Greener Journal of Agricultural Sciences

Vol. 9(4), pp. 382-395, 2019

ISSN: 2276-7770

Copyright 2019, the copyright of this article is retained by the author(s)

DOI Link: https://doi.org/10.15580/GJAS.2019.4.090219165

https://gjournals.org/GJAS

 

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Genetic Variability Analysis of Agro-Morphological Traits among Bread Wheat (Triticum aestivum L.) Genotypes at Raya Valley of Southern Tigray, Ethiopia

 

 

1Girma Degife*; 2Wassu Mohammed; 3Kebebew Assefa

 

 

1 Ethiopian Institute of Agricultural Research, Mehoni Agricultural Research Centre, P.O. Box 47, Mehoni, Ethiopia

2Haramaya University, P.O. Box 138, Haramaya, Ethiopia

3 Ethiopian Institute of Agricultural Research, Debre-Zeit Agricultural Research Centre, Debre-Zeit, Ethiopia

 

ARTICLE INFO

ABSTRACT

 

Article No.: 090219165

Type: Research

DOI: 10.15580/GJAS.2019.4.090219165

 

 

Assessment of genetic variability in crop species is one of the major activities of plant breeding which helps to design breeding methods and/or selection of genotypes for further evaluation to meet the diversified goals. Therefore, this field experiment was conducted to assess the genetic variability in bread wheat genotypes. The field evaluation of 32 genotypes and 4 released varieties was conducted in 6 x 6 Triple Lattice Design at Mehoni Agricultural Research Station in 2017 cropping season. Results of analysis of variance revealed the presence of significant differences among genotypes for 12 quantitative traits. The variation observed among genotypes for grain yield ranged from 2.80 to 5.33 t ha-1. The four genotypes (QAFZAH-2/FERRIUG-2, KAUZ'S'/FLORKWA1//GOUMRIA-3, ETBW5957and SERI 82/SHUHA'S'//PASTOR-2 had yield advantage of 7.58 to 12.21% over the high yielding check variety, GAMBO (4.75 t ha-1). Phenotypic (PCV) and genotypic (GCV) coefficient of variations ranged from 5.67 (plant height) to 14.74 (fertile tiller per plot) and 7.06 (days to maturity) to 19.08% (fertile tiller per plot), respectively. Heritability (H2) in broad sense and genetic advance as percent of mean (GAM) ranged between 41.46 (biomass yield) and 89.08 (days to heading) and 8.56 (plant height) and 24.09% (harvest index), respectively. High H2 estimates coupled with moderate or high GAM for plant height, days to heading, days to maturity, spike length, grain yield, spikelets per spike and harvest index suggested the higher chance of improving these traits through selection of genotypes for high mean performance.

 

Submitted: 02/09/2019

Accepted: 22/09/2019

Published: 01/11/2019

 

*Corresponding Author

Girma Degife

E-mail: girmadegife12@ gmail.com

 

Keywords: Heritability; Genotypic Coefficient of Variations; Genetic Advance

 

 

 


1. INTRODUCTION

 

Bread wheat (Triticum aestivum L.) is a hexaploid species with (2n=6x=42) having AABBDD with A, B and D genomes) (Sleper and Poehlman, 2006). It is one of one of the oldest domesticated grain crops for 8000 years which native to Middle East. It has been the basic staple food of many regions of the world, while it is grown under both irrigated and rain-fed conditions. It belongs to family Poaceae (formerly Graminae) (Yadawad et al., 2015). World wheat production in 2017 was 743.2 million tons with average yield (3.34 t ha-1) and it accounts for nearly 30% of global cereal (FAO, 2017). The hexaploid bread wheat accounts for 95% of the total wheat production; most of the remaining 5% is from tetraploid durum wheat (T. turgidum subsp. durum, 2n=4x=28, AABB) (Shewry, 2009).

