Agriculture, Ecosystems and Environment 149 (2012) 20–29
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Contrasting effects of warming and autonomous breeding on single-rice productivity in China Leilei Liu a,b , Enli Wang b,∗ , Yan Zhu a,∗∗ , Liang Tang a a National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu 210095, PR China b CSIRO Sustainable Agricultural Flagship, CSIRO Land and Water, GPO Box 1666, Black Mountain, Canberra, ACT 2601, Australia
a r t i c l e
i n f o
Article history: Received 22 September 2011 Received in revised form 13 December 2011 Accepted 14 December 2011 Available online 11 January 2012 Keywords: Climate change RiceGrow Model Potential yield Rainfed yield Rice variety change
a b s t r a c t China is one of the most important rice production countries in the world, and maintaining high rice productivity in China is very important for world food security. While previous studies showed that rice production in China has been and will be negatively impacted by global warming, the confounding effects of climatic change, variety improvement and agronomic managements have not been separately investigated. In this paper we combine an analysis of climate and rice growth data with crop modeling to investigate the impact of changes in climate, rice varieties, and agronomic management on rice productivity at four sites (Wuchang, Xinyang, Zhenjiang and Hanyuan) in China. The results showed a 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 led to a reduction in the length of rice growing period and a reduction in grain yield at all study sites, if no varietal 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 planned breeding effort may be needed to offset the negative impact of future climate change. © 2012 Elsevier B.V. All rights reserved.
1. Introduction The significant warming trend since the 1980s has been well documented at most locations around the world, and this trend is projected to accelerate in the future (Tao et al., 2006). During the last two decades, climate changes have been shown to impact on crop development and yields (Estrella et al., 2007, Liu et al., 2010; Lobell and Asner, 2003; Menzel et al., 2001). Lobell and Asner (2003) indicated a significant decline in maize and soybean yield in the United States as a result of increased temperature from 1982 to 1998. Peng et al. (2004) showed a 10% decline in rice yield with every 1 ◦ C increase in minimum temperature in the Philippines. In China, annual average air temperature has increased by 0.5–0.8 ◦ C during the past 100 years, which was slightly greater than the average global temperature rise (Ding et al., 2006). From 1955 to 2000, annual mean maximum and minimum temperatures increased by 0.13 ◦ C/decade and 0.32 ◦ C/decade for all of China (Liu et al., 2004). Rice is one of the main food crops in China, accounting for approximately 30% of the total planting acreage of food crops and half of the total grain production (Jing et al., 2007). Improved knowledge
∗ Corresponding author. Tel.: +61 2 62465964; fax: +61 2 62465965. ∗∗ Corresponding author. E-mail address:
[email protected] (E. Wang). 0167-8809/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.agee.2011.12.008
of how the past climate change has impacted rice production in China can provide insights into the likely impact of future climate change, and assist in the development of future climate adaptation strategies to ensure China’s food security. In China, a few studies have investigated the impact of past and possible future climate changes on rice production. Tao et al. (2006) studied the impact of climate changes on rice phenology and yield using correlation analysis with data collected at experimental stations from 1981 to 2000. They showed that changes in temperature over past decades had accelerated rice phenological development and decreased rice yields. Yao et al. (2007) assessed the impact of climate change on irrigated rice yield using the IPCC B2 climate change scenario (IPCC, 2007) and the CERES-Rice model for 2071–2090 in the main rice areas of China. Their results indicated that without the direct effect of CO2 on crop production, the B2 climate change scenario would have a negative impact on rice yield at most rice production regions. With the CO2 direct effect, however, rice yield was predicted to increase at all selected stations. Zhang et al. (2010) analyzed the recorded rice yields over the period of 1981–2005 in the main rice production areas of China, and reported that rice yields were positively correlated with solar radiation, which primarily drives yield variation. Similar results were also found by Sheehy et al. (2006), who reinvestigated the dataset of 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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in yield. Most of these previous studies either used rice yield data from many different cultivars together or used a simulation model with a single cultivar to analyze the impact of climate trends on rice growth and yield. The effects of changes in climate, variety and agronomic management (i.e. increased irrigation and fertilization, etc.) have not been separated. Recently, Liu et al. (2010) developed a method to combine long-term experimental data and crop modeling to investigate and separate the impact of changes in climate, crop varieties and agronomic management on wheat and maize production in China. In this study, we employ a method similar to that used by Liu et al. (2010) to investigate the impact of climate change, varietal change and agronomic management on rice production in China. We focused on the single rice production regions in China, combined with simulation modeling, to conduct a parallel analysis of climate and rice growth data collected from 1981 to 2009. Our objectives are to: (1) determine whether there were significant trends in climatic change during the main growth stages of rice for this period, (2) analyze the impact of climatic variables (temperature, precipitation, rainfall intensity and sunshine hours), variety change and agronomic management on rice growth and grain yield and (3) discuss the relative contribution of those factors to the changes in rice yield.
