Phenological trends of winter wheat in response to varietal and temperature changes in the North China Plain

Field Crops Research 144 (2013) 135–144

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Phenological trends of winter wheat in response to varietal and temperature changes in the North China Plain Jing Wang a,∗ , Enli Wang b , Liping Feng a , Hong Yin c , Weidong Yu a a b c

College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China CSIRO Land and Water, GPO Box 1666, Canberra, ACT 2601, Australia National Climate Center, China Meteorological Administration, Beijing 100081, China

a r t i c l e

i n f o

Article history: Received 23 August 2012 Received in revised form 30 December 2012 Accepted 31 December 2012 Keywords: Vernalization Photoperiod Thermal time Global warming Adaptation APSIM

a b s t r a c t Understanding the confounding impacts of climatic and varietal changes on crop phenology is of importance to help make appropriate adaptation measures and target future directions for crop breeding. This paper investigated the changes in winter wheat phenology and analysed the impact of climatic, varietal and sowing date changes on crop phenology during the last three decades by combining field data with modeling. The APSIM crop model was used to quantify the changes in wheat phenology in terms of vernalization and photoperiod sensitivity as well as the changes in thermal time of pre- and post-flowering stage among wheat varieties at six sites in the North China Plain (NCP). The results showed that APSIM model could capture the phenological changes of winter wheat caused by interannual climatic change. There was a large spatial difference in the response pattern of wheat phenology to climatic and varietal changes across NCP. At most sites the increase in temperature shortened the growth duration of winter wheat mainly by shortening the growth period from sowing to jointing. The delayed sowing of winter wheat further shortened the growth period from sowing to jointing at two northern sites (Tianjin and Huimin). Continuous adoption of new wheat varieties shortened the growth period from sowing to jointing at Huimin, Xinxiang and Zhumadian, from jointing to flowering at Tianjin, but helped increase the length of growth period from jointing to flowering at Huimin, Tangyin and Xinxiang and post-flowering growth period at all the sites except for Tangyin. The findings of the study suggest that past cultivar changes in wheat in the NCP have been varied geographically. © 2013 Elsevier B.V. All rights reserved.

1. Introduction The impacts of rising temperatures on the phenology of native plants have been reported worldwide (Bradley et al., 1999; Menzel et al., 2001; Fitter and Fitter, 2002; Matsumoto et al., 2003; Menzel, 2003). Advance of the growing season by 8 days over the period 1969–1998 was observed in Europe (Chmielewski and Rotzer, 2001). Similar acceleration of spring phenology was observed in Western Canada (Beaubien and Freeland, 2000), in Mediterrranean ecosystems (Krammer et al., 2000) and in China (Zheng et al., 2002; Lu et al., 2006). Recently, there is a growing concern on the effect of warming on the phenology of cultivated crops (Chmielewski et al., 2004; Hu et al., 2005; Estrella et al., 2007; Xiao et al., 2012) due to its potential impact on crop yield and farming practices. The majority of previous studies showed that increase in temperature shortened crop growing period, leading to reduced crop productivity (Sadras and Monzon, 2006; Tao et al., 2006; Monzon et al.,

∗ Corresponding author. Tel.: +86 10 6273 4636; fax: +86 10 6289 4117. E-mail address: [email protected] (J. Wang). 0378-4290/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.fcr.2012.12.020

2007; Wang et al., 2009, 2012). However, many of those studies did not explicitly consider the impact of cultivar changes under the warming climate. Crop improvement has played an important role in increasing crop yield under warming conditions (Zhang et al., 2005; Sun et al., 2007; Zhou et al., 2007; Yin and Struik, 2008; Wang et al., 2012). A few recent studies tried to combine long-term observational data and crop modeling together to separate the individual impact of both climate and crop varietal changes on phenology and yield of wheat and maize (Y. Liu et al., 2010) and rice (L. Liu et al., 2012) in China. They showed that past warming had an effect to shorten crop duration, but the effects varied across regions and with crop growth stages. Adoption of new varieties was able to stabilize crop growth duration at most sites studied in China. Crop modeling is an effective means to disentangle the influence of climatic trend and variety change on crop phenology and yield. They have been frequently used to assess the influence of climate, crop and management changes on crop production, and to assist crop breeding programs (Hammer et al., 2002; Hoogenboom et al., 2004; Sadras and Monzon, 2006; White et al., 2011). Different from most studies where crop models were used to simulate crop

