Acta Ecologica Sinica 31 (2011) 40–48

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Acta Ecologica Sinica

journal homepage: www.elsevier .com/ locate/chnaes

Simulating the rice yield change in the middle and lower reachesof the Yangtze River under SRES B2 scenario

Shen Shuang-He a, Yang Shen-Bin a,⇑, Zhao Yan-Xia b, Xu Yin-Long c, Zhao Xiao-Yan a, Wang Zhu-Yu a,Liu Juan a, Zhang Wei-Wei a

a College of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing, Jiangsu 210044, Chinab Chinese Academy of Meteorological Sciences, Beijing 100081, Chinac Institute of Agro-Environment and Sustainable Development, Chinese Academy of Agricultural Science, Beijing 100081, China

a r t i c l e i n f o a b s t r a c t

Article history:Accepted 8 November 2010

Keywords:RiceORYZA2000Yangtze River zoneClimate change

1872-2032/$ - see front matter � 2010 Ecological Socdoi:10.1016/j.chnaes.2010.11.007

⇑ Corresponding author.E-mail address: jaasyang@163.com (S.-B. Yang).

As one of the most important crops in China, rice accounts for 18% of the country’s total cultivated area.Increasing atmospheric CO2 concentration and associated climate change may greatly affect the rice pro-ductivity. Therefore, understanding the impacts of climate change on rice production is of great signifi-cance. This paper aims to examine the potential impacts of future climate change on the rice yield inthe middle and lower reaches of the Yangtze River, which is one of the most important food productionregions in China. Climate data generated by the regional climate Model PRECIS for the baseline (1961–1990) and future (2021–2050) period under IPCC SRES B2 scenario were employed as the input of the ricecrop model ORYZA2000. Four experimental schemes were carried out to evaluate the effects of futureclimate warming, CO2 fertilization and water managements (i.e., irrigation and rain-fed) on rice produc-tion. The results indicated that the average rice growth duration would be shortened by 4 days and theaverage rice yield would be declined by more than 14% as mean temperature raised by 1.5 �C during therice growing season in 2021–2050 period under B2 scenario. This negative effect of climate warming wasmore obvious on the middle and late rice than early rice, since both of them experience higher mean tem-perature and more extreme high temperature events in the growth period from July to September. Thesignificance effect of the enhanced CO2 fertilization to rice yield was found under elevated CO2 concen-trations in 2021–2050 period under B2 scenario, which would increase rice yield by more than 10%, but itwas still not enough to offset the negative effect of increasing temperature. As an important limiting fac-tor to rice yield, precipitation contributed less to the variation of rice yield than either increased temper-ature or CO2 fertilization, while the spatial distribution of rice yield depended on the temporal and spatialpatterns of precipitation and temperature. Compared to the rain-fed rice, the irrigated rice generally hadhigher rice yield over the study area, since the irrigated rice was less affected by climate change. Irriga-tion could increase the rice yield by more than 50% over the region north of the Yangtze River, with lesscontribution to the south, since irrigation can relieve the water stress for rice growing in the north regionof the study area. The results above indicated that future climate change would significantly affect therice production in the middle and lower reaches of the Yangtze River. Therefore, the adverse effect offuture climate change on rice production will be reduced by taking adaptation measures to avoid disad-vantages. However, there is uncertainty in the rice production response prediction due to the rice accli-mation to climate change and bias in the simulation of rice yield with uncertainty of parametersaccompanied with the uncertainty of future climate change scenario.

� 2010 Ecological Society of China. Published by Elsevier B.V. All rights reserved.

1. Introduction

Climate change has become a hot topic throughout the world.According to the 4th assessment report of Intergovernmental Panelon Climate Change (IPCC), the average global temperature has risen

iety of China. Published by Elsevie

0.74 ± 0.18 �C in the past 100 years, along with the intensive hu-man activities, continuous emissions of greenhouse gases, andincreasing extreme weather events, such as extreme high temper-ature and precipitation. The climate change has brought enormouseconomic losses to countries all around the world. However, sincemany uncertainties accompany the change of climate condition inthe future [1], forecast and assessment of the impact of climatechange on global environment are becoming a hotspot of presentresearch [2–6].

r B.V. All rights reserved.

