Genetic gains for physiological traits associated with yield in soft red winter wheat in the Eastern United States from 1919 to 2009

Europ. J. Agronomy 84 (2017) 76–83

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Genetic gains for physiological traits associated with yield in soft red winter wheat in the Eastern United States from 1919 to 2009 Maria Balota a,∗ , A.J. Green b , C.A. Griffey c , R. Pitman d , W. Thomason c a

Tidewater Agricultural Research and Extension Center, Suffolk, VA 23437, United States Dept. of Agronomy, Kansas State University, Manhattan, KS 66506, United States Dept. of Crop and Soil Environmental Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States d Eastern Virginia Agricultural Research and Extension Center, Warsaw, VA 22572, United States b c

a r t i c l e

i n f o

Article history: Received 25 January 2016 Received in revised form 21 November 2016 Accepted 24 November 2016 Keywords: 13 C isotope discrimination Canopy temperature depression Soft red winter wheat Genetic gain

a b s t r a c t Wheat (Triticum aestivum L.) breeding strategies can benefit from periodic evaluation of genetic gains for physiological and morphological traits, and their contribution to yield progress over time in a particular environment. The objective of this research was to expand the recent work at Virginia Tech on genetic yield improvement in soft red winter (SRW) wheat and determine the magnitude of progress for several physiological traits in 50 SRW wheat cultivars released from 1919 to 2009. Physiological traits evaluated here were extensively reported in the literature to be relevant for future wheat breeding as they directly contributed to yield increase under optimum and suboptimal environmental conditions; these traits include canopy temperature depression (CTD), flag leaf width (W), flag leaf area (LA), flag leaf dry weight (DW), flag leaf specific area (SLA), SPAD (soil plant analysis development) chlorophyll reading, and grain 13 C isotope discrimination (). Replicated experiments were performed at Warsaw and Holland, VA, in 2009–2010 and 2010–2011 growing seasons. Results showed that three traits consistently changed in magnitude over time and, at the same time, were significantly (p < 0.01) related to yield; they were LA, smaller leaf area-higher yields; DW, lighter leaves-higher yields; and , higher -higher yields. CTD decreased in magnitude and SLA, W, and SPAD chlorophyll reading did not significantly change over time. Our data suggest that further yield increase in the SRW wheat grown in eastern Unites States can be achieved through selection of cultivars with smaller leaves, and with high . © 2016 Elsevier B.V. All rights reserved.

1. Introduction Wheat (Triticum aestivum L.) is the most important food grain source for mankind with a current global production of 700 million metric tons (FAO, 2014). Even though this production has risen 3.5 times over the past 50 years, this is still insufficient to satisfy the projected food demand (Rosengrant et al., 2010). In the US, the largest wheat acreage of 33 million ha was harvested in 1981 but since it steadily declined to 43% less in 2014 (USDA-NASS, 2014). Because during the same time frame yield only increased by 21% and price declined, wheat productivity and economic returns have not kept pace with other major crops, in particular corn and soybean. To stabilize production and meet the increased demand for wheat products, wheat yield will have to increase through breeding

Abbreviations: , 13 C isotope discrimination; DW, dry weight; CTD, canopy temperature depression; LA, leaf area; SLA, specific leaf area; SPAD, soil plant analysis development; SRW, soft red winter; W, flag leaf width. ∗ Corresponding author. E-mail address: [email protected] (M. Balota). http://dx.doi.org/10.1016/j.eja.2016.11.008 1161-0301/© 2016 Elsevier B.V. All rights reserved.

and improved agronomic practices, but in this paper we will only address the breeding aspect. Wheat breeding strategies can benefit from periodic evaluation of genetic gains for agronomic and physiological characteristics, and their contribution to yield progress over time in a particular environment (Austin et al., 1980; Reynolds et al., 1999; Xiao et al., 2012). Recent studies suggest that future wheat yield gains must come from increasing biomass (Austin et al., 1980; Reynolds et al., 1999; Foulkes et al., 2009; Shearman et al., 2005; Donmez et al., 2001; Foulkes et al., 2011). This is supported by evidence that recent yield gains have been accompanied by slight increases in biomass while harvest index increases were minimal (Waddington et al., 1986; Sayre et al., 1997; Slafer et al., 1994; Calderini et al., 1999). For this, the National Institute of Food and Agriculture and International Wheat Yield Partnership launched a program for wheat yield improvement through biomass and photosynthesis https://nifa.usda.gov/sites/default/files/rfa/16 NIFA-IWYP.pdf, among other breeding strategies. 13 C isotope discrimination (), leaf characteristics, and cooler canopies were directly related to photosynthesis, biomass, and yield in wheat and