2. Materials and methods 2.1. Study sites Six rice cropping regions are classified across China, which cover single and double rice cropping areas (Fig. 1, from China National Rice Research Institute, 1989). Four sites were selected from the main single rice cropping system regions in this study (Fig. 1). They are Wuchang (44◦ 54 N, 127◦ 09 E, 194.6 m above sea level) in Heilongjiang province, Xinyang (32◦ 07 N, 114◦ 05 E, 114.5 m) in Henan province, Zhenjiang (32◦ 11 N, 119◦ 28 E, 27.3 m) in Jiangsu province and Hanyuan (29◦ 21 N, 102◦ 41 E, 795.9 m) in Sichuan province. These locations were chosen because they were typical single rice
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sites, representing three contrasting climatic regions: the northeast China cool temperate climate, the east China subtropical climate, and the southeast China mid-subtropical climate. Daily weather data and rice growth and yield data were recorded at each site. All of these data were collected by the National Meteorological Networks of 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 precipitation. They were recorded at each study site. Rice was planted from 1981 to 2009 at the Agrometeorological Experimental Station of each site, and was managed according to local practices for control of weeds and disease. Fertilizers and irrigation were applied according to local practices. Rice data include cultivars traits, sowing and harvesting dates, dates of growth stages, aboveground biomass, grain yield, and fertilizer and irrigation management. Crop harvest index (HI) was calculated as the ratio of grain yield to the total aboveground biomass. Three rice growth stages were used in this study, i.e., S1 – from sowing to jointing, S2 – from jointing to heading, and S3 – from heading to maturity. Rice cultivars that were grown frequently changed during the study period. At each site, the cultivars used in the experiments were representative of the most widely grown cultivars in each region. In total, eleven, twenty, thirteen and nine varieties were planted at Wuchang, Xinyang, Zhenjiang and Hanyuan, respectively, during the entire studied period. 2.3. Data analysis Trends over time for the mean of each climate variable during the 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 Student’s t test. The Stepwise Multi-Linear Regression (SMLR) was used to quantify the contributions of temperature and variety changes to variations in observed growth durations, and the effect of growth
Fig. 1. Rice cropping regions in China and the four study sites (Province names are shown in capital letters and in brackets).