S.-H. Shen et al. / Acta Ecologica Sinica 31 (2011) 40–48 41

China is a big agricultural country, and its agriculture has beensignificantly influenced by the climate change. For instance, withobvious temperature rise in northeast China in the past severaldecades, the production of rice and corn has experienced a trendof increase [7,8]; at the same time, the climate in the semiarid re-gion of highland areas in northwest China has become warmer andwetter, which led to a rise in the output of local wheat production[9]. In the past 50 years, the increasing precipitation as well asflood and continuous rain weather have affected the productionof rice [10,11], and the frequently occurred extreme weatherevents have caused serious losses to the output of crops in southChina [12]. Therefore, it is urgent and of great significance to assessthe impact of future climate change on the agricultural productionin China.

In the past 10 years, establishing a scenario of future climatechange by using Global Climate Model (GCM) and nesting withcrop models, has become an important method to study the impactof climate change on agricultural production [13–16]. However,the climate data obtained from GCM has low spatial and temporalresolution, which weakens the climate variation characteristics ofcertain areas. Therefore, downscaling method was always adoptedto resolve this issue [17]. Presently, based on the Special Report onEmissions Scenarios (SRES) of IPCC and regional climate models,many reports have been given about the research on assessingthe impact of climate change on crop production in the regionalscale. For example, according to the scenario of SRES A2, Chavaset al. [14] simulated the climate data in the period of 2071–2100by using the regional climate model RegCM3, and combined it withEPIC model to analyze the potential impacts of climate change tothe agricultural productivity in eastern China. It showed that with-out considering CO2 fertilization effect, the productivity of crops,would decrease by 2.5–12%. While considering CO2 fertilization ef-fect, the productivity would increase by 6.5–24.9%. Lin et al. [18]

Fig. 1. The study area (bold line) and the ag

analyzed the impact of climate change on the productivity of Chi-nese wheat, corn, and rice under the scenarios of SRES A2 and B2,by coupling regional climate model PRECIS with CERES series cropmodels. The result also demonstrated that CO2 fertilization effect isable to improve the yield of different crops notably. Similar resultscould also be seen in the researches completed by Yao et al. [19],Zhao [20], and Tian et al. [21]. Under the scenarios of SRES A2and B2, Xiong et al. [22] studied the threshold of temperature in-crease under climate change for Chinese food production and itsuncertainties based on the yield variation of three main food crops(rice, wheat and maize) during the simulated periods 2011–2040,2041–2070 and 2071–2100.

All the above-mentioned researches have suggested that theagricultural production in China, for different regions and differentcrops, would be seriously influenced by the climate change in thefuture. Taking the middle and lower reaches of the Yangtze Riveras the study area, this work combines the climate data under SRESB2 scenario, simulated from regional climate model PRECIS, withthe rice crop model ORYZA2000 [23] to assess the impact of cli-mate warming, CO2 fertilization effect, and water managements(i.e., irrigation and rain-fed) on rice production during the periodof 2021–2050. Meanwhile, adaptive measures were put forwardin order to weaken the adverse effect of climate change on rice pro-duction. And finally, the reliability of ORYZA2000 in the impactstudy is further verified.

2. Materials and methods

2.1. Study area

As an important food production region in China, the middleand lower reaches of the Yangtze River has a climate condition of

ro-meteorological stations (symbol ‘d’).

42 S.-H. Shen et al. / Acta Ecologica Sinica 31 (2011) 40–48

humid subtropical monsoon climate: annual average temperaturebetween 14 and 18 �C, frost free period as long as 210–270 days,accumulated temperature (P10 �C) as much as 4500–6500 �C,hours of sunshine as long as 700–1500 h, annual amount of precip-itation of 800–1400 mm, mostly in spring and autumn. The crop-ping system is two or three cropping a year, with rice season aslong as 210–260 days. The soil in study area is mainly paddy soil,while that in the South Rim is red soil. Yellow brown soil and yel-low-cinnamon soil is only in hilly area [24].

As shown in Fig. 1, the study area covers Shanghai City and sixprovinces: Jiangsu, Anhui, Zhejiang, Jiangxi, Hubei, and Hunan.According to the rice ecological zones in China [25], most of thestudy area belongs to the zone of double/single-season rice in cen-tral China, except the northern area in Anhui and Jiangsu province,which belongs to the zone of single-season rice in north China.

For several factors, such as planting structure adjustment andlabor force transfer, the area of double-season rice in the middleand lower reaches of the Yangtze River has experienced a declinein recent years, while the area of single-season rice increasesremarkably [25].

2.2. Climate scenario

According to IPCC SRES, B2 scenario was used in this paper. Thisscenario represents a world emphasizing the sustainable develop-ment of economics, society, and environment as well as a worldencouraging regionalization [26]. The main characteristic includes:continuous growth of global population, medium-speed economicdevelopment, and low-intermediate-level of CO2 emissions.

