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J. Crop Sci. Biotech. 2013 (December) 16 (4) : 233 ~ 242 DOI No. 10.1007/s12892-013-0106-6 RESEARCH ARTICLE
Global Warming Likely Reduces Crop Yield and Water Availability of the Dryland Cropping Systems in the U.S. Central Great Plains Jonghan Ko1, 2, *, Lajpat R. Ahuja1 USDA-ARS Agricultural Systems Research Unit, 2150 Centre Ave., Bldg D, Suite 200,Fort Collins, CO 80526, USA Chonnam National University, Department of Applied Plant Science, 77 Yongbong-ro, Buk-gu, Gwangju 500-757, Republic of Korea 1 2
Received: September 01, 2013 / Accepted: December 17, 2013 Ⓒ Korean Society of Crop Science and Springer 2013
Abstract We investigated the impact of GCM-projected climate change on dryland crop rotations of wheat-fallow and wheat-cornfallow in the Central Great Plains (Akron in Colorado, USA) using the CERES 4.0 crop modules in RZWQM2. The climate change scenarios for CO2, temperature, and precipitation were produced by 22 GCM projections for Colorado based on the A1B scenario. The climate change for years 2050 and 2075 was super-imposed on measured 30-year-baseline climate data (1989-2008). For all the cropping rotations and projection years, simulated yields of wheat and corn decreased significantly (P < 0.05) with increasing temperatures. The yield declines due to the elevated temperatures should be attributable to the shortening of crop maturity duration and concurrent decreases in soil water and evapotranspiration. The model was also projected to decrease crop yields for the combined climate change scenarios of CO2, temperature, and precipitation in the dryland cropping rotations. Key words: climate change, crop modeling, cropping rotation, simulation, water-use efficiency, yield
Introduction An increase in global mean surface temperatures by 0.74°C ± 0.18°C over the last 100 years (1906-2005) resulted from the build-up of anthropogenic greenhouse gases (GHG) in the atmosphere (IPCC 2007). The IPCC Special Report on Emission Scenarios (SRES) predicts a warming of ~ 0.2°C per decade over the next two decades. The probable doubling of the current atmospheric CO2 concentration due to the emissions and associated warming will likely impact on agricultural production due to changes in evapotranspiration, plant growth rates, plant litter composition, and nitrogen-carbon cycle (Long et al. 2006). The effect at any location of the world will depend on the magnitude of change and response Jonghan Ko ( ) E-mail:
[email protected] Tel: +82-62-530-2053 / Fax: +82-62-530-2059 The Korean Society of Crop Science
of the crops, forage or livestock species, and location-specific management. Soil-water-crop management practices that increase water-use efficiency and crop yield as well as add higher carbon residue to soil can potentially increase soil carbon and N storage to counteract the GHG build-up in the atmosphere (Smith et al. 2007). It is necessary to study the impacts of a projected increase in GHG and subsequent global climate change especially on the water-limited cropping systems to understand the influences and recommend remedial measures in the agricultural scenarios. Crop growth and development are environmentally dependent upon the integrated responses of various interacting variables such as temperature, CO2, nutrients, water, and agronomic management on various eco-physiological processes in an agricultural system. All these variables and
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Impacts of Climate Change on Cropping Systems
their interactions cannot be included in a field experiment to study their impacts on agricultural production. Well-calibrated and validated agricultural system models make it possible to integrate the various chemical, physical, and biological processes and their interactions in the system (Ahuja et al. 2000b; Ma et al. 2009). Using a validated system model, one can evaluate how temperature increases linked with elevated CO2 in experimental field studies will affect the response of crops to CO 2, water, and nitrogen. Adams et al. (1990) reported that GCM-based climate change scenarios in temperature and precipitation projected to decrease in crop yields and rise crop water demands, mitigating some or all of the CO2 enhanced crop yields. Tubiello et al. (2002) evaluated the projected climate change effects on US crop production of wheat, potato, maize, and citrus, based on two GCM scenarios. According to their study, climate change resulted in significant reductions of grain yield (30 to 40%) in some rainfed production areas, accompanied by increased year-toyear variability. Thompson et al. (2005) also summarized a US national assessment of dryland production of grain (corn, soybean, and winter wheat) and two forage (alfalfa and clover hay) crops based on climate change scenarios from three GCMs at two levels of CO2 concentrations (365 and 560 ppm). They projected overall US national production of the crops to change by ± 25% from present levels and to vary regionally by greater than ± 50%. Until recently, most assessment studies have been focused on single