Maize (Zea mays L.) genetic variability, character association, and path coefficient analysis, correlation and yield genotypes

Introduction

After rice and wheat, maize (Zea mays L.) is the third most significant cereal crop in the world. It was first grown in Mexico and Central America, but because of its increased productivity and broad range of adaptation, it is currently grown all over the world under a variety of environments (Yousuf et al., 2021) [20] . Because of its tremendous potential for output and ability to adapt to a variety of conditions among all the grains, maize is the most produced and is grown all over the world. Due to its high yield, greater economic return, and numerous applications, maize has become more and more popular among farmers in recent years. It is used for different applications including food, feed, industrial items, fodder and bio-fuel (Mishra et al., 2024) [6] . Maize is a major food crop and the main source of calories and protein for the majority of individuals who experience food insecurity globally (Shiferaw et al., 2011) [13] . Maize is commonly referred to as the "queen of cereals" due to its larger output potential than any other cereal crop. Although it may be produced year-round in southern India, maize is primarily planted in northern India during the Kharif (monsoon) season because it is a warmweather crop. Animal and poultry feed accounts for over 60% of India's maize grain production, with the remainder 14% going to the starch industry, 13% to food, 7% to processed food, and 6% to export (iimr.icar.gov.in). At current pricing, maize contributes approximately 9% of the country's food basket and over 400 billion to the agricultural GDP. Additionally, it generates over one billion labor hours of employment in the downstream industrial and agricultural sectors. The amount and quality of nutrient-rich feed given to meat and milch animals affects their output and productivity (Roy et al., 2019) [11] . For milch animals, maize is a high-quality feed that boosts body weight and produces higherquality milk. Since green fodder is thought to be the cornerstone of the dairy business, its availability is essential to encouraging sustainable dairy production (Naik et al., 2012) [8] .

However, in mixed crop-livestock systems, developing countries like India face a shortage of green and stored fodder. Roughly 23.4% of cattle in India lack dry fodder, whereas 11.4% need green fodder (Roy et al., 2019) [11] . Path coefficient analysis is a standardized partial regression coefficient that aids in evaluating the cause-and-effect relationship, effective selection, and the division of the correlation coefficient into direct and indirect effects of different traits toward the dependent variable. Path analysis is crucial for figuring out how closely yield and its constituent parts are related. The path coefficient (Wright, 1921; Dewey and Lu, 1959) [17,3] . The selection of optimal genotypes for rapid improvement through appropriate breeding techniques is aided by knowledge of genetic diversity, heritability, and genetic advancement. The improvement in the mean genotypic value is referred to as genetic progress of the chosen plants compared to the base population. According to Johnson et al. (1955) [4] , genetic gain should also be taken into account in order to reach a more trustworthy conclusion, even though the heritable estimates provide a helpful indication of relative values of selection based on phenotypic expression. Heritability estimates provide reliable information about the extent to which a particular genetic trait will be transmitted to the subsequent generations and thus could help significantly in making desirable selections. Breeders must therefore estimate heritability in order to assess the genetic potential of breeding materials, find attractive hybridization combinations, and use efficient selection strategies. The selection process will be easier and take less time if the heritability is larger, and the genetic improvement will be greater (Allard, 1960) [2] . Genetic metrics that are effective biometric instruments for evaluating genetic variability include genotypic coefficient of variance, phenotypic coefficient of variance, heritability, and genetic advancement. In addition to differentiation and diversity of genetic stocks, precise knowledge of genetic parameters like coefficient of variation, heritability, genetic advance, and the impact of environment on the expression of yield and yield components aids the breeder in choosing a variety of parents for the exploitation of heterosis (Singh et al., 2020) [14] .

Materials and Methods

The current experiment aims to assess fifteen maize germplasm lines along with one check variety using a Randomized Block Design at the Main Experiment Station Prof. Rajendra Singh (Rajju Bhaiya) University, Prayagraj. Sowing was done during the kharif season of 2025. The field was organized into 3 blocks, with each block containing sixteen plots (fifteen genotypes and one checks). Each plot will feature three rows, each plot two meters long, with 20 cm plant-to-plant spacing and 60 cm row spacing. Observations was taken from 5 randomly selected plants per plot, except for days to 50% tasseling and days to maturity, which was recorded on a plot basis. Grain yield was found to be strongly positively correlated with plant height, number of kernels per row, grain yield per ear, and shelling percentage, according to correlation analysis. Grain yield per plot had the largest direct impact on overall yield, followed by plant height and grain yield per ear, according to path analysis. Therefore, these characteristics can be employed as selection criteria to increase maize production. followed by day of 50%silking (7.64%). In the current studies except for day of 50% tasseling all the traits showed greater values of PCV%. Similar results were obtained by Ojo et al.

Results and Discussion

(2006) [10] ; Moulin, et al., (2009) [7] and Ali et al. (2012) [1] . The highest heritability values were found for grain yield/cob (68.68%), number of kernels /row (61.66%), grain yield /plot gm. (51.77%) and total yield kg/ha (51.25%) while the field emergence 15 DAS showed the lowest heritability value (3.47%). The higher values of heritability of aforementioned traits are indicative that selection can be made on the basis of these traits (Ojo et al. 2006; Moulin et al., 2009 [7] and Ali et al., 2012) [10,1] .

