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14 May 2022

19 Pages

Phenotypic Diversity Analysis of Lens culinaris Medik. Accessions for Selection of Superior Genotypes

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Department of Genetics & Plant Breeding, CCS Haryana Agricultural University, Hisar 125004, India
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Author to whom correspondence should be addressed.
Sustainability2022, 14(10), 5982;https://doi.org/10.3390/su14105982
This article belongs to the Special Issue Advance in Plant Breeding and Plant Biotechnology for Sustainable Agriculture
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Abstract

Knowledge of genetic diversity in lentil is imperative for selection of parental genotypes that could yield heterotic combinations. The aim of the present study was to investigate the genetic diversity among 43 diverse lentil genotypes to identify complementary and unique genotypes for breeding programmes. Field experimentation was carried out in two winter seasons (2019–2020 and 2020–2021) in Hisar (29°10′ N, 75°46′ E) using randomized block design (RBD) with three replications. The chi-square test analysis showed significant genotypic variation for qualitative traits. There was substantial genetic variation among the genotypes for most quantitative traits, connoting the need to exploit a high degree of genetic variation through selection. Multiple-trait selection would also be beneficial, as seed yield was positively associated with most quantitative traits. The principal component analysis recognized seed yield (SY), days to 50% flowering (DTF), days to maturity (DTM), number of pods per plant (NPP), number of primary branches (NPB), plant height (PH) and biological yield (BY) as target traits that prominently described variation within lentil genotypes. The cluster analysis discriminated the lentil genotypes into five discrete clusters. Cluster III and V were the most distant groups, implying wider diversity among the genotypes of these groups. Furthermore, cluster analysis identified genotypes IPL 316, LH 17-19, LH 18-04, LH 17-17, IPL 81 and Pant L-8 as high-yielding genotypes, while L 4717 was identified as an early-maturing genotype. Therefore, to obtain a broad spectrum of early-maturing high-yielding segregants, the selected genotypes may serve as superior parental lines for structuring breeding strategies.

1. Introduction

Lentil (Lens culinaris Medik. ssp. culinaris, 2n = 2x = 14) is the earliest domesticated grain legume and originated from L. culinaris Medik. ssp. orientalis in the Fertile Crescent of Eastern Asia [1]. It is the principal cool-season grain legume of the Indian subcontinent, North America, Middle East, Oceania, North Africa and Sub-Saharan Africa, grown for its nutritious lens-shaped seed. Its contribution to semi-arid ecologies is crucial due to its ability to yield a substantial return in marginal areas. It also has the capacity to resist the risks, especially drought stress, that are common in dryland farming [2]. It is crucial for human and animal nutrition, as well as improving soil health. Lentil seed is a rich source of protein (third-highest after soybean and hemp), soluble and insoluble fibre, minerals (K, Ca, Zn, Fe, P) and vitamins (thiamine, niacin and riboflavin) for balanced human nutrition [3,4]. Furthermore, due to its high lysine and tryptophan content, its consumption with cereals provides the perfect complementary amino acid profile for human consumption. Its ability to fix atmospheric nitrogen through symbiosis with Rhizobium leguminosarum and sequester carbon improves soil nutrient status and soil health [5].
Lentil accounts for 8% of global pulses production [6]. Its production is 5.73 million tons, which are yielded from 4.80 million hectares. Canada has become the dominant player in lentil production, accounting for about 38% of world production, followed by India and Australia [7]. Globally, lentils grew by 39% in production and over 100% in productivity from 1994 to 2019, with the majority of the growth taking place in Canada, India and Turkey [7]. In India, lentil occupies 1.30 million hectares with the production of 1.10 million tons [8]. The average productivity of lentil in India is 847 kg/ha, which is quite low as compared to the world average productivity of 1195 kg ha−1 [7]. The possible major constraints behind the yield gap include drought and heat stress, insect pests and diseases and a paucity of suitable cultivars for specific adaptation. Lentil production and productivity can be enhanced by breeding and deploying improved cultivars. The availability of adequate genetic variation is critical for the development of farmer- and market-driven superior lentil cultivars.
The narrow genetic base and high genotype × environment interactions serve as a bottleneck in the quest for tailoring high-yielding and stress-resilient cultivars of lentil. Reportedly, lentil accessions across India have exhibited relatively low levels of genetic diversity [9]. The continuous artificial selection and breeding with a primary focus on a few targeted traits to satisfy the ever-increasing demand has resulted in a lack of heterogeneity in the lentil primary gene pool [10]. A limited number of superior landraces and cultivars were repeatedly used as parental lines in hybridization programmes. Kumar et al. [11] reported that 30% of the genetic base in 35 Indian cultivars was contributed by only ten parental lines. Therefore, introgression of diverse and exotic genotypes has been recommended [12,13]. Through gene recombination and coherent selection, the genetic base of the primary gene pool of lentil could be widened. However, hybridization of exotic macrosperma lentils having late flowering with indigenous germplasm was not feasible on a vast scale due to cross-incompatibility until Precoz was found. Precoz, an Argentinian cultivar, was the first identified early-flowering macrosperma exotic germplasm that contributed to seed size and rust resistance [11,13].
Therefore, the development of locally adapted and high-yielding lentil cultivars requires a range of diverse genotypes to be used in breeding programmes to incorporate various traits as per the needs of farmers and end consumers [14]. The wild gene pool of lentil can serve as a potent source of genetic diversity that can be recombined with established local cultivars to broaden the genetic base and improve economic traits [15,16]. The genetic diversity among the genotypes is a valuable source of genes for breeding programmes, building new farming systems, diversifying production and producing new high-quality goods [17]. Recently, a photothermal model [14] and a multi-trait stability index [18] have been used to identify potential genotypes for lentil research and improvement. However, knowledge of genetic diversity aids in the selection of parental genotypes from random populations, and estimating the possibilities of heterotic combinations before starting crossing programmes can save time and resources if the levels and patterns of genetic diversity are accurately estimated [19]. Hence, the objective of the present study was to evaluate the genetic diversity among the promising lentil genotypes cultivated across India to identify unique, high-yielding and complementary genotypes for future utilization in breeding programmes.

