Conditional variations in temperature response of photosynthesis, mesophyll and stomatal control of water use in rice and winter wheat

Field Crops Research 199 (2016) 77–88

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Conditional variations in temperature response of photosynthesis, mesophyll and stomatal control of water use in rice and winter wheat Wei Xue a,b,∗ , Dennis Otieno b , Jonghan Ko a,∗ , Christiane Werner c , John Tenhunen b a b c

Department of Applied Plant Science, Chonnam National University, 500757 Gwangju, South Korea Department of Plant Ecology, BayCEER, University of Bayreuth, 95440 Bayreuth, Germany Department of Ecosystem Physiology, University of Freiburg, 79085 Freiburg, Germany

a r t i c l e

i n f o

Article history: Received 1 May 2016 Received in revised form 16 September 2016 Accepted 16 September 2016 Keywords: Wheat Rice Temperature response Photosynthesis Climate change

a b s t r a c t Environmental responses of photosynthesis and CO2 diffusive conductance as fundamental information for photosynthesis-transpiration coupled model have been increasingly concerned while are still research areas with unanswered questions in cereal crops. Photosynthesis (A), light utilization efficiency (␣), mesophyll conductance (gm ) and stomatal coefficient (gfac ) in temperate rice and winter wheat were investigated. There were no seasonal trend, no inter- and intra-species differences in relative temperature responses (activation energy Ha ) of Vcmax or Jmax . A phenomenon that grain-filling plants generally decreased gm and had a lower Q10 as compared to that at early growth stage existed, particularly in rice. Analyses of environmental influences indicated that optimal temperatures (Topt ) in Vcmax /Jmax depended on the prevailing temperature environment. Although prevailing temperature dependence in Topt of gm was not as profound as that of Vcmax or Jmax , Topt of gm in winter wheat was significantly lower than rice. Temperature response of Rdark in all sampled leaves shared a common trajectory. ␣ were almost invariant, while, high sensitivity to soil desiccation was observed. gfac in sunlit leaves was conservative during wet soil conditions. Shaded leaves with lower Na had higher gfac , resulting in a negative correlation between Na and gfac in canopy profiles. gfac was susceptible to fluctuations in soil water potential (␺s ), rapidly declined at a threshold of top-layer ␺s approx. −0.1 MPa. Numerical analyses regarding gm /Vcmax /Jmax effects on photosynthetic performance in rice and between rice and winter wheat documented that in context of climate change, consider growth environment-induced differences in temperature responses of photosynthetic parameters among cereal crops is indispensable to better predict interactions among soil-plant-atmosphere consortium. © 2016 Elsevier B.V. All rights reserved.

1. Introduction Understanding of leaf assimilatory processes and the mechanisms involved in the phenotypic plasticity of plants in response to their environment is an essential part for the application of photosynthesis-transpiration coupled model (PTC) to estimate the photosynthetic productivity (Lee et al., 2008; Bernacchi et al., 2013; Lombardozzi et al., 2015). Photosynthetic rates of individual leaves under given leaf nitrogen content (Na ) are determined by the light energy conversion efficiency (␣), the maximum electron transport rate (Jmax ), the maximum carboxylation rate (Vcmax ), CO2 diffusive conductance across the stomata and mesophyll tissues,

∗ Corresponding authors at: Department of Applied Plant Science, Chonnam National University, South Korea. E-mail addresses: [email protected] (W. Xue), [email protected] (J. Ko). http://dx.doi.org/10.1016/j.fcr.2016.09.016 0378-4290/© 2016 Elsevier B.V. All rights reserved.

dark respiration (Rdark ), light respiration and the characteristics of their environmental responses (Farquhar et al., 1980; Harley et al., 1986; Harley and Tenhunen, 1991). Of these key parameters, Vcmax and Jmax and their linkages to Na , leaf morphology, meteorological factors, and/or spectrum index have been widely investigated in C3 species, rice included (Wullschleger, 1993; Niinemets and Tenhunen, 1997; Wohlfahrt et al., 1998; Xu and Baldocchi, 2003; Wang et al., 2008; Jin et al., 2012; Noda et al., 2015; Xue et al., 2016a,b). Previous studies have shown the dependence of Vcmax and Jmax on leaf temperature (Farquhar et al., 1980; Harley and Tenhunen, 1991), increasing to maximum rates at the optimal leaf temperature and then declining again. Review by Medlyn et al. (2002a) reported a relatively consensus in shape of temperature response among tree species. Similar findings were reported by Lin et al. (2012, 2013), suggesting that shifts in the relative temperature response of photosynthesis among species are driven by

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prevailing climate environment and water vapor pressure deficit (VPD) and/or plant type. The shapes of temperature response of Vcmax and Jmax are commonly treated to be less variant as compared to absolute values during life cycles, and semi-empirically adjust the relative temperature curves based on absolute values at given leaf temperature was usually seen (Wang et al., 2003; Tenhunen et al., 2009; Ruidisch et al., 2014). Nevertheless, comparisons of the seasonal temperature responses amongst grass species showed significant differences (Wohlfahrt et al., 1998). Seasonal fluctuations in Populus trees were evident as well (Zhu et al., 2011). Temporal uncertainties in temperature response properties to be involved in PTC model may lead to an over- or under-estimation of photosynthesis at given temperature therefore biased understanding in satisfaction of PTC model application in cereal crop species under varying climate conditions. Potential risks imposed by the speculation of parameter estimation on evaluation of daily gross primary production in trees species and across different climate zones have been reported (Owen et al., 2007; Lombardozzi et al., 2015). Whereas, field reports to evaluate temperature response of photosynthesis and assess the extent to which the uncertainties influence temporal courses of photosynthetic capacity in response to varying temperatures in cereal crops especially rice and winter wheat are scarce. Stomatal coefficient (gfac ) links stomatal conductance to water vapor (gsw ), leaf transpiration and carbon dioxide assimilation in PTC model (Ball et al., 1987; Harley and Tenhunen, 1991). Relatively constant gfac over growing season were reported for populous (Zhu et al., 2011), mature maritime pine (Medlyn et al., 2002b), blue oak (Xu and Baldocchi, 2003), and alpine grasses (Wohlfahrt et al., 1998). Sala and Tenhunen (1994) suggested that large seasonal fluctuations in gfac may occur in leaves of plants that are exposed to drying soils. gfac as a measure of balance CO2 uptake and water use reflects long-term stomatal adaptation and its short-term variation in leaves grown under varying ecological conditions (Leuning, 1995). Variations of stomatal conductance to biotic and abiotic environment in rice and winter wheat have been widely reported, however, field evaluations of their gas-exchange performance and their comparisons remain sparse (Xu et al., 2014). Large shifts in gfac between paddy rice and winter wheat, among rice genotypes under different ecological conditions, and in canopy profiles may occur. The other factor that directly influences CO2 concentration inside chloroplasts is mesophyll conductance (gm ) which represents overall CO2 diffusion from intercellular air spaces to CO2 fixation sites. Ethier and Livingston (2004) and Niinemets et al. (2009a) reported that the shape of traditional A/Ci curves is largely influenced by variations of gm . Those studies have added to our understanding of gm and its possible determinants, as well as in shifts in gm that occur in response to the prevailing environment. Leaf anatomy and growth environment such as light intensity inside canopies are identified as important determinants of maximum gm across broad functional groups (Niinemets et al., 2009b; Flexas et al., 2012; Cano et al., 2013). Momentary gm may change largely within minutes that depends on fluctuations of temperature and CO2 concentration (Bernacchi and Long, 2002; Flexas et al., 2008, 2012; Xiong et al., 2015). Environmental response of gm and the extent to which temporal temperature responses of gm influence performance of PTC model and qualitative predictability in fluctuations of photosynthesis under varying climate among cereal crops are still insufficient but controversial (Bernacchi and Long, 2002; Niinemets et al., 2009a), but come into spotlight due to its important ecological implications such as water use strategies (Flexas et al., 2013; Cano et al., 2014). On the other hand, Werner and Beyschlag (2002) suggested to consider site/species specific light conversion efficiency, which is meaningful in evalua-

