Volume 47 Number 3 2013

Contents

Review Paper

Rice based cropping system and climate change 239-247R.K. Tiwari, Amit Jha, S.K. Tripathi, I.M. Khan, and S.K. Rao

Oncolytic virotherapy in veterinary practice 248-254Sonal Shrivastava, P.C. Shukla, Debosri Bhowmick and Manisha Nakul

Research Paper

Genetic analysis of CIMMYT based bread wheat genotypes for yield and its contributing traits 255-259R.S. Shukla and P.K.Moitra

Investigation on ethno medicinal remedies to cure diseases by tribes of eastern MadhyaPradesh with special reference to threat assessment of leguminosae family 260-262Karuna S. Verma and Lekhram Kurmi

Phytochemical screening of different plant parts of munga (Moringa oleifera Lam.) 263-268Karuna S. Verma and Rajni Nigam

Multiple regression analysis a selection criteria for wheat improvement 269-273Varsha Patil, P.K. Moitra and R.S. Shukla

Association analysis studies in indigenous and exotic germplasm lines of rice 274-277Pankaj Nagle, S.K. Rao, G.K. Koutu and Priya Nair

Influence of zinc application on yield attributes, yield, chemical composition and proteincontent of wheat grown on Typic Haplustert of Kymore plateau, Madhya Pradesh 278-283K.S. Keram and B.L. Sharma

Effect of in-situ moisture conservation for improving niger productivity in Kymore plateau,Madhya Pradesh 284-287M.R. Deshmukh, Alok Jyotishi and A.R.G. Ranganatha

Water productivity of early, medium and hybrid rice varieties under aerobic condition 288-290R.K. Tiwari, B.S. Dwivedi, I.M. Khan, S.K. Tripathi and Deepak Malviya

Wine production from over ripe guava fruits using Saccharomyces cerevisiae 291-297Yogesh Kalyanrao Patil, L.P.S. Rajput, Yogendra Singh and Keerti Tantwai

Investigations on the nutritional characteristics of kodo millet based traditional fermentedfood by tribals of Madhya Pradesh, India 298-302Deepali Agrawal, A. Upadhyay and Preeti Sagar Nayak

ISSN : 0021-3721 JNKVVVolume : 47 Research JournalNumber(3) : 2013 (September - December, 2013)

Effect of different micronutrients on the incidence of major sucking insect pests of tomato 303-307A.S. Thakur, S.K. Barfa, Amit Kumar Sharma and R. Pachori

Efficacy of some new molecules against the infestation of bringal shoot and fruitborer (Leucinodes orbonalis Guenee) 308-311R. Pachori, Sapna Tanve, Amit Kumar Sharma and A.S. Thakur

Insect pest complex on Acacia 312-314H. Dayma and R. Bajpai

Genetic resources of okra for the utilization in the management of Okra Yellow Vein MosaicVirus disease under climatic conditions of Kymore plateau zone, Madhya Pradesh 315-320Usha Bhale, Priyanka Dubey and S.P. Tiwari

Effect of weather parameters on development of ber powdery mildew and its controlby fungicides 321-324P.K. Amrate, Amarjit Singh and Chander Mohan

Population dynamics and management of lesion nematode (Pratylenchus thornei) in chickpea 325-329Jayant Bhatt, Arvind Jaware and S.P. Tiwari

Modelling and forecasting of area, production and yield of soybean in India 330-336P.Mishra, H.L.Sharma, R.B. Singh and Siddarth Nayak

SWOT analysis for lac cultivation in Madhya Pradesh 337-340Arvind Dangi Thakur, S.C. Meena and Ashutosh Shrivastava

Performance of National Agricultural Insurance Scheme in Raisen District of MadhyaPradesh- An economic evaluation 341-345Govind Prasad Namdev, P.K. Awasthi and N.K. Raghuwansi

Growing degree days (GDD) measurement system to predict plant stages 346-349Bharati Dass and A.K. Rai

Issued : 30 December 2013

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Chairman Dr. S.K. RaoDean, Faculty of Agriculture, JNKVV, Jabalpur

Members Dr. S.S. TomarDirector Research Services, JNKVV, JabalpurDr. O.P. VedaDirector Instruction, JNKVV, JabalpurDr. P.K. MishraDirector Extension Services, JNKVV, JabalpurDr. R.V. SinghDean, College of Agriculture, JNKVV, JabalpurDr. G.S. RajputDean, College of Agricultural Engineering, JNKVV, Jabalpur

Editor Mohan S. BhaleCo-Editor Abhishek Shukla

General Information: JNKVV Research Journal is the publication of J.N. Agricultural University (JNKVV),Jabalpur for records of original research in basic and applied fields of Agriculture, Agricultural Engineering, Vet-erinary Science and Animal Husbandry. It is published thrice a year (from 2012). The journal is abstracted in CABInternational abstracting system, Biological Abstracts, Indian Science Abstracts. Membership is open to all indi-viduals and organizations coping with the mission of the University and interested in enhancing productivity,profitability and sustainability of agricultural production systems and quality of rural life through education, re-search and extension activities in the field of agriculture and allied sciences.

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ISSN : 0021-3721 Registration No. : 13-37-67

Published by: Dr. SK Rao, Dean, Faculty of Agriculture, JNKVV, Jabalpur 482004 (M.P.), India

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Abstract

Rice (Oryza sativa L.) is the most important staple food cropin India that holds key to food security. Rice-based productionsystems provide livelihood for more than 50 millionhouseholds. In India, rice is grown on more than 44 m haunder three major ecosystems; rainfed uplands (16% area),irrigated medium lands (45%) and rainfed lowlands (39%),with a productivity of 0.87, 2.24 and 1.55 t ha-1, respectively.The change in climate has been attributed to global warmingand has many facts, including changes in long term trend intemperature and rainfall regimes as well as increasing year-to- year variability and a greater prevalence of extreme events.Agricultural systems will be affected by both short and longterm changes in climate, and will have serious implicationson rural livelihoods, particularly of the poor being the mostvulnerable. The impact of climate change poses seriousthreats to productivity and sustainability of various rice- basedcropping systems including rice- wheat cropping system, thebackbone of food security of India. Despite some projectedincrease in photosynthesis caused by increasedconcentrations of carbon dioxide, increased temperature willhave a far greater detrimental effect, resulting reduced cropproductivity. The rice - based cropping systems will continueto be important cropping systems in India in the years to come.Therefore, there is a strong need to monitor these systems interms of nutrient dynamics and to develop efficient integratednutrient supply and management system in different grgionsusing locally available resources like compost, farm yardmanure, farm wastes, crop residues and green manures.There is also a need to monitor insect, disease and weedproblems, water table and water harvesting techniques. Cropestablishment of succeeding crops after rice and dry seedingmethods of rice need greater attention. There is a need forthe choice of genotypes and introduction of short duration,photoperiod insensitive varieties, the possibilities for crop

intensification/ diversification have to be studied. Thus, amplescope exists for improving the total land productivity throughgeneration of appropriate production technologies for diverseagro-climatic situations.

Keywords : Rice, cropping system and climate

The continuing population pressure in the country willdemand substantial increase in food, feed, fodder, fiberand fuel production over the next few decades to beable to maintain self-sufficiency and also meet exportrequirement. Our population has already crossed onebillion mark and is estimated to stabilize around 1.5billion by the year 2030. The demand for food grains isestimated at 240 m t by the end of XI plan period.Keeping this in view, the government of India launchedthe National Food Security Mission to achieve theproduction of additional 10, 8 and 2 m t of rice, wheatand pulses, respectively. The task is quite challengingand the options available are very limited in view ofplateauing rend of yield in high productive areas,decreasing and degrading land, water, labour and otherinputs. Among the various possible approaches toachieve this target is to increase the productivity perunit time and area i.e.e, by raising two or more cropsper year through multiple, relay and intercropping bothin irrigated and rain fed areas; and by utilizing theavailable resources more efficiently. With the availabilityof shorter duration varieties of different crops the scopefor growing two or more crops in a year is continuouslyincreasing. Hence, emphasis needs to be laid onidentification of suitable cropping systems with higherand stable yields and / or profit in different agro-ecological regions. Further, in response to

Rice based cropping system and climate change

R.K. Tiwari, Amit Jha*, S.K. Tripathi, I.M. Khan, and S.K. Rao**Jawaharlal Nehru Krishi Vishwa VidayalyaCollege of Agriculture Rewa (MP)*College of Agriculture Jabalpur** Dean, Faculty of Agriculture JabalpurEmail: rktkvkrewa@rediffmail.com

JNKVV Res J 47(3): 239-247 (2013)

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commercialization of agriculture also, it is important toshift from routine food grain production system to nevercrops/ cropping systems depending upon the climaticconditions as well as agro- ecosystems under differentrice- based production system in order to makeagriculture an attractive, profitable and sustainablebusiness.

The change in climate has been attributed to globalwarming and has many facts, including changes in longterm trend in temperature and rainfall regimes as wellas increasing year- to- year variability and a greaterprevalence of extreme events. Agricultural systems willbe affected by both short and long term changes inclimate, and will have serious implications on rurallivelihoods, particularly of the poor being the mostvulnerable. The impact of climate change poses seriousthreats to productivity and sustainability of various rice-based cropping systems including rice- wheat croppingsystem, the backbone of food security of India. Despitesome projected increase in photosynthesis caused byincreased concentrations of carbon dioxide, increasedtemperature will have a far greater detrimental effect,resulting reduced crop productivity. Conservationagriculture involving continuous minimum mechanicalsoil disturbance, permanent organic soil cover anddiversified crop rotations provides opportunities formitigating green house gas emission and climatechange adaptation.

Rice (Oryza sativa L.) is the most important staplefood crop in India that holds key to food security. Rice-based production systems provide livelihood for morethan 50 million households. In India, rice is grown onmore than 44 m ha under three major ecosystems;rainfed uplands (16% area), irrigated medium lands(45%) and rainfed lowlands (39%), with a productivityof 0.87, 2.24 and 1.55 t ha-1, respectively. The crop inrabi/summer is grown on nearly 3 m has mostly with

irrigation in the eastern and southern states, but thekharif crop is grown under a wide range of soil andclimatic conditions throughout the country. Ricecultivation in eastern India is characterized bypredominantly rainfed culture (70%), mono-cropping,low fertilizer use and traditional varieties. On the otherhand under irrigated conditions, input intensive rice-based cropping systems involving cereals, pulses,oilseeds tuber and fiber crops are practiced.

Rice and rice- based cropping systems

Based on rational spread of crops in different agro-climatic regions of the country, about 500 croppingsystems have been identified by the PDCSR, but only30 cropping systems are important because of theirsizable area. (Yadav et al. 1998). Among them, differentrice based cropping systems such as rice-wheat, rice-rice, rice-chickpea/lentil, rice-mustard/linseed and rice-groundnut etc. Together occupy the largest area in thecountry. Rice-wheat and rice-rice cropping systemscontribute to major share of food grain pool of the nation,while other rice based cropping systems have theirsignificance to contribute the national production of oil-

Fig 1. Area, Production and Productivity of KharifRice during 2006-07 to 2010-11 of India

Fig 2. Area, Production and Productivity of Rabi/Sum-mer Rice during 2006-07 to 2010-11 of India

Fig 3. Rice area , production and productivity scenarioduring last 50 years

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seed and pulse crops.

With the introduction of high yielding photo andthermo insensitive rice varieties of relatively shorterduration, there was remarkable changes in the croppingsystem concept (Sharma et al, 2004). A large numberof crops are now being grown after rice under differentecologies based on soil and prevailing agro- climaticconditions in major rice growing states of the country.Out of the major cropping systems identified by theProject Directorate for Farming Systems Research, rice-based system occupies the largest area of about 28 mha in India. Among the rice- based cropping systems,the major ones are rice- wheat (9.8 m ha), rice-rice (5.9m ha), rice - fallow (4.4 m ha), rice-pulses (4.4 m ha),rice vegetables (1.2 m ha), rice groundnut (1.0 m ha),rice -mustard (0.5 m ha), rice- potato (0.5 m ha) andrice -sugarcane (0.4 m ha) Yadav & Subba Rao, 2001).

Issues in rice based cropping systems

Rice-wheat sequence is the most widely adoptedcropping system in the country and has becomemainstay of cereal production. The U.P., Punjab,Haryana, Bihar, M.P. and West Bengal state are theheartland of this cropping system with an estimated areaof about 12 million ha. This system is spreaded in 123districts of these states and contributes about 25 and

42% of the total national rice and wheat production,respectively. Rice-rice cropping system is next to it,covering an area of about 6 million ha. in Kerla,Tamilnadu, A.P. states during kharif and rabi seasons;and in Assam, West Bengal, Orissa, Maharashtra statesduring kharif and summer seasons. Among other ricebased cropping systems, rice-chickpea in Jharkhand,M.P., West Bengal and Bihar states; rice-mustard inWest Bengal, Orissa, Chhatisgarh and rice-groundnutin Tamilnadu, A.P. states are prevalent. All rice basedcropping systems are practiced under both irrigated andrainfed production systems depending on the agro-climatic suitability. Several issues/constraints such asland degradation, decline in soil productivity, inefficientland use pattern, low water use efficiency, build up ofdiseases/insects/weeds infestation and decline inenvironmental quality etc are emerging in the areascovered under different rice based cropping systems.

The specific issues needing careful attention ofresearchers for dominant rice based cropping systemscould be listed as below:-

1. Deterioration of soil physical properties byformation of hardpan in sub surface because ofsoil puddling for rice cultivation.

2. Difficulties for tillage and poor crop stand in wheatcultivation.

Table 1. Rice based cropping system in different agro-ecological zone of India

Agro ecological zone Soil Predominant rice based cropping systemWestern Himalayan region Hill soil, sub-mountain Rice-fellow, maize-wheat and rice-potatoEast Himalayan Broon hill, acidic soil, alluvial red Rice-fellow, rice-rice and rice-pulse/

sandy, red yellow oilseedLower gangetic plains Red alluvial, red, yellow loamy soil Rice-rice and rice-wheat, rice-potato-jute

and rice-potato-vegetablesMid gangetic plains Alluvial, calcareous, tarai soil Rice-wheat, rice maize, rice-potato-

sunflowerUpper gangetic plains Alluvial, tarai soil Rice-wheatTrans gangetic plains Alluvial, calcareous Rice-wheatEastern plateau and hills Red, yellow, sand loam to laterite Rice-black gram /niger/linseed,

rice-groundnut and rice-vegetablesSuthern plateau and hills Medium to deep black, red sandy to Rice-bengal gram/green gram, rice-rice

loamy coastal and deltaic alluviumEastern coast plains and hills Delta coastal, alluvial laterite red Rice-groundnut-green gram, rice-green

and medium black soil gram/black gram and rice-riceAndman and Nicobar island Medium to very deep red loamy and Rice-fellow

sandy soil

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3. Development of multiple nutrients (secondary andmicro) deficiency.

4. Declining water table in some areas due toindiscriminate exploitation of underground waterfor irrigation.

5. Buildup of infestation of Phalaris minor and wildout in wheat.

6. Imbalance or low use of fertilizers.

7. Lack of appropriate varietal adjustment for cropcomponents.

Rice Ecosystem

Preference of rice based cropping systems in differentparts is based on location advantage facilities. Forexample, rice - wheat and rice - rice systems arepracticed in irrigated ecology whereas rice - lathyrus,rice- gram or rice - blackgram etc. are practiced inrainfed uplands and lowlands.

Rainfed uplands

In India, 85% of the upland rice area is located in thestates of Assam, Bihar, West Bengal, Odisha andeastern Parts of Madhya Pradesh and Uttar Pradesh.The rainfall in this zone is in the range of 1000 to 2000mm or more and temperature ranges from 25 to 41 0Cin July and from 6 to 25 0C in January. Red lateritecand lateritic soil such as mixed red and yellow, redsandy, red loam, lateritic and mixed red and brown hillsoils account for about 55% of the total rice area in TheEast Zone .Next in the order of occurrence is alluvialsoil, which occupies about 27% of the total rice area.Rice is grown under rain fed condition in these uplandsin monsoon season.

In rain fed uplands, shorter duration (90-105days) rice varieties like Vandana, Dhanteshari, KalingaIII, Anjali, NDR 97, Annada are to be grown by sowingduring the onset of monsoon, so that the field shouldbe vacated early for the second crop (Saha et al. 2003).Crops like mustard, castor, linseed, safflower,balckgram, lentil, horsegram can be grown by takingadvantage of residual soil moisture and late monssomrains. The second crop should be sown as early aspossible (within a week) after harvest of rice to get theadvantage of residual soil moisture. Mulching by using

rice straw alsohelps to conserve the soil moisture undersuch situations. In bunded uplands, where there is stillpossibility for giving at least one or two irrigation throughharvested rain water, crops like sunflower, gram, tomato,etc. can be successfully grown after harvest of wetseason rice. The short duration impoved varieties ofthe above crops can give a good return under suchsitations, Intercropping of upland rice with pigeon pea(4:1 row ratio ) recorded higher rice equivalent yieldand net return over sole crop.

Irrigated medium lands

Under irrigated condition majority of rice is grown inwet season ( June to October) but around 4 m ha isunder dry season ( November to May) . The importantcropping systems followed under irrigated medium landsituations are rice- rice, rice - wheat , rice- winter maize,rice- groundnut , reice - sunflower, rice- potato, rice-mustard, rice- gram , rice - winter vegetables, etc. with200% cropping intensity. There is still scope to introducea third crop of short duration pulses like cowpea,greengram, blackgram or oilseed crops like sesame inareas where irrigation facilities are available to provide1-2 life saving irrigation to the third crop. In M.P., rice -potato- wheat, rice- wheat- grengram, onion- wheat -jute are found to be remunerative.

In Assam ( rice- rice - rice cropping sequencewith 300% cropping intensity) to meet household foodsecurity and year round employment generation forsmall and marginal farmers with land holding less than1 ha. In Punjab, the cropping system with 300% intensitysuch as rice- potato - sunflower, rice- potato- wintermaize and rice- toria- sunflower have been found to bemore productive than conventional systems of 200%cropping intensity with rice- wheat or rice - winger maize.Early medium to medium duration (120-135 days) ricevarieties like Naveen, Ratna, IR 36, Padmini, Khitish,Shatabdi, Tapaswini provides good scope to advancethe sowing time of the second crop by late October toearly November so that the third crop ( 70-90 days) canbe accommodated during February - April.

Rainfed lowlands

Rainfed lowland rice is grown in around 13 m ha, mostlyin eastern India, where soil moisture is available forlonger period, rice varieties of 140 days duration , mostlyphotosensitive types are grown and harvested from mid

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November to mid December. The water depth varies inrainfed lowlands and it can be shallow up to 25 cm, andmedium - deep waterlogged up to 50 cm. Deep- waterrice is grown in areas where water depth is more than50 cm up to 2 m, and around 4 m ha area is undercultivation in eastern India with an average productivityof 0.8 t ha-1. Most of the deep water rice area in WestBengal, Assam, North- east Bihar, and coastal Odishaisnow being under boro and dry season rice due to lowproductivity of deep water rice.

Farmers traditionally grow tall indica types, whichare prone to lodging and are of low productivity. Thevarieties grown in these land situations are generallymedium to long (130-180 days) duration dependingupon the water depths in the fields where they are grownand should have tolerance to drought initially and tosubmergence at later stage; photosensitivity; moderateto high tailoring abilities; tolerance to drought initiallyand to submergence at later stage; photosensitivity;moderate to high tillering abilities; tolerance to pestsand diseases and elongation ability in semi- deep anddeep situations. The ideal plant height for shallowlowlands is 110-130 cm, 130 -150 cm for medium deepsituations and > 150 cm for deep water areas.

Medium to long duration (140-155 days) ricevarieties like Swarna, Vijeta, Surendra, Moti, Pooja,Pankaj, etc. are usually grown in rainfed shallowlowlands of eastern India. Short duration pulses likegreengram, blackgram, etc. can be grown after riceharvest with residual soil moisture. There is a little scopeto take a second crop in areas where soil moisturerecedes fast during November onwards. Under suchsituation , crops like lathyrus, field pea, linseed, lentil,blackgram can be raised as relay / paira crop by sowingthe second crop in the standing crop of rice 10-15 daysbefore harvesting 9 (Saha & Moharana, 2005) . Incertain areas of eastern India, crops like sunflower,groundnut, watermelon, okra, sweet potato can beraised with limited irrigation (2-3) by utilizing theharvested rainwater stored in small farm ponds.

Long duration (155-180 days) photosensitive ricevarieties like Varshadhan, Gayatri, Savitri, sarala,Panidhan, Durga, Tulsi, Kalashree, Sabita and Naliniare grown predominantly intermediate deep and deepwater rice ecology of east coast and lower GangeticPlains of India. These areas are having potential toharvest rainwater during monsoon period ( June-September) that can help to grow several wintervegetables like pumpkin, bitter gourd , okra, chilli, alongwith other crops like blackgram, greengram, sunflower,

groundnut, watermelon, seasame, etc during the dryseason ( January- early April). The salt affected coastalareas are generally rainfed and mono- cropped withrice, Land mostly remains fallow during the dry seasondue to soil salinity and unavailability of fresh water.However, rice and certain salt tolerant crops likesunflower, chilli watermelon, sugat beet, cotton , etc.are grown in pockets depending on the availability ofharvested rainwater, soil and climatic conditions(Singhet al. 2006) Pulses like blackgram, greengram,cowpea, etc. and groundnut is also grown in some areashaving mild salinity.

Sustainability issues in cropping systems

• In semi- arid ecosystem intensive water use inrice - wheat cropping system led to increasedsalinization in many areas. There are indicationsof yield declines where balanced nutrientapplication has not been made. Deficiency ofmicronutrients has been observed.

• The problem of depletion of underground waterin semi- arid areas of Punjab and Haryana needsto be addressed through development ofalternate cropping system under limited watersupply.

• To reduce the use of purchased inputs by smallfarmers, green manure should be introduced inthe rice - wheat, rice- rice system. Fifty percentof nitrogen requirement of rice could besubstituted by growing sesbania beforetransplanting rice.

• In the sub- humid exosystem, reduction in wheatyield following rice is due to delayed wheatplanting, low plant stand and poor nutrientmanagement . Delayed wheat planting isassociated with excess soil moisture at the timeof rice harvest. Higher seed rate and nutrient cancompensate wheat yield losses to some extent.

Management of rice - based cropping systems

The rice - based cropping systems will continue to beimportant cropping systems in India in the years to come.Therefore, there is a strong need to monitor thesesystems in terms of nutrient dynamics and to developefficient integrated nutrient supply and managementsystem in different grgions using locally available

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resources like compost, farm yard manure, farm wastes,crop residues and green manures. There is also a needto monitor insect, disease and weed problems, watertable and water harvesting techniques. Cropestablishment of succeeding crops after rice and dryseeding methods of rice need greater attention. Thereis a need for the choice of genotypes and introductionof short duration, photoperiod insensitive varieties, thepossibilities for crop intensification/ diversification haveto be studied. Thus, ample scope exists for improvingthe total land productivity through generation ofappropriate production technologies for diverse agro-climatic situations.

Conservation agriculture

Conservation agriculture is characterized by threepriciples which are linked to each other, namelycontinuous minimum mechanical soil disturbance,permanent organic soil cover and diversified croprotations in the case of annual crps or plant associationsin case of perennial crops which provides opportunitiesfor mitigatin greenhouse gases (GHGs) emission andclimate change adaptation.. Recent research effortshave attempted to develop resource conservingtechnologies (RCTs), which are more resource efficient,use less inputs, improve production and income, andreduce GHGs emission compared to the conventionalpractices. Some of these technologies are beingadopted by the farmers on large scale, which wouldhelp farmers in combating climate chage to aconsiderable extent . Specific impacts of various RCTson GHGs mitigation are briefly discussed below.

Zero tillage

Conventional land preparation practices for wheat afterrice involves as many as 10-12 tractor passess.Changing to a zero- till system on 1 ha of land wouldsave 98 liters of diesel and approximately 1 million litersof irrigation water besides reducing about a quartertonne less emission of carbon dioside (CO2), Theprincipal contributor to global warming. However, impactor zero tillage on methane (CH4) and nitrous oxide (N20)emissions have showed contrasting results with lower,equal and higher compared to the conventional systemsdepending upon the soil type and water management.Zero tillage also allows rice - wheat farmers to so wheatsooner after rice harvest, so the crop heads and fill thegrain before the onset of pre monsoon hot weather.

Laser land leveling

Laser leveling of uneven field reduces water useallowing crop to grow in water limited condition. It alsoreduces fuel consumption because of efficient use oftractor and reduces GHGs emission , particularly CO2.Several other benefits such as operational efficiency,weed control efficiency, water use efficiency, nutrientuse efficiency, crop productivity and economic returnsand environmental benefits have also been reporteddue to laser aided land leveling compared toconventional practice of land leveling.

Direct Seeded Rice

Direct drill seeding of rice (DSR) could be a potentialoption for reducing Ch4 emission. Methane Is emittedfrom soil when it is continuously submerged as in thecase of conventional puddle transplanted rice. The DSRcrop does not require continuous soil submergence,thereby either reducing or totally eliminating CH4emission when it is grown as an aerobic crop. More-over, deeper root growth of DSR crop provides bettertolerance to water and heat stress. Besides theunpadded soil in DSR does not crack with moisturestress unlike puddle soil which helps to increase yieldsignificantly.

Crop Diversification

Diversification is growing a range of crops suited todifferent sowing and harvesting times, assists inachieving sustainable productivity by allowing farmersto employ biological cycles to minimize inputs, maximizeyields, conserve the resource base, reduce risk due toboth environmental and economic factors. The RCTssuch as bed planting and zero tillage expand thewindows of crop diversification. The farmers of rice-wheat belt have taken the initiative to diversify theiragriculture by including short duration crops such aspotato, soybean, blackgram, greengram, cowpea, pea,mustard, and maize into different combinations. Suchdiversification wouild not only improve income,employment and soil health but also reduce water useand GHGs emission and more adaptability to heat andwater stress.

Raised bed planting

In raised bed planting a part of soil surface always

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remains unsubmerged. Thus it not only reduces wateruse and improves drainage but also reduces methaneemission. Crops on beds with residue retained onsurface is less prone to lodging and more tolerant towater stress, thereby making it more adaptable tounfavorable climate.

Leaf color chart

The most efficient management practice to minimizeplant N uptake and minimize N loss is to synchronizesupply with plant demand. The use of leaf colour chart(LCC) promotes a need based N application to rice cropthat saves N and increases N use efficiency. As a resultthere will be less accumulation of mineral forms on N(NH4 and NO3) within the crop root zone and hence lesslosses of N and N2O emission. Besides, because ofhealthier plant growth due to timely application of Nfertilizer, damages caused by insects were reported tohave been reduced.

Integrated nutrient management

Food security and soil health are two important concernsin Indian agriculture. Integrated nutrient management(INM) in crop production, particularly in rice- basedcropping systems, plays a crucial role in the pursuit ofthese two set missions. Integrated nutrient managementis achieved through combined use of different sourcesof plant nutrients such as chemical fertilizers, organicmanures, green manures, crop residues, bio- fertilizers,industrial wastes and soil conditioners depending upontheir availability and suitability in a specific agro-ecological situation (Hegde and Dwivedi 1992), Pandaand Singh 1998). It also includes scientific managementof these sources of nutrients for securing optimum cropyield and soil fertility improvement. According to Royand Ange (1991). The basic concept underlyingintegrated plant nutrient supply and managementsystem (IPNS) is the maintenance or adjustment of soilfertility and of plant nutrient supply to an optimum levelfor sustaining the desired crop productivity throughoptimization of the benefits from all possible sources ofplant nutrients in an integrated manner. Economicviability and ecological sustainability are also majorconsiderations in INM. In a holistic approach , the INMpractices are designed and adpted to increase thequantity and of crop produce, decrease nutrient losses,increase the efficiency of applied and native nutrients,improve soil health , economize on fertilizer use, protectthe environment and minimize the energy consumption

in agriculture.

Takkar et al. (1998) considered a conceptualframework of IPNS which includes four distinct integralcomponents viz. (i) on-site nutrient resource generation.(ii) mobilization of off - site nutrient resources, (iii)resource integration and (iv) resource management. On- site nutrient resource generation is mostly achievedthrough green manuring and recycling of crop residues.Mobilization of off - site nutrient resources includes threecategories of sources of nutrients viz, bio- organicwastes (FYM and compost), bio- organisms (bio-fertilizers) and mineral resources (synthetic and mineralfertilizers). Resource integration, the guiding principleof INM not only supplements the fertilizer use but alsoprovides the benefits of positive interaction for variousnutrient sources in restoring soil fertility. It also ensuresbalanced crop nutrition and synergetic interaction in acropping system for sustainable agriculture. The nutrientresource management improves the nutrient useefficiency by decking nutrient losses from soil, optimizingnutrient resource combination and monitoring plantnutrient flows. It also addresses the sil related problemslimiting, crop growth such as soil acidity , salinity,alkalinity, soil compaction, etc. Ultimately, it impartsresilience against the soil degrading processes andpromotes quality of the environment.

Because of several reasons including those ofsoil bealth care and high crop yield, it is necessary tosupplement / complement chemical fertilizer applicationwith the other components of INM which are mostlyorganic in nature. Results of research on INM in irrigatedrice revealed that at N level of 60 kg ha-1, combinedapplication of urea and dhaincha green manure/ Azolla/FYM at 1:1 ratio on N level basis, produced comparablegrain yield to that of urea alone. However, at N level of90 kg ha-1, INM practices involving dhaincha greenmanure or Azolla dual crop were superior to thechemical source on N (Panda et al. 1991)

Weed Management

Climate change will also affect the weed communitiesin the rice based cropping system. A review on the effectof weed growth on yield suggested losses in the range28-74% in rice and 15-80% in wheat 5,6. Improvingweed control in farmers' field has shown to increaserice and wheat yield by 15-30%.Northwest Indiaannually contributes more than 50-60% of rice andwheat to the central food grain reserve, making it the'bread basket' of the country. Therefore, if productivityof these crops is affected, Indian food security is bound

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to be affected. Given that the demand for food isprojected to rapidly outpace increase in supply, effectiveweed control is a priority in this system. Important weedsof rice include Echinochloa crusgalli, E. colona, E.glabrescens, Ammanniaspp., Eragrostis spp., Ludwigiasp., Ischaemum rugosum, Leptochloa chinensis,Paspalum distichum, Cyperus iria, C.difformis,Fimbristylis miliacea, Scirpusmaritimus, Eleocharis spp.,Eclipta prostrata, Sphenoclea zeylanica andMonochoriavaginalis. Important weeds ofwheat includePhalaris minor, Avena ludoviciana, Poa annua,Loliumtemulentus, Chenopodium album,Rumexdentatus, R. spinosus, Medicago denticulata,Melilotus alba, Anagallis arvensis, Lathyrus aphaca,Fumaria parviflora, Vicia sativa, Coronopus didymus,Malvaparviflora and Cirsium arvense. Common weedmanagement practices in the rice based croppingsystem include soil tillage, flooding, summerploughing,crop rotation and use of herbicides; thesepractices are often used in combination. Integratedweed management strategies need to be developedwhich target the prevention of weed invasion,recruitment and reproduction. Such strategies mayinclude combination of optimal fertilizer schedule,summer ploughing, crop rotation, land preparation,modifying plant geometry, stale seedbed technique,planting time, seed rate and use of weed-competitivecultivars18. Knowledge of weed ecology and biologycould be used as a tool for effective weedmanagementin futuristic climate change.

Conclusion

The overwhelming importance of rice and rice - basedcropping systems for the food security of India requiresa through assessment of the rice resource base andthe impact of rice cultivation on the environment. Thedecline in soil and water quality in rice - based systemsis a major global issue. The situation is going to beworse in the event of possible global warming, whichhas negative impact on yield and soil fertility. Therefore,the systems should be constantly monitored in terms oftheir natural resource base. Suitable quantitative modelsthat incorporate the relevant bio-physical andsocioeconomic interactions to permit quantitativeassessment of rice cultivation in relation to theenvironment and natural resources need to bedeveloped. An environmental impact assessmentshould include a social impact assessment, strategicenvironmental assessment, and life cycle analysis ofthe implementation of rice technologies. Holistic andecoregional strategies to manage, preserve and improve

the nutrient resource base and soil qualities in rice-based cropping systems have to be strengthened.

Climate change poses serious threats toproductivity and sustainability of various croppingsystems. Recent efforts have attempted to develop anddeliver resource conservation technologies involving no- or minimum tillage with direct seeding and bed plantingwith residue mulch, innovations in residue managementto avoid straw burring and crop diversification asalternatives to the conventional management practicesfor improving productivity and sustainability of importantrice-based cropping systems. The wide scale adoptionof any improved cropping system by the farmingcommunity depends mostly on socio-economic factorssuch as labour availabiltity, credit requirement, cost ofinputs, processing, marketability and price of produce,risk involved and social acceptability of the new system.Thus before designing a particular cropping system,care should be given on its economic feasibility.Emphasis should also be given for developing suitablerice- based farming system model by incorporationanimal components into the system to enhance theoverall economy and standard of living of poor farmcommunity.

References

Hegde D M, Dwivedi B S (1992) Nutrient management in rice- wheat cropping system in India. Fertilizer News 37,27-41

Panda D, Singh D P (1998) In Rainfed rice for sustainablefood Security. (pp. 239-258). Cuttack: Central RiceResearch Institute

Panda D, Samantaray R N Mohanty S K, Patnaik S (1991)Green manuring with sesbania aculeate: Its role innitrogen nutrition and yield of rice. In S.K. dutta, C.Sloger (Eds.). Biological nitrogen fixation associatedwith rice production. (pp. 305-313). New Delhi: oxfordand IBH Pub Co

Roy R N, Ange A L (1991) Integrated plant nutrition systems(IPNS) and sustainable agriculture, In Proceedingsof FAI Annual Seminar. New Delhi: Fertilizerassociation of India

Saha S, Moharana M (2005) Utera cultivation - A viabletechnology option for rainfed shallow lowland ofcoastal Orissa. Indian Farming 56 (3) 13-15 19

Saha S, Dani R C, Beura J (2003) Integrated cropmanagement for rainfed upland rice NATP TechnicalBulletin No. 14. Central Rice Research Institute

Sharma S K , Subbaiah S V , Rao K S, Gangwar K S (2004)

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Rice- based cropping system for rainfed upland,rainfed lowland and irrigated areas of different statesof India. In Proceedings of National symposium on"Recent advances in rice - based farming systems".Central Rice Research Institute (Cuttack. P. 36-57

Singh D JP, Mahata KM R , Saha S, Is mail A M (2006) Cropdiversification options for rice- based croppingsystem for higher land and water productivity incoastal saline areas of eastern India. In Abrtr. 2ndInternational Rice Congress on "Science, Technologyand trade for peace and prosperity" 475 New DelhiIARI

Takkar P, Kundu S, Biswas A K (1998) In A Swarup et al.(Eds.), Long term soil fertility management throughIntegrated Plant Nutrient Supply . (pp. 78-88). Bhopal,India: Indian Institute of Soil Science

Yadav R , Subba Rao A V M (2001) Atlas of cropping systemsin India. (pp. 96) . Modipuram, Meerut, India: ProjectDirectorate for Cropping Systems Research

Yadav RL, Kamata Prasad and Singh R K (1998) Predominantcropping system of India. Project directorate croppingsystem research(PDCSR), Meerut

(Manuscript Receivd : 30.8.13; Accepted : 11.11.13)

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Abstract

Oncolytic virotherapy is an emerging treatment modality thatuses replication-competent viruses to destroy cancers.Oncolytic viruses are therapeutically useful viruses thatselectively infect and damage cancerous tissues withoutcausing harm to normal tissues. The specific tumour targetingcan be achieved by targeting various molecular steps/regulators of cell cycle eg. pro-apoptotic, translational,transductional and transcriptional targeting, and strategiesbased on tumour micro-environment and use of carrier cellsas cellular vehicle for oncolytic viruses. Oncolytic viruses suchas various human and canine adenoviruses, canine distempervirus and vaccinia virus strains have been preclinically testedfor canine cancer therapy. Several research groups andbiotechnology companies have engineered therapeutic virusesand armed them with genes that make the cells they infectuniquely susceptible to chemotherapy. These viruses areunder clinical trial phase I, II or III. Oncolytic viral therapy iscapable of increasing the therapeutic index between tumorcells and normal cells when viral replication proceedspreferentially in tumor cells. Armed therapeutic viruses orgenetically engineered viruses represent a very appealingtumor targeting approach and a novel opportunity to generateagents that could potentially cure canine cancers.

Keywords: Canine, Oncolytic virotherapy, Tumour

Oncolytic virotherapy is an emerging treatment modalitythat uses replication-competent viruses to destroycancers. Oncolytic viruses are therapeutically usefulviruses that selectively infect and damage canceroustissues without causing harm to normal tissues (Russelland Peng 2007). Oncolytic viruses have beensuggested to have great potential for cancer therapy,not only by direct destruction of the tumor cells, butalso to deliver other genes, for example genesexpressing anticancer proteins and as immunotherapy(Melcher et al. 2011).

Oncolytic virotherapy in veterinary practice

Sonal Shrivastava, P.C. Shukla, Debosri Bhowmick and Manisha NakulDepartment of Veterinary MedicineCollege of Veterinary Science & Animal HusbandryNanaji Deshmukh Veterinary Science UniversityJabalpur 482001 (MP)Email : sonal23987@gmail.com

Viral oncolytic therapy is under intense investigationas a novel anticancer strategy. Both alone and incombination with other conventional treatmentmodalities, viral oncolytics exploit the natural cytotoxicityof viruses to directly kill tumor cells. Results frompreclinical studies demonstrating the intricate interactionbetween oncolytic viruses, the targeted tumors and theirhosts, has resulted in new strategies being developedto overcome the challenges of maximizing oncolytic viralefficacy while ensuring safety (Woo et al. 2006).

History of oncolytic virotherapy

One of the first inklings that viruses could be useful incombating cancer came in 1912, when an Italiangynecologist observed the regression of cervical cancerin a woman who was inoculated with a rabies vaccinemade from a live, crippled form of the rabies virus(Nettelbeck et al. 2000). Physicians first injected virusesinto cancer patients intentionally in the late 1940s, butonly a handful appeared to benefit. In 1996, the firstapproval was given in Europe for a clinical trial usingthe oncolytic herpes simplex virus (HSV1716). From1997 to 2003, strain HSV1716 was injected into tumorsof patients with glioblastoma multiforme, a highlymalignant brain tumor, with no evidence of toxicity orside effects, and some long-term survivors. Other safetytrials have used HSV1716 to treat patients withmelanoma and squamous-cell carcinoma of head andneck (Rampling et al. 2000).

The first oncolytic virus to be approved by aregulatory agency was a genetically modifiedadenovirus named H101 by Shanghai Sunway Biotech.It gained regulatory approval in 2005 from China's StateFood and Drug Administration (SFDA) for the treatmentof head and neck cancer (Frew et al. 2008). Other

JNKVV Res J 47(3): 248- 254 (2013)

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oncolytic viruses based on HSV have also beendeveloped and are in clinical trials, most notablyOncoVex GM-CSF, developed by Amgen, which hassuccessfully completed a pivotal Phase III trial in March2013, for advanced melanoma with a very high degreeof statistical significance.

Characteristics of an ideal oncolytic virus

Oncolytic viruses induce an anti-tumor therapeutic effectthrough a subtle equilibrium between anti-viral and anti-tumor immune responses (Fulci et al. 2006). An idealoncolytic virus should demonstrate efficient, safe andcomplete destruction of tumor tissue by selectivereplication in cancer cells, eliciting strong immuneresponses against tumor cells and efficient clearancefrom the body preventing latent or recurrent infection. Itshould be propagation-deficient in immunocompromisedpatients with large recombinant gene carrying capacityand easily engineering to express antitumor agents.Furthermore, cost effectiveness and economy forwidespread use, easy monitoring with respect tosuccessful tumor colonization and potent anti-tumoractivity either alone or combined with conventionaltherapies, such as surgical resection, chemotherapy,and radiotherapy are desirable.

Mechanisms of oncolytic efficacy

Oncolytic viruses mediate the destruction of tumor cellsby several potential mechanisms (Table 1). In order toachieve efficient oncolytic activity a viral vector mustobey three main principles: (1) selectively target theneoplastic tissue while presenting minimal local andsystemic toxicity, (2) remain active despite inducing hostanti-viral immune response, and (3) reach all tumor focibeyond the tumor resection border (Dey et al. 2011).

Direct cell lysis due to viral replication

Viruses infect tumor cells and replicate themselves intumor cells. Upon lysis of infected tumor cells, new virionparticles burst out and proceed to infect additional tumorcells. This cycle then can repeat, by infection of adjacentcells and their subsequent destruction by the samemechanism. This feature of viral replication providescontinuous amplification of the input dose whichcontinues until stopped by the immune response or alack of susceptible cells.

Direct cytotoxicity of viral protein

Second mechanism, in which some oncolytic virusessynthesize certain proteins during replication that aredirectly cytotoxic to cancer cells e.g. adenovirusesgenerate the death protein E3 and the E4ORF4 proteinlate in the cell cycle; both these proteins are toxic tocell (Shtrichman and Kleinberger 1998).

Induction of antitumoral immunity

Tumor cells are inherently weakly immunogenicbecause they express low levels of majorhistocompatibility complex (MHC) antigens andstimulatory signals such as cytokines which activate alocal immune response. Adenoviruses express E1Aprotein during replication, which mediates killing oftumor cells by increasing their sensitivity to tumornecrosis factor (TNF). In addition, lysates of virus-infected tumor cells (oncolysates) have been used asactive specific immunotherapy in the treatment ofpatients with melanoma and ovarian carcinoma inclinical models. Lysates of virus-infected allogeneichuman tumor cells elicit humoral immune responsesagainst tumor-cell-associated antigens, virus-

Table 1. Mechanisms of antitumoral efficacy of oncolytic viruses (Kirn 1996)

Mechanism Examples

Direct cell lysis due to viral replication Adenoviruses, Herpes simplex virusesDirect cytotoxicity of viral protein Adenovirus E4ORF4Induction of antitumoral immunity Nonspecific (e.g., TNF): Adenovirus (E1A)

Specific (e.g., CTL response): Herpes simplex virusSensitization to chemotherapy and radiation therapy Adenovirus (E1A), Adenovirus (AdTK-RC)Transgene expression Herpes simplex virus (rRp450),

Vaccinia virus (GM-CSF)

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associated antigens, and antigens that may be virusinduced, and these immune responses can improve theoutcome of patients with melanoma in a surgicaladjuvant setting (Gooding 1994).

Sensitization to chemotherapy and radiation therapy

The adenovirus E1A gene product is a potentchemosensitizer, particularly in cells with functional p53(Lowe et al. 1994). The E1A gene product can inducehigh levels of p53 in these cells and render themsusceptible to DNA damage from chemotherapy andradiation. Enhanced chemosensitivity following viralinfection has been observed in vivo in a phase II clinicaltrial of intratumoral adenovirus (ONYX-015) incombination with cisplatin and 5-fluorouracil in patientswith head and neck cancer (Khuri et al. 2000).

Transgene expression

Some researchers have incorporated prodrugconverting enzymes, such as viral thymidine kinase andbacterial cytosine deaminase (CD), into replicationconditional adenoviruses to augment tumor cell killingvia the "bystander effect". Other groups have introducedvarious immune stimulatory genes such as interleukins-4 (IL-4) and -12 (IL-12) into oncolytic herpes viruses inan attempt to augment the antitumor immune responseof the host.

Modifications to improve oncolytic activity

Oncolytic viruses can be used against cancers in waysthat are additional to lysis of infected cells.

