UDC 633; DOI 10.18551/rjoas.2022-04.12
GENETIC VARIABILITY FOR GROWTH, YIELD, AND YIELD-RELATED TRAITS IN BLACKGRAM (VIGNA MUNGO (L.) HEPPER) GENOTYPES
Baral Roshani, Pokhrel Niranjan, Adhikari Nav Raj, Poudel Ankur
Institute of Agriculture and Animal Science, Tribhuvan University, Lamjung Campus, Nepal
Shrestha Jiban
National Plant Breeding and Genetics Research Centre, Nepal Agricultural Research Council, Lalitpur, Nepal
*E-mail: [email protected] ORCID:0000-0002-6189-8964
ABSTRACT
Blackgram (Vigna mungo (L.) Hepper) is an important legume crop extensively grown in Asia. It is one of the most abundant sources of protein, minerals, and vitamins. Studying genetic variability is an important task for genetic improvement of this crop. Ten blackgram genotypes were experimented on a randomized complete block design with three replications at Sundarbazar, Lamjung during July- October of 2019 to analyze their genetic variability. Plant height at maturity, days to 50% flowering, number of primary branches, branch length, number of pods bearing peduncles, the longest peduncle length, pod length, number of seeds per pod, 1000 seed weight, and grain yield per plant were significantly different between genotypes. BLG0076-2 had produced the highest grain yield per plant (6.19 g), followed by BLG0066-1-1 (5.42 g). The number of pods per plant had the highest genotypic coefficient of variation (GCV) (37.30 %) and phenotypic coefficient of variation (PCV) (46.44 %), followed by the number of pods bearing peduncles (GCV: 34.47, PCV: 46.44). Heritability values for branch length (0.94) are the greatest, followed by seeds per pod (0.92). Genetic advance (GA) was the highest in branch length (20.68), followed by the number of pods per plant (19.79). Similarly, the number of pod bearing peduncles had the highest genetic advance as a percent of the mean (GAM) (66.10), followed by branch length (63.59). Cluster analysis revealed that cluster 2 and cluster 3 had the greatest distance between cluster centroids (24.99), indicating genetic dissimilarity. This study suggests that blackgram genotypes, namely BLG0076-2 and BLG0066-1-1 were potential germplasm for varietal improvement programs.
KEY WORDS
Black gram, cluster analysis, grain yield, heritability, genetic variability.
Blackgram (Vigna mungo L.) Hepper, also known as mash or urd bean, is a widely cultivated grain legume belonging to the Fabaceae family, order Fabales, and class dicotyledon. Blackgram is one of the most abundant sources of vegetable protein, as well as certain important minerals and vitamins for the human body. Grain is composed of 56 % carbohydrates, 25% protein, 2% fat, 4% mineral, and 0.4% vitamins (Panigrahi et al., 2014). When supplied with most cereals (rice or wheat), blackgram complements the essential amino acids (arginine, leucine, lysine, isoleucine, valine, and phenylalanine) and serves a crucial dietary role (Paul, 2016). Pulses are consumed in Nepal at a rate of 24 grams per capita per day, compared to the 80 grams recommended by the World Health Organization (Shrestha et al., 2011). Blackgram can fix atmospheric nitrogen in a symbiotic relationship with nodule-producing bacteria, which aids in soil improvement (Pokhrel et al., 2019). It fixes roughly 50-55 kg of nitrogen per hectare. Blackgram contributes 5.21% of the country's pulse production (MoALD, 2019). It is grown on 23,492 hectares in Nepal, with a production of 19928 metric tons and productivity of 848 kg per hectare, respectively (MoALD, 2019). Many species of food legumes may be cultivated in Nepal's various climates and environments,
and blackgram is grown as a mixed, cash, and sequence crop in a variety of cropping systems. It is also produced as a solo crop under residual moisture conditions after rice harvest and as a relay crop before rice harvest (Parveen et al., 2011). Despite their importance in Nepalese farming systems, pulses are just a minor component of total agricultural systems due to the greater importance given to cereals as main food crops (Pandey et al., 2000). Blackgram is still grown in marginal areas in rainfed environments with terminal drought, which has a significant impact on its production (Patidar and Sharma, 2017). As a result, pulses have lower production stability and larger storage losses than cereals (Pandey et al., 2000). The lack of a suitable ideotype for varied cropping systems, low harvest indices, and sensitivity to diseases are all major obstacles to generating higher yields and stability in blackgram (Rana et al., 2019). Many attempts have been made to determine the degree of variability in black gram productivity and its component traits. However, research on this plant has lagged behind cereals and other legumes in some ways. The lack of adequate varieties and genotypes that are adapted to local environments is one of the issues that influence production and farmer preferences (Paul, 2016). In the face of disease epidemics and fluctuating environmental conditions, genetic variability is essential for improving yields and maintaining output. As a result, using available genetic diversity to improve this crop is required (Pyngrope et al., 2015).
