Screening of wheat genotypes for water stress tolerance.

The effect of water stress tolerance was checked in wheat genotypes, 12 newly evolved genotypes and three drought tolerant check varieties Sarsabz, Khirman and Chakwal-86 were screened under various water stress conditions at Nuclear Institute of Agriculture, Tando Jam. The experiments were conducted in RCBD using three irrigation levels viz. at residual moisture (no irrigation after sowing), single irrigation and three time irrigations. The effects of water stress were studied on grain yield, 1000 grain weight, biological yield and proline content (%). The newly evolved genotypes showed some genetic improvement in various traits as compared to commercial check varieties. At residual moisture, up to 3382 kg/ha was achieved from new genotypes. Genotypes BWM-3, NIA-8/7, NIA-9/5, NIA-25/5, ESW-9525, BWQ-4, Sarsabz and Khirman had comparatively higher mean yield (more than 2500 kg/ha) at residual moisture. This shows that the genotypes might have better tolerance to water stress. The proline level of wheat genotypes increased under severe water stress (residual moisture) as compared to well-irrigated experiments. The lines NIA-28/4, NIA-9/5, BWM-3, NIA-8/7, NIA-10/8, MSH-17, BWQ-4, BWS-78 and Chakwal-86 accumulated high level of proline under water stress which confirmed their tolerance to drought conditions. INTRODUCTION Bread wheat is a major cereal food crop of Pakistan. The shortage of irrigation water during wheat cropping season is one of the main yield limiting factors. The change in environmental factors such as drought, high and low temperatures and salinity are the main a-biotic stresses for wheat and other crops throughout the world (Khan et al., 2007, Reynolds et al., 1999). Water stress affects every aspect of plant growth during vegetative as well as reproductive phase (Richards et al., 2001, Kimurto et al., 2003). Wheat crop requires 4-6 irrigations at various critical growth stages during entire season. The studies have shown that water stress at any critical growth stage can affect substantially the morphology, physiology and finally the productivity of the crop (Richards and Townley-Smith, 1987, Zhang et al., 2004, Blum, 1996). Under water stress conditions, osmotic adjustment helps in maintaining growth and other physiological functions of plants and therefore, a genotype with high osmotic adjustment under drought could be the high yielding (Sivaramakrishnan et al., 1988, Akram et al., 2004). Although yield is a main objective, the selection for drought resistance would be useful in areas where constraints exist and where selected genotypes are supposed to be grown (Boyer, 1996). The environmental changes at global level address significant challenges to agriculture sector, but also provide opportunities to boost crop yields in water-stressed environments. Studies have shown that the number of spikes per square meter and kernels per spike significantly reduced in wheat due to 138 Mahboob Ali et al., Pak. J. Biotechnol. midseason (at the time of anthesis) drought (Strauss and Agenbag 2000). The severe water stress during anthesis stage of wheat crop cause serious loss in yield, decrease plant fertility, reduced number of spikes and spikelet’s per spike (Giunta et al., 1993, Mujtaba et al., 2007). The increased accumulation of proline under drought conditions may be either due to accelerated rate of synthesis of proline or due to inhibition of its oxidation resulting in large accumulation of proline in water stressed tissues (Stewart et al., 1997, Liu et al., 2007). The present studies were conducted to evaluate the performance of newly developed wheat genotypes under various water stress conditions and to select promising lines which possess tolerance to water stress conditions for future breeding. MATERIAL AND METHODS To determine the drought tolerance in newly developed wheat genotypes, 12 genotypes viz.,BWM-3,NIA-8/7,NIA-9/5, NIA 10/8,NIA-28/4,NIA-25/5,ESW-525, MSH-17, MSH36, MSH-22,BWQ-4, BWS-78 along with three drought tolerant check varieties Sarsabz, Khirman and Chakwal-86 were screened at three different irrigation levels at Nuclear Institute of Agriculture (NIA), Tando Jam. Three water stress experiments viz., T1: the experiment was conducted on residual moisture, no further irrigation was applied after sowing till maturity; T2: Single irrigation applied during seedling stage after 21 days of sowing and T3: Three irrigations applied i.e., first irrigation was applied at seedling stage, second at pre-anthesis (at heading) stage and third at post-anthesis stage during grain filling period. The experiments were conducted in randomized complete block design (RCBD) with three replications. Four rows (4 m long) of each of 15 genotypes were sown. Net plot size sown was 4.8m plot. The experiments were surrounded with 3.0 m buffer zone to protect from any seepage or leakage. The studies were conducted on morphological (grain yield, 1000-grain weight, biological weight and harvest index) and phenological traits (days to heading, days to maturity). A physiological parameter ‘proline’ from each genotype under each treatment was also studied as suggested by Bates et al., (1973). Two fresh fully expanded leaves were taken from each genotype. The leaves were chopped and 0.5g of each sample was grinded in a mortar with liquid nitrogen. Then 10ml (3%) sulphosalicylic acid was added. The extract was filtered through Whatman filter paper #2. 2.0 ml of filtrate solution was transferred in a test tube, added 2.0 ml of acidninhydrin solution (1.25g ninhydrin added in 30 ml glacial acetic acid and 20ml 6M orthphosphoric acid), then added 2.0ml of glacial acetic acid and heat at 100C for 1hour. The reaction was terminated in an ice bath. The mixture was mixed vigorously with 10ml toluene using Vortex. After warming at 25oC, chromophore was measured at 520nm absorbance using toluene as a blank on Spectrophotometer (DR/4000, HACH, USA). The proline concentration was then determined from standard curve and calculated on fresh weight basis. Data recorded was statistically analyzed using analysis of variance (ANOVA) and the means were compared using Duncan’s Multiple Range Test