Global Journal of Science Frontier Research: H Environment & Earth Science

Online ISSN : 2249-4626 Print ISSN : 0975-5896 DOI : 10.17406/GJSFR

Toxic Effect of Metal Ions Volatile Organic Compounds

Studying the Nature Relationship Regional Estimation of Flood Quantile

VOLUME 16 ISSUE 4 VERSION 1.0

Global Journal of Science Frontier Research: H Environment & Earth Science

Global Journal of Science Frontier Research: H Environment & Earth Science Volume 16 Issue 4 (Ver. 1.0)

Open Association of Research Society

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Contents of the Issue

i. Copyright Notice ii. Editorial Board Members iii. Chief Author and Dean iv. Contents of the Issue

1. Regeneration of the Molluscan Fauna of a Small Lowland River after Dredging. 1-13 2. Toxic Effect of Metal Ions in Water Resources.15-18 3. Studying the Nature Relationship between Climatic Factors and Cotton Production by Different Applied Statistical and Mathematical Ways. 19-47 4. Land use/Land Cover Dynamics and its Implication on Sustainable Land Management in Wallecha Watershed, Southern Ethiopia. 49-63 5. Landslide Hazard Zonation using Quantitative Methods in GIS, Pauri Garhwal District, Uttarakhand, India. 65-71 6. Regional Estimation of Flood Quantile at Ungauged Sites. 73-74 7. Total Volatile Organic Compounds (TVOCs) in Indoor and Outdoor Urban Atmospheres at a Terai Region of Northern India. 75-79

v. Fellows vi. Auxiliary Memberships vii. Process of Submission of Research Paper viii. Preferred Author Guidelines ix. Index

Global Journal of Science Frontier Research: H

Environment & Earth Science Volume 16 Issue 4 Version 1.0 Year 2016

Type : Double Blind Peer Reviewed International Research Journal

Publisher: Global Journals Inc. (USA)

Online ISSN: 2249-4626 & Print ISSN: 0975-5896

Regeneration of the Molluscan Fauna of a Small Lowland River after Dredging By Andrzej Zawal, Anna Sulikowska-Drozd, Edyta Stępień, Łukasz Jankowiak & Agnieszka Szlauer-Łukaszewska University of Szczecin, Poland Abstract- Dredging of the river to remove macrophytic vegetation and bottom sediment is a common anthropogenic disturbance in the river ecosystem that directly and indirectly influences benthic invertebrates, including molluscs. We assessed the effect of dredging on malacofauna during the year following such an intervention on the river Krąpiel (NW Poland) and describe the process of gradual recolonization of the dredged parts by gastropods and bivalves as well as its possible sources. Molluscs were adversely impacted immediately after the dredging: relative abundance of rheophilic and species typical of stagnant water or slow-flowing rivers changed and the overall species richness decreased. The fauna recovered to its pre-management state within a year. The BACI analysis showed no long-term effect of the intervention on the total abundance and diversity of the molluscs. As many as 17 mollusc species, among them Unio crassus, were present in the river before and after the dredging. Keywords: disturbance, recovery, dredging, diversity, abundance, mollusca. GJSFR-H Classification: FOR Code: 049999

RegenerationoftheMolluscanFaunaofaSmallLowlandRiverafterDredging

Strictly as per the compliance and regulations of :

© 2016. Andrzej Zawal, Anna Sulikowska-Drozd, Edyta Stępień, Łukasz Jankowiak & Agnieszka Szlauer-Łukaszewska. This is a research/review paper, distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License http://creativecommons.org/licenses/by-nc/3.0/), permitting all non commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Regeneration of the Molluscan Fauna of a Small Lowland River after Dredging

Andrzej Zawal α, Anna Sulikowska-Drozd σ, Edyta Stępień ρ, Łukasz Jankowiak Ѡ & Agnieszka Szlauer-Łukaszewska ¥

Abstract - D redging of the river to remove macrophytic structure and changes the availability of resources or the vegetation and bottom sediment is a common anthropogenic physical environment. Following the initial decreases in disturbance in the river ecosystem that directly and indirectly benthic diversity and abundance that immediately follow 201 influences benthic invertebrates, including molluscs. We a disturbance, aquatic organisms begin to colonize the r assessed the effect of dredging on malacofauna during the ea

sediments. This successional process, called benthic Y year following such an intervention on the river Kr ąpiel (NW Poland) and describe the process of gradual recolonization of recovery, is defined as a return of living resources to 11 the dredged parts by gastropods and bivalves as well as its pre-impact conditions, a reference condition (of a possible sources. Molluscs were adversely impacted neighbouring unaffected area), or both (Wilber & Clark immediately after the dredging: relative abundance of 2007). rheophilic and species typical of stagnant water or slow- There are several environmental conditions flowing rivers changed and the overall species richness identified as influencing benthic recovery rates (e.g. V decreased. The fauna recovered to its pre-management state

sediment type or the time of the disturbance), but it IV within a year. The BACI analysis showed no long-term effect of seems that lotic ecosystems regenerate relatively fast, ue ersion I the intervention on the total abundance and diversity of the s usually within months after dredging (Yount & Niemi s molluscs. As many as 17 mollusc species, among them Unio

1990; Wilber & Clark 2007). The natural succession of I crassus, were present in the river before and after the dredging. An additional 12 taxa were noted for the first time aquatic organisms in dredged areas seems congruent XVI following dredging indicating that the removal of with the process of recovery by benthic invertebrates deoxygenated sediments from the channel provided an after natural disturbances such as floods. The evidence opportunity for the establishment of more diverse mollusc shows that pre-flood conditions are usually re assemblages. Habitat preferences, mobility, and life cycle

established within weeks to months of a flood event characteristics of species determine how they survive )

which caused substantial losses of invertebrate diversity H disturbances and how fast they are able to recolonize the and reductions in density (Lepori & Hjerdt 2006; ( managed sites. Mundahl & Hunt 2011). Keywords: disturbance, recovery, dredging, diversity, Depending on their habitat preferences, abundance, mollusca.

mobility, or life cycle characteristics (e.g. winged adult Research Volume I. Introduction insects), it may be easier or more difficult for various groups of invertebrates to colonize parts of a river that redging of rivers and canals to enable navigation have been dredged. Molluscs, due to their low mobility, and agricultural land irrigation is a common potentially belong to groups which are not able to Frontier Dpractice worldwide. During dredging, rapidly recolonize dredged river segments (Aldridge macrophytic vegetation and bottom sediment are 2000), and work carried out in the river bed may lead to removed (St ępień at al. 2015; Stępień at al. 2016). These the destruction of populations of rare gastropod and Science procedures directly and indirectly affect communities of bivalve species (Layzer et al. 1993; Rauers et al. 2004). of aquatic organisms, by killing or damaging them, The problem of the destruction of malacofauna during destroying their hiding places and feeding places, and hydraulic engineering work and the process of recolo- altering hydrological conditions. Dredging is a nization of the bottom by molluscs requires in-depth Journal disturbance in the river ecosystem according to the research. definition given by Yount & Niemi (1990), i.e. a relatively Global discrete event that disrupts community or population

Author α ¥: Department of Invertebrate Zoology and Limnology, University of Szczecin, Waska 13, 71-415 Szczecin, Poland. e-mail: [email protected] Author σ: Department of Invertebrate Zoology and Hydrobiology, University of Lodz, Banacha 12/16, 90-237 Łód ź, Poland. Author ρ: Department of Plant Taxonomy and Phytogeography, University of Szczecin, Waska 13, 71-415 Szczecin, Poland. Author Ѡ: Department of Vertebrate Zoology and Anthropology, University of Szczecin, Waska 13, 71-415 Szczecin, Poland.

201 r ea

ue ersion I s s

XVI

)

H (

Research Volume

Frontier

Science Fig. 1 : Sampling localities on the Krąpiel River. K – control sites, undredged; P – dredged sites. Grey arrows indicate the connection between fish pond and the river channel of Assessing the recovery of benthic habitats Płaska et al. 2016; Dąbkowski et al. 2016). The aim of disturbed by dredging and the disposal of dredged the study was to assess the effect of dredging of a small

Journal material is an important and growing management issue lowland river on its Mollusca fauna during the year all over the world (Wilber & Clark 2007). A good following this intervention. We describe communities of understanding of this process may be helpful in molluscs in the river before and after the dredging and Global selecting methods for maintaining the navigability of follow the process of gradual recolonization of the rivers that would least affect the diversity and dredged segment by gastropods and bivalves. abundance of aquatic invertebrates, and through the II. Study Area food web, of other living resources as well. The research focused on the impact of The river Krąpiel is a tributary of the river Ina. dredging on some groups of macroinvertebrates: The segment studied (coordinates N: 53°25′ 17.38″; E: Hydrachnidia, Ostracoda, Odonata, Heteroptera, 15° 11′ 39.25 ″ – N: 53° 24′ 33.94″; E: 15° 11′ 59.31″) Trichoptera and Coleoptera (Szlauer-Łukaszewska & takes the form of a regulated channel 6-8 m wide, Zawal 2014; Zawal et al. 2015a; Zawal et al. 2015b; running alongside fish ponds (Fig. 1).

Before the dredging the river bed was densely shrubs were removed on both sides of the river, leaving overgrown with macrophytic vegetation, mainly only isolated trees (alders and willows). The spoil was Phragmites australis, and the bottom was covered with a deposited on the banks in the form of excavated thick layer of mud. The intervention involved cleaning out sediment. Sediment from the river was removed to such the river bed – removing the mud and vegetation a level as not to interfere with the natural slope of the covering it using an excavator with a dredge operating river bed, to avoid the formation of depressions filled from the bank of the river. The dredging was carried out with stagnant water. This resulted in the removal of in December 2008. about 80 % of the mud that had previously filled the river Following the dredging, the Krąpiel retained its bed, as well as the removal of silt and sand from some previous width. All of the rushes and macrophytic places. The openness of the channel increased 20 – 50 vegetation were removed from the river bed (except for % in places that were not previously overgrown and 80 the segment under the bridge, which was left % in places that had been overgrown with reeds untouched). In addition, a 5 m strip of rushes and willow (Phragmites). 201

r ea

Table 1: Characteristics of the sampled localities along the Krąpiel River; control (undredged) localities in bold Y

31 flow (m s–1) depth (m) bottom plants (%) shadow

Localities before after before after before after before after before after dredging dredging dredging dredging dredging dredging dredging dredging dredging dredging

K1/1 gravel, gravel, V 0.5 0.46 – 0.51 0.7 0.7 stones stones 0 0 lack lack K1 IV sand, silt, sand, silt,

K1/2 0.01 0.002 – 0.02 0.5 0.5 70 50 –70 partly partly ue ersion I

mud mud s s

sand, I D1/1 0.013 0.09 – 0.16 0.4 0.5 mud gravel 30 0 –10 lack lack

D1 XVI silt, sand, silt, D1/2 0.01 0.002 – 0.01 0.2 0.2 mud mud 90 0 – 40 partly lack

silt, D2/1 0.02 0.01– 0.05 0.2 0.5 mud silt, mud 90 0 –10 partly lack D2

D2/2 0.002 0.001– 0.002 0.1 0.2 mud mud 100 0 – 40 partly lack ) H ( D3/1 0.02 0.02 – 0.05 0.3 0.5 silt, mud sand, silt, 20 0 –10 partly lack mud D3 D3/2 0.002 0.001– 0.002 0.1 0.2 mud sand, silt, 80 0 – 40 partly partly

mud Research Volume

K2/1 0.14 0.09 – 0.2 0.5 0.5 sand, sand, 0 0 partly partly gravel gravel K2 sand, sand, K2/2 0.003 0.001– 0.003 0.2 0.2 70 30 –70 partly partly Frontier mud mud D4/1 0.001 0.001– 0.003 0.5 0.7 mud mud 40 0 – 40 partly lack D4 Science D4/2 0.04 0.03 – 0.06 0.5 1.0 mud mud 30 0 – 20 lack lack of Six sampling stations were established on a Additional mollusc samples were collected from segment of the river about 3 km long (Fig. 1). Two fish ponds (four stations) and from a small limnocrene stations were situated at undredged locations (control spring. Journal stations) – K1 upstream from the dredged segment and K2 near the bridge, and the remaining stations were at III. Methods Global dredged sites – P1, P2, P3 and P4. The investigation of molluscs in the river Krąpiel At each station, samples were taken from the was carried out in July 2008, before the dredging, and lentic (stagnant) and the lotic (drift) zone (Table 1). The from April to August 2009, after the dredging. One series former included shallow stretches, in some places of samples was collected from the sampling stations strongly overgrown with plants, and the river bottom before the dredging (total 12 samples), and after the contained a layer of deoxygenated sediment whose dredging material was collected 5 times in successive surface was covered with detritus. The latter included months (total 60 samples). Additionally, in July 5 stretches devoid of vegetation, with higher proportions samples were collected in fish ponds and 2 samples in of sand and gravel in the sediments. limnocren lying near the river.

The samples were taken using a hand dredge (ellipses in Fig. 5) using Van Dobben circles (Lepš & with 50 μm mesh net from a 1 m2 area marked by a Šmilauer 2003). metal square frame. To assess the impact of dredging on the The molluscs collected were preserved in 75 % mollusc community we used ‘before-after-control- alcohol. Species identification was carried out by impact’ (BACI) analysis, which makes it possible to Professor A. Piechocki. The specimens are kept in the compare data obtained in the control stations with data collection of the Department of Invertebrate Zoology and obtained in the impacted stations before and after the Hydrobiology, University of Lodz. intervention, i.e. in July 2008 and July 2009. There are a) Data analysis two aspects to be tested: BA – before and after – and The dominance and constancy of mollusc CI – control and impact site. BACI is the test for the BA species were classified according to Strzelec (1993). × CI interaction (Smith et al. 1993). The impact of The dominance categories were as follows: D– dredging was tested in two ways: (1) by testing 201 dominant species, constituting at least 5 % of the total Mollusca abundance, expressed as the number of r number of specimens, and d – rare species, constituting individuals at a given sampling station, with each ea

Y less than 5 %. The constancy categories were C – species analysed separately, and (2) by the Shannon- constant species, present in at least 50 % of sampling Wiener Index (Magurran 2004), with the Mollusca 4 stations, and c – accessory species, with frequency of biodiversity of each station analysed separately. When less than 50 % of the stations. the abundance of Mollusca was used as the dependent The mollusc species identified were divided into variable, BACI was tested using a generalized mixed three ecological groups according to the classification model (GLMM) with a log link and a negative binomial

V distribution. This should be used when the dependent by Ložek (1964): rheophilic species, associated with

IV variable shows high variation. We considered species a flowing water (R), species typical of lakes, ponds, and random effect (intercept) with scaled identity covariance. ue ersion I slow-flowing rivers (L), and species typical of small s

s In the second analysis, the Shannon-Wiener Index was temporary or overgrown pools (S).

I treated as a dependent variable and BACI was tested by The occurrence of molluscs during the period

XVI factorial ANOVA. following the dredging was analysed with respect to the following environmental factors: water flow velocity IV. Results (FLOW), plant cover (PLANT), dredging impact (DREDGING) and substrate composition (SAND, SILT, a) Composition of fauna and community structure During the entire study period a total of 1,034 ) MUD/DETRITUS). Flow velocity was measured with a

H live individuals belonging to 36 mollusc species were ( FlowTracker Acoustic Doppler Velocimeter. Vegetation cover was estimated visually by the phytosociological collected, of which 188 individuals belonging to 18 method developed by Braun-Blanquet (1964). Estimated species were collected in the river before the dredging, value of the percentage coverage arranged in bands 485 individuals belonging to 30 species were collected Research Volume including the lowest value coverage during the spring in the river after the dredging, 314 individuals belonging months to the highest in the summer months (Table 1). to 12 species were collected in the fish ponds, and 37 Bottom sediment components (stones, gravel, sand, silt individuals representing 7 species were collected from the limnocrene spring (Table 2). Frontier and mud) were assessed by allocating a numerical value to each component, where the sum of the values Taking into account only the samples from the always equaled 5, and the points allotted to respective river, 17 mollusc species were present in the samples before and after the dredging. The only species Science components reflected their shares in the sediment volume. The shaded area shows the presence of shrubs recorded in 2008 but not found later was Anodonta of or trees on the bank of the river, which for part of the day anatina. This bivalve was present in the fish ponds. As shadowing (partly) locality, or their absence (lack) (Table many as 12 taxa were noted for the first time following 1). Dredging impact was scored between 5 and 1; it was dredging of the river. It is worth noting that their Journal highest in April 2009 and lowest in August 2009. abundance was relatively low. Among the species found Substrate composition was visually estimated as the only after the dredging, only Gyraulus laevis and Physa fontinalis inhabited the fish ponds near the river, while Global proportions of fine and coarse sediment and organic matter. the genus Stagnicola (unidentified juvenile individuals) We used the DCA multivariate ordination was also found in the nearby spring. The only species method (Hill & Gauch 1980; ter Braak & Prentice 1988) that was found only in the fish ponds was Unio pictorum, to assess the range of the environmental gradient. while Valvata cristata, Bathomphalus contortus and Having verified by DCA that the environmental gradient Hippeutis complanatus were present only in the spring. covered was sufficiently long, we used CCA (ter Braak Taking into account all of the material collected, 1986; ter Braak & Verdonschot 1995) for community Anisus vortex, Bithynia tentaculata, Lymnea stagnalis, ordination of mollusc assemblages in relation to Planorbis planorbis and Radix balthica were included environmental variables. The species were grouped among the dominant and constant species (DC) (Table

2). The dominant but accessory species (Dc) were The dominance structure of the malacofauna Galba truncatula, Gyraulus laevis and Unio crassus. The community changed after the dredging. The presence of remaining species were rare and accessory (dc). Before 5 dominant and constant species was observed (DC: the dredging, two dominant and constant species were Bithynia tentaculata, Galba truncatula, Lymnaea noted in the malacofauna of the river (DC: Lymnaea stagnalis, Pisidium amnicum and Sphaerium corneum), stagnalis and Planorbis planorbis), and 5 dominant but and three dominant accessory species (Dc: Anisus accessory species (Dc: Anisus vortex, Galba truncatula, vortex, Unio crassus and Unio tumidus). Additionally, Planorbarius corneus, Radix balthica and Sphaerium Viviparus viviparus was a rare but constant species (dC). corneum).

Table 2: Molluscs overall abundance (ab), dominance (D) and frequency (C) in sampled sites; Ecol – habitat

preferences: R – fast flowing waters, L – stagnant and slowly flowing waters, S – ephemeral water bodies; * – only

empty shells found 201

river before river after r fish ponds limnocren ea Abbrev. dragging dragging No. Species ab D C Y of name ab D C ab D C ab D C ab D ECOL 51 1 Theodoxus fluviatilis (Linnaeus 1758) The flu 21 2.0 0.2 7 3.7 0. 2 14 2.8 0.3 R 2 Viviparus viviparus (Linnaeus 1758) Viv viv 29 2.8 0.4 7 3.7 0.3 22 4.4 0.7 R

Viviparus sp., juv. – 7 0.7 0.1 4 2.1 0.1 3 0.6 0.2

3 Potamopyrgus antipodarum (J. E. Gray 1843) Pot ant 10 1.0 0.2 10 2.0 0.3 R 4 Bithynia leachii (Sheppard 1823) – 1 0.1 0.1 1 0.2 0.1 S 5 Bithynia tentaculata (Linnaeus 1758) Bit ten 60 5.8 0.5 20 10.6 0.3 34 6.9 0.6 6 1.9 0.4 L V

6 Valvata cristata O. F. Müller 1774 – 3 0.3 0.1 0 3 8.1 S IV 7 Galba truncatula (O. F. Müller 1774) Gal tru 89 8.6 0.3 13 6.9 0.4 76 15.4 0.5 0 S

ue ersion I s

8 Lymnaea stagnalis (Linnaeus 1758) Lym sta 100 9.7 0.7 28 14.9 0.7 43 8.7 0.7 29 9.2 0.8 L s

9 Radix ampla (W. Hartmann 1821) – 4 0.4 0.1 2 1.1 0.1 2 0.4 0.1 R I 10 Radix auricularia (Linnaeus 1758) – 4 0.4 0.1 4 0.8 0.1 L XVI 11 Radix balthica (Linnaeus 1758) Rad bal 109 10.5 0.5 12 6.4 0.3 13 2.6 0.3 84 26.8 0.8 L Radix sp., juv. – 19 1.8 0.1 19 3.8 0.2 12 Stagnicola sp., juv. – 5 0.5 0.2 2 0.4 0.2 3 8.1 S 13 Planorbis carinatus O. F. Müller 1774 – 1 0.1 0.1 1 0.2 0.1 L

14 Planorbis planorbis (Linnaeus 1758) Pla pla 52 5.0 0.6 19 10.1 0.5 11 2.2 0.3 4 1.3 0.6 18 48.6 S )

15 Anisus vortex (Linnaeus 1758) Ani vor 167 16.2 0.6 19 10.1 0.2 53 10.7 0.4 95 30.3 0.8 L H ( 16 Bathyomphalus contortus (Linnaeus 1758) – 4 0.4 0.1 0 4 10.8 L 17 Planorbarius corneus (Linnaeus 1758) Pla cor 30 2.9 0.4 15 8.0 0.3 4 0.8 0.2 6 1.9 0.6 5 13.5 L 18 Gyraulus albus (O. F. Müller 1774) Gyr alb 13 1.3 0.3 5 2.7 0.2 7 1.4 0.3 1 0.3 0.2 L 19 Gyraulus crista (Linnaeus 1758) – 2 0.2 0.1 1 0.5 0.1 1 0.2 0.1 L Research Volume 20 Gyraulus laevis (Alder 1838) – 73 7.1 0.2 1 0.2 0.1 72 22.9 0.6 L 21 Hippeutis complanatus (Linnaeus 1758) – 3 0.3 0.1 0 0 3 8.1 L 22 Segmentina nitida (O. F. Müller 1774) – 2 0.2 0.1 1 0.5 0.1 1 0.2 0.1 S

23 Ancylus fluviatilis O. F. Müller 1774 – 1* 0 R Frontier 24 Physa fontinalis (Linnaeus 1758) – 3 0.3 0.2 2 0.4 0.2 1 0.3 0.2 L 25 Aplexa hypnorum (Linnaeus 1758) – 1 0.1 0.1 0 1 2.7 S 26 Anodonta anatina (Linnaeus 1758) – 8 0.8 0.3 2 1.1 0.1 0 6 1.9 0.8 R Science 27 Unio crassus Philipsson 1788 Uni cra 54 5.2 0.2 8 4.3 0.2 46 9.3 0.3 R

28 Unio pictorum (Linnaeus 1758) – 5 0.5 0.1 0 5 1.6 0.4 R of 29 Unio tumidus Philipsson 1788 Uni tum 35 3.4 0.3 5 2.7 0.1 25 5.1 0.3 5 1.6 0.4 R 30 Sphaerium corneum (Linnaeus 1758) Sph cor 48 4.6 0.4 17 9.0 0.2 31 6.3 0.6 L

31 Pisidium amnicum (O. F. Müller 1774) Pis amn 35 3.4 0.4 3 1.6 0.3 32 6.5 0.6 R Journal 32 Pisidium casertanum casertanum (Poli 1791) – 2 0.2 0.1 2 0.4 0.2 L 33 Pisidium henslowanum (Sheppard 1823) – 3 0.3 0.2 3 0.6 0.3 L 34 Pisidium casertanum ponderosum (Stelfox 1918) Pis pon 9 0.9 0.2 9 1.8 0.3 L Global 35 Pisidium subtruncatum Malm 1855 Pis sub 10 1.0 0.2 10 2.0 0.3 R 36 Pisidium supinum A. Schmidt 1851 Pis sup 13 1.3 0.3 13 2.6 0.4 R TOTAL 1034 188 495 314 37 b) Changes over time in the number of species was noted in May (to 16), then Before the dredging the presence of 18 mollusc in June the species richness remained at a similar level, species was noted in the river. Immediately following and the samples from July and August contained 20 completion of the work (in April), 10 species were noted species of molluscs. The number of individuals noted in in this same segment of the river. A substantial increase the samples increased continually from April to August.

T he Shannon-Wiener biodiversity index remained at a predominance of molluscs belonging to the first group similar level throughout the study period; seasonal (rheophiles) in the quantitative structure (68 %). The differences were not statistically significant. species whose dominance indices decreased the most Taking into account the habitat preferences of after the intervention are associated with standing or the species (R, L and S), we can observe pronounced slow-flowing water – Planorbis planorbis and changes in the fauna after the dredging (Fig. 2). Before Planorbarius corneus. In contrast, there was an increase the dredging the mollusc fauna of the Krąpiel consisted in the percentage of rheophilic bivalves in the mainly of rheophilic species and species characteristic community – Unio crassus, Unio tumidus and Pisidium of slow-flowing rivers (39 % and 44 %, respectively), amnicum. The number of species preferring standing while species associated with temporary or overgrown and slow-flowing water (L) continually increased after water bodies accounted for 17 %. Species typical of the dredging. In the quantitative structure, such slow-flowing rivers predominated in the quantitative molluscs were more abundant than rheophiles (R) from

201 structure (63 %). June to August. In August their share reached 63 %, as

r Immediately following the dredging only species before the intervention.

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Fig. 3: Changes in total number of specimens of selected gastropod and bivalve species after dredging

Frontier After the dredging, from 1 to 4 species the abundance of species preferring standing and slow- associated with small temporary and overgrown pools flowing water, such as Anisus vortex, Lymnaea stagnalis,

(S) were recorded in the river segment investigated. Sphaerium corneum and Unio tumidus. In contrast, the

These were not detected immediately after the changes in the abundance of typically rheophilic Science

intervention. Their presence was noted from May, and molluscs (V. viviparus, Unio crassus and P. amnicum) of they reached their greatest share of the quantitative showed no constant tendencies (Fig. 3). structure in June (50 %). This was linked to the increase

in abundance of Galba truncatula – this snail was Journal present in very high numbers in single samples from

June and July, but was not detected in August (Fig. 3).

The direction of the changes in the composition Global of the fauna was similar at the dredged and undredged sampling stations. The greatest differences were noted during the period immediately following the intervention (April-May), when the dredged sampling stations had a higher percentage of rheophilic species than the control stations (Fig. 2). The increase in the total number of molluscs in the period from May to August is linked to changes in

A 8 Control B 2 Control

7 Impact Impact 6 5 ener Index 4 i 1

3 2

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0 Shannon–W 0 Before Aft er 201 Before After r

ea Fig. 4: BACI analysis. A – Mean abundance of molluscs ± 1 SD; the interaction is not significant (p = 0.919); B – Y Shannon-Wiener Index of molluscs diversity; the interaction was not significant (p > 0.05)

8 Table 3: BACI analysis of impact of dredging on 11.1 % of the variation in the mollusc species Mollusca abundance. The tests of effects of GLMM composition, and the second 7.6 %. model The results of the CCA for the samples collected from the Krąpiel following the dredging show V Source F-statistics df1 df2 Significance that the variables used in the ordination explain 20 % of IV Corrected Model 5.634 3 128 0.001 the total variance of the mollusc species (Table 5).

ue ersion I Before-after 8.544 1 128 0.004 The results of the stepwise selection of s s Control-impact 7.596 1 128 0.007 environmental variables showed that of all the I BA × CI 0.010 1 128 0.919 environmental parameters considered only the degree XVI of bottom overgrowth by macrophytic vegetation Table 4: BACI analysis of impact of dredging on (PLANTS), dredging, silt content in bottom sediments Mollusca biodiversity expressed by Shannon-Wiener significantly statistically explained (p ≤ 0.05) the range Index. The tests of effects of factorial ANOVA of variance of occurrence of species, being responsible

) respectively for 7.8 – 4.6, 3 % of the variance (Table 6). Source Sum of df Mean F-statistic p H ( squares square The ordination diagram illustrating results of the

19.421 1 19.421 47.160 0.000 CCA shows that the first group of species is the most

Before-after 0.004 1 0.004 0.009 0.927 strongly positively correlated with the plants coverage Control-impact 0.154 1 0.154 0.373 0.548 and negatively correlated with flow, and the third group Research Volume BA × CI 0.005 1 0.005 0.012 0.914 is the most strongly negatively correlated with plants Error 8.236 20 8.236 and positively correlated with flow, but flow is not

statistically significant. The second group of species is

Frontier The BACI analysis showed no effect of the the most strongly positively correlated with dredging intervention on the total abundance and diversity of the process (Fig. 5). It consists of species that attained their molluscs (Tables 3 – 4, Fig. 4ab). Abundance greatest abundance in the first period following the decreased both at the dredged and control stations, Science dredging. These are mainly mollusks preferring a and the Shannon diversity index remained at the same

of substrate devoid of macrophytes and a stronger current. level. This group of molluscs gains a favourable habitat when

c) Ecological preferences of molluscs work that deepens the river bed and removes vegetation

Journal The DCA for the mollusc species after the is carried out, and many of the species found here were

intervention showed that the length of the gradient not noted before the intervention (P. subtruncatum, P.

represented by the first ordination axis is 5.426, which supinum and P. ponderosum), or were less abundant

Global means that the species covered a full Gaussian (e.g. U. tumidus, Pisidium amnicum, and V. viviparus).

spectrum. This in turn made it possible to conduct direct ordination analyses (CCA) to determine the relationships

between the occurrence of species and the

environmental parameters tested in the Krąpiel. The

eigenvalues of the axes show that the gradient

represented by the first ordination axis substantially

differentiates the occurrence of species (0.871), as its

eigenvalue is greater than 0.5. The first axis explains

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Table 5: Summary of CCA analysis between molluscs and environmental variables

Axes 1 2 3 4 Total inertia

)

Eigenvalues: 0.626 0.440 0.210 0.193 7.858 H ( Species-environment correlations: 0.907 0.837 0.701 0.661 Cumulative percentage variance

of species data: 8.0 13.6 16.2 18.7 of species-environment relation: 40.1 68.2 81.7 94.0 Research Volume Sum of all eigenvalues 7.858 Sum of all canonical eigenvalues 1.563

Table 6: Result of forward selection of environmental probably due to unfavourable oxygen conditions and a Frontier variables, using 499 permutations thick layer of deoxygenated mud on the bottom. A study of molluscs in a near-shore zone of the Włocławek Dam Conditional effects reservoir showed that on the muddy substrate greater Science Variable P F LambdaA of mollusc species richness was associated with a plants 0.61 0.002 4.20 stronger current, which probably meant better dredging 0.36 0.002 2.58 oxygenation of the water (Żbikowski et al. 2007). Richer silt 0.24 0.020 1.74 malacofauna has usually been noted in lowland rivers Journal flow 0.18 0.210 1.30 with a natural river bed, e.g. 38 species in the river sand 0.17 0.206 1.26 Krutynia (Jakubik & Lewandowski 2011) and 40 species Global in the river Wkra (Lewin 2014). The samples collected in V. Discussion the Krąpiel after the dredging contained 31 mollusc species. Because Krapiel is a small river, and studies

During the period before the dredging the have been conducted over a very short distance, mollusc fauna of the river Krąpiel was relatively poor in species richness is considered as high. The increase in qualitative terms (19 species; 2 –7 per sampling station) the number of species recorded may have resulted from and quantitatively very poor (average mollusc density of an improvement in living conditions in the river and the – 2 17 ind. m ). Species typical of standing water and restoration of the fauna (see below), but also from the severely eutrophic water were dominant. This was fact that more samples were taken during this period.

Despite the small number of species, the directly subject to the effect of the dredging. According

malacofauna of the Krapiel is interesting. It includes a to Diaz (1994), the effect of higher water turbidity could population of the mussel Unio crassus, typical of clean range from minor irritation or death for non-motile forms water (Zaj ąc 2004), which is protected in the EU (Annex unable to escape, to benefits for motile forms that enter II and IV of the EU Habitats and Species Directive). Unio the turbid water in search for food or protection. Layzer crassus was found both before and after the dredging of et al. (1993) pointed out that silt deposition associated the river. Sampling stations K1 and K2, where this with turbidity caused by dredging was a major factor in mussel was recorded in July 2008, were not dredged. the decline of mussels in regulated rivers, with juveniles After the intervention Unio crassus colonized the same being most heavily affected. It seems that filter feeding two places, and single individuals were also collected at snail species such as B. tentaculata also suffered from stations P1 and P3. The highest populati on density the increase in water turbidity (Jokinen 1992). Finally, –2 reached was 8 ind. m . It appears that the occurrence channel management may result in a reduction in the

201 of this species can be linked primarily to the increased number of potential host fish to which the mussels’ –1 r current in certain segments of the river (0.1– 0.5 m s ). glochidia must attach to fulfil their life cycle, thus ea

Y By comparison, in the river Wkra Unio crassus was disrupting the recruitment in the mussel population –1 recorded where the current was 0.06 – 0.6 m s , and its (Aldridge 2000). 10 density reached a maximum 20 ind. m– 2 (Le win 2014). VII. Recovery

VI. Disturbance In the river investigated, after a pronounced Management of the river with the dredge decrease in the occurrence of molluscs immediately

V affected the mollusc community in many ways. Molluscs following the dredging, a gradual regeneration of the

IV were physically eliminated from the dredged sites, which malacofauna was observed. In the summer (July– reduced overall abundance and species richness August 2009) the mollusc community in the investigated ue ersion I s s (samples from April 2009 included only 10 species; segment of the Krąpiel did not differ significantly (BACI – 2 I mean mollusc density was 5.5 ind. m ). The removal of analysis) in terms of species richness and abundance

XVI the vegetation and deepening of the river bed caused from its pre-dredging state (July 2008). The percentages an increase in the speed of the current, which in turn led of rheophilic species and species associated with to an increase in the surface area of habitats preferred standing water also returned to their prior state. The by rheophilic species such as Viviparus viviparus, Unio regeneration process of the malacofauna of this tumidus and Pisidium amnicum. This was accompanied segment of the Krąpiel lasted about 7 months. )

H by a marked decrease in the share of stagnophilic The process of regeneration of benthic ( species in the community, as their microhabitats were communities following various types of disturbances destroyed. (such as dredging, dredged spoil disposal or severe Monahan & Caffrey (1996) showed that benthic floods) has been described for flowing water bodies

Research Volume invertebrates are negatively affected by removal of (Yount & Niemi 1990, Mundahl & Hunt 2011) as well as plants from the channel even if the bottom sediment has for estuaries and tidal areas (Diaz 1994). It has been not been removed. Aquatic plants provide shelter from observed that in the case of hydraulic engineering work disturbances and predators, as well as a large surface the regeneration time for the benthos depends on the Frontier area for epiphytic algae, which molluscs utilize as a method of dredging and removal of macrophytic source of food, and sites for deposition of eggs. vegetation (Darby & Thorne 1995, Monahan & Caffrey A dramatic loss in the mussel population after 1996, Aldridge 2000). According to McCabe et al. Science dredging of a navigable waterway in England (up to 23 (1998), the negative effect of dredging on invertebrates of % of the unionid population found in the spoil on the was found to be apparent only immediately afterwards, river bank) was reported by Aldridge (2000). whereas a year later the same species of The high concentration of suspended sediment macroinvertebrates were common in the dredged area

Journal mobilized by dredging can alter the survival, growth and as well as in the reference area, and the total benthic invertebrate densities and biodiversity indices did not behaviour of stream biota. Increased water turbidity may have hindered respiration and food collection by differ, indicating that the dredging did not have a Global molluscs (especially suspension feeders) (Gulati et al. statistically significant effect on these parameters.