The climate data under B2 scenario for the baseline (1961–1990) and future period (2021–2050) were simulated by the regio-nal climate model PRECIS. The data have a grid resolution of50 km � 50 km, reflecting regional climate variation on a better le-vel. It includes daily maximum temperature, minimum tempera-ture, precipitation, solar radiation, wind speed, and relativehumidity. Table 1 demonstrates the relative changes of monthlymean climate variables during the rice season in 2021–2050 periodto that of the baseline.

2.3. Rice data

Rice experimental data from 1981 to 2006, including rice vari-ety, rice phenology, yield and field managements, were obtainedfrom 65 agro-meteorological stations of China MeteorologicalAdministration (Fig. 1). In order to improve the reliability of theexperimental data, annual fluctuation of rice yield has been exam-

Table 1Changes of monthly mean climate variables during the rice season of study area forB2 scenario (2021–2050) relative to baseline (1961–1990).

Month Baseline B2 scenario

Tmax ð�CÞ Tmin ð�CÞ P (mm) DTmax ð�CÞ DTmin ð�CÞ DP ð%Þ

3 13.7 5.4 96.9 1.6 1.5 �5.64 19.8 11.0 141.7 1.5 1.5 1.55 24.7 16.1 162.0 1.3 1.3 31.76 28.0 19.9 183.6 1.5 1.3 4.57 31.1 23.0 153.7 2.1 1.7 �3.78 30.8 22.5 131.5 1.7 1.8 25.49 26.4 18.3 98.4 1.5 1.9 34.3

10 21.3 12.7 70.3 1.4 1.3 �23.0

Annualmean

19.8 11.5 1222.7 1.6 1.5 6.2

Note: Tmax ; Tmin, P represents mean maximum temperature, mean minimum tem-perature, and precipitation respectively. DTmax ; DTmin; DP represents the change ofmean maximum temperature, mean minimum temperature, and precipitationrespectively.

ined. The rice observation data corresponding to the years and/orstations that have experienced serious disaster influence, or havelow rice production, or contain critical deficiency of informationsequence have been eliminated.

In addition, statistical data, such as rice planting area, rice yield,and nitrogenous fertilizer consumption for early rice, double crop-ping late rice, middle-season rice, and single cropping late rice ofevery province were obtained from China Agriculture Yearbooksfrom 1981 edition to 2006 edition, to provide basic informationon the rice production in the study area.

2.4. Soil data

The Harmonized World Soil Database (http://www.iiasa.ac.at/Research/LULC/External-World-Soil-database/HTML) establishedby Food and Agriculture Organization of the United Nations(FAO) and International Institute for Applied Systems Analysis,Vienna (IIASA), (http://www.iiasa.ac.at/Research/LULC/External-World-Soil-database/HTML) has been selected as the main sourcefor soil parameter for the regional simulation in this paper. The soildata is a 30 arc-second raster database with over 16,000 differentsoil mapping units that combines existing regional and nationalupdates of soil information worldwide. The soil profile in HWSDis divided into two layers: surface layer (0–30 cm) and base layer(30–100 cm). Physical and chemical characteristics for each layerinclude soil type, composition, texture, water storage capacity,bulk density, content of organic carbon, salinity, pH and so on.With the soil texture, bulk density and fraction of clay and sand,other soil parameters such as saturated volume water content,field water capacity, wilting coefficient and coefficient of conduc-tivity of saturated water for each layer, which are required bythe rice growth model, were estimated by using the Soil–Water-Characteristics (SWCT) provided by the SPAW model (http://hydrolab.arsusda.gov/SPAW/Index.htm).

2.5. ORYZA2000

2.5.1. Model introductionAs a dynamic and mechanismic rice growth model, ORYZA2000

can be used to simulate the rice growth under three conditions:potential growth, water stress, and nitrogen stress [23]. The modelcontains several modes to simulate the crop growth and develop-ment, evapotranspiration and water stress, nitrogen dynamics,and soil–water balance. ORYZA2000 divides the growth and devel-opment of rice into four phases: Basic Vegetative Phase (BVP, fromplant emergence to the starting of Photoperiod Sensitive Phase),Photoperiod Sensitive Phase (PSP, from the end of Basic VegetativePhase to panicle initiation), Panicle Formation Phase (PFP, frompanicle initiation to flowering), and Grain Filling Phase (GFP, fromflowering to physiological maturity). During the simulation, themodel follows the daily calculation scheme for the rates of drymatter production of the rice plant organs and the rate of pheno-logical development. By integrating these rates over time, dy mat-ter production of the crop is simulated throughout the growingseason. The total daily rate of canopy CO2 assimilation is calculatedfrom the daily incoming radiation, temperature, and leaf area in-dex. The model contains a set of subroutines that calculate the dai-ly rate by integrating instantaneous rates of leaf CO2 assimilationover time and depth within the canopy. The calculation is basedon an assumed sinusoidal time course of radiation over the dayand the exponential light profile within the canopy. After subtrac-tion of respiration requirements, the net daily growth rate is ob-tained. The dry matter produced is partitioned among variousplant organs. For the aboveground part, the accumulated dry mat-ter is distributed to stem, leaf, and panicle. Rice nitrogen contentsmainly influence the photosynthetic efficiency, leaf growth rate,

