Volume 49 Number 2 2015

Contents

Research Paper

Crop residue management with conservation agriculture for sustaining natural resources 125-136T.N.Thorat, K.K. Agrawal, M.L.Kewat, Girish Jha and Sandip Silawat

Assisted reproductive technology: Advances and applications in veterinary sciences 137-141Dharmendra Kumar, Rakesh Ranjan, Amit Kumar, S.N.S Parmar and Bikash Chandra Sarkhel

Impact of soil testing analysis in Madhya Pradesh 142-149H.O. Sharma, P.K. Mishra and R.S. Chouhan

Evaluation of molecular polymorphism among rice bean (Vigna umbellata) genotypes 150-153Aparna Pandey, Sharad Tiwari, A.K. Mehta and Niraj Tripathi

Bioethanol production from waste potato using co-culture of Saccharomyces cerevisiae 154-159and Zymomonas mobilisYogesh Sudam Patil, L.P.S. Rajput, Yogendra Singh and Keerti Tantwai

Estimation of genetic variability for grain yield and its attributes in aromatic rice genotypes 160-164under conditions of Jabalpur, Madhya PradeshNeha Sohgaura, G.K. Koutu, D.K. Mishra, S.K. Singh and Arpita Shrivasatava

Influence of organic, chemical and integrated nutrient management on biochemical 165-169parameters of Isabgol (Plantago ovata Forsk)Nisha Singh Keer, S.K. Dwivedi, Anubha Upadhyay, Preeti Sagar Nayak and R.K.Samaiya

Effect of organic manures, micronutrients and chemical fertilizers on growth and yield of Niger 170-174B.S. Solanki, M.R. Deshmukh, V.K. Katara and Alok Jyotishi

Use of biofertilizers, organic manures and inorganic fertilizers for autumn sown niger 175-177yield maximizationR.R. Badole, M.R. Deshmukh, B.S. Solanki, V.K. Katara and Alok Jyotishi

Effect of dates of sowing on chickpea production and productivity in rainfed rice fallow 178-179land in Madhya PradeshM.G. Usmani, S.K. Singh, R.K. Tiwari and S.K. Rao

Genetic divergence analysis in urdbean genotypes of India 180-184Rajmohan Sharma

Effect of nitrogen scheduling and dosages on aerobic rice 185-188Anjir Pandey, R.K. Tiwari, S.K. Tripathi, I.M. Khan and S. Singh

Computation of thermal indices for soybean in Madhya Pradesh 189-192H.K. Rai, Arpit Suryawanshi and D.D. Dangi

ISSN : 0021-3721 JNKVVVolume : 49 Research JournalNumber(2) : 2015 (May - August, 2015)

Issued : December 4, 2015

Available on website (www.jnkvv.org)

Effect of micronutrients and biofertilizer application on growth and yield contributing 193-199characters in onionPratibha Singh, S.K. Sengupta, P.K. Jain and B.K. Verma

Effect of micronutrient complex and biofertilizer application on growth and yield in 200-204onion (Allium cepa L.)Pratibha Singh, S.K. Sengupta, B.K. Verma and P.K. Jain

Effect of growth regulators, micronutrients and bio-fertilizers on fruiting of mango cv Langra 205-207under Jabalpur conditionAkshata Tomar and S.K. Pandey

Population dynamics of sugarcane early shoot borer in Madhya Pradesh 208-213A.K. Choudhary, P.K. Amrate and A. Chatterjee

Impact of harvesting stages on seed quality of soybean during storage 214-218V.R. Shelar, A.P. Karjule and K.C. Gagare

Mechanical damage due to threshing and processing methods and its effect on seed 219-227quality of soybean seedK.C. Gagare, R.W. Bharud, V.R. Shelar, A.P. Karjule and S.N. Mate

Family environment of girl students and its effect on extent of participation in sports and 228-233games at different educational level in Rewa Division of Madhya PradeshRachna Mishra

Estimation of rainfall erosivity factor (R) of universal soil loss equation for soil erosion 234-238modelling using GIS techniques in Shakkar River watershedA.P.M.Sharma, S.K.Sharma and R.J.Patil

Textural properties of extruded product prepared by using by-products of dhal milling industry 239-248Thongam Sunita Devi, A.K. Gupta, Sheela Pandey and A.P. Mahanta Sharma

Reassessing the efficacy of recommended insecticides against rice gall midge in different 249agro-climate zones of Andhra PradeshR. Bala Muralidhar Naik, D. Seshagiri Rao, L. Krishna and Md. Lathee Pasha

Population dynamics of pest on rabi groundnut crop at Kampasagar of Nalgonda district 250-251Andhra PradeshR. Bala Muralidhar Naik, L. Krishna, D. Bhadru and Md. Lathee Pasha

Light trap catches of major pests of rice in Nagarjuna Sagar Project Area of Nalgonda 252-254district of Andhra PradeshR.Bala Muralidhar Naik, Md.Latheef Pasha, L.Krishna, D.Bhadru and P.Rajani Kanth

A new record on association of phytoplasma with phyllody disease in 255Parthenium hysterophorus from Madhya Pradesh, IndiaK. N. Gupta

Ecological traits of yellow mosaic disease in relation to epidemics in soybean 256-261K.N. Gupta and R.K. Varma

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Abstract

India being an agriculture-dominant country produces morethan 500 million tons of crop residues annually. A largeportion of unused crop residues are burnt in the fields primarilyto clear the left-over straw and stubbles after the harvest.Non availability of labour, high cost of residue removal fromthe field and increasing use of combines in harvesting thecrops are main reasons behind burning of crop residues inthe fields. Burning of crop residues adversely affects the airquality, leads to nutrient loss like N,P, K and S, degradessoil properties and cause wastage of residue that is nowconsidered tremendous resource worldwide. Trace gas andaerosols emissions due to open field burning of such largequantity of residue leads to adverse implications of the localand regional environment, which also has linkages to theglobal climate change. The impacts of such burning need tobe arrested fast through various strategic policies, scientific,technical and social measures for sustaining conservationof environment and agricultural resources of the country. Thisarticle reviews the recent research efforts developed inconservation agriculture based crop residue managementtechnologies which are more resource efficient thanconventional agriculture.

Keywords: Crop residues, open field burning,conservation agriculture, sustainability practices

Indian agriculture produces about 500-550 million tonnes(Mt) of crop residues annually. There is production of 93.9million tons (Mt) of wheat, 104.6 Mt of rice, 21.6 Mt ofmaize, 20.7 Mt of millets, 357.7 Mt of sugarcane, 8.1 Mtof fibre crops (jute, mesta, cotton), 17.2 Mt of pulses and30.0 Mt of oilseeds crops, in the year 2011-12 (MoA 2012).This huge volume of crop residues are produced both on-farm and off-farm. These crop residues are used as animalfeed, soil mulch, manure, thatching for rural homes andfuel for domestic and industrial purposes and thus are of

Crop residue management with conservation agriculture forsustaining natural resources

T.N.Thorat, K.K. Agrawal, M.L.Kewat, Girish Jha and Sandip SilawatDepartment of AgronomyJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)Email: tnt161975@gmail.com

tremendous value to farmers. However, a large portion ofthese crop residues, about 90-140 Mt annually, is burnton-farm primarily to clear the fields to facilitate planting ofsucceeding crops. The problem of on-farm burning of cropresidues has intensified in recent years due to use ofcombines for harvesting and high cost of labours inremoving the crop residues by conventional methods. Theresidues of rice, wheat, cotton, maize, millet, sugarcane,jute, rapeseed-mustard and groundnut crops are typicallyburnt on-farm across the country. This problem is severein irrigated agriculture, particularly in northwest India wherethe rice-wheat system is mechanized (Anonymous 2012).Burning of crop residues leads to plethora of problemssuch as release of soot particles and smoke causinghuman health problems; emission of greenhouse gases(carbon dioxide, methane and nitrous oxide) adding toglobal warming; loss of plant nutrients; adverse impactson soil properties as well as soil flora and fauna andwastage of valuable crop residues.

It is a paradox that burning of crop residues andscarcity of fodder co-exists in this country, when fodderprices have surged significantly in recent years. Much ofthe paradox owes it to non-availability and easy accessof the quality crop planters which can seed into looseand anchored residues. Industrial demand for cropresidues is also increasing. To manage the residues in aproductive and profitable manner, conservation agriculture(CA) offers a good opportunity. With the adoption ofconservation agriculture-based technologies theseresidues can be used for improving soil health, increasingcrop productivity, reducing pollution and enhancingsustainability and climate resilience of agriculture. Theresource conserving technologies (RCTs) involving no orminimum tillage, direct seeding, bed planting and cropdiversification with innovations in residues managementare the possible alternatives to the conventional energy

JNKVV Res J 49(2): 125-136 (2015)

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and input-intensive agriculture (NAAS 2012).There areseveral other options such as animal feed, composting,energy generation, bio-fuel production and recycling insoil to manage the residues in a productive and profitablemanner. Use of crop residues as soil organic amendmentin the system of agriculture is a viable and valuable option.

Generation of crop residues in India and their nutritionalpotential

The Ministry of New and Renewable Energy, Govt. of Indiahas estimated that about 500 Mt of crop residues are

generated every year. As shown in Table 1, the generationof crop residues is highest in Uttar Pradesh (60 Mt)followed by Punjab (51 Mt) and Maharashtra (46 Mt).Among different crops, cereals generate maximumresidues (352 Mt), followed by fibres (66 Mt), oilseeds(29 Mt), pulses (13 Mt) and sugarcane (12 Mt). The cerealcrops (rice, wheat, maize, millets) contribute 70% whilerice crop alone contributes 34% to the crop residues.Wheat ranks second with 22% of the crop residueswhereas fibre crops contribute 13% to the crop residuesgenerated from all crops. Among fibres, cotton generatesmaximum (53 Mt) with 11% of crop residues. Coconutranks second among fibre crops with generation of 12 Mt

Table 1. State wise generation and remaining surplus of crop residues in India (Mt/yr)

State Crop residues Crop residues Crop residues Crop residuesgeneration surplus burnt burnt

(MNRE 2009) (MNRE 2009) (based on IPCC (Pathak et al. 2010)coefficients)

Andhra Pradesh 43.89 6.96 6.46 2.73Arunachal Pradesh 0.40 0.07 0.06 0.04Assam 11.43 2.34 1.42 0.73Bihar 25.29 5.08 3.77 3.19Chhattisgarh 11.25 2.12 1.84 0.83Goa 0.57 0.14 0.08 0.04Gujarat 28.73 8.9 9.64 3.81Haryana 27.83 11.22 6.06 9.06Himachal Pradesh 2.85 1.03 0.20 0.41Jammu and Kashmir 1.59 0.28 0.35 0.89Jharkhand 3.61 0.89 1.11 1.10Karnataka 33.94 8.98 3.05 5.66Kerala 9.74 5.07 0.40 0.22Madhya Pradesh 33.18 10.22 3.74 1.91Maharashtra 46.45 14.67 7.82 7.41Manipur 0.90 0.11 0.14 0.07Meghalaya 0.51 0.09 0.10 0.05Mizoram 0.06 0.01 0.02 0.01Nagaland 0.49 0.09 0.11 0.08Odisha 20.07 3.68 2.61 1.34Punjab 50.75 24.83 9.84 19.62Rajasthan 29.32 8.52 3.84 1.78Sikkim 0.15 0.02 0.01 0.01Tamil Nadu 19.93 7.05 3.62 4.08Tripura 0.04 0.02 0.22 0.11Uttarakhand 2.86 0.63 0.58 0.78Uttar Pradesh 59.97 13.53 13.34 21.92West Bengal 35.93 4.29 10.82 4.96India 501.76 140.84 91.25 92.81Source: MNRE (Ministry of New and Renewable Energy Resources) Govt. of India, New Delhi. (2009) www.mnre.gov.in/biomassrsources

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of residues. Sugarcane residues comprising of tops andleaves, generate 12 Mt, i.e., 2% of the crop residues inIndia. (MNRE, 2009). Among of these major crop residues,about 35-40 per cent of N, 60-85 per cent K, 30-35 percent of P and 40-50 per cent of S absorbed by rice remainsin the vegetative parts at maturity. One tone of rice strawcontains approximately 5-6 kg N, 0.8-0.9 kg P and 15-20kg K. Considering 90 per cent of rice straw and 30 percent wheat straw are available for recycling, the amountof nutrients recycled would be about 0.54 Mt. Besides N,P, K, a tone each of rice and wheat contains about 96,777, 745, 42, 55 and 4 g/ha of Zn, Fe, Mn, Cu, B and Mo,respectively.

Utilization and on-farm burning of crop residues in India

Traditionally crop residues have numerous competinguses such as animal feed, fodder, fuel, roof thatching,packaging and composting. The residues of cereal cropsare mainly used as cattle feed. Rice straw and husk areused as domestic fuel or in boilers for parboiling rice.Farmers use crop residues either themselves or sell it tolandless households or intermediaries, who further sellthem to industries. The surplus residues i.e., totalresidues generated minus residues used for variouspurposes, are typically burnt on-farm. Estimated totalamount of crop residues surplus in India is 91-141 Mtwhich is disposed of by burning each year. Pathak et al.(2010) have estimated that about 93 Mt of crop residuesare burnt on-farm in the country (Table 1). Presently, morethan 80 percent of total rice straw produced annually isbeing burnt by the farmers in 3-4 weeks during October-November (Singh et al. 2010a).

Adverse consequences of on-farm burning of cropresidues

Burning of crop residues leads to release of soot particlesand smoke causing human and animal health problems.It also leads to emission of greenhouse gases namelycarbon dioxide, methane and nitrous oxide, causing globalwarming and loss of plant nutrients like N, P, K and S.The burning of crop residues is wastage of valuableresources which could be a source of carbon, bio-active

compounds, feed and energy for rural households andsmall industries. Heat generated from the burning of cropresidues elevates soil temperature causing death of activebeneficial microbial population. The burning of cropresidues immediately increases the exchangeable NH4

+-N and bicarbonate-extractable P content, but there is nobuild up of nutrients in the profile. Long-term burningreduces total N and C, and potentially mineralizable N inthe upper soil layer.

The burning of agricultural residues leads tosignificant emission of chemically and radioactivelyimportant trace gases such as methane (CH4), carbonmonoxide (CO), nitrous oxide (N2O), oxides of nitrogen(NOx) and sulphur (SOx) and other hydrocarbons ofatmosphere. About 70%, 7% and 0.7% of C present inrice straw is emitted as carbon dioxide, carbon monoxideand methane, respectively, while 2% of N in straw isemitted as nitrous oxide upon burning. It also emits alarge amount of particulates that are composed of a widevariety of organic and inorganic species. One ton of ricestraw on burning releases about 3 kg particulate matter,60 kg CO, 1460 kg CO2, 199 kg ash and 2 kg SO2. (Guptaand Sahai 2005). Assuming that one fourth of the availableresidue is burnt in the field, it is estimated that theemissions of CH4, CO, N2O and NOx were 110 Gg, 2305Gg, 2 Gg and 84 Gg, respectively in the year 2000 fromrice and wheat (Table 2). Besides these other lighthydrocarbons, volatile organic compounds (VOCs) andsemi-volatile organic compounds (SVOCs) includingpolycyclic aromatic hydrocarbons (PAHs),polychlorinated biphenyls (PCBs), SOx and NOx are alsoemitted. These gases are of major concern for their globalimpact and may lead to increase in the levels of aerosols,acid deposition. These may subsequently undergo trans-boundary migration depending upon the wind speed/direction, reactions with oxidants like OH, leading tophysico-chemical transformation and eventually wash outby precipitation. The emission of gases from burning ofcrop residue are the major cause of concern for respiratorysymptoms, tuberculosis, asthma, and lung functioningof animals as well as human apart from the potential riskfor lung cancer as many pollutants found in large quantitiesin biomass smoke are known suspected carcinogens.

Table 2. Annual national emissions from rice and wheat straw open burning (All in Gg)

Years Production Quantity of dry residue CH4 CO N2O NOx

1994 145720 150576 102 2138 2.2 782000 156485 162125 110 2305 2.3 84

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Reasons for on-farm burning of crop residues

Farmers and policy makers are well-aware of the adverseconsequences of on-farm burning of crop residues.However, because of increased mechanization,particularly the use of combine harvesters, decliningnumbers of livestocks, long period required for compostingand unavailability of alternative economically viablesolutions, farmers are compelled to burn the residues.

The number of combine harvesters in the country,particularly in the Indo-Gangetic Plains (IGP) hasincreased dramatically from nearly 2000 in 1986 to over10000 in 2010. The north-western part (Punjab, Haryanaand Western Uttar Pradesh) of the IGP has about 75% ofthe cropped area under combine harvesting. Combineharvesters are used extensively in the central and easternUttar Pradesh, Uttarakhand, Bihar, Rajasthan, MadhyaPradesh and in the southern states as well for harvestingrice and wheat crops. Major reasons for rapid increase inthe use of combines are labour shortage, high wagesduring harvesting season, ease of harvesting and threshingand uncertainty of weather. On using combine harvesting;about 80% of the residues are left in the field as loosestraw that finally ends up being burnt on farm.

There are some other reasons also behindintentional burning of crop residues. On farm burningtraditionally provides a fast way to clear the fields off theresidual biomass, thus, facilitating land preparation andsowing/planting. It also provides a fast way of controllingweeds, insects and diseases, both by eliminating themdirectly or by altering their natural habitat. The time gapbetween rice harvesting and wheat sowing in north-westIndia is only 15-20 days. In this short duration, farmersprefer to burn the rice straw on-farm.

Competing uses of crop residues

The crop residues can be gainfully utilized for

i) Cop residues as livestock feed:

In India, the crop residues are traditionally utilized asanimal feed such as or by supplementing with someadditives. However, crop residues, being unpalatable andlow in digestibility, cannot form a sole ration for livestock.Crop residues are low-density fibrous materials, low innitrogen, soluble carbohydrates, minerals and vitaminswith varying amounts of lignin which acts as a physicalbarrier and impedes the process of microbial breakdown.To meet the nutritional requirements of animals, the

residues need processing and enriching with urea andmolasses, and supplementing with green fodders(leguminous/non-leguminous) and legume (sunhemp,horse gram, cowpea, gram) straws. Straws contain only3 to 5 per cent crude protein. For good growth on strawdiets, a level of 8 to 10 per cent protein is needed foryoung stock, this improves consumption and increasesenergy intake.

ii) Compost making

The crop residues have been traditionally used forpreparing compost. For this, crop residues are used asanimal bedding and are then heaped in dung pits. In theanimal shed each kilogram of straw absorbs about 2-3kg of urine, which enriches it with N. The residues of ricecrop from one hectare land, on composting, give about 3tons of manure as rich in nutrients as farmyard manure(FYM). The rice straw compost can be fortified with Pusing indigenous source of low grade rock phosphate tomake it value added compost with 1.5 per cent N, 2.3 percent P2O5 and 2.5 per cent K2O (Sidhu and Beri 2005).

iii) Bio-energy production:

Biomass can be efficiently utilized as a source of energyand is of interest worldwide because of its environmentaladvantages. In recent years, there has been an increasein the usage of crop residues for energy generation andas substitute for fossil fuels. In comparison with otherrenewable energy sources such as solar and wind,biomass source is storable, inexpensive, energy-efficientand environment-friendly. However, straw is characterizedby low bulk-density and low energy yield per unit weightbasis. The logistics for transporting large volumes of strawrequired for efficient energy generation represents a majorcost factor irrespective of the bio-energy technology.Availability of residues, transportation cost andinfrastructural settings (harvest machinery, modes ofcollection, etc.) are some of the limiting factors of usingresidues for energy generation.

iv) Bio-fuel and bio-oil production:

Conversion of ligno-cellulosic biomass into alcohol is ofimmense importance as ethanol can either be blendedwith gasoline as a fuel extender and octane-enhancingagent or used as a neat fuel in internal combustion engines.Theoretical estimates of ethanol production from differentfeedstock (corn grain, rice straw, wheat straw, bagasse

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and saw dust) vary from 382 to 471 l/t of dry matter. Thetechnology of ethanol production from crop residues is,however, evolving in India. There are a few limiting stepsin the process of conversion of crop residues into alcohol,which need to be improved. High energy requiring operatingconditions, costly hydrolytic cellulase enzyme, andunavailability of natural robust commercial organism toferment pentose and hexose sugars simultaneously eitheras single species or in combination of other species aresome of the constraints, which require additional researchefforts.

Bio-oil can be produced from crop residues by theprocess of fast pyrolysis, which requires temperature ofbiomass to be raised to 400-500 0C within a few seconds,resulting in a remarkable change in the thermaldisintegration process. About 75% of dry weight ofbiomass is converted into condensable vapours. If thecondensate is cooled quickly within a couple of seconds,it yields a dark brown viscous liquid commonly calledbio-oil. The calorific value of bio-oil is 16-20 MJ/kg.

v) Biogas generation

Gasification is a thermo-chemical process in which gasis formed due to partial combustion of crop residues. Themain problem in biomass gasification for power generationis the purification of gas for removal of impurities. Thecrop residues can be used in the gasifiers for 'producergas' generation. In some states, gasifiers of more than 1MW capacity have been installed for generation of'producer gas', which is fed into the engines coupled withalternators for electricity generation. One ton of biomasscan produce 300 kWh of electricity. The gasificationtechnology can be successfully employed for utilizationof crop residues in the form of pellets and briquettes. Thegenerated 'producer gas' is cleaned using bio-filters andused in specially designed gas engines for electricitygeneration.

vi) Biochar production

Biochar is a high carbon material produced through slowpyrolysis (heating in the absence of oxygen) of biomass.It is a fine-grained charcoal and can potentially play amajor role in the long-term storage of carbon in soil, i.e.,C sequestration and GHG mitigation. However, with thecurrent level of technology, it is not economically viableand cannot be popularized among the farmers. However,once all the valuable products and co-products such asheat energy, gas like H2 and bio-oil are captured and usedin the biochar generation process, it would becomeeconomically-viable. There is a need to develop low costpyrolysis kiln for the generation of biochar to utilize surpluscrop residues, which are otherwise burnt on-farm.

Management options of crop residues with conservationagriculture

Conservation agriculture, with the following three coreinter-linked principles, is a viable option for sustainableagriculture and is an effective solution to check landdegradation (Kassam 2011).

1. Minimizing mechanical soil disturbance andseeding directly into untilled soil to improve soil organicmatter content and soil health.

• Resource conservation technologies for cropresidue management

The RCTs (laser assisted precision land levelling,zero/reduced tillage, direct drilling of seeds, direct seedingof rice, unpuddled mechanical transplantation of rice,raised bed planting and crop diversification) withinnovations in residue management avoid straw burning,improve soil organic C, enhance input efficiency and havethe potential to reduce GHGs emissions (Pathak et al.2011).

Table 3. Performance of zero-till wheat sown into rice residue using happy seeder viv-a-vis conventional till wheat onfarmers fields in Punjab during 2007-2010

Year No. of experiments Grain yield (t/ha) Increase in yield withHappy seeder (HS) Conventional till (CT) HS over CT (%)

2007-08 46 4.59 4.50 2.02008-09 14 4.54 4.34 4.62009-10 94 4.42 4.30 2.8Mean 154 4.56 4.42 3.24

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Permanent crop cover with recycling of cropresidues is a pre-requisite and integral part of conservationagriculture. However, sowing of a crop in the presence ofresidues of preceding crop is a problem. But new variantsof zero-till seed-cum-fertilizer drill/planters such as HappySeeder, Turbo Seeder and rotary-disc drill have beendeveloped for direct drilling of seeds even in the presenceof surface residues (loose and anchored up to 10 t/ha).These machines are very useful for managing crop residuesfor conserving moisture and nutrients as well as controllingweeds in addition to moderating soil temperature. Datain Table 3 from 154 on-farm trial conducted during 2007-10 in different districts of Punjab showed that weightedaverage wheat yield for Happy Seeder sown plots wassignificantly more (3.24%) than the conventionally sownwheat Sidhu et al. (2011). Due to use of Happy Seedertechnology additional advantages like less weed growth,water saving and improvement in soil health andenvironmental qualities were also noted.

Jat et al. (2006) evaluated the second generationdrills (roto-disc drill, happy seeder, double disc drill andpunch planter) and found that wheat yield was comparableunder all the drills. Though the yield attributes and yieldunder all the drills was almost same but the highest yield(4.22 & 7.08 grain and straw respectively) was recorded

with roto-disc drill followed by double disc and lowestwith punch planter (Table 4).

2. Enhancing organic matter cover on soil using covercrops and/or crop residues. This protects the soil surface,conserves water and nutrients, promotes soil biologicalactivity and contributes to integrated pest management.

i) Crop residues as surface mulch:

Leaving crop residues on the soil surface seems to bebetter option as it conserves soil and water and reducesevaporation losses. Surface retained residues alsoreduce the germination of weeds leading to lower weedinfestation. Moreover, slow decomposition also helpsin building up of soil organic carbons-a direct indicatorof soil health. Sidhu and Beri (2005) conducted twoyear study on sandy loam soil at PAU Ludhiana andfound that rice straw mulching increased mean grainyield of wheat by 43 per cent compared to no mulchunder double zero till system. On the other hand, theincrease in wheat yield due to rice straw mulch in CT-puddled transplanted/ CT-direct seeded rice comparedto no mulch was 2.4 -5.4 per cent (Table 5).

Table 5. Effect of tillage and rice straw mulch on wheat yield (t/ha) in rice-wheat system

Rice treatments Wheat treatmentsConventional till Zero till ZT + rice straw

(CT) (ZT)-rice straw mulch (HS)removed

CT- Direct seeded rice (DSR) 5.05 4.03 5.17ZT + DSR 5.25 3.56 5.09CT- Puddled transplanted rice (PTR) 4.98 4.48 5.25Mean 5.09 a 4.02 b 5.17 a

Mean values followed by same letter do not differ significantly at P < 0.05

Table 4. Yield performance of wheat drilled with different new generation drills under full rice residues

Drills Plant height Effective Spike length Spiklets/ Yieldtillers spike (t/ha)

(cm) (m-2) (cm) Grain Straw

Happy seeder 84.6 388 9.81 17.5 4.22 7.08Roto-disc drill 86.8 395 9.53 17.2 4.21 7.05Double disc drill 86.2 375 9.46 16.9 4.03 7.00Mean 85.9 386 9.60 17.2 4.16 7.04CD at 5% NS NS NS NS NS NS

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Table 6. Effect of Sasbania green manure, nitrogen levels and rice residue on yield and yield attributes of rice-wheatcropping system

Treatment Sesbania aculeata Rice WheatDry N Effective 1000-grain Grain Effective 1000-grain Grain

biomass contribution tillers/m2 weight yield tillers/m2 weight yield(t/ha) (kg/ha) (g) (t/ha) (g) (t/ha)

Nitrogen and Green manureN0 - - 211.9 20.7 2.28 274.5 42.57 4.01N60 - - 241.9 21.0 3.51 299.4 43.27 4.06N120 - - 260.8 21.1 4.40 317.2 43.76 4.14G40N0 1.98 46.83 243.9 20.9 4.04 294.0 43.62 4.39G40N60 1.89 45.07 264.2 21.1 5.17 320.8 44.06 4.46G40N120 1.96 47.52 272.8 21.1 5.59 326.8 44.21 4.52G50N0 4.09 97.26 255.2 21.0 5.05 306.7 43.71 4.56G50N60 3.99 94.51 272.8 21.1 5.72 328.8 43.95 4.65G50N120 4.08 98.34 248.3 21.2 5.98 338.4 44.16 4.68CD at 5% - - 19.9 0.8 0.49 49.6 1.98 0.43Residue incorporation (%)0 290.2 43.49 4.3550 319.2 43.68 4.43100 326.1 43.93 4.38CD at 5% 32.2 NS NS

Table 7. System productivity and economics of pearlmillet-based systems as influenced by residue management

Treatment 2010-11 2011-12Productivity Net returns Net returns/ Productivity Net returns Net returns/

(t/ha) (x 103 Rs/ha) Rs invested (t/ha) (x 103 Rs/ha) Rs invested

Pearlmillet-wheatNo residue 2.03 0.67 0.03 2.53 -0.03 0.00Crop residue 3.03 6.35 0.23 4.80 20.09 0.56Leucaena twigs 3.52 11.92 0.48 4.13 15.56 0.47Pearlmillet-chickpeaNo residue 3.61 15.81 0.75 2.66 9.59 0.33Crop residue 5.08 24.52 0.92 5.69 32.60 0.92Leucaena twigs 6.32 37.16 1.54 5.75 17.42 0.53Pearlmillet-mustardNo residue 4.26 21.66 1.05 3.52 8.86 0.32Crop residue 5.54 29.98 1.15 5.68 23.79 0.69Leucaena twigs 6.95 44.29 1.87 4.45 13.88 0.44SE m + 0.10 0.14CD at 5% 0.29 0.41

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ii) In-situ incorporation of crop residues

It is another option to incorporate residues into fields toimprove soil organic matter levels and return to the soilswith the nutrients contained in straw. Yadvinder Singh etal. (2005) and Bijay Singh et al. (2008) have concludedthat application of rice residues to wheat typically hassmall effect on wheat yields in short term (1 to 3 years).From a four year study, Gupta et al. (2007) reported thatincorporation of rice straw showed no effect on wheatyield but wheat yield increased significantly in the 4th yearcompared with removal or burning of residues. Singh etal. (2006) studied the effect of in-situ green manuring ofSesbania and crop residue incorporation on yield of rice-wheat cropping system and found that incorporation of50 and 100% rice residue enhanced grain yield of wheatby 1.83 and 0.07% as compared to no residue (Table 6).

3. Diversification of crops in associations, sequencesand rotations to enhance system resilience

Amgain et al. (2013) assess the effect of residuemanagement practices on productivity and profitability ofrainy-season pearlmillet followed by winter-season cropsviz. wheat, chickpea and mustard and concluded that

pearlmillet should be followed by chickpea/mustard alongwith residue retention of crops/Leucaena twigs for higherproductivity and profitability under zero-till drylandconditions of North-Western India (Table 7).

