Evaluation of Bread Wheat ( Triticum aestivum L.) Germplasm at Kafa Zone, South West Ethiopia

Bread wheat germplasm has wide genetic diversity, which means it can withstand a lot of biotic and abiotic stresses. Despite the presence of bread wheat germplasm diversity in Ethiopia, wheat production in the Kafa Zone is signiﬁcantly lower than the national average. The ultimate goal of this research was to determine the genetic diversity of grain yield and yield components of bread wheat. One hundred bread wheat accessions with 3 local checks were evaluated in augmented randomized complete block design at Kafa Zone, Gewata Woreda Shupa site, during the 2018–19 growing season. The mean performance of the accessions revealed that accession number 29812 yielded more grain than the others. Spike length, number of seeds per spike, biomass yield, and harvest index all had moderate genotypic coeﬃcients of variation. Spike length, number of seeds per spike, thousand seed weight, biomass yield, and harvest index all had moderate-to-high heritability and also all the above-listed traits had moderate-to-high genetic advance as a percentage of the mean. This means that practical improvement of these essential traits can be achieved by eﬀective and satisfactory selection. Grain yield has positive correlations with grain ﬁlling period, number of productive tillers, spike length, number of seeds per spike, thousand seed weight, and biomass yield. The principal component analysis grouped all of the traits into four main components. Seven clusters and one ungrouped accession were formed from the accessions. Cluster IV and cluster VI had the greatest intercluster distance ( D 2 � 104.77) among the clustered groups, suggesting the probability of selecting a parental genotype for hybridization. However, the current result is merely indicative and cannot be used to draw ﬁrm conclusions. As a result, the experiment should be replicated in diﬀerent locations and seasons for greater consistency.


Introduction
Wheat has long been one of Ethiopia's most common cereals, dominating food habits and dietary practices alongside teff "injera" and considered to be a major source of energy and protein for the people [1].
Ethiopia produces the most wheat in sub-Saharan Africa, followed by South Africa [2]. In the 2016/17 cropping season, the crop ranked fourth in terms of area covered (1,696,082.59 ha) and quantity generated 4537853.339 metric tons, behind maize, teff, and sorghum [3].
Wheat grows in Ethiopia under a variety of environmental conditions, ranging from 1500 to 3200 meters above sea level [4], allowing for the existence of various wheat varieties. For genetic improvement programs and efficient genetic diversity utilization of plant materials, knowing the extent of heterogeneity among bread wheat (Triticum aestivum L.) accessions is extremely valuable [5]. High genetic diversity available in gene banks increases the chance of adaptability and plays a great role in crop breeding [6].
Regarding genetic diversity of bread wheat in Ethiopia, many studies have been conducted across various regions of the country [7][8][9][10]. Despite Ethiopia's bread wheat diversity, wheat is grown on 7137.64 hectares per year in the Kafa Zone, with a production of 1.902 tons per hectare, which is lower than the national production (2.675 tons per hectare) [3]. is lower production of bread wheat in the Kafa Zone is due to a lack of adaptable, high-yielding varieties. As a result, determining the variation present in collections conserved at the Ethiopian Biodiversity Institute is a crucial step toward crop improvement. With the above facts in mind, the following objectives were set for the current investigation to determine the genetic diversity of bread wheat yield and yield contributing traits.