Table 2Selected rice varieties and basic information on corresponding agro-meteorological stations.

Province Rice cropping system Rice variety Agro-meteorological station Years

Jiangsu (JS) Single cropping rice Shanyou 63 Xinghua 1991a, 1983–1993

Anhui (AH) Single cropping rice Zhongxian 898 Hefei 1996a, 1998–1999, 2001–2002, 2004–2006

Zhejiang (ZJ) Double cropping rice Zhe 733 (early rice) Jinhua 1996, 1997a, 1998–1999Lishui 1991–1992, 1994, 1999

Xieyou 46 (late rice) Jiaojiang 1992, 1996a, 1998–1999, 2001–2002Jinhua 1997–2000, 2002

Hubei (HB) Double cropping rice Ezao No. 7 (early rice) Jiangxia 1988–1990a, 2001Shanyou 63 (late rice) Jiangxia 1996a–1998, 2001–2004

Hunan (HN) Double cropping rice Zhefu 802 (early rice) Changde 1988a–1990, 1996Changsha 1987–1989

Yuchi 231 (late rice) Changde 1987–1990a, 1993, 1995–1996Changsha 1983, 1985, 1989

Jiangxi (JX) Double cropping rice Ganzaoxian 14 (early rice) Nanchang 1991, 1992a, 1993Erwan (late rice) Guangfeng 1982a–1984

a Indicates the experiment for ORYZA2000 calibration, and the other years are used for validation.

S.-H. Shen et al. / Acta Ecologica Sinica 31 (2011) 40–48 43

and wilting process of the rice leaves. While the growth of rice isunder water stress, the soil–water balance model is activated tosimulate such consequences as leaf rolling, low photosyntheticefficiency, low development rate, and decreased seed setting rate.ORYZA2000 model also considers the effect of CO2 fertilizationby introducing a corrected coefficient to the initial light-use effi-ciency of single leaf. The calculation of this value uses the formuladeduced by Jansen [27]:

e ¼ e3401� expð�0:00305� CCO2 � 0:222Þ1� expð�0:00305� 340� 0:222Þ

� �ð1Þ

where e is the value of CO2 effect; e340 represents the reference ef-fect value of CO2 with a concentration of 340 ppm (defined as 1);CCO2 is the CO2 concentration in the actual simulation environment.As a result, the model can be used to simulate the effect of CO2 fer-tilization on the rice growth.

2.5.2. Model calibration and validationA large number of parameters have been set in the ORYZA2000.

During the simulation, most of them can be set as default values,but the parameters reflecting rice heredity and variety characteris-tics, such as development rates, partitioning factors, relative leafgrowth rate, specific leaf area, leaf death rate, and fraction of stemreserves, require a large quantity of experiments to calibrate. How-ever, according to the observation data and statistical information,

4000 5000 6000 7000 80004000

4500

5000

5500

6000

6500

7000

7500

8000

8500

Observed rice yield (kg/ha)

Sim

ulat

ed ri

ce y

ield

(kg/

ha)

Shanyou 63 (JS)Zhongxian 898Erzao 7Shanyou 63 (HB)Zhefu 802Yuchi 231Ganzao 14ErwanZhe 733Xieyou 46

Fig. 2. Comparison between observed and simulated rice yield. Note: solid line isthe 1:1 reference line, and the dashed lines are lines with ±15% relative error.

rice varieties and cropping systems are different between agro-meteorological stations and between provinces, which increasesthe difficulty in calibration of ORYZA2000 for regional simulationand increases the uncertainties in rice yield estimation. Here,according to the actual changes of rice cropping system in studyarea, we assumed the rice cropping system in Jiangsu and Anhuiwas single-season rice, while it was double-season rice in Zhejiang,Jiangxi, Hubei, and Hunan. Then, representative rice varieties,which must have been grown in more than three agro-meteorolog-ical stations or grown more than 3 years in a same station, were se-lected for early rice and late rice for provinces with double-seasonrice, and selected for middle rice for provinces with single-seasonrice. Table 2 shows the selected representative rice varieties for dif-ferent provinces and the corresponding agro-meteorological sta-tions for calibration and validation.

