hortflora reserach spectrum vol. 6 (4) 2017

COMPLIMENTARY COPY

With Best Wishes of Happy New Year-2018

December 2017

Volume 6, Issue 4

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NAAS Rating : 3.78NAAS Rating : 3.78UGC Approved

(Jour. No. 45792)

Date of Publication : 28-12-2017Date of Publication : 28-12-2017

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CONTENTS

1. Application of Plastic Mulches for Vegetable Cultivation : A Review

Pramod Rai, Vikas Kumar Singh andDinmani

221-227

2. Decadal Trend Analysis of Weather Parameters in

Junagadh (Saurashtra) Region, Gujarat : A Case Study

Vishal Mehra, S.S. Chinchorkar and D.M.Paradava

228-233

3. Modelling Approach for In-situ Bioremediation ofcontamination Groundwater : A Review

Deepak Kumar, Sudheer Ch. and P.S.Kashyap

234-239

4. Seasonal Variations in Plant Environmental Parameters ofCustard Apple cv. Raydurg

Prerak Bhatnagar 240-246

5. Assessment of Genetic Variability for Different Characters of Dahlia Genotypes

H.M. Singh, Uma Shanshkar Mishra andTara Shankar Mishra

247-255

6. Growth and Flowering Behaviour of DendrobiumVarieties under Protected Condition in Gangetic AlluvialZone of West Bengal

Tapas Kumar Choudhuri and RaghunathSadhukhan

256-261

7. Growth of Kharif Onion (Allium cepa L.) in Response toPlanting Dates and Cultivars

Smaranika Mohanta, Joydip Mandal andDigvijay Sigh Dhakre

262-267

8. Growth, Productivity and Quality of Ber (Zizyphusmauritiana Lamk.) Cv. ‘Umran’ in Relation with SoilApplicatios of Phosphorus and Potassium.

Amritpal S. Randhawa, P.S. Aulakh andP.P.S. Gill

268-272

9. Studies on Effect of Foliar Application of Boron and GA 3

on Growth, Fruiting and Yield of Phalsa (Grewiasubinaequalis D.C.)

Mohd. Zeeshan and J.P. Singh 273-277

10. Genetic Variability, Character Association and PathCoefficient Analysis in China Aster [Callistephuschinensis (L.) Nees]

Pratiksha Kumari, Rajiv Kumar, T.Manjanatha Rao, M.V. Dhananjaya and V. Bhargav

278-282

11. Popularization of Protective Gloves throughDemonstrations

Rajdeep Kaur and Dimpy Raina 283-287

12. Effect of Integrated Nutrient Management onQuality Production of African Marigold (Tagetes erectaL.)

Suresh Kumar Sharma, Krishna Pal, K.P.Singh and S.K. Tewari

288-291

13. Ef fect of Exogenous Rooting Hormone on Bougainvilleacv. Thimma Propagation through Hard wood Cuttings

Babita Singh, S.S. Sindhu, Harendra Yadav and N.K. Saxena

292-295

14. Ef fect of Weed Green Manure on Rhizosphee Mycofloraof Spinach

R.L. Parbhankar and U.P. Mogle 296–299

15. Betelvine Cultivation : A New Avenue for LivelihoodSecurity

Shivnath Das, Ajit Kumar Pandey andPrabhat Kumar

300-303

16. Ef fect of Integrated Nutrient Management on Growth andFlowering Parameters of African Marigold (Tagetes erecta L.) cv. Pusa Narangi Gainda

Monbir Singh, Jitendra Kumar andPavitra Dev and Vijai Kumar

304-306

17. Cultural Management of Stem Rot of Rajmash Caused bySclerotinia sclerotiorum

Ramesh Singh, D.K. Tripathi and P.C.Singh

307-309

18. Effect of Cytokinin on Growth and Menthol Oil Content in Mentha piperita L. under Seasonal Variation

Awadhesh Kumar, L.P. Maurya, NeetuSingh and Balram Prasad Yadav

310-311

19. Reviewer’s List 312

www.hortflorajournal.com Volume 6, Issue 4 : December 2017 NAAS Rating : 3.78

HortFlora Research Spectrum ISSN: 2250-2823

AP PLI CA TION OF PLAS TIC MULCHES FOR VEG E TA BLE CUL TI VA TION : A

RE VIEW

Pramod Rai*, Vikas Kumar Singh and Dinmani

De part ment of Ag ri cul tural En gi neer ing, Birsa Ag ri cul tural Uni ver sity, Kanke, Ranchi, Jharkhand-834 006

*Cor re spond ing Au thor’s E-mail: pramod_kgp@ya hoo.co.uk

ABSTRACT : Mulching is the process or practice of covering the soil/ground to make more favourableconditions of root zone for plant growth, development and efficient crop production. The various types of plastic mulches are available in market and its selection depends upon the very purpose of mulch. The plastic mulchaffects below the soil microclimate, above the soil microclimate, weeds growth, soil moisture, insect behaviouretc. The soil temperature plays a very important role in crop productivity and the plastic mulch can be used toenhance or decrease the soil temperature as per requirements. Research continues on field evaluation of new

formulations of degradable, wavelength-selective, and coloured plastic mulches.

Keywords : Mulch, pro duc tiv ity, weeds growth, soil tem per a ture, degradable mulch

Mulch is any material applied to the soil surface to prevent loss of water by evaporation, to suppressweeds, to reduce temperature fluctuations to promoteproductivity. The advantages of using plastic mulchesfor the production of high-value vegetable crops havebeen recognized since the late 1950s (Waggoner et al.,

35).

Mulches can give excellent results when used inthe right circumstances, but the outcome will bedisappointing if proper selection of mulch is not donebased on environmental conditions and intendedpurpose of mulching. The mulching can be used forenhancing soil temperature, decreasing soil tempera-ture, insect repelling, reflection of light for enhancingphotosynthesis, enhancing the fruit quality, changingthe micro climate around the plant apart from other

usual benefits much provide.

However, the effect of mulching varies with soils,climate, kind of mulch material used and the rate ofapplication. Application of fertigation in combinationwith mulching can provide maximum yields ofvegetables and, from the aspect of sustainableagriculture, can contribute to a more economical use of water, decreased nutrient leaching from the soil andthereby reduced fertilizer requirements. Depending ontheir characteristics, different mulching materials,however, have different effects (Brault et al., 3). Theembossed plastic, diamond shape pattern visible onthe plastic gives it additional stretch compared to asmooth plastic. An embossed plastic acts like an‘accordion,’ stretching while lay it, then shrinkingslightly to form a tight seal over the surface of the bed.

Keeping the importance of mulch in vegetablecultivation discussion has been done on working andtypes of plastic mulch, advantages, waste issues and

disposal of plastic mulches.

1. WORKING OF PLASTIC MULCH

Plastic mulches can reflect, absorb and transmitincoming sunlight, the extent of which depends on thetype of mulch. The soil temperature under plasticmulch depends on the thermal properties (reflectivity,absorbitivity, or transmittancy) of particular mulch in

relation to incoming solar radiation. Gutkowski andTerranova (8) discussed the mechanisms thataffect the soil energy balance in the soil coveredwith plastic mulch to know the effect of differentfluxes in the soil. Soil energy balance can bemathematically described as follows:

R + R S H E = 0s 1 − − −Where Rs and RI are radiative fluxes, S is soil heat

flux, H is sensible heat flux and E is latent heat flux.These fluxes determine the temperature regime of thesoil, and can be manipulated by covering the soil with

appropriate mulches.

1.1. Radiative fluxes It is the net fluxes of short and long wave radiation

at the soil surface and is determined by the photometric characteristics (transmission, absorption and reflection of electromagnetic radiation) of both the soil and the

mulch.

1.2. Soil heat flux

It influences the heat storage capacity of the soiland depends on the thermal conductivity, specific heat

capacity and water content of soil.

HortFlora Research Spectrum www.hortflorajournal.com

Vol. 6, Issue 4; 221-227 (December 2017) ISSN: 2250-2823

Article’s History: Received: 09-10-17 Accepted: 03-11-17

NAAS Rating : 3.78

222 Rai et al. HortFlora Res. Spectrum, 6(4) : December 2017

1.3. Sensible heat flux

It is the net heat exchange due to convection.Mulching reduces sensible heat fluxes, because aircirculation between the soil and the atmosphere islimited. In mulched soils, sensible heat exchanges take place through the air layer trapped between the soiland the mulch and by convection from the mulch to theambient air. Sensible heat fluxes depend on the heattransfer capacity and the temperature of the mulch(determined by its optical properties), and on thethickness of the air layer between the soil and the

mulch.

1.4. Latent heat fluxes

It is the net heat exchange due to evaporation andcondensation of water and is determined by theevaporation and condensation of water from the soil,processes that require and release energy,respectively. Part of the energy lost throughevaporation is recovered as water condenses on themulch of soils under solarization. Water condensationon the mulch will modify its optical characteristics,

affecting both radiative and sensible heat fluxes.

2. TYPES OF PLASTIC MULCH

There are three primary non-degradable mulchhas been used in vegetable cultivation: black, clear(transparent), and white (or white-on-black) and soiltemperature follows the order: transparent mulch >

black mulch > white mulch (Haynes, 11).

Mulch’s color affects the temperature below andabove the mulch though the absorption, transmissionand reflection of solar energy. This affects themicroenvironment surroundings the plants and finallythe crop productivity (Lippert and Witing, 23). This iscaused primarily through changes in the componentsof the radiation balance, due to the effect of mulches on albedo, sensible heat flux, latent heat flux, and soil heat

flux (Liakatas et al., 22).

2.1 Clear

Clear plastic mulch absorbs little solar radiationbut transmits 85% to 95%, with relative transmissiondepending on the thickness and degree of opacity ofthe polyethylene. Clear plastic mulches generally areused in the cooler regions because it provides an evenwarmer soil environment (mini-greenhouse effect) than black plastic mulch. As the clear mulch transmits themajority of light in the PAR spectrum, weed growth canbe a problem and it will require the use of an herbicideor fumigation to prevent weed growth beneath themulch. The transmitted light is absorbed by the soil,resulting in increased daytime soil temperatures and at

night, condensation on the underside of the mulchprevents the long wave radiation emitted by the soilfrom escaping to the atmosphere. This further warmsthe soil (Lamont, 17). The clear (transparent) plasticfilm is generally used for soil solarization, a method ofheating the soil over moistened soil, to retain radiationfrom the sun during the hot season and usedsuccessfully to control soil-borne pathogens and

weeds (Katan, 15).

2.2 Black

Black plastic mulch, the predominate color used in vegetable production especially for weed control is anopaque black body absorber and radiator. Black mulchabsorbs most UV, visible, and infrared wavelengths ofincoming solar radiation and re-radiates absorbedenergy in the form of thermal radiation orlong-wavelength infrared radiation. The efficiency withwhich black mulch increases soil temperature can beimproved by optimizing conditions for transferring heatfrom the mulch to the soil. Because thermalconductivity of the soil is high, relative to that of air,much of the energy absorbed by black plastic can betransferred to the soil by conduction if contact is goodbetween the plastic mulch and the soil surface (Tarara,31; Lamont, 17). As light transmission by black mulchis limited, it is effective at preventing weed growth.Black polythene mulch reduced delayed preharvestfruit drop and fruit maturity, whereas transparent oneadvanced it in plum (Gupta, 7).

2.3 Reflective mulch (white, Co extruded white-black or silver-black or aluminum- black)

Light is reflected back into the atmosphere or theplant canopy from reflective mulches, resulting inslightly cooler soil temperatures. It can be used toestablish crops in the summer, when a reduced soiltemperature is required. The light reflected back intothe plant canopy with the reflective mulches is helpfulfor greenhouse crops that have limited light and alsoaffect the behavior of certain insects (Lamont, 18). Thereflective silver or aluminum mulches give cooler soiltemperatures and tend to repel aphids, which canserve as vectors for various viral diseases (Lamont etal., 21). Silver-on-black plastic film could be conductive and beneficial in enhancing the yield in watermelon

(Tyagi and Sharma, 34).

2.4 Wavelength-selective (photo selective or infra-red transmitting) mulches

The wavelength-selective mulches absorb PARand transmit solar infrared radiation, providing a

Applications of Plastic Mulches for Vegetable Cultivation : A Review 223

compromise between black and clear mulches. Thesemulches are a compromise between clear and blackmulch films, providing soil temperatures midwaybetween those under black and clear mulches, butlargely suppressing the weed growth, which is aproblem with clear mulches. The color of thesemulches can be blue-green or brown depending on themanufacturer. They contain very specific pigments thatgive them the unique ability to transmit a maximum ofnear infrared radiation and a minimum (14-16%) of

visible light.

2.5 Coloured mulch

The wavelength selective mulch films are oftenreferred to as colored mulches and are designed toabsorb specific wavelengths of the sun’s radiation andthus change the spectrum of the sunlight passing

through the film and/or being reflected back into theplant canopy. The various colors of woven and solidfilm plastics have been tested in the field and found that white and green coverings had little effect on the weeds but brown, black, blue, and white on black (doublecolour) films prevented weeds emerging (Horowitz,13). Green polythene mulch was found to be the best in terms of plant growth and flowering and fruiting in

strawberry (Sonkar et al., 29).

2.5.1. Red mulch : The red mulch partiallytranslucent allowing radiation to pass through andwarm the soil and also reflects radiation back into theplant canopy changing the ratio of R: FR light. Thisresult in changes in plant vegetative and flowerdevelopment and metabolism leading to early fruitingor increased yields in some fruit and vegetable crops.Red plastic mulch has been shown to increase tomatoyields and quality in some trials and reduce the severity of early blight in others. It also has been shown toincrease yields of honeydews, muskmelons and

zucchini (Lamont, 20).

2.5.2. Yellow mulch : It attracts certain insects,such as whitefly, cucumber beetle, some aphids andserves as a trap to prevent damage to the mainplanting and used as an insect monitoring tool(Lamont, 20). Sharma et al. (28) reported that yellowplastic mulches significantly extended the duration offlowering and fruiting and improved the fruit set ofstrawberry under protected cultivation compared to

control and black plastic mulch.

2.5.3. Blue mulch : It is found that in the green-house setting it attracts thrips and subsequently it helps to protect the main crop from thrips attack. Thewatermelon production increases with the use of blueplastic mulch film and research test results showed that

on average, zucchini and cucumber yields increased

20% over a three year period (Internet source, 14).

3. ADVANTAGES OF PLASTIC MULCHES

3.1. Primary Advantages

The color of mulch determines its energy-radiating behavior and its influences the microclimate around aplant. It affects the surface temperature of the mulchand the underlying soil temperature. The degree ofcontact between the mulch and soil is often quantifiedas a thermal contact resistance and it affect theperformance of mulch. If an air space is createdbetween the mulch and the soil due to rough soilsurface, soil warming will be less effective than wouldbe expected from particular mulch.

3.1.1. Soil heating/cooling : The heating proper-ties of plastic such as reflectivity, absorptivity, andtransmittancy and their interaction with the sun’sradiation will have a direct effect on the soiltemperatures beneath the plastic mulch (Schales andSheldrake, 26). Depending upon the season of cropcultivation and type of crop there is needed to enhanceor reduce the soil temperature to enhance the cropproductivity. To enhance the soil temperature, theabsorption of solar energy by the soil has to bemaximized while minimizing its heat losses and it canbe achieved by using mulch that is transparent tolong-wave radiation and reflective to short-wave

radiation.

3.1.2. Air Warming : Above ground effects ofplastic mulch are mainly due to the optical properties ofthe mulch and the fact that plastic acts as a barrier toevaporation (Waggoner et al., 35). Convective heattransport between the plastic and the atmosphere iscreated by the shape and properties of the plasticsurface and the temperature level between theboundary layer and the plastic as defined by (Ham and

Kluitenberg, 9).

H CT T

Rv

a m

a h

=−( )

.

Where, H is the convective heat transfer, Ta is airtemperature (K), Tm is the temperature of mulch, Cv is

the heat capacity of the air (Jm K )3 1− − , and Ra h, is the

aerodynamic resistance of heat transfer (sm )−1 .

Clarkson (4) was able to show that the greatesttemperature difference between mulched andnon-mulched areas occurred at two inches over theplastic & soil surfaces and in both cases the

temperature of the air above the plastic was higher

than the temperature of the air above the bare soil.

3.2. Secondary Advantages

3.2.1. Earlier Growth and Higher Production :

Polyethylene mulches have induced large increases ingrowth and yields for a variety of crops, includingtomato due to changes in soil and air temperature nearthe cover, soil water balance, improved aeration andnutrient availability compared to unmulched soil

(Haynes, 11; Lamont Jr., 18).

For enhancing the earliness of vegetable cropsduring winter season when soil temperature is low, themulch should maximize the transmission of thesoil-warming portion of sunlight (the near infra-redradiation) and minimize the transmission of visible light, can increase the weed growth under the plastic.Decoteau et al., 5) found earlier growth in tomatoeswith the use of plastic mulch. Many researchers havereported earlier (7 to 14 days and up to 21 days) andincreased yields (normally two to three times that ofunmulched soils) depending on geographic location,

soil type, plastic mulch used.

3.2.2. Greater Soil Moisture : One of the mostimportant reasons for using plastic mulch is its ability tomaintain soil moisture and it helps to improve plantgrowth and development. The surface mulch favorablyinfluences the soil moisture regime by controllingevaporation from the soil surface, improves infiltration,soil water retention, decreases bulk density andfacilitates condensation of soil water at night due totemperature reversals (Tisdall et al., 32). Liakatas et al. (22) reported plastic mulches alter the plant’smicroenvironment due to its ability to restrict soil waterevaporation. When comparing drip irrigation to furrowirrigation, Tiwari et al., 33) reported 40% reduction inirrigation water application with the use of black plastic

mulch in conjunction with drip irrigation.

3.2.3. Fewer Weeds : Unlike bare soil, plasticmulch depending upon its colour and properties, itreduces the amount of light in the photosyntheticallyactive range (PAR) of 400-700 nm from reaching thesoil beneath the plastic mulch and helps to prevent thegrowth and limit the germination of weeds (Ngouajioand Ernest, 25). The tight fit to the bed surfaceincreases heat transfer between the plastic and thesoil. It reduces weed pressure under the mulch, asweeds emerge, the cotyledons hit the hot plastic andmay be burned. Modification of the soil microclimate bymulching favors root proliferations and suppresses

weed population (Yaduraju and Ahuja, 37).

3.2.4. Less Leaching of Fertilizer : Plasticmulches have shown to improve the availability offertilizers to plants by reducing the leaching because itacts as a barrier to rainfall and thus prevents rainwaterfrom seeping through the soil and taking nutrientsbelow the point of contact for roots. Farias-Larios et al.(6) reported that use of plastic mulch reduced the use

of synthetic fertilizers in honey dew melon production.

3.2.5. Improved Fruit Quality : Fruit quality ismeasured by cleanliness, taste, insect damage, etc.Turnips grown on blue plastic mulch were found tohave a sharp taste while those grown on green plasticmulch were found to have a sweet taste (Antonious etal., 2). Sharma et al. (28) reported that red plasticmulch was found to be most effective for increasingyield and improving fruit quality (total soluble solids,total sugars, reducing sugars, ascorbic acid andanthocynin content) of strawberry under protected

cultivation.

3.2.6. Reduced soil compaction : Soil under theplastic mulch remains loose, friable, and well aerated.The roots have access to adequate oxygen and

microbial activity is enhanced (Hankin et al., 10).

3.2.7. Gas exchange : Mulch film is nearlyimpervious to CO2 released by roots or decompositionof organic matter in the soil, so it accumulates beneaththe plastic mulch. Because the mulch does not allowthe gas to penetrate, it has to escape through thetransplant holes and creates a “chimney effect,”resulting in higher levels of CO2 for the actively growing

leaves near the transplant hole (Hopen, 12).

3.2.8. Aids in fumigation and soil solarization :

Mulches increase the effectiveness of soil fumigantchemicals because, it acts as a barrier to gas escapeand keep gaseous fumigants in the soil. Plasticmulches, especially transparent/clear, are used in soil

solarization to control soil pests (Stapleton, 30).

3.2.9. Ability to double/triple crop : After theharvest of first crop, second or third crop can be grownon the same plastic mulch the annual expenses forplastic mulch and drip irrigation system. The second orthird crop can be fertilized through the drip irrigationline (fertigation) using soluble fertilizers and a fertilizer

injector (Marr and Lamont, 24).

3.2.10. Insect repelling : The insects are gettingresistance to chemicals after a certain time period andhence other chemicals having stronger insecticidalactivity must be developed. The development offunctional films having insect-repelling property maylead to a potential application (Schalk et al., 27).



Reflective polyethylene mulch has a repellent effect onpest and vector insects, such as aphids, whiteflies, and thrips. The reflective silver or aluminum mulches tendto repel aphids and thrips which can serve as vectors

for various viral diseases (Lamont et al., 21).

4. WASTE ISSUES AND DISPOSAL

Although use of plastic mulches are increasing incrop production due to several benefits but most ofthese mulches end up in landfills or are burned(Kyrikou and Briassoulis, 16). It causes pollutionproblems especially when plastic wastes were burned,producing irritant air like hydrogen chloride and otherpoisonous gases like methyl aldehyde. When buried,these plastic wastes required large areas, affecting thephysical and chemical features of the soil and pollutingit. Moreover, discarding undegradable plasticscarelessly resulted in blocked irrigation ditches andclogged drainages. None of these contributed orbenefited to the environment and the living things on it.Recycling is not typically an option as used mulchescontain dirt and debris from production fields that must

first be removed prior to the recycling process.

4.1. Degradable mulch

The limitation to use of plastic mulches forvegetable production has been the requirement toremove and dispose of the used plastic and it providedimpetus for the development of degradable mulches. In the early 1960’s, it was recognized that photo- orbiodegradable plastic could offer a solution to thedisposal problem associated with plastic mulches.Work on biodegradable starch-based film andphotodegradable polyolefin polymer and polyethylenepolymer films are under way but mulches have been

quite variable in their rate of degradation (Wilson, 36).

4.1.1. Photodegradable mulch

Photodegradable, where degradation takes placefrom the action of natural sunlight and disintegratesinto very small fragments of material caused bydegradation processes. The photodegradablepolymers break down in a two step process, an initialhydrolysis or photo-degradation stage followed byfurther biodegradation. ASTM (American Society forTesting and Materials) defined ‘photo-degradable’ as“Polymer capable of undergoing a significant loss ofproperties that can be measured by standardized tests

after exposure to representative amounts of sunlight.”

Factors that controlled and altered the rate ofbreakdown of photo-degradable mulch films included:formulation and quality control in film production,seasons of use, geographical region of use as it affects

film temperature, quantity, and quality (especially UV)of solar radiation related to day length, cloud cover, sun angle, etc. (Wilson, 36). The interval between mulchapplication and planting, crop canopy development,time of crop removal, and exposure of film to solarradiation after crop removal, also alter the time and rate

of film degradation.

However, some limitations still occur because, it is necessary to disk up the buried undegraded edges atthe end of the season and expose them to sunlightwhich is required for the degradation process to occur.Another limitation of degradable mulch is that double

cropping is made difficult.

4.1.2. Biodegradable mulch : Biodegradablemeans that a substance can be broken down into othersubstances, with a significant change of chemicalstructure, by the activities of living organisms and istherefore unlikely to persist in the environment. ASTM defined ‘biodegradable’ as “Polymer capable ofundergoing decomposition into CO2, methane, water,inorganic compounds or biomass in which thepredominant mechanism is the enzymatic action ofmicro-organisms, that can be measured bystandardized tests, in a specified period of time,

reflecting available disposal conditions.”

Biodegradable mulch allows growers to till themulch into the field at the end of the growing seasonrather than having to remove and dispose ofnon-degradable polyethylene mulches, often at

considerable cost (Anderson et al., 1).

CONCLUSION

Mulch colour determines its energy-radiatingbehaviour and its influence on the microclimate around

a vegetable plant, soil temperature etc. The blackplastic mulch can be used during winter season toenhance soil temperature and white-black/silver-blackduring summer season to reduce the soil temperature.The disposal of the used plastic mulches is the biggestlimitation to use of plastic mulches for vegetablecultivation and degradable plastic mulch offers a

solution to the disposal problem.

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DE CAD AL TREND ANAL Y SIS OF WEATHER PA RAM E TERS IN JUNAGADH

(SAURASHTRA) RE GION, GUJARAT: A CASE STUDY

Vishal Mehra1, S.S.Chinchorkar1* and D. M. Paradava 2

1Anand Ag ri cul tural Uni ver sity, Anand , Gujarat2PAE, AAU, Dahod, Gujrat.

*Cor re spond ing Au thor's E-maiIl: [email protected]

ABSTRACT : The rainfall is an important parameter for the well being of around 1.2 billion people of India.However, certain extreme rainfall events occurring in different seasons cause disastrous situation over someparts. The average rainfall during decades 1980-90, 1991-2000 and 2001-11 were 1034.68, 772.1 and1101.27 mm, respectively. The annual rainfall of Junagadh region shows increasing tendency reported higherrainfall in last decade 2001-2011. Similarly, past years BSS analysis significantly increasing trend and relativehumidity at increasing trend, which may in confirmation to increase in rainfall for this station. The maximumtemperature showed slightly increasing tendency, while minimum temperature revealed in significant increase. The average decadal minimum temperature for Junagadh region during decades 1980-1990, 91-2000 and2001-11 were 19.65, 20.26 and 21.08 °C, respectively. The average pan evaporation, trend analysis showsdecreasing trend and wind velocity, trend analysis shows significantly increasing trend. It is conjectured thatthese events may be associated with the global and the regional warming under the climate change scenario.In the event of their continuation, there would be severe impact on societal and environmental issueswarranting appropriate precautionary measures in near future to safeguard the interest of the vast populationof this region. The likely cause for increase in minimum temperature is global warming

Keywords : De cad al trend, weather pa ram e ters, stan dard de vi a tion, co ef fi cient of vari a tion

India is fortunate to enjoy the heavy rainfall spellsin all the seasons due to both tropical and extra-tropical weather systems. The summer or the southwestmonsoon season (June-September) is the main rainyseason contributing about 75-80 % of the annualrainfall.

Although, the contributions from other seasons,

viz. the winter (January-February), pre-monsoon

(March-May) and the post or north-east monsoon

(October-December) to all India rainfall are not very

significant, they are quite important for the particular

regions. Main weather systems which bring rainfall to

the region are monsoon low pressure areas, depre-

ssions, thunderstorms, tropical cyclones, western

disturbances etc. (Pant and Kumar, 8). The typical

orography of the region also influences the intensity

and distribution of the rainfall.

In view of the paramount importance of the rainfallfrom economic, societal and scientific points, extensive work has been carried out over the years on its variousfacets like trends, disaster events, spatio- temporalvariability, seasonal contributions etc. (Sinha Ray andDe, 8; Sen Roy and Balling, 9; Francis and Gadgil, 2;

Guhathakurta and Rajeevan, 3). Goswami et al. (4)used grid point data at 100 km resolution anddemonstrated a significant increasing trend in thefrequency and the magnitude of extreme monsoon rain events in central India over the past 50 years. Theseinstances are attributed to the warming global surface(Goswami et al., 4) and the tropical Indian ocean(Ajayamohan and Rao, 1)

The information of the peak rainfalls intensities atthe stations is instrumental for the planning of urbandevelopment, disaster management and for studyingthe environmental aspects pertaining to water runoffsin the vicinity of the stations.

Therefore, present study is carried out using thestation data. The domain is whole of Indian region andall the seasons are considered (Khaladkar et al., 5). Asustainable production system needs managementstrategies by using advance weather forecasting toolleading to precision farming, relevance in pest anddisease management approaches so as to reduce theinput cost and obtain higher profit.

By considering this view in mind the variability ofannual rainfall and its deviation from the normal rainfallin various decades at Junagadh of Saurashtra region is presented in this paper.



Article’s History:Received:01-10-17; Revised:23-11-17; Accepted : 03-12-17

NAAS Rating : 3.78

Decadal Trend Analysis of Weather Parameter in Junagadh (Saurashtra) Region, Gujarat : A Case Study 229

Study Area

The Junagadh city is located between latitudes21º 31'N and 70º 49' E in fig 1. The city is a gate way tofamous Gir Forest which is the natural habitat for thelast existing population of Asiatic Lion in the wild. Apartfrom Gir, there is Girnar Ranges, Barda Hills andextensive grasslands known as Vidis, which alsosupport a variety of wildlife especially avifauna.Junagadh has a tropical wet and dry climate, with three distinct seasons observed, a mild winter fromNovember to February, a hot summer from March toJune, and a monsoon from July to October. Junagadhfaces adverse climatic conditions in the summermonths with the temperature ranging from 28° Celsiusto 38° Celsius. In the winter months, the temperatureranges from 10° Celsius to 25° Celsius. Various factorssuch as its close proximity to the sea influence theweather of Junagadh. The latent winds from the seaaffect the climatic conditions in the region.

The daily weather data during the period1980-2011 (35 years) were obtained from Departmentof Agricultural Meteorology, Junagadh AgriculturalUniversity, Junagadh (Saurashtra Region) Gujaratwere utilized for preparing daily, weekly and monthlyaverages .The monthly averages of weatherparameters were analyzed to determine climatic trend.The climatic study was analyzed by calculating themean, standard deviation (SD), coefficient of variations (CV). The significant test (t) for all the weatherparameters has also been carried out (Kumar et al., 6).

1. In Saurashtra region rainfall is raining from400-800 mm per annum. Normal rainfall (1980-2009)of Junagadh district is about 952.1 mm. The averagerainfall during decades 1980-90, 1991-2000 and2001-11 [Fig.1] were 1034.68, 772.1 and 1101.27 mm,respectively with 484.96 standard deviation and 49.71CV% Table 1. The comparison of decadal and normalrainfall during 1980-90 and 1991-2000 shows

deficiency by 8.7% and -18.9% respectively, whileduring 2001-11 the rainfall was 15.66 % above thenormal.

The standard deviations and coefficients ofvariations were 714.85,251.46,313.87 and 69.09,

32.57,28.50 % during decades 1980-90, 1991-00 and2001-11, respectively (Table 2). During the decade2001-11 the coefficient of variation was quite low, itshows low variability in annual rainfall, while during1991-2000 and 1980-90 the CV% were higher due tohigh variability in annual rainfall. Overall annual rainfallvariation shows significant (at 10% probability level)increase in rainfall during past 32 years with annualincrease of 0.48 mm per year with R2 =0.00009 (y =0.4826x + 967.54) [Fig. 1]. The decadal trend analysisshows dissimilarities in trend as compare with overalldatabase.

2. The annual maximum temperature of Junagadh

region is oscillated between 40.98 °C during summer

and 7.0 °C in winter season. Temperature rises to its

maximum level during the months of March, April, May

and lowest in December-January. Annual average

Fig. 1 : Location map of Junagadh showing study area.

Table 1 : Decadal variations in weather parameters at Junagadh district.

Weather Parameters Decades S.D CV (%) Highest Lowest

1980-90 1991-2000 2001-11

Rainfall (mm) 1034.68 772.05 1101.27 484.9 49.71 2790.7 (1983) 139.0(1987)

B.S.S.(hrs) 7.69 7.26 7.04 0.49 6.71 8.67 (1987) 6.53 (1980)

Max.Temp.(0C) 34.11 33.9 33.98 0.59 1.73 36.17 (1987) 33.2(1984)

Mini. Temp (0C) 19.65 20.26 21.08 1.13 5.58 24.00 (1999) 17.99(1991)

Maxm.RH (%) 75.23 77.23 73.63 4.04 5.37 81.36 (1998) 64.54(1982)

Minim.RH (%) 43.41 43.02 40.23 3.15 7.48 48.25 (1985) 34.34(2002)

Epan (mm) 6.09 5.83 5.79 0.66 11.14 8.05 (1987) 4.68(2011)

W.D.(km/hr) 7.3 7.56 6.25 1.1 15.71 9.77 (1987) 4.13(1981)

Evapo-transpiration (mm) 2055.09 2094.99 2100.96 312.83 15.02 8.05 (1987) 4.68(2011)

maximum temperature during decades 1980-90,

1991-2000 and 2001-11 [Fig. 2] were 34.11, 33.90 and

33.98°C, respectively with 0.59 standard deviation and

1.73% CV (Table 1).The past 32 years trend analysis

shows the non-significant increase in maximum

temperature with the rate 0.003 °C with R2 0.0034 (y =

-0.0036x+34.062) [Fig. 2], while their decade wise

trend analysis shows slightly increasing then

230 Mehra et al. HortFlora Res. Spectrum, 6(4) : December 2017

0

200

400

600

800

1000

1200

May Jun Jul Aug Sep Oct Nov

MonthlyRainfall(mm)

Months

1980-1990 1991-2000 2001-2011

0

2

4

6

8

10

12

14

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

MonthlyBrightsunshine(hrs)

Months

1980-1990 1991-2000 2001-2011

y = -0.028x + 7.800R² = 0.289

5.0

6.0

7.0

8.0

9.0

10.0

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

AnnualBrightSunshine(hrs)

Years

BSS (hrs)

y = 0.482x + 967.5R² = 9E-05

0

500

1000

1500

2000

2500

3000

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

AnnualRainfall(mm)

Years

Rainfall (mm)

An

nu

al R

ain

fall

(mm

)

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

An

nu

al R

ain

fall

(mm

)

19

80

19

80

19

82

19

82

19

84

19

84

19

86

19

86

19

88

19

88

19

90

19

90

19

921

992

19

94

19

94

19

96

19

96

19

98

19

98

20

00

20

00

20

022

002

20

04

20

04

20

06

20

06

20

08

20

08

19

80

19

80

19

82

19

82

19

84

19

84

19

86

19

86

19

88

19

88

19

90

19

90

19

921

992

19

94

19

94

19

96

19

96

19

98

19

98

20

00

20

00

20

022

002

20

04

20

04

20

06

20

06

20

08

20

08

19

80

19

80

19

82

19

82

19

84

19

84

19

86

19

86

19

88

19

88

19

90

19

90

19

921

992

19

94

19

94

19

96

19

96

19

98

19

98

20

00

20

00

20

022

002

20

04

20

04

20

06

20

06

20

08

20

08

19

80

19

80

19

82

19

82

19

84

19

84

19

86

19

86

19

88

19

88

19

90

19

90

19

921

992

19

94

19

94

19

96

19

96

19

98

19

98

20

00

20

00

20

022

002

20

04

20

04

20

06

20

06

20

08

20

08

Fig. 1 : Rainfall and BSS pa tern and trend at Junaga district during last three decades.

28

30

32

34

36

38

40

42


MonthlyMaximumTemperature(0C)

Months

1980-1990 1991-2000 2001-2011

6

8

10

12

14

16

18

20

22

24

26

28

30

32


MonthlyMinimumTemperature(0C)

Months

1980-1990 1991-2000 2001-2011

y = -0.003x + 34.06R² = 0.003

32.0

32.5

33.0

33.5

34.0

34.5

35.0

35.5

36.0

36.5

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010A

nnualMaximumTemperature(0C)

Years

Maximum Temperature

An

nu

al M

axim

um

Tem

pea

ture

(°C

)

36.5

36.0

35.5

35.0

34.5

34.0

33.5

33.0

32.5

32.0

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

An

nu

al M

axim

um

Tem

pea

ture

(°C

)

32.0

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

y = 0.069x + 19.19R² = 0.326

16.0

18.0

20.0

22.0

24.0

26.0

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

AnnualMinimumTemperature(0C)

Years

Minimum Temperature

An

nu

al M

axim

um

Tem

pea

ture

(°C

)A

nn

ual

Max

imu

m T

emp

eatu

re (

°C)

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

Fig. 2 : Maximum and Minimum temperature pattern and trend at Junagadh district during last three decades.


decreasing trend during first two decades (1980-90

and 91-2000) and decreasing trend for last decade

(2001-11). During decades 1980-90, 1991-00 and

2001-11 the coefficients of variations were 2.45%,

0.72% and 1.59 % with standard deviation 0.84, 0.24

and 0.54, respectively (Table 2).

3. The average annual minimum temperature

during decades 1980-90, 1990- 2000 and 2001-11 [Fig.

2(c)] were 19.65, 20.26 and 21.08 °C, respectively and

having 1.13 standard deviation and 5.58% CV (Table

1). The 32 years trend analysis s hows significantly

increasing trend in minimum temperature at the rate of

0.069 °C per year with R2 = 0.3267 (y = 0.0691x +

19.19) [Fig.2]. The coefficients of variations for annual

minimum temperatures were 4.86%, 6.63% and 2.71%

with standard deviation 0.96, 1.34 and 0.57 during

decades 1980-90, 1991-00 and 2001-11, respectively

(Table 2).

4. The annual relative humidity of Junagadh

region is oscillated between 13 to 97.0%. Morning

relative humidity rises to its maximum level during the

months of June to September and lowest in

February-March. Annual average morning relative

humidity during decades 1980-90, 1991-2000 and

2001-11 [Fig. 3] were 72.23, 77.23 and 73.63%,

respectively with 4.04 standard deviation and 5.37%

CV (Table 1). The past 32 years trend analysis shows

the non significantly increase in morning humidity with

the rate of 0.0012 with R2 = 0.000008 (y = -0.0012x +

75.327) [Fig. 3]. During decades 1980-90, 1991-00 and

2001-11 the coefficients of variations were 6.98%,

3.76% and 4.02 % with standard deviation 5.25,2.91

and 2.96 respectively (Table 2).

5. The average annual afternoon relative humidity

during decades 1980-90, 1991-2000 and 2001-11 [Fig.

3(c)] were 43.41, 43.02 and 40.23 % respectively with

3.15 standard deviation and 7.48 % CV (Table 1). The

32 years trend analysis shows significantly increasing

trend in afternoon relative humidity at the rate of 0.10%

per year with R2 = 0.091 (y =–0.013x+43.765) [Fig. 3].

The coefficients of variations for annual afternoon

relative humidity were 7.30%, 7.16% and 6.34% with

standard deviation 3.15, 3.08 and 2.55 during 1980-90,

1991-00 and 2001-11, respectively (Table 2).

6. The average Pan Evaporation during decades

1980-90, 1991-2000 and 2001-11 [Fig. 3(c)] were 6.09,

5.83 and 5.79 mm respectively with 0.66 standard

deviation and 11.14 mm CV (Table 1). The 32 years

trend analysis shows decreasing trend in Pan

Evaporation at the rate of 0.0131 mm per year with R2

= 0.0348 (y =-0.0131x+6.1204) [Fig. 4]. The

coefficients of variations for annual afternoon relative

humidity were 13.64, 10.55 and 8.69 mm with standard

deviation 0.83, 0.61 and 0.55 mm during 1980-90,


50

60

70

80

90

100

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecMonthlyMaximumRelativeHumidity(%)

Months

1980-1990 1991-2000 2001-2011

y = -0.001x + 75.32R² = 8E-06

60.0

65.0

70.0

75.0

80.0

85.0

90.0

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010A

nnualMaximumRelativeHumidity(%)

Years

RH max (%)

10

20

30

40

50

60

70

80

90


MonthlyMinimumRelativeHumidity(%)

Months

1980-1990 1991-2000 2001-2011

y = -0.101x + 43.76

R² = 0.091

20.0

25.0

30.0

35.0

40.0

45.0

50.0

55.0

60.0

65.0

70.0

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010A

nnualMinimumRelativeHumidity(%)

Years

RH min (%)

An

nu

al M

axim

um

Rel

ativ

e H

um

idit

y (%

)A

nn

ual

Max

imu

m R

elat

ive

Hu

mid

ity

(%)

An

nu

al M

inim

um

Rel

ativ

e H

um

idit

y (%

)

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

Years

Fig. 3 : Morning and evening relative humidity pattern and trend at Junagadh district last three decades.

232 Mehra et al. HortFlora Res. Spectrum, 6(4) : December 2017

7. The average wind velocity during decades

1980-90, 1991-2000 and 2001-11 [Fig. 3(c)] were 7.30,

7.56 and 6.25 km/hr respectively with 1.10 standard

deviation and 15.71 mm CV (Table 1). The 32 years

trend analysis shows significantly increasing trend in

Wind Velocity at the rate of 0.0378 km/hr per year with

R2= 0.1032 (y =-0.0378x+7.6405) [Fig. 4]. The

coefficients of variations for annual wind velocity were

21.20, 6.24 and 6.27 km/hr with standard deviation

1.55, 0.47 and 0.39 km/hr during 1980-90, 1991-00

and 2001-11, respectively (Table 2).

