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Agricultural and Forest Meteorology 189–190 (2014) 91–104 Contents lists available at ScienceDirect Agricultural and Forest Meteorology jou rn al hom ep age: www.elsevier.com/locate/agrformet Responses of wheat growth and yield to climate change in different climate zones of China, 1981–2009 Fulu Tao a,, Zhao Zhang b , Dengpan Xiao a,c , Shuai Zhang a , Reimund P. Rötter d , Wenjiao Shi a , Yujie Liu a , Meng Wang a,c , Fengshan Liu a,c , He Zhang a,c a Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China b State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China c University of Chinese Academy of Sciences, Beijing 100039, China d MTT Agrifood Research Finland, Lönnrotinkatu 5, Mikkeli FI-50100, Finland a r t i c l e i n f o Article history: Received 18 July 2013 Received in revised form 20 December 2013 Accepted 14 January 2014 Keywords: Agriculture Climate change Crop Impact and adaptation Phenology Yield a b s t r a c t The experiment observations at 120 agricultural meteorological stations spanning from 1981 to 2009 across China were used to accelerate understandings of the response of wheat growth and productivity to climate change in different climate zones, with panel regression models. We found climate during wheat growth period had changed significantly during 1981–2009, and the change had caused measurable impacts on wheat growth and yield in most of the zones. Wheat anthesis date and maturity date advanced significantly, and the lengths of growth period before anthesis and whole growth period were significantly shortened, however the length of reproductive growth period was significantly prolonged despite of the negative impacts of temperature increase. The increasing adoption of cultivars with longer reproductive growth period offset the negative impacts of climate change and increased yield. Changes in temperature, precipitation and solar radiation in the past three decades jointly increased wheat yield in northern China by 0.9–12.9%, however reduced wheat yield in southern China by 1.2–10.2%, with a large spatial difference. Our studies better represented crop system dynamics using detailed phenological records, consequently better accounted for adaptations such as shifts in sowing date and crop cultivars photo-thermal traits when quantifying climate impacts on wheat yield. Our findings suggest the response of wheat growth and yield to climate change is underway in China. The changes in crop system dynamics and cultivars traits have to be sufficiently taken into account to improve the prediction of climate impacts and to plan adaptations for future. © 2014 Elsevier B.V. All rights reserved. 1. Introduction About 21% of the world’s food depends on wheat crop, which grows on 200 million hectares of cropland worldwide (FAO, 2012). Global wheat demand, particularly in developing counties, will con- tinue to increase in the near future (Ortiz et al., 2008). However climate change may negatively affect wheat yield in some major wheat production regions of the world (Ortiz et al., 2008; Challinor et al., 2010; Asseng et al., 2011; Olesen et al., 2011; Lobell et al., 2011, 2012; Licker et al., 2013), although the impacts have large uncertainties and remain inconclusive in terms of mechanisms, magnitude and spatial pattern (Ortiz et al., 2008; Asseng et al., 2013; Tao and Zhang, 2013). Since 1980s, China has continued to hold the greatest share of world wheat production, averaging 112 Mt from 2006 to 2010 Corresponding author. Tel.: +86 1064888269. E-mail address: taofl@igsnrr.ac.cn (F. Tao). (FAO, 2012). The impact of climate change/variability on wheat growth and productivity in China has been of key concern (Tao et al., 2006, 2008, 2012a; Xiong et al., 2008; You et al., 2009; Challinor et al., 2010; Tao and Zhang, 2013). Previous studies have tried to project long-term climate change on wheat productivity in future using crop models, driven by climate change scenarios output from global climate model or regional climate model (Xiong et al., 2008; Challinor et al., 2010; Liu and Tao, 2013; Tao and Zhang, 2013). Recently, a number of studies have also tried to investigate climate impacts on wheat yield in the past few decades based on reported harvest yield using statistical approaches (Tao et al., 2008, 2012; You et al., 2009; Zhang and Huang, 2013). Most of the statistical studies correlate crop yields at a province or county scale from census data with seasonal climate computed during a coarse and fixed crop growth period. In fact, cropping systems have shifted in the past few decades under the combined effects of climate change, cultivars turnover and agronomic management (Welch et al., 2010; Tao et al., 2012a,b; Tao and Zhang, 2013). There is a clear and present need to synthesize crop yield and climate data from 0168-1923/$ see front matter © 2014 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.agrformet.2014.01.013

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Agricultural and Forest Meteorology 189– 190 (2014) 91– 104

Contents lists available at ScienceDirect

Agricultural and Forest Meteorology

jou rn al hom ep age: www.elsev ier .com/ locate /agr formet

esponses of wheat growth and yield to climate change in differentlimate zones of China, 1981–2009

ulu Taoa,∗, Zhao Zhangb, Dengpan Xiaoa,c, Shuai Zhanga, Reimund P. Rötterd,enjiao Shia, Yujie Liua, Meng Wanga,c, Fengshan Liua,c, He Zhanga,c

Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, ChinaUniversity of Chinese Academy of Sciences, Beijing 100039, ChinaMTT Agrifood Research Finland, Lönnrotinkatu 5, Mikkeli FI-50100, Finland

r t i c l e i n f o

rticle history:eceived 18 July 2013eceived in revised form0 December 2013ccepted 14 January 2014

eywords:griculturelimate changerop

mpact and adaptationhenology

a b s t r a c t

The experiment observations at 120 agricultural meteorological stations spanning from 1981 to 2009across China were used to accelerate understandings of the response of wheat growth and productivity toclimate change in different climate zones, with panel regression models. We found climate during wheatgrowth period had changed significantly during 1981–2009, and the change had caused measurableimpacts on wheat growth and yield in most of the zones. Wheat anthesis date and maturity date advancedsignificantly, and the lengths of growth period before anthesis and whole growth period were significantlyshortened, however the length of reproductive growth period was significantly prolonged despite of thenegative impacts of temperature increase. The increasing adoption of cultivars with longer reproductivegrowth period offset the negative impacts of climate change and increased yield. Changes in temperature,precipitation and solar radiation in the past three decades jointly increased wheat yield in northern Chinaby 0.9–12.9%, however reduced wheat yield in southern China by 1.2–10.2%, with a large spatial difference.

ield Our studies better represented crop system dynamics using detailed phenological records, consequentlybetter accounted for adaptations such as shifts in sowing date and crop cultivars photo-thermal traitswhen quantifying climate impacts on wheat yield. Our findings suggest the response of wheat growthand yield to climate change is underway in China. The changes in crop system dynamics and cultivarstraits have to be sufficiently taken into account to improve the prediction of climate impacts and to planadaptations for future.

. Introduction

About 21% of the world’s food depends on wheat crop, whichrows on 200 million hectares of cropland worldwide (FAO, 2012).lobal wheat demand, particularly in developing counties, will con-

inue to increase in the near future (Ortiz et al., 2008). Howeverlimate change may negatively affect wheat yield in some majorheat production regions of the world (Ortiz et al., 2008; Challinor

t al., 2010; Asseng et al., 2011; Olesen et al., 2011; Lobell et al.,011, 2012; Licker et al., 2013), although the impacts have largencertainties and remain inconclusive in terms of mechanisms,agnitude and spatial pattern (Ortiz et al., 2008; Asseng et al., 2013;

ao and Zhang, 2013).Since 1980s, China has continued to hold the greatest share

f world wheat production, averaging 112 Mt from 2006 to 2010

∗ Corresponding author. Tel.: +86 1064888269.E-mail address: [email protected] (F. Tao).

168-1923/$ – see front matter © 2014 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.agrformet.2014.01.013

© 2014 Elsevier B.V. All rights reserved.

(FAO, 2012). The impact of climate change/variability on wheatgrowth and productivity in China has been of key concern (Tao et al.,2006, 2008, 2012a; Xiong et al., 2008; You et al., 2009; Challinoret al., 2010; Tao and Zhang, 2013). Previous studies have tried toproject long-term climate change on wheat productivity in futureusing crop models, driven by climate change scenarios output fromglobal climate model or regional climate model (Xiong et al., 2008;Challinor et al., 2010; Liu and Tao, 2013; Tao and Zhang, 2013).Recently, a number of studies have also tried to investigate climateimpacts on wheat yield in the past few decades based on reportedharvest yield using statistical approaches (Tao et al., 2008, 2012;You et al., 2009; Zhang and Huang, 2013). Most of the statisticalstudies correlate crop yields at a province or county scale fromcensus data with seasonal climate computed during a coarse andfixed crop growth period. In fact, cropping systems have shifted

in the past few decades under the combined effects of climatechange, cultivars turnover and agronomic management (Welchet al., 2010; Tao et al., 2012a,b; Tao and Zhang, 2013). There is a clearand present need to synthesize crop yield and climate data from
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92 F. Tao et al. / Agricultural and Forest Meteorology 189– 190 (2014) 91– 104

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ig. 1. Wheat cultivation fraction and major production zones of China, as well as the wheat cultivation fractions were based on Qiu et al. (2003).

ifferent areas, with more detailed information on crop systemnd management, to look insights into the response mechanisms ofrop growth and productivity to climate change (Lobell and Asner,003; Tao et al., 2006; Tao and Zhang, 2013; Welch et al., 2010;obell et al., 2011). Such studies can provide observed evidences tomprove the projections of climate impacts on future food produc-ion and to design adaptations (Lobell and Asner, 2003; Tao et al.,012b).

In the present study, the experiment observations at 120 agri-ultural meteorological stations (Fig. 1, Table 1) spanning from981 to 2009 across China were used to accelerate understandingsf the response and adaptation of wheat growth and productivity tolimate change in different climate zones. The long-term detailedxperiment records on cultivars, major phenological dates, yieldsnd management practices in each year provide us a unique oppor-unity to look insight into the dynamics of crop systems in response

o climate change in the past three decades. We aim to understandn the past three decades (1) how climate during wheat growtheriod has changed? (2) how wheat phenology and crop systemynamics has changed in response to climate change? and (3) to

able 1eneral information on wheat type, emergence date, anthesis date and maturity date ietween 1981 and 2009 at the agricultural experiment stations used in the study.

