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Agriculture, Ecosystems and Environment 149 (2012) 20– 29

Contents lists available at SciVerse ScienceDirect

Agriculture, Ecosystems and Environment

jo u r n al hom ep age: www.elsev ier .com/ locate /agee

ontrasting effects of warming and autonomous breeding on single-riceroductivity in China

eilei Liua,b, Enli Wangb,∗, Yan Zhua,∗∗, Liang Tanga

National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu10095, PR ChinaCSIRO Sustainable Agricultural Flagship, CSIRO Land and Water, GPO Box 1666, Black Mountain, Canberra, ACT 2601, Australia

r t i c l e i n f o

rticle history:eceived 22 September 2011eceived in revised form3 December 2011ccepted 14 December 2011vailable online 11 January 2012

eywords:

a b s t r a c t

China is one of the most important rice production countries in the world, and maintaining high riceproductivity in China is very important for world food security. While previous studies showed thatrice production in China has been and will be negatively impacted by global warming, the confoundingeffects of climatic change, variety improvement and agronomic managements have not been separatelyinvestigated. In this paper we combine an analysis of climate and rice growth data with crop model-ing to investigate the impact of changes in climate, rice varieties, and agronomic management on riceproductivity at four sites (Wuchang, Xinyang, Zhenjiang and Hanyuan) in China. The results showed a

limate changeiceGrow Modelotential yieldainfed yieldice variety change

significant increase in minimum temperature during all rice growth stages at Wuchang and Zhenjiang,and from heading to maturity at Xinyang, but little change at Hanyuan. Global warming would have ledto a reduction in the length of rice growing period and a reduction in grain yield at all study sites, if novarietal changes had occurred. However, the adoption of new rice varieties stabilized growing duration,increased harvest index and grain yield at three of the four sites. In the face of future warming, a plannedbreeding effort may be needed to offset the negative impact of future climate change.

. Introduction

The significant warming trend since the 1980s has been wellocumented at most locations around the world, and this trend isrojected to accelerate in the future (Tao et al., 2006). During the

ast two decades, climate changes have been shown to impact onrop development and yields (Estrella et al., 2007, Liu et al., 2010;obell and Asner, 2003; Menzel et al., 2001). Lobell and Asner (2003)ndicated a significant decline in maize and soybean yield in thenited States as a result of increased temperature from 1982 to998. Peng et al. (2004) showed a 10% decline in rice yield withvery 1 ◦C increase in minimum temperature in the Philippines. Inhina, annual average air temperature has increased by 0.5–0.8 ◦Curing the past 100 years, which was slightly greater than the aver-ge global temperature rise (Ding et al., 2006). From 1955 to 2000,nnual mean maximum and minimum temperatures increased by.13 ◦C/decade and 0.32 ◦C/decade for all of China (Liu et al., 2004).

ice is one of the main food crops in China, accounting for approx-

mately 30% of the total planting acreage of food crops and half ofhe total grain production (Jing et al., 2007). Improved knowledge

∗ Corresponding author. Tel.: +61 2 62465964; fax: +61 2 62465965.∗∗ Corresponding author.

E-mail address: Enli.Wang@csiro.au (E. Wang).

167-8809/$ – see front matter © 2012 Elsevier B.V. All rights reserved.oi:10.1016/j.agee.2011.12.008

© 2012 Elsevier B.V. All rights reserved.

of how the past climate change has impacted rice production inChina can provide insights into the likely impact of future climatechange, and assist in the development of future climate adaptationstrategies to ensure China’s food security.

