Biophysical Controls on Light Response of Net CO2 Exchange in a Winter Wheat Field in the North China Plain

Xiaojuan Tong; Jun Li; Qiang Yu; Zhonghui Lin

doi:10.1371/journal.pone.0089469

Abstract

To investigate the impacts of biophysical factors on light response of net ecosystem exchange (NEE), CO₂ flux was measured using the eddy covariance technique in a winter wheat field in the North China Plain from 2003 to 2006. A rectangular hyperbolic function was used to describe NEE light response. Maximum photosynthetic capacity (P_max) was 46.6±4.0 µmol CO₂ m⁻² s⁻¹ and initial light use efficiency (α) 0.059±0.006 µmol µmol⁻¹ in April−May, two or three times as high as those in March. Stepwise multiple linear regressions showed that P_max increased with the increase in leaf area index (LAI), canopy conductance (g_c) and air temperature (T_a) but declined with increasing vapor pressure deficit (VPD) (P<0.001). The factors influencing P_max were sorted as LAI, g_c, T_a and VPD. α was proportional to ln(LAI), g_c, T_a and VPD (P<0.001). The effects of LAI, g_c and T_a on α were larger than that of VPD. When T_a>25°C or VPD>1.1−1.3 kPa, NEE residual increased with the increase in T_a and VPD (P<0.001), indicating that temperature and water stress occurred. When g_c was more than 14 mm s⁻¹ in March and May and 26 mm s⁻¹ in April, the NEE residuals decline disappeared, or even turned into an increase in g_c (P<0.01), implying shifts from stomatal limitation to non-stomatal limitation on NEE. Although the differences between sunny and cloudy sky conditions were unremarkable for light response parameters, simulated net CO₂ uptake under the same radiation intensity averaged 18% higher in cloudy days than in sunny days during the year 2003−2006. It is necessary to include these effects in relevant carbon cycle models to improve our estimation of carbon balance at regional and global scales.

Citation: Tong X, Li J, Yu Q, Lin Z (2014) Biophysical Controls on Light Response of Net CO₂ Exchange in a Winter Wheat Field in the North China Plain. PLoS ONE 9(2): e89469. https://doi.org/10.1371/journal.pone.0089469

Editor: Ben Bond-Lamberty, DOE Pacific Northwest National Laboratory, United States of America

Received: June 1, 2012; Accepted: January 23, 2014; Published: February 20, 2014

Copyright: © 2014 Tong et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This research is jointly supported by National Natural Science Foundation of China (Grant No. 31100322), National Basic Research Program of China (Grant No. 2010CB428404 and Grant No. 2012CB955304) and the Fundamental Research Funds for the Central Universities (NO.YX2011-19). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Vegetation productivity is the foundation of carbon sequestration and grain yield formation. For the limit on field observation techniques, early studies mainly focused on photosynthesis at the leaf level. The biochemistry and ecophysiology of leaf photosynthesis have been well understood and parameterized [1], [2]. However, it is difficult to measure leaf photosynthesis over long periods and upscale photosynthetic rate from the leaf level to the canopy or ecosystem level because of the non-linear distribution of leaf area and radiation intensity within the vegetation canopy. With the development of micrometeorological techniques, especially the eddy covariance (EC) method, net photosynthetic rate of vegetation could be directly measured at the ecosystem level. The EC technique has been widely used in CO₂ flux measurements in forest, grassland and farmland ecosystems [3]–[9].

Plant photosynthesis is primarily driven by incident solar radiation. Light response models, including rectangular hyperbolic model [10], [11] and non-rectangular hyperbolic model [12], [13], were developed to describe the relationship between daytime net ecosystem CO₂ exchange (NEE) and photosynthetically active radiation (PAR). Besides solar radiation, factors influencing daytime NEE include environmental variables such as air temperature, vapor pressure deficit and soil water content and biological variables such as leaf area index and canopy conductance. These factors may be considered by (1) estimating the bias of simulated NEE as functions of influencing factors [14], [15], or (2) revising light response parameters as functions of influencing factors [9], [11], [16]–[19]. The effects of biophysical factors on light response parameters are often estimated using stepwise multiple linear regression models [9]. However, these models have not been used to assess the influence of variables on NEE residual so far.

Long-term observations and simulations have shown a worldwide decrease in surface solar radiation (global dimming) from 1950s to 1980s, with a partial recovery (brightening) in 1990s in some areas (e.g. high and middle latitude of the Northern Hemisphere) [20]. During the dimming period, direct radiation declined remarkably, whereas diffuse radiation enhanced. The increase in diffuse fraction of surface solar radiation may be attributed to increasing aerosol and/or cloudiness, as both factors tend to enhance scattering in the atmosphere [20]. Light responses of photosynthesis under various sky conditions were investigated in forests [8], [21]–[23] and croplands [24], [25]. For the tall and dense vegetations, photosynthesis may be enhanced by diffuse radiation because diffuse light distributes more effectively within the canopy compared with direct light [26]. After considering the effects of direct and diffuse radiation on canopy photosynthesis in the model, Mercado et al. [27] found that the increases in diffuse fraction enhanced the global land carbon sink by 23.7% during the period from 1960 to 1999.

China is the largest wheat producer and consumer in the world [28] and continuously attempts to increase its production to ensure national food security. As one of large food production regions in China, the North China Plain (NCP) produces about half of the country's wheat [29]. In this study, CO₂ flux was measured continuously using the EC technique in a winter wheat field in the North China Plain for 4 years. The objectives are to (1) investigate NEE light response and the influencing factors, and (2) assess the effects of sky conditions on NEE light response. This study will improve our knowledge on the parameterization of carbon cycle models and the scenario analyses of carbon sink in the future under changing climate.

Materials and Methods

Study site

This study was conducted at Yucheng Comprehensive Experiment Station, Chinese Academy of Sciences (36°57′N, 116°38′E, 23.4 m). It is located at the North China Plain, with a temperate monsoon climate. Mean annual temperature is 13.1°C and annual solar radiation is 5242 MJ m⁻². Annual precipitation is about 528 mm. Soil organic content is 1.21% and pH value is about 7.9. The typical cropping system in this region is the biannual rotation with winter wheat and summer maize. In this study, winter wheat was planted in mid/late October and harvested in early/mid June. The detailed field management was described by Tong et al. [30].

Field observations

CO₂ and latent heat fluxes were measured by the eddy covariance system with a three-dimensional sonic anemometer (model CSAT3, Campbell Sci. Inc., USA) and an infrared open-path CO₂/H₂O gas analyzer (model LI-7500, Li-Cor Inc., USA). The eddy covariance system, mounted at the height of 2.1 m, was used to measure 3-D wind speed, air temperature, humidity and CO₂ concentration above the canopy. Raw data were collected at 10 Hz and recorded by a CR5000 datalogger (model CR5000, Campbell Scientific Inc., USA). The CR5000 datalogger can store data and programs on a PC card. The card can be carried to a computer and the computer reads PC cards via its PCMCIA card slot directly.

Anemometers (model A100R, Vector, UK) and psychrometers (model HMP45C, Vaisala, Finland) were installed at heights of 2.05 and 3.25 m above the ground. Photosynthetically active radiation (PAR) was measured using a quantum sensor (model Li-190SB, Li-Cor Inc, USA). Solar radiation and net radiation (R_n) were measure by a pyranometer (model CM11, Kipp & Zonen, Delft, The Netherlands) and a net radiometer (model CNR-1, Kipp & Zonen, Delft, The Netherlands), respectively. Two soil heat flux plates were buried in the depth of 2 cm, one between rows and another between plants. Soil temperature was measured at 0, 5, 10, 30 and 50 cm depths. Soil water content (SWC) at the depths of 20 cm and 30 cm was measured with time domain reflectometers (TDR) (model CS616, Campbell Sci. Inc., USA). Rainfall was measured with a rain gauge (model 52203, Rm Young MI, USA). All meteorological data were recorded with a data logger (model CR23x, Campbell Sci. Inc., USA) and were stored at intervals of 30 min.

