Phenotypic Variability, Heritability, and Performance Evaluation of Bread Wheat (Triticum aestivum L.) Genotypes for Grain Yield and Yield-Related Characters in North West Ethiopia
Abstract
Bread wheat serves as a crucial staple crop globally, including in Ethiopia, but faces challenges related to yield reduction due to environmental constraints. Consequently, this research aimed to assess the phenotypic variability and performance of 12 bread wheat genotypes for grain yield (GY) and yield-related traits in northwest Ethiopia. The field experiment was conducted over 2 years in two environments (Ayehu Guagusa and Dangila). A third environment (Guagusa Shekudad) was added during the second cropping season, making the total number of environments three. These experiments were conducted using a randomized complete block design (RCBD) with three replications during the main cropping seasons of 2021/2022 and 2022/2023. The combined analysis of variance (ANOVA) showed a highly significant (p < 0.001) interaction between environments and genotypes for all traits, except plant height (PH). These results suggest that the genotype effect had a stronger influence than the environment, implying high heritability values and potential for successful genotype selection in broader adaptation areas. The combined analysis across two environments over 2 years showed that the broad-sense heritability values ranged from 58.82% for aboveground biomass (AGB) to 100% for GY. Notably, the ANOVA revealed high heritability associated with significant genetic advance (GA) as a percent of mean for traits such as the number of tillers per plant (TPP) (78.57%, 34.4%), PH (93.11%, 25.38%), spike length (SL) (71.08%, 20.25%), 1000-kernel weight (TKW) (71.04%, 24.38%), harvest index (HI) (71.11%, 22%), and GY (100% and 30.4%), respectively. Therefore, these traits should be a focus in the bread wheat breeding program, as their phenotypic expression is influenced by additive gene action. Consequently, direct selection for these traits will enhance GY. From the combined analysis of 2 years over two locations, and for the second cropping season at three locations for 1 year, the genotypes G6 (2.78 t/ha, 3.57 t/ha), G7 (2.84 t/ha, 3.58 t/ha), G8 (2.87 t/ha, 3.48 t/ha), and G12 (2.83 t/ha, 3.26 t/ha), respectively, demonstrated the highest GY performance across the environments and are recommended for local farmers. These findings provide valuable insights into the genetic potential of the tested bread wheat genotypes, which can be utilized in breeding programs to develop high-yielding and well-adapted cultivars for the study area.
1. Introduction
Bread wheat (Triticumaestivum L.) is a hexaploid plant with the chromosome number of 2n = 6x = 42. It is classified within the Triticeae group of the Poaceae family [1, 2]. It is globally recognized as the most extensively cultivated crop, supplying around 20% of the total human dietary protein and calorie consumption [3, 4]. Although there have been significant advancements in wheat productivity, further endeavors are required to address the challenge posed by the continuous growth of the global population, which is projected to reach 9.8 billion by the year 2050 [5]. It holds a critical role in ensuring food security in Ethiopia and serves as a significant provider of protein, dietary fibers, B vitamins, minerals, and other phytochemicals in the human diet [6, 7]. Additionally, it stands as one of the most economically and socially crucial cereal crops for both human food and animal feed [8]. Bread wheat holds a central role in the Ethiopian diet and is essential for producing various food items such as bread, porridge (genfo), local beer (tela), roasted grain (kolo), boiled grain (nifro), and injera [9, 10]. In addition, wheat straw is frequently utilized as roofing material and feed for animals [11].
Bread wheat holds a significant position among wheat species globally in terms of distribution and production [3, 12]. In Ethiopia, bread wheat cultivation spans ~2.1 million hectares of land annually, with 1.7 million hectares under rainfed conditions and 0.4 million hectares under irrigation. This cultivation yields around 6.7 million tons of grain each year [13]. Furthermore, a significant portion of sub-Saharan Africa’s wheat production originates from Ethiopia [14]. As a result of its importance in ensuring food security, reducing dependency on imported food, and supplying raw materials for the agro-processing industry, bread wheat is regarded as one of Ethiopia’s key strategic crops [15, 16].
Analyzing phenotypic variability in a crop is crucial for identifying key traits in plant breeding [17, 18]. Understanding heritability and genetic progress can aid in selecting specific features [19, 20]. A phenotype comprises an organism’s observable characteristics, influenced by genetic expression and environmental factors, as well as their interactions [21]. Heritability examines the correlation between the observed phenotypic values and their underlying genotypic values, considering phenotypic and genotypic variances [22]. Thus, the success of crop advancements relies on the genetic variability and heritability within plant materials. The extent of variability is assessed through genotypic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV), providing insights into the degree of variation in modified traits [23, 24]. Plant breeders focus on creating new cultivars with superior quality and stability. These new wheat cultivars undergo yield performance assessments in various locations before being released. Cultivars demonstrating high and consistent yields are preferred by farmers and breeders. The success of a new cultivar release hinges on its grain yield (GY) quantity, quality, and performance across different locations [25]. Enhancing crop production requires developing cultivars that can thrive in diverse environments, withstand biotic and abiotic stresses, and deliver quality product. Understanding the genetic diversity within genotype collections significantly influences these efforts [26, 27].
GY, a multifaceted quantitative trait, is influenced by various yield-contributing factors. Therefore, selecting desirable genotypes should consider not only yield but also other yield components [28]. Utilizing improved wheat varieties and appropriate planting schedules is crucial for enhancing bread wheat productivity [29]. Genetic variation forms the basis for selection response in plant breeding, where the differential reproduction of genotypes leads to changes in allele and gene frequencies within the population. This process alters genotypic and phenotypic values for the selected trait [30, 31]. Given the challenges of population growth and climate change, enhancing food availability is crucial. To address this, breeders must assess the phenotypic performance of crops to boost crop yields [32, 33]. The primary reason for low bread wheat yield is the absence of improved genotypes and seed quality [34]. Achieving high yield is indeed a primary and crucial goal of bread wheat breeding programs [12]. For developing countries like Ethiopia, the main objective is to enhance the seed yield of bread wheat. However, farmers in the study area have not selectively chosen the most productive and suitable bread wheat genotypes. Instead, they mainly use seeds from older, improved crop varieties. Several factors contribute to the unsatisfactory bread wheat production in this potentially high-yield area, including the lack of improved high-yielding and well-performed varieties, poor management practices, and both biotic and abiotic stresses. The most efficient way to address the biotic and abiotic challenges in bread wheat production is to create varieties that are suitable to the environments in which bread wheat is cultivated. Therefore, developing high-yielding varieties that are resistant to biotic and abiotic stress is essential for improving GY in bread wheat. At the same time, evaluating the variability in GY and other characteristics is crucial before devising a suitable breeding strategy for genetic enhancement. So, this study aims to evaluate the phenotypic variability, broad-sense heritability, and performance of bread wheat genotypes, focusing on GY and related traits to select high-yielding bread wheat varieties suitable for the study regions and comparable agro-ecological conditions.
