1.1. INTRODUCTION

1.1.1. Plant and human life

“We come on this earth as guests of plants” is a monumental ancient aphorism. Since time immemorial, nature’s own supreme creation, man, has completely learnt to exploit plant resources and to make use of every bit of it as civilization developed. In fact from the start of the life to the last breath, almost every aspect of human life is deeply associated with plants for all his needs (Bown, 1995). The plants are valuable natural renewable resource, and the most important producers of natural products including food, fiber, wood, oil and important life saving drugs. 1.1.2. Herbal medicines

Herbal medicines have been used since the dawn of civilization to maintain health and to treat diseases. The World Health Organization estimates that about three quarters of the world’s population currently use herbs and other forms of traditional medicines to treat their diseases. Even as we commence the new century with its exiting prospects of gene therapy herbal medicines remains as one of the common forms of therapy available to most of the world’s population (Kuruvilla, 2002).

Even today, majority of the medicines are prepared from the plant and plant products. Major pharmaceutical industries depend on the plant products for the preparation of various medicines. In the present context, the plant based system of medicine is widely accepted and practiced not only in the Indian peninsula but also in the developing and developed countries of the world. Thus plant derived medicines have been the first line of defense in maintaining health and combating diseases world over (John, 1984; Veale, 1992). 1.1.3 Indian scenario

India’s biodiversity is unmatched due to the presence of 16 different agro- climatic zones, 10 vegetation zones, 25 biotic provinces and 426 biomes (habitats of specific species). With only 2.4% of the land area, India already accounts for 7-8% of the recorded species of the world. Over 46,000 species of plants and 81,000 species of animals have been recorded in the country so far by the Botanical Survey of India, and the Zoological Survey of India, respectively. India is an acknowledged centre of crop diversity, and harbors many wild relatives and breeds of domesticated animals (NBA, 2005).

1 The Indian subcontinent is a vast repository of medicinal plants that are used in traditional medical treatments (Ballabh and Chaurasia, 2007). Many westerners have long regarded the Indian systems of medicine as a rich source of knowledge (Subhose, 2005). In India, around 25,000 medicinal plants have been recorded (Dev, 1997; Joy et al., 1998; FRLHT, 2009); however traditional communities are using only 7,000 - 7,500 plants for curing different diseases (Nayar, 1987; Samy, et. al., 1998; Samy and Ignacimuthu, 2000; Kamboj, 2000). The medicinal plants are listed in various indigenous systems such as Siddha (600), Ayurveda (700), Amchi (600) and Unani (700). The Allopathy utilizes (30) plant species for ailments (Rabe and Staden, 1997; FRLHT, 2009). 1.1.4. Market potential of herbal medicines

Recent times have witnessed increased sale of herbal products in the international market. Herbal medicines continue to be a major market in U.S. pharmaceuticals and constitute a multi-billion dollar business. According to the WHO, present demand for medicinal plants annually, is about US $ 14 billion. Traditional Chinese Medicine (TCM) has made tremendous advances in terms of modern scientific research, and according to the latest studies it contributes 80 % of the annual turnover of the total herbal drug industry (FRLHT, 2009).

Figure 1.1. Ayurvedic Product Market

The current world market potential of herbal medicine is estimated to be over $

60 billion per year; about $ 80-250 million in Europe and USA (El and Karakava, 2004). The turn over of the medicinal plant-related trade in India is about Rs. 2300

2 crores (US $ 551 million). Exports of Ayurvedic medicines from India have reached a value of 100 million dollars a year. About 60% of this is crude herbs and about 30% is finished product shipped abroad for direct sales to consumers (Fig. 1.1). The remaining 10% is partially prepared products to be finished in the foreign countries (Singh, 2008). Approximately 1500 botanicals are sold as dietary supplements, formulations which are not subjected to ‘Food and Drug Administration’s (FDA) clinical toxicity test to assure their safety and efficacy. Improvement in modern herbal medicine and reflective of their growing demand for natural medicines, 73 % of the respondents to a consumer survey indicated that they would depend more on herbal medicine in the future (Bouldin et al., 1999)

1.1.5. Active herbal constituents

The herbs contain ingredients known as active principles (phytochemicals) synthesized and stored by them. Some of the main active constituents found in the herbs are listed in Table 1.1. Table 1.1. Active principles found in the herbs (Bown, 1995)

SL.NO TYPES PROPERTIES EFFECTS SOURCE

1. Acids Sour antiseptic, cleansing Citrus species bitter, alkaline addictives, affects Papaver 2. Alkaloids nitrogenous central nervous system, somniferun compounds toxic 3. Anthraquinones Bitter irritant, laxative Rheum palmatum appetizer and improves 4. Bitters Bitter Gentiana lutea digestion a smell of new- antibacterial, Melilotus 5. Coumarins mown hay anticoagulant officinalis often diuretic, antiseptic, Fagopyrum 6. Flavones bitter or sweet antispasmodic, and anti- esculentum inflammatory anti-spasmodic, Digitalis lanata, carcinogenic, sedative, 7. Glycosides bitter, acrid Prunus serotina; affecting heart rate and Allium sativum, respiration, antibiotic 8. Gums and bland, sticky or soothing and softening Althaea officinalis

3 Mucilage slimy acrid, Commiphora 9. Resins antiseptic, healing astringent, myrrha sweet, often anti-inflammatory Saponaria 10. Saponins stimulant, or diuretic; soapy in officinalis hormonal water checking bleeding and Potentilla erecta 11. Tannins often antiseptic discharges antiseptic, fungicidal, Thymus vulgaris 12. Volatile oils Aromatic irritant and stimulant

1.1.6. How herbal ingredients work Phytochemicals, the herbal ingredients, have a measurable effect on the body when given internally or applied externally. The herbal ingredients act right from the fundamental systems such as digestive, respiratory, circulatory etc., to the complex systems such as the endocrine and reproductive (Chaudhury, 1992; Zhang, 1998). They act as anti-diarrhoeals (Acacia arabica and A. catechu), laxatives (Aloe ferox, Cassia acatifolia), carminatives (Cinnamon zeylancium, Ocimum sanctum), spasmolytics (Datura spp.), anti-emetics (Mentha spp.) etc. The herbal products can cure almost all kinds of ailments. The stomach, liver, kidney, skin, lungs, heart, bone and blood disorders are the common ailments cured by medicinal plants since ancient times. But specific compositions are formulated and administered for acute ailments such as cancer and AIDS (Chaudhury, 1992).

Herbal medicines differ greatly from the compounds synthesized within them and isolated from them. The whole plant (and extracts derived from it) contains many ingredients that work together and produce a quite different effect (synergistic effect) from that of an isolated constituent given on higher dosage. An example is meadowsweet containing healing ingredients (e.g. salicylates), and also buffering substances that protect the mucous membrane from the corrosive effects of salicylates. The complex chemistry of whole plant appears to lower the risk of side-effects, whereas isolated compounds may be surprisingly toxic. This is especially true of volatile oils derived from herbs (Bown, 1995).

4 1.1.7. Standards of curative principles Chemical principles from natural sources have become much simpler and have contributed significantly to the development of new drugs from medicinal plants (Cox, 1990 & 1994). In the last century, roughly 121 pharmaceutical products have been discovered from the plant source (Anesini, 1993).

Majority of the pharmaceutical companies promoting herbal products have fixed standards (percentage of active principle). Since the efficacy of herbal product is based on percentage of active principle, it becomes mandatory that claimed percentage of active principle should be present in the finished product. Consumer laboratory in America issued several alerts addressing batch to batch variability of the active constituents in commercial preparations for herbal remedies like Hypericum perforatum, Ginkgo biloba and Silybum marianum (Singh, 2008).

Central Council of Indian Medicine (CCIM) has developed agricultural techniques for prioritized Ayurvedic plants and commercialized the technology. Ayurveda recommends use of fresh herbs rather than stored herbs. Although it is not practically possible to have all herbs on store, one has to depend on the market for buying. Shelf-life and transportation are other factors responsible for variation of active principles in the herbs purchased from the market (Singh, 2008). Good Agricultural Practices (GAP) need to be standardized for enhancing quality of finished herbal products. With the introduction of organic farming and transgenic crops, it will be possible to get standardized raw material for therapeutically active finished products.

1.1.8. Environmental factors and plants

All organisms must live in some sort of environment. Their physiological processes, which are essential for the maintenance of life, are dependant upon environmental conditions and substances. In an ecosystem the species and its environment are taken as a complex “whole”. Environment, in itself is a complex of factors acting, reacting and interacting with the organism complex. Thus, the organisms and their environment are wedded together and are in a state of constant flux. The relationship between organisms and their environment is very complex (Clarke, 1954; Miller, 2003).

Plants are found all over the earth, growing in all kinds of situations. They are found even on the tops of high mountains that are covered with snow for the most of

5 the time in year and in the hot springs; they grow on the moist banks of the rivers and in the dry sand of deserts or even on hard rocks; they are found also growing in the crevices of walls and some lie perched on the trunks of trees, but the species that inhabit this earth vary from place to place.

The variation in the vegetation and species of different places are primarily due to the differences in the environment. Each component of the environment is known as an ecological factor. (Odum, 1971; Harborne, 1993). Ecological factors are many and diverse: - 1.Climatic factors (aerial environment), 2. Edaphic factors (soil conditions), 3. Physiographic factors (topographic discrepancies), and 4. Biotic factors (life form interactions). These ecological factors either work through one another or react together, as for example, the change in physiographic conditions at a place may bring about a change in local climate that, in turn, may affect the soil and soil nutrient based inter-plant competition resulting in the individual plant variations. These variations are felt right from the biochemical constituents, through the structure (anatomy) and functions (physiology) to the genetic makeup (Miller, 2003).

Each herb has a specific location and season for its maximum productivity which were strictly followed and practiced for its exploitation in the ancient days (Santillo, 2005). But modern herbal cultivation should also focus upon the abiotic factor under GAP for the production of most effective herbals. This would sustain the economy and health of the consumer. The present work has thus been designed to determine the influence of the abiotic factors viz. (climate and soil) on the popular and widely distributed, curative herb, “the giant milkweed” (Calotropis gigantea (L.) R.Br.).

1.2. AIM

The present work aims to understand the effects of different seasons and locations on the composition (phytochemical), structure (anatomical) and curative property (anticancer potential) of the popular curative herb, “the giant milkweed, (Calotropis gigantea (L.) R.Br).

6 1.3. OBJECTIVES ™ To record the climatic variations in the four seasons and five locations ™ To analyse the soil variations in the different seasons and locations ™ To assess the influence of the seasons and locations on the phytochemistry ™ To determine the effect of various seasons and locations on the anatomy ™ To evaluate the impact of the seasons and locations on the anticancer potential of C. gigantea.

1.4. SCOPE OF THE WORK The best location and season with the maximum productivity and growth of this plant would be evaluated through this study. The plant can be exploited effectively for its curative and economical potentials at that particular location and season.

1.5. SOCIAL RELEVANCE AND USEFULNESS OF THE WORK

™ This study would help to reveal the basic relationship between the plants and its environment, forming a fundamental database.

™ This current observation would enhance the sustainable production of natural products in an economic and ecofriendly way.

™ Uses of natural products are well established in the area of pharmaceuticals and pesticide industries. So, this study would help to maximize the uses of natural products in these industries.

™ This study may help the farmers to attain the maximum profits through proper GAP in terms of seasonal and soil managements for cultivating the medicinal plants.

™ Wastelands can be transformed into a productive land through the cultivation of

Calotropis over years. This would also bring down the CO2 content of the atmosphere mitigating global warming.

™ This study also may trigger the production of natural products from other plants and organisms.

™ Production of herbal medicines, biopesticides, organic fertilizers and ecofriendly fuel from Calotropis would reduce the pollution load in the biosphere and support the sustainable and harmonious way of life.

7 2.1. PLANT SELECTED FOR THIS STUDY

Plate.2.1. Calotropis gigantea (L.)R.Br.

[1. Flowering branch; 2. gynostegium in longitudinal branch; 3. pollinium; 4. follicle.]

From pre-historic times to the modern era in many parts of the world and India, plants, animals and other natural objects have profound influence on culture and civilization of man. Since the beginning of civilization, human beings have worshiped plants and such plants are conserved as a genetic resource and used as food, fodder, fibre, fertilizer, fuel, febrifuge and in every other way. Calotropis gigantea is one such plant (Royal Botanic Gardens, Kew, 1900). In ancient ayurvedic medicine the plant Calotropis gigantea is known as “sweta Arka” and Caotropis procera as “Raktha Arka”. Both of them are often similar in their botanical aspects and also have similar pharmacological effects (Mueen Ahamed et al., 2005). The systematic position, vernacular names, vegetative characters of the plant are given in the following Tables (2.1 -3). In the present study henceforth this plant is referred to as Calotropis.

8 Table 2.1. Systematic position of the selected plant

Kingdom Plantae Order: Gentianales Family: Asclepiadaceae Subfamily: Asclepiadoideae Genus: Calotropis Species: Gigantean

Table 2.2. Vernacular names

(Sanskrit) Arka,Ganarupa, Mandara, Vasuka, Svetapushpa, Sadapushpa, India Alarka, Pratapass, (Hindi) Aak, Madar, (Kannada) Ekka, (Tamil and Malayalam) Erukku, (Telugu) Jilledi Puvvu Malaysia Remiga, rembega, kemengu. English Crown flower, giant Indian milkweed. Indonesia Bidhuri (Sundanese, Madurese), sidaguri (Javanese), rubik (Aceh) Philippines Kapal-kapal (Tagalog). Laos Kok may, dok kap, dok hak. Thailand Po thuean, paan thuean (northern), rak (central). Vietnam B[oot]ng b[oot]ng, l[as] hen, nam t[it] b[at]. French Faux arbre de soie, mercure vegetal.

Table 2.3. Vegetative characters

Habit: Shrub or a small tree up to 2.5 m (max.6m) height. Simple, branched, woody at base and covered with a fissured; corky bark; branches Root: somewhat succulent and densely white tomentose; early glabrescent. All parts of the plant exude white latex when cut or broken. Opposite-decussate, simple, sub sessile, extipulate; blade-oblong obovate to broadly obovate, 5-30X2.5-15.5 cm, apex abruptly and shortly acuminate to apiculate, base Leaves: cordate, margins entire, succulent, white tomentose when young, later glabrescent and glacouse. Bracteate, complete, bisexual, actinomorphic, pentamerous, hypogynous, pedicellate, Flowers: pedicel 1-3 cm long. Floral Inflorescence: A dense, multiflowered, umbellate, peducled cymes, arising from the Characteristics: nodes and appearing axillary or terminal Sepal 5, Polysepalous, 5 lobed, shortly united at the base, glabrescent, quincuncial Calyx: aestivation. Corolla: Petals five, gamopetalous, five lobed, twisted aestivation. Androcium : Stamens five, gynandrous, anther dithecous, coherent. Bicarpellary, apocarpus, styles are united at their apex, peltate stigma with five lateral Gynoecium: stigmatic surfaces. Anthers adnate to the stigma forming a gynostegium. Fruit: A simple, fleshy, inflated, subglobose to obliquely ovoid follicle up to 10 cm or more in diameter. Many, small, flat, obovate, 6x5 mm, compressed with silky white pappus, 3 cm or Seeds: more long. (Gamble, 1923 and 1935; Lindley, 1985; Mueen Ahamed et al., 2005)

9 2.1.1. Ecology and Distribution

2.1.1.1. Natural habitat Calotropis is drought resistant, salt tolerant to a relatively high degree, grows wild up to 900 meters (msl) throughout the country (Sastry and Kavathekar, 1990) and. prefers disturbed sandy soils with mean annual rainfall: 300-400 mm. Through its wind and animal dispersed seeds, it quickly becomes established as a weed along degraded roadsides, lagoon edges and in overgrazed native pastures. It has a preference for and is often dominant in areas of abandoned cultivation especially disturbed sandy soils and low rainfall. It is assumed to be an indicator of over cultivation (Mueen Ahamed et al., 2005).

2.1.1.2. The chief features of the plant are:-

• The plant grows very well in a variety of soils and different environmental conditions • It does not require cultivation practices • It is one of the few plants not consumed by grazing animals (Sharma, 2003). • It thrives on poor soils particularly where overgrazing has removed competition from native grasses ( Smith, 2002) • Some times this plant is the only survivor in some areas, where nothing else grows (Sastry and Kavathekar, 1990; Oudhia, 2001). • It is drought tolerant and the pioneer vegetation in desert soil (Oudhia, 1997). • Presence of latex, extensively branched root system and thick leaves with waxy coverage are the xerophytic adaptations (Mueen Ahamed, 2005). • Hence, it is distributed in tropical and subtropical area of the world and throughout India. (Sastry and Kavathekar, 1990).

2.1.1.3. Geographic distribution It is a native of India, China and Malaysia and distributed in the following countries: Afghanistan, Algeria, Burkina Faso, Cameroon, Chad, Cote d’Ivoire, Democratic Republic of Congo, Egypt, Eritrea, Ethiopia, Gambia, Ghana, guinea- Bissau, India, Iran. Iraq, Israel, Kenya, Kuwait, Lebanon, Libyan, Arab Jamahiriya, Mali, Mauritania, morocco, Mozambique, Myanmar, Nepal, Niger, Nigeria, Oman, Pakistan, Saudi Arabia, Senegal, sierra Leone, Somalia, Sudan, Syrian Arab Republic, Tanzania, Thailand, Uganda, United Arab emirates, Vietnam, Yemen, Republic of

10 Zimbabwe, Exotic: Antigua and Barbuda, Argentina, Australia, Bahmas, Barbados, Bolivia, Brazil, chile, Colombia, Cuba, Dominica, Dominican Republic, Ecuador, French Guina, Grenada, Guadeloupe, Guatemala, Guyana, Haiti, Honduras, Jamaica, Martinique, Mexico, Montserrat, Netherlands Antilles, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, St Kitts and Nevis, St Lucia, St Vincent, and the Grenadines, Surinam, Trinidad and Tobago, Uruguay, Venezuela and Virgin Islands (US) (Mueen Ahmed et al., 2005).

2.1.1.4. Propagation and management The seeds freely float in the air and natural regeneration is very common. Vegetative propagation through stem and root cuttings is very useful in large scale multiplication of the superior genotypes.

Calotropis has been cultivated in South America and on the Caribbean Islands for the production of fibres at a spacing of 1-1.5m.When cultivated annualy yields of up to 500kg/ha are expected. A single harvest per season is preferable to a double or triple harvest; a single harvest would result in a net saving of energy input both on the form and in the processing plant. It is well suited for intensive energy farming in arid or semi-arid regions where frost is not a limiting factor (Mueen Ahamed et al., 2005).

2.1.2. Phytochemistry of Calotropis

The previous workers have reported many phytochemical constituents in the various parts of Calotropis gigantea especially in the leaves. Usharin, gigantin, calcium oxalate, alpha and beta-calotropeol, beta-amyrin., fatty acids (both saturated and unsaturated), hydrocarbons, acetates and the benzoates, a mixture of tetracyclic triterpene compounds, terols, giganteol and giganteol are also found to be present (Bhaskara Rama Murti and Seshadri, 1943, 1944, 1945a, b). Cardenolide calotropin (Kupchan et al., 1964), α-amyrin, β-amyrin, taraxasterol, β- sitosterol, α-amyrin methylbutazone, β-amyrin methylbutazone, α-amyrin acetate, β-amyrin acetate, taraxasteryl acetate, lupeol acetate B, gigantursenyl acetate A, gigantursenyl acetate B (Sen et al., 1992; Habib et al., 2007), flavonol glycoside, akundarol, uscharidin, calotropin, frugoside, calotroposides A to G (Kshirsagar, et al., 2010) are responsible for many of its activities. The following cardenolides are also described in the literature: calactin, calotoxin, calotropagenin, proceroside, syriogenine, uscharidin, uscharin, uzarigenin and voruscharin (Crout et al., 1963 a,b; Brischweiler et al., 1969a,

11 b; Lardon et al., 1970; Singh and Rastogi, 1972; Seiber et al., 1982). Other compounds found are benzoylisolineolon and benzoyllineolone (Chandler et al., 1968).

Flavonoids (Chopra et al., 1956; Singh and Rastogi, 1972), triterpenoids (Pal and Sinha, 1980), alkaloids, steroids, glycosides, saponins, terpenes, enzymes, alcohol, resin, fatty acids and esters of calotropeols (Seiber et al., 1982), volatile long chain fatty acids (Sen, et al., 1992), glycosides and proteases (Kitagawa et al., 1992) have been isolated from the various parts of the plant Calotropis gigantea.

Cleverson et al., (1996) worked out the laticifer fluid of Calotropis, and found to have strong proteolytic activity, having the enzyme cysteine proteinase and aspartic proteinase. Due to the presence of these components, the plants are resistant to phytopathogens and insects mainly in leaves where the latex circulates abundantly. The milky latex of the plant is rich in lupeol, calotropin, calotoxin, and uscharidin, the latex protein. Sharma and Sharma (1999) screened the major phytochemicals viz. alkaloids, carbohydrates, glycosides, phenolic compounds/tannins, proteins and amino acids, flavonoids, saponins, sterols, acid compounds, resins in flower, bud, root of Calotropis (Table 2.4).

Table 2.4. Phytochemical components in Calotropis

Class of Plant Part S.No. Tests performed Compounds Flower Bud Root Dragendorff’s test, 1. Alkaloids + + + Mayers test Molish test, 2. Carbohydrates + + + Fehling test 3. Glycosides + + + Keller killiani test Phenolic 4. compounds/tanni + + + Ferric chloride test ns Proteins and 5. + + + Xantho protein test amino acids 6. Flavonoids + + + Ammonia test With water 7. Saponins + + + With sodium bicarbonate Liebermann-Burchard test 8. Sterols + + + Salkowski reaction Hesse’sreaction With sodium bicarbonate 9. Acid compounds + + + With litmus paper With double distilled water 10. Resins + + + With acetone and

12 conc.Hydrochloric acid

11. Peroxides - - - Potassium Iodide test

12. Polyuronoids - - - Haemotoxylin test

(Sharma and Sharma, 1999).

2.1.3. Economic values of Calotropis

2.1.3.1. Medicinal properties

Different parts of the plant have immense potential to cure various diseases and disorders (Table 2.5). It is used in various polyherbal preparations (The Wealth of India, 1992; Tenpe, et al., 2007). There are more than hundred activities described in detail by Duke (1992). Calotropis is used alone and sometimes with other plants to cure variety of human and animals ailments. Table 2.5. Medicinal properties

S.No. Medicinal properties References Kirtikar and Basu, 1935; Shah and Joshi, 1971;Jain et al., 1973; Chaudhuri et al., 1975; Bhalla et al., 1. Asthma 1982; Saxena, 1986; Caius,1986; Das, 1996; Snigdha Roy 2008 2. Abortifacient Saha et al., 1961; Patel and Patel, 2004 Nadkarni,1976;Allen, 1994; Aminuddin Analgesic,anticonvulsant, 3. Girach,2001; Argal and Pathak ,2006; Pathak and anxiolytic and sedative Argal, 2007; Argal and Diwivedi, 2010 4. Antifertility and emmenagogue Patel and Patel, 2004

5. Anti-inflammatory activity Pardesi et al., 2008; Das et al., 2009 6. Antinociceptive activity Soares et al., 2005 7. Anthelmintic activity Zafar Iqbal et al., 2005 8. Anti cancer activity Choedon et al., 2006 Anti dote for Scorpion stings Hutt and Houghtom ,1998; Narumon., 2005; 9. and insect bites Kadhirvel et al., 2010 Dash, 1991; Jayaweera, 1980–1982; Dassanayake 10. Anti tumor activity , 1980–2000; Pal and Jain, 1998; Taylor et al., 1996 Satyavati et al., 1976; Dash, 1991; Jayaweera, 1980–1982; Dassanayake , 1980–2000; The Anti-diarrheal and anti 11. Wealth of India, 1992; Pal and Jain, 1998; Taylor dyssentry activities et al., 1996;Caius, 1986; Das, 1996;Havagiray et al., 2004 ; Chitme et al.,2004;Chitme et al., 2005 Valsaraj et al., 1997; Samy & Ignacimuthu, 2000; 12. Antimicrobial activity Rao, 2000; Ashraful et al., 2008

13 13. Antiviral activity Locher et al., 1995 14. Anxiety and pain Boericke, 2001; Sharma, 2001 15. CNS activity Argal and Pathak, 2006 16. Cold Caius, 1986; Das, 1996 Kirtikar and Basu, 1975; Shiddamallayya. et al., 17. Expectorant 2010 18. Cytostatic activity Smit et al., 1995 Ayoub and Kingston, 1981; Smit et al., 1995; 19. Cytotoxic activity Locher et al., 1995; Kupchan et al., 1964; Oliveira et al., 2007 20. Dyspepsia Blair, (1907).; Ghosh 1988 Caius, 1986; Das. 1996 ; Kirtikar KR and Basu, 21. Eczema 1998; Chitme et al., 2004; Chitme et al., 2005 22. Elephantiasis Caius, 1986; Das, 1996 23. Epilepsy Jain et al., 2001; Pathak and Argal, 2006 Elephantiasis of the legs and 24. Kirtikar and Basu, 1975 scrotum 25. Expectorant Kirtikar and Basu, 1935 26. Fever Caius, 1986; Das. 1996 27. Fibrinolytic activities Rajesh et al., 2005 Free radical Scavenging 28 Mueen Ahmed et al., 2003 activity 29. Healing the ulcers and blotches Blair, 1907; Ghosh 1988; Ferrington 1990 (Goat) Motility of mature 30. Haemonchus contortus of goat Sharma et al., 1971 origin

31. Indigestion Kirtikar and Basu, 1975 32. Kesarayer disease Kumar and Vallikannan, 2009 Shah and Joshi, 1971; Jain et al., 1973; Chaudhuri et al., 1975; Jayaweera, 1980–1982; Bhalla et al., 33. Leprosy 1982; Saxena, 1986; Dash, 1991; Dassanayake , 1980 –2000; Taylor et al., 1996; Pal and Jain, 1998; Kirtikar and Basu, 1998; Chitme et al., 2004 Liver injuries as well as on Jayaweera, 1980–1982; Dash, 1991; Dassanayake , 34. oxidative stress, 1980 –2000; Pal and Jain, 1998; Taylor et al., Hepatoprotective 1996; Lodhi et al., 2009 35. Mental disorders Upadhyaya et al., 1994; Sivastava et al., 2007 36. Migrine Prusti and Behera, 2007 Nasal ulcer, laxative, rheumatoid arthritis, bronchial 37. Narumon, 2005 asthma, diabetes mellitus, nervous disorders 38. Piles Shiddamallaya et al., 2010

39. Pregnancy interceptive activity Srivastava et al., 2007

14 40. Purgative Baldwin, 1979 41. Removing anemia Blair, 1907; Ghosh 1988; Ferrington 1990 42. Rheumatism Srivastava et al., 2007 43. Ringworm of the scalp Kirtikar and Basu, 1975 secondary syphilis, gonorrhea, 44. ascites, helminthiasis, and Kirtikar and Basu, 1998; Chitme et al., 2004 jaundice Dash, 1991; Jayaweera, 1980–1982; Dassanayake, 45. Skin diseases 1980 –2000; Taylor et al., 1996; Pal and Jain, 1998 46. Spleen disorder Shiddamallayya, et al., 2010 Swelling and inflammation in 47. Manandhar, 1990 sprain 48. TB and leprosy Kirtikar and Basu, 1935; Grange and Davey, 1990 49. Uterus stimulant Saha et al., 1961; Chopra et al., 1965 50. Vermicidal activity Garg and Atal, 1963 (Vertenery) Camel diseases Sharma et al., 1971; Antoine-Moussiaux et al., 51. treatment 2007 Dash, 1991; Jayaweera, 1980 – 1982; 52. Worms Dassanayake , 1980–2000; Taylor et al., 1996; Pal and Jain, 1998 Jayaweera, 1980–1982; Dassanayake, 1980–2000; 53. Wounds and ulcers Caius, 1986; Dash, 1991; Das, 1996; Taylor et al., 1996; Pal and Jain, 1998 Biswas and Mukherjee, 2003; Havagiray et al., 2004 ; Chitme et al.,2004; Rajesh et al., 2005; 54. Wound healing activity Snigdha Roy, 2008; Pradeep et al., 2009; Nalwaya et al., 2009

2.1.3.2. Various other uses

Calotropis is a highly potential plant resource. The various uses of this plant are given in the Table (2.6). Table 2.6. Other uses of Calotropis gigantea

S.No. Activities Parts Used References Ashok and Vaidya, 1998, 1. Arrow poison Latex sharma, 2003 2. Biocidal activity Latex Jain et al., 1989 Biogas and substitute for 3. Whole plant Shilpker et al., 2007 petroleum products 4. Brewing and to curdle milk The bark and latex Pereira and Seabrook, 1996 5. Cleansing water Leaves and its Saps Pereira and Seabrook, 1996

6. Energy plantation Whole Plant Pereira and Seabrook, 1996 Bark, and the silky 7. Fibers Nart et al., 1984 hairs from its seeds

15 Young pods, 8. Fodder Senescing leaves and Orwa et al., 2009 flowers Ganapathy and Narayanasamy., Fungicidal, insecticidal 1993; Haque et al., 2000; 9. Whole Plant properties Ashraful et al.,2009; Usha et al., 2009 10. Isomers Accumulation Whole Plant Abhilash and Singh (2008) 11. Larvicide Whole Plant Girdhar et al., 1984 12. Latex or rubber Latex Mueen Ahamed, 2005 Singh et. al. 1996, Mueen 13. Leather tanning Whole Plant Ahamed, 2005 Manna like sugar and liquor 14. Sap Pereira and Seabrook, 1996 (bar) Pereira and Seabrook, 1996; 15. Manure, Pest repellant Twigs and Leaves Rathood, 1998; TNAU, 2008 Hussein and El- Wakil, 1996; 16. Molluscicidal activity Whole plant Bakry, 2009 17. Indicators of Heavy Metals Leaf and Stem Samantaray et al., 1997 Whole plant Neraliya and Srivastava, 1996 18. Mosquitocidal potential Petroleum ether–

acetone extract, Poly aromatic hydrocarbon 19. Leaves Sharma & Tripathi, 2009 contamination 20. Pollution Monitoring Whole Plant Singh et al., 1995 21. Reclaiming salt lands Whole plant Pereira and Seabrook, 1996 Plantation of 22. To cool the air around homes Pereira and Seabrook, 1996 Calotropis 23. Substitute for paper Leaves Pereira and Seabrook, 1996

2.2. STUDY AREA

Seasonal and locational influences on the phytochemistry, the anatomy and the anticancer potential of Calotropis gigantea are studied during Sep 2007 - Aug 2008 in Tamil Nadu, India.

2.2.1. General status of Tamil Nadu

2.2.1.1 Climate

The climate of Tamil Nadu is tropical in nature with little variation in summer and winter temperatures. While April - June is the hottest summer period with the temperature rising up to the 40 ºC mark, November - February is the coolest winter period with temperature hovering around 20 ºC, making the climate quite pleasant. Surprisingly, Tamil Nadu gets all its rains from the northeast monsoon between

16 October and December, when the rest of Tamil Nadu remains dry. The average of annual rainfalls in Tamil Nadu ranges between 635 and 1,905 mm a year. During summer (April - June), the coastal regions of Tamil Nadu become uncomfortably warm and humid, but the cool sea breezes in the afternoon make the nights cool and pleasant. In this period the enchanting hill stations of the state provide much needed respite from heat and humidity of the plains.

2.2.1.2 Geography

India, which lies between 804’ N and 3706’ N latitude and 6807’ E and 97025' E longitude, has a total geographic area of 32, 87,782 km2. This is only 2.42 % of the total geographic area of the world. The Tamil Nadu is the southern most state of India, surrounded by Andhra Pradesh in the North, Karnataka and Kerala in the west, Indian Ocean in the South and Bay of Bengal in the East. Cape Comorin or Kanyakumari, the southern most point of India lies in the state of Tamil Nadu. The Eastern and Western Ghats (mountain ranges) run along the eastern and western borders of the state and meet at Sittlingi in Dharmapuri district, Tamil Nadu. The Western Ghats, bordering Tamil Nadu, breaks only at two points - Palakkad (25 km wide gap) and Shencottah, which connect the state with Karnataka and Kerala. The state of Tamil Nadu roughly extends between the 8° 04' N latitude (Cape Comorin) to 13.35" and the 78° 0' E to 80.20" longitude (Plate 2.2).

17

Plate 2.2. Location of Tamil Nadu in India Map (Govt. India, 2003)

2.3. STUDY DESIGN

2.3.1. Seasonal study

Table 2.7. Seasons and months Season Code Seasons Months S1. Northeast monsoon September 2007 - November 2007

S2. Pre-summer season December 2007 - February 2008

S3. Summer season March 2008 - May 2008

S4. Southwest monsoon June2008 - August 2008

The seasons chosen for the study are found in Table (2.7). The location, selected for the seasonal study, Thirumalai samudram village is situated on the northern side of Tiruchirappalli -Thanjavur highways (NH-47) between Sengipatti and

18 Vallam village. It was ensured that the sampling land was not under cultivation for the past five years.

2.3.2. Locational study

The samples for the locational studies were collected during August 2008 from five different places in Tamil Nadu, India (Table.2.8. and Plate 2.3 and 2.4).

Table 2.8. Details of the study area

Station Nature Of the Geographical Altitude Name of the Station Code Location extension [Meters (msl)]

Coastal tract Sothavilai beach, 8º07’28. 88” N, L1 (Plate 2.4.L1) Kanyakumari 77º29’38. 96”E 6

Anaimalayan patti 9º45’25. 71” N, L2 Hilly terrain (Pate 600 Hills, Theni 77 º21’49. 67”E 2.4 L2) Riverine zone Cauveri River bed - 10º50’07.19” N, L3 70 (Plate 2.4. L3) Tiruchirappalli 78º42’51.18” E Terrestrial -rural Thirumalai 10º43’42. 43”N, L4 stretch Samudram, Near 78 79º00’44. 60”E (Plate 2.4.L4) Tiruchirappalli Terrestrial -urban Ponmalai locoshed, 10º47’34.46”N, L5 area 85 Tiruchirappalli. 78º42’37.00”E (Plate 2.4.L5)

19

Plate 2.3. Study locations

20

L1 – Kanyakumari (Coastal tract)

L2 Anaimalayan Patti (Hilly terrain) Plate 2.4 (L1 & L2) Study locations

21 • Riverine area

L3- Tiruchirappalli (Riverine zone)

L4 – Thirumalaisamudram (Terrestrial – rural stretch) L5 – Tiruchirappalli (Terrestrial –urban area)

Plate 2.4 (L3, L4 & L5) Study locations

22 2.4. COLLECTION OF SOIL SAMPLES AND PLANT MATERIALS

The soil samples were collected to analyse the soil nutrients and heavy metal status; the aerial parts of the plant parts were collected to study the phytochemistry and anticancer potential. The stem, the root and the leaves from the third nodes are collected to study the anatomy in the four different seasons and five different locations. The sampling was performed between 3 pm to 4 pm on the respective days of sampling. The plant was vouched by the Department of Botany, Bishop Heber College, Tiruchirappalli. Tamil Nadu, India and a type specimen was submitted to the herbarium.

The specific data sources, methodology, and instruments for climatology, edaphic elements, phytochemistry, plant anatomy and anticancer potential are enumerated in the respective chapters.

2.5. STATISTICAL ANALYSIS

One sample‘t’ test is performed for the seasonal, soil, phytochemical, anatomical and invitro cytotoxicity data from the pooled sample. The Karl Pearson correlation analysis is performed where ever necessary to know the relationship among these parameters. All statistical analysis is performed by using SPSS (version15) package.

23 3.1. INTRODUCTION Weather and climate have a profound influence over the life on earth. They are a part of the daily activities of human beings and are essential for food production, health and the well-being. In common parlance, the notion’s “weather” and “climate” are loosely defined. The “weather”, is characterised by the short term physical properties such as temperature, wind, precipitation, clouds and other atmospheric elements at a particular place and time. “Climate” refers to the general pattern of the sum of atmospheric elements over a long period of time of a location. Though environmentally weather and climate are multifactorial, average temperature and precipitation are the two main factors determining a region’s climate (Miller, 2003). Variability is the fundamental property of the climate system. Climate varies from place to place, depending on the latitude, the distance from the sea, vegetation, presence or absence of mountains or other geographical factors. Climate varies also in time; from season to season, year-to-year, decade to decade or on much longer time-scales, era to era such as the ice ages. Statistically significant variations of the mean state of the climate or of its variability, typically persisting for decades or longer are referred to as “climate change”. The Glossary of IPCC gives definitions of these important and central notions of “climate variability” and “climate change” (IPCC, 1994) The traditional knowledge of weather and climate focuses on those variables that affect daily life most directly: average, maximum and minimum temperature, wind near the surface of the earth, precipitation in its various forms, humidity, cloud type and amount and solar radiation. These are the variables observed hourly by a large number of weather stations around the globe (IPCC, 1994). Multiple environmental changes and temporal variation patterns are the two important issues related to biological processes, which are believed to play a key role over the biological effects of climate change. The multiple environmental changes are of central importance because the climatic change will include simultaneous changes in at least three factors: relative proportion of gases, temperature and water availability. Each of these factors directly affect biological processes and there is increasing evidence that the combined effects of these changes will be very complex and include strong interactions between factors, and

24 that the combined effects will be difficult to predict from the effect of the individual factors. Changes in temporal variation patterns including extension of the growing season, increased frequency of freeze/thaw cycles, number of frost free days, frequency of extreme weather events etc. are believed to play significant roles over the biological functions as compared to just average changes in the affecting factors (Diodat and Bellocchi, 2010). Seasonal variations influence the plants, animals and their habitats. Climatic variation in factors such as temperature, humidity and air pressure probably underlies these observations. It has been shown also to predispose the development of a number of different diseases in animals and human beings (Khot, et al., 1984; Archimandritis, et al., 1995; Yen, et al., 1996; Boulay, et al., 2001; Gao, et al., 2001; Fink, et al., 2002; Stewart, et al., 2002 In plants variations in the productivity, seed quality, chemical composition, etc. also are noticed by many authors (Jensen, et al., 1996). In the present study efforts are made to understand the variations in the meteorological elements of the selected areas for one year (Sep2007-Aug 2008).

3.2. DATA COLLECTION AND PROCESSING

The following monthly meteorological facts for the respective study areas during the study period were collected from the Regional Meteorological Department Chennai, Tamil Nadu, India. According to the need the annual and seasonal averages and ranges were derived from the monthly facts.

1. Seasonal temperature facts a) Mean maximum temperature b) Mean minimum temperature c) Highest temperature d) Lowest temperature 2. Relative humidity records at Morning (08.30 a.m.) and evening (05.30 p.m.)

a) Mean relative humidity b) Highest relative humidity c) Lowest relative humidity

25 3. Monthly and seasonal rainfall information

a) Total rainfall b) Heaviest rainfall c) Numbers of rainy days 4. Monthly and seasonal mean wind speed

3.3. RESULTS AND DISCUSSIONS

3.3.1. Seasonal variability studies

The study area for the seasonal variability, exhibits a significant variations in the meteorological elements such as temperature, rainfall, humidity, and wind speed.

3.3.1.1. Variations in temperature

3.3.1.1a. Seasonal mean temperature variation

The seasonal mean maximum temperature differs by 5.6 °C ranging between 30.7 °C in pre-summer to 36.3 °C in southwest monsoon. The seasonal mean minimum temperature fluctuates by 7 °C ranging between 21.3 °C in pre-summer to 26 °C in southwest monsoon (Figure 3.1). These temperature variations are statistically significant among seasons (Table 3.1).

40 35.6 36.3 35 33.4 S1 30.7 30 26 24.4 25.1 25 S2 21.3 20 ° C

15 S3

10

5 S4

0 Mean Maximum Mean Minimum Temperature Temperature

Figure 3.1. Condition of mean temperature in seasons

26 Table 3.1. Seasonal mean temperature variation (One sample t - test)

Meteorological Std. N Mean t Statistical inference elements Deviation Mean maximum 4 34.00 2.5232 26.950 P<0.001 significant temperature Mean minimum 4 24.20 2.0412 23.711 P<0.001 Significant temperature DF= 3

3.3.1.1b. Variation in the seasonal highest and lowest temperatures A difference of 7 °C is recorded among the highest temperatures of the four seasons (Table 3.2). The hottest day of the study period (40.7 °C) occurred in the month of May of the summer season. The lowest temperature of the year occurred in the month of January (17.3 °C). The lowest temperature of other three seasons is given in this Table (3.2). These variations are statistically significant (Table 3.3). Table 3.2. Status of highest and lowest temperature in seasons Lowest Seasons Highest temperature °C Temperature °C Northeast 38.7 (October) 18.4 (November) monsoon Pre-summer 33.7 (February) 17.3 (January) Summer 40.7 (May) 21.8 (March) Southwest 39.2 (July) 20.6 (August) monsoon

The hottest period of the year is the southwest monsoon season (the mean maximum and minimum temperatures are 36.3 °C and 26 °C (Figure 3.1). Though the hottest season of the year is southwest monsoon the hottest day of the year occurred in summer (The maximum temperature is 40.7 °C and the minimum temperature is 21.8 °C). As per the data the ascending order of the mean maximum temperature among the four seasons is:

Pre-summer (30.7 °C) < northeast monsoon (33.4 °C) < summer (35.6 °C) < southwest monsoon (36.3 °C).

According to the report published by the Tamil Nadu government authority (2008), for this region variations of this sort is the regular pattern.

27 Table 3.3. Variations among the seasonal highest and lowest temperatures (one sample t - test)

Std. Meteorological Statistical N Mean Deviatio t elements inference n 25.06 Highest temperature 4 38.075 3.0380 P<0.001significant 6 27.34 Lowest temperature 4 18.825 1.3769 P<0.001Significant 4 DF=3

3.3.1.2. Relative humidity The relative humidity of an air-water mixture is defined as the ratio of the partial pressure of water vapor in the mixture to the saturated vapor pressure of water at the prescribed temperature. Relative humidity is normally expressed as percentage (Perry and Green, 2007). Relative humidity is an important metric used in forecasting weather. Humidity indicates the likelihood of precipitation, dew, or fog. The most humid cities on earth are generally located closer to the equator, near coastal regions. Cities in South and Southeast Asia are among the most humid, such as Kolkata and those in Kerala in India.

3.3.1.2.1. Variations in the highest relative humidity

Figure (3.2) shows the occurrence of the highest relative humidity (R.H.) at morning (08.30. a.m.) and evening (05.30 p.m.) during the study period. The highest relative humidity of the year with the difference of 6% is ranging between 92% (southwest monsoon) and 98 % (northeast monsoon and the pre-summer). The highest relative humidity recorded in the evening is ranging between 77% (southwest monsoon) to 98 % (northeast monsoon and pre-summer).The variations in the highest relative humidity among stations are statistically significant (Table 3.4).

28 98 98 98 92 98 98 96 100 77 90 S1 80 70 60 S2 50 40

Hi ghest R. H. (%) S3 30 20 10 S4 0 Morning Evening

Figure 3.2. Condition of highest relative humidity in seasons

Table 3.4. Seasonal variations in the highest relative humidity (One sample t - test)

Meteorological N Mean Std. t Statistical inference elements Deviation Highest R.H.morning 4 96.50 3.000 64.333 P<0.001significant Highest R.H.evening 4 92.25 10.210 18.070 P<0.001 Significant DF= 3

3.3.1.2.2. Lowest relative humidity The lowest R.H.morning ranges between 47 % (summer) - 66 % (pre- summer) with a difference of 19%, while the evening R.H. is with the difference of 13%. It ranges between 19% in summer and 32%in southwest monsoon (Figure 3.3).The variation is statistically significant (Table 3.5).