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phenology with derived cultivar parameters, Y. Liu et al. (2010) and L. Liu et al. (2012) used crop models to derive cultivar parameters based on observed phenology of various crop cultivars planted in long-term experiments, and used the derived cultivar parameters to quantify cultivar changes for wheat, maize and rice. They further used a crop model with a single fixed cultivar to simulate changes in crop yield as affected by climatic change, which excludes the impact of cultivar changes. These studies assumed that if a single cultivar was planted for many years, the crop model was able to capture the phenological changes caused by interannual climatic change. However, this assumption has not been tested against long-term datasets. Moreover, Y. Liu et al. (2010) indicated that the increase in vernalization sensitivity of wheat, i.e. the adoption of more winter type of wheat varieties, helped stabilize wheat growing season at a few sites in the NCP. However, other studies have found that wheat varieties requiring less vernalization have been adopted, especially in the warmer part of NCP under warmer climate conditions (Li et al., 2005). The aim of this paper is to complement the previous studies by testing the two assumptions for wheat phenology. The objectives were to combine long-term observational data and APSIM modeling to investigate: (1) whether the simulated phenology by APSIM for a single wheat cultivar follows the observed trend and (2) the consistency of varietal changes and their impact on wheat phenology as compared to those found in previous studies.

2. Materials and methods 2.1. Study sites, climate and crop data Six sites were selected in this study, i.e., Tianjin (39.08◦ N, 117.07◦ E, 3 m), Huanghua (38.37◦ N, 117.35◦ E, 7 m) in Hebei Province, Huimin (37.05◦ N, 117.52◦ E, 12 m) in Shandong Province, and Xinxiang (35.31◦ N, 113.88◦ E, 73 m), Tangyin (35.93◦ N, 114.35◦ E, 75 m) and Zhumadian (33◦ N, 114.02◦ E, 183 m) in Henan province. The sites roughly followed a north–south transect of the NCP (Fig. 1). The study period was from 1980 to 2009. At all the sites, except for Tianjin, the cropping system was a winter wheat (Triticum aestivum L.) – summer maize (Zea mays L.) double cropping rotation. At Tianjin, a winter wheat–summer soybean (Glycine max L.) cropping system was practiced before 1998, and converted to the winter wheat–summer maize double cropping rotation thereafter. Tianjin, Huimin, Xinxiang and Zhumadian, represent typical agricultural areas in the NCP, where crop varieties changed frequently. At Tangyin site, only one wheat variety (Baofeng 7228) was planted during a 14-year period from 1985 to 1999 except for 1991. The site was selected to investigate whether the simulated wheat phenology by APSIM followed the observed change in phenology, and the impact of climate change only on wheat phenology. Historical daily weather data from 1980 to 2009 were available for the six sites from the China Meteorological Administration, including daily average, maximum and minimum temperatures, rainfall, wind speed, sunshine hours, and relative humidity. Annual average air temperatures at Tianjin, Huanghua, Huiming, Tangyin, Xinxiang and Zhumadian were 13.0, 12.9, 13.0, 14.1, 14.5 and 15.1 ◦ C, while annual precipitation totals were 515, 538, 535, 588, 550 and 995 mm, respectively. Crop data including varieties, major phenological stages, yields and management practices from 1980 to 2009 were recorded at the agro-meteorological experimental station at each of the six sites. Winter wheat was flood irrigated three to four times with 250–300 mm of water. During the 29-year period, 10 wheat varieties were planted at Tianjin, 7 wheat varieties were planted at Huanghua, 13 wheat varieties were planted at Huimin, 8 wheat

Table 1 Wheat varieties used at Tianjin, Huanghua, Huiming, Tangyin, Xinxiang and Zhumadian and number of planting years (in brackets). Site

Varietal names of wheat (number of planting years)