2008). This factor influenced both dredged sites and the The most invasive dredging methods (removal

undredged sites downstream. The undredged sampling of macrophytes and a thick layer of bottom sediment in

station K2, situated between two dredged segments, the entire river bed) cause the river bottom to be

can be presumed to have been affected by the changes essentially devoid of macroinvertebrates, but many

caused by the intervention, including the temporary macroinvertebrates may recover relatively quickly owing

increase in the flow rate and the amount of suspended to their motility, which enables them to escape during

sediment in the water. Only sampling site K1 was the management regime and to recolonize afterwards situated upstream from the intervention and was not (Aldridge 2000). Recolonization of the river bottom takes

©2016 Global Journals Inc. (US) Regeneration of the Molluscan Fauna of a Small Lowland River after Dredging place in many ways, including via migration from The final potential means of colonization of a undredged segments of the river upstream and dredged river segment by molluscs is passive dispersal, downstream from the site of the dredging (Williams & which not only functions on a local scale, but also Hynes 1976). affects the spread of invasive species over a large area. According to Williams & Hynes (1976), most Molluscs are transported with fish (the parasitic colonizing macroinvertebrates come from drift (over 40 Unionidae larva – the glochidium) and by ships. %). This kind of passive dispersal occurs in mollusks Transport can even take place outside of the aquatic and is not limited to their larval stages (only a few environment. It cannot be ruled out that aerial dispersal freshwater molluscs have free living larvae, e.g. from other streams nearby played a role in the Dreissena polymorpha), but also affects juvenile and colonization of the Krąpiel by molluscs. In the direct adult individuals (Kappes & Haase 2012). Natural active vicinity of the river there are fish ponds inhabited by upstream movement is slow in molluscs. It is estimated molluscs. The literature contains reports of the spread of at 0.3 –1.0 km year–1 for most snails and below 0.1 km small bivalves and gastropods by birds, mammals and 201 year–1 for bivalves (Kappes & Haase 2012). aquatic insects. This usually involves shells attaching r ea

Observations by Aldridge (2000) indicate slow themselves to feathers or insect limbs, but cases have Y recolonization of dredged areas by adult mussels. The also been confirmed of molluscs being carried in the 111 author reports that the fastest migration noted for digestive tracts of other animals (Piechocki 1979; –1 European Unionidae species is ca. 5 m day , although Kappes & Haase 2012 and literature cited within). this is a response to environmental stress factors, such The rapidity of the regeneration of the as high temperature or low dissolved oxygen. Similar malacofauna observed in the investigated segment of results were obtained by Zając & Zając (2011), who the Krąpiel is within the time range reported in the V showed that adult specimens of U. crassus literature for flowing water affected by severe IV experimentally distributed in fast-flowing parts of the disturbances. For example, Mundahl & Hunt (2011)

ue ersion I s river channel moved shorter distances than mussels showed that taxa richness and community structure s distributed in slow deep parts, which try to move actively returned to pre -flood levels at most sites within a year. I toward more preferable environmental conditions (the These researchers observed that the recovery of XVI maximum distance recorded was 5.15 m). invertebrates (excluding flying insects) depends largely Another potential means of recolonization of the on their ability to survive the disturbance and on how river is movement of organisms up from within the quickly they reproduce. Thus, densities of some substrate. This direction is important, as the hyporheos invertebrate groups recovered within months of the flood ) has been shown to consist of immature stages of many Baetidae mayflies, Chironimidae midges, Simulidae H ( invertebrates, including unionids, which after leaving fish blackflies), while others (Ephemerellidae mayflies, live for 2 – 5 years buried in the bottom sediment Hydropsychidae caddisflies and Gammarus (Piechocki & Dyduch-Falniowska 1993). Many adult amphipods), required more than 2 years (Mundahl & molluscs (e.g. Viviparus, Theodoxus or Bithynia) also Hunt 2011 and literature cited within). Research Volume spend the winter in the sediment of deeper zones of Yount & Niemi (1990) enumerated several rivers and lakes (Piechocki 1979; Jakubik 2012). If the reasons for short recovery times of river communities: dredging of the river is carried out in the winter (as in the life history traits enabling rapid repopulation, Frontier river we investigated), these seasonal migrations of accessibility of unaffected upstream and downstream snails deep into the sediment may be conducive to the areas serving as sources of organisms, or high flushing survival of some individuals in situ, which is highly rates of lotic systems that allowed them to quickly dilute Science significant for the restoration of the population. or replace waters. According to the authors cited, the The composition and abundance of the fauna river biota possesses adaptations enabling it to survive of that remains in the river (remnant species) depends on disturbances such as natural floods. Similarly, fauna of the dredging method used (equipment and duration of other aquatic habitats exposed to regular natural the work). This is of fundamental importance in the disturbances, such as tidal freshwater, exhibits eurytopic Journal process of regeneration of the benthic community. tolerance and may recover within three weeks after a According to Ledger et al. (2006), remnant species disturbance caused by dredged spoil disposal (Diaz Global potentially facilitate or inhibit settlement of other 1994). The rapidity of fauna regeneration is affected not invertebrates or algae. The effect of remnant species on only by the fertility of individual species, but also by a immigrant colonization echoes differences in their life- certain elasticity in their reproductive strategy. In history traits and foraging behaviour. For example, the Viviparus viviparus, for example, earlier reproduction authors cited experimentally showed that Radix scraping (smaller females) was observed in individuals living in epilithon promoted settlement of filter feeders and more unstable habitats (Jakubik 2012). invertebrate predators, and strongly deterred settlement Our results and the review of the literature of nonpredatory chironomids. presented above indicate that in flowing water bodies

communities of the benthos, including molluscs, have the State of New York, The State Education the ability to regenerate quickly following disturbances, Department, The New York State Museum, Albany, both natural and of human origin. Although it was New York, pp. 1–122. evident that molluscs were adversely impacted 12. Magurran, A. E., 2004: Measuring Biological immediately after the dredging, our study indicated that Diversity. –Blackwell Publishing, Malden. after a year the malacofauna of the Krąpiel recovered to 13 . McCabe, G., Hinton, S. A. & Emmett, R. L., 1998: its premanagement state or was even enriched. The Benthic Invertebrates and Sediment Characteristics removal of a layer of deoxygenated sediments from the in a Shallow Navigation Channel of the Lower channel provided an opportunity for the establishment of Columbia River, Before and After Dredging.– more diverse and abundant mollusc assemblages. Northwest Science 72: 116 –125.

VIII. Ackowledgements 14. Monahan, C. & Caffrey, J. M., 1996: The effect of weed control practices on macroinvertebrate 201 This research was supported by the Ministry of communities in Irish Canals.–Hydrobiologia 340: r Science and Higher Education (contract no. N 305 ea 205 – 211. Y 222537). The authors thank Professor Andrzej Piechocki 15. Mundahl, N. D. & Hunt, A. M., 2011: Recovery of

12 for the identification of the species. stream invertebrates after catastrophic flooding in southeastern Minnesota, USA. – J. Freshw. Ecol. 26: References Références Referencias 445 – 457. 1. Aldridge, D. C., 2000: The impacts of dredging and 16 . Ledger, M. L., Harris, R. M. L., Milner, A. M. &

weed cutting on a population of freshwater mussels Armitage, P. D., 2006: Disturbance, biological V (Bivalvia: Unionidae).– Biol. Conserv. 95: 247– 257. legacies and community development in stream IV 2. Braun-Blanquet, J., 1964: Pflanzensoziologie: mesocosms. – Oecologia 148:682 – 691.

ue ersion I

s Grundzüge der Vegetationskunde. Zweite, 17. Lepori, F. & Hjerdt, N., 2006: Disturbance and s umgearbeitete und vermehrte Auflage. – Springer- Aquatic Biodiversity: Reconciling Contrasting Views. I Verlag, Wien. – BioScience 56:809 – 818. XVI 3. Darby, S. E. & Thorne, C. R., 1995: Fluvial 18. Lepš, J. & Šmilauer, P., 2003: Multivariate analysis maintenance operations in managed alluvial rivers. of ecological data using CANOCO. – Cambridge – Aquat. Conserv. 5: 37– 54. University Press, New York, pp. 1– 289.

4. Dąbkowski, P., Buczyński, P., Stępień, E., 19. Lewin, I., 2014: Mollusc communities of lowland ) Buczy ńska, E., Stryjecki, R., Czachorowski, S., H rivers and oxbow lakes in agricultural areas with ( Śmietana, P., Szenejko, M. & Zawal, A., 2016: The anthropogenically elevated nutrient concentration. – impact of dredging of a small lowland river on water Folia Malacologica 22:87–159. beetle fauna (Coleoptera). – J. Limnol. (in press). 20. Ložek, V., 1964: Die Quartärmollusken der 5. Diaz, R. J., 1994: Response of tidal freshwater

Research Volume Tschechoslovakei.– Geologische Zentralanstalt, macrobenthos to sediment disturbance. – Tschechoslovakische Akademie der Wissensch Hydrobiologia 78: 201– 212. aften, Praha, pp. 1– 375. 6. Gulati, R. D., Pires, L. M. D. & Van Donk, E., 2008:

Frontier Lake restoration studies: Failures, bottlenecks and 21. Piechocki, A., 1979: Mięczaki (Mollusca). Ślimaki prospects o new ecotechnological measures. – (Gastropoda). Fauna Słodkowodna Polski. Zeszyt 7.

Limnologica 38: 233 – 247. – PWN, Warszawa, Poznań. 22. Piechocki, A. & Dyduch-Falniowska, A., 1993: Science 7. Hill, M. O. & Gauch, H. G., 1980: Detrended Mięczaki (Mollusca). Małże (Bivalvia). Fauna

of correspondence analysis: an improved ordination technique. – Vegetatio 42:47– 58. Słodkowodna Polski 7 A.–Wydawnictwo Naukowe 8. Kappes, H. & Haase, P., 2012: Slow, but steady: PWN, Warszawa. 23. Płaska, W., Kurzątkowska, A., Stępień, E., Journal dispersal of freshwater molluscs. – Aquat. Sci. 74: 1–14. Buczyńska, E., Szlauer-Łukaszewska, A.,

9. Jakubik, B., 2012: Life strategies of Viviparidae Pakulnicka, J. & Zawal, A., 2016: The effect of

Global (Gastropoda: Caenogastropoda: Architaenioglossa) dredging of a small lowland river (Krąpiel – NW

in various aquatic habitats: Viviparus viviparous Poland) on aquatic Heteroptera. – Ann. Zool. Fenn. (in press). (Linnaeus, 1758) and V. contectus (Millet, 1813). –

Folia Malacologica 20: 145 –179. 24. Rauers, H., van de Weyer, K. & Pardey, A., 2004:

10. Jakubik, B. & Lewandowski, K., 2011: Molluscs of Biozönologische Untersuchungen zur Auswirkung

the Krutynia River (Masurian Lakeland). – Folia von Unterhaltungsmaßnahmen auf Flora und Fauna

Malacologica 19:19 – 29. von Gräben, dargestellt am Beispiel zweier

11. Jokinen, E., 1992: The freshwater snails (Mollusca: Grabensysteme in NRW. – Deutsche Gesellschaft

Gastropoda) of New York State. – The University of für Limnologie, Tagungsbericht Köln. pp. 140 –144.

25. Smith, E. P., Orvos, D. R. & Cairns, J. Jr, 1993: Myśliwy, M., Stryjecki, R., Buczyńska, E. & Impact assessment using the before-after-control- Buczyński, P., 2015: The influence of a lowland river impact (BACI) model : concerns and comments. – dredging (the Krąpiel in NW Poland) on water mite Can. J. Fish. Aquat. Sci. 50:627 – 637. fauna (Acari: Hydrachnidia). – Fundam. Appl. 26. Stępień, E., Zawal, A., Buczyński, P. & Buczyńska, Limnol. 186: 217 – 232. DOI: 10.1127/fal/2015/0735 E., 2015: Changes in the vegetation of the valley of 40. Zawal, A., Czachorowski, S., Stępień, E., Buczyńska, a small river (the Krąpiel in NW Poland) in the E., Szlauer-Łukaszewska, A., Buczyński, P., lowland river dredging.–Acta Biologica 22 (in press). Stryjecki, R. & Dąbkowski , P., 2015: Early post- 27. Stępień, E., Zawal, A., Buczyńska, E., Buczyński, P. dredging recolonization of caddisflies (Insecta: & Szenejko, M., 2016: Effects of dredging on the Trichoptera) in a small lowland river (NW Poland). – vegetation in a small lowland river. – Limnetica (in Limnology 17: 71 – 85. DOI: 10.1007/s10201-015- press). 0466-3 28. Strzelec, M., 1993: Ślimaki (Gastropoda) 201 antropogenicznych środowisk wodnych Wyżyny r ea

śląskiej. – Prace naukowe Uniwersytetu Śląskiego w Y Katowicach 1358, pp. 1–103. 131 29. Szlauer-Łukaszewska, A. & Zawal, A., 2014: The impact of river dredging on ostracod assemblages in the Krąpiel River (NW Poland). – Fundam. Appl. Limnol. 185: 295 – 305. DOI: 10.1127/fal/2014/0620

30. ter Braak, C. J. F., 1986: Canonical correspondence V

analysis: a new eigenvector technique for IV multivariate direct gradient analysis. – Ecology 67: ue ersion I s 1167–1179. s 31. ter Braak, C. J. F. & Prentice, I. C., 1988: A theory of I

gradient analysis. – Adv. Ecol. Res. 18, 271– 317. XVI 32. ter Braak, C. J. F. & Verdonschot, P. F. M., 1995: Canonical correspondence analysis and related multivariate methods in aquatic ecology. – Aquat.

Sci. 57, 255 – 289. )

33. Wilber, D. H. & Clark, D. G., 2007: Defining and H ( assessing benthic recovery following dredging and dredged material disposal. – Proceedings XXVII World Dredging Congress 2007, Orlando, Florida,

pp. 603 – 618. Research Volume 34. Williams, D. D. & Hynes, H. B. N., 1976: The recolonization mechanisms of stream benthos. – Oikos 27: 265 – 272. Frontier 35. Yount, J. D. & Niemi, G. J., 1990: Recovery of Lotic Communities and ecosystems from Disturbance – A Narrative Review of Case Studies. – Environmental Science Managements 14: 547– 569. 36. Zając, K., 2004: Unio crassus Philipsson, 1788. – In: of Głowaciński, Z. & Nowacki, J. (eds): Polish Red Data Book of Animals. Invertebrates. – Institute of Nature Conservation PAS, Cracow, pp. 353 – 351. Journal 37. Zając, K. & Zając, T., 2011: The role of active individual movement in habitat selection in the

Global endangered freshwater mussel Unio crassus Philipsson, 1788. – J. Conchol. 40: 446 – 461. 38. Żbikowski, J., Kakareko, T., Poznańska, M. & Kobak, J., 2007: Malacofauna of two hydrologically different habitats in the near-shore zone of the Włocławek Dam Reservoir (Vistula River, Poland). – Folia Malacologica 15: 25 – 38. 39. Zawal, A., Stępień, E., Szlauer-Łukaszewska, A., Michoński, G., Kłosowska, M., Bańkowska, A.,

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XVI This page is intentionally left blank ) H ( Research Volume Frontier Science of Journal Global

Global Journal of Science Frontier Research: H

Environment & Earth Science Volume 16 Issue 4 Version 1.0 Year 2016

Type : Double Blind Peer Reviewed International Research Journal

Publisher: Global Journals Inc. (USA)

Online ISSN: 2249-4626 & Print ISSN: 0975-5896

Toxic Effect of Metal Ions in Water Resources By Dr. Suranjana Chattopadhyay Maharaja Manindra Chandra College Metal Ions in Water Resources- Water is the most important resource. Without water life is not possible. From a chemical point of view, water, H2O, is a pure compound, but in reality, you seldom drink, see, touch or use pure water. Water from various sources contains dissolved gases, minerals, organic and inorganic substances. This photograph of Guilin shows the beauty of natural water. The rain curved an interesting landscape out of the lime stones in the area. Natural waters are often important parts of wonders of the world. GJSFR-H Classification: FOR Code: 090509

ToxicEffectofMetalIonsinWaterResources

Strictly as per the compliance and regulations of :

© 2016. Dr. Suranjana Chattopadhyay. This is a research/review paper, distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License http://creativecommons.org/licenses/by-nc/3.0/), permitting all non commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Toxic Effect of Metal Ions in Water Resources

Dr. Suranjana Chattopadhyay

I. Metal Ions in Water Resources II. Hydration and Hydrolysis of Metal Cations ater is the most important resource. Without water life is not possible. From a chemical point a) Hydration

of view, water, H O, is a pure compound, but in When sodium chloride dissolves in water, the W 2 reality, you seldom drink, see, touch or use pure water. sodium and chloride ions and the polar water molecules Water from various sources contains dissolved gases, are strongly attracted to one another by ion-dipole 201 r

minerals, organic and inorganic substances. This interactions. The solvent molecules (water in this case) ea photograph of Guilin shows the beauty of natural water. surround the ions removing them from the crystal and Y The rain curved an interesting landscape out of the lime forming the solution. As the dissolving process 151 stones in the area. Natural waters are often important proceeds, the individual ions are removed from the solid parts of wonders of the world. surface becoming completely separate, hydrated The total water system surrounding the planet species in the solution. Earth is called the hydrosphere . It includes freshwater In order to dissolve the sodium chloride in the V systems, oceans, atmosphere vapors, and biological water, three processes have to occur: IV waters. The Arctic, Atlantic, Indian, and Pacific oceans 1. The water molecules must be separated (H-bonds ue ersion I cover 71% of the Earth surface, and contain 97% of all s must be broken) s water. Less than 1% is fresh water, and 2-3 % is ice 2. the forces of attraction between the ions in the I caps and glaciers. The Antarctic Ice Sheet is almost the NaCl lattice must be broken XVI size of North America continent. These waters dominate 3. solute-solvent(ion-dipole)interactions must be our weather and climate, directly and indirectly affecting formed 8 2 our daily lives. They cover 3.35x10 km . The four The overall enthalpy change in forming a oceans have a total volume of 1.35x109 km3.

solution is the sum of energy changes for each of these Groundwater is an important part of the water system. ) processes. Whether the dissolution process will be H When vapor is cooled, clouds and rain develop. Some exothermic or endothermic depends on the relative ( of the rain percolates through the soil and into the magnitudes of the energy changes for the three steps. underlying rocks. The water in the rocks is groundwater, In general, a substance will be insoluble if the energy which moves slowly. Dust particles and ions present in

expended to break apart the solvent and solute particles Research Volume the air are nucleation center of water drops. Thus, 2+ is significantly greater than the energy given off when waters from rain and snow also contain such ions: Ca , 2+ + + + solute-solvent interactions are established. Mg , Na , K , NH4 . These cations are balanced by

- - - - - Frontier anions, HCO3 , SO4 , NO2 , Cl , and NO3 . The pH of rain b) Hydrolysis is between 5.5 and 5.6. Metal ions in aqueous solution behave as Lewis Rain and snow waters eventually become river acids. The positive charge on the metal ion draws or lake waters. When the rain or snow waters fall, they electron density from the O-H bond in the water. This Science increases the bond's polarity making it easier to break. interact with vegetation, top soil, bed rock, river bed and of lake bed, dissolving whatever is soluble. Bacteria, algae, When the O-H bond breaks, an aqueous proton is and water insects also thrive. Solubilities of inorganic released producing an acidic solution. Most chemical salts are governed by the kinetics and equilibria of elements are metallic and form simple aqua ions with Journal z+ dissolution. The most common ions in lake and river the formula [M(H2O)n] when the oxidation state is 1, 2 waters are the same as those present in rainwater, but or 3. With the higher oxidation states the simple aqua at higher concentrations. The pH of these waters ions dissociate losing hydrogen ions to yield complexes Global depends on the river bed and lake bed. Natural waters that contain both water molecules and hydroxide or contain dissolved minerals. Waters containing Ca2+ and oxide ions, such as the vanadium(IV) species 2+ 2+ Mg ions are usually called hard water. [VO(H2O)5] . In the highest oxidation state only − oxyanions, such as the permanganate (VII) ion, MnO4 are known.[1]

Author: Head of the Department, Maharaja Manindra Chandra College. e-mail: [email protected]

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Fig. : Hydration of metal ion c) Major Toxic ions and their effects Agency) is less than 0.003 mg/dm3. The maximum limit Environmental pollution by toxic metals occurs in drinking water is 0.003 mg/dm3. V globally through military, industrial, and agricultural Cadmium accumulates in kidneys, pancreas, IV processes and waste disposal (Duffus, 2002). Fuel and intestines and glands altering the metabolism of the

ue ersion I power industries generate 2.4 million tons of As, Cd, Cr, elements necessary for the body, such as zinc, copper, s s Cu, Hg, Ni, Pb, Se, V, and Zn annually The metal iron, magnesium, calcium and selenium. Damage to the I industry adds 0.39 million tons/yr of the same metals to respiratory tract and kidneys are the main adverse

XVI the environment, while agriculture contributes 1.4 million effects in humans exposed to cadmium compounds. In tons/yr, manufacturing contributes 0.24 million tons/yr humans exposed to fumes and dusts chronic toxicity of and waste disposal adds 0.72 million tons/yr. Metals, cadmium compounds is usually found after a few years. discharged or transported into the environment, may The main symptom of emphysema is that it often

) undergo transformations and can have a large develops without preceding bronchitis. The second H

( environmental, public health, and economic impact. basic symptom of chronic metal poisoning is kidney The pollutants of concern include cadmium, damage. It includes the loss and impairment of smell, lead, mercury, chromium, arsenic, zinc, cobalt and pathological changes in the skeletal system nickel as well as copper. They have a number of (osteoporosis with spontaneous fractures and bone Research Volume applications in basic engineering works, paper and pulp fractures), pain in the extremities and the spine, difficulty industries, leather tanning, petrochemicals, fertilizers, in walking, the formation of hypochromic anemia. The etc. Moreover, they have also negative impact on most known ‘Itai-Itai’ disease caused by cadmium

Frontier human health. exposure is mixed osteomalacia and osteoporosis. Cadmium is a metal of great toxicological Lead is a toxic metal, which accumulates in the concern. An important source of human exposure to vital organs of men and animals and enters into the

Science cadmium is food and water, especially for the body through air, water and food. According to the WHO population living in the vicinity of industrial plants, from (World Health Organization) standards, its maximum of which cadmium is emitted to the air. In the case of limit in drinking water is 0.05 mg/dm3 but the maximum exposure to occupational cadmium compounds, they discharge limit for lead in wastewater is 0.5 mg/dm3. are absorbed mainly by inhalation. However, an Lead is used as industrial raw material in the Journal important source of cadmium in soils is phosphate manufacture of storage batteries, pigments, leaded fertilizers. Large amounts of cadmium are also glass, fuels, photographic materials, matches and

Global introduced to soil together with municipal waste. The explosives. Lead being one of very important pollutants high mobility of cadmium in all types of soils is the comes from wastewaters from refinery, wastewaters reason for its rapid integration into the food chain. Daily from production of basic compounds containing lead, intake of cadmium from food in most countries of the wastewaters with the remains of after production world is 10-20 mg. Through the gastrointestinal tract solvents and paints. less than 10% cadmium is absorbed. An important The average collection of lead by an adult was source of human exposure to cadmium is food and estimated at 320-440 mg/day. Its cumulative poisoning water. In natural water, its typical concentration lies effects are serious haematological damage, anaemia, below 0.001 mg/dm3, whereas, the upper limit kidney malfunctioning, brain damage etc. Chronic recommended by EPA (Environmental Protection exposure to lead causes severe lesions in kidney, liver,

©2016 Global Journals Inc. (US) Toxic Effect of Metal Ions in Water Resources lungs and spleen. Acute poisoning with inorganic lead utilization of chromium compounds. Chromium is an compounds occurs rarely. In the case of acute important and widely applied element in industry. The poisoning in man, the symptoms are burning in the hexavalent and trivalent chromium is often present in mouth, vomiting, abdominal cramps, diarrhea, electroplating wastewater. Other sources of chromium constipation progressing to systolic, blood pressure and pollution are leather tanning, textile, metal processing, body temperature. At the same time there is hematuria, paint and pigments, dyeing and steel fabrication. proteinuria, oliguria, central nervous system damage. Chromium is associated with nucleic acids and Alkyl lead compounds are more toxic than inorganic is the subject to the concentration in liver cells. It plays lead connections. Tetraethyl lead toxicity manifested an important role in the metabolism of glucose, certain primarily in lead damage of the nervous system. Toxic proteins and fats, is part of enzymes and stimulates the effects of lead on the central nervous system are activity of others. All compounds of chromium, with the observed more in children. In adults, the effects of lead exception of chromate, are rapidly cleared from the toxicity occur in the peripheral nervous system. blood. Chromium also accumulates in the liver and 201 Symptoms of chronic poisoning may vary. The acute kidneys. High concentrations of chromium, observed in r ea form of poisoning known as lead colic is the general the lungs of people exposed to this metal, indicate that Y state of various spastic internal organs and neurological at least part of chromium is stored in this organ in the 171 damage in the peripheral organs. Long-term lead form of insoluble compounds. The binding of chromium poisoning can lead to organic changes in the central with the elements of the blood and transport of and peripheral nervous systems. Characteristic chromium by the blood depends mainly on its valence. symptoms include pale gray skin colour and the lead Hexavalent chromium readily crosses the membranes of line on the gums (blue-black border). red blood cells and after reduction to trivalent chromium V

In nature, natural circulation of mercury vapour is bound to hemoglobin. The reduction of hexavalent to IV

has a significant influence on the content of the soil and trivalent chromium, occurring within cells, considered as ue ersion I s water. Elemental mercury in the rain water creates the activation of the carcinogenic chromium, increases s compounds by oxidation to divalent mercury. Both the because the probability of interaction of trivalent I chemical reaction, and under the influence of biological chromium on the DNA. Clinical signs of acute toxicity of XVI factors, and especially the activity of bacteria in the chromium compounds are characterized by severe sediments of water bodies methyl and di-methyl abdominal pain, vomiting and bloody diarrhea, severe mercury compounds are formed. Mercury, a fixed kidney damage with hematuria leading to anuria,

component of the waste water treatment that may be observed gastrointestinal ulceration. Chromium ) used for soil fertilization is a major threat to the inclusion compounds and chromic acid are especially dangerous H ( of the metal in nutritional products. Drinking water may and cause serious damage to internal organs. Chronic contain up to 300 ng Hg/dm3, in highly industrialized exposure leads to chronic disorders in the body. areas it can reach up to 700 ng/dm3. Daily consumption Arsenic is present in over 160 minerals. It is of mercury from food in the general population is less readily bio-accumulative and therefore its concentration Research Volume than 20 μg/day. 80% of mercury absorbed by the in polluted waters may reach 430 mg/dm3 in plants and respiratory system is retained in the body. In the case of 2.5 mg/dm3 in fish.

ingestion of inorganic mercury salts, salivation, burning Arsenic accumulates in tissues rich in keratin, Frontier in the throat, vomiting, bloody diarrhea, necrosis of the like hair, nails and skin. Arsenic and its inorganic intestinal mucosa and kidney damage, leading to anuria compounds can cause not only cancer of the respiratory and uremia can occur. The concentration of mercury system and skin, but also neoplastic lesions in other Science vapour over 1 mg/m3 damages lung tissue and causes organs. Arsenic compounds enter the body from the severe pneumonia. The classic symptoms of metallic gastrointestinal tract and through skin and respiratory of mercury vapour poisoning are manifested by tremor, system. Arsenic compounds have affinity for many mental disorders, inflammation of the gums. Its enzymes and can block their action, and above all maximum limit in drinking water is 0.0005 mg/dm3. disturb the Krebs cycle. Inorganic arsenic compounds Journal

Chromium, occurring as Cr(III) or Cr(VI) in are more harmful than organic and among them AsH3 natural environments, is an important material resource, and As2O3 should be mentioned. 70-300 mg of As2O3 is Global an essential micronutrient or toxic contaminant. Cr(III) is considered to be the average lethal dose for humans. required for normal development of human and animal The dose of 10-50 ppb for 1 kg of body weight can organisms but Cr(VI) activates teratogenic processes, cause circulatory problems, resulting in necrosis and disturbs DNA synthesis and can give rise to gangrene of limbs. The dominant effects of arsenic in mutagenous changes leading to malignant tumours humans are changes in the skin and mucous (WHO, Report 1998). Natural sources of chromium membranes as well as peripheral nerve damage. There include weathered rocks, volcanic exhalations and are xerosis soles and palms, skin inflammation with biogeochemical processes and, in the man-polluted ulceration. In addition, there is perforation of the nasal environment, mainly wastes after processing and septum.

3 In nature, zinc occurs in the form of minerals. An mg/dm . The ma ximum limit in drinking water is 0.05 important source of zinc pollution is the burning of coal, mg/dm3 (Fewtrell et al. 1996).The demand for copper is petroleum and its products. Incineration of municipal increased in pregnant women, children and the elderly. solid waste can introduce about 75% zinc to urban air. Good dietary sources of copper include animal liver, Also, municipal wastewater generally contains shellfish, dried fruit, nuts and chocolate. In some cases significant amounts of zinc. The use of municipal and drinking water may also provide significant levels of industrial waste in agriculture results in the accumulation copper. Copper in the body is involved in oxidation- of zinc in the surface layers of soil. Another source of reduction processes, acts as a stimulant on the amount this metal in soils are some preparations of plant and activity of hemoglobin, in the process of hardening protection products, as well as phosphatic fertilizers. of collagen, hair keratinization, melanin synthesis as well The degree of toxicity of zinc is not big, but it depends as affects on lipid metabolism and properties of the on the ionic form, and changes under the influence of myelin sheath of nerve fibers. In animal cells it is mainly

201 water hardness and pH. The daily average download of concentrated in the mitochondria, DNA, RNA, and the

r zinc by an adult is estimated at about 10-50 mg /day. nucleus. Copper readily forms a connection with various ea The toxic dose is 150-600 mg. It is necessary for the Y proteins, especially those of sulphur. Although copper is proper functioning of living organisms and it is involved an essential metal, it can, in some circumstances, lead 18 in the metabolism of proteins and carbohydrates. High to toxic effects including liver damage and doses of zinc cause damage to many biochemical gastrointestinal disturbances. Wilson's disease (also processes followed by its deposition in the kidneys, known as hepatolenticular degeneration), Indian liver, gonads. Kidneys play an important role in Childhood Cirrhosis (ICC) are characterized by an

V maintaining zinc homeostasis in the body. Zinc is accumulation of copper -containing granules within liver relatively non-toxic to humans and animals. Hazard zinc IV cells. Ingestion of high levels of copper salts is known to mainly connected with secondary copper deficiency cause gastrointestinal upsets. Additionally, absorption of ue ersion I s

s does not give specific symptoms. copper compounds by inhalation causes congestion of

I Nickel is a moderately toxic element as the nasal mucosa, gastritis, diarrhea and toxic compared to other transition metals. It is a natural symptoms such as chronic lung damage. Copper XVI element of the earth’s crust; therefore its small amounts compounds act on the intact skin, causing it to itch and are found in food, water, soil, and air. Nickel occurs inflammation. They can cause conjunctivitis, ulceration naturally in the environment at low levels. Nickel and corneal opacity, nasal congestion and as well as concentrations in the groundwater depend on the soil sore throat and nasal septum.

) used, pH, and depth of sampling. In humans, the H ( absorption of nickel from the gastrointestinal tract is less III. Some Possible Remedies than 10%. Nickel taken with food and water is poorly Among different techniques used for removal of absorbed and rapidly excreted from the body. It high concentrations of heavy metals, precipitation-

Research Volume accumulates mainly in bones, heart, skin and various filtration, ion exchange, reverse osmosis, oxidation- glands. Nickel inhalation of atmospheric air is largely reduction, solvent extraction, as well as membrane accumulated in the lungs. Practically fatal or acute separation should be mentioned (Hubicki,et al. 1999; poisoning with nickel or its salts is not found. The most D browski et al. 2004). However, some of the wastes

Frontier ą toxic compound is carbonyl nickel. An excess of inhaled contain substances such as organics, complexing nickel causes damage to the mucous membranes. agents and alkaline earth metals that may decrease the Moreover, its symptoms are allergic disorders (protein metal removal and result in unacceptable Science metabolism disorder in plasma, changes in the concentrations of heavy metals in the effluents. of chromosomes and changes in bone marrow and Large toxicity of lead requires that its contents cancer. It is known that inhalation of nickel and its are reduced to the minimum (ppb level). To this end compounds can lead to serious problems, including, there are applied chelating ions with the functional Journal among others, respiratory system cancer. Moreover, phosphonic and aminophosphonic groups. Also weakly nickel can cause skin disorder which is a common basic anion exchangers in the free base form can be occupational disease in workers who handle its large used for selective removal of lead(II) chloride complexes Global amounts. Also dermatitis is the most common effect of from the solutions of pH in the range 4-6. Also a chronic dermal exposure to nickel. Chronic inhalation combined process of cation exchange and precipitation exposure to nickel in humans also results in detrimental is often applied for lead(II) removal form wastewaters respiratory effects. (Pramanik et al. 2009). Copper is an essential nutritional element being a vital part of several enzymes. It is one of the components of human blood. The estimated adult dietary intakes are between 2 and 4 mg/day. The upper limit recommended by WHO for copper is less than 1.3

Environment & Earth Science Volume 16 Issue 4 Version 1.0 Year 2016

Type : Double Blind Peer Reviewed International Research Journal

Publisher: Global Journals Inc. (USA)

Online ISSN: 2249-4626 & Print ISSN: 0975-5896

Studying the Nature Relationship between Climatic Factors and Cotton Production by Different Applied Statistical and Mathematical Ways By Zakaria M. Sawan Cotton Research Institute Abstract- This study investigates the statistical relationship between various climatic factors and overall flower and boll production. Also, the relationship between climatic factors and production of flowers and bolls obtained during the development periods of the flowering and boll stage. Further, predicting effects of climatic factors during different convenient intervals (in days) on cotton flower and boll production compared with daily observations. Furthermore, collects information about the nature of the relationship between various climatic factors and cotton boll development and the 15-day period both prior to and after initiation of individual bolls. And, provide information on the effect of various climatic factors and soil moisture status during the development stage on flower and boll production in cotton. Evaporation, sunshine duration, relative humidity, surface soil temperature at 1800 h, and maximum air temperature, are the important climatic factors that significantly affect flower and boll production. Keywords: cotton flower and boll production, evaporation, relative humidity, soil moisture status, sunshine duration, temperature. GJSFR-H Classification: FOR Code: 040199

StudyingtheNatureRelationshipbetweenClimaticFactorsandCottonProductionbyDifferentAppliedStatisticalandMathematicalWays

Strictly as per the compliance and regulations of :

© 2016. Zakaria M. Sawan. This is a research/review paper, distributed under the terms of the Creative Commons Attribution- Noncommercial 3.0 Unported License http://creativecommons.org/licenses/by-nc/3.0/), permitting all non commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Studying the Nature Relationship between

Climatic Factors and Cotton Production by

Different Applied Statistical and Mathematical

Ways

Zakaria M. Sawan 201 r

Abstract- This study investigates the statistical relationship practices affect crop growth interactively, sometimes ea between various climatic factors and overall flower and boll resulting in plants responding in unexpected ways to Y production. Also, the relationship between climatic factors and their conditions (Hodges et al. 1993). 191 production of flowers and bolls obtained during the Water is a primary factor controlling plant development periods of the flowering and boll stage. Further, growth. Xiao et al. (2000) stated that, when water was predicting effects of climatic factors during different convenient intervals (in days) on cotton flower and boll applied at 0.85, 0.70, 0.55 or 0.40 ET (evapotranspiration) to cotton plants grown in pots, production compared with daily observations. Furthermore, V collects information about the nature of the relationship there was a close relationship between plant between various climatic factors and cotton boll development development and water supply. The fruit-bearing IV

ue ersion I and the 15-day period both prior to and after initiation of branches, square and boll numbers and boll size were s s individual bolls. And, provide information on the effect of increased with increased water supply. Barbour and I various climatic factors and soil moisture status during the Farquhar (2000) reported on greenhouse pot trials

XVI development stage on flower and boll production in cotton. where cotton cv. CS50 plants were grown at 43 or 76% Evaporation, sunshine duration, relative humidity, surface soil relative humidity (RH) and sprayed daily with abscisic temperature at 1800 h, and maximum air temperature, are the important climatic factors that significantly affect flower and acid (ABA) or distilled water. Plants grown at lower RH boll production. The five-day interval was found to be more had higher transpiration rates, lower leaf temperatures adequately and sensibly related to yield parameters. and lower stomatal conductance. Plant biomass was ) H

Evaporation; minimum humidity and sunshine duration were also reduced at the lower RH. Within each RH ( the most effective climatic factors during preceding and environment, increasing ABA concentration generally succeeding periods on boll production and retention. There reduced stomatal conductance, evaporation rates, was a negative correlation between flower and boll production superficial leaf density and plant biomass, and

Research Volume and either evaporation or sunshine duration, while that increased leaf temperature and specific leaf area. correlation with minimum relative humidity was positive. Temperature is also a primary factor controlling Keywords: cotton flower and boll production, rates of plant growth and development. Burke et al.