tion of extent of interaction and adaption of plant photosynthetic physiology to varying environments. Lowland rice grown under rainfed conditions has been proposed to be a promising way to significantly improve agronomic water use efficiency while maintaining relatively high yield in the East Asia monsoon regions (Katsura et al., 2010; Nie et al., 2011; Okami et al., 2013; Xue et al., 2016a). Variations in plant photosynthesis productivity in response to growth environment may relate to ecological conditions where rice are planted such as field management practice, nutrient application rates (Martindale and Leegood, 1997), and endogenous differences among species/genotypes (Berry and Bjorkman, 2003). Intensive researches in trees (Medlyn et al., 2002a; Kattge and Knorr, 2007; Zhu et al., 2011; Lin et al., 2013; Jensen et al., 2015; Slot et al., 2016) but only few in crops excluding wheat and rice (Harley et al., 1985, 1992a,b) regards temperature response of photosynthesis have been reported. Therefore, intensive measurements in temperature responses of photosynthesis, ␣, and mesophyll and stomatal control for water use in tropical rice (Oryza sativa L. cv. IR-2793) and temperate rice (Oryza sativa L. cv. Unkwang), and in winter wheat at grain-filling stage were conducted, to test the following hypotheses: 1. As commonly suggested before, temperature response characteristics of photosynthesis and dark respiration vary depending on surrounding climate environment. 2. Variations in light energy conversion efficiency and gfac in rice and winter wheat canopies or over growing season are minimal. 3. Uniform temperature response function of photosynthesis, similar light energy conversion efficiency and gfac can be applied in cereal crops based on classification of ecological conditions, simplifying parameter selection of PTC model. 2. Materials and methods 2.1. Study site Field experiments were applied at the agricultural fields of Chonnam National University, Gwangju, South Korea (126◦ 53 E, 35◦ 10 N, altitude 33 m). More than 60% of annual precipitation here falls between June and September, during the East Asia monsoon season. Mean annual precipitation and mean annual air temperature during the past two decades are ca. 1400 mm and 13.8 ◦ C, respectively. The top soil layer (0–30 cm) is categorized as loam with sand of 388 g kg−1 , silt of 378 g kg−1 , clay of 234 g kg−1 , pH 5.5, organic C 12.3 g C kg−1 , available P 13.1 mg P2 O5 kg−1 , and total N before fertilization 1.0 g N kg−1 (Table 1). 30-day-old seedlings of Unkwang (Oryza sativa L. cv. Unkwang, Kim et al. (2006)) in 10 cm height were transplanted into flooded fields using an automatic rice planting machine on May 20, 2013 (DOY 140), with a row-line spacing of 12 × 30 cm. In average, 5 seedlings were planted in each hill. Fertilizer with a mass ratio of N–P–K of 11:5:6 was used to achieve fertilizer additions of 0 kg N ha−1 (no supplementary fertilization named as low nutrition group), 115 kg N ha−1 (normal nutrition group) and 180 kg N ha−1 (high nutrition group). 80% of N fertilizer was applied two days before transplanting (May 18, DOY 138), and the rest at the tillering stage 19 days after the transplanting (DOY 159). P fertilizer was applied as a 100% basal dosage, and 65% of K fertilizer as basal dosage and the rest during tillering were scheduled. Nutrient exchange between adjacent fields was minimized by establishment of cement walls 35 cm wide, inserted into a depth of one meter in the soil. Seeds of the same rice genotype were directly sown into the soil on April 22 (112 DOY) in a neighboring upland field, receiving fertilizer at the same level of 115 kg N ha−1 two times: 80% of total N before seeding and the rest on 160 DOY (tillering stage). No artificial water supply throughout growing seasons

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Table 1 Descriptions of soil and climate properties, and field management practices for IR-2793 and Unkwang rice and winter wheat planted in a controlled growth chamber in Bayreuth and in the field in Gwangju. Dates were expressed as dd/mm/yy, and as day of year (DOY). Crop species

Transplanting/Seeding date

Harvest

Growth conditions

Winter wheat (Triticum asetivum L. cv. Suan) Paddy rice (Oryza sativa L. cv. Unkwang) Rainfed rice (Oryza sativa L. cv. Unkwang) Paddy rice (Oryza sativa L. cv. IR-2793)

autumn, 2012 20/05/2013 (140) 22/04/2013 (112) 10/12/2013 (344)

May 2013 17/09/2013 (260) 10/10/2013 (283) 08/03/2014 (67)

Rainfed field in Gwangju 0, 115 and 180 kg N ha−1 in Gwangju 115 kg N ha−1 in Rainfed field in Gwangju 0, 100, 200, 300 kg N ha−1 in Bayreuth

Soil and climate properties Annual air temperature (◦ C) Annual precipitation (mm) Soil PH Total organic carbon (g kg−1 ) Total nitrogen (g kg−1 ) available P (mg kg−1 )

Growth chamber, Bayreuth 25 – – – – –

Gwangju 13.8 1391 5.5 12.3 1.0 13.1

was provided in the upland/rainfed field (hereafter call upland field as rainfed field). All field managements reflected the farming practice of the farmers in the region. Seeds of winter wheat (Triticum asetivum L. cv. Suan) were sown in autumn, 2012 in another neighboring rainfed field. To further evaluate the physiological hypothesis regards temporal variations in temperature response of photosynthesis in response to growth environment, one month old seedlings of tropical variety (Oryza sativa L. cv. IR-2793) was transplanted into plastic containers (length, width and height 50 × 50 × 48 cm) on December 10, 2013 in a controlled growth chamber with incident light intensity PAR of approx. 900 ␮mol m−2 s−1 (35.64 MJ m−2 d−1 ) at the University of Bayreuth, Germany (11◦ 34 E, 49◦ 56 N). Clay soil collected from local agricultural fields by logistics staffs were used to fully fill each container and then each of them was thoroughly irrigated with standing water layer until completion of growth chamber experiment. N, P and K fertilizer was mixed by a mass ratio similar to field compound fertilizer applied at Gwangju to generate four nutrient application rates 0, 100, 200 and 300 kg N ha−1 named low, normal, high, and heavy nutrient groups, applied at two times, before transplanting and at the active tillering stage. Three containers at each nutrient group were arranged, and 9 bundles at each container were established with 4 seedlings in each bundle. For gas exchange experiments, the plants were then acclimated in the growth chamber to daytime air temperature 30 ◦ C, relative humidity 60%. Air temperature during nighttime was constant at 25 ◦ C.