Any breeding program's success is primarily determined by how well existing variability in the base material is used, as well as the extent of variability in the targeted traits. Furthermore, crop improvement programs require the measurement of genetic diversity for both quantitative and qualitative features of economic value. As a result, collecting, evaluating, and utilizing available genetic diversity to meet specific ecosystem demands is desirable and should be encouraged to develop new varieties (Pokhrel et al., 2019). Similarly, phenotypic and genotypic coefficients of variation, genetic advance, and heritability all play a role in identifying superior genotypes, assisting in effective selection and improving existing cultivars (Reni et al., 2013). Evaluation of genetic diversity would aid in the efficient use of genetic variations in breeding programs. The current study was conducted to assess the growth, yield and yield-related performance of black gram genotypes and analyze thier variation, heritability and genetic advance in the hopes of developing superior varieties.
MATERIALS AND METHODS OF RESEARCH
Experimental location.This experiment was conducted in the research field of the Institute of Agriculture and Animal Science (IAAS), Lamjung Campus, Sundarbazar, Lamjung, Nepal from July to October 2019. The study site is located at 28.12°N latitude, 84.41°E longitude, and 620 m elevation. The experimental area's soil type was sandy loam. Table 1 shows the climatic data collected during the experiment.
Table 1 - Meteorological data of the experimental location in 2019
Months Maximum Temperature (oC) Minimum Temperature (oC) Rainfall (mm)
July 21 14 658.7
August 22 14 367.2
September 19 13 435.8
October 17 8 45.3
Table 2 - List of blackgram genotypes used in this study
SN Genotypes Source SN Genotypes Source
1 BLG0061-2-2 NGLRP 6 BLG0035-1 NGLRP
2 BLG0066-1-1 NGLRP 7 BLG0041-1 NGLRP
3 BLG0092-1 NGLRP 8 BLG0076-2 NGLRP
4 BLG0069-1 NGLRP 9 Shekhar-1 (Check) NGLRP
5 BLG0036-1 NGLRP 10 Khajura mash (Check) NGLRP
NGLRP: National Grain Legumes Research Program (NGLRP), Khajura, Banke, Nepal.
Experimental materials. Ten genotypes of blackgram were used for the experiment. The source of these genotypes was National Grain Legumes Research Program (NGLRP), Khajura, Banke (Table 2).
Experimental details and cultural practices. This experiment was conducted in a randomized complete block design (RCBD) with three replications and ten blackgram genotypes. Field preparation was performed on July 8, 2019, and seeding was completed on July 15, 2019, at a pace of two seeds per hill. Each 4m2 (2m x 2m) experimental plot included 100 plants with a crop geometry of 40cm x10cm (RRxPP), correspondingly. Inorganic fertilizer was applied at a rate of 20:40:20 NPK kg ha-1. At the time of planting, a full dose of nitrogen, phosphorus (P2O5), and potassium (K2O) was applied as the basal dose. The fertilizers were Urea, DAP (Diammonium Phosphate) and MOP (Muriate of Potash). In all plots, the entire agronomical package of procedures as recommended by NGLRP, Khajura was implemented uniformly.
Data Collection and analysis.The data was collected from ten plants chosen at random and left with two border rows on each side. After harvesting and drying at the optimum moisture level, yield and yield component traits such as pod length, no. of pods per plant, no. of seeds per pod, 1000 seed weight, and grain yield were measured. Data was collected independently for each genotype in each replication. The various genetic parameters, such as phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV), were computed using the formula given by Burton (1952), while heritability and GA as percent mean were determined using the formula adopted by Johnson et al. (1955). The average linkage clustering method was used for cluster analysis, and the Euclidian method was used to compute inter-cluster distance.
Statistical analysis.MS-Excel 2016 was used to enter the data. R Studio was used to analyze the data. A randomized complete block design (RCBD) with one-way ANOVA was used to evaluate the data. At 5% level of significance, the least significant difference (LSD) was employed to compare the treatment means (Gomez, Gomez, 1984).
RESULTS AND DISCUSSION
Agro-morphological performance of genotypes. Tables 3a and 3b show the agro-morphological variability among blackgram genotypes. Plant height at maturity, days to 50% flowering, number of primary branches, branch length, number of pods bearing peduncle, longest peduncle length, pod length, number of seeds per pod, 1000 seed weight, and grain yield were all substantially different between genotypes, except for the number of pods per plant. BLG0036-1 had the longest flowering days (29.3 days) and BLG0092-1 had the shortest flowering days (26 days). The number of primary branches ranged from 10.46 (Khajura mash) to 8.36 (BLG0069-1). The branch length was found to be the maximum in BLG0041-1(43.73cm), followed by check variety Shekhar-1 and the minimum in BLG0092-1(23.06cm). The number of pods bearing peduncles varied from 21.267 (Khajura mash-1; check variety) to 11.33 (BLG0092-1). The peduncle length varied from 10.62 (BLG0069-1) to 7.45 (Khajura mash-1; check variety). The longest days to 50% maturity was found in Shekhar-1 (56 days). Early maturing varieties were found to be BLG0036-1, BLG0092-1, BLG0069-1 and Khajura mash-1. The pod length ranged from 5.046 cm (Shekhar-1) to 4.26 cm (BLG0092-1). The value of no. of pods per plant ranged from 49.4 (BLG0069-1) to 25.26 (BLG0092-1). The maximum number of seeds per pod was found in Shekhar-1 (7.16), while the minimum was found in BLG0092-1 (5.50). The mean value for 1000 seed weight ranged from 47.78 g (BLG0069-1) to 40.96 g (BLG0041-1). Plant height varied from 50.86cm to 34.33cm. The highest grain yield per plant was found in genotype BLG0076-2 (6.19 g) and the lowest in Shekhar-1 (3.49 g). These findings were similar to findings found by Kumar et al. (2015), Thirumalai and Murugan (2020) and Patidar et al., (2018) who observed variability among the evaluated genotypes in their experiments.
