Variability in carbon dioxide fluxes among six winter wheat paddocks managed under different tillage and grazing practices

Atmospheric Environment 185 (2018) 100–108

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Variability in carbon dioxide fluxes among six winter wheat paddocks managed under different tillage and grazing practices

T

Pradeep Waglea,∗, Prasanna H. Gowdaa, Brian K. Northupa, Kenneth E. Turnera, James P.S. Neela, Priyanka Manjunathab, Yuting Zhoub a b

USDA, Agricultural Research Service, Grazinglands Research Laboratory, El Reno, OK 73036, USA Oklahoma State University, Department of Plant and Soil Sciences, Stillwater, OK 74078, USA

A R T I C LE I N FO

A B S T R A C T

Keywords: Ecosystem light use efficiency Eddy covariance Net ecosystem CO2 exchange No-till Spatial variability

Carbon dioxide (CO2) fluxes from six winter wheat (Triticum aestivum L.) paddocks (grain only, graze-grain, and graze-out) managed under conventional till (CT) and no-till (NT) systems were synthesized for the 2016–2017 growing season to compare the magnitudes and seasonal dynamics of CO2 fluxes and to investigate among-site variability of CO2 fluxes. Large variations in CO2 fluxes were observed among paddocks. Maximum daily (7-day averages) net ecosystem CO2 exchange (NEE) ranged from −3.39 to −8.68 g C m−2, gross primary production (GPP) ranged from 7.33 to 16.92 g C m−2, and ecosystem respiration (ER) ranged from 5.85 to 9.98 g C m−2. Seasonal sums of NEE ranged from −137 to −542 g C m−2. Optimum photosynthetically active radiation (PAR), air temperature (Ta), and vapor pressure deficit (VPD) for NEE were approximately 1700 μmol m−2 s−1, 22 °C, and 1.25 kPa, respectively. Across-site analysis showed percent of canopy cover (Canopy%) was strongly correlated with NEE (R2 = 0.76) and ecosystem light use efficiency (ELUE, R2 = 0.76). Integration of PAR with leaf area index (LAI) and integration of Ta with dry biomass weight (DW) explained 81% and 74% of variations in GPP and ER, respectively. Remotely-sensed enhanced vegetation index (EVI) explained 66% and normalized difference vegetation index (NDVI) explained 69% of the variations in NEE. Integration of PAR with NDVI or EVI explained ∼80% of variations in GPP, while NDVI × Ta explained 58% of variations in ER. Results illustrated that differences in wheat canopies related to paddock management, as indicated by differences in DW, LAI, Canopy%, NDVI, and EVI, must be accounted for explaining among-site variability of CO2 fluxes. Long-term measurements from our clustered and paired eddy covariance towers will provide insights into the effects of tillage and different grazing practices on CO2 dynamics in winter wheat.

1. Introduction There has been considerable interest in the measurement of net ecosystem exchange of carbon dioxide (CO2) between terrestrial ecosystems and the atmosphere due to the rising concentration of atmospheric CO2 and air temperature (Ta) (Liu et al., 2015). Consequently, the application of eddy covariance (EC) has rapidly increased in recent years (Baldocchi, 2014) to measure CO2, water vapor (H2O), and energy fluxes at the ecosystem level. Information gleaned from the analysis of EC data will be helpful for development of best management practices, identifying agricultural systems that are resilient under climate variability, and improving productivity and resource-use efficiencies while minimizing environmental impacts. However, it is not possible to measure fluxes at all sites at all times. Thus, the temporal and spatial variability of CO2 fluxes and its underlying mechanisms for

∗

Corresponding author. E-mail address: [email protected] (P. Wagle).

https://doi.org/10.1016/j.atmosenv.2018.05.003 Received 9 February 2018; Received in revised form 17 April 2018; Accepted 4 May 2018 Available online 07 May 2018 1352-2310/ Published by Elsevier Ltd.

major agroecosystems must be defined to extrapolate measured fluxes over large areas and long periods of time. Several studies have examined the spatial variability of CO2 fluxes and have shown that spatial variability was mostly associated with mean annual temperature or precipitation and/or canopy greenness and coverage (Churkina et al., 2005; Gilmanov et al., 2005; Kato and Tang, 2008; Wagle et al., 2015b; Yuan et al., 2009). However, most of these synthesis studies have examined fluxes across climatic gradients and/or assembled multiple biomes together. In contrast, synthesis of flux data for the same biome from multiple sites under different management practices within the same climatic condition allows investigation of the effects of non-climatic factors on the fluxes and an evaluation of different management practices. Wheat (Triticum aestivum L.) is one of the most widely grown grain crops in the world. Winter wheat is a major annual crop in the Southern