2. Material and Methods
2.1. Description of the Study Area
The research was conducted in three districts within the Awi Administrative Zone, namely, Ayehu Guagusa, Dangila, and Guagusa Shekudad districts, each characterized by unique environments (Figure 1). The Awi Zone is located between latitudes 10°23′N and 10°85′N and longitudes 36°35′E and 36°57′E, at altitudes ranging from 1800 to 3100 m above sea level. It experiences a mean annual rainfall of 1750 mm and a mean monthly temperature ranging from 17 to 27°C. The specific locations pertinent to this experiment are detailed and indicated in Table 1.
| S. No. | Location | Rainfall (mm) | Temperature (°C) | Altitude (m.a.s.l.) | Latitude | Longitude |
|---|---|---|---|---|---|---|
| 1 | Guagusa Shekudad | 1140–3572 | 10–25 | 2451–2537 | 11°09′60.00″N | 28°61′ to 28°87′ E |
| 2 | Dangila | 700–1200 | 16–35 | 2127–2200 | 11°16′N 36°50′E | 11°16′N 36°50′E |
| 3 | Ayehu Guagusa | 1500–2000 | 18–34 | 1600–1800 | 11°00′0.00″N | 36°39′59.99″E |
2.2. Experimental Material and Design
For this study, 12 varieties of bread wheat (Table 2), which were obtained from Kulumsa and Adet Agricultural Research Centers, were evaluated under rainfed condition in 2021/2022 and 2022/2023 for 2 years. The experiments were conducted at Dangila district and Ayehu Guagusa in both years, and during the 2022/2023 season a third location, Guagusa Shekudadad districts, was included. Eleven varieties from Kulumsa Agricultural Research Center, Oromia, Ethiopia, and one variety from Adet Agricultural Research Center, Amhara, Ethiopia, were used in this trial. The design was a randomized complete block design (RCBD) with three replications in all environments. Each experiment consisted of six rows with a spacing of 30 cm between rows and resulting in a plot area of 3.6 m2 (1.8 m × 2 m). The spacing between plots was 0.50 m, and there was a distance of 1 m between replications. For each plot, the recommended fertilizer application of 100 kg/ha (NPSZnB) and 150 kg/ha urea was applied. Half of the urea and the full amount of NPSZnB were added during planting, while the remaining half of the urea was applied during the mid-tillering stage. A seed rate of 150 kg/ha was used. Weeds were managed through manual weeding in all experimental fields across the three locations. No herbicides or fungicides were used for weed or disease control.
| S. No. | Genotype name | Code | Origin | Pedigree | Released by | Year of release |
|---|---|---|---|---|---|---|
| 1 | Shorima | G1 | ICARDA | UTQUE96/3/PYN/BAU//MILAN 2011 | KARC | 2011 |
| 2 | Biqa | G2 | ICARDA | PASTOR//HXL7573/2 ∗BAU/3/WBLL1 | KARC | 2014 |
| 3 | Lemu | G3 | CIMMYT | WAXWING ∗2/HEILO | KARC | 2012 |
| 4 | Kingbird | G4 | CIMMYT |
|
KARC | 2015 |
| 5 | Ogolcho | G5 | CIMMYT | WORRAKATTA/2 ∗PASTOR | KARC | 2012 |
| 6 | Tay | G6 | CIMMYT | ET-12 D4/HAR 604 or ET-12D4/4777(2)//FKN/GB/3/PAVON F76 “S” | AARC | 2005 |
| 7 | Honqolo | G7 | CIMMYT | NJORO SD-7 | KARC | 2016 |
| 8 | Danda’a | G8 | CIMMYT | KIRITATI//2 ∗PBW65/2 ∗SERI.1B | KARC | 2010 |
| 9 | Wane | G9 | CIMMYT | SOKOLL/EXCALIBUR | KARC | 2016 |
| 10 | Balcha | G10 | CIMMYT | CROC_1/AE.SQUARROSA (213)//PGO/10/ATTILA ∗2/9/KT/BAGE//FN/U/3/BZA/4/TRM/5/ALDAN/6/SERI/7/VEE#10/8/OPTA | KARC | 2019 |
| 11 | Hulluka | G11 | ICARDA | UTQUE96/3/PYN/BAU//MILAN 2012 | KARC | 2010 |
| 12 | Pavon-76 | G12 | CIMMYT | VICAM-71//CIANO-67/SIETE-CERROS-66/3/KALYANSONA/BLUEBIRD | KARC | 1982 |
- Note: CIMMYT, International Maize and Wheat Improvement Center.
- Abbreviations: AARC, Adet Agricultural Research Center; ICARDA, International Center for Agricultural Research in the Dry Areas; KARC, Kulumsa Agricultural Research Center.
2.3. Data Collection
Data were collected based on an average of 10 randomly selected plants per plot. Ten representative plants per plot were randomly selected from the central rows, excluding the two border rows and the 10 cm from both the left and right sides, for observations. For the data collected on a per-plant basis, 10 plants per plot were randomly selected for each of the measured traits. These traits included the number of tillers per plant (TPP), plant height (PH) (cm), number of kernels per spike (KPS), number of spikelet per spike (SkPS), and spike length (SL) (cm). The data for the number of days to 50% heading, number of days to 90% maturity, grain filling period (GFP) (the number of days from heading to maturity), 1000-kernel weight (TKW) (g), aboveground biomass (AGB) (kg), GY (t/ha), and harvest index (HI) (%)were collected on a plot basis.
2.4. Statistical Analysis
2.4.1. Analysis of Variance (ANOVA)
For the traits that showed significant differences among the genotypes (at the individual location) and genotype by environment interaction (over location), the mean separation test was computed by using Duncan’s multiple range tests (DMRTs) at the 5% level of significance.
2.4.2. Estimation of Genetic Parameters
Genetic parameters were estimated to identify the genetic variability among bread wheat genotypes and assess the impact of the environment on different traits. The phenotypic and genotypic coefficients of variation were determined using the method recommended by Burton and DeVane [38].
The heritability was classified as either low (0%–30%), moderate (30%–60%), or high (>60%), according to the categorization by Robinson et al. [40].
3. Results and Discussions
3.1. Mean, Range, and ANOVA
3.1.1. ANOVA
According to the combined ANOVA conducted at Ayehu Guagusa district and Dangila district for 2 years (Table 3), and Ayehu Guagusa district, Dangila District, and Guagusa Shekudadad district for 1 year (Table 4), the interaction between environments and genotypes was found to be highly significant (p < 0.001) in mean squares for all the studied traits among genotypes, indicating variation among the tested varieties for GY and yield-related traits. Previous studies on bread wheat also identified highly significant (p < 0.001), differences among genotypes, aligning with the similar outcomes by [9, 10, 43, 44]. This indicates that the expression of these traits is not consistent across locations. On the other hand, the genotypes selected for those characters at one location may not exhibit a similar relative performance at another location.