66 70 60 S1 60 52 47 50 S2 40 32 29 30 23 S3 19 Lowest R.H. (%) 20

10 S4 0 Morning Evening

Figure 3.3. Condition of lowest relative humidity in seasons

29 Table 3.5. Seasonal variations in the lowest relative humidity (One sample t - test)

Meteorological Std. N Mean t Statistical inference elements Deviation Lowest R.H. morning 4 56.25 8.421 13.359 P<0.001 significant Lowest R.H. evening 4 25.75 5.852 8.800 P<0.001 significant DF= 3

3.3.1.2.3. Seasonal mean relative humidity The seasonal mean relative humidity records a difference of 16% with the highest occurrence of 83.7 % (pre-summer) and the lowest occurrence of 67.7 % (southwest monsoon) in the morning. In evening the difference is 11.0% between 57.3% (northeast monsoon) and 46.3% (summer and southwest monsoon) (Figure 3.4). These variations are statistically significant (Table 3.6).

83.7 90 74.3 73 80 67.7 S1 70 57.3 56.3 60 46.3 46.3 S2 50

40

Mean R. H. ( %) 30 S3

20

10 S4 0 Morning Evening

Figure 3.4. Seasonal mean relative humidity conditions

Table 3.6. Seasonal variations among mean relative humidity (One sample t - test)

Meteorological Std. N Mean t Statistical inference elements Deviation Mean R.H.morning 4 74.675 6.6595 22.427 P<0.001 significant Mean R.H. evening 4 51.550 6.0759 16.969 P<0.001 Significant DF= 3

The relationship of rainfall and the R.H are directly proportional (Perry and Green, 2007). In general the rainy seasons of southwest and northeast monsoons expect heavy rains and a relatively high humidity after rainfall. The present

30 observation of the occurrence of low R.H. in southwest monsoon might be due to the occurrence of high temperature in this month. In pre-summer the temperatures is moderate and the mean maximum and minimum temperatures are 30.7 ° C and 21.3° C respectively. The heaviest rainfall of the year (172.6 mm) occurred in this pre- summer season. In contrast the mean highest maximum and minimum temperatures (36.3 ° C and 26° C) occurred in the southwest monsoon. These may be the reasons for the occurrence of highest R.H. in the pre-summer season and the lowest R.H. in northeast monsoon.

3.3.1.3.1. Seasonal rainfall pattern The seasonal rainfall pattern shows a distinct difference (Figure 3.5). The numbers of total rainy days recorded is 10 in pre-summer, summer and southwest monsoon. But in the northeast monsoon the total numbers of rainy days are 15. The highest total rainfall (384.7 mm) occurred in the southwest monsoon. The lowest total rainfall (220 mm) is recorded during summer. The difference is about 160mm. These variations are statistically significant (Table 3.7). The range of heaviest rainfall (within 24hrs) is between 172.6mm (pre- summer) and 53.8 mm (summer). The difference between the highest and lowest is 167.9mm.

450 16 15 384.7 400 14 Total rainfall

350 329.9 12 300 255.2 1010 10 10 250 220 Heaviest rainfall in 8 24 HRS(mm) 200 172.6 156 6 150 4 100 Number of rainy 60.2 53.8 days (2.5mm and 50 2 abve)

0 0 S1 S2 S3 S4

Figure 3.5. Seasonal rainfall pattern

31 Table 3.7. Seasonal variations in the rainfall (One sample t - test) Meteorological Std. Statistical N Mean t elements Deviation inference Total rainfall 4 297.45 74.0476 8.034 P<0.05 significant Heaviest rain 4 110.65 62.3740 3.548 P<0.05 significant Numbers of rainy days 4 11.25 2.500 9.000 P<0.05 significant DF= 3

3.3.1.4.1. Mean wind speed – Seasonal The seasonal mean wind speed is recorded between 7.3-11.7kmph. The highest mean wind speed is 11.7 kmph (southwest monsoon) and the lowest is 7.3 kmph (pre-summer) (Figure 3.6). These variations are statistically significant (Table 3.8).

14

11.7 12

) 10

p h 8.3 7.7 8 7.3

6 ind speed (km

W 4

2

0 S1 S2 S3 S4

Figure 3.6. Mean wind speed – Seasonal

Table 3.8. Seasonal variations among the wind speed (One sample t - test)

Meteorological Std. Statistical N Mean t elements Deviation inference Mean wind speed 4 8.750 2.0091 8.710 P<0.05 significant DF=3

3.3.1.5. Inference

In the pre-summer the temperature is considerably low and the R.H. and heaviest rainfall of the year are considerably high. The total rainfall of the southwest monsoon is higher than the other seasons. Indian climate is essentially monsoonal

32 (Chang, 1967; Hatwar, et al., 2005; Krishna kumar, et al., 2006) Physiography brings about changes in this general pattern. In South India the temperature is usually high throughout the year (Goswami, 1998, 2000).

The overall meteorological elements of the study area show that a distinct high temperature, low humidity, and low rainfall prevail in the summer. Since the southwest monsoon season is following the summer, the temperature is still high. The monsoon winds arising from Arabian sea produces heavy rains in South India with a lesser and discontinuous pattern in the interior (Tiruchirappalli). The range of variation in the maximum temperature is 3-4 °C higher than the range of variation in the minimum temperature. These feature show that the study area falls under dry tropical climate where the temperature is a dominant factor compared to other climatic elements.

3.3.2. Locational variability studies

The variations in maximum and minimum temperatures; relative humidity (morning and evening); number of rainy days and the amount of rainfall and wind speed show significant similarities and dissimilarities with in the year (September 2007-August 2008), among specific seasonal (southwest monsoon) and specific month (August) which is considered for the locational study (Chapter 2.3.2).

3.3.2.1. Temperature variation

3.3.2.1.1. Temperature variations among the locations during the sampling year

The annual temperature variations are depicted in Figure (3.7a and 3.7b). The temperature facts include the annual mean maximum temperature, mean minimum temperature, highest, and lowest temperatures of the selected study areas. The mean maximum temperature is ranging between 31.2 °C (L1) - 34 °C (L2- L5). The mean minimum temperature falls between 23.8 °C (L1) and 24.2 °C (L3-L5). The highest temperature fluctuates between 34.5 °C (L1- in July) to 40.7 °C (L3-L5 in May) and the lowest temperature is between 16.2 °C (L2 in December) to 19 °C (L1 in December). Thus, the study locations are significantly varying by the annual temperature elements (Table 3.9).

33 40

35 34 34 31.2 Mean 30 maximum temperature 24.2 25 23.8 24.1

C 20 º

15 Mean 10 minimum temperature 5

0 L1 L2 L3-L5

Figure 3.7a. Mean maximum and minimum temperature during the sampling year

45 40.7 39.2 40 34.5 35 Maximum temperatu 30 re

25 C

° 19 20 16.2 17.3 15 Minimum 10 temperatu re 5

0 L1 L2 L3-L5

Figure 3.7b.Highest and lowest temperature during the sampling year

Table 3.9. Temperature variation during the sampling year (One sample t - test) Meteorological Std. N Mean t Statistical inference elements Deviation Annual mean maximum 5 33.440 1.2522 59.714 P<0.001 significant temperature Annual mean minimum 5 24.100 .1732 311.130 P<0.001 significant temperature Annual highest 5 39.160 2.6848 32.615 P<0.001 significant temperature Annual lowest 5 17.42 1.003 38.817 P<0.001 significant temperature DF = 4

34 3.3.2.1.2. Temperature variations among the study locations during the sampling season

The mean maximum and minimum temperatures of the sampling season (southwest monsoon) fluctuates significantly (Figure 3.8a). The mean maximum temperature ranges between 31 °C (L1) and 36.5 °C (L2) with the difference of 5.5 °C. The mean minimum temperature is 23.7 °C (L1) to 26 °C (L3- L5). The highest and the lowest temperature of the sampling season (Figure 3.8b) are varying between 34.5 °C (L1 in July) and 39.2 °C (L3-L5 in July) and 20.6 °C (L3-L5 in August ) and 21.4 °C (L1 and L4 in July and August) respectively. The fluctuation in the highest temperature considerably high (4.7 °C) and in the lowest temperature it is very meager (0.8 °C). These variations are statistically significant (Table 3.10).

40 36.5 36.3 35 31 Mean 30 maximum 25.8 26 temperature 25 23.7

C 20 °

15 Mean 10 minimum temperature 5

0 L1 L2 L3-L5

Figure 3.8a. Mean maximum and minimum temperature during the sampling season

45 39 39.2 40 34.5 35 Highest temperat 30 ure

25 21.4 21.4 C 20.6 ° 20

15 Lowest 10 temperat ure 5

0 L1 L2 L3-L5

Figure 3.8b. Highest and lowest temperature during the sampling season

35 Table 3.10. Temperature variations among the locations during the sampling season (One sample t - test)

Meteorological Std. N Mean t Statistical inference elements Deviation Seasonal mean 5 35.28 2.394 32.950 P<0.01significant maximum temperature Seasonal mean minimum 5 25.500 1.0100 56.458 P<0.01significant temperature Seasonal highest 5 38.220 2.0813 41.061 P<0.01significant temperature Seasonal lowest 5 20.920 .4382 106.757 P<0.01significant temperature DF = 4

3.3.2.1.3. Temperature variations in the sampling month (August) The mean maximum temperature is 30.7 °C (L1) to 35.7(L2) and the mean minimum temperature is 23.5° C in L1 and 25.4 °C in L3 - L5 (Figure 3.9a). The highest temperature of the respective sampling month (Figure 3.9b) occurs between 33.1(L1) to 38.6 °C (L2) and the lowest temperature is between 20.6 °C (L3 - L5) and 21.9 °C (L1).These variations are statistically significant (Table 3.11).

40 35.7 34.9 35 30.7 Mean 30 maximum temperature 25.1 25.4 25 23.5

C 20 º

15 Mean 10 minimum temperature 5

0 123

Figure 3.9a. Mean maximum and minimum temperature during the sampling month

36 45 38.6 40 37.1 35 33.1 Highest temperature 30

25 21.9 C 21.4 20.6 ° 20

15 Lowest 10 temperature 5

0 L1 L2 L3-L5

Figure 3.9b. Highest and lowest temperature during the sampling month

Table 3.11. Temperature variations in the sampling month (One sample t - test)

Meteorological Std. N Mean t Statistical inference elements Deviation Monthly mean 5 34.220 1.9980 38.297 P<0.01 significant maximum Monthly mean minimum 5 24.960 .8264 67.533 P<0.01 significant temperature Monthly highest 5 36.600 2.0616 39.698 P<0.01 significant temperature Monthly lowest 5 21.020 .6017 78.120 P<0.01 significant temperature DF= 4

3.3.2.2. R.H.variations

3.3.2.2.1 Annual R.H. variation

The annual mean of R.H. morning (08.30 a.m.) is between 68.2 % in L2 and 75.7 % in L1 and in the evening (05.30 p.m.) it is between 51.6 % in L3- L5 and 73.6 % in L1 (Figure 3.10a).

The highest R.H. morning ranges between 98 % in L1 (March), L3 –L5 (100% L2 (in December) is 95 % in L1 (December and March) and - 98% (October and December). In evening the R.H. ranges between 95% in L1 (December) to 98% in L3-L5 (Oct and December) (Figure 3.10b).The annual lowest R.H.morning ranges between 39% (L2-May) to 47% (L3-L5 in May) and in evening the R.H. range is 19% (L3-L5 in March) - 31% (L2 in January) (Figure 3.10c).

37

80 75.7 73.6 74.7 70 68.2

60 Morning 51.7 51.6 50 )

40

R.H. (% 30

20 Evening

10

0 L1 L2 L3-L5

Figure 3.10a. Mean R.H. during the sampling year

101 100 100

99 98 98 98 morning 98 ) 97 96 96

R.H. ( % 95 95

94 Evening

93

92 L1 L2 L3-L5

Figure 3.10b Highest R.H. during the sampling year

50 47 45 43 39 40 Morning 35 30 31

) 30

. (% 25 19 R. H 20 15 Evening 10 5 0 L1 L2 L3-L5

Figure 3.10c Lowest R.H. during the sampling year

38 Statistically significant variations among the locations (Table 3.12) in the high, lowest and mean R.H. are evident.

Table 3.12. Variations in annual R.H. (%) (One sample t - test)

Meteorological Std. N Mean t Statistical inference elements Deviation Annual mean 5 73.600 3.0496 53.966 P<0.01 significant R.H.morning Annual mean 5 56.020 9.8276 12.746 P<0.01 significant R.H.evening Annual highest 5 98.40 .894 246.000 P<0.01 significant R.H.morning Annual highest 5 97.00 1.414 153.370 P<0.01 significant R.H.evening Annual lowest 5 44.60 3.578 27.875 P<0.01 significant R.H. morning Annual lowest 5 23.60 6.309 8.365 P<0.01 significant R.H. evening DF= 4

3.3.2.2.2. R.H. variations in the sampling season

The mean R.H. of the sampling season (southwest monsoon) shows a variation between 60% (L2) to 77.7 % (L1) in morning and 46.3% (L3-L5) to 78.7 % (L1) in evening (Figure 3.11a).The highest R.H. (95%) occurs in L1in evening (Figure 3.11b). The lowest R.H. recorded is 48% (L2) in morning and 31 % (L2) in the evening (Figure 3.11c). The variations in the R.H. facts among the locations are statistically significant (Table 3.13).

100 95 95 90 92 77.7 78.7 90 86 80 80 77 67.7 80 70 Morning 60 70 60 Morning 60 48.3 50 46.3 50 40 R.H. ( % ( ) R.H. 40 R.H. ( % ) ( R.H. 30 30 20 Evening Evening 20 10 10

0 0 L1 L2 L3-L5 L1 L2 L3-L5

Figure 3.11a. Mean R.H. Figure 3.11b. Highest R.H.

39 70 64

60 54 52 50 48 Morning

40 31 32 30 R.H. ( ) %

20 Evening 10

0 L1 L2 L3-L5

Figure 3.11c. Lowest R.H. during the sampling season

Table 3.13. Seasonal variations in the R.H. (%) (One sample t - test)

Meteorological Std. N Mean t Statistical inference elements Deviation Seasonal mean R.H. 5 68.160 6.2895 24.232 P<0.01 significant morning Seasonal mean R.H. 5 53.180 14.2924 8.320 P<0.01 significant evening Seasonal highest R.H. 5 91.40 3.286 62.190 P<0.01 significant morning Seasonal highest R.H. 5 81.20 7.823 23.209 P<0.01 significant evening Seasonal lowest 5 53.60 6.066 19.757 P<0.01 significant R.H.morning Seasonal lowest R.H. 5 36.20 9.960 8.127 P<0.01 significant evening DF = 4

3.3.2.2.3. R.H. variation in the sampling month

The mean R.H. variations in the morning and evening present a picture of high fluctuations among the locations studied. The mean R.H.morning ranges between 65% (L2) and 77 % (L1) and the difference is 12% (Figure 3.12a).The highest R.H. recorded in L1 at morning is 95% and evening is 92 % (Figure 3.12b). The lowest R.H recorded in L2 in the morning and the evening is 50% and 30% respectively (Figure 3.12c).These variations are statistically significant among the studied locations (Table 3.14).

40 90 82 80 77 72 70 65 Morning 60 52

) 50 50

40 R.H. ( % 30

20 Evening

10

0 L1 L2 L3-L5

Figure 3.12a. Mean R.H. during the sampling month

100 95 92 92 90 86 80 80 71 Morning 70

) 60 50

R.H. ( % 40 30 Evening 20 10 0 L1 L2 L3-L5

Figure 3.12b. Highest R.H. during the sampling month

70 65 61 60 54 50 Morning 50

) 40 33 31 30 R.H. ( %

20 Evening 10

0 L1 L2 L3-L5

Figure 3.12c. Lowest R.H. during the sampling month

41 Table 3.14. Variations in the R.H. (%) during the Sampling month (One sample t - test) Meteorological Std. Statistical N Mean t elements Deviation inference Monthly mean 5 71.60 4.278 37.426 P<0.01 significant R.H.morning Monthly mean 5 56.80 14.114 8.999 P<0.01 significant R.H.evening Monthly highest R.H. 5 91.40 3.286 62.190 P<0.01 significant morning Monthly highest R.H. 5 77.00 9.247 18.621 P<0.01 significant evening Monthly lowest R.H. 5 59.60 5.639 23.633 P<0.01 significant morning Monthly lowest R.H. 5 36.80 9.654 8.524 P<0.01 significant evening DF= 4

3.3.2.3. Rain fall pattern

Within the study period of one year the number of rainy days is highest in L1 (54 days); closely followed by L2 (53 days) (Table 3.15). In all the other locations (L3, L4 and L5) it is 45 days. Though the number of rainy days is low, the annual total rainfall is high in L3 - L5. It is due to the occurrence of the heaviest rainfall (172.6mm) in this area. The descending order of the total rainfall is as follows

L3=L4=L5= (1189.8mm) >L2 (1086mm)>L1 (788.7mm).

For the locational study sampling is done in the southwest monsoon season Within this season the number of rainy days is highest (17 days) in L1; the highest total rainfall (384 mm) and the heaviest rainfall (156 mm) occurred in L3 - L5. In the sampling month the number of rainy days is higher in L2 (8 days) and identical in all other locations (6 days). The heaviest rainfall of the season i.e. 156 mm (L3 - L5) is received in this month. The total rainfall is considerably high in L2 (248.8mm) during this month. The ascending order of the total rainfall during this month is as follows:

39.7 mm (L1) < 198.6mm (L3, L4 and L5) < 248.8 mm (L2).

All these variations are statistically significant (Table 3.16).

42 Table 3.15. Rainfall information of the studied locations

Elements of Rain fall L1 L2 L3 – L5

Annual total rainfall(mm) 788.7 1086.9 1189.8

Numbers of rainy days(2.5mm and above)In the year 54 53 45

Annual heaviest rainfall in 24 HRS(mm) 81.1 85.8 172.6

Seasonal total rainfall(mm) 113.8 288.6 384.7

Seasonal heaviest rainfall in 24 HRS(mm) 38.6 60.3 156 Numbers of rainy days in northeast monsoon season 17 13 10 (2.5mm and above) August- total rainfall(mm) 39.7 248.8 198.6

August-heaviest rainfall in 24 HRS(mm) 20 60.3 156

August-numbers of rainy days(2.5mm and above) 6 8 6

Table 3.16. Variations in the rainfall (One sample t - test)

Meteorological Std. Statistical N Mean t elements Deviation inference Annual total rainfall 1089 173.6854 5 6.302 P<0.01 significant (mm) Annual heaviest rainfall 5 136.940 48.8577 6.267 P<0.01 significant in 24 HRS(mm) Annual - numbers of rainy days(2.5mm and 5 48.40 4.669 23.179 P<0.01 significant above) Seasonal total 5 311.300 117.9875 5.900 P<0.05 significant rainfall(mm) Seasonal heaviest 5 113.380 58.8620 4.307 P<0.05 significant rainfall in 24 HRS(mm) Numbers of rainy days per season (2.5mm and 5 12.00 3.082 8.706 P<0.01 significant above) Monthly total 5 176.860 79.6965 4.962 P<0.01 significant rainfall(mm) Monthly heaviest rainfall 5 109.66 65.034 3.770 P<0.05 significant 24HRS(mm) Number of rainy days in this month[2.5MM and 5 6.40 .894 16.000 P<0.01 significant above] DF = 4

43 3.3.2.4. Variations in the mean wind speed The wind speed data shows that the highest mean annual wind speed (14.3kmph), the highest mean seasonal (northeast monsoon) wind speed (15.3 kmph) and the highest mean Monthly (August) wind speed (14.0 kmph) occurred in L1; and the lowest wind speed is occurred in L2 (Figure 3.13). These variations are statistically significant (Table 3.17).

18

16

14 Annual mean wind speed(kmph) 12 p h ) p h

10 Seasonal mean 8 windspeed(kmph) 6 ind speed (k m

W 4 Monthly mean wind 2 speed(kmph)

0 L1 L2 L3-L5 Figure 3.13. Mean wind speed

Table 3.17. Variations in the wind speed (One sample t - test) Meteorological Std. N Mean t Statistical inference elements Deviation Annual mean wind speed 5 9.100 3.3838 6.013 P<0.05 significant (kmph) Seasonal Mean wind 11.08 3.739251 3.302 5 P<0.05 significant speed (kmph) Monthly mean wind 5 10.40 3.286 7.076 P<0.05 significant speed (kmph) DF= 3

3.3.2.5. Inference

The comparison of meteorological elements in the study locations show that in coastal tract (L1) inspite of low temperature and low total rainfall , the humidity is highest which is due to more number of rainy days and high wind velocity. The hilly terrain (L2) shows a moderate temperature, R.H. and rainfall. The wind speed is very low. The ( riverine (L3), terrestrial-rural (L4) and terrestrial-urban (L5) locations show high temperature, flexible R.H. and heavy rainfall and highest total rainfall, with a less number of rainy days and moderate wind speed. These clearly picture out the climatic variation among the location of the study.

44 4.1. INTRODUCTION

Soil may be defined as the large friable unconsolidated top layer of the earth’s crust. It supports the growth of plants, which is the principal source of man’s food and clothing. Soil is a complex mixture of both living (bacteria, viruses, fungi, protozoan, nematodes and other living organisms) and nonliving elements (mixture of mineral particles of varying sizes, humus, water, and air). The soil consists of four major components (Figure 4.1) - air (20-30%), water (20-30%), minerals (45%) and organic matter (5%) (Brady, 1995).

Figure 4.1 Major components of soil

4.1.1. Topsoil and subsoil

The top soil or surface soil is the major zone of root development for plants. It contains many nutrients available to plants and supplies much of the water necessary for their growth. The subsoil is comprised of those soil layers underneath the topsoil. It is not seen from the surface and is not commonly distributed by the tillage, but there are few land uses that are not influenced by sub soil characteristics. Certainly crop production is affected by root penetration in to the subsoil and by the reservoir of moisture and nutrients it represents. Likewise downward movement of

45 rain water is sometimes impeded by impervious subsoil. (Brady, 1995; Akinrinde, 2004).

4.1.2. Soil and plant relations

In the plant world, soil serves as the primary transfer medium. A soil may be regarded as fertile when it supplies adequate plant nutrients. On the basis of the quantity requirements by plant, the minerals are classified into the macronutrients (C, H, O, N, P, K Ca, S and Mg) and the micronutrients (Fe, Bo, Mn, Zn, Mo and Cl). The absorption of essential elements is determined not only by the availability of soil-held nutrients but also by their proximity to the root surface. If roots penetrate the soil, they come in contact with the soil solution and absorption of the minerals occur from the soil solution as well as those held on the surface of the soil colloids. Most plants thrive best in an environment that balances water and nutrients. Plant and microbial processes coupled with soil processes ensure the effective use of essential elements for crop production (Wilkinson, 2000).

The effects of fertiliser application on type of vegetation, biomass, species composition and nutrient content are well documented, (e.g. Tallowin et al., 1990; Mountford et al., 1993; Smith et al., 1996; Schellberg et al., 1999). Typically, biomass increased, grasses grew at the expense of forbs, sown grasses increased substituting the indigenous species, species richness declined and the nutrient content of vegetation increased (Bakker, 1989; Olff and Bakker, 1991; Wilkinson, 2000). Application of composts and organic amendments often alters soil organic matter and nutrient cycling (Chang et al., 1991; Eghball, 2002), and increases soil nutrient levels (Chantigny et al., 2002; Gregorich et al., 1998). Immediate increases are often attributed to the presence of soluble materials in the composts (Chantigny et al., 2002).

4.1.3. Soil and environmental relationship

Soil is a habitat for plants. Climatic influences over soils were once upon a time largely ignored. Hillgard (1906) called attention to the relationships among climate, vegetation, rock materials and the kind of soils that developed. He conceived soils not merely as media for plant growth but as dynamic entities subject to study and classification in their field setting. Dokuchaev (1883) a Russian scientist

46 investigated unique horizontal layers in the soil associated with different combination of climate, vegetation and the underlying soil materials. The same sequence of layers was found in a widely separated geographical area that had similar climate and vegetation. The concept of soils as natural bodies was well developed by the Russian scientists.

4.1.4. Seasonal and locational variations in the soil

Plant zonation and species distribution in coastal tracts are influenced by the edaphic properties and the ionic composition in saline soils (Ukpong 1997; AIvarez- Rogel et al. 2001; Hoyer et al. 2004; Deegan et al. 2005). Biotic factors such as nutrient availability, competition, and facilitation also participate in controlling the distributional patterns of species (Silander and Antonovics 1982; Pennings and Callaway 1992; Bertness and Shumway 1993; Tessier et al. 2003). However, edaphic characterization, spatial, and seasonal variations in soil ionic composition in a coastal tract affected by the climate, groundwater levels, and microtopography, which, inturn, affect the distribution of coastal tract vegetation (Danin 1981; Snow and Vince 1984; Kruger and Peinemann 1996; Cemek et al. 2007). Human activities impact the soils and vegetation in the coastal salt marsh (Alvarez-Rogel et al. 2007). Adaptation to saline soils can be mostly due to specific biophysical, physiological, morphological, and biochemical variation in plants (Brugnoli and Lauteri 1991; Aragues et al. 2005; Maricle et al. 2007). Plants can simultaneously ameliorate soils and decrease salinity in soils when they adapt themselves to high salinity. In this study variation in some soil parameters have been studied at different seasons and locations.

4.2. MATERIALS AND METHODS

4.2.1. Collection of soil samples for seasonal studies The afternoon of the last day of every season (described in the Chapter 2.3.1) were preferred to collect the soil samples for the quantification of the essential and non essential elements.

4.2.2. Collection of soil samples for location studies Five different easily accessible locations which are varying from one another by altitude and other geographic conditions were selected for this study (described in

47 the Chapter 2.3.2). Soil samples from all the five locations were collected within the last week of Aug, 2008.

4.2.3. Soil sample collection and analysis

Soil samples were collected with wooden tools to avoid any contamination of the soils. Twenty five pits were dug for each sample. From each pit, soil sample was collected at a depth of about 30cm. A composite sample (Garner et al., 1988) of about 1kg was taken through mixing of represented soil sample. All composite samples were dried, ground with wooden mottle and passed through 2mm sieve. After sieving all the samples were packed in the polythene bags for laboratory investigations. Care was taken to maintain the homogeneity. The soil characters and nutrients are determined following standard methods (Table 4.1).

Table 4.1. The determination of soil characters and nutrients

Sl.No. Parameters Instruments Methods Thein, 1979 and 1. The soil texture Field method, Bouyoucos Bouyoucos, 1962. 2. pH pH meter (Elico -Model LI120) Jackson,1973 Electrical Conductivity Meter (Elico Model 3. Piper ,1966 conductivity CM180) Total organic Walkley and Black, 4. carbon and organic dichromate oxidation procedure 1934 matter Bashour and Sayegh, 5. Total nitrogen Kjeldahl Method 2007 Watanabe and Olsen, 6. Total phosphorous Colorie meter-Spectronic 20 1965 Bashour and 7. Total potassium Flame photometer (Elico-361) sayegh,2007 Krugel and Retter, 1934 8. Total sodium Flame photometer (Elico-361) Hesse,1972 Total calcium and Hesse, 1972; MoEn 9. AAS (Varion - 200AA) magnesium (2004). 10. Total sulphur Colorie meter-Spectronic 20 Hesse,1972

Total zinc, iron, l 11. manganese and AAS (Varion- 200AA) MoEn, 2004). copper

48 4.2.5. Determination and quantification of nonessential elements of the soil Chromium, Nickel, Cadmium, Lead, Cobalt, Mercury, Arsenic, Cyanide, Selenium and Silver were quantified using AAS (Varion- 200AA) by the protocol of MoEn (2004).

4.3. RESULTS AND DISCUSSION

4.3.1. Soil Texture The Soil texture is one of the most important properties, as it affects many other chemical, physical, and biological soil processes and properties such as the water-holding capacity, water movement through the soil, soil strength, leaching of pollutants into groundwater, and the natural soil fertility (Table 4.2) (Thien, 1979; Brady, 1995; Murphy et al., 2000).

Table 4.2. Soil properties related to textures (Presley and Thien, 2008)

In the present study through there is no seasonal change in soil texture (Table 4.3a). But the texture of the soil varies from locations to locations (Table 4.3b). This variation may be due to the parent materials and climatic conditions, soil water, etc. existing in the particular location (Jenny, 1941a, b; Thien, 1979; Brady 1990).

Table 4.3a. Seasonal variations of texture S.No. Seasons Textures of soil

1. S1 (northeast monsoon) Sandy loam

2, S2 (pre-summer) Sandy loam

3. S3 (summer) Sandy loam

4. S4 (southwest monsoon) Sandy loam

49 Table 4.3b. Locational variations of texture

S.No. Locations Textures of soil

1. L1 Sand

2. L2 loamy sand

3. L3 Sand

4. L4 sandy loam 5. L5 Silt

4.3.2a. Seasonal and Locational Variations of pH

The seasonal variations in the pH (Figure 4.2a.) of the study area (Tiruchirappalli) is ranging between 6.76 (pre-summer) - 6.96 (southwest monsoon), which is very close to the neutral condition. Statistically the variation among seasons is significant (Table 4.4a.). Though, all the values are near to the neutral condition. The locational variation of soil has significant impact on the pH (Figure 4.2b and Table 4.4b). The descending order of the study site based on the soil pH is L1 (8.57) > L5 (8.27) > L3 (7.9) > L2 (7.5) >L4 (6.96). Except L4, in all other areas studied, the pH is above 7. Among the other four, L1 and L5 have a pH value above 8. This level is slightly higher than the optimum for the growth of most plants (5.5 to 7.5). This high pH may be due to the salt water influence at the coastal tract (L1) and the pollution load at terrestrial- urban area (L5). Soil pH directly affects the solubility of many of the nutrients in the soil, needed for proper plant growth and development. As the soil pH decreases, the availability of nutrients, such as phosphorus, to roots usually decreases because of the precipitation reactions with iron and aluminum. However, plants can affect their micro-environment and are often found to grow well over a range of soil pH. In general, many plants will do well in a soil pH range of 5.5 to 7.5. (Hanlon et al., 1990).

50 7 6.82 6.76 6.81 6.96

6

5

H 4 p

3

2

1 S1 S2 S3 S4

Figure 4.2a. Soil pH seasonal

Table 4.4a. Seasonal variations in the pH (One sample t - test)

Meteorological Std. N Mean t Statistical inference elements Deviation Seasonal pH 4 6.8375 .08578 159.42 P<0.01 significant DF= 4

9 8.57 8.27

8 7.9

7.5 H p

6.96 7

6 L1 L2 L3 L4 L5 Figure 4.2b. Soil pH in the locations

Table 4.4b. Locational variations in the pH (One sample t - test)

Std. soil parameter N Mean t Statistical inference Deviation

Locational pH 5 7.8400 .63470 27.620 P<0.01 significant

D F= 4

51 Soils are reservoirs of nutrients and water for plant growth. Nutrient availability to plants depends on the concentration, content and activity of each nutrient in the soil (Mayland and Wilkinson, 1989). The pH and soil organic matter change throughout the growing season and such changes affect the availability of mineral nutrients (Gardner et al., 1985; Marschner, 1995). In temperate climates, a drop in pH will increase the availability of micronutrients such as manganese, iron, zinc and copper, and decrease the availability of nitrogen and phosphorus (Marschner, 1995). As the soil pH increases above 6.5, manganese, a micronutrient, may become limiting to plant growth. Phosphorus and micronutrients such as copper and zinc also decrease in their availability to plants at high pH (8 and above). Soils composed of limestone have an alkaline soil pH (8.3). Plants grow in these pH of the soils, but nutrient deficiencies are common (Myung 2008; Hanlon et al., 1990).

4.3.3. Electrical Conductivity (EC) variation

In this study the seasonal variations in the electrical conductivity (EC) ranges between 0.05 ds/m (S4) - 0.11 ds/m (S1) (Figure 4.3a). Electrical conductivity variations are very high within the studied locations (Figure 4.3b). The highest electrical conductivity is observed at L5 (0.18ds/m) and the lowest value is at L4 (0.05ds/m). These seasonal and locational variations in the electrical conductivities are statistically significant (Table 4.5a and 4.5b)

0.12 0.11

0.1

0.08 0.06 0.06 0.06 0.05 EC(ds/m) 0.04

0.02

0 S1 S2 S3 S4

Figure 4.3a Electrical conductivity –Seasonal study

52 Table 4.5a Seasonal variations in electrical conductivity (One sample t - test)

soil Std. N Mean t Statistical inference parameter Deviation

EC 4 .0700 .02708 5.170 P<0.05 significant

D F= 3 The Electrical Conductivity (EC) of a soil solution is a measure of the ability of the solution to conduct electricity. Traditionally it is used to measure soil salinity. Also EC measurements have the potential for estimating the variations in some of the physical properties of the soil (Tom 1999). The EC indicates the presence or absence of salts in general, but does not indicate specific types of salt. In actuality, the interpretation of EC of a soil or media must be made considering the plant(s) to be grown, since EC directly affects the plants growing in the soil or media. The impact of EC on plants is also directly affected by water management (Hanlon et al., 1993).

0.2 0.18 0.18 0.16 0.16 0.14 0.12 0.1 0.09 0.08 0.06 E C (E C d s / ) m 0.06 0.05 0.04 0.02 0 L1 L2 L3 L4 L5 Locations

Figure 4.3b. The condition of electrical conductivity in the locations

Table 4.5b Locational variations in electrical conductivity

(One sample t - test)

Std. soil parameter N Mean t Statistical inference Deviation Electrical 5 .1080 .05891 4.100 P<0.05 significant Conductivity (ds/m) D F= 4

53 4.3.4 Variations in organic carbon and organic matter

The seasonal variations in the organic carbon and organic matter fluctuates between 0.52% (S2) to 0.56% (S1) and 1.04% (S2) to 1.12% (S1) respectively (Figure 4.4a). The observations on the locational variations are 0.39% (L5) to 0.56 % (L1) of organic carbon and 0.78% (L5) to 1.12 % (L1) of organic matters (Table 4. 4b).The variation in the organic carbon and matter in seasons and locations are statistically important (Table 4.6a and 4.6b).

1.12 1.2 1.08 1.08 1.04

1 Organic Carbon (%) 0.8 0.56 0.54 0.54 ( % ) 0.6 0.52

0.4 Organic Matter (%) 0.2

0 S1 S2 S3 S4

Figure 4.4a. Organic carbon and organic matter in soil (seasonal study)

Table 4.6a. Seasonal variations in the organic carbon and organic matter (One sample t - test)

Std. soil parameter N Mean t Statistical inference Deviation Organic carbon 4 .5400 .01633 5.170 P<0.05 significant

Organic matter 4 1.0800 .03266 66.136 P<0.01 significant DF= 3

54 1.2 1.12 1.08 1.04 0.98 1 Organic Carbon (%) 0.78 0.8

0.6 0.56 0.54 0.49 0.52 ( % ) ( % 0.39 0.4 Organic Matter (%) 0.2

0 L1 L2 L3 L4 L5

Figure 4.4 b. Organic carbon and organic matter position in the locations

Table 4.6b Locational variations in Organic carbon and organic matter (One sample t - test)

soil parameter N Mean Std. Deviation t Statistical inference

Organic carbon 5 .5000 .06671 16.760 P<0.01 significant

Organic matter 5 1.0000 .13342 16.760 P<0.01 significant DF= 4

Soil organic carbon (SOC) is an important indicator of soil quality and productivity. Organic carbon occurs in the form of organic matter. However, it is a key element for healthy soil (Kumar et al., 2006). The quantification of soil organic carbon (SOC) has recently attracted the attention of many researchers as it is the largest terrestrial carbon (C) pool in addition to being an important indicator of soil quality and productivity. Soil organic matter (SOM) comprises an accumulation of partially disintegrated and decomposed plant and animal residues and other organic compounds synthesized by the soil microbes as the decay occurs (Brady, 1990). Soil humus (or humic material) is that portion of SOM that is a heterogeneous mixture of organic compounds formed by degradation and synthesis. Soil humus makes up about 60-80% of SOM. The amount of the organic matter and the surface inorganic minerals can vary from less than 1% in coarse-textured sandy soils to more than 5% in fertile prairie grasslands. The amount is influenced by all soil forming factors. Jenny (1941) arranged the order of importance of these factors as: Climate > vegetation > topography = parent material > age

55 Rasmussen and Collins, (1991) found out some general characteristics of SOM. They are:- 1. Grassland soils have higher SOM than forest soils. 2. SOM increases with increasing precipitation and decreases with increasing temperature. 3. Fine-textured soils have higher SOM than coarse-textured soils. 4. Poorly drained soils have higher SOM than well drained soils. 5. Soils in lowlands have higher SOM than soils on upland positions.6.The carbon mineralization was faster in dry seasons than wet seasons (Olive et al., 2003).

4.3.5 Variations in the primary macronutrients (N, P & K)

4.3.5.1 Seasonal variations in the primary macronutrients (N, P & K)

The seasonal observation of the essential macronutrients - total nitrogen, phosphorous, and potassium are high during summer (0.64 %, .22% and 0.87% respectively) compared to the other seasons (Figure 4.5a to 4.5c). The minimum level of nitrogen and potassium were recorded in the southwest monsoon (0.51% and 0.11% respectively).But in the case of Phosphorous the minimum level was observed at northeast monsoon. Variations observed is statistically significant (Table 4.7).Thus, it is evident from the study that seasons play a vital role in mineralization process. Nutrient availability to plants depends on the concentration; content and activity of each nutrient in the soil (Wilkinson, 2000).

0.7 0.64 0.62 0.59 0.6 0.51 0.5

0.4

0.3 Nitrogen ( ) % 0.2

0.1

0 S1 S2 S3 S4

Figure 4.5a. Nitrogen in soil (seasonal study)

56 0.24 0.22 0.22 0.21 0.2 0.2 0.19

0.18

0.16

Phosphorous ( % ) (Phosphorous % 0.14

0.12

0.1 S1 S2 S3 S4

Figure 4.5b. Phosphorous in soil (seasonal study)

0.88 0.87 0.86 0.86

0.84

0.82

0.8 0.79 0.78 0.76

Potassium ( % ) ( % Potassium 0.76

0.74

0.72

0.7 S1 S2 S3 S4

Figure 4.5c. Potassium in soil (seasonal study)

Table 4.7. Seasonal variations in the in primary macronutrients (N, P & K) (One sample t - test)

Std. soil parameter N Mean t Statistical inference Deviation Total Nitrogen 4 .5900 .05715 20.646 P<0.01 significant Total Phosphorous 4 .2050 .01291 31.758 P<0.01 significant Total Potassium 4 .8200 .05354 30.631 P<0.01 significant D F= 3

57 4.3.5.2. Locational variations in the primary macronutrients (N, P & K)

In locational studies the nitrogen, phosphorous and potassium are varying from location to location (Figure 4.6a to 4.6c). These variations are statistically significant (Table 4.8). The highest level of the nitrogen was present in the coastal tract (0.74%) and the lowest was observed in the soil of terrestrial - rural stretch (0.51%). The compositions of soil are strongly affected by the seasonality of rainfalls and the atmospheric temperatures. The dry season had a higher C and N mineralization than those of the wet season. Chemical changes across seasons suggest that soil organic matter associated with macro-aggregates represents the main source of energy for microbial activity at the beginning of the wet season, while micro-aggregates protect the labile nutrient forms during the growing season (Oliva et al., 2003). The phosphate content of the rhizosphere soil fluctuates widely since the availability of phosphate content depends on various factors such as pH, moisture rhizoexudates etc., (Richardson, 2001). Numata et al. (2006 a, b) investigated the physical and chemical properties of the soil and nutrients of grasses under different climatic and edaphic conditions and management practices in Rondoˆnia state, in the southwestern Amazon region.

The present observation supports the variations of nutrients among seasons. Nitrogen status in soil and has been reported to increase through the decomposition of litter. Jamali (1992) also reports the litter decomposition as a cause for an increase in the total nitrogen content from 0.11% to 0.12% under Akashmoni (Acacia auriculiformis) and 0.06% to 0.07% under Garjan (Dipterocarpus turbinatus) plantations. The soil nitrogen concentration is altered by soil moisture, pH and biological processes. Inturn the biological process are affected by pH, moisture temperature and soil organic matter (Marschner, 1995). Microbes not only convert essential nutrients like nitrogen to usable forms, helping its growth but also can inhibit plant growth by immobilizing nutrients. Dramatic changes in soil organic nitrogen both spatially and throughout the seasons were attributed in part to microbial activity (Gardner et al., 1985).

58 0.8 0.74 0.7 0.7 0.7 0.6 0.52 0.51 0.5 0.4 0.3 Nitrogen (%) Nitrogen 0.2 0.1 0 L1 L2 L3 L4 L5

Figure 4.6a. Nitrogen in soil (locational study)

0.25 0.23 0.23 0.22 0.2 0.2 ) 0.18

0.15

0.1

P h o s p h o r s o s h o u h (p P % 0.05

0 L1 L2 L3 L4 L5

Figure 4.6b. Phosphorous in soil (locational study)

1.2

0.98 1 0.91 0.87 0.82 0.8 0.76

0.6

0.4 P o t a s s I u m ( % ) ( o t % P a s sI m u 0.2

0 L1 L2 L3 L4 L5

Figure 4.6c. Potassium in soil (locational study)

59 Table 4.8. Locational variations in the in primary macronutrients (N, P & K) (One sample t - test)

Std. soil parameter N Mean t Statistical inference Deviation Total Nitrogen (%) 5 .6340 .10991 12.899 P<0.01 significant Total Phosphorous (%) 5 .2120 .02168 21.866 P<0.01 significant Total Potassium (%) 5 .8680 .08408 23.083 P<0.01 significant DF= 4

4.3.6. Variations in the secondary macronutrients (Mg, Ca & S)

4.3.6.1 Seasonal Variations

Some seasonal variations were observed in the magnesium, calcium and sulphur contents (Figure 4.7a to 4.7c). In summer (S3) except sodium all other elements are at lower levels. In this season, status of the calcium is 5.16%, the magnesium 2.26% and sulphur 0.26%. In S1 the calcium and magnesium are found to be in higher amounts (5.69% and 2.64% respectively). The sulphur is highest in the S4. This inequality is statistically significant (Table 4.9).

2.7 2.64 2.59 2.6

2.5 2.48

2.4

2.3 2.26

2.2

M a g n eM s ) i ( u m % 2.1

2 S1 S2 S3 S4

Figure 4.7a. Seasonal facts of magnesium

60 5.8 5.69 5.7

5.6

5.5 5.42 5.4

5.3 5.19 5.2 5.16

C a l c i u ( ) m % 5.1 5

4.9

4.8 S1 S2 S3 S4

Figure 4.7b. Seasonal facts of calcium

0.7

0.59 0.6 0.56 0.54

0.5

0.4

0.3 0.26

S u l ) p h u r ( % 0.2

0.1

0 S1 S2 S3 S4

Figure 4.7c. Seasonal facts of sulphur

Table 4.9. Seasonal variations in the in secondary macronutrients (Mg, Ca and S) (One sample t - test)

Std. soil parameter N Mean t Statistical inference Deviation Total Magnesium 4 2.4925 .16879 29.533 P<0.01 significant Total Calcium 4 5.3650 .24583 43.648 P<0.01 significant Total Sulphur 4 .4875 .15305 6.370 P<0.05 significant DF =3

61 4.3.6.2 Locational variations Locational observation (Figure 4.8a to 4.8c) shows some variations in these parameters. Calcium is high in L3 (5.48%) and low in L1 (5.16%). Magnesium is higher in L5 (2.59%) and low in L2 (2.25%). Sulphur is higher in L4 (0.59%) and low in L1 (0.51%). These dissimilarities are statistically significant (Table 4.10).