2.2. Field measurements of diurnal courses of leaf CO2 exchange Diurnal gas exchange and chlorophyll fluorescence measurements in sunlit (uppermost) leaves of winter wheat at grain-filling stage (116 DOY) were carried out using a portable gas-exchange and chlorophyll fluorescence system (GFS-3000 and PAM Fluorometer 3050-F, Heinz Walz GmbH, Effeltrich, Germany) to track ambient environmental conditions external to leaf cuvette. Repeated measurements of diurnal gas exchange and chlorophyll fluorescence in the sunlit (uppermost), second, third and fourth mature leaves of the high nutrient group in Unkwang canopy profiles were conducted on 57 and 73 DAT (Day after transplanting) (197 and 213 DOY) were conducted. The measurement dates of diurnal courses of the sunlit (uppermost) leaves in the low nutrient group were 171, 172, 179, 180 and 199 DOY (31, 32, 39, 40 and 59 DAT), in the normal nutrient group on 175, 177, 195 and 211 DOY (35, 37, 55, and 71 DAT), in the high nutrient group on 170 and 178 DOY (30 and 38 DAT), and in rainfed field on 157, 181, 201, 205, 222, 223, 227, 231, 235 and 238 DOY. Middle parts of two or three intact and healthy leaves were enclosed from sunrise to sunset. Photosynthesis rates and micrometeorological factors such as incident light intensity, air/leaf temperature, air humidity, and CO2

concentration were recorded every 5 min. Automatic calibration executed by a user-defined program was repeated every 15 min. 2.3. Measurements of CO2 response curves of net assimilation rate CO2 response curves under saturating light intensities (PAR of 1500 ␮mol m−2 s−1 ) and different leaf temperatures at vegetative and grain-filling stages (here referred to a time period of several days after flowering: milky stage (Yoshida, 1981)) in sunlit leaves of the field paddy at each nutrient group, of rainfed field and of growth chamber IR-2793 rice at each nutrient group were obtained using the GFS-3000 and PAM Fluorometer 3050-F. Each CO2 response curve was commenced after selected leaves had acclimated to leaf chamber microenvironment (CO2 of 400 ␮mol mol−1 , PAR of 1500 ␮mol m−2 s−1 , and relative humidity of ca. 60%) by step by step adjustment of CO2 concentration in leaf chamber from a high level of 1600, to 1200, 800, 600, 400, 200, 150, 100, and 50 ␮mol mol−1 . Assimilation rates were recorded when gsw reached steady states, then advanced to next CO2 concentration and repeated the same procedure to acquire a complete CO2 response curve. Similar measuring steps were repeated for CO2 and light response curves in sunlit and third leaves numbered from canopy top to bottom in winter wheat canopy profiles (measurements were available only at grain-filling stage). Chlorophyll fluorescence was measured parallel with CO2 /light curves. At least three replicated CO2 /light curves (control incident light intensity and control CO2 concentration and relative humidity at normal levels) at each leaf temperature were obtained, which was varied in 5 ◦ C from 20 to 35 ◦ C at early in the season and from 25 to 35 ◦ C at grain-filling stage. Leaf physiology was not altered by the experimental manipulations at different CO2 concentrations as indicated by testing measurements, which showed that sampled leaves after experiencing changes in CO2 concentration could indicate assimilation rate at CO2 of 400 ␮mol mol−1 similar to the initial levels. After accomplishing one CO2 response curve, sampled leaves were completely dark-adapted for at least 8 min under normal CO2 concentration for measurement of dark respiration (Rdark ). Attempts to best fit temperature response of photosynthetic parameters ideally require gas exchange measurements at higher leaf temperature such as 40 ◦ C. Nevertheless, it is impossible to achieve in the fields at Gwangju region even though the relative humidity and flow rate can be adjusted to avoid serious condensation problems, due to limited time in the field measurements and trade-off consideration between data quality required under rapid fluctuations in micrometeorology and data quantity. 2.4. Estimation of temperature response function of photosynthesis Mesophyll conductance (gm ) was estimated by using the variable JP method proposed by Harley et al. (1992a,b). The proportion

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of generated electron transport rate that is used by CO2 fixation process (Jp ) was quantified by a previous method referred to our previous research (Xue et al., 2016b). Assimilation rate and ETR in the region from 240 to 600 ␮mol mol−1 were used, because (1) gm estimates over this range of intercellular CO2 concentration (Ci ) values are reliable, since photosynthesis is limited by regeneration of RuBP and limitations by triose phosphate utilization that usually occur only at higher CO2 concentrations (Harley et al., 1992a,b; Flexas et al., 2007; Sharkey et al., 2007); and (2) the determinations are appropriate for the values observed for Ci under natural field conditions (ca. 250 ␮mol mol−1 ). Photosynthetic values at Ci of ca. 250 ␮mol mol−1 were used for gm estimation. Thus, gm =

A Ci −

 ∗ (Jp +8A+8Rday ) Jp −4A−4Rday

,

(1)

where A and Ci are measured directly. Chloroplast CO2 compensation point (*) at leaf temperature 30 ◦ C was 44.4 ␮mol mol−1 determined before and its temperature function referred to Bernacchi and Long (2002). Rday , non-photorespiratory CO2 evolution in the light, was approximately 60% of dark respiration as measured by the gas exchange system. In order to ensure the compatibility of parameter estimation method and data comparisons across literature reports, a widely used estimation protocol described by Sharkey et al. (2007) was used to estimate Jmax (J product by their method was then converted to Jmax by apply Smith equation (Harley and Tenhunen, 1991)) and Vcmax for each CO2 response curve at respective leaf temperature. Jmax and Vcmax as well as gm are temperature dependent and are better described by using a peaked Arrhenius function (Medlyn et al., 2002a; Lin et al., 2013), f (TK ) =

k25 eHa (TK −298.1)/298.1RTK 1 + e(STK −Hd )/RTK

(1 + e(298.1S−Hd )/298.1R ),

(2)

where TK is the leaf temperature (K); R is the gas constant (8.314 J K−1 mol−1 ); Ha is the activation energy describing the rate of increase in the process at low temperature (kJ mol−1 ), and Hd the deactivation energy equal to 200 kJ mol−1 as precious suggestions by Medlyn et al. (2002b); S is an entropy item (kJ K−1 mol−1 ), and k25 is value at leaf temperature 25 ◦ C. The temperature dependency of Rdark using an Arrhenius equation, optimum temperature (Topt ) for Vcmax and Jmax , and Q10 depicting increment of every 10 ◦ C in gm were fitted by Eqs. (3)–(5), f (TK ) = k25 eHa (TK −298.1)/298.1RTK ,