 

In Ethiopia, wheat is one of the major staple and strategic food security crops, and accounts for approximately 11% of the national calorie intake. Ethiopia is the second largest wheat producer in sub-Saharan Africa after South Africa. It is cultivated on 1.7 million hectares of land and has the production of 4.54 million tons with remain low productivity of 2.67 t ha-1 (CSA, 2017) in the country as compared to the world average yield (3.34 t ha-1) (FAO, 2017).

Wheat is producing in Tigray regions which relatively lager as compared to other crop of the area. The total wheat area and production in Tigray region was 107,724.17 hectares and 212,867.26 tons with the average yield of 1.98 t ha-1. Wheat stands second both in area and production among all crops followed by barely and tef. In the southern zone, the area coverage and productivity of wheat was 49,189.20 ha and 1,019,11.14 tons with the average yield of 2.072 t ha-1 respectively which is lower than from national (CSA, 2017).

 

Genetic improvement to develop varieties with high yield potential and resistance/tolerance to abiotic and biotic stresses, with acceptable end-use quality, is the most viable and environment-friendly option to sustainably increase wheat yield. Selection for grain yield improvement can only be effective if sufficient genetic variability is present in the genetic material (Ali et al., 2008). Therefore, knowledge of genetic variability present in a given crop species for the character under improvement is of paramount importance for the success of any plant breeding program (Bisne, 2009). Variability is the occurrence of differences among individuals due to differences in their genetic composition and/or the environment in which they are raised (Allard, 1960; Falconer and Mackay, 1996). In order to bring the heritable improvements in economic characters through selection and breeding, estimation of genetic parameters must be made before starting a program. There are different techniques available to compute the genetic parameters and the index of transmissibility of characters (Waqar- Ul-Haq et al., 2008).

 

The existence of variation alone in the population is not sufficient to improve desirable characters. High heritability is also needed to have better opportunity to select directly for the traits of interest. This is mainly because of the opportunity associated with high heritability incorrect identification and measurement of the genotypes based on phenotypic values and in avoiding errors in genotypic classification (Welsh, 1981). In breeding programs, selection is an integral part by which genotypes with high productivity in a given environment could be developed. However, selection for high yield is difficult because yield is the end product of components of several characteristics, polygenic inheritance, and highly influenced by environment and genotype x environment interaction.

 

In the Southern Zone of Tigray Regional State, at mid and highland areas, some genetic variability studies in wheat genotypes have been made to develop varieties (Adhiena et al., 2016). Raya Valley is the part of Southern Zone of Tigray Regional State; however, neither genetic variability studies in wheat genotypes nor introduction of improved wheat varieties were attempted. This is due to the insufficient rain fall to support the growth and yield production of wheat in the area and the largest part of the valley is at low altitude (1600 m.a.s.l.) experiencing warm to hot weather conditions. But, the dependence on rainfall alone in the area has in recent years been gradually replaced by supplemental irrigation and irrigated crop production. The number of farmers and investors using irrigation and supplemental irrigation is increasing. However, the absence of recommended varieties for the area remains as one of the major wheat production constraints in the area. Therefore, it is necessary to undertake research to develop wheat varieties for which genetic variability study is the first step. Thus, the present research was undertaken with the following objectives:-

 

          To assess genetic variability in selected bread wheat genotypes

 

 

3. MATERIALS AND METHODS

 

3.1. Description of the Experimental Area

 

The study was carried at the research station of Mehoni Agricultural Research Center (MhARC) under supplemental irrigation in the 2017 main cropping season. Mehoni is located in Raya Valley in the northern parts of Ethiopia about 668 km from countys capital city of Addis Ababa, and about 120 km South of Mekelle, the capital city of Tigray regional state, Northern Ethiopia. Geographically, the experimental site is located at 12 41'50'' N latitude and 39 42'08'' E longitude with an altitude of 1578 m.a.s.l. The site receives mean annual rainfall of 750 mm with an average minimum and maximum temperature of 22 C and 32 C, respectively. The soil type and textural class of the experimental area is verty soil and clay loam respectively with pH of 7.9-8.1 (Haileslassie et al., 2015).

