Evaluation of drought tolerance in bread wheat using water relations and integrated selection index

Transcription

1 Journal of Biodiversity and Environmental Sciences (JBES) ISSN: (Print) (Online) Vol. 6, No. 1, p , RESEARCH PAPER OPEN ACCESS Evaluation of drought tolerance in bread wheat using water relations and integrated selection index Ezatollah Farshadfar 1*, Meysam Ghasemi 1 1 Campus of Agriculture and Natural Resources, Razi University, Kermanshah, Iran Article published on January 05, 2015 Key words: bread wheat, drought tolerance, integrated selection index (ISI), water relations. Abstract In order to evaluate drought tolerance in bread wheat genotypes an experiment was conducted in a randomized complete block design with three replications under rainfed and irrigated conditions during growing season. Drought stress significantly increased relative water protection (RWP), water saturation deficit (WSD), initial water content (IWC), leaf water content (LWC) and excised leaf water retention (ELWR), while decreased leaf water Loss (LWL), relative water content (RWC) and excised leaf water loss (ELWL). Principal component analysis (PCA) showed that integrated selection index (ISI) was correlated with relative water content (RWC), water saturation deficit (WSD), initial water content (IWC), leaf water content (LWC), stress yield (Ys) and yield potential (Yp) indicating that ISI was able to distinguish group A genotypes (drought tolerant with high grain yield under rainfed and irrigated conditions). Screening drought tolerant genotypes using mean rank, standard deviation of ranks and rank sum (RS), discriminated genotypes 9, 3, 12 and 2 as the most drought tolerant. * Corresponding Author: Ezatollah Farshadfar e_farshadfar@yahoo.com 77 Farshadfar and Ghasemi

2 Introduction Climate change, climate variability and environmental stress play an important role in determining physiological performance, vulnerability and productivity of crop plants in their environment (Marian Brestic and Marek Zivcak, 2013). Drought is considered as the main environmental factor limiting plant growth and yield worldwide. Drought stress results in stomatal closure and reduced transpiration rates; a decrease in the water potential of plant tissues; growth inhibition and decrease in photosynthesis; accumulation of abscisic acid (ABA), sorbitol, mannitol and proline; and formation of radical scavenging compounds, e.g. ascorbate, glutathione and a-tocopherol (Yordanov et al., 2003). Besides these physiological responses, plants also undergo morphological changes (Vassileva et al., 2012). The impact of water shortage and lower rainfall during the sowing period seems to be the main reason for lesser acreage under wheat crop and reduction in wheat production. Therefore, breeding for drought tolerant wheat is an important task and objective in the present scenario (Anwar et al., 2011). Since the yield is a trait with low heritability, therefore selection based on that alone can not be reliable (Blum, 1992). Many morphological, biochemical and molecular responses on crop plants, such as stomatal closure and reduced transpiration rates contribute towards adaptation to such unavoidable environmental constraints (Sairam, 1994; Mohamed and Ismail, 2009). Breeding for drought tolerance by selecting solely for yield may not be successful, because the heritability of yield under drought conditions is controlled at independent genetic loci. Therefore, plant breeders have always looking appropriate and repeatable indicators to screening for drought tolerance (Zobel et al., 1988; Hasheminasab et al., 2014). Physiologists have often suggested that the detection and selection of physiological traits related to plant water status are reliable methods to breeding for higher yield, and could be a valuable strategy for use in conjunction with normal methods of plant breeding (El Jaafari et al., 1993; Blum, 2005). RWP is an important physiological indicator in assessing the degree of water stress. RWP is also among the main physiological criteria that influence plant water relations and have been using for screening drought tolerant genotypes (Hasheminasab et al., 2012). Relative water content (RWC), Relative Water Loss (RWL), Excised leaf water retention (ELWR), Excised Leaf Water Loss (ELWL), Initial Water Content (IWC), Leaf Water Content (LWC), Water Saturation Deficit (WSD), Leaf Water Loss (LWL) are among the main physiological criteria that influence plant water relations and have been used for assessing drought tolerance (Barrs, 1968; Clark and Maccaig, 1982; Manette et al., 1988; Xing et al., 2004). The objective of the present investigation were to: (i) better understand the effect of drought stress on some physiological traits associated with leaves water status (ii) identify the efficent physiological traits for screening drought tolerant genotypes and (iii) introduction of a new integrated selection index for screening drought tolerant genotypes. Materials and methods Nineteen bread wheat genotypes (Triticum aestivum L.) listed in Table 1 were assessed in a randomized complete block design with three replications under two irrigated and rainfed conditions during growing season in the experimental field of College of Agriculture, Razi University, Kermanshah, Iran. Mean precipitation in was mm. Sowing was done by hand in plots with four rows 2 m in length and 25 cm apart. The seeding rate was 400 seeds per m 2 for all plots. Besides the stress yield (Ys) and yield potential (Yp), the following physiologic characters were also measured in the stress condition. 78 Farshadfar and Ghasemi