2.7

2.59 2.6

2.5 2.48

2.4 2.38

2.3 2.26 2.25

2.2 M a g n e s i u m ( % ) ( e % s a g n i m u M

2.1

2 L1 L2 L3 L4 L5

Figure 4.8a. Magnesium in soil (locational study)

5.6

5.5 5.48

5.4

5.29 5.3

5.19 5.19 5.2 C a l c I u ( m ) % 5.16

5.1

5 L1 L2 L3 L4 L5

Figure 4.8b. Calcium in soil (locational study)

62 0.6 0.59 0.58 0.58

0.56 0.54 0.54 0.53

0.52 0.51

S u l p h u ) r ( % 0.5

0.48

0.46 L1 L2 L3 L4 L5

Figure 4.8c. Sulphur in soil (locational study)

Table 4.10. Locational variations in the in secondary macronutrients (Mg, Ca and S) (One sample t - test)

soil parameter N Mean Std. Deviation t Statistical inference

Total Magnesium 5 2.3920 .14550 36.761 P<0.01 significant Total Calcium 5 5.2620 .13142 89.534 P<0.01 significant Total Sulphur 5 .5500 .03391 36.266 P<0.01 significant DF= 4

Small amounts of Mg are adsorbed to soil organic matter. Increasing soil organic matter concentrations increases the exchangeable cation capacity and improves the Mg supply available for plants. The organically complexed Mg is an important source of Mg in some soils ((Mathan and Rao, 1982). Mg, Ca, K and Na, on the average, make up 21, 36, 26 and 36 g/kg of the lithosphere, respectively (Baker and Risser, 1983). Magnesium is readily leached upon weathering from most Mg-bearing minerals. As a result, soils contain an average of only 5 g/kg (Mayland and Wilkinson, 1989). Previous reports point out the existence of time and spatial variability in the available soil nutrients (P, K, and Mg). Although Kowalenko (1991) and Hoskinson et al. (1999) detected distinct variation in available soil nutrient content of spring and autumn samples, Dampney et al. (1997), Pierce and Nowak (1999) and Stipek (2003) stated that the spatial variability of available soil nutrients was not influenced by the different sampling time.

63 4.3.7 Seasonal and locational variations in micronutrients

8.79 8.8

8.6

8.4 8.16 8.12

8.2

8 7.69 7.8

Iron ( p p m ) m p ( p Iron 7.6

7.4

7.2

7 S1 S2 S3 S4

Figure 4.9a. Total iron in soil (seasonal study)

1.69 1.8 1.52 1.6 1.29 1.3 1.4

) 1.2

1

0.8

Copper (ppm 0.6

0.4

0.2

0 S1 S2 S3 S4

Figure 4.9b. Total copper insoil (seasonal study)

10.36 10.26 10.4 10.2 9.78 10 9.8 9.6 9.19 9.4 9.2 Manganese (ppm ) (ppm Manganese 9 8.8 8.6 S1 S2 S3 S4

Figure 4.9c. Total manganese in soil (seasonal study)

64 1.8 1.62 1.56 1.49 1.6 1.32 1.4

1.2

1

0.8 zinc (ppm ) (ppm zinc 0.6

0.4

0.2

0 S1 S2 S3 S4

Figure 4.9d. Total zinc in soil (seasonal study)

Table 4.11. Seasonal variations in the in the micronutrients (Fe, Cu, Mn &Zn) (One sample t - test)

soil parameter N Mean Std. Deviation t Statistical inference

Iron 4 8.1900 .45306 36.154 P<0.01 significant Copper 4 1.4500 .19201 15.104 P<0.01 significant Manganese 4 9.8975 .53531 36.978 P<0.01 significant Zinc 4 1.4975 .12971 23.090 P<0.01 significant DF =3

10.98 12 10.57 10.59

10 7.95 8.12

8

6 Iron) (ppm 4

2

0 L1 L2 L3 L4 L5

Figure 4.10a. Total iron in soil (locational study)

65 1.92 1.95 1.91 1.87 1.9

1.85

1.8

1.75 1.69 1.67 1.7

Copper ) ( ppm 1.65

1.6

1.55

1.5 L1 L2 L3 L4 L5

Figure 4.10b. Total copper in soil (locational study)

9.19 10 9 8 7 5.89 5.68 5.49 6 4.23 5 4

Manganese (ppm ) (ppm Manganese 3 2 1 0 L1 L2 L3 L4 L5

Figure 4.10c. Total manganese in soil (locational study)

1.65 1.62 1.68 1.8

1.6 1.26 1.32 1.4

1.2

1

0.8

Zinc ) ( ppm 0.6

0.4

0.2

0 L1 L2 L3 L4 L5

Figure 4.10d. Total zinc in soil (locational study)

66 Table 4.12. Locational variations in the in the micronutrients (One sample t - test)

Soil parameter N Mean Std. Deviation t Statistical inference

Iron 5 9.6420 1.47728 14.594 P<0.01 significant Copper 5 1.8120 .12215 33.171 P<0.01 significant Manganese 5 6.0960 1.84645 7.382 P<0.01 significant Zinc 5 1.5060 .19945 16.884 P<0.01 significant DF =4

The studied micronutrients exhibit seasonal (Figure 4.9a to 4.9d) and locational variations (Figure 4.10a to 4.10d). These variations are statistically significant (Table 4.11 and 4.12). The status of manganese is about 9.19 ppm (S4) to 10.36 ppm (S1); iron 7.69ppm (S1) to 8.79ppm (S2); zinc 1.32ppm (S4) to 1.69 (S1); and the copper 1,29 ppm (S1) to 1.69 ppm (S4). Many factors influence soil micronutrient dynamics, such as pH, organic matter, vegetation and management practice, viz. addition of organic amendments (McDowell, 2003). Seasonal variation of soil micronutrient availability may be related to plant growth patterns, microbial activity, and temperature effects on the decomposition of soil organic matter and compost (Akinrinde, 2004) The locational observation also shows considerable variations in these microelements. The status of manganese is higher (5.89 ppm) at L1 and lower (4.23ppm) at L2; iron is higher (10.98ppm) at L1 and low (7.95ppm) atL2; zinc is high 1.68 at L5and low (1.26 ppm) at L2; and the copper is high (1.92ppm) at L3 and low (1.67 ppm) at L2 .This seasonal and locational variation of minerals in soil are in agreement with the results of the previous soil worker (Hlavay et al., 2004). It can be inferred that the distribution difference of soil ionic composition is the consequence of the corporate impact of climate and vegetation, with out respect to vegetation seasonal salt ionic concentration. In the 0–5 cm topsoil varies in NP plot Soil salinity in some coast flats is regulated by seasonal rainfall patterns (de Leeuw et al. 1991; Silvestri et al. 2005; Tho et al. 2008), which is proven by the present study. The distribution characteristic of sodiumchloride is that higher concentration occurs in spring and winter and lower concentration in summer and autumn. The solubility of sodiumchloride is small compared to potassium sulfate. Sodiumchloride is prone to be accumulated in the topsoil (0-5 cm) during the winter

67 and spring, due to small rainfall and low temperature. The lower concentration of salinity in topsoil (0–5 cm) in summer and autumn resulted from the increase in rainfall and the dilution of salinity. In the locational study sodium is higher in L1 and L5. It might be due to the coastal impacts in L1 and the urban impacts in L5.

4.3.8. Variations in the non essential element

0.3 0.29 0.27 0.26 0.25 0.24

0.2

0.15

Sodium (% ) Sodium(% 0.1

0.05

0 S1 S2 S3 S4

Figure4.11a. Seasonal facts of total sodium

Table 4.13a. Seasonal variations in the in the sodium (One sample t - test)

Std. soil parameter N Mean t Statistical inference Deviation Sodium 4 .2650 .02082 25.460 P<0.01 significant DF= 3

0.3 0.29 0.29

0.25 0.25 0.24 0.23

0.2 )

0.15

Sodium (% 0.1

0.05

0 L1 L2 L3 L4 L5

Figure 4.11b. Sodium in soil (locational study)

68 Table 4.13b. Locational variations in the in the sodium (One sample t - test)

Soil parameter N Mean Std. Deviation t Statistical inference

Sodium 5 .2600 .02828 20.555 P<0.01 significant DF= 3

The analysed non essential elements of sodium found to be higher (0.29%) in the summer season (Figure 4.11a).In locational sample the Coastal area (L1) and the Terrestrial-urban area (L5) possesses the higher amount of 0.29 % (Figure 4.11b).These variations are statistically significant (Table 4.13a and 4.13b) The other nonessential elements such as chromium, nickel, cadmium, lead, cobalt, mercury, arsenic, cyanide, selenium and silver were not found in detectable levels. It may be due to the nature of parent materials of the soil and the soil reaction.

4.4. Inference The difference in nutrient patterns of soil directly affects the plant. Specific variation occur in uptake, translocation, accumulation, and use of mineral elements required for plant growth (Clark, 1983). Except the trace elements analysed the soil samples in the present study exhibit seasonal and locational variations in the macro and micro nutrients. The analysed nonessential elements (Chromium, Nickel, Cadmium, Lead, Cobalt, Mercury, Arsenic, Cyanide, Selenium and Silver) are not present in the detectable levels in all the sampling locations. Soils are reservoirs of nutrients and water for plant growth. Nutrient availability to plants depends on the concentration, content and activity of each nutrient in the soil (May land and Wilkinson, 1989). . Several changes in the variability of soil nutrient concentrations during four seasons and five different locations have been shown. Under controlled laboratory conditions, crop growth and development can be described in detail (Gardner et al., 1985), but in the natural systems such descriptions are compounded by changing environmental factors and the genetic and physiological abilities of plants to adapt to these complex cropping environments. Many theories about the availability of plant growth factors (e.g., nutrients, water, light, temperature, etc.) and their potential limiting effects on plant growth

69 have been put forward. In most general terms, some of these theories have been referred to as laws, such as Liebig’s “Law” of the minimum, based on his statement that “growth of a plant is dependent on the amount of foodstuff which is presented to it in minimum quantity” (Odum, 1971). The complexity of plant growth and development, compounded by the non- steady-state of the production agriculture environment, the interaction of growth factors, and the ability of crop plants to adjust or omit physiological pathways in response to environmental conditions results in a myriad of possible interactions that are too extensive to be predicted by such simplistic models (Gardner et al., 1985). As mineral nutrition management becomes spatially and temporally more refined, the interacting affect of many factors will have significant implications in site- specific crop nutrient management. The availability or unavailability of nutrients can be dramatically affected by a combination of factors including soil pH, EC, microbial activity and other factors (Marschner, 1995). Variations in pH and soil organic matter change throughout the growing season and such changes affect the availability of mineral nutrients (Gardner et al., 1985; Marschner, 1995). In temperate climates, a drop in pH will increase the availability of micronutrients such as manganese, iron, zinc and copper, and decrease the availability of nitrogen and phosphorus (Marschner, 1995). The soil present in the area is sandy loam, through out the year the texture of the soil is not affected by the season. The seasonal variation in the pH is not significant. The pH is very close to the neutral condition. The electrical conductivity shows significant variation 0.05ds/m (S4) to 0.11ds/m (S1). The range of organic carbon was 0.52% (S2) to 0.56% (S1) and the range of organic matter was 1.04 % (S2) to 1.12 (S1). In this the difference is meager. The seasonal observation of the primary macronutrients total nitrogen, phosphorous, and potassium were high at summer (0.64 %, .22% and 0.87% respectively) compared to the other seasons. The minimum level of nitrogen and potassium were recorded in the southwest monsoon (0.51% and 0.11% respectively). But in the case of Phosphorous the minimum level was observed at northeast monsoon. In the case of secondary nutrients some seasonal variations were observed in the calcium, magnesium, sulphur and sodium contents. In summer (S3) except sodium all other elements are at lower levels. In this season the calcium was 5.16%,

70 the magnesium was 2.26% and sulphur was 0.26%. In S1 the calcium and magnesium were found to be in higher amounts (5.69% and 2.64% respectively). The sulphur was highest in the S4. In the analyses of micro nutrients, some of they are not found in the detectable quantity in all the seasons and locations. The Manganese was about 9.19ppm (S4) to 10.36ppm (S1); iron was ranging between 7.69ppm (S1) and 8.79ppm (S2); zinc was about 1.32ppm (S4) to 1.69 (S1); and the copper was about 1,29ppm (S1) to 1.69 ppm (S4).

71 5.1. INTRODUCTION At present naturally occurring phytochemicals are of major scientific interest. Technically, the term ‘‘phytochemicals’’ refers to every naturally occurring chemical substance present in plants, especially to those that are biologically active (Caragay, 1992) .They occur in small amounts in all groups of plants and in all parts of plants- woods, barks, stems, pods, leaves, fruits, roots, flowers, pollens and seeds (Pratt and Hudson, 1990; Pratt et al., 1992; Macias et al., 2007). All living organisms are composed of chemical substances from both the inorganic and organic world that appear in roughly the same proportions, and performs the same general tasks. Hydrogen, oxygen, nitrogen, carbon, phosphorus, and sulfur form normally a major portion of all the living cells. The primary metabolic routes use these compounds and produce primary metabolites. The primary metabolites are present almost everywhere in nature and are essential for all life forms. The primary metabolites include the common carbohydrates, fats, proteins and nucleic acids that are needed to procreate and maintain life. Typically they are involved in the energy regulation of organisms, growth and development of tissues. In short, they are the building blocks of the organisms. (Salisbury and Ross, 1974; Taiz and Zeiger, 2003; Ari Tolenen, 2003). According to Mallette et al (1960) the chemical constituents present in the plants can be considered from the standpoint of utility to the cell, viz. structural materials, food reserves, metabolic machinery, incidental and special substances.

5.1.1. Structural chemicals The organic biomolecules are initially utilized in the synthesis of a small number of building blocks that are, in turn, used in the construction of a vast array of vital macromolecules. Some of these materials are providing rigid structure to the cells and organisms. In the young cells the quantity of structural materials is very low. On the other hand, in the matured cells the walls are so thick as to leave small cavities within the cells. Such cells frequently die and then serve only in supporting and protecting the softer tissues. The polysaccharides constitute approximately 75 percent of the dry weight of higher plants for example cellulose, hemi cellulose, pectin, gum arabic, gum tragacanth, mucilage, and lignin. Most of these polysaccharides are components of cell wall. Once formed, the structural materials are quite inert when considered from the metabolic standpoint (Mallete et al., 1960).

72 5.1.2. Food reserves Plants prepare their own food (e.g. photosynthetic process), which serve as reserves during the periods of surplus synthesis. This will be stored as carbohydrates, lipids, and proteins. These reserves are stored (1) in the active cells as amyloplasts and oil vacuoles, (2) in special storage organs for the purpose of providing new vegetative growth (tuber, bulb, etc.) or, (3) in the fruit and seed for the next generation (Mallete et al., 1960).

5.1.2.1. Carbohydrate

A carbohydrate is an organic compound with the general formula Cn (H2O) n i.e. consists only of carbon, hydrogen and oxygen, with the last two in the 2:1 atomic ratio. The carbohydrates (saccharides) are classified into monosaccharide, oligosaccharides, and polysaccharides. The plant uses solar energy to oxidize water, releasing oxygen and reduce carbon dioxide there by farming large carbon compounds, primarily sugars. This complicated process can be summarized as.

CO2 + H2O Æ (C6 H12 O6) n + O2 At the time of energy surplus these sugar molecules are polymerised and stored as a reserve material. Carbohydrates serve various functions in a living cell; they transport energy and supply C to other metabolic pathways. Starch and sucrose are the storage forms of reserve carbohydrates. In the major economically valuable timber plants, which are commercially, exploited cellulose and hemicellulose are the carbohydrate. A number of other carbohydrates (D-fructofuranose, fructosans, D- galactose, D-mannose, D-galactopyranose, galactomannans etc. are found in a variety of plants (Mallete et al., 1960; Salisbury and Ross, 1974; Taiz and Zeiger, 2003).

5.1.2.2. Lipids

The term lipids include a variety of organic compounds found in the plants. In many cases the term is synonymous to the “ether extractable,” and includes the triglycerides, phospholipids, and waxes as well as the non- fatty acid containing substances, resins, resin acids, terpenes (essential oil), and plant sterols, which possess diversified biological and commercial applications.

73 Lipids are classified into three main groups, namely, simple lipids, compound lipids, and hydrolytic products of lipids or derived lipids. Simple lipids are defined as organic esters which, upon hydrolysis, yield only aliphatic alcohols and aliphatic monocarboxylic acids. Examples are fats and waxes. Upon hydrolysis, compound lipids yield aliphatic alcohols, aliphatic monocarboxylic acids, and other products such as carbohydrates, phosphoric acid, or nitrogen bases. Examples of this group are the phospholipids and the glycolipids. The hydrolytic products of derived lipids include the fatty acids, various alcohols, such as glycerols and the sterols, and a number of nitrogenous compounds such as cholins or sphingosine (Mallete et al., 1960; Salisbury and Ross, 1974; Taiz and Zeiger, 2003).

5.1.2. 3. Proteins

Proteins (also known as polypeptides) are organic compounds made of amino acids arranged in a linear chain and folded into a globular form. The amino acids in a polymer are joined together by the peptide bonds between the carboxyl and amino groups of adjacent amino acid residues. The sequence of amino acids in a protein is defined by the sequence of a gene, which is encoded in the genetic code (Ridley, 2006).

In general, the genetic code specifies 20 standard amino acids; however, in certain organisms the genetic code can include selenocysteine and pyrrolysine. Shortly after or even during synthesis, the residues in a protein are often chemically modified by post-translational modification, which alter the physical and chemical properties, folding, stability, activity, and ultimately the functions of the proteins. Proteins can also work together to achieve a particular function, and they often associate to form stable complexes (Maton et al., 1993). Like other biological macromolecules such as polysaccharides and nucleic acids, proteins are essential parts of organisms and participate in virtually every process within the cells. Many proteins are enzymes that catalyze biochemical reactions and are vital to metabolism. Proteins also have structural or mechanical functions. Other proteins are important in cell signaling, immune responses, cell adhesion, and the cell cycle. Proteins are also necessary as fodder; since animals cannot synthesize all the amino acids they need and must obtain essential amino acids from food. Through the process of digestion, animals break down ingested

74 protein into free amino acids that are then used in the metabolism (Taiz and Zeiger, 2003; Nelson, 2005). Proteins from different vegetable sources are so similar in chemical composition that they are used interchangeably for many industrial purposes. Ease of isolation and cost of the starting material are the determining factors. The most important industrial uses of proteins are for the production of plastics and adhesives, in coatings for the paper products, and in bonding ply wood veneers. Artificial textile fibers can be prepared from vegetable proteins (Mallete et al., 1960; Salisbury and Ross, 1974)

5.1.3. Metabolic machinery and cofactors

Associated with the reserve proteins in the seed, and widely distributed throughout the remainder of the plant, are the proteins which serve to catalyze the multitude of reactions involved in the complicated process termed metabolism. Enzymatic proteins require the assistance of large numbers of cofactors in the performance of their roles as catalysts. Included in this class are the inorganic elements (macro and micro elements), vitamins, nucleotides, and the plant growth regulators (Mallete et al., 1960).

5.1.4. Incidental substances

In addition to the above mentioned, essential elements, the plant absorbs many of the diverse inorganic ions found in the soil (ex. selenium, mercury, cadmium, arsenic, lead, cobalt, etc.). Plants grown in soils containing appreciable quantities of these and similar elements accumulate measurable amounts. In most cases such absorption is inconsequential, both to the plant and to the animal which consumes it; however, selenium being a well-known exception (Mallete et al., 1960).

75 5.1.5. The special substances

The special substances are the substances which are produced by the plants. Plants produce a large, diverse array of organic compounds that appear to have no direct function in the growth and development. These substances are known as secondary metabolites, secondary products, or natural products. Secondary metabolites have no generally recognized, direct role in the process of photosynthesis, respiration, solute translocation, protein synthesis, nutrient assimilation, differentiation, or the formation of carbohydrates, proteins, and lipids. But their roles as plant protectants and chemical reservoir have been later on brought to light (Harborne, 1993). Secondary metabolites also differ from primary metabolites in having a restricted distribution in the plant kingdom. That is, particular secondary metabolites are often found in only one plant species or related group of species, whereas primary metabolites are found throughout the plant kingdom (Mallete et al., 1960).

5.1.6. Factors affecting the chemical composition

The evolutionary history of the plant kingdom is a story of constant adaptations to the changing environmental conditions. Plants, as sedentary organisms, have to adjust to the surrounding environment during their life cycle (Harborne, 1993; Ferni., 2007). Plants have adapted by modifying morphological and anatomical features, through physiological variation or by biochemical means. Biochemical adaptation may involve both basic / primary metabolism and special/ secondary metabolism (Harborne, 1993). This renders metabolic responses to stress and to interactions with its environment primordial features (Pichersky et al., 2006; Goff and Klee, 2006; Kappers et al., 2005). To meet such demands it has been estimated that the plant kingdom contains upwards of 200,000 metabolites (De Luca and St Pierre, 2000) with values for a single species being given in the order of 15,000 (Dixon, 2001; Hartmann et al., 2005). In recent years, increasing attention has been paid to the ways the plants are biochemically adapted to their differing environments. Many studies have been conducted by biochemists world over to explore the biochemical adaptation of the plants at various environmental variables like seasonal effects, drought, frost,

76 salinity, mineral nutrients, heavy metal toxicity, altitude, various soil textures, radiation, etc. The biotic variations such as age of the plant, microbial attack, grazing, competition, and individual nutritional status, have been proven to have an impact on the secondary metabolite profile in higher plants (Harborne, 1982). Among the studies on the impact of abiotic factors over the secondary plant products the contribution of the following phytochemists are noteworthy: Korner, (1999); Spitaler, (2006), Owuor et al., (2008) have proved the altitudinal impacts on plant materials. Krishnan et al., (2000) and Chen et al., (2009) implicated seasonal changes along with altitudinal variations. The seasonal and climatic variations have been strongly witnessed by many scientists (Adam, 1970; Kramer and Kozlowski, 1979; Bonicel et al., 1987; Ashworth et al., 1993; Rinne et al., 1994; Zidorn and Stuppner, 2001; Barbarox and Breda, 2002; Bhowmik and Matsui, 2003; Geography and climate- Namdeo et al., 2010). The impact of seasonal and locational influences on the metabolites of a popular native curative plant Calotropis has so far not been reported.

5.2. MATERIALS AND METHODS

5.2.1. Plant material collection for the seasonal and locational studies

The collection of plant materials is identical to what has been described in the previous Chapter (2.4).

5.2.2. Drying of plant materials

This process is done to ensure good keeping qualities and suppress the action of enzymes on the sampled plants. The collected plant materials were washed thoroughly to remove the dirt and other contaminations. Then the collected leaves were dried carefully under shade, at room temperature so as to retain their fresh green colour, and also to prevent decomposition of active compounds. The dried leaves were powdered using a stone grinder. The powdered materials are henceforth termed as crude drugs. Crude drugs were stored in airtight, dark, glass container.

77 5.2.3. Phytochemical analysis The crude drugs were used to perform the following analyses:- Table 5.1. Phytochemical analysis

Extraction / analytical S.No. Parameters References method Walkley and Black, 1. Organic Carbon Volumetric method 1934 Hydrolysed by 2.5 N HCl – Hedge and 2. Total Carbohydrate Anthrone method - Hofreiter,1962 colorimetry Buffer Extract- Lowry et al., 1951; 3. Total Protein Lowrys method - colorimetry Mattoo,1970 4. Total Lipid Soxhlet method - gravimetry Folch et al 1957 Calorific value (Carbohydrate x 4.15)+ (Fat 5. Phillips, 1969 x 9.4) +(Protein x 5.65) 6. Ash content Dry ash – gravimetry Renaud et al., 1994. Pellettand young,1980; Micro-Kjeldahl method - 7. Total Nitrogen Thenmoli Titrimetry Balasubramanian and Sadasivam,1987 Triple acid digestion- 8. Total Phosphorous colorimetry (Spectronic 20 Jackson,1973 at 540 nm +) Triple acid digestion-Flame 9 Total Potassium, Jackson,1973 photometry (ELICO-361) Calcium, magnesium, zinc, copper, iron, Triple acid Digestion -AA manganese, ,boron, Spectrophotometry (Varion molybdenum, chromium, Baker and Suhr, 1982; 10 200AA) nickel, cadmium, lead, Allen ,1989

cobalt, mercury, arsenic,

cyanide, Selenium and silver

5.2.4. Elemental analysis

Two grams of ash were digested with mixture of nitric acid, sulphuric acid, and perchloric acid in the ratio 11:6:3, for 24 hours to remove the organic matters. The digested sample was made up to 100 ml and used for the assay of the trace elements through Atomic Absorption Spectrophotometer (AAS- Varion 200AA) using suitable hollow-cathode lamps. Appropriate working standard was prepared for each element. All elements were determined through this procedure. A blank reading was also obtained.

78 5.2.5. Extraction and GC-MS analysis

The crude drug was subjected to extraction with analytical grade solvent of chloroform for GC-MS analysis. 25 g of the crude drug was taken in a round bottom flask and 50ml of analytical grade chloroform was added and refluxed for 8 hrs. After completion of the 8hrs the round bottom flask was cooled and the extract was filtered through the Buchner funnel. The extract was evaporated to dryness under nitrogen atmosphere using turbo evaporator. The residue obtained was dissolved in 2ml chloroform and transferred into the GC vial and injected into the GC-MS port.

GC–MS analysis was performed on an Agilent gas chromatograph model 6890 N coupled to an Agilent 5973 N mass selective detector. Analytes were separated on an HP-5MS capillary column (30 m X 0.25 mm X 1.0 μl) by applying the following temperature program: 40 °C for 5 min, 40–70 °C at 2 °C /min, 70°C for 2 min, 70–120 °C at 3 °C /min, 120–150 °C at 5 °C /min, 150–220 °C at 10°C /min and then 220 °C for 2 min. Transfer line temperature was 280 °C . Mass detector conditions were: electronic impact (EI) mode at 70 eV; source temperature: 230 °C; scanning rate 2.88 scan S-1; mass scanning range: m/z 29–540. Carrier gas was helium at 1.0 ml min-1. The tentative identification of volatile components was achieved by comparing the mass spectra with the data system library (NIST 98) and other published spectra (Mass Spectrometry Data Centre., 1974), supported by retention index data, which were compared with available literature retention indices (NIST Chemistry Web Book, 2005). All compounds were quantified as 3-octanol equivalents.

5.3. RESULTS AND DISCUSSIONS

5.3.1. Seasonal variations

5.3.1.1 Seasonal variations in the organic compounds

5.3.1.1.1 Organic carbon

Organic carbon is one of the most stable elements of plant biomass. The present observation shows that the organic carbon differs significantly between seasons (Figure 5.1 and Table 5.2a). In the southwest monsoon season (S4) the plant

79 sample possesses the highest organic carbon (27.5%) and in the summer season (S3) the lowest (24.8%).

Though significant the current study recorded a little variation in the organic carbon. The difference between the highest and lowest values is only 2.7%. The organic carbon shows a positive relationship only with the highest R.H.and the nitrogen (Table 5.2b and 5.2c).

27.5 27.5 27 26.5 25.6 26 25.4

25.5 24.8 25 24.5

Organic carbon ( %) 24 23.5 23 S1 S2 S3 S4

Figure 5.1. Organic Carbon in the plant – seasonal study

Table 5.2a. Seasonal variations in organic carbon (One sample t - test) Std. Statistical Parameter N Mean t Deviation Inference Organic 44.24 P<0.01 4 25.825 1.1673 Carbon (%) 9 significant DF = 3 Table 5.2b. Karl Pearson correlation between season and organic carbon

Organic carbon vs. Correlation value Statistical inference Highest R.H. morning -.957* P<0.05 Significant N= 4 Table 5.2c. Karl Pearson correlation between soil and organic carbon

Organic carbon vs.. Correlation value Statistical inference Total Nitrogen -.959* P<0.05 Significant N= 4

80 The current global stock of soil organic carbon is estimated to be 1,500– 1,550 Pg (Batjes, 1996; Post, 2001; and Lal, 2004). This constituent of the terrestrial carbon stock is twice as that of the earth’s atmosphere (720 Pg), and more than triple the stock of organic carbon in terrestrial vegetation (560 Pg) (Bolin, 1970; Baes et al., 1977). Baties and Sombroek, (1997) reported the quantity of C stored in soils to be about three times more than the vegetation and twice as much as that which is present in the atmosphere (Batjes and Sombroek, 1997). But, in the case of Calotropis the amount of organic carbon is higher than of the soil (Chapter 4.3.4). Brogowski, et. al., (2002) observed a significant loss in the carbon content during the winter in crop plants. This was related to the weight loss of the crop.

5.3.1.1.2. Seasonal variations in the carbohydrate, protein and lipid The variations in the quantity of primary metabolites (carbohydrate, protein and lipid) among seasons are statistically significant (figure 5.2 and Table 5.3a). The carbohydrate, protein and lipids are slightly higher in southwest monsoon and lower in summer. The average contents of carbohydrate is between 9.3% (L3) to 10.2% (L4); the protein is between 5.3 % (L3) to 6.2 % (L4) and the lipid is 2.7 % (L2) to 3 %( L4). The correlation analysis expresses a significant positive relation of protein with the highest R.H. morning and evening and lipids with the lowest R.H. evening. Highest R.H. morning is related to the protein and the lowest R.H. evening is significantly correlating with the lipids (Table 5.3b). This suggests that the R.H is the main factor which highly influences the primary metabolism than the other meteorological parameters. Among the soil components, nitrogen is correlated with the protein, the potassium is linked with the lipid and the sodium is connected with the carbohydrate and lipids (Table 5.3c).

81 12 10.2 9.7 9.8 10 9.3 Total Carbohydrate

8 6.2 ) 6 5.5 5.4 5.3 Total

( % Proteins

4 2.9 2.7 2.8 3

2 Total Lipids

0 1234

Figure 5.2. Carbohydrate, protein and lipid in the plants (seasonal study)

Table 5. 3a.Variations in the Carbohydrate, protein and lipids (One sample t - test) Std. Parameter N Mean t Statistical Inference Deviation 9.75 0.369 Carbohydrate 4 52.748 P<0.01 significant

5.6 0.408 Protein 4 27.434 P<0.01 significant

2.85 Lipid 4 0.1291 44.152 P<0.01 significant

DF = 3

In the earlier studies contents of the carbohydrates were found to vary between the summer and winter samples. Yoo et al. (1996) found the seasonal variations in carbohydrate, (starch and other soluble sugars) in the White forsythia. Carbohydrate loss in winter was integrated with the nucleic acids, and the physiological processes (Brogowski, 2002). Arbutus unedo and Olea europaea contained higher amounts of total lipids at the beginning of the growing season (Christou et al., 1994). Similarly the current observations of these compounds show significant increase in the southwest monsoon and decrease in summer. This decrease may be, to maintain the physiological processes.

82 Table 5.3b. Karl Pearson correlation between meteorological elements and the seasonal variation in the Carbohydrates, Proteins and Lipids Correlation value Statistical inference Parameter Carboh Carbohydrate Protein Lipid Protein Lipid ydrate Highest R.H. -0.812 -.980* -.775 P>0.05 P<0.05 P>0.05 morning N.S. Significant N.S. Highest R.H. -0.755 -.960* -.721 P>0.05 P<0.05 P>0.05 evening N.S Significant N.S P<0.01 Lowest R.H. 0.855 .837 .993** P>0.05 P>0.05 Signific evening N.S N.S ant Mean Wind 0.785 .983* .835 P>0.05 P<0.05 P>0.05 Speed N.S. Significant. N.S. N=4; N.S= Not significant

Table 5.3c. Karl Pearson correlation between soil parameters and the seasonal variation in the Carbohydrates, Proteins and Lipids

Correlation value Statistical inference Parameter Carbohyd Carbohy Protein Lipid Protein Lipid rate drate P<0.05 P>0.05 P>0.05 Total nitrogen -.899 -.986* -.949 Significa N.S. N.S. nt P>0.05 P>0.05 P<0.05 Potassium -.775 -.854 -.964* N.S N.S Significant P<0.05 P>0.05 P<0.01 Sodium -.953* -.902 -.992** Significa N.S Significant nt N=4; N.S= Not significant

5.3.1.1.3. Seasonal variations in the energy value

Several publications deal with differences in caloric values of many species from different sex classes, nutritional status and different stages of life history are the main reasons for this variability of the energy contents (Wiegert and Hasler, 1965; Gyllenberg, 1969; wising, 1971; Benedetto Castro, 1975). In the higher plants, there is a distinct dependence of caloric values up on the climatic conditions, the availability of water and the concentration of dissolved salts in the soil (Malone, 1968; Caspers, 1975). Considerable variations of energy values between the individual components of plant species have been reported by Runge (1971), Larcher et al., (1973) and Caspers (1975). The endogenous, climate dependent

83 energy value was previously recorded in eight species of a meadow and an old-field community (Caspers, 1977). The seasonal variations in the energy potential of the plants are depicted in figure 5.3. The highest energy potential (9.46 kcal.) was found in southwest monsoon season and the lowest (8.36 kcal) in summer seasons. At the time of adverse conditions the plant spends much of the energy to maintain the basic metabolism (Brogowski et al., 2002). In the present study also the content of carbohydrate, protein and lipids are significantly reducing at summer, which may be due to the adaptive mechanism to escape from the dry-hot summer. As a result of these metabolites, the energy contents are also coming down in summer. The variation found is statistically significant (Table 5.4a).

108

106 105.56 104 102

100 98.59 98 96.56 96 94.86 (Kcal /100g) 94 92 90 88 S1 S2 S3 S4

Figure 5.3. Energy content in the plant (seasonal study)

Table 5.4a. Seasonal variations in the energy content (One sample t - test) Std. Parameter N Mean t Significance Deviation Calorific 4 8.6600 .40620 42.639 P<0.01 significant value DF = 3

The calorie value of the plant is associated with the highest R.H. (morning and evening) and wind speed (Table 5.4b) and the nitrogen content of the soil (Table5.4c).

84 Table 5.4b. Karl Pearson correlation between season and calorie value Correlation Calorie value vs. Statistical inference value Highest R.H.morning -.985 P<0.05 Significant Highest R.H. evening -.964 P<0.05 Significant. Mean Wind Speed .968 P<0.05 Significant N=4

Table 5.4c. Karl Pearson correlation between soil and Calorie value Correlation Calorie value vs. Statistical inference value Total nitrogen -.969* P<0.05 Significant N=4

5.3.1.1.4. Seasonal variations in the yield of extract

The extract yield potential of the plant varies significantly from season to season (Figures 5.4 and Table 5.5a). The southwest monsoon sample provided the maximum quantity of the extract (6.95%) and the pre-summer sample yielded the lowest amount (4.95%). The yield of extracts is only connected with mean maximum temperature and minimum temperature and mean R.H morning (Table 5.5b).

8 6.85 6.95 7 6.4 6 4.95 5

4 ( % ) ( % 3

2

1

0 S1 S2 S3 S4

Figure 5.4. Yield of extracts in seasons

85 Table 5.5a Seasonal variations in the yield of extract (One sample t - test) Std. Parameter N Mean t Significance Deviation Yield of Extract 4 6.288 .9232 13.621 P<0.01 significant DF = 3 Table 5.5b. Karl Pearson correlation between season and yield of extract

Correlation Yield of extract vs. Statistical inference value Mean Max Temperature 969* P<0.05 Significant Mean Minimum Temperature .991** P<0.01Significant Highest Temperature .968* P<0.05Significant Mean R.H. morning -.958* P<0.05Significant N= 4

5.3.1.1.5. Compounds present in the chloroform extract

The analysis of the organic compounds present in the chloroform extract of Calotropis through GC-MS revealed the presence of 64 compounds. Out of these the summer sample seems to possess the maximum number of compounds (50) (Figure 5.5, 5.5a and Table 5.5, 5.6a). As per the numbers of the compounds synthesized, the ascending order of the seasons is as follows: Pre- summer, northeast monsoon, southwest monsoon and summer (S2 - 35) < (S1 - 37) < (S4 - 40) < (S3 - 50). The variation observed in the number of compounds is statistically significant (Table 5.6b.). Out of the 64 compounds, 24 show statistically significant seasonal variations (1.Tetradecane 2. Hexadecane 3. Bicyclo[3.1.1]heptane,2,6,6-trimethyl- ,(1alpha,2alpha,5alpha) 4. 9-Octadecyne 5. n-Hexadecanoic acid 6. Phytol 7. 9, 12, 15-Octadecatrienoic acid,(Z,Z,Z)- 8. 9-Octadecenamide,(Z)- 9. Heptacosane 10. 13- Docosenamide,(Z)- 11. Squalene 12. Nonacosane 13. Heptacosane,1-chloro- 14. Tricosane 15. Vitamin E 16. Campesterol 17. Stigmasterol 18. Alpha-Amyrin 19. 4,4,6a,6b,8a,11,12,14b-Octamethyl-1,4,4a,5,6,6a,6b,7,8,8a,9,10,11,12,12a,14,14a, 14b-octadecahydro-2H-picen-3-one 20. .alpha-Amyrin 21. 12-Oleanen-3-yl acetate, (3alpha) - 22. 9, 19-Cyclolanost-24-en-3-ol, acetate 23. Urs-12-en-24-oic acid, 3- oxo-, methyl ester, (+) - 24. Taraxasterol) (Table 5.6c).

86 The correlation analysis shows the numbers of compounds are associated only with the soil (Table 5.6d).

60

50 50

40 40 37 35

30

20 Number ofcompounds Number

10

0 S1 S2 S3 S4

Figure 5.5. Number of compounds identified in seasons

87 S1 (Northeast monsoon) S2 (Pre-Summer)

S3 (Summer) S4 (Southwest monsoon) Figure 5.5a. GC - MS Chromatogram - seasonal samples

88 Table 5.6a. The composition of chloroform extract of Calotropis through GC-MS in seasons

S.No. Rt. Compound name S1 S2 S3 S4 1 7.9 Dodecane - 0.28 0.29 0.14 2 9.1 2-Methoxy-4-vinyl phenol 0.35 - 0.11 - 3 9.7 Tetradecane 0.41 0.16 0.15 0.21 4 10.7 phenol, 2,4-bis(1,1-dimethylethyl) 0.24 0.12 0.11 - 2(4H)-Benzofuranone,5,6,7,7a-tetrahydro-4,4,7a- 5 11.1 0.08 - 0.06 - trimethyl- 6 11.5 Hexadecane 0.21 0.15 0.24 7 12.7 Tridecanoic acid, 12-methyl-methylester - - 0.14 - 8 13.1 Tetradecanoic acid 0.48 - 0.39 - 9 13.5 5-Ethylcyclopent-1-enecarboxaldehyde - - - 0.23 Bicyclo[3.1.1]heptane,2,6,6-trimethyl- 10 13.8 0.53 0.52 0.25 0.28 ,(1alpha,2alpha,5alpha) 11 14.2 9-Octadecyne 0.23 0.15 0.15 0.28 12 14.8 Hexadecanoic acid,methyl ester 0.17 0.19 1.3 - 13 15.3 n-Hexadecanoic acid 9.68 6.33 4.33 2.68 14 16.7 9-Octadecanoic acid,methylester,(E)- - - 0.73 - 15 16.8 Phytol 1.11 1.05 0.76 0.56 16 17 Octadecanoic acid,methyl ester - - 0.46 - 17 17.3 9,12,15-Octadecatrienoic acid,(Z,Z,Z)- 3.3 2.36 2.05 0.55 18 17.5 9,12-Octadecadienoic acid(z,z)- 0.91 - - - 19 17.7 Dodecanamide 0.53 0.53 - 0.34 20 17.8 Hexadecanamide - - 0.37 - 21 19.2 Octacosane - - 1.19 - A'-Neogammacer-22(29)-en-3- 22 19.5 - 1.39 - 1.55 ol,acetate,(3beta,21beta)- 23 20 9-Octadecenamide,(Z)- 4.42 2.51 2.47 3.99 24 20.4 Urs-20-en-3-ol,(3alpha,18alpha,19alpha)- - - - 1.09 25 20.6 Lup-20(29)-en-3-ol, acetate,(3beta)- - 2.57 1.57 2.1 26 21.6 Hop-22(29)-en-3alpha-ol - 14.19 7.29 9.67 1,2-Benzenedicarboxylic acid, mono(2- 27 22.3 0.37 - - - ethylhexyl)ester 28 23.2 Nonadecane,1-chloro- - - 0.71 - 29 23.6 Ergost-22-en-3-0l,(3alpha,5alpha,22E,24R)- - - 0.32 - 30 23.7 Stigmastane-3,6-dione,(5alpha) - 1.29 - - 31 24.2 Z-12-Pentacosene - - 0.08 - 32 24.6 Heptacosane 0.26 0.16 0.37 0.17 33 25 Tetracosanoic acid,methyl ester - - 0.07 - 34 25.6 13-Docosenamide,(Z)- 0.23 0.19 0.12 0.14 35 26 Squalene 1.76 0.89 1.84 0.97 36 26.9 Z-14-Nonacosane - - 1.16 1.84 37 27.2 Nonacosane 1.97 1.21 1.53 1.88 Tricyclo[4.3.0.0(7,9)]nonane, 2,2,5,5,8,8-hexamethyl- 38 27.4 0.14 0.16 0.43 1.49 ,(1alpha,6beta, 7alpha,9alpha)- 39 27.6 Bicyclo[4.2.0]oct-2-ene, 3,7-dimethyl-7-(4-methyl-3- - - 0.92 -

89 pentenyl)-8-(2,6,10-trimethyl-1,5,9-undecatrienyl)- ,[1alpha,6alpha,7alpha,8alpha(1E,5E)]- 2,6,10,14,18,22-tetracosahexanone,2,6,10,15,9,23- 40 28.1 - - - 0.89 hexamethyl-,(all-E)- 41 28.6 Heptacosane,1-chloro- 0.67 0.5 0.61 0.37 42 29.4 gamma-Tocopherol 0.25 - 0.26 - 43 30 17-Pentatriacontene - - 0.23 - 44 30.4 Tricosane 8.52 4.84 6.27 5.17 45 30.8 Vitamin E 1.15 3.86 3.85 1.47 2(1H)Naphthalenone,3,5,6,7,8,8a-hexahydro-4,8a- 46 31 - - - 3.25 dimethyl-6-(1-methylethenyl)- 47 31.2 Desmosterol - - 0.2 - 48 32.6 Campesterol 1.7 2.06 2.57 1.57 49 33.2 Stigmasterol 1.21 1.1 1.73 1.09 Ergost-8,24(28)-dien-3-ol,4,14-dimethyl- 50 34.1 0.26 - 0.42 0.59 ,(3alpha,4alpha,5.alpa.,)- 51 34.7 Stigmasterol,22,23-dihydro- 3.59 3.56 - 1.7 52 34.8 gamma-Sitosterol - - 3.86 - 53 34.9 Heneicosane,11-decyl- - - - 0.89 Pyridine-3-Carboxamide,oxime,N-(2-trifluromethyl 54 35.1 - - - 2.53 phenyl)- 56 35.2 Stigmasta-5,24(28)-dien-3-ol,(3alpha,24Z)- - - 2.04 - 57 35.7 alpha-Amyrin 5.76 4.07 4.02 4.2 4,4,6a,6b,8a,11,12,14b-Octamethyl- 58 36 1,4,4a,5,6,6a,6b,7,8,8a,9,10,11,12,12a,14,14a,14b- 1.36 1.45 0.63 0.98 octadecahydro-2H-picen-3-one 59 36.5 4,22-Stigmastadiene-3-one 1.69 0.89 - 1.68 60 37 .alpha-Amyrin 8.42 6.04 13.35 13.67 61 38.4 12-Oleanen-3-yl acetate, (3alpha)- 16.9 12.44 9.58 10.16 62 38.7 9,19-Cyclolanost-24-en-3-ol,acetate 3.12 2.23 1.88 1.98 63 39.9 Urs-12-en-24-oic acid, 3-oxo-,methyl ester,(+)- 13.6 15.5 13.03 15.11 64 40.2 Taraxasterol 4.16 5 3.6 2.3

Table 5.6b. Number of compounds identified in seasons through GC-MS (One sample t - test)

Std. Parameter N Mean t Significance Deviation Total Number of P<0.01 4 40.50 6.658 12.165 compounds significant DF= 3

90 Table 5.6c. Seasonal variation in the composition of chloroform extract (One sample t - test) Std. S.No. Parameter N Mean t Significance Deviation P<0.05 1 Tetradecane 4 .2325 .12121 3.836 significant P<0.05 2 Hexadecane 4 .2675 .14009 3.819 significant. Bicyclo[3.1.1]heptane,2,6,6- P<0.05 3 trimethyl- 4 .3950 .15067 5.243 significant ,(1alpha,2alpha,5alpha) P<0.05 4 n-Hexadecanoic acid 4 5.7550 3.01234 3.821 significant P<0.01 5 Phytol 4 .8700 .25703 6.770 significant 9,12,15-Octadecatrienoic P<0.05 6 4 2.065 1.1413 3.619 acid,(Z,Z,Z)- significant P<0.05 7 Heptacosane 4 .2400 .09764 4.916 significant P<0.01 8 13-Docosenamide,(Z)- 4 .1700 .04967 6.846 significant P<0.05 9 Squalene 4 1.3650 .50441 5.412 significant P<0.01 10 Nonacosane 4 1.6475 .34798 9.469 significant P<0.01 11 Heptacosane,1-chloro 4 .5375 .13200 8.144 significant P<0.01 12 Tricosane 4 6.2000 1.66311 7.456 significant P<0.05 13 Vitamin E 4 2.5825 1.47516 3.501 significant P<0.01 14 Campesterol 4 1.975 .4475 8.826 significant P<0.01 15 Stigmasterol 4 1.2825 .30325 8.458 significant 10.80 P<0.01 16 alpha-Amyrin 4 4.5125 .83512 7 significant 4,4,6a,6b,8a,11,12,14b- Octamethyl-1,4,4a,5,6,6a,6b, P<0.05 17 4 1.1050 .37652 5.870 7,8,8a,9,10,11,12,12a,14,14a, significant 4b-octadecahydro-2H-picen-3-1 P<0.05 18 .alpha-Amyrin 4 10.370 3.75596 5.522 significant 12-Oleanen-3-yl acetate, P<0.01 19 4 12.270 3.3244 7.382 (3alpha)- significant 9,19-Cyclolanost-24-en-3- P<0.01 20 4 2.3025 .56453 8.157 ol,acetate significant Urs-12-en-24-oic acid, 3-oxo- 24.19 P<0.01 21 4 14.310 1.1830 ,methyl ester,(+)- 2 significant P<0.01 22 Taraxasterol 4 3.7650 1.13353 6.643 significant DF=3

91 Table 5.6d. Karl Pearson correlation between soil and the total numbers of compound

Total numbers of compound Correlation Statistical inference vs. value Total magnesium -.965* P<0.05 Significant N=5

5.3.1.1.6. Groups of phytochemicals

The identified 64 compounds (Table 5.6a) belong to different phytochemical groups such as, terpenes, sterols, fatty acids, hydrocarbons, heterocyclic compounds, phenolics and hydroxylamines (Table 5.7) Terpenes are the major constituents of the chloroform extract. According to the percentage of compounds it can be arranged as follows; Terpene Fatty acids>Hydrocarbon> Sterols>Heterocyclic compounds>Phenolics>Hydroxylamines. According to the number of compounds it can be arranged as follows; Hydrocarbon (8 - 14) > Terpenes (9 - 13) > Fatty acids (7 -12) >sterols (6 - 8)>Phenolics (1 - 2)> Heterocyclic compounds (1). The number within the parentheses represents the number of component ranging between seasons.

