Contrasting effects of warming and autonomous breeding on single-rice productivity in China
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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 .co
Contra us produc
Leilei Liua National Eng or Inf210095, PR Chib CSIRO Sustain berra
a r t i c l
Article history:Received 22 SeReceived in re13 December Accepted 14 DAvailable onlin
Keywords:Climate changRiceGrow ModPotential yieldRainfed yieldRice variety change
ice pant fo
willrovene anges inXinyaperatang, b
to 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.
2012 Elsevier B.V. All rights reserved.
The signdocumenteprojected tolast two decrop develoLobell and Aindicated aUnited Stat1998. Pengevery 1 C iChina, annuduring the page global tannual mea0.13 C/decRice is one imately 30%the total gr
icant warming trend since the 1980s has been welld at most locations around the world, and this trend is
accelerate in the future (Tao et al., 2006). During thecades, climate changes have been shown to impact onpment and yields (Estrella et al., 2007, Liu et al., 2010;sner, 2003; Menzel et al., 2001). Lobell and Asner (2003)
signicant decline in maize and soybean yield in thees as a result of increased temperature from 1982 to
et al. (2004) showed a 10% decline in rice yield withncrease in minimum temperature in the Philippines. Inal average air temperature has increased by 0.50.8 Cast 100 years, which was slightly greater than the aver-emperature rise (Ding et al., 2006). From 1955 to 2000,n maximum and minimum temperatures increased byade and 0.32 C/decade for all of China (Liu et al., 2004).of the main food crops in China, accounting for approx-
of the total planting acreage of food crops and half ofain production (Jing et al., 2007). Improved knowledge
ding author. Tel.: +61 2 62465964; fax: +61 2 62465965.ding author.ress: Enli.Wang@csiro.au (E. Wang).
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 Chinas 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 20712090 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 19812005 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
see front matter 2012 Elsevier B.V. All rights reserved.agee.2011.12.008sting effects of warming and autonomotivity in Chinaa,b, Enli Wangb,, Yan Zhua,, Liang Tanga
ineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory fnaable Agricultural Flagship, CSIRO Land and Water, GPO Box 1666, Black Mountain, Can
e i n f o
ptember 2011vised form2011ecember 2011e 11 January 2012
a b s t r a c t
China is one of the most important rproductivity in China is very importrice production in China has been andeffects of climatic change, variety impinvestigated. In this paper we combiing to investigate the impact of chanproductivity at four sites (Wuchang, signicant increase in minimum temand from heading to maturity at Xinym/locate /agee
breeding on single-rice
ormation Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu
, ACT 2601, Australia
roduction countries in the world, and maintaining high ricer world food security. While previous studies showed that
be negatively impacted by global warming, the confoundingment and agronomic managements have not been separately
analysis of climate and rice growth data with crop model- climate, rice varieties, and agronomic management on riceng, Zhenjiang and Hanyuan) in China. The results showed aure during all rice growth stages at Wuchang and Zhenjiang,ut little change at Hanyuan. Global warming would have led
L. Liu et al. / Agriculture, Ecosystems and Environment 149 (2012) 20 29 21
in yield. Most of these previous studies either used rice yield datafrom many different cultivars together or used a simulation modelwith a single cultivar to analyze the impact of climate trends onrice growth and yield. The effects of changes in climate, variety andagronomic management (i.e. increased irrigation and fertilization,etc.) have not been separated. Recently, Liu et al. (2010) developeda method to combine long-term experimental data and crop mod-eling to investigate and separate the impact of changes in climate,crop varieties and agronomic management on wheat and maizeproduction in China.
In this study, we employ a method similar to that used by Liuet al. (2010) to investigate the impact of climate change, varietalchange and agronomic management on rice production in China.We focused on the single rice production regions in China, com-bined with simulation modeling, to conduct a parallel analysis ofclimate and rice growth data collected from 1981 to 2009. Ourobjectives are to: (1) determine whether there were signicanttrends in climatic change during the main growth stages of rice forthis period, (2) analyze the impact of climatic variables (temper-ature, precipitation, rainfall intensity and sunshine hours), varietychange and agronomic management on rice growth and grain yieldand (3) discuss the relative contribution of those factors to thechanges in rice yield.
2. Materials and methods
2.1. Study sites
Six rice cropping regions are classied across China, which coversingle and double rice cropping areas (Fig. 1, from China NationalRice Research Institute, 1989). Four sites were selected from themain singleare Wuchanlongjiang prprovince, Zhand HanyuThese locat
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
nds oee mage wt testtify ons i
ames rice cropping system regions in this study (Fig. 1). Theyg (4454N, 12709E, 194.6 m above sea level) in Hei-ovince, Xinyang (3207N, 11405E, 114.5 m) in Henanenjiang (3211N, 11928E, 27.3 m) in Jiangsu provincean (2921N, 10241E, 795.9 m) in Sichuan province.ions were chosen because they were typical single rice
Trethe threach stdents to quanvariati
Fig. 1. Rice cropping regions in China and the four study sites (Province nver time for the mean of each climate variable duringain growth stages (S1, S2 and S3) of rice and the length ofere tested for signicance at the 5% level using the Stu-
. The Stepwise Multi-Linear Regression (SMLR) was usedthe contributions of temperature and variety changes ton observed growth durations, and the effect of growth
are shown in capital letters and in brackets).