3 Table 1. Genotype codes. code Genotype code Genotype 1 Ilam-zagros 11 WC WC WC-4953s 3 Pishgam 13 WC WC Ilam-Nastoor 5 Ilam- kohdasht 15 Rigav 6 WC Karim 7 WC Sardari 8 Ilam- cross sabalan 18 WC WC Azar2 10 WC Relative water content (RWC) A sample of 5 leaves were taken randomly from the flag leaves of each genotype and fresh weight (FW) was measured. Then, samples were placed in distilled water for 4 h and reweighed to obtain turgid weight (TW). Leaf samples were oven dried and weight in 70 C for 48 h (DW). RWC was calculated using the following formula [Barrs, 1968]. RWC (%) = [(FW-DW)/(TW-DW)] x 100 Relative water protection (RWP) Five leaves were taken randomly from each genotype and weighted (Fresh weight= FW). The leaves were then wilted at 25 C for 6 h (This time can be different for various plant species) and weighed again (Withering weight= WW). Then the samples were oven dried in 70 C for 48 h and reweighed (Dry weight= DW). RWP was calculated using the following formula ( Hasheminasab et al., 2012). RWP (%) = [(WW-DW)/(FW-DW)] x 100 Relative water loss (RWL) Five fully expanded leaves were sampled at anthesis stage. The leaf samples were weighed (FW), wilted for 2, 4 and 6 hour at 25 C, reweighed (WW2h, WW4h, WW6h), and oven dried for 48 h at 70 C to obtain dry weight (DW). Time interval between two subsequent measurements 2h (T2-T1). The RWL was calculated using the following formula (Barrs, 1968) : RWL= [(FW-WW2h) + ( WW2h-WW4h) + (WW4h- WW6h)]/[ 3 DW (T2 T1)] Excised leaf water retention (ELWR) Excised leaf water retention was determined according to Clark and Mccaig (1982), where the youngest leaves before anthesis stage were collected and weighed (FW), left for 6 h, then wilted at 25 C and reweighed (W6h). ELWR was calculated using the following formula: ELWR = 1 - [(FW-W6h)/FW] Excised leaf water loss (ELWL) A sample of 5 leaves were from the flag leaves of each genotype and fresh weight (FW) was measured. wilted for 6 hour at 25 C, reweighed (W6h). Leaf samples were oven dried and weight in 70 C for 48 h (DW). ELWL was calculated using the following formula (Manette et al., 1988). ELWL = (FW-W6h)/(FW-DW) Leaf water content (LWC) Fresh leaves were taken from each genotype and each replication after anthesis stage and weighed immediately to record fresh weight (FW). Then subjected to oven drying at 70 C for 48 h to record dry weight (DW). The LWC were calculated using the following equation (Clark and Maccaig, 198): LWC = (FW-DW)/FW Leaf water loss (LWL) Five young fully expanded leaves were sampled for each of three replications at anthesis stage. The leaf samples were weighed (FW), wilted for 2 hour at 25 C, reweighed (W2h) (Xing et al., 2004). LWL = (WF-W2h)/WF Water saturation deficit (WSD) A sample of 5 leaves were taken randomly from the flag leaves of each genotype and fresh weight (FW) was measured. Then, samples were placed in distilled water for 4 h and reweighed to obtain turgid weight (TW). Leaf samples were oven dried and weight in 70 C for 48 h (DW) (Barrs, 1968). WSD = [(TW-FW)/(TW-DW)] Initial water content (IWC) Fresh leaves were taken from each genotype and each replication after anthesis stage and weighed 79 Farshadfar and Ghasemi