| Character | Source of variation | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Env | Rep(Env) | Env ∗Gen | Env(yr) | Gen | Gen ∗yr | Gen ∗Env ∗yr | Error | CV | LSD | ||
| (df = 1) | (df = 5) | (df = 11) | (df = 1) | (df = 11) | (df = 11) | (df = 11) | (df = 92) | — | — | ||
| TPP | 5.36 ∗∗ | 0.45 | 0.24 ∗∗ | 10.8 ∗∗ | 0.71 ∗∗ | 0.24 ∗∗ | 0.57 ∗∗ | 0.06 | 10 | 0.2 | |
| PH | 306.54 ∗∗ | 2.28 | 12.88ns | 51.72 ∗ | 352.37 ∗∗ | 25.02 ∗∗ | 34.17 ∗∗ | 7.45 | 3.26 | 2.2 | |
| KPS | 123.06 ∗∗ | 1.65 | 34.63 ∗∗ | 240.46 ∗∗ | 70.44 ∗∗ | 20.9 ∗∗ | 22.6 ∗ | 4.8 | 2.2 | 1.8 | |
| SkPS | 2.95 | 0.59 | 2.55 ∗∗ | 85.56 ∗∗ | 5.4 ∗∗ | 3.82 ∗∗ | 1.69 ∗∗ | 0.5 | 0.71 | 0.6 | |
| SL | 4.5 ∗∗ | 0.05 | 0.57 ∗∗ | 0.76 ∗∗ | 4.2 ∗∗ | 1.09 ∗∗ | 1 ∗∗ | 0.18 | 4.76 | 0.35 | |
| DTH | 136 ∗∗ | 18.15 | 21.02 ∗∗ | 156.25 ∗∗ | 148 ∗∗ | 28.91 ∗∗ | 11.98 ∗∗ | 4 | 2 | 1.62 | |
| DTM | 191.36 ∗∗ | 23.74 | 14.48 ∗∗ | 330.03 ∗∗ | 251.86 ∗∗ | 40.27 ∗∗ | 26.21 ∗∗ | 6.7 | 2.3 | 2.1 | |
| GFP | 637.56 ∗∗ | 2.66 | 36 ∗∗ | 35ns | 98.92 ∗∗ | 33.44 ∗∗ | 24.66 ∗∗ | 7.3 | 5.6 | 2.2 | |
| GY | 0.05 ∗∗ | 0.0001 | 0.002 ∗∗ | 0.06 ∗∗ | 0.02 ∗∗ | 0.01 ∗∗ | 0.009 ∗∗ | 0.0004 | 3.8 | 0.02 | |
| TKW | 841 ∗∗ | 17.58 | 90.73 ∗∗ | 300.44 ∗∗ | 206 ∗∗ | 161.85 ∗∗ | 237.08 ∗∗ | 22.87 | 8.7 | 3.9 | |
| AGB | 1.13 ∗∗ | 0.03 | 0.1 ∗∗ | 1.86 ∗∗ | 0.18 ∗∗ | 0.11 ∗∗ | 0.03 ∗∗ | 0.008 | 6 | 0.07 | |
| HI | 96.92 ∗∗ | 22.63 | 69 ∗∗ | 264.53 ∗∗ | 78 ∗∗ | 75 ∗∗ | 38 ∗∗ | 7.27 | 7.23 | 2.19 | |
- Note: Env, mean square due to environment; Rep(Env), mean square due to replication by environment; Env ∗Gen, mean square due to interaction between environments and genotypes; Env(yr), mean square due to environment by year; Gen, mean square due to genotypes; Gen ∗yr, mean square due to interaction between year and genotypes; Gen ∗Env ∗Yr, mean square due to interaction between environments and genotypes and years, error, mean square due to error.
- Abbreviations: AGB, aboveground biomass; CV, coefficient of variation; df, degree of freedom; DTH, days to heading; DTM, days to maturity; GFP, grain filling period; GY, grain yield; HI, harvest index; KPS, kernels per spike; LSD, least square difference; ns, nonsignificant; PH, plant height; SkPS, spikelet per spike; SL, spike length; TKW, 1000-kernel weight; TPP, tillers per plant.
- ∗Significant at p < 0.05.
- ∗ ∗Significant at p < 0.001.
| Trait | Mean | Range | δ2g | δ2p | PCV% | GCV% | GA | GAM | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Minimum | Maximum | |||||||||||
| Score | Genotype | Score | Genotype | |||||||||
| TPP | 2.5 | 2.06 | G11 | 2.95 | G7 | 0.22 | 0.28 | 21.17 | 18.76 | 78.57 | 0.86 | 34.4 |
| PH | 83.6 | 77.13 | G9 | 94.67 | G6 | 115 | 122.45 | 13.24 | 12.83 | 93.11 | 21.22 | 25.38 |
| KPS | 50.24 | 44.98 | G10 | 54.04 | G8 | 21.88 | 26.68 | 10.28 | 9.31 | 77.34 | 8.23 | 16.38 |
| SkPS | 16.76 | 15.87 | G9 | 18.47 | G8 | 1.63 | 2.13 | 8.71 | 7.62 | 66.34 | 2 | 11.93 |
| SL | 8.94 | 7.75 | G9 | 9.82 | G5 | 1.34 | 1.52 | 13.8 | 12.95 | 71.08 | 1.81 | 20.25 |
| DTH | 65 | 59.25 | G9 | 69.67 | G8 | 48 | 52 | 11.1 | 10.66 | 91.16 | 13.54 | 20.83 |
| DTM | 113.04 | 106.2 | G9 | 123 | G6 | 81.72 | 88.42 | 8.32 | 8 | 90.88 | 17.6 | 15.57 |
| GFP | 48.1 | 44.58 | G3 | 54.83 | G6 | 30.54 | 37.84 | 12.79 | 11.5 | 71.89 | 9.11 | 19 |
| GY | 2.6 | 2.23 | G9 | 2.87 | G8 | 0.007 | 0.007 | 15 | 15 | 100 | 0.17 | 30.4 |
| TKW | 55 | 46.12 | G11 | 59.42 | G5 | 61.04 | 83.91 | 16.65 | 14.21 | 71.04 | 13.41 | 24.38 |
| AGB | 1.53 | 1.39 | G9 | 1.81 | G6 | 0.06 | 0.07 | 17.3 | 16 | 58.82 | 0.32 | 21 |
| HI | 37.04 | 33.69 | G6 | 42.28 | G12 | 23.58 | 30.85 | 15 | 13.11 | 71.11 | 8.14 | 22 |
- Abbreviations: AGB, aboveground biomass; DTH, days to heading; DTM, days to maturity; GFP, grain filling period; GY, grain yield; HI, harvest index; KPS, kernels per spike; PH, plant height; SkPS, spikelet per spike; SL, spike length; TKW, 1000-kernel weight; TPP, tillers per plant.
Based on the combined ANOVA conducted across two locations over 2 years (Table 3), the interaction between the environment and genotypes was highly significant (p < 0.001) for all traits, except PH (12.88 ns), which showed nonsignificant differences for the GxE effect. These traits indicated that the environment had less influence compared to the genotype effect, suggesting high heritability value and potential for successful genotype selection in broader adaptation areas. Similar result was reported by Alambo et al. [45], for the performance evaluation of Ethiopian bread wheat genotypes. The significant genotype, environment, and their interactions indicate the possibility of pinpointing high GY and other favorable traits. This suggests that the varieties tested display variation in their performance for GY and related characteristics in various environments, enabling the identification of genotypes with high GY and other desirable attributes.