crops such as wheat or corn, while much less is known about potential effects of climate change on crop production under various cropping rotation systems. The RZWQM (Root Zone Water Quality Model) is a process-oriented agricultural system model, which includes various physical, chemical, and biological processes (Ahuja et al. 2000). RZWGM is designed to simulate the impacts of soil-crop-nutrient management practices on soil water, crop productivity, and water quality. The crop simulation modules (CSM) in the DSSAT 4.0 package include modules able to simulate detailed growth and development of 16 different crops (Jones et al. 2003). The soil and water routines of RZWQM are interconnected with the CSM-DSSAT 4.0 crop modules in current RZWQM2 (Ma et al. 2009). This brings the advantages of linking the detailed soil water and nitrogen modules of RZWQM2 to the detailed crop modules of DSSAT 4.0. The combined RZWQM2 model has been tested for crop production at various locations worldwide (Hu et al. 2006; Ma et al. 2005, 2006, 2008 and 2009; Saseendran et al. 2007; Yu et al. 2006). The model was also applied to simulate impacts of climate change on crop production and soil water availability using the FACE experiment (Ko et al. 2010). Field experiments have been conducted on several no-till dryland cropping systems of increasing cropping intensity involving winter wheat in rotation with various summer crops (i.e., corn, proso-millet, sunflower, canola) since 1991 at the USDA-ARS Central Great Plains Research Station at Akron, Colorado, USA (Anderson et al. 1999). Saseendran et al. (2005, 2008, and 2009) simulated some results from these
experiments using the DSSAT-CERES models in RZWQM. Using the updated version RZWQM2, Saseendran et al. (2010) also reproduced the experimental data obtained from the crop rotations of wheat-follow (WF) under both conventional tillage (CT) and no tillage (NT), and wheat-corn-follow (WCF) and wheat-corn-millet under NT. A recent effort by Ko et al. (2012) followed it to simulate effects of climate change on crop production and transpiration in the above rotations using the calibrated and validated wheat, corn, and proso millet crop modules in RZWQM2. The objectives of this study were to simulate the effects of climate change, based on GCM projections in CO2, temperature, and precipitation for the years 2050 and 2075, on crop productivity, soil water availability, and plant water uses under the WF-CT, WF-NT, and WCF-NT cropping systems using the same crop modules in RZWQM2 in the US Central Great Plains.
Materials and Methods RZWQM2 model The DSSAT4.0-CERES crop simulation modules for wheat and maize in RZWQM2 were used (Ma et al. 2009; Saseendran et al. 2009) in this study. RZWQM2 can simulate a detailed soil-water balance module using the Green-Ampt equation for infiltration and the Richards’ equation for redistribution of water among different soil layers (Ahuja et al. 2000a). RZWQM2 uses the extended Shuttleworth-Wallace equation to simulate potential evapotranspiration, which is modified to include the surface crop residue dynamics on aerodynamics and energy fluxes (Farahani and DeCoursey 2000). The soil carbon and nitrogen dynamic module contains two surface residue pools, three soil humus pools, and three soil microbial pools. RZWQM2 can simulate nitrogen mineralization, nitrification, denitrification, ammonia volatilization, urea hydrolysis, methane production, and microbial population processes in what is measured to be a reasonable degree of detail (Shaffer et al. 2000). The model is able to deal with management practices such as tillage, applications of manure and fertilizers, planting and harvesting operations, irrigation, and surface crop residue dynamics (Rojas and Ahuja 2000). The DSSAT4.0-CERES crop models in RZWQM2 can simulate crop yield and yield components, leaf numbers, and phenological stages. The CERES models in RZWQM2 calculate net biomass production using the radiation use efficiency (RUE) approach. The effects of elevated CO2 on RUE are modeled empirically using curvilinear multipliers based on a modified Michaelis-Menten equation to fit responses of crop growth to CO2 concentration (Allen et al. 1987; Peart et al. 1989): RUEm • CO2 RUE=–––––––––––––– + RUEi CO2+Km where RUEm is the asymptotic response limit of (RUE – RUEi) at high CO2 concentration, RUEi is the intercept on the
JCSB 2013 (December) 16 (4) : 233~242
y-axis, and Km is the value of the substrate concentration, i.e., CO 2, at which (RUE – RUE i) = 0.5 RUE m. Similar methodologies were applied for simulations of CO2 effects on cropping systems in EPIC (Williams et al. 1989), APSIM (Reyenga et al. 1999), and the Sirius (Jamieson et al. 2000). CERES determines impacts of water stress on photosynthesis using empirically calculated stress factors, in association with potential transpiration and crop water uptake (Ritchie and Otter-Nacke 1985). Elevated CO2 concentration declines stomatal conductance in the Shuttleworth–Wallace equation for potential transpiration (Allen et al. 1987). This decreases water stress at a given soil water content.