Wheat cultivation zones Wheat type Number of stations

Zone I Winter wheat 16

Zone II Winter wheat 30

Zone III Winter wheat 10

Zone IV Spring wheat 20

Zone V Winter wheat 13

Zone VI Spring wheat 10

Zone VII Spring wheat 6

Zone VIII Winter wheat 15

ricultural meteorological stations in the major production zones used in the study.

what extent the climate change has affected wheat growth andproductivity in a setting in which farmers make decisions basedon the weather they observe every day, across the major wheatproduction regions of China (Fig. 1)?

2. Materials and methods

2.1. Stations and data

The study areas include the major wheat production regions inChina. According to wheat cultivation zones in China (Jin, 1961;Zhao, 2010), the agricultural meteorological stations were groupedinto eight zones, i.e., Zone I, Zone II, Zone III, Zone IV, Zone V, Zone VI,Zone VII and Zone VIII (Fig. 1). These zones were created to reflectdifferences in biophysical conditions such as soil and climate andcropping system (Jin, 1961; Tong, 1992; Zhao, 2010).

Wheat trial data on wheat cultivars, phenology, yields andmanagement practices from 1981 to 2009 were from Chinaagricultural meteorological experiment stations, which are main-tained by China Meteorological Administration (CMA). The detailed

n major wheat production zones of China. The data were based the observations

Emergence date Anthesis date Maturity date

Oct 5th May 15th Jun 20thOct 20th May 2nd Jun 5thNov 5th Apr 25th May 30thApr 10th Jun 13th Jul 17thOct 8th May 16th Jun 20thApr 17th Jun 12th Jul 17thApr 24th Jul 9th Aug 27thNov 11th Apr 3rd May 12th

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F. Tao et al. / Agricultural and Forest Meteorology 189– 190 (2014) 91– 104 93

Table 2Mean of Tmean, Tmax, precipitation and SRD during each growth period of wheat, as well as mean and trend of wheat yield, in the major wheat production zones of China,between 1981 and 2009. The data were based the observations between 1981 and 2009 at the agricultural experiment stations used in the study.

Zone Period Mean yield (kg/ha) Yield trend (kg/ha/year) Tmean (◦C) Tmax (◦C) Precipitation (mm) SRD (MJ/m2/day)

Zone IVGP2 – – −2.19 3.99 15.23 9.11VGP3 – – 11.71 18.28 61.30 16.41RGP – – 20.84 27.38 66.06 19.60GPw 3818.84 50.81 6.53 12.97 181.58 12.68

Zone II VGP1 – – 6.76 12.51 43.83 8.78VGP2 – – -0.14 5.48 19.12 8.53VGP3 – – 10.45 16.57 73.75 14.40RGP – – 19.82 26.06 74.42 17.95GPw 4600.69 84.57 7.79 13.75 205.60 11.71

Zone III VGP1 – – 6.41 11.24 75.81 8.00VGP2 – – 2.73 7.44 38.82 8.84VGP3 – – 11.19 16.34 133.83 12.88RGP – – 20.49 25.62 104.03 16.00GPw 3810.3 88.36 9.71 14.47 379.09 10.79

Zone IV VGP – – 15.70 22.99 45.47 21.62RGP – – 21.80 28.66 46.14 22.60GPw 5081.92 60.41 17.84 24.98 90.50 21.96

Zone V VGP1 – – 6.73 14.45 13.02 11.25VGP2 – – -5.02 0.94 22.85 8.58VGP3 – – 13.16 20.40 25.99 17.81RGP – – 22.33 29.85 15.99 22.88GPw 4627.14 147.92 5.87 12.72 77.06 13.51

Zone VI VGP – – 17.21 24.20 46.67 21.35RGP – – 22.92 29.94 36.85 22.99GPw 4009.48 111.23 19.32 26.33 83.66 21.95

Zone VII VGP – – 13.32 20.59 93.50 22.43RGP – – 16.98 24.31 83.48 21.78GPw 4562.92 56.27 14.68 21.97 175.56 22.16

Zone VIIIVGP2 – – – – – –VGP3 – – – – – –

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henological records at the experiment stations allow us to matchxactly weather variables with farm-specific sowing, anthesis andarvesting dates, subsequently specific wheat growth phases, inach year. Crop management practices at the experiment stationsere generally same as or better than the local traditional practices.ccording to the long-term experiment records, the traditionalanagement practices did not change much during the study

eriod, although the cultivars were frequently shifted. Irrigationnd fertilizer was used for several times every year, and pesticidesere also used to control pests and diseases.

We selected the agricultural meteorological experiment sta-ions that had good records of crop growth and weather parametersor more than 10 years. Finally we used total 120 geographicallynd climatologically different agricultural meteorological experi-ent stations across the major wheat production regions of China

Fig. 1). There were 16, 30, 10, 20, 13, 10, 6 and 15 stations forone I, Zone II, Zone III, Zone IV, Zone V, Zone VI, Zone VII and ZoneIII, respectively. The general information on wheat cultivation inach zone was presented in Table 1. Winter wheat was cultivated inone I, Zone II, Zone III, Zone V, and Zone VIII; and spring wheat wasultivated in Zone IV, Zone VI and Zone VII. The general informa-ion on mean and trend of wheat yield and seasonal climate during

ach growth stage across the stations in each zone was presentedn Table 2.