22 L. Liu et al. / Agriculture, Ecosystems and Environment 149 (2012) 20 29
duration, harvest index, and agronomic factors on observed riceyields. In addition, we further analyzed the correlation betweenharvest index and the length of S3 or the ratio of S3 over the totalgrowth duration. We also used Analysis of Variance (ANOVA) toanalyze difThe varietie
To quanfour sites, descriptionical stages avalues to mrity.
2.4. Crop m
The Ricerice phenoleach study opment tim1997; Wanmodel to pbiomass intThe RiceGrosimulating ring (Meng eIn general, tnology, biomfactors (cul
The RiceFirstly, we quantify thsites. RiceGphenologictoperiod sefactor (BFF)emergence a range of 0i.e., shortesreects theof the basicof 00.2, wvars. TS detcultivars antemperaturlittle from crice develoIE and PS afsitivity anato IE. BFF da range of 0grain lling
Secondlyopment, biocultivar undand managbe analyzedeffects of ctices. For eaused for thefor Wuchanand Shanyoducted at twconditions, from diseasbetween diexperiment
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.
for um t/dece tem/decum ttivele inimum.al sust 29
Wucl inteuringnjianm soin m
dec 1). Hng aer ctly djian
m joraturimue inne han, wm he
3 shne cwinenjiaal simthe prowiof sigcreasurity
4 shold) at alial anThis ference in harvest index among varieties at each site.s that were planted for only one year were excluded.tify the varietal changes during the study period at thethe cultivar parameters in the RiceGrow model (see
below) were derived based on the observed phenolog-t each site. This was achieved by adjusting parameteratch the simulated dates of jointing, heading and matu-
Grow model (Tang et al., 2009) was used to simulateogical development, biomass growth and grain yield atsite. The model uses the concept of physiological devel-e to simulate the phenology of rice (Cao and Moss,g and Engel, 1998). It uses a canopy photosynthesisredict rice biomass accumulation. The partitioning ofo different organs is controlled by developmental stage.w model has been calibrated and validated in China forice phasic development, biomass growth and partition-t al., 2004, 2003), leaf age, and leaf area (Ye et al., 2008).he model was able to reproduce the observed crop phe-ass and yield as affected by climatic and management
tivar, fertilizer, irrigation) in China.Grow modeling serves two purposes in this study.used the model to derive the cultivar parameters toe varietal changes in the study period at the fourrow model has four cultivar parameters that affect riceal development. They are intrinsic earliness (IE), pho-nsitivity (PS), thermal sensitivity (TS), and basic lling. IE affects the duration of the vegetative stage fromto the start of the photoperiod sensitive stage. It has1.0, with 1.0 representing the least number of days,t duration of that stage intrinsic of the cultivar. PS
photoperiod sensitivity of the cultivars from the end vegetative phases to the jointing stage. It has the rangeith larger values for more photoperiod-sensitive culti-ermines the temperature sensitivity for different riced has a range of 1.06.0, with larger values for moree-sensitive cultivars. Temperature response changesultivar to cultivar, and a TS value of 2.0 described thepmental response to temperature well. Although bothfect the length of S1 (from emergence to jointing), sen-lysis showed that the duration of S1 is most sensitiveetermines the length of the grain lling period and has1.0, with greater values for cultivars having a shorter
period., the model was used to simulate phenological devel-mass growth and grain yield of single rice for a givener a dened management level. This allows varieties
ement to be kept constant, so that climatic impact can from the simulation results, without the confoundinghanging cultivars and agronomic management prac-ch study site, a single cultivar from the early 80 s was
entire simulation period (19812009), i.e., Xixuan 14g, Guizhao 2 for Xinyang, Zhuziqing for Zhenjiangu 2 for Hanyuan, respectively. Simulations were con-o levels of water supply, i.e., full irrigation and rainfedboth assuming ample nutrient supply, and no impacte, pests, or weeds. Sowing dates did not change muchfferent years and were xed for each year, as per thes.
Theing thexceptminim0.33 Caverag0.34 Cmaximrespecincreasin minp < 0.05
Totthe paonly atrainfaldays dfor Zhe
Frotrend for the(TableWuchaThe othnicanat Zhen
Froimum,At XinyThere signicicantly
Fig.using othe groand Zhthe totreach lated gresult the deto mat
Fig.tial yiesites. Apotentjiang. ic trends during crop stages
n maximum, average and minimum temperature dur-e growing period increased in all the study sites,minimum temperature at Hanyuan (Fig. 2ad). Theemperature increased by 0.94 C/decade at Wuchang,ade at Xinyang, and 0.69 C/decade at Zhenjiang. Theperature increased by 0.72 C/decade at Wuchang,
ade at Xinyang and 0.60 C/decade at Zhenjiang, andemperature increased by 0.43, 0.32 and 0.57 C/decad...