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The impacts of climate change on wheat yield in the Huang-Huai-Hai Plain of China using DSSAT-CERES-Wheat model under different climate scenarios
Journal of Integrative Agriculture 2019, 18(6): 1379–1391 Available online at www.sciencedirect.com
ScienceDirect
RESEARCH ARTICLE
The impacts of climate change on wheat yield in the Huang-HuaiHai Plain of China using DSSAT-CERES-Wheat model under different climate scenarios QU Chun-hong1*, LI Xiang-xiang2, 3*, JU Hui4, LIU Qin4 1
Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China Agro-meteorological Center of Jiangxi Province, Nanchang 330096, P.R.China 3 Meteorological Science Institute of Jiangxi Province, Nanchang 330096, P.R.China 4 Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China 2
Abstract Climate change has been documented as a major threat to current agricultural strategies. Progress in understanding the impact of climate change on crop yield is essential for agricultural climate adaptation, especially for the Huang-Huai-Hai Plain (3H Plain) of China which is an area known to be vulnerable to global warming. In this study, the impacts of climate change on winter wheat (Triticum aestivum L.) yield between the baseline period (1981–2010) and two Representative Concentration Pathways (RCP8.5 and RCP4.5) were simulated for the short-term (2010–2039), the medium-term (2040–2069) and the long-term (2070–2099) in the 3H Plain, by considering the relative contributions of changes in temperature, solar radiation and precipitation using the DSSAT-CERES-Wheat model. Results indicated that the maximum and minimum temperatures (TMAX and TMIN), solar radiation (SRAD), and precipitation (PREP) during the winter wheat season increased under these two RCPs. Yield analysis found that wheat yield increased with the increase in SRAD, PREP and CO2 concentration, but decreased with an increase in temperature. Increasing precipitation contributes the most to the total impact, increasing wheat yield by 9.53, 6.62 and 23.73% for the three terms of future climate under RCP4.5 scenario, and 11.74, 16.38 and 27.78% for the three terms of future climate under RCP8.5 scenario. However, as increases in temperature bring higher evapotranspiration, which further aggravated water deficits, the supposed negative effect of increasing thermal resources decreased wheat yield by 1.92, 4.08 and 5.24% for the three terms of future climate under RCP4.5 scenario, and 3.64, 5.87 and 5.81% for the three terms of future climate under RCP8.5 scenario with clearly larger decreases in RCP8.5. Counterintuitively, the impacts in southern sub-regions were positive, but they were all negative in the remaining sub-regions. Our analysis demonstrated that in the 3H Plain, which is a part
Received 9 April, 2018 Accepted 19 December, 2018 QU Chun-hong, E-mail: [email protected]; LI Xiang-xiang, E-mail: [email protected]; Correspondence LIU Qin, E-mail: [email protected] * These authors contributed equally to this study. © 2019 CAAS. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/). doi: 10.1016/S2095-3119(19)62585-2
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of the mid-high latitude region, the effects of increasing thermal resources were counteracted by the aggravated water deficits caused by the increase in temperature. Keywords: climate change, relative contribution, wheat yield, DSSAT-CERES-Wheat model, Huang-Huai-Hai Plain
1. Introduction Agriculture is one of the most sensitive systems to climate change, in part because it is directly exposed to environmental conditions (Parry and Ruttan 1991; Piao et al. 2010). Global warming increases the thermal resources for high latitudes causing arable regions to expand (Ju et al. 2013), while exacerbating heat and drought stress for arid and semi-arid regions with fluctuating food supplies (Yuan et al. 2016). Thus, investigations of the impacts of climate change on agriculture are crucial in order to avoid negative impacts on national food security (Piao et al. 2010; Ju et al. 2013). Statistical models and numerical simulations have been widely used to detect relationships between the climate and crops (Shi et al. 2013). Based on long term yield data, time-series regression models at the station level which consider yield series and sets of climate variables can be established with regression coefficients that represent the impacts of climate change (David and Christopher 2007; Tao et al. 2008). Recently, statistical models that rely on information from multiple stations were shown to be better at predicting crop responses to temperature changes (Schlenker and Roberts 2009; Wolfram and David 2010; Tao et al. 2014). However, the influence of non-climatic factors (such as cultivar and fertilizer changes) on yield need to be eliminated. Although some elimination methods, such as the first-difference yield and the linear detrended yield, have been widely used in various studies, their consistency remains questionable. For example, the linear detrended method is based on the notion that crop management and variety renewal changed linearly with time (Xiong et al. 2014). Thus, whether these results are realistic remains questionable and opposing effects have been documented (Shi et al. 2013). In recognizing the complex interactions between crop growth and environmental factors, numerical simulation models have become popular research tools for agro-meteorological researchers in recent years. Dynamic crop models, such as the Erosion Productivity Impact Calculator (EPIC) model (Williams et al. 1983), the Agricultural Production Systems sIMulator (APSIM) (Keating et al. 2003), and the Decision Support System for Agrotechnology Transfer (DSSAT) (Jones et al. 1998),
have been tested and used in quantifying water, nitrogen and weather responses at field or regional scales around the world. Among them, DSSAT is a platform that contains individual models for different crops and can quantitatively predict the growth and production of annual field crops with the interactions of aerial and soil environments, cultivar factors and management practices (Ritchie et al. 1998). DSSAT has been used for climate change and climate extreme impact assessments for rice, wheat and maize for different zones in China and for historical and future scenarios. However, few studies have focused on the impact of changes in individual climate variables in isolation. The Huang-Huai-Hai Plain (3H Plain) is a vital crop area that plays a key role in guaranteeing Chinese wheat supplies. Annual rainfall is unevenly distributed and has caused a dry climate during the wheat growing season. Water shortage is one of the major factors threatening high and stable production of wheat. Furthermore, climate change in future projections shows an increasing risk of drought in the 3H Plain (Liu et al. 2010). Thus, understanding the relationship between climate and wheat yield is essential for agricultural climate adaptation. Traditional climate-crop relationships based on statistical analyses often produce conflicting conclusions because of the effects of different elimination methods or the consideration of different predictor variables (Shi et al. 2013). Many studies using both a statistical approach and numerical simulation have proven that climate change has threatened the current agricultural strategies. Consequently, the objectives of this research were to: (1) Investigate the characteristics of the meteorological climatic variables during the wheat growth period over the 3H Plain under differing RCP scenarios. (2) Establish a DSSAT-CERES-Wheat simulation for wheat yield under differing RCP scenarios. (3) Identify the relative impacts of shifts in each variable on winter wheat yield in isolation.
2. Materials and methods 2.1. Study region The 3H Plain is widely accepted to be the major grain producing area in China (Yong et al. 2013), as it provides about 70% of wheat and 30% of maize production in China
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(Yang et al. 2015). Due to the extratropical monsoon climate, more than 70% of annual precipitation falls during the summer season (July to September) (Zhang et al. 2011). Thus, the winter wheat growing period has suffered from serious water deficits, with only 25–40% of water demand being satisfied by precipitation (Mei et al. 2013). To maintain high yield, wheat is irrigated with pumped groundwater, which has led to social environment threats, such as lowering of the groundwater level and surface subsidence (Zhang et al. 2005). Additionally, the increase in temperature and changing precipitation patterns have perturbed regional crop production (Yu et al. 2014). The 3H Plain can be divided into six sub-regions in terms of the climatic conditions and agricultural management practices. Detailed information on the six sub-regions is described by Li et al. (2015).
2.2. CERES-Wheat model The CERES-Wheat model is a simulation system that predicts daily wheat growth, development and yield based on information on the aerial and soil environments, cultivar factors and management information (Ritchie et al. 1998). CERES-Wheat, along with other cereal crop models included in DSSAT such as CERES-Maize and CERES-Rice, has been widely used in optimizing the use of resources and quantifying risks related to weather variations at field or regional scales around the world (Timsina and Humphreys 2006). Additionally, the applicability of CERES-Wheat has been tested over a wide range of field trials (Xiong et al. 2014). Weather, soil and management information Input data sets for model operation require weather information (daily solar radiation, maximum and minimum temperatures, precipitation), soil information (classification and basic profile characteristics by soil layer) and management information (e.g., cultivar, planting, irrigation and fertilization information). The 0.5°×0.5° daily weather data for future climate scenarios was derived from the HadGEM2-ES model, and divided into baseline period (1981–2010), shortterm period (2010–2039), medium-term period (2040–2069) and long-term period (2070–2099). The 0.5°×0.5° soil classification and profile characteristics were collected from the Harmonized World Soil Database (HWSD). In this study, nitrogen stress was not included and no irrigation was applied in order to fully capture the impact of climate change on water availability. In this study, all simulations were nitrogen-free and under rainfed conditions. For the planting date for each grid and each year, the final date of 15°C in each year was considered as the suitable sowing date, which was counted by the method of 5-day gliding average.
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Model calibration and evaluation In the CERES-Wheat model, coefficients that control the development and growth of wheat (Ritchie et al. 1998) must be calibrated and evaluated under specific environmental conditions before being used for climate impact analysis (Hunt and Boote 1998). Based on the principles that: 1) the station must have at least three growing seasons cultivated with one cultivar; 2) the chosen growing seasons should have no missing data for irrigation, fertilization, growth period and yield; and 3) there should be no natural disasters such as drought and heat stress during the chosen growing seasons, six agro-meteorological stations were selected as the representatives for each sub-region (Fig. 1). The field observed anthesis date, maturity date and the yield data used for calibrating the cultivar coefficients were obtained from the agro-meteorological stations of the China Meteorological Administration (Fig. 1). The general information on the six stations with chosen seasons and representative cultivar names for model calibration and evaluation, and the average information of growth stages and yields are shown in Table 1.