Impact of crop residue management on soil health

Crop residues are an important constituent in nutrientcycling and also play an important role in maintainingsoil physical, chemical and biological condition.

a. Chemical soil health

The most important factor in determining soil health issoil organic matter. Long term incorporation of cropresidues build soil organic matter level and also increasethe availability of macro and micro nutrients. Gupta et al.(2007) found that the application of crop residue for threeyears increased availability of P and K in soil over strawburned. Besides a direct supply of P, crop residues canlower the P sorption capacity and enhance nutrientavailability. Both inorganic and organic P contents in soilincreased with straw incorporation. Naresh (2013) reportedthat before wheat planting, burning of residues results

Table 9. The effect of residue management on soil microbial biomass C and N in zero till wheat

Condition Residue management SMB C (mg C/kg soil) SMB N (mg N/kg soil)

Rainfed Removal 288 b 22 aFull retention 453 a 20 a

Irrigated Burning 540 b 22 cRemoval 617 ab 25 bPartial retention 681 a 25 bFull retention 687 a 31 a

Table 8. Effect of crop residues management in rice wheat rotation (3 years) on physicochemical properties of soil

Parameter Initial status Retained Incorporation Removed Burnt(Depth 10-20 cm)

pH 7.83 7.95 7.35 7.40 7.65Water stable aggregates > 250 µm 51.9 57.4 56.9 46.3 38.2Organic carbon (%) 0.46 0.53 0.58 0.43 0.47Available N (kg/ha) 64.6 89.0 83.0 32.0 21.0Available P (kg/ha) 25.8 39.0 42.0 21.0 29.0Available K (kg/ha) 52.1 67.0 69.0 48.0 55.0

133

Tabl

e 10

. Effe

ct o

f cro

ppin

g sy

stem

s, re

sidu

e m

anag

emen

t and

tilla

ge p

ract

ices

on

phys

ical

pro

perti

es o

f soi

l (0-

15 c

m)

Trea

tmen

tO

rgan

ic c

arbo

nO

rgan

ic m

atte

rW

ater

sta

ble

Poro

sity

Bulk

den

sity

Wat

er h

oldi

ngag

greg

ates

capa

city

(%)

(%)

(%)

(%)

(mg/

m3 )

(%)

2004

-05

2005

-06

2004

-05

2005

-06

2004

-05

2005

-06

2004

-05

2005

-06

2004

-05

2005

-06

2004

-05

2005

-06

Cro

ppin

g sy

stem

Coc

onut

+ b

anan

a1.

231.

252.

122.

1538

.72

42.3

039

.21

41.3

11.

401.

3439

.34

40.4

3

Coc

onut

+ M

aize

1.18

1.21

2.03

2.08

38.4

541

.00

39.0

740

.77

1.40

1.35

38.2

739

.01

Coc

onut

+ P

inea

pple

1.25

1.30

2.15

2.24

39.5

243

.15

39.3

741

.21

1.38

1.31

39.7

841

.18

SEm

±0.

022

0.02

10.

031

0.03

10.

300.

252

0.27

10.

293

0.01

00.

011

0.39

10.

343

CD

at 5

%0.

051

0.04

20.

082

0.08

20.

881

0.73

50.

735

NS

NS

0.02

21.

144

1.00

8

Res

idue

man

agem

ent

Surfa

ce m

ulch

ing

1.31

1.37

2.25

2.37

39.6

442

.69

39.1

941

.22

1.39

1.33

39.6

640

.94

Res

idue

inco

rpor

atio

n1.

131.

131.

951.

9438

.16

41.6

039

.24

40.9

81.

401.

3538

.60

39.4

7

SEm

±0.

011

0.01

10.

022

0.02

10.

250

0.20

10.

223

0.23

20.

010

0.00

10.

322

0.28

1

CD

at 5

%0.

042

0.04

20.

073

0.06

50.

720

0.59

2N

SN

SN

S0.

010

0.93

40.

822

Tilla

ge

Con

vent

iona

l tilla

ge1.

221.

222.

002.

0338

.24

41.6

238

.69

40.5

81.

401.

3539

.11

40.0

9

Red

uced

tilla

ge1.

221.

292.

112.

2339

.56

42.6

839

.74

41.6

21.

381.

3239

.15

40.3

7

SEm

±0.

010

0.01

10.

021

0.02

00.

250

0.20

10.

223

0.23

20.

010

0.00

10.

322

0.28

0

CD

at 5

%N

S0.

042

0.05

30.

061

0.72

00.

592

0.65

10.

684

NS

0.01

1N

SN

S

Initi

al v

alue

s fo

r SO

C c

onte

nt-1

.08%

, SO

M-1

.86%

, WSA

-35.

24%

, Por

osity

-36.

38%

. Bul

k de

nsity

- 1.5

0 M

g/m

3 , W

ater

hol

ding

cap

acity

-36.

5%

134

huge loss of N (up to 75%), P (25%) and K (21%) and byincorporation of residues of both crops in rice-wheatrotation increased the available N, P and K contents insoil over removal and burn of residues. Surface retentionof residues increases soil N, P and K uptake by 14.6,28.5 and 17.7 per cent (Table 8).

b. Biological soil health

Crop residues provide energy for growth and activities ofmicrobes and substrate for microbial biomass, and provideconditions for source-sink of nutrients. Availability ofnutrients like N, P, and S is particularly dependent uponsoil microbial biomass (SMB) and microbial activity, whichin turn depend on the supply of organic substrates insoil. Verhulst et al. (2009) reported that soil microbialbiomass (C and N) decreased with decreasing amount ofresidue retained on the soil surface in the zero tilltreatments of both rainfed and irrigated long term trial.The SMB reflects the soil's ability to store and cyclenutrients (C, N, P and S) and organic matter and playsan important role in physical stabilization of aggregates(Table 9)

c. Physical soil health

Crop residues are important source of soil organic matterand upon incorporation may lead to improve soil physicalparameters. Singh et al. (2010b) found that incorporationof crop residues decreased BD and increased infiltrationrate, WHC, microbial population, soil fertility as comparedto no residue treatment. The residue incorporation withNPK fertilizer resulted in the highest yield, nutrient uptake,improved residual soil fertility and soil microorganism'sstatus. Sudha and George (2011) studied the effect ofcropping systems, residue management and tillagepractices on organic carbon sequestration in soils andfound improvement in soil properties like aggregatestability, porosity, bulk density, water holding capacitywhich reflects better yield and returns in the two years ofstudy (Table 10).

Constraints of using crop residues with CA

• Difficulties in sowing and application of fertilizer,pesticides and problem of pest infestation

• Requires more attention on timings and placementof nutrients, pesticides and irrigation

• Weed control is the major bottleneck in the rice-

wheat system

• Excessive use of chemical herbicides createsunhealthy environment

• Nutrient management becomes complex becauseof higher residues levels

• Loss in basal application of N fertilizers at the timeof seeding and hence less efficiency and environmentalpollution

• Specialized equipments are required for fertilizerplacement which contributes high costs

• For adoption of conservation agriculture higheramount of herbicides are used which creates problemsof pollution and environment hazards

• Requires management skills

• Apprehensive of lower crop yields and /or economicreturns

• Negative attitudes or perceptions, and institutionalconstraints

• Farmers have strong preferences for clean andgood looking fields as against untilled shabby lookingfields

Research needs for efficient CRM with CA

• Generation and utilization of crop residues

• Basic and strategic research

• Optimizing competing uses of crop residues

• Water and nutrient management with conservationagriculture

• Pest management in conservation agriculture

• Machinery for conservation agriculture

Conclusion

• India has the challenging task of ensuring foodsecurity for the 'most' populous country by 2050 with oneof the largest malnourished population. Besides, farmingin future has to be multi-functional and ecologicallysustainable so that it can deliver ecosystem goods andservices as well as livelihoods to producers and society.

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Hence farming should effectively address local, nationaland international challenges of food, water and energyinsecurity; issues related to climate change; anddegradation of natural resources.

• For ensuring the country's food security both inshort and long term perspectives and making agriculturesustainable, the soil resource base must be strong andhealthy.

• Conservation agriculture, with crop residues as anintegral component, is an effective solution to the aforesaidchallenges and ensures a strong natural resource base.

• Crop residues are of great economic values aslivestock feed, fuel and industrial raw material, and inconservation agriculture for which it is a pre-requisite.

• Crop residues, either partly or entirely must be usedfor conservation agriculture for ensuring the country's foodsecurity, making agriculture sustainable and the soilresource base healthy.

References

Amgain LP, Shrma AR, Das TK, Behera UK (2013) Effect ofresidue management on productivity andeconomics of pearlmillet based cropping systemunder zero till condition. Indian J Agron 58 (3):298-302

Anonymous (2012) Crop residue management withconservation agriculture: Potential, constraints andpolicy needs. IARI, New Delhi pp 1-30

Gupta PK, Sahai S (2005) Residue open burning in rice-wheat cropping system in India: An agenda forconservation of environment and agriculturalresources. In: Conservation Agriculture- Status andProspects (Eds Abrol IP, Gupta RK and Malik RK)CASA, New Delhi pp 50-54

Gupta RK, Singh Y, Ladha JK, Singh B, Singh J, Singh G,Pathak H (2007) Yield and phosphorustransformations in a rice-wheat system with cropresidue and phosphorus management. J AmericanSoc Soil Sci 71:1500-07

Jat ML, Gupta RK, Sharma SK, Gill MS (2006) Drilling wheat(Triticum aestivum) in loose residues: Experienceswith participatory research on rice-wheat croppingsystem in western Indo-Gangetic plains. In: NationalSymposium on Conservation Agriculture andEnvironment, October 26-28, 2006, BHU, Varanasipp 59-60

Kassam A (2011) The Future of Farming: What Needs ToChange? The Sixth Hugh Bunting Memorial Lecture,

University of Reading, UKMNRE (Ministry of New and Renewable Energy Resources)

(2009) Govt. of India, New Delhi. www.mnre.gov.in/biomassrsources

MoA (Ministry of Agriculture) (2012) Govt. of India, New Delhi.www.eands.dacnet.nic.in

Naresh RK (2013). Rice residues: From waste to wealththrough environment friendly and innovativemanagement solutions, its effects on soil propertiesand crop productivity. Int J LifeSc Bt and Pharm Res.2 (1):133-141

National Academy of Agricultural Sciences (NAAS) (2012)Management of crop residues in the context ofconservation agriculture. Policy paper 58

Pathak H, Bhatia A, Jain N, Aggarwal PK (2010) Greenhousegas emission and mitigation in Indian agriculture -A review, In ING Bulletins on Regional Assessmentof Reactive Nitrogen, Bulletin No. 19 (Ed. Bijay-Singh), SCON-ING, New Delhi p 34

Pathak H, Saharawat YS, Gathala M, Ladha JK (2011) Impactof resource-conserving technologies in the rice-wheat system. Greenhouse Gas Sci Tech 1: 261-277

Sidhu BS, Beri V (2005) Experience with managing riceresidue in intensive rice-wheat cropping system inPunjab. In: Conservation Agriculture- Status andProspects (Eds Abrol IP, Gupta RK and Malik RK)CASA, New Delhi. pp 55-63

Sidhu HS, Singh M, Singh Y, Blackwell J, Singh V, Gupta N(2011) Machinery development for crop residuemanagement under direct drilling. In: Resilient FoodSystems for a Changing World: Proceedings of the5th World Congress on Conservation Agriculture.Incorporating 3 rd farming Systems DesignConference, 25-29th September 2011, Brisbane,Australia. pp 157-158

Singh B, Shan YH, Johnson-beebout SE, Singh Y, BureshRJ (2008) Crop residue management for lowlandrice-based cropping systems in Asia. Advances inAgron 98:118-99

Singh R, Jat ML, Biswas C (2006) Effect of in-situ greenmanuring of Sesbania and crop residueincorporation on yield of rice (Oryza sativa)-wheat(Triticum aestivum) cropping system. In: NationalSymposium on Conservation Agriculture andEnvironment, October 26-28, 2006, BHU, Varanasipp 238-239

Singh Y, Singh B, Timsina J (2005) Crop residuemanagement for nutrient cycling and improving oilproductivity in rice based cropping system in tropics.Advances in Agron 85:269-407

Singh Y, Singh M, Sidhu HS, Khanna PK, Kapoor S, Jain AK,Singh AK, Sidhu GK, Singh A, Chaudhary, DP,

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Minhas PS (2010a) Options for effective utilizationof crop residues. Directorate of Research, PunjabAgricultural University, Ludhiana, India pp 32

Singh SK, Kumar D, Lal SS (2010b) Integrated use of cropresidues and fertilizers for sustainability of potato(Solanum tuberosum) based cropping systems inBihar. Indian J Agronomy 55 (3): 203-208

Sudha B and George Annamma (2011) Tillage and residuemanagement for organic carbon sequestration incoconut (Cocus nucifera)- based cropping system.Indian J Agron 56(3):223-227

Verhulst N, Govaerts B, Verachtert E, Kienle F, Limon-OrtegaA, Deckers J, Raes D, Sayre KD (2009) In:Innovations for Improving Efficiency, Equity andEnvironment. Proceedings of the 4 th WorldCongress on Conservation Agriculture, February,4-7, 2009 New Delhi India pp 71-79

(Manuscript Receivd : 17-01-2015; Accepted : 30-06-2015)

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Abstract

In livestock, genetic improvement and quick disseminationare prime concern over the years for researchers. To achieveit, assisted reproductive technology (ART) is considered asa major tool which includes artificial insemination (AI),multiple ovulations, embryo transfer, sex determination ofsperm or embryos, in vitro fertilization (IVF), intra cytoplasmicsperm injection (ICSI), somatic cell nuclear transfer (SCNT)etc. These techniques have been introduced to overcomereproductive problems, to increase the offspring fromselected females and to reduce the generation intervals infarm animals. AI is the most effective method being used forthe genetic improvement of animals. Reproductive capacityand efficiency has been improved tremendously since theintroduction of artificial insemination. Super ovulation andembryo transfer technology increased the opportunity toobtain multiple progeny in less generation interval. Ovumpick up associated with IVF/ICSI could be applied fortreatment of sterility or to disseminate the superiorgermplasm in short time interval. Polymerase chain reactiontechnology and flow cytometer are currently being used forsexing embryos on a small scale, and it is likely that thistechnology will be used for embryo diagnostics' in the future.The cloning through nuclear transfer and the production ofcloned, transgenic animal has been technically achieved.Recently, many livestock species have been cloned usinghandmade cloning technique. Advances in animalreproductive technology promise new possibilities includingtherapeutic cloning, stem cell biology, cell and gene therapyand transgenesis.

Keywords: Reproduction, Genetics, Artif icialinsemination, In vitro fertilization, Cloning

Assisted reproductive technology (ART) is considered asa major tool for accelerating the genetic improvements in

JNKVV Res J 49(2): 137-141 (2015)

livestock with the applications of advance techniques overtraditional reproduction. Major techniques under ARTincludes artificial insemination (AI), cryopreservation ofgametes or embryos, multiple ovulations, embryo transfer,sex determination of sperm or embryos, in vitro fertilization(IVF), intra cytoplasmic sperm injection (ICSI), somaticcell nuclear transfer (SCNT) etc. These reproductivetechnologies were developed to offer possibilities for wideruse of superior germplasm in short time intervals. To takefull advantage of the benefits of ART, one must understandthe basic physiology of the male and female reproductivesystems as well as various methods to synchronizereproductive cycles. As novel findings emerge, newperspectives and applications are proposed, tested, refinedand, finally applied to fields. In the present scenario, theuse of biotechnology in reproduction enables to manipulatethe animal's genetics using genetic engineeringtechniques in animal husbandry with consideration of herdhealth, nutrition, growth, and reproduction. In this view,genetic improvement is seldom introduced without otheraspects such as animal management, disease control,nutrition, and reproduction. Furthermore, the recentadvances as genomics, transcriptomes, proteomics,metabolomics and bioinformatics in the study of ART willallow a greater understanding of the limitations to efficientreproductive processes.

Different Techniques under ART

Artificial insemination

Artificial insemination has been in use worldwide for morethan half century. As a modern technology, AI with freshor frozen semen has been considered the most successful

Assisted reproductive technology: Advances and applications inveterinary sciences

Dharmendra Kumar*, Rakesh Ranjan*, Amit Kumar**, S.N.S Parmar** and Bikash Chandra Sarkhel**Animal Biotechnology CentreNDVSU, Jabalpur 482 004 (MP)**Department of Animal Breeding and GeneticsNDVSU, Jabalpur 482 004 (MP)Email : dr.dharmendra.pat@gmail.com

138

and efficient reproductive technology in animal productionand reproduction (Gordon 1994). The opportunity forgenetic improvement through progeny testing andbreeding programmes would be extremely limited withoutAI especially in remote places. Using advance techniquesviz, multiple ovulation, estrous synchronization andembryo transfer technology (ETT), AI increased theopportunity to obtain multiple progeny in short generationinterval. For estrous synchronization systems,prostaglandin F2 and its analogues are used to assistproducers to incorporate AI into their operations byreducing time and labor associated with estrus detection,even in anestrous animals. Recently, with betterunderstanding of endocrine profiles of females throughoutthe estrous cycle, economical and efficient systems havebeen developed for the synchronization of ovulation, whichallows producers to AI animals at a predetermined fixed-time, eliminating oestrus detection.

Oestrous synchronization

Oestrous synchronization is a major step for AI andembryo transfer protocol which includes the manipulationof the oestrus cycle results in standing oestrus in majorityof animals, within a short period of time. It is a very effectivemethod to increase the proportion of animals that arebred at the beginning of the breeding season.Physiologically, it is based on synchronization of thefollicular waves and/or luteal regression. Thesynchronization programme is based on hormonalregimen including PGF2 , GnRH and controlledIntravaginal Drug (Progesterone) Releasing device (CIDR).

Embryo transfer

Development of embryo transfer technology allowsproducers to obtain multiple progeny from geneticallysuperior females. Depending on the species, fertilizedembryos can be recovered from females (embryo donors)of superior genetic merit by surgical or nonsurgicaltechniques and subsequently transferred to recipients oflesser genetic merit. In large animal's viz., cattle andhorses, fertilized embryos could recover without surgerywhereas, in case of small animals viz., goat and sheep,embryos must be recovered by surgical techniques.Normally only one or two embryos are produced duringeach normal reproductive cycle in cattle or buffaloes. Toincrease the number of embryos, the embryo donor istreated with a hormone regimen to induce multipleovulations and subsequently inseminate artificially ornaturally.

In vitro fertilization

In vitro fertilization (IVF) is a technique of fertilization ofoocyte by sperm under in vitro condition. Under IVF,oocytes are recovered either by ovum pick up (vanWagtendonk-de Leeuw et al. 2006) or simply by ovariesof slaughtered females. The use of slaughterhouse ovariesin IVF techniques for livestock reproduction overcomesthe limitation of oocyte number. The process of in vitromaturation of oocytes involves the use of optimizedhormonal combination (Oestradiol, leutinizing and folliclestimulating hormone) and growth factors. Likewise certainchemicals viz. heparin, caffine etc. are used for capacitaionof sperm of desired sires. The in vitro matured oocytesare inseminated and subsequently co-incubated under

Fig 1. Process of in vitro fertilization showing (a) co-incubation of oocyte and sperm, and (b) blastocyst stage ofembryos

139

specific fertilization media in humidified CO2 incubator(Fig 1a&b). Different types of fertilization media arereported by different workers viz., Bracket and Oliphant(BO) (Cognie et al. 1995), synthetic oviductal fluid (mSOF)(Rho et al. 2001), Tyrode's medium (Wang et al. 2002).

Intracytoplasmic sperm injection (ICSI)

Intracytoplasmic sperm injection is the newest and themost successful micromanipulation technique for treatingmale factor infertility. It entails the mechanical insertionof a chosen spermatozoon directly in to the cytoplasm ofan oocyte (Fig 2). ICSI involves direct injection of a singlesperm or sperm head (nucleus) into the ooplasm, bypassing natural process of sperm oocyte interaction.Hence, fertilization process taking place via ICSI is differentfrom in vivo or in vitro fertilization. It is a microfertilizationtechnique that is used to curve the male infertilityproblems in animals and also in cases where eggs arenot easily perpetrated by sperm. ICSI is considered as alast resort when all other conventional methods ofinsemination fail.

This technique is considered to be efficient thanIVF and AI in terms that in ICSI only one intactspermatozoon is sufficient to fertilize an ovum while AIand IVF require millions of spermatozoa. In the veterinaryfield, micromanipulation in domestic animal species e.g.bovine (Gotto et al. 1990; Suttner et al. 2000), equine(Dell'Aquila et al. 1997), ovine (Catt and Rhodes 1995),swine (Probst and Rath 2003) etc., has been used for thepast two decades as an experimental means and in thecommercial field.

Sperm sexing

By adopting the molecular techniques like Polymerasechain reaction (PCR) and flow cytometer/cell sorter, onecan select the sex of the offspring as per need of industry.For example, the beef industry prefers male calves, forthe growing and finishing stages of meat production.Whereas, the dairy industry prefers heifer calves for milkand offsprings. Thus, methods are needed to determinethe sex of sperm or embryos so producers can controlthe sex of the offspring of their livestock. Using a specificdye that binds to DNA (Hoechst 33342 stain) and a flowcytometer/cell sorter, the DNA content of individual spermis measured. The ability to sex semen has a largepotential for commercialization; thus, much of the researchto develop and refine sperm sexing technology has beenconducted in the private sector. XY, Inc., a company inFt. Collins, CO, has been the leader in developing spermsexing technology in cattle, horses, and pigs.Researchers at the USDA Agricultural Research Servicehave also played a major role in developing sperm sexingtechnology for poultry and swine. Apart from sperm sexing,now a day basic research has been emphasized to embryosexing by polymerase chain reaction (PCR). In thistechnique, a blastomere is aspirated or isolated from theearly stage of embryo, DNA isolated, and with specificset of X/Y primers PCR is carried out to determine thesex of the embryo.

Somatic cell nuclear transfer or Cloning

Since the mid 1980s, technology has been developed totransfer the nucleus from either a blastomere or a somaticcell to an enucleated oocyte). The first livestock animal

Fig 2. Process of ICSI showing microinjection of sperminto oocyte

Fig 3. Process of cloning showing transfer of cell inperivitelline space of enucleated oocyte

140

(sheep) was cloned in 1986 using cells from early embryos(Willadsen 1986). Then, the birth of a sheep "Dolly" bytransfer of a somatic-cell nucleus (SCNT) of an adult(Wilmut et al. 1997) represented the fall of an importantbiological dogma, i.e., that differentiated somatic cellscould not be reprogrammed to a toti- or pluripotent statethat would allow development of a new individual. This"nuclear transfer" produces multiple copies of animalsthat are themselves nearly identical copies of donor. Thecloning technique involves culturing somatic cells froman appropriate tissue of the animal to be cloned. Nucleifrom the cultured cells are transferred into an enucleatedoocyte obtained from another animal (Fig 3). Thereconstructed oocytes are cultured and transferred to arecipient female. However, the efficiency of the cloningtechnology remains low due to increased rates ofpregnancy losses, placental and fetal alterations,dystocia and birth of large offspring with lower postnatalsurvival. In a study, a relatively low number of clonedembryos survive to term (1 to 5%), with approximately athird of cloned calves not surviving to weaning, and morethan 8% dying before reaching 4 years of age (Well et al.2004). Till date, SCNT has been successfully used inmany animal species to produce cloned offspring. Thistechnique may be accomplished for advance reproductivepurposes, i.e., to produce a genetically identical copy ofthe individual that supplied the donor cell, or for therapeuticpurposes, i.e., to produce cells or tissue fortransplantation back to the individual that supplied thedonor cell. Somatic-cell cloning is a rapidly developingarea and a valuable technique to copy superior genotypesand to produce or copy transgenic animals.

Hand-made cloning technique: Although somaticcell nuclear transfer is being performed in manylaboratories all over the world, successful application ofthe embryo reconstruction technology is still a difficultand demanding task. The conventional technique of cloningincludes complicated and time consuming processes,requiring expensive equipments like micromanipulatorsand highly qualified and skilled personnel. Thecomplications and less efficiency of micromanipulatorbased SCNT technique pave the way of "Handmadecloning" (HMC). The first published report to excludemicromanipulators from the NT was the bovine zona-freeembryonic cell nuclear transfer (Peura et al. 2001). Forsomatic cell nuclear transfer, Vajta et al. (2003) firstreported the use of HMC for the production of zona freecloned embryos in cattle. In HMC, oocytes were bisectedinto two halves with the hand guided blades under stereomicroscope. The enucleated half oocyte (demicytoplast)was attached with somatic cell to form couplet. Each

couplet was electrofused with another enucleateddemicytplast to form full reconstructed oocyte, chemicallyactivated and subsequently cultured in vitro. Thistechnology of SCNT allows more efficient production ofembryos for transfer and to study the basic scientificaspects of the critical limiting steps in nuclear transfer.

Future prospects of ART

ART boost up the embryonic stem cell technology throughin vitro embryo production. Cloning and IVF are thetechniques that are continuously providing the embryosfor stem cell culture. Further transgenesis, one of themost fascinating areas of modern research involvesdifferent techniques of ART viz., cloning (Schnieke et al.1997; Cibelli et al. 1998), DNA microinjection (Nottle etal. 2001) to generate transgenic embryos or live animals.

The controlled reproduction in animals hascontributed tremendously to the satisfaction of theincreasing demands of the modern society (Kues andNiemann 2004). Farmers are now adopting more advancedtechniques to enhance reproductive efficiency of animals,which may further increase potential economic efficiencyto livestock sectors. Different ART techniques viz.,artificial insemination, estrous synchronization, superovulation, embryo transfer, in vitro fertilization, sexedsemen, and cloning have influenced the livestock industry.The integrated use of molecular techniques, moderninstruments and other allied livestock disciplines, thepotential of farm animals for improving human health isgrowing. Advances in animal reproductive technologypromise new possibilities, but many ethical challengeshave emerged with the development of the fourthgeneration technologies, including therapeutic andreproductive cloning, stem cell biology, cell and genetherapy and transgenesis. The overall geneticadvancement in animals can only be attained when goodpractices in livestock management are improved. To besuccessful, the application of biotechnologies mustinclude good practices in animal husbandry, animal healthand nutrition, and reproduction.

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(Manuscript Receivd :15-03-2015; Accepted : 30-08-2015)

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Abstract

Looking to the importance of soil testing analysis in famers'field this particular study has been conducted to evaluate theadequacy, usefulness effectiveness and contribution inreturns in farmer's field. The primary data were collectedfrom the 100 respondents through pre tested interviewschedule by personal investigation from two locations i.e.Sagor and Dhar districts of Madhya Pradesh for the year2010-11. It is observed from the study that out of 100respondents who have submitted soil samples to soil testinglabs for its analysis only 71 received soil testing report. Outof these 71 only 49 respondents adopted therecommendations of soil heath card and applied nutrientsin crops for improvement in yield levels. The per hectareexpenditure on seed, fertilizer and plant protection measuresof adopted farmers increased for all the crops after adoptingrecommendation of soil testing analysis. The per hectareexpenditure on labour was also found to increased in all thecrops (wheat, gram, potato) except soybean. The cost ofcultivation (Rs. /ha) and cost of production (Rs. /q) of all thecrops reduced drastically, while benefit cost ratio was foundto be increased after adoption of soil testingrecommendation. The lack of knowledge about soil testingtechnology (70%), non-availability of soil testing report in time, less cooperation from the officers of agriculture department(46%) and complicated method of soil testing sample werefound to be main constraints in adoption of soil testingrecommendations as reported by majority of respondents inthe area under study. It was also observed during theinvestigation that there is an ample scope to improve theanalyzing capacity as well as dissemination ability of soiltesting laboratories. If this, couple with professionalmanagement through proper linkages, can bring radicalchanges in soil testing services in the state to the extent offarmers' satisfaction. The results of the research undertakenmade it clear that adoption of recommendation of soil testingreduced the cost of production and increased returns overcost of cultivation of crops. This fact may be popularizedamongst the farmers so that they can take benefit of soiltesting analysis. Sufficient field staff with trained personal

Impact of soil testing analysis in Madhya Pradesh

H.O. Sharma, P.K. Mishra* and R.S. ChouhanAgro-Economic Research CentreJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482 004 (MP)*Director ExtensionJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482 004 (MP)

JNKVV Res J 49(2): 142-149 (2015)

should be kept at village level and method as well as resultdemonstrations of these recommendations may be takenup in farmers' field for its wide adoption.

Keywords: Impact, soil testing, analysis

Soil testing till today has been used mainly to formulateprecise recommendations for the major nutrients i.e.Nitrogen, Phosphorus and Potassium fertilization of cropsin different soils and to recommend appropriate doses ofamendments for salt-affected and acidic soils.Micronutrients, comprising Zinc, Copper, Iron,Manganese, Boron and Chlorine, though required byplants in much smaller amounts, yet are as essential forthem as the major nutrients. Despite that, little attentionhas been paid to employ the soil testing for assessingthe micronutrient status of soils and determining soilsrequirement for micronutrient fertilizers for growing crops.With an objective to extent the advisory service to thefarmers of the state regarding the nutrient problems ofsoils and crops and suggest appropriate remedialmeasures for efficient correction of the same. JawaharlalNehru Agriculture University Jabalpur and the Departmentof Agriculture Madhya Pradesh Bhopal have establishedsoil testing laboratories for nutrient. Some privatelaboratories are also available in the state. Farmers areadvised to make the best use of this service rendered bythese laboratories.

