Genotypic correlation and path coefficient analysis of yield and associated traits in sweet corn genotypes

Introduction

Sweet corn (Zea mays L. var. saccharata) is an important specialty maize type valued for its tender kernels, sweetness, palatability and nutritional quality. Its characteristic sweetness is mainly associated with soluble sugars accumulated during the milk stage, with sucrose, glucose and fructose being the major sugars (Hale et al., 2005) [6] . Improvement of cob yield along with desirable quality traits is therefore an important objective in sweet corn breeding. Cob yield is a complex quantitative trait influenced by several interrelated morphological, phenological and yield-component characters. Understanding their association with cob yield is essential for identifying suitable selection criteria and improving breeding efficiency. Significant associations among yield components have been reported in sweet corn and maize (Kashiani and Saleh, 2010; Suhaisini et al., 2016; Vani Praveena et al., 2022) [8,22,24] . Correlation analysis helps determine the direction and magnitude of association among traits; however, it cannot distinguish between direct and indirect effects, particularly when yield components are interrelated. Path coefficient analysis overcomes this limitation by partitioning correlations into direct and indirect effects, thereby helping to identify traits with greater direct association with yield (Dewey and Lu, 1959) [4] . Previous studies have successfully used path analysis to identify important yield-contributing traits in maize and sweet corn (Raghu et al., 2011; Khazaei et al., 2010; Vani Praveena et al., 2022) [9,16,24] . Therefore, the present study was undertaken to estimate genotypic correlations and path coefficients among cob yield and associated quantitative and quality traits in sweet corn genotypes and to identify important traits useful as selection criteria for improving cob yield under the conditions of the present study.

Materials and methods

The experiment was carried out in a Randomized Block Design (RBD) with three replications at the R.K. Nagar Farm of the All India Coordinated Research Project on Maize, Shenda Park farm, Kolhapur during Kharif 2025. The experimental material consisted of 20 sweet corn genotypes (including 17 genotypes and 3 check varieties) obtained from the AICRP on Maize.

Each entry was sown in plot of 4.00 m length, adopting a spacing of 60 cm between rows and 20 cm between plants. The recommended fertilizer dose of 60 kg N, 60 P2O5 and 40 kg K2O ha -1 was applied as basal, and the remaining 60 kg N ha -1 was top-dressed at 45 days after sowing. Standard agronomic practices and plant protection measures were uniformly followed during the entire crop growth period. In each replication, five plants were selected at random and tagged at 35 days after sowing for recording observations on quantitative traits, viz., days to 50% tasseling, days to 50% silking, plant count at harvest, number of cobs per plot at harvest, mean cob length with husk (cm), mean cob length without husk (cm), mean cob girth with husk (cm), mean cob girth without husk (cm), number of kernel rows per cob, number of kernels per row, plant height (cm), cob height (cm), 100-kernel weight (g) and cob yield per plant with husk, along with biochemical traits, viz., total soluble solids (TSS; °Brix), reducing sugar (%) and sucrose (%). TSS was measured using a hand refractometer, while reducing sugar was estimated by Fehling's reagent method. The genotypic correlation coefficient worked out as described by Singh and Chaudhary (1985) [20] . The Path coefficient analysis done as per the method suggested by Dewey and Lu (1959) [4] . [10] . Direct selection for maximum length and girth cobs bearing higher kernel counts efficiently boosts overall yield, consistent with reports by Wannows et al., (2010) [25] and Barua et al., (2017) [2] . The primary influence of ear dimensions and kernel weight in driving final yield is further corroborated by Ram Reddy et al., (2012) [18] and Vani Praveena et al., (2022) [24] . Furthermore, the linear relationships established between husked and dehusked ear traits parallel the phenotypic patterns detailed by Rajasekar et al., (2024) [17] . The robust association between harvest plant count and total cobs per plot underscores the necessity of maintaining optimal plant populations to maximize unit yield, agreeing with Khazaei et al., (2010) [9] . Positive interrelationships between flowering parameters and vegetative height accord with findings by Raghu et al., (2011) and Vani Praveena et al., (2022) [16,24] , though environmental variation can occasionally weaken silking-yield relationships, as observed by Natraj et al., (2014). Regarding quality traits, the high positive correlations among brix, reducing sugars and sucrose validate brix as a dependable indirect selection metric for endosperm sweetness, supporting conclusions by Kumari et al., (2007) [11] and St. [5,7] . Crucially, the absence of significant negative correlations between sugar parameters and total cob yield indicates that selecting for high endosperm sugar concentration does not incur a yield. This confirms the feasibility of concurrently breeding high-yielding and highsugar sweet corn hybrids, as previously demonstrated by Suhaisini et al., (2016) and A. Thanga Hemavathy (2020) [1,7,22] .