evaporation, relative humidity, soil moisture status, Frontier sunshine duration, temperature. (1988) has defined the optimum temperature range for biochemical and metabolic activities of plants as the I. Introduction thermal kinetic window (TKW). Plant temperatures above or below the TKW result in stress that limits growth and Science limate affects crop growth interactively, yield. The TKW for cotton growth is 23.5 to 32°C, with an of sometimes resulting in unexpected responses to optimum temperature of 28°C. Biomass production is prevailing conditions. Many factors, such as C directly related to the amount of time that foliage length of the growing season, climate (including solar temperature is within the TKW. Hodges et al. (1993) Journal radiation, temperature, light, wind, rainfall, and dew), found that the optimum temperature for cotton stem and cultivar, availability of nutrients and soil moisture, pests leaf growth, seedling development, and fruiting was

and cultural practices affect cotton growth (El-Zik 1980). Global almost 30°C, with fruit retention decreasing rapidly as The balance between vegetative and reproductive the time of exposure to 40°C increased. Reddy et al. development can be influenced by soil fertility, soil (1998) found that when Upland cotton (G. hirsutum) cv. moisture, cloudy weather, spacing and perhaps other DPL-51 was grown in naturally lit plant growth chambers factors such as temperature and relative humidity at 30/22°C day/night temperatures from sowing until (Guinn 1982). Weather, soil, cultivars, and cultural flower bud production, and at 20/12, 25/17, 30/22, 35/27 and 40/32°C for 42 days after flower bud production, Author: Cotton Research Institute, Agricultural Research Center, Ministry of Agriculture & Land Reclamation, Gamaa Street, Giza, Egypt. fruit retention was severely curtailed at the two higher e-mail: [email protected] temperatures compared with 30/22°C. Species/cultivars

that retain fruits at high temperatures would be more cotton. Minimizing the deleterious effects of the productive both in the present-day cotton production factors through utilizing proper cultural practices will environments and even more in future warmer world. lead to improved cotton yield. Schrader et al. (2004) stated that high temperatures that II. ata and ethods plants are likely to experience inhibit photosynthesis. D M Zhou et al. (2000) indicated that light duration is

the key meteorological factor influencing the wheat- Two uniform field trials were conducted at the

cotton cropping pattern and position of the bolls, while experimental farm of the Agricultural Research Center, o o temperature had an important function on upper (node 7 Ministry of Agriculture, Giza, Egypt (30 N, 31 : 28’E at an to 9) and top (node 10) bolls, especially for double altitude of 19 m), using the cotton cultivar Giza 75 (Gossypium barbadense L.) in 2 successive seasons (I cropping patterns with early maturing varieties. and II). The soil texture was a clay loam, with an alluvial The objectives of this investigation were to study: substratum (pH = 8.07, 42.13% clay, 27.35% silt, 201 A- The effect of various climatic factors on the overall

r 22.54% fine sand, 3.22% coarse sand, 2.94% calcium flower and boll production in Egyptian cotton. This ea carbonate and 1.70% organic matter) (Sawan et al. Y could pave the way for formulating advanced 2010). predictions as for the effect of certain climatic 20 In Egypt, there are no rain-fed areas for conditions on cotton production of Egyptian cotton. cultivating cotton. Water for the field trials was applied It would be useful to minimize the deleterious effects using surface irrigation. Total water consumed during of the factors through utilizing proper cultural practices which would limit and control their each of two growing seasons supplied by surface -1

V irrigation was about 6,000-m³ h . The criteria used to negative effects, and this will lead to an increase in

IV determine amount of water applied to the crop cotton yield. depended on soil water status. Irrigation was applied ue ersion I B- Also, this study investigated the relationship

s when soil water content reached about 35% of field between climatic factors and production of flowers I capacity (0-60 cm). In season I, the field was irrigated on and bolls obtained during the development periods

XVI 15 March (at planting), 8 April (first irrigation), 29 April, of the flowering and boll stage, and to determine the 17 May, 31 May, 14 June, 1 July, 16 July, and 12 August. most representative period corresponding to the In season II, the field was irrigated on 23 March (planting overall crop pattern. date), 20 April (first irrigation), 8 May, 22 May, 1 June, 18 C- Further, this study aimed at predicting effects of June, 3 July, 20 July, 7 August and 28 August. ) climatic factors during different convenient intervals H (in days) on cotton flower and boll production Techniques normally used for growing cotton in Egypt ( were followed. Each experimental plot contained 13 to compared with daily observations. The study 15 ridges to facilitate proper surface irrigation. Ridge presents a rich effort focused on evaluating the width was 60 cm and length was 4 m. Seeds were sown efficacy of regression equations between cotton

Research Volume on 15 and 23 March in seasons I and II, respectively, in crop data and climatic data grouped at different hills 20 cm apart on one side of the ridge. Seedlings time intervals, to determine the appropriate time were thinned to 2 plants per hill 6 weeks after planting, scale for aggregating climate data to be used for resulting in a plant density of about 166,000 plants ha-1. Frontier predicting flower and boll production in cotton. Phosphorus fertilizer was applied at a rate of 54 kg P O D- Furthermore, this study investigates and collects 2 5 ha-1 as calcium super phosphate during land information about the nature of the relationship between various climatic factors and cotton boll preparation. Potassium fertilizer was applied at a rate of

Science -1 development and the 15-day period both prior to 57 kg K2O ha as potassium sulfate before the first

of irrigation (as a concentrated band close to the seed and after initiation of individual bolls of field grown cotton plants in Egypt. This could pave the way for ridge). Nitrogen fertilizer was applied at a rate of 144 kg -1 formulating advanced predictions as for the effect of N ha as ammonium nitrate in two equal doses: the first

Journal was applied after thinning just before the second certain climatic conditions on production of irrigation and the second was applied before the third Egyptian cotton. It would be useful to minimize the deleterious effects of the factors through utilizing irrigation. Rates of phosphorus, potassium, and Global nitrogen fertilizer were the same in both seasons. These proper cultural practices which would limit and amounts were determined based on the use of soil tests control their negative effects, and this will lead to an (Sawan et al. 2010). improvement in cotton yield. E- And provide information on the effect of various After thinning, 261 and 358 plants were randomly climatic factors and soil moisture status during the selected (precaution of border effect was taken into development stage on flower and boll production in consideration by discarding the cotton plants in the first Egyptian cotton. This could result in formulating and last two hills of each ridge) from 9 and 11 inner advanced predictions as for the effect of certain ridges of the plot in seasons I, and II respectively. Pest climatic conditions on production of Egyptian control management was carried out on an-as-needed

©2016 Global Journals Inc. (US) Studying the Nature Relationship between Climatic Factors and Cotton Production by Different Applied Statistical and Mathematical Ways basis, according to the local practices performed at the (Sawan et al. 2005). Range and mean values of the experimental (Sawan et al. 2010). climatic parameters recorded during the production Flowers on all selected plants were tagged in stage for both seasons and overall data are listed in order to count and record the number of open flowers, Table 1 (Sawan et al. 2006). Daily number of flowers and and set bolls on a daily basis. The flowering season number of bolls per plant which survived till maturity commenced on the date of the first flower appearance (dependent variables) during the production stage in the and continued until the end of flowering season (31 two seasons are graphically illustrated in Figures 1 and August). The period of whole September (30 days) until 2 (Sawan et al. 2005). the 20th of October (harvest date) allowed a minimum of 50 days to develop mature bolls. In season I, the III. Results and Discussion flowering period extended from 17 June to 31 August, a) Response of flower and boll development to climatic whereas in season II, the flowering period was from 21 factors on the anthesis day June to 31 August. Flowers produced after 31 August 201 Daily number of flowers and number of bolls per were not expected to form sound harvestable bolls, and r plant which survived to maturity (dependent variables) ea therefore were not taken into account (Sawan et al. Y during the production stage of the two seasons (68 days 2010). and 62 days in the first and the second seasons, 211 For statistical analysis, the following data of the respectively) are graphically illustrated in Figures 1 and dependent variables were collected: number of tagged 2 (Sawan et al. 2005). The flower- and boll-curves flowers separately counted each day on all selected reached their peaks during the middle two weeks of plants (Y ), number of retained bolls obtained from the 1 August, and then descended steadily till the end of the total daily tagged flowers on all selected plants at V season. Specific differences in the shape of these harvest (Y ), and (Y ) percentage of boll retention IV 2 3 curves in the two seasons may be due to the growth-

([number of retained bolls obtained from the total ue ersion I reactions of environment, where climatic factors (Table s number of daily tagged flowers in all selected plants at s 1) (Sawan et al. 2006) represent an important part of the harvest]/[daily number of tagged flowers on each day in I environmental effects (Miller et al. 1996). all selected plants] x 100). XVI

As a rule, observations were recorded when the i. Correlation estimates number of flowers on a given day was at least 5 flowers Results of correlation coefficients [correlation found in a population of 100 plants and this continued and regression analyses were computed, according to

Draper and Smith (1966) by means of the computer for at least five consecutive days. This rule omitted eight ) observations in the first season and ten observations in program SAS package (1985). between the initial group H ( the second season. The number of observations (n) of independent variables and each of flower and boll was 68 (23 June through 29 August) and 62 (29 June production in the first and second seasons and the through 29 August) for the two seasons, respectively. combined data of the two seasons are shown in Table 2

Variables of the soil moisture status considered were, (Sawan et al. 2002). Research Volume the day prior to irrigation, the day of irrigation, and the The correlation values indicate clearly that first and second days after the day of irrigation (Sawan evaporation is the most important climatic factor

et al. 2010). affecting flower and boll production as it showed the Frontier

The climatic factors (independent variables) highest correlation value. This factor had a significant considered were daily data of: maximum air temperature negative relationship with flower and boll production.

(°C, X ); minimum air temperature (°C, X ); maximum- Sunshine duration showed a significant negative relation 1 2 Science minimum air temperature (diurnal temperature range) with fruit production except for boll production in the first

of (°C, X ); evaporation (expressed as Piche evaporation) season, which was not significant. Maximum air 3 (mm day-1, X ); surface soil temperature, grass temperature, temperature magnitude, and surface soil 4 temperature or green cover temperature at 0600 h (°C, temperature at 1800 h, were also negatively correlated Journal X ) and 1800 h (°C, X ); sunshine duration (h day-1, X ); with flower and boll production in the second season 5 6 7 maximum relative humidity (maxRH) (%, X ), minimum and the combined data of the two seasons. Minimum 8 relative humidity (minRH) (%, X ) and wind speed (m s-1, humidity in the second season, the combined data of 9 Global X ) in season II only. The source of the climatic data the two seasons, and maximum humidity in the first 10 was the Agricultural Meteorological Station of the season were positively and highly correlated with flower

Agricultural Research Station, Agricultural Research and boll production. Minimum air temperature and soil

Center, Giza, Egypt. No rainfall occurred during the two surface temperature at 0600 h showed low and growing seasons (Sawan et al. 2005). insignificant correlation to flower and boll production

Daily records of the climatic factors (independent (Sawan et al. 2002). variables), were taken for each day during production The negative relationship between evaporation stage in any season including two additional periods of with flower and boll production, means that high

15 days preceding and after the production stage evaporation rate significantly reduces cotton flower and

boll production. This may be due to greater plant water would cause a significant reduction in boll number. deficits when evaporation increases. Also, the negative Thus, applying specific treatments such as an additional relation between each of maximum temperature, irrigation, and use of plant growth regulators, would temperature magnitude, surface soil temperature at decrease the deleterious effect of evaporation after boll 1800 h, or sunshine duration, with flower and boll formation and hence contribute to an increase in cotton production revealed that the increase in the values of boll production and retention, and the consequence is these factors had a detrimental effect upon fruit an increase in cotton yield (Sawan et al. 2002). In this production in Egyptian cotton. On the other hand, there connection, Moseley et al. (1994) stated that methanol was a positive correlation between each of maximum or has been reported to increase water use efficiency,

minimum humidity with flower and boll production growth and development of C3 plants in arid conditions, (Sawan et al. 2002). under intense sunlight. In field trials cotton cv. DPL-50 Results obtained from the production stage of (Gossypium hirsutum), was sprayed with a nutrient

201 each season individually, and the combined data of the solution (1.33 lb N + 0.27 lb Fe + 0.27 lb Zn acre-1) or r two seasons, indicate that relationships of some climatic 30% methanol solution at a rate of 20 gallons acre-1, or ea

Y variables with the dependent variables varied markedly sprayed with both the nutrient solution and methanol from one season to another. This may be due to the under two soil moisture regimes (irrigated and dry land). 22 differences between climatic factors in the two seasons The foliar spray treatments were applied 6 times during as illustrated by the ranges and means shown in Table 1 the growing season beginning at first bloom. They found (Sawan et al. 2006). For example, maximum that irrigation (a total of 4.5 inches applied in July) temperature, minimum humidity and soil surface increased lint yield across foliar spray treatments by

V temperature at 1800 h did not show significant relations 18%. Zhao and Oosterhuis (1997) reported that in a

IV in the first season, while that trend differed in the second growth chamber when cotton (Gossypium hirsutum cv.

ue ersion I season. The effect of maximum humidity varied Stoneville 506) plants were treated with the plant growth s s markedly from the first season to the second one. regulator PGR-IV (gibberellic acid, IBA and a proprietary I Where it was significantly correlated with the dependent fermentation broth) under water deficit stress and found

XVI variables in the first season, while the inverse pattern significantly higher dry weights of roots and floral buds was true in the second season. This diverse effect may than the untreated water-stressed plants. They be due to the differences in the mean values of this concluded that PGR-IV can partially alleviate the factor in the two seasons; where it was, on average, detrimental effects of water stress on photosynthesis

) about 86% in the first season, and about 72% on and dry matter accumulation and improves the growth

H average in the second season, as shown in Table 1 and nutrient absorption of growth chamber-grown cotton ( (Sawan et al. 2006). plants. Meek et al. (1999) in a field experiment in Boll retention ratio [(The number of retained Arkansas found that application of 3 or 6 kg glycine bolls obtained from the total number of each daily betaine (PGR) ha-1, to cotton plants had the potential for

Research Volume tagged flowers in all selected plants at harvest/Total increasing yield in cotton exposed to mild water stress. number of daily tagged flowers of all selected plants) x ii. Multiple linear regression equation 100] curves for both of the two seasons are shown in By means of the multiple linear regression

Frontier Figures 3 and 4 (Sawan et al. 2002). Also, these curves analysis, fitting predictive equations (having good fit) describe why the shapes and patterns associated with were computed for flower and boll production per plant the flower and boll curves for I and II seasons were using selected significant factors from the nine climatic different. It seems reasonable that the climatic data that Science variables studied in this investigation. Wind speed were collected in these two experiments (I and II evaluated during the second season had no influence

of seasons) could provide adequate information for on the dependent variables. The equations obtained for describing how these two seasons differed and how the each of the two dependent variables, i.e. number of crop responded accordingly (Sawan 2014 a & b).

Journal flowers (Y1) and bolls per plant (Y2) in each season and These results indicate that evaporation is the for combined data from the two seasons (Table 2) most effective and consistent climatic factor affecting (Sawan et al. 2002) are as follows: boll production. As the sign of the relationship was Global negative, this means that an increase in evaporation First Season: (n = 68)

Y1 = 21.691 - 1.968 X4 - 0.241 X7 + 0.216 X8, R = 0.608** and R² = 0.3697, While R² for all studied variables was 0.4022.

Y2 = 15.434 - 1.633 X4 + 0.159 X8, R = 0.589** and R² = 0.3469 and R² for all studied variables was 0.3843. Second Season: (n = 62)

Y1 = 77.436 - 0.163 X1 - 2.861 X4 - 1.178 X7 + 0.269 X9, R = 0.644**, R² = 0.4147.

Y2 = 66.281 - 0.227X1 - 3.315X4 - 2.897X7 + 0.196X9, R = 0.629**, R² = 0.3956.

In addition, R² for all studied variables was 0.4503 and 0.4287 for Y1 and Y2 equations respectively. Combined data for the two seasons: (n = 130)

Y1 = 68.143 - 0.827 X4 - 1.190 X6 - 2.718 X7 + 0.512 X9, R = 0.613**, R² = 0.3758 Y = 52.785 - 0.997 X - 0.836 X - 1.675 X + 0.426 X , R = 0.569**, R² = 0.3552 2 4 6 7 9 While R² for all studied variables was 0.4073 for Y and 0.3790 for Y . 1 2 Three climatic factors, i.e. minimum air deficit for cotton based on cultivar earliness, growing - temperature, surface soil temperature at 0600 h, and season length, and availability of irrigation water. wind speed were not included in the equations since The negative relationship between sunshine they had very little effect on production of cotton flowers duration and cotton production may be due to the fact and bolls. The sign of the partial regression coefficient that the species of Gossypium used is known to be a for an independent variable (climatic factor) indicates its short day plant (Hearn and Constable 1984), so, an effect on the production value of the dependent variable increase of sunshine duration above that needed for 201 (flowers or bolls). This means that high rates of humidity cotton plant growth will decrease flower and boll r ea and/or low values of evaporation will increase fruit production. Oosterhuis (1997) studied the reasons for Y production (Sawan et al. 2002). low and variable cotton yields in Arkansas, with 231 iii. Contribution of selected climatic factors to variations unusually high insect pressures and the development of in the dependent variable the boll load during an exceptionally hot and dry August. Relative contributions (RC %) for each of the Solutions to the problems are suggested i.e. selection of selected climatic factors to variation in flower and boll tolerant cultivars, effective and timely insect and weed V production is summarized in Table 3 (Sawan et al. 2002). control, adequate irrigation regime, use of proper crop IV Results in this table indicate that evaporation was the monitoring techniques and application of plant growth

regulators. ue ersion I most important climatic factor affecting flower and boll s s production in Egyptian cotton. Sunshine duration is the b) Effect of climatic factors during the development I second climatic factor of importance affecting periods of flowering and boll formation on the XVI production of flowers and bolls. Relative humidity and production of cotton temperature at 1800 h were factors of lower contribution Daily number of flowers and number of bolls per than evaporation and sunshine duration/day. Maximum plant that survived to maturity (dependent variables) temperature made a contribution less than the other during the production stage of the two growing seasons affecting factors. are graphically illustrated in Figures 5 and 6 (Sawan et ) H The highest contribution of evaporation to the al. 1999). Observations used in the statistical analysis ( were obtained during the flowering and boll stage (60 variation in both flower and boll production (Sawan et al. 2002) can, however, be explained in the light of results days for each season), which represent the entire production stage. The entire production stage was found by Ward and Bunce (1986) in sunflower Research Volume (Helianthus annuus). They stated that decreases of divided into four equivalent quarter's periods (15 days humidity at both leaf surfaces reduced photosynthetic each) and used for correlation and regression analyses. rate of the whole leaf for plants grown under a moderate Independent variables, their range and mean Frontier temperature and medium light level. Kaur and Singh values for the two seasons and during the periods of (1992) found in cotton that flower number was flower and boll production are listed in Table 4 (Sawan et decreased by water stress, particularly when applied at al. 1999). Both flower number and boll production show flowering. Seed cotton yield was about halved by water the higher value in the third and fourth quarters of Science stress at flowering, slightly decreased by stress at boll production stage, accounting for about 70% of total of formation, and not significantly affected by stress in the production during the first season and about 80% of the vegetative stage (6-7 weeks after sowing). Orgaz et al. total in the second season. (1992) in field experiments at Cordoba, SW Spain, grew Linear correlation between the climatic factors Journal cotton cultivars Acala SJ-C1, GC-510, Coker-310 and and the studied characteristics, i.e. flower, boll Jean cultivar at evapotranspiration (ET) levels ranging production and boll retention ratio, were calculated Global from 40 to 100% of maximum ET (ETmax) which were based on quarters of the production stage for each generated with sprinkler line irrigation. The water season. Significant relationships (< 0.15) are shown in production function of Jean cultivar was linear; seed Tables 5 and 6 (Sawan et al. 1999). Examining these -1 yield was 5.30 t ha at ETmax (820 mm). In contrast, the tables, it is clear that the fourth quarter of production production function of the three other cultivars was linear stage consistently exhibited the highest R² values up to 85% of ETmax, but leveled off as ET approached regardless of the second quarter for boll retention ratio; ETmax (830 mm) because a fraction of the set bolls did however, less data pairs were used (n = 30 for not open by harvest at high ET levels. These authors combined data of the fourth quarter “n = 15 for each concluded that it is possible to define an optimum ET quarter of each season”) to calculate the relations.

Results obtained from the four quarters of the ranges and means shown in Table 4 (Sawan et al. 1999). production period for each season separately and for For example, maximum temperature and surface soil the combined data of the two seasons, indicated that temperature at 1800 h did not show significant effects in relationships varied markedly from one season to the first season, while this trend differed in the second another. This may be due to the differences between the season. climatic factors in the two seasons; as illustrated by its Multiple linear regression equations obtained from data of the fourth quarter, for: 1. Flower production,

Y = 160.0 + 11.28X1 - 4.45X3 - 2.93X4 - 5.05X5 - 11.3X6 - 0.962X8 + 2.36X9 And R²= 0.672** 2. Boll production,

Y = 125.4 + 13.74X1 - 6.76X3 - 4.34X4 - 6.59X5 - 10.3X6 - 1.25X8 + 2.16X9 201

r With an R² = 0.747**

ea 3. Boll retention ratio, Y Y = 81.93 - 0.272X3 - 2.98X4 + 3.80X7 - 0.210X8 - 0.153X9 24 And its R² = 0.615** The equation obtained from data of the second quarter of production stage for boll retention ratio, Y= 92.81 - 0.107X - 0.453X + o.298X - 0.194X + 0.239X 3 4 7 8 9 And R² = 0.737**

V R² values for these equations ranged from boll production (Table 6). These erratic correlations may

IV 0.615 to 0.747. It could be concluded that these be due to the variations in the values of this factor equations may predict flower and boll production and between the quarters of the production stages, as ue ersion I

s s boll retention ratio from the fourth quarter period within shown from its range and mean values (Table 4) (Sawan

I about 62 to 75% of its actual means. Therefore, these et al. 1999).

XVI equations seem to have practical value. Comparing Burke et al. (1990) pointed out that the Tables 6 and 7 (Sawan et al. 1999), it can be seen that usefulness of the 27.5°C midpoint temperature of the differences in R² between the fourth quarter and the TKW of cotton as a baseline temperature for a thermal entire production period of the two seasons for each of stress index (TSI) was investigated in field trials on flower, boll production, and boll retention ratio were cotton cv. Paymaster 104. This biochemical baseline ) H large (0.266, 0.325, and 0.279 respectively). These and measurements of foliage temperature were used to ( differences are sufficiently large to make a wide gap compare the TSI response with the cotton field under a typical field sampling situation. This could be performance. Foliage temperature was measured with due to the high percentage of flower and boll production hand-held 4°C field of view IR thermometer while plant

Research Volume for the fourth quarter. biomass was measured by destructive harvesting. The Equations obtained from data of the fourth biochemical based TSI and the physically based crop quarter explained more variations of flower, boll water stress index were highly correlated (r² = 0.92) for production and boll retention ratio. Evaporation, cotton across a range of environmental conditions. Frontier humidity and temperature are the principal climatic Reddy et al. (1995) in controlled environmental factors that govern cotton flower and boll production chambers pima cotton cv. S-6 produced less total during the fourth quarter; since they were most strongly biomass at 35.5°C than at 26.9°C and no bolls were Science correlated with the dependent variables studied (Table produced at the higher temperature 40°C. This confirms of 6) (Sawan et al., 1999). the results of this study as maximum temperature Evaporation, that seems to be the most showed negative significant relationship with production important climatic factor, had negative significant variables in the fourth quarter period of the production Journal relationship which means that high evaporation ratio stage. Zhen (1995) found that the most important reduces significantly flower and boll production. factors decreasing cotton yields in Huangchuan County, Maximum temperature, temperature-differentiates and Henan, were low temperatures in spring, high Global maximum humidity also showed negative significant link temperatures and pressure during summer and the with fruiting production, which indicates that these sudden fall in temperature at the beginning of autumn. climatic variables have determinable effect upon Measures to increase yields included the use of the Egyptian cotton fruiting production. Minimum humidity more suitable high-oil cotton cultivars, which mature was positively high correlated in most quarter periods early, and choosing sowing dates and spacing so that for flower, boll production and boll retention ratio. This the best use was made of the light and temperature means that an increase of this factor will increase both resources available. flower and boll production. Maximum temperature is It may appear that the grower would have no sometime positively and sometime negatively linked to control over boll shedding induced by high temperature,

©2016 Global Journals Inc. (US) Studying the Nature Relationship between Climatic Factors and Cotton Production by Different Applied Statistical and Mathematical Ways but this is not necessarily the case. If he can irrigate, he climatic factors for different intervals of days (combined can exert some control over temperature since data of the 2 seasons) (Table 8) (Sawan et al. 2006). transpiring plants have the ability to cool themselves by Evaporation was the most important climatic evaporation. The leaf and canopy temperatures of factor affecting flower and boll production in Egyptian drought-stressed plants can exceed those of plants with cotton. The negative correlation means that high adequate quantity of water by several degrees when air evaporation ratio significantly reduced flower and boll humidity is low (Ehrler 1973). The grower can partially production. High evaporation rates could result in water overcome the adverse effects of high temperature on stress that would slow growth and increase shedding net photosynthesis by spacing plants to adequately rate of flowers and bolls (Sawan et al. 2006). Kaur and expose the leaves. Irrigation may also increase Singh (1992) found in cotton that flower number was photosynthesis by preventing stomata closure during decreased by water stress, particularly when existing at the day. Adequate fertilization is necessary for maximum flowering stage. Seed cotton yield was decreased by rates of photosynthesis. Finally, cultivars appear to differ about 50% when water stress was present at flowering 201 in their heat tolerance (Fisher 1975). Therefore, the stage, slightly decreased by stress at boll formation r ea grower can minimize boll abscission where high stage, and not significantly affected by stress in the Y temperatures occur by selecting a heat-tolerant cultivar, vegetative stage (6-7 weeks after sowing). 251 planting date management, applying an adequate The second most important climatic factor was fertilizer, planting or thinning for optimal plant spacing, minimum humidity, which had a high positive correlation and irrigating as needed to prevent drought stress with flower and boll production, and retention ratio. The (Sawan 2014b). positive correlation means that increased humidity would bring about better boll production. V c) Appropriate time scale for aggregating climatic data IV to predict flowering and boll setting behavior of The third most important climatic factor in our

study was sunshine duration, which showed a ue ersion I s cotton s significant negative relationship with flower and boll

I i. Statistical Analysis production only. The negative relationship between

Statistical analysis was conducted using the sunshine duration and cotton production may be due to XVI procedures outlined in the general linear model (GLM, the fact that the species of the genus Gossypium are SAS Institute, Inc. 1985). Data of dependent and known to be short day plants (Hearn and Constable independent variables, collected for each day of the 1984), so, an increase of sunshine duration above that production stage (60 days in each season), were sufficient to attain good plant growth will decrease ) summed up into intervals of 2, 3, 4, 5, 6 or 10 days. H

flower and boll production. Bhatt (1977) found that ( Data from these intervals were used to compute exposure to daylight over 14 hours and high day relationships between the dependent variables (flower temperature, individually or in combination, delayed and boll setting and boll retention) and the independent flowering of the Upland cotton cv. J34. Although average variables (climatic factors) in the form of simple sunshine duration in our study was only 11.7 h, yet it Research Volume correlation coefficients for each season. Comparisons could reach 13 h, which, in combination with high between the values of “r” were done to determine the maximum temperatures (up to 38.8°C), may have best interval of days for determining effective adversely affected reproductive growth. Frontier relationships. The α-level for significance was P < 0.15. Maximum air temperature, temperature

The climatic factors attaining a probability level of magnitude and surface soil temperature at 1800 h show significance not exceeding 0.15 were deemed important significant negative relationships with flower and boll Science

(affecting the dependent variables), selected and production only. Meanwhile, the least important factors of combined with dependent variable in multiple regression were surface soil temperature at 0600 h and minimum analysis to obtain a convenient predictive equation air temperature. Our results indicate that evaporation (Cady and Allen 1972). Multiple linear regression

was the most effective climatic factor affecting cotton Journal equations (using stepwise method) comprising selected boll production. As the sign of the relationship was predictive variables were computed for the determined negative, this means that an increase in evaporation interval and coefficients of multiple determinations (R²) caused a significant reduction in boll number (Sawan et Global were calculated to measure the efficiency of the al. 2006). Thus, applying specific treatments, such as an regression models in explaining the variation in data. additional irrigation or the use of plant growth regulators

Correlation and regression analyses were computed (PGR) that would decrease the deleterious effect of according to Draper and Smith (1966) (Sawan et al. evaporation after boll formation, could contribute to an

2006). increase in cotton boll production and retention, and a. Correlation estimates consequently an increase in cotton yield. In this Significant simple correlation coefficients were connection, Meek et al. (1999) in a field experiment in estimated between the production variables and studied Arkansas found that application of 3 or 6 kg glycine

betaine (PGR) ha-1 to cotton plants under mild water opportunity to avoid any adverse effect for weather stress increased yield. factors on cotton production through applying Comparing results for the different intervals of appropriate cultural practices such as adequate days with those from daily observation (Table 8) (Sawan irrigation regime or utilization of plant growth regulators. et al. 2006), the 5-day interval appeared to be the most This proposal would be true if the fluctuations in weather suitable interval, which actually revealed a more solid conditions were not extreme. Our recommendation and more obvious relationships between climatic factors would be the accumulation 5-day climatic data, and use and production characters. This was in fact indicated by this information to select the adequate cultural practices the higher R2 values obtained when using the 5-day (such as an additional irrigation or utilization of plant intervals. The 5-day interval may be the most suitable growth regulators) that would help circumvent the interval for diminishing the daily fluctuations between the unfavorable effects of climatic factors. In case of sharp factors under study to clear these relations comparing fluctuations in climatic factors, data could be collected

201 with the other intervals. However, it seems that this daily, and when stability of climatic conditions is r conception is true provided that the fluctuations in restored, the 5-day accumulation of weather data could ea

Y climatic conditions are limited or minimal. Therefore, it be used again (Sawan et al. 2006).

would be the most efficient interval used to help 26 d) Response of flower and boll development to climate circumvent the unfavorable effect of climatic factors. factors before and after anthesis day This finding gives researchers and producers a chance The effects of specific climatic factors during to deal with condensed rather than daily weather data. both pre- and post-anthesis periods on boll production b. Regression models and retention are mostly unknown. However, by V Multiple linear regression equations were determining the relationship of climatic factors with IV estimated using the stepwise multiple regression flower and boll production and retention, the overall level

ue ersion I technique to express the relation between cotton s of production can be possibly predicted. Thus, an s production variables [number of flowers (Y1); bolls per understanding of these relationships may help

I plant (Y2); and boll retention ratio (Y3)] and the studied physiologists to determine control mechanisms of XVI climatic factors (Table 9) (Sawan et al. 2006). production in cotton plants (Sawan et al. 2005). Daily Evaporation and surface soil temperature at records of the climatic factors (independent variables), 1800 h, sunshine duration and minimum humidity were taken for each day during production stage in any accounted for a highly significant amount of variation (P season including two additional periods of 15 days ) < 0.05) in cotton production variables, with the equation before and after the production stage (Table 10) (Sawan H ( obtained for the 5-day interval showing a high degree of et al. 2005). certainty. The R² values for the 5-day interval were higher In each season, the data of the dependent and than those obtained from daily data for each of the independent variables (68 and 62 days) were regarded cotton production variables. Also, the 5-day interval as the original file (a file which contains the daily Research Volume gave more efficient and stable estimates than the other recorded data for any variable during a specific period). studied intervals (data not shown) (Sawan et al. 2006). Fifteen other files before and another 15 after the The R² values for these equations clearly indicate the production stage were obtained by fixing the dependent Frontier importance of such equations since the climatic factors variable data, while moving the independent variable involved explained about 59 to 62% of the variation data at steps each of 1 day (either before or after found in the dependent variables. production stage) in a matter similar to a sliding role Science During the production stage, an accurate (Sawan et al. 2005). The following is an example (in the

of weather forecast for the next 10 days would provide an first season):

Data of any dependent Any independent variable variable (for each

Journal (for each climatic factors) flowers and bolls)

In case of original file and In case of original file and

Global Production stage files before production files after production File stage stage Date Days Date Days Date Days Original file 23 Jun-29 Aug 68 23 Jun-29 Aug 68 23 Jun-29 Aug 68 st 1 new file 23 Jun-29 Aug 68 22 Jun-28 Aug 68 24 Jun-30 Aug 68 nd 2 new file 23 Jun-29 Aug 68 21 Jun-27 Aug 68 25 Jun-31 Aug 68 th 15 new file 23 Jun-29 Aug 68 8 Jun-14 Aug 68 8 Jul -13 Sept 68 Thus, the climate data were organized into days the first year and 62 days the second year) and 15 records according to the complete production stage (68 day, 14 day, 13 day,….and 1 day periods both before

©2016 Global Journals Inc. (US) Studying the Nature Relationship between Climatic Factors and Cotton Production by Different Applied Statistical and Mathematical Ways and after the production stage. This produced 31 maximum humidity showed insignificant correlation with climate periods per year that were analyzed for their both flower and boll production except for one day relationships with cotton flowering and boll production period only (the 15th day). Generally, the results in the (Sawan et al. 2005). two seasons indicated that daily evaporation, sunshine i. Correlation estimates duration and minimum humidity were the most effective A. Results of the correlation between climatic factors and consistent climatic factors, which exhibited and each of flower and boll production during the significant relationships with the production variables for 15 day periods before flowering day (Tables 11 and all the 15 day periods before anthesis in both seasons 12) revealed the following (Sawan et al. 2005): (Sawan et al. 2005). The factors in this study which had been found First season to be associated with boll development are the climatic Daily evaporation and sunshine duration factors that would influence water loss between plant showed consistent negative and statistically significant and atmosphere (low evaporation demand, high 201

correlations with both flower and boll production for r humidity, and shorter solar duration). This can lead to each of the 15 moving window periods before anthesis ea direct effects on the fruiting forms themselves and Y (Table 11). Evaporation appeared to be the most inhibitory effects on mid-afternoon photosynthetic rates important climate factor affecting flower and boll 271 even under well-watered conditions. Boyer et al. (1980) production. found that soybean plants with ample water supplies Daily maximum and minimum humidity showed can experience water deficits due to high transpiration consistent positive and statistically significant rates. Also, Human et al. (1990) stated that, when correlations with both flower and boll production in most sunflower plants were grown under controlled V of the 15 moving window periods before anthesis (Table temperature regimes, water stress during budding, IV 11) (Sawan et al. 2005). Maximum daily temperature

anthesis and seed filling, the CO2 uptake rate per unit ue ersion I showed low but significant negative correlation with s s leaf area as well as total uptake rate per plant,

flower production during the 2-5, 8, and 10 day periods I significantly diminished with stress, while this effect before anthesis. Minimum daily temperatures generally resulted in a significant decrease in yield per plant. XVI showed insignificant correlation with both production variables. The diurnal temperature range showed few B. The correlation between climatic factors and each of correlations with flower and boll production. Daily soil boll production and boll retention over a period of surface temperature at 0600 h showed a significant 15 day periods after flowering (boll setting) day

) positive correlation with boll production during the (Tables 13 and 14) (Sawan et al. 2005) revealed the H period extending from the 11-15 day period before following: ( anthesis, while its effect on flowering was confined only First season to the 12 and the 15 day periods prior anthesis. Daily Daily evaporation showed significant negative soil surface temperature at 1800 h showed a significant correlation with number of bolls for all the 15 day Research Volume negative correlation with flower production during the 2- periods after flowering (Table 13). Meanwhile its 10 day periods before anthesis. relationship with retention ratio was positive and

Second season significant in the 9-15 day periods after flowering. Daily Frontier Daily Evaporation, the diurnal temperature sunshine duration was positively and significantly range, and sunshine duration were negatively and correlated with boll retention ratio during the 5-13 day significantly correlated with both flower and boll periods after flowering. Daily maximum humidity had a

Science production in all the 15 day periods, while maximum significant positive correlation with the number of bolls daily temperature was negatively and significantly during the first 8 day periods after flowering, while daily of related to flower and boll formation during the 2- 5 day minimum humidity had the same correlation for only the periods before anthesis (Table 12) (Sawan et al. 2005). 11, and 12 day periods after flowering. Daily maximum

Journal Minimum daily temperature showed positive and minimum temperatures and the diurnal temperature and statistically significant correlations with both range, as well as soil surface temperature at 1800 did production variables only during the 9-15 day periods not show significant relationships with both number of