Materials and Methods
e experiment was conducted in the Kafa Zone, Gewata Woreda, Bonga Agricultural Research Center, Shupa substation in the 2018/19 cropping season. e planting material used in the study comprised a hundred accessions of bread wheat collected from different regions of Ethiopia and obtained from the Ethiopian Biodiversity Institute. ree varieties were used as a check. e accessions were selected randomly from major wheat-producing regions. e experiment was laid out in an Augmented Randomized Complete Block Design (ARCBD) with ten blocks. Each block comprised ten accessions and three checks, a total of thirteen accessions in one block. All the checks were repeated in all the blocks randomly, while the accessions were unreplicated. Each accession was grown in 2 rows of 1.5 m long plots with 20 cm distance between rows. Data were recorded on plant height, the number of productive tillers per plant, spike length, number of seed per spike, days to heading, days to maturity, grain filling period, thousandgrain weights, grain yield, biomass yield, and harvest index. Harvest index was calculated as grain yield divided by biological yield multiplied by hundred. All of the usual recommended agronomic practices and plant protection measures were implemented, as recommended by EIAR [11]. Statistical analysis for ANOVA, Pearson's correlation, clustering, and principal component analysis were analyzed by using SAS software version 9.3. e significance of correlation and cluster distance was determined by using the r-table from Gomez and Gomez [12]. Genetic variance, phenotypic variance, genetic coefficient of variance, and phenotypic coefficient of variance were calculated based on the formula proposed by Burton and De Vane [13]. Heritability and genetic advance as percent of mean were determined based on the formula proposed by Falconer et al. [14].

Analysis of Variance.
e mean square of all the traits studied showed the presence of significant differences (P < 0.05) among the tested accessions (Table 1). is suggests that the studied breeding materials have an adequate genetic variation for all of the traits. is indicates that crop improvements through selection are possible. e result of the present study agrees with the finding of [15] who reported the presence of high variation among the genotypes for days to heading and days to maturity. Accession number 222785 was the earliest to fill the grain with 26 days, which is 37 days earlier than the late grain filling period while accession number 214115 was late to fill the grain with 63 days. e differences in days to heading, days to maturity, and grain filling period that are seen among the accessions are attributable to the combined effect of genetic and environmental variables.

Number of Productive Tillers.
e mean value of all the accessions for the number of productive tillers was 4.1 with a range from 1.5 to 7.2. e lowest number of productive tillers was observed in accession number 7341, whereas accession number 29812 recorded the highest number of productive tillers while the check had 3.6 and 5.4 minimum and maximum tiller numbers, respectively. A number of reports [16][17][18] stated the presence of a high range of variation for the number of productive tillers. e genetic and environmental factor is the main reason for the variation observed among accessions for the number of productive tillers. e cumulative influence of genetic and environmental variables is responsible for the differences in plant height and quantity of seeds per spike seen among the accessions.

ousand Seed Weight and Grain Yield.
e overall mean value for thousand seed weight was 23 g. e range value of thousand seed weight was 7 g to 39.5 g. e lowest seed weight was exhibited by accession number 213309 while the highest thousand seed weight was recorded by accession number 6885. e minimum grain yield recorded was 1.047 tons ha −1 for accession number 7341, whereas a maximum of 5.70 tons was for accession number 29812 with a mean value of 3.35 tons ha −1 . e checks had 2.791 and 4.678 tons ha −1 lowest and the highest grain yields, respectively. Some accessions, 6883, 6884, 29811, 29813, 242429, and 243702, exhibited higher grain yield than the check variety (Shorima). Many studies [15,[20][21][22] reported a high range of variation among genotypes for grain yield. In general, a higher range of variation among accession for days to heading, days to maturity, grain filling period, number of productive tillers, plant height, spike length, number of seeds per spike, thousand seed weight, and grain yield was due to genetic and environmental variation.

Genotypic and Phenotypic Coefficients of Variation.
According to Burton and Devane [13], GCV and PCV are classified as high (>20%), medium (10-20%), and low (<10%). In the present study, GCV ranged from 3.173 for days to heading to 15.279 for grain yield. PCV ranged from 3.553 for days to heading to 21.818 for the number of productive tillers. High PCV values were observed for the number of productive tillers (21.81%) and grain yield (20.28) ( Table 2). e present result revealed that the magnitude of the difference was relatively low for days to heading, days to maturity, plant height, spike length, thousand seed weight, number of seeds per spike, and thousand seed weight. is suggested that the marked influence of environmental factors for the phenotype expression of genotypes was low and the higher chance of improvement of these traits through selection. In support of the present result, Arya et al. [16] and Adhiena et al. [23] reported low magnitude of differences between PCV and GCV that was observed for days to heading, days to maturity, plant height, spike length, thousand seed weight, and harvest index.
In the present study, the magnitude of differences between PCV and GCV was high for the grain filling period, the number of tillers, grain yield, and biomass yield. is implies the greater influence of environmental factors for the phenotypic expression of these traits that enhances breeder to use heterosis/hybridization/breeding strategy. In relation to the present result [24][25][26], a high magnitude of differences between PCV and GCV was observed for the number of productive tillers, grain, and biomass yield.