Biomass, leaf area index (LAI) and plant height were measured every 5 days during the growing season of winter wheat. There were three sampling plots (replications) for each measurement. Twenty plants were sampled continuously for each plot and the plots were selected randomly. Leaf area was measured with Leaf area instrument (model Li-3100, Campbell Sci. Inc., USA). In this study, the main growing season began when the cropland turned from the carbon source to the sink (5-day moving mean CO₂ flux from positive to negative), and it ended when shifted reversely.

Flux data quality control

CO₂ and latent heat fluxes were calculated as follows:(1)

(2)where ρ is air density, w' the vertical wind velocity, c' CO₂ concentration, λ the latent heat of vaporization, E water vapor flux and q the specific humidity. Overbars indicate an averaging operation and primes denote deviations from the mean.

Raw data were conducted by two dimension coordinate rotations [31] and Webb-Pearman-Leuning (WPL) correction [32] to obtain 30-min mean flux data. CO₂ and water vapor fluxes could be affected by rain and dew. The abnormal data were eliminated following the method used by Falge et al. [33]. In the daytime, data gaps were 29%, 8%, 10% and 16% in the growing seasons of winter wheat in 2003, 2004, 2005 and 2006, respectively. Gap filled data were not used in this paper.

Light response of NEE

In most ecosystems, the relationship between daytime NEE and PAR can be expressed by a rectangular hyperbolic function [33], [34]:(3)where α is the initial slope of the light response curve (initial light use efficiency), P_max the maximum photosynthetic capacity, R_d the daytime ecosystem respiration rate under dark conditions. The model (Eq. (3)) was fitted by the software “Origin 7.0” (Microcal Software Inc.). In this study, negative NEE means net CO₂ uptake by the cropland and positive NEE indicates net CO₂ emission from the cropland.

To study the environmental factors influencing daytime NEE besides PAR, NEE was computed using Eq. (3) and the residual (NEE_r) was obtained as measured NEE minus simulated NEE. Stepwise multiple linear regression models were used to estimate the integrative influences of biophysical factors on NEE_r. Statistical analyses were performed using the software “SPSS 13.0” (SPSS Inc.). According to the method used by Carrara et al. [17], Powell et al. [18] and Teklemariam et al. [7], air temperature (T_a), vapor pressure deficit (VPD), soil water content (SWC), leaf area index (LAI) and canopy conductance (g_c) were divided into many classes and mean NEE_r was obtained for each class to show the trends in NEE_r versus variables.

To investigate seasonal patterns of light response parameters, a rectangular hyperbola (Eq. (3)) was used to describe the light response of NEE every five days and a time series of α, P_max and R_d were obtained. Meanwhile, the biophysical variables in the daytime with the 5-day interval were averaged. Stepwise multiple linear regression analyses were applied to distinguish the key drivers for α, P_max and R_d. The relationships among light response parameters were also investigated at the 5-day scale.

Light response parameters varied among years. Standard errors (e₁ and e₂) were calculated for sunny and cloudy sky conditions and total standard error (e) was obtained for both sky conditions using the equation as follows:(4)

The difference between sunny and cloudy sky conditions would be pronounced if it was larger than the total standard error for two sky conditions.

Canopy conductance

According to Monteith and Unsworth [35], canopy conductance (g_c) was calculated as follows:(5)where λE is the latent heat flux (W m⁻²), R_n net radiation (W m⁻²), G soil heat flux (W m⁻²), △ the slope of saturation vapor pressure to the air temperature curve (kPa K⁻¹), ρ air density (mol m⁻³), C_p the specific heat capacity of air (J mol⁻¹ K⁻¹), VPD vapor pressure deficit (kPa), γ the psychrometric constant (kPa K⁻¹), g_c canopy conductance (m s⁻¹), and g_a aerodynamic conductance (m s⁻¹) which can be given as follows [36]:(6)where ra is aerodynamic resistance (s m−1), z the height where wind velocity measured (m), d zero-plane displacement height (m), z0 roughness length (m), k the von Karman constant (0.41) and u wind velocity (m s−1). z0 and d can be calculated as [37], [38]:

(7)(8)where h is the crop height (m).

Defining sunny and cloudy sky conditions

The clearness index (k_t) is used to describe sky conditions and the degree of impact of cloudiness on the solar radiation received at the Earth's surface [22]. It can be given by [39]:(9)

(10)

(11)where S is global solar radiation (W m⁻²), S_e the extraterrestrial irradiance at a plane parallel to the earth surface (W m⁻²), S_sc the solar constant (1370 W m⁻²), t_d the day of year, β the solar elevation angle, φ the local latitude, δ the declination of the sun, and ω hour angle. The sky conditions were classified at a half-day scale because the days without clouds for the whole daytime were rare [39]. In this study, sunny skies were simply identified when the half-day mean k_t was larger than a threshold in the morning and afternoon, respectively. 1.2 times of two-month running average k_t was used as the threshold so that the number of obtained sunny half-days was close to the mean value observed in recent decades. Clearness index were plotted against solar elevation angles and fitted by cubic polynomials in the sunny mornings and afternoons, respectively (Fig. 1). The data falling away from the major patterns were excluded [39]. Different from the studies in the forests [22], [39], k_t observed in the wheat field was lower in the afternoon than in the morning (Fig. 1a). Air pollution may reduce the atmospheric transparency, resulting in a small incident solar radiation at the land surface. The reduction was more evident in the afternoon than in the morning (Fig. 1b).

Download:

Figure 1. Scatter plots and regressions between (a) the clearness index (k_t) and the sine of solar elevation angles (sinβ), and (b) global solar radiation (S) and sinβ for a winter wheat field in April-May 2003.

The data were fitted by cubic polynomials in the morning (solid line) and afternoon (dashed line), respectively.

https://doi.org/10.1371/journal.pone.0089469.g001

Results

NEE Light response and influencing factors

The relationship between daytime NEE and PAR in a winter wheat field is shown in Figure 2. Light response curves in April and May were similar but both of them differed from those in March. From 2003 to 2006, P_max was 46.6±4.0 µmol CO₂ m⁻² s⁻¹, α 0.059±0.006 µmol µmol⁻¹ and R_d 5.3±0.3 µmol CO₂ m⁻² s⁻¹ in April−May, two or three times as high as those in March (Table 1). However, the inter-annual coefficients of variation (CV) for α, P_max and R_d were great in March due to large variations of LAI, daytime mean T_a, VPD and SWC among years. The variations in VPD and SWC resulted from significant changes in precipitation in March among years (Tables 1 and 2). In March 2005, α was so small and P_max was so large that the light response curves were actually close to a line (Table 1 and Fig. 2).

Download:

Figure 2. Response of net ecosystem CO₂ exchange (NEE) to photosynthetically active radiation (PAR) in a winter wheat field.

a–c: NEE obtained by eddy covariance technique; d–f: light response curves fitted monthly using the rectangular hyperbolic function (Eq. (3)). The values of light response parameters were shown in Table 1.

https://doi.org/10.1371/journal.pone.0089469.g002

Download:

Table 1. Monthly NEE light response parameters (P_max, α and R_d) derived from Eq. (3) for a winter wheat field.

https://doi.org/10.1371/journal.pone.0089469.t001

Download:

Table 2. Monthly biophysical variables in the winter wheat field from March to May in the year 2003–2006.

https://doi.org/10.1371/journal.pone.0089469.t002

Table 3 indicates that NEE_r declined with the increases in LAI, g_c, T_a and SWC (P<0.001) but increased with the increase in VPD (P<0.001). Positive/negative NEE_r means measured NEE was higher/lower than simulated NEE; or measured CO₂ uptake was lower/higher than simulated CO₂ uptake. The impacts of biophysical factors on NEE_r varied in different months. Factors influencing NEE_r were sorted as LAI, SWC, g_c, T_a and VPD in March; g_c, LAI, VPD and T_a in April; and LAI, g_c, VPD and T_a in May. The effects of SWC on NEE_r were significant in March but insignificant in April and May (Table 3).