The combined ANOVA conducted at Ayehu Guagusa district for 2 years (Table 5) and Dangila district for 2 years (Table 6) also revealed significant differences among the tested genotypes (p < 0.001) for various traits. These include TPP, PH, KPS, SkPS, SL, spikes per plant, days to heading (DTH), days to maturity (DTM), GFP, GY, TKW, AGB and HI. This indicates the presence of ample variability in the selected material for the study, highlighting its suitability for crop improvement. Previous studies on bread wheat [46, 47] also showed highly significant differences among various traits, which aligns with the findings of our study. Hence, the combined ANOVA illustrated substantial genotypic diversity in GY and its components, suggesting significant potential among the examined varieties for enhancing these attributes through focused selection in breeding initiatives.
| Trait | Mean | Range | Source of variation | δ2g | δ2p | PCV% | GCV% | GA | GAM | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Minimum | Maximum | Rep | Gen | Yr ∗Gen | Error | |||||||||||||
| Score | Gen | Score | Gen | (df = 2) | (df = 11) | (df = 11) | (df = 46) | CV | LSD | |||||||||
| TPP | 2.28 | 1.9 | G11 | 2.8 | G10 | 0.2 | 0.34 ∗∗ | 0.22 ∗∗ | 0.04 | 9.03 | 0.24 | 0.1 | 0.14 | 16.41 | 13.87 | 71.43 | 0.55 | 24.12 |
| PH | 85.06 | 79.2 | G9 | 97.8 | G6 | 2.3 | 201.87 ∗∗ | 32.65 ∗∗ | 4.46 | 2.5 | 2.45 | 65.8 | 70.26 | 9.85 | 9.54 | 93.65 | 16.17 | 19 |
| KPS | 49.31 | 46.7 | G10 | 52.3 | G5 | 1.5 | 35.8 ∗∗ | 22.7 ∗∗ | 3.67 | 3.6 | 2.07 | 10.71 | 14.38 | 7.7 | 6.64 | 74.48 | 5.82 | 11.8 |
| SkPS | 16.9 | 16.07 | G1 | 18 | G6 | 0.67 | 2.3 ∗∗ | 2.27 ∗∗ | 0.41 | 3.8 | 0.75 | 0.63 | 1.04 | 6.03 | 4.7 | 60.58 | 1.27 | 7.5 |
| SL | 9.1 | 7.45 | G9 | 10.07 | G5 | 0.07 | 3.36 ∗∗ | 0.73 ∗∗ | 0.27 | 5.7 | 0.6 | 1.03 | 1.3 | 12.53 | 11.15 | 79.23 | 1.86 | 20.45 |
| DTH | 66 | 58.8 | G9 | 71.5 | G6 | 6 | 96 ∗∗ | 14.48 ∗∗ | 2.74 | 2.5 | 1.92 | 31.09 | 33.83 | 8.8 | 8.45 | 92 | 11.02 | 16.7 |
| DTM | 111.89 | 106 | G9 | 123 | G6 | 1.43 | 119.71 ∗∗ | 17.83 ∗∗ | 5.94 | 2.2 | 2.83 | 37.92 | 43.86 | 5.92 | 5.5 | 86.46 | 11.8 | 10.55 |
| GFP | 45.97 | 41.67 | G1 | 56.17 | G12 | 4.6 | 61.74 ∗∗ | 20.17 ∗∗ | 8.18 | 6.2 | 3.32 | 17.85 | 26.03 | 11.1 | 10 | 68.57 | 7.21 | 15.68 |
| GY | 2.69 | 2.27 | G9 | 3.01 | G12 | 0.0002 | 0.01 ∗∗ | 0.02 ∗∗ | 0.0007 | 4.5 | 0.03 | 0.003 | 0.004 | 11 | 9.44 | 75 | 0.1 | 17.24 |
| TKW | 52.53 | 43.5 | G10 | 60.5 | G5 | 25.1 | 175.09 ∗∗ | 354.28 ∗∗ | 36.4 | 11.5 | 7.01 | 46.23 | 82.63 | 17.3 | 12.94 | 56 | 10.48 | 19.95 |
| AGB | 1.62 | 1.4 | G1 | 1.75 | G11 | 0.0003 | 0.06 ∗∗ | 0.044 ∗∗ | 0.007 | 5.2 | 0.1 | 0.02 | 0.03 | 10.7 | 8.73 | 66.67 | 0.24 | 14.81 |
| HI | 36.22 | 30 | G11 | 42 | G12 | 1.8 | 91.15 ∗∗ | 59 ∗∗ | 7.75 | 7.7 | 3.23 | 27.8 | 35.55 | 16.46 | 14.56 | 78.2 | 9.6 | 26.5 |
- Abbreviations: AGB, aboveground biomass; DTH, days to heading; DTM, days to maturity; GFP, grain filling period; GY, grain yield; HI, harvest index; KPS, kernels per spike; PH, plant height; SkPS, spikelet per spike; SL, spike length; TKW, 1000-kernel weight; TPP, tillers per plant.
- ∗∗Significant at p < 0.001.
| Trait | Mean | Range | Source of variation | δ2g | δ2p | PCV% | GCV% | GA | GAM | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Minimum | Maximum | Rep | Gen | Yr ∗Gen | Error | |||||||||||||
| Score | Gen | Score | Gen | (df = 2) | (df = 11) | (df = 11) | (df = 46) | CV | LSD | |||||||||
| TPP | 2.67 | 2.2 | G11 | 3.32 | G7 | 0.03 | 0.61 ∗∗ | 0.6 ∗∗ | 0.08 | 10.6 | 0.33 | 0.18 | 0.26 | 19.1 | 15.89 | 69.23 | 0.73 | 27.34 |
| PH | 82.14 | 75.11 | G9 | 91.5 | G6 | 2.3 | 150.4 ∗∗ | 26.53 ∗∗ | 10.44 | 4 | 3.76 | 46.65 | 57.09 | 9.199 | 8.315 | 81.71 | 12.72 | 15.48 |
| KPS | 51.16 | 45.3 | G10 | 57.54 | G8 | 1.8 | 69.28 ∗∗ | 20.8 ∗∗ | 6.42 | 4.95 | 2.94 | 20.95 | 27.37 | 10.23 | 8.947 | 76.55 | 8.25 | 16.13 |
| SkPS | 16.62 | 15.34 | G9 | 19.13 | G8 | 0.52 | 5.65 ∗∗ | 3.23 ∗∗ | 0.58 | 4.61 | 0.89 | 1.69 | 2.27 | 9.065 | 7.822 | 74.45 | 2.3 | 13.84 |
| SL | 8.76 | 8.05 | G9 | 9.57 | G5 | 0.03 | 1.42 ∗∗ | 1.35 ∗∗ | 0.1 | 3.53 | 0.36 | 0.44 | 0.54 | 8.389 | 7.572 | 81.48 | 1.2 | 13.7 |
| DTH | 64 | 59.67 | G2 | 70.67 | G8 | 30.37 | 73 ∗∗ | 29.4 ∗∗ | 5.23 | 3.57 | 2.66 | 22.59 | 27.82 | 8.241 | 7.426 | 81.2 | 8.8 | 13.75 |
| DTM | 114.2 | 106.3 | G9 | 123.33 | G6 | 46.06 | 146.63 ∗∗ | 48.65 ∗∗ | 7.45 | 2.4 | 3.17 | 46.39 | 53.84 | 6.425 | 5.964 | 86.16 | 13.02 | 11.4 |
| GFP | 50.2 | 46.67 | G3 | 58.67 | G6 | 0.72 | 73.15 ∗∗ | 37.93 ∗∗ | 6.45 | 5.06 | 2.95 | 22.23 | 28.68 | 10.67 | 9.393 | 77.51 | 8.55 | 17.03 |
| GY | 2.5 | 2.18 | G9 | 2.87 | G7 | 0.00007 | 0.01 ∗∗ | 0.04 ∗∗ | 0.0002 | 2.7 | 0.02 | 0.003 | 0.004 | 11.71 | 10.58 | 81.67 | 0.11 | 20.37 |
| TKW | 57.36 | 47.83 | G11 | 64.83 | G7 | 10.06 | 121.75 ∗∗ | 44.65 ∗∗ | 9.35 | 5.33 | 0.35 | 37.47 | 46.82 | 11.93 | 10.67 | 80.03 | 11.28 | 19.67 |
| AGB | 1.44 | 1.2 | G9 | 1.9 | G6 | 0.06 | 0.21 ∗∗ | 0.09 ∗∗ | 0.009 | 6.7 | 0.11 | 0.07 | 0.08 | 19.64 | 18.37 | 87.5 | 0.83 | 57.64 |
| HI | 37.86 | 30 | G6 | 42.6 | G12 | 43.44 | 55.86 ∗∗ | 54.25 ∗∗ | 6.78 | 6.88 | 3.03 | 16.36 | 23.14 | 12.71 | 10.68 | 70.7 | 7.01 | 18.52 |
- Abbreviations: AGB, aboveground biomass; DTH, days to heading; DTM, days to maturity; GFP, grain filling period; GY, grain yield; HI, harvest index; KPS, kernels per spike; PH, plant height; SkPS, spikelet per spike; SL, spike length; TKW, 1000-kernel weight; TPP, tillers per plant.