2. Materials and Methods

2.1. Plant Materials and Study Location

The study investigated 43 lentil genotypes, encompassing 33 promising released cultivars, nine advanced breeding lines and one exotic genotype (Table 1), which were procured from the Indian Institute of Pulses Research (IIPR), Kanpur, and the Department of Genetics and Plant Breeding, Chaudhary Charan Singh Haryana Agricultural University (CCS HAU), Hisar. The field experiments were conducted on research farms of Pulses Section, Department of Genetics and Plant Breeding, CCS HAU, Hisar, during the 2019–2020 and 2020–2021 Rabi cropping seasons. The research site is in a subtropical region with a dry environment characterized by extremely hot summers, chilly winters and little rainfall. The meteorological conditions (temperature and rainfall) that prevailed during the two cropping seasons were recorded according the procedure given by Khichar and Niwas [20]. Since cropping season 2020–2021 had considerably higher daytime (maximum) temperatures throughout and after blooming up to maturity, as well as significantly lower cumulative rainfall when compared with cropping season 2019–2020, both seasons were identified as separate environments for evaluating the genotypes (Figure 1).
Table 1. Description of the lentil genotypes used in the study.
Figure 1. Comparison of weather parameters across two years. Max = maximum temperature, Min = minimum temperature, °C = degree Celsius, mm = millimetre.

2.2. Experimental Design and Data Collection

The experiment was laid out in randomized block design (RBD) with three replications. Each genotype was planted on a plot comprising six rows. The rows were 4 m long and 0.30 m apart, resulting in a plot size of 7.2 m2. All recommended agronomic practices were followed for lentil production [21]. List of different qualitative and quantitative traits for which data were collected is presented in Table 2. Qualitative characterization was done according to lentil descriptors of the Protection of Plant Varieties and Farmers’ Rights Act (PPV and FRA) [22].
Table 2. Descriptors for the lentil qualitative and quantitative traits as per PPV and FRA.

2.3. Statistical Analysis

Data collected for qualitative traits (Table 2) were analysed for frequency distribution and chi-square test analysis. The quantitative data for each variable were tested for analysis of variance (ANOVA) to determine variability among genotypes. Subsequently, data were pooled across season for further analysis. To assess trait relationships and identify influential components among 11 quantitative traits, Pearson’s correlation coefficient and Principal component analysis (PCA) were analysed. The city block (Manhattan) distance was further used for the construction of dendrogram following unweighted pair-group method with arithmetic-average (UPGMA)-based hierarchical clustering of genotypes. IBM SPSS Statistics version 26 was used to conduct the above-mentioned analyses on data gathered for qualitative and quantitative traits.

3. Results

3.1. Evaluation of Genotypes Based on Qualitative Traits

Almost all assessed qualitative traits, such as foliage green colour intensity, stem anthocyanin colouration, leaflet size, growth habit, flower standard colour, tallness and seed traits, exhibited significant variations among the genotypes (Table 3). However, significant variations were not found for time of flowering and two qualitative traits: leaf pubescence and pod anthocyanin colouration did not yield any variation, and hence chi-square test could not be performed. Morphological trait variations observed in lentil genotypes are also depicted in Figure 2 and Figure 3. A large proportion of genotypes (58.1%) were late-flowering, while 41.9% of genotypes were medium-flowering and none of the genotypes were early-flowering (Table 3). About 76.9% of genotypes were semi-erect, while 23.3% of genotypes were erect in growth habits (Table 3, Figure 2). The majority of the genotypes (79.1%) had medium plant height, while 20.9% had short plant height (Table 3, Figure 3). About 44.2% genotypes had medium seed size, and the rest of the genotypes had large, small and very large seed size at 25.6%, 18.6% and 11.6%, respectively (Table 3). Grey seed test colour was found in a high proportion of genotypes (44.2%), followed by brown (37.2%), green (9.3%), pink (7.0%) and black (2.3%) (Table 3, Figure 3). The most common cotyledon colour was orange, as it was exhibited by 93.0% of the genotypes (Table 3).
Table 3. Distribution of phenotypic classes and significance tests among qualitative traits.
Figure 2. Morphological trait variation observed in lentil genotypes across two years. (A) Foliage: Intensity of green colour (light, medium and dark). (B) Stem: Anthocyanin colouration (present and absent). (C) Plant: Growth habit (erect and semi-erect). (D) Leaflet size (small, medium and large).
Figure 3. Morphological trait variation observed in lentil genotypes across two years. (A) Flower: Colour of standard (violet and white). (B) Seed: Testa mottling (present and absent). (C) Seed: Testa colour (brown, grey, pink, green and black). (D) Tallness (short and medium).

3.2. Genotype and Genotype × Environment Variations Based on Quantitative Traits

The analysis of variance (ANOVA) for eleven quantitative traits exhibited highly significant differences (p < 0.001) among all the genotypes under study (Table 4). The variation across the seasons was also significant (p < 0.001) for almost all the traits except number of pods per plant (NPP) and hundred-seed weight (HSW). The genotype × season interactions had significant (p < 0.001) effect on all the assessed traits except seeds per pod (SP) (Table 4).
Table 4. Mean squares of analysis of variance for seed yield and its components measured in 43 lentil genotypes across two years.