Table 5.7. Groups of compounds identified in seasons

Compound S.no. S1 S2 S3 S4 group 9 *(53.65 12 * 1 Terpenes 11*(55.92**) 13*(62.44**) **) (65.14**)

2 Sterols 6*(11.54**) 6*(11.12**) 8 *(13.02**) 6*(8.61**)

3 Fatty acids 9*(19.94**) 7*(12.24**) 12*(12.56**) 7*(8.86**) 12 4 Hydrocarbons 8*(12.81**) 8 *(7.52**) 14*(14.10**) *(15.85**) Heterocyclic 5 3 *(1.47**) 1 *(3.86**) 3*(4.18**) 1*(1.47**) compounds 6 Phenolics 2 *(0.59**) 1*(0.12**) 2 *( 0.21**) -

7 Hydroxylamines - - - 1*(2.52**) (* Total number; ** Tentative Quantity)

92 5.3.1.1.7. Seasonal variations in terpenoids

Terpenoids or terpenes comprise one of the most important groups of active compounds in plants with over 20000 known structures. All terpenoid structures may be derived from isoprene (five-carbon) units containing two unsaturated bonds. They are synthesized from acetate via the mevalonic acid pathway (Pengelly, 2006). The terpenes form the major portion of the chloroform extract of Calotropis. There are 13 terpenes identified in the seasonal samples which can be classified as monoterpenes, diterpenes, and triterpenes. The triterpenes are 11 in number (Table 5.8a). As per the number of terpene compounds present they are as follows: (S4)13> (S2)12> (S3)11> (S1)9. Quantitatively they are in the following order: (S2)65.14% > (S4)62.44% > (S3)55.92% > (S1)53.65%. The ‘Urs-20-en-3-ol, (3 beta 18 alpha, 19 alpha)’ are found only in the southwest monsoon. The seasonal variations in the number and the quantity of terpenes are statistically significant (Table 5.8b). The correlation analysis shows that the quantity of terpenes is significantly associated with the heaviest rainfall (Table 5.8c). There is no correlation between the soil parameters and the terpenes. Triterpenoid compounds are derived from a C30 precursor, squalene (Bruneton, 1995). They have similar configurations to steroids (found in plants and animals) whose C27 skeletons are also derived from squalene (Pengelly, 2006). Triterpenes attract attention because of their biological activities; e.g. taraxasterol, beta-amyrine and alpha-amyrine, Taraxasterol was shown to exhibit considerable activity against 12-O-tetradecanoylphorbol-13-acetate (TPA)-induced inflammatory ear oedema in mice and tumor promotion in mouse skin (Akihisa et al., 1996). Triterpene alcohols from the flowers of Compositae were demonstrated to possess marked anti-inflammatory activity (Akihisa et al., 1996). Taraxerol possesses antiulcer properties. α -Amyrine, lupeol and cycloartan-type triterpenes are cytotoxic agents (Banskota et al., 1999).

Table 5.8b. Terpenoids identified in seasons

S.No. Compounds Name S1 S2 S3 S4

Monoterpenes Bicyclo[3.1.1]heptane,2,6,6-trimethyl-,(1alpha ,2beta 1. 0.53 0.52 0.25 0.28 5alpha)

93 Diterpenes (Acyclic) 2. Phytol 1.11 1.05 0.76 0.56 Triterpenes 3. Hop-22(29)-en-3.beta.-ol - 14.19 7.29 9.67 4. Urs-12-en-24-oic acid, 3-oxo-,methyl ester,(+)- 13.6 15.5 13.03 15.11 5. Squalene 1.76 0.89 1.84 0.97 6. Taraxasterol 4.16 5 3.6 2.3 A'-Neogammacer-22(29)-en-3- 7. - 1.39 - 1.55 ol,acetate,(3.beta.,21.beta.)- Pentacyclic triterpene 8. Alpha-Amyrin 8.42 6.04 13.35 13.67 9. Beta-Amyrin 5.76 4.07 4.02 4.2 10. Lup-20(29)-en-3-ol,acetate,(3.beta.)- - 2.57 1.57 2.1 11. 12-Oleanen-3-yl acetate, (3.alpha.)- 16.9 12.44 9.58 10.16 12. Urs-20-en-3-ol,(3.beta.,18.alpha.,19.alpha.)- - - - 1.09 1.36 1.45 0.63 0.98 Beta-Amyrene derivatives-4,4,6a,6b,8a,11,12,14b-

13. Octamethyl-1,4,4a,5,6,6a, 6b,7,8,8a,9, 10,11,12,12a,

14, 14a,14b - octadecahydro-2H-picen-3-one

Table 5.8b. Seasonal variations in the quantity of Terpenes (One sample t - test) Std. Parameter N Mean t Statistical inference Deviation

Number of 4 11.25 1.708 13.175 P<0.01 significant Terpenes quantity of 59.28 4 5.39480 21.980 P<0.01 significant Terpenes 75 DF= 3

Table 5.8c. Karl Pearson correlation between season and Number and quantity of the terpenes

Correlation value Statistical inference Parameters Numbers of Quantity of Numbers of Quantity of Terpenes Terpenes Terpenes Terpenes P>0.05 P<0.05 Heaviest Rain .793 .972* Not Significant Significant N= 4

94 5.3.1.1.8. Hydrocarbons Large numbers of the long chain hydrocarbons constitute the chloroform extract (Table 5.9a). There are about 17 hydrocarbons present in the chloroform extract. The numbers of hydrocarbons ranges between 8 (northeast monsoon and pre- summer) to 14 (summer). The quantity is about 7.52% in pre-summer and 15.85% in (southwest monsoon). In summer 14.10% of Hydrocarbons are present. The major types of hydrocarbon are found arer Alkanes, Alkenes, Carbocyclic acids and Pthalic acids. A trace amount of Bicyclo [4.2.0] oct-2-ene, 3,7-dimethyl-7-(4-methyl-3- pentenyl)-8-(2,6,10-trimethyl-1,5,9-undecatrienyl)-,[1.alpha.,6.alpha., 7.beta.,8.alpha (1E,5E)]-; Octacosane17-Pentatriacontene; Nonadecane,1-chloro-; Z-12-Pentacosene found only in the summer season. Heneicosane, 11-decyl-; 5-Ethylcyclopent-1- enecarboxaldehyde are found in the southwest monsoon. 1, 2-Benzenedicarboxylic acid, mono (2-ethylhexyl) ester is found only in the northeast monsoon. It may be due to the favorable climate and soil conditions of the particular season. The numbers and quantities of hydrocarbons exhibit statistically significant variations among seasons (Table 5.9b). The correlation analysis reveals that the numbers of hydrocarbons are associated with mean R.H. evening and the lowest R.H.morning. The quantities of hydrocarbons are linked with mean maximum and minimum temperatures and mean R.H. morning (Table 5.9c). The numbers of hydrocarbons are associated with the magnesium of the soil (Table 5.9d).

Table 5.9a. Hydrocarbons in seasons

S.No. Types of hydrocarbon S1 S2 S3 S4 Cyclic hydrocarbon Bicyclo[4.2.0]oct-2-ene, 3,7-dimethyl-7- (4-methyl-3-pentenyl)-8-(2,6,10-trimethyl 1. - - 0.92 - -1, 5, 9-undecatrienyl)-, [1.alpha., 6.alpha., 7.beta.,8.alpha.(1E,5E)]- Tricyclo[4.3.0.0(7,9)]nonane, 2,2,5,5,8,8- 2. hexamethyl-,(1.alpha.,6.beta., 0.14 0.16 0.43 1.49 7.alpha.,9.alpha.)- Alkanes 3. Tricosane 8.52 4.84 6.27 5.17 4. Nonacosane 1.97 1.21 1.53 1.88 5. Hexadecane 0.47 0.21 0.15 0.24 6. Tetradecane 0.41 0.16 0.15 0.21

95 7. Dodecane - 0.28 0.29 0.14 8. Z-14-Nonacosane - - 1.16 1.84 9. Octacosane - - 1.19 - 10. 17-Pentatriacontene - - 0.23 - 11. Heneicosane,11-decyl- - - - 0.89 Chlorinated 12. Nonadecane,1-chloro- - - 0.71 - 13 Heptacosane,1-chloro- 0.67 0.5 0.61 0.37 14 Z-12-Pentacosene - - 0.08 - 15 Heptacosane 0.67 0.5 0.61 0.37 Cyclopentanes 16 5-Ethylcyclopent-1-enecarboxaldehyde - - - 0.23 Carbocyclic acids pthalic acids 1,2-Benzenedicarboxylic acid, mono(2- 17 0.37 - - - ethylhexyl) ester

Table 5.9b. Seasonal variations in the Numbers and quantity of the Hydrocarbons (One sample t - test)

Std. Parameter N Mean t Statistical inference Deviation Numbers of 4 10.50 3.000 7.000 P<0.01 significant Hydrocarbons Quantity of 12.57 4 3.58977 7.003 P<0.01 significant Hydrocarbons 00 DF= 3

Table 5.9c. Karl Pearson correlation between season and numbers and quantity of the Hydrocarbons

Correlation value Statistical inference Parameters Numbers of Quantity of Numbers of Quantity of Hydrocarbons Hydrocarbons Hydrocarbons Hydrocarbons Mean max P>0.05 Not .828 .976 P<0.05Significant Temperature significant Mean Minimum P>0.05 Not .686 1.000 P<0.01Significant temperature Significant P>0.05 P<0.01 Mean R.H.morning -.633 -.991 Not Significant Significant P<0.05 P>0.05 Mean R.H. evening -.960 -.731 Significant Not Significant Lowest R.H. P<0.05 P>0.05 -.957 -.843 morning Significant Not Significant N=4

96 Table 5.9d. Karl Pearson correlation between soil and numbers and quantity of the Hydrocarbons

Correlation value Statistical inference Parameters Numbers of Quantity of Numbers of Quantity of Hydrocarbons Hydrocarbons Hydrocaron Hydrocarbon P>0.05 P<0.05 Magnesium .951* -.470 Not Significant Significant N= 4 5.3.1.1.9. Fatty acids The fatty acids are varying significantly among the seasons (Table 5.10a). There are about 15 fatty acids identified in the chloroform extract. A maximum number of 12 compounds are present in the summer samples. The lowest number of 7 compounds are present in the pre-summer and northeast monsoon seasons. Also, they are varying in the peak area percentage. The descending order of the number of fatty acids is as follows:- 12 (S3) > 9 (S1) > 7 (S2) = (S4). In the quantity the descending order is 19.94 (S1) >12.56(S3)>S2 (12.24)>S4 (8.86). Tetracosanoic acid; methyl ester; Hexadecanamide; Tridecanoic acid, 12- methyl-methylester; 9-Octadecanoic acid, methylester,(E); Octadecanoic acid, methyl ester are present only in the summer season.2,6,10,14,18,22- tetracosahexanone,2,6,10,15,9,23-hexamethyl-,(all-E) is present only in the southwest monsoon. 9, 12-Octadecadienoic acid (z, z) is present only in the northeast monsoon. The total number and quantity of fatty acids exhibits distinct variations among seasons (Table 5.10b). The correlation analysis with seasons shows that there is no relationship between meteorological elements. The quantity is linked with the soil parameter - electrical conductivity and the numbers are linked with the sulphur content of the soil (Table 5.10c). The fatty acids are the well known active metabolites. They serve as an important energetic substrate for the cells. Linoleic acid is essential for the maintenance of growth and α- linolenic acid for neural functions. Both acids were shown to be potent cycloxygenase-2 (COX-2) catalyzed prostaglandin biosynthesis inhibitors (Ringbom et al., 2001).

97 Table 5.10a. Fatty acids in seasons

S.No. Types of Fatty acids S1 S2 S3 S4 1. Tetracosanoic acid,methyl ester - - 0.07 - Unsaturated 2. 9,12,15-Octadecatrienoic acid,(Z,Z,Z)- 3.3 2.36 2.05 0.55 aturated 3. 9-Octadecenamide,(Z)- 4.42 2.51 2.47 3.99 Erucic acid 4. 13-Docosenamide,(Z)- 0.23 0.19 0.12 0.14 linoleic acids 5. 9,12-Octadecadienoic acid(z,z)- 0.91 - - - Palmitic acids 6. Hexadecanamide - - 0.37 - 7. n-Hexadecanoic acid 9.68 6.33 4.33 2.68 Esters, methyl 8. Hexadecanoic acid,methyl ester 0.17 0.19 1.3 - Myrsitic acids 9. Tetradecanoic acid 0.48 - 0.39 - Lauric acids 10. Dodecanamide 0.53 0.53 - 0.34 Stearic acids esters Tridecanoic acid, 12-methyl- 11. - - 0.14 - methylester 12. 9-Octadecanoic acid,methylester,(E)- - - 0.73 - 13. Octadecanoic acid,methyl ester - - 0.46 - Omega-3 Derivatives

Docosahexaenoic Acids 2,6,10,14,18,22- 14. tetracosahexanone,2,6,10,15,9,23- - - - 0.89 hexamethyl-,(all-E)- Polymeric fatty acids 15 9-Octadecyne 0.23 0.15 0.15 0.28

Table 5.10b. Seasonal variations in the Numbers and quantity of the Fatty acids (One sample t - test)

Std. Parameter N Mean t Statistical inference Deviation Numbers of 4 8.75 2.363 7.406 P<0.01 significant Fatty acids Quantity of 13.40 4 4.67027 5.738 P<0.05 significant Fatty acids 00 DF= 3

98 Table 5.10c. Karl Pearson correlation between soil parameters and the fatty acids

Correlation value Statistical inference Parameters Numbers of Quantity of Numbers of Quantity of fatty fatty acids fatty acids fatty acids acids P>0.05 P< 0.05 EC 0.156 0.982* Not Significant Significant P< 0.05 P>0.05 Sulphur -.952* -007 Significant Not Significant N= 4

5.3.1.1.10. Sterol composition

The next large numbers of compounds belongs to sterols (Table 5.11a). The maximum numbers of compounds (8) is recorded in summer and other three seasons are identical in the numbers of compounds (6). There are only four sterol compounds present in the summer. Quantitatively the order is S3 (13.02) > S1 (11.54)>S2 (11.12)>S4 (8.61). The quality and quantity of sterols exhibit statistically significant seasonal variations (Table 5.11b). The quality and quantity of sterols are not linked with meteorological elements. The number is linked only with the sulphur content of the soil (Table 5.11b). Table 5.11a. Sterols in seasons TYPES OF S.NO. S1 S2 S3 S4 STEROLCOMPOUND 1. 9,19-Cyclolanost-24-en-3-ol,acetate 3.12 2.23 1.88 1.98 Phytosterols 2 Campesterol 1.7 2.06 2.57 1.57 3 Stigmasterol 1.21 1.1 1.73 1.09 4 gamma-Sitosterol - - 3.86 - 5 Stigmasterol,22,23-dihydro- 3.59 3.56 - 1.7 Cholesterols

(Dehydrocholesterols) 6 Desmosterol - - 0.2 - Stigmasterols Analogs/Derivatives Stigmasta-5,24(28)-dien-3- 7 - - 2.04 - ol,(3.beta.,24Z)- 8 4,22-Stigmastadiene-3-one 1.69 0.89 - 1.68 Ergosterols Analogs/Derivatives

(Withanolides) Ergost-22-en-3- 9 - - 0.32 - 0l,(3.beta.,5.alpha.,22E,24R)- Ergost-8,24(28)-dien-3-ol,4,14- 10 0.26 - 0.42 0.59 dimethyl-,(3.beta.,4.alpha.,5.alpa.,)-

99 Steroids

(Cholestenones) 11 Stigmastane-3, 6-dione, (5.alpha.) - 1.29 - -

Table 5.11b. Seasonal variations in the Numbers and quantity of the Sterols (One sample t - test)

Parameter N Mean Std. Deviation t Statistical inference Numbers of Sterols 4 6.50 1.000 13.000 P<0.01 significant Quantity of Sterols 4 11.0725 1.83280 12.083 P<0.05 significant DF= 3 Table 5.11c. Karl Pearson correlation between soil and numbers and quantity of the Sterols

Correlation value Statistical inference Parameters Numbers of Quantity of Quantity of Numbers of Sterols Sterols Sterols Sterols P>0.05 Not Sulphur -.991** -.793 P<0.05 significant significant N=4

Sterols are important constituents of all eukaryotes. They play a vital role in plant cell membranes. Plant sterols are physiologically very active; they are precursors of many hormones and oviposition stimulants of some insects (Harborne, 2001). Phytosterols such as stigmasterol and sitosterol are essential components of cell membranes, and they are also used as the starting material in the production of steroidal drugs. Phytosterols are characterised by a hydroxyl group attached at C-3 and an extra methyl or ethyl substituent in the side chain which are not present in the animal sterols (Harborne and Baxter, 1993). Phytosterols are minor but beneficial components of the human diet. Since they inhibit growth of tumours and help in the regulation of blood cholesterol, they are therapeutically important. Many herbs e.g. Withania somnifera (Pengelly, 2005); Urtica dioica (Hirano et al., 1994) and Commiphora mukul (Bruneton, 1995) are rich some of steroidal compounds. The typical plant sterols, sitosterol and stigmasterol, appeared as main sterol components (Nes and Parish, 1989). Sitosterol possesses antihyperlipoproteinaemic, antibacterial and antimicotic activities and has been shown to act as inhibitor of tumor cells (Yasukawa et al., 1991; Kasahara et al., 1994); cancer cells (Raicht et al., 1980).

100 They also exhibit significant inhibitory effect on HIV reverse transcriptase (Akihisa et al., 2001).A mixture of stigmasterol and sitosterol was shown to possess anti- inflammatory activity upon tropical application (Gomez et al., 1999). They are used for the treatment of prostate problems (Gomez et al., 1999).

5.3.1.1.11. Heterocyclic Compounds

There are three biologically important heterocyclic compounds identified (Table 5.12a). These three compounds occur only in the northeast monsoon and the summer. Quantitatively it can be arranged as S3 (4.18) >S2 (3.86) S1 & S4 (1.47). The quality and quantity of these compounds displays statistically significant seasonal variations (Table 5.12b). The quality of compounds shows significant relationship with heaviest the rainfall and the quantity is linked with lowest R.H. (Table 5.12c). Table 5.12a. Heterocyclic compounds in the plants – seasonal study

S.No. Compound name S1 S2 S3 S4 Benzopyrans 1. Vitamin E 1.15 3.86 3.85 1.47 2(4H)-Benzofuranone,5,6,7,7a- 2. 0.08 - 0.06 - tetrahydro-4,4,7a-trimethyl- Tocopherols 3. gamma-Tocopherol 0.25 - 0.26 -

Table 5.12b. Seasonal variations in the Numbers and quantity of the Heterocyclic Compounds (One sample t - test)

Std. Parameter N Mean t Statistical inference Deviation Numbers of Heterocyclic 4 2.00 1.155 3.464 P<0.05 significant Compounds Quantity of Heterocyclic 4 2.7450 1.47803 3.714 P<0.05 significant Compounds DF = 3

101 Table 5.12c. Karl Pearson correlation between season and numbers and quantity of the Heterocyclic Compounds

Correlation value Statistical inference Parameters Numbers of Quantity of Numbers of Quantity of Heterocyclic Heterocyclic Heterocyclic Heterocyclic Compounds Compounds Compounds Compounds P>0.05 P<0.05 Lowest R.H. evening -.345 -.958* Not Significant Significant P>0.05 P>0.05 Total Rainfall -.933 -.403 Not Significant Not Significant P<0.01 P>0.05 Heaviest Rain -.993** -.022 Significant Not Significant N=4

5.3.1.1.12. Phenolics

Two phenolics are recorded through the GCMS analysis (Table 5.13a.). The 2- Methoxy vinyl phenol is present in northeast monsoon and summer. The 2, 4-bis (1, 1-dimethyl) is not present in the southwest monsoon. The quantity varies as follows S1 (0.59)>S3 (0.21)>S2 (0.12).The seasonal variations in the phenolics compounds are statistically not significant (Table5.13b).

Table 5.13a. Phenolic compounds in seasons

S.No. Types of phenolics S1 S2 S3 S4 phenol, 2,4-bis(1,1- 1 0.24 0.12 0.11 - dimethylethyl) Catechols 2-Methoxy-4-vinyl 2 0.35 - 0.11 - phenol

Table 5.13b. Seasonal variations in the Numbers and quantity of the Phenolics Compounds (One sample t - test)

Parameter N Mean Std. Deviation t Statistical inference Numbers of Phenolics 4 1.25 .957 2.611 P>0.05 Not significant Compounds Quantity of Phenolics 4 .2300 .25495 1.804 P>0.05 Not significant Compounds DF = 3

102 Phenolics usually possess antimicrobial and antifungal activities and consequently defensive in function. Aldehydes and ketones often act as allelochemicals. Such activities are reported for all the three aldehydes identified in the present study. Decanal is an attractant for some insects (Mattiacci et al., 2001; Wang et al., 1999), while nonanal is a repellent (Huber and Borden, 2001; Wang et al., 1999). Decanal has some pheromone- like activity (Cosse et al., 2002).

5.3.1.1.13. Hydroxylamine

Only one Hydroxylamine Oxime - Pyridine-3-Carboxamide, oxime, N-(2- trifluromethyl phenyl - is identified in the southwest monsoon. The variations in the number and quantity are statistically not significant (Table 5.14a).

Table 5.14a. Seasonal variations in the Numbers and quantity of the Hydroxylamine Compounds (One sample t - test)

Std. Parameter N Mean t Statistical inference Deviation Numbers of 4 .25 .500 1.000 P>0.05 Not significant Hydroxylamine Quantity of 4 .63 1.260 1.000 P>0.05 Not significant Hydroxylamine DF=3

5.3.1.2. Seasonal variations in the inorganic Compounds

5.3.1.2.1. Seasonal variations in the ash content

Total ash value of plant material represents the amount of minerals and earthy materials attached. The presence of ash shows a significant difference between seasons (figure 5.6 and Table 5.15a). The samples of southwest monsoon season (S4) possesses highest ash content (1.79 %) and that of summer season (S3) possess the lowest amount of ash (1.58%), Amin et al., (2007) have reported variation in the percentage of ash matter, between dry season and green seasons in Sudan. The ash content shows significant relationship with lowest R.H. evening and the nitrogen, potassium and sulphur contents of the soil (Table 5.15b and 5.15c)

103 1.85 1.8 1.79 1.75 1.7 1.69

1.65 1.62 1.58

Ash (%) 1.6 1.55 1.5 1.45 S1 S2 S3 S4 Seasons

Figure 5.6. Presence of ash in seasons

Table 5.15a. Variations in ash content (One sample t - test) Std. parameter N Mean T Statistical inference Deviation Ash (%) 4 1.6700 .09201 36.299 P<0.01 significant DF= 3

Table 5.15b. Karl Pearson correlation between season and variations in the ash content

Ash content vs. Correlation value Statistical inference

Lowest R.H. evening .966* P<0.05 Significant N= 4

Table 5.15c. Karl Pearson correlation between soil and variations in the ash content

Ash content vs. Correlation value Statistical inference

Total Nitrogen -.989* P<0.05 Significant Potassium -.968* P<0.05 Significant Sulphur -.975* P<0.05 Significant N= 4

5.3.1.2.2. Essential macro nutrients

104 The nitrogen, Phosphorous, potassium, calcium, magnesium, and sulphur show significant seasonal variations (Table 5.16a). Nitrogen is the second most important element of organic matter in plants. Nitrogen content in the plants of red clover is relatively lower after the main crop (spring barley) harvest; it reaches its peak at the start of the vegetation period in the spring. In the subsequent stages of biomass growth, nitrogen content in red clover becomes stable till the blooming is completed, (Brogowski, 2002). The nitrogen (Figure 5.7a) is slightly high in the summer season (2.24%) and low in the pre- summer (2.13%). The phosphorous (figure 5.7b) and potassium (Figure 5.7c) are slightly higher in the southwest monsoon (S4) and slightly lower in pre-summer season. Yoo et al., (1996) observed the seasonal variation in the nitrogen content. In winter the nitrogen content was high in his study.

Phosphorus (H2PO4) content shows similar trends to that of nitrogen. Its highest content occurs after the winter and further decreases very slowly till the stage of blossom shed. This trend has been most likely due to carbohydrate loss as observed along the winter time. The stems contain relatively low amounts of phosphates, richer are leaves, and inflorescence are the richest (Brogowski, 2002). The correlation analysis shows that the nitrogen is linked with lowest R.H. morning and heaviest rainfall. The phosphorous is associated with highest R.H. (morning and evening) and the wind speed. The potassium is associated with the lowest temperature and the wind speed (Table 5.16b). The potassium and the phosphorous are connected with the pH of the soil (Table 5.16c).

2.26 0.6 2.24 0.53 2.24 0.5 2.22 0.42 0.43 0.4 2.2 2.18 0.4 2.18 2.16 2.16 0.3 2.14 2.13 0.2 2.12 N I t r o g e n ( % ) 2.1 0.1 P % oh o u s p ( s h o r ) 2.08 2.06 0 S1 S2 S3 S4 S1 S2 S3 S4 Seasons Seasons

Figure 5.7a.Nitrogen Figure 5.7b. phosphorous

105 3.4 3.28 3.3 3.2 3.1 2.97 3 2.94 2.9 2.81 2.8

P o t a s s iu m( % ) % m( iu s s t a o P 2.7 2.6 2.5 S1 S2 S3 S4 Seasons

Figure 5.7c. Potassium

Table 5.16a. Seasonal variations in nitrogen, phosphorous and potassium (One sample t - test) Std. Statistical parameter N Mean T Deviation inference Total Nitrogen P<0.01 4 2.1775 .04646 93.741 (%) significant Total P<0.01 Phosphorous 4 .4450 .05802 15.339 significant (%) Total Potassium P<0.01 4 3.0000 .19916 30.126 (%) significant DF= 3

Table 5.16b. Karl Pearson correlation between seasonal elements and variations in the nitrogen, phosphorous and potassium

Correlation value Statistical inference Parameters phospho phosphoro Nitrogen potassium Nitrogen potassium rous us Lowest P<0.05 .496 .949 .959* P>0.05 N.S P>0.05 N.S temperature Significant Highest R.H. P<0.05 -.036 -.977* -.937 P>0.05 N.S P>0.05 N.S morning Significant Highest R.H. P<0.05 -.125 -.987* -.944 P>0.05 N.S P>0.05 N.S evening Significant Lowest R.H. P<0.05 -.952* -.515 -.517 P>0.05 N.S P>0.05 N.S morning Significant Heaviest P<0.01 -.633 .293 .157 P>0.05 N.S P>0.05 N.S Rain Significant Mean Wind P<0.05 P<0.05 .066 .981* .981* P>0.05 N.S Speed Significant Significant N= 4; N.S= Not significant

106 Table 5.16c. Karl Pearson correlation between soil and variations in the nitrogen, phosphorous and potassium Correlation value Statistical inference Parameters phospho Nitrogen potassium Nitrogen phosphorous potassium rous

P<0.05 P<0.05 pH .215 988* .999** P>0.05 N.S Significant Significant N= 4; N.S= Not significant The other macronutrients, calcium, magnesium, and sulphur are depicted in figures 5.7d to7.7f. The calcium is high in summer (4.84%) and low in the southwest monsoon season (3.64%). where as the magnesium is high in the pre-summer season (3.62%) and also low in southwest monsoon (2.59%). On the other hand the sulphur is very high (0.98%) in southwest monsoon and low (0.47%) in summer. These elements are significantly varying seasons to seasons (Table 5.16d). The Calcium variation is subject to the highest R.H. (morning and evening) and the wind speed. The calcium is associated with the nitrogen .The magnesium is related to the lowest temperature and mean R.H. morning and the soil pH. The sulphur is grately related to highest R.H. (morning and evening) and wind speed (Table 5.16e and 5.16f).

6 4 3.62 3.5 5 4.84 3.16 3.19 4.59 4.62 ) 3 % )

( 2.59 4 3.64

% 2.5 (

3 2

n e s iu m iu n e s 1.5 2 g C ac l ium 1 M a M a 1 0.5 0 0 S1 S2 S3 S4 S1 S2 S3 S4 Seasons Seasons

Figure 5.7d. Calcium Figure 5.7e. Magnesium

107 1.2 0.98 1

0.8

0.6 0.51 0.48 0.47 0.4 S u lS p h u r (%)

0.2

0 S1 S2 S3 S4 Seasons

Figure 5.7f. Sulphur

Table 5.16d. Variations in the macro nutrients (One sample t - test) Std. parameter N Mean t Statistical inference Deviation Total Calcium (%) 4 4.4225 .53344 16.581 P<0.01 significant Total Magnesium (%) 4 3.1400 .42261 14.860 P<0.01 significant Total Sulphur (%) 4 .6100 .24725 4.934 P<0.05 significant DF= 3

Table 5.16e. Karl Pearson correlation between season and variations in the calcium, magnesium and sulphur

Correlation value Statistical inference Parameters Magnes Calcium Sulphur Calcium Magnesium Sulphur ium Lowest P>0.05 P<0.05 P>0.05 -.766 -.978* .822 temperature N.S Significant N.S Mean R.H. P>0.05 P<0.05 P>0.05 .608 .957* -.648 morning N.S Significant N.S Highest R.H. P<0.05 P>0.05 P<0.01 .978* .868 -.998** morning Significant N.S Significant Highest R.H. P<0.05 P>0.05 P<0.05 .955* .885 -.989* evening Significant N.S Significant Mean Wind P<0.05 P>0.05 P<0.05 Speed -.967* -.934 .968* Significant N.S Significant N=4; N.S. = Not significant

108 Table 5.16f. Karl Pearson correlation between soil and variations in the Calcium, Magnesium and sulphur

Correlation value Statistical inference Parameters Magnes Calcium Sulphur Calcium Magnesium Sulphur ium P>0.05 P<0.05 P>0.05 pH 915 -.977* .930 N.S Significant N.S P<0.05 P>0.05 P>0.05 Total Nitrogen .977* .840 -.934 Significant N.S N.S N=4; N.S. = Not significant

5.3.1.2.3 Essential micro nutrients The significant impact was observed in the micronutrients. Zn, Cu, Fe, Mn, Bo, and Mb are low in the southwest monsoon. Except Fe, Mn and Bo all the other micronutrients are higher in summer. Fe, Mn and Bo are higher in pre-summer. The contnent of zinc is 3.46 ppm (Figure 5.8a). The highest concentration of copper is 1.12 ppm. (Figure 5.8b). The (Figure 5.8c) highest iron concentration is 159.3 ppm and the Mn (Figure 5.8d) is 26.34 ppm. The highest value of Bo is 0.13 ppm. (Figure 5.8e).The molybdenum (Figure 5.8f) is high in the S3 (0.13 ppm). The seasonal variations are statistically significant (Table 5.17a and 5.17b). The variation of zinc is not linked with any of the climataological elements. The copper is associated with highest R.H. morning and mean wind speed. The iron is subjected to the highest R.H. (morning and evening) and the mean wind speed (Table 5.17c). The manganese and boron show significant relationship with the highest R.H. (morning and evening) and the mean wind speed. The zinc and copper are associated with the total nitrogen content of the soil. (Table 5.17d). The manganese is relevant to the pH, total nitrogen. The molybdenum is associated with the total nitrogen and the sodium present in the soil (Table 5.17e.)

109

4 1.2 1.12 3.46 1.06 3.5 3.29 3.19 1 0.97 3 2.84 )

) 0.8

2.5 m m p

(pp 0.56 (p 2 0.6 er

1.5 pp Z in c Z in Co 0.4 1 0.5 0.2

0 0 S1 S2 S3 S4 S1 S2 S3 S4 Seasons Seasons

Figure 5.8a. Zinc Figure 5.8b. Copper

180 30 156.3 159.3 157.2 26.34 160 25.87 25 22.64 140 ) m

120 110.2 (pp 20 )

m 100

(pp 15 80 12.36 a n e s e a n e s Iron g 60 10 40 M a n 5 20

0 0 S1 S2 S3 S4 S1 S2 S3 S4 Seasons Seasons

Figure 5.8c. Iron Figure 5.8d. Manganese

0.14 0.13 0.14 0.13 0.12 0.12 0.12 0.12 )

m 0.1 0.1 p 0.1 0.1 p

) ( m 0.08 0.08 (pp 0.06 0.05 0.06

Boron 0.05 b d e n u m

0.04 y 0.04 0.02 M o l l M o 0.02 0 S1 S2 S3 S4 0 S1 S2 S3 S4 Seasons Seasons

Figure 5.8e. Boron Figure 5.8f. Molybdenum

110 Table 5.17a. Variations in the micronutrients (One sample t - test) Std. Parameter N Mean t Statistical inference Deviation Total Zinc (ppm) 4 3.1950 .26160 24.427 P<0.01 significant Total Copper (ppm) 4 .9275 .25264 7.343 P<0.01 significant Total Iron (ppm) 4 145.750 23.7333 12.282 P<0.01 significant Total Manganese (ppm) 4 21.8025 6.50630 6.702 P<0.01 significant Total Boron (ppm) 4 .2175 .18839 2.309 P>0.05 Not significant Total Molybdenum 4 .095 .0332 5.729 P<0.05 significant (ppm) DF= 3 Table 5.17b. Karl Pearson correlation between season and variations in the zinc, copper and Iron

Correlation value Statistical inference Parameters Zinc Copper Iron Zinc Copper Iron Highest R.H. P>0.05 P<0.05 P<0.01 .905 .970* .999** morning N.S Significant Significant Highest R.H. P>0.05 P>0.05 P<0.01 .864 .948 .995** evening N.S N.S Significant Mean Wind P>0.05 P<0.05 P<0.05 -.927 -.983* -.988* Speed N.S Significant Significant N= 4; N.S= Not significant

Table 5.17c. Karl Pearson correlation between season and variations in the manganese, boron and molybdenum

Correlation value Statistical inference Parameters molyb molybden Manganese Boron Manganese Boron denum um Highest R.H. P<0.05 P<0.01 .968* -1.000** .905 P>0.05 N.S morning Significant Significant Highest R.H. P<0.05 P<0.01 .954* -.994** .861 P>0.05 N.S evening Significant Significant P<0.01 P<0.05 Mean Wind Speed -.997** .974* -.895 P>0.05 N.S Significant Significant N= 4; N.S= Not significant

Table 5.17d. Karl Pearson correlation between soil and variations in the zinc, copper and Iron

Correlation value Statistical inference Parameters Zinc copper iron Zinc copper iron P<0.05 P<0.01 P>0.05 Total nitrogen .992** .993** 939 Significant Significant N.S N= 4; N.S= Not significant

111 Table 5.17e. Karl Pearson correlation between soil and variations in the, Manganese, Boron and the Molybdenum

Correlation value Statistical inference Parameters Mangane Molybd Molybdenu Boron Manganese Boron se enum m P<0.05 pH -.976* .944 -.814 P>0.05 N.S P>0.05 N.S Significant P<0.05 P<0.05 Total nitrogen .981* -.932 .967* P>0.05 N.S Significant Significant P<0.05 sodium .877 -.803 .966* P>0.05 N.S P>0.05 N.S Significant N= 4; N.S= Not significant

5.3.1.2.4. Seasonal variations in the non essential element

The chromium (Figure 5.9a) shows meager variations among the seasonal samples. The highest value of the chromium is 0.005 mg/g (southwest monsoon and the lowest value is recorded in the pre-summer (0.1 mg/g). the difference is 0.4 mg/g. Nickel is high (0.04 mg/g) in the southwest monsoon and low in the northeast monsoon and pre-summer (0.01 mg/g) (Figure 5.9b). The cadmium is high (0.05 mg/g) in northeast monsoon and low in summer and southwest monsoon seasons (0.03 mg/g) (Figure 5.9c). The lead is high (0.16 mg/g) in the southwest monsoon and low (0.12 mg/g) in northeast monsoon and summer seasons (Figure 5.9d). Except northeast monsoon (0.2 mg/g) all other seasons possessed the equal quantities of cobalt (Figure 5.9e). In the content of mercury (Figure 5.9f) there is meager difference found. In the pre-summer and southwest monsoons the mercury is 0.002 mg/g and in the northeast monsoon it is 0.001 (mg/g). The Silver (Figure 5.9g) is ranging between 0.05 (northeast monsoon and summer) to 0.06 mg/g (pre-summer and southwest monsoon). In all the seasons selenium (Figure 5.9h) is found to be higher. Among these the highest concentration is recorded in the summer and the lowest concentration (0.57 mg/g) is recorded in the southwest monsoon. The difference is 0.08g. The silver concentration is high (0.06 mg/g) in the pre-summer and the southwest monsoon and low in the northeast monsoon and the summer seasons. Arsenic and Cyanide are not present at all.

112 Except chromium, nickel and the other analysed non essential elements cadmium, lead, cobalt, mercury, silver and selenium show significant seasonal variations (Table 5.18a). The lead is highly correlated with the R.H (morning and evening). Cobalt is highly subjected to the number of rainy days. Mercury is related to the heaviest rainfall. Silver is linked to the R.H (morning and evening) and heaviest rainfall (Table 5.18c). The variations in the cadmium and selenium are not subjected to the meterological elements (Table 5.18b and d). Selenium (Se) is an essential micronutrient for many organisms, including plants, animals and humans. The concentration of Se in plant varies between areas (Zhu et al., 2009). Too much Se can lead to toxicity and the low level causes deficiency. Worldwide, interest in the biological impacts of Se on the environment and food chains is increasing because it is an essential micronutrient for many organisms, including humans and other animals (although it is toxic at higher concentrations) ( Terry et al., 2000). It is also a beneficial nutrient for many plants, including higher plant taxa (Pilon Smits et al., 2009; Lyons et al., 2009). In organisms that require Se, selenocysteine (Se Cys) is an essential component in the so-called selenoproteins or selenoenzymes (Rayman 2002), 25 of which have been identified in humans (Kryukov et al., 2003; Lu and Holmgren, 2009). Selenoproteins have a redox function involved in free-radical scavenging (Lu and Holmgren, 2009), and several studies have shown that improving Se status can lower the risk of cancer (Clark et al., 1996; Wallace et al., 2009). Se speciation varies with plant species and the form of Se fed to the plant (Reid et al., ,2008; Sors et al., 2005; Ximenez-Embun et al., 2004; Kapolna and fodder, 2006; Kapolna et al., 2007; Grant et al., 2004). It is well documented that inter- and intraspecific variation in Se accumulation in plants exists (Zhu et al., 2009).