tems by Saseendran et al. (2010). Simulated grain yields of wheat and corn corresponded to the measured grain yields mostly within ± 1 SD with root mean squared error (RMSE) and model efficiency (E) (Nash and Sutcliffe 1970) of all the two crops less than 462 kg ha-1 and 0.76, respectively (data now shown). Table 1. Generic coefficients or cultivar parameters for winter wheat and corn developed for simulation using the CERES-maize and CERES-wheat modules in RZWQM2 Parameter P1
Cropping system data Experimental field data were obtained from the long-term dryland alternative crop rotation practices at the Central Great Plains research station of USDA Agricultural Research Service at Akron in Colorado, USA (40o 09' N, 103o 09' W; 1,384 m) since 1991. The mean annual precipitation was 420 mm at the research station. These experiments were performed on a Weld silt loam soil in 9.1 × 30.5 m plots laid out in east-to-west direction with three replications in a randomized complete block design. Twenty crop rotations and three tillage treatments were initially established, which include combinations of six crops and fallow. Detailed information can be found from the reports by Bowman and Halvorson (1997) and Anderson et al. (1999), which include cultural practices, plot area, and experiment design. In the current study, we used data from the wheat-fallow (WF) and wheat-corn-fallow (WCF) cropping systems. The WF and WCF data were available for 17 years from 1992 to 2008. The WF cropping system was conducted under both conventional tillage (CT) and no tillage (NT) scenarios, while the WCF was practiced under NT only. Three winter wheat cultivars were planted as follows: ‘TAM 107’ from 1991 to 1995’, ‘Akron’ from 1996 to 2005, and ‘Danby’ from 2006 to 2008. Five corn hybrids were as follows: ‘Pioneer Hybrid 3732’ from 1992 to 1997, ‘DK493 BT’ from 1998 to 1999, ‘DKC49-92’ in 2000, ‘NK4242 BT’ from 2001 to 2003, and ‘N42B7’ from 2004 to 2008. Further descriptions of cultural practices for simulations using the model are available from Saseendran et al. (2010).
Model parameterization and calibration Minimum variables for model simulations are daily solar radiation, maximum and minimum temperature, precipitation, soil texture, and initial soil nitrogen and soil water conditions. Required crop management practices include typically planting dates, planting depth, plant population, the amounts and methods of irrigation, and fertilizer applications. Cultivar parameters for the wheat and corn cultivars used in the simulations (Table 1) were calibrated for the location as described in Saseendran et al. (2010). The model was previously calibrated and validated for wheat and corn grain yields under the different cropping sys-
P1V
P1D
P2
P5 G1 G2
G3
PHINT
Corn† Wheat‡ C1 C2 C3
Definitions Thermal time from seedling emergence to the end of Juvenile phase during which the plants are not responsive to changes in photoperiod (degree days). Relative amount that development is slowed for each day of unfulfilled vernalization, assuming that 50 days of vernalization is sufficient for all cultivars. Relative amount that development is slowed when plants are grown in a photoperiod 1 hour shorter than the optimum (which is considered to be 20 h). Extent to which development is delayed for each hour increase in photoperiod above the longest photoperiod at which development is at maximum rate, which is considered to be 12.5 h (days). Thermal time from silking (or begin grain filling) to physical maturity. Kernel number per unit weight of stem (less leaf blades and sheaths) plus spike at anthesis (1 g-1). Maximum possible number of kernels per plant (corn) or kernel-filling rate under optimum conditions (mg day-1) (wheat). Kernel-filling rate during the linear grain-filling stage and under optimum conditions (mg day-1) (corn) or non-stressed dry weight of a single stem (excluding leaf blades and sheaths) and spike when elongation ceases (g) (wheat). Phyllochron interval (°C).
