Research Article

Bread Wheat Germplasm Evaluation for Soil Moisture Stress Tolerance under Rainfed Condition 

Arvind Kumar, P. C. Sharma, R. Singh and J. Kumari

  • Page No:  754 - 761
  • Published online: 31 Dec 2018
  • DOI : HTTPS://DOI.ORG/10.23910/IJBSM/2018.9.6.1926a

  • Abstract

A preliminary yield trial was conducted with 1358bread wheat germplasm including four national checks (C-306, KRL 210, HD 2967 and Kharchia, 65) for identifying moisture stress tolerant genotypes. The experiment was conducted at experimental farm, Central Soil Salinity Research Institute, Karnal under rainfed conditions during 2015-16 cropping season. The new germplasm were compared against four standard varieties using an augmented design with fifteen blocks. Evaluation of germplasm accessions revealed considerable variation among grain yield (g)-m, effective tillers per meter and days to 50% heading traits studied. The adjusted grain yield of sixty two germplasm (38 accessions of NBPGR and 24 salt tolerant lines of CSSRI) were out yield the best check (C-306). However seventeen genotypes, namely, EC463441, EC534443, EC609338, IC543290, IC321905, IC535493, IC547643, KRS-611, KRL-112, KRL-105, KRL-136, KRL-119, KRS-621, KRL2-10, KRL-340, KRL-90 and KRL-35 were found most desirable for yield potential. These germplasm can be considered as an important source of genes for earliness, higher grain yield and for improving number of effective tillers in wheat improvement programme for drought tolerance. Non-significant differences for grain yield between the salt tolerant (KRL 210 and Kharchia 65) and drought tolerant (C-306) checks shows co-existence of salt and water stress tolerance in KRL 210 and Kharchia 65, although both the cultivars were developed for salt prone area, it is, as such, they are likely fit for cultivation under conditions with low soil moisture.