Global before anthesis, while daily minimum humidity showed bolls and retention ratio. Daily soil surface temperature the same correlation trend in all the 15 moving window at 0600 h had a significant negative correlation with boll periods before anthesis. Daily soil surface temperature retention ratio during the 3-7 day periods after anthesis. at 0600 h was positively and significantly correlated with Second season flower and boll production for the 12, 14, and 15 day Daily evaporation, soil surface temperature at periods prior to anthesis only. Daily soil surface 1800 h, and sunshine duration had a significant negative temperature at 1800 h showed negative and significant correlation with number of bolls in all the 15 day periods correlations with both production variables only during after anthesis (Table 14) (Sawan et al. 2005). Daily the first and second day periods before flowering. Daily maximum and minimum temperatures and the diurnal

temperature range, and soil surface temperature at 0600 humidity and evaporation is less than 4:1 (Sawan et al. h had a negative correlation with boll production. Their 2005). significant effects were observed during the 1, and 10- Evaporation appeared to be the most important 15 day periods for maximum temperature, and the 1-5, climatic factor (in each of the 15-day periods both prior and 9-12 day periods for the diurnal temperatures to and after initiation of individual bolls) affecting number range. Meanwhile, the daily minimum temperature and of flowers or harvested bolls in Egyptian cotton. High soil surface temperature at 0600 h had a significant daily evaporation rates could result in water stress that negative correlation only during the 13-15 day periods. would slow growth and increase shedding rate of Daily minimum humidity had a significant positive flowers and bolls. The second most important climatic correlation with number of bolls during the first 5 day factor in our study was humidity. Effect of maximum periods, and the 9-15 day periods after anthesis. Daily humidity varied markedly from the first season to the maximum humidity showed no significant relation to second one, where it was significantly correlated with 201 number of bolls produced, and further no significant the dependent variables in the first season, while the r relation was observed between any of the studied inverse pattern was true in the second season. This ea

Y climatic factors and boll retention ratio. diverse effect may be due to the differences in the The results in the two seasons indicated that 28 values of this factor in the two seasons; where it was on evaporation and humidity, followed by sunshine duration average 87% in the first season, and only 73% in the had obvious correlation with boll production. From the second season (Table 10) (Sawan et al. 2005). Also, was results obtained, it appeared that the effects of air found that, when the average value of minimum humidity temperature, and soil surface temperature tended to be exceeded the half average value of maximum humidity, V masked in the first season, i.e. did not show any the minimum humidity can substitute the maximum

IV significant effects in the first season on the number of humidity on affecting number of flowers or harvested

ue ersion I bolls per plant. However, these effects were found to be bolls. In the first season (Table 10) the average value of s s significant in the second season. These seasonal minimum humidity was less than half of the value of I differences in the impacts of the previously mentioned maximum humidity (30.2/85.6 = 0.35), while in the

XVI climatic factors on the number of bolls per plant are second season it was higher than half of maximum most likely ascribed to the sensible variation in humidity (39.1/72.9 = 0.54). evaporation values in the two studied seasons where -1 -1 The third most important climatic factor in our their means were 10.2 mm.d and 5.9 mm d in the first study was sunshine duration, which showed a

) and second seasons, respectively (Sawan et al. 2005). significant negative relationship with boll production. The H There is an important question here concerning, ( r values of (Tables 11-14) (Sawan et al. 2005) indicated if there is a way for forecasting when evaporation values that the relationship between the dependent and would mask the effect of the previous climatic factors. independent variables preceding flowering (production The answer would be possibly achieved through relating stage) generally exceeded in value the relationship Research Volume humidity values to evaporation values which are between them during the entire and late periods of naturally liable to some fluctuations from one season to production stage. In fact, understanding the effects of another (Sawan et al. 2005). It was found that the ratio climatic factors on cotton production during the

Frontier between the mean of maximum humidity and the mean previously mentioned periods would have marked of evaporation in the first season was 85.8/10.2 = 8.37, consequences on the overall level of cotton production, while in the second season this ratio was 12.4. On the which could be predictable depending on those other hand, the ratio between the mean minimum Science relationships. humidity and the mean of evaporation in the first season of ii. Regression models was 30.8/10.2 = 3.02, while in the second season this An attempt was carried out to investigate the ratio was 6.75 (Table 13) (Sawan et al. 2005). From these ratios it seems that minimum humidity which is effect of climatic factors on cotton production via

Journal prediction equations including the important climatic closely related to evaporation is more sensitive than the factors responsible for the majority of total variability in ratio between maximum humidity and evaporation. It can be seen from the results and formulas that when the cotton flower and boll production. Hence, regression Global models were established using the stepwise multiple ratio between minimum humidity and evaporation is regression technique to express the relationship small (3:1), the effects of air temperature, and soil between each of the number of flowers and bolls/plant surface temperature were hindered by the effect of and boll retention ratio (Y), with the climatic factors, for evaporation, i.e. the effect of these climatic factors were each of the a) 5, b) 10, and c) 15 day periods either prior not significant. However, when this ratio is high (6:1), the to or after initiation of individual bolls (Tables 15 and 16) effects of these factors were found to be significant. (Sawan et al. 2005). Accordingly, it could be generally stated that the effects of air, and soil surface temperatures could be masked Concerning the effect of prior days the results by evaporation when the ratio between minimum indicated that evaporation, sunshine duration, and the

©2016 Global Journals Inc. (US) Studying the Nature Relationship between Climatic Factors and Cotton Production by Different Applied Statistical and Mathematical Ways diurnal temperature range were the most effective and humidity appeared to have secondary effects, yet they consistent climatic factors affecting cotton flower and are in fact important players. The importance of boll production (Table 15). The fourth effective climatic sunshine duration has been alluded to by Moseley et al. factor in this respect was minimum humidity. On the (1994) and Oosterhuis (1997). Also, Mergeai and Demol other hand, for the periods after flower the results (1991) found that cotton yield was assisted by obtained from the equations (Table 16) indicated that intermediate relative humidity. evaporation was the most effective and consistent e) Cotton (Gossypium barbadense) flower and boll climatic factor affecting number of harvested bolls. production as affected by climatic factors and soil Regression models obtained demonstrate of moisture status each independent variable under study as an efficient i. Basic Variables and important factor (Sawan et al. 2005). Meanwhile, A. Dependant variables as defined above: (Y ) and (Y ) they explained a sensible proportion of the variation in 1 2 (Sawan et al. 2010). flower and boll production, as indicated by their R², 201

B. Independent variables (Xs): r which ranged between 0.14-0.62, where most of R2 prior 1. Irrigation on day 1 = 1. Otherwise, enter 0.0 (soil ea to flower opening were about 0.50 and after flowering all Y moisture status) (X1) but one are less than 0.50. These results agree with 2. The first and second days after the day of irrigation 291 Miller et al. (1996) in their regression study of the relation (soil moisture status) = 1. Otherwise, enter 0.0 (X2). of yield with rainfall and temperature. They suggested 3. The day prior to the day of irrigation (soil moisture that the other 0.50 of variation related to management status) to check for possible moisture deficiency on practices, which can be the same in this study. Also, the that day = 1. Otherwise, enter 0.0 (X3). regression models indicated that the relationships V 4. Number of days during days 1 (day of flowering)-12 between the number of flowers and bolls per plant and IV (after flowering) that temperature equaled or

the studied climatic factors for the 15 day period before ue ersion I exceeded 37.5 °C (high temperature) (X4). s s or after flowering (Y3) in each season explained the

5. Range of temperature (diurnal temperature) [°C] on I highly significant magnitude of variation (P < 0.05). The day 1 (day of flowering) (X5). R² values for the 15 day periods before and after XVI 6. Broadest range of temperature [°C] over days 1 flowering were higher than most of those obtained for (day of flowering)-12 (after flowering) (X6). each of the 5 and the 10 day periods before or after 7. Minimum relative humidity (minRH) [%] during day 1 flowering. This clarifies that the effects of the climatic (day of flowering) (X7).

factors during the 15 day periods before or after ) 8. Maximum relative humidity (maxRH) [%] during day flowering are very important for Egyptian cotton boll H 1 (day of flowering) (X8). ( production and retention. Thus, an accurate climatic 9. Minimum relative humidity (minRH) [%] during day 2 forecast for the effect of these 15 day periods provides (after flowering) (X9). an opportunity to avoid any possible adverse effects of 10. Maximum relative humidity (maxRH) [%] during day unusual climatic conditions before flowering or after boll Research Volume 2 (after flowering) (X10). formation by utilizing additional treatments and/or 11. Largest maximum relative humidity (maxRH) [%] on adopting proper precautions to avoid flower and boll days 3-6 (after flowering) (X11). reduction. Frontier 12. Lowest minimum relative humidity (minRH) [%] on The main climatic factors from this study days 3-6 (after flowering) (X12). (Sawan et al. 2005) affecting the number of flowers and 13. Largest maximum relative humidity (maxRH) [%] on bolls, and by implication yield, is evaporation, sunshine days 7-12 (after flowering) (X13). Science duration and minimum humidity, with evaporation (water

14. Lowest minimum relative humidity (minRH) [%] on of stress) being by far the most important factor. Various days 7-12 (after flowering) (X14). activities have been suggested to partially overcome 15. Lowest minimum relative humidity (minRH) [%] on water stress. Temperature conditions during the days 50-52 (after flowering) (X15). Journal reproduction growth stage of cotton in Egypt do not 16. Daily light period (hour) (X16). appear to limit growth even though they are above the optimum for cotton growth (Sawan 2013). This is ii. Statistical analysis Global contradictory to the finding of Holaday et al. (1997). A Simple correlation coefficients between the possible reason for that contradiction is that the effects initial group of independent variables (climatic factors of evaporation rate and humidity were not taken into and soil moisture status) (X’s) and the corresponding consideration in the research studies conducted by dependent variables (Y’s) were computed for each other researchers in other countries. The matter of fact is season and the combined data of the two seasons. that temperature and evaporation are closely related to These correlation coefficients helped determine the each other to such an extent that the higher evaporation significant climatic factors and soil moisture status rate could possible mask the effect of temperature affecting the cotton production variables. The level for (Sawan 2014a). Sunshine duration and minimum significance was P < 0.15. Those climatic factors and

soil moisture status attaining a probability level of correlation with flower and boll production. The positive significance not exceeding 0.15 were deemed important relationship between relative humidity with flower and (affecting the dependent variables) (Sawan et al. 2010). boll production means that low relative humidity rate Those factors were combined with dependent variables reduces significantly cotton flower and boll production. in multiple regression analysis to obtain a predictive This may be due to greater plant water deficits when model as described by Cady and Allen (1972). Multiple relative humidity decreases. Also, the negative linear regression equations (using the stepwise method) relationship between the variables of maximum comprising selected predictive variables were computed temperature exceeding 37.5 °C (X4), range of diurnal for the determined interval. Coefficients of multiple temperature on flowering (X5), and sunshine duration determinations (R2) were calculated to measure the (X16) with flower and boll production revealed that the efficiency of the regression models in explaining the increased values of these factors had a detrimental variation in data. Correlation and regression analysis effect upon Egyptian cotton fruit production. Results

201 were computed according to Draper and Smith (1985) obtained from the production stage of each season, and r using the procedures outlined in the general linear the combined data of the two seasons showed marked ea

Y model (GLM) (SAS Institute 1985). variability in the relationships of some climatic variables with the dependent variables. This may be best 30 a. Correlation estimates Simple correlation coefficients between the explained by the differences between climatic factors in independent variables and the dependent variables for the two seasons as illustrated by the ranges and means flower and boll production in each season and shown in Table 1. For example, maximum temperature combined data of the two seasons are shown in Tables exceeding 37.5 °C (X4) and minRH did not show V 17-19 (Sawan et al. 2010). The simple correlation values significant relations in the first season, while that trend IV indicated clearly that relative humidity was the most differed in the second season. These results indicated

ue ersion I important climatic factor. Relative humidity also had a that relative humidity was the most effective and s s significant positive relationship with flower and boll consistent climatic factor affecting boll production. The I production; except for lowest minRH on days 50-52 second most important climatic factor in our study was XVI (after flowering). Flower and boll production were sunshine duration, which showed a significant negative positively and highly correlated with the variables of relationship with boll production. largest maxRH (X11, X13) and lowest minRH (X14, X15) b. Multiple linear regression models, beside in the first season, minRH (X7, X9), largest maxRH contribution of climatic factors and soil moisture

) (X11), and lowest minRH (X12, X14, X15) in the second status to variations in the dependent variables H

( season, and the combined data of the two seasons. Regression models were established using the Effect of maxRH varied markedly from the first to the stepwise multiple regression technique to express the second season. MaxRH was significantly correlated relationship between the number of flowers and bolls with the dependent variables in the first season, while per plant-1 (Y) with the climatic factors and soil moisture Research Volume the inverse pattern was true in the second season. This status (Table 20). Relative humidity (%) was the most diverse effect may be best explained by the differences important climatic factor affecting flower and boll of 87% in the first season, and only 73% in the second production in Egyptian cotton [minRH during day 1 (X7),

Frontier season (Table 1). Also, when the average value of minRH during day 2 (X9), largest maxRH on days 3-6 minRH exceeded the half average value of maxRH, the (X11), lowest minRH on days 3-6 (X12), largest maxRH minRH can substitute for the maxRH on affecting on days 7-12 (X13), lowest minRH on days 7-12 (X14)

Science number of flowers or harvested bolls. In the first season and lowest minRH on days 50-52 (X15)]. Sunshine (Table 1) the average value of minRH was less than half duration (X16) was the second climatic factor of

of of the value of maxRH (30.2/85.6 = 0.35), while in the importance affecting production of flowers and bolls. second season it was higher than half of maxRH Maximum temperature (X4), broadest range of

Journal (39.1/72.9 = 0.54). Sunshine duration (X16) showed a temperature (X6) and soil moisture status (X1) made a significant negative relation with fruit production in the contribution affecting flower and boll production. The first and second seasons and the combined data of the soil moisture variables (X2, X3), and climatic factors (X5,

Global two seasons except for boll production in the first X8, X10) were not included in the equations since they season, which was not significant. Flower and boll had very little effects on production of cotton flowers production were negatively correlated in the second and bolls. season and the combined data of the two seasons with Relative humidity showed the highest the number of days during days 1 -12 that temperature contribution to the variation in both flower and boll equaled or exceeded 37.5 °C (X4), range of temperature production (Table 20). This finding can be explained in (diurnal temperature) on flowering day (X5) and the light of results found by Ward and Bunce (1986) in broadest range of temperature over days 1-12 (X6). The sunflower (Helianthus annuus). They stated that soil moisture status showed low and insignificant decreases of relative humidity on both leaf surfaces

©2016 Global Journals Inc. (US) Studying the Nature Relationship between Climatic Factors and Cotton Production by Different Applied Statistical and Mathematical Ways reduced photosynthetic rate of the whole leaf for plants minimum relative humidity value and flower and boll grown under a moderate temperature and medium light production, indicate that low evaporation rate, short level. period of sunshine duration and high value of minimum Reddy et al. (1993) found that cotton humidity would enhance flower and boll formation. The (Gossypium hirsutum) fruit retention decreased rapidly 5-day interval was found to give adequate and sensible as the time of exposure to 40°C increased. Gutiérrez relationships between climatic factors and cotton and López (2003) studied the effects of heat on the yield production growth under Egyptian conditions when of cotton in Andalucia, Spain, during 1991-98, and compared with other intervals and daily observations. It found that high temperatures were implicated in the may be concluded that the 5-day accumulation of reduction of unit production. There was a significant climatic data during the production stage, in the negative relationship between average production and absence of sharp fluctuations in these factors, could be number of days with temperatures greater than 40°C satisfactorily used to forecast adverse effects on cotton and the number of days with minimum temperatures production and the application of appropriate 201 greater than 20°C. Wise et al. (2004) indicated that production practices circumvent possible production r ea restrictions to photosynthesis could limit plant growth at shortage. Y high temperature in a variety of ways. In addition to Finally, the early prediction of possible adverse 311 increasing photorespiration, high temperatures (35- effects of climatic factors might modify their effect on 42°C) can cause direct injury to the photosynthetic production of Egyptian cotton. Minimizing deleterious apparatus. Both carbon metabolism and thylakoid effects through the application of proper management reactions have been suggested as the primary site of practices, such as, adequate irrigation regime, and injury at these temperatures. utilization of specific plant growth regulators could limit V

Regression models obtained explained a the negative effects of some climatic factors. IV

sensible proportion of the variation in flower and boll ue ersion I s production, as indicated by their R2, which ranged References Références Referencias s between 0.53-0.72. These results agree with Miller et al. I 1. Barbour MM, Farquhar GD (2000) Relative humidity- (1996) in their regression study of the relation of yield XVI and ABA-induced variation in carbon and oxygen with rainfall and temperature. They suggested that the 2 isotope ratios of cotton leaves. Plant, Cell and other R 0.50 of variation was related to management Environment 23: 473-485. practices, which coincide with the findings of this study. 2. Bhatt JG (1977) Growth and flowering of cotton

Thus, an accurate climatic forecast for the effect of the ) (Gossypium hirsutum L.) as affected by daylength 5-7 day period during flowering may provide an H ( opportunity to avoid possible adverse effects of unusual and temperature. Journal of Agricultural Science 89: 583-588. climatic conditions before flowering or after boll 3. Boyer JS, Johnson RR, Saupe SG (1980) Afternoon formation by utilizing additional treatments and/or

water deficits and grain yields in old and new Research Volume adopting proper precautions to avoid flower and boll soybean cultivars. Agron J 72: 981-986. reduction (Sawan 2013). 4. Burke JJ, Mahan JR, Hatfield JL (1988) Crop

IV. Conclusions specific thermal kinetic windows in relation to wheat Frontier and cotton biomass production. Agron J 80: 553- Evaporation, sunshine duration, relative 556. humidity, surface soil temperature at 1800 h, and 5. Cady FB, Allen DM (1972) Combining experiments maximum temperature, were the most significant Science to predict future yield data. Agron J 64: 211-214. climatic factors affecting flower and boll production of

of Egyptian cotton. Also, it could be concluded that the 6. Draper NR, Smith H (1966) Applied Regression fourth quarter period of the production stage is the most Analysis. John Wiley & Sons Ltd., New York, NY. 407 appropriate and usable production time to collect data pp. Journal for determining efficient prediction equations for cotton 7. El-Zik KM (1980) The cotton plant - its growth and flower and boll production in Egypt, and making development. Western Cotton Prod. Conf. Summary Proc., Fresno, CA, p. 18-21. valuable recommendations. Further, it could be Global concluded that during the 15-day periods both prior to 8. Guinn G (1982) Causes of square and boll shedding and after initiation of individual bolls, evaporation, in cotton. USDA Tech. Bull. 1672. USDA, minimum relative humidity and sunshine duration, were Washington, DC. the most significant climatic factors affecting cotton 9. Gutiérrez Mas JC, López M (2003) Heat, limitation of flower and boll production and retention in Egyptian yields of cotton in Andalucia. Agricultura, Revista cotton. The negative correlation between each of Agropecuaria 72: 690-692. evaporation and sunshine duration with flower and boll 10. Hearn AB, Constable GA (1984) The Physiology of formation along with the positive correlation between Tropical Food Crops. Chapter 14: Cotton. P. 495-527

(Edited by Goldsworth, P.R.; Fisher, N.M.), John growth. Agriculture Ecosystems & Environment 54: Wiley & Sons Ltd., NY. 664 pp. 17-29. 11. Hodges HF, Reddy KR, McKinion JM, Reddy VR 23. Reddy KR, Robana RR, Hodges HF, Liu XJ, (1993) Temperature effects on cotton. Bulletin Mckinion JM (1998) Interactions of CO2 enrichment Mississippi Agricultural and Forestry Experiment and temperature on cotton growth and leaf Station No. 990: 15. characteristics. Environ Exp Bot 39: 117-129. 12. Holaday AS, Haigler CH, Srinivas NG, Martin LK, 24. SAS Institute, Inc (1985) SAS User’s Guide: Taylor JG (1997) Alterations of leaf photosynthesis Statistics. 5th ed. SAS Institute, Inc., Cary, NC. pp. and fiber cellulose synthesis by cool night 433-506. temperatures. In Proceedings Beltwide Cotton 25. Sawan ZM (2013) Studying the relationship between Conferences, January 6-10, New Orleans, LA, climatic factors and cotton production by different National Cotton Council, TN pp 1435-1436. applied methods. Journal of Stress Physiology &

201 13. Human JJ, Du Toit D, Bezuidenhout HD, De Bruyn Biochemistry 9: 251-278. r LP (1990) The influence of plant water stress on net 26. Sawan ZM (2014a) Climatic factors: evaporation, ea

Y photosynthesis and yield of sunflower (Helianthus sunshine, relative humidity, soil and air temperature annuus L.). J Agron Crop Sci 164: 231-241. and cotton production. Annual Research & Review 32 14. Kaur R, Singh OS (1992) Response of growth in Biology,4: 2835-2855. stages of cotton varieties to moisture stress. Indian 27. Sawan ZM (2014b) Nature relation between climatic J Plant Physiol 35: 182-185. variables and cotton production. Journal of Stress 15. Meek CR, Oosterhuis DM, Steger AT (1999) Drought Physiology & Biochemistry 9: 251-278.

V tolerance and foliar sprays of glycine betaine. In 28. Sawan ZM, Hanna LI, Gad El Karim GhA,

IV Proceedings Beltwide Cotton Conferences, January McCuistions WL (2002) Relationships between

ue ersion I 3-7, Orlando, FL, USA. Memphis, USA. National climatic factors and flower and boll production in s s Cotton Council, 559-561. Egyptian cotton (Gossypium barbadense). Journal I 16. Mergeai G, Demol J (1991) Contribution to the study of Arid Environment 52: 499-516.

XVI of the effect of various meteorological factors on 29. Sawan ZM, Hanna LI, McCuistions WL (1999) Effect production and quality of cotton (Gossypium of climatic factors during the development periods hirsutum L.) fibers. Bulletin des Recherches of flowering and boll formation on the production of Agronomiqued de Gembloux 26: 113-124. Egyptian cotton (Gossypium barbadense).

) 17. Miller JK, Krieg DR, Paterson RE (1996) Relationship Agronomie 19: 435-443.

H ( between dryland cotton yields and weather 30. Sawan ZM, Hanna LI, McCuistions WL (2005) parameters on the Southern Hig Plains. In Response of flower and boll development to climatic Proceedings Beltwide Cotton Conferences, January factors before and after anthesis in Egyptian cotton. 9-12, Nashville, TN, USA, Memphis, USA, National Climate Research 29: 167-179.

Research Volume Cotton Council, 1165-1166. 31. Sawan ZM, Hanna LI, McCuistions WL (2006) 18. Moseley D, Landivar JA, Locke D (1994) Evaluation Appropriate time scale for aggregating climatic data of the effect of methanol on cotton growth and yield to predict flowering and boll setting behaviour of

Frontier under dry-land and irrigated conditions. In cotton in Egypt. Communication in Biometry and Proceedings Beltwide Cotton Conferences, January Crop Science 1: 11-19. 5-8, San Diego, CA, USA. Memphis, USA. National 32. Sawan ZM, Hanna LI, McCuistions WL, Foote RJ

Science Cotton Council, 1293-1294. (2010) Egyptian cotton (Gossypium barbadense) 19. Oosterhuis DM (1997) Effect of temperature flower and boll production as affected by climatic of extremes on cotton yields in Arkansas. In factors and soil moisture status. Theoretical and Proceedings of the Cotton Research Meeting, held Applied Climatology 99: 217-227. at Monticello, Arkansas, USA, February 13 [Edited 33. Schrader SM, Wise RR, Wacholtz WF, Ort DR, Journal by Oosterhuis, D.M.; Stewart, J.M.]. Special Report- Sharkey TD (2004) Thylakoid membrane responses Agricultural Experiment Station, Division of to moderately high leaf temperature in Pima Cotton.

Global Agriculture, University of Arkansas, No. 183: 94-98. Plant, Cell and Environment 27: 725-735. 20. Orgaz F, Mateos L, Fereres E (1992) Season length 34. Ward DA, Bunce JA (1986) Responses of net and cultivar determine the optimum evapotranspira- photosynthesis and conductance to independent tion deficit in cotton. Agron J 84: 700-706. changes in the humidity environments of the upper 21. Reddy KR, Hodges HF, McKinion JM (1993) and lower surfaces of leaves of sunflower and Temperature effects on Pima cotton leaf growth. soybean. J Exper Botany 37: 1842-1853. Agron J 85: 681-686. 35. Wise RR, Olson AJ, Schrader SM, Sharkey TD 22. Reddy KR, Hodges HF, McKinion JM (1995) Carbon (2004) Electron transport is the functional limitation dioxide and temperature effects on pima cotton of photosynthesis in field-grown Pima cotton plants

at high temperature. Plant, Cell and Environment 27: the plant growth regulator PGR-IV under water- 717-724. deficit stress. Environ Exper Botany 38: 7-14. 36. Xiao J-F, Liu Z-G, Yu X-G, Zhang J-Y, Duan A-W 38. Zhou Z-G, Meng Y-L, Shi Pei, Shen Y-Q, Jia Z-K (2000) Effects of different water application on lint (2000) Study of the relationship between boll weight yield and fiber quality of cotton under drip irrigation. in wheat-cotton double cropping and Acta Gossypii Sinica 12: 194-197. meteorological factors at boll-forming stage. Acta 37. Zhao D-L, Oosterhuis D (1997) Physiological Gossypii Sinica 12: 122-126. response of growth chamber-grown cotton plants to

i ure e ends 201 r ea Y

331 V IV ue ersion I s s I XVI Average number of flowers and bolls per plant

) H (

Days after begining of flowering

Figure 1 : Daily number of flowers and bolls during the production stage (68 days) in the first season (I) for the Research Volume Egyptian cotton cultivar Giza 75 (Gossypium barbadense L.) grown in uniform field trial at the experimental farm of the Agricultural Research Centre, Giza (30°N, 31°:28'E), Egypt. The soil texture was a clay loam, with an alluvial substratum, (pH = 8.07). Total water consumptive use during the growing season supplied by surface irrigation was Frontier 3 -1 about 6000 m ha . No rainfall occurred during the growing season. The sampling size was 261 plants (Sawan et al. 2005)

Science

Journal

Global

201 r ea Y

34 Average number of flowers and bolls per plant V

IV Days after begining of flowering ue ersion I

s Figure 2 : Daily number of flowers and bolls during the production stage (62 days) in the second season (II) for the s Egyptian cotton cultivar Giza 75 (Gossypium barbadense L.) grown in uniform field trial at the experimental farm of I the Agricultural Research Centre, Giza (30°N, 31°:28'E), Egypt. The soil texture was a clay loam, with an alluvial XVI substratum, (pH = 8.07). Total water consumptive use during the growing season supplied by surface irrigation was about 6000 m3ha-1. No rainfall occurred during the growing season. The sampling size was 358 plants (Sawan et al. 2005) ) H

( Research Volume Frontier Boll of retention retio (%) Science of

Journal Days after begining of flowering

Global Figure 3 :Daily boll retention ratio during the production stage (68 days) in the first season (I) for the Egyptian cotton cultivar Giza 75 (Gossypium barbadense L.) grown in uniform field trial at the experimental farm of the Agricultural

Research Centre, Giza (30°N, 31°:28'E at an altitude 19 m), Egypt. The soil texture was a clay loam, with an alluvial substratum, (pH = 8.07). Total water consumptive use during the growing season supplied by surface irrigation was about 6000 m3 ha-1. No rainfall occurred during the growing season. The sampling size was 261 plants (Sawan et al. 2002)

Boll retention retio (%) 201 r ea Y

351 Days after begning of flowering

Figure 4 : Daily boll retention ratio during the production stage (62 days) in the second (II) for the Egyptian cotton

cultivar Giza 75 (Gossypium barbadense L.) grown in uniform field trial at the experimental farm of the Agricultural V Research Centre, Giza (30°N, 31°:28'E at an altitude 19 m), Egypt. The soil texture was a clay loam, with an alluvial IV substratum, (pH = 8.07). Total water consumptive use during the growing season supplied by surface irrigation was

3 -1 ue ersion I s about 6000 m ha . No rainfall occurred during the growing season. The sampling size was 358 plants (Sawan et al. s

2002) I XVI

) H (

Research Volume Frontier Science of Average number of flowers and bolls per plant Journal Days after begining of flowering Figure 5 : Daily number of flowers and bolls during the production stage (60 days) in the first season (I) for the Egyptian cotton cultivar Giza 75 (Gossypium barbadense L.) grown in uniform field trial at the experimental farm of Global the Agricultural Research Centre, Giza (30°N, 31°:28'E), Egypt. The soil texture was a clay loam, with an alluvial substratum, (pH = 8.07). Total water consumptive use during the growing season supplied by surface irrigation was about 6000 m3ha-1. No rainfall occurred during the growing season. The sampling size was 261 plants (Sawan et al.. 1999)

201 r ea Average number of flowers and bolls per plant Y

Days after begining of flowering

V Figure 6 : Daily number of flowers and bolls during the production stage (60 days) in the second season (II) for the

IV Egyptian cotton cultivar Giza 75 (Gossypium barbadense L.) grown in uniform field trial at the experimental farm of

ue ersion I the Agricultural Research Centre, Giza (30°N, 31°:28'E), Egypt. The soil texture was a clay loam, with an alluvial s s substratum, (pH = 8.07). Total water consumptive use during the growing season supplied by surface irrigation was I about 6000 m3ha-1. No rainfall occurred during the growing season. The sampling size was 358 plants (Sawan et al.

XVI 1999)

Table 1 : Range and mean values of the independent variables for the two seasons and over all data Over all data ) First season* Second season**

H Climatic factor's (Two seasons) ( Range Mean Range Mean Range Mean

Max Temp (°C), (X 1) 31.0-44.0 34.3 30.6-38.8 34.1 30.6-44.0 34.2 Min Temp (°C), (X 2) 18.6-24.5 21.9 18.4-23.9 21.8 18.4-24.5 21.8 ♦ Max-Min Temp (°C), (X 3) 9.4-20.9 12.4 8.5-17.6 12.2 8.5-20.9 12.3 Research Volume -1 Evap (mm d ), (X 4) 7.6-15.2 10.0 4.1-9.8 6.0 4.1-15.2 8.0 0600 h Temp (°C), (X 5) 14.0-21.5 17.8 13.3-22.4 18.0 13.3-22.4 17.9 1800 h Temp (°C), (X6) 19.6-27.0 24.0 20.6-27.4 24.2 19.6-27.4 24.1 -1 Frontier Sunshine (h d ), (X7) 10.3-12.9 11.7 9.7-13.0 11.9 9.7-13.0 11.8 Max RH (%), (X8) 62-96 85.4 51-84 73.2 51-96 79.6 Min RH (%), (X9) 11-45 30.8 23-52 39.8 11-52 35.1 -1 Wind speed (m s ) , (X10) ND ND 2.2-7.8 4.6 ND ND Science (Sawan et al. 2006). of ♦Diurnal temperature range. ND not determined. * Flower and boll stage (68 days, from 23 June through 29 August). **Flower and boll stage (62 days, from 29 June through 29 August).

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Global

Table 2 : Simple correlation values for the relationships between the independent variables and the studied dependent variable Dependent variable Independent variables First season Second season Combined data (Climatic factors) Flower Boll Flower Boll Flower Boll ** ** ** ** Max Temp [°C] (X1) –0.07 –0.03 –0.42 –0.42 –0.27 –0.26 M in Temp [°C] (X2) –0.06 –0.07 0.00 0.02 –0.03 –0.02 ** ** ** ** Max-Min Temp [°C] (X3) –0.03 –0.01 –0.36 –0.37 –0.25 –0.24 -1 ** ** ** ** ** ** Evapor [mm d ] (X4) –0.56 –0.53 –0.61 –0.59 –0.40 –0.48 0600 h Temp [°C] (X5) –0.01 –0.06 –0.14 –0.13 –0.09 –0.09 ** ** ** ** 1800 h Temp [°C] (X 6) –0.02 –0.16 –0.37 –0.36 –0.27 –0.25 -1 * ** ** ** ** Sunshine [h d ] (X 7) –0.25 –0.14 –0.37 –0.36 –0,31 –0.25 ** ** Max RH [%] (X 8) 0.40 0.37 0.01 0.01 0.04 –0.06 201 ** ** ** ** Min RH [%] (X 9) 0.14 0.10 0.45 0.46 0.33 0.39 r Wind speed [m s-1] (X ) ND ND –0.06 –0.04 ND ND ea 10 Y (Sawan et al. 2002). 371 ND not determined * P < 0.05; ** P < 0.01.

Table 3 : Selected factors and their relative contribution to variations of flower and boll production V Flower production Boll production * R.C. (%) R.C. (%) IV

Selected climatic factors ue ersion I s

First Second Combined First Second Combined s

season season data season season data I

Max Temp [°C] (X1) – 5.92 – – 5.03 – -1 XVI Evapor [mm d ] (X4) 19.08 23.45 16.06 23.04 22.39 22.89 1800 h Temp [°C] (X6) – – 5.83 – – 2.52 -1 Sunshine [h d ] (X7) 9.43 7.77 8.31 11.65 7.88 5.47 Max RH [%] (X8) 8.46 – – – – –

Min RH [%] (X9) – 4.37 7.38 – 4.26 4.64 )

** R² % for selected factors 36.97 41.47 37.58 34.69 39.56 35.52 H R² % for factors studied 40.22 45.03 40.73 38.43 42.87 37.90 ( R² % for factors deleted 3.25 3.56 3.15 3.74 3.31 2.38 (Sawan et al. 2002).

* R.C. % = Relative contribution of each of the selected independent variables to variations of the dependent variable. Research Volume ** R² % = Coefficient of determination in percentage form.

Table 4 : Range and mean value of the independent variables (climatic factors) during the four periods of flower and boll production stage Frontier ______First priod Second period Third period Fourth period Climatic ______Science factors Range Mean Range Mean Range Mean Range Mean

______of

First season Max Temp °C, (X1) 31.0-37.3 33.7 33.0-37.3 34.7 32.4-37.2 34.5 32.0-38.4 33.8 Journal Min Temp °C, (X2) 18.6-23.5 21.4 20.6-23.5 22.3 18.9-24.4 21.6 19.6-23.8 21.8 Max-Min °C, (X ) 9.4-14.8 12.3 9.8-15.6 12.4 9.7-18.3 12.9 9.5-14.6 12.0 3 Evapor. mm/d, (X4) 10.2-15.2 11.7 8.0-13.2 `10.1 7.6-11.2 9.1 7.7-11.1 9.2 Global 0600 h Temp. °C, (X5) 14.2-19.9 16.8 15.8-21.5 18.9 13.9-21.1 17.4 15.4-20.8 18.0

1800 h Temp.°C, (X6) 22.0-25,2 23.8 22.2 -27.0 24.2 19.6-25.6 24.1 21.8-26.0 23.9 Sunshine h/d, (X7) 11.4-12. 9 12.4 10. 4-12.4 11.5 10.5-12.4 11.6 9.9-12.2 11.4 Max Hum %, (X8) 62-88 80.7 84-94 88.4 85-96 89.9 76-96 87.4

Min Hum %, (X9) 21-37 28.2 22-43 31.4 17-42 29.9 24-45 34.0 S econd Season

Max Temp °C, (X1) 31.4-38.8 35.5 31.4 -35.5 33.4 32.6-37.9 34.4 30.6-34.6 32.8 ______Min Temp °C, (X2) 20.1-23.4 21.3 19.6 -23.1 21.7 18.4-24.3 22.3 18.6-23.9 21.7

______

Max-M in °C, (X3) 9 .4-17.6 14.2 10. 1-15.0 11.7 9.6 -17.0 12.1 8.5 -12.6 11.0

Evapor. mm/d, (X4) 5.9-9.8 7.5 5.0-7.0 6.0 4.3-7.1 5.6 4.1-6.1 4.9

0600 h Temp. °C, (X5) 15. 5-20.4 17.5 15.2 -21.4 18.4 12.9 -22.4 18.7 13. 3-21.0 17.5

1800 h Temp. °C, (X6) 22.8-26.5 24. 4 22.2 -26.5 24.2 22.9 -27.4 24.4 20.6 -25.8 23.6

Sunshine h/d, (X 7) 11. 2-13.0 12. 4 10. 9-12.6 11. 9 10. 6-12.4 11.6 10.3 -12.3 11.5 Max-Hum %, (X ) 62-83 71.7 51-82 72.8 59-81 74.7 64-84 73.3 8 Min Hum %, (X9) 23-44 33.1 32-50 41.3 29-51 39.9 37-52 44.7

_Windspeed______m/s, (X10) 2.8-6.8 5.1 3.4-6.6 4.5 2.2-7.8 4.4 3.4-5.8 4.5

(Sawan et al. 1999).

201 Table 5 : Significant simple correlation values between the climatic factors and flower, boll production and boll

r retention ratio due to quarters of production stage

ea ______Y Flower Boll Ratio:Bolls/Flowers (100) Climatic factors ______38 ______1st 2nd 3rd 4th 1st 2nd 3rd 4th 1st 2nd 3rd 4th

First season (n by quarter = 15) Ma xTem p °C, (X1) n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s.. * * * ** ** Min Temp °C, (X2) 0.516 0.607 n.s. n.s. 0.561 0.638 n.s. n.s. n.s. 0.680 n.s. n.s. V * * * Max-Min °C, (X3) n.s. n.s. 0.538 n.s. n.s. n.s. 0.494 n.s. 0.515 n.s. n.s. n.s.