3.3. Mean Performance of Lentil Genotypes across the Environments

As shown in Table 5 and Table S1, genotype WBL 77 was the earliest to attain 50% flowering at 73 days, which was followed by L 4717 at 74.3 days and IPL 220 at 75 days. However, genotype L 4717 was earliest to attain maturity at 112.3 days, followed by L 4727 at 117.1 days and RVL 11-6 at 119.3 days. Pant L-8 was the latest genotype to flower at 85.5 days, while DPL 15 was the latest to mature at 128.1 days. The mean DTF and DTM were 79 and 124.3 days, respectively (Table 5 and Table S1). The mean plant height of test genotypes was 44 cm, and it varied from 27.7 cm to 53.6 cm (Table 5 and Table S1). The shortest genotype across the seasons was L 4717. The tallest genotypes, with plant height exceeding 50 cm, were RLG 5, LH 18-05, LH 82-6, IPL 81, LH 17-19, LH 17-17 and LH 18-04. The mean number of pods per plant of the test genotypes was 115 (Table 5 and Table S1). The most productive genotypes, with high number of pods per plant, were LH 17-19, IPL 316, Pant L-8 and Pant L -7 with 140.1, 139.9, 131.7 and 131.1, respectively. The mean number of primary branches varies from 2.4 to 3.53, with mean value for test genotypes of 2.93 (Table 6 and Table S1). Genotypes DPL 58, LH 17-19 and LH 18-04 were the best performing genotypes with 3.53, 3.53 and 3.47 primary branches per plant, respectively. There was a wide variation in number of fruiting branches, ranging from 11.3 to 20.6, with mean value of 15.9 (Table 6 and Table S1). The highest numbers of fruiting branches per plant were 20.6, 20.1, 19.3 and 18.3, observed on the genotypes LL 1373, DPL 62, HUL 57 and LH 84-8, in that order. The genotype × season interaction effects were non-significant for number of seeds per pod. The mean number of seeds per pod was 1.64. The hundred-seed weight varied from 1.7 to 3.5 g/100 seed (Table 6 and Table S1). Genotypes LL 1373, Precoz, IPL 406, DPL 62 and RVL 13-5 expressed the highest HSW: 3.0 g/100 seed. The mean value of biological yield per plot of the test genotypes was 3.224 kg (Table 7 and Table S1). The mean harvest index (%) varied from 28.9 to 44.2%, with a grand mean of 34.6% (Table 7 and Table S1). The highest harvest index was achieved by genotype L 4717 (44.2%), which is followed by LH 18-04 (43.5%) and LH 17-19 (40.4%). There was a marked genetic difference for seed yield per plot ranging from 0.606 to 1.373 kg/plot with a mean of 1.072 kg/plot. Genotypes IPL 316, LH 18-04 and LH 17-19 were the best-performing genotypes, with mean seed yield of 1.373, 1.347 and 1.301 kg/plot. The lowest yield response was recorded for genotypes RVL 13-7 and RVL 31, with seed yield less than 0.8 kg/plot.
Table 5. Mean performance for days to 50% flowering, days to maturity, plant height and number of pods per plant among the ten best- and five worst-performing genotypes after assessing 43 lentil genotypes across two years.
Table 6. Mean performance for number of primary branches, number of fruiting branches, seeds per pod and 100-seed weight among the ten best- and five worst-performing genotypes after assessing 43 lentil genotypes across two years.
Table 7. Mean performance for biological yield, harvest index and seed yield among the ten best- and five worst-performing genotypes after assessing 43 lentil genotypes across two years.

3.4. Interrelations among Agro-Morphological Traits

Positive correlation was observed among most of the traits (Table 8). DTF had significant positive association with DTM, PH, NPP, BY and SY (p < 0.01). Similarly, NPB exhibited positive correlation with PH, NPP, HI and SY (p < 0.01). Assessed traits revealed a variable degree of relationships with seed yield. SY was highly significantly (p < 0.01) and positively correlated with DTF, DTM, PH, NPP, NPB, BY and HI. SY and BY exhibited the strongest correlation (r = 0.77, p < 0.01). However, SY exhibited weak negative correlation with HSW (r = −0.18). Moreover, HSW had significant negative relationship with SP (r = −0.45, p < 0.01).
Table 8. Phenotypic correlation coefficients among the eleven quantitative traits of 43 lentil genotypes.

3.5. Principal Component Analysis

The principal component analysis (PCA) was performed to obtain a better understanding of sources of variance among lentil genotypes. PCA reduces the number of traits influencing the maximum percentage of total variance. Of the 11 principal components (PCs) produced, only first three PCs are discussed, as their eigen values were more than one. A total of 67.5% variation was explained by the first three PCs (Table 9). PC1 explained 39.2% of total variation and was positively influenced by BY (0.89), DTM (0.79), NFB (0.72), PH (0.70), SY (0.64), DTF (0.63) and NPP (0.59). PC2 added 16.8% of total variation, and the traits HI, NPB and SY were the highest contributors, with contributions of 0.88, 0.62 and 0.57, respectively. The third PC explained 11.5% of total variation, SP was the only major positive contributor with PC loading of 0.84, and HSW was the only major negative contributor with PC loading of −0.75.
Table 9. Eigen values, eigen vectors and proportion of variation accounted for by first three principal components.
Three groups of characters were identified on the basis of trait biplot, as shown in Figure 4. The first group was composed of NPB, SY, NPP, PH, DTF and DTM, which showed positive association with the first two PCs. The second group was negatively correlated with PC2 and comprised BY, NFB, SP and HSW. The third group contained HI that was negatively correlated with PC1. Furthermore, traits such as NPB, NPP, PH, DTF, DTM, BY, NFB and SY were positively correlated to each other, since vectors of these traits were in the same direction and formed acute angles between each other. However, HSW was negatively associated with SY, as depicted by the obtuse angle between them.
Figure 4. Trait biplot ordination depicting the association among quantitative traits in 43 lentil genotypes assessed across two years. For trait code and genotype description, refer to Table 1 and Table 2.

