Ethiopia is not self-sufficient to meet its increasing wheat demand from domestic production partly due to a lack of improved seeds. Efforts are undertaken to fill the gap through off-season production of wheat using supplemental irrigation and modern cultivars adapted to arid areas. This study was carried out to evaluate the genetic variability and adaptability of 15 Ethiopian bread wheat genotypes at different agroecologies in Wolaita and Dawuro zones, Ethiopia. The field experiment was conducted at three locations using a randomized complete block design with three replications during the 2019/2020 main cropping season. Analysis of variance based on 11 morphological agronomic traits and two major wheat diseases revealed that there were highly significant differences () among the genotypes for all the traits studied at each location and combined over locations. The top three cultivars viz. Alidoro, Galema, and Honqolo exhibited higher average grain yield (GY) of 4.54 t/ha, 4.36 t/ha, and 4.0 t/ha, respectively, combined over locations. Eight of the traits (72.73%) exhibited moderate (30–60%) to high broad-sense heritability ( > 60%) values. High associated with high genetic advance as percent of mean was observed for the severity of both stem and yellow rust diseases combined over locations. GY was significantly related to aboveground biomass at all locations. This study depicted that cultivar Alidoro had wider adaptability for grain yield and resistance to wheat rusts.

1. Introduction

Wheat (Triticum aestivum L.) is the most widely grown cereal crop in the world. It is the second major food crop next to rice [1]. It is widely cultivated for its grain for do mestic consumption in various recipes and industrial uses [24]. It is the major staple food for 40% of the world’s population [5]. The global current (2020/2021) wheat utilization was forecasted at about 758 million tons, that is, 1.5 percent higher than in 2018/19, where the increment was mostly associated with growth in food consumption [6]. To feed the world’s growing population, the global demand for wheat yield should increase by 50% in 2050 as estimated by Allen et al. [7].

China and India are the world’s largest wheat producers, annually producing 134,340,630 and 98,510,000 tons of wheat, respectively. Africa’s average wheat production from 2014/15 to 2016/17 was 71.7 million tons, whereas that of 2017/2018 and 2018/2019 cropping seasons was 74.8 and 75.2 million tons, respectively [6]. Ethiopia is the second-largest wheat producer in Africa with annual production amounting to approximately 4.54 million tones and an average grain yield of 2.67 t/ha [8]. The annual average wheat utilization in Ethiopia in 2016/17, 2017/2018, and 2018/2019 was 5.6, 6.0, and 6.1 million tons, respectively [6], clearly showing the deficit and need for additional import to meet the domestic demand. In the Southern Nations Nationalities People Region (SNNPR) of the country, wheat covered an area of 133,419.80 ha with a total production of 334,633.93 tons. Of which, 3,092.39 tons was obtained from Wolaita Zone on 1,630.25 ha cultivated land and about 4207 tons was obtained from Dawuro Zone on the estimated cultivated area of 2274.05 ha [9].

Wheat adapts to a wide range of environmental conditions mainly due to the complex nature of its genome, which provides great flexibility to the crop [10, 11]. In Ethiopia, wheat can grow in highlands, which are located between 6° and 16°N latitude and 35° and 42°E longitude and at altitudes ranging from 1500 to 3000 m.a.s.l. However, the most suitable altitude zones of wheat fall between 1900 and 2700 m.a.s.l [12].

Breeders are continuously working for the improvement of grain yield, with better quality (bread-making quality, seed color, seed size, protein content, etc.) and resistance to both biotic and abiotic stresses. In Mexico (CIMMYT), the segregating wheat populations, which were widely adapted, high yielding, and with stable performance, would be selected from two environments with differing disease and abiotic stresses by the shuttle breeding methods [13]. This breeding method is further corroborated by international multilocation testing of advanced lines, particularly in Kenya and Ethiopia, for screening to stem rust, leaf rust, and stripe rust diseases [14]. However, breeding for wide adaptation has not been very successful because in most areas temperatures and rainfall patterns shift annually including edaphic factors (soil acidity, salinity, alkalinity, and fertility problems) and vary from region to region. In addition, farmers could not be able to maintain the same agronomic practices consistently, since diseases and pest pressures vary from year to year.

