The Role of Leaf Epicuticular Wax in the Adaptation of ...

RESEARCH

The Role of Leaf Epicuticular Wax in the Adaptation of Wheat (Triticum aestivum L.) to High Temperatures and Moisture Deficit Conditions Suheb Mohammed,* Trevis D. Huggins, Francis Beecher, Chris Chick, Padma Sengodon, Suchismita Mondal, Ashima Paudel, Amir M.H. Ibrahim, Michael Tilley, and Dirk B. Hays

ABSTRACT Water deficit is one of the primary causes of decreasing wheat (Triticum aestivum L.) yields. Previous studies have identified associations in genomic regions with cooler canopies, the heat-susceptible index, and grain yield in spring wheat. This project aimed to define the role of leaf epicuticular wax (EW) as a drought-adaptive trait for improving the production and stability of yield attributes. A recombinant inbred line (RIL) population created from two spring wheat cultivars (‘Halberd’ and ‘Len’) was used. The parent lines were selected because of their different responses to drought, with Halberd exhibiting better water deficit tolerance. In five environments, an α lattice design with two replications and two distinct moisture treatments (water deficit and irrigated) were implemented. The RILs exhibited significant segregation for leaf EW, canopy temperature (CT) and drought susceptibility index (DSI). The inheritance of leaf EW was low (0.15) because of significant environment interactions. The RILs grown under water deficit produced significantly higher EW content (19–30%) compared with those under irrigation. The leaf EW significantly correlated with plot yield (r = 0.32) and leaf CT (r = -0.32) and the DSI for mean single head weight (r = -0.23) at Uvalde 2012 under water deficit. In addition, EW and CT correlated with stability parameters (DSI, regression of coefficient, and regression mean square) of different yield components within and across water deficit environments. This study explains the inter-relationship between leaf EW and CT in improving wheat adaptability to moisture and heat stress.

S. Mohammed, Daylight Foods, 660 Vista Way, Milpitas, CA 95035; T.D. Huggins, C. Chick, A. Paudel, A.M.H. Ibrahim, D.B. Hays, Dep. of Soil and Crop Sciences, Texas A&M Univ., College Station, TX 77840; F. Beecher, Monsanto Company, Williamsburg, IA 52361; P. Sengodon, Monsanto Company, Evansville, IN 47725; S. Mondal, CIMMYT, Elbatan, Mexico; M. Tilley, USDA-ARS, Center for Grain and Animal Health Research, Manhattan, KS 66502. Received 27 July 2017. Accepted 4 Oct. 2017. *Corresponding author (suheb65@gmail. com). Assigned to Associate Editor Vasu Kuraparthy. Abbreviations: CT, canopy temperature; DSI, drought susceptibility index; EW, epicuticular wax; H2 , broad-sense heritability; KNS, kernel number per spike; MSHW, mean single head weight; RIL, recombinant inbred line; TKW, 1000-kernel weight.

W

heat is grown across large areas of the tropical and temperate regions of the world, with an approximate production of 709.4 Tg in 2015–2016 (USDA-NASS, 2014). The world wheat production in 2015 was 735 Tg, with an increase of only 3 Tg production over 2014 (Food and Agriculture Organization of the United Nations, 2015). World water demand has tripled over the past 50 yr, with water tables being depleted at a faster rate in heavily irrigated regions, such as the Southern Great Plains of the United States (Brown, 2003). In the past 10 yr, aquifers have been depleted at depths of 31 to 76 m, where a 4-m depletion leads to a 9% reduction in aquifer storage (US Global Change Research Program, 2009). Wheat production has been, and will continue to be, greatly impacted by the depletion of subsurface water resources and inadequate precipitation. Water stress to wheat during preanthesis increases maturation, inhibits starch synthesis, and enhances seed abortion, resulting in poor seed set (Bhullar and Jenner 1985; Hays et al., 2007; Weldearegay et al., 2012). Drought-susceptible genotypes exposed to water and heat stress exhibited impaired photosynthesis, reduced chlorophyll content, and lower biomass (Mohammed et al., 2012), with an early transition to the dry seed Published in Crop Sci. 58:1–11 (2018). doi: 10.2135/cropsci2017.07.0454 © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA All rights reserved.

crop science, vol. 57, march– april 2018

www.crops.org 1

stage and poor seed set (Yang et al., 2002). Although the demand for wheat has been increasing yearly, few genetic gains in heat and water stress tolerance have been achieved (Reynolds et al., 2007). Leaf EW is often expressed as a bluish-green covering on the adaxial and abaxial leaf surfaces and appears during the early reproductive stages. It is associated with increased drought tolerance in rice (Oryza sativa L.) (Haque et al., 1992), maize (Zea mays L.) (Meeks et al., 2012), barley (Hordeum vulgare L.) (Febrero et al., 1998), wheat (Bennett et al., 2012), sorghum [Sorghum bicolor (L.) Moench.] ( Jordan et al., 1983), and many other crops (Baenziger et al., 1983). In addition to the leaf surface, EW is present on the peduncle, leaf sheath, stem sheath, glumes, and other areas of the plant, acting as a hydrophobic barrier between the leaf epicuticle and the surrounding environment (Bird et al., 2007). It may resist the movement of moisture flow, and impede leaf-feeding insects and pathogenic fungi (Eigenbrode and Espelie 1995). Leaf EW and its varied composition have a significant impact on stomatal or epidermal conductance in wheat (Araus et al., 1991; Huggins et al., 2017) relative water content or decreased transpiration in jatropha ( Jatropha mollissima (Pohl) Baill.) (Figueiredo et al., 2012), and improved water-use efficiency in peanut (Arachis hypogea L.) (Samdur et al., 2003), and wheat ( Johnson et al., 1983). It also influences canopy light reflectance in wheat (Huggins et al., 2017) of highenergy wavelengths; studies on barley concluded that the reflectance of photosynthetically active regions reflectance differed between glaucous and nonglaucous lines at the 560-nm wavelength (Febrero et al., 1998). Epicuticular wax is interrelated with the different physiological traits affecting plant water use and therefore it may decrease leaf CT and enhance drought resistance, resulting in an increase in yield stability. Leaf CT is a physiologically integrated trait that is used as an early generation selection tool (Olivares-Villegas et al., 2007; Pinto et al., 2010) and is also significantly correlated with the DSI (Blum et al., 1989; Rashid et al., 1999) and leaf EW (Mondal et al., 2015; Huggins et al., 2017). In pea (Pisum sativum L.) cultivars, EW indirectly influences grain yield by improving harvest index under water-deficit conditions, decreasing residual transpiration rates as well as leaf CT (Sánchez et al., 2001). Lower CT is also strongly associated with increased grain yield, serving as a highthroughput phenotyping tool for mapping populations under moisture stress conditions (Olivares-Villegas et al., 2007). Increased leaf EW may compensate for increased stomatal conductance, thereby increasing leaf temperature depression and yield stability under heat stress conditions (Mondal and Hays 2007; Mondal et al., 2015; Huggins et al., 2017). Yield stability can be determined within an environment (the DSI and the heat susceptible index) (Blum et al., 1989; Fischer and Maurer 1978) and between different 2