3.6. Cluster Analysis

Eleven agro-morphological traits delineated 43 lentil genotypes into five major clusters. From Table 10 and Figure 5, it is clearly evident that Cluster I was the largest (28 genotypes) group. Clusters III and IV contained 10 and 3 genotypes, respectively. Cluster II as well as Cluster V were the smallest groups, containing one genotype each. The allocation of genotypes into different clusters was not specific, as clusters consisted of a mixture of cultivars and advanced breeding lines. However, Precoz, the only exotic cultivar, was solely placed in Cluster II. The minimum inter-cluster distance was 36.40 units between Cluster I and II (Table 11). Cluster III and Cluster V were the most diverse groups, with 94.69 units inter-cluster distance. Furthermore, the maximum intra-cluster distance was recorded in Cluster I (24.85 units), while minimum intra-cluster distance was found in Cluster IV, which congregated three genotypes (Table 11). Cluster III had the highest mean values for six traits (Table 12): DTM (125.07 days), PH (47.50 cm), NPP (130.28), NPB (3.08), NFB (16.42) and SY (1.15 kg). The second group (Cluster II) included the highest average for three traits: DTF (80.67 days), SP (1.67) and HSW (3.39 g). On the contrary, Cluster V recorded the lowest mean values for the traits DTF (74.33 days), DTM (112.33), PH (27.67 cm), NPB (2.63), NFB (11.25), HSW (1.79 g) and BY (2.20 kg). However, Cluster V revealed the highest average for HI (44.25%).
Table 10. Grouping of 43 lentil genotypes into different clusters using between-group method when evaluated for eleven quantitative traits.
Figure 5. Hierarchical clustering depicting genetic similarity matrix of 43 lentil genotypes evaluated across two years.
Table 11. Inter- and intra-cluster (diagonal) distance for 43 genotypes of lentil.
Table 12. Mean values of 11 quantitative traits for five clusters revealed by cluster analysis among 43 lentil genotypes.