To increase the production of crops, which adapt to diverse environmental conditions (resistant to both biotic and abiotic stresses), and to improve the quality of the product through developing more adaptive cultivars, the knowledge of the genetic diversity within a germplasm collection has a significant impact [15]. While new wheat cultivars are developed by the breeders, the new cultivars are tested for their yield performances in multilocation trials. The success of releasing a new wheat cultivar depends upon its quantity and quality of grain yield and adaptation potential in those locations. Finally, cultivars with high and stable yields are highly preferred by both farmers and breeders.

In Ethiopia, wheat production and productivity have been increasing significantly although it is still insufficient to meet the increasing demand for the ever-increasing population [7]. The wheat production of the country covers 75% of the national demand, while the remaining 25% of the wheat is imported from abroad [16]. This is due to the influence of diseases such as stem rust, yellow rust, septoria leaf spot, shortage or lack and suboptimal use of production inputs (e.g., improved seeds and fertilizers), and breakdown of disease resistance genes of the released cultivars after few years of production [17].

Therefore, evaluation of the performance of recently released improved bread wheat cultivars compared with relatively older ones and estimating their genetic variability and adaptability across diverse environmental conditions is very important to identify the most adaptable, stable, disease-resistant, and high yielder cultivar across a range of environments [18]. This requires a careful investigation to identify the most important yield and yield-related quantitative traits to select the most productive and wide range adaptable cultivar and suggest useful indexes to the wheat improvement program. Hence, this study was conceived to assess the genetic variability and adaptability of Ethiopian bread wheat (Triticum aestivum L.) genotypes and select the ones with higher grain yield and better adaptation across the test locations in Wolaita and Dawuro zones of southern Ethiopia.

2. Materials and Methods

2.1. Descriptions of the Study Area

The genetic variability and adaptability of Ethiopian bread wheat were assessed at farmer’s field in three locations (Table 1). Two of the locations were in Wolaita Zone (in Ade Koysha Kebele, Damot Gale District, and in Sunkale Kebele, Damot Sore District). The third location is in Dawuro Zone at Wolaita Sodo University Tercha Campus Research Site (Kechi woreda).

2.2. Plant Materials and Experimental Design

Seeds of all 15 bread wheat genotypes (Table 2) were obtained from Kulumsa Agriculture Research Center (KARC), Ethiopia. The genotypes were planted in the randomized complete block design (RCBD) with three replications. Each plot was composed of six rows of 0.2 m spaced, with 2.5 m length and 1.2 m width. Therefore, the area of each experimental plot was 3 m2 (1.2 m × 2.5 m).

2.3. Sowing and Crop Management

The experimental field was well tilled (ploughed three times before sowing), and planting rows were prepared using hand-pulled row marker. Seeds were sown by hand drilling method at a planting depth of ∼5 cm and 10 cm intra-raw spacing between plants. Planting was carried out at the appropriate planting time for each location (at Damot Gale on July 23, 2019, Kechi on July 26, 2019, and Damot Sore on July 31, 2019). For all plots, inorganic fertilizer was uniformly applied at the rate of 100 kg ha−1 di-ammonium phosphate (DAP) and 150 kg urea ha−1 as recommended for bread wheat by KARC. The whole rate of DAP and half of the urea were applied at planting time, and the remained half was added at the mid-tillering stages. The seed rate was 150 kg/ha. Hand weeding was done to control weeds in the experimental fields in all the three locations. However, neither herbicides nor fungicides were applied to control weeds and diseases, respectively.

2.4. Data Collection for Agronomic Traits

All necessary data were collected from the four middle rows of each plot. Data were collected on the plant and plot bases. For the data collected on a plant basis, 10 plants per plot were randomly selected for each of the traits; that is, number of tillers per plant, number of kernels per spike, number of spikelets per spike, plant height (cm), peduncle length (cm), and spike length (cm). The data for number of days to heading (75%), number of days to maturity (90%), grain filling period, number of productive tillers per meter square, thousand-kernel weight (gm), aboveground biomass (kg), grain yield (t/ha), and harvest index (%) were collected on a plot basis.

2.5. Data on Disease Parameters

Disease severity data were recorded for stem rust and stripe rust when the disease severity reached between 60% and 100% in the field on susceptible cultivars. The data were recorded every week until the susceptible plants showed 100% susceptibility. The 1–9 scoring scale was adopted to record the data as described in Bariana et al. [19].