environments (Eberhart and Russell 1966). The possible phenotypic correlations among EW, CT, DSI, and stable yields may be expressed in the genetic colocalization of different traits. These pleiotropic loci may provide markerassisted selection tools and aid in the rapid advancement of water-deficit and heat-tolerant wheat cultivars. The inheritance of leaf glaucousness, which is an expression of EW composition is influenced by a single codominant allele in durum wheat(Triticum durum Desf.) (Clarke et al., 1994). Liu et al. (2007) reported that nonglaucousness was controlled by a single dominant allele in synthetic hexaploid wheat, whereas the degree of glaucousness was the result of additive gene action in durum wheat (Clarke et al., 1994; Clarke and Richards 1988) and bread wheat (Stuckey 1972). However, the EW trait, which is quite different from the glaucousness trait, was reported to be inherited as a polygenic trait in wheat (Mondal et al., 2015) and rice (Haque et al., 1992). Leaf EW heritability was low to moderate in some crops such as wheat (0.21–0.27) (Mondal et al., 2015), and maize (0.41 for inbred; 0.59 for hybrid lines) (Meeks et al., 2012), whereas wax glaucousness has high heritability (0.72 to 0.88) (Bennett et al., 2012). Epicuticular wax content is influenced by differences in the environment and plant tissue type. For instance, under water-deficit conditions, EW increases in various crops, such as peanut (Samdur et al., 2003), oat (Avena sativa L.) (Bengtson et al., 1978), wheat ( Johnson et al., 1983), rice (Haque et al., 1992), and sorghum (Blum et al., 1989). The abaxial leaf surface has a higher wax content and lower stomatal conductance than the adaxial leaf surface (Araus et al., 1991). The present study used a RILs population: (i) to evaluate the genetic variability and inheritance of leaf EW and (ii) to determine the drought-adaptive role of the increased leaf EW under different water-deficit and its correlation with lower leaf CT, DSI, Eberhart stability levels, and improved yield components.

MATERIALS AND METHODS Parents’ Pedigree and Traits A RIL population of 180 individuals was derived from the parent lines Halberd and Len. Halberd is an Australian spring wheat cultivar developed at Roseworthy Agricultural College in 1969 with the pedigree Scimitar/ KenyaC6042//Bobin/3/Insignia-49 (Paull et al., 1998). Halberd was one of the dominant Australian spring wheat cultivars during the 20th century, possessing boron tolerance (Paull et al., 1992), durable rust resistance alleles (Bariana et al., 2007), drought tolerance, and the ability to maintain carbohydrate accumulation during moisture stress ( Ji et al., 2010). Len (originally called ND543) is a semidwarf hard red spring wheat cultivar developed in North Dakota in 1979 with the pedigree ND499/3/

www.crops.org


Justin/RL4205//Wisc261 (USDA-ARS, 2015). Len was developed from the sister line ‘Thatcher’, which possesses the adult plant resistance gene Lr34 and is considered to have moderate leaf and stem rust resistance (Kolmer et al., 2011). Len is moderately susceptible to drought and heat (Hossain et al., 2012), but has good agronomic traits.

Population Development and Experimental Design The RILs were advanced via single-seed descent from single seed or plant bulking and planting from F2 to F5 generation. Seeds from the F5 generation were bulked to develop 180 F5:6 RILs. The F6 lines were advanced in the field and were evaluated during 2010 as an F5:7 generation. The F8 (2011) and F9 (2012) generations were used to conduct the experiments. The 2011 yield trials were conducted at the Texas A&M AgriLife Research Stations in Uvalde, College Station, and Chillicothe, and at the CIMMYT in Ciudad de Obregon, Mexico. During 2012, trials were conducted at the AgriLife Research Stations in Uvalde and College Station, TX. A drip irrigation system (I-tape, ATS irrigation, Inc. Brenham, TX) 2.54 cm in diameter and 15-cm emitter spacing was used. Each plot was 1.5 by 3 m in dimension with six rows, each row spaced 10 cm apart. The plot planting was standardized with 1800 kernels. At each location, two treatments were used (irrigated and water deficit conditions), with each treatment replicated twice. Halberd, Len, and the RILs were organized in an α lattice design (13 × 24) within each replication. Irrigation was similar for both treatments until the initiation of stem elongation (Feekes growth stage 6), at which point irrigation was withheld from the water deficit treatment, and the irrigated treatment received additional (≈ 228 mm) irrigation (Table 1). The Feekes 6 stage differs across the population within 6 to 7 d and the irrigation was withheld once all the lines attained the Feekes 6 growth stage. Annual precipitation, irrigation supplied, and the total water received were recorded during the crop growing season in each of the environment (Table 1). The College Station trial in 2012 was not irrigated and the Obregon trial in 2011 was floodirrigated three times throughout the growing period.

Agronomic and Physiological Measurements At the grain-filling stage; leaf CT, and leaf EW were measured. The 10 d after pollination was standardized as the point when the kernel growth had reached full sheath size and was closer to soft dough stage. The 10 d after pollination and physiological maturity varied significantly across RILs, so the measurements (EW and CT) were done in two to three consecutive trips. Leaf CT was measured with a Fluke 561 IR portable infrared thermometer (Fluke Corp. Everett, WA). The leaf EW and CT were sampled from all the environments except Chillicothe 2011 and College Station 2012. The thermometer crop science, vol. 58, march– april 2018

gun was focused at the lateral side of the plot canopy at a 45° angle horizontally for approximately 3 s at each plot. Measurements were taken between 13:00 and 15:30 on a hot, sunny, noncloudy, and low wind days.

Leaf EW Quantification At 10 d after pollination, 1-cm leaf discs were collected from five independent flag leaves per RIL. The leaf disc punches were collected into vials without disturbing the adaxial and abaxial leaf EW content, air-dried to avoid pathogen infections, and stored at -20° for wax extraction. A colorimetric wax quantification method based on the change in wax color, which is produced by a reaction with acidified K 2Cr2O7 reagent (Ebercon et al., 1977). To extract wax, the samples were immersed in 1 mL chloroform for 30 s and transferred into a separate 2-mL vial. The solution was subsequently air-dried in a fume hood. Next, 300 μL of the acidic K 2Cr2O7 was added to each vial and then heated at 100° in a water bath for 30 min. After heating, 700 μL of deionized water was added to each of the vials and the color was allowed to develop for 1 h. Three 100-μL samples from each vial were then loaded in a 96-well U-shaped enzyme-linked immunosorbent assay microplate (Greiner Bio-One, Monroe, NC) and the optical densities of the samples were measured at 590 nm with a PHERAstar plus microplate reader (BMG-Labtech, Offenburg, Germany). A standard curve was prepared by following a serial dilution technique from EW extracted from 20 randomly selected Halberd flag leaves (Mondal et al., 2015). The resulting EW linear standard curve equation was used to determine the wax concentration of samples.

