International Journal of Microbiology and Biotechnology
Volume 1, Issue 1, November 2016, Pages: 1-9

Genetic Variability, Heritability and Genetic Advance in Bread Wheat (Triticumaestivum.L) Genotypes at Gurage Zone, Ethiopia

Kifle Zerga1, *, Firew Mekbib2, Tadesse Dessalegn3

1Department of Horticulture, College of Agriculture and Natural Resource Management, Wolkite University, Wolkite, Ethiopia

2School of Plant Science, College of Agriculture and Environmental Science, Haramaya University, Dire Dawa, Ethiopia

3Department of Plant Science, College of Agriculture, Bahirdar University, Bahirdar, Ethiopia

Email address:

(K. Zerga)
(F. Mekbib)
(T. Dessalegn)

*Corresponding author

To cite this article:

Kifle Zerga, Firew Mekbib, Tadesse Dessalegn. Genetic Variability, Heritability and Genetic Advance in Bread Wheat (Triticumaestivum.L) Genotypes at Gurage Zone, Ethiopia. International Journal of Microbiology and Biotechnology. Vol. 1, No. 1, 2016, pp. 1-9. doi: 10.11648/j.ijmb.20160101.11

Received: July 14, 2016; Accepted: July 26, 2016; Published: November 3, 2016


Abstract: In Ethiopia, a number of improved bread wheat (TriticumaestivumL.) varieties have been released by different research centers for the existence of genetic variability, heritability and genetic advance. However nothing has been done at Gurage Zone and therefore a total of twenty five bread wheat (TriticumaestivumL.) genotypes were evaluated for genetic variability, heritability and genetic advance at Gurage zone at two different environments. The genotypes were grown in randomized complete block design. Data were collected on 13 agronomic characters. Analysis of variance at each location showed highly significant (P≤ 0.01) difference for all characters, except harvest index at Fereziye, and harvest index and days to heading at Kotergedra. The combined analysis of variance over the two locations showed highly significant (P≤0.01) variations among the genotypes in all studied traits. The medium values of PCV and GCV were recorded from above ground biomass and tillers per plant across two locations. High estimates of heritability across a location were obtained in the case of spikelets per spike (98.06%), 1000 kernel weight (93.01%) and plant height (85.08%). Across location high values of genetic advance was obtained from above ground biomass (22.83%) and tillers per plant (21.61%).

Keywords: Wheat, Genetic Advance, GCV, PCV, Heritability


1. Introduction

Wheat, a self-pollinating annual plant in the true grass family Gramineae (Poaceae), is extensively grown as staple food sources in the world [20]. It is exclusively produced under rain fed conditions in meher and belg (long and short rainy seasons), respectively.

China is the leading wheat producing country in the world with a production of 121.023, million tons or 18% of the world total. India, United States of America, France and Russian Federation follow respectively. Wheat is a strategic crop in Africa, as an essential element of food security, yet African countries spend more on importing wheat every year. North Africa is the largest wheat producing part of Africa and the production is estimated at about 20.2 million tons. Egypt is the leading wheat producing country in Africa, Morocco, Algeria and Tunisia are the respective countries [10].

Wheat is grown at an altitude ranging from 1500 to 3000 m.a.s.l, between 6-160 N latitude and 35-420 E longitude in our country. The most suitable agro-ecological zones, however, fall between 1900 and 2700 m.a.s.l [1]. Wheat in Ethiopia is an important cereal crop and it ranks fourth in total area coverage next to teff, maize and sorghum; also fourth in total production next to maize, teff and sorghum. 4.23 million tons of wheat is produced on an area of 1.7 million ha and about 4.6 million farmers are involved. Oromia, Amhara, SNNP and Tigray are the major wheat producing regions in the country with area coverage of 875641.45, 529609.63, 137294.72 and 108865.39 ha respectively. Furthermore, 47259 farmers were involved with unestimated area coverage in Gurage Zone in 2015 main production season [7].

In Ethiopia, bread wheat improvement has started in 1949 and up to now many varieties have been released by the national and regional research institutes. However, those varieties are not widely distributed to all parts of the country. This is because of several constraints including the remoteness and inaccessibility of the growing areas that limited testing of adaptability and yields of the varieties in such areas. It is necessary to evaluate varieties for the intended growing regions since varieties are recommended as high yielding after evaluating at a few representative wheat growing areas. At Gurage Zone of South Nation, Nationality Region is one of the areas where improved varieties are not widely distributed so far, most probably due to the above indicated problems. Particularly, the potential of the area to wheat crop is not exploited due to lack of improved varieties. There is no detailed information indicating the adaptability and production status of the improved bread wheat varieties in the area. Therefore, it is important to evaluate those varieties at Gurage Zone to study the genetic variability, heritability and genetic advance since it provides information that can be utilized to improve wheat yield through breeding and to identify high yielding and more adaptable varieties to improve productivity and production of wheat.