(3)

where k25 is dark respiration under leaf temperature 25 ◦ C. Hd

Topt =

S − R ln

 Q10 =



Ha Hd −Ha

Tk + 10 exp Tk



,

10Ha RTk (Tk + 10)

(4)

stomatal conductance to water vapor (gsw ), which depends on environmental drivers and net assimilation rate. A widely used semi-empirical model with recommended parameters of Leuning (1995) was selected, gsw = gmin + gfac

A , (Cs − 44)(1 + VPD/350)

(6)

where Cs is CO2 concentration at leaf surface, approximating to ambient CO2 concentration in leaf cuvette due to huge boundary layer conductance. gmin is residual value of gsw when assimilation rate is zero, and gfac represents the stomatal sensitivity to assimilation rate. VPD is water vapor pressure deficit between leaf surface and leaf tissues. Estimation of gfac in field paddy and rainfed rice was derived from diurnal course of gas exchange, and gfac in winter wheat was calculated based on photosynthesis rates of CO2 and light response curves when Ci fluctuated between 100 and 400 ␮mol m−2 s−1 which are normal ranges commonly observed in field-grown leaves. 2.6. Measurements of soil water content and leaf water potential Diurnal course for leaf water potential in rainfed rice plants were measured with a pressure chamber (PMS Instruments, Corvallis, OR). Measurements were conducted hourly from sunrise to sunset (Xue et al., 2016a). Volumetric soil water content (SWC, m3 m−3 ) at 10, 30 and 60 cm depth in the rainfed field were measured at several locations close to the plants sampled for leaf gas exchange measurements using EC-5 soil moisture sensors (Decagon, WA, USA). The sensors were installed at the initial growth stage and kept in the field throughout the season. Data were logged every 30 min using EM50 data-logger (Decagon, WA, USA) and calibrated based on actual soil water measurements conducted in the laboratory. SWC was converted to soil-water tension, s (kPa) with standard soil-water retention curves of van Genuchten (1980), 

  s

= r +



s − r 1+

 n m ,

(7)

s

where ␪(s ) is soil water content at matrix potential s ; ␪r and ␪s represent residual and saturating soil water content (m3 m−3 ), and ␴ relates to the inverse of the air entry suction, which are optimally estimated by Hydrus 1D model (Schaap et al., 1998) based on the local investigation of soil texture and soil water content observations: ␪r = 0.12 m3 m−3 , ␪s = 0.57 m3 m−3 , ␴ = 0.0096 cm−1 , n = 1.49, and where m is (1-1/n). 3. Results 3.1. Temperature response of assimilation rate in rice and winter wheat

 ,

(5)

2.5. Estimation of light use efficiency (˛) and stomatal coefficient (gfac ) When photosynthesis is primarily limited by electron transport rate in low-light environment without occurrence of stressesinduced stomatal closure, photosynthetic efficiency derived from a linear portion of light response curve is suggested to approximate the maximum level at given leaf temperatures (Skillman, 2008). ␣ defined as ratio of electron transport rate to incident PAR were decided by values of ETR under incident PAR not larger than 200 ␮mol m−2 s−1 in early morning and late afternoon. To predict water vapor exchange, the photosynthesis model needs to be coupled with a submodel predicting the behavior of

Temperature responses of Amax in rice and winter wheat were illustrated in Fig. 1. Apply a polynomial model well fitted Amax vs. leaf temperature correlation, with high statistical correlation coefficients and significant levels (Table 2, p < 0.05). Maximum Amax and the corresponding optimal temperature (Topt ) were well grabbed by measurements of Amax vs. leaf temperature across all data sets (Fig. 1a and b). Enhanced Amax at Topt in IR-2793 (not shown in Fig. 1) and field paddy rice Unkwang promoted by nutrient additions were not observed at principal growth stages (p = 0.31). Under each measuring temperature, Amax in sunlit leaves of winter wheat at grain-filling stage were profoundly greater than Unkwang and IR-2793 (Fig. 1b, p = 0.0006). There were no significant differences in Topt between vegetative and reproductive plants in IR-2793 that were planted in controlled growth chamber with constant air temperature (Table 2, p = 0.53;

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data not shown here). While, Topt in field-grown Unkwang rice were significantly improved from 30.04 ◦ C of the vegetative stage to 32.8 ◦ C of grain-filling stage by 2.4 ◦ C (p = 0.003). The lowest Topt in winter wheat by almost 10 ◦ C as compared to grain-filling rice occurred parallel with seasonal changes in lower mean air temperature of approx. 14.2 ◦ C in April (grain-filling stage in winter wheat) and that of 27.8 ◦ C in August (grain-filling stage in rice).

3.2. Temperature responses of Vcmax, Jmax , and Rdark in rice and winter wheat

Fig. 1. Variations of Amax (assimilation rate under normal CO2 concentration of 400 ␮mol mol−1 and saturating light of 1500 ␮mol m−2 s−1 ) in dependence on leaf temperature (Tleaf ) in Unkwang and in winter wheat. Abbreviations: Veg., vegetative; GF, grain-filling.

Vcamx,25 , the maximum rate of Rubisco activity at 25 ◦ C, changed across all data sets by a factor of two (Table 3). Generally speaking, Vcamx,25 were higher at the vegetative stage than grain-filling stage in Unkwang rice, and leaves of winter wheat exhibited the highest Vcamx,25 . No significant discrepancies among nutrient groups at principal growth stages existed in either IR-2793 or Unkwang (ANOVA, p > 0.05). As for the relative temperature response of Vcmax , Ha throughout growing season in rice (Unkwang and IR-2793) and wheat were not significantly different (ANOVA, p > 0.05), falling into a range between 78 and 91 kJ mol−1 (Table 3). They suggested a convergence in relative temperature responses of Vcmax across data sets. S in winter wheat were highest while progressively declined in field-grown rice over time. Topt in winter wheat was lowest, and increased in field rice from the vegetative to grain-filling phase, which was parallel with seasonal changes of S across wheat and rice and well corresponded to increment of mean air temperature over seasons. The variability in the temperature response of Vcmax was illustrated in Fig. 2a (raw data in Supplementary Fig. S1 in the online version at DOI:10.1016/j.fcr.2016.09.016), showing the temperature responses normalized to 1 at 25 ◦ C. Leaves at grain-filling phase in field rice had a much steeper Vcmax -T response owing to high

Table 2 Temperature response functions of Amax (assimilate rate under normal CO2 concentration and saturating light) in winter wheat, Unkwang under low and normal nutrient groups, and in IR-2793 subject to normal, high and heavy nutrient groups. Abbreviations: Veg., vegetative; GF, grain-filling. Crops*treatment

Temperature response function

Topt (◦ C)

Adj R2

IR-2793 Normal-Veg. IR-2793 High-Veg. IR-2793 Heavy-Veg.

Amax = −17.63 + 3.13Tleaf − 0.057Tleaf 2 Amax = −28.22 + 4.06Tleaf − 0.077Tleaf 2 Amax = −15.21 + 3.11Tleaf − 0.059Tleaf 2

27.45 26.36 26.35

0.63 0.75 0.77

Unkwang Paddy Low-Veg. Unkwang Paddy Normal-Veg. Unkwang Rainfed-Veg.