8. Annual bright sunshine hours (BSS) of

Junagadh region is oscillated between 0 hr in monsoon

to 11.26 hrs in summer season. Annual average BSS

during decades 1980 -90, 1991- 2000 and 2001-11

[Fig. 1(c)] were 7.69, 7.26 and 7.04 hours, respectively

with 0.49 standard deviation and 6.71% CV (Table 1).

The BSS at Junagadh shows significantly increasing

trend with the annual rate 0.028 hours per year with R2

0.2899 (y = -0.0282x + 7.8004) [Fig.1] .The coefficients

of variations were 7.42, 3.82,4.80 hrs with standard

deviations are 0.57,0.28 and 0.34 hrs during 1980-90,


0

2

4

6

8

10

12


MonthlyPanEvaporation(mm)

Months

1980-1990 1991-2000 2001-2011

y = -0.013x + 6.120R² = 0.034

3.0

4.0

5.0

6.0

7.0

8.0

9.0

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

AnnualPanEvaporation(mm)

Years

PE (mm)

2

4

6

8

10

12

14

16


MonthlyWindVelocity(km/hr)

Months

1980-1990 1991-2000 2001-2011

y = -0.037x + 7.640R² = 0.103

0

2

4

6

8

10

12

14

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

AnnualWindVelocity(km/hr)

Years

WV (km/hr)

An

nu

al P

er E

vap

ora

tio

n (

mm

)A

nn

ual

Win

d V

elo

city

(km

/hr)

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

Table 2 : Decadal analysis of weather parameters at Junagadh district.

Weather Parameters Decades

(1980-1990)

Decades

(1990-2000)

Decades

(2000-2011)

SD CV (%) SD CV (%) SD CV (%)

Rainfall (mm) 714.85 69.09 251.46 32.57 313.87 28.50

Bright sun shine (hrs) 0.57 7.42 0.28 3.82 0.34 4.80

Maximum Temperature (0C) 0.84 2.45 0.24 0.72 0.54 1.59

Minimum Temperature (0C) 0.96 4.86 1.34 6.63 0.57 2.71

Maximum Relative Humidity (%) 5.25 6.98 2.91 3.76 2.96 4.02

Minimum Relative Humidity (%) 3.15 7.30 3.08 7.16 2.55 6.34

Pan Evaporation (mm) 0.83 13.64 0.61 10.55 0.50 8.69

Wind Velocity (km/hr) 1.55 21.20 0.47 6.24 0.39 6.27

Evapotranspiration (mm) 444.75 21.64 312.95 14.94 126.75 6.03


CONCLUSION

The average rainfall during decades 1980-90,1991-2000 and 2001-11 [Fig.1 ] were 1034.68, 772.1and 1101.27 mm, respectively. The annual rainfall ofJunagadh region shows increasing tendency reportedhigher rainfall in last decade 2001-2011. Similarly, pastyears BSS analysis significantly increasing trend andrelative humidity at increasing trend, which may inconfirmation to increase in rainfall for this station. Themaximum temperature showed slightly increasingtendency, while minimum temperature revealed insignificant increase. The average decadal minimumtemperature for Junagadh region during decades1980-1990, 91-2000 and 2001-11 were 19.65, 20.26and 21.08 °C, respectively. The average PanEvaporation, trend analysis shows decreasing trendand wind velocity, trend analysis shows significantly

increasing trend. It is conjectured that these eventsmay be associated with the global and the regionalwarming under the climate change scenario. In theevent of their continuation, there would be severeimpact on societal and environmental issueswarranting appropriate precautionary measures in near future to safeguard the interest of the vast population of this region. The likely cause for increase in minimumtemperature is global warming.

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Ocean dipole modulates the number of extremerainfall events over India in a warming

environment, J. Met. Soc. Japan, 86 : 245-252.

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Atmosp. Phys., 94 : 27-45.

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J..Climatol.,28: 1453-1469, DOI : 10.1002/joc.1640.

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5. Khaladkar R.M., Mahajan P.N. and Kulkarni J. R.2009 Alarming Rise in the Number and Intensityof Extreme Point Rainfall Events over the Indian Region under Climate Change Scenario,Contribution from IITM Research Report No.RR-123, AUGUST 2009

6. Kumar N., Pisal, R. R. Shukla S. P. and Patel S. S.

(2015). Analysis of climatic variability at heavy

rainfall zone of South Gujarat. Mausam, 66 (4) :551.583: 551.577.37; October 2015)

7. Pant G.B., Rupa Kumar K. (1997). Climates ofsouth Asia. Eds. J. Wiley and Sons, Chichester,320P

8. Sinha Ray K.C. and De U.S. (2003). Climatechange in India as evidenced from instrumental

records. WMO Bulletin., 52 : 53 -59.

9. Sen Roy S. and Balling R.C. (2004). Trends inextreme daily precipitation indices in India. Int. J.

Climatol., 24 : 457-466.

q

Citation : Mehra V., Chinchorkar S.S. and Paradava D.M. (2017). Decadal trend analysis of weather parameters in

Junagadh (Saurashta) region, Gujarat : A case study. HortFlora Res. Spectrum, 6(4) : 228-233

MOD EL LING AP PROACH FOR IN-SITU BIOREMEDIATION OF CON TAM I -

NATED GROUND WA TER : A REVIEW

Deepak Kumar1* , Sudheer Ch. 2 and P.S. Kashyap1

1Department of Soil & Wa ter Con ser va tion En gi neer ing, GBPUA&T, Pantnagar2Ministry of For est and En vi ron ment, New Delhi

*Cor re spond ing Au thor’s E-mail- [email protected]

ABSTRACT : Groundwater contamination is a big challenge all over the world. Ex-situ remediation is generaland most common remediation technique as far as groundwater remediation is concerned. Ex-situremediation is a costly procedure and it can’t control the movement of contaminant plume in an aquifer. Inrecent decade, in-situ bioremediation has been proved to be cost effective and eco friendly technology forremediation of groundwater. Organic contaminant e.g. gasoline compound, is remediated most effectivelyusing this technique. In-situ bioremediation technique encourages growth and reproduction of indigenousmicroorganism, which enhances biodegradation of organic constituents in the subsurface. To inject oxygenand other nutrient at the contaminated plume, injection and extraction wells are used. In-situ bioremediationconsist of a set of injection and extraction wells and it’s very important to decide optimal number of these wellsto minimize the overall cost. Thus, simulation-optimization approach is important to decide variablesconcerned with in-situ bioremediation system. Simulation and optimization is a technique which increases thesystem efficiency. This paper will review various optimization techniques used for in-situ bioremediation andpresent a brief study on single and multi-objective optimization used for in-situ bioremediation system.

Keywords : In-situ bioremediation, groundwater, injection well, ex trac tion well, or ganic con tam i nant

Water is the main element of human civilization.Scarcity of drinkable water is a threat to existence ofliving on the earth. Groundwater is one of the mainsources of drinking water on the earth. Groundwater isthe renewable source of water supply and it is alsoconsidered as the safe source of water for drinking andfor all domestic uses. In India about 50% of the totalirrigated area is dependent upon groundwater (CWC,4) and about 60% of irrigated food production dependson irrigation from groundwater wells (Shah et al., 24). But due to excessive leaching of toxic chemicals intothe subsurface from industries (e.g. heavy metals andhydrocarbons), agricultural fields (e.g. arsenic,ammonia etc), and from sewage dump sites can causegroundwater contamination. Its contamination cancause severe health problems. In the past fewdecades, researchers has been working ongroundwater remediation techniques and developedPump-and-treat system, air sparging, vapour extraction system, in-situ bioremediation system for groundwaterremediation. Pump and treat method is one of the mostcommonly used method for groundwater remediationbut the cost of remediation in this case is very high andit showed poor performance too (Wang and McTernan, 27). This led many researchers to consider naturalattenuation as an alternative technology for

groundwater remediation (Newell et al. 20). Bioremed-iation of groundwater is one such alternate to pumpand treat system. It is relatively new technology whichhas more advantage e.g. more rapid cleanups, lesstransfer of contaminants and its lower cost thanpump-and-treat methods and other conventionalmethods. Bioremediation is the use of living organisms, primarily microorganisms, to degrade theenvironmental contaminants into less toxic forms. Ituses indigenous bacteria and fungi or plants todegrade or detoxify substances hazardous to humanhealth and/or the environment. In a typical in-situbioremediation process, groundwater is extracted tothe surface by using extraction wells, extracted water is than mixed with electron acceptor and nutrient andthen re-injected. Injection wells are also used tosimulate growth of a microbial population to acceleratethe degradation of the pollutants by injecting anincreased supply of electron acceptors or nutrients.The injection wells are located up gradient of thecontaminated source. The process of in-situbioremediation involves complex and uncertainrelationships among biomass, contaminant, nutrientsand appropriate control actions. Costs of remediation,efficiency of the system, time of remediation are someof the factors which affect the overall remediationprocess both economically and socially. Thus thesefactors should be properly optimized to get a system



Article’s History: Received : 11-11-17 Accepted : 09-12-17

NAAS Rating : 3.78

Modelling Approach for In-situ Bioremediation of Contaminated Groundwater 235

which is more cost effective and whose efficiency ishigh. Both single and multi-objective optimization hasbeen done to optimize the in-situ bioremediation ofgroundwater to get more efficient system. The purposeof optimal design of in-situ bioremediation is todetermine number of injection and extraction wells, it’slocation, it’s pumping rate such that the design satisfyminimum cost of remediation.

In this manuscript, a review study on theoptimization technique for in-situ bioremediation hasbeen done. Since no such review on optimal design ofin-situ bioremediation is available till now, so this paperwill provide a brief study of optimization technique(both single objective and multi-objective) used forin-situ bioremediation of groundwater.

Governing Equations for Simulation of in-situBioremediation

For in-situ bioremediation of contaminated aquifer, a set of injection and extraction wells are installed (Fig.1). Optimal numbers of wells are function ofcontaminant concentration, groundwater flow and itsdirection, total area to be remediated. Decisionvariables for this system may be pumping rate, numberof injection/extraction wells, contaminant concen-tration. In most of the recent research work (Ch et al., 5; Raei et al., 22; Akbarenjaol et al., 2) researchers try tominimize the total cost of operation of in-situbioremediation system. To find an optimal number ofinjection and extraction wells, researcher has used

several optimization tools and techniques (Ch. et al., 5;

Kumar et al., 15 and 16) Injection wells are used toincorporate nutrients and oxygen through water inaquifer. Extraction wells are used to check themigration of plume in downstream direction.

The governing equation for simulating

groundwater flow (Eq. 1) in two dimensions is given by :

δδ

δδ

δδ

δδ

δδx

Th

x yT

h

yS

h

tWx y

+

= +

K( )1

Where, T K bx x= =. transmissivity in the x

direction [ ]; .L T T K by y2 = = transmissivity in the y

direction [ ];L T b2 = aquifer thickness [L]; h = hydraulic

head [L]; S = storage coefficient; W=source or sinkterm [L/T].

The governing equation for groundwater flow

(Eq.2) must be solved before solving the transport

equation for contaminant. The governing transport

equation in two dimensions is given by :

δδ

δδ

δδ

δδ

∂∂x

DC

x yD

C

y xC Vx y x

+

− ( . )

− =∂∂

δδy

C vC

ty( . ) K( )2

here, C=concentration of solute [ ]M L3 ;

D Dx y, = coefficient of dispersion in x, y direction

[ ]L T2 ; v vx y, = seepage velocity [L/T]

Borden and Bedient (3) gave mathematical

expressions (Eq. 3 to Eq. 5) for removal of hydrocarbon

and oxygen from the plume and growth of

microorganisms in the plumes. It was simulated using

Monod function.

dC

dtM k

C

K C

O

K Ot

h O

= −+

+

. . . K( )3

dO

dtM k F

C

K C

O

K Ot

h O

= −+

+

. . . . K( )4

dM

dtM k Y

C

K C

O

K Ot

th O

=+

+

. . . .

+ ′ −K Y C b Mc t. . . K( )5

Where, C = hydrocarbon (Contaminant)

concentration; O = oxygen concentration; Mt = total

microbial concentration; k = maximum hydrocarbon

utilization rate per unit mass microorganisms; Y =microbial yield coefficient (g cells/g hydrocarbon);

Kh = hydrocarbon half-saturation constant; K0 =oxygen half-saturation constant; Kc = first-order decay

rate of natural organic carbon; C′ = natural organic

carbon concentration; b = microbial decay rate,

F = ratio of oxygen to hydrocarbon consumed.

Fig. 1: In-situ bioremediation system for contaminated aquifer.

236 Kumar et al. HortFlora Res. Spectrum, 6(4) : December 2017

Optimal Design Considering Single ObjectiveFunction

In-situ bioremediation is relatively nascenttechnology as compared to other remediationtechnology. Lot of work has been done on optimizationof pump-and-treat system of groundwater remedi-

ation. (Ahlfeld et al., 1; Dougherty and Marryott 6;

Haggerty and Gorelick 9; McKinney and Lin 17; Rogers and Dowla, 23 ; Huang and Mayer, 13) In this section,only single objective optimization for in-situbioremediation has been reviewed.

Minsker (18) studied the application of optimiza-tion in in-situ bioremediation. Use of Optimizationtechnique in this research work has been consideredas the first approach towards in-situ bioremediation ofgroundwater. Finite element biodegradation simulationmodel called BIO2D (Minsker and Shoemaker, 19) hasbeen used for solving the governing equations ofcontaminant transport. SALQR (Successiveapproximation linear quadratic regulator) has beenused as optimization tool for minimizing cost. Ahypothetical site having phenol as contaminant hasbeen considered and it has been studied thatmodification in pumping strategy, cleanup duration andwell locations could affect the cost of remediation andan optimal solution for in-situ bioremediation can beachieved. Eq. 6 and 7 present the objective functionand the constraint considered (Eq. 8 and Eq. 9) bythem are as follows :

minv v J U G X U ki k K k kk

KK ( ) ( , , )= =∑ 1

K( )6

Gk is the cost of a strategy during managementperiod k. Further, Gk has been assumed as hyperbolathat converges to a linear function at large pumpingrates

G X U k a uk k k ii U i k( , , )

,= +∈∑ 1 2

K( )7

Subjected to

X Y X U kk k k+ =1 ( , , ) K( )8

L X U k k Kk k( , , ) , , ..........≤ =0 1 K( )9

Where, J U( ) is the total cost of a pumpingstrategy, Uk is the control vector during managementperiod k ui k, , is the pumping rate at ( , )x yi i inmanagement period k. Xk is the state vector ofhydraulic heads and concentrations of contaminant,oxygen and biomass at the beginning of management

period k .L is the set of constraints on the control andstate vectors.

For optimization of any system there may bedifferent approach and technique. All the techniquecannot be evaluated with equal weighted. Comparisonbetween various optimization methods for optimalsolution for in-situ bioremediation of groundwater hasbeen done by Yoon and Shoemaker (29). The work has been done on a hypothetical site with toluene andphenol as the contaminant in two different cases. Theyhave set performance index (which is the total pumping cost) as the objective function. Optimization techniquecompared were NSLX(Nelder-Mead Simplex), MSLX(Modified simplex),PDS(Parallel Directive Search),IFFCO (Implicit Filtering for Constrained Optimization),DES (Derandomized evolutionary Strategy),BIGA(Binary Coded Genetic Algorithm),RGA(RealCoded Genetic Algorithm)and SALQR. Among allthese techniques, their numerical results showed thatSALQR was the fastest method and MSLX was themost consistently accurate method. Their results alsostated that BIGA was not as effective method forbioremediation optimization. The objective functionworked out by them has been shown in Eq. (3). Onlyinjection wells have been considered as the decisionvariable (Eq. 10).

min PI u a u Pik

ik

k

N

i

M m( ) . ( )= +== ∑∑ 211

K( )10

Where PI is the performance index; M is thenumber of potential wells; Nm is the number of

simulation time steps; a is the unit pumping cost; uik is

the pumping rate at well i during management period k;P is the penalty function.

Use of Genetic algorithm (GA) for optimization ofgroundwater remediation has been proved to be moresuccessful than nonlinear derivative based algorithmbecause it is computationally faster than othernonlinear simulation models. Moreover, GA does notrequire the complicated groundwater mass transferequations which are difficult to code. Garrett et al. (8)applied Parallel real valued Genetic algorithms (PRGA) instead of BIGA for optimal design of in-situbioremediation of groundwater for most suitable cost of remediation for Edwards Air Force Base (AFB) whichwas contaminated by trichloroethylene. They came tothe conclusion that PRGA provided more cheaper andeffective solution than the system implemented inEdward AFB demonstration. Yoon and Shoemaker (29) have also applied real-coded GA (RGA) consideringEq. 3 as the objective function. In his work, BIGA hasbeen compared with RGA for a hypothetical site and


they came to the conclusion that RGA solve the optimal problem much quicker than BIGA.

Hu et al. (10) has constructed an on-line real-timeprocess control system known as a hybrid fuzzypredictive control system. Hybrid fuzzy predictivecontrol can provide a cost effective solution to processcontrol of in-situ bioremediation processes. Thiscontrol system can positively respond to theunpredicted events such as sudden change oftemperature, pH etc. For solving groundwater flowequations Galerkin finite element method has beenused and for microbial reaction equations, fourth-orderRunge-Kutta method has been used. Fuzzy set theoryhas been used to model uncertainty. Again, Hu et al.(12) applied dynamic predictive control system to a labexperiment and tested the lab data using GA forminimization of overall operational cost.

The objective functions of Hu et al. (10 and 11)were (Eq. 11) and Eq. 12))

min [ ( ) ]G I Gk k lek

K +=∑ 1 K( )11

G I aC

aN

C

aEk k i

Ni k

i

Ei k

ii U

( ) . ., ,= + +

∈∑ 1

1 2+ ui k,

K( )12

Where, G Ik k( ) is dependent cost of a controlaction Ik of the control sequence l during period k; Gleis a constant cost in each period k ; is pumping rate ofwell i in period k ai; is constant relative cost coefficient;

C CNi k Ei k, ,, are the amount of nutrient and electronacceptor additions of well i in period k; N,E is the unitcost of the selected nutrient addition n and electronaddition E. K is the total remediation period.

GA has been combined with simulated annealingto form GAA (genetic annealing algorithm). ParallelRecombinative Simulated Annealing (PRSA) retainsthe desirable asymptotic convergence of SA and addsthe GA’s population approach and recombinativeoperator. Shieh et al. (25) studied PRSA for searchingoptimal control of in-situ bioremediation. BIOPLUME IIhas been used for simulating the aquifer hydraulics and bioremediation process. Two stage managementapproaches for cost minimization has beenconsidered. In first stage total remediation cost hasbeen minimized (Eq.13) and in later time-varying

pumping strategy has been used for minimizing cost ofremediation(Eq.14). Optimization results showed thatPRSA performs better than simulated annealing andgenetic algorithms for optimizing system design byminimizing the total system cost.

Minimize Z W C e P efp

e

M p

= +=∑ ( ) . ( )1

( ) ( ) ( )e D p e E p ee

M

e

M

e

M P eP

+

+

= == ∑ ∑∑ 1 11

K( )13

Minimize

Ui

C e P e t

rt y

pe

M

t

M

p

Pn

=+

+

== ∑∑ 1

111

( )( ) . ( , )

.

Max D p e E p ee

M

e

Mi e

( ) ( )= =∑ ∑

+

1 1

Max

K( )14

Where, Z is total present worth of in-situbioremediation system; Wf is factor used for converting pumping/treatment cost to present worth; ir is discountrate ;e is index denoting a potential injection andextraction location; p(e) is injection or extraction rate at

location e; C ep ( ) cost coefficient for injection or

extraction; M p is total number of injection and

extraction wells; is installation cost of well at location e;IP(e) is zero-one integer for injection or extraction well

existence at location e; D p ee

M i

( )=∑

1 is oxygen and

nutrient injection facility cost; M i is total number of

injection wells; E p ee

M e

( )=∑

1 is treatment facility

capital cost; Me is total number of extraction wells; yp

is stress period duration; U is total present worth ofpumping and facility capital cost; p(e, t) is the injection

or extraction rate at location e for stress period t; Mn is

the total number of stress period.

Optimal Design Considering Multi-ObjectiveFunction

With increasing scare resources and othercomplexity, while designing the in-situ remediationsystem, only cost of remediation can’t be the onlyobjective. Simultaneously, other objectives such asarea of site remediated, reduction in contaminantconcentration etc. can also be the objective function.Thus, there may be trade off between two objectives.To formulate it and to solve such problems, one canmodel such problems in terms of multi-objectiveproblems. In multi-objective optimization models, trade of optimal solution will be obtained.

238 Kumar et al. HortFlora Res. Spectrum, 6(4) : December 2017

A general multi-objective problem can be statedas: (Eq. 15 and Eq. 16).

O f d d d s s

s

n

n

1 1 1 2 1 21

2

= max min [ ( , , ...., ; , ,

.... .... .... )] K( )15

O f d d d s s

s

n

n

2 1 1 2 1 21

2

= max min [ ( , , ...., ; , ,

.... .... .... )] K( )16

The number of objective functions may be morethan two and the objective functions may subject to thefollowing set of constraints. (Eq 17 and Eq. 18)

d d d d d dn n1 1 2 2 1 1≤ ≤ ≤* *, , ................. * K( )17

s s s s s sn n1 1 2 2 1 1≤ ≤ ≤* *, , ................. * K( )18

Where there are n1 decision variables and n2 state variables.

In real world groundwater remediation involvesmulti-objective problems (e.g. minimization of cost andmaximization of efficiency). For pump-and treat method of groundwater remediation, researchers like (Erickson

et al., 7; Ren and Minsker, 21; Singh and Minsker, 26)has used multi-objective approach, but for in-situbioremediation only few work is available. Knarr et al.(14) used multi-objective evolutionary algorithm forin-situ bioremediation system for groundwater whichwas contaminated with perchlorate. Hu et al. (11)developed control system for in-situ bioremediation ofgroundwater using multi-objective optimizationmethod. Minimizing overall cost and maximizing theefficiency of the system under uncertain data of abioremediation site has been studies simultaneously.The objective functions considered by Hu et al. (11) are as follows (Eq. 19 and Eq. 20):

Objective 1 :

Min Min( ). .

, ,F

a

C

aN

C

aE

ii U

Ni k

i

Ei k

i

k=

+ +

∑ ε

1=∑ 1

K

1 2+ +

u Gi k ie,

K( )19

Objective 2 :

MIn ( )K

The abbreviation for Eq. (19) has been stated inthe earlier paragraph; K is the efficiency of the system.

CONCLUSION

In-situ bioremediation has been proved to bemore cost effective, eco-friendly and efficient

technology for groundwater remediation, but still thistechnology has not became a hot research topic tillnow. Most of the research work for optimal design ofin-situ bioremediation is concentrated for singleobjective optimization. Single objective optimization forin-situ bioremediation using RGA, PRSA, and Processcontrol system has been successfully implemented foroptimization. For better management of groundwaterremediation, multi-objective optimization is animportant tool. Lot of work has been done for pump-and-treat system but for in-situ bioremediation,research on Multi-objective optimization for in-situbioremediation is still in its nascent stage.

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Citation : Kumar D., Ch. S. and Kashyap P.S., (2017). Modelling approach for in-situ bioremediation of

contaminated groundwater : a review. HortFlora Res. Spectrum, 6(4) : 234-239

SEA SONAL VARI A TIONS IN PLANT EN VI RON MEN TAL PA RAM E TERS OF CUS -

TARD AP PLE CV. RAYDURG

Prerak Bhatnagar*

De part ment of Fruit Sci ence, Col lege of Hor ti cul ture and For estry, Jhalawar-326001 (Ag ri cul ture Uni ver sity, Kota).

*Cor re spond ing Au thor’s E-mail : prerakb_22@ya hoo.co.in

ABSTRACT : In horticulture, water has always been important constraint and in many cases, a rare resource.Certainly, the precise knowledge of the physiological basics of all aspects of water use relations is essential forbetter understanding of horticultural crops. The following variables viz., photosynthetic rate, stomatalconductance, photosynthetic active radiation, relative humidity percentage of leaves, internal CO2concentration, transpiration rate and leaf temperature were evaluated. In this experiment, studies were aimedto evaluate the physiological characteristics of custard apple cv. Raydurg in response to fluctuations inparameters month wise during August to December, 2015. Photosynthetic rates (Pn) ranged from 2.98 to 9.3

mmol m s2 1− − ; Photosynthetic Active Radiation (PAR) ranged from 859 to 2087; stomatal conductance (Gs)

varied between 3.0 to 17.0 m smol2 1− − ; relative humidity percentage between 5.60 to 12.57 over an active

growth season from August to December, 2015.

Keywords : Photosynthetic rate, tran spi ra tion rate, PAR, stomatal con duc tance, in ter nal CO2 con cen tra tion.

Leaves are the principal sites of photosynthesis inplants. Photosynthesis rate increases when the leavesattain the full physiological maturity (Kramer andKozlowski, 4; Flore and Lakso 3). Custard apple(Annona squamosa L.) is native to Tropical Americaand is found naturally under the lower canopies ofAravalli forest range in the districts of Udaipur,Rajsamand, and Chittorgarh as well as in foothills oftrees like Butea monosperma, Kadam, the Malwaplateau districts of Baran and Jhalawar districts ofRajasthan state of India. The plants flourish well undersemi arid environments of these districts with an annual average rainfall range between 500-800 mmduring the rainy season of South West Monsoonextending from July to September every year. Yet today in Rajasthan state, Custard apple has not attained thestatus of commercial crop due to its natural habitat intribal areas and people exploit it mainly at theirsubsistence level owing to lack of knowledge,adequate management and understanding of itsphysiology. Custard apples being deciduous in natureunder North Indian conditions, the shrubs becomeleafless during winter (December-January) followed byresumption of new foliage in February-March. Thefoliage cover persists from March onwards throughrenewal of growth and development up to Decembermonth. During severe summers growth slows downand again the plants started rapid phase of growth

during the period of maximum moisture availability.With intent to understand the physiology of custardapple plants, the plant canopy studies comprisingenvironmental variables were undertaken in the newlyestablished orchard of 4 years age of custard apple cv.

Raydurg.

MATERIALS AND METHODS

The study was conducted from August toDecember 2015 in the orchard of custard apple cv.Raydurg spaced at 5 × 5 m under the vertisols ofJhalawar district at Fruit Instructional Farm of Collegeof Horticulture and Forestry, Jhalawar. Leaf gas

exchanges were measured periodically from 8.00 amto 6.00 am month wise with the help of PortablePhotosynthesis System of CIRAS, USA. Measure-ments were taken from three mature fully exposed

leaves located at 5 th or 6 th node above the basal shoot

of three plants per replication during the study period.During measurements, the photosynthetic photon flux

density (PPFD) was adjusted to 1000 µmol m s2 1− − ,

and the rate of air flow into the leaf chamber was set at

500 ml min−1. All the plant environment variables were

recorded periodically at 8.00 am, 10.00 am, 12.00noon, 2.00 pm, 4.00 pm and 6.00 pm on clearcloudless days. Data were subjected to analysis ofvariance (ANOVA) using Indostat statistical software totest for significance of seasonal variations on plantenvironment variables.




NAAS Rating : 3.78

Seasonal Variations in Plant Environmental Parameters of Custard Apple cv. Raydurg 241

RESULTS AND DISCUSSION

The data pertaining to photosynthesis in (Table 1)revealed that during August month, the peakphotosynthesis increased exponentially and occurred

at 10.00 am (9.3 mmol m s2 1− − ) followed by a sharp

decline in Pn during 12.00 noon (4.9 mmol m s2 1− − )

and 2.00 pm (4.3 mmol m s2 1− − ) and again rose at 4.00

pm and then declined. The better photosynthesisduring 10.00 am could be attributed to better stomatal

conductance (gs = 13 mmol m s2 1− − ) and a strong

correlation with gs during this period and rapid gaseous exchange from the custard apple leaves in the morninghours. Leaves begin to contribute of the carboneconomy of the plant when they are fully expanded and dark green as reported by Hieke et al. (2). There isdecline in photosynthetic rates during mid period of theday (12.00 noon to 2.00 pm) in consonance with thedecline in stomatal conductance during this period.

Net assimilation rates also increasedexponentially during September month. The maximal

photosynthesis (8.3 mmol m s2 1− − ) was found to be

observed at 10.00 am followed by a successive decline

in Pn during mid noon (5.5 mmol m s2 1− − ), 2.00pm (5.3

mmol m s2 1− − ) and again slight increase at 4.00 pm

(5.9 mmol m s2 1− − ) and then declined in the evening.

The Pn values showed positive correlation withstomatal conductance values period wise.

The photosynthetic rates in custard apple duringOctober month exhibited an ascending trend duringearly morning hours (8.00 to 10.00 am) drop down atmid day (12.00 noon to 2.00pm) and rose slightlyduring early evening (4.00 pm) followed by a slightdecline in the evening. The assimilation rates duringthe October month shows a positive correlation withstomatal conductance as higher stomatal openingfacilitates better gaseous exchange at mesophyll levelfor stronger source-sink ratio. The data in Fig.1revealed a linear positive regression trend of photo-synthetic rate with stomatal conductance. An increasein stomatal conductance shows an exponentialincrease in carboxylation efficiency of assimilateproduction. The pronounced photosynthetic activity ofcustard apple leaves was observed during earlymorning hours at 8.00-10.00 am. The trend in Fig.2revealed negative regression with acclimation of lightbeing highest at lower levels of photosynthetic activeradiation during early morning hours at 8.00 am fromAugust to December month. The trend in Fig.3 alsorevealed negative regression of photosynthetic rate

versus relative humidity % of custard apple leaves andit supports the fact that custard apple assimilates foodunder very low water content and has inherent geneticmechanism for its survival under drought or waterstress conditions. The trend in Fig.4 reflects theconstant and linear trend of photo synthetic ratesversus internal CO2 concentration and custard appleplants has limited effect on photosynthetic ratesirrespective of the concentration of internal CO2synthesized in varying amounts during the growingseason (August to December months). The trend inFig.5 reflects the linear regression trend ofphotosynthesis versus transpiration rate and revealedthat lower transpiration rates contribute to higher Pnrates during early morning hours and highertranspiration rates during mid noon and post noonhours (2.00-6.00 pm) contributed to decline in Pn rates

as observed during the study period i.e., (August-December months). The trend in Fig.6 reflects negative and inverse regression between photosynthetic rateand leaf temperature of custard apple leaves during the growing season as Pn rates were recorded higher atlow leaf temperatures of custard apple during earlymorning hours (8.00-10.00am) and Pn rates dip downat higher leaf temperatures during mid noon and postnoon hours (2.00-6.00pm).

Photosynthetic Active Radiation (PAR) : Thedata exhibiting photosynthetic active radiation (Table 2) revealed that during August month, the peak PAR was

Fig.1 Photosynthetic rate versus stomatal conductance (8.00-6.00pm).

Fig. 2 Photosynthetic rate versus photosynthetic active radiation (8.00-6.00pm).

242 Bhatnagar HortFlora Res. Spectrum, 6(4) : December 2017

recorded at 10.00 am (1042 ) and decreased at 12.00noon (842) due to cloudy weather at the time ofrecording observations, it increased slightly at 2.00 pm(994 ) followed by a slight reduction at 4.00 pm (938)

and decreased at 6.00 pm during evening hours.

Studies during September month revealed highPAR (1010 ) at 8.00 am and it increased significantly at10.00 am (1668) followed by a steep increase at 12.00noon (1847) and reached at its peak at 2.00 pm (2026),

subsequently it decreased at 4.00 pm (1825) and at6.00 pm (1644) during evening hours. The high PARvalues observed during September month is due toclear sky conditions noted at the time of recordingobservations. Further data during October monthrevealed high PAR throughout the study period. Thevalues observed a rapid increase in trend with theprogression of increase in time intervals. The values ofPAR observed at 8.00 am (1010), 10.00 am (1793),12.00 noon (2026), 2.00 pm (2087), 4.00 pm (1714)and 6.00 pm (1628) got contributed to good photosyn-thetic rates during October month as better lightavailability to leaves led to better production ofassimilates. The present investigations duringNovember month revealed slight reduction in PARthroughout the day length period. The values observeda steady increase of PAR at different time intervals.

The values of PAR recorded at 8.00 am (994), 10.00am (1093), 12.00 noon (1139), 2.00 pm (938), 4.00 pm(936) and 6.00 pm (917) showed consonance withbetter assimilation rates recorded during Novembermonth. Carboxylation efficiency parameter like PARwas also observed maximum due to application ofvermicompost alongwith PSB (Sharma et al., 7).

Stomatal Conductance (gs) : The data presen-ted in Table 3 revealed the opening of stomata as afunction of stomatal conductance (gs) during the studyperiod. During August month, the values of gs recorded at different time intervals show a steady increase

during early morning hours i.e (9 m smol2 1− ) at 8.00 am

and (13m 2 smol −1) at 10.00 am, followed by a dip (7

m smol2 1− ) at 12.00 noon and (6m smol2 1− ) at 2.00 pm,

it rose slightly at 4.00 pm (8 m smol2 1− ) and then dips

down at 6.00 pm (5 m smol2 1− ). The low values of

stomatal conductance (gs) observed during August

month might be due to low values of PAR observedduring the August month and it leads to mesophylllimitation of leaves.

Stomatal conductance rates increasedexponentially during September month. The maximum

gs (14 m smol2 1− ) were found to be observed at 10.00

am followed by a successive decline in mid noon

(11m smol2 1− ), it further remained constant at 2.00 pm

(11m smol2 1− ), increased slightly at 4.00 pm

(12 m smol2 1− ) and then decrease at 6.00 pm

(10 m smol2 1− ). The better values of gs observed during

September month got contributed to better assimilation

rates (Pn) indicates the active phase of source-sinkratio and assimilation translocation to the growingparts. The stomatal conductance rates in October

Fig.3 Photosynthetic rate versus relative humidity% of custard apple leaves (8.00am-6.00pm).

Fig.4 Photosynthetic rate versus internal CO2 concentration ( (8.00am-6.00pm).

Fig.5 Photosynthetic rate versus transpiration rate (8.00am-6.00pm).

Fig.6 Photosynthetic rate versus leaf temperature (8.00am-6.00pm).


month revealed an ascending trend during early

morning hours i.e. (11m smol2 1− ) at 8.00 am and

(17 m smol2 1− ) at 10.00 am followed by a slight

reduction (12 m smol2 1− ) at 12.00 noon, subsequently it

decreased to (11m smol2 1− ) at 2.00 pm and 4.00 pm, it

further dips to (9 m smol2 1− ) at 6.00 pm during evening

hours. The optimal stomatal conductance gotcontributed to good carboxylation efficiency manifested

in terms of photosynthetic rates during October month.The variation in stomatal conductance (gs) rates during November month revealed an increasing trend during

Table 1 : Photosynthetic rates (Pn) of custard apple cv. Raydurg.

Pn(mmol-2s-1) 8.0 am 10.0 am 12.00 noon 2.0 pm 4.0 pm 6.0 pm

August 5.90 9.30 4.90 4.30 5.80 3.2

September 6.60 8.30 5.50 5.30 5.90 4.3

October 6.90 8.60 5.00 4.20 4.80 4.25

November 6.70 8.50 5.20 4.10 4.70 4.16

December 5.70 7.90 3.20 3.00 3.10 2.98

CD (P=0.05) 0.49 0.56 0.23 0.23 0.45 0.21

Table 2 : Photosynthetic Active Radiation (PAR) of custard apple cv. Raydurg.

PAR 8.0 am 10.0 am 12.00 noon 2.0 pm 4.0 pm 6.0 pm

August 862 1042 842 994 938 859

September 1028 1668 1847 2026 1825 1644

October 1010 1793 2026 2087 1714 1628

November 994 1093 1139 938 936 917

December 1134 1685 2026 1878 1793 1780

CD (P=0.05) 11.48 156.27 91.49 499.79 94.03 35.22

Table 3 : Stomatal conductance (gs) of custard apple cv. Raydurg.

Gs (m2smol-1) 8.0 am 10.0 am 12.00 noon 2.0 pm 4.0 pm 6.0 pm

August 9.00 13.00 7.00 6.00 8.00 5.00

September 12.00 14.00 11.00 11.00 12.00 10.00

October 13.00 17.00 12.00 11.00 11.00 9.00

November 15.00 16.00 13.00 12.00 13.00 11.00

December 9.00 13.00 6.00 5.00 5.00 3.00

CD (P=0.05) 3.67 4.08 3.09 1.57 2.52 5.48

Table 4 : Leaf temperature (Tleaf) of custard apple cv. Raydurg.

Tleaf(oC) 8.0 am 10.0 am 12.00 noon 2.0 pm 4.0 pm 6.0 pm

August 24.10 26.00 28.00 33.00 31.00 30.00

September 22.70 26.00 29.00 33.40 32.50 31.40

October 21.50 26.50 31.00 34.40 33.60 32.20

November 21.00 26.80 28.80 32.10 31.40 30.80

December 21.00 26.50 28.60 30.60 28.00 26.00

CD (P=0.05) 1.40 3.09 2.85 3.09 3.67 3.52

Table 5 : Transpiration rate (E) of custard apple cv. Raydurg.

E(molm-2s-1) 8.0 am 10.0 am 12.00 noon 2.0 pm 4.0 pm 6.0 pm

August 0.80 0.80 1.50 1.40 1.40 1.20

September 0.60 0.80 1.30 1.20 1.10 1.00

October 0.40 0.60 1.40 1.60 1.80 1.20

November 0.80 0.90 1.70 1.40 1.50 1.40

December 0.40 0.50 1.40 1.30 1.50 1.20

CD (P=0.05) 0.19 0.23 0.24 0.04 0.48 0.04


early morning hours i.e. (15 m smol2 1− ) at 8.00 am and (

(16 m smol2 1− ) at 10.00 am, thereafter it showed mid

day depression (13 m smol2 1− ) at 12.00 noon, followed

by values of (12 m smol2 1− ) at 2.00 pm, (13 m smol2 1− )

at 4.00 pm and (11m smol2 1− ) at 6.00 pm. The better

values of stomatal conductance got contributed tobetter photosynthetic rates during November monthand augmented the active mobilization of assimilates in the custard apple plant system. The data on stomatalconductance during December month showed valuesof sub-optimal performance in custard apple leaves.

The maximum gs was found to be (13 m smol2 1− ) at

10.00 am and values of (6 m smol2 1− ) at 12.00 noon,

(5.00 m smol2 1− ) at 2.00 pm, (5.00 m smol2 1− ) at 4.00

pm and (3 m smol2 1− ) at 6.00 pm were observed. The

low values of stomatal conductance observed might bedue to physiological ageing of custard apple leavesand slow down of metabolic activities in plant with theonset of winter and change in weather conditions.

Leaf temperature (Tleaf) : The data presented inTable 4 indicates variation in leaf temperature ofcustard apple plants at different months and timeduring study period. The leaf temperature oscillationsduring August showed a gradual trend of increase from early morning hours and reaches its maxima at 2.00pm (33°C) followed by successive decrease in leaftemperatures at 4.00 pm(31°C) and 6.00 pm (30.0°C).The leaf temperature data during September monthrevealed gradual increase of temperature from earlymorning hours and reaches its peak at 2.00 pm(33.4°C) followed by slight decrease in leaftemperature at 4.00 pm (32.5°C) and 6.00 pm (31.4°C).

The high evening temperatures contributed to lowphotosynthetic rates at 4.00 pm and 6.00pm underpresent study. The leaf temperature oscillations duringOctober months revealed that it reaches maxima at2.00 pm (34.4°C) followed by a slight decrease in leaftemperatures at 4.00 pm (33.6°C) and 6.00 pm(32.2°C). The high leaf temperatures during Octobermonth led to mesophyll limitation of gaseous diffusionand low photosynthetic rates were observed duringevening hours (2.00-6.00pm). The leaf temperaturevariations during November month showed a steadytrend of increase from early morning hours andreaches its maxima at 2.00 pm (32.1°C) followed byslight decline in leaf temperature at 2.00 pm (31.4°C)and 4.00 pm (30.8°C). The increase in leaf surfacetemperature was due to closure of stomata asfeedback response to high ambient temperatureleading to high vapour pressure deficit which in turn

restricted the thermoregulatory capacity of leaves asreported by Bhatnagar and Kaul (1) in Kinnowmandarin. The high leaf temperatures during eveninghours contributed to high PARs during correspondingduration and led to low photosynthetic rates duringNovember month. The leaf temperature fluctuationsduring December month revealed the same trend andreaches its maxima at 2.00 pm (30.6°C) followed by adecrease in leaf temperature at 4.00 pm (28.0°C) and6.00pm (26.0°C). The decline of leaf temperaturesduring winter coincides with onset of winter and slowsdown the leaf physiological and metabolic activities due to ageing and completion of growth phase is reflectedin reduction of photosynthetic rates recorded during the month of December.