Download:

Table 3. Influences of biophysical factors (T_a, VPD, SWC, g_c and LAI) on NEE residual in a winter wheat field from March to May in the year 2003–2006, estimated by the stepwise multiple linear regression models.

https://doi.org/10.1371/journal.pone.0089469.t003

Shifted trends in NEE_r versus variables illustrated the limit in linear regression analysis (Fig. 3). With the increase in T_a and VPD, NEE_r turned to increase significantly (P<0.001) when T_a was more than 25°C or VPD more than 1.1−1.3 kPa. With an increase in g_c, the NEE_r decline disappeared, or even turned into an increase (P<0.01) when g_c exceed 26 mm s⁻¹ in April or 14 mm s⁻¹ in March and May (Fig. 3 and Table 4). No reverse trends were found for NEE_r versus LAI and SWC.

Download:

Figure 3. Effects of biophysical factors on NEE residual (NEE_r) in a winter wheat field in March (a–e), April (f–j) and May (k–o).

NEE residual was measured NEE minus simulated NEE. The simulated NEE was obtained by Eq. (3). Biophysical factors include canopy conductance (g_c), Leaf area index (LAI), daytime air temperature (T_a), vapor pressure deficit (VPD) and soil water content at the depth of 20 cm (SWC).

https://doi.org/10.1371/journal.pone.0089469.g003

Download:

Table 4. Correlation coefficients between NEE residual and influencing variables when the variables were less or more than the thresholds.

https://doi.org/10.1371/journal.pone.0089469.t004

Light response parameters: seasonal variation and influencing factors

Light response parameters varied seasonally, with peaks in April or May ranged from 64.2 to 86.1 µmol CO₂ m⁻² s⁻¹ for P_max, from 0.070 to 0.087 µmol µmol⁻¹ for α and from 6.5 to 8.5 µmol CO₂ m⁻² s⁻¹ for R_d during the year 2003−2006 (Fig. 4). The seasonal patterns of P_max were similar with LAI but different from T_a and VPD which have an increasing tendency from March to May. Compared with other years, P_max peaked earlier in 2004 when LAI reached the maximum in mid-April due to fast warming in spring (Fig. 4).

Download:

Figure 4. Seasonal variation of initial light use efficiency (α), maximum photosynthetic capacity (P_max), daytime ecosystem respiration under dark conditions (R_d), air temperature (T_a), vapor pressure deficit (VPD) and leaf area index (LAI) in a winter wheat field during the main growing season.

α, P_max and R_d were obtained at 5-day intervals using a regression model (Eq. (3)).

https://doi.org/10.1371/journal.pone.0089469.g004

Figure 5 shows the scattered points of biophysical factors (LAI, g_c, T_a, VPD and SWC) versus NEE light response parameters (α, P_max and R_d) at 5-day scale during the main growing season of winter wheat. Stepwise multiple linear regression models were used to assess their relationships (Table 5). P_max increased with the increase in LAI, g_c and T_a (P<0.001) but reduced with the increase in VPD (P<0.001). The factors influencing P_max were sorted as LAI, g_c, T_a and VPD. α was proportional to ln(LAI), g_c, T_a and VPD (P<0.001). The impacts of LAI, g_c and T_a on α were larger than that of VPD. R_d was proportional to T_a (P<0.001). It was better to express the relationship between R_d and T_a using exponential equation instead of linear equation. However, the influences of SWC on all light response parameters were insignificant (Table 5).

Download:

Figure 5. Effects of biophysical factors (g_c, LAI, T_a, VPD and SWC) on light response parameters (α, P_max and R_d) at 5-day intervals during the main growing season of winter wheat.

The meanings of abbreviates were the same as those in Figures 3 and 4.

https://doi.org/10.1371/journal.pone.0089469.g005

Download:

Table 5. Effects of biophysical drivers (LAI, g_c, T_a, VPD and SWC) on light response parameters (P_max, α and R_d) at the 5-day scale estimated by the stepwise multiple linear regression models during the growing seasons of 2003–2006.

https://doi.org/10.1371/journal.pone.0089469.t005

During the growing season of winter wheat, α increased linearly with an increase in R_d (P<0.001) (Fig. 6). α and R_d enhanced firstly and then declined with the increase in P_max. The maximum α and R_d appeared when P_max was around 50 µmol CO₂ m⁻² s⁻¹. Their relationships could be expressed by quadratic polynomials (P<0.001) (Fig. 6).

Download:

Figure 6. Relationships among initial light use efficiency (α), maximum photosynthetic capacity (P_max), and daytime ecosystem respiration under dark conditions (R_d) at 5-day intervals during the main growing season of winter wheat.

https://doi.org/10.1371/journal.pone.0089469.g006

NEE Light response and NEE−g_c relationships under various sky conditions

Light response curves of NEE were determined under sunny and cloudy sky conditions, respectively (Fig. 7 and Table 6). The analyses were only conducted during the period with large LAI (0.8LAI_max<LAI<LAI_max) to limit the influences of crop growth on NEE light response. Compared with sunny sky conditions, P_max under cloudy conditions was 19%, 12% and 27% higher in 2003, 2004 and 2006 but 14% lower in 2005; α under cloudy skies was 24% and 64% larger in 2003 and 2005, but 5% and 10% less in 2004 and 2006 (Table 6). On average, P_max, α and R_d under cloudy sky conditions were 9%, 11% and 2% higher than those under sunny sky conditions, respectively. However, owing to large variation in α, P_max and R_d among years, their differences between two sky conditions were smaller than their total standard errors calculated by Eq. (4) (Table 7), indicating unremarkable differences in light response parameters between sunny and cloudy sky conditions.

Download:

Figure 7. Response of NEE to PAR under sunny and cloudy sky conditions in a winter wheat field during the period of large leaf area index (0.8LAI_max<LAI<LAI_max).

Light response curves (solid curves) were fitted using the rectangular hyperbolic function (Eq. (3)). The values of light response parameters were shown in Table 3. Between the upper and lower dotted curves were 95% confidence intervals.

https://doi.org/10.1371/journal.pone.0089469.g007

Download:

Table 6. NEE light response parameters (P_max, α and R_d) derived from Eq.(3) under sunny and cloudy sky conditions in a winter wheat field during the period of large leaf area index (0.8LAI_max<LAI<LAI_max).

https://doi.org/10.1371/journal.pone.0089469.t006

Download:

Table 7. Comparisons of light response parameters and simulated NEE (NEE_r) under sunny and cloudy sky conditions.

https://doi.org/10.1371/journal.pone.0089469.t007

NEE Light response in cloudy days differed from that in sunny days. The differences between two light response curves were significant in 2003 and 2005 but insignificant in 2004 and 2006. Cloudy and sunny light response curves were so close in 2004 and 2006 that their differences were within the confidence intervals due to scattered points of NEE versus PAR (Fig. 7). At the same PAR, simulated net CO₂ uptake was 33%, 13%, 23% and 8% (averaged 18%) higher under cloudy sky conditions than under sunny sky conditions in 2003, 2004, 2005 and 2006, respectively. The difference between two sky conditions was more than their standard errors (Table 7), suggesting that net CO₂ uptake under cloudy sky conditions was significantly higher than that under sunny sky conditions.

