Response of rice yield and yield components to elevated [CO2]: A synthesis of updated data from FACE experiments

European Journal of Agronomy 112 (2020) 125961

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Response of rice yield and yield components to elevated [CO2]: A synthesis of updated data from FACE experiments

T

⁎

Chunhua Lva,b, Yao Huanga,b, , Wenjuan Suna, Lingfei Yua, Jianguo Zhuc a

State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, China University of Chinese Academy of Sciences, Beijing, China c State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Cultivar type Free-air CO2 enrichment Nitrogen management Pre- and post-heading Temperature Rice yield components

Rice is the most widely consumed staple food for more than half of the world’s population. Rising atmospheric carbon dioxide concentration [CO2] is expected to improve crop yields in the future. Rice responds to elevated [CO2] through photosynthesis improving yield components. This response depends on rice types, climate and fertilizers. However, the determinants of rice yield and the contribution of yield components at elevated [CO2] are far from certain. We extracted data from articles published before the end of 2018. These articles reported the responses of rice yield and yield components to elevated [CO2] at FACE conditions across four locations in China and Japan. Using CART (Classification and regression tree, a nonparametric modeling approach to recursively partition predictor variables) and regression models, we identified the principal determinants and the contribution of yield components to yield at elevated [CO2]. Elevated [CO2] (∼200 μmol mol−1 above ambient) increased rice yields by 13.5% (n = 93), 22.6% (n = 10) and 32.8% (n = 17) for japonica, indica and hybrid cultivars. The type of rice cultivars dominantly determined the response of spikelets per panicle, while temperature is of greatest importance in determining the response of filled spikelets percentage to elevated [CO2]. Optimal nitrogen rates at elevated [CO2] are site-specific, depending on local soil fertility and temperature. The contribution of post-heading elevated [CO2] to yield is higher for indica and hybrid (24%) than for japonica cultivars (13%). Lower benefit of japonica cultivars from post-heading elevated [CO2] is likely attributed to an intensive photosynthetic acclimation. Our findings highlight the importance of pre- and post-heading CO2 fertilization effect and nitrogen management in yield benefits from elevated [CO2], and the necessity for crop modelers to incorporate the knowledge from FACE studies into models so that models become more accurate, rigorous and robust.

1. Introduction Rice (Oryza sativa L.) is one of the world’s three major crops and is consumed by more than half of the world’s population. Global food demand in 2050 is projected to increase by at least 60 percent above 2006 levels to meet the world’s food needs for growing populations (FAO, 2016). Meeting this 60% increase in demand will require significant improvements in rice production. Atmospheric carbon dioxide concentration ([CO2]) increased to 403.3 ± 0.1 μmol mol−1 in 2016, approximately 145% of pre-industrial level (WMO, 2017). This increase will inevitably continue (IPCC, 2013). Crops respond directly to rising [CO2] through photosynthesis and stomatal conductance (Ainsworth and Rogers, 2007; Franks et al., 2013), which hence promotes crop yield (Amthor, 2001; Long et al., 2006; Ainsworth, 2008). The direct fertilization effect of ⁎

rising atmospheric [CO2] is expected to offset the reduction of crop yields induced by climate change (Fischer, 2009; Müller et al., 2010). Using the data from growth chambers, sunlit controlled-environment chambers, greenhouses, open-top chambers and FACE (free-air CO2 enrichment) facility, a meta-analysis conducted by Ainsworth (2008) suggested that elevated [CO2], on an average, increased rice yields by 23%, but FACE experiments showed only a 12% increase. In a similar manner, Wang et al. (2015) reported an average increase of 16% in rice yield under FACE, lower than those with greenhouses, growth chambers, and open-top chambers. Long et al. (2006) recommended that the FACE results were more reasonable because FACE conditions are more natural than those in chambers. The yield benefits observed under FACE are at least as large as one can expect in open fields under higher atmospheric [CO2] in the future (Kimball, 2016). Since the first rice FACE project was commenced in Japan in 1996

Corresponding author at: State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, China. E-mail addresses: [email protected], [email protected] (Y. Huang).

https://doi.org/10.1016/j.eja.2019.125961 Received 24 July 2019; Received in revised form 19 September 2019; Accepted 26 September 2019 1161-0301/ © 2019 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).