Tianjin

Dongfanghong 3(4), Fengkang 8(6), Nongda 146(2), Fengkang 15(2), Jing 411(1), Jingdong 6(1), Jingdong 8(8), Jinghe 3(1), Zhonghan 110(1), Jing 9428(3) Taishan 1(1), Keyi 26(3), Jianding 187(2), Kefan 26(1), Hengshui 187(1), 71321(20), Xiaohongmang(1) Zixuan 2(2), Fu 63(3), Fu 6(1), Lumai 5(3), Jinghua 1(4), 215953(1), Yan 1604(1), 831(1), Wenqian 1(1), 915091(1), Baiyu 119(1), Lumai 23(10) Zhengyin 1(3), Beinong 3217(2), Baofeng 7228(13), Yumai 13(1), Wenmai 4(2), Wenmai 6(2), Yumai 58(1), Zhoumai 16(2) Yuyuan 1(1), 3039(3), Baofeng 7228(2), Baizhengyin(3), Yanshi 4(1), 79201(3), 891(1), Zhoumai 9(4), Bainong 62(1), Aizhao(1), Wenmai 6(2), Zhengmai 9023(1), Shanxuan 1(2), Xinmai 6(2), Luomai 21(2) Boai 7422(2), Beinong 3217(5), Baofeng 7228(1), Yanshi 9(1), Xian 8(7), Yumai 21(1), Yumai 22(1), Beinong 64(3), Zhengmai 9023(5)

Huanghua Huiming

Tangyin

Xinxiang

Zhumadian

varieties were planted at Tangyin, 15 wheat varieties were planted at Xinxiang and 10 wheat varieties were planted at Zhumadian (Table 1). Fertilizer applications and other management practices followed those used by local farmers. The timing of six wheat growth stages was recorded each year. They were: sowing, overwintering, turning-green, jointing, flowering and maturity. Crop growth period was therefore divided into five phases according to the observed stages: WS1 – sowing to overwintering; WS2 – over-wintering to turning-green; WS3 – turning-green to jointing; WS4 – jointing to flowering; and WS5 – flowering to maturity. We use WS to denote the period from sowing to maturity. 2.2. The agricultural system model and its validation The Agricultural Production System sIMulator (APSIM, version 5.3) was used to simulate wheat phenology from 1980 to 2009. APSIM was well-tested and widely used in Australia (Keating et al., 2003; Wang et al., 2003). It has also been tested and applied in the NCP (Wang et al., 2007a; Chen et al., 2010). These studies in NCP indicated that once the model was calibrated for crop phenology, it was able to reproduce the observed crop growth, yield and water use in the study areas. The model could explain more than 90% of the variation in crop biomass and more than 80% of the variation in soil water content for the wheat–maize double cropping system. Here, we further tested the performance of APSIM in simulating wheat phenology based on the long-term observational data from 1986 to 1999 at Tangyin for a single wheat variety, before we used the model to quantify cultivar changes of wheat at the other study sites. The key growth stages simulated in APSIM include sowing, emergence, floral initiation, flowering and maturity. We approximated the floral initiation stage in APSIM with the observed jointing stage. APSIM was firstly calibrated based on observed days from sowing to emergence, jointing, flowering and maturity (days after sowing, DAS) in the period of 1985–1989 to derive the crop parameters with the method described below. The calibrated model was then tested against the observed phenology in the period from 1990 to 1999, i.e., data independent of model calibration. 2.3. Derivation of cultivar parameters for all wheat cultivars For the whole study period at all the other sites and the period from 1985 to 1989 at Tangyin, the three wheat phenology parameters, i.e. vernalization sensitivity, photoperiod sensitivity and

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Fig. 1. The North China Plain (NCP) and the study sites: Tianjin, Huanghua in Hebei province, Huimin in Shandong province, Tangyin, Xinxiang and Zhumadian in Henan province.