Transpiration rate (E) : The data presented intable 5 indicates the variation in transpiration raterecorded during varying time periods. Transpirationrate was found minimum (0.8) during August month,however it was found maximum (1.5) at 12.00 noonand higher transpiration rates were observed at 2.00

pm (1.4 mol m s2 1− − ), 4.00 pm (1.4 mol m s2 1− − ) and at

6.00 pm (1.2 mol m s2 1− − ). Higher transpiration rates

recorded during mid day and evening hourscorresponds to high leaf transpiration values and highPAR values for this duration. The transpiration rateduring September month revealed that minimum value

(0.6 mol m s2 1− − ) was observed at 8.00 am and it rose

to (0.8 mol m s2 1− − ) at 10.00 am and reaches peak

(1.3 mol m s2 1− − ) at 12.00 noon, however it remained

significantly higher at 2.00 pm (1.2 mol m s2 1− − ), 4.00

pm (1.1mol m s2 1− − ) and (1.0 mol m s2 1− − ) as

compared to morning hours. The present studiesduring October month revealed low transpiration rate

values of (0.4 mol m s2 1− − ) at 8.00 am and

(0.6 mol m s2 1− − ) at 10.00 am during morning hours

and rose steadily at 12.00 noon (1.4 mol m s2 1− − ), 2.00

pm (1.6 mol m s2 1− − ) with maximum value (1.8 mol

m s2 1− − ) at 4.00 pm and then decline at 6.00 pm

(1.2 mol m s2 1− − ). The reason for this might be

attributed to high PAR values during the correspondingduration as observed during October month. Thetranspiration rate in custard apple during Novembermonth revealed an ascending trend during early

morning hours i.e. (0.8 mol m s2 1− − ) at 8.00 am and

(0.9 mol m s2 1− − ) at 10.00 am followed by a peak value

at 12.00 noon (1.7 mol m s2 1− − ) and then declined

slowly at 2.00 pm (1.4 mol m s2 1− − ), rose at 4.00 pm


(1.5 mol m s2 1− − ) and then slightly decline at 6.00 pm

(1.4 mol m s2 1− − ). Higher transpiration rates observed

during evening hours might be due to correspondinglyhigh leaf temperatures and lower relative humiditypercentage of custard apple leaves recorded duringthis duration. The transpiration rate in custard appleduring December month revealed lower transpiration

values during early morning hours i.e (0.4 mol m s2 1− − )

at 8.00 am and (0.5 mol m s2 1− − ) at 10.00 am, it

reaches its peak at 12.00 noon (1.4 mol m s2 1− − ),

declined slightly at 2.00 pm (1.3 mol m s2 1− − ), again

rose to (1.5 mol m s2 1− − ) at 4.00 pm and then drop

down to (1.2 mol m s2 1− − ) at 6.00 pm. The higher

transpiration rates recorded during mid noon andonwards up to evening hours are supported by the low

relative humidity percentage recorded during thecorresponding duration of December month. Similarfindings have been reported by Sharma and Bhatnagar (6) in custard apple.

Relative humidity% of custard apple leaves

(Rh) : The relative humidity percentage of custardapple leaves are presented in table-6. The data duringAugust month revealed that during morning hours, Rhpercentage values were high during morning hours i.e.(12.41%) at 8.00 am and (12.57%) at 10.00 amfollowed by mid day depression (11.98%) at 12.00noon, consequently upsurge in Rh values were notedat 2.00 pm (12.01%), 4.00 pm (12.40%) and 6.00 pm(12.50%). The overall high Rh values observed duringAugust month might be attributed to better soil moisture conserved during the prevailing rainy season. Thebetter Rh values contribute to better photosyntheticrates during the August month.

The data recorded during September monthrevealed the similar trend and Rh percentage valueswere high during morning hours i.e. (12.20%) at 8.00am and (12.40%) at 10.00 am followed by mid daydepression (11.82%) at 12.00 noon, consequentlyupsurge in Rh values were noted at 2.00 pm (11.80%),4.00 pm (11.94%) and 6.00 pm (12.00%). The betterRh percentage values of custard apple leaves might be attributed to better post monsoon conserved moisturestatus of clayey soils of plant rhizosphere. The overallbest Pn rates recorded during September month mightbe attributed to relative humidity percentage contentrecorded during the month of September. The dataestimated during October month revealed decline in Rh

percentage values from morning to evening hours witha range (9.25 to7.97%). The decline in relative humidity during October month might be attributed to decline in

water uptake and beginning of senescent phasetowards physiological ageing of custard apple leaves.The data during November month showed furtherreduction in relative humidity percentage of custardapple leaves exhibited from 8.00 am (8.03%), 10.00 am (7.04%), 12.00 noon (5.60%), 2.00 pm (5.02%) with aslight increase at 4.00 pm (6.14%) and 6.00 pm(6.39%). The reduction in relative humidity duringNovember month might be attributed to lesser wateruptake and further progression of leaf senescence andonset of dormancy period just before winter. The dataduring December month showed slight increase inrelative humidity percentage of custard apple leavesprogressing from 8.00 am (8.79%), 10.00 am (8.29%),12.00 noon (5.81%), 2.00 pm (5.75%) and linear trendat 4.00 pm (5.80%) and 6.00 pm (5.6%). The overallless values of relative humidity during December

month might be attributed to start of dormancy periodwith change in weather conditions and hastening ofsenescence in custard apple leaves.

Internal CO2 concentration (Ci) : The internal

CO2 concentration in ppm of custard apple leavesduring study period (Table 7) revealed that duringAugust month Ci values were high in the morning hours i.e (430 ppm) at 8.00 am and (434 ppm) at 10.00 amfollowed by a mid day depression due to stomatalclosure with a value (330 ppm) at 12.00 noon, followedby a slight increase at 2.00 pm (335 ppm), 4.00 pm(372 ppm) and declined in the evening at 6.00 pm (256ppm). The high Ci values during early morning hourscorrelate strongly with the photosynthetic rates duringAugust month.

The data during September month revealed highCi values during morning hours i.e. (428 ppm) at 8.00am and (437 ppm) at 10.00 am followed by a severemid day depression due to low stomatal conductancewith a Ci value (217 ppm) at 12.00 noon, followed by aslight increase (220.00 ppm) at 2.00 pm, suddenupsurge (298 ppm) at 4.00 pm and low (240.00 ppm) at 6.00 pm. The stomatal conductance trend duringSeptember month was uniform from 12.00 noon to 4.00 pm and Ci increases linearly with the reduction in Pnvalues at 12.00 noon and 2.00 pm.

The data during October month showed similartrend of high Ci values during early morning hours i.e(385.00 ppm) at 8.00 am and (397.66 ppm) at 10.00 am followed by a mid day depression with a Ci value (287ppm) at 12.00 noon, followed by a slight reduction (266ppm) at 2.00 pm, upsurge (290 ppm) at 4.00 pm anddeclined during evening hours (256 ppm). The high Civalues during early morning hours correlate stronglywith the carboxylation efficiency during October month. The decrease in photosynthetic rates during evening

hours is often attributed to stomatal and non stomatallimitations as reported by Lauer and Boyer (5). Thevariation in internal CO2 concentration duringNovember month revealed that there was high Ci rates during early morning hours i.e. (415 ppm) at 8.00 amand (420 ppm) at 10.00 am followed by a dip (326 ppm) during mid day hours at 12.00 noon, subsequently alinear trend (330 ppm) at 2.00 pm, (335 ppm) at 4.0 pmand (310 ppm) at 6.00 pm, respectively. The stomatalconductance trend during September month wasuniform from 12.00 noon to 4.00 pm and Ci maintainedlinear trend with the reduction in Pn values at 12.00noon, 2.00 pm and 6.00 pm hours.

The data during December month revealed highCi values during morning hours i.e. (391.56 ppm) at8.00 am and (397.33 ppm) at 10.00 am followed by asevere mid day depression due to low stomatalconductance with a Ci value (221.66 ppm) at 12.00noon, followed by a continuous decrease (217.00 ppm) at 2.00 pm, (189 ppm) at 4.00 pm and (179 ppm) at6.00 pm, respectively. The decline in Ci rates duringDecember month might be attributed to physiologicalsenescence and stomatal patchiness of custard appleleaves and this led to decline in Pn rates during thismonth.

CONCLUSION

Custard apple crop is potential fruit crop in termsof rate of photosynthesis during August to Novembermonth; there is maximum CO2 accumulation in theleaves which results in higher biomass productionduring this period.

REFERENCES

1. Bhatnagar P. and Kaul M.K. (2006). Diurnalvariation in gas exchange of Kinnow under dripirrigation system. Indian J. Arid Hort., 1 (2), 14-16.

2. Hieke S., Menzel C.M and Ludders P. (2002).Effects of leaf shoot and fruit development onphotosynthesis of litchi trees. Tree Physiol, 22955-961.

3. Flore, J.A. and Lakso A.N. (1989). Environmentaland physiological regulation of photosynthesis infruit crops. Hort. Rev., 11 : 111–157.

4. Kramer P.J. and Kozlowski T.T. (1979).Physiology of woody plants. Academic Press,New York, 811 p.

5. Lauer M.J. and Boyer J.S. (1992). Internal CO2measured directly in leaves; abscissic acid andlow leaf water potential cause opposing effects. Plant Physiol. 98 : 1310-1316.

6. Sharma A. and Bhatnagar P. (2011). INM incustard apple cv. Arka Sahan. MSc. Thesissubmitted to College of Horticulture and Forestry,Jhalawar, MPUAT, Udaipur, pp: 110.

7. Sharma A., Bhatnagar P., Singh. J. and Jain M.C.(2016). Growth and physiological correlations incustard apple cv. Raidurg under consortium ofvermicompost and PSB. HortFlora Res.Spectrum, 5(2) : 89-98.

q

Citation : Bhatnagar P. (2017). Seasonal variations in plant environmental parameters of custard apple cv.

Raydurg. HortFlora Res. Spectrum, 6(4) : 240-246.


Table 6 : Relative humidity (%) of custard apple leaves of cv. Raydurg.

Rh (%) 8.0 am 10.0 am 12.00 noon 2.0 pm 4.0 pm 6.0 pm

August 12.41 12.57 11.98 12.01 12.4 12.5

September 12.20 12.40 11.82 11.80 11.94 12.00

October 9.25 9.40 8.03 7.69 7.88 7.97

November 8.03 7.04 5.60 5.02 6.14 6.39

December 8.79 8.29 5.81 5.75 5.80 5.60

CD (P=0.05) 2.46 2.85 3.09 2.30 3.09 2.86

Table 7 : Internal CO2 concentration (ppm) of custard apple leaves of cv. Raydurg.

Ci (ppm) 8.0 am 10.0 am 12.00 noon 2.0 pm 4.0 pm 6.0 pm

August 430.00 434.00 330.00 335.00 372.00 256.00

September 428.00 437.00 217.00 220.00 298.00 240.00

October 385.00 397.66 287.00 266.00 290.00 256.00

November 415.00 420.00 326.00 330.00 335.00 310.00

December 391.56 397.33 221.66 217.00 189.00 179.00

CD (P=0.05) 21.75 16.52 20.62 8.92 13.60 16.32

AS SESS MENT OF GE NETIC VARI ABIL ITY FOR DIF FER ENT CHAR AC TERS OF

DAHLIA GE NO TYPES

H. M. Singh1* , Uma Shanshkar Mishra 2 and Tara Shankar Mishra 3

1Na tional Hor ti cul tural Re search and De vel op ment Foun da tion, Indore (M.P.)2Ma hatma Gan dhi Chitrakoot Gramodaya Vishwavidyalaya, Chitrakoot, Satna (M.P.)3KVK Arunachal Pradesh

*Cor re spond ing Au thor’s Email : [email protected]

AB STRACT : The study was un der taken on ge netic vari abil ity in dahlia ge no types for veg e ta tive, flo ral andtu bers char ac ters of Dahlia (Dahlia variabilis) for two suc ces sive years. Forty va ri et ies viz. Kenya Bi-col our,Gamki Sport, Croy don Gaint, Golden Glory, Duston Stone, Park Beauty, Eter nity, Prime Min is ter, Alden Gal axy, Sil ver city, From by Su preme, Se nior Ball, Snow Hill Rose, Do ris Day, Sun Set, Rob ert Walker, Mi chael, Corton Lina, Sandhya, Pow der Puff, Lord Budha, Mis tral De light, Kel vin, Rust ing, Moon Place, Snow Top, RoyalRose, Sun Rise, Duccan Magic, Pom pon, Alpana, Aditya, Black Out, Bara Kanchan, Kalvin Rose, Eter nitySpot, Hara Gauri, Indira, Chi cago and Krishna were eval u ated. A wide range of vari a tion in the meanper for mance of dif fer ent pa ram e ters in va ri et ies was ob served for all the traits taken in the pres entin ves ti ga tions Traits like plant height, num ber of branches/plant num ber of flow ers/plant, num ber of daysre quired for bud emer gence and its ma tu rity, length of flower bud, num ber of flow ers/plant, di am e ter of flower,num ber and weight of tu ber etc. showed rel a tively larger variations, compared to other during both years ofinvestigations.

Keywords : Dahlia, ge netic vari abil ity, plant growth, flo ral traits, tu ber yield.

Dahlia is one of the important bulbous floweringcrop and its flowers are used for various purposes ofdecoration. It belongs to the family Compositeae.Dahlia has a large group of beautiful striking colours –bi-colours and multicoloured cultivars (Basu and Bose,1). Its varieties have considerable importance andscope in economic field. In recent years dahlia farmingalong with other flowers has picked up very well both inthe hills and plains. Its flowers of giant decorative, large decorative, medium decorative, small decorative,

pompon and cactus types are grown in Uttar Pradesh,Delhi, Rajasthan, Punjab, Tamil Nadu, HimanchalPradesh, Gujarat, Karnataka, Andhra Pradesh, Sikkim, Madhya Pradesh, Kolkata, Orissa, Assam, States andNorth Eastern Hill regions in sporadic cultivation.

In view of its importance and scope in floriculture,research work on dahlia, becomes essential.Interrelationship of different vegetative andreproductive traits is very important. Flower charactersare expressed on the basis of genetic constitution of aplant, species/varieties. Since breeding process is apromising tool for selection programme of the plantmaterial. Prior to plan research programme needfulinformations in respect of the aims and objectives for

making improvement in available plant materials aremain essential points.

Therefore for this purpose information on differentvegetative and reproductive characters must be known to the breeder. Dahlia germplasm contains varioustypes, which indicate the need of investigations. In view of above facts, present study on phenotypic andgenotypic variability was undertaken.


The studies were undertaken at instructionalFarm, Department of Horticulture, College ofAgriculture, Chandra Shekhar Azad University ofAgriculture and Technology, Kanpur U.P. during winterseasons of 2011-12 and 2012- 2013. The experimentwas laid out in a Randomized Block Design with threereplications. Forty varieties of dahlia viz., KenyaBi-colour, Gamki Sport, Croydon Gaint, Golden Glory,Duston Stone, Park Beauty, Eternity, Prime Minister,Alden Galaxy, Silver city, From by Supreme, SeniorBall, Snow Hill Rose, Doris Day, Sun Set, RobertWalker, Michael, Corton Lina, Sandhya, Powder Puff,Lord Budha, Mistral Delight, Kelvin, Rusting, MoonPlace, Snow Top, Royal Rose, Sun Rise, DuccanMagic, Pompon, Alpana, Aditya, Black Out, BaraKanchan, Kalvin Rose, Eternity Spot, Hara Gauri,Indira, Chicago and Krishna were taken for their




NAAS Rating : 3.78

248 Singh et al. HortFlora Res. Spectrum, 6(4) : December 2017

evaluation under open field condition. Uniform-sized

dahlia tubers (3.0-4.0 cm diameter) were planted in 1st

week of November, on raised bed at a spacing 60 cm x60 cm. Each entry comprised 12 plants and all therecommended agronomic package of practices werefollowed. The observations were recorded on fiverandomly selected plants per replication for eachgermplasm on sixteen important traits (Table 1, 2 and5). The data collected were pooled and analyzedstatistically.


The variability present in forty varieties of dahliawas measured in vegetative characters which has itsown specific quality on the basis of its geneticalconstitution (Table 1 and 2). The maximum tubersprouting time was 16.867 days in variety Park Beauty(V6) followed by 15.600, 15.400 and 14.95 days in V7,

V1 and V38, respectively. The time taken to tuber

sprouting ranged from 14.800 days in Kenya Bi-colour

(V1) to 16.867 days in Park Beauty (V6) during2011-2012. In second year it varied from 14.433 days

(V16 ) to 14.967 days (V25). Statistically significantdifference for days taken to tuber sprouting was alsoobserved for rest of the varieties. Considerablevariation in plant height during both years wasobserved. In first year it varied from 56.978 in KenyaBi-colour to 82.598 cm in Rusting (V24). The maximumplant height was followed by 81.199, 80.463 and79.944 cm in V20, V2 and V25, respectively in 2011-12.During second year’s trial also, plant height varied from 59.792cm in Senior Ball (V12) to 81.873cm in PowerPuff (V20). The plant height parameter has its ownimpact on the plant growth which would give aconsiderable chance of desirable character. Thenumber of branches/plant varied from 5.267 (Kelvin) to

9.737 (Golden Glory) during 2011-12, whereas itranged from 5.360 (Kelvin) to 9.358 (Gamki Sport)

Table 1 : Estimation of variability in different vegetative growth characters for the year 2011-12.

Varieties/Treatment No.of daysfor tubersprouting

Plantheight(cm)

No. ofbranches

/plant

Length ofbranch

(cm)

No. ofleaves/plant

Length ofleaf (cm)

Diameter of leaf-stalk

(cm)

V1Kenya Bi-colour 14.800 77.309 9.133 22.432 21.990 17.833 0.637

V2 Gamki Sport 13.833 80.463 9.472 24.488 25.362 17.673 0.757

V3Croydon Gaint 13.500 73.683 9.553 25.519 24.642 18.639 0.760

V4Golden Glory 13.667 74.414 9.737 23.562 21.872 17.062 0.900

V5Duston Stone 12.700 60.360 8.719 23.579 20.957 18.125 0.790

V6Park Beauty 16.867 73.639 8.522 23.995 19.128 19.318 0.793

V7Eternity 15.600 73.463 8.620 21.681 20.225 19.801 0.737

V8Prime Minister 14.333 63.395 8.738 21.280 20.437 20.739 0.737

V9Alden Galaxy 14.367 73.553 7.323 21.392 21.585 23.184 0.913

V10Silvercity 12.833 70.080 7.585 21.883 19.938 17.641 0.660

V11From by Supreme 14.467 56.978 6.922 19.603 18.391 20.805 0.707

V12Senior Ball 14.100 59.542 7.213 18.675 21.143 23.267 0.727

V13Snow Hill Rose 11.867 62.376 6.677 18.673 20.637 23.262 0.690

V14Daris Day 12.733 58.562 6.450 18.883 20.060 22.327 0.560

V15Sun Set 15.400 70.367 .790 19.022 19.617 19.939 0.603

V16Robert Walker 14.433 68.499 .712 19.203 18.515 18.269 0.500

V17Michael 13.767 64.799 .075 18.955 17.373 20.537 0.523

V18Corton Lina 12.200 67.803 6.797 19.854 6.862 18.861 0.550

V19 Sundhya 12.333 69.962 6.680 19.384 17.072 19.697 0.480

V20 Powder Puff 13.633 81.199 7.640 19.507 21.372 22.263 0.490

V21Lord Budha 12.567 77.933 7.613 21.071 20.670 23.498 0.777

Assessment of Genetic Variability for Different Characters of Dahlia Genotypes 249

during 2012-13. The range for production of branchesalso exhibits for a further prospect of the growth leading to flowering. The results are inconsonance with Beuraand Maharana (2) and Misra and Singh (6). The length

of branches ranged from 15.722 (Royal Rose) to25.519 cm (Croydon Giant) during first year. Themaximum length was followed by 24.488, 23.95 and23.579 cm in V2, V6 and V5 treatments, respectively.

V22 Mistral Delight 11.600 73.225 6.283 18.850 20.923 21.473 0.830

V23 Kelvin 11.033 68.055 5.267 17.557 21.066 20.699 0.583

V24 Rusting 14.333 82.598 6.737 16.568 23.720 23.313 0.633

V25 Moon Place 14.800 79.944 7.120 18.113 23.270 23.530 0.553

V26 Snow Top 12.800 69.770 6.787 16.517 22.049 23.694 0.513

V27 Royal Rose 13.833 69.105 7.110 15.722 20.782 20.869 0.577

V28 Sun Rise 11.400 70.570 6.582 16.770 16.893 21.935 0.663

V29 Duccan Magic 12.633 71.230 7.157 16.583 17.367 21.020 0.553

V30 Pompon 12.767 71.378 7.077 16.092 19.393 20.475 0.660

V31 Alpana 11.967 67.043 6.920 16.992 18.273 18.515 0.650

V32 Aditya 12.733 65.988 6.687 16.035 19.408 17.683 0.650

V33 Black Out 14.433 67.957 6.267 16.699 16.770 21.733 0.683

V34 Bara Kanchan 10.033 73.667 6.737 21.172 20.631 21.752 0.723

V35 Kalvin Rose 12.333 76.512 7.120 22.117 19.660 22.930 0.610

V36 Eternity Spot 11.633 75.803 7.310 20.955 20.062 22.265 0.760

V37 Hara Gauri 11.767 75.915 6.848 19.903 19.478 22.820 0.750

V38 Indira 14.967 60.852 8.144 20.442 17.827 22.485 0.630

V39 Chicago 11.833 72.540 7.923 18.735 17.950 22.375 0.613

V40 Krishna 12.267 71.471 6.651 18.804 20.818 22.555 0.580

C.D. (P=0.05) 1.4658 3.0122 0.8602 1.9228 1.4257 1.500 0.080

Table 2 : Estimation of variability in different vegetative growth characters for the year 2012-13.

Varieties/Treatment No. ofdays for

sprouting

Plantheight

(cm)

No. ofbranches/

plant

Length of branch

(cm)

No. ofleaves/plant

Length of leaf (cm)

Diameterof

leaf-stalk(cm)

V1Kenya Bi-colour 12.300 74.833 7.960 22.750 24.539 18.945 0.597

V2Gamki Sport 12.033 75.825 9.358 25.415 22.602 17.920 0.770

V3Croydon Gaint 11.533 73.897 7.865 26.065 21.802 19.800 0.763

V4Golden Glory 11.300 71.288 7.988 25.503 21.199 18.359 0.797

V5Duston Stone 12.900 63.611 7.440 24.427 21.038 18.180 0.790

V6Park Beauty 14.967 75.852 7.919 25.333 17.723 18.886 0.757

V7Eternity 13.567 72.323 7.520 23.837 17.561 19.045 0.707

V8Prime Minister 13.433 71.323 7.694 23.117 16.995 21.778 0.730

V9Alden Galaxy 12.300 67.818 6.335 22.200 16.768 23.320 0.787

V10Silvercity 12.533 65.736 6.952 25.600 18.407 18.393 0.640

V11From by Supreme 12.833 60.433 6.857 21.180 19.618 20.419 0.730

V12Senior Ball 11.933 59.792 6.873 21.702 17.447 24.620 0.690

V13Snow Hill Rose 11.733 60.747 6.202 20.190 18.773 24.952 0.673


Like the first year, results of second year also showed a considerable variation for length of branch where itvaried from 14.872 (Duccan Magic) to 26.065 cm

(Croydon Giant). Variability in number and length ofbranches had also been reported by Misra et al. (8) and Prasad et al. (9).

V14 Daris Day 11.800 62.139 6.727 19.380 19.302 26.769 0.577

V15 Sun Set 15.267 66.462 6.807 18.678 18.693 24.892 0.567

V16 Robert Walker 14.433 69.954 6.939 17.358 17.178 21.401 0.510

V17 Michael 13.967 62.442 6.550 19.752 17.043 24.185 0.513

V18 Corton Lina 12.333 70.876 6.775 19.359 17.243 23.487 0.530

V19 Sundhya 12.100 80.134 5.405 20.118 17.150 22.060 0.507

V20 Powder Puff 13.267 81.873 7.032 20.117 18.717 26.122 0.497

V21 Lord Budha 12.300 69.780 6.830 19.808 18.017 26.986 0.780

V22 Mistral Delight 11.733 66.569 6.456 18.690 19.980 23.188 0.753

V23 Kelvin 11.033 72.456 5.360 17.336 20.384 23.338 0.580

V24 Rusting 13.733 71.595 6.415 18.600 20.772 24.897 0.557

V25 Moon Place 14.900 79.788 6.393 16.673 20.760 26.693 0.540

V26 Snow Top 13.667 69.854 6.538 16.517 20.033 24.663 0.533

V27 Royal Rose 15.567 65.698 6.638 15.875 20.235 22.435 0.540

V28 Sun Rise 11.533 70.750 6.497 16.517 17.170 25.947 0.567

V29 Duccan Magic 12.700 70.030 6.237 14.872 17.153 24.205 0.617

V30 Pompon 13.33 71.489 6.338 16.250 17.065 21.845 0.633

V31 Alpana 12.467 68.108 6.495 16.407 17.382 22.005 0.637

V32 Aditya 11.867 64.673 6.590 16.789 17.727 20.638 0.597

V33 Black Out 13.467 67.402 6.828 16.212 18.398 18.583 0.603

V34 Bara Kanchan 11.200 77.093 6.918 20.198 22.338 20.007 0.727

V35 Kalvin Rose 12.200 75.335 6.393 20.692 22.238 20.581 0.657

V36 Eternity Spot 11.933 72.792 6.650 20.218 21.517 19.683 0.790

V37 Hara Gauri 14.067 70.998 6.462 19.490 21.140 20.290 0.753

V38 Indira 14.833 65.621 6.827 19.138 20.813 18.000 0.677

V39 Chicago 12.700 70.912 6.833 18.687 19.803 19.574 0.647

V40 Krishna 14.767 72.433 6.638 17.818 20.058 19.382 0.657

C.D. (P=0.05) 0.9780 3.0615 0.7968 1.5214 1.2601 1.394 0.300

Table 3 : Estimation of variability in different reproductive parameters of dahlia varieties for the year

2011-12.

Varieties/Treatment No. of days for bud

emergence

No. of daysfor budmaturity

Length offlower bud

(cm)

No. offlowers/

plant

Diameter of

flower (cm)

No. offlowers

/head

V1 Kenya Bi-colour 33.700 20.045 4.187 11.187 25.227 40.337

V2 Gamki Sport 33.472 20.602 4.410 10.207 21.333 44.588

V3 Croydon Gaint 33.210 20.005 4.700 8.578 26.315 41.825

V4 Golden Glory 31.533 20.033 4.767 7.673 16.598 31.173

V5 Duston Stone 34.583 19.538 3.750 8.210 16.372 40.232

V6 Park Beauty 33.721 19.284 3.917 10.110 25.875 39.989


The varieties under experiments indicated the leaf growth as an effective parameter for food material. It is

evident from the Table 1 & 2 that number ofleaves/plant varied from 16.770 (Black Out) to 25.362

V7 Eternity 31.838 16.838 4.450 10.320 24.502 41.515

V8 Prime Minister 34.517 22.617 4.433 10.389 27.528 42.823

V9 Alden Galaxy 36.273 20.630 4.833 10.053 19.734 32.838

V10 Silvercity 37.247 20.460 3.850 7.707 21.403 35.220

V11 From by Supreme 36.790 21.610 4.133 7.478 21.060 35.767

V12 Senior Ball 39.348 19.157 4.450 6.788 19.878 30.788

V13 Snow Hill Rose 41.351 18.922 3.900 6.948 18.867 32.838

V14 Daris Day 38.035 18.467 4.090 8.140 17.210 33.575

V15 Sun Set 35.943 19.138 3.990 7.285 20.525 29.180

V16 Robert Walker 33.900 18.515 4.093 6.761 21.170 29.537

V17 Michael 30.593 18.127 4.443 10.782 25.098 34.945

V18 Corton Lina 33.706 18.498 4.533 10.288 25.642 34.004

V19 Sundhya 36.130 17.540 4.533 11.287 25.715 39.060

V20 Powder Puff 37.380 20.033 4.727 11.183 25.750 40.597

V21 Lord Budha 34.527 20.367 4.693 10.117 24.333 28.572

V22 Mistral Delight 37.412 21.511 4.657 9.456 23.625 40.658

V23 Kelvin 35.087 21.158 4.333 8.352 17.183 21.537

V24 Rusting 31.278 20.457 3.200 8.168 17.542 26.035

V25 Moon Place 30.518 20.518 3.273 6.737 22.907 29.027

V26 Snow Top 31.943 20.123 4.177 5.834 12.255 20.508

V27 Royal Rose 31.922 18.620 3.233 5.757 21.607 29.073

V28 Sun Rise 35.019 18.393 4.250 5.710 20.910 25.953

V29 Duccan Magic 34.293 20.393 3.903 6.688 17.483 27.698

V30 Pompon 34.047 19.133 3.350 7.793 23.400 37.353

V31 Alpana 34.573 17.298 4.433 5.309 21.013 22.088

V32 Aditya 30.754 18.503 3.953 5.364 22.848 22.770

V33 Black Out 31.635 20.342 4.293 6.500 24.178 37.671

V34 Bara Kanchan 34.776 21.498 3.810 6.713 22.534 39.643

V35 Kalvin Rose 33.518 21.757 3.740 6.527 22.930 30.140

V36 Eternity Spot 36.070 22.777 3.757 5.487 21.772 31.792

V37 Hara Gauri 35.805 21.455 3.827 6.879 20.792 31.880

V38 Indira 33.222 21.528 4.250 9.357 22.508 28.978

V39 Chicago 33.503 21.522 3.903 8.755 20.207 30.140

V40 Krishna 33.510 22.147 3.350 8.172 19.565 30.613

C.D. (P=0.05) 2.145 1.2621 5.247 0.7879 1.4627 2.3578


(Gamki Sport) during 2011-12. The maximum numberof leaves/plant was followed by 24.990, 24.642 and

23.720 in V1, V3 and V24, respectively. In the secondyear, it was ranging from 16.768 (Alden Galaxy) to

Table 4 : Estimation of variability in different Reproductive Parameters for the year 2012-13.

Varieties/Treatment No. of days forbud emergence

No. of days for bud

maturity

Length offlower bud

(cm)

No. offlowers/

plant

Diameterof flower

(cm)

No. of flowers/

head

V1 Kenya Bi-colour 35.107 21.657 3.717 10.117 22.600 37.443

V2 Gamki Sport 35.987 21.762 3.847 9.650 21.047 40.737

V3 Croydon Gaint 34.122 20.950 4.190 8.368 23.127 36.938

V4 Golden Glory 32.658 21.207 3.673 7.502 15.825 31.373

V5 Duston Stone 33.718 20.220 4.333 7.759 16.305 34.620

V6 Park Beauty 34.505 20.257 3.517 9.129 23.567 36.667

V7 Eternity 34.083 20.157 3.900 8.379 22.441 40.335

V8 Prime Minister 33.332 25.328 4.583 9.386 24.103 38.250

V9 Alden Galaxy 36.348 24.232 3.600 8.552 20.052 31.383

V10 Silvercity 37.995 22.105 3.950 7.589 19.560 35.382

V11 Fromby Supreme 39.050 24.195 3.650 6.591 20.626 34.215

V12 Senior Ball 39.986 21.838 3.673 6.585 18.871 30.442

V13 Snow Hill Rose 40.993 21.888 3.483 6.390 17.630 31.035

V14 Daris Day 38.588 21.123 4.117 7.078 17.308 32.410

V15 Sun Set 35.933 20.992 3.600 6.495 18.392 32.678

V16 Robert Walker 35.328 22.318 4.557 6.205 19.663 28.585

V17 Michael 32.827 22.107 4.553 9.423 22.022 31.183

V18 Corton Lina 35.653 21.815 3.500 9.409 23.657 30.792

V19 Sundhya 36.287 23.195 3.667 9.661 23.153 35.737

V20 Powder Puff 37.278 23.220 3.677 9.948 23.600 39.495

V21 Lord Budha 36.662 23.048 3.523 7.446 23.411 29.477


V23 Kelvin 38.297 21.577 4.040 7.578 18.839 19.987

V24 Rusting 33.521 22.838 3.583 7.359 16.940 25.193

V25 Moon Place 31.903 22.166 3.783 5.150 20.603 26.897

V26 Snow Top 34.212 21.580 3.363 5.698 11.746 19.607

V27 Royal Rose 35.162 27.598 4.223 5.293 19.532 30.000

V28 Sun Rise 37.312 22.208 3.457 5.247 19.821 21.898

V29 Duccan Magic 37.053 21.760 3.407 6.652 17.810 27.268

V30 Pompon 37.720 21.186 3.553 7.268 22.105 31.337

V31 Alpana 37.506 22.070 3.510 5.930 20.520 21.455

V32 Aditya 34.218 23.043 4.233 5.823 21.375 21.303

V33Black Out 32.937 23.412 4.350 6.500 21.708 36.517

V34Bara Kanchan 30.563 17.003 4.417 7.730 24.828 36.523

V35Kalvin Rose 31.318 17.885 4.033 7.105 25.017 27.070

V36Eternity Spot 32.422 19.283 4.233 6.192 24.403 37.318

V37 Hara Gauri 32.408 19.318 3.933 8.005 21.982 26.390

V38 Indira 30.584 19.117 4.000 10.432 25.190 41.185

V39Chicago 30.283 18.559 4.003 9.338 20.716 28.165

V40Krishna 31.011 18.508 4.103 9.292 20.842 26.302

C.D. (P=0.05) 1.759 0.8431 0.4943 0.7955 1.1614 2.3202

24.539 leaves/plant (Kenya Bi-colour). Length of leaves of both years revealed a significant variation in


Table 5 : Estimation of variability in different traits of tuber production in dahlia during 2011-12

and 2012-13.

Varieties/Treatment 2011-12 2012-13

No. of

tubers/plant

Diameter of

tuber (cm)

Weight

/tuber (Kg)

No. of

tubers/ plant

Diameter of

tuber (cm)

Weight/

Tuber (Kg)

V1 Kenya Bi-colour 10.163 2.212 0.600 10.262 2.178 0.498

V2 Gamki Sport 6.983 2.090 0.440 8.337 1.738 0.317

V3 Croydon Gaint 5.653 2.055 0.228 8.068 1.475 0.224

V4 Golden Glory 9.477 2.507 0.522 9.025 2.067 0.438

V5 Duston Stone 9.318 2.652 0.413 8.253 2.067 0.385

V6 Park Beauty 6.298 2.305 0.217 5.430 1.972 0.272

V7 Eternity 5.133 2.175 0.415 5.357 2.208 0.322

V8 Prime Minister 8.685 1.668 0.435 3.605 1.972 0.288

V9 Alden Galaxy 5.367 1.807 0.350 5.427 1.782 0.337

V10 Silvercity 6.637 2.233 0.382 6.732 2.093 0.258

V11 Fromby Supreme 7.685 1.750 0.433 7.363 1.493 0.348

V12 Senior Ball 8.475 1.658 0.358 8.430 1.682 0.238

V13 Snow Hill Rose 8.977 1.439 0.213 7.455 1.323 0.255

V14 Daris Day 8.783 1.268 0.188 7.643 1.441 0.233

V15 Sun Set 9.178 2.268 0.452 7.057 1.160 0.393

V16 Robert Walker 10.050 1.557 0.350 9.552 1.417 0.218

V17 Michael 9.578 1.670 0.233 9.318 1.347 0.215

V18 Corton Lina 8.610 1.433 0.245 7.562 2.643 0.205

V19 Sundhya 9.335 2.502 0.302 7.452 2.818 0.197

V20 Powder Puff 11.298 2.983 0.587 11.197 1.323 0.410

V21 Lord Budha 12.413 2.265 0.613 11.835 2.350 0.433


V23 Kelvin 9.435 1.905 0.232 9.593 1.687 0.233

V24 Rusting 9.323 2.033 0.337 9.078 2.442 0.262

V25 Moon Place 9.368 1.792 0.232 8.703 1.367 0.177

V26 Snow Top 10.672 1.463 0.283 10.327 1.223 0.193

V27 Royal Rose 10.390 2.070 0.418 9.690 2.317 0.348

V28 Sun Rise 9.584 2.407 0.305 9.286 2.317 0.198

V29 Duccan Magic 10.375 2.117 0.230 9.125 2.127 0.225

V30 Pompon 9.583 2.345 0.242 8.693 2.413 0.326

V31 Alpana 9.680 1.936 0.255 8.652 1.433 0.305

V32 Aditya 8.543 1.783 0.228 9.385 1.272 0.235

V33 Black Out 8.440 1.668 0.235 8.407 1.250 0.325

V34 Bara Kanchan 7.593 2.000 0.214 8.243 2.227 0.227

V35 Kalvin Rose 7.743 1.535 0.233 8.802 1.885 0.317

V36 Eternity Spot 7.702 1.527 0.208 8.798 1.693 0.170

V37 Hara Gauri 8.738 1.547 0.198 9.295 1.393 0.180

V38 Indira 7.683 1.433 0.332 8.795 2.035 0.228

V39 Chicago 7.640 1.870 0.211 9.085 1.770 0.200

V40 Krishna 8.915 2.108 0.163 8.647 2.003 0.177

V40 C.D. (P=0.05) 0.8607 0.3894 0.0676 0.7463 0.4015 0.0734


all the varieties examined, which ranged from17.062cm (Golden Glory) to 23.694 cm in Snow Topvariety during first year. During second year, the lengthof leaf varied from 17.920cm in Gamki Sport to 26.993cm in Kelvin. The maximum length was followed by26.769cm (V14), 26.686cm (V21), 26.122cm (V20) and25.947 cm (V26) in promising varieties of dahlia.

It is apparent from Table 1 that diameter of leafstalk ranged from 0.500cm to 0.913 cm in RobertWalker to Alden Galxy during first year trial. Themaximum thickness of leaf-stalk was followed by0.900, 0.830 and 0.793 cm in the treatment V4, V22and V6, respectively. Variability in flowering traits are insupport of reports of John et al. (3), Kumar et al. (4) and Misra et al. (7 and 8).

Data summarized in Table 3 & 4 revealed aconsiderable range of variation for the time periodrequired for flower bud emergence. Number of daystaken to bud emergence showed variation by rangingfrom 30.518 days (Moon Place) to 41.350days (SnowHill Rose) during 2011-12. The minimum number ofdays taken to bud emergence is a desirable characterfor availability of early flowers in a crop. In the secondyear, the minimum days for flower bud emergencewere recorded as 30.283 (Chicago), 30.563 (BaraKanchan) and 30.584 (Indira). Bud maturity period was recorded in days and the data indicated the usefulresults (Table 3 & 4). The minimum period for budmaturity to flower is useful for early flowering of avariety. Amongst dahlia varieties, minimum budmaturity period (16.838 days) was observed in varietyEternity (V7) followed by 17.298 days in V3, 17.540days in V1 and 17.127 days in V17 during first yeartrial. In the second year, again the minimum period forbud maturity was observed 20.157 days in varietyEternity (V7). The next best variety for earlier budmaturity (20.220 days) was Duston Stone (V5) followed by V6 (20.257 days). A significant variability in length of flower bud was also observed (Table 3 & 4) whichforms the size of the flower bud leading to its fulldeveloped flower. The maximum length of flower budwas recorded as 4.833 cm in Alden Galaxy (V9)followed by 4.767, 4.700 and 4.693 cm in V3, V20 and

V21, respectively during first year. Similarly the lengthof flower bud was also varied from 3.363cm to 4.583cm in V26 to V8, respectively during second year.