M. Balota et al. / Europ. J. Agronomy 84 (2017) 76–83

used as proxy selection tools for the development of improved cultivars (Brennan et al., 2007; Dalal et al., 2013; Farquhar et al., 1989; Li et al., 2012; Lopez and Reynolds, 2010; Merah et al., 2001; Merah and Monneveux, 2015; Motzo et al., 2013; Royo et al., 2002; Shahinnia et al., 2016; Willson et al., 2015). For example, flag leaf photosynthetic rate, stomatal conductance, canopy temperature depression (CTD), and  measured during grain filling had significant contribution to yield gain increase of semidwarf wheat varieties released from 1962 to 1988 at the International Maize and Wheat Improvement Center (CIMMYT) (Fischer et al., 1998). CTD was further documented as a useful selection tool for yield potential and general fitness of wheat cultivars within given environments (Reynolds et al., 1999). CTD was described as an integrated canopy response derived from whole plant stomatal conductance (Reynolds et al., 1994, 1999), leaf architecture, i.e., leaf width (W) and specific leaf area (SLA) (Balota et al., 2008), carbon fixation (Fischer et al., 1998), and growth rate (Babar et al., 2006). Genetic progress in wheat yield has been reported to vary from 0.5% (1982–1985) in Canada (Hucl and Baker, 1987) to 1.0% (1920–1989) in Argentina (Calderini et al., 1995) and to 1.5% (1950–1982) in Mexico (Waddington et al., 1986). In China, Xiao et al. (2012) reported a 0.85% yr−1 genetic yield gain for thirteen wheat cultivars and two advanced lines released from 1969 to 2006 in Shandong province. Yield gain was largely associated with agronomic characteristics, i.e., increased kernels per square meter, biomass and harvest index, and reduced plant height, but significant genetic changes over time were also observed for physiological characteristics, i.e., leaf area index, SPAD (soil plant analysis development) chlorophyll reading, carbon assimilation, and watersoluble carbohydrates content in these environments. In the United States, genetic yield progress ranged from 0.48% yr−1 in Nebraska (Fufa et al., 2005) to 4.0% yr−1 in Oklahoma (Khalil et al., 1995). Numerous other studies were performed nationwide (Cox et al., 1988; Donmez et al., 2001; Feyerherm et al., 1984; Graybosch and Peterson, 2010) reporting yield gains around 1.0% yr−1 but these studies almost exclusively involved hard red winter wheat and did not evaluate the physiological characteristics that could heave contributed to increased yield such as , leaf characteristics and cooler canopies. Recently, Green and coworkers (2012) evaluated genetic yield improvement in soft red winter wheat (SRW), the most common wheat type grown in the mid-Atlantic region of the USA. These authors reported rates of genetic yield improvement from 0.56 to 1.4% yr−1 , depending on location, and identified flag leaf angle, kernel weight, spikes per square meter, lodging, flowering date, harvest index, normalized difference vegetative index, and green leaf retention as the major agronomic characteristics associated with yield variation. The objective of this research was to expand Green’s work and determine the magnitude of progress for several physiological characteristics associated with photosynthesis, biomass accumulation, and yield, i.e., , CTD, W, flag leaf area (LA), flag leaf dry weight (DW), SLA, and SPAD, in 50 SRW wheat cultivars released from 1919 to 2009. Green et al. (2012) hypothesized that agronomic traits in addition to yield changed with breeding over years. This paper is hypothesizing that physiological traits also changed through breeding over time and that these traits were related to and contributed to yield increase.

77

pared yield and agronomic traits, and in this paper we analyze and compare yield and physiological characteristics. In short, experiments were conducted at the Eastern Virginia Agricultural Research and Extension Center, Warsaw, VA (Kempsville loam, 37◦ 59 N, 76◦ 46 W, 40.5 m elevation) in 2009/2010 and 2010/2011 growing seasons, and at the Tidewater Agricultural Research and Extension Center, Holland, VA (Eunola loamy fine sand, 36◦ 68 N, 76◦ 77 W, 18.9 m elevation) in 2010/2011. At both locations, 49 soft red winter (SRW) wheat cultivars released from 1950 to 2009 and one historical SRW cultivar, Red May (1919), were chosen to represent the most historically significant cultivars grown in the mid-Atlantic region. A detailed list of these cultivars including name, state of origin, year of release and dwarfing gene, and the base for their selection was reported by Green et al. (2012). Here we only report the names and year of release (Table 1). Cultivars were planted in replicated plots on 23 Oct 2009 and 17 Oct 2010 at Warsaw and on 2 Nov 2010 at Holland. Seven-row plots with 2.7 m in length and 15.2 cm row spacing at Warsaw and 17.8 cm at Holland were seeded at 520 seed m−2 seeding rate, after seed was treated with Baytan (triadimenol; Bayer Crop Science), Captan 400 (Captan; Bayer Crop Science), and Gaucho (imidacloprid; Bayer Crop Science) for pest and disease control. Both, seeding rate and plot size are typical for the wheat breeding program at Virginia Tech. Weeds were controlled with Finesse (DuPont) in 2010 and with Harmony-Extra SG (DuPont) in 2011. Fertility program was based on soil test recommendations from the Virginia Cooperative Extension Soil Testing Laboratory and management practices by Brann et al. (2000). In each field, soil nutrient levels were tested prior to planting. P2 O5 and K2 O ranging from 45 to 90 kg ha−1 each were applied at this time based on soil levels. Also at planting 22 kg N ha−1 was applied, followed by a split spring application of 34 kg N ha−1 at GS 25 and 134 kg N ha−1 at GS 30. To minimize lodging that could result from wheat varieties grown under currently recommended agronomic management as opposed to historical practices, trinexipac-ethyl growth regulator was applied between growth stage (GS) 25 and 30 (Zadoks et al., 1974). Foliar fungicides Tilt (propiconazole; Syngenta) and Prosaro (prothioconazole and Tebuconazole; Bayer Crop Science) were effectively applied to control leaf diseases. Physiological maturity (50% of the plants in a plot had yellow upper peduncle) was recorded at GS 90; at this time a 0.305 m2 subsample from three center rows was harvested for  evaluations. The remaining plants within each plot were harvested at GS 92 using a plot combine. Grain yield was calculated using entire plot minus the subsample area and was adjusted to 13.5% standard grain moisture. For the physiological measurements, we have used similar measuring procedures with what was described in the literature, using whether canopy, flag leaf, or seed, whichever was the most relevant for variety comparisons determined through an abundance of publications in this field (Austin et al., 1980; Reynolds et al., 1999; Foulkes et al., 2009; Donmez et al., 2001; Foulkes et al., 2011). For example, in wheat, flag leaf characteristics more than other leaves on the plant were closely related to yield (Shahinnia et al., 2016). However, the timing of sampling could not be perfectly synchronized with the individual cultivar’s growth stages due to logistics of taking measurements at two locations 430 km apart from each other and the complexity and duration of the physiological measurements. 2.2.