Historical daily weather data, including daily mean tempera-ure (Tmean), maximum temperature (Tmax), minimum temperature

17.57 22.70 132.26 12.8210.57 14.67 272.24 8.01

(Tmin), solar radiation (SRD), precipitation, and sunshine durationat the agricultural meteorological stations from 1980 to 2009 werealso obtained from CMA. At the stations without SRD observations,SRD was estimated using sunshine duration observation and theÅngström–Prescott (A-P) equation (Prescott, 1940):

SRD =(

a + bn

N

)Ra (1)

where Ra is extraterrestrial SRD (MJ m−2 d−1); a and b are the APcoefficients; and n and N is actual and theoretical sunshine duration,respectively.

2.2. Analyses

Statistical models that relied on information from multiple sta-tions, namely panel models, were documented to be better atpredicting crop responses to temperature change than time-seriesstatistical model at each station (Lobell and Burke, 2010). Therefore,for each zone, the time-series data from the respective weatherstations were combined into a panel dataset. A panel analysis wassubsequently carried out for each zone.

To investigate climate change and its impact on crop yield at

each growth stage, the whole growth period (GPw) of winter wheatwere divided into four growth periods, i.e., VGP1 (from emer-gence to winter dormancy), VGP2 (from winter dormancy to greenup), VGP3 (from green up to anthesis), and RGP (from anthesis to
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94 F. Tao et al. / Agricultural and Forest Meteorology 189– 190 (2014) 91– 104

Fig. 2. Trends in Tmean, precipitation, and SRD during different growth periods of wheat from 1981 to 2009 in each wheat production zone. The error bar represents thestandard error of the estimates. The trends with a mark ‘a’ are significant at 0.05 level, and with a mark ‘b’ are significant at 0.01 level.

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F. Tao et al. / Agricultural and Forest Meteorology 189– 190 (2014) 91– 104 95

Table 3Trends in wheat phenological dates and the lengths of each growth period during 1981–2009, in the major wheat production zones of China. Trends with ‘*’ and ‘**’ aresignificant at the 0.05 and 0.01 level, respectively.

Zone I Zone II Zone III Zone IV Zone V Zone VI Zone VII Zone VIIIEmergence date (days/decade) 1.45* 0.81 −3.17** −1.37** 4.03** 1.46 1.22 −0.65Dormancy date (days/decade) 1.51* 0.78 −1.63 – 2.37** – – –Greenup date (days/decade) −1.40 −1.44** 0.88 – −2.05 – – –Anthesis date (days/decade) −2.93** −4.50** −4.85** −2.84** −4.78** 0.60 −0.07 −3.27**Maturity date (days/decade) −2.72** −3.61** −3.11** −2.41** −3.75** 2.28 0.57 −2.84**VGP1 length (days/decade) −0.03 −1.07 −0.38 – −1.66* – – –VGP2 length (days/decade) −2.93* −2.32* 1.75 – −4.39** – – –

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VGP3(VGP) length (days/decade) −2.55** −3.13** −4.68RGP length (days/decade) 0.21 0.70** 1.73GPw length (days/decade) −4.19** −4.58** 0.34

aturity). The GPw of spring wheat was divided into two growtheriods, i.e., vegetative growth period (VGP from emergence tonthesis) and reproductive growth period (RGP, from anthesis toaturity).For each zone, time trends in wheat emergence dates, anthe-

is dates and maturity dates during 1981–2009, as well as timerends in Tmean, precipitation and SRD during each growth period,.e., VGP1, VGP2, VGP3, RGP, and GPw for winter wheat, VGP, RGPnd GPw for spring wheat, were analyzed using linear regressionethod.To investigate the correlations between annual yields with cli-

ate variables during each growth period across the stations forach zone, the yields were firstly linearly de-trended to get thee-trended yield series that were mainly affected by seasonal cli-ate variability. Then the climate variables were also linearly

e-trended, and the partial correlation analysis was applied tonvestigate the correlation between the de-trended yields seriesnd the de-trended climate variables series during 1981–2009. Sta-istical significance was tested using the two-tailed t-test.

To avoid the confounding effects of highly correlated climateariables, for each zone and each growth period, seven panelegression models with different predictors were estimated touantify the uncertainties in estimating crop responses to major cli-ate variables. The seven panel regression models were as follows:

di,t = ˇi,0 + ˇ1t + ˇ2�Tmeani,t+ εi,t (2)

di,t = ˇi,0 + ˇ1t + ˇ2�Tmeani,t+ ˇ3�Pi,t + εi,t (3)

di,t = ˇi,0 + ˇ1t + ˇ2�Tmeani,t+ ˇ4�SRDi,t + εi,t (4)

di,t = ˇi,0 + ˇ1t + ˇ2�Tmeani,t+ ˇ3�Pi,t + ˇ4�SRDi,t + εi,t (5)

di,t = ˇi,0 + ˇ1t + ˇ3�Pi,t + εi,t (6)

di,t = ˇi,0 + ˇ1t + ˇ3�Pi,t + ˇ4�SRDi,t + εi,t (7)

di,t = ˇi,0 + ˇ1t + ˇ4�SRDi,t + εi,t (8)

able 4orrelations between length of each growth period and Tmean, as well as between length.05 level, and with a mark ‘**’ are significant at 0.01 level.