2.3. Simulation design To investigate the response of wheat yield to changes in each climate variable between baseline and future climate scenarios, five simulation scenarios were set, as shown in Table 2. Scenario S0 simulates the wheat yield under the basic climate which represents the baseline daily maximum and minimum temperatures (TMAX and TMIN), precipitation (PREP), and solar radiation (SRAD). Scenarios S1, S2, S3 and S4 simulate the wheat yield using one or more climatic variables of future climate (temperature (T), T+SRAD, T+SRAD+PREP, T+SRAD+PREP+CO2), with the remaining variables held constant. For example, if we want to simulate the effects of an increase in temperature in the short-term (2010–2039), i.e., S1, we reconstruct the 2010–2039 climate data series with the maximum and minimum temperatures from RCP4.5 or RCP8.5 scenarios while moving the solar radiation and precipitation data from baseline (1981–2010) to 2010–2039 conditions. The impacts of climate change on yield can be calculated by: FT (%)=
FS (%)=
FP (%)=
YS1–YS0 YS0 YS2–YS1 YS0 YS3–YS2 YS0
×100
(1)
×100
(2)
×100
(3)
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112°E
114°E
116°E
118°E
120°E
122°E
Beijing Baodi I Tianjin
40°N
N W
III 38°N
Taiyuan
124°E
E S
Shijiazhuang
Huimin
II
Zibo Jinan IV
36°N
Agro-meteorogical stations Capital cities
Zhengzhou
32°N
Suzhou
V
34°N
VI 0
75 150
300 km
Agricultural sub-regions Provincial boundaries
I Huai’an
Nanjing
Hefei
Shanghai
Fig. 1 The inset map shows the location of Huang-Huai-Hai (3H) Plain of China and its six agricultural sub regions. The subregions of 3H Plain include a coastal land-farming-fishing area (sub-region I), piedmont plain-irrigable land (sub-region II), low plain-hydropenia irrigable land and dry land (sub-region III), hill-irrigable land and dry land (sub-region IV), basin-irrigable land and dry land (sub-region V) and hill-wet hot paddy-paddy field (sub-region VI).
Table 1 The average planting date, anthesis days, maturity days and yield of the seasons selected for model calibration and evaluation1) Sub-region2) I II III IV V VI
Representative station Shenzhou Baodi Huimin Zibo Suzhou Huai’an
Season3)
Cultivar
2003, 2004, 2005, 2007, 2009* 1996*, 1997*, 2000, 2001, 2002, 2004 2004, 2005, 2006, 2007, 2008* 2002*, 2005, 2006 2001, 2002, 2003, 2004* 2001*, 2002*, 2004, 2005, 2006, 2007
SX733 JD8 LM23 JM20 WM19 WM6
Planting date (mon/d) 10/07 10/03 10/12 10/12 10/15 10/21
ADAP (d) 210 220 201 205 191 180
MDAP (d) 244 254 234 236 225 214
HWAM (kg ha–1) 4 285 5 300 4 586 5 271 5 122 5 460
1)
ADAP, anthesis days after planting; MDAP, maturity days after planting; HWAM, harvest weight at maturity. I, the coastal land-farming-fishing area; II, the piedmont plain-irrigable land; III, the low plain-hydropenia irrigable land and dry land; IV, the hill-irrigable land and dry land; V, the basin-irrigable land and dry land; VI, the hill-wet hot paddy-paddy field. 3) The seasons with * were chosen for model evaluation. 2)
Table 2 Simulation scenarios used to identify the impact of climate changes on winter wheat yield1) Scenario S0 S1
TMAX TMIN Baseline Baseline Future climate Future climate
S2
Future climate Future climate
S3 S4
Future climate Future climate Future climate Future climate
1)
SRAD Baseline Baseline
PREP Baseline Baseline
Purpose Simulate yield under baseline climate conditions Simulate yield under future temperatures, with the SRAD and PREP held constant Future climate Baseline Simulate yield under future temperatures and SRAD, with the PREP held constant Future climate Future climate Simulate yield under future climate conditions Future climate Future climate Simulate yield with the fertilizer efficiency of CO2
TMAX, the maximum temperature; TMIN, the minimum temperature; SRAD, solar radiation; PREP, precipitation.
FC (%)=
YS4–YS3 YS0
×100
(4)
Where, FT, FS, FP and FC represent the relative impacts of temperature changes, SRAD changes, precipitation changes and the fertilizer efficiency of elevated CO2, respectively.
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3. Results 3.1. Model calibration and validation The coefficients of the chosen variety, which determine the development and growth of wheat, were calibrated and validated using at least three seasons of field observation data (Table 1). The definitions of the wheat coefficients are shown in Table 3. The three coefficients (P1V, P1D and P5) that affect the phenological development were estimated using the observed phenological dates (anthesis and maturity dates). G1, G2, and G3 determine the grain number, grain weight, and spike number, respectively. However, due to the lack of yield analysis data, these coefficients were estimated using harvest yield data only. PHINT is the thermal time between successive tip leaf appearances. The normalized root mean square error (NRMSE) listed in Table 3 represents the error between the simulated anthesis date (ADAP), maturity date (MDAP) and yield (HWAM) and the observations. Overall, the simulated phenological development agreed well with the observations (Fig. 2). The NRMSE ranged from 0.3 to 1.3% for the anthesis date and from 0.8 to 1.5% for
the maturity date (Table 3). Due to the lack of yield analysis data in the process of calibration, the NRMSE values for yield ranged from 6.0 to 11.5%. While higher than those for phenological development, the NRMSE values were still below the acceptable level of 15%. This suggests that the calibrated coefficients can be applied for the purposes of the present study.
3.2. Changes of climatic variables during the wheat growing period Temperature Table 4 shows the differences in the TMAX and TMIN during the winter wheat growing seasons between future climate scenarios and the baseline period. The TMAX and TMIN of most sub-regions would increase, except for the mountainous northwest areas, such as the coastal landfarming-fishing area (sub-region I). The average increment of TMAX is 0.78, 2.17 and 2.61°C for the short-term, medium-term and long-term under the RCP4.5 scenario and 0.55, 2.17 and 3.86°C for these three terms of future climate under the RCP8.5 scenario, with the highest increment in the low plain-hydropenia irrigable land and dry land (subregion III). Minimum temperature increased by 0.36, 1.11
Table 3 Genetic coefficients and the normalized root mean square error (NRMSE, %) of anthesis days after planting (ADAP), maturity days after planting (MDAP) and harvest weight at maturity (HWAM)1) Representative station Shenzhou Baodi Huimin Zibo Suzhou Huai’an
Sub-region2) I II III IV V VI
P1V (d) 19.6 13.2 13.7 9.1 15.0 10.1
P1D (%) 38.8 63.3 57.9 79.0 63.0 62.0
P5 (°C d) 557.0 634.1 560.2 602.4 583.1 685.3
G1 (no. g–1) 30.0 17.2 29.0 20.9 15.4 16.0
G2 (mg) 65.0 46.3 64.2 43.8 57.4 50.5
G3 (g) 1.9 1.4 1.1 1.0 1.2 1.1
PHINT 95.0 95.0 95.0 95.0 95.0 95.0
ADAP 0.9 1.2 0.9 0.3 1.3 1.9
NRMSE (%) MDAP HWAM 1.1 6.8 1.3 10.7 0.8 11.4 0.9 11.5 1.5 8.9 1.4 6.0
1)
7 5 3 3 5 7 9 Measured HWAM (t ha–1)
230 Simulated ADAP (d)
270
9
Simulated MDAP (d)
Simulated HWAM (t ha–1)
P1V, days required for vernalization (optimum vernalizing temperature); P1D, photoperiod response (% reduction in rate/10 h drop in photoperiod); P5, grain filling phase duration; G1, kernel number per unit canopy weight at anthesis; G2, standard kernel size under optimum conditions; G3, standard non-stressed mature tiller weight (including grain); PHINT, interval time between successive leaf tip appearances. 2) I, the coastal land-farming-fishing area; II, the piedmont plain-irrigable land; III, the low plain-hydropenia irrigable land and dry land; IV, the hill-irrigable land and dry land; V, the basin-irrigable land and dry land; VI, the hill-wet hot paddy-paddy field.
250 230 210 210
230
250
Measured MDAP (d)
270
210 190 170 170
190
210
230
Measured ADAP (d)
Fig. 2 Comparison between measured and simulated value of harvest yield at maturity (HWAM), maturity days after planting (MDAP) and anthesis days after planting (ADAP). The dotted line is the 1:1 reference line.