13 C

isotope discrimination () analysis

2. Materials and methods 13 C

2.1. Plant materials and growth conditions This experiment used the same wheat plots published by Green et al. (2012) but emphasis here was on wheat physiology. That is, using one experiment Green et al. (2012) evaluated and com-

isotope discrimination was determined on dried ground grain samples (2 mg, ±0.2 mg) collected at physiological maturity (GS 90) from a composite sample of fifteen plants taken from the 0.305 m subsample area described above. These samples were used by Green et al. (2012) for collection of agronomic plant traits including harvest index after being threshed in a Vogel style nursery

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M. Balota et al. / Europ. J. Agronomy 84 (2017) 76–83

Table 1 Year of release for 50 soft red winter wheat cultivars used in this study. Cultivar

Release year

Cultivar

Release year

Cultivar

Release year

Cultivar

Release year

Red May Seneca Wakeland Redcoat Blueboy Arthur Coker 68-15 Potomac Coker 747 Coker 916 Tyler Wheeler Massey

1919 1950 1959 1960 1966 1968 1971 1975 1976 1980 1980 1980 1981

Saluda Coker 983 Pioneer 2555 Pioneer 2548 Coker 9803 Coker 9835 Madison Wakefield FFR 555W Coker 9134 Pioneer 2684 Jackson Pioneer 2580

1983 1983 1986 1988 1990 1990 1990 1990 1991 1992 1992 1993 1993

Pioneer 2643 Florida 302 Coker 9663 Pioneer 26R24 Roane USG 3209 AGS 2000 Sisson SS 520 SS 560 Tribute Pioneer 25R47 Pioneer 26R15

1993 1994 1997 1999 1999 1999 2000 2000 2000 2001 2002 2003 2003

Coker 9436 Branson MPV 57 Pioneer 26R31 Dominion Panola USG 3555 SS 5205 Shirley Oakes Merl

2004 2004 2005 2005 2005 2005 2007 2008 2008 2009 2009

thresher (Almaco). The seed was ground and shipped to the Colorado Plateau Stable Isotope Laboratory, Department of Biological Sciences, Northern Arizona University for elemental analysis using the stable isotope abundance technique. Stable carbon isotope composition was calculated as ␦13 C where ␦13 C (‰) = [(R sample/R standard) − 1] × 100, and where R is the 13 C/12 C ratio. Vienna Pee Dee Belemnite was used as the standard. Results are reported as  values and were calculated assuming a 13 C isotope composition of air of −8‰ (Farquhar et al., 1989). 2.3. Canopy temperature depression assessment Canopy temperature depression (CTD) of each plot was measured with a hand-held infra-red thermometer (IRT) (Agri-Therm Model 6110L, Everest Interscience Inc., Tucson, AZ) with field of view of 4◦ at approximately 50 cm above the canopy. Four measurements per plot (two facing east and two facing west) were taken around noon and averaged to give one reading per plot. The CTD is reported here as the difference between air temperature (Ta ) and canopy temperature (Tc ) with positive values when canopies were cooler than the air. In 2010 at Warsaw, CTD was measured in all plots weekly at five growth stages, i.e., early heading, anthesis (50% of the spikes in a plot had extruded anthers), 1-wk, 2-wks, and 3wks post anthesis. In 2011, CTD measurements were collected 1-wk post anthesis at Warsaw, and at heading and 1-wk post anthesis at Holland.

at different growth stages starting at heading through 3-wks post anthesis. Because these measurements were repeated over several growth stages, they were analyzed as repeated measures design in a split plot arrangement. The univariate model for the repeated measures data was: yij =  + c i + sj + (cs)ij + eij where ␮ is the overall mean effect, ci the main effect of the ith cultivar (j = 1–50), sj the growth stage effect (j = 1–5), (cs)ij the interaction of the ith cultivar and jth growth stage, and eij the random error associated with the ijth experimental unit. In this repeated measures model, the growth stage effect was a repeating measure factor. The cultivar effect was treated as fixed effect. The data were analyzed using the MANOVA option for repeated measures in SYSTAT 12 statistical software (SYSTAT Software Inc., San Jose, CA). Grain yield and  were analyzed by ANOVA with cultivars and environments as fixed effects using the same software. Environment was defined as year-location. Cultivar mean differences were tested by Tukey’s honestly significant difference (HSD) (␣ = 0.05). Annual genetic gains for yield and the physiological traits were estimated from the linear regression of selected traits on year of release in SYSTAT. The percent genetic gain per year was additionally calculated relative to the historical cultivar Red May, where% genetic gain yr−1 = {[(XG − XRM )/XRM ]/[YG − YRM ] × 100}, and where X is the mean value of a given characteristic, and Y is the year of release for each genotype (G) and Red May (RM) (Green et al., 2012).