With the meteorological data obtained from local meteorologi-cal bureaus, two calibration programs, DRATES and PARAM, built inthe ORYZA2000 model were used to calibrate the rice geneticparameters. Validation was then carried out to check the calibratedparameters, as shown in Fig. 2. It shows that the relative error ofmost simulated rice yield is less than 15%, with a correlation coef-ficient of 0.67. The absolute error for each simulated developmentstage is less than 5 days, and the average error for the wholegrowth duration is 1.4 days. The results suggest that ORYZA2000model can be used to simulate the representative rice varieties ina relative high accuracy.

2.6. Regional simulation schemes

In order to simulate the impact of climate change on regionalrice production, ORYZA2000 was first upscaled to regional scaleand then coupled with the climate data under B2 scenario. Here,four experimental schemes (considering or without consideringCO2 fertilization effect, irrigation or rain-fed) were established tosimulate the change of rice yield under B2 scenario by consideringthe climate warming, CO2 fertilization and different water manage-ments (irrigation or rain-fed).

Each experimental scheme was carried out grid by grid with aresolution of 50 km � 50 km. For each grid, the input data files ofORYZA2000 were created, which contain experimental conditions,crop characteristics, soil properties, and weather data. All modelparameter values were read from these files. The genetic parame-ter values were set according to the calibration result for each rep-resentative rice variety. In all experimental schemes, the initial daythat the daily average temperature steadily reaches 13 �C (with

44 S.-H. Shen et al. / Acta Ecologica Sinica 31 (2011) 40–48

5-day moving average method) was set as the seeding date of earlyrice each year, the seeding date of double-season late rice was setat June 22, and that of single-season rice was set at May 10. Totally200 kg/hm2 nitrogenous fertilizer was applied during each rice sea-son with 30%, 30%, 30%, and 10% fertilized in transplanting stage,tillering stage, jointing stage, and booting stage respectively. Forthe irrigated rice, automatic irrigation system was used with75 mm deep water automatically irrigated when the soil watercontent is lower than 0.3 m3/m3. At last, the mean atmosphericCO2 concentration in baseline was set at 330 ppm, while in the per-iod from 2021 to 2050 under B2 scenario, the mean concentrationwas set at 450 ppm. For the experimental scheme of without con-sidering CO2 fertilization effect, the CO2 concentration in 2021–2050 period was set as the same level as that of the baseline.

ORYZA2000 model is run at each grid for each of the dominantsoils. Considering the difference of grid resolution between soildata and climate data, we generated a new soil vector data in Arc-GIS software by adding vectorized soil data on the grided climatedata with the help of IDENTITY tool. Then, with the new data, fol-lowing rules were used to select the dominant soils for each grid:(1) If the predominant soil proportion is at least 50%, it is the onlysoil used; otherwise (2) if it is less than 50%, then simulation is car-ried out using all soils with proportions of at least 20%, but only thesoil with maximum simulated rice yield is selected.

2.7. Yield statistics and analysis

For the single cropping rice region, the average rice yield in30 years was calculated using simulated middle rice yield, whilefor the double cropping rice region, the average rice yield in30 years was calculated by averaging the sum of simulated earlyrice yield and late rice yield. The variation coefficient of riceyield was also calculated (i.e. the ratio of the standard deviationto the average yield in 30 years). Then, results were analyzed

JS-S AH-S HB-E HB-L HN-E HN-L JX-E JX-L ZJ-E ZJ-E-8

-7

-6

-5

-4

-3

-2

-1

0

Province

Cha

nge

of g

row

th d

urat

ion

(d)

Fig. 3. Changes of rice growth duration in six provinces. Note: ‘‘JS’’: Jiangsu, ‘‘AH’’:Anhui, ‘‘HB’’: Hubei, ‘‘HN’’: Hunan, ‘‘JX’’: Jiangxi, ‘‘ZJ’’: Zhejiang, ‘‘S’’: Single-seasonrice, ‘‘E’’: double-season early rice, and ‘‘L’’: double-season late rice.

Table 3Changes of rice yield in four different experimental schemes under B2 scenario relative to

Experimental scheme Change of rice yie

Mean value

Irrigation and without considering CO2 fertilization effect �14.8Rain-fed and without considering CO2 fertilization effect �15.2Irrigation and considering CO2 fertilization effect �3.3Rain-fed and considering CO2 fertilization effect �4.1

with ANOVA and correlation analysis using statistical softwareSPSS 13.0.