thermal time from flowering to maturity, were derived by matching the simulated and observed flowering and maturity dates (DAS) with a trial-and-error method. In the APSIM model, changes in either vernalization sensitivity or photoperiod sensitivity affect the simulated flowering time. Without knowing which process was actually responsible for the observed changes in phenology, we started with the cultivar parameter values for a typical winter wheat variety (vern sens = 4 and photop sens = 4.1) to derive the vernalization sensitivity and photoperiod sensitivity parameters for each wheat cultivar planted in the experiments. Vernalization sensitivity parameter was firstly adjusted aiming to match simulated and observed flowering date (DAS). If the simulated flowering dates could not match the observed dates by only adjusting the vernalistaion sensitivity, the photoperiod sensitivity parameter was then modified until the best agreement was achieved. Changes in growing period for each cultivar were analyzed based on the change in total thermal time (TTd in degree-days) from sowing to flowering and from flowering to maturity. The total thermal time was calculated as:

TTd =

n 

DTT

(1)

i=1

where DTT is thermal time per day (◦ Cd), n is number of days of a certain growth stage. DTT is calculated from 3-hourly air temperatures interpolated from the daily maximum and minimum temperatures above 0 ◦ C, i.e., the same method as in the APSIM model.

2.4. Statistical analysis Linear regression analysis was used to analyze the change trend in climatic variables, length of phenological stages (DAS) (both the total length from sowing to jointing, flowering and maturity and the length of each phase separately) as well as changes in the required thermal time for pre- and post-flowering stages of wheat. The slope of linear regression line was evaluated using Student’s t-test at 95 and 99% levels. The performance of APSIM model in simulating wheat phenology was evaluated by using three statistics: the root mean square

error (RMSE), Eq. (2); the mean bias error (MBE), Eq. (3) and the model efficiency (ME), Eq. (4).

  n 1 RMSE =  (Yi − Xi )2 n

(2)

i=1

1  MBE = ( ) (Yi − Xi ) n n

(3)

i=1

n (Yi − Xi )2 ME = 1 − n i=1 2 i=1

(Xi − Xaver )

(4)

where Xi and Yi are the observed and simulated values respectively, Xaver is the average of the observed values, and n is the number of observations (Wang et al., 2007b). 3. Results 3.1. Climate trends within the growing season of winter wheat in the NCP Maximum, minimum and average temperatures during the whole growing period of winter wheat increased at all the study sties (P < 0.05) except for Tianjin (Table 2). The largest increase occurred at Zhumadian. Minimum temperature increased for all the growth stages at Huimin (P < 0.01), Tangyin (P < 0.01), Xinxiang (P < 0.05) and Zhumadian (P < 0.05), but only for the growth stages from overwintering to jointing at Huanghua (P < 0.05). The increase in temperature during the stages from turning-green to jointing was higher than the other growth stages. Y. Liu et al. (2010) found that warming increased from south to north based on the data at three study sites in the NCP. However, our results showed the largest increase occurred at the southern site Zhumadian, which suggested there was a lager spatial difference in temperature change trend in the NCP. 3.2. Performance of APSIM model in simulating wheat phenology Using the data from 1985 to 1989 at Tangyin, the cultivar parameters for Baofeng 7228 were: vern sens = 3.0, photop sens = 3.8, tt stgf mat = 490. The model calibration performance in terms of RMSE, MBE and ME was shown in Table 3. Simulated durations of phenological stages (DAS) were close to the observed ones with

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Table 2 Trend of change in maximum, minimum and average temperatures (represented as the slope of regression line) during key developmental stages of winter wheat at Tianjin, Huanghua, Huimin, Tangyin, Xinxiang and Zhumadian from 1980 to 2009. WS1: sowing to overwintering; WS2: over-wintering to turning-green; WS3: turning-green to jointing; WS4: jointing to flowering; WS5: flowering to maturity; WS: sowing to maturity. Phenological stages

Air temperature

Tianjin (◦ C/y)

Huanghua (◦ C/y)

Huimin (◦ C/y)

Tangyin (◦ C/y)

Xinxiang (◦ C/y)

Zhumadian (◦ C/y)

WS1

Tmax Tmin Taver Tmax Tmin Taver Tmax Tmin Taver Tmax Tmin Taver Tmax Tmin Taver Tmax Tmin Taver

0.011 −0.017 −0.002 0.023 0.006 0.012 0.068 0.03 0.054* 0.023 0.024 0.026 0.035 0.012 0.032 0.032 0.008 0.021