Suicide genes

Viruses can be used as vectors for delivery of suicidegenes, encoding enzymes that can metabolise aseparately administered non-toxic pro-drug into a potentcytotoxin, which can diffuse to and kill neighbouringcells. One herpes simplex virus, encoding a thymidinekinase suicide gene, has progressed to phase III clinicaltrials. The herpes simplex virus thymidine kinasephosphorylates the pro-drug, ganciclovir, which is thenincorporated into DNA, blocking DNA synthesis(Freeman et al. 1996). The tumor selectivity of oncolyticviruses ensures that the suicide genes are onlyexpressed in cancer cells, however a 'bystander effect'on surrounding tumor cells has been described with

several suicide gene systems (Duarte et al. 2012).

Suppression of angiogenesis

Angiogenesis is an essential part of the formation oflarge tumor masses. Angiogenesis can be inhibited bythe expression of several genes, which can be deliveredto cancer cells in viral vectors, resulting in suppressionof angiogenesis, and oxygen starvation in the tumor.Enhanced antitumor activities have been demonstratedin a recombinant vaccinia virus encoding anti-angiogenic therapeutic antibody and with an HSV1716variant expressing an inhibitor of angiogenesis (Connerand Braidwood 2012).

Expression of sodium-iodide symporter

Addition of the sodium-iodide symporter (NIS) gene tothe viral genome causes infected tumor cells to expressNIS and accumulate iodine. When combined withradioiodine therapy it allows local radiotherapy of thetumor, as used to treat thyroid cancer. The radioiodinecan also be used to visualise viral replication within thebody by the use of a gamma camera. This approachhas been used successfully preclinically withadenovirus, measles virus and vaccinia virus (Li et al.2010).

Mechanisms of oncolytic specificity

There are two general mechanisms that are employedto achieve tumor-selective viral replication:

Deletion of viral genes that are dispensable uponinfection of neoplastic cells but are critical for viralreplication in non-neoplastic cells

An elegant example of this strategy is theoncolytic adenovirus ONYX-015, which is an attenuatedadenovirus with two mutations in the E1B-55 kD gene.

Placement of tumor-specific promoters upstreamof viral genes that are critical for efficient viral replication

An oncolytic adenoviral mutant has beendeveloped in which the E1A gene, the expression ofwhich is critical for viral replication, is under the controlof the tumor-specific a-fetoprotein (AFP) gene promoter.This mutant, AvE1A04i, replicates preferentially in AFP-expressing cells such as hepatocellular carcinoma(HCC) cells (Hallenbeck et al. 1999).

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Engineering of oncolytic viruses

The specific tumour targeting can be achieved bytargeting various molecular steps/regulators of cell cycleeg. pro-apoptotic, translational, transductional andtranscriptional targeting, and strategies based ontumour micro-environment and use of carrier cells ascellular vehicle for oncolytic viruses.

Pro-apoptotic targeting

Many viruses delay apoptosis of infected cells in orderto assist their replication. These encode certain proteinswhich alter the activity of important regulators ofprogrammed cell death such as p53 and pRb.Adenoviral proteins E1A and E1B inactivate pRb andp53 in normal cells, respectively, to delay prematureapoptosis (Russell and Peng 2007).

Translational targeting

HSV-1 can be made tumour selective by mutating the134.5 gene (designated as R3616). The product of this

gene (ICP34.5) binds with protein phosphatase-1 andinhibits phosphorylation of eukarykotic initiation factor-2 (eIF-2) by activated PKR (ds RNA induced proteinkinase). This unphosphorylated eIF-2 cannot inhibittranslation of viral transcripts unlike its phosphorylatedcounterpart. Cancer cells are resistant to the PKRactivated inhibition of viral replication due to the highlevel of Ras activity which inhibits autophosphorylationof PKR. Thus mutant having deleted 134.5 cannotmultiply in normal cells but tumour cells remainpermissive (Sarinella et al. 2006).

Transductional targeting

This approach to tumor selectivity has mainly focusedon adenoviruses and HSV-1, although it is entirely viablewith other viruses. For instance, many cancer cells over-express intracellular adhesion molecule-1 (ICAM-1) anddecay accelerating factor (DAF), the receptors forcoxsackie virus A21 (CAV21). Transductional targetingcan be done in one of two ways: Bi-specific adaptermolecules administered along with the virus to redirectviral coat protein tropism; and Coat-protein modificationinvolving genetically modifying the fiber knob domainof the viral coat protein to alter its specificity (Wickham2003).

Non-transductional targeting/ Transcriptional targeting

Oncolytic viruses can be rendered tumour selective byplacing essential viral gene under the regulation oftumour specific promoter. However, this technique islimited to DNA viruses (excluding pox viruses). Certaintumour specific gene promoters like human telomerasereverse transcriptase (hTERT) and survivin are activein a variety of tumour types while others are specific forparticular tumours, e.g. Prostrate specific antigen (PSA)for prostrate, foetoprotein for liver and tyrosinase forskin (Dalba et al. 2005).

Double targeting

It is unlikely to be possible to make a virus entirelyspecific toward any tissue type by using just one formof targeting. Double targeting with both transductionaland non-transductional targeting methods is moreeffective than any one form of targeting alone.

Targeting strategies based on tumour microenvironment

To support uncontrolled growth and tissue invasion,tumour cells develop a modified microenvironment suchas hypoxia, activation of certain proteases andangiogenesis. This can be harnessed for developingstrategies for tumour targeting. A dual regulatedoncolytic Ad CNHK500 was developed in which the E1bgene is controlled by a hypoxia responsive promoterand the E1a gene is controlled by a human telomerasereverse transcriptase (hTERT) promoter (Singh et al.2012).

Targeting tumour using carrier cells as cellular vehiclefor oncolytic viruses

Cancer cell secretes a number of chemokines whichhelps in trafficking of immune cells to tumour. Theseimmune cells can be used as cellular vehicle for efficientdelivery of OVs to tumour cells. Other types of cellssuch as stem cells (mesenchymal, endothelialprogenitor cells) have also been developed as cellularvehicles to deliver OVs (Komarova et al. 2006).

Oncolytic virotherapy in veterinary medicine

Cancer still remains frequently lethal disease of humanas well as animals, especially pet animals, despite the

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significant progress made in its diagnosis and treatmentin recent years. It is a leading cause of death in animalsand endemic in both developed and developingcountries (Merlo et al. 2008). Cancer is considered asthe second most frequent cause of death in humansand the first one in canines and felines. Spontaneouscases of tumors in domestic animals especially in caninetumors of which are mostly similar to those of humans,offer an interesting opportunity for comparative studiesand to understand cancer biology and drug development(Pawaiya and Kumar 2008).

Canine Adenoviruses

Adenoviruses are being tested as therapeutic agentsfor canine cancers. Human adenovirus 5 has beenshown to productively replicate in canine osteosarcomaand canine mammary carcinoma cells. Furthermore,canine adenovirus 2 (CAV-2), transcriptionally targetedto canine osteosarcoma cells by inserting osteocalcinpromoter, was tested as therapeutic agent for canineosteosarcoma (Smith et al. 2006).

Canine Distemper Virus

Canine distemper virus binds to a cellular receptor,Signalling Lymphocyte Activation Molecule (SLAM orCD150). Canine lymphoid cell lines and B and Tlymphocytes established from dogs with lymphoma havebeen shown to express CD150 receptors. AttenuatedCDV has been tested for oncolytic property in thelymphoma cells and was able to infect and induceapoptosis in these cells. It may therefore be used totreat canine lymphoma patients.

Vaccinia Virus

Two oncolytic vaccinia virus strains, namely JX-594(Jennerex Biotherapeutics, Inc. USA) and GLV-1h68(Genelux Corporation, USA), have shown promisingpreclinical data and are now undergoing clinical trialsin humans (Dranoff 2002). Significant inhibition of tumorgrowth and damage of tumor tissues was observed aftersystemic administration of GLV-1h68 in tumor bearingnude mice (Gentschev et al. 2010). Additionally, theopportunity to localize GLV-1h68 viruses via opticalimaging might be utilized in metastasis detection (Kellyet al. 2009).

Canary Pox Viruses

The effect of recombinant canary pox viruses (ALVAC)was analyzed clinically in canine cancer patients.Intratumoral administration of this recombinant poxvirusin dogs with melanoma revealed localized distributionof virus into tumor tissue (Jourdier et al. 2003).

Translation of oncolytic virotherapy from dogs to humansand the reverse

Canine cancers share many features in common withhuman cancers including histological appearance,tumor genetics, molecular targets and response toconventional therapy. In both species, tumor initiationand progression is influenced by similar factors like age,nutrition, sex and environmental exposure (Khanna etal. 2006). Furthermore, carcinogenesis and tumorbiologic behaviour in dogs have more features incommon with humans than with laboratory rodents.Despite evidence of oncolytic virus efficacy in mousemodels of cancers, many viruses fail in human trialsdue to unacceptable toxicity or lack of efficacy (Wildneret al. 2003). Hence, pet dogs with tumors are necessarymodels to demonstrate efficacy of oncolytic viruses forhuman cancers.

Many of the treatment options used in veterinarymedicine resemble protocols used to treat humancancer patients. In addition, public release of nearly99% canine genome sequences provided a window ofopportunity to expand the scope of comparativeoncology. Comparison of canine genome sequenceswith the human genome suggests that around 19000genes identified in the dog match to similar ororthologous genes in the human genome (Lindblad etal. 2005). Taking into consideration the value ofcomparative oncology, data obtained from humanclinical trials can be effectively transferred to canines.

Biosafety

It is important that precautions for infectious materialand biological safety, and biosafety guidelines or theirequivalent, be followed when administering oncolyticviruses. Respective institutional, country, state, andlocal regulations should be followed. Generally, as partof the clinical protocol, all regulatory authorities requiresome form of barrier contraception for the duration ofthe clinical trial as a standard precaution to preventperson-to-person transmission. Non-clinical viral

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shedding studies can be useful in preparing for clinicalstudies and evaluating detection methods. It is advisableto integrate monitoring for shed virus into the clinicaldevelopment plan (Vile et al. 2002).

Conclusion

Chemotherapy and radiation therapy are currentmainstays in the treatment of advanced cancers but arelimited by tumor cell resistance to these agents and arelatively narrow therapeutic index. Thus, dose-escalation or combination therapies designed toovercome resistance or increase tumor cell kill arelimited by toxicity to normal tissues. Oncolytic viraltherapy, on the other hand, is capable of increasingthe therapeutic index between tumor cells and normalcells when viral replication proceeds preferentially intumor cells. Armed therapeutic viruses or geneticallyengineered viruses represent a very appealing tumortargeting approach and a novel opportunity to generateagents that could potentially cure canine cancers. It ishoped that collective efforts will contribute to thedevelopment of effective and safe viruses for bothhuman and animal cancer therapy.

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(Manuscript Receivd : 30.8.13; Accepted : 19.12.13)

255

Abstract

The experimental materials comprised of 76 CIMMYT basedpromising lines of wheat received from Mexico and 6 checksthese lines were planned to assess their potential under highfertility timely sown condition during rabi 2012-13 under wheatImprovement Project Department of Plant Breeding andGenetics, JNKVV, Jabalpur. The experiment were laid outwith three replication under randomized complete block deignand observations were recorded on yield and its contributingtraits and subjected to analysis for the genetic analysis. Theanalysis of variance for 14 characters revealed highlysignificant differences for all the characters. Phenotypic andgenotypic coefficient of variation was found to be higher fornumber of tillers per plant, spike density, number of ears perplant and high PCV % for grain yield per plant. Heritabilityestimates was high for tillers per meter number of grains perear ,harvest index, biological yield per plant, seed yield perplant, spike length, 1000 grain weight and days to flowerinitiation. High heritability coupled with high genetic advanceas percentage of mean was observed for tillers per meter,numbers of grains per ear, harvest index, biological yield perplant, seed yield per plant and numbers of ears per plant andspike density. It indicates role of additive gene action andselection of promising genotypes and its use in hybridizationprogramme shall be effective.

Keywords: Bread wheat, genetic analysis, CIMMYT,Genetic variability, genotypic and phenotypic coefficientof variation, genetic advance

Wheat (Triticum aestivum L emThell.) is the world'ssecond most important staple food crop for more than35 percent of world's population next to the rice. It iscultivated under a wide range of climatic conditionsfavored by cool, moist weather followed by dry warmweather. Wheat generally accorded a special emphasisamong cereal crops.It produces about 20% foodresources of the world, high productivity and theprominent position it holds in the international food graintrade.

Genetic analysis of CIMMYT based bread wheat genotypesfor yield and its contributing traits

R.S.Shukla and P.K.MoitraDepartment of Plant Breeding and GeneticsJawaharlal Nehru KrishiVishwavidyalayaJabalpur 482004 (MP)

In India wheat is being grown in 29.9 millionhectare area and produce 93.9 million tones and 3.1tones/ha productivity. Madhya Pradesh area,productionand productivity is 53 lakh hectare, 131 lakh tones andproductivity 3.1tones/ha (Anonymous, 2013).Rain-fedarea in India and MP is 67 and 70% respectively out ofwhich 60 - 65 percent is under restricted irrigation. Wateruse efficient wheat varieties for such large areas arethe need of the state for sustainable wheat productionand food security.

The productivity of wheat MP is increasinggradually due to increase in irrigation facilities but thearea under restricted irrigation is still needs to beenhanced by developing suitable wheat varieties.Thereason of low productivity in these areas is lack of highyielding varieties for restricted irrigation, hightemperature during early vegetative phase,unavailability of water and power etc. Many moderncultivars in wheat and in other crops as well, are oftengenetically similar, with a rather narrow genetic base.Therefore, in wheat breeding there is needed to utilizesources of new diversity. New variation can be createdby hybridization between different parental cultivars.Yield is a complex polygenic quantitative trait,considerably affected by environment. Therefore,selection of genotypes based on yield is not effective.Selection has to be made for the components of yield.The availability of genetic variability is the basic pre-requisite for any genetic improvement throughsystematic breeding programme.

The correlation between traits reveals the type,nature and magnitude of association between yieldcomponents with yield and among themselves. Toincrease the yield, contributes of direct and indirecteffects of yield components provides the basis for itssuccessful breeding programme and hence the problemof yield increase can be more effectively tackled on the

JNKVV Res J 47(3): 255-259 (2013)

256

basis of performance of yield components and selectionfor closely related characters (Choudhary et al 1986).On the other hand, path coefficient analysis measuresthe direct and indirect effect for one variable uponanother and permits the partitioning of thetotalcorrelation coefficient into direct and indirect effect(Dewey and Lu 1959).

Yield being a complex character is a function ofseveral component characters and their interaction withenvironment. Probing of structure of yield involvesassessment of mutual relationship among variouscharacters contributing to the yield. In this regardgenotypic and phenotypic correlation reveals the degreeof association between different characters and thusaid in selection to improve the yield and yield attributingcharacters simultaneously. Further, path coefficientanalysis helps in partitioning of correlation coefficientsinto direct and indirect effects and in the assessment ofrelative contribution of each component character tothe yield.Considering the above facts in the proposedstudy, an effort is made to screen advance and uniformelite bread wheat lines.

Material and methods

The experimental material comprises of 75 elite wheatdiverse lines (31st ESWYT- 20, 5EBWYT-6, 2CSISA-HT-EM-4, 32nd ESWYT-20 and 6EBWYT-25) receivedfrom CIMMYT with six check varieties viz. GW-366,GW273, GW322, HI1544, MP1201 and UFAN. Theexperiment was laid out in randomized complete blockdesign of two rows of 2.5m length and 20 cm apart withthree replications under high fertility timely sowingcondition under Wheat Improvement Project to explorethe genetic potential.The seed was hand dibbled andrecommended cultural packages were followed to raisethe healthy crop.

The observations were recorded on five randomlyselected plants from each plot and from each replicationfor characters viz; days to flower initiation (days) daysto maturity (days), plant height (cm), number of tillers /plant, ear length (cm), number of grains/ear, numberof spikelets/spike, spike density, number of spikelets/spike, thousands grain weight (g), seed yield/plant(g), biological yield/plant (g), harvest index (%) andtillers/meter. The data were subjected to statisticalanalysis for PCV and GCV (Burton, 1952), heritabilityin broad sense (Allard, 1960 and Hanson et al. 1956)and expected genetic advance as suggested byJohnson et al. (1955).

Result and discussion

One of the important purposes of present investigationwas to find out the extent of variability present in 81advance generation genotypes of wheat with regardsto 14 characters (Table 1).

The analysis of variance for 14 charactersrevealed highly significant differences for all thecharacters. It indicated the existence of sufficient geneticvariability for the characters studied which providesample scope for selecting superior and desiredgenotypes by the plant breeders for furtherimprovement.

The assessment of heritable and non-heritablecomponents in the total variability observed isindispensable in adapting suitable breeding procedure.The heritable portion of the overall observed variationcan be ascertained by studying the components ofvariation such as GCV, PCV, heritability and predictedgenetic advance.

Present study revealed that phenotypic andgenotypic coefficient of variation was found to be higherfor number of tillers/plant,spike density, number of ears/plant and high PCV % for grain yield /plant. Similarobservations were reported by Shukla et al (2000),Kumar et al (2003), Singh and Chaudhary (2006), Khanet al (2007), Ali et al (2008), Riaz et al (2010), Zecevicet al (2010), Tripathi et al (2011), they have reportedhigh genotypic coefficient of variation for number ofgrains per spike, grain yield/plant and harvest index(Table 2).

The coefficient of variation indicates only theextent of variability present for the characters and doesnot indicate the heritable portion. This could beascertained from heritability estimates which in broadsense. Phenotypic variation in the population is theresult of genotypic and environmental effects. Theportion of total variation caused by genotypes is calledheritability and estimated as the ratio of genotypicvariance to the total phenotypic variance. Broad senseheritability includes both additive and non-additive geneeffects. The knowledge of heritability is helpful inassessing merits and demerits of a particular trait as itenables the plant breeder to decide the course ofselection procedures to be followed under a givensituation.

In present investigation heritability estimates washigh for tillers/meter, number of grains/ear, harvestindex, biological yield /plant, seed yield/plant, spike

257

Tabl

e 1.

: Ana

lysi

s of

var

ianc

e fo

r Yie

ld a

nd y

ield

attr

ibut

es fo

r 81C

IMM

YT b

ased

bre

ad w

heat

gen

otyp

es

Sou

rces

of

Deg

ree

ofM

ean

sum

of s

quar

eva

riatio

nfre

edom

X 1X 2

X 3X 4

X 5X 6

X 7X 8

X 9X 10

X 11X 12

X 13X 14

Rep

licat

ion

(r-1

)=2

19.3

890

.52

158.

6412

9.97

1.86

5.37

12.5

218

.61

5.99

16.0

712

.07

35.6

20.

307

23.9

5G

enot

ypes

(v-1

)=80

94.3

7**

129.

81**

157.

45*

24.8

5**

5.19

**4.

70**

11.3

0**

47.5

4**

12.0

1**

81.0

6**

48.8

0**

119.

65**

1.07

3*35

4.37

**E

rror

(r-1

) (v-

1)=1

6049

.05

84.3

314

9.25

21.9

22.

203.

246.

6518

.02

4.98

26.9

422

.50

29.9

31.

0076

.82

SE

of D

iffer

ence

5.71

7.49

9.97

3.82

1.21

1.47

2.10

3.46

1.82

4.23

3.87

4.46

0.81

67.

15C

D a

t 5%

11.2

914

.80

19.7

07.

552.

392.

904.

166.

843.

598.

377.

658.

821.

6114

.13

CD

at 1

%7.

9310

.40

13.8

45.

301.

682.

042.

924.

812.

525.

885.

376.

201.

139.

93E

CV

%9.

710

6.88

12.5

863

.16

23.0

519

.01

14.2

113

.33

19.0

714

.16

12.0

912

.90

54.4

212

.07

X 1= D

ays

to fl

ower

initi

atio

nX 8 =

Bio

logi

cal y

ield

/ pl

ant.

X 2 = D

ays

to m

atur

ityX 9 =

See

d yi

eld

/ pla

ntX 3 =

Pla

nt h

eigh

t (cm

)X 10

= H

arve

st in

dex

(%)

X 4 = N

umbe

r of t

iller

/pla

ntX 11

= 10

00 g

rain

wei

ght i

n (g

)X 5 =

Num

ber o

f ear

/pla

ntX 12

= N

umbe

r of g

rain

s / e

arX 6 =

Ear

leng

th (c

m)

X 13 =

Spi

ke d

ensi

tyX 7 =

Num

ber o

f spi

klet

s /e

arX 14

= T

iller

s / m

eter

s

* S

igni

fican

t at 5

% le

vel *

* S

igni

fican

t at 1

% le

vel

Tabl

e 2.

Par

amet

ers

of G

enet

ic v

aria

bilit

y fo

r yie

ld a

nd it

s co

mpo

nent

cha

ract

ers

of C

IMM

YT b

ased

bre

ad w

heat

gen

otyp

es

Cha

ract

erM

ean

Ran

ge2 g

2 e2 p

GC

VPC

Vh2 (

bs)

GA

GA

as

%M

inim

umM

axim

um(%

)(%

)(%

)of

mea

n

Day

s to

flow

er in

itiat

ion

(X1)

72.1

343

.66

79.0

026

.00

16.3

542

.35

7.06

9.02

61.3

98.

2311

.41

Day

s to

mat

urity

(X2)

133.

4612

5.00

138.

6633

.90

28.1

162

.01

4.36

5.90

54.6

78.

876.

64P

lant

hei

ght (

cm) (

X3)

97.2

064

.80

104.

2335

.90

49.7

585

.65

6.16

9.52

41.9

17.

998.

22N

o. o

f till

ers

/ pla

nt (X

4)7.

412.

8810

.63

5.85

7.31

13.1

632

.64

48.9

544

.45

3.32

44.8

0N

o of

ear

/ pl

ant (

X5)

6.43

1.43

8.93

1.49

0.73

2.22

18.9

823

.17

67.1

12.

0632

.04

Ear

leng

th c

m) (

X6)

9.48

3.23

11.8

01.

211.

082.

2911

.60

15.9

652

.83

1.65

17.3

6N

o of

spi

klet

h/ e

ar (X

7)18

.14

8.83

21.7

63.

032.

225.

259.

5612

.63

57.7

12.

7215

.01

Bio

logi

caly

ield

/pla

nt (g

) (X

8)31

.83

19.0

643

.30

13.8

46.

0119

.85

11.6

913

.99

69.7

26.

3920

.10

See

d yi

eld/

pla

nt (X

9)11

.69

4.91

15.6

43.

451.

665.

1115

.89

19.3

467

.51

3.14

26.8

9H

arve

st in

dex

(%) (

X 10)

36.6

520

.85

45.4

624

.03

8.98

33.0

013

.38

15.6

772

.82

8.62

23.5

110

00 g

rain

wei

ght (

g) (X

11)

39.2

221

.20

45.3

313

.77

7.50

21.2

79.

4611

.76

64.7

46.

1515

.68

No

of g

rain

s /e

ar (X

12)

42.4

017

.06

56.1

636

.56

9.98

46.5

414

.26

16.0

978

.56

11.0

426

.04

Spi

ke d

ensi

ty (X

13)

1.83

1.61

2.48

0.26

0.33

0.56

27.8

640

.89

46.4

20.

7239

.11

Tille

rs /

met

er (X

14)

72.6

045

.33

96.3

310

9.36

26.2

813

5.64

14.4

016

.04

80.6

319

.34

26.6

4

258

length, number of ear/plant, 1000 grain weight and daysto flower initiation. In confirmation with results of earlierworkers Krishnawat and Sharma (1998) for weight ofgrain/spike, grain yield/plant, biological yield/plant andharvest index, Prasad and Pandey (2001) for plantheight, productive tillers/plants, 1000-grains weight,Pawar et al (2002) for length of spike, number ofspikelet/spike, grains/ear, number of tillers/m, Shuklaand Singh (2004) for number of grains/spike, grain yield/plant, Kumar et al (2010) for grain yield/plant, Sahu(2011) for number of grains/ear, biological yield/plant,harvest index, Nafde (2012) for number of grains/ear,grain yield/plant, biological yield/plant, harvest index,Tsegaye et al (2012) 1000 grain weight, biological yieldalso reported high heritability estimates in wheat.

Genetic advance is the difference of meangenotypic value of the selected genotypes over the basepopulation. Heritability estimates are useful in decidingthe characters to be considered while making selection,but selection based on this factor alone may limit theprogress, as it is prone for changes with environment,material etc. so, heritability needed sufficient geneticadvance attributable to additive gene action fordesirable gain from selection. Therefore, geneticadvance as a percentage of mean worked out thusgenetic information would support for an effectiveselection.An estimate of genetic advance is valid foronly one generation and largely depends of intensity ofselection. Heritability for traits and phenotypic varianceare available in population.

In the present study high heritability coupled withhigh genetic advance as percentage of mean wasobserved for tillers/meter, numbers of grains/ear,harvest index, biological yield/plant, seed yield/plant andnumbers of ears/plant and spike density.

The results were in accordance with the resultsof various research workers on wheat viz. Krishnawatand Sharma (1998) for weight of grain/spike, grain yield/plant and biological yield/plant and harvest index, Pawaret al. (2002) for plant height, Mondal et al. (2004) forroot weight and grain yield/plot. Shukla and Singh(2004) for number of grains/spike, grain yield/plant, totalbiomass/plant and spike weight, Singh and Chaudhary(2006) Ali et al. (2008) for plant height, number ofspikelets/spike, spike length, number of grains/spike,1000 grain weight and yield/plant. Bakshi et al. (2008)for number of spikelets/panicle and grain yield/plant,Manal (2009) for spike length and 1000 grain-weight,Tripathi et al. (2011) for plant height, grain yield/plant

biological yield, harvest index and test weight.

lhehV esfDldks vk/kkfjr xsgw¡ dh 76 vk'ktud fd'eksa dk 6 fu;a=.kfd'eksa ds lkFk mudh mit {kerk vkdyu dk v/;;u 2012&13esa xsgq¡ vuqla/kku ifj;kstuk] ikS/k iztuu ,oa vuqoaf'kdh foHkkx] t-us-—-fo-fo-] tcyiqj esa fd;k x;k A mijksDr v/;;u rhu jsIyhds'kuesa mit ,oa mit esa lgk;d ?kVdks ds vuqokaf'kd fo'kys"k.k gsrq fd;kx;k A fo'kys"k.k ds rgr mit c<+kus okys xsgq¡ ds 14 fofHkUu ?kVdksaesa egRoiq.kZ fofo/krk ikbZ xbZ A QhuksVhfid ,oa thuksVhfid lwpukadesa 'kk[kk,a izfr ikS/k] ckyh /kuRo] ckyh izfr ikS/k] gsrq mPp fofof/krrk ikbZ xbZ A

'kka[kk,a izfr ikS/k] nkuk izfr ckyh] mit lw¡ph] tSfod mitizfr ikS/k] Qwy f[kyus dk le;] mit izfr ikS/k] ckyh dh yackbZ ,oagtkj nkus dk otu esa mPp ca'kkxr vuqekfur ik;k x;k A blhizdkj oa'kkuqxr ,oa vuqokaf'kd izxfr nksuksa ds vk/kkj ij 'kk[kk izfrehVj] nkus izfr ckyh] mit lw¡ph] tSfod mit] cht mit izfrikS/k ckyh izfr ikS/k] rFkk ckyh /kuRo esa mPp ,d :irk ikbZ xbZ tksbu ?kVdks ds peu esa ,oa ladj }kjk okaf'kd lq/kkj dks n'kkZrh gS A

Acknowledgement

Authors are thankful to BISA, CIMMYT, Lakhanwara,Jabalpur for providing the promising genotypes.

References

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Nafde A (2012) Studies on growth and yield components ofbread wheat under restricted irrigation. M Sc (Ag)Thesis JNKVV Jabalpur

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(Manuscript Receivd : 16.8.13; Accepted : 22.12.13)

260

Abstract

An ethno botanical study was conducted from 2009 to 2012to investigate the uses of threatened medicinal plants by localtribal people in Eastern Madhya Pradesh. The results obtainedrevealed that 27 plants were used as a cure of 15 ailmentsbelonging to Leguminosae (Fabaceae) family. All the plantscollected from the study area were either endemic orthreatened. The need for the conservation of these threatenedplants cannot be over emphasized as most tribal people inthe study area depend mostly on Plants of these species.Proper conservation and management plans are suggestedto conserve the medicinal plant resources before it lost forever.

Keywords: Conservation, Threatened medicinal plants,Eastern Madhya Pradesh.

The increase of human population in the last fewdecades demanding development in various sphereshas resulted directly or indirectly in the sudden and oftenfar-reaching disturbances in natural ecosystems, theEastern Madhya Pradesh is one of them it has 10,160sq. km. with of population 2,460,714 of which male andfemale were 1,278,448 and 1,182,266 respectively(2011 Census). Geographically it is located by 230 -100 North & 79°57° East & 411 meters high above meansea level.

Jabalpur has Geological formations such as;Archeans, Gondwanas, Lametas, Decan traps andVindhyans, the forest division with Jabalpur, Sihora andBargi occupies the 1551.78 sq.km. area under reserveand protected forest, the forest division is classified asDry Tropical forests (Champion and Seth 1968). Due toincreasing threats to plant diversity in the area include

Investigation on ethno medicinal remedies to cure diseasesby tribes of eastern Madhya Pradesh with special referenceto threat assessment of leguminosae family

Karuna S. Verma and Lekhram KurmiAeroallergens, Immunology and Angiosperms Diversity LaboratoryRani Durgawati UniversityJabalpur 482001 (MP)

loss of habitat through increasing livestock,deforestation, a requirement for more land for housingand cultivation.

Less-than-threatened categories are NearThreatened, Least Concern, and the no longer assignedcategory of Conservation Dependent. Species whichhave not been evaluated (NE), or do not have sufficientdata (Data Deficient) also are not considered"threatened" by the IUCN. In the present study plantswere categorized according to Red List categories ofIUCN version 4.0 (2001 - 2012). The list of plants whichhave been considered as CR- Critically Endangered,EN- Endangered, VU- Vulnerable are given on the basisof frequent survey and available literature.

Material and methods

Survey and collection of plants - Extensive surveys andfield work involved collection of plants for preparing anaccount of the threatened Leguminous (Fabacious)plants field trips were undertaken in tribal areas of theEastern Madhya Pradesh during 2009 to 2012. At eachtime of visit, different tribal hamlets and forest pocketswere choose in different seasons to collect moreinformation on plants. Information was compiled throughscientifically guided questionnaires (Jain 1991),interviews and general conversations with several tribalherbal healers, village heads, elder women and otherlocal informants collected by interview. The plants wereinitially identified by their vernacular names throughconsultations with the local people. Voucher specimenswere prepared and deposited in the Herbarium cumMuseum, Department of Biological Science RaniDurgawati Vishva Vidyalaya Jabalpur for further recordand references.

JNKVV Res J 47(3): 260-262 (2013)

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Identification of plant specimen - After collection anattempt was made to identify plants. From fresh materialthose could not be identified with the help of "Flora ofBritish India, by Sir Hooker (1872) Flora of Bhopal(Oomachan 1977), Flora of Jabalpur (Oommachan andShrivastava 1996), B.S.I. (Madhya-Pradesh Vol. I -III.1993-2001), Khanna et al. (2001).The threat status ofthe identified plant species in the study area was definedafter consultation with relevant literature (Jain and Rao1983, Nayar and Shastry 1990, Jadhav et al. 2001,Leaman 2005) and Conservation Assessment andManagement Planning (CAMP) reports of India. Thesource of plant collection from respective forest types

was recorded. The plants were enumeratedalphabetically with their botanical name with authorcitation, family name, local name, habit, source ofcollection, part used, medicinal uses and threat status(Table 1).

Result and Discussion

The results of the study have revealed 27 plant speciesbelonging to Fabaceae family distributed in 15 generathat are frequently used for treatment of 15 diseasesby local tribes, herbalists and traditional healers (Table

Table 1. Threatened plants used ethno medicinally in Eastern Madhya Pradesh.

Sub-family - PapilionaceaeS.N. Botanical name Local Name Habit Part used Ethno -medicinal uses TS

1. Abrus precatorius L. Gumchi, Ratti C R., Sd. Cough & Cold EN2. Alhagi maurorum Medic D.C. Jawasa S R Piles EN3. Butea monosperma Lam. Taub. Palash ,Teshu, T Rb Haematuria,Piles VUL4. Butea superva Roxb. Safed palashbel C Fl Skindisoder CR5. Canavalia gladiate (Benth)Baker Jangli sem C L Gonorrhea VUL6. Clitoria ternata L. Aprajita C L Fever VUL7. Clitoria biflora Dalz. Kajroti H Sd Inflammation VUL8. Dalbergia latifolia Roxb. Safed Shisham T R Diarrhea EN9. Dalbergia paniculata Roxb. Dhobin T Sd Fever VUL10. Dalbergia lanceolaria (L.F.) Dhamosi T Rb Skin- disease EN11. Erythrina indica Lamk. Pangara T L Urinary troubles EN12. Erythrina suberosa L. Gadha palash T L Menstrual flow CR13. Mucuna pruriens L. Dc.Prodr. Kauch H Sd Dysentery EN14. Pueraria tuberosa Roxb.willd Bidari kand C Rb Foothache VUL

Sub family Caesalpiniaceae15. Bauhinia racemosa Lamk. Asta T Sb Teethache VUL16. Bahunia purpurea L. Sp. Keolar T Rb Diarrhoea VUL17. Bahunia variegate L. Sp. Kachnar T Sd Skin- disease VUL18. Bahunia vahlii Wight &Arn. Mahul patta C L Fever EN19. Cassia occidentalis L. Sp Chirotha S S Skin- disease EN20. Cassia javanica L. Sp. Java cassia T Sb Skin- disease VUL

SubFamily - Mimosaceae21. Acacia catechu ( L.f) Willd Sp. Khair T Sb Skin- disease VUL22. Acacia pennata L. Willd. Sp. Chhoti Chilati S Sb Diarrhoea VUL23. Albizia lebbeck L. Benth. KalaSiris T L Skin- disease VUL24. Albizia procera Roxb. , Benth. Safed Siris T L Skin- disease VUL25. Neptunia triquetra Benth. Lajalu H Fl Eye disease CR26. Prosopis juliflora (SW.) DC. Pro Khejra T Sb Stomach pain VUL27. Prosopis cineraria (L.) Druce. Shami T Rb Fever CR

H = Herb, C = Climber, S =Shrub, Fl= Flower, L= Leaf, R= Root, Rb= Root bark, S= Stem, Sb= Stem bark, Sd=Seed, EN = Endangerd, CR = Critical Endangerd, VUL = Vulnerable. TS= Threat status.

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1). Among them 03 were herbs, 15 were trees, 06 wereclimbers and 3 were shrubs. Members of the familyFabaceae are the most commonly used. As seen in Table1, common health ailments in the study area were skinproblems such as eczema, wounds and cuts. This isbecause of Tribals are maintaining ancient style of livingi.e., forest dwelling and hence are more prone to getskin cuts and skin allergies because of spiny and thornyplants and so also due to the different pollen grains orstinging hairs of some plants.

The second important disorder observed is ofstomach complaints viz. dysentery, Diarrhoea, stomachpain, etc. This may be because of poor hygiene andsometimes use of contaminated water. A total of 5 plantsare employed for various stomach complaints. Hence,there is always search for powerful remedies by trialand error method, which has resulted in thedevelopment of reliable ethnomedicine for treatingdifferent diseases.

Present study has revealed that medicinal plantsstill play a vital role in the primary healthcare of thepeople of this region. During the survey, it was observedthat more than half of the total number of peoplequestioned regularly used medicinal plants to treat manyailments. Therefore, this study is important to preservethe knowledge of medicinal plants used by the peoplein the Eastern Madhya Pradesh. Also, it is of significanceto exploit novel pharmacological compounds from theseplants for various treatments of diseases.

The threatened categories have been assessedusing the IUCN Red List Criteria, Version 4.0. (2001-12). All the species identified in the present study wereendemic and/or threatened. Out of 27 plant species,15 are of Vulnerable, 08 Endangered and 4 criticallyendangered.

Acknowledgements

The authors are thankful to Head, Department of PGStudies and Research in Biological Science, and DeanFaculty of Life Science Rani Durgawati University,Jabalpur (MP) for generous help during the executionof the work.

References

Champion H G, Seth, S K (1968) A Revised Survey of theForest Types of India. Govt. of India. Publications,New Delhi

Hooker J D (1872) The flora of British India. 1 (7): 1904, Asketch of the flora of British India. In the imperialGazette, London

Jadhav S N, Ved D K, Ghate U, Reddy K N, Reddy C S (2001)Proceedings of the Conservation

Assessment and Management Planning Workshop: MedicinalPlants of Andhra Pradesh. EPTRI Hyderabad

Jain S K, Sastry A R K (1980) Threatened plants of India-Astate of the Art Report. Botanical Survey of India.Calcutta

Jain S K (1991) Dictionary of Indian Folk Medicine andEthnobotany. Deep Publi., New Delhi p 311

Jain S K, Rao R R (1983) An Assessment of ThreatenedPlants of India. Botanical Survey of India, Calcutta

Kala C P (2000) Status and conservation of rare andendangered medicinal plants in the Indian Trans-Himalaya. Biological Conservation 93 (3): 371-379

Khanna K K, Kumar A, Dixit R D, Singh N P (2001) Supplementto the Flora of Madhya Pradesh. BSI, Calcutta

Leaman D (2005) International standard for sustainable wildcollection of medicinal and aromatic plants (ISSC-MAP). Medicinal Plant Conservation Newsletter 11:4-5

Mudgal V, Khanna K K, Hajara P K (1997) Flora of MadhyaPradesh Vol. II.BSI, Calcutta

Nayar M P, Shastry A R K (1990) Red Data Book of IndianPlants, Vol. III. BSI Calcutta

Oommachan M, Srivastava J L (1996) Flora of Jabalpur, Sci.Pub. Jodhpur p 1 - 354

Oommachan M (1977) The Flora of Bhopal, J.K. Jain Bro.Bhopal p 1- 475

Pattanaik C, Reddy C S, Reddy K N (2009) Ethno-medicinalSurvey of Threatened Plants in Eastern Ghats, IndiaOur Nature 7:122-128

Singh N P, Khanna K K, Mudgal V, Dixit, R D (2001) Flora ofMadhya Pradesh Vol. III.BS, Calcutta

Verma D M , Balakrishan N P, Dixit R P (1993) Flora of MadhyaPradesh Vol. I.BSI, Calcutta.

Verma K S, Dahake D, Sinha R (2010) Survey of Ethno-medicinal plants of selected sites of Jabalpur district(MP). Indian Journal of Tropical Biodiversity 196/10

Verma K S, Iqbal Y, Khare D (2010) Ethno- medicinalimportance of weeds and their present status inJabalpur. Vegetos 23 (2)

(Manuscript Receivd : 11.9.13; Accepted : 30.12.13)

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Abstract

Aqueous, methanol, ethyl acetate and petroleum ether extractsof leaf, stem, root, fruit and seeds of a common tree "Munga"(Moringa oleifera Lam.) were used for determination ofphytochemical constituents. In the present study thirteenprinciples phytochemicals were investigated. Aqueous extractshowed presence of saponins, tannins and sterols in all plantparts. Cardiac glucosides were present in fruit and seed only.Methanolic extract showed sterols in all plant parts. Ethylacetate and petroleum ether extract showed presence ofglycerol, starch and sterol in plant parts. Out of all, lipids andsterols were found to be positive in all four extracts of leaf.Alkaloids, anthraquinones and flavonoids were other importantsecondary metabolites.

Keywords: Phytochemistry, secondary metabolites,bioactive metabolites, Moringa oleifera Lam

Phytochemistry is concerned with compoundssynthesized and accumulated by plants with thestructural characterization of these molecules (Awoyinkaet al. 2007, Edeoga et al. 2005, Kalkar et al. 2009).Although Moringa oleifera is native to the sub Himalayatracts of India, Pakistan, Bangladesh and Afghanistan,where it is used as folk medicine (Fahey 2005), it isnow widely distributed all over the world (Lockelt et al.2000). M. oleifera is referred as a "miracle tree" or a"wonder tree" (Fuglie 2001) with significant socio-economic importance because of its several nutritional,pharmacological (Caceres et al. 1991, Fuglie 2001) andindustrial applications (Makkar and Becker 1997, Foidl2001). The leaves of this plant contain a profile ofimportant trace elements, and are a good source ofproteins, vitamins, b-carotenes, amino-acids andvarious phenolics (Anwar 2007).

The present study is aimed at comparing all themajor plant parts of Moringa oleifera for its

Phytochemical screening of different plant parts of munga(Moringa oleifera Lam.)Karuna S. Verma and Rajni NigamAeroallergens Immunology & Angiosperm's Diversity laboratoryDepartment of Post Graduate Studies and Research in Biological ScienceRani Durgawati UniversityJabalpur 482001 (MP)

phytochemical constituents. The plant has beendescribed traditionally for the various medicinal andgeneral purpose uses. However, the link between thetraditional knowledge and the current scientificperspective is missing. To create the link, different partsof the plant were screened for the presence of primaryand secondary metabolites. Since this plant is native tomost of the Indian regions, this comprehensivecomparative study will add to the current knowledgebank of Moringa oleifera.

Material and methods

The whole plants of Moringa oleifera were purchasedfrom the local nursery of Jabalpur (MP) during rainyseason of 2012. From these young plants (approx.length of 1 to 1.5 meter) roots, stem and leaves wereobtained. The whole plants were taken out of the soil,washed and root, stem and leaves were separatedmanually. For fruit and seeds, the fully grown pods ofthe M. oleifera from fully grown trees were used. Thesepods were picked during the summer of 2012. Fromthe fully developed pods (drumsticks), the seeds wereseparated and rest of the pod was used a fruit source.The plant parts were dried under shade till constantweight is achieved. The dried parts were cut into smallpieces (wherever required), dried and grounded to afine powder of less than 100 µM as described byHarborne (1998). The dried powder was stored in a cooland dry place in an airtight container until used.

Phyto-extracts were prepared from 10 g of drypowder of each plant part in a sequential manner withdifferent polar to non-polar solvents i.e. water, methanol,ethyl acetate and petroleum ether (Mdlolo et al. 2008,Sreelata and Padma 2009). Aqueous extracts wereprepared by cold percolation method whereas methanol,ethyl acetate and petroleum ether extracts were

JNKVV Res J 47(3): 263-268 (2013)

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prepared using Soxhlet extractor. All the extracts wereconcentrated under vacuum to 20 ml to get aconcentration of 500 mg dry weight ml-1.

The qualitative phytochemical tests for majorprimary and secondary metabolites were performed asdescribed by Trease and Evans (1983), Harborne (1998)and Thimmaiah (2004).

Results and discussion

In the present study, the phytochemical constituents ofMoringa oleifera were sequentially extracted withsolvents of different polarity. The results suggest thatthe aqueous, ethyl acetate and petroleum ether couldextract more numbers of primary and secondarymetabolites than the methanol. The various plant parts(leaf, stem, root, fruit and seed) of M. oleifera varied incomposition of primary and secondary metabolites. Table1 shows the presence of phytochemicals in aqueousextracts of M. oleifera. Protein was completely absentin all five plant parts. Among lipids, only presence ofglycerol could be found in fruit, seed and leaf aqueousextracts. The presence of carbohydrates in all plant partwas reported through Molisch's, Benedict's andFehling's test. Only fruit and seed showed positive testfor carbohydrates and these plant parts were found richsources for sugars. Molisch's test was found positiveonly in root extract.

Alkaloids were reported through Mayer's,Dragendroff's and Wagner's test. All these tests werepositive for seed extracts. Remaining plant parts showedabsence of alkaloids. Presence of saponins and sterolswere found in all plant parts' aqueous extracts whereasflavonoids, resins, triterpenes, coumerins andanthraquinones were found absent in all aqueousextracts. Among tannins, presence was found in all fiveparts through lead acetate test. Gelatin test was positivewith fruit aqueous extracts while ferric chloride test waspositive for root and fruit extracts. Presence of cardiacglucosides was reported through Keller- Killiani test andit was positive for fruit and root aqueous extract.