Number of flowers/plant ranged from 5.309 inAlpana (V31) to 11.187 in Kenya Bi-colour (V1) and themaximum number of flowers was followed by V20(11.183), V17 (10.782), V8 (10.389) and V7 (10.320)during 2011-12. In second year also, the flower

production was recorded variable where it wasmaximum 10.432 flowers/plant in Indira (V38) followedby 10.117 (V1), 9.948 (V20) and 9.661 (V19). Flowersize as diameter of head of whole flower was found tohave a considerable variability. Diameter of flower wasnoted maximum to 27.528 cm in Prime Minister (V8)followed by 26.315 (V2), 25.642 (V18), 25.750 (V20),25.715 (V19), 25.227 cm (V1). The minimum diameterwas 12.255 cm in Snow Top (V26). In second yearflower diameter was observed maximum 25.190 cm invariety Indira (V38) and was minimum 11.746 cm inV26. The maximum number of flowers/head (44.588)was observed in Gamki Sport (V2) followed by 42.823

(V8 ), 41.825 (V3), 41.515 (V7) and 40.658 (V22) during2011-12. During second year’s trial, the number offlowers/head ranged from 19.607 (V26) to 40.737 (V2).The maximum number of flowers/head was followed by

40.335 (V7), 39.495 (V20), 39.450 (V22) and 38.250

(V8 ). Results on varietal variability for flowering traitsare in support of Kumar et al. (5) and Misra et al. (7)and Prasad et al. (9).

Observations on tuber production/plant (Table 5)revealed that during first year tuber production(Nos./plant) ranged from 5.133 in Eternity (V7) to12.413 in Lord Budha (V21). There was a greatvariation in number of tubers/plant in both years of theexperiments. The varieties revealed a great rangewhich indicated the chance for improvement in dahliagermplasm. In second year, it also revealed variation in tuber production. The tuber size as diameter variedfrom 1.268cm in Daris Day (V14) to 2.983cm in PowerPuff (V20) in first year. The maximum diameter wasfollowed by 2.652cm (V5), 2.502 cm (V19), 2.265cm

(V21) and 2.407cm (V28). Variation in diameter oftubers in second year was also observed significant indifferent varieties of dahlia. The average data forweight of single tuber (Table 5) showed the variationfrom 0.163kg (V40) to 0.613 kg (V21). The maximumweight/tuber was followed by 0.600 (V1), 0.587 (V20 ),0.522 (V4) and 0.452 kg (V15) during 2011-12. Duringsecond year, it ranged from 0.170 kg/tuber to 0.498kg/tuber in V36 to V1 respectively. The range ofvariation in tuber weight has a promising effect onsprouting and plant production in next generation of the crop.

REFERENCES

1. Basu A. and T.K. Bose (1970). The ornamental

plants. Indian Hort. 277 : 21-22.

2. Beura, S. and Maharana T. (1990). Geneticvariance in different dahlia varieties. Orissa J.

Agric. Res., 3 (2) : 169-172.


3. John A.Q., Paul P.M. and Neelofar S. (1995).Genetic variability and correlation studies in

Zinnia. Indian J. Orna. Hort. 2 (1-2) : 1-4.

4. Kumar R., Prasad A. L. Singh and Singh K.(2003). Genetic divergence studies in Dahlia. Nat. Symp. Orna. Hort. Plant held at Kerala Agric Univ. Trichur. PP : 13.

5. Kumar R., Singh L. and Arya S. (2004). Studies

on varietal performance of Dahlia. 91st Indian Sci.

Cong. pp : 37-38.

6. Misra H.P. and Singh K. B. (1987). Dahlia

varieties for late planting. Indian Hort., 31 (3) :3-4.

7. Misra R.L. and Singh B. (1986). Variability and

correlation studies in Dahlia. Indian J. Hort., 43(3-4) : 269-273.

8. Misra R.L., Verma, T.C. Thakur, P.S. and SinghB. (1987). Variability and correlation studies in

Dahlia. Indian J. Hort., 44 (3 & 4) : 269-273.

9. Prasad, A., Bihari M. Kumar R. and Singh L.(2007). Studies on genetic divergence in Dahlia

genotypes. Plant Sci., 40 : 9-12.

q

Citation : Singh H.M., Mishra U.M. and Mishra T.S. (2017). Assessment of genetic variability for different characters of dahlia genotypes. HortFlora Res. Spectrum, 6(4) : 247-255

GROWTH AND FLOW ER ING BE HAV IOUR OF Den dro bium VA RI ET IES UN DER

PRO TECTED CON DI TION IN GANGETIC AL LU VIAL ZONE OF WEST BEN GAL

Tapas Kumar Choudhuri* and Raghunath Sadhukhan

All In dia Co or di nated Re search Pro ject on Flori cul ture, BCKV, Mohanpur, Nadia, West Ben gal

*Cor re spond ing Au thor’s E-mail : [email protected]

ABSTRACT : Six varieties of Dendrobium spp. (Big White Sanan, Peach, Pink Strip, Morning Glory, Sonia andAiyara Pink) were evaluated at the Horticultural Research Station, Mondouri, BCKVV, Nadia, West Bengal,during 2015-16 and 2016-17 under protected condition in naturally ventilated polyhouse. Maximum number ofspike/plant was noticed in Morning Glory (5.67) and Sonia (5.67). Number of florets/spike was maximum inPink Strip (13.67) followed by Morning Glory (13.33) and Peach (12.0). Morning Glory also had maximum spike length (58 cm) and rachis length (25 cm). Taking into consideration of both the quantitative and qualityparameters, it may be concluded that Morning Glory and Pink Strip were better for pot plant production,whereas, Peach and Sonia were better for cut spike production. Big White Sanan and Aiyara Pink were verypoor performer.

Keywords : Or chid, Den dro bium nobilis, pro tected cul ti va tion, cut flower pro duc tion.

Orchid is a handsome flower having highest pricein the market among the flowers grown in the world due its incredible range of diversity in terms of colour offlower, shape and size, appearance and post harvestlife. Asia continent is the prime source of Dendrobiumspp. and is mainly found in different countries likeKorea, Japan, Indonesia, Thiland and Malaysia(Mukherjee, 4). At present orchids of nearly 1300species and 1,50,000 varieties are found in the world,85% of which comes under Dendrobium spp.Dendrobium orchids are used mainly in Asiatic regionfor production as well as consumption because it ispreferably grown in the Sub-tropical region. In Indiacoastal part of the states like West Bengal, Kerala,Karnataka and Tamil Nadu have a greater scope of

Dendrobium cultivation for consumption of local market and export. At present India is having very meagershare of orchid in terms of area as well as production.There is a huge demand of this flower in this countrywhich is fulfilled through importing. These orchids aregrown well in high humidity condition and according toDe et al. (1) the single dominant factor which affects the cultivation of this orchid is humidity which should be ashigh as (50-75%). Gangetic West Bengal has highaverage rainfall (1500 mm), moderate temperature and high humidity which are preferred by this tropicalorchid. Identification of suitable varieties for better cutflower or pot plant production will help the growers takethis crop in challenging mode in cultivation practice forhigher profit. So, the present investigation wasundertaken to evaluate different varieties of

Dendrobium for cut flower production under protected

condition to identify the potential varieties for cut flowerproduction in this belt.


The experiment was carried out under naturallyventilated polyhouse at Mondari Farm of BidhanChandra Krishi Viswavidyalaya, Mohanpur, Nadia,West Bengal, during two consecutive years 2015-16and 2016-17. Tissue cultured seedlings were planted in September, 2013. Seedlings were brought from theSTK Orchid Farm, Thailand. Experiment was set up inwell equipped open ventilated hi-tech polyhouse (1000sq.mt) with 50% black shade net inside. The



Article’s History:Received : 30-09-17 Accepted : 31-10-17

NAAS Rating : 3.78

Fig. 1 : A field of Dendrobium orchid different varieties growing under polyhouse.

Growth and Flowering Behaviour of Dendrobium Varieties under Protected Condition 257

experiment was laid out in Completely RandomizedDesign with six varieties replicated thrice and thestatistical analysis of the data was carried out followingFisher’s analysis of Variance Technique as describedby Gomez and Gomez (2). Details of treatmentsconsisting of varieties are mentioned below (Table 1).

The growing environment inside had partiallymechanized control system through foggers and wasregulated based on plant’s requirement. This poly

house also had sprinkler irrigation system for wateringand fertigation. A platform in north and south directionwas made with galvanized iron structure inside thepolyhouse. The size of platform in terms of width,height and length was 3.5, 2.5 and 75 ft. respectivelyand 2.5ft space was left in-between the two platformsfor intercultural operation. Dendrobium orchid isepiphytic in nature and preferably grows in soillessmedia. Here coco blocks (Length x Width=30cmx20cm) were used for growing media and 150numbers of blocks were placed on each platform,where four number of tissue culture plants wereplanted in a each block. So approximately10,000numbers of plants were accommodated in a 1000sq.mt area of polyhouse.

To cope with high temperature in summer monthsduring April-May gyro net and foggers were functioning at an interval of half an hour starting from 10am to 2pm. Here 12-14 hours day length and light intensity of

2500-3000 foot candle during summer months wasprovided to the plants. Too much light on the plantsduring summer had scorching effect or leathery leavesand low light intensity during rainy season resulted indark green leaves.

There is a close relationship between fertigationand watering to the orchid plants, because of fertilizersand micro-nutrients are applied with water throughsprinkler. Dendrobium orchid require very little amount

on nutrients, but it to be provided continuously exceptduring dormancy period in severe winter. Most of thewater soluble fertilizers were used for fertigation like N(Sources: ammonium nitrate, ammonium sulphate andcalcium nitrate), P (Sources: mono potassiumphosphate, mono ammonium phosphate, phosphoricacid) and K (Sources: potassium sulphate andpotassium nitrate). There are so many micro-nutrients(Ca, Mg, Fe, Zn, Bo) were used for better quality offlower production.

There were no major disease and pest during thestudy, but at regular fortnight interval CopperOxychloride @ 2gm/lit. of water was sprayed on theplant as preventive measure.


(a) Vegetative growth

Table 1 : Characteristics of different varieties of Dendrobium nobilis.

Name of the variety Stem/pseudo bulb Leaves Flower

V1: Big White Sanan Erect, Medium height,thin, Cylindrical, Caneshaped, stout

Dark Green, Light green,Glossy, Lanceolate,Apex-acute, No. ofLeaves:6

Erect, Inflorescence appear in termination portion, two time flowering, No. of flower:,4, No. offlorets/spike:6 Colour of flower: Greenish white

V2:Peach Erect, Tall, Thick, Cylindrical, Cane shaped,stout

Light Green, dull appear,Lanceolate, Apex-acute,No. of leaves:12

Erect, Inflorescence appear in termination portion, two time flowering, No. of flower:,4, No. offlorets/spike:12 Colour of flower: Pinkish -white

V3:Pink Strip Erect, Tall, Thick, Cylindrical, Cane shaped,stout

Dark Green, Glossy,Cuspidate, Apex-acute, No. of leaves:13

Erect, Inflorescence appear in termination portion, two time flowering, No. of flower:,4, No. offlorets/spike:13 Colour of flower: Pinkish withdark pink strips

V4:Morning Glory Erect, Tall, Thick, Cylindrical, Cane shaped,stout

Dark Green, Glossy,Cuspidate, Apex-acute,No. of leaves:9

Arching, Inflorescence appear in termination,once in a year of flowering, No. of flower:,04, No.of florets/spike:13, Colour of flower.Mauve withcentre violet

V5: Sonia Erect, Medium Tall,Medium Thick,Cylindrical, Cane shaped,stout

Dark Green, Glossy, Lanceolate, Apex-acute,No. of leaves:11

Arching, Inflorescence appear in termination,Twice in a year of flowering, No. of flower:,08,No. of florets/spike:8, Colour of flower.Violet with centre light pink

V6: Aiyara Pink Erect, Tall, Thick,Cylindrical, Cane shaped,stout

Dark Green, Glossy, Lanceolate, Apex-acute,No. of leaves:6

Erect, Inflorescence appear in termination portion, two time flowering, No. of flower:,4, No. offlorets/spike:8 Colour of flower: Dark pink

258 Choudhari and Sadhukhan HortFlora Res. Spectrum, 6(4) : December 2017

The effect of vegetative growth of differentvarieties of Dendrobium orchid in terms of plant height,number leaves, leaf area, intermodal length and stemgirth revealed significantly variance among thevarieties in two consecutive years reflected in Table 2.

The plant height was obtained maximum in Peach(68.0cm), whereas lowest was in Morning Glory(45.33cm) and at par with Big White Sanan (46.00 cm)

over others varieties. Another important findingrecorded that ‘Pink Strip’ variety of plant height wassame between consecutive years, whereas Peachimprove plant height up to 7cm and others showed very negligible differences. Regarding leaf production wasobserved highest (12.00/plant) in Peach and Sonia,whereas fewer number of leaves was recorded 6.33and 6.67 in ‘Big White Sanan’ and ‘Aiyara Pink’respectively during 2016-17. All most all the varieties

were increased their single leaf to next year. Thevarieties of ‘Morning Glory’ brought maximum leaf area(84.00 sq.cm) followed by ‘Pink Strip’(71.90sq.cm) and‘Sonia’(70.90 sq.cm) incomparision to others varietiesand very poor performance was recorded in ‘Big WhiteSanan’(44.07 sq.cm)in 2016-17. All varieties showedimprovement of leaf area between two years and that is more or less same magnitude. In case of intermodallength development of Dendrobium varieties ‘Big White Sanan’ showed very lanky growth to have maximumintermodal length (6.27cm), whereas very densefoliage like compactness of leaves in the stem wasfound in three varieties (Sonia: 3.10cm, Morning Glory:3.17cm and Pink Strip: 3.60cm) and intermediatelength was found in ‘Aiyara Pink’ (4.37cm) and‘Peach’(5.17cm). Here, ‘Big White Sanan’ and “Peach’varieties were increased intermodal length from

2015-16 to 2016-17, but rest of the varieties wereshowed very negligible difference. The most luxuriantgrowth in terms of girth of stem was greatly improved in Morning Glory: 6.40cm and Pink Strip: 6.13cm andthere is little differences among the others varieties(4.53-5.03cm). ‘Pink Strip’ and ‘Morning Glory’ bothwere markedly increased of the stem girth during

2015-16 to 2016-17 over others varieties.

(b) Flowering behaviour

The varieties of Dendrobium orchid wereindividually showed pronounced effect on flowering(Table 3 and 4). There was significant variation amongthe varieties during flowering days taken fromvegetative sprouting to flowering (84.67-88.67 days).Regarding this aspect, during 2016-17 all most all thevarieties showed early flowering was observed overforegoing year (2015-16). There is significant variationamong the varieties on flower spikes production perplant per year and studied that highest number offlower spikes (Fig.2) was harvested (5.66/plant/year)from the varieties of ‘Morning Glory’ and ‘Sonia’followed by ‘Pink Strip’ (5.00/plant/year) and fewernumber(2.00/plant/year) from ‘Big White Sanan’ and‘Aiyara Pink’, whereas intermediate magnitude wasnoted in ‘Peach’ by 4.33. There is no improvement ofspike production/plant/year in ‘Big White Sanan’ and‘Aiyara Pink’from 2015-16 to 2016-17, but on anaverage increase single number /plant in rest of thevarieties and greater improvement was found in‘Sonia’(1.33).

The performance of different varieties ofDendrobium on quality flower production in terms offlorets number, flower size, spike length, rachis length

Table 2 : Vegetative growth of different varieties of Dendrobium spp.

Treatments Plant height(cm)

No. of leaves/plant

Leaf area(sq.cm)

Internodallength(cm)

Stem girth (cm)

2015-16 2016-17 2015-16 2016-17 2015-16 2016-17 2015-16 2016-17 2015-16 2016-17

V1: Big White Sanan 43.07 46.00 5.33 6.33 41.73 44.07 5.80 6.27 4.20 4.53

V2:Peach 61.33 68.00 11.00 12.00 53.00 56.60 4.67 5.17 4.80 5.03

V3:Pink Strip 54.00 54.00 9.33 10.33 83.86 87.46 3.30 3.60 5.57 6.13

V4:Morning Glory 44.00 45.33 8.33 9.33 76.46 80.53 3.03 3.17 5.90 6.40

V5: Sonia 56.33 57.00 11.00 12.00 57.60 61.26 3.10 3.10 4.27 4.60

V6: Aiyara Pink 47.00 49.00 5.67 6.67 58.76 61.97 4.13 4.37 4.33 4.60

C.D. (P = 0.05) 2.061 4.251 1.341 1.149 3.788 4.991 0.206 0.37 0.254 0.367

C.V. 2.249 4.335 8.827 6.603 3.402 4.244 2.797 4.804 2.919 3.913


as well as diameter of spike were found significantlydifferences. Regarding number of florets productionper spike was appreciably increase in ‘Pink Strip’(13.66 florets/spike)and at par with ‘Morning Glory’(13.33) followed by ‘Peach’ (12.00florets/spike)over

other varieties (6.33-8.33). All varieties showed

continuous improvement of number of floretsproduction per spike from 2015-16 to 2016-17. Spikeand rachis length of the flower spike were observedmaximum in ‘Morning glory (58.07 and 25.40cm) incomparison to others varieties during 2016-17. Spikeand rachis length of flower growth was noted about4cm more between two years of all varieties.

This study revealed that among different varietiesduring 2016-17, some varieties like ‘Aiyara Pink’,‘Sonia’, ‘Peach’ and ‘Big White Sanan’ were producedhighest size of florets all most at par from 7.0-7.53cm,whereas better performing variety of “Morning Gloryreduces of its flower size (5.8cm). From 2015-6 to2016-17, all varieties increase number of florets per

spike, but maximum improvement was noted in ‘Peach’ (1.13 cm). Out of the six varieties studied, ‘Peach’ (1.17 cm) exhibited very strong thickened flower spikefollowed by ‘Sonia’ (1.10 cm) and it is registered thatvery lanky flower spike were produced by ‘Big WhiteSanan” (0.50cm) and same trend was found in both the

Table 3 : Flowering behaviour of different varieties of Dendrobium spp.

Treatments Days to flowering No. ofspikes/Plant

/year

No. offlorets/spike

Spike length

(cm)

Rachis length

(cm)

2015-16 2016-17 2015-16 2016-17 2015-16 2016-17 2015-16 2016-17 2015-16 2016-17

V1: Big White Sanan 88.67 84.67 2.00 2.00 6.00 7.66 37.67 41.60 19.80 21.33

V2:Peach 92.00 88.67 3.33 4.33 11.00 12.00 41.67 45.73 14.00 16.00

V3:Pink Strip 91.67 88.67 4.33 5.00 12.00 13.66 40.33 44.93 18.27 20.40

V4:Morning Glory 92.67 88.67 4.66 5.66 13.33 13.33 53.00 58.07 23.40 25.40

V5: Sonia 94.00 87.33 4.33 5.66 5.33 6.33 26.67 30.67 16.00 18.13

V6: Aiyara Pink 94.50 88.33 2.00 2.00 7.33 8.33 39.67 44.40 17.67 20.20

C.D. (P = 0.05) 4.26 5.62 0.84 0.73 1.46 1.40 3.25 3.57 1.69 1.22

C.V. 2.54 3.53 13.68 9.93 8.90 7.68 4.54 4.49 5.17 3.36

Fig. 2 : Flower production and spike length of different varieties of Dendrobium during 2016-17.

Table 4 : Flowering behaviour of different varieties of Dendrobium spp.

Treatments Flower size(cm)

Spike diameter

(cm)

Field life(days)

Vaselife in tap water

2015-16 2016-17 2015-16 2016-17 2015-16 2016-17 2015-16 2016-17

V1 : Big White Sanan 5.93 7.00 1.33 1.50 17.00 21.67 15.00 17.00

V2 : Peach 6.00 7.13 2.07 2.27 31.33 36.00 25.33 28.67

V3 : Pink Strip 5.60 6.27 1.63 1.83 26.67 31.33 22.00 24.33

V4 : Morning Glory 5.27 5.80 1.50 1.57 32.33 37.33 24.33 28.67

V5 : Sonia 6.70 7.53 1.93 2.13 25.00 29.33 21.00 24.67

V6 : Aiyara Pink 6.90 7.50 2.37 2.57 31.00 34.67 24.00 29.00

C.D. (P = 0.05) 0.35 0.422 0.175 0.19 2.322 2.613 2.033 1.989

C.V. 3.204 3.413 5.382 5.33 4.742 4.58 5.151 4.354

260 Choudhari and Sadhukhan HortFlora Res. Spectrum, 6(4) : December 2017

years of study. Field life and vase life in tap water, bothwere promising performance found in “Morning Glory”(37.33 and 28.67 days) followed by ‘Peach”(36 and28.67 days) and ‘Aiyara Pink’(34.67 and 29 days).From 2015-16 to 2016-71, both the flower life improveby on an average 4 and 2 days, respectively in allvarieties.

Performance of all varieties were improved from2015-16 to 2016-17 in all respect. From the above cited results, it has been found that vegetative growth wasexcelled in Morning Glory, Peach, Pink Strip and Soniaand intermediate growth was recorded in ‘Aiyara Pink’,whereas ‘Big White Sanan’ showed very weakened

and lanky growth. So, prevailing micro-climate in thepolyhouse affects on growth on all varieties ofDendrobium, which reflected on vegetative growth inthe polyhouse and this climate supported to thevarieties of Morning Glory, Peach, Pink Strip and Soniafor proper growth and development. In this aspectSugapriya et al. (5) reported that at Bangalorecondition Sonia recorded maximum plant height(54.57cm), leaf area (61.06 sq.cm) length ofinternode(5.0cm) among the nine varieties and in 2014at Bangladesh, Mehraj et al. (3) noticed that among thethree varieties Sonia-17 performed better in terms ofPseudobulb height(78.9cm) and girth (19.7mm).

Photographs Showing of Six Varieties of Dendrobium spp.


Regarding flower production in relation to quantityand quality, both the parameters clearly distinguishedamong the six varieties of Dendrobium. Here MorningGlory showed excellent performance in all aspect, butspike is showing arch like structure, which is not goodfor cut flower production may be treated as pot plantproduction and same trend was also found with ‘Pinkstrip. Whereas second best group performers are‘Peach and Sonia’ spikes are very strong and straightand may be used for cut flower production formarketing. As reported by Sugapriya et al. (5) thatSonia produced maximum number of flower/plant/year(8.67) among the nine varieties in Karnataka. SimilarlySonia-17 also found better in terms of quality flowerproduction like spike length(44.6cm) and number offlorets per spike(12.2)observed by Mehraj et al. (3) inBangladesh.

CONCLUSION

Thus, from the above results and discussion it isapparent that for commercial aspect among the sixvarieties of Dendrobium, ‘Morning Glory’ and ‘PinkStrip’ are the best for pot plant production for gardendecoration, whereas for cut flower production ‘Peach’and ‘Sonia’ are the best to obtain maximum production

for marketing of quality flower under polyhouse ‘BigWhite Sanan’ and ‘Aiyara Pink’are not so good for thiszone.

REFERENCES1. De L.C., Barman D., Medhi R.P., Chhetri G. and

Pokhrel H. (2013). Production technology ofDendrobium, Tech. Bull. No. 3, Pub: NRC Orchid, ICAR, Pakyong, Sikkim, India. pp.22.

2. Gomez K.A. and Gomez A.A. (1984). StatisticalProcedures for Agricultural Research (Secondedition). John Wiley &sons, New York USA.

3. Mehraj H., Shikha K.J., Nusrat, A., Shiam I.H. and Jamal Uddin A.F.M. (2014). Growth and flowering behavior of Dendrobium cultivars. J. Biosci.

Agric. Res., 2 (2) : 90-95.

4. Mukherjee S.K. (2002). Distribution of Orchids,Orchids, Pub : ICAR, Krishi AnusandhanBhawan, Pusa New Delhi, pp.2.

5. Sugapriya S., Mathad J.C., Patil A.A., HegdeR.V., Lingaraju S. and Biradar M.S.(2012).Evaluation of Dendrobium orchids for growth andyield grown under green house. Karnataka J.

Agric.Sci., 25(1) : pp.104-107.

q

Citation : Choudhuri T.K. and Sadhukhan R. (2017). Growth and flowering behaviour of Dendrobium varieties

under protected condition in Gangetic Alluvial Zone of West Bengal. HortFlora Res. Spectrum, 6(4) :256-261.

GROWTH OF KHARIF ON ION (Allium cepa L.) IN RE SPONSE TO PLANT ING

DATES AND CULTIVARS

Smaranika Mohanta1, Joydip Mandal1* and Digvijay Singh Dhakre 2

1De part ment of Hor ti cul ture and Post Har vest Tech nol ogy;2De part ment of Ag ri cul tural Eco nom ics and Ag ri cul tural Sta tis tics,

In sti tute of Ag ri cul ture, Visva-Bharati, Sriniketan - 731236 (West Ben gal)

*Cor re spond ing Au thor’s E-mail: [email protected]

ABSTRACT : A research work was conducted at Horticulture Farm, Sriniketan during kharif 2013 to study thegrowth performance of five kharif onion cultivars (Agrifound Dark Red, Arka Kalyan, Arka Niketan, Indam

Marshal and Red Stone) in four planting dates (15th August, 30th August, 15th September and 3 th0 September) under Red and Laterite Zone of West Bengal. Overall growth performance of onion plants were improved asthe planting dates were shifted from August to September. Maximum plant height, number of leaves and neck

diameter were recorded on 3 th0 September planting followed by planting on 15th September. Agrifound DarkRed and Indam Marshal excelled other cultivars in different growth parameters. Onion cultivars Agrifound Dark Red or Indam Marshal were suggested to be planted during September to get vigourous plants, whichassumed to give more bulb yield or growers may also sell the plants as green onion in the market.

Keywords : On ion, kharif, plant ing, cultivars, growth.

In India, the main season or rabi crop of onion isharvested during summer, stored and slowly madeavailable for domestic supply as well as export. Thereis critical gap in supply of onion in the country fromOctober to December and as a result the prices shootup. Importance of kharif cultivation of onion to stabilizethe prices is well accepted. West Bengal has to depend on the other states which produce kharif and late kharifonion for supply of bulb during lean period (October toMarch). In recent years West Bengal has taken a newstrategy to produce kharif onion to minimize the gapand thereby stabilize the prices during these months ofshortfall. The recent studies showed that the overallperformance of kharif onion is satisfactory in WestBengal (Dhar et al., 3; and Mohanta and Mandal, 10).Again, in West Bengal, the growing environment is toomuch different in kharif season than that of rabi season, unlike in Peninsular India where a more or lessequitable climate prevails round the year. Suitableagro-techniques are, therefore, needed to get aremunerative return from large scale kharif seasoncultivation in West Bengal. Good production oftenassociated with good growth and development of anycrop. The growth and yield of cultivated crops areinfluenced by genotype, growing environment andagronomic practices. Planting time is one of theimportant factors that greatly influence the growth, yield and quality of onion (Abd EI-All et al., 1 and Kandil et

al., 8). Most onion cultivars are sensitive to photoperiod and their range of adaption is limited. Kharif onion is anoff-season cultivation of the crop for which standardiz-ation of varieties is of immense utility (Hirave et al., 6).Thus, it is imperative to assess the stability inperformance of recommended varieties of onion for aspecific location, especially for kharif onion (Haldar etal., 5). At present, very little information is available ondifferent aspects of kharif onion cultivation in WestBengal. The present research work was thereforedesignated to study the growth performance of somekharif onion cultivars in different dates of planting under Red and Laterite Zone of West Bengal.


The field experiment was conducted during kharif2013-2014 at the Horticulture Farm (23°42’ N latitudeand 87°40’ 30’’ E longitude with an average altitude of40 m above mean sea level), Sriniketan. Theexperimental site was situated in the sub-humid,subtropical lateritic belt of West Bengal which locatedin the eastern part of India and characterized by hotsummer and moderately cold and short winter. Soil ofthe experimental site was loamy sand in texture with6.40 pH and 0.47 % organic carbon. The availablenitrogen content was 218.5 kg/ha, availablephosphorus content was 11.6 kg/ha and availablepotassium content was 72.8 kg/ha. FYM @ 25 t/ha andNPK @ 100-60-100 kg/ha was applied to the crop. Thetreatments comprised of four different planting dates



Article’s History:Received : 14-09-17 Accepted: 04-02-17

NAAS Rating : 3.78

Growth of Kharif Onion (Allium cepa L.) in Response to Planting Dates and Cultivars 263

viz. 15 th August (D1), 3th0 August (D2), 15 September

(D3 ) and 3 th0 September (D4) and five onion cultivars

viz. Agrifound Dark Red (V1), Arka Kalyan (V2), ArkaNiketan (V3), Indam Marshal (V4) and Red Stone (V5).45 days old healthy seedlings were transplanted at aspacing of 15 cm × 10 cm in a plot of 2m × 1.5m for

each treatment. The experiment was arranged in aFactorial Randomized Block Design with threereplications. Recommended cultural practices werefollowed to raise the crop. Ten plants were selected atrandom in each plot to record the observations on plant height, number of leaves/plant and neck diameter. Data

Table 1: Effect of date of planting and cultivar on plant height (cm).

45 days 60 days 75 days 90 days 105 days

Dates of Planting

D1 24.4c 33.9b 41.3b 49.0c 55.7d

D2 25.6b 35.3ab 38.6b 45.4d 59.5c

D3 26.3b 35.6a 40.3b 52.6b 63.0b

D4 27.5a 35.0a 49.3a 57.1a 66.1a

C.D. (P=0.05) 0.9 1.2 3.3 1.7 1.2

Cultivars

V1 29.5a 38.8a 43.8ab 52.5b 61.8b

V2 24.9b 33.9b 41.8bc 49.1bc 59.8c

V3 20.3c 29.4c 38.5c 48.2c 59.3c

V4 30.2a 38.7a 47.3a 57.3a 68.4a

V5 25.1b 33.7b 40.5c 48.1c 55.6d

C.D (P=0.05) 1.0 1.3 3.6 3.4 1.4

Dates of Planting × Cultivars

D1V1 26.5gh 36.0cde 40.6 45.8defg 55.6ij

D1V2 23.4ij 33.2fgh 44.6 51.5cde 54.4jk

D1V3 19.1k 28.7j 38.3 49.7cdef 53.0kl

D1V4 29.5bcd 38.8ab 42.9 52.7bcd 64.3cdef

D1V5 23.4ij 32.7fghi 39.9 45.3efg 51.2l

D2V1 28.7def 37.5bcd 38.3 47.0cdefg 62.7f

D2V2 24.6hi 33.6efg 34.7 40.8g 56.2i

D2V3 20.6k 30.2ij 37.3 44.2fg 58.3hi

D2V4 29.2cde 39.8ab 46.3 51.5cde 66.3bc

D2V5 25.4ghi 34.8ef 36.3 43.7fg 53.8jkl

D3V1 31.1abc 40.1ab 40.1 52.8bcd 63.2def

D V3 2 24.1ij 34.1efg 41.7 52.3bcde 63.1ef

D V3 3 22.0jk 30.6hij 35.6 49.1cdef 62.5fg

D V3 4 29.5bcd 38.0bc 43.0 59.2ab 69.0b

D V3 5 25.0hi 34.9def 41.2 49.8cdef 57.2hi

D V4 1 31.6ab 41.4a 56.3 64.6a 65.5cde

D V4 2 27.3efg 34.7efg 46.0 51.6cde 65.5cde

D V4 3 19.6k 28.2j 42.9 50.0cdef 65.9cd

D V4 4 32.6a 38.3bc 56.8 65.7a 73.8a

D4V5 26.6fgh 32.2ghi 44.5 53.7bc 59.9gh

GM 26.0 34.9 42.3 51.0 61.1

C.D (P=0.05) 2.1 2.6 NS 7.0 2.7

CV (%) 4.8 4.5 10.4 8.2 2.7

Note: D1 (15th August 2013), D2 (30th August 2013), D3 (15th September 2013), D4 (30th Sep. 2013) and V1 (ADR), V2

(Arka Kalyan), V3 (Arka Niketan), V4 (Indam Marshal) and V5 (Red Stone); Different alphabets denotes they arestatistically different to each other.

264 Mohanta et al. HortFlora Res. Spectrum, 6(4) : December 2017

was recorded at fortnight interval starting from 45 daysup to 105 days after transplanting.


Three growth parameters i.e. plant height, number of leaves and neck diameter of onion has beendiscussed. The novel technology for off-season onionbulb production offer good opportunity for increasingthe income of farmers. This technology also brings

scope for production of green onion which has greatdemand in local markets of West Bengal.

Plant height

The plant height is one of the important characters which influence the performance of the plant in terms of growth, vigour and survival. In onion, marketable yieldwas significantly and positively correlated with plantheight (Singh et al., 13). Vigorous plant often fetches

Table 2 : Effect of date of planting and cultivar on number of leaves /plant.


Dates of Planting

D1 4.0ab 5.0bc 6.0bc 7.0bc 7.9bc

D2 3.8b 4.7c 5.6c 6.6c 7.7c

D3 4.2a 5.2ab 6.2ab 7.2ab 8.3ab

D4 4.3a 5.5a 6.5a 7.5a 8.5a

C.D (P=0.05) 0.3 0.3 0.4 0.4 0.4

Cultivars

V1 4.5b 5.5a 6.6a 7.6a 8.5b

V2 3.6d 4.6b 5.5b 6.5c 7.5c

V3 3.5d 4.6b 5.5b 6.5c 7.6c

V4 4.8a 5.9a 6.9a 7.9a 9.1a

V5 4.0c 4.9b 5.9b 6.9c 7.8c

C.D (P=0.05) 0.3 0.4 0.4 0.4 0.4

Dates of Planting x Cultivars

D V1 1 4.3 5.3 6.7 7.7 8.3

D V1 2 3.3 4.3 5.3 6.3 7.3

D V1 3 3.3 4.7 5.3 6.3 7.3

D V1 4 4.7 5.7 6.7 7.7 8.7

D V1 5 4.0 5.0 6.0 7.0 8.0

D V2 1 4.3 5.3 6.3 7.3 8.3

D V2 2 3.3 4.3 5.0 6.0 7.0

D V2 3 3.0 4.0 5.0 6.0 7.3

D V2 4 4.3 5.3 6.0 7.0 8.3

D V2 5 4.0 4.7 5.7 6.7 7.3

D V3 1 4.7 5.7 6.7 7.7 8.7

D V3 2 3.7 4.7 5.7 6.7 7.7

D V3 3 3.7 4.7 5.7 6.7 7.7

D V3 4 5.0 6.0 7.0 8.0 9.3

D V3 5 4.0 5.0 6.0 7.0 8.0

D V4 1 4.7 5.7 6.7 7.7 8.7

D V4 2 4.0 5.0 6.0 7.0 8.0

D V4 3 4.0 5.0 6.0 7.0 8.0

D V4 4 5.0 6.7 8.0 9.0 10.0

D V4 5 4.0 5.0 6.0 7.0 8.0

GM 4.1. 5.1 6.1 7.0 8.1

C.D (P=0.05) NS NS NS NS NS

CV (%) 10.0 8.6 8.3 7.2 6.6

Note: D1 (15th August 2013), D2 (30th August 2013), D3 (15th September 2013), D4 (30th Sep. 2013) and V1 (ADR), V2(Arka Kalyan), V3 (Arka Niketan), V4 (Indam Marshal) and V5 (Red Stone); Different alphabets denotes they arestatistically different to each other.


good market return as green onion. Plant heightshowed good variation due to various dates of plantingand different cultivars. Data on plant height at 45, 60,75, 90 and 105 DAP has been presented in Table 1.Planting dates significantly influenced the plant heightin all the days. Data taken on 45 days after planting

showed highest plant height in 30 th September

planting, followed by 15 th September planting. Similar

result also noticed for 60, 75, 90 and 105 DAP. Thus,

delay in planting increased plant height. Planting in themonth of August received full monsoon. Weather aswell as soil condition was not supportive for good plantgrowth. On the other hand, plants get more congenialweather condition in later date of planting that washelpful for better growth and development. It wasindicated the suitability of late September planting thanAugust planting for kharif onion in this region. Thesimilar results also noted by Gautam et al. (4),

Table 3 : Effect of date of planting and cultivar on neck diameter (mm).


Dates of Planting

D1 4.0b 5.9 7.6 9.2b 10.1c

D2 4.9a 5.8 6.7 8.0c 10.1c

D3 4.1b 5.3 6.8 10.3b 12.6b

D4 4.1b 5.5 9.6 12.1a 15.1a

C.D (P=0.05) 0.5 NS NS 1.1 1.0

Cultivars

V1 4.9a 6.5a 8.6a 11.4a 13.4a

V2 3.8b 5.1b 6.7c 9.0c 11.0b

V3 4.0b 5.1b 6.9bc 9.0c 11.0b

V4 4.8a 6.3a 8.5a 10.6ab 12.9a

V5 3.9b 5.0b 7.6b 9.7bc 11.5b

C.D (P=0.05) 0.6 0.6 0.7 1.2 1.0

Dates of Planting × Cultivars

D V1 1 4.8abcd 7.4 9.0 11.3bcd 11.4

D V1 2 2.9h 4.8 7.6 8.8def 10.1

D V1 3 3.3gh 4.9 6.6 8.1ef 9.3

D V1 4 5.8a 7.5 8.1 9.9cde 10.4

D V1 5 3.2gh 4.8 6.6 8.1ef 9.3

D V2 1 5.5ab 6.3 7.0 8.5ef 11.0

D V2 2 4.8abcd 5.5 6.1 7.3f 9.3

D V2 3 4.9abc 5.5 7.3 8.6ef 10.5

D V2 4 4.3cdefg 5.7 6.2 7.8ef 10.0

D V2 5 5.2abc 5.9 6.7 8.0ef 9.8

D V3 1 4.5bcdef 5.8 7.9 11.4bc 13.8

D V3 2 4.1cdefg 5.3 5.7 10.2cde 11.7

D V3 3 3.4fgh 4.9 5.8 8.9cdef 10.8

D V3 4 4.6bcde 5.6 7.7 11.2bcd 15.2

D V3 5 3.7defgh 4.7 6.7 9.7cdef 11.3

D V4 1 4.7abcde 6.6 10.6 14.4a 17.5

D V4 2 3.6efgh 4.8 7.3 9.8cdef 13.1

D V4 3 4.2cdefg 5.3 7.8 10.3cde 13.3

D V4 4 4.6bcde 6.4 11.7 13.2ab 16.0

D V4 5 3.4fgh 4.5 10.6 13.0ab 15.6

GM 4.3 5.6 7.6 9.8 12.0

C.D (P=0.05) 1.1 NS NS 2.5 NS

CV (%) 15.8 13.2 11.3 15.3 10.5

Note: D1 (15th August 2013), D2 (30th August 2013), D3 (15th September 2013), D4 (30th Sep. 2013) and V1 (ADR), V2(Arka Kalyan), V3 (Arka Niketan), V4 (Indam Marshal) and V5 (Red Stone); Different alphabets denotes they arestatistically different to each other.

266 Mohanta et al. HortFlora Res. Spectrum, 6(4) : December 2017

Khodadadi (9) and Nayee et al. (11). Different cultivarsalso significantly influenced the plant height. Amongthe five cultivars, Indam Marshal produced highestplant height throughout the growing period. The cultivar Agrifound Dark Red is the second best performer forthis trait. However, the lowest plant height wasproduced by cultivars Arka Niketan. The differences inplant height among these five cultivars may be due todifferences in their inherent genetic configuration. Thisresult is in conformity with that of Chandrika and Reddy (2) and Jogdande et al. (7). The interaction effect ofplanting date and cultivar on plant height showedsignificant variation except 75 DAPS. The cultivarIndam Marshal produced maximum plant height when

planted on 3 th0 September throughout the crop

growing period. Agrifound Dark Red also performedwell in late planting.