Success or failure of soil testing programmeslargely depends on providing correct information to farmers,ability of the programme to provide service to a large groupof farmers in a particular area, proper analysis andinterpretation of results and recommendations that whenfollowed are profitable for the farmer. Then only will thisservice be effectively utilized to improve local agriculturalproduction Time and quality consciousness in the service

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is a real challenge for the analysts in the new millennium.This compels laboratory to adopt rapid, reliable, time savingprocedures and methods to meet future requirements.The farmer's confidence in the programme can beestablished only by demonstrating that it actually providesa means of improving his profit. Looking to the importanceof the soil testing in farmers' field this study had beenconducted as the review of various studies reported thatthe recommendations of soil testing laboratories are usefulfor increasing the levels of output but the majority of thefarmers have not been interested in this due to lack ofknowledge about soil testing facilities, testing of soils isincredible, laboratories are situated far away, and nonavailability of soil testing report etc.

Objectives

The present study was planned to focus the impactassessment of soil testing analysis in Sagar and Dhardistricts of Madhya Pradesh with the following specificobjectives:

• To assess the soil testing infrastructure availableacross different agro-climatic regions / districts of MadhyaPradesh.

• To determine the growth of sample target, andachieved by soil testing laboratory.

• To identify the gaps in sample target, and achievedby Sagar and Dhar soil testing laboratories andrecommendation adopted by the farmers.

• To evaluate the cost effectiveness of the soil testinganalysis.

• To identify constraints in adoption soil testingtechnology and suggest ways and means for properutilization of these soil testing laboratories.

Materials & methods

In Madhya Pradesh total numbers of laboratories are 70,out of which Soil Testing laboratories of Sagar & Dhar(MP) have been selected purposively for the study. Thesoil testing laboratory of Sagar district covers farmers ofSagar and Damoh districts and Soil testing laboratorysituated at Dhar covers Dhar district. The laboratoryworking under the direct control of the Joint Director SoilTesting, Department of Agriculture Madhya Pradesh, andSub Divisional Agriculture Office, Senior Agriculture

Development Officer. The Rural Agriculture ExtensionOfficer (RAEO) helps in the collection of soil sample atfield level and sends these samples to soil testinglaboratory. The soil testing reports are also provided toRAEOs for its distribution among the recipient farmers.

Both primary and secondary data collected for thestudy. The primary data were recorded on generalinformation of farmers who tested there soil and adoptedthe recommendation of soil testing report, land use andcropping pattern, incremental cost and return obtainedbefore and after adopting recommendation of soil testing,constraints in adoption of soil testing recommendation.The secondary data were collected on infrastructure facilityavailable in different agro climatic region in MadhyaPradesh, sample collected, analyzed and reported duringthe year 2001-02 to 2010-2011 by the soil testinglaboratory. Year 2001-02 and 2010-2011 were treated asbase and current year respectively for analyzing ofsecondary data. The survey method was used forcollection of the relevant data from selected cultivatorsby using pre-tested interview schedule. The investigatorsbriefly explained about the objectives of the study to eachrespondent and assured them that the suppliedinformation will be used only for research purpose. Thesecondary data were also collected by personal visit inthe office of Director of Agriculture and Joint Director ofAgriculture, Soil Testing, Vindhyachal Bhavan, Bhopal andalso from the published and unpublished record of SoilTesting laboratories of Dhar and Sagar districts.

A list of all the farmers who tested their soil samplein the year 2008- 09 has been collected from the respectivesoil testing laboratory and 50 farmers from each laboratoryhave been selected for the study. Thus, the total numberof respondents were 100, (50 each from Sagar and Dhardistricts) of Madhya Pradesh.

To assess the impact of soil testing analysis beforeand after technique has been followed and the years 2008-09 and 2009-10 were treated as before and after yearrespectively. The collected primary data pertain to theagriculture year 2010-11. While, the required secondarydata are pertain to year from 2001-02 to 2010-11. Theanalysis of the collected data was done on the basis ofstated objectives. The growth of sample targeted andachieved and absolute change analyzed with the help ofsecondary data. In this triennium average ending year2003-04 was treated as base and triennium averageending 2010-11 was treated as Current year. The datawere classified into two groups, i.e. before and afteradoption of soil testing technology by the respondents.

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Results & discussion

The available soil testing structure, gap in sample targetand achievements, incremental returns received afteranalyzing of soil sample are considered for in-depth study.

Soil testing infrastructure in the state

The soil testing facilities available across the state hasbeen given in the Table 1. The table reveled that therewere 70 soil testing labs exist in the state under differentagro-climatic regions. The numbers of labs were foundmaximum in Malwa Plateau (13) followed by Kymore

Plateau and Satpura Hills (11) and Vindhya Plateau (10).The other agro climatic zone also had more then one soiltesting labs.

The coverage or catchments of per lab was 0.63lakh farmers and 0.47 lakh hectares land or cultivableland. The maximum farmers covered by labs was foundin Central Narmada Valley (1.15 lakh) followed by VindhyaPlateau (1.06 lakh) Chhattisgarh Plains (0.70 lakh) andKymore Plateau and Satpura Hills (0.67 lakh).

As for as coverage of area under each lab isconcerned labs situated in Chhattisgarh plain (Bhalaghatdistrict) covered 0.72 lakh hectare, followed by CentralNarmada Valley (0.65 lakh hectare), Northern Hills ofChhattisgarh (0.60 lakh hectare) and Kymore Plateauand Satpura Hills (0.51 lakh hectares). Other labs alsocovered a significant area and provide service to needyfarmers. (Table 1) It is also observed from the data thatlabs situated in Satpura Plateau (0.34 lakh hectares)covered the lowest area, which is appreciable in terms ofavailability of infrastructure facilities.

On an average 0.50 lakh ha area and 0.66 lakhfarmers are being covered under a lab in the stateindicating an urgent need to establish more and moresoil testing lab not only to reduce the pressure on exitinglabs but also to improve the access and incrasing therate of adoption thereby reducing the transportation costfor benefit of the farming community in particular andincreasing the fertility of soil.

Table 1. Soil Testing Infrastructure in Madhya Pradesh (2010 - 11)

Agro climatic zones Districts (No.) Soil testing No. of Net area Lab availablelabs (No.) farmers sown per lakh per lakh

(lakh) (lakh/ha) farmers hectare

Chhattisgarh plains 1 2 2.88 2.75 0.70 0.72Northern Hill of CG 6 5 8.12 8.34 0.62 0.60Kymore Plateau & Satpura Hills 7 11 16.37 21.55 0.67 0.51Central Narmada Valley 2 4 3.47 6.10 1.15 0.65Vindhya Plateau 6 10 9.42 24.38 1.06 0.41Gird Region 7 9 13.50 17.85 0.67 0.50Bundelkhand 3 4 10.89 8.84 0.37 0.45Satpura Hills 2 3 5.64 8.70 0.53 0.34Malwa Plateau 9 13 23.37 31.14 0.56 0.42Nimar Plains 5 7 11.80 14.46 0.59 0.48Jhabua Hills 2 2 5.10 4.00 0.39 0.50Total 50 70 110.56 148.11 0.66 0.51

Fig. 1. Agro-climatic zone wise Soil testing Infrastructurein Madhya Pradesh

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Gap in sample target and achievement

The gap in soil sample targeted and achieved has beenpresented in Table 2. The gaps between target andachievement were recorded to be 19.95 & 21.18 per centduring the current year (2011) and 63.47 & 41.41 per centduring the base year (2004) in Sagar and Dhar districtsrespectively. The target of 10000 sample remain unchagedduring the base and current year in Sagor districts ,while ib case of Dahar the target was found to be reducefrom 15000 (2004) to 11000 (2011).

Target and achievement of samples

The target of soil samples were found to be stagnant to15000 with the growth of -3.55% per year during the periodunder study. It is also noted that the target were decreasedby -496.97 soil samples per year in Dhar district ofMadhya Pradesh, while, the achievement were found tobe increased from 9811 (2001-02) to 13581 (2010-11) witha rate of 24.25 soil samples and growth of 0.25% peryear. The gap between target and achievement rangesfrom - 9.46% (2010-11) to -51.71% (2008-09) and could

Table 2. Gap in Sample Targeted and Achievement, Sagar District of MP

Particular Sagar Dhar Total

A) The base year (TE 2004) Target 10000 15000 25000Achieved 3653 8785 12438

Gap 6347 6215 12562(63.47) (41.43) (50.25)

B) The current year (TE 2011) Target 10000 11000 21000Achieved 8005 8670 16675

Gap 1995 2330 4325(19.95) (21.18) (20.60)

Change over base year Target 0 -4,000 -4000Achieved 4352 -115 4237

(119.13) (-1.31) (34.06)Figures in parenthesis show percentages to total

Table 3. Growth and Gap of Sample Targeted and Achieved in Dhar District of MP

Year Target Achievement Gap % gap

2001 - 02 15000 9811 -5189 -34.592002 - 03 15000 7269 -7731 -51.542003 - 04 15000 9274 -5726 -38.172004 - 05 15000 11411 -3589 -23.932005 - 06 15000 12355 -2645 -17.632006 - 07 20000 10014 -9986 -49.932007 - 08 12000 9500 -2500 -20.832008 - 09 12000 5795 -6205 -51.712009 - 10 6000 6632 632 10.532010 - 11 15000 13581 -1419 -9.46Mean 14000 9564 -4436 -Standard Deviation 3559.03 2489.04 3153.13 -Coefficient of Variance (%) 0.25 0.26 0.71 -Regression Coefficient -496.97 24.25 -521.22 -Growth (%) -3.55 0.25 -11.75 -

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Table 4. Growth and Gap of Sample Targeted and Achieved in Sagar District of MP

Year Target Achievement Gap % gap

2001 - 02 10000 2197 -7803 78.032002 - 03 10000 3215 -6785 67.852003 - 04 10000 5548 -4452 44.522004 - 05 10000 5312 -4688 46.882005 - 06 10000 6310 -3690 36.902006 - 07 10000 7072 -2928 29.282007 - 08 10000 6778 -3222 32.222008 - 09 10000 7019 -2981 29.812009 - 10 10000 7381 -2619 26.192010 - 11 10000 9615 -385 3.85Mean 10000 6045 -3955 -Standard Deviation 0.00 2127.62 2127.62 -Coefficient of Variance (%) 0.00 0.35 0.54 -Regression Coefficient 0.00 657.21 -657.21 -Growth (%) 0.00 10.87 -16.62 -

Table 5. Incremental cost after adoption of soil testing recommendation by the farmers in different crops (Rs/ha)

Particulars Soybean Wheat Gram PotatoBefore After Before After Before After Before After

Seed & seed treatment 3667.95 3667.95 2107.00 2563.93 2881.67 2680.96 41990.00 41990.00(0.00) (21.69) (-6.97) (0.00)

Manures & fertilizer 2229.59 1577.84 1557.15 3384.79 401.00 315.00 618.74 1920.43(-29.23) (117.37) (-21.45) (210.38)

Plant Protection 0.00 489.88 0.00 0.00 422.00 1321.00 0.00 0.00( ) (0.00) (213.03) (0.00)

Weedicides 0.00 494.00 331.10 496.65 0.00 0.00 0.00 0.00( ) (50.00) (0.00) (0.00)

Labour 6064.55 6898.48 7826.36 7917.41 6380.83 6833.67 10127.00 10744.50(5.53) (1.16) (7.10) (6.10)

Interest on working capital 464.94 337.27 393.70 478.30 335.86 371.34 1756.10 1820.01(-27.46) (21.49) (10.56) (3.64)

Depreciation 1344.25 1344.25 1382.20 1382.20 278.28 278.28 423.59 423.59(0.00) (0.00) (0.00) (0.00)

Total Variable cost 13771.28 14309.67 13597.51 16223.28 10699.64 11800.25 54915.43 56898.53(3.91) (19.31) (10.29) (3.61)

Total Fixed Cost 8010.81 10357.50 8323.80 9672.40 4791.43 6703.91 14898.34 16684.92(2.29) (16.20) (39.91) (11.99)

Total Cost of Cultivation 21782.09 24667.17 21921.31 25895.68 15491.07 18504.16 69813.77 73583.45(-28.18) (18.10) (19.45) (5.40)

Cost of Production (Rs/q) 1430.21 1248.39 567.30 551.90 1253.76 1069.66 565.29 425.58(-12.72) (-2.71) (-14.68) (-24.71)

Figures in parenthesis show percentages difference to before

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not even full fill in any year during the period under study(Table 3).

The target of soil sample to be tested remain thesame in each year during the period under study butachivement were found to be increased from 2197 (2001-02) to 9615 (2010-11) by the rate of 657.21 sample andgrowth of 10.87% per annum along with gaps of -3.85(2010-11) to -78.03 (2001-02) per cent during the periodunder study in sagor distruct of MP (Table 4).

Incremental cost & return after adoption of soil testingrecommendation

Impact of soil testing analysis has been done by analysiscost and return incurred in before and after the adoptionof soil testing recommendation. Although, there were nosignificant difference found in different locations. Hencethere pooled analysis has been taken into considerationfor all the crops. In which farmers adopted the

recommendation of soil testing considering the rateprevailing in the year 2010-2011.

The cost of cultivation (Rs./ha) of all the crops i.ewheat (18.10%), gram (19.45%) and potato (5.40%) exceptsoybean ( -28.18%) were found to increased, while thecost of production (Rs./q) of all the crops were found todecreased from -2.71per cent (wheat ) to - 24.71 per cent(potato) after adoption of soil testing analysis report bythe cultivators. The per hectare expenditure on fertilizerof increased for wheat (117.37%) and potato (210.38%)was found to be increased, whereas the for soybean(-29.23%) and gram (-21.45%) was found to be decreasedafter adoption of soil testing report (Table 5).

In sum the per hectare expenditure on seed,fertilizer and plant protection measures of adopted farmersincreased for all crops after adopting soil testing analysisrecommendation. The per hectare expenditure on labourwas also found increased in all crops. The cost ofcultivation and cost of production of all the crops reduced

Table 6. Incremental return after adoption of soil testing recommendation by the farmers in different crops (Rs/ha)

Particulars Soybean Wheat Gram PotatoBefore After Before After Before After Before After

Yield physical unit (q/ha)Main product 15.23 19.76 38.61 46.88 12.35 17.29 123.50 172.90

(29.74) (21.42) (40.00) (40.00)By product 22.84 27.78 19.30 22.44 7.41 10.37 0.00 0.00

(21.63) (16.27) (39.95) (0.00)Gross return (Rs/ha)Main product 44171.83 57304.00 46343.29 53860.57 27170 38038 86450.00 121030

(29.73) (16.22) (40.00) (40.00)By product 2284.75 2778.75) 1930.97 2244.19 592.80 829.92 0 0

(21.62) (16.22) (40.00) (0.00)Gross returns 46456.58 60082.75 48274.26 56104.76 27762.80 38867.92 86450.00 121030

(29.20) (16.22) (40.00) (40.00)Net income (Rs/ha)At variable cost 32685.30 45773.08 34676.69 39881.50 17062 27067 31534.57 64131

(40.04) (15.01) (58.63) (103.37)At total cost 24685.30 35415.58 26352.85 30209.12 12271.40 20363.16 16636.23 47446.56

(43.53) (14.63) (65.94) (185.20)Cost - Benefit ratioAt variable cost 3.37 4.19 3.55 3.46 2.59 3.29 1.51 2.13

(40.04) (-2.59) (26.94) (35.12)At total cost 2.17 2.43 2.20 2.17 1.79 2.10 1.24 1.64

(43.53) (-1.62) (17.20) (32.83)Figures in parenthesis show percentages difference to before

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drastically after adaption of recommendation of soiltesting.

The gross return, net return at variable cost andnet return at total cost related to all the crops were foundto be increased 16.22 per cent (Wheat ) to 40.00 percent (gram & potato), 15.01 per cent (Wheat) to 103.37per cent ( potato) and 14.63 per cent (wheat) to 185.20per cent ( potato) after adoption of soil testing analysis.The cost benefit ratio at variable as well as total cost wasalso found to be increased for all the crops expect wheat( Table 6).

Constraints in adoption of soil testing technology

The constraints reported by the sample cultivators inadoption of soil testing technology are presented in Table7. It is observed from the data that lack of knowledgeabout soil testing facility among cultivators (70%) wasfound the main constraint in adoption of soil testingtechnology followed by non availability of soil testingreports in time to cultivator (62%), less cooperation fromAgriculture Officers/Staff of Agriculture Department (46%),complicated method of taking soil sampling (30%),technology totally different from farming practices (26%),lack of training about soil testing technology (22%), highcost of adoption of recommended practices (20%,)difficulty in adoption of recommendations (20%),incredibility of soil testing report (12%) and situation ofsoil testing labs not with the reach of cultivators (12%),were the other main constraints reported by farmers duringthe course of investigation.

Conclusion

The following conclusion are made from the above results

The present infrastructure of soil testing facility is foundto be insufficient in different agro climatic regions ofMadhya Pradesh. Whatever infrastructure is available isnot functioning properly hence, coverage of target/achievement needs to be increased by employing skilland trained staff in these labs. This is needs to beincreased quantity as quality of soil sample testing.

There is an ample scope to improve the analyzingcapacity as well as dissemination ability of the soil testinglaboratories. If this, coupled with professional managementthrough proper linkages, can bring radical changes in thesoil testing service in the state to extent the farmers'satisfaction.

The Department of Agriculture ensures an effectiveand live linkage between the field and the laboratory. It isto be appreciable if each lab may adopt at least onenearby village from where sample may be collected bythe laboratory staff and recommendations are alsocommunicated / handed over directly by the laboratorystaff to the farmers and to follow the outcome of theprogramme. Each lab can take up one village as a missionto see the utility of the programme by itself and find outshortcomings so that the whole programme can beimproved on the basis of such direct observation / study.Presently, the labs are literally cut off from the field andwork in isolation of the whole programme.

Soil analysis and fertilizer recommendation is onlya part of the soil testing service. To a good measure, theefficiency of the service depends upon the care and effortsput forth by extension workers and the farmers in collectionand dispatch of the samples to the laboratories andobtaining reports timely. Its effectiveness also dependsupon the proper follow up in conveying therecommendations to the farmers, including the actual useof fertilizer according to the recommendations. The roleof extension service, soil chemists and the agronomistsin the field is important. The service is suffering both fromtechnological aspect and due to inadequate and untrainedmanpower. Weakness of the programme in its variousaspects as discussed above needs improvement.

If the fertilizer industry will venture to produce andpromote the products on the basis of requirement ofspecific soil nutrient deficiency, the industry will have toget into the soil testing programme in a big way andgenerate such information as a measure of goodsupplement to soil testing programme basically being runby the Government. The fertilizer industry may adopt at

Table 7. Constraints in adopting of soil testing technology

Constraints Respondents(%)

Lack of knowledge about testing facility 70Non availability of soil testing report in time 62Less cooperation from Agriculture 46Officers/staffComplicated methods of Soil Sampling 30Technology is far different from farming 26practicesLack of Training for testing 22High cost of recommendation 20Difficulty in adoption of recommendation 20Soil testing is incredible 12Lab situated far away from the village 12

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least one district in a State and ensure and monitor thatthe fertilizer in the adopted district is used on the basis ofplant nutrient deficiency as determined through accuratesoil testing.

The awareness about soil testing facility, its needand importance is at the farmers' level hence, awarenessbuilding must be taken up by extension activities. As theadoption of recommendations of soil testing reduces costof production of crops and increases returns. This factmay be popularized among the farmers' so that they canbe benefited. Sufficient field staff with trained personalshould be kept at village level and method as well asresult demonstrations of these technologies may be takenup at the village level which popularized the impact ofthese technologies in front of the cultivators.

e`nk ijh{k.k ds fo'ys"k.k ds egRo dks ns[krs gq, ;g v/;;u fdlku ds[ksr esa e`nk ijh{k.k ls gksus okys ykHk dh i;kZIrrk] mldh mi;ksfxrkizHkko'khyrk vkSj ;ksxnku dk ewY;kadu djus ds fy, fd;k x;k gS Ablds fy, izkFkfed vkadMs O;kfDrxr lk{kkRdkj vuqlwph ds ek/;e lso"kZ 2010&11 ds fy, e/; izns'k ds lkxj vkSj /kkj ftyksa ds 100—"kdksa ls ,df=r fd, x, A bu —"kdksa ls e`nk ijh{k.k ds iwoZ rFkki'pkr~ dh tkudkjh ,d= dh x;h A v/;;u ls ;g Kkr gqvk fd ftu—"kdksa us fo'ys"k.k ds fy, enk ijh{k.k iz;ksx'kkykvksa dks feÍh ds uewusizLrqr fd;s Fks] muesa ls dsoy 71 izfr'kr —"kdksa dks enk ijh{k.k fjiksVZizkIr gqbZ A bu 71 esa ls dsoy 49 —"kdksa n~okjk e`nk LokLF; dkMZ dhflQkfj'kksa dks viuk;k x;k vkSj mit ds Lrj esa lq/kkj dj Qlyksa esaiks"kd rRoksa dk mi;ksx fd;k A e`nk ijh{k.k ds fo'ys"k.k dh flQkfj'kdks viukus ds ckn lHkh Qlyksa ds fy, cht] moZjd vkSj ikS/k laj{k.kds mi;ksa esa izfr gsDVs;j [kpZ esa o`f) ik;h x;h A Je ij izfr gsDVs;jO;; Hkh lks;kchu dks NksM+dj lHkh Qlyksa ¼xsgq¡] puk] vkyw½ esa vf/kdik;k x;k A e`nk ijh{k.k dh flQkfj'k viukus ds ckn lHkh Qlyksa esadk'rdkjh dh ykxr ¼:@gsDVs;j½ vkSj mRiknu dh ykxr ¼:@fDoaVy½dkQh de gbZ tcfd mRiknu ykxr vuqikr c<+k ik;k x;k A v/;;u

ds rgr vf/kdkj —"kdksa esa enk ijh{k.k rduhd ds ckjs esa Kku dh deh¼70%½] le; esa e`nk ijh{k.k fjiksVZ dh vuqiyC/krk ¼62%½] —f"kfoHkkx ds vf/kdkfj;ksa ls Hkh de lg;ksx ¼46%½] vkSj feÍh ijh{k.kuewus dh tfVy fof/k ¼30%½] e`nk ijh{k.k flQkfj'kksa ds viukus esaeq[; ck/kkvksa ds :i esa crk;k A bl 'kks/k ds ifj.kkeksa ls ;g Li"V gSfd e`nk ijh{k.k dh flQkfj'k ls mRiknu dh ykxr ds lkFk dk'r dhykxr de gksrh gS ,oa vf/kd vk; izkIr gksrk gS bl rF; dks fdlkuksads chp yksdfiz; cuk;k tk ldrk gS rkfd os feÍh ijh{k.k ds fc'ys"k.kdk ykHk ys ldrs gS lkFk gh bu flQkfj'kksa ds ifj.kke izn'kZuksa dksizf'kf{kr O;fDrx.k ds lkFk i;kZIr QhYM LVkQ dks xk¡o Lrj rdfof/k ds :i esa vPNh rjg ls igqpkuk pkfg, rkfd fdlku bls vkSjvf/kd ek=k esa viuk ldsa A tk¡p ds nkSjku ;g Hkh ik;k fd enk ijh{k.kiz;ksx'kkykvksa dh fo'ys"k.k {kerk ds lkFk&lkFk izpkj&izlkj dh {kerkesa lq/kkj djus dh i;kZIr vko';drk gS lkFk gh mfpr la;kstu dsek/;e ls fdlkuksa dh larqf"V ds fy, O;kolkf;d izca/ku ds lkFk jkT;esa e`nk ijh{k.k esa Økafrdkjh ifjorZu yk;k tk ldrk gS A

References

Anonymous (2000) Relevance of soil testing of agricultureand the environment. Issue Paper Council forAgricultural Science and Technology 15 : 12

Biswas PP (2002) Soil testing at farmers door step. FertilizerNews 47 (10): 21-24

Rao AS, Sanjay Shrivastava (1999) Experiences on currentstatus of crop responses to fertilizers in differentagro-climatic zones as learnt from All IndiaCoordinated Research Project on soil test cropresponse correlation. Fertilizer News 44 (4): 83-95

Sharma HO, Yadav Rajeev, Nahatkar SB (2005) AdoptionPattern and Constrains of Soybean ProductionTechnology in Malawa Platues of MP Agril. Situationin India 61(4): 3-17

(Manuscript Receivd : 22-02-2015; Accepted : 30-08-2015)

150

Abstract

Rice bean is important crop and is used as vegetable, folkmedicine and fodder. 38 accessions were selected formolecular analysis using RAPD and ISSR markers.Randomly selected 8 decamer primers amplified 49 RAPDmarker loci. Out of them, 26 loci were found to be polymorphic.Average bands per primer were 6.125, while averagepolymorphic bands per primer were 3.25. The PIC value ofRAPD markers was with an average of 0.27. Five ISSRprimers amplified 31 ISSR marker loci. Among these, 9 werepolymorphic across all the rice bean accessions. Percentpolymorphism ranged up to 83.33. Average bands per primerwere 6.20, while average polymorphic bands per primer were1.80. The average of PIC scores for ISSR markers was 0.104.

Keywords: Genetic variation, ISSR, Phenotypiccoefficient, RAPD, Rice bean

Rice bean (Vigna umbellata) belongs to genus Vigna,subgenus Ceratotropis (Piper) Verdc this includes azukibean group. Rice bean is a traditional crop grown acrossSouth, Southeast and East Asia and its wild form isdistributed across a wide area of the tropical monsoonforest zone from eastern India, Nepal, Myanmar, Thailand,Laos and Southeast China (Seehalak et al. 2006). It isan important tropical legume plant, identified as an underexploited species. It is used as vegetable, folk medicineand fodder in Southeast Asian countries (Wu et al. 2001).Rice bean is a good sources of protein; essential aminoacids, essential fatty acids and minerals, and the driedseeds make an excellent addition to a cereal based diet.

Evaluation of molecular polymorphism among rice bean (Vignaumbellata) genotypes

Aparna Pandey, Sharad Tiwari*, A.K. Mehta† and Niraj TripathiBiotechnology CentreJawaharlal Nehru Krishi VishwavidyalayaJabalpur 482 004 (MP)†Department of AgronomyCollege of AgricultureJawaharlal Nehru Krishi VishwavidyalayaJabalpur 482 004 (MP)Email: shtiwari@gmail.com

JNKVV Res J 49(2): 150-153 (2015)

Molecular technique is a tool to detect the extentand structure of genetic variation applying molecularmarkers, providing insights into the diversity of cropvarieties and potential contributions represented by theirwild relatives. In this paper we report the variability atphenotypic and molecular level in rice bean. The objectivesof the present study were to determine the nature andamount of genetic variability for fodder yield and itscomponents and to detect the genetic diversity amongthe germplasm by RAPD and ISSR markers.

Material and methods

Leaf samples of rice bean genotypes (Table 1) werecollected after 25-30 days of sowing. Genomic DNA wasisolated using procedure described earlier by Saghai-Maroof et al. (1984). To evaluate genetic differences amongrice bean genotypes, 20 RAPD and 15 ISSR markerswere tested. Out of these 35 markers, eight RAPD andfive ISSR primers were selected on the basis of capabilityto produce bands in all the genotypes. Selected primerswere confirmed by repeating the reaction twice beforeemploying them for diversity analysis. Amplified productswere separated on 1.5% agarose gel (Sigma, USA) in 1XTAE buffer and the size of amplified DNA fragments wasestimated on the basis of 1Kb plus ladder (Fermentas,USA). Gels stained with ethidium bromide were run forapproximately 4h at 65V.

Evaluation of fragment patterns was carried out bysimilarity index. Reproducible bands were scoredmanually as '1' or '0' for presence or absence of the bands.

151

The final RAPD and ISSR generated data were used tocalculate pair wise similarity coefficients (Jaccard 1908)using the Similarity for Qualitative Data (SIMQUAL) formatof NTSYS-pc version 2.1 (Numerical Taxonomy andMultivariate Analysis System) software package (Rohlf2002). Cluster analysis was performed on the basis ofgenetic similarity matrix and the resulting similaritycoefficients were used for constructing dendrogram usingthe Unweighted Pair Group Method with ArithmeticAverage (UPGMA) with the SAHN module of NTSYS-pc.

Results and discussion

Genetic information at DNA level could be more reliableto reflect the suitable genetic difference between theaccessions as it is normally not subjected toenvironmental variation. During the present investigationout of 94 accessions of rice bean, 38 were selected onthe basis of diversity analysis at phenotypic levelexhibiting better fodder and crude protein yields. Thegenomic DNA of 38 accessions was explored using ISSRand RAPD markers to assess genetic variability amongthem.

RAPD analysis

The RAPD analysis was carried out using decamerprimers from Operon Technologies Inc. for DNA

amplifications through PCR. Out of 15 decamer primersonly 8 primers responded to all the accessions. These 8decamer primers amplified 49 RAPD marker loci (Table2). The size of amplified fragments ranged from 240-1600bp. Out of these 49 bands, 26 bands (53.06%) werepolymorphic revealing presence of diversity among theaccessions under study. Muthusamy et al. 2002 alsoobserved high degree of polymorphism using RAPDmarkers in rice bean landraces. Other pulses exhibitingsubstantial diversity within germplasm are Italian commonbean (Marotti et al. 2006), domesticated cowpea and itswild progenitors (Ba et al. 2004) and common beanlandraces (Maciel et al. 2001).

