Evaluation of drought tolerance indices in wheat recombinant inbred line population

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1 Available online at Annals of Biological Research, 2013, 4 (3): ( ISSN CODEN (USA): ABRNBW Evaluation of drought tolerance indices in wheat recombinant inbred line population Mojtaba Nouraein 1, Seyed Abolghasem Mohammadi 1,2*, Saeid Aharizad 1, Mohammad Moghaddam 1,2 and Behzad Sadeghzadeh 3 1 Department of Plant Breeding and Biotechnology, Faculty of Agriculture, University of Tabriz, Tabriz, IRAN 2 Center of Excellence in Cereal Molecular Breeding, University of Tabriz, Tabriz, IRAN 3 Dryland Agricultural Research Institute (DARI), Maragheh, IRAN ABSTRACT In a two years experiment, 149 bread wheat recombinant inbred lines (RILs) derived from a cross between of US variety Yecora Rojo and Iranian local genotype NO.49 along with parental lines were grown under well-watered and water deficit stress conditions, imposed at panicle initiation stage. Eight drought tolerance indices including Stress Tolerance Index (STI), Mean Productivity (MP), Geometric Mean Productivity (GMP), Stress Tolerance (TOL), Stress Susceptibility Index (SSI), Yield Stability Index (YSI), Relative drought index (RDI), and Drought resistance index (DI) were used to identify high yielding and drought tolerant RILs. Significant and positive correlation was found between grain yield under stress condition (Ys) and STI, MP, GMP, SSI, YSI, RDI, as well as DI indicating the efficiency of these indices for screening drought tolerant genotypes. Grain yield under drought stress condition was associated with yield under well-watered condition (r = 0.66 ** and 0.84 ** in 2009 and 2010, respectively). Reduction in plant height due to water stress was negatively correlated with grain yield, biomass, spike length, grain number per spike, and 1000 grain weight in 2009 and yield reduction percentage in Among the drought tolerance indices, GMP showed higher correlation with grain yield (r = 0.94 ** and r = 0.97 ** ), biomass (r = 0.73 ** and r = 0.81 ** ), and harvest index (r = 0.7** and r = 0.76**) under stress condition in 2009 and 2010, respectively. The biplot based on components showed that the most appropriate indices to identify genotypes were GMP, MP, and STI. Key words: Drought, panicle initiation stage, potential yield, tolerance indices, water deficit INTRODUCTION Wheat is widely grown as a rain-fed crop in semi-arid areas, where large fluctuations occur in the amount and frequency of rainfall events from year to year and among sites within years [21]. In arid and semiarid regions with Mediterranean climate, wheat crops usually encounter drought during the grain filling period [10]. Therefore, wheat production and its sustainability are largely influenced by drought stress which cause reduced crop yield [32]. Drought is one of the most common environmental stresses worldwide that affects growth and development of plants through alternations in metabolism and gene expression [23]. It was estimated that about 65 million hectares of wheat grown area world-wide are affected by this environmental constraint and under water-limited environments, wheat yields are commonly reduced to 50 percent or even less of its yield potential under normal irrigation [6]. 113

2 Identification and development of drought tolerant genotypes is one of the wheat breeding programs challenges worldwide. However, development of drought tolerant cultivars is hampered by low heritability of related traits and lack of effective selection strategies [21]. In the most of field based experiments, drought tolerance indices, based on yield reduction under drought conditions in comparison to non-stress conditions are commonly used to identify drought tolerant genotypes [25, 35, 4]. The relative yield performance of genotypes in drought stressed and more favorable environments seems to be a common starting point in identification of traits related to drought tolerance and selection of genotypes for use in breeding for dry environments [8]. Various quantitative criteria have been proposed for selection of genotypes based on their yield performance in stress and non-stress environments [31]. Rosielle