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2.2. Statistical analysis and regression models

(Kobayashi et al., 2006), pertinent papers have been published that focused on different rice cultivars (e.g. Hasegawa et al., 2013; Zhang et al., 2015), and various rates of nitrogen (N) application (e.g. Kim et al., 2003; Yang et al., 2006). In a FACE experiment with eight rice cultivars, Hasegawa et al. (2013) observed a range of yield enhancement from 3% to 36%. Hybrid rice cultivars generally showed a large yield response to FACE (∼30% increase), approximately twice as high as for japonica rice cultivars (Zhu et al., 2014). Rice grown under low N conditions weakened yield response to elevated [CO2]. Grain yield increased by a mean value of 15% when the application rates of N ranged from 80 to 150 kg ha−1, but increased only by 7% under 40 kg ha−1 N rate (Kim et al., 2003). A four-year FACE experiment (∼100 μmol mol-1 enrichment) indicated that CO2 enrichment significantly increased rice yield by a mean of 8%, but decreased by 4.7% under warming (+1.6–1.8 °C) (Wang et al., 2016). The components of harvestable yield in rice are the number of panicles on unit ground area, the number of filled spikelets per panicle, and single grain weight. A change in any of these parameters at elevated [CO2] will alter the final yields (Ainsworth, 2008). Prior metaanalysis studies encompassed various fumigation methods (Ainsworth, 2008; Wang et al., 2015) have provided an insight into the mechanisms in the response of crop production to elevated [CO2], while a relatively small portion of FACE experiments was included. Furthermore, the determinants of rice yield and the contribution of yield components at elevated [CO2] are far from certain. Since the previous meta-analysis was conducted by Wang et al. (2015) who extracted data from publications prior to July 2014, publications that focused on different cultivars and fertilizer rates from rice FACE have been increasing (e.g. Lai et al., 2014; Usui et al., 2014; Zhu et al., 2015; Chen et al., 2015; Li et al., 2017; Nakano et al., 2017; Wang et al., 2018). This offers a great opportunity to refresh our understanding of the determinants of rice yield at elevated [CO2], and thereby to improve modeling efforts in the future. The objectives of this study are to synthesize the response of rice yield and yield components to elevated [CO2] under different experimental conditions, and to quantify the contribution of yield components to harvested yield using updated data from FACE experiments.

A relative response ratio (RRRX) was defined to determine the effects of elevated [CO2] on rice yield and yield components as follows:

Xface − Xamb ⎞ RRRX = ⎛ × 100% Xamb ⎝ ⎠ ⎜

⎟

(1)

where Xface and Xamb are the values of rice yield or yield components at FACE and ambient conditions, respectively. Classification and regression tree (CART; Breiman et al., 1984), a popular data mining technique based on recursive binary partitioning, was used to recursively identify the importance of variables that may determine RRRx. The variables included the type of rice cultivars (i.e. japonica, indica and hybrid indica), the application rates of nitrogen, and mean temperature during growing season. One-way ANOVA was performed to test the difference in the mean values of RRRx among sites. Pearson correlation was applied to investigate the relationship between the values of RRRx. The majority of data come from japonica cultivars with multiyear and different N rates (Table S1). Backward stepwise regression (F-statistic to enter and remove in the model with a p-value = 0.1) was used for these data to quantify the dependence of RRRx on soil properties, temperature, nitrogen rate, and their interactions. Candidate predictors include fractions of soil sand and silt, soil total nitrogen (STN), soil C:N ratio, growing season temperature (T), N rate, and the interactions of T × STN and T × N. To eliminate varietal interference in the quantification, the RRRx from different cultivars in the same year with the same N rate were averaged at a given site before the performance of stepwise regression. Backward stepwise regression was also employed to determine the relationship between changes in grain yield and yield components in terms of RRRx as follows:

