Genetic variability and correlation studies in bread wheat (Triticum aestivum L.) for seed yield attributing traits

Introduction

Wheat (Triticum aestivum L., 2n=42) is one of the most important cereal crops of the world and contributes nearly 20% of the total caloric requirement of the global population. It belongs to the family Poaceae and is mainly cultivated as a self-pollinated crop. Among cultivated wheat species, hexaploid wheat occupies more than 90% of the cultivated area and is widely utilized for bread and bakery products due to the presence of gluten proteins (Debasis and Khurana, 2001) [4] . Wheat grains are rich source of carbohydrates, proteins, vitamins and minerals, making it an important component of human nutrition (Shewry, 2007) [14] . India is the second largest producer as well as consumer of wheat after China and contributes more than 12% to the global wheat production. In India, wheat occupies about 33.4 million hectares area and plays a vital role in national food security (Mahajan Sunali, 2025) [7] . Uttar Pradesh is the leading wheat producing state with high productivity under irrigated conditions (Anonymous, 2024-25) [1] . Genetic variability is the basic requirement for any successful crop improvement programme because it provides opportunity for effective selection and development of superior genotypes (Sabharwal and Lodhi, 1995) [13] .

Estimates of heritability and genetic advance help breeders in understanding the inheritance pattern and expected genetic gain under selection (Songsri et al., 2008) [15] . Further, correlation and path coefficient analyses are important tools for identifying yield contributing traits and their direct or indirect influence on grain yield. Therefore, the present investigation was conducted during Rabi 2025-26 at Rama University, Kanpur to study genetic variability and character association in wheat genotypes.

Materials and methods

The present investigation was carried out at the research farm of FASAI, Rama University, Kanpur. Twenty-three diverse wheat (Triticum aestivum L.) genotypes were collected from GPB department and evaluated in RBD field experimental design with three replications. All recommended agronomic practices have been performed time to time to raise the good crop. data for 14 important traits viz., Days to 50% Flowering (DFF), Days to Maturity (DTM), Plant Height (PHT), Flag Leaf Length (FLL) cm, Productive Tillers Per sq. Meters (PTPM), Peduncle Length (PL) cm, Spike Length (SL) cm, Spike weight (g) (SW), Spikelet Per Spike (SPS), Grains Per Spike (GPS), Test Weight (g) (TW), Grain yield per plant (g) (GYPP), Biological Yield Per Plant (BYPP) and Harvest Index (HI) % has been recorded from 5 randomly selected competitive plant and their mean value was calculated and subjected to further statistical analysis using Windo-stat 9.0 and Opstat.

Result and Discussion

Analysis of Variance

The analysis of variance revealed highly significant differences among the wheat genotypes for all the studied characters, indicating presence of substantial genetic variability in the experimental material (Table 1). The genotype mean square values were considerably higher than the corresponding error variances for most traits, suggesting that variability was mainly genetic in nature and effective selection can be practiced. For flowering and maturity traits, genotype mean squares of 62.15 for days to 50% flowering and 15.99 for days to maturity were highly significant over their respective error values (1.906 and 1.248). Plant height also showed high variability with genotype mean square of 207.40 against error variance of 6.984. Among yield contributing traits, productive tillers per m² (2283.84), grains per spike (62.27) and test weight (59.57) exhibited highly significant variation among genotypes. Similarly, spike related traits such as spike length (3.28), spikelets per spike (10.38) and spike weight (0.30) also showed significant differences, indicating good scope for selection. The most important trait, grain yield per plot, recorded highly significant genotype mean square value of 39690.24 compared to error variance of 6094.148, suggesting wide genetic diversity for yield potential. Biological yield per plot (75188.81) and harvest index (39.46) also exhibited significant variability among the genotypes. Overall, the ANOVA results confirmed the presence of adequate genetic variation for further wheat improvement programme. Overall, the ANOVA results clearly demonstrated that sufficient amount of genetic variability was present among the evaluated genotypes for all the studied characters. The low magnitude of error variance for most traits further indicated reliability and precision of experimental data. Since genotype mean squares were highly significant for all characters, it may be concluded that the breeding material possesses wide genetic diversity and selection based on these traits would be effective in future crop improvement programme. Such findings are commonly reported in cereal breeding experiments where significant variability forms basis for further studies like heritability, genetic advance, correlation and path coefficient analysis.