Primer OPC-15 amplified a specific electromorphof 245bp in accession JRB07-54-3 (Sample 37). As thisspecific accession exhibited highest green fodder yield,dry fodder yield per day and crude fiber percent amongall the accessions under study, it can be predicted that

Table 1. List of rice bean genotypes used forpolymorphism study

S. Genotype S. Genotype S. Genotype

1. BFRB-3 14. JRB06-3 27. JRB07-1-12. BFRB-3-1 15. JRB06-8-1 28. JRB07-33-13. BFRB-6 16. JRB06-8-2 29. JRB07-33-24. KRB-167 17. JRB06-9 30. JRB07-34-15. Bidhan-1 18. JRB06-9-1 31. JRB07- 35-16. Bidhan-1-1 19. JRB06-11 32. JRB07-35-27. BFRB-8 20. JRB06-13 33. JRB07-35-38. BFRB-8-1 21. JRB06-13-1 34. JRB07-36-19. JRB-06 22. JRB06-10 35. JRB07-39-110. JRB-06-1 23. JRB06-114 36. JRB07-54-211. BFRB-9 24. JRB04-1 37. JRB07-54-312. KRB-19 25. JRB04-2 38. JRB07-1-113. KRB-19-1 26. JRB07-1

Table 2. Number of bands obtained using RAPD andISSR markers

Primer TB MB PB PP PICRAPD

OPC-09 10 0 10 100.00 0.683OPC-10 4 4 0 0.0 0.000OPC-11 10 0 10 100.00 0.679OPC-13 4 4 0 0.0 0.000OPC-15 5 0 5 100.00 0.450OPC-16 4 4 0 0.0 0.000OPC-19 4 4 0 0.0 0.000OPH-20 8 7 1 12.5 0.0841Total 51 23 28 - -Average 6.375 3.5 39.06 0.237

ISSR

UBC-880 7 3 4 57.14 0.260UBC-885 6 1 5 83.33 0.259UBC-821 7 7 0 0.0 0.000UBC-851 5 5 0 0.0 0.000UBC-853 6 6 0 0.0 0.000Total 31 22 9 - -Average 6.20 1.8 28.09 0.104

TB-Total bands, MB-Monomorphic bands, PB-Polymorphic bands, PP-Percentage polymorphism, PIC-Polymorphic Information Content

152

marker OPC-15 may be associated with these traits andwill be a suitable object for further investigation to revealits linkage with specific trait/gene. The cluster analysisfor selected 38 accessions of rice bean using 8 RAPDmarkers was done using NTSYS-pc software programme.The cluster analysis grouped accessions into six clusters(Fig 1). The first cluster contained three accessions 1, 9,and 16 while the second cluster occupied thirteenaccessions 3, 5, 6, 11, 19, 13, 14, 35, 22, 30, 27, 18 and2. The third cluster consisted of seven accessions 4, 15,7, 12, 8, 10 and 17 and the fourth one had sevenaccessions 20, 28, 26, 29, 34, 21 and 37. The fifth clustercontained three accessions 23, 31 and 36 and the lastcluster consisted of five accessions 24, 25, 33, 32 and38. Among these accessions JRB06-9-1 (sample 18) andBFRB-3-1 (Sample 2) were closely related with each otherbut differed from all other accessions. Accession JRB07-1-1 (sample 38) showed the highest diversity from all otheraccessions as per dendrogram analysis. This accessionalso produced highest number of leaves and can be usedas a parent in a hybridization programme for developingfodder rice bean varieties.

ISSR analysis

A set of 12 ISSR primers (UBC primers of 16-20 bases)were used to analyze genetic diversity among selected38 accessions. Out of 12 ISSR primers only 5 primersresponded with all the accessions. These five primersamplified 31 marker loci with amplified fragments sizebetween 230-1100 bp. Out of these 31 loci, 9 were found

to be polymorphic (22.6%) across all the rice beanaccessions under study. Ajibade et al. (2000) reportedthat ISSR primers generate 3 to 26 markers with anaverage of 12.94 per accession in Vigna (Table 2). Similarresults have been reported also with ISSR markers byMarotti et al. (2006) among Italian common beanlandraces and Muthusamy et al. (2002) in rice beanlandraces.

Based on ISSR markers, genetic diversity among38 accessions of rice bean was estimated and adendrogram was generated by UPGMA cluster analysisbased on similarity coefficient. The cluster analysisgrouped 38 accessions of rice bean into five major clusters(Fig 2). The first cluster contained two accessions 1 and6 .The Second cluster comprised seven accessions 3,24, 8, 9, 20, 21 and 23. The third cluster occupied six 4,5, 25, 26, 31 and 32. The fourth cluster contained elevenaccessions 7, 10, 13, 14, 15, 16, 17, 18, 19, 11 and 12while the last cluster with eleven accessions 22, 29, 30,33, 35, 36, 27, 28, 34, 37 and 28. Accession JRB06-10(sample 22) exhibited highest diversity among 38accessions in UPGMA analysis. Fang et al. (2006) incowpea and Ajibade et al. (2000) in Vigna also observedhigh diversity with resembling cluster patterns.

The total number of polymorphic and discriminantfragments were found to be higher with RAPD as comparedto ISSR. Similar results were also obtained in rice beanas more polymorphic loci were detected with RAPD(70.30%) than with ISSR fingerprinting (61.79%)(Muthusamy et al. 2008).

Fig. 1. Dendogram generated showing relationshipamong rice bean germplasm using RAPD markers

Fig. 2. Dengrogram genrrated showing relationshipamong rice bean germplasm using ISSR markers

153

During the present investigation with rice beangermplasm, considerable level of diversity was revealedby the dendrogram developed from molecular analysis.In addition, the excellent attributes of RAPD and ISSRmarkers could be of great importance for cultivaridentification and estimating genetic variability among theselected rice bean accessions. It will provide a means toformulate a breeding strategy for the genetic enhancementof rice bean, a potential pulse fodder crop.

jkbl chu ,d egRoiq.kZ Qly gS tks lCth] yksd fpfdRlk vkSj pkjsds :i esa iz;ksx dh tkrh gS A blds 38 foHksnksa dks vkj-,-ih-Mh- rFkkvkbZ-,l-,l-vkj- dk mi;ksx dj vf.od fo'ys"k.k gsrq p;fur fd;kx;k Fkk A vVdyksa ij vk/kkfjr 8 izkbelZ us 49 vkj-,-ih-Mh- ekdZlZfcUnqiFkksa dks ifjyf{kr fd;k ftuesa ls 26 ikyhekfQZd Fks vkSj 6-125cS.M izfr izkbej Fks tcfd ikyhekfQZd cS.M izfr izkbej 3-25 Fks Avkj-,-ih-Mh- ekdZlZ dh ih-vkb-lh- dk vkSlr 0-27 Fkk A ik¡pvkbZ-,l-,l-vkj- izkbelZ us 31 ekdZj fcUnqiFkksa dks ifjyf{kr fd;kftuesa ls 9 ikyhekfQZd Fks A vkSlr ikyhekfQZTe 83-33 izfr'kr~ rdFkk ,oa izfr izkbej vkSlr cS.M 6-20 tcfd izfr izkbej vkSlrikyhekfQZd cS.M 1-80 Fks A vkbZ-,l-,l-vkj- ds fy, vkSlr ih-vkbZ-lh- dk eku 0-104 Fkk A

References

Ajibade SR, Weeden NF, Michite S (2000) Inter simplesequence repeat analysis of genetic relationshipsin the genus Vigna. Euphytica 111(1):47-55

Ba FS, Remy SP, Gepts P (2004) Genetic diversity in cowpea[Vigna unguiculata (L.) Walp.] As revealed by RAPDmarkers. Genetic Resour Crop Evol 51:539-550

Fang J, Chao CT, Robertsand PA, Ehlers JD (2007) Geneticdiversity of cowpea [Vigna unguiculata (L.) Walp.] infour West African and USA breeding programs asdetermined by AFLP analysis. Genet Resour CropEvol 54:1119-1209

Maciel FL, Garald LTS, Echevarrigaray S (2001) RAPDmarkers variability among cultivars and landracesof common beans (Phaseolus vulgaris L.) of SouthBrazil. Euphytica 120(2):257-263

Marotti I, Bonetti A, Minelli M, Cizonea P Dinelli G (2006)Characterization of some Italian common bean(Phaseolus vulgaris L.) landraces by RAPD, semi-random and ISSR molecular markers. GenetResour Crop Evol 54:175-188

Muthusamy S, Kanagarajan S, Ponnusamy S (2008)Efficiency of RAPD and ISSR markers system inaccessing genetic variation of rice bean (Vignaumbellata) landraces. Electronic J Biotech 11:3451-3458

Rohlf FJ (2002) NTSYS-pc: numerical taxonomy systemver.2.1. Exeter Publishing Ltd, Setauket

Saghai-Maroof MA, Soliman KM, Jorgensen RA, Allard RW(1984) Ribosomal DNA spacer-lengthpolymorphism in barley: Mendelian, inheritance,chromosomal location and population dynamics.Proc Natl Acad Sci 81:8014-8018

Seehalak W, Tomooka N, Waranyuwat A, Thipyapong P,Laosuwan P, Kaga A, Vaughan DA (2006) Geneticdiversity of the Vigna germplasm from Thailand andneighbouring

(Manuscript Receivd : 17-02-2015; Accepted : 28-08-2015)

154

Abstract

The present study was carried out with the objectives ofoptimization of fermentation variables for maximum yield ofbioethanol using co-culture of Saccharomyces cerevisiaeand Zymomonas mobilis and quality evaluation of bioethanolproduced. In this investigation on optimization of fermentationvariables for maximum recovery of bioethanol using co-cultureof the yeast and bacteria,key factors were optimized in solidstate fermentation (SSF) and simultaneous saccharificationand fermentation (SiSF) method for obtaining better recoveryof bioethanol. The results of various experiments revealedthat with the SSF technique using co cultures of yeast andbacteria the highest yield of bioethanol (5.9%) was obtainedat incubation temperature of 30°C after 96 hr of incubationperiod whereas the residual sugar level after fermentationat incubation temperature of 30°C was obtained at a low of0.202 mg/ml of fermented broth after 96 hr of incubationperiod with respect to initial sugar level of 0.408 mg/ml ofmedium. In case of SiSF, highest yield of bioethanol (5.5%)was obtained at a pH of 4.5 with incubation temperature of25°C after 96 hr of incubation period with the residual sugarleft to a minimum of 0.218 mg/ml of fermented broth from theinitial sugar level of 0.412 mg/ml of medium under a set ofabove mentioned fermentation variables. On qualityevaluation of bioethanol produced , the results analysedshowed that with the SSF technique, the values of density ,viscosity and boiling point of bioethanol produced by co-culture were found to be 1.0218g/ml, 0.98 centipoise and78.3°C respectively . Like wise in case of SiSF method, thevalues of density, viscocity and boiling point were found to be1.0245 g/ml, 0.99 centipoise and 78.1°C respectively .

Keywords: Bioethanol, Bio fuel, Ligno-cellulose,Fermentation, Yeast, Bacteria, Co-culture

Ethyl alcohol produced from biomass based wastematerial is called bioethanol. It can be produced from alarge variety of natural renewable materials such asagricultural crops, land forest products, as well as from

Bioethanol production from waste potato using co-culture ofSaccharomyces cerevisiae and Zymomonas mobilis

Yogesh Sudam Patil, L.P.S. Rajput, Yogendra Singh and Keerti TantwaiBiotechnology CentreJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482 004 (MP)

industrial and domestic waste such as paper, textile andbeverages. In the present scenario, there is a growinginterest for ecologically and economically sustainablebiofuel production. In the past years, feasibility oflignocelluloses containing materials for ethyl alcoholproduction has been explored depending upon theavailability in the region (Shindo and Tachibana 2006).An ethanologenic microorganisms capable of fermentingall of the sugars released from lignocellulosic biomassthrough a saccharification process is essential forsecondary bioethanol production (Yanase et al. 2010).The rapidly growing demand for energy, a dwindling andunstable supply of petroleum and the emergence of globalwarming from the use of fossil fuels have rekindled a stronginterest in pursuing alternative and renewable energysources. Presently, fermentation of sugar to ethanol isbest established process for conversion of biomass toenergy (Classen et al. 1999). Fermentation of starchbased raw material leading to the production of biofuel iseconomical and should be followed in India (Sharma etal. 2006). In our country, ethanol is usually produced byfermentation of molasses and its less availability hasresulted in nonviability of molasses based industries. Itis therefore necessary to explore the possibilities for theproduction of bioethanol from starch rich waste materialsas it is available in plenty in our country.

A large quantity of inexpensive waste potatoes fromfarm is available in surplus in the state of Madhya Pradeshfor utilization as substrate in the production of bioethanol.These cheaply and easily available waste potatoes containa considerable amount of starchy material required foroptimum growth of microorganism used for production ofbioethanol. Not much work has been done on theutilization of waste potatoes in our country. Hence thereis an urgent need to explore the possibility for utilizationof waste potatoes in the production of bioethanol as plentyof waste potato is available in the state of MP. keeping inview the above fact following research work was planned

JNKVV Res J 49(2): 154-159 (2015)

155

with objectives as to analyse the proximate compositionof waste potatoes collected from different locations, alongwith optimize the fermentation variables for maximum yieldof bioethanol using co-culture of Saccharomycescerevisiae and Zymomonas mobilis and evaluate thequality of bioethanol produced.

Materials and methods

The present study was conducted in theFermentation Technology Laboratory, BiotechnologyCentre, Jawaharlal Nehru Krishi Vishwa Vidyalaya,Jabalpur (M.P.).Waste potato tubers (Fig.1) werepurchased from Adhartal vegetable market Jabalpur (MP).The bioethanol producing microorganisms co-culture viz.Saccharomyces cerevisiae MTCC 170 and Zymomonasmobilis MTCC 2427 were obtained from Institute ofMicrobial Technology (IMTECH) Chandigarh, Punjab (Fig2 and 3). In this experiment, waste potatoes were takenas starch source (substrate). Theculture ofSaccharomyces cerevisiae and Zymomonas mobilis weregrown and maintained on Yeast Extract Peptone Dextrose(YEPD) and Rich medium (RM) media respectively. Theculture of yeast and bacteria were maintained by subculturing them every 15 days on YEPD and RM agar plates,incubating for 24 and 48 hrs respectively at 30°C andthereafter storing in a refrigerator at 4°C until further use.Inoculum was prepared separately in YEPD and RM broth.These inoculums were used to inoculate sterilizedpotatoes sample.

Fermentation methods were used for productionof bioethanol from waste potatoes by employing solid statefermentation (SSF) as described by Sharma et al. (2006)and simultaneous saccharification and fermentation (SiSF)as described by O'Leary (2000) was adopted forconducting the experiment. For solid state fermentation(SSF) and simultaneous saccharification and fermentation(SiSF) method, different variables viz. temperature, pH,

and incubation period were studied for better recovery ofbioethanol. In SSF, by maintaining the optimum conditionof moisture content at 60% level, production of bioethanolwas carried out at different incubation temperatures viz.25, 30 and 35°C for different incubation periods viz. 3, 4,5 and 6 days in order to attain for maximum recovery ofbioethanol using co-culture of Saccharomyces cerevisiaeMTCC 170 and Zymomonas mobilis MTCC 2427. In SiSF,The process of fermentation was carried out at differenttemperatures viz. 25, 30 and 35°C for different incubationperiods viz. 3, 4, 5 and 6 days with different ranges of pHviz. 4.5, 5.0 and 5.5 pH for maximum recovery of bioethanolusing co-culture of Saccharomyces cerevisiae MTCC 170and Zymomonas mobilis MTCC 2427. The yield ofbioethanol was determined by distillation and dehydrationprocess adopted by O'Leary (2000). Distillation anddehydration was done using rotatory evaporator at 78 ±2°C under vacuum. Potato tubers were analysed forvarious chemical constituents like moisture, dry mattercontent, amylase and amylopectin contents accordingto AOAC (1980). Total starch content (Keer, 1950), totalsugar (Miller 1959) were also recorded. Quality ofbioethanol produced was assessed using three differentparameters like density determination using pycnometer(Caylak and Sukan 1998), viscosity by Ostwaldviscosimeter (Bernnan and Tipper 1967) and Boiling pointdetermination as par O' Leary (2000).

Results and discussion

The effect of incubation temperatures and incubationperiods on yield of bioethanol using co-culture of the yeast(Saccharomyces cerevisiae MTCC 170) and bacteria(Zymomonas mobilis MTCC 2427) applying the processof solid state fermentation (SSF) is summarized in (Table1). This depicts that co-culture gave maximum yield (5.9%)of bioethanol at a incubation period of 96 hr havingmaintained optimum incubation temperature of 30°C. Thevalues of bioethanol yield were found to be lowest and

Fig.1. waste potatoes used forbioethanol production

Fig.2. Strains of Saccharomycescerevisiae (MTCC 170)

Fig.3. Strains of Zymomonas mobilis(MTCC 2427)

156

Table 1. Effect of incubation temperature on bioethanolyield at different incubation period in SSF method

Substrate taken - 50 g, Water added - 30 ml

Incubation period (hr) Yield of bioethanol (%)Temperature (°C)

25 30 35

72 4.1 4.7 2.8

96 5.8 5.9 4.6

120 4.3 5.6 3.9

144 2.5 5.0 2.9

* Values are average of triplicates

Table 2. Effect of different pH on yield* of Bioethanol inSimultaneous Saccharification and Fermentation (SiSF)at different incubation temperatures and incubation periodsusing co-culture of yeast (Saccharomyces cerevisiaeMTCC 170) and bacteria (Zymomonas mobilis MTCC2427)

Substrate taken - 50 g, Water added - 30 ml

Incubation Period pH Yield of bioethanol (%)(hr) Temperature (0C)

25 30 35

72 4.5 3.5 3.0 2.6

5.0 2.8 3.1 2.4

5.5 2.8 2.6 2.7

96 4.5 5.5 4.1 3.2

5.0 3.5 3.9 3.3

5.5 3.7 3.4 3.3

120 4.5 4.5 3.7 4.0

5.0 4.2 3.5 4.4

5.5 4.1 4.1 4.3

144 4.5 3.4 3.4 3.3

5.0 3.9 2.6 3.4

5.5 3.5 3.6 3.5

*Values are average of triplicates

Table 3. Effect of incubation temperature on residual sugarin solid state fermentation (SSF) at different incubationperiods

Substrate taken - 50 g, Water added - 30 ml

Incubation period (hr) Residual sugar afterfermentation

Temperature (°C)25 30 35

72 0.321 0.372 0.387

96 0.121 0.202 0.254

120 0.095 0.087 0.097

144 0.077 0.056 0.067

* Values are average of triplicates

Table 4. Effect of diffrent pH on residual sugar inSimultaneous Saccharificaiton and Fermentation (SiSF)at different incubation temperatures and incubation periods

Substrate taken - 50 g, Water added - 30 ml

Incubation period pH Residual sugar after(hr) Fermentation (mg/ml of

fermented broth)Temperature (oC)

25 30 35

72 4.5 0.312 0.352 0.321

5.0 0.332 0.334 0.365

5.5 0.314 0.384 0.344

96 4.5 0.124 0.218 0.134

5.0 0.132 0.221 0.214

5.5 0.112 0.231 0.212

120 4.5 0.092 0.103 0.109

5.0 0.102 0.097 0.112

5.5 0.092 0.105 0.101

144 4.5 0.042 0.063 0.082

5.0 0.053 0.074 0.084

5.5 0.063 0.074 0.032

*Values are average of triplicates

157

recorded as 2.5% from the co-culture of yeast and bacteriaat incubation temperature of 25°C with incubation periodof 144 hr. It was interesting to note that with theadvancement in incubation period from 72 to 96 hrs, therewas a relative increase in bioethanol yield and thereafterit got reduced for both the strains of yeast and bacteria.Various workers have also reported the similar findingsusing yeast and bacteria (Liimatainen et al. 2004 Ming-Xong et al. 2009). Similarly, Ming-Xiong et al. (2009)reported 10.53 g/L of bioethanol yield from raw sweet potatostarch (20 g starch/lit) using genetically engineeredZymomonas mobilis after 96 hr of incubation period. Thefindings in the present investigation indicated that theefficiency of bioconversion of starch into bioethanol wasgreater due to maximum enzymatic activity at aincubation period of 96 hr with a incubation temperatureof 30°C for the co-culture of yeast (Saccharomycescerevisiae MTCC 170) and bacteria (Zymomonas mobilisMTCC 2427). Hence it was concluded that the incubationtemperature of 30°C for the co-culture of yeast(Saccharomyces cerevisiaeMTCC 170) and bacteria(Zymomonas mobilis MTCC 2427) with the incubationperiod of 96 hr for found to be optimum under SSFtechnique for achieving the maximum yield of bioethanol.The findings obtained in the present investigation showedthat these are in agreement with the reported observationsby earlier workers. Although some variations observed inthe values in present investigation might be due to thegenetic variability of the strains used and cultureconditions maintained.

The effect of pH, incubation temperature andincubation period on yield of bioethanol in SimultaneousSaccharification and Fermentation (SiSF) was alsostudied taking 50g substrate with 50ml distilled water (Table2). The maximum yield of bioethanol (5.5%) at aincubation temperature of 25°C with incubation period of96 hr and having maintained the pH at 4.5. It was alsoobserved that there was a relative increase in bioethanolyield at incubation temperature of 25°C with the relativeincrease in incubation period upto 96 hr. However thebioethanol yield further got decreased at a incubation

period of 120 and 144 hr. The decrease in bioethanol yieldmight be due to less enzymatic activity after an incubationperiod of 96 hr. Several workers have also reported thebioethanol yield almost in the similar range frombioconversion of starch rich substrates using yeast andbacteria (Abouzied et al. 1986 O'Leary 2000).

Initial sugar present in the fermentation mediumgot reduced relatively with the progressive increase inthe incubation period upto 120 hr irrespective of the co-culture (yeast and bacteria) and incubation temperature(25, 30 and 35° C) used in the method of solid statefermentation (SSF). (Table3). It was also observed thatthere was a relative decrease in the level of residual sugarafter a fermentation period of 72 hrs proceeding to 144 hr.In case of the co-culture of yeast (Saccharomycescerevisiae MTCC 170) and bacteria (Zymomonas mobilisMTCC 2427), the level of residual sugar after fermentationwas found to be minimum (0.067 mg/ml of medium) at aincubation temperature of 35° C and incubation period of144 hr. These observations indicated that the enzymatichydrolysis of sugar must have taken place at a higherrate under the above mentioned fermentation conditionsresulting in maximum reduction in the level of residualsugar and in turn giving rise to maximum production ofbioethanol. Various workers have also reported the levelof residual sugars with respect to initial sugar level,incubation temperature and period of incubation (Hoskinsand Lyons 2009). The similar trend of bioconversion ofsugar into ethanol resulting in the reduction of residualsugar level at different time intervals.

Different observations depicted in table 4 ondifferent levels of pH (4.5, 5.0 and 5.5) at differentincubation temperatures (25, 30 and 35° C) and incubationperiods (72, 96 120 and 144 hrs) showed that thebioconversion of sugar into bioethanol was relatively lowat a pH of 5.0 as compared to pH of 4.5 and 5.5irrespective of the incubation temperature, incubationperiod used in the Simultaneous Saccharificaiton andFermentation (SiSF) process of fermentation. It was alsoobserved that pH of 4.5 was found to be optimum for betterconversion of sugar using co-culture at a incubationtemperature of 25° C and incubation period of 96 hr. Thefindings in the present investigation showed that the levelsof residual sugar were found to be minimum under abovementioned fermentation conditions. The reason for higherefficiency of conversion of sugar might be the higher activityof enzymes involved in the hydrolysis of sugar intobioethanol. Ado et al. (2009) reported that using co-culture

Table 5. Quality attributes* of bioethanol produced fromco-culture of two different strains using SSF and SiSFmethods

Quality attributes SSF SiSF

Density (g/ml) 1.0218 1.0245Viscosity (centipoise) 0.98 0.99Boiling point (° C) 78.3 78.1

* Values are average of triplicates

158

of A. niger and S. cerevisiaeunder optimized conditionssuch as incubation temperature, incubation period andpH, the sugar concentration present in cassava starchreduced from 0.24 g/100 ml on first day of fermentation to0.01 g/100 ml on the seventh day in simultaneoussaccarification and fermentation (SiSF) method. It wasfurther reported that the concentration of sugar gotreduced rapidly and consistently during 24 hr offermentation and thereafter decrease was found to begradual upto 96 hr of incubation period. These findings inthe present investigations are in agreement with the resultof earlier workers as reported above.

The quality of bioethanol, produced using twodifferent methods (SSF) and (SiSF) of fermentation wasassessed by various quality attributes such as density,viscosity and boiling point .The results analysed showed(Table 5) that with the SSF technique, the density ofbioethanol produced by co-culture was found to be 1.0218g/ml, whereas the viscosity value for bioethanol producedby co-culture was found to be 0.98 centipoise. Likewise,the value of boiling point of bioethanol produced by co-culture was found to be 78.3°C. In case of SiSF method,the density of bioethanol produced from co-culture wasfound to be 1.0245 g/ml. Likewise, the viscosity ofbioethanol produced by co-culture was found to be 0.99centipoise. Similarly, the value of boiling point ofbioethanol produced from co-culture was found to be78.1°C. Several workers have also reported the density,viscosity and boiling point of bioethanol under variedfermentation conditions (Caylak and Sukan 1998Ghobadian et al. 2008 O'Leary 2000). Bioethanolproduction from waste potato has also been reportedusing yeast (S.cerevisiae) and bacteria (Z. mobilis MTCC2427) separately by Rai et al. 2013 a, Rai et al. 2013 brespectively.

bl 'kks/k dk;Z esa ;hLV ,oa thok.kq ds lg&dYpj ds }kjk ck;ks bZFksukWydh vf/kdre mRikndrk ,oa xq.koRrk dk v/;;u fd;k x;k A bldsvUrxZr ,l-,l-,Q- o ,l-vkbZ-,l-,Q- fof/k dk mi;ksx ck;ks&bZFksukWydh vf/kDre izkfIr ds fy;s fd;k x;k A ,l-,l-,Q- fof/k dks viukdj ck;ks&bZFksukWy dk vf/kDre mRiknu 5.9% izkIr gqvk ftlds fy;srkiØe dks 30 0C ,oa le;kof/k 96 ?k.Vs ntZ dh x;h A ,l-vkbZ-,l-,Q- fof/k ds }kjk vf/kDre mRiknu 5.5% izkIr gqvk ftlds fy;srkiØe 25 0C o 96 ?k.Vs dh le;kof/k ntZ dh xbZ A xq.koRrk dkv/;;u djus ij ,l-,l-,Q- fof/k }kjk izkIr ck;ks&bZFksukWy dk /kuRo¼1.0218 g/ml½ foLdksflVh ¼0.98 lsaVh iksvkbt½ o DoFkkuad ¼78.3

0C½ tcfd ,l-vkbZ-,l-,Q- fof/k }kjk izkIr ck;ks&bZFksukWy ds fy;smijksDr ekud Øe'k% 1.0245 (g/ml), 0.99 ,oa 78.1 0C izkIr gq;sA

References

Abouzied MM, Adinarayna R (1986) Direct fermentation ofpotato starch to ethanol by coculture of Aspergillusniger and Sacchromyces cerevisiae. Appl EnvironMicrobiol 52(5):1055-1059

Ado SA, Olukotun GB, Ameh JB, Yabaya A (2009)Bioconversion of Cassava starch to ethanol in asimultaneous saccharification and fermentationprocess by co-cultures of Aspergillus niger andSaccharomyces cerevisiae. Sci World J 4(1):19-22

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

Brennan D, Tipper CFH (1967) A manual of laboratory ofexperiments in physical chemistry, Graw-HillPublishing Company, p.19

Caylak B, Sukan FV (1998) Comparison of differentproduction process for bioethanol. Turk J Chem22:351-359

Claassen PAM, Lier JBV, Contreras AML, Niel EWJV, SijtsmaL, Stams AJM, de Vries SS, Weusthuis RA (1999)Utilisation of biomass for the supply of energycarriers. Appl Microbiol Biotech 52:741-755

Ghobadian B, Rahimi H, Hashjin TT, Khatamifar M (2008)Production of bioethanol and sunflower methyl esterand investigation of fuel blend properties. J Agri Sciand Tech 10:225-232

Hoskins B, Lyons M (2009) Improving bioethanol yield: Theuse of solid-state fermentation products grown onDDGS. J Inst Brew115:64-70

Keer RW (1950) Chemistry and industry of starch. Academicpress, Inc, New York. pp 659- 672

Liimatainen H, Kuokkanen T, Kaariainen J (2004)Development of bio-ethanol production from wastepotatoes. In: Pongrácz E (ed.) proceedings of thewaste minimization and resources use optimizationconference. University of Oulu, Finland. OuluUniversity Press: Oulu. Pp 123-129

Miller G (1959) Use of di nitro salicylic acid reagent fordetermination of reducing sugar. Ana chem319:426-428

Ming-xiong H, Feng H, Bai F, Li Y, Liu X, Zhang Y ( 2009)Direct production of ethanol from raw sweet potatostarch using genetically engineered Zymomonasmobilis. Afri J Microbiol Res:721-726

O'Leary D (2000). Ethanol online: available on http://www.Ethanol.org

Sharma V, Kent D, Rausch M, Tumbleson E, Singh V (2006)

159

Starch Fermentation Characteristics for DifferentProportions of Amylose and Amylopectin. Ameri SociAgril Bio Eng St. Joseph, Michigan www.asabe.org

Shindo S, Tachibana T (2006) Production of bioethanol fromspent grain - a by-product of beer production. MasterBrew Assoc Am Tech Q 43: 189-193

Rai SK, Rajput LPS, Singh Y, Tantwai K (2013a) Studies onBioethanol production from Waste Potatoes usingyeast (S.cerevisiae) Plant Arch 13 (02): 847-853

Rai SK, Rajput LPS, Singh Y, Tantwai K (2013b) Studies onBioethanol production from Waste Potatoes usingBacteria (Z. mobilis MTCC 2427)". Appl Biol Res 15(2):154-158

Yanase S,Yamada R, Kaneko S, Noda H , Hasunuma T,Tanaka T, Ogino C, Fukuda H and Kondo A (2010)Ethanol production from cellulosic materials usingcellulose expressing Yeast. Biotechnol J 5 : 449-455

(Manuscript Receivd :17-0-2015; Accepted :25-08-2015)

160

Abstract

Present investigation was carried out to elucidate informationon nature and magnitude of genetic variation observed foryield and its attributing traits in 103 aromatic rice genotypescollected from different geographical locations. Analysis ofvariance revealed significant differences for all the traits understudy. High genotypic and phenotypic coefficient of variationwas recorded for spikelet density, harvest index, number offertile spikelets panicle-1, length breadth ratio of cooked rice,length breadth ratio, length breadth ratio of decorticated grain,number of spikelets panicle-1, panicle weight plant-1, grainyield plant-1, number of tillers plant-1, number of productivetillers plant-1 , biological yield plant-1, grain length of cookedrice, plant height, grain length, panicle index and decorticatedgrain length. The traits spikelet density, harvest index, numberof fertile spikelets per panicle, length breadth ratio of cookedrice, length breadth ratio, length breadth ratio of decorticatedgrain, number of spikelet per panicle, panicle weight plant-1,grain yield plant -1 number of tillers plant -1, number ofproductive tillers plant-1, biological yield plant-1, grain lengthafter cooking, grain length, plant height, panicle index,decorticated grain length, days to maturity, days to fifty percentflowering, grain breadth after cooking, thousand grain weight,elongation index, grain breadth, decorticated grain breadth,panicle length plant -1, days to fifty percent flowering,elongation ratio, amylose content and head rice recovery %exhibited high heritability coupled with high genetic advance.It indicates that the heritability is most likely due to additivegene effect and direct selection may be effective for thesetraits.