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Great Plains (SGP - Kansas, Oklahoma, and Texas) of the United States. The SGP accounts for approximately 30% of wheat production in the United States and the majority of the winter wheat is grown under rainfed conditions (Lollato and Edwards, 2015). Although conservation tillage (e.g., no or minimum tillage, residue management) has gained more interest among producers to improve production and reduce negative impacts on the environment, both conventional and conservation tillage are common for winter wheat in the region (Hossain et al., 2004). Wheat is also an adaptable cereal crop that is managed by producers for a range of potential commodities, and often generates multiple products in a single year (Redmon et al., 1995). Winter wheat management in the SGP includes grain-only (no grazing), graze-grain (dual purpose wheat–grazing from mid-November to late February by beef stockers), and graze-out (no grain production–grazing from mid-November through April by beef stockers) (Phillips et al., 1999). It is well recognized that CO2 dynamics of ecosystems are influenced by management practices and disturbances (Angers et al., 1997; Moinet et al., 2017; Wagle et al., 2017; Zhou et al., 2017). Although some studies have reported CO2 dynamics of wheat paddocks under individual forms of management (Anthoni et al., 2004; Fischer et al., 2007; Gilmanov et al., 2003; Schmidt et al., 2012), there is a scarcity of comparative studies of CO2 fluxes in winter wheat paddocks experiencing the same climate in response to different grazing and tillage practices. Direct measurement of CO2 fluxes associated with major climatic variables by EC systems, biometric measurements at study sites, and remotely-sensed vegetation indices from satellites allow an examination of spatial and temporal variability of CO2 fluxes in response to major controlling factors, and an investigation of the functional response of CO2 fluxes to major climatic variables at the ecosystem level. This study reports CO2 fluxes from multi-purpose production-scale paddocks of rainfed winter wheat (grain-only, graze-grain, and graze-out) managed under conventional till (CT) and no-till (NT) systems. The objectives of this study were to: 1) compare seasonal dynamics and magnitudes of CO2 fluxes in grain-only, graze-grain, and graze-out winter wheat paddocks managed under CT and NT systems; 2) examine the responses of net ecosystem CO2 exchange (NEE) to major climatic variables; and 3) investigate the major controlling factors for among-site variability (spatial patterns) of CO2 fluxes across management systems.

Fig. 1. Experimental site and location of eddy covariance flux towers. CT and CNT are canola-till and canola no-till, respectively. See Table 1 or Appendix for definition of abbreviations for other paddocks.

grain winter wheat (3rd year); and graze-out winter wheat (4th year). The tillage component of the experiment was initiated in 2015 and grazing was begun in 2016 with groups of yearling stocker cattle assigned to remove wheat biomass. A cluster of EC towers (Fig. 1) were deployed at the beginning of the 2016–2017 growing season (September/October 2016). Winter wheat (cv. Gallagher at approximately 90 kg seeds ha−1) was sown at 7.5 cm row spacing. Wheat paddocks were managed for high production potential using practices common to the region, including fertilizer, herbicide, and pesticide applications. Detailed management practices for the paddocks during the 2016–2017 growing season are presented in Table S1. The weather at the study site is characterized as temperate continental, with 30-year mean (1981–2010) annual temperature of ∼15 °C and precipitation of 925 mm. The winter wheat growing season (October 2016–May 2017) received 517 mm of total precipitation, while the 30-year mean for the same period was 567 mm. Rainfall was lower from October 2016 to January 2017, but higher in February and April 2017 when compared to the long-term means. Soil moisture had close correspondence with rainfall patterns (Fig. 2). Soil moisture was lower than 0.1 m3 m−3 for most part until mid-January due to low rainfall and increased substantially after rainfall. It decreased sharply at the beginning of the fast crop growth during mid-February and increased again after rainfall. Compared to the long-term means, October–November 2016 and January–March 2017 periods were slightly warmer. Although there were some drier and warmer periods, overall the crop did not experience long severe droughts during the growing

2. Materials and methods 2.1. Site description and weather conditions The study site (178 ha) is situated on the USDA-ARS Grazinglands Research Laboratory (GRL), El Reno, Oklahoma (35°33′29″ N, 98°1′50″ W, and ∼414 m above sea level). A common experiment named the “Grazinglands Research on agroEcosystems and the ENvironment (GREEN) farm” was developed and initiated in 2014. The primary soils within the area were a complex of Renfrow-Kirkland silt loams, Bethany silt loams, and Norge silt loams (Mollisols) with an average pH ≤ 5.8, electrical conductivity < 300 μS cm−1, and soil bulk density (ρb) of 1.34–1.45 g cm−3 (USDA-NRCS, 1999). The study sites are also a part of the USDA Long-Term Agroecosystem Research (LTAR) network and GRL-FLUXNET (a network of integrated flux measurement systems at the GRL) project. The landscape of the site contained five zones with different exposures; northern, southern, and eastern exposures, a rolling, and undulating landscape without a dominant exposure (Fig. 1). The area of each zone was sub-divided into two paired paddocks, resulting in 10 total paddocks that were assigned to either CT or NT management. Management also included production of different products each year by the paired paddocks. Two paddocks were assigned to graze-out winter wheat each year (as controls), while the rest of the remaining pairs of paddocks followed a four-year crop rotation: canola (Brassica napus L.) for grain (1st year); grain-only winter wheat (2nd year); graze-

Fig. 2. Seasonal patterns of soil moisture (5 cm depth) during the 2016–2017 growing season. See Table 1 or Appendix for definition of abbreviations for paddocks. 101