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and 1.99°C for these three terms of future climate under
differences of SRAD and PREP during the winter wheat
the RCP4.5 scenario and 0.31, 1.62 and 3.56°C under the
growing seasons between the future scenarios and baseline
RCP8.5 scenario, also showing the highest increment in
period for different future time periods. Likewise, the
sub-region III. Thus, the difference of warming amplitude
seasonal SRAD and PREP are also expected to increase,
between RCP4.5 and RCP8.5 mainly occurred during
and the increment of PREP is much higher than that of
2070–2099, while in the 2010–2069 range, the temperature
SRAD. However the SRAD of the northern areas of the
warmed at a mostly equal rate. Additionally, as shown in
study region exhibited a decreasing trend, especially for the
Table 4, the warming tendency is larger for TMAX than
short-term, such as in sub-region VI. The increasing rate
TMIN in the future scenarios, which is inconsistent with the
for SRAD was higher in the RCP4.5 scenario than in the
historical observations that typically show a higher warming
RCP8.5 scenario, while the increase was higher for PREP
speed for TMIN.
under RCP8.5. For the SRAD, the average increase rates
Solar radiation and precipitation Table 5 shows the
were 2.16, 5.99 and 5.29% for RCP4.5 and 1.70, 3.96 and
Table 4 Changes in the maximum temperature (TMAX) and the minimum temperature (TMIN) during the winter wheat growing season between future climate scenarios and baseline period Sub-region1) I II III IV V VI Average 1)
2)
Scenario2) RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5
Changes in TMAX (°C) 2010–2039 2040–2069 2070–2099 0.28 1.62 1.90 0.11 1.51 3.02 0.84 2.39 3.08 0.86 2.47 4.24 1.12 2.76 2.94 0.83 2.68 4.36 0.67 2.24 2.61 0.41 2.17 4.05 0.97 2.27 2.77 0.63 2.26 3.96 0.71 1.77 2.36 0.41 2.00 3.68 0.78 2.17 2.61 0.55 2.17 3.86
Changes in TMIN (°C) 2010–2039 2040–2069 2070–2099 –0.84 –0.23 0.69 –0.72 0.54 2.47 0.42 1.37 2.47 0.55 1.97 4.07 0.76 1.59 2.51 0.71 2.24 4.23 0.87 1.72 2.60 0.79 2.13 4.05 0.55 1.28 2.06 0.38 1.62 3.52 0.57 1.18 1.94 0.44 1.60 3.44 0.36 1.11 1.99 0.31 1.62 3.56
I, the coastal land-farming-fishing area; II, the piedmont plain-irrigable land; III, the low plain-hydropenia irrigable land and dry land; IV, the hill-irrigable land and dry land; V, the basin-irrigable land and dry land; VI, the hill-wet hot paddy-paddy field. RCP, Representative Concentration Pathway.
Table 5 Changes in solar radiation (SRAD) and precipitation (PREP) during the winter wheat growing season between future climate scenario and baseline period Sub-region1) I II III IV V VI Average 1)
Scenario2) RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5
Changes in SRAD (%) 2010–2039 2040–2069 2070–2099 5.94 9.39 8.36 5.50 6.03 3.81 4.19 6.69 6.18 3.65 4.74 3.33 2.59 6.74 5.84 2.13 3.86 1.89 2.26 5.97 5.20 1.96 4.16 2.24 1.32 5.47 4.89 0.84 3.92 1.84 –3.16 1.54 1.04 –3.68 0.73 –0.55 2.16 5.99 5.29 1.70 3.96 2.10
Changes in PREP (%) 2010–2039 2040–2069 2070–2099 17.57 3.95 22.68 19.18 33.47 43.98 6.06 3.55 11.67 8.92 23.92 21.02 13.12 –4.89 19.34 14.80 24.99 40.33 20.30 9.18 34.71 21.06 30.55 44.67 14.84 15.73 31.59 17.60 20.30 42.34 9.98 16.77 30.22 13.15 16.02 28.13 13.68 8.48 25.58 15.95 24.24 37.44
I, the coastal land-farming-fishing area; II, the piedmont plain-irrigable land; III, the low plain-hydropenia irrigable land and dry land; IV, the hill-irrigable land and dry land; V, the basin-irrigable land and dry land; VI, the hill-wet hot paddy-paddy field. 2) RCP, Representative Concentration Pathway.
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2.10% for RCP8.5 for the different terms, while the PRCP increased by 13.68, 8.48 and 25.58% for RCP4.5 and 15.95, 24.24 and 37.44% for RCP8.5 for the different terms.
3.3. Impacts of different climate variables on wheat yield Wheat yields were simulated using one or more climatic variables for the future climate (temperature, temperature+radiation, temperature+radiation+precipitation) with the remaining variables held at the levels of the baseline period. The relative changes of the yield calculated by eqs. (1–4) are shown below. Impact of increasing temperature The impact of increases in temperature varied between the southern and northern 3H Plain under both RCP4.5 and RCP8.5 (Fig. 3). On average, the increasing temperature would decrease wheat yield by –1.92, –4.08 and –5.24% for the three terms of future climate under RCP4.5 and –3.64, –5.87 and –5.81% for RCP8.5 scenario, with higher rates of decline in RCP8.5. Interestingly, the impacts in southern sub-regions (V and VI) were positive but they were all negative in the remaining sub-regions (Table 6). Impact of increasing solar radiation The expected changes of solar radiation would positively impact wheat
yield (Fig. 4). The increasing solar radiation would elevate wheat yield by 4.41, 2.81 and 3.67% for the three terms of future climate under RCP4.5 and 5.04, 4.37 and 7.14% for RCP8.5 scenario (Table 7), with no obvious spatial distribution. However, the elevated yield rates were higher under RCP8.5, but rates at which the solar radiation increased were lower than under RCP4.5. The reason for this observation is that perhaps the increasing solar radiation also aggravated water deficits by increasing evapotranspiration, which would lead to a reduced yield increase rate in the RCP4.5 with a higher increase in the magnitude of solar radiation than RCP8.5. Impact of increasing precipitation Similar to solar radiation, changes of precipitation would positively impact wheat yield (Fig. 5). Increasing precipitation would elevate wheat yield by 9.53, 6.62 and 23.73% for the three terms of future climate under RCP4.5 and 11.74, 16.38 and 27.78% for RCP8.5 scenario, with higher rates under the RCP8.5 scenario. However, the impact of increasing precipitation was negative in the southern part of study region (Fig. 5; Table 8), mainly because the precipitation in these grids was originally higher than in other grids, so the increasing precipitation would create higher risks of water-logging damage. Impact of increasing CO2 As depicted in Fig. 6, compared
A
B
C
D
E
F
<–30 –20 –10
0
10
20
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30
40
50
60 >70
%
Fig. 3 Spatial distribution of the relative impact of warming temperature on winter wheat yield. A, B, and C represent the impacts for the short-term period (2010–2039), medium-term period (2040–2069) and long-term period (2070–2099), respectively under RCP4.5 scenario, while D, E and F represent the impacts for these three periods, respectively, under RCP8.5 scenario.
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to the normal CO2 concentration of 380 ppm, the wheat yields tended to increase with higher CO2 concentrations. Compared to the baseline CO2 concentration (380 ppm),
Table 6 Relative impact of changes in temperature on winter wheat yield for different sub-regions Subregion1) I II III IV V VI Average
Scenario2) RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5
Relative impact of temperature change 2010–2039 2040–2069 2070–2099 –4.08 –8.10 –11.21 –3.65 –8.23 –11.47 –8.15 –21.97 –27.08 –10.40 –22.89 –30.28 –8.33 –21.62 –28.38 –10.71 –22.14 –25.06 –8.92 –16.60 –20.35 –10.08 –16.97 –14.58 1.69 7.29 9.13 0.20 4.18 7.03 11.06 22.09 28.62 7.07 17.87 24.88 –1.92 –4.08 –5.24 –3.64 –5.87 –5.81
1)
I, the coastal land-farming-fishing area; II, the piedmont plainirrigable land; III, the low plain-hydropenia irrigable land and dry land; IV, the hill-irrigable land and dry land; V, the basinirrigable land and dry land; VI, the hill-wet hot paddy-paddy field. 2) RCP, Representative Concentration Pathway.
the CO2 concentrations of 422, 495 and 532 ppm for the three terms of future climate under RCP4.5 and 423, 571 and 798 ppm for RCP8.5 scenario would increase yields by 3.65, 8.35 and 13.80% and then 3.68, 17.47 and 41.87%, respectively, with a higher spatial distribution in the south (Table 9).
4. Discussion 4.1. The impact of increases in temperature Previous analysis of the climate-crop relationship showed that global warming can bring both positive and negative effects, but negative impacts tend to dominate (Lobell and Field 2007; Wang et al. 2008; You et al. 2009; Schlenker and Lobell 2010; Lobell et al. 2011; Knox et al. 2012). The general consensus is that the impact of global warming has improved crop yield at high latitudes, but decreased it at low latitudes (Xiong et al. 2012). In this study, based on the CERES-Wheat model, the increases in temperature in the 3H Plain would reduce wheat yield by 1.92, 4.08 and 5.24% under RCP4.5 and by 3.64, 5.87 and 5.81% under RCP8.5 for the short-term, medium-term and long-term, respectively, which is not consistent with the previous consensus. By building the empirical statistical climate-crop relationship,
A
B
C
D
E
F
<–30 –20 –10
0
10
20
30
40
50
60 >70
%
Fig. 4 Spatial distribution of the relative impact of increasing solar radiation on winter wheat yield. A, B, and C represent the impacts for the short-term period (2010–2039), medium-term period (2040–2069) and long-term period (2070–2099), respectively under RCP4.5 scenario, while D, E and F represent the impacts for these three periods, respectively, under RCP8.5 scenario.