3. Result and analysis

3.1. The impact of climate change on rice growth duration

The climate warming accelerates the process of rice develop-ment and reduces rice growth duration. Compared with the cli-mate condition under the baseline, the temperature under B2scenario increases by 1.5 �C during the rice season. Especially fromJuly to September, the increasement reaches about 1.8 �C onaverage.

Fig. 3 shows the change of rice growth duration compared withthat under the baseline. It shows that the average rice growthduration would be shortened by 4 days in 2021–2050 period, witha maximum of 7 days found in Jiangsu province for middle rice,and a minimum of 1 day in both Jiangxi and Hubei province forearly rice. On average, the rice growth duration for both middleand late rice would be shortened by 5 days, while it would be3 days for early rice. The reason for the difference was that the hightemperature and high temperature increase in July and August in2021–2050 period strikingly accelerate the rice development ofboth middle and late rice. Moreover, the number of days with ex-treme high temperature (i.e., daily high temperature >35 �C) fromJuly to September in 2021–2050 period under B2 scenario was26 days, 10 days more than that under the baseline, and theincreases for each province ranged from 52.6% (Hubei) to 80.0%(Zhejiang). Zhao et al. [11] has analyzed the impact of historicalclimate change on rice production in the Yangtze River zone. Itpointed out that under the climate warming, high temperature,especially continuous high temperature during the period of flow-ering stage and late milk stage, mainly affected middle rice. There-fore, the remarkable increase of number of days with extreme hightemperature under B2 scenario would further threaten the produc-tion of single-season rice and double-season late rice in the studyarea.

3.2. Effect of climate change on rice yield

Table 3 shows the change of rice yield in 2021–2050 period un-der B2 scenario comparing with that under the baseline. The sim-ulated rice yield under B2 scenario experiences a clear drop in allthe experimental schemes. Without considering CO2 fertilizationeffect, the yield of irrigated rice decreases by 14.8%, and the yieldof rain-fed rice decreases by 15.2% on average. It indicates that cli-mate warming not only shortens the rice growth duration, but alsoreduces the rice yield remarkably. With CO2 fertilization effect con-sidered, the yield of irrigated rice decreases by 3.3% and the yield ofrain-fed rice decreases by 4.1% on average. It indicates that CO2 fer-tilization has a certain positive effect on rice yield, but cannot com-pensate the negative effect caused by temperature increase yet.The results also show that the standard deviation of rain-fed riceyield is higher than that of irrigated rice whether the effect ofCO2 fertilization is considered or not. Under the irrigation, the

baseline.

ld (%)

Median value Maximum value Minimum value Std.

�14.7 3.6 �35.2 5.9�16.1 20.7 �40.6 9�3.2 20.3 �32.5 7.9�4.2 46.2 �36.6 11.5

S.-H. Shen et al. / Acta Ecologica Sinica 31 (2011) 40–48 45

growth of rice can hardly be affected by precipitation, while rain-fed rice is susceptible to the temporal and spatial variation ofprecipitation, which leads to a more remarkable difference of inter-annual variation and spatial variation of the yield.

Fig. 4 shows the spatial distribution of rice yield change underB2 scenario in four experimental schemes. It can be seen fromFig. 4a and b that without considering CO2 fertilization effect, therice yield in most regions of the study area decreases by more than10%. The region with a significant yield reduction for irrigated riceconcentrates in the central and southern part of Anhui, central areain Hubei and eastern area in Hunan province. As for rain-fed rice,the significant yield reduction region concentrates in the northernpart of both Jiangsu and Anhui, central and northern part of Hubei,and central and western part of Hunan province. With the effect ofCO2 fertilization considered, the area with significant yield reduc-tion clearly decreases, as shown in Fig. 4c and d. Since the positiveeffect of CO2 fertilization to the rice growth, areas with yield riseincrease in the study area, which mainly appears in Hunan prov-

Fig. 4. Spatial variation of rice yield change in 2021–2050 period under B2 scenario relativfed and without considering CO2 fertilization effect; (c) irrigation and considering CO2 f

ince, and the central and southern area of Jiangxi, and some partsof Jiangxi and Hubei province.

For irrigated rice, increased CO2 concentration in 2021–2050period significantly promotes the overall level of rice yield, reduc-ing the magnitude of yield decrease by 11.5%. However, the spatialvariation of yield change is mainly related to the spatial distribu-tion of temperature increase in the study area. In Fig. 4b, grids withthe color of light-green and yellow correspond to the yield increaseareas, which are mainly distributed in regions with relatively high-er altitude, suggesting that under the joint effect of CO2 fertiliza-tion and climate warming, these regions become more conduciveto rice growth under the future climate change.