0.016 0.006 0.013 0.039 0.044* 0.043 0.088** 0.067** 0.079** 0.027 0.035 0.03 −0.005 0.024 0.017 0.038* 0.045** 0.043**

0.02 0.089** 0.059* 0.047 0.104** 0.084** 0.066* 0.117** 0.097** 0.025 0.071** 0.045 −0.011 0.058** 0.028 0.035* 0.096** 0.07**

0.008 0.077** 0.042 0.017 0.084** 0.051* 0.121** 0.128** 0.124** 0.04 0.091** 0.066** 0.017 0.079** 0.048* 0.008** 0.008** 0.064**

0.021 0.072** 0.056* 0.013 0.055* 0.04 0.127** 0.148** 0.14** 0.018 0.076** 0.039 0.027 0.077** 0.05* 0.036* 0.081** 0.062**

0.054 0.071* 0.068* 0.019 0.075** 0.053 0.123** 0.157** 0.138** 0.126** 0.136** 0.124** 0.046 0.112** 0.071* 0.05** 0.102** 0.075**

WS2

WS3

WS4

WS5

WS

* **

Significant at P < 0.05. Significant at P < 0.01.

small RMSE and MBE. Days from sowing to both flowering and maturity (DAS) were accurately simulated with a RMSE < 2.0 days. Performance of the APSIM model against independent phenological data from 1990 to 1999 was shown in Fig. 2 and Table 3. The simulated duration to emergence, jointing and flowering (DAS) followed closely the observed changes, except for the 1990 growing season where simulated duration to jointing was longer than the observed value (Fig. 2). Simulated durations to maturity (DAS) from 1997 to 1999 were shorter than the recorded ones. In general, the model slightly overestimated the days to jointing and flowering (DAS) and slightly underestimated the days to emergence and maturity dates (Table 3). The results indicate that the APSIM model, once calibrated, is able to capture the phenological changes of wheat in response to inter-annual changes in climate and sowing dates. 3.3. Impact of climate change on wheat phenology During last three decades, the recorded sowing dates were delayed significantly at Tianjin and Huimin (P < 0.01), but not at other sites (Fig. 3). This was mainly attributed to the change from winter wheat–summer soybean to winter wheat–summer maize double cropping at Tianjin. Summer maize has a longer growing period than summer soybean, delaying sowing of winter wheat. At other sites, maize varieties with longer growing period were gradually introduced, which delayed sowing of winter wheat to allow more time for maize to mature, but the delay was only significant at Huimin (P < 0.01). Fig. 4 shows the trends of change in the growth durations from sowing to jointing, sowing to flowering and sowing to maturity simulated by APSIM with one single wheat cultivar and one sowing date at each site (wheat variety Dongfanghong 3 and sowing

date September 24 at Tianjin, wheat variety Keyi 26 and sowing date October 4 at Huanghua, wheat variety Zixuan 2 and sowing date September 28 at Huimin, wheat variety Zhengyin 1 and sowing date October 4 at Tangyin, wheat variety 3039 and sowing date October 19 at Xinxiang and wheat variety Boai 7422 and sowing date October 8 at Zhumadian). Assuming no varietal and sowing date changes in the past, the simulated change in phenology would reflect only the impact of temperature change. The simulated growth duration from sowing to jointing was shortened by 3.6, 3.1, 4, 4 and 6 days/decade at Huanghua, Huimin, Tangyin, Xinxiang and Zhumadian (P < 0.01), but not at Tianjin (Fig. 4). The simulated growth durations from sowing to flowering and from sowing to maturity were also shortened at all sites (P < 0.05). However, there was no such trend from jointing to flowering and from flowering to maturity at any site (data not shown here, but also Fig. 6), suggesting that climate change affected mainly the growth stage from sowing to jointing of winter wheat (Fig. 4), i.e. the jointing of winter wheat became earlier with time. These changes were in agreement with the temperature changes in Table 2. 3.4. Impact of sowing date on wheat phenology Fig. 5 showed the trends of change in the growth durations from sowing to jointing, sowing to flowering and sowing to maturity simulated using APSIM with the same cultivar at each site as in Fig. 4, but with different sowing dates each year. Sowing date was set equal to that recorded in the agro-meteorological stations. The simulated growth durations from sowing to jointing was shortened by 4.2, 5, 5.6, 4.6, 4.4 and 5.6 days/decade at Tianjin (P < 0.01), Huanghua (P < 0.05), Huimin (P < 0.01), Tangyin (P < 0.01), Xinxiang (P < 0.01) and Zhumadian (P < 0.01), respectively (Fig. 5). Comparison of the results in Figs. 4 and 5 showed that delaying the sowing