4. Discussion

With limited availability of genetic variability in the primary gene pool of lentil, selection of superior and complementary genotypes may be used effectively to aid in varietal development as per the needs of farmers and consumers. Knowledge of genetic diversity assists in the selection of parental genotypes from random populations and enables the establishment of heterotic groups. The present study assessed 43 lentil genotypes across two cropping seasons to evaluate the extent of genetic diversity and inter-relatedness among traits and to select potential genotypes with good complementation to develop transgressive segregates. The genotypes exhibited significant variation in qualitative traits (Table 3), which connotes the presence of important genetic variation in test genotypes that fosters morphological variation. Similar findings have been reported by Gaad et al. [23], who found significant variation in qualitative traits among lentil accessions procured for Algeria, ICARDA and USDA genebanks. The heterogeneity in morphological traits such as plant height, growth habit and seed traits is important for tailoring cultivars that meet farmers’ needs. For instance, breeding cultivars adapted for mechanical harvesting require screening of genotypes with tall stature and erect or semi-erect growth habit. Since tallness and erect growth habit are among the traits, in addition to non-lodging and even ripening, to be incorporated into genotypes to assist harvest mechanization [24]. However, Jawad et al. [25] reported that genotypes suitable for mechanized harvesting are low yielders and thus require hybridizing with high-yielding genotypes. The variation in lentil seed traits such as seed testa colour and cotyledon colour also helps in recognizing genotypes preferred by local farmers and consumers. For example, red lentils that have orange cotyledon colour are preferred by farmers in South Asia [26]. This preference for red lentils is associated with the fact that lentil is grown in South Asia as a winter crop, where temperature increases as crop heads toward maturity, resulting in a shorter seed-filling period in which red lentils yield higher than green lentils [24]. Similarly, Choudhary et al. [27] reported a preponderance of red lentils genotypes with brown or grey testa colour and orange cotyledons in the Indian subcontinent, indicating the farmers’ colour preference. Therefore, knowledge of farmers’ and consumers’ preferences, the needs of mechanized harvesting and variation in qualitative traits among the genotypes becomes useful for structuring appropriate breeding objectives and thus suitable breeding programmes.
Being highly self-pollinated (out-crossing less than 0.08%) and inbreeding species, presence of genetic variation is indispensable for improvement of quantitative traits in lentil. The ANOVA for tested traits exhibited significant differences among the genotypes (Table 4), highlighting the presence of an ample amount of genetic variation for exploitation in future breeding programmes. The differences in genetic composition among the genotypes are largely responsible for expressed genetic variation, which is imperative for crop improvement [28]. Similar findings have been reported by many lentil researchers for different traits [29]. The genotype performances were also not consistent across two cropping seasons, as indicated by significant genotype and season interactions for most of the quantitative traits (Table 4). Temperature and rainfall are among the elements that influence phenotypic expression in the environment. Because of the strong effect of the environment on phenotypic expression, genotype–phenotype correlation is known to be reduced [30], making the identification of stable and superior genotypes more difficult. Nevertheless, significant genotype × environment interactions affecting different quantitative traits have been described previously for various legumes including lentil, pigeonpea and cowpea [31,32,33].
In the present study, there was a significant difference in genotype performance for seed yield and its attributing traits across the two cropping seasons. For the majority of genotypes, days to maturity, seed yield, biological yield, plant height and number of branches were considerably lower in cropping season 2020–2021 than in cropping season 2019–2020 (Table 5, Table 6 and Table 7). This was due to markedly higher daytime temperatures throughout and after blooming up to maturity in cropping season 2020–2021, as well as significantly lower cumulative rainfall when compared with cropping season 2019–2020 (Figure 1). High temperatures post-flowering restrict vegetative growth, accelerate crop towards maturity and thus ultimately reduce seed yield [34]. Furthermore, about 50% accumulation of stem dry matter in lentil occurs after peak flowering [35]; thus, water deficits during the reproductive and grain filling stages reduce biological yield and thereby substantially decrease seed yield [36,37,38]. Similarly, Sehgal et al. [39] reported in their research about the adverse effects of drought and heat stress at time of grain filling on seed yield in lentil.
In order to initiate an effective crop improvement program, knowledge of inter-trait correlation among the traits is imperative. Directly selecting for complex traits such as yield, which are expressed late, may end up making the selection process complicated and time-consuming. Moreover, yield is a quantitatively inherited trait and is influenced by genetic effects as well as genotype and environment interactions. Therefore, indirect selection is preferred, because indirect character is considerably easier to quantify than direct character. Hence, it’s a good idea to seek and employ strongly associated characters [40]. In the present study, highly positively significant association of SY with DTF, DTM, PH, NPP, NPB, BY and HI (p < 0.01) was observed (Table 8). In indeterminate legumes, seed yield is a function of number of branches, number of pods, biological yield and proportion of flowers that end up forming mature pods [41]. Multiple-trait selection is also feasible, as most of the traits exhibit positive association. Thus, selection for these positively associated yield-attributing traits could bring about sufficient gain in seed yield. Similar results have been reported by [42,43] in their studies. However, SY was found to be weakly and negatively correlated with HSW, which may result in inefficient selection or low genetic gain. Furthermore, HSW was found negatively associated with SP, but SP was positively correlated with SY. A weak correlation between SY and HSW was also reported by [44,45]. Conversely, Sharma et al. [29] and Kumar et al. [42] reported a positive association between HSW and SY. The significant positive correlation exhibited between DTF, DTM, PH and BY suggests that indirect selection for earliness could be performed using plant height and biological yield as reference traits. Since winter lentil, especially in South Asia, is frequently exposed to terminal heat and drought stress, there is a need to develop short-duration cultivars to mitigate yield losses [46]. The significant relationship between DTF, DTM, PH, NPP, NPB, BY, HI and SY connotes that these quantitative traits would be a good index to screen genotypes for higher grain yield. The genetic cause of notable association between traits was likely attributable to the pleiotropic effect rather than linkage between the genes controlling various characters [47].
The reduction of number of traits responsible for maximum variation and identification of important traits with high variability among the genotypes was made possible by using principal component analysis. In this study, there were considerable morphological differences among the genotypes, as a few eigen vectors elucidated the majority of observed variation (Table 9). The results further revealed that SY, DTF, DTM, NPP, NPB, PH and BY were the most important traits due to their high contribution to PC1. This suggests that selection should be performed for those genotypes that report high and desirable mean performances for these targeted traits. These findings are in agreement with [29,48], who found that the first three PCs accounted for the majority of variation and traits associated with them were vital for crop improvement. Furthermore, the positive association between SY, NPB, NPP, PH, DTF, DTM, BY and NFB was again confirmed by biplot ordination (Figure 4), in which the angle between vectors of these correlated traits was acute. Therefore, indirect selection for seed yield could be practised using these associated traits, as they also exhibited high contribution to PCs.
UPGMA dendrogram delineated the lentil genotypes into five groups (Table 10, Figure 5), which further implied broad genetic diversity among the genotypes. However, the presence of cultivars and advanced breeding lines from different institutes across India (from where genotypes were sourced) in each group could be attributed to the use of a few superior parental lines in lentil breeding programmes. According to Kumar et al. [11], ten parental lines contributed approximately 30% of the genetic base in 35 lentil cultivars. In addition, Precoz, an exotic line, has been extensively used in recombination programmes to broaden the genetic base of microsperma Indian germplasm since the 1990s. Precoz was the first identified exotic macrosperma early-flowering genotype [11,13,15]. Mean values of clusters connoted that Cluster III was outstanding due to highest trait values, principally for NPP, NPB, NFB, PH and SY, while Cluster V was the earliest to flower and mature and had highest mean performance for HI (Table 12). Moreover, Cluster III and V were the most distant groups, implying wider diversity among the genotypes of these groups (Table 11). Therefore, lentil breeders should choose genotypes from Cluster III and V during breeding experiments to produce sufficient variability and screen out transgressive segregants. The present findings are in agreement with previous works, one of which reported that on the basis of 11 quantitative traits, 50 lentil accessions were clustered into seven groups [29], while Maurya et al. [49] found that 74 lentil genotypes were discriminated into nine different clusters based on 11 agro-morphological traits.