The presence of phytochemicals in methanolicextracts of M. oleifera. Only seed extract showedpresence of protein through xanthoprotic test. Proteinwas completely absent in all remaining plant parts.Among lipids, only presence of glycerol could beestablished in extract of leaf, stem and root. Seed showspositive test for carbohydrates through Molisch's andFehling test and fruit shows positive through Benedict'stest (Table 2).

Methanolic extracts of root, fruit and seed showedpresence of alkaloids through Mayer's test and only thefruit extract showed Dragendroff's test positive.Saponins were found in root and fruits extract. Sterolswere reported in all plant parts whereas resin,triterpenes and coumerins were found absent in allmethanolic extracts. Extract of leaf, root, fruit and seedshow presence of flavonoids. Presence of tannins wasfound in stem, root and seed extract. Gelatin test waspositive only in root extracts while lead acetate test waspositive for stem, root and seed. Presence of cardiacglucosides was reported through Keller- Killiani test andwas positive for root, fruit and seed methanolic extracts.

The presence of phytochemicals in ethyl acetateextracts of M. oleifera. Only leaf and root extract showedpresence of protein. Among lipids, only presence ofglycerol could be established in all extracts except fruit.The seed extract also showed positive Sudan III testfor lipids. For carbohydrates, all plant part showedpositive results through Molisch's test and only leafshowed additional positive Fehling's test (Table 3).

Extract of leaf, root, fruit and seed showedpresence of flavonoids. Tannin presence was found instem, root and seed extract. Gelatin test was positiveonly in root extracts while lead acetate test was positivefor stem, root and seed. Leaf and seed extract showedpositive test for sterols. Alkaloid, saponins, flavonoids,resins, tannins, cardiac glucosides, triterpenes andcoumerins were found absent in all ethyl acetateextracts.

The presence of phytochemicals in petroleumether extracts of different plant parts of M. oleifera.Protein was reported only in root extract through biurettest and was completely absent in all remaining plantparts. Solubility test for lipids was positive for leaf andstem extracts. Presence of glycerol could be found inextract of leaf and root while Sudan III test was positivefor leaf and stem. Only root extract showed positiveresult through Molisch's test for carbohydrates and forpresence of flavonoids. Tannins, resins, cardiacglucosides, coumerins and anthraquinone were absentin all plant part extracts. Presence of triterpenes couldbe seen only in seed extract (Table 4).

This study established the presence of majorphytochemicals which have been identified by otherresearches in various plants and in different parts ofplants (Benett et al. 2003, Hassan et al. 2007, Devbhutiet al. 2009). Santos et al. (2005) discovered that extractsobtained by water soaking of M. oleifera intact seedsshowed presence of tannin as well as antioxidantactivity. Similarly leaves of Moringa oleifera were shown

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Table 1. Phyto-chemical screening of different part ofMunga (Moringa oleifera Lam.) aqueous extracts

Qualitative test Aqueous extractsLeaf Stem Root Fruit Seed

Alkaloids

Mayer' test - - - - +

Dragendroff's - - - - +

Wagner's test - - - - +

Carbohydrates

Molisch's test - - + + +

Benedict's test + - - + +

Fehling's test - - - + +

Protein

Xanthoprotic test - - - - -

Biuret test - - - - -

Lipids

Solubility test - - - - -

Glycerol test + - - + +

Sudan III test - - - - -

Saponins -

Foam test + + + + +

Flavinoids - - - - -

Resins - - - - -

Tannins

Gelatin test - - - + -

Lead acetate test + + + + +

Ferric chloride test - - + + -

Sterols

Salkowski's test + + + + +

Liebermann's test + + + + +

Cardiac glucosides

Keller-Killiani test - - - + +

Triterpenes - - - - -

Coumerins - - - - -

Anthraquinone + - - - -

+ = Phytochemical detected, - = Not detected

Table 2. Phyto-chemical screening of different part ofMunga (Moringa oleifera Lam.) methanolic extract

Qualitative test Methanol extractLeaf Stem Root Fruit Seed

Alkaloids

Mayer' test - - + + +

Dragendroff's - - - + -

Wagner's test - - - - -

Carbohydrate

Molisch's test - - - - +

Benedict's test - - - + -

Fehling's test - - - - +

Protein

Xanthoprotic test - - - - +

Biuret test - - - - -

Lipids

Solubility test - - - - -

Glycerol test + + + - -

Sudan III test - - - - -

Saponins -

Foam test - - + + -

Flavinoids + - + + +

Resins - - - - -

Tannins

Gelatin test - - + - -

Lead acetate test - + + - +

Ferric chloride test - - - - -

Sterols

Salkowski's test + + + + +

Liebermann's test + + + + +

Cardiac glucosides

Keller-Killiani test - - + + +

Triterpenes - - - - -

Coumerins - - - - -

Anthraquinone - - - - -

+ = Phytochemical detected, - = Not detected

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Table 3. Phyto-chemical screening of different part ofMunga (Moringa oleifera Lam.) ethyl acetate extract

Qualitative test Ethyl acetate extractLeaf Stem Root Fruit Seed

Alkaloids

Mayer' test - - - - -

Dragendroff's - - - - -

Wagner's test - - - - -

Carbohydrate

Molisch's test + + + + +

Benedict's test - - - - -

Fehling's test + - - - -

Protein

Xanthoprotic test - - - - -

Biuret test + - + - -

Lipids

Solubility test - - - - -

Glycerol test + + + - +

Sudan III test - - - - +

Saponins -

Foam test - - - - -

Flavinoids - - - - -

Resins - - - - -

Tannins

Gelatin test - - - - -

Lead acetate test - - - - -

Ferric chloride test - - - - -

Sterols

Salkowski's test + - - + -

Liebermann's test + - - - -

Cardiac glucosides

Keller-Killiani test - - - - -

Triterpenes - - - - -

Coumerins - - - - -

Anthraquinone - - - - -

+ = Phytochemical detected, - = Not detected

Table 4. Phyto-chemical screening of different part ofMunga (Moringa oleifera Lam.) petroleum ether extracts

Qualitative test Petroleum ether extractLeaf Stem Root Fruit Seed

Alkaloids

Mayer' test - - - - -

Dragendroff's - - - - -

Wagner's test - - - - -

Carbohydrate

Molisch's test - - + - -

Benedict's test - - - - -

Fehling's test - - - - -

Protein

Xanthoprotic test - - - - -

Biuret test - - + - -

Lipids

Solubility test + + - - -

Glycerol test + - + - -

Sudan III test + + - - -

Saponins -

Foam test - - - - -

Flavinoids + - - - -

Resins - - - - -

Tannins

Gelatin test - - - - -

Lead acetate test - - - - -

Ferric chloride test - - - - -

Sterols

Salkowski's test + - - - -

Liebermann's test + - - - -

Cardiac glucosides

Keller-Killiani test - - - - -

Triterpenes - - - - +

Coumerins - - - - -

Anthraquinone - - - - -

+ = Phytochemical detected, - = Not detected

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to contain kaempferol, which is a known phenolic groupphytochemical (Bajpai et al. 2005). Sultana et al. (2009)investigated effects of four extracting solvents [absoluteethanol, absolute methanol, aqueous ethanol (ethanol:water, 80:20 v/v) and aqueous methanol (methanol:water, 80:20 v/v)] and two extraction techniques(shaking and reflux) on the antioxidant activity of extractsof roots of Moringa oleifera along with other plants. Thetested plant materials contained appreciable amountsof total phenolic contents and flavonoids. Singh et al.(2009) investigated the aqueous extract of leaf, fruit andseed of Moringa oleifera. The HPLC and MS/MSanalysis showed the presence of gallic acid, chlorogenicacid, ellagic acid, ferulic acid, kaempferol, quercetin andvanillin. The leaf extract was with comparatively highertotal phenolics content, flavonoids content and ascorbicacid content.

Most of the earlier studies related tophytochemical screening from Moringa tree haveconcentrated either on one plant part i.e. seed or forthe presence of certain phytochemicals only i.e.phenolic group compounds that possess antioxidantactivity. The present study presents a comprehensivephytochemical screening of all major plant parts of acommon tree; Munga (Moringa oleifera Lam.) usingdifferent solvents and thereby extracting most of thephytochemicals. The findings in this study agree withearlier studies which found that not all phytochemicalsare present in all plant parts and that those presentdiffer according to the type of the extracting solventsused (Ayinde et al. 2007).

Acknowledgement

The authors are thankful to Dean, Faculty of life scienceand Head, Department of Post Graduate Studies andResearch in Biological Science, Rani DurgawatiUniversity, Jabalpur for providing all facilities andencouragement.

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(Manuscript Receivd : 21.9.13; Accepted : 30.12.13)

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Abstract

Multiple regression analysis was carried out taking 40genotypes of wheat which are in seed production chain takingseven important traits viz flag leaf length (X1), flag leaf width(X2), number of seeds/pike (X3), 1000 seed weight (X4),biological yield/plant (X5) and harvest index (X6) and spikelength (X7) and seed yield/ plant (y). Multiple regressionequations were construction taking 2,3,4,5,6 and all the seventraits at a time. Relative construction in predicting yield/plantwas calculated for each situation. Among individual traitsbiological yield/plant (X5) and harvest index (X6) contributed47.60 and 49.70% respectively in predicting yield/plant. Amongtwo character combination biological yield/plant (X5 & X6) andharvest index was the best combinations which predicted98.20% variation in yield. Among 3 characters flag leaf breadth(X2), biological yield/plant (X5) & harvest index (X6) contributed98.20% in yield prediction. Hence it can be concluded thatbiological yield/plant (X5) and harvest index (X6) percent arethe most important traits, since these traits has positivesignificant correlation with yield high heritability values. Hencedue emphasis should be given to biological yield/plant andharvest index while selecting genotypes for wheatimprovement.

Keywords: Multiple regression analysis, correlation,heritability, coefficient of determination, additive geneaction.

Wheat (Triticm aestivum L.) is grown under wide rangeof climatic condition but the favorable one adapted forgrowing in cool and dry environment. It is staple foodfor nearly 40 per cent of world population covering atleast, 43 countries and provides 20 per cent of foodcalories to the mankind. India is maintaining its secondposition of wheat producing nations since last 10 yearsand continuous record breaking wheat harvest to thetune of 93 million tones during 2011-12 crop seasonsfrom 29.3 million ha (Anonymous 2012). It is now

Multiple regression analysis a selection criteriafor wheat improvement

Varsha Patil, P.K.Moitra and R.S.ShuklaDepartment of Plant Breeding and GeneticsJawaharlal Nehru Krishi VishwavidyalayaJabalpur 482004 (MP)

realized that sustaining the productivity of wheatgrowing areas in existing cropping system and underclimate change of country particularly in MP essentialto provided food security to the population of India whichby the year 2020 A.D. will be 1.25 billion and thus theprojected demand for wheat by the year 2020 A.D. willbe 95-109 million tones.

Wheat is the world's second most importantstaple food crop and contributes to the extent of about27% of the total food grain production. The productivityof wheat in M.P. is low (2.7) in comparison of productivityat national level (3.1 tones/ha. (Anonymous 2012)

Yield is a quantitatively inherited complex traitwhich is governed by polygenes, with very minute effectand as such governed by environment effect. In orderto improve wheat yield it is essential to know the relativecontribution of yield component characters viz, 1000seed weight, biological yield/plant, number of seeds/spike, spike length, flag leaf length and flag leaf breadth.Morphological features of seed and plant parts are themajor components of identification of cultivar. Thesuccess of wheat breeding programme depends up onthe magnitude and nature of genetic variability in thedesired direction is the prerequisite for planning anysuccessful and effective crop improvement programme.In order to judge the role of yield components biometricalstudies such as correlation analysis and path analysisis practiced.

Expressed correlation is due to linkage of genesor pleiotropic effect and it expresses nature ofassociation between variables. However, regressionanalysis measure changes in dependent variable dueto unit change in independent variable (yieldcomponents). In the present study multiple regressionequation considering taking yield as dependent variableand set of independent yield contributing traits couldexplain the total variation in yield. Individual contribution

JNKVV Res J 47(3): 269-273 (2013)

270

towards yield is also expressed taking single characterat a time. Any multiple regression equation for set ofindependent traits is more effective which can explainhigher percentage of variation in plant yield of wheat.Hence present investigation was under taken todetermine relative contribution of set of independenttraits through multiple regression analysis.

Material and methods

Forty wheat varieties in seed production chain and somepromosing lines were used as planting materialexperiment was carried out at Wheat ImprovementProject at seed breeding farm, College of Agriculture,Jabalpur (MP) during rabi 2010-2011 in randomizedblock design with three replications under irrigatedtimely sown condition. The recommended package ofpractices was followed to raise the good crop.Observations were recorded on ten randomly selectedcompetitive plants for Flag leaf length (X1), flag leafbreadth (X2) number of seeds per spike (X3), 1000 seedweight (X4), biological yield/ plant (X5) harvest index(X6) and spike length (X7) and seed yield/ plant (y).The data were analyzed to workout multiple regressionanalysis as per method suggested by Panse andSukhatme (1967)

Result and discussions

Analysis of variance for all the traits under studyrevealed men square due to genotypes were highlysignificant for all the traits indicating presence ofsufficient genetic variability in material used under study.

Multiple regression analysis was carried outtaking yield/plant as dependent variable and all othercharacters as independent variables. Multipleregression analysis has become one of the most widelyused statistical tools for analyzing functional relationshipamong variables, which is expressed in the form ofequations connecting dependent variable and one andmore independent variable (Table -1). Multiple regressionequations were constructed taking 2,3,4,5 and 6character combinations together.

Among various two characters (21 combinations)combination relative contribution towards yield/plantranged from 7.40 to 98.20 percent. The best twocharacter combination was biological yield/plant (X5)and harvest index (X6) which accounted for 98.20percent of total variation in seed yield/plant. Othercombinations among two character combinations whichwere next to best combination were 1000 seed wt (X4)

& harvest index (X6) followed by flag leaf length (X1)and biological yield/plant (X5), flag leaf breadth (X2)and harvest index (X6), 1000 seed weight (X4) andbiological yield (X5) and number of seeds/pike (X3) andharvest index (X6). It is evident from two charactercombinations that the value of expressed variability inseed yield was higher when ever one of the characterout of two was harvest index (X6) or biological yield/plant (X5). When harvest index and biological yield/plant were considered alone were able to express 49.70and 47.60 percent of variation in seed yield respectivelywhich was maximum among all traits when singlecharacter was taken into account.

Among three traits combinations flag leaf length(X1) biological yield /plant (X5) & harvest index (X6)98.20%, flag leaf breadth (X2), biological yield/plant (X5)and harvest index (X6) 98.20% and number of seeds/spike (X3) biological yield/plant (X5) and harvest index(X6) 98.20% were the best combinations in explainingtotal variability in seed yield/plant.

A total of twenty five multiple regressionequations taking four characters together wereconstructed. The best combination was 1000 seedweight (X4), coupled with biological yield/plant (X5),harvest index (X6) and spike length (X7) which explains98.20% variability in seed yield/plant. Other fourcharacter combinations flag leaf breadth (X2) 1000 seedweight (X4), biological yield/plant (X5), harvest index(X6) and spike length (X7), number of seeds/spike (X3),1000 seed weighs (X4), biological yield/plant (X5) andharvest index (X6), all of them were able to express98.30% of total variation in yield.

Among five character combination flag leafbreadth (X2) 1000 seed weight (X4), biological yield/plant (X5), harvest index (X6) and spike length (X7)and number of seeds/spike (X3), 1000 seed wt (X4),biological yield/plant (X5), harvest index (X6) and spikelength (X7) were best able to express 98.40% ofvariation in yield/plant. There was no further increasedin prediction power for 6 and all 7 characters consideredtogether in construction of multiple regression equation.

It is clear that biological yield/plant (X5) andharvest index (X6) were the most important traits inexplaining the variation in seed yield, which clearlysuggests that where ever biological yield/plant (X5) andharvest index (X6) were included in equation it resultedin increased value of coefficient of determination. Thesefinding are in agreement with findings of Batter et al(1984), Budak and yildirim (1995) and Rastogi (1997)also reported that these two traits viz biological yield/plant and harvest index shall be given due importance

271

Tabl

e 1.

Mul

tiple

regr

essi

on e

quat

ions

mul

tiple

cor

rela

tion

coef

ficie

nts

rela

tive

cont

ribut

ion

of im

porta

nt tr

aits

to e

xpre

ss v

aria

tion

in y

ield

/pla

nt in

whe

at

S.N

o.Eq

uatio

nsR

Rel

ativ

eS

. No.

Equa

tions

RR

elat

ive

cont

ribut

ion

cont

ribut

ion

to y

ield

to y

ield

per p

lant

per p

lant

01Y=

61.1

99+2

.591

x10.

326

10.6

002

Y=54

.906

+54.

500x

20.

248

6.10

03Y=

71.3

25+0

.94x

30.

222

4.90

04Y=

18.4

58+2

.771

x40.

439

19.2

005

Y=-3

.818

+0.3

99x5

0.69

047

.60

06Y=

-0.4

26+3

.334

x60.

705

49.7

007

Y=12

6.98

8+0.

177x

70.

267

9.00 Tw

o ch

arac

ter c

ombi

natio

ns01

Y=24

.875

+2.1

80x1

+34.

647x

20.

358

12.8

002

Y=36

.451

+2.2

56x1

+0.5

46x3

0.34

812

.10

03Y=

-36.

771+

2.33

3x1+

2.62

6x4

0.52

727

.80

04Y=

-66.

861+

2.46

4x1+

0.39

5x5

0.75

657

.20

05Y=

-2.1

12+0

.107

x1+3

.306

x60.

705

49.7

006

Y=71

.479

+2.8

52x1

-1.5

29x7

0.34

011

.60

07Y=

9.46

2+50

.002

x2+0

.840

x30.

317

10.0

008

Y=-0

.149

+20.

286x

2+2.

546x

40.

447

20.0

009

Y=-6

.276

+2.3

26x2

+0.3

97x5

0.69

047

.60

10Y=

-74.

176+

54.3

11x2

+3.3

33x6

0.74

755

.70

11Y=

58.7

38+6

7.33

1x2-

1.90

4x7

0.27

27.

4012

Y=-4

8.61

5+1.

040x

3+2.

852x

40.

502

25.2

013

Y=-3

1.82

6+0.

513x

3+0.

389x

50.

700

49.0

014

Y=-2

0.93

4+0.

386x

3+3.

252x

60.

710

50.5

015

Y=76

.209

+0.9

95x3

-0.7

40x7

0.22

751

.00

16Y=

-41.

839+

1.33

2x4+

0.35

4x5

0.71

751

.40

17Y=

-65.

055+

1.91

5x4+

0.03

1x6

0.76

458

.40

18Y=

26.6

76+2

.845

x4-1

.002

x70.

443

19.7

019

Y=-1

35.7

25+0

.403

x5+3

.365

x60.

991

98.2

020

Y=0.

981+

0.40

1x5-

0.46

3x7

0.69

147

.70

21Y=

-2.6

35+3

.334

x6+0

.197

x70.

705

49.7

0Th

ree

char

acte

r com

bina

tions

01Y

=-0.

176+

1.84

1x1+

34.7

90x2

+ 0.

550x

30.

379

14.3

002

Y=-

35.0

09+2

.362

x1-2

.668

x2+

2.65

3x4

0.52

727

.80

03Y

=-47

.462

+2.7

60x1

-25.

510x

2 +0

.418

x50.

763

58.3

004

Y=-

71.3

79-0

.807

x1+6

1.65

0x2

+3.5

47x6

0.75

156

.50

05Y

=26.

674+

2.47

2x1+

51.4

85x2

-2.8

93x7

0.39

615

.70

06Y

=-71

.811

+1.8

92x1

+0.7

05x3

+2.

708x

40.

550

30.3

007

Y=-

70.3

01+2

.411

x1+8

.816

x3 +

0.39

4x5

0.75

757

.30

08Y

=-19

.875

-0.1

22x1

+0.4

02x3

+3.

281x

60.

710

50.5

009

Y=4

5.02

0+2.

525x

1+0.

643x

3 -1

.926

x70.

368

13.6

010

Y=-

97.9

21+2

.361

x1+1

.181

x4 +

0.35

5x5

0.77

660

.20

11Y

=-67

.594

+0.1

56x1

+1.9

18x4

+2.

989x

60.

765

58.5

012

Y=-

25.3

75+2

.765

x1+2

.795

x4 -2

.636

x70.

553

30.5

013

Y=-

134.

437-

8.51

2x1+

0.40

4x5

+3.3

88x6

0.99

198

.20

14Y

=-53

.858

+2.8

29x1

+0.4

00x5

-2.1

54x7

0.76

959

.10

15Y

=-3.

309+

7.40

3x1+

3.31

4x6

+0.1

53x7

0.70

549

.70

16Y

=-58

.301

+12.

683x

2+1.

010x

3 +2

.710

x40.

505

25.5

017

Y=-

32.6

01+0

.773

x2+0

.513

x3 +

0.38

8x5

0.70

049

.00

18Y

=-86

.909

+52.

829x

2+0.

278x

3 +3

.274

x60.

749

56.2

019

Y=5

.643

+68.

437x

2+1.

019x

3 -2

.877

x70.

358

12.8

020

Y=-

31.8

52-1

2.29

5x2+

1.43

7x4

+0.3

62x5

0.71

951

.70

21Y

=-97

.795

+33.

858x

2+1.

523x

4 +3

.092

x60.

777

60.4

022

Y=3

.686

+33.

103x

2+2.

546x

4 -1

.901

x70.

461

21.2

023

Y=-

137.

333+

1.52

7x2+

0.40

2x5

+3.3

65x6

0.99

198

.20

24Y

=-4.

472+

7.26

8x2+

0.39

4x5

-0.6

78x7

0.69

147

.80

25Y

=-70

.352

+66.

921x

2+3.

331x

6 -1

.871

x70.

755

57.0

026

Y=-

79.0

48+0

.621

x3+1

.449

x4 +

0.33

7x5

0.73

253

.50

27Y

=-94

.900

+0.5

15x3

+1.9

90x4

+2.

910x

60.

774

59.8

028

Y=-

41.5

63+1

.210

x3+3

.029

x4 -2

.193

x70.

521

27.2

029

Y=-

132.

042-

7.97

8x3+

0.40

5x5

+3.3

82x6

0.99

198

.20

30Y

=-25

.556

+0.5

86x3

+0.3

90x5

-0.9

86x7

0.70

349

.50

31Y

=-19

.720

+0.3

99x3

+3.2

49x6

-0.1

71x7

0.71

050

.50

32Y

=-14

0.68

2+0.

223x

4+0.

386x

5 +3

.329

x60.

991

98.3

033

Y=-

33.8

56+1

.405

x4+0

.354

x5 -0

.970

x70.

720

51.8

034

Y=-5

9.76

3+1.

964x

4+3.

023x

6-0.

618x

70.

766

58.6

035

Y=-

131.

066+

0.40

4x5+

3.36

5x6

-0.4

48x7

0.99

198

.30

272

S.N

o.Eq

uatio

nsR

Rel

ativ

eS

. No.

Equa

tions

RR

elat

ive

cont

ribut

ion

cont

ribut

ion

to y

ield

to y

ield

per p

lant

per p

lant

Four

cha

ract

er c

ombi

natio

ns01

Y=69

.388

+1.9

33x1

-3.8

18x2

+0.

707x

3+2.

748x

40.

550

30.3

002

Y=-4

9.91

6+2.

722x

1-25

.304

x2 +

5.88

8x3+

0.41

7x5

0.76

358

.30

03Y

=-88

.843

-1.0

32x1

+6.5

67x2

+0.

398x

3+3.

523x

60.

757

57.2

004

Y=-

5.97

7+2.

080x

1+54

.784

x2 +

0.72

4x3-

3.42

8x7

0.42

718

.20

05Y=

-107

.449

+2.2

28x1

+0.2

12x3

+1.

229x

4+0.

349x

50.

777

60.4

006

Y=-

93.6

68-0

.145

x1+0

.534

x3 +

1.99

1x4+

2.94

4x6

0.77

459

.90

07Y

=-66

.557

+2.3

13x1

+0.8

81x3

+2.

937x

4-3.

236x

70.

585

34.2

008

Y=-7

5.03

6+2.

812x

1-41

.354

x2 +

1.50

6x4+

0.38

1x5

0.79

262

.70

09Y=

-95.

383-

0.42

7x1+

38.5

48x2

+1.

463x

4+3.

215x

60.

779

60.6

010

Y=-3

3.46

1+2.

663x

1+14

.412

x2 +

2.66

7x4-

2.96

7x7

0.55

530

.80

11Y=

-67.

798-

15.7

97x2

+0.6

48x3

+1.

589x

4+0.

346x

50.

734

53.9

012

Y=-

119.

113+

30.1

53x2

+0.4

29x3

+1.

628x

4+2.

984x

60.

784

61.4

013

Y=-6

3.10

1+31

.797

x2+1

.200

x3 +

2.74

0x4-

3.04

7x7

0.53

528

.60

14Y

=-13

3.82

1+1.

770x

2-8.

160x

3 +0

.403

x5-3

.383

x60.

991

98.2

015

Y=-

33.5

60+9

.923

x2+0

.598

x3 +

0.38

1x5-

1.29

0x7

0.70

449

.60

16Y=

-139

.995

-0.8

06x2

+0.2

30x4

+0.

396x

5+3.

328x

60.

991

98.3

017

Y=-

29.9

44-6

.648

x2+1

.448

x4 +

0.35

8x5-

0.78

9x7

0.72

051

.90

18Y=

-135

.629

+6.1

17x2

+0.3

99x5

+3.

364x

6-0.

629x

70.

992

98.3

019

Y=-8

8.96

5+67

.392

x2+0

.424

x3 +

3.24

1x6-

2.27

7x7

0.76

157

.90

20Y=

-137

.667

-5.7

70x3

+0.2

07x4

+0.

397x

5+3.

344x

60.

992

98.3

021

Y=-

73.1

62+0

.759

x3+1

.604

x4 +

0.33

3x5-

1.72

0x7

0.74

054

.70

22Y

=-12

9.26

5-4.

883x

3+0.

405x

5 -0

.405

x7+3

.376

x60.

991

98.3

023

Y=-

90.2

46+0

.618

x3-1

.247

x7 +

2.86

9x6+

2.10

2x4

0.77

860

.50

24Y

=-12

9.26

5-4.

883x

3-0.

405x

7 +3

.376

x6+0

.405

x50.

991

98.3

025

Y=-

135.

986+

0.26

6x4+

0.39

6x5

-3.3

22x6

-0.5

45x7

0.99

298

.40

Five

cha

ract

er c

ombi

natio

ns01

Y=-

84.0

02+2

.686

x1-4

1.11

6x2

+0.1

96x3

+1.5

490.

793

62.9

002

Y=-

118.

838-

0.68

8x1+

37.0

83x2

0.5

02x3

+1.5

490.

787

61.9

0x4

+0.3

75x5

x4+3

.165

x603

Y=-

76.5

66+2

.187

x1+1

6.77

8x2

+0.8

94x3

+2.7

900.

588

34.6

004

Y=-

133.

886-

8.22

2x1+

2.86

3x2

-7.1

35x3

+0.4

030.

991

98.2

0x4

-3.6

30x7

x5+3

.402

x605

Y=-

52.0

87+2

.837

x1-1

2.69

5x2

-0.1

68x3

+0.4

070.

771

59.5

006

Y=-

139.

530-

7.67

5x1+

6.30

4x2

+0.2

20x4

+0.3

960.

991

98.3

0x5

-1.9

34x7

x5+3

.350

x607

Y=-

73.2

27+3

.00x

1-29

.401

x2 +

1.53

8x4+

0.37

30.

800

64.0

008

Y=-

137.

255-

4.77

9x1-

5.12

1x3

+0.2

08x4

+0.3

970.

992

98.3

0x5

-1.9

43x7

x5+3

.356

x609

Y=-

102.

310+

2.59

7x1+

0.37

4x3

+1.4

52x4

+0.3

450.

797

63.6

010

Y=-9

0.28

3-0.

827x

1+72

.752

x2 +

0.50

4x3+

3.44

4x6-

2.02

0x7

0.76

558

.60

x5-2

.874

x711

Y=-

93.0

93-0

.182

x1+4

7.97

7x2

+1.4

98x4

+3.1

430.

786

61.7

012

Y=-1

35.4

58-4

.861

x1+6

.467

x2 +

0.39

9x5+

3.37

7x6-

0.61

1x7

0.99

298

.30

x6-1

.798

x713

Y=-

137.

334-

0.44

7x2-

5.67

9x3

+0.2

11x4

0.39

80.

992

98.3

014

Y=-7

0.01

7-5.

096x

2-5.

121x

3 +0

.756

x4+0

.337

x5-1

.578

x70.

740

54.7

0x5

+3.3

43x6

15Y

=-12

2.09

3+45

.187

x2+0

.590

x3

+1.6

68x4

+2.9

420.

796

63.4

016

Y=-1

33.9

79+5

.932

x22-

4.10

1x3

+0.4

00x5

+3.3

73x6

-0.5

87x7

0.99

298

.30

x6-2

.436

x717

Y=-1

38.3

53+3

.824

x2+0

.240

x4 +

0.39

3x5+

3.32

6x0.

992

98.4

018

Y=-1

35.5

06-1

.104

x3+0

.262

x4 +

0.39

6x5+

3.32

5x6-

0.53

4x7

0.99

298

.40

6-0.

648x

7Si

x ch

arac

ter c

ombi

natio

ns01

Y=-

87.9

01+2

.817

x1-2

7.34

2x2

+0.3

27x3

+1.6

13x4

+0.

803

64.5

002

Y=-

121.

743-

0.44

1x1+

48.7

50x2

+0.

627x

3+1.

615x

4+3.

060

0.79

763

.60

0.36

3x5

-2.2

13x7

x6 -2

.294

x703

Y=-

137.

924+

3.81

3x2-

9.71

7x3

+0.2

37x4

+0.3

93x5

+0.

992

98.4

03.

329x

6 -0

.638

x7Se

ven

char

acte

r com

bina

tions

01Y

=-13

7.93

8+1.

437x

1+3.

689x

2 -1

.104

x3+0

.238

x4+0

.394

0.99

298

.40

x5 +

3.32

5x6-

0.64

2x7

273

while selecting genotypes for wheat improvement.These two traits also showed highly positive significantcorrelation with yield/plant, high positive direct effectfrom path analysis was maximum for harvest index andbiological yield/plant. These two character alsoexhibited high broad sense heritability estimates whichindicates that due emphasis should be given for wheatimprovement as these traits are governed by additivegene action.

References

Anonymous (2012) Project Directorate Report, DWR, Karnalp 3

Batten GD, Khan MA, Cullis BR (1984) Yield responses bymodern wheat genotypes to phosphate fertilizer andtheir implication for breeding. Euphytica 33 (1): 81-89

Budak N, Yildirim MB (1995). Harvest index, biomassproduction and their relationships with grain yield inwheat. Ege universities Ziraat Fakultesi Dergisi 33(2): 25-28

Panse VG, Sukhatme PV (1667) Stastical method foragricultural workers. ICAR publication 152-161

Rastogi NK (1997) Multiple regression analysis as screeningtool for yield improvement in wheat. Advances inplant sciences 10 (2): 211-213

(Manuscript Receivd : 30.8.13; Accepted : 30.12.13)

274

Abstract

Rice is the staple food crop of India, providing 43% of caloricrequirement for more than 70% Indian population. The successof any crop improvement programme depends on nature andmagnitude of genetic variability, heritability, genetic advance,characters associations of yield and its component traits.Association analysis study provide better understanding ofyield components and furnishes information of influence ofeach contributing trait to yield directly as well as indirectlyand also enables breeders to rank the genetic attributesaccording to their contribution. The estimates of coefficientof correlation revealed the highest significant positivecorrelation of grain yield plant-1 was obtained with biologicalyield plant-1, harvest index, number of grains panicle-1, numberof filled grains panicle-1, panicle length, panicle index andspikelet density. Positive association of these traits with grainyield plant-1 revealed that these characters can be used asarchitecture for yield improvement.

Keywords: Germplasm characterization, correlationanalysis, Oryza sativa

Rice (Oryza sativa L.) is the staple food crop of India,providing 43% of caloric requirement for more than 70%Indian population. The success of any crop improvementprogramme depends on nature and magnitude ofgenetic variability, heritability, genetic advance,characters association, direct and indirect effects onyield and its attributing traits of the genotypes. Yield ofpaddy is a complex quantitative character controlledby many genes interacting with the environment and isthe product of many factors called yield components.Selection of parents based on yield alone is oftenmisleading. Hence, the knowledge about relationshipbetween yield and its contributing characters is neededfor an efficient selection strategy for the plant breedersto evolve an economic variety. Association analysis

Association analysis studies in indigenous and exoticgermplasm lines of rice

Pankaj Nagle, S. K. Rao, G. K. Koutu and Priya NairDepartment of Plant Breeding and GeneticsJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)Email : priya_nair_04@yahoo.co.in

provides better understanding of yield and itscomponents and its association with each other. Basedon these important aspects, the present study wasundertaken to estimate association amongst seed yieldand its various attributing traits.

Material and methods

The material used in the present study comprised of 71exogenous and 9 indigenous lines (Table1) received fromIRRI, Philippines under the project INGER. Theexperiment was carried out at Seed Breeding Farm,Department of Plant Breeding and Genetics, Collegeof Agriculture, JNKVV, Jabalpur (M.P.) in randomizedcomplete block design with three replications. Theobservations was recorded on fifteen quantitative traitsviz., plant height (cm), panicle length (cm), panicleweight plant-1 (g), number of tillers plant-1, number ofeffective tillers plant-1, 1000 grain weight (g), number offilled grains panicle-1, number of unfilled grains panicle-

1, number of grains panicle-1, spikelet density, days to50 % per cent flowering, biological yield plant-1 (g), grainyield plant-1 (g), harvest index (%) and panicle index.

Correlation coefficients were calculated for allquantitative characters combinations at phenotypic,genotypic and environmental level by the formula givenby Miller et al. (1958).

Result and discussion

The development of a high yielding genotype throughrice breeding which is an autogamic species requires athorough knowledge of the association of yieldcomponents. Grain yield in rice is a complex character,quantitative in nature and an integrated function of a

JNKVV Res J 47(3): 274-277 (2013)

275

Tabl

e 1.

Ger

mpl

asm

s us

ed in

stu

dy

S.N

o.N

ame

of G

erm

plas

mS

.No.

Nam

e of

Ger

mpl

asm

S.N

o.N

ame

of G

erm

plas

mS

.No.

Nam

e of

Ger

mpl

asm

1O

M 5

936

21IR

774

98-1

27-3

-2-3

-241

IR 7

9253

-55-

1-4-

661

IR 7

3885

-1-4

-3-2

-1-6

(MA

TATA

G 9

)2

KO

NAW

E22

OM

563

642

IR 8

1350

-95-

2-1-

262

IR 8

1330

-19-

2-1-

33

IR 6

423

IR 8

2355

-9-1

-243

IR 7

7542

-167

-1-1

-1-1

-363

BO

ND

OYU

DO

4A

NG

KE

24IR

806

92-6

4-3-

2-1

44IR

809

14-6

-3-1

-264

IR 8

0694

-44-

1-2-

25

IR 7

7542

-127

-1-1

-1-1

-225

IR 8

1350

-9-3

-3-3

45IR

792

53-1

9-3-

3-5

65IR

780

91-1

20-3

-2-2

-36

IR 7

1677

-161

-2-3

26IR

809

22-3

-2-2

-346

IR 7

9193

-83-

1-1-

166

IR 7

9482

-106

-2-2

-17

IR-5

027

IR 7

9532

-21-

2-2-

147

IR 8

2355

-5-2

-367

CIM

ELA

TI8

IR 8

0914

-8-3

-2-1

28IR

729

06-3

2-1-

3-3

48IR

753

86-1

4-3-

2-2

68IR

792

47-1

07-1

-2-1

9IR

809

09-8

-2-2

-329

IR 8

1852

-120

-2-1

-349

IR 8

0397

-87-

1-2-

369

IR 8

1173

-33-

1-2-

310

IR 7

7504

-36-

3-3

30IR

818

90-2

6-3-

3-1

50IR

563

81-1

39-2

-2 (P

SB

RC

28)

70IR

769

39-9

8-1-

1-1

11P

SB

RC

68

31IR

785

45-4

9-2-

2-2

51IR

781

19-2

4-1-

2-2-

271

IR 7

1186

-122

-2-2

-3 (N

SIC

RC

158

)12

Loca

l Che

ck J

R 2

0132

SU

NG

GA

L52

IR 7

6993

-49-

1-1

72IR

CE

LEB

ES

13IR

811

71-4

2-1-

2-3

33IR

795

4-65

-1-3

-253

IR O

M 5

625

73O

M 5

900

14IR

785

85-9

8-2-

2-1

34IR

796

48-3

5-2-

1-1

54IR

742

84-1

0-1-

2-3-

274

Bal

agha

t15

IR 7

2176

-307

-4-2

-2-3

35IR

730

04-3

-1-2

-155

IR 8

1373

-119

-2-2

-175

Jeer

a S

hank

ar16

IR 7

9088

-36-

1-1-

3-2

36IR

752

88-3

8-3-

156

BA

TAN

G G

AD

IS76

Ram

ker

17IR

72

37IR

798

54-3

82-1

-457

IR 7

8555

-68-

3-3-

377

Pee

so18

IR 7

6928

-74-

3-2-

138

IR 7

9089

-149

-2-3

-3-3

58IR

811

74-1

25-2

-3-1

78K

aket

i19

IR 8

1166

-60-

3-1-

239

IR 7

9643

-39-

2-2-

359

IR O

M 5

935

79P

andr

i20

IR 7

7542

-234

-1-1

40IR

780

91-6

-2-3

-1-1

60IR

811

73-6

4-2-

1-2

80U

rai B

indu

number of component traits. Therefore, selection foryield per se may not be much rewarding unless yieldcomponents are taken into consideration.

Correlation coefficient analysis

The correlation coefficient estimates the degree anddirection of association between a pair of charactersand helps simultaneous improvement of the correlatedtraits through selection. High magnitude of positivecorrelation coefficient at genotypic level indicates stronglinkage at genetic level, but high values at phenotypiclevel may not always show strong association and itmay be broken up with change in environment. Theestimates of genetic correlation coefficients along withphenotypic ones, also gives a clear picture of the extentof inherent association and also indicates to what extentthe phenotypic correlation coefficients are influencedby the environment.

The results of correlation coefficients revealedthe characters viz., days to 50 % flowering and numberof unfilled grains panicle-1 showed no correlation withgrain yield plant-1(Table 2). The characters viz., plantheight,; panicle length, number of tillers plant-1, numberof effective tillers plant-1, number of filled grains panicle-

1, number of grains panicle-1, 1000 grain weight, spikeletdensity, biological yield plant-1, harvest index , panicleindex and spikelet fertility % had significant positiveassociations with grain yield plant-1.These results werein conformity with the results of Basavaraja et al. (1997),Kumar et al. (1998), Samonte et al. (1998), Bagali et al.(1999), Gupta et al. (1999), Bastian et al. (2000), Rao(2000), Tomar et al. (2000), Nayak et al. (2001), Babu etal. (2002), Islam et al. (2002), Samo et al. (2002),Chaudhary and Motiramani (2003), Chand etal.(2004),Tyagi et al. (2004), Madhavilatha et al.(2005b), Satyanarayana et al. (2005), Shashidhar etal. (2005), Vaithiyalingan and Nadarajan (2005),Gazafrodi et al. (2006), Muthuswamy and AnandaKumar (2006b), Agahi et al. (2007), Khan et al. (2009),Sabu et al.( 2009) and Chakraborty et al. (2010).Spikeletsterility % showed a very low negative association.

gekjk ns'k ,d —f"k iz/kku ns'k gS A gekjs ns'k dh 70% vkcknh vius43% Å"ek mko';drk dh iwfrZ ds fy, bl Qly ij fuHkZj gS Afdlh Hkh Qly ds mUufrdj.k dk;ZØe esa ml Qly esa ikbZ tkusokyk vkuqokaf'kd fofo/kRrk] vkuqokaf'kd fodkl dk egRoiw.kZ ;ksxnkugksrk gS A

lglac/k fo'ys"k.k ,oa i; xq.kkad ,slh egRoiw.kZ i)fr gS ftuds }kjk

276

Tabl

e 2.

Gen

otyp

ic (G

) and

Phe

noty

pic

(P) c

orre

latio

n fo

r mor

phol

ogic

al a

nd fl

oral

trai

ts o

f ger

mpl

asm

line

s of

rice

D50

%F

PHP

LN

OT/

PN

OE

T/P

NFG

/PN

UFG

/PN

G/P

BY/

PH

I%PI

%SF

%S

S%

SD%

GW

D50

%F

G1.

000

-0.3

060.

2651

0.05

450.

0637

-0.0

876

0.31

140.

0018

0.15

86-0

.205

90.

0111

-0.3

647

0.36

04-0

.145

4-0

.590

P1.

000

-0.0

295

0.24

88**

-0.0

357

0.04

33-0

.086

00.

2759

**-0

.002

70.

1523

*-0

.187

3**

0.01

00-0

.144

5**

0.33

92**

-0.1

415*

-0.0

453

PHG

1.00

00.

4550

-0.1

830

-0.1

788

0.21

590.

3591

0.28

950.

4791

-0.2

951

0.10

56-0

.230

80.

2303

0.06

110.

1708

P1.

000

0.42

08**

-0.1

213

-0.1

223

0.20

48**

0.32

40**

0.27

36**

0.44

40**

-0.2

556*

*0.

1028

-0.2

183*

*0.

2172

**0.

0618

0.14

89*

PL

G1.

000

-0.0

251

-0.0

555

0.40

560.

3894

0.46

950.

5047

-0.1

577

-0.0

157

-0.2

059

0.20

34-0

.073

10.

1033

P1.

000

0.00

72-.0

201

0.37

78**

0.33

94**

0.43

44**

0.44

61**

-0.1

000

-0.0

137

-0.1

851*

*0.

1834

**-0

.068

90.

0823

NO

T/P

G1.

000

1.00

29-0

.096

70.

1148

-0.0

578

0.26

800.

0002

-0.4

585

-0.1

879

0.18

92-0

.099

8-0

.464

0P

1.00

00.

9682

**-0

.070

60.

0831

-0.0

412

0.25

56**

0.00

87-0

.366

2**

-0.1

451*

0.14

63*

-0.0

277

-0.3

250*

*N

OE

T/P

G1.

000

-0.0

862

0.11

57-0

.048

00.

2572

**0.

0338

-0.4

585

-0.1

964

0.19

77-0

.073

2-0

.428

9P

1.00

0-0

.060

60.

0977

-0.0

282

0.23

91**

0.02

84-0

.378

2**

0.15

56*

0.15

67*

-0.0

151

-0.3

246*

*N

FG/P

G1.

000

0.23

040.

9672

0.38

980.

0338

0.19

510.

1686

-0.1

670

0.87

52-0

.041

5P

1.00

00.

2161

**0.

9640

**0.

3705

**0.

0369

0.19

20**

0.16

32*

-0.1

615*

0.78

18**

-0.0

342

NU

FG/P

G1.

000

0.46

990.

3767

-0.0

334

-0.0

617

-0.2

298

0.92

960.

2944

-0.2

887

P1.

000

0.46

80**

0.33

93**

-0.0

442

-0.0

577

-0.2

468*

*0.

8464

**0.

2510

**-0

.222

4**

NG

/PG

1.00

00.

4518

0.02

190.

1609

-0.0

896

0.09

100.

8707

-0.1

129

P1.

000

0.42

78**

0.02

140.