 

When there was cessation of rainfall during the execution of the experiment, the crop was affected by moisture stress. During this time supplementary irrigate was provided using ground water resource to compensate the amount of water needed by the crop and also to provide the essential moisture for normal growth. This practice helps in alleviating the adverse effects of unfavorable rain patterns and improves crop yields. Therefore, amount of irrigation water to supplement to each experimental plot was directed using drip irrigation which was installed in the experimental site, and the amount of water was measured using soil squeezed method to test soil moisture manually by hand and the irrigating started from booting stage at 55 days after sowing.

 

3.2. Experimental Plant Materials

 

A total of 36 bread wheat genotypes including four standard checks (Table.1) obtained from the National Wheat Research Program specifically from Werer (WARC) and Kulumsa (KARC) Agricultural Research Centers. The genotypes were selected based on adaptation to low moisture stress and classified under lowland types. In this experiment, four released for moisture stress bread wheat varieties were included as standard checks


.

Table 1.List and pedigree of the thirty six bread wheat genotypes including four released varieties

G*

Genotype (Pedigree)

Origin

G1

HUBARA-3*2/SHUHA-4

CIMMYT/ICARDA

G2

Atila-7

CIMMYT/ICARDA

G3

ETBW5535

EIAR/KARC

G4

ETBW5957

EIAR/KARC

G5

ATILA/AWSEQ-4

CIMMYT/ICARDA

G6

FENTALLE (CHECK)

CIMMYT/ICARDA

G7

ADEL-2

CIMMYT/ICARDA

G8

DAJAJ-1//VEE'S'/SAKER'S'

CIMMYT/ICARDA

G9

PASTOR-2/HUBARA-5

CIMMYT/ICARDA

G10

HIDDAB/ATTILA-7

CIMMYT/ICARDA

G11

PASTOR-2/HUBARA-3

CIMMYT/ICARDA

G12

HUBARA-5/ANGI-1

CIMMYT/ICARDA

G13

GAMBO (CHECK)

CIMMYT/ICARDA

G14

ANGI-2/HUBARA-3

CIMMYT/ICARDA

G15

ETBW 5898 (SETII C1)

EIAR/KARC

G16

QAFZAH-2/FERRIUG-2 (SET II C1)

CIMMYT/ICARDA

G17

TAGANA

CIMMYT/ICARDA

G18

JNRB.5/PIFED

CIMMYT/ICARDA

G19

KINGBIRD (CHECK)

EIAR/KARC

G20

OGOLCHO (CHECK)

EIAR/KARC

G21

ETBW5955 SET II C2)

EIAR/KARC

G22

REYNA-28

CIMMYT/ICARDA

G23

ETBW5963(SET II C3)

EIAR/KARC

G24

PRINIA-1//NESMA*2/14-/3/DUCULA

CIMMYT/ICARDA

G25

FRANCOLIN #1/BAJ #1

CMSS09B00490S-099M-099Y-2WGY-0B

G26

KAUZ'S'/FLORKWA1//GOUMRIA-3

CIMMYT/ICARDA

G27

BJY/COC//PRL/BOW/3/BLOYKA-1

CIMMYT/ICARDA

G28

KUBSA

CIMMYT/ICARDA

G29

PBW343*2/KUKUNA//KIRITATI

CIMMYT/ICARDA

G30

HUBARA-2/QAFZAH-21//DOVIN-2

CIMMYT/ICARDA

G31

INQALAB 91*2/TUKURU//WHEAR

CIMMYT/ICARDA

G32

ATILA*2//CHIL/BUC*2/3KUKUNA

CIMMYT/ICARDA

G33

SERI 82/SHUHA'S'//PASTOR-2 (SET I)

CIMMYT/ICARDA

G34

florkwa2/6/saker's'/5/rbs /anza/3/kvz/hys/ymh/tob /4/bow

CIMMYT/ICARDA

G35

katila17/deek2/8vee's'/7/cebeco148/3/ron/cha//nor67/5/hk/38m

CIMMYT/ICARDA

G36

attila 50y//attila/bcn/3/star*3/ musk-3

CIMMYT/ICARDA

Source: G*= genotype code number used in the thesis.

