Next Article in Journal
Livelihood Resilience and Its Influencing Factors of Worker Households in the Face of State-Owned Forest Areas Reform in China
Next Article in Special Issue
Phenotypic Diversity Analysis of Lens culinaris Medik. Accessions for Selection of Superior Genotypes
Previous Article in Journal
Exploring the Research Regarding Frugal Innovation and Business Sustainability through Bibliometric Analysis
Previous Article in Special Issue
Assessing the Genetic Divergence of Onion (Allium Cepa L.) through Morpho-Physiological and Molecular Markers
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Assessment of Morphological Diversity of Colchicum luteum L., an Economically Important Threatened Medicinal Plant of Kashmir Himalaya

1
Division of Environmental Sciences, Sher-e-Kashmir University of Agricultural Sciences and Technology, Srinagar 190025, Jammu & Kashmir, India
2
Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
3
Division of Genetics and Plant Breeding, Faculty of Agriculture, Sher-e-Kashmir University of Agricultural Sciences and Technology, Wadura, Kashmir 193201, Jammu & Kashmir, India
4
Division of Basic Sciences & Humanities, Sher-e-Kashmir University of Agricultural Sciences and Technology, Srinagar 190025, Jammu & Kashmir, India
5
Department of Saidla, Aligarh Muslim University, Aligarh 21589, Uttar Pradesh, India
6
Princess Dr. Najla Bint Saud Al-Saud Center for Excellence Research in Biotechnology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
7
Department of Public Health, Daffodil International University, Dhaka 1341, Bangladesh
*
Authors to whom correspondence should be addressed.
Sustainability 2022, 14(3), 1327; https://doi.org/10.3390/su14031327
Submission received: 20 December 2021 / Revised: 14 January 2022 / Accepted: 17 January 2022 / Published: 25 January 2022

Abstract

:
Colchicum luteum L. is an economically important and endangered medicinal plant of the Kashmir Himalaya. The corm extract is used for the treatment of rheumatism, gout, Behcet’s syndrome, and Alzheimer disease. It is also used extensively in plant breeding programs for the doubling of chromosomes. The present study was carried out for two years (2017–2019) to study the genetic diversity of C. luteum, an economically important and endangered medicinal plant of Kashmir Himalaya. The mapping of genetic diversity of C. luteum was estimated using Mahalanobis D2 analysis in the Aharbal (Kulgam), Dhara (Theed), and Baera Baal Hills (Harwan) of Kashmir Valley. The results showed the presence of 5 clusters for 30 populations. There were 17 populations in cluster-1, 1 in cluster-2, 2 in cluster-3, 3 in cluster-4, and 7 in cluster-5. The majority of the population was a group in cluster-1 followed by cluster-5. The maximum intracluster distance (D2 values) was observed in cluster-5 (46.55588) followed by cluster-3 (41.61871), and the maximum inter-cluster distance (D2 values) was observed in cluster-3 (46.55588) followed by cluster-5 (41.61871). Our study revealed that plant species possessed sufficient genetic diversity among the populations. Cluster-5 showed superiority in plant−1 respect of the maximum mean plant height (28.46 cm), leaf area (47.0 cm2), number of seeds plant−1 (26.85), corm length (5.15 cm), corm width (3.17 cm), fresh weight of corm plant (6.87 g), and dry weight of corm plant (4.81 g) as compared to other clusters. Out of five clusters, cluster-5 is a promising one for better yield and yield attributing traits. The present study revealed that plant species possessed sufficient genetic diversity among the populations as 30 populations were arranged into 5 clusters. Therefore, cluster-5, consisting of seven populations from the undisturbed area of Harwan, and consequently the populations from the same cluster can be multiplied for initiating a conservation and breeding program and can serve as a tool for the scientific community to evolve better contemporary varieties of C. luteum with profitable characters such as more yield of corms, etc. This will assist farmers, particularly the marginal farmers, to alleviate their income.