1580

*-0

.082

90.

0843

0.77

60**

-0.0

915

BY/

PG

1.00

0-0

.405

4-0

.256

6-0

.227

70.

2253

0.19

440.

0625

P1.

000

-0.4

045*

*-0.

2467

**-0

.212

4**

0.21

00**

0.18

19**

0.05

43H

I%G

1.00

0-0

.474

6-0

.054

0-0

.050

80.

1294

0.05

63P

1.00

0-0

.239

7**

0.04

66-0

.044

20.

1153

0.04

33PI

%G

1.00

00.

1385

-0.1

399

0.21

610.

1406

P1.

000

0.13

43*

-0.1

357*

0.19

70**

0.11

94SF

%G

1.00

0-1

.000

0.04

620.

2574

P1.

000

-0.2

991*

*0.

0431

0.2

195*

*S

S%

G1.

000

-0.0

436

-0.2

632

P1.

000

-0.0

402

-0.2

241*

*SD

%G

1.00

0-0

.178

5P

1.00

0-0

.118

8G

WG

1.00

0P

1.00

0*

Sig

nific

ant a

t 5 p

er c

ent *

* S

igni

fican

t at 1

per

cen

t

277

ge mit ij izR;{k ,oa vizR;{k vlj Mkyus okys xq.kksa dk v/;;'udjrs gS ,oa mu dkjdksa dk cks/k djrs gS ftuds }kjk vit c<+kbZ tkldrh gS A

iFk xq.kkad fo'ys"k.k ls ;g Kkr gksrk gS fd cht mit izfr ikS/kktSfod mit izfr ikS/kk] iSnkokj lwphdkad] Hkjs nkus izfr iq"i xqPN]nkus izfr iq"i&xqPN] iq"i xqPN lwpdkad dqN ,ls dkjd gS gks mitij vfr egkRoiw.k ,oa ldkjkRed izHkko Mkyrs gS A vr% bl lQymUufrdj.k dk;ZØe esa bu dkjdksa vFkok xq.kksa dk n{krk ls iz;ksx djge /kku mit dks c<+k ldys gS A

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Bastian D, Rangasamy P, Sakila M, Backiyarani S (2000)Correlation studies in rice. Res on Crops 1(2):261 -262

Chakraborty R, Chakraborty S (2010) Genetic variability andcorrelation of some morphometric traits with grainyield in bold grained rice (Oryza sativa L.) gene poolof Barak valley. American- Eurasian J Sustain Agric4(1): 26-29

Chand SP, Roy SK, Mondal GS, Mahato PD, Panda S, SarkarG, Senapati BK (2004) Genetic variability andcharacter association in rainfed lowland Aman paddy(Oryza sativa L.). Environment and Ecology 22 (2):430 - 434

Chaudhary M, Motiramani NK (2003) Variability andassociation among yield attributes and grain qualityin traditional aromatic rice accessions. Crop Improv30 (1): 84 - 89

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Islam A, Duara PK, Barua PK (2002) Genetic variability in a

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Khan S, Imran AM, Ashfaq M (2009) Estimation of geneticvariability and correlation for grain yield componentsin rice (Oryza sativa L.). American-Eurasian J AgricEnviron Sci 6 (5):585 - 590

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(Manuscript Receivd : 10.9.13; Accepted : 5.12.13)

278

Influence of zinc application on yield attributes, yield, chemicalcomposition and protein content of wheat grown onTypic Haplustert of Kymore plateau, Madhya Pradesh

K. S. Keram and B. L. SharmaDepartment of Soil Science and Agricultural ChemistryJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)

Abstract

A field experiment was conducted for two successive yearson a Typic Haplustert at the Research Farm of Department ofSoil Science and Agricultural Chemistry, J.N. Krishi VishwaVidyalaya, Jabalpur (M.P.) to study the effect of zincapplication on yield attributes, yield, chemical composition andprotein content of wheat. Wheat variety GW-273 was sownduring Rabi season, 2010-11 and 2011-12 with recommendedinputs. The recommended doses of fertilizers were applied@ 120 N: 60 P2O5: K2O 40 kg ha-1 in all treatments. Zn wasapplied @ 1.25, 2.5, 5.0, 10.0 and 20.0 kg ha-1 as zinc sulphateat the time of sowing in all treatments except control(recommended NPK). The results indicated that combinedapplication of recommended NPK and Zn significantly andpositively affected the yield attributing characters, yield andchemical composition as well as protein content of wheat, ascompared to NPK alone. The maximum number of effectivetillers, 1000-grain weight, yield (grain and straw), chemicalcomposition (N, K and Zn), crude and true protein wasachieved by the application of 20 kg Zn ha-1 with recommendedNPK as compare to control, except P concentration in grainand straw that was reduced at highest level of Zn.

Key words: Zinc, yield attributes, yield, chemicalcomposition, protein, wheat, Kymore plateau.

Wheat (Triticum aestivum L.) is cultivated worldwideprimarily as a food commodity and a strategiccommodity. The acceptance of wheat in Asia as a basicfood stuff led to its widespread dissemination as foodaid to developing countries. Wheat is an importantstaple food crop of the entire world as well as India.Wheat is the world's leading cereal crop cultivated overan area of about 226.54 m ha-1 with a production of161.9 m tonnes. In India, the production is about 72 mtonnes from an area of 25 m ha (Singh et al. 2011). Thecurrent problem of wheat contributing in low yield is the

use of old technology like unawareness about theapplication of balanced micro and macro-nutrients aremore effective in terms of getting maximum yield andreduce losses. But the average yield of wheat is lowdue to many factors. Nutrient deficiency is one of theimportant factors. Zinc is the most common deficientmicronutrient elements in soil in the world and about50% soils of India are deficient in Zn (Sarkar and Singh2003).

Zinc is one of the eight important essential traceelement for both plants and humans (Hao et al., 2007).Zinc plays an important role in the production of biomass(Cakmak 2008). Zinc is essential for the synthesis ofplant growth regulator also called auxin (IAA); suchcompound regulates the growth and development ofplants. Zinc is involved in a large number of enzymesas a cofactor. For example, it is involved in activationof different enzymes such as dihydrogenase, aldolase,isomerase, transphosphorase and DNA polymerase(Marschner 1995). Total Zn concentration is sufficient inmany agricultural areas, but available Zn concentrationis deficient because of different soil and climaticconditions. Soil pH, lime content, organic matter content,clay type and amount and the amount of appliedphosphorus fertilizer affect the available Znconcentration in soil.

However, Zn deficiency appears to be the mostwidespread and frequent micronutrient deficiency incrops worldwide, resulting in severe losses in yield andnutritional quality. The Zn deficiency in soils of Kymoreplateau of Madhya Pradesh is about 70.3% (Khampariaet al. 2010). Limited information is available regardingzinc requirement of wheat crop in soils of the Kymoreplateau. Therefore, the present investigation wasundertaken to contemplate the optimum dose of zincunder the semi-arid and sub-tropical climate for

JNKVV Res J 47(3): 278-283 (2013)

279

Tabl

e 1.

Yie

ld a

ttrib

utin

g ch

arac

ters

, yie

ld a

nd q

ualit

y of

whe

at g

row

n in

Typ

ic H

aplu

ster

t as

influ

ence

by

diffe

rent

leve

ls o

f Zn

durin

g ye

ar 2

010-

11 a

nd 2

011-

12

Trea

tmen

tsN

o. o

f effe

ctiv

e til

lers

(m-2

)100

0-gr

ain

wei

ght (

g)Yi

eld

(t ha

-1)

Prot

ein

(%)

Gra

inSt

raw

Cru

deTr

ue20

10-1

120

11-1

220

10-1

120

11-1

220

10-1

120

11-1

220

10-1

120

11-1

220

10-1

120

11-1

220

10-1

120

11-1

2

NPK

(C

ontro

l)34

6.33

364.

6435

.45

37.7

73.

754.

004.

604.

939.

5010

.55

8.88

9.35

NPK

+1.2

5 kg

Zn

ha-1

349.

1336

8.55

36.1

438

.05

3.80

4.06

4.66

4.96

9.83

11.0

09.

109.

71

NPK

+2.5

0 kg

Zn

ha-1

359.

2537

5.73

36.6

639

.11

3.89

4.19

4.73

5.04

10.4

311

.74

9.50

10.2

2

NPK

+5 k

g Zn

ha-1

371.

9839

4.18

40.0

740

.90

3.98

4.49

4.80

5.37

11.2

112

.51

10.0

510

.66

NPK

+10

kg Z

n ha

-141

4.40

409.

0542

.43

44.4

84.

484.

775.

285.

5611

.50

12.9

410

.54

11.4

7

NPK

+20

kg Z

n ha

-141

4.45

428.

1842

.58

44.5

94.

524.

795.

295.

5911

.59

13.0

810

.67

11.7

6

Mea

n37

5.92

390.

0538

.89

40.8

24.

074.

384.

895.

2410

.68

11.9

79.

7810

.53

C.D

. (5%

)64

.66

66.2

16.

626.

900.

700.

740.

880.

901.

171.

311.

111.

18

Tabl

e 2.

Che

mic

al c

ompo

sitio

n of

whe

at g

row

n in

Typ

ic H

aplu

ster

t as

influ

ence

by

diffe

rent

leve

ls o

f Zn

durin

g ye

ar 2

010-

11 a

nd 2

011-

12

Trea

tmen

tsN

(%)

P (%

)K

(%)

Zn (m

g kg

-1)

Gra

inSt

raw

Gra

inSt

raw

Gra

inSt

raw

Gra

inSt

raw

2010

-11

2011

-12

2010

-11

2011

-12

2010

-11

2011

-12

2010

-11

2011

-12

2010

-11

2011

-12

2010

-11

2011

-12

2010

-11

2011

-12

2010

-11

2011

-12

NPK

(C

ontro

l)1.

671.

850.

570.

660.

330.

310.

098

0.08

80.

510.

540.

740.

7820

.76

22.2

38.

999.

43

NPK

+1.2

5 kg

Zn

ha-1

1.73

1.93

0.58

0.67

0.32

0.30

0.09

50.

085

0.51

0.55

0.75

0.79

22.3

625

.02

9.24

9.77

NPK

+2.5

0 kg

Zn

ha-1

1.83

2.06

0.60

0.69

0.30

0.29

0.09

30.

083

0.52

0.56

0.76

0.82

24.5

827

.96

9.54

10.1

2

NPK

+5 k

g Zn

ha-1

1.97

2.20

0.63

0.73

0.28

0.28

0.08

80.

073

0.53

0.58

0.77

0.84

27.6

231

.92

10.1

911

.06

NPK

+10

kg Z

n ha

-12.

022.

270.

660.

770.

260.

260.

080

0.07

00.

580.

620.

820.

8930

.89

35.3

210

.94

11.6

0

NPK

+20

kg Z

n ha

-12.

032.

300.

690.

800.

240.

240.

078

0.06

80.

600.

650.

840.

9134

.81

39.4

911

.42

12.2

4

Mea

n1.

872.

100.

620.

720.

290.

280.

088

0.07

80.

540.

580.

780.

8426

.84

30.3

210

.05

10.7

0

C.D

. (5%

)0.

210.

230.

070.

08N

SN

SN

SN

S0.

060.

070.

090.

102.

963.

351.

171.

24

280

obtaining sustainable wheat yield at Kymore plateau.

Materials and methods

This research work was carried out on a Typic Haplustertat the Research Farm of Department of Soil Scienceand Agricultural Chemistry, J.N. Krishi VishwaVidyalaya, Jabalpur (MP) which lies between 23o10" Nlatitude and 79o57" E longitude, during the successiveyear. The experiment was laid out in completerandomized block design with four replications. Theexperimental soil (0-15 cm depth) was analyzed for initialsoil physico-chemical properties. Soil texture was clayeyhaving the following characteristics: sand-25.3%, silt-17.90%, clay-56.8%, pH-7.2, OC-4.5 g kg-1, CaCO3-20.5g kg-1, EC-0.22 dS m -1, available N 223.0-kg ha-1,available P 18.9-kg ha-1, available K-314.3 kg ha-1,DTPA extractable Zn-0.66 mg kg-1. Wheat GW-273 wassown during Rabi season, 2010-2011 and 2011-2012on 15 and 20 December, respectively, with hand drillusing seed rate 120 kg ha-1. A basal dose of 60:60:40N, P2O5, and K2O was applied before sowing of wheat,through urea, super phosphate and muriate of potashfertilizers. Remaining 60 kg N was applied to wheat cropin two split doses during crop growth. The doses of Zn@ 0, 1.25, 2.50, 5, 10 and 20 kg ha-1 were given throughzinc sulphate fertilizer before sowing of wheat alongwithbasal dose of N, P2O5, and K2O. All crop managementand protection measures were followed. Weed controlpractices were included physical method i.e., hoeingalong with weedicides. The crop was harvested atmaturity, 120 days after sowing. At harvesting time, onemeter square was randomly selected from eachexperimental plot to estimate the yield attributingcharacters viz. number of effective tillers and 1000-grain

weight. Grain and straw yield were recorded andsamples of grains and straws were kept at 600C for 48hrs and then ground with a grinding mill and analysedfor N, P, K and Zn content by adopting standardprocedure. Nitrogen in grain and straw was determinedby microkjeldal method (AOAC 1965), phosphorus wasdetermined by Vanadomolybdate yellow colour methodof Koenig and Johnson (1942) on spectrophotometer,potassium as described by Black (1965) and Zn by usingatomic absorption spectrophotometer. Crude proteinpercentage was calculated multiplying the N contentby constant factor of 5.70 and true protein wascalculated by deducing non- protein nitrogen form crudeprotein. The entire data was analysed statistically byusing ANOVA technique.

Results and discussion

Yield attributes and yield

The results indicated that the yield attributing characters,yield and quality expect of number of effective tillers,1000-grain weight, grain and straw yield in wheat wereinfluenced by Zn application in both year (Table 1 andFig. 1).

It is evident from the data of two consecutive year2010-11 and 2011-12, that the number of effective tillers(414.45 and 428.018) and 1000-grain weight (42.58 and44.59 g) was recorded with treatment comprisingNPK+20 kg Zn ha-1, which was significantly higher thancontrol at maturity stage, respectively. The treatmentwith 10 kg Zn ha-1 was statistically at par with 20 kg Znha-1 in number of effective tillers and 1000-grain weight,during 2010-11 only. The lowest number of effective

0.00

1.00

2.00

3.00

4.00

5.00

6.00

2010-11 2011-12

Yie

ld t

ha-1 NPK (Control)NPK+1.25 kg Zn ha-1NPK+2.50 kg Zn ha-1NPK+5 kg Zn ha-1NPK+10 kg Zn ha-1NPK+20 kg Zn ha-1

0

2

4

6

8

10

12

14

2010-11 2011-12 2010-11 2011-12

Crude True

Pro

tein

(%)

Treatments NPK (Control)Treatments NPK+1.25 kg Zn ha-1Treatments NPK+2.50 kg Zn ha-1Treatments NPK+5 kg Zn ha-1Treatments NPK+10 kg Zn ha-1Treatments NPK+20 kg Zn ha-1

Fig 1. Effect of different levels of Zn on Grain yield ofwheat during year 2010-11 and 2011-12

Fig 2. Effect of different levels of Zn on protein contentof wheat during year 2010-11 and 2011-12

281

Fig

3. E

ffect

of d

iffer

ent l

evel

s of

Zn

on N

con

tent

of w

heat

dur

ing

year

2010

-11

and

2011

-12

Fig

4. E

ffect

of d

iffer

ent l

evel

s of

Zn

on P

con

tent

of w

heat

dur

ing

year

2010

-11

and

2011

-12

Fig

5. E

ffect

of d

iffer

ent l

evel

s of

Zn

on K

con

tent

of w

heat

dur

ing

year

2010

-11

and

2011

-12

Fig

6. E

ffect

of d

iffer

ent l

evel

s of

Zn

on Z

n co

nten

t of w

heat

dur

ing

year

201

0-11

and

201

1-12

282

tillers (346.33 and 364.64) and 1000-grain weight (35.45and 37.77 g) was recorded in control at maturity stagein both the years, respectively. The treatments 5, 2.50and 1.25 kg Zn ha-1 was statistically at par with controlat maturity stages in both number of effective tillers and1000-grain weight.

Similarly, the highest the grain (4.52 and 4.79 tha-1) and straw (5.29 and 5.59 t ha-1) yield was observedin treatment consisting NPK+20 kg Zn ha-1, which wassignificantly higher than the control during the year2010-11 and 2011-12. The treatment with 10 kg Zn ha-

1 was at par to 20 kg Zn ha-1 only in grain yield. No Znfertilization treatment showed significant result towardsstraw yield. The lowest grain (3.75 and 4.00 t ha-1) andstraw (4.60 and 4.93 t ha-1) yield was recorded in control.The treatments with application of 5, 2.50 and 1.25 kgZn ha-1 seems equally in their effect and the differencebetween them were insignificant and statistically at parwith control in grain yield. Such effect of Zn fertilizationmight be due to its critical role in crop growth, involvingin photosynthesis process, chlorophyll formation, N-fixation, respiration and other biochemical andphysiological activities and thus their importance inachieving higher and sustainable yield. The results arein conformity with the findings of Seadh et al. (2009)and Gul et al. (2011).

Chemical compositions and quality

Chemical composition i.e. N, K and Zn concentration ingrains and straw and quality parameter of grain i.e.crude and true protein percentage show positiveresponse with increasing levels of Zn fertilizer in bothyears as shown in Table 1,2 and Fig.2 to 6, whereas,the magnitude of P concentration was in decreasingorder with increasing levels of Zn.

The application of recommended dose of NPKalongwith 20 kg Zn ha-1 resulted in the maximum valuesof traits N, K and Zn concentration as well as crude andtrue protein percent with significant differencescompared with control in two growing seasons.Generally, it was observed that the importance of Znfertilization with recommended NPK in terms of N, K,Zn and crude protein content assorted as: NPK+20 kgZn > NPK+10 kg Zn > NPK+5 kg Zn > NPK+2.50 kg Zn> NPK+1.25 kg Zn > NPK alone in both season, whileP concentration was in decreasing order andinsignif icant with increasing levels of Zn. Thisimprovement in grain quality and chemical compositionmay be due to the role of Zn in maintaining balancedplant physiological growth. Even though Zn is present

in small amounts in plants, it involved and activates alarge number of enzymes as a cofactor. For example, itis involved in activation of different enzymes such asdihydrogenase, aldolase, isomerase andtransphosphorase. It is inferred that plant not to be ableto survive with inadequate Zn because they areessential to the synthesis of DNA and RNA and tometabolizing protein. Decline in P concentration in grainand straw may be due to antagonistic effect betweenZn and P. The results achieved in this work are partiallycompatible with those obtained by Alam et al (2000),Habib (2009) and Seadh et al (2009).

vuqla/kku iz{ks=] e`nk foKku ,oa d`f'k jlk;u"kkL= foHkkx]t-us-d`-fo-fo- tcyiqj ¼e-iz-½ ds fVfid gsIywLVVZ e`nk esaflafpr xsgw¡ esa vuq'kaflr moZjdksa dh ek=k¼120%60%40 u=tu % LQqj % iksVk'k fd- xzk- izfr gs-½ds lkFk 10 ,oa 20 fd-xzk- tLrk izfr gs- dk mi;ksx djus ijmit fu/kkZfjr djus okys dkjd ¼dkjxj fdYyks dh la[;k izfroxZ eh- rFkk 1000&nkus dk Hkkj½] mit ¼vukt ,oa HkwlkVu izfr gs-½] jlk;fud laxBu ¼u=tu] iksVk'k ,oa tLrk½ viDorFkk ;FkkFkZ izksVhu dh izfr'kr vfHkO;atd ,oa vf/kdreik;k x;k gS] tcfd tLrk ds mi;ksx ls Qly esa LQqj izfr"kresa deh ikbZ xbZ gSA

Reference

AOAC (1965) Official methods of analysis of the associationof official agricultural chemists. 10th Ed., 744

Alam SM, Zafar I, Latif A (2000) Effect of P and Zn applicationby fertigation on P use efficiency and yield of wheat.Trop Agric Res and Exten 3(2): 115-120

Black CA (1965) Methods of soil analysis. Part I and part IIAgronomy series No. 9, Ame Soc Agron Inc.Madison, Wisconsin, USA

Cakmak I (2008) Enrichment of cereal grains with zinc:Agronomic or genetic biofortification. Plant and Soil302: 1-17

Gul H, Said A, Saeed B, Ahmad I, Ali K (2011) Response ofyield and yield components of wheat towards foliarspray of nitrogen, potassium and zinc. ARPN J Agricand Bio Sci 6(2): 23-25

Habib M (2009) Effect of foliar application of Zn and Fe onwheat yield and quality. African J Biotech 8(24): 6795-6798

Hao MD, Wei XR, Dang TH (2003) Effect of long-term applyingzinc fertilizer on wheat yield and content of zinc indryland. Plant Nut and Ferti Sci 9(3): 377-380

Khamparia RS, Singh MV, Sharma BL, Kulhare PS, SharmaGD (2010). Four decades of research on micro andsecondary nutrients and pollutant elements in soil ofM.P. Res Publi No. 9 AICRP micro- and secondarynutrients and pollutant elements in soil and plant,IISS Bhopal 6: 1-113

283

Koenig D, Johnson PP (1942). Distribution of phosphorus inbiological materials. Ind Eng Chem 14: 155

Marschner H (1995) Mineral Nutrition of Higher Plants. 2nded. Acad. Press, London. 301-306

Sarkar, Singh (2003) Crop response of secondary andmicronutrient in acidic soil of India. Ferti News 48:47-54

Seadh SE, El-Abady MI, El-Ghamry AM and Farouk S (2009).Influence of micronutrient application and nitrogenfertilization on wheat yield, quality of grain and seed.J Bio Sci 9(8): 851-858

Singh CM, Sharma PK, Kishor P, Misra PK (2011). Impect ofintegrated nutrient manage ment on growth, yieldand nutrient uptake by wheat. Asian J Agric Res5(1):76-82

(Manuscript Receivd : 10.8.12; Accepted : 30.12.13)

284

Effect of in-situ moisture conservation for improving nigerproductivity in Kymore plateau, Madhya Pradesh

M.R. Deshmukh, Alok Jyotishi and A.R.G. RanganathaProject Coordinating Unit (Sesame and Niger)Jawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482 004 (MP)Email : deshmukhmohan24@gmail.com

Abstract

Experiments were conducted on niger with the objective toevaluate suitable moisture conservation practices forimproving the productivity and monetary returns. Resultsrevealed that normal sowing of seed with one handweeding(HW) at days after sowing (DAS) followed by vegetativemulching (use of incorporated weeds) proved significantlysuperior over normal sowing followed by two handweedingsat 15 and 30 DAS with regard to seed yield and monetaryreturns. It produced seed yield of 650 kg/ha with NMR of Rs5214/ha and B:C ratio of 1.66 against normal sowing withseed yield of 541 kg/ha fetching NMR of Rs 2942/ha and B:Cratio of 1.37. Other moisture conservation practices viz.,normal sowing with one H.W. at 15 DAS followed by sawdust mulching or soil stirring also proved equally effective tofarmers practice with regard to seed yield and monetaryreturns.

Keywords: Moisture conservation, Mulch, Stirring,Economics, Niger

Niger is an important oilseed crop for tribals of Jabalpurarea in the kymore plateau zone of Madhya Pradesh.Generally it is grown in hill-slopes, hillocks anddegraded lands, where soil depth is very shallow havingvery low moisture holding capacity. The rainfall of theniger growing area is often highly erratic and results inmoisture stress during growing season of croppingfrequently because of long dry spells. It causes greatreduction in yield of niger. As niger crop is sensitive tomoisture stress particularly increases if desirablemoisture condition is maintained at critical growth stagesof crop-growth (Rath et al 2006). Agricultural droughtoccurred due to early cessation of monsoon rains canbe managed for growth through conserved rain water(Grewal et al 1989). Therefore, the present study wastaken up to find out the efficient method of moistureconservation for improving niger productivity of the zone.

Materials and methods

Field experiments were conducted on niger cv. JNC-6at Research Farm of Project Coordinating Unit (Sesameand Niger), JNKVV, Jabalpur for two consecutive yearsduring winter season of the year 2007 and 2008 withthe objective to evaluate the moisture conservationtechniques. The soil of the experimental field was clayloam in texture, neutral in reaction (pH 7.2) andanalyzing in low organic carbon (0.39%), available N220 kg/ha and available P 7.85 kg/ha and high availableK (345 kg/ha) contents. Six treatments of moistureconservation techniques viz., normal sowing with twohand weedings (H.W.)-T1; one H.W. at 15 days aftersowing (DAS) + dust mulching within and between rowsafter weeding-T2; one H.W. at 30 DAS + dust mulching-T3; one H.W. at 15 DAS + vegetative mulch 4 t/ha-T4;one H.W. at 15 DAS + soil stirring after each irrigationupto 50 DAS-T5 and keeping dead furrow after 6 rows-T6 were tested in randomized block design with threereplications. The seeds were treated with Thiram 3 g/kg seed and sown on October 16, 2007 and October,6, 2008 in rows 30 cm apart by drilling 5 kg seed/ha atabout 3 cm depth. Just after sowing the seeds werewell covered in the soil and a light irrigation was givenfor germination of seed. A uniform dose of fertilizers as40 kg N + 30 kg P2O5 + 20 kg K2O/ha was applied toeach plot. Half quantity of N and total P and K fertilizerswere applied as basal, while remaining N was given astop dressing. Data on various yield attributes and yieldwere recorded. The oil yield was also determinedtreatment wise on the basis of oil content in seed. Theeconomics was calculated using the prevailing pricesfor the inputs and produce during that period of time.Finally data were statistically analysed for the

JNKVV Res J 47(2): 284-287 (2013)

285

Tabl

e 1.

Effe

ct o

f diff

eren

t moi

stur

e co

nser

vatio

n pr

actic

es o

n pl

ant-h

eigh

t, ba

sal b

ranc

hes

and

yiel

d at

tribu

tes

of n

iger

dur

ing

2007

and

2008

at

Jaba

lpur

Trea

tmen

tP

lant

hei

ght

Bas

al b

ranc

hes/

Cap

itule

/pla

ntS

eeds

/cap

itula

Test

wei

ght

(cm

)pl

ant (

#)(#

)(#

)(g

)20

0720

08M

ean

2007

2008

Mea

n20

0720

08M

ean

2007

2008

Mea

n20

0720

08M

ean

T 1 -N

orm

al s

owin

g w

ith tw

o ha

nd w

eedi

ngs

110

122

116

6.5

7.5

7.0

32.3

33.6

32.9

23.1

23.9

24.5

4.3

4.5

4.4

T 2 -O

ne h

and

wee

ding

at 1

5 D

AS

+ s

aw d

ust m

ulch

ing

126

141

134

8.2

8.9

8.6

34.6

35.3

34.9

34.9

35.7

24.7

4.4

4.6

4.5

T 3 -O

ne h

and

wee

ding

at 3

0 D

AS

+ s

aw d

ust m

ulch

ing

120

144

132

8.3

9.2

8.8

35.3

35.6

35.5

35.5

36.8

25.1

4.6

4.7

4.6

T 4 -O

ne h

and

wee

ding

at 1

5 D

AS

+ v

eget

ativ

e m

ulch

ing

130

142

136

8.5

9.3

8.9

35.7

36.1

35.9

35.9

36.9

25.4

4.9

4.8

4.8

T 5 -O

ne h

and

wee

ding

at 1

5 D

AS

+ s

oil s

tirrin

g up

to 5

0 D

AS

125

143

134

8.3

8.6

8.5

34.5

34.6

35.0

35.0

36.2

24.9

4.4

4.3

4.3

T 6 -K

eepi

ng d

ead

furr

ow a

fter s

ixth

row

122

139

131

8.4

8.6

8.3

35.4

34.7

35.6

35.6

36.1

25.0

4.2

4.4

4.3

SE

m±

7.9

6.3

8.1

0.36

0.23

0.31

0.39

0.31

0.28

0.20

0.24

0.19

0.11

0.13

0.12

CD

(P=0

.05)

24.8

19.2

25.4

1.16

0.71

0.95

1.24

0.96

0.85

0.62

0.73

0.59

NS

NS

NS

# =

Num

ber

Tabl

e 2.

Effe

ct o

f diff

eren

t moi

stur

e co

nser

vatio

n pr

actic

es o

n se

ed y

ield

(kg/

ha),

oil y

ield

and

eco

nom

ics

of n

iger

dur

ing

2007

and

2008

at

Jaba

lpur

See

d yi

eld

Oil

cont

ent

Oil

yiel

dN

et m

onet

ary

B:C

Trea

tmen

t(k

g/ha

)(%

)(k

g/ha

)re

turn

sra

tio20

0720

08M

ean

2007

2008

Mea

n20

0720

08M

ean

(Rs/

ha)

T 1 -N

orm

al s

owin

g w

ith tw

o ha

nd w

eedi

ngs

440

642

541

34.8

936

.79

35.8

415

423

619

529

421.

33

T 2 -O

ne h

and

wee

ding

at 1

5 D

AS

+ s

aw d

ust m

ulch

ing

571

675

623

35.2

936

.82

36.0

621

024

322

548

241.

63

T 3 -O

ne h

and

wee

ding

at 3

0 D

AS

+ s

aw d

ust m

ulch

ing

548

672

610

35.3

236

.39

35.8

619

424

521

942

641.

53

T 4 -O

ne h

and

wee

ding

at 1

5 D

AS

+ v

eget

ativ

e m

ulch

ing

572

729

650

35.7

136

.31

36.0

120

426

523

452

141.

66

T 5 -O

ne h

and

wee

ding

at 1

5 D

AS

+ s

oil s

tirrin

g up

to 5

0 D

AS

552

663

607

35.4

335

.76

35.6

019

523

721

641

461.

51

T 6 -K

eepi

ng d

ead

furr

ow a

fter s

ixth

row

431

589

510

35.0

636

.03

35.5

515

121

218

125

561.

37

SE

m±

2128

25-

--

1411

1228

60.

3

CD

(P=0

.05)

6586

78-

--

4334

3786

40.

11

286

interpretation of the results.

Results and discussion

Seed yield

The moisture conservation practices viz., one H.W. at15 DAS followed by saw dust mulching - (T2), H.W. at30 DAS followed by saw dust mulch - (T3), one H.W. 15DAS followed by vegetative mulching - (T3) and oneH.W. 15 DAS followed by soil stirring upto 50 DAS -(T5) produced significantly higher seed yield over normalsowing with 2 H.W. - (T1) during both years ofinvestigation (Table 1 and Fig 1). Based upon two yearsdata on seed yield, keeping dead furrow after six rows.T6 produced lowest seed yield 510 kg/ha which was atpar to T1 (541 kg/ha). Seed yield significantly increasedwith the treatments adopting soil conservation practicesas T2 (623 kg/ha), T3 (610 kg/ha), T4 (650 kg/ha) and T5(607 kg/ha) over the former two treatments. However,variation in seed yield were not significant between foursoil conservation practices, however T4 producednumerically higher seed yield. The superiority in seedyield due to different soil conservation practices overcontrol (normal sowing with H.W. only) were mainlyattributed to higher plant-height, basal branches/plant,capitula/plant, seeds/capitula (Table 1). The test weightof seed was not affected by different soil conservationpractices. Different soil conservation practices viz., useof dust mulching (T2 and T3), vegetative mulching (T4)and soil stirring (T5) helped to improve the growthparameters viz., plant-height and branches/plant whichalso improved the yield attributes viz., capitule/plant andseeds/capitula. Keeping dead furrow after each sixthrow-T6 did not compensate the losses caused byreducing the population. Stirring of soil after H.W. at 15DAS upto 50 DAS - T5 and dust mulching after the H.W.at 30 DAS-T3 probably conserved lesser moisture and

produced numerically inferior yield attributes and thus,resulting in lesser seed yield. These results are in closeconformity with the findings of Panda and Mohanty(2009).

Oil yield

Oil yield depend on the seed yield and oil content ofseed. Oil content of seed did not vary due to differentmoisture conservation practices, but oil yieldsignificantly varied, mainly due to variations in seed yield(Table 2 and Fig 2). Because of significantly higher seedyield with T2, T3, T4 and T5 moisture conservationpractices, oil output were also significantly higher, overT1 - control as well as T6 - keeping dead furrow after 6th

row for sowing of seed. These results also corroboratedthe findings of Trivedi and Ahlawat (1991 and 1993)and Deshmukh et al (2007).

Economics

Different moisture conservation practices viz., T4 (Rs5214/ha), T2 (Rs 4824/ha), T3 (Rs 4264/ha) and T5 (Rs4146/ha) fetched significantly higher net monetaryreturns (NMR) over normal sowing - T1 (Rs 2942/ha).Though, cost of cultivation slightly increased with theinclusion of different moisture conservation practicesover T1, significant increase in seed yield resulted intomarked increase in the NMR values. Consequently, theB:C ratio were also significantly greater with differentwater conservation practices (T2, T3, T4 and T5) overcontrol.

Hkwfe esa ueh laj{k.k djus ds fofHkUu rjhdksa dk ewY;kadu jkefry dhQly ls vf/kd mRikndrk ,oa vkfFkZd vk; izkIr djus ds mn~ns';ls o"kZ 2007&08 ds nkSjku lrr~ ijh{k.k iz;ksx fd;s x;s A ifj.kkeksa

0

20

40

60

80

100

120

140

Yie

ld a

ttri

bute

s

T1 T2 T3 T4 T5 T6

Treatments

Fig 1. Effect of different moisture conservation practices onmean ancillary characters and yield attributes of niger

Plant height (cm) Basal branches/plant (#) Capitule/plant (#)

Seeds/capitula (#) Test weight (g)

Fig 2. Effect of different moisture conservation practices onmean seed, oil yield and economics of niger

0100020003000400050006000

T1 T2 T3 T4 T5 T6Treatments

Yie

ld a

ndE

cono

mic

s

0

0.5

1

1.5

2

B:C

rat

io

Seed yield (kg/ha) Oil content (%) Oil yield (kg/ha)

NMR (Rs/ha)

B:C ratio

287

ls Kkr gqvk dh jkefry dh cksuh ds 15 fnu i'pkr~ funakbZ ds lkFkokuLifrd iyokj dk 4 Vu@gsDVs;j ds eku ls iz;ksx djus ijjkefry dh cksuh ds 15 ls 30 fnu i'pkr~ nks ckj fuankbZ ds djusds mipkj ds rqyuk esa vf/kd mit ,oa vkfFkZd vk; izkIr djus esmi;qDr ik;k x;k A igys mipkj ls 650 fdxzk@gSDVs;j mit]5214 :Ik;s 'kq) ykHk rFkk 1-66 ykHk&O;; vuqikr nqljs mipkjls izkIr mit 541 fdxzk@gSDVs;j] 2942 :i;s@gSDVs;j 'kq) ykHk,oa 1-37 ykHk&O;; vuqikr ds fo:) ntZ fd;k x;k A vU; uehlja{k.k djus ds rjhdkass ;Fkk jkefry dh lkekU; cksuh ds 15 fnui'pkr~ fuankbZ ds lkFk ydMh ds Hkqls dk iyokj vFkok Hkwfe foyksMudjus ls izkIr mit LFkkuh; —"kd rjhdksas ls izkIr gksus okyh chtmit rFkk 'kq) ykHk ds lerqY; gksuk ik;k x;kA

References

Deshmukh MR, Pandey A.K, Sharma RS, Duhoon S.S (2007)Effect of integrated nutrient management onproductivity and economic viability of niger. JNKVVRes J 41(1):32-35

Grewal S.S, Mittal SP, Agnihotri Y, Dubey LN (1989) Rainwater harvesting for the management of agriculturaldroughts in the foot hills of northern India. Agril WaterMang 16:309-322

Panda S, Mohanty LK (2009) In-situ moisture conservationtechniques for improving niger, [Guizotia abyssinica(L.f.)] Cass productivity. J Oilseeds Res 26:316-317

Rath BS, Garhayak LM, Sahoo J, Swain NC, Mishra HP,Mohapatra PC (2006) Oilseed production technologyin Orissa. Agricultural Technology Information Centre,Directorate of Extension Education Orissa Universityof Agriculture and Technology Bhubaneshwar(Odisha) p 23

Trivedi SJ, Ahlawat RPS (1991) Effect of nitrogen andphosphorus on growth and yield on niger (Guizotiaabyssinica Cass). Indian J Agron 36(3):432-433

Trivedi SJ, Ahlawat RPS (1993) Quality studies in niger(Guizotia abyssinica Cass) in relation to nitrogen andphosphorus. Gujrat Agric Univ Res J 18(2):92-93

(Manuscript Receivd : 20.7.12; Accepted : 11.12.13)

288

Abstract

A field experiment was conducted in Kharif 2012 underAICRIP-Rice at JNKVV College of Agriculture Farm, RewaMP in aerobic condition. Three dates of sowing i.e. D1, D2 andD3 as main treatment and six rice genotypes two in each groupof early, (Danteshwari and Narendra-97), medium (Govindaand Sahabhagi) and hybrid (PHB 71 and BH 21) as subtreatment. Direct seeding method was used in rainfed aerobiccondition. It was found that D2 (30th June) seeding date wasfound suitable for rice direct seeding in upland aerobiccondition and among the different group of genotypes,Danteshwari in early, Sahabhagi in medium and BH-21 inhybrid exhibited higher water productivity and grain yield.

Rice (Oryza sativa L.) is the main food crop in Asia wheremore that 90% of the world's rice is produced andconsumed which provides on an average of 35% of totalcalorie intake (Bauman 2001). The 40% of totalcultivated area of rice is under fragile ecosystems whichinclude the rain fed upland, lowland and deepwater ricewhere yield are both low and extremely variable. Inupland ecosystem where sustainability is threatened byfresh water scarcity, water pollution and competition forwater use (Gleick 1993). It is difficult to have water forirrigated rice system which consumes two three timesmore water than other cereals therefore majorchallenges are to produce more rice in aerobic conditionwith increase water productivity and reduce water input(Postel 1997). Aerobic condition help in water savingin terms of water use efficiency. In the present scenario,water productivity is more important and thus, need tofind out suitable time and genotypes for efficient wateruse and productivity. Hence, the present study wasdesign to find out the suitable time of seeding andgenotypes of early, medium and hybrid rice for efficientwater use and productivity.

Water productivity of early, medium and hybrid rice varieties underaerobic condition

R.K. Tiwari, B.S. Dwivedi*, I.M. Khan, S.K. Tripathi and Deepak MalviyaAll India Coordinated Rice Improvement ProjectCollege of Agriculture Rewa 486001 (MP)*Department of Soil Science and Agricultural Chemistry, College of AgricultureJabalpur 482 004 (MP)Email : rktkvkrewa@rediffmail.com

Material and methods

A field experiment was conducted in Kharif 2012 underAICRIP-Rice at JNKVV, College of Agriculture Farm,Rewa (M.P.) in aerobic condition. Three dates of sowingi.e. D1, D2 and D3 (20th June, 30th June and 10th July2012 respectively) as main treatment and six ricegenotypes two in each group of early, (Danteshwari andNarendra 97), medium (Govinda and Sahabhagi) andhybrid (PHB 71 and BH 21) as sub treatment. Directseeding method was used in rainfed aerobic condition.Uniform dose of 100 kg N, 60 kg P2O5 and 40 kg K2O/ha along with 20 kg ZnSO4 was applied to all 25 m2

plots through urea, SSP and MOP. Application ofnitrogen was done in 3 split i.e. 50% as basal, 25% attillering and remaining 25% at PI stage of crop growth.Soil of experimental field was silty clay loam having 6.7pH, low in available nitrogen and medium in phosphorusand high in potassium with 0.52% organic carbon.Observations were recorded on yield attributingcharacters in all treatments and water productivity wascalculated on agronomic yield (g of grain)/unit of wateruse (kg of water) by using the following formula (Grassi2009).

YWP =

(I+R)

Where,WP = Water productivityY = YieldI = Water irrigation appliedR = amount of rainfall

JNKVV Res J 47(3): 288-290 (2013)

289

Tabl

e 1.

Yiel

d at

tribu

ting

char

acte

rs, y

ield

and

wat

er p

rodu

ctiv

ity o

f ear

ly, m

ediu

m a

nd h

ybrid

rice

gen

otyp

es in

aer

obic

con

ditio

n

Trea

tmen

t mai

n pl

otSu

b pl

otD

ay o

fP

anic

leW

eigh

t of

Test

wei

ght

Gra

in y

ield

Wat

er(D

ate

of s

owin

g)(g

enot

ypes

)M

atur

ityno

./m2

pani

cle

(g)

pani

cle

(g)

(kg/

ha)

prod

uctiv

ity(k

g/m

2 )D

1 (20

th J

une

2012

)E

arly

Dan

tesh

awar

i94

303

2.44

27.2

730

222.

379

Ear

lyN

-97

9238

91.

9726

.46

3277

2.58

0

med

ium

Gov

endr

a10

531

32.

6428

.67

3744

2.94

8

med

ium

Sah

abha

gi10

931

52.

8426

.87

3888

3.06

1

Hyb

ridPH

B-71

120

321

2.74

27.3

440

223.

166

Hyb

ridBH

-21

122

330

2.85

27.2

738

723.

048

D2 (

30th J

une

2012

Ear

lyD

ante

shaw

ari

9532

02.

9427

.60

3722

2.93

0

Ear

lyN

-97

9630

72.

3426

.60

3555

2.79

9

med

ium

Gov

endr

a10

632

62.

9028

.14

3927

3.09

2

med

ium

Sah

abha

gi11

032

82.

8426

.67

4000

3.14

9

Hyb

ridPH

B-71

120

330

3.27

27.8

740

383.

179

Hyb

ridBH

-21

121

335

3.31

27.0

740

943.

225

D3 (

10th J

uly

2012

Ear

lyD

ante

shaw

ari

9532

02.

6126

.50

3705

2.91

7

Ear

lyN

-97

9832

32.

1427

.00

3538

2.78

5

med

ium

Gov

endr

a11

032

22.

8426

.77

3983

3.13

6

med

ium

Sah

abha

gi11

231

72.

6227

.50

3944

3.10

5

Hyb

ridPH

B-71

121

330

3.20

27.4

739

883.

150

Hyb

ridBH

-21

125

338

3.17

26.3

439

833.

136

LSD

(D)

1.02

5.95

0.22

0.85

249.

120.

05

LSD

(G)

2.15

9.76

0.46

1.71

192.

310.

12

(DX

G)

NS

14.1

2N

S2.

4830

1.26

0.46

290

Results

Effect of date of seeding on maturity and cropproductivity

The crop duration was increased by 1 to 6 days in earlyduration genotypes form D1 to D3. Maturity of N-97 wasdelayed by 6 days in D3. In medium duration genotypesmaturity was delayed by 4-5 days from first date ofseeding (D1) to 3rd date of sowing (D3) and maximumdelayed was occurred in cv. Govinda while minimumdelayed (1 to 3 days) in maturity was found in hybridgroup from D1 to D3 dates of seeding. The mean rainfallreceived by all genotypes was 1269.6 mm during cropgrowth period.

Highest mean water productivity (3.06 kg/m3) wasassociated with D2 i.e. 30th June 2012 followed by D3(3.03) and D1 (2.86). Genotypes Danteshwari in earlygroup, Sahabhagi in medium and BH-21 in hybrid grouphad mximum water productivity (Table 1) in D2 and amongthe different group of genotypes hybrids had higherwater productivity followed by medium and early group.

Effect of date of seeding on yield parameters and yield

Significant variation was recorded for various yieldparameters and yield among the different dates ofsowing and genotypes. The mean maximum panicles/m2, panicle weight and test weight (g) was found in D2followed by D3 while, the minimum values of these yieldattributing characters was found in D1. Among thedifferent group of genotypes, panicle No/m2, panicleweight, test weight and grain yield was superior inhybrids followed by medium and early group genotypes.In early duration group genotype Danteshwari hadmaximum panicles/m2, panicle weight and test weight,while in medium duration genotypes Sahabhagiexhibited superiority over Govinda. In the hybrids BH-21 had maximum number of panicles, panicle weightand test weight.