In China, a few studies have investigated the impact of pastand possible future climate changes on rice production. Tao et al.(2006) studied the impact of climate changes on rice phenologyand yield using correlation analysis with data collected at experi-mental stations from 1981 to 2000. They showed that changes intemperature over past decades had accelerated rice phenologicaldevelopment and decreased rice yields. Yao et al. (2007) assessedthe impact of climate change on irrigated rice yield using the IPCCB2 climate change scenario (IPCC, 2007) and the CERES-Rice modelfor 2071–2090 in the main rice areas of China. Their results indi-cated that without the direct effect of CO2 on crop production, theB2 climate change scenario would have a negative impact on riceyield at most rice production regions. With the CO2 direct effect,however, rice yield was predicted to increase at all selected sta-tions. Zhang et al. (2010) analyzed the recorded rice yields over theperiod of 1981–2005 in the main rice production areas of China, andreported that rice yields were positively correlated with solar radi-

ation, which primarily drives yield variation. Similar results werealso found by Sheehy et al. (2006), who reinvestigated the datasetof Peng et al. (2004) and pointed out a decrease in solar radiation,instead of increase in temperature, could also explain the reduction

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L. Liu et al. / Agriculture, Ecosyste

n yield. Most of these previous studies either used rice yield datarom many different cultivars together or used a simulation modelith a single cultivar to analyze the impact of climate trends on

ice growth and yield. The effects of changes in climate, variety andgronomic management (i.e. increased irrigation and fertilization,tc.) have not been separated. Recently, Liu et al. (2010) developed

method to combine long-term experimental data and crop mod-ling to investigate and separate the impact of changes in climate,rop varieties and agronomic management on wheat and maizeroduction in China.

In this study, we employ a method similar to that used by Liut al. (2010) to investigate the impact of climate change, varietalhange and agronomic management on rice production in China.

e focused on the single rice production regions in China, com-ined with simulation modeling, to conduct a parallel analysis oflimate and rice growth data collected from 1981 to 2009. Ourbjectives are to: (1) determine whether there were significantrends in climatic change during the main growth stages of rice forhis period, (2) analyze the impact of climatic variables (temper-ture, precipitation, rainfall intensity and sunshine hours), varietyhange and agronomic management on rice growth and grain yieldnd (3) discuss the relative contribution of those factors to thehanges in rice yield.

. Materials and methods

.1. Study sites

Six rice cropping regions are classified across China, which coveringle and double rice cropping areas (Fig. 1, from China Nationalice Research Institute, 1989). Four sites were selected from theain single rice cropping system regions in this study (Fig. 1). They

re Wuchang (44◦54′N, 127◦09′E, 194.6 m above sea level) in Hei-

ongjiang province, Xinyang (32◦07′N, 114◦05′E, 114.5 m) in Henanrovince, Zhenjiang (32◦11′N, 119◦28′E, 27.3 m) in Jiangsu provincend Hanyuan (29◦21′N, 102◦41′E, 795.9 m) in Sichuan province.hese locations were chosen because they were typical single rice

Fig. 1. Rice cropping regions in China and the four study sites (Pr

d Environment 149 (2012) 20– 29 21

sites, representing three contrasting climatic regions: the northeastChina cool temperate climate, the east China subtropical climate,and the southeast China mid-subtropical climate. Daily weatherdata and rice growth and yield data were recorded at each site. All ofthese data were collected by the National Meteorological Networksof the China Central Meteorological Agency (CMA).

2.2. Climate and crop data

Daily weather data for the period of study include maximum,average and minimum temperatures, sunshine hours and precipi-tation. They were recorded at each study site. Rice was planted from1981 to 2009 at the Agrometeorological Experimental Station ofeach site, and was managed according to local practices for controlof weeds and disease. Fertilizers and irrigation were applied accord-ing to local practices. Rice data include cultivars traits, sowing andharvesting dates, dates of growth stages, aboveground biomass,grain yield, and fertilizer and irrigation management. Crop har-vest index (HI) was calculated as the ratio of grain yield to thetotal aboveground biomass. Three rice growth stages were usedin this study, i.e., S1 – from sowing to jointing, S2 – from jointingto heading, and S3 – from heading to maturity.

Rice cultivars that were grown frequently changed during thestudy period. At each site, the cultivars used in the experimentswere representative of the most widely grown cultivars in eachregion. In total, eleven, twenty, thirteen and nine varieties wereplanted at Wuchang, Xinyang, Zhenjiang and Hanyuan, respec-tively, during the entire studied period.