Estimation of Heritability in the Broad Sense and Genetic
Advance.
e estimated heritability was studied for all traits ( Table 2). e heritability values ranged from 40.186 for the grain filling period to 87.042% for the number of seeds per spike. Robinson et al. [27] classified heritability values as low (0-30%), moderate (30-60%), and high (60 and above).
us, high heritability was observed for days to heading (79.75%), days to maturity (81.61%), plant height (71.19%), spike length (66.80%), number of seeds per spike (87.04%), thousand seed weight (70.31%), biomass yield (62.45%), and harvest index (76.21%), which indicates that environment had a low influence on the expression of the traits suggesting direct selection for improvement. In support of the present study, Alemu et al. [28] reported high heritability for days to heading and spike length. Many studies [8,29,30] and [31] reported heritability from low to high.
Heritability estimates appear to be more meaningful when accompanied by estimates of genetic advance. In the present study, high heritability coupled with high genetic advance as percent of the mean was observed for spike length, the number of seeds per spike, biomass yield, and harvest index ( Table 2).
is suggests that these traits are not much influenced by environmental factors and substantial improvement for these traits could be achieved through direct selection and also these traits are considered to be governed by additive genes. In support of the present finding, Arya et al. [16] found high heritability coupled with high genetic advance as percent of the mean for spike length, the number of seeds per spike, biomass yield, and harvest index.
Moderate heritability coupled with high genetic advance as percent of the mean was observed for the number of productive tillers and grain yield indicating improving these traits through selection would be effective. In agreement with these, Kumar et al. [18] reported moderate heritability coupled with high genetic advance as a percent of the mean for the number of productive tillers. Alemayehu et al. [24] reported moderate heritability coupled with high GAM for grain yield. e present study was supported by the work of [17,28,30,32,33].

Correlation of Grain Yield with Other
Traits at the Genotypic Level. Grain yield exhibited a positive significant correlation with the grain filling period, number of productive tillers, spike length, number of seeds per spike, thousand seed weight, and biomass yield at both genotypic levels (Table 3). erefore, any improvement of these traits would result in a substantial increment in grain yield. is also implies that selection of accessions based on the grain filling period, number of productive tillers, number of seeds per spike, and biomass yield would be beneficial for increasing wheat grain yield. Grain filling period, number of tillers, spike length, thousand seed weight, and biomass yield had a positive correlation with grain yield, according to Alemu et al. [34]; Din et al. [31]; and Salehi et al. [35]. 129 * � significant at probability level of 0.05 and * * � significant at probability level of 0.01, SV � source of variation, Nacc � new accessions, Ch vs. acc � check vs. accessions, ns � nonsignificant, df � degree of freedom, CV% � coefficient of variation in percentage, DH � days to heading, DM � days to maturity, GFP � grain filling period, NPT �number of productive tillers, PH � plant height (cm), SPL � spike length (cm), NSPS � number of seeds per spike, TSW � thousand grain weight (g), GY (t ha −1 ) � grain yield in tons per hectare, BY (t ha −1 ) � biomass yield in tons per hectare, and HI � harvest index.