Significant variation was found in grain yield indifferent dates of sowing and genotypes. Meanmaximum grain yield (3889.33 kg/ha) was found in D2closely followed by D1 (3856.83 kg/ha) while, minimumgrain yield was noted in D1 because there was no rainsafter dry seeding. Among the genotypes group, hybridHB-21 yielded maximum (4094 kg/ha) followed bySahabhagi (4000 kg/ha) in medium group andDanteshwari (3788 kg/ha) early groups.

It is concluded from the study that D2 (30th June)seeding date was found suitable for rice direct seedingin upland aerobic condition and among the differentgroup of genotypes, Danteshwari in early, Sahabhagiin medium and BH-21 in hybrid exhibited higher waterproductivity and grain yield.

vf[ky Hkkjrh; vuqla/kku ifj;kstuk ds varxZr d`f'k egkfo|ky;]jhok ds iz{ks= esa [kjhQ 2012 ds /kku dh lh/kh cqokbZ ds fy;s rhufofHkUUk cqokbZ fof/k;ks ds lkFk /kku dh N% iztkfr;ks dk v/;;u fd;kx;kA ftlesa 2 iztkfr;k¡ ¼/kUrs"ojh] ujsUnz 97½ de vof/k esa idusokyh] nks iztkfr;k¡ ¼xksfoUnk] lgHkkxh½ e/;e vo/kh esa idus okyh ,oanks iztkfr;k¡ ¼ih-,p-ch- 71] ch-,p- 21½ "kadj iztkfr;k¡ FkhA v/;;u ls ;g irk pyk fd /kku dh lh/kh cqokbZ ds fy;s Mh&2 ¼30twu½ lcls mfpr Fkh] ftles /kku dh iztkfr;k¡ ds rhuks lewgks esalkFkZd mit izkIr gqbZA

Reference

Bouman BAM (2001) Water efficient management strategiesin rice production. IRRI Notes, 26 (2): 17-22

Gleick PH (1993) Water crisis: a guide to the world fresh waterresources. PISDES, Stockholm EnvironmentInstitute, New York (USA) Oxford Univ Press 473

Grassi C, Bouman BAM, Castaneda AR, Manzelli M, VecchioV (2009) Aerobic rice: Crop performance and wateruse efficiency. J Agric & Environ Internat Develo 103(40) : 259-270

Postel S (1997) Last oasis facing water scarcity, Norton andcompany, New Yark (USA) 239p

(Manuscript Receivd : 5.9.13; Accepted : 30.12.13)

291

Abstract

The present study was conducted with the objectives toanalyse the chemical composition of guava fruits collectedfrom different locations, optimization of the fermentationvariables for maximum yield of alcohol using Saccharomycescerevisiae MTCC 170 and evaluation of the sensory qualityof fruit wine produced. Firstly, different chemical constituentsof guava fruit pulp were analysed which showed that the guavafruit pulp was found to contain a good amount of TSS requiredfor bioconversion into alcohol. Secondly , investigations weredone to get maximum recovery of alcohol yield at standardTSS of 20oBrix, incubation temperature of 30oC and pH of3.76 (original pH of guava fruit juice) with different ranges ofincubation periods viz. 24, 48, 72, 96, 120, 144, 168 and 192hr. The higher yield (10.5%) of alcohol was recorded atincubation period of 168 hr and found the same at furtherincubation period of 192 hr. The results of various experimentsrevealed that the culture of yeast gave maximum yield ofalcohol (13.2%) at TSS level of 22oBrix, pH of 4.0 withmaintaining the incubation temperature of 27°C and incubationperiod of 168 hr. Third investigation on the sensory qualityevaluation of guava fruit wine revealed that guava fruit winesample with alcohol yield of 13.2% was found to be moreacceptable with respect to all the sensory attributes incomparison to other samples of guava wine.

Kewwords: Guava fruit, Wine, Saccharomycescerevisiae MTCC 170, TSS, Sensory attributes

Guava (Psidium guajava L.) is one of the most importantcommercial fruit crops consumed locally in India. It isfourth most important fruit in our country after citrus,mango and banana. It is a good source of ascorbic acid,pectin, sugars and certain minerals (Adree et al. 2010).Guava is completely edible fruit and considered as"apple of the poor" due to its low cost, easy availabilityand high nutritive value. It plays an important role inreducing nutritive disorders due to deficiency of vitamin

Wine production from over ripe guava fruits usingSaccharomyces cerevisiae

Yogesh Kalyanrao Patil, L.P.S. Rajput, Yogendra Singh and Keerti TantwaiBiotechnology CentreJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)

C in human health. Guava is consumed mainly as freshfruit or processed juice products. Guava wine is theproduct of anaerobic fermentation by yeast in which thesugars are converted into alcohol and carbon dioxide.Despite that, several studies have been carried out toknow the suitability of other fruits as substrates for thepurpose of wine production (Okunowo et al. 2005).Guava is easy to grow, possesses high nutritive valueand its products like juices, beverages, nectars etc. arelargely appreciated by the consumers. Guava juicerequires 'chaptalization' so as to adjust its Brix andprepare a perfect wine out of it. The chaptalized juice("must") is treated with pectinase or a combination ofenzymes and fermented with traditional yeasts at atemperature range of 22 to 30°C and inoculum size of6 to 11% (v/v). The addition of N and P improves ethanolproduction and various consumer quality parametersof guava wine. Racking and ageing of guava wine alsoimproves the sensory and organoleptic characteristicsof guava wine (Kocher and Pooja 2009). Moreover, theseasonal availability and high cost of grapes in Indiahas also necessitated the search for alternative fruitsources viz. guava, bel, jamun etc. (Alobo and Offonry2009). High rate of wastage of these fruits especially attheir peak of production season necessitates the needfor alternative preservation and post harvesttechnologies towards their value addition that canreduce the level of post harvest losses besidesincreasing diversity of wine (Okoro 2007 Alobo andOffonry 2009).

The guava fruit is available in plenty during theseason of production causing glut in the market. Inaddition to this, fruits are highly perishable in natureand there is a lot of spoilage in production season dueto insects, pests, diseases in addition to losses duringtransportation and storage. Not much work has beendone on preparation of wine from guava fruits. Hencethere is an urgent need to develop the production

JNKVV Res J 47(2): 291-297 (2013)

292

technology for such type of products from over ripenedguava fruits. Keeping in view the above fact thisresearch work was planned with the objectives toanalyse the chemical composition of guava fruitscollected from different locations and optimize thefermentation variables for maximum yield of alcoholusing Saccharomyces cerevisiae MTCC 170 and thereafter to evaluate the sensory quality of fruit wineproduced.

Materials and methods

The present study was conducted in the FermentationTechnology Laboratory, Biotechnology Centre,Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur(M.P).Over ripe guava fruits were purchased from

different market locations of Jabalpur city and pooledthem to get pulp for research work .The wine producingmicroorganism's culture viz. Saccharomyces cerevisiaeMTCC 170 was obtained from Institute of MicrobialTechnology (IMTECH) Chandigarh, Punjab. The strainwas selected due to high yielding capacity with noproduction of any unwanted substances. The culture ofSaccharomyce cerevisiae was grown and maintainedon Yeast Extract Peptone Dextrose (YEPD) media. Thehomogenized pulp of guava was incubated at 45oC withpectinase 0.50mg/100g pulp for 6 hours to obtain juicefrom the pulp. The juice was separated by filtration. Theclear juice was used for chemical analysis andpreparation of wine.The fermentation process describedby Kocher and Pooja (2011) was used for production ofwine from over ripe guava fruits using Saccharomycescerevisiae (Fig 1).

The above mentioned fermentation method wasalso used for carrying out the experiments onoptimization of different fermentation variables(incubation period, TSS, pH and incubationtemperature) for achieving the better recovery of wine.Different samples of guava fruit pulp and wine wereanalysed for TSS, pH, titrable acidity, total sugar andascorbic acid also. Total soluble solids (TSS) in guavafruit pulp and wine were determined with the help ofErma Hand Refractometer. Acidity of guava fruit pulpand wine were determined by A.O.A.C (1990) method.The pH was measured by pH meter afterstandardization. Total sugar was estimated by themethod described by Rangana (1997). The yield ofalcohol was determined by distillation and dehydrationprocess adopted by O'Leary (2000). Various sensoryquality parameters such as colour, flavor, taste andoverall quality characteristics of wine were assessed inorder to know the consumers acceptability for furtherapplication on large scale. Wines were evaluated by apanel of 8 Judges according to the method of Amerineet al. (1965) on a 9 point hedonic scale.

Results and discussion

Chemical composition of guava fruit revealed that guavafruit pulp contained TSS 13.2oBrix, pH 3.76, titrableacidity 0.71%, Ascorbic acid 237.5 mg/100ml, totalsugar 10.70 %, reducing sugar 7.53 % and non-reducingsugar 3.17 %. Data presented in Table 1 on yield ofalcohol using culture i.e. Scaccharomyces cerevisiaeMTCC170 employing the process of fermentationshowed that there was a gradual increase in alcoholyield up to incubation period of 168hr. At a incubationperiod of 168hr, culture produced maximum yield

Guava fruits

Washing

Pulping

Pectinase treatment(0.50mg/100g pulp at 45° C for 6 hours)

Filtration

Juice

Adjustment of TSS and pH(TSS 22oBrix and pH 4.0)

Addition of DAHP and KMS

(DHAP 0.05 %and KMS 100ppm)

Pasteurization of juice(85°C for 30 minute)

Addition of yeast culture

(Saccharomyces cerevisiae)

Fermentation for 168hr(at 27oC)

Pasteurized wine(85°C for 20 minute)

Addition of clearing agent Bentonite

(0.1g/100ml wine)

Guava wine

293

Table 1. Effect of incubation period on the yield of alcohol at incubation temperature of 30oC, pH 3.76 and TSS20oBrix

Incubation Alcohol yield Incubation Alcohol yield Incubation Alcohol yieldperiod (hr) % (v/v) period(hr) % (v/v) period(hr) % (v/v)

0 0 72 5.6 144 9.9

24 2.2 96 6.8 168 10.5

48 4.1 120 8.6 192 10.5*Values are average of triplicates

Table 2. Effect of incubation period on TSS Content* at incubation temperature of 30oC, pH 3.76 and TSS 20oBrix

Incubation TSS Incubation TSS Incubation TSSperiod (hr) (oBrix) period (hr) (oBrix) period (hr) (oBrix)

0 20 72 11.5 144 5.2

24 17.1 96 8.2 168 4.1

48 14.3 120 6.9 192 4.1*Values are average of triplicates

Table 3. Effect of different pH and TSS levels on alco-hol yield* at different incubation temperature with anoptimum incubation period of 168hr

S.No. Temp. TSS Alcohol yield (%)(oBrix) pH

3.0 3.5 4.0 4.5

1 24oC 18 7.8 7.8 9.3 8.62 20 7.3 8.6 6.4 10.53 22 7.8 9.3 11.8 9.94 24 8.6 9.9 11.3 11.85 27oC 18 7.8 8.6 9.3 7.36 20 6.4 6.4 10.5 11.37 22 8.6 10.5 13.2 12.68 24 8.6 7.8 11.3 12.39 30oC 18 7.3 8.6 9.3 9.910 20 8.6 9.3 9.9 10.511 22 8.6 9.9 11.5 11.312 24 5.8 7.8 9.3 11.813 33oC 18 7.3 9.3 9.9 8.614 20 8.6 9.9 10.5 5.915 22 8.6 10.5 11.3 9.316 24 7.3 7.8 9.3 9.9* Values are average of triplicates

Table 4. Effect of different pH and TSS levels on titrableacidity* of guava wine at different incubation tempera-tures 24 0C with an optimum incubation period of 168hrs

S.No. Temp.TSS (oBrix) Titrable acidity (%)pH

3.0 3.5 4.0 4.5

1 24oC 18 1.73 1.64 1.44 1.422 20 1.69 1.60 1.46 1.393 22 1.74 1.62 1.40 1.374 24 1.70 1.69 1.37 1.335 27oC 18 1.60 1.58 1.40 1.376 20 1.78 1.64 1.56 1.427 22 1.63 1.60 1.47 1.468 24 1.71 1.58 1.55 1.429 30oC 18 1.58 1.44 1.39 1.3210 20 1.67 1.46 1.41 1.3611 22 1.72 1.69 1.59 1.4412 24 1.69 1.68 1.52 1.3813 33oC 18 1.64 1.60 1.57 1.4614 20 1.69 1.56 1.42 1.3615 22 1.74 1.69 1.58 1.4916 24 1.79 1.68 1.59 1.44* Values are average of triplicates

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(10.5%) of alcohol whereas at a incubation period of192 hr, it remained the same (10.5%). Data presentedin Table 2 on the effect of incubation period on TSScontent showed that there was a gradual decrease inTSS content up to a incubation period of 168hr. At anincubation period of 168hr, TSS was found to be 4.1oBrixwhereas at a incubation period of 192 hr, it was thesame (4.1oBrix). The Effect of different pH and TSSlevels on alcohol yield at different incubationtemperatures with an optimum incubation period of168hr was also recorded. The findings in thisinvestigation revealed that alcohol yield varied to a greatextent employing yeast in the process of fermentation.

The observations depicted in Table 3 indicatedthat the culture of yeast (Saccharomyces cerevisiaeMTCC170) gave maximum yield of alcohol (13.2%) ata TSS level of 22oBrix, pH of 4.0 with maintaining the

incubation temperature of 27°C and incubation periodof 168 hr. The value of alcohol yield was found to belowest and recorded as 5.8% at a TSS level of 24oBrix,pH of 3.0 with maintaining the incubation temperatureof 30°C and incubation period of 168 hr. Several workershave also reported the alcohol yield almost in the similarrange from bioconversion of TSS rich substrates usingyeast (Shankar et al. 2006 Reddy and Reddy 2011).Jawahar (1999) reported that using strainSaccharomyces cerevisiae 3287, 22% sugar, 4.0 pHand 0.05% DHAP were found to be optimum for theproduction of good quality wine from guava juice. Sevdaand Rodrigues (2011) reported that the fermentationtemperature of 25°C, pH 4.0, DAP 0.6% and 6%inoculum level gave the better results. The findingsobtained in this investigation showed that these resultsare in agreement with the reported observations byearlier workers. Although some variations observed in

Table 5. Changes in TSS, pH, alcohol yield, titrable acidity, ascorbic acid and total sugar of guava wine during thefermentation period of 192 hr with an interval of 24 hr at incubation temperature of 27oC, TSS 22oBrix and pH 4.0

Incubation TSS pH Alcohol yield Titrable acidity Ascorbic acid Total sugarperiod (hr.) (oBrix) (%) (%) (mg/100ml) (%)

0 22 4.0 0.0 0.79 237.5 19.1724 17.8 3.93 2.7 0.89 217.2 17.348 13.10 3.89 4.4 0.97 179.1 13.272 11.6 3.81 6.8 1.06 154 10.796 8.6 3.74 8.6 1.19 127.6 7.8120 7.2 3.71 10.5 1.28 112.7 6.1144 5.7 3.66 11.8 1.36 102 4.2168 4.3 3.61 13.2 1.47 94.3 3.42192 4.3 3.60 13.2 1.48 94.0 3.40* Values are average of triplicates

Table 6. Chemical composition* of guava fruit wine

S.No. Constituents Amount

1. TSS (oBrix) 4.3

2. pH 3.61

3. Alcohol yield (%) 13.2

4. Titrable acidity (%) 1.47

5. Ascorbic acid (mg/100ml) 94.3

6 Total sugar (%) 3.42* Values are average of triplicates

the values in this study might be due to the geneticvariability of the strains used and fermentationconditions maintained.

In the present investigation, differentobservations have been made on titrable acidity ofguava wine at different TSS levels (18, 20, 22 and24oBrix) under different pH conditions (3.0, 3.5, 4.0 and4.5) with different incubation temperatures (24, 27, 30and 33°C) at an optimum incubation period of 168 hr.The findings in the investigation revealed that titrableacidity varied to a great extent under differentfermentation conditions. The observations (Table 4)indicated that the titrable acidity was found to be

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maximum (1.79%) at a TSS level of 24oBrix, pH of 3.0with maintaining the incubation temperature of 33°C andoptimum incubation period of 168 hr. The value oftitrable acidity was found to be lowest and recorded as1.32% at a TSS level of 18oBrix, pH of 4.5 withmaintaining incubation temperature of 30°C andincubation period of 168 hr. Several workers have alsoreported the titrable acidity almost in the similar range(Divya and Kumari 2009; Reddy and Reddy 2009 ).Diwan and Shukla (2005) reported that guava winefound to contain titrable acidity between 1.11 to 1.95%.Shankar et al. (2006) reported that total acidity and fixedacidity of wine increased as fermentation progresseddue to the presence of organic acid formed as by-product. The findings obtained in this investigationshowed that these are in agreement with the reportedobservations made by earlier workers. Although somevariations observed in the values in presentinvestigation might be due to the genetic variability ofthe strains used and culture condition maintained in theearlier studies.

There was a gradual decrease in TSS level, pH,ascorbic acid and total sugar contents with a relativeincrease in incubation period up to 192 hr. On the otherhand, there was a gradual increase in alcohol yield andtitrable acidity with a relative increase in incubationperiod upto 192 hr (Table 5). It was also observed thatthese changes became less pronounced after 168 hrof fermentation. The similar observations have alsobeen reported in the literature with some minorvariations in their values (Okoro et al. 2007; Alobo andOffonry 2009; Divya and Kumari 2009; Masyimi et al.2013). Shankar et al. (2006) reported that reducingsugar and total sugar contents of guava must gotdecreased upon fermentation as sugars present are

converted into alcohol and carbon dioxide. Similarly,pH of guava must got decreased due to increase intitrable acidity as fermentation progressed. Theseobservations also revealed that the total acidity and fixedacidity of wine increased as fermentation progresseddue to the presence of organic acid formed as by-product. Reddy and Reddy (2009) reported that uponguava must fermentation, sugar got decreased from14.2 to 1.2%, acidity increased from 2.5 to 2.8% andethanol increased up to 7.3%(w/v). Saveda andRodergues (2011) also reported that TSS level gotdecreased upon guava must fermentation. Kocher andPooja (2011) reported that during the guava mustfermentation, pH and ascorbic acid got decreased upto 3.6 and 83.6mg/100ml respectively and alcohol yieldincreased up to 13.8%. Andri et al. (2012) observedthat with the increase in fermentation periods, sugarconcentration decreased and ethanol concentration gotincreased almost linearly when 0.5%(w/v) Baker yeastwas used in the Jackfruit wine making procedure. Thefindings obtained in the present investigation showedthat these are in agreement with the reportedobservations by earlier workers. Although somevariations observed in the values in presentinvestigation might be due to the genetic variability ofthe strains used and culture condition maintained duringthe fermentation process.

Chemical composition of guava fruit wine wasalso studied. The data presented in Table 6 on chemicalcomposition of guava fruit wine revealed that variousimportant chemical constituents such as TSS, pH,alcohol yield, titrable acidity, ascorbic acid and totalsugar present in guava fruit wine were having the similarcomposition as reported in the literature, although someminor variation in the values were observed (Kocher

Table 7. Mean score values of sensory quality characteristics of guava fruit wine with maximum alcohol yield

Sample Code Sensory quality characteristics of guava fruit wineColour Flavour Taste Overall acceptability

A 8.4 8.1 8.0 8.2

B 7.8 7.6 7.7 7.8

C 7.1 7.0 7.4 7.3

SEm + 0.05 0.05 0.02 0.03

CD 5% 0.13 0.14 0.09 0.11

Sample A - with maximum alcohol yield (13.2%)Sample B - with second maximum alcohol yield (12.6%)Sample C - with third maximum alcohol yield (12.3%)

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and Pooja 2011; Reddy and Reddy 2011). Kocher andPooja (2011) reported that wine prepared from Panjabpink variety of guava contained 13.8% alcohol yield,3.6 pH and 83.6mg/100ml ascorbic acid. Similarly,Reddy and Reddy (2011) also reported that guava winecontained 1.2% sugar, 4.5 pH, 2.8% acidity and 7.3%alcohol yield. The findings obtained in the presentinvestigation showed that these are in agreement withthe reported observations by earlier workers. In thepresent investigation, the slight variation in the valuesof various chemical constituents observed in the guavafruit wine might be due to the genetic variability of thestrains used and culture condition maintained. Inaddition to these, environmental conditions and otherfactors might have also played some role in influencingthe composition of various constituents.

The sensory quality characteristics of guava fruitwine (Table 7) revealed that guava fruit wine samples(A) were found to be more acceptable with respect toall the sensory attributes in comparison to samples 'B'and 'C'. Guava wine (Sample A) was found to contain13.2% alcohol under optimum conditions of fermentationviz. TSS 22oBrix, pH of 4.0, incubation temperature of27oC and incubation period of 168 hr. Many workershave also reported the sensory quality characteristicsof fruit wine as the sensory quality analysis of wine isan important parameter in determining its quality (Attri2009). Pooja (2011) reported that guava wine preparedfrom three varieties (Panjab pink, Arka amuiya andLacknow-49) had enhanced taste, aroma and flavor withaging of three month. The finding of earlier workers haveshown that guava wine was acceptable by a panel ofjudges indicating the possibility of using guava fruitsfor commercial production for the growing market of winein our country. Various reports have been published inthe literature indicating the variation in thephysiochemical, processing and sensory qualitycharacteristics of the guava fruit variety and processedproducts (Jain and Nema 2007; Sharma et al. 2010 ).

Since the beginning of the 1980s, the use ofSaccharomyces cerevisiae yeast starters has beenextensively applied in the industrial and homemadebeverage production processes. Currently, most of thewine production processes rely on various strains ofSaccharomyces cerevisiae that allow rapid and reliablefermentation, reduce the risk of sluggish or stuckfermentation and prevent microbial contamination(Romano et al. 2003). Yeast starter cultures that arespecifically selected for the winemaking process on thebasis of scientifically verified characteristics typicallycomplement and optimise the raw material quality andindividual characteristics of the wine, creating a more

desirable product (Swiegers et al. 2005). In modernwinemaking, specific yeast strains have beenpreferentially used to guarantee the desired quality ofthe product. Yeasts are the prominent organismsinvolved in wine production and determine severalcharacteristics of the wine including the flavour by arange of mechanisms and activities (Fleet 2003).

References

AOAC (1990) Official method of analysis, 23th Ed., Associationof official analytical chemists, Washington, DC

Adree M, Younis M, Farooq U, Hussain K (2010) Nutritionalquality evaluation of different guava varieties. Pak JAgri Sci 47(1) : 1-4

Alobo AP, Offonry SU (2009) Characteristics of coloured wineproduced from Roselle Hibiscus sabdariffa) calyxextract. J Inst Brew 115: 91-94

Amerine MA, Roessler EB (1965) Principles of sensoryevaluation of foods. Academic press, New York

Andri CK, Sari DR, Pinandita APP, Retnowati DS, BudiyatiCS (2012) Preparation of wine from jackfruit(Artocarpus heterophyllus lam) juice using bakeryeast: effect of yeast and initial concentrations. WorldApplied Sci J 16(9): 1262-1268

Attri BL (2009) Effect of initial concentration on the physico-chemical characteristics and sensory qualities ofcashew apple wine. Nat Prod Radiance 8: 374-379

Divya Kumari A (2009) Effect of different temperatures, timingsand storage periods on the physico-chemical andnutritional characteristics of whey-guava beverage.World J Dairy & Food Sci 4(2): 118-122

Diwan A, Shukla SS (2005) Process development for theproduction of clarified guava juice. J Food Sci Tech42: 245-249

Fleet H (2003) Yeast interactions and wine flavor. Int J FoodMicrobiol 86: 11-22

Jain PK, Nema PK (2007) Processing of pulp of variouscultivars of guava (Psidium guajava L.) for leatherproduction. Agri Engg Int: the CIGR E J 9: 1-9

Jawahar A (1999) Studies on preparation of wine from guavajuice. M.Sc. Thesis, MPKV, Rahuri (MH)

Kocher GS, Pooja (2011) Status of wine production from guava(Psidium guajava L.): A traditional fruit of India. AfricJ of Food Sci 5(16): 851-860

Musyimi SM, Sila DN, Akoth EM, OnyangoCM, Mathooko FM(2013) The influence of process optimization on thefermentation profile of mango wine prepared fromthe apple mango variety. J Ani Plant Sci 17(3): 2600-2607

OkoroCE (2007) Production of red wine from roselle (Hibiscussabdariffa) and pawpaw (Carica papaya) using palm-wine yeast (Saccharomyces cerevisiae). Niger FoodJ 25: 158-164

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Okunowo WO, Okotore RO, Osuntoki AA (2005) The alcoholicfermentative efficiency of indigenous yeast strainsof different origin on orange juice. Afr J Biotechnol4:1290-1296

Pooja (2011) Optimization of fermentation conditions forproduction of wine from guava (Psidium guajava L.).MSc Thesis Punjab Agricultural University LudhianaIndia

Rangana S (1997) Hand book of analysis and quality controlfor fruit and vegetable product Tata McGraw Hill PubCo

Reddy LV, Reddy OS (2009) Production optimization andcharacterization of wine from mango (Mangiferaindica Linn.) Nat Prod Rad 8(4): 426-435

Reddy LVA, Reddy LPA (2011) Preliminary study onpreparation and evaluation of wine from guava(Psidium guajava L.) fruit. Int J Food and FermTechnol 1(2): 261- 266

Romano PC, Fiore M, Paraggio M, Caruso M, Capece A (2003)Function of yeast species and strains in wine flavor.Int J Food Microbiol 86: 169-180

Sevda SB, Rodriguess L (2011) Fermentative behavior ofSaccharomyces strains using guava (Psidiumguajava L.) must fermentation and optimization ofguava wine production. J Food Process and Technol2: 118-127

Shankar S, Dilip J, Narayana RY (2006) Fermentation of guavapulp with grape grown yeast (S. cerevisae var.ellipsoideus) for wine production. Ind J Hort 60: 171-173

Sharma A, Sehrawat SK, Singhrot RS, Tele A (2010)Morphological and chemical characterization ofPsidium species. Pro Nat Bot Hor Agrobot Cluj 38:28-32

Swiegers JH, Bartowsky EJ, Henschke PA, Pretorius IS (2005)Yeast and bacterial modulation of wine aroma andflavour. Australian J Grape and Wine Res 11: 139-173

(Manuscript Receivd : 30.9.13; Accepted : 17.12.13)

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Abstract

Kodo (Paspalum scorbiculatum L.) is popular millet grown inMadhya Pradesh. Tribals of Madhya Pradesh use kunaita (mudmill) for dehsking of kodo and get creamish white grain. In thepresent study kodo based fermented food 'idli' was developed.Idli was made with different proportion of kodo in place of rice(25, 50, 75 and 100%) and blackgram dhal and a control withrice and blackgram (100:50). Changes in pH and batter volumewere note down before and after fermentation and also theacceptability of 'idli' produced was ascertained and comparedwith the control. pH of the batter was found to decreased inall types of idli batter, whereas batter volume increasedsignificantly after 16 hour fermentation at 300C. Sensorycharacteristics like appearance, colour, texture, flavor andoverall acceptability were lower in kodo based 'idli', howeverthe product was found acceptable. Thus the neglected graincan be utilized as value added product in terms of nutritionalquality acceptability for all age group of people.

Keywords: Kodo, Idli, Fermentation, nutricereals

The Millets are a group of variable, small-seeded,annual grasses that are native to many parts of theworld. Millets provides a nutritious, staple source ofmillions of people in India. However, realizing thenutrient composition of the grains they are nowconsidered as nutricereals. Millets helps to lower bloodglucose levels and improves insulin response (Lakshmiet al 2002). Whole grains like millet may have healthpromoting effects equal to or even in higher amountthan fruits and vegetables and have a protective effectagainst insulin resistance, heart diseases, diabetes,

Investigations on the nutritional characteristics of kodomillet based traditional fermented food by tribalsof Madhya Pradesh, India

Deepali Agrawal, A. Upadhyay* and Preeti Sagar Nayak**

Krishi Vigyan Kendra Powarkheda (Hoshangabad)*Department of Food Science**Department of Plant PhysiologyJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482 004 (MP)

ischemic stroke, obesity, breast cancer, childhoodasthma and premature death (Cade et al 2007). Minormillets are hardy and have a marvelous storability. Theminor millets a group includes several food cropsnamely finger millet, foxtail millet, proso millet, barnyardmillet and kodo millet. Kodo millet (Paspalumscorbiculatum L.) generally used by Goand and Baigatribe of Madhya Pradesh. They used kunaita (mud mill)for dehusking of kodo and get creamish white grain theyfeed their child with gruel of this kodo millet. Low contentof protein and certain anti-nutritional factors of this foodare causing malnutrition problem in children in tribalareas of MP. Consumption of cereals with legumes, ageold tested practice, take care of deficiencies of eachother and make the dual more balanced. However,improvement in the quality of the diet can be realizedwith simple inexpensive fermentation technology(Agrawal et al. 2003). Hence the present study wasconducted planned to find out the feasibility of the kodo:black gram mixture for production of traditionalfermented food 'idli'.

Materials and method

Rice, dehusked kodo and blackgram dhal purchasedlocally, were washed and soaked separately in distilledwater. Idli was prepared by mixing of kodo with rice andblackgram dhal in different ratio 100:00:50 (control),75:25:50, 50:50:50, 25:75:50 and 00:100:50respectively. Washed and soaked separately in watertill they become soft according to different proportion.The ingredients ground separately were mixed and

JNKVV Res J 47(3): 298-302 (2013)

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allowed to ferment for 16 hr at 370C. Rise in battervolume and change in pH was noted. The batter wasfilled in idli pot and steam cooked.

Total sugar and free sugar were estimated byCarrol et al. (1956) and Dubois et al. (1951) respectively,amino nitrogen by microkjeldhal digestion procedure asgiven in A.O.A.C. (1980) with the help of PelicanNitrogen Analyzer. Water soluble vitamin like vitaminC was assayed by the methods given in Methods ofvitamin assay (1966). Calcium was estimated by usingsystronic-128 flame photometer. Iron was determinedby atomic absorption spectrophotometer. Sensoryevaluation test of 'Idli' was done by the procedure givenby Amerine et al. (1965) using 9 point hedonic scale forlike, dislike taking into account the various qualityattributes like colour and appearance, shape and size,texture, taste and overall acceptability. The panel wassupplied with the basic information about the productand was asked to write down the result on score card.The ratings were given on the sensory attributes likeappearance, colour, texture, taste, flavour and overall

acceptability as per the hedonic rating mentioned (Table2).

Results and discussion

The chemical compositions of millet grains and theirfood products were found to be modified byfermentation. Therefore, millet grains are used toproduce different kinds of traditional fermented foodsin developing countries in Africa and Asia. Fermentationis one of the processes that decrease the levels ofantinutrients in food grains and increase the proteinavailability, in vitro protein digestibility (IVPD), andnutritive value. Fermented foods like Dosa and Idli arepopular in many parts of India. These are very commonas breakfast foods and even as the evening meals insouthern part of the country. Millet is widely used asone of the ingredient for these kinds of fermented foods.It not only improves the taste but at the same timeenriches the food value in terms of protein, calcium andfibre.

All the combination of fermented food items was foundto be acidic in nature and pH evaluated was found tobe in between 4.3 to 4.6 (Table 3). The acidic nature ofthese products is probably due to the production oforganic acids during fermentation by acid producingmicroorganisms, as the fermentation is carried out underunhygienic and uncontrolled dominance of these generain other cereal based beverages has also beendiscussed earlier (Bassapa 2002; Muyanja et al. 2003).Among all the combination studied Rice: Kodo: Blackgram dhal (00:100:50) was found to be most acidichaving pH value 4.3 while Rice: Kodo: Black gram dhal(100:00:50) was found to have higher pH value i.e. 4.6.Venkatasubbaiah et al. (1984) reported that loweringof pH (4.20 to 4.82) to be a common feature in idli batterafter fermentation. According to Steinkraus et al. (1967)

Table 1. Combination of rice, kodo and blackgram dhalfor preparation of idlis

Treatment Rice De husked Black gram(%) kodo (%) dhal (%)

T1(control) 100 00 50

T2 75 25 50

T3 50 50 50

T4 25 75 50

T5 00 100 50

Table 2. Rating of Idli using nine point Hedonic scale

Like extremely : 9

Like very much : 8

Like moderately : 7

Like slightly : 6

Neither like nor dislike : 5

Dislike slightly : 4

Dislike moderately : 3

Dislike very much : 2

Dislike extremely : 1

Table 3. Physico-chemical characteristics of Idli Batter

Ingredients pH Batter volume(ml)(Rice: Kodo: Unfermented Fermented Unfermented Fermented

Black gram dhal)100:00:50 6.5 4.6 100 195

75:25:50 6.3 4.5 100 189

50:50:50 6.2 4.5 100 182

25:75:50 6.3 4.4 100 182

00:100:50 6.3 4.3 100 178

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idlis prepared from batter in the pH range of 4.1-5.3had a satisfactory flavour when steamed. The resultsshowed that the pH of all combination of the batter waswithin this ranges after 16 hours fermentation.Nagarathamma and Siddappa (1965) suggested a pHof 5.0 to be most an optimum for obtaining satisfactoryidli.

The combination of Rice: Kodo: Black gram dhal(00:100:50) was found to have maximum value for TotalSugar, Free sugar, Vitamin C, Iron and minimum forAmino nitrogen and Calcium as given in the Table 4.The calcium and iron contents of the millets wereanalyzed as these two minerals are of nutritionalimportance in the diets of population who consume milletas staple food. Fermentation of idli batter has asignificant effect on the increase of vitamins B, C andessential amino acids and in the reduction of antinutrients (Phytate-50% hydrolyzed), enzyme inhibitorsand flatus sugars (Steinkraus 1983).

The mean acceptability scores obtained by thesensory evaluation of millet idlis are in Table 5. Amongthe different variations standard idli has got a highestscores of 9.0 followed by the variation mixed idli with ascore of 8.2 and the least score 6.5 is obtained by theKodo idli for the colour attributes. The texture attributeswas found to be maximum for the standard with the scoreof 9.0 followed by the mixed idli (7.9). Regarding thetaste attributes the highest score of 8.8 is obtained bythe standard which is followed by the mixed idli with thescore of 7.5. The overall acceptability scores of standardwere found to be slightly higher (8.8) than the mixedidli with the score of 7.0 and the lowest was obtainedby Kodo idlis (6.3). Although, colour and appearanceof millet idli were rated as less attractive compared torice idli, the products were acceptable. The white colourimparted on idli and any deviation from white colour

Table 4. Nutrient composition of fermented idli batter made from different proportion

Ingredients Total sugar Free sugar Amino nitrogen Vitamin C Calcium Iron(Rice: Kodo: (mg/g) (mg/g) (g) (mg) (mg) (mg)Black gram dhal)100:00:50 55.2 4.25 0.14 5.28 39.4 5.58

75:25:50 51.3 3.23 0.14 5.10 40.6 5.93

50:50:50 52.4 2.69 0.15 5.20 48.3 5.29

25:75:50 50.62 2.24 0.16 5.10 55.3 5.25

00:100:50 48.40 2.16 0.17 5.10 65.1 5.15

S.Ed 0.82 0.92 0.02 0.91 0.88 1.05

CD@ 5% 1.82 2.05 0.04 2.03 1.96 2.34

Table 5. Mean score of quality attributes of Idli

Ingredients Parameters(Rice: Kodo: Black gram dhal) Colour Texture Taste Over all acceptability

100:00:50 9.0 9.0 8.8 8.8

75:25:50 8.2 7.9 7.5 7.0

50:50:50 7.2 7.0 7.0 7.0

25:75:50 6.8 7.0 6.8 6.8

00:100:50 6.5 6.8 6.6 6.3

S.Ed 0.85 0.78 0.76 0.82

CD@ 5% 1.88 1.75 1.69 1.84

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has been reason for low mean score for idli based onkodo millets (00:100:50) which was natural dull creamcolour however kodo base idli were acceptable asindicated by the mean score of overall acceptability.Similar results were found by Veena et al. (2004). Theidli batter comprises lactic acid bacteria and causes animprovement in the nutritional, textural and flavourcharacteristics of the final product. The sensoryattributes of idli (final product) prepared from the Maizeand rice based batter related well to the determinedflavour profile (Agrawal et al. 2003).

Conclusion

In the developed countries, due to large obesity problemand also for maintaining normal and sound health,different formulations and activities are coming up,specially delivering soluble fibres to the consumers viadifferent foods like cereals and cereal productscontaining antioxidants. The ethnic small millets provedto have a good scope for enhancing nutrition security,marketing and income generation of communitymembers, particularly rural women. The chemicalchanges during fermentation include an increase in freesugar indicating partial breakdown of carbohydrates.An increase in amino nitrogen indicates a similarbreakdown of proteins. It is likely that the intermediatesin these conversions such as simpler starches, dextrin,maltose and peptides also increase. Increase in thenumber of bacteria also involved in increasing niacin,riboflavin and vitamin C. These changes serve to makeidli more nutritious, palatable and digestible which isbeneficial for children. To combat the malnutrition interms of protein, calorie and micro nutrient problems,the use of kodo millets with pulse are effective choicein tribal areas in domestic preparation of fermented foodidli are effective choice in tribal areas. Formulation alsoshowed to be a highly strategic intervention in thepopularization of nutritionally and technologically richlocal crops which are currently largely neglected andunderutilized.

dksnks ¼Paspalum scorbiculatum L.½ e/;izns'k esa mxkbZ tkusokyh yksdfiz; Qly gSA e/;izns'k ds vkfnoklh dksnks dks dquSrh¼feV~Vh dh pDdh½ esa ihykiu fy;k gqvk lQsn vukt izkIr djrs gSAorZeku v/;;u esa dksnks ls cuh fd.ou bMyh rS;kj dh xbZ gSA bMyh

esa fofHkUu ek=k esa pkaoy ds LFkku ij dksnks dk ¼25, 50, 75 ,oa100½ ek=k ,oa mjn nky yh xbZ gSA fu;a=.k ds fy, ¼Control

sample½ pkaoy ,oa mjn nky ¼100:50½ yh xbZA blesa ih-,p- ,oadksnks dh ek=k ¼Value½ dks fHkUu & fHkUu eki esa Mkydj fd.ou lsiwoZ o ckn esa mldh fd.ou voLFkk dks ns[kk x;k ,oa mldhLohdk;Zrk dks Hkh tkuk x;k fu;a=.k ds lkFk Hkh rqyuk dh xbZA 300C ij 16 ?kaVs esa bMyh ds ?kksy ¼Batter½ dk PH tSls&tSls degksrk x;k oSls&oSls mldh Qwyus dh ek=k c<+rh xbZA rS;kj ?kksy esa30 0C ij 16 ?kaVs ds fd.ou ds ckn dkQh o`f) gqbZ] tcfd ?kksyds ih,p] bMyh vkVk ds lHkh izdkjksa esa deh ikbZ xbZA mifLFkfr] jax]cukoV] Lokn vkSj lexz Lohdk;Zrk laosnh fo'ks"krkvksa bMyh vk/kkfjrdksnks esa de Fks] ysfdu mRikn Lohdk;Z fd;k x;k FkkA lHkh vk;q lewgds fy;s iks"kd rRoksa dh xq.koRrk Lohdk;Zrk ds ekeys esa mRikn tksM+hds :Ik esa bl izdkj ds misf{kr vukt dk mi;ksx fd;k tk ldrk gSA

References

Agrawal Deepali, Pandey Sheela, Gupta OP (2003)Biochemical and organoleptic studies on thefeasibility of maize based fermented food. JNKVVRes J 37(2):25-27

Amerine MA, Pangborn, RM Rosster, EB (1965) Principles ofsensory evaluation of foods. Academic Press NewYork. 275

AOAC (1980) Official methods of analysis, 23rd Ed.Association of Official Analytical Chemists,Washington, DC

Bassapa SC (2002) Investigations on Chhang from fingermil let (Eleucine Coracena Gaertn.) and itscommercial prospects. Indian Food Ind 21(1) 46-53

Cade JE, Berley VJ, Greenwood DC (2007) Dietary fibre andrisk of breast cancer in the UK womens's Cohortstudy. Int J Epidemiol 36:431-438

Carrol MV, Lonely RN, Roe TJ (1956) Determination of totalsugar in cereals. J Biochem 220:580

Dubois MK, Gilles MK, Hamilton PA, Robers F, Smith W (1951)A colorometric method for the determination of sugar.Nature 168:167

Lakshmi KP, Sumathi S (2002) Effect of consumption of fingermillet on hyperglycemia in non-insulin dependentdiabetes mellitus (NIDDM) subjects. Food Nutr Bull23(3) 241-245

Methods of Vitamin assay (1966) The association of vitaminChemists, Interscience Publishers, New York, 3rdedn; 287

302

Muyanja BK, Naruhus JA, Langsrud T (2003) Isolation,characterization and identification of lactic acidbacteria from Bushera: A Ugandan traditionalfermented beverage. Food Microbiology 80(3) 201-210

Nagarathanamma K, Siddappa GS, (1965) Canning of idli. JFood Sci Tech 2(3)132

Steinkraus KH, Vanveen AG, Thiebean OB (1967) Studieson idli-An Indian fermented black gram rice food.Food Technol 21: 916-919

Steinkraus KH (1983) Handbook of Indigenous FermentedFoods. Marcel Dekker Inc, 304 New York.

Veena B, Bharati V, Chimmad Rama, Naik K, Malagi Usha(2004) Development of Barnyard Millet BasedTraditional Foods in barnyard millet based idli.Karnataka J Agri Sci 17 (3) 522-527

Venkatasubbaiah P, Dwarakanath ET, Murthy V (1984)Microbiological and Physico-chemical changes in idlibatter during fermentation. J Food Sci Tech 21: 61

(Manuscript Receivd : 20.8.13; Accepted : 30.12.13)

303

Abstract

The study on effect of different micronutrients on the incidenceof sucking pests of tomato crop during the rabi 2006-07,revealed that mixture of all micronutrients was found mosteffective treatment in reducing the population of white fly (0.83white fly/10cm twig/plant) which was at par with Manganesesulphate 100 ppm (1.11 white fly/10cm twig/plant) whileremaining treatments (T1, T2, T3, T4, T5 and T7) were foundat par to each other but superior to untreated control in respectto white fly population. In case of aphid the treatments ofManganese sulphate 100 ppm (1.05 aphid/10cm twig/plants)and mixture of all micronutrients (1.22 aphid/10cm twig/plants)were found most effective in reducing the aphid populationand at par to each other. The next effective treatments inorder of effectiveness were Ferrous sulphate 100 ppm,Commercial formulation-Multiplex 100 ppm, Zinc sulphate 100ppm, and Copper sulphate 100 ppm

Keywords: Micronutrients, sucking pests,management, tomato

Tomato (Lycopersicone esculentum) is one of the mostpopular and widely grown vegetable in the world. It isgrown in all seasons and is the second most importantcrop among vegetables. The total area under tomato incountry is assessed 4.66 million ha with a total yield ofabout 8.272 million tones and an average yield of 16-17 tones/ha In Madhya Pradesh, tomato is cultivated inan area of 23 thousand ha. with an average yield ofabout 0.52 million tones. Tomato farming for profit cannotimagined without adequate protection from principleenemies such as insects, fungus, weeds and mites.Among the these enemies insect pests are majorbecause all parts of the plant offer food, shelter andreproduction site for them. Whitefly and aphid are themajor sucking pests of tomato. Both these pests arepolyphagous in nature and vector of many viral diseases

Effect of different micronutrients on the incidenceof major sucking insect pests of tomato

A.S. Thakur, S. K. Barfa, Amit Kumar Sharma and R. PachoriDepartment of EntomologyCollege of AgricultureJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)Email : amitsharma2375@gmail.com

like leaf curl, mosaic etc. Nymphs and adults both causethe damage by sucking the sap from leaves and tenderparts of the plants. The severe infestation resulting inpremature curling of leaves and plant become unablefor flowering and fruiting. The losses caused by theseinsects and diseases varies from 50 to 92%. Variousmethods are used for controlling the insect pests intomato like chemicals, botanicals, use of resistance/tolerance varieties etc., of which only the practicalmethod to control the pests is by chemical insecticides.However, it leaves a film of persistence insecticidalpoison over the foliage and fruits which is hazardousand uneconomical too. Therefore alternative methodslike application of micronutrients are cheap andaffordable for small scale farmers compared to syntheticpesticides. Micronutrients are safer to use and theyindirectly affect different insect pests. Keeping thesefacts in view, the present study was under taken toevaluate the effect of different micronutrients on theincidence of major sucking pests of tomato.