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Tao et al. (2014) demonstrated that the warming climate in the past three decades increased wheat yield in northern China by 0.9–12.9%, but reduced wheat yield in southern
Table 7 Relative impact of changes in solar radiation on winter wheat yield for different sub-regions Subregion1) I II III IV V VI Average
Scenario2) RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5
Relative impact of radiation change 2010–2039 2040–2069 2070–2099 3.94 0.49 1.23 4.85 3.36 5.94 6.17 5.91 9.51 7.01 8.48 14.72 6.19 2.61 4.93 7.41 7.12 9.43 4.50 2.62 5.92 6.16 4.96 9.70 4.25 1.76 1.69 4.25 1.64 4.35 1.74 4.96 1.81 1.75 3.81 2.44 4.41 2.81 3.67 5.04 4.37 7.14
1)
I, the coastal land-farming-fishing area; II, the piedmont plainirrigable land; III, the low plain-hydropenia irrigable land and dry land; IV, the hill-irrigable land and dry land; V, the basinirrigable land and dry land; VI, the hill-wet hot paddy-paddy field. 2) RCP, Representative Concentration Pathway.
China by 1.2–10.2%, with a large spatial difference. Xiong et al. (2012) also found using the CERES models that due to the warming climate, cereal crops in China exhibited yield losses in areas at low latitudes, while experiencing gains at high latitudes. Obviously, increasing temperatures during recent decades in the mid to high latitudes (such as in the 3H Plain) is beneficial to enriching thermal resources, and the cold stress events which threaten overwintering crops also decreased significantly (Tao et al. 2014; Xiao and Tao 2014). Thus, the yields increased with warmer temperatures in the southern part of the 3H Plain (Fig. 3). On the other hand, climate warming could increase crop water requirements and drought risks by increasing potential evapotranspiration (Yang et al. 2015), which may counteract the advantages of increasing thermal resources. However, many previous studies ignored the relationship between increases in temperature and water status, either by simulating crop yield under non-water stress conditions or by building statistical regression models using the yield under partial to full irrigation. In this study, wheat yield was simulated under rain-fed conditions, which fully consider how temperature warming would affect crop water requirements. Maintaining precipitation at the baseline level, water deficit (the ratio of precipitation minus evapotranspiration to
A
B
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D
E
F
<–30 –20 –10
0
10
20
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30
40
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60 >70
%
Fig. 5 Spatial distribution of the relative impact of increasing precipitation on winter wheat yield. A, B, and C represent the impacts for the short-term period (2010–2039), medium-term period (2040–2069) and long-term period (2070–2099), respectively, under RCP4.5 scenario, while D, E, and F represent the impacts for these three periods, respectively, under RCP8.5 scenario.
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A
B
C
D
E
F
<–30 –20 –10
0
10
20
30
40
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60 >70
%
Fig. 6 Spatial distribution of the relative impact of increasing CO2 on winter wheat yield. A, B, and C represent the impacts for the short-term period (2010–2039), medium-term period (2040–2069) and long-term period (2070–2099), respectively under RCP4.5 scenario, while D, E and F represent the impacts for these three periods, respectively, under RCP8.5 scenario. Table 8 Relative impact of changes in precipitation on winter wheat yield for different sub-regions Subregion1) I II III IV V VI Average
Scenario2) RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5
Relative impact of precipitation change 2010–2039 2040–2069 2070–2099 5.32 2.02 16.12 7.01 17.11 31.39 6.62 4.81 17.18 7.53 17.85 23.62 5.52 0.67 21.46 11.14 17.01 34.20 20.10 15.24 49.55 27.18 32.61 48.74 13.40 10.56 29.00 14.23 15.22 26.91 5.74 5.41 11.88 5.75 3.68 7.59 9.53 6.62 23.73 11.74 16.38 27.78
Table 9 Relative impact of changes in CO2 on winter wheat yield for different sub-regions Subregion1) I II III IV V VI Average
Scenario2) RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5
Relative impact of increasing CO2 2010–2039 2040–2069 2070–2099 4.27 8.87 12.76 3.79 17.98 40.84 3.47 5.83 8.47 2.88 18.92 11.08 3.37 4.42 7.53 3.07 9.05 28.78 3.56 7.06 13.99 2.41 8.88 30.62 3.76 10.27 17.91 6.46 30.34 90.19 3.23 11.19 18.07 2.67 15.88 40.64 3.65 8.35 13.80 3.68 17.47 41.87
1)
I, the coastal land-farming-fishing area; II, the piedmont plainirrigable land; III, the low plain-hydropenia irrigable land and dry land; IV, the hill-irrigable land and dry land; V, the basinirrigable land and dry land; VI, the hill-wet hot paddy-paddy field. 2) RCP, Representative Concentration Pathway.
1)
evapotranspiration), which has already been the limiting factor to wheat production in the northern part of the 3H Plain, would greatly increase (Fig. 7, taking RCP8.5 as an example). However, due to the sufficient supply of
precipitation in the southern part of the 3H Plain, increasing evapotranspiration had an insignificant influence on water deficit (Fig. 7). Thus, in this study, increases in temperature, with precipitation and radiation held at the level of baseline
I, the coastal land-farming-fishing area; II, the piedmont plainirrigable land; III, the low plain-hydropenia irrigable land and dry land; IV, the hill-irrigable land and dry land; V, the basinirrigable land and dry land; VI, the hill-wet hot paddy-paddy field. 2) RCP, Representative Concentration Pathway.
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A
B
C
<–0.5 –0.4 –0.3 –0.2 –0.1 0
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Fig. 7 Water deficit condition (the ratio of precipitation minus evapotranspiration to evapotranspiration) under RCP8.5 scenario with precipitation to be constant at baseline level. A, baseline period (1981–2010). B, short-term period (2010–2039). C, medium-term period (2040–2069). D, long-term period (2070–2099).
period, negatively affected wheat yield in the northern part of 3H Plain, but positively affected it in the southern part of 3H Plain. Additionally, climate warming also shortened the length of the wheat growing period, which may also be disadvantageous to wheat yield. However, the CERESWheat model ignores the output of the regreening stage that indicates the starting date of vegetative growth. Thus, changes in the length of the vegetative growth period under climate changes is not clear, and the quantitative impact of a shortened growing period on wheat yield needs further analysis.
4.2. The fertilizer efficiency in elevated CO2 Higher CO 2 concentrations can lead to higher net photosynthesis rates. Meanwhile, higher CO2 concentrations also improve water use efficiency (WUE) by reducing transpiration per unit leaf area, as higher CO2 concentrations could induce stomata closure (Rosenzweig and Iglesias 1998). Kimball (1983) reported that when the CO 2 concentration was increased from 300 to 600 ppm in a chamber test, the yield of cereals could increase about (33±6)%, which agrees with our results. Our results showed that the CO2 concentrations of 422, 495 and 532 ppm for the three terms of future climate under RCP4.5 would increase yields by 3.65, 8.35 and 13.80% and then 423, 571 and 798 ppm for RCP8.5 scenario would increase yields by 3.68, 17.47 and 41.87% compared to baseline (the CO2 concentrations of 380 ppm). However, the results from either chamber tests or crop models are based on the ideal situations. For example, the positive impacts of higher CO2 concentrations on pests and weeds are neglected. In addition, the negative effects of water or nitrogen stress on
the fertilizer efficiency in elevated CO2 was also ignored in the model. Tubiello et al. (2008) pointed out that the yield-increasing effect of higher CO2 in the Free-Air CO2 Enrichment (FACE) could only reach half of that in the chamber test. Thus, the fertilizer efficiency in elevated CO2 in the crop model was overestimated.
4.3. Sources of uncertainty In this study, the CERES-Wheat model was used to estimate the impacts of climate change on winter wheat yield in the 3H Plain. However, different crop models have different simulation algorithms for dealing with leaf development, light interception, yield formation, crop phenology and so on (Palosuo et al. 2011). For example, in the CERES-Wheat model, the growth stage depends on thermal time, and ignores the stresses from water or fertilizer deficits. Also, CERES-Wheat only simulates nitrogen efficiency while the effects of phosphate and potassium are not considered. Thus, comparisons of different crop models can reveal and quantify the uncertainties related to crop growth and yield predictions (Yao et al. 2011; Tan et al. 2016; Liu et al. 2017). Such comparisons of different crop models show that in simulating yield responses to climate change, the variance between models increases with warmer temperatures and higher concentrations of CO2 (Martre et al. 2015). Additionally, the fertilizer efficiency in elevated CO2 in the crop model was overestimated compared to the FACE experiments (Tubiello et al. 2008). In this study, our results were based on the mechanism from the Open-Top Chamber (OTC) or Environment Chamber. Thus, comparison analysis between crop models, especially the variance of CO 2 contributions to fertilizer efficiency, is necessary to quantify the uncertainties related to specific crop model assumptions.
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5. Conclusion In this study, the variations of temperature, solar radiation and precipitation under two RCP scenarios (RCP4.5 and RCP8.5) were revealed, and the relative contributions of these changes to winter wheat yield were untangled. Climate trend analysis showed that TMAX, TMIN, SRAD and PREP of the winter wheat growing season all increased. Yield analysis showed that wheat yield increased with the increases in solar radiation, precipitation and CO2, but decreased with increases in temperature. However, the increase in temperature decreased wheat yield by 1.92, 4.08 and 5.24% for the three terms of future climate under RCP4.5 scenario, and 3.64, 5.87 and 5.81% for the three terms of future climate under RCP8.5 scenario. Interestingly, the impact in the southern sub-region V, in which the water resource is comparatively abundant. Our analysis demonstrated that in the 3H Plain, which belongs to the mid-high latitude region, advantageous increases of thermal resources were counteracted by disadvantageous aggravated water deficits caused by increases in temperature.
Acknowledgements This research was supported by the National Natural Science Foundation of China (41401510 and 41675115) and the Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences (2017–2020).
