maximum yields by planting date vary spatially across the study region under the future climate scenarios. For southern areas (eastern Illinois, southern Illinois, and southwest Indiana) maximum yields resulted from later planting dates. East-central Indiana showed no discernable pattern in maximum yields or planting dates under future climate scenarios. For central and northern locations (northwest Ohio, southwest Wisconsin, south-central Michigan, eastern Wisconsin, and the Michigan thumb) earlier planting dates produced maximum yields. This CHANGES IN SOYBEAN YIELDS IN THE MIDWESTERN UNITED STATES 467 north-south gradient results in a shift to predominantly earlier planting dates, with late- and mid-maturing soybean cultivars producing maximum yields, and allowing large yield increases in the northern locations. This is a potentially important factor for the logistics of spring planting on farms with competing labor and machinery requirements.

3.4. THE IMPACT OF EXTREME MOISTURE AND TEMPERATURE REGIMES ON SOYBEANS

DSSAT results did not indicate any extreme moisture stress at either of these locations that represent the northern and southern extent of our study region, under the HadCM2-SUL scenarios, even though Table II does indicate decreased precipitation in the month of August. The temperature effect is much more important in causing these shifts in yields. However, for southern Illinois, under the doubled variability HadCM2-GHG run, increased moisture stress is evident. As Table III indicates, the number of days with extremely high temperatures at each site will increase dramatically under the future climate scenarios. Incidents of extremely high temperatures at the eastern Wisconsin site under the HadCM2- SUL conditions will approach that of southern Illinois under current conditions. Of particular interest, though, is the temperature during the various growth stages of the plant. To analyze this more clearly we compared growth stages under current climate conditions (VEMAP) and under the most extreme climate scenario (doubled variability HadCM2-GHG). The average maximum and minimum temperatures by growth stage at the eastern Wisconsin and southern Illinois sites for these climate scenarios are given in Figure 9. The planting dates presented for the southern Illinois site (Figure 9a) indicate the optimum period currently (144) and under the doubled variability HadCM2-GHG scenario (193). The doubled variability HadCM2-GHG conditions force higher maximum temperatures throughout the growing season, exacerbating moisture stress, particularly during seed ﬁll. Equally dramatic is the increase in minimum temperatures. The opposite effect is seen at eastern Wisconsin (Figure 9b). Higher maximum temperatures and higher minimum temperatures, particularly late in the season, result in signiﬁcant yield increases. In fact, the temperatures under the doubled variability HadCM2-GHG scenario at eastern Wisconsin are in the range of those of current conditions at southern Illinois.

3.5. CO2 FERTILIZATION EFFECTS

To evaluate the effect of CO2 fertilization within our study region we selected a single location (eastern Illinois) and simulated yields for ambient CO2 levels (360 ppmv) and under future CO2 levels (555 ppmv) for VEMAP, HadCM2-GHG, and HadCM2-SUL climate scenarios (Figure 10). The difference in yields between 360 ppmv CO2 and 555 ppmv CO2 is greatest under the HadCM2-SUL scenario. The SOYGRO model results did not indicate any signiﬁcant water stress during 468 JANE SOUTHWORTH ET AL. Mean maximum and minimum temperatures by developmental stage for (a) Southern Illinois, and (b) Eastern Wisconsin, for current (VEMAP) climate conditions and for the doubled variability HadCM2-GHG climate scenario. Figure 9. CHANGES IN SOYBEAN YIELDS IN THE MIDWESTERN UNITED STATES 469

Figure 10. A comparison of soybean yields (bu/ac) for 20 different planting dates for Eastern Illi- nois, under current (360 ppmv) and future (555ppmv) CO2 levels, for VEMAP, HadCM2-GHG, and HadCM2-SUL climate scenarios. the growing season. With sufﬁcient water availability and no nutrient stress, the CO2 fertilization effect resulted in soybean yield increases of about 30% at this site. Where soybeans are planted early and under the HadCM2-GHG scenario, the difference in yields was not as great. Other conditions, such as the extremely high temperatures, were limiting and so the full effect of CO2 fertilization was not seen.

4. Discussion

4.1. CROP YIELD CHANGES BY REGION

At southern locations, where later planting dates produced higher yields, and later- maturing soybean were the dominant cultivars producing maximum yields, there appears to be a limit in the amount of yield increase. In these locations there also were yield decreases under more extreme future climate scenarios, in part due to the limits in yield of the late-maturing cultivars planted later in the season. The yield responses to increasing temperatures found in this research matches those found by previous researchers (Jones et al., 1999; Lal et al., 1999; Ferris et al., 1998; Hoogenboom et al., 1995). High temperatures limit soybean growth in the southern locations, and especially under HadCM2-GHG runs, due to the greater frequency of higher temperature events (Table III) and concomitant increases in 470 JANE SOUTHWORTH ET AL. moisture stress under the doubled variability HadCM2-GHG scenario. Addition- ally, the higher yields produced by later planting dates reflect the results of Jones et al. (1999). In the central and northern locations increases in yield are much greater due in part to (i) more mid-maturing cultivars as the producers of maximum yields, and (ii) to the shift to earlier planting dates. The maturity group influences the flexibility of length of growing period. These changes in planting dates and cultivars allow for a greater potential yield, as represented in this research, by significantly increased potential future yields in these locations. Such shifts in planting dates reflect the same north-south patterns as yields, and thus help to explain the shifts in yields. This research indicated a dominance of mid-maturing cultivars in central and northern regions, and we also found a simultaneous shift in planting dates to earlier dates. The differences in our central and northern location from the results of Jones et al. (1999) are likely because their sites were predominantly in southern locations, whereas our study included more northern locations. The differential impacts upon the climate, and hence crop yields from north to south illustrate the need for such analyses to be conducted across larger spatial areas, as results are highly variable spatially.