Number of leaves/plant

Number of leaves /plant has shown aconsiderable variation under various dates of plantingand different varieties. However, the interaction effectswere non-significant for all the observations at 45, 60,75, 90 and 105 DAP. The data on number leaves /planthas been presented in Table 2. The number of leavesper plant was progressively increased with theadvancement of time. In kharif onion, the propersenesces of the leaves never attended, unlike rabionions. Among the various dates of planting, the

highest number of leaves /plant was noted in 4 th

planting (30 th September), closely followed by 3rd

planting (15 th September). These two results were

statistically at par. The number of leaves/plant obtained in the August planting differed statistically fromSeptember planting. The August planting receivedsome unfavorable climatic conditions which might have attributed to lowering of leaves /plant. Similar resultwas also reported by Mohanta and Mandal (10). Datataken at fortnight interval revealed that the leafnumbers per plant was increased gradually as plantmatures for all the studied cultivars. The probablereason is that the growth of onion plant had not seizedas favourable conditions prevailed. The cultivar IndamMarshal, has proved itself as the best, with respect ofnumber of leaves/plant, followed by the cultivar Agri-found Dark Red. The inherent genetic configurationcoupled with environmental influences might beresponsible for the difference in leaves /plant in oniongenotypes (Kandil et al., 8). In onion, the varietaldifferences in leaf numbers per plant have beenreported by Chandrika and Reddy (2), Hirave et al. (6)and Jogdande et al. (7). Interaction effect of dates ofplanting and onion varieties for this trait was statistically

non-significant. This finding was similar to Nayee et al.(11).

Neck diameter

Data on neck diameter has been represented inTable 3. Neck diameter differed significantly in differentdates of planting, except 60 and 75 DAP. Data revealed that the neck diameters were increased as the plantmatures. In kharif onion, overall congenial weather forvegetative growth helps to increase the neck diameterof the plants. However, thicker neck diameter is one ofthe reasons of lack of storability of kharif onion. Datarevealed that the neck diameter increased as date ofplanting shifted from mid August to end September in alinear fashion. The maximum neck diameter was

recorded on 3 th0 September planting (at 105 DAP).

Similar observations also reported by Nayee et al. (11),Mohanta and Mandal (10) and Reisizadeh et al. (12).The five genotypes under observation in this presentinvestigation showed significant variation in neckdiameter. Agrifound Dark Red, followed by IndamMarshal noted highest neck diameter in all theobservations at 45, 60, 75, 90 and 105 DAP.Considerable differences in this trait among these fivecultivars may be due to their genetic configuration.Interaction effect of date of planting and cultivar hadsignificant influences on neck diameter at 45 and 90DAP. The highest neck diameter was obtained incultivar Agrifound Dark Red (20.8 mm) and Indammarshal (19.1 mm) on 30th September planting.

The study revealed that the growth of onion plantin kharif season positively influenced by differentplanting dates and cultivars. September planting wassuperior to the August planting in terms of more plantheight, greater number of leaves per plant and thicken

neck diameter. Among the cultivars, Agrifound DarkRed and Indam Marshal excelled other cultivars indifferent growth parameters. Thus, onion cultivarsAgrifound Dark Red or Indam Marshal should beplanted during September to get vigorous plants, which may ultimately leads to better bulb production.Additionally, growers may also sell the plants directlyas green onion in the market.

REFERENCES1. Abd EI-All H.M., Seham M.A. and Sahim S.M.

(2012). Effect of planting dates on growth, yieldand quality of some green onion (allium cepa l.)cultivars for local marketing and exportation. J.

Biol. Chem. Envron. Sci., 7(1): 33-47.

2. Chandrika V. and Reddy D.S. (2011). Responseof onion genotypes (Allium cepa L.) to variedplanting patterns in Southern Agro-climatic zone


of Andhra Pradesh. J. Res. ANGRAU, 39 (3) :21-25.

3. Dhar M., Mandal J. and Mohanta, S. (2016).Prospects of onion cultivation (Allium cepa L.) inWest Bengal. Rural health, Womenempowerment and Agriculture Issues andChallenges. New Delhi Publishers, pp: 257-274.

4. Gautam I.P., Khatri B. and Paudel G.P. (2006).Evaluation of different varieties of onion and theirtransplanting times for off-season production in

Mid Hills of Nepal. Nepal Agric. Res. J., 7 : 21-25.

5. Haldar A., Karak C., Naik A., Samanta M.K andHazra P. (2009). Identification of suitable earlyrabi onion varieties under West Bengal condition.

J. Crop and Weed, 5 (1): 124-129.

6. Hirave P.S., Wagh A.P., Alekar A.N. and KhardeR.P. (2015) Performance of red onion varieties in

Kharif season under Akola conditions. J. Hort., 2 :132. doi:10.4172/2376-0354.1000132

7. Jogdande N.D., Gajbhiye R.P. and Ganvir G.B. (2011). Performance of onion genotypes forgrowth and yield parameters under Akola

conditions. Green Farming, 2 (5) : 548-549.

8. Kandil A.A., Sharief A.E. and Fathalla F.H.(2013). Effect of transplanting dates of someonion cultivars on vegetative growth, bulb yield

and its quality. E.Sci J. Crop Production, 02 (03) :72-82.

9. Khodadadi M. (2012). The effects of planting dateand mother bulb size on quantitative andqualitative seed traits of onion red rey variety. Int.

J. Agri. Res. Rev., 2 (4) : 324-327.

10. Mohanta S. and Mandal J. (2014). Growth andyield of Kharif onion (Allium cepa L.) as influenced by dates of planting and cultivars in Red andLaterite Zone of West Bengal. HortFlora Res.

Spectrum, 3(4): 334-338.

11. Nayee D.D., Verma L.R. and Sitapara H.H.(2009). Effect of various planting materials anddifferent dates of planting on growth and bolting of Kharif onion (Allium cepa L) cv. Agrifound Dark

Red. Asian Sci., 4 (1): 13-15.

12. Reisizadeh A., Naderidarbaghshahi M. andGolparvar, A. (2011). Effect of sowing date andplanting methods on some qualitative andquantitative traits of onion (Texas Early Grano

502 cv.) in Iran. Technical J. Engg. Appl Sci., 2

(11): 401-403.

13. Singh R.K., Bhonde S.R. and Gupta R.P. (2011).Genetic variability in late Kharif (Rangada) onion

(Allium cepa L.). J. Appl. Hort., 13 (1) : 74-78.

q

Citation : Mohanta S., Mandal and Dhakre D.S. (2017). Growth of kharif onion (Allium cepa L.) in response to

planting dates and cultivars. HortFlora Res. Spectrum, 6(4) : 262-267.

GROWTH, PRO DUC TIV ITY AND QUAL ITY OF BER (Zizyphus mauritiana Lamk.)

CV. ‘UMRAN’ IN RE LA TION WITH SOIL AP PLI CA TIONS OF PHOS PHO RUS

AND PO TAS SIUM

Amritpal S. Randhawa*, P.S. Aulakh and P.P.S. Gill

De part ment of Fruit Sci ence, Punjab Ag ri cul tural Uni ver sity, Ludhiana 141004, Punjab, In dia


ABSTRACT : The present studies on “growth, productivity and quality of ber (Zizyphus mauritiana Lamk.) cv.‘Umran’ in relation with soil applications of phosphorus and potassium” were carried out at Fruit ResearchFarm, Department of Fruit Science, Punjab Agricultural University, Ludhiana during the year 2014-15. Twelveyears old ‘Umran’ ber plants growing under uniform cultural practices underwent various phosphorus andpotassium fertilizer treatments. Three levels each of phosphorus (1.0, 1.5 and 2.0 kg SSP/plant) andpotassium (0.5, 1.0 and 1.5 kg MOP/plant) were applied as soil application. Various physiological parameters(vegetative growth, fruit size, fruit weight, fruit yield, fruit drop) and biochemical parameters (soluble solids,sugars, titratable acidity, ascorbic acid) were recorded. There was no significant effect of the treatments on thegrowth parameters of the ber trees. However, the fruit parameters were significantly affected. The treatment T7(1.0 kg SSP + 1.5 kg MOP) showed minimum fruit drop. The treatment T6 (2.0 kg SSP + 1.0 kg MOP) showedmaximum fruit yield, fruit weight, TSS and matured the earliest.

Keywords : Ber, Umran, phos pho rus, po tas sium, fer til izer.

The ber (Zizyphus mauritiana Lamk.) is one of themost ancient fruit crops belonging to the familyRhamnaceae. Due to its wider adaptability to adverseclimatic and soil conditions, ber is largely grown on vast drylands and wastelands where most of the other fruitplants fail to grow. On this account it is also called as‘the apple of arid zone’. Ber fruit is very rich withrespect to its nutritive value. The ripe ber fruits are richin vitamin C, A and B complex as well as in mineralslike calcium, phosphorus and iron.

Mineral nutrition during the growth of the fruit isone of the key factors that affect fruit yield and quality.

Growth, yield and quality parameters of the fruit cropsare influenced by phosphorus and potassiumapplication because of their role in various metabolicprocesses of the plant. Phosphorus is a constituent ofnucleic acid, phytin, phospholipids, coenzymes andhigh energy compounds like NAD, NADP and ATP. Italso helps in root growth, development of reproductivestructures and crop maturity. Potassium is known asthe quality element for crops especially fruits. Itimproves the fruit colour, flavour, size, shelf life, proteinand starch content and reduces the physiologicaldisorders as well as insect and disease incidence. So,adequate amount of these nutrients is essential forproper growth of the plant.

Dhatt et al. (5) reported the highest yield in ber cv.Umran with 400 g N, 100 g P O2 5 and 200 g K O2 pertree. Hudina and Stampar (8) reported a significantimprovement in the fruit quality with respect to thecontents of glucose, sorbitol, malic acid, citric acid,TSS and potassium with foliar applications ofphosphorus and potassium (15 % P O2 5 and 20 % K O2 ) on pear cv. ‘Williams’.

Little scientific work on soil application ofphosphorus and potassium fertilizers on ‘Umran’ berhas been carried out under sub-tropical conditions ofPunjab. Keeping this in view, the present studies were

planned to study the effects of soil application ofdifferent levels of P and K on growth, yield and qualityof ‘Umran’ ber.


Twelve years old plants of ber cv. Umran, buddedon Katha ber rootstock and planted at the spacing of7.5 × 7.5 m were used as the experimental material forconducting the present studies. All the selected plantswere maintained under the recommended culturalpractices.

The trees were fertilized with FYM (100 kg/tree)during the first fortnight of June. The dose of Urea 46%N (1000 g/tree) was split up in two parts - one part wasapplied during the second fortnight of July and theother part was applied soon after the fruit set. The




NAAS Rating : 3.78

Growth, Productivity and Quality of Ber in Relation with Soil Applications of P & K 269

variable doses of phosphorus and potassium wereapplied during the second fortnight of July.

Three levels each of phosphorus and potassiumwere taken for soil application. Single superphosphate(SSP) 16% P O2 5 was used as a fertilizer source forphosphorus, while muriate of potash (MOP) 60% K O2was used as the fertilizer source for potassium. A totalof 9 combinations of different concentrations ofphosphorus and potassium fertilizers were made forsoil application and were replicated three times andone plant was taken under each treatment (Table 1). Inaddition to these treatments, a control treatment,consisting of the recommended doses of FYM andurea was also made. In all, a total of 30 plants werestudied for the experiment. The experiment was laidout according to Randomized Block Design. The datawas analyzed using CPCS1 software.

Table 1 : Treatment details

Treatments Combination SSP

(kg/tree)

MOP(kg/tree)

T1P1K1 1.0 0.5

T2P2K1 1.5 0.5

T3P3K1 2.0 0.5

T4P1K2 1.0 1.0

T5P2K2 1.5 1.0

T6P3K2 2.0 1.0

T7P1K3 1.0 1.5

T8P2K3 1.5 1.5

T9P3K3 2.0 1.5

T10Control - -

The tree height was measured from ground levelto the top of the tree, ignoring the off-type shoots withgraduated poles. The tree spread was measured bymeasuring the distance between points to which mostof the branches of the tree had grown in the east-west

and north-south directions.

The trunk girth was measured at 25 cm from theground level. The trunk growth during the growingseason was calculated by measuring the trunk girthtwice during the course of the study, once before thestart of the experiment and again at the end of theexperiment.

The current season shoot growth was measuredby randomly selecting four current season shoots ineach of the four directions and tagging them at the timeof fruit set. The maximum length attained by theseshoots from their respective bases was measured withthe help of a metre rod in the second week of May after

harvesting the fruits. The mean shoot length wasexpressed in cm.

The fruit yield was estimated from the averagefruit weight of ten fruits multiplied by the total number of fruits under each replication and calculated inkilograms per tree. Four randomly selected branches,one on each of the four sides of the tree were taggedand the number of fruits per branch were recorded inthe month of November. The number of fruits retainedon each tagged branch were counted ten days beforeharvest. The percent fruit drop was calculated as:

Fruit drop (%)

=

−Total number of fruits set

Number of fruits retained

Total number of fruits set× 100

The mean fruit size (length and breadth) of 10randomly selected fruits in each replication wasrecorded with the help of a digital Vernier’s calliper. The fruit size (length and breadth) was expressed incentimetres per fruit. The mean fruit weight wasdetermined by weighing the fruits on an electronicbalance and expressed in grams.

The fruit firmness was determined with the help ofa pressure tester penetrometer, using 8 mm stainless

steel probe. About 1cm2 skin from the shoulder end of

each fruit was removed with the help of a peeler andthe fruit firmness was recorded and expressed as

Kg/cm 2.

The total soluble solids content of the fruit juicewas determined with the help of Bausch and Lombhand refractometer at room temperature and thereadings thus obtained were corrected to 20°C with the help of temperature correction chart (AOAC, 1). It wasexpressed in °Brix. The titratable acidity wasdetermined by titrating 10 ml of the extracted juiceagainst N/10 NaOH solution using phenolphthalein asan indicator.

The total sugars and ascorbic acid were

determined as per standard method (AOAC, 2).


The data presented in Table 2 shows that the soilapplication of phosphorus and potassium fertilizers had a non-significant effect on the plant growth parametersin ber cv. ‘Umran’. However, maximum tree height wasobserved in the treatment T9, while the maximum treespread was observed in the treatments T4 and T7. Themaximum growth in trunk girth was observed in the

270 Randhawa et al. HortFlora Res. Spectrum, 6(4) : December 2017

treatment T9, which also showed maximum shootgrowth in the current season.

The results are in accordance with the previousfindings of Mimoun (11) who did not find any significanteffect of potassium fertilizers on the tree height andtree spread in olives. Sharma and Singh (14) alsoreported a slight but non-significant increase in trunk

girth with the increasing dose of potassium fertilizer.

The perusal of data in Table 3 shows a significanteffect of phosphorus and potassium fertilizers on thefruit physiological parameters in ber cv. ‘Umran’. Thehighest fruit yield was observed in the treatment T6,which was statistically at par with that of the treatments

T7 and T8.

The increase in fruit yield might be attributed to the corresponding increase in the different yieldcomponents, viz. fruit weight and total number of fruitsper plant. The potassium application might havestimulated the rate of photosynthesis, phloem loadingand translocations, and subsequently greater partitionof photosynthates to the developing fruits. This is inaccordance with the findings of Kumar et al. (10).These observations also correlate with the findings ofZhong et al. (18) in pear and Kashyap et al. (9) inpomegranate cv. ‘Ganesh’.

The maximum fruit length was observed in thetreatment T7, which was statistically at par with that inthe treatments T6 and T8. The maximum fruit breadthand fruit weight was observed in the treatment T6,

while the minimum fruit size and weight was observedin the control treatment. The fruit weight might beattributed to the significantly larger sized fruits, which is further dependent on higher cell division, cellenlargement and increased photosynthetic activitiesamong others. The availability of phosphorus andpotassium during the fruit development significantlyimprove upon the above parameters. The translocation of photosynthates, namely sugars and othercarbohydrates, to the fruits is increased by theavailability of potassium, thus increasing the fruit sizeand weight. This is in agreement with the findings ofTaiz and Zeiger (16). The above observations aresupported by the previous findings by Dhatt (4) in‘Kinnow’ mandarin and Gill et al. (7) in ‘Patharnakh’pear.

The maximum fruit firmness was observed in thecontrol treatment. The plants applied with higher dosesof phosphorus and medium levels of potassiumfertilizers (T6) showed lowest fruit firmness. This mightbe due to the fruits maturing earlier than the fruits of the other plants. The results are in complete confirmationwith the findings of Neilsen et al. (13) in ‘McIntosh’ and

‘Delicious’ apple trees.

The minimum fruit drop was observed in thetreatment T7, which was statistically at par with that inthe treatment T8, while the control treatment showedthe maximum fruit drop.

The data presented in the Table 4 shows asignificant increase in the total soluble solids (TSS)content of fruits on soil application of phosphorus andpotassium fertilizers. The maximum TSS (16.53°Brix)was observed in the treatment T6, which is statisticallyat par with the treatments T8 and T9. There is asignificant increase in the TSS with increase in dose ofpotassium fertilizer. This might be due to the role ofpotassium in photosynthetic rate, i.e. synthesis ofcarbohydrates, and their translocation. These are inagreement with the previous observations by Hudinaand Stampar (8) in ‘Williams’ pear. Dilmaghani et al. (6)reported a significant corresponding increase in thetotal soluble solids in ‘Golden Delicious’ apples withincrease in potassium application.

Application of phosphorus and potassiumfertilizers had a non-significant effect on the titratableacidity content of ber cv. ‘Umran’ (Table 1). Themaximum titratable acidity (0.31 %) was observed inthe treatments T2 and T5. The minimum titratableacidity (0.28 %) was observed in the treatments T7 and

T8. However, Bhati and Yadav (3) also reported a

Table 2 : Effect of application of phosphorus and

potassium fertilizers on the tree growth

parameters in ber cv. ‘Umran’

Treatments Treeheight

(m)

Treespread

(m)

Trunkgirth(cm)

Shootgrowth

(cm)

T1 4.49 8.33 7.17 368.33

T2 4.47 8.27 7.23 368.00

T3 4.45 8.33 7.17 366.33

T4 4.51 8.47 7.27 370.67

T5 4.53 8.43 7.33 372.67

T6 4.52 8.40 7.33 371.67

T7 4.51 8.47 7.43 372.67

T8 4.54 8.37 7.37 373.67

T9 4.56 8.43 7.47 374.67

Control 4.45 8.30 7.13 366.67

CD (P = 0.05)

NS NS NS NS

Growth, Productivity and Quality of Ber in Relation with Soil Applications of P & K 271

decrease in titratable acidity with increase in potassium

content.

The application of phosphorus and potassiumsignificantly increased the total sugars content in berfruits (Table 4). The maximum total sugars content wasrecorded in the treatment T8, which was statistically atpar with the treatments T6 and T9. The trend observedfrom Table 4 shows that the increasing levels ofpotassium lead to an increase in total sugars content.The potassium availability facilitates the efflux ofsucrose to apoplast, thereby increasing thetranslocation of sugars to sink tissues (Taiz and Zeiger

16). The results find favour with the previousobservations made by Verbric et al. (17) in ‘Golden

Delicious’ apple trees, and Neva et al. (2008) in appletrees.

Ascorbic acid content of fruits was significantlyincreased with the application of phosphorus andpotassium fertilizers. The maximum ascorbic acid wasrecorded in the treatment T9, which was statistically atpar with the treatments T6 and T8. This significantincrease in the ascorbic acid content was studied byBhati and Yadav (3) and they found that this might bedue to the perpetual synthesis of glucose-6-phosphate,

Table 3 : Effect of application of phosphorus and potassium fertilizers on the fruit physiological parameters

in ber cv. ‘Umran’.

Treatments Fruit Yield(kg/tree)

Fruit Length(cm)

Fruit Breadth (cm)

Fruit weight(g)

Fruit firmness (Kg/cm2)

Fruit drop(%)

T1 81.53 4.56 3.21 22.47 0.92 82.80

T2 79.97 4.59 3.19 23.13 0.92 83.59

T3 79.75 4.54 3.16 21.93 0.91 83.03

T4 83.78 4.62 3.22 23.40 0.85 82.86

T5 84.23 4.64 3.25 23.60 0.85 83.20

T690.75 a 4.71 a 3.39 a 26.40 a 0.83 82.39

T790.08 a 4.75 a 3.29 24.80 a 0.90 80.40 a

T8 86.20 a 4.70 a 3.32 a 24.37 0.92 81.02 a

T9. 83.92 4.67 3.27 23.73 0.91 82.41

Control 77.56 4.48 3.12 20.93 0.97 84.70

CD (P = 0.05) 5.77 0.06 0.09 1.84 0.04 0.68

Table 4 : Effect of application of phosphorus and potassium fertilizers on the fruit biochemical parameters

in ber cv. ‘Umran’.

Treatments TSS (°Brix) Titratable acidity(%)

Total sugars (%) Ascorbic acid(mg/100 g pulp)

T1 14.67 0.30 9.74 80.40

T2 14.53 0.31 9.50 77.70

T3 15.33 0.29 10.09 81.60

T4 15.40 0.30 10.30 82.80

T5 14.73 0.31 9.90 79.20

T6 16.53a 0.29 11.09a 87.00a

T7 15.53 0.28 10.49 82.80

T8 16.33a 0.28 11.26a 84.90a

T9. 15.87a 0.29 10.86a 88.20a

Control 13.93 0.30 9.37 76.80

CD (P = 0.05) 0.84 NS 0.73 4.55

272 Randhawa et al. HortFlora Res. Spectrum, 6(4) : December 2017

which is thought to be the precursor of ascorbic acid,throughout the fruit growth and development. Theseresults are in accordance with the findings of Singh etal. (15) who reported a significant increase in ascorbicacid in ber cv. Gola on application of nitrogen,

phosphorus and potassium.

CONCLUSION

As per the findings of the study conducted, we can conclude that the treatment T6 (2.0 Kg SSP + 1.0 KgMOP) showed the best results with respect to the fruitsize and fruit yield.

REFERENCES1. AOAC (1990). Official Methods of Analysis.

Association of Official Analytical Chemist, 15thEd. Washington, DC, USA.

2. AOAC (2000). Official Methods of Analysis ofAnalytical Chemists. Association of OfficialAnalytical Chemists, Washington, DC, USA.

3. Bhati B. S. and Yadav P. K. (2003). Effect of foliarapplication of urea and NAA on quality of ber(Zizyphus mauritiana Lamk.) cultivar Gola.

Haryana J. Hort. Sci., 32 (1-2): 32-33.

4. Dhatt A. S. (1990). Nutrient management in citruswith special reference Kinnow. In: Gill K S,Kanwar J. S. and Singh R. (ed) Citriculture in N-W India, pp 157-69.

5. Dhatt A. S., Grewal G. P. S. and Dhillon W. S.(1993) Effect of N, P and K treatments on growthand quality of Umran ber (Zizyphus mauritiana

Lamk.). Punjab Hort. J., 33 : 70-75.

6. Dilmaghani M. R., Malakouti M. J., Neilsen G. H.and Fallahi E. (2005). Interactive effects ofpotassium and calcium on K/Ca ratio and itsconsequences on apple fruit quality in calcareous

soils of Iran. J. Plant Nutr., 27 (7): 1149-62.

7. Gill P. P. S., Ganaie M. Y., Dhillon W. S. andSingh N. (2012). Effect of foliar spraysof potassium on fruit size and quality of

‘Patharnakh’ pear. Indian J. Hort., 69 : 512-16.

8. Hudina M. and Stampar F. (2002). Effect ofphosphorus and potassium foliar fertilization on

fruit quality of pears. Acta Hort., 594 : 487-93.

9. Kashyap P., Pramanick K. K., Meena K. K. andMeena V. (2012). Effect of N and K application onyield and quality of pomegranate cv. Ganesh

under rainfed conditions. Indian J. Hort., 69 (3) :322-27.

10. Kumar N., Selvi R,, Meenakshi N. Balasubra-manyan S. and Ebert G, (2006), Foliar application of potash (SOP) on yield and quality of fruits in

mango cv. Alphonso. Intl, Symp, Bal, Fert., 2 :446-48.

11. Mimoun M. B. (2004). Foliar potassiumapplication on olive tree. PI regional workshop onPotassium and Fertigation development in WestAsia and North Africa; Rabat, Morocco, pp 24-28.

12. Neva G., Dechen A. R. and Nachtigall G. R.(2008). Nitrogen and potassium fertilization affect

apple fruit quality in southern Brazil. Commun.

Soil Sci. Plant Analysis, 39 (2) : 96-107.

13. Neilsen G. H., Stevenson D. S., Fitzpatrick J. Jand Brownlee C. H. (1989). Nurition and yield ofyoung apple trees irrigated with municipal waste

water. J. Amer. Soc. Hort. Sci., 114 : 377-83.

14. Sharma K. K. and Singh N. P. (2011). Soil andOrchard Management. Daya Publishing House,New Delhi. pp 342-54.

15. Singh R. R., Chauhan K. S. and Singh H. K.(1986). Effect of various doses of N, P and K onphysico-chemical composition of ber fruit cv.

Gola. Prog. Hort., 18 (1-2) : 35-38.

16. Taiz Z. and Zeiger E. (2004). Plant Physiology..Porto Alegre, Artmed. Pp 23-45

17. Verbric R., Stamper F. and Vodnik D. (2002).influence of the foliar application of phosphorusand potassium on photosynthetic intensity inapple tree (Malus domestica Borkh.). Acta Hort.,

594 : 165-70.

18. Zhong J. H., Yan-an T., Guo W. Z., Lu J. M., XiaoP. L., Guang L. Z. (2002). Effect of potashapplication on the output and quality of DangshanSuli pear variety in loess area. J. Fruit Sci., 19 (1) : 8-11.

q

Citation : Randhawa A.S., Aulakh P.S. and Gill P.P.S. (2017). Growth, productivity and quality of ber (Zizyphusmauritiana Lamk.) cv. ‘Umran’ in relation with soil applications of phosphorus and potassium. HortFlora

Res. Spectrum, 6(4) : 268-272

STUD IES ON EF FECT OF FO LIAR AP PLI CA TION OF BO RON AND GA3 ON

GROWTH, FRUIT ING AND YIELD OF PHALSA (Grewia subinaequalis D.C.)

Mohd. Zeeshan* and J.P. Singh

De part ment of Hor ti cul ture, Chandra Shekhar Azad Uni ver sity of Ag ri cul ture and Tech nol ogy Kanpur-208002

*Cor re spond ing Au thor’s E-mail : [email protected];[email protected]

ABSTRACT : The studies on the effect of foliar application of boron and GA3 on fruiting and yield of phalsa was

under taken in Horticulture Garden of Department of Horticulture CSAUA&T, Kanpur during 2015-16 and2016-17.There were 4 levels each of GA3 and Boron i.e., 0,10,20,30 ppm and 0,30,40,50 ppm, respectivelytried in a Factorial Randomized Block Design with three replications. First foliar application of the treatmentswas given when the flower buds were fully swollen and it was super imposed after three weeks. GA3 inincreasing concentrations maximized fruit set, fruit diameters, fruit weight of 100 fruits, volume of fruit and yield per plant significantly during both the years recording 72.01, 72.74%; 0.92, 0.93 cm; 119.04, 123.13g; 1.25,

1.26 cm3 and 5.39, 5.70 kg values, respectively with 30 ppm of GA3 treatment. Similarly, boron also increased

these attributes with increasing doses showing 70.02, 70.72; 0.89,0.89; 11.058, 114.38g; 1.24, 1.25 cm3 and5.13, 5.40 kg. values, respectively. Interactive effect of 30 ppm GA3 and 50 ppm boron improved all theparameters numerically.

Keywords : GA3 bo ron, fruit ing, yield, phalsa.

Phalsa (Grewia subinaequalis D.C.) belonging tofamily Tiliaceae, is an important minor fruit crop ofIndia. It is native to the Indian sub-continent andSouth-East Asia. The genus contains 140 species ofwhich mostly Grewia subinaequalis is of commercialimportance. Phalsa plant is bushy in nature and bearssmall berry like fruits of deep reddish brown colour.This sub-tropical fruit in central Uttar Pradesh flowersin the month of February and fruit ripen in the secondfortnight of April which continues up to first week of June. Commercially it is grown in Punjab, Haryana,Rajasthan, Uttar Pradesh, Madhya Pradesh and Bihar.Phalsa is a drought resistant and most suitable for hotwastelands, arid and semi-arid regions. Ripe fruits aresub acidic and good source of vitamin A and C. Fruitcontains 50-60% juice,10-11% sugar and 2.25% acidsas reported by Aykroyd (2). Fruits are used for makingexcellent juice and squash. The fruits posses highmedicinal properties. Its ripe fruit exert cooling effects,cure inflammation, heart and blood diseases, fever and constipation (Salunkhe and Desai, 13).The leaves arebelieved to have antibiotic properties hence, applied on skin eruptions.

Uneven ripening, small fruits and high perishability restrict its popularity. Phalsa is generally grown on poor lands where nutrient availability is limited. Under thesecircumstances, role of growth regulators particularly

GA 3 and boron concentrations may prove fruitful in

improving its fruiting and yield as GA 3 is mainly usedfor manipulating many physiological eventsresponsible for enhancing these attributes assuggested by Raghava and Tiwari (11). It would bebetter with foliar feeding of vital nutrient for sustainingthe plant against any nutrient deficiency. Among thetrace elements boron in different concentration hasbeen reported to influence flowering and fruiting ofmany crops,as reported by Ahmadi andMohammadkhani (1) and Kumar et al. (8). It regulatescarbohydrate metabolism and is involved intranslocation of photosynthates, cell wall developmentand R.N.A. metabolism.

In view of the above facts an experiment entitlled‘‘studies on foliar application of boron and GA 3 ongrowth, fruiting and yield of phalsa (Grewiasubinaequalis D.C.)’’ was planned to ascertaininfluence of GA 3 and boron concentrations on fruitingand yield of phalsa.


The present investigation was carried out inHorticulture Garden of Department of HorticultureChandra Shekhar Azad University of Agriculture andTechnology Kanpur during two consecutive years i.e.2015-16 and 2016-17. Kanpur is geographicallysituated in Gangetic plains of Uttar Pradesh. Theclimate is sub-tropical characterized by hot dry summer and cold winter. The rainfall is mostly received fromJuly to September with scattered showers in winter.



Article’s History:Received: 09-11-17 Accepted: 12-12-17

NAAS Rating : 3.78

274 Zeeshan and Singh HortFlora Res. Spectrum, 6(4) : December 2017

There were sixteen treatments comprising fourconcentrations each of GA 3 (0,10,20 and 30 ppm) andboron (0,30,40 and 50 ppm) tried in FactorialRandomized Block Design replicating thrice. Theexperiment was conducted on well established phalsaplants of Sharbati variety. Plants received culturalpractices and plant protection requirement includingweeding hoieng and irrigation timely. Uniform dose(R.D.F.) along with F.Y.M. @10 kg per bush wasapplied during both the years. Pruning of phalsa shoots was done on December, 20 during both the years ofstudy. After pruning, profuse swallen buds appear. First spraying of GA 3 and boron was given at this stage (5February) and second spraying was super imposedafter three weeks during both the years under eachreplication as per schedule with a foot sprayer havingfine nozzle. Flowers and set fruits were counted on the

tagged shoots produced to ascertain percentage of fruit set. The diameter of ripe fruits at harvest wasmeasured with the help of vernier calipers. The weightof 100 fruits was recorded under each treatment on aphysical balance. Volume of phalsa fruits was workedout by water displacement method.The weight of eachharvest of different replication were summed up todetermine the yield per plant.


Effect of GA 3

A perusal of Table 1 revealed that foliar application of GA3 in increasing concentrations increased fruit setin phalsa during both the years of investigation.Treatment of 30 ppm exerted significant response ascompared to rest of doses during both the years. Therewas 72.01 and 72.74 percent fruit set in first andsecond years of trial against the minimum recorded63.71 and 64.34 percent under control respectively.The results are in agreement with the reports of Syamal and Chhonker (18) in aonla, Singh et al. (15, 16) inPhalsa and Gogoi et al. (4) in Assam lemon.

The diameter of phalsa fruit measured at harvestindicated that highest concentration of GA 3 producedbiggest fruits (0.92 and 0.93 cm) during both first andsecond years of trial, respectively. It is obvious from the data that 30 ppm being statistically at par with 20 ppmduring both the years proved significantly moreeffective than control and 10 ppm treatment. Smallestberries were, howewer, registered under control (0.76and 0.77 cm), respectively. The weight of 100 berriesas influenced by GA 3 doses varied significantly whichprogressively increased with increasing concentra-tions.

The findings are in conformity with the reports ofSingh et al. (15 and16) in Phalsa, Khot et al. (7) ingrape and Gill and Bal (3) in ber. Application of 30 ppm

GA 3 remaining at par with 20 ppm level during both the years recorded highest of 119.04 and 123.13 g fruitweight against the minimum of 89.78 and 92.85gregistered under control, respectively. All GA 3 dosestried in the present investigation proved significantlyeffective in this regard under both the years of trial.Theresults are in line with the reports of Singh et al. (15)and Kacha et al. (5) in Phalsa. The volume of fruit wasdetermined at harvest by water displacement method.

GA 3 30 ppm obviously recorded greater fruit volume1.25 and 1.26 cc respectively. However, 20 and 30 ppm did not differ significantly in former year but it excelledrest of the treatment in respect of fruit volume in thelater year. The fruits under control showed significantlylesser volume 1.164 and 1.182 cc obviously owing tosmaller berries. The finding of the present investigation are in accordance with the reports of Masalkar andWavhal (9) and Sandhu et al. (14) in ber.

The data on the sum of accelerated yield indicated that increasing concentrations increased yield ofberries significantly with the maximum of 5.395 and5.700 kg per bush under 30 ppm against the minimumof 3.355 and 3.713 kg recorded under control duringrespective years.All the levels of GA 3 gave significantly higher yield as compared with control. The findings arein agreement with the reports of Singh et al. (15) andKacha et al. (5) in Phalsa, Gill and Bal (3) and Katiyaret al. (6) in ber and Rajput et al. (12) in guava.

The application of GA 3 in the presentinvestigation has given marked increase in fruitset,size,weight,volume of fruit and yield per plant inphalsa.All of these attributes enhanced progressivelywith increasing concentrations.The maximum values

were noted when the plants were treated with 30 ppm

GA 3.

Gibbrellic acid is effective in manipulatingphysiological event e.g. fruit set, weight and size offruits which obviously influence the yield. Now it iscommercially used to improve the fruiting and yieldattributes in many crops like grape, ber, citrus, aonlaand phalsa. Its significant use has been exploited in cell elongation, formation of chlorophyll and photosyntheticactivities, Singh et al. (16) and Katiyar et al. (6) alsoemphasized the beneficial improvement due to GA 3treatment.

Effect of Boron

Foliar application of boron progressivelyincreased the fruit set with increasing concentration

Studies on Effect of Foliar Application of Boron and GA3 on Fruiting and Yield Traits of Phalsa 275

during both the years of study. Treatment of Boron (50ppm) gaved significantly higher fruit set remaining atpar with its immediately lower level i.e. 40 ppm. It wassignificantly maximum to the tune of 70.03 and 70.72%during first and second years of trial respectively.Theplants under control expressing 66.87 and 67.55% setbeing significantly lowest remained statistically at parwith 30 ppm treatment during respective years of study. Similar results have been reported by Katiyar et al. (6)and Ahmadi and Mohammadkhani (1) in ber andPrasad et al. (10) in guava.

The fruit size in term of diameter was recordedhighest (0.885 and 0.895 cm) under 50 ppm treatmentduring first and second years of trial, respectively being statistically at par with 40 ppm treatment. Untreatedplants registered lowest fruit diameter (0.82 and 0.83cm) remaining at par with 30 ppm treatment. Asregards the weight of 100 fruits the trend was quitesimilar to fruit size recording maximum fruit weightunder 50 ppm treatment (110.58 and 114.38 g)

remaining at par with 40 ppm treatment under both thetrials. Plants under control being at par with 30 ppmshowed minimum fruit weight (99.78 and 103.21 g).The fruit weight, however,was greater under second yearas compared to respective treatment of first year.Trendof increase in fruit volume obviously tallied with fruitsize. The plants under control during both the yearsremaining at par with 30 ppm gave minimumvalues(1.190 and 1.200 cc). Similarly,40 and 50 ppmwere noted statistically at par when compared witheach other exhibiting 1.22,1.23 and 1.24,1.25 volume

respectively. The data on the yield of phalsa fruit ofdifferent harvest were summed up during both theyears. The variation in yield of different treatments ofboron was quite similar to size and weight of fruit.Boron (50 pmm) treatment recording 5.125 and 5.395kg yield per plant did not differ with its immediatelylower level (5.010 and 5.258 kg).As exhibited by theabove parameters plants under control showing 4.213and 4.430 kg yield did not differ with 30 ppm treatment

Table 1 : Effect of boron,GA3 and their interaction on fruit set percentage, diameter of fruit and fruit weight

of phalsa (Grewia subinaequalis D.C.).

Treatment Fruit set percentage

2015-16 2016-17

B G

B0 (Control) B1

(30ppm)

B2

(40ppm)

B3 (50ppm)

Mean B0

(Control)

B1

(30ppm)

B2

(40ppm)

B3 (50ppm)

Mean

G0 (Control) 62.15 63.30 64.55 64.85 63.71 62.75 63.91 65.20 65.50 64.34

G1 (10ppm) 66.75 68.45 70.35 70.95 69.13 67.40 69.15 71.10 71.65 69.83

G2 (20ppm) 68.15 69.40 70.75 71.15 69.86 68.90 70.15 71.50 71.90 70.61

G3 (30ppm) 70.45 71.65 72.80 73.15 72.01 71.15 72.40 73.55 73.86 72.74

Mean 66.88 68.20 69.61 70.03 67.55 68.90 70.34 70.72

C.D.(P = 0.05)

B G B×G B G B×G

1.76 1.76 NS 1.91 1.91 NS

Diameter of fruit (cm)

G0 (Control) 0.74 0.76 0.77 0.78 0.76 0.75 0.77 0.78 0.79 0.77

G1 (10ppm) 0.81 0.83 0.86 0.87 0.84 0.82 0.89 0.87 0.88 0.87

G2 (20ppm) 0.85 0.88 0.91 0.92 0.89 0.86 0.89 0.92 0.93 0.90

G3 (30ppm) 0.87 0.91 0.94 0.97 0.92 0.88 0.92 0.95 0.98 0.93

Mean 0.82 0.85 0.87 0.89 0.83 0.87 0.88 0.89

C.D.(P = 0.05)

B G B × G B G B×G

0.04 0.04 NS 0.04 0.04 NS

Weight of 100 fruits

G0 (Control) 85.25 88.65 91.75 93.50 89.79 88.15 91.67 94.88 96.70 92.85

G1 (10ppm) 91.75 94.80 98.25 100.65 96.36 94.95 98.20 101.65 104.10 99.73

G2 (20ppm) 109.65 115.40 119.35 122.40 116.70 113.45 119.35 123.45 126.60 120.71

G3 (30ppm) 112.45 116.30 121.65 125.75 119.04 116.30 120.35 125.80 130.10 123.14

Mean 99.78 103.79 107.75 110.58 103.21 107.39 111.45 114.38

C.D.(P = 0.05)

B G B×G B G B×G

4.54 4.54 NS 5.62 5.62 NS

276 Zeeshan and Singh HortFlora Res. Spectrum, 6(4) : December 2017

under both the years of study.Above results are in linewith the reports of Kumar et al. (8) in guava,Verma etal. (19) and Shukla et al. (17) in aonla.

Many functions have been ascribed to B becauseof its presence especially at the growing points and inthe conducting tissues. This element does not being apart of enzyme but it may influence the effect ofactivities of several enzymes such as oxidase andsucrose. Boron also plays a part in flowering and

fruiting processes, nitrogen metabolism, harmonemovement and cell division. Improvement in floweringand fruiting due to boron has been studied by Verma etal. (19) and Kumar et al. (8).

Interactive effect of boron and GA 3 showingnumerical improvement in all the parameters failed totouch the level of significance. Application of 50 ppmboron in conjuction with 30 ppm GA 3, however,registered maximum values in respect of fruit set,diameter, weight and volume of fruit and yield per plantduring both the years of study. Plants under controlexpressed poor fruit set, lowest yield and yieldattributes during both the year of experiment. Thesefindings are inconformity with the reports of Shukla etal. (17) in aonla and Rajput et al. (12) in guava.

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Table 2 : Effect of boron, GA3 and their interaction on volume (cm3) and yield of phalsa fruit per plant.

