Character Association and Causation Analysis of Certain Quantitative Traits in Maize (Zea may L.)

Maize (Zea mays L.) is one of the most important cereal crops in the world. In India the average production of maize is 28.77 million tonnes from an area of 9.57 million hectare with the average productivity of 3.006 tonnes per hectare (DES, 2021). Grain yield in maize and most of the crops is a quantitative trait which controlled polygenically so effective yield improvement and simultaneous improvement in yield components are imperative (Bello and Olaoye, 2009). Selection on the basis of grain yield alone is usually not very effective and efficient. However, selection based on its component characters could be more efficient and reliable (Muhammad et al., 2003). This is the reason that several workers emphasized the use of component approach for successful breeding programme for higher yield (Moll et al., 1962). Therefore, the knowledge of interrelationship between different characters and the direct and indirect contribution of such characters on yield is crucial for making efficient selection strategy to develop the inbreds and/or hybrids having desirable combination of the character.

The experiment was conducted using 77 genotypes including twenty-one parental lines (eighteen females and three males), their Fifty-four F1 hybrids (produced by crossing eighteen lines (female) and three testers (male) in line × tester mating design during Kharif-2018) and two checks (Bharat Kaveri and Don 1588). The experiment was carried out in randomized block design (RBD) with three replications during Rabi 2018-19 at Student Instructional Farm CS Azad University of Agriculture and Technology, Kanpur (UP) India. The plot length was 4 m and inter and intra row spacing was 60 x 25 cm. Data on flowering and maturity traits were recorded on plot basis while, data on yield traits were recorded on plant basis (Five plants from each genotype from each replication were randomly selected and tagged for recording the observations). Analysis of variance was done as per Panse and Sukhatme, 1985. Data on 17 different characters were collected and analysized to identify the characters having significant desirable relationship with grain yield and the direct and indirect contribution of such characters on grain yield. The simple correlation coefficient between different characters was estimated according to Searle, (1961). Path coefficient analysis was carried out according to Dewey and Lu, (1959).

Success of any breeding programme largely depends on the available variability for different characters among the genotypes studied. The analysis of variance for different quantitative traits was done and presented in Table 1 which revealed significant amount of variability in the genotypes for all the traits, indicating the great opportunity of selection for elite genotypes. Thakur et al., (2016), Patil et al., (2016) and Dar et al., (2018)a have also reported highly significant variation for all the characters under study.



The interrelationship between different characters was worked out and given in Table 2. The genotypic correlation coefficients were found greater than phenotypic correlation coefficients for most of the characters that showed true relationship. So the selection of elite genotypes on the basis of such characters would be rewarding. Raghu et al., (2011) and Singh et al., (2022) also reported the same results. Grain yield per plant showed highly significant positive correlation with cob weight (0.801, 0.796), kernels per row (0.752, 0.740), kernels per cob (0.736, 0.729), shelling percentage (0.725, 0.699), cob diameter (0.590, 0.536), plant height (0.523, 0.515), 100-kernel weight (0.460, 0.456), kernel rows per cob (0.425, 0.404), cob length (0.343, 0.318) and days to 75% dry husk (0.233, 0.207) at genotypic level and phenotypic level respectively. However, grain yield had significant positive correlation with germination percentage (0.149, 0.161) and cobs per plant (0.137, 0.148). Highly significant positive association of grain yield per plant with days to 75% dry husk showed that long duration (late maturity) improves the grain yield per plant in maize. Positive association of grain yield per plant with days to maturity, plant height, ear height, ear length, ear girth, number of kernel rows per plant, number of kernels per row, 100- kernel weight and shelling percentage in also reported by Kumar et al., (2014). Positive and highly significant correlation of grain yield with ear height, days to 50% male flower initiation, days to 50% female flower initiation, days to maturity, 1000 grain weight, ear weight at genotypic and phenotypic level also reported by Shukla, (2017). Positive relationship of ear girth, kernels per row and ear length with grain yield per plant has also reported by Sumalini and Manjulatha, (2012). Prakash et al., (2019) and Raghu et al., (2011) also reported significant and positive relationship of grain yield per plant with plant height, ear length, ear girth, kernel rows per ear, kernels per row and 100-grain weight. Significant positive association of kernel rows per cob and kernels per row with grain yield per plant have also reported by Pahadi and Sapkota, (2016). Similar results have also been reported by Kinfe and Tsehaye, (2015); Kumar et al., (2015); Patil et al., (2016); Bartaula et al., (2019); Prakash et al., (2019). Seedling length and seed vigour index were found to be negatively associated with grain yield per plant but the relation was non-significant. This indicates the need of causation analysis to conclude the result since correlation gives only an idea about the yield contributing characters but does not provide the exact picture of direct and indirect contributions to yield.



Path analysis partitions the correlation coefficient into direct and indirect effects of component characters (independent variables) on yield (dependent variable). It gives the understanding of cause-and-effect relationship between different character combinations. The direct and indirect effects of different characters on grain yield per plant are presented in Table 3. High direct positive impact on grain yield per plant was exhibited by seed vigour index (1.063, 0.518), cobs per plant (0.542, 0.540), cob weight (0.540, 0.537) and kernels per row (0.403, 0.300) at genotypic and phenotypic level respectively. Bello et al., (2010) also reported highest direct effects on grain yield per plant by ear weight. The grain yield of a population of maize was improved markedly through indirect selection for the number of ears per plant (Lonnquist, 1967).Thus, seed vigour index, cobs per plant, cob weight and kernels per row emerged as most important direct yield contributors. Similar findings have also reported by Shukla, (2017), Raghu et al., (2011) and Singh et al., (2022).

Thus the combined study of correlation and path coefficient analysis indicating that the cob weight, shelling percentage, cob diameter, germination percentage and seed vigour index should be utilized in formulation of selection strategy for evaluation of parental lines and/or hybrids in maize.