Zone I Zone II Zone III

VGP1 length and Tmean −0.02 0.07* −0.14

VGP2 length and Tmean −0.44** −0.58** −0.29**

VGP3 (VGP) length and Tmean −0.62** −0.46** −0.77**

RGP length and Tmean −0.45** −0.49** −0.49**

GPw length and Tmean −0.60** −0.44** −0.01

VGP1 length and yield −0.08 −0.003 −0.01

VGP2 length and yield −0.25** −0.14** 0.08

VGP3 (VGP) length and yield 0.04 −0.13** 0.11

RGP length and yield 0.19** 0.06 0.10

GPw length and yield −0.27** −0.11** 0.08

−1.39** −2.73** −0.87 −1.33* –0.35 1.03** 1.69** 0.67 0.43

−1.04 −7.75** 0.82 −0.65 −2.16*

where Ydi,t is annual yield observations (not detrended) at stationi in year t. ˇi,0 represents an intercept for each station i. ˇ1 rep-resents the linear time trend of observed yields mainly due tothe long-term climatic and no-climatic trends including improve-ments in varieties, technology, management and policy during thestudy period. ˇ1–4 are model parameters to be fit, and εi,t is anerror term. �Tmeani,t, �Pi,t, and �SRDi,t represent the linearly de-trended growing season averages for Tmean, precipitation, and SRD,respectively, at station i in year t.

The parameter ˇ2 in four panel regress models, i.e., Eqs. (2)–(5),represents different estimates of yields sensitivity to Tmean duringa growth period. Likewise, the parameter ˇ3 in four panel regressmodels, i.e., Eqs. (6), (3), (7) and (5), represents different estimatesof yields sensitivity to precipitation during a growth period. Theparameter ˇ4 in four panel regress models, i.e., Eqs. (8), (4), (7)and (5), represents different estimates of yields sensitivity to SRDduring a growth period.

For each zone, the sensitivity of yield change to Tmean, precipi-tation and SRD change during a growth period, i.e. the panel modelparameter ˇ2, ˇ3, and ˇ4, respectively, was estimated using multi-ple regression method based on the trial data on yield and climatefrom 1981 to 2009 across the stations in the zone. The sensitivitywas further expressed in percentage of actual mean yield acrossthe stations during the study period as

ˇiYdmean

× 100%,where ˇi is the panel model parameter ˇ2, ˇ3,and ˇ4. Ydmean is the actual mean yield across the stations in azone during 1981–2009.

For each zone and each growth period, the impact of changein a climate variable (i.e., Tmean, precipitation and SRD) on cropyield during 1981–2009 (expressed in percentage of actual meanyield) was estimated by multiplying the sensitivity of yield changeto the climate variable with the magnitude of change in the climatevariable across the stations in the zone during the study period. Thelater was estimated by a linear trend.

For each zone and each growth period, the joint impact ofclimate change on crop yield during 1981–2009 (expressed in per-

centage of actual mean yield) was computed by summing theimpacts of changes in Tmean, precipitation and SRD on crop yieldduring the study period.

of each growth period and yield. The correlations with a mark ‘*’ are significant at

Zone IV Zone V Zone VI Zone VII Zone VIII– 0.38** – – –– −0.62* – – –

−0.69** −0.36** −0.71** −0.22** –−0.36** −0.05 −0.80** −0.45** −0.30**−0.65** −0.52** −0.86** −0.14 −0.36**− −0.19** − − −− −0.26** − − −0.05 0.04 0.02 0.05 −0.22** 0.22** 0.19** 0.05 −0.060.15** −0.33** 0.13 0.08 −0.08

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96 F. Tao et al. / Agricultural and Forest Meteorology 189– 190 (2014) 91– 104

Fig. 3. Correlations between wheat yield and Tmean, precipitation, and SRD during different growth periods from 1981 to 2009 in each wheat production zone. The correlationswith a mark ‘a’ are significant at 0.05 level, and with a mark ‘b’ are significant at 0.01 level.

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F. Tao et al. / Agricultural and Forest Meteorology 189– 190 (2014) 91– 104 97

F C incre

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ig. 4. Estimated wheat yield changes by four panel regression models for each 1 ◦

rror bar represents the standard error of the estimates.

. Results

.1. Climate trends in the major wheat production zones from981 to 2009

From 1981 to 2009, climate during wheat growth period hadhanged significantly in the major wheat production zones. Ineneral, Tmean increased significantly while precipitation and SRDhanged diversely across the zones (Fig. 2). During GPw, in Zone, Zone II, Zone III, Zone IV and Zone VIII, Tmean increased sig-ificantly while precipitation decreased. In Zone V and Zone VII,oth Tmean and precipitation increased. In Zone VI, Tmean decreasedlightly while precipitation increased significantly. In addition, pre-

ipitation change little during VGP1 and VGP2, however decreasedignificantly during VGP3(VGP) and RGP in Zone I, Zone II, ZoneII and Zone IV. SRD during GPw did not change significantlyn most of the zones, except in Zone VII where it decreased

ease in Tmean during different growth periods in each wheat production zone. The

significantly by 0.63 MJ m−2 day−1 decade−1. Nevertheless, SRDduring VGP1 and VGP2 decreased notably in Zone I, Zone II, ZoneIII and Zone V.