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ranged from 0.65 to 0.69 for four grazed paddocks. Energy balance of EC measurements ranged from 0.7 to 0.9 in most cases (Foken, 2008; Wilson et al., 2002). The EC systems were fenced at the grazed paddocks to protect from cattle. Due to fencing, wheat biomass was higher inside enclosures than the outside grazed paddocks. All soil sensors were installed inside enclosures for protection but net radiometers were directed outside to grazed areas from the mounting poles on the edges of enclosures. Slightly lower values of energy balance at our grazed paddocks can, therefore, be attributed to underestimation of G fluxes due to more biomass inside fenced areas.

season. 2.2. Eddy covariance and meteorological data collection, and processing of fluxes An EC system, comprised of a 3-D sonic anemometer (CSAT3, Campbell Scientific Inc., Logan, Utah, USA) and an open path infrared gas analyzer (LI-7500-RS, LI-COR Inc., Lincoln, Nebraska, USA), was installed near the center of each six paddocks, ranging from ∼10 to 28 ha. The EC towers were 2.5 m tall and fluxes were collected at 10 Hz frequency. The wheat planting area was large enough to provide adequate fetch length for these short towers. The fetch length for the EC tower ranged from more than 100 m to few hundred meters (in most paddocks). When checked for a month of peak growth (April), 86–91% of CO2 fluxes with quality flag 0 (best quality) were obtained from inner 100 m radius of the EC tower (data not shown). Supporting meteorological variables measured included Ta and relative humidity (using HPM45C, Vaisala, Helsinki, Finland), soil heat (G) fluxes (using HFT3, REBS Inc., Bellevue, Washington, USA) at 8 cm depth, soil moisture content (using Hydra Probe, Stevens Water Monitoring Systems Inc., Portland, Oregon, USA) at 5 cm, soil temperature (using thermocouples) at 2 and 6 cm depths, land surface temperature (using infrared thermometer, Apogee Instruments Inc., Logan, Utah, USA), photosynthetically active radiation (PAR, using a quantum sensor LI-190, LI-COR Inc., Lincoln, Nebraska, USA), and net radiation (Rn, using NRLite, Kipp & Zonen, Delft, The Netherlands). The 30-min values of these meteorological variables were collected using a CR1000 datalogger (Campbell Scientific, Logan, UT, USA). Rainfall data for the El Reno Mesonet Station were obtained from the Oklahoma Mesonet website (www.mesonet.org, accessed April 7, 2017). Raw eddy fluxes were processed to compute 30-min fluxes using the EddyPro software version 6.2.0 (LI-COR Inc., Lincoln, Nebraska, USA). Flux datasets were screened by removing fluxes with quality flag 2 (fluxes that should be discarded), unreliable fluxes, and statistical outliers (i.e., beyond ± 3.5 standard deviation) based on a two-week running window (Wagle et al., 2015a, 2017). Sensible (H) and latent (LE) heat fluxes were further screened to keep in the range of −200 to 500 W m−2 and -200 to 800 W m−2, respectively (Sun et al., 2010; Wagle and Kakani, 2014). We screened fluxes for calm conditions (u* < 0.1 m s−1) during nighttime. Gaps in fluxes were filled and NEE was partitioned into its components (gross primary production, GPP and ecosystem respiration, ER) using the REddyProc package from the Max Planck Institute for Biogeochemistry, Germany. The tool employs gap filling methods as mentioned in a previous paper (Falge et al., 2001) and also accounts for the temporal auto-correlations of fluxes and the co-variation of fluxes with meteorological variables (Reichstein et al., 2005). The tool follows the methods similar to Reichstein et al. (2005) for NEE partitioning where ER is estimated from the relationship between Ta and nighttime NEE (Lloyd and Taylor, 1994). More details on this tool can found at https://www.bgc-jena.mpg.de/bgi/index.php/ Services/REddyProcWebRPackage. The sign convention for NEE in this study is that negative NEE is gain of carbon (carbon sinks) and positive NEE is loss of carbon (carbon sources) by the paddocks. Ecosystem light use efficiency (ELUE, g C mol−1 PAR) was computed as the ratio of sum of GPP to PAR (Wagle et al., 2016).