2.3. Data analysis

Trends over time for the mean of each climate variable duringthe three main growth stages (S1, S2 and S3) of rice and the length of

each stage were tested for significance at the 5% level using the Stu-dent’s t test. The Stepwise Multi-Linear Regression (SMLR) was usedto quantify the contributions of temperature and variety changes tovariations in observed growth durations, and the effect of growth

ovince names are shown in capital letters and in brackets).

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2 L. Liu et al. / Agriculture, Ecosyste

uration, harvest index, and agronomic factors on observed riceields. In addition, we further analyzed the correlation betweenarvest index and the length of S3 or the ratio of S3 over the totalrowth duration. We also used Analysis of Variance (ANOVA) tonalyze difference in harvest index among varieties at each site.he varieties that were planted for only one year were excluded.

To quantify the varietal changes during the study period at theour sites, the cultivar parameters in the RiceGrow model (seeescription below) were derived based on the observed phenolog-

cal stages at each site. This was achieved by adjusting parameteralues to match the simulated dates of jointing, heading and matu-ity.

.4. Crop modeling

The RiceGrow model (Tang et al., 2009) was used to simulateice phenological development, biomass growth and grain yield atach study site. The model uses the concept of physiological devel-pment time to simulate the phenology of rice (Cao and Moss,997; Wang and Engel, 1998). It uses a canopy photosynthesisodel to predict rice biomass accumulation. The partitioning of

iomass into different organs is controlled by developmental stage.he RiceGrow model has been calibrated and validated in China forimulating rice phasic development, biomass growth and partition-ng (Meng et al., 2004, 2003), leaf age, and leaf area (Ye et al., 2008).n general, the model was able to reproduce the observed crop phe-ology, biomass and yield as affected by climatic and management

actors (cultivar, fertilizer, irrigation) in China.The RiceGrow modeling serves two purposes in this study.

irstly, we used the model to derive the cultivar parameters touantify the varietal changes in the study period at the fourites. RiceGrow model has four cultivar parameters that affect ricehenological development. They are intrinsic earliness (IE), pho-operiod sensitivity (PS), thermal sensitivity (TS), and basic fillingactor (BFF). IE affects the duration of the vegetative stage frommergence to the start of the photoperiod sensitive stage. It has

range of 0–1.0, with 1.0 representing the least number of days,.e., shortest duration of that stage intrinsic of the cultivar. PSeflects the photoperiod sensitivity of the cultivars from the endf the basic vegetative phases to the jointing stage. It has the rangef 0–0.2, with larger values for more photoperiod-sensitive culti-ars. TS determines the temperature sensitivity for different riceultivars and has a range of 1.0–6.0, with larger values for moreemperature-sensitive cultivars. Temperature response changesittle from cultivar to cultivar, and a TS value of 2.0 described theice developmental response to temperature well. Although bothE and PS affect the length of S1 (from emergence to jointing), sen-itivity analysis showed that the duration of S1 is most sensitiveo IE. BFF determines the length of the grain filling period and has

range of 0–1.0, with greater values for cultivars having a shorterrain filling period.

Secondly, the model was used to simulate phenological devel-pment, biomass growth and grain yield of single rice for a givenultivar under a defined management level. This allows varietiesnd management to be kept constant, so that climatic impact cane analyzed from the simulation results, without the confoundingffects of changing cultivars and agronomic management prac-ices. For each study site, a single cultivar from the early 80 s wassed for the entire simulation period (1981–2009), i.e., ‘Xixuan 14’or Wuchang, ‘Guizhao 2’ for Xinyang, ‘Zhuziqing’ for Zhenjiangnd ‘Shanyou 2’ for Hanyuan, respectively. Simulations were con-ucted at two levels of water supply, i.e., full irrigation and rainfed

onditions, both assuming ample nutrient supply, and no impactrom disease, pests, or weeds. Sowing dates did not change muchetween different years and were fixed for each year, as per thexperiments.

d Environment 149 (2012) 20– 29

The simulated durations of each growth stage and grain yieldwere analyzed using the same methods as for the observed data,to determine their trend of change and the impact of climatic vari-ability/change in the past decades on yield.