Materials and methods

The field experiment was conducted during rabi seasonof the year 2006-07 in Randomized Block Design (RBD)with three replications at the JNKVV research farm,Jabalpur. The tomato variety Jawahar Tomato 99 wasgrown. The micronutrients (Boric acid 100 ppm, Zincsulphate 100 ppm, Ammonium molybdate 50 ppm,Copper sulphate 100 ppm, Ferrous sulphate 100 ppm,Manganese sulphate 100 ppm, commercial formulationmultiplex 100 ppm and Mixture of all micronutrients)were applied three times as foliar application using FootSprayer. Total quantity of spray solution required foruniform coverage of the crop on per plot basis wasworked out for each treatment separately. Thetreatments were prepared by mixing desired quantity

JNKVV Res J 47(3): 303-307 (2013)

304

Tabl

e 1.

Effe

ct o

f diff

eren

t mic

ronu

trien

t on

the

inci

denc

e of

tom

ato

whi

te fl

y

Trea

tmen

tsD

ose

inW

hite

fly

popu

latio

n on

10

cm/ t

wig

/ pla

ntO

vera

ll m

ean

ppm

Pre

Trea

tmen

tFi

rst S

pray

Seco

nd S

pray

Third

spr

ay3r

d da

y7t

h da

y3r

d da

y7t

h da

y3r

d da

y7t

h da

yaf

ter

afte

raf

ter

afte

raf

ter

afte

rT 1 -

Bor

ic a

cid

100

2.66

2.00

2.33

1.66

2.33

1.00

2.00

1.83

(1.7

7)(1

.55)

(1.6

7)(1

.46)

(1.6

7)(1

.16)

(1.5

5)(1

.52)

T 2- Zi

nc s

ulph

ate

100

2.33

2.00

1.33

2.66

2.00

1.66

2.33

1.99

(1.6

7)(1

.55)

(1.3

4)(1

.77)

(1.5

5)(1

.46)

( 1.6

7)(1

.57)

T 3- Am

mon

ium

mol

ebda

te50

3.00

1.66

2.00

3.66

4.00

1.33

2.00

2.27

(1.8

5)(1

.46)

(1.5

5)(1

.85)

(2.1

1)(1

.34)

(1.5

5)(1

.66)

T 4 - C

oppe

r sul

phat

e10

02.

331.

332.

331.

662.

331.

332.

001.

8(1

.67)

(1.3

4)(1

.67)

(1.4

6)(1

.67)

(1.3

4)(1

.58)

(1.5

2)T 5 -

Fer

rous

sul

phat

e10

02.

661.

331.

332.

662.

001.

662.

001.

83(1

.77)

(1.3

4)(1

.34)

(1.7

7)(1

.67)

(1.4

3)(1

.58)

(1.5

2)T 6 -

Man

gane

se s

ulph

ate

100

3.00

1.00

0.66

1.33

1.33

1.00

1.33

1.11

(1.8

5)(1

.22)

(1.0

4)(1

.34)

(1.3

4)(1

.16)

(1.3

4)(1

.26)

T 7 - C

omm

erci

al fo

rmul

atio

n (M

ultip

lex)

100

2.66

2.00

1.66

2.33

2.33

1.66

2.00

1.99

(1.7

6)(1

.58)

(1.4

6)(1

.67)

(1.6

7)(1

.46)

(1.5

5)(1

.57)

T 8 - M

ixtu

re o

f all

mic

ronu

trien

ts10

03.

330.

660.

660.

661.

330.

661.

000.

83(1

.94)

(1.0

4)(1

.04)

(1.0

4)(1

.34)

(1.0

4)(1

.16)

(1.1

5)T 9 -

Con

trol

3.00

3.66

3.33

4.33

3.33

3.33

4.33

3.61

(1.8

5)(2

.03)

(1.9

5)(2

.19)

(1.9

5)(1

.95)

(2.1

9)(4

.11)

S.Em

+0.

970.

150.

130.

100.

120.

130.

150.

05C

.D. a

t 5%

NS

0.45

0.41

0.31

0.38

0.41

0.45

0.17

()=

Figu

res

in p

aren

thes

is a

re a

rcsi

n tra

nsfo

rmed

val

ues

305

Tabl

e 2.

Effe

ct o

f diff

eren

t mic

ronu

trien

t on

the

inci

denc

e of

tom

ato

aphi

d

Trea

tmen

tsD

ose

inAp

hid

popu

latio

n on

10

cm/ t

wig

/ pla

ntO

vera

ll m

ean

ppm

Pre

Trea

tmen

tFi

rst S

pray

Seco

nd S

pray

Third

spr

ay3r

d da

y7t

h da

y3r

d da

y7t

h da

y3r

d da

y7t

h da

yaf

ter

afte

raf

ter

afte

raf

ter

afte

rT 1-

Boric

aci

d10

04.

665.

334.

001.

332.

661.

001.

662.

66(2

.26)

(2.4

0)(2

.11)

(1.3

4)(1

.77)

(1.1

6)(1

.46)

(1.7

7)T 2-

Zinc

sul

phat

e10

05.

002.

331.

662.

002.

661.

332.

662.

11(2

.30)

(1.6

4)(1

.43)

(1.6

7)(1

.73)

(1.3

4)(1

.77)

(1.6

1)T 3-

Amm

oniu

m m

oleb

date

505.

332.

661.

663.

005.

661.

332.

002.

71(2

.37)

(2.7

7)(1

.46)

(1.8

5)(2

.47)

(1.3

4)(1

.55)

(1.7

9)T 4-

Cop

per

sulp

hate

100

5.33

2.66

1.33

2.00

3.00

1.33

2.33

2.11

(2.4

0)(1

.76)

(1.3

4)(1

.67)

(1.8

5)(1

.34)

(1.6

7)(1

.61)

T 5 - F

erro

us s

ulph

ate

100

5.00

1.33

1.00

2.00

2.66

2.00

2.00

1.83

(2.3

3)(1

.34)

(1.1

6)(1

.67)

(1.7

6)(1

.55)

(1.5

5)(1

.52)

T 6 - M

anga

nese

sul

phat

e10

05.

330.

660.

661.

332.

000.

661.

001.

05(2

.40)

(1.0

4)(1

.04)

(1.3

4)(1

.67)

(1.0

4)(1

.22)

(1.2

4)T 7 -

Com

mer

cial

form

ulat

ion

(Mul

tiple

x)10

05.

662.

001.

332.

332.

661.

661.

661.

94(2

.44)

(1.5

5)(1

.34)

(1.6

7)(1

.76)

(1.4

6)(1

.46)

(1.5

6)T 8 -

Mix

ture

of a

ll m

icro

nutri

ents

100

5.00

1.00

1.00

1.00

2.00

0.66

1.00

1.22

(2.2

2)(1

.16)

(1.1

6)(1

.16)

(1.6

7)(1

.04)

(1.2

2)(1

.31)

T 9 - C

ontro

l6.

006.

338.

006.

007.

005.

335.

006.

27(2

.48)

(2.6

0)(2

.90)

(2.5

3)(2

.72)

(2.4

0)(2

.33)

(2.6

0)S.

Em+

0.30

0.17

0.17

0.16

0.17

0.12

0.15

0.04

C.D

. at 5

%)

NS

0.57

0.53

0.50

0.52

0.38

0.47

0.14

()=

Figu

res

in p

aren

thes

is a

re a

rcsi

n tra

nsfo

rmed

val

ues

306

of water for each plot separately. The crop was sprayedthrice with each of the micronutrient at an interval of 7days on the appearance of the pests. The observationswere recorded on the number of aphids and white fliesa day before the spray and 3rd, and 7th day afterspraying. The observations on white flies were takenon one selected twig of 10 cm in each 5 randomlyselected plants. These twigs were covered carefully withtransparent polythene bags. The number of nymph andadults were counted at each twig. The population ofaphid was recorded at 10cm/twig on 5 randomlyselected plants from each plot.

Result and discussion

White fly

The pre-treatment observations indicated non-significant differences among the experimental plot inrespect to white fly population (ranged between 2.33 to3.33 white fly/ 10 cm twig/ plant). All the micronutrientswere found to be significantly superior over untreatedcontrol in reducing white fly population (Table1). Themean white fly population in different treatmentsrevealed that the treatment Mixture of all micronutrients(0.83 white fly/10cm twig/plant) was found most effectivein reducing white fly population which was at par withManganese sulphate @ 100ppm (1.11 white fly/10cmtwig/plant) while remaining treatments were found atpar to each other in respect to white fly population. Thehighest number of white fly population was recordedfrom the untreated control plot (3.61 white fly) (Fig.1).The result is conformity with the result of Inbar et al(2001), they reported that leaf minor feeding &

oviposition and corn earworm larval growth rates werehigher on the vigorous plants and lower on the punched.Berlinger and Wermelinger (2001) also reported thatthe plant nutrient have their impact on insect growthand development. They studied life history parametersof white fly and reported that the longevity and fecundityincreased with higher nitrogen level in the nutrientsolution, whereas the developmental time decreased.Over all growth was faster on high nitrogen plants thanon low nitrogen plants. El Rafie (2000) also reportedthat the plants treated with high levels of nitrogen(ammonium sulphate 21% nitrogen) had increasednumbers of Bemisia tabaci and decreased yields. Amixture of moderate levels of nitrogen with potassiumsulphate and phosphorous resulted in low populationof B. tabaci and increased yield. Chatterjee et al. (2013)reported that significant reduction in whitefly populationwas observed in treatments containing higher amountof FYM or vermicompost as compare to sole inorganicfertilizers

Aphid

The pretreatment observations indicated non-significantdifferences among the experimental plots. The aphidpopulation was in range of 4.66 to 6.00 aphid/10cm twig/plant (Table 2). All the micronutrients had their impacton aphid population and they were found significantlysuperior over untreated control in reducing the aphidpopulation. Among the micro nutritional treatments thetreatment of Manganese sulphate @100 ppm was foundmost effective in reducing the aphid population (1.05aphid/10cm twig/plants) which was at par with Mixtureof all the micronutrient (1.22 aphid/10cm twig/plant). The

0

0.5

1

1.5

2

2.5

3

3.5

4

No. of nym

phs

& a

dult

s/10

cm t

wig

s/pla

nt

T1 T2 T2 T4 T5 T6 T7 T8 T9

Treatments

Fig 1. Effect of diffenet micronutrient on incidence oftomato whitefly

0

1

2

3

4

5

6

7

No. of nym

phs &

adults/1

0cm

tw

igs/p

lant

T1 T2 T2 T4 T5 T6 T7 T8 T9

Treatments

Fig 2. Effect of different micronutrient on incidence oftomato aphid

307

next effective treatments in order of effectiveness wereFerrous sulphate 100 ppm, Commercial formulationMultiplex 100 ppm, Zinc sulphate 100ppm, and Coppersulphate 100 ppm, they were at par to each other.Highest number of aphid population was recorded fromthe untreated control plot (6.27 aphid/10cm twig/plant)(Fig. 2). Leite et al (1999) reported that there was adirect relationship between mite infestation and the levelof phosphorous applied to the soil. Leite et al (1998)recorded that increasing nitrogen and potash fertilizationincreased the leaf miner oviposition rate onLycopersicon hirsutum.

o"kZ 2006&07 ds jch ekSle esa VekVj ds jlpwld dhVkas ¼lQsneD[kh ,oa ekgq½ ds fu;a=.k gsrq lw{e iks"kd rRoksa ¼cksfjd ,flM 100ih-ih-,e-] ftad lYQsV 100 ih-ih-,e-] veksfu;e ekWfyCMsV 50

ih-ih-,e-] dkWij lYQsV 100 ih-ih-,e-] Qsjl lYQsV 100 ih-ih-,e-] eSXuht+ lYQsV 100 ih-ih-,e-] O;kikfjd QkeZwys'ku eYVhIYsDl 100 ih-ih-,e-] rFkk lw{e iks"kd rRoksa dk feJ.k½ dkewY;kadu fd;k x;k A mijksDr iz;kksx ls izkIr ifj.kkeksa ds vuqlkjVekVj dh lQsn eD[kh ds fu;a=.k gsrq lw{e iks"kd rRoksa dk feJ.krFkk esXuht+ lYQsV 100 ih-ih-,e lokZf/kd izHkko'kkyh ik;k x;k Ablh izdkj VekVj ds ekgq dhV ds fua;=.k gsrq eXuht lYQsV 100ih-ih-,e & lw{e iks"kd rRoksa dk feJ.k lokZf/kd izHkko'kkyh fln~/kgq,A

Pic. 1 White fly infestation on tomato leaf

References

Berlinger M J, Wermelinger B (2001) N-nutrition of tomatoplants affects life table parameters of the greenhouse white fly. Mitteilungender- Schweizerishen-Entomolgischen-Gesellschaft 74 (1-2): 69-75

El Rafie (1999) Effect of different rates of (N, P and K) fertilizeron Bemisia tabaci Genn. Infestation on tomato andits effect on the yield. Egyptial J Agril Res 77 (3):1067-1073.

Inbar M, Doostdar H, Mayer RT (2001) Suitability of stressedand vigorous plants to various insect hervivorus.Oikos 94 (2): 228-235

Leite GLD, Picano M, Zanuncio JC, Jham GN, Moura, MF(1999) Effect of the levels of fertilization on theintensity of attack by Tuta absuluta in Lycopersiconesculentum. Manejo Integrado de plagas 53: 72-76

Leite GLD, Picano M, Azevedo AA, Zurita Y, Marauini F (1998)Oviposition and mortility of Tuta obsoluta onLycopersicone esculentum. Menjo Integrado deplagas 49: 26-35

Chatterjee Ranjit, Choudhuri Partha, Nripendra Laskar (2013)Influence of nutrient management practices forminimizing whitefly (Bemisia tabaci Genn.)population in tomato (Lycopersicon esculentum Mill.).Int J Sci Env Tech 2: 956 - 962

Pic. 2 Aphid infestation on tomato leaf

(Manuscript Receivd : 30.8.13; Accepted : 19.12.13)

308

Abstract

The study on the investigation of some newer insecticides formanaging brinjal shoot and fruit borer (Leucinodes orbonalisGuenee) was made during rabi 20011-12. Application ofEmmamectin Benzoate 5 SG @ 200g/ha was significantlysuperior with the highest marketable fruit yield of 551.27q/ha,followed by Profenofos 50% EC @ 2000ml/ha (398.72q/ha)which was at par with Rynaxypyr 20% EC @150ml/ha (394.56q/ha). The next in order of comparative effectiveness werePyriproxyfen 10% EC @ 500ml/ha (318.17 q/ha) andDifenthiuron 50% WP@ 600g/ha (316.66 q/ha). The leasteffective insecticidal treatment was Pyriproxyfen 5% EC +Fenpropathrin 15% EC @ 500ml/ha (243.28 Q/ha) and thelowest yield 141.43 q/ha was recorded from the untreatedcontrol plots.

Keywords: Leucinodes orbonalis, Efficacy, insecticides,shoot and fruit borer, brinjal

The brinjal shoot and fruit borer, Leucinodes orbonalisGuenée is a potential pest. Brinjal plants are very muchsusceptible to insect attack right from seedling to finalharvesting stage. Brinjal is attacked by 53 species ofinsect pests of which 8 are considered as major pestscausing enormous damage to the crop in every seasonin every year (Biswas et al. 1992 and Nayer et al. 1995).Among the major insect pests, brinjal shoot and fruitborer is the most destructive pest of brinjal inBangladesh and India (Tewari and Sandana 1990). Theproductivity of brinjal in Madhya Pradesh is 0.07 t/haagainst that of the country (0.06 t/ha), and world's(16.90t/ha). Infestation of insect pests and its poormanagement is the major cause of the low productionof brinjal in Madhya Pradesh. White fly (Bemicia tabaci),leaf hopper (Amrasca biguttula biguttula) and shoot andfruit borer (Leucinodes orbonalis) infestation have amajor role in lowering the marketable yield of the brinjal.

Efficacy of some new molecules against the infestation of bringalshoot and fruit borer (Leucinodes orbonalis Guenee)

R. Pachori, Sapna Tanve, Amit Kumar Sharma and A.S. ThakurDepartment of EntomologyColllege of AgricultureJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)Email : amitsharma2375@gmail.com

The apparent loss of fruits has been reported to bevarying from 20-90% in various parts of the country (Rajuet al. 2007).Chemicals are widely used for managinginsect pests in brinjal but the use of inappropriatepesticides, incorrect timing of application and improperdoses have resulted in high pesticides costs with littleor no appreciable reduction of pest damage. Further ithas been reported that due to indiscriminate use ofinsecticides, Leucinodes orbonalis has developedresistance to the conventional toxic insecticides (Rajuet al. 2007 Hegde et al. 2009). In addition, the residuesof chemical pesticides on the edible parts are more thanthe tolerable level (Jha et al. 2006). The presentinvestigation was made to test the efficacy of somenewer insecticides for the effective management ofbringal shoot and fruit borer.

Materials and methods

The experiment was conducted at experimental field ofthe Department of Entomology, Live Stock Farm,Adhartal, JNKVV, Jabalpur (M.P.) during rabi 2011-12.,using randomized block design. The plot size was 3.6 x2.4 m. The bringal crop ( A. K. 123) was transplanted inthe fourth week of November.The treatments consistedof spraying the crop for four times with Pyriproxyfen10% EC (500ml/ha), Pyriproxyfen 5% EC +fenpropathrin 15% EC (500ml/ha), Difenthiuron 50%WP (600gm/ha), Emmamectin Benzoate (Proclaim)5%SG (200gm/ha), Rynaxypyr 20%EC (150ml/ha),Profenofos 50% EC (2000ml/ha) and untreated control.Fruit infestation by shoot and fruit borer was assessedby counting the total number of damage and healthyshoot/ fruit at each picking per plot. The percentagedata on damaged fruits and fruit yield loss data weretransformed to arcsin transformation and statisticallyanalyzed as per the method advocated by Snedecor

JNKVV Res J 47(3): 308-311 (2013)

309

and Cochran (1967). Similarly, data on healthymarketable fruit yield were also subjected to statisticalanalysis at 5% level of significance.

Results and discussion

Fruit damage percentage

All the insecticidal treatments were significantly superiorover untreated control (Table 1). Emmamectin benzoate(4.77%) was superior followed by Profenofos (8.09%)which was at par with Rynaxypyr (9.35%). The next besttreatment was Pyriproxyfen (12.95%) which was alsoat par with Difenthiuron (14.33%). Other treatments wereleast effective but superior over control. SimilarlySharma (2010) reported that Emmamectin benzoatewas highly effective in terms of reduction in fruitinfestation, Sarkar et al. (2011) also reported that fairlygood and healthy yields of bringal was produced by theapplication of new generation pesticide molecules likeRynaxypyr and Emmamectin benzoate.

Healthy fruit yield

Different treatments were found to have an effect onthe yield of marketable fruits of brinjal (Table 2). Themarketable fruit yield ranged from 141.43 to 551.27 q/ha. The maximum fruit yield of 551.27 q/ha was recordedfrom the plot treated with Emmamectin benzoate wasfound significantly superior over other treatments. It wasfollowed by Profenofos 50% EC (398.72q/ha) which wasat par with Rynaxypyr 20 EC (394.56 q/ha). Similar

Table 2. Mean marketable fruit yield in different insecti-cidal treatments

Treatments Dose/ha Av yield/ha (q)

T1 Pyriproxyfen 10% EC 500ml 318.17

T2 Pyriproxyfen 5% + Fenpropathrin 15% 500ml 243.28

T3 Difenthiuron 50% WP 600g 316.66

T4 Emmamectin Benzoate 5 SG 200g 551.27

T5 Rynaxypyr 20% EC 150ml 394.56

T6 Profenofos 50% EC 2000ml 398.72

T7 Control 141.43

SEm+ 2.19

CD at 5% 6.06Tabl

e 1.

Fru

it in

fest

atio

n (%

) in

diffe

rent

inse

ctic

idal

trea

tmen

ts

Pre-

Per c

ent f

ruit

dam

age

per p

icki

ngO

vera

ll m

ean

Trea

tmen

tsD

ose/

hatre

atm

ent

(Mea

n of

thre

e re

plic

atio

n)of

four

spr

ays

Pick

ings

12

34

56

7Py

ripro

xyfe

n 10

% E

C50

0 m

l23

.31

14.1

114

.03

13.5

613

.33

12.1

112

.21

11.3

112

.95

(28.

87)

(22.

07)

(21.

99)

(21.

60)

(21.

41)

(20.

36)

(20.

45)

(19.

65)

(22.

52)

Pyrip

roxy

fen

5% +

Fenp

ropa

thrin

15%

500

ml

23.4

717

.45

16.9

615

.93

15.8

214

.98

14.5

913

.96

15.7

1(2

8.97

)(2

4.69

)(2

4.32

)(2

3.53

)(2

3.44

)(2

2.77

)(2

2.45

)(2

1.94

)(2

4.40

)D

ifent

hiur

on 5

0% W

P60

0g24

.11

15.8

115

.63

14.7

414

.67

13.7

113

.56

12.2

114

.33

(29.

41)

(23.

43)

(23.

29)

(22.

58)

(22.

52)

(21.

74)

(21.

60)

(20.

45)

(23.

60)

Emm

amec

tin B

enzo

ate

5 %

SG20

0g24

.04

8.72

7.52

6.62

5.70

3.41

1.12

0.28

4.77

(29.

36)

(17.

18)

(15.

99)

(14.

91)

(13.

81)

(10.

65)

(6.0

8)(3

.05)

(16.

60)

Ryn

axyp

yr 2

0% E

C15

0ml

24.3

510

.73

10.5

79.

729.

528.

668.

477.

759.

35(2

9.57

)(1

9.12

)(1

8.97

)(1

8.17

)(1

7.97

)(1

7.11

)(1

6.92

)(1

6.16

)(2

0.02

)Pr

ofen

ofos

50%

EC

2000

ml

24.5

29.

629.

128.

568.

227.

457.

296.

368.

09(2

9.68

)(1

8.07

)(1

7.58

)(1

7.01

)(1

6.66

)(1

5.84

)(1

5.67

)(1

4.61

)(1

9.08

)C

ontro

l24

.64

23.4

920

.32

21.7

419

.86

18.2

314

.89

18.1

019

.48

(29.

76)

(28.

99)

(26.

79)

(27.

71)

(26.

46)

(25.

28)

(22.

70)

(25.

18)

(26.

86)

SEm

+0.

440.

140.

380.

400.

270.

110.

680.

150.

70C

D a

t 5%

NS

0.43

1.17

1.23

0.84

0.33

2.10

0.47

1.94

() F

igur

e in

par

enth

esis

are

arc

sin

trans

form

ed v

alue

s

310

Healthy fruits Damage fruit

Fruit damage by shoot and fruit borer Larva of shoot and fruit borer

311

finding have been reported by Suganya Kanna et al.(2005), Dutta et al. (2007) and Sarkar et al. (2011).Thenext in order of comparative effectiveness werePyriproxyfen 10% EC (318.17 q/ha) and Difenthiuron(316.66 q/ha). The least effective insecticidal treatmentwas Pyriproxyfen 5% EC + Fenpropathrin 15% EC(243.28 Q/ha) and the lowest yield 141.43 q/ha wererecorded from the untreated control plots (Fig. 1).

o"kZ 2011&12 dh joh ekSle esa cSaxu ds ruk ,oa Nsnd dhV dsfu;s=.k gsrq ,ekdsfDvu csatk,V 5 ,l-th- izksQsuksQkl 50% bZ-lh-] jkbusfDlikj 20% bZ-lh-] ikbjhizkDlhQsu 10% bZ-lh-] MkbQsuF;wjkWu50% MCyw ih- ,oa ikbjhizkDlhQsu 5 $ QsuizksisfFkzu 15% bZ-lh-dk ewY;kadu fd;k x;k izkIr ifj.kkeksa ds vuqlkj ,ekesfDVu csatks,V5 ,l-th- 200 xzke@gs- cSxu ds ruk ,oa Qy Nsnd dhV ds izdksidks fu;af=r djus esa lokZf/kd izHkko'kkyh ik;k x;k rFkk blds mipkjLo:i cSxu dh 551-27 fdo-@gs- mit izkIr gqbZ A f}rh; izHkko'kkyhdhVuk'kd ds :i esa izksQsuksQkl 50% bZ-lh- 2000 fe-yh-@gs-ik;k x;k ftles mipkj }kjk 398-72 fdo-@gs- mit izkIr gqbZ AU;wure mit 141-43 fDo-@gs- vumipkfjr fu;a=.k ls izkIr gqbZA

Reference

Biswas GC, Sattar MA, Seba MC (1992) Survey andmonitoring of insect pests of brinjal Khagrachari HillyRegion. pp: 40-42. Annual Report 1991-92, EntomolDiv BARI Joydebpur Gazipur

Dutta NK, Alam Nasiruddin MS, Das M, Munmun AK (2007)Efficacy of new chemical insecticides against brinjalshoot and fruit borer L. orbonalis (Guen). J SubtropAg. Res and Dev 5(3) : 301-304.

Hegde JN, Girish R, Chakravarthy AK (2009) Integatedmanagement of brinjal shoot and fruit borer,Leucinodes orbonalis (Guen.). ProceedingInternational Conference on Horticulture on"Horticulture for Livelihood Security and EconomicGrowth", November 9-12, 2009, Bangalore Universityof Agricultural Science 1103-1107

Jha SK Jaikrishnan S, Gopal Madhuban (2006) Persistenceof chloropyrifos on egg plant for the management ofshoot and fruit borer. Ann Pl Protec Sci 14:116-118

Nayer KK Ananthakrishnan TN, David BV (1995) Generaland Applied Entomology. 11edn. Tata McGraw- Hillpub Co Ltd 4/12, New Delhi 557 p

Raju SVS Bar UK Shankar Uma, Kumar Sailendra (2007)Scenario of infestation and management of eggplantshoot and fruit borer, Leucinodes orbonalis (Guen.)in India. Resis Pest Manag. N Let 16(2):14

Sarkar Sudarshan, Chakraborti, Kanti P (2011) Managementof Leucinodes orbonalis Guenee on eggplants duringthe rainy season in India. J Pl Protec Res 51(4) :325-328

Sharma Anil (2010) Bioefficacy of insecticides againstLeucinodes orbonalis on brinjal. J Env Bio 31(4) :399-402

Snedecor GW, Cochran WG (1967) Statistical Methods,Oxford and IBH Publishing Company, New Delhi 1-292

Suganya Kanna S, Chandra Sekaran S, Regupathy A, StanlyJ (2005) Field efficacy of emamectin 5 SG againsttomato fruit borer, Helicoverpa armigera Pestology4: 21-22

Tewari GC, Sandana HR (1990) An unusual heavyparasitization of brinjal shoot and fruit borer,Leucinodes orbonalis Guen by a new braconidparasite. Ind J Entomol 52(2): 338-341

(Manuscript Receivd : 30.8.13; Accepted : 19.12.13)

312

JNKVV Res J 47(3): 312-314 (2013)

Abstract

Acacia nilotica is an important species under agroforestrysystem. It contributes for railway slipper, heavy constructionetc. It is attract by wide spectrum of insect pest. In the presentstudy 30 provenance were evaluated against insect pest undernatural field conditions. The results revealed that nine insectpest were found associated. Maximum and minimuminfestation was recorded on treatment Mashra khurd Laitpur& Chsistoor Bhaswara Road Warda i.e. 72.4 and 40.4respectively.

Key words: [Acacia nilotica, provenances, infestation]

The National Forest cover is about 78.29 m/ha orapproximately 23.81% of total land covers. Acacianilotica is a tree species largely belonging to theplantation, Agroforestry system, and homesteadplantation, which are mostly artificial systems ofecosystem therefore the tree is prone to damage byinsect pest and disease. It is therefore, it is necessaryto determine all damaging agency and their controlmeasure for obtaining maximum production from forestand plantation of babul. There is little informationavailable about insects and diseases impacting forestsand the forest sector. Acacia nilotica is the most valuabletimber-producing plant species. It contributes anestimated 40-50 percent to the total timber production.The tree occurs in pure, even age stands which havebeen artificially regenerated by direct seeding in floodplains.

Materials and methods

The field investigation was conducted at Dusty AcreResearch Farm, Department of Forestry, JawaharlalNehru Krishi Vishwa Vidyalaya, Jabalpur (M.P.) duringKharif 2011-12 under the AICRP Project.

Insect pest complex on Acacia

H. Dayma and R. BajpaiDepartment of ForestryCollege of AgricultureJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)

The investigation was laid out in RandomizedBlock Design with three replication. In a RandomizedBlock Design each replication was divided in 30 plotsto the different provenance of Acacia nilotica. Seed werecollected on the basis of phenotypic characters/yieldper tree. To note the pest complex and incidence of insectpest on Acacia nilotica the observation were recordedat weekly interval in the field conditions starting fromthe first week of August at different stage of plant/cropgrowth up to 4th week of November on the five randomlyselected plants.

Results and discussion

Prevalence of insect pest complex on Acacia

Nine insect pests were recorded on thirty provenances,four have been found major that includes Bag worm(Eumeta crameri) Westwood (Lepidoptera: Psychidae),Green beetle, (Mimela junii). (Coleoptera :Scarabeidae), Webworm, (Ethmia hiramella)(Lepidoptera : Ethmiidae), Hairy caterpillar, (Calliteragrotei) Moore (Lepidoptera: Tortricidae), However, Greenbug, Coreidae Leach. (Hemiptera : Coreidae),Geomatrid caterpillar, Ascotis infixaria Bagnall(Thysanoptera: Thripidae), Black carpenter bee,Xylocopa latipes. Drury Hymenoptera : Apidae ), Larvaeof butterfly, Lymantria incerta. (Lepidoptera :Lymantriidae), Tree hopper, Oxyrachis tarandus(Homoptera: Membracida)

Successional investigations revealed thatbagworm was observed first, followed hairy caterpillar,web worm, green beetle, tree hopper and butterfly oflimitaria, during the month of November. Pillai and Gopi(1984) reported the web worm larvae as a pest of foliageand tender bark. Singh et al (1989) listed 6 species ofScarabaeid beetle on Acacia nilotica viz; C.scabrater,

313

Pteroma playgiophleps, Clyta succinata, Diapromorphoturcia, Dereadus denticollis, Homoeoserus signatus,Cysocories purpureous, Acrida lugubris and Orthacrisruficarnis as major pest. They reported Araecerusfascuculater as a major pest of seeds of Acacia nilotica.Maximum infestation was recorded during October.Beeson (1941) recorded larvae of Selpa celtis feedingon saplings in and young plantation during rainy season.Pillai and Gopi (1989) reported Lepidoptera as a majororder causing infestation.

Seasonal Incidence and Population Dynamics

Maximum and Minimum infestation recorded during theperiod of observation by the major insect was in thetreatment Mashra khurd Laitpur & Chsistoor BhaswaraRoad Warda that is 72.4 % and minimum 40.4 %respectively the maximum average temperature andhumidity recorded during the month of November was30.9 and 88% similar work has been by El- Atta andAbdel nour (1995) at lambwa forest Sudan. The effectof larvae of (Heteroropsylla incisa) was assessed onthe diameter at breast height tree height volume andon mean annual increment of acacia. Over 4 year periodthere was 30 % reduction in dbh 21 % decrease in heightgrowth. 54 % decrease in volume and 60 % decreasein mean annual increment under condition of Sudan.Maxine F- Miller (1993) recorded damage by Bruchidbeetle on seeds from 4-100 % germination percentageof the seed in the range of 1-6 %. Banga (1999) recorded62 and 55 % damage by insect borer i.e. Cerconataanninella and Trigonaspini Rohanar et al (1999)recorded more than 95 % damage by Bruchid beetle

Population dynamics

Maximum population was of web worm and Hairycaterpillar, Webworm was recorded during month of Mayand green beetle was recorded during July and Augustmonth. The period of infestation, population deviationwas maximum temperature/humidity was 310c and 90%)respectively. Walter (1994) finding indicated thatadaption and ecology must be considered in eachspecial species present which may vary with changingenvironmental condition form area to area and from timeto time. Jayanthi et al. (2006) concluded that pestincidence was not influenced by plant phenology.

—f"k okfudh esa vdsf'k;k fuyksfVdk o`{k dk ,d egRoiw.kZ LFkku gSaA]bldh ydM+h jsyos rFkk fuek.kZ ds mi;ksx esa ykbZ tkrh gSaA dhVksa dkvkd'kZ.k bl o`{k cgqr vf/kd ik;k x;k gSaA 'kks/k dk;Z ds nkSjkuvdsf'k;ka fuyksfVdk ds 30 izkfousUll esa dqy uks dhVksa ls uqdlku dkizHkko ik;k x;k gSaA vf/kdrd rFkk U;wure uqdlku Mashrakhurd Laitpur & Chsistoor Bhaswara Road Warda esadze'k% 72-4 rFkk 40-4 izfr'kr ik;k x;kA

References

Beeson CFC (1941) The ecology and control of the forestinsects of India and the neighboring countries Vasant

Table 1. Acacia nilotica provenances collected from fivedifferent states

PT1 Mashara Khurd Jakhara Lalitpur UPPT2 Ragoli After Ramoli station sagar MPPT3 Gadhakota after river Damoh MPPT4 Kumaria Parsoria Damoh MPPT5 Majoli to tihar Jabalpur MPPT6 Choubatia mandla MPPT7 Mangoli Cijara Mandla MPPT8 23 km before Nagpur MSPT9 Chsistoor Bhaswara Road Warda MSPT10 Anandwadi Bhaswara Road Warda MSPT11 Agril. university pt. Buldhana MSPT12 Kolarigram after akola buldhana MSPT13 Rustampur Pandhana Khandwa MPPT14 Dasooda Uniyalfarm Indore MPPT15 Mangalia Devas Road Indore MPPT16 Abhaypur Shajapur MPPT17 Shantinagar Bhopal MPPT18 Purvalia Bhopal MPPT19 Shyampur Sehore MPPT20 Penchi Guna MPPT21 Badarvas Shivpuri MPPT22 Hal Colum Nasik MSPT23 Zars Dindori MPPT24 Rau Pusa Faizabad UPPT25 College Of Agriculture Nagpur MSPT26 College Of Agriculture Raipur CGPT27 Firozpur (R1l9 P4)Firojpur PunjabPT28 Bilaspur CGPT29 Jhansi UPPT30 College Of Agriculture Jabalpur MP

314

Press, Dehra Dun pp 1007Braga Sobrinho Banderira CT Mosquita ALM (1999)

Occurrence and disease of Indian forest trees Forest21(2): 213-238

EI-Atta HA Abdel Nour HO (1995) Forest pests in Sudan: theireconomic importance and control. United Pub. ofTenzonia FAO, Rome Tenzania Forestry Res. Inst.Marogaro Tenzania pp 82-91

Jayanthi PD Kamala Verghese Abraham, Rani Haonnarrmma,Nugraju, DK (2006) Damage potential andseasonability of the sapodilla bad borer Anarsiaachrasella (Lepidoptera: Gelechidae) in IndiaInternational Tropical Insect Sci 26 (2): 86-91

F Miller Maxine (1993) Large African herbivores, bruchidbeetles and their interactions with acasia. Springer-Verlag Oecologia 97: 265- 270

Pillai SRM Gopi KG(1989) Further record of insect and pestson Acacia nolotica ucid. ex Del in Nurseries andyoung plantation and the needs for control measure.in Seminar on Forest Protection, June 29-30 (1989)Dehra Dun India

Singh MP, Satyvir, DR Parihar (1989) A note on thecoleopterous pest of forest plants in the Indian desertIndian. J For 12(4): 330-331

Rohner Christoph Ward David (1999) Large mammalianherbivores and the conservation of arid Acaciastands in the middle east. Spa: memiferos Herriboresgrandesyla conservation des acacia enel mediooriente. Conservation Biology

Walter GH (1994) Species concepts and the natural ofecological generalization about diversity in lamberts(eds), Specncer HG speciation and the recognitionconcept. Theory and Application Johns HopkinsUniversity Press, Baltimore

(Manuscript Receivd : 1.10.13; Accepted : 10.12.13)

315

Abstract

Evaluation of 16 varieties, 15 exotic and 34 indigenouscollections of okra was made under natural high diseasepressure conditions of Kymore plateau zone of MadhyaPradesh, India against Okra Yellow Vein Mosaic Virus disease.Base upon the coefficient of infection 4 varieties (ParbhaniKranti, Arka Anamika, Shrawan, JAE 9457003) and sixindigenous (IC 99746, IC 112481, IC 111511, IC 90175, IC326083, IC 433686) collections were found resistant .

Keywords: Varietals evaluation, okra, yellow veinmosaic virus, disease measuring scale

Okra (Abelmoschus esculentus (L.) Moench), aflowering plant in the mellow family is commerciallyvalued for its edible green tender fruits. Seeds are agood source of oil (13-22%) and protein (22-24%) anda rich source of iodine (Baloch et al.1990).The oil isalso used in soap, cosmetic industry and as Vanaspati,while protein for fortified feed preparation. The okra fruitand seed fiber is often utilized in jute, textile and paperindustry (http://en.wikipfdia.org./wiki/okra). The OkraYellow vein Mosaic Disease is the widest spreaddestructive problem, infects at all the stages of cropstage. Initially, diseases appear as diffuse and mottledappearance of younger leaves that may turn intoirregular inter venial yellow islands in older leaves.Homogeneous interwoven network of yellow veinsenclosing islands of green tissues is a commonsymptom. Infected plants remain stunted and bear veryfew deformed and small fruits (Singh 2004; Sastry and

Genetic resources of okra for the utilization in the managementof Okra Yellow Vein Mosaic Virus disease under climaticconditions of Kymore plateau zone, Madhya Pradesh

Usha Bhale, Priyanka Dubey and S. P. TiwariDepartment of Plant PathologyJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)

Singh 1973; Capoor and Varma 1950).

The monopartite begomo virus ( Geminiviridae)and a small satellite DNA- induces the typicalsymptoms of yellow vein mosaic disease in okra(Mansoor et al. 2001).The most destructive and widespread disease is transmitted through white fly (Bemisiatabaci).The size of the virus particle ranges 18X 30 nm.Presence of small, spherical particles reveals in thephloem sieve nuclei (Dahal et al. 1993; Faquet et al.2005). Plants infected 50 and 60 day after germinationsuffers a loss of 84 and 48% respectively. Yield losesto the tune of 49.3 to 93.8 % coupled with reduction innumber of fruits and seeds per plant have been reported(Sastry and Singh 1974; Gupta and Thind 2006).

Materials and methods

Evaluation of genetic resources

Under natural field conditions 16 varieties, 15 exoticand 34 indigenous collections were (obtained throughDr AK Nigam) and tested against the high diseasepressure conditions at Maharjpur farm, Department ofHorticulture, JNKVV, Jabalpur, when the crop attainedthe age of 45 day. The coefficient of the infection (CI)was measured (Prabhu et al. 2007; Singh and Singh2000) and as per scale on 10 randomly selected plants.

Coefficient of the infection (CI) =% plant diseaseincidence X response value to each severity grade

JNKVV Res J 47(3): 315-320 (2013)

316

Results and discussion

Disease incidence was measured on 10 randomlyselected plants of each entry. During the period (15November) the average temperature was 22C with 69%relative humidity. In the particular standardmeteorological week no rainfall was received. Theaverage population of the vector ranged from 3 to 8 perleaf.

Evaluation of varieties

The disease incidence was variable and ranged from20 to 50% (Table 1) in 16 varieties. The coefficient ofinfection value ranged from 5.0 to 37.5. least coefficientof infection was recorded in Parbhani Kranti, ArkaAnamika, JAE 6, JAE 9457003, while it was maximum(37.5%) in A4, JAE 7, VRO6 under Jabalpur conditions.

Appearance of disease Symptoms Response value Coefficient of the Reactionsymptoms infection

Absent 0 0.00 0.0-4.0 HR< 25% leaves 1 0.25 4.1-9.0 R25-50% leaves 2 0.50 9.0-19.0 MR51-75%leaves 3 0.75 19.1-39.0 MS76-90% leaves 4 1.00 39.1-69 S> 90% leaves 5 1.00 69.0-100 HS

Scale

Table 1. Incidence of okra yellow vein mosaic virus disease in different varieties grown under natural field condi-tions

Variety % disease Response Co-efficient Reactionincidence value of infection

Pusa Green 40 0.5 20.0 Moderately SusceptibleParbhani Kranti 20 0.25 05.0 ResistantArka Anamika 20 0.25 05.0 ResistantVarsha Uphar 30 0.50 15.0 Moderately ResistantArka Abhay(JAE 1) 30 0.50 15.0 Moderately ResistantSonal (JAE 2) 40 0.50 20.0 Moderately SusceptibleKanchan (JAE 8) 40 0.50 20.0 Moderately SusceptibleTulsi (JAE 7) 50 0.75 37.5 Moderately SusceptibleShrawan (JAE 6) 20 0.25 05.0 ResistantMAHYCO(JAE 9457003) 20 0.25 05.0 ResistantMAHYCO (JAE 511010) 40 0.50 20.0 Moderately SusceptibleA 4 50 0.75 37.5 Moderately SusceptibleVRO 6 50 0.75 37.5 Moderately SusceptibleSB 2 30 0.50 15.0 Moderately ResistantSB 4 30 0.50 15.0 Moderately ResistantSB 6 40 0.50 20.0 Moderately Resistant

317

Evaluation of exotic collections

Among 15 exotic collections the coefficient of infectionranged from 15.0 to 56.25 and diseases incidenceranged from 30.0 to 75.0% .Least disease incidence(30%) was recorded in EC 169341 and EC 169337 whilemaximum (75%) was in EC 169355 (Table 2). In EC169337, EC 169515, EC 169341, EC 169319 and EC169366, the coefficient of infection was below 20.

Evaluation of indigenous collections

Among 34 indigenous collections the disease incidenceranged from 20(in IC 112481) to maximum (60%)in IC433682, IC 433715, IC 433718, IC 433720 (Table3).Thecoefficient of infection was less than 20 in IC 99693, IC99746, IC 105544, IC 117223, IC 112481, IC 111511,IC 111484, IC 117229, IC 117216, IC90175, IC 282286and IC 326083.

Based upon the reactions, okra genetic resourceswere categorized under different categories (Table 4).Among the varieties, 4 were found under resistantcategory while from indigenous collections only 6 entrieshad shown the desired grade. None of the entry fromexotic collections exhibited the resistant reaction under

prevailing set of environment at Jabalpur. VarietyParbhani Kranti, Arka Anamika, Shrawan and MAHYCO(JAE 9457003) exhibited the resistant reaction .Sixentries IC 99746, IC 112 481, IC 111511, IC 9175, IC366083 and IC 433686 were resistant. Among thevarieties, Varsha Uphar, Arka Abhay, SB 2, SB4 haveshown the moderately resistant reaction while 5 exoticcollections and 11 indigenous collections have shownthe reaction. Among the exotic and indigenouscollections, 2 entries were highly susceptible(EC169456-A, IC 1117251) while 5 entries in each fromEC and IC have shown the susceptible reaction.