3.2. Changes in wheat phenology and the relations totemperature change

Wheat sowing date and subsequently emergence date wasdelayed in Zone I, Zone II, Zone V, Zone VI and Zone VII, howeveradvanced significantly in Zone III and Zone IV (Table 3). Anthe-sis date and maturity date advanced significantly in all the zonesexcept Zone VI and Zone VII (Table 3). As a result, the length of each

growth period before anthesis was generally shortened, howeverthe length of RGP was prolonged in all the zones (Table 3). Finallythe GPw was significantly shortened in Zone I, Zone II, Zone V andZone VIII.
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98 F. Tao et al. / Agricultural and Forest Meteorology 189– 190 (2014) 91– 104

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ig. 5. Estimated wheat yield changes by four panel regression models for 10% incrrror bar represents the standard error of the estimates.

Tmean was significantly negatively correlated with the lengthf each growth period at most of the cases (Table 4); suggestingncrease in Tmean reduced the length of each growth period.

.3. Correlations between wheat yields and climate variablesuring each growth period

Winter wheat yield was positively correlated with Tmean inorthern China including Zone I and Zone II, however was nega-ively correlated with Tmean in southern China including Zone IIInd Zone VIII (Fig. 3). In northwestern China, spring wheat yieldas negatively correlated with Tmean in Zone VI, but positively

orrelated with Tmean in high altitude Zone VII. The positive (neg-tive) correlations suggested that yield was positively (negatively)ffected by increase in Tmean.

Wheat yield was generally negatively correlated with pre-ipitation in southern China, was insignificantly correlated withrecipitation in northern China (Fig. 3).

Wheat yield was positively correlated with SRD for winterheat in eastern China including Zone II and Zone III, and for springheat in northwestern China including Zone IV and Zone VI (Fig. 3).owever it was negatively correlated with SRD for winter wheat in

precipitation during different growth periods in each wheat production zone. The

Zone I and Zone V, as well as for spring wheat in high altitude ZoneVII.

3.4. Sensitivity of wheat yield to climate variables during eachgrowth period

The estimates on sensitivity of wheat yield to climate variablesfrom the four panel regression models were generally consistent(Fig. 4). For each 1 ◦C increase in Tmean during GPw, yield increasedfor winter wheat in Zone I, Zone II and Zone V of northern Chinaby 9.4%, 1.7% and 0.7%, respectively (Fig. 4). By contrast, yielddecreased for winter wheat in Zone III and Zone VIII of southernChina by 3.1% and 2.3%, respectively (Fig. 4); as well as for springwheat in Zone IV and Zone VI of northwestern China by 1.3% and6.1%, respectively. Spring wheat yield in high altitude Zone VIIincreased by 6.8% (Fig. 4).

For precipitation increase by 10% during GPw, yield decreasedfor winter wheat in Zone I, Zone II and Zone V of northern China

by 0.4%, 0.4% and 0.2%, respectively; and in Zone III and Zone VIIIof southern China by 1.9%, and 1.7%, respectively. In northwesternChina, spring wheat yield decreased in Zone IV and Zone VII by 0.6%,however increased in Zone VI by 1.3% (Fig. 5).
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ig. 6. Estimated wheat yield changes by four panel regression models for 10% increepresents the standard error of the estimates.

For SRD increase by 10% during GPw, yield increased for winterheat in Zone II and Zone III of eastern China by 6.5% and 23.1%,

espectively; as well as for spring wheat in Zone IV and Zone VI oforthwestern China by 4.9% and 6.9%, respectively (Fig. 6). By con-rast, yield decreased for winter wheat in Zone V of western Chinand Zone VIII of southwestern China by 5.4% and 0.7%, respectively.n addition, yield decreased for winter wheat in Zone I by 12.2%,nd for spring wheat in high altitude Zone VII by 7.6%.

.5. Yield change due to climate change over each growth periodrom 1981 to 2009

Climate change from 1981 to 2009 had caused measurablempacts on wheat yield in most of the zones. Due to increase inmean during GPw in the period, yield increased for winter wheat inone I, Zone II and Zone V of northern China by 13.3%, 6.5% and 0.4%,

espectively. By contrast, yield decreased for winter wheat in ZoneII and Zone VIII of southern China by 3.8% and 4.0%, respectivelyFig. 7). For spring wheat in northwestern China, yield decreased inone IV by 2.3% but increased in high altitude Zone VII by 5.9% due

SRD during different growth periods in each wheat production zone. The error bar

to increase in Tmean during GPw; yield increased in Zone VI by 2.6%due to decrease in Tmean during GPw.

Due to decrease in precipitation during GPw from 1981 to 2009,yield increased for winter wheat in Zone I and Zone II of northernChina by 1.2% and 0.5%, respectively; in Zone III and Zone VIII ofsouthern China by 1.5% and 3.1%, respectively; and for spring wheatin Zone IV of northwestern China by 1.6% (Fig. 8). Due to increase inprecipitation during GPw, yield increased for spring wheat in ZoneVI by 8.3%, however decreased for winter wheat in Zone V and forspring wheat in Zone VII by 0.3% and 3.3%, respectively.