Keywords :   Wheat, soil moisture stress and germplasm

  • Introduction

    Wheat is the most widely grown cereal crop, as it is a staple food for about 40% of the world population (Malik et al, 2015). It is exposed to numerous edaphic stressors i. e. biotic and abiotic (Water scarcity, high temperature, problematic soils and freezing etc.) during the cropping season. Although, among all abiotic stresses, moisture stress is the single most important stress leading to maximum reduction in crop productivity (Kang et al., 2009). Irregular and unpredictable rainfall caused by climate change is likely to further aggravate water stress leading to a decline in productivity of all cereals including wheat in many areas around the world (Collins et al., 2008, Reynolds and Ortiz, 2010). In most of the irrigated regions of the India, receding water tables (Rodell et al., 2009), non-availability of irrigation water, irregular and unpredictable rainfall leads also soil moisture stress from mild to severity (drought) and ultimately affect the productivity of the wheat crop. The North-Western plains (wheat bowl of India) are too seriously challenged by the twin threat of heat and drought. On the other hand nearly 2.5 mha area of wheat in central India is grown under residual moisture regimes and suffers from stress of drought, heat and salinity(Anonymous, 2015).Approximately 20% of the area of wheat under cultivation is under rainfed environment (Reynolds et al., 1999). Even under irrigated conditions, 66% area of wheat receives only 1-2 irrigations (Joshi et al., 2007) subjecting to the crop to moisture stress and resulting in reduced grain yield. Further depletion of underground water and limited rainfall exacerbate the scarcity of water due to climate change in near future has been predicted. Severe soil moisture stress during anthesis could decrease the grain yield of wheat crop up to 50% by reducing the number of grains (Joshi et al., 2007). Soil moisture stress is managed by drought avoidance or dehydration tolerance (Kramer and Boyer, 1995). Drought avoidance includes root depth of the plant, maximum use of available water by plant, however dehydration tolerance include the capability of the plant to partially dehydrate and grow again when moisture received through rainfall. In the rain-fed environments of developing countries, national average yields of wheat differ from 0.8 to 1.5 t ha-1 (Morris et al., 1991). The reaction of plants to water stress is important from two stand-points, survival and productivity. While survival is important for native vegetation, productivity or grain yield is the major concern for most crop plants (Begg, 1980). Thus, tolerance to a moderate water stress is of significance to crop plants, whereas plants which can survive a severe water stress would be expected to have low productivity (Kramer, 1980). Consequently, breeding for enhanced moisture stress tolerance is essential for achieving greater food security for the hundreds of millions of rural poor (Ortiz et al., 2008). Intended for a successful breeding program, the presence of broader genetic variability plays a vital role. Development of higher-yielding wheat cultivars for water-limited environments is the most viable solution to stabilizing and increasing wheat production under current climatic conditions. The development of high yielding and stable varieties requires a continuous supply of new germplasm as a source of desirable genes and/or gene combinations (Ayana and Bekele 1998). The genetic potential for high productivity is already present in crops, but productivity is limited due to stressors, therefore selections should be made under conditions more likely to be encountered in the field rather than under ideal conditions (Boyer, 1982). More diverse parents create the greater chance of exploiting productive recombinants and broad variability in segregating generations during genetic improvement of crop plants (Mohammadi and Prasanna, 2003). Hence precise information on the nature and degree of genetic variability and divergence among the germplasm is a pre requisite for crop improvement programs as it would help in the development of superior recombinants adaptable for different abiotic stresses(Rauf et al., 2012). Although grain yield is a complex trait dependent on several contributing factors which are in turn highly susceptible to environment. Presently, in India the progress for breeding drought tolerant varieties has been rather slow. The wheat variety C 306 released in the year 1969 is still considered the most droughts tolerant under soil moisture deficit condition and is used as a standard check throughout the country (Joshi et al., 2007a).The first approach to assist breeders in their efforts is screening of the germplasm for the identification of good donor for yield potential. Germplasm either for potential varieties or for useful parents must be based on multiple breeding traits (Yan and Fregeau-Reid, 2008). Therefore genetic variability in the germplasm for yield potential under soil moisture stress was investigated in order to breed superior moisture stress tolerant genotypes. The main objectives of this study were to (i) examine the variation in yield potential within the bread wheat germplasm using the univariate technique and (ii) compare the germplasm with the currently grown bread wheat cultivar and national checks.

  • Materials and Methods

    In this study a set of 1354 bread wheat germplasm including 1213 germplasm (780 indigenous collections and 433 exotic collections) received from National Bureau Plant Genetic Resources (NBPGR), New Delhi and 141 salt tolerant germplasm from Central Soil Salinity Research Institute (CSSRI), Karnal along with four national checks were evaluated under rainfed condition at experimental farm, CSSRI, Karnal during October to April in 2015-2016 cropping season. The farm is situated at 29°43’ N latitude and 70°58’ E longitude and 245 m altitude. The site is representative of the areas with semi-arid to dry sub humid climate with hot summer and a cold winter, the cropping season characterized by minimum and maximum temperature of 4.6 and 36.4 °C respectively. The seasonal precipitation of 46.2 mm in 5 rainy days of the site is recorded. The soil at the site was reclaimed sodic (pH: 7.80-8.21) clay loam. The planting was carried out after applying a pre-planting soaking irrigation of approximately 50 mm. The plant materials (100 seeds per entry) were sown in 1 row of 2 meter length with 30 cm apart in a non- replicated trial. The experiment was laid out in augmented randomized complete block design (Federer and Raghavarao, 1975) with fourteen blocks with 94 accessions and  15th block with 38 accessions and four standard varieties (checks) were repeated in every block randomly. These standard varieties, namely, C-306, KRL 210, HD 2967 and Kharchia 65, represented selective tolerance to particular osmotic stresses,  highly drought tolerant (C-306) to highly salt tolerant (Kharchia 65). However HD 2967 and KRL 210 were chosen because they were the pioneer varieties extensively grown by the farmers and commonly used as standard varieties in irrigated high fertility and salinity/alkalinity tolerance evaluation trials respectively. These four varieties were sown in each block, but each entry to be evaluated was sown only once in the experiment. The positions of the 94 varieties and entries in each block were fully randomized. Weeds were controlled manually. Fertilizer rates were 90kg N, 60 kg P and 40 kg K ha-1 were applied at the time of sowing. The crop was maintained completely under rain-fed conditions. The main traits, which are important for preliminary evaluation of germplasm under rainfed condition, were recorded for each entry. These traits include, days to heading (DH), number of effective tillers (NET) and grain yield (YLD). Days to heading was designated as the day until 50% of the plant in the plot had at least one open flower. Numbers of effective tillers were counted at the time of physiological maturity.  The yield after harvesting was measured for each genotype at each plot.