2.6. Data Analysis
2.6.1. Analysis of Variance

The agro-morphological data including disease parameters of the three locations were subjected to the variance analysis using GenStat 16th edition statistical software package (VSN International Ltd., London, UK) following the standard procedures described by Gomez and Gomez [20] to evaluate the performance of genotypes for each trait and location and calculate the error variances for each of the environments. For combined analysis of variance over locations, the homogeneity of error variance was tested using Bartlett’s test for homogeneity of variances using the same software. The difference between treatment means was compared using the least significant difference (LSD) test at 5% level of significance when the ANOVA showed a significant difference among genotypes.

2.6.2. Genetic Parameters

The estimation of genetic parameters was done to identify and ascertain genetic variability among the bread wheat genotypes and to determine the extent of environmental effect on various traits. By considering all the genotypes tested in the uniform environment, the mean square for error (MSe) for each trait was assumed to be purely a random environmental variance . The genotypic variance was calculated from the ANOVA table for each trait by adopting the formula described in Singh and Chaudhary [21], and the phenotypic variance was computed by adopting the following formula as suggested below by Burton and Devane [22].

Genotypic variance () =  and phenotypic variance () =   + , where MS—mean squares of genotypes, MSe—mean square due to error, and r—number of replications.

Genotypic and phenotypic coefficients of variations were calculated using the formula described in Singh and Chaudhary [21] as follows.

Genotypic coefficients of variations and phenotypic coefficients of variations , where is genotypic variance, is phenotypic variance, and x is the grand mean value of the trait.

The combined genetic variance components across locations were computed using a similar approach as for individual locations using the following formula adopted from Allard [23].

Environmental variance , genotype variance , genotype-by-location interaction variance , and phenotypic variance , where MSe = mean square for error, MSgl = mean square of genotype-by-locationinteraction, MS = mean square of genotype, r = replication, and l = location.

2.6.3. Estimation of Heritability and Genetic Advance

Heritability in broad sense () for all traits was computed using the formula adopted by Allard [23]. Heritability in a broad sense (%) = , where is genotypic variance and is phenotypic variance. Genetic advance was computed using the formula adopted from Johnson et al. [24] and Allard [23]. Genetic advance (GA) = (k) (σp) × (), where is genotypic variance, is phenotypic variance, δp is the standard deviation of phenotypic variance, and k is the selection differential at a particular selection intensity, i.e., 2.06, suggested by Falconer at 10% selection intensity. Genetic advance as percent of mean was calculated to compare the extent of predicted advances of different traits under selection using the formula given by Falconer and Mackey [25]. GAM =  , where GAM = genetic advance as percent of mean, GA = genetic advance, and x = mean value of the trait.

3. Results and Discussion

3.1. Analysis of Variance

The analyses of variance showed significant differences among the tested genotypes. Except stem rust, which was nonsignificant, all other traits showed significant differences between genotypes when subjected to the combined analysis of variance over the three locations (Table 3). Similar findings have been reported by other authors that bread wheat exhibited significant differences among genotypes for number of days to maturity, number of days to heading, plant height, spike length, number of seeds per spike, thousand seed weight, harvest index, and grain yield per plant as reviewed by Majumder et al. [26]. The nonsignificant difference obtained in the combined ANOVA (for location and genotype-by-location interaction effect) for stem rust disease severity data (Table 3) could be the presence of major genes (all stage resistance gene/seedling resistance gene) in the majority of the Ethiopian bread wheat cultivars, which are less influenced by environmental effects compared with adult plant resistance (slow rusting resistance) genes [27]. These findings encourage carrying out additional genetic studies to improve the cultivars through hybridization and selection programs.

The interaction between locations and genotypes was significant for the majority (61.5%) of the traits such as plant height, number of tillers per plant, number of effective tillers per plant, number of seeds per spike, biomass yield (t/ha), grain yield (t/ha), thousand seed weight (g), and harvest index. Desalegn and Dinesh [28] reported a significant genotype-by-environment (location) (GxE) effect for days to heading, days to maturity, plant height, grain filling period, spike length, aboveground biomass yield, thousand seed weight, and harvest index, most of which were in agreement with the present findings. Traits such as days to maturity, resistance to both stem and yellow rust diseases, thousand-seed weight, and spike length were less influenced by the effect of environmental differences indicating that such traits could contribute to the genotypes for wider adaptation or uniform performance of genotypes across environments. Traits that showed nonsignificant differences for GxE effect (Table 3) indicated that there was less influence on environment compared with the genotype effect. Such traits would have high heritability value and could contribute to the successful selection of genotypes for wider adaptation areas [29].

