Genetic variability studies on bread wheat ( Triticum aestivum L . ) genotypes

Thirty bread wheat genotypes were tested to assess the genetic variability, among studied genotypes using alpha-lattice design at Tongo sub-center of Assosa Agricultural Research Center and Kulumsa Agricultural Research Center in 2015. Analysis of variance revealed that there were statistically significant differences among the genotypes for most of the traits at individual and across locations. From the combined analysis of variance, significant (p≤0.05) effect due to location, varieties and G×E was observed for most of the traits. The varieties showed wider variability in mean grain yield of 1284.43788.7 kg ha -1 , 2588.3-4683.3 kg ha -1 and 1936.4 4095.6 at Tongo, Kulumsa and across location, respectively. Moderate PCV values (>10%) were obtained for grain yield, 1000 kernel weight, harvest index, tillers per plant and spikes per plant at individual location and across location including days to heading, above ground biomass yield, spike length and kernels per spike at Tongo and above ground biomass yield at Kulumsa. Similarly, moderate GCV values (>10%) were obtained for grain yield, 1000 kernel weight, tillers per plant and spikes per plant at individual location including days to heading, harvest index and kernels per spike at Tongo and above ground biomass yield at Kulumsa. Lower (<10%) was obtained for all traits across location. High heritability estimates (>80%) were obtained for days to heading (86.0%) and days to maturity (85.1%) at Tongo and days to heading (86.2 and 82.69%) and spikes length (80.1 and 82.85%) at Kulumsa and across location. But relatively high genetic advance (>20%) was obtained for grain yield (28.5%) and harvest index (24.3%) at Tongo. Moderate genetic advance (10-20%) was observed for 1000 kernel weight, spikes length and days to heading at individual location and across location including spikelets per spike, tillers per plant, above ground biomass, spikes per plant and plant height at individual location. Generally, it has been observed the presence of variability among the genotypes, heritability in the tested traits of the genotypes studied. Hence, Selection and hybridization on those genotypes based on the trait with high GCV, heritability and genetic advance can be recommended for farther yield improvement of bread wheat at respective location.