Principal Component Analysis.
In the present investigation, only the first four principal components showed eigenvalues more than one and cumulatively they explained 73.77% of the entire variability available among accessions (Table 4). According to Chahal and Gosal [36], traits with the largest absolute values closer to unity within the first principal component influence the clustering more than those with lower absolute values closer to zero. e first two principal components are more important as revealed by their higher eigenvalues. e principal component analysis showed that 31.96% of the total variation in the germplasm for the traits was explained by PC1. e higher contribution of PC1 was loaded by grain yield, biomass yield, thousand seed weight, number of productive tillers, number of seeds per spike, and days to heading. PC2 contributed 17.96% to the total variation of the accessions. e 17.96% contribution of PC2 was due to high variation for days to maturity, plant height, and spike length. Around 14.14% variation was accounted for by PC3, which was loaded by grain-filling period. PC4 accounted for 9.71% of total variation, which was loaded by harvest index (Table 4). Alemayehu et al. [24] found the most contributing traits were above-ground biomass, spike length, and plant height in durum wheat. Poudel et al. [37] reported that days to heading, maturity, and grain filling period contributed more to the total diversity. e traits far from the origin contributed more to the total diversity. Accordingly, the primary traits that contributed more to total diversity are plant height, days to maturity, days to heading, the number of seeds per spike, and grain filling period (Figure 1). In other way, the trait nearest to the x-axis contributed to PC1 and that nearest to the y-axis contributed to PC2. e traits which lie on the origin (spike length, number of productive tillers, thousand seed weight, grain yield, biomass yield, and harvest index) contributed less to total diversity.

Cluster Mean Analysis of Major Contributing Traits to
Diversity. In the present study, cluster IV was characterized as the lowest cluster means for days to heading, which were considered to be the early heading accessions found in this cluster. e early maturing accessions were represented in cluster II, with recorded mean days to maturity of 95 days, whereas late maturing with mean days to maturity of 131 days was found in cluster VI (Table 5). Cluster II [27] exhibited the lowest grain filing period against the highest of cluster V (58.67). Cluster VI consists of the tallest accessions DH: days to heading, DM: days to maturity, GFP: grain filling period, NPT: number of productive tillers, PH: plant height (cm), SPL: spike length (cm), NSPS: number of seeds per spike, TSW: thousand seed weight (g), GY: grain yield (t ha −1 ), BY: biomass yield (t ha −1 ), and HI: harvest index.   Advances in Agriculture with a mean plant height of (139 cm), whereas the shortest with a mean height of 98.91 cm was found in cluster VII. e highest mean performances of the number of seeds per spike were recorded for cluster IV (56.24), while the smallest numbers of seeds per spike were for cluster VII (29.16). is result implies sufficient scope for genotypic improvement through hybridization between the accessions from divergent clusters. In general, cluster IV exhibited the highest cluster mean value for the number of productive tillers, number of seeds per spike, and thousand seed weight,   Advances in Agriculture 5 whereas cluster V exhibited the highest grain yield which indicates the accessions present in clusters IV and V may be used as parents in hybridization programs for developing high-yielding wheat varieties.

Intra-and Inter-Cluster
Distances. e highest average intercluster D 2 was recorded between cluster IV and cluster VI (D 2 � 104.77) followed by between cluster II and cluster VI (D 2 � 103.880), cluster IV and cluster VII (D 2 � 92.492), and cluster III and cluster IV (D 2 � 74.423) ( Table 6). is revealed that these clusters were genetically more divergent from each other and had the tendency of obtaining promising parents for crossing. e minimum intercluster distance was observed between cluster II and cluster VII (D 2 � 14.91223) ( Table 6), indicating that accessions of these two clusters were closely related, which suggests the presence of gene flow. us, the crossing of accessions belonging to the same cluster is not expected to yield superior hybrids.

Conclusion
e present study indicated the presence of variability among the tested accessions that can be exploited in the wheat improvement program. e existence of variability among accessions for quantitative traits shows the direction for the direct selection of parental genotypes to develop hybrids. e top five accessions that performed better than the released check varieties for grain yield were 29812, 29811, 29813, 242427, 242429, and 243702. erefore, for grain yield production, direct selection of those accessions can be possible. In general, the presence of genetic variability creates enormous opportunities for the improvement of bread wheat genotypes. erefore, the information generated from this study can be used by breeders who are interested in different traits. However, the present result is only an indication and cannot draw a definite conclusion. Since the experiment was carried out at one location and in one season, it is recommended to further evaluate high-yielding accessions over locations and seasons to check the stability of the accession.

Data Availability
e data supporting the findings of this study are available on request from the corresponding author.