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RESEARCH ARTICLE
The impacts of climate change on wheat yield in the Huang-HuaiHai Plain of China using DSSAT-CERES-Wheat model under different climate scenarios QU Chun-hong1*, LI Xiang-xiang2, 3*, JU Hui4, LIU Qin4 1
Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China Agro-meteorological Center of Jiangxi Province, Nanchang 330096, P.R.China 3 Meteorological Science Institute of Jiangxi Province, Nanchang 330096, P.R.China 4 Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China 2
Abstract Climate change has been documented as a major threat to current agricultural strategies. Progress in understanding the impact of climate change on crop yield is essential for agricultural climate adaptation, especially for the Huang-Huai-Hai Plain (3H Plain) of China which is an area known to be vulnerable to global warming. In this study, the impacts of climate change on winter wheat (Triticum aestivum L.) yield between the baseline period (1981–2010) and two Representative Concentration Pathways (RCP8.5 and RCP4.5) were simulated for the short-term (2010–2039), the medium-term (2040–2069) and the long-term (2070–2099) in the 3H Plain, by considering the relative contributions of changes in temperature, solar radiation and precipitation using the DSSAT-CERES-Wheat model. Results indicated that the maximum and minimum temperatures (TMAX and TMIN), solar radiation (SRAD), and precipitation (PREP) during the winter wheat season increased under these two RCPs. Yield analysis found that wheat yield increased with the increase in SRAD, PREP and CO2 concentration, but decreased with an increase in temperature. Increasing precipitation contributes the most to the total impact, increasing wheat yield by 9.53, 6.62 and 23.73% for the three terms of future climate under RCP4.5 scenario, and 11.74, 16.38 and 27.78% for the three terms of future climate under RCP8.5 scenario. However, as increases in temperature bring higher evapotranspiration, which further aggravated water deficits, the supposed negative effect of increasing thermal resources decreased wheat yield by 1.92, 4.08 and 5.24% for the three terms of future climate under RCP4.5 scenario, and 3.64, 5.87 and 5.81% for the three terms of future climate under RCP8.5 scenario with clearly larger decreases in RCP8.5. Counterintuitively, the impacts in southern sub-regions were positive, but they were all negative in the remaining sub-regions. Our analysis demonstrated that in the 3H Plain, which is a part
Received 9 April, 2018 Accepted 19 December, 2018 QU Chun-hong, E-mail: [email protected]; LI Xiang-xiang, E-mail: [email protected]; Correspondence LIU Qin, E-mail: [email protected] * These authors contributed equally to this study. © 2019 CAAS. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/). doi: 10.1016/S2095-3119(19)62585-2
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of the mid-high latitude region, the effects of increasing thermal resources were counteracted by the aggravated water deficits caused by the increase in temperature. Keywords: climate change, relative contribution, wheat yield, DSSAT-CERES-Wheat model, Huang-Huai-Hai Plain
1. Introduction Agriculture is one of the most sensitive systems to climate change, in part because it is directly exposed to environmental conditions (Parry and Ruttan 1991; Piao et al. 2010). Global warming increases the thermal resources for high latitudes causing arable regions to expand (Ju et al. 2013), while exacerbating heat and drought stress for arid and semi-arid regions with fluctuating food supplies (Yuan et al. 2016). Thus, investigations of the impacts of climate change on agriculture are crucial in order to avoid negative impacts on national food security (Piao et al. 2010; Ju et al. 2013). Statistical models and numerical simulations have been widely used to detect relationships between the climate and crops (Shi et al. 2013). Based on long term yield data, time-series regression models at the station level which consider yield series and sets of climate variables can be established with regression coefficients that represent the impacts of climate change (David and Christopher 2007; Tao et al. 2008). Recently, statistical models that rely on information from multiple stations were shown to be better at predicting crop responses to temperature changes (Schlenker and Roberts 2009; Wolfram and David 2010; Tao et al. 2014). However, the influence of non-climatic factors (such as cultivar and fertilizer changes) on yield need to be eliminated. Although some elimination methods, such as the first-difference yield and the linear detrended yield, have been widely used in various studies, their consistency remains questionable. For example, the linear detrended method is based on the notion that crop management and variety renewal changed linearly with time (Xiong et al. 2014). Thus, whether these results are realistic remains questionable and opposing effects have been documented (Shi et al. 2013). In recognizing the complex interactions between crop growth and environmental factors, numerical simulation models have become popular research tools for agro-meteorological researchers in recent years. Dynamic crop models, such as the Erosion Productivity Impact Calculator (EPIC) model (Williams et al. 1983), the Agricultural Production Systems sIMulator (APSIM) (Keating et al. 2003), and the Decision Support System for Agrotechnology Transfer (DSSAT) (Jones et al. 1998),
have been tested and used in quantifying water, nitrogen and weather responses at field or regional scales around the world. Among them, DSSAT is a platform that contains individual models for different crops and can quantitatively predict the growth and production of annual field crops with the interactions of aerial and soil environments, cultivar factors and management practices (Ritchie et al. 1998). DSSAT has been used for climate change and climate extreme impact assessments for rice, wheat and maize for different zones in China and for historical and future scenarios. However, few studies have focused on the impact of changes in individual climate variables in isolation. The Huang-Huai-Hai Plain (3H Plain) is a vital crop area that plays a key role in guaranteeing Chinese wheat supplies. Annual rainfall is unevenly distributed and has caused a dry climate during the wheat growing season. Water shortage is one of the major factors threatening high and stable production of wheat. Furthermore, climate change in future projections shows an increasing risk of drought in the 3H Plain (Liu et al. 2010). Thus, understanding the relationship between climate and wheat yield is essential for agricultural climate adaptation. Traditional climate-crop relationships based on statistical analyses often produce conflicting conclusions because of the effects of different elimination methods or the consideration of different predictor variables (Shi et al. 2013). Many studies using both a statistical approach and numerical simulation have proven that climate change has threatened the current agricultural strategies. Consequently, the objectives of this research were to: (1) Investigate the characteristics of the meteorological climatic variables during the wheat growth period over the 3H Plain under differing RCP scenarios. (2) Establish a DSSAT-CERES-Wheat simulation for wheat yield under differing RCP scenarios. (3) Identify the relative impacts of shifts in each variable on winter wheat yield in isolation.
2. Materials and methods 2.1. Study region The 3H Plain is widely accepted to be the major grain producing area in China (Yong et al. 2013), as it provides about 70% of wheat and 30% of maize production in China
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(Yang et al. 2015). Due to the extratropical monsoon climate, more than 70% of annual precipitation falls during the summer season (July to September) (Zhang et al. 2011). Thus, the winter wheat growing period has suffered from serious water deficits, with only 25–40% of water demand being satisfied by precipitation (Mei et al. 2013). To maintain high yield, wheat is irrigated with pumped groundwater, which has led to social environment threats, such as lowering of the groundwater level and surface subsidence (Zhang et al. 2005). Additionally, the increase in temperature and changing precipitation patterns have perturbed regional crop production (Yu et al. 2014). The 3H Plain can be divided into six sub-regions in terms of the climatic conditions and agricultural management practices. Detailed information on the six sub-regions is described by Li et al. (2015).
2.2. CERES-Wheat model The CERES-Wheat model is a simulation system that predicts daily wheat growth, development and yield based on information on the aerial and soil environments, cultivar factors and management information (Ritchie et al. 1998). CERES-Wheat, along with other cereal crop models included in DSSAT such as CERES-Maize and CERES-Rice, has been widely used in optimizing the use of resources and quantifying risks related to weather variations at field or regional scales around the world (Timsina and Humphreys 2006). Additionally, the applicability of CERES-Wheat has been tested over a wide range of field trials (Xiong et al. 2014). Weather, soil and management information Input data sets for model operation require weather information (daily solar radiation, maximum and minimum temperatures, precipitation), soil information (classification and basic profile characteristics by soil layer) and management information (e.g., cultivar, planting, irrigation and fertilization information). The 0.5°×0.5° daily weather data for future climate scenarios was derived from the HadGEM2-ES model, and divided into baseline period (1981–2010), shortterm period (2010–2039), medium-term period (2040–2069) and long-term period (2070–2099). The 0.5°×0.5° soil classification and profile characteristics were collected from the Harmonized World Soil Database (HWSD). In this study, nitrogen stress was not included and no irrigation was applied in order to fully capture the impact of climate change on water availability. In this study, all simulations were nitrogen-free and under rainfed conditions. For the planting date for each grid and each year, the final date of 15°C in each year was considered as the suitable sowing date, which was counted by the method of 5-day gliding average.
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Model calibration and evaluation In the CERES-Wheat model, coefficients that control the development and growth of wheat (Ritchie et al. 1998) must be calibrated and evaluated under specific environmental conditions before being used for climate impact analysis (Hunt and Boote 1998). Based on the principles that: 1) the station must have at least three growing seasons cultivated with one cultivar; 2) the chosen growing seasons should have no missing data for irrigation, fertilization, growth period and yield; and 3) there should be no natural disasters such as drought and heat stress during the chosen growing seasons, six agro-meteorological stations were selected as the representatives for each sub-region (Fig. 1). The field observed anthesis date, maturity date and the yield data used for calibrating the cultivar coefficients were obtained from the agro-meteorological stations of the China Meteorological Administration (Fig. 1). The general information on the six stations with chosen seasons and representative cultivar names for model calibration and evaluation, and the average information of growth stages and yields are shown in Table 1.
