Estimation of direct and indirect effects of yield-contributing traits on seed yield in greengram (Vigna radiata (L.) Wilczek)

Introduction

Greengram (Vigna radiata L.) is one of India's major Zaid and Kharif pulse crops and belong to family Fabaceae with a diploid Chromosome number. it can be cultivated as a catch crop in between the Rabi and Kharif seasons. It is mostly grown in East Asia, Southeast Asia, and the Indian subcontinent, with India as its principal origin. India produces 24% of the world's pulses, making it the world's top producer. It is a significant pulse crop with a high nutritional content. A cheap source of protein and other nutrients, it is also regarded as such. Its short duration makes it a great crop to plant alongside other important crops in an intercropping system. It is a significant traditional pulse crop in India. It is especially significant in India, where the majority of people are vegetarians, as it provides a plentiful supply of high-quality, easily digestible protein. About 334-344 kcal per serving are provided by its seeds, which normally contain 59-65% carbs, 22-28% total protein, 21-25% amino acids, 1.5-2.63% lipids, 1.0-1.5% fat, 3.5-4.5% fiber, and 4-5% ash (Asari et al., 2019) [1] Furthermore, Mungbean is well known for having a higher iron and folate content than many other legumes (Vara and Shiva, 2013) [2] . Essential amino acids including phenylalanine, isoleucine, leucine, and lysine are present in considerable amounts in it. Pulses, which are grown all over the world and have enormous nutritional and economic value, are produced and consumed most in India. Pulses are utilized as green manure, feed, and soil fertility enhancers in addition to being a significant source of plant-based protein for people. In terms of nutrition, they offer vital minerals like iron, zinc, folate, and magnesium, as well as proteins, carbs, dietary fibers, vitamins, and phytochemicals. Legumes, which rank second only to grasses in agricultural importance, are an essential part of world agriculture, human and cattle nutrition, and environmental sustainability. They are part of the third biggest family of higher plants, which includes over 20,000 species. According to Das and Barua (2015) [3] , India accounts for 54% of global production and 65% of the total land used for mungbean farming. According to the Directorate of Economics & Statistics (2020) [4] , the crop occupies around 4.34 million hectares and yields 2.12 million tonnes, with an average productivity of 489 kg/ha. Self-pollinating and short-duration, it boosts rural livelihoods and provides 22-24% protein (Degefa et al., 2014) [5] . According to Desai et al. (2020) [6] , mature grains possess 347 kcal, 1.2 g fat, 16.3 g fiber, 23.9 g protein, 62.6 g carbohydrates, and 1.2 g fat per 100 g. After chickpeas and pigeon peas, mungbean is the third most significant pulse in India. Over 35.58 lakh hectares were under cultivation in 2024-2025, yielding 3.10 million tons. Rajasthan led the region in both area and yield, followed by Maharashtra and Karnataka (Directorate of Economics & Statistics, 2024) [7] . Evaluating genetic variability, heritability, and genetic advance remains fundamental to identifying superior genotypes in self-pollinated crops like mungbean, where natural variability is often limited by floral structure and specific microclimatic requirements. Parameters such as genotypic and phenotypic coefficients of variation, heritability, and genetic advance provide insights into trait architecture and guide effective selection (Johnson et al., 1955) [8] . Combining correlation with path coefficient analysis offers a systematic approach to untangle direct and indirect trait effects, thereby supporting more precise selection strategies (Rahman et al., 1982; Dewey & Lu, 1959) [9][10] . Correlation coefficient is the degree and amplitude of link between important plant features can be evaluated with correlation coefficient analysis, which also provides crucial selection criteria and helps create models based on yield and its contributing elements in field trials. However, the precise nature of these relationships is frequently not made clear by it. The robust statistical method known as path coefficient analysis, on the other hand, is intended to separate correlations and measure the direct and indirect effects of many variables on seed production.

Materials and Methods

A experiment was conducted at the Crop Research Fields, Department of Genetics and Plant Breeding, Naini Agricultural Institute, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, U.P., in Kharif 2024, a randomized block design with three replications was used to investigate correlation and path analysis for yield and its attributing traits in greengram. The crop covered a gross area of 167.6 m² with a net seeded area of 96 m², with rows spaced 30 cm apart and plants spaced 10 cm apart. The essential plant protection measures were implemented to guard against pests and diseases, and recommended agronomic techniques were adhered to to guarantee healthy crop growth.

Experimental Material

The experimental material for the present study was procured from the Department of Genetics and Plant Breeding, SHUATS, Prayagraj, and comprised 30 diverse Greengram lines, the details of which are presented in Table 1. For each replication, the following fourteen characters were observed on five randomly chosen individual plants of each genotype viz., Days till 50% of the flowers bloom, 50% of the pods develop, Days till maturity Biological yield (g), seed index (g), harvest index (%), number of seeds per pod, number of major branches per plant, number of clusters per plant, number of pods per cluster, number of pods per plant, and plant height (cm). [11,12,13] .

Correlation Analysis

The genotypic and phenotypic correlation coefficients were computed among all characteristics presented in Tables 3 and4 [14,15,16] .

Path Analysis

We are often concerned with boosting yield because the outcome of a breeding program depends on a number of morpho-physiological features. Other factors also affect these features' impact on yield because they are often linked.

Separating the direct effects of a component character on yield from indirect effects through other features is made easier with the help of path coefficient analysis. The genotypic and phenotypic correlation coefficients of seed yield with its contributing factors were separated into direct and indirect impacts using route coefficient analysis. The findings are displayed in Figs. 2,3, and Tables 5 and6. Both directly and indirectly, through connections with other variables, each component affected the yield. The number of pods per cluster (0.5365) had the greatest direct positive impact on seed yield per plant, according to the phenotypic path coefficient results. This was followed by the number of primary branches per plant (0.2074), pod length (0.0240), harvest index (0.1089), seed index (0.0105), and number of pods per plant (0.0154). The number of pods per cluster (0.5372) had the greatest direct positive impact on seed yield per plant, according to the genotypic path coefficient results. This was followed by the number of primary branches (0.1896), harvest index (0.0576), seed index (0.0206), pod length (0.0283), and number of pods per plant (0.0151). [14,17,18] .

Conclusion

Genotypes perform better on average under yield traits, genotypes that for the various sowing conditions should be evaluated. Before being released, they must to go through varietal testing if they do better than the typical check. In hybridization programs, these genotypes can also be employed as parents to create superior, high-yielding genotypes. In order to determine the component traits that selection might be relied on to improve genetic yield, this approach quantifies the correlations between different plant characters. Positive genetic correlations between desired features are advantageous to plant breeders because they enable the simultaneous enhancement of both traits. Direct impacts and indirect effects via other qualities are separated by partitioning the correlation. While features with strong indirect effects can be selected indirectly through linked qualities, traits with high direct effects can be targeted directly to increase yield.

References

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