and Hamblin [30] defined stress tolerance (TOL) as the differences in yield between the stress (Ys) and non-stress (Yp) environments and mean productivity (MP) as the average yield of Ys and Yp. Fischer and Maurer [14] proposed a stress susceptibility index (SSI) of the cultivar. Geometric mean productivity (GMP) which used by breeders is interested in relative performance under various conditions, since drought stress can vary in severity in the field environment over years [29]. Fernandez [13] defined a new advanced index (STI: stress tolerance index), which can be used to identify genotypes that produce high yield under both stress and non-stress conditions and Fischer and Wood [15] introduced relative drought index (RDI). Yield stability index (YSI) also was computed and suggested by Bouslama and Schapaugh [3]. This parameter is calculated for a given genotype using grain yield under stress condition relative to its grain yield under non-stressed conditions. The genotypes with high YSI is expected to have high yield under stressed and low yield under non-stressed conditions [26]. Clarke et al. [8] used SSI for evaluation of drought tolerance in wheat genotypes and found year-to-year variation in SSI for genotypes and their ranking pattern. In spring wheat cultivars, Guttieri et al. [17] used SSI criterion and suggested that SSI higher than one indicates above-average susceptibility to drought stress. Lan [22] with applying drought resistance index (DI) in wheat could identify genotypes with high yield under both stress and non-stress conditions. The aim of the present study was to indentify drought tolerance RILs obtained from a cross between two diverse parents based on various drought tolerance indices for further uses in Iranian wheat breeding program. MATERIALS AND METHODS Experimental design and genetic materials One hundred and forty nine recombinant inbred lines developed by single-seed descent (SSD) method from an F2 population derived from a cross between Yecora Rojo (Male parent, a dwarf wheat, CIMMYT-derived cultivar grown locally in California) and genotype NO.49 (a tall Iranian landrace, well adapted to the Mediterranean climate, early-flowering) a long with parental lines were evaluated under irrigated and water deficit conditions during two years. Each genotype was directly seeded in three-row plots in randomized block design with two replications for each condition. In the both experiments, water was fully supplied during the seedling and vegetative growth periods to keep the plants growing under well-watered conditions. When the majority of the lines reached the panicle initiation stage, irrigation was stopped for stress condition and water stress was allowed to develop. The stressed plants were irrigated when 140 mm water evaporated as measured by the Class A evaporation pan. Full irrigation was resumed for well-watered condition. Measurements and data analysis Putative drought tolerance relative traits, days to flowering, spike number per m 2, grain number per spike, plant height and spike length, 1000 grain weight, biomass, grain yield and harvest index were measured in both conditions. Pearson s correlation coefficient was used to calculate the association between drought tolerance indices and grain yield; grain yield in stress and non-stress condition and reduction in plant height as well as GMP and measured traits of RILs. Drought resistance indices were calculated using the following relationships: (1) Stress Tolerance Index STI = [(Yp) (Ys)] / (Ῡp) 2 [13] (2) Mean Productivity MP = (Yp + Ys) / 2 [30] (3) Geometric Mean Productivity GMP = [(Yp) (Ys)] 0.5 [13] (4) Stress Tolerance TOL = (Yp - Ys) [30] (5) Stress Susceptibility Index SSI = 1 - [(Ys) / (Yp)] / SI (Stress Intensity) SI = 1 - [(Ῡs) / (Ῡp)] [14] (6) Yield Stability Index YSI = Ys / Yp [3] (7) Relative drought index RDI = (Ys / Yp) / (Ῡs / Ῡp) [15] (8) Drought resistance index DI = Ys (Ys / Yp) / Ῡs [22] 114