Treatment Volume of fruit (cm3)

2015-16 2016-17

B G

B0

(Control)

B1

(30ppm)

B2

(40ppm)

B3 (50ppm)

Mean B0

(Control)

B1

(30ppm)

B2

(40ppm)

B3 (50ppm)

Mean

G0 (Control) 1.140 1.227 1.190 1.200 1.164 1.150 1.170 1.197 1.210 1.182

G1 (10ppm) 1.170 1.190 1.200 1.220 1.195 1.180 1.200 1.220 1.230 1.207

G2 (20ppm) 1.220 1.230 1.240 1.250 1.235 1.230 1.240 1.250 1.260 1.245

G3 (30ppm) 1.230 0.240 1.260 1.270 1.250 1.240 1.250 1.270 1.280 1.260

Mean 1.19 1.20 1.22 1.24 1.20 1.22 1.23 1.25

C.D.(P = 0.05)

B G B×G B G B×G

0.032 0.032 NS 0.022 0.022 NS

Yield/plant(kg)

G0 (Control) 3.150 3.450 3.750 3.830 3.355 3.300 3.610 3.930 4.010 3.713

G1 (10ppm) 4.250 4.650 5.060 5.170 4.782 4.450 4.870 5.300 5.420 5.010

G2 (20ppm) 4.650 5.080 5.530 5.660 5.230 4.870 5.320 5.800 5.950 5.485

G3 (30ppm) 4.800 5.240 5.700 5.840 5.395 5.10 5.50 6.00 6.20 5.70

Mean 4.213 4.605 5.010 5.125 4.430 4.825 5.258 5.395

C.D.(P = 0.05)

B G B × G B G B×G

0.463 0.463 NS 0.525 0.525 NS

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q

Citation : Zeeshan M. and Singh J.P. (2017). Studies on effect of foliar application of boron and GA 3 on fruiting

and yield traits of phalsa (Grewia subinaequalis D.C.). HortFlora Res. Spectrum, 6(4) : 273-277

GE NETIC VARI ABIL ITY, CHAR AC TER AS SO CI A TION AND PATH CO EF FI -

CIENT ANAL Y SIS IN CHINA AS TER [Callistephus chinensis (L.) Nees]

Pratiksha Kumari*, Rajiv Kumar, T. Manjanatha Rao, M.V. Dhananjaya and V. Bhargav

Di vi sion of Flori cul ture and Me dic i nal Crops, ICAR-In dian In sti tute of Hor ti cul tural Re search, Hesaraghatta Lake Post, Bengaluru, Karnataka 560 089


ABSTRACT : Eight genotypes of China aster were evaluated to determine genetic variability, heritability,correlation and path coefficient analysis, for 13 growth, flowering and post-harvest traits. Significantdifferences among genotypes for all the traits were observed through analysis of variance. Higher genotypicand phenotypic coefficient of variation was recorded for number of leaves/plant and number of rayflorets/flower head. High heritability (>60%) was recorded for all traits. The genetic advance ranged from 0.97(flower head diameter) to 143.01 (number of leaves/plant). High genetic gain was recorded for number ofleaves/plant and number of ray floret/flower head. Weight of flowers/plant was significant and positivelycorrelated both at genotypic and phenotypic level for earliness, duration of flowering, and number of flowersper plant, 100 flower weight and shelf life. Path coefficient analysis using correlation coefficients revealed that100 flowers weight contributed highest positive direct effect on weight of flowers/plant followed by number ofleaves/plant and number of flowers/plant. This study suggests that effective selection for desirable traits canbe achieved in China aster.

Keywords : China as ter, GCV, PCV, Heritability, Cor re la tion, Path co ef fi cient anal y sis.

China aster is an important commercial cropgrown for cut flowers, loose flowers and for landscapegardening. It belongs to the family Asteraceae and isnative of China. The genus Callistephus derives itsname from two Greek words ‘Kalistos’ and ‘Stephos’meaning ‘most beautiful’ and ‘a crown’, respectively.The botanist Cassini described China aster asCallistephus hortensis and Linnaeus named it as Asterchinensis. However, the present name of China asterhas been given by Nees.

Crop improvement of China aster is essential forincreasing the varietal wealth. Development of newervarieties with improved traits will open novel vistas forcommercial cultivation of China aster. Success of cropimprovement program depends on the extent ofgenetic variability prevailing in the germplasm.Understanding of the variability present in the availablegenotypes will lead to effective selection. It isnecessary to work out observed variability intophenotypic coefficient of variation and genotypiccoefficient of variation, which ultimately indicates theextent of variability existing for various traits.

The genetic variability in terms of GCV and PCV is not sufficient enough for determination of heritablevariability. In addition, estimation of heritability andgenetic gain is also needed to assess the extent of

genetic gain expected from effective selection. Asheritability in broad sense includes both additive andepistatic gene effects, it will be reliable only when it isaccompanied with high genetic advance (Burton, 5 and Johnson et al., 8).

Relationships among different traits throughcorrelation co-efficient at genotypic and phenotypiclevels as well as direct and indirect effects of traits onweight of flowers per plant through path analysis areessential to understand character association.


The present investigation was conducted at theexperimental field of Division of Floriculture andMedicinal Crops, ICAR-Indian Institute of HorticulturalResearch, Hesaraghatta Lake Post, Bengaluru duringthe year 2016-17. Eight genotypes namely ArkaKamini, Arka Poornima, Arka Aadya, Arka Archana,Arka Violet Cushion, Arka Shashank, Local Violet andLocal Pink were evaluated for thirteen vegetativegrowth, flower quality and postharvest traits. Allgenotypes are being maintained at ICAR-IIHR,Bengaluru. The experiment was laid out in randomizedcompletely block design under open field conditions.The 30 days old seedlings with 4 to 5 leaves of all eightgenotypes were planted at a spacing of 30 cm × 30 cm.

The observations were recorded on plant height (cm),number of leaves/plant, number of branches/plant,days to first flowering, flower head diameter (cm),



Article’s History: Received : 25-11-17 Accepted: 04-12-17

NAAS Rating : 3.78

Genetic Variability, Character Association and Path Coefficient Analysis in China Aster 279

flower stalk length (cm), number of ray florets/perflower head, duration of flowering, number of flowers/plant, weight of flowers/plant (g), 100 flowers weight(g), vase life (days) and shelf life (days). Data collectedwere averaged and analyzed statistically.

The genotypic and phenotypic coefficients ofvariance were calculated as suggested by Burton andDeVane (6) and heritability (broad sense), geneticadvance and genetic gain were calculated using theformulae given by Johnson et al. (8). The correlationsand path analysis were calculated as suggested byAl-Jibouri et al. (1) and Dewey and Lu (7), respectively.


GCV, PCV, heritability and genetic gain

The estimates of genetic parameters likephenotypic coefficient of variation (PCV), genotypic

coefficient of variation (GCV), heritability (h2 broad

sense), genetic advance and genetic gain arepresented in Table 1. The analysis of variance revealed highly significant variation among the genotypes for allthe characters.

Phenotypic coefficient of variation was recordedhigher than genotypic coefficient of variation for all thecharacters which indicated influence of environment for the expression of these character or genotype andenvironmental interactions. Negi et al. (12), Kumar andPatil (11) and Khangjarakpam et al. (10) also reportedhigher PCV over GCV. Narrow differences betweengenotypic and phenotypic coefficients of variation wererecorded for all the characters except flowering

duration, number of flowers per plant and shelf lifeindicating little environmental influence on theexpression of characters.

Higher phenotypic and genotypic coefficients ofvariation were observed for number of leaves per plant(35.93 and 36.08, respectively) followed by number ofray floret per flower head (31.90 and 31.96,respectively). The values for these estimates werefound to be low for flower head diameter (8.71 and8.75, respectively) and duration of flowering (15.33 and 16.40, respectively). The genotypic coefficient ofvariation alone is not enough to measure the heritablevariations. The genotypic coefficient of variationtogether with heritability estimates gives the bestpicture of the amount of advance to be expected fromany selection (Burton, 5).

High heritability estimates were observed for all13 traits (Table 1). Highest heritability was recorded for100 flowers weight (99.87%) followed by weight offlowers per plant (99.66%). Rai et al. (15) reported high heritability (>80%) estimates for various traits exceptfor number of harvests of loose flower. Khangjarakpamet al. (9) also reported high heritability (>60%) forvarious vegetative and flowering traits except floweringduration in China aster.

The estimates of genetic gain were comparativelyhigher for number of leaves per plant (73.72%),number of ray floret per flower head (65.58%) andweight of flowers per plant (62.36%), however,minimum genetic gain was recorded for flower headdiameter (17.85%). High heritability and geneticadvance as per cent over mean was observed for most

Table 1 : Estimates of genetic parameters for 13 quantitative traits in China aster.

Characters Mean Range GCV PCV Heritability(%)

Geneticadvance

Genetic gain(%)

Plant height (cm)53.81 41.88-65.71 14.43 14.83 94.72 15.57 28.93

Number of leaves/plant193.98 47.82-262.69 35.93 36.08 99.20 143.01 73.72

Number of branches/plant13.39 9.74-16.70 17.92 18.22 96.71 4.86 36.30

Days to first flowering79.20 61.15-98.80 15.24 15.45 97.36 24.54 30.99

Flower head diameter (cm)5.45 4.72-6.10 8.71 8.75 99.03 0.97 17.85

Flower stalk length (cm)41.42 31.97-56.47 20.12 20.62 95.19 16.75 40.44

Number of ray floret/flower head101.56 33.55-147.10 31.90 31.96 99.61 66.60 65.58

Duration of flowering (days)16.00 13.51-21.37 15.33 16.40 87.40 4.72 29.53

Number of flowers/plant55.75 40.93-87.65 25.72 26.32 95.54 28.88 51.79

Weight of flowers/plant (g)170.10 98.46-260.35 30.33 30.38 99.66 106.08 62.36

100 flowers weight (g)309.56 230.50-379.50 18.48 18.49 99.87 117.77 38.05

Vase life (days)7.64 6.05-9.57 13.42 13.59 97.52 2.09 27.29

Shelf life (days)3.60 2.84-5.15 21.09 22.57 94.72 1.48 40.57

280 Kumari et al. HortFlora Res. Spectrum, 6(4) : December 2017

of the characters studied. Hence, simple selectionsmay be useful for improving these characters.

Negi et al. (12) reported high heritability with highgenetic gain for flower weight and stalk length in Chinaaster. High heritability along with high genetic advanceas per cent of mean for weight of flowers per plant(Kumar and Patil, 11), flower diameter and number ofray florets/flower head (Raghava and Negi, 14) andplant height, flower stalk length and number ofbranches/plant (Ashwath and Parthasarathy, 2) hasalso been reported in China aster.

Phenotypic and genotypic correlationcoefficients and path correlation

Phenotypic and genotypic correlation coefficientsfor various traits in China aster are presented in Table2. The overall genotypic correlation coefficients were

found to be higher than phenotypic correlationcoefficients. This indicates the interaction of genotypesand environment. These correlation coefficientsprovide a measure of relationship among traits and areuseful for selection of genotype. Weight of flowers/plant was taken as a measure of flower yield. Theweight of flowers/plant was significantly and positivelycorrelated to duration of flowering, number of flowersper plant, 100 flower weight and shelf life. It wassignificantly and negatively correlated to days to firstflowering.

Days to first flowering are a negative traits, sinceearly flowering is preferred over late flowering. Similarresults have been reported by Khangjarakpam et al.(9). Days to first flowering showed significant negativecorrelation with number of branches per plant, numberof flowers per plant and shelf life. It was significantly

Table 2 : Phenotypic and genotypic correlation matrix for 13 quantitative traits in China aster.

Character 1 2 3 4 5 6 7 8 9 10 11 12 13

1 P G

1.000

1.000

2 P G

-0.124NS

-0.143NS

3 P G

-0.781**

-0.785**

0.453NS

0.475NS

4 P G

0.580*

0.584*

0.085NS

0.078NS-0.653**

-0.663**

5 P G

-0.215NS

-0.236NS0.055NS

0.054NS0.111NS

0.114NS

-0.104NS

-0.104NS

6 P G

0.650**

0.677**0.320NS

0.316NS

-0.396NS

-0.401NS

0.631**

0.649**

-0.665**

-0.684**

7 P G

-0.043NS

-0.050NS-0.323NS

-0.329NS

-0.301NS

-0.303NS

-0.023NS

-0.029NS

0.629**

0.632**

-0.431NS

-0.449NS

8 P G

0.181NS

0.234NS-0.284NS

-0.305NS

-0.125NS

-0.157NS

0.011NS

0.019NS

-0.378NS

-0.401NS0.324NS

0.345NS0.186NS

0.202NS

9 P G

-0.447NS

-0.469NS0.359NS

0.376NS0.533*

0.539*-0.521*

-0.522*0.045NS

0.034NS-0.167NS

-0.165NS0.102NS

0.105NS0.317NS

0.389NS

10 P G

-0.249NS

-0.249NS-0.023NS

-0.022NS

0.302NS

0.306NS

-0.583*

-0.589*

-0.022NS

-0.020NS-0.170NS

-0.174NS0.365NS

0.366NS0.645**

0.694**0.823**

0.841**

11 P G

0.259NS

0.264NS -0.638**

-0.641**

-0.297NS

-0.303NS-0.293NS

-0.295NS-0.035NS

-0.037NS-0.122NS

-0.122NS0.537*

0.539*0.611*

0.655**-0.004NS

-0.007NS0.545*

0.548*

12 P G

0.052NS

0.062NS-0.574*

-0.584*

-0.060NS

-0.073NS-0.230NS

-0.233NS-0.636**

-0.646**0.079NS

0.060NS-0.470NS

-0.477NS0.329NS

0.343NS-0.017NS

-0.020NS0.089NS

0.093NS0.209NS

0.211NS

13 P

G

-0.409NS

-0.454NS

-0.469NS

-0.495NS0.316NS

0.330NS-0.648**

-0.673**0.226NS

0.218NS-0.596*

-0.616*0.488NS

0.528*0.409NS

0.434NS0.253NS

0.245NS0.600*

0.641**0.710**

0.756**0.128NS

0.128NS1.0001.000

Note 1: Plant height (cm), 2: Number of leaves/plant, 3: Number of branches/plant, 4: Days to first flowering, 5: Flower head diameter (cm), 6: Flower stalk length (cm), 7: Number of ray floret/flower head, 8: Duration of flowering, 9: Num- ber of flowers/plant, 10: Weight of flowers/plant (g), 11: 100 flowers weight (g), 12: Vase life (days), 13: Shelf life(days)

Genetic Variability, Character Association and Path Coefficient Analysis in China Aster 281

and positively correlated with plant height and stalklength. It is understood that plants with delayedflowering attain more height with longer stalk. Similarly,number of branches/plant was found to be significantlypositively correlated to plant height, days to firstflowering and number of flowers/plant. These resultsare in confirmation with the findings of Sharma (16) inmarigold, and Sreenivasulu et al. (17) in China aster.

Considering weight of flowers per plant to be adependent trait, phenotypic and genotypic coefficientsof correlation between weight of flowers/plant andother traits were further partitioned into direct andindirect effects (Table 3). On portioning the phenotypic correlation into direct and indirect effects, maximumpositive direct effect on weight of flowers per plant wasrecorded for 100 flowers weight (1.358) followed bynumber of leaves per plant, number of flowers per plant and days to first flowering, vase life and flower headdiameter.

Maximum negative direct effect was recorded forplant height followed by number of branches per plant,number of ray florets per flower head and flower stalklength. It can be attributed that the taller plants witherect growth habit led to lower yield in comparison to

short spreading type plants. For genotypic contributiontoo, highest positive and direct contribution wasrecorded for 100 flowers weight, followed by number ofleaves per plant, number of flowers per plant, flowerhead diameter, days to first flowering and vase life.Whereas, number of branches per plant recorded themaximum negative direct effect on weight of flowersper plant, followed by plant height, number of ray florets per flower head and days to first flowering. The resultssuggest that both magnitude and direction ofcontribution varied. Similar results have been reportedby Baweja (3), Phule (13) in China aster and Bharathiet al. (4) in marigold.

CONCLUSION

The present study revealed existence ofconsiderable variation among China aster genotypesand presence of highly heritable traits such as numberof leaves per plant, number of ray florets per plant,number of flowers per plant and weight of flowers perplant, which can be used for developing newgenotypes with superior growth and flowering traits inChina aster.

Table 3 : Phenotypic and genotypic direct and indirect effects among different quantitative traits in China

aster.

Character 1 2 3 4 5 6 7 8 9 10 11 12

1 P G

-0.743-0.742

-0.110-0.129

0.5750.586

0.1580.163

-0.063-0. 076

-0.239-0.251

0.0260.032

0.0350.044

-0.299-0.313

0.3520.369

0.0140.017

0.0470.052

2 P G

0.0930.106

0.8870.899

-0.334-0.354

0.0230.022

0.0160.017

-0.118-0.117

0.1910.208

-0.055-0.057

0.2400.251

-0.867-0.894

-0.152-0.160

0.0540.056

3 P G

0.5810.583

0.4020.427

-0.736-0.746

-0.177-0.185

0.0320.037

0.1460.148

0.1780.192

-0.024-0.029

0.3560.360

-0.404-0.422

-0.016-0.020

-0.036-0.038

4 P G

-0.432-0.434

0.0760.070

0.4810.495

0.2720.280

-0.030-0.034

-0.232-0.240

0.0140. 018

0.0020.004

-0.349-0.349

-0.398-0.411

-0.061-0.064

0.0740.076

5 P G

0.1600.175

0.0490.049

-0.082-0.085

-0.028-0.029

0.2910.323

0.2450.253

-0.372-0.400

-0.073-0.075

0.0300.023

-0.047-0.051

-0.169-0.177

-0.026-0.025

6 P G

-0.483-0.503

0.2840.284

0.2910.299

0.1710.181

-0.194-0.221

-0.368-0.370

0.2550.284

0.0620.064

-0.112-0.110

-0.166-0.171

0.0210.016

0.0680.070

7 P G

0.0320.037

-0.286-0.296

0.2220.226

-0.006-0.008

0.1830.204

0.1590.166

-0.592-0.633

0.0360.038

0.0680. 070

0.7300.752

-0.125-0.131

-0.056-0.060

8 P G

-0.135-0.173

-0.252-0.275

0.0920.117

0.0030.005

-0.110-0.129

-0.119-0.127

-0.110-0.128

0.1930.186

0.2120.260

0.8310.913

0.0870.094

-0.047-0.049

9 P G

0.3330.348

0.3180.338

-0.392-0.403

-0.142-0.146

0.0130.011

0.0620. 061

-0.060-0.066

0.0610.072

0.6690.667

-0.005-0.010

-0.004-0.006

-0.029-0.028

10 P G

-0.193-0.196

-0.566-0.576

0.2190.226

-0.080-0.082

-0.010-0.012

0.0450.045

-0.318-0.341

0.1180.122

-0.002-0.005

1.3581.395

0.0550.058

-0.081-0.086

11 P G

-0.039-0.046

-0.509-0.525

0.0440.054

-0.062-0.065

-0.185-0.209

-0.029-0.022

0.2780.302

0.0630.064

-0.011-0.014

0.2830.294

0.2650.274

-0.011-0.015

12 P G

0.3040.337

-0.415-0.445

-0.232-0.247

-0.176-0.188

0.0660.070

0.2190.228

-0.289-0.334

0.0790.081

0.1690.163

0.9651.054

0.0250.035

-0.114-0.114

Note 1: Plant height (cm), 2: Number of leaves/plant, 3: Number of branches/plant, 4: Days to first flowering, 5: Flower head diameter (cm), 6: Flower stalk length (cm), 7: Number of ray floret/flower head, 8: Duration of flowering, 9: Num- ber of flowers/plant, 10: Weight of flowers/plant (g), 11: 100 flowers weight (g), 12: Vase life (days), 13: Shelf life(days)

282 Kumari et al. HortFlora Res. Spectrum, 6(4) : December 2017

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16. Sharma P. (2014). Evaluation of genotypes ofFrench marigold (Tagetes patula L.) under Nauni, Solan conditions. M.Sc. Thesis, Dr. Y. S. ParmarUniversity of Horticulture and Forestry, Nauni,Solan.

17. Sreenivasulu G. B., Kulkarni B. S., Nataraj S. K.,Reddy B. S., Naik K. M. and Chandan K. (2007).Correlation studies for yield and yield contributingcharacters in China aster (Callistephus chinensis

Nees.). Asian J. Hort., 2 (2) : 192-194.

Citation : Kumari P., Kumar R., Rao T.M., Dhananjaya M.V. and Bhargav V. (2017). Genetic variability, characterassociation and path coefficient analysis in China aster [Callistephus chinensis (L.) Nees]. HortFlora

Res. Spectrum, 6(4) : 278-282

POP U LAR IZA TION OF PRO TEC TIVE GLOVES THROUGH DEM ON STRA TIONS

Rajdeep Kaur1 and Dimpy Raina 2

1Deptt of Ap parel and Tex tile Sci ence, PAU, Ludhiana2KVK Ferozepur, PAU, Ludhiana


ABSTRACT : Front line demonstrations conducted under the close supervision of scientists is one of theimportant tools of extension to demonstrate new innovations at farmers’ field. The present study is an attemptto compare the farmer’s practice with the demonstrated technology. A total sample of fifty five respondents who had been given front line demonstrations was selected to get the required information. The farmer does theplucking of thorny vegetables bare handedly. All the respondents had problems of mechanical injury (cuts andrashes) and skin problems. All the respondents working on different operations of chilli faced the problem ofeyes and nose like sneezing, while 54.17% suffered from respiratory problems. Protective gloves were givento these selected farmers to get rid of the rashes and other associated problems. All the respondentsperformed plucking activity with the protective gloves. Sorting, grading and weeding activities were alsoperformed by wearing these gloves. The respondents reported that the gloves protected the skin of thewearer, provided abrasion protection, were easy to don and doff, they felt ease while working and the gloves

provided protection against prickly nature of the plant.

Keywords : Gloves, okra, pluck ing, protection.

The history of Indian agriculture is marked withrepeated challenges and the ways and means toovercome them. The Krishi Vigyan Kendras (KVKs)have been addressing these challenges effectively atgrassroot level along with farmers. To address theissues related to technology dissemination inagriculture, the KVK, a grassroot level scheme hasbeen designed. One of the mandates of the KVKs istechnology dissemination through frontline

demonstrations.

Vegetable cultivation has become highly commer-cialized. With the view to achieve high level ofproduction, it is not only enough to develop farminnovations but is also necessary to transfer the latesttechnology from the research system to ultimate usersi.e., farmers and farm women. The nature and extent of the involvement of Indian rural women in agriculturalactivities differ with the variations in agro-productionsystems. The women perform the maximum farmoperations thereby contributing a lot towards theupliftment of the economic and social status of theirfamilies and finally, accelerating the pause of ruraldevelopment (Singh, 3). They play a significant andcrucial role in vegetable production which includesmost of the activities done with the vegetables. About60% of agricultural operations like sowing seeds,transportation of sapling, winnowing, storage of grains

etc are handled exclusively by women (Aggarwal1).Women work is getting harder and more timeconsuming due to ecological degradation andchanging agriculture technology and practices.

Taking into account the above considerations,frontline demonstrations (FLD) were carried out in asystematic manner on farmer’s field to show the worthof the new technology and convincing the farmers toadopt protective gloves for plucking of vegetables.Looking into the problems of the vegetable pluckers the study was conducted to compare the farmer’s practicewith the demonstrated technology.


Front line demonstrations on the use of protectivegloves for plucking of vegetable crops were conductedduring the years 2014-2016 at farmer’s fields inFerozepur. These protective gloves were full armlength having double layer on the inner side made fromknitted fabric. Simple purposive sampling techniquewas used to select the respondents. A total sample offifty five respondents who had been given FLDs wasselected to get the required information. Thirty onerespondents were working on okra, cucurbits, brinjal,corn fields while twenty four respondents were workingon chilli fields. The workers working on these fieldswere personally contacted to inquire the difference felt

by them with the use of the demonstrated technology.They were asked to provide unbiased and independent opinions regarding the desired information needed for




NAAS Rating : 3.78

284 Kaur and Raina HortFlora Res. Spectrum, 6(4) : December 2017

this study. A structured schedule was used to collectthe data by personal interview method. The datacollected was processed, tabulated and presented inthe form of table. To find out the acceptability of theprotective gloves, evaluation of different attributes wasdone on 3 point scale with scores designated as 3 for

highly satisfied, 2 for satisfied and 1 for not satisfied.


Frontline demonstration technology andfarmers practice

The data in Table 1 shows the comparisonbetween the front line demonstration (FLD) andfarmer’s practice and it was noticed that protectivegloves were brought into use under the guidance ofKVK scientist as earlier the plucking of vegetable cropswas done barehanded. The respondents experiencedinjury on their hands while performing various activitiesin okra, brinjal, cucurbits, corn and chilli crops withoutthe use of gloves. The yield of the crop with the use ofthese gloves increased by 1 kg per hour per person asthe speed of plucking increased. In a study by Bains etal, (2). It was observed that the speed of plucking okraremained same as compared to while wearingconventional plastic gloves and only a few (16%)reported that the speed of plucking okra increasedwhile wearing knitted gloves (Bains et al., 2). It wasdifficult for the respondents to pluck the vegetablewhen the temperature was beyond 30 oC and humiditywas more, whereas these gloves protected the armsand hands of the users from high temperature and

humid weather.

It was all the more difficult for the respondents tocarry out household activities after working in thesecrops due to the prickly nature of the plants. Thecondition of the female respondents was worse thanthe males as they had to perform household choressuch as cooking, washing clothes, dishes etc. It wasfound that with the use of these protective gloves thephysiological condition of the respondents during andafter work remained good. The physical activity tooremained strenuous with the farmers practice as theirarms were exposed to the plant directly whereas it wasless strenuous after using gloves as arms were fullycovered with cotton knitted gloves.

Table 1 : Comparison between demonstrated

technology and farmer’s practice.

Intervention Demonstratedtechnology

Farmer’spractice

Plucking of crop With protectivegloves

Bare handed

Problem faced - Injury on hands

Yield (kgs/ hr/person)

2-4 kg/ hr/ person 1-3 kg/ hr/ person

Speed ofplucking

Increased Less as comparedwith use of gloves

Temperature Protected arms and hands

Difficult to workabove 30 oC

RelativeHumidity

No ill effect ofhumidity with theuse of gloves

Very difficultwhen humiditywas more

PhysiologicalCondition duringwork

Good Became difficult to carry outhouseholdactivities

Physical activity Less strenuouswith the use ofgloves

Strenuous

Yield of okra, cucurbits, brinjal and corn on anaverage (Table 2) was 1-3 kg/ acre per person while itwas 2-3 kg/ acre per person for chilli crop during greenstage of chilli (Table 2). The respondents worked fortwo to six hours daily for plucking okra, cucurbits,brinjal, corn crops and for alternate five days for eight to ten hours to pluck chillies. All the respondents were

daily paid workers who got ̀ 5 per kilogram of crop.

Table 2 : Yield attributes and working conditions of

workers before use of technology.

Attributes Okra, cucurbits,brinjal, corn

Chillies

Yield attributes

Yield/acre(kgs) 1-3 kg/acre/ person 2-3 kg/ acre/ person

Working conditions

No. of days Daily Daily (Alternate 5days)

No. of Hours 2-6 hrs 8-10 hrs

Income (`) ` 5 per kg crop ` 5 per kg crop

As apparent from Table 3 that all the respondentshad problems of mechanical injury (cuts and rashes)and skin problems. All the respondents working ondifferent operations of chilly faced the problem of eyesand nose like sneezing, while 54.17% suffered fromrespiratory problems. Almost half of the respondents ofokra, cucurbits, brinjal and corn pluckers faced eyeproblems with touch. Data in the table shows that incase of okra, cucurbits, brinjal, corn, arms and hands of all the respondents (100%) were most affected duringworking with all these crops followed by 93.55% of therespondents who had problem on face. The skin offace being sensitive was the most affected when it wastouched by hands. Face, arms and hands of all therespondents (100%) working with chillies were affected

Popularization of Protective Gloves Through Demonstrations 285

followed by 79.17% of the respondents who sufferedfrom rashes etc on other body parts too. It wasobserved that the protective gloves were preferred bythe chilly growers particularly after the month of June,at the red ripe stage. The factors responsible for theproblems faced by the respondents working with okra,cucurbits, brinjal and corn were prickly nature of theplant (100%), humidity (90.32%) and high temperatureand heat scorch (22.58%). The respondents workingwith chillies when at red ripe stage considered humidity (87.50%) as the main factor responsible for theproblems followed by high temperature and heatscorch (79.17%) while only 12.50% of the respondentsconsidered prickly nature of the plant responsible for

the problem.

All the respondents performed plucking activitywith the protective gloves (Table 4). Sorting of okra,cucurbits, brinjal and corn by wearing protective gloves was done by 6.45% of the respondents and 79.17% of

the respondents for chillies. To separate the stem of the chilli is otherwise a tedious job as the respondentsexperienced irritation on hands while performing thistask. But these gloves proved to be useful for this tasktoo. Grading by wearing protective gloves was done by22.58% and 37.50% of the respondents for okra,cucurbits, brinjal and corn and chillies, respectively.One fourth of the respondents used gloves whileweeding of chillies while 54.84% of the respondentsused them for weeding in okra, cucurbits, brinjal andcorn crops.

While studying different attributes of protectivegloves, the respondents reported that the glovesprotected the skin of the wearer, provided abrasionprotection, were easy to don and doff, they felt easewhile working and the gloves provided protectionagainst prickly nature of the plant (Table 5). Theweighted mean score for all these attributes was 3.0and was ranked I. It was further observed that the

Table 3 : Problems faced during work with farmer’s practice.

Features Okra, cucurbits, brinjal, corn (31) Chillies when at red ripe stage(24)

No. of respondents Percentage No. of respondents Percentage

Problems faced

Mechanical injury 31 100.00 24 100.00

Skin problems 31 100.00 24 100.00

Nose problems(Sneezing)

0 0 24 100.00

Respiratory problems 0 0 13 54.17

Eye problems 17 (with touch) 54.84 24 100.00

Body parts affected

Face 29 93.55 24 100.00

Arms and hands 31 100.00 24 100.00

Body 9 29.03 19 79.17

Factors responsible for the problems

Humidity 28 90.32 21 87.50

High temperature andheat scorch

7 22.58 19 79.17

Prickly nature of plant 31 100.00 3 12.50

Table 4 : Work activities done by the workers with gloves.

Actions performed in protective gloves

Okra, cucurbits, brinjal (31) Chillies (24)

No. of Respondents % No. of Respondents %

Plucking 31 100.00 24 100.00

Sorting 2 6.45 19 79.17

Grading 7 22.58 9 37.50

Weeding 17 54.84 6 25.00

286 Kaur and Raina HortFlora Res. Spectrum, 6(4) : December 2017

durability of the gloves was given rank VI by therespondents with weighted mean score 2.71. Thus, the respondents were highly satisfied with the above saidattributes of the gloves as observed from the weightedmean scores. However, the respondents were notsatisfied with the moisture absorption and gave thisattribute the last rank with weighted mean score of

1.14.

As preventive measures, prior to thedemonstration, all the respondents used to anoint theirbody with mustard oil, butter, buttermilk, curd etc. to get rid of irritation, whereas only 10.91% of therespondents anointed their body after use ofdemonstrated gloves as a risk cover. Regardingchanging clothes daily to avoid the health problems,

12.73% changed prior to the demonstration while5.45% changed after the use of gloves (Fig 1). It wasfurther observed that with farmer’s practice, 67.27% ofthe respondents dipped their hands in cold water ascold water reduced the feeling of irritation for sometimebut none of the respondents needed dipping hands inwater after the use of gloves. The household activitiesof all the respondents suffered due to the abovementioned problems. The women workers were moreaffected as they were unable to do the cooking and

other household chores due to these problems.

CONCLUSION

Front line demonstrations conducted under theclose supervision of scientists is one of the important

Table 5 : Level of protection provided by protective gloves.

Attributes Highlysatisfied (3)

Somewhatsatisfied (2)

Not satisfied(1)

WMS Ranks

Protection of Skin 55 0 0 3.00 I

Moisture absorption 0 8 47 1.14 VIII

Provide abrasion protection 55 0 0 3.00 I

Durable 39 16 0 2.71 VI

Easy to don and doff 55 0 0 3.00 I

Ease of working 55 0 0 3.00 I

Protection against prickly nature of plant 55 0 0 3.00 I

Social acceptance 37 18 0 2.67 VII

Fig 1 : Preventive measures taken to avoid health problems.

Popularization of Protective Gloves Through Demonstrations 287

tools of extension to demonstrate new innovations atfarmers’ field. FLD’s are playing important role inmotivating the farmers for adoption of improvedtechnology resulting in increasing their profits. Keepingin view the importance in transfer of technology, FLD’sshould be designed and conducted carefully andeffectively and provisions should be made for othersupportive activities for speedy dissemination ofdemonstrated technology among farming community.The benefits under FLD created awareness andmotivated the other farmers to adopt the technology.The front line demonstration and farmer’s practicewere compared and it was noticed that protectivegloves were brought into use under the guidance ofKVK scientist as earlier the plucking of vegetable cropswas done barehanded. The respondents experiencedinjury on their hands while performing various activities

in okra, brinjal, cucurbits, corn and chillies. With theuse of demonstrated technology it was noticed that the

yield of the crop increased by 1 kg per hour per personas the speed of plucking increased apart from makingthe working conditions comfortable by reducing the

injuries on hands etc.

REFERENCES 1. Aggarwal M. (2003), Economic participation of

rural women in agriculture. Empowerment ofRural Women in India, Edited by Gopal Singh2003, RBSA Publications Jaipur, Rajasthan.http:// knowledge centre. drwa.org.in/ womenagri.

htm

2. Bains S., Kaur R., and Kaur D. (2011).Development of protective gloves for okra

pluckers of Punjab. J. Res. Punjab Agric Univ. 48

(1 & 2) : 101-104.

3. Singh S. (2003). The Gender Agenda.Kurukshetra (March 2003) p. 6-7.

Citation : Kaur R. and Raina D. (2017). Popularization of protective gloves through demonstrations. HortFlora

Res. Spectrum, 6(4) : 283-287.

EF FECT OF IN TE GRATED NU TRI ENT MAN AGE MENT ON QUAL ITY PRO DUC -

TION OF AF RI CAN MARI GOLD (Tagetes erecta L.)

Suresh Kumar Sharma1* , Krishan Pal 2 , K P Singh 2 and S K Tewari1

1CSIR-Na tional Bo tan i cal Re search In sti tute, Rana Pratap Marg, Lucknow (U.P.), 226001 India2 IFTM Uni ver sity, Lodhipur Rajput, Delhi Road (NH-24), Nagla Needer, Moradabad (U.P).244102


ABSTRACT : A field experiment on marigold crop was carried out during 2015-16 with the concept of integrated nutrient management under partially reclaimed sodic soils. Results indicated that highest nutrient availability

(N-405.80 kg ha−1, P- 22.44 kg ha−1, K 212.48 kg ha−1) and bacterial population (26.40105 to 64.16*105 pergram of soil) was recorded in T8 (75%N + Azotobacter + Phosphate Solubilizing Bacteria-PSB) while thelowest was observed in control. Among the treatments, application of 75%N + Azotobacter + PhosphateSolobulizing Bacteria-PSB (T8) noted less time to first bud initiation (35.26 day), less time for first flowering bud opening (57.87 day), long flowering period (74.13 days), more no. of flowers/plant (53.66), maximum size offlower (7.80 cm), and maximum flower weight (10.55 g) as compared to other treatment, and the lowest wasobserved in control.

Keywords : Af ri can mari gold, bio-fer til izer, nu tri ent man age ment, flower yield.

Marigold (Tagetes erecta L.) is one of the mostpopular and commercial flowering annual cultivated indifferent parts of the country. It has great demand forgarland, cut flowers and decorative purposes atvarious kinds of religious and social functions.Nutrients play an important role in growth anddevelopment of marigold crop. Continuous andindiscriminate use of chemical fertilizers alters the soilfertility, leading to soil pollution and ultimately poor crop yield. It is therefore, necessary to restrict their use.However, considering recent concept of integratednutrient management system, which has currently aspecial significance in crop production to address thesustainability problem and is being practiced in severalcrops. Integration of biofertilizers and organic manuresreduce the consumption of inorganic fertilizers andincrease the quality and quantity of flower. Efficacy ofthe inorganic fertilizers was increased when they arecombined with organic manures. Application offarmyard manure (FYM) increased the population ofmicro-flora mainly Azotobacter (Gupta et al., 2). TheIntegrated Nutrient Management (INM) means thesupply of nutrients to the plants from various sources.INM includes the intelligent and efficient use ofinorganic, organic and biological resources so assustain optimum yield, improve or maintain the soilchemical and physical properties and provide cropnutrition package which are technically sound,economically viable, practically feasible and

environmentally safe. The main aim of INM is to utilizeall the sources of plant nutrients in a judicious andefficient manner. Keeping above benefit points in viewan investigation was carried out on Integrated NutrientManagement in African marigold (Tagetes erecta L.) tofind out the effect of INM on quality production ofAfrican marigold.


The present investigation was laid out at Banthra

Research Station Unit (Gharu Campus) of

CSIR-National Botanical Research Institute, Lucknow

(U.P.). To study the integrated nutrient management in

African marigold under partially reclaimed soil, a field

experiment was conducted during 2015-16. The farm

site is located between latitude 26°43’03”N and

longitudes 80° 50’ 02”E at an altitude of 120 m above

the mean sea level. The climate of this region is

characterized by long and intensive hot summer low

and irregular rainfall and long mild winter. The area

receives an annual rainfall of 80 -100 cm, 70% of which

is concentrated in the month of July-September. The

initial properties of the soil were pH- 8.4, EC- 0.30,

organic carbon- 4.2g kg −1, available N-159 kg ha −1,

available P-12.8 kg ha −1, Available K-180 kg ha −1. The

experiment comprising 14 treatment combinations viz.,

T1-100% N, T2-100% N + Azotobacter, T3-100% N +

PSB, T4-100% N + Azotobacter + PSB, T5-75% N, T6-

75% N + Azotobacter, T7-75% N + PSB, T8-75% N +




NAAS Rating : 3.78

Effects of Integrated Nutrients Management on Quality Production of African Marigold (Tagetes erecta L.) 289

Azotobacter + PSB, T9-50% N, T10-50% N +

Azotobacter, T11-50% N + PSB, T12-50% N +

Azotobacter + PSB, T13-Azotobacter + PSB and T14-

Control with 3 replications under randomized block

design. The nitrogenous fertilizer was given through

Urea and Urea was applied at the time of sowing and

nitrogen applied in three splits i.e. 1/3 at the time of

sowing, 1/3 at the time of tillering and rest 1/3 at the

time of bud initiation. Azotobacter @ 2 kg/ha was

applied through seedlings root treatments for few

minutes before transplanting while PSB @ 2 kg/ha was

applied through soil treatment at the time of

transplanting. Seedlings treated by the bio-fertilizers as

per treatment were transplanted in ridges and furrows

with a spacing of 50 cm × 20 cm between row to row

and plant to plant, respectively. Uniform cultural

practices like irrigation, hoeing and weeding, plant

protection measures were adopted. Effect of different

nutrient management which involved in different

treatment was tested and data on various vegetative

and flowering parameters was recorded and

statistically analyzed using standard method

assuggested by (Panse and Sukhatme, 5).


Nutrient availability and bacterial population

The results regarding plant height as presented inTable 1 showed significant effect of organic andinorganic fertilizer application. The results indicatedthat varying doses of organic, inorganic and theircombinations of plant nutrients significantly influencednutrient availability and bacterial population (Table 1).Results exhibited that all the treatment showedsignificant response over control (T14). Resultsindicated that significantly highest nutrient availability

of nitrogen (405.80 kg ha −1), P (22.44 kg ha −1), and K

(212.48 kg ha −1) was observed in T8 (75%N +

Azotobacter + Phosphate Solobulizing Bacteria-PSB)while the lowest availability of nitrogen (220.14 kg

ha −1), P (15.84 kg ha −1), and K (166.64 kg ha −1) was

observed in control. The bacterial population (26.40 ×

105 to 64.16 × 105 per gram of soil) was recorded in T8

(75%N + Azotobacter + Phosphate Solobulizing

Table 1 : Effect of integrated nutrient management on nutrient availability (N,P,K kg/ha) and total

bacterial population (cell × 10 5/g of soil) in African marigold rhizosphere.