Grain Yield Components Quantification For each plot, 50 grain heads were harvested and then plot yield (g m–2) was harvested. The 50 heads were used to estimate yield attributes: mean single head weight (MSHW), 1000-kernel weight (TKW) and kernel number per spike (KNS). The MSHW is the average weight of seed from fifty heads harvested from each plot. The test weight TKW was derived from the MSHW. The KNS was calculated from TKW and MSHW. The plot yield (g m–2) was harvested from the Obregon 2011 and Uvalde 2012 environments, although MSHW or 50 heads were harvested from all the environments, except College Station 2012 and Obregon 2011. For each RIL, heads (without secondary tiller heads) were uniformly harvested from the central region of the plots. The TKW was measured with a Seedburo TM 801 Count-a-Pak seedcounting machine (Seedburo Equipment Co., Des Plains, IL). The MSHW is part of plot yield, although MSHW is more accurate than plot yield. The plant height (cm) was measured by placing a ruler in the center of each plot from the ground to the main spike. The plant height measurements were taken at plant maturity. The awn data were taken to keep track down of the RILs, as Halberd is awnless and Len is awned.

www.crops.org 3

Table 1. Growth, weather, and irrigation data for each environment during the 2011 and 2012 growing seasons for 180 Len × Halberd spring wheat recombinant inbred lines (RILs). Environment

Phenology

Growth period

GDD

Tmax

Tmin

Tavg

Days >30°

————————— ° ————————— CS 2011

Vegetative 1–110 2750.0 Reproductive 111–161 3406.9 Total – – Vegetative 1–97 2375.7 Reproductive 98–182 3575.0 Total – – Vegetative 1–109 2391.0 Reproductive 110–164 3575.0 Total Vegetative 1–111 2803.5 Reproductive 112–177 3666.7 Total – – Vegetative 1 to 97 2696.5 Reproductive 98–161 3941.7 Total – – Vegetative 1–97 2436.8 Reproductive 98–159 2856.9 Reproductive 98–161 3941.7

CS 2012

UV 2011

UV 2012

CH 2011

OB 2011

Max. RH

Min. RH Precipitation

——— % ———

mm

27.8 37.8 – 31.1 36.1 – 35.5 40.5

19.7 25.0 – 20.5 24.4 – 20.0 25.0

23.8 30.5 – 25.0 30.0 – 27.7 31.1

0 10 – 0 1 – 0 8

85.5 87.4 – 89.5 90.8 – 80.5 84.9

39.6 34.0 – 50.3 42.1 – 30.0 30.9

109.0 77.5 – 198.6 29.2 – 21.6 61.2

30.5 37.7 – 32.2 42.2 – 30.3 34.2 42.2

20.0 22.7 – 15.0 26.1 – 14.0 17.8 26.1

25.0 30.0 – 21.1 29.4 – – – 29.4

0 6 – 0 6 – 11 40 6

91.5 91.0 – 81.7 62.8 – 80.3 78.8 62.8

42.3 32.8 – 35.0 52.2 – 16.2 16.4 52.2

17.0 73.9 – 3.6 36.1 – 0.0 0.0 36.1

Irrigation

Total water

——————— ≈ mm ——————— C D C D 167.7 167.7 276.7 276.7 227.5 0 305.0 77.5 395.2 167.7 581.7 354.2 0 0 198.6 198.6 0 0 29.2 29.2 – – 227.8 227.8 167.7 167.7 189.3 189.3 227.5 0 288.9 61.2 167.7 478.2 250.5 167.7 167.7 184.7 184.7 227.5 0 301.4 73.9 395.2 167.7 486.1 258.6 167.7 167.7 171.3 171.3 227.5 0 263.6 36.1 395.2 167.7 444.9 207.4 Flood (2) N/A – – Flood (1) N/A – – 227.5 0 263.6 36.1

† CS, College Station; UV, Uvalde; CH, Chillicothe; OB, Ciudad de Obregon; GDD, growing degree-days, Tmax, maximum temperature; Tmin, minimum temperature; Tavg, average temperature; RH, relative humidity; C, irrigation and treatment; D, drought (water deficit treatment).

For each RIL, the DSI was calculated from the individual RILs and the mean plot yield and grain yield components (MSHW and TKW) of all RILs in the irrigated vs. water-deficit treatments (Fischer and Maurer 1978) in a particular environment. The RILs with a DSI 1 are considered water-deficitsusceptible (low stability). The DSI was calculated for MSHW, TKW, and plot yield; it was not available for the Obregon 2011 and College Station 2012 environments. In Fig. 2, the plot yield and yield components’ DSI results for Uvalde 2011 and Uvalded 2012 environments are shown. The DSI was calculated with the following formula: Ys 1 Y p1 DSI = Y 1- s2 Yp 2 1-

[1]

where Ys1 indicates an individual RIL’s plot yield or grain yield components (MSHW, TKW) under the water deficit treatment (stressed environments); Yp1 is the mean of plot yield or the grain yield components (MSHW, TKW) of all RILs under the irrigated treatment (nonstressed environments); and 1 – (Ys2 ÷ Yp2) is the stress intensity calculated from the mean of plot yield or the grain yield components (MSHW and TKW) of all RILs under stressed (Ys2) and nonstressed (Yp2) conditions.

4

Statistical Analysis PROC CORR (Pearson’s correlation method) (SAS Institute Inc., Cary, NC) was used to analyze the agronomic and physiological trait correlations across different environments, as Pearson’s correlation is preferred for parametric data (Isobe et al., 1986). The uniformity of the frequency distributions of the traits across the population was analyzed with Proc Univariate SAS codes. A test for normality was performed for each of the traits across individual location and years. Variance across environments was homogeneous, combined analysis was performed together across all environments. The PROC GLM model was used to perform the ANOVA test: Yij = μ + Ej + Gi + GEij + εij,

[2]

where Yij is the phenotype trait value for genotype i and environment j, Ej is the environmental main effect, G i is the genotypic main effect, GEij is the genotype × environment interaction, and εij is the environmental error, which is assumed to be normally and independently distributed. The variance components were calculated from the genotypic, environmental, and genotype × environment interaction main effects. The variance components [genotypic variance (σ2g), the variance of genotype × environment (σ2gxe) interactions, and the variance of experimental error (σ2error)] were calculated from the mean squares (SAS version 9.1). The variance components were used to calculate the broad-sense heritability (H2) (Eq. [3]). Genotypes (g), replications (r), and environments (e) were considered to be random variables.

www.crops.org


Fig. 1. Epicuticular wax content (A, C, E, G, I) and leaf canopy temperature (CT) (B, D, F, H) were plotted against plot yield and yield components with regression analysis (R2) and Pearson’s correlations (r) for 180 recombinant inbred lines (RILs) of Len × Halberd population during 2011 and 2012 under water deficit. MSHW, mean single head weight; KNS, kernel number per spike; TKW, 1000-kernel weight; Hal, Halberd. A, B, D, F, H, I: Uvalde (UV) 2012; C, E, G: Chillicothe (CH) 2011, UV 2012, College Station (CS) 2011, and CS 2012; J: UV 2011, UV 2012).

The H2 was calculated via an entry mean method with the following formula: H2 =

s2 g 2

s error s gXe + + s2 g r xe e 2

.