4 immediately to record fresh weight (FW). Then subjected to oven drying at 70 C for 48 h to record dry weight (DW). The IWC were calculated using the following equation (Clark and Maccaig, 1982): IWC = (FW-DW)/DW Integrated selection index (ISI) Based on statistical analysis of physiological traits under water deficit and the following three formulas, ISI was calculated: (1) Sij= (Xi j µj)/ δj (2) MPij = (Sijd + Sijw)/2 (3) ISIi = b1mpi1 + b2mpi2 + + bjmpij Statistical analysis Analysis of variance, mean comparison using Duncan s multiple range test, correlation analysis between mean of the characters measured and principal component analysis (PCA), based on the rank correlation matrix were performed by SAS (package 9.1.3), SPSS ver. 16 and STATISTICA ver. 8. Standard deviation of ranks (SDR) was measured as: Where Rij is the rank of in vivo drought tolerance indicator and i. is the mean rank across all in vivo drought tolerance indicators for the its genotype and SDR= (S 2 i) 0.5. Where Sij = standardized physiologic value of trait j (j Rank sum (RS) = Rank mean ( ) + Standard deviation = 1 to 11, i.e. RWC, LWC, ELWR, IWC, RWP, RWL, of rank (SDR) (Farshadfar and Elyasi, 2012). WSD, ELWL, LWL, Yp and Ys) in genotype i under irrigated (w) and drought (d) conditions, Xij = physiological value of genotype i on trait j, µj = mean value of trait j in all genotypes, δj = the standard deviation of trait j, Mpij = the mean productivity of trait j on genotype i, bj the weight value of trait j, bj was populated from the average contribution to factor 1 and ISI = integrated selection index. Results and discussion Analysis of variance In developing breeding program to improve the drought tolerance of a crop plants it is necessary to gain knowledge about the physiological mechanisms of tolerance (Blum, 1985; Manette et al., 1988). The results of analysis of variance showed significant differences for Yp, Ys, WSD, IWC, LWC, ELWR, Formula (1) standardizes the value of different traits to the same unit of measure; formula (2) evaluates the appearance of genotypes for each trait; and formula (3) integrates the appearance of genotypes for all traits. ELWL, LWL, RWP and RWC in the rainfed condition (Table 2) indicating the presence of genetic variation between the genotypes for these attributes. Similar observations have been reported in bread wheat (Farshadfar, 2012). Table 2. Analysis of variance for physiological characteristics under rainfed conditions. Mean squares Grain Yield RWC LWC ELWR IWC S.O.V. df rainfed irrigated rainfed rainfed rainfed rainfed Replication * Genotype ** ** 0.015** 0.001** 0.005** 0.087** Error Table 2 continued Mean squares RWP RWL WSD ELWL LWL S.O.V. df rainfed rainfed rainfed rainfed rainfed Replication * Genotype ** ** 0.015** 0.006** Error * and **: Significant at 1% and 5% level of probability respectively; S.O.V: Source of variation, d.f: Degree of freedom. 80 Farshadfar and Ghasemi