Due to increase in SRD during GPw from 1981 to 2009, yieldincreased for spring wheat in Zone IV and Zone VI of northwest-ern China by 1.6% and 1.3%, respectively (Fig. 9). By contrast, yielddeceased for winter wheat in Zone I by 1.4%. Due to decrease in SRD,yield increased for winter wheat in Zone V and for spring wheat inZone VII of western China by 1.0% and 6.2%, respectively; however,

deceased for winter wheat in Zone II and Zone III by 3.9% and 7.9%,respectively.

From 1981 to 2009, changes in Tmean, precipitation and SRDjointly increased wheat yield in northern China, however decreased

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ig. 7. Estimated wheat yield changes by four panel regression models due to obseroduction zone. The error bar represents the standard error of the estimates.

heat yield in southern China. For example, wheat yield in Zone I,one II, Zone IV, Zone V, Zone VI and Zone VII of northern Chinancreased by 12.9%, 7.3%, 0.9%, 1.1%, 12.3% and 8.9%, respectively;owever, decreased in Zone III and Zone VIII of southern China by0.2% and 1.2%, respectively (Fig. 10).

. Discussion

.1. Spatial pattern of wheat yield response to climate changerom 1981 to 2009

Impacts of climate change from 1981 to 2009 on wheat yieldad an explicit spatial pattern. The impacts were positive in north-rn China including Zone I, Zone II, Zone IV, Zone V, Zone VI andone VII, however negative in southern China including Zone IIInd Zone VIII (Fig. 10). The spatial pattern is well consistent with

ao et al. (2012a) that used the census yield data at county scalend spatially extrapolated phenological data based on stations data.lthough the final impacts were dependent on the joint roles of

he changes in all climate variables, the roles of one or two climate

hanges in Tmean during different growth periods from 1981 to 2008 in each wheat

variables dominated in a zone. Yield increase in Zone I was mainlyascribed to increase in Tmean, where temperature was generallyless than the optimum temperature for wheat (Table 2). The meanof optimum temperatures for wheat was 22.0 ◦C, 4.9 ◦C, 10.6 ◦C,21.0 ◦C and 20.7 ◦C for sowing to emergence, vernalization, terminalspikelet, anthesis and grain-filling phase, respectively (Porter andGawith, 1999). In Zone II, winter wheat yield was most sensitive toSRD during GPw, which however changed only slightly during thestudy period. Increase in Tmean, together with decrease in precipita-tion, increased wheat yield slightly. In Zone III, winter wheat yieldwas most sensitive to SRD, and secondary to temperature. Tem-perature during anthesis and grain-filling period was approachingor above the optimum temperature. Decrease in SRD particularlyduring RGP and increase in temperature particularly during VGP3reduced yield. Irrigated spring wheat was sensitive to SRD and tem-perature in Zone IV where Tmean during RGP was 21.8 ◦C, above

the optimum temperature. Increase in temperature reduced theduration of RGP at some stations in the zone. From 1981 to 2009,SRD during RGP increased significantly which offset the negativeimpacts of temperature increase on final yield. Winter wheat yield
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ig. 8. Estimated wheat yield changes by four panel regression models due to obseheat production zone. The error bar represents the standard error of the estimate

n Zone V was most sensitive to SRD, where SRD and Tmean duringGP was the largest among all the zones; however SRD and Tmean

hanged slightly during the study period and had slight impacts onheat yield. As in Zone IV, irrigated spring wheat in Zone VI was

lso sensitive to SRD and Tmean, where Tmean during RGP was 22.9 ◦C.uring the study period, slight decrease in Tmean and slight increase

n SRD, together with significant increase in precipitation, jointlyncreased wheat yield in the zone. In high altitude Zone VII, climate

as characterized by high irradiance, low atmospheric pressurend low temperature, photo-inhibition on plant photosynthesisccurred sometimes (Zhang and Tang, 2005). Spring wheat yieldn the zone increased due to increase in temperature and decreasen SRD, however decreased due to increase in precipitation. In ZoneIII, high temperature during RGP, water-logging, insects and dis-ase were the major problems with wheat production (Jin, 1961).

ncrease in temperature reduced yield however decrease in pre-ipitation increased yield, and finally climate change during thetudy period had slight negative impacts on wheat yield in theone.

hanges in precipitation during different growth periods from 1981 to 2008 in each

The results showed that precipitation had relative less impacton yields than SRD and temperature. Wheat at the stations waswell irrigated. Yields were not sensitive to precipitation because themajority of the ˇ3 in the multiple regression models were not sta-tistically significant in Zones I–VII (Supporting Information). Wheatwas prone to water-logging, insects and disease during rainy seasonand high precipitation reduced crop yields particularly during RGP.In Zone VI, temperature and SRD were quite high, which causedhigh evapotranspiration and water requirements. The results alsoshowed that yields decreased with SRD increase in Zone I, Zone Vand Zone VII. Excess SRD can photoinhibit photosynthesis and maylead to photooxidative destruction of the photosynthetic appara-tus (Long et al., 1994), such as in Zone V and high altitude ZoneVII (Zhang and Tang, 2005). In Zone I, there was a negative corre-lation between SRD and minimum temperature, decreases of SRD

benefited yields because of the associated reduction in frost occur-rence. In addition, there is experimental evidence that yields ofsome crops can rise if small reductions in total radiation coincidewith increases in diffuse radiation (Stanhill and Cohen, 2001).
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F erved

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ig. 9. Estimated wheat yield changes by four panel regression models due to obsroduction zone. The error bar represents the standard error of the estimates.