RRRGY = C1 × RRRPD + C2 × RRRSPP + C3 × RRRPFS + C4 × RRRSGW (2) where RRRGY, RRRPD, RRRSPP, RRRPFS and RRRSGW represent the relative response ratio in grain yield, panicle density, spikelets per panicle, percentage of filled spikelets and single grain weight, respectively. Ci (i = 1, 2, 3, 4) are corresponding regression coefficients. The contribution of changes in yield components to yield at elevated [CO2] was then calculated by Eq. (3):

2. Materials and methods 2.1. Data sources We searched peer-reviewed journal articles published prior to the end of 2018 in Web of Science, Google Scholar and China Knowledge Resource Integrated Database (CNKI) to obtain the data associated with rice FACE experiments. To be comparable, the FACE experiments with elevated ∼200 μmol mol1 [CO2] were selected. Twenty-three peer-reviewed papers were obtained with the following criteria: (i) geographical location of the experiments, climate and soil properties, rice variety, and fertilization were described; and (ii) measurements included at least one of the following parameters: grain yield, panicle density or panicles per unit area, spikelets per panicle, spikelet density or spikelets per unit area, percentage of filled spikelets, and single grain weight. The raw data were either obtained from tables or extracted from figures using the GetData Graph Digitizer v. 2.26 (free software downloaded from http://getdata-graph-digitizer.com/). The FACE experiments which met above two criteria were located at four sites, two in China and two in Japan (Supporting Information Table S1, Table 1). Mean annual temperature ranged from 9.4 to 16.0 °C, and mean annual precipitation ranged from 980 to 1545 mm. Mean air temperature during rice growing season ranged from 20.0 °C at Shizukuishi to 25.1 °C at Wuxi. Soil properties are quite different at the four sites, with the highest organic carbon (77.6 g kg−1) and high sand fraction (43%) at Shizukuishi and the lowest organic carbon (15.0 g kg−1) and sand fraction (9.2%) at Wuxi (Table 1).

FCi =

Ci × RRRi C1 × RRRPD + C2 × RRRSPP + C3 × RRRPFS + C4 × RRRSGW × 100%

(3)

where FCi is the contribution of the ith yield component change (RRRi) to grain yield change (%) at elevated [CO2]. Statistical analysis was performed using IBM SPSS 21.0 (IBM Crop., Armonk, NY, USA).

3. Results 3.1. Response of rice yield and yield components to elevated [CO2] Elevated [CO2] increased rice yield and yield components in general (Table 2). The [CO2] effect on grain yield was significantly higher at Yangzhou (24.8%) than other three sites (12.8–15.0%). As far as yield components were concerned, the effect of elevated [CO2] on panicle density and spikelet density was generally higher than those on spikelets per panicle, percentage of filled spikelets, and single grain weight. Moreover, elevated [CO2] did not significantly increase single grain weight (-0.1–1.9%). It is also noteworthy that elevated [CO2] reduced spikelets per panicle by 7.9% at Wuxi (Table 2). 2

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Table 1 Summary of geographical location, climate and soil properties at four FACE sites. Item

Wuxi, China

Yangzhou, China

Tsukuba, Japan

Shizukuish, Japan

Geographical location

31°37′ N 120°28′ E 5 2001–2003 16.0 25.1 1100–1200 Stagnic Anthrosols 25.1 65.7 9.2 6.8 15.0 1.59

32°35′ N 119°42′ E 5 2004–2016 14.9 25.0 980 Shajiang-Aquic Cambiosol 13.7 28.5 57.8 7.2 18.4 1.45

35°58′ N 139°60′ E 10 2010–2013 13.8 24.4 1280 Fluvisols 23.0 40.0 36.0 5.9 21.4 1.97

39°38′ N 140°57′ E 210 1998–2008 9.4 20.0 1545 Andosols 26.0 31.0 43.0 5.6 77.6 4.83

Latitude Longitude Altitude (m) Time span of the experiments Climate MAT (ºC)a MTGS (ºC)b MAP (mm)c Soil Classification Clay (%) Silt (%) Sand (%) pH Organic carbon (g kg−1) Total nitrogen (g kg−1) a b c

Mean annual temperature. Mean temperature during growing season. Mean annual precipitation.