2.4. Biometric measurements Periodic measurements (approximately every two weeks, coinciding with Landsat overpass dates during the active growing season) of leaf area index (LAI, using LAI-2200C plant canopy analyzer, LI-COR Inc., Lincoln, Nebraska, USA) and percent of canopy cover (Canopy%, using Canopeo app, http://www.canopeoapp.com/) were collected from five random locations within a paddock at each sampling date. Aboveground biomass samples were destructively collected from 0.5 × 0.5 m2 quadrats. Both fresh weight and dry weight (DW) were recorded for biomass, with DW determined after samples were oven dried at 70 °C for a minimum of 48 h. Grain-only and graze-grain winter wheat paddocks were harvested for grain during June 8–14, 2017. 2.5. Landsat vegetation indices Landsat-derived vegetation indices (enhanced vegetation index, EVI and normalized difference vegetation index, NDVI) were computed from Landsat surface reflectance product (Landsat 7 ETM+ and Landsat 8) for the study area. The reflectance product was downloaded from the USGS EarthExplorer (http://earthexplorer.usgs.gov/). The data quality control approach that excluded the effect of clouds and cirrus on observations was provided in a previous paper (Zhou et al., 2016). 2.6. Examining the responses of NEE to major climatic variables (PAR, Ta, and VPD) To examine the response of NEE to major climatic variables [PAR, Ta, and vapor pressure deficit (VPD)], daytime NEE (PAR > 5 μmol m−2 s−1) for the active growing season (November 2016–April 2017) were binned in 12 classes of PAR (< 100, 100–200, 200–400, 400–600, 600–800, 800–1000, 1000–1200, 1200–1400, 1400–1600, 1600–1800, 1800–2000, and > 2000 μmol m−2 s−1), 13 classes of Ta (< −5, -5-0, 0–3, 3–6, 6–9, 9–12, 12–15, 15–18, 18–21, 21–24, 24–27, 27–30, and > 30 °C), and 10 classes of VPD (< 0.5, 0.5–1, 1–1.5, 1.5–2, 2–2.5, 2.5–3, 3–3.5, 3.5–4, 4–4.5, and > 4.5 kPa). Non-gap filled NEE data were pooled across all paddocks for the analyses. 2.7. Statistical analysis Quantitative relationships of daily (2-week averages, 1-week before and 1-week after the sampling or Landsat overpass dates) values of NEE, GPP, ER, and ELUE were examined with biometric variables (DW, LAI, and Canopy%) and Landsat-derived vegetation indices (EVI and NDVI) across all six winter wheat paddocks. Since biometric variables and vegetation indices can be correlated to each other, we did not use any two of them in the same regression to avoid multicollinearity. The most significant biometric variable or vegetation index was integrated with Ta or PAR to examine if there was a significant improvement on their relationships with CO2 fluxes and ELUE. Finally, we selected the model based on the highest level of significance and fit statistics such as the coefficient of determination (R2) and root mean square error (RMSE).

2.3. Energy balance The energy balance for the growing season was computed from a linear regression (i.e., energy balance = slope of the regression) between 30-min values (not gap-filled, 0 quality flag only) of available energy (Rn - G) vs. sum of turbulent fluxes (H + LE) for each paddock separately. Stored energy in soil (above the soil heat flux plates) and plant canopy were not accounted in the calculation of energy balance. The energy balance was 0.75 and 0.76 for grain-only CT and NT paddocks, respectively (data not shown). Similarly, the energy balance 102

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Fig. 3. Seasonal evolution of aboveground dry biomass, leaf area index (LAI), and percent of canopy cover for six winter wheat paddocks during the 2016–2017 growing season. See Table 1 or Appendix for definition of abbreviations for paddocks.

3. Results and discussion

increased with increasing crop growth during October/November, depending on sowing time. Cold weather reduced NEE and ELUE during December–January due to low photosynthetic activity. Both increased with rising temperature and increasing crop growth after mid-January. Both NEE and ELUE reached maximums during peak growth in April and then declined with the advance of crop senescence with maturity. Less pronounced differences in NEE and ELUE were observed among paddocks during winter. Grain-only paddocks were carbon sinks from mid-November to the first week of May. However, they were near carbon neutral (average NEE < −0.7 g C m−2 d−1) during mid-December to mid-January. Graze-out and graze-grain paddocks alternated between functioning as carbon sinks and sources. These paddocks were carbon sinks from mid/ late October through mid-December, switched to acting as carbon sources from mid-December to late January, and then switched back to carbon sinks through April. Results showed that all four winter wheat paddocks (both CT and NT) that were grazed functioned as carbon sources from mid-December to late January due to removal of biomass by cattle when grain-only paddocks were still small carbon sinks. Maximum NEE (7-day averages) reached approximately −8 g C m−2 d−1 and ELUE reached approximately 0.44 g C mol−1 PAR in April for both (CT and NT) grain-only paddocks (Fig. 4). Maximum NEE for graze-out paddocks reached up to −5.93 ± 0.35 g C m−2 d−1 (CT) and −3.14 ± 0.55 g C m−2 d−1 (NT) during mid-March. Maximum ELUE reached up to 0.24 g C mol−1 PAR for graze-out CT paddock in mid-April when it reached up to 0.17 g C mol−1 PAR for graze-out NT paddock. Maximum NEE reached approximately −5.25 ± 0.43 g C m−2 d−1 and maximum ELUE reached approximately 0.30 g C mol−1 PAR for graze-grain CT paddock in April when NEE reached up to

3.1. Vegetation characteristics and grain yield at six winter wheat paddocks Large variability was observed in aboveground DW, LAI, and Canopy% across six winter wheat paddocks (Fig. 3). Graze-out and graze-grain paddocks under NT management had poor stands due to an armyworm infestation at the beginning of the season, which necessitated a second planting for stand establishment. Maximum DW was about 0.3 kg m−2, LAI was < 4 m2 m−2, and Canopy% was 50–60% for these paddocks. Maximum DW reached about 1.3 kg m−2 for both (CT and NT) grain-only paddocks and 0.6 kg m−2 for graze-out and grazegrain paddocks under CT management. Maximum LAI reached 7.53 m2 m−2 for grain-only CT, 6.95 m2 m−2 for grain-only NT, 4.02 m2 m−2 for graze-out CT, and 4.22 m2 m−2 for graze-grain CT paddocks. Maximum Canopy% was around 97% for both (CT and NT) grain-only paddocks, 74% for graze-out CT, and 86% for graze-grain CT paddocks. Canopy height reached around 1 m in grain-only paddocks and 0.75 m in the graze-out and graze-grain paddocks. As expected, canopy height and DW showed a strong linear relationship (R2 = 0.82, data were pooled across six paddocks, data not shown). Grain yield averaged approximately 4.86 and 3.53 t ha−1 for grain-only CT and NT paddocks, respectively. Yields for graze-grain paddocks were 1.08 t ha−1 (CT) and 0.98 t ha−1 (NT). Graze-out paddocks were not harvested for grain. 3.2. Seasonal dynamics of NEE and ELUE at six winter wheat paddocks Both NEE and ELUE showed clear seasonality (Fig. 4). They 103

