Genetic variability, heritability, correlation, path analysis and genetic divergence studies for yield and yield contributing traits in pea (Pisum sativum L.)

Introduction

The Pea (Pisum sativum L.) is one of the most important cool-season vegetable crops in the Fabaceae family. Its dry seeds, which are high in protein, vitamins, and minerals, and green pods are the main reasons it is produced (Anonymous, 2021). Pea growing is important for India's agricultural economy and nutritional security. Is an essential and nutritious winter food that is cultivated all throughout the country. India produces 5524 thousand metric tonnes of peas annual on an area of approximately 563 thousand hectares (Anonymous 2019). The first cultivation of peas appears to have been in western Asia, from where it spread to Europe, China and India. India is the second largest producer of pea in the world after Russia (Vasanthrao et al., 2025). Currently pea is being produced in more than 95 Countries across the world, with an annual production of around 15.7 million tons (FAO, 2023). The evaluation of genetic variability is important to know the source of genes for a particular trait within the available germplasm. There was no common criterion for selection of genotypes based on specific objectives among the plant materials (Kumar et al., 2022). Pea provided protein and fiber for human and livestock diets (Hood-Nieferet et al., 2012), Variability indices such GCV and PCV, heritability (h2), and genetic advance (GA) are necessary when creating an ideal breeding program targeted at utilizing the inherent variability found in the original population (Raj et al., 2020). According to the assumption that yield components are mutually related, path coefficient analysis determines the direct and indirect contributions of different independent characters on a dependent component (Jeberson et al., 2016;Sharma et al., 2023). Any crop needs to have genetic variability, heritability, and genetic advancement in order to identify superior genotypes and enhance any attributes (Prabhu M et al., 2009). The activities and interactions of different component characteristics culminate in [4, 3, 32, 10, 15, 11, 21, 12, 25, 10] the complex character that is called yield (Uhlarik et al., 2022). Along with to functioning as a method for identifying a genotype with a high yield potential, path analysis permits breeders to analyze the nature of forces interacting to form the given relationship. Therefore, the results of correlation and path analysis for yield and yield con attributing traits in pea are the primary objective of this analysis. (Kumar et al., 2014). Genetic divergence can be used to identify desirable parents for the aim of improving a specific trait through hybridization when selection is ineffective. Mahalanobis developed D2 statistics, that is a useful tool to estimate how much genetic diversity is present in a population (Sharma M. 2023). The experiment was also conducted to evaluate the yield performance of different pea genotypes in order to identify superior genotypes for higher productivity. In addition, genotypic and phenotypic correlation coefficients were estimated to determine the association among yield and yield contributing traits, which would help in understanding the nature and magnitude of relationships among different characters influencing seed yield in pea [21, 17, 23] .

Materials and Methods Experimental Location and Plant Material

The current research investigation was carried out at the Agricultural Research Farm of Prof. Rajendra Singh (Rajju Bhaiya) University in Naini, Prayagraj, Uttar Pradesh, India, during the Rabi season of 2025-2026. The present study, a total of 19 pea genotypes used as experimental material. Kashi Mukti (V1), Kashi Ageti (V2), AP-3 (V3), AP-1 (V4), AP-2 (V5), K-6 (V6), AP-4 (V7), Kashi Uday (V8), GS-10 (V9), M-7 (V10), AP-5 (V11), Kali Naina (V12), Anzel (V13), Pant Matar-155 (V14), IPFD 9-2 (V15), Swami Aparna (V16), Phool Bahar (V17), Rachna (V18), and Kashi Nandini (V19) were among the genotypes.

Experimental Design

The experiment was laid out in a Randomized Block Design (RBD) with three replications. A total of 19 field pea genotypes (18 genotypes along with one check variety) were evaluated. Each plot measured 2 × 1.5 m², maintaining a spacing of 30 cm between rows and 10 cm between plants.