1. Introduction

The use of medicinal herbs by humans to cure different ailments is time immemorial. These herbs have large curative significance and use as medium and equitable sources for a better and healthy life [1,2,3,4,5]. Medicinal plants are an invaluable source of new drugs and their overexploitation for commercial application is decreasing their density and natural diversity in the wild [6,7,8,9]. Eighty per cent of individuals in poor nations rely entirely on herbal remedies for their primary health and more than 25 per cent of recommended products are sourced from wild species in developing countries [6]. There are about 50,000 to 80,000 floral plant species used in therapeutic applications according to the International Union for the Conservation of Nature and World Wildlife Fund statistics on medicinal plants in the world. Due to overharvesting and habitat loss, some 15,000 species are on the verge of disappearing. With rising global population and crop consumption, 20% of their natural resources have been previously wiped out [10,11,12]. Though this threat is not new, the rate of species loss and habitat destruction is increased several folds worldwide. It is moving toward the risk of extinction of medicinal plants, especially in India and China along with some African nations [6,13,14,15,16]. The Department of Ecology, Environment and Remote Sensing, Jammu and Kashmir, reported that the species C. luteum comes under rare and threatened status [17]. The State Forestry Department believes that during the previous 2 decades, these plants have been massively extracted and smuggled from the Kashmir woods, resulting in their diminished wildlife levels. Colchicum luteum L., commonly known as Suranjan-e-Talkh (Urdu), comes under the family Liliaceae [18]. There are around 280 genera and 4000 species in the Liliaceae family, which are primarily perennial plants with starchy rhizomes, corms, or bulbs. Colchicum species are split into two groups based on the flowering period. Their leaves and flowers occur in distant seasons. C.luetum is a ‘monocot plant’ and is collected in quantity from the Kashmir meadows. It has tuberous roots which are oval in shape and brown in colour. The leaves are 6–12 inches in length and round in shape with small flowers. The flowers are 1–2 inches in length and around half an inch in width. The blossoming season begins between the middle of February and April and the seeds are matured between April and June. The flowers are hermaphrodite, having female and male organs, and are bee and fly pollinated. It is ideal for low-loam soils and loves well-drained soils which do not grow in shade [19]. These herbal species are commonly present on the edges of the forest or in open grasslands and temperate western Himalayas, from Kashmir to Chamba, at altitudes ranging from 700 to 2800 m in India. It extends into the other Indian states of Punjab, Himachal Pradesh, and Sikkim, and neighboring countries such as Nepal, Afghanistan, Pakistan, and the Hindu Kush Mountains. In general, it thrives well where climate conditions are low and have a temperature less than 15 °C. Its natural habitat conditions are highlighted by physiologically temperate conditions such as snow, severe winter, as well as lower humidity using corms, which may also be obtained by snow melting in April and May, from natural environments. The plant corms must not be damaged or infected [20,21,22,23,24].
Colchicine is the main alkaloid recovered from all Colchicum species [25]. There are 31 kinds of tropane alkaloids identified from C. luteum [25,26,27]. Colchicine is a common compound and is renowned for its antimitotic and its hereditary inflammatory disease such as acute gout and family-based Mediterranean fever. [28,29]. The earliest use of colchicine as mitotic inhibitors and for induction of polyploids is reported in 1937 [30,31] and is still popular today in various plant breeding programs [31,32,33]. It is also used for the treatment of Behcet’s syndrome [34,35] and Alzheimer disease [36]. It is also reported that crude ethanolic extracts and different fractions showed lower activity (29–61%) against acetylcholinesterase and no activity against urease [37,38]. The derivatives of colchicine such as demecolcine are used against myeloid leukemia [39] and allocolchinoid phosphate-derivative ZD6126 is tested in cancer therapy [40]. Despite all its uses, a cost-effective and productive in vitro synthesis method of Colchicum alkaloids is far from reality and the corms of Colchicum are the source of colchicine extraction. C. luteum species are taxonomically very challenging to class, especially autumn flowering species [41]. The species is facing various biotic stresses such as uncontrolled grazing and habitat loss due to various anthropogenic activities such as illegal harvesting, deforestation, construction of roads, and tourism.
Keeping in view the rare and threatened status and enormous medicinal potential of C.luteum, there is no available report on the genetic diversity of the species in Kashmir Himalaya. The success of a conservation program of a species depends on different parameters, including genetic diversity. Therefore, it is the need of the hour to study the genetic diversity of this threatened plant species. The present study was conducted by a comparative analysis of the morphological features of vegetative and reproductive parts of C. luteum. It helps in ascertaining the genetic diversity of C. luteum in Kashmir Himalaya.

2. Material and Methods

The study on estimation of the genetic diversity of C. luteum was conducted for two years (2017–2019) at Division of Environmental Sciences, SKUAST-K, Shalimar, J&K. In Kashmir Himalaya, three areas, namely Aharbal (Kulgam), Dhara (Theed), and Baera Baal Hills (Harwan) (Figure 1), were selected for an exclusive survey of C. luteum population. The data were recorded on ten competitive plant populations from each sub-location and mean values were calculated from three sub-locations in each location. The following morphological characters of the vegetative and reproductive stages were calculated to differentiate the variation in a population.
(a) Plant height (cm): The plant height was drawn first from the bottom to the tip of the leaf, using a measuring scale, of all populations from selected plants. (b) Leaf area plant−1 (cm2): The area of the leaf was recorded by using the graph method. An outline of a leaf on the graph paper was drawn and a number of squares were calculated. (c) Leaf length (cm): The length of the leaf was determined from the point of emergence of leaf to the apex of the leaf with the help of scale from the selected plants in all the populations. (d) Leaf width (cm): The leaf width was measured horizontally from the center of the leaf using a scale on selected plants from all populations. (e) Number of seeds plant−1: The number of seeds from each plant was counted at the time of harvesting from the selected plants in all the populations. (f) Corm length (cm): The corm length was recorded by using a scale from the selected plants in all the populations. (g) Corm width (cm): The width of the corm was measured with the help of measuring tape from the selected plants in all the populations. (Corm width (cm) = X/2). (h) Fresh weight of corm (g): ‘The fresh weight of the corm per plant was taken separately for each plant by the help of electronic weighing balance’. (i) Dry weight of corm (g): The dry weight of the corm per plant was taken after oven it completely for 24 h, with the help of an electronic weighing balance from the selected plants in all the populations.

3. Statistical Analysis

D2 statistics were used to determine the competitive representative diversity of plant populations (Mahalanobis, 1936). Mahalanobis D2 analysis evaluated the data. Computation of D2 values.
The differences in transformed values for various characters were computed and D2-values were calculated according to the following formula:
D 2 = i 1 p ( Y ij Y ik ) 2
where
  • P = number of characters studied, and
  • Yij and Yik = are two transformed variables of the ith character for two genotypes.
All the morphological characters were analyzed for the thirty populations of C. luteum. Using the D2 was adopted between every feasible combination and was calculated. The populations were categorized into different clusters and intra- and intercluster distances were calculated. The data were analyzed and Mahalanobis D2 analysis was performed by using Torcher’s method as suggested by Rao [42].