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duration, harvest index, and agronomic factors on observed rice yields. In addition, we further analyzed the correlation between harvest index and the length of S3 or the ratio of S3 over the total growth duration. We also used Analysis of Variance (ANOVA) to analyze difference in harvest index among varieties at each site. The varieties that were planted for only one year were excluded. To quantify the varietal changes during the study period at the four sites, the cultivar parameters in the RiceGrow model (see description below) were derived based on the observed phenological stages at each site. This was achieved by adjusting parameter values to match the simulated dates of jointing, heading and maturity. 2.4. Crop modeling The RiceGrow model (Tang et al., 2009) was used to simulate rice phenological development, biomass growth and grain yield at each study site. The model uses the concept of physiological development time to simulate the phenology of rice (Cao and Moss, 1997; Wang and Engel, 1998). It uses a canopy photosynthesis model to predict rice biomass accumulation. The partitioning of biomass into different organs is controlled by developmental stage. The RiceGrow model has been calibrated and validated in China for simulating rice phasic development, biomass growth and partitioning (Meng et al., 2004, 2003), leaf age, and leaf area (Ye et al., 2008). In general, the model was able to reproduce the observed crop phenology, biomass and yield as affected by climatic and management factors (cultivar, fertilizer, irrigation) in China. The RiceGrow modeling serves two purposes in this study. Firstly, we used the model to derive the cultivar parameters to quantify the varietal changes in the study period at the four sites. RiceGrow model has four cultivar parameters that affect rice phenological development. They are intrinsic earliness (IE), photoperiod sensitivity (PS), thermal sensitivity (TS), and basic filling factor (BFF). IE affects the duration of the vegetative stage from emergence to the start of the photoperiod sensitive stage. It has a range of 0–1.0, with 1.0 representing the least number of days, i.e., shortest duration of that stage intrinsic of the cultivar. PS reflects the photoperiod sensitivity of the cultivars from the end of the basic vegetative phases to the jointing stage. It has the range of 0–0.2, with larger values for more photoperiod-sensitive cultivars. TS determines the temperature sensitivity for different rice cultivars and has a range of 1.0–6.0, with larger values for more temperature-sensitive cultivars. Temperature response changes little from cultivar to cultivar, and a TS value of 2.0 described the rice developmental response to temperature well. Although both IE and PS affect the length of S1 (from emergence to jointing), sensitivity analysis showed that the duration of S1 is most sensitive to IE. BFF determines the length of the grain filling period and has a range of 0–1.0, with greater values for cultivars having a shorter grain filling period. Secondly, the model was used to simulate phenological development, biomass growth and grain yield of single rice for a given cultivar under a defined management level. This allows varieties and management to be kept constant, so that climatic impact can be analyzed from the simulation results, without the confounding effects of changing cultivars and agronomic management practices. For each study site, a single cultivar from the early 80 s was used for the entire simulation period (1981–2009), i.e., ‘Xixuan 14’ for Wuchang, ‘Guizhao 2’ for Xinyang, ‘Zhuziqing’ for Zhenjiang and ‘Shanyou 2’ for Hanyuan, respectively. Simulations were conducted at two levels of water supply, i.e., full irrigation and rainfed conditions, both assuming ample nutrient supply, and no impact from disease, pests, or weeds. Sowing dates did not change much between different years and were fixed for each year, as per the experiments.