290
290
290
-
-
-
-
40.0
-
-
-
65.0
0.8
0.8
0.8
-
615
615
615
370
-
-
-
24
690
690
590
18.0
9.6
7.0
7.0
1.0
38.0 48.0 88.0 60.0
† Corn cultivars: C1 = Pioneer 3732, Dekalb 493 BT; C2 = DKC-49-92, N4242BT; and C3 = N42B7. ‡ Wheat cultivars: TAM 107, Akron, and Danby.
Simulation of climate change impacts for future projections Climate projections were produced in response to the radiative forcing due to atmospheric CO2 concentrations up to the year 2100 based on A1B scenario (IPCC, 2007) by 22 different GCMs (Table 2) for Akron, Colorado. Forty-eight model runs were produced with varying run numbers for each model because of different spin-up conditions of each model run. Out of all the model outputs, 22 runs of each GCM model were obtained by statistically comparing simulated climate data with the measured data during the past 95 years from 1912 to 2008. Using the selected climate projections, monthly mean climate change scenarios of 30 year variations, centered in 2050 and 2075, were determined
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Impacts of Climate Change on Cropping Systems
Table 2. Global Circulation Models (GCM) used for the climate change projections No. GCM name 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
bccr_bcm2_0 cccma_cgcm3.1 (t47) cnrm_cm3 csiro_mk3.0 fgoals-g1.0 gfdl_cm2.0 gfdl_cm2.1 giss_model_aom giss_model_eh giss_model_er ingv-sxg inmcm3_0 ipsl_cm4 miroc3_2_hires miroc3_2_medres miub_echo_g
17 18 19 20 21 22
mpi_echam5 mri_cgcm2_3_2a ncar_ccsm3_0 ncar_ pcm1 ukmo_hadcm3 ukmo_hadgem1
Host Center Institution
Atmospheric resolution (lat, long, o)
Bjerknes Center for Climate Research, Norway Canadian Centre for Climate Modeling and Analysis, Canada CERFACS, National Weather Research Center, METEO-FRANCE, France CSIRO Atmospheric Research, Australia Institute for Atmospheric Physics, China Geophysical Fluid Dynamics Laboratory, USA Geophysical Fluid Dynamics Laboratory, USA NASA Goddard Institute for Space Studies, USA NASA Goddard Institute for Space Studies, USA NASA Goddard Institute for Space Studies, USA Intituto Nazionale di Geofisicia e Vulcanologia, Italy Institute for Numerical Mathematics, Russia Pierre Simon Laplace Institute, France Center for Climate Systems Research/ NIES/JAMSTEC, Japan Center for Climate Systems Research / NIES/JAMSTEC, Japan Meteorological Institute of the University of Bonn/ Meteorological Research Institute of KMA/Model Data Group, Germany/Korea Max Planck Institute for Meteorology, Germany Meteorological Research Institute, Japan National Center for Atmospheric Research, USA National Center for Atmospheric Research, USA Hadley Centre for Climate Prediction, Met Office, UK Hadley Centre for Climate Prediction, Met Office, UK
1.8 × 2.8 3.75 × 3.75 2.8 × 2.8 1.88 × 1.88 2.8 × 2.8 2 × 2.5 2 × 2.5 3×4 4×5 4×5 1.125 × 1.125 4×5 2.5 × 3.75 2.8 × 2.8 2.8 × 2.8 3.75 × 3.75 1.878 × 1.88 2.8 × 2.8 1.4 × 1.4 2.8 × 2.8 2.5 × 3.75 2.5 × 3.75
Table 3. Statistical analysis for the simulation data (Fig. 1) of the climate change (CC) effects on winter wheat yield in conventional tillage under the wheat-fallow cropping system CC effec† Year Comb
Temp
CO2
Baseline 2050 2075 Baseline 2050 2075 Baseline 2050 2075
Yield‡ K-S test (Pr)
kg ha-1
0.063 0.014 0.031 0.013 0.782 0.367
2503a 2407a 2263a 2503a 2219ab 1999b 2503a 2632a 2721a