Variability is the occurrence of differences among individuals due to differences in their genetic composition and/or the environment in which they are raised [2], [9]. Heritability in broad sense can be defined as the proportion of the total genetic variability to the total phenotypic variance [2]. Heritability estimates can be used to predict genetic advance under selection so that breeders can anticipate improvement from different types and intensities of selection. Heritability values (estimates) vary not only within the environment but also with the nature of the test population [11]. In view of this the present study was undertaken with the following specific objectives to:

(1)  Quantify the variability among genotypes for yield and yield related traits.

(2)  Estimate heritability and genetic advance among genotypes for yield and yield contributing characters.

2. Materials and Methods

2.1. Experimental Materials

Experimental materials comprised of twenty five bread wheat genotypes released from different agricultural research centers (Table 1).

Table 1. List of Genotypes.

Entry Variety Name Source Center Year of Release
1 ETBW 5879 Kulumsa 2014
2 ETBW 6095 Kulumsa 2014
3 WORRAKATTA/PASTOR Sinana 2014
4 UTQUE96/3/PYN/BAU//MILLAN Sinana 2014
5 Hidasse Kulumsa 2012
6 Ogolcho Kulumsa 2012
7 Hoggana Kulumsa 2011
8 Hulluka Kulumsa 2012
9 Mekelle-3 Mekelle 2012
10 Mekelle-4 Mekelle 2013
11 Shorima Kulumsa 2011
12 Mekelle-1 Mekelle 2012
13 Mekelle-2 Mekelle 2011
14 Ga'ambo Werer 2011
15 Kakaba Kulumsa 2010
16 Danda'a Kulumsa 2010
17 Gassay Adet 2007
18 Alidoro Holleta 2007
19 Digelu Kulumsa 2005
20 Tay Adet 2005
21 Sofumar Sinana 1999
22 Mada-Wolabu Sinana 1999
23 Pavon-76 Kulumsa 1982
24 Jefferson Kulumsa 2012
25 King Bird Kulumsa 2014

2.2. Experimental Design and Field Management

The genotypes were planted in early July 2015 at Wolkite University stations (Kotergedra and Fereziye) (Table 2 & Fig. 1). The genotypes were grown in randomized complete block design (RCBD) with three replications. Each plot consisted of six rows spaced 20cm X 2.5m long. The plot area was 3m2 (2.5m X 1.2m). A 1.5 meter distance was maintained between replication and 50cm between plots used for both sites.

Fertilizers (both N and P2O5) was applied at the rate of 150 kg/ha urea and 100 kg/ha DAP at the time of planting and tillering. Seeding was done at the rate of 125Kg/ha. Seed and fertilizer was drilled uniformly by hand. Weeding and other agronomic practice was carried out as per recommendations of the respective sites.

Table 2. Location and descriptions of weather conditions for the two testing sites.

Sites Seasonal Temperature (°C) Soil type Soil PH Seasonal Rainfall (mm) Location
Max Min Latitude Longtiude Altitude
Fereziye 24.37 10.2 EutricNitisols 5.4 1336.8 8.20N 37.90E 1980 masl
Kotergedra 23 8 EutricNitisols 5.7 1450 8.050N 37.50E 2600 masl

Figure 1. Geographical Map of Gurage Zone for the study area.

2.3. Data Collection

The data on the following attributes was collected on the basis of the central four rows in each plot.

1.  Days to heading (DTH): The number of days from date of sowing to the stage where 75% of the spikes have fully emerged.

2.  Days to maturity (DTM): The number of days from sowing to the stage when 90% of the plants in a plot have reached physiological maturity.

3.  Grain filling period (GFP): The number of days from heading to maturity, i.e. the number of days to maturity minus the number of days to heading.

4.  Grain yield (GY): Grain yield in grams obtained from the central four rows of each plot and converted to kilograms per hectare at 12.5% moisture content.

5.  1000-kernel weight (TKW): Weight of 1000 seeds in gram.

6.  Above ground biomass (AGB): The plants within the four central rows were harvested and weighed in grams.

7.  Harvest index (HI): On a plot basis, the ratio of dried grain weight to the dried above ground biomass weight multiplied by 100.

Ten plants were randomly selected from the four central rows for recording the following observations:

1.  Tillers/plant (TPP): The average number of tillers

2.  Plant height (PHT): The average height in cm from ground level to the tip of the spike.

3.  Kernels per spike (KPS): The average number of kernels per spike.

4.  Spikelet per spike (SkPS): The average number of spikelet's per spike.

5.  Spike length (SL): The average spike length in cm from its base to the tip.

6.  Spikes per plant (SPP): The average number fertile spikes per plant including tillers.

2.4. Data Analysis

The data were recorded were subjected to analysis by using General Linear Model procedure and the statistical package SAS version 9.1 was used for the following statistical procedures.