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Fig. 4. Seasonal dynamics (7-day average) of net ecosystem CO2 exchange (NEE) and ecosystem light use efficiency (ELUE) for six winter wheat paddocks during the 2016–2017 growing season. Error bars were not plotted to improve the readability of the graph. See Table 1 or Appendix for definition of abbreviations for paddocks.

−2.23 ± 0.46 g C m−2 d−1 and ELUE reached up to 0.18 g C mol−1 PAR for graze-grain NT paddock. Identical seasonal dynamics and magnitudes of NEE and ELUE for grain-only CT and NT paddocks (Fig. 4) indicated no impact of tillage systems on NEE and ELUE. Due to this reason, substantially smaller magnitudes of NEE and ELUE for graze-out and graze-grain paddocks under NT management as compared to their CT counterparts from February to May (Fig. 4) cannot solely be attributed to tillage practices. As mentioned earlier, graze-out and graze-grain paddocks under NT management had poor stands due to armyworm infestations, as reflected by lower biomass, LAI, and Canopy%. The poor vegetation stands resulted in smaller magnitudes of NEE and ELUE. Since tillage practices were initiated in 2015 (one year prior to measurement), more years may be required to see actual effects of tillage practices on CO2 fluxes and ELUE. The maximum daily NEE in our study approximated −9 g C m−2 d−1 for grain-only winter wheat, which was similar to those reported (< −10 C m−2 d−1) for winter wheat in Ponca City and Billings, Oklahoma (Arora, 2003; Fischer et al., 2007). However, NEE rates in our study were lower than those (approximately −12 g C m−2 d−1) reported for winter wheat in Germany (Anthoni et al., 2004; Schmidt et al., 2012) despite similar maximum DW of around 1.3 kg m−2 and LAI > 6 m2 m−2. Grain yield was lower (∼5 t ha−1 or less) in Oklahoma compared to ∼8 t ha−1 in Germany (Anthoni et al., 2004).

saturated at PAR of ∼1500 μmol m−2 s−1 for wheat grown even under elevated CO2 (Bunce, 2000). Similarly, NEE increased almost linearly with increasing Ta up to ∼22 °C, then declined sharply as Ta increased further. The NEE increased rapidly with increasing VPD up to ∼1.25 kPa, plateaued up to ∼1.75 kPa, and then declined almost linearly as VPD increased further. Activity of Ribulose-1, 5-bisphosphate carboxylase/oxygenase (RuBISCO) is enhanced by simultaneously increasing Ta and VPD, resulting in higher carbon uptake up to certain thresholds of Ta and VPD. Thereafter, carbon uptake is suppressed due to stomatal regulation (Turner et al., 1984). Other non-stomatal effects (Morison and Gifford, 1983) such as increased photorespiration due to greater increase in oxygenating reaction of RuBISCO than the carboxylating reaction (Long, 1991) or increased heterotrophic respiration at high Ta can also reduce net carbon uptake by the ecosystem. Optimum values for threshold Ta and VPD are different for different crop species. Our results showed that optimum thresholds for Ta and VPD for NEE in winter wheat were approximately 22 °C and 1.25 kPa, respectively. A review article reported an optimum Ta range of 17–23 °C for wheat over the course of the growing season (Porter and Gawith, 1999). Another study reported an optimum Ta of 25 °C for photosynthesis in wheat (Wardlaw, 1974). Note that optimum Ta for photosynthesis changes according to environmental conditions such as mean growth Ta or the degree of water stress. For example, the optimum Ta for photosynthesis in winter wheat increased at a rate of 3 °C for each 4 °C increase in mean growth Ta (Sawada, 1970), most likely due to acclimation of stomatal conductance, RuBISCO carboxylation efficiency, and photosynthetic apparatus to high Ta. In comparison, the mean daily Ta for the growing season (October 2016–May 2017) in our study was ∼11.5 °C. In addition, optimum Ta varies with genotype and stage of development in

3.3. Responses of NEE to major climatic variables Response of NEE to major climatic variables (PAR, Ta, and VPD) is shown in Fig. 5. The NEE increased almost linearly with increasing PAR up to ∼1700 μmol m−2 s−1, then plateaued or declined as PAR increased further. A previous study showed that stomatal conductance 104

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Fig. 5. Response of net ecosystem CO2 exchange (NEE) to photosynthetically active radiation (PAR), air temperature (Ta), and vapor pressure deficit (VPD) for winter wheat. Daytime NEE for the active growing season (November 1, 2016–April 30, 2017) were pooled across six winter wheat paddocks. Bars represent standard errors of the means.

wheat (Slafer and Rawson, 1995). Like Ta, optimum VPD for photosynthesis will vary according to environmental conditions even for the same species. In addition, functional responses at the ecosystem-level will greatly differ from traditional leaf-level measurements. For example, stomatal response to VPD can differ for leaves at different positions/heights. Thus, study of functional responses at the ecosystemlevel will be helpful for modeling at larger spatial scales (i.e., regional and global) under changing climate.