3.2. Mean Performance of Genotypes

The mean performance of 15 bread wheat genotypes for 13 agro-morphological traits at Damot Gale, Damot Sore, and Kechi locations is presented in Tables 46, respectively. The result showed the presence of significant differences for all studied traits at all locations (). The number of days to heading (DH) was the highest at Kechi (78) as compared to Damot Gale (73) and Damot Sore (69). Days to maturity (DM) followed a similar fashion as DH where Kechi exhibited the highest DM (127.5), followed by Damot Gale (110) and Damot Sore (105), respectively. The genotypes grouped as early and late heading corresponded with the respective early and late maturity characteristics of each location. In a similar study, Birhanu et al. [30] and Mollasadeghi et al. [31] reported that the days to heading and maturity of bread wheat genotypes corresponded with each other. However, a recent study on bread wheat traits showed the absence of significant association between number of days to heading and days to maturity [32]. Days to maturity (DM) data showed that nearly similar genotypes were grouped uniformly as early maturing, intermediate maturing, and late maturing types across locations, implying genetic factors contributed to the lions’ share for the variations in DH and DM among the genotypes. The causes of variations in DH could be the differences in the number of days that the genotypes have taken at the three locations as there were variations in average rainfall, temperature, and light intensity at the three locations. The maturity date is an important trait for farmers of the study areas in which they are interested in identifying early and late maturing wheat varieties. Hidase variety was among the early maturing bread wheat genotypes at all locations. This result agrees with the study result reported by Bekele et al. [33] in which the Hidasse variety was preferred by farmers because of its early maturity.

The mean value of bread wheat genotypes for plant height (PH) showed that the genotypes evaluated at Damot Sore location (79.8 cm) were shorter than those evaluated at Damot Gale (91.6 cm) and Kechi (96.6 cm) locations. This could be related to higher rainfall and relatively lower temperature at Damot Gale and Kechi sites that might have increased the height of bread wheat plants at these locations. It was reported that the performance of bread wheat genotypes for PH was significantly and uniformly influenced by average temperature, altitude, and precipitation of the environment they were evaluated [34]. Similar trends were also observed for traits such as number of tillers/plant, effective number of tillers/plant, spike length, aboveground biomass (g), and grain yield (g). These traits were best performed at Damot Gale location. Two traits namely number of seeds/spike and thousand seed weight were best performed at Kechi location. The highest mean value of harvest index (49.2%) was obtained at Damot Sore location, while the lowest harvest index (39.7%) was obtained at Kechi location. The maximum and minimum grain yields per hectare were recorded from genotype Galema (6.25 t/ha) at Damot Gale location and from Pavon-76 (2.45 t/ha) at Kechi location, respectively.

Analysis of combined data of the three locations showed that the genotype Alidoro exhibited the highest average grain yield (4.54 t/ha) (Table 7), indicating its wider adaptability and resistance to the rusts. The mean score values of stem rust and yellow rust severity data showed little variation across locations, which could be attributed to the specificity of genes possessed by genotypes. Qualitative genes are less likely influenced by environmental effect [35]. In general, the mean performance of genotypes with respect to grain yield, plant height, and aboveground biomass indicated that bread wheat genotypes were better performed at Damot Gale (4.8 t/ha of grain yield) compared with those at Kechi (3.24 t/ha of grain yield) (Tables 46). This result is similar to the findings of Allison et al. [36], who reported that wheat grain yield was low at high elevations where temperatures were too low to allow the crops to mature. The highest yields occurred at intermediate elevations with sufficient precipitation and mild temperatures.