The simulated durations of each growth stage and grain yield were analyzed using the same methods as for the observed data, to determine their trend of change and the impact of climatic variability/change in the past decades on yield. 3. Results 3.1. Climatic trends during crop stages The mean maximum, average and minimum temperature during the rice growing period increased in all the study sites, except for minimum temperature at Hanyuan (Fig. 2a–d). The minimum temperature increased by 0.94 ◦ C/decade at Wuchang, 0.33 ◦ C/decade at Xinyang, and 0.69 ◦ C/decade at Zhenjiang. The average temperature increased by 0.72 ◦ C/decade at Wuchang, 0.34 ◦ C/decade at Xinyang and 0.60 ◦ C/decade at Zhenjiang, and maximum temperature increased by 0.43, 0.32 and 0.57 ◦ C/decade, respectively, for these sites. At Hanyuan, there was a trend of increase in maximum and average temperature and of decrease in minimum temperature, but the change was not significant at p < 0.05. Total sunshine hours exhibited a negative trend with time over the past 29 years at all sites (Fig. 2e–h), but decreased significantly only at Wuchang (p < 0.05) and Xinyang (p < 0.01). Precipitation and rainfall intensity (calculated as total rainfall divided by total rainy days during the rice growing period) had a decreasing trend except for Zhenjiang (Fig. 2i–l), but was only significant at Wuchang. From sowing to jointing (S1), there was a general increasing trend in maximum, average and minimum temperatures, except for the decreasing trend in minimum temperature at Hanyuan (Table 1). However, only the increase in minimum temperature at Wuchang and average temperature at Zhenjiang were significant. The other climatic variables during the S1 stage did not change significantly during the study period, except for the rainfall intensity at Zhenjiang. From jointing to heading (S2), both maximum and minimum temperatures showed an increasing trend, but only the increase in minimum temperature at Wuchang and Zhenjiang, and the increase in average temperature at Zhenjiang were significant. Sunshine hours generally decreased, but was only significantly at Hanyuan, where the precipitation also decreased significantly. From heading to maturity (S3), at Wuchang and Zhenjiang, maximum, average and minimum temperatures increased significantly. At Xinyang, only the minimum temperature increased significantly. There was little change at Hanyuan. Sunshine hours decreased significantly only at Xinyang, while precipitation decreased significantly only at Wuchang. 3.2. Simulated rice phenology and yield Fig. 3 showed the simulated lengths of the rice growing stages using one cultivar for each study site. The simulated total length of the growing period decreased significantly at Wuchang, Xinyang and Zhenjiang. At Hanyuan, while there was a decreasing trend in the total simulated growing period duration, the decrease did not reach the p < 0.05 significance level. The reduced length of simulated growing periods at Wuchang, Zhenjiang and Xinyang were a result of significant reduction in S1 and S3 together. At Hanyuan, the decrease in minimum and average temperature from heading to maturity led to a prolonged duration of S3. Fig. 4 shows the simulated rice yield under fully irrigated (potential yield) and rainfed conditions (rainfed yield) at the four study sites. At all sites, a significant decrease was simulated for both potential and rainfed production at Wuchang, Xinyang and Zhenjiang. This implies that the past climate change had a negative
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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) during the rice growing period at four study sites. Straight line is the linear regression line against years. ** Significant at p < 0.01; * Significant at p < 0.05. In a–d, indicates maximum temperature; ♦ indicates the minimum temperature and × indicates the average temperature. In i–l, indicates the precipitation and indicates the rainfall intensity.
impact on both potential and rainfed yield at the four sites. The decline in rainfed yield was a result of both reduced potential yield and reduced precipitation. The difference between potential and rainfed yield showed an increasing trend at all sites, indicating
increased water deficit and demand for irrigation at these study sites. During the past 29 years, the highest simulated rice yields occurred at Hanyuan in Sichuan province, with a potential and
Table 1 Trends in daily maximum, average and minimum temperature (T max, T avg and T min), sunshine hours (SH), precipitation (P) and rainfall intensity(RI) during the growth stages from sowing to jointing (S1), jointing to heading (S2) and heading to maturity (S3) of rice at the four study sites (1981–2009). Stage
Climatic variables
Wuchang Trend
Xinyang Trend
Zhenjiang Trend
Hanyuan Trend
S1
T max T min T avg SH P RI
0.04 0.53** 0.32 −0.36 −0.03 0.15
0.02 0.07 0.02 −0.16 0.09 0.11
0.35 0.36 0.37* −0.28 0.09 0.38*
0.36 −0.15 0.21 0.11 0.12 0.04
S2
T max T min T avg SH P RI
0.07 0.49** 0.31 −0.17 −0.13 −0.21
0.20 0.26 0.23 −0.24 0.01 −0.05
0.28 0.56** 0.42* −0.16 0.15 −0.06
0.22 0.06 0.18 −0.43* −0.49** −0.12
S3
T max T min T avg SH P RI
0.52** 0.54** 0.56** −0.02 −0.42* −0.25
0.02 0.50** 0.01 −0.45* −0.04 −0.21
0.39* 0.51** 0.47** −0.14 −0.13 −0.02
0.00 −0.06 −0.03 −0.01 0.04 −0.26
** *
Significant at p < 0.01. Significant at p < 0.05.