2.3. Simulation design To investigate the response of wheat yield to changes in each climate variable between baseline and future climate scenarios, five simulation scenarios were set, as shown in Table 2. Scenario S0 simulates the wheat yield under the basic climate which represents the baseline daily maximum and minimum temperatures (TMAX and TMIN), precipitation (PREP), and solar radiation (SRAD). Scenarios S1, S2, S3 and S4 simulate the wheat yield using one or more climatic variables of future climate (temperature (T), T+SRAD, T+SRAD+PREP, T+SRAD+PREP+CO2), with the remaining variables held constant. For example, if we want to simulate the effects of an increase in temperature in the short-term (2010–2039), i.e., S1, we reconstruct the 2010–2039 climate data series with the maximum and minimum temperatures from RCP4.5 or RCP8.5 scenarios while moving the solar radiation and precipitation data from baseline (1981–2010) to 2010–2039 conditions. The impacts of climate change on yield can be calculated by: FT (%)=
FS (%)=
FP (%)=
YS1–YS0 YS0 YS2–YS1 YS0 YS3–YS2 YS0
×100
(1)
×100
(2)
×100
(3)
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112°E
114°E
116°E
118°E
120°E
122°E
Beijing Baodi I Tianjin
40°N
N W
III 38°N
Taiyuan
124°E
E S
Shijiazhuang
Huimin
II
Zibo Jinan IV
36°N
Agro-meteorogical stations Capital cities
Zhengzhou
32°N
Suzhou
V
34°N
VI 0
75 150
300 km
Agricultural sub-regions Provincial boundaries
I Huai’an
Nanjing
Hefei
Shanghai
Fig. 1 The inset map shows the location of Huang-Huai-Hai (3H) Plain of China and its six agricultural sub regions. The subregions of 3H Plain include a coastal land-farming-fishing area (sub-region I), piedmont plain-irrigable land (sub-region II), low plain-hydropenia irrigable land and dry land (sub-region III), hill-irrigable land and dry land (sub-region IV), basin-irrigable land and dry land (sub-region V) and hill-wet hot paddy-paddy field (sub-region VI).
Table 1 The average planting date, anthesis days, maturity days and yield of the seasons selected for model calibration and evaluation1) Sub-region2) I II III IV V VI
Representative station Shenzhou Baodi Huimin Zibo Suzhou Huai’an
Season3)
Cultivar
2003, 2004, 2005, 2007, 2009* 1996*, 1997*, 2000, 2001, 2002, 2004 2004, 2005, 2006, 2007, 2008* 2002*, 2005, 2006 2001, 2002, 2003, 2004* 2001*, 2002*, 2004, 2005, 2006, 2007
SX733 JD8 LM23 JM20 WM19 WM6
Planting date (mon/d) 10/07 10/03 10/12 10/12 10/15 10/21
ADAP (d) 210 220 201 205 191 180
MDAP (d) 244 254 234 236 225 214
HWAM (kg ha–1) 4 285 5 300 4 586 5 271 5 122 5 460
1)
ADAP, anthesis days after planting; MDAP, maturity days after planting; HWAM, harvest weight at maturity. I, the coastal land-farming-fishing area; II, the piedmont plain-irrigable land; III, the low plain-hydropenia irrigable land and dry land; IV, the hill-irrigable land and dry land; V, the basin-irrigable land and dry land; VI, the hill-wet hot paddy-paddy field. 3) The seasons with * were chosen for model evaluation. 2)
Table 2 Simulation scenarios used to identify the impact of climate changes on winter wheat yield1) Scenario S0 S1
TMAX TMIN Baseline Baseline Future climate Future climate
S2
Future climate Future climate
S3 S4
Future climate Future climate Future climate Future climate
1)
SRAD Baseline Baseline
PREP Baseline Baseline
Purpose Simulate yield under baseline climate conditions Simulate yield under future temperatures, with the SRAD and PREP held constant Future climate Baseline Simulate yield under future temperatures and SRAD, with the PREP held constant Future climate Future climate Simulate yield under future climate conditions Future climate Future climate Simulate yield with the fertilizer efficiency of CO2
TMAX, the maximum temperature; TMIN, the minimum temperature; SRAD, solar radiation; PREP, precipitation.
FC (%)=
YS4–YS3 YS0
×100
(4)
Where, FT, FS, FP and FC represent the relative impacts of temperature changes, SRAD changes, precipitation changes and the fertilizer efficiency of elevated CO2, respectively.
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3. Results 3.1. Model calibration and validation The coefficients of the chosen variety, which determine the development and growth of wheat, were calibrated and validated using at least three seasons of field observation data (Table 1). The definitions of the wheat coefficients are shown in Table 3. The three coefficients (P1V, P1D and P5) that affect the phenological development were estimated using the observed phenological dates (anthesis and maturity dates). G1, G2, and G3 determine the grain number, grain weight, and spike number, respectively. However, due to the lack of yield analysis data, these coefficients were estimated using harvest yield data only. PHINT is the thermal time between successive tip leaf appearances. The normalized root mean square error (NRMSE) listed in Table 3 represents the error between the simulated anthesis date (ADAP), maturity date (MDAP) and yield (HWAM) and the observations. Overall, the simulated phenological development agreed well with the observations (Fig. 2). The NRMSE ranged from 0.3 to 1.3% for the anthesis date and from 0.8 to 1.5% for
the maturity date (Table 3). Due to the lack of yield analysis data in the process of calibration, the NRMSE values for yield ranged from 6.0 to 11.5%. While higher than those for phenological development, the NRMSE values were still below the acceptable level of 15%. This suggests that the calibrated coefficients can be applied for the purposes of the present study.
3.2. Changes of climatic variables during the wheat growing period Temperature Table 4 shows the differences in the TMAX and TMIN during the winter wheat growing seasons between future climate scenarios and the baseline period. The TMAX and TMIN of most sub-regions would increase, except for the mountainous northwest areas, such as the coastal landfarming-fishing area (sub-region I). The average increment of TMAX is 0.78, 2.17 and 2.61°C for the short-term, medium-term and long-term under the RCP4.5 scenario and 0.55, 2.17 and 3.86°C for these three terms of future climate under the RCP8.5 scenario, with the highest increment in the low plain-hydropenia irrigable land and dry land (subregion III). Minimum temperature increased by 0.36, 1.11
Table 3 Genetic coefficients and the normalized root mean square error (NRMSE, %) of anthesis days after planting (ADAP), maturity days after planting (MDAP) and harvest weight at maturity (HWAM)1) Representative station Shenzhou Baodi Huimin Zibo Suzhou Huai’an
Sub-region2) I II III IV V VI
P1V (d) 19.6 13.2 13.7 9.1 15.0 10.1
P1D (%) 38.8 63.3 57.9 79.0 63.0 62.0
P5 (°C d) 557.0 634.1 560.2 602.4 583.1 685.3
G1 (no. g–1) 30.0 17.2 29.0 20.9 15.4 16.0
G2 (mg) 65.0 46.3 64.2 43.8 57.4 50.5
G3 (g) 1.9 1.4 1.1 1.0 1.2 1.1
PHINT 95.0 95.0 95.0 95.0 95.0 95.0
ADAP 0.9 1.2 0.9 0.3 1.3 1.9
NRMSE (%) MDAP HWAM 1.1 6.8 1.3 10.7 0.8 11.4 0.9 11.5 1.5 8.9 1.4 6.0
1)
7 5 3 3 5 7 9 Measured HWAM (t ha–1)
230 Simulated ADAP (d)
270
9
Simulated MDAP (d)
Simulated HWAM (t ha–1)
P1V, days required for vernalization (optimum vernalizing temperature); P1D, photoperiod response (% reduction in rate/10 h drop in photoperiod); P5, grain filling phase duration; G1, kernel number per unit canopy weight at anthesis; G2, standard kernel size under optimum conditions; G3, standard non-stressed mature tiller weight (including grain); PHINT, interval time between successive leaf tip appearances. 2) I, the coastal land-farming-fishing area; II, the piedmont plain-irrigable land; III, the low plain-hydropenia irrigable land and dry land; IV, the hill-irrigable land and dry land; V, the basin-irrigable land and dry land; VI, the hill-wet hot paddy-paddy field.
250 230 210 210
230
250
Measured MDAP (d)
270
210 190 170 170
190
210
230
Measured ADAP (d)
Fig. 2 Comparison between measured and simulated value of harvest yield at maturity (HWAM), maturity days after planting (MDAP) and anthesis days after planting (ADAP). The dotted line is the 1:1 reference line.