[3]

Stability Analysis To analyze the water deficit tolerance and stability of different genotypes across environments (locations and years), the Eberhart–Russell equation (Eberhart and Russell 1966) was used to calculate the regression slope (β) and the SD values for each RIL for leaf EW and different yield components. The AGROBASE generation II system (Agronomix Software, Inc., Winnepeg, MB, Canada) (Mulitze, 2004) and SAS version 9.3 (SAS Institute, 2011) were used to analyze the different stability indices for each trait across environments. The stability of each genotype is judged by its least variation in yield loss across the environments. It is determined as the regression slope of each individual entry by the mean grain yield components of all crop science, vol. 58, march– april 2018

entries recorded at the various moisture levels of all different locations. Individuals with an Eberhart stability index value (β) of 1 (Eberhart and Russell 1966) and a SD of 0 are more stable than individuals with a β above or below 1 and a SD above or below 0. Individuals with a value of β = 1 and SD = 0 (Lin et al., 1986) were ranked as 10 (high stability) and individuals with β and SD values deviating from 1 and 0 respectively were considered to be less stable and were ranked as 1 (Fig. 2, Supplemental Table S4 and Supplemental Table S5). Eberhart’s index calculates RIL stability across environments, and the DSI calculates stability within an environment for different moisture regimes. Eberhart’s stability analysis was calculated for MSHW, TKW, and EW. Equation [4] [deviation from the regression mean square (Lin et al., 1986] and Eq. [5] [regression coefficient (Ali et al., 2012; Finlay and Wilkinson 1963)] were used to calculate Eberhart’s stability index. é 1 ù 2 ú éY -Y ù -å Y - Y d 2 = êê ji i. ú .j ê ú ë û 2 q ) ûú ëê (

(

) ; 2

[4]

www.crops.org 5

Fig. 2. Mean grain yield, leaf canopy temperature (CT) (Uvalde), and leaf epicuticular wax contents plotted against yield components’ stability index with regression analysis and Pearson’s correlations for 180 recombinant inbred lines (RILs) of a Len × Halberd population during 2011 and 2012 under water deficit. MSHW, mean single head weight; KNS, kernel number per spike; DSI, drought susceptibility index; UV, Uvalde. A, C, E, G, I, J: College Station (CS) 2011, Chillicothe (CH) 2011, CS 2012, UV 2012; D,F: UV 2011; B, H: UV 2012.

RESULTS

0 cm at Obregon during the growing season (Table 1). Maximum air temperature during the reproductive stage ranged between 31 and 35°C at Obregon, 35 and 40° at Uvalde, 33 and 42°C at Chillicothe, and between 33 and 38°C at College Station. For the physiological and agronomic variables, numerous significant differences were observed between the water deficit (»170 mm) and the irrigated (»395 mm) treatments for parents and RILs (Table 1, Supplemental Table S1 and Supplemental Table S2). In Obregon 2011, flood irrigation was performed and there was no moisture stress treatment at this location.

Precipitation

Wax Inheritance

In 2011, the environmental conditions were ideal for a water deficit experiment with precipitation £10 cm at Uvalde and Chillicothe, £20 cm at College Station, and

The RILs across environments varied significantly for measured EW. Broad-sense heritability was calculated on the basis of the entry means for phenotypic traits under

æ ö ç Y ji - Yi - Y. j + Y.. )(Y. j + Y.. ) ÷÷ ,[5] ÷÷ b= 1 + å ççç ÷÷ çè å (Y. j + Y.. ) ø

where Yij is the response variable for individual RIL i in environment j, b is the regression slope, q is the number of environments, Ȳ.i is the mean of family i, Ȳ.j is the RIL mean of environment j, Ȳ is the predicted variable, and Ȳ.. is the overall RIL mean.

6

www.crops.org


Table 2. Combined mean square variances of entries and genotype × environment interactions over five environments and broad-sense heritability (H2)of agronomical and physiological traits for 180 Len × Halberd spring wheat recombinant inbred lines (RILs) under water deficit and heat + irrigation conditions during 2011 and 2012. Genotype MSHW TKW Height KNS CT EW

D

C

0.039** 31.94** 304.6** 38.91*** 5.10** 3.03**

0.04** 55.2** 149.96** 10.87** 20.28** 3.19**

Variance components Genotype × environment D C 0.024** 24.69** 50.45** 25.81** 3.07** 2.66*

Experimental error D C

0.018** 14.5** 44.64** 17.4** 17.6** 2.95**

0.015** 11.01** 43.00** 18.04 2.22** 2.28**

0.014** 10.84** 18.07** 10.87** 8.07** 2.65*

H2 D

C

0.39 0.23 0.80 0.39 0.40 0.15

0.55 0.74 0.70 0.49 0.13 0.08

*** Significant at the ≤0.001 probability level. ** Significant at the ≤0.01 probability level. * Significant at the ≤0.05 probability level. † C, irrigated and heat; D, water deficit; MSHW, mean single head weight; TKW, 1000-kernel weight; PlHt, plant height; KNS, kernel number per spike; CT, canopy temperature; EW, leaf epicuticular wax.

Table 3. Pearson’s phenotypic correlation coefficients between agronomic and physiological traits for a Len × Halberd spring wheat recombinant inbred line (RIL) population combined across five environments (water deficit) during 2011 and 2012. Variables

MSHW

TKW

MSHW TKW KNS PlHt DSI-MSHW DSI-TKW CT EW

1 – – – – – – –

0.38*** 1 – – – – – –

KNS 0.69*** –0.26*** 1 – – – – –

PlHt 0.42*** 0.23*** 0.60*** 1 – – – –

DSI-MSHW

DSI-TKW

–0.17*** NS –0.15*** –0.09** 1 – – –

NS NS NS NS NS 1 – –

CT –0.28*** –0.34*** –0.18*** –0.18*** NS NS 1 –

EW 0.58*** 0.39*** 0.32*** –0.15*** NS NS –0.25*** 1

*** Significant at the ≤0.001 level. ** Significant at the ≤0.01. † NS, nonsignificantl MSHW, mean single head weight (g); TKW, 1000-kernel weight (g per 1000 seeds); KNS, kernel number per spike; PlHt, plant height (cm); CT, canopy temperature (°C); EW, epicuticular wax (mg dm–2); DSI-MSHW, drought susceptibility index for mean single head weight; DSI-TKW, drought susceptibility index for 1000-kernel weight.

the water deficit and irrigated treatments by considering all factors (replications, genotype, environments, and genotype × environment) as random (Table 2). Heritability of the yield components and plant height was higher in the irrigated than in the water-deficit treatment. In contrast, CT and EW traits expressed higher inheritance under water deficit conditions than under irrigated conditions (Table 2). Leaf EW correlated significantly with potential and stable yield components but it was highly influenced by the environment (Fig. 2J; Table 3). The strong environmental interactions and low genetic stability decreased EW inheritance under water deficit (H 2 = 0.15) and irrigated (H 2 = 0.08) conditions across the tested environments (Table 2). The Uvalde 2011 season was drier and produced significantly more wax than the Uvalde 2012 season (Fig. 1J). The wax CV for the water deficit and irrigated treatments were also high (28.4 and 35.9% respectively). The Eberhart stability values for EW also decreased when EW content on the leaf surface increased, which helps explain the unstable nature of the wax across environments (Fig. 2J) and the environmentally adaptive nature of the leaf EW content. All other traits displayed crop science, vol. 58, march– april 2018

low to high inheritance with significant genotype × environment interactions across the five environments under the water deficit (MSHW = 0.39, TKW = 0.23, KNS = 0.39, plant height = 0.80, and CT = 0.40) and irrigated (MSHW = 0.55, TKW = 0.74, KNS = 0.49, plant height = 0.70, and CT = 0.13) treatments (Table 2).