4. Results and Discussion

In the present study, variability and diversity (D2 statistic) at the phenotypic level were estimated at three locations (Aharbal-Kulgam, Dhara Theed, Baera Bal Harwan) of Kashmir Valley in thirty populations of C. lutuem (Figure 2). Dendrogram depicting relationship among 30 populations of Colchicum luteum with total of Five clusters showed in Figure 3. Data were recorded on nine phenotypic traits, viz. plant height, leaf area, leaf length, leaf width, number of seeds plant−1, corm length, corm width, fresh weight of corm, and dry weight of corms. Genetic diversity was estimated from the data pooled over years using Mahalanobis’ D2-statistic which indicated the presence of sufficient genetic diversity among the populations. D2-statistics revealed a total of 5 clusters (Figure 1 and Table 1) with 17 populations in cluster-1, 1 (cluster-2), 2 (cluster-3), 3 (cluster-4), and 7 (cluster-5). The majority of populations was grouped in cluster-1, followed by cluster-5 (Table 1). According to Rao [43], no formal rules can be laid down for forming a cluster, yet any two genotypes belonging to the same cluster should at least, on average, show a smaller D2 value as compared to two genotypes falling into many clusters. Work from Ahmad and Borah [44] observed that the clustering pattern reflected considerable influence on genetic diversity. There is no similarity between genetic diversity and geographic location, the contributions of different genotypes in many different clusters at times. In this study (Phaseolus volgaris L.), the genetic diversity of 36 six genotypes were also evaluated for 13 yields and yield attributive properties and grouped into six clusters via Mahalanobis. The largest cluster-1 was followed by cluster-6 and cluster-2 and -3 with 19 different genotyping types. The genetic divergence of sesame (Sesamum indicum L) in 2019, a set of 96 advancing breeding lines of genotype, was classified in 15 clusters, as per Table 1 and Figure 1, by Hukumchand and Parameshwarappa [45]. Cluster-I was the highest of the 68 line clusters, cluster-2 (11), cluster-5 (4), and cluster-11, respectively.
The mean performance of the traits in a cluster and different phenotypes falling within a cluster are presented in Table 2. The maximum mean plant height (28.46 cm) was recorded in cluster-5, followed by cluster-4 (27.71 cm), and the lowest plant height mean was found in cluster-3 (13.0). The maximum mean leaf area (47.0 cm) was recorded in cluster-5, followed by cluster-4, while the minimum leaf area was found in cluster-1 (28.79 cm). The maximum mean leaf length (19.51 cm) was recorded in cluster-5, followed by cluster-4, and the minimum leaf length (11.6 cm) was found in cluster-1. The maximum leaf width (1.32 cm) was recorded in cluster-5, followed by cluster-4, while the minimum leaf width (0.97) was found in cluster-1. The maximum number of seeds (26.85) was recorded in cluster-5, followed by cluster-4, while the minimum number of seeds (11.41) was found in cluster-1. The maximum corm length (5.15 cm) was recorded in cluster-5, followed by cluster-4, while the minimum corm length (3.06) was found in cluster-2. The maximum corm width plant (3.17 cm) was recorded in cluster-5, followed by cluster-4, while minimum corm width (2.05) was in cluster-1. The maximum fresh weight of the corm plant (6.87 g) was recorded in cluster-5, followed by cluster-4, while the minimum fresh weight of corm (2.27 g) was found in cluster-1. The maximum dry weight of the corm plant (4.81 g) was in cluster-5, followed by cluster-4, while minimum dry weight of corm (1.58 g) was found in cluster-1 (Table 2). The descriptive statistics for the parameters considered in the study are given in Table 3.
The maximum cluster mean of different phenotypic traits comprises of plant height, leaf area, leaf length, leaf width, number of seeds−1, corm length, corm width, fresh weight of corm, and dry weight of corm, and were predominately found at undisturbed areas recorded in cluster-5 and cluster-4, and the lowest values were found at disturbed areas recorded in cluster-3, cluster-2, and cluster 1 (Table 2). In accordance with other crops, work performed by Hukumchand and Parameshwarappa [45] on Sesamum indicum L.), in which they assessed the traits seed yield per plant (42.43%), 1000 seed weight (18.44%), number of capsules per plant (9.25), and height to the first capsule (6.82), have contributed the maximum to genetic divergence. In addition, [46] worked on (Phaseolus vulgaris L.), in which they studied the traits green pod yield contributed diversity (40.17%) followed by plant height (18.89 per cent). The highest green pod yield was 165 g per plant, while it recorded an average number of pods (23.85). Increasingly, the possibilities of heterotic expression in F1 and broad variation across segregating progeny are seen to be the more diversified parents within their fitness overall bounds [47,48,49,50,51,52]. The cluster pattern might be utilized to choose parents for the hybridization procedure to give the greatest potential diversity to various economic characteristics [53,54,55]. Furthermore, this should work to interconnect genotypes across a wide range of variabilities and to construct transgressive segregates for C, such as cluster (I and V), clusters (I and V), and clusters (I and IV). In addition, the cluster value performance of 30 populations of C. luteum at different locations of Kashmir is validated in the DMRT (Table 4).
The mean intracluster and intracluster distances (D2) values for the data revealed the highest intrinsic distance (D2) (6.97640) of cluster-1, followed by cluster-5 of the cluster-5 (5.10289), with the highest interclusters (D2) (46.55588) in the cluster-5 and the subcluster 3 (41.61871) (Table 5), respectively, with the Cabsicum annuum L. [56]. Between clusters 1 and 3, the maximum intercluster distance was found. Shayam [52] evaluated the 32 genotype features of the sunflower where the maximum interface between cluster-6 and cluster-7 was reported. The work assisted to identify the superior genotype among diverse genotypes.
Among the clusters with more than one population, the maximum intracluster distance included high yielding populations. Selection of parents from different clusters having wider intercluster distance and also showing good intracluster diversity for a set of economic traits have been useful in creating broad genetic base segregants after this hybridization [52,57,58]. Cluster means for different characters help in choosing the diverse parents for an initiating hybridization program which will eventually broaden the genetic base of the species. Further, there are chances of obtaining transgressive segregants with high heterotic effects [43,59,60,61,62]. Sardana et al. (1997) observed that cluster means and genotypic variation reveal interesting pictures about the nature of diversity and having maximum opportunities crossing between genotypes of different clusters and are expected to give maximum heterosis
The DMRT has been performed using the “agricolae” package of R studio software version 3.6.3. In this method, all the possible differences between the means of the genotypes for various parameters under study have been calculated and are given in Table 4.