3 In the above formulas, Ys, Yp, Ῡs, and Ῡp represent yield under stress, yield under non-stress for each genotype, yield mean in stress and non-stress conditions for all genotypes, respectively. The biplot display was also used to identify tolerant and high yielding genotypes using STATISTICA software, based on principal component analysis. RESULTS Correlation between grain yields and drought tolerance indices Table 1 shows correlations between drought tolerance indices and grain yield under normal and water deficit conditions in 2009 and Significant correlations were observed between grain yield under non-stress and stress conditions in both years (0.66 ** and 0.84 ** in 2009 and 2010, respectively). Grain yield in non-stress condition (Yp) was significantly correlated with drought tolerance indices in both years except SSI, YSI, and RDI in However, correlation coefficients between Yp and MP (0.94 ** and 0.97 ** ), GMP (0.89 ** and 0.95 ** ) as well as STI (0.87 ** and 0.94 ** ) were higher in two years of experiment. Correlations between grain yield under stressed condition and drought tolerance indices except TOL were significant in both years. Similar to Yp, MP (0.88 ** and 0.95 ** ), GMP (0.94 ** and 0.97 ** ), and STI (0.92 ** and 0.96 ** ) showed higher positive correlations with Ys. In addition, correlations between Ys and DI were also high (0.92 ** and 0.94 ** ). Table 1. Correlation coefficients between drought tolerance indices and grain yield under water deficit and well watered conditions in 2009 (below diagonal) and 2010 (above diagonal) experiments. Ys Yp SSI MP TOL STI GMP YSI RDI DI Ys ** ** 0.95 ** ** 0.97 ** 0.43 ** 0.43 ** 0.94 ** Yp 0.66 ** ** 0.58 ** 0.94 ** 0.95 ** ** SSI -0.5 ** 0.28 ** ** * * -1 ** -1 ** -0.7 ** MP 0.88 ** 0.94 ** ** 0.99 ** 0.99 ** ** TOL ** 0.84 ** 0.41 ** ** 0.31 ** ** ** ** STI 0.92 ** 0.87 ** ** 0.97 ** 0.27 ** ** 0.19 * 0.19 * 0.82 ** GMP 0.94 ** 0.89 ** * 0.99 ** 0.29 ** 0.98 ** * 0.17 * 0.82 ** YSI 0.5 ** ** -1 ** ** 0.18 * 0.17 * 1 1 ** 0.7 ** RDI 0.5 ** ** -1 ** ** 0.18 * 0.17 * 1 ** ** DI 0.92 ** 0.33 ** ** 0.63 ** ** 0.71 ** 0.71 ** 0.78 ** 0.78 ** 1 *,**: significant at 5 and 1% probability levels, respectively. The estimates of stress tolerance attributes indicated that the identification of drought-tolerant genotypes based on a single criterion was contradictory. For example, according to STI and GMP and MP genotypes number 52, 65, and 86 in 2009 and 28, 65, and 156 in 2010 were the most drought tolerant lines and genotypes number 140, 95, and 15 in 2009 and 14, 89, and 39 in 2010 were the most drought sensitive lines. Based on the TOL index, the desirable drought tolerant genotypes were 139, 121, and 145 in 2009 and 163, 131, and 152 in 2010, but using SSI, 107, 130, and 121 in 2009 and 163, 113, and 87 in 2010 were identified as desirable lines. According to YSI and RDI indices, genotypes number 15, 79, and 29 in 2009 and 37, 51, and 55 in 2010 were detected as the tolerant lines to drought stress. Considering DI, genotypes number 58, 37, and 81 in 2009 and 65, 86, and 101 in 2010 were the most drought tolerant lines. Grain yield under stress and non-stress conditions High phenotypic variation was observed for grain yield occurred under normal and drought stress conditions in both years. In 2009, the mean grain yield was and g/m 2 under well-watered and water deficit conditions. Water deficit during the flowering stage reduced mean grain yield by 36% in average. In 2010, the grain yield under drought stress g/m 2 ) was 27.1% lower on average than its potential yield under well-watered conditions ( g/m 2 ). There was a positive relationship between grain yield under well-watered and drought stress conditions in 2009 and 2010 (r = 0.66 ** and r = 0.84 ** ) (Figure 1). Although few lines showed high grain yield under both conditions (lines number 28, 44, 52, 54, 65, 136 and 156), the majority of lines performed well only under favorable condition. Reduction in plant height Water deficit resulted in plant height reduction in all the studied RILs compared with normal condition. In average, plant height was reduced under water deficit by 5.2 and 6.3 cm in 2009 and 2010, respectively. Correlations between reduction in plant height and the measured traits were inconsistent across the years. The reduction in plant height 115