5. Conclusions

In current study, 43 lentil genotypes were examined for their diversity to identify complementary and unique genotypes for lentil breeding programmes. The data obtained showed that genotypes possessed a wide range of diversity for qualitative traits. The study would also be beneficial for choosing appropriate genotypes with suitable plant types and seed traits that align with the interests of farmers and consumers. There was substantial genetic variation among the genotypes and significant genotype × environment effects for most traits, connoting the urgency of exploiting a high degree of genetic variation through selection. Multiple-trait selection would be highly advantageous, as seed yield and most other quantitative traits were positively and significantly correlated. The PCA analysis recognized SY, DTF, DTM, NPP, NPB, PH and BY as target traits that prominently described variation within lentil genotypes. Hence, these traits should serve as useful selection indices for lentil improvement. The cluster analysis discriminated the lentil genotypes into five discrete clusters and advocated for hybridization between genotypes present in Cluster III and V to yield superior transgressive segregants. Since drought and heat stress are frequent occurring phenomena due to global warming and climate change, the need to develop early-maturing cultivar is inevitable. Therefore, to obtain a broad spectrum of early-maturing high-yielding segregants, the genotypes IPL 316, LH 17-19, LH 18-04, LH 17-17, IPL 81 and Pant L-8 grouped in Cluster III with high mean performances for target traits such as yield and its attributing traits should be hybridized with L 4717 (Cluster V), which was the earliest to flowering and maturity. The results of the present study would help to identify heterotic clusters and superior parents for structuring breeding strategies to develop improved lentil cultivars.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su14105982/s1, Table S1: Pooled mean performance of forty-three genotypes of lentil for eleven characters.

Author Contributions

Each author has participated sufficiently in the completion of this work. L.C., M.K. and R.Y. contributed to the experimental design, data analysis and review of manuscript. U.D. contributed to methodology and visualization. R.S. implemented the experiment, analysed the data and wrote up the manuscript. A. and V.K. contributed to data collection and visualization. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data are contained within the article and Supplementary Materials.