As mentioned above, LAI and g_c were key factors affecting NEE. After neglecting the effects of LAI on NEE during the period with large LAI, the NEE−g_c relationships were investigated in several radiation classes (Fig. 8). The correlations between NEE and g_c were described by logarithmic equations under strong, moderate and weak radiation, respectively (P<0.001). With the increase in ln(g_c), net CO₂ uptake enhanced more quickly under strong radiation than under low radiation. At the same g_c, net CO₂ uptake was higher in cloudy days than in sunny days. Nevertheless, the differences between two regression lines were insignificant for all PAR classes because they were within the wide confidence intervals owing to scattered points of NEE versus ln(g_c) (Fig. 8).

Download:

Figure 8. Linear relationship between NEE and logarithmic canopy conductance (g_c) under sunny and cloudy sky conditions (solid lines) in a winter wheat field during the period of large LAI (0.8LAI_max<LAI<LAI_max).

Between the upper and lower dotted lines were 95% confidence intervals.

https://doi.org/10.1371/journal.pone.0089469.g008

Discussion

Factors influencing NEE light response

In boreal and temperate forests, NEE_r generally declines with increasing T_a and VPD. These trends turn to rise when T_a exceeds 20−25°C [7], [17] and VPD exceeds 1.1−1.3 kPa [7], [18]. In this study, similar phenomena were observed beyond the same thresholds in a temperate cropland (Fig. 3 and Table 4). However, in the tropical forest, NEE_r always increases with the increase in T_a and VPD without reverse trends [40] (Loescher at al., 2003). It may be ascribed to high air temperature and humidity with a narrow range in the tropic region.

Net CO₂ uptake decreased with the increase in T_a and VPD (Fig. 3), indicating that temperature and water stress occurred. Under high temperature (T_a>25°C), photosynthesis was prohibited and soil and plant respirations were great, resulting in a lower net CO₂ uptake than the one simulated by Eq. (3). On the other hand, VPD controls photosynthetic rate through influencing stomatal closure. Under higher VPD, stomatal opening is smaller, leading to the decrease of photosynthetic rate [41]. Moreover, the decline of photosynthetic rate was likely due to low leaf water potential caused by high transpiration rates [42]. Ecosystem respiration is large under higher VPD because of high air and soil temperatures [41]. Hence, net carbon uptake by the cropland dropped. In this study, lower VPD threshold in April than in May (Table 4) implied that wheat plants were more sensitive to the drought at the fast growing stage than at the senescence stage.

Around the critical value, the reverse trends in NEE_r versus g_c (Fig. 3 and Table 4) suggested a shift from stomatal limitation to non-stomatal limitation on daytime net CO₂ exchange. At dawn, dusk or in cloudy days, photosynthesis was inhibited by weak radiation even though g_c was large. Higher g_c threshold in April than in March and May (Table 4) indicated that net CO₂ uptake was more sensitive to stomatal behavior at the rapid growth stage than at the slow growth senescence stage. Therefore, NEE_r was mainly controlled by g_c instead of LAI in April. However, when g_c was limited in colder March or drier May, LAI became the most important factor affecting NEE_r in these months.

The significant impacts of SWC on NEE_r in March (Table 3) may be ascribed to large variation in soil moisture before and after irrigation at the turning green stage of winter wheat (Fig. 4). Nevertheless, the sufficient irrigation concealed these effects in the following two months (Table 3).

Factors influencing light response parameters

Photosynthetic light response parameters were not constant but varied seasonally [9], [11], [19]. For wheat canopy, mean P_max in April−May observed in this study (49 µmol CO₂ m⁻² s⁻¹) was lower than the results (62−67 µmol CO₂ m⁻² s⁻¹) obtained in a winter wheat field in central Germany [5] and a spring wheat field in Manitoba, Canada [43]. Mean α in April−May obtained in this study (0.056 µmol µmol⁻¹) was lower than the value (0.063 µmol µmol⁻¹) observed in a winter wheat field in central Germany [5], but higher than that (0.036 µmol µmol⁻¹) obtained in a spring wheat field in Manitoba, Canada [43]. The magnitudes of light response parameters varied among different studies might be attributed to the discrepancy in temperature, humidity, canopy structure or/and other factors.

P_max had positive correlations with LAI [9], [16], [18], [19], air temperature [17], [18] and SWC [9], [18] but negative correlation with VPD [9], [44]. A similar phenomenon was found in this study (Fig. 5 and Table 5). Among all factors, LAI was regarded as a key factor controlling P_max [9], [16], [18]. P_max increased linearly [18] or nonlinearly [16], [19] with increasing LAI. The nonlinear relationship was expected as the leaves shade each other in the ecosystem with higher vegetation density [16]. In the cropland, the linear correlation between P_max and LAI (Fig. 5 and Table 5) illustrated a suitable density range for canopy light intercept.

LAI determines photosynthetic area while g_c controls the photosynthetic intensity. Owing to the strong link between g_c and photosynthetic rate, the influences of environmental factors (T_a, VPD and SWC) on photosynthesis may be ascribed to their effects on g_c [45]. Hence, factors influencing P_max was sorted by LAI, g_c, T_a and VPD (Table 5). Our results in an irrigated wheat field differed from those obtained by Zhang et al. [9] who found that P_max was only affected by LAI and VPD in a dry wheat field.

In this study, α was affected by LAI, T_a, g_c and VPD in an irrigated wheat field (Table 5), differing from the result reported by Zhang et al. [9] who found that α was only influenced by LAI in a dry wheat field. α represents weak light use efficiency by the plants. At dawn and dusk, the weak light is mainly composed of diffuse radiation. Under low LAI, less diffuse radiation was obtained by the short canopy. When LAI enlarged, the canopy structure was beneficial to receiving scatter light from each direction. Less diffuse radiation was intercepted by the sparse canopy when LAI decreased with leaf senescence. Moreover, at dawn and dusk, low radiation led to small g_c. Under cold and moist conditions, α was more sensitive to T_a and g_c than to VPD.

Generally, the key factor influencing respiration was temperature (Table 5). It likely changed to moisture under dry conditions. For instance, Zhang et al. [9] obtained that R_d had positive correlations with LAI and soil moisture in a dry wheat field. In this study, R_d was correlated with T_a significantly but not related to VPD and SWC (Table 5 and Fig. 5) due to sufficient irrigation in the winter wheat field. Furthermore, the effect of LAI on R_d was unremarkable (Table 5 and Fig. 5) because R_d was composed of soil and plant respiration and only the later part was related to LAI [16].

After investigating the flux data observed in the grasslands and croplands over the world, Gilmanov et al. [19] pointed out that light response parameters were correlated with each other. Because P_max was proportional to LAI, α and R_d correlated to P_max was the same as correlated to LAI (Table 5, Figs. 5 and 6). Positive correlation between α and R_d may result from a relatively stable critical PAR (PAR under zero NEE) of 110±6, 99±6, 102±7 and 101±9 µmol m⁻² s⁻¹ in 2003, 2004, 2005 and 2006, respectively.

NEE Light response and NEE−g_c relations under various sky conditions

P_max in a wheat field was greater under cloudy skies than under sunny skies in most of years (Table 6), in agreement with the studies in the temperate forests [8], [21], [22]. Different from the temperate area, Zhang et al. [22] observed a reverse phenomenon in a subtropical forest: net CO₂ uptake under cloudy skies was less than that under sunny skies. It may be owing to much lower radiation intensity in cloudy days compared with sunny days in the subtropical forest. In the winter wheat field, differences of P_max and α between two sky conditions were insignificant (Table 6) due to large variations in P_max and α among years. The values of α in this study were consistent with those reported by Dengel and Grace [8] and Zhang et al. [22] for forests, but different from the results observed by Hollinger et al. [10], Rocha et al. [21] and Suyker et al. [24] who obtained a larger α under cloudy conditions in the forests and an irrigated maize cropland.