Comb = combined effect of CO2, temperature, and precipitation; Temp = temperature. ‡ Yield data were analyzed with both Kolmogorov-Smirnov (K-S) test and Duncan's Multiple Rage Test (DMRT). a,b,c The values with the same superscript letters are not significantly different (Duncan's Multiple Rage Test at 95 % confidence intervals). †
Fig. 1. Wheat grain yield in wheat-fallow under conventional tillage (CT) and no tillage (NT) for combinations of CO2, temperature, and precipitation (a and b), with only temperature (c and d) and only CO2 (e and f) in the baseline (BL) and years 2050 and 2075. The two future years are represented as the combined effects. Open circles, error bars, and a box represent the 5th, 10th, 25th, 75th, 90th, and 95th percentiles of the yield data, showing the median (solid line) and mean (broken line) in the box
based on the 30-year baseline (1988-2008). The average of the GCM projections was determined as an ‘ensemble’ member (Table 3). The GCM models simulated the mean of monthly mean temperatures for 2050 and 2075 to be increased 1.86 to 2.83°C and 2.57 to 4.07°C, respectively.
Mean of monthly total precipitations from the baseline varied from -28.4 to +36.6 % and -14.7 to +14.2 %, respectively. While the individual GCM projections differed from their mean temperature and precipitation from one to another, the mean values of temperature and precipitation projections correlated well with the projections results for the Southwest of the USA (Lenart 2007). The model projections of temperature and precipitation were then superimposed to the 30 year climate data at Akron, Colorado. The average temperature increase of each month was equally added to the daily minimum and maximum temperatures in the corresponding month. Likewise, the percent change in precipitation of each month was used to change the
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Fig. 2. Soil water, evapotranspiration (ET), evaporation, and transpiration of wheat in wheat-fallow under conventional tillage for combinations of CO2, temperature, and precipitation (a-d), only temperature (e-h), and only CO2 (i-l) in the baseline (BL) and years 2050 and 2075. The two future years are represented as the combined effects. Open circles, error bars, and a box represent the 5th, 10th, 25th, 75th, 90th, and 95th percentiles of the yield data, showing the median (solid line) and mean (broken line) in the box.
Fig. 3. Soil water, evapotranspiration (ET), evaporation, and transpiration of wheat in wheat-fallow under no tillage for combinations of CO2, temperature, and precipitation (a-d), only temperature (e-h), and only CO2 (i-l) in the baseline (BL) and years 2050 and 2075. The two future years are represented as the combined effects. Open circles, error bars, and a box represent the 5th, 10th, 25th, 75th, 90th, and 95th percentiles of the yield data, showing the median (solid line) and mean (broken line) in the box.
Table 4. Statistical analysis for the simulation data (Fig. 2) of the climate change (CC) effects on soil water, evapotranspiration (ET), evaporation, and transpiration of winter wheat in conventional tillage under the wheat-fallow cropping system
dence level for both DMRT and K-S test, so differences are reported below as ‘significant’ based on this criterion.