Correlations

It is evident from the correlation analysis that several yield contributing traits exhibited positive and significant association with grain yield at both genotypic and phenotypic levels. Grain yield per plot showed a strong and positive significant correlation with total yield (1.00 & 1.00) at both levels.

Similarly, grain yield per ear exhibited a positive and significant association with total yield (0.7591 & 0.6296), indicating its direct contribution towards productivity. Similar findings were also reported by Ojo et al. (2006) [10] and Malik et al. (2005) [5] . Plant height at 60 DAS and 90 DAS showed positive and significant correlation with total yield (0.6451 & 0.6311; 0.7148 & 0.4922) at genotypic and phenotypic levels, respectively. Number of kernels per row also exhibited strong positive and significant association with total yield (0.6137 & 0.3656), while number of rows per ear showed moderate positive association (0.5157 & 0.2591) at both levels. These results are in agreement with the findings of Yousuf and Saleem (2001) [19] and Ojo et al.

(2006) [10] , who also reported similar associations in maize.

[5] . On the other hand, days to 50% tasseling, silking and anthesis exhibited negative and significant correlation with total yield (-0.8206 & -0.6907; -0.8604 & -0.7023; -0.8889 & -0.7342) at genotypic and phenotypic levels, respectively. This indicates that early flowering genotypes are more productive compared to late maturing ones. These findings are in conformity with the reports of Ojo et al. (2006) [10] and Yousuf and Saleem (2001) [19] . Shelling percentage showed a positive and significant association with total yield ( [5] . Field emergence showed a negative association with total yield at genotypic level (-0.5604) but weak positive association at phenotypic level (0.2166), indicating environmental influence on trait expression. In general, genotypic correlation coefficients were higher than phenotypic correlations for most of the traits, indicating that the observed associations are largely governed by genetic factors with minimal environmental interference. These findings are in close agreement with earlier reports of Yousuf and Saleem (2001) [19] and Ojo et al. (2006) [10] .

Path coefficient analysis

Path coefficient analysis at both genotypic and phenotypic levels revealed that grain yield per plot had the highest positive direct effect on total yield (1.0032 and 0.9952), indicating that it is the most important yield contributing trait. Similarly, grain yield per ear also exhibited a positive direct effect on total yield (0.0043 and 0.0109), though its magnitude was comparatively lower. Similar results were also obtained by Ojo et al. (2006) [10] ; Rehman et al. (2007) and Ali et al., (2011). Among growth traits, plant height at 60 DAS showed a positive direct effect (0.0042 and 0.0397), followed by plant height at 90 DAS (0.0019 and 0.0090) at phenotypic and genotypic levels, respectively. Ear height also exhibited a positive direct contribution (0.0014 and 0.0268), suggesting that plant stature plays an important role in determining yield. Yield attributing traits such as number of kernels per row showed a small but positive direct effect (-0.0003 and -0.0147 showing slight variation across levels), whereas number of rows per ear exhibited positive direct effects (0.0019 and -0.0063), indicating mixed influence depending on genetic and environmental interaction. Similar results were also reported by Taiwo et al.

(2020) [16] and Saritha et al. (2022) [12] in maize Days to 50% tasseling, silking and anthesis exhibited negative direct effects on total yield at both levels (e.g., -0.0058, -0.0057, -0.0058 at phenotypic level and -0.0217, -0.0212, -0.0050 at genotypic level), indicating that delayed flowering reduces yield potential. This supports the earlier correlation findings. A similar kind of positive direct effect of plant height was found by Yaregal et al.

(2021) [18] and Sirigireddy et al. (2022) [15] . Ear diameter showed a low but positive direct effect (0.0033 and 0.0183), while seed index also contributed positively (0.0013 and 0.0120). However, shelling percentage exhibited a negative direct effect (-0.0014 and -0.0046), suggesting that its contribution to yield is mainly through indirect effects. Similar results were obtained by O' Regan et al. (1992) [9] ; Malik et al. (2005) [5] and Ali et al. (2011).

Indirect Effects

Most traits influenced yield indirectly through key components like grain yield per plot and grain yield per ear. For example. Plant height (60 & 90 DAS) contributed indirectly via grain yield per plot and kernels per row. Number of kernels per row showed strong indirect effects via grain yield per ear. Number of rows per ear contributed indirectly through grain yield per plot. Ear height influenced yield indirectly through plant height and grain yield traits. Flowering traits (tasseling, silking, anthesis) showed negative indirect effects through grain yield per plot and kernel traits, reinforcing their overall negative association with yield.

Field emergence and number of cobs per plant had negligible direct effects but contributed indirectly through yield components, indicating their limited importance in direct selection.

Acknowledgement

AcknowledgementThe authors gratefully acknowledge the Rajendra Singh (Rajju Bhaiya) University, Prayagraj, UP, India, and Sciences for providing all necessary facilities and resources.

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