5. Conclusions

This study highlighted the presence of considerable morphological variations in 30 populations of C. luteum collected from three different agro-climatic regions of Kashmir Himalaya. The majority of the populations were grouped in cluster-1, followed by cluster-5. The plant species possess sufficient genetic diversity among the populations. Cluster means of different characters help in choosing the diverse parents for hybridization. Keeping in view the ruthless overexploitation of endangered plant species, it is important to safeguard them. A hybridization program between identified diverse parents will help to broaden the genetic base and to identify the transgressive segregants with high heterotic segregants. Parent investigation has attempted to group the genotypes in different clusters which can serve as a base for the selection of parents. Cluster-5 is a promising one for better yield and yield attributing traits. The present study concluded that plant species possessed sufficient genetic diversity among the 30 populations, 7 populations from cluster-5 of the undisturbed area in Harwan, and consequently the populations from the same cluster can be multiplied for an initiating conservation and breeding program and can serve as a tool for the scientific community to evolve better contemporary varieties of C. luteum with profitable characters such as more yield of corms, etc. This will assist farmers, particularly the marginal farmers, to alleviate their income. Further studies are required to study the population’s C. luteum extensively to devise a package for strengthening the conservation measures of this endangered plant species.

Author Contributions

R.A.R. and H.B. drafted the experimental design and R.A.R., M.A.B., H.B., S.A.P. and A.F. performed the statistical analysis. R.A.R. also performed the lab experimental analysis, and R.A.R., A.F., H.M.A., K.R.H., H.N., M.A.B. and S.A.P. helped in data collection, data analysis, and the initial draft of the manuscript text. All authors have read and agreed to the published version of the manuscript.

Funding

This Project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under grant no. DF-752-130-1441. The authors, therefore, acknowledge with thanks DSR for technical and financial support.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data sharing not applicable to this article.

Acknowledgments

This Project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under grant no. DF-752-130-1441. The authors, therefore, acknowledge with thanks DSR for technical and financial support. The authors would like to acknowledge the Division of Environmental Sciences, FoH, SKUAST-K, J&K, India, for help provided in carrying out this study.

Conflicts of Interest

All authors agree to submit this manuscript in this periodical. There is no conflict of interest among the authors or with any external agency.