2.5. Analysis of Variance

The analysis of variance was conducted using randomized complete block design (RCBD). Before computing the combined analysis, error variance homogeneity test was performed using the procedure suggested by [12]. In the combined analysis of variance, locations were considered random and genotypes were considered fixed. The least significant difference (LSD) was used to compare two means at the 5% and 1% level of significance.

Analysis of variance of randomized complete block design for each test location was computed using the following mathematical model:

Where: Yij =the observed value of the trait Y for the ith genotype in jth replication

µ= the general mean of trait Y

rj = the effect of jth replication

gi= the effect of ith genotypes and

εij= the experimental error associated with the trait y for the ithgenotype in jth replication.

Analysis of variance of randomized complete block design for combined location was computed using the following mathematical model:

Where: = observed value of genotype i in block k of location j

µ = 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

Least significant Difference (LSD) among genotypes and coefficient of variation in percent (CV%) for all characters was computed [12].

LSD= α (2σ2e/r)½

CV%= [(σ2e) ½/) x100] where, α = t- value at 5% and 1% probability level.

2.6. Estimation of Genetic Parameters

The genotypic and phenotypic variance components and coefficient of phenotypic and genotypic variability was estimated as follows:

Where: = mean square due to genotypes,

= error mean square

r = the number of replication

Variance components for the data combined over locations were computed in a similar fashion as for individual locations by using the following formula [14], [15].

 =

 =

=

Where: σ2gl = variance of genotype by location interaction

MSe = error mean square

MSgl = genotype by location interaction mean square

MSg = genotype mean square

r = replication

l = location

Where: = grand mean of character

Heritability (H2): heritability in broad sense for all characters was computed using the formula given by [9]. Broad sense heritability (h2) expressed as a percentage of the ratio of the genotypic variance (σ2g) to the phenotypic variance (σ2p) and was estimated on genotype mean base as described by [2] as:

Where: H2 = heritability in broad sense

 = Phenotypic variance

 = Genotypic variance

Genetic advance in absolute unit (GA) and present of the mean (GAM), assuming selection of superior 5% of the genotypes was estimated in accordance with the methods illustrated by [14] as:

Where: K=the standardized selection differential at 5% selection intensity (k=2.06)

sp =phenotypic standard deviation on mean basis

H2=heritability in broad sense

Genetic advance as percent of mean was calculated to compare the extent of predicted advance of different traits under selection, using the following formula.

Where: GAM= genetic advance as percent mean

GA= genetic advance under selection

= Mean of the population in which selection employed

3. Results and Discussion

3.1. Analysis of Variance

The analysis of variance for different characters at Fereziye and Kotergedra are presented in Appendices Table A1 and A2, respectively. Genotypes differed highly significantly (p≤0.01) at Fereziye in all measured characters, except harvest index. Highly significant (p≤0.01) variation was observed for days to maturity, 1000 kernel weight, above ground biomass, plant height, kernels per spike, spikelets per spike, spike length and spikes per plant and significant (p≤0.05) variation was observed for grain filling period, grain yield and tillers per plant but non-significant variation was observed for days to heading and harvest index at Kotergedra. [17], observed non-significant differences among bread wheat genotypes for days to maturity, spike length, fertile tillers, spikelets per spike, grains per spike and grain yield per plot in contrary to the present study. [22], reported that significant differences among genotypes for all the characters to support this result.

Before pooling of the data across environments, the ANOVA assumption was tested for its homogeneity using Bartlet test. The ratio of the highest error means square and the smallest error mean square the value was compared with the F table and, if it is significant no need of pooling across the environments [12]. Hence, the test of homogeneity of variance showed uniformity except kernel per spike, grain yield and harvest index and the data were pooled across environments and analyzed. The results of the combined analysis of variance across the two locations are presented in Table 3. The combined analysis of variance over the two locations showed highly significant (P≤0.01) variations among the genotypes in all studied traits. However, location effects were highly significant (P≤0.01) for days to heading, days to maturity, grain filling period, above ground biomass, tillers per plant, plant height and spikes per plant.

Locations x genotype interaction was highly significant (P≤0.01), for days to heading, days to maturity, grain filling period, above ground biomass, tillers per plant, spike length and spikes per plant and plant height (p≤0.05). However significant interaction was not evident for 1000 kernel weight and spikelets per spike.

Table 3. Analysis of variance for 10 characters across the two locations.

