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Evaluation of seedling characteristics of wheat (Triticum aestivum L

By Tracy Adams,2014-07-19 20:57
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Evaluation of seedling characteristics of wheat (Triticum aestivum L

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    Evaluation of seedling characteristics of wheat (Triticum aestivum L.) through canonical

    correlation analysis

     1223Mustafa Erayman, Bekele Geleta Abeyo, P. Stephen Baenziger*, Hikmet Budak, Kent 4 M. Eskridge

    1- Department of Crop Science, Faculty of Agriculture, Mustafa Kemal University,

    31034 Antakya, Hatay, TURKEY

    2- Department of Agronomy and Horticulture, University of Nebraska- Lincoln, NE

    68583, USA

    3- Biological Sciences and Bioengineering Program, Faculty of Engineering and

    Natural Sciences, Sabanci University, Orhanli, 34956, Tuzla, Istanbul / TURKEY

    4- Department of Statistics, University of Nebraska- Lincoln, NE 68583, USA

    * Correspondence: P.S. BAENZIGER, Department of Agronomy and Horticulture,

    University of Nebraska- Lincoln, NE 68583, USA. E-mail: pbaenziger1@unl.edu.

Abstract

    To examine the seedling characteristics of nine different bread wheat (Triticum aestivum

    L.) varieties, several variables regarding seedling size and germination characteristics were analyzed using canonical correlation analysis. Significantly correlated first canonical variate pairs indicated that the variables within each set such as coleoptile length, shoot length and fresh weight within size set, and emergence rate index and germination percentage can be regarded as main factors for vigorous wheat seedlings. The variables such as root number, root weight and dry weight did not seem to have predictive power on seedling size measurements of wheat. Both emergence rate index and somewhat germination percentage within the first canonical variate of germination set appeared to be the correct factors for vigorous germination of wheat seed. Our analysis revealed that compared to other variables, coleoptile length and emergence rate index are powerful determinants of reliable germination, in turn better wheat stand establishment. Selecting for these traits in early generation is expected to increase the seedling vigor of wheat. Canonical correlation analysis was shown to be suitably sensitive to detect relationships between seedling variables in bread wheat.

Keywords: coleoptile length, emergence rate index, seedling vigor

Introduction

    Adequate wheat (Triticum aestivum L.) stand establishment is major requirement for high

    grain yield (Paulsen, 1987). Cultivars emerging rapidly are valuable because rainfall after sowing can result in a soil crust that prevents the wheat coleoptile or first leaf to emerge. Additionally, early emerging crops can maximize the water utilization leading to better stand establishment and grain yield. Therefore, identifying the seedling characteristics associated with the best seedling vigor will be very important. Varying seedling vigor determinants have been correlated to find better selection criteria for crop stand establishment in wheat fields. For selection purposes, seed size, protein content and initial root and above-ground biomass were correlated in wheat (Ries and Everson, 1973). Several studies have revealed the positive effects

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    of larger seed size on wheat germination and establishment (Singh, 1970; Ries and Everson, 1973; Hampton, 1981; Kalakanavar et al., 1989; Aparicio et al., 2002).

    Another essential seedling trait useful to improve wheat stand establishment is length of coleoptile, which is a protective sheath encasing shoot during emergence. Coleoptile length (CL) is of great importance when considering variable seeding depth, soil surface temperature and moisture, which affect the coleoptile development, in turn seedling emergence and crop stand establishment. Coleoptile length is a highly heritable character and could be efficiently used in selection programs in early segregating generations (Hakizimana et al., 2000; Chowdry and Allan, 1963). Although the main variation in the coleoptile length is genetic (ICARDA, 1987), the trait was significantly affected by genotype x environment interaction (Hakizimana et al., 2000). Boyd, Gordon and Lacroix, (1971) considered germination capability and rate of germination as important factors in assessing seedling vigor. Emergence capability of winter wheat was also associated with coleoptile length and stand establishment (Schillinger et al., 1998). A positive association was reported between CL and winter wheat emergences indicating that CL affected total plant emergence (Burleigh et al., 1965; Sunderman 1964). Additionally, CL was found to be positively correlated with plant height indicating that shorter plants had shorter coleoptiles (Whan 1976; Hoff et al., 1973; Sunderman 1964) as would be expected since the reduced height genes that control plant height in semidwarf wheat also affect CL (Rebetzke et al. 2001; Ellis et al. 2005). Allan et al. (1962) also found a positive correlation between CL and emergence rate index. Several other seedling characteristics such as emergence percentage, emergence rate index, dry root and shoot, fresh root and shoot weights and lengths were correlated and found to be heritable seedling traits (Khan et al. 2002). The studies, which associated seedling vigor to other plant characteristics or germination properties, used either simple linear regression or correlation for their analyses (Schillinger et al., 1998; Rebetzke and Richards, 1999; Khan et al., 2002; Cisse and Ejeta, 2003). Simple correlation and even principal component analyses do not make the distinction between variables, namely, linear and multiple regression analyses allow only a single dependent variable. Canonical correlation analysis (CCA) is a multivariate linear statistical method used to describe the linear relationship between two sets of variables, and understand the multidimensional relations between these two sets of variables (Hotelling, 1936). Canonical correlation analysis was used for end-use quality variables of bread wheat (Butt et al., 2001), texture and sensory characteristics of different potato cultivars (Vainionpaa et al., 2000) and various other disciplines to analyze multidimentional relations between multiple dependent and multiple independent variables (Shafto et al., 1997). However, to our best knowledge canonical correlation analysis has not been used to investigate relationships between seedling and germination characteristics.