- ∗∗Significant at p < 0.001.
3.1.2. Mean Performance and Range of Morphological Traits of the Genotypes
The mean performance of 12 bread wheat genotypes for 12 morphological traits is shown in Tables 5–8. A combined analysis conducted over 2 years in the Ayehu Guagusa and Dangila districts reveals that the highest GY recorded was 2.87 t/ha for G8, while the lowest yield was 2.23 t/ha for G9. The range was from 2.23 to 2.87 t/ha, with a mean of 2.6 t/ha (Table 7). The second combined analysis was performed on data obtained from the Ayehu Guagusa, Dangila, and Guagusa Shekudad districts over 1 year since one location was included at the second season. From this analysis, the maximum GY of 3.58 t/ha was obtained from G7, while the minimum yield of 2.71 t/ha was from G2. This resulted in a yield range from 2.71 to 3.58 t/ha, with a mean of 3.29 t/ha (Table 8).
| No. | Genotypes | Trait | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TPP | PH | KPS | SkPS | SL | DTH | DTM | GFP | GY | TKW | AGB | HI | |||
| 1 | Shorima | 2.51c,d | 82.68d,e | 49.88b,c | 16.29d,e | 9.258b,c | 66.33c | 113.3c | 47c | 2.5c,d,e | 51.75cd | 1.387h | 39.23b | |
| 2 | Biqa | 2.48c,d | 79.05f,g | 47.51d | 16.26d,e | 9.25bc | 60.25f | 109.6d | 49.33b | 2.48d,e | 56.33a,b | 1.525d,e,f | 36.38c,d,e | |
| 3 | Lemu | 2.55b,c,d | 82.19d,e | 50.01b,c | 16.94b,c | 9.358b,c | 68.75a,b | 113.3c | 44.58c | 2.556b,c,d | 57.25a,b | 1.454f,g,h | 37.69b,c,d | |
| 4 | Kingbird | 2.66b,c | 81.69d,e | 50.74b,c | 16.96b,c | 8.717d | 62.67e | 109.2d | 46.58c | 2.5c,d,e | 53.92b,c | 1.5e,f,g | 35.51d,e,f | |
| 5 | Ogolcho | 2.34d,e | 89.01c | 51.44b | 16.85b,c,d | 9.817a | 64.42d | 110.7d | 46.25c | 2.6b,c | 59.42a | 1.433g,h | 39.51b | |
| 6 | Tay | 2.32d,e | 94.67a | 53.39a | 17.07b | 9.108c | 68.08a,b,c | 122.9a | 54.83a | 2.78a | 55.08a,b,c | 1.808a | 33.69f | |
| 7 | Honqolo | 2.95a | 78.7f,g | 49.61b,c | 16.99b,c | 8.508d,e | 67.33b,c | 113.9c | 46.58c | 2.84a | 58.75a | 1.642b | 37.71b,c,d | |
| 8 | Danda’a | 2.51c,d | 91.83b | 54.04a | 18.47a | 9.183b,c | 69.67a | 116.7b | 47c | 2.87a | 57.33a,b | 1.621b,c | 38.23b,c | |
| 9 | Wane | 2.38d,e | 77.13g | 49.19c,d | 15.87e | 7.75f | 59.25f | 106.2e | 46.92c | 2.23f | 58.67a | 1.392h | 35.04e,f | |
| 10 | Balcha | 2.75a,b | 80.57e,f | 44.98e | 16.16e | 8.336e | 63.08d,e | 109.3d | 46.25c | 2.6b | 48.75d,e | 1.6b,c,d | 35.2e,f | |
| 11 | Hulluka | 2.06f | 83.1d | 51.5b | 16.92b,c | 9.522a,b | 67.92a,b,c | 118.2b | 50.25b | 2.45e | 46.17e | 1.55c,d,e | 33.97e,f | |
| 12 | Pavon-76 | 2.2e,f | 82.6d,e | 50.55b,c | 16.36c,d,e | 8.413d,e | 61.92e | 113.2c | 51.5b | 2.83a | 55.92a,b,c | 1.45f,g,h | 42.28a | |
- Note: Superscript lowercase letters indicated that the significance level between treatments.
- Abbreviations: AGB, aboveground biomass; DTH, days to heading; DTM, days to maturity; GFP, grain filling period; GY, grain yield; HI, harvest index; KPS, kernels per spike; PH, plant height; SkPS, spikelet per spike; SL, spike length; TKW, 1000-kernel weight; TPP, tillers per plant.