Phenotypic and genotypic coefficients of variation. Table 4 shows that the number of pods per plant (143.09 and 221.85) had the highest genotypic and phenotypic variation, followed by branch length (106.6 and 112.75). For all of the characters, phenotypic variance
was higher than genotypic variance, showing that environmental factors had an impact on these traits. Konda et al. (2009), Senthamizhselvi et al. (2019), Sabesan et al. (2018) and Reddy et al. (2018) all found similar results. According to Sivasubramanian and Menon (1973), phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) were classified as low (less than 10%), moderate (10-20%), and high (more than 20%). PCV values varied from 46.44 for no. of pods per plant to 7.402 for days to 50 percent mature pods, while GCV values ranged from 37.297 for number of pods per plant to 7.049 for days to 50% mature pods. Except for no. of pods per plant, the difference between GCV and PCV was small for all traits studied, indicating that traits other than no. of pods per plant are more genetically controlled and less influenced by environmental factors.
Table 3a - Mean values of various growth and yield traits of ten genotypes of blackgram
at Lamjung in 2019
Genotypes Plant height at maturity (cm) Days to 50% flowering Number of primary branches Branch length (cm) Number of pods Longest bearing peduncle peduncle length (cm) Days to 50% matured pod
BLG0061 -2-2 45.87ab 28.67ab 8.80cd 35.27bc 15.97bcd 8.84bc 53.33bc
BLG0066-1-1 45.20abc 26.67bc 8.90cd 30.70cd 12.37de 8.28cd 54.00ab
BLG0092-1 34.33e 26.00c 9.27bcd 23.07e 11.33e 8.84bc 50.67d
BLG0069-1 37.23de 27.67abc 8.37d 25.93e 13.73cde 10.62a 50.67d
BLG0036-1 43.07bcd 29.3337a 8.53cd 33.00bc 17.33bc 9.80ab 49.67d
BLG0035-1 42.87bcd 28.00abc 8.53cd 36.37b 13.73cde 8.39cd 54.67ab
BLG0041-1 50.87a 28.00abc 9.60abc 43.73a 14.93bcde 8.63bcd 53.00bc
BLG0076-2 37.93de 27.33abc 10.00ab 27.43de 18.63ab 7.93cd 51.33cd
Shekhar-1 39.63cde 29.33a 8.73cd 36.40b 12.83de 8.09cd 56.00a
Khajura mash-1 39.07de 26.00c 10.47a 33.33bc 21.27a 7.46d 50.00d
Grand Mean F test LSD(5%) CV% 41.6 *** 5.54 7.77 27.7 ** 1.83 3.86 9.12 ** 0.98 6.29 32.52 *** 4.25 7.62 15.21 *** 3.52 13.49 8.69 *** 1.13 7.62 52.33 *** 1.39 2.26
Note: ** =significant at P <0.01. ***=significant at P <0.001. Different letters represent significant differences based on LSD test at p<0.05 .CV: Coefficient of variation, LSD: Least significant difference.
Table 3b - Mean values of various growth and yield traits of ten genotypes of blackgram
at Lamjung in 2019
Genotypes Pod length (cm) Number of pods per plant Number of seeds per pod 1000 seed weight (g) Yield per plant (g)
BLG0061 -2-2 4.34cd 36.6 6.13de 45.21b 4.50def
BLG0066-1-1 4.63bc 26.33 6.80ab 45.04b 5.42b
BLG0092-1 4.26d 25.27 5.50f 44.30bc 5.32bc
BLG0069-1 4.33cd 49.4 5.87ef 47.79a 4.40ef
BLG0036-1 4.67b 25.53 6.43bcd 45.33b 5.26bc
BLG0035-1 4.53bcd 29.33 6.27cde 43.79bcd 3.92fg
BLG0041-1 4.69b 31.5 6.33cd 40.96e 5.19bcd
BLG0076-2 4.40bcd 38.43 6.00de 42.52cde 6.19a
Shekhar-1 5.05a 29.4 7.17a 45.41b 3.494g
Khajura mash-1 4.48bcd 28.93 6.60bc 41.89de 4.66cde
Grand Mean 4.54 32.07 6.3 44.22 4.84
F test *** NS *** *** ***
LSD(5%) 0.27 17.93 0.41 2.05 0.66
CV% 3.49 27.67 3.81 2.70 7.91
Note: Ns=Not significant. ***=significant at P <0.001Different letters represent significant differences based on LSD test at p<0.05 . CV: Coefficient of variation, LSD: Least significant difference.