Table 1 Growing season sums of net ecosystem CO2 exchange (NEE, g C m−2), gross primary production (GPP, g C m−2), and ecosystem respiration (ER, g C m−2), and growing season average ecosystem light use efficiency (ELUE, computed as the ratio of GPP to PAR, g C mol−1 PAR) during the 2016–2017 growing season for six winter wheat paddocks.

3.4. Variability in NEE, GPP, ER, and ELUE across six winter wheat paddocks Cumulative NEE, GPP, and ER for the growing season are presented in Table 1. Results showed that all paddocks were carbon sinks at the seasonal scale. However, large variations in seasonal sums of CO2 fluxes (NEE ranged from −137 to −542 g C m−2, GPP ranged from 893 to 1643 g C m−2, and ER ranged from 694 to 1145 g C m−2) were observed among paddocks. Based on the level of biomass removal by grazing cattle, seasonal sums of NEE (negative sign convention) were in the expected order of grain only > graze-grain > graze-out for CT paddocks, where we did not have issues of armyworm infestation, even

Sites

Day of the Year

NEE

GPP

ER

ELUE

Grain-only Till (GT) Grain-only No-till (GNT) Graze-out Till (GOT) Graze-out No-till (GONT) Graze-grain Till (GGT) Graze-grain No-till (GGNT)

306–163 306–163 301–163 301–163 273–163 273–163

−542 −469 −200 −158 −303 −137

1643 1571 893 918 1147 1113

1101 1102 694 760 1145 976

0.24 0.23 0.12 0.12 0.18 0.14

though grain-only paddocks had shorter growing seasons (i.e., planted late). However, NEE sum was higher in graze-out paddock than armyworm infested graze-grain paddock under NT system. Large difference in vegetation stand due to armyworm damage caused a huge difference in NEE sum between graze-grain paddocks under CT (−303 g C m−2) and NT (−137 g C m−2) systems for the same period. 105

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Table 2 Maximum daily (7-day average, g C m−2 d−1) net ecosystem CO2 exchange (NEE), gross primary production (GPP), and ecosystem respiration (ER) during the 2016–2017 growing season for six winter wheat paddocks. Sites

NEE

GT GNT GOT GONT GGT GGNT

−7.79 −8.68 −5.93 −3.54 −5.25 −3.39

± ± ± ± ± ±

1.08 0.87 0.35 0.23 0.43 0.26

GPP

ER

16.62 ± 0.95 16.92 ± 0.49 12.39 ± 0.58 7.33 ± 0.24 12.31 ± 0.34 7.9 ± 0.30

9.47 ± 0.23 9.98 ± 0.51 8.78 ± 0.34 5.85 ± 0.31 9.1 ± 0.30 6.94 ± 0.21

Table 3 A summary of regression analyses for among-site variability of net ecosystem CO2 exchange (NEE), gross primary production (GPP), ecosystem respiration (ER), and ecosystem light use efficiency (ELUE) against biometric variables [leaf area index (LAI), dry weight (DW), and percent of canopy cover (Canopy %)]. Most significant, and simple and robust regressions are highlighted in bold. S. No.

Regression equation

Response variable: NEE 1 4.38 DW2–9.37 DW–0.16 2 −0.88 LAI–0.11 3 −0.07 Canopy% + 1.3 4 −0.004 Canopy% × Ta–0.12 5 −0.002 Canopy% × PAR +0.64 Response variable: GPP 1 −10.42 DW2 + 22.18 DW + 1.05 2 1.84 LAI +1.44 3 0.002 Canopy%2–0.07 Canopy% + 5.03 4 0.11 LAI × Ta + 3.09 5 0.05 LAI × PAR + 1.97 Response variable: ER 1 −6.04 DW2 + 12.81 DW + 0.89 2 0.96 LAI +1.33 3 0.05 Canopy% + 1.49 4 −0.004 (DW × PAR)2 + 0.31 (DW × PAR) + 1.5 5 −0.024 (DW × Ta)2 + 0.73 (DW × Ta) + 1.93 Response variable: ELUE 1 −0.19 DW2 + 0.42 DW + 0.1 2 0.045 LAI + 0.08 3 6E-05 Canopy%2–0.004 Canopy% + 0.22

Large variations in the magnitudes of CO2 fluxes were observed among paddocks (Table 2). Maximum daily (7-day averages) NEE ranged from −3.39 ± 0.26 to −8.68 ± 0.87 g C m−2 d−1, GPP ranged from 7.33 ± 0.24 to 16.92 ± 0.95 g C m−2 d−1, and ER ranged from 5.85 ± 0.31 to 9.98 ± 0.51 g C m−2 d−1. Grain-only paddocks had the largest rates of CO2 fluxes due to higher biomass. Like seasonal NEE sums, the magnitudes of NEE were also substantially lower in armyworm infested graze-out and graze-grain NT paddocks as compared to their CT counterparts. Growing season average ELUE values were 0.23–0.24 g C mol−1 PAR for grain-only CT and NT paddocks (Table 2). In comparison, they were 0.12 g C mol−1 PAR for both (CT and NT) graze-out paddocks, and 0.18 and 0.14 g C mol−1 PAR for graze-grain CT and NT, respectively. These among-site differences in CO2 fluxes and ELUE can be attributed to large discrepancies in vegetation stands and ecosystem structures among paddocks due to grazing and other factors. For example, maximum LAI ranged from < 4 to 7.5 m2 m−2, maximum DW ranged from 0.3 to 1.3 kg m−2, and maximum Canopy% ranged from 50 to 97% among the paddocks. Thus, we examine the relationships between among-site variability in vegetation characteristics/ecosystem structures and CO2 fluxes in the following sections.