Leaf EW and CT versus Plot Yield and Grain Yield Components The leaf EW and CT were negatively correlated (r = -0.25, P ≤ 0.001) and had a significant association with different yield components under water deficit conditions (Table 3 and Fig. 1I). The RILs with high amounts of EW were unstable across environments and significantly influenced different yield components (Fig. 1, Fig. 2). In addition to EW, cooler canopies, which are partly regulated by EW, also act as a drought-adaptive trait and improve yield components under water deficit (Table 3; Fig. 1B, D, F, H). Thus leaf EW and CT have a synergistic impact on yield components under water deficit conditions at different environments (Supplementary Table S3).

www.crops.org 7

Leaf EW and CT versus Grain Yield Component Stability Yield component (MSHW, TKW) stability varied significantly within locations (DSI: -2 to 2) and across locations (Eberhart’s: 0–10) (Fig. 2). Across all locations and treatments, Halberd had higher yield component stability than Len (Fig. 1 and 2) (Hays et al., 2007; Mason et al., 2011; Mason et al., 2010; Mondal and Hays 2007). The RILs with higher plot yield or yield components potential displayed higher stability within an environment (DSI) (Fig. 2B, 2D, 2F) and across environments (Eberhart’s rank) (Fig. 2C, 2G). The leaf EW correlated negatively with DSI for MSHW (r = -0.40, P ≤ 0.001) and DSI for TKW (r = -0.35, P ≤ 0.001) and correlated positively with Eberhart’s stability rank for MSHW (r = 0.36, P ≤ 0.001), KNS (r = 0.24, P ≤ 0.01), and TKW (r = 0.37, P ≤ 0.001) (Fig. 2A, D, E, F) during the reproductive stages. These results clearly reveal the importance of leaf EW in maintaining grain yield component stability across different water deficit conditions (Fig. 2). In addition to the leaf EW content, leaf CT improved grain yield component stability and correlated positively with plot yield stability during water deficit as measured by the plot yield DSI (r = 0.37, P ≤ 0.001) (Fig. 2H). There was a positive relationship between the leaf EW Eberhart stability rank and the MSHW Eberhart stability rank, with most RILs concentrating towards the center closer to stability (r = 0.39, P ≤ 0.001) (Fig. 2I). Increased leaf EW influenced CT and was highlighted as an important target to improve the stability and potentiality of grain yield components under water deficit conditions (Fig. 2B, 2D, 2F, 2H; Table 3). These results help to elucidate the association of higher wax stability with higher MSHW and TKW stability. In conclusion, leaf EW is associated with cooler canopies, as well as a lower DSI and Eberhart stability index, indicating its importance in imparting water deficit tolerance within and across environments.

DISCUSSION Variation among RILs and Heritability of Leaf EW A RIL population was developed from a cross between two spring wheat lines (Halberd and Len) to investigate phenotypic and genotypic correlations between physiological and agronomic traits under water deficit conditions in southern Texas and northern Mexico. Halberd exhibits significantly (P ≤ 0.01) higher EW load and lower CT, with relatively higher yield component stability (Fig. 1 and Fig. 2). The present investigations revealed significant genetic variability and trait segregation for leaf EW (Table 2), with similar results of trait segregation having been identified in other studies (Araus et al., 1991; Uddin and Marshall 1988). The low to high inheritance of leaf EW was also noted in various studies on other species including wheat (0.12) (Clarke et al., 1994), maize (0.17) (Meeks 8

et al., 2012), wild rye grasses (Elymus triticoides Buckley) ( Jefferson 1994), alfalfa (Medicago sativa L.) (0.35) ( Jefferson et al., 1989), sorghum (0.36) ( Jordan et al., 1983), and rice (0.77) (Haque et al., 1992). For sorghum, the genetic stability of leaf EW varied significantly and was highly adapted to environmental conditions ( Jordan et al., 1983). The mean leaf EW content for the RILs in the water deficit treatment (0.30 mg dmˉ 2) was statistically greater than that for the RILs in the irrigated treatment (0.19 mg dmˉ 2) (Fig. 1J). Overall, the greater the heat or moisture stress, the greater the production of EW (Huggins et al., 2017). Obregon was the hottest location and had 0 mm of precipitation during the growing season, which resulted in the highest overall leaf EW load (ranging from 2.99 to 12.81 mg dm-2). Uvalde and College Station had the next highest leaf EW loads. Similarly, a previous study on tobacco (Nicotiana glauca L. cv. Graham) revealed a sixfold increase of lipid transfer protein gene transcripts and a 1.5to twofold increase in wax accumulation leaves exposed to increased periodic drying (Cameron et al., 2006). Overall, major genotype × environment interactions mask the effect of genetic variance, leading to a decrease in trait inheritance and a reduction in the correlations between genotype and phenotype (Romagosa and Fox 1993).

Leaf EW and CT to Improve Potential Yield Components Across the different field trials, leaf EW and CT had significantly improved yield components. Although a cooler canopy is a physiologically integrated trait, under water deficit conditions, it is also correlated with increased EW load. Our study revealed a significant association between EW load and leaf CT in different environments. A negative correlation exists between EW and CT (Table 3), with an increase in wax load from 1 mg dm-2 to 4.5 mg dm-2, leading to a decrease of 1.8° in CT (Fig. 1I). In general, we found that the greater the water deficit, the stronger the correlation. Obregon, which had the driest environment, leads with strong negative correlations between cooler canopies and EW, followed by Uvalde and College Station (data not shown). Similar negative correlations were identified in a winter wheat population subjected to heat stress conditions (Mason et al., 2013; Huggins et al., 2017) and a positive correlation was observed between leaf temperature depression and EW (Mondal et al., 2015; Huggins et al., 2017). The association between leaf EW and grain yield components or plot yield depends on the severity of the water deficit and the impact of the field environmental conditions. In our study, there was a stronger correlation between plot yield and EW at Uvalde 2012and Obregon (Fig. 1A). Under drier conditions, glaucous wheat lines tend to reflect high-energy radiation, maintain water use efficiency, and improve yields (Febrero et al., 1998). Across all environments, EW was positively correlated with MSHW, KNS,