The PCV and GCV of no. of pods per plant, branch length, number of pods bearing peduncle, and yield per plant were all found to be high. PCV and GCV were found to be moderate in the number of primary branches, peduncle length, and seeds per pod, indicating that direct selection via phenotype observation is effective. Plant height was shown to have a high value of PCV when the GCV was moderate, whereas days to 50% flowering, days to 50% mature pods, pod length, and thousand seed weight were found to have low GCV and PCV, indicating that direct selection may not be profitable. For no. of pods per plant, number of clusters per plant, and yield per plant, Panigrahi et al. (2014), Patel et al. (2014), and Shoba (2018) found similar results. Kumar et al. (2015) found similar moderate GCV and PCV of seeds per pod. Chowdhury et al. (2020) found similar results for plant height, no. of
seeds per pod, days to 50% flowering, and black gram test weight. Gowsalya et al. (2016) reported higher GCV and PCV of branch length in a previous experiment. Lower GCV and PCV estimations for days to 50% flowering, days to maturity, hundred seed weight, and pod length matched the findings of Reddy et al. (2018), Bandi et al. (2018) and Bishnoi et al. (2017). The GCV for seed yield was found to be relatively high in this study, indicating that there is a lot of scope for yield increase by direct selection.
Table 4 - Estimates of Genotypic variance (Vg), Phenotypic variance (Vp), Genotypic coefficient of variation (GCV), Phenotypic coefficient of variation (PCV), Broad sense heritability (h2bs), Genetic advances (GA) and genetic advances as percent of mean (GAM)for growth, yield and its attributing
traits of ten blackgram genotypes
Traits Vg Vp GCV PCV h2 bs GA GAM
Days to 50% flowering 4.10 5.24 7.31 8.26 0.78 3.69 13.31
No. of Primary branches 1.36 1.68 12.77 14.23 0.81 2.15 23.59
Branch length (cm) 106.60 112.75 31.75 32.65 0.95 20.68 63.59
No. of pod bearing Peduncle 27.49 31.70 34.46 37.01 0.87 10.06 66.11
Peduncle length (cm) 2.43 2.87 17.94 19.50 0.85 2.96 34.02
Days to 50% mature pods 13.61 15.01 7.05 7.40 0.91 7.24 13.83
Pod length (cm) 0.16 0.18 8.66 9.34 0.86 0.75 16.56
No. of pods per plant 143.10 221.86 37.30 46.44 0.65 19.79 61.70
No. of seeds per pod 0.67 0.72 12.93 13.48 0.92 1.61 25.55
1000 seeds weight (g) 11.58 13.02 7.70 8.16 0.89 6.61 14.96
Plant height at maturity (cm) 68.64 79.10 19.91 21.38 0.87 15.90 38.21
Yield per plant (g) 1.85 1.99 28.10 29.20 0.93 2.70 55.73
Note: Vg - genotypic variance, Vp - phenotypic variance, h2bs - heritability in the broad sense, GCV - genotypic coefficient of variation, PCV - phenotypic coefficient of variation, GA - genetic advance at 5% intensity of selection, GAM - genetic advance as per cent of mean
Heritability and genetic advance.The degree of similarity between parents and their offspring is determined by heritability. As proposed by Robinson et al. (1949), an attempt was made in this study to assess heritability in a broad sense and characterize it as low (50 %), moderate (50-70 %), and high (>70 %). For branch length and no. of pods per plant, the heritability estimates ranged from 0.945 to 0.645. Days to 50% flowering(78.2 %), number of primary branches (80.5 %), branch length(94.5 %), number of pod bearing peduncles (86.7 %), peduncle length (84.7 %), days to 50% mature pods(90.7 %), pod length (86.0 %), no. of seeds per pod(92.0 %), thousand seed weight (89.0 %), plant height(86.8%), and yield per plant (92.7 %) were among the traits studied with high heritability.Table 4 summarizes these findings. No. of pods per plant had the lowest heritability among the traits, which was similar to the findings of Siddique et al. (2006). The findings were similar to those of Panigrahi and Baisakh (2014) and Baisakh et al. (2014), except for no. of sees per pod, which had low heritability. Blackgram previously found high heritability of pod length, seed yield, plant height, 100 seed weight, clusters per plant, no. of seeds per plant, and branches per plant (Kumar et al., 2015). Veni et al. (2015) also found that all nine quantitative traits evaluated had a high heritability, indicating less environmental influence. Kuralarasan et al. (2018) found that all of the studied traits, including peduncle length, have a high heritability. In a general sense, high heritability levels indicate that character is less influenced by environmental factors and that phenotype and breeding values may be more strongly associated.
Genetic advance and genetic advance as percent of mean.Under one cycle of selection at a given selection intensity, genetic advance (GA) under selection refers to the improvement of traits in genotypic value for the new population compared to the base population (Wolie et al., 2013). The genetic advance as a percent of the mean (GAM) ranged from 13.308 for days to 50% flowering to 66.107 for the number of pod bearing peduncles. Days to 50% flowering, number of branches, days to 50% mature pods, pod length, no. of seeds per pod, and thousand seed weight were all used to classify GAM as low (10%), medium (30%-10%), and high (>30%). GAM was high in branch length, no. of pod bearing peduncles, peduncle length, no. of pods per plant, plant height, and yield per plant. These findings are very similar to those of Sushmitharaj et al. (2018) and Gowsalya et al. (2016).