Model

R2

RMSE

PR LR LR LR LR

0.44 0.63 0.76 0.65 0.71

1.45 1.04 0.89 1.09 1.24

PR LR PR LR LR

0.67 0.73 0.67 0.78 0.81

2.09 1.74 3.46 1.57 1.47

PR LR LR PR

0.70 0.54 0.30 0.72

1.13 1.38 1.69 1.09

PR

0.74

1.05

PR LR PR

0.39 0.65 0.76

0.074 0.051 0.048

LR: linear regression, PR: polynomial regression, R2: the coefficient of determination, and RMSE: root mean square error.

models (Ruimy et al., 1999). Consequently, we observed strong relationships of ELUE with LAI (R2 = 0.65) and Canopy% (R2 = 0.76). Aboveground DW was the most significant biometric variable to influence ER (R2 = 0.70, RMSE = 1.13 g C m−2 d−1, P < 0.0001). The DW-ER relationship was slightly improved when DW was integrated with PAR (R2 = 0.72 and RMSE = 1.09 g C m−2 d−1) or Ta (R2 = 0.74 and RMSE = 1.05 g C m−2 d−1). It is important to note that ER includes both autotrophic and heterotrophic respiration, and it is difficult to separate heterotrophic respiration from plant-based respiration (Reichstein et al., 2014).

3.5. Quantitative relationships of NEE, GPP, ER, and ELUE with biometric variables (LAI, DW, and canopy%) A summary of regression analyses for among-site variability of CO2 fluxes and ELUE against biometric variables is provided in Table 3. Aboveground DW showed polynomial relationships, best described by quadratic functions, with NEE (R2 = 0.44), GPP (R2 = 0.67), ER (R2 = 0.70), and ELUE (R2 = 0.39) since CO2 fluxes and ELUE remained relatively constant or declined above DW of 0.8 kg m−2. These relationships are supported by aboveground DW reaching a maxima in May at full maturity, while CO2 fluxes peaked in April. The LAI showed linear relationships with NEE (R2 = 0.63), GPP (R2 = 0.73), ER (R2 = 0.54), and ELUE (R2 = 0.65) since both peaked in April and declined with increasing vegetation senescence in May. Canopy% showed linear relationships with NEE (R2 = 0.76) and ER (R2 = 0.30), and polynomial relationships with GPP (R2 = 0.67) and ELUE (R2 = 0.76). Canopy% was the most significant biometric variable to influence NEE (R2 = 0.76, RMSE = 0.89 g C m−2 d−1, and P < 0.0001) and ELUE (R2 = 0.76, RMSE = 0.048 g C mol−1 PAR, and P < 0.0001). High canopy coverage allows for high light absorption by the ecosystem, resulting in greater photosynthetic activity or carbon uptake. The LAI was the most significant biometric variable to influence GPP (R2 = 0.73, RMSE = 1.74 g C m−2 d−1, and P < 0.0001). The LAI-GPP relationship was improved when LAI was integrated with Ta (R2 = 0.78 and RMSE = 1.57 g C m−2 d−1) or PAR (R2 = 0.81 and RMSE = 1.47 g C m−2 d−1). As green leaf area increases, PAR absorption by the canopy increases. The close relationship between LAI and PAR absorption found in this study supports the calculation of the fraction of PAR absorbed by the canopy (FAPARcanopy) as a function of LAI and light extinction coefficient in light use efficiency based GPP

3.6. Quantitative relationships of NEE, GPP, ER, and ELUE with Landsat vegetation indices Quantitative relationships of NEE, GPP, ER, and ELUE were examined with Landsat-derived EVI and NDVI across six paddocks (Table 4). The EVI and NDVI explained 66% and 69% of the variations in NEE, respectively. The NEE-EVI and NEE-NDVI relationships were not improved when EVI and NDVI were integrated with Ta or PAR (data not shown). However, the relationships of GPP with NDVI and EVI were improved substantially when integrated with Ta or PAR. The EVI × PAR or NDVI × PAR explained 80–81% of variations in GPP. Both EVI and NDVI showed poor relations with ER (R2 < 0.21). The integration of Ta with NDVI (NDVI × Ta) explained 58% of variations in ER. The EVI and NDVI explained 62% and 72% of the variations in ELUE. The ELUE-EVI or ELUE-NDVI relationships were not improved when EVI and NDVI were integrated with Ta or PAR (data not shown). Strong correspondence between CO2 fluxes and remotely-sensed vegetation indices showed the potential of vegetation indices for identifying winter wheat phenology and modeling of CO2 fluxes. Our results suggest that differences in ecosystem structure such as DW, LAI, Canopy% or remotely-sensed vegetation indices must be taken into account to explain among-site variability of CO2 fluxes for winter wheat. Since in-situ measurement of ecosystem structure is difficult and time consuming, accurate estimations of DW, LAI, and Canopy% from 106

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Table 4 A summary of regression analyses for among-site variability of net ecosystem CO2 exchange (NEE), gross primary production (GPP), ecosystem respiration (ER), and ecosystem light use efficiency (ELUE) against Landsat-derived enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI). Most significant, and simple and robust regressions are highlighted in bold. S. No.