IV * * ++ + * + + + Evapor. mm/d, (X4) 0.512 -0.598 n.s. 0.424 0.397 -0.500 -.0321 n.s. n.s. -0.387 -0.287 n.s.

ue ersion I 0600 h Temp. -0.352+ 0 .534* -0.358+ 0.301+ 0.402+ 0.516*-0.441++ n.s. n.s. 0.440++ n.s. -.292+ s °C,(X5) s 1800 h Temp. °C,(X6) n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. I + ++ * Sunshine h/d, (X7) n.s. n.s. 0.346 n.s. n.s. n.s. n.s. 0.430 n.s. n.s. n.s. 0.480

XVI + + ++ + ++ + + Max Hum %, (X8) -0.316 -0.260 0.461 0.283 n.s. n.s. 0.410 n.s. .389 n. s. n. s. -0.322 + ++ ++ ++ ++ * Min Hum %, (X9) n.s. 0.309 -0.436 n.s. n.s. 0.436 -0.316 n.s. -0.473 0.527 n.s. n.s.

Second season (n by quarter = 15)

Ma xTem p °C, (X ) n.s. n.s. n.s. -0.730** n.s. n.s. n.s. -0.654** n.s. n.s. 0.407++ n.s. 1

) ++ + Min Temp °C, (X2) n.s. n.s. n.s. -0 .451 n.s. n.s. n.s. - 0.343 n.s. n.s. n.s. n.s. H ( Max-Min °C, (X ) n.s. n.s. 0.598* n.s. n.s. n.s. 0.536* n.s. 0.456++-0.416++ n.s. n.s. 3 Evapor. mm/d, (X ) n.s. n.s. 0.640** n.s. n.s. n.s. 0.580* n.s. n.s. -0.318+ n.s. n.s. 4 0600 h Temp. °C,(X ) -0.397+ -0.301+-0.407++-0.506* -0.380+-0.323+-0.332+-0.426++ n.s. n.s. 0.283+ n.s. 5 ++ ** ++ * ** 1800 h Temp. °C,(X6) n.s. -.0440 n.s. -0.656 n.s. -0.410 n.s. -0.582 -.0626 n.s. n.s. n.s. Research Volume Sunshine h/d, (X ) 0.362+ n.s. n.s. n.s. 0.340+ 0.308+ .354+ n.s. n.s. 0.409++ n.s. n.s. 7 Max Hum %, (X ) -0.523* 0.424++ -0.587* n.s. -0530* 0.431++-0.586* n.s. n.s. n.s. n.s. n.s. 8 Min Hum %, * ** * ** ++ (X9) n.s. n.s. -0.585 0.639 n.s. n.s. -0.517 0.652 n.s. n.s. n.s. 0.420 Frontier ______

n.s.Means simple correlation coefficient is not significant at the 0.15 alpha level of significance. ** S ignificant at 1% probability level, * Significant at 5% probability level. ++ Science Significant at 10% probability level, + Significant at 15% probability level. n Nu mber of data pairs used in calculation. of Wind speed did not show significant effect upon the studied production variables. (Sawan et al. 1999).

Journal Table 6 : Significant simple correlation values between the climatic factors and flower, boll production, and boll

retention ratio due to quarters periods of production stage for the combined data of the two seasons (n =30) ______

Global ___Fl______ower Boll Ratio:Bolls/Flowers (100) Climatic factors ______1st 2nd 3rd 4th 1st 2nd 3rd 4th 1st 2nd 3rd 4th + ** ++ ** + MaxTemp °C, (X ) n.s. n.s. 0.29 -0.48 n.s. n.s. 0.38 -0.47 0.27 n.s. n.s. n.s. 1 ++ + Min Temp °C, (X2) n.s. n.s. -0.35 n.s. n.s. n.s. -0.28 n.s. n.s. n.s. n.s. n.s. * + ** ++ ** ** ++ * ** + Max-Min °C, (X3) -0.40 -0.30 0.59 -0.36 n.s. -0.48 0.52 -0.38 -0.40 -0.47 n.s. -0.28 ** ++ ** ** ** ** ** ** ** Evapor. mm/d, (X4) 0.78 n.s. 0.32 -0.67 0.67 -0.51 n.s. -0.74 n.s. -0.82 -0.49 -0.72 + * + ++ ++ ______0600 h Temp.______°C,(X5) n.s. 0.27 -0.43 -0.31 n.s. n.s. -0.37 -0.37 n.s. n.s. n.s. n.s.

______* ++ 1800 h Temp. °C,(X6) n.s. n.s. n.s. -0.42 n.s. n.s. n.s. -0.37 n.s. n.s. n.s. n.s. ++ ++ + + Sunshine h/d, (X7) n.s. n.s. 0.38 n.s. n.s. n.s. 0.32 n.s. n.s. 0.30 n.s. 0.27 ** ** ** * ** Max Hum %, (X8) n.s. n.s. n.s. -0.64 n.s. n.s. n.s. -0.71 n.s. -0.60 -0.44 -0.70 ** ** ++ * ++ ** ** * ** Min Hum %, (X9) n.s. n.s. -0.54 0.69 -0.32 0.42 -0.37 0.72 n.s. 0.72 0.40 0.56 2 ______R 0.667 0.116 0.496 0.672 0.446 0.335 0.389 0.747 0.219 0.737 0.269 0.615 (Sawan et al. 1999). Table 7 : Significant simple correlation values between the climatic factors and flower, boll production and boll retention ratio for combined data of the two seasons (n = 120) ______Cli ______matic______factors Flower Boll Ratio M axTem p °C, (X1) -0.152++ n.s. n.s.

Min Temp °C, (X2) n.s. n.s. n.s. 201 ** ** Max-Min °C, (X 3) -0.259 -0.254 n.s . r ** ** ** Evapor.mm/d, (X4) -0.327 -0.429 -0.562 ea Y 0600 h Temp. °C, (X5) n.s. n.s. n.s. * 1800 h Temp. °C, (X6) -0.204 -0.190++ n.s. 391 * Sunshine h/d, (X7) -0.227 -0.180++ n.s. ** M ax Hum %, (X8) n.s . n.s -0.344 . ** ** ** Min Hum %, (X9) 0.303 0.364 0.335 2 ** ** * ______R 0.406 0.422 ____0.336 (Sawan et al. 1999). V IV Table 8 : Significant simple correlation coefficient values between the production variables and the studied climatic ue ersion I s

factors for the daily and different intervals of days combined over both seasons s

I Climatic factorsz Surface soil Relative XVI Air temp (°C) Sunshine Daily and Production Evap temp (°C) humidity (%) -1 duration intervals of days variables (mm d ) Max Min Max- Min 0600 h 1800 h (h d-1) Max Min

(X ) (X ) (X ) (X ) (X ) (X ) (X ) ) 1 2 (X3) 4 5 (X6) 7 8 9 ++ ** ** * * ** H Daily (n = 120) Flower -0.15 NS -0.26 -0.33 NS -0.20 -0.23 NS 0.30 ( Bo ll NS NS -0.25** -0.43** NS -0.19++ -0.18++ NS 0.36** ** ** Boll ret. rat. NS NS NS -0.56 NS NS NS NS 0.34 2 Days (n# = 60) Flower -0.31++ NS -0.32* -0.36** NS -0.24+ -0.36 ** NS 0.37** Boll -0.29++ NS - 0.30++ -0.46** NS -0.21+ -0.31* NS 0.44** Research Volume Boll ret. rat. NS NS NS -0.61** NS NS NS NS 0.40**

3 Days (n# = 40) Flower * * * ++ * * -0.34 NS -0.34 -0.33 NS -0.28 -0.39 NS 0.34 Boll * * ** + * ** Frontier -0.32 NS -0.32 -0.48 NS -0.24 -0.36 NS 0.45 Boll ret. rat. ** * NS NS NS -0.63 NS NS NS NS 0.40 # 4 Days (n = 30) Flower -0.31++ NS -0.35++ -0.33++ NS -0.28+ -0.39* NS 0.34++ ++ ++ ** + * * Boll -0.31 NS -0.33 -0.48 NS -0.23 -0.38 NS 0.45 Science Boll ret. rat. NS NS NS -0.64** NS NS NS NS 0.42* of 5 Days (n# = 24) Flower ++ ++ ++ ++ ** * -0.35 NS -0.37 -0.39 NS -0.39 -0.52 NS 0.41 Boll -0.33+ NS -0.35++ -0.49* NS -0.35++ -0.44* NS 0.47** Boll ret. rat. NS NS NS -0.66** NS NS NS NS 0.43* Journal 6 Days (n# = 20) Flower -0.37++ NS -0.41++ -0.38++ NS NS -0.54** NS 0.42* Boll -0.37++ NS -0.40++ -0.49* NS NS -0.46* NS 0.49* Boll ret. rat. ** *

NS NS NS -0.69 NS NS NS NS 0.45 Global # ++ + * * ++ 10 Days (n = 12) Flower NS NS -0.45 -0.40 NS -0.55 -0.65 NS 0.43 ++ ++ ++ * ++ Boll NS NS -0.43 -0.51 NS -0.53 -0.57 NS 0.51 Boll ret. rat. ** * NS NS NS -0.74 NS NS NS NS 0.55

(Sawan et al. 2006). z Wind speed did not show significant effect upon the studied production variables, so is not reported. ** Significant at 1 % probability level, * Significant at 5 % probability level.

++ Significant at 10 % probability level, + Sign ificant at 15 % probability level. NS Means simple correlation coefficient is not significant at the 15% probability level. #n = Number of data pairs used in calculation.

Table 9 : The equations obtained for each of the studied cotton production variables for the five-day intervals and daily intervals combin ed over both seasons

z Equation R² Significance Five-day intervals Y1 = 23.78 – 0.5362X 4 – 0.142 9X6 – 0.1654X 7 + 0.0613X 9 0.6237 ** Y2 = 15.89 – 0.4762X 4 – 0.158 3X6 – 0.1141X 7 + 0.0634X 9 0.5945 ** Y3 = 72.65 – 0.0833X 4 – 0.164 7X6 + 0.2278X9 0.6126 ** Daily intervals Y1 = 19.78 – 0.181X3 – 0.069X 4 – 0.164X6 – 0.182X7 + 0.010X 9 0.4117 ** Y2 = 14.96 – 0.173X3 – 0.075X 4 – 0.176X6 – 0.129X7 + 0.098X 9 0.4461 ** Y3 = 52.36 – 3.601X 4 – 0.2352 X7 + 4.511X 9 0.3587 **

(Sawan et al. 2006).

201 z Where Y1 = number of flowers per plant, Y2 = number of bolls per plant, Y3 = boll retention ratio, X3 = maximum – r -1 minimum temperature °C, X4 = evaporation mm day , X6= surface soil temperature °C at 1800 h., X7 = sunshine duration ea -1 Y h day and X = minimum relative humidity %. 9 40 Table 10 : Mean, standard deviation, maximum and minimum values of the climatic factors during the flower and boll stage (initial time) and the 15 days prior to flowering or subsequent to boll setting for I and II season at Giza, Egypt

First season* Second season** Climatic factors Mean S.D. Max. Min. Mean S.D. Max. Min.

V Max temp [°C] (X1) 34.1 1.2 44.0 31.0 33.8 1.2 38.8 30.6

IV Min temp [°C] (X2) 21.5 1.0 24.5 18.6 21.4 0.9 24.3 18.4 ♦ Max-Min temp [°C] (X3) 12.6 1.1 20.9 9.4 12.4 1.3 17.6 8.5 ue ersion I -1 s

s Evapor [mm d ](X4) 10.6 1.6 16.4 7.6 6.0 0.7 9.8 4.1 0600 h temp [°C] (X ) 17.5 1.1 21.5 13.9 17.6 1.2 22.4 13.3 I 5

1800 h temp [°C] (X6) 24.2 1.9 32.3 19.6 23.7 1.1 27.4 20.6 XVI -1 Sunshine [h d ] (X7) 11.7 0.8 12.9 9.9 11.7 0.4 13.0 10.3

Max hum [%] (X8) 85.6 3.3 96.0 62.0 72.9 3.8 84.0 51.0 Min hum [%] (X9) 30.2 5.2 45.0 11.0 39.1 5.0 52.0 23.0 -1 Wind speed [m s ] (X10) ND ND ND ND 4.6 0.9 7.8 2.2

) *Flower and boll stage (68 days, from 23 June through 29 August). H

( **Flower and boll stage (62 days, from 29 June through 29 August). ♦ diurnal temperature range. ND not determined

(Sawan et al. 2005).

Research Volume Table 11 : Simple correlation coefficients (r) between climatic factors and number of flower and harvested bolls in initial time (0) and each of the 15 –day periods before flowering in the first season (I)

Frontier Air temp. Evap. Surface soil Sunshine Humidity -1 Climate ______(°C) (mm d ) ______temp. (°C) duration ______(%)______-1 period ♦ (h d ) Max. Min. Max-Min 0600 h 1800 h Max. Min. Science (X ) (X ) (X ) (X ) (X ) (X ) (X ) (X ) (X ) 1 2 3 4 5 6 7 8 9 of 0# Flower -0.07 -0.06 -0.03 -0.56** -0.01 -0.20 -0.25* 0.40** 0.14 Boll -0.03 -0.07 -0.01 -0.53** -0.06 -0.16 -0.14 0.37** 0.10

1 Flower -0.15 -0.08 -0.11 -0.64** -0.01 -0.17 - 0.30* 0.39** 0.20 Journal Boll -0.07 -0.08 -0.02 -0.58** -0.06 -0.10 -0.23* 0.36** 0.13 2 Flower -0.26* -0.10 -0.22 -0.69** -0.07 -0.30* -0.35** 0.42** 0.30* Boll -0.18 -0.08 -0.14 -0.64** -0.05 -0.21 -0.25* 0.40** 0.20 Global 3 Flower -0.28* -0.02 -0.31** -0.72** 0.15 -0.29* -0.37** 0.46** 0.35** Boll -0.19 -0.02 -0.21 -0.65** 0.11 -0.20 -0.30* 0.37** 0.25*

4 Flower -0.26* -0.03 -0.26* - 0.67** 0.08 - 0.24* -0.41** 0.46** 0.35** Boll -0.21 -0.04 -0.21 -0.63** 0.04 -0.18 - 0.35** 0.39** 0.29* 5 Flower -0.27* -0.02 -0.27* -0.68** 0.16 -0.29* - 0.45** 0.49** 0.38**

Boll -0.22 0.00 -0.24* -0.63** 0.16 -0.21 -0.39** 0.44** 0.32**

6 Flower -0.21 0.05 -0.25* -0.73** 0.16 -0.28* - 0.46** 0.47** 0.42** Boll -0.15 0.08 -0.21 -0.67** 0.19 -0.19 - 0.46** 0.43** 0.35**

7 Flower -0.17 -0.01 -0.17 -0.69** 0.10 -0.27* - 0.43** 0.46** 0.35** Boll -0.11 -0.06 -0.15 -0.64** 0.14 -0.19 - 0.46** 0.43** 0.32**

8 Flower -0.24* -0.03 -0.24* -0.71** 0.09 -0.30* -0.44** 0.45** 0.45** Boll -0.14 0.04 -0.17 -0.63** 0.16 -0.17 -0.48** 0.44** 0.39**

9 Flower -0.23 -0.10 -0.19 -0.68** 0.05 -0.33** -0.32** 0.43** 0.44** Boll -0.14 0.04 -0.17 -0.61** 0.15 -0.21 -0.40** 0.42** 0.41**

10 Flower -0.26* 0.05 -0.30* -0.67** 0.13 -0.29* -0.29* 0.40** 0.48**

Boll -0.14 0.13 -0.22 -0.58** 0.22 -0.17 -0.36** 0.46** 0.41**

11 Flower -0.20 0.10 -0.27* -0.62** 0.21 -0.19 -0.29* 0.42** 0.44**

Boll -0.04 0.22 -0.16 -0.53** 0.27* -0.04 -0.38** 0.45** 0.36** 12 Flower -0.17 0.16 -0.26* -0.62** 0.29* -0.15 -0.40** 0.44** 0.45** 201 Boll 0.00 0.25* -0.13 -0.51** 0.35** -0.04 -0.45** 0.40** 0.30* r 13 Flower -0.13 0.16 -0.22 -0.62** 0.23 -0.12 -0.42** 0.43** 0.45** ea Y

Boll 0.00 0.22 -0.11 -0.51** 0.30* - 0.03 -0.49** 0.41** 0.33**

14 Flower -0.08 0.18 -0.18 -0.56** 0.21 -0.15 -0.44** 0.41** 0.46** 411

Boll 0.01 0.21 -0.10 -0.47** 0.26* -0.09 -0.49** 0.42** 0.33** 15 Flower -0.08 0.22 -0.21 -0.51** 0.24* -0.22 -0.42** 0.39** 0.38**

Boll -0.03 0.19 -0.13 -0.45** 0.24* -0.17 -0.44** 0.43** 0.30* V *: Significant at 5% level and **: significant at 1% level. IV # 0 = Initial time. ♦ ue ersion I

diurnal temperature range. s s (Sawan et al. 2005). I

XVI

) H

(

Research Volume

Frontier

Science

Journal

Global

z Table 12 : Simple correlation coefficients (r) between climatic factors and number of flower and harvested bolls in initial time (0) and each of the 15–day periods before flow ering in the second season (II)

Climate Air temp. Evap. Surface soil Sunshine Humidity -1 period ______(°C) (mm d ______) temp. (°C) duration __(%)___ -1 ♦ (h d ) Max. Min. Max-Min 0600 h 1800 h Max. Min.

(X1) (X2) (X3) (X4) (X5) (X6) (X7) (X8) (X9) # 0 Flower -0.42** 0.00 -0.36** -0.61** -0.14 -0.37** -0.37** 0.01 0.45** Boll -0.42** 0.02 -0.37** -0.59** -0.13 -0.36** -0.36** 0.01 0.46** 1 Flower -0.42** 0.10 -0.42** -0.63** -0.08 -0.29* -0.41** 0.05 0.48**

Boll -0.41** 0.11 -0.42** -0.62** -0.07 -0.28* -0.41** 0.05 0.47**

201 2 Flower -0.40** 0.08 -0.43** -0.65** -0.09 -0.27* -0.39** 0.02 0.49**

r Boll -0.40** 0.08 -0.43** -0.64** -0.08 -0.26* -0.40** 0.03 0.49** ea Y 3 Flower -0.38** 0.13 -0.43** -0.61** -0.06 -0.17 -0.38** 0.00 0.45**

Boll -0.37** 0.15 -0.44** -0.61** -0.05 -0.15 -0.38** 0.01 0.46** 42 4 Flower -0.36** 0.17 -0.41** -0.61** -0.04 -0.18 -0.38** 0.02 0.45**

Boll -0.35** 0.18 -0.41** -0.60** -0.03 -0.16 -0.36** 0.03 0.44**

5 Flower -0.30* 0.13 -0.36** -0.60** -0.07 -0.23 -0.32** -0.05 0.43**

V Boll -0.28* 0.15 -0.35** -0.58** -0.05 -0.21 -0.31** -0.05 0.41**

IV 6 Flower -0.24 0.21 -0.38** -0.61** -0.02 -0.12 -0.28* 0.02 0.40**

ue ersion I Boll -0.22 0.24 -0.38** -0.59** 0.00 -0.07 -0.29* 0.02 0.40**

s s 7 Flower -0.19 0.23 -0.29* -0.54** -0.03 -0.05 -0.26* -0.04 0.32** I Boll -0.18 0.23 -0.27* -0.53** -0.02 -0.03 -0.27* -0.04 0.30*

XVI 8 Flower -0.15 0.24 -0.25* -0.52** -0.03 -0.07 -0.24* -0.05 0.28*

Boll -0.14 0.22 -0.22 -0.51** -0.03 -0.06 -0.22* -0.05 0.26*

9 Flower -0.16 0.34** -0.32** -0.56** 0.08 -0.02 -0.25* 0.05 0.30*

Boll -0.14 0.34** -0.31** -0.56** 0.09 -0.01 -0.23* 0.07 0.29* )

H 10 Flower -0.16 0.31** -0.30* -0.56** 0.11 -0.06 -0.27* 0.11 0.33** ( Boll -0.14 0.28* -0.27* -0.55** 0.09 -0.07 -0.25* 0.09 0.31**

11 Flower -0.16 0.31** -0.27* -0.55** 0.10 -0.02 -0.31** 0.08 0.32**

Boll -0.15 0.29* -0.26* -0.53** 0.10 0.00 -0.29* 0.08 0.29*

Research Volume 12 Flower -0.17 0.44** -0.37** -0.57** 0.26* 0.02 -0.36** 0.17 0.34**

Boll -0.17 0.42** -0.36** -0.55** 0.25* 0.01 -0.34** 0.16 0.32**

13 Flower -0.14 0.40** -0.33** -0.56** 0.21 0.03 -0.28* 0.10 0.34** Frontier Boll -0.15 0.38** -0.34** -0.56** 0.21 0.01 -0.27* 0.09 0.33**

14 Flower -0.19 0.39** -0.38** -0.59** 0.25* 0.04 -0.34** 0.16 0.35**

Boll

Science -0.20 0.39** -0.40** -0.59** 0.26* 0.03 -0.36** 0.17 0.36**

Flower

of 15 -0.24 0.49** -0.45** -0.62** 0.37** 0.16 -0.38** 0.27* 0.42**

Boll -0.24 0.51** -0.48** -0.63** 0.40** 0.15 -0.40** 0.26* 0.43**

*: Significant at 5% level and **: significant at 1% level. # Journal 0 = Initial time.

♦ diurnal temperature range.

z Wind speed did not show significant effect upon the studied production variables, so it is not reported.

Global (Sawan et al. 2005).

Table 13 : Simple correlation coefficient (r) values between climatic factors and number of harvested bolls and retention ratio in initial time (0) and each of the 15–day periods after flowering in the first season (I)

Climate Air temp. Evap. Surface soil Sunshine Humidity period (°C) (mm d-1) temp. (°C) duration (%) -1 ______(h d ) ______♦ Max. Min. Max. -Min . 0600 h 1800 h Max. Min. (X1) (X2) (X3) (X4) (X5) (X6) (X7) (X8) (X9) 0# Retention ratio• -0.05 -0.03 -0.03 -0.10 -0.11 0.10 0.20 -0.04 -0.02 No. of bolls -0.03 -0.07 -0.01 -0.53** -0.06 -0.16 -0.14 0.37** 0.10 1 Retention ratio -0.07 -0.08 -0.01 -0.10 -0.16 0.04 0.15 0.04 0.05 No. of bolls 0.02 -0.08 0.08 -0.49** -0.09 -0.05 -0.20 0.35** 0.09 201

2 Retention ratio -0.08 -0.14 0.02 -0.08 -0.19 0.03 0.17 0.02 -0.02 r ea

No. of bolls 0.02 -0.04 0.07 -0.46** -0.06 -0.01 -0.19 0.33** 0.09 Y

3 Retention ratio -0.09 -0.21 0.06 -0.08 -0.24* 0.02 0.19 0.01 -0.10 431 No. of bolls 0.03 -0.03 0.06 -0.44** -0.04 0.05 -0.18 0.32** 0.08 4 Retention ratio -0.05 -0.20 0.09 -0.01 -0.24* 0.01 0.22 0.00 -0.15 No. of bolls 0.01 -0.05 0.05 -0.40** -0.03 0.04 -0.16 0.31* 0.08 V 5 Retention ratio -0.03 -0.21 0.13 0.07 -0.25* 0.00 0.26* -0.02 -0.22 No. of bolls 0.00 -0.07 0.05 -0.37** -0.02 0.03 -0.13 0.29* 0.07 IV ue ersion I s 6 Retention ratio 0.01 -0.19 0.15 0.12 -0.24* 0.02 0.27* -0.03 -0.20 s

No. of bolls -0.01 -0.08 0.04 -0.38** -0.02 0.04 -0.15 0.31* 0.13 I

7 Retention ratio 0.05 -0.17 0.17 0.18 -0.25* 0.05 0.29* -0.02 -0.21 XVI No. of bolls -0.03 -0.09 0.03 -0.39** -0.04 0.06 -0.14 0.34** 0.18 8 Retention ratio 0.06 -0.08 0.13 0.21 -0.20 0.07 0.28* -0.06 -0.19

No. of bolls -0.05 -0.07 -0.01 -0.35** -0.02 0.02 -0.17 0.28* 0.17 ) H

9 Retention ratio 0.08 0.00 0.08 0.26* -0.14 0.08 0.29* -0.12 -0.20 ( No. of bolls -0.08 -0.06 -0.05 -0.33** -0.01 0.00 -0.23 0.20 0.16

10 Retention ratio 0.06 -0.02 0.05 0.27* -0.13 0.09 0.27* -0.10 -0.08

No. of bolls -0.11 -0.10 -0.07 -0.34** -0.03 -0.03 -0.19 0.18 0.21 Research Volume 11 Retention ratio 0.04 -0.04 0.08 0.28* -0.12 0.08 0.26* -0.09 -0.05 No. of bolls -0.18 -0.18 -0.06 -0.37** -0.10 -0.04 -0.14 0.15 0.28* Frontier 12 Retention ratio 0.02 0.01 -0.08 0.32** -0.05 0.05 0.25* -0.08 -0.03 No. of bolls -0.17 -0.13 -0.08 -0.32** -0.06 -0.07 -0.11 0.16 0.24*

13 Retention ratio -0.04 0.04 -0.09 0.38** 0.00 0.01 0.27* -0.09 -0.02 Science No. of bolls -0.15 -0.09 -0.09 -0.29* -0.03 -0.10 -0.08 0.18 0.20 of 14 Retention ratio -0.07 0.04 -0.13 0.34** 0.06 -0.02 0.18 -0.08 -0.01 No. of bolls -0.15 -0.10 -0.10 -0.28* -0.01 -0.10 -0.15 0.17 0.17 Journal 15 Retention ratio -0.13 0.03 -0.18 0.33** 0.09 -0.04 0.06 -0.07 0.00 No. of bolls -0.16 -0.10 -0.11 -0.28* 0.00 -0.11 -0.13 0.17 0.15

* and ** Significant at 5% and 1% levels of significance, respectively. Global # 0 = Initial time • Retention ratio: (the number of retained bolls obtained from the total number of each daily tagged flowers in all selected plants at harvest/each daily number of tagged flowers in all selected plants) x 100. ♦ diurnal temperature range. (Sawan et al. 2005).

Table 14 : Simple correlation coefficient (r) values between climatic factorsz and number of harvested bolls and retention ratio in initial time (0) and each of the 15–day periods after flowering in the second season (II)

Air temp. Evap. Surface soil Sunshine Humidity (°C) (mm d-1 ) temp . (°C) duration (%) Climate ______(h d -1) ______period Max. Min. Max.-Min♦ 0600 h 1800 h Max. Min.

(X1) (X2) (X3) (X4) (X5) (X6) (X7) (X 8) (X9) 0# Retention ratio• -0.04 0.20 -0.31* -0.14 0.12 -0.20 0.01 -0.04 0.17 No. of bolls -0.42** 0.02 -0.37** -0.59** -0.13 -0.36** -0.36** 0.01 0.46** 1 Retention ratio -0.10 -0.03 -0.22 -0.21 -0.15 -0.05 -0.04 -0.02 0.23 No. of bolls -0.25* -0.01 -0.36** -0.63** -0.15 -0.30* -0.25* 0.06 0.44** 201 2 Retention ratio -0.15 -0.06 -0.10 -0.15 -0.08 -0.21 -0.01 -0.04 0.12 r

ea No. of bolls -0.18 -0.01 -0.34** -0.65** -0.11 -0.25* -0.32* 0.13 0.43** Y 3 Retention ratio -0.03 -0.01 -0.02 -0.21 -0.01 -0.17 -0.08 0.09 0.12 44 No. of bolls -0.15 -0.06 -0.30* -0.62** -0.05 -0.28* -0.31* 0.14 0.33** 4 Retention ratio 0.08 -0.02 0.07 -0.09 -0.03 -0.09 -0.10 0.05 -0.04 No. of bolls -0.15 -0.05 -0.28* -0.63** -0.06 -0.25* -0.33** 0.15 0.32*

V 5 Retention ratio 0.23 -0.03 0.12 -0.06 -0.06 -0.01 -0.11 0.01 -0.16

IV No. of bolls -0.14 -0.05 -0.25* -0.62** -0.06 -0.24* -0.35** 0.15 0.31* ue ersion I

s 6 Retention ratio 0.09 -0.08 0.12 -0.09 -0.07 -0.01 -0.09 0.00 -0.05 s No. of bolls -0.15 -0.04 -0.22 -0.61** -0.08 -0.25* -0.34** 0.13 0.22 I

XVI 7 Retention ratio -0.03 -0.12 0.12 -0.10 -0.11 -0.01 -0.04 -0.03 0.02 No. of bolls -0.15 -0.02 -0.19 -0.60** -0.10 -0.29* -0.32* 0.10 0.18 8 Retention ratio -0.02 0.05 0.03 -0.10 -0.04 -0.03 -0.02 -0.01 0.01 No. of bolls -0.20 -0.03 -0.23 -0.61** -0.10 -0.28* -0.32* 0.19 0.22 )

H 9 Retention ratio -0.02 0.13 -0.05 -0.10 0.08 -0.05 -0.01 0.03 0.00 ( No. of bolls -0.24 -0.04 -0.29* -0.62** -0.11 -0.30* -0.33** 0.13 0.27* 10 Retention ratio -0.04 0.12 -0.08 -0.09 0.05 0.11 -0.02 0.04 0.02 No. of bolls -0.27* -0.07 -0.30* -0.60** -0.16 -0.34** -0.34** 0.11 0.26* Research Volume 11 Retention ratio -0.07 0.10 -0.10 -0.08 0.03 0.20 -0.03 0.05 0.04 No. of bolls -0.30* -0.12 -0.30* -0.61** -0.18 -0.39** -0.36** 0.10 0.27*

Frontier 12 Retention ratio -0.11 0.09 -0.14 -0.11 0.04 0.13 -0.08 0.11 0.09 No. of bolls -0.32* -0.19 -0.26* -0.60** -0.22 -0.42** -0.37** 0.09 0.27* 13 Retention ratio -0.14 0.09 -0.17 -0.18 0.06 -0.06 -0.14 0.16 0.12 Science No. of bolls -0.33** -0.26* -0.23 -0.59** -0.28* -0.48** -0.39** 0.08 0.27* of 14 Retention ratio -0.11 -0.04 -0.10 -0.13 -0.15 -0.05 -0.09 0.01 0.12 No. of bolls -0.34** -0.32* -0.21 -0.61** -0.32* -0.48** -0.38** 0.06 0.27*

Journal 15 Retention ratio -0.08 -0.11 0.02 -0.08 -0.22 -0.05 -0.02 -0.03 0.12 No. of bolls -0.35** -0.37** -0.18 -0.61** -0.38** -0.48** -0.37** 0.03 0.27*

Global * and ** Significant at 5% and 1% levels of significance, respectively.

# 0 = Initial time

• Retention ratio: (the number of retained bolls obtained from the total number of each daily tagged flowers in all selected plants at harvest/each daily number of tagged flowers in all selected plants) x 100.

♦ diurnal temperature range.

z Wind speed did not show significant effect upon the studied production variables, so it is not reported.

(Sawan et al. 2005).

Table 15 : The models obtained for the number of flowers and bolls per plant as functions of the climatic data derived from the 5, 10, and 15 day periods prior to flower opening in the two seasons (I, II)

Season Model z R² Significance

First Flower

Y1 = 55.75 + 0.86X3 – 2.09X4 – 2.23X7 0.51 **

Y2 = 26.76 – 5.45X4 + 1.76X9 0.42 **

Y3 = 43.37 – 1.02X4 – 2.61X7 + 0.20X8 0.52 ** Boll 201 Y1 = 43.69 + 0.34X3 – 1.71X4 – 1.44X7 0.43 ** r

Y = 40.11 – 1.82X – 1.36X + 0.10X 0. 48 ** ea

2 4 7 8 Y Y = 31.00 – 0.60X – 2.62X + 0.23X 0. 47 ** 3 4 7 8 451 Se cond Flower

Y1 = 18.58 + 0.39X3 – 0.22X4 – 1.19X7 + 0.17X9 0.54 **

V Y2 = 16.21 + 0.63X3 – 0.20X4 – 1.24X7 + 0.16X9 0.61 **

IV Y3 = 14.72 + 0.51X3 – 0.20X4 – 0.85X7 + 0.17X9 0.58 **

ue ersion I Boll s s

Y1 = 25.83 + 0.50X3 – 0.26X4 – 1.95X7 + 0.15X9 0.61 ** I

Y2 = 19.65 + 0.62X3 – 0.25X4 – 1.44X7 + 0.12X9 0.60 ** XVI

Y3 = 15.83 + 0.60X3 – 0.22X4 – 1.26X7 + 0.14X9 0.59 **

z Where Y1, Y2, Y3 = number of flowers or bolls per plant at the 5, 10 and 15 day periods before flowering, -1 respectively X2 = minimum temperature (°C), X3 = diurnal temperature range (°C), X4 = evaporation (mm day ), -1 ) X 7 = sunshine duration (h day ), X8 = maximum humidity (%) and X9 = minimum humidity (%). H (Sawan et al. 2005). (

Table 16: The models obtained for the number of bolls per plant as functions of the climatic data

derived from the 5, 10 and 15 day periods after flower opening in the two seasons (I, II)

Research Volume Season Model z R² Significance

Frontier FirstY1 = 16.38 - 0.41X4 0.14 **

Y2 = 16.43 - 0.41X4 0.14 **

Science Y3 = 27.83 - 0.60X4 - 0.88X9 0.15 **

of Second Y = 23.96 - 0.47X - 0.77X 0.44 ** 1 4 8

Y2 = 18.72 - 0.58X4 0.34 ** Journal

Y3 = 56.09 - 2.51X4 - 0.49X6-1.67X7 0.56 **

Global z Where Y1, Y2, Y = 3number of bolls per plant at the 5, 10, and 15 day periods after flowering, respectively, -1 -1 X 4 = evaporation (mm day ), X6 = soil surface temperature (°C) at 1800, X7 = sunshine duration (h day ),

X 8 = maximum humidity (%) and X9 = minimum humidity (%). (Sawan et al. 2005).