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Fig. 3. Simulated duration of rice stages from sowing to jointing (S1, ), from jointing to heading (S2, ♦), from heading to maturity (S3, ), and from sowing to maturity (S1 + S2 + S3, ) at the four study sites. Straight lines show the linear trends against year. ** Significant at p < 0.01; * Significant at p < 0.05.
rainfed yield of 13,744 and 11,872 kg ha−1 , respectively. The lowest rice yields occurred at Wuchang in Heilongjiang province, with a potential and rainfed yield of 5781 and 4335 kg ha−1 , respectively. The maximum decrease in simulated yield occurred at Xinyang. From 1981 to 2009, the potential and rainfed yield decreased by 358 and 417 kg ha−1 decade−1 at Wuchang, 630 and 698 at Xinyang, 326 and 349 at Zhenjiang and 33 and 290 at Hanyuan, respectively.
trend in biomass at Xinyang, Zhenjiang and Hanyuan. The changes in observed grain yield were also different from simulated results. There was a significant increasing trend at Wuchang, Xinyang and Zhenjiang, while a decreasing trend at Hanyuan (though not significant) in Sichuan province. The reasons for the contrasting change in grain yield at different sites are analyzed in the following sections. 3.5. Impact of rice varietal changes on phenology and grain yield
3.3. Observed rice phenology, 1981–2009 The changes in observed phenology of rice (Fig. 5) are different form that predicted by the simulation model (Fig. 3). The total growth duration of rice tended to increase significantly at Xinyang (p < 0.01) and Zhenjiang (p < 0.05) during the study period. At Wuchang, S1 increased significantly. At Xinyang, all the three stages (S1, S2 and S3) showed an increased trend, though not significant at p < 0.05, leading to a significant increase in the total growing period. At Zhenjiang, there was a significantly increasing trend from heading to maturity (S3), contributing to the significant increase in total growth duration. At Hanyan, S2 (jointing to heading) was shortened significantly (p < 0.01) while other stages increased slightly, leading to a nearly stable total length of growing period. The different trends between simulated and observed growth period are mainly due to the changes in cultivars from 1981 to 2009. 3.4. Observed grain yields, 1981–2009 A significant increase in recorded biomass was observed at Wuchang during the study period (Fig. 6). There was no significant
Table 2 shows the results from SMLR analysis on factors that contributed significantly to change or stabilize each of the growth stage duration. The results show that both temperature and cultivars affected the duration of developmental stages. The varietal changes are mainly due to changes in intrinsic earliness (IE) affecting S1 and basic filling factor (BFF) affecting S3. At Wuchang, increases in temperature during all three stages had significant impact to shorten the durations. However, the reduction in IE compensated the negative impact of warming, leading to an extended S1. At Xinyang, temperature change only impacted on S1, which was compensated by changes in IE, leading to a stabilized S1. Changes in BFF led to an extended S3 stage. At Zhenjiang, temperature change affected S1 and S3, leading to a shortened S1, while change in BFF significantly extended S3. At Hanyuan, both IE and BFF changes affected S1 and S3, while temperature increase shortened the duration of S2. These results are consistent with what is shown in Fig. 5. As a combined effect of the temperature and varietal changes, the total length of growth stage increased, except at Hanyuan (Fig. 5), the extension was significant at Xinyang (p < 0.01) and Zhenjiang (p < 0.05). Rice harvest index showed a significant increasing trend at Wuchang, Xinyang and Zhenjiang, but no significant trend at
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Fig. 4. Simulated rice yields under full irrigation and rainfed conditions at the four sites (1981–2009). Straight lines show the linear trends. ** Significant at p < 0.01; * Significant at p < 0.05. indicates potential yield, ♦ indicates rainfed yield, × indicates the difference between the two.