 

 


3.3. Experimental Design and Layout

 

The field experiment was laid out in 6x6 triple Lattice design. The width of 1.2 m and length of 2.5 m and a total 3 m2 area was allocated for each plot in each incomplete block of replication. Each plot had six rows at the spacing of 20 cm between rows, 0.5 m path between plots, 1 m spacing between sub-blocks (incomplete block) and 1.5 m distance between replications with total area of 19.5 m x 41.6 m. The net plot size of experimental plot was 1 m x 2.5 m (2.5 m2) since the plants in the two outer most rows were treated as border plants and excluded.

 

3.4. Land Preparation, Sowing and Management

 

The experimental field was prepared by using farm tractor plough. It was ploughed two times, the first at the beginning of May the second at the middle of June and the third manually using labor worker during planting in early July 2017.

 

The full dose of blended fertilizer recommended for the study area are NPSzn (19% N, 38%P: 7% S and 2.5% Zn) at the rate of 100 kg ha-1 was applied as band application at planting time under supplemental irrigation. Nitrogen fertilizer in the form of Urea (46% N) at a rate of 150 kg ha-1 was applied in two split doses; with half applied two weeks after sowing and remaining half after early booting stage. The seeds (125 kg ha-1 rate) were sown by hand drilling in the rows as uniformly as possible. All other necessary field management practices were carried out as per the recommendations.

3.5. Data Collection

 

Data were collected both on plot and plant bases. The four central rows were used for data collection on plot basis, whereas 10 randomly selected plants from the four central rows of each plot were used for data collection on plant basis. Mean data of the 10 sample plants were used for data analyses.

 

 

3.5.1. Data collected on plot basis

 

Days to heading: The number of days from the date of sowing to the stage where 50% of the plants have fully emerged spikes.

Days to physiological maturity: The number of days from the date of sowing to the stage where 90% of the plants in the plot reached physiological maturity.

 

Grain filling period: The number of days from heading to maturity obtained by subtracting the number of days to heading from the number of days to maturity.

 

Thousand kernel weight (g): The weight of one thousand randomly taken kernels from each experimental plot and adjusted to 12.5% moisture content.

 

Grain yield plot-1(g plot-1): Grain yield in grams obtained from the central four rows of each plot, and adjusted to 12.5% moisture content.

Grain yieldha-1(t ha-1): Grain yield obtained from each plot was used to calculate grain yield in tons per hectare.

 

Biomass yields (t ha-1): The plants in the four central rows were harvested at the point of attachment to the ground, collected, sun-dried and weighed to obtain the biological yield.

 

Harvest index (HI%): Calculated on a plot basis, as the ratio of dried grain weight adjusted to 12.5% moisture content to the dried total above ground biomass weight and multiplied by 100.

 

3.5.2. Data collected on plant basis

 

Data for the following characters were recorded on 10 randomly selected plants from each experimental plot. The averages of the ten plants in each experimental plot were used for data analysis.

 

Plant height (cm): This was measured from the soil surface to the tip of the spike excluding the awns at physiological maturity.

 

Number of fertile tillers per plant: The average number of fertile tillers per plant

 

Kernels per spike: The average number of kernels per spike

 

Spikelet per spike: The average number of spikelet per spike

 

Spike length (cm): This was measured in cm from the base of the spike to the top of the last spikelet excluding the awns.