    Improved seedling response to adverse environmental conditions likely results in increased vigor of growing plants and increased yield. How seedling characteristics affect differences in early vigor among genotypes is not well understood. Therefore, the objectives of our study were; i) to examine factors that may contribute to wheat growth advantage which are the principal determinants of early seedling vigor in wheat, and ii) to investigate plant characteristics important for increased seedling vigor, which helps as selection criteria in the development of vigorous high yielding varieties. We used canonical correlation analysis to see how morphological characteristics were associated with the germination properties of wheat plants during seedling formation.

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    Materials and methods

Plant material

    Nine wheat cultivars (Table 1) from diverse backgrounds that were widely grown in Nebraska were used in this study. Centura, Pronghorn, and Scout 66 were conventional height cultivars. Vista, Alliance, Arapahoe, Nekota, and Niobrara were semidwarf cultivars (most likely containing Rht). Cougar has the phenotype of lines containing the Rht allele. All seed was 18

    derived from field grown plants in 1996.

    Table 1. The Pedigree /Cross and some agronomic traits of varieties released by University of Nebraska- Lincoln from 1966-1996.

Name Pedigree /Cross Year Ht(cm) Maturity

    released

    Ne68513/Ne68457//Centurk/3/Brule 1992 Short med.-late Vista

    Arkan/Colt//Chisholm sib 1993 med. med.- Alliance

    Brule/3/Parker*4/Agent//Belot.198/3/Lancer 1988 med. med.-late Arapahoe

    Bennett/TAM107 1994 med. med.- Nekota

    TAM105*4/Amigo/Brule sel.5 1994 med. med.- Niobrara

    Warrior*5/Agent//Ne68457/3/Centurk78 1983 tall med.-early Centura

    Centura/Dawn//Colt sib. 1996 tall med.-early Pronghorn

    selection from scout 1966 tall med.-early Scout66

    Thunderbird/NE87505 - med. Med.-early Cougar

Growth system

    Thirty seeds of each variety with similar seed size were sown in a flat box (83 cm long by 10 cm wide and 15 cm depth) filled with moistened vermiculite at 2.5, 5.0, 7.5 or 10 cm depths and covered to the desired level after labeling. The boxes were watered and put in a dark growth chamber set at a constant temperature of 20 ?C for 12 hr and 12.8 ?C for 12 hr. A split plot design with two replications was used considering four planting depths as main plot and 9 bread wheat varieties as sub-plot. Continuous watering was done to maintain the required moisture for germination.

Measurements

    Seedlings were counted when the shoots emerged. Two weeks after planting, 10 seedlings were removed, the vermiculite was washed away and CL, shoot length (SL), and root length (RL) were measured to the nearest millimeter and number (RN) was counted. In addition, fresh weights (FW) and dry weights (DW) of the sampled seedlings were taken. Means of the 10 seedlings were used for the analyses of CL, SL, RL, RN, FW and DW. Emergence rate index (ERI) was determined by counting daily the emerged shoots of 30 plants and multiplying the number of shoots, which emerged on the first day, by six, plus the 5 times the number of shoots emerged on the second day, and so on until multiplying the shoots, which emerged on the sixth

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    day, by one. Germination percentage (GP) was the number of germinated plants out of 30 seed. Following oven drying at 70 ?C for 48 hours the dry weight was recorded for each variety.

Statistical analyses

Variety differences were analyzed using standard analysis of variance (ANOVA) and Duncan‟s

    multiple range test procedures, and PROC GLM command was used for this purpose (SAS Institute, Inc., Cary, NC). Variance components were estimated from the expected mean squares (Comstock and Moll, 1963) and heritabilities of the dependent variables were estimated as a ratio of genotypic variance to phenotypic variance where the depth effects were removed by not including the depth variance component in the estimate of phenotypic variance (Fehr, 1983).

    Canonical correlation analysis, developed by Hotelling (1936), often allows a more meaningful interpretation of interrelations between variables than simple correlation and multiple regression analyses. Canonical correlation analysis utilizes 2 sets of variables and forms linear indices from each of the sets of variables so that the correlation between the two indices is maximized. Several pairs of linear indices are derived and are named canonical variates th(Warwick, 1975). Specifically, the i pair of canonical variate is described as:

    qp

    ;; and , i = 1,2,3…p and j= 1,2,3, …q VbzjiijUazjiij

    th where qp, z is the j variable (standardized or not), p is the number of variables in the first j

    set, q is the number of variables in the second set, a and b are the variable coefficients (or ijij

    loadings) in the first and second sets, respectively. Because we were only interested in describing associations due to variability across genotypes, we generated a data matrix corrected for depth effect by subtracting the different sowing depth means from the original data. This resulted in 36 observations for each of the 8 variables. Using the variables corrected for depth was reasonable since depth x variety interaction was insignificant for all but one of the variables (Table 2).

    As basic inputs we utilized two sets of variables, one set was based on seedling morphology variables and the other was based on germination variables. Coleoptile length, SL, RL, RN, FW, and DW variables are related to morphology or size of the seedlings, therefore the set was named as “size”(U). Emergence rate index and GP constituted the “germination” (V) ii

    set. In order to determine the relationship between these two sets, we employed the canonical correlation analysis using PROC CANCORR command (SAS Institute, Inc., Cary, NC). Canonical correlation computations were performed on the correlation matrix, computed from the adjusted data matrix previously described from SAS Institute.

Results and discussion

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