References

  1. Shinwari, Z.K.; Malik, S.; Karim, A.M.; Faisal, R.; Qaiser, M. Biological activities of commonly used medicinal plants from Ghazi Brotha, Attock district. Pak. J. Bot. 2015, 47, 113–120. [Google Scholar]
  2. Wani, M.Y.; Ganie, N.A.; Rather, R.A.; Rani, S.; Bhat, Z.A. Seri biodiversity: An important approach for improving quality of life. J. Ent. Zool. Stud. 2018, 6, 100–105. [Google Scholar]
  3. Yeshiwas, Y.; Tadele, E.; Tiruneh, W. The dynamics of medicinal plants utilization practice nexus its health and economic role in Ethiopia: A review paper. Int. J. Biodiv. Conserv. 2019, 11, 31–47. [Google Scholar]
  4. Howland, O. Fakes and chemicals: Indigenous medicine in contemporary Kenya and implications for health equity. Int. J. Equity Health 2020, 19, 1–2. [Google Scholar] [CrossRef]
  5. Kurnaz, M.L.; Kurnaz, I.A. Commercialization of medicinal bioeconomy resources and sustainability. Sust. Chem. Pharm. 2021, 22, 100484. [Google Scholar] [CrossRef]
  6. Hamilton, A.C. Medicinal plants, conservation and livelihoods. Biodiv. Conserv. 2004, 13, 1477–1517. [Google Scholar] [CrossRef]
  7. Balunas, M.J.; Kinghorn, A.D. Drug discovery from medicinal plants. Life Sci. 2005, 78, 431–441. [Google Scholar] [CrossRef]
  8. Aftab, T.; Hakeem, K.R. Medicinal and Aromatic Plants: Healthcare and Industrial Applications; Springer Nature: Basingstoke, UK, 2021. [Google Scholar]
  9. Rather, R.A.; Wani, A.W.; Mumtaz, S.; Padder, S.A.; Khan, A.H.; Almohana, A.I.; Almojil, S.F.; Alam, S.S.; Baba, T.R. Bioenergy Bioenergy: A foundation to environmental sustainability in a changing global climate scenario. J King Saud Univ. Sci. 2021, 34, 101734. [Google Scholar] [CrossRef]
  10. Ross, I.A. Medicinal Plants of the World (Volume 3): Chemical Constituents, Traditional and Modern Medicinal Uses; Humana Press Inc.: Totowa, NJ, USA, 2005; pp. 110–132. [Google Scholar]
  11. Malik, S.; Bano, H.; Rather, R.A.; Ahmad, S. Cloud seeding; Its prospects and concerns in the modern world–A review. Int. J. Pure Appl. Biosci. 2018, 6, 791–796. [Google Scholar] [CrossRef]
  12. Edison, L.K.; Kumar, S.P.; Pradeep, N.S. Educating biodiversity. In Bioresources and Bioprocess in Biotechnology; Springer: Singapore, 2017; pp. 143–165. [Google Scholar]
  13. Heywood, V.H.; Iriondo, J.M. Plant conservation: Old problems, new perspectives. Biol. Conserv. 2003, 113, 321–335. [Google Scholar] [CrossRef]
  14. Zerabruk, S.; Yirga, G. Traditional knowledge of medicinal plants in Gindeberet district, Western Ethiopia. S. Afr. J. Bot. 2012, 78, 165–169. [Google Scholar] [CrossRef]
  15. Ripple, W.J.; Abernethy, K.; Betts, M.G.; Chapron, G.; Dirzo, R.; Galetti, M.; Levi, T.; Lindsey, P.A.; Macdonald, D.W.; Machovina, B.; et al. Bushmeat hunting and extinction risk to the world’s mammals. R. Soc. Open Sci. 2016, 3, 160498. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Ma, S.; Khayatnezhad, M.; Minaeifar, A.A. Genetic diversity and relationships among Hypericum L. species by ISSR Markers: A high value medicinal plant from Northern of Iran. Caryologia 2021, 20, 97–107. [Google Scholar] [CrossRef]
  17. Rasool, R.; Fayaz, A.; Ul Shafiq, M.; Singh, H.; Ahmed, P. Land use land cover change in Kashmir Himalaya: Linking remote sensing with an indicator based DPSIR approach. Ecol. Indic. 2021, 125, 07447. [Google Scholar] [CrossRef]
  18. Shinwari, Z.K.; Gilani, S.S. Sustainable harvest of medicinal plants at Bulashbar Nullah, Astore (northern Pakistan). J. Ethnopharmacol. 2003, 84, 289–298. [Google Scholar] [CrossRef]
  19. Ahmed, S.N.; Ahmad, M.; Shinwari, Z.K.; Shinwari, S. Taxonomic, pharmacognostic and physicochemical authentication of Colchicum luteum Baker (Suranjantalkh) from its commercial adulterant. Pak. J. Bot. 2016, 48, 2039. [Google Scholar]
  20. Bhattachar, S.K. Hand Book of Medicinal Plants; Pointer Publishers: Jaipur, India, 1998; p. 110. [Google Scholar]
  21. Dulloo, M.E.; Estrada Carmona, N.; Rana, J.C.; Yadav, R.; Grazioli, F. Varietal Threat Index for Monitoring Crop Diversity on Farms in Five Agro-Ecological Regions in India. Diversity 2021, 11, 514. [Google Scholar] [CrossRef]
  22. Bano, H.; Lone, F.A.; Bhat, J.I.; Rather, R.A.; Malik, S.; Bhat, M.A. Hokersar wet land of Kashmir: Its utility and factors responsible for its degradation. Plant Arch. 2018, 18, 1905–1910. [Google Scholar]
  23. Goded, S.; Ekroos, J.; Azcárate, J.G.; Guitian, J.A.; Smith, H.G. Effects of organic farming on plant and butterfly functional diversity in mosaic landscapes. Agric. Ecosyst. Environ. 2019, 284, 106600. [Google Scholar] [CrossRef]
  24. Franklin, C.M.; Harper, K.A.; Clarke, M.J. Trends in studies of edge influence on vegetation at human-created and natural forest edges across time and space. Can. J. For. Res. 2021, 51, 274–282. [Google Scholar] [CrossRef]
  25. Capraro, H.G. In the Alkaloids; Brossi, A., Ed.; Academic Press: Cambridge, MA, USA, 1984; Volume 23, pp. 1–70. [Google Scholar]
  26. Ondra, P.; Valka, I.; Vicar, J.; Sütlüpinar, N.; Simasnek, V. Chromatographic determination of constituents of the genus Colchicum (Liliaceae). J. Chromatogr. 1995, 704, 351–356. [Google Scholar] [CrossRef]