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and 1.99°C for these three terms of future climate under
differences of SRAD and PREP during the winter wheat
the RCP4.5 scenario and 0.31, 1.62 and 3.56°C under the
growing seasons between the future scenarios and baseline
RCP8.5 scenario, also showing the highest increment in
period for different future time periods. Likewise, the
sub-region III. Thus, the difference of warming amplitude
seasonal SRAD and PREP are also expected to increase,
between RCP4.5 and RCP8.5 mainly occurred during
and the increment of PREP is much higher than that of
2070–2099, while in the 2010–2069 range, the temperature
SRAD. However the SRAD of the northern areas of the
warmed at a mostly equal rate. Additionally, as shown in
study region exhibited a decreasing trend, especially for the
Table 4, the warming tendency is larger for TMAX than
short-term, such as in sub-region VI. The increasing rate
TMIN in the future scenarios, which is inconsistent with the
for SRAD was higher in the RCP4.5 scenario than in the
historical observations that typically show a higher warming
RCP8.5 scenario, while the increase was higher for PREP
speed for TMIN.
under RCP8.5. For the SRAD, the average increase rates
Solar radiation and precipitation Table 5 shows the
were 2.16, 5.99 and 5.29% for RCP4.5 and 1.70, 3.96 and
Table 4 Changes in the maximum temperature (TMAX) and the minimum temperature (TMIN) during the winter wheat growing season between future climate scenarios and baseline period Sub-region1) I II III IV V VI Average 1)
2)
Scenario2) RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5
Changes in TMAX (°C) 2010–2039 2040–2069 2070–2099 0.28 1.62 1.90 0.11 1.51 3.02 0.84 2.39 3.08 0.86 2.47 4.24 1.12 2.76 2.94 0.83 2.68 4.36 0.67 2.24 2.61 0.41 2.17 4.05 0.97 2.27 2.77 0.63 2.26 3.96 0.71 1.77 2.36 0.41 2.00 3.68 0.78 2.17 2.61 0.55 2.17 3.86
Changes in TMIN (°C) 2010–2039 2040–2069 2070–2099 –0.84 –0.23 0.69 –0.72 0.54 2.47 0.42 1.37 2.47 0.55 1.97 4.07 0.76 1.59 2.51 0.71 2.24 4.23 0.87 1.72 2.60 0.79 2.13 4.05 0.55 1.28 2.06 0.38 1.62 3.52 0.57 1.18 1.94 0.44 1.60 3.44 0.36 1.11 1.99 0.31 1.62 3.56
I, the coastal land-farming-fishing area; II, the piedmont plain-irrigable land; III, the low plain-hydropenia irrigable land and dry land; IV, the hill-irrigable land and dry land; V, the basin-irrigable land and dry land; VI, the hill-wet hot paddy-paddy field. RCP, Representative Concentration Pathway.
Table 5 Changes in solar radiation (SRAD) and precipitation (PREP) during the winter wheat growing season between future climate scenario and baseline period Sub-region1) I II III IV V VI Average 1)
Scenario2) RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5
Changes in SRAD (%) 2010–2039 2040–2069 2070–2099 5.94 9.39 8.36 5.50 6.03 3.81 4.19 6.69 6.18 3.65 4.74 3.33 2.59 6.74 5.84 2.13 3.86 1.89 2.26 5.97 5.20 1.96 4.16 2.24 1.32 5.47 4.89 0.84 3.92 1.84 –3.16 1.54 1.04 –3.68 0.73 –0.55 2.16 5.99 5.29 1.70 3.96 2.10
Changes in PREP (%) 2010–2039 2040–2069 2070–2099 17.57 3.95 22.68 19.18 33.47 43.98 6.06 3.55 11.67 8.92 23.92 21.02 13.12 –4.89 19.34 14.80 24.99 40.33 20.30 9.18 34.71 21.06 30.55 44.67 14.84 15.73 31.59 17.60 20.30 42.34 9.98 16.77 30.22 13.15 16.02 28.13 13.68 8.48 25.58 15.95 24.24 37.44
I, the coastal land-farming-fishing area; II, the piedmont plain-irrigable land; III, the low plain-hydropenia irrigable land and dry land; IV, the hill-irrigable land and dry land; V, the basin-irrigable land and dry land; VI, the hill-wet hot paddy-paddy field. 2) RCP, Representative Concentration Pathway.
QU Chun-hong et al. Journal of Integrative Agriculture 2019, 18(6): 1379–1391
2.10% for RCP8.5 for the different terms, while the PRCP increased by 13.68, 8.48 and 25.58% for RCP4.5 and 15.95, 24.24 and 37.44% for RCP8.5 for the different terms.
3.3. Impacts of different climate variables on wheat yield Wheat yields were simulated using one or more climatic variables for the future climate (temperature, temperature+radiation, temperature+radiation+precipitation) with the remaining variables held at the levels of the baseline period. The relative changes of the yield calculated by eqs. (1–4) are shown below. Impact of increasing temperature The impact of increases in temperature varied between the southern and northern 3H Plain under both RCP4.5 and RCP8.5 (Fig. 3). On average, the increasing temperature would decrease wheat yield by –1.92, –4.08 and –5.24% for the three terms of future climate under RCP4.5 and –3.64, –5.87 and –5.81% for RCP8.5 scenario, with higher rates of decline in RCP8.5. Interestingly, the impacts in southern sub-regions (V and VI) were positive but they were all negative in the remaining sub-regions (Table 6). Impact of increasing solar radiation The expected changes of solar radiation would positively impact wheat
yield (Fig. 4). The increasing solar radiation would elevate wheat yield by 4.41, 2.81 and 3.67% for the three terms of future climate under RCP4.5 and 5.04, 4.37 and 7.14% for RCP8.5 scenario (Table 7), with no obvious spatial distribution. However, the elevated yield rates were higher under RCP8.5, but rates at which the solar radiation increased were lower than under RCP4.5. The reason for this observation is that perhaps the increasing solar radiation also aggravated water deficits by increasing evapotranspiration, which would lead to a reduced yield increase rate in the RCP4.5 with a higher increase in the magnitude of solar radiation than RCP8.5. Impact of increasing precipitation Similar to solar radiation, changes of precipitation would positively impact wheat yield (Fig. 5). Increasing precipitation would elevate wheat yield by 9.53, 6.62 and 23.73% for the three terms of future climate under RCP4.5 and 11.74, 16.38 and 27.78% for RCP8.5 scenario, with higher rates under the RCP8.5 scenario. However, the impact of increasing precipitation was negative in the southern part of study region (Fig. 5; Table 8), mainly because the precipitation in these grids was originally higher than in other grids, so the increasing precipitation would create higher risks of water-logging damage. Impact of increasing CO2 As depicted in Fig. 6, compared
A
B
C
D
E
F
<–30 –20 –10
0
10
20
1385
30
40
50
60 >70
%
Fig. 3 Spatial distribution of the relative impact of warming temperature on winter wheat yield. A, B, and C represent the impacts for the short-term period (2010–2039), medium-term period (2040–2069) and long-term period (2070–2099), respectively under RCP4.5 scenario, while D, E and F represent the impacts for these three periods, respectively, under RCP8.5 scenario.
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to the normal CO2 concentration of 380 ppm, the wheat yields tended to increase with higher CO2 concentrations. Compared to the baseline CO2 concentration (380 ppm),
Table 6 Relative impact of changes in temperature on winter wheat yield for different sub-regions Subregion1) I II III IV V VI Average
Scenario2) RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5
Relative impact of temperature change 2010–2039 2040–2069 2070–2099 –4.08 –8.10 –11.21 –3.65 –8.23 –11.47 –8.15 –21.97 –27.08 –10.40 –22.89 –30.28 –8.33 –21.62 –28.38 –10.71 –22.14 –25.06 –8.92 –16.60 –20.35 –10.08 –16.97 –14.58 1.69 7.29 9.13 0.20 4.18 7.03 11.06 22.09 28.62 7.07 17.87 24.88 –1.92 –4.08 –5.24 –3.64 –5.87 –5.81
1)
I, the coastal land-farming-fishing area; II, the piedmont plainirrigable land; III, the low plain-hydropenia irrigable land and dry land; IV, the hill-irrigable land and dry land; V, the basinirrigable land and dry land; VI, the hill-wet hot paddy-paddy field. 2) RCP, Representative Concentration Pathway.
the CO2 concentrations of 422, 495 and 532 ppm for the three terms of future climate under RCP4.5 and 423, 571 and 798 ppm for RCP8.5 scenario would increase yields by 3.65, 8.35 and 13.80% and then 3.68, 17.47 and 41.87%, respectively, with a higher spatial distribution in the south (Table 9).
4. Discussion 4.1. The impact of increases in temperature Previous analysis of the climate-crop relationship showed that global warming can bring both positive and negative effects, but negative impacts tend to dominate (Lobell and Field 2007; Wang et al. 2008; You et al. 2009; Schlenker and Lobell 2010; Lobell et al. 2011; Knox et al. 2012). The general consensus is that the impact of global warming has improved crop yield at high latitudes, but decreased it at low latitudes (Xiong et al. 2012). In this study, based on the CERES-Wheat model, the increases in temperature in the 3H Plain would reduce wheat yield by 1.92, 4.08 and 5.24% under RCP4.5 and by 3.64, 5.87 and 5.81% under RCP8.5 for the short-term, medium-term and long-term, respectively, which is not consistent with the previous consensus. By building the empirical statistical climate-crop relationship,
A
B
C
D
E
F
<–30 –20 –10
0
10
20
30
40
50
60 >70
%
Fig. 4 Spatial distribution of the relative impact of increasing solar radiation on winter wheat yield. A, B, and C represent the impacts for the short-term period (2010–2039), medium-term period (2040–2069) and long-term period (2070–2099), respectively under RCP4.5 scenario, while D, E and F represent the impacts for these three periods, respectively, under RCP8.5 scenario.