3. Results

3.1. Climatic trends during crop stages

The mean maximum, average and minimum temperature dur-ing the rice growing period increased in all the study sites,except for minimum temperature at Hanyuan (Fig. 2a–d). Theminimum temperature increased by 0.94 ◦C/decade at Wuchang,0.33 ◦C/decade at Xinyang, and 0.69 ◦C/decade at Zhenjiang. Theaverage temperature increased by 0.72 ◦C/decade at Wuchang,0.34 ◦C/decade at Xinyang and 0.60 ◦C/decade at Zhenjiang, andmaximum temperature increased by 0.43, 0.32 and 0.57 ◦C/decade,respectively, for these sites. At Hanyuan, there was a trend ofincrease in maximum and average temperature and of decreasein minimum temperature, but the change was not significant atp < 0.05.

Total sunshine hours exhibited a negative trend with time overthe past 29 years at all sites (Fig. 2e–h), but decreased significantlyonly at Wuchang (p < 0.05) and Xinyang (p < 0.01). Precipitation andrainfall intensity (calculated as total rainfall divided by total rainydays during the rice growing period) had a decreasing trend exceptfor Zhenjiang (Fig. 2i–l), but was only significant at Wuchang.

From sowing to jointing (S1), there was a general increasingtrend in maximum, average and minimum temperatures, exceptfor the decreasing trend in minimum temperature at Hanyuan(Table 1). However, only the increase in minimum temperature atWuchang and average temperature at Zhenjiang were significant.The other climatic variables during the S1 stage did not change sig-nificantly during the study period, except for the rainfall intensityat Zhenjiang.

From jointing to heading (S2), both maximum and minimumtemperatures showed an increasing trend, but only the increasein minimum temperature at Wuchang and Zhenjiang, and theincrease in average temperature at Zhenjiang were significant.Sunshine hours generally decreased, but was only significantly atHanyuan, where the precipitation also decreased significantly.

From heading to maturity (S3), at Wuchang and Zhenjiang, max-imum, average and minimum temperatures increased significantly.At Xinyang, only the minimum temperature increased significantly.There was little change at Hanyuan. Sunshine hours decreasedsignificantly only at Xinyang, while precipitation decreased signif-icantly only at Wuchang.

3.2. Simulated rice phenology and yield

Fig. 3 showed the simulated lengths of the rice growing stagesusing one cultivar for each study site. The simulated total length ofthe growing period decreased significantly at Wuchang, Xinyangand Zhenjiang. At Hanyuan, while there was a decreasing trend inthe total simulated growing period duration, the decrease did notreach the p < 0.05 significance level. The reduced length of simu-lated growing periods at Wuchang, Zhenjiang and Xinyang were aresult of significant reduction in S1 and S3 together. At Hanyuan,the decrease in minimum and average temperature from headingto maturity led to a prolonged duration of S3.

Fig. 4 shows the simulated rice yield under fully irrigated (poten-

tial yield) and rainfed conditions (rainfed yield) at the four studysites. At all sites, a significant decrease was simulated for bothpotential and rainfed production at Wuchang, Xinyang and Zhen-jiang. This implies that the past climate change had a negative

L. Liu et al. / Agriculture, Ecosystems and Environment 149 (2012) 20– 29 23

Fig. 2. Trends in maximum, minimum, average temperature (T max, T min and T avg) (a–d), sunshine hours (SH) (e–h), precipitation and rainfall intensity (P and RI) (i–l)d on linm e averi

idar

TTs

uring the rice growing period at four study sites. Straight line is the linear regressiaximum temperature; ♦ indicates the minimum temperature and × indicates th

ntensity.

mpact on both potential and rainfed yield at the four sites. Theecline in rainfed yield was a result of both reduced potential yieldnd reduced precipitation. The difference between potential andainfed yield showed an increasing trend at all sites, indicating

able 1rends in daily maximum, average and minimum temperature (T max, T avg and T min),tages from sowing to jointing (S1), jointing to heading (S2) and heading to maturity (S3)