| No. | Genotypes | Trait | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TPP | PH | KPS | SkPS | SL | DTH | DTM | GFP | GY | TKW | AGB | HI | ||
| 1 | Shorima | 2.544c,d | 86.97e | 50.18a,b | 16.75a,b,c,d | 9.678d,e | 67.89e | 118.9d | 51a | 3.39e | 67.44e,f | 1.906b,c | 38.44a,b |
| 2 | Biqa | 1.999a | 81.11a,b,c | 50.1a,b | 16.48a,b,c | 9.522d,e | 60.67a,b | 114.9b,c | 54.22b,c | 2.71a | 61.78b,c | 1.689a | 36.22a |
| 3 | Lemu | 2.248a,b | 83.97c,d,e | 50.19a,b | 17.08b,c,d,e | 9.678d,e | 69.22e | 120.6d,e | 51.33a | 3.56f | 68.89f | 1.906b,c | 40.55b,c |
| 4 | Kingbird | 2.214a,b | 83.39b,c | 51.88b,c | 16.79b,c,d | 9.144b,c | 63.33c | 115.3b,c | 52a,b | 3.47f | 60.44a,b | 1.856b | 40.65b,c |
| 5 | Ogolcho | 2.237a,b | 90.17f | 51.21b | 17.27c,d,e | 9.678d,e | 65.22d | 116.2c | 51a | 3.16c,d | 66.56e,f | 1.889b,c | 36.83a |
| 6 | Tay | 2.429b,c | 97.42g | 56.13d,e | 19.21f | 9.822e | 69.56e | 126.4g | 56.89d | 3.57f | 62.67b,c,d | 2.089d | 36.87a |
| 7 | Honqolo | 2.689d | 79.23a | 51.94b,c | 17.79e | 8.811a,b | 68.89e | 121.9e,f | 53a,b,c | 3.58f | 72.67g | 2.056d | 37.79a,b |
| 8 | Danda’a | 2.537c,d | 92.46f | 57.8e | 19.44f | 9.6d,e | 71.22f | 124f,g | 52.78a,b,c | 3.48e,f | 68.44e,f | 2.083d | 36.13a |
| 9 | Wane | 2.403b,c | 79.77a | 50.67b | 16.3a,b | 8.6a | 59.67a | 110.6a | 50.89a | 2.83b | 66.11d,e,f | 1.711a | 37.17a |
| 10 | Balcha | 2.733d | 83.62b,c,d | 47.99a | 15.92a | 9.07b,c | 62.44c | 113.4b | 51a | 3.42e | 57.11a | 2.011c,d | 37.08a |
| 11 | Hulluka | 2.059a | 86.6d,e | 54.42d | 17.37d,e | 9.34c,d | 72.78f | 125.3g | 52.56a,b,c | 3.11c | 59.67a,b | 1.911b,c | 35.92a |
| 12 | Pavon-76 | 2.118a | 80.7a,b | 53.87c,d | 17.26c,d,e | 8.573a | 61.78b,c | 116.2c | 54.44c | 3.26d | 64.78c,d,e | 1.656a | 42.69c |
| Mean | 2.4 | 85.5 | 52.2 | 16.76 | 17.3 | 66.06 | 118.65 | 52.6 | 3.29 | 64.7 | 1.9 | 38.03 | |
| Range | 1.99–2.7 | 79–97.42 | 48–58 | 16.3–19.4 | 8.6–9.8 | 60–72.8 | 110–126.4 | 50.89–57 | 2.71–3.58 | 57–72.67 | 1.6–2.1 | 35–43 | |
| CV | 9.8 | 3.6 | 4.6 | 4.75 | 3.78 | 2.64 | 1.95 | 3.9 | 3.4 | 5.8 | 6.6 | 7.4 | |
| LSD | 0.22 | 2.92 | 2.26 | 0.77 | 0.33 | 1.64 | 2.2 | 2 | 0.02 | 3.6 | 0.12 | 2.66 | |
- Note: Superscript lowercase letters indicated that the significance level between treatments.
- Abbreviations: AGB, aboveground biomass; DTH, days to heading; DTM, days to maturity; GFP, grain filling period; GY, grain yield; HI, harvest index; KPS, kernels per spike; PH, plant height; SkPS, spikelet per spike; SL, spike length; TKW, 1000-kernel weight; TPP, tillers per plant.
The combined analysis data from the Ayehu Guagusa location over 2 years showed that the maximum GY 3.01 t/ha was obtained from G12, while the minimum 2.27 t/ha was obtained from G9, with a mean GY of 2.69 t/ha (Table 5). At the same time, the combined analysis data from the Dangila location over 2 years revealed that the highest GY 2.87 t/ha was obtained from G7, while the minimum 2.18 t/ha was obtained from G9, with a mean GY of 2.5 t/ha (Table 6). The top four yielder genotypes on average were G7, G6, G8, and G12. This finding suggests that the genotype with the highest yield performance exhibits strong adaptability to diverse environmental conditions. Such a genotype could be a valuable asset for crop breeding programs and agricultural practices, as it demonstrates the ability to thrive in various settings while maintaining high productivity. The identification of genotypes with superior yield potential is crucial for developing improved wheat varieties that can perform consistently across different locations and seasons. These high-yielding genotypes can serve as valuable genetic resources for breeders to incorporate desirable traits for stakeholders, ultimately enhancing overall wheat production and food security.
Days to start heading and DTM are an important plant trait to find whether crop is early maturing or not. Wheat variety taking less number of days for heading is categorized as early maturing genotype [48]. G9 was the earliest, while G6 was the latest for DTH and DTM, respectively, at all experimental sites and years (Tables 5–9). The number of days required for a plant to reach heading and maturity is an essential characteristic in agricultural studies. These traits play a crucial role in determining when to plant, how the crop grows, its potential yield, and guide breeding efforts to improve productivity and resilience of the crop [49].
| Trait | Mean | Source of variation | δ2g | δ2p | PCV% | GCV% | GA | GAM | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Env | Rep(Env) | Gen | Env ∗Gen | Error | |||||||||
| (df = 2) | (df = 6) | (df = 11) | (df = 22) | (df = 66) | |||||||||
| TPP | 2.35 | 0.38 ∗ | 0.07 | 0.53 ∗∗ | 0.43 ∗∗ | 0.05 | 0.15 | 0.23 | 20.41 | 16.48 | 65.22 | 0.64 | 27.23 |
| PH | 85.45 | 210.3 ∗∗ | 7.61 | 278.66 ∗∗ | 33.26 ∗∗ | 9.63 | 89.41 | 99.85 | 11.69 | 11.07 | 89.54 | 18.4 | 21.53 |
| KPS | 52.2 | 649 ∗∗ | 6.58 | 72.19 ∗∗ | 33.97 ∗∗ | 5.79 | 21.92 | 28.34 | 10.2 | 8.97 | 77.35 | 8.5 | 16.28 |
| SkPS | 17.3 | 40.66 ∗∗ | 0.48 | 10.38 ∗∗ | 2.63 ∗∗ | 0.67 | 3.27 | 3.85 | 11.34 | 10.45 | 84.94 | 3.43 | 19.83 |
| SL | 9.3 | 3.69 ∗∗ | 0.03 | 1.78 ∗∗ | 0.6 ∗∗ | 0.12 | 0.56 | 0.66 | 8.736 | 8.05 | 84.85 | 1.42 | 15.27 |
| DTH | 66.06 | 50.19 ∗∗ | 2.44 | 175.79 ∗∗ | 12.04 ∗∗ | 3.03 | 56.85 | 62.08 | 11.93 | 11.41 | 91.58 | 14.86 | 22.5 |
| DTM | 118.7 | 704 ∗∗ | 22.2 | 272.27 ∗∗ | 19.93 ∗∗ | 5.38 | 88.27 | 95.72 | 8.242 | 7.92 | 92.22 | 18.6 | 15.67 |
| GFP | 52.6 | 441.6 ∗∗ | 12.13 | 30.43 ∗ | 27.62 ∗∗ | 4.24 | 8 | 14.45 | 7.227 | 5.38 | 55.36 | 4.34 | 8.25 |
| GY | 0.71 | 0.07 ∗∗ | 0.0002 | 0.04 ∗∗ | 0.01 ∗∗ | 0.0006 | 0.013 | 0.013 | 16.34 | 16.22 | 98.51 | 0.24 | 33.8 |
| TKW | 64.7 | 600.5 ∗∗ | 7.37 | 181.4 ∗∗ | 97.11 ∗∗ | 14.23 | 57.35 | 66.7 | 12.62 | 11.7 | 85.98 | 14.47 | 22.36 |
| AGB | 1.9 | 1.55 ∗∗ | 0.02 | 0.02 ∗∗ | 0.14 ∗∗ | 0.02 | 0.003 | 0.013 | 6.077 | 3.04 | 25 | 0.06 | 3.16 |
| HI | 38.03 | 171.1 ∗∗ | 11.68 | 41.65 ∗∗ | 48.5 ∗∗ | 8 | 11.62 | 18.4 | 11.28 | 9 | 63.16 | 5.58 | 14.67 |
- Abbreviations: AGB, aboveground biomass; DTH, days to heading; DTM, days to maturity; GFP, grain filling period; GY, grain yield; HI, harvest index; KPS, kernels per spike; PH, plant height; SkPS, spikelet per spike; SL, spike length; TKW, 1000-kernel weight; TPP, tillers per plant.