2.4. Flag leaf characteristics measurement 3. Results Leaf characteristics LA, W, DW, SLA and SPAD chlorophyll readings were evaluated on flag leaves randomly collected from five main stems per plot and transported to the laboratory in moistened paper towels. Measurements were taken from each leaf and averaged to give one measurement per plot. Leaf area was measured with a LI-3000 leaf area meter (LICOR Inc., Lincoln, NE). Leaf width was measured at its max W, approximately 2 cm upwards from the base. SPAD chlorophyll readings were taken with a SPAD meter (SPAD-502, Konica Minolta Optics Inc., Japan). Specific leaf area was calculated as cm2 leaf area g−1 leaf weight for each plot after weighing the dried leaves in an oven at 65 ◦ C for 24 h. In 2010 and 2011 at Warsaw, leaf characteristics were determined weekly from anthesis to 2-wks post anthesis stages. In 2011 at Holland measurements were taken weekly, at anthesis and 1-wk post anthesis. 2.5. Statistical analysis The experiment was a randomized complete block with two replications in 2009/2010 and three in 2010/2011 growing seasons. Each individual plot was used as the experimental unit. With the exception of , all other physiological measurements were taken

3.1. Effect of growth conditions on yield Weather conditions were different among environments for the entire wheat growing season, the grain filling period, and the AprilMay period when the physiological measurements were taken (Green et al., 2012). Warsaw 2010 was warm (max 27.9 ◦ C; min 14 ◦ C) and dry (107 mm) from heading to harvest. During the physiological measurements, average temperature was 21 ◦ C and precipitation 40 mm. It was the driest season for this study and because of high temperatures in May and June grain filling period was reduced (Green et al., 2012). Consequently, average yield was the smallest, 5144 kg ha−1 . Holland 2011 was also hot (max 28 ◦ C; min 12.7 ◦ C) from heading to harvest but more precipitation was recorded for this period (119 mm) and for the period when physiological measurements were taken (57 mm) than in 2010. This was reflected by an average yield of 5965 kg ha−1 , 821 kg ha−1 more than in 2010. Warsaw 2011 was cooler, in particular during the day when max temperature did not exceed 27 ◦ C (min temperature was 15.8 ◦ C), and the wettest (161 mm) during April through June. The average yield for this environment was the greatest, 7410 kg ha−1 .

M. Balota et al. / Europ. J. Agronomy 84 (2017) 76–83

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Table 2 Analysis of variance and F-values for physiological characteristics for 50 soft red winter wheat cultivars across three environments. Source

dfa

13

Cdiscrimination

Canopy temperature depression

SPAD chlorophyll reading

Flag leaf area

Flag leaf dry weight

Specific leaf area

Flag leaf width

Yield

Environment (E) Genotype (G) G×E Error

2 49 98 250

204.76*** 2.95*** 0.69 –

560.19*** 1.20 0.90 –

202.03*** 9.67*** 1.32* –

171.71*** 32.77*** 2.21*** –

49.65*** 21.01*** 1.73*** –

138.28*** 7.47*** 1.37*

1302.58*** 19.73*** 1.54** –

941.29*** 15.96*** 1.71*** –

*, **, *** Significant at the 0.05, 0.01, and 0.001 probability levels, respectively. a Degrees of freedom. Table 3 Univariate repeated measures analysis of flag leaf width, canopy temperature depression, flag leaf area, SPAD (soil plant analysis development) chlorophyll reading, flag leaf dry weight, and specific leaf area of 50 soft red winter wheat cultivars used in this study. Source of variation

Warsaw 2010 a

Warsaw 2011 b

Holland 2011

df

F-ratio

df

F-ratio

df

F-ratio

Canopy temperature depression Cultivar (C) Growth stage (GS) C × GS

49 4 196

0.99 401.15*** 0.78

– – –

– – –

49 1 49

0.47 650.64*** 0.81

SPAD-Chlorophyll reading Cultivar (C) Growth stage (GS) C × GS

49 2 98

4.63*** 180.30*** 1.39

49 2 98

5.10*** 202.04*** 1.22

49 1 49

4.51*** 530.28*** 2.21**

Leaf area Cultivar (C) Growth stage (GS) C × GS

49 2 98

8.27*** 163.17*** 1.60*

49 2 98

29.08*** 26.96*** 1.40*

49 1 49

11.56*** 0.92 1.31

Flag leaf dry weight Cultivar (C) Growth stage (GS) C × GS

49 2 98

7.02*** 34.35*** 1.66**

49 2 98

20.81*** 17.40*** 0.97

49 1 49

13.00*** 23.83*** 1.33

Specific leaf area Cultivar (C) Growth stage (GS) C × GS

49 2 98

2.57*** 81.98*** 0.85

49 2 98

3.98*** 135.42*** 0.90

49 1 49

11.56*** 0.92 1.31

Flag leaf width Cultivar (C) Growth stage (GS) C × GS

49 2 98

8.43*** 82.56*** 1.51*

49 2 98

6.94*** 180.42*** 1.14

49 1 49

17.82*** 47.61*** 1.95*

*, **, *** Significant at the 0.05, 0.01, and 0.001 probability levels, respectively. a Degrees of freedom. b The adjusted univariate Greenhouse-Geisser (G-G) and Huynh-Feldt (H-F) F-ratio.