Amax = −18.88 + 2.97Tleaf − 0.050Tleaf 2 Amax = −46.21 + 4.75Tleaf − 0.077Tleaf 2 Amax = −27.69 + 3.73Tleaf − 0.063Tleaf 2

29.7 30.84 29.60

0.32 0.62 0.38

Unkwang Paddy Low-GF Unkwang Paddy Normal-GF Unkwang Rainfed-GF

Amax = −120.11 + 8.51Tleaf − 0.13Tleaf 2 Amax = −96.40 + 7.13Tleaf − 0.11Tleaf 2 Amax = −79.65 + 6.05Tleaf − 0.091Tleaf 2

32.73 32.50 33.24

0.78 0.60 0.46

Winter wheat-GF

Amax = 2.75 + 2.19Tleaf − 0.047Tleaf 2

23.29

0.66

Table 3 Parameters in temperature response functions of maximum carboxylation rate (Vcmax ) in rice Unkwang and IR-2793 under nutrient and water availability and in winter wheat. Abbreviations: Veg. vegetative; GF, grain-filling. mean ± S.D. Crops*treatment

Vcmax,25 (␮mol m−2 s−1 )

Ha (J mol−1 )

S (J K−1 mol−1 )

Topt (◦ C)

IR-2793 Normal-Veg. IR-2793 High-Veg. IR-2793 Heavy-Veg.

132.27 ± 2.58

77758 ± 3915

635 ± 3

39.94 ± 0.65

Unkwang Paddy Low-Veg. Unkwang Paddy Normal-Veg. Unkwang Rainfed-Veg.

131.98 ± 6.88

91413 ±17193

642 ±6

37.67 ± 2.84

Unkwang Paddy Low-GF Unkwang Paddy Normal-GF Unkwang Rainfed-GF

94.75 ± 7.92

90549 ± 18059

635 ± 5

40.96 ± 3.02

Winter wheat-GF

160.89 ± 20.89

83813 ± 16313

655 ± 6

26.38 ± 2.55

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Fig. 2. Sample responses of (a) Vcmax , (b) Jmax , and (c) gm to leaf temperatures. Values are normalized to 1 at 25 ◦ C. Abbreviations: Veg. vegetative; GF, grain-filling. Table 4 Parameters in temperature response functions of maximum carboxylation rate, maximum electron transport rate (Jmax ) in rice Unkwang and IR-2793 under nutrient and water availability and in winter wheat. Abbreviations: Veg. vegetative; GF, grain-filling. mean ± S.D. Crops*treatment

Jmax,25 (␮mol m−2 s−1 )

Ha (J mol−1 )

S (J K−1 mol−1 )

Topt (◦ C)

IR-2793 Normal-Veg. IR-2793 Hhigh-Veg. IR-2793 Heavy-Veg.

227.07 ± 5.54

36905 ± 3136

645 ± 2

31.06 ± 0.79

Unkwang Paddy Low-Veg. Unkwang Paddy Normal-Veg. Unkwang Rainfed-Veg.

177.46 ± 14.03

65321 ± 20938

646 ± 7

33.09 ± 2.12

253.04 ± 26.36

65636 ± 29148

648 ± 8

31.20 ± 2.91

Unkwang Paddy Low-GF Unkwang Paddy Normal-GF Unkwang Rainfed-GF

133.83 ± 5.38

62702 ± 15235

639 ± 6

36.41 ± 2.90

Winter wheat-GF

264.02 ± 32.73

44212 ± 15371

655 ± 5

27.58 ± 2.80

value of Ha independent of nutrient additions, and the Topt in winter wheat was clearly lower as compared to those in rice. Seasonal fluctuation in Jmax,25 similar to Vcmax,25 in field rice was evident with higher values at the vegetative phase than the grain-filling (Table 4). Leaves of winter wheat at filling stage had the highest Jmax,25 (Table 4). Plants grown in rainfed field at the vegetative stage exhibited relatively high Jmax,25 as compared to paddy rice. The variability of temperature response in Jmax was shown in Fig. 2b (raw data in Supplementary Fig. S2 in the online version at DOI:10.1016/j.fcr.2016.09.016). Ha were similar in sampled leaves irrespective of growing seasons and genotypes as well as crop species, with average of 52.82 ± 14.75 kJ mol−1 . The Topt of Jmax were not significantly different in IR-2793 over time (not shown here). Leaves of field plants at grain-filling stage exhibited higher Topt than those in leaves of vegetative plants, and winter wheat showing the lowest Topt , which well correspond to monthly changes in Topt of Amax (Tables 2 and 4). Seasonal changes in S when compare field rice and winter wheat were in line with those changes in Topt , namely higher S leading to lower Topt . Shapes of temperature response of Rdark in field rice and winter wheat were completely different from observations in tempera-

Table 5 Parameters in temperature response functions of dark respiration (Rdark ) in rice Unkwang and IR-2793 under nutrient and water availability and in winter wheat. mean ± S.D. Crops

Rdark,25 (␮mol m−2 s−1 )

Ha (J mol−1 )

Q10

Rice and winter wheat

0.83 ± 0.03

49608 ± 10953

1.92 ± 0.53

ture responses Vcmax and Jmax (Table 5). A common temperature trajectory with average Ha of 49.61 ± 10.95 kJ mol−1 and Rdark,25 at 0.83 ± 0.03 ␮mol m−2 s−1 well fitted all data sets, yielding Q10 of 1.92. No significant correlation between Rdark,25 and Na in rice existed (not shown here). 3.3. Temperature response of mesophyll conductance (gm ) in rice and winter wheat gm,25 , mesophyll conductance at 25 ◦ C, presented a seasonal tendency assembling Vcmax,25 and Jmax,25 , declining from the vegetative to grain-filling stage (Table 6). Most variations of gm,25 may relate to Na documented by our previous research and/or to differences in leaf anatomy among crops. Interestingly, leaves of

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Table 6 Parameters for temperature response of mesophyll conductance (gm ) in rice Unkwang and IR-2793 under nutrient and water availability and in winter wheat. Abbreviations: Veg. vegetative; GF, grain-filling. mean ± S.D. Crops*treatment

gm,25 (mmol m−2 s−1 )

Ha (J mol−1 )

S (J K−1 mol−1 )

Q10

Topt (◦ C)

IR-2793 Normal-Veg. IR-2793 High-Veg. IR-2793 Heavy-Veg.

245.51 ± 37.30

76837 ± 25192

652 ± 7

2.9 ± 1.6

31.34 ± 4.58

Unkwang Paddy Low-Veg. Unkwang Paddy Normal-Veg. Unkwang Rainfed-Veg.