Hanyuan (Fig. 7). Results from ANOVA indicate that changes in harvest index between cultivars are significant, and there was a significant increasing trend in harvest index at Wuchang and Zhenjiang (Fig. 8). Further analysis on harvest index and the length of growth stages showed that HI changes were neither related to the changes in duration from heading to maturity, nor to the ratio of duration from heading to maturity (S3) over the total growth duration (S1 + S2 + S3). This implies that changes in harvest index are independent of changes of growth durations. Table 3 shows the impact of harvest index, total growth duration, nitrogen application and irrigation on rice grain yield from the SMLR analysis. Only at Hanyuan was the change in rice yield
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 of S3 duration on yield was found at all study sites. The lack of significant impact of either total or S3 duration on grain yield at Wuchang, Xinyang and Zhenjiang indicates that the stabilization of growth durations due to rice varietal changes may have prevented yield from declining, but did not lead to significant yield increase. At all sites, harvest index significantly impacted on grain yield. The increase in harvest index at Wuchang, Xinyang and Zhenjiang (Fig. 7) contributed significantly to the increase in rice grain yield (Table 3).
Table 2 Results of stepwise multi-linear regression (above 95% significant level) of climatic and non-climatic parameters vs. the lengths of three growth stages (S1, S2 and S3) of rice (1981–2009) (T avg is average temperature, IE is intrinsic earliness, BFF is basic filling factor). Sites
Duration
T avg
Wuchang
S1 S2 S3
−2.70 −1.60 −2.30
Xinyang
S1 S2 S3
−1.86
Zhenjiang
S1 S2 S3
−3.59
Hanyuan
S1 S2 S3
B0 , intercept of the linear regression equation.
B0
R2
−30.01
97.55 56.72 107.36
0.63 0.40 0.19
−59.53
82.51
0.34
IE
BFF
−30.71
−0.85
36.98
0.34
174.30
0.26
−139.65
117.74
0.68
−86.55
23.09 77.71 8.50
0.16 0.42 0.15
−68.45 −1.95
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Fig. 5. Observed duration of rice stages from sowing to jointing (S1, ), from jointing to heading (S2, ♦), from heading to maturity (S3, ), and from sowing to maturity (S1 + S2 + S3, ) at the four study sites. Straight lines show the linear trends against year. ** Significant at p < 0.01; * Significant at p < 0.05.
3.6. Impact of irrigation and fertilizer application on rice yield Changes in nitrogen application positively impacted the yield at Wuchang and Hanyuan, but the impact was much less compared with that of the HI (Table 3). Irrigation showed a negative trend against the yield at Zhenjiang and Hanyuan, and this was mainly caused by the declining trend in irrigation amount from 1981 to 2009 (a larger amount of irrigation water applied at the earlier stages of the study period), which indicates inefficient use of irrigation water at earlier stages and improved irrigation management with time. Lack of significant impact for irrigation and fertilization in this analysis indicated that from 1981 onwards, sufficient irrigation and fertilization had been applied in the rice field and the variations in crop grain yields were mainly impacted by climatic and varietal factors. 4. Discussion and conclusions This paper combines data analysis and crop modeling together to investigate the impact of past changes in climate, rice variety and agronomy on productivity of single rice in China. The method
used is an extension of that of Liu et al. (2010), applied to rice crops. Our methodology differs from those used in most previous climate change impact studies in that it enables quantification of individual contributions from climatic changes and changes in crop variety and agronomic management, to the observed changes in rice yield. While the results of climate change impact studies based on 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 ignore these factors. For future climate change impact on crop yield, a number of studies have adopted a modeling approach combined with future climate scenarios (developed using GCM predictions) to simulate possible yield change under future climates. Most of these studies use a fixed crop cultivar in the modeling (Matthews, 1995; Wang et al., 2011; Xiong et al., 2007), thus are not able to capture the potential mitigating impact of varietal changes. A few modeling studies have shown that the crop cultivar changes could have significant impact on simulated yield for a given climate change scenario (Wang and Wang, 2007; Wang et al., 2009). Our results suggest that while global warming can lead to reduction in growing period and decrease rice yield in China, improvement in
Table 3 Results of stepwise multi-linear regression (above 95% significant level) between rice yield and crop harvest index (HI), total growth duration, irrigation input and nitrogen fertilizer application at the four study sites, 1981–2009. Sites
HI
Wuchang Xinyang Zhenjiang Hanyuan
0.70 0.50 0.63 0.07
Total growth duration (days) – – – 1.36
B0 , intercept of the linear regression equation.