Stage Climatic variables Wuchang

Trend

S1 T max 0.04

T min 0.53**

T avg 0.32

SH −0.36

P −0.03

RI 0.15

S2 T max 0.07

T min 0.49**

T avg 0.31

SH −0.17

P −0.13

RI −0.21

S3 T max 0.52**

T min 0.54**

T avg 0.56**

SH −0.02

P −0.42*

RI −0.25

** Significant at p < 0.01.* Significant at p < 0.05.

e against years. ** Significant at p < 0.01; * Significant at p < 0.05. In a–d, � indicatesage temperature. In i–l, � indicates the precipitation and © indicates the rainfall

increased water deficit and demand for irrigation at these studysites.

During the past 29 years, the highest simulated rice yieldsoccurred at Hanyuan in Sichuan province, with a potential and

sunshine hours (SH), precipitation (P) and rainfall intensity(RI) during the growth of rice at the four study sites (1981–2009).

Xinyang Zhenjiang HanyuanTrend Trend Trend

0.02 0.35 0.360.07 0.36 −0.150.02 0.37* 0.21

−0.16 −0.28 0.110.09 0.09 0.120.11 0.38* 0.04

0.20 0.28 0.220.26 0.56** 0.060.23 0.42* 0.18

−0.24 −0.16 −0.43*

0.01 0.15 −0.49**

−0.05 −0.06 −0.12

0.02 0.39* 0.000.50** 0.51** −0.060.01 0.47** −0.03

−0.45* −0.14 −0.01−0.04 −0.13 0.04−0.21 −0.02 −0.26

24 L. Liu et al. / Agriculture, Ecosystems and Environment 149 (2012) 20– 29

F ting t( year.

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ig. 3. Simulated duration of rice stages from sowing to jointing (S1, �), from joinS1 + S2 + S3, ©) at the four study sites. Straight lines show the linear trends against

ainfed yield of 13,744 and 11,872 kg ha−1, respectively. The lowestice yields occurred at Wuchang in Heilongjiang province, with aotential and rainfed yield of 5781 and 4335 kg ha−1, respectively.he maximum decrease in simulated yield occurred at Xinyang.rom 1981 to 2009, the potential and rainfed yield decreased by58 and 417 kg ha−1 decade−1 at Wuchang, 630 and 698 at Xinyang,26 and 349 at Zhenjiang and 33 and 290 at Hanyuan, respectively.

.3. Observed rice phenology, 1981–2009

The changes in observed phenology of rice (Fig. 5) are dif-erent form that predicted by the simulation model (Fig. 3). Theotal growth duration of rice tended to increase significantly atinyang (p < 0.01) and Zhenjiang (p < 0.05) during the study period.t Wuchang, S1 increased significantly. At Xinyang, all the threetages (S1, S2 and S3) showed an increased trend, though not sig-ificant at p < 0.05, leading to a significant increase in the totalrowing period. At Zhenjiang, there was a significantly increasingrend from heading to maturity (S3), contributing to the signifi-ant increase in total growth duration. At Hanyan, S2 (jointing toeading) was shortened significantly (p < 0.01) while other stages

ncreased slightly, leading to a nearly stable total length of grow-ng period. The different trends between simulated and observedrowth period are mainly due to the changes in cultivars from 1981o 2009.

.4. Observed grain yields, 1981–2009

A significant increase in recorded biomass was observed atuchang during the study period (Fig. 6). There was no significant

o heading (S2, ♦), from heading to maturity (S3, �), and from sowing to maturity ** Significant at p < 0.01; * Significant at p < 0.05.

trend in biomass at Xinyang, Zhenjiang and Hanyuan. The changesin observed grain yield were also different from simulated results.There was a significant increasing trend at Wuchang, Xinyang andZhenjiang, while a decreasing trend at Hanyuan (though not signifi-cant) in Sichuan province. The reasons for the contrasting change ingrain yield at different sites are analyzed in the following sections.