- ∗Significant at p < 0.05.
- ∗∗Significant at p < 0.001.
Including all other characters like PH, total number of TPP, and others used for this study, the variability in agronomic traits suggests that there is sufficient genetic diversity to develop high-yielding varieties of bread wheat. Previous researchers have supported this study and reported significant differences among the bread wheat tested genotypes in all the studied traits, as indicated by Geneti et al., Alambo et al., Gebremariam et al., and Ferede et al. [9, 45, 47, 50]. Generally, variations in the average performance of genotypes for GY and related traits suggest differences in the genetic potential of the genotypes, indicating the existence of a broad spectrum of genetic variability among them.
3.2. Estimates of Genetic Parameters
Understanding the genetic parameters in crop improvement is crucial for comprehending the transmission of quantitative traits and forecasting the performance of breeding materials. These parameters encompass Genotypic and Phenotypic Variance, heritability, and GA. Understanding these genetic parameters is vital for identifying the superior plant varieties for crop enhancement.
3.2.1. Estimation of GCV and PCV
GCV and PCV are important in crop improvement by revealing insights into genetic and environmental influences on traits. GCV assesses genetic variability, while PCV considers both genetic and environmental factors. These coefficients aid plant breeders in identifying the impact of genetics and environment on traits, assisting in the selection of superior cultivars for crop enhancement [24]. Based on Deshmukh et al. [51], PCV and GCV values exceeding 20% are classified as high, while values below 10% are considered to be low, and values falling between 10% and 20% are considered moderate. In the current study, utilizing combined ANOVA across locations and years, there was little difference between the GCV and PCV (Tables 4–6 and 9). A slight difference between PCV and GCV indicates that breeders must take into account genetic and environmental factors when choosing top performing genotypes for crop enhancement efforts [52, 53]. It emphasizes the need to assess bread wheat varieties not just based on genetic capacity but also on their performance in various environmental settings. This knowledge helps breeders in developing varieties that display consistency across various environments, ensuring the adaptability of bread wheat cultivars in a wide range of growing conditions. This result was in agreement with the study of Bedada et al. [54].
Most of the traits showed that moderate PCV and GCV values like TPP (21.17 and 18.76), high PCV PH (13.24 and 12.83), DTH (11.11 and 10.66), GFP (12.79 and 11.5), GY (15 and 15), TKW (16.65 and 14.21), AGB (17.3 and 16), and HI (15 and 13.11), respectively, were studied for two locations with 2 years (Table 9). In the present study, exactly similar result of PCV (15) and GCV (15) was recorded from GY (Table 9). Therefore, similar PCV and GCV values in the GY of bread wheat suggest that genetic factors are the primary drivers of variability in GY over environmental influences. When PCV and GCV values align, it indicates that genetic variations significantly influence the performance and variability of GY in bread wheat varieties. This insight enables breeders to select genotypes with high genetic potential for GY and resilience to different environmental challenges, leading to the development of improved bread wheat cultivars with high-yield characteristics.
3.2.2. Estimates of Heritability in a Broad-Sense GA and GAM
Heritability is a crucial concept in the study of adaptability and stability of bread wheat genotypes, as it provides insights into the genetic potential of these plants to express desirable traits across varying environments. Understanding heritability helps researchers and breeders identify which traits are genetically controlled and how these traits can be reliably passed on to subsequent generations [55]. In the performance of bread wheat, broad-sense heritability provides valuable insights into the genetic factors influencing the traits of interest [56].
In the present study, heritability ranged from 58.82% for AGB to 100% for GY in the combined analysis of 2 years at two locations. Estimates of heritability in the broad sense (h2b), GA, and GAM were calculated for all the traits studied (Table 4). For the combined ANOVA at three locations for 1 year, heritability ranged from 25% for AGB to 98.51% for GY, as shown in Table 9. In the combined ANOVA for 2 years in Ayehu Guagusa district, heritability ranged from 56% for TKW to 93.65% for PH, as presented in Table 5. Finally, the analysis conducted at Dangila district over 2 years showed that heritability ranged from 69.23% for TTP to 86.16% for DTM, as depicted in Table 6.
The value of heritability calculated for each trait was categorized into high heritability (>60%), moderate heritability (30%–60%), and low heritability (<0%–30%) following the classification proposed by Robinson et al. [40]. In the present study, high estimates of heritability were observed for nearly all the traits investigated, except for ABG (25% and 58.82%) (Tables 4 and 9), which are classified as low and moderate, respectively. Additionally, moderate heritability values were reported for GFP (55.36%) and TKW (56%) (Tables 5 and 9), respectively.
When the PCV and GCV values are closer to each other compared to other traits studied in the given experiment, the highest heritability was observed from those traits compared to others. According to the results observed in Table 4, the highest heritability ranged from 66.34% to 100% for SkPS and GY, respectively.
The top four highest heritability were recorded for GY (100%) with PCV (15) and GCV (15) values, PH (93.11%) with PCV (13.24) and GCV (12.83) values, DTH (91.16%) with PCV (10.28) and GCV (9.31) values, and DTM (90.88%) with PCV (8.32) and GCV (8) values (Table 4). According to the results shown in Table 9, the highest heritability ranged from 63.16% to 98.51% for HI and GY, respectively. The top four heritabilities were similar to the results observed in Table 4 for traits GY (98.51%) with PCV (16.34) and GCV (16.22) values, PH (89.54%) with PCV (11.69) and GCV (11.07) values, DTH (91.58%) with PCV (11.93) and GCV (11.41) values, and DTM (92.22%) with PCV (8.2) and GCV (7.92) values (Table 9).
Previous researchers [57] supported these results by demonstrating a high heritability of 99.08% for GY, with PCV (64.3) and GCV (64.04) values. They also found a high heritability of 98.6% for PH, with PCV (17.3) and GCV (17.2). This supporting evidence suggests that when PCV and GCV values are closely matched, the heritability becomes significantly high compared to other traits, suggesting that the traits being studied are less affected by the environment. In general, high heritability was noted for all the traits examined as shown in Table 6. The high heritability estimates for these traits suggest that the observed variation is primarily genetically controlled and less affected by the environment [58]. This also implies that a significant portion of the variance seen is inheritable, making selection for these traits potentially effective. However, the effectiveness of selection depends on the extent of dominance and epistasis effects, which represent a portion of genetic variance that is nonheritable [28].
In their study [59], they highlighted the importance of high broad-sense heritability values in relation to the performance of bread wheat. This suggests that a significant portion of trait variation is attributed to genetic factors, indicating a high heritability of the trait. This knowledge is valuable for breeders as it enables them to pinpoint genotypes with favorable traits for the development of enhanced bread wheat varieties. By focusing on genotypes with high broad-sense heritability, breeders can select those with consistent performance across diverse environments, thereby enhancing the success of crop improvement programs.
The selection process is not only dependent on heritability but also on GA and GAM. According to Johnson et al. [60], GA and GAM were classified as high (>20%), medium (10% to 20%), and low (<10%). It is crucial to select parents with greater GAs and higher heritability for GY and yield-related traits [61]. Simultaneously, the combination of high heritability with moderate GA suggests that the traits analyzed in the study are highly heritable. This indicates that selecting high-performing genotypes could lead to improvements in these traits.