The response of percentage of filled spikelets (RRRPFS) to elevated [CO2] was principally determined by temperature. The RRRPFS averaged 6.7% when the temperature ranged from 23.8 °C to 25.4 °C, but showed less significant (0.3–1.3%) when the temperature was out of this range (Fig. S5). Single grain weight showed a small response for hybrid cultivars (+3.1%), but nearly zero (+0.3%) for japonica and indica cultivars (Fig. S6). Importance of variables that determined RRRX showed different orders in grain yield and yield components (Table 3). Relative response ratios in grain yield (RRRGY), spikelets per panicle (RRRSPP) and single grain weight (RRRSGW) were dominantly determined by the type of rice cultivars, while temperature was of greatest importance in RRRPD and RRRPFS. Nitrogen input showed the most important in the determination of RRRSD. In addition to the type of rice cultivars, nitrogen input also determined RRRSPP greatly.

3.2. Factors determining the relative response ratios The type of rice cultivars determined the response of grain yield to elevated [CO2]. Hybrid and indica cultivars showed higher relative response ratios than japonica cultivars, with the mean RRRGY values of 29.0% and 13.5%, respectively (Supporting Information Fig. S1). Furthermore, the mean RRRGY value for hybrid cultivars was 32.8% (n = 17), higher than that for indica cultivars (22.6%, n = 10). The N rates higher than 90 kg ha−1 further promoted RRRGY for hybrid and indica cultivars (Fig. S1). The rates of nitrogen (N) application higher than 80 kg ha−1 improved the response of panicle density to elevated [CO2]. Panicle density increased by 12.6% at the N rates > 80 kg ha−1, approximately 1.4 times of that at the N rates < = 80 kg ha−1 (Fig. S2). The effect of temperature on RRRPD was inconsistent between two groups of N rates. Higher RRRPD occurred at temperature < = 24.6 °C when the N rates were < = 80 kg ha−1. In contrast, higher temperature improved the RRRPD at the N rates > 80 kg ha−1 (Fig. S2). On an average, elevated [CO2] did not promote the spikelets per panicle in japonica cultivars, but the RRRSPP showed a significant increase (+11.2%) for hybrid and indica cultivars (Fig. S3). Moreover, the RRRSPP in japonica cultivars was depressed (-5.3%) under FACE when the N rates were higher than 135 kg ha−1. Nevertheless, the RRRSPP in japonica cultivars benefited from the temperatures higher than 25.1 °C when the N rates were < = 135 kg ha−1. RRRSD showed a higher increase for hybrid (21.4%), nearly 1.9 times of that for japonica and indica cultivars (Fig. S4). N rates and temperature determined RRRSD for japonica and indica cultivars. The spikelet density at N rates ≤ 135 kg ha−1 showed high benefit from elevated [CO2], particularly at temperatures ≤ 20.7 °C (Fig. S4).

3.3. Dependence of RRRx on soil properties, temperature, nitrogen and their interactions for japonica cultivars The relative response ratios in yield components can be determined by a set of parameters of soil properties, growing season temperature (T), nitrogen input (N) and their interactions. The values of adjusted R2 suggest that 62% and 74% of the variability in RRRPD and RRRSPP, and approximately 48% of the variability in RRRSD and RRRPFS can be explained by a linear combination of these parameters (Table 4). However, the variability in RRRSGW and RRRGY could not be explained by these parameters (i.e., no variables entered in the stepwise regression).

Table 2 Relative response ratio (%) to elevated [CO2] at four sites. Item

RRRGY RRRPD RRRSPP RRRSD RRRPFS RRRSGW

Wuxi, China

Yangzhou, China

Tsukuba, Japan

Shizukuishi, Japan

Mean

SE

n

Mean

SE

n

Mean

SE

n

Mean

SE

n

12.8A 18.3A −7.9A 9.0A 6.1A 1.0AB

1.0 1.2 0.8 1.1 2.0 0.6

12 12 12 12 12 14

24.8B 10.6BC 7.0B 14.2AB 4.0A 1.9A

2.4 1.2 2.5 3.1 1.1 0.5

33 18 18 12 15 27

15.0A 7.3B 4.7BC 11.6AB 5.2A 0.0B

1.4 1.1 1.2 0.8 1.7 0.4

39 27 19 25 23 35

13.3A 12.1C 1.5C 13.8B 0.2B −0.1B

1.1 1.2 0.8 1.4 0.7 0.4

36 28 27 27 27 27

RRR: relative response ratio; GY: grain yield; PD: panicle density; SPP: spikelets per panicle; SD: spikelet density; PFS: percentage of filled spikelets; SGW: single grain weight. The same as below. Different capital letters in the same row indicate significant difference (p < 0.05) among sites. 3