For rain-fed rice, precipitation is an important factor that limitsthe yield, but its contribution is much lower than that of climatewarming and CO2 fertilization effect. According to the analysis,the rice yield under B2 scenario did not increase remarkably afterthe rise of precipitation in 2021–2050 period, so the effect of CO2

fertilization is still a determinant to improve the overall rice yield

e to baseline. (a) Irrigation and without considering CO2 fertilization effect; (b) rain-ertilization effect; (d) rain-fed and considering CO2 fertilization effect.

JS AH HB HN JX ZJ-40

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Yiel

d ch

ange

(%)

JS AH HB HN JX ZJ

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JS AH HB HN JX ZJ-40

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JS AH HB HN JX ZJ-40

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d ch

ange

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(a) (b)

(c) (d)

Fig. 5. Changes of rice yield in 2021–2050 period under B2 scenario in six provinces relative to baseline. (a) Irrigation and without considering CO2 fertilization effect; (b)rain-fed and without considering CO2 fertilization effect; (c) irrigation and considering CO2 fertilization effect; (d) rain-fed and considering CO2 fertilization effect. In thefigure, ‘‘Minimum value’’, ‘‘1/4 percentile value’’, ‘‘Median’’ (red line), ‘‘3/4 percentile value’’, ‘‘Maximum value’’, and ‘‘Abnormal value’’ (blue spot) are presented from bottomto top. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

46 S.-H. Shen et al. / Acta Ecologica Sinica 31 (2011) 40–48

in study area. However, the temporal and spatial variation of pre-cipitation and temperature during the rice growth period mainlyinfluence the spatial variation of rain-fed rice yield, which conse-quently has a more significant spatial difference than that of irri-gated rice.

Fig. 5 shows the rice yield variation for all provinces under eachexperimental scheme. The dispersion of the yield changes as wellas the difference of the changes between provinces for irrigatedrice are less than that of rain-fed rice. It can be explained by thetemporal and spatial variation of precipitation resulting in largespatial variation of the yield change for rain-fed rice. However,the difference of yield changes between each province is not onlyaffected by the variation of climate factors, but also by the differentresponse of representative rice varieties to the climate warmingand CO2 fertilization effect. For example, ignoring the temperatureincrease, the yield of Shanyou 63 and Erwan would be increased by22.6% and 19.7% respectively when the CO2 concentration risesfrom 330 ppm to 450 ppm. While keeping the CO2 concentrationconstant, the yield of the two rice varieties would be decreasedby 12.8% and 7.1%, respectively, with temperature increased by3 �C. Therefore, the spatial variation of rice yield changes can alsobe related to the different genetic characteristics of rice varieties.

Fig. 6 demonstrates the relative change of rice yield betweenirrigated and rain-fed rice under B2 scenario when CO2 fertilizationeffect is considered. According to the statistics, the relative changeranges from �7.0% to 143.4%, with 45.9% on average. As seen fromthe figure, the irrigated rice yield in Jiangsu, Anhui, and most ofHubei is 50% higher than that of rain-fed rice. Moreover, the irri-gated rice yield is doubled in coastal and north regions of Jiangsu,northwest of Hubei, and the central and northern Hunan. However,in the most part of the southern Yangtze River area, the relativechange is less than 30%, suggesting that increase of irrigation areaand guarantee of water supply in the north of Yangtze River caneffectively contribute to the rice yield, while in the south of Yan-gtze River, since the precipitation in growth period is relativelyhigher, the effect of irrigation here is not as significant as that inthe north of Yangtze River.

4. Discussion

Four experimental schemes have been carried out in this studyto simulate the impact of future climate change on rice yield in themiddle and lower reaches of the Yangtze River. The analysis were

Fig. 6. Relative change of rice yield in irrigation mode to rain-fed mode under B2scenario with CO2 fertilization effect.

S.-H. Shen et al. / Acta Ecologica Sinica 31 (2011) 40–48 47

focused on the effect of temperature increase, CO2 fertilization andwater management on rice production in future climate.