0.006 0.045 0.04 0.005 0.04 0.005 0.035

0.004 0.03

0.025 0.003 0.02 0.02 0.002 0.002 0.002 (mg/g) Nickel 0.015

Chromium ( mg/ g ) 0.01 0.01 0.001 0.01 0.001 0.005

0 0 S1 S2 S3 S4 S1 S2 S3 S4 Figure 5.9a. Chromium Figure 5.9b.Nickel

113 0.06 0.18 0.16 0.05 0.16 0.05 0.14 0.13 0.04 0.12 0.12 0.04 0.12

0.03 0.03 0.1 0.03 0.08

0.02 g ) / (mg Lead 0.06 Cadmium (mg / g) 0.04 0.01 0.02

0 0 S1 S2 S3 S4 S1 S2 S3 S4 Figure 5.9c. Cadmium Figure 5.9d. Lead

0.035 0.0025 0.03 0.03 0.03 0.03 0.002 0.002 0.002 0.025 0.02 0.02 0.0015

0.015 0.001 0.001 0.001 Cobalt (mg / g )

0.01 g ) (mg / Mercury 0.0005 0.005

0 0 S1 S2 S3 S4 S1 S2 S3 S4 Figure 5.9e. Cobalt Figure 5.9f. Mercury

0.062 0.66 0.65 0.06 0.06 0.06 0.64 0.63 0.058 0.62 0.62 0.056

0.054 0.6

0.052 0.58 0.05 0.05 0.57

Silver (mg /g ) 0.05 Selenium (mg /g ) (mg/g Selenium 0.56 0.048 0.54 0.046

0.044 0.52 S1 S2 S3 S4 S1 S2 S3 S4 Figure 5.9g. Silver Figure 5.9h. Selenium

Table 5.18a. Variations in the nonessential elements (One sample t - test) parameter N Mean Std. Deviation t Statistical inference Total chromium 4 .00250 .001732 2.887 P>0.05 Not significant Total Nickel 4 .0200 .01414 2.828 P>0.05 Not significant Total Cadmium 4 .0375 .00957 7.833 P<0.01 significant Total lead 4 .1325 .01893 13.999 P<0.01 significant Total Cobalt 4 .0275 .00500 11.000 P<0.01 significant Total Mercury 4 .00150 .000577 5.196 P<0.05significant Total Silver 4 .0550 .00577 19.053 P<0.01significant Total Selenium 4 .6175 .03403 36.287 P<0.01significant DF= 3

114 Table 5.18b. Karl Pearson correlation between season and variations in cadmium, lead and cobalt

Correlation value Statistical inference Parameters cadmium lead cobalt cadmium lead cobalt Highest R.H. P<0.05 P>0.05 .522 -.968* -.333 P>0.05 N.S morning Significant N.S Highest R.H. P<0.05 P>0.05 .588 -.953* -.375 P>0.05 N.S evening Significant N.S Numbers of - P<0.01 .870 -.440 P>0.05 N.S P>0.05 N.S Rainy Days 1.000** Significant N=4; N.S= not significant

Table 5.18c. Karl Pearson correlation between season and variations in mercury, silver and selenium

Correlation value Statistical inference Parameters mercury silver selenium mercury silver selenium Highest R.H. P>0.05 P<0.05 P>0.05 -.577 -.577 .930 morning N.S Significant N.S Highest R.H. P>0.05 P<0.05 P>0.05 -.537 -.537 .894 evening N.S Significant N.S Heaviest P<0.01 P<0.01 P>0.05 .993** .993** -.527 Rainfall Significant Significant N.S N=4; N.S= not significant

Table 5.18d.Karl Pearson correlation between soil and variations in cadmium, lead and cobalt

Correlation value Statistical inference Parameters cadmium lead cobalt cadmium Lead cobalt P<0.05 EC .900 -.585 -.985* P>0.05 N.S P>0.05 N.S Significant P<0.05 Manganese .525 -.970* -.576 P>0.05 N.S P>0.05 N.S Significant N=4; N.S= Not significant

5.3.1.3 Discussion The seasonal carbohydrate cycles are particularly well defined in many deciduous trees of the temperate zone. Total carbohydrate contents of stems and branches reach a maximum in autumn at the time of leaf fall, begin to decrease in late winter, and decrease rapidly in early spring when carbohydrates are being depleted by accelerated respiration and used in growth of new tissues (Kramer and Kozlowski, 1979).Seasonal changes in carbohydrate concentrations have been reported is many

115 plants (Bonicel et al., 1987; Ashworth et al., 1993; Rinne et al., 1994; Barbarox and Breda, 2002; Bhowmik and Matsui, 2003). The lowest nitrogen contents of branchlets at the different development stages occurred in summer, when the growth is active in Cassuarina equisetifolia (Zhang et al., 2009). A portion of N was allocated to other portions (e.g., roots and flowers); for example, the peak of the flowering period appears from April to June for C. equisetifolia (Morton, 1980). Similarly, Aerts et al., 1999) suggested that summer warming reduced N contents of mature and senescent leaves in Rubus. Second, N contents was diluted by branchlet’s mass accumulation during summer when C. equisetifolia grew rapidly. Changes in leaf N contents has direct impact on the photosynthetic capacity of the species involved, as there is usually a direct relation between leaf N content and the maximum rate of photosynthesis (Lambers et al., 1998). This may be true of Calotropis also. It is common to find a negative correlation between N and secondary compound contents, such as phenolics and tannins (Horner et al., 1987; Mansfield et al., 1999).The carbon nutrient balance (CNB) hypothesis postulates that phenolic levels in plants are determined by the balance between carbon and other nutrient availability (Bryant et al., 1983). The growth-differentiation balance (GDB) hypothesis (Loomis, 1932; Lorio, 1986; Herms and Mattson, 1992) considers factors that limit growth and differentiation (the sum of chemical and morphological changes that occur in maturing cells, including carbon-based secondary synthesis) also limit N. The production of phenolics dominates when factors other than photosynthate supply are suboptimal for growth. This pattern tend to support source-sink hypotheses, such as the carbon nutrient balance hypothesis (Bryant et al., 1983) and the growth-differentiation balance hypothesis (Herms and Mattson, 1992) that predict increased C allocation to secondary C compounds under low nutrient conditions. In this study at summer the nitrogen concentration is high. At the time of increased nitrogen the hydrocarbons, the fatty acids and sterols also tend to be at the higher side. Several researchers have noted pronounced seasonal variation in the elemental concentration in plants elsewhere in the world, with the largest trace metal concentrations generally occurring in the springtime (Ashton & Riese 1989, Stednick et al. 1987).

116 Leaves and twigs from various shrub species were evaluated for comparative seasonal contents of Ca, Mg, Na, K, P, Cu, Fe, Mn and Zn. Plants were collected in summer, autumn, winter and spring in the counties: Linares, Santiago, Iturbide and Montemorelos belonging to the state of Nuevo Leon, Mexico. During summer, mineral concentrations were higher in general. Only Ca, Mg, K, Mn and Fe were in substantial amounts (Ram´ırez et al., 2006). The seasonal variation of the essential oil extracts from the aerial parts of a Santolina rosmarinifolia population has been studied in detail by Paul et al., (2001). He found that the oil yields increased in the months of March, April, May and June. Oil concentration showed significant correlations with both precipitation (positive) and temperature (negative). The main components of the essential oil showed a significant negative correlation with temperature, while capillene offered a strong positive correlation with precipitation. The rest of the essential oil components did not show any noticeable trend. Faini et al., (1999) observe that the production of epicuticular waxes to be at a minimum level in winter when the relative percentage of less polar compounds (waxes and hydrophobic solids) increases in comparison to the other seasons. A high production of epicuticular waxes in Summer, when drought and solar radiation is highest, suggests that the polar solids (triterpenes, flavonoids, diterpene acids) present in high concentrations might act as a physical barrier to prevent water permeation and dehydration of the leaves and filtering or reflecting of incident light (Harborne et al., 1975). By contrast, during the winter season, production of waxes decreases as a result of the low vegetative activity and changes in chemical composition could then be explained in terms of a thermoregulatory action of the hydrophobic compounds which would also protect the plant from desiccation due to the wind. Costa et al.,

(2009) found a clear seasonal influence on the essential oil compositions. Variations of the Camptothecin content of Nothapodytes nimmoniana according to the seasons and locations are well documented (Namdeo et al., 2010). The overall seasonal variation in the macro and micro nutrients and the secondary metabolites shows that some prominent differences occur between seasons. The organic carbon (2.75%), carbohydrate (1.02%), protein (0.62%), lipids (0.3%), ash content (1.79%) phosphorous (0.53%), potassium (3.28%), sulphur (0.98%) and lead (0.16mg/g) are higher in southwest monsoon. At the same time the calcium (3.64%), magnesium (2.59%), zinc (2.84 ppm), copper (0.56 ppm), iron (110.2ppm),

117 manganese (12.36ppm), boron (0.05ppm), molybdenum (0.05ppm) and selenium (0.57mg/g) are found to be low in the southwest monsoon. The meteorological elements such as mean maximum temperature (36.3 °C) and rain fall (384 mm) and mean wind speed (11.7kmph) are high in this season. The mean R.H morning (67.7%) and evening (46.3 %) are comparatively low in this season. The variation in this analysed parameters may be due to the mean maximum and minimum temperature, Highest temperature, highest and lowest R.H. (morning and evening), mean R.H. (morning and evening), heaviest rainfall, numbers of rainy days, heaviest rainfall and mean wind speed.

5.3.2. Locational variations

5.3.2.1. Locational variations in the organic compounds

5.3.2.1.1. Organic carbon

The quantity of organic carbon is high (27.5%) in the coastal tract (L1) and the terrestrial-rural stretch (L4). In the riverine zone (L3) it is meager (26.2 %) (Figure 5.10). The organic carbon shows statistically significant variations among locations (Table 5.19). The organic carbon shows no correlation with meteorological and soil parameters.

27.5 27.5 27.6 27.4 27.2 27 26.5 26.8 26.4 26.6 26.2 26.4 26.2

Organic carbon (%) carbon Organic 26 25.8 25.6 25.4 L1 L2 L3 L4 L5

Figure 5.10. Organic carbon in the plant (locational study)

118 Table 5.19. Locational variations in the organic carbon (One sample t - test) Std. Parameter N Mean t Statistical Inference Deviation Organic Carbon (%) 5 26.82 .6301 95.18 P<0.01 significant DF = 4 5.3.2.1.2. Locational variations in carbohydrate, protein and lipid

Statistically significant variations are noticed in the quantity of macromolecules (carbohydrate, protein and lipid) among the locations studied (Table 5.20). The maximum amount of carbohydrate (10.6%) is present in the coastal tract (L1) and the lowest level (10.2%) is present in the terrestrial-rural stretch (L4). The protein is also slightly higher (6.9 %) in the coastal tract (L1) and low (5.8%) in riverine zone (L3). The lipid is slightly higher (3.3%) in the hilly terrain (L2) and low (2.8%) in the terrestrial -urban area (L5) (Figure 5.11). The locational variations in the carbohydrate, protein, and lipid show no relationship with the meteorological elements and the soil parameters.

12 10.3 10.6 10.3 10.5 10.2 10 Carbohy drate

8 6.9 6.4 6.2 6.4 5.8 ( % ) 6 Proteins

3.3 3.1 4 2.9 3 2.8

2 Lipids

0 12345

Figure 5.11. Carbohydrate, protein and lipid in the plant- locational study

Table 5.20. Locational variations in the carbohydrate, protein and lipid (One sample t - test) Statistical Parameter N Mean Std. Deviation t Inference P<0.01 Carbohydrate 5 10.380 0.1643 141.254 significant P<0.01 Protein 5 6.340 0.3975 35.665 significant P<0.01 Lipid 5 3.020 0.1924 35.107 significant DF = 4

119 5.3.2.1.3. Locational variations in the energy content The energy values of the plant samples are varying significantly in accordance with the macro molecules (Table.5.21). The range is 105.225 calories/ 100 g (L5- terrestrial-urban area) to 110.235 calories/ 100 g (L1- coastal tract) (Figure 5.12).

110.235 111 109.925 110 109 108

107 105.56 105.485 105.225 106 105 104 103 102 L1 L2 L3 L4 L5 Figure 5.12. Locational variations in the energy content

The locational variation in the energy content is highly correlated with the meteorological elements (Table 5.22). The energy content of the plant sample shows no relationship with the soil.

Table 5.21. Locational variations in the energy value Std. Parameter N Mean t Statistical Inference Deviation Calorific value 5 9.4060 .20864 100.808 P<0.01 significant DF = 4

Table 5.22. Karl Pearson correlation between meteorological elements and calorific value Statistical Calorie value vs. Correlation value inference Seasonal lowest temperature .980** P<0.01 Significant Monthly lowest temperature .917* P<0.05 Significant. Annual highest R.H. evening -.932* P<0.05 Significant Annual lowest R.H. morning -.927* P<0.05 Significant Annual lowest R.H. evening .982** P<0.01 Significant Annual heaviest rainfall -.977** P<0.01 Significant Seasonal heaviest rainfall -.963** P<0.01 Significant Monthly heaviest rainfall -941* P<0.05 Significant Annual numbers of rainy days 972** P<0.01 Significant N=4

120 5.3.2.1.4. Locational variations in the yield of extract The extract yield potential of the plant varies significantly from location to location (Figures 5.13). The plant collected at the coastal tract provided the maximum quantity of the extract (6.9%) and the terrestrial urban area sample yielded the lowest amount (4 %). This variation is statistically significant (Table 5.23) The yield of extracts is not correlated with any of the meteorological or soil parameters.

8 6.95 6.9 7 6.7

6 5.4 ) 5 4 4

3 Extract yield ( % yield Extract 2

1

0 L1 L2 L3 L4 L5

Figure 5.13. Yield of extracts in locations

Table 5.23. Locational variations in the yield of extract Parameter N Mean Std. t Statistical Inference Deviation Yield of extract 5 5.990 1.28082 10.457 P<0.01 significant

DF = 4

5.3.2.1.5. Compounds present in the chloroform extract The GC-MS analysis of the chloroform extract of the Calotropis which is collected from various locations has been identified to contain a complex mixture of 64 compounds (Figure 5.14 – 5.14e). They are varying from 39 (L2) to 43(L1) compounds (Table 5.25). As per the numbers of compounds isolated the location can be arranged as L5 (38) > L4 (40)> L1 (41) > L2 and L3 (42). The variations in the numbers of compounds are statistically significant (Table 5.24 a) Out of these 64 compounds 31 compounds possesses significant variations among locations (2-Methoxy-4-vinyl phenol; Hexadecane; Tridecanoic acid, 12- methyl-methylester; Tetradecanoic acid ; 9-Octadecyne ; n-Hexadecanoic acid ; 9- Octadecanoic acid,methylester,(E)- ; Octacosane; A'-Neogammacer-22(29)-en-3-

121 ol,acetate,(3beta,21beta)-; 9-Octadecenamide,(Z)-; Lup-20(29)-en-3-ol, acetate,(3beta)-; Z-12-Pentacosene; Heptacosane; Tetracosanoic acid,methyl ester; 13-Docosenamide,(Z)-; Squalene; Nonacosane; Bicyclo[4.2.0]oct-2-ene, 3,7- dimethyl-7-(4-methyl-3-pentenyl)-8-(2,6,10-trimethyl-1,5,9-undecatrienyl)-,[1alpha, 6alpha, 7alpha,8alpha(1E,5E)]-;2,6,10,14,18,22-tetracosahexanone,2,6,10,15, 9, 23- hexamethyl- ,(all-E)- ; Heptacosane,1-chloro-; gamma-Tocopherol ; 17- Pentatriacontene; 2(1H)Naphthalenone,3,5,6,7,8,8a-hexahydro-4,8a-dimethyl-6-(1- methylethenyl)-; Desmosterol; gamma-Sitosterol; Pyridine-3-Carboxamide,oxime,N- (2-trifluromethyl phenyl)- ; alpha-Amyrin ; ,4,6a,6b,8a,11,12,14b-Octamethyl- 1,4,4a,5,6,6a,6b,7,8,8a,9,10,11,12,12a,14,14a, 4b -octadecahydro-2H-picen-3-one ; .alpha-Amyrin;12-Oleanen-3-yl acetate, (3alpha)-;9,19-Cyclolanost-24-en-3- ol,acetate ;Urs-12-en-24-oic acid, 3-oxo-,methyl ester,(+)) (Table 5.24b ) . The total numbers of compounds are interconnected to some of the meteorological parameters (annual lowest temperature, seasonal and monthly mean R.H morning, seasonal lowest R.H (morning and evening), monthly lowest R.H. evening, seasonal and monthly total rainfall, and the numbers of rainy days) and the soil parameters (Total nitrogen and the iron content) (Table 5.24c and 5.24d).

43 42 42 42 41 41 40 40

39 38 38

37

36 L1 L2 L3 L4 L5

Figure 5.14. Numbers of compounds identified in locational samples

122

GC-MS Chromotogram for L1 GC-MS Chromotogram for L2

GC-MS Chromotogram for L3 GC-MS Chromotogram for L4 Figure 5.14a. GC MS chromatogram for locational study (L1 to L4)

123

GC-MS Chromotogram for L5 Figure 5.14b. GC - MS chromatogram for locational study (L5)

124 Table 5.25. Compounds identified in the chloroform extract

S.No. Rt. Compound name L1 L2 L3 L4 L5 1 7.9 Dodecane 0.43 0.57 - 0.14 - 2 9.1 2-Methoxy-4-vinyl phenol - 0.09 - - - 3 9.7 Tetradecane 0.19 0.11 0.14 0.21 0.19 4 10.1 Caryophyllene 0.15 - - - - 2(4H)-Benzofuranone,5,6,7,7a-tetrahydro-4,4,7a- 5 11.1 - - - - 0.12 trimethyl- 6 11.5 Hexadecane 0.31 - - 0.24 - 7 13.1 Tetradecanoic acid 0.26 0.37 0.44 - 0.49 8 13.5 5-Ethylcyclopent-1-enecarboxaldehyde 0.18 0.19 0.11 0.23 0.18 Bicyclo[3.1.1]heptane,2,6,6-trimethyl- 9 13.8 0.23 0.43 0.47 0.28 1.09 ,(1.alpha.,2.beta.,5.alpha.) 10 14.2 9-Octadecyne 0.15 0.62 0.28 0.28 1.16 11 14.8 Hexadecanoic acid,methyl ester 0.15 0.23 - - - 12 15.3 n-Hexadecanoic acid 4.95 6.57 4.34 2.68 6.76 13 16.7 9-Octadecanoic acid,methylester,(E)- - 0.48 - - 0.35 14 16.8 Phytol 1.45 0.76 0.94 0.56 1.79 15 17.3 9,12,15-Octadecatrienoic acid,(Z,Z,Z)- 2.58 4.37 1.58 0.55 3.42 16 17.5 Olean-12-ene,3-methoxy-,(3.beta.)- 0.66 - 0.67 - - 17 17.5 2-Methyl-Z,Z-3,13-octadecadienol - - - - 0.68 18 17.7 Dodecanamide 1.27 - 0.46 0.34 - 19 17.8 Hexadecanamide - 0.76 0.51 - 0.44 20 18.7 12-Oleanen-3-yl acetate, (3.alpha.)- - - 1.47 - - 21 19.2 Octacosane 1.86 1.35 0.71 - 0.5 A'-Neogammacer-22(29)-en-3- 22 19.5 1.19 - 1.09 1.55 1.24 ol,acetate,(3.beta.,21.beta.)- 23 20 9-Octadecenamide,(Z)- 3.08 2.78 2.31 3.99 4.24 24 20.4 Urs-20-en-3-ol,(3.beta.,18.alpha.,19.alpha.)- 0.96 - - 1.09 - 25 20.6 Lup-20(29)-en-3-ol,acetate,(3.beta.)- - 2.9 - 2.1 - 26 21.6 Hop-22(29)-en-3.beta.-ol 8.13 9.01 11.67 9.67 14.55 1,2-Benzenedicarboxylic acid, mono(2- 27 22.3 - - 0.21 - - ethylhexyl)ester Oxirane,2,2-dimethyl-3-(3,7,12,16,20- 28 23 pentamethyl-3,7,11,15,19-Heneicosapentaenyl)- - 0.25 - - - ,(all-E)- 29 23.1 11,13-Dimethyl-12-tetradecen-1-ol acetate - - 0.71 - - 30 23.2 Nonadecane,1-chloro- - 1.06 0.82 - - 31 24.2 Z-12-Pentacosene 0.73 0.19 0.09 - 0.19 32 24.6 Heptacosane 0.33 0.62 0.44 0.17 0.59 33 25.6 13-Docosenamide,(Z)- 0.08 0.21 - 0.14 0.18 34 26 Squalene 0.9 1.24 1.45 0.97 2.14 35 26.9 Z-14-Nonacosane - 1.98 - 1.84 2.69 36 27.2 Nonacosane 2.37 1.96 6.37 1.88 2.27 Tricyclo[4.3.0.0(7,9)]nonane, 2,2,5,5,8,8- 37 27.4 0.29 - - 1.49 0.49 hexamethyl-,(1.alpha.,6.beta., 7.alpha.,9.alpha.)- Bicyclo[4.2.0]oct-2-ene, 3,7-dimethyl-7-(4- 38 27.6 methyl-3-pentenyl)-8-(2,6,10-trimethyl-1,5,9- 1.09 1.33 2.4 - 0.87 undecatrienyl)-

125 ,[1.alpha.,6.alpha.,7.beta.,8.alpha.(1E,5E)]- 2,6,10,14,18,22-tetracosahexanone,2,6,10,15,9,23- 39 28.1 - - - 0.89 - hexamethyl-,(all-E)- 40 28.6 Heptacosane,1-chloro- 0.43 0.43 0.77 0.37 0.47 41 29.4 gamma-Tocopherol 0.23 0.16 0.32 - 0.25 42 30 17-Pentatriacontene 0.13 0.37 0.34 - 0.25 43 30.4 Tricosane 4.63 4.81 6.18 5.17 5.35 44 30.8 Vitamin E 2.42 1.53 2.02 1.47 2.8 45 30.9 Olean-12-ene 1.79 - - - - 2(1H)Naphthalenone,3,5,6,7,8,8a-hexahydro-4,8a- 46 31 - 4.04 - 3.25 - dimethyl-6-(1-methylethenyl)- 47 32.6 Campesterol 1.78 1.91 2.16 1.57 1.83 48 33.2 Stigmasterol 1.06 1.79 1.35 1.09 1.4 Ergost-8,24(28)-dien-3-ol,4,14-dimethyl- 49 34.1 0.56 - - 0.59 - ,(3.beta.,4.alpha.,5.alpa.,)- 50 34.7 Stigmasterol,22,23-dihydro- - - - 1.7 1.74 51 34.8 gamma-Sitosterol 2.76 3.35 2.14 - - 52 34.9 Heneicosane,11-decyl- - - 1.21 0.89 0.92 Pyridine-3-Carboxamide,oxime,N-(2- 53 35.1 - 1.47 1.8 2.53 0.98 trifluromethyl phenyl)- 54 35.2 Stigmasta-5,24(28)-dien-3-ol,(3.beta.,24Z)- 1.71 - - - - 56 35.7 .beta.-Amyrin 3.78 3.17 6.61 4.2 4.31 4,4,6a,6b,8a,11,12,14b-Octamethyl- 57 36 1,4,4a,5,6,6a,6b,7,8,8a,9,10,11,12,12a,14,14a,14b- 0.49 0.67 - 0.98 - octadecahydro-2H-picen-3-one 58 36.5 4,22-Stigmastadiene-3-one - 1.37 1.38 1.68 2.33 59 37 .alpha-Amyrin 12.36 8.8 12.65 13.67 11.76 60 37.8 9,19-Cyclolanost-24-en-3-ol,acetate,(3.beta.)- - - 0.67 - - 61 38.4 12-Oleanen-3-yl acetate, (3.alpha.)- 12.75 4.37 7.55 10.16 7 62 38.7 9,19-Cyclolanost-24-en-3-ol,acetate 2.18 0.81 1.5 1.98 1.37 63 39.9 Urs-12-en-24-oic acid, 3-oxo-,methyl ester,(+)- 16.97 18.95 9.84 15.11 9.24 64 40.2 Taraxasterol - 1.15 1.83 2.3 1.38

Table 5.25a. Variations in the numbers of compounds identified in locations through GC-MS (One sample t - test)

Std. Parameter N Mean t Significance Deviation Total Numbers of P<0.01 5 41.00 1.581 57.983 compounds significant DF=4 Table 5.25b. Locational variation in the composition of chloroform extract (One sample t - test) Std. S.No. Parameter N Mean t Significance Deviation P>0.05Not 1 Dodecane 5 .2280 .25956 1.964 significant

126 P<0.05signifi 2 2-Methoxy-4-vinyl phenol 5 .02 .040 1.000 cant P>0.05Not 3 Tetradecane 5 .1680 .04147 9.058 significant phenol, 2,4-bis(1,1- P>0.05Not 4 5 dimethylethyl) .0300 .06708 1.000 significant 2(4H)-Benzofuranone,5,6,7,7a- P>0.05Not 5 5 tetrahydro-4,4,7a-trimethyl- .02 .054 1.000 significant P<0.05 6 Hexadecane 5 .1100 .15264 1.611 significant. Tridecanoic acid, 12-methyl- P<0.05 7 5 methylester .3120 .19460 3.585 significant. P<0.05 8 Tetradecanoic acid 5 .1780 .04324 9.204 significant. 5-Ethylcyclopent-1- P>0.05Not 9 5 enecarboxaldehyde .5000 .34467 3.244 significant Bicyclo[3.1.1]heptane,2,6,6- P>0.05Not 10 5 trimethyl-,(1alpha,2alpha,5alpha) .4980 .40905 2.722 significant P<0.05 11 9-Octadecyne 5 .0760 .10784 1.576 significant P>0.05 Not 12 Hexadecanoic acid,methyl ester 5 5.060 1.68560 6.712 significant P<0.05 13 n-Hexadecanoic acid 5 .17 .232 1.601 significant 9-Octadecanoic P<0.05 14 5 acid,methylester,(E)- 1.100 .50779 4.844 significant P>0.05 Not 15 Phytol 5 2.500 1.49988 3.727 significant P>0.05 Not 16 Octadecanoic acid,methyl ester 5 .2660 .36425 1.633 significant 9,12,15-Octadecatrienoic P>0.05 Not 17 5 acid,(Z,Z,Z)- .14 .304 1.000 significant P>0.05 Not 18 9,12-Octadecadienoic acid(z,z)- 5 .4140 .52037 1.779 significant P>0.05 Not 19 Dodecanamide 5 .34 .334 2.289 significant P>0.05 Not 20 Hexadecanamide 5 .29 .657 1.000 significant P<0.05 21 Octacosane 5 .8840 .72954 2.709 significant A'-Neogammacer-22(29)-en-3- P<0.05 22 5 ol,acetate,(3beta,21beta)- 1.014 .59231 3.828 significant P<0.05 23 9-Octadecenamide,(Z)- 5 3.280 .81495 9.000 significant Urs-20-en-3- P>0.05 Not 24 5 ol,(3alpha,18alpha,19alpha)- .4100 .56329 1.628 significant Lup-20(29)-en-3-ol, P<0.05 25 5 acetate,(3beta)- 1.00 1.398 1.599 significant P>0.05 Not 26 Hop-22(29)-en-3alpha-ol 5 10.61 2.56130 9.259 significant 1,2-Benzenedicarboxylic acid, P>0.05 Not 27 5 mono(2-ethylhexyl)ester .04 .094 1.000 significant 28 Nonadecane,1-chloro- 5 .05 .112 1.000 P>0.05Not

127 significant Ergost-22-en-3- P>0.05Not 29 5 0l,(3alpha,5alpha,22E,24R)- .14 .318 1.000 significant P>0.05Not 30 Stigmastane-3,6-dione,(5alpha) 5 .38 .522 1.611 significant P<0.05 31 Z-12-Pentacosene 5 .2400 .28513 1.882 significant P<0.05 32 Heptacosane 5 .4300 .18668 5.151 significant P<0.05 33 Tetracosanoic acid,methyl ester 5 a .00 .000 3.256 significant P<0.01 34 13-Docosenamide,(Z)- 5 .1220 .08379 6.015 significant P<0.05 35 Squalene 5 1.340 .4981 2.364 significant P>0.05 Not 36 Z-14-Nonacosane 5 1.30 1.231 3.474 significant P<0.01 37 Nonacosane 5 2.970 1.91169 1.650 significant Tricyclo[4.3.0.0(7,9)]nonane, P>0.05 Not 38 2,2,5,5,8,8-hexamethyl- 5 .4540 .61517 2.939 significant ,(1alpha,6beta, 7alpha,9alpha)- Bicyclo[4.2.0]oct-2-ene, 3,7- dimethyl-7-(4-methyl-3- pentenyl)-8-(2,6,10-trimethyl- P<0.05 39 5 1,5,9-undecatrienyl)- 1.138 .86583 1.000 significant ,[1alpha,6alpha,7alpha,8alpha(1E ,5E)]- 2,6,10,14,18,22- P<0.05 40 tetracosahexanone,2,6,10,15,9,23 5 .18 .398 6.975 significant -hexamethyl-,(all-E)- P<0.01 41 Heptacosane,1-chloro- 5 .4940 .15837 3.533 significant P<0.05 42 gamma-Tocopherol 5 .1920 .12153 3.175 significant P<0.05 43 17-Pentatriacontene 5 .2180 .15353 19.370 significant P>0.05 Not 44 Tricosane 5 5.228 .60351 8.011 significant P>0.05 Not 45 Vitamin E 5 2.048 .57164 1.000 significant 2(1H)Naphthalenone,3,5,6,7,8,8a P<0.05 46 -hexahydro-4,8a-dimethyl-6-(1- 5 .3580 .80051 1.617 significant methylethenyl)- P<0.05 47 Desmosterol 5 1.46 2.016 19.319 significant 1.850 P>0.05 Not 48 Campesterol 5 .21413 10.157 0 significant 1.338 P>0.05 Not 49 Stigmasterol 5 .29457 1.632 0 significant Ergost-8,24(28)-dien-3-ol,4,14- P>0.05 Not 50 5 a dimethyl- .00 .000 1.633 significant

128 ,(3alpha,4alpha,5.alpa.,)- P>0.05 Not 51 Stigmasterol,22,23-dihydro- 5 .2300 .31512 2.356 significant P<0.05 52 gamma-Sitosterol 5 .69 .942 2.389 significant

1.650 P>0.05 Not 53 Heneicosane,11-decyl- 5 1.56582 3.210 0 significant

Pyridine-3- P<0.05 54 Carboxamide,oxime,N-(2- 5 .60 .565 1.000 significant trifluromethyl phenyl)- Stigmasta-5,24(28)-dien-3- P>0.05 Not 56 5 ol,(3alpha,24Z)- 1.36 .945 7.554 significant P<0.01 57 alpha-Amyrin 5 .3420 .76474 2.235 significant 4,4,6a,6b,8a,11,12,14b- Octamethyl- P<0.05 58 1,4,4a,5,6,6a,6b,7,8,8a,9,10,11,12 5 4.414 1.30657 3.555 significant ,12a,14,14a,14b-octadecahydro- 2H-picen-3-one P>0.05 Not 59 4,22-Stigmastadiene-3-one 5 .4280 .42822 14.409 significant P<0.05 60 alpha-Amyrin 5 1.35 .850 1.000 significant 12-Oleanen-3-yl acetate, P<0.01 61 5 (3alpha)- 11.85 1.83869 5.848 significant 9,19-Cyclolanost-24-en-3- P<0.01 62 5 ol,acetate .13 .300 6.503 significant Urs-12-en-24-oic acid, 3-oxo- P<0.01 63 5 ,methyl ester,(+)- 8.366 3.19913 7.264 significant P>0.05Not 64 Taraxasterol 5 1.568 .53914 3.442 significant

Table 5.25c. Karl Pearson correlation between meteorological parameters and variations in the total numbers of compounds

Numbers of compounds vs. Correlation value Statistical inference Annual Lowest Temperature .882* P<0.05 significant Seasonal Mean R.H.morning .890* P<0.05 significant Monthly Mean R.H.mornin .887* P<0.05 significant Seasonal Lowest -.958* P<0.05 significant R.H.morning Seasonal Lowest R.H. -.999** P<0.01 significant evening Monthly Lowest R.H. -.996** P<0.01 significant evening Seasonal Total rainfall(mm) .936* P<0.05 significant Monthly total rainfall(mm) .962** P<0.01 significant Numbers of rainy days per -.907* P<0.05 significant season (2.5mm and above) N=5

129 Table 5. 25d. Karl Pearson correlation between soil parameters and variations in the total numbers of compounds

Numbers of compounds Statistical Correlation value vs.soil parameters inference Total Nitrogen (%) .906* P<0.05 significant Iron (ppm) .911* P<0.05 significant N=5

5.3.2.1.6. Groups of chemical The total numbers of compounds identified (64) belong to the families of terpenes, sterols, fatty acids, hydrocarbons, heterocyclic compounds, phenolics and hydroxylamines (Table 5.26). The terpenes are the major group of compound among the identified ones.

Table 5.26. Chemical groups

Compound group L1 L2 L3 L4 L5

Terpenes 13*(61.81**) 11*(42.5**) 11*(56.26**) 13*(62.44**) 9*(54.53**)

Sterols 6*(10.06**) 5*(11.38**) 8*(9.2**) 6*(8.61**) 5*(8.67**)

Fatty acids 8*(12.52**) 8*(15.63**) 8*(10.49**) 7*(8.87**) 8*(17.28**)

Hydrocarbons 2*(12.97**) 14*(19.01**) 12*(19.79**) 12*(15.88**) 12*(14.96**)

Heterocyclic 2*(2.65**) 2*(2.08**) 2*(2.34**) 2*(1.47**) 3*(3.17**) compounds Phenolics - 1*(0.10**) - - -

Hydroxylamines - 1*(1.81**) 1*(1.80**) 1*(2.52**) 1*(0.98**)

(* Total number; ** tentative Quantity)

5.3.2.1.7. Terpenoids There are 17 compounds identified in the locational samples. They are of to five types (1. Monoterpene, 2. Diterpenes, 3. Sesquiterpene 4. Triterpenes, 5. Pentacyclic Triterpenes). Based on the numbers of compounds the locations can be descendingly arranged as L1 (14)>L4 (13)>L3 (12)> L2 (11)>L5 (10). According to the quantity of the phytochemicals the descending order of the locations is L4 (62.44)>L1 (61.81)>L3 (56.26)>L5 (54.53)>L2 (42.5)

130 The 12-Oleanen-3-yl acetate, (3.alpha.) is found only in hilly terrain and Caryophyllene is identified only in the coastal tract. The variations in the numbers of compound and the quantity are statistically significant (Table 5.27a). The variation in the numbers of compounds are associated with the soil parameters (soil organic carbon and the organic matter) and not associated with any of the meteorological elements (Table 5.27b). The quantity of the terpenoids is associated with the meteorological elements of relative humidity only (Table 5.27c).

Table 5.27.Terpenoids

S.no. Compounds name L1 L2 L3 L4 L5

MONOTERPENES Bicyclo[3.1.1]heptane,2,6,6-trimethyl- 1. 0.23 0.43 0.47 0.28 1.09 ,(1alpha ,2beta 5alpha) DITERPENES (ACYCLIC) 2. Phytol 1.45 0.76 0.94 0.56 1.79 SESQUITERPENE 3. Caryophyllene 0.15 - - - - TRITERPENES 4. Olean-12-ene 1.79 - - - - Urs-12-en-24-oic acid, 3-oxo-,methyl 5. 16.97 18.95 9.84 15.11 9.24 ester,(+)- 6. Hop-22(29)-en-3.beta.-ol 8.13 9.01 11.67 9.67 14.55 7. Squalene 0.9 1.24 1.45 0.97 2.14 8. Taraxasterol - 1.15 1.83 2.3 1.38 A'-Neogammacer-22(29)-en-3- 9. 1.19 - 1.09 1.55 1.24 ol,acetate,(3.beta.,21.beta.)- 10. Olean-12-ene,3-methoxy-,(3.beta.)- 0.66 - 0.67 - - Oxirane,2,2-dimethyl-3-(3,7,12,16,20- 11. pentamethyl-3,7,11,15,19- - 0.25 - - - Heneicosapentaenyl)-,(all-E)- PENTACYCLIC TRITERPENE 12. Alpha-Amyrin 12.36 8.8 12.65 13.67 11.76 13. Beta-Amyrin 3.78 3.17 6.61 4.2 4.31 14. Lup-20(29)-en-3-ol,acetate,(3.beta.)- - 2.9 - 2.1 - 15. 12-Oleanen-3-yl acetate, (3.alpha.)- - - 1.47 10.16 - Urs-20-en-3- 16. 0.96 - - 1.09 - ol,(3.beta.,18.alpha.,19.alpha.)- Beta-Amyrene derivatives- 4,4,6a,6b,8a,11,12,14b-Octamethyl- 17. 1,4,4a,5,6,6a, 6b,7,8,8a,9, 10,11,12,12a, 0.49 0.67 - 0.98 - 14, 14a,14b - octadecahydro-2H-picen-3- one

131 Table 5.27a. Seasonal variations in the number and quantity of terpenoids (One sample t - test) Std. Parameter N Mean t Statistical inference Deviation

Numbers of 5 12.00 1.581 16.971 P<0.01 significant Terpenes quantity of 55.49 5 8.02332 15.465 P<0.01 significant Terpenes 20 DF=4

Table 5.27b. Karl Pearson correlation between numbers of terpenoids and soil parameters

Numbers of terpenes vs. Soil Correlation value Statistical inference parameters Organic Carbon (%) .924* P<0.05 significant Organic Matter (%) .924* P<0.05 significant N=5

Table 5.27c. Karl Pearson correlation between quantity of terpenoids and meteorological parameters Quantity of terpenes vs. Correlation value Statistical inference Meteorological parameters Annual Mean R.H. morning .927* P<0.05 significant Monthly Mean R.H. morning .890* P<0.05 significant Annual Highest R.H. -.905* P<0.05 significant morning Seasonal Highest R.H. .917* P<0.05 significant morning Monthly Highest R.H. .917* P<0.05 significant morning N=5

5.3.2.1.8. Hydrocarbons In the locational comparison, 18 hydrocarbons are identified (Table 5.28). But for L2 (13 compounds) other location possess 12 compounds. According to the quantity they can be arranged as L3 (19.79) > L2 (19.01)>L4 (15.88) > L5 (14.96)> L1 (12.97). Bicyclo [4.2.0] oct-2-ene,3, 7-dimethyl- 7- (4-methyl-3-pentenyl) -8- (2,6,10-trimethyl-1,5,9-undecatrienyl) -, [1alpha., alpha, 7beta, 8.alpha (1E,5E)]-; 1,2- Benzenedicarboxylic acid, mono(2-ethylhexyl) ester ; is present only in the riverine zone. The locational variation in the hydrocarbon is statistically significant (Table

132 5.28a). The variations in the number is associated with the meteorological parameter of Annual Mean R.H. morning (Table 5.28b) and the soil parameters of nitrogen, phosphorous, iron, zinc and copper (Table 5.28c) . The quantity is related to the soil sodium (Table 5.28d) and it is not associated with any of the meteorological elements.

Table 5.28. Locational variations in the Hydrocarbons

S.no. Compound L1 L2 L3 L4 L5

Bicyclo[4.2.0]oct-2-ene, 3,7-dimethyl-7- (4-methyl-3-pentenyl)-8-(2,6,10-trimethyl- 1. 1.09 1.33 2.4 - 0.87 1,5,9-undecatrienyl)- ,[1.alpha.,6.alpha.,7.beta.,8.alpha.(1E,5E)]- Tricyclo[4.3.0.0(7,9)]nonane, 2,2,5,5,8,8- 2. hexamethyl-,(1.alpha.,6.beta., 0.29 - - 1.49 0.49 7.alpha.,9.alpha.)- 2(1H)Naphthalenone,3,5,6,7,8,8a- 3. hexahydro-4,8a-dimethyl-6-(1- - 4.04 - 3.25 - methylethenyl)- ALKANES 4. Tricosane 4.63 4.81 6.18 5.17 5.35 5. Nonacosane 2.37 1.96 6.37 1.88 2.27 6. Hexadecane 0.31 - - 0.24 - 7. Tetradecane 0.19 0.11 0.14 0.21 0.19 8. Dodecane 0.43 0.57 - 0.14 - 9. Z-14-Nonacosane - 1.98 - 1.84 2.69 10. Octacosane 1.86 1.35 0.71 - 0.5 11. 17-Pentatriacontene 0.13 0.37 0.34 - 0.25 12. Heneicosane,11-decyl- - - 1.21 0.89 0.92 CHLORINATED 13. Nonadecane,1-chloro- - 1.06 0.82 - - 14. Heptacosane,1-chloro 0.43 0.43 0.77 0.37 0.47 ALKENES 15. Z-12-Pentacosene 0.73 0.19 0.09 - 0.19 DICARBOXYLIC ACIDS 16. Heptacosane 0.33 0.62 0.44 0.17 0.59 CYCLOPENTANES 17. 5-Ethylcyclopent-1-enecarboxaldehyde 0.18 0.19 0.11 0.23 0.18 CARBOCYCLIC ACIDS PTHALIC

ACIDS 1,2-Benzenedicarboxylic acid, mono(2- 18. - - 0.21 - - ethylhexyl) ester

133 Table 5.28a. Locational variations in the numbers and quantity of the Hydrocarbons (One sample t - test)

Std. Parameter N Mean t Statistical inference Deviation

Numbers of 5 12.40 .894 31.000 P<0.01 significant Hydrocarbons Quantity of 16.72 5 2.56771 14.564 P<0.01 significant Hydrocarbons 40 DF = 4

Table 5.28b. Karl Pearson correlation between meteorological parameters and numbers of the hydrocarbons

Parameters Correlation value Statistical inference

Annual Mean R.H. morning .889* P<0.05 significant N=5

Table 5.28c. Karl Pearson correlation between soil parameters and the numbers of hydrocarbons Parameters Correlation value Statistical inference

Total Nitrogen (%) .895* P<0.05 significant Total Phosphorous (%) .980** P<0.01 significant Iron .928* P<0.05 significant Zinc .950* P<0.05 significant Copper .929* P<0.05 significant

Table 5.28d. Karl Pearson correlation between soil parameters and quantity of the hydrocarbons

Parameters Correlation value Statistical inference Total Sodium (%) 893* P<0.05 significant

5.3.2.1.9. Fatty acids The locational samples consist of 13 fatty acids (Table 5.29). Except L4 (7- compounds) all the other locations possesses 8 compounds. The maximum quantity is present in L5 (17.28) and the minimum is present in the L4 (8.87). According to the quantity they can be descendingly arranged as L5 (17.28) >L2 (15.63) >L1 (12.52) >L3 (10.49) >L4 (8.87). The Tetracosanoic acid, methyl ester, Hexadecanamide,

134 11,13-Dimethyl-12-tetradecen-1-ol acetate are present only in riverine zone; 2,6,10,14,18,22-tetracosahexanone, 2,6,10,15,9,23- hexamethyl-, (all-E)- appear only in terrestrial – rural stretch. The locational variations found in the fatty acids are statistically significant (Table 5.29a). The numbers of compounds is associated only with the soil parameters of total potassium. The quantity is associated neither with climate nor soil parameters (Table 5.29b). Table 5.29. Fatty acids

S.No. Compound L1 L2 L3 L4 L5 UNSATURATED 9,12,15-Octadecatrienoic 1. 2.58 4.37 1.58 0.55 3.42 acid,(Z,Z,Z)- MONOUNSATURATED 2. 9-Octadecenamide,(Z)- 3.08 2.78 2.31 3.99 4.24 Erucic Acid 3. 13-Docosenamide,(Z)- 0.08 0.21 - 0.14 0.18 LINOLEIC ACIDS PALMITIC ACIDS 4 Hexadecanamide -- - 0.37 - - 5 n-Hexadecanoic acid 4.95 6.57 4.34 2.68 6.76 ESTERS, methyl 6 Hexadecanoic acid,methyl ester 0.15 0.23 - - - MYRSITIC ACIDS 7 Tetradecanoic acid 0.26 0.37 0.44 - 0.49 LAURIC ACIDS 8 Dodecanamide 1.27 - 0.46 0.34 - STEARIC ACIDS

ESTERS 9-Octadecanoic 9 - 0.48 - - 0.35 acid,methylester,(E)- Omega-3 Derivatives

Docosahexaenoic Acids 2,6,10,14,18,22- 10 tetracosahexanone,2,6,10,15,9,23- - - - 0.89 - hexamethyl-,(all-E)- FATTY ALCOHOLS 11 2-Methyl-Z,Z-3,13-octadecadienol - - - - 0.68 11,13-Dimethyl-12-tetradecen-1- 12 - - 0.71 - - ol acetate POLYMERIC FATTY ACIDS 13 9-Octadecyne 0.15 0.62 0.28 0.28 1.16

135

Table 5.29a. Locational variations in the numbers and quantity of the Fatty acids (One sample t - test)

Std. Parameter N Mean t Statistical inference Deviation Numbers of Fatty 5 8.00 .707 25.298 P<0.01 significant acids Quantity of Fatty 13.32 5 4.68214 6.365 P<0.05 significant acids 80 DF= 4

Table 5.29b. Karl Pearson correlation between soil parameters and numbers of fatty acids

Parameters Correlation value Statistical inference Total Potassium (%) .925* P<0.05 significant

5.3.2.1.10. Sterol composition There are 12 sterols identified in the locational samples (Table 5.30). The location sample 1, L3, and L4 possess 6 compounds and the others possess 5 compounds. As per their quantity the descending order is as follows L2 (11.38)> L1 (10.06)>L3 (9.2)>L5 (8.67)> L4 (8.61). The following compounds are present in any one location only 9, 19-Cyclolanost-24-en-3-ol, acetate, (3.beta.)-, Desmosterol,Stigmasta-5,24(28)-dien-3-ol,(3.beta.,24Z)-,Ergost-22-en-3-l, (3beta, 5alpha, 22E, 24R)- (riverine zone ); stigmasterol, 22, 23 – dihydro - (coastal tract). The locational variations among the sterol compounds are qualitatively and quantitatively statistically significant (Table 5.30a). The quantities of the sterol compounds are associated only with the meteorological elements such as seasonal lowest temperature and the annual lowest R.H. morning. The number of the compounds is not associated with the meteorological elements and the soil parameters (Table 5.30b).