4.2. IMPACTS OF CHANGES IN CLIMATE VARIABILITY

Increasing the variability of the future climate scenarios, by using the doubled climate variability scenarios, decreases the mean decadal crop yields. Overall, yields associated with these doubled climate variability scenarios have lower mean decadal yields due to some years having very low yields, i.e., the year-to-year variability of yields is much greater under these scenarios. The greater variance of yields associated with the doubled variability scenarios, and especially for the HadCM2-GHG climate scenario, is due to the more extreme increases in temperatures and moisture stress associated with these climate scenarios (Tables II and III), compared to the lesser increases for the HadCM2-SUL climate scenarios.

4.3. IMPACTS OF CO2 FERTILIZATION AND CLIMATE CHANGE

Our results are consistent with those reported elsewhere for increased temperature and CO2 concentrations (Jones et al., 1999; Lal et al., 1999; Rosenzweig and Hillel, 1998; Phillips et al., 1996; Kaiser et al., 1995; Wittwer, 1995; Rosenzweig et al., 1994; Siqueira et al., 1994), suggesting that the impacts of climate change resulting in a warmer, wetter Midwestern Great Lakes Region with atmospheric CO2 concentrations of 555 ppmv, will result in large increases in soybean yields across more central and northern locations, and some limited decreases in yields in more southern locations. These yield decreases are more likely under conditions of extreme heat (HadCM2-GHG) and increased climate variability. As these scenarios are much more extreme, and considered less likely to occur, moderate warming and CHANGES IN SOYBEAN YIELDS IN THE MIDWESTERN UNITED STATES 471 increased CO2 concentrations in this region will result in greatly increased soybean yields.

5. Conclusions

The main conclusions of this research are: • Under future climate scenarios central and northern locations in the study region will experience large increases in soybean yields, and southern locations will experience moderate decreases in yield. • High temperatures appear to limit soybean yields under the future climate scenarios. • For soybeans, increased atmospheric CO2 concentration and changing climate variability appear to be major drivers of yield change. • CO2 fertilization produces a yield increase of approximately 20%, increasing to 30% under moderate (HadCM2-SUL halved and normal variability runs) future climate scenarios. • Only under extreme climate change scenarios, as represented by the HadCM2- GHG and doubled variability scenarios, will soybean yields decrease in all locations in this study region due to high temperatures and, at southern Illinois, due to high temperatures and the resultant moisture stress. • Increasing the variability of the future climate scenarios increases the variability of the year-to-year crop yields, and results in lower mean decadal yields, compared to unchanged or halved variability analyses. • Planting dates may shift. To obtain maximum yields planting dates must shift to earlier in the central and northern locations, and later in the southern locations. Higher yields come from earlier maturity groups. Farmers can adapt and adjust their management practices as a result of climate change (Schimmelpfennig et al., 1996; Rosenberg, 1992). Typical options include changing planting dates, using cultivars better adapted to new conditions (increased heat tolerance, increased drought tolerance, etc.,), planting alternate crops which may be better adapted, and using supplemental irrigation. While farmers in the southern locations of our study region may require some or all of these options (e.g., more heat tolerant cultivars), farmers in the central and northern areas of this region may not require adaptation strategies because their soybean yields tend to increase under conditions of future climate. However, there will be incentives to take advantage of increased yields from earlier planting dates. Our results demon- strate the importance of including planting dates and other production concerns, as well as climate variability and CO2 fertilization effects, in climate change and potential adaptation integrated research assessments. The DSSAT suite of models provides an effective tool to study the potential of climate change on crop production (Hoogenboom et al., 1995). Our research 472 JANE SOUTHWORTH ET AL. also examined the impact of climate change and changing climate variability on maize (Southworth et al., 2000) and wheat in this study region. We are currently evaluating the impact of changes in yields and optimum planting periods due to climate change upon the economics and logistics of these farms. Future results will allow us to discern likely changes in current production practices and in crop mix within this very important agricultural study region.

Acknowledgements

This research was funded by grant number R 824996-01-0 from the Science to Achieve Results (STAR) Program of the United States Environmental Protection Agency. The overall quality of this manuscript was greatly improved by two anony- mous reviewers whose comments we greatly appreciate. We thank Dr. David Viner for his help and guidance with the use of the HadCM2 data, which was provided by the Climate Impacts LINK Project (DETR Contract EPG 1/1/68) on behalf of the Hadley Center and the United Kingdom Meteorological Ofﬁce. We thank Dr. Joe Ritchie at Michigan State University for his help and advice on SOYGRO throughout this project and Dr. Susan Riha at Cornell University for her comments on the temperature sensitivity of SOYGRO. We thank Drs. Jim Jones and Ken Boote of the University of Florida for sharing a manuscript and several stimu- lating discussions with us. We thank Dr. Timothy Kittel at the National Center for Atmospheric Research (NCAR), Boulder, Colorado for recommendations, help and guidance with VEMAP data acquisition and use. We greatly appreciate the assistance of Dr. Linda Mearns at NCAR in discussions on climate variability and for her comments regarding this manuscript and our research. In addition we thank Michael R. Kohlhaas for his editing of this manuscript.

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(Received 15 August 2000; in revised form 20 August 2001)