Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52411113EnglishN2019July6HealthcareAlcohol Use Disorder in Adolescents from Network Theory Perspective
English0106Kimasha BorahEnglish Kalyan BhuyanEnglish Dhrubajyoti BhuyanEnglishAlcohol use disorders in adolescents has been a major health and social concern across the globe demanding timely intervention in order to prevent the great deal of morbidity and mortality that are often associated with maladaptive pattern of substance use. Understanding the causation, maintenance and progression of alcohol use disorder is of utmost importance in formulating strategies for its treatment and prevention. There has been a major paradigm shift in understanding of the complex phenomenon of alcohol use in the form of application of concept of network theory. In this review a sincere attempt has been made to give insight into the various issues related to alcohol use disorders in adolescents.
EnglishSocial network, Alcohol dependence, Drinking habitIntroduction:
There have been a tremendous progress in the field of networkwhich is viewed as the science of connectivity,interactions and interdependence over last few decades[1]. It is an interdisciplinary field covering many areas including telecommunications, computer, biological and social networks. [2,3]. Goh et.al illustrated that a human disease network comprises of human disorder and diseases and two diseases are linked if they share at least one associated gene [4].Recently the studies related to network analysis of various disorders have gained popularity amongst the researchers and scholars. Construction and analysis of complex network has been the area of interest and research in many fields dealing with complex organizations of interacting entities[5]. One such complex area is the substance use disorder in which symptoms in diagnostic criteria, behavioural, psychological and social factors and consequences do show a complex interaction. Thus making it a potential area for application of network analysis.
Alcohol consumption is a major social and health issue of this era and has been regarded as an important contributor to death and disability. Alcohol has been one of the major contributor of many diseases like oesophageal cancer, liver cancer, cirrhosis of the liver, homicide, epilepsy, and has been linked to various adverse foetal and maternal outcomes such as sudden infant death syndrome, fetal alcohol syndrome, malnutrition, sexually transmitted diseases and many more. Alcohol has been estimated to be associated with as many as 1.8 million deaths each year across the globe and has been ranked as the fourth leading cause of disability adjusted life years (DALY) lost.[6,7,8]
International Classification of Diseases and Related Health Problems 10th edition (ICD10) and Diagnostic and Statistical Manual edition 5 (DSM5) have recognised alcohol use disorders as valid psychiatric diagnosis. Though Alcohol abuse and alcohol dependence were classified as two different entities in DSM IV, DSM 5 has integrated them into a single disorder called Alcohol Use disorder [9]. Apart from the nosological change there is definitely a need to change our approach in understanding the cause, effect and progress of this major health and social concern in order to deal effectively with the menace of alcohol use. One of such approach may be application of network theory. Network theory helps in revealing alcohol related problem and also their relations. This review is an attempt to look into the complex problem of alcohol use disorder in terms of network theory in general and social network in particular.
Basics of network theory and social network:
NETWORK is a collection of points joined together in pairs by lines. The points are referred to as vertices or nodes and the lines are referred to as edges. Vertices and edges carries different information s such as names or strengths, to capture more details of the system [10]. Networks describe not only components connection, interactions but also their pattern of connections[1].Network theory utilizes the concept of graph theory and become essential to uncovering the deeper interdependencies found in many complex systems.[ 2,3]
Social network Analysis:
Social network Analysis is multidisciplinary area covering social psychology,mathematics,statistics, anthropology,biology, communication studies, economics, geography, information science, organizational studies andcomputer sciences[11,12]
A social network is a social structure comprises of nodes or actors (individuals or organizations) and edges among them.Edge indicates their different relations such as friendship, kinship, common interest, financial exchange, dislike, sexual relationships or relationships of beliefs, knowledge or prestige [12,13]
Nodes contain different sizes. They may represent individual, groups, organizations or societies. Relations among nodes indicate level of analysis. Some relation may be individual to individual and some are individual to group[13]. Social network is network of human society,where several people are linked by acquaintance or social interactions[10]. Network study illustrates different aspects of individuals and their nature. It indicates the pattern of connections results in big effect in behaviour of the system. In social network,pattern shows how one person affect the next one, form opinion, gather news, which node affect mostly the others and so on. To understand one system fully, it is necessary to know the structure of the system. Network reduces a system to an abstract structure capturing only the basics of connection patterns and little else [10].Social networks operate on many levels, from families up to the level of nations. In simple language social network is the map of individuals and their relations [12]
Social network can be broadly classified as whole network or sociocentric network and personal network or egocentric network. Sociocentric network specifies the relationship among the defined population whereas egocentric network ties specify people such as their "personal communities [12].
Egocentric network can provide individual information, social support, access to resources, sense-making, normative pressures, influence and these factors affect Ego’s behaviour and this kind of network is formed based on selection and influence. Individual taking alcohol tends to select his friends who also takes alcohol and thereby forms an ego centric network where he is the ego and his friends are the alters.This kind of network is dominated by the people with particular views [14].
Matrices and measures of social network analysis:
SNA can provide a platform for better understanding in terms of which actors are involved in a network, their links, how influential each actor is; what their motivations are and how the network is structured (15) .Measures and matrices are needed in network analysis in order to understand fundamental concepts. Centrality is considered to be an important network measure. Other measures of network analysis arerobustness, efficiency, effectiveness and diversity [16].
Centrality: Centrality indicates the most important or central nodes in a network. There are four basic concepts of centrality. The simplest form of network measure is degree centrality. Degree centrality indicates the number of connections a node has [10].
A second approach of centrality measure is closeness centrality. It indicates the length of the shortest paths to all other actors in the network [17].
Betweenness centrality: The extent to which an actor lies on path between other actors is the betweenness centrality. In social network, messages and news are being passed from one person to another.Messages, news always take the shortest (geodesic) path though the network or one such path, chosen at random, if there are several.Betweenness centrality measures the influence of an actor over all others within the network which implies higher the betweenness centrality, higher is the influence of the actor within the network. [10]
A third approach of centrality measure is eigenvector centrality. It is an extension of degree centrality. In a network, all nodes are not equivalent .Some are more relevant than others. Eigenvector centrality refers to the importance of a node if it is linked to by other important nodes. Itdoes notindicates the nodes with high degree but indicates node that connected with other important nodes[17]
Network Density is the measure of the connectedness in a network.
Robustness is defined as the tendency of the individual nodes in a social network to form local clusters. The robustness is the measure of how fragmented a network will be if the fraction of nodes is removed from it.
Network efficiency measures how one actor is effectively connected with other actors. This indicates efficient node or actor can easily access information, knowledge and status through minimum connections [16].
Effectiveness measures the cluster of nodes that can be reached by efficient nodes. Efficient nodes can easily gain information without much effort.