INTRODUCTION
Wheat (Triticum aestivum L.) is one of the world's major cereal crops and stable food of many regions grown under both irrigated and rain fed conditions.Unlike rice and maize, which prefer tropical environment, wheat is extensively grown in temperate regions occupying 17% of all crop acreage worldwide.It is the staple food for 40% of the world's population (Goyal and Prasad, 2010;Peng et al., 2011).Currently it is also becoming most important cereals grown on a large scale (Fassil et al., 2000), because of its significance as cash crop, high level of production per unit area, its major role in supplying the dietary requirements of the society.Wheat is the second only to rice which provides 21% of the total food calories and 20% of the protein for more than 4.5 billion people in 94 developing countries (Braun et al., 2010).Food consumption of wheat is projected at 488 million tonnes, 1.3% higher than in the 2014 season, keeping the average per capital level steady at 67.6 kg (FAO, 2015).Global wheat grain production must increase 2% annually to meet the requirement of consistently increasing world population (around 9 billion) till 2050 (Rosegrant and Agcaoili, 2010).
The leading wheat producing countries are China, India, United States, France, and Russia Federation (FAO, 2015).The Wheat Yield Consortiums an integral part of the wheat strategy to break the genetic yield barrier.In March 2012, 34 research organizations finalized a 10-year integrated research plan.The organizations agreed for sharing advanced scientific expertise, facilities and germplasm, to improve the wheat plant's photosynthesis, ear size and stalk strength working together to succeed in raising the genetic yield potential by up to 50% in the next 20 years.The wheat yield consortium findings will be incorporated into the wheat breeding platform, to deliver high-yielding varieties to farmers' fields in wheat target regions (CGIAR, 2013) In Ethiopia, bread wheat is an introduced crop, although its time of introduction is immemorial (Hailu, 1991).Wheat can grow in the Ethiopian highlands, which are situated between 6 o and 16 o N and 35 o and 42 o E, at altitude ranging from 1500 to 3000 m.However, the most suitable altitude zones of wheat fall between 1900 and 2700 m.a.s.l (Bekele et al., 2000).
Wheat is an important staple food crop and the third highest source of grain-based calories behind corn and sorghum in Ethiopia.It accounts for a little more than 20% of the total calorie supply.60% of production is used for household consumption, 20% is sold to the market, while the balance is used for seed, in-kind wages, animal feed and other uses.Wheat bran from commercial wheat millers is used as one of the ingredients in commerciallyproduced, compound animal feed (GAIN, 2015).It grows on 1.6 million hectares of production area with a total production of 3.8 million metric tons and ranks fourth in both area and production among cereal crops in different regions of Ethiopia (CSA, 2015).Ethiopian wheat production self-sufficiency is only 75% and the remaining 25% of wheat imported commercially and through food aid and shares of total cereal consumption is increased by 20% in resent year, making it the second most consumed cereal in Ethiopia after corn (USDA, 2016).Therefore, to meet the self-sufficiency, growing demand of manufacturing industries and reduce the importing, increasing the yield potential would be the solution in the long-run.Farther more increasing wheat production is important to the economic stability and food security of Ethiopia.
Although the productivity of wheat has increased in the last few years in Ethiopia, it is still very low as compared to other wheat producing countries.The national average productivity is estimated to 2.4 tons/ha (CSA, 2015) which is by far below the world's average of 3.27 tons/ha (USDA, 2016).The low productivity is attributed to a number of factors including: Biotic (Diseases, insect pests, and weeds), abiotic (moisture, soil fertility, etc.,) (Zegeye et al., 2001).Among biotic factors, rusts are the most important diseases of wheat, which cause up to 60% loss of wheat yield for leaf or stripe (yellow) rust and 100% loss for stem rust (Park et al., 2007).Wheat and rusts have coevolved for thousand years and resulted in the accumulation of wide spectrum of the pathogens in Ethiopia (Mengistu et al., 1991).Therefore, developments of new varieties which are resistance to different diseases and adaptable to environments with abiotic stress could be a solution for farther grain yield improvement in wheat.
Grain yield and its quality are the principal characters of a cereal crop (Ullah et al., 2010).They are complex quantitative characters, which are influenced by a number of yield contributing characters.Hence, the selection for desirable genotypes should not only be based on yield alone, and the other yield components should also be considered.Direct selection for yield is often misleading in wheat because wheat yield is polygenically controlled.For effective utilization of the genetic stock in crop improvement, information of mutual association between yield and yield components is necessary.It is therefore, necessary to know the correlation of various component characters with yield and among themselves.The correlation coefficients between yield and yield components usually show a complex chain of interacting relationship.Path coefficient analysis partitions the components of correlation coefficient into direct and indirect effects and illuminates the relationship in a more meaningful way.The success of a breeding program depends largely upon the amount of genetic variability present in the population and the extent to which the desired traits are heritable (Majumder et al., 2008).
Several genetic variability studies have been conducted on different crop species based on quantitative and qualitative traits in order to select genetically distant parents for hybridization (Daniel et al., 2011).Genetic improvement to develop varieties with high yield potential and resistance/tolerance to a biotic and biotic stresses, with acceptable end-use quality, is the most viable and environment-friendly option to sustainably increase wheat yield.Such improvement of crops requires creation and introduction of genetic variation, inbreeding coupled with selection, and extensive evaluation of breeding materials at multiple locations to identify adapted and stable genotypes with desirable agronomic traits.Several genetic variability studies have been conducted on bread wheat at the different regions of Ethiopia (Adhiena, 2015;Awale et al., 2013;Gezahegn et al., 2015;Mitsiwa, 2013;Obsa, 2014).However, no variability studies have been conducted at Benishangul Gumze Regional State.Therefore, such information is essential for creation of genetic variation and further bread wheat improvement particularly, in the region and generally in the country.Therefore, the current study was carried out to estimate the genetic variability of bread wheat genotypes for yield and yield related traits.