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Table 3 Importance of variables determining RRRX. The dimensionless values were determined by CART. Independent variable

Type of rice cultivars N-input Temperature

Table 6 Regression models of grain yield in terms of relative response ratio. Effect

RRRGY

RRRPD

RRRSPP

RRRSD

RRRPFS

RRRSGW

42.58 16.08 11.66

4.46 8.05 10.83

21.88 11.37 10.52

8.88 9.96 4.07

1.41 2.80 10.18

0.91 0.04 0.36

RRRPD RRRSPP RRRPFS RRRSGW Adjusted R2 p-value

Table 4 Regression coefficients of RRRx against soil parameter, growing season temperature (T), nitrogen input (N), and their interactions (n = 31). Effect

RRRPD

RRRSPP

RRRSD

RRRPFS

Constant Soil sand (%) Soil silt (%) STN (g kg−1)§ Soil C:N ratio T (oC) N (kg ha−1) T×N T × STN Adjusted R2 p-value

97.06 −4.47 −4.03 n.e. ¶ 19.60 4.58 0.014 n.e. −1.852 0.623 0.0000

203.88 −2.73 −3.57 n.e. n.e. 2.29 0.247 −0.010 −0.262 0.741 0.0000

−185.85 n.e. n.e. 55.93 −1.96 8.52 0.482 −0.019 −2.265 0.473 0.0011

98.88 n.e. n.e. −24.17 −1.35 −2.91 0.220 −0.010 1.006 0.489 0.0008

§ ¶

¶

Indica and Hybrid (n = 12)

Coefficient

Standardized Coefficient

Coefficient

Standardized Coefficient

0.969 0.906 0.708 n.e. ¶ 0.661 < 0.0001

0.86 0.37 0.33

1.142 1.278 1.081 0.546 0.992 < 0.0001

0.43 0.41 0.21 0.07

did not enter in stepwise regression.

Table 7 Contribution of yield components to grain yield (%) in terms of RRRX using Eq. (3). Type of cultivars

Statistics

RRRPD

RRRSPP

RRRPFS

RRRSGW

Japonica

Arithmetic Mean 95% LCL of AM ¶ 95% UCL of AM § Arithmetic Mean 95% LCL of AM 95% UCL of AM

85.6 32.8 138.4 40.5 22.5 58.5

1.9 −40.4 44.1 35.7 22.5 48.8

12.5 −3.8 28.8 16.8 7.2 26.3

7.1 5.1 9.0

Indica and Hybrid

¶ 95% lower confidence limits of arithmetic mean; limits of arithmetic mean.

soil total nitrogen. did not enter in stepwise regression.

Relative response ratio in grain yield (RRRGY) was positively correlated with RRRSPP, RRRSD, RRRPFS and RRRSGW, while did not significantly correlate with RRRPD (Table 5). Moreover, the RRRPD was found to negatively correlate with RRRSPP, and the RRRSD was positively correlated with RRRPD and RRRSPP. No significant correlations were tested between RRRSGW and RRRPD, RRRSPP, RRRSD, or RRRPFS. The RRRGY for japonica cultivars was well quantified by a linear combination of RRRPD, RRRSPP and RRRPFS (R2 = 0.661, n = 58, p < 0.0001). For indica and hybrid cultivars, the adjusted R2 of RRRGY against RRRX in yield components is 0.992 (n = 12, p < 0.0001) (Table 6). The values of standardized regression coefficients suggested a relative high importance of RRRPD and RRRSPP in the determination of RRRGY at elevated [CO2]. Increases in panicles at elevated [CO2] contributed about 86% to the yield change for japonica cultivars, while only 41% for hybrid and indica cultivars (Table 7). In contrast, changes in spikelets per panicle contributed more than one third to the yield change for hybrid and indica but less than 2% for japonica cultivars. The contribution of panicle density (RRRPD) together with spikelets per panicle (RRRSPP), determined pre-heading, to the yield change (RRRGY) was estimated to be 87.5% for japonica and 76.2% for hybrid and indica cultivars. It is also noteworthy that changes in single grain weight at elevated [CO2]