The impacts of long-term climate change we investigated hereo not explicitly account for the impact of extreme weather.xtreme weather such as high temperature at crop critical growthtages can play a critical role in affecting wheat growth and yieldChallinor et al., 2010; Asseng et al., 2011; Lobell et al., 2012), whichhould be investigated in further study.

.2. Cultivar turnover, phenology change and yield change during981-2009, as well as the implications to adaptation

In the study, the adaptations including shifts in sowing datend changes in crop cultivars photo-thermal traits were explicitlyccounted for when quantifying climate impacts on crop growthnd yields. The contributions of other adaptations such as improve-

ents in varieties, technology, management and policy to crop

ield were implicitly described by a non-climatic factor linearrend, i.e., ˇ1 in Eqs. (2)–(8). Using data on management prac-ices from experimental stations may have impacted the results

changes in SRD during different growth periods from 1981 to 2008 in each wheat

and obscure our ability to detect adaptation to climate change inan explicit manner.

Wheat cultivars were shifted frequently, which, together withimprovement of agronomic management practices, contributednotably to yield increase in the past few decades (Zhang et al., 2005;Zhou et al., 2007). For example, Zhang et al. (2005) indicated thatwinter wheat yield at an experiment station in the North ChinaPlain (i.e., Zone II in this study) increased by 50% from 1982 to2002 or 2.38% per year, and yield increase was associated with theincrease in kernel numbers per unit area without alternation of theweight of kernels. Field trials using 47 leading common wheat cul-tivars released during 1960–2000 in the North China Plain showedthat average annual genetic gain in grain yield ranged from 32.07 to72.11 kg/ha/year or from 0.48% to 1.23% (Zhou et al., 2007). Largelybecause of successful utilization of dwarfing genes and the 1B/1R

translocation, the genetic improvement in grain yield was primar-ily attributed to increased grain weight per spike, reduced plantheight, and increased harvest index (Zhou et al., 2007). These stud-ies support our results that wheat yields generally increased from
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ig. 10. Estimated wheat yield changes by four panel regression models due to joi008 in each wheat production zone. The error bar represents the standard error of

0.81 kg/ha/year to 147.92 kg/ha/year or from 1.19% to 3.20% perear, with an average of 85.6 kg/ha/year or 1.98% per year, acrosshe major wheat production zones except Zone VIII where yieldecreased by 2.16 kg/ha/year (Table 2).

We found that wheat anthesis date and maturity date advancedenerally, and length of growth period before anthesis and GPw washortened, however length of RGP was significantly prolonged. Theength of each growth period was significantly negatively corre-ated with temperature, suggesting wheat growth was significantlyffected by temperature change. In most cases, increase in tem-erature reduced the length of growth period in the past threeecades, nevertheless the length of RGP increased significantly dueo adoption of cultivars with high thermal-requirement and late-

aturity cultivars, as well as slight decrease in temperature duringGP resulted from advancement of anthesis date (see also Tao et al.,012b). This adaptation option indeed worked since length of RGPas significantly correlated with wheat yield in Zone I, Zone IV,

one V and Zone VI (Table 4). The length of GPw was significantlyhortened in Zone I, Zone II, Zone V and Zone VIII (Table 3), howeverhe shortening was beneficial to yield because there was a negativeorrelation between the length of GPw and yield in these zones

nges in Tmean, precipitation and SRD during different growth periods from 1981 tostimates.

(Table 4). The reasons underlying may be that early maturity canprevent grain-filling period from high temperature stress or rainyseason (Tao and Zhang, 2013). The length of GPw was significantlypositively correlated with yield in Zone IV, however the length ofGPw was shortened because spring wheat in the zone was moresensitive to temperature increase (Tao et al., 2012b).

5. Conclusion

The experiment observations at 120 agricultural meteorologicalstations spanning from 1981 to 2009 across China were used to lookinsights into the response and adaptation of wheat growth and pro-ductivity to climate change in different climate zones. Our studiesbetter represented crop system dynamics by using detailed phe-nological records, consequently better accounted for adaptationssuch as shifts in sowing date and crop cultivars, when quantifyingclimate impacts on wheat yield. We found that climate change had

caused notable impacts on wheat growth and productivity acrossthe major wheat production regions in China although agronomicmanagement and cultivars turnover were continuing to play animportant role in increasing productivity and adapting to climate
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hange. Our findings suggest the changes in crop system dynamicsnd cultivars photo-thermal traits in the past decades, as well asheir possible changes in future; have to be sufficiently taken intoccount to improve the prediction of climate impacts and to plandaptations for future.

cknowledgements

This study was supported by the National Science Founda-ion of China (Project No. 41071030) the strategic pilot scientificrojects of the Chinese Academy of Science (Project NumberDA05090308), and the National Key Programme for Developingasic Science (Project Number 2010CB950902). We acknowledgereatly the anonymous referees for their valuable comments on thearly version of this manuscript.

ppendix A. Supplementary data

Supplementary data associated with this article can be found,n the online version, at http://dx.doi.org/10.1016/j.agrformet.014.01.013.

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