 


 

 


3.6. Data Analyses

 

3.6.1. Analysis of variance

 

The data were subjected to analysis of variance (ANOVA) using SAS statistical software version (9.2) (SAS, 2008) as per the expectations shown on Table 2. The comparison of mean performance of genotypes was done following the significance of mean squares using Duncans Multiple Range Test (DMRT).


 

 

Table 2. Analysis of variance in triple lattice design and expected mean square

 

Source of variation

DF

Sum of squares (SS)

Mean square

MS=

Computed F

Expected mean squares

Replication

r 1

SSR

MSR

Treatment (unadj.)

k2 1

SST (unadj.)

MST (unadj.)

 

σ2 () m σt2

Blocks within replication (adj.)

r(k-1)

SSB (adj.)

 

MSB (adj.)

 

Intra block error

(k-1)(rk-k-1)

SSE

MSE

 

RCB Error

(t-1) (r-1)

SSe

MSe

 

s2e

Total

rk2 1

SSTO

 

 

 

r = Number of replications. k2 = Number of treatments, k = Number of plots in a block, SS = Sum square, MS = Mean square, s2 = Variance, t = Number of genotypes, MSE = Mean squares for error and s2e= Error variance.

 

Relative efficience=according to (Gomez and Gomez (1984).

 

 


The characters that exhibited significant mean squares in general ANOVA were further subjected to genetic analyses. Phenotypic and genotypic variance and coefficient of variation, heritability, and genetic advance were computed using the Excel Microsoft Program. Genetic diversity was estimated from quantitative traits of genotypes using Euclidean distance computed by Statistical Software.

 

3.6.2. Phenotypic and genotypic variability

 

The phenotypic and genotypic variability of each quantitative trait were estimated as phenotypic and genotypic variances and coefficients of variation. The phenotypic and genotypic coefficients of variation were computed using the formula suggested by Burton and de Vane (1953) as follows.

 

Genotypic variance (σ2g)

 

Where, σ2g = genotypic variance, Msg= mean square of genotype, Mse = mean square of error, r = number of replications

 

Phenotypic Variance (σ2p) = σ2g + σ2e

Where, σ2g = Genotypic variance, σ2e = Environmental variance in which Environmental variance = mean square of error and

 

σ2p = phenotypic variance

 

 

 

 

 

Where: PCV= Phenotypic coefficient of variation, GCV= Genotypic coefficient of variation

= population mean of the character being evaluated

 

PCV and GCV values were categorized as low, moderate, and high values as indicated by Sivasubramaniah and Menon (1973) as follow. > 0 - 10% = Low, > 10 20% = Moderate; and > 20% = High

 

3.6.3. Heritability and genetic advance

 

Broad sense heritability values were estimated using the formula adopted from Falconer and Mackay (1996).

H2 = (σ2g/σ2p) x 100

 

Where, H2 = heritability in broad sense

 

σ2p = phenotypic variance

σ2g = Genotypic variance

 

The heritability percentage was categorized as low, moderate and high as suggested by Robinson et al. (1955) as follows:-

 

> 0 - 30% = Low, > 30 60% = Moderate; and > 60% = High

 

Expected genetic advance under selection (GA)

 

Genetic advance in absolute unit (GA) and as percent of the mean (GAM), assuming selection of superior 5% of the genotypes were estimated in accordance with the methods illustrated by Johnson et al. (1955) as:

 

GA = K * SDp * H2;

 

Where, GA = Genetic advance, SDp = Phenotypic standard deviation on mean basis, H2 = Heritability in the broad sense and K = the standardized selection differential at 5% selection intensity (K = 2.063).