RRRPD

RRRSPP

RRRSD

RRRPFS

RRRSGW

1 0.177 0.489*** 0.710*** 0.443*** 0.243*

1 −0.535*** 0.418*** −0.109 −0.057

1 0.540*** 0.022 0.105

1 −0.084 0.059

1 −0.057

1

95% upper confidence

4. Discussion The majority of cultivars planted at Yangzhou FACE were hybrid and indica rice, accounting for two-thirds (22/33) of the total cases of recorded grain yield, which led to a large yield response to elevated [CO2] (Table 2). This agrees with the recursive binary partitioning by CART (Fig. S1) and the meta-analyses conducted by Ainsworth (2008) and Wang et al. (2015). The net photosynthetic rates (Pn) of japonica rice leaves at FACE were significantly lower than those at ambient CO2 concentration when measured at the same CO2 concentration, particularly after heading (Chen et al., 2005; Yong et al., 2007; Zhu et al., 2012; Cai et al., 2018). Data from a 2-year FACE experiment conducted by Chen et al. (2014) indicated that the response of Pn to elevated [CO2] at mid-grain filling stage was significantly lower than at booting and heading stages, though an indica variety showed consistently higher photosynthesis than a japonica variety under both ambient and FACE growth conditions. The post-heading photosynthetic acclimation was also found for a hybrid indica variety (Zhu et al., 2014). Moreover, the response of Pn to post-heading elevated [CO2] for a hybrid indica variety was significantly higher than a japonica variety, and the increases in filled spikelet percentage and single grain weight at elevated [CO2] were higher for hybrid cultivar than for japonica cultivar (Zhu et al., 2014). These findings suggest that the lower yield benefit from elevated [CO2] for japonica cultivars (Fig. S1) is likely attributed to an intensive postheading photosynthetic acclimation, and that the CO2 fertilization effect not only in pre-heading but in post-heading contributed to the higher yield for indica and hybrid cultivars (Table 7). Higher response of rice yield to elevated [CO2] was observed at the N rates of 90 kg ha−1 and 120 kg ha−1 at Shizukuishi site. In contrast, the RRRGY did not show significant difference (p = 0.48) when the N rates increased from 150 kg ha−1 to 250 kg ha−1 at Wuxi and Yangzhou sites, although a higher RRRGY was observed at a N rate of 350 kg ha−1 (Fig. 1). Note that the soil total nitrogen, representing an indigenous supply of N (Dobermann et al., 2003), is much higher but the growing

Table 5 Pearson correlation matrix between relative response ratios in yield and yield components (n = 70). RRRGY

§

contributed 7.1% to yield change for indica and hybrid cultivars, but not for japonica cultivars (Table 7).

3.4. Contribution of yield components to grain yield in terms of RRRX

RRRGY RRRPD RRRSPP RRRSD RRRPFS RRRSGW

Japonica (n = 58)

Importance

* and *** represents significant level of p < 0.05 and p < 0.001, respectively. 4

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Fig. 1. Relative response ratio in japonica grain yield under various rates of nitrogen application at Shizukuishi, Japan, and Wuxi and Yangzhou, China. Values in parentheses are the number of cases. Vertical bars show standard error of the mean.