Yield information for each environment and cultivar was provided by and is available at Green et al. (2012). Here, yield is used only for comparisons with the physiological traits evaluated in this study.

characteristics evolution through breeding, averages across growth stages within each environment were used for further analysis. 3.3. Changes in physiological characteristics over time

3.2. Analysis of variance for plant characteristics Environment had a significant (p < 0.001) effect on all plant characteristics evaluated in this study (Table 2). There were no significant differences among cultivars for CTD but there were for all other traits. The interaction between cultivar and environment was not significant for  and CTD, but it was for all the other traits. The repeated measures analysis showed significant cultivar and growth stage main effects with a few exceptions, i.e., there were no significant differences among cultivars for CTD in any environment, and there were no significant differences among growth stages for LA and SLA at Holland in 2011 (Table 3). The interaction between cultivar and growth stage was absent for CTD and SLA in all environments. For the other physiological characteristics, this interaction was significant but seemed to be depended upon the environment. For example, a significant interaction for SPAD chlorophyll reading was recorded at Holland in 2011 but not in the other two environments (Table 3). To allow better understanding of the physiological

Mean, range, SEM, and absolute and relative (to Red May) genetic gains per year within each environment are presented in Table 4. For each environment, the coefficients of determination (r2 ) from Pearson correlation of selected traits on the year of release are shown in Table 5. Traits showing consistent and significant trends over time and environments were grain yield and , i.e., significantly increased over time, and flag LA and DW, i.e., significantly decreased over time. Estimated annual yield gains ranged from 24.8 to 46.7 kg ha−1 yr−1 , depending on environment (Table 4). For , increases ranged from 0.011 to 0.018‰ yr−1 . Depending on the environment, the range of LA decrease was from 0.07 to 0.13 cm2 yr−1 and for DW from 2 to 3 mg yr−1 . Canopy temperature depression showed significant decline over time with a range of 0.005–0.02 ◦ C yr−1 at Warsaw 2010 and Holland 2011; no change in CTD over time was recorded at Warsaw in 2011. Flag leaf width, SLA, and SPAD chlorophyll reading did not significantly change over time (Table 5). From the grouping of cultivars per decades of their

−0.001 46.7*** −0.176 1.112

release, it seems that the greatest change through breeding from the 1919 through 1970-time frame, and until after 2001 was for LA (26% decrease), followed by flag leaf DW (22% decrease) (Table 6). CTD was reduced by 16%, while much smaller overall changes were recorded for  (4% increase), leaf green color (4% increase), SLA (4% decrease), and flag leaf width (5% decrease). During the same time, yield was overall increased by 27%. 3.4. Phenotypic associations among yield and physiological characteristics

18.8

13 C discrimination (‰) Canopy temperature depression (◦ C) SPAD chlorophyll reading (relative) Flag leaf area (cm2 ) Flag leaf dry weight (g) Flag leaf specific leaf area (g cm2 ) Flag leaf width (cm) Yield (kg ha−1 )

*, **, *** Significant at the 0.05, 0.01, and 0.001 probability levels, respectively. Yield data is from Green et al., 2012.

0.000 26.5*** −0.122 0.654 0.009 65 0.7−1.2 3408–6516 0.97 5145

1.39 5965

1.1−1.8 3914–8022

0.011 55

−0.113 0.570

−0.001 24.8***

1.27 7410

0.9−1.9 3889–8960

0.011 79

−0.128*** −0.003*** −0.198 −0.684 −0.663 −0.111 −0.066*** −0.002*** 0.042 −0.308 −0.312 0.011 0.257 0.007 1.456 11.8–17.9 0.3−0.6 151–270 17.9 0.43 211

19.5 0.42 233

12.5–31.4 0.3−0.7 197–271

0.312 0.007 1.240

−0.465 −0.419 −0.008

−0.124*** −0.002*** −0.173

16.5 0.38 282

11.4–31.8 0.3−0.7 182–314

0.278 0.007 1.266

0.034 0.044 0.019 0.085 0.258 38.0−49.0 43.5

38.9

28.9–47.5

0.271

0.115

0.012

43.0

36.4–51.3

0.257

0.002 0.248 −0.005* −0.780 0.041 −0.6−1.3 0.37

1.00

−1.25−3.95

0.066

−0.654

−0.017***

2.71

−0.65−3.9

0.068

0.018*** 0.111 0.011** 0.094 0.055 17.5–20.0

20.0

17.9–21.8

0.065

0.096

0.016***

20.6

18.2–22.2

0.071

Genetic gain yr−1 Range Mean Range Mean Genetic gain yr−1 Genetic gain % yr−1 SE Range Mean Select traits

Warsaw 2009–2010

Holland 2010–2011

SE

Genetic gain % yr−1

Genetic gain yr−1

Warsaw 2010–2011

SE

Genetic gain % yr−1

M. Balota et al. / Europ. J. Agronomy 84 (2017) 76–83 Table 4 Mean, range, SE, mean percent genetic gain (relative to cultivar Red May), and mean absolute genetic gain from linear regression of select traits on year of release for 50 wheat cultivars grown in three environments in eastern Virginia.