Path Analysis

From Table No. 3, path coefficient analysis, executed as per the methodology of Dewey and Lu (1959) [4] , was applied to partition the phenotypic correlation coefficients into direct and indirect effects, offering a clearer understanding of the primary yield-contributing traits in sweet corn. The evaluation revealed that the strongest positive direct effects on cob yield per plant with husk were exerted by the number of kernels per row (0.6626), number of cobs per plot at harvest (0.3998) and number of kernel rows per cob (0.3187). Additionally, mean cob girth with husk (0.2358), reducing sugar percentage (0.1102), Brix percentage (0.0918) and days to 50 per cent silking (0.0540) demonstrated positive direct contributions to yield. These results underline the critical role of kernel parameters and ear characteristics as primary criteria for direct selection in yield improvement programs, aligning with prior studies by Bello et al., (2010) [3] , Raghu et al., (2011) [16] and Reddy et al., (2022) [19] . Conversely, direct negative effects were recorded for plant count at harvest (-0.2571), mean cob girth without husk (-0.2151), sucrose percentage (-0.1612), cob length with husk (-0.0628) and cob length without husk (-0.0401), corroborating the observations of Matin et al., (2017) [14] . Assessment of indirect pathways indicated that key structural traits such as cob length, cob girth, plant height, and cob height exerted significant positive indirect contributions to cob yield through the number of kernels per row and number of cobs per plot. Flowering attributes, including days to 50 per cent tasseling and silking, indirectly supported yield by lengthening the reproductive phase and enhancing sink capacity, consistent with findings by Singh et al., (1999) [21] and Kumar et al., (2006) [11] . Furthermore, biochemical traits such as brix, reducing sugars and sucrose exhibited favorable positive mutual indirect pathways without compromising kernel development, supporting reports by Lertrat and Pulam (2007) [13] and St. Subaedah et al., (2021). Overall, path analysis demonstrates that simultaneous selection for number of kernels per row, number of cobs per plot and kernel rows per cob provides the most effective selection strategy for maximizing cob yield in sweet corn.

Conclusion

The study revealed that number of kernels per row, number of cobs per plot and number of kernel rows per cob were the most important traits contributing to cob yield in sweet corn, as evidenced by their strong positive genotypic correlations and high positive direct effects. These traits can therefore be considered effective selection criteria for improving cob yield. Sugar traits showed weak or negligible association with yield, indicating the possibility of simultaneous improvement of yield and sweetness in sweet corn.