Regression equation

Response variable: NEE 1 9.42 EVI2–19.42 EVI +5.16 2 −11.24 NDVI + 5.79 Response variable: GPP 1 20.54 EVI2–8.97 EVI +4.83 2 0.04 (EVI × Ta)2 + 0.53 (EVI × Ta) + 1.49 3 0.014 (EVI × PAR)2 + 0.01 (EVI × PAR) + 2.1 4 32.53 NDVI2–27.51 NDVI + 8.94 5 0.06 (NDVI × Ta)2–0.001 (NDVI × Ta) + 2.09 6 0.02 (NDVI × PAR)2–0.29 (NDVI × PAR) + 4.02 Response variable: ER 1 29.95 EVI2–28.38 EVI + 9.99 2 0.49 (EVI × Ta) + 1.79 3 0.18 (EVI × PAR) + 1.72 4 28.30 NDVI2–33.66 NDVI + 13.34 5 0.02 (NDVI × Ta)2 + 0.2 (NDVI × Ta) + 1.56 6 0.008 (NDVI × PAR)2–0.13 (NDVI × PAR) + 3.41 Response variable: ELUE 1 0.33 EVI2 + 0.07 EVI + 0.06 2 0.82 NDVI2–0.58 NDVI + 0.19

Model

R2

RMSE

PR LR

0.66 0.69

1.43 1.34

PR PR PR

0.38 0.73 0.81

2.69 1.78 1.5

PR PR PR

0.48 0.70 0.80

2.45 1.90 2.63

PR LR LR PR PR PR

0.21 0.38 0.27 0.18 0.58 0.48

1.94 1.72 1.87 1.98 1.41 1.59

PR PR

0.62 0.72

0.06 0.05

Acknowledgments This study was supported in part by a research grant (Project No. 2013-69002) through the USDA-NIFA's Agriculture and Food Research Initiative (AFRI). The authors would like to thank the University of Oklahoma team for two EC systems, Craig Mittelstaedt for management of the study paddocks, and Jeff Weik, Kory Bollinger, and graduate students for assistance in data collection. Appendix A. Supplementary data Supplementary data related to this article can be found at http://dx. doi.org/10.1016/j.atmosenv.2018.05.003. AppendixDefinition of abbreviations

LR: linear regression, PR: polynomial regression, R2: the coefficient of determination, and RMSE: root mean square error.

Canopy% CO2 CT DW EC ELUE ER EVI G GGNT GGT GNT GONT GOT GPP GT H H2O LAI LE NDVI NEE NT PAR R2 RMSE Rn RuBISCO SGP Ta VPD

remotely-sensed spectral reflectance (Asrar et al., 1984) can be viable options for upscaling in-situ flux measurements at larger spatial and finer temporal scales. 4. Conclusion Highly variable rates of CO2 fluxes were found across six winter wheat paddocks managed under different grazing practices (grain-only, graze-grain, and graze-out) and tillage (CT and NT) systems. Winter wheat NEE peaked when PAR was ∼1700 μmol m−2 s−1, Ta was ∼22 °C, and VPD was ∼1.25 kPa. Regression analyses showed that Canopy%, LAI, and aboveground DW were key variables for explaining among-site variations in NEE, GPP, and ER, respectively. Adding Ta or PAR to the model improved the model performance in most cases. Canopy% was found to have the most significant relationship with ELUE. A strong correspondence between CO2 fluxes and remotelysensed vegetation indices (NDVI and EVI) was also found. These results provide insights into spatial variations of CO2 fluxes and offer a potential for extrapolation of CO2 fluxes over the large areas of winter wheat. Our results indicate that the effects of changing climate and management practices on vegetation properties or ecosystem structures will have huge implications on CO2 fluxes in winter wheat cropping systems. However, measurements across multiple years are necessary to examine how long-term carbon dynamics will compare with this one year measurement and to provide more insights into the effects of tillage and different grazing practices on CO2 dynamics in winter wheat. Disclaimer “Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture.” “The U.S. Department of Agriculture (USDA) prohibits

Percent of canopy cover Carbon dioxide Conventional till Dry biomass weight Eddy covariance Ecosystem light use efficiency Ecosystem respiration Enhanced vegetation index Soil heat flux Graze-grain No-till Graze-grain Till Grain-only No-till Graze-out No-till Graze-out Till Gross primary production Grain-only Till Sensible heat flux Water vapor Leaf area index Latent heat flux Normalized difference vegetation index Net ecosystem CO2 exchange No-till Photosynthetically active radiation Coefficient of determination Root mean square error Net radiation Ribulose-1, 5-bisphosphate carboxylase/oxygenase Southern Great Plains Air temperature Vapor pressure deficit

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