Table 3 Root mean square errors (RMSE), mean bias error (MRE), and model efficiency (ME) of APSIM simulations for the duration of key phenological stages (DAS) of wheat at Tangyin. Calibration period: 1985–1989, validation period: 1990–1999. Phenological stage

Calibration

(DAS)

RMSE (d)

MBE (d)

ME

Validation RMSE (d)

MBE (d)

ME

Emergence date Jointing date Flowering date Maturity date

2.18 2.92 1.22 1.87

−1.75 −2.5 1 1.5

−1.11 −0.36 0.31 −0.3

1.45 3.49 2.72 2.85

−1.1 1.8 1.8 −1.1

−2.28 0.54 0.86 0.75

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Fig. 2. Comparison of simulated and observed emergence, jointing, flowering and maturity dates (days after sowing, DAS) of winter wheat from 1990 to 1999 at Tangyin.

of winter wheat at Tianjin and Huimin shortened the growth period from sowing to jointing significantly. Fig. 6 shows the change trend in simulated duration from jointing to flowering and from flowering to maturity of winter wheat at the six study sites. No change trend in the duration of the above two growth stages was detected at any site.

3.5. Impact of cultivar change on wheat phenology The recorded duration from sowing to jointing was shortened by 3.8, 4, 7.7, 5.2, 6.2 and 12 days/decade at Tianjin (P < 0.01), Huanghua (P < 0.05), Huimin (P < 0.01), Tangyin (P < 0.01), Xinxiang (P < 0.01) and Zhumadian (P < 0.01) (Fig. 7). The period from sowing

Fig. 3. Changes in recorded sowing dates (day of year) of winter wheat at Tianjin, Huanghua, Huimin, Tangyin, Xinxiang and Zhumadian from 1980 to 2009. ** Significant at P < 0.01.

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Fig. 4. Changes in simulated durations from sowing to jointing, from sowing to flowering and from sowing to maturity (DAS) of winter wheat (single cultivar and the same sowing date, see text) at Tianjin, Huanghua, Huimin, Tangyin, Xinxiang and Zhumadian, respectively from 1980 to 2009. ** Significant at P < 0.01; * significant at P < 0.05.

Fig. 5. Changes in simulated durations from sowing to jointing, from sowing to flowering and from sowing to maturing (DAS) of winter wheat (single cultivar and different sowing date, see text) at Tianjin, Huanghua, Huimin, Tangyin, Xinxiang and Zhumadian, respectively from 1980 to 2009. ** Significant at P < 0.01; * significant at P < 0.05.

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Fig. 6. Changes in simulated durations from jointing to flowering and from flowering to maturing (days) of winter wheat (single variety and different sowing date, see text) at Tianjin, Huanghua, Huimin, Tangyin, Xinxiang and Zhumadian from 1980 to 2009.

Fig. 7. Changes in recorded durations from sowing to jointing, from sowing to flowering and from sowing to maturity (DAS) of winter wheat at Tianjin, Huanghua, Huimin, Tangyin, Xinxiang and Zhumadian from 1980 to 2009. ** Significant at P < 0.01; * significant at P < 0.05.