References

1. Thanga Hemavathy A. Genetic diversity studies in sweet corn (Zea mays L. saccharata). An Int Q J Life Sci. 2020;15(2):267-269.
2. Barua NS, Chaudhary VP, Hazarika GN. Genetic variability and correlation studies for morphological traits in maize (Zea mays L.) genotypes. Indian Res J Genet Biotechnol. 2017;9(1):38-48.
3. Bello OB, Abdulmaliq SY, Afolabi MS, Ige SA. Correlation and path coefficient analysis of yield and agronomic characters among open pollinated maize varieties and their F1 hybrids in a diallel cross. Afr J Biotechnol. 2010;9(18):2633-2639.
4. Dewey DR, Lu KH. A correlation and path coefficient analysis of components of crested wheatgrass seed production. Agron J. 1959;51:515-518.
5. Geeta HP, Palanimuthu V, Srinivas G. Study of some physico-chemical properties of sweet corn. Int J Agric Sci Res. 2017.
6. Hale TA, Hassell RL, Phillips T. Refractometer measurements of soluble solid concentration do not reliably predict sugar content in sweet corn. Horttechnology. 2005;15(3):668-672.
7. Hemavathy AT, Balaji K, Ibrahim S, Anand G, Sankar D. Genetic variability and correlation studies in maize (Zea mays L.). Agric Sci Dig. 2008;28(2):112-114.
8. Kashiani P, Saleh G. Estimation of genetic correlations on sweet corn inbred lines using SAS mixed model. Am J Agric Biol Sci. 2010;5:309-314.
9. Khazaei F, Alikhani MA, Yari L, Khandan A. Study the correlation, regression and path coefficient analysis in sweet corn (Zea mays var. saccharata) under different levels of plant density and nitrogen rate. J Agric Biol Sci. 2010;5(6):14-19.
10. Kumar MVN, Kumar SS. Studies on character association and path coefficients for grain yield and oil content in maize (Zea mays L.). Ann Agric Res. 2000;21(1):73-78.
11. Kumar S, Shahi JP, Singh J, Singh SP. Correlation and path analysis in early generation inbreds of maize (Zea mays L.). Crop Improv India. 2006;33(2):156.
12. Kumari J, Gadag RN, Jha GK. Genetic analysis and correlation in sweet corn (Zea mays L. saccharata) for quality traits, field emergence and grain yield. Indian J Agric Sci. 2007;77:613-615.
13. Lertrat K, Pulam T. Breeding for increased sweetness in sweet corn. Int J Plant Breed. 2007;1(1):27-30.
14. Matin MQI, Uddin MS, Rohman MM, Amiruzzaman M, Azad AK, Banik BR. Genetic variability and path analysis studies in hybrid maize (Zea mays L.). Am J Plant Sci. 2017;8(12):3101-3109.
15. Nataraj V, Shahi JP, Agarwal V. Correlation and path analysis in certain inbred genotypes of maize (Zea mays L.) at Varanasi. Int J Innov Res Dev. 2014;3(1):21-25.
16. Raghu B, Suresh J, Kumar SS, Saidaiah P. Character association and path analysis in maize (Zea mays L.). Madras Agric J. 2011;98(3):7-9.
17. Rajasekar, Kumar AA, Akilan M, Nirmalaruban R. Morphological characterization and genetic diversity analyses of yield and yield attributing traits in sweet corn (Zea mays var. saccharata). J Sci Res Rep. 2024;30(7):635-646.
18. Ram Reddy, Seshagiri Rao V, Sudarshan MR. Heritability and character association among grain yield and its components in maize (Zea mays L.). J Res ANGRAU. 2012;40(2):45-49.
19. Reddy SGM, Lal GM, Krishna TV, Reddy YVS, Sandeep N. Correlation and path coefficient analysis for grain yield components in maize (Zea mays L.). Int J Plant Soil Sci. 2022;34(23):24-36.
20. Singh RK, Chaudhary DD. Biometrical methods in quantitative genetic analysis. New Delhi: Kalyani Publishers; 1985.
21. Singh PK, Prasad MK, Chaudhari LB. Diversity study in maize (Zea mays L.). J Appl Biol. 1999;9:129-132.
22. Suhaisini B, Ravikesavan R, Yuvaraja A. Genetic variability and correlation among yield and yield contributing traits in sweet corn. Madras Agric J. 2016;103(10-12):293-296.
23. St Subaedah, Edy E, Mariana K. Growth, yield, and sugar content of different varieties of sweet corn and harvest time. Int J Agron. 2021;2021:8882140. 7 p.
24. Vani Praveena M, Talekar SC, Kachapur RM. Studies on correlation and path co-efficient analysis of single cross hybrids in sweet corn. Int J Plant Sci. 2022;17(2):173-179.
25. Wannows AA, Azzam HK, Al-Ahmad SA. Genetic variances, heritability, correlation and path coefficient analysis in yellow maize crosses (Zea mays L.). Agric Biol J N Am. 2010;1(4):630-637.