Genetic variability, correlation and path coefficient analysis for yield traits in groundnut (Arachis hypogaea L.)

Introduction

Groundnut (Arachis hypogaea L.) is an essential and profitable oilseed crop also known as King of Oilseeds. It has a chromosome number (2n = 4x = 40) and is a self-pollinated crop with cleistogamous flowers and belongs to family Fabaceae. Groundnut supplies oil, protein, dietary fibre, vitamins B-complex, vitamin E, and minerals including iron, zinc, potassium, and magnesium (Mondal et al., 2018) [11] . Groundnut also contains trace minerals such as selenium, manganese, and copper, along with antioxidant polyphenolic materials such as flavonoids and resveratrol, which help keep cellular functioning normal. Globally groundnut is cultivated in area of 359.84 lakh hectares and total production reaches 770.69 lakh tonnes, while the average productivity stands at 2109.47 kg ha⁻¹. In India, groundnut occupies 53.51 lakh hectares, producing 120.91 lakh tonnes, and the average productivity is around 2260 kg ha⁻¹ during 2025-26 (upag.gov.in, 2025-26). The crop was grown on 4.87 lakh hectares in Bundelkhand, with total production of 3.61 lakh tonnes, crop together has average productivity of 1580 kg ha -1 in 2022-23 (desagri.gov.in, 2022-23). Groundnut (Arachis hypogaea L.) has huge economic value as a major oilseed, and also as a food legume crop but it has narrow genetic base. Because of that, there is a need for a systematic characterization and screening of the existing germplasm, for agronomically relevant traits, and ability to cope with the biotic and abiotic stresses (Reddy et al., 2023) [14] . The overall success of any breeding programme depends a lot on finding better genotypes, through evaluating genetic variability and the amount of diversity that exists in the breeding material.

Measuring the genotypic coefficient of variation (GCV) and the phenotypic coefficient of variation (PCV) gives useful insight into how much variation is actually available for each trait. Heritability, along with genetic advance, also plays a role in anticipating how well selection might work in practice. Correlation analysis measures the strength of the relationship between yield, and its component traits. In path coefficient analysis, the correlation coefficients are split into direct and indirect effects, which highlight the traits that are required to enhance the yield. So, the current study was carried out to estimate the genetic variability, to look into the association among pod yield and its component traits, and to figure out the direct along with indirect effects of these traits on pod yield. In the end, it should offer practical knowledge that can support future groundnut improvement programmes (Birthal et al., 2010) [2] .

Materials and Methods

This study was carried out during kharif season of 2024 at the agricultural Farm, RLBCAU, Jhansi. The experiment was arranged in a randomized complete block design, with four replications. The experimental material consisted of 20 genotypes and 4 checks of groundnut (Table 1). Usual recommended package of practices was followed for crop establishment. The observations were taken from five randomly chosen competitive plants for each genotype in each replication, except days to 50% flowering which was noted on plot basis. Later, the data was handled for statistical analysis, and the analysis of variance (ANOVA) was computed. Based on that, the following genetic parameters were worked out for the traits that showed significant mean square effects due to genotypes. Phenotypic and genotypic variances were obtained based on the formula explained by Johnson et al. (1955) [7] . The phenotypic coefficient of variation and genotypic coefficient of variation were estimated by following Burton (1952) [3] . Broad sense heritability was computed in percent using the approach given by Allard (1960) [1] . The expected genetic advance from selecting five percent of the superior progeny was calculated with the help of Allard (1960) [1] . The genotypic correlation coefficient was determined using the formula recommended by Hazel et al. (1943) [6] . For correlation significance, the coefficient was tested against the 'r' value reported by Fischer and Yates (1963) [5] . Further, path coefficient analysis was done by employing the correlation coefficients to understand the direct along with indirect impacts of these variables on yield as suggested by Wright (1921) [20] and demonstrated by Dewey and Lu (1959) [4] .

Result and Discussion

Genetic variability

The analysis of genetic variability showed variation among the groundnut genotypes for all the characters that were observed (Table 2). PYQPH, recorded the top mean value 23.99 g, and ranged from 8.80-57.20 g. In the same way, broad ranges were observed for PH (24.28-61.70 cm), TNOB (4.99-29.40), TNOP (8.20-33.20) [15] , Kumari et al., (2023) [9] , Saiteja et al., (2023) [16] .

The phenotypic coefficient of variation (PCV) was only slightly higher than the genotypic coefficient of variation (GCV) for all traits, showed that the environment does not interfere too much with how these characters are expressed. The strongest GCV and PCV values were noted for PYQPH (44.71% and 45.03%), TNOB (31.81% and 41.05%), SW (29.58% and 32.76%), TNOP (25.03% and 27.44%), HSW (24.72% and 25.96%), and SP (24.21% and 27.51%). On the other hand, SPP had the smallest GCV at 4.92% and PCV at 8.64%, meaning this particular trait has narrow genetic variability, comparatively. PYQPH showed the highest heritability (98.6%) followed by PL (96.0%), SP (94.3%), HSW (90.6%), TNOP (83.2%), SW (81.6%), SL (77.5%), PH (70.5%), and PW (70.0%). On the other hand, TNOB (60.0%) had moderate heritability and SPP (32.5%), recorded the smallest heritability. Highest GAM value was recorded for, PYQPH (91.45%) followed by SW (55.04%), TNOB (50.77%), HSW (48.47%) and TNOP (47.02%). SL (43.90%), PL (31.00%), PH (29.17%), PW (28.16%), SP (16.95%) and SPP (5.79%) showed lowest GAM value. When heritability is coupled with genetic advance, the highest combination observed in PYQPH, HSW, TNOP, SW, SL, and PL showed that additive gene action is predominant in all these traits. But for SPP it was different, it had low h² (32.5%) and also low GAM (5.79%) indicating involvement of non-additive gene action. The present investigation is in agreement with Reddy et al., (2025) [15] , Kumari et al., (2023) [9] , Saiteja et al., (2023) [16] , Mallepogu et al., (2025) [10] .

