Exploring the Genetic Variability of Blackgram [Vigna mungo (L.) Hepper] Genotypes Over Environments

Among pulses, blackgram (Vigna mungo L.) is an important short-duration legume crop that is extensively grown in India. Blackgram covers 4.63 million ha of area with production and productivity of 2.77 million tonnes and 599 kg/ha respectively (Indiastat-2021-22). It provides us with a high-quality, readily digestible source of protein and can restore soil fertility through symbiotic nitrogen fixation. The main obstacles to increasing this crop’s output are its low harvest index, lack of genetic variability and the use of genotypes and varieties that are suited to the environment in the area (Hadimani et al., 2016; Gangadhar et al., 2023). The outcome of several interconnected plant activities is regarded as yield. Since it is a very complicated trait regulated by multiple genes and interconnected with other yield components, directly increasing yield can be exceptionally difficult. Enhancing traits that are closely linked can help attain it. Thus, genetic variation is a fundamental prerequisite for crop breeding programs (Appalaswamy and Reddy, 2004). The success of the breeding program is mostly determined by the degree of genetic variation that may be achieved in the population (Narayanan et al., 2021).  The wide range of genetic variability assists in choosing the best genotype easier. Phenotypic and genotypic variation are the causes of the observed population variances.  Phenotypic variance is the term used to describe the physically evident diversity within a population. This variation in phenotype is the result of the interplay between environment and genotype. Genetic variation is an innate trait that is unaffected by external factors. Variability is necessary for the effective selection of any trait during a breeding program. Planning future breeding programs requires an understanding of genetic variability for economically significant traits, as well as their heritability and genetic advancement, to improve any crop species (Singh et al., 2007). Thus, the present investigation was carried out to estimate the genetic variability among 50 blackgram genotypes in three different environments and hence study the effect of environment on variability analysis.

The experimental material comprised of 50 blackgram genotypes raised in randomized complete block design during rabi 2021-22 in three different environments viz., Agricultural College and Research Institute, Madurai (E1), National Pulses Research Institute, Vamban (E2) and a farmer’s field at Vizhudhudayan, Ariyalur district (E3). The details of the environments under study are presented in Table 1. The recommended package of practices was followed according to the crop production guide. Ten quantitative traits viz., days to 50% flowering, plant height (cm), number of branches per plant, number of clusters per plant, number of pods per cluster, total number of pods per plant, pod length (cm), hundred seed weight (g) and seed yield per plant (g) were recorded. The phenotypic and genotypic coefficients of variation (PCV and GCV) were calculated based on Burton (1952). Heritability (h2) and genetic advance as a percent of the mean (GAM) were worked out by methods advocated by Johnson et al., (1955).


 

In the investigation of the extent of genetic variability among the genotypes, the genetic parameters PCV, GCV, heritability (h2) and genetic advance as a percent of the mean (GAM) are much noteworthy. The amount of variation in the base population determines the potential of the experimental material  (Chauhan et al., 2020). The PCV and GCV of ten quantitative traits over three environments are presented in Table 2. The PCV of all the traits over the environments was observed to be higher than the GCV indicating the environmental influence on the traits under study. This outcome is in accordance with the Ramya et al., (2014), Deepshika et al., (2014), Priyanka et al., (2016), Narayanan et al., (2021) and Gangadhar et al., (2023).


 
The graphical representation of PCV and GCV are presented. The trait; days to 50% flowering recorded the lowest PCV in all three environments. The highest PCV was observed in hundred seed weight (40.64%) in E1; number of branches per plant (50.03%) in E2 and seed yield per plant (33.78%) in E3. The traits viz., plant height, number of branches per plant, number of clusters per plant, total number of pods per plant, hundred seed weight (g) and seed yield per plant (g) were found to have high PCV in all the three environments suggesting that they have higher environmental influence. These results were in corroboration with Partap et al., (2019) for plant height and number of branches per plant Charitha and Lal (2021) and Yergude et al., (2021) for number of clusters per plant, total number of pods per plant, hundred seed weight (g) and Gomathi et al., (2023) for seed yield per plant (g).
 
In case of GCV; the trait, days to 50% flowering exhibited lowest in all three environments except for E2 in which the number of pods per cluster recorded the lowest GCV (8.62%). Hundred seed weight (38.24%) showed highest GCV in E1; number of branches per plant in E2 (45.52%) and E3 (36.10%). The traits viz., plant height, number of branches per plant, number of clusters per plant, the total number of pods per plant, hundred seed weight (g) and seed yield per plant (g) were found to have high GCV over environments. Similar results were reported by Jeberson et al., (2019) for plant height and number of branches per plant, total number of pods per plant, hundred seed weight (g) and seed yield per plant (g); Anuradha et al., (2020) for plant height and number of branches per plant, total number of pods per plant and seed yield per plant. The trait; pod length was observed to have moderate PCV and GCV over all environments. This outcome is in agreement with Charitha and Lal (2021) and Teja and Lal (2021).
 
Regarding heritability (h2), days to 50% flowering reported the highest heritability in all three environments  (Table 3). The lowest h2 was expressed by the number of pods per cluster in E1 (52.10%) and E2 (24.01%) whereas by plant height in E3 (66.06%). All the traits under study exhibited high heritability over all environments except for the number of pods per cluster in E2 (52.10%) and E2 (24.01%). In the case of genetic advance as a per cent of the mean (GAM), the lowest was recorded by the trait, days to 50% flowering in E1 (16.93%) and E3 (15.42%); whereas in E2, it was observed in the number of pods per cluster (8.70). This is in accordance with Yergude et al., (2021) for days to 50% flowering. The highest GAM was obtained by hundred seed weight in E1 (74.11%); the number of branches in E2 (85.32%) and E3 (65.20%). All of the traits except days to 50% flowering in E1 and E2; the number of pods per cluster in E2 exhibited high GAM (Table 3). Toppo et al., (2019) and Teja and Lal (2021) also observed low GAM for days to 50% flowering and Anuradha et al., (2020) for the number of pods per cluster. All studies subjected to multi-environmental data provide much more reliable and efficient conclusions than single environmental data. Hence, in this study genetic variability studies are performed for three different environments and the characters, plant height (cm), number of branches per plant, number of clusters per plant, total number of pods per plant, hundred seed weight (g) and seed yield per plant (g) exposed high PCV, GCV, heritability and genetic advance in all three environments denoting that they are less influenced by environment and can be used for selection in crop improvement.

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The outcome of the study denotes that, the environment poses a great influence in genetic variability analysis. However, the traits; plant height (cm), number of branches per plant, number of clusters per plant, the total number of pods per plant, hundred seed weight (g) and seed yield per plant (g) showed high PCV, GCV, h2 and GAM in all the three environments. The variability of these traits was found to be reproducible in all three environments. Results pertaining to more locations are highly reliable than a single environment data. Hence these traits should be given a great emphasis as selection indices in future crop improvement programmes for enhancing crop yield.

The authors were thankful to the Agricultural College and Research Institute, Tamil Nadu Agricultural University, Madurai and National Pulses Research Centre, Vamban for rendering the facility and giving technical support. The research work was catalyzed and financially supported by the Tamilnadu State Council for Science and Technology, Department of Higher Education, Government of Tamilnadu under Research Funding for Research Scholars (RFRS 2021-2022).

The authors declare that they have no conflict of interest.