4 was negatively associated with grain yield (r = 0.22 ** ), biomass (r = 0.2 ** ), spike length (r = 0.16 ** ), grain number per spike (r = 0.37 ** ), thousand kernel weight (r = 0.13 ** ), in 2009 and with yield reduction percentage (r = 0.15 * ) but in Figure 1. Relationship between grain yield of each genotype grown under well-watered conditions and under water deficit conditions in 2009 and Geometric mean productivity The mean of GMP of the studied RILs in 2009 ranged from to and based on GMP, RILs number 52, 65 and 86 were drought tolerant and RILs number 140, 95 and 15 were drought sensitive genotypes. In 2010, GMP varied from to and RILs number 28, 65 and 156 were identified as drought tolerant and RILs number 14, 89 and 39 as drought susceptible genotypes. There was strong positive correlation between grain yield under drought stress and GMP in 2009 (r = 0.94 ** ) and 2010 (r = 0.97 ** ) (Figure 2). Based on two years data, RIL number 65 has the highest GMP. Correlation analysis revealed significant positive association between GMP and biomass (0.73 ** and 0.81 ** ), grain number per spike (0.62 ** and 0.43 ** ), harvest index (0.7 ** and 0.76 ** ), spike length (0.33 ** and 0.24 ** ), thousand kernel weight (0.42 ** and 0.54 ** ) and spike number per m 2 (0.43 ** and 0.55 ** ) in 2009 and 2010 (figure 3 and figure 4). In the present study, yield potential and GMP were strongly associated with grain yield, grain number per spike, harvest index and biomass. Figure 2. Relationship between Geometric Mean Productivity and grain yield of genotypes in 2009 and

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6 Figure 3. Relationship between Geometric Mean Productivity and biomass, grain number per spike, harvest index, spike length, thousand kernel weight, and spike number per m 2 of genotypes in

7 Figure 4. Relationship between Geometric Mean Productivity and biomass, grain number per spike, harvest index, spike length, thousand kernel weight, and spike number per m 2 of genotypes in Principal component analysis Principal components analysis was performed to group drought tolerance indices based on their function as well as genotypes using their drought tolerance indices. Two first principal components explained 98.52% and 99.37% of the total variation of indices in 2009 and 2010, respectively. In the both years, PC1 could separate TOL and SSI from other indices and PC2 distinguished DI, YSI and RDI from others (Figure 5). Biplot based on PC1 and PC2 assigned drought tolerance indices into three groups. Group 1 with high value for PC1 and PC2 consisted of TOL and SSI. Yp, MP, GMP, STI and Ys were clustered in group 2 presented with low and high values for PC1 and PC2, respectively. Group 3 included DI, YSI and RDI which characterized with low value for the both PCs. 1.0 TOL Yp SSI 1.0 TOL SSI Second component: 42.81% MP 0.5 GMP STI Ys 0.0 DI -0.5 G2 G3 G1 Second component: 40.09% Yp 0.5 MP GMP STI Ys 0.0 DI -0.5 G2 G3 G1 YSI RDI First component: 55.71% YSI RDI First component: 59.28% Figure 5. Principal component analysis of drought tolerance indices in 2009 (left) and 2010 (right). Figure 6 indicates the distribution of RILs based on their drought tolerance indices. The results revealed higher association between MP, GMP, and STI indices and yield in the two environments and these were the most appropriate indices to screen genotypes response to drought. Based on biplot of two PCs, RILs number 86, 65, 52, 84, and 37 in 2009 and 28, 156, 52, 101, 86, and 65 in 2010 were identified as drought tolerant and 141, 39, 140, 164, and 14 in 2009 and 14, 39, 89, 140, 24, and 15 drought sensitive genotypes. Considering only Ys, Yp, and GMP (Figure 7) RILs number 52, 65, 86, 123, and 155 in 2009 and 28, 65, 156, 52, and 136 in 2010 were identified as drought tolerance genotypes characterized with the highest GMP, Ys, and Yp. 119