Keywords: variability, heritability, genetic advance,aromatic rice

Rice is the world's most important crop and a primarysource of food for more than half the world's population. It

Estimation of genetic variability for grain yield and its attributes inaromatic rice genotypes under conditions of Jabalpur, MadhyaPradesh

Neha Sohgaura, G.K. Koutu, D.K. Mishra, S.K. Singh and Arpita ShrivasatavaDepartment of Plant Breeding and GeneticsJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)Email: sohgauraneha@gmail.com

JNKVV Res J 49(2): 160-164 (2015)

is one of the most diversified crop species due to itsadaptation to a wide range of geographical, ecologicaland climatic regions. Aroma in cultivated rice is beingappreciated by consumers and represents a high-value-added trait. Also, high milling returns and good cookingquality are often associated with aromatic rice varieties.After the achievement of production goals, acquisition ofbetter intrinsic quality of rice has been identified as oneof the possible avenues and challenges of the next decade.For the development of high yielding varieties with quality,the information on variability and genetic parameters ofgrain quality attributes and their association with eachother including grain yield is essential to formulatebreeding strategies for grain quality improvement,Assessment of variability for yield and its componentcharacters is of utmost importance prior to planning foran appropriate breeding strategy. Genetic parameterssuch as genotypic coefficient of variation (GCV) andphenotypic coefficient of variation (PCV) are helpful toolsin detecting the amount of variability present in thegermplasm. Heritability and genetic advance are importantselection parameters in estimating the resultant effect inselection of best genotypes for yield and its attributingtraits. Keeping eye on these objectives the presentinvestigation was carried out to know the extent of geneticvariability in genetically diverse group of aromaticgenotypes collected from different geographical locations

Material and methods

The experimental material consisted of 103 aromatic ricegenotypes planted in a randomized complete block designwith three replication at Seed Breeding Farm, Departmentof Plant Breeding and Genetics, JNKVV, Jabalpur during

161

Kharif season 2013 and 2014. Twenty four day oldseedlings of each genotype were transplanted in threerow of 2.0 m length with a spacing of 22.5 cm betweenrows and 10 cm between plants with in rows at the rate of20 plants per row. The crop was grown with thesupplementation of fertilizer N, P and K @ 80, 60 and 40kg ha-1, respectively. Standard agronomic practices werefollowed. A composite sample of 10 plants from the middlerow was chosen to record observations on these plantsfor yield and its contributing characters. The geneticparameter for grain yield and its attributes were calculatedby employing standard statistical parameters.

Result and discussion

According to results of pooled analysis of two years,analysis of variance indicated that the differences amonggenotypes were highly significant for all the traits studied.This indicated that the genotypes had sufficient amountof variability. Phenotypic coefficient of variation estimateswere higher than the genotypic coefficient of variation forall character under study. This is due to the occurrenceof error variance into the phenotypic coefficient of varianceand indicating that they all interacted with the environmentto some degree. High genotypic and phenotypiccoefficient of variation was recorded for spikelet density,harvest index, number of fertile spikelets panicle-1, lengthbreadth ratio of cooked rice, length breadth ratio, lengthbreadth ratio of decorticated grain, number of spikeletspanicle-1, panicle weight plant-1, grain yield plant-1, numberof tillers plant-1, number of productive tillers plant-1,biological yield plant-1, grain length of cooked rice, plantheight, grain length, panicle index and decorticated grainlength, suggesting that, these characters are under theinfluence of genetic control. Hence, these characters canbe relied upon and simple selection can be practiced forfurther improvement. These results are in consonancewith Buu and Tuan (1991), Ganesan et al. (1995), Vermaet al. (2000) and Jaiswal et al. (2007) for grain yield plant-1,Chauhan (1996) and Singh and Choudhary (1996) forharvest index, Verma et al. (2000) and Gangashetty etal. (2013) for number of tillers plant-1 and number ofproductive tillers plant-1, Chauhan (1996) and Gangashettyet al. (2013) for plant height, Buu and Tuan (1991) andGangashetty et al. (2013) for Panicle weight plant-1 andgrain length, Singh and Choudhary (1996), Ashvani et al.(1997) and Verma et al. (2000) for biological yield plant-1,Ashvani et al. (1997) for number of fertile spikeletspanicle-1, Ganesan et al. (1995) and Veni et al. (2006) forlength breadth ratio, length breadth ratio of decorticatedgrain and decorticated grain length. Ta

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162

The estimates of genotypic and phenotypiccoefficient of variations were moderate for grain breadthof cooked rice, test grain weight, elongation index, grainwidth, decorticated grain width, panicle length plant-1,days to fifty percent flowering, elongation ratio, amylosecontent and head rice recovery percent. However, lowgenotypic and phenotypic coefficient of variations wereobserved for Milling percent, spikelet fertility percent,hulling percent and days to maturity which indicates theexistence of comparatively moderate variability for thesetraits, which could be exploited for improvement through

selection in advanced generations. This result was inagreement with the findings of Gangashetty et al. (2013)for grain breadth and panicle length plant-1 and Karim etal. (2007) for days to maturity.

Results of pooled analysis showed that all the traitsunder study exhibited the high heritability. However, highgenetic advance was expressed by the traits spikeletdensity, harvest index, number of fertile spikeletspanicle-1, length breadth ratio of cooked rice, lengthbreadth ratio, length breadth ratio of decorticated grain,

Table 2. Parameters of genetic variability for yield and quality attributing traits (Pooled)

Traits Mean Range GCV (%) PCV (%) h2 (bs) (%) GA GA asMin max % of mean

DTF 103.50 77.24 134.79 13.19 13.22 99.41 28.03 27.08DTM 140.28 114.09 163.29 8.86 8.88 99.42 25.52 18.19NOT 10.27 4.76 17.50 22.26 22.41 98.62 4.67 45.53NOPT 9.36 4.20 16.98 22.95 23.11 98.63 4.40 46.96PH 105.66 63.71 169.16 21.67 21.73 99.45 47.03 44.51PL 27.30 18.12 36.25 12.87 13.03 97.58 7.15 26.19PW 23.26 12.13 45.50 27.20 27.23 99.78 13.02 55.97NOS 143.01 59.40 342.76 28.77 28.88 99.23 84.44 59.04NOFS 118.23 33.68 314.67 32.88 33.02 99.18 79.75 67.45SFP 82.08 56.69 96.46 7.34 7.45 96.99 12.22 14.89SD 535.05 197.65 1227.65 32.86 32.97 99.31 360.88 67.45TGW 21.15 11.00 32.68 17.03 17.05 99.81 7.42 35.06GYPP 17.38 9.03 33.11 26.24 26.33 99.31 9.36 53.86BYPP 69.83 36.50 114.50 22.93 22.98 99.55 32.91 47.13PI 76.91 31.33 98.84 20.97 21.09 98.83 33.03 42.94HI 26.05 11.89 54.32 32.66 32.71 99.70 17.50 67.18GL 8.74 5.50 14.04 21.58 21.58 99.98 3.89 44.45GW 2.52 1.80 4.00 15.88 15.89 99.86 0.83 32.69LBR 3.57 1.96 6.00 29.44 29.45 99.94 2.17 60.63H% 72.18 60.00 87.79 8.75 8.76 99.87 13.01 18.02M% 63.23 50.92 78.73 9.66 9.68 99.63 12.56 19.86HRR% 54.99 40.76 71.49 10.50 10.52 99.65 11.87 21.59DGL 6.21 3.70 10.09 20.33 20.33 99.99 2.60 41.88DGW 2.13 1.31 3.22 14.71 14.72 99.87 0.65 30.29LBRDG 3.00 1.59 6.11 28.49 28.50 99.94 1.76 58.67GLAC 8.80 5.28 16.90 22.04 22.04 99.99 4.00 45.40GWAC 2.59 1.70 4.01 19.42 19.43 99.91 1.03 39.99LBRC 3.56 1.43 6.00 30.77 30.78 99.93 2.25 63.37ER 1.42 1.11 1.95 11.73 11.74 99.90 0.34 24.15EI 1.19 0.78 1.70 16.47 16.49 99.71 0.40 33.88AC 20.33 15.00 25.30 11.33 11.33 99.94 4.74 23.33

163

number of spikelet panicle-1, panicle weight plant-1, grainyield plant-1, number of tillers plant-1, number of productivetillers plant-1, biological yield plant-1, grain length aftercooking, grain length, plant height, panicle index,decorticated grain length, days to maturity, days to fiftypercent flowering, grain breadth after cooking, thousandgrain weight, elongation index, grain breadth, decorticatedgrain breadth, panicle length plant-1, days to fifty percentflowering, elongation ratio, amylose content and head ricerecovery percent indicating that the heritability is mostlikely due to additive gene effect and selection may beeffective. This result was in consonance with Girish et al.(2006), Chandra et al. (2009), Nandan et al. (2010) andGolam et al. (2014) for grain yield plant-1, Girish et al.(2006) and Chandra et al. (2009) for biological yield plant-1and harvest index, Mishra and Verma (2002) and Nandanet al. (2010) for thousand grain weight, Nandan et al.(2010) and Verma et al. (2014) for number of spikeletspanicle-1, Hasib et al. (2004) and Panwar (2005) for numberof fertile spikelets panicle-1, Mishra and Verma (2002) forplant height and spikelet density, Mishra and Verma(2002), Jaiswal et al. (2007) and Nandan et al. (2010) fornumber of tillers plant-1 and number of productive tillersplant-1, Mishra and Verma (2002), Hasib et al. (2004),Chaurasia et al. (2012) and Golam et al. (2014) for paniclelength plant-1 and panicle weight plant-1, Fatema et al.(2011) for grain length and amylose content, Jaiswal etal. (2007), for grain breadth and grain length after cooking,Jaiswal et al. (2007) and Fatema et al. (2011) for lengthbreadth ratio, Nandan et al. (2010) grain length beforecooking and Chaurasia et al. (2012) for Days to 50%flowering.

Conclusion

On the basis of the results of pooled analysis of two years,it is concluded that aromatic rice genotypes included instudy revealed a high degree of variability. Less differencebetween PCV and GCV indicates that these charactersare mainly controlled by the genetic factor and selectionbased on these characters will be rewarding. The presentinvestigation revealed high heritability coupled with highgenetic advance as per cent of mean for most of thecharacters indicating the presence of considerablevariation and additive gene effects. Hence, improvementof these characters could be effective through phenotypicselection.

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(Manuscript Receivd : 28-04-2015; Accepted : 30-05-2015)

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Abstract

Isabgol (Plantago ovata Forsk) belongs to the familyPlantaginaceae and commonly known as blonde psyllium,is grown for husk and seeds. The present investigation wasconducted at Research Farm, Department of Crop and HerbalPhysiology, JNKVV, Jabalpur (M.P) during the Rabi season2008-09. The treatment combination showed a significantlywide variability in biochemical parameters of Isabgol.Maximum Chlorophyll a, Chlorophyll b, chlorophyll total,Carotenoids content and Chlorophyll 'a/b' ratio was estimatedwith T12 (50% NPK of RDF + FYM + PSB+ Zn + Azotobactor)significantly followed by T7 ( FYM + PSB + Azotobactor ) at 90DAS. Significantly maximum nutrient contents viz. N (3.66%),P (0.460%), K (0.93%), Zn (40.26 mg /kg), Sulphur (0.85)and proximate parameters viz. Protein (22.88%),Carbohydrate (26.57%), Fat (16.49%), Fibres (33.01%), Ash(3.21%), Moisture (8.65%), Swelling (18.79%), Husk(37.50%), were estimated in T12 (50% NPK of RDF + FYM +PSB+ Zn + Azotobactor), which were at par with T1 (100% ofRDF 50:25:30 NPK kg/ha) T2 (50:25:30 kg/haNPK of 100%RDF + Zn 5 kg/ha) and T7 (FYM + PSB + Azotobactor ).

Keywords: Isabgol, fertilizers, Plantago ovata

The Plantago ovata belongs to the family Plantaginaceaeand is commonly known as blonde psyllium. India is thesole exporter of psyllium husk and seed to the worldmarket. In India, at present the crop is grown in an areaover 50,000 ha and about Rs.160 billions is earned annuallythrough its export. In the world psyllium is commerciallycultivated in rabi season. India is the world's largestproducer and exporter of Isabgol and considered as soleproducer of isabgol in the world. In India, Isabgol havingmajor belt spread from semi arid to arid covering largerarea in Rajasthan and Gujarat. India ranks first in Isabgolproduction (around 1 lakh tones) in world than Pakistan.

Influence of organic, chemical and integrated nutrient managementon biochemical parameters of Isabgol (Plantago ovata Forsk)

Nisha Singh Keer, S.K. Dwivedi, Anubha Upadhyay, Preeti Sagar Nayak and R.K.SamaiyaDepartment of Plant PhysiologyCollege of AgricultureJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)Email-skdwivedi_jnau@rediffmail.com

JNKVV Res J 49(2): 165-169 (2015)

Gujarat grows isabgol in 24199 hectares and producesabout 26092 MT with a productivity of about 1078 kg/ha(Anonymous 2010a). The share of Gujarat in an areaand production in the country is about 30 to 35 per cent.About 90 % of the country's production is exported andIndia is earning more than 200 crores rupees foreignexchange (Anonymous, 2010b). In Madhya PradeshIsabgol was grown on area of 4372 ha and Mandsour,Neemuch, Jabalpur and Ratlam are found to be the majorIsabgol growing districts of the State (Sharma et al. 2008).

The Isabgol husk is a membranous covering of theseed, white to light pink in colors. It is 2 to 3 mm x 0.5 to1.0 mm in dimension, translucent and odorless. It absorbsmoisture and forms a tasteless mucilaginous substancewhich constitutes the drugs. Isabgol mucilage has aremarkable property as a thicker and is therefore also formaking of ice cream in the west. The dehusked seed isaround 69% by weight of the total seed crop and is foundrich in starch and fatty oil (Atal et al. 1964). The dehuskedis used as a bird feed. Kanitkar and Pendse (1969) foundthe seed oil possessing properties of reducing cholesterollevel of serum in rabbits.

The seed contains mucilage, fatty acid and largequantities of albuminous matter which is apharmacologically active glucoside, Acubin (C13H19O8H2O)and sugar. The seed also contains some essential aminoacid like valine, alanine, glutamic acid, glycine, cystine,leucine, and tyrosine. The seed husk (epicarp of seed) ismedicinally important part which is mainly used fortreatment of number of stomach disorder viz., chronicconstipation, dysentery (Voderholzer et al. 1997) and manyothers like inflammation of the mucous membrane ofgastro-intestinal, genito-urinary tracts, ulcerative colitisand internal bleeding hemorrhoids (Thomas and Anon1992; Fernandez and Hinojosa 1999; Perez and Gomez

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1996). The medicinal nature of husk is because of itsability to form a gel in water, emollient poultice (Mhaskaret al. 2000). The mucilage acts very much like liquidparaffin. It is cheaper and is free from side effects producedby habitual use of liquid paraffin like eczema, malignantdisease of the colon, pain, etc.

Modern green revolution targeted at increased foodgrain production was large dependent on chemicalfertilizer, and high yielding crop varieties. There was asteady increase in the global use of chemical fertilizersin different agricultural systems. Continuous and largescale use of chemical fertilizer led to yield stagnationand decrease in fertilizer responsiveness of the crops.The excessive applications of chemical fertilizer causesoil sealing, fertility diminishing and residual problemsand also damaged the soils original micro-ecologicalbalance. These ill effects of chemical fertilizers emphasizea need for changes in agricultural production technologies.(Chonkar and Rattan 2000). Keeping this in view presentresearch investigation was conducted to evaluate the effectof chemical, organic and integrated nutrient managementpractices on biochemical estimations in Isabgol.

Material and methods

The present investigation conducted Dusty Acre, atResearch Farm Department of Crop and HerbalPhysiology, JNKVV, Jabalpur (MP) during the rabi seasonof 2008-2009. The soil of experimental plot was sandyloam with good drainage capacity and its analysis revealedthat it had nitrogen 206 kg/ha availability with 27 kg/haPhosphorus, 300 kg/ha potash, presence of organiccarbon (0.60%) and pH (7.5). The experiment was laidout in Randomized Block Design with twelve treatmentsand three replications with the Variety Gujrat Isabgol 1.The date of sowing and harvesting were 11th November2008 and 19th march 2009.

Details of treatments

T1- 50:25:30 kg/ha of NPK (100% recommended doseof fertilizers)

T2- 50:25:30 kg/haNPK of 100% RDF + Zn 5 kg/haT3- 10 tones FYM/haT4- 10 tonnes FYM/ha + PSB 3 kg/haT5- 10 tonnes FYM/ha + Azotobactor 3 kg/haT6- 10 tones FYM/ha + PSB 3 kg/ha+ Azotobactor 3

kg/haT7- 10 tonnes FYM/ha + PSB 3 kg/ha+ Azotobactor

3 kg/ha+ Zn 5 kg/ha

T8- NPK kg/ha of 50% RDF + 5 tonnes FYM/haT9- NPK kg/ha of 50% RDF + 5 tonnes FYM/ha +

PSB 3 kg/haT10- NPK kg/ha of 50% RDF + 5 tonnes FYM/ha +

Azotobactor 3 kg/haT11- NPK kg/haof 50% RDF + 5 tonnes FYM/ha + PSB

3 kg/ha+Azotobactor 3 kg/haT12- NPK kg/ha of 50% RDF + 5 tonnes FYM/ha +

PSB 3 kg/ha+ Zn 5 kg/ha Azotobactor 3 kg/ha

Biochemical parameters

The chlorophyll a, b and carotenoids content of leaveswere estimated at 60, 75, 90, 105 and 120 days aftersowing by Yoshida et al. 1972. Nitrogen was estimatedby AOAC (1995), Phosphorus by Koing and Johnson(1942), Potassium by Black (1965), Zinc & Sulphur byterbidiametric method and Carbohydrates by Anthronemethod. Moisture, Fat, Fibre and Ash were estimated byAOAC (1980), Husk and Swelling were estimated byThanki and Talati (1983).

Results and discussion

The leaf chlorophyll 'a' content varied significantly due tothe effect of Chemical fertilizer, FYM and Biofertilizer atdifferent stages of crop growth. The maximum Chlorophylla content was obtained in T12 (0.061, 1.27, 1.99) at 6075, 90 and T7 (1.60) 105 DAS and minimum was in T10(0.054, 0.81, 0.63) at 60, 90, 105 and T8 (0.75) 75 DASrespectively. Maximum chlorophyll 'b' was obtained in T12(0.090, 1.28, 0.67) at 60, 90, 105 and T7 (0.46) 75 DASand minimum was noted in T5 (0.070), T10 (0.16), T8 (0.36)and T10 (0.29) at 60, 75, 90 and 105 DAS respectively.

The leaf chlorophyll (a/b) ratio varied significantlydue to the effects of Chemical fertilizer FYM andBiofertilizer treatments. T5 (0.779), T8 (7.529), T12 (1.04)and T7 (2.475) exhibited the highest values and lowestvalues were noted in T12 (0.671), T7 (2.780), T10 (0.47) &T12 (0.237) at 60, 75, 90 and 105 DAS respectively.Maximum chlorophyll 'a+b' content was registered in T12(1.51), T7 (1.750), T12 (3.270), T7 (2.243) at 60, 75, 90 and105 DAS respectively. Minimum was registered in T10(0.124, 1.207, 0.923) at 60, 90 and 105 and T8 (0.850) 75DAS respectively. Significantly higher carotenoid wasnoted in T12 (0.215, 0.98, 1.04, 0.77) at 60, 75, 90 and105 DAS respectively. Minimum was exhibited in T8 (0.154,0.42, 0.42) and T10 (0.47) at 60, 75, 105 and 90 DASrespectively.

The leaf serves as the major photosynthetic organs

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Table 1. Effect of chemical fertilizer FYM and biofertilizer on biochemical parameters

Treatment 90 DAS (mg/g fresh wt.) Zinc Swelling HuskChlorophyll Chlorophyll Chlorophyll Chlorophyll Carotenoids (mg/kg) (%) (%)

'a' 'b' 'a/b' 'a+b'

T1 1.58 1.10 3.137 2.678 0.92 35.97 13.81 35.33

T2 1.87 1.11 2.903 2.977 0.98 37.18 13.82 36.36

T3 1.22 0.74 3.420 1.957 0.74 30.38 18.79 30.36

T4 1.22 0.62 3.522 1.843 0.76 28.96 18.77 31.83

T5 1.01 0.44 3.381 1.453 0.68 23.87 18.76 29.50

T6 1.61 1.09 3.266 2.702 0.64 35.93 13.81 35.16

T7 1.96 1.30 2.780 3.260 1.02 37.68 13.82 36.16

T8 1.76 0.36 7.529 2.117 0.58 20.49 18.76 26.16

T9 1.29 0.86 3.400 2.150 0.80 33.64 18.79 32.83

T10 0.81 0.40 4.876 1.207 0.47 22.67 18.76 29.16

T11 1.37 0.94 3.212 2.307 0.87 33.55 13.80 35.00

T12 1.99 1.28 2.921 3.270 1.04 40.26 13.83 37.50

Mean 1.474 0.853 3.696 2.327 0.791 31.71 16.29 32.97

SEm± 0.022 0.016 0.254 0.024 0.015 0.51 0.05 0.78

CD5% 0.066 0.046 0.744 0.069 0.045 1.49 0.16 2.30

Table 2. Effect of chemical fertilizer FYM and biofertilizer on biochemical parameters

Treatments Moisture Ash Fat Fiber Nitrogen Protein Carbohydrate Potassium Phosphorus Sulphur(%) (%) (%) (%) (%) (%) (%) (%) (%) (%)

T1 8.62 2.91 15.71 31.89 3.42 21.37 25.49 0.85 0.287 0.69

T2 8.64 3.07 16.12 32.76 3.51 21.94 26.32 0.91 0.314 0.76

T3 8.60 3.07 14.63 30.72 2.78 17.30 23.89 0.81 0.270 0.64

T4 8.60 2.65 14.37 30.64 2.64 16.50 22.62 0.80 0.260 0.73

T5 8.59 2.46 13.84 29.78 2.64 16.51 22.41 0.79 0.250 0.61

T6 8.63 3.00 15.96 32.45 3.12 21.62 25.81 0.86 0.290 0.65

T7 8.64 3.19 16.32 32.81 3.55 22.40 26.48 0.91 0.330 0.81

T8 8.54 2.26 13.48 29.02 2.62 16.40 26.09 0.75 0.240 0.53

T9 8.61 2.74 14.77 31.21 2.84 17.45 24.61 0.83 0.270 0.67

T10 8.57 2.37 13.62 29.37 2.63 17.45 21.75 0.78 0.240 0.58

T11 8.62 2.81 15.45 31.65 3.24 20.12 25.22 0.84 0.280 0.65

T12 8.65 3.21 16.49 33.01 3.66 22.88 26.57 0.93 0.460 0.85

G.Mean 8.61 2.89 15.06 31.27 3.05 19.33 24.77 0.84 0.290 0.68

SEm ± 0.04 0.05 0.09 0.07 0.10 0.23 0.03 0.01 0.033 0.02

C.D.5% 0.14 0.17 0.28 0.22 0.28 0.69 0.10 0.01 0.096 0.05

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of Isabgol and much of the difference in the rate ofphotosynthetic are due to variation in leaf chlorophyllcontents. The photosynthetic capability of plant increasewith chlorophyll concentration, Chloroplast account for25% of total dry matter production and 40% of the totalnitrogen of the leaves. The maximum leaf chlorophyllcontents was achieved in young fully expanded leaf andgradually started to decline with the advancement of ageand senescence. The leaf chlorophyll content also varieddue to soil moisture status and nutrient management etc.The treatment combination of various nutrient sourcesaffected the photosynthetic pigments significantly at allthe four crop growth stages viz. 60, 75, 90 and 105 DAS.Maximum amount of leaf chl a, b and carotenoid wereestimated with T12 (NPK kg/ha of 50% RDF + 5 tonnesFYM/ha + PSB 3 kg/ha+ Zn 5 kg/ha Azotobactor 3 kg/ha) followed by T7 at almost all the important growth stages.Both T12 and T7 were able to support nutritional needs ofplants. The nutrient application also enhanced thebiosynthesis of photosynthetic pigments by creatingfavorable cellular environment and providing nutrients.Nitrogen is involved in chloroplast development and is anessential unit of chlorophyll molecule. Similar thephosphorus and potassium which also the major nutrientand are involved in various vital process. These resultconfirmed the findings of Pirjal et al (1971) and Yuzhenwenet al (1995).

The result revealed that significantly maximummoisture and ash content was attained by T12 (8.65 &3.21) and minimum was recorded in T8 (8.54 & 2.26).Significantly maximum fat and fiber was noted in T12 (16.49& 33.01), and minimum in T8 (13.48 & 29.02) respectively.The influence of different fertilizer doses with significantmaximum nitrogen and protein content was registered inT12 (3.66 & 22.88) and minimum was registered in T8 (2.62& 16.40).

Maximum carbohydrate and potassium wasobtained in T12 (26.57 & 0.93) and minimum was obtainedin T10 (21.75) and T8 (0.75) respectively. Significantlyhigher phosphorus and sulphur content was noted in T12(0.460 & 0.85) and the lowest was recorded in T8 (0.240&0.53). Maximum Zinc, husk and swelling factor contentwas recorded in T12 (40.26, 37.50) and T3 & T9 (18.79).The minimum content was recorded in T8 (20.49, 26.16)and T11 (13.80) respectively.

Ash and moisture (%) in Isabgol also affected bytreatment combination significantly, maximum ash andmoisture were estimated in T12 at par with T7. Maximumswelling (%) was noted in T3 and T9 which was at par withT5, T8 and T10, and higher husk content was obtained in

T12 which was at par with T1, T2 and T7. These results arein conformity with the findings of Yadav et al. (2006). Thetreatment combination had a significant effect on nitrogen,phosphorus, potassium, sulphur, and zinc. Maximumnitrogen content was estimated in T12 at par with T1, T2

and T7. Maximum phosphorus, potassium and sulphurcontent was noted in T12 followed by T2 and T7. Theseresults corroborates with the findings of Kalyansundaraet al. (1984), Patel et al. (1996). Maximum Zn contentwas recorded in T12 which was at par with T2 and T7. Theresults confirmed the finding of Singh and Chouhan (1994),Mann and Vyas (1999), Solanki and Shaktawt (1999),Maheshwari et al. (2000) and Rathore and Chudawast(2003). The biochemical estimation was also made onIsabgol seed for other macro-molecules likecarbohydrates, proteins and fat. The treatmentcombinations had maximum carbohydrates, protein, fatand fiber (%) in T12 (T11 + Zn) which is at par with T7.These results are in conformity with the findings of Pendseet al. (1976), Jenner et al. (1990), Schipper (1990), Galovaet al. (1999), Yoeng (2001) and Yadav et al. (2006).

It is concluded that the treatment combinations.T12 (50% NPK of RDF + FYM + PSB + Zn + Azotobactor)and T7 ( FYM + PSB + Azotobactor ) were able to causea significant improvement in various physiologicalparameters like leaf photosynthetic pigment contents,plant nutrient contents, biochemical parameters and huskyield.

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169

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(Manuscript Receivd :05-04-2015; Accepted : 30-07-2015)

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Abstract

Field experiment was conducted on niger cv JNC-1 atResearch Farm of Project Coordinating Unit (Sesame andNiger), JNKVV, Jabalpur during winter season of 2007 underirrigated production system to evaluate the effect of combineduse of fertilizers, organic manures and micronutrients onthe growth, yield, oil content and oil yield of niger. Resultsrevealed that application of combined nutrient managementas 100% NPK + 2.5 t FYM + 20 kg ZnSO4 + 2.5 kg FeSO4/ha(A) or 100% NPK + 2.5 t/ha FYM + 20 kg ZnSO4/ha (B) or100% NPK + 2.5 t FYM + 2.5 kg FeSO4/ha (C) significantlyimproved the branches/plant, capitulate/plant, seeds/capitulam and seed yield of niger over application 100%NPK or 50% of NPK + organic manures as well asmicronutrients. Combined nutrient management as A or Bor C were equally good and effective for increasing the NMRand B:C ratio. These nutrient managements wereremunerative as compared to application 100% NPK or 50%of NPK + organic manures + micronutrients.