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Fig. 8. Changes in recorded durations from jointing to flowering and from flowering to maturity (days) of winter wheat at Tianjin, Huanghua, Huimin, Tangyin, Xinxiang and Zhumadian from 1980 to 2009. ** Significant at P < 0.01; * significant at P < 0.05.

to flowering was shortened by 6.1, 5.6 and 9.4 days/decade, and the period from sowing to maturity was shortened by 3.6, 5.3 and 7.5 days/decade, respectively, at Tianjin, Huimin and Zhumadian (P < 0.01), but not at other sites. Fig. 8 showed the change trend in observed durations from jointing to flowering and from flowering to maturity of winter wheat at the six study sites. The observed duration from jointing to flowering was shortened by 2.3 days/decade at Tianjin (P < 0.01) while increased by 2.1, 3.6 and 3.9 days/decade at Huimin, Tangyin and Xinxiang (P < 0.01), respectively. The periods from flowering to maturity increased by 2.5 and 1.9 days/decade at Tianjin (P < 0.01) and Zhumadian (P < 0.05). The difference between simulated and observed change trend in phenology reflects the impact of cultivar change. Comparing the results in Figs. 5–6 and Figs. 7–8, it appeared that continuous adoption of new wheat cultivars further shortened the growth period from sowing to jointing at Huimin, Xinxiang and Zhumadian, from jointing to flowering at Tianjin, but helped increase the duration from sowing to jointing at Huanghua, jointing to flowering at Huimin, Tangyin, Xinxiang and post-flowering growth duration at Tianjin and Zhumadian. Cultivar changes were further reflected by the changes in thermal time needed to complete the pre-flowering and post-flowering phases (Fig. 9). The duration of the pre-flowering phase decreased by 80.8 ◦ Cd per decade at Tianjin (P < 0.01), but increased by 29.1 ◦ Cd per decade at Huanghua (P < 0.01), 68.6 ◦ Cd per decade at Tangyin (P < 0.05) and 62.1 ◦ Cd per decade at Xinxiang (P < 0.01). This implies that in the past three decades, early flowering cultivars have been adopted at Tianjin, while later flowering cultivars have been adopted at Huanghua, Tangyin and Xinxiang. Thermal time required for post-flowering stage increased by 41.4, 8.6, 16.2, 11.9 and 41.2 ◦ Cd per decade at Tianjin (P < 0.01), Huanghua (P < 0.01), Huimin (P < 0.01), Xinxiang (P < 0.05) and Zhumadian (P < 0.01) (Fig. 10). The increase in thermal time of

pre- and post-flowering stages of wheat helped stabilize the length of growing period and alleviate the negative impact of increase temperature on growth duration o of winter wheat. The duration of grain-filling increased at all sites, which made a large contribution to the extension of the reproductive stage (Fig. 10). 4. Discussion The study confirmed that APSIM is able to capture the changes in phenology of a given wheat variety caused by inter-annual climatic change. Therefore, APSIM can be used to assess the impact of climate change on crop growth and development. The comparison between the key phonological stages simulated with a single wheat cultivar and the observed stages at the study sites indicated that varietal change had affected on wheat phenology. Y. Liu et al. (2010) found that the adoption of new wheat varieties helped stabilize the length of pre-flowering period against the shortening effect of warming at three sites in the NCP. Our study confirmed their findings for Huanghua, Tangyin and Xinxiang. Furthermore, our study found that the impact of wheat varieties on the duration of the pre-flowering period varied with site. Improved wheat varieties helped increase the length of growth period from sowing to jointing for Huanghua, and the length of growth period from jointing to flowering for Tangyin and Xinxiang. However, our results were significantly different from the results of Y. Liu et al. (2010) at Tianjin and Zhumadian. For Tianjin, cropping system changed from winter wheat–summer soybean to winter wheat–summer maize. To ensure sufficient growth duration of summer maize to increase yield, new wheat varieties had shorter jointing to flowering phases. For Zhumadian, new wheat varieties shortened the phase from sowing to jointing, indicating that wheat varieties with less vernalization requirement or photoperiod sensitivity were used. The findings of the study further suggest that different varietal changes happened at different sites.

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Fig. 9. Trend in thermal time required for pre-flowering stage (sowing to flowering) of winter wheat varieties at Tianjin, Huanghua, Huimin, Tangyin, Xinxiang and Zhumadian from 1980 to 2009. ** Significant at P < 0.01; * significant at P < 0.05. Variety names are given in the graph. Thermal time was calculated based on observed phenological dates. The error bars indicate the range of thermal time of the same variety. The blank circles indicate the mean value of thermal time of the same variety. The solid circles indicate thermal time of each variety calculated based on observed phenology.