www.crops.org


and TKW (Fig. 1A, E, F; Table 3). Similar trait associations and genetic overlap exist among stay-green, leaf EW, and CT in sorghum (Awika et al., 2017; Ehleringer 1980; Mkhabela 2012), and in wheat (Araus et al., 1991; Fischer and Wood 1979; Mondal and Hays 2007). In previous studies, genotypes with greater wax load were observed to restrict residual water transpiration in peas (Sánchez et al., 2001) and reflect high energy radiation (Vanderbilt et al., 1991). In another study, different wax-rich sorghum genotypes reflected high energy radiation and reduced their transpiration rate (Premachandra et al., 1994). In wheat, a 1° decrease in CT increased the grain weight by 4 mg (Ishag et al., 1998; Olivares-Villegas et al., 2007). In pea plants, leaf EW positively correlated with harvest index and negatively correlated with CT, suggesting that increased EW may have increased the reflection of high energy radiation, thereby preventing photo-oxidative damage (Sánchez et al., 2001). These waxy cuticle layers may affect transpirational cooling needs and stomatal conductance by acting as a reflective surface to high temperatures and high-energy radiation, thereby reducing unnecessary water loss while still allowing canopies to stay cool. Overall, leaf EW and cooler canopy temperatures during the reproductive stage could be important drought-adaptive traits and these traits could be effectively incorporated into a high-yielding genetic background.

Leaf EW and CT to Improve Yield Components and Plot Yield Stability

Conflict of Interest Disclosure The authors declare that there is no conflict of interest.

Supplemental Information Supplemental Table S1. Means of parents and range of RIL (Len × Halberd) across different environments for agronomic and physiological traits under control and moisture deficit conditions during 2011 and 2012. Supplemental Table S2. Means of RILs for different traits in different environments of irrigation and heat vs. water deficit during 2011 and 2012. Supplemental Table S3. Coefficient correlation calculated for each environment for different physiological and agronomical traits under moisture deficit environments. Supplemental Table S4. Top and bottom 10 Eberhart’s stability rankings for Len × Halberd RILs under waterdeficit conditions across all environments and years. Supplemental Table S5. Top and bottom 10 wax amount produced for Len × Halberd RILs under water-deficit conditions across all environments and years. Acknowledgments

The role of leaf EW in maintaining stable yields across varying water deficit environments has been poorly documented. Another physiological trait, leaf CT, correlated positively with plot yield DSI (r = 0.37, P ≤ 0.001) (Fig. 2H), with most of the RILs’ stability index values concentrated close to 1 (Ishag et al., 1998). A previous study of wheat genotypes revealed the role of cooler canopies in keeping DSI close to 1 in water deficit environments (Rashid et al., 1999). For plot yield and MSHW, almost 75% of the RIL populations showed an Eberhart stability index of £1 across environments and 61% of individual RILs showed a DSI rating of £1 within an environment (Fig. 1B, 1C). In addition to leaf EW and CT studies, other studies have made similar conclusions, with osmotic adjustment improving yield stability within the environment (DSI) (Khanna-Chopra 1999) and across different environments (Eberhart stability) (Moinuddin et al., 2005).

CONCLUSIONS The goal of this study was to investigate drought-adaptive traits with significant genetic variation. The significant phenotypic correlations of agronomic and physiological traits provide a clue to the existence of genetic linkages for drought-adaptive traits and potential yield attributes across different environments. Leaf EW had a significant crop science, vol. 58, march– april 2018

association with cooler canopies, which was probably caused by the reflection of high-energy wavelengths and a reduction of excess heat energy on the leaf surface. Genetic loci that regulate high and stable levels of leaf EW and cooler canopies could be integrated into the genetic background of drought- and heat-susceptible elite lines.

This research was supported by the Agriculture and Food Research Initiative Competitive Grant #2010-6514-20389 from the USDA National Institute of Food and Agriculture to DBH and AI, Texas A & M University. 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 USDA. The USDA is an equal opportunity provider and employer.

References Ali, A., M.A. Masood, and M.A. Zahid. 2012. Identifying the most promising genotypes in lentil for cultivation in a wide range of environments of Pakistan using various yield stability measures. Pak. J. Bot. 44:1919–1922. Araus, J.L., A. Febrero, and P. Vendrell. 1991. Epidermal conductance in different parts of durum wheat grown under Mediterranean conditions: The role of epicuticular waxes and stomata. Plant Cell Environ. 14:545–558. doi:10.1111/j.1365-3040.1991. tb01525.x Awika, H.O., D.B. Hays, J.E. Mullet, W.L. Rooney, and B.D. Weers. 2017. QTL mapping and loci dissection for leaf epicuticular wax load and canopy temperature depression and their association with QTL for staygreen in Sorghum bicolor under stress. Euphytica 213:207. doi:10.1007/s10681-017-1990-5 Baenziger, P.S., D.M. Wesenberg, and R.C. Sicher. 1983. The effects of genes controlling barley leaf and sheath waxes on agronomic performance in irrigated and dryland environments. Crop Sci. 23:116–120. doi:10.2135/cropsci1983.0011183X002300010034x

www.crops.org 9

Bariana, H.S., H. Miah, G.N. Brown, N. Willey, and A. Lehmensiek. 2007. Molecular mapping of durable rust resistance in wheat and its implication in breeding. In: H.T. Buck, J.E. Nisi, and N. Salomon, editors, Wheat production in stressed environments. Springer, Rotterdam. p. 723–728. Bengtson, C., S. Larsson, and C. Liljenberg. 1978. Effects of water stress on cuticular transpiration rate and amount and composition of epicuticular wax in seedlings of six oat varieties. Physiol. Plant. 44:319–324. doi:10.1111/j.1399-3054.1978.tb01630.x Bennett, D., A. Izanloo, J. Edwards, H. Kuchel, K. Chalmers, M. Tester, et al. 2012. Identification of novel quantitative trait loci for days to ear emergence and flag leaf glaucousness in a bread wheat (Triticum aestivum L.) population adapted to southern Australian conditions. Theor. Appl. Genet. 124:697–711. Bhullar, S., and C. Jenner. 1985. Differential responses to high temperatures of starch and nitrogen accumulation in the grain of four cultivars of wheat. Funct. Plant Biol. 12:363–375. Bird, D., F. Beisson, A. Brigham, J. Shin, S. Greer, R. Jetter, et al. 2007. Characterization of Arabidopsis ABCG11/WBC11, an ATP binding cassette (ABC) transporter that is required for cuticular lipid secretion. Plant J. 52:485–498. doi:10.1111/j.1365313X.2007.03252.x Blum, A., L. Shpiler, G. Golan, and J. Mayer. 1989. Yield stability and canopy temperature of wheat genotypes under droughtstress. Field Crops Res. 22:289–296. doi:10.1016/03784290(89)90028-2 Brown, L.R. 2003. World creating food bubble economy based on unsustainable use of water. Earth Policy Institute. Rutgers University. http://www.earthpolicy.org/plan_b_updates/2003/ update22# (accessed 15 Dec. 2017) Cameron K.D., M.A. Teece, and L.B. Smart. 2006. Increased accumulation of cuticular wax and expression of lipid transfer protein in response to periodic drying events in leaves of tree tobacco. Plant Physiol. 140:176–183. Clarke, J.M., T.N. McCaig, and R.M. Depauw. 1994. Inheritance of glaucousness and epicuticular wax in durum wheat. Crop Sci. 34:327–330. doi:10.2135/cropsci1994.0011183X003400020002x Clarke, J.M., and R.A. Richards. 1988. The effects of glaucousness, epicuticular wax, leaf age, plant height, and growth environment on water loss rates of excised wheat leaves. Can. J. Plant Sci. 68:975–982. doi:10.4141/cjps88-118 Ebercon, A., A. Blum, and W. Jordan. 1977. A rapid colorimetric method for epicuticular wax contest of sorghum leaves. Crop Sci. 17:179–180. doi:10.2135/cropsci1977.0011183X001700010047x Eberhart, S.A., and W.A. Russell. 1966. Stability parameters for comparing varieties. Crop Sci. 6:36–40. doi:10.2135/cropsci1966.00 11183X000600010011x Ehleringer, J. 1980. Leaf morphology and reflectance in relation to water and temperature stress. In: N. Turner and P. Kramer, editors, Adaptation of plants to water and high temperature stress. Wiley-Interscience, New York. p. 295–308. Eigenbrode, S.D., and K.E. Espelie. 1995. Effects of plant epicuticular lipids on insect herbivores. Annu. Rev. Entomol. 40:171–194. doi:10.1146/annurev.en.40.010195.001131 Food and Agricultural Organization of the United Nations. 2015. FAO cereal supply and demand brief. Food and Agricultural Organization of the United Nations. http://www.fao.org/ worldfoodsituation/csdb/en/ (accessed 15 Dec. 2017). Febrero, A., S. Fernández, J.L. Molina-Cano, and J.L. Araus. 1998. Yield, carbon isotope discrimination, canopy reflectance and cuticular conductance of barley isolines of differing glaucousness. J. Exp. Bot. 49:1575–1581. doi:10.1093/jxb/49.326.1575 Figueiredo, K.V., M.T. Oliveira, A.F.M. Oliveira, G.C. Silva, and