3.5. Impact of rice varietal changes on phenology and grain yield

Table 2 shows the results from SMLR analysis on factors that con-tributed significantly to change or stabilize each of the growth stageduration. The results show that both temperature and cultivarsaffected the duration of developmental stages. The varietal changesare mainly due to changes in intrinsic earliness (IE) affecting S1 andbasic filling factor (BFF) affecting S3. At Wuchang, increases in tem-perature during all three stages had significant impact to shortenthe durations. However, the reduction in IE compensated the neg-ative impact of warming, leading to an extended S1. At Xinyang,temperature change only impacted on S1, which was compensatedby changes in IE, leading to a stabilized S1. Changes in BFF led to anextended S3 stage. At Zhenjiang, temperature change affected S1and S3, leading to a shortened S1, while change in BFF significantlyextended S3. At Hanyuan, both IE and BFF changes affected S1 andS3, while temperature increase shortened the duration of S2. Theseresults are consistent with what is shown in Fig. 5. As a combinedeffect of the temperature and varietal changes, the total length of

growth stage increased, except at Hanyuan (Fig. 5), the extensionwas significant at Xinyang (p < 0.01) and Zhenjiang (p < 0.05).

Rice harvest index showed a significant increasing trend atWuchang, Xinyang and Zhenjiang, but no significant trend at

L. Liu et al. / Agriculture, Ecosystems and Environment 149 (2012) 20– 29 25

F ur sitS tes th

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ig. 4. Simulated rice yields under full irrigation and rainfed conditions at the foignificant at p < 0.05. � indicates potential yield, ♦ indicates rainfed yield, × indica

anyuan (Fig. 7). Results from ANOVA indicate that changes inarvest index between cultivars are significant, and there was aignificant increasing trend in harvest index at Wuchang and Zhen-iang (Fig. 8). Further analysis on harvest index and the length ofrowth stages showed that HI changes were neither related to thehanges in duration from heading to maturity, nor to the ratio ofuration from heading to maturity (S3) over the total growth dura-ion (S1 + S2 + S3). This implies that changes in harvest index are

ndependent of changes of growth durations.

Table 3 shows the impact of harvest index, total growth dura-ion, nitrogen application and irrigation on rice grain yield fromhe SMLR analysis. Only at Hanyuan was the change in rice yield

able 2esults of stepwise multi-linear regression (above 95% significant level) of climatic and no1981–2009) (T avg is average temperature, IE is intrinsic earliness, BFF is basic filling fac

Sites Duration T avg IE

Wuchang S1 −2.70 −30.01S2 −1.60

S3 −2.30

Xinyang S1 −1.86 −59.53S2S3

Zhenjiang S1 −3.59

S2S3 −0.85

Hanyuan S1 −68.45S2 −1.95

S3

0, intercept of the linear regression equation.

es (1981–2009). Straight lines show the linear trends. ** Significant at p < 0.01; *e difference between the two.

significantly related to the variation in total growth duration,implying that the shortening trend in total growth duration (Fig. 5d)led to reduction in rice yield (Fig. 6d). No significant impact ofS3 duration on yield was found at all study sites. The lack of sig-nificant impact of either total or S3 duration on grain yield atWuchang, Xinyang and Zhenjiang indicates that the stabilizationof growth durations due to rice varietal changes may have pre-vented yield from declining, but did not lead to significant yield

increase. At all sites, harvest index significantly impacted on grainyield. The increase in harvest index at Wuchang, Xinyang and Zhen-jiang (Fig. 7) contributed significantly to the increase in rice grainyield (Table 3).

n-climatic parameters vs. the lengths of three growth stages (S1, S2 and S3) of ricetor).

BFF B0 R2

97.55 0.6356.72 0.40

107.36 0.19

82.51 0.34

−30.71 36.98 0.34

174.30 0.26

−139.65 117.74 0.68

23.09 0.1677.71 0.42

−86.55 8.50 0.15

26 L. Liu et al. / Agriculture, Ecosystems and Environment 149 (2012) 20– 29

F ting t( year.