  27. Karlik, E.; Deger, M.; Erdal, U.Z.; Gozukirmizi, N. Pioneering in vitro studies for callus formation of colchicum chalcedonicum azn. Trakya Univ. J. Nat. Sci. 2020, 21, 131–137. [Google Scholar]
  28. Massarotti, A.; Coluccia, A.; Silvestri, R.; Sorba, G.; Brancale, A. The tubulin colchicine domain: A molecular modeling perspective. ChemMedChem 2012, 7, 33–42. [Google Scholar] [CrossRef] [PubMed]
  29. Naaz, F.; Haider, M.R.; Shafi, S.; Yar, M.S. Anti-tubulin agents of natural origin: Targeting taxol, vinca, and colchicine binding domains. Eur. J. Med. Chem. 2019, 1, 310–331. [Google Scholar] [CrossRef] [PubMed]
  30. Blakeslee, A.F.; Avery, A.G. Methods of inducing doubling of chromosomes in plants: By treatment with colchicine. J. Hered. 1937, 28, 393–411. [Google Scholar] [CrossRef]
  31. Manzoor, A.; Ahmad, T.; Bashir, M.A.; Hafiz, I.A.; Silvestri, C. Studies on colchicine induced chromosome doubling for enhancement of quality traits in ornamental plants. Plants 2019, 8, 194. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Fatima, B.; Usman, M.; Khan, M.S.; Khan, I.A.; Khan, M.M. Identification of citrus polyploids using chromosome counts, morphological and SSR markers. Pak. J. Agric. Sci. 2015, 52, 1. [Google Scholar]
  33. Mehta, I.; Chaudhary, H.K.; Sharma, P.; Manoj, N.V.; Singh, K.; Sran, R.S. In vivo colchicine manipulation for enhancing DH production efficiency in Triticum durum using Imperata cylindrica-mediated chromosome elimination approach. Cereal Res. Commun. 2020, 48, 217–224. [Google Scholar] [CrossRef]
  34. Wechsler, B. Colchicine and Behcet’s disease: An efficacious treatment finally recognized! La Rev. De Med. Interne 2002, 23, 3. [Google Scholar]
  35. Anzengruber, F.; Graf, V.; Hafner, J.; Meienberger, N.; Guenova, E.; Dummer, R. Efficacy and safety of colchicine in inflammatory skin diseases: A retrospective, monocentric study in a large tertiary center. J. Dermatol. Treat. 2021, 2, 104–109. [Google Scholar] [CrossRef]
  36. Aisen, P.S.; Marin, D.B.; Brickman, A.M.; Santoro, J.; Fusco, M. Pilot tolerability studies of hydroxychloroquine and colchicine in Alzheimer disease. Alzheimer Dis. Assoc. Disord. 2001, 15, 96–101. [Google Scholar] [CrossRef] [PubMed]
  37. Ahmad, B.; Khan, H.; Bashir, S.; Ali, M. Antimicrobial bioassay of Colchicum luteum Baker. J. Enzym. Inhib. Med. Chem. 2006, 21, 765–769. [Google Scholar] [CrossRef] [PubMed]
  38. Yakoubi, R.; Megateli, S.; Sadok, T.H.; Gali, L. Photoprotective, antioxidant, anticholinesterase activities and phenolic contents of different Algerian Mentha pulegium extracts. Biocatal. Agric. Biotechnol. 2021, 34, 102038. [Google Scholar] [CrossRef]
  39. Leonard, B.J.; Wilkinson, J.F. Desacetylmethylcolchicine in treatment of myeloid leukaemia. Br. Med. J. 1955, 1, 874. [Google Scholar] [CrossRef] [Green Version]
  40. Yue, Q.X.; Liu, X.; Guo, D.A. Microtubule-binding natural products for cancer therapy. Planta Medica 2010, 76, 1037–1043. [Google Scholar] [CrossRef] [Green Version]
  41. Toplan, G.G.; Gürer, C.; Afife, M. Importance of Colchicum species in modern therapy and its significance in Turkey. J. Pharm. Istanb. 2016, 46, 129–144. [Google Scholar]
  42. Rao, C.R. Advanced Statistical Methods in Biometrical Research, 1st ed.; John Wiley and Sons: New York, NY, USA, 1952. [Google Scholar]
  43. Rao, T.P.; Gomathinayagam, P. Genetic diversity in semi dry rice bunder different environments. Madras Agric. J. 1997, 84, 314–317. [Google Scholar]
  44. Ahmad, T.; Borah, P. Genetic diversity in glutinous rice germplasm of Assam. Oryza 1999, 36, 74–75. [Google Scholar]
  45. Hukumchand, P.S. Genetic diversity analysis for quantitative traits in advanced breeding lines of sesame (Sesamum indicum L.). Int. J. Curr. Microbiol. Appl. Sci. 2019, 8, 2970–2979. [Google Scholar]
  46. Haralayya, D.; Salimath, P.M.; Aghora, T.S.; Adivappar, N.; Ganga, P.S. Genetic diversity analysis by D2 clustering of yield and yield attributing traits in French bean (Phaseolus vulgaris L.). J. Pharmacogn. Phytochem. 2017, 6, 1331–1335. [Google Scholar]
  47. Arunachalam, V. Genetic distance in plant. Ind. J. Genet. Plant Breed. 1981, 41, 226–236. [Google Scholar]
  48. Bano, H.; Rather, R.A.; Bhat, J.I.A.; Bhat, T.T.; Azad, H.; Bhat, S.A.; Hamid, F.; Bhat, M.A. Effect of pre-sowing treatments using phytohormones and other dormancy breaking chemicals on seed germination of Dioscorea deltoidea Wall. Ex Griseb.: An Endangered Medicinal Plant Species of North Western Himalaya. Ecol. Environ. Conserv. 2021, 27, 253–260. [Google Scholar]
  49. Wani, M.Y.; Rather, R.A.; Bashir, M.; Shafi, S.; Rani, S. Effect of zinc on the larval growth and quality cocoon parameters of silkworm (Bombyx mori L.): A review. Int. J. Fauna Biol. Stud. 2018, 5, 31–36. [Google Scholar]
  50. Wani, M.Y.; Mir, M.R.; Mehraj, S.; Rather, R.A.; Ganie, N.A.; Baqual, M.F.; Sahaf, K.A.; Hussain, A. Effect of different types of mulches on the germination and seedling growth of mulberry (Morus SP.). Int. J. Chem. Stud. 2018, 6, 1364. [Google Scholar]