Seed yield per plant, clusters per plant, no. of pods per plant, plant height, test weight, days to 50% blooming, and days to 50% pod setting were all found to be identical by Aftab et al. (2018). A trait with a moderate to high GAM indicates that the character is governed by additive genes and that improvement of such traits will be favored by selection.
Heritability estimations combined with genetic advance is usually more accurate in predicting selection gain than heritability alone. Branch length, number of pods bearing a peduncle, peduncle length, plant height, and single plant yield all had high heritability and genetic advance as a percentage of the mean, indicating that these traits were controlled by additive gene action in inheritance and indicating the potential for improvement using simple selection procedures. For these traits, Gowsalya et al. (2016), Venkatesan et al. (2005) and Sushmitharaj et al. (2018) reported similar findings. Kuralarasan et al. (2018) also supported it for plant height, peduncle length, number of clusters per plant, and single plant yield.
Cluster analysis.Genetic divergence analysis was frequently utilized to establish the genetic relationship between genotypes and to identify the genotypes that would be appropriate for future breeding programs. Divergence was calculated for all twelve characters of ten blackgram genotypes. Table 5 and Figure 1 show all genotypes were divided into three clusters based on the traits.
Based on growth and yield traits, the 10 blackgram genotypes were divided into four groups (Table 5). Twenty-two black gram genotypes were grouped into five clusters by Vyas et al. (2018). Similarly, in a study conducted by Ahmadikhah et al. (2008), fifty-eight rice varieties were categorized into four clusters based on 18 morphological traits. Rahman et al. (2011) used 14 physiological traits to divide 21 rice varieties into five clusters. Anandan et al. (2011) also showed similar results in rice.
Table 5 - Grouping of ten blackgram genotypes by Euclidean average linkage method
Cluster number No. of genotypes Genotypes
1. Clusterl 6 BLG0061 -2-2, BLG0066-1-1, BLG0036-1, BLG0035-1, Shekhar-1, BLG0041-1,
2. Cluster 2 1 BLG0092-1
3 Cluster 3 1 BLG0069-1,
4. Cluster 4 2 BLG0076-2, Khajura mash-1
Cluster1 was the largest one, having 6 genotypes, i.e., BLG0061-2-2, BLG0066-1-1, BLG0036-1, BLG0035-1, BLG0041-1 and Shekhar-1, which represented 60% of the genotypes. This cluster had the highest days to 50% flowering (28.33), branch length (35.91 cm), days to 50% mature pods (53.44), pod length (4.65 cm), seeds per pod (6.52) and plant height at maturity (44.58 cm) (Table 6). Cluster 2 consisted of BLG0092-1 and this cluster was characterized by the highest peduncle length (8.84 cm), yield per plant (5.32 g) with the lowest days to 50% flowering (26), branch length (23.07 cm), number of pods bearing peduncle (11.33), days to 50% mature pods (50.67) and plant height at maturity (34.33 cm) (Table 6). Cluster3 had one genotype, BLG0069-1. Genotypes grouped in this cluster had the highest number of pods per plant (49.40), thousand seed weight (47.79 g), peduncle length (10.62 cm) (Table 6). Cluster 4 had two genotypes, BLG0076-2 and Khajura mash-1. Genotypes grouped in this cluster had the highest number of primary branches (10.23), No. of pod bearing peduncles number (19.95) and yield per plant (5.42 g) (Table 6). Chakma et al. (2012) came up with similar results. For cluster 1, traits including days to 50% flowering, branch length, days to 50% mature pods, pod length, no. of seeds per pod, and plant height at maturity can be used to improve yield based on centroid values. Peduncle length and yield per plant can also be selected for cluster 2 to improve yield. Cluster 3 genotypes can be utilized for hybridization to increase economically important traits such as no. of pods per plant, thousand seed weight, and peduncle length. Furthermore, genotypes for a hybridization program can be chosen from clusters with a high inter-cluster distance and high genetic diversity. Selection between clusters with the shortest inter-cluster distance, on the other hand, may not be particularly useful in hybridization programs.
Distance between cluster centroids was found to be greater in between cluster 2 and cluster 3 (24.99) as presented in Table 7. Such genotypes can also be used in breeding programs for developing biparental crosses between the most diverse and closest groups to
break the undesirable linkages between yield and its associated traits. The distances between cluster centroids ranged from 13.95 to 24.99 (Table 7). The lowest distance between cluster centroids was found in the cluster 1 and cluster 4 (13.95) indicating genetic similarity and the highest was found in the cluster 2 (BLG0092-1) and cluster 3 (BLG0069-1) (24.99) indicating genetic dissimilarity (Table 7).