Diversity: Social Network maps the social interactions and these interactions can be established by observing different factors. One of the factor is the history of the individual actors. Social network analysis focuses mainly on this aspect i.e. diversity of each nodes [17]
Alcohol use disorders and network:
Alcohol use disorder is the maladaptive patterns of alcohol use. Alcohol isone of the most commonly misused drugs in both developed and underdeveloped Countries. Practice of drinking causes multiple medical conditions. Alcohol has been attributed to the direct and indirect causation of more than 60 diseases including HIV/AIDS infection and unintended pregnancy[8]. According to National House Hold Survey, 21.4% of adult males are current user of alcohol and 43.9% of treatment seeking person at any Drug de addiction Centre in India are Alcohol Dependent[19]. A number of factors are contributed to alcohol addiction and alcohol dependence. The genesis of most alcohol and drug problems rests with a complex interaction between biological, psychological and environmental factors [20]. Understanding of social network can be helpful in understanding and identification of alcohol use behaviour and to develop better prevention and intervention programs to reduce alcohol-related harm [21]. The problem of alcohol dependence is often associated with various physical and mental comorbidities. Alcohol use disorders are often known to cause physical, mental, social, legal, financial and familial harms and the levels of harm are dependent on levels of use, patterns of use, Individual and social factors. Thus making it a potential ground for application of network theory with numerous hubs. Adolescents being the period of stress and storm is often the most crucial phase of human life and most of the psychiatric illnesses including alcohol use disorder have onset traced back to this stage. Most of the patients with alcohol use disorder often reported to have their first drink during adolescents and nowadays much focus has been given on adolescents’ alcohol use.
Social network analysis helps in understanding the influence of friendship ties among adolescents alcohol use[18]. S R Sznitman (2013) in his paper , Peer social network and adolescent alcohol use reviewed the influence of social networks in adolescent alcohol use and concluded that peer selection is important in development of substance use disorders in adolescents. This knowledge may be proved important in formulating preventive strategies in a broader public health perspective. [21]
Adolescent can adopt others behaviour very easily as they are very good observer and learner. Earlier literature demonstrate that, adolescent drinking behaviour was highly influential by friends or friendship networks as they are easily inspired by friends and other social groups outside their own family member.Social network is the interactions among individuals. Network centrality, density and efficiency are the key measuring factor of adolescent and their friendship network. Centrality indicates the number of connections a person has i.e how influential the person is. The ties among adolescent shows the node which lies on the shortest paths linking other adolescents. Utilizing the concept of network theory, researchers can examine the connections or the set-up of the network, their density, connections efficiency and its influence upon adolescents’ risky behaviours. [18].
The national Youth Risk Behavior Survey (YRBS) of USA has documented that 9th–12th grade students in high school have engaged in many risky health behaviours and identified age, out degree and betweenness as important factors associated with risky behaviours like sexual intercourse, drinking alcohol and other substance abuse ( Jeon KC et al). In this context following network measures need to be defined –
Degree: It is defined as the number of connections an actor has. The degree is of two types: In degree and out degree. The in degree is the number of connections an ego responds while out degree is the number of connections the ego receives
Density is the ratio of the number of actual connections divided by the total possible connections in the network.
According to Youth Risk Behavior Survey (YRBS) data
70.8% of teenagers reported having consumed at least one alcoholic drink( Centers for Disease
Control and Prevention, 2012a)
47.4% of adolescents had engaged in sexual intercourse(Centers for Disease Control and Prevention, 2012c).
44.7% engaged with tobacco use (Centers for Disease Control and Prevention, 2012d)
39.9% marijuana use (Centers for Disease Control and Prevention, 2012b).
22.1% drink alcohol or use drug before had sexual intercourse last time
In their experiment with two schools, one with smaller sample size but with a denser friendship network than the other, demonstrated that more dense the friendship network higher the incidence of risk behaviour suggesting that larger network have less risk behaviours depending on how densely the actors are connected.( Jeon KC et al). Like in all other networks various centrality measures have been identified in adolescent substance use network. Betweenness centrality allows us to identify the individuals in the network that are likely exert control over others. This centrality measures indicates that individuals in the network are likely to be influenced by the risky behaviours of others as they are connected by a greater number of geodesic paths. Adolescent with higher betweenness in the network control or influence others easily. Ennett et al. (2008) , have found significant correlation between friend’s cigarette use and betweenness centrality. Higher betweenness centrality was related to an increased risk for engaging in smoking behaviour also. Another centrality measure is Bonacich centrality which measures not only a function of how many friends an individual has but also the number of friends one’s friends have.[18]
Parenting style and its effect on network:
One of the major causes of adolescent alcohol use behaviour is the style of parenting. Parental control, warmth and negligence affect highly on adolescent alcohol use behaviour.Parenting style means the way of upbringing the child and is mainly governed by two factors namely warmth and control. Based on the levels of parental warmth and control there are four different styles of parenting which are summarized in table 1.
Authoritative parenting is proved to be optimal and children of such parents are less likely to have delinquent peer network and lower level of substance abuse (Fletcher et al 1995 and Sharley et al 2012)
Adolescent behaviour of substance use is determined by two factors –
behaviour of the friend
parenting style of friends mother
Influence of substance use habit on adolescent behaviour:
It was estimated that probability of drinking to the point of drunkenness increases by 32% if he has a friend who has the habit of doing so. Similarly having a friend with history of binge drinking increases in adolescent by 47%.
Parenting style of friends mother:
The strong contributing factor towards the network behaviour of alcohol use disorder in adolescent is the parenting style. Shaley et al 2012 demonstrated that parenting style of friends mother is the determining factor of adolescent substance abuse and concluded that “ the adolescent who do not engage in substance abuse are often connected to authoritative parents via their friends even if their own parents are not authoritative. Having a friend of whose mother is authoritative decreases the likely hood of drinking to point of darkness and binge drinking by 40% and 38% respectively. [22]
Interactions among symptoms of SUD form psychopathological networks:
It includes interactions such as a strong predictive relation between tolerance and more-than-planned substance use. Network theory helps in analyzing symptoms and their associations to achieve new insight into the mechanisms of SUD[23]. Rheumtulla et al(2017) applied the concept of network analysis in substance use disorder by using Diagnostic and Statistical Manual 4th Edition (DSM IV)Abuse and dependence criteria in twins with life time drug use(mainly cannabis, sedatives, stimulants, cocaine, opioids and hallucinogens) and concluded that three different types of network analysis can be performed
Individual substance class network
Cross substance class network
Cross substance class variability network
Cross substance class network analysis revealedthat using a substance more than planned is the most central symptom indicating its status as a gateway symptom: losing control over how much or how long one takes drug,leading to host of other abuse and dependence symptoms. Different symptoms were found to be central to different substance network which indicates that different symptoms have specifically in triggering other symptoms and predicting negative clinical outcome across different class of substance. Association between various symptoms do predict the existence of a particular pathway common to all substances. Individual class network was formed by using this model to estimate one network for each substances. Some notable similarities like association between unable stop and hazardous use is present across the substances were noted apart from some striking differences in the form that edges between hazardous use and legal consequences are absent for opium, cocaine and hallucinogens while it is strong for the sedative use indicating the importance of context in which these substances. Mundt et al(2011) while systematically examining the impact of peer social network concluded that adolescents are at higher risk of alcohol use owing to their relative position in the social network comprising of friends and friends of their friends. Peer social network impacts onset of alcohol use in adolescent .Most of the studies revelled that friends alcohol use and adolescent social network characteristics exerts an independent effects on adolescent alcohol initiation. Having friends with more friends regardless of their drinking status increases the likelihood of initiation of alcohol use. Moreover for every additional friend with high in degree (likelihood of being nominated as friend), initiation of alcohol use in adolescent increases by 13%.[24]
Social network revels the spreading of behaviours of adolescent through social ties. Adolescents are more attracted towards similar people. Mundt et al(2012) illustrated about an analytical approach for social network analysis was actor based model. It is a powerful tool for agent’s selection of friends based on alcohol use and changes in alcohol use behaviour over time.Influence and selection are two key factor for adolescent alcohol use behaviour. This model can disentangle selection and influence and determinetheir relative contribution to similarities in alcoholuse behaviour among friends. Study have found that selection is the strongest factor of alcohol use in early adolescent while selection and influence are the two factors effecting alcohol use behaviour on later adolescent. This model comprises of two parts:Friendship network evolution and alcohol use behaviour evolution.Friendship network evolution is formed by friendship ties depending on a list of friendship choice variables. Three variables effect on this part:
Adolescent alcohol use behaviour on the number of friendship selection (ego and their alters)
Probability of being influenced by alters drinking habits
Similarities of alcohol use between Ego and alters.
Other control variables are connectedness of friendship ties within the network (density), reciprocity, transitive triplets, 3 cycles, in degree and out degree popularity. Reciprocity is the likelihood to reciprocate friendship nomination while transitive triplets mean the tendency for the friends to be friends and the propensity for closure in three-person friendships is defined as 3-cycles. Age, gender, race/ethnicity, parental drinking, family bonding, alcohol use similaritywerealso shaped network structure of adolescent alcohol use. Family bonding was one of the main protective factors for adolescent alcohol use behaviour.
Alcohol use behaviour evolution part contains friendship-related influence Component. This component indicates the tendency for alcohol use to change based on the average drinking of immediate friends. Other control variables are age, gender, race/ethnicity, parental drinking, family bonding and linear and quadratic shape effects modelling average drinking across the network. The network selection pattern in adolescent alcohol use provides the much needed platform to understand the dynamics of initiation and maintaining of alcohol abuse in adolescents which can be proved to be pivotal in formulating intervention strategies of the global epidemic of alcohol use disorder [25]
Conclusion:
Thus social network analysis can give an insight into various factors determining the initiation, progress and maintenance of alcohol use disorders in adolescents. Without clear guidance on the causal pathway between peers and alcohol use, interventions aimed at tackling alcohol use disorders in adolescents may not be effective. Network analysis approach can be an effective alternate measure in understanding the complex nature of the problem and thereby may be considered as a path breaking approach to preventive strategies.
Englishhttp://ijcrr.com/abstract.php?article_id=2615http://ijcrr.com/article_html.php?did=2615
Sayama H, Cramer C, Porter M A, Sheetz L, Uzzo S, What are essential concepts about networks?, Journal of Complex Networks (2016) 4, 457–474, doi:10.1093/comnet/cnv028
Telesford Q; Network Theory Analysis Of Ethanol Self-Administering Nonhuman Primates ,Dessertation, retrieved from https://wakespace.lib.wfu.edu/bitstream/handle/.../Telesford_wfu_0248D_10506.pdf
Telesford, Qawi K; Laurienti, Paul J; Davenport, April T et al. (2015) The effects of chronic alcohol self-administration in nonhuman primate brain networks. Alcohol Clin Exp Res 39:659-71
Goh K et al , The human disease network, PNAS 2007, vol. 104 (21) 8685–8690
Borsboom D and Cramer A OJ. Network analysis: An integrative approach to the structure of Psychology,Annu.Rov Click Psychol 2013.9−91−121
Pal HR, Kumar A, Chapter 3: Epidemiology of Substance Use in Lal R (Editor). Substance Use Disorder Manual for Physicians, National Drug Dependence Treatment Centre, All India Institute of Medical Sciences 2005 pp 1-13
Pullen, Erin L., "Social Networks, Drug Use, And Drug Abuse Help-Seeking: A Test of the Network Episode Model Among African American Women" (2014).Theses and Dissertations--Sociology. 15. https://uknowledge.uky.edu/sociology_etds/15
Bryant K J, Expanding Research on the Role of Alcohol Consumption and Related Risks in the Prevention and Treatment of HIV/AIDS, 2006 Substance Use & Misuse, 41:1465–1507
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author.
Newmann M E J, Network: An Introduction, New York Oxford University Press 2010
David Knoke, Song Yang; Social Network Analysis: 2nd Edition, Sage publications, Thousand Oaks 2008
Social Network Analysis Theory and Applications retrieved from https://www.politaktiv.org/documents/10157/29141/SocNet_TheoryApp.pdf
Katz N, Lazer D, Arrow H, and Contractor N; Network Theory and Small Groups, Small Group Research Vol 35, Issue 3, pp. 307 – 332
DeJordy R, Halgin D; Introduction to Ego Network Analysis, Boston College and the Winston Center for Leadership & Ethics, Academy of Management PDW 2008
International Rescue Committee: Social Network Analysis Handbook, Connecting the dots in humanitarian programs, July 2016 retrieved from https://www.rescue.org/sites/default/files/document/1263/socialnetworkanalysise-handbook.pdf
Ghali N, Panda M, Hassanien, Abraham A and Snasel V; Social Networks Analysis: Tools, Measures and Visualization, in A. Abraham (ed.), Computational Social Networks: Mining and Visualization, DOI 10.1007/978-1-4471-4054-2 © Springer-Verlag London 2012
https://www.sci.unich.it/~francesc/teaching/network/eigenvector.html, cited on May 2, 2019
Kwon Chan Jeon & Patricia Goodson (2016) Alcohol and sex: friendship networks and co-occurring risky health behaviours of US adolescents, International Journal of Adolescence and Youth 2016, 21:4, 499-512, DOI: 10.1080/02673843.2015.1110954
Ray R (2004). The Extent, Pattern and Trends of Drug Abuse in India, National Survey.
Schuckit, M. A., Smith, T. L., Eng, M. Y., & Kunovac, J. Women who marry men with alcohol-use disorders. Alcoholism: Clinical & Experimental Research 2002, 26:1336–1343
Sznitman SR. Peer social network and adolescent alcohol use. OA Alcohol 2013 Jun 01;1(1):9.
Shakya HB, Christakis NA, Fowler JH. Parental Influence on Substance Use in Adolescent Social Networks. Archives of pediatrics& adolescent medicine. 2012;166(12):1132-1139. doi:10.1001/archpediatrics.2012.1372.
Rhemtulla M, Fried EI, Aggen SH, Tuerlinckx F, Kendler KS, Borsboom D. Network analysis of substance abuse and dependence symptoms. Drug and alcohol dependence. 2016;161:230-237. doi:10.1016/j.drugalcdep.2016.02.005.
24.Mundt M.P , The Impact of Peer Social Networks on Adolescent Alcohol Use Initiation, Academic Dediaturts 2011;11:414−42
25.Mundt MP, Mercken L, Zakletskaia L, Peer selection and influence effects on adolescent alcohol use: a stochastic actor-based model BMC Pediatrics 2012, 12:115
Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52411113EnglishN2019July6Life SciencesEffect of Organic Manures on Growth, Yield Attributes and Yield of Babycorn (Zea mays L.)
English0712Snehaa A.English C. RavikumarEnglish M. GanapathyEnglish S. ManimaranEnglish G. B. Sudhagar RaoEnglish A. KarthikeyanEnglishAim: The present investigation was carried out to evaluate the suitable organic manure to sustain the soil fertility, yield of baby corn and also opt a best alternate crop for rice fallow pulses presumably in missing season..
Materials and Methods: Field experiments were conducted in the Experimental Farm of the Department of Agronomy, Annamalai University, Annamalai Nagar during summer and kharif season of 2017 in Randomized block design to study the response of baby corn (Zea mays L.) to different organic manures for their growth, yield attributes and cob and green fodder yield..
Result: Among the different treatments, RDF (150:60:40 kg NPK ha-1) had a positive effect on the growth, yield attributes, cob and green fodder yield in baby corn for I and II crops which was at par with the application of vermicompost @ 5 t ha-1. The lowest values of growth, yield attributes and yield were recorded by Farm compost @ 5 t ha-1.
Conclusion: Application of fertilizer may be good in the short term for getting maximum yield and net income to the farmers; but, in the long run, to increase the corn quality and sustain the soil fertility, T3 vermicompost @ 5 t ha-1 treatment is the best and thus this practice can be recommended to the maize growing farmers in Tamil Nadu.
EnglishOrganic manure, Growth, Yield, Baby corn, Vermicompost, FYMIntroduction
Maize is the third most important cereal crop in India as well as in the world. A recent trend is of growing maize for vegetable purpose, which is commonly known as ‘baby corn’. It is a small young cob or ear or the female inflorescence before pollination or fertilization. The important attributes relevant to baby corn are early maturity, synchronized ear emergence and small palatable yellow kernels (Kumar and Kalloo, 1998). The early harvest and sale of baby corn ears before dry spells provides higher profits and untranslocated photosynthates left in green stover becomes valuable source for nutritious green fodder to live stock giving impetus to diary, meat production. Thus baby corn is safe for consumption in fresh state. Its economic potential is further enhanced owing to the availability of green, soft, succulent nutritious, palatable fodder with higher digestibility (Ramachandrappa
et al., 2004). Baby corn provides the valuable nutrition which lack in most people’s diet. It is extremely high in potassium, B vitamins, thiamine, riboflavin, niacin, folates that helps in brain function and improves memory and low in fat that helps in weight loss goals. The yellow corn contains more carotenoid content, but baby corn is plucked white which contains lesser content of carotenoid. The lesser quantity of carotenoid reduces the risk of heart diseases and cancer. Also it has better glycemic index than the regular corn making it a good substitute.
Baby corn has a short growing period (60-75 days), so that a farmer can grow four or more crops per year depending upon the agro-climatic conditions. Corn has always had high nutrient demands and already puts a great strain on soil and fertilizer nutrient sources. Large quantities of N, P, K, Ca, Mg, and S are removed with the grain and stover from the soil. Trace elements are also removed and must be replaced. Use of inorganic fertilizers for increasing cereal production is inevitable in the present circumstances where cereal crop needs and livelihood issues of the people have sustained national priority. But this has declined the soil fertility in the long term. The only way out to this gloomy scenario is to develop sustainable and nutrient balance through organic farming, which would increase the cereal crop production substantially without harming the precious environment. Organic manures and bio-fertilizers serve as an alternate to chemical inputs and are being increasingly used in vegetable production today. Organic manure serves as an alternate practice to mineral fertilizers for improving soil structure (Dauda et al., 2008) and microbial biomass (Suresh et al., 2004). In addition to that, the cultivation of baby corn can lead to at least double return to the farmer unlike normal grain maize (Dass et al., 2004).
One hundred grams of baby corn are found to be rich in 89.1% Moisture, 1.9 g Protein, 0.2 g Fat, 0.06 g Ash, 8.2 mg Carbohydrate, 28 mg Calcium, 86 mg Phosphorus and 11mg Ascorbic Acid (Thavaprakash et al., 2005). Keeping this in view, the present investigation was under taken to identify and quantify the suitable organic source for the cultivation of organic babycorn which can minimize the consumption of time, labour, energy and concomitantly increased the growth and yield of baby corn.
Materials and Methods
Field experiments were conducted during summer and kharif seasons of 2017 in the experimental farm, Annamalai Univeristy, Tamilnadu, (11024 North latitude. 78041 East longitude and +5.79 MSL) in order to study the effect of different organic manures on growth and yield of organic baby corn in north Cauvery deltaic regions where the baby corn can be act as a best alternate crop for rice fallow pulses presumably in missing season. The climate at the experimental farm is moderately warm with hot summer months. The maximum temperature ranged from 30.1°C to 39.2°C with a mean of 34.47°C and the minimum temperature ranged from 18.9°C to 28.6°C with a mean of 24.1°C. The relative humidity ranged from 79 to 88 per cent with a mean of 84.8 per cent. The mean hours of bright sunshine were 7.9 hrs for the study period. The textural class of experimental soil was clay loam with 43.1% of clay, 14.2% silt and 41.8% of sand in the surface (0-15cm) soil. The surface soil posses pH 7.8, Electrical conductivity 0.72, organic carbon of 0.52 and the available N, P and K Viz., 162.4, 24, 285 kg/ha respectively. The experiments were laid in RBD, comprising 8 treatments with three replications. T1 - RDF (150:60:40 NPK kg ha-1), T2 - Farm yard manure @ 12.5 t ha-1, T3 - Vermicompost @ 5 t ha-1, T4 - Fish Amino Acid @ 10 l ha-1, T5 - Farm Compost @ 12.5 t ha-1, T6 - Neem Cake @ 1 t ha-1, T7 - Mahua Cake @ 1 t ha- , T8 - EM Inoculated Farm Yard Manure @ 12.5 t ha-1.
2.1. Fish amino acid
Materials required:
1. Fish trash (head, bone, intestine, etc)
2. Jaggery
3. Clay pot/plastic jar or glass jar
4. Net rubber band/thread
Preparation:
Remove the fish intestines and chop into fine pieces (10kg of fish waste with 2kg of jaggery) powder the jaggery and add it. Add these 2 to broad mouthed glass jar or plastic jar that is just the right size (not too big) (ratio up to 2/3 of its volume). Cover the jar with the lid or net, tighten it and mix it well by shaking the jar. Within 30 days it will be fermented, filter it using nylon mesh to get 300-500ml solution changed into honey like syrup.
2.3. Enriched Microorganism inoculated farm yard manure:
Materials required:
1. 1litre of EM solution
2. 1kg of jaggery
3. 180kg of Farm yard manure
4. 10 litres of water.
Procedure:
Farm yard manure (180kg) is heaped in 4×2×1 dimension.1 litre of EM solution diluted in 10 litres of water and 1 kg of jaggery is added to the solution. 1:10 ratio of EM :water and jaggery mixed solution is sprinkled on the heaped farm yard manure and mixed well thoroughly. This farm yard manure inoculated with EM solution is covered with gunny bags and left up to 7-10 days. EM solution helps for easy decomposition of farm yard manure.
Crop management
The experimental field was ploughed to a depth of 15 to 20 cm two weeks before sowing by tractor and levelled. The soil in the field was brought in to a fine filth. Laying of plots and allocation of treatments were carried out according to the treatment schedule which were randomized. Channels were laid to facilitate irrigation of plots individually. The fertilizer recommendation for baby corn is 150:60:40 kg of N, P2O5 and K2O ha-1 respectively. Nitrogen was applied as urea (46 per cent N), phosphorous as single super phosphate (16 per cent P2O5) and potassium as muriate of potash (60 per cent K2O) half dose of N and half dose of K2O were applied on 20 DAS only on the controlled plot. Baby corn seeds were treated with Azospirillum (600 g ha-1) and phosphobacteria (600 g ha-1) for 24 hours before sowing as per treatment schedule. Seeds were dibbled with a spacing of 60 cm between the rows and 20 cm within the plants. Two seeds were dibbled at a depth of 1-2 cm and then covered with the soil.
Plant protection measures against pest and diseases were taken up as and when required. The tassels were removed immediately after their emergence and before the tassels turned to pink colour to avoid fertilization of the cob. If the silk gets pollinated, the kernel starts developing within hours and the cob would become hard and unfit for consumption of baby corn as vegetable. Hence, detaselling was done as and when emergence of tassel. Topping refers to nipping or the removal of terminal portion from the uppermost node to induce better cob development and to avoid fertilization of the cob. Topping beyond 9th, 10th, and 11th internodes was done at 47, 50, and 53 DAS respectively. From each net plot area, young ears together with the sheath were harvested immediately after emergence (1 - 3cm) of silk. Five to six harvests with in an interval of two days were carried out. The ears from net plot area of each plot were harvested separately, weighed and expressed as green cob yield in kg ha-1. Green fodder was harvested at the time of every topping treatment imposed and after the last harvest of ears, weighed and expressed in t ha-1. Five plants from each plot were chosen by simple random sampling method and were tagged. These tagged plants were used for recording all biometric observations at different stages of crop growth.
Statistical analysis
The data recorded were statistically analysed and whenever the results were found significant, the critical differences were arrived at 5 per cent level and drawn statistical calculations (Panse and Sukhatme, 1978).
Results
Growth Attributes
Statistically analyzed results described that the effect of different sources of organic manure application had a positive influence on all growth traits. Application of recommended dose of inorganic fertilizer (T1) (150:60:40 kg ha-1 NPK) recorded the higher values of plant height (61.4 and 64.7cm), LAI (3.6 and 4.1), DMP (850.3 and 920.7 kg ha-1) and CCI (16.0 and 16.8) for summer and kharif seasons. Among the different organic sources (T3) vermicompost @ 5 t ha-1 recorded the higher values of growth attributes Viz., plant height (59.6 and 62.21cm), LAI (3.5 and 3.8), DMP (812.3 and 893.5 kg ha-1) and CCI (15.5 and 16.3) @ 30 DAS for summer and kharif seasons and it was at par with T1. Application of vermicompost recorded high degree of aggressiveness with inorganic fertilizers.
Yield attributes
Organic sources significantly influenced the yield components and yield in both the crops. Among the different treatments T1(150:60:40 kg ha-1 NPK) recorded the higher values of yield attributes viz., the number of cobs (2.98 and 3.14), cob length (26.1 and 26.4 cm), cob girth (2.1 and 2.2 cm), cob weight (28.9 and 30.2 g), cob yield (6565.0 and 7020.8 kg ha-1) and fodder yield (3300 and 3546 kg ha-1) for summer and kharif seasons and that was on par with T3 (vermicompost @ 5 t ha-1).
Discussion
Growth Attributes
Among the different organic sources (T3) vermicompost @ 5 t ha-1 recorded the higher values of growth attributes Viz., plant height, LAI, DMP kg ha-1 and CCI @ 30 DAS for summer and kharif seasons and it was at par with inorganic fertilizers applied treatment (T1). Application of vermicompost recorded high degree of aggressiveness with inorganic fertilizers. This might be due to better enhancement of physico - chemical properties of soil which leads to impart soil structure as well as slow releasing pattern and steady supply of nutrients thorough out the period of crop growth. Application of organic manures may have helped improve physico-chemical properties of the soil, imparting favourable soil structure for root growth and soil enzymes (the latter continue to break down organic matter in the soil to release nutrients and make them available near the rhizosphere for absorption by plant roots, thereby improving fruit quality) (Chaoui et al, 2003).
In addition to that the influence of organic fertilization through vermicompost on LAI could be attributed by increment of metabolic process in plants which seems to have promoted meristematic activities causing apical growth. This result is in agreement with the findings of Atarzadeh et al. (2013). This was in line with the studies of Choudhary and Jat (2004) and Jinjala et al. (2016).
Yield attributes
Among the different treatments T1(150:60:40 kg ha-1 NPK) recorded the higher values of yield attributes viz., the number of cobs, cob length, cob girth, cob weight , cob yield and fodder for summer and kharif seasons and that was on par with T3 (vermicompost @ 5 t ha-1). Application of recommended dose of fertilizers increased the number of cobs per plant, cob girth and individual cob weight. This might be due to wide availability of nutrients throughout its growth period resulting in huge biomass production that leads to availability of photosynthates, metabolites and nutrients to develop reproduction structure. This present results are in line with the findings of Edwin et al. (2003), Thavaprakash et al. (2008) and in addition increment of fodder yield Singh et al. (2011) and Lone et al. (2013). Apparently, the higher yield (cob yield and fodder) and yield attributes viz., number of cobs per plant, cob girth, cob length and individual cob weight in vermicompost received plots could be due to better interception, absorption and utilization of radiation energy leading to higher photosynthetic rate and finally more accumulation. The overall improvement reflected into better source- sink relationship, which in turn enhanced the yield and yield attributes (Madhavi et al., 1995), similar results were also reported by Gurmeet et al. (2016), Thavaprakash et al. (2007), Uwah et al. (2014).
The fodder yield increased in both inorganic and vermicompost received plots due to higher plant height and dry matter production per plant. Also the two possible mechanisms was due to the regulatory role of nitrogen in production of amino acids and plant hormones responsible for cell division and enlargement and higher nitrogen facilitating optimum development of photosynthetic apparatus captures the incident light more efficiently. This was in concomitant with the findings of Tariq et al., (2011).
Conclusion
From the results of the experiments it can be concluded that application of recommended dose of fertilizer and also organic treatment vermicompost @ 5 t ha -1 which was on par with chemical fertilizer was found to be the most efficient in increasing the corn yield, green fodder yield in baby corn. Application of fertilizer may be good in the short term for getting maximum yield and net income to the farmers; but, in the long run, to increase the corn quality and soil quality vermicompost treatment is best and thus this practice can be recommended to the maize growing farmers in Tamil Nadu.
Acknowledgement
Authors wish to acknowledge the immense help received from the scholars whose articles are cited and included in the references of this manuscript. The authors are also grateful to authors /editor/publishers of all those articles, journals and books from where the literature for this article has been reviewed and discussed. Authors wish to acknowledge the Annamalai University for the conduct of experimental trial. Authors acknowledge the immense help received from the scholars whose articles are cited and included in references of this manuscript. The authors are also grateful to authors / editors / publishers of all those articles, journals and books from where the literature for this article has been reviewed and discussed.
Englishhttp://ijcrr.com/abstract.php?article_id=2616http://ijcrr.com/article_html.php?did=2616Atarzadeh, S.H., M. Mojaddam and T. Saki Nejad. 2013. The interactive effects, humic acid application and several of nitrogen fertilizer on remobilization star wheat. Int. J. Biosci., 3(8): 116-123.
Chaoui, H.I., L.M. Zibilske and T. Ohno. 2003. Effects of earthworm casts and compost on soil microbial activity and plant nutrient availability. Soil Biol. Biochem., 35: 295-302.
Choudhary, G.R. and N.L. Jat. 2004. Response of coriander (Coriandrum saivum) to inorganic nitrogen, farm yard manure and biofertilizer. Indian J. of Agric. Sci., 78: 761-763.
Dass, S., V.K. Yadav, A. Kwatra, M.L Jat, S. Rakshit, J. Kaul, O. Parkash, I. Singh, Edwin Luikham, J. Krishna Rajan, K. Rajendran and P.S. Mariam Anal. 2003. Agric. Sci. Digest., 23(2): 119-121.
Dauda, S.N., F.A. Ajayil and E. Ndor. 2008. Growth and yield of water melon (Citrullus lanatus) as affected by poultry manure application. Journal of Agriculture & social sciences, 4(3):121-124.
Edwin Luikham, J. Krishna Rajan, K. Rajendran and P.S. Mariam Anal. 2003. Agric. Sci. Digest., 23(2): 119-121.
Gurmeet Singh, Navtej Singh and Ramandeep Kaur. 2016. Effect of integrated nutrient management on yield and quality parameters of baby corn (Zea mays L.) Intl. J. of App. and pure Sci. and Agriculture (IJAPSA) Vol 2(2), pp: 161-166.
Jinjala, V. R., H.M. Virdia, N.N. Saravaiya and A.D. Raj. 2016. Effect of integrated nutrient management on baby corn (Zea mays L.). Agric. Sci. Digest., 36(4): 291-294.
Kumar, S and G. Kalloo. 1998. Attributes of maize genotype for baby corn production. Maize genetics News Letter, pp: 74.
Lone A.A., B.A. Allai and F.S. Nehviu. 2013. Growth, yield and economics of baby corn (Zea mays L.) as influenced by integrated nutrient management (INM) practices. African J. Agric. Res., 8(37): 4537-4540.
Madhavi, B.L., M.S. Reddy, P.C. Rao. 1995. Integrated nutrient management using poultry manure and fertilizers for maize. Ind. J. of Agron.vol.40:1-4.
Panse, V.G. and P.V. Sukhatme. 1978. Statistical methods for Agricultural methods for agricultural workers, ICAR, New Delhi, p.361.
Ramachandrappa, B.K., Nanjappa, H.V. and Shivakumar, H.K. 2004. Yield and quality of baby corn (Zea mays L.) as influenced by spacing and fertilization levels. Acta Agron. Hung.52:237-43.
Singh, M. K., Singh R. N. and Singh, V. K. 2011. Effect of organic and inorganic sources of nutrient on growth, yield, quality and nutrient uptake by baby corn (Zea mays L.) Ann. Agric. Res. 32 (3&4): 93-99.
Suresh, K.D., G. Sneh, K.K. Krishna and C.M. Mool. 2004. Microbial biomass carbon and microbial activities of soils receiving chemical fertilizers and organic amendments. Archives Agron. Soil. Sci., 50: 641-647.
Tariq, M., M. Ayub, M. Elahi, A.H. Ahmad, M.N. Chaudhary and M.A. Nadeem. 2011. Forage yield and some quality attributes of millet (Pennisetum americanum L.) hybrid under various regimes of nitrogen fertilization and harvesting dates. African J. of Agric. Res., 6 (16):3883-3890.
Thavaprakash, N., K. Velayudham, V.B. Muthukumar. 2007. Effect of crop geometry, intercropping systems and integrated nutrient management practices on productivity of baby corn (Zea mays L.) based intercropping systems. Res. J. of Agrl. Biol. Sci., 1(4): 295-302.
Thavaprakash, N., K. Velayudham, V.B. Muthukumar. 2007. Effect of crop geometry, Intercropping system and Integrated Nutrient management practices on productivity of Baby Corn (Zea mays L.) based inter cropping systems. Research journal of Agricultural and biological sciences, 1(4): 295-302.
Thavaprakash, N., Velayudham, K. and Muthukumar, V.B. 2008. Response of crop geometry, intercropping systems and INM practices on yield and fodder quality of baby corn. Asian J. Scient. Res., 1(2): 146-152.
Uwah, D.F., U.L. Undie and N.M. John. 2014. Comparative evaluation of animal manures on soil properties, growth and yield of sweet maize (Zea mays L. saccharata Strut.). J. of Agriculture and Environ. Sci., 3(2): 315-331.
Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-52411113EnglishN2019July6Life SciencesSpecies Diversity and Vegetation Structure of Coal Mine Generated Wasteland of Raniganj Coal Field, West Bengal, India
English1325Saikat MondalEnglish Debnath PalitEnglish Pinaki ChattopadhyayEnglishAim: The main aim of the study was to study the vegetation structure and species diversity of coal mine generated waste land, located in Raniganj coal field area, West Bengal.
Methodology: The survey of vegetation was conducted at both study sites by using standard quadrat method. Study of different phytosociological attribute and species diversity analysis was done using standard methods. Statistical analysis was performed to represent the importance of different phytosociological attributes.
Result: Distribution pattern in both wasteland indicate contagious or clumped type. Butea monosperma and Streblus asper was the most dominant tree species in the two study area respectively whereas, Cynodon dactylon was the most dominant herb species in the study areas. The diversity of herbs was much higher than the others layer of vegetation in both waste lands. Concentration of dominance or Simpson Dominance Index also exhibits variation among the vegetation layers. The Jaccard’s Index of similarity for tree, herb shrub and climber vegetation was 57.14%, 71.11%, 50% and 33.33% respectively between the two waste lands. Hierarchical cluster analysis highlights 13 and 7 primary cluster in the two study area respectively based on their phytosociological attributes. Principle component analysis reveals 97.57% and 92% variance for the first two principle components in the study areas respectively.
Conclusion: The present investigation can be concluded that the data of vegetation analysis might be utilized as baseline information and tool to predict the best and effective reclamation procedure of these coal mined areas.
EnglishCoal mining, Reclamation, Wasteland, VegetationINTRODUCTION
The resources from our mother earth are rapidly utilized for improvisation and maintaining the quality of the life in different way. Mining operation for extraction of coal is one of the most familiar and oldest activity but it tend to bring a notable impact on the environment by damaging landscapes and local floral population (Bell et al., 2001; Sarma, 2005). Moreover extensive mining activity can lead to massive destruction of natural ecosystem along with the biodiversity of the area (Ezeaku and Davidson, 2008). In this scenario, full recovery of these ecosystems with their biodiversity may take several years (Cooke, 1999). Therefore, needful attempts have to take to minimize the negative impacts as well as restoration of the degraded environments and these might highlights a significant contribution of the mining sector towards proper development of the impacted area in a sustainable way (Hoadley et al., 2002). The open coal excavations generate wide areas of degraded land or we can say it as wasteland, had gained primary succession conditions but the colonization process is very low, probably due to unfavorable conditions or minimum pioneer plants which suits the environment (Jochimsen et al., 1995). The Damalia and Nimcha-Harabhanga area (Raniganj block of Barddhaman Paschim District, West Bengal) is well known for open cast coal mining. Large-scale open cast mining of these area produced vast barren and unproductive lands and extensive damage to the vegetation. Hence, to counter ecological hazards and restoration of ecological balance, proper reclamation and basic knowledge about it is the priority for these mine area. Better and effective restoration and reclamation process requires detailed concept about the native vegetation and processes of their natural recovery. This study was conducted in coal mine generated waste land of Damalia and Nimcha-Harabhanga area with an aim of gaining knowledge and provide data of natural and compatible vegetation and to formulate any difference in the vegetation composition of these two mine areas of Raniganj Coal Field, West Bengal, India
Materials and Methods:
Study area
Damalia and Nimcha-Harabhanga coal mine generated waste land were selected as the study sites under Satgram mining area and are situated at Raniganj block and Asansol subdivision of West Bengal, India. Damalia waste land is located in between 23?36´31.9´´N and 87?4´6.1´´E at 80.2m elevation and Nimcha-Harabanga waste land is located between 23?36'32.9''N and 87?4'2.4''E at 83.8m elevation (Figure 1).
Vegetation Analysis
The survey of vegetation was conducted at both study sites by using standard quadrat method (Srivastava 2001) during peak growth season. Sums of 5 sites in each wasteland were selected for sampling. In each sites, 10 quadrats (10m X 10m for trees), within these 100 m2 quadrats, 5 m X 5 m quadrats for shrubs and climbers, and 1m x 1 m quadrats for herbs) were laid to quantify various layers of vegetation. Quantitative community characteristics such as frequency, density, abundance and importance value index (IVI) of each plant species were determined, following Misra (1968) and A/F value (Whiteford, 1949). The resultant frequency values were classified into frequency classes following Raunkiaer, 1934 frequency class analysis, such as: class A (1%–20%), class B (21%–40%), class C (41%–60%), class D (61%–80%) and class E (81%-100%) (Hewit and Kellman, 2002).
Diversity indices analysis
Species diversity (Shannon and Weiner, 1963), Concentration of dominance (Simpson, 1949), Species richness (Margalef, 1978) and Evenness index (Pielou, 1966) were calculated for undisturbed and disturbed sites. The distribution pattern of the species was studied by using Whiteford’s index (Whiteford, 1949). Similarity index of different layer of wasteland vegetation between two study areas was determined following Jaccard’s index of similarity (Krebs, 1999).
Statistical analysis
Hierarchial cluster analysis was performed to interpret the similarity level of the tree species based on their phytosociological parameters for both the waste land and principle component analysis through statistical computer software.
Results
The density, frequency, frequency class, abundance, importance value index (IVI), Whiteford’s index of vegetation at two study area are shown in Table 1 and 2 respectively. The A/F ratio showed Contagious or clumped distribution pattern in both wasteland which stipulates fragmented and patchy type of natural vegetation because of mining. Similar types of distribution pattern were also observed by Sarma (2005) in the coal mining areas of Nokrek biosphere reserve of Meghalaya. At Damalia wasteland area, the most dominating tree species was Butea monosperma with the highest IVI value, whereas, Streblus asper was the most dominant one in Nimcha-Harabhanga (Table 1 and 2). Cynodon dactylon was the most dominant herb species in both Damalia and Nimcha-Harabhanga wasteland in terms of IVI value (Table 1 and 2). Tephrosia purpuria and Jatropha gossipyfolia was the dominant shrub species in Damalia and Nimcha-harabhanga waste land respectively (Table.1 and 2) Higher importance value indicated its ability to grow in the degraded environment. Species, family compositions of Damalia and Nimcha-Harabhanga waste land are represented in table 3. The study highlights that asteraceae and fabaceae (7 species each) are the most dominant family in Damalia waste land and asteraceae in case of Nimcha-Harabhanga waste land. Present study reflected density of tree in Damalia was higher than in Nimcha- Harabhanga but the density of herbs was lower in Damalia.
Diversity indices analysis
Species diversity indices (Shannon-Weaver) reveal variation among the tree, herb, shrubs and climber species (Table 4). Herbs species shows higher diversity than the other types of vegetation in both study area. Concentration of dominance or Simpson Dominance Index also exhibits variation among the vegetation layers. The Evenness Index (Pielous Index) and Margalef Index for species richness also highlights variation among different vegetation layers in both waste lands (Table 4). The Jaccard’s Index of similarity for tree, herb shrub and climber vegetation was 57.14%, 71.11%, 50% and 33.33% respectively between the two waste lands (Table 4) The present study of the two wasteland flora according to Raunkiaer’s life form (Raunkiaer 1934) reveals that the dominance of Phanerophyte in Damalia wasteland and therophytes in Nimcha-Harabhanga wasteland.(Table 5 & Fig.2).
Statistical analysis
Hierarchial cluster analysis based on different phytosociological attributes was done for the tree and herb layer of both waste lands (Figure 3 and 4). In Damalia waste land 13 primary clusters and in Nimcha-Harabhanga 7 primary cluster are formed. The more the distance scale of the clusters the more the plant species are remotely related to each other. In Damalia, the 1st cluster shows close similarity among the Heliotropium indicum, Amaranthus spinosus, Acacia auriculiformis, Alstonia scholaris, Crotalaria juncea, Aerva lanata, Blumea lacera, Oldenlandia corymbosa plant species. Cluster 2 comprises of Hyptis suaveolens, Anisomeles indica, Melochia corchorifolia, Oxalis corniculata, Mimosa pudica Desmodium gangeticum. Cluster 3 comprises of Ailanthus excelsa, Albizzia lebbek, Azadirachta indica, Ziziphus jujube, Dalbergia sissoo, Phoenix dactylifera. Cluster 4,7,8,9,10,11,12 and 13 comprises only 2 plant species in each. Cluster 5 composed of Boerhaavia repens, Achyranthus aspera, Alternanthera sessilis, Cleome viscose and cluster 6 composed of Parthenium histerophorus, Evolvulus nummularis, Gomphrena serrata, Euphorbia hirta, Eclipta alba, Oplismenus composites. In Nimcha-Harabhanga waste land cluster 1 comprises Heliotropium indicum, Amaranthus spinosus, Sida acuta, Eucalyptus globules, Senna siamea, Borassus flabellifer, Dalbergia sissoo, Phoenix dactylifera, Azadirachta indica, Crotalaria juncea, Oxalis corniculata, Mimosa pudica, Solanum virginianum, Desmodium gangeticum, Cyperus rotundus, Ailanthus excels, Alstonia scholaris, Acacia nilotica, Hyptissu aveolens, Anisomeles indica, Melochia corchorifolia, Senna obtusifolia, Albizzia lebbek. Cluster 2 composed of Acacia nilotica and Alangium salviifolium, cluster 3 contains Triumfetta rhomboidea, Achyranthus aspera, Desmodium triflorum, Parthenium histerophorus, Evolvulus nummularis, Euphorbia hirta, Eclipta alba, Oplismenus composites, Gomphrena serrata, Alternanthera sessilis, Aerva lanata, Boerhaavia repens and Cleome viscose. Cluster 4 comprised of Sida cordata and Blumea lacera. Cluster 5 includes Coldenia procumbens, Centella asiatica, Gmelina arborea, Lagerstroemia speciosa, Ficus religiosa and Ficus benghalensis. Cluster 6 composed of Eragrostis cilianensis, Dactyloctenium aegyptium and Eupatorium odoratum. Cluster 7 composed of Streblus asper and Ziziphus jujube.
Principle component analysis was done for the tree layer of different phytosociological attributes for two coal mines generated waste lands. For the tree layer of Damalia waste land (Table 6 and Figure 5), the first two principle components account for 97.57% of the total variance in the data set. Therefore, 65 and 31% of variance were calculated for the first two principle components respectively. From this it can be concluded that the first principle component is probably the most important to represent the variation within the phytosociological attributes in the tree layer of Damalia. In Damalia, Streblus asper (12) and Butea monosperma (8) have similarity regarding their phytosociological attributes and exhibit high correlation with the first axis but Borassus flabellifer (7) and Moringa oleifera (10) shows negative correlation with first axis for the same phytosociological attributes. In Nimcha-Harabhanga waste land (Table 7 and Figure 6), the total variance were 92% for the first two principle components. Therefore, 65 and 26% of variance were calculated for the first and second principle components and first principle is important for representing the variation within the phytosociological attributes of the tree layer like Damalia. Maximum tree species of Nimcha-Harabhanga were located on or near of axis 1 and 2. Hence, indicates strong positive correlation of the concerned species along with the phytosociological attributes represented by the axis respectively. Due to higher distance from both the axis, Butea monosperma (9) has a weak relation with the phytosociological attributes of the respective axis.
CONCLUSION
The principle objective of this research was to analyze the natural occurrence of different plant species native to different habitats across two different coal mine generated waste land of Raniganj Coalfield, West Bengal aiming to enhance diversity and functioning of huge area of coal mine generated waste land. The potentiality of vegetation of an area is based on various environmental constrain and regional variables (Nath 2004). The present study shows that phytosociological analysis can be utilized as important tools to predict the nature of mine soil for the growth of vegetation as well as eco-restoration. The waste land areas of coal field are invaded by some stress tolerant floras which are able to initiate ecological succession and gathering data and information of such kind of stress tolerant plant species have enormous practical application in terms of eco-restoration. The study of natural vegetation in details of coal mine affected area can be implicit to formulate and conduct revegetation programme in any coal mine generated waste lands. Furthermore, this type of data can be utilized to maintain genetic diversity and equally to confirm the use of ecosystem in a sustainable way (Jha and Singh, 1990; Bannerjee et al., 1996).
ACKNOWLEDGEMENT
Authors express their deep sense of gratitude to Department of Botany, Durgapur Govt. college, West Bengal and Department of Zoology, Raghunathpur College, West Bengal, India for their support to conduct the study. Authors also acknowledge the immense help received from the scholars whose articles are cited and included in references of this manuscript. The authors are also grateful to authors / editors / publishers of all those articles, journals and books from where the literature for this article has been reviewed and discussed.
CONFLICT OF INTEREST
As an author we do not have any conflict of interest in the present communication
Englishhttp://ijcrr.com/abstract.php?article_id=2617http://ijcrr.com/article_html.php?did=2617Banerjee SK, Mishra TK; Singh AK. Restoration and reconstruction of coal mine spoils: an assessment of time prediction for total ecosystem development. Advances in Forestry Research in India 2000; 23: 1–28.
Bell FG, Bullock SET, Halbich TFJ, Lindsey P. Environmental impacts associated with an abandoned mine in the Witbank Coalfield, South Africa. International Journal of Coal Geology 2001; 45: 195–216.
Cooke JA. Mining, Ecosystems of the World 16 - Ecosystems of Disturbed Ground, ed. L.R. Walker, Elsevier, Amsterdam, the Netherlands, 1999; p.365-384.
Ezeaku PI. and Davidson A. Aanalytical situations of land degradation and sustainable management strategies in Africa. J. Agri. Soci. Sci. 2008; 4: 42-52.
Hewit N. and Kellman M. True seed dispersal among forest fragments: dispersal ability and biogeographical controls. Journal of Biogeography 2002; 29(3), 351–363.
Hoadley M, Limpitlaw D, Weaver A. Mining, Minerals and Sustainable Development in Southern Africa, the report of the regional MMSD process, 1, 2 and 3. 2002.
Jha, AK. and Singh JS.. Revegetation of mine spoils: Review and case study. In: Dhar, B.B. (ed.), Environmental Management of Mining Operations. Ashish Publishing House. New Delhi. 1990; p. 300-326.
Jochimsen M, Hartung J, Fischer I. Spontane und kunstliche Begrunung der Abraumhalden des Stein-und Braunkohlenbergbaus. Ber. d. Reinhold Tuxen-Ges. 1995; 7: 69–88.
Krebs CJ. Ecological methodology. 2nd. ed. Menlo Park, CA: AddisonWesley Longman; 1999.
Margalef FR. Information theory in ecology. Gen Syst 1978; 3: 36–71.
Misra R.. Ecology Work Book. Oxford & IBH Publication, New Delhi; 1968.
Nath. Changes in Soil Attributes Consequent upon Differences in Forest Cover in a Plantation Area. Indian Soc Soil Sc 1998; 36: 515-521.
Pielou EC. The measurements of diversity in different types of biological collections. J Theor Biol 1966; 13: 131–144.
Raunkiaer C. The Life Forms of Plants and Statistical Plant Geography. Oxford, U. K: Oxford University Press 1934; 632.
Sarma K. Impact of coal mining on vegetation: a case study in Jaintia Hills District of Meghalaya, India. Thesis for partial fulfilment of the requirements for the degree of Master of Science International Institute For Geo-Information Science And Earth Observation Enschede, The Netherlands. 2005.
Shannon CE, Weiner W. The Mathematical Theory of Communication, University of Illinois Press, Urbana, USA; 1963.
Simpson EH. Measurment of diversity. Nature 1949; 163: 688.
Srivastva HN. Practical Botany. Pradeep Publications, Jalandhar; 2001.
Whiteford PB. Distribution of woodland plants in relation to succession and clonal growth. Ecology 1949; 30: 199–208.
Whittakker RH. Dominance and diversity in land plant communities. Science 1965; 14: 250–259.