Acknowledgments

The authors acknowledge the station managers and technical staff of Pulses Section, Department of Genetics and Plant Breeding, CCS HAU, for technical assistance and overall support. All India Coordinated Projects (AICRP) on MULLaRP stationed at Indian Institute of Pulses Research, Kanpur, are sincerely thanked for providing the genotypes used in the study. The authors duly acknowledge the Department of Agricultural Meteorology for providing data for different weather parameters.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Cubero, J.I. Origin, domestication and evolution. In Lentils; Webb, C., Hawtin, G.C., Eds.; Commonwealth Agricultural Bureau: Slough, UK, 1981; pp. 15–38. [Google Scholar]
  2. Kumvanshi, S.M.; Shukla, K.C.; Valenkar, S.V.; Saraf, R.K. Crop modeling to sustain better crop harvest out of fluctuating climatic conditions of Sagar Region of Madhya Pradesh. In Proceedings of the National Conference on Biodiversity and Sustainable Utilization of Biological Resources, Sagar, Madhya Pradesh, India, 16–18 March 2001; pp. 193–197. [Google Scholar]
  3. Bhatty, R.S. Composition and Quality of Lentil (Lens culinaris Medik): A Review. Can. Inst. Food Sci. Technol. J. 1988, 21, 144–160. [Google Scholar] [CrossRef] [Scilit]
  4. Savage, G.P. The composition and nutritive value of Lentils (Lens culinaris). Nutr. Abstr. Rev. (Ser. A) 1988, 58, 320–343. [Google Scholar]
  5. Quinn, M.A. Biological nitrogen fixation and soil health improvement. In The Lentil: Botany, Production and Uses; Erskine, W., Muehlbauer, F.J., Ashutosh, S., Sharma, B., Eds.; CABI Publishing: Oxfordshire, UK, 2009; pp. 229–247. ISBN 9781845934873. [Google Scholar]
  6. Szczebyło, A.; Halicka, E.; Jackowska, M.; Rejman, K. Analysis of the Global Pulses Market and Programs Encouraging Consumption of This Food. Zesz. Nauk. SGGW W Warszawie—Probl. Rol. Światowego 2019, 19, 85–96. [Google Scholar] [CrossRef] [Scilit]
  7. FAOSTAT. 2019. Available online: https://www.fao.org/faostat/en/#home (accessed on 1 January 2022).
  8. INDIASTAT. 2020. Available online: https://www.indiastat.com/data/agriculture/agricultural-production (accessed on 1 January 2022).
  9. Kumar, J.; Gupta, S.; Dubey, S.; Gupta, P.; Sen Gupta, D.; Singh, N.P. Genetic diversity changes in Indian lentils over the times. J. Plant Biochem. Biotechnol. 2018, 27, 415–424. [Google Scholar] [CrossRef] [Scilit]
  10. Dikshit, H.K.; Singh, A.; Singh, D.; Aski, M.S.; Prakash, P.; Jain, N.; Meena, S.; Kumar, S.; Sarker, A. Genetic diversity in Lens species revealed by EST and genomic simple sequence repeat analysis. PLoS ONE 2015, 10, e0138101. [Google Scholar] [CrossRef] [Scilit]
  11. Kumar, S.; Gupta, S.; Chandra, S.; Singh, B.B. How wide is the Genetic Base of Pulse Crops? In Pulses in New Perspective; Ali, M., Singh, B.B., Kumar, S., Dhar, V., Eds.; ISPRD: Kanpur, India, 2004; pp. 188–210. [Google Scholar]
  12. Erskine, W. Lessons for breeders from land races of lentil. Euphytica 1997, 93, 107–112. [Google Scholar] [CrossRef] [Scilit]
  13. Erskine, W.; Chandra, S.; Chaudhry, M.; Malik, I.A.; Sarker, A.; Sharma, B.; Tufail, M.; Tyagi, M.C. A bottleneck in lentil: Widening its genetic base in South Asia. Euphytica 1998, 101, 207–211. [Google Scholar] [CrossRef] [Scilit]
  14. Neupane, S.; Dhakal, R.; Wright, D.M.; Shrestha, D.K.; Dhakal, B.; Bett, K.E. Strategic Identification of New Genetic Diversity to Expand Lentil (Lens culinaris Medik.) Production (Using Nepal as an Example). Agronomy 2021, 11, 1933. [Google Scholar] [CrossRef] [Scilit]
  15. Ferguson, M.E.; Robertson, L.D. Genetic diversity and taxonomic relationships within the genus Lens as revealed by allozyme polymorphism. Euphytica 1996, 91, 163–172. [Google Scholar] [CrossRef] [Scilit]
  16. Singh, M.; Bisht, I.S.; Kumar, S.; Dutta, M.; Bansal, K.C.; Karale, M.; Sarker, A.; Amri, A.; Kumar, S.; Datta, S.K. Global wild annual Lens collection: A potential resource for lentil genetic base broadening and yield enhancement. PLoS ONE 2014, 9, e107781. [Google Scholar] [CrossRef] [Scilit]
  17. Jing, R.; Vershinin, A.; Grzebyta, J.; Shaw, P.; Smkal, P.; Marshall, D.; Ambrose, M.J.; Ellis, T.N.; Flavell, A.J. The genetic diversity and evolution of field pea (Pisum) studied by high throughput retrotransposon based insertion polymorphism (RBIP) marker analysis. BMC Evol. Biol. 2010, 10, 44. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  18. Sellami, M.H.; Pulvento, C.; Lavini, A. Selection of Suitable Genotypes of Lentil (Lens culinaris Medik.) under Rainfed Conditions in South Italy Using Multi-Trait Stability Index (MTSI). Agronomy 2021, 11, 1807. [Google Scholar] [CrossRef] [Scilit]
  19. Halluer, A.R.; Miranda, J.B. Quantitative Genetics in Maize Breeding, 2nd ed.; Iowa State University Press: Ames, IA, USA, 1988. [Google Scholar]
  20. Khichar, M.L.; Niwas, R. Practical Manual on Weather Measurement; Department of Agricultural Meteorology, CCS Haryana Agricultural University: Hisar, India, 2013. [Google Scholar]
  21. CCS HAU. Package of Practices for Crops of Haryana-Rabi; Chaudhary Charan Singh Haryana Agricultural University: Hisar, India, 2017; pp. 61–65. [Google Scholar]
  22. PPV & FRA. Guidelines for the Conduct of Test for Distinctiveness, Uniformity and Stability on Lentil (Lens culinaris Medik); Protection of Plant Varieties and Farmers’ Rights Authority (PPV & FRA), Government of India: New Delhi, India, 2007.
  23. Gaad, D.; Laouar, M.; Gaboun, F.; Abdelguerfi, A. Characterization of some algerians and foreign lentil accessions by qualitative traits. Hortic. Biotechnol. Res. 2018, 4, 43–47. [Google Scholar]
  24. Materne, M.; Siddique, K.H.M. Agroecology and Crop Adaptation. In The Lentil: Botany, Production and Uses; Erskine, W., Muehlbauer, F.J., Sarker, A., Sharma, B., Eds.; CABI Publishing: Oxfordshire, UK, 2009; pp. 47–63. [Google Scholar]
  25. Jawad, M.; Malik, S.R.; Sarwar, M.A.; Asadullah, M.; Hussain, I.; Khalid, R. Genetic Analysis of Lentil (Lens culinaris) Exotic Germplasm to Identify Genotypes Suitable for Mechanical Harvesting. Pakistan J. Agric. Res. 2019, 32, 152–158. [Google Scholar] [CrossRef] [Scilit]