    2.1.  Statistical analysis

    Several simple univariate statistics including minimum, maximum, range, mean, standard deviation and coefficient of variation were used to describe the variability among the genotypes, which were calculated for each trait based on the genotypes (Prosperi et al., 2006, Morris, 2009).Combinations of mean and SD for each attributes was also used to identify superior genotypes. In this case five categories of genotypes for each studied trait can be formed. In the case of YLD and NET, the genotypes with values >(mean+SD) and>(mean+2SD) can be identified as desirable genotypes and those with values<(mean-SD) and (mean-2SD) can be identified as undesirable ones (Shakhatreh et al., 2010). In the case of DH   the genotypes with values less than mean, (mean –SD) and (mean-2SD) will be more desirable. The genotypes with values between mean±SD were average in the performance. Analysis of variance (ANOVA) was carried out based on augmented complete block design using Windostat (Indostat service Hyderabad, India)accounting for both inter- and intra- block differences. The following linear model was used: yij=μ+gi+cjj+ εij  Where: yij is the observation of treatment i in jth block, μ is the general mean gi is the effect of test treatment, cj is the effect of control treatments in jth block, βjis the block effects and εij is the residual error. The plot numbers were considered in the residual to account for any measurement errors. Means were adjusted for inter and intra block variations and were compared based on the standard errors of the differences between two means with controls and with new entries. Due to the imbalance created owing to the occurrence of new entries in a block, four different standard errors (Federer, 1956; Federer and Raghavarao, 1975) were computed as follows:

    1.Between means of two check varieties(Ci – Cj): Sc=√2MSE/r

    2. Between adjusted yields of two new selections in the same block(BiTi–BiTj): Sd=√2MSE

    3. Between adjusted yields of two new selections in different blocks(BiTi–BjTj): Sv=√2(C+1)MSE/c

    4. Between adjusted yield of a new selection and a check mean(Ci – Ti:): Svc=√2(r+1) (c+1)MSE/rc

    The phenotypic, genotypic and environmental variances were calculated according Weber and Stamp (1988).

    From the genotypic, phenotypic, and environmental variances, different genetic parameters like (a) phenotypic coefficient of variation (PCV), (b) genotypic coefficient of variation (GCV), which has paramount importance in the selection process of breeding experiments were calculated according to (Singh and Chaudhury, 1985):

    GCV and PCV values were categorized as low (0-10%), moderate (10-20%) and high (20% and above) values as indicated by Burton and de vane (1953).Broad sense heritability (h2b) for all characters was estimated as the ratio of genotypic variance to the phenotypic variance and expressed in percentage according to the methods suggested by Falconer and Mackay (1996) h2 b=σ2 g/σ2p, where σ2 g and σ2p are the components of variance for the genotypic and genotypic effects respectively. Robinson et al.(1949) classified heritability values as high (>60%),moderate (30-60%) and low (0-30%).The difference between the mean of the selected individuals () and the mean of the population from which the selection has been made () i.e. genetic gain or advance is worked out as  