Table 6 - Cluster means for twelve traits of ten blackgram genotypes
Variable Clusterl Cluster2 Cluster3 Cluster4 centroid
Plant height at maturity 44.58 34.33 37.23 38.50 41.61
Days to 50% flowering 28.33 26.00 27.67 26.67 27.70
No. of primary branches 8.85 9.27 8.37 10.23 9.12
Branch length (cm) 35.91 23.07 25.93 30.38 32.52
No. of pod bearing peduncle number 14.53 11.33 13.73 19.95 15.21
Peduncle length (cm) 8.67 8.84 10.62 7.69 8.69
Days to 50% mature pods 53.44 50.67 50.67 50.67 52.33
Pod length (cm) 4.65 4.26 4.33 4.44 4.54
No. of pods per plant 29.78 25.27 49.40 38.68 33.07
No. of seeds per pod 6.52 5.50 5.87 6.30 6.31
1000 seeds weight (g) 44.29 44.31 47.79 42.20 44.22
Yield per plant (g) 4.63 5.32 4.40 5.42 4.84
Table 7 - Distances between cluster centroids in ten blackgram genotypes
Cluster1 Cluster2 Cluster3 Cluster 4
Cluster1 Cluster2 Cluster3 Cluster4 - 17.77 23.75 24.99 13.95 18.25 14.84
Table 8 - Different statistics of Euclidean distance and cluster analysis of ten blackgram genotypes
Cluster .. , . Within clusters sum Average distance No. of genotypes 3 3r of squares from centroid Maximum distance from centroid
Cluster1 Cluster2 Cluster3 Cluster4 6 316.79 6.86 1 0.00 0.00 1 0.00 0.00 2 25.16 3.55 10.77 0.00 0.00 3.55
Dendrogram Average Linkage, Euclidean Distance
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Figure 1 - Dendrogram of ten blackgram genotypes using Euclidean average linkage method
Similar results were found by Mohanlal et al. (2018) who found the highest intra- and inter-cluster distances between cluster III and cluster V in their multivariate analysis of 21 black gram genotypes. Anandan et al. (2011) and Latif et al. (2011) both found similar results in rice. Vennila et al. (2011) and Latif et al. (2011) proposed using distantly placed cluster genotypes in hybridization programs to get a wide spectrum of variation among genotypes. Cluster 1, the largest, is comprised of six genotypes, indicating all the genotypes had some similar characteristics and crossing among the genotypes in this cluster may give transgressive segregants. The genotypes present in cluster 2 and cluster 3 indicate that they could be more divergent from other genotypes and that crossing with the genotypes of other different clusters would be suitable for obtaining heterosis for some of the important traits.
The maximum distance of cluster 1 from centroids was 10.77 followed by cluster 2 (3.55) (Table 8). Chakma et al. (2012) discovered similar results.
CONCLUSION
Plant height at maturity, days to 50% flowering, number of primary branches, branch length, number of pods bearing peduncles, the longest peduncle length, pod length, number of seeds per pod, 1000 seed weight, and grain yield all differed significantly between genotypes. Such variation showed that the genotypes had great potential in future breeding programs when it came to selection. PCV was greater than GCV in all traits, showing that there was an environmental influence. Cluster 2 and cluster 3 had the greatest gap between cluster centroids, indicating genetic distance. BLG0076-2 and BLG0066-1-1 were the genotypes with the highest yield potential. These genotypes can be grown for higher production.
ACKNOWLEDGEM ENTS
The authors are grateful to National Grain Legume Research Program, Banke, Nepal for providing the genotypes needed for conducting this research and we express our gratitude to Mr. Kosh Raj Upadhyay, Assistant Professor from Gauradhaha Campus, for his guidance and support on the use of statistical software and analysis.
REFERENCES
1. Aftab, N., Lal, G. Sheera, A., Bose, N.C., & Tripathi, A.M. (2018). Evaluation of genetic variability in black gram (Vigna mungo L. Hepper) germplasm. Journal of Plant Development Sciences,10, 445-452.
2. Ahmadikhah, A., Nasrollanejad, S., & Alishah, O. (2008). Quantitative studies for investigating variation and its effect on heterosis of rice. International Journal of Plant Production, 2(4), 297-308.
3. Anandan, A., Eswaran, R., & Prakash, M. (2011). Diversity in rice genotypes under salt affected soil based on multivariate analysis. Pertanika Journal of Tropical Agricultural Science, 34(1), 33-40.
4. Baisakh, B., Das, T.R., & Panigrahi, K.K. (2014). Genetic variability and correlation analysis for yield and yield contributing traits in advanced mutant lines of blackgram. Food Legumes, 27(3), 202-205.
5. Bandi, H.R.K., Rao, K.N., Krishna, K.V., & Srinivasulu, K. (2018). Variability, heritability and genetic advance for quantitative characters in rice fallow blackgram [Vigna mungo (L.) Hepper]. International Journal of Current Microbiology and Applied Sciences, 7(2), 171-176. DOI: https://doi.org/10.20546/ijcmas.2018.702.022
6. Bishnoi, A., Gupta, P., Meghawal, D.R., & Lal, G.M. (2017). Evaluation of genetic variability and heritability in blackgram (Vigna mungo (L.) Hepper) genotypes. Journal of Pharmacognosy and Phytochemistry, 6(4),493-496.
7. Burton, G.H. (1952). Quantitative inheritance in grasses. Proceedings; 6th International Grassld. Congress, 1,277-283.