 

Genetic advance as percent of mean (GAM)

 

Genetic advance as percent of mean was estimated as follows:

 

GAM = X100

 

Where, GAM = Genetic advance as percent of mean, GA = Genetic advance

 

 

The GA as percent of mean was categorized as low, moderate and high as suggested by Johnson et al. (1955) as follows. 0 - 10% = Low, 10 20% = Moderate, and >20% = High

 

 

4. RESULTS AND DISCUSSION

 

4.1. Analysis of Variance

 

The analyses of variance (Table 3) showed highly significant differences (P0.01) among wheat genotypes for all studied traits. Such considerable range of variations would provide a good opportunity for yield improvement. The results also justifies carrying out further genetic analysis by considering all (12) agro-morphology traits. Adhiena et al. (2016) conducted genetic variability study in 26 bread wheat genotypes considering twelve traits in Southern Zone of Tigray Regional State, at mid and highland areas. They reported the presence of significant differences among genotypes for all traits except for plant height and number of spikelets per plant. Many authors also revealed highly significant differences among all the wheat genotypes for all the characters (Mohammed et al., 2011; Dergicho et al., 2015; Gezahegn et al., 2015). However, days to maturity, number of tillers per plant, biological yield and harvest index were not significantly different in durum wheat genotypes (Dawit et al., 2012). This disparity may be due to the differences in the genotypes and test environments used in the different studies.

 

The relative efficiency of triple lattice design was greater than one for more than half quantitative traits but it was greater than 0.95 for all quantitative traits indicating triple lattice design is advantage as over RCBD in increasing in experimental precision (Table 3). Masood et al. (2008) and Idrees and Khan, (2009) reported alpha lattices design were on the average more efficient in reducing the experimental error and hence provide the efficient estimation of treatment contrasts. Thus, the present analysis were done by using triple lattice design. Coefficients of variation in percent were also used to compare the precision of the experimentation i.e. means with lower CV% for most of the characters revealed the reliability of the data collected from the experiment (Gomez and Gomez, 1984).



 

Table 3. Mean squares from analysis of variance for twelve traits of thirty six bread wheat genotypes evaluated at Mehoni in 2017

 

Traits

Replications(d.f =2)

Block Within replication (Adj.)(df=15)

 

Treatments (d.f=35)

 

Intra block Error

(d.f=55)

RCBD

Error

Rel. to effic.

(%)

CV (%)

(Unadj)

(Adj)

 

Plant height (cm)

1293.51**

20.43**

94.92

91.22**

17.49

18.12

100.51

4.88

Days to heading

7.06**

4.82**

142.80

122.83**

4.04

4.21

100.64

2.80

Grain filling period (days)

2.26ns

5.71**

28.04

27.50**

6.12

6.08

98.39

8.59

Days to maturity

1.69ns

9.04**

157.00

132.74**

8.13

8.32

100.24

2.84

No. of fertile tillers/plant

0.11ns

0.07ns

0.50

0.38**

0.09

0.08

95.67

13.53

Spike length (cm)

0.14ns

0.24ns

2.70

2.17**

0.25

0.25

98.89

5.28

No. of spikelets/spike

0.22ns

0.98ns

7.31

6.48**

1.21

1.16

95.92

5.98

No. of kernels/ spike

15.53**

17.32**

65.39

56.24**

12.22

13.31

102.49

7.54

1000-kernel weight (g)

3.77ns

12.70**

58.66

53.33**

12.99

12.93

99.51

10.07

Grain yield (t/ha)

0.14ns

0.09ns

1.13

0.96**

0.12

0.12

94.46

8.38

Biomass yield (t/ha)

0.04ns

0.08ns

0.25

0.25**

0.10

0.09

95.31

10.26

Harvest index (%)

2.09ns

7.83**

82.19

74.19**

6.91

7.11

100.33

7.60

Note, ** and * indicates highly significant at (1%) and significant at (5%) probability levels, respectively. DF= degree freedom

Rel.effic. = relative efficiency, RCBD=completely randomized design, CV= coefficient of variations and adj. and uadj. = adjusted or unadjusted treatment

 


 


 


4.2. Mean Performance of Genotypes

4.2.1. Phenology and Growth Characters

 