Table 5.30. Sterol composition

S.no. Compound L1 L2 L3 L4 L5 1. 9,19-Cyclolanost-24-en-3-ol,acetate 2.18 0.81 1.5 1.98 1.37 2. 9,19-Cyclolanost-24-en-3-ol,acetate,(3.beta.)- - - 0.67 - - PHYTOSTEROLS 3. Campesterol 1.78 1.91 2.16 1.57 1.83

136 4. Stigmasterol 1.06 1.79 1.35 1.09 1.4 5. gamma-Sitosterol 2.76 3.35 2.14 - - 6 Stigmasterol,22,23-dihydro- - - - 1.7 1.74 CHOLESTEROLS -(Dehydrocholesterols) 7. Desmosterol - - 0.2 - STIGMASTEROLS Analogs/Derivatives 8. Stigmasta-5,24(28)-dien-3-ol,(3.beta.,24Z)- - - 2.04 - - 9. 4,22-Stigmastadiene-3-one 1.69 0.89 - 1.68 1.69 ERGOSTEROLS Analogs/Derivatives

(Withanolides) 10. Ergost-22-en-3-0l,(3.beta.,5.alpha.,22E,24R)- - - 0.32 - - Ergost-8,24(28)-dien-3-ol,4,14-dimethyl- 11. 0.56 - - 0.59 - ,(3.beta.,4.alpha.,5.alpa.,)-

Table 5.30a. Locational variations in the numbers and quantity of the Sterols (One sample t - test)

Parameter N Mean Std. Deviation t Statistical inference Numbers of Sterols 5 5.60 .548 22.862 P<0.01 significant Quantity of Sterols 5 9.5840 1.16014 18.472 P<0.05 significant DF= 4 Table 5. 30b. Karl Pearson correlation between meteorological elements and quantity of sterols Statistical Parameters Correlation value inference Seasonal Lowest .894* P<0.05 significant Temperature Annual Lowest R.H. -.980** P<0.01 significant morning DF= 4

5.3.2.1.11. Heterocyclic Compounds

There are three heterocyclic compounds present in the locational samples. L5 possesses all the three compounds and the other locations possess two compounds. The quantity of the compound ranges between 2.65(L1) to 3.17(L5). 2(4H)- Benzofuranone, 5, 6, 7, 7a-tetrahydro-4, 4,7a-trimethyl- is present only in the sample L5- terrestrial urban area (Table 5.31). The locational variations among these compounds are significant (Table 5.31a). The variation in the numbers is correlated with potassium content of the soil (Table 5.31b). The quantity is associated with pH and potassium content of the soil (Table 5.31c).

137 Table 5.31. Heterocyclic compounds

S.no. Compound L1 L2 L3 L4 L5 BENZOPYRANS 1. Vitamin E 2.42 1.53 2.02 1.47 2.8 2(4H)-Benzofuranone,5,6,7,7a- 2. - - - - 0.12 tetrahydro-4,4,7a-trimethyl- TOCOPHEROLS 3. gamma-Tocopherol 0.23 0.16 0.32 - 0.25

Table 5.31a. Locational variations in the numbers and quantity of the Heterocyclic compounds (One sample t - test)

Parameter N Mean Std. t Statistical inference Deviation Numbers of 5 2.00 .707 6.325 P<0.01 significant Heterocyclic compounds Quantity of 5 2.342 .63433 8.256 P<0.01 significant Heterocyclic 0 compounds DF= 4 Table 5.31b. Karl Pearson correlation between soil parameters and number of heterocyclic compounds Statistical Parameters Correlation value inference Total Potassium (%) .925* P<0.05 significant N=5

Table 5.31c. Karl Pearson correlation between soil parameters and number of heterocyclic compounds

Parameters Correlation value Statistical inference pH .892* P<0.05 significant Total Potassium (%) .996** P<0.01 significant N=5

5.3.2.1.12. Phenolics Only one phenolic compound and hydroxyl amine are identified in the locational samples (Table 5.32 and 5.33). The phenolic compound is found only in the hilly terrain. The hydroxyl amine is found in all the locations except L1.The quantity is 0 (L1) to 2.53 (L4). The variation in this compound is not associated with the meteorological elements. The quantity of the hydroxylamine is correlated with the pH of the soil (Table 5.33a).

138 Table 5.32. Phenolics and hydroxylamine

S.no. Compound L1 L2 L3 L4 L5 Phenolics - Catechols 1 2-Methoxy-4-vinyl phenol - 0.09 - - -

Table 5.33. Hydroxylamine

S.no. Compound L1 L2 L3 L4 L5 Hydroxylamine- Oximes Pyridine-3-Carboxamide,oxime,N-(2- 2 - 1.47 1.8 2.53 0.98 trifluromethyl phenyl)-

Table 5.33 a. Karl Pearson correlation between soil parameters and quantity of hydroxylamines compounds

Parameters Correlation value Statistical inference pH -.941* P<0.05 significant N=5

5.3.2.2. Locational variations in the inorganic compounds

5.3.2.2.1. Locational variations in the ash content

The ash composition is high in the L5 (1.89%) and low in the L1 (1.72 %) (Figure 5.14). The locational variations in the ash composition are statistically significant (Table 5.34). The variations noticed are not associated with any meteorological and soil factors.

1.89 1.9

1.85 1.81 1.79 1.8 1.74 1.75 1.72 Ash (%) 1.7

1.65

1.6 L1 L2 L3 L4 L5

Figure 5.15. Locational variations in the ash content

139 Table 5.34. Variations in ash content (One sample t - test)

parameter N Mean Std. Deviation t Statistical inference

Ash (%) 5 1.7900 .06671 60.001 P<0.01 significant DF=3

5.3.2.2.2. Locational variations in the essential macro nutrients The essential macronutrients present in the plant samples collected at five different locations show distinct variations. The figure (5.15a) exhibits the variations in the nitrogen which is between 2.05 %( L1) to 2.18% (L2 and L4). The nitrogen is comparatively rich in hilly terrain and the terrestrial - rural stretch and lower in L1 (coastal tract). The figure (5.15b) shows the ranging of phosphorous between 0.53% (L4) to 0.58% (L5). The phosphorous is rich in the L5 (terrestrial-urban area) and low in L4 (the terrestrial-rural stretch). The potassium is rich in the terrestrial –rural stretch (L4) and lower in the coastal tract (L1) (Figure 5.15c).

0.58 2.18 2.18 2.2 0.58 0.57 0.57

0.57 2.15 0.56 2.09 0.54 2.1 2.07 0.55 0.54 0.53 2.03 2.05 0.53 Nitrogen ( % ) Nitrogen

Phosphorous ( % ) 0.52 2 0.51

1.95 0.5 L1 L2 L3 L4 L5 L1 L2 L3 L4 L5

Figure 5.15a. Nitrogen Figure 5.15b. Phosphorous

3.3 3.28

3.22 3.25 3.19

) 3.2

( % 3.12 3.15 3.1

3.1 Potassium

3.05

3 L1 L2 L3 L4 L5

Figure 5.15c. Potassium

140 The other macronutrients, calcium, magnesium, and sulphur are depicted in figures (5.15d to 5.15f). The variations in these compounds (N, P, K, Ca, Mg, and S) are statistically significant (Table 5.35). The variations found in the essential macronutrients are only associated with the soil elements (Table 5.35 to 5.38). The nitrogen content of plant is associated with the soil nitrogen, phosphorous, potassium, sodium, iron, zinc and copper of the soil (Table 5.36).The phosphorous content of plant is related to the pH, nitrogen, potassium iron, zinc and copper of the soil (Table 5.37). The potassium content of plant is correlated with the soil nitrogen, while sulphur is associated with the pH, electrical conductivity, and potassium. The calcium and magnesium are not associated with any of these soil parameters (Table 5.36 and 5.37).

2.65 4 3.94 2.65 2.61 2.59 3.9 2.6 3.79

3.8 2.55 2.51

3.64 2.48 3.7 3.64 2.5 3.59

Calcium ( % ( ) Calcium 3.6 2.45 Magnesium ( % ) ( Magnesium

3.5 2.4

3.4 2.35 L1 L2 L3 L4 L5 L1 L2 L3 L4 L5 Figure 5.15d. Calcium Figure 5.15e. Magnesium

0.98 1 0.84 0.9 0.78

0.8 0.64 0.7 0.56 0.6 0.5 0.4 Sulphur ) ( % 0.3 0.2 0.1 0 L1 L2 L3 L4 L5

Figure 5.15f. Sulphur

141 Table 5.33. Seasonal variations in the essential macronutrients (One sample t - test) Statistical Parameter N Mean Std. Deviation t inference Total Nitrogen P<0.01 5 2.1100 .06745 69.946 (%) significant Total P<0.01 5 3.182 .0736 96.646 Potassium (%) significant Total P<0.01 Phosphorous 5 .5580 .02168 57.553 significant (%) Total Calcium P<0.01 5 3.7200 .14405 57.746 (%) significant Total P<0.01 Magnesium 5 2.5680 .07085 81.045 significant (%) Total Sulphur P<0.01 5 .7600 .16553 10.267 (%) significant DF= 4

Table 5.34. Karl Pearson correlation between soil parameters and Nitrogen

Plant nitrogen vs. Correlation value Statistical inference Total Nitrogen (%) -.927* P<0.05 significant Total Phosphorous (%) -.889* P<0.05 significant Total Potassium (%) -.952* P<0.05 significant Total Sodium (%) -.904* P<0.05 significant Iron -.936* P<0.05 significant Zinc -.970** P<0.01 significant Copper -.935* P<0.05 significant N=5

Table 5.35. Karl Pearson correlation between soil parameters and phosphorous

Plant phosphorous vs. Correlation value Statistical inference pH .903* P<0.05 significant Total Nitrogen (%) .948* P<0.05 significant Total Potassium (%) .944* P<0.05 significant Iron .945* P<0.05 significant Zinc .957* P<0.05 significant Copper .955* P<0.01 significant

Table 5.36. Karl Pearson correlation between soil parameters and potassium

Plant potassium vs. Correlation value Statistical inference Total Nitrogen (%) -.888* P<0.05 significant N=5

142 Table 5.37. Karl Pearson correlation between soil parameters and sulphur

Plant sulphur vs. Correlation value Statistical inference pH -.879* P<0.05 significant Electrical Conductivity -.964** P<0.05 significant (ds/m) Total Potassium (%) -.934* P<0.05 significant N=5

5.3.2.2.3 Essential micro nutrients

The micronutrients show some variations among the locations 9 Figure 5.16a to 5.16f). The Zn ranges from 2.69 ppm (coastal tract) to 2.89 (riverine zone) and the copper is 0.57ppm (hilly terrain) to 0.81(coastal tract). The iron ranges between 106.3ppm (coastal track) and 112.6 ppm (hilly terrain). The manganese was about 10.36 ppm (hilly terrain) and 12.64 ppm (coastal tract). The boron was about 0.05 (terrestrial- rural stretch) to 0.08 ppm (hilly terrain and riverine zone). The molybdenum was about 0.03 ppm (coastal tract) and 0.06 ppm (riverine zone and the terrestrial-urban area). The analysed micronutrients show significant variations between places (Table 5.37a). Zinc and molybdenum are mostly pertinent to the meteorological parameters like temperature, R.H., and rainfall (Table 5.37b and c). The other parameters show no relevance to the meteorological and soil parameters.

2.89 2.87 2.9 0.9 0.81 2.84 0.79 2.83 2.85 0.8 0.7 0.57 2.8 0.59 0.56 0.6 2.75 2.69 0.5 2.7 0.4 Zinc (ppmZinc )

2.65 ) ( ppm Copper 0.3 0.2 2.6 0.1 2.55 0 L1 L2 L3 L4 L5 L1 L2 L3 L4 L5

Figure 5.16a. Zinc Figure 5.16b. Copper

143 112.6 113 14 12.64 12.49 12.36 112 110.3 110.2 12 10.36 10.48 111 110 10 109 106.9 8 108 106.3 107 6 Iron ( ppm ) 106

Manganese ( ppm ) 4 105 104 2 103 L1 L2 L3 L4 L5 0 L1 L2 L3 L4 L5

Figure 5.16c. Iron Figure 5.16d. Manganese

0.08 0.08 0.06 0.06 0.06 0.08 0.07 0.05 0.05 0.07 0.06 0.05 0.06 0.05 0.05 0.04 0.03 0.04 0.03 0.03 Boron ppm ( ) 0.02 0.02 Molybdenum ( ppmMolybdenum( ) 0.01 0.01 0 L1 L2 L3 L4 L5 0 L1 L2 L3 L4 L5

Figure 5.16e. Boron Figure 5.16f. Molybdenum

Table 5.37a. Variations in the micronutrients (One sample t - test) Std. parameter N Mean t Statistical inference Deviation Total zinc (ppm) 5 2.824 .07861 80.326 P<0.01 significant Total copper (ppm) 5 .6640 .12482 11.895 P<0.01 significant Total iron (ppm) 5 109.260 2.6197 93.259 P<0.01 significant Total manganese (ppm) 5 11.6660 1.14253 22.832 P<0.01 significant Total boron (ppm) 5 .0680 .01304 11.662 P<0.01 significant Total molybdenum 5 .0500 .01225 9.129 P<0.01significant (ppm) DF=4 Table 5.37b. Karl Pearson correlation between Meteorological parameters and Zinc Correlation Parameters Statistical inference value Annual mean maximum temperature 953* P<0.05 significant Seasonal mean maximum 945* P<0.05 significant temperature Monthly mean maximum .903* P<0.05 significant temperature Annual mean minimum .973** P<0.01 significant temperature

144 Seasonal mean minimum .967** P<0.01 significant temperature Monthly mean minimum .973** P<0.01 significant temperature Annual highest temperature .973** P<0.01 significant Seasonal highest temperature .960 P<0.05 significant Monthly lowest temperature -.895* P<0.05 significant Annual mean R.H.evening -954* P<0.05 significant Seasonal mean R.H.morning -963** P<0.01 significant Monthly mean R.H.evening 963** P<0.01 significant Seasonal highest R.H. evening 973** P<0.01 significant Monthly highest R.H. evening 949* P<0.05 significant Seasonal lowest R.H. evening -.943* P<0.05 significant Monthly lowest.R.H. Evening -.931* P<0.05 significant Seasonal total rainfall(mm) .963** P<0.01 significant Number of rainy days per season 949* P<0.05 significant (2.5mm and above) N=5

Table 5.37c. Karl Pearson correlation between Metereological parameters and Molybdenym Correlation Molybdenym vs. Statistical inference value Annual mean maximum temperature .913* P<0.05 significant Seasonal mean maximum .904* P<0.05 significant temperature Annual mean minimum .943* P<0.05 significant temperature Seasonal mean minimum .930* P<0.05 significant temperature Monthly mean minimum .939* P<0.05 significant temperature Annual highest temperature .943* P<0.05 significant Seasonal highesttemperature .922* P<0.05 significant Monthly lowest temperature -.882** P<0.01 significant Annual mean R.H.evening -.914* P<0.05 significant Seasonal mean R.H.morning -.925* P<0.05 significant Monthly mean R.H.evening -.926 P<0.05 significant Seasonal highest R.H. evening -.973** P<0.01 significant Monthly highest R.H. evening -.949* P<0.05 significant Seasonal lowest R.H. evening -.902* P<0.05 significant

145 Monthly lowest R.H. evening -.888* P<0.05 significant Seasonal total rainfall(mm) .937* P<0.05 significant Numbers of rainy days per season -.927* P<0.05 significant (2.5mm and above) N=5

5.3.2.2.4. Locational variations in the nonessential element

Among the nonessential elements analysed Cr is high (0.005 mg/g) in terrestrial-rural stretch and low (0.002 mg/g) in the coastal tract. The Ni is 0.02 mg/ g (coastal tract and terrestrial – urban area) to 0.05 mg/ g. The Cd is about 0.02 (hilly terrain) to 0.04 coastal tract mg/g. The Pb was about 0.12mg/g (terrestrial –urban area) to 0.16 (coastal tract and terrestrial-rural stretch). The Co is about 0.02 mg/ g (terrestrial-urban area) to) 0.06 mg/g (riverine zone). Except terrestrial-rural stretch (0.002) all other samples contain equal mercury (0.001mg/ g). The silver is about 0.02 mg/ g (coastal tract) to 0.06 mg/ g (terrestrial rural stretch). The selenium is present in very high level. It is about 0.52 (riverine zone) to 0.59 mg/ g (coastal tract).The arsenic and cyanide are not present in any samples (Figures 5.17a to 5.17h). The nonessential elements show (except cadmium) no correlation among the meteorological and the soil parameters. The cadmium is correlated to the temperature and the R.H., rainfall and wind speed (Table 5.38).

0.005 0.005 0.05 0.0045 0.004 0.05 0.04 0.004 0.045 0.04 0.0035 0.003 0.003 0.035 0.03 0.003 0.002 0.03 0.0025 0.02 0.025 0.02 0.002 0.02

0.0015 Nickel ( mg /g ) Chromium) g (mg/ 0.015 0.001 0.01 0.0005 0.005 0 0 L1 L2 L3 L4 L5 L1 L2 L3 L4 L5

Figure 5.17a.Chromium Figure 5.17b.Nickel

146 0.04 0.16 0.16 0.15 0.04 0.16 0.035 0.03 0.03 0.13 0.03 0.14 0.12

0.03 0.12 0.025 0.02 0.1 0.02 0.08 0.015 0.06 Lead ( Lead mg / g)

Cadmium Cadmium ( mg /g ) 0.01 0.04 0.005 0.02 0 L1 L2 L3 L4 L5 0 L1 L2 L3 L4 L5

Figure 5.17c.Cadmium Figure 5.17d. Lead

0.06 0.06 0.002 0.05 0.002 0.0018 0.05 0.04 0.0016 0.04 0.0014 0.03 0.001 0.001 0.0012 0.001 0.001

0.03 0.02 0.001 0.0008

Cobalt Cobalt mg( / g ) 0.02

Mercury ( mg / g ) ( mg Mercury 0.0006 0.0004 0.01 0.0002

0 0 L1 L2 L3 L4 L5 L1 L2 L3 L4 L5

Figure 5.17e. Cobalt Figure 5.17f. Mercury

0.06 0.59 0.06 0.6

0.57 0.05 0.58

0.04 0.56 0.54 0.03 0.03 0.03 0.53 0.03 0.54 0.02 0.52

Silver mg ( / g ) 0.02 0.52 Selenium ( mg ( Selenium g ) /

0.01 0.5

0 0.48 L1 L2 L3 L4 L5 L1 L2 L3 L4 L5

Figure 5.17g. Silver Figure 5.17h. Selenium

Table 5.38a. Variations in the nonessential elements (One sample t - test) Std. Statistical parameter N Mean t Deviation inference Total chromium 5 .0034 .001140 6.668 P<0.01 significant Total Nickel 5 .0320 .01304 5.488 P<0.01 significant Total Cadmium 5 .0300 .00707 9.487 P<0.01 significant Total lead 5 .1440 .01817 17.725 P<0.01 significant Total Cobalt 5 .0400 .01581 5.657 P<0.01 significant Total Mercury 5 .0012 .000447 6.000 P<0.01significant Total Silver 5 .6175 .03403 36.287 P<0.01significant Total Selenium 5 .5500 .02915 42.183 P<0.01significant DF= 4

147 Table 5.38b. Karl Pearson correlation between Meteorological parameters and cadmium

Parameters Correlation value Statistical inference Monthly Mean Maximum -.885* P<0.05 significant Monthly Highest temperature -.943* P<0.05 significant Annual Lowest Temperature .987** P<0.01 significant Seasonal Mean R.H. morning .995** P<0.01 significant Monthly Mean R.H.morning .992** P<0.01 significant Seasonal Highest R.H. morning .968** P<0.01 significant Monthly Highest R.H. morning .968** P<0.01 significant Seasonal Lowest R.H.morning 933* P<0.05 significant Seasonal Lowest R.H. evening 940* P<0.05 significant Seasonal heaviest rainfall in 24 HRS(mm) -.928* P<0.05 significant Annual mean wind speed(kmph) 993** P<0.01 significant Monthly mean wind speed(kmph) .968** P<0.01 significant

5.3.2.3. Discussion On a comprehensive view the locational variations in phytochemicals is markable. The plant sample of L1 (coastal tract) reveals high amount of carbohydrate, protein, organic carbon, copper, manganese, and the cadmium and the maximum numbers of terpenes, sterols and hydrocarbon content. The ash value, potassium, zinc, iron, molybdenum, chromium and nickel are lower than the other locations. The L2 (hilly terrain) samples possesses lipids, nitrogen, calcium, magnesium; iron, boron, nickel, manganese, and the cadmium in lower quantity and the other parameters analysed are in moderate level. In the L3 (riverine zone) zinc, boron, cobalt, are higher and the organic carbon, magnesium, protein, mercury, selenium are lower and the other parameters are moderate. In the L4 (Terrestrial-rural) area organic carbon , nitrogen, potassium, sulphur, chromium lead mercury, silver are considerably higher than other locations and the Carbohydrates, phosphorous, sodium, iron, boron, are lower than the other locations and the other factors are in moderate level. L5 (Terrestrial-urban) area shows high contents of ash, phosphorous, sodium, molybdenum, and the low contents of lipids, nitrogen, calcium, sulphur, nickel, lead, cobalt, and mercury. The other parameters are at moderate level. Mohamed and Alain (1995) suggested that accumulation of carbohydrates under salinity stress being due to reduction in their utilization, either as a source of

148 energy or for the formation of new cells and tissues. On the other hand, Cornic and Massacci (1996) and Abo Kassem et al (2002) reported that high salt concentration can result in osmotic adjustment by regulating the accumulation of solutes especially sugars and proteins. In calotropis procera seedlings the total soluble and insoluble carbohydrates content in the shoot and root tended to increase with increasing salinity stress in the solution culture and also with the age of the plant which were considered to play an important role in the osmotic adjustment (Al-Sobhi et al., 2006). In this connection, Ahmed and Girgis (1979) emphasized the importance of nitrogen intermediates as osmotically active ingredients in plant metabolism and showed that desert plants depend, to a large extent, on the accumulation of organic intermediates in building up their osmotic pressure. Nilsen and Orcutt (2000) reported that plants frequently produce a number of unique proteins in their response to environmental stresses. The rate of element uptake by plant is substantially affected by plant species grown on different soils (Tlustoš et al. 2001). Khanzada et al., (2008) worked on Calotropis procera (Ait). R.Br. (Ascelpiadaceace) which are collected from, the different locations of Sindh shows significant variations in the composition of As, Ca, Cd, Cr, Cu, Fe, K, Mg, Mn, Na, Pb and Zn elements. The amount of Ca was the highest among them. Ca varied according to the collection point. Maximum amount (1481.2 mg/g) of Ca was present in the samples collected from Daulatpur Saffan and minimum amount 9.0 mg/g was present in the samples from Jamshoro. Khanzada et al., (2008), also reported the variations in the protein content of calotropis procera which collected from different places. The highest value of total protein recorded was 50.80% of dry weight (Daulatpur), 32.11% (NawabShah), 25% (Hyderabad) and (Jamshoro) and 29.45% from different sites. Previously the highest total protein was reported in Calotropis procera in leaf extracts 23.94, stem 8.94, bark 12.69 (Kalita et al., 2004). Samat et al.,(2009) found that the seasonal and locational variations in the minerals by analyzing Twenty-nine of browse plant species that recommended by camel herders (trees and shrubs) and 24 forage types of crop residues, grasses and forbs were collected by hand plucking and clipping from different part of Sudan during dry and wet seasons.

149 The concentration of Mg, K, Fe, Zn was higher than other elements and the amount of Cd, As, Pb and Cr was minimum 0.12 to 0.97 mg/g, whereas Cu and Mn was11.9 to 12.33 mg/g and Zn 5.15 to 2.022 ppm As 40.2 μg to 30.11 μg in C. procera from Sindh. Variations of elemental concentrationsvaried from high in Ca (1481.2 ppm) and low in K (387.8ppm) where the K, Mg, Ca. was reported in maximum values, wile Na, Mn, Zn, Cr are present in minimum range. Spitaler et al., (2006) found that the total contents of sesquiterpene lactones and flavonoids were not positively correlated with the altitude of the growing site. However, the proportion of flavonoids with vicinal free hydroxy groups in ring B to the flavonoids lacking this feature significantly increased with elevation. Additionally, the level of caffeic acid derivatives also positively correlated with the altitude of the growing site. In particular the amounts of 1-methoxyoxaloyl-3, 5- dicaffeoylquinic acid significantly increased in elevated sites and samples from the summit region contained 85% more of this compound than samples from valley sites.

A study was carried out by Negi et al., (2009) to determine the accumulation and variation of trace elements in roots and leaves of Asparagus racemosus collected from four different altitudes in Uttarakhand, India. The metals investigated were Zn, Cu, Mn, Fe, Co, Na, K, Ca, and Li. The concentration level of Fe was found to be highest at an altitude of 2,250 m, whereas the level of Cu was lowest. Chieh et al., (2006) found that the concentration of synephrine, evodiamine, dehydroevodiamine and rutaecarpine are highly varying from location to location. In plants, polyphenol synthesis and accumulation is generally stimulated in response to biotic/abiotic stresses (Dixon and paiva, 1995; Naczk, and shahidi, 2004) such as salinity (Navarro et al., 2006). Ksouri et al., (2007) found that Jerba and Tabarka accessions differed in their growth response to salinity level, and the poly phenol content. Oliveira et al. (2006) studied that the Mikania cordifolia which are collected from different locations and found that there are no significant qualitative differences related to the presence of triterpenes and steroids. Finally they concluded that all collected specimens of M. cordifolia presented similar constitution of triterpenoids, despite some possible differences in proportions.

150 6.1. INTRODUCTION The environmental or the habitat factors influence the characters and composition of individual plants and the plant communities. Any feature of an organism or its parts which enables the organism to exist under conditions of its habitat is called adaptation. An organism accumulates many adaptive features in it. Such features may ensure a degree of success either by allowing the plant to make full use of the amounts of nutrient, water, heat and light available to it or by providing a significant amount of protection against some unfavorable or adverse factors, such as very high or very low temperature, drought, nutrients and so on. The adaptive features of an organism may be hereditary (i.e., genetically controlled) or they may be induced by the habitat factors (i.e., environmentally controlled) (Brady et al., 2005; Shukla and Chandel, 2007). According to Odum (1971), an ecosystem is a complex in which the habitat, plants and animals are considered as one interesting unit, the minerals and energy of one passing in and out of the other species. Thus, the organisms and their environment are wedded together and are in a state of constant flux. The relationship between organisms and their environment is based on certain principles (Shukla and Chandel, 2007). 1. An organism cannot exist in vacuum. 2. An organism is a product of nature (genetic-setup) and nurture (environmental upbringing). The inherited qualities are unfolded in proper environment. 3. Everything influencing the life processes of organisms constitutes its environment. 4. Environment in a habitat may be considered in to biotic and abiotic components and the activities of the organisms are influenced by the combined effects of various environmental factors. 5. The environmental requirements of different organisms differ from individual to individual and also with age and need. 6. An organism may show different tolerance limits for a particular environmental factor in different habitats and at different age and stage of life history. 7. Organisms react with the external stimuli caused by the environmental changes. The reactions may be exhibited by movements (migration) or adaptational changes in the body or physiological activities. All such adaptations have survival value.

151 8. Widely distributed species are adapted to various habitat conditions by evolving ecotypes.

6.1.1. Plant anatomy and Environment Plant Anatomy refers to study of internal morphology, pertaining to different tissues. Most plants are immobile and therefore have to quickly and efficiently adapt to changing environments. In general, plant body in Angiosperms is differentiated into root, stem, leaf, flower and fruit. The roots anchor the plant in the soil and are required to absorb and transport water and nutrients from the soil to the other parts of the plant. The stems hold up the leaves for optimal exposure to sunlight and transport water, nutrients, and photosynthates from source to sink. The leaves are the photosynthetic machinery also regulating plant temperature. The flower and fruits are the reproductive units transforming the genetic feature through generation through generation through pollination and fertilization. All the plant parts are made up of different types of tissues containing different cell types according to their function. A tissue is a mass of similar or dissimilar cells performing a common function. The body of a vascular plant is composed of dermal tissue (skin), ground tissue (inner to dermal tissue) and vascular tissue (conducting elements (xylem, phloem). The stems hold up the leaves for optimal exposure to sunlight and transport water, nutrients, and photosynthates from source to sink. The leaves are the photosynthetic machinery also regulating plant temperature. The flower and fruits are the reproductive units transforming the genetic feature through generation through generation through pollination and fertilization. With increasing environmental variations a better understanding of plant response to environmental stresses is vital for the economical exploitation. Improving management practices for cultivation and predicting the dynamics of natural vegetation under climate change (Chaves et al., 2003) are essential for such exploitation.

6.1.2. Factors affecting the Anatomy of plants “Nothing can be more abrupt than the change often due to diversity of soil, a sharp line dividing a pine- or heather-clad moor from calcareous hills.”

-Alfred Russel Wallace (1858)

152 Wallace (1858) and Darwin (1859) put forth the idea that adaptation is the signature of evolution by the hand of natural selection, and that adaptation to novel environments results in the origin of new species. The role of adaptive evolution in shaping the earth’s diverse biota is undisputed (Schluter, 2001). Wallace (1858) recognized that plant adaptation to different soil types is an evidence of the strong natural selection imposed by ecological discontinuities.

Apart from evolutionary adaptations, all traits have an enormous spatial and temporal variability. The evolutionary, developmental and environmental variations in traits and the large number of potentially important traits and trait combinations complicate predictions of relevant plant functions. However, many traits that alter the same plant functions covary along the environmental gradients. The trait assemblage may also significantly simplify projections of plant functioning in changing environmental conditions. Analyses of the trait co variations have identified a series of general correlations among relevant structural and functional traits of plants (Niinemets and Sack, 2006). Plants with unusual or localized distribution patterns have always fascinated botanists and other natural historians. The study of such plants has provided information on the history and evolution of certain regions and their floras. Further, these plants have provided opportunities to investigate aspects of evolutionary ecology and population dynamics unique to such plant populations (Liu and Godt, 1983; Linhart and Grant, 1996). Climate sets the limits for biota; however, geology enriches discontinuity and habitat diversity (Jenny, 1941). The classic generalizations on the distribution of plants (Cain, 1944), place the edaphic factor second only to climate as the major environmental determinants of plant distribution. The edaphic factor pertains to the substratum upon which the plant grows and from which it derives its mineral nutrients and much of its water supply. It involves physical, chemical, and biological properties of soils (Mason, 1946a, b). When physical and chemical properties of substrate are arrayed discontinuously, opportunities for colonization by different species as well as events leading to speciation can occur (Kruckeberg, 1986). Many authors have described the various factors of growth which affect the anatomy of plants such as soil, seasons, pollutions, etc. Some plants are growing widely in various types of biotic and abiotic conditions due to their

153 high resistance and adaptability. Adaptation of a species to this variation may reflect different morphological and physiological characteristics, resulting in the development of ecotypes (Kubiske and Abrams 1992, Zheng et al. 2000). This capability may have high survival value in plants from environments that experience frequent episodes of drought and flooding (Sarmiento, 1984; Soriano, 1992). Chen and Wang, (2009) investigated the anatomical and physiological differences between two Leymus chinensis ecotypes which coexisted in semi-humid meadow and semi-arid steppe. The study addressed the hypothesis that, at the same habitat, the two ecotypes exhibit remarkable divergences in adaptive strategies under drought and salinity, and the function of these strategies is compensatory. There exist significant differences in anatomical and physiological strategies between the two ecotypes and the compensatory effects of these strategies enable the two ecotypes to coexist at a similar habitat. Elevated levels of heavy metals can have detrimental effects on plants at the cellular and at the whole-plant levels (Barcelo et al., 1988; Burzynski and Klobus, 2004; Shaw et al., 2004). Metal ions are known, for example, to inhibit root elongation, photosynthesis, enzyme activity, and cause oxidative damage to membranes (Hernandez and Cooke, 1997; Hartley et al., 1999; Shawe et al., 2004; Sheoran et al., 1990). These effects may make plants more susceptible to additional stresses such as drought due to the reduced water uptake capacity of the smaller root system and possibly blocked aquaporins (integral membrane proteins belonging to the major intrinsic protein (MIP) family) and due to decreased water use efficiency (Patterson and Olson, 1983; Arduini et al., 1994; Moustakas et al., 1997; Yang et al., 2004; Ionenko et al., 2006; Ryser and Emerson, 2007). Under the salt stress, the important mechanisms of plant tolerance involve Na+ exclusion and K+/Na+ selectivity. Roots must exclude most of the Na+ and Cl+ in the soil solution and maintain high selectivity of K+ over Na+ to avoid ion toxicity in shoot tissues (Peng et al., 2004; Garthwaite et al., 2005; Munns et al., 2006; Sarita et al., 2009). These strategies for the plant to successfully endure drought and salinity are widespread. At the time of water scarcity to minimize water loss and maximise water uptake plants use some adoptive traits. Water uptake is maximized by increasing the vessel number, reducing the vessel size in stems or increasing investment in the roots (Jackson et al., 2000; Sobrado, 2007).Water loss is minimised by closing stomata,

154 thickening the leaf blade and the epidermal cell dimensions, increasing LMA (leaf mass per unit area) or reducing light absorbance through rolled leaves (Cellier et al., 2000; Ehleringer and Cooper, 1992; Pena- Rojas et al., 2005; Stefanos et al., 2008). Studies concerning the anatomy and ultra structure of the leaf blade under conditions of pollution have also been carried out (Mudd and Kozlowski, 1975; Soikkeli, 1981; Huttunen and Laine, 1983; Barnes et al., 1988; Ebel et al., 1990; Zobel and Nighswander, 1991). Chen et al., (2006) investigated the differences in the anatomical characteristics of the foliar vascular bundles in four ecotypes of Phragmites communis Trin. Psaras and Sofroniou (2004) have studied the differences in the stem and root wood anatomy of the shrub, Phlomis fruticosa at two different seasons of Mediterranean. Vascular systems are responsible for the transport of water and solutes in plants. However, because of its special anatomical structure, the vascular system also functions as an apoplastic barrier for plants in the acquisition of water and solutes (Hose et al., 2001; Steudle, 2000). Although some studies have suggested that changes in the vascular architecture, such as modifications to the wall architecture, ion composition, and protein expression, are involved in the resistance of plants to environmental stresses (Orians and Jones, 2001; Saijo et al., 2001; Equiza and Tognetti, 2002; Child et al., 2003; Engloner et al., 2003; Zwieniecki et al., 2003; Cholewa and Griffith, 2004), information is lacking on the functions of anatomical and chemical modifications of foliar vascular bundles in plant resistance to these stresses. He and Zhang, (2003) reported that in the shrub Sabina vulgaris, which grows in the semi-arid Mu Us Sandland of China, the size of the vascular bundle is strongly negatively correlated with the soil water content, whereas net photosynthesis, night respiration, and stomatal conductance are highly positively correlated with soil water content. Ogle, (2003) found that photosynthetic carbon reduction is primarily restricted to specialized bundle sheath cells, and suggested that a smaller interveinal distance would increase the number of veins and thus increase the density of bundle sheath cells, thereby potentially enhancing photon capture. The present study explores the possible environmental factors that can influence the adaptation in the morphology and anatomy of the vegetative organs of Calotropis at the studied seasons and areas.

155 6.2 MATERIALS AND METHODS

6.2.1. Collection of Plant materials

Collection of plant materials and conditions are given in the chapter 2.2

6.2.2 Sectioning and Photomicrographs for Anatomical study

The fixed leaf (midribs and lamina), stem and root specimens were processed as per the method of Sass, (1940) and sectioned in a rotary microtome. The sections were dewaxed and stained with toludine blue (Johansen, 1940; O’Brien et al. 1964), wherever necessary sections were also stained with safranin, fast green and IKI for studying the leaf, stem and root anatomy. Photographs of different magnifications were taken with Nikonlabphoto 2 microscopic unit for the microscopic descriptions of tissues wherever necessary. Descriptive terms of anatomical features are as given in the standard anatomy books (Esau, 1964).

6.3. RESULTS AND DISCUSSIONS

6.3.1. Seasonal and locational variations in the plant morphology

6.3.1.1 Seasonal variations in the plant morphology

The seasonal variations in the numbers of leaves within 30 cm from the shoot apex, leaf width, length and leaf area; flower bunches /twig, flowers per bunch shows some differences among the seasons (Table. 6.1a). In S1 all these variables are towards the higher side (4.5, 7.0, 32.0, 26.0, 3.0, 12.0, 1.45 and 2.83 respectively). The mean leaf width, length and area are in the following descending order:- S1>S4>S2>S3.The height and canopy of the plant also shows similar pattern of occurrence. The variations in all the morphological features are statistically significant among the seasons (Table 6.1b). The observed seasonal vitiations in the mean leaf length (cm), mean numbers of leaves / 30 cm twig), mean flower bunches are associated with the meteorological elements of lowest R.H. evening. The other morphological variations are not associated with any of the meteorological parameters (Table 6.1c).

156 The variations in the mean leaf width (cm), mean leaf length (cm), mean leaf area (cm2), mean numbers of leaves /30 cm twig, mean flower / bunch , height of the plant (m), canopy cover of the plant (m2) are associated only with the phosphorous content of the soil (Table 6.1d).

Table 6.1a. Seasonal variations in plant morphology

S.No. Parameters S1 S2 S3 S4 1. Mean Leaf Width(cm) 4.5 3.4 2.6 3.9 2. Mean Leaf Length(cm) 7.0 4.8 4.0 6.8 3. Mean Leaf Area(cm2) 32.0 16.0 14.0 26.0 4. Mean Numbers of leaves /30 cm twig 26.0 14.0 8.0 24.0 5. Mean Flower bunches / 30 cm twig 3.0 2.0 1.0 3.0 6. Mean Flower / bunch 12.0 9.0 6.0 10.0 7. Height of the plant(m) 1.45 1.28 1.15 1.37 8. Canopy cover of the plant/Plant circumference (m 2 ) 2.83 2.11 1.67 2.54

Table 6.1b. Seasonal variations in plant morphology (One sample t - test) Std. Statistical Parameter N Mean t Deviation Inference Mean Leaf Width(cm) 4 3.600 .8042 8.953 P<0.01 significant Mean Leaf Length(cm) 4 5.65 1.482 7.624 P<0.01 significant Mean Leaf Area(cm2) 4 22.00 8.485 5.185 P<0.05 significant Mean Numbers of leaves /30 cm twig 4 18.00 8.485 4.243 P<0.05 significant Mean Flower bunches / 30 cm twig 4 2.25 .957 4.700 P<0.05 significant Mean Flower / bunch 4 9.25 2.500 7.400 P<0.01 significant 20.40 Height of the plant(m) 4 1.3125 .12868 P<0.01 significant 0 Canopy cover of the plant/Plant 4 2.2875 .50691 9.025 P<0.01 significant circumference (m 2 ) DF = 3 Table 6.1c. Karl Pearson correlation between seasonal parameters and the plant morphology

Correlation Parameters Statistical inference value Mean Leaf Length vs. Lowest R.H. evening 963* P<0.05 Significant Mean Numbers of leaves / 30 cm twig vs. .953* P<0.05 Significant Lowest R.H. Evening Mean Flower bunches / 30 cm twig vs. lowest .967* P<0.05 Significant R.H. evening N= 4

157 Table 6.1d. Karl Pearson correlation between soil and the plant morphology

Correlation Statistical Parameters value inference

Mean Leaf Width vs. Phosphorous -.995** P<0.01 significant Mean Leaf Length vs. Phosphorous -.958* P<0.05 significant Mean Leaf Area vs. Phosphorous -.974* P<0.05 significant Mean Numbers of leaves /30 cm twig vs. -.974* P<0.05 significant Phosphorous Mean Flower / bunch vs. Phosphorous -.981* P<0.05 significant Height of the plant vs. Phosphorous -.993** P<0.01 significant Canopy cover of the plant vs. Phosphorous -.996** P<0.01 significant N=4 6.3.1.2. Locational variations The locational variation (Table 6.2a) shows a visible difference in the leaf size, numbers of leaves and flowers. In the terrestrial-urban area (L5) plant leaf size is bigger than the other sites. The leaf size (cm2) is decreasing as follows L5 (66)> L3 (64)>L1 (44)>L4 (24)> L2 (22); The trend of the distribution in the number of leaves is L4=L1 (24)> >L2 (20)>L3 (16>L5 (12).The flower bunches and numbers of flowers/bunches, height of the plant, and canopy cover of the plant are high in the riverine zone (L3). The locational variations among these morphological parameters are statistically significant (Table 6.2b). The morphological characters in these locational variations are only associated with the soil parameters. The mean leaf width is associated with phosphorous, zinc and copper; the mean leaf length is associated with the nitrogen, phosphorous, iron, zinc and copper; the mean leaf area is associated with the phosphorous, zinc and copper; the mean flower/bunch is associated with the phosphorous (Table 6.2c.).This proves the importance of phosphorous, zinc and copper in the plant morphology.