Table 17 : Simple correlation coefficient (r) values between the independent variables and the dependent variables in the first season (I) Dependent variables Independent variables (First season) (Irrigation and climatic factors) Flowers Bolls (X1) Irrigation on day 1 -0.1282 -0.0925 (X2) Irrigation on day 0 or –1 (1st and 2nd day after irrigation) -0.1644 -0.1403 (X3) 1 is for the day prior to irrigation - 0.0891 -0.0897 (X4) Number of days that temperature equaled or exceeded 37.5 °C 0.1258 0.1525 (X5) Range of temperature [°C] on day 1 -0.0270 -0.0205 (X6) Broadest range of temperature [°C] over days 1 -12 0.0550 0.1788d (X7) MinRH [%] during day 1 0.1492 0.1167 c 201 (X8) MaxRH [%] during day 1 0.2087 0.1531 r (X9) MinRH [%] during day 2 0.1079 0.1033 ea

Y (X10) MaxRH [%] during day 2 0.1127 0.0455 (X11) Largest maxRH [%] on days 3-6 0.3905a 0.2819b 46 (X12) Lowest minRH [%] on days 3-6 0.0646 0.0444 (X13) Largest maxRH [%] on days 7-12 0.4499a 0.3554b (X14) Lowest minRH [%] on days 7-12 0.3522a 0.1937d (X15) Lowest minRH [%] on days 50-52 -0.3440a -0.4222a (X16) Daily light period (hour) -0.2430b -0.1426 V (Sawan et al. 2010). IV aSignificant at 1 % probability level

ue ersion I b s Significant at 5 % probability level s c Significant at 10 % probability level

I d Significant at 15 % probability level XVI Table 18: Simple correlation coefficient (r) values between the independent variables and the dependent variables in the second season (II) Dependent variables ) Independent variables

H (Second season) ( (Irrigation and climatic factors) Flowers Bolls (X1) Irrigation on day 1 -0.0536 -0.0467 (X2) Irrigation on day 0 or –1 -0.1116 -0.1208 (X3) 1 is for the day prior to the day of irrigation -0.0929 -0.0927 Research Volume (X4) Number of days that temperature equaled or exceeded 37.5 °C -0.4192a -0.3981a (X5) Range of temperature [°C] on day 1 -0.3779a -0.3858a (X6) Broadest range of temperature [°C] over days 1-12 -0.3849a -0.3841a a a Frontier (X7) MinRH [%] during day 1 0.4522 0.4665 (X8) MaxRH [%] during day 1 0.0083 0.0054 (X9) MinRH [%] during day 2 0.4315a 0.4374a (X10) MaxRH [%] during day 2 0.0605 0.0532 Science (X11) Largest maxRH [%] on days 3-6 0.2486c 0.2520b

of (X12) Lowest minRH [%] on days 3-6 0.5783a 0.5677a (X13) Largest maxRH [%] on days 7-12 0.0617 0.0735 (X14) Lowest minRH [%] on days 7-12 0.4887a 0.4691a a a Journal (X15) Lowest minRH [%] on days 50-52 -0.6246 -0.6113 (X16) Daily light period (hour) -0.3677a -0.3609a (Sawan et al. 2010). Global a Significant at 1 % probability level b Significant at 5 % probability level c Significant at 10 % probability level

T able 19 : Simple correlation coefficient (r) values between the independent variables and dependent variables in the combined two seasons (I and II) Dependent variables Independent variables (Combined two seasons) (Irrigation and climatic factors) Flowers Bolls (X1) Irrigation on day 1 -0.0718 -0.0483 (X2) Irrigation on day 0 or –1 -0.1214 -0.1108 (X3) 1 is for the day prior to the day of irrigation -0.0845 -0.0769 (X4) Number of days that temperature equaled or exceeded 37.5 °C -0.2234b -0.1720c (X5) Range of temperature [°C] on day 1 -0.2551a -0.2479a (X6) Broadest range of temperature [°C] over days 1-12 -0.2372a -0.1958b (X7) MinRH [%] during day 1 0.3369a 0.3934a

(X8) MaxRH [%] during day 1 0.0032 -0.0911 201 a a (X9) MinRH [%] during day 2 0.3147 0.3815 r

(X10) MaxRH[%] during day 2 -0.0094 -0.1113 ea Y (X11) Largest maxRH [%] on days 3-6 0.0606 -0.0663 (X12) Lowest minRH [%] on days 3-6 0.3849a 0.4347a 471 (X13) Largest maxRH [%] on days 7-12 -0.0169 -0.1442d (X14) Lowest minRH [%] on days 7-12 0.3891a 0.4219a (X15) Lowest minRH [%] on days 50-52 -0.3035a -0.2359a (X16) Daily light period (hour) -0.3039a -0.2535a V (Sawan et al. 2010). IV a Significant at 1 % probability level

b ue ersion I Significant at 5 % probability level s s c

Significant at 10 % probability level I d Significant at 15 % probability level XVI Table 20 : Model obtained for cotton production variables as functions of climatic data and soil moisture status in individual and combined seasons

2 Season Model R )

Seas on I Y = – 557.54 + 6.35X + 0.65X + 1.92X + 4.17X + 0.63 H 1 6 7 11 13 ( (n = 68) 2. 88X14 – 1 .90X15 – 5.63X16

Y2 = – 453.93 + 6.53X6 + 0.61X7 + 1.80X11 + 2.47X13 + 0.53

1. 87X14 – 1.85X15 Research Volume

Y1 = –129.45 + 25.36X1 + 37.02X4 + 1.48X7 + 1.69X 9 + 0.72

Season II 4.46X12 + 2.55X 14 – 4.73X15 Frontier (n = 62)

Y2 = – 130.23 + 24.27X1 + 35.66X4 + 1.42X7 + 1.61X 9 + 0.71 4.00X + 2. 18X – 4.09X 12 14 15 Science

of Y1 = – 557.36 + 6.82X6 + 1.44X7 + 0.75X9 + 2.04X11 + 2. 55X12 0.57 + 2.01X13 + 3.27X 14 – 2.15X15 Combined data: Journal I & II Y2 = – 322.17 + 6.41X6 + 1.20X7 + 0.69X9 + 1.81X11 + 2. 12X12 0.53 (n = 130) + 2.35X 14 – 2.16X15

(Sawan et al. 2010). Global

(Y1) Number of cotton flowers; (Y2) Number of cotton bolls.

(X1) Irrigation on day 1; (X4) Number of that temperature equaled or exceeded 37.5 °C; (X6) Broadest range of temperature [°C] over days 1-12; (X7) MinRH [%] during day 1; (X9) MinRH [%] during day 2; (X11) Largest maxRH [%] on days 3-6; (X12) Lowest minRH [%] on days 3-6; (X13) Largest maxRH [%] on days 7-12; (X14) Lowest minRH [%] on days 7-12; (X15) Lowest minRH [%] on days 50-52; (X16) Daily light period (hour). All entries significant at 1% level.

201 r

ea Y 48

V IV ue ersion I s s I

XVI This page is intentionally left blank ) H ( Research Volume Frontier Science of Journal Global

Global Journal of Science Frontier Research: H

Environment & Earth Science Volume 16 Issue 4 Version 1.0 Year 2016

Type : Double Blind Peer Reviewed International Research Journal

Publisher: Global Journals Inc. (USA)

Online ISSN: 2249-4626 & Print ISSN: 0975-5896

Land use/Land Cover Dynamics and its Implication on Sustainable Land Management in Wallecha Watershed, Southern Ethiopia By Barana Babiso, Senbetie Toma & Aklilu Bajigo Wolaita Sodo University Abstract- This study was conducted at Wallecha Watershed in the middle course of Bilate River Basin in Wolayitta Zone, Southern Ethiopia with the main objective of assessing the patterns of spatial and temporal dynamics of land use/land cover (LU/LC) and implications on sustainable land management (SLM). The study area was covered 10116.7ha, and had previously undergone substantial LU/LC changes. The changes in LU/LC which occurred between 1984 and 2010 were monitored in a context of geographical information system (GIS) and remote sensing (RS). The Lands at imageries of 1984, 2000 and 2010 were used to produce three land cover maps of the respective years using GIS and RS techniques with field verification. Changes in the density of vegetation cover and land degradation over time in the watershed were also estimated with the help of Normalized Difference Vegetation Index (NDVI). The general trend was observed as there is decrease in forest lands and shrub-grasslands at a rate of 34.27and 15.63ha per year respectively, and a decrease in degraded lands at a rate of 7.63 ha per year. Keywords: dynamics, land cover, land use, watershed, SLM. GJSFR-H Classification: FOR Code: 120504

LanduseLandCoverDynamicsanditsImplicationonSustainableLandManagementinWallechaWatershedSouthernEthiopia

Strictly as per the compliance and regulations of :

© 2016. Barana Babiso, Senbetie Toma & Aklilu Bajigo. This is a research/review paper, distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License http://creativecommons.org/licenses/by-nc/3.0/), permitting all non commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Land use/Land Cover Dynamics and its Implication on Sustainable Land Management in Wallecha Watershed, Southern Ethiopia

Barana Babiso α, Senbetie Toma σ & Aklilu Bajigo ρ

Abstract - This study was conducted at Wallecha Watershed in

the burning of areas to enhance the availability of wild 201 the middle course of Bilate River Basin in Wolayitta Zone, games and secure essential resources (Ellis and r

Southern Ethiopia with the main objective of assessing the Pontius, 2012). It is worth mentioning here, however, ea patterns of spatial and temporal dynamics of land use/land Y that the current rates, extents and intensities of LU/LC cover (LU/LC) and implications on sustainable land 491 management (SLM). The study area was covered 10116.7ha, changes are far greater than ever in history, driving and had previously undergone substantial LU/LC changes. unprecedented changes in the ecosystems and The changes in LU/LC which occurred between 1984 and environmental processes at local, regional and global 2010 were monitored in a context of geographical information scales, LC conversions (i.e., the complete replacement

system (GIS) and remote sensing (RS). The Lands at of one cover type by another) are measured by a shift V imageries of 1984, 2000 and 2010 were used to produce three from one LC category to another as is the case in IV land cover maps of the respective years using GIS and RS agricultural expansion, deforestation, or urban ue ersion I

techniques with field verification. Changes in the density of s

expansion (Liu et al., (2007). LC modifications are more s vegetation cover and land degradation over time in the

subtle changes that affect the character of the land I watershed were also estimated with the help of Normalized cover without changing its overall classification. Difference Vegetation Index (NDVI). The general trend was XVI observed as there is decrease in forest lands and shrub- Monitoring of land-cover conversion can be performed grasslands at a rate of 34.27and 15.63ha per year by a simple comparison of successive land cover maps. respectively, and a decrease in degraded lands at a rate of By contrast, the detection of subtle changes within land- 7.63 ha per year. A corresponding increase was observed in cover classes requires a representation of LC where, the tree plantations and cultivated lands at a rate of 30.07 and surface attributes vary continuously in space and time ) H 27.46 ha per year respectively. The decrease in forest lands (De Fries et al., 1995). Remotely sensed (RS) time series ( and shrub-grasslands partly reflects the considerable data reveals that LC changes do not always occur in a degradation of natural vegetation in the watershed. Three state progressive and gradual way, but they may show of vegetation disturbance levels viz. Highly, Moderately and periods of rapid and abrupt change followed either by a Less stressed areas were identified and delineated using NDVI Research Volume values which were measured through the analysis of the quick recovery of ecosystems or by a non-equilibrium spectral reflectance of the red and the near infrared bands for trajectory (Lambin et al., 2003); (Liu et al., (2007). In net the imageries. This finding has highlighted that the changes shell, both land-cover modifications and rapid land- were not in favor of the natural ecosystem, rather triggered cover changes need to be better taken into account in Frontier large scale clearing of forests and shrub-grasslands. Hence, LC change studies. Climate-driven land-cover greater emphasis must be given to wise use and SLM modifications do interact with land-use changes. practices, regulated population growth and integrated Science LU/LC changes play a pivotal role in environmental rehabilitation programs in the studied of watershed. environmental and ecological changes and have both Keywords: dynamics, land cover, land use, watershed, positive and negative impacts on a particular watershed. SLM. They also alter a catchment area's hydrological cycle by modifying rainfall, evaporation and runoff, particularly in Journal small catchment areas (Cao et al., (2009). I. Introduction In Ethiopia, the amount, rate and intensity of LU ith the birth of agriculture, changes in land Global changes are very high and variable, implying that it is use/land cover (LU/LC) might first have dynamic. For instance, over the past 41 years, occurred during the prehistoric period as the W agricultural land areas increased significantly at the cost direct and indirect consequences of human actions with of natural vegetation (woodland and shrub land) and in recent decade reductions in woodland (Kiros, 2008). Author α σ: Wolaita Sodo University, Department of Geography and Observations by (Kebrom & Hedlund, 2000). shows a Environmental Studies. Author ρ: Jigjiga University, Department of Natural Resource decrease in the coverage of shrub lands, riverine Management. e-mail: [email protected] vegetation, and forests, but areas under cultivation

remained more or less unchanged on the highlands of of past LU practices, current LU/LC patterns, and Kallu District. The finding in the Derekolli Catchment, projections of future LU/LC. South Wollo also shows that the shrub lands declined by In Ethiopia, various studies have been 58 percent between 1957 and 1986, with an increase in conducted on LU/LC changes mainly in Northern cultivated land by only 7 percent (Belay, 2002). LU/LC highlands. However, very few studies have been changes that occurred from 1971/72 to 2000 in Yerer conducted in Southern Ethiopian highlands to monitor Mountain and its surroundings manifested through an and quantitatively describe LU/LC dynamics. Even the increase in cultivated land at the expense of the few studies conducted in Southern Ethiopian highlands grasslands (Kahsay, 2004). In the semi- arid areas of the have not so far been studied with requisite focus to Central Rift Valley, in Keraru and Gubeta- Arjo, during Wallecha watershed. Basic data on the extent and trend the period 1973-2000, cropland coverage has increased of LU/LC changes in wallecha watershed that would and woodland cover lost (Ephrem, 2010). help for planning and adoption of SLM practice to

201 LU changes have also been significant in recent minimize the extent and curve the trend of dynamic years in Southern Ethiopia, including the area where this LU/LC changes are scanty. It is, therefore, the intention

Year study was conducted (Gole and Denich, 2001). Large of this study to narrow, if not to fill, this gap and provide scale plantation expansion, communities’ crop field an in depth understanding of the patterns of LU 50 expansion, and change in farming system was observed dynamics and implications for planning appropriate land in Silte Zone (Daniel, 2008). The study at the Umbulo management in the watershed, so that ways will be Catchment revealed the loss of 100 percent of high sought to reverse the trend. vegetation and a slight (10%) increase in vegetation

V between 1986 and 2000 (Awdenegest and Holden, II. Objectives IV 2009). Expansion of crop land area from 4,688ha in In the present study an attempt has been made

ue ersion I 1994 to 5,717ha in 1997, was in Chencha Zuria Woreda to assess the spatial and temporal dynamics of LU/LC s s (Dwivedi et al., 2005). at Wallecha Watershed during the period from 1984 to

I As a result of these land resource degradation 2010; and examine the implications of observed LU/LC XVI processes, persistent negative changes are taking place changes on sustainable land management (SLM). in socio-economic well-being of the people and the environment, and land resources consequently giving III. Materials and Methods birth to various forms of socio-economic challenges a) The study Area

) (Blaikie and Brookfield, 1987). Poor households are Wallecha Watershed is found to the North West H

( often virtually forced to over utilize natural resources for of Lake Abaya in the Southern Highlands of Ethiopia. their daily subsistence. Thus, landless farmers colonize Astronomically, it is located between 6º53ʹ30ʺ and forests and even highly erodible hillsides. Rural 7º4ʹ30ʺN latitudes and 37º48ʹ0ʺ and 37º59ʹ0ʺE households in fuel wood-deficit areas strip foliage and longitudes with a total area of 10,116 hectares (Figure Research Volume burn crop and animal residues for fuel rather than using 1). It located at about 350 Km south of Addis Ababa, the them for fertilizer and this contributes to land national capital, and 153 Km southwest of Hawassa, the degradation. A cycle of poverty and natural resource regional capital.

Frontier degradation is thus established (Repetto, 1987).

In the highlands of Ethiopia, LU/LC changes have reduced water retention capacity and increased

Science the surface run-off and stream flow leading to loss of wetlands and dying of lakes (Alemayehu and Arnalds of 2011). Indeed such complications imply the need for call for adoption for sustainable land management (SLM) practices. Hence, LU/LC change time series analysis is Journal significant, not only for the sustainable management of land resources, but also for the projection of future LU

Global trajectories (Long et al., 2008). Therefore, knowledge of LU/LC change is essential in decision-making in relation

to a wide range of issues, such as for reversing land degradation, deforestation, and climate change (Kiros, 2008). Improving the understanding of LU/LC dynamics can lead to the projection of future LU/LC changes and to more appropriate policy interventions for achieving better land management. Generally, determining the effects of LU/LC change depends on an understanding

201 r ea Y

511 V IV ue ersion I s s

Figure 1: Location Map of Wallecha Watershed (Source: Extracted from Ethio-GIS) XVI

i. Physical Characteristics 89.2 percent of the watershed. However, these It is demonstrated with the DEM (Digital diversified landforms are highly interactive and related to Elevation Model) of the watershed, which is clipped from each other via drainage systems and socio-cultural

SRTM (Shuttle Radar Topographic Mapping), that the condition. ) H northern and the central parts of the study watershed The watershed experiences bi-modal rainfall ( are flat with little variation in the altitude, whereas most with annual average of about 1250mm. This enables a of the south western part is highly rugged. The altitude length of growing period of almost 300 days. The ranges from 1750 m.a.s.l, at the north eastern part to farmers carry out two cycles of seasonal cropping (the Research Volume 2835 m.a.s.l in south western part around the gabba during the short rainy season from February to escarpment. The slope steepness of the area ranges July and the sila during the long rainy season from from 0 to 68 percent ranging from gentle / flat to very August to December) and sometimes an inter season steep slopes. Larger part of the watershed in fact lies on cycle from December to March. The mean annual Frontier slope of greater than 15 percent and each having its temperatures fluctuate between 16 and 20°C all along own implications for conservation treatments. the year.

As to the drainage of the watershed, numerous The original vegetation type of the watershed Science

small temporary/ ephemeral streams which appear in was woodland savanna vegetation where scattered of the rainfall season and some permanent rivers enable trees and shrubs occurred in herbaceous elements efficient system of drainage. Major intermittent streams (Zeleke and Bliss, 2010). Today, the land is intensively

that join the watershed include: Borodo, Helame and used whereby large natural forest cover is virtually Journal Kiljijo at the foot of Mount Damota, but flow longer absent. The remnants of these original vegetation types distances in Damot Gale and Damot Woyde Woredas in are found in small patches only around sacred places the north easterly direction before joining Bilate River and on the steeper slopes of the escarpment, and also Global which ends in Lake Abaya. along stream courses as riverine trees. Very big and old According to the traditional agro-climatic scattered wild trees observed in agricultural fields, zonation of Ethiopia, which is based on overlaying woodlots, front yards, and grazing lands are indicators rainfall, temperature and altitude, the study watershed is for the previous vegetation cover that consisted of identified as having Dega and Weina Dega agro-climatic various indigenous and endemic plant species. The zones. The Dega comprises areas elevated above portion of land under forest is very small and even here 2300m and covers about 10.8 percent of the area. The only a few stands of indigenous trees such as Cordia Weina Dega ranging from 1751-2300m covers about Africana, Croton Macrostachyus and Podocarpus

falcatus are observed. Trees and shrubs are sparsely classifications and post classification comparison covering the largest part of the watershed. Exotic trees, analysis. To increase the accuracy of supervised specifically eucalyptus species are planted extensively classification, more than 5,000 pixels were used to around settlements and along river courses. The LU in create a signature for each LU/LC classes. the watershed is based on mixed rain-fed agriculture. d) Post classification / Supervised More specifically, it is enset-coffee-livestock system that After selectively combining classes, classified combines annual and perennial crops with livestock images were sieved, clumped and filtered before production (Le Gal and Molinier, 2006). producing final output. Sieving removes isolated b) Image Pre-processing classified pixels using blob grouping, while clumping LC changes in the study area for over 26 years helps maintain spatial coherency by removing period were extracted from digital satellite imageries of unclassified black pixels (speckle or holes) in classified Landsat Thematic Mapper (TM,1984), and Landsat images. Finally, a 3 x 3 median filter was applied to 201 Enhanced Thematic Mapper Plus (ETM+, 2000 and smoothen the classified images. Classified images were 2010). These Multi-temporal Remote Sensing (RS) data then exported to ArcGIS (V.10) from ENVI. The polygons Year were imported to ERDAS (v.10) Imagine software. The of <0.5 ha in size were eliminated to minimize the 52 imageries were first radiometrically and geometrically effects of classification errors arising from resolution corrected and georeferenced with the help of 1:50,000 differences among the satellite images while at the topographic maps. Image mosaicking, clipping of area same time without significantly altering the area under of interest (AOI), enhancement and rectification were each LU class. The resultant polygon themes were used applied on the images. Accuracy assessment for the in further analyses. The LU polygon themes for the V LU/LC maps was based on 34-ground truthing points. imageries were then overlaid at a time in ArcGIS and the IV The ground-truth information required for the area converted from each of the classes to any of the ue ersion I

s classification and accuracy assessment of imageries other class was computed. s was collected through the field observations during Shuttle Radar Topographic Mapping (SRTM) I March - April, 2013 which also helped to rectify GCPs for image having 30m resolution and Ethio-GIS database XVI geo-referencing, visual interpretation of the images and were used to delineate the watershed and produce select reference areas. All referenced areas were different maps respectively. digitally documented and localized by GPS e) Vegetation Stress Detection measurements. Normalized Difference Vegetation Index (NDVI) )

H c) Image Transformation and Classification is one of the most common indices of analysis of ( In order to enhance visual identification, image density of vegetation cover and has been used in order transformations were applied on each of the FCC to measure changes in the density of vegetation cover images. To aid the discrimination of the different land and land degradation in the watershed. It is preferred to

Research Volume cover classes Tasseled Cap Transformation (TCT) was the simple index (EVI-Enhanced Vegetation Index) as it applied. The maximum likelihood classifications were corrects reflectance distortions caused by the particles applied for the image classification after selecting in the atmosphere as well as the ground cover below the training areas. Because of powerful classification vegetation. In this research, NDVI (Lu et al., 2004) was Frontier capacity of the software, ENVI 4.3 (Environment for computed as: Visualizing Images) was used for supervised LU/LC

Science − RNIR )( Where: NIR: reflectance in the near infrared portion of the of NDVI = + RNIR )( electromagnetic spectrum R: red band value recorded by satellite sensor.

Journal NDVI values range from -1.0 to +1.0. Negative consideration of present LU and/or potential LU; that is, values approaching -1 correspond to water or values the land capability. This study, however, focused on how close to zero (-0.1 – 0.1) represent no vegetation / bare land is being used at present compared to the use of Global land, and low positive values correspond to shrub & the same land in the past. With little modification to grass lands. Values close to +1.0 represent luxurious (FAO, 2004) LC classification legends, five major LU/LC vegetation. In general, the cover density increases as types: forest land, shrubs and grassland, tree the positive value increases. plantations, cultivated land and degraded land have been identified in Wallecha watershed based on the IV. Results And Discussion characteristics of Landsat imageries and repetitive

a) Land use/Land Cover classification results obtained with the help of ground surveys (Table Classification of LU in order to facilitate its 1). analysis is always contentious as it collates

As the LU/LC classification from 1984 landsat4 markedly increased than it was in 1984. However, the TM image is presented in Table 1, the greatest share of remaining LU/LC types: forest lands, shrubs and grass LU/LC from all classes is cultivated land covering about lands, and degraded lands found decreased in their 37.36 percent of the total area, followed by exotic tree areal coverage (Figure 2b). plantations, and forest and wetlands covers (Figure 2a). As shown in Table 1, in the year 2000, the coverage of cultivated land and exotic tree plantations

Table 1: Areas of LU/LC Classes of Wallecha Watershed for Study Period

1984 2000 2010 LU/LC Class Ha % ha % ha % Forests and Wetlands 2153.34 21.29 1405.26 13.89 1262.16 12.48 201 r

Shrubs and Grasslands 1134.36 11.21 1082.43 10.70 728.10 7.20 ea Y Tree Plantations 2561.22 25.32 3030.57 29.96 3343.14 33.05 531 Cultivated land 3779.91 37.36 4271.85 42.23 4493.79 44.42 Degraded land 487.89 4.82 326.61 3.23 289.53 2.86 Total 10116.70 100.00 10116.70 100.00 10116.70 100.00

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Figure 2a : LU/LC Map of 1984

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Figure 2b : LU/LC Map of 2000 Science In the year 2010, cultivated land took the of highest share covering 44.42 percent of the total watershed area, followed by tree plantations. Only 12.47 percent and 7.2 percent from the total coverage was by Journal forests and shrub & grass lands respectively while the least areal extent covered by degraded land (Figure 2c).

Global

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An important aspect of change detection is to 2000. It can also be seen from Table 2 that, while larger Science

determine which land use class was actually changed to portions of forest and woodlands, shrub and grass of what another LU/LC type. This process involves a pixel lands, and cultivated lands converted to tree to pixel comparison of the study period images through plantations, portion of degraded areas were converted overlay analysis. The LU/LC change matrices presented to cultivated lands between 1984 and 2000. Journal in Table 2, 3 and 4 depict the direction of change and The conversion of forest and woodlands, shrub the land use type that remained unchanged (Figure 3). and grass lands to cultivated land is mainly associated

It is found that of the total area of forest and with the land demand for crop production to satisfy the Global woodlands, shrub and grass lands, and tree plantation food demand of increasing human population, and loss in 1984, only about 33, 31 and 39 percent of areas of land productivity in relation to unsustainably managed respectively were found unchanged in 2000. Whereas, crop cultivation approach. As a result of land the rest great portions of these LU/LC areas were degradation, i.e., the land with gullies and outcrops of converted to other types (Table 2). As to the area of stones due to runoff, it is common practice to plant tree degraded lands, only about 27 percent remained species, dominantly Eucalyptus saligna and Cupressus unchanged between 1984 and 2000. However, much lusitanica Gravellia robusta in the area. (62.94%) of the cultivated land areas were not

Table 2: LU/LC Conversion Matrix between 1984 and 2000

LU/LC Class in 2000 LU/LC classes Forests Shrubs & grass Tree Cultivated Degraded Total lands Plantations lands lands Forests ha 711.00 98.91 737.82 598.41 7.20 2153.34 % 33.02 4.59 34.26 27.79 0.33 100.00

Shrub & grass ha 124.02 351.36 467.55 187.47 3.96 1134.36 lands % 10.93 30.97 41.22 16.53 0.35 100.00

ss in 1984 in ss Tree Plantations ha 356.49 301.59 1004.04 871.02 28.08 2561.22 a % 13.92 11.78 39.20 34.01 1.10 100.00

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Cultivated lands ha 199.08 275.04 772.11 2378.9 154.8 3779.91

Le/LC Cl Le/LC Year % 5.27 7.28 20.43 62.94 4.10 100.00

56 Degraded lands ha 14.67 55.53 49.05 236.07 132.57 487.89

% 3.01 11.38 10.05 48.39 27.17 100.00

Total ha 1405.30 1082.43 3030.57 4271.9 326.61 10116.72 % 13.89 10.70 29.96 42.23 3.23 100.00

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Figure 3 : LU/LC Change Map between 1984 and 2010

Table 3: Description for legend colors of LU/LC Change Map

1. Forest to Forest 6. Shrub & grass to 11. Tree Plantations 16. Cultivated lands 21. Degraded lands to Forest to Forest to Forest Forest 2. Forest to Shrub 7. Shrub & grass to 12. Tree Plantations 17. Cultivated lands 22. Degraded lands to & grass Shrub & grass to Shrub & grass to Shrub & grass Shrub & grass 13. Tree Plantation 3. Forest to Tree 8. Shrub & grass to 18. Cultivated lands 23. Degraded lands to to Tree Plantations Tree Plantations to Tree Plantations Tree Plantations Plantations 14. Tree Plantations 4. Forest to 9. Shrub & grass to 19. Cultivated lands 24. Degraded lands to to Cultivated Cultivated lands Cultivated lands to Cultivated lands Cultivated lands lands 15. Tree Plantations 201 5. Forest to 10. Shrub & grass to 20. Cultivated lands 25. Degraded lands to r

to Degraded ea

Degraded lands Degraded lands to Degraded lands Degraded lands Y lands 571 As to LU/LC conversion between 2000 and the period, larger areas of forests and cultivated lands 2010, small portion of forests, shrub & grass lands, tree were converted to tree plantations, and more of shrub & plantations and degraded lands was remained grass lands and degraded lands were converted to unchanged (Table 4). As to the converted LU/LC size in cultivated lands. V

Table 4: LU/LC Conversion Matrix between 2000 and 2010 IV ue ersion I s

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LU/LC class Forests Shrubs & Tree Cultivated Degraded Total I

grass Plantations lands lands XVI lands Forests ha 470.07 55.17 588.24 283.23 8.55 1405.26 % 33.45 3.93 41.86 20.15 0.61 100.00 )

Shrub & grass lands ha H (

s in 2000 in s 81.54 266.04 340.74 351.09 43.02 1082.43 s % 7.53 24.58 31.48 32.44 3.97 100.00 Tree Plantations ha 496.53 253.35 1257.48 989.19 34.02 3030.57

% Research Volume 16.38 8.36 41.49 32.64 1.12 100.00 LU/LC Cla LU/LC Cultivated lands ha 208.62 143.37 1118.34 2668.41 133.11 4271.85 % 4.88 3.36 26.18 62.46 3.12 100.00 Degraded lands ha Frontier 5.4 10.17 38.34 201.87 70.83 326.61 % 1.65 3.11 11.74 61.81 21.69 100.00 Total ha 1262.16 728.1 3343.14 4493.79 289.53 10116.72 Science

% 12.48 7.20 33.05 44.42 2.86 100.00 of

Table 5 shows that of the total forest and accounting only about 58.4 percent. As to exotic tree

Journal wetlands area in 1984, only about 637.38ha (30%) plantations, about 59.26 percent of the 1984 area was remained unchanged in 2010. The rest forest area was found converted to other LU/LC types in 2010. converted to other LU/LC types as shown in the Table 4. Of the total cultivated area in 1984, about 34.5 In contrast, conversion of other LU/LC types to forests percent converted to other LU/LC types until 2010. On Global amounted only 29 percent compared with 70 percent the other hand, about 44.91 percent of the cultivated that was lost to other LU/LC types. area in 2010 was converted from other LU/LC types. It Furthermore, only about 302.94 ha (26.7%) of is worth mentioning here that about 64.87 percent of the shrub -grasslands in 1984 was still under the same cover degraded lands in 2010 was due to the conversion of condition in 2010, and the rest 831 ha (73.3%) of shrub- other LU/LC types. Thus, it is apparent that the forest grasslands were transformed to other LU/LC types. The land and shrubs and grass land LU/LCs were most at area of shrub-grasslands that changed from other risk of undergoing change. LU/LC types in 2010 was comparatively small

In general, the natural forest lands were highly use system, clearing the natural vegetation and disturbed and the resource base has been damaged. converting to crop cultivation, and later in to tree Basically the leading reason for this conversion is plantation with exotic tree species, and recently with

demand of cultivation land. Rather than improving the different fruit trees. productivity of the land under use with integrated land Table 5: LU/LC Conversion Matrix between 1984 and 2010

LU/LC Class in 2010

LULC class Forests Shrubs & Tree Cultivated Degraded Total grass lands Plantations lands lands

Forests Ha 637.38 63.9 933.21 508.23 10.62 2153.34 % 29.60 2.97 43.34 23.60 0.49 100.00 201 Shrub & grass lands Ha 127.17 302.94 374.76 295.65 33.84 1134.36 % 11.21 26.71 33.04 26.06 2.98 100.00 s in 1984 in s Year Tree Plantations Ha 346.95 190.44 1043.37 952.11 28.35 2561.22

58 % 13.55 7.44 40.74 37.17 1.11 100.00 Cultivated lands Ha 138.06 139.95 911.07 2475.81 115.02 3779.91

% 3.65 3.70 24.10 65.50 3.04 100.00

LU/LC Clas LU/LC Degraded lands Ha 12.60 30.87 80.73 261.99 101.7 487.89

% 2.58 6.33 16.55 53.70 20.84 100.00

V Total Ha 1262.16 728.1 3343.14 4493.79 289.53 10116.72

IV % 12.48 7.20 33.05 44.42 2.86 100.00

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s The results of image processing for the rate of agricultural expansion. Shrubs and grasslands also I LU/LC changes between 1984 and 2000 have been reduced at rate of 3.25 ha/year. Whereas, the degraded XVI presented in Table 6 and indicated that cultivated land lands decreased by 10.08 ha/year mainly due to and tree plantation increased with a rate of 30.75 and massive afforestation and preservation of common tree 29.33 ha/year respectively consuming areas largely from species such as Eucalyptus and pine through campaign forest lands, and shrub & grassland (Table 6). On the programs by then regime. ) other hand, forest lands dramatically reduced at the H

( Table 6: Pattern of LU/LC change between 1984 and 2000 in Wallecha watershed

Change from 1984 to 2000 Mean Annual Rate of Change

Research Volume LU/LC Classes Ha % ha % Forests -748.08 -34.74 -46.76 -2.17 Shrubs and grasslands -51.93 -4.58 -3.25 -0.29 Frontier Tree plantations 469.35 18.33 29.33 1.15 Cultivated land 491.94 13.01 30.75 0.81 Degraded land -161.28 -33.16 -10.08 -2.07 Science

of In the period between 2000 and 2010, exotic and shrub & grassland was also experienced at the rate tree plantations increased with a mean annual rate of of 14.31 and 34.43 ha/year respectively. However, 31.25 ha/year, followed by 22.19 ha/year increase in degraded lands slightly decreased at the rate of 3.71

Journal cultivated lands (Table 7). The reduction in forest lands ha/year.

Table 7: Pattern of LU/LC Changes between 2000 and 2010 in Wallecha Watershed

Global Change from 2000 to 2010 Mean Annual Rate of Change LU/LC Classes Ha % ha % Forests -143.10 -10.22 -14.31 - 1.02 Shrubs and grasslands -354.33 - 32.62 -35.4 -3.27

Tree plantations 312.57 10.31 31.25 1.03 Cultivated land 221.94 5.2 22.19 0.52

Degraded land -37.08 -11.45 -3.71 -1.14

Between 1984 and 2010, tree plantation and ha/year. Degraded lands were transformed to other land cultivated land still continued to increase with the mean LU/LCs mainly as the case of government interventions annual rate of 30.07 and 27.45 ha/year respectively and community initiatives to rehabilitate bare lands in (Table 8). The reduction in forests and shrubs and the watershed in the period. grassland also continued with more or less similar In general, the pattern showed a tendency pattern; i.e. with about -1.59 and -1.35 percent per towards more land being brought under cultivated land, annum. Whereas, reduction in degraded land increased while tree plantations became more important at the as compared to previous years with the rate of -7 expense of shrub-grassland and riverine trees.

Table 8 : Pattern of LU/LC Changes between 1984 and 2010 in Wallecha watershed Change from 1984 to 2010 Mean Annual Rate of Change LU/L C Classes Ha % ha % Forests -891.18 -41.39 -34.27 -1.59 201 Shrubs and grassland -35.81 -15.63 -1.38 r -406.26 ea Tree plantations 30.53 30.07 1.17 Y 781.92 Cultivated land 713.88 18.89 27.46 0.73 591

Degraded land -198.36 -40.66 -7.63 -1.56

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0 1984 2000 2014 Frontier Forests and Wetlands 21.29 13.89 12.48 Shrubs and Grasslands 11.21 10.70 7.20 Tree Plantations 25.32 29.96 33.05 Science of Cultivated land 37.36 42.23 44.42 Degraded land 4.82 3.23 2.86

Journal

Figure 4 : Pattern of LU/LC Changes between 2000 and 2010 in Wallecha Watershed Global d) The State of Vegetation Cover Density series class signatures involving secondary data to

On the basis of the NDVI values drawn through resolve mixed classes (Table 9). the analysis of spectral reflectance of the red and the near infrared bands for the imageries, three state of vegetation disturbance levels (Highly, Moderately and Less stressed) have been estimated for the watershed and the classes were identified based on the class identification and labeling process to generate time-

Table 9 : NDVI based Estimation of Land Degradation Levels

No. NDVI Value Land Use/Land Cover type Estimated Stress level 1 < 0.15 Degraded lands and Settlement areas, parts of Highly Stressed intensively cultivated lands

2 0.15 – 0.3 Shrubs and grassland, cultivated lands, parts of Moderately Stressed natural vegetation

3 > 0.3 Natural forests, Plantations and healthy Less Stressed vegetation’s

The NDVI values of the different cover types for watershed have revealed that 2420.9 ha (23.9 %),

201 the year 1984, 2000 and 2010 imageries clearly show 4273.23 ha (42.2 %), and 3422.58 ha (33.8%) of the highest value for vegetation cover as 0.71, 0.58 and 0.60 watershed area falls within highly, moderately and less

Year respectively with the mean value 0.31, 0.13 and 0.27 for stressed respectively in the year 1984 (Figure 5a). For the year 1984, 2000 and 2010 respectively. The lowest the year 2000, the values for highly stressed class 60 cover values have also been measured as -0.09, -0.31 became 4908.96 ha (48.5%), moderately stressed and -0.07 for 1984, 2000 and 2010 respectively. These became 3852.8 ha (38%) and the remaining 1354.96 ha indicate that the state of the vegetation cover was better (13.39%) was less stressed (Figure 5b). In the year during the 1980s and poor around 2000 and getting 2010, 2644.33 ha (26%), 6196.34 ha (61%) and 1276

V improved during 2010. Thus, the final watershed (13%) of the watershed were found highly, moderately

IV disturbance maps generated for the Wallecha and less stressed respectively (Figure 5c). ue ersion I s s I XVI

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Figure 5a : NDVI map of the Wallecha Watershed for 1984

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Figure 5c : NDVI map of the Wallecha Watershed for 2010

V. Conclusion resources in the Motueka river catchment, New Zealand: Water Resource Management. 23: 137- The observed LU/LC changes and associated 151. problems in Wallecha Watershed has sustainable land 6. Daniel Ayalew. (2008). Remote Sensing and GIS- management and environmental implications. The based LU/LC Change Detection in the Upper Dijo LU/LC changes occurred in the study area either River Catchment, Silte Zone, Southern Ethiopia: degraded or enhanced the land’s capacity for sustained Working papers on population and land use change use and regaining its natural cover. The removal of in central Ethiopia # 17, Addis Ababa University. vegetation cover in the landscape by implication 7. De Fries, R.S., Field, C.B., Justice, C.O. and Los, S. increased the risk of soil erosion. Before 1984, the (1995). Mapping the land surface for global current cover types (short grasses and remnant bamboo atmosphere-biosphere models: Toward continuous forest) were occupied by highland forests, bush; distributions of vegetation’s functional properties. suggesting substantial rate of forest degradation in the

201 Journal of Geophys. Res. Atmos. 100:867–882. watershed. Generally, the increasing dynamics of LU/LC 8. Dwivedi, R. S., Sreenivas, K. and Ramana, K. V. leads to an increase in the vulnerability of the landscape

Year (2005). Land-Use/Land-Cover Change Analysis in (reduction in vegetation cover, soil degradation and the Part of Ethiopia using Landsat Thematic Mapper depletion of biodiversity, which in turn leads to 62 data. International Journal of RS, 26: 1285–87. environmental deterioration) although there is some positive impacts from tree plantations. Choices of the 9. Ellis, E. and Pontius, R. (2012). Land-use and land- land users are influenced by driving forces: a web of cover change. In: Encyclopedia of Earth. (Cutler J. economic, social and bio physical factors that frame Cleveland, Eds.), Washington, D.C. V their livelihood patterns. Therefore, scale up of 10. Ephrem Garedew. (2010). Land-Use and Land- IV sustainable land management practices through Cover Dynamics and Rural Livelihood Perspectives

ue ersion I in the Semi-Arid Areas of Central Rift Valley of

s adaptive management can only be achieved if s concerned farmers and other direct stakeholders Ethiopia. Umeå, Swedish University of Agricultural I perceive SLM to be in their own best interest (i.e. Sciences. XVI adoption of improved systems and practices provides a 11. FAO (Food and Agriculture Organization). (2004). tangible return for their efforts and investment within a Methodological Framework for Land Degradation reasonable time frame. Hence, greater emphasis must Assessment in Drylands (LADA). Rome, Italy. be given to wise use and SLM practices, and integrated 12. Gole, T. and Denich, M. (2001). Subsistence in

) environmental rehabilitation programs have to be Mountain Forest Ecosystem: The Case of SW H

( implemented. Ethiopia. Proceedings of the World Mountain Symposium. Interlaken, Switzerland. V. Acknowledgements 13. Kahsay Berhe. (2004). Land Use and Land Cover We would like to thank the staff of Damot Gale Changes in the Central Highlands of Ethiopia: The Research Volume Woreda Agriculture Office, local people of the case of Yerer Mountain and its Surroundings (Msc watershed, and field assistants for taking time to share thesis), Addis Ababa University. their rich indigenous knowledge, and cooperation for 14. Kebrom Tekle & Hedlund, L. (2000). Land Cover Frontier field data collection. Changes between 1958 and 1986 in Kalu District, Southern Wello, Ethiopia. Mountain Research and References Références Referencias Development, 20(1): 42-51. Science 1. Alemayehu Muluneh and Arnalds O. (2011). 15. Kiros Meles. (2008). Temporal and Spatial Changes

of Synthesis of Research on Land Use and Land Cover in Land Use Patterns and Biodiversity in Relation to Dynamics in the Ethiopian Highlands. Geophysical Farm Productivity at Multiple Scales in Tigray, Research, 112: 1-39. Ethiopia. (Unpublished).