8. Chakma, S. P., Huq, H., Mahmud, F., & Husna, A. (2012). Genetic diversity analysis in rice (Oryza sativa L.). Bangladesh Journal of Plant Breeding and Genetics, 25(1), 31-39.DOI: https://doi.org/10.3329/bjpbg.v25i1.17010
9. Chowdhury, T., Das, A., Mandal, G.S., Bhattacharya, S., & Chatterjee, S. (2020). Genetic Variability, Character Association and Divergence Study in Urdbean [Vigna mungo (L). Hepper]. International Journal of Current Microbiology and Applied Sciences, 9(2), 17261734. DOI: https://doi.org/10.20546/ijcmas.2020.902.198
10. Gomez, K.A., & Gomez, A.A. (1984). Statistical procedures for agricultural research. John Wiley & Sons.
11. Gowsalya, P., Kumaresan, D., Packiaraj, D., & Kannan B.J. R. (2016). Genetic variability and character association for biometrical traits in blackgram (Vigna mungo (L.) Hepper). Electronic Journal of Plant Breeding, 7(2), 317-324. DOI: https://doi.org/10.5958/0975-928X.2016.00039
12. Johnson, H.W., Robinson, H.F., & Comstock, R.E. (1955). Estimates of genetic and environmental variability in soybeans 1. Agronomy Journal, 47(7), 314-318. DOI: https://doi.org/10.2134/agronj1955.00021962004700070009x
13. Konda, C.R., Salimath, P.M., & Mishra, M.N. (2009). Genetic variability studies for productivity and its components in blackgram [Vigna munga (L.) Hepper]. Legume Research-An International Journal, 32(1), 59-61.
14. Kumar, G.V., Vanaja, M., Lakshmi, N.J., & Maheswari, M. (2015). Studies on variability, heritability and genetic advance for quantitative traits in black gram [Vigna mungo (L.) Hepper]. Agricultural Research Journal, 52(4), 28-31. DOI: https://doi.org/10.5958/2395-146X.2015.00056.3
15. Kumar, L.Y., Anuradha, C.H., Reddy, S.S., Srinivas, A.M.N., & Subbaiah, V.K. (2015). Estimation of correlation and path analysis for yield and yield attributing traits in blackgram [Vigna mungo (L.) Hepper]. Research Journal of Biotechnology, 10(5), 48-54.
16. Kuralarasan, V., Vanniarajan, C., Kanchana, S., Veni, K., & Lavanya, S.A. (2018). Genetic divergence, heritability and genetic advance in mutant lines of urdbean [Vigna mungo (L.) Hepper]. Legume Research-An International Journal, 41(6), 833-836. DOI: https://doi.org/10.18805/LR-3794
17. Latif, M.A., Rahman, M.M., Kabir, M.S., Ali, M.A., Islam M.T., & Rafii, M.Y. (2011). Genetic diversity analyzed by quantitative traits among rice (Oryza sativa L.) genotypes resistant to blast disease. African Journal of Microbiology Research, 5(25), 4383-4391.
18. MoALD. (2019). Statistical Information on Nepalese Agriculture. Ministry of Agricultural Development, Singh Durbar, Kathmandu, Nepal. P.80-81.
19. Mohanlal, V. A., Saravanan, K., & Sabesan, T. (2018). Multivariate analysis in blackgram (Vigna mungo L. Hepper) genotypes. Journal of Pharmacognosy and Phytochemistry,7(6), 860-863.
20. Pandey, S.P., Yadav, C.R., Sah, K., Pande, S., & Joshi, P.K. (2000). Legumes in Nepal. In: Legumes in rice and wheat cropping systems of the Indo-Gangetic Plain - constraints and opportunities. International Crops Research Institute for the Semi-Arid Tropics, Patancheru, Andhra Pradesh, India. pp. 71-97.
21. Panigrahi, K.K., & Baisakh, B. (2014). Variability and association studies in mutants and landraces of blackgram (Vigna mungo L. Hepper) of Odisha. Research Journal of Agricultural Sciences, 5(4), 817-821. DOI: https://doi.org/1798-1604-2014-210
22. Panigrahi, K.K., Mohanty, A., & Baisakh, B. (2014). Genetic divergence, variability and character association in landraces of blackgram (Vigna Mungo [L.] Hepper) from Odisha. Journal of Crop and Weed, 10(2), 155-165.
23. Parveen, S.I., Sekhar, M.R., Reddy, D.M., & Sudhakar, P. (2011). Correlation and path coefficient analysis for yield and yield components in blackgram (Vigna mungo (L.) Hepper). International Journal of Applied Biology and Pharmaceutical Technology, 2(3), 619-625.
24. Patel, R.V, Patil, S.S., Patel, S.R., & Jadhav, B.D. (2014). Genetic variability and character association in blackgram (Vigna mungo (L.) Hepper) during summer. Trends Bio., 7, 3795-3798.
25. Patidar, M., & Sharma, H. (2017). Correlation and path coefficient studies in Blackgram (Vigna mungo (L.) Hepper). Journal of Pharmacognosy and Phytochemistry, 6(4), 16261628.
26. Patidar, M., Sharma, H., & Haritwal, S. (2018). Genetic variability studies in blackgram (Vigna mungo (L.) Hepper). International Journal of Chemical Studies, 6(2), 1501-1503.
27. Paul, S. (2016). Genetic diversity of blackgram (Vigna mungo L.). Department of Genetics and Plant Breeding, Sher-E-Bangla Agricultural University.