Data collection

Data were collected for a range of yield and related traits which included plant height at 30, 60 and 90 days after sowing (DAS), DAS to first flowering, DAS to 50% flowering, DAS to first pod setting, number of branches per plant, pod length (cm), number of pods per plant, number of seeds per pod, DAS to maturity, seed yield per plot (kg), biological yield (kg), seed index, seed yield (q/ha), harvest index (%), and seed yield per plant (g).

Statistical analysis

The data thus collected were statistically analysed using method proposed by (Steel et al., 1980). To find actual differences in traits between plant types (genotypes), we used ANOVA testing.

Mean Performance

Arithmetic mean was calculated using the formula: Analysis of Variance (ANOVA)-The ANOVA was performed according to Randomized Block Design (RBD).

Results and Discussion

Mean Performance of Genotypes

Genotype V17 recorded the highest plant height at 30 DAS (27.3 cm), whereas V14 showed maximum plant height at 60 DAS (99.1 cm) and V18 at 90 DAS (174.7 cm). The earliest flowering was observed in V17 for days to first flowering (41.0 days), while V19 showed the earliest 50% flowering (48.7 days). Genotypes V10 and V13 recorded the earliest first pod setting (53.7 days), whereas V16 was identified as the latest flowering and maturing genotype with maximum days to maturity (99.3 days). Among the yield attributing traits, genotype V14 produced the highest number of branches per plant (3.6) and maximum number of pods per plant (15.5). The longest pod length (10.7 cm) and highest number of seeds per pod (8.9) were recorded in genotype V9. For yield traits, genotypes V3 and V11 recorded the highest seed yield per plot (0.8 kg), while genotype V5 showed maximum biological yield (1.7 kg). Genotype V3 recorded the highest seed index (26.7 g), seed yield per hectare (27.8 q/ha), harvest index (55.0%) and seed yield per plant (18.0 g). Overall, genotype V3 exhibited superior mean performance for most of the yield and yield contributing traits, indicating its potential usefulness in future pea breeding programmes. Similar results were obtained by Abdulla et al., (2014); Bueckert et al., (2015). Table 1. showed the mean values of different traits of nineteen genotypes of pea [1] .

Analysis of variance

The significant genetic variability in the experimental material was indicated by the analysis of variance, that showed substantial differences among the genotypes for each characteristic examined. In line with earlier findings, this suggests that additive genetic effects predominate and that simple selection can be useful for improvements (Maurya et al., 2018) [22] . The magnitude of variance due to genotypes was particularly high for plant height at 90 DAS (5592.888), plant height at 60 DAS (967.823), days to maturity (239.864) and harvest index (144.452), reflecting substantial differences among the genotypes for these traits. The analysis of variance for a Randomized Block Design was carried out for the data recorded on each character as per standard procedure given by Panse and Sukhatme (1967) [19] for individual characters for each environment. The ANOVA skeleton is given below: [13, 36, 30, 26, 33] . [5, 6] . Seed yield per plant (g) exhibited highly significant positive association with number of branches, pod length (cm), number of seeds per pod, seed yield per plot (kg), seed index (g), harvest index (%). Seed yield per plant had maximum positive direct effect at genotypic level viz. harvest index (%) (0.780) followed by seed yield q/ha. (0.764), seed yield per plot (0.668), seed index (g) (0.649), number of pod (0.561), pod length (0.489), number of branches per plant (0.043). AP-3, GS-10, AP-2, AP-5, Kashi Uday these varieties are recommended to farmers on the basis of seed yield per plant, seed yield per plot, number of seeds per pod, biological yield per plot, number of pods per plant seed yield q/ha.

Acknowledgement

AcknowledgementsThe authors thank the Department of Genetics and Plant Breeding, Faculty of Agriculture, Prof. Rajendra Singh (Rajju Bhaiya) University, Naini, Prayagraj for providing experimental facilities. Seed material received from Indian Institute of Vegetable Research Varanasi and Chandra Shekhar Azad University of Agriculture & Technology Kanpur, is gratefully acknowledged.  Conflict of interest: The authors declare no conflict of interest.  Data availability: No datasets were generated or analyzed during the current study.  Clinical Trial Number: Not applicable  Funding Declaration: This work was not supported by any funding sources.

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