80

When individual physiological characteristics were regressed on yield, grain yield was positively associated with  (r2 ranged from 0.25 to 0.50, p < 0.001) in all environments (Table 5) (Fig. 1). Combining all locations for , which is permissible in absence of a significant G × E interaction (Table 2), the linear relationship with yield was even stronger with an r2 of 0.70 (Fig. 1). In all environments, yield was negatively correlated with LA (r2 ranged from −0.14, p < 0.01, to −0.48, p < 0.001) and DW of the flag leaf (r2 ranged from −0.11, p < 0.05, to −0.43, p < 0.001). At Warsaw in both years, grain yield was not associated with CTD (Table 5). In the hot and humid environment at Holland in 2011, yield was negatively correlated with CTD (r2 = −0.24, p < 0.001). There was no relationship between grain yield and SLA, SPAD, and width in any of these environments. Cultivars released prior to 1971 had less , and greater LA and DW than the cultivars released after 2001, and yielded in average 1532 kg ha−1 less (Table 6). Among them, Red May had least  and greatest LA, and yielded least across environments, 3760 kg ha−1 (Green et al., 2012). Seneca, Redcoat, Wakeland, Blueboy, and Arthur, which were released from 1950 through 1970, were similar with Red May for , LA, and DW, and had average yields from 4292 to 5475 kg ha−1 (Green et al., 2012). Under the same environmental conditions and using the current cultural practices, Sisson, Pioneer 25R47, Pioneer 26R15, SS 520, and SS 560 had highest average yields in excess of 6900 kg ha−1 across environments and , and smaller leaves than other cultivars. They were released in early 2000 s. The flag leaf was slightly greener, indicated by a higher SPAD reading, for the newer than for older cultivars (Table 6). SS 560 (released in 2001) and MPV 57 (released in 2005) were the greenest cultivars from this set and both had 47 SPAD relative units across environments; the mean of all cultivars was 42. 4. Discussion As in many examples of modern scientific research (de Wit, 1958; Tanner and Sinclair, 1983; Sinclair, 2012), in this paper we used the same experiment and previously published data of yield for comparisons with the physiological characteristics analyzed here. From all physiological measurements considered in this study, only  significantly increased over time through breeding in all environments. Delta is expected to change in magnitude over time very little (Farquhar et al., 1989). Indeed, in this experiment, delta changed from 0.011 to 0.018‰ y−1 , and the range among cultivars was up to 4.73‰ (2.5‰ in average). This is large enough to differentiate among cultivars and warrant for a successful screening, as vastly described in the literature (Brennan et al., 2007; Motzo et al., 2013; Li et al., 2012). Carbon isotope discrimination () change with breeding of the RSW wheat over time is the major discovery of this paper; and this has not been reported in the literature in wheat before (at least not for the RSW wheat). This is an independent trait from all other traits Green and co-authors published in 2012. Delta, is a major trait associated with photosynthesis, biomass, yield, and water use efficiency

M. Balota et al. / Europ. J. Agronomy 84 (2017) 76–83

81

9,000 y = 578x - 5705 p=0.0001 (W2010) y = 547x - 4934 p=0.0001 (H2011)

Yield kg ha

-1

y = 1336x - 20085 p=0.0001 (W2011)

7,000

5,000

3,000

17

18 13

19

20

21

22

C discrimination (‰)

Fig. 1. Linear regression of wheat yield on 13 C discrimination with equation of the best-fit line for each of the three environments. W2010 is Warsaw, VA, in 2010 (circles); H2011 is Holland, VA, in 2011 (triangles); and W2011 is Warsaw, VA, in 2011 (diamonds). When locations are combined, y = 1118 x − 15973, p = 0.0001, and r2 = 0.70. Table 5 Trait association (coefficient of determination, r2 ) with the year of release and yield for 50 wheat cultivars grown in three environments in eastern Virginia. Select traits

Warsaw 2009–2010

13

C discrimination Canopy temperature depression SPAD chlorophyll reading Flag leaf area Flag leaf dry weight Flag leaf specific leaf area Flag leaf width Yield

Holland 2010–2011

Warsaw 2010–2011

Year of release

Yield

Year of release

Yield

Year of release

Yield

0.160** −0.104* 0.020 −0.226*** −0.209*** 0.004 0.000 0.762***

0.253*** −0.002 0.056 −0.144** −0.112* 0.001 0.008 –

0.240*** −0.314*** 0.006 −0.376*** −0.241*** −0.077 −0.037 0.583***

0.294*** −0.241*** 0.035 −0.226*** −0.161*** 0.061 −0.003 –

0.421*** 0.008 0.050 −0.453*** −0.377*** −0.075 −0.029 0.797***

0.500*** 0.032 0.054 −0.477*** −0.434*** −0.037 −0.000 –

*, **, *** Significant at the 0.05, 0.01, and 0.001 probability levels, respectively.

Table 6 Decadal mean and SE for physiological characteristics and yield of 50 SRW wheat cultivars across three environments. Decade

n

b

1919–1970 1971–1980 1981–1990 1991–2000 2001–2009 a b

15 6 12 30 36

13

C discrimination

CTDa

‰

◦

19.11 ± 0.15 19.80 ± 0.14 19.72 ± 0.11 20.05 ± 0.11 19.90 ± 0.10

1.60 ± 0.16 1.70 ± 0.18 1.50 ± 0.16 1.49 ± 0.11 1.34 ± 0.12

SPAD Chlorophyll reading

C 41.2 ± 0.55 40.5 ± 0.43 40.7 ± 0.39 41.4 ± 0.31 42.8 ± 0.39

Flag leaf area

Flag leaf dry weight

Specific leaf area

Flag leaf width

Yield

cm2

g

g cm2

cm

kg ha−1

22.7 ± 0.76 18.4 ± 0.56 17.6 ± 0.32 17.4 ± 0.24 16.7 ± 0.23

0.50 ± 0.02 0.41 ± 0.01 0.40 ± 0.01 0.39 ± 0.01 0.39 ± 0.01

227 ± 2.77 224 ± 2.59 222 ± 1.95 223 ± 1.59 218 ± 1.61

1.27 ± 0.04 1.24 ± 0.04 1.22 ± 0.02 1.27 ± 0.02 1.21 ± 0.02

4889 ± 133 5965 ± 152 6300 ± 132 6582 ± 113 6655 ± 106

CTD, canopy temperature depression. Yield data is from Green et al., 2012. Because of a reduced number of total entries, entries from 1919 through 1970 were combined.