Nitrogen (kg ha−1 )
Irrigation (mm)
B0
R2
0.07 – – 0.02
– – −0.24 −0.17
0 0 0 0
0.75 0.52 0.61 0.44
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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 aboveground biomass, • 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 also shown. ** Significant at p < 0.01; * Significant at p < 0.05.
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Fig. 8. Harvest index of different rice cultivars at the four study sites. The cultivars are listed in a chronological order from left to right. The different letters above the bars indicate significant difference at p < 0.05.
rice varieties in the past 29 years were able to stabilize the growth duration and to increase rice grain yield. The results of this paper reveal that the changes in climatic variables were not uniform during the different rice stages across the main single-rice regions in China. For the past 29 years, the major significant change was the increase in minimum temperature in all rice growth stages at Wuchang and Zhenjiang, but only in stage from heading to maturity at Xinyang. At Hanyuan, there had been little change in temperature in the past 29 years. The sunshine hours showed a decreasing trend at all sites. Annual precipitation and rainfall intensity showed a slightly decreasing trend except for Zhenjiang, but only significant at the 95% level at Wuchang. Our approach of using crop modeling with a single cultivar for rice, without nutrient or water limitations, enables quantification of the impact of past climate change on the length of growth stages (S1, S2 and S3), and the potential and rainfed yield of single rice in the absence of varietal and other management changes. The results show that if there were no cultivar changes in the past, the changes in the climate would already have negatively impacted crop development and yield, leading to reduced rice yield at all sites. Except for Hanyuan, the observed trend in both phenology and rice yields were significantly different from the simulated ones, showing stabilized growth durations and increased yield. The reasons for these differences are mainly due to the cultivar changes (IE in S1, BFF in S3 and HI). Our analysis revealed that the major changes in rice variety were reduced IE (intrinsic earliness) at Wuchang, Xining and Hanyuan, reduced BFF (basic filling factors) at Xinyang, Zhenjiang and Hanyuan, and increased harvest index at Wuchang, Xinyang and Zhenjiang. As a result, the reduction of IE and BFF prolonged or stabilized the total length of the growth period, stabilized the total biomass production and grain yield. The increase in observed rice yields was mainly attributable to increased harvest index (HI) (Table 3). The difference in the trends of simulated and observed growth stages and grain yield of rice highlighted contrasting effects of climatic change, varieties and management options.
Changes in harvest index were not related to changes in the duration of growth stages, but appeared to be a separate varietal change of rice at the study sites. The highest harvest index reached 0.56–0.57 at the study sites. Other studies on wheat indicated that dry matter costs of reducing stem lodging in wheat limits HI for further increase to closer to 0.5 (Berry et al., 2007), which may imply limitations for increase in HI of rice as well. Further, Peng et al. (2000) indicated that in the Philippines increased HI was important from 1966 to 1980, but since 1980 total biomass and a slightly longer duration seem to be associated with higher yields. Recent evidence also shows that high yield of modern rice varieties was associated with higher biomass production around flowering time and higher non-structural carbohydrate content at heading (Takai et al., 2006). Therefore, further genetic progress will be possibly linked to increased biomass accumulation, given the limits to increased harvest index (Fischer and Edmeades, 2010). Adoption of new rice cultivars at the study sites was a type of autonomous adaption response to climatic trend (Smith and Lenhart, 1996), because no planned breeding efforts were developed to target impact of warming. There has been a general warming trend in China over the last 100 years and the warming trend is predicted to continue on the future (Tao et al., 2008). Such changes have been suggested to have a negative impact on agriculture. Planned breeding efforts to target the mitigation of the negative impact of future climate change are therefore a key element in the development of adaptation strategies to increase or maintain high rice productivity and to guarantee future food security. In conclusion, the adoption of new rice cultivars in many sites of China has been able to compensate the potential negative impact of global warming in the past 3 decades. The major cultivar changes involved reduced intrinsic earliness, extended grain filling period, and improved harvest index. Continuing global warming in the future will necessitate targeted breeding efforts. Combing
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