Genetic Divergence Studies in Cucumber (Cucumis sativus L.) Genotypes for Yield and Quality

The cucumber (Cucumis sativus L.), with its chromosome number of 2n = 14, is a member of the family Cucurbitaceae, which includes 117 genera and 825 species native to warmer regions of the world. Native to the Indian sub-continent, it is now cultivated in many tropical and subtropical regions (Sebastian et al., 2010). It is typically monoecious but is a strongly cross-pollinated crop. Cucumbers exhibit a wide spectrum of sexual dimorphism, including androecious, gynoecious, hermaphrodite and andromonoecious (Bailey, 1969). India is home to numerous species of Cucumis sativus and allied plants, each with its own unique set of botanical and agronomic characteristics. In the South Asian region, cucumbers are planted commercially across the board. Although several landraces and wild variants of cucumber have been reported (Sebastian et al., 2010), they have not been utilized to the same extent as the high-yielding cultivars. Lack of exploitable genetic variability, lack of appropriate genotypes for different cropping systems, sensitivity to biotic and abiotic stresses, lack of quality seeds of improved varieties and a narrow genetic base due to the repeated use of few parents with a high degree of pertinence in crossing programmes are the major constraints in achieving higher and profitable productivity of cucumber. Cucumber’s low levels of variability have been used in successful varietal development initiatives. The output of this crop has reached a plateau, despite numerous breeding efforts. In order for a breeding program to be successful, genetic diversity and variability in the germplasm must already exist. In order to determine the extent of the variation available for yield and yield attributing features, it is necessary to have an accurate estimation of the nature and size of diversity in a crop. A plant breeder’s ability to pick superior parents for hybridization is aided by their familiarity with the genetic variation present in the available germplasm. It is expected that offspring from genetically varied parents will be more successful. “It is well-established that choosing parents with a wider range of characteristics increases the likelihood of producing offspring with high heterotic F₁ scores and a broad range of segregation-based variability. When attempting to measure phenotypic variation, multivariate analysis using Mahalanobis’s D² statistic is a powerful tool. This study was initiated to evaluate the type and extent of genetic divergence among thirty -two cucumber genotypes in light of the foregoing. The breeder might utilize the results of a study like this to choose economically and genetically advantageous genotypes to include into the breeding plan in order to achieve the desired gains in Odisha.

The research work was carried out during Rabi, 2019, at the OUAT’s Department of Vegetable Science’s experimental field in Bhubaneswar, Odisha with a plot size of 3.2 m × 3.0 m. A Randomized Block Design with 32 genotypes and 3 replicates was used in the study. Characters such as node at which first female flower appears, number of days to first harvest, fruit length (cm), fruit diameter (cm), average fruit weight (g), number of fruits per plant, vine length (m), TSS (0B), shelf life (days), severity of downy mildew (%) and fruit yield per vine were recorded from five randomly selected plants in each plot to reduce the likelihood of bias. (kg). Mahalanobis D² (1936) statistics were used in Windostat 9.30 to calculate genetic divergence (D²) and a dendrogram, Mahalanobis Tocher distance, was created and displayed in Fig 1 and 2.
 

 

D² analysis
 
D² values were used to divide the 32 genotypes into five quite different groups, as depicted in (Table 1). Within each cluster, D² values ranged from 0.00 to 45.08. Table 2 showed that the intra cluster diversity was greatest in cluster I (with 25 genotypes), next in cluster III (with 23.94) and finally in clusters II, IV and V (with 1 genotype each), where the intra cluster distance was zero. Similar results were found by Suma et al., (2021); Kumar et al., (2013) and Hasan et al., (2015) in cucumber and Indraja et al., (2022). Genotypes that clustered together had originated from several locations. Genotypes from the same geographic location clustered separately, contrary to expectations. This revealed that there was no certain connection between spatial and genetic diversity. Materials with the same origin were clustered together, indicating that the genotypes from that origin have a diverse genetic background.
 

 

       
The D² values, which were used to determine the statistical separation of the clusters, are also depicted visually (Fig 2). The most divergent clusters were Cluster I and Cluster V (D²=133.68), then Cluster II and Cluster V (D²= 122.47), Cluster II and Cluster IV (D²= 120.70) and Cluster I and Cluster III (D²= 110.29). It was determined, however, that the shortest distance (D² = 24.55) existed between Clusters II and III. The different genotypes indicated by maximal inter cluster distance will differ in phenotypic performance, hence it is vital to select divergent parents based on these distances in order to develop favorable hybrids and transgressive segregants in cucumber. So, in addition to selecting genotypes from clusters that have substantial inter cluster distance for hybridization, it is also conceivable to think about selecting parents based on the level of genetic divergence with reference to a particular character of interest. This means that, depending on the breeder’s intentions, one may select parents that are extremely different from one another in terms of productivity, fruit size, or shelf life.
 
Cluster means for different characters
 
Table 3 displays the mean values for 12 quantitative traits of 32 genotypes of Cucumber. Mean values for node at which the first female flower appears (6.59), number of days to first female flower production (40.67), days to first harvest (40.67) and total soluble solids (4.82) were all highest for Cluster II genotypes. Cluster IV had the longest fruit length (21.31 cm), high fruit weight (210.42 g), longest vines, highest yields per plant (2.27 g) and highest total soluble solids. (3.08). Among the five clusters, Cluster V had the highest mean cluster values for number of fruits per plant (11.89), fruit diameter (3.53), fruit shelf life (5.30), downy mildew severity (9.24 %) and the lowest mean cluster values for node at which first female flower appears (2.88) and days to first fruit harvest (35.74). Several studies, including those by Hasan et al., (2015), Ahirwar et al., (2017), Shewta et al., (2018) and Kumar et al., (2019), have highlighted the significance of cucumbers genetic diversity.
 

       
Clusters were created using the following characteristics to determine their relative contributions to the variation: fruit yield per vine (16.50%), number of fruits per plant (10.80%), average fruit weight (9.80%), node at which first female flower appears (8.50%), severity of downy mildew percentage (8.06%), total soluble solids (7.15%), days to first harvest (7.20%), fruit length (7.10%), and fruit width (7.10%) displayed in Table 4. Hanchimani (2006) observed that clustering in cucumber was most affected by factors such as fruit number per vine, fruit length, average fruit weight, and yield per vine. Similar trends have been observed by previous studies who examined both the average fruit weight (Staub et al., 1997; Rasheed et al., 2002, in Pumpkin; Praveen Kumar, 2011, in muskmelon) and the number of fruits per vine in bottle gourd (Badade et al., 2001).
 

Genotypes collected from different eco-geographic regions were found to be highly variable. Genotypes from clusters IV and V, which showed the highest mean for yield per plant, were also distinguished by their longer fruits, heavier average fruits, higher total number of fruits per plant, longer vines, lower incidence of downy mildew percentage, and earlier ripening”. Selection from cluster I is effective for breeding programs targeting the small-fruited group, and it is also effective for generating long-storage fruit varieties. High-diversity cluster genotypes may be used in a breeding program to create high-yielding varieties with desirable qualities, and they may also be used in a heterosis breeding program to create F₁ hybrids with exceptional yield and quality characteristics. Screening for significant features, such as average fruit weight, fruit length, and fruit production, is necessary when selecting genetically varied genotypes for hybridization. These characters can then be exploited for further development by phenotypic selection.

The authors would like to thank the College of Agriculture, Department of Vegetable Science and Odisha University of Agriculture and Technology in Odisha, India for their assistance with this experiment.

All authors declare that they have no conflicts of interest.