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ig. 5. Observed duration of rice stages from sowing to jointing (S1, �), from joinS1 + S2 + S3, ©) at the four study sites. Straight lines show the linear trends against

.6. Impact of irrigation and fertilizer application on rice yield

Changes in nitrogen application positively impacted the yield atuchang and Hanyuan, but the impact was much less comparedith that of the HI (Table 3). Irrigation showed a negative trend

gainst the yield at Zhenjiang and Hanyuan, and this was mainlyaused by the declining trend in irrigation amount from 1981 to009 (a larger amount of irrigation water applied at the earliertages of the study period), which indicates inefficient use of irriga-ion water at earlier stages and improved irrigation managementith time. Lack of significant impact for irrigation and fertilization

n this analysis indicated that from 1981 onwards, sufficient irri-ation and fertilization had been applied in the rice field and theariations in crop grain yields were mainly impacted by climaticnd varietal factors.

. Discussion and conclusions

This paper combines data analysis and crop modeling togethero investigate the impact of past changes in climate, rice varietynd agronomy on productivity of single rice in China. The method

able 3esults of stepwise multi-linear regression (above 95% significant level) between rice yie

ertilizer application at the four study sites, 1981–2009.

Sites HI Total growth duration (days) N

Wuchang 0.70 – 0.Xinyang 0.50 – –

Zhenjiang 0.63 – –

Hanyuan 0.07 1.36 0.

0, intercept of the linear regression equation.

o heading (S2, ♦), from heading to maturity (S3, �), and from sowing to maturity ** Significant at p < 0.01; * Significant at p < 0.05.

used is an extension of that of Liu et al. (2010), applied to ricecrops. Our methodology differs from those used in most previousclimate change impact studies in that it enables quantification ofindividual contributions from climatic changes and changes in cropvariety and agronomic management, to the observed changes inrice yield. While the results of climate change impact studies basedon statistical analysis of past climate and yield data (Tao et al.,2006) include the impacts of changed crop varieties and agronomy,many modeling studies on future climate change impacts ignorethese factors. For future climate change impact on crop yield, anumber of studies have adopted a modeling approach combinedwith future climate scenarios (developed using GCM predictions)to simulate possible yield change under future climates. Most ofthese studies use a fixed crop cultivar in the modeling (Matthews,1995; Wang et al., 2011; Xiong et al., 2007), thus are not able tocapture the potential mitigating impact of varietal changes. A fewmodeling studies have shown that the crop cultivar changes could

have significant impact on simulated yield for a given climatechange scenario (Wang and Wang, 2007; Wang et al., 2009). Ourresults suggest that while global warming can lead to reduction ingrowing period and decrease rice yield in China, improvement in

ld and crop harvest index (HI), total growth duration, irrigation input and nitrogen

itrogen (kg ha−1) Irrigation (mm) B0 R2

07 – 0 0.75– 0 0.52

−0.24 0 0.6102 −0.17 0 0.44

L. Liu et al. / Agriculture, Ecosystems and Environment 149 (2012) 20– 29 27

Fig. 6. Observed aboveground biomass and grain yield and change of rice at study sites (1981–2009). ** Significant at p < 0.01; * Significant at p < 0.05. © indicates abovegroundbiomass, • indicates grain yield.

Fig. 7. Changes in harvest index (HI) of rice varieties at study sites (1981–2009). The bars indicate the range of the same variety. Trend lines of the observed HI are alsoshown. ** Significant at p < 0.01; * Significant at p < 0.05.

28 L. Liu et al. / Agriculture, Ecosystems and Environment 149 (2012) 20– 29

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ig. 8. Harvest index of different rice cultivars at the four study sites. The cultivarsndicate significant difference at p < 0.05.

ice varieties in the past 29 years were able to stabilize the growthuration and to increase rice grain yield.