The genotypes variation for days to heading, grain filling period and days to maturity ranged from 58 to 81, 21 to 33 and 89 to 115, respectively. The mean performances of genotypes for plant height and number of fertile tillers per plot ranged from 74 to 97, and 1.4 to 2.9, respectively (Table 4). Adhiena et al. (2016) reported a wide range of variations among 26 bread wheat genotypes for days to heading ranged from 49.3 to 63 days with a mean of 56.9 days and days to maturity ranged from 102.7 to 129.7 days with a mean value of 114 days. Alemu et al. (2016) also reported wide range of variation between 48 and 66, and 97 and 108 for days to heading and days to maturity, respectively, among 30 bread wheat genotypes.

 

One (G1), three (G3) and two (G2) genotypes had mean performances lower than the earliest standard check (Kingbird) for days to heading, grain filling period and days to maturity, respectively (Table 4). None of new entry genotypes showed superiority over highest performing check (Gambo) for plant height while four genotypes had mean number of fertile tillers per plot greater than the highest performing check variety (Fentalle). Three (G3) and five (G5) genotypes were shorter than the check with shortest plant height among check varieties and low number of fertile tillers per plot, respectively. Among 36 genotypes, 47.22% exhibited days to heading lower than the genotypes mean indicating those genotypes were early heading as compared to the others (Table 4). Grain filling is also an important trait that ultimately affects the overall grain yield by increasing grain weight. The results in agreement with the findings of Mollasadeghi et al. (2012) in which days to heading and days to maturity showing similar parallelism to each other. However, some authors also reported non-significant differences among bread wheat genotypes for days to maturity and number of fertile tillers (Khan, 2013). This result suggested that the higher chance of selecting early genotypes which can escape the terminal moisture stress which is one of the wheat production problems in the study area. In this study, the genotypes with early heading also showed early maturity and late maturing ones exhibited correspondingly late days to heading. The differences of different authors report for the performance of bread wheat genotypes for maturity, plant height and number of fertile tillers for varied number of bread wheat genotypes might be due to the differences in the genetic factors carried by the genotypes included in each experiment, growing seasons and environments where the genotypes evaluated. The early maturity, plant height and number fertile tillers were reported as a function of both genetic and environmental factors (Berhanu, 2004; Obsa, 2014; Alemu et al., 2016).

 

4.2.2. Spike, biomass, harvest index and grain yield characters

Traits like, the number of grains per spike is an important plant attribute that depends upon spike length, spikelets per spike and spike density. Accordingly, the wide genotypes variation for spike length, spikelets per spike, number of kernels per spike and thousand-kernel weight ranged from 7.6 cm to 11.4 cm, 15 to 22, 35 to 59, and 27g to 44g, within an average value of 9.46, 18.38, 46.10 and 35.79 respectively (Table 4). Similarly, Maqbool et al., (2010) reported wide range of variation for plant height, grain filling period, number of spikelets per spike, biological yield, grain yield and thousand-kernel weight. The mean performances of genotypes for biomass yield (plot-kg) and harvest index were ranged from 2.1 to 3.5 and 21.9-42.1% respectively. Current modern wheat varieties have harvest index (HI) of c. 0.45-0.50 (spring type) and 0.50-0.55 (winter type), approaching its theoretical maximum value (c. 0.64 in winter wheat) (Foulkes et al., 2011; Reynolds et al., 2012). This wide ranges of mean values these traits depicted that bread wheat germplasm possess good amount of genetic variability.

Three genotypes; viz ATILA*2//CHIL/BUC*2/3KUKUNA, ETBW5535 and ETBW5963 (SET II C3) genotypes with longer spike length than the check (Gambo), the longest spike length among check varieties. High number of kernels per spike was recorded for genotypes 6 and 9 respectively than the standard check (Gambo) (Table 4). Eight, twenty nine and thirty three entry genotypes showed superior for spikelets per spike than highest performing check (Gambo). Among 36 genotypes, 25% of the genotypes showed highest thousand-kernel weight than the highest performing check (Ogolcho) variety. In the present result for thousand kernel weight, comparative result with Obsa (2014) report for 1000-seed weight with values ranging from 25 to 46.67 g with a mean value of 39.67g.