10

M.G. Santos. 2012. Epicuticular-wax removal influences gas exchange and water relations in the leaves of an exotic and native species from a Brazilian semiarid region under induced drought stress. Aust. J. Bot. 60:685–692. doi:10.1071/BT12168 Finlay, K., and G. Wilkinson. 1963. The analysis of adaptation in a plant-breeding programme. Crop Pasture Sci. 14:742–754. doi:10.1071/AR9630742 Fischer, R., and R. Maurer. 1978. Drought resistance in spring wheat cultivars. I. Grain yield responses. Crop Pasture Sci. 29:897–912. doi:10.1071/AR9780897 Fischer, R., and J. Wood. 1979. Drought resistance in spring wheat cultivars. III. Yield associations with morpho-physiological traits. Crop Pasture Sci. 30:1001–1020. doi:10.1071/AR9791001 Haque, M.M., D.J. Mackill, and K.T. Ingram. 1992. Inheritance of leaf epicuticular wax content in rice. Crop Sci. 32:865–868. doi:10.2135/cropsci1992.0011183X003200040006x Hays, D.B., J.H. Do, R.E. Mason, G. Morgan, and S.A. Finlayson. 2007. Heat stress induced ethylene production in developing wheat grains induces kernel abortion and increased maturation in a susceptible cultivar. Plant Sci. 172:1113–1123. doi:10.1016/j. plantsci.2007.03.004 Hossain, A., J. Teixeira da Silva, M. Lozovskaya, V. Zvolinsky, and V. Mukhortov. 2012. High temperature combined with drought affect rainfed spring wheat and barley in south-eastern Russia: Yield, relative performance and heat susceptibility index. J. Plant Breed. Crop Sci. 4:184–196. Huggins T, Mohammed S, Sengodon P, Ibrahim AMH, Tilley M, Hays DB (2017) Changes in leaf epicuticular wax load and its effect on leaf temperature and physiological traits in wheat cultivars (Triticum aestivum L.) exposed to high temperature during anthesis. J. Agron. Crop Sci. doi:10.1111/jac.12227 Ishag, H.M., B.A. Mohamed, and K.H.M. Ishag. 1998. Leaf development of spring wheat cultivars in an irrigated heat-stressed environment. Field Crops Res. 58:167–175. doi:10.1016/S03784290(98)00092-6 Isobe, T., E. Feigelson, and P. Nelson. 1986. Statistical methods for astronomical data with upper limits. II—Correlation and regression. Astrophys. J. 306:490–507. doi:10.1086/164359 Jefferson, P., D. Johnson, M. Rumbaugh, and K. Asay. 1989. Water stress and genotypic effects on epicuticular wax production of alfalfa and crested wheatgrass in relation to yield and excised leaf water loss rate. Can. J. Plant Sci. 69:481–490. doi:10.4141/ cjps89-059 Jefferson, P.G. 1994. Genetic variation for epicuticular wax production in Altai wildrye populations that differ in glaucousness. Crop Sci. 34:367–371. doi:10.2135/cropsci1994.0011183X0034 00020011x Ji, X., B. Shiran, J. Wan, D.C. Lewis, C.L. Jenkins, A.G. Condon, et al. 2010. Importance of pre-anthesis anther sink strength for maintenance of grain number during reproductive stage water stress in wheat. Plant Cell Environ. 33:926–942. doi:10.1111/ j.1365-3040.2010.02130.x Johnson, D.A., R.A. Richards, and N.C. Turner. 1983. Yield, water relations, gas exchange, and surface reflectances of near-isogenic wheat lines differing in glaucousness. Crop Sci. 23:318–325. doi:10.2135/cropsci1983.0011183X002300020033x Jordan, W., R. Monk, F. Miller, D. Rosenow, L. Clark, and P. Shouse. 1983. Environmental physiology of sorghum. I. Environmental and genetic control of epicuticular wax load. Crop Sci. 23:552– 558. doi:10.2135/cropsci1983.0011183X002300030025x Khanna-Chopra, R. 1999. Osmotic adjustment and yield stability in wheat genotypes and species grown in water limited environments. J. Plant Biol. 26:173–178.