et al., 2006), and dry matter allocation and translocation (Cheng et al., 2010; Roy et al., 2015). These would lead to changes in yield and yield components. Simulation models are widely used to examine the climate change impact on crop production. The main effect of elevated [CO2] was simulated within the processes of leaf-scale photosynthesis and water use acted through effects on stomatal conductance as well as tissue growth over a growing season (White et al., 2011), though modeling approaches differ among models (Tubiello and Ewert, 2002; Boote et al., 2013; Vanuytrecht and Thorburn, 2017). The effect of elevated [CO2] on crop yields is one of the most uncertain and influential parameters in models to assess climate change impacts and adaptations (Lobell and Field, 2008). This uncertainty, being attributed to the limited availability of experimental data on CO2 responses for crops grown under typical field conditions (Lobell and Field, 2008), can promote mistrust in model results and make it difficult for policymakers to act on the information (Rötter et al., 2011). Our synthesis showed that the yield increases at elevated [CO2] were principally determined by pre-heading conditions and the photosynthetic acclimation during post-heading may depress the CO2 fertilization effect for japonica cultivars. Moreover, the responses of yield components to elevated [CO2] are dependent on SOC, temperature, nitrogen input and their interactions (Table 4). The contribution of yield components to grain yield also varied greatly among the types of cultivar (Table 7). Moreover, a two-year T-FACE (temperature and freeair controlled CO2 enhancement) experiments showed that rice yield reduced by 12.3% and 6.6% under the combination of elevated CO2 (200 μmol mol−1 higher than ambient CO2) and temperature (1 °C above the ambient temperature) in comparison with ambient conditions (Li et al., 2017). Another 2-year T-FACE experiment indicated that

season temperature is lower at Shizukuishi than at Wuxi and Yangzhou (Table 1), suggesting that the N rates to maximize rice yield at elevated [CO2] are site-specific. The optimal N rates should be lower at the sites with higher STN than at the sites with lower STN as far as yield components are concerned. Taking RRRSPP which shows a fairly good fitness with soil properties, temperature, and nitrogen input (Table 4) as an example, we estimated RRRSPP at Shizukuishi and Yangzhou. The RRRSPP at Shizukuishi was estimated to increase with the N rates at temperatures between 18 °C and 24 °C, but trends to decline at T = 26 °C (Fig. 2a). Similar to the trend at Shizukuishi, the RRRSPP at Yangzhou site declined with the N rates at T = 26 °C and 27 °C (Fig. 2b). The cross point of N rate is 110 kg ha−1 at Shizukuishi and 200 kg ha−1 at Yangzhou, respectively. Interestingly, the cross points of N rate matched the RRRGY under various rates of nitrogen application (Fig. 1), which may suggest that the optimal N rate would be 110 kg ha−1 at Shizukuishi and 200 kg ha−1 at Yangzhou, respectively, as far as RRRSPP is concerned. The N rates higher than 200 kg ha−1 may not be optimal at Yangzhou, though two cases showed a higher RRRGY at a nitrogen rate of 350 kg ha−1 (Fig. 1). Nitrogen rates higher than the optimum may also depress the response of filled spikelet percentage and single grain weight to elevated [CO2] (Yang et al., 2006), and increase risk of lodging as well as damage from pests and diseases (Yoshida, 1981; Nie and Peng, 2017). The N rates should thus take local conditions into consideration (Table 4; Figs. 1 & 2) to get large benefit of yield components and consequent yield from elevated [CO2]. Elevated [CO2] not only affects leaf photosynthesis through changing stomatal conductance (Ainsworth and Rogers, 2007), but also alters leaf area (Kim et al., 2003; Roy et al., 2012), leaf and plant nitrogen (Sakai et al., 2006; Roy et al., 2012), tiller numbers per unit area (Yang

Fig. 2. Estimated RRRSPP with various N rates at (a) Shizukuishi, Japan, and (b) Yangzhou, China. Soil total nitrogen was assigned as 4.8 g kg−1 at Shizukuishi and 1.5 g kg−1 at Yangzhou, respectively. 5

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the yield reduction under the combination of elevated CO2 and temperature was mainly attributed to a decrease in the number of filled grains per m2 (Cai et al., 2016). This is in agreement with our study that increase in temperature could reduce RRRPFS (Table 4). However, the simulated CO2 responses in current models neither take the responses of yield components to elevated [CO2] into consideration nor validate against experimental data on CO2 responses for crops, though the data from crop FACE studies have been increased since the late 1980s. White et al. (2011) denunciated insufficient detail in the description of modelled responses in simulation studies. Rötter et al. (2011) urged for more quality (through improved validation), transparency and consistency in modelling approaches. Boote et al. (2013) made a call for intensive testing of simulated CO2 responses. Recently, Vanuytrecht and Thorburn (2017) also pointed the lack of centralized documentation on implemented CO2 responses in crop models and on the extent of validation against experimental data. We urge crop modelers to incorporate the knowledge from FACE studies into their models so as to make the models more accurate, rigorous and robust.