in many crops including wheat. Particularly, it has been related to stomatal conductance and carboxylation efficiency and literature shows all these traits to have valuable contribution to increased biomass and yield in wheat. For example, the 13 C discrimination is a measure of CO2 diffusion rate through stomata and CO2 fixation efficiency over the plant growth period (Farquhar et al., 1989). Because of this,  has been positively correlated with photosynthesis and stomatal conductance in numerous wheat studies (Condon et al., 1990; Fischer et al., 1998; Monneveux et al., 2004), wheat yield (Balota et al., 2008; Fischer et al., 1998; Condon et al., 1987; Royo et al., 2002), and yield and harvest index in barley (Teulat et al., 2001). In wheat and other crops,  appeared to be under strong genetic control with broad sense heritability values ranging from 60 to 90% (Condon et al., 1987; Hubick et al., 1988), even though environment, i.e., water and irradiance, had significant effects on its magnitude (Acevedo, 1993; Araus et al., 1999; Craufurd et al., 1991; Royo et al., 2002; Vogel, 1978). In agreement with earlier research, our study showed positive correlations of  with grain yield of the SRW wheat cultivars in all three environments. Average  values also mirrored well the rainfall amounts received during grain filling with lowest values of 18.8‰ in the dry 2010 season and highest (20.6‰) in the cool and humid War-

saw 2011; at Holland in 2011  was 20.0‰. Genotypic differences for  were significant and the G × E interaction was not (Table 2), which denotes strong genetic control as previously shown. However, based on the Tukey’s HSD test, cultivars were better separated for their mean  in the drier seasons. Unlike in the other two environments, in the most humid season at Warsaw in 2011 no significant differences among cultivars were recorded. F values for the genotypic effects on  were 3.3 (p < 0.0001) at Warsaw in 2010, 2.06 (p < 0.0001) at Holland in 2011, and 0.86 (ns) at Warsaw in 2011. This could be the result of stomata becoming less sensitive to daily fluctuations of VPD, irradiance, and wind in drier weather (Flexas et al., 2006); or could be the result of root system differences between modern and old cultivars more evident during dry seasons (Nakhforoosh et al., 2014). CTD showed inconsistent trends and magnitudes between environments. Our data showed absence of significant differences among cultivars while the estimated direction of selection interestingly showed negative relationships with yield. CTD has been positively correlated with yield and considered as an effective trait to predict performance of wheat breeding lines in nurseries at CIMMYT (Amani et al., 1996; Fischer et al., 1998; Reynolds et al., 1994, 1998, 1999). This apparent contrast with previous

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literature showing mostly positive relationships deserves further investigation and clarification of the reasons. Recently, Rebetzke and colleagues (2013) have shown that canopy temperature (CT) is genetically associated with stomatal conductance and grain yield in wheat. In their study, time of sampling for CT screening was critical. At pre-anthesis stage, increased CT was associated with reduced plant height at maturity, increased harvest index, and yield; at post-anthesis stage, cooler canopies were rather associated with increased yield. They also noted that semi-dwarf wheats (Rht-B1b and Rht-D1b) had higher stomatal conductance (and presumably increased photosynthesis and yield) but warmer canopies. In our study, CTD was measured pre- and post-anthesis, and semidwarf cultivars were mixed with tall varieties, which could have caused the CTD inconsistencies and hindered its true association with yield. More importantly, Rebetzke and colleagues (2013) separately grouped the wheat lines by height to minimize confounding CT response when genotypes with large height differences, like in this experiment, are next to each other in small plots, i.e., short genotypes tend to be warmer due to less wind exposure when planted next to tall genotypes. Indeed, in this experiment plant height ranged from 105 to 140 cm for Red May (1919); from 93 to 116 for the cultivars released during 1950–1970; and from 68 to 83 cm for the shortest cultivar SS 5205, a 2008 release; significant genotypic differences of plant height for these 50 SRW wheat cultivars (Supplemental Table 1) and its decrease over time was documented by Green et al. (2012). However, Green et al. (2012) did not present genotypic means for plant height, like we show in Supplemental Table 1. For our story, Supplemental Table 1 is important at allowing to, at least in part, explain the CTD result in this experiment. This is the second paper that we are aware of after Rebetzke et al. (2013) suggesting potential effect of neighbor plots’ plant height on canopy temperature; and this information may have significant implications in variety selection based on CTD. In the view of these recent discoveries our CTD protocol seems to have masked genotypic responses and relationships with yield, and needs further refining. On the other hand, using other statistical procedures besides GLM, may help explaining these differences. For example, meta-analysis is a statistical procedure that combines data from multiple studies and, when the treatment response is inconsistent from one study to another, meta-analysis can identify the reason for variation. Meta-analysis has been used for decades in medical and pharmaceutical fields and now increasingly more in agronomy. From all leaf characteristics measured in this study, LA and DW of the flag leaf significantly decreased over time through breeding and since width did not change, shaped changed, allowing for shorter and more erect leaves (Green et al., 2012). LA and DW were consistently correlated with grain yield in all environments with smaller and lighter leaves producing greater yields. LA and DW were significantly correlated (r2 ranged from 0.81 to 0.89). Green et al. (2012) showed that over time and through breeding flag leaves became significantly more erect; and they detected significant cultivar differences for leaf angle among the 50 SRW wheat cultivars. The highest yielding cultivars had the most erectile leaf shape and this characteristic had significant contribution to yield in all environments, Warsaw 2010, 2011, and Holland 2011 (Green et al., 2012). It has been shown that erectile leaf-types of wheat could have improved RUE from better PAR distribution through the canopy (Reynolds et al., 1999). In combination with green leaf retention, which may be favored by humid seasons of the midAtlantic region, leaf erectile shape through selection for shorter leaves with reduced LA could be a venue for further yield improvement of SRW wheat in this region. For SPAD chlorophyll reading, W, and SLA no significant changes over time and no consistent relationships with yield were detected in this experiment.