Treatment 2015-16 Bacterial Population2015-16

Nitrogen Phosphorus Potassium January February

T1371.40 17.84 201.42 13.12 32.16

T2401.62 18.20 207.42 15.83 40.16

T3374.78 20.20 205.80 15.24 38.12

T4404.82 21.42 210.42 19.46 52.14

T5330.42 17.62 189.43 14.42 34.12

T6344.16 17.92 193.80 16.44 44.16

T7325.14 18.80 190.42 16.12 42.18

T8405.80 22.44 212.84 26.40 64.16

T9282.84 17.21 178.34 14.86 36.80

T10294.36 17.50 183.48 18.66 50.12

T11284.16 18.74 183.62 17.48 48.16

T12300.16 19.20 186.42 23.20 58.46

T13234.86 16.00 170.82 25.60 60.88

T14220.14 15.84 166.64 12.82 30.14

CD (P = 0.05) 2.036 1.817 0.609 1.655 1.929

T1-100% N, T2- 100% N + Azotobacter, T3- 100% N + PSB, T4 - 100% N + Azotobacter + PSB, T5- 75% N, T6- 75% N + Azotobacter, T7- 75% N + PSB, T8- 75% N + Azotobacter + PSB, T9- 50% N, T10- 50% N + Azotobacter, T11-50% N + PSB, T12- 50% N + Azotobacter + PSB, T13- Azotobacter + PSB, T14 - Control.

290 Sharma et al. HortFlora Res. Spectrum, 6(4) : December 2017

Bacteria-PSB) while the lowest bacterial populationwas observed in control.

Growth, yield, and quality parameter

The observation related to days taken to first

flower bud initiation, days taken to first flower bud

opening, duration of flowering, number of flowers per

plant, size of flower, and weight of flower are presented

in Table 2. The results showed that varying doses of

organic, inorganic and their combinations of plant

nutrients significantly influenced growth, yield, and

quality of African marigold (Table 2). Results showed

that all the treatment showed significant response over

control (T1). Early flowering was noted under T8 (75%N

+ Azotobacter + Phosphate Solobulizing Bacteria-

PSB) as compared to other treatment. Application of

75%N + Azotobacter + Phosphate Solubilizing

Bacteria-PSB (T8) provided early first flower bud

initiation (35.26 day), less time for first flower bud

opening (47.80 day) as compared to control.

Significantly longer duration (74.13 day) of flowering

was also observed in T8. The significantly maximum

no. of flowers/plant (53.66), size of flower (7.80 cm),

and weight of flower (10.55 g/flower) were observed in

T8 (75%N + Azotobacter + Phosphate Solubilizing

Bacteria-PSB), as compared to other treatments, while

minimum no. of flowers/plant (34.80), size of flower

(5.22 cm), and weight of flower (8.35 g per flower) was

observed in control. These findings corroborate with

that of Yadav et al. (9) in marigold and Shashidara and

Gopinath (7) in calendula. Similar observations have

also been reported by Kumar et al. (3) in aster, Gupta et

al. (2), Chandrikapure et al. (1), Kumar et al. (4) and

Syamal et al. (8) in marigold on economic basis

Rolaniya et al. (6) have reported that combined

application of 100% RDF of NPK and Azotobacter and

PSB resulted the highest benefit : cost ration in

maigold.

CONCLUSION

It is concluded from this experimentation thatapplication of 75%N + Azotobacter + PhosphateSolubilizing Bacteria-PSB (T8) provides early andquality flowering of African marigold with maximum no.of flowers/plant (53.66), size of flower (7.80 cm), andweight of flower (10.55 g/flower). It is recommended for commercial cultivation of African marigold to get higheryield and return.

Table 2 : Effect of integrated nutrient management on quality production of African marigold.

Treatment Days taken to first flower

bud initiation

Days taken to first flower

bud opening

Duration offlowering

(days)

No. offlowers/Plant

Size of flower (cm)

Weight offlower (g)

T138.73 52.73 66.27 45.60 6.48 9.60

T238.40 52.33 68.53 47.73 7.20 9.76

T337.93 51.60 71.40 50.46 6.70 9.00

T437.13 50.53 72.26 52.00 7.44 8.50

T536.86 49.87 67.37 47.60 5.44 10.18

T636.53 49.40 70.40 49.60 6.04 10.27

T736.00 48.80 73.33 51.40 5.04 9.65

T835.26 47.80 74.13 53.66 7.80 10.55

T940.80 55.47 61.20 40.33 7.34 9.94

T1040.33 54.93 63.00 42.66 6.86 9.22

T1139.80 54.26 65.06 44.20 7.60 8.34

T1239.06 53.33 66.13 45.26 5.48 10.42

T1341.40 57.33 60.00 36.40 6.20 10.50

T1442.46 57.87 58.27 34.80 5.22 8.35

CD (P = 0.05) 1.30 1.832 1.627 3.54 0.44 0.36

T1-100% N, T2- 100% N + Azotobacter, T3- 100% N + PSB, T4 - 100% N + Azotobacter + PSB, T5- 75% N, T6- 75% N + Azotobacter, T7- 75% N + PSB, T8- 75% N + Azotobacter + PSB, T9- 50% N, T10- 50% N + Azotobacter, T11-50% N + PSB, T12- 50% N + Azotobacter + PSB, T13- Azotobacter + PSB, T14 - Control.

Effects of Integrated Nutrients Management on Quality Production of African Marigold (Tagetes erecta L.) 291

Acknowledgements

The authors are thankful to Director,CSIR-National Botanical Research Institute, Lucknow(Uttar Pradesh) for providing necessary facilities toconduct and report this research.

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q

Citation : Sharma S.K., Pal K., Singh K.P. and Tewari S.K. (2017). Effect of integrated nutrient management on

quality production of African marigold (Tagetes erecta L.) HortFlora Res. Spectrum, 6(4) : 288-291.

EF FECT OF EX OG E NOUS ROOT ING HOR MONE ON Bou gain vil lea CV. THIMMA

PROP A GA TION THROUGH HARD WOOD CUT TINGS

Babita Singh*, S. S. Sindhu, Harendra Yadav and N. K. Saxena

ICAR-In dian Ag ri cul tural Re search In sti tute, Pusa, New Delhi -110 012

*Cor re spond ing Au thor’s E-mail:[email protected]

ABSTRACT : An experiment on stimulation of rooting of Bougainvillea peruviana cv. Thimma using two different

rooting hormones (IBA and NAA) was conducted at Bougainvillea Repository, Division of Floriculture and

Landscaping, IARI, New Delhi between the months of February to April, 2017. The experiment was laid out in

Completely Randomized Design (CRD) with three replicates. Data were collected on days to first sprout, no. of

vegetative buds/plant, rooting percentage, length of longest shoot, shoot fresh wt./plant, shoot dry wt./plant ,

length of longest root/plant, fresh wt. of root/plant and dry wt. of root/plant. The results showed that the

treatment of cuttings with IBA (6000 ppm) was significantly better than the control and all the other treatments

with respect to rooting of cuttings like days to first sprout (11.66 days), no. of vegetative buds/plant (4.333),

rooting percentage (90.00%), length of longest shoot (38.0 cm), shoot fresh wt./plant (22.333 g), shoot dry

wt./plant (3.843 g), length of longest root/plant (18.233 cm), fresh wt. of root/plant (7.527 g) and dry wt. of

root/plant (1.58 g) followed by IBA (5000) ppm and IBA ( 2500ppm) + NAA (2500 ppm) compared to control and

other treatments, irrespective of the use of plant growth regulators.

Keywords : Bou gain vil lea, IBA, NAA, growth reg u la tors, root ing hor mone.

Bougainvillea is an important ornamental plant oftropical and subtropical North Indian conditionscommonly used as a shrub, hedge and climber in thegarden. It bears varied coloured bracts with profuseflowering in dry and hot weather conditions. Themodernization and urbanization have enhanced theuse of bougainvillea, because landscape horticulture is getting lot of attention and also high in demand due tolarge scale plantation in urban areas, drought andpollution tolerant, low maintenance requirement in

comparison to other plants and various uses. Thedifficult to root cultivars which are difficult to propagateand whose market demand is increasing day by dayapplication of exogenous rooting hormone on hardwood cutting or on air layering propagation isapplicable alternative. Certain nursery men who workat small scale and cannot afford to choose the tissueculture techniques, in such areas work on suitableproductive method is very successful and important.

Bougainvillea var. Thimma is a bud sport of ‘MaryPalmer’ with double coloured bracts purple and whiteon the same branch and same times, a member of thePeruviana group is a popular and free flowering. In difficult-to-root bougainvillea cultivars (Baraskar et al.,1) like B. peruviana cv. Thimma (Variegated flower), the rooting success through conventional method of

rooting of hardwood cuttings is very low. However,treatment of cuttings with auxins (NAA or IBA) hasbeen reported to improve rooting in many woodyspecies (Singh et al., 14 and 15) including Bougain-villea alba (Hassan and Abou-Taleb, 5). The presentstudy was, therefore, undertaken to standardize thegrowth regulator treatment and method of plantation for improving the rooting of cuttings and establishment ofplants.


The experiment was carried out in Bougainvillearepository of the Division of Floriculture andLandscaping, Indian Agricultural Research Institute,New Delhi during the year 2016-2017. The hardwoodcuttings (20 cm, pencil thickness) of B. peruviana cv.Thimma, prepared in February, and quick dip treatment were given (10 sec.) with different concentrations ofIBA (Indole-3 butyric acid), NAA (Naphthalene aceticacid) and combinations of both. The stock solution ofppm was prepared by dissolving NAA or IBA 1mg/literdistilled water. IBA and NAA directly not dissolve indistilled water so ethyl alcohol was used. The requiredconcentrations were prepared by diluting the stocksolution with distilled water. The pH was adjusted to5.8-6.0 by using the 1N HCl or NaOH. The hard woodcuttings were treated with growth regulator with




NAAS Rating : 3.78

Effect of Exogenous Rooting Hormone on Bougainvillea cv. Thimma Propagation 293

different concentrations as per following treatmentcombinations by quick dip method for 10 seconds :

T1 : Control (Without Hormones); T2 : IBA

1000ppm; T3 : IBA 2000ppm; T4 : IBA 3000ppm; T5 :

IBA 4000ppm; T6 : IBA 5000ppm; T7 : IBA 6000ppm;

T8 : NAA 1000ppm; T9 : NAA 2000ppm; T10 : NAA3000ppm; T11 : NAA 4000ppm; T12 : NAA 5000ppm;

T13 : NAA 6000ppm; T14 : IBA 1000 + NAA 1000 ppm;

T15 : IBA 1000 + NAA 1500 ppm; T16 : IBA 1500 + NAA1000 ppm; T17 : IBA 1500 + NAA 1500 ppm; T18 : IBA2000 + NAA 2000 ppm; T19 : IBA 2500 + NAA 2500ppm

The cuttings were planted in polybag with mixture

of cocopeat, vermiculite, vermicompost and sand (2 : 1

: 1 : 1) in month of February and kept in shade net

conditions. The experiment was laid out in completely

randomized design with three replications to determine

the statistical significance of treatment effects.

Differences were considered significant at 5% level of

significance.


The hardwood cuttings treated with growth

regulators (quick dip) were significantly better than the

control with respect to rooting of cuttings. (i.e. days to

first sprout, no. of vegetative bud/plants, rooting

percentage, length of longest shoot, shoot wt./plant,

dry shoot wt./plant, length of longest root/plant, fresh

wt. of root/plant and dry wt. of roots/plant.)

Effect of rooting hormones on survivalpercentage

The survival percentage and number of sprouts

per cutting observed after 75 days of cutting planting.

The results showed (Table 1) that the survival

percentage of bougainvillea varies from 0 to 90 %. The

maximum survival percentage (90%) observed in IBA

5000 and 6000ppm, while the maximum number of

sprouts per cutting (4.33) was observed in IBA 6000

ppm and it was minimum in control. Its might be due to

that auxin application has been found to enhance the

histological features like formation of callus tissue and

differentiation of vascular tissues (Mitra and Bose, 9). It

had been reported that IBA at 6000 ppm was found

significantly superior for increasing sprouting

percentage and number of shoots of Bougainvillea

buttiana cv. Mahara (Deshmukh and Barad, 3) and

Masoodi et al. (7) in case of Glycyrrhiza glabra L.

Effect of rooting hormones on shoot growth

Shoot character (Table 1) showed that minimum

days to first sprout (11.66 days), length of longest

shoot/plant (38 cm), maximum shoot wt./plant (22.333

g) and dry shoot wt./plant (3.843 g) was recorded in

IBA 6000 ppm followed by IBA 5000 ppm and IBA 2500

+NAA 2500 ppm and the minimum was recorded in

control. This might be due to its high concentration

which would have stimulated emergence of sprouts in

cuttings. Similar information was also reported by

Parmar et al. (11) in Bougainvillea peruviana cv. Tourch

Glory and Masoodi et al. (7) in case of Glycyrrhiza

glabra L. It was reported that IBA at 6000 ppm was

found significantly superior for increasing sprouting

percentage, number of shoots, length of shoot, fresh

and dry weight of shoots of Bougainvillea buttiana cv.

Mahara (Deshmukh and Barad, 3).

Effect of rooting hormones on root growth

The quick dip treatment of hardwood cuttings with

IBA 6000 ppm resulted in maximum percentage of

rooted cutting (90.00%), length of longest root/plant

(18.233 cm), fresh wt. of root/plant (7.527 g) and dry wt.

of root/plant (1.58 g)), followed By IBA 5000 ppm and

IBA 2500 + NAA 2500 ppm irrespective to the use of

plant growth regulators (Table 1). The minimum

percentage of rooted cutting (0%), length of longest

root/plant (0 cm), fresh wt. of root/plant (0.0 g) and dry

wt. of root/plant (0.0 g) was recorded in control. Similar

information was also reported by Parmar et al. (11) and

Gupta and Kher (4). The results are in conformity with

the earlier findings of beneficial effect of IBA on rooting

with quick dip treatment in Bougainvillea (Mishra and

Sharma, 8, Chovatia et al., 2; Joshi et al., 6, Philip and

Gopalakrishnan, 13 and Peshker, 12). Panwar et al.

(10) also reported significantly higher per cent of

rooting in Bougainvillea var. Alok with IBA 2000 ppm

with quick dip method.

The results of the present investigation getsupport from the findings of above said workers andalso suggest that the variation of bract colour is themain reason of attraction of bougainvillea. Consideringits huge demand and short supply application of rooting hormones on hard wood cuttings would be inevitablepractice in the years to come for commercialproduction of beautiful bougainvillea on small scale fornursery men.


REFERENCES1. Baraskar S.D., Bhatt N.R., Kale P.N. and

Choudhary K.G. (1990). Rooting in difficult-to-root bougainvillea cultivars. J. Maharashtra Agric.

Univ., 15 (2) : 268-269.

2. Chovatia V.P., Poshiya V.K. and Shukla P.T.1995. Root initiation studies in Bougainvillea(Bougainvillea peruviana L.) var. “Mary Palmer.

Gujarat Ag. Univ. Res. J., 20 (2) : 167-169.

3. Deshmukh K.K. and Barad A.V. (2006). Effect ofgrowth regulators on rooting of stem cutting inBougainvillea buttiana var. Mahara. Crop Res.,

Hisar. 32 (3) : 391-393.

4. Gupta V.N. and Kher M.A. (1991). A note on theinfluence of auxins in regeneration of roots in the

tip cutting of Bougainvillea sp. var. Garnet Glory

under intermittent mist. Haryana J. Hort. Sci., 20

(1-2) : 85-87.

5. Hassan S.M. and Abou-Taleb N.S. (1996).Studies on the propagation of Bougainvillea alba

plants by stem cuttings. Ann. Agric. Sci. Cario., 40(2): 841-851.

6. Joshi A.R., Mahorkar V.K. and Sadawarte K.T.(1989). Studies on rooting of cutting in someBougainvillea varieties as influenced by plant

growth regulators. PKV Res. J., 13 (2) : 166-167.

7. Masoodi N.A., Shrivastava L.J. and Mir, N.A.(1994). Studies on the effect of growth regulatorson initiation of rooting in cuttings of Gylcyrrhiza

glabra Linn. Indian J. Plant Physiol., 1 : 28-29.

Table 1 : Effect of exogenous rooting hormone on survival percentage, growth and rooting characters of

bougainvillea cv. Thimma.

TreatmentDays to

firstSprout

No ofvegetativ

ebud/plan

ts

Survival %

Lengthof

longestshoot(cm)

Shootfresh wt.

/plant(g)

Shootdry wt./plants

(g)

Lengthof

longestroot(cm)

Fresh wt of root/plants

(g)

Dry wtof

root/plants (g)

T1 32.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

T224.00 2.33 40.00 9.66 5.66 0.58 6.66 0.25 0.16

T322.00 2.66 50.00 12.83 6.66 1.05 8.23 0.90 0.29

T415.66 3.66 70.00 20.66 12.66 2.28 13.43 2.70 0.79

T515.33 4.00 80.00 24.00 14.66 2.51 15.43 4.04 0.84

T613.66 4.33 90.00 30.66 21.00 3.83 17.53 5.09 1.57

T711.66 4.33 90.00 38.00 22.33 3.84 18.23 7.52 1.58

T827.66 2.00 30.00 4.16 3.00 0.37 4.66 0.17 0.00

T926.00 2.00 30.00 9.00 5.66 0.58 6.53 0.19 0.14

T1017.33 3.00 50.00 14.23 9.00 1.72 10.00 1.56 0.42

T1116.00 3.33 50.00 16.16 10.66 1.77 10.30 2.20 0.54

T1216.00 3.66 50.00 17.16 11.66 1.99 13.16 2.55 0.59

T1315.00 4.00 80.00 24.66 15.0 2.89 16.13 4.75 0.88

T1423.66 2.33 50.00 11.50 6.0 0.66 7.00 0.44 0.19

T1517.66 2.66 50.00 12.83 8.66 1.37 9.00 1.44 0.39

T1616.66 3.33 50.00 15.00 9.66 1.74 10.13 2.04 0.42

T1716.00 3.66 60.00 17.50 12.0 2.01 13.23 2.58 0.66

T1815.33 4.00 80.00 23.33 13.8 2.47 14.50 3.33 0.84

T1914.66 4.00 80.00 27.33 17.0 3.00 16.94 5.03 0.94

Mean 18.75 3.12 56.84 17.30 10.55 1.82 11.11 2.46 0.59

C.D.(P=0.05)

6.747 1.731 16.085 14.639 9.699 2.174 6.933 3.669 0.930

Effect of Exogenous Rooting Hormone on Bougainvillea cv. Thimma Propagation 295

8. Mishra S.N. and Sharma C.P. (1995). Effect ofplant growth regulators on rooting of stem

cuttings of Bougainvillea. Prog. Hort., 12 (1-2) :33-38.

9. Mitra G.C. and Bose N. (1954). Rooting andhistological responses of detached leaves to

β–Indole butyric acid with special reference to

Boerhavia diffusa Linn. Phytomorphol., 7 : 370.

10. Panwar R.D., Gupta A.K. and Sharma J.R. andRakesh (1994). Effect of growth regulators onrooting in Bougainvillea var. Alok. Intern. J.

Tropical Agri., 12 (3-4) : 255-261.

11. Parmar B.R., Patel V.B., Bhalerao P.P. and TankR.V. (2010). Effect of different plant growthregulators on vegetative propagation ofBougainvillea peruviana cv. Touch Glory through

hard wood cutting. The Asian J. Hort. 5 (1) :222-224

12. Peshker S. (1982). Studies on propagation of

Bougainvillea by stem cuttings. Thesis Abstr. 8 :47.

13. Philip J. and Gopalakrishnan P.K. (1982). Effectof certain plant growth regulating substances onrooting of cutting in Bougainvillea var. Mahara.

South Indian Hort., 30 (1) : 56-57.

14. Singh K.K., Rawat V., Rawat J.M.S., Tomar Y.K.and Kumar P. (2013). Effect of IBA and NAAconcentration on rooting in stem cuttings of nightqueen (Cestrum nocturnum L.) under sub-tropical

valley conditions. HortFlora Res. Spectrum, 2 (1) : 81-83.

15. Singh K.K., Kumar A., Tomar Y.K. and Kumar P.(2013). Effect of length of cutting andconcentration of IBA on rooting in shoot tip cuttingof Sawani (Lagerstroemia indica L.) under mist

condition. HortFlora Res. Spectrum, 2 (2) :153-157.

q

Citation : Singh B., Sindhu S.S., Yadav H. and Saxena N.K. (2017). Effect of exogenous rooting hormone in

Bougainvillea cv. Thimma propagation through hard wood cuttings. HortFlora Res. Spectrum, 6(4) :292-295.

EF FECT OF WEED GREEN MA NURE ON RHIZOSPHERE MYCOFLORA OF

SPIN ACH

R. L. Parbhankar1 and U. P. Mogle 2

De part ment of Bot any, J. E. S. Col lege, Jalna


ABSTRACT : The present investigation was carried out in pot experiment. Collection of rhizosphere and nonrhizosphere soil was done at 41 and 111 days of sowing. In the present study association of mycoflora inrhizosphere under the influence of different manures was investigated in spinach plant. A total of 14 fungalspecies belonging to different groups viz., Aspergillus niger, A. flavus, A. terreus, A. roseus, Trichodermaviride, Penicillium sp., colletotrichum spinacease, Rhizoctonia solani, Fusarium oxysporum, Alternaria sp.,Rhizopus mucar, Penicillium, Chaetomium sp., Cladosporium sp. were isolated. Among them Aspergillus wasthe most predominant genus. Analysis of soil samples from all treated as well as control plants showed that the saprophytic fungi were more frequent than pathogenic fungi due to effect of weed green manures.

Keywords : Spin ach, rhizosphere mycoflora, weed green ma nure.

Living plants create a unique habitat around theroots which is favourable for the growth andmetabolism of numerous microorganisms. Themicroorganisms living in this region influences thehealth of a plant and also surrounding soil ecosystems. The rhizosphere is directly influenced by plant rootsand root secretions, can contain up to 1011microbialcells per gram of root (Egamberdieva et al., 5) andmore than 30,000 prokaryotic species. It is regarded as an important active zone for microbial colonization andactivity (Hiltner, 8; Hartmann et al., 7). Most of thenutrient cycling and disease suppression needed bythe plant occurs in the rhizosphere regions.

Many evidences highlights the importance of theroot micro biome in determining plant health andproductivity (Berendsen et al., 2). The rhizospheremicro biome can help plants in nutrient uptake

(Lugtenberg et al., 11; Morrissey et al., 13), and abioticand biotic stress tolerance by increasing plantimmunity (Zamioudis and Pieterse, 20) thus leading toincrease in plant productivity (Berg, 3).

Abundance of microorganisms in soil isinfluenced by many factors like organic matter, soilreaction, moisture, temperature and nature of plantgrown (Waksman, 18). High microbial density inrhizosphere is due to the presence of the organiccompounds exuded by roots (Alaxender, 1;Rangaswami and Bagyaraj, 15). Microorganismsgrowing on plant roots can influence plant growth(Tapwal et al., 17). Keeping the above facts in mind,

this study was undertaken to evaluate the effect ofweed manures on rhizosphere mycoflora of spinach.


Collection of soil samples

Soil samples were collected from the rhizosphereand non rhizosphere regions from each experimentalpot at 41 and 111days intervals. The soil samples werestored in sterile polythene bags for laboratory study.

Isolation of Fungi

A serial dilution method (Warcup, 19) wasfollowed for the isolation of rhizosphere fungi usingPotato Dextrose Agar (Dubey and Maheshwari, 4).

For the determination of mycoflora, 1 g of soilsample was dissolved in 100 ml of distilled water.Potato Dextrose Agar medium was poured in sterilizedpetriplates and solidified. Soil samples were collectedand used for fungal analysis. At the time of serial

dilution labeled the dilution blank, as 10 −1, 10 −2, 10 −3,

and 10 −4 and marked with marker pencil. Finally from

the dilution number 10 −4 1ml of suspension was

transferred with the help of sterile pipette to sterilizedPetri dishes containing PDA medium. The 1 ml of soilsample suspension was added in three sterile pouredpetriplates. The inoculated petriplates were incubatedat room temperature 27ºC ± 2° for 2-7 days.

Identification of Fungi

The fungal species were identified on the basis ofcolony and morphological characters up to specieslevel as per Gilman (6) and Mukadam (14).




NAAS Rating : 3.78

Effects of Weed Green Manure on Rhizosphere Mycoflora of Spinach 297

RESULT AND DISCUSSION

In the study of rhizosphere mycoflora of spinach14 species of fungi viz. Aspergillus niger Link., A.Flavus Link., A. terreus Link., A. roseus, Tricodermaviride Pers, Colletotrichum spinaceae, Rhizoctoniasolani, Rhizoctonia solani Dc. Ex fries, Alternariaspinaciae, Rhizopus oryzae, Mucor sp. Penicilliumnotatum Link., Chaetomium globosum Kunze andSchmidt and Cladosporium fulvum Link were recorded(Table 1 and 2). The results indicate that the populationof A. niger and A. flavus increased significantly in all the treatments as compared to control and nonrhizosphere. This may be due to the root exudates ofspinach which might have enhanced the population offungi. A. terreus, found highest in the treatment of GMC and COC. It was absent in all other treatments andcontrol. Trichoderma viride, a well-known soil

inhabitant possesses antagonism against several plant pathogenic fungi (Simmons, 16; Howell, 9) were foundin the treatment of COC in both harvests. It was mostlyobserved in remaining treatments. Pathogenic fungilike Colletotrichum spinaceae, Rhizoctonia solani,

Fusarium oxysporum, Alternaria sp. were more incontrol, non rhizosphere and CFU in bothharvests.These were found minimum after 41 days and were totally absent after 111 days due to increasedmicrobial activity. Rhizopus, a saprophytic as well aspathogenic fungus was maximum in GMB in bothharvest and it was observed irregularly in remainingtreatments. Chaetomium and Cladosporium wereobserved in control, non rhizosphere and most of thetreatments but chaetomium was maximum in COP andCladosporium was maximum in the treatment of GMC.

From the results it can be concluded thatmanuring affects qualitative and quantitative

population of microorganisms in the rhizosphereof spinach. Application of weed organic manures, notonly increased microbial population, but also improvedfertility of the soil due to the nitrification processbrought about by some Aspergillus species. Thepresent results are in agreement with earlier findings of Malwar et al, 12, and Kowalchuk et al. 10).

Table 1 : Impact of Parthenium, Cassia and Ipomoea weeds on Rhizosphere mycoflora of spinach (41 days).

Fungi isolated

(CFU × 10-4)

Nonrhizosphere

Rhizosphere

Age of the plant (41Days)

GMP GMC GMI COC COI COP CFU CON

Aspergillus niger 3.3 3.3 3.7 4.0 4.0 4.7 4.3 4.7 3.7

Aspergillus flavus 2.3 4.0 3.3 3.7 2.7 4.0 4.3 2.3 4.0

A. terrus 2.7 3.3 2.3 2.7

A. roseus 1.0 1.0 3.7 3.0 1.0 2.0

Trichoderma viride 1.0 3.0 2.3 2.0 1.0

Colletotrichum spinaceae 1.3 1.0 1.3 1.0 1.3

Rhizoctonia solani 0.7 1.0 0.3 0.3 1.0

Fusarium oxysporum f.sp. spinacia 1.0 1.0 2.0

Alternaria sp. 1.3

Rhizopusoryzae 2.3 2.3 2.0 2.0 2.0 1.0

Mucar 1.7 2.7 2.0

Penicillium notatum 1.0 1.3 1.7 2.3 1.0

Chaetomium sp. 1.7 2.0 2.0 2.0

Cladosporium sp. 3.7 2.0

Sterile mycelium 1.0 1.0

Treatments : CON = Control; CFU = Chemical fertilizer urea; GMP = Green manure of Parthenium hysterophorous;GMC = Green Manure of Cassia tora; GMI = Green manure of Ipomoea carnea; COP = Compost of Partheniumhysterophorous; COC = Compost of Cassia tora; COI = Compost of Ipomoea carnea;

298 Prabhankar and Mogle HortFlora Res. Spectrum, 6(4) : December 2017

REFERENCES1. Alexander M. (1977). Introduction to Soil

Microbiology. John Willey and Sons, New York.

2. Berendsen R.L., Pieterse C.M.J. and BakkerPAHM (2012). The rhizosphere microbiome and

plant health. Trends Plant Sci., 17 : 478-486.

3. Berg G. (2009). Plant-microbe interactionspromoting plant growth and health; perspectivefor controlled use of microorganisms in

agriculture. Appl. Microbial. Biotech., 84 : 11-18.

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Microbiol 10 : 1-9.

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(1999). J. Maharashtra Agri. Univ., 24 (2) :121-124.

Table 2 : Rhizosphere mycoflora of spinach (111 days).

Fungi isolated

(CFU × 10-4)

Nonrhizosphere

Rhizosphere

Age of the plant (111 Days)

GMP GMC GMI COC COI COP CFU CON

Aspergillus niger 3.0 4.7 7.0 4.7 6.0 5.3 5.7 3.3 3.3

Aspergillus flavus 1.0 3.7 4.7 4.3 5.7 6.3 6.0 1.3 1.0

A. terrus 2.0 3.7 2.3 1.0

A. roseus 1.0 3.0 2.0 2.7 2.3 1.0

Trichoderma viride 1.3 2.3

Colletotrichum spinaceae 1.3 1.0 1.3

Rhizoctonia solani 0.3 1.0

Fusarium oxysporum f.sp. spinacia 1.0 2.0

Phytophthora cryptogea

Alternaria sp.

Rhizopusoryzae 2.7 1.3 2.3 2.0 1.0 1.0

Mucar 2.0 1.7

Penicilliumnotatum 1.0 2.0 2.0 2.3

Chaetomium sp. 1.7 2.0 2.7 3.3 2

Cladosporiumsp 1.7 4.0 2.3 3.0 2

Sterile mycelium 1.0 1.0

Effects of Weed Green Manure on Rhizosphere Mycoflora of Spinach 299

13. Morrissey J.P., Dow J.M., Mark G.L.and O’GaraF. (2004). Are microbes at the root of a solution to

world food production? EMBO Rep., 5 : 922-926.

14. Mukadam D. S. (1997). The Illustrated Kingdomof Fungi. Akshar Ganga Prakashan, Aurangabad.

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Agricultural Microbiology. 2nd Edition. Prince Hall

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16. Simmons E. G. (1970). Second InternationalMycological Congress, Jampa, Florida, Coated

By Dube, 1993.

17. Tapwal A., Sharma Y.P. and Lakhanpal T.N.(2003). Seasonal variation in apple rhizosphere

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Mycology 2 (1) :45.

18. Waksman S.A. (1952) Microbial Analysis of Soilas Index of Fertility III. Influence of Fertilization

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19. Warcup J.H (1960). Methods for Isolation andEstimation of Activity of Soil. In : The Ecology ofSoil Fungi’. Liverpool Univ. Press, Liverpool.

20. Zamioudis C. and Pieterse C.M.J. (2012).Modulation of host immunity by beneficial

microbes. Mol. Plant Microbes, 25 : 139-150.

q

Citation : Parbhankar R.L. and Mogle U.P. (2017). Effect of weed green manure on rhizosphere mycoflora of

spinach. HortFlora Res. Spectrum, 6(4) : 296-299.

BETELVINE CUL TI VA TION : A NEW AV E NUE FOR LIVE LI HOOD SE CU RITY

Shivnath Das, Ajit Kumar Pandey* and Prabhat Kumar

Betelvine Re search Cen tre, Islampur, Nalanda-801303 In dia (Bihar Ag ri cul tural Uni ver sity, Sabour, Bhagalpur)


ABSTRACT : This paper critically examines how betel vine cultivation can be a viable livelihood option for ruralpoor in Bihar. Betel vine cultivation by rural farmers in Bihar shows a pathway for reducing their poverty andenabling upward social mobility. There by betel vine cultivation might be play key role in economicdevelopment of Bihar by unlocking the lock of rural entrepreneurship to some extent. Apart from their pivotalrole in cultivation of staple crops, they are primarily responsible for the production of secondary crops such asbetel vine cultivation which are often the only source of income available to their families. Farmers also oftenpossess unique knowledge about betel vine cultivation and handle most of the work associated with it. Thebetel farming activities can generate employment opportunities for throughout the year. It is one of the mostimportant cash crops and adequately justifies its nomenclature as the “green ATM for rural women”. Thus,government needs to recognise betel leaves as an important trading commodity and offer them support. Iffarmers have given a little support in terms of insurance or infrastructure then betel leaf trade will flourish toboost up the national economy and generate huge employment opportunities for the rural people.

Keywords : Betelvine, Magahi Paan, rural peo ple, livelihood se cu rity.

Despite of various social, economic and other

constraints, rural people have high level participation in

agriculture and they are very committed in their

agricultural activity. The multitasking potentiality of rural

labour bought significant propositions for agricultural

productivity, economic viability, household food

security, family health, family economic security and

welfare (Balasubrahamanyam, 1) . Betelvine cultivation

can be a viable livelihood option for rural poor people in

Bihar. Potentialities of betelvine cultivation for

livelihood security to rural people are discussed in the

following heads.

Betelvine Cultivation has Potential to providelivelihoods security

To the farmers, a betel leaf garden, onceestablished, is like a perennial fountain providing cashfor meeting their everyday requirements. Betelvinecultivation is highly labour intensive crop which acts asa source of employment generation for livelihoodssecurity in Bihar. Approximately, 20.8 lakh rural peoplederive their livelihood directly or indirectly fromproduction, processing, handling, transportation andmarketing of betel leaves in Bihar (Table 1). The tradeof this crop is estimated about 100 corers rupeesannually in the state (Srivastava and Prasad, 18).Women are engaged in different operation of betelvine

cultivation whole of the year and earned income atregular interval.

Table 1 : Labourers involve in betelvine cultivation

in Bihar (per hectare basis)

Culturaloperation

No. of mendays

Cultural operation No. ofmendays

BarejaConstruction

75 Manual weeding 100

LandPreparation

10 Tying up vine with kans

100

Planting of vine 10 Lowering of vine 20

NutrientManagement

5 Leaf plucking (harvesting)

40

Crop Protection 20 Packaging 40

IrrigationManagement

100

Total 520 men days involve in 1 hectare area of

betel vine cultivation annually

Suitable cash crop for small farmers, despitehigh input requirement

Fresh leaves of betel vine are recognized asgreen ATM for the growers yet it is neglected due tolack of improved technology transfer in farmer fields(Das et al., 6). Betelvine considers as a suitable cashcrop for small farmers, despite high input needs. Evenone can set up a betel garden on a 3.0 decimal area. Afarm of 10-15 decimals can provide considerable net




NAAS Rating : 3.78

Re search Note :

Betelvine Cultivation : A New Avenue for Livelihood Security 301

profit for a family of five members for 10 to 30 years(Guha, 9).

Tremendous potential in the earning offoreign exchange

The economic potentiality of the crop can be

imagined by the fact that around 20 million people

consume fresh leaves of betelvine in India. The

demand for fresh betel leaves is not only limited to

Nepal, Canada and Gulf countries but also there is

tremendous demand from the European countries

(Balasubramanian et al., 2). Leaves worth about `

30-40 million are exported to the countries like Bahrain,

Canada, Great Britain, Hong Kong, Italy, Kuwait, Nepal,

Pakistan, Saudi Arab and many other European

countries (Jana, 10; Singh et al., 17). It is estimated

that about 20 million people derive their livelihood

directly or indirectly from production, processing,

handling, transplantation and marketing of betel leaves

in India. In this way, the crop provides a National

Income to the tune of ̀ 6,000-7,000/- million/ year. This

clearly indicates that this crop has a tremendous

potentiality in earning the foreign exchange which will

strengthen the nation in many ways. The major betel

vine growing states of India are given in Table 2.

Table 2 : Area under betelvine cultivation in major

states of India.

Name of theStates

Area in (ha)

Name of theStates

Area in(ha)

Andhra Pradesh 2900 Madhya Pradesh 1250

Tamil Nadu 5500 Rajasthan 50

Karnataka 8700 Uttar Pradesh 2000

Kerala 3300 Bihar 3200

Orissa 5000 West Bengal 3000

Gujarat 200 Assam 3000

Maharashtra 2700 Others 9200

Total area under betelvine cultivation in India is

50000 haSource: Balasubrahamanyam (1)

Leaves of magahi pan has potential to earnbetter market price than other pan

Betel vine crop (Piper betle L.) occupied an area

about 4000 ha in Bihar out of which Magahi pan

covered about 439 ha area (Table 3). This is most

popular betel vine cultivar of Bihar mainly grown in the

Magadha region comprisining of Aurangabad, Gaya,

Nawada and Nalanda districts (O’Malley, 15). Magahi

pan is a high quality paan and earns better market price

than other pan (Das et al., 5). The estimated annual net

income worked out to the tune of ` 50,000 per hectare

for “Desi,”and “Bangla,” pan etc whereas, in case of

“Magahi” pan, the estimated annual net income is very

high up to ` 80,000 per hectare. The demand of

“magahi” pan in foreign countries is very high making it

an important source of foreign exchange.

Table 3 : Area under betelvine cultivation in major

districts of Bihar

Name of the district

Area in (ha)

Name of thedistrict

Area in(ha)

Bhagalpur 150 Vaishali 300

Munger 100 Sitamarhi 150

Begusarai 136 East Champaran 250

Khagaria 140 WestChamparan

225

Katihar 100 Darbhanga 250

Purnea 200 Madhubani 275

Madhepura 75 Nawada 90 (Magahi pan)

Saharsa 70 Gaya 74 (Magahi pan)

Saran 100 Aurangabad 150 (Magahi pan)

Samastipur 200 Nalanda 125 (Magahi pan)

Muzaffarpur 90 Others districts 750

Source : Jha and Kumar (11)

Huge potential in the industrial market

Betel leaves serves as a cheap source of

medicine which is also easily available in the market.

Mastication of betel leaves produces a sense of

freshness, alertness, salivation, energetic feeling with

enhanced mental and physical response of the human

body (Guha, 9). Betel leaves have been reported to

exhibit antioxidant, anti-inflammatory, immune-modul-

atory and antitumor activities (Khanra, 13). The

extracted essential oil also possesses anti-fungal and

anti-bacterial properties. This indicates that essential

oil is the powerful inhibitor of pathogens causing

cholera, typhoid, tuberculosis etc. (CSIR, 3). Owing to

huge potential in the industrial market, on account of its

medicinal benefits indicates a promising industrial

future of betel vine cultivation (Table 4). If a

well-coordinated effort by the farmers, traders,

scientists, technologists and policy makers are made, it

will not only help reduce the post-harvest losses of

betel leaves but also boost the national economy as

well as generate huge employment opportunities for

the rural people.

302 Das et al. HortFlora Res. Spectrum, 6(4) : December 2017

Table 4 : Different products manufactured from

betel leaves.

Products manufactured from betel leaves on industrialscale

Tooth-pastes Cold drinks

Skin emollients Chocolates

Tooth-powder Appetizers

Paan masala Digestive agents

Deodorants Tonics and medicines

Mouth fresheners Beauty and cosmetics products

Facial creams Betel leaf essential oil

Anti-septic lotions Ice-cream

Source : Guha (8)

Processed betel leaves (cured betel leaf) havepotential to earn more money

The curing process for betel leaves was probablyfirst invented at Varanasi, India where the techniqueswere traditionally used for making Banarasi paan(cured betel leaf). The green leaves are treated withsmoke, high temperature and pressure for improvingorganoleptic qualities and ultimately the green leavesare converted to white or yellowish white colour leaves. Although, there is no standard method been reportedfor curing process of betel leaves, Betelvine ResearchCentre, Islampur under Bihar Agricultural University,Bhagalpur, Bihar (India) took initiation from the year2013 in the studied of curing of betelvine andconstructed two paan bhati at the centre (Kumar andPandey, 14). It also facilitates the training to betelgrowers coming from different parts of the state. Themethod of curing the betel leaves are alternate heatingof 6 hours at 50-60°C and cooling of 12 hours, two tothree time following aeration of leaves by turning andstored under dark condition. It took 15-20 days formaking complete white or yellowish from green betelleaves. In this process, the shelf life of betel leaves isextended up to one month and curing imparts softnessand sweet taste in betel leaves (Pandey et al., 16). After completion of the curing process, the leaves aregraded, spoiled leaves are discarded, cured leaves are taken out and the uncured green leaves are curedagain for 8-24 hrs depending upon the colour of theleaves. Changes occurred in sweetness of leaves aftercuring leads to earn more money (` 0.50 per leaf in thelocal market) by the farmers. The processed leaf alsoimparts a sweet taste, making it a favourite amongconnoisseurs due to its high quality.