Source Loc (df=1) Rep (df=2) Genotype (df=24) Loc*Genotype(df=24)  Error (df=98) CV%
Days to heading 7017.80** 14.13 70.87** 68.38** 7.79 3.86
Days to maturity 40574.00** 51.81 231.04** 125.15** 17.22 3.14
Grain filling period 13843.00** 25.85 95.68** 48.82** 20.43 7.55
1000-kernel weight 8.17 7.17 67.06** 4.69 7.68 6.33
Above ground biomass 497078424.00** 3438273.50 15499552.40** 4597724.30** 1575433.00 12.43
Tillers/plant 15.68** 4.84* 9.92** 3.87** 1.03 13.69
Plant height 3864.87** 29.74 166.67** 24.86* 12.52 5.77
Spikelets per spike 0.00 0.06 12.35** 0.24 0.94 5.88
Spike length 1.06 0.37 3.95** 0.98** 0.36 7.08
Spikes per plant 23.36** 3.45* 3.20** 2.03** 0.78 12.36

*, ** Indicate significant and highly significant at the 0.05 and 0.01 probability levels, respectively

3.2. Phenotypic and Genotypic Variations

Estimated variance components, phenotypic coefficient of variability (PCV) and genotypic coefficient of variability (GCV) of the characters studied at Fereziye, Kotergedra and combined locations are presented in Tables 4, 5 and 6 respectively.

According to [8], 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% as medium. High PCV values were observed at Fereziye for spikes per plant (29.80%), grain yield (26.61%), tillers per plant (26.33%), above ground biomass (26.03%) and kernels per spike (20.99%). Highest PCV was also reported by [18] for number of tillers (40.4%) followed by grain yield per plant (16.97%), spike length (12.99%), plant height (10.37%) and days to flowering (7.69%). The PCV values for grain filling period (13.57%), spike length (11.77%), days to heading (10.27%) and plant height (10.12%) were medium at Fereziye. Days to maturity (9.54%) and 1000 kernel weight (9.52%) had low PCV values. Spikes per plant (26.15%), grain yield (25.29%), tillers per plant (23.49%) and above ground biomass (23.13%) had higher GCV values at Fereziye. The high PCV and GCV observed indicate their high variability that in turn offers good scope for selection. [18], has reported GCV of 37%) observed for tiller number per plant.

Kernels per spike (11.39%) and spike length (10.10%) had medium GCV values. Days to heading (9.67%), grain filling period (9.64%), plant height (8.86%), spikelets per spike (8.61%), days to maturity (8.51%) and 1000 kernel weight (7.11%) had low GCV values at Fereziye. Low GCV and PCV values indicate lesser scope of selection as they are under the influence of environment. Therefore, the high values of PCV and GCV for grain yield, spikes per plant, above ground biomass and tillers per plant indicated the existence of high variability and therefore, can respond positively to selection at Fereziye. Similar result was found by [21], who reported high values of GCV and PCV recorded for tiller number and grain yield. Higher magnitudes of GCV and PCV were recorded for grain yield, biological yield, productive tillers per plant and plant height [5].

Higher PCV and GCV values were not estimated at Kotergedra. Medium PCV values on tillers per plant (19.34%), spikes per plant (19.19%), above ground biomass (17.14%), grain yield (14.75%), kernels per spike (12.46%), spike length (12.43%) and plant height (10.59%) were estimated at Kotergedra that in turn offers good scope for selection. [19], reported medium PCV and GCV obtained from plant height and kernels number per spike.

Spikelets per spike (9.66%), 1000 kernel weight (9.42%), grain filling period (7.73%) and days to maturity (3.02%) showed low PCV values at Kotergedra. Similarly [19], reported low PCV values obtained from days to heading, days to maturity, spikelets per spike, spike length and 1000 kernel weight. In the present study medium GCV values were estimated on spikes per plant (12.97%), above ground biomass (11.74%), tillers per plant (11.66%) and kernels per spike (10.89%) that in turn offers good scope for selection. Low GCV values on grain yield (9.64%), spike length (9.54%), plant height (8.11%), spikelets per spike (7.52%), 1000 kernel weight (6.87%), grain filling period (4.79%) and days to maturity (2.11%) at Kotergedra indicating that less scope of selection as they were under the influence of environment. Similar studies by [5] reported that number of spikelets per spike, days to heading, test weight, harvest index, grain filling period and days to maturity exhibited least genotypic and phenotypic coefficients of variation.

Estimates of phenotypic coefficients of variation ranged from 9.52% (1000 kernel weight) to 29.80% (spikes per plant). The genotypic coefficients of variation also ranged from 7.11% (1000 kernel weight) to 26.15% (spikes per plant) at Fereziye. PCV value ranged from 3.02% (days to maturity) to 19.34% (tillers per plant) and the GCV values ranged from 2.11% (days to maturity) to 12.97% (spikes per plant) at Kotergedra.