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Declaration of Competing Interest None. Acknowledgements This work was supported by the National Natural Science Foundation of China (Grant No. 41530533). We thank the authors whose data are included in this synthesis. Thanks are also dedicated to Prof. Kazuhiko Kobayashi at Ibaraki University, Japan for his valuable comments on the draft of this manuscript, and to Dr. Toshihiro Hasegawa at the National Institute for Agro-Environmental Sciences, Japan for his help to confirm the type of rice cultivars planted at the Tsukuba FACE. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.eja.2019.125961. References Ainsworth, E.A., 2008. Rice production in a changing climate: a meta-analysis of responses to elevated carbon dioxide and elevated ozone concentration. Glob. Change Biol. 14, 1642–1650. https://doi.org/10.1111/j.1365-2486.2008.01594.x. Ainsworth, E.A., Rogers, A., 2007. The response of photosynthesis and stomatal conductance to rising [CO2]: mechanisms and environmental interactions. Plant Cell Environ. 30, 258–270. https://doi.org/10.1111/j.1365-3040.2007.01641.x. Amthor, J.S., 2001. Effects of atmospheric CO2 concentration on wheat yield: review of results from experiments using various approaches to control CO2 concentration. Field Crops Res. 73, 1–34. https://doi.org/10.1016/S0378-4290(01)00179-4. Boote, K.J., Jones, J.W., White, J.W., Asseng, S., Lizaso, J.I., 2013. Putting mechanisms into crop production models. Plant Cell Environ. 36, 1658–1672. https://doi.org/10. 1111/pce.12119. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J., 1984. Classification and Regression Trees. Chapman and Hall (Wadsworth, Inc.), New York, USA, pp. 254. Cai, C., Li, G., Yang, H., Yang, J., Liu, H., Struik, P.C., Luo, W., Yin, X., Di, L., Guo, X., Jiang, W., Si, C., Pan, G., Zhu, J., 2018. Do all leaf photosynthesis parameters of rice acclimate to elevated CO2, elevated temperature, and their combination, in FACE environments? Glob. Change Biol. 24, 1685–1707. https://doi.org/10.1111/gcb. 13961. Cai, C., Yin, X., He, S., Jiang, W., Si, C., Struik, P.C., Luo, W., Li, G., Xie, Y., Xiong, Y., Pan, G., 2016. Responses of wheat and rice to factorial combinations of ambient and elevated CO2 and temperature in FACE experiments. Glob. Change Biol. 22, 856–874. https://doi.org/10.1111/gcb.13065. Chen, C., Jiang, Q., Ziska, L.H., Zhu, J.G., Liu, G., Zhang, J.S., Ni, K., Seneweera, S., Zhu, C.W., 2015. Seed vigor of contrasting rice cultivars in response to elevated carbon dioxide. Field Crops Res. 178, 63–68. https://doi.org/10.1016/j.fcr.2015.03.023. Chen, C.P., Sakai, H., Tokida, T., Usui, Y., Nakamura, H., Hasegawa, T., 2014. Do the rich always become richer? Characterizing the leaf physiological response of the highyielding rice cultivar Takanari to free-air CO2 enrichment. Plant Cell Physiol. 55, 381–391. https://doi.org/10.1093/pcp/pcu009. Chen, G.Y., Yong, Z.H., Liao, Y., Zhang, D.Y., Chen, Y., Zhang, H.B., Chen, J., Zhu, J.G., Xu, D.Q., 2005. Photosynthetic acclimation in rice leaves to free-air CO2 enrichment

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