166.65 ± 13.05

98845 ± 23176

647 ± 6

2.4 ± 1.2

35.88 ± 3.55

Unkwang Paddy Low-GF Unkwang Paddy Normal-GF Unkwang Rainfed-GF

143.90 ± 10.21

54620 ± 20383

636 ± 20

1.6 ± 0.6

36.46 ± 3.88

Winter wheat-GF

296.74 ± 35.46

49641 ± 21937

660 ± 8

1.3 ± 0.3

24.17 ± 3.01

grain-filling plants in rice had a lower Q10 by 73.3% as compared to that at initial growth stage. The Topt of gm in winter wheat at filling stage was approximately 24.17 ◦ C which was significantly lower than those in rice. However, prevailing temperature dependence was not clearly evident in field-grown rice over growing season. Normalized temperature curve of gm to 1 at 25 ◦ C was illustrated in Fig. 2c (raw data in Supplementary Fig. S3 in the online version at DOI:10.1016/j.fcr.2016.09.016). There was a rapid increment with temperature before occurrence of decline at the vegetative stage in rice. The lower Topt in winter wheat was visualized (p < 0.01). 3.4. Light conversion efficiency in rice and winter wheat Light energy conversion efficiency (␣) in field-grown Unkwang under three nutrient groups and two water supply treatments and in winter wheat were illustrated in Fig. 3a. Temporal values of ␣ in sampled sunlit and shaded leaves in canopy profiles tended to be invariable approximating 0.25 mol e− mol−1 photons. However, decline of ␣ in the afternoon induced by lowing leaf water potential (␺leaf ) appeared during a prolonged drought period that did not show a complete recovery as compared to ␣ measured in the morning (Fig. 3a and b), showing a negative correlation between ␣ and minimum leaf water potential (␺leaf,min ) (R2 = 0.73, p = 0.02). ␺leaf,min less than −1.0 MPa resulted in a dampened ␣ (Fig. 3b). A parallel change in diurnal course of gas exchange and electron transport rate in sunlit leaves of rainfed rice suffered water stress was observed (not shown here), documenting that photosynthesis inhibition at midday was partially ascribed to down-regulation of photochemical efficiency of photosystem.

3.5. Stomatal coefficient (gfac ) in rice and winter wheat Comparisons in within-canopy gfac and in gfac of sunlit leaves over the growing season between field rice and winter wheat were shown in Fig. 4a. Sunlit leaves in Unkwang and winter wheat across three nutrient groups and water treatments showed a conservative gfac , while, gfac increased in leaves grown from canopy top to bottom parallel with a decline of Na in both rice and wheat, which leads to a negative correlation for gfac -Na in canopy profiles (Fig. 4b). Measurements of Na in winter wheat were missing, but similar spatial correlation for gfac -Na was anticipated as well in winter wheat canopies. During soil desiccation gfac was significantly constrained less than 3 in sunlit leaves (Fig. 4a), which were positively correlated to ␺leaf,min (Fig. 4c). Furthermore, the later was very sensitive to variations in soil water potential (␺s ) and rapidly declined at a threshold of approx. −1.0 MPa (Fig. 4d).

3.6. Imply temperature response of photosynthesis Comparisons between observations in temperature response of Amax and predictions were illustrated in Fig. 5. Striking correspondence between the observations and predictions at prevailing temperatures found in both rice and winter wheat, and a close correlation between predictions and observations in diurnal gas exchange in leaves of rice canopies (y = 1.0 × −0.5; not shown here) documented that CO2 response curves measured in the field and key parameters estimated were reliable, represented daily dynamics of leaf photosynthetic physiology.

Fig. 3. (a) Seasonal development of light energy conversion efficiency (LCE), (c) quantum yield (QY), and (b) discontinuous measurements of minimum (leaf,min ) and predawn (maximum, leaf,max ) leaf water potential in Unkwang rice subjected to nutrient and water treatments and in winter wheat. Shading area indicated occurrence of water stress during growing season. Abbreviations: GF, grain-filling; Morni., morning; After., afternoon.

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Fig. 4. (a) Seasonal development of stomatal coefficient (gfac ) in sunlit leaves and canopy profiles of Unkwang rice under different water and nutrient treatments and in winter wheat. Shading area indicated occurrence of water stress in rainfed rice; (b) relationship between gfac and leaf nitrogen content per area (Na ) in Unkwang rice; (c) correlations between gfac and minimum leaf water potential (␺leaf,min ), (d) between minimum leaf water potential (␺leaf,min ) and soil water potential (␺s ) in rainfed rice. Abbreviations: GF, grain-filling.

Traditional methodology used before in literature to parameterize PTC model by directly using the A/Ci curves (assuming infinite gm ) could occasionally predict Amax at prevailing temperatures well. Whereas, overestimation of Amax was appeared at temperatures lower than prevailing ones (Fig. 5a–c). Alternative methodology seen in literature adopting constant gm , in example of gm of 300 mmol m−2 s−1 , significantly overestimated Amax at lower/chilling temperature events and largely overestimated Amax in winter wheat at higher temperatures that exceeded the Topt of gm due to greater gm artificially set than real gm . Biased estimation of Amax in another methodology by using constant gm of 800 mmol m−2 s−1 occurred as well. To further highlight ecological implications of consider conditional variations of environmental response of key parameters in crop modelling researches, constant S of 645 similar to observations in rice was incorporated into modelling practice in winter wheat. The artificial manipulation greatly overestimated Amax at warming temperatures as compared to normal predictions (Fig. 5c), which emphasizes the importance to account for differences in temperature response traits among crops under varying climate conditions.

4. Discussion Photosynthesis-transpiration coupled model consists of two principal components, photosynthetic rate which is regulated by Rubisco activity related to CO2 concentration and/or electron transport rate resting on incident light intensity, the other one stomatal submodel that mechanically couples photosynthesis and transpiration processes. To accurately capture interaction and adaption of plant growth to ambient climate change and understand seasonal courses of carbon and water fluxes in agroecosystems, indepen-

dent measurements accounting for those processes in cereal crops growing under different methods of field management and anthropogenic interventions are required. In this research, we highlighted in context of climate change, consider growth environmentinduced differences in temperature responses of photosynthetic parameters among cereal crops is mandatory to better predict interactions among soil-plant-atmosphere consortium.

4.1. Temperature responses of photosynthesis and dark respiration and ambient growth temperature environment The shift in the Topt of photosynthesis shown in this study (Fig. 1) has also been observed in most other studies of seasonal temperature acclimation in field-grown plant species (Medlyn et al., 2002b; Lin et al., 2013). A large range from 5 to 10 ◦ C in the Topt recorded by them showed a good correlation with monthly mean temperature. The Topt in leaves of winter wheat at grain-filling stage registered at the end of April (spring season) was approx. 23.29 ◦ C, and seasonal Topt in field rice profoundly increased from the vegetative of 30.04 ± 0.68 ◦ C to grain-filling of 32.82 ± 0.37 ◦ C (Table 2). Correspondingly, daily average temperature raised from 14.2 ◦ C in April to∼23.10 ◦ C in mid-June (vegetative stage) and 27.8 ◦ C at the beginning of August (grain-filling stage). Moreover, IR-2793 grown in controlled growth chamber with constant air temperature did not exhibit large shifts in Topt of photosynthesis throughout growing seasons, which performs in contrast to field observations in Unkwang rice. Parallel development in seasonal Topt and mean air temperature suggested that the prevailing climate environment as an operative factor directly influences temperature acclimation of photosynthesis.