8 163 S eco n d co m p o n en t DI Ys STI GMP MP male female S econd com ponent DI Ys STI GMP MP male female Yp TOL SSI,YSI,RDI First component Yp TOL SSI,YSI,RDI First component Figure 6. Biplot of wheat recombinant inbred lines based on the first and second principal components resulted from principal component analysis of drought tolerance indices in 2009 (left) and 2010 (right). Figure 7. Three-dimensional representation of recombinant inbred lined based on their Yp, Ys and GMP in 2009 (left) and 2010 (right). DISCUSSION AND CONCLUSION Climate change is causing more frequent and intense periods of drought as overall rainfall levels decline. Dry areas cover more than 40% of the world s land surface and are home to 2.5 billion people; one-third of the global population. Drought is currently one of the main constraints that prevent crop plants from expressing their full genetic potential. The identification of drought tolerance genotypes is crucial to secure productivity. In the present study, we evaluated 149 RILs derived from a cross between two spring wheat genotypes with differential characteristics for drought response. To identify drought tolerance RILs, several drought tolerance indices were used. Our results showed that STI, GMP and MP were able to identify RILs producing high yield in both conditions. However, MP was not significantly correlated to YSI, SSI, and RDI. Low correlations between various indices suggest that each index may be a potential indicator of different biological response to drought. Majidi et al. [24] reported positive and significant correlations between Yp and TOL, MP, GMP, STI as well as SSI indices. They also found significant correlations between Ys and GMP and STI indicated that selection based on these indices may increase yield in stress and nonstress conditions. Under severe stress, TOL, YSI and SSI were found to be more useful for discriminating tolerant genotypes, however, none of the indices could clearly identify high yielding genotypes under both stress and nonstress conditions. Fernandez [13] in mung bean, Farshadfar and Sutka [11] in maize and Golabadi et al. [16] in durum wheat also reported the relative effectiveness of indices such as STI, MP and GMP in discriminating drought 120

9 tolerant genotypes. In our study, RILs with high STI, MP and GMP but low TOL and SSI were performed well in both conditions. In the present study, grain yield under normal irrigation was significantly correlated with grain yield water deficit in both years. Blum [2] and Panthuwan et al. [27] believe that potential yield has a large impact on yield only under moderate drought stress conditions. Several researchers have concluded that selection will be most effective when the experiments are done under both favorable and stress conditions [14, 8, 13]. However, some researchers concluded due to genotype by environmental interaction, indirect selection for stress environment based on nonstress environment data will not be efficient. Ceccarelli and Grando [7] and Bruckner and Frohberg [5] reported that in barley and wheat genotypes with low yield potential were more productive under stress condition. Plant height (PH) reduction has obviously contributed to lodging resistance and yield increase of wheat in the last decades. In our study, significant correlation was found between plant height reduction and some traits; however, the magnitude of the correlations was not high to be predictive. In general, genotypes with high reduction in plant height showed large decrease in grain yield, biomass and grain number per spike. This supports the hypothesis that improved drought tolerance is compatible with the semi-dwarf plant type. Plant height reduction may be associated with internal plant water status, particularly turgor pressure to exsert the panicle. Internal plant water deficit inhibiting cell expansion and growth of plants has long been recognized [19]. In the present study, PCA and biplot analysis were used to group drought tolerance indices. The result showed strongly associated between RDI and DI indices and YSI (yield stability index). But MP, STI, GMP, TOL and SSI either did not show association with YSI (right angle) or showed negative correlation with YSI (obtuse angle). Therefore, these indices are suitable for selection of drought tolerance genotypes and based on these indices genotypes with acceptable yield performance under stress and high yield performance under non-stress environments will be selected [12, 34, 28]. Selection based on a combination of indices may provide a more useful criterion for improving drought resistance in cereals. Biplot of genotypes based on two PCs obtained from PCA of