    (Allard, (1960) where k is the standardized selection differential having constant values at 5% selection intensity (K=2.063) and  h2b and σP are the heritability in broad sense and phenotypic standard deviation respectively. Genetic advance as % of mean was calculated to compare the extent of predicted advance of different traits under selection, using the GAM= GA/ x̅ ×100 Where, GA, represent genetic advance under selection and x̄, represent grand mean of the population. Genetic advance as % mean was categorized as low (0-10%), moderate (10-20%) and high (>20%) according to Falconer and Mackay (1996). To pick the winners national checks are included along with the new genotypes to provide a base against which yield superiority could be measured. Ultimate goal is to find new entries that are out yielder the checks. The breeder is normally not interested in entries those having the yield less than the checks, whether difference is significant or not. In the study we rejected H0GC in favor of H1GC, where µGis the true mean of the new genotype/germplasm and µC is the true mean of the check. Statistic, least significant increase(LSI or one-sided least significant difference) was used against which we could compare yield increases for those entries that out yield the checks (Petersen, 1994). LSI=SEd×tα(at error df) Where t, is the one-tailed t test at α probability level at error df.  In practice, if a new germplasm mean, YG, is greater than ȳ C+LSI  the increase yield of the germplasm over the check yield is said to be significant at the 100% level.

  • Results and Discussion

    Univariate statistics of recorded data on 1358 genotypes (1354 germplasm, four checks) for three traits are summarized in Table 1. For each trait, the overall means, maximum, minimum, range, and SD are given. All the trait values were checked for the normality through Shapiro-Wilks test (Shapiro and Wilk, 1965). Test showed that all the recorded traits followed normal distribution. For days to heading (DH), the values ranged from 72-128 days with a mean value of 93. The DH values for the national checks ranged from 87 to 102 days. This data showed the existence of 190 genotypes heading, 7 days earlier than the national check (C 306) for rainfed condition. High variation was found among the genotypes for number of effective tillers (NET). The values recorded for the NET varied from 7 to 190 with a mean of 63 (Table 1) and highest value (190) recorded for the indigenous germplasm IC 534198. The grain yield (YLD) varied from 13 to 459 g m-1 

    with average mean of  117 gm-1. Among the national checks, the YLD varied from 111 to 147 g m-1, while 643 experimental entries had yield performance greater than the mean, showing the potential to select higher yielding genotypes from the germplasm. Number of effective tillers and yield showed symmetrical histogram about the mean, and about 95% of the population observations lie within 2 standard deviations of the mean in the large population.Therefore the superior genotypes can be identified for each trait based on integrating of mean values with SD. The groups which include genotypes with a trait value of (mean+SD) and (mean+2SD) will provide an indication of the superior genotypes and also indicate high probability of success in selection in the case of sufficient high heritability (Shakhatreh et al. 2010). The number and percentage of bread wheat genotypes which had a better performance than mean, (mean+SD) and (mean+2SD) based on each trait are presented in Table 2. The percentage of genotypes with values higher than the mean across all traits varied from 31% (for DH) to 47% (for YLD) (Table 2). Based on these three agronomic traits, in general, about 40% of the genotypes had a better performance than the overall mean.