It shows that the average temperature of rice season in 2021–2050 period under B2 scenario would be raised by 1.5 �C, withmaximum increase appearing during the summer, which wouldcause a 62.5% increase in number of days with extreme high tem-perature. Therefore, it is necessary to cultivate rice varieties withresistance to high temperature so as to reduce the harm to theyield formation. The results also showed that the increase of CO2

concentration under future climate is conducive to raise the riceyield, but it still cannot compensate the negative effect of climatewarming. In addition, irrigation can significantly contribute tothe increase of rice yield in regions north of Yangtze River. How-ever, with climate changed in the future, the difference of time–space distribution of precipitation would be increased, and theoccurrence of extreme ones would be more frequent. Accordingto the statistics of extreme precipitation events from May to Sep-tember, with the index of daily precipitation higher than 50 mm,it is shown that there were more than 10 days, on average, haveexperienced extreme precipitation in the study area under B2 sce-nario from May to September, and the increase rate is up to 28.2%.Therefore, increasing irrigation area can effectively prevent theinfluence of precipitation distribution difference and extreme pre-cipitation events on rice production.

Climate change impact studies are plague with uncertainties.For instance, ORYZA2000 model considers the effect of CO2 fertil-ization by introducing a corrected coefficient to the initial light-use efficiency of single leaf. However, Bannayan et al. [28] pointedout that ORYZA2000 may greatly overestimate the increase in peakgreen leaf area index due to elevated CO2 concentration, and fail toreproduce the observed interaction with nitrogen in the rice yieldresponse to elevated CO2 concentration. Furthermore, representa-tive rice varieties show different reaction to temperature increaseand CO2 fertilization during the model simulation, indicating thatthe difference of yield variation between different provinces is af-fected not only by climate factors, but also by the genetic charac-teristics of different rice varieties.

The SWCT usually provides hydrological models with estimatedvalues of critical soil parameters [29], but its application in the

field of agricultural ecosystem is scarce, suggesting that it couldcontribute to more uncertainties in this study. In addition, themethod to select predominant soils for each grid may fail in themountainous area where the soils may unsuitable for agriculture.

In spite of many uncertainties in our schemes, the results ob-tained reasonably agree with other similar studies [14,18–20],demonstrating that future climate change would significantly af-fect the rice production, with shortened growth period and in-creased spatial variation of rice yield changes. However, theextent of climate change impact on rice yield simulated in this pa-per differs from that in other similar studies [14,19]. For example,the difference between reported yield variation and simulatedyield variation in this paper can be up to ±15%, and the spatial dis-tribution of rice yield changes can be different [14,19].

5. Conclusion

The impact of climate change on rice production in the middleand lower reaches of the Yangtze River has been simulated byupscaling ORYZA2000 to regional scale, with the input climate datafrom PRECIS for baseline (1961–1990) and future period (2021–2050) under IPCC SRES B2 scenario.

The results show that the climate warming would acceleratethe rice development and reduce the rice yield significantly. CO2

fertilization effect can promotes the overall level of rice yield inthe study area, but it still cannot compensate the negative effectof temperature increase. For rain-fed rice, precipitation is a criticalfactor limiting the rice yield, but it seems that its contribution isstill far less than the effect of climate warming and CO2 fertiliza-tion. For irrigated rice, the average rice yield is higher than thatof rain-fed rice. But in regions south of Yangtze River, irrigationhas relatively small contribution to rice yield, while in north re-gions, it can contribute more than 50% increase to the rice yield.

The above results indicate that future climate change can nota-bly affect the rice production in the middle and lower reaches ofYangtze River. Therefore, adaptive measures should be studiedand put forward to weaken the adverse effect of climate warming,but to take the advantages of CO2 fertilization effect and watermanagement. For example, it is feasible to cultivate varieties withresistance to high temperature or to postpone the seeding date soas to reduce the harm to middle and late rice caused by high tem-peratures. Enlarging the irrigation area in the north of Yangtze Riv-er can be an effective measure to promote the rice yield in thisregion. However, such future incidents as continued cloudy–rainyweather, flood, and extreme precipitation would further affectthe rice production in study area, especially in the south regionof Yangtze River.

Overall results simulated by ORYZA2000 agree reasonably wellwith other similar studies, especially that simulated by CERES-RICEmodel [18–20], indicating the ORYZA2000 is suitable for predictingthe change of rice production in response to future climate changein further studies. However, extreme weather events under futureclimate change have features of increasing frequency, duration andintensity, so further research and analysis are needed to under-stand how the meteorological disasters influence the rice yieldvia models.

Acknowledgements

This research was funded by the Special Program forScientific Research in Public Welfare Meteorological Services(GYHY200806008) and (GYHY200906022), and by Climate ChangeSpecial Program of China Meteorological Administration (CCSF-09-12), and by the National Science Foundation of China (40901238).

48 S.-H. Shen et al. / Acta Ecologica Sinica 31 (2011) 40–48

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