In the current study, high heritability was observed coupled with high or moderate values of GA and GAM for various traits. These included high heritability (78.57%) with high GAM (34.4) for TPP, high heritability (93.11%) with high GA (21.22) and high GAM (25.38) for PH, high heritability (77.34%) with moderate GAM (16.38) for KPS, high heritability (66.34%) with moderate GAM (11.93) for SkPS, high heritability (71.08%) with high GAM (20.25) for SL, high heritability (91.16%) with moderate GA (13.54) and high GAM (20.83) for DTH, high heritability (90.88%) with moderate GA (17.6) and moderate GAM (15.57) for DTM, high heritability (71.89%) with moderate GAM (19) for GFP, high heritability (100) with high GAM (30.4) for GY, high heritability (71.04%) with moderate GA (13.41) and high GAM (24.38) for TKW, and high heritability (71.11%) with high GAM (22) for HI (Table 4).
After conducting a combined analysis across two locations for 2 years, the following results were documented in Table 9. The study revealed high heritability, accompanied by high or moderate values of GA and GAM for various traits. Notably, traits such as TPP showed high heritability (65.22%) with high GAM (27.2); PH exhibited high heritability (89.54%) with high GAM (21.5) and moderate GA (18.4); KPS displayed high heritability (77.35%) with moderate GAM (16.2); SkPS demonstrated high heritability (84.94%) with moderate GAM (19.8); SL showed high heritability (84.85%) with moderate GAM (15.2), indicated high heritability (91.58%) with moderate GA (14.86) and high GAM (22.5), and was recorded from DTH; DTM presented high heritability (92.22%) with moderate GA (18.6) and moderate GAM (15.6); GY exhibited high heritability (98.51%) with high GAM (33.8); TKW showed high heritability (85.98%) with moderate GA (14.47) and high GAM (22.3); and HI displayed high heritability (63.16%) with moderate GAM (14.6) (Table 9).
Finally, from the result obtained from the combined analysis of Ayehu Guagusa district and Dangila district, the following results were recorded (Tables 5 and 6). The study identified traits with high heritability, besides with high or moderate values of GA and GAM. Notably, TPP exhibited high heritability (71.43% and 69.23%) with high GAM (24.12 and 27.34); PH showed high heritability (93.65% and 81.71%) with moderate GA (16.17 and 12.72) and moderate GAM (19 and 15.48); KPS displayed high heritability (74.48% and 76.55%) with moderate GAM (11.8 and 16.13); SL demonstrated high heritability (79.23% and 81.48%) with high GAM (20.45) and moderate GAM (13.7); DTH revealed high heritability (92% and 81.2%) with moderate GAM (16.7 and 13.75); and DTM showed high heritability (86.46% and 86.16%) with moderate GA (11.8 and 13.02) and moderate GAM (10.55 and 11.4). Additionally, traits like GFP, high heritability (68.57% and 77.51%) with moderate GAM (15.68 and 17.03); GY, high heritability (75% and 81.67%) with moderate GAM (17.24) and high GAM (20.37); AGB, high heritability (66.67% and 87.5%) with moderate GAM (14.81) and high GAM (57.64); HI, high heritability (78.2% and 70.7%) with high GAM (26.5) and moderate GA (18.52), indicate these traits were less affected by environmental fluctuations which suggests that selecting for these traits could be relatively straightforward. This idea was in agreement with a study conducted by Roba et al. [62]. Their findings indicated high heritability estimates and substantial GA, suggesting that these traits were primarily governed by additive genes. This implies that additive genes contribute equally to the phenotype, without one gene dominating over the other, as described by Sewore et al. [63]. The high GA and high heritability values suggest that environmental factors have minimal influence on the expression of specific traits, indicating the involvement of additive gene action. Consequently, these traits can be enhanced through natural selection [64].
Information from previous studies of Ethiopian bread wheat genotypes [57] indicated that traits such as TPP, DTH, DTM, PH, SL, GY, TKW, AGB, and HI in bread wheat genotypes showed high heritability with high GA as percent of mean supported this study. High heritability coupled with high GA as percent of mean for TPP, DTH, PH, SL, and AGB in this study was also supported by Mullualem et al. [65].
The high heritability with moderate GA observed in the current study (Table 6) was in line with the findings of [66]. Generally, traits showing high heritability with high GA as a percentage of the mean were indicative of additive gene action [65], while traits with moderate heritability and low GA as a percentage of the mean were primarily influenced by nonadditive gene action. Direct selection might not be feasible for these traits as most of the variation is attributed to environmental effects.
In the present study, moderate heritability with low GAM was recorded from GFP (Table 4). Moderate heritability conjunction with moderate GA as a percentage of the mean was observed in the case of TKW, suggesting that both additive and nonadditive gene actions contribute to the expression of these traits, as shown in Table 5. In the present study, low heritability (25%) and low GA (0.06) were recorded for ABG in Table 9, suggesting that nonadditive gene action influences the expression of these traits, making effective phenotypic selection challenging. Alambo et el. [45] also reported low heritability (20) and low GA (0.8) in support of these findings in the performance evaluation of Ethiopian bread wheat genotypes.
4. Conclusion and Recommendations
The result obtained in this study indicated that there is significant variability in bread wheat genotypes in terms of agronomic traits related to GY of tested lines planted under rainfed conditions in Ethiopia. The combination of high heritability and GA, expressed as a percentage of the mean, suggests that selecting for these traits could effectively enhance bread wheat yield. The study’s outcomes confirmed the influence of additive gene action and indicated that these traits are less affected by environmental fluctuations, making them relatively easy to select for bread what genotypes to improve GY. Therefore, high heritability and GA as percent of mean are essential parameters that help breeders make informed decisions during the performance evaluation of bread wheat genotypes, leading to the development of superior bread wheat cultivars adapted to various environments. This finding indicates that a particular bread wheat genotype performs well and adapts effectively to various environments. This genotype is valuable for bread wheat production because it maintains high yield across different conditions and resistant for biotic and abiotic stresses. Accordingly both combined and separate analysis confirmed that the genotypes Tay, Honqolo, Danda’a, and Pavon-76 genotypes showed the best performance on average in terms of GY and related traits compared to others. Finally, we recommend these genotypes for broader cultivation in the study area and related agro-ecologies.
Conflicts of Interest
The authors declare no conflicts of interest.
Author Contributions
This work was carried out in collaboration among all authors. Destaw Mullualem proposed the study, developed the proposal, and wrote the manuscript, including the techniques and all steps of the research up to the final submission. Alemu Tsega, Tesfaye Mengie, Desalew Fentie, Zelalem Kassa, and Amare Fassil designed the study, wrote the proposal, collected and analyzed the data, interpreted the results, and wrote both the first draft and the revised versions of the manuscript. Yitayih Dessie, Amare Aleminew, Demekech Wondaferew, Esubalew Sintie, and Belsti Atnkut supervised the study design, oversaw the research activities and data collection, and prepared and commented on the manuscript. All authors read and approved the final manuscript.
Funding
No funding was received for this study.
Acknowledgments
The authors express their gratitude to the Kulumsa Agricultural Research Center and Adet Agricultural Research Center of Ethiopian Institute of Agricultural Research for providing the bread wheat genotypes used in this study. They also appreciate the support from Injibara University Research and Community Service for facilitating the field experiments and providing equipment for the research work over 2 years.
Open Research
Data Availability Statement
The data are available on request from the authors.