In spite of no significant differences in light response parameters between sky conditions, simulated net CO₂ uptake under cloudy sky conditions was greater than that under sunny sky conditions at the same PAR (Fig. 7). A similar phenomenon was observed in the temperate forests [8], [21] and the irrigated cropland [24]. More CO₂ uptake by the canopy under cloudy skies than that under sunny skies was owing to many reasons. Firstly, the proportion of diffuse radiation increases under cloudy sky conditions and more light can reach below leaves of the canopies [46]. Photosynthetic rates of shaded leaves are promoted by the delivery of diffuse radiation [47]. In addition, compared with shaded leaves, the phenomenon of saturating photosynthesis easily happens for sunlit leaves because shaded leaves often illuminate brightly [48]. Secondly, canopy conductance was usually higher under cloudy sky conditions than that under sunny sky conditions. Diffuse radiation enhances canopy stomatal conductance mainly due to the reduction in VPD and blue light enrichment within the canopy during cloudy and overcast weather [49], [50]–[52]. Both low VPD [53] and blue light [54], [55] can stimulate stomatal opening and then enhance photosynthetic rate.

In this study, NEE−g_c relationships were expressed by logarithmic equations (Fig. 8), differing from the result obtained by Dengel and Grace [8] who pointed out that net CO₂ uptake enhanced linearly with the increase in g_c. Under strong radiation, net CO₂ uptake enhanced quickly with increasing g_c (Fig. 8), indicating that g_c was the main factor affecting NEE. Compared with cloudy days, net CO₂ uptake was more sensitive to the variation in g_c for higher temperature and VPD in sunny days.

Acknowledgments

We are particularly grateful to Prof. Xiaomin Sun, Assoc. Prof. Zhilin Zhu and Mr. Zhenmin Liu for their assistance with field measurements and instrumentation maintenance, and the editor and two anonymous reviewers for their constructive comments on the manuscript.

Author Contributions

Conceived and designed the experiments: XT JL QY. Performed the experiments: JL XT. Analyzed the data: JL XT. Contributed reagents/materials/analysis tools: QY JL ZL. Wrote the paper: XT JL.