3.3. Genotypic and Phenotypic Coefficients of Variations

Most of the traits considered in this study had a high phenotypic and genotypic coefficient of variation (>20%) according to the categorization given by Deshmuk et al. [37] as low (<10%), moderate (10–20%), and high (>20%). The combined analysis of variance over three locations revealed that the majority of traits showed a significant difference among locations and genotypes. Higher values of GCV were recorded from stem rust (55.9%) and yellow rust (38%), indicating that the traits are controlled by genetic factor, and hence, there is a higher chance of improvement of the crop through selection. Only stem rust (56.7%) and yellow rust (37.2%) showed moderate values of GCV (Table 8). The rest of the traits displayed lower-to-moderate values of GCV that ranged from 2.7% (days to maturity) to 10.7% (aboveground biomass). This study’s result agrees with Ibrahim et al. [38], suggesting that there may also be some chance of improving traits with moderate GCV through phenotypic selection. However, the selection is practically impossible in traits with low genotypic coefficient of variation. Higher values of PCV were recorded by stem rust (68.7%), yellow rust (62.3%), aboveground biomass (23.5%), and grain yield (22.1%). Moderate values of PCV were shown by the major components of yield, whereas the growth (plant height) and phenological traits exhibited low value of PCV (Table 8). The traits that exhibited low estimates of GCV and PCV are difficult or virtually impractical to improve through selection due to the masking effect of environment on the genotypic effect [39]. In our study, the PCV values were also higher than the corresponding GCV values for all the traits. This indicates the observed variations between the genotypes for each trait were not only due to genotypic effect but also due to environmental influences.

3.4. Broad-Sense Heritability ()

Estimates of heritability in broad sense () were calculated for all the traits studied. The value of heritability calculated for each trait was grouped into high heritability (>60%), moderate heritability (30–60%), and low heritability (<0–30%) as per the classification suggested by Robinson et al. [40]. Accordingly, high estimates of heritability were recorded only for stem rust (68%), whereas moderate heritability was obtained for spike length (49%), number of days to maturity (43%), number of days to heading (43%), yellow rust (36%), and plant height (51%) (Table 8). Studies conducted by Gergana and Bozhidar [41] showed high heritability values for spike length. Singh [39] stated that for a character with high heritability (≥80%), the selection is fairly easy, because there would be a close correspondence between genotype and phenotype due to a relatively smaller contribution of environmental factors to the expression of the phenotype. However, very low heritability estimates were recorded for number of seeds spike−1 (8%), total number of tillers plant−1 (6%), and effective number of tillers plant−1 (6%) (Table 8).

3.5. Genetic Advance

It is important to find out the genetic gains likely to be achieved in the next generation that is classified as high (>20%), medium (10 to 20%), and low (<10%) as suggested by Johnson et al. [24]. The genetic advance as percent of means (GAM) expressed ranged from 2.55% (number of days to maturity) to 93.04% (stem rust) at Damot Gale and Damot Sore sites, respectively (Tables 911). This refers to the improvement of the traits in genotypic value for the new population compared with the base population in one cycle of selection that is within the range of 2.55% (for number of days to maturity) to 93.04% (stem rust resistance) at 5% selection intensity.

High heritability () accompanied by high genetic advance as percent of means (GAM) was exhibited from stem rust (68%, 96.3%) and moderate accompanied by high GAM was obtained from yellow rust (36%, 45.8%), respectively. In addition, spike length (49%, 10.9%) exhibited moderate heritability () and moderate genetic advance as percent of means (GAM), respectively (Table 8). Gezahegn et al. [42] also reported that high heritability coupled with moderate GAM for spike length (63.66%, 10.34%), which was in line with this study.

Even though heritability estimates provide the basis for the success of selection on the phenotypic performance, the estimates of heritability and genetic advance should always be considered simultaneously, since high heritability alone will not always be associated with high genetic advance [24]. The estimates of GA help in understanding the type of gene action involved in the expression of various polygenic traits. High values of genetic advance are indicative of the involvement of additive gene action, whereas low values are indicative of nonadditive gene action [43]. Thus, the heritability estimates will be reliable if accompanied by a high genetic advance.

4. Conclusion and Suggestions

The research results indicated the presence of significant variations among bread wheat genotypes for yield and yield-related traits. This variability can potentially be exploited in future improvement of bread wheat breeding programs. However, it was evident that cultivar “Alidoro” showed outstanding performance in terms of grain yield and other components of yield including resistance to both stem and yellow rust diseases. Hence, we recommend cultivar “Alidoro” for wider cultivation across Wolaita and Dawuro zones and similar agroecologies.

Data Availability

All the necessary data are included in the manuscript. If additional data are required, the corresponding author can be contacted.

Conflicts of Interest

The authors declare that they have no conflicts of interest.


The authors acknowledge the Kulumsa Agricultural Research Center of Ethiopian Institute of Agricultural Research Center for supplying the seeds of wheat genotypes used in this study. The authors thank Wolaita Sodo University Research and Community Service for funding the research work.