    In the case of days to heading, 62% of the genotypes had DH values less than the mean. In screening about 96 genotypes tend to headed and mature before other genotypes and this set of genotypes will be suitable for improving the ruling varieties based on earliness. However 13% of genotypes had YLD better than the (mean+SD), while 14% of the genotypes showed NET higher than the (mean+SD). In contrast, just 7 % of the genotypes had DH values less than the (mean-SD) (Table 3). The percentage of genotypes with values better than the (mean+2SD) for YLD, and NET were 3 and 4% respectively. However no genotype was found for the DH with values less than the (mean-2SD). Mean squares of the 3 characters from analysis of variance (ANOVA) are presented in Table 3. Significant differences were observed among germplasm for all three characters studied. This indicating the presence of variability, which can be exploited through selection for further breeding programs. Similarly, works of Kalimullahet al. (2012) reported that number of tillers per plant, grain yield per plant showed significant differences between forty one bread wheat genotypes were studied. Shashikala (2006) reported significant differences for days to heading, number of effective tillers-m and grain yield(g)-m. The PCV ranged from 40.64 for YLD to 45.94 for NET whereas 37% GCV estimated for YLD and NET. Phenotypic coefficients of variation were generally higher than genotypic coefficients of variation for all traits studied indicating the influence of environment. Burton and devane (1953) classified PCV and GCV values as high (>20%), medium (10-20%) and low (<10%). Accordingly, high PCV and GCV were observed in all the three (DH, NET and YLD) traits. The high PCV and GCV indicate that selection may be effective based on these traits. In support of this study Tarekegne et al. (1994) reported high PCV and GCV for grain yield in wheat. The bread wheat genotypes showed array of variations in grain yield. Broad sense heritability estimates ranged from 66.8% for NET to 99.9% for DH. The high heritability for all the three traits is encouraging from the breeding standpoint for high value of studied traits. Robinson et al.(1949) classified heritability values as high (>60%), moderate (30-60%) and values less than 10% low. Accordingly, high heritability was observed for all three characters of 1354 bread wheat germplasm. High heritability values for these traits indicated that the variation observed was mainly under genetic control and was less influenced by the environment and the possibility of progress from selection. This may be attributed due to uniform environmental conditions during the conduct of the experiment. The obtained results were in agreement with results of Rasal et al. (2008) which also noticed higher heritability values for DH, number of productive tillers and grain yield per plot. Genetic advance expressed as a percentage of the mean ranged from 63.23% for NET to 86.42% for DH. Falconer and Mackay (1996) classified genetic advance as % of mean as low (0-10%), medium (10-20%) and high (20% and above). Accordingly, genetic advance as percentage of mean was higher for all characters studied. The estimates of genetic advance help in understanding the type of gene action involved in the expression of various polygenic characters. High values of genetic advance are indicator of additive gene action whereas low values are indicator of non-additive gene action (Singh and Narayanan, 1993). Therefore, heritability and genetic advance are important selection parameters. The estimate of genetic advance is more useful as a selection tool when considered jointly with heritability estimates (Johnson et al., 1955). High heritability associated with high genetic advance was observed for all the three traits.  The positive correlation (r=0.55***)between YLD and NET

    would be useful if we areselecting for high levels of both traits. However, the strongnegative correlation (r=-0.23***) between yield and days to heading(DH) under rainfed conditions would be helpfulwhen selecting for high yield andearliness.

    LSD values were computed for testing intra and inter block variations. For this purpose we used two-sided tα test (Table 4). For this trial, however, the most useful comparison would be between the adjusted yield of a new selection and a check mean. Therefore to find those new selections that out yield the checks, we compute the LSI for this purpose: LSI =tα×Svc, For the 0.5% LSI we have the 0.5% one tailed t at 42

    df t (α=0.005)=2.704 and LSI=(2.704) (22.04)=59.58 Then any adjusted selection yield greater than

    1. 139.52+59.58=199.40 significantly out yields ‘KRL 210'

    2. 145.67+59.58=205.55 significantly out yields ‘C 306'

    3. 111.49+59.58=171.37 significantly out yields 'HD 2967'

    4. 146.91+59.58=206.79 significantly out yields ‘Kharchia 65'

    The adjusted and observed grain yield of 62 germplasm (38 germplasm from NBPGR and 24 salt tolerant germplasm from CSSRI) which was greater than observed grain yield of C-306 (drought tolerant check) is given in Table 5.

    Non-significant difference between the salt tolerant (KRL 210 and Kharchia 65) and drought tolerent (C-306) checks (Table 6) shows co-existence of salt and water stress tolerance in KRL 210 and Kharchia 65 although both the cultivars were developed for salt affected areas, it is, as such, they are likely fit for cultivation under conditions with low water potential, which in turn creates high osmotic pressure in the outer environment. The most desirable genotypes were identified for each trait based on integrating of mean-value with SD.