5. Conclusions All physiological characteristics included in this study were selected based on previous findings showing significant contributions to wheat yield increases. Some of these characteristics, CTD and SPAD for example, were proposed and are being routinely used to empirically select wheat and other crops for better yields by breeding programs. In this study, , LA, and DW of the flag leaves significantly changed over time due to breeding, and all explained a significant portion of yield variation at all locations. Selection based on  in the grains, LA, and DW could be used for further yield improvement in SRW wheat; when using  relatively drier environments may help to identify genetic differences for this trait. CTD cannot be used for wheat yield improvement in the Mid-Atlantic region yet and refining the methodology to improve its efficacy for this environment is needed. Acknowledgements This work was supported by the funding from the Virginia Small Grains Board and Virginia Crop Improvement Association. We thank the staff at the Tidewater Agricultural Research and Extension Center and Virginia Tech Blacksburg campus for their technical help on this study. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.eja.2016.11.008. References Acevedo, E., 1993. Potential of carbon isotope discrimination as a selection criterion in barley breeding. In: Ehleringer, J.R., Hall, A.E., Farquhar, G.D. (Eds.), Stable Isotopes and Plant Carbon/water Relations. Academic Press, New York, NY, pp. 399–417. Amani, I., Fischer, R.A., Reynolds, M.P., 1996. Canopy temperature depression association with yield of irrigated spring wheat cultivars in hot climate. J. Agron. Crop Sci. 176, 119–129. Araus, J.L., Febrero, A., Catala, A., Molist, M., Voltas, J., Romagosa, I., 1999. Crop water availability in early agriculture: evidence from carbon isotope discrimination of seeds from a tenth millennium BP site on the Euphrates. Glob. Change Biol. 5, 233–244. Austin, R.B., Bingham, J., Blackwell, R.D., Evans, L.T., Ford, M.A., Morgan, C.L., Taylor, M., 1980. Genetic improvement in winter wheat yields since 1900 and associated physiological changes. J. Agric. Sci. 94, 675–689. Babar, M.A., Reynolds, M.P., van Ginkel, M., Klatt, A.R., Raun, W.R., Stone, M.L., 2006. Spectral reflectance to estimate genetic variation for in-season biomass, leaf chlorophyll, and canopy temperature in wheat. Crop Sci. 46, 1046–1057, http://dx.doi.org/10.2135/cropsci2005.0211. Balota, M., Payne, W.A., Evett, S.R., Peters, T.R., 2008. Morphological and physiological traits associated with canopy temperature depression in three closely related wheat lines. Crop Sci. 48, 1897–1910, http://dx.doi.org/10.2135/ cropsci2007.06.0317. Brann, D.E., Holshouser, D.L., Mullins, G.L., 2000. Agronomy Handbook Pub. No. 424-100. Virginia Coop. Ext., Blacksburg, VA. Brennan, J.P., Condon, A.G., van Ginkel, M., Reynolds, M.P., 2007. An economic assessment of the use of physiological selection for stomatal aperture-related traits in the CIMMYT wheat breeding program. J. Agric. Sci. 145 (3), 187–194. Calderini, D.F., Dreccer, M.F., Slafer, G.A., 1995. Genetic improvement in wheat yield and associated traits. A re-examination of previous results and the latest trends. Plant Breed. 114, 108–112, http://dx.doi.org/10.1111/j.1439-0523. 1995.tb00772.x. Calderini, D.F., Reynolds, M.P., Slafer, G.A., 1999. Genetic gains in wheat yield and main physiological changes associated with them during the 20th century. In: Satorre, E.H., Slafer, G.A. (Eds.), Wheat: Ecology and Physiology of Yield Determination. Food Products Press, New York, pp. 351–377. Condon, A.G., Richards, R.A., Farquhar, G.D., 1987. Carbon isotope discrimination is positively correlated with yield and dry matter production in field grown wheat. Crop Sci. 27, 996–1001. Condon, A.G., Farquhar, G.D., Richards, R.A., 1990. Genotypic variation in carbon isotope discrimination and transpiration efficiency in wheat. Leaf gas exchange and whole plant studies. Aust. J. Plant Physiol. 17, 9–22. Cox, T.S., Shroyer, R., Ben-Hui, L., Sears, R.G., Martin, T.J., 1988. Genetic improvement in agronomic traits of hard winter wheat cultivars from 1919 to

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