Genetic variability and trait association among yield and related quantitative traits in Maize (Zea mays L.) testcross hybrids and parental lines

Introduction

Maize (Zea mays L.) is one of the most important cereal crops cultivated worldwide and serves as a major source of food, feed, fodder and industrial raw material. Owing to its remarkable adaptability, high yield potential and diverse end uses, maize is often referred to as the "Queen of Cereals." Globally, maize is cultivated across a wide range of agro-climatic conditions and plays a crucial role in ensuring food and nutritional security. In addition to human consumption, a substantial proportion of maize production is utilized in the livestock and poultry sectors, while its industrial applications include the manufacture of starch, ethanol, corn oil, sweeteners and various value-added products. According to FAOSTAT (2023) [6] , maize is grown in more than 160 countries with a global production exceeding 1.2 billion tonnes. In India, maize ranks third after rice and wheat among cereal crops and occupies an area of about 10.04 million hectares with an average productivity of 3.35 t ha⁻¹ (Agricoop, 2023-24). The growing demand for maize driven by population expansion, rapid urbanization, increasing livestock production and industrial utilization necessitates continuous improvement in productivity. Since the scope for increasing cultivated area is limited, enhancement of grain yield through genetic improvement remains one of the most effective approaches to meet future demand. Hybrid breeding has played a pivotal role in increasing maize productivity worldwide by exploiting heterosis and combining favorable alleles from diverse parental lines. The success of any breeding programme largely depends on the availability and utilization of sufficient genetic variability within the breeding material.

Assessment of genetic variability is therefore an essential prerequisite for the development of superior cultivars and hybrids. The magnitude of variability present in a population determines the effectiveness of selection and the potential for genetic improvement. Genetic parameters such as genotypic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV) provide valuable information regarding the extent of variability available for selection. However, variability alone is not sufficient to predict the response to selection. Estimates of heritability and genetic advance help breeders understand the proportion of variation that is heritable and the expected improvement achievable through selection (Dudly and Moll, 1969). High heritability coupled with high genetic advance generally indicates the predominance of additive gene action and suggests that selection based on phenotypic performance can be highly effective (Nwangburuka et al., 2012) [11] . Grain yield is a complex quantitative trait governed by several component characters and influenced by environmental factors. Therefore, direct selection for yield alone may not always be effective. Understanding the relationships among yield and its contributing traits through correlation analysis provides an opportunity to identify key characters that can be used as indirect selection criteria. Traits exhibiting strong and positive associations with grain yield can be targeted in breeding programmes to improve selection efficiency and accelerate genetic gain. The evaluation of hybrids and their parental lines provide valuable insights into the expression of yield and related traits, helping breeders identify superior combinations and important yield determinants. In the present investigation, 100 maize genotypes comprising 64 testcross hybrids, 34 parental lines and 2 commercial checks were evaluated to assess the extent of genetic variability, heritability, genetic advance and trait associations for grain yield and its related characters. The study was undertaken to identify important yield-contributing traits and generate information useful for the development of highyielding maize hybrids suitable for future breeding programmes.

Material and Methods

The material for the present study comprised of 100 maize genotypes, which include in 64 maize testcross hybrids along with 34 parents and two checks. These genotypes were evaluated in randomized complete block design (RCBD) with two replications during kharif, 2023 at Maize Research Centre, Rajendranagar. Each entry was raised in two rows of four-meter length with a spacing of 60 cm × 20 cm and following all the recommended package of practices. The observations were recorded from each genotype for twelve quantitative traits viz., days to 50 per cent tasseling (DTT), days to 50 per cent silking (DTS), anthesis-silking interval (ASI), leaf angle (LA), plant height (PH) (cm), ear height (EH) (cm), cob length (CL) (cm), cob girth (CG) (cm), kernel row number (KRN), number of kernels per row (NKPR), hundred grain weight (TW) (g), shelling percentage (SP) and grain yield per hectare (GYPHA) (t/ha). The mean values of the data from these genotypes were subjected to analysis of variance (ANOVA) using variability package (Popat et al. 2020) [13] in R-studio Rx643.6.2 and ANOVA was tested for significance. Genotypic and phenotypic variances were determined according to Singh and Chaudhary (1985) [15] using MS-Excel programme. The genotypic coefficient of variation and phenotypic coefficient of variation were computed according to Burton and De vane (1953) [3] using the following formula. Where, σ 2 g = genotypic variance, σ 2 p = phenotypic variance and X ̅ = sample mean. The GCV and PCV were classified according to Sivasubramanian and Menon (1973) [16] as low (less than 10%), moderate (10-20%) and high (more than 20%). Heritability in broad sense (h 2 bs ) was computed for each character as the ratio of genotypic variance to the total variance as suggested by Hanson et al. (1956) [7] .