For combined analysis PCV ranged from 4.69% to 17.37% and GCV ranged from 0.89% to 13.56%. With this range medium value of PCV was obtained by tillers per plant (17.37%) followed by above ground biomass (15.91%) and spikes per plant (10.21%). Low PCV values obtained from spike length (9.52%), spikelets per spike (8.69%), plant height (8.59%), 1000 kernel weight (7.64%), grain filling period (6.67%), days to heading (4.75%) and days to maturity (4.69%). Medium GCV values were obtained for tillers per plant (13.56%) and above ground biomass (13.34%). Low GCV was obtained for spikelets per spike (8.61%), spike length (8.26%), plant height (7.93%), 1000 kernel weight (7.37%), spikes per plant (6.18%), grain filling period (4.67%), days to maturity (3.18%) and days to heading (0.89%). [16], has reported low GCV obtained for days to maturity (7.91%) and this supports this result. The medium values of PCV and GCV in the case of above ground biomass and tillers per plant reveal the existence of high amount of variability in the case of these characters, so that they can respond positively to selection across a location.

3.3. Heritability

According to [23], if heritability of a character is very high, say 80% or more, selection for such characters could be fairly easy because there would be a close correspondence between genotype and phenotype due to a relatively smaller contribution of environment to phenotype. But, for a character with low heritability, say less than 40%, and selection may be considerably difficult or virtually impractical due to the masking effect of the environment on genotypic effects. Estimated heritability in the case of the characters studied are presented in Tables 4, 5 and 6.

Heritability estimates are expected to be lower in poor environments where heritability is concealed due to a greater genotype x environment interaction component. Lower heritability was obtained for kernels per spike (29.45%) at Fereziye. Tillers per plant (36.34%) and grain filling period (38.37%) had lower heritability at Kotergedra and therefore there is less scope of selection in the case of such characters.

Grain filling period (50.50%), 1000 kernel weight (55.83%), spikelets per spike (68.76%), spikes per plant (76.97%), spike length (73.65%), plant height (76.67%), tillers per plant (79.61%) and days to maturity (79.67%) had medium heritability at Fereziye. Days to maturity (48.84%), grain yield (42.74%), 1000 kernel weight (53.16%), above ground biomass (46.86%), plant height (58.57%), spikelets per spike (60.56%), spike length (58.87%), spikes per plant (45.68%) and kernels per spike (76.40%) at Kotergedra had medium heritability estimate indicating low influence of external environment. At Fereziye higher heritability values were found for grain yield (90.33%) and days to heading (88.64%) and therefore these characters show potential to respond positively to selection due to additive gene effect and low environmental influence. Similar results were obtained by [3], for higher heritability of days to heading (89.08%) and grain yield plot-1 (84.64%), indicating the possibility of success in selection. Grain filling period (62.72%), days to maturity (76.17%), plant height (74.04%), number of tillers plant-1 (69.23%), number of grains spike-1 (68.34%) and 1000 grain weight (60.23%) showed moderately higher heritability.

Higher estimates of heritability across a location was obtained for spikelets per spike (98.06%), 1000 kernel weight (93.01%) and plant height (85.08%) indicating their potential to respond positively to selection across the locations. [19], reported high estimate of heritability obtained from plant height (98.3%). [4], [6] and [3] also reported high level of heritability for days to heading, days to maturity, grain filling period, plant height and grain yield/ plot and moderate heritability estimates for number of spikelets per spike and tillers per plant. Medium heritability was obtained for spike length (75.19%), above ground biomass (70.34%), tillers per plant (60.99%), grain filling period (48.98%) and days to maturity (45.83%). Low estimates of heritability have been obtained for spikes per plant (36.56%) and days to heading (3.51%) from the present study.

3.4. Estimates of Expected Genetic Advance

The estimated genetic advance and expected genetic advance as percent of the mean for the characters considered at Fereziye, Kotergedra and combined location are also presented in Tables 4, 5 and 6, respectively. Expected genetic advance as percent of the mean was generally medium for most characters at both locations. [8], classified genetic advance as percent of mean as low (<10%), moderate (10-20%) and high (>20%). Among the characters, the highest genetic advance as percent of mean was recorded for grain yield (49.04%), spikes per plant (46.79%), tillers per plant (42.76%), above ground biomass (41.95%) and medium genetic advance was recorded for days to heading (18.57%), spike length (17.68%), plant height (15.83%) and days to maturity (15.50%) at Fereziye. [18], reported that high genetic advance was observed for grain yield per plant (32.84%), plant height (20.35%), spike length (16.30%) and flowering day (14.36%). In the case of kernels per spike (19.42%), spikes per plant (17.88%) and above ground biomass (16.39%) it was moderately high when compared to other traits at Kotergedra. Emphasis should be given to these characters which showed high genetic advance for formulating reliable selection indices for the development of high yielding bread wheat genotypes.

Estimates of genetic advance as percentage of mean ranged from 10.84% for 1000 kernel weight to 49.04% for grain yield at Fereziye, and from 3.01% for days to maturity to 19.42% for kernels per spike at Kotergedra. Generally, grain filling period and days to maturity at Kotergedra depicted genetic advance values lower than 10%. [3], reported high genetic advance as percent of mean for days to heading, grain filling period, number of tillers, 1000 seed weight, plant height, peduncle length and spike length in contradiction to the present study.