5 Mean comparisons In our study, genotypes 6, 11 and 7 had higher RWC content while genotypes15, 17 and 13 exhibited lower RWC under water stress condition (Table 3). In general, this genotypic variation in RWC may be attributed to differences in the ability to absorb more water from the soil and or the ability to control water loss through the stomata's. Higher Relative water content (RWC) had been reported to play a role in the stress tolerance in wheat (Nouri et al., 2011) and barley (Kocheva and Georgiev, 2003). Sairam and Srivastava (2001) observed variation in relative water content in wheat genotypes and suggested that RWC was a suitable indicator for screening drought tolerant genotypes. According to WSD, genotypes 17, 15 and 13 exhibited the most and genotypes 6, 7, 11 and 10 the least WSD, respectively. For Leaf LWC the genotypes 3, 17 and 6 had the most and 1, 4 and 18 had the least LWC and for ELWR genotypes 18, 7 and 10 exhibited the most and genotypes 3, 11 and 4 displayed the least ELWR. Genotypes 11, 12 and 15 revealed high ELWL, while genotypes 18, 17 and 5 showed the lowest amount. With regard to LWL accession 7 indicated the most and accession 4 revealed the least LWL. Based on RWP genotypes 18, 10, 5 and 17 exhibited the most while genotypes 12, 11, 15 and 13 indicated the lowest amount of RWP. Based on the RWL genotypes 3, 2 and 12 showed the most and genotypes 17, 19 and 7 exhibited the least RWL content. Initial water content was highest in genotypes 3, 17, 6 and 19 and lowest in genotypes 1, 18 and 10. Maximum LWC belonged to genotype 3, while minmum was attributed to genotype 1 (Table 3). Table 3. Ranks (R), ranks mean ( ) and standard deviation of ranks (SDR) of physiological indicators of drought tolerance. Genotype code Yp R Ys R RWC R LWC R ELWR R IWC R RWP R Farshadfar and Ghasemi

6 Table 3 continued Genotype code RWL R WSD R ELWL R LWL R ISI R Ṝ SDR RS Integrated selection index An integrated selection index (ISI) based on combination of water relations, Yp and Ys may provide useful criterion for improving drought tolerance in crop plants (Farshadfar, 2012). Estimation of integrated selection index (ISI) for genotypes was carried out by merging physiological traits relative water content (RWC), relative water loss (RWL), relative water protection (RWP), water saturation deficit (WSD), excised leaf water retention (ELWR), excised leaf water loss (ELWL), leaf water content (LWC), leaf water content (LWL), initial water content (IWC) and grain yield. The results exhibited that the highest values of ISI were attributed to genotypes 3, 9 and 11 (Table 3). Principal component analysis To better understand the relationships, similarities and dissimilarities among the in vivo indicators of drought tolerance, principal component analysis (PCA) was used. The main advantage of using PCA over cluster analysis is that each statistics can be assigned to one group only (Khodadadi et al., 2011).The relationships among different physiological criteria of drought tolerance are graphically displayed in a biplot of PCA1 and PCA2 (Fig. 1). The first and second components accounted for 61.05% of variations among the indices. The PCA1 and PCA2 mainly distinguish the indices in different groups. One interesting interpretation of biplot is that the cosine of the angle between the vectors of two indices approximates the correlation coefficient between them. The cosine of the angles does not precisely translate into correlation coefficients, since the biplot does not explain all of the variation in a data set. Nevertheless, the angles are informative enough to allow a whole picture about the interrelationships among the drought indices (Yan and Kang, 2003). Ys, Yp, RWC, WSD and ISI we refer to group 1= G1 revealed positive correlation (acute angle). The G1 indices identified genotypes 9, 4, 14 and 20 as drought tolerant. The PCs axes separated IWC, LWC and ISI in group (G2) and ELWL, ELER, LWL, RWL and RWP in group (G3). This procedure was employed in bread wheat (Farshadfar et al., 2012) for screening selection criteria of drought tolerance. 82 Farshadfar and Ghasemi

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