References

1. Farquhar GD, Caemmerer S, Berry A (1980) A biochemical model of photosynthetic CO₂ assimilation in leaves of C₃ species. Planta 149: 78–90.
- View Article
- Google Scholar
2. Harley PC, Thomas RB, Reynolds JF, Strain BR (1992) Modelling photosynthesis of cotton grown in elevated CO₂. Plant Cell Environ 15: 271–282.
- View Article
- Google Scholar
3. Hollinger DY, Kelliher FM, Schulze E-D, Bauer G, Arneth A, et al. (1998) Forest-atmosphere carbon dioxide exchange in eastern Siberia. Agric For Meteorol 90: 291–306.
- View Article
- Google Scholar
4. Flanagan LB, Wever LA, Carlson P J (2002) Seasonal and interannual variation in carbon dioxide exchange and carbon balance in a Northern temperate grassland. Global Change Biol 8: 599–615.
- View Article
- Google Scholar
5. Anthoni PM, Knohl A, Rebmann C, Freibauer A, Mund M (2004) Forest and agricultural land-use-dependent CO₂ exchange in Thuringia, Germany. Global Change Biol 10: 2005–2019.
- View Article
- Google Scholar
6. Li J, Yu Q, Sun X, Tong X, Ren C, et al.. (2006) Carbon dioxide exchange and the mechanism of environmental control in a farmland ecosystem in North China Plain. Sci China Ser D 49 (Suppl. II): 226–240.
7. Teklemariam T, Staebler RM, Barr AG (2009) Eight years of carbon dioxide exchange above a mixed forest at Borden, Ontario. Agric For Meteorol 149: 2040–2053.
- View Article
- Google Scholar
8. Dengel S, Grace J (2010) Carbon dioxide exchange and canopy conductance of two coniferous forests under various sky conditions. Oecologia 164: 797–808.
- View Article
- Google Scholar
9. Zhang P, Chen SP, Zhang WL, Miao HX, Chen JQ, et al. (2012) Biophysical regulations of NEE light response in a steppe and a cropland in Inner Mongolia. J Plant Ecol 5(2): 238–248.
- View Article
- Google Scholar
10. Hollinger DY, Kelliher FM, Byers JN, Hunt JE, McSeveny TM, et al. (1994) Carbon dioxide exchange between an undisturbed old growth temperate forest and the atmosphere. Ecology 75: 134–150.
- View Article
- Google Scholar
11. Aubinet M, Chermanne B, Vandenhaute M, Longdoz B, Yernaux M, et al. (2001) Long term carbon dioxide exchange above a mixed forest in the Belgian Ardennes. Agric For Meteorol 108: 293–315.
- View Article
- Google Scholar
12. Gilmanov TG, Verma SB, Sims PL, Meyers TP, Bradford JA, et al. (2003) Gross primary production and light response parameters of four Southern Plains ecosystems estimated using long-term CO₂-flux tower measurements. Global Biogeochem Cycles 17(2): 1071
- View Article
- Google Scholar
13. Hirata R, Hirano T, Saigusa N, Fujinuma Y, Inukai K, et al. (2007) Seasonal and inter-annual variations in carbon dioxide exchange of a temperate larch forest. Agric For Meteorol 147: 110–124.
- View Article
- Google Scholar
14. Giasson M-A, Coursolle C, Margolis HA (2006) Ecosystem-level CO₂ fluxes from a boreal cutover in eastern Canada before and after scarification. Agricultural and Forest Meteorology, 140 23–40.
15. Migliavacca M, Meroni M, Manca G, Matteucci G, Montagnani L, et al. (2009) Seasonal and interannual patterns of carbon and water fluxes of a poplar plantation under peculiar eco-climatic conditions. Agricultural and Forest Meteorology 149: 1460–1476.
- View Article
- Google Scholar
16. Laurila T, Soegaard H, Lloyd C R, Aurela M, Tuovinen J-P, et al. (2001) Seasonal variations of net CO₂ exchange in European Arctic ecosystems. Thero Appl Climatol 70: 183–201.
- View Article
- Google Scholar
17. Carrara A, Janssens IA, Curiel Yuste J, Ceulemans R (2004) Seasonal changes in photosynthesis, respiration and NEE of a mixed temperate forest. Agric For Meteorol 126: 15–31.
- View Article
- Google Scholar
18. Powell TL, Bracho R, Li J, Dore S, Hinkle CR, et al. (2006) Environmental controls over net ecosystem carbon exchange of scrub oak in central Florida. Agric For Meteorol 141: 19–34.
- View Article
- Google Scholar
19. Gilmanov TG, Aires L, Barcza Z, Baron VS, Belelli L, et al. (2010) Productivity, respiration, and light-response parameters of world grassland and agroecosystems derived from flux-tower measurements. Rangeland Ecol Manage 63: 16–39.
- View Article
- Google Scholar
20. Wild M (2009) Global dimming and brightening: a review. J Geophys Res 114: D00D16
- View Article
- Google Scholar
21. Rocha AV, Su HB, Vogel CS, Schmid HP, Curtis PS (2004) Photosynthetic and water use efficiency responses to diffuse radiation by an aspen-dominated northern hardwood forest. Forest Sci 50 (6): 793–801.
- View Article
- Google Scholar
22. Zhang M, Yu GR, Zhang LM, Sun XM, Wen XF, et al. (2010) Impact of cloudiness on net ecosystem exchange of carbon dioxide in different types of forest ecosystem in China. Biogeosciences 7: 711–722.
- View Article
- Google Scholar
23. Urban O, Klem K, Ač A, Havránková K, Holišová P, et al. (2012) Impact of clear and cloudy sky conditions distribution of photosynthetic CO₂ spruce canopy. Funct Ecol 26: 46–55.
- View Article
- Google Scholar
24. Suyker AE, Verma SB, Burba GG, Arkebauer TJ, Walters DT, et al. (2004) Growing season carbon dioxide exchange in irrigated and rainfed maize. Agric For Meteorol 124: 1–13.
- View Article
- Google Scholar
25. Béziat P, Ceschia E, Dedieu G (2009) Carbon balance of a three crop succession over two cropland sites in South West France. Agric For Meteorol 149: 1628–1645.
- View Article
- Google Scholar
26. Wild M, Roesch A, Ammann C (2012) Global dimming and brightening – evidence and agricultural implications. CAB Reviews 7(003): 1–7.
- View Article
- Google Scholar
27. Mercado LM, Bellouin N, Sitch S, Boucher O, Huntingford C, Wild M, et al. (2009) Impact of changes in diffuse radiation on the global land carbon sink. Nature 458: 1014–1017.
- View Article
- Google Scholar
28. Wang F, He ZH, Ken Sayre K, Li SD, Si JS, et al. (2009) Wheat cropping systems and technologies in China. Field Crop Res 111: 181–188.
- View Article
- Google Scholar
29. NBSC, National Bureau of Statistics of China, 1998. China Statistical Yearbook. China Stat. Press, Beijing.
30. Tong XJ, Li J, Zhang XS, Yu Q, Qin Z, et al. (2007) Mechanism and bio-environmental controls of ecosystem respiration in a cropland in the North China Plains. New Zeal J Agr Res 50 (5): 1347–1358.
- View Article
- Google Scholar
31. McMillen RT (1988) An eddy correlation technique with extended applicability to non-simple terrain. Boundary Layer Meteorol 43: 231–245.
- View Article
- Google Scholar
32. Webb EK, Pearman GI, Leuning R (1980) Correction of flux measurements for density effects due to heat and water vapor transfer. Quart J R Meteorol Soc 106: 85–100.
- View Article
- Google Scholar
33. Falge E, Baldocchi D, Olson R, Anthoni P, Aubinet M, et al. (2001) Gap filling strategies for defensible annual sums of net ecosystem exchange. Agric For Meteorol 107: 43–69.
- View Article
- Google Scholar
34. Ruimy A, Jarvis PG, Baldocchi DD, Saugier B (1995) CO₂ fluxes over plant canopies and solar radiation: A review. Adv Ecol Res 26: 1–68.
- View Article
- Google Scholar
35. Monteith JL, Unsworth MH (1990) Principles of Environmental Physics, second ed. Edward Arnold, London.
36. Monteith JL (1965) Evaporation and environment, in The state and Movement of Water in Living Organisms, Sym. Soc. Exp. Biol., Vol XI X, 205−234, Company of Biologists, Cambridge, England.
37. Rutter AJ, Kershaw KA, Robins PC, Morton AJ (1971) A predictive model of rainfall interception in forests, I. Derivation of the model from observations in a plantation of Corsican pine. Agric Meteorol 9: 367–384.
- View Article
- Google Scholar
38. Lhomme J-P (1991) The concept of canopy resistance: historical survey and comparison of different approaches. Agric For Meteorol 54: 227–240.
- View Article
- Google Scholar
39. Gu L, Fuentes JD, Shugart HH, Staebler RM, Black TA (1999) Response of net ecosystem exchanges of carbon dioxide to changes in cloudiness: results from two North American deciduous forests. J Geophys Res 104 (D24): 31,421–31,434.
40. Loescher HW, Oberbauer SF, Gholz HL, Clark DB (2003) Environmental controls on net ecosystem-level carbon exchange and productivity in a Central American tropic wet forest. Global Change Biol 9: 396–412.
- View Article
- Google Scholar
41. Anthoni PM, Law BE, Unsworth MH (1999) Carbon and water vapor exchange of an open-canopied ponderosa pine ecosystem. Agric For Meteorol 95: 151–168.
- View Article
- Google Scholar
42. Hirasawa T, Hsiao TC (1999) Some characteristics of reduced leaf photosynthesis at midday in maize growing in the field. Field Crop Res 62: 53–62.
- View Article
- Google Scholar
43. Glenn AJ, Amiro BD, Tenuta M, Stewart SE, Wagner-Riddle C (2010) Carbon dioxide exchange in a northern Prairie cropping system over three years. Agric For Meteorol 150: 908–918.
- View Article
- Google Scholar
44. Dufranne D, Moureaux C, Vancutsem F, Bodson B, Aubinet M (2011) Comparison of carbon fluxes, growth and productivity of a winter wheat crop in three contrasting growing seasons. Agr Ecosyst Environ 141: 133–142.
- View Article
- Google Scholar
45. Flexas J, Medrano H (2002) Drought-inhibation of photosynthesis in C3 Plants: Stomatal and Non-stomatal limitations revisited. Ann Bot 89: 183–189.
- View Article
- Google Scholar
46. Jarvis PG, James GB, Landsberg JJ (1976) Coniferous forest. In: Vegetation and the Atmosphere (ed. Monteith J L), 171–240. Academic Press, London.
47. Gu L, Baldocchi DD, Wofsy SC, Munger JW, Michalsky JJ, et al. (2003) Response of a deciduous forest to the Mount Pinatubo eruption: enhanced photosynthesis. Science 299: 2035–2038.
- View Article
- Google Scholar
48. Urban O, Janouš D, Acosta M, Czerný R, Marková I, et al. (2007) Ecophysiological controls over the net ecosystem exchange of mountain spruce stand. Comparison of the response in direct vs. diffuse solar radiation. Global Change Biol 13: 157–168.
- View Article
- Google Scholar
49. Kuiper PJC (1964) Dependence upon wavelength of stomatal movement in epidermal tissue of Senecio odoris. Plant Physiol 39: 952–955.
- View Article
- Google Scholar
50. Mansfield TA, Meidner H (1966) Stomatal opening in light of different wavelengths: effects of blue light independent of carbon dioxide concentration. J Exp Bot 17: 510–521.
- View Article
- Google Scholar
51. Morison JIL, Jarvis PG (1983) Direct and indirect effects of light on stomata. I. In Scots pine and Sitka spruce. Plant Cell Environ 6: 95–101.
- View Article
- Google Scholar
52. Karlsson PE (1986) Blue light regulation of stomata in wheat seedlings. II. Action spectrum and search for action dichroism. Physiol Plant 66: 207–210.
- View Article
- Google Scholar
53. Pejam MR, Arain MA, McCaughey JH (2006) Energy and water vapour exchanges over a mixedwood boreal forest in Ontario, Canada. Hydrol Process 20: 3709–3724.
- View Article
- Google Scholar
54. Aphalo PJ, Jarvis PG (1993) Separation of direct and indirect responses of stomata to light: results from a leaf inversion experiment at constant intercellular CO₂ molar fraction. J Exp Bot 44: 791–800.
- View Article
- Google Scholar
55. Matsuda R, Ohashi-Kaneko K, Fujiwara K, Goto E, Kurata K (2004) Photosynthetic characteristics of rice leaves grown under red light with or without supplemental blue light. Plant Cell Physiol 45: 1870–1874.
- View Article
- Google Scholar

[ref1] 1. Farquhar GD, Caemmerer S, Berry A (1980) A biochemical model of photosynthetic CO₂ assimilation in leaves of C₃ species. Planta 149: 78–90.
View Article
Google Scholar

[2] View Article

[3] Google Scholar

[ref2] 2. Harley PC, Thomas RB, Reynolds JF, Strain BR (1992) Modelling photosynthesis of cotton grown in elevated CO₂. Plant Cell Environ 15: 271–282.
View Article
Google Scholar

[5] View Article

[6] Google Scholar

[ref3] 3. Hollinger DY, Kelliher FM, Schulze E-D, Bauer G, Arneth A, et al. (1998) Forest-atmosphere carbon dioxide exchange in eastern Siberia. Agric For Meteorol 90: 291–306.
View Article
Google Scholar

[8] View Article

[9] Google Scholar

[ref4] 4. Flanagan LB, Wever LA, Carlson P J (2002) Seasonal and interannual variation in carbon dioxide exchange and carbon balance in a Northern temperate grassland. Global Change Biol 8: 599–615.
View Article
Google Scholar

[11] View Article

[12] Google Scholar

[ref5] 5. Anthoni PM, Knohl A, Rebmann C, Freibauer A, Mund M (2004) Forest and agricultural land-use-dependent CO₂ exchange in Thuringia, Germany. Global Change Biol 10: 2005–2019.
View Article
Google Scholar

[14] View Article

[15] Google Scholar

[ref6] 6. Li J, Yu Q, Sun X, Tong X, Ren C, et al.. (2006) Carbon dioxide exchange and the mechanism of environmental control in a farmland ecosystem in North China Plain. Sci China Ser D 49 (Suppl. II): 226–240.