Keywords : Organic manure, micronutrients, fertilizer,oil content, economics

Niger [Guizotia abyssinica (L.f.) Cass] is one of the oilseedcrops predominantly grown in tribal areas of the MadhyaPradesh and India. Its seeds contain a considerablequantity of edible oil (38 to 43%), protein (20%) and (12%)sugar (Gentinet and Teklewold 1995). India is the chiefproducer of niger seeds in the world by contributing secondand fourth position in the acreage, and annual productionof the world. It is grown in an area of nearly 4.14 lakhhectare in the country with an annual production of 1.08lakh tonnes and productivity of 2.61 kg seeds/ha. MadhyaPradesh covers nearly 1.06 lakh hectare area under this

JNKVV Res J 49(2): 170-174 (2015)

Effect of organic manures, micronutrients and chemical fertilizerson growth and yield of Niger

B.S. Solanki*, M.R. Deshmukh** , V.K. Katara* and Alok Jyotishi***AICRP on Niger, ZARS, Chhindwara 480 001 (MP)**Project Coordinating Unit, Sesame and Niger,Jawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482 004 (MP)Email : deshmukhmohan24@gmail.com

crop with annual production of 0.24 lakh tonnes andproductivity of 224 kg seeds/hectare (Damodaram andHedge 2007).

Niger cultivation is confined on marginal and sub-marginal lands with the use of negligible agro-inputs, owingto its low in productivity. Niger is widely grown duringkharif (rainy season and rainfed crop in Madhya Pradesh,but it performs well during winter season also due to itsphoto incentive nature under irrigated production system.Its productivity is better with superior quality seeds duringwinter season crop than kharif season crop in MadhyaPradesh (Agrawal et al. 1996). It has potential to produceseed yields upto 800 kg/ha on the research farms withthe adoption of improved crop varieties and productiontechnologies. It responds very well to fertilizer applicationconsiderably higher quantity in balanced manner.Application of adequate quantity of fertilizers isunaffordable to most of the niger growers, hence combinednutrient management consisting of various organic,inorganic and biological sources appears to be an efficientalternative for its appropriate nutrient management.Information pertaining to nutrient management in niger ismeager for the Jabalpur region of the state. Therefore toevaluate the effect of combined use of fertilizers, organicmanures and micronutrients on the growth and yield ofwinter sown niger under irrigated production system thepresent investigation was carried out.

Material and methods

Field experiment was conducted on niger cv. JNC-1 atResearch Farm, Project Coordinating Unit (Sesame and

171

Niger) during winter season of the year 2007. The soil ofthe experiment field was clay loam in texture, neutral inreaction (pH 7.50) low in OC (0.44%) content with normalEC (0.43 dS/m) and analyzing in low available N (198 kg/ha) and available S (8.2 kg/ha), medium in available P(18.6 kg/ha) and available Zn (2.16 kg/ha); high in availableK (322.6 kg/ha) and available Fe (17.86 kg/ha) contents.Twelve treatments consisting with different nutrientmanagement (Table 1) were tested in a randomized blockdesign with four replications. The seeds were treated withThiram @ 3 g/kg seed and sown on October 16, 2007 inrows 40 cm apart by drilling 5 kg seeds/ha at a depth of3 cm. A light irrigation was given for germination of seed.Then subsequent three irrigations were given at an intervalof 20 days through flood method of irrigation. Therecommended dose of fertilizers (RDF) was 40 kg N + 30kg P2O5 + 20 kg K2O/ha. The FYM and Neem Oil Cake(NOC) contained 0.5% N, 0.2% P and 0.5% K and 5.22N, 1.08% P and 1.48% K respectively. The full quantityof FYM, NOC, ZnSO4 and FeSO4, phosphorus, potassiumand one third quantity of nitrogen was applied as basaldose as per the treatments. Remaining two third quantityof nitrogen was top dressed at 30 DAS. FYM and NOCwere applied as per treatment by broadcast method andthen well mixed in the soil, whereas inorganic fertilizerswere placed in rows and mixed with soil at the time ofsowing. Plant population was maintained by thinning at12 DAS. The crop was kept weed free by manual handweedings twice at 20 and 40 days after sowing (DAS).The crop was harvested on February 2, 2008. Data onvarious growth parameters, yield attributes and finallyseed yields were recorded. The oil content of seeds wasestimated by using NMR equipment in the laboratory ofProject Coordinating Unit (Sesame and Niger), JNKVV,Jabalpur. The oil yield was also determined treatmentwise on the basis of oil content in seed. The economicswas calculated using the prevailing prices for the inputsand produce during that period of time. Finally data werestatistically analysed for the interpretation of the results.

Results and discussion

Effect on growth parameters and yield attributes

The plant height significantly varied due to the effect ofdifferent treatments. Application of 50% RDF - T2significantly produced shorter plants (130.1 cm) than allother treatments which were at par. Remaining treatmentshad plant-height ranging from 137.2 to 138.7 cm (Table1). This may be due to variations in total nutrientsapplication were much high, which affected the plant-height invariably. Thus, it could be said that the nutrient

supplied under different treatments can not be fulfilledoptimum requirement by the crop, therefore plant heightvaried due to effect of different treatments. Application50% NPK (T2) produced minimum number of branches(5.80/plant) whereas remaining treatments producedmaximum number of branches/plant (6.86 to 7.08) whichdid not vary with each other. It is evident from the earlierdiscussion that these treatments synthesized more foodmaterials and photosynthates and ultimately producedsignificantly more DMP/m2 by the plants than other nutrientmanagement. Because of the facts these treatmentsmight have resulted in the production of higher number ofbranches/plant than other plants. The marked superiorityin these growth parameters with adequate nutrient supplyis advocated in niger by earlier researchers (Kachapurand Radder 1983 a&b and Trivedi et al. 1988).

The number of capitulae/plant, seeds/capitulamsignificantly varied due to different nutrient managementtreatments, whereas 1000-seed weight and harvest-indexwere unaffected (Table 1). The number of capitulae/plantwas maximum (33.60) with T9 - 100% NPK + 2.5 t FYM +20 kg ZnSO4 + 2.5 kg FeSO4/ha which was at par tothose recorded with T7 (33.25), T8 (33.05) and T12 (30.08).It means all treatments receiving 100% NPK or plusorganic manures as well as micronutrients producedsignificantly higher number of capitulae/plant than thoseobtained with 50% NPK or plus other nutrients except toT12. Similarly, T9 produced maximum number of seeds/capitulam (26.06) closely followed by rest of thetreatments except T2 (22.37), T6 and T10 (22.60), T11(23.55), T4 (23.61) and T12 (23.66) which had significantlylesser number of seeds/capitulam. Application of 50%NPK (T2) was the poorest for this character. Number ofseeds/capitulam was significantly superior with thetreatments associated with 100% NPK or plus than thatof with 50% NPK or plus. The values pertaining to 1000-seed weight was superior numerically with T9 (4.16g) andharvest-index with T11 (14.34g) treatment than the others.The treatment T9 produced more dry matter production(DMP/m2) by the plants than other treatments becauseof more accumulation of food materials andphotosynthates by this treatment. This treatmentproduced better plant growth in terms of more branchingand in slightly taller plants than the treatments receivingother nutrients management. Therefore, T9 resulted inproduction of superiority in above said yield attributes overothers. These results also corroborated the findings ofTrivedi (1988) and Paikary et al. (2001).

Effect on seed, stover and oil yields

Seed yields are governed by various yield attributes.

172

Tabl

e 2.

Effe

ct o

f nut

rient

man

agem

ent t

reat

men

ts o

n oi

l con

tent

, oil

yiel

d an

d ec

onom

ics

of n

iger

at J

abal

pur (

Mad

hya

Prad

esh)

Nut

rient

man

agem

ent

Oil

Oil

Cos

t of

Gro

ssN

etBe

nefit

:co

nten

tyi

eld

cultiv

atio

nm

onet

ary

mon

etar

yco

st(%

)(k

g/ha

)(R

s/ha

)re

turn

sre

turn

sra

tio(R

s/ha

)(R

s/ha

)T 1 -

100

% N

PK32

.70

143

1123

018

840

7610

1.68

T 2 - 5

0% N

PK31

.65

108

1071

014

680

3970

1.37

T 3 - T

1 + 2

.5 t/

ha F

YM32

.17

150

1248

020

092

7612

1.61

T 4 - T

2 + 2

.5 t/

ha F

YM31

.76

114

1196

015

409

3449

1.29

T 5 - T

1 + 2

.5 t/

ha N

OC

32.1

015

012

730

2015

274

221.

58T 6 -

T2 +

0.5

t/ha

NO

C31

.04

110

1221

015

220

3010

1.27

T 7 - T

1 + 2

.5 t/

ha F

YM +

20

kg/h

a Zn

SO4

32.6

916

012

580

2103

284

121.

67T 8 -

T1 +

2.5

t/ha

FYM

+ 2

.5 k

g/ha

FeS

O4

32.4

915

512

505

2058

080

751.

64T 9 -

T1 +

2.5

t/ha

FYM

+ 2

0 kg

/ha

ZnSO

4 + 2

.5 k

g/ha

FeS

O4

32.6

016

012

605

2122

986

241.

68T 10

- T 2 +

2.5

t/ha

FYM

+ 2

0 kg

/ha

ZnSO

432

.45

120

1206

015

980

3920

1.33

T 11 -

T 2 + 2

.5 t/

ha F

YM +

2.5

kg/

ha F

eSO

431

.85

117

1198

515

819

3834

1.32

T 12 -

T 2 + 2

.5 t/

ha F

YM +

20

kg/h

a Zn

SO4 +

2.5

kg/

ha F

eSO

432

.13

124

1208

516

587

4502

1.37

SEm

±0.

462.

0-

649

308

0.01

CD

(P=0

.05)

NS

5.8

-18

7088

90.

04Sa

le p

rice

of n

iger

see

d R

s 40

/kg

and

stov

er 0

.50/

kg

Tabl

e 1.

Effe

ct o

f nut

rient

man

agem

ent t

reat

men

ts o

n gr

owth

par

amet

ers,

yie

ld a

ttrib

utes

, see

d yi

eld

and

stov

er y

ield

of n

iger

at J

abal

pur (

Mad

hya

Prad

esh)

Nut

rient

man

agem

ent

Plan

tBr

anch

esC

apitu

lae

Seed

sTe

stSe

edSt

over

Har

vest

heig

ht/p

lant

/ pla

nt/c

apitu

law

eigh

tyi

eld

yiel

din

dex

(cm

)(#

)(#

)(g

)(k

g/ha

)(k

g/ha

)(%

)T 1 -

100

% N

PK13

7.8

6.93

32.5

025

.04

4.02

436

2800

13.4

7T 2 -

50%

NPK

130.

15.

8024

.10

22.3

74.

0434

120

8014

.06

T 3 - T

1 + 2

.5 t/

ha F

YM13

8.0

6.91

32.8

525

.36

4.11

466

2905

13.8

2T 4 -

T2 +

2.5

t/ha

FYM

137.

26.

8627

.60

23.6

14.

0835

821

7914

.11

T 5 - T

1 + 2

.5 t/

ha N

OC

138.

36.

9532

.80

25.3

64.

0246

729

4513

.68

T 6 - T

2 + 0

.5 t/

ha N

OC

138.

76.

9026

.50

22.6

04.

1235

421

2014

.30

T 7 - T

1 + 2

.5 t/

ha F

YM +

20

kg/h

a Zn

SO4

138.

77.

0833

.25

25.2

64.

1148

830

2513

.89

T 8 - T

1 + 2

.5 t/

ha F

YM +

2.5

kg/

ha F

eSO

413

8.6

7.04

33.0

525

.96

4.00

477

3000

13.7

1T 9 -

T1 +

2.5

t/ha

FYM

+ 2

0 kg

/ha

ZnSO

4 + 2

.5 k

g/ha

FeS

O4

138.

67.

0533

.60

26.0

64.

1649

230

9813

.69

T 10 -

T 2 + 2

.5 t/

ha F

YM +

20

kg/h

a Zn

SO4

137.

56.

9429

.90

22.6

04.

1237

122

8013

.99

T 11 -

T 2 + 2

.5 t/

ha F

YM +

2.5

kg/

ha F

eSO

413

7.5

7.00

28.1

023

.55

4.10

368

2193

14.3

4T 12

- T 2 +

2.5

t/ha

FYM

+ 2

0 kg

/ha

ZnSO

4 + 2

.5 k

g/ha

FeS

O4

137.

07.

0030

.08

23.6

64.

0838

523

7513

.94

SEm

±1.

60.

121.

281.

060.

0417

.964

.40.

86C

D (P

=0.0

5)4.

90.

343.

703.

21N

S51

.519

5.1

NS

(#) =

Num

ber;

Rec

omm

ende

d do

se o

f fer

tiliz

er (R

DF)

= 4

0 kg

N +

30

kg P

2O5 +

20

kg K

2O/h

a

JNK

VV

Res

J 4

9(2)

: (

2015

)

173

Consequence upon the superiority in yield attributes withT9 recorded significantly maximum seed yield (492 kg/ha) among all treatments which was statistically at parto all those treatments receiving 100% NPK plus othernutrients viz., T7 (488 kg/ha), T8 (47 kg/ha), T5 (467 kg/ha)and T3 (466 kg/ha). But application of 100% NPK (T1)produced significantly lesser seed yield (436 kg/ha) thanT9, being at par to remaining treatments as mentionedabove (Table 1). The treatments associated with 50% NPKor plus to it produced significantly lesser seed yields thanall treatments receiving 100% NPK or plus to it. Amongthem T2 yielded minimum seed yield (341 kg/ha) whichwas at par to all the treatments of 50% NPK or plus.However, T12 gave the higher seed yield (385 kg/ha). Thesuperiority in seed yield due to micronutrient applicationsupplemented with 100% RDF over 100% RDF aloneattributed mainly due to effect of additional quantity ofmicronutrients and organic manures by the crop. Thus, itcould be concluded that the present recommended doseof fertilizer was not enough to meet the nutrientrequirement of niger crop under existing agroclimaticconditions. The superiority in higher seed yields with T9,T7, T8, T5 and T3 treatments mainly attributed to thesuperiority in different growth parameters and yieldattributes with the efficient utilization of the nutrients bythe plants. Several workers have emphasized for suchimproved nutrient use efficiency through the applicationof integrated use of fertilizers, organic manures andmicronutrients in niger under varying agroclimaticconditions (Guggari et al. 1995 and Paikary et al. 2001).

The stover yields were also significantly maximum(3098 kg/ha) with T9 among all treatments but it was atpar to T7 (3025 kg/ha), T8 (3000 kg/ha), T5 (2945 kg/ha)and T3 (2905 kg/ha), than 100% RDF T1 (2800 kg/ha)Table 1. Nutrient use efficiency was improved by combineduse of fertilizers, organic manures and micronutrients.Better vegetative growth of plants interms of morebranching and taller plants resulted into higher stoveryields. Similar results are also reported by severalresearchers (Deshmukh et al. 2002 and 2007, Thakurand Umat 2007) from their investigations. Though oilcontents in seed ranging from 31.04 to 32.70% underdifferent treatment did not differ with each other (Table 2).The oil yield/ha significantly varied with them mainly dueto variations in seed yields. The oil yield was higher (160kg/ha) with T9 and T7 treatments which were at par to 155kg/ha in T8. The next best treatments were T5 andT3 withsimilar oil yields of 150 kg/ha. Application of 100% NPK(T1) produced significantly lesser oil yields (143 kg/ha)than above treatments, but it was significantly higher thanother treatments (108 to 124 kg/ha) receiving 50% NPK+ FYM or NOC or ZnSO4 or FeSO4. The oil yield wasminimum (108 kg/ha) with 50% NPK (T2) which was at

par to the treatments associated with the application of50% N + organic manures as well micronutrients viz., T4(114 kg/ha), T6 (110 kg/ha), T11 (117 kg/ha), T10 (120 kg/ha) and T12 (124 kg/ha). These treatments produced thelowest seed yield, hence they produced the lower oil yields(Table 2). Similar results are also reported by Paikary etal. (2001), Trivedi and Ahlawat (1991 and 1993), andDeshmukh et al. (2007).

Effect of Economic viability

The common cost for niger cultivation was Rs 10190/hafor all treatments, the cost of fertilizers/manures/micronutrients whatever applied under a particulartreatment alongwith cost of their application was addedwith common cost of cultivation. Thus, the cost ofcultivation varied from Rs 10780/ha to 12605/ha bysupplementing 50% NPK and 100% NPK + 2.5 t FYM +20 kg ZnSO4 + 2.5 kg FeSO4/ha respectively (Table 2).The cost of cultivation increased due to increased quantityof NPK fertilizers, FYM as well as NOC and micronutrients(ZnSO4 or FeSO4 or both). T9 led to record maximum GMRof (Rs 20152/ha) and T3 (Rs 20082/ha). The next besttreatment with regard to GMR (Rs 18840/ha) was 100%NPK (T1) which was also significantly higher than all 50%NPK or plus viz., T12 (Rs 16587/ha), T10 (Rs 15980/ha),T11 (Rs 15819/ha), T6 (Rs 15220/ha), T4 (Rs 15409/ha)and T2 (Rs 14680/ha). Application of 50% NPK - T2 alonehad minimum GMR (Rs 14680/ha). Application of 50%NPK (T2) alone had minimum GMR (Table 2). The highestNMR (Rs 8624/ha) was recorded with T9 among alltreatments which was at par to T7 (Rs 8472/ha) and T8(Rs 8075/ha). Application of 50% NPK + 0.5 t NOC/ha(T6) led to register the lowest NMR (Rs 3010/ha). Theprofitability (1.68) was maximum with application of 100%NPK T9, which was at par to T7 (1.67) and T8 (1.64). TheB:C ratio was minimum with T6 (1.27).

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174

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References

Agrawal KK, Jain KK, Sharma RS, Kashyap ML (1996)Response of winter niger [Guizotia abyssinica (L.f.)Cass] to time of sowing and fertility levels. Journalof Oilseeds Research 13(1):122-123.

Gentinet A, Teklewold (1995) An agronomic and seed qualityevaluation of niger [Guizotia abyssinica (L.f.) Cass]germplasm grown in Ethiopia. Plant Breed 144:375-376

Damodaram T, Hegde DM (2007) Oilseeds Situation : AStatistical Compendium. Directorate of OilseedsResearch, Hyderabad 128-136

Kachapur MD, Radder GD (1983a) Response of nigergenotype with varying levels of row spacing andfertility. Mysore J agric Sci 17:115-120

Kachapur MD, Radder GD (1983b) Studies on growthanalysis in niger [Guizotia abyssinica (Lf.) Cass].Mysore J agric Sci 17:225-229

Deshmukh MR, Jain HC, Duhoon SS, Goswami U (2002)Performance of niger (Guizotia abyssinica (L.f.)Cass] influenced by inorganic fertilizers, FYM andbio-fertilizers in different soil types. J Oilseeds Res19(1):79-81

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

Guggari AK, Chandranath HT, Manjappa K, Pujari BT (1995)Response of niger to application of macro and micronutrients in combination with farm yard manure.Farming Systems 11(3/4):51-53

Paikary RK, Mishra PJ, Mohapatra AK, Halder J, Panda S(2001) Response of niger to secondary andmicronutrients in acid red soil. Annals Agric Res22(1):140-142

Thakur NS, Umat Rajeev (2007) Integrated nutrientmanagement in niger. JNKVV Res J 41(1):36-39

Trivedi SJ (1988) Effect of different levels of nitrogen andphosphorous on growth, yield attributes and yieldof niger [Guizotia abyssinica (L.f.) Cass]. MSc(Ag)Thesis Gujarat Agricultural University, Ahmedabad,India, p 71

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

Trivedi SJ, Ahlawat RPS (1993) Quality studies in niger[Guizotia abyssinica (L.f.) Cass] in relation tonitrogen and phosphorus. Gujarat agric Univ Res J18(2):92-93

(Manuscript Receivd : 30-04-2015; Accepted : 23-07-2015)

175

Abstract

Field experiment on niger cv. JNC-9 was conducted duringautumn season of 2008 at Research Farm, ProjectCoordinating Unit (Sesame & Niger) JNKVV, Jabalpur (MP)with objective to evaluate the suitable integrated nutrientmanagement for remunerative productivity of autumn sownseason niger crop under irrigated production system. Resultsrevealed that integrated nutrient management consisting of75% RDF + Azotobacter + PSB, 50% RDF + 2 tVermicompost/ha and 50% RDF + 5 t FYM/ha werecomparable with each other for growth parameters, yieldattributes and seed yields. Application of 100% RDF recordedmaximum seed and stover yields (665 and 3689 kg/ha),fetched higher NMR and B:C ratio (Rs 8996/ha and 1.94)and found remunerative for niger production.

Keywords: Biofertilizers, organics, inorganic fertilizers,Seed, Oil yield and Economics

Niger [Guizotia abyssinica (L.f.) Cass] is one of theimportant oilseed crops of tribal areas in the country andstate. Niger seeds contain a considerable quantity of edibleoil (38 to 43%), protein (20%), sugar (12%) and mineralsfor human and animal meals (Gentient and Teklewod1995). Its cakes obtained after extraction of oil are usedfor cattle feed and the low grade oil-cake is used as organicmanure in the agricultural lands. It is cultivated in 4.14lakh hectare area in the country with annual productionof 1.08 lakh tonnes and productivity of 261 kg seeds/ha.Madhya Pradesh covers nearly 1.06 lakh hectare areawith annual production of 0.24 lakh tonnes and productivityof 224 kg seeds/hectare (Damodaram and Hegde 2007).

Use of biofertilizers, organic manures and inorganic fertilizers forautumn sown niger yield maximization

R.R. Badole, M.R. Deshmukh, B.S. Solanki, V.K. Katara and Alok JyotishiProject Coordinating Unit (Sesame and Niger)Jawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482 004 (MP)AICRP - Niger*ZARS Chhindwara (MP)Email : deshmukhmohan24@gmail.com

It can be sown from July to August months underrainfed conditions as well as during autumn season withirrigation facilities. It has tolerance to streams of weatherfluctuations with less susceptibility to damages causedby animals, birds, insects and diseases etc. (Sharmaand Kewat 1998). These features lure the farmers for itscultivation in different parts of the country. In spite of thesepeculiarities, the cultivation of this crop is still confinedon marginal and sub-marginal lands with the use ofnegligible agro-inputs, which results in low productivity. Itis widely grown during kharif (rainy) season, but itperforms well during autumn season also due to itsphotoinsentive nature. Its productivity is better withsuperior quality seeds during autumn than kharif seasonin Madhya Pradesh (Agrawal et al. 1996).

This crop has potential to produce seed yields upto800 kg/ha on the research farms with the adoption ofimproved crop varieties and production technologies. Itcan be grown with the use of negligible quantity of manuresand fertilizers, it responds well to considerably higherquantity of chemical fertilizers in balanced manner. Since,the adequate quantity of fertilizer application isunaffordable by the most of the niger growers, hence,this quantity can be fulfill through using various organic,inorganic and biological sources which appears to be analternative sources of nutrients for its proper nutrientmanagement of nutrients. The information pertaining tomanagement of nutrients in proper and balanced mannerin niger is not available for Kymore plateau zone of MadhyaPradesh. Keeping these facts in view a field experimentwas conducted during the autumn season of 2008 toevaluate suitable integrated nutrient management forremunerative productivity of niger under irrigated productionsystem.

JNKVV Res J 49(2): 175-177 (2015)

176

Material and methods

A field experiment was conducted on niger cv JNC-9 atResearch Farm, Project Coordinating Unit (Sesame andNiger) during autumn season of 2008. The soil of theexperimental field was clay loam in texture, neutral inreaction (pH 7.40), low in OC (0.44%) content with normalEC (0.43 dS/m) and analysing low in available N (220 kg/ha, medium in available P (19 kg/ha) and high in availableK (323 kg/ha) contents. Ten treatments consisting withdifferent nutrient management (Table 1) were tested in arandomized block design with four replications .Therecommended dose of fertilizers (RDF) was 40 kg N + 30kg P2O5 + 20 kg K2O/ha. The Vermicompost and FYMcontained 2.1% N, 1.25% P and 0.84% K and 0.5% N,0.8% P and 0.50% K respectively. The full quantity ofVermicompost, FYM, phosphorus, potassium and onethird quantity of nitrogen was applied as basal dose asper the treatments. Remaining two third quantity of nitrogenwas top dressed at 30 DAS. FYM @ 5 t/ha was appliedas per treatment by broadcasting and well mixing in thesoil whereas inorganic fertilizers were placed in rows. TheVermicompost was applied in furrows before sowing @ 2t/ha in allotted plots. The seeds treated with Thiram @ 3g/kg seed were sown on October 6, 2008 in rows 30 cmapart by drilling 5 kg seeds/ha at about 3 cm depth in allplots. Just after the sowing a light irrigation was given forgermination of seeds. Then subsequent three irrigationswere given at an interval of 20 days through flood irrigation.Plant population was maintained by thinning at 12 DAS.The crop was kept weed free by hand weeding twice at20 and 40 DAS. The crop was harvested on January 28,2009. Data on growth parameters, yield attributes andfinally seed yields were recorded. The oil content of seedswas estimated by using NMR equipment in the laboratoryof Project Coordinating Unit (Sesame and Niger), JNKVV,Jabalpur. The treatmentwise oil yield was also determinedon the basis of oil content in seed. The economics wascalculated using the prevailing prices for the inputs andproduce during that period of time. Finally data werestatistically analysed for the interpretation of the results.

Results and discussion

Effect on growth parameters and yield attributes

Significantly the highest plants were noted in T5 - 50%RDF + 2 t VC/ha (194 cm) which were higher than T2, T4,T7, T8, T9 and T10 being comparable with each other. Themaximum number of branches/plant (13.63) was recordedin T1 - (40 + 30 + 20 N:P:K kg/ha) among all the treatmentsbeing significantly superior over T8 and T9 only. It is

noteable here that nutrient management under T3 (75%RDF + Azotobacter + PSB), T5 (50% RDF + 2 tVermicompost/ha), T7 (50% RDF + 5 t FYM/ha) and evenT2 (50% RDF + Azotobacter + PSB) were comparable toT1 (100% RDF) with regard to these both growthparameters. Thus, it is obvious that combined use ofinorganic, organic and biological sources of nutrient withjudicious proportion might be advantageous for the propergrowth of crop. Though, application of nutrients throughorganic manures only under T10 (5 t FYM/ha + 1 tVermicompost/ha) was inferior to T1 (100% RDF), it wasat par to T2 (50% RDF + Azotobacter + PSB), T5 (50%RDF + 2 t Vermcompost/ha) and T7 (50% RDF + 5 tFYM/ha). Thus, it could be said that average growth ofcrop could be achieved even in the first cropping seasonwith the use of organic manures and biological fertilizers.Similar results are also reported in niger crop by Trivedi(1988).

The yield attributes i.e. number of seeds/capitula,test-weight and harvest-index did not differ significantlydue to effect of different nutrient management. However,values were numerically superior with T1, T3, T5 and T7than other treatments being at par with each otherestablished their marked superiority with regard of numberof capitulae/plant than the remaining nutrient managementtreatments. These treatments received desired quantityof nutrients, which resulted into production of superiorgrowth parameters due to greater of dry matter production,which ultimately attributed to produce superior yieldattributes particularly significantly more number ofcapitulate/plant. The treatments T8, T9, T10, T4 and T6 couldnot fulfil the nutrient requirement of plants as per theirneeds. Hence, they produced inferior growth parametersand ultimately attributed to produce lesser values ofdifferent yield attributes. Similar findings are also quotedby the earlier researchers from their investigations (Trivedi1988).

Effect on seed, stover and oil yields

Nutrient management through application of 100% RDFproduced maximum seed and stover yields (665 and 3689kg/ha) among all the treatments being very close to theseobtained with the application of 75% RDF + Azotobacter+ PSB (658 and 3737 kg/ha), 50% RDF + 2 t VC/ha (646and 3730 kg/ha) and 50% RDF + 5 t FYM/ha (629 and3679 kg/ha). These treatments had better growth of plantswhich resulted superiority in producing the superior yieldattributes and finally attributed to produce significantlyhigher seed and stover yields. The superiority in growthparameters like plant-height and branches/plant directlyattributed to produce higher stover yield while superiority

177

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References

Agrawal KK, Jain KK, Sharma RS, Kashyap ML (1996)Response of winter niger [Guizotia abyssinica (L.f.)Cass] to time of sowing and fertility levels. JOilseeds Res 13(1):122-123

Damodaram T, Hegde DM (2007) Oilseeds Situation : AStatistical Compendium. Directorate of OilseedsResearch, Hyderabad, 128-136

Deshmukh MR, Jain HC, Duhoon SS, Goswami U (2002)Performance of niger (Guizotia abyssinica (L.f.)Cass] influenced by inorganic fertilizers, FYM andbio-fertilizers in different soil types. J Oilseeds Res19(1):79-81

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

Gentinet A, Teklewold (1995) An agronomic and seed qualityevaluation of niger [Guizotia abysinica (L.f.) Cass]germplasm grown in Ethiopia. Plant Breed 144:375-376

Sharma RS, Kewat ML (1998) Niger does well under farmingsituation constraints. Indian Farming 47(11):15-24

Trivedi SJ (1988) Effect of different levels of nitrogen andphosphorous on growth, yield attributes and yieldof niger [Guizotia abyssinica (L.f.) Cass]. MSc(Ag)Thesis submitted to Gujarat Agricultural University,Ahmedabad, India, p 71

Trivedi SJ, Ahlawat RPS (1993) Quality studies in niger[Guizotia abyssinica (L.F.) Cass] in relation tonitrogen and phosphorus. Gujarat agric Univ Res J18(2):92-93

in yield attributes particularly significantly more numberof capitulae/plant resulted into production of significantlyhigher seed yields. Thus, it could be believed that nutrientmanagement under 75% RDF + Azotobacter + PSB, 50%RDF + 2 t VC/ha and 50% RDF + 5 t FYM/ha were equallygood to application of recommended dose of nutrientsthrough fertilizers only. These results also corroboratedthe findings of other workers from their studies on nutrientmanagement from different locations of the country(Deshmukh et al. 2002). Though, oil contents in seedwas ranging from 32.76 to 33.80 did not deviate markedlydue to various nutrient management treatments. Oil yieldsignificantly varied with them ranging from minimum oilyield of 197 kg/ha with 5 t FYM/ha + Azotobacter + PSBto maximum yield of 221 kg/ha with application of 100%RDF due to higher seed yields. Similar results are alsoreported in niger crop by earlier researchers [Trivedi andAhlawat (1993) and Deshmukh et al. (2007)].