  51. Lakshman, S.S.; Chakraborty, N.R.; Debnath, S.; Kant, A. Genetic variability, character association and divergence studies in sunflower (Helianthus annuus L.) for improvement in oil yield. Afr. J. Biol. Sci. 2021, 3, 129–145. [Google Scholar] [CrossRef]
  52. Shyam, C.; Chandrakar, P.K.; Rastogi, N.K.; Banjare, U. Evaluation of Genetic Divergence Analysis in Wheat for Yield and its Component Characters. Int. J. Agric. Environ. Biotechnol. 2018, 11, 829–834. [Google Scholar] [CrossRef]
  53. Hazra, P.; Sahu, P.K.; Roy, U.; Dutta, R.; Roy, T.; Chattopadhyay, A. Heterosis in relation to multivariate genetic divergence in brinjal (Solanum melongena). Ind. J. Agric. Sci. 2010, 80, 119–124. [Google Scholar]
  54. Kumar, S.; Rattan, P.; Sharma, J.P.; Gupta, R.K. D2 analysis for fruit yield and quality components in tomato (Lycopersicon esculentum Mill.). Ind. J. Plant Gen. Res. 2010, 23, 318–320. [Google Scholar]
  55. Padder, S.A.; Mansoor, S.; Bhat, S.A.; Baba, T.R.; Rather, R.A.; Wani, S.M.; Popescu, S.M.; Sofi, S.; Aziz, M.A.; Hefft, D.I.; et al. Bacterial endophyte community dynamics in apple (Malus domestica Borkh.) germplasm and their evaluation for scab management strategies. J. Fungi 2021, 7, 923. [Google Scholar] [CrossRef]
  56. Rahevar, P.M.; Patel, J.N.; Axatjoshi, S.; Gediya, L.N. Genetic diversity study in chilli (Capsicum annuum L.) using multivariate approaches. Electr. J. Plant Breed. 2021, 12, 314–324. [Google Scholar]
  57. Singh, A.K.; Singh, S.B.; Singh, S.M. Genetic divergence in scented and fine genotypes of rice (Oryza sativa L.). Ann. Agric. Res. 1996, 17, 163–166. [Google Scholar]
  58. Wani, M.Y.; Mehraj, S.; Rather, R.A.; Rani, S.; Hajam, O.A.; Ganie, N.A.; Mir, M.R.; Baqual, M.F.; Kamili, A.S. Systemic acquired resistance (SAR): A novel strategy for plant protection with reference to mulberry. Int. J. Chem. Stud. 2018, 2, 1184–1192. [Google Scholar]
  59. Qian, Y.W.; He, K.M. Utilization of exotic rice germlasm resources in Guang-dong province. Crop. Genet. Resour. 1991, 2, 36–37. [Google Scholar]
  60. Ibrahim, A.U.; Yadav, B.; Anusha, R.; Magashi, A.I. Heterosis studies in durum wheat (Triticum durum L.). J. Genet. Genom. Plant Breed. 2020, 4, 2–8. [Google Scholar]
  61. Rather, R.A.; Bano, H.; Padder, S.A.; Perveen, K.; Al Masoudi LM Alam, S.S.; Hong, S.H. Anthropogenic Impacts on Phytosociological Features and Soil Microbial Health of Colchicum luteum L. An Endangered Medicinal Plant of North Western Himalaya. Saudi J. Biol. Sci. 2022, 10. [Google Scholar] [CrossRef]
  62. Sardana, S.; Borthakur, D.N.; Lakhanpal, T.N. Genetic divergence in rice germplasm of Tripura. Oryza 1997, 34, 201–208. [Google Scholar]
Figure 1. Location map of the selected study areas in Kashmir, Himalaya, viz. Aharbal, Dhara Theed, and Harwan.
Figure 1. Location map of the selected study areas in Kashmir, Himalaya, viz. Aharbal, Dhara Theed, and Harwan.
Sustainability 14 01327 g001
Figure 2. Images of C. luteum during flowering stage from the three locations, (a) Harwan, (b) Dhara Theed, and (c) Aharbal, showing morphological variations from undisturbed to disturbed sites of Kashmir Himalaya.
Figure 2. Images of C. luteum during flowering stage from the three locations, (a) Harwan, (b) Dhara Theed, and (c) Aharbal, showing morphological variations from undisturbed to disturbed sites of Kashmir Himalaya.
Sustainability 14 01327 g002
Figure 3. Dendrogram depicting genetic relationship amongst 30 populations of Colchicum luteum of forest hills of Kashmir Himalaya.
Figure 3. Dendrogram depicting genetic relationship amongst 30 populations of Colchicum luteum of forest hills of Kashmir Himalaya.
Sustainability 14 01327 g003
Table 1. Classification of Colchicum luteum populations of forest hills of Kashmir Himalaya into different clusters on the basis of the genetic diversity.
Table 1. Classification of Colchicum luteum populations of forest hills of Kashmir Himalaya into different clusters on the basis of the genetic diversity.
Cluster No.Number of Populations/LandracesName of the Population/Landrace
Cluster-117P1, P2, P3, P4, P5, P6, P7, P8, P9, P10, P12, P13, P14, P15, P17, P19, P20
Cluster-21P16
Cluster-32P11, P18
Cluster-43P22, P23, P29
Cluster-57P21, P24, P25, P26, P27, P28, P30
Table 2. Cluster mean performance of morphological, yield and yield component traits of populations of C. luteum of forest hills of Kashmir Himalaya.
Table 2. Cluster mean performance of morphological, yield and yield component traits of populations of C. luteum of forest hills of Kashmir Himalaya.
Cluster No.Plant Height
Plant−1 (cm)
Leaf Area
Plant−1 (Sq.cm)
Leaf Length
Plant−1 (cm)
Leaf Width (cm) Plant−1No. of Seeds Plant−1Corm Length Plant−1 (cm)Corm Width Plant−1 (cm)Fresh Weight of Corm Plant−1 (g)Dry Weight of Corm Plant−1 (g)
114.32 d28.79 e111.66 e20.97 e311.41 e43.08 e52.05 e62.27 e71.58 ed
219.20 c35.52 d115.31 D21.16 d312.02 d43.06 d52.07 d63.82 c72.21 c8
313.01 e36.03 c112.18 c21.15 c324.03 c43.50 c52.40 c62.55 d71.62 d8
427.71 b44.50 b115.54 b21.20 b324.05 b44.30 b52.63 b65.37 b72.94 b8
528.46 a47.01 a119.51 a21.32 a326.85 a45.15 a53.17 a66.87 a74.81 a8