[ref7] 7. Teklemariam T, Staebler RM, Barr AG (2009) Eight years of carbon dioxide exchange above a mixed forest at Borden, Ontario. Agric For Meteorol 149: 2040–2053.
View Article
Google Scholar

[18] View Article

[19] Google Scholar

[ref8] 8. Dengel S, Grace J (2010) Carbon dioxide exchange and canopy conductance of two coniferous forests under various sky conditions. Oecologia 164: 797–808.
View Article
Google Scholar

[21] View Article

[22] Google Scholar

[ref9] 9. Zhang P, Chen SP, Zhang WL, Miao HX, Chen JQ, et al. (2012) Biophysical regulations of NEE light response in a steppe and a cropland in Inner Mongolia. J Plant Ecol 5(2): 238–248.
View Article
Google Scholar

[24] View Article

[25] Google Scholar

[ref10] 10. Hollinger DY, Kelliher FM, Byers JN, Hunt JE, McSeveny TM, et al. (1994) Carbon dioxide exchange between an undisturbed old growth temperate forest and the atmosphere. Ecology 75: 134–150.
View Article
Google Scholar

[27] View Article

[28] Google Scholar

[ref11] 11. Aubinet M, Chermanne B, Vandenhaute M, Longdoz B, Yernaux M, et al. (2001) Long term carbon dioxide exchange above a mixed forest in the Belgian Ardennes. Agric For Meteorol 108: 293–315.
View Article
Google Scholar

[30] View Article

[31] Google Scholar

[ref12] 12. Gilmanov TG, Verma SB, Sims PL, Meyers TP, Bradford JA, et al. (2003) Gross primary production and light response parameters of four Southern Plains ecosystems estimated using long-term CO₂-flux tower measurements. Global Biogeochem Cycles 17(2): 1071
View Article
Google Scholar

[33] View Article

[34] Google Scholar

[ref13] 13. Hirata R, Hirano T, Saigusa N, Fujinuma Y, Inukai K, et al. (2007) Seasonal and inter-annual variations in carbon dioxide exchange of a temperate larch forest. Agric For Meteorol 147: 110–124.
View Article
Google Scholar

[36] View Article

[37] Google Scholar

[ref14] 14. Giasson M-A, Coursolle C, Margolis HA (2006) Ecosystem-level CO₂ fluxes from a boreal cutover in eastern Canada before and after scarification. Agricultural and Forest Meteorology, 140 23–40.

[ref15] 15. Migliavacca M, Meroni M, Manca G, Matteucci G, Montagnani L, et al. (2009) Seasonal and interannual patterns of carbon and water fluxes of a poplar plantation under peculiar eco-climatic conditions. Agricultural and Forest Meteorology 149: 1460–1476.
View Article
Google Scholar

[40] View Article

[41] Google Scholar

[ref16] 16. Laurila T, Soegaard H, Lloyd C R, Aurela M, Tuovinen J-P, et al. (2001) Seasonal variations of net CO₂ exchange in European Arctic ecosystems. Thero Appl Climatol 70: 183–201.
View Article
Google Scholar

[43] View Article

[44] Google Scholar

[ref17] 17. Carrara A, Janssens IA, Curiel Yuste J, Ceulemans R (2004) Seasonal changes in photosynthesis, respiration and NEE of a mixed temperate forest. Agric For Meteorol 126: 15–31.
View Article
Google Scholar

[46] View Article

[47] Google Scholar

[ref18] 18. Powell TL, Bracho R, Li J, Dore S, Hinkle CR, et al. (2006) Environmental controls over net ecosystem carbon exchange of scrub oak in central Florida. Agric For Meteorol 141: 19–34.
View Article
Google Scholar

[49] View Article

[50] Google Scholar

[ref19] 19. Gilmanov TG, Aires L, Barcza Z, Baron VS, Belelli L, et al. (2010) Productivity, respiration, and light-response parameters of world grassland and agroecosystems derived from flux-tower measurements. Rangeland Ecol Manage 63: 16–39.
View Article
Google Scholar

[52] View Article

[53] Google Scholar

[ref20] 20. Wild M (2009) Global dimming and brightening: a review. J Geophys Res 114: D00D16
View Article
Google Scholar

[55] View Article

[56] Google Scholar

[ref21] 21. Rocha AV, Su HB, Vogel CS, Schmid HP, Curtis PS (2004) Photosynthetic and water use efficiency responses to diffuse radiation by an aspen-dominated northern hardwood forest. Forest Sci 50 (6): 793–801.
View Article
Google Scholar

[58] View Article

[59] Google Scholar

[ref22] 22. Zhang M, Yu GR, Zhang LM, Sun XM, Wen XF, et al. (2010) Impact of cloudiness on net ecosystem exchange of carbon dioxide in different types of forest ecosystem in China. Biogeosciences 7: 711–722.
View Article
Google Scholar

[61] View Article

[62] Google Scholar

[ref23] 23. Urban O, Klem K, Ač A, Havránková K, Holišová P, et al. (2012) Impact of clear and cloudy sky conditions distribution of photosynthetic CO₂ spruce canopy. Funct Ecol 26: 46–55.
View Article
Google Scholar

[64] View Article

[65] Google Scholar

[ref24] 24. Suyker AE, Verma SB, Burba GG, Arkebauer TJ, Walters DT, et al. (2004) Growing season carbon dioxide exchange in irrigated and rainfed maize. Agric For Meteorol 124: 1–13.
View Article
Google Scholar

[67] View Article

[68] Google Scholar

[ref25] 25. Béziat P, Ceschia E, Dedieu G (2009) Carbon balance of a three crop succession over two cropland sites in South West France. Agric For Meteorol 149: 1628–1645.
View Article
Google Scholar

[70] View Article

[71] Google Scholar

[ref26] 26. Wild M, Roesch A, Ammann C (2012) Global dimming and brightening – evidence and agricultural implications. CAB Reviews 7(003): 1–7.
View Article
Google Scholar

[73] View Article

[74] Google Scholar

[ref27] 27. Mercado LM, Bellouin N, Sitch S, Boucher O, Huntingford C, Wild M, et al. (2009) Impact of changes in diffuse radiation on the global land carbon sink. Nature 458: 1014–1017.
View Article
Google Scholar

[76] View Article

[77] Google Scholar

[ref28] 28. Wang F, He ZH, Ken Sayre K, Li SD, Si JS, et al. (2009) Wheat cropping systems and technologies in China. Field Crop Res 111: 181–188.
View Article
Google Scholar

[79] View Article

[80] Google Scholar

[ref29] 29. NBSC, National Bureau of Statistics of China, 1998. China Statistical Yearbook. China Stat. Press, Beijing.