CC effec†
Year
Soil water
ET
Evaporation Transpiration
Results and Discussion
(cm3 cm-3) Comb
Temp
CO2
Baseline 2050 2075 Baseline 2050 2075 Baseline 2050 2075
0.200a 0.187b 0.184b 0.200a 0.189bc 0.187c 0.200a 0.199a 0.198a
268.0a 248.1ab 239.2b 268.0a 248.1ab 238.7b 268.0a 269.8a 270.7a
111.8a 95.4b 93.3b 118.8a 99.7ab 97.2b 118.8a 107.8a 105.7a
156.2a 152.7a 146.0a 156.2a 148.4a 141.5a 156.2a 162.0a 165.0a
Comb = combined effect of CO2, temperature, and precipitation; Temp = temperature. a,b,c The values with the same superscript letters are not significantly different (Duncan's Multiple Rage Test at 95 % confidence intervals). †
daily precipitations in the corresponding month. These projected climate data were used to simulate the climate change effects of CO 2, temperature, and precipitation on wheat, sorghum, and proso millet using the validated model simulation conditions for the cropping system data set.
Statistical evaluation The mean values for different projection years as described were tested for significance of differences from the mean of the baseline using the Duncan’s Multiple Range Test (DMRT, Duncan 1955) using PROC GLM (SAS version 9.2, Cary, NC). Kolmogorov-Smirnov (K-S) test using PROC NPAR1WAY was performed between the baseline and each of the projection year’s data. For this purpose, we assumed that year to year values within a CDF were statistically independent, as we simulated each year separately (not in a continuous simulation for all years) that minimized the dependence among years. All significance testing used a 95% confi-
Effects of projected climate change on the WF under NT and CT Simulated wheat yield in WF-CT and WF-NT for the baseline years were compared with the projections for 2050 and 2075 for effects of individual factors of temperature and CO2 as well as their combinations including precipitation (Fig. 1). With increasing CO2 concentrations alone (i.e., 550 ppm for 2050 and 693 ppm for 2075), the yield increased. However, this yield increase was not significantly different in both K-S test and DMRT at 95% confidence intervals (Table 3). With increasing temperatures, the average yields decreased in both WF-CT and WF-NT. The differences were statistically significant in both WF-CT (Table 3) and WF-NT (data not shown). With precipitation change scenarios, small numerical increases in yield were projected, through the differences were statistically insignificant (data not shown). With all three factorscombined, the yield generally decreased to degrees that were significant in both WF-CT and WF-NT. Climate change impacts on the wheat yield in WF-NT were similar to those in WF-CT. The impacts of CO2, temperature, precipitation, and all three of these factors combined on grain yield were comparable between the two rotations (WF-CT and WF-NT). The crop yield was higher under no tillage for the baseline, and for years 2050 and 2075. The soil water in the years 2050 and 2075 in WF-CT was simulated to decrease in comparison with the baseline years, under the combined effects of CO2, temperature, and precipitation as well as with increasing temperatures (Fig. 2). These decreases were significantly different at 95% confidence intervals according to Duncan’s Multiple Range Tests (Table
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Impacts of Climate Change on Cropping Systems
Fig. 5. Soil water, evapotranspiration (ET), evaporation, and transpiration of wheat in wheat-corn-fallow under no tillage for combinations of CO2, temperature, and precipitation (a-d), only temperature (e-h), and only CO2 (i-l) in the baseline (BL) and years 2050 and 2075. The two future years are represented as the combined effects. Open circles, error bars, and a box represent the 5th, 10th, 25th, 75th, 90th, and 95th percentiles of the yield data, showing the median (solid line) and mean (broken line) in the box.
Fig. 4. Wheat and corn grain yields in wheat-corn-fallow under no tillage for combinations of CO2, temperature, and precipitation (a and b), with only temperature (c and d) and only CO2 (e and f) in the baseline (BL) and years 2050 and 2075. The two future years are represented as the combined effects. Open circles, error bars, and a box represent the 5th, 10th, 25th, 75th, 90th, and 95th percentiles of the yield data, showing the median (solid line) and mean (broken line) in the box.
Table 5. Statistical analysis for the simulation data (Fig. 4) of the climate change (CC) effects on wheat and corn yields in no tillage under the wheatcorn-fallow cropping system Crop
CC effec†
Year
Wheat
Comb
Baseline 2050 2075 Baseline 2050 2075 Baseline 2050 2075 Baseline 2050 2075 Baseline 2050 2075 Baseline 2050 2075
Temp
CO2
Corn
Comb
Temp
CO2
Yield‡ K-S test (Pr)
kg ha-1
0.219 0.366 0.030 0.005 0.945 0.564