Mean performance of 23 diverse wheat genotypes

The mean performance of 23 wheat genotypes revealed considerable variability for all the studied traits, indicating presence of good genetic diversity among the breeding material (

Estimates of genetic parameters of 23 wheat genotypes for 14 characters under study

The estimates of genetic parameters revealed considerable amount of variability among the wheat genotypes for all the studied traits (Table 3). In general, the Phenotypic Coefficient of Variation (PCV) was slightly higher than Genotypic Coefficient of Variation (GCV) for all characters, indicating some influence of environment on trait expression. However, the narrow difference between PCV and GCV for most traits suggested that environmental influence was comparatively low and variability was mainly genetic in nature. Moderate to high PCV and GCV values were observed for traits like spike weight (PCV = 14.66%, GCV = 11.00%), number of grains per spike (PCV = 14.20%, GCV = 12.35%) and flag leaf length (PCV = 12.88%, GCV = 10.89%), indicating presence of sufficient variability for effective selection. On the other hand, lower estimates of PCV and GCV were recorded for days to maturity (2.28% and 2.04%), suggesting comparatively less variability for this trait.

High heritability coupled with high genetic advance as percent of mean generally indicates additive gene action and effectiveness of direct selection. In the present study, high heritability was observed for days to 50% flowering (91.33%), plant height (90.54%) and test weight (83.97%). Among these traits, plant height also exhibited relatively high genetic advance as percent of mean (18.47%), suggesting good scope for improvement through selection. The trait number of grains per spike showed high heritability (75.56%) along with high GA percent mean (22.10%), indicating predominance of additive gene effects and effectiveness of phenotypic selection. Similarly, spike length recorded heritability of 77.38% with GA percent mean of 19.92%, while spikelets per spike showed 67.56% heritability and 19.28% GA over mean. These traits may therefore serve as important selection criteria for yield improvement. Similar findings has also been reported by reported by Bhardwaj (2018) [2] , Rathwa et al. (2018) [12] , Thapa et al. (2018) [16] , and Poudel et al. (2021) [11]

Correlation estimates of 14 characters for grain yield and its attributing traits in wheat genotypes during rabi 2025-26 A. Genotypic correlation estimates

The correlation analysis revealed significant association among different yield and yield contributing traits in wheat under study. Grain Yield Per Plot (GYPP) exhibited strong and positive correlation with important traits such as number of productive tillers per square meter (0.976), number of spikelets per spike (0.960), harvest index (0.834), number of grains per spike (0.827) and spike weight (0.808), indicating that these traits contributed substantially towards yield improvement (Table 4). Grain yield also showed significant positive association with flag leaf length (0.551), days to 50% flowering (0.549), spike length (0.533), test weight (0.492) and biological yield per plot (0.487), suggesting that simultaneous improvement of these characters may enhance wheat productivity. Among inter-character relationships, plant height had strong positive correlation with spike length (0.940), grains per spike (0.801) and test weight (0.610), while productive tillers/m² showed highly significant association with spike weight (0.892), spikelets per spike (0.859) and grains per spike (0.846). Harvest index also exhibited strong positive relationship with spike weight (0.825) and spikelets per spike (0.874). Some negative correlations were observed, particularly days to maturity with plant height (-0.247) and spikelets per spike (-0.259), though their effect on grain yield was comparatively low.

Overall, the study indicated that productive tillers/m², spikelets per spike, grains per spike, spike weight and harvest index were the major yield determining traits and therefore should be given due emphasis during selection in wheat breeding programme.

B. Phenotypic correlation estimates

The correlation analysis revealed significant association among grain yield and its contributing traits in wheat (Table 5). Grain yield per plot showed strong positive correlation with number of productive tillers per square meter (0.866), followed by harvest index (0.743), number of grains per spike (0.623), spike weight (0.617) and number of spikelets per spike (0.610), indicating that these traits are major contributors towards yield improvement. Grain yield also exhibited positive association with plant height (0.452), days to 50% flowering (0.424), spike length (0.398), flag leaf length (0.390), test weight (0.346) and biological yield per plot (0.513), suggesting that simultaneous improvement in these characters may enhance productivity. Among intercharacter relationships, plant height showed strong positive correlation with spike length (0.792) and grains per spike (0.691), while productive tillers/m² had significant positive association with harvest index (0.645), grains per spike (0.658) and spikelets per spike (0.622). Spike length also exhibited positive correlation with grains per spike (0.632) and test weight (0.370). Some negative associations were observed, particularly days to maturity with plant height (-0.222) and harvest index (-0.127). Overall, the study indicated that productive tillers, grains per spike, spike weight and harvest index were the most important traits influencing grain yield in wheat. Similarly, Chitralekha et al. (2018) [3] , Pooja et al. (2018) [10] , Kumar et al. (2018) [6] , and Meles et al. (2017) [9] reported significant positive correlations of grain yield per plot with number of productive tillers per plot, plant height, and biological yield per plot. Mondal et al. (1994), Mecha et al. (2017) [8] , and Dogan (2009) [5] also reported significant positive correlations of grain yield per plot with number of productive tillers per plot, plant height, and biological yield per plot. Overall, the correlation analysis indicated that productive tillers per square meter, harvest index, grains per spike, spike weight and spikelets per spike were the most important traits influencing grain yield. Therefore, emphasis on these traits during selection may be effective for developing high yielding wheat genotypes.

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