their drought tolerance indices is an acceptable method to identify superior genotypes under stress and non-stress environments. Kaya et al. [20], Abdolshahi et al. [1] and Dadbakhsh and YazdanSepas [9] reported that the bread wheat genotypes with high PC1 and low PC2 scores were high yielding (stable genotypes) whereas genotypes having low PC1 and high PC2 scores were low yielding genotypes (unstable genotypes). The correlation coefficient between two indices is almost angle cosine of their vectors [33]. In biplot of genotypes based on drought tolerance indices, the cosine of the angle between the vectors of two indices approximates the correlation coefficient between them. In a three-dimensional representation of genotypes based on their Ys, Yp, and GMP, they could be categorized into four groups based on their performance in stress and non-stress environments; genotypes expressing uniform superiority in both stress and non-stress environments (Group A); genotypes performing with high performance in non-stress environments (Group B); genotypes with relatively high yield only in stress environments (Group C), and genotypes performing poorly in both stress and non-stress environments (Group D). The optimal selection criteria should distinguish group A from the other three groups [13]. GMP is more powerful in separating group A and has a lower susceptibility to different amounts of Ys and Yp so; GMP will be bias when the difference between Ys and Yp is less. As described by Hohls [18], GMP can select high yielding genotypes in both stressed and non-stressed environments. Acknowledgment This study was funded by the Center of Excellence in Cereal Molecular Breeding, University of Tabriz. The authors would like to acknowledge and appreciate the generosity of Dr B. Ehdaei, for providing RIL population. REFERENCES [1] R. Abdolshahi, M. Omidi, A.R. Talei, B. Yazdi Samadi, EJCP, 2010, 3, [2] A. Blum, Plant Growth Regul, 1996, 20, [3] M. Bouslama, W.T. Schapaugh, Crop Sci, 1984, 24, [4] H. Boussen, M. Ben Salem, A. Slama, E. Mallek-Maalej, S. Rezgui, Options Mediterraneennes, 2010, 95, [5] P.L. Bruckner, R.C. Frohberg, Crop Sci, 1987, 27, [6] D. Byerlee, M, Morris, Food Policy, 1993, 18, [7] S. Ceccarelli, S. Grando, Euphytica, 1991, 57,

10 [8] J.M. Clarke, R.M. DePauw, T.F. Townley-Smith, Crop Sci, 1992, 32, [9] A. Dadbakhsh, A. YazdanSepas, Adv Environ Biol, 2011, 5, [10] B. Ehdaie, J.G. Waines, J Genet Breed, 1996, 50, [11] E. Farshadfar, J. Sutka, Acta Agron Hung, 2002, 502, [12] E. Farshadfar, M. Zamani, M. Matlabi, E. Emam-Jome, Iranian J Agric Sci, 2001, 32, [13] G.C.J. Fernandez, In Proceeding of Symposium, Taiwan, 1992, pp [14] R.A. Fischer, R. Maurer, Aust J Agric Res, 1978, 29, [15] R.A. Fischer, J.T. Wood, Aust J Agric Res, 1979, 30, [16] M. Golabadi, A. Arzani, S.A.M. Mirmohammadi Maibody, Afri J Agri Res, 2006, 1, [17] M.J. Guttieri, J.C. Stark, K.O. Brien, E. Souza, Crop Sci, 2001, 41, [18] T. Hohls, Euphytica, 2001, 120, [19] T.C. Hsiao, Ann Rev Plant Physiol, 1973, 24, [20] Y. Kaya, C. Plta, S. Taner, Turk J Agric For, 2002, 26, [21] F.M. Kirigwi, M. VanGinkel, R. Trethowan, R.G. Sears, S. Rajaram, G.M. Paulsen, Euphytica, 2004, 135, [22] J. Lan, Acta Agri Bor-occid Sinic, 1998, 7, [23] A.C. Leopold, Adaptation and Acclimation Mechanisms, Wily-Liss, New York, 1990, pp [24] M. Majidi, V. Tavakoli, A, Mirlohi, M.R. Sabzalian, Aust J Crop Sci, 2011, 5, [25] J. Mitra, Curr Sci, 2001, 80, [26] R. Mohammadi, M. Armion, D. Kahrizi, A. Amri, Int J of Plant Product, 2010, 4, 1, [27] G. Panthuwan, S. Fokai, M. Cooper, S. Rajatasereekul, J.C. O Toole, Field Crops Res, 2002, 41, [28] M. Pouresmael, M. Akbari, S. Vaezi, S. Shahmoradi, Iran J Crop Sci, 2009, 11, [29] P. Ramirez, J.D. Kelly, Euphytica, 1998, 99, [30] A.A. Rosielle, J. Hamblin, Crop Sci, 1981, 21, [31] A.S. Taghian, A. Abo-Elwafa, Assiut J Agric Sci, 2003, 34, [32] R.M. Trethowan, M. Reynolds, K. Sayre, I. Ortiz-Monasterio, Annal Appl Biol, 2005, 146, [33] W. Yan, I. Rajcan, Crop Sci, 2002, 42, [34] M. Zabet, A.H. Hoseinzade, A. Ahmadi, F. Khialparast, Iran J Agric Sci, 2003, 34, [35] G.H. Zou, H.Y. Liu, H.W. Mei, G.L. Liu, X.Q. Yu, M.S. Li, J.H. Wu, L. Chen, L.J. Luo, J Inter Plant Biol, 2007, 49,