Across locations high values of genetic advance were obtained for above ground biomass (22.83%) and tillers per plant (21.61%). Medium genetic advance was obtained from spikelets per spike (17.49%), plant height (14.92%), spike length (14.61%) and 1000 kernel weight (14.49%) but minimum genetic advance was obtained for spikes per plant (7.62%), grain filling period (6.66%), days to maturity (4.39%) and days to heading (0.34%). Therefore tillers per plant and above ground biomass were positively selected across different environments because of their high genetic advance. [13], has reported high genetic advance for biological yield (46.6%). [19], reported high expected genetic advance estimates for plant height, kernel number per spike, productive tillers and thousand kernel weight. According to [14], high heritability estimates along with high genetic advance is usually more helpful in predicting gain under selection than heritability estimates alone.

Table 4. Range, mean, variance, broad sense heritability, genotypic and phenotypic coefficients of variability and genetic advance as percent of mean for the 12 characters of bread wheat genotypes tested at Fereziye.

Characters Range Mean±SE mean σ2g σ2 p H2(%) GCV% PCV% GA GA%
Days to heading 53.00-75.33 65.53±0.77 40.14 45.28 88.64 9.67 10.27 12.17 18.57
Days to maturity 101.00-135.33 115.80±1.26 97.16 121.96 79.67 8.51 9.54 17.95 15.50
Grain filling period 40.00-61.33 50.27±0.78 23.50 46.55 50.50 9.64 13.57 7.03 13.98
Grain yield 1983.30-4941.70 2941.00±86.23 515429.06 570615.87 90.33 25.29 26.61 1391.96 49.04
1000-kernel weight 35.00-50.00 43.53±0.47 9.58 17.17 55.83 7.11 9.52 4.72 10.84
Above ground biomass 4858.30-12508.30 8281.20±246.18 3676343.17 4653064.67 79.01 23.13 26.03 3476.78 41.95
Tillers/plant 5.03-12.83 7.73±0.23 3.24 4.07 79.61 23.49 26.33 3.28 42.76
Plant height 54.70-82.57 66.40±0.77 34.63 45.17 76.67 8.86 10.12 10.51 15.83
Kernels per spike 35.83-66.00 47.92±1.16 29.80 101.17 29.45 11.39 20.99 6.04 12.61
Spikelets per spike 13.53-21.30 16.50±0.19 2.02 2.94 68.76 8.61 10.38 2.40 14.56
Spike length 6.73-11.30 8.61±0.12 0.76 1.03 73.65 10.10 11.77 1.52 17.68
Spikes per plant 4.03-11.83 6.69±0.12 3.03 3.94 76.97 26.15 29.80 3.12 46.79

σ2g=Genotypic Variance, σ2p=Phenotypic Variance, H2=Heritability, GCV%=Genotypic Coefficient of Variation, PCV%=Phenotypic Coefficient of variation, GA=Genetic Advance

Table 5. Range, mean, variance, broad sense heritability, genotypic and phenotypic coefficients of variability and genetic advance as percent of mean for the 11 characters of bread wheat genotypes tested at Kotergedra.

Characters Range Mean±SE mean σ2g σ2 p H2 (%) GCV% PCV% GA GA as%
Days to maturity 142.67-156.00 148.69±0.52 9.86 20.19 48.84 2.11 3.02 4.48 3.01
Grain filling period 64.33-78.00 69.48±0.62 11.06 28.82 38.37 4.79 7.73 4.20 6.05
Grain yield 3166.70-5366.70 3742.33±64.38 130230.67 304710.67 42.74 9.64 14.75 481.28 12.86
1000-kernel weight 33.33-53.33 44±0.47 9.13 17.17 53.16 6.87 9.42 4.49 10.21
Above ground biomass 9842.00-16992.00 11922±235.29 1957369.67 4176786.67 46.86 11.74 17.14 1953.81 16.39
Tillers/plant 5.40-10.43 7.08±0.16 0.68 1.88 36.34 11.66 19.34 1.02 14.34
Plant height 46.00-66.83 56.25±0.68 20.79 35.50 58.57 8.11 10.59 7.12 12.66
Kernels per spike 44.17-70.60 53.11±0.78 33.46 43.79 76.40 10.89 12.46 10.31 19.42
Spikelets per spike 14.20-21.40 16.51±0.18 1.54 2.54 60.56 7.52 9.66 1.97 11.93
Spike length 6.87-10.07 8.44±0.12 0.65 1.10 58.87 9.54 12.43 1.26 14.93
Spikes per plant 5.30-10.27 6.75±0.15 0.77 1.68 45.68 12.97 19.19 1.21 17.88

σ2g=Genotypic Variance, σ2p=Phenotypic Variance, H2=Heritability, GCV%=Genotypic Coefficient of Variation, PCV%=Phenotypic Coefficient of variation, GA=Genetic Advance

Table 6. Range, mean, variance, broad sense heritability, genotypic and phenotypic coefficients of variability and genetic advance as percent of mean for the 10 characters of bread wheat genotypes for combined locations.