Correlation analysis

Phenotypic and genotypic correlation coefficients (Table 3) showed that PYQPH had positive and highly significant Overall, among SL, SW, SP, and HSW, positive correlations were also significant at both phenotypic and genotypic levels. In general, the magnitude of rg was usually higher than the rp across most trait pairings, so the correlation looks like they are mainly governed by genetic factors, with less impact from the environment. So, the strong positive correlation pattern involving PYQPH with TNOP, PL, PW, SL, SW, SP and HSW means these traits could work as suitable selection yardsticks for improving yield in groundnut. Zaman et al., (2012) [22] , Kumar et al., (2014) [8] and Shoba et al., (2012) [17] finds similar results in their investigation.

Path coefficient analysis

Genotypic and phenotypic path coefficient (Table 4a and4b) analyses showed differences in how large the direct and indirect effects of the studied traits are on PYQPH. At the genotypic level, the highest positive direct effect on PYQPH was contributed by SL (0.95484), then TNOP (0.57900), PL (0.37160), PH (0.11099), PW (0.05699), SPP (0.03559) and, finally DT50% (0.02566). Meanwhile the negative direct effects for SW (-0.42652), SP (-0.29591), HSW (-0.24896) and TNOB (-0.02460). The residual effect stayed at 0.0326 which showed that most of the variability in PYQPH is covered by the traits. At the phenotypic level, PL (0.59288) had the highest positive direct effect on PYQPH, and after that HSW (0.25090), TNOP (0.08767), PW (0.08531), SW (0.08200), SL (0.04401), TNOB (0.00308), and DT50% (0.00088). Negative direct effects were noticed for SP (-0.13423), PH (-0.03403) and SPP (-0.00969). In addition, many yields related traits gave large positive indirect effects through PL, emphasizing its influence on PYQPH when conditions are phenotypic. The residual effect was 0.0551, pointing towards the selected characters accounting for a big share of the phenotypic variation in PYQPH. Patel et al., (2021) [12] , Tirkey et al., (2021) [18] found similar results as the present study.

Genetic divergence analysis

Using Mahalanobis D² statistics, 20 groundnut genotypes with 4 checks were grouped into four clusters, by Tocher's method (Table 5). Cluster I was the largest, with 18 genotypes (2, 12, 1, 17, 10, 21, 4, 7, 13, 15, 23, 24, 20, 16, 3, 8, 6, and 9), then Cluster II had only three (11, 19, and 22). Cluster III held two genotypes (5 and 18) and Cluster IV was more like a solo cluster, only genotype 14. This spread across clusters suggests that there is quite a lot of genetic diversity. Looking at the mean intra-cluster D² distance (Table 6), it went from 0.00 up to 103.80. Cluster III showed the highest intra-cluster value (103.80), and after that Cluster I (56.87), while Cluster II came next (38.03). Cluster IV, since it had just one genotype, naturally had an intra-cluster distance of 0.00. The fact that Cluster III had the larger intra-cluster distance means the genotypes inside it are more genetically diversity. For the mean inter-cluster D² distance, the range was 139.48 to 1722.22. The largest inter-cluster distance was between Clusters III and IV (1722.22), then Clusters I and III (1131.45) and finally Clusters II and IV (645.71). So, in general, these clusters are quite genetically far apart. Meanwhile the smallest inter-cluster distance came from Clusters I and IV (139.48), then Clusters I and II (288.43), and then Clusters II and III (371.96). This pattern implies the genetic relation between Clusters I, II, and IV are somewhat less distant compared with the other pairings. The present finding is in agreement with the results of Zaman et al., (2010) [21] , Vivekananda et al., (2015) [19] , Raghuwanshi et al., (2016) [13] .

Conclusion

This present investigation showed wide, genetic variability among 24 groundnut genotypes for pod yield and the traits that come with it, so that overall, there is a real scope for genetic improvement via selection. Genotypes RLBGN5, TG88, RLBGN6, TAG24 and TG37A did well in pod yield per plant, and they also looked good for several yield contributing characters, so they can be considered useful genetic resources for future breeding programmes.

Heritability coupled with high genetic advance for pod yield per plant, hundred seed weight, total number of pods per plant, seed weight, seed length, and pod length, indicated that additive gene action is playing the major role, and this also suggests that direct selection on these phenotypes would be beneficial. Then correlation and path coefficient analyses highlighted total number of pods per plant, pod length, seed length, pod width, seed weight, shelling percentage, and hundred seed weight as the main selection criteria to improve pod yield, but pod length, seed length, and total number of pods per plant gave the highest positive direct effects, sort of the strongest push. Using Mahalanobis D², the genotypes were divided into four separate clusters, and the largest inter cluster distance was between Clusters III and IV, which clearly points toward wide genetic divergence. So, genetically divergent and high performing genotypes like RLBGN5 and TG88 from Cluster III with RLBGN1 from Cluster IV, or from Cluster II, like TAG24, RLBGN6, and TG37A, are expected to create broad genetic variability and also desirable recombinants. In the end these genotypes may help in developing new high yielding groundnut varieties, with better yield traits overall.

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