QU Chun-hong et al. Journal of Integrative Agriculture 2019, 18(6): 1379–1391
Tao et al. (2014) demonstrated that the warming climate in the past three decades increased wheat yield in northern China by 0.9–12.9%, but reduced wheat yield in southern
Table 7 Relative impact of changes in solar radiation on winter wheat yield for different sub-regions Subregion1) I II III IV V VI Average
Scenario2) RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5
Relative impact of radiation change 2010–2039 2040–2069 2070–2099 3.94 0.49 1.23 4.85 3.36 5.94 6.17 5.91 9.51 7.01 8.48 14.72 6.19 2.61 4.93 7.41 7.12 9.43 4.50 2.62 5.92 6.16 4.96 9.70 4.25 1.76 1.69 4.25 1.64 4.35 1.74 4.96 1.81 1.75 3.81 2.44 4.41 2.81 3.67 5.04 4.37 7.14
1)
I, the coastal land-farming-fishing area; II, the piedmont plainirrigable land; III, the low plain-hydropenia irrigable land and dry land; IV, the hill-irrigable land and dry land; V, the basinirrigable land and dry land; VI, the hill-wet hot paddy-paddy field. 2) RCP, Representative Concentration Pathway.
China by 1.2–10.2%, with a large spatial difference. Xiong et al. (2012) also found using the CERES models that due to the warming climate, cereal crops in China exhibited yield losses in areas at low latitudes, while experiencing gains at high latitudes. Obviously, increasing temperatures during recent decades in the mid to high latitudes (such as in the 3H Plain) is beneficial to enriching thermal resources, and the cold stress events which threaten overwintering crops also decreased significantly (Tao et al. 2014; Xiao and Tao 2014). Thus, the yields increased with warmer temperatures in the southern part of the 3H Plain (Fig. 3). On the other hand, climate warming could increase crop water requirements and drought risks by increasing potential evapotranspiration (Yang et al. 2015), which may counteract the advantages of increasing thermal resources. However, many previous studies ignored the relationship between increases in temperature and water status, either by simulating crop yield under non-water stress conditions or by building statistical regression models using the yield under partial to full irrigation. In this study, wheat yield was simulated under rain-fed conditions, which fully consider how temperature warming would affect crop water requirements. Maintaining precipitation at the baseline level, water deficit (the ratio of precipitation minus evapotranspiration to
A
B
C
D
E
F
<–30 –20 –10
0
10
20
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30
40
50
60 >70
%
Fig. 5 Spatial distribution of the relative impact of increasing precipitation on winter wheat yield. A, B, and C represent the impacts for the short-term period (2010–2039), medium-term period (2040–2069) and long-term period (2070–2099), respectively, under RCP4.5 scenario, while D, E, and F represent the impacts for these three periods, respectively, under RCP8.5 scenario.
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A
B
C
D
E
F
<–30 –20 –10
0
10
20
30
40
50
60 >70
%
Fig. 6 Spatial distribution of the relative impact of increasing CO2 on winter wheat yield. A, B, and C represent the impacts for the short-term period (2010–2039), medium-term period (2040–2069) and long-term period (2070–2099), respectively under RCP4.5 scenario, while D, E and F represent the impacts for these three periods, respectively, under RCP8.5 scenario. Table 8 Relative impact of changes in precipitation on winter wheat yield for different sub-regions Subregion1) I II III IV V VI Average
Scenario2) RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5
Relative impact of precipitation change 2010–2039 2040–2069 2070–2099 5.32 2.02 16.12 7.01 17.11 31.39 6.62 4.81 17.18 7.53 17.85 23.62 5.52 0.67 21.46 11.14 17.01 34.20 20.10 15.24 49.55 27.18 32.61 48.74 13.40 10.56 29.00 14.23 15.22 26.91 5.74 5.41 11.88 5.75 3.68 7.59 9.53 6.62 23.73 11.74 16.38 27.78
Table 9 Relative impact of changes in CO2 on winter wheat yield for different sub-regions Subregion1) I II III IV V VI Average
Scenario2) RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5 RCP4.5 RCP8.5
Relative impact of increasing CO2 2010–2039 2040–2069 2070–2099 4.27 8.87 12.76 3.79 17.98 40.84 3.47 5.83 8.47 2.88 18.92 11.08 3.37 4.42 7.53 3.07 9.05 28.78 3.56 7.06 13.99 2.41 8.88 30.62 3.76 10.27 17.91 6.46 30.34 90.19 3.23 11.19 18.07 2.67 15.88 40.64 3.65 8.35 13.80 3.68 17.47 41.87
1)
I, the coastal land-farming-fishing area; II, the piedmont plainirrigable land; III, the low plain-hydropenia irrigable land and dry land; IV, the hill-irrigable land and dry land; V, the basinirrigable land and dry land; VI, the hill-wet hot paddy-paddy field. 2) RCP, Representative Concentration Pathway.
1)
evapotranspiration), which has already been the limiting factor to wheat production in the northern part of the 3H Plain, would greatly increase (Fig. 7, taking RCP8.5 as an example). However, due to the sufficient supply of
precipitation in the southern part of the 3H Plain, increasing evapotranspiration had an insignificant influence on water deficit (Fig. 7). Thus, in this study, increases in temperature, with precipitation and radiation held at the level of baseline
I, the coastal land-farming-fishing area; II, the piedmont plainirrigable land; III, the low plain-hydropenia irrigable land and dry land; IV, the hill-irrigable land and dry land; V, the basinirrigable land and dry land; VI, the hill-wet hot paddy-paddy field. 2) RCP, Representative Concentration Pathway.
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A
B
C
<–0.5 –0.4 –0.3 –0.2 –0.1 0
1389
D
0.1 >0.2
Fig. 7 Water deficit condition (the ratio of precipitation minus evapotranspiration to evapotranspiration) under RCP8.5 scenario with precipitation to be constant at baseline level. A, baseline period (1981–2010). B, short-term period (2010–2039). C, medium-term period (2040–2069). D, long-term period (2070–2099).
period, negatively affected wheat yield in the northern part of 3H Plain, but positively affected it in the southern part of 3H Plain. Additionally, climate warming also shortened the length of the wheat growing period, which may also be disadvantageous to wheat yield. However, the CERESWheat model ignores the output of the regreening stage that indicates the starting date of vegetative growth. Thus, changes in the length of the vegetative growth period under climate changes is not clear, and the quantitative impact of a shortened growing period on wheat yield needs further analysis.
4.2. The fertilizer efficiency in elevated CO2 Higher CO 2 concentrations can lead to higher net photosynthesis rates. Meanwhile, higher CO2 concentrations also improve water use efficiency (WUE) by reducing transpiration per unit leaf area, as higher CO2 concentrations could induce stomata closure (Rosenzweig and Iglesias 1998). Kimball (1983) reported that when the CO 2 concentration was increased from 300 to 600 ppm in a chamber test, the yield of cereals could increase about (33±6)%, which agrees with our results. Our results showed that the CO2 concentrations of 422, 495 and 532 ppm for the three terms of future climate under RCP4.5 would increase yields by 3.65, 8.35 and 13.80% and then 423, 571 and 798 ppm for RCP8.5 scenario would increase yields by 3.68, 17.47 and 41.87% compared to baseline (the CO2 concentrations of 380 ppm). However, the results from either chamber tests or crop models are based on the ideal situations. For example, the positive impacts of higher CO2 concentrations on pests and weeds are neglected. In addition, the negative effects of water or nitrogen stress on
the fertilizer efficiency in elevated CO2 was also ignored in the model. Tubiello et al. (2008) pointed out that the yield-increasing effect of higher CO2 in the Free-Air CO2 Enrichment (FACE) could only reach half of that in the chamber test. Thus, the fertilizer efficiency in elevated CO2 in the crop model was overestimated.
4.3. Sources of uncertainty In this study, the CERES-Wheat model was used to estimate the impacts of climate change on winter wheat yield in the 3H Plain. However, different crop models have different simulation algorithms for dealing with leaf development, light interception, yield formation, crop phenology and so on (Palosuo et al. 2011). For example, in the CERES-Wheat model, the growth stage depends on thermal time, and ignores the stresses from water or fertilizer deficits. Also, CERES-Wheat only simulates nitrogen efficiency while the effects of phosphate and potassium are not considered. Thus, comparisons of different crop models can reveal and quantify the uncertainties related to crop growth and yield predictions (Yao et al. 2011; Tan et al. 2016; Liu et al. 2017). Such comparisons of different crop models show that in simulating yield responses to climate change, the variance between models increases with warmer temperatures and higher concentrations of CO2 (Martre et al. 2015). Additionally, the fertilizer efficiency in elevated CO2 in the crop model was overestimated compared to the FACE experiments (Tubiello et al. 2008). In this study, our results were based on the mechanism from the Open-Top Chamber (OTC) or Environment Chamber. Thus, comparison analysis between crop models, especially the variance of CO 2 contributions to fertilizer efficiency, is necessary to quantify the uncertainties related to specific crop model assumptions.
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5. Conclusion In this study, the variations of temperature, solar radiation and precipitation under two RCP scenarios (RCP4.5 and RCP8.5) were revealed, and the relative contributions of these changes to winter wheat yield were untangled. Climate trend analysis showed that TMAX, TMIN, SRAD and PREP of the winter wheat growing season all increased. Yield analysis showed that wheat yield increased with the increases in solar radiation, precipitation and CO2, but decreased with increases in temperature. However, the increase in temperature decreased wheat yield by 1.92, 4.08 and 5.24% for the three terms of future climate under RCP4.5 scenario, and 3.64, 5.87 and 5.81% for the three terms of future climate under RCP8.5 scenario. Interestingly, the impact in the southern sub-region V, in which the water resource is comparatively abundant. Our analysis demonstrated that in the 3H Plain, which belongs to the mid-high latitude region, advantageous increases of thermal resources were counteracted by disadvantageous aggravated water deficits caused by increases in temperature.
Acknowledgements This research was supported by the National Natural Science Foundation of China (41401510 and 41675115) and the Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences (2017–2020).
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