New possibilities of agricultural diversification

Replacement of rainfed upland rice with low waterrequiring high value cash crop like betelvine may beone of the best option to increase the production,

productivity, income and employment in rainfed uplandrice areas in south Bihar. Besides, it can beintercropped in mango orchard in open cultivationwhich reduces the cost of bareja construction. Thefresh leaves of betel vine (Paan) cultivation in rainfedupland area or intercropping in mango orchard meansthere are new possibilities of agricultural diversification

and livelihoods under climate change (Das et al., 7)

Potential to earn money from every sphere of human life

The significance of leaves has been explained inrelation to every sphere of human life. For example, awell-prepared betel quid is still regarded as anexcellent mouth freshener and mild vitalizer, routinelyserved on the social, cultural and religious occasionslike marriage, Puja (religious festivals), Sraddhaceremony (religious function performed aftercremation) etc. It is also used as a special item offeredto the guests in order to show respect in the Indiansociety. This clearly showed that this crop has atremendous potentiality in earning the money fromsocial, cultural, religious occasions and evenday-to-day life (Guha, 9) which will strengthen the ruralpeople economically at local level.

Major constraints in production andmarketing of betelvine

According to Kaleeswari and Sridhar (12), themajor problems in betel leaf farming were traditionallymanagement operations, unskilled labour, pest anddisease problem, non existence of regulated market,presence of too many middlemen and price fluctuationdue to seasonality in production of betel leaves.However, Zn-deficient soil also act as limiting factor forsustainable production of betelvine in agro-climaticzone III B of Bihar (Das et al., 4)

CONCLUSION

The betel farming activities can generateemployment opportunities for throughout the year. It isone of the most important cash crop and adequatelyjustifies its nomenclature as the “green ATM for ruralwomen” which is unlocking the lock of womenentrepreneurship in Bihar. The central and stategovernment should jointly take appropriate steps toimproving pest management of betel farm activities, toestablish a Research and Development Board; toenhance export oriented activities meeting globalstandards, to reduce intermediaries in marketing, tostabilize the betel prices, to increase farm cultivationand awareness among betel growers. Thus,government needs to recognise betel leaves as an

Betelvine Cultivation : A New Avenue for Livelihood Security 303

important trading commodity and offer them support. Iffarmers have given a little support in terms of insurance or infrastructure then betel leaf trade will flourish toboost up the national economy and generate huge

employment opportunities for the rural women.

REFERENCES1. Balasubrahamanyam V.R. (Ed.). (1994). “Betel

vine”. National Botanical Research Institute,Lucknow 6-7.

2. Balasubramanian S., Sharma R., Gupta R.K. andPatil R.T. (2011). Validation of drying models andrehydration characteristics of betel (Piper betel L.)

leaves. J. Food Sci. Techn., 48 (6) : 685-691.

3. CSIR (1969). The Wealth of India. Council ofScientific and Industrial Research (CSIR), NewDelhi 84-94.

4. Das S.N., Kumar P. and Pandey A. K. (2017).Effect of zinc application on marketable leafproduction of betelvine, In: Proc. of National Conf.on Climate Change and Agricultural Production.Adapting Crops to Climate Variability andUncertainty held at BAU, Sabour during April 6-8,2017. Excel India Publishers, New Delh pp.220-221.

5. Das S.N., Kumar P., Pandey A. and Ranjan R.D.(2016). Enhancement of betelvine productionthrough integrated crop management practices.

Extended Summaries Vol. 2 : 4th Intern. Agron.

Congress, Nov. 22–26, 2016, New Delhi, India

6. Das S.N., Ranjan R.D., Azad C. and Kumar A.(2014). Integrated crop management for susta-inable production of Betelvine in Bihar.NASA-2014 Compedium-cum Abstract. Interna-tional symposium on New-Dimension in Agrome-teorology for sustainable Agriculture. GBPUA and Technology, Pantnager, India during 16-18

October, 2014. Pp 353.

7. Das S.N., Ranjan R.D. and Thorat T.N. (2015).Productivity and profitability as influenced bydiversification of betelvine under climate change.Book of Abstract. National seminar on weatherand climate risks in agriculture under changingclimate: Manage- ment and Mitigation. Tikamgarh (MP), India during 12-13 March, 2015. Pp 37.

8. Guha P. (2000). Commercial exploitation of oilfrom betel leaves. Proc of Sixth RegionalWorkshop on Oil Seeds and Oils. IIT, Kharagpur(Ed.). Kharagpur, India 56-57.

9. Guha P. (2006). Betel leaf : the neglected green

gold of India. J. Human Ecol., 19 (2) : 87-93..

10. Jana B.L. (1996). Improved technology for betelleaf cultivation. Seminar-cum-Workshop on Betelleaf Marketing. 5-6 June. Midnapur (W. B.).

11. Jha P.K. and Kumar N. (2014). Status of betelvine crop in Bihar, pp.102-105. In: Hima B K,Surayanarayana M A, and Vasantha K T (eds.)Compendium, National meet on Betelvine-Farmers, Traders and Researchesr Interface,February 22-23,2014 IIHR, Bengaluru.

12. Kaleeswari V. and Sridhatr T.S. (2013). A studyon betel vne cultivation and market crisis in Kaur

district, Indian J. Appl. Res., 3 (10) :1-3

13. Khanra S. (1997). Betel Leaf Based Industry.

Nabanna Bharati 30 (2) : 169.

14. Kumar P. and Pandey A.K. (2014). Status ofbetelvine cultivation in Bihar. Compendium,National Meet on Betelvine, IIHR, Bagaluru,February 22-23, 2014. Pp. 106-110.

15. O’Malley L.S.S. (1906). Bengal DistrictGazetteers: Gaya. Lagoss Press, New Delhi(Reprint 2007). P. 113.

16. Pandey A.K., Kumar P., Singh N.K. and ChoubeyM. (2016). Management of Post-harvest losses inBetel (P. betle L.) leaves by various techniques,pp. 30-35. In: Nation Conference of RuralLivelihood Security through InnovativeAgri-entrepreneurship, 12-13 March 2016 held atICAR Central Potato Research station, Patna.

17. Singh K.K., Balasubrahamanyam V.R. andKochha, V. (1990). Effect of different packingmethods, temperature conditions, treatment withchemicals on the senescence and storagebehaviour of betel (Piper betle L.) leaves.

18. Srivastava C. and Prasad B. (1996). Aneconomic analysis of marketing of betel vine in

Bihar, The Bihar J. Agric. Marketing, 4(2) :157-165.

q

Citation : Das S.N., Pandey A.K. and Kumar P. (2017). Betelvine cultivation : A new avenue for livelihood security

HortFlora Res. Spectrum, 6(4) : 300-303.

EF FECT OF IN TE GRATED NU TRI ENT MAN AGE MENT ON GROWTH AND

FLOW ER ING PA RAM E TERS OF AF RI CAN MARI GOLD (Tagetes erecta L.) CV.

PUSA NARANGI GAINDA

Monbir Singh, Jitendra Kumar*, Pavitra Dev and Vijai Kumar1

De part ment of Hor ti cul ture, Chaudhary Charan Singh Uni ver sity, Cam pus, Meerut-250 0041Deptt. of Hor ti cul ture, CSSS(PG) Col lege, Machhra, Meerut


ABSTRACT : An effort was made to study the effect of integrated nutrient management on the growth andflowering parameters of marigold (Tagetes erecta L.) cv. Pusa Narangi Gainda at the Horticultural ResearchFarm, Department of Horticulture, Chaudhary Charan Singh University Campus, Meerut (U.P.). Theexperiment was laid out in randomized block design with ten treatments replicated thrice. The treatmentscomprised of NPK, vermicompost and Azotobacter in different combinations and control (no fertilizers). Themaximum plant height (67.14 cm), no. of primary branches/plant (16.54), flower diameter (6.27 cm), freshweight/flower (8.70 g) and flower yield (212.52 q/ha) were recorded under the combined application of 100%NPK + Azotobacter. While, maximum number of secondary branch/plant (44.28), spread of plant (45.41 cm),stem diameter (1.66 cm) and number of flower/plant (63.15) were recorded under the 100% Vermicompost +

Azotobacter (5.0 kg/ha) treatment.

Keywords : Mari gold, ni tro gen, phos pho rus, po tas sium, vermicompost, Azotobacter.

African marigold (Tagetes erecta L.) belonging to

family Asteraceae is one of the most important

commercial flower crops grown all over the world and

in India as well; due to its easy cultivation and wide

adaptability to soil and climatic conditions. At the time

of blooming, marigold enhance the decorative value of

a garden within a short span of time. It is commonly

cultivated as a loose flower crop throughout India. The

loose flowers of marigold are utilized for making

garlands, baskets; veni etc., and having great demand

in various kinds of religious and social functions. The

petals are often used for embedding in handmade

paper such as greeting cards. Carotenoids extracts

from Tagetes erecta, used for poultry feed as additive

to enhance chicken skin and egg yolk coloration (Scott

et al., 9) at considerable lower cost than synthetic or

other natural carotenoids (Seemann, 10).

The successful cultivation of marigold depends on many factors amongst which nutrients play animportant role. The continuous and imbalance use ofchemical fertilizers deteriorate the soil health, groundwater, atmospheric pollution and deposition ofnon-available phosphorus of soil, resulting decreasethe yield (Lindsay and Norvell, 6). No single source of

nutrient is capable of supplying plant nutrients inadequate amount and in balance proportion.Therefore, it is essential to use balance nutrientmanagement in crop production for increasing qualityproduce with sustainable soil and eco health throughthe use of appropriate combination of organic,inorganic and natural sources. NPK is essential forproper growth, development and photosyntheticactivity. Chemical fertilizers give quick response ascompaire to organic and bio-fertilizers. Vermicompostis rich in all essential plant nutrients and improves soilstructure, texture, aeration, and water holding capacityand prevents soil erosion. It is a stable and enrichedsoil conditioner. It helps in reducing population ofharmful pathogenic microbes and provides good soilconditions for beneficial microorganism such asAzotobacter. Azotobacter is a free living nitrogen fixingbacteria and plays a vital role in achieving sustainable

production at low cost (Alacron and Ferrera, 1).

A field experiment was conducted at theHorticultural Research Farm, Department ofHorticulture, Chaudhary Charan Singh UniversityCampus, Meerut (U.P.) in 2013. The experiment wasdesigned in randomized block design (RBD) with threereplications. The seeds were sown in the nursery andtwenty five days old seedlings (4-5 leaf sage) weretransplanted at 45 × 45 cm spacing. The treatmentsinvolved in the study were ten in numbers i.e. 100%




NAAS Rating : 3.78

Re search Note :

Effect of Integrated Nutrient Management on Growth and Flowering Parameters of African Marigold 305

NPK (100 : 200 : 50 kg/ha), 100 % Vermicompost (8.5t/ha), Azotobacter (5.0 kg/ha), 100% NPK +Azotobacter, 100 % Vermicompost + Azotobacter, 50% NPK + 50% Vermicompost, 50% NPK + 50%Vermicompost + Azotobacter, 50% NPK + Azotobacter,50 % Vermicompost + Azotobacter and Control (nofertilizers). Nitrogen, phosphorus and potassium wereapplied through the source of chemical fertilizers in theform of urea, single super phosphate and muriate ofpotash, respectively. One half dose of nitrogen, fulldose of phosphorus and potassium were applied at thetime of seedlings transplanting. The remaining halfdose of nitrogen was applied in two split dosed at 15and 30 days after transplanting. The data recorded onvarious parameters of growth and flowering viz., plantheight (cm), number of primary branches, number ofsecondary branches, spread of plant (cm), diameter of

stem (cm), days taken to flowering, flower diameter(cm), fresh weight of flower (g), number of flowers perplant and flower yield (q/ha) were subjected tostatistical analysis. All the recommended practices

were followed during investigation.

Experimental findings clearly revealed that allgrowth parameters were significantly influenced byintegrated nutrient management practices. The

maximum plant height (67.12 cm) and number ofprimary branches per plant (16.54) were observed dueto the combined effect of 100% NPK + Azotobacterfollowed by 100% NPK treatment (Table 1). Similarfindings were also reported by Thumar et al. (11). Thismight be due to the inorganic fertilizers provide thequick supply of essential major nutrients i.e. NPK,where nitrogen is an essential for cell division and cellenlargement and promote photosynthetic activity forfood manufacture. Phosphorus is needed for utilizationof carbohydrates and encourages cell division andpotassium play important role in root development andleaf stomata activity. Similarly Azotobacter fixedatmospheric nitrogen in the plant root zone, which

promotes vegetative growth of plant.

The maximum stem diameter (1.66 cm), plantspread (45.41 cm) and number of secondary brachesper plant (44.28) were recorded under the 100%Vermicompost + Azotobacter treatment followed by100 % vermicompost treatment. These findings are inclose conformity with the reports of Bhat et al. (2). Thismight be due to the vermicompost having excellenteffect on overall plant growth encourages the growth ofnew shoots or leaves. Beside major nutrients,vermicompost is the rich source of trace elements viz.,

Table 1 : Effect of integrated nutrient management on growth and flowering parameters of African marigold

(Tagetes erecta L.) cv. Pusa Narangi Gainda.

Treatments Plantheight(cm)

Plantspread(cm)

Stemdiameter

(cm)

No. ofprimarybranches/plant

No. ofsecond

arybranches/plant

Flowerdiameter (cm)

No. offlowers/plant

(g)

Freshweight/flower

(g)

Freshfloweryield(q/ha)

100% NPK (100:200:50kg/ha)

62.11 41.43 1.28 14.51 31.33 5.57 57.30 7.58 198.93

100 % Vermicompost(8.5 t/ha)

49.72 42.19 1.47 12.43 40.46 4.67 60.50 5.52 119.66

Azotobacter (5.0 kg/ha) 45.24 41.29 1.31 11.26 34.43 4.77 55.80 5.37 105.81

100% NPK + Azotobacter 67.14 43.80 1.37 16.54 39.38 6.27 59.90 8.70 212.52

100 % Vermicompost + Azotobacter

48.17 45.41 1.66 12.12 44.28 4.90 63.15 5.27 111.92

50 % NPK + 50% Vermicompost

51.55 43.09 1.39 12.56 35.77 4.97 53.52 6.80 146.73

50% NPK + 50% Vermicompost + Azotobacter

56.25 39.51 1.35 13.62 36.53 5.31 60.18 6.90 173.53

50% NPK + Azotobacter 53.66 40.79 1.29 13.28 37.75 4.99 50.82 6.82 151.57

50 % Vermicompost + Azotobacter

46.62 38.61 1..36 12.74 35.56 5.30 55.91 4.70 98.06

Control (no fertilizers) 40.24 34.31 1.21 9.74 27.34 4.03 43.44 3.89 84.66

C.D. (P=0.05) 2.61 4.58 0.21 1.87 5.01 0.83 7.66 1.40 22.67


calcium, magnesium, zinc, copper, iron andmanganese (Prabha et al., 7). Vermicompost also actas chelating agent and regulate the availability ofnutrients. Azotobacter helps in synthesis of growthregulating substances like auxins, cytokinin andGiberellic Acid (GA). In addition, it stimulatesrhizospheric microbes, protects the plants fromphyto-pathogens, improves nutrient uptake andultimately boost up biological nitrogen fixation. HighestB : C ratio and maximum net returns in marigold due tocombined application of NPK and biofertilizers havealso been reported by Rolaniya et al. (8).

Flowering behavior of any plant depends on thegenetics of plant but flowering can be increase throughthe appropriate nutrient management. It observed from the investigation that maximum flower diameter (6.27cm), fresh weight per flower (8.70 g) and flower yield(212.52 q/ha) significantly observed due to thecombined application of 100% NPK + Azotobacter(Table 1). Similar finding was also reported by Kausiket al. (5). Chandrikapure et al. (3) also reported that the application of nitrogen, phosphorus, potash along withazotobacter significantly increased the flower size, percent petal weight and flower yield. The increased flower production might be due to application of biofertilizerwith combination of inorganic fertilizes significantlyincreased plant growth as well as number of brancheswhich directly stimulate flower yield. While, maximumnumber of flower per plant (63.15) was recorded fromthe 100% Vermicompost + Azotobacter treatment. This might be due to vermicompost creating favorablecondition for nitrogen fixation at higher rate and supplyof other nutrients, bacterial secretion, hormoneproduction and supply of antibacterial and antifungalcompounds, which were favourable for growth and

ultimately increased yield. These findings corroboratewith that of Thumar et al. (11) and Idan et al. (4).

REFERENCES1. Alacron A. and Ferrera C. R. (2000). Biofertilizer

importance and management in agriculture. Agri.

Technica-en-Maxico, 26 (2): 1991-203.

2. Bhat D. J., Dogra S., Pandey R.K., Sharma J.P.and Jamwal S. (2010). Influence of integratednutrient management on growth, flowering andyield of African marigold cv. Pusa Narangi

Gainda. Environ. Ecol., 28 (1A): 466-468.

3. Chandrikapure K. R., Sadawarte K.T., PanchabhD. M. and Shelke B. D. (1999). Effect ofbioinoculants and graded doses of nitrogen ongrowth and flower yield of marigold (Tagetes

erecta L.). Orissa J. Hort., 27 (2) : 31-34.

4. Idan R. O., Prasad V. M. and Saravanan S.(2014). Effect of organic manures on flower yieldof African marigold (Tagetes erecta L.) cv. Pusa

Narangi Gaina. Intern. J. Agric. Sci. Res., 4 (1) :

39-49.

5. Kausik H., Singh J. P., Mohan B., Rajbeer andNathiram (2013). Effect of inorganic fertilizer(nitrogen) and bio-fertilizer (Azospirillum) ongrowth and flowering in African marigold (Tagetes erecta L.) cv. Pusa Narangi Gainda. Intern. J.

Agric. Sci., 9 (1) : 189-192.

6. Lindsay W. L. and Norvell W. A. (1978).Development of DTPA soil test for zinc, iron,manganese and copper. Soil Sci. Soc.Amer.

Proc., 15 : 149-151.

7. Prabha L. Indira M., Jeyaraaj A. and Jeyarraj R.(2005). Macro and micronutrient changes invermicomposting of vegetable waste usingEudrilus eugeniae. South Asian J. Socio Pol.

Studies, pp. 129.

8. Rolaniya M.K., Khandelwal S.K., Koodi S., JatP.K. and Choudhary A. (2017). Effect of NPKbiofertilizers and Plant spacing on economics ofAfrican marigold (Tagetes erecta L.). HortFlora

Res. Spectrum, 6 (1) : 63-65.

9. Scott M. I., Ascarelli I. and Olson G. (1968).

Studies on egg-yolk pigmentation. Poultry Sci., 47

: 863.

10. Seemann M. (1968). Latest trends in layernutrition. How does the yellow get into egg?

Lohmann Info., 21 : 7-11.

11. Thumar B. V., Barad A. V., Neelima P. andBhosale N. (2013). Effect of integrated system ofplant nutrition management on growth, yield andflower quality of African marigold (Tagetes erecta

L.) cv. Pusa Narangi Gainda. Asian J. Hort., 8 (2) :

466-469.

q

Citation : Singh M., Kumar J., Dev. P. and Kumar V. (2017). Effect of integrated nutrient management on growth

and flowering parameters of African marigold (Tagetes erecta L.). HortFlora Res. Spectrum, 6(4) :304-306.

CUL TURAL MAN AGE MENT OF STEM ROT OF RAJMASH CAUSED by Sclerotinia sclerotiorum

Ramesh Singh*, D. K. Tripathi and P. C. Singh

De part ment of Plant Pa thol ogy,T.D.P.G. Col lege, Jaunpur (U.P.)-222 002


ABSTRACT : Stem rot caused by Sclerotinia sclerotiorum is an important disease of Rajmash (Phaseolusvulgaris L.) in Eastern U.P. Stem rot could be managed by the integration of various cultural practices like,different date of sowing and uses of different type of soil amendments. Among six different sowing dates viz.,

Ist and III rd week October, I st and III rd week of November and I st and III rd week of December, the lowest

average of disease incidence (11.30%) and higher crop yield (27.60 q/ha) was obtained in I st week of October

sowing during 2014-15. The maximum average disease incidence (29.60%) was observed in IIIrd week ofDecember sowing in the year 2015-16. Seven different types of soil amendments viz. pyrite and Gypsum (@ 2.0 t/ha each) and Neem cake, Paddy straw, Ground nut cake, Caster cake and Mustard cake (@ 20.0 t/haeach) were used as soil application. All the amendments were significantly effective in reducing the diseaseover the control. The minimum disease incidence (8.30%) and maximum its yield (29.60 q/h) in I year and9.50% disease incidence and yield of 28.30q/ha in II year was recorded by the application of pyrite. Gypsumwas least effective soil amendment which showed maximum (25.00%) disease incidence in I year and II yearwith minimum yield (17.0 q/ha & 16.80 q/ha) in I and II year.

Keywords : Phaseolus vulgaris, stem rot, Sclerotinia sclerotiorum, sow ing dates, soil amend ment.

Rajmash (Phaseolus vulgaris L.) is an importantlegumenous vegetable crop used as green pod asvegetable. Vegetables are an integral part of Indiadietary. They are an important source of protein andessential adjuncts to a predominantly cereal based diet and enhance the biological value of protein consumed.They are thus, not dependent on industrially fixednitrogen, a process requiring energy, but add up to30kg N/ ha to the soil and improve its fertility. Being amajor source of protein, rajmash provide all the eightbasic form of amino acid or the eight essential aminoacids. These amino acids act against a number ofdiseases and are important to maintain a healthyimmune system. It is essential that a single cup ofuncooked beans provide around 85% of the dailyprotein requirement.

It suffers from a number of viral, bacterial,nemtodal and fungal diseases. Among the fungaldiseases, Sclerotinia sclerotiorum (Lib.) de Barycausing stem rot of Rajmash has been observed to bemore destructive causing losses at 20-40 per cent during the recent years under favourable environ-mental conditions. However, very meager informationis available on this disease. Therefore, it was felt

necessary to explore the possibility for the control ofthe soil borne disease through the use of differentsowing dates and uses of different soil amendments.

The experiments were conducted at StudentsResearch Farm Peelikothi, Jaunpur in two successiveyears during 2014-2015 and 2015-2016. Theexperiments comprised of six different sowing dates

viz., I st and III rd week October, I st and III rd week of

November and I st and III rd week of December were

taken in RBD with three replications in 3 × 2 m plot sizeand irrigated as and when required. The experimentwas carried out in a sick plot. The seeds of PDR-14were used for these experiments. The observations ondisease incidence were recorded at after two monthfrom the date of sowing and presented diseaseincidence and yield after crop maturity.

Seven types of soil amendments like pyrite andGypsum (applied in soil @ 2.0 t/ha) and Neem cake, Paddy straw, Ground nut cake, Caster cake andMustard cake @ 20.0 tonne/ha were incorporated tendays before sowing of seeds. The disease incidencewere recorded when the plants of Rajmash attained the age of two month and suitable control was alsomaintained with or without any amendments in the soil.Disease incidence and yield data were recorded after

crop maturity and analyzed statistically.




NAAS Rating : 3.78

Re search Note :


Effect of different sowing dates on diseaseincidence and yield of Rajmash

The results obtained (Table 1) revealed that theminimum average disease incidence (11.30%) with

maximum yield (27.60 q/ha) was found in Ist week of

October of sowing dates in the year of 2014-15. The

next best effective sowing dates were IIIrd week of

October and Ist week of November in both the years in

respect to disease incidence and yield also howeverthey were statistically at par with each others. Duringsecond year (2015-16) The maximum diseaseincidence (29.60%) with minimum yield (15.20 q/ha)

was found in IIIrd week of December date of sowing.

The average disease incidence decreased with

increased yield at Ist week October to IIIrd week of

December in both the years. Singh (2) had alsoreported that delayed sowing helped in lowering downdisease incidence and increased crop yield. Singh andSingh (3) recorded that the stem rot of berseem caused by Sclerotinia sclerotiorum, was effectively controlled

by sowing the crop in October and November. Theeffect of sowing date on severity of Sclerotinia rot ofsun flower caused by Sclerotinia sclerotiorum wasdetermined with significant reduction in disease

severity in late season crop (Singh and Tripathi, 4).

Effect of different soil amendments ondisease incidence and yield of Rajmash

Observations on disease incidence (Table 2)

showed that all the amendments were significantly

effective in reducing the disease over them control.

Among them incorporation of Pyrite was found

significantly superior and showed minimum disease

incidence (8.30%) and maximum yield (29.60 q/h) in

the year of 2014-15 and 9.50% disease incidence and

yield of 28.30q/ha in the year 2015-16. Next best soil

amendment was Neem cake followed by Paddy straw,

Ground nut cake, Caster cake, Mustard cake. Neem

cake, Paddy straw and Ground nut cake were

statistically at par in both the years in respect to

Table 1 : Effect of different sowing dates on disease incidence and yield of Rajmash.

Dates of Sowing Average disease incidence (%) Yield (q/ha)

2014-15 2015-16 2014-15 2015-16

Ist week of October 11.30 (19.64)* 12.40 (20.62) 27.60 (31.69) 26.30 (30.85)

IIIrd week of October 13.65 (21.64) 14.30 (22.22) 26.33 (30.87) 25.60 (30.40)

Ist week of November 15.95 (23.58) 17.10 (24.33) 25.80 (30.53) 24.30 (29.53)

IIIrd week of November 24.30 (29.53) 26.15 (30.75) 20.30 (26.78) 19.60 (26.28)

Ist week of December 25.15 (30.79) 26.30 (30.85) 18.60 (25.55) 20.30 (26.78)

IIIrd week of December 28.60 (32.33) 29.60 (32.96) 16.90 (26.49) 15.20 (22.95)

CD (P=0.05) (2.64) (2.96) (1.26) (1.30)

*Angular transformed value in parenthesis

Table 2 : Effect of different soil amendments on disease incidence and yield of Rajmash.

Soil Amendments Average disease Incidence (%) Yields (q/ha.)

2014-15 2015-16 2014-15 2015-16

Pyrite 8.30 (16.74)* 9.50 (17.95) 29.60 (32.96) 28.30 (28.86)

Neem cake 9.85 (18.28) 10.30 (18.72) 28.30 (32.14) 27.00 (31.31)

Paddy straw 12.20 (20.44) 12.90 (21.05) 26.60 (31.05) 26.30 (30.85)

Ground nut cake 15.60 (23.26) 14.30 (22.22) 25.30 (30.20) 24.60 (29.73)

Caster cake 18.90 (25.77) 20.66 (27.03) 21.30 (27.49) 20.60 (26.99)

Mustard cake 21.85 (27.86) 23.10 (28.73) 19.40 (26.13) 18.30 (25.33)

Gypsum 25.00 (30.00) 26.10 (30.72) 17.00 (24.35) 16.80 (24.20)

Control 28.30 (32.14) 29.70 (33.02) 15.30 (23.03) 14.60 (22.46)

CD (P=0.05) (3.56) (2.60) (1.76) (1.80)

*Angular transformed value in parenthesis.

Cultural Management of Stem Rot of Rajmash caused by Sclerotinia sclerotiorum 309

disease incidence and yields also. Gypsum was least

effective soil amendment which showed maximum

disease incidence (25.00%) in 2014-15 and 26.10 % in

2015-16 with minimum yield (17.0 q/ha and 16.80 q/ha,

respectively). The present findings coincide with the

observation made by Basu and Maiti (1) who reported

that stem rot of potato was reduced by the

amendments of NPK + FYM. The effect of sowing date

on incidence of Sclerotinia blight of brinjal caused by

Sclerotinia sclerotiorum was determined with

significant reduction in disease incidence in late

season crop (Singh and Singh, 5).Singh and Sachan

(6) observed that stem rot of sunflower caused by

Sclerotinia sclerotiorum reduced, when the sunflower

crop was with Pyrite followed by Neem cake and

Mustard cake.

REFERENCES1. Basu A. and Maiti M. K. (2006). Role of host

nutrition and varieties on the development of stem

rot of potato. Ann. Pl. Protec. Sci., 14 : 479-480.

2. Singh D. (1996). Effect of sowing date on white rot (Sclerotinia sclerotiorum) development in pea.

Indian J. Agric. Sci., 65 : 621-623.

3. Singh H. and Singh H.(1995). Cultural control ofstem rot of berseem caused by Sclerotinia

sclerotiorum. Pl. Dis. Res., 10 : 28-32.

4. Singh R. and Tripathi N.N. (1995).Effect of date of sowing and stage of crop growth on severity ofSclerotinia rot of sunflower. Haryana Agric. J.

Res., 25 : 131-132.

5. Singh R. and Singh L.B. (2007). Evaluation ofdifferent soil amendments against Sclerotinia

blight of brinjal. Ann. Pl. Protec. Sci., 15 : 265-266.

6. Singh R. and Sachan N.S (2014). Management of Sclerotinia stem rot of sunflower through nitrogensources and soil amendments. Ann. Pl. Protec.

Sci., 22 (2) : 440-441.

q

Citation : Singh R., Tripathi D.K. and Singh P.C. (2017). Cultural management of stem rot of Rajmash caused by

Sclerotinia sclerotiorum. HortFlora Res. Spectrum, 6(4) : 307-309.

EF FECT OF CYTOKININ ON GROWTH AND MEN THOL OIL CON TENT IN

Mentha piperita L. UN DER SEA SONAL VARI A TION

Awadhesh Kumar1* , L. P. Maurya1, Neetu Singh 2 and Balram Prasad Yadav 3

1Department of Bot any, Dr. Akhtar Hasan Rizvi Shia De gree Col lege, Jaunpur 222 002 (U.P.)2TDPG Col lege Jaunpur-222 002(UP)3N.K.Girls, De gree Col lege, Aflepur, Malhni Bazar, Jaunpur(U.P.)


ABSTRACT : In pot culture studies, metabolic activities were correlated with the effect of exogenousapplication of cytokinin at certain level (0.0, 1.0, 4.0, and 10.0 ppm) of cytokinin in mint (Mentha piperita L.)under variable seasons. In summer season, the higher level of cytokinin i.e. 4.0ppm resulted in vigorousvegetative growth. All the parameters studied were also affected in winter season also, but values were too low than those in summer season. It showed a positive correlation with the growth attributes and menthol oilcontents in trichome glands of mint under the influence of variable seasons. Thus, both cytokinin and long daytreatment provides a great significance in controlling the growth and yielding capacity of mint plant.

Keywords : Mentha piperita, cytokinin, men thol oil, trichome glands.

The growth and essential oil production in menthaare affected by variable factors, such as photosynthetic rate, photoperiod, light intensity, climatic and seasonalchanges, ontogeny, nutrition, salinity, temperature,humidity and growth regulators (Kumar et al., 3; Povh and Ono, 4).

Cytokinin (kinitin) functions in whole plant fromembryo to mature plant. It plays a vital role inprocesses like cell division, shoot initiation and growth,delay senescence, photomorphogenic developmentunder response to environmental stimulus (Farooqi

and Sharma, 2; Scravoni et al., 5).

The present study on effect of exogenousapplication of cytokinin on growth and menthol oilcontents in pot grown mentha (Mentha piperita L.)under two variable seasons i.e. summer (hightemperature-long photoperiod) and winter (lowtemperature–short photoperiod) was conducted inDepartment of Botany, Dr. A.H. Rizvi Shia DegreeCollege, Jaunpur. Pot experiments were carried duringJanuary-March 2017 (winter season) and April-June2017 (summer season). Three levels of cytokinin i.e.1.0 ppm, 4.0 ppm and 10.0 ppm were sprayed twiceafter 15 and 30 days of planting of mentha plants inpots of 12 inch size. Control plants were sprayed withdistilled water only. Growth parameters (Table 1) weremeasures physically as per the nature of trait at 75

days after planting. Essential oil (menthol) contents infresh herb was estimated by steam distillation method.

Results (Table 1) exhibited that in summerseason, plant stem height (56.41cm) and number ofstolon branches/plant (44.51) were found maximum byexogenous application of cytokinin @ 4 ppm level.These values were much higher than in winter seasonin respect of above mentioned traits. Total leaf area

(3648.25cm 2/plant) and total biomass (9.67g) in

summer season was maximum due to higher level (10ppm) of cytokinin. Menthol contents in summer couldnot be influenced by cytokinin application, and it wasfound maximum (0.92%) in control plants. In winterseason, cytokinin spray increased the menthol content

at all levels over control, though menthol content wastoo low than in plants grown in summer. Spray ofcytikinin @ 4 ppm resulted in the highest content(0.48%) in winter season. Number of leaves (274.25)and total biomass (8.46 g) per plant were foundmaximum with 10 ppm concentration of cytokininduring winter season. Reports of Garg et al. (1),Farooqi and Sharma (2), Kumar et al. (3), and Scravoni et al. (5) are in close conformity with present results.

Exogenous application of growth stimulatorshowed significant influences on menthol oil contentsas regard the seasonal variation is concerned.Summer season (high temperature and longphotoperiod treatments) is most suitable environmentfor growth as compared to winter season (low




NAAS Rating : 3.78

Re search Note :

Effect of Cytokinin on Growth and Menthol Oil Content in Mentha under Seasonal Variation 311

temperature and short day). It concluded that cytokininand long day treatment provides a great significance ingrowth and yielding capacity of mint plant.

REFERENCES1. Garg, O.K., Hemantaranjan, A. and Gupta, R.

C. (1985). Response of Japanese mint (Menthaarvensis L.) to gibberellic acid under inductiveand non inductive day length condition. Abstracts- Symposium on medicinal and aromaticplants, Feb. 25-27, 1985, Mungpoo,Darjeeling.

2. Farooqi A.H.A. and Sharma S. (1988). Effect ofgrowth retardants on growth and essential oil

content in Japanese mint. Plant Growth Regul., 7(1) : 39–45.

3. Kumar A., Mouzzam H. and Singh P. (2012).Response of gibberellic acid on growth behaviour and menthol oil yield of mentha (Mentha piperita

L.). HortFlora Res. Spectrum, 1(3) : 284-285

4. Povh J.A. and Ono E.O. (2006). Rendimento doóleo essencial de Salvia officinalis L. sob ação de

reguladores vegetais. Acta Sci. Biol. Sci., 28(3):189-193

5. Scravoni J., Vasconcellos M.C., Valmorbida J.,Ferri A.F., Marques M.O.M., Ono E.O. andRodrigues J.D. (2006). Rendimento ecomposição química do óleo essencialde Mentha piperita L. submetida a aplicações de

giberelina e citocinina. Rev. Bras. Pl. Med., 8(4):40-43.

q

Citation : Kumar A., Maurya L.P., Singh N. and Yadav B.P. (2017). Effect of cytokinin on growth and menthol oil

content in Mentha piperita L. under seasonal variation. HortFlora Res. Spectrum, 6(4) : 310-311

Table 1 : Growth and menthol oil content of Mentha as influenced by cytokinin levels under

variable seasons.

Cytokinin

Height of plant (cm)

No. of stolonbranches/plant

No. of leaves/plants

Total leaf area/plant (cm2)

Biomass/plant (g)

Menthol oil content (%)

S W S W S W S W S W S M

00 ppm 48.25 45.34 36.80 112.40 305.11 242.21 3518.25 1615.52 9.45 6.89 0.92 038

1.0 ppm 52.16 44.41 40.11 114.51 374.20 262.35 3735.22 1695.34 9.34 7.46 0.89 0.36

4.0 ppm 56.41 42.21 44.51 118.61 383.31 270.51 3385.42 1881.20 9.63 8.13 0.84 0.48

10 ppm 54.21 51.14 50,18 120.11 375.20 274.25 3648.25 1872.40 9.67 8.46 0.68 0.36

S-Summer, W-Winter

Re viewer’s List 2017 1. Dr. Kavita Arvindaksharan

Asstt. Pro fes sor

Deppt. of Veg e ta ble Sciece,C.H.F. Jhalawar

2. Dr. Gopal SinghPro fes sor

Deptt. of Plant Pa thol ogyS.V.P.U.A.&T., Modipuram, Meerut

3. Dr. Nishant A. Deshmukh

Sci en tist (Hort.)

ICAR-Res. Com plex for NEH Re gion, Umiam PO-BhoiDistt-Meghalaya

4. Dr. Tanjeet Singh Chahal

Fruit Res. Sta tion (PAU),Jallowal-Lesriwal, Punjab

5. Er. Prem Kumar Sundaram

Sci en tist

Deptt. of Farm Mach. & Power Engg.ICAR Re search

Com plex for East ern Re gion, ICAR Parishar, P.O-Bihar

Vet er i nary Col lege, Patna-800014 (Bihar)

6. Dr. Madhubala ThakreScientist Div. of Fruits & Hortic, TechnologyIARI, Pusa, New Delhi.

7. Dr. Ajay Kr. TiwariSr. Scientist (Flori.)Div. of Floriculture & Landscaping IARI, Pusa New Delhi

8. Dr. Sanjay Kr. PatelAsstt. Pro fes sorKrishi Vigyan Kendra (Anand Agri. Univ.,Muvaliya Farm), Dahod-389151 (Guj.)

9. Dr. Manish Srivastava

Sr. Sci en tist

Div. of Fruits & Hortic. Tech.,IARI, Pusa, New Delhi-110-012

10. Dr. Rajesh Kumar Shukla

Asstt. Pro fes sor

Deptt. of Hor ti cul ture, Col lege of Ag ri cul ture,GBPUA&T, Pantnagar-263145

11. Dr. Rachna Arora

Asstt. Pro fes sor (Hort)Deptt. of Horticul ture, KVK, Langroya, Distt.- SBS Nagar, Punjab

12. Dr. S. K. Lodhi

Asstt. Pro fes sor (Hort.)

Di rec tor ate of Ex ten sion, SVPUA&T, Meerut

13. Dr Priyamvada Sonkar

As sis tant Pro fes sor (Fruit Sci ence)KNK College of Horticulture, Mandsaur (M.P.)

14. Dr. Sasmita Behera

Jr. Sci en tist (Hor ti cul ture)AICRP on Agroforestry, OUAT, Bhubaneshwar(Orissa)

15. Dr. Muzaffar Mir

Sci en tist (Fruit Sci ence)

KVK Poonch (SKUAST-J), Jammu

16. Dr. C. N. Panchal

Assistatnt Pro fes sor (Hort.)

Vanbandhu Agri. Poly tech nic (S. D. Agric. Univ.)

Amirgarh, Banaskantha (Gujrat)

17. Dr. J. L. Nag

As sis tant Pro fes sor (Hort.)College of Agriculture & Research Station, Kanker (C.G.)

18. Dr. Sanjay Kumar

Prof. (Hor ti cul ture)

Deptt. of Ap plied Plant Sci ence (Hort.) B.B.A.Uni -

ver sity,Rae Barelly Road, Lucknow-226025 (U.P.)

19. Dr. Satya Prakash

PC/Assoc. Dir. (Hort)Krishi Vigyan Kendra (SVPUAT, Meerut)Saharanpur

20. Dr. Rupa Upadhyay

Asstt. Pro fes sor

Deptt. of Hor ti cul ture Lady Irwin Col lege (DU),

New Delhi

21. Dr. M. L. Meena

Assoc. Pro fes sor

Deptt. of Ap plied Plant Sci ence (Hort.)

BBA Univ. Raibarely Road, Lucknow

22. Dr. Amitava Paul

Assoc. Prof.

Deptt. of Crop Im prove ment (Horti. & Ag. Bot any),

Palli Siksha Bhawan (Instt. of Ag ri cul ture), Vishva

Bharti, Sriniketan-731236 (W.B.)

312

The support provided by above reviewers and all the members of Editorial Bord (2017) by the way of peer review of thepapers published in 'HortFlora Research Spectrum' Vol. 6 (1–4), 2017 is duly acknowledged and appreciated. We look forwardto their continued assistance. —Dr. V.K. Umrao, Chief Ed i tor, HRS, Sec re tary, BAAS

Team HortFlora..........withes a very Happy, Healthy, Prosperous and Peaceful New Year -2018 to all the esteemedMembers of Advisory Board & Executive Council of BAAS, Editorial Board, Reviewers, Contributors and well wishers.