Journal 2. Awdenegest Moges and Holden, N. M. (2009). Land 16. Lambin E.F., Geist, H.J. and Lepers, E. (2003). Cover Change and Gully Development between Dynamics of Land Use and Land Cover Change in 1965 and 2000 in Umbulo Catchment, Ethiopia. Tropical Regions. Annual Review of Environment and Global Mountain Research and Development, 29: 265–276. resources. 28:205-241. 3. Belay Tegene. (2002). Land-cover/land-use 17. Le Gal, E. and Molinier, N. (2006). Agricultural and changes in the Derekolli catchment of the South economic analysis-diagnosis of Obe Jage (Damot Welo zone of Amhara region, Ethiopia. Eastern Gale, Wolayta). Institut National Agronomique. Africa Social Science Research Review. 18:1–20. Grignon, Paris. 4. Blaikie, P. and Brookfield, H. (1987). Land 18. Liu, J., Dietz, T., Carpenter, S., Alberti, M., Folke, C., Degradation and Society. Methuen, New York. Moran, E. and Taylor, W. (2007), ‘‘Complexity of 5. Cao, W., Davie, T., Fenemor, A. (2009). Modeling Coupled Human and Natural Systems,’’ Science, impacts of land cover change on critical water 317, Pp.1513–1516.

19. Long, H.L., Wu, X.Q.,Wang, W.J. and Dong, G.H. (2008). Analysis of Urban-Rural LUC during 1995- 2006 and its Policy Dimensional Driving Forces in Chongqing, China. Sensors, 8: 681 -699. 20. Lu, D., Mausel, P., Brondizio, E. and Moran, E. (2004). Relationships between Forest Stand Parameters and Landsat TM spectral responses in the Brazilian Amazon Basin. Forest Ecology and Management.198: 149-167. 21. Repetto, R. (1987): Population and Environment: an Uncertain Future. Population Bulletin, Vol. 42(2). 22. Zeleke Ewnetu and Bliss J.C. (2010). Small Scale

Forestry in a Changing World: Opportunities and 201

Challenges and the Role of Extension and r ea Technology Transfer. Proceedings of the Y conference/ IUFRO Conference, 06-12 June 2010 Bled, Slovenia. 631

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Publisher: Global Journals Inc. (USA)

Online ISSN: 2249-4626 & Print ISSN: 0975-5896

Landslide Hazard Zonation using Quantitative Methods in GIS, Pauri Garhwal District, Uttarakhand, India By Ramlakhan Yadav & Dr. Neelam Rawat lmora Kumaun University Abstract- Landslides are one of the critical natural processes, which cause enormous damage to life and property. These include roads, railways, bridges, dams, houses and also lead to loss of life. Hence, there is a need for landslide hazard zonation for identification of potential landslide areas. The present study is an attempt towards development of a landslide model by using multi- criteria decision analysis in GIS and remote sensing techniques for landslide hazard zonation. Pauri district was selected for this project. ResourceSAT-2 LISS- 4-Mx satellite imageries, SOI topographical maps, and ancillary data were used as inputs to the study. The data layers of Landuse-landcover and Geology were interpreted from satellite image and available ancillary data. Other raster thematic layers i.e. Slope, Aspect, Elevation and Drainage density have been generated in Arc info 3D Analyst Tool using ASTER DEM of 30 m. resolution. Keywords: pauri garahwal region the landslide hazard zonation, multi- criteria decision analysis quantitative methods in remote sensing and GIS. GJSFR-H Classification: FOR Code: 961099

LandslideHazardZonationusingQuantitativeMethodsinGISPauriGarhwalDistrictUttarakhandIndia

Strictly as per the compliance and regulations of :

© 2016. Ramlakhan Yadav & Dr. Neelam Rawat. This is a research/review paper, distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License http://creativecommons.org/licenses/by-nc/3.0/), permitting all non commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Landslide Hazard Zonation using Quantitative Methods in GIS, Pauri Garhwal District, Uttarakhand, India

Ramlakhan Yadav α & Dr. Neelam Rawat σ

Abstract- Landslides are one of the critical natural processes, I. Introduction which cause enormous damage to life and property. These 201 include roads, railways, bridges, dams, houses and also lead andslide one of the natural catastrophies, always r

ea to loss of life. Hence, there is a need for landslide hazard cause a major problem in the Himalayas by killing Y zonation for identification of potential landslide areas. The L hundreds of people every year besides damaging present study is an attempt towards development of a 651 the properties and blocking the communication links. landslide model by using multi-criteria decision analysis in GIS Landslides in the mountainous terrains are natural and remote sensing techniques for landslide hazard zonation. degradational processes, and one of the most important Pauri district was selected for this project. ResourceSAT-2 LISS- 4-Mx satellite imageries, SOI topographical maps, and landscape building factors. Most of the terrains in the ancillary data were used as inputs to the study. The data mountainous areas have been subjected to slope failure V

layers of Landuse-landcover and Geology were interpreted at least one under the influence of a variety of factors, IV from satellite image and available ancillary data. Other raster and triggered by event such as extreme rainfall or ue ersion I s thematic layers i.e. Slope, Aspect, Elevation and Drainage earthquakes. The frequency and magnitude of slope s density have been generated in Arc info 3D Analyst Tool using failures can increase due to human activities, such as I ASTER DEM of 30 m. resolution. A numerical rating scheme

deforestation or urban expansion. The problem of XVI for the factors was developed for spatial data analysis in GIS. landslides becomes more aggravated, especially during First of all, delineation of landslides (167 Nos.) from high the rainy season, through the main causative factor for resolution satellite data was carried out and verification from Google Earth data was done. Extraction of relevant the stability are often geological and geomorphological

in nature. As such there is an urgent need on part of the

parameters was done from the remotely sensed data using )

digital and visual interpretation techniques. A statistical scientific community to formulate strategies for H relationship was established between landslides and selected minimizing the societal impact of landslides. One of the ( terrain parameters. Weights were assigned to different layer first steps in this direction preparation of Landslide depending on their impact on occurrence of landslide. Hazard Zonation (LHZ) maps. Landslide Hazard Zonation map was prepared based on integration of remotely sensed data derived layers and terrain Aim and Scope of the Project Research Volume characteristics derived topographic data. Validation of the The aim of the project is to operationalise a results was done through ground checks and finally the working methodology wherein a geoenvironmental

Landslide Hazard Zonation map and Digital database was parameters are analysed to develop models of for Frontier created in GIS environment. The resulting landslide hazard mapping landslide prone areas using remotely sensed zonation map delineates the area into three different zones of data, geographical Information System (GIS). Earlier relative Hazard (HZ) classes: High, Moderate and Low. 110

studies have demonstrated the capability of remote Science landslide fall in the High HZ category while 43 in the Moderate sensing based techniques in extraction of landslide HZ category. 14 landslides fall in the Low HZ category. The of High HZ class was located in the Amsaur, Chowki Ghata while related information using visual as well as digital image Dugadda and Kandikhal falls in moderate HZ class. processing techniques, in which integration of remote sensing derived information with relevant terrain Keywords: pauri garahwal region the landslide hazard Journal zonation, multi- criteria decision analysis quantitative parameters and other ancillary/field data have been methods in remote sensing and GIS. made in GIS environment. It is proposed to provide LHZ

maps showing the probable areas of landslide Global occurrence based on geological/geomorphological conditions. bjectives Author α: M.sc Remote Sensing & GIS Dept. of Geography Kumaun II. O University, S. S. J. Campus, Almora Kumaun University, Naninital a) Delineation of landslides from high resolution Uttarakhand University. e-mail: [email protected] Author σ: Scientist(SC) Uttarakhand Space Application Center satellite data and verification from Google Earth Uttarakhand Dehradun. data.

b) Extraction of relevant parameters from the remotely 53K/13, 53K/14,53N/4, 53O/1 and 53O/2. Pauri district sensed data using digital and visual interpretation forms boundaries with Dehradun & Haridwar in west, techniques. Tehri, Rudraprayag and Chamoli in north, Naintal and c) Establishing statistical relationship between Almora in east and Bijnor(U.P.) in south. The District is landslides and selected terrain parameters. administratively divided into nine tehsils, viz., Pauri, d) Preparation of Landslide Hazard Zonation based on Lansdown, Kotdwar, Thalisain, Dhumakot, Srinagar, integration of remotely sensed data derived layers Satpuli, Chaubatakhal & Yamkeshwar and fifteen and terrain characteristics derived topographic data. developmental blocks, viz., Bironkhal, Dwarikhal, e) Validation of the results through ground checks. Dugadda, Ekeshwer, Jaihrikhal, Kaljikhal, Khirsu, Kot, f) Compilation of Landslide Hazard Zonation map and Nainidanda,Pauri, Pabo, Pokhra, Rikhnikhal, Thalisain, creation of Digital database. Yamkeswar. The area of Pauri district is 5230 sq.kms. The population is 687,271 which are distributed in 3473 a) Description of the study area 201 villages. Rivers Alaknanda, Purvi Nayar, Hewal, Pauri District lies between Longitudes 78o 11' Ramganga, Malan, Sona, Pachhmi Nayar, Gawana Gad, 30'' E and 79o 14' 20'' E & Latitudes 29o 26' 45''N and Year Nawalka, Sukro and Kho drain the district. 30o 14' 42''N and falls under Survey of India toposheets 66 Nos. 53J/8, 53K/5, 53J/12, 53K/9, 53K/10, 53J/16, V IV ue ersion I s s I XVI ) H (

Research Volume Figure 1

coordinate system (depicted on SOI topographical III. Methodology maps). Thus it was imperative to follow the standards of Frontier The satellite data has been georeferenced SOI map sheets. the chosen scale for input data is

using the standard coordinate system of the 1/50000. topographic maps using Erdas Imagine software. The

Science georeferenced satellite data is opened in the Arc GIS

of 10.2.2 Arcview and visual interpretation techniques were used to demarcate the existing landslides. The standard image interpretation keys like tone, colour, texture,

Journal association were used. In total 167 landslides have been marked. These landslides were converted to Kmz files and overlaid on the Google Earth software. The

Global locations were verified in the 3 dimensions and checked from all the angles. Based on the objectives and

information needs spatial data- consisting of thematic maps prepared form remotely sensed data and other /conventional sources. Geographical Information System (GIS) Package is kept as core of the database. The spatial data domain forms the set of layers

prepared form RS as well as from other sources, and the Survey of India (SOI) longitude and latitude

671 V IV ue ersion I s s I XVI

) H

( Figure 2

Research Volume Frontier Science of Journal Global Figure 3

68 V IV ue ersion I s s I XVI ) H ( Figure 3 IV. Landslide Mapping assigned corresponding to landslide occurrence in these layers. The influence of the layer in causing Research Volume Using High resolution LISS-IV & Cartosat-2A landslide has also assigned. The details are as under merged data of 2011-12 Landslides were mapped by visual interpretation of satellite image. Total 167

Frontier Landslides have identified. a) Landslide Hazard Zonation Map Base map(Roads, District, Block, Rivers) has Science been prepared from available data from the centre.

of Followingvector thematic layers have been

prepared by interpreting satellite image. Land use/land cover

Journal 1. Geology

Other raster thematic layers have been

generated in Arc info 3D Analyst Tool using from ASTER Global DEM of 30 m. resolution.

2. Slope 2.Aspect 3. Elevation Drainage density for generation of Landslide Hazard Zonation maps. Thematic maps/layer generated

are

b) Integration Of Maps

The above 6 layers were integrated in the

Weighted Overlay model of ArcInfo and weights were

Table 1 Sr. No. Layer Influence Class Weight 1 Slope(Degrees) 25% 0-5 1 5-10 2 10-20 7 20-30 8 30-40 6

40-50 3 2 Aspect(Direction) 9 North 2 North-East 2 East 3 South-East 7 201 South 8 South-West 7 r ea West 2 Y North-West 1 691 3 Elevation(metre above 13% 185-709m. 6

m.s.l.) 710-1134m. 8

1135-1542m. 3

1542-2023m. 2

2024-3069m. 1 V

Sr. No. Layer Influence Class Weight IV

4 Drainage 10% 0-2 2

ue ersion I s

Density(km/sq. kms) 2-4 7 s

4-6 7 I

6-8 8 XVI More than 8 3

5 Landuse/Landcover 15% Water Body 2

Forest Scrub 3

Wasteland-Scrub 1

Treeclad 1 )

Forest Evergreen 3 (

Agriculture 4

Built up NIL

Forest Deciduous Open 8

Forest Deciduous Dense/Close 3 Research Volume

6 Geology 25% Phyllites-Quartzites-Slates 7

Shale-Siltstones 5

Sandstone-Graywacks 8 Frontier Alluvium 2

Sandstone-Conglomerate 8

Ganga Alluvium 1

Science Granitoids 3

Crystallines-Metamorphics 4 of

Krol Limestone 6

V. Results and Discussion Himalaya,This map has been prepared by integrating 06 layers. More layers need to be added in the model to Journal The Final Landslide Hazard Zonation map obtain more precision. This map is not checked in the generated after processing 6 themes in Arc Info is as field ground truth. under. The final map has been classified in 3 categories Global viz. High, Moderate and Low Hazard categories. a) Slope map Geology in order to understand the landslide Slope is a very important factor in determining geomorphology of the study area, general lithological the slope stability. Substrate rock characteristics map has been prepared with the help of World View -2 determine the slope stability relationship between the MS satellite imagery (0.46 cm) and Pauri Garhwal in slope, lithology and frequency of landslide is complex Uttarakhand –Geological survey of India map the study many workers have tried to work out this statistically area is located in the lesser Himalaya of Pauri Garhwal using criteria either individually or cumulatively for Uttarakhand the krol group rocks of the lesser examining the slope factors like slope steepness (in

degrees or per cent) and slope aspect- direction in different slope categories is also done. Maximum which a slope face and curvature are considered. Slope landslides have occurred in aspect categories South- map has been generated and classified into 6 slope West (14.37%), South (35.97%) and South East classes i.e. 0-5o, 5-10o, 10-20o, 20-30o, 30-40o and (15.57%) directions. The Southern aspect(SW-S-SE) more than 400. Distribution of landslides in different contribute for 65.91% of total landslides. slope categories is also done. Maximum landslides have occurred.

b) Aspect map

Aspect map has been generated and classified into 8 aspect classes. Distribution of landslides in

Table 2 201 Landslides % Aspect Landslides %

Year 12 7.19 S(157.5-202.5) 60 35.97 10 5.99 SW(202.5-247.5) 24 14.37 70 18 10.78 W(247.5-292.5) 13 7.78 26 15.57 NW(292.5-337.5) 04 2.40

c) Elevation map f) Landuse-Landcover

Elevation map of Pauri district was prepared by Landuse-Landcover map of Pauri district has V classifying Digital Elevation model into 5 elevation been adopted from Landuse-Landcover layer prepared IV categories. Distribution of landslides in different under USAC Project. Classes are Water body, Forest ue ersion I

s elevation categories is also done. Maximum landslides Scrub, Wasteland/Scrub, Treeclad, Forest Evergreen, s have occurred in elevation category (710-1134m) and Agriculture land, Built up, Forest Deciduous Open, I (185-709m). Forest Deciduous Dense/Close. Maximum landslides XVI have occurred in Forest Deciduous Open category(84) d) Drainage Density map followed by Agriculture Land(22), Forest Scrub(14), Drainage density is the total length of all the Forest Evergreen & Forest Deciduous Dense/Close(13 streams and rivers divided by the total area of the each) and Water Body(11).-Landslide hazard zonation

) drainage basin. It is a measure of how well or how map Geology in order to understand the landslide H

( poorly a watershed is drained by stream channels. It is geomorphology of the study area , general lithological also directly proportional to the magnitude of erosion map has been prepared with the help of World View -2 thus effecting the incidences of landslides. Its unit is MS satellite imagery (0.46 cm) and Pauri Garhwal in kms/sq.kms. Drainage density map has been generated Uttarakhand –Geological survey of India map the study Research Volume from ASTER DEM using 3D Analyst tool and classified area is located in the lesser Himalaya of Pauri Garahwal into 6 classes viz. 0-2, 2-4, 4-6, 6-8 and more than Uttarakhand the krol group rocks of the lesser Himalaya, 8(kms/sq.kms). Distribution of landslides in different This map has been prepared by integrating 06 layers.

Frontier drainage density categories is also done. Maximum landslides have occurred in Drainage Density category Table 3 : The Landslide hazard zonation details as 2-4, 4-6 and 6-8(kms/sq.kms). under

Science e) Geology Sr. No. Hazard category No. of landslides

of 1 Low 14 Lithology plays a very important role in the occurrence of landslides. Interaction between local 2 Moderate 43 3 High 110 geology and the long-term climatic conditions result in Journal 4 Total 167 significantly different landforms with varying degree of susceptibility to land sliding. Geological map of Pauri district has been adopted from Geological map from Global Geological Survey of India. Rock types Phyllites, Quartzites, Slates, Siltstone, Sandstones, Graywacks, Granitoids, Alluvium, Crystallines & Metamorphics and Limestones are present in the district. Maximum landslides have occurred in Sandstone-Graywacks category (Siwalik formation). The area is also traversed Himalayan Frontal Thrust, Main Boundary Thrust and Ramgarh/North Almora Thrust.

711 V IV ue ersion I s s I XVI

) Figure 4 H ( VI. Conclusion Research Council. Washington, DC:National Academy Press, pp. The present study brings out a definite 2. 2006 S. K Investigation of Triggering factors on relationship between the Landslide occurrence and the Landslide Occurrence and landslide Hazard Research Volume Remote Sensing generated thematic layers. The Zonation A GIS Based Approach B.Sc. Thesis Landslide Hazard Zonation is categorized in three Institute of forestry pokhara Kathmandu Nepal. categories High, Moderate and Low Hazard zones. Out 3. 1996 Cruden D.M. and Varnes, D.J Landslide types of 167 toal landslides, 110 Landslides fall in the High HZ Frontier and processes. In turns A.K Schuster R.L.(E.D.) and 43 Landslides fall in Moderate HZ category while landslide investigation and mitigation special report only 14 landslides fall in the Low HZ category. 34.01% of transportation Research board, National Research the district falls under Low HZ category and 40.73% falls Science council. Washington National Academy pres. under Moderate HZ category while 25.26 under High HZ 4. 2002 Yung A.Y... And pai H.H.Application of remote of category. sensing and GIS in landslide detection International journal of remote sensing. VII. Acknowledgments Journal 5. 1988 Yin. K.L and Yan Yan T.Z Statistical prediction The authors are especially thankful to Dr. model for Slope Instability of Metamorphosed Rocks in Bonnard C (Ed) Proceedings of the 5th Durgesh Pant, Director USAC, Dehradun for providing Global the permission to use USAC lab facilities and satellite International Symposium on landslide. datasets for the research work. 6. 2008 Sharma M, KUMAR, R GIS – Landslide hazard Zonation a C ASE study from the parwanoo Area

References Références Referencia s lesser and outer Himalaya, H.P.., India Bulletins of Engineering Geology and the Environment. 1. Cruden, D. M., and Varnes, D. J., 1996. Landslide 7. 2007 Choubey, V. M .P.K.Mukherjee, B.S. Bajwa types andprocesses. In: Turner A.K., Schuster R. L., and walia, V. Geological and Tectonic Influence on (ed.) Land slide investigation and mitigation special water –Soil- Radon Relationship in mandi –manali report 247, Transportation Research Board, National Area Himachal Himalaya Environmental Geology.

201 Year 72

V IV ue ersion I s s I

XVI This page is intentionally left blank ) H ( Research Volume Frontier Science of Journal Global

Global Journal of Science Frontier Research: H

Environment & Earth Science Volume 16 Issue 4 Version 1.0 Year 2016

Type : Double Blind Peer Reviewed International Research Journal

Publisher: Global Journals Inc. (USA)

Online ISSN: 2249-4626 & Print ISSN: 0975-5896

Regional Estimation of Flood Quantile at Ungauged Sites By Basri Badyalina, Ani Shabri & Nur Amalina Mat Jan Univerisiti Teknologi Malaysia Abstract- In this study, Linear Regression (LR) is performance is investigated with and without implementation of Topological Kriging (TK). The aims of this study to determine the used of TK can improve the performance of LR by grouping the basin which have similar hydrology characteristics. Then LR only model the relationship inside the regions. The result show that LR based TK is more reliable in term of estimation accuracy. Keywords: linear regression, ungauged, kriging. GJSFR-H Classification: FOR Code: 300105

RegionalEstimationofFloodQuantileatUngaugedSites

Strictly as per the compliance and regulations of :

© 2016. Basri Badyalina, Ani Shabri & Nur Amalina Mat Jan. This is a research/review paper, distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License http://creativecommons.org/licenses/by-nc/3.0/), permitting all non commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Regional Estimation of Flood Quantile at Ungauged Sites

α σ ρ Basri Badyalina , Ani Shabri & Nur Amalina Mat Jan

Abstract - In this study, Linear Regression (LR) is performance leave-one-out cross-validation procedure is generally is investigated with and without implementation of Topological used to measure the validity of the model proposed [4]. Kriging (TK). The aims of this study to determine the used of TK can improve the performance of LR by grouping the basin II. Data and Methodology which have similar hydrology characteristics. Then LR only 201 model the relationship inside the regions. The result show that a) Data r ea

LR based TK is more reliable in term of estimation accuracy. The data were acquired from Department of Y Keywords: linear regression, ungauged, kriging. Irrigation and Drainage, Ministry of Natural Resources and Environment, Malaysia. There are 70 station chosen 731 I. Introduction whereas all the stations are located in Peninsular lood quantile estimation is an vital concern in Malaysia. They are located within latitude 1° N-5° N and longitudes of 100° N-104° N. hydrology, especially in ungauged catchments. V According to Sivapalan et al. (2003), ungauged b) Regionalization for Linear Regression

F IV catchments are catchments that had insufficient data The variation in streamflow characteristics such histories (in terms of both data quantity and quality) [1]. ue ersion I as flood quantiles are very related to the variations of s s The majority of rivers and stream reaches and tributaries physiographic and climatic factors. Using this fact I in the Peninsular Malaysia are ungauged or poorly emperical equations develop to relate streamflow gauged [2]. Trustworthy estimation of flood quantile is a chrateristics with the meteorogical and physiographic XVI vital aspect in watershed development, as it is influential variables. There are many models whereby the in gaining a deeper sense of flow variability in ungauged relationship between catchment streamflow and basins. Regionalization refers to the method of catcthment charateristics can be expressed. However, relocating the information (hydrological information) in practice the most commonly used relationship )

H from one catchment (location) to another. Agreeing to ( between the flood quantiles ()Q and catchment this explanation, the ungauged catchments should be T located in a region homogeneous with the gauged characteristics is the power form function. The power basins. The hypothesis behind the homogeneous is that function has the following form: Research Volume identical geology, climate, topography and soils in the αα α 12 n homogeneous area would generally yield identical QT = αε0AA1 1 A2 0 (1) responses, but not essentially in geographically neighboring catchments [3]. Regionalization technique where αα12,,,αn are the model parameters, Frontier is to decide and clarify hydrological variables of AA12, ,..., An are the site characteristics, εo is the interests. In flood quantile regionalization, the recorded multiplicative error term, n is the number of sites stream flow data of neighboring or similar gauged characteristics and Q is the flood quantile of T-year Science catchments will be used to create hydrological model T return period. The power form model can be linearized of parameters or relationships with catchment by a logarithimic transformation whereas the parameters characteristics. Then relationship between stream flow of the linearized model can be estimated by a linear and catchment characteristics are then develop to regression model. In other word, taking logs on both Journal obtain hydrological models. This relationship is known sides, as regionalization method. For the latter, model

performance are evaluated using simulated ungauged Global =αα+++α ε catchments, before the model can be used in real log(Qt) log(01 ) log(AA1 )nn log( ) log(0 ) ungauged catchments. After a relationship between catchment characteristics and hydrological variables is or generally acceptable, it is important to authenticate the YX = β + e (2) model before it can be applied in ungauged basins. The where = for  vector of flood Author α σ ρ: Faculty of Science, Univerisiti Teknologi Malaysia, Johor, YQlog( T ) im=1, 2,,: Malaysia. e-mail: [email protected] qunatiles from m sites.

β = [log(αα01), ,,]: αn vector of coefficients; References Références Referencias

X = [(1, logAi )]: matrix of the logarithm of the 1. Sivapalan M, Takeuchi K, Franks SW, Gupta VK, physiographic and meteorological charateristics with the Karambiri H, Lakshmi V, et al. IAHS Decade on first column being equal to one. Predictions in Ungauged Basins (PUB), 2003–2012: Shaping an exciting future for the hydrological = ε matrix of the logarithm of the error terms e [log( 0 )]: sciences. Hydrological Sciences Journal. 2003; 48(6):857-80. ε 0 , which are assumed to be independent, identically 2. Mamun A, Hashim A, Amir Z. Regional Statistical distributed with 0 mean and constant variance. Models for the Estimation of Flood Peak Values at m = total number sites Ungauged Catchments: Peninsular Malaysia. = number of independent variables excluding the n Journal of Hydrologic Engineering. 2011;17(4):547- constant term

201 53.

r 3. Smakhtin, V. U. (2001). Low flow hydrology: a c) Topological Kriging (TK) ea TK applies kriging methods over a geographical review. Journal of hydrology, 240(3), 147-186. Y space and combines two groups of forcing for 4. Samuel, J., Coulibaly, P., & Metcalfe, R. A. (2011). 74 hydrological variability (Archfield et al., 2013). TK is then Estimation of continuous streamflow in Ontario applied to build region on Peninsular Malaysia based on ungauged basins: comparison of regionalization

rainfall data. methods. Journal of Hydrologic Engineering, 16(5), 447-459. III. Results 5. Archfield, S. A., Pugliese, A., Castellarin, A., Skøien, V J. O., & Kiang, J. E. (2013). Topological and IV Table 1 : Leave-One-Out validation result in term of prediction accuracy canonical kriging for design flood prediction in ue ersion I s ungauged catchments: an improvement over a s Hydrological

I traditional regional regression approach?. LR R-LR Variables Hydrology and Earth System Sciences, 17(4), 1575-

XVI NASH q10 0.6831 0.7012 1588. q50 0.6722 0.6836 q100 0.6324 0.6539 RMSE q10 825.9714 796.5721 q50 874.1341 858.6972 )

H q100 912.2361 895.9628

( Tab. 1 show the comparison between linear regression without regionalization (LR) and linear regression with regionalization (R-LR). Both of model Research Volume performance are measure using NASH and RMSE. R-LR is a procedure where the region of Peninsular Malaysia is divided according it hydrology similarities and then LR

Frontier is applied between each region obtain form TK. From Tab. 1, the result indicated R-LR is perform better than LR. The used of regionalization is important because it

Science will group the catchments with similar hydrology attributes and make the model easier to build a suitable of relationship between input and output. Thus Regionalization Linear Regression is better than benchmark model linear regression.

Journal

IV. Conclusion

Global The used of Topological Kriging (TK) for flood quantile estimation at ungauged basin is presented in this study. Each of the region obtain from TK are then applied LR. Three various return period in this study were used to see the capability of the model to estimate for short term and long term. R-LR are then compare with LR witohot the implementation of TK. The result LR with implementation of TK produce more reliable estimation of flood quantile at ungauged basin.

Environment & Earth Science Volume 16 Issue 4 Version 1.0 Year 2016

Type : Double Blind Peer Reviewed International Research Journal

Publisher: Global Journals Inc. (USA)

Online ISSN: 2249-4626 & Print ISSN: 0975-5896

Total Volatile Organic Compounds (TVOCs) in Indoor and Outdoor Urban Atmospheres at a Terai Region of Northern India By Amit Masih & Anurag S. Lall St. Andrews College Abstract- Volatile organic compounds (VOCs) are an important class of air pollutants and even at a trace amounts; these compounds have a high potential hazard to human health due to their carcinogenic nature. In particular, highly reactive VOCs, which are reported to be toxic and also may participate in numerous reactions in the atmosphere to form secondary air pollutants including ground level ozone and secondary organic fine particles. Thus, an investigation of indoor/outdoor TVOC was conducted at selected locations in Gorakhpur in order to ascertain the contamination levels. The concentrations of TVOC were measured at two locations in the city of Gorakhpur, which covers residential and roadside areas. The samples were collected for the period of three consecutive days for indoors and outdoors, at each microenvironment. TVOC levels were measured using a portable data logging Ion Science PhoCheck+ photo-ionization detector (PID). TVOC concentration for combined indoor/outdoor air was 65.03 ppb and 161.08 ppb at residential and roadside site respectively. At residential site, the indoor and outdoor mean concentration of TVOC was 90.45 ppb and 39.62 ppb respectively. The average indoor concentration at roadside site was 173.52 ppb whereas at outdoor it was 148.68 ppb. At both the sites, the indoor TVOC levels were higher than that at outdoors. Keywords: TVOC, indoor, outdoor, residential, roadside, terai region.