Economics of the treatments

The cost of cultivation was minimum (Rs 9480/ha) with100% RDF, which slightly increased as Rs 9550/ha andRs 9865/ha when Azotobacter + PSB were combinedwith 50% and 75% RDF, respectively. It was againincreased from Rs 10535/ha to Rs 11720/ha, whendifferent nutrient management were applied by integrationof organic, inorganic and biological sources as well astotal nutrients through organic sources only. The GMRwas minimum (Rs 16650/ha) with application of 5 t FYM/ha + Azotobacter + PSB because of the lowest seed andstover yields, while it was maximum (Rs 18488/ha) withthe application of 100% RDF. The later treatment wascomparable to those realized with 75% RDF + Azotobacter+ PSB (Rs 18318/ha), 50% RDF + 2 t VC/ha (Rs 18027/ha) and 50% RDF + Azotobacter + PSB. Thesetreatments were markedly higher than rest of thetreatments. Application of 100% RDF recorded highestNMR (Rs 8996/ha) among all the treatments whereas,the minimum NMR of Rs 5230/ha was realized with theapplication of 5 t FYM/ha + Azotobacter + PSB.Application of 100% RDF led to register significantlymaximum B:C ratio (1.94) among all the treatments, whileit was the minimum (1.44) with 5 t FYM/ha + 1 t VC/habeing at par with the application of 5 t FYM/ha +Azotobacter + PSB (1.45).

ifj;kstuk leUo;u bZdkbZ fry ,oa jkefry t-us-d`f"k fo'o fo|ky;tcyiqj ¼e/;izns'k½ ds vuqla/kku iz{ks= Ikj flafpr mRiknu fof/k dsvarxZr 'kjn ekSle o"kZ 2008 esa jkefry dh mUur fdLe ts-,u-lh-& 9 ij ijh{k.k iz;ksx fd;k x;k A ftldk m|s'; 'kjn ekSle esa cksbZ (Manuscript Receivd : 30-04-2015; Accepted :05-08-2015)

178

Abstract

The effect of dates of sowing on chickpea production andproductivity in rainfed rice fallow of Madhya Pradesh wasinvestigated using three sowing dates within each sowingdate during the 2007-08, 2008-09 and 2009-10 seasons atSatna district of Madhya Pradesh (MP). Sowing timesindependently influenced yield in chickpea under semi-aridconditions in M.P. The results were season dependent. Therewere differences in the total yield of chickpea between thesowing dates. These results indicate that there is goodpotential for chickpea production (up to 4169.44 kg/ha) insowing date in 1st fortnight of November this area whichcould be exploited to diversify grain legume production inMP.

Chickpea [Cicer arietinum (L.) family leguminaceae] isone of the important pulse crop cultivated during postrainy season under rainfed condition. In India, chickpeais cultivated over an area of 6.31 m ha (52.5% of world)producing 5.08m t (55% of world) with an averageproductivity of 806 kg/ha. Historically, Madhya Pradeshhas been the major pulse producing state in the country.The primary purpose of this study is to diagnose effect ofdates of sowing on chickpea inhibiting production andproductivity of pulses in the chief pulse producing stateof Madhya Pradesh. Madhya Pradesh ranked first bothin terms of area (19.8 percent) and production (20.9percent) of pulses in India. In Madhya Pradesh, nearly90% of chickpea is cultivated under rainfed condition grownunder residual soil moisture during rabi. Date of sowingis one of the important agronomic factors affectingproductivity of most of arable crops owing to changes inenvironmental conditions to which phenological stagesof crop are exposed. The modified environment resultingfrom different dates of sowing may thus influence the crop

JNKVV Res J 49(2): 178-179 (2015)

Effect of dates of sowing on chickpea production and productivityin rainfed rice fallow land in Madhya Pradesh

M.G. Usmani, S.K. Singh, R.K. Tiwari and S.K. RaoCollege of AgricultureRewa 486001 (MP)Jawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482 004 (MP)

growth and development. Chickpea is usually sownbetween mid October to mid November. However, sowingsare often delayed when grown in sequence with kharifcrops. The delayed sowing in such cases results in drasticreduction in yield. Several research workers have reportedthe suitability of early maturing chickpea varieties fordelayed sowing, Hence, it was desired to workout theoptimum date of sowing for the chickpea. With this view,an attempt was made to study the determination ofsuitable planting date in order to increase chickpea yieldin Kymore and Satpura hills of MP under rainfed condition.

Material and methods

An experiment was conducted in Satna district of MadhyaPradesh to study the yield and suitable planting date inchickpea as influenced by dates of sowing during period2008-09 to 2010-11. The soil of the experiment site wasclassified under Vertisols with 7.8 pH, EC of 0.25 dS/mand 0.56% organic carbon. The available N, P2O5 andK2O status of soil was 285, 28.2 and 325 kg per ha,respectively. Rice was incorporated before sowing theexperiment crop. The seeds of chickpea were treated withThiram @ 3g/kg seeds along with rhizobium @ 375g/habefore sowing. During the crop period, the total rainfallreceived was (2.0 to 37.8 mm) in 2008-09, (0.5 to 87.3mm) in 2009-10 and (2.62 to 79.4 mm) during 2008-11.The fertilizer dose of 25 kg N and 50 kg P2O5 /ha wasapplied at sowing. The crop was harvested at 85-90 daysafter sowing (DAS). At 30, 60 DAS and at harvest, fivefarmers were randomly selected from district for recordingyield and sowing dates.

The dates of sowing levels showed significanteffects on yield and suitable planting date [Table 1].

179

Result and discussion

Significant difference was observed in seed yield due todifferent dates of sowing (Table 1). The result showed thedifferent sowing date affect the yield performance inchickpea and it was observed that the yield performedbest in 1st fortnight of November i.e. 09-13 Novemberfollowed by 2nd fort night of November i.e. 19-23 Novemberand 1st fort night of December i.e. 01-05 December. The,yield significantly differed due to dates of sowing (Table1). The chickpea sowing on 1st fort night of Novemberrecorded significantly higher seed yield over 2nd fort nightof November and 1st fort night of December. Thus, it canbe inferred that sowing of Chickpea on I fortnight ofNovember found optimum as evidenced by suitableplanting date and yield. These findings were more or lessin line with the result obtained by Agarwal et al. (1997),Anwar et al. (2003), Mehar et al. (2000) and Onyari et al.(2010).

Acknowledgement

The authors are grateful to ICRISAT, Patancheru,Hyderabad in collaboration with JNKVV for their financialassistance and special guidance under the researchproject entitled' Enhancing of Chickpea production inrainfed rice fallow land adopting improved pulse productionand protection technology (IPPPT).

Table 1. Influence of dates of sowing on yield anddetermination of suitable planting dates of chickpea

Rainfall (mm)Month 2008 2009 2010

October 0 110 0November 0 109 0December 0 0 0January 21.7 0 0February 0 0 0March 0 0 0

Yield (kg/ha)Sowing date 2008-09 2009-2010 2010-11Early (09-13Nov) 4169.44 3600.88 3567.92Mid (19-23Nov) 3205.36 3559.68 3312.48Late (01-05Dec) 2233.04 2851.04 2867.52CD (0.05) 827.73 1281.58 1751.71CV 11.39 16.92 23.75

References

Agarwal MC, Dhindiwal AS, Jaiswal CS, Prabhakar A, AujaMS (1997) Status of research on Agriculture WaterManagement of Northern Region. All Indiacoordinated project for Research on WaterManagement, Directorate of Water ManagementResearch (ICAR) Patna, pp 138

Anwar MR, Mckenzie BA, Hill GD (2003) The effect of irrigationand sowing date on crop yield and yieldcomponents of Kabuli chickpea (Cicer arietinumL.) in a cool-temperate subhumid climate. J AgricSci 141: 259-271

Mehar Singh, Rakeshkumar, Singh RC (2000)Agrotechnology for kabuli chickpea. In Proc NationSymp Agron : Challenges Strat Mill, Nov 15-18,Gujarat Agric Univ Junagadh, Gujarat

Onyari CAN, Ouma JP, Kibe AM (2010) Effect of tillage methodand sowing time on phenology, yield and yieldcomponents of chickpea (Cicer arietinum L.) undersemi-arid conditions in Kenya. J Appl Biosci 34:2156 - 2165

(Manuscript Receivd : 25-03-2015; Accepted :20-06-2013)

180

Abstract

Genetic divergence among 35 genotypes was estimated overfour environments. These genotypes were grouped in 4, 14,10 and 7 clusters during the four environments viz. zaid 2010,kharif 2010, zaid 2011 and kharif 2011 respectively. Maximumnumbers of 14 clusters were formed during kharif 2010. ADT-5 appeared as most diverse genotype. Azad urd 1, TU 65-1,IU 65-2, IU 83-5, IU 94-3, BARC urd-1, TU 92-3 and TU 98-14were also exhibited true diversity. Four canonical rootsaccounted for 65.78 % of total variation.

Keywords: Vigna mungo, D2 analysis, genetic divergence,cluster analysis, canonical root analysis.

Black gram (Vigna mungo L.) or urdbean is basically awarm season crop, however in India it is grown in zaidand kharif, even up to 1800 m altitude. It is grown incropping as a sole crop, mixed crop, catch crop andsequential crop. It is quite drought resistant but intolerantto prolonged cloudiness. It is normally grown in areaswith an average temperature of 25-35°C and an annualrainfall of 600-1000 mm. In higher rainfall areas it may begrown in the dry season on residual moisture. It isconsidered to have been domesticated in India from itswild ancestral form Vigna mungo var. silvestris. Center ofgenetic diversity is found in India (Zeven and de Wet 1982).Natural distribution of V. mungo var. silvestris ranges fromIndia to Myanmar (Tateishi 1996). In India, it is grown inan area about 3.06 m ha. with a total production of 1.7 mt with an average productivity of 555 kg/ha (2013-14 sourcewww.iipr.res.in). Andhra Pradesh ranks first in area andproduction followed by Madhya Pradesh, Odisha andMaharashtra, while Karnataka leads in productivityfollowed by Andhra Pradesh. The Guntur District ranksfirst in Andhra Pradesh for the production of black gram.

JNKVV Res J 49(2): 180-184 (2015)

Genetic divergence analysis in urdbean genotypes of India

Rajmohan SharmaDirectorate of Research ServicesJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482 004 (MP)Email:sharma.rajmohan@gmail.com

Material and methods

The experimental material used in the present studycomprised of thirty five genotypes of urdbean collectedfrom the genetic stock maintained at Department ofGenetics and Plant Breeding, JNKVV, Jabalpur. Theobservations were recorded for days to 50% flowering,number of branches per plant, number of pods per plant,yellow vein mosaic incidence, days to maturity, plantheight, biological yield per plant, 100 seed weight, harvestIndex and seed yield per plant for five randomly chosenplants per replication per genotype. The data weresubjected to Mahalanobis's (1936) D2 analysis and thegenotypes were grouped by Tocher's method assuggested by Rao (1952.)

Result and discussion

The analysis of variance revealed a significant differenceamong the thirty five genotypes for all the charactersindicating the existence of high genetic variability amongthe genotypes. The genotypes evaluated are presentedin Table 1. These genotypes were grouped in 4, 14, 10and 7 clusters during the four environments viz. zaid 2010,kharif 2010, zaid 2011 and kharif 2011 respectively.Maximum number of 14 clusters were formed during kharif2010 indicating that climatic conditions prevailing duringcrop season were favourable for better expression of seedyield per plant and its attributes. Cluster I contained themaximum number of genotypes (29, 20, 16, and 29 duringzaid 2010, kharif 2010, zaid 2011 and kharif 2011respectively) followed by cluster II and cluster III (Table2). ADT - 5 formed single cluster in three out of fourenvironments indicating that it is most divergent genotype.Other genotypes formed single cluster in twoenvironments were Azad Urd 1, TU 65-1, IU 65-2, IU 83-

181

5, IU 94-3, BARC Urd-1, TU 92-3 and TU 98-14. Effortshave been made by Singh et al. (2011), Neelavathi andGovindarasu (2010), Chauhan et al. (2008),Elangaimannan et al. (2008), Konda et al. (2007), Shanthiet al. (2006) and Ghafoor et al. (2001) to asses the geneticdiversity in urd bean but most of the studies are based onsingle environment.

The range of intra cluster and inter cluster distanceduring kharif 2010 was 0 to 40.13 and 18.58 to 271.05(Table 3) respectively. Maximum inter cluster distancewas observed between cluster X and XIII followed by clusterVII and cluster XIII (215.50). Minimum inter cluster distancewas observed between cluster III and IV followed by clusterII and cluster VI (20.13).

During the season zaid 2011 the intra clusterdistance ranged between 0 to 32.43 and the inter clusterdistance was in between 39.9 to 175.2. Cluster III andcluster VII were the most divergent clusters having highestinter cluster distance followed by cluster III and cluster V(166.41). Contrary to this cluster I and cluster IV followedby cluster IV and cluster IX were least divergent clusters.

In kharif 2011 the intra cluster distance varied from0 to 53.93 while inter cluster distance varied from 37.61to 435.42. Maximum estimate of inter cluster distance

was recorded between cluster III and VII followed by clusterII and VII (371.72) and cluster IV and VII (207.89). Thecluster II and III were the least divergent clusters. Selectionof genotypes from diverse clusters will be helpful inexploitation of heterosis for seed yield per plant in urdbean.

Canonical root analysis (Rao 1952) was carriedout to asses the extent of variation for different characters.Four canonical roots accounted for 65.78 % of totalvariation (Table 4). First canonical vector explained 32.16% variation followed by second vector which explained15.81 % variation whereas third and fourth vectorsexplained 9.82 % and 7.99 % of total variation respectively.The results revealed that number of branches per plantfollowed by harvest index and 100 seed weight contributedlargely to major axis i.e. first vector of differentiation. Butin secondary axis plant height followed by harvest indexand number of pods per plant indicated their role insecondary differentiation. 100 seed weight followed byplant height and number of branches per plant contributedlargely to the differentiation in the tertiary axis. Days to50% flowering, plant height, yellow vein mosaic incidenceand number of branches per plant contributed largely tothe differentiation in the fourth axis. Singh et al. (2011)have also reported similar results.

Table 1. Description of genotypes

Genotype Origin Genotype Origin

Pant Urd 19 GBPUA & T, Pantnagar IU86-1 IIPR, KanpurNarendra - 1 NDUA & T, Faizabad IU98-843 IIPR, KanpurTVM-1 BARC, Mumbai IU-65-2 IIPR, KanpurBARC Urd-1 BARC, Mumbai TAU-1-1 PDKVV Akola and BARC, MumbaiPU -13 PAU, Ludhiana TU-92-3 BARC, MumbaiU-10 PAU, Ludhiana TPU-4 MPKVV, Rahuri and BARC, MumbaiADT-5 TNAU, Coimbatore TAU-4 PDKVV Akola and BARC, MumbaiKU-301 CSAU&T, Kanpur TU 98-14 BARC, MumbaiMash 404 PAU, Lidhiana JU-2 JNKVV, JabalpurLBG 623 ANGRAU, RARS, LAM, Guntur JU-8-6 JNKVV, JabalpurCo-5 TNAU, Coimbatore IU 83-5 IIPR, KanpurT-91 IIPR, Kanpur IU 62-219 IIPR, KanpurT-9 IIPR, Kanpur IU 83-4 IIPR, KanpurTAU-1 PDKVV Akola and BARC, Mumbai IU 94-3 IIPR, KanpurLBG-20 ANGRAU, RARS, LAM, Guntur IU 88-10 IIPR, KanpurTU 92-14 BARC, Mumbai PDU-1 IIPR, KanpurTU-65-1 BARC, Mumbai Azad Urd - 1 CSAUA & T, KanpurTU31-13 BARC, Mumbai

182

Table 2. Cluster composition in urdbean

Cluster Season GenotypesNo. Name

I E1 29 TAU-1, LBG-20, Mash 404, KU 301, Narendra - 1, PDU-1, IU-65-2, T-9, TVM1, T-91, TU-92-3, LBG623, PU -13, IU 83-5, TU-65-1, TAU-1-1, ADT 5, U-10, TU98-14, IU86-1, TAU-4, IU94-3, IU62-219,Pant Urd 19, IU83-4, Azad Urd 1, TU 92-14, IU98-843, IU 83-5

E2 20 TVM1, TU31-13, U-10, LBG-20, TPU-4, Pant Urd 19, IU86-1, KU 301, IU62-219, TAU-1, Co-5, IU88-10,Mash 404, TAU-1-1, IU83-4, T-91, TAU-4, T-9, TU 92-14, Narendra - 1

E3 16 TAU-4, JU-2, TVM 1, IU-65-2, Narendra - 1, IU94-3, IU88-10, TAU-1-1, TU31-13, Pant Urd 19, IU62-219, Mash 404, TPU-4, JU-8-6, PU -13, LBG 623

E4 29 IU98-843, TU98-14, PDU-1, LBG 623, TU 92-14, JU-2, TAU-1-1, TU31-13, JU-8-6, TPU-4, IU88-10,TAU-4, IU83-4, T-9, TAU-1, LBG-20, KU 301, IU86-1, IU62-219, T-91, Co-5, TU-92-3, Pant Urd 19, U-10, Mash 404, Narendra - 1, IU 83-5, PU -13, TVM 1

II E1 1 BARC Urd1E2 1 PDU-1E3 5 KU 301, TAU-1, T-9, LBG-20, IU83-4E4 1 TU-65-1

III E1 3 JU-8-6, IU88-10, JU-2E2 1 IU-65-2E3 3 BARC Urd 1, TU-92-3, IU86-1E4 1 Azad Urd 1

IV E1 2 TU31-13, Co-5E2 1 IU 83-5E3 1 U-10E4 1 IU-65-2

V E1 -E2 1 JU-8-6E3 1 TU98-14E4 1 ADT 5

VI E1 -E2 1 IU98-843E3 5 TU-65-1, Azad Urd 1, PDU-1, IU98-843, TU 92-14E4 1 IU94-3

VII E1 -E2 1 ADT 5E3 1 ADT 5E4 1 BARC Urd1

VIII E1 -E2 1 TU98-14E3 1 T-91E4 -

IX E1 -E2 - PU -13E3 1 Pant Urd 19E4 -

X E1 -E2 3 BARC Urd1, TU-92-3, JU-2E3 1 IU 83-5E4 -

XI E1 -E2 1 Azad Urd 1E3 -E4 -

XII E1 -E2 1 IU94-3E3 -E4 -

XIII E1 -E2 1 TU-65-1E3 -E4 -

XIV E1 -E2 1 LBG 623E3 -E4 -

Where E1- Zaid 2010, E2- Kharif 2010, E3- Zaid 2011, E4- Kharif 2011

183

Tabl

e 3.

Intra

and

inte

r clu

ster

dis

tanc

es in

urd

bea

n fo

r fou

r sea

sons

(zai

d 20

10, k

harif

201

0, z

aid

2011

and

kha

rif 2

011)

Clu

ster

Sea

son

III

IIIIV

VVI

VII

VIII

IXX

XIXI

IXI

IIXI

VI

E 138

.69

74.7

661

.25

69.1

964

.32

53.9

217

6.10

114.

9980

.11

93.2

5-

--

-E 2

40.1

364

.91

62.5

269

.25

57.3

669

.33

67.3

693

.17

61.8

772

.29

121.

8858

.51

133.

5112

7.01

E 328

.42

50.9

311

3.32

39.9

051

.84

69.7

348

.52

48.2

165

.68

62.0

2E 4

53.9

310

1.09

126.

8587

.33

109.

9993

.21

169.

69-

--

--

--

IIE 1

-50

.72

126.

8572

.18

129.

1387

.67

80.8

213

4.90

177.

8812

4.51

--

--

E 2-

0.00

26.2

456

.83

61.1

320

.13

92.7

423

.61

75.7

112

8.49

25.4

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184

Table 4. Value of canonical vectors and % of variation explained

Character CR1 CR2 CR3 CR4

Days to 50 % Flowering -0.099 -0.053 -0.159 0.356No. of branches / plant 0.839 0.065 0.293 0.146No. of pods/ plant -0.088 0.118 0.081 -0.152YMV incidence -0.117 -0.006 -0.117 0.171Days to maturity -0.015 0.094 0.091 -0.868Plant height -0.403 0.488 0.539 0.203Biological yield/plant -0.098 -0.804 0.196 -0.034100 seed weight 0.165 -0.020 0.542 -0.010Harvest Index 0.241 0.288 -0.488 -0.052Seed yield/plant 0.078 0.036 -0.488 -0.056% of variation explained 32.16 15.81 9.82 7.99Cumulative variation 32.16 47.97 57.79 65.78

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References

Chauhan MP, Mishra AC, Singh, Ashok Kumar (2008) Geneticdivergence studies in urd bean (Vigna mungo L.)Legume Res 31 (1) : 63-67

Elangaimannan R, Anbuselvam, Y and Karthikeyan, P (2008)Genetic diversity in blackgram [Vigna mungo (L.)Hepper]. Legume Res 31 (1) : 57-59

Ghafoor A, Sharif A, Ahmad Z, Zahid MA, Rabbani MA (2001)Genetic diversity in blackgram (Vigna mungo L.Hepper). Field Crops Res 69 (2) : 183-190

Ghafoor A, Zubair M, Malik BA (1990) Path analysis in mash(Vigna mungo L.). Pakistan J Botany 22 (2) : 160-167

Konda CR, Salimath PM, Mishra MN (2007) Genetic diversityin blackgram (Vigna mungo L.). Legume Res 30(3) : 212-214

Mahalanobis PC (1936) On the generalized distance instatistics. Proc Natl Acad Sci India 12 : 49-55

Neelavathi R, Govindarasu (2010) Analysis of variability anddiversity in rice fallow blackgram [Vigna mungo (L.)Hepper]. Legume Res 33 (3) : 206-208

Rao CR (1952) Advanced statistical methods in biometricalresearch. Whiley & Sons, New York

Shanthi P, Jebaraj SN, Manivannan (2006) Genetic diversityin urdbean (Vigna mungo L. Hepper). Legume Res29 (3) : 186-190

Singh G and Rai V K (1980) Response of two Cicer arietinumL. varieties to water. Ind Ecol 7: 246-253

Singh Mohar, Sharma SK, Singh TP, Dutta M (2011) Factoranalysis of components of yield and some growthparameters in urdbean (Vigna mungo (L.) Hepper).Indian J Plant Genetic Resources 24 (3) : 346-348

Tateishi (1996) Systematic of the species of Vigna subgenusCeratotropis. In "Mungbean Germplasm :Collection, Evaluation and Utilization for BreedingProgram" JIRCAS Working Report No. 2 : 9-24

Zeven, A C and de Wet J M J (1982) Dictionary of cultivatedplants and their regions of diversity. Centre forAgricultural Publication and Documentation,Wageningen

(Manuscript Receivd :28-03-2015; Accepted :05-07-2015)

185

Abstract

A field experiment was conducted during kharif season of2014 on sandy clay-loam soil at Rewa (MP) to study nitrogenscheduling and dosages on aerobic rice. the application of125 kg N/ha resulted in significantly higher growthparameters, yield attributes and yield (28.19 q/ha),consequently maximum net income upto Rs 21671/ha withB:C ratio 1.91 from aerobic rice var. Pusa Sugandha-3. Outof different N-scheduling treatments, nitrogen applied in 3splits (1/3 10-12 days after emergence + 1/3 after tillering +1/3 at panicle initiation stage) recorded maximum growthparameters, yield attributes and yield (34.62 q/ha), therebygave highest net income upto Rs.31545/ha with B:C ratio2.34. There was no interaction effect between treatments foryield parameters.

Keywords: Nitrogen scheduling, dosages, aerobic rice

Rice (Oryza sativa) is an important kharif cereal crop ofMadhya Pradesh covering an area of 15.59 lakh .hectareswith production upto 14.12 lakh tonnes. It is sown withdifferent methods under upland and lowland conditions.The nitrogen requirement of rice plqnts is rather high andmost of the Indian soils are very low in its content. Theapplied nitrogen is rapidly mineralized in soil and eitherabsorbed by the rice crop in the ammonical form orescapes in gaseous state or leaches down into sub-soillayer beyond root-zone of the plants.

Nitrogen is a key nutrient III determining the levelof crop productivity. The efficiency of applied nitrogen isvery low and varies from 20 to 25% in upland rice cropdue to the oxidized condition prevailing in uplands andconcomitant heavy nitrogen loss through percolatingwater. Hence, fractional (split) application of nitrogen inright amount and proportion, and when it is needed themost, seems to be a practical preposition. Splitting theapplication of nitrogen at the appropriate physiological

Effect of nitrogen scheduling and dosages on aerobic rice

Anjir Pandey, R.K. Tiwari*, S.K. Tripathi, I.M. Khan and S. SinghDepartment of AgronomyJawaharlal Nehur Krishi Vishwa VidyalayaCollege of AgricultureRewa 486001 (MP)

JNKVV Res J 49(2): 185-188 (2015)

growth stages of kharif crops increased its productionefficiency compared to application of all the quantity offertilizer-N at sowing (De Rajat, 1979). In view of the abovefacts, the present research on N-scheduling for aerobicrice was taken up.

Material and methods

The field experiment was conducted during kharif seasonin 2014 at the JNKVV College of Agriculture, Rewa (MP).The soil was sandy clayloam in texture having pH 6.7,electrical conductivity 0.40 dS/m, organic carbon 6.62 g/kg, available N, P2O5 and K2O 236, 18.5 and 352 kg/ha,respectively. The total rainfal lduring June to October was814.2 mm. The treatments comprised two nitrogen levels(120 and 150 kg/ha) in the main-plots and six N-scheduling and dosages in the sub-plots as detailedbelow:

Nitrogen IeveIs-2 (Main plots)

120 kg/ha

150 kg/ha

Nitrogen scheduling-6 (sub plots)

N-2 splits (1/2 Basal + 1/2 PI stage)

N-2 splits (1/210-12 DAE +1/2 PI stage)

M-3 splits (1/3 Basal + 1/3 AT+ PI stage)

N-3 splits (1/3 10-12 DAE + 1/3 AT +1/3 PI stage)

N-4 splits (1/4 Basal + 1/4 AT stage + PI stage +Y. at Flowering stage)

N-4 splits (1/4 DAE + 1/4 AT stage + PI stage +Y. at Flowering stage)

186

The experiment as laid out in split-plot design with threereplications. The rice variety Pusa Sugandha-3 was sownon 4th July 2014 @ 50 kg seed/ha in rows 20 cm apartand keeping 10 cm distance between plants. The cropwas grown as per recommended package of practices.An uniform dose of P60 K50 was applied in all thetreatments. The crop was harvested on 25 October, 2014.

Results and discussion

Yield attributes

The data (Table 1) reveal that all the yield - attributingcharacters were enhanced significantly due to increasedsupply of nitrogen upto 150 kg N/ha over 120 kgN/ha.The number of panicles were 274/m2 in 150 kg N/ha asagainst only 244/m2 in 120 kg N/ha. The number of grainswas 115/panicle over 75/panicle in case of 120 kg N/ha.Similarly the number of filled grains was upto 93/panicle,while it was only 53/panicle in 120 kg N/ha. The increasedyield - attributes under 150 kg N/ha was due to significantlyincreased growth parameters in this N level. The numberof unfilled grains/panicle as well as 1000-grain weight didnot change upto significant extent due to lower and highernitrogen levels. These findings are in close agreementwith those of Singh et al. (2008), Singh Braham (2008),Reddy et al. (2011) and Reddy et al. (2013).

The splitting and timing of N application treatmentsexerted significant influence upon yield - attributingcharacters of aerobic rice. The nitrogen splitted three times(1/3 10-12 DAE + 1/3 AT + 1/3 PI stages ) as in S4 provedthe best which enhanced the yield- attributes uptomaximum extent. The number of panicles were 265/m2,number of grains upto 1 16/panicle and number of filledgrains (89/panicle). The number of unfilled grains was alsofound highest in case of S4 (N-scheduling treatment),however the test weight of 1000-grains remained identicalin all the N scheduling treatments.

The second best N-scheduling treatment was S3having 3 splits at 1/3 basal + 1/3 AT + 1/3 PI stages. Thistreatment gave 263 panicles/m2, 100grains/panicle and75 filled grains/panicle. On the other hand, SI treatmenthaving 2 splits of N at 1/2 basal + 1/2 PI stage recordedsignificantly lowest 249 panicles/m2, 76 grains/panicleand 52 filled grains/panicle. The other treatments viz. S2,S5 and S6 attained the intermediate position with respectto yield- attributes, giving identical influence to each other.The best performance of S4 and then S3 N-schedulingtreatments on yield - attributes was exactly in accordancewith the growth characters recorded in these treatments.The present results are in close agreement with those of

Sathiya et al. (2008), Ayub Muhammad et al. (2008),Sathiya and Ramesh (2009) and Singh et al. (2013).

Productivity and Economics

The data (Table 2) indicate that grain and straw yield aswell as harvest index of aerobic rice did not influence dueto fertility levels. However, grain yield was higher in caseof 150 kg N/ha (28.19 q/ha) as against 25.22 q/ha from120 kg N/ha. Similarly straw yield was also higher fromdue to 150 kg N/ha (73.52 q/ha) as against 55.80 q/hafrom 120 kg N/ha. The position of harvest index wasreverse. It was higher (31 %) in case of 120 kg N/ha over28% in case of 150 kg N/ha. The increased grain andstraw yield due to increase N supply was exactly inaccordance with the yield attributing characters underincreased supply of nitrogen. Consequently the netincome values (Rs 21671/ha) and B:C ratio (1.91) werefound maximum due to increased .supply of nitrogen upto150 kg/ha. This net income was higher by Rs.5382/ha asagainst lower dose of nitrogen supply.