Experimental sites
The experiments were conducted at two locations, Kulumsa Agricultural Research Center (KARC) and Tongo, under Assosa Agricultural Research Center (AsARC) (Table 1).
The bread wheat genotypes to be studied were given in Table 2.

Experimental design, data collected and field management
The trials were planted in July 04, 2015 at Kulumsa and August 18, 2015 at Tongo.Masood et al. (2008) reported Alpha lattice design provided smaller standard errors of differences, coefficients of variation and error mean squares as compared to randomized complete block design providing efficiency in comparing different entries/lines.Therefore in the current study, thirty genotypes were grown in alpha-lattice design with three replications.Each experimental unit consisted six rows of 2.5 m length with 20 cm spacing between rows.Data were collected from the central four rows for the parameters days to heading, days to maturity, grain filling period, grain yield, 1000 kernel weight, above ground biomass yield, harvest index, hectoliter weight and from randomly sampled plants for the characters; tillers per pant, plant height, kernel per spike, spikelet per spike, spike length and spikes per plant.1.5 m alleys were left between reps.Non-experimental variables such as seed and fertilizer rates were used as recommended for the specific testing sites.Hence, 73/69 kgha -1 N/P 2 O 5 were used for Kulumsa and 60/69 kg ha -1 N/P 2 O 5 for Tongo.
A seed rate of 125 kg ha -1 was used at both locations.

Analysis of variance (ANOVA)
The analysis of variance (ANOVA) was performed using the SAS version 9.1.3software for Alpha-Lattice Design.For each location and combined data over locations, analyses of variances, were done using the mean of ten sample plants for the characters like plant height, tillers per plant, spikelets per spike, spike length, kernels per spike and spikes per plant.However, plot values were used for the characters such as days to heading and maturity, grain yield per hectare, harvest index, grain filling period, hectoliter weight, thousand kernels weight, and above ground biomass yield for analysis of variance.The Least Significant Difference (LSD) was used to compare two means at the 5 and 1% level of significance.Individual locations ANOVA were computed using the following mathematical model: Where: =the observed value of the trait Y for the genotype in replication; = the general mean of trait Y; = the effect of replication; = the effect of genotypes and =block within replicate effect; = the experimental error associated with the trait y for the genotype in l th block with in replication and replication.Combined ANOVA model: Where, = observed value of genotype i in block k of location j; = grand mean; = effect of genotype I; = environment or location effect; = the interaction effect of genotype i with location/environment j; = effect of block k in location/environment j; = random error or residual effect of genotype i in block k of location j.

Estimation of phenotypic and genotypic parameters
Genotypic variance ( 2 g ) = r MS MS e g  (Burton and De vane, 1953) Where:

  
Variance components for the data combined over locations were computed in a similar fashion as for individual locations by using the following formulae (Johnson et al., 1955) Where: = Genetic by location interaction; = error mean square; = genotype by location interaction mean square; = genotype mean square; r = replication and l = location Coefficient of variation at phenotypic, genotypic and environmental levels was estimated as: Where: x = grand mean of character.

Estimation of heritability in broad sense
Heritability (H): in broad sense for all characters was computed using the formula given by Falconer (1989).Broad sense heritability (H) expressed as a percentage of the ratio of the genotypic variance (σ 2 g ) to the phenotypic variance (σ 2 p ) was estimated on genotype mean base (Allard, 1960) as: Where: H 2 = heritability in broad sense;

Estimation of genetic advance
Genetic advance in absolute unit (GA) and percent of the mean (GAM), were estimated in accordance with the methods illustrated by Johnson et al. (1955) as: GA = Kσ P H Where, K=the standardized selection differential at 5% selection intensity (k=2.063); p =phenotypic standard deviation on mean basis; H=heritability in broad sense.