Fig. 10. Trend in thermal time required for post-flowering stage (flowering to maturity) of winter wheat varieties at Tianjin, Huanghua, Huimin, Tangyin, Xinxiang and Zhumadian from 1980 to 2009. ** Significant at P < 0.01; * significant at P < 0.05. Variety names are given in the graph. Thermal time was calculated based on observed phenological dates. The error bars indicate the range of thermal time of the same variety. The blank circles indicate the mean value of thermal time of the same variety. The solid circles indicate the observed value of thermal time of each variety.

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Previous studies showed that the warming in the NCP mainly occurred during the vegetative (pre-flowering) growth stage of wheat and the magnitude of the increase in minimum temperature was larger than maximum temperature since 1980s (Tao et al., 2006; Wang et al., 2012). Similar phenomenon was also observed in the growth season of winter wheat in Australia and Argentina (Sadras and Monzon, 2006). However, our results showed that temperature increased during all growth stages with the highest increases occurring in the period from turning-green to jointing. This is similar to the results reported from Northwest China (Wang et al., 2008). Sowing date also affects growth duration. Delayed sowing further shortened the growth period of winter wheat, accompanied by the effect of increased temperature at Tianjin and Huimin. This result is consistent with experimental studies in the NCP (Sun et al., 2007; Yang et al., 2009). The shortened growth period of wheat due to delayed sowing normally decreases wheat yield. However, delayed sowing of wheat allows a longer duration of maize growth and enables planting of longer-season maize varieties. The increase in maize grain yield due to extended grain-filling period has increased the total productivity of the wheat–maize cropping system (Wang et al., 2012). Combining observed data and APSIM modeling enabled the quantification of the cultivar changes of wheat and their impact on phenology during the study period. However, we were unable to separately quantify the changes in vernalization requirements and photoperiod sensitivity of the cultivars, because changes in the thermal time requirement of pre-flowering stage could be a consequence of changes in either of the two. Whether the reduction in the growth duration before jointing was caused by reduced vernalization or photoperiod sensitivity remains to be explained. The study provides a sound approach to disentangling the influence of climatic trends, cultivar selection and sowing date on crop phenology. The approach could be used to further study the contribution of individual and compound changes in climatic, varietal, and management options to yield change of winter wheat–summer maize cropping system in the NCP. It is also a useful tool to quantify the impact of adaptation options on crop production. Acknowledgements This work is supported by National Science Foundation of China (41101046), National Basic Research Program of China (2009CB118608) and CMA/Henan Key Laboratory of Agrometeorological Support and Applied Technique (AMF201203). We would like to thank China Meteorological Administration for providing the historical climate data and agro-meteorological data. The authors acknowledge the anonymous referees for their valuable comments. References Beaubien, E.G., Freeland, H.J., 2000. Spring phenology trends in Alberta, Canada: links to ocean temperature. Int. J. Biometeorol. 44, 53–59. Bradley, N.L., Leopold, A.C., Ross, J., Huffaker, W., 1999. Phenological changes reflect climate change in Wisconsin. Proc. Nat. Acad. Sci. USA 96, 9701–9704. Chmielewski, F., Muller, A., Bruns, E., 2004. Climate changes and trends in phenology of fruit trees and field crops in Germany, 1961–2000. Agr. Forest Meteorol. 121, 69–78. Chmielewski, F., Rotzer, T., 2001. Response of tree phenology to climate change across Europe. Agr. Forest Meteorol. 108, 101–112. Chen, C., Wang, E.L., Yu, Q., 2010. Modelling the effects of climate variability and water management on crop water productivity and water balance in the North China Plain. Agr. Water Manage. 97, 1175–1184. Estrella, N., Sparks, T.H., Menzel, A., 2007. Trends and temperature response in the phenology of crops in Germany. Glob. Change Biol. 13, 1737–1747. Fitter, A.H., Fitter, R.S.R., 2002. Rapid changes in the flowering time in British plants. Science 296, 1689–1691. Hammer, G.L., Kropff, M.J., Sinclair, T.R., Porter, J.R., 2002. Future contributions of crop modelling: from heuristics and supporting decision-making to understanding genetic regulation and aiding crop improvement. Eur. J. Agron. 18, 15–31.

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