 

Grain yield per plant is also a character of prime importance and of special interest to a wheat breeder. Accordingly, highly significant variability was observed among genotypes for grain yield t ha-1, which ranged from 2.9 to 5.3 with the mean value of 4.18 t ha-1 and coefficient of variation of 8.38%. Depending on the mean performances, genotypes such as QAFZAH-2/FERRIUG-2 (SET II C1), KAUZ'S'/FLORKWA1//GOUMRIA-3, SERI 82/SHUHA'S'//PASTOR-2 (SET I) and ETBW5957HUBARA-3*2/SHUHA-4 had mean performances higher than the highest performing check variety (Gambo=4.75 t ha-1) for grain yield (t ha-1) with 5.33, 5.16, 5.15 and 5.11 while lower yielder were obtained from genotypes G30 (3.33 t ha-1), G11 (2.98 t ha-1) and G8 (2.87 t ha-1) (Table 4). Generally, the range of variation was wide for all the characters studied. Berhanu et al. (2017) conducted genetic variability among 49 bread wheat genotypes at Axum, Northern, Ethiopia and reported a wide range of grain yield from 2.37 to 5.44 t ha-1 with a mean of 3.95 t ha-1 and the maximum grain yield obtained was 5.44 t ha-1 and 5.37 t ha-1), 4.64 t ha-1) and 4.56 t ha-1 respectively. Gezahegn et al. (2015) reported a wide variation of grain yield per hectare which ranged from 2.11 to 5.95t ha-1 while Alemu et al. (2016) also reported that 2.59 to 4.68 t ha-1 and 1.28 to 3.79 tones ha-1 at Kulumsa and Tongo site for bread wheat in Ethiopia respectively. Regarding biomass yield, 25% of the genotypes were greater than the highest biomass yielder check Gambo (13.08 t ha-1). These high yielding genotypes could be utilized in further breeding.


 

 

 


Table 4. Mean performance of thirty six bread wheat genotypes evaluated at Mehoni in 2017

G*

HD

GFP

MD

PHT

FTPP

SL

SPS

KPS

TKW(g)

GY(t/ha)

BY(kg p)

Hi (%)

G1

70.00h-k

29.33ah

99.33g-j

82.88e-h

2.57a-d

9.21g-l

19.13b-f

46.53b-i

32.73g-m

4.75a-d

3.30a-c

36.35c-k

G2

78.67a-c

26.67dj

105.33c-f

85.34d-f

2.27d-i

10.55b-d

19.47b-d

49.60b-e

35.33c-l

3.83h-m

3.03a-c

31.85j-n

G3

73.33e-i

25.00gk

98.33g-k

94.23ab

2.07d-k

10.37a-c

19.27b-d

49.67b-e

30.87j-m

4.59b-g

3.00a-c

37.92a-g

G4

65.67lm

24.00i-j

89.67o

83.32d-h

2.60ac

8.90i-o

15.00k

39.73jk

42.13a-c

5.15ab

3.10a-d

41.53a-c

G5

78.00a-d

29.00a-h

107.00b-d

87.55b-f

1.90h-n

10.20c-f

18.67b-g

47.67b-g

29.20l-k

4.11e-j

3.20a-d

32.19h-n

G6

68.33k-l

29.67a-g

98.00g-l

94.87ab

2.67ab

10.06c-g

17.87c-h

48.90b-e

31.20j-m

4.26e-i

3.17a-d

33.66f-l

G7

71.00f-k

21.33k

92.33m-o

81.60e-h

2.9a

8.29m-q

15.17j

35.30k

38.33a-h