GJSFR-H Classification: FOR Code: 059999p

TotalVolatileOrganicCompoundsTVOCsinIndoorandOutdoorUrbanAtmospheresat aTeraiRegionofNorthernIndia

Strictly as per the compliance and regulations of :

© 2016. Amit Masih & Anurag S. Lall. This is a research/review paper, distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License http://creativecommons.org/licenses/by-nc/3.0/), permitting all non commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Total Volatile Organic Compounds (TVOCs) in Indoor and Outdoor Urban Atmospheres at a Terai Region of Northern India

α σ Amit Masih & Anurag S. Lall

Abstract- Vo latile organic compounds (VOCs) are an important degradation processes for aromatic VOCs in the class of air pollutants and even at a trace amounts; these atmosphere and the resulting products contribute to 201 compounds have a high potential hazard to human health due secondary organic aerosol (SOA) formation by r to their carcinogenic nature. In particular, highly reactive ea nucleation and condensation (Brocco et al., 1997). The Y VOCs, which are reported to be toxic and also may participate major sources of VOCs in ambient air include vehicular in numerous reactions in the atmosphere to form secondary 751 air pollutants including ground level ozone and secondary and industrial emissions, petrol refineries and usages of organic fine particles. Thus, an investigation of indoor/outdoor solvents. (de Blas et al., 2012). However, in indoor air, TVOC was conducted at selected locations in Gorakhpur in the sources of VOCs are diverse. Major sources include order to ascertain the contamination levels. The concentrations the combustion processes like cooking, heating and of TVOC were measured at two locations in the city of smoking. In addition, several domestic products such as V

Gorakhpur, which covers residential and roadside areas. The cleansers, stain removers, paints, varnishes, furnishings IV samples were collected for the period of three consecutive

and certain building materials also have a significant ue ersion I s days for indoors and outdoors, at each microenvironment. contribution. (de Blas et al., 2012). Exposure to elevated s

TVOC levels were measured using a portable data logging Ion I levels of VOCs may cause several adverse health effects Science PhoCheck+ photo-ionization detector (PID). TVOC concentration for combined indoor/outdoor air was 65.03 ppb such as irritation in mucous membrane, weakness, XVI and 161.08 ppb at residential and roadside site respectively. difficulty in concentrating, nausea, discomfort and At residential site, the indoor and outdoor mean concentration headache. It may also cause some serious health of TVOC was 90.45 ppb and 39.62 ppb respectively. The effects such as lung cancer and leukemia. Although average indoor concentration at roadside site was 173.52 ppb large number of individual VOCs has been identified in ) whereas at outdoor it was 148.68 ppb. At both the sites, the air, our knowledge regarding the health effects caused H ( indoor TVOC levels were higher than that at outdoors. by some of these individual VOCs is very limited. (WHO, Keywords: TVOC, indoor, outdoor, residential, roadside, 1989). Hence, in order to illustrate the pollutant load in terai region. terms of VOCs, the individual VOC concentrations

should be added together to obtain an entity called Total Research Volume I. Introduction Volatile Organic Compounds (TVOC). (Molhave, 1990; dramatic impact on air quality world-wide was Andersson et al., 1997). In India, the monitoring of TVOC

created by the rapid urbanization and has become more important because this country is in Frontier Aindustrialization over the past decades. Recently, developing phase & in urban area there is very close Asia alone has more than 10 megacities, including proximity of residence and busy road with high traffic is Delhi, and Mumbai, India. The impact of air pollution on quite often. People residing in an urban area especially Science human health has stimulated much interest for both in India spend more time while commuting for their work public and scientific communities. A wide range of and residence. Thus, it becomes more important to get of organic and inorganic pollutants have been recognised. the actual air quality parameters for Indian cities. To our Amongst these, the volatile organic compounds (VOCs) knowledge, there has been a shortage of TVOC studies have gained special attention because they not only especially in this particular part of northern India. Journal deteriorate human health, but also contribute Therefore, this paper describes a monitoring study significantly to major environmental problems such as conducted at Gorakhpur in which the indoor and Global global warming, stratospheric ozone depletion as well outdoor TVOC levels were measured along with as ground level ozone formation. VOCs are carbon- seasonal variation in residential and roadside compounds that have boiling point lower than or equal atmospheres of a terai region of northern India. to 250◦C at an atmospheric pressure of 1atm. (EU, 2004). The reaction of VOCs with hydroxyl radicals (OH) II. Methods and Materials and/or nitrate (NO3) radicals serves as the dominant a) Sampling site description Author α σ: Environmental Research Lab, Department of Chemistry, St. Gorakhpur (26°45′32″N 83°22′11″E) is located Andrew’s College, Gorakhpur, India. e-mail: [email protected] in the terai region of eastern Uttar Pradesh in northern

India, near the border of Nepal, in the foothills of the and total suspended particulate matter from engine Shiwalik Himalayas. Situated on the basin of rivers Rapti exhausts (Masih et al., 2016). and Rohini, the geographical shape of the Gorakhpur

city is of bowl, surrounded by the river and other small a) BTEX sampling Samples were collected for the period of three streams from three sides. River Rapti is interconnected consecutive days for indoors and outdoors, at each through many other small rivers. The present district of microenvironment. Sampling duration was for 8 h from Gorakhpur, 265Kms east of capital Lucknow, on 10:00 am to 6:00 pm during all the seasons. TVOC National Highway (NH-28), covers geographical area of levels were measured using a portable data logging Ion

3483.8 Sq. km having total population of about + ) . . Science PhoCheck photo-ionization detector (PID) 4,440,895 Masih and Lall., 2016). In winter season, the equipped with 10.6eV ultra-violet lamp (NIOSH, 2003). temperature ranges from 3.5°C to 29.5°C with an The sampling instrument was placed above 1.5 m average of 18.6°C and humidity is 71%. During summer

201 (breathing zone) from floor level at indoor and outdoor season, average temperature was 35.5°C with the range locations. The instrument uses ultra-violet light to break from 20.5°C to 48.5°C having 82% humidity, whereas in ear down VOCs in the air into positive and negative ions. Y ° monsoon season the temperature ranges from 18.2 C Then, it measures the flow of electric current, which is 76 to 43.8°C with an average of 30.8°C and humidity is proportional to the concentration of contaminants. The 89% as represented in Table 2. Figure 1 illustrates that concentration is displayed on the monitor. About 220 the prevailing summer and monsoon winds are east- ml/min of air was drawn through the instrument’s internal northeast (ENE-30.1% & 26.3%) and west-southwest pump. The PID data logger was set for 1-sec (WSW-14.1% & 19.4%) with wind speeds of 0.1 to 12.2 measurement interval. Before each sampling, the V and 0.1 to 14.1 meters per second respectively, while instrument was calibrated by 100 ppm isobutylene IV during winters, the wind direction are towards east- according to manufacturer’s instructions (Henderson,

ue ersion I southeast (ESE-21.9%) and west (W-20.1%) with wind s 1999; ASSE, 2000). s speeds of 0.1 to 10.1 meters per second. Air sampling I was accomplished at two sites of Gorakhpur city III. Results and Discussion XVI namely, ‘Taramandal’ which is exclusively a residential area, and ‘Golghar’ which is representative of a roadside The indoor-outdoor combined average TVOC area, since it is situated by the side of a road which concentration was 65.03 ppb and 161.08 ppb at

carries high traffic density resulting in emission of smoke residential and roadside site respectively. ) H

( RDS RES

Summer Research Volume

Frontier Monsoon Science

of Winter Journal 0 50 100 150 200

Global TVOC concentrations (ppb) Figure 1 : TVOC levels at residential and roadside sites during different seasons

Figure 1 shows the pattern of average TVOC a) Spatial Variation levels during different seasons. From the figure, it is At residential site, the indoor concentration of evident that in each season the TVOC levels at roadside TVOC ranged from 66.07 ppb to 116.06 ppb with a site were higher than that at residential site. Higher mean value of 90.45 ppb, while for outdoor air the mean concentrations at roadside site may result from the concentration was 39.62 ppb with a range of 34.3 ppb

proximity of intense automobile traffic. to 46.89 ppb. The average indoor concentration at

©2016 Global Journals Inc. (US) Total Volatile Organic Compounds (TVOCs) in Indoor and Outdoor Urban Atmospheres at a Terai Region of Northern India roadside site was 173.52 ppb with a range of 145.66 indoor air through various vents present in the building ppb to 206.10 ppb; however the ambient TVOC (Batterman et al., 2007). This is also evident from the concentration ranged from 134.94 ppb to 163.88 ppb indoor-outdoor (I/O) ratios which show that at roadside with a mean value of 148.68 ppb. At both the sites, the site I/O ratios were lower than that observed at indoor TVOC levels were higher than that at outdoors. residential site. Mean I/O ratio at residential site was 2.3 This may be probably due to indoor combustion with a range of 1.7 to 2.7 while at roadside site I/O activities like cooking, smoking and heating as well as ranges from 0.9 to 1.3 with a mean value of 1.1. diverse indoor sources of emission like fabrications and b) Seasonal Variation furnishings used within the house. The indoor TVOC During winters the TVOC concentration at concentration at roadside site was higher than that at residential and roadside were 44.13 ppb and 157.14 residential site, which suggests that roadside indoors ppb for outdoors while it was 106.46 ppb and 192.66 were under combined influence of indoor emissions and ppb for indoors respectively. outdoor vehicular emissions. This is supported by the 201

r fact that VOCs from outdoor air may also enter the ea Y

771 V IV ue ersion I s s I XVI

) H Figure 2 : Indoor and outdoor TVOC seasonal variation at both sites (

In summer season, the TVOC levels were 38.43 monsoon season may also be explained due to the ppb (residential) and 150.48 ppb (roadside) for wash out effect on outdoor VOCs. Research Volume outdoors whereas 73.57 ppb and 155.26 ppb at indoors respectively. During monsoons, the TVOC for residential and roadside were 36.29 ppb and 138.38 ppb at outdoors, while 91.33 ppb and 172.68 ppb at indoors Frontier respectively. Seasonal variation of indoor and outdoor TVOC levels at both sites is depicted in Figure 2(a) and (b). Seasonal trend for TVOC at outdoors was in the Science order of winter > summer > monsoon whereas for of indoors it was winter > monsoon > summer at both the sites. In winters, high TVOC levels at outdoors might be due to the decrease in the rate of photochemical Journal degradation due to OH radicals, whereas at indoors it may be explained on account of relatively higher emissions in winters (Na et al., 2005) due to frequent Global combustion activities like room heating. (Masih and Lall., 2016). The wash out effect due to frequent rain showers may be the probable reason for lowest outdoor TVOC levels in monsoon season at both theasites. Seasonal variation of I/O ratios at both sites is shown in Figure 3(a) and (b). It is evident from the figure, that at both the sites, the I/O ratios were highest in monsoon season followed by winter and summers. Highest I/O ratios in

78 Figure 3 : Seasonal variation of I/O ratios at both sites The results of the present study have been Development Conference and Exposition, June 25 - compared with other studies on TVOC in Table 1 28, Orlando, Florida, USA. V showing that most of the countries have higher TVOC 3. Batterman S, Jia C, Hatzioasilis G. (2007). Migration IV levels than India. of volatile organic compounds from attached ue ersion I

s garages to residences: A major exposure source.

s Table 1 : Average TVOC (ppb) levels compared from Environmental Research 104(2), 224-240.

I literature 4. de Blas M, Navazo M, Alonso L, Durana N, Gomez XVI Country TVOC* MC, Iza J. (2012). Simultaneous indoor and outdoor (ppb) on-line hourly monitoring of atmospheric volatile USA 195.23 organic compounds in an urban building. The role Brazil 195.15 of inside and outside sources. Science of the Total

) India 150.75 Environment 426, 327-335. H ( This Study 113.04 5. EU. (2004). “ Directive 2004/42/CE of the European Sweden 86.13 Parliament and of the Council of 21 April 2004 on Greece 67.33 the limitation of emissions of volatile organic *A verage TVOC compounds due to the use of organic solvents in Research Volume certain paints and varnishes and vehicle refinishing IV. Acknowledgements products and amending Directive 1999/13/EC.”

Financial support from Department of Science 6. Henderson R. E. (1999). Portable Gas Detectors Frontier and Technology (DST), New Delhi, India in Project No. used in Confined Space and Other Industrial SR/FTP/ES-77/2013 is duly acknowledged. Authors Atmospheric Monitoring Programs. Safety and gratefully acknowledge Revd. Prof. J. K. Lal (Principal) Health in Confined Spaces, McManus, Neil, Lewis

Science and Dr. S. D. Sharma (Head) Chemistry Department, St Publishers, Boca Raton, FL.

of Andrew’s College, Gorakhpur, UP, India, for providing 7. Masih A., Lall Anurag S., Singhvi R., Taneja A. necessary facilities. Authors are also thankful to Mr. Jay (2016) Inhalation exposure and related health risks Patel, ERT, USEPA for providing technical support of BTEX in ambient air at different

Journal during the analysis of samples. microenvironments of a terai zone in north India.

Atmospheric Environment 147, 55-66.

References Références Referencias 8. Masih A. & Lall A.S. (2016). Measurements of BTEX Global 1. Andersson K., Bakke J.V., Bornehag C.G., Clausen in the Vicinity of Roadside at Gorakhpur, India. G., Skerfving S., Sundell J. (1997). TVOC and Health Proceedings of the International Conference on in Non-Industrial Indoor Environments. Indoor Air 7, Sciences, Engineering and Technical Innovations, Jalandhar, Punjab, India, Vol.2, 195-197. 78-91. 2. American Society of Safety Engineers (ASSE) 9. Molhave L., (1990). Volatile organic compounds, (2000). Use of a Portable Photo-Ionization Detector indoor air quality and health. In: Walkinshaw, (PID) in Determining Occupational Exposure to D.S.(ed.) Proceedings of Indoor Air ’90, Ottawa, Volatile Organic Compounds (VOCs) in a Print Canada Mortgage and Housing Corporation, Vol. 1, Shop. Proceeding of the ASSE Professional 15-33.

10. Na K, Moon KC and Kim YP. (2005). Source contribution to aromatic VOC concentration and ozone formation potential in the atmosphere of Seoul. Atmospheric Environment. 39, 5517-5524. 11. NIOSH. (2003). Hydrocarbons, aromatic: method 1501. NIOSH Manual of Analytical Methods (NMAM). 12. World Health Organization (1989). Indoor Air Quality: Organic Pollutants, Copenhagen, WHO Regional Office for Europe (EURO Reports and Studies No. 111).

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Page Size: 8.27" X 11'"

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2. Ethical Guidelines,

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4. Manuscript’s Category,

5. Structure and Format of Manuscript,

6. After Acceptance.

1. GENERAL

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Scope

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Review papers: These are concise, significant but helpful and decisive topics for young researchers.

Research articles: These are handled with small investigation and applications

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The recommended size of original research paper is less than seven thousand words, review papers fewer than seven thousands words also.Preparation of research paper or how to write research paper, are major hurdle, while writing manuscript. The research articles and research letters should be fewer than three thousand words, the structure original research paper; sometime review paper should be as follows:

Papers: These are reports of significant research (typically less than 7000 words equivalent, including tables, figures, references), and comprise:

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(h) Brief Acknowledgements.

(i) References in the proper form.

Authors should very cautiously consider the preparation of papers to ensure that they communicate efficiently. Papers are much more likely to be accepted, if they are cautiously designed and laid out, contain few or no errors, are summarizing, and be conventional to the approach and instructions. They will in addition, be published with much less delays than those that require much technical and editorial correction.

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Format

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Abstract, used in Original Papers and Reviews:

Optimizing Abstract for Search Engines

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Key Words

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One must be persistent and creative in using keywords. An effective keyword search requires a strategy and planning a list of possible keywords and phrases to try.

Search engines for most searches, use Boolean searching, which is somewhat different from Internet searches. The Boolean search uses "operators," words (and, or, not, and near) that enable you to expand or narrow your affords. Tips for research paper while preparing research paper are very helpful guideline of research paper.

Choice of key words is first tool of tips to write research paper. Research paper writing is an art.A few tips for deciding as strategically as possible about keyword search:

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• One should start brainstorming lists of possible keywords before even begin searching. Think about the most important concepts related to research work. Ask, "What words would a source have to include to be truly valuable in research paper?" Then consider synonyms for the important words. • It may take the discovery of only one relevant paper to let steer in the right keyword direction because in most databases, the keywords under which a research paper is abstracted are listed with the paper. • One should avoid outdated words.

Keywords are the key that opens a door to research work sources. Keyword searching is an art in which researcher's skills are bound to improve with experience and time.

Numerical Methods: Numerical methods used should be clear and, where appropriate, supported by references.

Acknowledgements: Please make these as concise as possible.

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The Editorial Board and Global Journals Inc. (US) recommend that, citation of online-published papers and other material should be done via a DOI (digital object identifier). If an author cites anything, which does not have a DOI, they run the risk of the cited material not being noticeable.

The Editorial Board and Global Journals Inc. (US) recommend the use of a tool such as Reference Manager for reference management and formatting.

Tables, Figures and Figure Legends

Tables: Tables should be few in number, cautiously designed, uncrowned, and include only essential data. Each must have an Arabic number, e.g. Table 4, a self-explanatory caption and be on a separate sheet. Vertical lines should not be used.

Figures: Figures are supposed to be submitted as separate files. Always take in a citation in the text for each figure using Arabic numbers, e.g. Fig. 4. Artwork must be submitted online in electronic form by e-mailing them.

Preparation of Electronic Figures for Publication Even though low quality images are sufficient for review purposes, print publication requires high quality images to prevent the final product being blurred or fuzzy. Submit (or e-mail) EPS (line art) or TIFF (halftone/photographs) files only. MS PowerPoint and Word Graphics are unsuitable for printed pictures. Do not use pixel-oriented software. Scans (TIFF only) should have a resolution of at least 350 dpi (halftone) or 700 to 1100 dpi (line drawings) in relation to the imitation size. Please give the data for figures in black and white or submit a Color Work Agreement Form. EPS files must be saved with fonts embedded (and with a TIFF preview, if possible).

For scanned images, the scanning resolution (at final image size) ought to be as follows to ensure good reproduction: line art: >650 dpi; halftones (including gel photographs) : >350 dpi; figures containing both halftone and line images: >650 dpi.

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Color Charges: It is the rule of the Global Journals Inc. (US) for authors to pay the full cost for the reproduction of their color artwork. Hence, please note that, if there is color artwork in your manuscript when it is accepted for publication, we would require you to complete and return a color work agreement form before your paper can be published.

Figure Legends: Self-explanatory legends of all figures should be incorporated separately under the heading 'Legends to Figures'. In the full-text online edition of the journal, figure legends may possibly be truncated in abbreviated links to the full screen version. Therefore, the first 100 characters of any legend should notify the reader, about the key aspects of the figure.

6. AFTER ACCEPTANCE

Upon approval of a paper for publication, the manuscript will be forwarded to the dean, who is responsible for the publication of the Global Journals Inc. (US).

6.1 Proof Corrections The corresponding author will receive an e-mail alert containing a link to a website or will be attached. A working e-mail address must therefore be provided for the related author.

Acrobat Reader will be required in order to read this file. This software can be downloaded

(Free of charge) from the following website: www.adobe.com/products/acrobat/readstep2.html. This will facilitate the file to be opened, read on screen, and printed out in order for any corrections to be added. Further instructions will be sent with the proof.

Proofs must be returned to the dean at [email protected] within three days of receipt.

As changes to proofs are costly, we inquire that you only correct typesetting errors. All illustrations are retained by the publisher. Please note that the authors are responsible for all statements made in their work, including changes made by the copy editor.

6.2 Early View of Global Journals Inc. (US) (Publication Prior to Print) The Global Journals Inc. (US) are enclosed by our publishing's Early View service. Early View articles are complete full-text articles sent in advance of their publication. Early View articles are absolute and final. They have been completely reviewed, revised and edited for publication, and the authors' final corrections have been incorporated. Because they are in final form, no changes can be made after sending them. The nature of Early View articles means that they do not yet have volume, issue or page numbers, so Early View articles cannot be cited in the conventional way.

6.3 Author Services Online production tracking is available for your article through Author Services. Author Services enables authors to track their article - once it has been accepted - through the production process to publication online and in print. Authors can check the status of their articles online and choose to receive automated e-mails at key stages of production. The authors will receive an e-mail with a unique link that enables them to register and have their article automatically added to the system. Please ensure that a complete e-mail address is provided when submitting the manuscript.

6.4 Author Material Archive Policy Please note that if not specifically requested, publisher will dispose off hardcopy & electronic information submitted, after the two months of publication. If you require the return of any information submitted, please inform the Editorial Board or dean as soon as possible.

6.5 Offprint and Extra Copies A PDF offprint of the online-published article will be provided free of charge to the related author, and may be distributed according to the Publisher's terms and conditions. Additional paper offprint may be ordered by emailing us at: [email protected] .

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Before start writing a good quality Computer Science Research Paper, let us first understand what is Computer Science Research Paper? So, Computer Science Research Paper is the paper which is written by professionals or scientists who are associated to Computer Science and Information Technology, or doing research study in these areas. If you are novel to this field then you can consult about this field from your supervisor or guide.

TECHNIQUES FOR WRITING A GOOD QUALITY RESEARCH PAPER:

1. Choosing the topic: In most cases, the topic is searched by the interest of author but it can be also suggested by the guides. You can have several topics and then you can judge that in which topic or subject you are finding yourself most comfortable. This can be done by asking several questions to yourself, like Will I be able to carry our search in this area? Will I find all necessary recourses to accomplish the search? Will I be able to find all information in this field area? If the answer of these types of questions will be "Yes" then you can choose that topic. In most of the cases, you may have to conduct the surveys and have to visit several places because this field is related to Computer Science and Information Technology. Also, you may have to do a lot of work to find all rise and falls regarding the various data of that subject. Sometimes, detailed information plays a vital role, instead of short information.

2. Evaluators are human: First thing to remember that evaluators are also human being. They are not only meant for rejecting a paper. They are here to evaluate your paper. So, present your Best.

3. Think Like Evaluators: If you are in a confusion or getting demotivated that your paper will be accepted by evaluators or not, then think and try to evaluate your paper like an Evaluator. Try to understand that what an evaluator wants in your research paper and automatically you will have your answer.

4. Make blueprints of paper: The outline is the plan or framework that will help you to arrange your thoughts. It will make your paper logical. But remember that all points of your outline must be related to the topic you have chosen.

5. Ask your Guides: If you are having any difficulty in your research, then do not hesitate to share your difficulty to your guide (if you have any). They will surely help you out and resolve your doubts. If you can't clarify what exactly you require for your work then ask the supervisor to help you with the alternative. He might also provide you the list of essential readings.

6. Use of computer is recommended: As you are doing research in the field of Computer Science, then this point is quite obvious.

7. Use right software: Always use good quality software packages. If you are not capable to judge good software then you can lose quality of your paper unknowingly. There are various software programs available to help you, which you can get through Internet.

8. Use the Internet for help: An excellent start for your paper can be by using the Google. It is an excellent search engine, where you can have your doubts resolved. You may also read some answers for the frequent question how to write my research paper or find model research paper. From the internet library you can download books. If you have all required books make important reading selecting and analyzing the specified information. Then put together research paper sketch out.

9. Use and get big pictures: Always use encyclopedias, Wikipedia to get pictures so that you can go into the depth.

10. Bookmarks are useful: When you read any book or magazine, you generally use bookmarks, right! It is a good habit, which helps to not to lose your continuity. You should always use bookmarks while searching on Internet also, which will make your search easier.

11. Revise what you wrote: When you write anything, always read it, summarize it and then finalize it.

12. Make all efforts: Make all efforts to mention what you are going to write in your paper. That means always have a good start. Try to mention everything in introduction, that what is the need of a particular research paper. Polish your work by good skill of writing and always give an evaluator, what he wants.

13. Have backups: When you are going to do any important thing like making research paper, you should always have backup copies of it either in your computer or in paper. This will help you to not to lose any of your important.

14. Produce good diagrams of your own: Always try to include good charts or diagrams in your paper to improve quality. Using several and unnecessary diagrams will degrade the quality of your paper by creating "hotchpotch." So always, try to make and include those diagrams, which are made by your own to improve readability and understandability of your paper.

15. Use of direct quotes: When you do research relevant to literature, history or current affairs then use of quotes become essential but if study is relevant to science then use of quotes is not preferable.

16. Use proper verb tense: Use proper verb tenses in your paper. Use past tense, to present those events that happened. Use present tense to indicate events that are going on. Use future tense to indicate future happening events. Use of improper and wrong tenses will confuse the evaluator. Avoid the sentences that are incomplete.

17. Never use online paper: If you are getting any paper on Internet, then never use it as your research paper because it might be possible that evaluator has already seen it or maybe it is outdated version.

18. Pick a good study spot: To do your research studies always try to pick a spot, which is quiet. Every spot is not for studies. Spot that suits you choose it and proceed further.

19. Know what you know: Always try to know, what you know by making objectives. Else, you will be confused and cannot achieve your target.

20. Use good quality grammar: Always use a good quality grammar and use words that will throw positive impact on evaluator. Use of good quality grammar does not mean to use tough words, that for each word the evaluator has to go through dictionary. Do not start sentence with a conjunction. Do not fragment sentences. Eliminate one-word sentences. Ignore passive voice. Do not ever use a big word when a diminutive one would suffice. Verbs have to be in agreement with their subjects. Prepositions are not expressions to finish sentences with. It is incorrect to ever divide an infinitive. Avoid clichés like the disease. Also, always shun irritating alliteration. Use language that is simple and straight forward. put together a neat summary.

21. Arrangement of information: Each section of the main body should start with an opening sentence and there should be a changeover at the end of the section. Give only valid and powerful arguments to your topic. You may also maintain your arguments with records.

22. Never start in last minute: Always start at right time and give enough time to research work. Leaving everything to the last minute will degrade your paper and spoil your work.

23. Multitasking in research is not good: Doing several things at the same time proves bad habit in case of research activity. Research is an area, where everything has a particular time slot. Divide your research work in parts and do particular part in particular time slot.

24. Never copy others' work: Never copy others' work and give it your name because if evaluator has seen it anywhere you will be in trouble.

25. Take proper rest and food: No matter how many hours you spend for your research activity, if you are not taking care of your health then all your efforts will be in vain. For a quality research, study is must, and this can be done by taking proper rest and food.

26. Go for seminars: Attend seminars if the topic is relevant to your research area. Utilize all your resources.

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27. Refresh your mind after intervals: Try to give rest to your mind by listening to soft music or by sleeping in intervals. This will also improve your memory.

28. Make colleagues: Always try to make colleagues. No matter how sharper or intelligent you are, if you make colleagues you can have several ideas, which will be helpful for your research.

29. Think technically: Always think technically. If anything happens, then search its reasons, its benefits, and demerits.

30. Think and then print: When you will go to print your paper, notice that tables are not be split, headings are not detached from their descriptions, and page sequence is maintained.

31. Adding unnecessary information: Do not add unnecessary information, like, I have used MS Excel to draw graph. Do not add irrelevant and inappropriate material. These all will create superfluous. Foreign terminology and phrases are not apropos. One should NEVER take a broad view. Analogy in script is like feathers on a snake. Not at all use a large word when a very small one would be sufficient. Use words properly, regardless of how others use them. Remove quotations. Puns are for kids, not grunt readers. Amplification is a billion times of inferior quality than sarcasm.

32. Never oversimplify everything: To add material in your research paper, never go for oversimplification. This will definitely irritate the evaluator. Be more or less specific. Also too, by no means, ever use rhythmic redundancies. Contractions aren't essential and shouldn't be there used. Comparisons are as terrible as clichés. Give up ampersands and abbreviations, and so on. Remove commas, that are, not necessary. Parenthetical words however should be together with this in commas. Understatement is all the time the complete best way to put onward earth-shaking thoughts. Give a detailed literary review.

33. Report concluded results: Use concluded results. From raw data, filter the results and then conclude your studies based on measurements and observations taken. Significant figures and appropriate number of decimal places should be used. Parenthetical remarks are prohibitive. Proofread carefully at final stage. In the end give outline to your arguments. Spot out perspectives of further study of this subject. Justify your conclusion by at the bottom of them with sufficient justifications and examples.

34. After conclusion: Once you have concluded your research, the next most important step is to present your findings. Presentation is extremely important as it is the definite medium though which your research is going to be in print to the rest of the crowd. Care should be taken to categorize your thoughts well and present them in a logical and neat manner. A good quality research paper format is essential because it serves to highlight your research paper and bring to light all necessary aspects in your research.

,1)250$/*8,'(/,1(62)5(6($5&+3$3(5:5,7,1* Key points to remember:

Submit all work in its final form. Write your paper in the form, which is presented in the guidelines using the template. Please note the criterion for grading the final paper by peer-reviewers.

Final Points:

A purpose of organizing a research paper is to let people to interpret your effort selectively. The journal requires the following sections, submitted in the order listed, each section to start on a new page.

The introduction will be compiled from reference matter and will reflect the design processes or outline of basis that direct you to make study. As you will carry out the process of study, the method and process section will be constructed as like that. The result segment will show related statistics in nearly sequential order and will direct the reviewers next to the similar intellectual paths throughout the data that you took to carry out your study. The discussion section will provide understanding of the data and projections as to the implication of the results. The use of good quality references all through the paper will give the effort trustworthiness by representing an alertness of prior workings.

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Writing a research paper is not an easy job no matter how trouble-free the actual research or concept. Practice, excellent preparation, and controlled record keeping are the only means to make straightforward the progression.

General style:

Specific editorial column necessities for compliance of a manuscript will always take over from directions in these general guidelines.

To make a paper clear

· Adhere to recommended page limits

Mistakes to evade

Insertion a title at the foot of a page with the subsequent text on the next page Separating a table/chart or figure - impound each figure/table to a single page Submitting a manuscript with pages out of sequence

In every sections of your document

· Use standard writing style including articles ("a", "the," etc.)

· Keep on paying attention on the research topic of the paper

· Use paragraphs to split each significant point (excluding for the abstract)

· Align the primary line of each section

· Present your points in sound order

· Use present tense to report well accepted

· Use past tense to describe specific results

· Shun familiar wording, don't address the reviewer directly, and don't use slang, slang language, or superlatives

· Shun use of extra pictures - include only those figures essential to presenting results

Title Page:

Choose a revealing title. It should be short. It should not have non-standard acronyms or abbreviations. It should not exceed two printed lines. It should include the name(s) and address (es) of all authors.

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Abstract:

The summary should be two hundred words or less. It should briefly and clearly explain the key findings reported in the manuscript-- must have precise statistics. It should not have abnormal acronyms or abbreviations. It should be logical in itself. Shun citing references at this point.

An abstract is a brief distinct paragraph summary of finished work or work in development. In a minute or less a reviewer can be taught the foundation behind the study, common approach to the problem, relevant results, and significant conclusions or new questions.

Write your summary when your paper is completed because how can you write the summary of anything which is not yet written? Wealth of terminology is very essential in abstract. Yet, use comprehensive sentences and do not let go readability for briefness. You can maintain it succinct by phrasing sentences so that they provide more than lone rationale. The author can at this moment go straight to shortening the outcome. Sum up the study, wi th the subsequent elements in any summary. Try to maintain the initial two items to no more than one ruling each.

Reason of the study - theory, overall issue, purpose Fundamental goal To the point depiction of the research Consequences, including definite statistics - if the consequences are quantitative in nature, account quantitative data; results of any numerical analysis should be reported Significant conclusions or questions that track from the research(es)

Approach:

Single section, and succinct As a outline of job done, it is always written in past tense A conceptual should situate on its own, and not submit to any other part of the paper such as a form or table Center on shortening results - bound background informati on to a verdict or two, if completely necessary What you account in an conceptual must be regular with what you reported in the manuscript Exact spelling, clearness of sentences and phrases, and appropriate reporting of quantities (proper units, important statistics) are just as significant in an abstract as they are anywhere else

Introduction:

The Introduction should "introduce" the manuscript. The reviewer should be presented with sufficient background information to be capable to comprehend and calculate the purpose of your study without having to submit to other works. The basis for the study should be offered. Give most important references but shun difficult to make a comprehensive appraisal of the topic. In the introduction, describe the problem visibly. If the problem is not acknowledged in a logical, reasonable way, the reviewer will have no attention in your result. Speak in common terms about techniques used to explain the problem, if needed, but do not present any particulars about the protocols here. Following approach can create a valuable beginning:

Explain the value (significance) of the study Shield the model - why did you employ this particular system or method? What is its compensation? You strength remark on its appropriateness from a abstract point of vision as well as point out sensible reasons for using it. Present a justification. Status your particular theory (es) or aim(s), and describe the logic that led you to choose them. Very for a short time explain the tentative propose and how it skilled the declared objectives.

Approach:

Use past tense except for when referring to recognized facts. After all, the manuscript will be submitted after the entire job is done. Sort out your thoughts; manufacture one key point with every section. If you make the four points listed above, you will need a

least of four paragraphs.

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Present surroundings information only as desirable in order hold up a situation. The reviewer does not desire to read the whole thing you know about a topic. Shape the theory/purpose specifically - do not take a broad view. As always, give awareness to spelling, simplicity and correctness of sentences and phrases.

Procedures (Methods and Materials):

This part is supposed to be the easiest to carve if you have good skills. A sound written Procedures segment allows a capable scientist to replacement your results. Present precise information about your supplies. The suppliers and clarity of reagents can be helpful bits of information. Present methods in sequential order but linked methodologies can be grouped as a segment. Be concise when relating the protocols. Attempt for the least amount of information that would permit another capable scientist to spare your outcome but be cautious that vital information is integrated. The use of subheadings is suggested and ought to be synchronized with the results section. When a technique is used that has been well described in another object, mention the specific item describing a way but draw the basic principle while stating the situation. The purpose is to text all particular resources and broad procedures, so that another person may use some or all of the methods in one more study or referee the scientific value of your work. It is not to be a step by step report of the whole thing you did, nor is a methods section a set of orders.

Materials:

Explain materials individually only if the study is so complex that it saves liberty this way. Embrace particular materials, and any tools or provisions that are not frequently found in laboratories. Do not take in frequently found. If use of a definite type of tools. Materials may be reported in a part section or else they may be recognized along with your measures.

Methods:

Report the method (not particulars of each process that engaged the same methodology) Describe the method entirely To be succinct, present methods under headings dedicated to specific dealings or groups of measures Simplify - details how procedures were completed not how they were exclusively performed on a particular day. If well known procedures were used, account the procedure by name, possibly with reference, and that's all.

Approach:

It is embarrassed or not possible to use vigorous voice when documenting methods with no using first person, which would focus the reviewer's interest on the researcher rather than the job. As a result when script up the methods most authors use third person passive voice. Use standard style in this and in every other part of the paper - avoid familiar lists, and use full sentences.

What to keep away from

Resources and methods are not a set of information. Skip all descriptive information and surroundings - save it for the argument. Leave out information that is immaterial to a third party.

Results:

The principle of a results segment is to present and demonstrate your conclusion. Create this part a entirely objective details of the outcome, and save all understanding for the discussion.

The page length of this segment is set by the sum and types of data to be reported. Carry on to be to the point, by means of statistics and tables, if suitable, to present consequences most efficiently.You must obviously differentiate material that would usually be incorporated in a study editorial from any unprocessed d ata or additional appendix matter that woul d not be available. In fact, such matter should not be submitted at all except requested by the instructor.

Content

Sum up your conclusion in text and demonstrate them, if suitable, with figures and tables. In manuscript, explain each of your consequences, point the reader to remarks that are most appropriate. Present a background, such as by describing the question that was addressed by creation an exacting study. Explain results of control experiments and comprise remarks that are not accessible in a prescribed figure or table, if appropriate. Examine your data, then prepare the analyzed (transformed) data in the form of a figure (graph), table, or in manuscript form. What to stay away from Do not discuss or infer your outcome, report surroundings information, or try to explain anything. Not at all, take in raw data or intermediate calculations in a research manuscript. Do not present the similar data more than once. Manuscript should complement any figures or tables, not duplicate the identical information. Never confuse figures with tables - there is a difference. Approach As forever, use past tense when you submit to your results, and put the whole thing in a reasonable order. Put figures and tables, appropriately numbered, in order at the end of the report If you desire, you may place your figures and tables properly within the text of your results part. Figures and tables If you put figures and tables at the end of the details, make certain that they are visibly distinguished from any attach appendix materials, such as raw facts Despite of position, each figure must be numbered one after the other and complete with subtitle In spite of position, each table must be titled, numbered one after the other and complete with heading All figure and table must be adequately complete that it could situate on its own, divide from text Discussion:

The Discussion is expected the trickiest segment to write and describe. A lot of papers submitted for journal are discarded based on problems with the Discussion. There is no head of state for how long a argument should be. Position your understanding of the outcome visibly to lead the reviewer through your conclusions, and then finish the paper with a summing up of the implication of the study. The purpose here is to offer an understanding of your results and hold up for all of your conclusions, using facts from your research and generally accepted information, if suitable. The implication of result should be visibly described. Infer your data in the conversation in suitable depth. This means that when you clarify an observable fact you must explain mechanisms that may account for the observation. If your results vary from your prospect, make clear why that may have happened. If your results agree, then explain the theory that the proof supported. It is never suitable to just state that the data approved with prospect, and let it drop at that.

Make a decision if each premise is supported, discarded, or if you cannot make a conclusion with assurance. Do not just dismiss a study or part of a study as "uncertain." Research papers are not acknowledged if the work is imperfect. Draw what conclusions you can based upon the results that you have, and take care of the study as a finished work You may propose future guidelines, such as how the experiment might be personalized to accomplish a new idea. Give details all of your remarks as much as possible, focus on mechanisms. Make a decision if the tentative design sufficiently addressed the theory, and whether or not it was correctly restricted. Try to present substitute explanations if sensible alternatives be present. One research will not counter an overall question, so maintain the large picture in mind, where do you go next? The best studies unlock new avenues of study. What questions remain? Recommendations for detailed papers will offer supplementary suggestions. Approach:

When you refer to information, differentiate data generated by your own studies from available information Submit to work done by specific persons (including you) in past tense. Submit to generally acknowledged facts and main beliefs in present tense.

XXI

THE $'0,1,675$7,2158/(6

Please carefully note down following rules and regulation before submitting your Research Paper to Global Journals Inc. (US):

Segment Draft and Final Research Paper: You have to strictly follow the template of research paper. If it is not done your paper may get rejected.

The major constraint is that you must independently make all content, tables, graphs, and facts that are offered in the paper. You must write each part of the paper wholly on your own. The Peer-reviewers need to identify your own perceptive of the concepts in your own terms. NEVER extract straight from any foundation, and never rephrase someone else's analysis.

Do not give permission to anyone else to "PROOFREAD" your manuscript.

Methods to avoid Plagiarism is applied by us on every paper, if found guilty, you will be blacklisted by all of our collaborated research groups, your institution will be informed for this and strict legal actions will be taken immediately.) To guard yourself and others from possible illegal use please do not permit anyone right to use to your paper and files.

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CRITERION FOR GRADING A RESEARCH PAPER (COMPILATION) BY GLOBAL JOURNALS INC. (US) Please note that following table is only a Grading of "Paper Compilation" and not on "Performed/Stated Research" whose grading solely depends on Individual Assigned Peer Reviewer and Editorial Board Member. These can be available only on request and after decision of Paper. This report will be the property of Global Journals Inc. (US).

Topics Grades

A-B C-D E-F

Clear and concise with Unclear summary and no No specific data with ambiguous appropriate content, Correct specific data, Incorrect form information Abstract format. 200 words or below Above 200 words Above 250 words

Containing all background Unclear and confusing data, Out of place depth and content, details with clear goal and appropriate format, grammar hazy format appropriate details, flow and spelling errors with specification, no grammar unorganized matter Introduction and spelling mistake, well organized sentence and paragraph, reference cited

Clear and to the point with Difficult to comprehend with Incorrect and unorganized well arranged paragraph, embarrassed text, too much structure with hazy meaning Methods and precision and accuracy of explanation but completed Procedures facts and figures, well organized subheads

Well organized, Clear and Complete and embarrassed Irregular format with wrong facts specific, Correct units with text, difficult to comprehend and figures precision, correct data, well Result structuring of paragraph, no grammar and spelling mistake

Well organized, meaningful Wordy, unclear conclusion, Conclusion is not cited, specification, sound spurious unorganized, difficult to conclusion, logical and comprehend concise explanation, highly Discussion structured paragraph reference cited

Complete and correct Beside the point, Incomplete Wrong format and structuring References format, well organized

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Index

A T

Abscission · 30 Tentaculata · 11, 18

Combustion · 13, 15, 16

Delineates · 2 Dermatitis · 4 Dredging · 6, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21

Gangrene · 3

Hematuria · 3

Incineration · 4

Juvenile · 10, 18

Osteoporosis · 2

Percolates · 1 Planorbis · 11, 12

Seldom · 1 save our planet

Global Journal of Science Frontier Research

Visit us on the Web at www.GlobalJournals.org | www.JournalofScience.org or email us at [email protected]

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ISSN 9755896 7 0 1 1 6 5 8 6 9 8 >72416