Table 6.2a. Locational variations in plant morphology

S.No. Parameters L1 L2 L3 L4 L5

1. Leaf Width(cm) 4.9 2.6 6.6 3.9 7.0

2. Leaf Length(cm) 10.3 6.5 12.2 6.8 12.5

3. Leaf Area(cm2) 44 22 64 26.0 66

4. Numbers of leaves / 30 cm twig 24 20 16 24 12

158 5. Flower bunches / 30 cm twig 5 2 7 3.0 4

6. Flower/bunch 12 5 17 10 13

7. Height of the plant(m) 1.3 0.7 1.4 1.37 0.9 Canopy cover of the plant/Plant 8. 3.14 0.48 5.31 2.54 0.79 circumference (m 2 )

Table 6.2b. Locational variations in the plant morphology (One sample t - test)

Std. Statistical Parameter N Mean t Deviation Inference Leaf Width(cm) 5 5.000 1.8398 6.077 P<0.01 significant Leaf Length(cm) 5 9.660 2.8763 7.510 P<0.01 significant Leaf Area(cm2) 5 44.40 20.562 4.828 P<0.05 significant Numbers of leaves / 30 cm 5 19.20 5.215 8.232 P<0.05 significant twig Flower bunches / 30 cm twig 5 4.20 1.924 4.882 P<0.05 significant Flowers / bunch 5 11.40 4.393 5.802 P<0.01 significant Height of the plant(m) 5 1.134 .3151 8.048 P<0.01 significant Canopy cover of the plant/ (m 5 2.4520 1.95578 2.803 P<0.05 significant 2 ) DF =4

Table 6.2c. Karl Pearson correlation –Locational variations in the plant morphology vs. soil parameters Statistical Parameters Correlation value inference Mean Leaf Width (cm) vs. .965** P<0.01 significant Phosphorous Mean Leaf Width(cm) vs. Zinc .893* P<0.05 significant Mean Leaf Width(cm) vs. copper .932* P<0.05 significant Mean Leaf Length (cm) vs. .900* P<0.05 significant Nitrogen Mean Leaf Length(cm) vs. .952* P<0.05 significant Phosphorous Mean Leaf Length(cm) vs. Iron .918* P<0.05 significant Mean Leaf Length(cm) vs. Zinc .952* P<0.05 significant Mean Leaf Length (cm) vs. .987** P<0.01 significant copper Mean Leaf Area(cm2) vs. .940* P<0.05 significant Phosphorous Mean Leaf Area(cm2) vs. Zinc .906* P<0.05 significant Mean Leaf Area(cm2) vs. copper .959** P<0.01 significant Mean Flower / bunch vs. .934* P<0.05 significant Phosphorous N=5

159 6.3.1.3. Discussion Plants respond to variations in the content of soil water and oxygen through morphological, anatomical and physiological adjustments that help them cope with such variations. This capability may have high survival value in plants from environments that experience frequent episodes of drought and flooding, such as some tropical savannas and temperate sub-humid grasslands (Sarmiento, 1984; Soriano, 1992). Leaves are the main organs of assimilation in many higher plants. Leaf photosynthetic capacity is connected with the primary production (Reich et al. 1997), which to a great extent determines the plant’s competitive ability (Grime 1997). However, environmental constraints have limited the tendency to maximise photosynthetic capacity through plant evolution, as resources must also meet other plant functions. For example, leaves must defend themselves against herbivores and other physical hazards (Coley 1983; Herms and Mattson 1992), or they must store their assimilates to be consumed during future unfavorable periods (Bloom et al., 1985; Meletiou-Christou et al., 1992). Each species’ pattern of allocation between protection and production must reflect the balance between different selective forces which have acted on the whole-plant life strategy through evolution. Therefore, the search for leaf traits indicative of leaf performance is crucial to the understanding of the functional ecology of plant species (Diez et al., 2000). Variations in several leaf morphological parameters may serve as adaptations for plants to a hot, dry environment. Reduced leaf size, contributing to a lower total leaf area per plant during the summer months, appears to be common for several broadleaf perennial shrubs (Evenari 1938, Orshan 1964, Cunningham and strain1969, Evenari et al., 1971). The present observation shows the similar trend in the summer season (S3). Increases in pubescence during dry periods led to substantial decreases in the leaf absorptance to solar irradiation (from 0.61 to 0.43). Similar alterations occurred in Hyptis, but with less month-to-month variation, while Mirabilis had comparatively small changes in these leaf parameters (Smith and Nobel, 1977). Nisar et al., (2010) have studied the morpho-anatomical adaptability potential of fourteen accessions of Lasiurus scindicus (Henr.) collected from the Cholistan desert, Pakistan during 2005. Data collected revealed considerable variation for leaf area.

160 Numerous laboratory experiments with herbaceous plants and tree seedlings or saplings have shown that leaf area reduction is a common response to soil water shortage (Fischer and Turner 1978; Begg 1980; Poorter 1989; Lof and Welander 2000; Pedrol et al., 2000; Otieno et al., 2005), thereby reducing the transpiring surface area and avoiding severe decreases in cell water potential and turgidity (Hinckley and others 1981; Kozlowski and Pallardy 1997). High salt tolerance of the ‘Salt Range’ ecotype was associated with the increased succulence in root and leaf (mainly midrib), formation of aerenchyma in leaf sheath, increased vascular bundle area, metaxylem area and phloem area, highly developed bulliform cells on leaves and increased sclerification in root and leaf. Furthermore, both stomatal density and stomatal area were considerably reduced under high salinities in the Salt Range ecotype (Hameed et al., 2009). With increasing altitude, atmospheric pressure and air temperature decrease. The atmospheric pressure and temperature are associated with high irradiance, wind velocity, and great diurnal fluctuation at higher altitudes (Larcher, 1980; Friend and Woodward, 1990). High irradiance results in altered stomatal density (Körner et al., 1989). Low temperature at high elevations is responsible for small organ size (small leaves and shoots), increased cell wall thickness, and leaf thickness. According to the study of Körner et al., (1989) the leaves in the higher altitude is small. Similar trends were observed in the present observation in the hilly terrain. The leaves in this area are comparatively smaller than the other area studied. Gates et al., (1968) elucidated the quantitative influence of reduced leaf size on transpiration for desert plants using heat balance analyses, and Taylor (1971, 1975) reported that calculated water-use efficiencies (mass CO2 fixed/mass H2O transpired) were maximal for leaves, 2cm in length under desert conditions. Additional studies evaluating the influence of leaf dimension on heat transfer have shown that the more deeply lobed sun leaves of Quercus alba had enhanced convective heat dissipation (Vogel 1968, 1969) as did tattered leaves for selected species of Musaceae (Taylor and Sexton 1972). In addition to reduced leaf size, other morphological characteristics have also been associated with the leaves developing in full sunlight (sun leaves) compared with larger ones developing under shaded conditions (shade leaves) on the sample plant. In the present observation of the bigger leaf at the terrestrial-urban area (L5) may be due to the prevalence of shade in this locats.

161 Specifically sun leaves tend to be thicker with more highly developed mesophyll regions (Hanson 1917, Wylie 1951, Walter 1973), which increases the ratio of mesophyll cell surface area to external leaf surface area (Turrell 1965, Nobel et al., 1975, Nobel, 1976). This increase in internal area per unit leaf area(A mes/ A), which leads to a decrease in the liquid phase resistance to CO2 uptake, accounted for virtually all of the increase in photosynthesis per unit leaf area measured for the leaves of Plectranthus paviflorus (Nobel et al., 1975) and Hyptis emoryi (Nobel 1976).

6.3.2. Seasonal and locational variations in the leaf anatomy

6.3.2.1a. Seasonal variations in midrib anatomy

The Plate 6.1 and the Table 6.3a show the seasonal variations in the midrib. The midrib is plano convex in sectional view with flat adaxial side and convex abaxial side. The size of the midribs is between 1.35mm (S2) and 3 mm (S1). The widths of the midrib are about 2.3 mm (S2) to 4.5mm (S1). Vascular bundles also vary slightly in thickness and width. The thickness is about 200 μm (S3) to 400μm (S2). In the xylem elements, numbers of rows are about 2 (S4) to 5 (S1and S2). The width of xylem elements are about 20 μm (S3) to 40μm (S1, S2 and S4). In the ground tissue parenchyma are thin walled, less compact in S4, thin walled and compact in S2 and S3. But in the S1 they are wide. Laticifers are abundant in S1 and S2 but in the S3 they are less frequent and in S4 they are not evident. Inner and outer phloem strands are well preserved in S1and S3, In the S4 they are found as small discrete series. But in the S2 they are not preserved well.

The variation in the leaf midrib is statistically significant (Table 6.3b). Thickness of the midrib shows association with the numbers of rainy days; the vascular bundle thickness is linked with lowest R.H. morning; vascular bundle width is connected to the highest temperature and lowest R.H. morning; the numbers of rows of xylem elements are connected with the mean R.H.evening (Table 6.3c). The width of the midrib is associated with the organic carbon and the organic matter. The width of the xylem elements is related to the sulphur contents of the soil (Table 6.3d).

162

S1.1 S1.2

S2.1 S2.2

S3.1 S3.2

S4.1 S4.2 Plate 6.1. Seasonal variations in the midrib

S1.1, S2.1, S3.1 & S4.1: T.S. of leaf through midrib (4X) S1.2, S2.2, and S3.2 & S4.2: T.S. of leaf through midrib (10X) [Abs-abaxial side; Abph-abaxial phloem;Adph-adaxial phloem; Ads-adaxial side; Col-Collenchyma; Gp-Ground parenchyma; Iph-Inner phloem; Lf- Laticiferous cell; Oph- Outer phloem; PGT-Parenchymatous ground tissue, V-vascular bundle; X- Xylem]

163 Table 6.3a. Seasonal variations in midrib

S.No. Leaf Organs parameters S1 S2 S3 S4 Thickness(mm) 3 1.35 1.5 1.8 1. Midrib Width (mm) 4.5 2.3 3 3 Thickness(µm) 350 400 200 300 2. Vascular bundle Width (mm) 3 4 2 2.5 Numbers of rows 5 5 4 4 3. Xylem elements Width((µm) 40 40 20 40 Numbers of collenchyma 4. Ground tissue 5 6 4 6 layers

Table 6.3b. Seasonal variations in leaf midrib anatomy (One sample t - test)

Std. Statistical Leaf Organs Parameter N Mean t Deviation Inference Thickness(mm) 4 1.91 .749 5.109 P<0.05 significant Midrib Width (mm) 4 3.200 .9274 6.901 P<0.01 significant Thickness(µm) 4 312.50 85.391 7.319 P<0.01 significant Vascular bundle Width (mm) 4 2.88 .854 6.734 P<0.01 significant Numbers of 15.58 4 4.50 .577 P<0.01 significant Xylem elements rows 8 Width((µm) 4 35.00 10.000 7.000 P<0.01 significant Numbers of 10.96 Ground tissue collenchyma 4 5.25 .957 P<0.01 significant 7 layers DF=3

Table 6.3c. Karl Pearson correlation –Seasonal variations in leaf midrib anatomy VS meteorological parameters

Correlation Statistical Parameters value inference Midrib thickness vs. Numbers of Rainy Days .968* P<0.05 significant Vascular bundle thickness (µm) vs. Lowest R.H. .968* P<0.05 significant morning Vascular bundle width vs. Highest temperature -.972* P<0.05 significant Vascular bundle width vs. Lowest R.H. morning -.979* P<0.05 significant Xylem elements numbers of rows vs. Mean R.H. evening .998** P<0.05 significant N=4

164 Table 6.3d. Karl Pearson correlation –Seasonal variations in leaf midrib anatomy vs. soil parameters

Parameters Correlation value Statistical inference

Midrib Width vs. Organic carbon .968* P<0.05 significant Midrib Width vs. Organic matter .968* P<0.05 significant Xylem elements width vs. Sulphur .991** P<0.01 significant N=4

6.3.2.1b. Seasonal variations in lamina and stomatal anatomy Thickness of lamina is ranging between 400 μm (S3 and S4) and 570μm (S2). The epidermal layers are about 20μm (S3) to 50 μm (S2) at the adaxial side and 10μm (S3 and S4) to 25μm (S1) at abaxial side. Numbers of layers in the palisade zone is similar in all the four seasons studied, but its height differs between 100 μm (S4) to 150 μm (S2). The epidermises are prominent with parallel cuticular striations in S1, S2 and S3 except S 4. In S4 the epidermis is not evident. Stomata are paracytic in all the seasons. The size of the guard cells are about 10X20 (S4) to 15X25 (S3) (Plate 6.2 and Table 6.4a). The seasonal variation in the lamina of leaf is statistically prominent (Table 6.4b). The lamina thickness is associated with the mean minimum temperature and highest temperature; the thickness of epidermal layers (adaxial side) is correlated with the highest temperature, palisade zone height is related with the mean minimum temperature, lowest temperature, and mean R.H. morning (Table 6.4c). The thickness of the epidermal layers shows association with the presence of calcium in the soil (Table 6.4d).

165

S1.3 S1.4

S2.3 S2.4

S3.3 S3.4

S4.3 S4.4

Plate 6.2 Anatomy of the lamina and stomatal morphology S1.3, S2.3, S3.3 & S4.3: T.S. of lamina (10X) S1.4, S2.4, S3.4 & S4.4: T.S. of Abaxial epidermis with stomata (40X) [ Abe-Abaxial epidermis; ade- Adaxial epidermis; Abs- Abaxial side; Ads- Adaxial side; Bs-Bundle sheath; Ec- Epidermal cells; Lv-Lateral vein;h-Phloem; Pm- Palisade mesophyl; Rc-Rosette cells; Sc- Subsidiary cell; Sm-Spongy mesophyl; Tr-Trichome bearing epidermal cell; X-Xylem]

166 Table 6.4a Seasonal variations in lamina and stomata

S.No. Leaf Organs S1 S2 S3 S4 1. Lamina Thickness(mm) 420 570 400 400 Thickness of epidermal Adaxial 30 50 20 25 2. layers (µm) Abaxial 30 20 10 10 Numbers of layers 3 3 3 3 3. Palisade zone Height(µm) 120 150 120 100 4. Guard cell size (µm) size(µm) 15X20 15X20 15X25 10X20

Table 6.4b Seasonal variations in leaf lamina and stomata anatomy (One sample t - test)

Std. Leaf Organs N Mean t Statistical Inference Deviation 10.88 Lamina Thickness(mm) 4 447.50 82.209 P<0.01 significant 7 Thickness of Adaxial 4 31.25 13.150 4.753 P<0.05 significant epidermal layers (µm) Abaxial 4 17.50 9.574 3.656 P<0.05 significant Numbers of 11.88 4 3.00 .000 P<0.01 significant layers 4 Palisade zone Height(µm) 4 122.50 20.616 2.330 P>0.05 not significant Guard cell size (µm) size(µm) 4 293.75 71.81 10.07 P<0.01 significant DF=3

Table 6.4c. Karl Pearson correlation – Seasonal variations in leaf lamina and stomata anatomy vs. meteorological parameters

Correlation Statistical Parameters value inference Lamina Thickness (mm) vs. Mean Minimum temperature -.971* P<0.05 significant Lamina Thickness(mm) vs. Highest temperature -.976* P<0.05 significant Thickness of epidermal layers Adaxial side vs. Highest -.996** P<0.01 significant temperature Palisade zone Height (µm) vs. Mean Minimum temperature -.974* P<0.05 significant Palisade zone Height (µm) vs. Lowest temperature -.954* P<0.05 significant Palisade zone Height (µm) vs. Mean R.H.morning .996** P<0.01 significant N=4

Table 6.4d. Karl Pearson correlation – Seasonal variations in leaf lamina and stomata anatomy vs. soil parameters

Correlation Statistical Parameters value inference Thickness of epidermal layers Abaxial side vs. .998** P<0.05 significant Calcium N=4

167 6.3.2.2a. Locational variations in midrib anatomy

The loctional variation of the midrib (Plate 6.3 and Table 6.5a) shows that the thickness of the midrib is about 1.65 mm (L1) to 2.3mm (L5). The widths of the midribs are between 2.4mm (L2) and 4mm (L5). The thickness of the vascular bundles are 190μm (L3) to 300μm (L1, L2, L4, and L5). 4 to 6 rows of xylem elements which are 35 µm to 51 µm in thickness are observed. The collenchyma of the ground tissue are about 6 to 8 layers and the parenchyma are found to be inner circular, thin walled and compact(L1, L2 and L3) to less compact (L4 and L5). The laticifers are ranging as frequent (L2 and L3), less frequent (L1 and L5) and not evident (L4). The inner phloem is not well preserved in L1 and L2, and well preserved in L3 and L5. In L4 the phloem is found to be smaller in discrete series. In the case of outer phloem the L1 and L2 are found to be less distinct and L3 and L5 are well preserved. In the L4 outer phloem is found to be in large isolated nests. The locational variation in the midrib is statistically significant (Table 6.5b).The midrib the vascular bundles are associated with only some of the meteorological elements such as seasonal and monthly lowest temperature, annual and monthly R.H. evening, annual, seasonal and monthly heaviest rainfall and annual, seasonal total rainy days (Table 6.5c). The midrib variation among the locations are linked with the soil content of organic carbon, organic matter, nitrogen, magnesium, calcium, iron and copper (Table 6.5d). Table 6.5a. Locational variations in midrib

S.No. Leaf Organs L1 L2 L3 L4 L5 Thickness (mm) 1.75 1.65 1.8 1.8 2.3 1. Midrib size Width (mm) 2.75 2.4 2.6 3 4 Thickness(µm) 300 300 190 300 300 2. Vascular bundle Width (mm) 1.9 2 2.8 2.5 2.7 Numbers of rows 4 5 5 4 6 3. Xylem elements Width((µm) 35 44 45 40 51 Numbers of collenchyma 4. Ground tissue 8 6 8 6 7 layers

168

L1.1 L1.2

L2.1 L2.2

L3.1 L3.2

L4.1 L4.2

L5.1 L5.2

Plate 6.3. Locational variations in the midrib L1.1, L2.1, L3.1, L4.1& L5.1: T.S. of leaf through midrib (4X) L1.2, L2.2, L3.2, L4.2 & L5.2: T.S. of leaf through midrib (10X) (Ads- Adaxial Side; Col-Collenchyma; Gp-Ground parenchyma; Iph-Inner phloem; Lf- Laticiferous cell; VS-Vascular strand; Oph- Outer phloem; X-Xylem)

169 Table 6.5b.Locational variations in mid rib anatomy (One sample t - test)

Std. Leaf Organs Parameter N Mean t Statistical Inference Deviation Thickness(mm) 5 1.86 0.254 5.109 P<0.05 significant Midrib 10.52 Width (mm) 5 2.95 0.626 P<0.01 significant 9 12.63 Thickness(µm) 5 278 49.19 P<0.01 significant 6 Vascular bundle 13.02 Width (mm) 5 2.38 0.408 P<0.01 significant 3 Numbers of 12.82 5 4.8 0.836 P<0.01 significant rows 9 Xylem elements 16.13 Width((µm) 5 43 5.958 P<0.01 significant 8 Numbers of 15.65 Ground tissue collenchyma 5 7 1 P<0.01 significant 2 layers DF=4

Table 6.5c. Karl Pearson correlation –Locational variations in leaf midrib anatomy vs. meteorological parameters Correlat Statistical Parameters ion value inference P<0.01 Vascular bundle width (mm) vs. Seasonal Lowest temperature -.961** significant P<0.05 Vascular bundles width (µm) vs. Monthly Lowest temperature -.944* significant P<0.05 Vascular bundle width (µm) vs. Annual Highest R.H. evening .952* significant P<0.05 Vascular bundle width (µm) vs. Monthly Highest R.H. evening -.893* significant P<0.05 Vascular bundle width (µm) vs. Annual Lowest R.H. evening -.954* significant Vascular bundle Width (mm) vs. Annual Heaviest rainfall in P<0.01 .963** 24 HRS(mm) significant Vascular bundle width (µm) vs. Seasonal heaviest rainfall in 24 P<0.01 .964** HRS(mm) significant

Vascular bundle width (µm) vs. Monthly heaviest rainfall P<0.05 .956* 24HRS(mm) significant

Vascular bundle width (µm) vs. Annual -Numbers of rainy P<0.01 -.964** days(2.5mm and above) significant

Vascular bundle width (µm) vs. Numbers of rainy days per P<0.05 -.893* season (2.5mm and above) significant N=5

170 Table 6.5d. Karl Pearson correlation – Locational variations in leaf midrib anatomy vs. soil parameters Correlati Statistical Parameters on value inference Midrib Width vs. Total Magnesium .880* P<0.05 significant Vascular bundle Thickness vs. Total Calcium -.927* P<0.05 significant Xylem elements Numbers of rows vs. Organic Carbon -.941* P<0.05 significant Xylem elements Numbers of rows vs. Organic Matter -.941* P<0.05 significant Xylem elements Width vs. Organic Carbon -.912* P<0.05 significant Xylem elements Width vs. Organic Matter -.912* P<0.05 significant Ground tissue Numbers of collenchyma layers vs. Total .933* P<0.05 significant Nitrogen Ground tissue Numbers of collenchyma layers vs. Iron .927* P<0.05 significant Ground tissue Numbers of collenchyma layers vs. Copper .880* P<0.05 significant N=5

6.3.2.2b. Locational variations in lamina and stomatal anatomy

The thickness of the lamina is 270μm (L2) to 450μm (L3). The thickness of epidermal layers is 15μm (L5) to 25μm (L3) in adaxial side and except L3 (20μm) others are 10μm in the abaxial side. The palisade layers are about 2-3 in all the areas. The height of palisade zones are about 50μm (L2) to 150μm (L1). The epidermal cells are small, polyhedral, cuticular walls thick, cuticular striations prominent in L1and L5.The cuticular striations are not developed in L3 and absent in L2 and L4. The stomata are paracytic in L1, L2, L4 and cyclocytic in GC 3 in L5 they are both paracytic and cyclocytic. The size of the guard cell size is 10X20 (L4) and 20X25 L3 and L5 (Table 6.6a). Locational variation in lamina and stomatal anatomy is statistically significant (Table 6.6b). The heights of the number of cells in the palisade zone are associated with the climatical elements -R.H., numbers of rainy days and mean wind speed. (Table 6.6c and 6d).

171

L1.3 L1.4

L2.3 L2.4

L3.3 L3.4

L4.3 L4.4

L5.3 L5.4 Plate 6.4. Locational variations in the lamina and stomatal morphology

L1.3, L2.3, L3.3, L4.3 &L5.3: T.S. of lamina (10X) L1.4, L2.4, L3.4, L4.4, &L5.4: T.S. of Abaxial epidermis with stomata (40X) [ Abe-Abaxial epidermis; ade- Adaxial epidermis; Abs- Abaxial side; Ads- Adaxial side; Bs- Bundle sheath; Ec- Epidermal cellL; Lv-Lateral vein; Ph-Phloem; Pm- Palisade mesophyl; Rc-Rosette celL; Lc-Subsidiary cell; Sm-Spongy mesophyl; Tr-Trichome bearing epidermal cell; X-Xylem]

172 Table 6.6a. Locational variations in Lamina and stomatal anatomy

S.No. Leaf Organs L1 L2 L3 L4 L5 1. Lamina Thickness(mm) 400 270 450 400 350 Thickness of Adaxial 20 20 25 25 15 2. epidermal layers (µm) Abaxial 10 10 20 10 10 Numbers of 3 2 3 3 3 3. Palisade zone layers Height(µm) 150 50 100 100 100 Guard cell 15X 4. size(µm) 20X20 20X25 10X20 20X25 size((µm) 25

Table 6.6b.Locational variations in lamina and stomatal anatomy (One sample t - test)

Std. Statistical S.No. Leaf Organs N Mean t Deviation Inference Thickness P<0.01 1. Lamina 5 374.00 68.044 12.290 (mm) significant P<0.01 Thickness of Adaxial 5 21.00 4.183 11.225 significant 2. epidermal P<0.01 layers(µm) Abaxial 5 12.00 4.472 6.000 significant Numbers P<0.01 5 2.80 .447 14.000 of layers significant 3. Palisade zone Height(µ P<0.01 5 100.00 35.355 6.325 m) significant Guard cell P<0.01 4. size(µm) 5 395 122.98 8.647 size (µm) significant DF=3

Table 6.6c. Karl Pearson correlation –Locational variations in leaf stomatal anatomy vs. meteorological elements Correlatio Statistical Parameters n value inference Palisade zone Numbers of layers vs. Annual Mean .990** P<0.05 significant R.H.morning Palisade zone Numbers of layers vs. Annual Highest R.H. -1.000** P<0.05 significant morning Palisade zone Numbers of layers vs. Seasonal Highest R.H. .919* P<0.05 significant morning Palisade zone Numbers of layers vs. Monthly Highest .919* P<0.05 significant R.H. morning Palisade zone Numbers of layers vs. Monthly Lowest R.H. .952* P<0.05 significant morning Palisade zone Numbers of layers vs. Number of rainy -1.000** P<0.05 significant days[2.5MM and above per month

173 Palisade zone Numbers of layers vs. Monthly mean wind .919* P<0.05 significant speed(kmph) Palisade zone height vs. Monthly total rainfall (mm) -.928* P<0.05 significant Palisade zone height vs. Annual mean wind speed (kmph) .993** P<0.01 significant Palisade zone height vs. Monthly mean wind speed .968** P<0.01 significant (kmph) N=5

Table 6.6d. Karl Pearson correlation –Locational variations in leaf stomatal anatomy vs.soil elements Correlation Statistical Parameters value inference Thickness of epidermal layers- Adaxial side vs. Electrical P<0.05 -.903* Conductivity significant P<0.05 Thickness of epidermal layers- Abaxial side vs. Total Calcium 927* significant N=5 The thickness of the adaxial side of the epidermal layers only shows correlation with the soil parameters of electrical conductivity and the calcium content of the soil (Table 6.6d.)

6.3.2.3. Discussion

Calotropis exhibited compact arrangement of palisade mesophyll cells. Calotropis possesses some interesting anatomical traits that help the plant grow in various climates and especially on poor soils of the wasteland where nothing else can survive. The presence of thick cuticle and highly developed multicellular epidermal trichomes on both the upper and lower leaf surfaces can be regarded as xeromorphic adaptations. They likely have a role in protecting the plant from excessive water loss and help to hide it from excess light. However, the presence of stomata both on the abaxial and adaxial surfaces was interesting. This trait is mesomorphic (Rudall., 2007) and is inconsistent with the above xeromorphic traits. It seems logical that in order to reduce water loss, stomata would be found only on the lower surface of the leaf where they are not exposed to direct sunlight and would therefore experience less transpiration. That is not the case in Calotropis. Perhaps this plant has evolved some other means of reducing water loss with its slightly succulent leaves.

174 The bi-facial nature of the Calotropis mesophyll was indeed characteristic of dicot plants. The highly organized palisade mesophyll was located just below the adaxial epidermis. It contained many chloroplasts. This is not surprising, as in most dicots this is the major site for photosynthesis. The spongy mesophyll layer was located below the palisade layer and had much airspace. This is another mesomorphic trait of the dicots (Rudall, 2007). Vascular tissues observed in the mid vein of the plant are arranged roughly in the form of a shallow crescent. As expected, the tracheary elements in the xylem appeared to have thick, lignified walls that were birefringent under polarized light. The phloem is located below the xylem. There are other tissues in the midvein which appear birefringent with polarized light. These occupy most of the area in the midvein surrounding the vascular tissue. It is likely that these cells will become lignified in order to provide structural support for the midvein. The limited amount of vascular tissue in the mid vein plus its central location could be regarded as a xeromorphic trait. The abundance of thick walled cells in the midrib could also be xeromorphic. (Fahn and Cutler, 1992). Laticifers were found in the leaf, especially in the midrib. This is not unexpected because Calotropis belongs to the milkweed family, the Asclepiadaceae where laticifers are common. The leaf anatomy in Calotropis is very much like a typical dicot plant with a few xerophytic adaptations (thick cuticle, adaxial / abaxial trichomes and stomata, a highly sclerotic midvein and laticifers). The leaf contains a mixture of xeromorphic and mesomorphic traits. Plant as a sedentary organism, is adjusting with the existing edaphic and seasonal variables. eg. countenance of high irradiance alters the mass of stomata (Körner et al. 1989). Some key anatomical traits play a substantial role in water stress tolerance. Increased palisade mesophyll height, higher leaf strength index (LSI), higher number of conducting tissues with increased diameter in leaf, stem and root and controlled transpiration rate due to a lower number of stomata per unit leaf area along with the increased guard cell size (Rudall, 2007). The present observation of the seasonal variations exhibits slight modificationsin the midrib and the lamina. The S1 specimen possesses substantial size of mid rib than other seasons. S2 and S3 possess midrib of smaller size. The laticifers

175 are frequent in S1 and S2 and less frequent in S3 but not evident in S4. It may be due to the relative humidity and the rainfall rate of this season. The hilly terrain plant exhibits a diminutive midrib. Korner et al., (1989) also observed similar trend in his study. Hameed et al., 2009 reported that midrib and lamina thicknesses were increasing with increasing salt level. The Salt Range ecotype had considerably thicker leaves than those recorded in the other ecotypes (Hameed et al., 2009). Though the L1 sample is collected in coastal tract the midrib size is not substantial than the other locations. In regions that experience high pollution emissions, such as in mining areas and industrialized zones, or where agricultural soils are contaminated by phosphoric fertilizers and/or sewage sludge, toxic trace metals, etc. affect plant development (Khudsar et al., 2000). In this study, the terrestrial-urban area sample shows comparatively thick midrib than other area. The widths of the vascular bundles are also about 1.9mm (L1) to 2.8mm (L3). The numbers of rows of xylem elements are about 2- 6. The widths of xylem elements are between 35μm to 51μm. In the ground tissue the numbers of collenchymas layers are about 6(L2 and L4) to 8 (L1 and L3). The parenchymas are circular, thin walled cells and compact in L1 and L3. Wide zone and thin walled in L2 and circular, thin walled and less compact in L4 and L5. The laticifers are not evident in L4, less frequent in L1 and L5 and abundant in L2, L4). The phloem strands are less prominent (L1, L2), small discrete nests (L4) and well preserved in L3 and L5. The outer phloems are less prominent in L1 and L2 well preserved in others. Evans (1996) reported that the increase in leaf area (or decrease in specific leaf weight, SLW) with shade for several species was caused mainly by an increase in mesophyll cell size, larger vascular bundles and sclerenchyma tissue, and thicker epidermal layers. This is coinciding with current observations. Salt stress, like other abiotic factors, affects leaf size through a decrease of cell expansion (Ticha, 1982; Curtis and Lauchli, 1987) and cell division (Hasegawa et al., 2000; Zhu, 2001; Fricke and Peters, 2002). Moreover, cellular differentiation is also affected (Assmann and Wang, 2001; Bird and Gray, 2003), altering the spatial relationship between the different types of leaf cells. To mitigate salt stress effects, plants can regulate their transpiration flux through a better control of the stomatal opening (as a short-term response) and through modifications of leaf anatomy (as a longterm response). Stomata are the main structures responsible for gas exchange.

176 Control and changes in stomatal density and size can contribute to regulate water use efficiency (Radoglou and Jarvis, 1990; Ceulemans and Mousseau, 1994). Anyway, the relationship between stomatal functionality and plant water status is very complex and several factors are involved (Buckley, 2005). Poplars are isohydric species because during the day they can control their stomatal openings to maintain a leaf water status potentially unaffected by changes in environmental conditions until plants are close to death (Tardieu and Simonneau, 1998). The Populus genus exhibits wide inter specific, as well as intra specific, variation in the stomatal characteristics such as guard cell length, density, and index (Ceulemans et al., 1995; Pearce et al., 2006; Al Afas et al., 2006; Monclus et al., 2006). These traits vary significantly according to leaf conformation and geographical provenance. Ecotypes adapted to more xeric conditions, show leaf xeromorphic characteristics like small blade surface, small stomata, and high stomatal density (Kuzminsky and Sabatti, 1994; Dunlap and Stettler, 2001). In particular, P. alba has hypostomatic leaves with a higher stomatal density than other poplar species. The stomatal density is controlled by genetic factors (Ticha, 1982; Sparks and Black, 1999; Dunlap and Stettler, 2001; Casson and Gray, 2008), and the production of this specialized epidermal cell occurs during the first developmental stages of the leaf (Pieters, 1974; Radoglou and Jarvis, 1990). Moreover, stomatal density and cell size can be modified by environmental factors such as drought (Aasamaa et al., 2001; Bosabalidis and Kofidis, 2002; Xu and Zhou, 2008), light (Ticha, 1982), and atmospheric CO2 concentration (Ferris and Taylor, 1994; Ferris et al., 2002). The control and transmission of signals from mature to developing leaves could be a valid mechanism to explain these changes (Lake et al., 2001). Elevated midrib and lamina thicknesses were observed with increasing salt level. The Salt Range ecotype had significantly thicker leaves than those recorded in the other ecotypes (Hameed et al., 2009). The present observation of high Lamina size, palisade zones are wider in the coastal tract plant. This is coinciding with the previous report.

177 6.3.3. Seasonal and locational variations in stem anatomy

6.3.3.1 Seasonal variations in stem anatomy

The cortex of all the stems is heterogeneous except S2 (homogenous). The width of cortex ranges from 100μm (S1) to 850μm (S4). The collenchyma is about 5(S3) to 8(S4). The parenchyma is thin walled and less compact in S1, S2 and S4. Vascular cylinder thickness varies from 300 μm (S2) to 550μm (S1). The width of the xylem cylinder ranges between 180 μm (S2) to 400μm (S1 and S4). Fibre masses are distinct in S1 and small and less prominent in S2 and small and not prominent in S3. At S4 it is dispersed in clusters with thick walls and narrow lumen. Inner and outer phloems are well preserved in S1 and in S4 the inner phloem is more prominent than outer phloem. But in S2 and S3 the phloem appears as circular discrete masses. Laticifers are well developed in all the seasons. The width of the xylem elements of vessels and fibres were 40 μm (S4) to 80μm (S2) and 15μm (S1) to 25μm (S2) respectively (Plate 6.5a, 5b and Table 6.7a) The seasonal variations in the stem organs (except width of the cortex) are statistically significant (Table 6.7b). The seasonal variation in the cortex of the stem is associated with the highest R.H. evening and total rainfall and the xylem cylinder are correlated with the heaviest rainfall (Table 6.7c). The seasonal variations in the thickness of the vascular cylinder and the width of the xylem elements of the stem are highly associated with the soil parameters the

organic carbon, organic matter and the iron content) (Table 6.7d).

178

S1.5 S1.6

S2.5 S2.6

S3.5 S3.6

S4.5 S5.6 Plate 6.5a. T.S. of Stem S1.5 S2.5, S3.5 & S4.5: T.S. of stem a sector enlarged

(4X) S1.6, S2.6, S3.6 & S4.6: T.S. of stem cortex, secondary phloem and secondary xylem enlarged (10X) [ Cf-Cortical fibre; Co= Corex; Col- Collenchyma;Ep-Epidermis; Ic-Inner cortex; Iph- Inner phloem; Oco- Outer cortex; Oph-Outer phloem; Pa- Prenchymacells; Pi- Pith; Sx-Secondary xylem; Ve-Vessels; Xf- Xylem fibre]

179

S1.7 S1.8

S2.7 S2.8

S3.7 S3.8

S4.7 S4.8

Plate 6.5b. Structure of the secondary xylem and medullary phloem S1.7 S2.7, S3.7 & S4.7: T.S. of secondary xylem (40X) S1.8, S2.8, S3.8 & S4.8: T.S. of medullary phloem and laticifers cells (40X) [Co-Cortex; Ep-Epidermis; Lf-Laticifer cell; Iph- Inner phloem; Mph- Medullary phloem; Oph-Outer phloem; Pi-Pith; Sx-Secondary xylem; Ve-Vessel; X-Xylem; Xf- Xylem fibre]

180 Table 6.7a. Seasonal variations in the stem anatomy

S.No. Stem organs S1 S2 S3 S4 Width(µm) 100 200 400 850 1. Cortex Numbers of Collenchyma 6 7 5 8 2. Vascular cylinder thickness(µm) 550 300 400 400 3. Xylem cylinder Width(µm) 400 180 400 200 Vessels Width (µm) 50 80 70 40 4. Xylem elements Fibres Width (µm) 15 25 20 20

Table 6.7b. Seasonal variations in the stem anatomy (One sample t - test) Std. Stem Organs Parameter N Mean t Statistical Inference Deviation P>0.05 not width(µm) 4 387.50 332.603 2.330 significant Cortex numbers of 4 6.50 1.291 10.07 P<0.01 significant Collenchyma Vascular cylinder thickness(µm) 4 412.50 103.078 8.004 P<0.01 significant Xylem cylinder width(µm) 4 295.00 121.518 4.855 P<0.05 significant vessels Width 4 60.00 18.257 6.573 P<0.01 significant Xylem elements (µm) fibres Width (µm) 4 20.00 4.082 9.798 P<0.01 significant DF=3

Table 6.7c. Karl Pearson correlation –Seasonal variations in stem anatomy vs. meteorological elements Correlation Parameters Statistical inference value Cortex - numbers of Collenchyma vs. Total Rainfall .992** P<0.01 significant Xylem cylinder Width(µm) vs. Heaviest Rain -.998** P<0.05 significant N=4

Table 6.7d. Karl Pearson correlation –Seasonal variations in stem anatomy vs. soil parameters Correlation Parameters Statistical inference value Vascular cylinder thickness vs. Organic carbon .990** P<0.01 significant Vascular cylinder thickness vs. Organic matter 990** P<0.01 significant Vascular cylinder thickness vs. Iron -.964* P<0.05 significant Xylem elements fibers Width vs. Organic carbon -1.000** P<0.01 significant Xylem elements fibers Width vs. Organic matter -1.000** P<0.01 significant Xylem elements fibers Width vs. Iron .991** P<0.01 significant N=4

181 6.3.3.2. Locational variations in stem anatomy

In the stem, the cortex was heterogeneous in L1, L4 and L5 but homogenous in L2 and L3. The cortex is about 400μm (L2 and L5) to 850μm (L4). There are 5 (L5) to 8 (L3 and L4) collenchyma layer present. In the inner parenchyma the cells are small and almost compact. Thickness of the vascular cylinder is about 500μm (L4) to 800μm (L5). The thickness of the xylem cylinders are about 200μm (L4) to 410μm (L3). The fibre masses are prominent and lignified in L1 and L3 but in L2 it is less lignified and in L4 it is in dispersed clusters with thick walls and narrow lumen. In L5 large circular masses of scattered fibres were noticed. The inner and outer phloems are well developed in L1, L3, L4 and L5 .In L4 the inner phloem is more prominent than outer phloem. But in L 2 it is less prominent. The vessel elements of the xylem are about 50μm (L4) and 100 μm (L5). The fibres of the xylem are about 20μm (L1, L4 and L5). But in the L3 it is 25(µm) (Plate 6.6a, 6.6b and Table 6.8a). The locational variations in the tissues of the stem are statistically prominent (Table 6.8b). The locational variations in the tissues of the stem show correlation only with the soil parameters. In the cortex the numbers of collenchyma and the width of the xylem cylinder are regulated by the manganese content of the soil. The thickness of the vascular cylinder and the width of the vessel elements are linked with the potassium and width of the fibers are correlated with the calcium content of the soil (Table 6.8c).

182

L1.5 L1.6

L2.5 L.2.6

L3.5 L3.6

L4.5 L4.6

L.5.5 L5.6 Plate 6.6a. Anatomy of the stem L1.5 L2.5, L3.5, L4.5 & L5.5: T.S. of stem a sector enlarged (4X) L1.6, L2.6, L3.6, L4.6&L5.6: T.S of stem cortex, secondary phloem and secondary xylem enlarged (10X) [ Cf-Cortical fibre; Co= Corex; Col- Collenchyma;Ep-EpidermiL; Ic-Inner cortex; Iph- Inner phloem; Oco- Outer cortex; Oph-Outer phloem; Pa- Prenchyma cellL; Pi-Pith; Lx-Lecondary xylem; Ve- Vessel; Xf- Xylem fibre]

183

L1.7 L1.8

L2.7 L2.8

L3.7 L3.8

L4.7 L4.8

L5.7 L5.8

Plate 6.6b. Structure of the secondary xylem and medullary phloem L1.7 L2.7, L3.7, L4.7 & L5.7: T.S. of secondary xylem (40X) L1.8, L2.8, L3.8, L4.8 & L5.8: T.S. of medullary phloem and laticifers cellL (40X) [Co-Cortex; Ep-EpidermiL; Lf-Laticifer cell; Iph- Inner phloem; Mph- Medullary phloem; Oph-Outer phloem; Pi-Pith; Sx-Secondary xylem; X-Xylem; Xf- Xylem fibre ;Ve-Vessel]

184 Table 6.8a. Locational variations in the stem anatomy

Stem S.No. parameter L1 L2 L3 L4 L5 Organs

Width(µm) 500 400 500 850 400 1. Cortex Numbers of 7 6 8 8 5 Collenchyma Vascular Thickness 2. 600 600 650 500 800 cylinder (µm) Xylem 3. Width (µm) 300 400 410 200 350 cylinder Vessels 80 80 70 50 100 Xylem Width (µm) 4. elements Fibres Width 20 20 25 20 20 (µm)

Table 6.8b. Locational variations in the stem anatomy (One sample t - test) Std. Statistical Stem Organs Parameter N Mean t Deviation Inference P<0.05 width(µm) 5 530.00 185.742 6.380 significant Cortex numbers of P<0.01 5 6.80 1.304 11.662 Collenchyma significant P<0.01 Vascular cylinder thickness(µm) 5 630.00 109.545 12.860 significant P<0.01 Xylem cylinder width(µm) 5 332.00 85.849 8.647 significant vessels Width P<0.01 5 76.00 18.166 9.355 (µm) significant Xylem elements fibers Width P<0.01 5 21.00 2.236 21.000 (µm) significant DF=4 Table 6.8c. Karl Pearson correlation - Locational variations in the stem anatomy vs. Soil elements Correlation Statistical Parameters value inference P<0.01 Cortex - numbers of Collenchyma vs. Manganese .970** significant P<0.05 Vascular cylinder thickness vs. Total Potassium .904* significant P<0.05 Xylem cylinder width vs. Manganese -.902* significant Xylem elements -vessels Width vs. Total P<0.05 .894* Potassium significant P<0.05 Fibers width vs. Total Calcium .927* significant N=5

185 6.3.3.3. Discussion The stems have two major functions, firstly, to hold up the leaves for optimal exposure to sunlight and secondly, to transport water and nutrients via the xylem and soluble carbon sources and hormones via the phloem to all parts of the plant. In contrast, the main function of leaves is to ‘host’ the process of photosynthesis. Photosynthesis occurs in chloroplasts, specialized green cellular compartments where light energy is captured for the production of glucose from CO2 and water (Roessner and Pettolino, 2007). Physiological and morphological aspects like transpiration efficiency (Condon et al. 1990), dissociation tolerance mechanism (Zhang et al., 1999), grain filling under stress (Blum, 2000), stem reserve (Kulkarni and Deshpande 2006), stay greenness (Borrel et al., 2000), seedling thermo tolerance (Yadav et al., 1999), root traits (Shashidhar et al. 1991), root morphology (Kamoshita et al. 2002) and root length Kulkarni and Deshpande, (2007) are earlier well established at different condition. The effects of water regime on the structures in stem were greater than those in leaf. Except for principal vein diameter and stoma density on leaf surfaces, all other structural traits were significantly affected by water regime (Tao et al., 2009).The number of xylem vessels in the stem also plays an important role in the stress tolerance mechanism being higher in tolerant genotype as compared to susceptible genotype. Blum (2000) discussed genotypic variation for the ability to store and mobilizes carbohydrates for seed filling during terminal moisture stress. In potato plant the xylem vessels of increased diameter and their higher number were observed in the stem of the polyploid mutant as compared to cultivated genotypes, which indicates the adaptability of the wild plant to drought (Kulkarni et al., 2008). The number of xylem vessels (r = 0.703) and secondary phloem width (r = 0.706) are key stem anatomical features which show significant positive correlations with dry matter production and ultimately with drought tolerance in tomato (Kulkarni and Deshpande 2006). Similar results were observed in chilli (Kulkarni et al., 2008). The present observation shows the width of cortex to be higher in S4 and low in S1. The number of collenchyma also fluctuates season to season. In summer it is found to be lesser in number (5) and in the southwest monsoon it is in higher number (8). The vascular cylinder thickness is greater in S1 (Northeast monsoon) and lesser in pre-summer. The Xylem cylinder is wide in S1 and S3 and in pre-summer it found to be narrow. The width of vessels is increased (80) in pre-summer and reduced (40) in

186 southwest monsoon. The fibre is wide in pre-summer and narrow in Northeast monsoon. These are all might be due to the seasonal fluctuations of the area. The locational changes exhibit remarkable variations in the structure of the cortex. The cortex of the L4 sample is wider than other locations. The numbers of collenchymas are also higher in this area. But, the vascular cylinder and the xylem cylinder, xylem vessels are found to be narrow. This may be due to the nature of the soil and the climatic condition of the area.