Analysis of variance of studied traits
Individual location (Table 3) and across locations (Table 4) ANOVA was carried out for 14 characters recorded at Tongo and Kulumsa.There was a highly significant difference among the genotypes for all traits including days to heading, days to maturity, grain filling period, plant height, grain yield, 1000 kernel weight, hectoliter weight, biological yield, harvest index, tillers per plant, spikes per plant, spikes length, spikelets per spike and kernels per spike studied at individual locations confirming the genetic variability for yield and its components.Obsa (2014) and Awale et al. (2013) also reported considerable genetic variability for grain yield and its component characters in studied bread wheat genotypes in Ethiopia.Other authors also reported considerable genetic variability for grain yield and its component characters in durum wheat (Khan et al., 2013;Mohammed et al., 2011).Gezahegn et al. (2015) reported highly significant and significant differences among genotypes (P<0.01) for days to heading, days to maturity, grain filling period, 1000 kernel weight, plant height, spike length, number of productive tillers per plant, number of spikelet's per spike and number of grains per plant, grain yield per plot, harvest index and hectoliter weight.However, Mitsiwa (2013) reported non-significant differences among bread wheat genotypes for plant height and spike length and Adhiena (2015) for plant height and number of tillers per plant.
Twelve quantitative characters which had homogeneous error variances were subjected for combined ANOVA over locations (Table 4).Significant location effects were observed for all the traits except number of spike per plant indicating the differences in growth conditions exhibited at the two locations.
Mean squares of genotypes were significant (P≤0.01) for all characters including days to heading, days to maturity, plant height, grain yield, 1000 kernel weight, hectoliter weight, harvest index, spikes per plant, spikes length, spikelets per spike and kernels per spike except for number of tillers per plant indicating variability in studied genotypes.Hence, selection could be effective for different quantitative characters or for inclusion in crossing program for creating variability.Such variability with in studied genotypes was also reported by Navin et al. (2014).
The location × genotype interaction was significant for days to heading, days to maturity, plant height, grain yield, 1000 kernel weight, hectoliter weight, harvest index, spikes length, spikelets per spike and kernels per spike except number tiller per plant and spike per plant indicating different performance of bread wheat genotype across the two locations or genotypes responded differently to the different environmental conditions suggesting the importance of the assessment of genotypes under different environments in order to identify better performing genotypes for a particular environment.In accordance with Tesfaye et al. (2014) who reported significant differences among genotypes for most of the traits including day to heading, days to maturity, plant height, Septoria disease, thousand seed weight and hector liter weight across environments

Mean and range of grain yield and yield components
Range and mean values for the 14 characters are shown in Tables 5 and 6 for Tongo and Kulumsa, respectively.The mean performance of the 30 genotypes for 14 traits is presented in Appendix Tables 5 and 6.Coefficients of variation (CV %) were used to compare the precision of the experimentation, that is, means with lower CV% for most of the characters revealed existence of reliability of the data (Gomez and Gomez, 1984).A range for days to heading at Tongo was 46 to 70 days with minimum values in genotypes ETBW 8518 and the maximum in ETBW 6940 with an average value of 55 days.46.6% of the genotypes need above the grand mean (55 days) days to heading.The range for days to heading at Kulumsa was 48 to 66 days relatively narrow than days to heading at Tongo with minimum values in genotypes ETBW 8518 and the maximum in ETBW 7213 with an average value of 56 days.30.0% of the genotypes need above the grand mean (56 days) days to heading.Days to maturity at Tongo and Kulumsa also ranged from 97 (ETBW 7101) to 117 (ETBW 6940) and 97 (ETBW 7101 and ETBW 8517) to 108 (ETBW 6940, ETBW 7147 and ETBW 7213) days, respectively, with an average value of 105 and 102 days, respectively, indicating that the tested genotypes were early to medium maturing category.Grain felling period ranged from 42 to 54.7 and 35 to 49 at Tongo and Kulumsa, respectively, indicating long grain filling period is required at Tongo relative to Kulumsa.
Plant height varied from 63.3 to 83.5 cm at Tongo and 67.2 to 88.7 cm at Kulumsa with a mean height of 75.7 and 78.8 cm, respectively.Number of tillers per plant and spikes per plant were ranged from 2 to 4 and 2 to 4, respectively, at Tongo with a mean of 3 for number of tillers per plant and 3 for number of spike per plant.Similarly, these traits ranged from 2 to 4 and 2 to 4, respectively, at Kulumsa with a mean of 3 for number of tillers per plant and 3 for number of spike per plant.Both number of tillers per plant and number of spike per plant showed similarity in values at both locations indicating most of the tillers were fertile.Spike length ranged from 6.6 to 9.7cm at Tongo and 6.7 to 10.0 cm at Kulumsa with a mean length of 7.9 and 8.5 cm, respectively.The mean number of spikelets per spike and number of kernel per spike were ranged 15 to 22 and 27 to 54, respectively, at Tongo with a mean of 18 for spikelet per spike and 41 for kernels per spike.Similarly, these traits ranged from 15 to 21 and 40 to 54, respectively, at Kulumsa with a mean of 18 for spikelet per spike and 46 for kernels per spike.
The mean 1000 kernel weight ranged from 24.7 g (ETBW 7194) to 38.7 g (ETBW 7364) with an average value of 33.5 g at Tongo and ranged from 27.6 g (ETBW 7058) to 51.3 g (ETBW 8518) with an average value of 43.3 g at Kulumsa.Hectoliter weight provides a rough estimate of flour yield potential in wheat and is important to millers just as grain yield is important to wheat producer.This variable ranged from 71.3 kg/hl (ETBW 8516) to 81.9 kg/hl (ETBW 8510) with an average value of 78.7 kg/hl at Tongo and ranged from 65.4 kg/hl (ETBW 8511) to 75.9 kg/hl (ETBW 8506) with an average value of 73.2 kg/hl at Kulumsa.
Above ground biomass showed a wide range of variation 9000 to 14166.7 kg ha -1 with the mean value 11627.8 and 6000 kg ha -1 to 15000 kg ha -1 with the mean value 10816.7 kg ha -1 at Tongo and Kulumsa, respectively.Harvest index (HI) has been used to describe the proportion of harvestable biomass.Current modern wheat varieties have HI of c. 0.45 to 0.50 (spring type) and 0.50 to 0.55 (winter type), approaching its theoretical maximum value (c.0.64 in winter wheat) (Foulkes et al., 2011;Reynolds et al., 2012).In this study, harvest index ranged from 0.1 to 0.3 with an average value of 0.2 at Tongo and ranged from 0.2 to 0.4 with an average value of 0.3 at Kulumsa.The score of the variable was lower than its theoretical maximum value (0.64) at both locations.

Estimates of genetic parameters
The amount of genotypic and phenotypic variability that exist in a species is of utmost importance in breeding to select better varieties and initiating a breeding program.Genotypic and phenotypic coefficients of variation are used to measure the variability that exists in a given genotypes.Estimated genotypic coefficient of variability (GCV) and phenotypic coefficient of variability (PCV), broad sense heritability as well as genetic advance for selection of the traits studied are presented in Tables 5 to  7.

Phenotypic and genotypic coefficients of variation:
In general, estimates of phenotypic coefficient of variation in this study were higher than their corresponding genotypic coefficient of variation indicating the influence of environment on the expression of these characters although the differences were small at both locations.Narrower difference between the values of GCV and PCV indicated that the environmental effect was small for the expression of these characters.According to Deshmukh et al. (1986) PCV and GCV values greater than 20% are regarded as high, whereas values less than 10% are considered to be low and values between 10 and 20% to be moderate.At Tongo the GCV ranged from 2.51% (Hectoliter weight) to 17.62% (Grain yield), whereas PCV ranged from 3.71% (Hectoliter weight) to 22.27% (Grain yield).Among all characters, moderate GCV and PCV values (>10%) were observed for days to heading (10.42 and 11.23%), grain yield (17.62 and 22.27%), 1000 kernel weight (10.45 and 12.63%), harvest index (14.41and 17.45%), tillers per plant (11.26 and 19.23%), spikes per plant (13.00 and 20.97%), kernels per spike (11.04 and 14.42%), respectively, suggesting sufficient variability and thus scope for genetic improvement through selection for these traits.Navin et al. (2014) reported higher magnitude of GCV and PCV for grain yield per plant, harvest index, tillers per plant, spike length and test weight which support this finding.The rest of the characters grouped under low phenotypic and genotypic coefficients of variation, indicating less scope of selection as they were under the influence of environment.
At Kulumsa the GCV ranged from 0.11% (harvest index) to 13.57% (tillers per plant), whereas PCV ranged from 0.15% (harvest index) to 20.89% (tillers per plant).Moderate GCV and PCV values were observed for grain yield (10.32 and 14.59%), thousand-kernel weight (10.47 and 11.83%), above ground biomass yield (11.46 and 14.95%), tillers per plant (13.57and 20.89%) and spikes per plant (12.65 and 19.50%), respectively.This indicated that selection will be effective based on these characters and their phenotypic expression would be good indication of the genotypic potential.Similar results of moderate PCV and GCV has been reported for 1000 kernel weight and grain yield in wheat (Gezahegn et al., 2015).The characters days to maturity, grain filling period, plant height, hectoliter weight and harvest index were grouped under low phenotypic and genotypic coefficients of variation.The result is in line with the finding of Mohammed et al. (2011) andGezahegn et al. (2015) for characters days to maturity, number of spikelets per spike and test weight showed low PCV and GCV (<5%) values.Mitsiwa (2013) also reported low PCV and GCV for grain filling period (1.82 and 1.59%) and days to maturity (3.63 and 3.50%), respectively.
For combined analysis the estimated heritability for the studied traits is presented in Table 7.The heritability values ranged from 8.51 to 82.85 %.High heritability (>80%) was computed for days to heading and spike length indicating selection could be fairly easy and improvement is possible using these traits in breeding.Adhiena (2015) reported high heritability for days to heading which support this finding.Similarly, Gergana and Bozhidar (2015) and Desheva and Cholakov (2014) reported high heritability value for spike length.In the same year Gergana and Bozhidar (2015) reported high estimates of heritability (above 60%) for five characters spike length with awns (74.93%), spike length without awns (80.48%), spikelets per spike (63.96%), grain weight per spike (67.47)% and thousand grain weight (73.51%) in their study on variability, heritability, genetic advance and associations among characters in emmer wheat genotypes.Medium to moderate heritability was recorded for days to maturity (75.65%), plant height (61.99%), 1000 kernel weight (69.96%), hectoliter weight (49.79%), spikelets per spike (61.19%) and kernels per spike (47.51%).Arati et al. (2015), Navin et al. (2014) and Ali et al. (2008) also reported high heritability estimates for grain yield per plant, number of seeds per spike, plant height and 1000 seed weight which support the present findings.Low heritability was recorded for the characters grain yield (29.28%), harvest index (28.21%),tiller per plant (8.51%) and spikes per plant (20.66%).This result is contradicted with the finding of Gergana and Bozhidar (2015) who reported high heritability for tillers per plant and spikes per plant.Selection may be considerably difficult or virtually impractical for less heritable due to the masking effect of the environment.
Estimates of expected genetic advance: Genetic advance as percent mean was categorized as low (0-10%), moderate (10-20%) and high 20% and above (Johnson et al., 1955).Accordingly, the expected genetic advance as the percent of means expressed as a percentage of the mean ranged from 3.46% for hectoliter weight to 28.45% for gain yield at Tongo (Table 5).High GAM was observed in grain yield (28.45%) and harvest index (24.28%).In accordance with finding of Arati et al. (2015) and Navin et al. (2014) who reported similar result with this study.GAM was moderate for days to heading (19.71%), 1000 kernel weight (17.63%), above ground biomass yield (10.88%), tillers per plant (13.44%), spikes per plant (16.45%), spikelets per spike (15.73%) and kernels per spike (10.98%).GAM was low for days to maturity, grain filling period, plant height and hectoliter weight.
At Kulumsa the expected genetic advance expressed as a percentage of the mean ranged from 0.17% for harvest index to 19.14% for 1000 kernel weight (Table 6), indicating that selecting the top 5% of the base population could result in an advance of 0.17 to 19.14% over the respective population mean.GAM was moderate for 1000 kernel weight plot (19.14%) followed by spikelets per spike, tillers per plant, above ground biomass yield, spikes per plant, spikes length, days to heading, plant height in conformity with the findings by Gezahegn et al. (2015) and Awale.et al. (2013) for the traits, 1000 kernel weight per plot (20.13%), grain yield (14.85%), days to 50% heading (14.70%) and number of grains per plant(14.65%)except for harvest index (15.68%).
Genetic advance expressed as percentage of mean from the combined analysis (Table 7) was moderate for days to heading (14.56%), 1000 kernel weight (15.31%) and spikes length (14.62).Gergana and Bozhidar (2015) reported moderate for spikes length (31.83%) and thousand grains weight (33.76%).Mohammed et al. (2011) and Navin et al. (2014) also reported high genetic advance (as percentage of mean) for grain yield and yield related traits like thousand kernel weight and harvest index which are similar with the present finding.Awale et al. (2013) reported high genetic advance for days to heading, grain filling period, number of tillers, 1000 seed weight, plant height, peduncle length and spike length which are similar with this study except for number of tillers and grain filling period.This suggested selection could be effective in genotypes for these traits and the possibility of improving bread wheat grain yield through direct selection for grain yield related traits.Low genetic advance as percent of the means were recorded for the characters grain yield, harvest index, plant height, spikes per plant, spikelets per spike and kernels per spike, days to maturity, hectoliter weight and tillers per plant.The result is not in line with finding of Gergana and Bozhidar (2015) who reported high genetic advance as a percent of the mean for the characters, number of productive tillers per plant and plant height which are low in this study.Characters like days to heading, 1000 seed weight and spike length showed high heritability coupled with moderate genetic advance.Therefore, these characters should be given top priority during selection breeding in wheat.The results are in accordance with reports of Navin et al. (2014) for the character 1000 kernels weight and Desheva and Cholakov (2014) for spike length indicated that heritability was due to additive gene effects and selection may be effective in early generations for these traits.Gezahegn et al. (2015) reported that high heritability couple with moderate genetic advance as percent of mean for days to 50% heading (82.06 and 14.70%), 1000 kernel weight (74.28 and 20.13%), plant height (69.43 and 10.27%) and spike length (63.66 and 10.34%), respectively, which support the present study.High heritability associated with low genetic advance was exhibited by days to maturity (85.93 and 9.26).This may be because of predominance of non-additive gene action in the expression of this character.The high heritability of these traits was due to favorable influence of environment rather than genotypic and selection for these traits may not be rewarding.

Conclusion
The study revealed the existence of significant genetic variability among the tested genotypes and heritability for different traits confirmed possibility to increase wheat productivity in target area.Attention should be given for traits which has moderate to high heritability and genetic advance in order to bring an effective response of grain yield improvement.Hence, selection and hybridization on those genotypes based on the trait with high GCV, heritability and genetic advance can be recommended for farther yield improvement of bread wheat at respective location.
genetic advance as percent mean; GA=genetic advance under selection, and X = Mean of the population in which selection employed.

Table 1 .
Experimental site analysis.

Table 2 .
The listed of bread wheat genotypes to be studied.

Table 3 .
Mean squares of the 14 traits of bread wheat genotypes tested at Kulumsa and Tongo in 2015/16.

Table 4 .
Mean squares of the 12 traits of bread wheat genotypes tested across location in 2015/16.

Table 5 .
Range , mean, variance, broad sense heritability, genotypic and phenotypic coefficient of variability, genetic advance as of mean for the 14 characters of bread wheat genotypes tested at Tongo in 2015/16.

Table 6 .
Range, mean, variance, broad sense heritability, genotypic and phenotypic coefficient of variability, genetic advance as of mean for the 14 characters of bread wheat genotypes tested at Kulumsa in 2015/16.

Table 7 .
Range, mean, variance, broad sense heritability, genotypic and phenotypic coefficient of variability, genetic advance as of mean for the 12 characters of bread wheat genotypes tested at across location in 2015/16.