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winter wheat and in rice at the vegetative stages (Fig. 1) Several factors were suggested to be responsible for variations in Jmax,25 and Vcmax,25 among and within crop species, including growth light intensity (Ellsworth and Reich, 1993), and leaf morphology (Wang et al., 2008; Noda et al., 2015). These factors are typically associated with variations in Na within species over time (Niinemets and Tenhunen, 1997). In our case, those two processes were tightly related to changes of Na (Xue et al., 2016a,b), and to endogenous differences in capacity of leaf nitrogen accumulation between winter wheat and rice. Carbon losses from dark canopy respiratory account for approximate 40% of gross primary production (Yoshida, 1981). As a result, it is vital to estimate environmental response of carbon release from dark respiration. Rdark rates raised strongly with short-time rise of leaf temperature, which exerts a linear or exponential trajectory until ∼50–55 ◦ C which are extremely high temperatures that can not commonly occur in temperate regions (O’Sullivan et al., 2013; Slot et al., 2016). Therefore, gas exchange measurements usually can not generate a fully peaked temperature response curve of Rdark because of a serious condensation problem caused by relatively high dew point. Seasonal pictures of Rdark temperature response in rice and winter wheat over growing season were quantified (Table 5), showing a uniform temperature trajectory with Q10 – the proportional increase in Rdark with 10 ◦ C warming – at 1.92. Huve et al. (2012) suggested that temperature dependent changes in leaf sugar concentrations were primarily responsible for temperature response of dark respiration, which may interpret the similarity in relative temperature response of dark respiration among cereal crops.

Fig. 5. Comparisons among observations of Amax temperature response, prediction, predictions assuming gm to be infinite, constant at 300 and 800 mmol m−2 s−1 , and prediction with S = 645 in rice Unkwang at vegetative and grain-filling stages and in winter wheat. Abbreviations: Veg. vegetative; GF, grain-filling.

Hd the deactivation energy, and S the entropy factor combine to determine the Topt for the process. Hd slightly differed among species (Zhu et al., 2011; Harley and Tenhunen, 1991). Large fluctuations in S reported before were primarily mediated by the prevailing growth environment (Lin et al., 2013). Kattge and Knorr (2007) suggested that S for both Vcmax and Jmax processes decreased with growth temperature. In line with them, leaves of winter wheat experienced chilling environment exhibited the lowest S, followed by increasing S for Vcmax and Jmax in field rice from vegetative to grain-filling stage together with seasonal temperature warming (Tables 3 and 4). Ha between winter wheat and rice, among genotypes and across nutrient and water treatments were conservative. As a result, relative temperature response for Vcmax and Jmax was respectively grouped, yielding Ha 86.27 ± 12.98 and 52.82 ± 14.75 kJ mol−1 . They are significantly higher than those reported in tree species, 69.9 ± 4.2 and 27.9 ± 1.9 kJ mol−1 in Eucalyptus species (Lin et al., 2013), 57 and 39 kJ mol−1 in maritime pine (Medlyn et al., 2002b), and other tree species of 52.11 ± 11.06 kJ mol−1 (Medlyn et al., 2002a). Shifts in optimal temperature of photosynthesis are suggested to be related to surrounding air temperatures probably due to temperature acclimation of temperature function of Vcmax and Jmax . There were significant differences in Vcmax,25 over growing seasons in rice and between rice and winter wheat at the grain-filling stage (Table 3). Large discrepancies in Vcmax,25 among nutrient groups existed at neither vegetative nor grain-filling stages (Table 3). Similar to Vcmax,25 , significantly higher Jmax,25 in sunlit leaves of winter wheat than rice was present. Leaves of rice at the grain-filling stage exhibited lower values as compared to those at vegetative stage (Table 4), which result in greater Amax in

4.2. Significant differences in temperature response of mesophyll conductance (gm ) between rice and winter wheat At saturating light and current atmosphere CO2 concentration gm exponentially increased with temperature arming and arrived a plateau at higher temperatures and then declined (Fig. 2c; Table 6). Leaves of rice at early growth stage showed a greater Q10 than grain-filling stage, and no significant discrepancies among nutrient treatments existed. Q10 of plants grown vegetatively was slightly higher than Q10 reported in other crops (Bernacchi and Long, 2002; Scafaro et al., 2011) but Q10 of grain-filling plants was compatible. Phenological differences between species and leaf nitrogen allocation among photosynthetic components over rice growth season may be responsible for variations of seasonal Q10 (Xue et al., 2016a). Absolute values of gm at 25 or 30 ◦ C were concordant with previous reports by Yong et al. (2009) and Adachi et al. (2013), and lower than reports by Scafaro et al. (2011). The differences can be acceptable because a potential acclimation of relative temperature response of gm to growth temperature would result in unexpected variations of photosynthetic physiology (Flexas et al., 2012) of which leaf anatomy such as cell internal architecture, chloroplast number and size, positioning of chloroplasts along the cell walls, nitrogen allocation concurrently determine CO2 diffusion conductance (Niinemets et al., 2009b; Xiong et al., 2015). Lower values of gm at milky stage may result from superimposed impacts by leaf anatomy and/or gm -related proteins. gm,25 in sampled leaves of winter wheat was relatively lower than those reported values in six wheat varieties growth in controlled growth chamber (Barbour and Kaiser, 2016). Vegetatively growing plants were sampled for measurements of gm by them. As we discussed previously in rice, gm,25 progressively declined over the growing seasons. Differences in gm,25 are likely ascribed to phenology dependent leaf anatomy differences in sampled plants. gm,25 in leaves of winter wheat significantly greater than that of paddy rice at grain-filling stage may contribute to observed large assimilation rate and even larger leaf water use efficiency, since our