www.crops.org


Kolmer, J., D. Garvin, and Y. Jin. 2011. Expression of a Thatcher wheat adult plant stem rust resistance QTL on chromosome arm 2BL is enhanced by Lr34. Crop Sci. 51:526–533. doi:10.2135/ cropsci2010.06.0381 Lin, C.-S., M.R. Binns, and L.P. Lefkovitch. 1986. Stability analysis: Where do we stand? Crop Sci. 26:894–900. doi:10.2135/cropsci 1986.0011183X002600050012x Liu, Q., Z. Ni, H. Peng, W. Song, Z. Liu, and Q Sun. 2007. Molecular mapping of a dominant non-glaucousness gene from synthetic hexaploid wheat (Triticum aestivum L.): Molecular mapping of non-glaucousness gene in wheat. Euphytica 155:71–78. doi:10.1007/s10681-006-9302-5 Mason, R.E., D.B. Hays, S. Mondal, A.M. Ibrahim, and B.R. Basnet. 2013. QTL for yield, yield components and canopy temperature depression in wheat under late sown field conditions. Euphytica 194:243–259. doi:10.1007/s10681-013-0951-x Mason, R.E., S. Mondal, F.W. Beecher, and D.B. Hays. 2011. Genetic loci linking improved heat tolerance in wheat (Triticum aestivum L.) to lower leaf and spike temperatures under controlled conditions. Euphytica 180:181–194. doi:10.1007/s10681-011-0349-6 Mason, R.E., S. Mondal, F.W. Beecher, A. Pacheco, B. Jampala, A.M. Ibrahim, et al. 2010. QTL associated with heat susceptibility index in wheat (Triticum aestivum L.) under short-term reproductive stage heat stress. Euphytica 174:423–436. doi:10.1007/ s10681-010-0151-x Meeks, M., S.C. Murray, S. Hague, D. Hays, and A.M.H. Ibrahim. 2012. Genetic variation for maize epicuticular wax response to drought stress at flowering. J. Agron. Crop Sci. 198:161–172. Mkhabela, S.M. (2012) Genetic variation, heritability estimates, and yield relationship of pre-flowering and post-flowering drought resistant traits in grain sorghum. Ph.D. diss., Texas Tech Univ., Lubbock, TX. Mohammed, S., B.C. Blaser, and B.A. Stewart. 2012. Planting geometry and plant population affect dryland maize grain yield and harvest index. J. Crop Improv. 26(1):130–139. doi:10.1080/1542 7528.2011.618241 Moinuddin, R.A. Fischer, K.D. Sayre, and M.P. Reynolds. 2005. Osmotic adjustment in wheat in relation to grain yield under water deficit environments. Agron. J. 97:1062–1071. doi:10.2134/ agronj2004.0152 Mondal, S., and D. Hays. (2007) Leaf epicuticular wax improves heat tolerance in wheat. Presented at ASA-CSSA-SSSA 2007 International Annual Meeting, 4–8 Nov. 2007. New Orleans, LA. Mondal, S., R.E. Mason, T. Huggins, and D.B. Hays. 2015. QTL on wheat (Triticum aestivum L.) chromosomes 1B, 3D and 5A are associated with constitutive production of leaf cuticular wax and may contribute to lower leaf temperatures under heat stress. Euphytica 201:123–130. Mulitze, D.K. 2004. Agrobase generation II. Users’ manual. Agronomix Software Inc., Winnepeg, MB, Canada. Olivares-Villegas, J.J., M.P. Reynolds, and G.K. McDonald. 2007. Drought-adaptive attributes in the Seri/Babax hexaploid wheat population. Funct. Plant Biol. 34:189–203. doi:10.1071/FP06148 Paull, J., K. Chalmers, A. Karakousis, J.M. Kretschmer, S. Manning, and P. Langridge. 1998. Genetic diversity in Australian wheat varieties and breeding material based on RFLP data. Theor. Appl. Genet. 96:435–446. doi:10.1007/s001220050760 Paull, J., R. Nable, and A. Rathjen. 1992. Physiological and genetic control of the tolerance of wheat to high concentrations of boron and implications for plant breeding. Plant Soil 146:251–260. doi:10.1007/BF00012019


Pinto, R.S., M.P. Reynolds, K.L. Mathews, C.L. McIntyre, J.-J. Olivares-Villegas, and S.C. Chapman. 2010. Heat and drought adaptive QTL in a wheat population designed to minimize confounding agronomic effects. Theor. Appl. Genet. 121:1001–1021. doi:10.1007/s00122-010-1351-4 Premachandra, G., D. Hahn, J. Axtell, and R. Joly. 1994. Epicuticular wax load and water-use efficiency in bloomless and sparse-bloom mutants of Sorghum bicolor L. Environ. Exp. Bot. 34:293–301. doi:10.1016/0098-8472(94)90050-7 Rashid, A., J. Stark, A. Tanveer, and T. Mustafa. 1999. Use of canopy temperature measurements as a screening tool for drought tolerance in spring wheat. J. Agron. Crop Sci. 182:231–238. doi:10.1046/j.1439-037x.1999.00335.x Reynolds, M.P., C.S. Pierre, A.S. Saad, M. Vargas, and A.G. Condon. 2007. Evaluating potential genetic gains in wheat associated with stress-adaptive trait expression in elite genetic resources under drought and heat stress. Crop Sci. 47:172–189. doi:10.2135/ cropsci2007.10.0022IPBS Romagosa, I., and P. Fox. 1993. Genotype × environment interaction and adaptation. In: M.D. Hayward, N.O. Bosemark, and I. Romagosa, editors, Plant breeding principles and prospects. Chapman & Hall, London. p. 373–390. Samdur, M., P. Manivel, V. Jain, B. Chikani, H. Gor, S. Desai, et al. 2003. Genotypic differences and water-deficit induced enhancement in epicuticular wax load in peanut. Crop Sci. 43:1294– 1299. doi:10.2135/cropsci2003.1294 Sánchez, F.J., M. Manzanares, E.F. de Andrés, J.L. Tenorio, and L. Ayerbe. 2001. Residual transpiration rate, epicuticular wax load and leaf colour of pea plants in drought conditions. Influence on harvest index and canopy temperature. Eur. J. Agron. 15:57–70. doi:10.1016/S1161-0301(01)00094-6 SAS Institute. 2011. The SAS system for windows: Release 9.3. SAS Inst., Cary, NC Stuckey, J.R. 1972. Inheritance of glaucousness in wheat. PhD thesis, Univ. of New South Wales, Sydney, NSW, Australia Uddin, M.N., and D. Marshall. 1988. Variation in epicuticular wax content in wheat. Euphytica 38:3–9. USDA-ARS. 2015. Germplasm resources information network. USDA-ARS. http://www.ars-grin.gov (accessed 12 Dec. 2017), USDA-NASS. 2014. Agricultural statistics. USDA-NASS, U.S. Gov. Print. Office, Washington, DC. US Global Change Research Program. (2009) Global climate change impacts in the United Nations. Union of Concerned Scientists. http://www.ucsusa.org/assets/documents/global_ warming/us-global-climate-change-report-greatplains.pdf (accessed 15 Dec. 2017) Vanderbilt, C.V., L. Grant, and S.L. Ustin. 1991. Polarization of light by vegetation. In: R.B. Myeneni and J. Ross, editors, Ross Photon-Vegetation Interactions. Springer, Berlin, Heidelberg. p. 191–228. doi:10.1007/978-3-642-75389-3_7 Weldearegay, D.F., F. Yan, D. Jiang, and F. Liu. 2012. Independent and combined effects of soil warming and drought stress during anthesis on seed set and grain yield in two spring wheat varieties. J. Agron. Crop Sci. 198:245–253. doi:10.1111/j.1439037X.2012.00507.x Yang, J., J. Zhang, L. Liu, Z. Wang, and Q. Zhu. 2002. Carbon remobilization and grain filling in japonica/indica hybrid rice subjected to postanthesis water deficits. Agron. J. 94:102–109. doi:10.2134/agronj2002.0102

www.crops.org 11