Characters Range Mean±SE mean σ2gl σ2g σ2p H2(%) GCV PCV GA GA as% mean
Days to heading 64.67-78.17 72.37±0.71 20.20 0.42 11.81 3.51 0.89 4.75 0.25 0.34
Days to maturity 122.17-145.67 132.25±1.51 35.98 17.65 38.51 45.83 3.18 4.69 5.80 4.39
Grain filling period 53.67-69.5 59.87±0.93 9.46 7.81 15.95 48.98 4.67 6.67 3.99 6.66
1000-kernel weight 34.17-51.67 43.77±0.33 -1.00 10.40 11.18 93.01 7.37 7.64 6.34 14.49
Above ground biomass 7487.5-13920.8 10101.60±225.91 1007430.43 1816971.35 2583258.73 70.34 13.34 15.91 2306.19 22.83
Tillers/plant 5.62-11.63 7.40±0.14 0.95 1.01 1.65 60.99 13.56 17.37 1.60 21.61
Plant height 50.93-74.7 61.33±0.66 4.11 23.64 27.78 85.08 7.93 8.59 9.15 14.92
Spikelets per spike 13.87-21.35 16.50±0.13 -0.23 2.02 2.06 98.06 8.61 8.69 2.87 17.39
Spike length 6.85-10.45 8.52±0.08 0.21 0.50 0.66 75.19 8.26 9.52 1.24 14.61
Spikes per plant 5.98-9.18 7.15±0.10 0.42 0.20 0.53 36.56 6.18 10.21 0.54 7.62

σ2gl= Genotype x location variance, σ2g=Genotypic Variance, σ2p=Phenotypic Variance, H2=Heritability, GCV%=Genotypic Coefficient of Variation, PCV%=Phenotypic Coefficient of variation, GA=Genetic Advance

4. Conclusions

This study generally indicated that there was significance genetic variability among the genotypes studied. Thus, there is an opportunity in selection of superior varieties among advanced bread wheat genotypes through direct selection at the study locations as short term strategy rather than a lengthy crossing program.

Abbreviation

CSA Central Statistical Agency
FAOSTAT Food and Agriculture Organization Statistics
GCV Genotypic Coefficient of Variation
PCV Phenotypic Coefficient of Variation
RCBD Randomized Complete Block Design
SAS Statistical Analysis System
SNNP South Nation, Nationality and People

Acknowledgement

First I would like to thank Ethiopian Ministry of Education for financial support and Haramaya University for hosting the study. I am grateful to Wolkite University for giving me the opportunity to use research field, allocating the required labor, materials for field work and vehicle for the research field supervision.

Appendices

Table A1. Analysis of variance for 13 characters at Fereziye site.

Source Rep (df=2) Gen (df=24) Error (df=48) CV%
Days to heading 27.21** 125.56** 5.14 3.46
Days to maturity 29.64 316.28** 24.79 4.30
Grain filling period 32.69 93.56** 23.04 9.55
Grain yield (GY): 91975 1601474** 55186.81 7.98
1000-kernel weight 1.33 36.33** 7.58 6.33
Above ground biomass 668367 12005751** 976721.5 11.93
Harvest index 0.01 0.01 0.002 14.59
Tillers/plant 0.43 10.55** 0.83 11.79
Plant height 21.85 114.44** 10.54 4.89
Kernels per spike 67.57 160.77** 71.37 17.63
Spikelet per spike 0.07 6.97** 0.92 5.80
Spike length 0.35 2.54** 0.27 6.04
Spikes per plant 0.13 10.004** 0.91 14.23

*, ** Indicate significant and highly significant at the 0.05 and 0.01 probability levels, respectively

Table A2. Analysis of variance for 13 characters at Kotergedra site.

Source Rep (df=2) Gen (df=24) Error (df=48) CV%
Days to heading 2.29 13.69 10.11 4.01
Days to maturity 23.05 39.91** 10.33 2.16
Grain filling period 14.68 50.95* 17.76 6.06
Grain yield (GY): 532908 565172* 174480 11.16
1000-kernel weight 7 35.42** 8.04 6.44
Above ground biomass 3258775 8091526** 2219417 12.49
Harvest index 0.001 0.001 0.001 7.49
Tillers/plant 6.13* 3.23* 1.19 15.43
Plant height 15.65 77.08** 14.71 6.81
Kernels per spike 92.96** 110.70** 10.33 6.05
Spikelet per spike 0.06 5.62** 1.00 6.06
Spike length 0.52 2.39** 0.45 7.97
Spikes per plant 5.90** 3.21** 0.91 14.14