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relevant research stated that leaf water use efficiency was tightly related to ratio of gm /gsw in cereal crops (Xue et al., 2016a). Significant differences in relative temperature response of gm between paddy rice and winter wheat existed in Topt rather than Ha (Table 6). Grain-filling plants in winter wheat exerted a Ha of 49.64 ± 21.93 kJ mol−1 similar to that of paddy rice. Nevertheless, Topt in wheat leaves was significantly lower than rice, which was in line with changes in Amax and seasonal changes in mean monthly air temperature, likely owning to higher S. 4.3. Soil water content dependent changes in photosynthetic efficiency Prior information indicated that light utilization efficiency (LUE) in C3 species averaged 0.0733 mol CO2 mol−1 absorbed photons at 30 ◦ C under 2% O2 conditions (Ehleringer and BjoRkman, 1977). Quantum absorptances ranged between 76% and 88% depending on sampled species. Assuming a conservative value for the number of electrons required for NADPH synthesis and the amount of NADPH required for regeneration of one molecule of ribulose-1,5bisphosphate (RuBP), i.e., 4 electrons consumed for one molecule of CO2 fixed in the absence of photorespiration (Ting, 1981; Sharkey, 1985). LUE values based on incident light in C3 varied from 0.224 to 0.258 mol e− mol−1 photons. A larger range of 0.223 to 0.32 mol e− mol−1 photons in C3 was reported (Long et al., 1993), may due to temperature dependence of PSII photochemical efficiency under low light (Yin et al., 2014) which may be one of potential factor that could cause differences between LUE of early morning and late afternoon. Nevertheless, light conversion efficiency was more sensitive to soil water content. Constrained values in leaves of rice suffered drought stress were evident. Generally speaking, the thylakoid membrane complex involved in RuBP regeneration is more sensitive to high temperatures or stress environment because thermal sensitivity of membranebound proteins in whole electron transport chain are higher due to high-temperature induced alterations in thylakoid membrane composition (Martindale and Leegood, 1997; Sage and Kubien, 2007; Xue et al., 2011). Light conversion efficiency is physiologically related to photochemical efficiency of photosystem which is susceptible to adverse environment. 4.4. Stomatal coefficient (gfac ) varies according to leaf position in canopy profiles and soil moisture environment Although different mechanisms from photosynthesis such as K ion concentration in guard cells or ABA content are involved in regulations of gsw , stomatal conductance derived via gas exchange technique commonly co-vary with assimilation rate under various environments in plant species. gfac as one of staple input parameters in canopy photosynthesis model is considered to deepen understandings of the coordinated adjustment between photosynthesis and stomatal openness at given environment within and among plant species (Ball et al., 1987), reflecting adaptive capacity of photosynthetic physiology responding to environmental gradients. Previous reports indicated that gfac was relatively constant over growing seasons under various growth conditions (Wohlfahrt et al., 1998; Medlyn et al., 2002a; Xu and Baldocchi, 2003; Zhu et al., 2011). In our research, sunlit leaves over growing season had a conservative gfac , which is in line to their reports. However, gfac were substantially higher in low-light adapted leaves which have lower leaf nitrogen content (Fig. 4b), highlighting the distinctly adaptive strategies of stomatal openness in cereal crops in response to light climate changes. Furthermore, leaves suffered water stress indicated dampened gfac (Fig. 4c). Similar findings in Quercus ilex in a Mediterranean watershed were also reported by

Sala and Tenhunen (1994). We found that gfac presented a rapid decline with increasing soil desiccation at a threshold of −1.0 MPa.

4.5. Recommendations for modelling soil-plant-atmosphere consortium Key parameters estimated directly from traditional A/Ci curves in some of currently prevailing modelling methodologies assuming constant gm have been suggested for field modelling campaigns. Three numerical categories of gm the infinite, higher gm of 800, and moderate gm of 300 mmol m−2 s−1 were applied to evaluate effects of traditional methods on estimation of photosynthesis in field-grown rice and winter wheat. Substitute three types of gm in steady-state model Cc = Ci -A/gm , get A/Cc curve, then calculate Vcmax and Jmax at each measuring temperature. In general, predicted Amax for the three constant gm methods overweighted observations (Fig. 5). Deviations of Amax estimation, in case of the grain-filling stage, at temperatures from 20 to 35 ◦ C in 5 increment were 38.4%, 31.3%, 22.1% and 18.8% by the infinite gm method, for gm = 300 method the deviation of Amax being 31.4%, 16.7%, 4.6% and −2.02%, and for gm = 800 method the deviation of Amax being 46.7%, 23.6%, 8.7% and −2.1% (Fig. 5b). Adopt constant gm especially the infinite one in photosynthesis model can not be a reliable solution. Give gm one intermediate value of 300 mmol m−2 s−1 , large deviations were only observed at lower temperatures. And, similar phenomena occurred in the method adopting a higher gm of 800 mmol m−2 s−1 as well. Rubisco activity was suggested to be primary factor limiting Amax at low temperatures (Sage and Kubien, 2007; Lin et al., 2013). CO2 concentration inside chloroplasts is one of two causes that determine Rubisco activity. Therefore, model sensitivity at low temperatures largely depends on quantitative gm at respective temperatures. Although using constant gm for A/Ci conversion thereafter estimation of photosynthetic parameters may be occasionally acceptable, prediction of constant gm methods would largely deviate from in vivo photosynthesis in plants exposed to stress environment. Those suggested that constant gm and infinite gm methodologies are not always highly reliable to mimic photosynthesis in species especially for those grown under chilling stress environment. It was clear that temperature responses of Vcmax and Jmax were largely affected by the prevailing climate environment (Tables 3 and 4). Substitute S of winter wheat at grain-filling stage by those observed in rice largely overestimated Amax at higher leaf temperatures (Fig. 5c), suggesting that growth temperature dependent changes in the relative temperature response of Vcmax and Jmax and their differences among crops and over growing season must be incorporated into modelling practice in monitoring of leaf and ecosystem photosynthetic productivity. Another suggestion is to consider environmental response of gfac serving as the key linkage between photosynthesis and transpiration processes. gfac in shaded leaves was significantly higher as compared to sunlit leaves while was vulnerable to drought stress, interprets the observed phenomena that transpiration rate at saturating light in shaded leaves was relatively higher/equal to that of uppermost leaves in paddy rice. Reduction of gfac during drought period in rainfed rice also led to lowering stomatal conductance thereby intercellular CO2 concentrations which finally limit photosynthesis rate. Results based on this research suggested that take growth environment dependent differences in temperature response of photosynthesis and adaption of stomatal regulation into account could better understand and interpret response of crop plants in photosynthetic productivity to changing climate.

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5. Conclusions As far as we know, this is the first field reports that quantitatively quantify mechanisms involved in regulation of photosynthesis over growing season and environmental responses of those key components, aiming to highlighting parameter selection of photosynthesis-transpiration coupled model in agricultural ecosystems. We found that Topt of photosynthesis strongly depended on the prevailing climate environment. Temperature response of Rdark in sampled plants shared a common trajectory. Light conversion efficiency was less variant while sensitive to soil desiccation. gfac in rice and wheat sunlit leaves was relatively conservative, although shaded leaves with lower Na had higher gfac . gfac was susceptible to fluctuations of leaf and soil water potential. Selection of photosynthetic parameters when apply process-based model in monitoring of crop photosynthesis in multiple-crop grown agro-ecosystem requires the understanding of environment-induced acclimation of temperature response of photosynthesis. Acknowledgments This study was carried out as part of the International Research Training Group TERRECO (GRK 1565/1) funded by the Deutsche Forschungsgemeinschaft (DFG) at the University of Bayreuth, Germany and the Korean Research Foundation (KRF) at Kangwon National University, Chuncheon, S. Korea. We thank the agricultural logistics group of CNU for the field management and for the rice seedling cultivation in the nursery. W. Xue thanks financial support from the program of China Scholarships Council (CSC No. 201204910156). We do acknowledge the helps in the field by Seung Hyun Jo, Toncheng Fu, Fabian Fischer, Nikolas Lichtenwald and Yannic Ege. We gratefully acknowledge the technical assistance of Ms. Margarete Wartinger for all her support in the field and laboratory.

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