Subscripts indicate that within the column the values with same letters did not differ y Tukey’s test.
Table 3. The descriptive statistics for the 30 populations of Colchicum luteum.
Table 3. The descriptive statistics for the 30 populations of Colchicum luteum.
MinimumMaximumMeanStd. Deviation
Plant height10.2135.3019.87717.99874
Leaf area22.5054.5034.77869.08518
Leaf length8.4420.1013.90623.46952
Leaf width0.685.251.22320.80632
No. of seeds7.0028.0015.67866.63355
Corm length1.505.173.60340.95686
Corm width1.103.242.32390.59166
Fresh weight of corm1.108.453.71551.95302
Dry weight of corm0.676.322.33481.38865
Table 4. DMRT test validate the cluster value performance of 30 populations of Colchicum luteum.
Table 4. DMRT test validate the cluster value performance of 30 populations of Colchicum luteum.
Populations
Characters
/Codes
Plant HeightLeaf AreaLeaf LengthLeaf WidthNo. of SeedsCorm LengthCorm WidthFresh Weight of CormDry Weight of Corm
P(1)11.35 jkl24.25 op9.15 n0.71 b8.00 ef2.62 m1.43 bc1.08 f0.79 lm
P(2)10.70 kl23.00 pq8.35 o0.70 b8.00 ef1.77 o1.11 c1.72 ef1.16 kl
P(3)14.00 hi28.50 jk11.60 ij0.97 b11.00 def3.21 ghij2.05 abc2.43 def1.43 hijk
P(4)12.40 ijk26.50 lmn10.70 k0.89 b10.00 def3.20 hijk1.93 abc2.16 def1.09 klm
P(5)16.40 efg29.05 j11.95 i1.10 b14.00 d3.42 fg2.01 abc3.36 cde2.04 fg
P(6)12.35 ijkl24.50 op9.83 m0.81 b12.00 def2.46 m3.03 ab1.10 f0.67 m
P(7)13.30 ij27.50 kl10.75 k5.25 a10.00 def3.01 jk1.26 c3.03 de1.57 ghij
P(8)12.25 ijkl25.50mno10.14 lm0.88 b10.00 def2.72 lm1.79 abc2.91 de1.63 ghij
P(9)10.21i22.50 q8.45 o0.69 b7.00 f1.50 o1.11 c1.59 ef0.77 lm
P(10)16.80 efg29.75 ij11.70 i0.96 b14.00 d3.38 fg2.06 abc3.33 cde2.15 f
P(11)12.50 ijk27.00 klm12.00 i1.06 b20.00 b3.06 jk1.51 abc2.17 def1.27 jk
P(12)11.30 jkl25.00 no10.42 kl0.89 b8.00 ef2.20 n1.89 abc1.22 f0.72 lm
P(13)16.00 fgh33.00 g12.80 g1.01b12.00 def3.14 hijk2.26 abc1.95 def1.30 jk
P(14)12.05 ijkl26.50 lmn11.20 j0.89 b7.00 f2.94 kl1.91 abc2.33 def1.40 hijk
P(15)15.40 gh31.00 hi12.20 h1.09 b12.00 de3.21 ghij2.31abc3.47 cd1.78 fghi
P(16)18.55 e34.50 f14.80 de1.15 b20.00 b3.30 ghi2.30 abc3.52 cd2.21fg
P(17)16.70 efg32.50 gh13.75 f1.04 b12.00 def3.37 gh2.36 abc3.11 de1.87 fghi
P(18)12.90 ijk27.00 klm11.80 i1.13 b24.00 ab3.06 jk1.95 abc1.95 def1.53hijk
P(19)15.85 fgh32.00 gh12.90 g1.38 b10.00 def3.11ijk2.01 abc1.84 def1.45 ijk
P(20)18.25 ef33.00 g13.90 f1.26 b12.00 def3.63 f2.67 abc3.28 cde1.98 fgh
P(21)29.20 bc44.00 cd18.80 b1.17 b28.00 a5.12 ab3.16 ab6.16 b4.10 cd
P(22)26.70 d41.00 e14.75 e1.06 b12.00 def4.25 e2.26 abc4.78 bc2.14 fg
P(23)28.40 cd44.50 cd15.15 d1.10 b14.00 de4.11 e2.42 abc4.71 bc3.05 e
P(24)28.85 bc45.00 c19.95 a1.11 b28.00 a4.59 d3.05 ab5.31 b3.58 d
P(25)35.30 a48.00 b17.25 c1.26 b18.00 bc5.05 abc3.06 ab5.77 b3.99 c
P(26)30.95 b48.00 b19.20 b1.37 b20.00 b4.86 bc3.21 a8.12 a5.18 b
P(27)30.60 bc54.50 a19.00 b1.20 b26.00 a5.17 a3.25 a8.45 a6.32 a
P(28)31.35 b43.00 d19.30 b1.22 b28.00 a5.01 abc3.14 ab5.90 b3.52 d
P(29)28.90 bcd45.00 c15.00 de1.11 b18.00 cd4.10 e2.47 abc4.74 bc3.00 e
P(30)29.10 bc45.00 c20.10 a1.45 b28.00 ab4.78 cd2.90 abc5.72 b4.24 cd
Critical Value1.7261.4340.3451.2954.1520.190.1870.2820.346
SE(m)0.6080.5050.1220.4561.4630.0670.0660.0990.122
Means followed by the same letter are not significantly different at p = 0.05. DMRT = Duncan’s multiple range test. CD (p ≤ 0.05) = critical difference, SE (m) = standard error. P = plant populations from three sites (1–30) (plant populations of Colchicum luteum).
Table 5. Mean intra (diagonal) and inter (above diagonal) cluster distances (D2) among 30 populations of Colchicum luteum of forest hills of Kashmir Himalaya.
Table 5. Mean intra (diagonal) and inter (above diagonal) cluster distances (D2) among 30 populations of Colchicum luteum of forest hills of Kashmir Himalaya.
Cluster No.Cluster-1Cluster-2Cluster-3Cluster-4Cluster-5
Cluster 16.9764032.8360638.7711823.7461330.81826
Cluster 2-0.0000041.6187134.4419837.65239
Cluster 3--0.58275238.3500346.55588
Cluster 4---3.3220831.43609
Cluster 5----5.10289
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Rather, R.A.; Bano, H.; Firoz, A.; Mohammed Ali, H.; Bhat, M.A.; Padder, S.A.; Nafees, H.; Hakeem, K.R. The Assessment of Morphological Diversity of Colchicum luteum L., an Economically Important Threatened Medicinal Plant of Kashmir Himalaya. Sustainability 2022, 14, 1327. https://doi.org/10.3390/su14031327

AMA Style

Rather RA, Bano H, Firoz A, Mohammed Ali H, Bhat MA, Padder SA, Nafees H, Hakeem KR. The Assessment of Morphological Diversity of Colchicum luteum L., an Economically Important Threatened Medicinal Plant of Kashmir Himalaya. Sustainability. 2022; 14(3):1327. https://doi.org/10.3390/su14031327

Chicago/Turabian Style

Rather, Rauoof Ahmad, Haleema Bano, Ahmad Firoz, Hani Mohammed Ali, M. Ashraf Bhat, Shahid Ahmad Padder, Huda Nafees, and Khalid Rehman Hakeem. 2022. "The Assessment of Morphological Diversity of Colchicum luteum L., an Economically Important Threatened Medicinal Plant of Kashmir Himalaya" Sustainability 14, no. 3: 1327. https://doi.org/10.3390/su14031327

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop