Volume 3, Issue 1 of Tropical Plant Research

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www.tropicalplantresearch.com 1 Received: 13 October 2015 Published online: 29 February 2016

ISSN (E): 2349 – 1183ISSN (P): 2349 – 9265

3(1): 01 – 09, 2016

Research article

Tree species diversity in tropical forests of Barak valley

in Assam, India

Nepolion Borah1*, Debajit Rabha

2 and Florida Devi Athokpam

2

1School of Environmental Sciences, Jawaharlal Nehru University, New Delhi, India

2Department of Ecology and Environmental Science, Assam University Silchar, Assam, India

*Corresponding Author: [email protected] [Accepted: 01 February 2016]

Abstract: To enumerate the tree species diversity of tropical forests, 89 belt-transects was laid in

different reserve forests and private forests of the Barak Valley, Assam, Northeast India. A total of

222 tree species were recorded from 152 genera and 65 families. Euphorbiaceae was the most

species rich family with 23 species. Out of 65 families, 30 families were recorded with only one

species while 10 families were recorded with two species. Artocarpus chama was the most

abundant and frequently occurred species. Podocarpus nerifolia was the only gymnosperm tree

recorded in this study while Caryota urena and Pleomele spicata were the monocot tree species.

Five threatened species were recorded from the Valley.

Keywords: Belt-transect - Threatened species - Frequency - Barak valley.

[Cite as: Borah N, Rabha D & Athokpam FD (2016) Tree species diversity in tropical forests of Barak valley in

Assam, India. Tropical Plant Research 3(1): 1 – 9]

INTRODUCTION

Assam is part of Indo-Burma the biodiversity hotspot regions, which is situated in the north-eastern corner of

Indian subcontinent and considered one of the richest occurrences of angiosperm plants. The Southern part ofAssam, which is popularly known as Barak Valley, consists of three districts namely Cachar, Karimganj and

Hailakandi. The vegetation of this region is mostly represented by tropical moist evergreen and tropical moist

semi-evergreen forest types (Champion & Seth 1968). The forests of this region are relatively unexplored

harbouring rich plant diversity. The vegetation of this region has been free from anthropogenic disturbances

over centuries. But due to rapid population growth and development activities, some parts of the forests are

under huge anthropogenic pressure such as over exploitation of species for timber, fuel-wood, fodder, bamboo

cutting, settlement etc. (Borah & Garkoti 2011). The floristic composition is one of the major anatomical

characters of the forest community (Dansereau 1960). So it is very important to know the species composition

and its distribution of these forests to take proper management strategies.

A good number of scientific literatures are available on angiosperm flora of Assam (Kanjilal et al. 1934 –

1940, Hooker 1872 – 1887, Rao & Verma 1969, 1976, Choudhury 1982, Dam & Dam 1984). For a modern

floristic assessment, it is important to know the tree wealth of a forest along with their ecological amplitude as

they are the backbone of any forest and provides the microclimate suitable for the survival of other small plants

as well as animals (Bajpai et al. 2015, Dular 2015).When we see the tree diversity exclusively, very few studies

are available from the state (Sarkar & Devi 2014, Rabha 2014).The literature dealing with the tree diversity and

their ecological standings is either very old (Choudhury 1982, Dam & Dam 1984) or focused to a specific area

(Borah & Garkoti 2011, Borah 2012, Borah et al. 2014). Thus, the present study was performed to enumerate

the tree species composition and their ecological status from the tropical forests of this region.

MATERIAL AND METHODS

Geographically Barak Valley of Northeast India (Fig. 1) is surrounded by North Cachar Hills and Jaintia

Hills in the north, in east by Manipur, in the south by Mizoram and in the west by Tripura and Sylhet district of

Bangladesh.


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www.tropicalplantresearch.com 2

Figure 1. Map of Barak valley, Assam, India.

The soil of the region is sandy clay loam and sandy loam in texture and slightly acidic in nature (pH ranges

from 5.35 to 6.1) with an average bulk density of 1.08 gcm-3

and water holding capacity of 38.75% (Athokpam

et al. 2013). The area has a tropical monsoon climate with high annual precipitation and high temperature.

Climate during April – October is characterized by rainy season with an average rainfall 2330.50 mm. The region

is characterized by moderate temperature with monthly average temperature ranging from 11.9 – 32.7 oC (Borah

2012). Climatically, the year may be divided into four seasons. December to February is the winter season,

followed by spring or early summer from March to April/May, then June to September is the South WestMonsoon rainy season or late summer, and October and November constitute the post monsoon or autumn

season (Athokpam & Garkoti 2013).

Different reserve forest of Barak Valley are Innerline Reserve Forest, Barak Reserve Forest, Borail Reserve

Forest, Sonai Reserve Forest, Upper Jiri Reserve Forest, Katakhal Reserve Forest, Longai Reserve Forest,

Badshahi-tilla Reserve Forest, Duhalia Reserve Forest, Patharia Reserve Forest, Tilbhoom Reserve Forest and

Singla Reserve Forest.

Present study was carried out during the years 2010 to 2013 by laying 89 belt-transects of 10 m × 500 m

sized in different reserve forests and private forests of Barak Valley. Out of 89 transects 35 were delimited in

Cachar, 29 in Karimganj and 15 in Hailakandi districts. The belt transects were laid in such way that it covers

different microclimates of the studied forest so that different types of vegetation comes within transects. After

lying transect, all the plant species of >10 cm GBH trees sampled and some specimens of each species were

brought to laboratory to prepare herbarium following Jain & Rao (1977). The species were identified with the

help of „Flora of Assam‟ (Kanjilal et al. 1934 – 1940) and the herbarium of Botanical Survey of India, Shillong.


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Species nomenclatures were followed as per “Assam‟s Flora (present status of vascular plants)” (Chowdhury et

al. 2005). After identifying all the species, physiognomy type, growth form and IUCN status were studied by

available literature. The frequency and abundance were estimated as follows-

RESULTS AND DISCUSSION

Table 1. Number of genus (GN) and species (SN) in different families recorded in Barak valley, Assam, India.

S. No. Family Name GN SN S. No. Family Name GN SN

1 Euphorbiaceae 15 23 34 Simaroubaceae 2 22 Lauraceae 7 17 35 Urticaceae 2 2

3 Moraceae 4 14 36 Agavaceae 1 14 Verbenaceae 5 10 37 Alangiaceae 1 1

5 Mimosaceae 4 9 38 Araliaceae 1 16 Rubiaceae 9 9 39 Arecaceae 1 17 Caesalpiniaceae 5 8 40 Bixaceae 1 18 Meliaceae 6 8 41 Boraginaceae 1 19 Myrsinaceae 3 7 42 Bromeliaceae 1 1

10 Myrtaceae 3 7 43 Burseraceae 1 111 Sapindaceae 5 7 44 Cannabaceae 1 112 Anacardiaceae 4 5 45 Capparaceae 1 113 Rutaceae 5 5 46 Datiscaceae 1 114 Annonaceae 4 4 47 Ebenaceae 1 115 Clusiaceae 2 4 48 Ehretiaceae 1 116 Dipterocarpaceae 3 4 49 Elaegnaceae 1 117 Magnoliaceae 2 4 50 Fagaceae 1 1

18 Papilionaceae 4 4 51 Juglandaceae 1 119 Sterculiaceae 3 4 52 Leeaceae 1 120 Symplocaceae 1 4 53 Lythraceae 1 121 Bignoniaceae 3 3 54 Moringaceae 1 122 Fagaceae 2 3 55 Oxalidaceae 1 123 Myristicaceae 2 3 56 Podocarpaceae 1 124 Sapotaceae 3 3 57 Rhamnaceae 1 125 Theaceae 3 3 58 Rhizophoraceae 1 1

26 Apocynaceae 2 2 59 Sabiaceae 1 127 Bombacaceae 1 2 60 Saurauiaceae 1 128 Combretaceae 1 2 61 Sonneratiaceae 1 129 Dilleniaceae 1 2 62 Styraceae 1 130 Elaeocarpaceae 1 2 63 Thymelaeaceae 1 131 Flacourtiaceae 2 2 64 Tiliaceae 1 132 Malvaceae 2 2 65 Ulmaceae 1 1

33 Memecylaceae 1 2

A total of 222 tree species were recorded from present study belonging to 152 genera and 65 families. Out of

65 families, Euphorbiaceae was the most species rich family (15 genus and 23 species) followed by Lauraceae

(7 genus and 17 species), Moraceae (4 genus and 14 species) etc. (Table 1). Among them 30 families contained

only one species while 10 families contained 2 species. Out of 222 species, 146 species were evergreen tree

while 76 species were deciduous tree species. Among the recorded species, 18% were large, 43% were medium

and 39% were small size trees. Artocarpus chama was the most abundant and frequently occurred species

(Table 2). In the present study five species were recorded in the IUCN Red List of Threatened Species. Out of 5

threatened species Dipterocarpus turbinatus was critically endangered, and 2 species were vulnerable namely

Saraca asoca and Aquilaria malaccensis, 1 species was Lower Risk/least concern namely Mangifera sylvatica

and other 1 species was data deficient namely, Hydnocarpus kurzii. Podocarpus nerifolia was the only


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gymnosperm tree species recorded in this study while Caryota urena and Pleomele spicata were the monocot

tree species. Some of these species were recorded only in few numbers. A good number of endemic, primitive

angiosperm and threatened species were not recorded in this survey which were recorded from this region in

earlier works of Hooker (1972 – 1887), Kanjilal (1934 – 1940), Chowdhury et al. (2005) etc. In earlier surveys

some species like Canarium bengalensis, Quercus semiserrata, Garcinia keeniana, Embelia parviflora,

Calliandra umbrosa, Magnolia pterocarpa, Caesalpinia digyna, Ixonanthes khasiana, Acranthera tomentosa

etc. were recorded from this region but in present study these were not recorded. This may be due to that the

population of these species become very less or distribution of these species restricted to only some particular

pockets or these species may be loss from this region. The main cause behind this is destruction and degradation

of forest area by rapid urbanization, human settlement, industrialization (mainly tea industry and paper mills),

shifting cultivation, rubber plantation, reckless and ruthless exploitation of plants of potential economic

importance, fuel wood collection, raising of artificial forests by monoculture of some important species such as

Tectona grandis etc. Socioeconomic condition might be responsible for enhanced utilization of the forest

resources and this may eventually lead to a species-poor state (Murali et al. 2014). It is very important to take

proper management strategies for those less abundant and less frequently occurred species, otherwise these

species will also lost from this region in near future.

Table 2. Physiognomic type (PhT), growth form (GrF), abundance (Ab) and frequency in % (Fr) of encountered species in

Barak valley, Assam, India. (E-evergreen, D-deciduous, L-large tree, M-medium sized tree, S-small tree)

S.No. Species Name Family PhT GrF Ab Fr

1 Acacia auriculiformis A. Cunn. ex Benth. Mimosaceae E M 1.50 2.252 Acacia sinuata (Lour.) Merr. Mimosaceae D M 2.50 2.253 Actinodaphne angustifolia Nees Lauraceae E M 18.00 12.364 Actinodaphne obovata (Nees) Bl. Lauraceae E S 18.50 13.485 Adenanthera pavonina L. Mimosaceae D S 1.00 2.25

6 Aegle marmelos (L.) Corr. Rutaceae D M 3.00 3.377 Ailanthus integrifolia Lam. Simaroubaceae D M 63.00 30.348 Alangium chinensis (Lour.) Rehder Alangiaceae E S 1.50 1.129 Albizia chinensis (Osbeck) Merr. Mimosaceae D M 67.00 37.08

10 Albizia lebbeck (L.) Benth. Mimosaceae D M 57.00 29.2111 Albizia lucidior (Steud.) Nielson ex Hara Mimosaceae E L 19.00 15.7312 Albizia odoratissima (L. f.) Benth. Mimosaceae D M 8.00 6.7413 Albizia procera (Roxb.) Benth. Mimosaceae D M 44.50 26.97

14 Allophylus triphyllus (Burm. f.) Merr. Sapindaceae E M 11.00 8.9915 Alseodaphane owdenii Parker Lauraceae E M 43.50 31.4616 Alseodaphne andersonii (King ex Hook. f.) Kostel. Lauraceae E L 10.50 8.9917 Alstonia scholaris (L.) R.Br. Apocynaceae E L 46.50 43.8218 Amoora hiernii Visw. & Ramech. Meliaceae E L 3.00 3.3719 Ananas sp. Bromeliaceae E S 4.00 1.1220 Annona reticulata L. Annonaceae E S 2.00 4.4921 Anthocephalus chinensis (Lam.) A. Rich. ex Walp. Rubiaceae E L 14.00 17.98

22 Aporusaaurea Hook. f. Euphorbiaceae E S 21.00 16.85

23 Aporusa octandra (Buch.-Ham.ex D.Don) Vick. Euphorbiaceae E S 21.00 12.3624 Aquilaria malaccensis Lam. Thymelaeaceae D M 7.50 11.2425 Ardisia calorata Roxb. Myrsinaceae D S 2.50 2.2526 Artocarpus chamaBuch.-Ham. Moraceae D L 396.0 97.7527 Artocarpus heterophyllus Lam. Moraceae E M 3.00 3.3728 Artocarpus lacuchaBuch.-Ham. Moraceae D L 141.0 74.1629 Averrhoa carambola L. Oxalidaceae E M 1.50 3.3730 Baccaurea ramiflora Lour. Euphorbiaceae E S 79.50 41.57

31 Bauhinia purpurea L. Caesalpiniaceae D M 12.00 2.2532 Bauhinia variegata L. Caesalpiniaceae D M 76.00 29.21

33 Bischofia javanica Bl. Euphorbiaceae E M 4.50 5.6234 Bixa orellana L. Bixaceae D M 5.00 3.3735 Bombax ceiba L. Bombacaceae D L 67.50 40.45

36 Bombax insigne Wall. Bombacaceae D L 12.00 12.3637 Bridelia monoica (Lour.) Merr. Euphorbiaceae D S 32.00 14.61


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38 Bridelia Montana (Roxb.) Willd. Euphorbiaceae D S 9.00 6.7439 Bridelia vomentisa Bl. Euphorbiaceae D S 0.50 1.1240 Butea monosperma (Lam.) Taub. Papilionaceae E S 1.00 2.2541 Callicarpa arborea Roxb. Verbenaceae E S 91.00 42.7042 Callistemon citrinus (Curtis.) Stapf Myrtaceae E S 0.50 1.12

43 Camellia sinensis (L.) O. Cuntz Theaceae E S 2.50 5.6244 Carallia branchiata (Lour.) Merr. Rhizophoraceae E M 76.00 44.9445 Caryota urena L. Arecaceae E M 9.50 11.2446 Cassia fistula L. Caesalpiniaceae D M 8.50 8.9947 Cassia javanica L. Caesalpiniaceae D M 1.50 2.2548 Cassia siamea Lam. Caesalpiniaceae D M 12.50 5.6249 Castanopsis indica (Roxb.) DC. Fagaceae E M 33.00 19.1050 Castanopsis purpurella (Miq.) Balak. Fagaceae E M 140.5 66.2951 Cedrela microcarpa C. DC. Meliaceae D M 3.50 4.4952 Celtis australis L. Ulmaceae D M 9.50 11.24

53 Chukrasia tabularis A. Juss. Meliaceae D L 8.50 4.4954 Cinnamomum cacharensis Parker Lauraceae E S 18.00 8.99

55 Cinnamomumglaucescens (Nees) Hand.-Mazz. Lauraceae E L 31.50 26.97

56 Cinnamomum tamala (Buch.-Ham.) Nees & Eberm. Lauraceae E S 4.00 7.8757 Cordia dichotoma Forst. f. Boraginaceae E M 46.00 29.2158 Crateva religiosa G. Forst. Capparaceae E S 2.00 3.3759 Croton joufra Roxb. Euphorbiaceae D M 44.00 29.2160 Cryptocarpa sp. Lauraceae E M 9.00 5.6261 Crysophyllum roxburghii G. Don Sapotaceae E L 24.00 8.99

62 Cynometra polyandra Roxb. Caesalpiniaceae E L 238.0 50.5663 Derris rubusta (Roxb. ex DC.) Benth. Papilionaceae D M 1.50 2.2564 Desmos longiflorus (Roxb.) Safford. Annonaceae E S 25.00 19.1065 Dillenia indica L. Dilleniaceae E L 48.00 44.9466 Dillenia pentagyna Roxb. Dilleniaceae E L 14.00 19.1067 Diospyros toposia Buch.-Ham. Ebenaceae E L 28.50 20.2268 Dipterocarpus turbinatus Gaertn. Dipterocarpaceae E L 158.0 15.73

69 Duabanga grandiflora (Roxb. ex DC.) Walp. Sonneratiaceae D L 111.5 51.6970 Dysoxylum alliaria (Buch.-Ham.) Balak. Meliaceae E M 16.50 8.9971 Dysoxylum binectariferum (Roxb.) Hook. f. Meliaceae E M 172.0 59.5572 Dysoxylum gobara (Buch.-Ham.) Merr. Meliaceae E M 133.5 38.2073 Elaeagnus sp. Elaegnaceae E S 74.50 38.2074 Elaeocarpus floribundus Bl. Elaeocarpaceae E M 32.50 23.6075 Elaeocarpus sphaericus (Gaertn.) K. Schum. Elaeocarpaceae E M 7.50 5.6276 Embelia nutans Wall. Myrsinaceae E S 8.00 6.7477 Embelia ribes Burm. f. Myrsinaceae D S 20.50 12.36

78 Embelia tsjeriam-cottam DC. Myrsinaceae E S 17.00 11.2479 Endospermum antiquorum L. Euphorbiaceae E M 13.50 15.7380 Engelhardtia spicata Lech. ex Bl. Juglandaceae E S 1 1.1281 Erythrina variegataL. Papilionaceae D M 23.50 16.85

82 Eugenia grandis Wight Myrtaceae E M 7.00 4.4983 Euphoria longan (Lour.) Steud. Sapindaceae E S 3.00 4.4984 Eurya acuminata DC. Theaceae E S 104.0 50.56

85 Evodia meliaefolia Benth. Rutaceae D M 10.00 10.1186 Excoecaria oppositifolia Griff. Euphorbiaceae E S 15.50 10.11

87 Ficus auriculataLour. Moraceae E S 18.00 15.7388 Ficus benghalensis L. Moraceae E L 2.50 5.62

89 Ficus heterophylla L. f. var. repens Willd. Moraceae E S 7.50 3.3790 Ficus hirta Vahl Moraceae E S 28.00 19.1091 Ficus hispida Vahl Moraceae E S 72.00 38.2092 Ficus lepidosa Wall. Moraceae E S 116.5 46.0793 Ficus racemosa L. Moraceae D M 90.50 55.0694 Ficus religiosa L. Moraceae D L 32.50 37.08

95 Ficus semicordata Buch.-Ham. ex J. E. Sm Moraceae E S 1.50 1.1296 Flacourtia indica (Burm. f.) Merr. Flacourtiaceae D S 3.00 4.49


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156 Pajanelia longifolia (Willd.) Schum. Bignoniaceae E S 11.00 13.48157 Palaquium polyanthum Benth. Sapotaceae E M 197.5 47.19158 Parkia timoriana (DC.) Merr. Fagaceae D M 0.50 1.12159 Persea bombycina (King ex Hook. f.) Kostel Lauraceae E M 11.00 11.24160 Phoebe attenuate Nees Lauraceae E M 13.50 8.99

161 Phoebe goalparensis Hutchinson Lauraceae E M 15.50 7.87162 Phyllanthus emblica L. Euphorbiaceae D M 14.50 8.99163 Picrasma javanica Bl. Simaroubaceae E S 8.50 5.62164 Pithecelobium heterophyllum (Roxb.) Benth. Mimosaceae D S 5.00 4.49165 Pleome lespicata (Roxb.) N.E. Brown Agavaceae E S 38.00 12.36166 Podocarpus nerifolia D. Don Podocarpaceae E M 34.50 17.98167 Polyalthia sp. Annonaceae E S 0.50 1.12168 Premna benghalensis Cl. Verbenaceae D M 16.00 13.48169 Premna milleflora Cl. Verbenaceae D M 19.50 19.10170 Psychotria monticolaKurz Rubiaceae E S 16.50 10.11

171 Pterospermum acerifolium (L.) Willd. Sterculiaceae D L 7.50 5.67172 Pterospermum lanceaefolium Roxb. Sterculiaceae E M 79.00 33.71

173 Pterygota alata (Roxb.) R. Br. Sterculiaceae D L 97.50 44.94

174 Quercus griffithii Hook. f. & Th. Fagaceae E M 18.00 10.11175 Randia racemosa (Cav.) f. Vill. Rubiaceae E S 7.50 7.87176 Samanea saman (Jack.) Merr. Papilionaceae E L 5.00 4.49177 Sapindus attenuatus wall. Sapindaceae D M 8.50 6.74178 Sapindus mukorossi Gaertn. Sapindaceae D M 0.50 1.12179 Sapindus sp. Sapindaceae D M 8.50 6.74

180 Sapium baccatum Roxb. Euphorbiaceae E L 136.0 60.67181 Saprosma ternatum Hook. f. Rubiaceae E S 11.50 8.99182 Saraca asoca (Roxb.) de Wilde. Caesalpiniaceae E M 33.50 22.47183 Saurauia roxburghii Wall. Saurauiaceae E S 78.50 32.58184 Schima wallichii (DC.) Kuntze Theaceae E M 376.5 65.17185 Schleichera trijuga Willd. Sapindaceae D L 7.00 6.74186 Semecarpus anacardium L. Anacardiaceae E M 187.5 53.93

187 Shorea robusta Gaertn. Dipterocarpaceae E L 33.50 3.37188 Spondias pinnata (L. f.) Kurz Anacardiaceae D M 109.5 53.93189 Sterculia urens Roxb. Sterculiaceae D M 8.00 7.87190 Sterculia villosa Roxb. Sterculiaceae D M 49.50 35.96191 Stereospermum personatum (Hassk.) Chatterjee Bignoniaceae E L 191.0 62.92192 Streblus asper Lour. Moraceae E M 49.00 28.09193 Styrax serrulatum Roxb. Styraceae E S 10.00 10.11194 Symplocos cochinchinensis (Lour.) Moore ssp.

Cochinchinensis Lour.Symplocaceae E S 6.00 5.62

195 Symplocos khasiana (Cl.) Brand. Symplocaceae E S 10.50 10.11196 Symplocos sp. Symplocaceae E S 2.50 3.37197 Symplocos cochinchinensis (Lour.) Mooressp. Laurina

(Retz.) NooteboomSymplocaceae E S 4.00 3.37

198 Syzygium cumini (L.) Skeels Myrtaceae E M 93.00 49.44199 Syzygium jambos (L.) Alston Myrtaceae E S 30.00 16.85200 Syzygium kurzii (Duthie) Balak. Myrtaceae E M 16.00 8.99

201 Syzygium oblatum (Roxb.) Wall. ex A.M. & Cowan Myrtaceae E M 1.50 3.37202 Syzygium praetermissum (Gage) Balak. Myrtaceae E M 73.50 30.34

203 Syzygium syzygioides (Miq.) Merr. Myrtaceae E M 67.00 44.94204 Magnolia hookeri (Cubitt & Smith) Raju&Nayar Magnoliaceae E S 18.50 16.85

205 Tamarindus indica L. Caesalpiniaceae D M 0.50 1.12206 Tectona grandis L. f. Verbenaceae D L 145.0 21.35207 Terminalia bellirica (Gartn.) Roxb. Combretaceae D L 114.5 48.31208 Terminalia chebula Retz. Combretaceae D L 36.50 29.21209 Tetramelesnudiflora R. Br. Datiscaceae D L 109.0 51.69210 Toona ciliata M. Roem. Meliaceae D M 161.5 66.29

211 Trema orientalis (L.) Bl. Cannabaceae E M 2.00 1.12212 Trewia nudiflora L. Euphorbiaceae D M 58.00 28.09


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213 Vatica lancaefolia (Roxb.) Bl. Dipterocarpaceae E M 138.0 50.56214 Oreocnide sp. Urticaceae E S 2.50 4.49215 Vitex altissima L. f. Verbenaceae E M 49.00 25.84216 Vitex peduncularis Wall. ex Schauer. Verbenaceae E M 89.00 58.43217 Vitex pinnata L. Verbenaceae E M 5.50 7.87

218 Vitex sp. Verbenaceae E M 30.50 24.72219 Wendlandia sp. Rubiaceae E S 4.50 5.62220 Xerospermum glabratum (Kurz) Radlk. Sapotaceae E L 62.00 21.35221 Zanthoxylum rhetsa (Roxb.) DC. Rutaceae E M 57.50 40.45222 Zizyphus mauritiana Lam. Rhamnaceae D M 20.50 13.48

CONCLUSION

The Barak Valley of Assam has good number of tree species, the major component of the forests ecosystem.

Depletion of species number and frequency due to the different anthropogenic pressure are the main disquiet.

Utilization of traditional knowledge and legal and full involvement of the local communities in conservation

practices might be very effective to conserve the forests in this region. Despite of rich tree species diversity it

provides various ecosystem services such as habitat to other species, carbon storage, carbon sequestration etc.

and environmental benefits which needs further study.

ACKNOWLEDGEMENTS

Authors thank to Botanical Survey of India, Shillong for species identification. Authors are grateful to the

forest departments of Cachar, Karimganj and Hailakandi districts of Assam for permission and support during

the study.

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in Hollongapar Gibbon Wildlife Sanctuary, Assam, Northeast India. Tropical Plant Research 1(2): 26 – 36.


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www.tropicalplantresearch.com 10 Received: 19 October 2015 Published online: 29 February 2016

ISSN (E): 2349 – 1183ISSN (P): 2349 – 9265

3(1): 10 – 17, 2016

Research article

Soil organic carbon stocks in different land uses in Pondicherry

university campus, Puducherry, India

SM. Sundarapandian*, S. Amritha, L. Gowsalya, P. Kayathri, M. Thamizharasi, Javid

Ahmad Dar, K. Srinivas, D. Sanjay Gandhi and K. Subashree

Department of Ecology and Environmental Sciences, Pondicherry University, Pondicherry - 605014


Abstract: Soil Organic Carbon (SOC) stocks (30 cm soil depth) were assessed in different land

uses (teak plantation, eucalyptus plantation, acacia plantation, shrub land and grass land) in

Pondicherry University campus by using Walkley & Black’s method. The soil bulk density was

found to increase significantly (P


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Sundarapandian et al. (2016) 3(1): 10 – 17.


with alarming population growth rate. This leads to conversion of agricultural land and areas of aquifers into

real estates, industrialized and institutional areas. Pondicherry University is one of the institutions with a large

land cover of 760 acres. Hence, the university has taken steps to green the entire campus except the built-areas

and paths. Even though the disaster of Thane cyclone in December 2011 uprooted and damaged several large

and very old trees, huge patches of forest still exists. The Flora of the university campus has been documented

by Parthasarathy et al. (2010). Influence of Thane cyclone on tree damage has been assessed by Sundarapandian

et al. (2014a). Biomass and carbon stock assessments of woody vegetation in the University campus have been

done by Sundarapandian et al. (2014b). Recently, many educational institutes in the western world have

assessed their carbon footprints. Recent advances reveal that instead of carbon footprint, ecological footprint

would be more reasonable and applicable. Based on the assessment of ecological footprint, Canada decided to

withdraw from the Kyoto Protocol. At present, Indian institutes also take initiatives to green their campuses and

assess their carbon footprints. Pondicherry University has endeavoured to construct a solar campus in silver

jubilee buildings. Several initiatives are under discussion. At this crucial time, baseline data of carbon stocks of

the campus is one of the important parameters to plan green campus initiatives and estimate ecological footprint.

The present study will be more important in terms of baseline data generation and documentation. This baseline

data will also be helpful to estimate the carbon sequestration potential of forest ecosystems in the campus in the

near future. Therefore, the present study was intended to evaluate the following objectives: (1) to examine thevariations in soil C stock in different land uses and (2) to examine the relationship of soil C stock with various

edaphic factors.


Study area

Figure 1. Aerial view of Pondicherry University Campus. (Source: Google Earth)

Pondicherry University (12o

0.97' N, 79o

51.33' E) is situated 10 km north of Puducherry town, on the

Coromandel coast (Fig. 1) and spans an area of 780 acres, of which the built-areas occupy approximately

1,80,000 m2. The climate is tropical with most rainfall during northeast monsoon (October – December) and very

less and inconsistent rainfall during southwest monsoon (June – September). The mean annual rainfall is 1282

mm for the last two decades (1990 – 2010).The mean annual maximum and minimum temperatures of

Puducherry are 32.58oC and 24.51

oC. The soil is red ferrilitic, sandy and heavily drained. The vegetation of

Pondicherry University is mainly composed of tropical dry evergreen scrub and palm savannas in the west and

south and cashew plantations, rice, sugarcane and groundnut cultivation in the east. For the present study, five


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different land uses in the university viz., teak plantation, eucalyptus plantation, acacia plantation, shrub land and

grass land were selected.

Soil sampling and laboratory analysis

Soil samples were collected at 0 – 10, 10 – 20 and 20 – 30 cm depths from each land use using a core sampler

during January and February of the year 2013. Ten sets of samples were collected from each study site and aremixed together to form a composite soil sample, from which six replicate samples were brought to the

laboratory for further analysis. Before analysis, soil samples were sieved through a 2 mm mesh and then mixed

thoroughly. Soil organic carbon was estimated by using Walkley and Black’s method (Walkl ey 1947). In this

method, about 60 – 86% of SOC is oxidized and therefore a standard correction factor of 1.32 was used to obtain

the corrected SOC values (De Vos et al. 2007).

For bulk density, in each site, six aggregated undisturbed soil cores were taken by a soil corer with 5 cm

internal diameter. The soil samples were weighed immediately and transported to the laboratory where they

were oven-dried at 105oC for 72 h and re-weighed. In the soils containing coarse rocky fragments, the coarse

fragments were separated by a sieve and weighed. The bulk density of the mineral soil core was calculated with

the help of the formula described by Pearson et al. (2005). Soil carbon stocks were then calculated for each soil

depth based on the thickness of the soil layer, bulk density and carbon concentration. The total carbon content

upto 30 cm depth was finally estimated by summing the carbon concentration of all the layers (Pearson et al.

2005).

Statistical Analysis

The variation in SOC stocks among different land uses and soil depths (0 – 10, 10 – 20, 20 – 30 cm) was

examined with analysis of variance (ANOVAs). The relationship between SOC stock and three edaphic factors

(soil moisture, soil pH and bulk density) were examined with correlation analysis followed by linear regression.

RESULTS

The soil moisture (%) in different land uses of Pondicherry University campus ranged from 2.98 (shrub land)

to 5.63 (acacia) up to 30 cm soil depth (Fig. 2). The soil moisture (%) was found to significantly vary (P


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Figure 3. pH in different land uses in Pondicherry University campus, Puducherry, India.

Figure 4. Soil bulk density in different land uses in Pondicherry University campus, Puducherry, India.

The SOC percent in different land uses of Pondicherry University campus ranged from 1.53 (teak) to 2.1

(acacia) up to 30 cm soil depth (Fig. 5). The SOC stock percent significantly (P


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to 2.96 (acacia) at 0 – 10 cm, 1.38 (teak) to 1.86 (shrub land) at 10 – 20 cm and 0.80 (teak) to 1.70 (shrub land) at

20 – 30 cm.

Figure 6. Soil organic matter (SOM) in different land uses in Pondicherry University campus, Puducherry, India.

The total SOC stocks up to 30 cm soil depth in the studied different land uses ranged from 19.47 (teak) to

27.06 (grass land) Mg C ha-1

(Fig. 7). The mean range of total soil carbon in different depths was 7.39 (shrub

land) to 12.56 (acacia) Mg C ha-1 at 0 – 10 cm, 6.35 (teak) to 10.74 (shrub land) Mg C ha -1 at 10 – 20 cm and 3.69

(teak) to 8.72 (shrub land) Mg C ha-1 at 20 – 30 cm. The total SOC stocks were significantly greater in grass land

and shrub land than the other study sites.

Figure 7. Total soil organic carbon (t/ha) in different land uses in Pondicherry University campus, Puducherry, India.

Regression analysis indicated that soil pH and soil moisture had a negative relationship with SOC percent,SOM and total carbon (Mg C ha-1) in Pondicherry University campus (Fig. 8). However, bulk density showed a

positive relationship with total carbon (Mg C ha-1).

DISCUSSION AND CONCLUSION

SOC showed a decreasing trend with increasing soil depth in all the study sites. This may be due the greater

decomposition rate in the upper layer compared to other layers. Similar results have been observed by other

workers as well (Jobbagy & Jackson 2000). The higher percentage of carbon in acacia and eucalyptus

plantations may be due to high litter inputs and more biological activity. In addition, the leaves of acacia and

eucalyptus trees have high lignin content which slows down the decomposition rate, which might have led to the

accumulation of humus throughout the year. This might be one of the reasons for high SOC stocks in these sites.

The range of SOC in tropical dry deciduous and moist forests ranged between 8.9 and 177 Mg C ha-1

in the top

50 cm soil depth (Chhabra et al. 2003) and our results are consistent with the above-stated values. The changes

in SOC stocks might also be due to different vegetation types, litter quality and quantity, soil type and texture,

0

1

2

3

4

5

6

7

0-10 10-20. 20-30 0-30

S O M (

% )

Soil depth (cm)

Teak Eucalyptus Acacia Grassland Shrubland

0

5

10

15

20

25

30

35

0-10 10-20. 20-30 0-30

T o t a l O r g a n i c C a r b o n ( t / h a )

Soil depth (cm)

Teak Eucalyptus Acacia Grassland Shrubland


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soil chemistry, soil moisture, decomposition rates and also landscape position as stated by Beets et al. (2002),

Vesterdal et al. (2008) and Twongyirwe et al. (2013). The change in total SOC stocks (Mg C ha-1) could also be

due to differences in soil bulk density at different soil depths (Jobbagy & Jackson 2000).

Figure 8. Regression analysis of soil organic carbon (SOC), soil organic matter (SOM) and total carbon (TC) with bulk

density, soil moisture and soil pH in different land uses in Pondicherry University campus, Puducherry, India.

y = -0.1824x +

0.2915

R² = 0.0219

0

0.05

0.1

0.15

0.2

0.25

0.30.35

0 0.1 0.2

L o g a r i t h m o

f S O C ( % )

Logrithm of Bulk density

(g/cm3)

y = 0.0392x + 0.2432

R² = 0.0063

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 0.5 1

L o g a r i t h m o

f S O C ( % )

Logarithm of soil moisture

(%)

y = -0.6958x +

0.8111

R² = 0.7201

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.7 0.8 0.9

L o g a r i t h m o

f S O C ( % )

Logarithm of pH

y = -0.1824x +

0.7636

R² = 0.0219

0.64

0.660.68

0.7

0.72

0.74

0.76

0.78

0.8

0 0.1 0.2

L o

g a r i t h m o

f S O M (

% )


(g/cm3)

y = 0.0392x +

0.7153

R² = 0.0063

0.64

0.660.68

0.7

0.72

0.74

0.76

0.78

0.8

0 0.5 1

L o

g a r i t h m o

f S O M ( % )


(%)

y = -0.6958x +

1.2832

R² = 0.7201

0.64

0.660.68

0.7

0.72

0.74

0.76

0.78

0.8

0.7 0.8 0.9

L o

g a r i t h m o

f S O M (

% )

Logarithm of pH

y = 0.6696x +

1.3044

R² = 0.2092

1.28

1.3

1.32

1.34

1.36

1.38

1.4

1.42

1.44

0 0.1 0.2

L o g a r i t h m o

f T C

( t C / h a )


(g/cm3)

y = -0.284x + 1.5691

R² = 0.2362

1.28

1.3

1.32

1.34

1.36

1.38

1.4

1.42

1.44

1.46

0 0.5 1

L o g a r i t h m o

f T C

( t C / h a )


(%)

y = -0.7012x +

1.9397

R² = 0.519

1.28

1.3

1.32

1.34

1.36

1.38

1.4

1.42

1.44

0.7 0.8 0.9

L o g a r i t h m o

f T C

( t C / h a )

Logarithm of pH


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SOC (%) showed a negative correlation with soil pH, soil bulk density and soil moisture. SOM (%) also

showed the same trend. To the contrary, the total carbon showed a positive correlation with bulk density, but not

with soil pH and soil moisture. Jobbagy & Jackson (2000) and Li et al. (2010) have also observed a negative

correlation of soil organic carbon with bulk density. The overall SOC was highest in grass land- than the other

sites. Shoji et al. (1993) in Central France have also reported high SOC values in grass land. Gupta & Sharma

(2014) have also concluded that grass lands have the maximum SOC pool based on their assessment of SOC

stocks in different land use systems of Uttarakhand. The present study suggests that maintaining diverse land

uses enriches the soil carbon stocks of the institution in addition to preserving biodiversity.

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www.tropicalplantresearch.com 18 Received: 04 November 2015 Published online: 29 February 2016

ISSN (E): 2349 – 1183

ISSN (P): 2349 – 9265

3(1): 18 – 32, 2016

Review article

Predicting suitability of tree species in various climatic conditions

Sharad Tiwari1

and Rajesh Kumar Mishra2*

1Institute of Forest Productivity, Lalgutwa, Ranchi, Jharkhand

2Tropical Forest Research Institute, P.O. RFRC, Mandla Road, Jabalpur, Madhya Pradesh


Abstract: Climate is a key factor shaping the forest environment; thus changes in the climate are

likely to strongly affect forest ecosystems by altering the physiology, growth, mortality and

reproduction of trees, the interactions between trees and pathogens, and ultimately the disturbance

regimes (winds, wildfires, insect attacks, etc.). The sensitivity to such changes depends on the

level that is considered (landscapes vs. forest, stands vs. single trees) and on the specific site

conditions. These complex influences indicate that a changing climate may lead to non-linear

responses, tipping points, etc., particularly since the longevity of trees implies that many

individuals present today will experience substantial changes of the climate before they will be

replaced by the next generation. Thus, the question arises to what degree current trees and forest

ecosystems are able to cope with a changing climate. In the present work a user-friendly package

“PLANTPAK” has been developed and tested successfully to evaluate the climatic suitability of

forestry species in central Indian region. The package can be used to store, retrieve and display

information based on simple key strokes. The package provides query on textural as well as map

basis. The package is tested with 15 data records and all the features including data entry,

information retrieval based on species name, location wise, climatic as well as edaphic fields are

working properly. Further the package is also successfully tested for providing map based retrieval

of information of suitable species.Keywords: Forest ecosystem - Central India - Climatic suitability - PLANTPAK.

[Cite as: Tiwari S & Mishra RK (2016) Predicting suitability of tree species in various climatic conditions.

Tropical Plant Research 3(1): 18 – 32]

INTRODUCTION

The world’s rapidly rising population requires most countries to make the best possible use of their land

resources for agriculture, horticulture, forestry and conservation. Being able to predict where and how well

particular plants are likely to grow in different regions is vital for land use planning. Linking GIS and modules

can help to answer these questions, but decision makers and researchers in developing countries have limited

access to these technologies. Climate has an important influence on tree growth it is particularly useful as a

means to predict where particular tree will grow, as mean climatic condition can now be reliably estimated formost locations around the world. Being able to identify where particular trees (or plants) will grow is useful, but

many people need to know how well they will grow on particular sites. Generally they do not require highly

precise predictions of yield, but they do need to know whether growth will be good, fair, poor or useless.

Therefore the development of interactive and user friendly decision support system is being proposed, which

will help in predicting suitability of important forestry species of central India in varied climatic and edaphic

conditions.

Dr. Michael Hutchinson (Center for Resource and Environmental Studies, Australian national University)

has developed a package known as ANUSPLIN, which uses Laplacian smoothing splines to interpolate spatially

between data recorded at meteorological stations (Hutchinson 1989, 1992). As part of ACIAR project 9127

mean monthly data were collated from meteorological stations in a single area including China, Thailand,

Vietnam, Laos, Cambodia and Peninsula Malaysia (Zuo et al . 1996). A digital elevation model was prepared byZuo et al . (1996) and monthly mean values for all five climatic factors were estimated for a 1/20

th of a degree


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Tiwari & Mishra (2016) 3(1): 18 – 32

.


grid (ca.5 km) of approximately 400 000 points across China and mainland South east Asia. As part of ACIAR

Project 9127 climatic mapping programs were prepared at the CSIRO Division of Forestry for China (100 000

grid points) and Latin America (66000 grid pints – interpolated climatic data kindly supplied by Dr. Peter Jones,

CIAT, Colombia).

Most of the programs have been developed for the MS-DOS environment, which is the most common

operating system on PC’s used in the developing world. However, a version of the THAI program has recently

also been developed for the Windows environment. Windows allows multitasking which makes it easy to

compare maps produced by different descriptions on the computer’s screen, as well as providing built-in support

for hundreds of different printers. Significant progress has been made in the development of climatic mapping

software in recent years (Hackett 1988, Booth 1990, Hackett 1991, Booth 1996a, b).

METHODOLOGY

For the present work climatic and edaphic data for entire central region has been collected. Basic database

structure and data retrieval algorithm has been developed. Initially records for 15 selected species with reference

to their climatic and edaphic suitability has been collected. All the forms including Main form, user

management form, data entry and edit form, query shell form has been designed and tested successfully. The

package has been developed and tested successfully for all the operations including data entry, data modificationand retrieval of information.

Requirement analysis

The brief study of the areas involved including the potential of the work, target user group, infrastructure

requirement and other related issues. It was attempted to create an easy to use and user friendly package, which

not only allows maintaining records but also provides for data retrieval, data entry, search for any specific data

among the entire database. It was decided that this database package will be made available to entire institute

and to others interested in deriving information or consultation. The field or parameters of input of information

were discussed at large and at various levels. The database structure was suitably modified to incorporate the

suggestions made to widen the applicability and usefulness of the package. Table 1 containing information

about species was designed for entering species records. Table 2 is also depicted the location details from the

fields.

Table 1. Species information. Table 2. Location details and climatic conditions.

Species Id Location ID

Species Botanical Name Location name

Genus Soil Type

Vernacular Name Temp min

Family Temp max

Uses Rainfall min

Combination Rainfall max,

Soil- Poorly suitable Suitable Species

Soil- Moderately Suitable Remark

Soil- Most Suitable

Temp Mean Min- Poorly suitable

Temp Mean Min- Moderate suitable

Temp Mean Min- suitable

Temp Mean Max- Poorly suitable

Temp Mean Max- Moderate suitable

Temp Mean Max- suitable

Rainfall- Poorly suitable

Rainfall- Moderate suitable

Rainfall- Most Suitable

Remark


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Tiwari & Mishra (2016) 3(1): 18 – 32

.


Database structure

The database should be prepared as showed in tables 3 – 6.

Table 3. Species master.

Field name Data type & Width

Species_ID Number (7) Primary key

Species_Botanical_Name Varchar2 (50)

Genus Varchar2 (40)Vernacular_Name Varchar2 (40)

Family Varchar2 (40)

Uses Varchar2 (100)

Combination Varchar2 (100)

Soil_Poorly_suitable Varchar2 (100)

Soil_Moderately_suitable Varchar2 (100)

Soil_Most_Sutable Varchar2 (100)

Temp_Mean_Min_Poorly_l Number (2)

Temp_Mean_Min_Moderate_l Number (2)

Temp_Mean_Min_suitable_l Number (2)

Temp_Mean_Max_Poorly_l Number (2)

Temp_Mean_Max_Moderate_l Number (2)

Temp_Mean_Max_suitable_l Number (2)

Temp_Mean_Min_Poorly_u Number (2)

Temp_Mean_Min_Moderate_u Number (2)

Temp_Mean_Min_suitable_u Number (2)

Temp_Mean_Max_Poorly_u Number (2)Temp_Mean_Max_Moderate_u Number (2)

Temp_Mean_Max_suitable_u Number (2)

Rainfall_Poorly_l Number (4)

Rainfall_Moderate_l Number (4)

Rainfall_Most_Sutable_l Number (4)

Rainfall_Poorly_u Number (4)Rainfall_Moderate_u Number (4)

Rainfall_Most_Sutable_u Number (4)

Remark varchar2 (100)

Preference varchar2 (55)

Table 4. User master.


USERID VARCHAR2 (25) primary key

PASSWORD VARCHAR2 (20)FNAME VARCHAR2 (20)

LNAME VARCHAR2 (20)

DESIGNATION VARCHAR2 (25)

PREVILLAGE VARCHAR2 (20)

REMARK VARCHAR2 (50)

Table 5. Location master.


location_ID Number (6) primary key

location_name varchar2 (35)

soil_type varchar2 (40)

temp_min varchar2 (5)

temp_max varchar2 (5)

rain_min varchar2 (9)

rain_max varchar2 (9)

suitable_species varchar2 (35)

remark varchar2 (50)


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Tiwari & Mishra (2016) 3(1): 18 – 32

.


Table 6. Error master.


error_ID Number (7) primary keyerror_date date

user_ID varchar2 (25)

error_details varchar2 (100)

error_status varchar2 (15)

solution varchar2 (100)

remark varchar2 (100)

Functional identification

The system consists of selections made by the user at different stages in application and the entries that the

user makes. Different selections and entries that the user can make were finalized. It is usually very difficult to

specify system characteristics accurately without actually doing much of the proposed work. Thus, a quick guess

about the systems characteristics was all that was possible at this point. To systematically plan the output of the

proposed system, thorough interaction with some potential target group had been done which resulted in

finalization of procedure of the output display on monitor or printing as reports. The inputs required to produce

the required outputs were listed and the sources of these inputs determined. A tentative, general schedule for

developing the package was decided as follows:

1. User login ID and password.

2. User selects what he wants to do.

3. Data Entry: User enters the data regarding the database.

4. Editing: User can add, edit, species and location details.

5. Information Retrieval.

6. User can report an error.

Data collection

Data were collected from various sources viz. literature, books and journals. The district wise climatic and

edaphic data were collected from NIC site. The state maps were downloaded from state NIC website. Species

data were collected through extensive review of relevant literature.

Feasibility study

Feasibility study was conducted to select the best system meeting performance requirements. Once it was

determined that the project was feasible, project specifications which finalize project requirements were

prepared. Three key considerations were involved in feasibility analysis: Economic, Technical & Operational.

Economic analysis also known as cost / benefit analysis, determined, whether the adoption of the system was

cost justified or not. Technical consideration evaluated existing hardware and software and future requirement.

Operational feasibility specified that whether the proposed system will meet the operating requirements of the

organization.

System design life cycle

In order to transform requirements into a working system both the customer/user and the developer has to be

satisfied. The user/employee has to understand that what the system is suppose do and at the same time the

system developer is to know as to how the system is to work.

Conceptual design

The Conceptual Design tells what the system will do. The System is described in term of it’s boundary,

entities, attributes and relationship. In this phase it was determined that-

1. The Data comes from field level survey.

2. The Data is fed to the system.

3. The front end designed, links the user to the database.

4. The user is offered a number of choices like simple viewing of records searching for a particular record,

record entry, deletion of record etc.

5.

The format of reports output screen.


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Algorithms:

I. Algorithm for adding user

1. START

2. INPUT:

{USERID, PASSWORD, USERNAME, DESIGNATION, REMARK}

3. FORM LEVEL VALIDATION :

i. IF THEN

GO To NEXT STEP

ELSE

User is prompted to correct the entries / data in the field / fields.

4. DATABASE LEVEL VALIDATION:

{As user ID primary key therefore two user’s IDs can’t be identical.}

i. IF THEN

GO To NEXT STEP

ELSE

User is prompted to correct the entries / data in the field / fields.5. MESSAGE : RECORD ADDED.

6. END.

II. Algorithm for Adding Species

1. START

2. INPUT:

{

Species ID

Species Botanical Name

Genus

Vernacular NameFamily

Uses

Combination

Soil- Poorly Suitable

Soil- Moderate Suitable

Soil- Most Suitable

Temp Mean Min- Poorly Suitable

Temp Mean Min- Moderate Suitable

Temp Mean Min- Most suitable

Temp Mean Max- Poorly Suitable

Temp Mean Max- Moderate SuitableTemp Mean Max- Most Suitable

Rainfall- Poorly Suitable

Rainfall- Moderate Suitable

Rainfall- Most Suitable

Remark

Preference

}


i. IF THEN

GO To NEXT STEP

ELSE



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4. DATABASE LEVEL VALIDATION:

i. IF THEN

GO To NEXT STEP

ELSE


5.

MESSAGE: RECORD ADDED.

6. END.

III. Algorithm for adding location

1. START

2. INPUT :

{LOCATION NAME, RAINFALL, TEMPERATURE, SUITABLE SPECIES, SOIL TYPE, REMARK}


i. IF THEN

GO To NEXT STEP

ELSE

User is prompted to correct the entries / data in the field / fields.4. DATABASE LEVEL VALIDATION:

i. IF THEN

GO To NEXT STEP

ELSE


5. MESSAGE: RECORD ADDED.

6. END.

IV. Algorithm for search / query operations

1.

START2. INPUT:

{BOTANICAL NAME, VARNACULAR NAME, GENUS, RAINFALL, TEMPERATURE, WHATEVER

CARITERIA USER WANTS TO USE.}

3. FORM LEVEL VALIDATION:

i. IF THEN

GO To NEXT STEP

ELSE


4. FORMING SQL QUERY AND SENDING IT TO RDBMS (ORACLE):

i. IF < Data Found > THEN

GO To NEXT STEPELSE

User is prompted no records found try again.

5. DISPLAYING Message.

6. END.

Design approach

Modular is the design approach. The entire system works due to the functionality of its component modules.

Each module was designed to be complete in itself. It’s the user action, however that decides the flow of control

in the system since the entire programming is event based. For each action of the user, a particular module is

associated to starts functioning generating the desired results.


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1

3

2

Add User

START

Manage Accounts Data Entry Query shell ABOUT S/W

Logout

Accessories

Edit User Privileges

Delete User

View User

Add Species

Add Location

Edit Species

Edit Location

View Records

Change PasswordMap

Image Gallery

By Location

B S ecies

Calculator

Paint Brush

Related Site

Error Re ort

View ErrorReport Error

S/W Details

STOP

Advanced Search

Figure 1. Flow chart of program.


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Search in Database table

BOF

Data not found

Show Results

3

Enter the soil Type or Rainfall Rang

or Temperature Range or use

No

Records found

Yes

Figure 2. Flow chart for advance search.

If


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BOF

Data not found

Show Results

2

Enter the Local Name or soil Type

No

Records found

Yes

Figure 3. Flow chart to search species by local details.

If


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BOF

Data not found

Show Results

1

Enter the Botanical Name or Vernacular

Name or Soil Type

No

Records found

Yes

Figure 4. Flow chart to search by species details.

If


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O LEVEL DFD

1 LEVEL DFD

SEARCH

DATABASE

(Backend)

ADD

EDIT

VIEW

Add Species & Location

Edit Species & Location Details

Search in Database by Species & Location Criteria

View Species & Location Details

Plant-PakSURVEY

Figure 6. Flow chart of 1 level DFD.

Figure 5. Flow chart of O level DFD.


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2 - LEVEL DFD

The database contains important new features that optimize traditional business applications, facilitatecritical advancement for internet-based business, and stimulate the emerging hosted application market. New

database features deliver the performance, scalability, and availability essential to hosted service software made

available to anyone, anywhere. The database offers new transparent, rapid growth clustering capabilities, along

with powerful and cost-effective security measures, zero-data-loss safeguards, and real-time intelligence to

deliver the power needed in today's dynamic marketplace.

Data retrieval

The package provides retrieval of information based on following fields:

By Species Name: User can get access to records by simply selecting the species either by

Local_ Name or by Vernacular_ Name: On selection of this choice, a drop down menu containing all

the available species stored in the database is displayed. User can select the species of his interest from

the drop down menu.

By Family Type: On selection of this option, all the species belongs to a particular family are displayed.

By Location Name: On selection of this option, a window as containing list of all the locations stored in

the database is displayed. User can select the location of his interest from the drop down menu. By Climatic and Edaphic Condition: On selection of this option, a user friendly window is displayed.

User can enter the Soil type or climate range to retrieve information belonging to those climatic and

edaphic conditions.

Through Map: The package has the option to provide information also through the map. On selection of

this option, the map of selected state is displayed. The user can browse through the map and can retrieve

species suitable to a particular place by simply clicking at that place on the map).

The package has been successfully implemented and tested for 15 species ( Acacia catech, Albizia lebbec,

Albizia procer, Ailanthus excels, Boswellia serrata, Gmelina arborea, Holoptelea integrifolia, Lagerstroemia

parviflora, Madhuca latifolia, Moringa oleifera, Pongamia pinnata, Pterocarpus marsupium, Dalbergia

latifolia, Sterculia urens, Emblica officinalis) in Madhya Pradesh and Maharashtra region (Appendix I – IV).

The strength of this package is that it is very user friendly more intended to not only function to the best ofuser satisfaction but its workability also. It allows the users to not only retrieve data but also manage the

Location

Table

ADD

EDIT

SEARCH

VIEW

Species

Table

Figure 7. Flow chart of 2 level DFD.


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database by updating to whenever necessary. The user is provided with all the relevant option of adding,

deleting, making changes to and also updating the records. The point of vulnerability of this application is that

care has to be taken while feeding data to the database. The database incorporates two table and care should be

taken that while one table is being fed the relevant filed of the second table also be fed at the same time

otherwise it may cause inconvenience and irrelevant data retrieval.

This application on integration with GIS application can be enhanced to work like a Specialist Decision

Support System. The structure of the database can be planned in a better and efficient way; there still is a scope

to redefine it. This application was tested to work successfully with a fifteen tree species. The workability of the

system has been checked from each and every aspect and was found to work fine generate the desired output.

The application of this model shows good results in the research resulted in satisfactory results for most of

the species, but not for others. Hence, the need and justification that if further developed it can be proved as

quite robust and applicable at any level and with high percentage of accuracy. The evaluation of the results can

still be debated, and various experts will have a say regarding their respective field of expertise. However, this

Decision Support System, with further improvement, i.e. providing more detailed information, will cover the

gaps which can be seen here in some of the suitability maps for the species. The lack of “proved” mathematic al

formula is also the issue in this DSS; a formula was created which would grow more complex and accurate as

further input is provided. This way of producing suitability maps for tree species purposes has not been carriedout in the past in our country. The method needs a lot more input data in order for it to be deemed as absolutely

reliable (or close to that). Such input would require implementing detailed species characteristics, detailed

climate-vegetation data, habitat specifications, slope and aspect data, various constraint data (agricultural land,

urban areas, industrial areas, and protected areas), etc. However, this research shows that this is a very good

approach for creating suitability of tree species in different climatic conditions which provide satisfactory

accuracy. That was the case in our research, where some of the suitability resulted in very good spatial

distribution and accuracy, while other for some of the species deemed areas as suitable when in fact they aren’t

such. The tree species used in this paper has justified their choice, in terms of offering various possibilities for

afforestation throughout the country. Finally, the approach used in this research resulted in the creation of a

model which was carried out as such for the first time in our country, and with its development will prove to be

robust enough to be used during the afforestation of any localities. In the end it can be concluded that the qualityof the model is dependent on the quality of the input. Therefore, more research should be done in improving the

theoretical background of the modelling process and the input parameters.

Conclusion

The decision support systems make use of variety of technology and new technologies playing important

role in decision making. PLANTPAK as this new decision support system has been named will be immensely

useful for farmers, tree planters and entrepreneurs in the arena of plantation forestry to decide the suitability of

tree species according to locality, climate and edaphic characteristics in central India. However, the package

could easily be enriched by incorporation of other area and species. In the recent times, the need of site specific

information along with geographical details has grown up immensely. The textual data coupled with geographic

detail can provide a very clear picture of the object under consideration. Many areas of DSS application are

concerned with geographic details. GIS based DSS can make use of spatial data processing. Clearly SDSS will

be an important subset of DSS in future. The trend in the future is likely to be growth in the use of SDSS by

users without any special skill in GIS applications. SDSS don't require in-depth commands to operate, yet allow

users to negotiate very sophisticated geographic analysis. In the future, the use of SDSS will be extended to

applications where the spatial information is only an interim stage. Users dealing with this broader set of

applications need to be given control over the important variables in the decision, while other processing is

performed without the need for extensive user interaction. With the development of such systems, new classes

of decision and new class of users will be supported effectively.

REFERENCES

Anonymous (1993) Plant Resources of South-East Asia. No. 5 (1&2). Pudoc Sc. Publication, Wegeningen.

Booth TH (1990) Mapping regions climatically suitable for particular tree species at the global scale. Forest Ecology & Management 36: 47 – 60.


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Booth TH (1996a) The development of climatic mapping programs and climatic mapping in Australia. In: Booth

TH (ed) Matching Trees and Sites. Proceedings of an International Workshop held in Bangkok, Thailand,

27 – 30 March 1995. ACIAR Proceedings No. 63, pp. 38 – 42.

Booth TH (1996b) The development of climatic mapping programs and climatic mapping in Australia. In:

Booth TH (ed) Matching Trees and Sites. ACLAR Proceedings No. 63.

Brandis D (1971) Indian Trees. Bishen Singh Mahendra Pal Singh, Dehradun.

Burley J & Styles BT (1976) Tropical Trees: Variation, Breeding and Coservation. Academic Press, London.

Gamble JS (1984) A Manual of Indian Timbers. Bishen Singh Mahendra Pal Singh, Dehradun.

Hackett C (1988) Matching Plants and Land: Development of a broad scale system from a crop project for

Papua New Guinea. CSIRO Division of Water and Land Resources. Natural Resources Series no.11,

Melbourne, 82 p.

Hackett C (1991) Plantgro: a software package for the prediction of plant growth. CSIRO, Melbourne.

Hutchinson MF (1989) A new objective method for spatial interpolation of meteorological variables from

irregular network applied to the estimation of monthly mean solar radiation, temperature, precipitation and

wind run. CSIRO Division of Water and Land Resources, Tech.Memo.89/5, CSIRO, Canberra 10 p.

Hutchinson MF (1992) Documentation for SPLINA and SPLINB- two programming the ANUSPLIN software

package. CRES, Australian, National University Canberra.Luna RK (1996) Plantation Trees. International Book Distributors, Dehradun.

Maslekar AR (1996) Foresters Companion. Surya International Publications, Dehradun.

McCann C (1985) Trees of India. Periodical Expert Book Agency, New Delhi.

Prakash R & Hocking D (1986) Some Favourite Trees For Fuel And Fodder . Society For Promotion of

Wasteland Development, New Delhi.

PurKayastha SK (1996) A Manual of Indian Timbers. Sribhumi Publication Company, Kolkata.

Seth SK, Raizada MP & Waheed Khan MA (1962) Trees for Van Mahotsava. FRI and Colleges, Dehradun.

Singh RV (1982) Fodder Trees of India. Oxford and IBH Publication Company, New Delhi.

Singhal RM & Khanna P (1991) Multipurpose Trees and Shrubs. ICFRE, Dehradun.

Troup RS (1986) The Silviculture of Indian trees. International Book Distributors, Dehradun.

Zuo H, Hutchinson MF, McMahon JP & Nix HA (1996) Developing a mean monthly climatic database forChina and Southeast Asia. In: Booth TH (ed) Matching Trees and Sites. ACIAR Proceedings No. 63.


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Appendix I: Classification of different districts of Madhya Pradesh according to rainfall (mm).

S. No. Rain fall range Districts

1. 800 – 1000 Gwalior, Bhind, Shivpuri, Morena, Sheopurkala,

Khandwa, Burhanpur, Khargone, Jhabua

2. 800 – 1200 Mandsaur, Ratlam, Ujjain, Dewas, Indore, Sajapur,

Rajgarh, Dhar3. 800 – 1400 Chhattarpur, Datia, Tikamgarh,

4. 1000 – 1200 Betul, Chhindwara5. 1000 – 1400 Rewa, Satna, Panna, Seoni, Katni

6. 1200 – 1400 Bhopal, Sehore, Raisen, Vidisha, Guna, Ashoknagar,

Sagar, Damoh

7. 1200 – 1600 Balaghat, Shahdol, Anuppur, Sidhi, Mandla, Jabalpur,

Narsinghpur, Hoshangabad, Harda

Appendix II: Soil types of different districts of Madhya Pradesh.

S. No. Soil Type Districts

1. Shallow & medium black Betul, Chhindwara, Seoni

2. Deep medium black Narsinghpur, Hoshangabad, Harda, Shahdol, Umaria, Jabalpur, Katni,Sagar, Damoh, Vidisha, Raisen, Bhopal, Sehore, Rajgarh, Ujjain,

Dewas, Shajapur, Mandsaur, Neemach, Ratlam, Jhabua, Dhar, Indore,

Khargone, Barwani, Khandwa

3. Alluvial soil Gwalior, Morena, Sheopurkala, Bhind

4. Mixed red & black Mandla, Dindori, Balaghat, Rewa, Satna, Panna, Chhatarpur,

Tikamgarh, Shivpuri, Guna, Datia, Sidhi

Appendix III: Classification of different districts of Maharashtra according to rainfall (mm).

S. No. Rain fall range Districts

1. 500 – 800 Ahmednagar, Aurangabad, Akola, Beed, Buldhana,

Dhule, Jalgaon, Jalna, Osmanabad, Pune, Sangli, Solapur

2. 800 – 1200 Amravati, Chandrapur, Hingoli, Latur, Nashik, Nanded,

Parbhani, Washim, Wardha, Yavatmal

3. 1200 – 1500 Bhandara, Gondia, Gadchiroli, Kolhapur, Nagpur, Satara

4. 2500 – 3000 Sindhudurg, Thane

5. 3000 – 3500 Raigad, Ratnagiri

Appendix IV: Soil types of different districts of Maharashtra.

S. No. Soil Type Districts

1. Black to red Akola, Amravati, Beed, Buldhana, Jalgaon,

Latur, Osmanabad, Solapur

2. Light laterite, Reddish brown, Greyish black Ahmednagar, Satara

3. Brown to red Bhandara, Gadchiroli

4. Reddish brown to black, Greyish black Kolhapur, Nashik, Pune, Sangli

5. Coarse & shallow Raigad, Thane

6. Black soils, Black to red Aurangabad, Jalna, Nanded, Parbhani,

Yavatmal

7. Laterite, light laterite & reddish brown Ratnagiri, Sindhudurg

8. Black Soils, Brown to red Chandrapur, Nagpur, Wardha

9. Black to red, Greyish black Dhule


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www.tropicalplantresearch.com 33 Received: 18 November 2015 Published online: 29 February 2016

ISSN (E): 2349 – 1183ISSN (P): 2349 – 9265

3(1): 33 – 39, 2016

Research article

Eight new records of fresh water filamentous algae

(Oedogonium Link) from India

Priya Jitendra* and V. K. Anand

Department of Botany, University of Jammu, Jammu & Kashmir, India


Abstract: The present paper deals with the eight species of Oedogonium which are being reportedfor the first time from India. All the taxa are arranged in broad groups according to their

morphological peculiarities and sexual reproduction i.e. Macrandrous (Homothallic andHeterothallic). During the present investigation 5 macrandrous homothallic {Oedogonium

subvaucherii Claass., O. pseudofragile Claass., O.upsaliense (Wittr.) Hirn., O.visayenseBritt., O.amplius (Tayl.) Tiff.} and 3 macrandrous heterothallic forms {O. cf capillare (L)Kutz., O. angustistomum Hoff ., O. magnusii Wittr. var. major Bock and Bock} have beencollected for the first time from India.

Keywords: Filamentous algae - Oedogonium - Homothallic - Heterothallic - New record.

[Cite as: Jitendra P & Anand VK (2016) Eight new records of fresh water filamentous algae (Oedogonium Link)from India. Tropical Plant Research 3(1): 33 – 39]

INTRODUCTION

Oedogoniales, an order of filamentous freshwater green algae is well defined with several unique features,

including asexual reproduction through the production of zoospores that possess a subapical ring of many short

flagella called as stephanokont. Oedogoniales exhibited a specialized type of oogamy and an elaborate methodof cell division which results in the accumulation of apical caps. The order is comprised of one family

Oedogoniaceae which includes three genera namely Oedogonium Link , Bulbochaete Agardh and Oedocladium Stahl (Silva & Moe 2003). These three genera are different from each other morphologically, but also share the

several characteristics that distinguish Oedogoniales from rest of the green algae. On the basis of their peculiar

characteristics, members of the Oedogoniales are important not only from academic point of view but also are of

great ecological significances especially in the field of limnology since they occupy specific niches, food for a

number of aquatic organisms (Olojo et al. 2003, Kone & Teugels 2003, Awasthi et al. 2006), used for theremoval of heavy metals, production of antibiotics (Redondo et al. 2006) and being used as indicator of waterquality (Bajpai et al. 2013, Srivastava et al. 2014). This large, economically important order with its uniquefeatures attracted the phycologists of all over the world including India, but unfortunately this order remained

unexplored in India. Many stray reports on the Oedogoniales have appeared from various parts of India(Randhawa 1940, Kamat 1967, Kamat & Patel 1973, Shukla 1971, Bharati & Pai 1972, Shukla et al. 1988, Saha& Pandit 1987, Mahato et al. 1998, Prasad & Misra 1992, Misra et al. 2002) but no consolidated work has ever been done on Oedogoniales and Oedogonium in particular. Keeping in mind, the paucity of work done onOedogonium, the present research problem has been undertaken to work out the Oedogonium species of Jammuregion. Hence, a survey has been made of six districts of Jammu.

Jammu, the winter capital of Jammu & Kashmir State, is situated at a longitude 74° –76° 15’ E and latitude

32° 15’ to 30° 30’ N, and altitude 304.8 to 3658.5 metres above the mean sea level, It represents subtropical to

temperate climate with plains to mountainous terrain, which exhibiting remarkable longitudinal variations and

vegetation types. Due to the prevalent of varied climatic conditions, there exist number of natural and manmade

water bodies like ponds, rivers, ditches, pools, streams, nallahas, lakes and temporary water bodies. These water

bodies inhabit a great deal of algal diversity, of which Chlorophyceae is the most dominating.


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Algal samples were collected from different localities of Jammu. Algae were picked up simply by hand from

shallow edges of the ponds or occasionally by plankton net and then preserved in 4% formaldehyde solution.

Preliminary screening of Oedogonium species from the samples was made in the Aquatic biology lab,Department of Botany, University of Jammu, Jammu. Microphotographs of vegetative and reproductive

structures of different species of Oedogonium were taken using a microphotographic camera PM6 type,Olympus make and Nikon FM3A (E). The film roll used was Nova Silver Plus, 125 ASA black and white.

Laboratory cultureIn the laboratory, samples were first observed under a compound microscope for the presence of

reproductive structures. The vegetative filaments were transferred to petri dishes or flasks, rich in CO 2

atmosphere for the formation of reproductive structures (Kumar & Singh 1984). The cultures were kept in the

culture room, illuminated with a fluorescent, white, cool 15w tube light of 464 lux with a temperature of 15°C –

25°C and a photo period of 16:8 L/D regime. After 10 – 20 days, the crude cultures were examined

microscopically. The incubated cultures were observed by pulling some filaments from several locations from

the same sample and made a whole mount on the glass slide. Samples were checked regularly for the next

following few weeks particularly for the late bloomers since each sample may contain many species and also all

species may not fruit at the same time (different species of Oedogoniales undergo fertility at different interval).

RESULTS

Enumeration

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