Heritability broad sense (h 2 bs ) =

Where, σ 2 g = genotypic variance and σ 2 p = phenotypic variance. It was categorized by Robinson et al. (1951) [14] as low (less than 0.30), moderate (0.31 -0.60) and high (more than 0.60). Genetic advance and genetic advance as percent of mean were estimated according to Johnson et al. (1955) [9] using the following formula,

Where, i = 2.06 (selection intensity at 5%), σ p = Phenotypic standard deviation, h 2 bs = Heritability in broad sense.

Genetic advance as per cent of mean (GAM) = × 100

The GAM is grouped as low (less than 10%), moderate (10-20%) and high (more than 20%) respectively, according to Johnson et al. (1955) [9] . Karl Pearson's simple correlation coefficients for all the eleven characters were worked out using variability package (Popat et al. 2020) [13] and the heatmap of the correlation was made using function corrplot in R-studio Rx643.6.2.

Results

The analysis of variance revealed highly significant differences among the 100 maize genotypes for all the twelve quantitative traits studied, indicating the presence of substantial genetic variability within the experimental material (Table 1). The significant genotypic mean squares observed for all traits suggest that the germplasm possesses adequate variability for effective selection and further genetic improvement. Particularly large genotypic mean squares were recorded for plant height (1777.40), ear height (541.85), number of kernels per row (121.12), test weight (63.61) and grain yield per hectare (20.79), indicating considerable divergence among genotypes for these economically important traits. Such significant variation among genotypes forms the basis for successful selection and breeding programmes aimed at yield enhancement in maize. A wide range of variation was observed for all the traits under study, indicating the existence of substantial phenotypic diversity among the evaluated maize genotypes. Grain yield per hectare varied from 2.07 to 13.46 t ha⁻¹ with a mean value of 8.14 t ha⁻¹ (Table 1). Similarly, considerable variation was observed for flowering traits, plant architecture traits, cob traits and yield-contributing characters. The phenotypic variance was generally higher than the corresponding genotypic variance for all traits, suggesting some degree of environmental influence on trait expression (Table 2). However, the differences between phenotypic and genotypic variances were relatively narrow for most characters, indicating that the observed variability was predominantly genetic in nature. Among the traits studied, grain yield per hectare exhibited the highest genotypic coefficient of variation (39.23%) and phenotypic coefficient of variation (39.95% The correlation analysis revealed several significant associations among grain yield and its component traits (Fig. 1). Grain yield per hectare exhibited strong and highly significant positive correlations with cob length (r = 0.89), number of kernels per row (r = 0.85), cob girth (r = 0.83), plant height (r = 0.81), ear height (r = 0.68), test weight (r = 0.67) and kernel rows per cob (r = 0.61). These associations indicate that improvement in these component traits is likely to contribute positively towards grain yield enhancement. Among the yield-contributing traits, cob length showed strong positive associations with number of kernels per row (r = 0.92), cob girth (r = 0.81), plant height (r = 0.82), ear height (r = 0.73), test weight (r = 0.63) and grain yield per hectare (r = 0.89). Cob girth was positively associated with number of kernels per row (r = 0.81), kernel rows per cob (r = 0.70), test weight (r = 0.65) and grain yield per hectare (r = 0.83). Likewise, number of kernels per row exhibited significant positive relationships with cob length, cob girth, kernel rows per cob, test weight and grain yield.

Plant height and ear height displayed a strong positive association (r = 0.88), indicating that taller plants generally possessed higher ear placement. Flowering traits, namely days to 50% tasseling and days to 50% silking, showed highly significant positive correlation with each other (r = 0.96), reflecting the synchronized nature of flowering in maize. However, both flowering traits exhibited significant negative correlations with grain yield per hectare, suggesting that earlier flowering genotypes tended to be more productive under the experimental conditions. Shelling percentage also showed a negative association with several yield-contributing traits and grain yield. Overall, the correlation analysis identified cob length, cob girth, number of kernels per row, kernel rows per cob and test weight as major contributors to grain yield improvement.

The comparative distribution analysis between the 64 testcross hybrids and the parental lines revealed clear differences in the expression of agronomic and yield-related traits (Fig. 2). Hybrids generally exhibited superior performance for plant height, ear height, cob length, cob girth, kernel rows per cob, number of kernels per row and grain yield per hectare compared to the parental lines. The higher median values and wider distribution observed in hybrids for these traits reflect the manifestation of heterosis and enhanced genetic complementation. Conversely, parental lines tended to exhibit higher values for flowering duration and shelling percentage. The most pronounced difference was observed for grain yield per hectare, where hybrids substantially outperformed the parental lines, confirming the effectiveness of hybrid breeding in exploiting heterosis for yield enhancement. Similar trends were also evident for major yield-contributing traits such as cob length, cob girth and number of kernels per row, further emphasizing their importance in determining hybrid performance. The comparative boxplot analysis clearly demonstrates the superiority of hybrids over their parental lines for yield and related attributes and validates the usefulness of the testcross breeding approach in maize improvement.