  26. Pratap, A.; Kumar, J.; Kumar, S. Evaluation of wild species of lentil for agro-morphological traits. Legum. Res. 2014, 37, 11–18. [Google Scholar] [CrossRef] [Scilit]
  27. Choudhary, R.; Panwar, R.; Pandey, D.; Rakesh Choudhary, C.; Verma, S.; Chourasiya, V. Morphological characterization of lentil (Lens culinaris Medikus.) varieties based on six qualitative traits. J. Pharmacogn. Phytochem. 2017, 6, 1611–1615. [Google Scholar]
  28. Vollmann, J.; Grausgruber, H.; Ruckenbauer, P. Genetic variation for plant breeding. In Proceedings of the 17th EUCARPIA General Congress, Tulln, Austria, 8–11 September 2004. [Google Scholar]
  29. Sharma, S.R.; Singh, S.; Gill, R.K.; Kumar, R.; Parihar, A.K. Selection of promising genotypes of lentil (Lens culinaris Medik.) by deciphering genetic diversity and trait association. Legum. Res. 2020, 43, 764–769. [Google Scholar] [CrossRef] [Scilit]
  30. Bustos-Korts, D.; Romagosa, I.; Borràs-Gelonch, G.; Casas, A.M.; Slafer, G.A.; Van Eeuwijk, F. Genotype by environment interaction and adaptation. In Encyclopedia of Sustainability Science and Technology; Savin, R., Slafer, G.A., Eds.; Springer: New York, NY, USA, 2018; pp. 29–71. [Google Scholar]
  31. Nath, U.K.; Rani, S.; Paul, M.R.; Alam, M.N.; Horneburg, B. Selection of superior lentil (Lens esculenta M.) genotypes by assessing character association and genetic diversity. Sci. World J. 2014, 2014, 372405. [Google Scholar] [CrossRef] [Scilit]
  32. Gerrano, A.S.; Jansen van Rensburg, W.S.; Mathew, I.; Shayanowako, A.I.T.; Bairu, M.W.; Venter, S.L.; Swart, W.; Mofokeng, A.; Mellem, J.; Labuschagne, M. Genotype and genotype × environment interaction effects on the grain yield performance of cowpea genotypes in dryland farming system in South Africa. Euphytica 2020, 216, 80. [Google Scholar] [CrossRef] [Scilit]
  33. Vales, M.I.; Srivastava, R.K.; Sultana, R.; Singh, S.; Singh, I.; Singh, G.; Patil, S.B.; Saxena, K.B. Breeding for Earliness in Pigeonpea: Development of New Determinate and Nondeterminate Lines. Crop Sci. 2012, 52, 2507–2516. [Google Scholar] [CrossRef] [Scilit]
  34. Summerfield, R.J.; Muehlbauer, F.J.; Short, R.W. Controlled environments as an adjunct to field research on lentils (Lens culinaris). V. Cultivar responses to above- and below-average temperatures during the reproductive period. Exp. Agric. 1989, 25, 327–341. [Google Scholar] [CrossRef] [Scilit]
  35. Saxena, M.C.; Hawtin, G.C. Morphology and growth patterns. In Lentils; Webb, C., Hawtin, G.C., Eds.; Food and Agriculture Organization: Slough, UK, 1981; pp. 39–52. [Google Scholar]
  36. Farooq, M.; Wahid, A.; Kobayashi, N.; Fujita, D.; Basra, S.M.A. Plant drought stress: Effects, mechanisms and management. In Sustainable Agriculture; Springer: Dordrecht, The Netherlands, 2009; pp. 153–188. ISBN 9789048126651. [Google Scholar]
  37. Pushpavalli, R.; Zaman-Allah, M.; Turner, N.C.; Baddam, R.; Rao, M.V.; Vadez, V. Higher flower and seed number leads to higher yield under water stress conditions imposed during reproduction in chickpea. Funct. Plant Biol. 2015, 42, 162–174. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  38. Shrestha, R.; Turner, N.C.; Siddique, K.H.M.; Turner, D.W. Physiological and seed yield responses to water deficits among lentil genotypes from diverse origins. Aust. J. Agric. Res. 2006, 57, 903–915. [Google Scholar] [CrossRef] [Scilit]
  39. Sehgal, A.; Sita, K.; Bhandari, K.; Kumar, S.; Kumar, J.; Vara Prasad, P.V.; Siddique, K.H.M.; Nayyar, H. Influence of drought and heat stress, applied independently or in combination during seed development, on qualitative and quantitative aspects of seeds of lentil (Lens culinaris Medikus) genotypes, differing in drought sensitivity. Plant Cell Environ. 2019, 42, 198–211. [Google Scholar] [CrossRef] [Scilit]
  40. Ahmadikhah, A.; Nasrollanejad, S.; Alishah, O. Quantitative studies for investigating variation and its effect on heterosis of rice. Int. J. Plant Prod. 2008, 2, 297–307. [Google Scholar] [CrossRef] [Scilit]
  41. Egli, D.B. Seed Biology and the Yield of Grain Crops; CAB International: Wallingford, UK, 1998. [Google Scholar]
  42. Kumar, A.; Gill, R.K.; Singh, M. Genetic variability and association analysis for various agro morphological traits in lentil (Lens culinaris m.). Legum. Res. 2020, 43, 776–779. [Google Scholar] [CrossRef]
  43. Kumar, P.; Vimal, S.C.; Kumar, A. Study of simple correlation coefficients for yield and its component traits in lentil (Lens culinaris Medik.). Int. J. Curr. Microbiol. Appl. Sci. 2017, 6, 3260–3265. [Google Scholar]
  44. Al-Aysh, F.M. Genetic variability, correlation and path coefficient analysis of yield and some yield components in landraces of lentil (Lens culinaris Medik.). Jordan J. Agric. Sci. 2014, 10, 737–750. [Google Scholar] [CrossRef] [Scilit]
  45. Kumar, J.; Srivatava, E. Impact of reproductive duration on yield and its component traits in lentil. Legum. Res. 2015, 38, 139–148. [Google Scholar] [CrossRef] [Scilit]
  46. Kumari, V.V.; Roy, A.; Vijayan, R.; Banerjee, P.; Verma, V.C.; Nalia, A.; Pramanik, M.; Mukherjee, B.; Ghosh, A.; Reja, M.H.; et al. Drought and heat stress in cool-season food legumes in sub-tropical regions: Consequences, adaptation, and mitigation strategies. Plants 2021, 10, 1038. [Google Scholar] [CrossRef] [Scilit]
  47. Singh, P.; Pandey, A. Correlation between different traits of aromatic rice (Oryza sativa L.) and their cause-effect relationship. Appl. Biol. Res. 2012, 14, 47–53. [Google Scholar]
  48. Bilal, M.; Jamil, M.; Ali, A.; Ilyas, M.K.; Ghafoor, A. Multivariate analysis of genetic diversity in lentil (Lens culinaris) Germplasm. Fresenius Environ. Bull. 2018, 27, 2151–2159. [Google Scholar]
  49. Maurya, K.S.; Kumar, S.; Choudhary, A.M.; Singh, V.; Chauhan, M.P. Diversity analysis in lentil (Lens culinaris Medik.). J. Pharmacogn. Phytochem. 2018, 7, 1958–1961. [Google Scholar]
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