    The groups including genotypes with a trait value of (mean+2.576 SD) will give an indication of the superior donors ( Table 7).

    In normally distributed population 99% of the individuals lies within 2.576 standard deviations of the population mean.  With respect to early heading ( mean-1.96SD) IC282300, IC539399, IC111959, IC111914, IC128388, IC398067, IC402048, IC543367, IC415861, IC524288, IC408334 and EC578119 accessions were most desirable. These germplasm can be considered as an important source of genes for earliness under rainfed conditions. Accessions IC534198, IC279060, IC564126, IC78926, IC279328, IC543290, IC542983,IC310071, IC430369, IC536339, IC533809, IC585637, IC138543, IC469480, IC393128 IC535350, IC542968, IC279030, IC543262 EC217847, EC575756, EC218116, EC575648, EC374915, EC534433, EC534528, EC557001, EC575737, EC575712, EC217899 KRL-136, KRL-112 and  KRL-2-22 would be most desirable for number of the effective tillers which are positively correlated with grain yield under rainfed condition. With respect to high grain yield per plot EC463441, EC534443, EC609338, IC543290, IC321905, IC535493, IC547643 KRS-611, KRL-112, KRL-105, KRL-136, KRL-119, KRS-621, KRL2-10, KRL-340, KRL-90 and KRL-35 accessions were identified as most potential donors. Results showed high and significant variation among genotypes compared with local check for the traits examined. It is well documented that in a breeding program, the first priority trait is economic yield (García del Moral et al., 2003). Any cultivar with good agronomic traits will notbe successful if it has low yield potential. The screening of germplasm helped in identifying promising genetic donors for different characters. This result is supported by Mittal and Brar 2008, Ali et al.,2010.

  • Conclusion

    Screened out germplasm can be considered as an important source of genes for earliness, number of effective tillers and higher grain yield under rain fed situation, where terminal drought stress is common phenomenon. Identified out yielder accessions may be used for introgression of desirable genes in the background of elite varieties to enhance yield under moisture stress environments.

  • Acknowledgement

    The authors are highly thankful to Director, Central Soil Salinity Research Institute and Head, Division of Crop Improvement for providing necessary facilities to carry out this work and Director, NBPGR for providing bread wheat germplasm. The financially support by the Indian Council of Agricultural Research, Government of India, New Delhi is gratefully acknowledged.

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Kumar A, Sharma PC, Singh R, Kumari J. Bread Wheat Germplasm Evaluation for Soil Moisture Stress Tolerance under Rainfed Condition  IJBSM [Internet]. 31Dec.2018[cited 8Feb.2022];9(1):754-761. Available from:

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Phenotypic Screening of F3 Rice (Oryza sativa L.) Population Resistance Associated with Sheath Blight Disease

Ashmita Timsina, Uday Kumar Thera and Naveenkumar Ramasamy

Biplot-analysis, cluster analysis, IC277275, IC277332, Sheath blight, UPGMA

Published Online : 31 May 2022

Research Article

Detection of B. anthracis from Environmental Samples during Outbreak in Tamilnadu by Molecular Methods

K. Senthilkumar, G. Ravikumar and K. G. Tirumurugaan

Anthrax, Biosafety, Cattle, Environment, PCR, Phylogenesis, Soil, Zoonoses

Published Online : 31 May 2022

Research Article

Phenotypic Stability for Fruit yield and its Components of Brinjal

V. Chaitanya and   R. V. S. K. Reddy

Brinjal, environment, genotypes, hybrids, quality parameters, stability, yield

Published Online : 27 May 2022

Short Research

Effect of Water Stress on Yield and Seed Quality of Coriander (Coriandrum sativum L.)

Priyanka Thakur and Anju Thakur

Biological yield, dry matter index, harvest index (HI)

Published Online : 07 Feb 2018