Throughout the world, search of host resistanceusing genetic resources has been considered as thecheapest and most effective method for themanagement of OYVMV disease (Abdul and Waqar2002; Chandra et al. 2000; Batra et al. 2000; Pandaand Singh 2003; Khan and Mukopadhyay 1986).Evaluation of 97 genotypes was made by Dhankar etal.(1989) and IC 9273, Baunia 3(1) and IC 23592 wasidentified as resistant while Bora et al. (1992) reportedArka Anamika and five other genotypes free fromdisease among 22 genotypes. Khan and Mukopadhyay(1986) screened 5 varieties and S1-1 exhibited minimumincidence. Genotypes No 6, LORMI, VRO3, hybrid DVR1, DVR2 were found free from disease while VRO4,exhibited mild reactions (Batra et al. 2000). Raghupati

Table 2. Incidence of okra yellow vein mosaic virus disease in different exotic collections under natural fieldconditions

Exotic collection % disease Response Co-efficient of Reactionincidence value infection

EC 169463 70.0 0.75 52.5 SusceptibleEC 169536 50.0 0.50 25.0 Moderately SusceptibleEC 169366 37.5 0.50 18.75 Moderately ResistantEC 169319 33.5 0.50 16.75 Moderately ResistantEC 169399 70.0 0.75 52.50 SusceptibleEC 169341 30.0 0.50 15.00 Moderately resistantEC 169374 42.8 0.50 21.40 Moderately SusceptibleEC 169357 66.6 0.75 49.95 SusceptibleEC 169355 75.0 0.75 56.25 SusceptibleEC 169515 37.5 0.50 18.75 Moderately ResistantEC 169496 50.0 0.50 25.00 Moderately SusceptibleEC 169337 30.0 0.50 15.00 Moderately resistantEC 169456-A 90.0 1.00 90.00 Highly SusceptibleEC 169334 50.0 0.50 25.00 Moderately SusceptibleEC 169481 6.6 0.75 49.95 Susceptible

318

Table 3. Incidence of okra yellow vein mosaic virus disease in different indigenous collections under natural fieldconditions

Indigenous collections % disease Response Co-efficient of Reactionincidence value infection

%

IC 99693 37.5 0.50 18.75 Moderately ResistantIC 99729 50.0 0.50 25.0 Moderately SusceptibleIC 99746 20.0 0.25 05.0 ResistantIC 105544 30.0 0.50 15.0 Moderately ResistantIC 117223 22.2 0.50 11.0 Moderately ResistantIC 112481 20.0 0.25 05.0 ResistantIC 113904 50.0 0.50 25.0 Moderately SusceptibleIC 111511 22.2 0.25 05.0 ResistantIC 117229 44.4 0.50 22.2 Moderately SusceptibleIC 111484 30.0 0.50 15.0 Moderately ResistantIC 117229 30.0 0.50 15.0 Moderately resistantIC 111484 40.0 0.50 20.0 Moderately SusceptibleIC 111478 40.0 0.50 20.0 Moderately SusceptibleIC 117222 44.4 0.50 22.2 Moderately SusceptibleIC 117216 37.5 0.50 18.75 Moderately resistantIC 90175 22.2 0.25 5.5 ResistantIC 90134 33.3 0.50 16.65 Moderately resistantIC 90170 33.3 0.50 16.65 Moderately resistantIC 433660 50.0 0.50 25.0 Moderately SusceptibleIC 433662 50.0 0.50 25.0 Moderately SusceptibleIC 433682 66.6 0.75 49.9 SusceptibleIC 433715 66.6 0.75 49.9 SusceptibleIC 433686 25.0 0.25 6.25 ResistantIC 433370 40.0 0.50 20.0 Moderately susceptibleIC 433671 60.0 0.75 45.0 SusceptibleIC 433718 60.0 0.75 45.0 SusceptibleIC 433720 60.0 0.75 45.0 SusceptibleIC 111483 30.0 0.50 15.0 Moderately ResistantIC 117251 100.0 1.00 1.00 Highly SusceptibleIC 111481 50.0 0.50 25.0 Moderately SusceptibleIC 112456 50.0 0.50 25.0 Moderately SusceptibleIC 282286 30.0 0.50 15.0 Moderately ResistantIC 282288 37.5 0.50 18.75 Moderately ResistantIC 326083 22.2 0.25 05.0 Resistant

319

et al.(2000) reported that disease was absent in Bo1,HRB 55, KS404, HRB 9-2, Hy 8, Parbhani Kranti , S10and S4. Azad Bhendi 1 has been reported to be moreresistant than Pusa Sawani, and Parbhani Kranti (Yadavet al. 2004). Safadar et al. (2005) observed that SurkhBhendi as highly resistant, Sabz Pari and Safal asmoderately resistant and Pahuja as tolerant to okrayellow vein mosaic disease.

e/; izns'k jkT; ds dSeksj IysV;q esas [ksrks dh voLFkk esa fHkaMh dh 16iztkfr;k¡] 15 ,XlksfVd rFkk 34 bafMftul ,d=hdj.k dh xbZiztkfr;k¡ dks fHkaMh ifRr f'kjk jksx ds fo:/k ijh{k.k fd;k x;k AdksfQf'k;aV vkWQ baQs'ku ds vuqlkj ijHkuh Økafr] vjdk vukfedk]Jo.k] th-,-bZ- 9457003 rFkk Ng bafMftful ¼vkbZ-lh- 99746]vkbZ-lh- 112481] vkbZ- lh- 111511] vkbZ- lh- 90175] vkbZ-lh- 326083] vkbZ- lh- 433686½ jksx izfrjks/kd ikbZ xbZ A

Acknowledgement

Authors are thankful to Dr A K Nigam for providing thegermplasm of okra for the purpose and Professor &Head, Department of Horticulture, JNKVV, Jabalpur forfacilities.

References

Abdul Rehman, Waqar Ahmed (2002) Screening of okragenotypes for resistance to yellow vein mosaic virusunder field conditions. Pakistan J Phytopath 14(1):84-87

Batra V K, Singh J, Singh J (2000) Screening of okra varietiesto yellow vein mosaic virus under field conditions.Veg Sci 27(2): 192-193

Bora GC, Saikia AK, Shadeque A (1992) Screening of okragenotypes for resistance to yellow vein mosaic virusdisease. Ind J Virol 8(1):55-57

Capoor SP, Varma PM (1950) ellow vein mosaic of Hibiscusesculentus (L.) Ind J Agric Sci 20: 217-230

Chandra Deo, Singh KP, Panda KP, DeoC (2000) Screening

Table 4. Reaction of genetic resources of okra to yellow vein mosaic virus disease

Reaction Variety / Collection

Resistant Variety Parbhani Kranti, Arka Anamika, Shrawan, MAHYCO (JAE9457003)

Exotic collection NilIndigenous collection IC 99746, IC 112481, IC 111511, IC 90175, IC 326083, IC

433686Moderately resistant Variety Varsha Uphar, Arka Abhay, SB2, SB4

Exotic collection EC 169366, EC 169319, EC 169341, EC 169515, EC169337

Indigenous collection IC 99693, IC 105544, IC 117223, IC 111484, IC 117229,IC 117216, IC 90134, IC 90170, IC 111483, IC 282286, IC282288

Moderately Susceptible Variety Pusa Green , Sonal, Kanchan Tulsi, MAHYCO(JAE511010), A4, VRO6, SB6

Exotic collection EC 169536, EC 169374, EC 169496, EC 169334Indigenous collection IC 99729, IC 113904, IC 117229, IC 111484, IC 111478,

IC 117222, IC 433660, IC433662, IC433670, IC111481,IC112456

Susceptible Variety NilExotic collection EC 169463, EC 169399, EC 169357, EC 169355,

EC169481Indigenous collection IC 433270, IC 433718, IC 433671, IC 433715, IC 433662,

IC 433682Highly susceptible Variety Nil

Exotic collection EC169456-AIndigenous collection IC 1117251

320

of Okra parental lines and their FLS for resistanceagainst yellow vein mosaic virus. Veg Sci 27(1): 78

Dhal G, Neupane FP, Baral DR (19920 Effect of planting andinsecticides on the incidence and spread of yellowvein mosaic of okra in Nepal. Int J Tropical Plant Dis10(1): 109-124

Dhankar BS, Chauhan MS, Kishore N (1989) Reaction ofdifferent genotypes of okra (Abelmoschus esculentus(L.) Moench) to yellow vein mosaic virus .Ind J Virol5(1-2):94-98

Fauqet CM , Mayao MA, Moriloff J, Desselbeyar U, Ball LA(eds) (2005) Virus taxonomy VIII Report of ICTV.Elsevier Press London UK

Gupta S K, Thind TS (2006) Disease problem in vegetableproduction. Scientific Publishers Jodhpur (India)576p

Khan MA, Mukopadhyay S (1986) Screening of okra(Abelmoschus esculentus (L.) Moench) varietiestolerant to yellow vein mosaic virus (YVMV) .Res &Dev Rept 3(1): 86-87

Mansoor SP, Amin M, Hussain Y,Zafar S Bull , Briddar RW,Markam PG (2001) Association of disease complexinvolving a Begamo virus DNA 1 and distinct DNA-?with leaf curl disease of okra in Pakistan . Plant Dis85 (8)922

Panda PK, Singh KP (2003) Resistance in okra genotypes toyellow vein mosaic virus Veg Sci 30(2):171-172

Prabhu T, Warde SD, Ghante PH (2007) Resistance of okrayellow vein mosaic virus in Maharashtra .Veg Sci35(2): 119-122

Raghupati N, Veeraghavthatham D, Thamburaj S (2000)Reaction of okra (Abelmoschus esculentus (L.)Moench) cultures of bhendi yellow vein mosaic virusdisease south. Indian Hort 48 (1-6):103-104

Safadar A, Khan MA, Habib A, Rasheed S, Iftkhar Y (2005)Management of yellow vein mosaic disease of okrathrough pesticides / biopesticides and suitablecultivars. Int J Agric & Biol 7(1):145-147

Sastry KSM, Singh SJ (1973) Restriction of yellow vein mosaicvirus spread in okra through the control of vectorwhite fly (Bemisia tabaci)Ind J Mycol & Pl Path 3(1):76-80

Sastry KSM, Singh SJ (1974) Effect of yellow vein mosaicvirus infection on growth and yield of okra crop .IndianPhytopath 27(3): 294-297

Singh AK, Singh KP (2000) Screening for diseases incidenceof YVMV in okra treated with Gamma rays and CMS.Veg Sci 27(1): 72-75

Singh RS (2004) Plant diseases (VIII ed) Oxford & IBH PubCo New Delhi

Yadav JR, Shrivastava JP, Singh B, Kumar R (2004) Azadbhendi 1 (Azad Ganga ) a disease resistant varietyof bhendi . Plant Achieves 4(1):205-207

(Manuscript Receivd : 30.9.11; Accepted : 16.8.13)

321

Abstract

The correlation studies indicated that air temperature(maximum, minimum and average) significant negativelywhereas morning relative humidity positively related withprogression of ber powdery mildew. The favourable weathercondition for very rapid progress of disease included airtemperature (maximum and minimum) ranged from 24 to 280C and 8 to 13 0C, respectively and high morning relativehumidity coupled with less rainfall. Four fungicides namely,Bayleton 25 WP, Score 25 EC (difenconazole), Tilt 25EC(propiconazole) and tebuconazole 25 EC @ 0.05 and 0.10per cent were also tested on cv. Umran and Bayleton 25 WPsignificantly checked the disease and increased the fruit yield.

Keywords: Ber, powdery mildew, weather parameters,fungicides

Powdery mildew of ber (Zizyphus mauritiana) causedby Oidium erysiphoides f. sp zizyphi Yen and Wang isan important disease and commercially grown varietiesare highly susceptible leading to qualitative andquantitive losses upto 35-45 % to the ber growers(Rawal and Saxena 1996). The fungus produces whitepowdery mass of spores on all the aerial plant partsresulting in pre-mature drop of flower buds and fruits(Rawal 1988). Infected fruit show discolouration,cracking and become mummified and fail to develop.The time of appearance and disease severity vary andis affected by the prevailing climatic factors.Investigations were thus conducted on the seasonaloccurrence of disease, its correlation with weatherparameters and an attempt was also made to find outefficacy of some fungicides against the disease.

Material and methods

The role of abiotic factors on the progress of berpowdery mildew under field condition was studied in

Effect of weather parameters on development of ber powderymildew and its control by fungicides

P.K. Amrate, Amarjit Singh and Chander MohanDepartment of Plant PathologyPunjab Agricultural UniversityLudhiana 141004Email : pawanamrate@gmail.com

during 2010-11 and 2011-12 in the new orchard, PunjabAgricultural University (PAU) Ludhiana. Theobservations on powdery mildew appearance on theber fruit cv. Umran were recorded at weekly intervalcommencing from the 40 standard meteorological week(SMW) during each year and continued up to the 05SMW in to next year. Disease severity was recordedon randomly selected five ber trees with three fruitingtwigs tagged per plant using 0-5 grade (0= no disease;1= 1-20; 2= 21-40; 3= 41-60; 4= 61-80 and 5= 81-100per cent fruit area covered with powdery mildew). Theper cent disease severity was calculated. Data onweekly temperature (maximum, minimum and average),RH (morning, evening and average) and rainfall (mm)were obtained from Agro meteorological departmentobservatory. Correlation and regression analysis wereperformed between development of disease andweather parameters.

For the control of the disease, four fungitoxicants,namely Bayleton 25 WP, Score 25 EC (difenconazole),Tilt 25EC (propiconazole) and tebuconazole 25 EC @0.05 and 0.10 per cent were sprayed during flowering(September), mid - October, mid - November and early- December on 15-year-old ber cv. Umran for two fruitingseasons (2010-11 and 2011-12) in the New orchard ofP.A.U. , Ludhiana. Each treatment was replicated thriceby keeping single tree per replication. An equal numberof unsprayed plants were kept as control. The data onthe development of powdery mildew on ber fruits wererecorded on the three marked fruiting twigs per plant atthe end of December using 0-5 grade. The per centdisease severity and per cent disease control werecomputed. The yield was recorded in March at the timeof harvest.

JNKVV Res J 47(3): 321-324 (2013)

322

Table 1. Correlation matrix showing relationship among disease severity with weather parameters during 2010-11

Severity Temperature (0C) Relative humidity (%)Maximum Minimum Average Morning Evening Average

Max Tem -0.625**Min Tem -0.803** 0.890**Ave Tem -0.730** 0.974** 0.970**Mor Rh 0.478* -0.552* -0.545* -0.566*Eve Rh -0.012 -0.690** -0.330 -0.532* 0.389Ave Rh 0.079 -0.725** -0.408* -0.589* 0.607** 0.961**Rainfall -0.015 -0.182 -0.141 -0.166 -0.063 0.179 0.148

*Significance at 5 per cent; **Significance at 1 per cent

Table 2. Correlation matrix showing relationship among disease severity with weather parameters during 2011-12

Severity Temperature (0C) Relative humidity (%)Maximum Minimum Average Morning Evening Average

Max Tem -0.555*Min Tem -0.641** 0.903**Ave Tem -0.607** 0.978** 0.972**Mor Rh 0.733** -0.635** -0.770** -0.719**Eve Rh 0.106 -0.652** -0.292 -0.494* 0.246Ave Rh 0.264 -0.740** -0.452 -0.620** 0.478* 0.965**Rainfall 0.019 -0.495* -0.160 -0.345 -0.048 0.822** 0.740**

*Significance at 5 per cent; **Significance at 1 per cent

Table 3. Efficacy of different fungicides against powdery mildew disease of Ber during 2010-11 and 2011-12

Treatments Concentrations(%) Powdery mildew Per cent diseasel Fruit yield/Per cent Per cent control trees (kg)incidence severity

Bayleton 0.05 6.5 1.3 96.4 91.00.10 0.0 0.0 100.0 92.5

Tilt 0.05 18.6 6.7 81.7 87.00.10 9.0 2.0 94.5 90.0

Score 0.05 20.4 7.8 78.7 86.00.10 13.0 3.7 89.9 87.5

Folicur 0.05 29.0 11.4 68.9 82.00.10 21.4 9.0 75.4 85.5

Unsprayed - 74.0 36.6 0.0 58.5CD (0.05) 1.87 1.68 - 1.88

323

Results and discussion

The severity of powdery mildew was low during firstcouple of week in October during both the seasons (Fig1). During 2010-11, the severity increased sharply from14.6 to 33.5 per cent between 44 and 47 SMW andreaching a peak of 37.8 per cent in the 48 SMW. Themax temp between 44 to 48 SMW was 29.0 to 24.2 0Cwhile the min temp ranged from 13.6 to 8.2 0C. MorningRelative humidity during this period 92 to 94 per centwhereas in the evening 37 to 44 per cent and no rainfallhas appeared. During 2011-12, the severity of thedisease increased rapidly between the 45 to 48 SMWand reached the highest of 40.6 per cent at 48 SMW.The max temp between 28.3 to 24.6 0C whereas themin temp during this period ranged from 14.0 to 9.8 0C.Morning RH remained between 91 to 98 per cent whileevening RH was varied from 38 to 55 per cent and norainfall has appeared.

Simple correlation: The simple correlationcoefficient matrices calculated between dependent(powdery mildew severity) and independent variablesviz. temperature (max, min and average), relativehumidity (morning, evening and average) and rainfallof the time course under investigation are presented(Table 1 and 2). The correlation between severity andthe variables viz. min and average temp were highlysignificant (0.01) and negatively correlated (-0.803,-0.641 and -0.730, -0.607) during both the year,respectively max temp was also significant andnegatively correlated. Morning RH was significantpositively (0.478 and 0.733) correlated during both theyear. The variables viz. RH (evening and average) andrainfall exhibited a non-significant and very weakrelationship with severity during both the year.

The regression analysis was performed afterpooling the both years (2010-11 and 2011-12) data tofind out the relationship between weather parametersand powdery mildew severity. The R2 value (coefficientof determination) indicated that 78.6 per cent variationin the disease severity could governed by temperature,relative humidity and rainfall. The multiple regressiongave linear equation for per cent disease severity (Y)with respect to weather parameters (X1 = max temp,X2 = min temp, X3 = average temp, X4 = morning RH,X5 = evening RH, X6 = average RH and X7 = rainfall).

Y= -155.10 -13.55 X1 -11.19 X2 + 23.92 X3 + 6.59 X4+2.80 X5 -7.69 X6 + 0.647 X7 .…(Eq.1)(R2 = 0.786; SE = 7.32)

All four fungicides were found to be effective incontrolling the powdery mildew. The average diseaseseverity and fruit yield varied from 0 to 11.4 per centand 82.0 to 92.5 Kg/tree in different treatment ascompared to 36.6 per cent and 58.5 Kg/tree in control.A highest disease control (96.4 and 100.0 per cent) andfruit yield (91.0 and 92.5 Kg/tree) were observed fromboth the concentration (0.05 and 0.10 per cent) ofBayleton, respectively whereas tebuconazole 25 ECwas found to be least effective (Table 3).

The significant and negative correlation withtemperature (minimum, maximum and average) andpositive with morning relative humidity are in agreementwith the finding of Rawal and Sexana (1996) and Thindand Kaur (2005). The superiority of bayleton incontrolling powdery mildew is in fair accordance withJamadar and Desai (1998), Munshi and Bal (2004) andThind and Kaur (2006).

Fig 1. Weather data and ber powdery mildew severity during 2010-11(A) and 2011-12 (B)

324

References

Jamadar M M, Desai S A (1998): Chemical control of powderymildew of ber Karnataka. J Agric Sci 11: 415-18

Munshi GD, Bal JS (2004) Spray schedules for the control ofpowdery mildew of ber with fungicides. Pl Dis Res19: 97

Rawal RD (1988) Assessment of yield losses in ber fruit dueto powdery mildew. Pl Dis Res 3:138

Rawal RD, Saxena AK (1996) Diseases of dryland horticultureand their management. In: Proc Silver Jubilee NatSymp Arid Hort, HAU Hisar Dec 5-6 127-39

Thind SK, Nirmaljit Kaur (2005) Correlation matrix of berpowdery mildew with weather parameters and itsprediction model. Pl Dis Res 20(2): 192-193

Thind SK, Nirmaljit Kaur (2006) Management of ber powderymildew with fungicides. Indian J Hort 63(3): 267-269

(Manuscript Receivd : 15.9.13; Accepted : 16.12.13 )

325

JNKVV Res J 47(2): 325-329 (2013)

Abstract

The population of lesion nematode fluctuates several timesduring the season. There was a gradual increase in soil androot population from seedling to flowering stage and declinedfor a short period of time at pod formation stage was noted.The population again increased and reached to its maximumat harvesting and declined as there was no crop in the fieldthere after. Neem cake @ 10g /m2 as soil amendment andTrichoderma harzianum ( 5g/Kg) as seed treating agent werefound most effective.

Keywords: population dynamics, soil amendments,seed treatment, Pratylenchus thornei

The lesion nematode Pratylenchus thornei is describedas a major limiting factor in chickpea production whichreduces the yield to the tune of 26 per cent(Anon 2000).The nematode has gained an alarming situation in thestate due to monocropping of chickpea and posed athreat to chickpea cultivation.

The application pattern of chemical pesticides tomanage the nematode has increased particularly whereproduction methods were intensified to increaseagricultural output. Use of chemicals is costly, harmfulfor the microflora and fauna with long residual effects.Amendment of soil with decomposable organic matterand bio-control agents is recognized as the mosteffective methods of changing soil and rhizosphereenvironments there by adversely affecting the life cycleof pathogens and enabling the plant to resist attack ofpathogens through better vigour and/or altered rootphysiology.

Keeping this in view an attempt has been madeto delineate the population dynamics of P. thornei andto develope economically feasible and viable technologyto manage P. thornei under field conditions

Population dynamics and management of lesionnematode (Pratylenchus thornei) in chickpea

Jayant Bhatt, Arvind Jaware and S.P. TiwariDepartment of Plant PathologyJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)

Materials and methods

Seasonal fluctuation:

The experiment was conducted under field conditionsnaturally infested with lesion nematode (P. thornei). Thesoil samples were collected at an interval of 15 daysstarting from fallow to sowing of chickpea to harvest.The population of lesion nematode was extractedfollowing the Cobb's sieving and decanting method.Nematode population was assessed by suspending thenematodes in 100ml water and population was counted,by taking five aliquents of one ml. using stereoscopicbinocular microscope. Later nematode population wascalculated taking average of five aliquants.

Evaluation of different plant bi-products:Theexperiment was conducted under field conditions in plotsmeasuring 2.75m×3.50m naturally infested with P.thornei. The initial population ranged from 270-345N/200cm3. The oil cekes viz., Neem, Jatropha, NSKP andmustard were individually mixed with the plot soil @10g/m2 and pulverized. Chickpea seeds (ver. JG74)were sown in each plot with row to row distance 40 cm.and plant to plant distance 30 cm along with a standardcheck of carbofuran (@ 1kg ai./ha) and one untreatedcontrol. The experiment was designed underRandomized Block Design (RBD) with five treatmentsand four replications. Adequate plant protectionmeasures were adopted to grow healthy crop.Experiment was allowed to run till harvest andobservations on final nematode population in soil/200cm3 and roots/5g, nodulation/plant, plant height (at 30days), yield (q/ha) and final population of nematodewere recorded at the end of experiment.

326

Evaluation of bio-agents as seed treatment

The experiment was conducted under field conditions,in plots measuring 2.75m×3.50m naturally infested withlesion nematode. The initial population ranged from 260-315N/200cm3 soil. The experiment was designedfollowing RBD with five treatments viz., Trichodermaharzianum, T. viride, Pochonia chlamydosporia ,Paecilomyces lilacinus and an untreated control.

Seeds of chickpea were treated with the talcformulation of bio-agents @ 5g/kg seeds (2×108 spores/g talc). The plots were sown with chickpea seeds (var.JG-74) and irrigated. The experiment was allowed torun till harvest and observations on final nematodepopulation (soil/200 cm3 and root/5g), nodulation/plant,plant height (at 30 days) and yield (q/ha) were recordedat the time of harvest.

Results and discussion

Seasonal fluctuation

The experiment was conducted in naturally infested fieldwhere the crop is being grow continuously. The datapresented in the Table 1 revealed that the initial

population of lesion nematode (280N) declines atseedling stage however penetration started and theroots showed presence of (90N) nematodes within 15days after germination.

There was a gradual increase in the soilpopulation during growth and pre-flowering (150 and

Table 1. Seasonal fluctuation in the population of mi-gratory nematode in chickpea

Month Date Nematode population Crop stageSoil/200cm3 Root/5gm

Nov. 30 280 GerminationDec. 15 200 90 Seedling

30 150 96 GrowthJan. 15 290 110 Pre- flowering

30 410 122 FloweringFeb. 15 360 105 Pod-formation

30 400 91 MaturityMarch 15 620 75 Harvesting

30 740April 15 345

30 255

Table 2. Evaluation of different plant bi-products against Pratylenchus thornei in chickpea

Treatment Initial nematode Final nematode population Nodulation/ Plant height Yieldpopulation plant (cm) (q/ha)

(Soil/200cm3) Soil/200cm3 Root/5g

Neem cake @ 10g/m2 315* 345.75 86.05 88.05 9.98 13.1(17.76)** (18.61) (9.30)

Jatropha cake @ 10g/m2 280 387.55 105.25 84.75 9.95 11.97(17.75) (19.70) (10.28)

NSKP @ 10g/m2 290 359.57 92.83 86.39 9.18 36.25(17.04) (18.98) (9.66)

Mustered @10g/m2 270 410.15 110.25 83.15 9.25 12.36(16.45) (20.26) (10.52)

Carbofuran @ 1 kg ai./ha. 310 185.35 50.15 90.89 9.65 14.55(17.62) (13.63) (7.12)

Control 345 550.25 156.26 57.25 8.28 11.25(18.59) (23.47) (12.52)

S.Em.(±) (0.36) (0.54) (0.34) 5.08 0.83 1.01CD(P=0.05) (1.10) (1.06) (1.06) 15.60 2.56 1.43* Mean of four replications** Figures in parenthesis are square root transformed values

327

290N) stage and a sudden increase (410N) was notedat flowering stage. Maximum (122N) nematodepopulation was recorded in roots during this period.

The nematode population in soil declined bothin soil (360N) and in roots (105N) at pod formation andagain increased in soil (400N) but declined in root (91)during pod formation and maturity. Root populationshowed drastic decline at harvest period but maximum(620 and 740N) population of P. thornei in soil wasrecorded at the time of harvesting (March 15).

Thereafter, the soil population declined andreached to its minimum (255N) when there was no cropin the field. The data revealed the population in soilgradually increased up to pre-flowering with a slightdecrease during seedling and growth stages and attainsa high level at flowering stage and reaches to its peakduring harvesting. Sebastian and Gupta (1995) reportedthe population of P. thornei in soil to increasecorresponding to the presence of crop and a gradualreduction in following months. The observationsrecorded during the investigations confirm the abovefindings. The highest population at the time of harvestmay be due to decortications of roots leading to releaseof nematodes.

Evaluation of different plant bi-products

The influence of various oil cakes and bi-products ofplant on nematode multiplication and growth of chickpea

is presented ( Table 2 ). Treatment with carbofuran (1kgai./ha) resulted with minimum population of lesionnematode in soil (185.35) and in roots (50.15) followedby neem cake where the nematode population wasrecorded to be 345.75 in soil and 86.05 in roots. Theseresults are in accord with the findings of Tiyagi andShamim (2004) on chickpea. The antinemic propertiesof neem ascribed to the presence of oleic acid, sulphurand flavonoides as well as extra cellular enzyme toxin,siderophore and phytochrome compounds of thepotential bio control agents may be exploited inbiological control leading to an ecofriendly, low costtechnology for developing an appropriate integratedmanagement system (Bandopadhyay 2002)

Jatropha and mustard recorded significantlyreduced nematode population in soil and root whencompared with control (550.25 and 156.26). Reducedsoil (359.57) and root (92.83) population was alsorecorded with NSKP but was inferior in its efficacy whencompared to neem cake.

The plant height in all the treatments wasrecorded to be non significant among themselves butsignificantly superior over control. Maximum (90.89)number of rhizobial nodules were noted with carbofuranfollowed by neem cake (88.05), NSKP (86.39), jatropha(84.75) and mustard (83.15). Minimum nodulation(57.25) was recorded with control. Significant increasein yield (14.55 q/ha) was recorded in carbofuran (1kgai./ha) followed by neem cake (13.10 q/ha), NSKP(12.43 q/ha) and mustard cake (12.36 q/ha) minimum

Table 3. Effect of bio-agents on the multiplication of Pratylenchus thornei in chickpea

Treatment Initial nematode Final nematode population Nodulation/ Plant height Yieldpopulation plant (cm) (q/ha)

(Soil/200cm3) Soil/200cm3 Root/5g

Trichoderma harzianum 310* 150.55 57.43 86.81 9.61 22.12@ 5g/kg seed (17.63)** (12.29) (7.61)Trichoderma viride 275 206.61 72.32 83.69 9.32 20.85@ 5g/kg seed (16.60) (14.39) (8.53)Pochonia chlamydosporia 285 310.25 118.65 79.95 9.57 19.66@ 5g/ kg seed (16.90) (17.63) (10.92)Paceilomyces lilacinus 315 194.11 59.26 85.50 9.29 21.16@ 5g/kg seed (17.76) (13.95) (7.73)Control 260 437.82 137.25 60.38 8.08 18.12

(16.14) (20.60) (11.74)S.Em. (±) (0.54) (0.80) (0.65) 6.38 0.83 1.69CD(P=0.05) (1.66) (2.24) (1.99) 19.58 2.57 5.20*Mean of four replications**Figures in parentheses are square root transformed values

328

(11.25 q/ha) yield was recorded with untreated control.Jatropha stood next in order of efficacy in reducing thenematode population and increasing the yield along withplant growth condition. Efficacy of Jatropha was alsoreported by Patel and Patel (2007) on tomato and Vermaand Nandal (2007) on bottle gourd against root knotnematode.

Mustard cake however, reduced the nematodepopulation and increased yield along with nodulationbut it was inferior over neem and Jatropha. Sebastianand Gupta (1996) reported the efficacy of mustard cakeand demonstrated that root population of P. thorneideclined at 120 days after sowing.

Effect of bio-agents

Minimum soil (150.55) and root population (57.43) wasrecorded with Trichoderma harzianum followed by P.lilacinus which recorded 194.11 nematodes in soil and59.26 in roots. Trichoderma viride stood next in orderof efficacy where 206.61 nematode in soil and 72.32nematodes in roots were recorded (Table 3). The soil(310.25N) and root (118.65) population of P. thorneiwas recorded higher than other treatments but thesignificant effect of the fungus was noted whencompared with control which recorded maximumpopulation of nematode in soil (437.82) and in roots(137.25).

Maximum 86.81 nodulation was recorded withTrichoderma harzianum followed by P. lilacinus (85.50)and P. chlamydosporia (79.95). Minimum (60.38) rootnodules were recorded in control. The effect oftreatments on the formation of root nodules wasrecorded to be non significant but they are significantlysuperior over control

Maximum plant height (9.61cm) was noted in T.harzianum which was observed to be statistically at parwith P. chlamydosporia (9.57 cm) followed by T. viride(9.32 cm) and P. lilacinus (9.29cm) against minimum(60.38) in control.

Increased yield was noted in T. harzianum (22.12q/ha) followed by P. lilacinus (21.16) and T. viride (20.85)against minimum (18.12 q/ha) in control. All thetreatments were superior over control.

These results are in accord with the findings ofHari Chand and Singh (2005) and Khan et al. (2004)on chickpea Paecilomyces lilacinus stood next in theorder of efficacy where the soil and root population ofP. thornei declined drastically over control along withsignificant improvement on nodulation, plant height and

yield. Similar results have also been reported by Mishraet al. (2003) who observed reduction in nematodepopulation in chickpea when seeds were treated withP. lilacinus.

The effectivity of P. lilacinus against reproductionof nematode and improvement in plant growth wasreported by Khan and Goswami (2000) on tomatoinfected with Meloidogyne incognita. Further, themicroscopic examination revealed empty eggs in theroots of plants grown in P. lilacinus treated plots. Similarresults have also been reported by Sharma and Trivedi(1997) . The fungus penetrated the eggs and fed upontheir contents leaving empty shels.

The efficacy of T. viride against P. thornei wasalso noted in terms of reduction in nematode populationand increase n plant growth parameters. Similar findingshave also been reported by Sankarnarayan et al. (1999)who observed increase in plant growth parameters andreduction in nematode multiplication in sunflower treatedwith T. viride against Meloidogyne incognita. The resultsare also in accord with the finding of Pandey et al. (2003)on chickpea against M. incognita.

Pochonia chlamydosporia @ 10 g/kg seed alsoworks well in reducing the chickpea. The effect of P.chlamydosporia was observed to be inferior than T.harzianum, T. viride and P. lilacinus but superior overcontrol. The results are in conformation with the findingsof Dhawan et al. (2007) on okra. Kerry and Diaz (2004)reported that P. chlamydosporia significantly reducednematode infestation in vegetables.

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References

Ali MS, Nath P, Gogoi KK (2004) Botanical management ofsheath blight disease of winter rice in Assam.Bioprospecting of commercially important plantproceeding of the national symposium onBiochechemical approaches for utilization andExploitation of commercially important plants. JohatIndia 12-14 Nov 2003, 2004: 207-212

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Anon (2000) Quiquiennial Report. All India Co-ordinatedResearch Project on Nematode, Center JNKVV,Jabalpur

Bandopadhyay A K (2002) A current approach to themanagement of root disease in bast fibre plant withconservation of natural and microbial agents. JMycopathol Res 40(1): 57-62

Dhawan SC, Babu NP, Singh S (2007) Bio-management ofroot knot nematodes (Meloidogyne incognita) on okraby egg parasitic fungus Pochonia chlamydosporia,National. Symposium. on Nematology in 21st CenturyEmerging Paradigms, Assam. Agricultural UniversityJorhat 22-23 Nov pp 102

Govindachari TR, Suresh G, Masilamani S (1996) Antifungalactivity of Azadirachta indica leaf hexane extract.Fitoterapia 70 (4): 417-420

Hari Chand, Surender Singh (2005) Control of chickpea wilt(Fusarium oxysporium f. sp. ciceri) using bioagentsand plant extracts. Indian J Agril Sci 75(2) : 115-116

Kerry B, Hidalgo-Diaz L (2004) Application of Pochoniachlamydosporia in the integrated control of root knotnematode on organically grown vegetable crops incuba. Bulletin-OILB/SROP 27:123-126

Khan MR, Goswami BK (2000) Effect of different doses ofPaecilomyces lilacinus isolate 6 on Meloidogyneincognita infecting tomato. Indian J Nematol 30:5-7

Mishra SD, Dhawan SC, Tripathi MN, Saswati Nayak (2003)Field evaluation of bio-pesticides, chemicals and bio-agents on plant parasitic nematodes infestingchickpea. Curr Nematol 14(1/2) : 89-91

Pandey Gopal RK, Hemlata Pant (2003) Efficacy of differentlevels of Trichoderma viride against root-knotnematode in chickpea (Cicer arietinum L.). AnnalsPlant Protec Sci 11(1) : 101-103

Patel BA, Patel SK (2007) Efficacy of Jatropha formulationagainst root knot nematodes in tomato nursery andfield condition, National Symposium on Nematologyin 21st Century: Emerging Paradigms (22-23) : 33

Sebastian S, Gupta P (1996) Evaluation of oil cakes againstPratylenchus thornei on chickpea. Intn chickpeaPigeonpea Newsl (3): 40-41

Sebastian S, Gupta P (1995) Population dynamics ofPratylenchus thornei in infested fields at Allahabad.Indian J Mycol Pl Path 25 (3): 270-271

Sharma W, Trivedi PC (1997) Concomitant effect ofPaecilomyces lilacinus and Vesicular ArbuscularMycorrhizal fungi on root-knot nematode infectedokra. Annals Plant Protec Sci 5:70-74

Tiyagi SA, Ajaz Shamim (2004) Biological control of plantparasitic nematodes associated with chickpea usingoil cakes and Paecilomyces lilacinus. Indian JNematol 34(1) : 44-48

Verma KK, Nandal SN (2007) Efficacy of organic cakes in themanagement of root knot nematode (Meloidogynejavanica) in bottlegourd, National. Symposium. onNematology. in 21st Century: Emerging Paradigm22-23 Nov p 58

(Manuscript Receivd : 22.9.13; Accepted : 20.12.13)

330

Abstract

Soybean is an important crop in India. This study focuses onforecasting the cultivated area and production of soybean inIndia using Autoregressive Integrated Moving Average(ARIMA) model. Time Series data covering the period of 1970-2010 was used for the Study. The data were obtained fromAgriculture at Glance 2010. The result shows soybeanproduction forecast for the year 2020 to be about 12.29 millionstonns. The model also shows that the soybean area wouldbe 11.70 million hectares in 2020. In case of yield model shownthat the yield of soybean would be 1237 Kg/ha in 2020. Thisprojection is important as it helps inform good policies withrespect to relative production, price structure as well asconsumption of soybean in the country. The conclusion fromthe study is that, total cropped area can be increased in future,if land reclamation and conservation measures are adopted.The projection shown that soybean will play vital role to solvefood security problem in India in future.

Keywords: ARIMA, forecasting, production, soybean

India is one among the largest vegetable oil economiesin the world. Soybean (Glycine max) is an importantvegetable oilseed crop. It is considered to be a cashcrop. It is a major source of edible vegetable oils andproteins which contains about 40% protein and 20%oil. Soybean plays a major role in the world food trade.It constitutes about 42% and 56% of area and productionrespectively of total oilseeds. The current globalproduction of soybean is around 176.64 million MT withUSA being the largest producer (Satyawathi 2005) Indiaranks 5th in the area and production of soybean in theworld after USA, Brazil, Argentina and China. In recentyears, soybean has assumed important position in thecountry, as it is one of the most stable kharif cropsyielding cost effective production under varied agroclimatic conditions unlike other kharif pulses and

Modelling and forecasting of area, production and yieldof soybean in India

P.Mishra, H.L.Sharma*, R.B. Singh* and Siddarth Nayak**Bidhan Chanda Krishi VishwavidyalayaNadia 741252 (WB)*Jawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)Email : pradeepjnkvv@gmail.com

oilseeds. (Chauhan and Singh, 2004). Madhya Pradeshstate contributes about 67% and 56% in total area andproduction of soybean and is called as 'Soya state'.Madhya Pradesh, Maharashtra and Rajasthan togethercontribute about 97% to total area and 96% productionof soybean in the country. The soybean seeds, oil andoil cake are economically useful in various ways.Soybean oil is used as cooking medium and formanufacturing several industrial products, such asvanaspati ghee, paints, linoleum oilcloth, printing inks,soaps, insecticides, disinfectants etc. Soybean seedsare used for preparation of soytofu (Paneer), soya milk,soya sprouts, immature pods, soya nuts, etc. Soybeanoil cake is used for preparation of biscuits, protein richbread and other confectionary, bakery, high proteinlivestock feed etc. Iqbal et al. (2005) attempted toforecast the area and production of wheat in Pakistanup to 2022 using last thirty years data of area andproduction of wheat for modeling purpose. AutoRegressive Integrated Moving Average (ARIMA) is themost general class of models for forecasting a timeseries. Appearance of lags of the forecast errors in themodel is called "moving average". (ARIMA) model wasintroduced by Box and Jenkins in 1976 for forecastingvariables. Badmus and Ariyo (2011) forecasted thecultivated area, production and yield for year 2020 ofmaize in Nigeria using ARIMA model taking time seriesdata for 1970-2005. In the present study, an effort hasbeen made to study the production scenarios of totalsoybean in India. Mishra et al. (2012) made teaproduction in India forecasts for 1918-2010 usingARIMA model

Material and methods

Data related to area, production and yield of soybeanin India since 1970 to 2010 were collected from

JNKVV Res J 47(3): 330-336 (2013)

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Agriculture at Glance, 2010. Statistical tools used todescribe the above series are minimum, maximum,average, standard error, skewness, kurtosis; Box-Jenkins (1976) ARIMA modelling has been used toforecast series under consideration.

Descriptive statistics

To examine the nature of each series these have beensubjected to get various statistics. Descriptive statisticsare used to describe the basic features of the data in astudy. They provide simple summaries about the sampleand the measures. Together with simple graphicsanalysis, they form the basis of virtually everyquantitative analysis of data. Descriptive statistics aretypically distinguished from inferential statistics. Withdescriptive statistics we are simply describing what isor what the data shows. With inferential statistics, youare trying to reach conclusions that extend beyond theimmediate data alone. For instance, we use inferentialstatistics to try to infer from the sample data what thepopulation might think. Or, we use inferential statisticsto make judgments of the probability that an observeddifference between groups is a dependable one or onethat might have happened by chance in this study. Thus,we use inferential statistics to make inferences fromour data to more general conditions; we use descriptivestatistics simply to describe what's going on in our data.

Parametric Trends Models

To get an overall movement of the time series data, trendequations are fitted. In this exercise different idea aboutthe models like, polynomial, exponential, linear,compound etc are used for the purpose.

After the evaluation of trend of each and everyseries, our next task is to forecast the series for theyear to come. For the purpose the study adopted theBox -Jenkins methodology. Data for the period 1970-2006 have been used for the model building, while datafor years 2007-10 are taken for model validation. Modelsare again compared according to the minimum valuesof Root Mean Square Error (RMSE), Mean AbsoluteError (MAE), Mean Square Error (MSE) and maximumvalue of Coefficient of determination (R2).

Autoregressive model

ARIMA models which stands for AutoregressiveIntegrated Moving Average models. Integrated meansthe trends have been removed; if the series has nosignificant trend, the models are known as ARMAmodels.

The notation AR (p) refers to the autoregressivemodel of order p. The AR (p) model is written

where p are the parameters of the model, c isa constant and t is white noise. Sometimes the constantterm is omitted for simplicity.

Moving Average model

The notation MA (q) refers to the moving average modelof order q:

The Box-Jenkins type ARIMA process (Box andJenkins, 1976) can be defined as (B)( d yt - ) = (B) t,Here, yt denotes soybean area and production in mil-lion hectares and million tons respectively, is the meanof dyt , (B) = 1 - 1B - …….… pB

p , (B) = 1 - 1B - ...- q B

q , i denotes the ith moving average parameter, i

denotes the ith autoregressive parameter and B denotethe difference and back-shift operators respectively.

Different trend models used

Polynomial Yt= b0+ b1 t + b2t2 + b2t

3+……+ bktk

Linear Yt = b0+b1tQuadratic Yt = b0+b1t+b2t

2

Cubic Model Yt= b0+ b1t + b2t2 + b3t

3

Compound Yt= b0eb1t

Exponential Yt = b0e(b1t)

Logarithmic Yt = b0 + b1ln(t)

Growth Yt e 0 1In tb b ( )

tt

P

iit XcX

1

tit

q

iitX

1

332

Model selection and diagnostic check

Among the competitive models, best models areselected based on minimum value of Root Mean SquareError (RMSE), Mean Absolute Error (MAE), MeanSquare Error (MSE) and maximum value of Coefficientof Determination (R2) and of course the significance ofthe coefficients of the models. Best fitted models areput under diagnostic checks through auto correlationfunction (ACF) and partial autocorrelation function(PACF) of the residuals.

R-squared

An estimate of the proportion of the total variation inthe series that is explained by the model. This measureis most useful when the series is stationary.R-squaredcan be negative with a range of negative infinity to 1.Negative values mean that the model underconsideration is worse than the baseline model. Positivevalues mean that the model under consideration isbetter than the baseline model.