*, ** Indicate significant and highly significant at the 0.05 and 0.01 probability levels, respectively


References

  1. Abu T (2012). Grain and feed annual report. Grain report number: ET1201, Addis Ababa, Ethiopia (triticum aestivum L.).Research in Plant Biology, 3(1): 33-36.
  2. Allard RW (1960). Principles of Plant Breeding. John Wiley and Sons. Inc. New York. P430.
  3. Awale D,Takele D, Mohammed S (2013). Genetic variability and traits association in bread wheat (Triticum aestivum L.) genotypes.International Research Journal of Agricultural Sciences, 1(2): 19-29.
  4. Berhanu M (2004). Genetic variability and character associations in bread wheat (Triticum aestivumL.) genotypes developed for semiarid areas. An MSc Thesis Presented to the School of Graduate Studies of Alemaya University, Ethiopia.
  5. Bharat B, Gaurav SS, Ravindra K, Rishi P, Manoj P, Anant K, Sonu B, Nagar SS, Rahul VP (2013). Genetic Variability, Heritability and Genetic Advance in Bread Wheat (Triticum aestivum L.).Environment & Ecology,31 (2): 405—407.
  6. BirhanuB (2010). Assessment of Bread Wheat Production, Marketing and Selection of N-Efficient Bread Wheat (Tritium aestivum L.) Varieties for Higher Grain Yield and Quality in North Western Ethiopia.M.Sc. Thesis, Bahirdar University,Bahirdar, Ethiopia.
  7. CSA (Central Statistical Agency (2015). Agricultural Sample Survey. Report on Area, Production and Yield of Meher Season Crops for Private Peasant Holdings. Statistical Bulletin 578, CSA, Addis Ababa, Ethiopia.
  8. Deshmukh SNSN, Basu MS, Reddy PS (1986). Genetic variability, character association and path coefficient analysis of quantitative traits in Viginia bunch varieties of ground nut. Indian Journal of Agricultural Science, 56:515-518.
  9. Falconer DS, Trudy FC Mackay (1996). Introduction to Quantitative Genetics 4th ed.
  10. FAOSTAT (Food and Agricultural Organization Statistics) (2014). Last accessed by top five of anything, December 19thNew Work.
  11. Gemechu K (1996). Variability and interrelationship of some metric characters in Groundnut (Arachis hypogea L).MSc. thesis, AUA, Ethiopia.
  12. Gomez KA, Gomez AA (1984). Statistical Procedures for Agricultural Research, 2nd edit. John Wiley and Sons, New York.
  13. Jimera H, Hirpa L, Rao CP (2015). Genetic Variability, Character Association and Genetic Divergence in Barley (Hordeum vulgare L.) Genotypes Grown at Horo.
  14. Johnson HW, Robinson HF, Comstock RE (1955a). Estimates of genetic and environmental variability in soybeans. Agronomy Journal, 47:314-318.
  15. Johnson HW, Robinson HF, Comstock RE (1955b). Estimates of genetic and environmental variability in soybeans. Agronomy Journal, 47: 314-318.
  16. Kassahun A (2011). Variability for yield, yield related traits, protein content and association among traits of sorghum (sorghum bicolor (L) moench varieties in Wollo, Ethiopia, Msc thesis, Haramaya university, Haramaya, Ethiopia.
  17. Khan SA (2013). Genetic Variability and Heritability Estimates in F2 wheat Genotypes. International Journal of Agriculture and Crop Sciences, 5 (9):983-986.
  18. Mitsiwa A (2013). Genetic Variability and Association among agronomic Characters in some wheat (triticumaestivum) genotypes in Arsi zone, Oromia region, Ethiopia. M.sc.Thesis, Haramaya University, Haramaya, Ethiopia.
  19. Mohammed A, Amsalu A, Geremew B (2011). Genetic variability, heritability and trait associations indurum wheat (TriticumturgidumL. var. durum) genotypes.African Journal of Agricultural Research, 6(17): 3972-3979.
  20. Mollasadeghi V, Shahryari R (2011). Important morphological markers for improvement of yield in bread wheat. Advances. Environmental Biology, 5 (3): 538–542.
  21. Nukasani V, Potdukhe NR, Swati B, Deshmukh S, Shinde SM (2013). Genetic variability, correlation and path analysis in wheat.India Journal of Wheat Resurch, 5 (2): 48-51.
  22. Obsa C (2014). Genetic variability among bread wheat (triticum aestivuml.) Genotypes for growth characters yield and yield components in bore district, oromia regional state.M.Sc. Thesis,Haramaya University, Haramaya, Ethiopia.
  23. Singh BD (2001). Plant Breeding: Principles and methods. Kalyani publishers, New Delhi. 896p.

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