[ref30] 30. Tong XJ, Li J, Zhang XS, Yu Q, Qin Z, et al. (2007) Mechanism and bio-environmental controls of ecosystem respiration in a cropland in the North China Plains. New Zeal J Agr Res 50 (5): 1347–1358.
View Article
Google Scholar

[83] View Article

[84] Google Scholar

[ref31] 31. McMillen RT (1988) An eddy correlation technique with extended applicability to non-simple terrain. Boundary Layer Meteorol 43: 231–245.
View Article
Google Scholar

[86] View Article

[87] Google Scholar

[ref32] 32. Webb EK, Pearman GI, Leuning R (1980) Correction of flux measurements for density effects due to heat and water vapor transfer. Quart J R Meteorol Soc 106: 85–100.
View Article
Google Scholar

[89] View Article

[90] Google Scholar

[ref33] 33. Falge E, Baldocchi D, Olson R, Anthoni P, Aubinet M, et al. (2001) Gap filling strategies for defensible annual sums of net ecosystem exchange. Agric For Meteorol 107: 43–69.
View Article
Google Scholar

[92] View Article

[93] Google Scholar

[ref34] 34. Ruimy A, Jarvis PG, Baldocchi DD, Saugier B (1995) CO₂ fluxes over plant canopies and solar radiation: A review. Adv Ecol Res 26: 1–68.
View Article
Google Scholar

[95] View Article

[96] Google Scholar

[ref35] 35. Monteith JL, Unsworth MH (1990) Principles of Environmental Physics, second ed. Edward Arnold, London.

[ref36] 36. Monteith JL (1965) Evaporation and environment, in The state and Movement of Water in Living Organisms, Sym. Soc. Exp. Biol., Vol XI X, 205−234, Company of Biologists, Cambridge, England.

[ref37] 37. Rutter AJ, Kershaw KA, Robins PC, Morton AJ (1971) A predictive model of rainfall interception in forests, I. Derivation of the model from observations in a plantation of Corsican pine. Agric Meteorol 9: 367–384.
View Article
Google Scholar

[100] View Article

[101] Google Scholar

[ref38] 38. Lhomme J-P (1991) The concept of canopy resistance: historical survey and comparison of different approaches. Agric For Meteorol 54: 227–240.
View Article
Google Scholar

[103] View Article

[104] Google Scholar

[ref39] 39. Gu L, Fuentes JD, Shugart HH, Staebler RM, Black TA (1999) Response of net ecosystem exchanges of carbon dioxide to changes in cloudiness: results from two North American deciduous forests. J Geophys Res 104 (D24): 31,421–31,434.

[ref40] 40. Loescher HW, Oberbauer SF, Gholz HL, Clark DB (2003) Environmental controls on net ecosystem-level carbon exchange and productivity in a Central American tropic wet forest. Global Change Biol 9: 396–412.
View Article
Google Scholar

[107] View Article

[108] Google Scholar

[ref41] 41. Anthoni PM, Law BE, Unsworth MH (1999) Carbon and water vapor exchange of an open-canopied ponderosa pine ecosystem. Agric For Meteorol 95: 151–168.
View Article
Google Scholar

[110] View Article

[111] Google Scholar

[ref42] 42. Hirasawa T, Hsiao TC (1999) Some characteristics of reduced leaf photosynthesis at midday in maize growing in the field. Field Crop Res 62: 53–62.
View Article
Google Scholar

[113] View Article

[114] Google Scholar

[ref43] 43. Glenn AJ, Amiro BD, Tenuta M, Stewart SE, Wagner-Riddle C (2010) Carbon dioxide exchange in a northern Prairie cropping system over three years. Agric For Meteorol 150: 908–918.
View Article
Google Scholar

[116] View Article

[117] Google Scholar

[ref44] 44. Dufranne D, Moureaux C, Vancutsem F, Bodson B, Aubinet M (2011) Comparison of carbon fluxes, growth and productivity of a winter wheat crop in three contrasting growing seasons. Agr Ecosyst Environ 141: 133–142.
View Article
Google Scholar

[119] View Article

[120] Google Scholar

[ref45] 45. Flexas J, Medrano H (2002) Drought-inhibation of photosynthesis in C3 Plants: Stomatal and Non-stomatal limitations revisited. Ann Bot 89: 183–189.
View Article
Google Scholar

[122] View Article

[123] Google Scholar

[ref46] 46. Jarvis PG, James GB, Landsberg JJ (1976) Coniferous forest. In: Vegetation and the Atmosphere (ed. Monteith J L), 171–240. Academic Press, London.

[ref47] 47. Gu L, Baldocchi DD, Wofsy SC, Munger JW, Michalsky JJ, et al. (2003) Response of a deciduous forest to the Mount Pinatubo eruption: enhanced photosynthesis. Science 299: 2035–2038.
View Article
Google Scholar

[126] View Article

[127] Google Scholar

[ref48] 48. Urban O, Janouš D, Acosta M, Czerný R, Marková I, et al. (2007) Ecophysiological controls over the net ecosystem exchange of mountain spruce stand. Comparison of the response in direct vs. diffuse solar radiation. Global Change Biol 13: 157–168.
View Article
Google Scholar

[129] View Article

[130] Google Scholar

[ref49] 49. Kuiper PJC (1964) Dependence upon wavelength of stomatal movement in epidermal tissue of Senecio odoris. Plant Physiol 39: 952–955.
View Article
Google Scholar

[132] View Article

[133] Google Scholar

[ref50] 50. Mansfield TA, Meidner H (1966) Stomatal opening in light of different wavelengths: effects of blue light independent of carbon dioxide concentration. J Exp Bot 17: 510–521.
View Article
Google Scholar

[135] View Article

[136] Google Scholar

[ref51] 51. Morison JIL, Jarvis PG (1983) Direct and indirect effects of light on stomata. I. In Scots pine and Sitka spruce. Plant Cell Environ 6: 95–101.
View Article
Google Scholar

[138] View Article

[139] Google Scholar

[ref52] 52. Karlsson PE (1986) Blue light regulation of stomata in wheat seedlings. II. Action spectrum and search for action dichroism. Physiol Plant 66: 207–210.
View Article
Google Scholar

[141] View Article

[142] Google Scholar

[ref53] 53. Pejam MR, Arain MA, McCaughey JH (2006) Energy and water vapour exchanges over a mixedwood boreal forest in Ontario, Canada. Hydrol Process 20: 3709–3724.
View Article
Google Scholar

[144] View Article

[145] Google Scholar

[ref54] 54. Aphalo PJ, Jarvis PG (1993) Separation of direct and indirect responses of stomata to light: results from a leaf inversion experiment at constant intercellular CO₂ molar fraction. J Exp Bot 44: 791–800.
View Article
Google Scholar

[147] View Article

[148] Google Scholar

[ref55] 55. Matsuda R, Ohashi-Kaneko K, Fujiwara K, Goto E, Kurata K (2004) Photosynthetic characteristics of rice leaves grown under red light with or without supplemental blue light. Plant Cell Physiol 45: 1870–1874.
View Article
Google Scholar

[150] View Article

[151] Google Scholar

Correction

Figures

Abstract

Introduction

Materials and Methods

Study site

Field observations

Flux data quality control

Light response of NEE

Canopy conductance

Defining sunny and cloudy sky conditions

Results

NEE Light response and influencing factors

Light response parameters: seasonal variation and influencing factors

NEE Light response and NEE−gc relationships under various sky conditions

Discussion

Factors influencing NEE light response

Factors influencing light response parameters

NEE Light response and NEE−gc relations under various sky conditions

Acknowledgments

Author Contributions

References

NEE Light response and NEE−g_c relationships under various sky conditions

NEE Light response and NEE−g_c relations under various sky conditions