Root Mean Square Error (RMSE)

The square root of mean square error is called RMSE.A measure of how much a dependent series varies fromits model-predicted level, expressed in the same unitsas the dependent series.

Mean Absolute Percentage Error (MAPE)

A measure of how much a dependent series varies fromits model-predicted level. It is independent of the unitsused and can therefore be used to compare series withdifferent units.

Mean Absolute Error (MAE)

Measures how much the series varies from its model-predicted level, MAE is reported in the original seriesunits.

With the help of SPSS 16 computer package ARIMAmodels was found to be estimated for tea in India.

Model Formulation

The whole period under consideration (1970-2010) hasbeen divided into two parts.

(a) The model formulation period (1970-2006)

(b) Model validation period (2007-2010)

On the basis of best fitted model forecasting has beenmade up to 2020.

Results and discussion

Since 1970 the area under soybean has increased from0.03million ha to 9.79 million ha registering a growth ofalmost 746%.The average area under soybean being3.60 million ha. In fact the effect of green revolution isbeing reflected. The effect of expansion of area is clearlyvisible in the production scenario of soybean. With amere 0.01 million tonnes of production it has reachedto 10.97million tonnes during the year 2010. Platykurticnature of production indicates that there has beencontinuous force on enhancing production of thesecrops during the period. Increased production ofsoybean would not been possible without a substantialincreasing per ha yield of the crop. Starting with only426kg of Soybean per ha, it has reached to 1235kg/haduring the year 2010. Thus the joint effect of expansionarea and yield has resulted in a brighter picture ofsoybean production scenario in India.

1

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i ii

X XM AE

n

2

1

ˆn

i ii

X XR M S E

n

1

ˆ

x 1 0 0

ni i

i i

X XX

M A P En

2

2 1

2

1

ˆn

ii

n

ii

X XR

X X

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Table 1. Descriptive Statistics

Area Production Yield(MH) (MT) (Kg/ha)

Mean 3.60 3.58 879.95Standard Error 0.51 0.55 30.70Kurtosis -1.25 -1.01 -0.18Skewness 0.44 0.62 -0.50Minimum 0.03 0.01 426Maximum 9.79 10.97 1235SGR (%) 746.34 2450 3.75CGR (%) 1.56 1.72 1.01

Area and production are in respectively in millionhectare and million tonnes and yield in kg/ha.

Trends in production behaviour of soybean

To workout the trends in area, production and yield ofsoybean in India different parameter model like Linear,Logarithmic, Quadratic, Cubic, Compound growth andExponential model where attempted to among the

competitive models. The best model was selected onthe basis of the maximum R2 value, significance of themodel and its coefficient. In the following few sectionswe shall present (Table-2) the result of these exercises.

Box-Jenkins modelling and forecasting

After the evaluation of trend of each and every series,our next task is to forecast the series for the year tocome. For the purpose we adoptated the Box -Jenkinsmethodology and forecasting has discuss in the materialand method section. Data for the period 1970-2006 hasbeen used for the model building, while data for years2007-10 are taken from model validation. (as describethe material and method section ) Best fitted modelsare used to forecast the series for the years to come.Though different series has been fitted with differentARIMA models but one thing is clear that none of theseries is stationary in nature and first order differencingis required for all the series. The selected models areARIMA (0,1,0), ARIMA (0,1,3) ARIMA (0,1,4) andARIMA (0,1,5). These four models are again comparedaccording to the minimum values of RMSE, MAE, MSEand MAPPE and maximum value of R2 which are given

Table 2. Model Summary and Parameter Estimates of parametric trend models

Equation R2 F Sig. Constant b1 b2

Area Cubic 0.987 918.918 0 0.396 -0.174 0.019

Production Cubic 0.955 259.528 0 0.51 -0.21 0.02

Yield Linear 0.419 28.1 0 656.938 10.62

Fig 1. Line graph showing the observed and expectedvalue of area under soybean

Fig 2. Line graph showing the observed and expectedvalue of production of soybean

334

in Table 3. Hence, it can be concluded that ARIMA (0,1,5) is the best fitted model for forecasting the Area ofsoybean in India. Production behaviour of soybean hasbeen modelled with the help of Box- Jenkins's ARIMAmodelling technique. This justified that the selection ofARIMA (1, 1, 3) is the best model to represent the datagenerating process very precisely. Yield behaviour of

Fig 3. Line graph showing the observed and expectedvalue of yield of soybean

Table 4. Forecasting of area, production and yield of soybean of India

Area Production YieldYear Observed Predicated Observed Predicated Observed Predicated

2007 8.88 8.83 10.97 10.62 1235 1211

2008 9.51 9.06 9.91 9.32 1041 1097

2009 9.79 9.25 10.05 9.59 1026 1061

2010 9.21 9.46 9.81 9.84 1065 1120

2015 10.57 11.06 1179

2020 11.70 12.29 1237

Table 3. Model selection criteria for area, productionand yield of soybean in India

Best R2 RMSE MAPE MAEARIMAmodel

Area (0,1,5) 0.994 0.246 20.59 0.17

Production (1,1,3) 0.958 0.65 23.39 0.40

Yield (0,1,3) 0.269 163.06 14.55 118.75

soybean has been modelled with the help of Box-Jenkins's ARIMA modelling technique. This justified thatthe selection of ARIMA (0, 1, 3) is the best model torepresent the data generating process very precisely.

Area and production are in respectively in millionhectare and million tonnes and yield in kg/ha.

Conclusions

ARIMA model offers a good method for predicting themagnitude of any variable. Its strength lies in the factthat the method is suitable for any time series with anypattern of change and it does not require the forecasterto choose a prior value of any parameter. In our studyARIMA (0, 1, 5) model is best suited for estimation ofsoybean area data. From the forecast values obtainedthe regression model, it can be said that forecasted areawill increases to some extent in future i.e. In 2010-11area of soybean was 10.97 million ha. Up to the year2020-21 it will be 11.70 million ha.In case of productionof soybean the ARIMA (1, 1, 3) model is best fitted. , itcan be said that forecasted production will increases tosome extent in future i.e. In 2010-11 production ofsoybean was 9.81 million tonnes. Up to the year 2020-21 it will be 12.29 million tonnes. In case of yield ofsoybean the ARIMA (0, 1, 3) model is best fitted. , itcan be said that forecasted yield will increases to someextent in future i.e. In 2010-11 production of soybeanwas 1065kg/ha. Up to the year 2020-21 it will be 1237kg/ha.The projection of area, production and yieldshown that soybean will play vital role to solve foodsecurity problem in India in future.

335

Fig 4. ACF and PACF graphs of residuals for the bestfitted (ARIMA 0, 1,5) of area under soybean

Fig 5. Observed and Predicated / Forecasted area un-der soybean

Fig 6. ACF and PACF graphs of residuals for the bestfitted (ARIMA 1, 1,3) of production of soybean

Fig 7. Observed and predicated / forecasted produc-tion of soybean

Fig 8. ACF and PACF graphs of residuals for the bestfitted (ARIMA 0,1,3) of yield of soybean

Fig 9. Observed and Predicated / Forecasted Yield ofsoybean

336

lks;kchu Hkkjr esa ,d egRoiw.kZ Qly gS A ;g v/;;u LolekJ;h,dh—r xfreku vkSlr ds izk:i dk iz;ksx djrs gq, Hkkjr esalks;kchu ds —"V {ks= ,oa mRiknu ds Hkfo";ok.kh ij izdk'k MkyrkgS A bl v/;;u ds fy, 1970&2010 ds dky Js.kh ds vk¡dM+ksa dksiz;ksx fd;k x;k Fkk A vk¡dM+s 2010 ds —f"k ,d utj ls izkIr fd;kx;k A o"kZ 2020 ds fy, lks;kchu mRiknu vuqeku ds ckjs esa 12-29 n'kyk[k gksus dk irk pyrk gS A ekWMy Hkh lks;kchu {ks= 2020esa 11-70 yk[k gsDV;j gksxk] irk pyrk gS A lks;kchu dh iSnkokj2020 esa 1237 fdyksxzke@gsDVs;j gksxk irk pyk gS fd mit ekWMyds ekeys esa ;g lkis{k mRiknu] fdey lajpuk ds lkFk gh ns'k esalks;kchu dh [kir ds fy, lEeku ds lkFk esa vPNh uhfr;ksa dks lwfpr,oa enn~ gS ds :i esa ;g iz{ksi.k egRoiw.kZ gS A v/;;u ls fu"d"kZ]Hkwhe lq/kkj vkSj laj{k.k ds mik;ksa dks viuk;k tkrk gS A dqy Qly{ks=] Hkfo"; esa c<+k;k tk ldrk gS] lks;kchu iz{ksi.k dh Hkfo"; esaHkkjr esa [kk| lqj{kk dh leL;k dks gy djus ds fy, egRoiw.kZHkwfedk gS A

References

Agricultural Statistics at a Glance (2010) Directorate ofEconomics and Statistics, Department of Agricultureand Cooperation, Ministry of Agriculture, Govt. ofIndia

Box GEP, Jenkins GM (1976) Time Series Analysis:Forecasting and Control, Holden-Day San Fransisco

Badmus MA, Ariyo OS (2011) Forecasting Cultivated Areasand Production of Maize in Nigerian using ARIMAModel. Asian J Agric Sci 3(3): 171-176

Chauhan, GS, Singh NB (2004) Present status of soybeanproductionk and uses in India, Proceedings ofsoybean production and improvement in India, Indore: NRCS p 1-9

Iqbal NBK, Maqbool Asif, Shohab Abid Ahmad (2005) Use ofthe ARIMA Model for Forecasting Wheat Area andProduction in Pakistan. J Agric Social Sci 1(2): 120-122

Mishra P, Sahu PK, Bajpai P, Nirnjan HK (2012) Past Trendsand Future Prospects in Production, and ExportScenario of Tea in India. International Review ofBusiness and Finance 4(1):25-33

Satyawathi TC (2005) Improved soybean varieties of India,Indore National Research Centre for Soybean p 1-30

(Manuscript Receivd : 20.8.13; Accepted : 17.12.13)

337

Abstract

Madhya Pradesh (14.48%) is the third largest state of lacproduction, and Balaghat, Mandla, Chhindwara, Seoni,Narsinghpur, Dindori, Anuppur, Shahdol, Hoshangabad,Khargone and Dewas are the major lac producing districts inthe state. It is assumed that 80 to 95 percent of the potentialof lac host trees is not being utilized., Low cash and labourinput activity with high returns which is generates ruralemployment and income are the strength of lac cultivation inM.P. Lac has significant climatic risks and up to 50 percent ofthe potential crop is commonly lost in poor seasons. Afavorable export market scope exists for greater value addedactivity within the State. Shortage of supply and high lac exportprices over years have reduced market uptake in somemarkets and encouraged substitutes. The effective cultivationof lac production, Technical training, monitoring, assistance tounify producers for brood lac distribution and marketing, amarket information system and a strong state level planningand monitoring organization are required for lac cultivationand development.

Keywords : SWOT, cultivation, lac, Madhya Pradesh

In Madhya Pradesh, forestry is the second major landuse after agriculture. About 83 percent of the 33 millionpeople engaged in agriculture practice rain-fed farming.Rain-fed farming is always associated with risk and lowproductivity. Erratic and uneven distribution of rainaffects the crop growth and its productivity. The climatein rain fed areas is semi arid and soils are usuallydeficient in nutrients as well as moisture. Suchconditions are not conducive to improving cropperformance. Under such rain-fed agriculture, whereKharif (wet season -July to October) is the maincultivation season for agricultural crops, followed by afallow or less productive season under crops in Rabi(winter season-November to June), the cropping systemleads to a prolonged lean period from November toJune. This lean period is characterized by migration,

SWOT analysis for lac cultivation in Madhya Pradesh

Arvind Dangi Thakur, S. C. Meena and Ashutosh ShrivastavaAgro-Economic Research Centre for MP & CGJawaharlal Nehru Krishi VishwavidyalayaJabalpur 482 004 (MP)

illicit felling and related desperate measures to obtaincash to overcome household food insecurity and othercontingencies (repairing roofs, marriage, preparationfor Kharif cropping etc,). The main Lac crop during themonths of May/ June months assist households toovercome these difficulties and can also be a significantcontributor to reducing migration.

There are 56,069 villages in the state; thesevillages are the primary production centers of food,fodder, fiber and fuel. Although these villages are alsothe owners of natural resources in the state, about 37percent of the people in the rural sector continue to livebelow poverty level. Most of the poor in the state liveeither in the fringes of forest or near the forest. Farmersin these rain-fed areas over recent years have faced adecline in their farm income. Their main option has beento divert effort towards off-farm income. Off farm incomehas become a necessity among the resource poorsection in the rural sector to meet their household foodsecurity.

Lac production is a complimentary orsupplementary form of income to the existing livelihoodactivities of households. The harvesting periods of lac(October and May/ June for rangeeni Lac, and Juneand December for kusumi lac) coincides with the stressperiod of the majority in the rain-fed parts of MadhyaPradesh. Lac is relatively low cash and labour inputcrop with high returns. It is generally compatible withexisting rural livelihood activities in terms of its labourrequirement. Lac cultivation also encouragesconservation of host trees and leads to a re-greeningof the land.

Lac are scale insects (Laccifer Lacca) which liveon trees called lac host trees where they secrete thelac resin which is scraped off and manufactured intoshellac. To produce just 1 kilogram of lac resin around300,000 insects lose their tiny lives. A scale insect is a

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common name for any of about 2000 insect speciesfound all over the worlds that attach themselves in greatnumbers to plants and trees. Scale insects range froman almost microscopic size to more than 2.5 cm. They

can be very destructive totrees - stunting or killingtwigs and branches bydraining the sap.

India is the foremost lacproducing country of theworld with an annualproduction of about 21,300tones and it is worthnoticing here that MadhyaPradesh stands thirdlargest producer of lac inthe country. It produced

approximately 2,870 tones of scrapped lac coming about

13.5 percent of India's total lac production. Experts saythat the state is poised to emerge as Agri-business huband this would help poor lac farmers share handsomeprofits. The home of richest biodiversity of economicallyimportant lac insects. Lac of commerce is derived froma few species belonging to the genus Kerria. Lac yieldsthree basic components of economical value, i.e., resin,wax and dye. In India, lac cultivation is widely practicedin the states of Jharkhand (50.6%), West Bengal (6.5%),Chhattisgarh (20%), Madhya Pradesh (13.5%), Orissa(1.9%), Maharashtra (4.1%) and parts of Uttar Pradesh(2.3%), Andhra Pradesh (0.2%) and Gujarat (0.4%).

The principal districts that are currently producinglac in MP are: Balaghat, Mandla, Chhindwara, Seoni,Narsinghpur, Dindori, Anuppur, Shahdol, Hoshangabad,Khargone, Dewas. These districts are in the south andeast of the State.

Considering lac cultivation is an employment and

Pic. B: Host plant of Lac Pic. C: Different uses of lac

Fig. Lac Production Scenario in M.P. (2010-11) Pic. D: Lac Cultivation

339

income generation activity, the SWOT analysis of thisenterprise has been done to drawn conclusions.

SWOT Analysis

SWOT is for Strengths, Weaknesses, Opportunities,and Threats. Using the SWOT Analysis would beevaluating these areas. A project or enterprises needsto have an objective and they need to identify the areasof enterprises.

The usefulness of SWOT analysis is not limitedto profit-seeking organizations. SWOT analysis may beused in any decision-making situation when a desiredend-state (objective) has been defined. Examplesinclude: non-profit organizations, governmental units,and individuals. SWOT analysis may also be used inpre-crisis planning and preventive crisis management.SWOT analysis may also be used in creating arecommendation during a viability study/survey.

Strengths

Lac production has a long tradition in MP and the activityavoids many of the risks associated with "new" incomeearning activities., it is assumed that 80 to 95 percentof the potential of lac host trees are not being utilized.,Low cash and labour input activity with high returns andgenerates rural employment and income are thestrength of lac cultivation in M.P., It encourages re-greening and forest conservation and the State of MPhas a generally favorable climate for production. It is

not rain dependent and provides income at critical timesof the year in rain-fed dependent agricultural areas.

Export markets appear strong an unlikely to beseriously affected by an increase in production fromMP of say 3000 to 5000 MT and It reduces migrationoutside the State during the lean income months of theyear and over the past four years 13,000 householdshave already commenced lac production with a doublingof lac production from MP. In many cases surveyed,lac is providing around 50 percent of rural householdcash expenditure needs.

It is compatible with existing land based activities- minor shading does not appear to affect paddyproduction or any other crop production. It provides animportant livelihood activity for women who in manycases insist on their share of income for their input.Rural youth are more attracted to lac than other group.

Weaknesses

Lac has significant climatic risks from heat, rain, hailand prolonged fog and up to 50 percent of the potentialcrop is commonly lost in poor seasons., slow andrequires technical training and follow-up technicalassistance one year lead-time before significant incomeand brood lac has often been in short supply and needscareful co-ordination and organized transport as timingis critical and more than 80 percent of the lac producedin MP receives its primary processing through outside

Table 1. Production scenario of Lac in Madhya Pradesh (in tons)

Name of Districts Total Production % to State Total Production % to Statein 2009-10 in 2009-10

Anuppur & Shahdol 28 1.17 15 2.19

Balaghat 547 22.89 217 31.68

Chhindwara 65 2.72 15 2.19

Dindori 27 1.13 20 2.92

Hoshangabad 120 5.02 95 13.87

Mandla 105 4.39 50 7.30

Narsinghpur 18 0.75 13 1.90

Seoni 1375 57.53 225 32.85

Others 105 4.39 35 5.11

Madhya Pradesh 2390 100.00 685 100.00

340

the State. Prices fluctuate up to +/-40 percent in a yearbecause of price manipulation by export traders.

The shelf life of scraped lac is short (maximumof two months without cold storage conditions) soproducers cannot easily hold back selling during lowprice periods. Trading practices work unfairly againstproducers with under weighing, unfair grading andopportunist pricing in many instances and Theft is aproblem in most producing areas. It is perceived to bea crop of backward tribal communities and sometimesdifficult to attract other new producers.

Taxes (VAT and mandi tax) on lac in MP reducegrower returns compared with neighboring states.Chhattisgarh has removed both Vat and CESS on lac.Maharashtra has removed VAT. Unfortunately lac istreated as both a NTFP and agricultural crop undertaxation (and other) laws. Inoculation for the katki cropin July comes at a time when labour is short in someintensive agricultural cropping areas. No minimum pricesupport or crop insurance schemes operate for lac. andNo crop credit facilities exist for lac producer inputrequirements.

Opportunities

The doubling of lac production that has taken place inthe past four years could relatively easily be doubledagain and there is a favorable export market outlookwith increasing interest in natural and sustainableproducts. Scope exists for greater value added activitywithin the State - including possibly a special exportzone for lac industries and Opportunity for someproducers/districts to specialize on brood lac production.

The opportunity to unify produce through supportto encourage increased production, collective marketingand possibly processing., The opportunity to use lac inconjunction with Joint Forest Management (JFM) as amajor forest conservation tool.

Threats

Other States in India could also quickly increaseproduction and possibly threaten export market stability.Little is known about the lac end uses and risks ofsubstitution in export markets and the similarly little isknown about the plans of other producing countries.

A shortage of supply and high lac export pricesover the past 4 years are stated by exporters to havereduced market uptake in some markets andencouraged substitutes. Global warming and morevariable climates could increase climatic risk.

e/;izns'k ¼14.48%½ yk[k mRiknu es rhljk c³k jkT; gS! e-iz- esaorZeku le; esa eq[; yk[k mRiknu dj jgs feyksa esa eq[;r%ckyk?kkV] e.Myk] flouh] ujflgaiqj] fMaMksjh] vuwiiqj] 'kgMksy]gks'kaxkckn] [kjxksu o nsokl gS A

;g ekuk tkrk gS fd yxHkx 80 ls 90 izfr'kr yk[k dsvkfJr isMksaa dh {kerk dk mi;ksx ugha gks ikrk] e/; izns'k esa deuxnh ,oa FkksMs etnwj vkxrksa ds lkFk vf/kdre ykHk ds lkFk jkstxkj,oa vk; mRikfnr djuk gh xzkeh.k {ks=ksa es yk[k dh [skrh dh 'kfDrgS A

yk[k ekSleh vfuf'prrk] ls lh/ks :Ik ls izHkkfor gksrk gS ,oa50 izfr'kr izHkkoh Qly lk/kkj.kr% [kjkc ekSle ls u'V gks tkrhgS A

jkT; esa yk[k gsrq ewY; ls loa/kZu ,oa fu;kZr cktkj dh HkjiwjlaHkouk,a O;kIr gS A vkiwfrZ dh deh ,oa mPp fu;kZr ykxr ds dkj.kfoxr 4 o'kksZa ls fu;kZrd dqN cktkjksa ls [kjhnh de djrh gS rFkk;gka ij fodYiksa dks izksRlkgu nsuk vko';d gS A e/; izns'k esa yk[kmRiknu] rduhd izf'k{k.k ,oa fuxjkuh yk[k dhV forj.k ¼Broodlack½ vkSj foi.ku] cktkj lwpuk iz.kkyh vkSj ,d etcwr jkT;Lrjh; fu;kstu vkSj fuxjkuh laxBu] yk[k dh [ksrh ds fodkl dsfy, vko';d gS A

Reference

MP Forest Department: www.forest.mp.gov.inMP Minor forest Produce Federation: www.mfpfederation.comIndian Lac Research Institute Ranchi: www.icar.org.in/ilri/

default.htmwww.kvkjabalpur.orgwww.pradan.org.inwww.zeezivisa.nic.in

(Manuscript Receivd : 16.12.12; Accepted : 8.10.13)

341

Performance of National Agricultural Insurance Scheme in RaisenDistrict of Madhya Pradesh- An economic evaluation

Govind Prasad Namdev, P. K. Awasthi and N. K. RaghuwansiDepartment of Agricultural Economics and Farm ManagementJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 MPEmail : gnamdev58@gmail.com

Abstract

Agricultural production in Madhya Pradesh involve severalrisks partly due to uncertain weather. NAIS is vital mechanismfor providing insurance coverage to farmers and safeguardesagainst production risk. Against this backdrop the presentstudy has examined are performance of NAIS operating inRaisen district of Madhya Pradesh and some suggestablesuggestion are given to make it more effective.

Keywords: Crop Insurance, Agriculture, NAIS, MadhyaPradesh

Risk and uncertainty are twin dangers, which hamperagricultural production and bring about instability in ruraleconomy of the state. Inadequate and uneven rainfall,hail-storm, incidence of insect pest and diseases etc.are important factors, which cause considerable lossesin agriculture. Farmer and nature are the oppositeplayers in crop production. Raisen district of MadhyaPradesh is an agricultural important district of Narmadavalley. Rice, Wheat, Chickpea, soybean and pigeonpea. their production level fluctuated widely due to theseclimatic changes, thus, farmer loose considerableamount of farm income.

In order to mitigate these risk arising due tovarious factors, Government of India introduced newinsurance scheme called "National AgriculturalInsurance Scheme from rabi 1999- 2000 season in placeof the old Comprehensive Crop Insurance Scheme(CCIS) which was implemented in rabi since 1985. Itprovides coverage to all food crops, oilseed,horticultural/ commercial crops (banana, cotton) andlivestock. Keeping in this view an attempt was made in

present study to evaluate progress and performance ofNAIS in Raisen district of Madhya Pradesh.

Material and method

The study was confined to Raisen District of MadhyaPradesh. The objective function of the study was toevaluate the coverage and performance of the NAIS, inthe study area. The relevant macro level parametersviz. number of farmers benefited area covered, sum-insured and premium and claim compensated etc werecollected from Annual Progress Report of theimplementing agency covering a period from 2006 to2011. Absolute change, Relative Change (%) andtabular analysis statistical techniques were used toanalysed the collected data.

Performance of NAIS

The insured farmers increased every year except 2008from 2006 up to 2011(105.75%). The lowest and highestnumber of farmers were insured during 2006 (19987)and 2011 (41125) respectively.

The area insured was maximum in the year2011(166018.78 ha) followed by 2010 (155943.4 ha).Whereas, it was minimum in the year 2008 (78959.31ha) followed by 2006 (96478.14 ha). It is also inferredfrom the table that maximum sum insured was recordedfor the year 2011 (Rs 17057.45 lakh) followed by 2010(Rs 12979.56 lakh) and 2009 (Rs 10969.69 lakh). Onthe other hand the lowest sum insured of Rs 4443.63lakh was noted for the year 2006 followed by 2008 (Rs4471.74 lakh) and 2007 (Rs 6112.67 lakh). The premiumcollected was the highest in the year 2011 (Rs 697.01lakh) while, it was lowest during 2008 (Rs 1.56 lakh).

JNKVV Res J 47(3): 341-345 (2013)

342

During rest of the period of study it ranged from Rs155.52 lakh to 454.28 lakh. The subsidy given was Rs1.43 lakh in 2006 which increased up to Rs 5.19 lakh in2011. The highest claim of Rs 8005.79 lakh was paid in2011 which was much higher than rest of the years. Alowest claim of Rs 13.05 lakh has been paid in 2008followed by 2007(Rs 80.42 lakh). Similarly;36204farmers were benefited in 2011 which is thehighest. The number of farmers benefited during restof the period of study ranged from 396 to 6465 beinglowest in 2008 (Table 1).

Subsidy to premium under NAIS

In the year 2008 percentage of subsidy to premium washighest (130.78%) which is much higher than it isrecorded for other years. The lowest value wasobserved in 2011(0.74%) followed by 2006 (0.92%).Percentage of subsidy to premium was 0.97 in 2007 &2009 and 1.03 in 2010, that claim to premium ratioranged between 0.21 to 11.49 being lowest in 2009 andhighest in 2011 respectively. This ratio was 8.34 in 2008which was lower than noted in 2011 but, much greaterthan recorded for rest of the years. Maximum percentageof claim to sum assured was noticed for 2011 (46.93%)which was much higher than rest of the years followedby 2010 (6.56%).on the other hand it was the lowest for2008 (0.29%) followed by 2009 (0.75) (Table1).

Category wise farmers are benefitted

Category wise farmers covered in 2006 were 50740which increased by 104.9% up to 2011 (103970).Thisincrease was noticed 22.55% and 164.82% for small/marginal and other categories of farmers respectively.It is also observed that farmers covered under small/marginal farmers were much less from 2006 to 2011than farmers of other category. Regarding the total areacovered it increased from 209510 ha (2006) to 371510ha (2011), but it decreased for farmers under small/marginal categories by 61.2% (from 101100 ha to 39160ha). Thus increase in total area is due to more coverageof the farmers of other category (from 108400 ha to332350 ha). The sum insured of Rs 800987570 wasrecorded for the year 2006 which increased up to Rs3849508040 in the year 2011. Similar to area coveredsum insured also decreased by 25.1% from 2006 (Rs464189910) to 2011 (Rs 347643300) for small/marginalfarmers while, it increased from Rs 336797650 to3501860730 for farmers of other category. Similarresults were obtained for premium collected, it also

Tabl

e 1.

Per

form

ance

of N

AIS

Year

Farm

erA

rea

Sum

Prem

ium

Sub

sidy

Cla

ims

paid

Ben

efite

dPe

rcen

tage

Cla

ims/

Cla

ims

/in

cure

din

cure

din

cure

dfa

rmer

sof

sub

sidy

prem

ium

sum

(ha)

(Rs

in la

kh)

(Rs

in la

kh)

(Rs

in la

kh)

(Rs

in la

kh)

assu

red(

%)

2006

1998

796

478

4443

.615

5.5

1.43

125.

422

930.

920.

812.

82(1

1.47

)20

0727

714

1291

1661

12.7

213.

92.

0880

.414

180.

970.

381.

32(5

.12)

2008

1853

578

959

4471

.71.

562.

0413

.039

613

0.78

8.34

0.29

(2.1

4)20

0932

105

1423

2010

969.

738

3.9

3.74

82.1

709

0.97

0.21

0.75

(2.2

1)20

1039

316

1559

4312

979.

645

4.3

4.65

851.

664

651.

031.

876.

56(1

6.44

)20

1141

125

1660

1917

057.

469

7.0

5.19

8005

.836

204

0.74

11.4

946

.93

(88.

03)

Sour

ce: A

gric

ultu

re In

sura

nce

Com

pany

of I

ndia

Lim

ite

343

decreased for small/marginal farmers from Rs 16048380(2006) to 8710410 (2011). Increase in total premiumcollected from 2006 to 2011 (from Rs 21269100 to98282800) is due to increase in premium collected fromfarmers of other category (Rs 5220720 to 89572390respectively for 2006 and 2011).

Both absolute and relative changes were higherfor farmers of other category than small/marginal. Thearea covered was declined for small/marginal farmersand the absolute and relative changes were declined61.94 and 61.26% respectively. But the area coveredincreased for farmers of other category which in turnincreased the total area covered. Negative changeswere observed for sum insured and premium collectedtoo for the farmers of small/marginal category. On theother hand positive changes were observed for thefarmers of other category (Table 2).

Claim disbursement of NAIS

Performance and claim disbursement of NAIS in boththe seasons was studied and data has been presented.

It could be noted from the table that farmers coveredwere slightly higher in rabi season as compared to kharifin each year except 2003 where more farmers werecovered in kharif (17580) as compared to rabi (4300).A gradual increase in number of farmers was observedin both the season from 2001 to 2011 with the exceptionof kharif 2008, rabi 2008 and rabi 2011 where it declinedslightly than preceding year. During kharif seasonnumber of farmers covered under NAIS ranged between17580 (2003) to 51440 (2011) whereas during rabiseason they ranged between 4300 (2003) to 60680(2010). The range of area covered was 84.44 thousandha to 202.32 thousand ha and 15.24 thousand ha to213.00 thousand ha during kharif and rabi seasonrespectively. Area per farmer ranged between 3.93 ha(2011) to 4.92 ha (2001) during kharif season and 2.94ha (2004) to 4.18 ha (2001) during rabi season.

Maximum sum insured was noted during kharif2011(Rs 21137.79 lakh) followed by rabi 2011(Rs17358.63 lakh) while minimum was recorded duringrabi 2001 (Rs159.12 lakh). Similarly maximum suminsured per hectare was also recorded during kharif

Table 2. Category wise farmers are benefitedNo. of Farmers: (000)

Area: - (000 ha)Sum Insured & Premium: (000)

Year Particular Small/marginal Others Total

2006 Farmers covered 21.37 29.37 50.74Area covered 101.10 108.40 209.51Sum insured 464189.91 336797.65 800987.57Premium 16048.38 5220.72 21269.10

2011 Farmers covered 26.19 77.78 103.97Area covered 39.16 332.35 371.51Sum insured 347647.30 3501860.73 3849508.04Premium 8710.41 89572.39 98282.80

ChangeFarmers covered A.C. 4.81 48.41 53.22

R.C. % 22.51 164.83 104.88Area covered A.C. -61.94 223.94 161.99

R.C. % -61.26 206.57 77.32Sum insured A.C. -116542.61 3165063.08 3048520.47

R.C. % -25.10 939.75 380.59Premium A.C. -7337.97 84351.66 77013.69

R.C. % -45.72 1615.70 362.09

A.C. : Absolute Change , R.C.: Relative Change * Source: Agriculture Insurance Company Of India Limited

344

Tabl

e 3.

Cla

im d

isbu

rsem

ent o

f NAI

SN

o. o

f Far

mer

s : -

(000

)Ar

ea: -

(000

ha)

Sum

Insu

red

& Pr

emiu

m :

- (R

s 00

0)

Year

Farm

ers

Are

aPe

r far

mer

Sum

Per h

a su

mPr

emiu

mPe

r ha

Cla

imFa

rmer

sco

vere

dco

vere

dar

ea (

ha)

insu

red

insu

red

(`)

prem

ium

bene

fitte

d

Kha

rif20

0118

.39

90.4

64.

9217

6328

1949

5996

6610

1741

420

0219

.26

84.4

44.

3823

1057

2736

7893

9319

622

9543

2003

17.5

885

.41

4.86

2816

3632

9796

6111

394

314

6620

0426

.56

96.7

63.

6442

7242

4415

1495

315

50

020

0526

.410

1.13

3.83

5164

4651

0718

075

179

00

2006

21.3

810

1.1

4.73

4641

9045

9116

048

159

1255

623

0820

0732

.80

151.

184.

6171

6935

2185

824

036

733

1772

82.

8520

0821

.49

91.3

94.

2551

7018

2405

917

397

810

1333

0.40

2009

40.9

217

8.30

4.36

1411

780

3450

146

264

1131

8358

0.89

2010

47.1

818

6.52

3.95

1564

324

3315

652

088

1104

1316

879.

8220

1151

.44

202.

323.

9321

1377

941

092

6990

213

5981

0109

37.9

4C

hang

e20

0118

.41

86.7

74.

7222

9674

2661

7850

9171

9438

0820

1146

.51

189.

054.

0816

9662

836

250

5608

511

9831

6718

16.2

2A.

C.

24.3

849

7026

1297

013

854

12-0

.33

-1.9

1-2

.614

14R

.C.%

152.

6511

7.87

-13.

5663

912

6261

4.4

1217

4302

-99.

5R

abi

2001

20.9

287

.44.

1815

913

182

2445

2817

1711

2520

0228

.88

108.

273.

7525

9593

2398

4098

3844

776

2003

4.3

15.2

43.

5425

396

1666

509

3311

851

2004

28.6

184

.16

2.94

3081

8636

6247

6757

104

1953

2005

30.8

99.2

43.

2238

8310

3913

5968

6023

319

2220

0629

.37

108.

43.

6933

6798

3107

5220

4883

7321

1720

0741

.02

153.

693.

7580

0249

1950

912

898

314

5638

612

.320

0826

.91

90.9

13.

3842

9133

1594

771

5126

620

792.

1920

0944

.66

146.

023.

2785

7440

1919

914

036

314

1625

0.99

2010

60.6

821

3.00

3.51

1564

508

2578

326

124

430

1042

3117

.93

2011

52.5

216

9.18

3.22

1735

863

3305

128

381

540

2867

33.

7C

hang

e20

0118

.03

70.3

03.

8210

0301

1415

2351

3376

141

720

1152

.62

176.

073.

3313

8593

726

011

2284

742

844

843

7.55

A.C

.34

.59

105.

76-0

.49

1285

636

2459

520

496

395

4408

2-4

10R

.C.%

191.

7915

0.44

-12.

8212

8217

3887

211

9557

96-9

8.2

345

2011 (Rs 41092.13) followed by kharif 2009 (Rs34500.97) while minimum was noted during rabi 2001(Rs 182.07). It is observed that maximum premium ofRs 699.01 lakh was collected in kharif 2011 whileminimum was noted for rabi 2003 (5.09 lakh). Perhectare premium ranged between 66.28 to 1358.90during kharif seasons of study and Rs 27.98 to Rs540.38 during rabi seasons. As regards the claim it wasnoted maximum for kharif 2011(Rs 8101.09 lakh).Number of farmers benefited were highest in kharif 2002(9543) followed by kharif 2006 (Rs 2308) (Table 3).

Conclusion

The study leads to concluded that the NAIS coveragein terms of area covered, Premium collected, Claimsettlement etc. is small, and thus the present level ofcoverage will have to be improved for agricultural riskmanagement. Efforts by the government be requires interms of designing appropriate mechanism and alsoproviding financial support to the insurance agencies.

e/;izns'k esa vfuf'pr ekSle ds dkj.k d`f"k mRiknu rFkk iz{ks= vk;esa tksf[ke cuk jgrk gS jkf"V; d`f"k chek ;kstuk ftldk fdz;kUo;uo"kZ 1999&2000 ls Hkkjr ljdkj }kjk fd;k x;k gSaA ;g ;kstukd`"kdks dks Qly chek lqfo/kk iznku djrh gSa vkSj fdluks dks mRiknutksf[ke lss lqj{kk iznku djrh gSa A izLrqr v/;;u e/;izns'k dsjk;lsu ftys esa jkf"V; d`f"k chek ;kstuk dh izxfr dk eqY;kadu fd;kx;k gSaA rFkk ;kstuk ds laHkkfor fdz;kUo;u gsrq vko';d lq>koks dksHkh fn;k x;k gSa A

References

Abbaspour (1996) Bayesian risk methodology for insurancedecisions. World Agril. Economics and Rural SocAbst 38 (8):486

Ahsan SM (1983) Crop insurance in Bangladesh: Anassessment of the pilot programme. Internat Agric22 (3): 251-262

Bruce J (2009) Factors Affecting Farmers Utilization ofAgricultural Risk Management Tools: The Case ofCrop Insurance, Forward Contracting, and SpreadingSales: Agril and Applied Econo 41(1) 107-123

Chaubey P, Chesneau Doosti (2011) On Linear WaveletDensity Estimation : Some Recent DevelopmentsInstitute South Asian Studies 65(2):169-179

(Manuscript Receivd : 30.3.13; Accepted : 1.8.13)

346

Abstract

Crops grown in winter season such as wheat, garden peas,chick peas etc requires specific amount of energy in caloriesfor attending maturity. The longer the period for acquiringrequired energy greater will be the biomass leading to higherproductivity. The above said varies from variety. Weak winterseason with intermittent high temperature days leads to earlyflowering and maturity, results in loss in yields. There arecertain ways to take corrective steps to prevent early maturityby the said reasons. Presently there is no equipment tocalculate amount of energy absorbed day-to-day wise in crops,preventing farmers to take appropriate steps. People oftenuse a calendar to predict plant development for managementdecisions. However, calendar days can be misleading,especially for early crop growth stages. Measuring the heataccumulated over time provides a more accurate physiologicalestimate than counting calendar days. The ability to predict aspecific crop stage, relative to insect and weed cycles, permitsbetter management. This is especially important when morethan two crops are being grown on same field, each with adifferent management schedule for pesticide application,fertility management, irrigation scheduling and harvest.Growing degree days (GDD)_ sometimes called heat unitsare used to relate plant growth, development and maturity toair temperature. GDD is based on the idea that developmentof a plant will occur only when the temperature exceeds aspecific base temperature for certain number of days. Eachtype of plant is adapted to grow best over its own specificbase temperature, called Tbase. Keeping above in view theauthors are proposing to develop a suitable system whichwill predict plant stages based on GDD.

Keywords: Heat energy, growing degree days, plantgrowth, base temperature.

Plant development depends on temperature. Itsdevelopment is closely related to the daily accumulation

Growing degree days (GDD) measurement system to predict plantstages

Bharati Dass and A. K. RaiInstrument Development and Service CentreJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)

of heat. A certain amount of heat is required to provideenough energy for the plants to move to the nextdevelopment stage. The amount of heat requiredremains constant from year to year, but depending onweather conditions, the amount of actual time can vary.Each plant has a minimum base temperature orthreshold below which development does not occur.

Plants require physical and chemicalenvironment/inputs for growth. Equipments are availablefor estimating chemical parameters such as Nitrogen,phosphorus, Potassium etc from soil and plants.Similarly equipments are available for soil moistureestimation and photo radiations in visible and IR regions.However in addition to above certain crops grown inwinter season such as wheat, garden peas, chick peas,mustard etc requires specific amount of heat energy incalories for attending maturity. The amount of heatrequired remains constant from year to year, butdepending on weather conditions, the period foracquired energy may vary and is not constant in termsof days. Each organism has a minimum basetemperature or threshold below which developmentdoes not occur. The longer the period for acquiringrequired energy greater will be the biomass leading tohigher productivity. This may vary from crop to crop andvariety to variety. Weak winter season with intermittenthigh temperature days leads to early flowering andmaturity, results in loss in yields.

Growing Degree Day

The heat units accumulated over the growing seasonfor a particular crop is defined as Growing degree Day.GDD is based on the idea that development of a plant

JNKVV Res J 47(3): 346-349 (2013)

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will occur only when the temperature exceeds a specificbase temperature for certain number of days. Each typeof plant is adapted to grow best over its own specificbase temperature, called Tbase.

Daily GDD=((Tmax+Tmin)/2)-Tbase

where,

Tmax = the daily maximum air temperature.

Tmin = the daily minimum air temperature.

Tbase = the GDD base temperature for the plant beingmonitored.

If daily GDD calculates to a negative number it is madeequal to zero.

Present Status

People often use a calendar to predict plantdevelopment for management decisions. However,calendar days can be misleading, especially for earlycrop growth stages.

Use of Degree Day

Measuring the heat accumulated over time provides amore accurate physiological estimate than countingcalendar days. The ability to predict a specific cropstage, relative to insect and weed cycles, permits bettermanagement. This is especially important when morethan two crops are being grown on same field, eachwith a different management schedule for pesticideapplication, fertility management, irrigation schedulingand harvest.

Fig 1. Thresholds and degree days

Need of the Equipment

Presently there is no equipment to calculate amount ofenergy absorbed day-to-day wise in crops, preventingfarmers to take appropriate steps.

Importance

Accurate prediction of crop stages can determine thegrowth progress of crops in relation to temperature andmoisture.

Predicts and defines the time when herbicidesor insecticides can be applied for optimum activity,efficacy and control.

Permits accurate comparisons of cropdevelopment in different years at widely separatedlocations.

Predicts and determines when nutrient andirrigation scheduling can correspond to cropdeficiencies. Fertilizers can be added during earlygrowth stages to correct deficiencies and increaseyields.

Methodology proposed

Basically to calculate GDD information needed are:

• Tmax

• Tmin

• Tbase

For measuring temperature electronically followingsensors are commonly used:

• Platinum Resistance

• Thermisters

• IC based sensors

• IR sensors

Some of them are having non-linear response.In addition to above Solid State Sensors are availablesuch as analog (LM-35) and digital (LM-95234).

Sensor will be selected after testing theiraccuracy and power requirement. Electronic interfaceof sensors will be designed keeping in view the

348

ADC

MUX16channel

DisplayPanel

T1

T2T3

T8

ClockDigital code

Microcontroller+

Memory

Digital data

Start conversion

Fig 2. Hardware schematic block diagram

environmental conditions of the field such as humidity,temperature etc. The digital interface will be in terms of12 bit ADC and 8051 microcontroller or any other lowpower microcontroller. 8051 consists of four-8bit parallelports with a total of 32 I/O lines, 8bit data bus, 16 bitaddress bus, 4KB on chip program memory, 128 byteson chip data memory, 32 general purpose register eachof 8 bits, two 16 bit timers, five interrupts, one 16 bitprogram counter and one 16 bit data pointer register,one 8 bit stack pointer, 12MHz crystal, one full duplexserial communication port.

Conclusion

GDD measurement system will be very useful forpredicting the plant stages and maturity of crops duringwinter season.

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References

Pal SK, Verma VN, Singh MK, Thakur R (1996) Heat unitrequirement for phonological development of wheat(Triticum aestivum L.) under different levels ofirrigation, seeding date and fertilizer. Indian J AgricSci 66: 397-400

349

Peterson RF (1965) Wheat Crop Series, Ed Polunin N, InterScience Publication Inc. New York 422

Phadnawis NB, Saini AD (1992) Yield models in wheat basedon sowing time and phonological development. AnnalP Physiolo 6: 52-59.

Tewari SK, Singh M (1993) Yielding ability of wheat at differentdates of sowing: a temperature developmentperformance. Indian J Agric Sci 38: 204-209

Wilsie CP (1962) Crop Adoptation and Distribution. FreemanW H and Co., London pp.52-59

Miller P, Lanier W, Brandt S (2001) Using Growing DegreeDays to Predict plant stages, Montana StateUniversity, Mont Guide MT200103 AG 7/2001

B

R e a d T m a x ,T m i n , T b a s e

C a l c u la t eG D D

I sG D D– v e ?

H = H + G D D

S t o r e H

S t a r t

S e le c t ac r o p

G D D = 0

H = 0I s H

A t t a in e dM a t u r i t y

v a l u e ?

A

D i s p la y

S t o p

A

B

(Manuscript Receivd : 3.10.13; Accepted : 26.12.13)

Fig 3. Flow diagram