6.3.4. Seasonal and locational variations in root anatomy

6.3.4.1. Seasonal variations in root anatomy

The thicknesses of the roots are between 1.5mm (S2) to 4mm (S3). The periderms are deeply fissured in S1, S2 and S3. In S4 the periderms is V shaped and deeply fissured. They are about 200μm (S3) to 800μm (S4) in size. The width of the cortex was about 300μm (S2) to 100μm (S1). Starch grains are abundant in S1, S3 and S4 but in S2 the starch grains are not evident. The width of the secondary phloems are ranging between 350μm (S1, S2 and S3) and 500μm (S4). Thickness of secondary xylem are about 1mm (S2) and 1.6mm (S3) and the diameter of the vessels are about 110μm (S4) to 250 μm (S2) Xylem rays are prominent in S1, S2 and S4 but in S2 it is not distinct. Xylem fibres are found circular with thin walled lumen in S1, angular with thin walled lumen in S2, angular with thick walled in S3and thin walled with wide lumen in (S4) are observed. Tyloses are absent in all the seasons (Plate 6.7 and table 6.9a). The seasonal variation in the root anatomy is statistically prominent (Table 6.9b). Thickness of the root is associated with the mean maximum and minimum temperature and the highest temperature. The width of secondary phloem is correlated with the highest R.H. (morning & evening) and the mean wind speed. The diameter of the secondary xylem vessels are linked with the mean maximum and minimum temperature and the mean R.H. morning (Table 6.9c). The secondary phloem width is associated with the pH of the soil (Table 6.9d).

187

S1.9 S1.10

S2.9 S2.10

S3.9 S3.10

S4.9 S4.10

Plate 6.7. Seasonal variations in the root anatomy S1.9 S2.9, S3.9 & S4.9: T.S. of root (4X) S1.10, S2.10, S3.10 & S4.10:T.S. of root showing starch grains in the cortical tissue (10X) [Cf-Cortical fibre; Co- cortex; Fi- Fissure; Pe-Periderm; Sg-Starch grains; Sph- Secondary phloem; Sx-Secondary xylem; Ve-Vessels; Xf-Xylem fibre]

188 Table 6.9a. Seasonal variations in the root anatomy

S.No. Root S1 S2 S3 S4 1. Thickness (mm) 3.2 1.5 4 3.8 2. Periderm Width (µm) 550 350 200 800 3. Cortex Width (µm) 1000 300 400 350 4. Secondary phloem width (µm ) 350 350 350 500 Secondar Thickness (mm) 1.5 1 1.6 1.2 5. y Xylem Vessels diameter (µm) 170 250 140 110

Table 6.9b. Seasonal variations root anatomy (One sample t - test) Root N Mean Std. t Statistical Deviation Inference Thickness (mm) 4 3.125 1.135415 5.505 P<0.05 significant Periderm Width (µm) 4 475 259.8076 3.654 P<0.05 significant Cortex Width (µm) 4 512.5 327.5541 3.129 P>0.05 not significant Secondary phloem width (µm ) 4 387.50 75.000 10.333 P<0.01 significant Secondary Thickness (mm) 4 1.325 .2754 9.623 P<0.01 significant Xylem Vessels diameter (µm) 4 167.50 60.208 5.564 P<0.05 significant DF= 3 Table 6.9c. Karl Pearson correlation –Seasonal variations in root anatomy vs. meteorological elements Correlation Statistical Parameters value inference P<0.05 Root thickness vs. Mean max Temperature 962* significant P<0.05 Root thickness vs. Mean Minimum temperature . .968* significant P<0.05 Root thickness vs. Highest temperature .987* significant P<0.01 Secondary phloem width vs.Highest R.H. morning -1.000** significant P<0.01 Secondary phloem width vs.Highest R.H. evening -.996** significant P<0.05 Secondary phloem width vs. Mean Wind Speed .979* significant P<0.05 Secondary Xylem thickness vs. Heaviest Rain -.975* significant Secondary Xylem Vessels diameter vs. Mean max P<0.05 -.987* Temperature significant Secondary Xylem Vessels diameter vs. Mean Minimum P<0.01 -.995** temperature significant Secondary Xylem Vessels diameter vs. Mean R.H. P<0.05 .990* morning significant N=4

189 Table 6.9d. Karl Pearson correlation –Seasonal variations in the root anatomy vs. soil parameters Correlation Statistical Parameters value inference P<0.05 Secondary phloem width vs. pH .952* significant N=4

6.3.4.2. Locational variations in the root anatomy

Thicknesses of the roots are about 3.8mm (L4) to 7mm (L2). Widths of the periderm are between 170μm (L1) and 800μm (L4). Periderm is deeply fissured in L3, L4 and L5but in L1 the fissures are not prominent and L2 the fissures are thin walled. Cortical widths are about 400μm (L1, L5) to 550μm (L4).In the cortex, the starch grains are circular and concentric in L1, L3, L4 and L5 but in the L2 it was not evident. Widths of the secondary phloem are between 100μm (L2) to 550μm (L5).Thicknesses of the secondary xylem are about 1.2mm (L4) to 3mm (L2, L3).The diameters of the vessels are about 70μm(L1) to 140μm(L2, L3). The xylem rays are prominent only in L2, L4 and L5. Others are not prominent. Xylem fibres are thin walled with lumen narrow in L1. Thick walled and wide lumen are observed in L2, Thin walled with wide lumen are found in L3, L4 and L5. The Tyloses was found only in L2.Tyloses are not present in others (Plate 6.8 and Table 6.10a). The locational variation observed in the root anatomy is statistically significant (Table 6.10b). Root thickness is associated with the annual mean and highest RH, seasonal, monthly highest and lowest RH at morning (Table 6.10c). The cortex width is linked with most of the climatical elements (Table 6.10d). The secondary phloem width is associated with the annual lowest relative humidity morning and total magnesium content of the soil (Table 6.10c and 6.10d). The secondary xylem thickness is correlated with the annual lowest relative humidity morning (Table 6.10c).

190

L1.9 L1.10

L2.9 L2.10 L2.11

L3.9 L3.10

L 4.9 L4.10

L5.9 L5.10 Plate 6.8. Locational variations in the Root Anatomy L1.9 L2.9, L3.9, L4.9& L5.9: T.S. of root (4X) L1.10, L2.10, L3.10, L4.10 & L5.10: T.S. of root showing starch grains in the cortical tissue (10X) L2.11: T.S. of root showing tyloses [Cf-Cortical fibre; Co- cortex; Fi- Fissure; Pe-Periderm; Sg-Starch grains; Sph- Secondary phloem; Sx-Secondary xylem; Xf-Xylem fibre; Ty- Tyloses; Ve-Vessels ]

191 Table 6.10a. Locational variations in the root anatomy

S.No. Root organs L1 L2 L3 L4 L5

1. Thickness (mm) 4 7 4 3.8 4.6 2. Periderm 170 400 200 800 400 3. Cortex 400 500 500 550 400 4. Secondary phloem width (µm) 300 100 350 500 550 Thickness (mm) 2 3 1.2 1.2 2 Secondar 5. Vessels diameter y Xylem 70 140 140 110 100 (µm

Table 6.10b. Locational variations in the root anatomy (One sample t - test)

Std. Statistical Root N Mean t Deviation Inference P<0.01 Thickness (mm) 5 4.68 1.331 7.861 significant P<0.05 Periderm Width (µm) 5 394.00 251.356 3.505 significant P<0.05 Cortex Width (µm) 5 490.00 288.097 3.803 significant P<0.05 Secondary phloem width (µm ) 5 360.00 178.185 4.518 significant P<0.01 Thickness (mm) 5 1.88 5.658 5.658 Secondar significant y Xylem Vessels diameter P<0.01significan 5 112.00 8.491 8.491 (µm) t DF= 4

Table 6.10c. Karl Pearson correlation – Locational variations in root anatomy vs. meteorological elements Correlation Statistical Parameters value inference Root thickness vs. Annual mean R.H.m Morning -.971** P<0.01 significant Root thickness vs. Annual highest R.H. morning .974** P<0.01 significant Root thickness vs. Seasonal highest R.H. morning -.912* P<0.05 significant Root thickness vs. Monthly highest R.H. morning -.912* P<0.05 significant Root thickness vs. Monthly Lowest R.H. morning -.941* P<0.05 significant Cortex Width vs. Annual Mean maximum temperature -.990** P<0.01 significant Cortex Width vs. Seasonal Mean maximum -.993** P<0.01 significant Temperature Cortex Width vs. Monthly Mean Maximum -.996** P<0.01 significant Cortex Width vs.Annual Mean Minimum temperature -.927* P<0.05 significant Cortex Width vs. Seasonal Mean minimum temperature -.975** P<0.01 significant Cortex Width vs. Monthly Mean Minimum Temperature -.958* P<0.05 significant Cortex Width vs. Annual Highest Temperature -.930* P<0.05 significant

192 cortex width vs. Seasonal Highest temperature -.984** P<0.01 significant Cortex Width vs. Monthly Highest temperature -.979** P<0.01 significant Cortex Width vs.Annual Lowest temperature .930* P<0.05 significant Cortex Width vs. Seasonal Mean R.H. morning .905* P<0.05 significant Cortex Width vs. Annual Mean R.H. evening 989** P<0.01 significant Cortex Width vs. Seasonal Mean R.H. morning .980** P<0.01 significant Cortex Width vs. Monthly Mean R.H. morning .980** P<0.01 significant Cortex Width vs. Seasonal Lowest R.H. morning 955* P<0.05 significant Cortex Width vs. Seasonal Highest R.H. evening .984** P<0.01 significant Cortex Width vs. Seasonal Lowest R.H. evening .994** P<0.01 significant Cortex Width vs. Monthly Lowest R.H. evening .997** P<0.01 significant Cortex Width vs. Seasonal Total rainfall -.882* P<0.05 significant Cortex Width vs. Monthly total rainfall -.986** P<0.01 significant Cortex Width vs. Annual mean wind speed .914* P<0.05 significant Secondary phloem width vs. Annual Lowest R.H. .910* P<0.05 significant morning Secondary Xylem Thickness vs. Annual Lowest R.H. -.888* P<0.05 significant morning N=5

Table 6.10d. Karl Pearson correlation – Locational variations in root anatomy vs. soil elements Correlation Statistical Parameters value inference P<0.05 Secondary phloem width vs. Total Magnesium .915* significant N=5

6.3.4.3. Discussion

According to Jackson et al., 2000; Sobrado, 2007 the vessels number increases but the vessels size reducing in stems reducing the vessels size in stems or the root investment increases when the water uptake is maximized. Tolerance to low tissue water potential may involve osmotic adjustment, accumulation of high proline and soluble sugar contents. Accumulation in cells is associated with the prevention of protein denaturation under the drought stress (Rajendrakumar et al., 1994; Saradhi et al., 1995). Under the salt stress, the important mechanisms of plant tolerance involve Na+ exclusion and K+/Na+ selectivity, roots must exclude most of the Na+ and Cl+ in the soil solution and maintain high selectivity of K+ over Na+ to avoid ion toxicity in shoot tissues (Peng et al., 2004; Garthwaite et al., 2005; Munns et al., 2006; Sarita et al., 2009). These strategies for plant successfully to endure drought and salinity are widespread successfully in plants.

193 In many woody temperate plants cambial activity is seasonal (usually annual), which results in the formation of growth rings. The secondary xylem formed in the early part of the season (early wood or spring wood) is generally less dense and consists of thinner-walled cells than the xylem formed later in the growing season (late wood or summer wood). In ring-porous woods the vessels are considerably larger in early wood than in late wood .In diffuse porous woods the main distinction between early and late wood is in the size and wall thickness of the fibres. As woody plants age and their trunks increase in girth, the central area becomes non-functional with respect to water transport or food storage, and the vessels frequently become blocked by tyloses. Tyloses are formed when adjacent parenchyma cells grow into the vessels through common pit fields. The central non-functional area of the trunk, the heartwood, is generally darker than the outer living sapwood (Rudall, 2007). The tyloses found in the location L2, suggests the occurrence of aging environment in L2 this needs further investigation. Calotropis root vessels are wider than stem a vessel, which agrees with reports of other species (Carlquist 1975; Ewers et al. 1997; Psaras & Sofroniou 1999). Root vessels are almost twice as wide as stem vessels (52 VS 27 μm). Considering that the conducting capacity of a tube is proportional to the 4th power of its radius (Hagen- Poiseuille equation; Tyree et al. 1994), the conducting capacity of a 52 μm root vessel is about 14 times higher than that of a 27 μm stem vessel). The seasonal variations show the wide root (800 µm), Secondary phloem (500 µm) are present in the S4. The cortex is wider in S1 and the secondary xylem i s wider in S3 (1.6 mm). The vessels are wider in S2 (250 µm). These variations might be due to the rate of rain fall and temperature occurrence of the particular seasons. In the case of locational variations the thickness is higher in Hilly terrain (L2) and the periderm and cortex are wider in L4 (800 µm, 550 µm). These variations also might be due to the soil conditions and the climatical variations of the area.

194 7.1. INTRODUCTION

All innovations, developments and advances in the field of science and technology aim at making the life comfortable and worth living for man. Man as an integral part of nature has been depending upon plants for food, clothing, shelter and medicines. Theophrastus (370-285 B.C) regarded as the “Father of Botany” has highlighted the dependence of man on plants in his “Enquiry into plants” and “The causes of plants”.

Everlasting human desire to acquire sound health and long life forced him to search for the fountain of youth which could prolong his life or amazing herbs which could exert their effects for the preservation of human health. The art of healing has, therefore, its origin in the antiquity of human civilizations. Old historical references show that plant derivatives have been in use by the mankind even 6000 years ago for various medical purposes. The first known physician was evidently Chen Nung (Chen, 1925, 1927; Marse, 1934), emperor of China around 3000 B.C., who recorded a number of drugs and poisonous, in his compilation known as “Pen tsau”- “The Great Herbal”. However, the first systematic record of the Chinese medicine entitled ‘Shen Nong Ben Cao Fing” was compiled during the first century describing physiological and pharmaceutical effects through 365 entries related to 252 plant materials. Later,” Ben Cao Gang Mu”, which was written by Li Shizhen provided 1894 entries, which still serves as valuable reference for practicing medicinal research in China .The ancient civilization of Sumer, Babylon and Mohenjo-daro also made sizeable contribution in the medicinal field. Later, during the Hellenic and Greco- Roman Period, a great reservoir of medical knowledge was provided by world famous physician like Hippocrates (Sigeristh, 1934), Theophrastus (Hort, 1916) and Galen (Walsh, 1937). However, the most significant pharmacological compilation of the Greeks was the authoritative text of Discordies (Walsh, 1939). After him, Pliny the elder wrote “Natural History” in 37 volumes. Galen wrote some 30 books on pharmacology along with his “galenicals” preparations.

195 7.1.1. Herbs curing diseases

‘Herbs’ apart from the botanical definition refer to a wide range of plants having therapeutic properties. The herbs cure our ailments and keep our body in order and hence are curative herbs. The herbs can cure almost all kind of ailments. The digestive system, respiratory system, liver, kidney, skin, bone and blood disorders are the common ailments cured by medicinal plants since ancient times. But specific compositions are formulated and administered for acute ailments such as cancer and AIDS.

7.1.2. Cancer and its harshness

Cancer is one of the most dreaded diseases of the 20th century and spreading further with continuance and increasing incidence in 21st century. In the United States, as the leading cause of death, it accounts for 25% of all the deaths in humans presently. It is considered as an adversary of modernization and advanced pattern of socio-cultural life dominated by Western medicine. Multidisciplinary scientific investigations are making best efforts to combat this disease, but the sure-shot, perfect cure is yet to be brought into world medicine (Balachandran and Govindarajan, 2005).

7.1.3. Photochemistry-based cancer therapy

Cancer is a major cause of death and the number of new cases, as well as the number of individuals living with cancer, is expanding continuously. Due to the enormous propensity of plants that synthesize mixtures of structurally diverse bioactive compounds, the plant kingdom is potentially a very diverse source of chemical constituents with tumor cytotoxic activity. Despite the successful utilization of few phytochemicals, such as vincristine and taxol, into mainstream cancer chemotherapy, commercial plant-derived anticancer formulations represent only one- fourth of the total repertoire of the available treatment options. Though significant progress has been made towards the characterization of isolated compounds and their structure-related activities, the complex composition of plant extracts, along with the lack of reproducibility of activity and the synergy between different, even unidentified, components of an extract, prohibits the full utilization of plants in pharmaceutical research. A total of 187 plant species, belonging to 102 genera and 61 families have been identified as an active or promising source of phytochemicals with

196 antitumor properties, corresponding to a 41 percent increase during the last five years. Among them, only 15 species (belonging to ten genera and nine families) have been utilized in cancer chemotherapy at a clinical level, whereas the rest of the identified species are either active against cancer cell lines or exhibit chemotherapeutic properties on tumor-bearing animals under experimental conditions. Phenylpropanoids are the most widely distributed compounds (18 families), followed by terpenoids (14 families), and alkaloids (13 families) (Kintzios, 2006).

7.1.4. Commercial plant-derived anticancer formulations

The greatest recent impact of plant-derived drugs is probably felt in the antitumor area, where compounds such as taxol, vinblastine, vincristine, and camptothecin have dramatically improved the effectiveness of chemotherapy against some of the deadliest cancers (Rates, 2001). Indeed, several commercial plant-derived anticancer formulations exist, the most important of which are listed in Table 7.1. To a certain extend, the lack of reproducibility of activity for more than 40 percent of plant extracts is one of the major setbacks in using plants in pharmaceutical discovery (Cordell, 2000). In addition, the complex composition of plant extracts is not compatible with high- throughput assays used in drug discovery experiments (Raskin et al., 2002). Another reason is the frequently observed synergy between different, even unidentified components of an extract in the expression of the optimal pharmacological effect (Liu, 2004; Kumi-Diaka et al., 2004). On the other hand, there exists a far more extensive list of compounds with cancer chemopreventive, rather than chemotherapeutic, properties, i.e., they act as inhibitors of early stages of carcinogenesis (Costa et al., 1990). Cancer development is a multi step procedure that includes the following: activation of oncogenes, inactivation of tumor suppressor genes, and modulation of mitogenic signal transduction pathways that are critical in cancer progression and present potential targets for cancer prevention/intervention (Singh et al., 2002). A number of phytochemicals inhibit cell cycle progression in cancer cells, yet their clinical applications are still exploratory.

197 Table 7.1. List of approved, plant-derived oncology drugs

Active Trade Source species anticancer Manufacturer formulation ingredient

Topotecan Irinotecan Glaxo Smith Kline Campothecaacuminata Hycamtin Camptosar Pharmacia and Upjohn

Vepesid Bristol Myers,Squibb

Genpharm ,Bedford, AmPharm,Partners Sicor Pharms Etoposide Etoposide Podophyllumpeltatum ,Pharmachemie, Marsam Pharm, Supergen Toposar Sicor Pharms Etopophos Bristol Myers, Squibb Teniposide Vumon Bristol MyersSquibb Abraxane Am Bioscience Taxol Bristol Myers, Squibb Paclitaxel Mayne Pharma,USA Mylan Paclitaxel Supergen, Ivax Pharms,Bedford Baker Norton Akorn ,Novex,Bausch and Lomb Betaxolol ,Amide Pharm Taxus sp. Betaxolol Betaoptic Alcon, Kerlone Lorex Docetaxel Taxotere Aventis Nolvadex AstraZeneca citrate Aegis, Andrx, Ivax Pharms, Tamoxifen Tamoxifen Bar,Mylan, Pharmachemie, Roxane, Teva Iscador Cancer Research Association Helixor Viscum album Iscisin- Wala-Heilmittel Viscum Isorel GmbH, Novipharm Vinblastin Mayne Pharma,USA Bedford Vinblastin sulfate ,AmPharmPartners Velban Eli Lilly Vinca rosea Vincristine Mayne Pharma, USA Sicor Vincristine sulfate PharmsAmPharm Partners, Eli Lilly Oncovin Valrubicin Anthra Valstar

(FDA Electronic Orange Book, 2005)

198 7.1.5. Plant metabolites with antitumor properties

Plant metabolites with antitumor properties are primarily cytotoxic, probably due to their evolution-driven development as natural pesticides for the self-defense of plant organisms. Their accumulation in the plant body is usually the result of stress induced elicitation of specific biosynthetic pathways (Dixon, 1986). For example, it has been proven that lignans present in the roots of Anthriscus sylvestris have insecticidal activity (Kozawa et al., 1982). Alkaloids are also common constituents of poisonous plants, often found in meadows where they are exposed to frequent grazing by animals. However, some plant metabolites exert cytotoxic effects in less direct ways. For example, flavonoids (such as baicalin from Scutellaria baicalensis) can inhibit cancer cell proliferation by modulating the activity of cyclin-dependent kinases (Dai and Grant, 2003; Chang et al., 2004), it also demonstrates a cytotoxic estrogen- like activity in high concentrations (Nair et al., 2004; Oh and Chung, 2004; Woo et al., 2005). In other words, some plant metabolites can act as chemotherapeutical agents due to their growth-regulatory properties. Plant-derived natural products with antitumor properties can be classified into thirteen distinct chemical groups (Kintzios and Barberaki, 2004), which are as follows; 1.alkaloids, 2. phenyl propanoids and its derivatives, 3. terpenoids, 4. aldehydes, 5. Annonaceous acetogenins, 6. Glycosides, 7. Lignans, 8. Lipids (fatty acids (aliphatic carboxylic acids), fatty acid esters, phospholipids and glycolipids) 9. Unsaponified lipids (Naphthoquinones, Phenanthraquinones, Anthraquinones) 10. Nucleic acids (purine and pyrimidine bases),11. Polysaccharides 12. Proteins and 13. unidentified complex substances present in plants. Among them, best documented for their tumor-cytotoxic activity are alkaloids (Facchini, 2001), phenylpropanoids (Dixon and Paiva, 1995) and terpenoids (Trapp and Croteau, 2001).

7.1.6. In vitro cytotoxic study

Plant materials under consideration for efficacy testing are usually composed of complex mixtures of different compounds with different solubility in aqueous culture media. The first step is selection of starting materials, primarily based on the ethnobotanical information (i.e., reputation of therapeutic action in a traditional medicine sense). The second step is identification of biological activity (in the case of cancer chemotherapy, this certainly includes selective cytotoxicity) of the extracts

199 derived from the selected plant material. Frequently, extracts are pre fractionated by means of chromatography and then fractions are screened for biological activity in vitro (Constant and Beecher, 1995). The combination of different in vitro assay systems may not only enhance the capacity to screen for active compounds, but may also lead to better conclusions about possible mechanisms and therapeutic effects. Thus, preclinical tests usually evaluate the cytotoxicity of a candidate antitumor agent in vitro, that is, on cells cultured on a specific nutrient medium under controlled conditions. Certain neoplastic animal cell lines have been repeatedly used for this purpose. Alternatively, animal systems bearing certain types of cancer have been used (Gebhardt, 2000). A more efficient, disease-oriented screening strategy should employ multiple disease-specific (e.g., tumortype specific) models and should permit the detection of either broad-spectrum or disease-specific activity. The use of multiple in vivo animal models for such a screen is not practical, given the scope of requirements for adequate screening capacity and specific tumor-type representation (Kintzios and Barberaki, 2004). The availability of a wide variety of human tumor cell lines representing many different forms of human cancer, however, offers a suitable basis for development of a disease-oriented in vitro primary screen (Miyairi et al., 1991; Mockel et al., 1997; Gebhardt, 2000). According to Kintzios and Barberaki (2004) some of the most common animal and cell culture lines used for primary screening are listed in Table 7.2.

Table7.2. Common Animal & Cell Culture Lines Used For Primary Screening S.No. Most commonly used animal and cell culture lines used for primary screening 1. P-388 lymphocytic leukemia bearing mice 2. 9KB carcinoma of the nasopharynx cell culture assay 3. Human erythroleukemia K562 cell line 4. MOLT-4 leukemic cell line 5. RPMI, and TE671 tumor cells 6. ras-expressing cells 7. Alexander cell line (a human hepatocellular carcinoma cell line secreting HbsAg) 8. Human larynx (HEp-2) and lung (PC-13) carcinoma cells 9. Mouse B16 melanoma, leukemia P-388, and L5178Y cells 10. Liver-metastatic variant (L5) 11. 7,12-dimethyl benzanthracene (DMBA) induced rat mammary tumors 12. Ehrlich ascites tumor-bearing mice 13. Ehrlich ascites carcinoma (EAC), Dalton’s lymphonia ascites 14. (DLA), and Sarcoma-180 15. (S-180) cells 16. MCA-induced soft tissue sarcomas in albino mice

200 7.1.7. Antitumor and anticancer activity of Calotropis

The cytotoxicity of extracts of its root, leaves and flowers of Calotropis has been shown (Kupchan, et. al., 1964; Smit et al., 1995; Locher et. al., 1995; Kruchi et. al., 1998; Rutten and Statius, 1998).Previous experiments with this plant, extracts of the root and the leaves showed cytotoxic activity against human epidermal nasopharynx carcinoma (Ayoub and Kingston, 1981). Cytostatic activity of C. procera, was earlier reported (Smit et al., 1995). In vitro anti-tumour activity and a high level of in vivo tolerance of this plant was recorded by Van Quaquebeke et al., (2005) .The whole latex of Calotropis gigantia possesses anticancer and cytotoxic activity against hepatocellular carcinoma (Choedon et al., 2006). The water-soluble protein fraction of the latex was evaluated and found to possess a potential cytotoxic activity against different human cancer cell lines. (De Oliveira et al., 2007). This study explores the possible inflection in in-vitro anticancer potential of the chloroform extract of C.gigantea, collected during different seasons and locations on the Ehrlich Ascites Carcinoma cell lines.

7.2. MATERIALS AND METHODS

7.2.1. Plant material collection and preparation of the extract:

7.2.2. Procuring and maintenance of cell lines:

Ehrlich Ascites Carcinoma cells were obtained from Amla Cancer Research Centre, Thrissur and were maintained by weakly intraperitoneal inoculation of 1X106cells/mouse.

7.2.3. In-vitro cytotoxicity (tryphan blue)

Short- term in-vitro cytotoxicity was assessed (Sheeja et al., 1997). The tumor cells were aspirated from peritoneal cavity of tumor bearing mice using an insulin syringe and transferred to a test tube containing isotonic saline. The cells were then washed in normal saline and cell number was determined using a haemocytometer and adjusted at 10x106 cells/ml. For the cytotoxic assay, different concentrations of the extracts (25-400 µg/ml) were added to each tube and the final volume was adjusted to one ml with normal saline. Control tubes were kept with the saline, tumor

201 cells and without the extracts. All the tubes were incubated at 370c for 3 hours. After incubation 0.1ml of 0.4% tryphan blue dye in isotonic saline was added to each tube and the number of viable (unstained) and dead (stained) cells were counted using haemocytometer.

7.4. RESULTS AND DISCUSSION

7.4.1. Seasonal variation

The chloroform extract of different concentration (25 μg/ml to 400 μg/ml). Calotropis exhibits a significant cytotoxicity against the EAC cells (Figure 7.3). The highest concentration (400 μg/ml) shows significant effect (44.7% to 72.5%) against to the tested EAC cells. The significant fluctuation is recorded in the seasonal variations. The maximum cytotoxic potential was recorded in the season S3 (summer) (Figure 7.1 -7.4 & plate 7.1a to 7.1d). The variations in the cytotoxicity among locations are statistically significant (Table 7.3).

44.7 45 60 56.2 36.5 40 50 35 40.6 28.5 30 40 32.2 25 20.7 30 23.4 20 16.3

Cell Death ( % ) ( Death Cell 20 Cell Death ( % ) 15 10 10 4.62 10 4.62 5

0 0 Control 25μg50μg100μg200μg400μg Control 25μg50μg100μg200μg 400μg Figure 7.1.Cytotoxic potential of S1 Figure 7.2. Cytotoxic potential of S2

56.4 80 72.5 60

70 59.8 50 42.8 60 52.3 40 32.7 50 39.7 28.6 40 30 19.3 30 23.6 CellDeath ( % )

Cell Death ( % ) 20 20 4.62 4.62 10 10

0 0 Control 25μg50μg 100μg200μg 400μg Control 25μg50μg 100μg 200μg 400μg Figure 7.3. Cytotoxic potential of S3 Figure 7.4.Cytotoxic potential of S4

202

Plate 7.1a. S1-Cytotoxicity (400μg) Plate 7.1b. S2-Cytotoxicity

Plate 7.1c. S3-Cytotoxicity Plate 7.1d S4-Cytotoxicity

Table 7.3. Seasonal variation in the cytotoxicity (One sample t - test)

Concentrations N Mean Std. Deviation t Significance 25μg 4 17.30 5.715 6.054 P<0.01 significant 50μg 4 28.100 8.3996 6.691 P<0.01 significant 100μg 4 36.425 10.7478 6.778 P<0.01 significant 200μg 4 44.925 10.2546 8.762 P<0.01 significant 400μg 4 57.450 11.4270 10.055 P<0.01 significant DF= 3

7.4.2. Locational variation The cytotoxicity observed among the locations falls between 51.5% (L1) and 57.3 %( L3). (Figure 7.5a and plate 7.5a to 7.5f). This locational variation is statistically significant among the cytotoxic potential (Table 7.4). Though the variations are significant among the locations, the season three samples cytotoxicity is considerably higher than all the other samples tested (Fig. 7.4).

60 60 51.5 52.7

50 50

37.7 38.3 40 40 31 27.8 26.7 30 30 21.7 Cell Death( ) % Cell Death ( ) % 20 12.8 20 13.4

4.62 10 4.62 10

0 0 Control 25μg50μg 100μg200μg400μg Control 25μg50μg100μg200μg400μg Figure 7.5a. Cytotoxicity of L1 Figure 7.5b. Cytotoxicity of L2

203 57.3 56.4 60 60

50 44.1 50 42.8 37.7 40 40 32.7 28.6 26.4 30 30 17.5 19.3

Cell Death (% ) Cell Death (% 20 Cell Death ( % ) 20

4.62 10 10 4.62

0 0 Control 25μg50μg100μg 200μg 400μg Control 25μg50μg100μg200μg400μg Figure 7.5c. Cytotoxicity of L3 Figure 7.5d. Cytotoxicity of L4

60 54.5

50 47.5

) 40 37.3

30 25.3

18.8 Cell Death ( % ( Death Cell 20

10 4.62

0 Control 25μg50μg 100μg 200μg 400μg

Figure 7..5e. Cytotoxicity of L5

Plate 7.2a. Cytotoxicity of L1 Plate 7.2b.Cytotoxicity of L2

Plate 7.2 c. Cytotoxicity of L3 Plate7.2d. Ctotoxicity of L4 Plate7.2e Cytotoxicity of L5

204 Table 7.4. Locational variation in the cytotoxicity (One sample t - test)

Concentrations N Mean Std. Deviation t Significance

25μg 5 16.360 11.975 3.0550 P<0.01 significant

50μg 5 25.740 22.553 2.5521 P<0.01 significant

100μg 5 33.300 17.642 4.2208 P<0.01 significant

200μg 5 42.080 22.914 4.1063 P<0.01 significant

400μg 5 54.480 50.060 2.4335 P<0.01 significant DF= 3

7.4.3. Discussion The anticancer potential of Calotropis has been established by many researchers (Smith et al., 1995; Locher et al., 1995; Kupchan et al., 1964; Ayoub and Kingston, 1981; Van Quaquebeke et al., 2005; Choedon et al., 2006; De Oliveira et al., 2007; Wang et al., 2008; Pardesi et al., 2008). This activity is implicated with the presence of alpha- amyrin, .beta amyrin, lupeol, sitosterol, beta-Sitosterol, linoleic- acid, oleic-acid, Stigmasterol, caryophyllene, etc. (Yasukawa et al., 1991; Duke, 1992 ; Zheng et al., 1992; Kasahara et al., 1994; Banskota et al., 1999). These compounds have also been found to present in chloroform extract of the Calotropis in this study (Table 5.6a and 5.23) The S3 (summer) plant sample exhibits a maximum cytotoxic effect against the EAC cell lines (72.5%) than the other seasons. The range of cytotoxic potential is 44.7% to 72.5% at 400 μg/ml of the extract. The descending order of the cytotoxicity is

Summer (72.5 %) > Southwest monsoon (56.4%) > Pre-summer (56.2 %) > Northeast monsoon (44.7 %) There are about 64 phytochemicals found in the chloroform extract of Calotropis (Table 5.6a and 5.23). The maximum number - 50 compounds (about 75% of the total compounds) are present in the summer season (S3). According to the occurrence of the number of compounds the seasons can be arranged as follows:-

Summer (50) > Southwest monsoon (40) > Northeast monsoon (37) > Pre- summer (35)

205 The exhibited cytotoxic potential matches almost with the number of cytotoxic principles present in the tested samples of the four seasons implicating their role in the activity. Seasonal changes influence various abiotic factors such as temperature, pH, salinity etc. as well as the biotic factors like micro faunal and microbial diversity of the soil which ultimately is responsible for the biosynthesis of the secondary metabolites with in the plants growing in the soil (Chennubhotla et al., 1982; König and Wright, 1996; Baig and Zehra, 1997). All the tested extracts of Calotropis collected from the five different locations but in the same season were found to possess significant cytotoxic effects with meager variations. The descending orders of the cytotoxicic effect of the locational samples at 400 μg/ml are L3 (51.5 %) >L4 (52.7 %) >L5 (57.3 %) >L2 (56.4 %) >L1 (54.5%) The mean difference in the number of phytochemicals of the locational samples is meager and is not concurrence with the anticancer potential of the sample. The locational variation observed in the phytochemicals as well as the anticancer potential in Calotropis is not as distinct as the variations observed through the seasons. Also the chemogeographical variations of the oil composition in Heteropyxis natalensis from five distinct localities contained 1, 8-cineole and limonene as major constituents in all the samples. The antimicrobial study of these samples indicated little variability between locations but higher variability among seasons. (Vuuren et al., 2007). According to Vuuren et al., (2007).The production of phytochemicals is (often) governed by external factors such as soil quality and climate. The chemical composition of a plant is thus subject to quantitative and qualitative variation. Biological activity which is dependent on the chemical composition is similarly subject to variation. This finding is identical to the present investigation. The seasonal and spatial differences have profound effects on the biomass and chemical constituents at different stages of the plant and ultimately on the life cycle. The biotic (morphology, type, physiology etc.) and abiotic factors (soil, salinity, temperature etc.) indeed influence the expression of active principles of the plants during different seasons and at different locations (Correa Junior et al. 1991). Freire et al., (1993) found out in the winter extract of Sargassum wightii that the inflammatory edema was inhibited for a longer time. This reflects that the plant

206 extracts of winter collection contains compounds that maintain the magnitude of activity for longer duration. Thus not only the active principles but also the curative properties are influenced by the environmental factors. The other findings in a similar line is antimicrobial and anti oxidant activity of the plant Artemisia princeps var. orientalis fractions differed depending on the growth season: that is, the activity was weaker in April (sprouting stage) and October (fading stage), but more pronounced in July, August and September (vegetative stage) (Yun et al., 2008). Thus chemically and curatively seasonal variations are more pronounced that the locational variations in Calotropis. Summer is most active season for the production of secondary metabolites and the anticancer activity.

207 8.1. CONCLUSIONS Despite the relationship between the plant and its environment is incredibly

complex, the following conclusion can be drawn from the present study executed to

comprehend the effects of different seasons and locations on the phytochemical,

anatomical and anticancer properties of Calotropis gigantea (L) R.Br. a multi

potential plant.

™ Objective 1

¾ The recorded climatic variations in the four seasons seem to be

statistically significant.

9 Northeast monsoon (S1) – Medium temperature with greatest

difference between the highest and lowest temperatures – Maximum

number of rainy days – Highest mean RH in the evening – Medium

wind speed.

9 Pre-summer (S2) – Lowest temperature of the year with the smallest

difference between the highest and lowest temperatures – Heaviest

rainfall of the year – Highest mean RH in the morning – Minimum

wind speed.

9 Summer (S3) – Highest temperature of the year with maximum

difference between the maximum and minimum temperatures – Lowest

total rainfall and minimum rainfall day – Lowest mean RH in the

evening.

9 Southwest monsoon (S4) – Highest mean maximum temperature -

Maximum total rainfall, Moderate number of rainy days – Lowest

mean RH morning and evening – Highest wind speed.

208 ¾ The climatic variations among the studied locations are also statistically significant:- 9 Coastal tract (L1) - The mean maximum and the minimum temperatures and the total rainfall are low. The numbers of rainy days, the mean RH and the wind speed are the highest. 9 Hilly terrain (L2) - Maximum high and mean temperatures, lowest mean RH in the morning and evening – Medium total rainfall and number of rainy days with minimum wind speed. 9 Riverine zone (L3), Terrestrial rural stretch (L4) & Terrestrial urban area (L5) - Medium, maximum, minimum and mean temperatures - Medium mean RH in the morning and evening- Maximum total rainfall, minimum number of rainy days- Medium wind speed.

♦ The study area during the study period has experienced a dry tropical climate with all above variations. ™ Objective2 ¾ The seasonal variations in all the soil parameter studied (except soil texture) are statistically significant. 9 Except EC, Fe, Mn and S all other parameters (Soil texture, pH, organic carbon, and organic matter, N, P, K, Na, Ca, Mg, Zn and Cu) exhibit only a meager seasonal variations. 9 There is a statistically significant difference in all the locational parameters studied 9 The locational variation is considerable in pH, EC, organic carbon, organic matter, Fe and Mn. 9 N, P, K, Mg, Ca, S, Cu, Zn and Na exhibit meager locational variations. ♦ The impact of both season and location is considerably high upon EC, Fe & Mn and low upon N, P, K, Ca, Mg, Zn, Cu and Na of the soil. The locational impact is more on soil pH, Organic Carbon and Organic Matter while the seasonal impact is more on S.

209 ™ Objective 3

¾ The resolution based up on the phytochemical studies are:-

9 The impact of seasons upon the organic and the inorganic plant constituents are diversified. 9 Among the organic constituents meager variations in the primary metabolites, moderate changes in the energy content and the yield of extract and considerable variations among the secondary metabolites are evident. 9 Similarly among the inorganic constituents the impacts of the seasons on the essential macro nutrients are meager and the essential micronutrients are higher while up on the nonessential elements it is moderate. 9 In Calotropis 64 secondary metabolites are identified. A maximum of 50 compounds occurred in summer (S3) and minimum 35 compounds in pre-summer (S2). 9 The exclusive summer compounds are either, sterols, hydrocarbons or fatty acids. 9 Among the identified secondary metabolites, 25 compounds are common to all seasons (Terpenes – 9; Hydrocarbons – 7; Fatty acids – 5; Sterols – 3; Heterocyclic compounds – 1). 9 The locational samples express only meager variations in all the organic components, macro and micro essential elements. A moderate variation is observed in the nonessential elements. 9 21 compounds are common to all locations (Terpenes – 7; Hydrocarbons – 6; Fatty acids – 4; Sterols – 3; Heterocyclic compounds – 1). 9 Calotropis is accumulating considerable amount of selenium within the plant body during all seasons and locations. This is interesting and needs further study. 9 Among the climatic factors temperature, RH and rainfall influence the chemical composition of the plant. 9 Organic carbon, N, P, Fe, Zn and Cu of the soil manipulate the chemical constituents of the plant.

210 ♦ The impact of seasons is more pronounced over the phytochemicals than the locations. ™ Objective 4 ¾ The morpho anatomical variations reckon to the following:- 9 The morphological and anatomical features are significantly altered by the seasons except the palisade zone of the leaf and cortex of the root and stem. 9 Leaf area, number of leaves and flowers per branch and size of the midrib are the morphological features with the drastic changes. While number of floral bunches per twig, height of the plant and cover of the canopy show meager changes. 9 Size of the midrib and its vascular bundles, xylem of the vascular bundle, size of the leaf lamina, thickness of the epidermal layer, palizade zone height, and guard cells size are the leaf parameters with the drastic change due to seasonal variations. But the ground tissue of the midrib and the number of palizade layers do not express high variations. 9 In the stem the size of the vascular cylinder, xylem cylinder and individual xylem elements vary considerably due to seasons. The variations are not so high in the number of collenchyma and width of the xylem fibre. 9 The periderm and the cortical width along with the size of the xylem are highly influenced by the seasons while secondary phloem is not. 9 All the morphological and anatomical features exhibit statistically significant alterations due to locational variations. 9 The locations considerably influence all the morphological features of the plant. 9 The size of the leaf lamina, vascular bundle and xylem elements, height of the palizade zone and size of the gourd cell in the leaf have been influenced by the locational variations considerably. All other features of the leaf are not that much variable. 9 In the anatomical features of the stem the locational variations are identical to that of the seasonal variations.

211 9 Excepting the root cortex all the other root features are also highly influenced by the changes in the locations. 9 The tyloses occurred only in the specimen of the Hilly terrain which needs further scrutiny. 9 The morphological and the anatomical features are highly associated with the climatical factors (temperature, R.H and rainfall) and the soil nutrients (phosphorous, zinc iron, calcium and copper).

♦ The locational variations seem to have more influence on the structure of the plant (morphology and anatomy) than the seasons. ™ Objective 5 ¾ The inference on the anticancer studies are:- 9 All the plant samples possesses substantial level of concentration dependant cytotoxicity against the Ehrlich Ascites Cancer (EAC) cell lines at all the experimental concentrations. 9 The activity is extensively high in the summer samples which might be the expression of the synergistic interaction of the additional sterols, hydrocarbons and fatty acids. 9 The number of active principles in the plant and the cytotoxicity almost coincide with each other. 9 The locational samples also exhibit statistically significant variations. However the mean difference is less among them compare to the seasonal samples. ♦ Probably due to the presence of terpenes, hydrocarbons, heterocyclic compounds, sterols and fatty acids, Calotropis is a potential anticancer medicinal plant, especially more vigorous during summer.

The seasons as well as locations manipulate the composition (phytochemistry), structure (anatomy) and the curative property (anticancer potential) of Calotropis gigantea. The composition and in turn the curative property are more dependant on the periodicity of the seasons while the structure is more influenced by the relative stability of the locations.

212 8.2. SUGGESTIONS ™ For the pharmacological applications the plants can be harvested in summer. ™ For the biomass value of the plant, harvest at monsoon is advocated ™ In order to use any specific compound of the plant, appropriate seasons / locations must be selected. ™ Secondary metabolites can be utilized as markers of seasonal variations because of the prominent changes express by them. ™ The phytochemicals of the other potential plant must be standardized. ™ Wastelands can be transformed into a productive land through the

cultivation of Calotropis over years. This would also bring down the CO2 content of the atmosphere mitigating global warming. ™ Production of herbal medicines, biopesticides, organic fertilizers and ecofriendly fuel from Calotropis would reduce the pollution load in the biosphere and support the sustainable and harmonious way of life.

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