The results of this paper reveal that the changes in climatic vari-bles were not uniform during the different rice stages across theain single-rice regions in China. For the past 29 years, the major

ignificant change was the increase in minimum temperature inll rice growth stages at Wuchang and Zhenjiang, but only in stagerom heading to maturity at Xinyang. At Hanyuan, there had beenittle change in temperature in the past 29 years. The sunshineours showed a decreasing trend at all sites. Annual precipitationnd rainfall intensity showed a slightly decreasing trend except forhenjiang, but only significant at the 95% level at Wuchang.

Our approach of using crop modeling with a single cultivar forice, without nutrient or water limitations, enables quantificationf the impact of past climate change on the length of growth stagesS1, S2 and S3), and the potential and rainfed yield of single rice inhe absence of varietal and other management changes. The resultshow that if there were no cultivar changes in the past, the changesn the climate would already have negatively impacted crop devel-pment and yield, leading to reduced rice yield at all sites. Exceptor Hanyuan, the observed trend in both phenology and rice yieldsere significantly different from the simulated ones, showing sta-

ilized growth durations and increased yield. The reasons for theseifferences are mainly due to the cultivar changes (IE in S1, BFF in S3nd HI). Our analysis revealed that the major changes in rice vari-ty were reduced IE (intrinsic earliness) at Wuchang, Xining andanyuan, reduced BFF (basic filling factors) at Xinyang, Zhenjiangnd Hanyuan, and increased harvest index at Wuchang, Xinyangnd Zhenjiang. As a result, the reduction of IE and BFF prolongedr stabilized the total length of the growth period, stabilized theotal biomass production and grain yield. The increase in observed

ice yields was mainly attributable to increased harvest index (HI)Table 3). The difference in the trends of simulated and observedrowth stages and grain yield of rice highlighted contrasting effectsf climatic change, varieties and management options.

sted in a chronological order from left to right. The different letters above the bars

Changes in harvest index were not related to changes in theduration of growth stages, but appeared to be a separate vari-etal change of rice at the study sites. The highest harvest indexreached 0.56–0.57 at the study sites. Other studies on wheat indi-cated that dry matter costs of reducing stem lodging in wheat limitsHI for further increase to closer to 0.5 (Berry et al., 2007), whichmay imply limitations for increase in HI of rice as well. Further,Peng et al. (2000) indicated that in the Philippines increased HIwas important from 1966 to 1980, but since 1980 total biomassand a slightly longer duration seem to be associated with higheryields. Recent evidence also shows that high yield of modern ricevarieties was associated with higher biomass production aroundflowering time and higher non-structural carbohydrate content atheading (Takai et al., 2006). Therefore, further genetic progresswill be possibly linked to increased biomass accumulation, giventhe limits to increased harvest index (Fischer and Edmeades,2010).

Adoption of new rice cultivars at the study sites was a typeof autonomous adaption response to climatic trend (Smith andLenhart, 1996), because no planned breeding efforts were devel-oped to target impact of warming. There has been a generalwarming trend in China over the last 100 years and the warm-ing trend is predicted to continue on the future (Tao et al., 2008).Such changes have been suggested to have a negative impact onagriculture. Planned breeding efforts to target the mitigation ofthe negative impact of future climate change are therefore a keyelement in the development of adaptation strategies to increaseor maintain high rice productivity and to guarantee future foodsecurity.

In conclusion, the adoption of new rice cultivars in many sitesof China has been able to compensate the potential negative

impact of global warming in the past 3 decades. The major cultivarchanges involved reduced intrinsic earliness, extended grain fillingperiod, and improved harvest index. Continuing global warmingin the future will necessitate targeted breeding efforts. Combing

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bservations and modeling is an effective means to tease out thempact of climatic, varietal and management changes on cropield.

cknowledgments

We gratefully acknowledge the funding support from the Newentury Exceptional Talent Program of China (NCET-08-0797), theational Natural Science Foundation of China (30871448), theational Basic Research program of China (2009CB118608), theatural Science Foundation of Jiangsu Province (BK2008330) and

he CSIRO-Chinese Ministry of Education (MOE) PhD Research Fel-owship Program.

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