28. Pokhrel, A., Aryal, L., & Poudel, P. (2019). A Review on Research Work of Grain Legumes Research Program, NARC.
29. Pyngrope, A.H., Noren, S.K., Wricha, T., Sen, D., Khanna, V.K., & Pattanayak, A. (2015). Genetic diversity analysis of blackgram [Vigna mungo (L.) Hepper] using morphological and molecular markers. International Journal of Applied and Pure Science and Agriculture, 1(8),14-113.
30. Rahman, A., & Vincent N. (2011). Narrowing the modeling gap: A cluster-ranking approach to coreference resolution. Journal of Artificial Intelligence Research, 40, 469521. DOI: https://doi.org/10.1613/jair.3120
31. Rana, M.S., Hossain, M.A., Urmi, T.A., Ahmed, S., Haque, M.M., & Islam, M.M. (2019). Evaluation of Blackgram (Vigna mungo L.) Genotypes for their Tolerance to Flooding. The Agriculturists, 17, 89-101. DOI: https://doi.org/10.3329/agric.v17i1-2.44699
32. Reddy, A.K., Priya, M.S., Reddy, D.M., & Reddy, B.R. (2018). Genetic divergence studies in blackgram (Vigna mungo (L.) Hepper). International Journal of Pure and Applied Bioscience, 6(5), 232-237. DOI: http://dx.doi.org/10.18782/2320-7051.6957
33. Reni, Y.P., Rao, Y.K., Satish, Y., & Babu, J.S. (2013). Estimates of genetic parameters and path analysis in blackgram (Vigna mungo (L.) Hepper). International Journal of Plant, Animal and Environmental Sciences, 3(4), 231-234.
34. Robinson, H.F., Comstock, R.E., & Harvey, P.H. (1949). Estimates of heritability and the degree of dominance in corn. Agronomy Journal, 41(8), 353-359. DOI: https://doi.org/10.2134/agronj1949.00021962004100080005x
35. Sabesan, T., Mohanlal, V.A., & Saravanan, K. (2018). Studies on genetic correlation and path coefficient analysis of blackgram (Vigna mungo [L.] Hepper) genotypes under salinity. Journal of Phytology, 10(1), 09-11. DOI: https://doi.org/10.25081/jp.2018.v10.3407
36. Senthamizhselvi, S., Muthuswamy, A., & Shunmugavalli, N. (2019). Genetic variability, correlation and path coefficient analysis for yield and yield components in blackgram (Vigna mungo (L.) Hepper). Electronic Journal of Plant Breeding, 10(4), 1600-1605. DOI: https://doi.org/10.5958/0975-928X.2019.00206.0
37. Shoba, D. (2018). Genetic variability and correlation studies in black-gram [Vigna mungo (L.) Hepper]. Electronic Journal of Plant Breeding, 9(4), 1583-1587.
38. Shrestha, R., Neupane, R.K., & Adhikari, N.P. (2011). Status and future prospects of pulses in Nepal. Regional Workshop on Pulse Production Held at Nepal Agricultural Research Council (NARC), Kathmandu, Nepal. pp.24-25.
39. Siddique, M., Malik, M.F.A., & Awan, S.I. (2006). Genetic divergence, association and performance evaluation of different genotypes of mungbean (Vigna radiata). International Journal of Agriculture and Biology, 8(6), 793-795.
40. Sivasubramanian, S., & Menon, M. (1973). Heterosis and inbreeding depression in rice. Madras Agricultural Journal, 60(7), 1139-1140.
41. Sushmitharaj, D., Shoba, D., & Arumugam Pillai, M. (2018). Genetic variability and correlation studies in blackgram (Vigna mungo [L.] Hepper) with reference to ymv resistance. International Journal of Current Microbiology and Applied Sciences, 6, 28492856. DOI: https://doi.org/10.13140/RG.2.2.29631.61602
42. Thirumalai, R., & Murugan, S. (2020).Variability studies on blackgram (Vigna mungo (L.) Hepper) genotypes for mungbean yellow mosaic virus. Plant Archives, 20(1), 2535-2539
43. Veni, K., Murugan, E., & Radhamani, T. (2015). Genetic variability in black gram (Vigna munga (L.) Hepper). Paper presented at the III International Symposium on Underutilized Plant Species. P. 1241. DOI: https://doi.org/10.17660/ActaHortic.2019.1241.38
44. Venkatesan, N., Thangavel P., & Ganesan J. (2005). Genetic variability, heritability and genetic advance analysis in segregating generation of blackgram {Vigna mungo (L.) Hepper}. Legume Research-An International Journal, 28, 49-51.
45. Vennila, S., Anbuselvam, Y., & Palaniraja, K. (2011). D2 analysis of rice germplasm for some quantitative and quality traits. Electronic Journal of Plant Breeding, 2(3), 392-396.
46. Vyas, D., Joshi, A., & Kedar, O.P. (2018). Genetic diversity analysis of black gram (Vigna mungo L.). Journal of Pharmacognosy and Phytochemistry, 7(3), 2535-2538.
47. Wolie, A., Dessalegn, T., & Belete, K. (2013). Heritability, variance components and genetic advance of some yield and yield related traits in Ethiopian collections of finger millet (Eleusine coracana (L.) Gaertn.) genotypes. African Journal of Biotechnology, 12(36), 5529-5534.