Discussion

The highly significant differences observed among genotypes for all twelve quantitative traits indicate the existence of substantial genetic variability in the experimental material, which is a prerequisite for effective selection and genetic improvement. Similar observations of significant genetic variation among maize genotypes have been reported by Magar et al. (2021) [10] , Antony et al. (2024) [2] and Suresh et al. (2021) [17] . The presence of considerable variability among hybrids, parental lines and checks suggests that the germplasm evaluated in the present study offers ample opportunities for identifying superior genotypes and developing high-yielding hybrids. The magnitude of genotypic and phenotypic coefficients of variation provides valuable information regarding the extent of variability available for selection. The high GCV and PCV observed for grain yield per hectare, ear height, number of kernels per row, plant height and cob length indicate substantial genetic variability for these traits. The relatively narrow differences between GCV and PCV for most characters suggest limited environmental influence on their expression, thereby increasing the effectiveness of phenotypic selection. Similar findings were reported by Jilo et al. (2018) [8] , Wedwessen and Zeleke (2020) [19] and Magar et al. (2021) [10] , who observed high variability for grain yield and major yield components in maize. Heritability estimates alone do not provide a complete picture of the inheritance of a trait; therefore, they should be interpreted together with genetic advance. In the present investigation, grain yield per hectare, ear height, number of kernels per row, plant height and cob length exhibited high heritability coupled with high genetic advance as percent of mean. This combination suggests that additive gene effects play a major role in the inheritance of these traits and that substantial improvement can be achieved through direct selection. Similar conclusions were drawn by Wali, (2019) [18] and Wedwessen and Zeleke (2020) [19] , who reported that traits exhibiting high heritability and high genetic advance are predominantly governed by additive genetic effects and respond effectively to selection. The correlation analysis provided valuable insights into the relationships among yield and its component traits. Grain yield per hectare exhibited strong positive associations with cob length, number of kernels per row, cob girth, plant height, test weight and kernel rows per cob. These relationships suggest that improvement in these component traits is likely to result in a corresponding increase in grain yield. The exceptionally high positive association between cob length and number of kernels per row further indicates that longer cobs facilitate greater kernel development, thereby contributing directly to yield enhancement. Similar positive relationships among grain yield, cob traits and kernel traits have been reported by Pavan et al. (2011) [12] , Devasree et al. (2020) [4] , Magar et al. (2021) [10] and Antony et al. (2024) [2] . The significant negative correlations observed between flowering traits and grain yield indicate that early flowering genotypes tended to produce higher yields under the experimental conditions. Early flowering is often advantageous because it enables efficient utilization of available resources and helps plants escape terminal stress conditions. Therefore, selection for moderate earliness combined with superior yieldcontributing traits could be beneficial in maize breeding programmes. The comparative analysis between hybrids and parental lines clearly demonstrated the superiority of hybrids for grain yield and most yield-contributing traits. The enhanced performance of hybrids for plant height, ear height, cob length, cob girth, kernel rows per cob and number of kernels per row reflects the successful exploitation of heterosis and favorable gene interactions. The substantially higher grain yield observed in hybrids confirms the effectiveness of hybrid breeding in maize and highlights the importance of selecting parents with superior combining ability for developing productive hybrids. These findings are consistent with the fundamental principles of heterosis breeding and support the continued use of hybrid development as a major strategy for maize yield improvement.

Conclusion

The present investigation revealed substantial genetic variability among the 100 maize genotypes comprising testcross hybrids, parental lines and checks for all twelve quantitative traits studied. High genotypic and phenotypic variability, coupled with high heritability and high genetic advance, were observed for grain yield per hectare, ear height, number of kernels per row, plant height and cob length, indicating the predominance of additive gene action and the effectiveness of direct phenotypic selection for these traits. Correlation analysis identified cob length, number of kernels per row, cob girth, kernel rows per cob and test weight as major yield-contributing traits owing to their strong positive associations with grain yield. The comparative distribution analysis further demonstrated the superiority of hybrids over parental lines for grain yield and most yield-related attributes, highlighting the successful exploitation of heterosis in the testcross population. Therefore, selection based on cob length, cob girth, number of kernels per row, kernel rows per cob and test weight, along with grain yield, would be highly effective for developing superior maize hybrids.

The identified variability and trait associations provide valuable information for future maize breeding programmes aimed at enhancing productivity and developing high-yielding hybrids adapted to diverse growing environments.

Acknowledgement

AcknowledgementThe authors acknowledge the support of Maize Research Centre, Rajendranagar, PJTAU for providing all the logistic support in conduct of the experiment, and IIMR-Winter Nursery Centre and CIMMYT for providing the genetic material under study.

References

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