The beneficial effect of higher dose of nitrogen upto150 kg ha -I has also been reported by Singh et al. (2008),Singh Braham (2008), Reddy et al. (2011) and Reddy etal. (2013).

As regards with the N-scheduling treatments, S4having 3 N splits (1/3 10-12 DAE + 1/3 AT + 1/3 PI stage)recorded significantly higher grain and straw yield i.e.34.62 and 83.43 q/ha, respectively over all the remainingNscheduling treatments. This was however, followed byS3, S4 and S6 treatments producing identical grain andstraw yield. On the other hand, SI and S2 recorded thelowest productivity parameters. These productivityparaineters are exactly in accordance with the yield-attributing parameters recorded from these treatments.The beneficial effect of 2 to 4 splitting of N for increasedrice yield has also been reported by Sathiya et al. (2008)and Ayub Muhammad et al. (2008), Sathiya and Ramesh(2009) and Singh et al. (2013).

The economical gain was also found according tothe grain yield under different N-scheduling treatments.Accordingly, S4 having 3 N splits resulted in maximumnet income (Rs 3l545/ha) as well as B:C ratio (2.34).This was almost equally followed by S3, Ss and S6 (Rs18673 to Rs 19791/ha) and B:C ratio (1.79 to 1.83). Theminimum net income (Rs 11353/ha) and B:C ratio (1.49)was recorded from S2 treatment having 2 splits of Napplication. This was, however, followed by SI giving lowerincome (Rs I309l/ha) and B:C ratio (1.57).

187

Table 1. Yield attributes of aerobic rice as influenced by nitrogen levels and scheduling of N application

Treatments Panicles Grains Filled Unfilled 1000-grain/m2 /panicle grain grain weight

/panicle /panicle (g)

Nitrogen levels

F1 - 120:60:50 N:P:K kg/ha (100% of RDF) 244 75 53 21 19F2 - 150:60:50 N:P:K kg/ha (125% of RDF) 274 115 93 22 22

CD (P-0.05) 10.18 28.02 35.56 0.72 1.33Schedulling of N application

S1 - 2 splits (½ basal + ½ PI stage) 249 76 52 24 20S2 - 2 splits (½ 10-12 DAE + ½ PI stage) 257 85 74 12 20

S3 - 3 splits (1/3 basal + 1/3 AT + 1/3 PI stage) 263 100 75 25 20S4 - 3 splits (1/3 10-12 DAE + 1/3 AT + 1/3 PI stage) 265 116 89 27 21

S5 - 4 splits (1/4 basal + 1/4 AT stage + 1/4 PI stage 258 98 74 24 21+ ¼ flowering stage)

S6 - 4 splits (1/4 DAE + 1/4 AT stage + 1/4 PI stage 262 93 74 19 21+ ¼ flowering stage)

CD (P=0.05) 9.70 11.09 15.62 1.58 NSInteraction Sig Sig Sig Sig NS

DAE = Days after emergence, AT = Active tillering stage, PI = Panicle initiation stage

Table 2. Productivity and economics of aerobic rice as influenced by nitrogen levels and scheduling of N application

Treatments Grain Straw Harvest Net B:Cyield yield index income ratio(q/ha) (q/ha) (%) (q/ha)

Nitrogen levelsF1 - 120:60:50 N:P:K kg/ha (100% of RDF) 25.2 55.80 31 16289 1.70

F2 - 150:60:50 N:P:K kg/ha (125% of RDF) 28.19 73.52 28 21671 1.91CD (P-0.05) NS NS NS - -

Schedulling of N applicationS1 - 2 splits (½ basal + ½ PI stage) 23.00 52.83 31 13091 1.57

S2 - 2 splits (½ 10-12 DAE + ½ PI stage) 22.11 47.47 32 11353 1.49S3 - 3 splits (1/3 basal + 1/3 AT + 1/3 PI stage) 26.38 73.63 27 19427 1.83

S4 - 3 splits (1/3 10-12 DAE + 1/3 AT + 1/3 PI stage) 34.62 83.43 30 31545 2.34S5 - 4 splits (1/4 basal + 1/4 AT stage + 1/4 PI stage + ¼ flowering stage) 26.79 63.49 30 18673 1.79

S6 - 4 splits (1/4 DAE + 1/4 AT stage + 1/4 PI stage + ¼ flowering stage) 27.35 67.10 29 19791 1.83CD (P=0.05) 6.32 12.73 2.89 - -

Interaction NS NS NS - -

DAE = Days after emergence, AT = Active tillering stage, PI = Panicle initiation stage

188

References

Ayub Muhammad, Tahir Nadeem, Nazir MA, Muhammad MA(2008) Growth and yield response of fine to splitapplication of nitrogen. Pakistan J Life Soc Sci 6(1): 14-17

De Rajat (1979) Time and method of application. FertilizerNews 24 : 2140

Reddy MD, Ramulu V, Reddy SN (2011) Response of rice(Oryza sativa L.) cultivars to nitrogen fertilizationunder aerobic and transplanted condition.International J Bio-resource and StressManagement 2 (1): 78-82

Reddy MM, Padmaja B, Veeranna G, Reddy DVV (2013)Response of aerobic rice to irrigation schedulingand nitrogen doses under drip irrigation. J Res41(2):144-148

Sathiya K, Ramesh T (2009) Effect of split application ofnitrogen on growth and yield of aerobic rice. JExperimental Sci 23(1):303-306

Sathiya K, Sathyamoorti K, Martin GJ (2008) Effect of nitrogenlevels and split doses on the productivity of aerobicrice. Res on Crops 9(3):527-530

Singh MV, Mishra BN, Neeraj Kumar (2013) Effect of nitrogenscheduling on rabi maize. Annals of Plant and SoilResearch 15(2) : 171-172

Singh Aruna, Pandey Girish, Singh RP (2008) Effect of greenmanuring and nitrogen on productivity of rice-wheatcropping system. Annals of Plant and Soil Res 10(2): 191-192

Singh Braham, Srivastav SK (2008) Performance of new ricevarieties at different nitrogen levels undertransplanted condition. International J Agric Sci 4(2): 417-420

(Manuscript Receivd : ; Accepted : )

189

Abstract

Field experiment was conducted during Kharif season of2012 at research farm of JNKVV, Jabalpur (230 13' N latitude,790 57' E longitudes and elevation of 393 meter amsl) toestimate the different thermal indices including Growingdegree days (GDD), Heliothermal unit (HTU), Heat useefficiency (HUE) and Photothermal index (PTI) for soybeanCv. JS 97-52 using standard procedures. The resultsshowed that maximum values of GDD (392.1 0C), HTU(2347.66 0C hours), PTI (18.4 0C/day) and HUE (5.95 kg/0C)were required for emergence to end of true vegetative, seedfilling to physiological maturity, sowing to emergence andfirst flower to full bloom stages, respectively. The result furtherindicates that total thermal unit (GDD) required for soybeanwas 1776.1 0C, while average heat use efficiency for entiregrowing period was 3.01 kg/0C.

Keywards : Soybean, GDD, HTU, PTI and HUE

Temperature plays a key role in the physiological andmorphological development of the crops. Temperatureprimarily affects growth duration with lower temperatureincreasing the length of time that the crop could interceptradiation. The effect of temperature on crop is accountedthrough the concept of heat unit which is based on thefact that crops have a certain amount of temperaturerequirement for the completion of each stages of itsontogeny. The seasonal variation in crops and varietiescan be effectively answered through its heat unitrequirement. Varughese and Iruthayaraj (1995) observedhowed that heat unit requirements for Rabi crops wereless than for kharif crops for all physiological phases. Inthe present investigation, different thermal indices forsoybean was estimated under Jabalpur conditions usinga set of data observed through experiment conductedduring kharif season of 2012.

Computation of thermal indices for soybean in Madhya Pradesh

H.K. Rai, Arpit Suryawanshi and D.D. DangiDepartment of Soil Science and Agricultural ChemistryJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482 004 (MP)E-mail: rai.hkr.hitendra@gmail.com

JNKVV Res J 49(2): 189-192 (2015)

Material and methods

A field experiment with soybean (Glycine max L. Merril)Cv. JS 97-52 was conducted during kharif season of 2012at the research farm of JNKVV, Jabalpur (230 13' N, 790

57' E and 393.0 meter altitude) in the south-eastern partof the Madhya Pradesh. The soil of the experimental sitewas Vertisols belonging to fine Montmorillonitic,Hypothermic family of Typic Haplustarts. The experimentalsite has sub-tropical climate characterized by hot drysummers and cool dry winter. It lies under the "KymorePlateau and Satpura hills" agro climatic zone of MadhyaPradesh. The mean maximum temperature during themonth of May-June varies between 42.5 to 46.4 0C and isthe hottest month, while the mean minimum temperaturevaries between 4.2 to 8.7 0C during December-January,which are the coldest month of the year. The averageannual rainfall of the region is about 1200 mm which ismostly received between June to September and a littlerainfall (75 to 175 mm) received in the month from Octoberto May. The mean maximum and minimum relativehumidity of the region varies between 86.16 to 91.2%and 62.7 to 67.3%, respectively. Average pan evaporationis about 3.93 mm/day. Thermal indices including growingdegree days (GDD), heliothermal unit (HTU), heat useefficiency (HUE) and photothermal index (PTI) for differentphenophases of soybean crop were estimated usingfollowing equations:

1. Growing degree days (GDD)

i=dsGDD (0C day) = (Tm - Tb)

2. Heliothermal Unit (HTU)

i=dsHTU (0C day) = (Tm - Tb)i x Di

i=de

190

3. Heat use efficiency (HUE)

Biological yield (kg/ha)HUE (kg/ha per 0C day) = --------------------------------------

GDD (0C day)

4. Photothermal Index (PTI)

Accumulation of GDD for a phenophase (0C day)

PTI (0C day/day) = ---------------------------------------------------- Days taken for the phenophase

(day)

Where,

- Summation of

i - Day (number)

Ds - Day of start for the phenophase

De - Day of end for the phenophase

Tm - Mean temperature for the day

Tb - Base temperature (for soybean- 10 0C)

Di - Sunshine hour of the day

Results and discussion

Various thermal indices (GDD, HTU, HUE and PTI) wereestimated for soybean crop and the results thus obtainedare described in this section of the manuscript underfollowing heads:

Accumulation of GDD at different phenophases

Accumulation of heat unit in terms of growing degree days(GDD) was estimated for different phenophases of soybeancrop and the results thus obtained are depicted in figure1. The data clearly reveled that accumulation of GDD fromsowing to emergence was lowest (92 0C day) and it wasmaximum (392.1 0C day) for emergence to completion ofvegetative phase. Almost comparable accumulation ofGDD was obtained for reproductive phases includingvegetative to first flower, first flower to full bloom, full bloomto poding, poding to pod filling and pod filling to maturitystages of soybean. Similar pattern of GDD accumulationin soybean have been also reported by variousresearchers from different parts of the world (Mcnaughtonet al. 1985; Dhingra et al. 1985, Sharma 1994).

Accumulation of heliothermal unit in soybean

Accumulation of Helio-thermal unit (HTU) was alsodetermined and the results are graphically presented inthe Figure 2. The results revealed that the accumulatedhelio-thermal unit for different phenophases was rangedfrom 237.7 to 2347.7 °C days. The highest value ofaccumulated HTU (2347.7 °C days) was recorded for podfilling to maturity stage of soybean. Similar finding wasalso reported by Kumar et al. (2008). It was further noticedthat accumulated helio-thermal unit between sowing toemergence, emergence to vegetative phase, first flowerto full bloom and full bloom to poding phases werecomparables. It may be because of cloudy conditionsduring these phenophases resulting in lower helio-thermalunit accumulation.

Heat use efficiency for different phenophases of soybean

Heat use efficiency (HUE) was also computed usingperiodically measured values of the dry matter

Fig. 1. Accumulation of heat units during differentphenophases of soybean

Fig. 2. Accumulation of helio-thermal units duringdifferent phenophases of soybean

191

accumulation and GDD for different phenophases ofsoybean crop and the results thus obtained are depictedin figure 3. Result clearly indicated that HUE ranged from0.97 to 5.95 kg/ha per 0C day with lowest value for sowing- emergence and highest for first flower- full bloom stages.Further, a sharp increase in HUE from end of vegetativephase till full bloom stage and thereafter a consistentdecrease was noticed till maturity stage. It may be dueto higher photosenthates accumulation of photosenthatesper unit time during first flower to full bloom stage. Theresults obtained in the present study in confirmation withthe findings of Balakrishnan and Naterajaratanam (1981).

(17.76 0C day/day) and poding to pod filling (17.15 0Cday/day) phases and lowest (14.61 0C day/day) for podfilling to maturity phase, respectively.

Fig. 3. Variation in heat use efficiency of soybeanduring different phenophases

Fig. 4. Photothermal Index for different phenophases ofsoybean

Photothermal Index for different phenophases of soybean

Photo thermal index (PTI) for soybean was estimatedusing accumulated GDD for different phenophases andthe number of days required completing the correspondingphenophase and the results thus obtained are depictedin Fig 4. It is evident for the data that maximumphotothermal index (18.4 0C day/day) was obtained forsowing to emergence followed by first flower to full bloom

Fig. 5. Relationship between GDD and plant biomassof soybean

Relationship between GDD and plant biomass of soybean

Accumulation of growing degree days and photosenthatesin soybean was regressed and is depicted in Fig 5. It isclearly evident for the figure that a linear relationship(R2=0.963) exists between GDD and plant biomass.

Conclusion

The results of the present study showed that rapidaccumulation of heat unit (GDD) takes place during thevegetative phases while buildup of photothermal units(HTU) was more pronounced during the reproductivephases. Further, the heat use efficiency (HUE) washighest for full bloom stage of the crop and a linearrelationship exists between accumulation of thermal unitsand plant biomass in soybean. Therefore, it can beconcluded from the study that consideration of differentthermal indices has greater importance for enhancing theproductivity of soybean crop and need to be matchedthrough agronomic manipulations.

,d iz{ks= iz;ksx }kjk o"kZ 2012 esa lks;kchu dh Qly esa o`f+) ,oafodkl gsrq vko';d rkiØe ds fofHkUu ?kVdksa dh x.kuk dh x;h ftllsirk pyrk gS fd e?;izns'k esa lks;kchu dh Qly esa mfpr o`f) ,oafodkl gsrq vf?kdre xzksbax fMxzh Mst ¼392-2 fMxzh lsaVhxzsM½] gsfy;ksFkeZy;wfuV ¼2347-66 fMxzh lsaVhxzsM /kaVs½] QksVksFkeZy baMsDl ¼18-4 fMxzhlsaVhxzsM izfr fnu½ rFkk m"ek iz;ksx {kerk ¼5-95 fdyksxzke izfr fMxzhlsaVhxzsM½ Øe'k% Qly mxus ls ysdj iw.kZ okuLifrd o`f)] nkuk Hkjko

192

ls fQft;ksyksftdy esP;ksfjVh] cqokbZ ls ikS?k mxus rd rFkk izFke iq"Ikfudyus ls iw.kZ iq"iu rd dh voL;kvksa ds fy, Fkh A tcfd lks;kchudh Qly dky esa dqy m"ek dh vko';drk 1776-1 fMxzh lsaVhxzsM ,oavkSlr m"ek iz;ksx {kerk 3-01 fdyksxzke izfr fMxzh lsaVhxzsM ikbZ xbZ A

References

Varughese K and Iruthayaraj MR (1995) Seasonal variationin yield and heat unit requirement of maize (Zeamays L.). Crop Res 10(1):67-73

Mcnaughton HG, Warrington IJ and Turnbulk HL (1985) Theeffects of temperature and day length on the rate ofdevelopment of pigeonpea. Annals of Bot 56(5):597-611

Dhingra KK, Kaur H, Dhaliwal LK and Singh J (1985).Photological behaviour and heat unit requirementof soybean genotype under different date of sowing.J Res Panjab Agril Univ 32: 129-135

Sharma NN (1994). Response of soybean to sowing date inthe hill zone of Assam. Ann Agril Res 15: 489-490

Balakrishnan K and Naterajaratanam N (1981). Influence ofsowing dates on the phenology and transpirationrate in pigeonpea. Madras Agric J 74 (3):121-128

Kumar A, Pandey V, Shekh AM and Kumar M (2008). Growthand yield response of soybean (Glycine max L.) inrelation to temperature, photoperiod and sunshineduration at Anand, Gujarat, India. American-Eurasian J Agronomy 1(2): 45-50

(Manuscript Receivd : 30-04-2015; Accepted : 17-06-2015)

193

Abstract

An investigation was made to determine the effect ofmicronutrients and biofertilizers application on growth andyield contributing characters in onion (Allium cepa L.) atHorticulture Complex, Maharajpur, Jawaharlal Nehru KrishiVishwa Vidyalaya, Jabalpur (MP). Treatments were arrangedin Factorial RBD during Rabi season of 2013-14. Resultwere found to be significant in most of the growth and yieldattributing parameters of onion. The number of leaves perplant (15.57), plant Height (72.92 cm) neck thickness of bulb(6.12 mm), average weight of bulb (58.56 g), bulb Diameter(5.17 cm), bulb yield per plot (15.17 kg) and bulb yield perhectare (303.42 q/ha) were observed significantly maximumin treatment M2 - (Fe 0.5%, B 0.1%, Zn 0.5 %, Cu 0.2%, Mn0.2%). Number of leaves per plant (15.13), plant Height(73.38 cm), neck thickness of bulb (6.19 mm), average weightof bulb (57.87 g), bulb Diameter (5.23 cm), bulb yield per plot(15.03 kg) and bulb yield per hectare (300.71 q/ha) wererecorded significantly maximum in treatment B2 - Azospirillum5Kg/ha as soil application before transplanting. Howeverminimum values were found under the control i.e. M1 and B0.

Keywords: Onion, biofertilizer, micronutrients.

Onion (Allium cepa L.) is one of the most importantcommercial vegetable crops and is widely grown in almostall over the world (Mishra et al. 2013). Onion has its owndistinctive flavour and is used in soups, different dishes,salads, sandwiches and is also cooked alone as avegetable. Its pungency is due to the presence of Allylpropyl disulphide, a volatile oil. It contains carbohydrates,protein, vitamin A, thiamine, riboflavin, niacin and ascorbicacid.

Effect of micronutrients and biofertilizer application on growth andyield contributing characters in onion

Pratibha Singh, S.K. Sengupta, P.K. Jain and B.K. VermaDepartment of HorticultureCollege of AgricultureJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)E-mail:pratibha_atul08@yahoo.com

JNKVV Res J 49(2): 193-199 (2015)

The area and production of onion in India is about1.203 million hectares and 19.4 million tonnes of bulb,respectively, with an average yield of 16.1 MT /ha (NHB,2014). The yield is very low as compared to the worldaverage yield of 19.1 t /ha. Intensive cropping, imbalancedfertilization and minimal usage of micro nutrients andlimited application of organic manures have resulted inthe depletion of soil fertility in India. Boron and zinc arethe most important micro-nutrients and are essential forcell division, nitrogen and carbohydrate metabolism andwater relation in plant growth. Application of boron canincrease bulb size and yield of onion (Smriti et al. 2002).Response of onion to zinc application has also beenreported (Lal and Maurya 1981). Mishra et al. (1990) haveshown that application of ZnSO4 (0.5%) and FeSO4 (1.0%)as foliar spray recorded significantly higher plant heightand other growth parameters in onion. Bio fertilizers referto living organisms, which augment plant nutrient suppliesin symbiotic or asymbiotic way. Among the asymbiotic,nitrogen fixing-bacteria, Azotobacter and Azospirillumcontribute to significant improvement in crops yield by15-20 per cent while reducing the depletion of soil nutrients.In addition to these beneficial effects, biofertilizers allowthe saving of at least 20-30 kg/ha inorganic N fertilizers,as they possess a tremendous potentiality in nitrogenfixation.

Material and methods

The experiment was conducted at Horticulture Complex,Department of Horticulture, JNKVV, Jabalpur (MP) during2013-14 to study the effect of micronutrients andbiofertilizer application on growth and yield attributingcharacters in onion (Allium cepa L.). The two factors werechosen as a treatment. Factor A consisted of

194

micronutrient and Factor B related with biofertilizers. Thetreatments consist of foliar spray of five micronutrientscombination M1 - (Fe 0%,B 0%, Zn 0%, Cu 0%, Mn 0%),M2 - (Fe 0.5%, B 0.1%, Zn 0.5 %, Cu 0.2%, Mn 0.2%),M3 - (Fe 1.0%, B 0.2%, Zn 1.0 %, Cu 0.4%, Mn 0.4%),M4 - (Fe 1.5%, B 0.3%, Zn 1.5%, Cu 0.6 %, Mn 0.6 %),M5 - (Fe 2.0%, B 0.4%, Zn 2.0 %, Cu 0.8 %,Mn 0.8 %),M6 - (Fe 2.5%, B 0.5%, Zn 2.5%, Cu 1.0 %,Mn 1.0 %).water spray as control and along with soil application ofbiofertilizers viz. B0-Control, B1-Azotobacter 5 kg/ha andB2 - Azospirillum 5Kg/ha. Foliar sprays of micronutrientswere done at 45 days after transplanting. The nurserysowing was done on 15 November 2013. The crop wastransplanted (spacing of 15 cm × 10 cm), fertilized andirrigated as per the recommended practices. Theexperiment was laid out in a Factorial randomized blockdesign with three replications. The source of micronutrientfor iron, boron, zinc, copper and maganese were Ferroussulphate, Borax, Zinc Sulphate, Cupper Sulphate andMaganese Sulphate respectively. Ten plants wereselected from each plot as a unit for all observations ongrowth and yield. Based on the net plot yield, yield perhectare was calculated and expressed in q/ha.

Results and discussion

Growth Parameters

Plant Height

The application of micronutrients Fe 0.5%, B 0.1%, Zn0.5 %, Cu 0.2%, Mn 0.2% (M2) was recorded significantlymaximum plant height (72.92 cm) which was closelyfollowed by M3 - (Fe 1.0%, B 0.2%, Zn 1.0 %, Cu 0.4%,Mn 0.4%) (71.67 cm) and M4 - (Fe 1.5%, B 0.3%, Zn1.5%, Cu 0.6 %, Mn 0.6 %) (70.93 cm) as compared toM1 - (Fe 0%, B 0%, Zn 0%, Cu 0%, Mn 0%) (controlwithout micronutrients) which was 63.06 cm zinc andboron play an essential role in improving plant growth,through the biosynthesis of endogenous hormones whichis responsible for promotion of plant growth (Bhatt et al.2004, Hansch et al. 2009). The improved growthcharacters as a result of foliar application of micronutrientwhich would have enhanced photosynthesis and othermetabolic activities, which lead to increase in cell divisionand elongation. This result are in agreement with theresults reported by Smriti et al. (2002) and Manna (2013)in onion crop.

Application of Azospirillum 5 kg/ha (B2) was notedsignificantly maximum (73.38 cm) plant height followedby Azotobacter 5 kg/ha (B1 ) ( 70.38 cm) as compared Ta

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72.9

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303.

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M3 -

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71.6

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70.9

314

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,Mn

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M6 -

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66.5

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11.8

5

195

to (B0 - no biofertilizer) (62.59 cm). Increase in plant heightdue to Azospirillum inoculation have also been reportedby (Mengistu and Singh 1999) in onion. The similar resultswere observed by, Chattoo et al. (2007), Ranjan et al.(2010), Jawadagi et al. (2012), Ghanti and Sharangi (2009),Navale and Wani (2006) and Kumar (2010).

Number of leaves

The application of micronutrients Fe 0.5%, B 0.1%, Zn0.5 %, Cu 0.2%, Mn 0.2% (M2) was recorded significantlymaximum (15.57) number of leaves which was closelyfollowed by M3 - (Fe 1.0%, B 0.2%, Zn 1.0 %, Cu 0.4%,Mn 0.4%) (14.55 leaves) and M4 - (Fe 1.5%, B 0.3%,Zn1.5%, Cu 0.6 %, Mn 0.6 %) (14.51 leaves ) as comparedto M1 - (Fe 0%,B 0%, Zn 0%, Cu 0%, Mn 0%) which was13.33 leaves. Increase in number of leaves/plant this maybe due to the improved growth characters as a result offoliar application of micronutrient which would haveenhanced photosynthesis and other metabolic activities,which lead to increase in cell division and elongation.The same trends were recorded by in onion Sliman et al.(1999), EL-Gamelli et al. (2000), El-Shafie et al. (2002),Chattoo et al. (2007), El-Tohamy et al. (2009), Alam etal. (2010), Abd El-Samad et al. (2011), Ballabh et al.(2012), Jawadagi et al. (2012), Manna (2013) and Trivediand Dhumal (2013).

Application of Azospirillum (5 kg /ha) (B2) was notedsignificantly maximum (15.13) number of leaves followedby Azotobacter 5 kg/ha (B1 ) (14.50 leaves) as comparedto (B0 - no biofertilizer) (13.58 leaves). Application ofAzospirillum and PSB enhanced the leaf number. Theenhanced plant growth characters might be due to highernutrient availability as well as better nutrient uptake bythe crops. Similar results were recorded by Mengistu andSingh (1999), Navale and Wani (2006), Ranjan et al.

(2010), Singh and Sachan (1998), Naruka and Singh(2002) in garlic and Shanti and Balakrishnan (1989) inaggregatum onion.

Neck thickness of bulb

Application of micronutrients Fe 0.5%, B 0.1%, Zn 0.5%, Cu 0.2%, Mn 0.2% (M2) was recorded significantlyminimum neck thickness of bulb (6.12 mm) which wasfollowed by M3 (Fe 1.0%, B 0.2%, Zn 1.0 %, Cu 0.4%,Mn 0.4%) (6.23 mm) and M4 - (Fe 1.5%, B 0.3%,Zn1.5%, Cu 0.6 %, Mn 0.6 %) (6.33 mm) as compared toM1 - (Fe 0%,B 0%, Zn 0%, Cu 0%, Mn 0%) (controlwithout micronutrients) (8.04 mm). It may be due to zincand boron play an essential role in improving plant growth,through the biosynthesis of endogenous hormones whichis responsible for promotion of plant growth (Bhatt et al.2004; Hänsch et al. 2009).

Treatment Azospirillum 5 kg /ha (B2) applicationwas noted significantly minimum (6.19 mm) neckthickness of bulb followed by Azotobacter 5 kg/ha (B1)(6.40 mm) as compared to (B0 - no biofertilizer) (7.42mm). The similar results were observed by Chattoo et al.(2007), Ranjan et al. (2010) and Kumar (2010).

Yield attributing parameters

Average weight of bulb

Foliar application of micronutrients Fe 0.5%, B 0.1%, Zn0.5 %, Cu 0.2%, Mn 0.2% (M2) was recorded significantlymaximum average weight of bulb (58.56 g) followed byM3 (Fe 1.0%, B 0.2%, Zn 1.0 %, Cu 0.4%, Mn 0.4%)(52.86 g) and M4 - (Fe 1.5%, B 0.3%, Zn 1.5%, Cu 0.6 %,

Fig. 1. Effect of micronutrients application on vegetativegrowth and yield attributes parameters of onion

Fig. 2. Effect of Biofertilizers application on vegetativegrowth and yield attributes parameters of onion

196

Mn 0.6 %) (50.97 g) as compared to M1 - (Fe 0%, B 0%,Zn 0%, Cu 0%, Mn 0%) (45.31 g). The similar resultswere observed by Yadav et al. (2003) and Singh and Tiwari(1995).

Azospirillum 5 kg /ha (B2 ) soil application wasnoted significantly maximum (57.87 g) average weight ofbulb followed by Azotobacter 5Kg/ha (B1) (53.22 g) ascompared to (B0 - no biofertilizer) (41.94 g). The similarresults were observed by Mengistu and Singh (1999),Ranjan et al. (2010), Aswani et al. (2005), Kumar (2010),Jawadagi et al. (2012), Saranghem and Singh (2014) andMeena et al. (2015).

Bulb Diameter

Application of micronutrients Fe 0.5%, B 0.1%, Zn 0.5%, Cu 0.2%, Mn 0.2% (M2) was recorded significantlymaximum bulb diameter (5.17 cm) which was closely

followed by M3 (Fe 1.0%, B 0.2%, Zn 1.0 %, Cu 0.4%,Mn 0.4%) (5.15 cm) and M4 - (Fe 1.5%, B 0.3%, Zn 1.5%,Cu 0.6 %, Mn 0.6 %) (5.14 cm) as compared to M1 - (Fe0%, B 0%, Zn 0%, Cu 0%, Mn 0%) (4.49 cm). This maybe due to the micronutrient application especially boronwhich enhances the enzyme activity which in turn triggerthe physiological processes like protein and carbohydratemetabolism in plants. Similar results were reported byLal and Maurya (1981), Alam et al. (2010), Manna (2013)and Singh and Tiwari (1995).

Application of Azospirillum 5 kg /ha (B2) was notedsignificantly maximum (5.23 cm) bulb diameter followedby Azotobacter 5 kg/ha (B1) (5.14 cm) as compared to(B0 - no biofertilizer) (4.62 cm). The diameter influencethe yield of onion and consumer preference. The similarresults were observed by Mengistu and Singh (1999),Navale and Wani (2006), Kumar (2010), Jawadagi et al.(2012).

Table 2. Effect of Biofertilizers application on vegetative growth and yield attributes parameters of onion

Treatments Plant height Number of Neck Average wt Bulb Bulb yield Bulb yield(cm) leaves/ thickness of bulb diameter /plot (q/ha)

plant of bulb (g) (cm) (kg)(mm)

BiofertilizerB0 - No biofertilizer 62.59 13.58 7.42 41.94 4.62 13.16 263.33B1 - Azotobacter 5Kg/ha 70.38 14.50 6.40 53.22 5.14 14.28 285.68B2 - Azospirillum 5Kg/ha 73.38 15.13 6.19 57.87 5.23 15.03 300.71SEm± 0.31 0.09 0.09 0.56 0.04 0.14 2.90CD (P=0.05) 0.89 0.27 0.26 1.63 0.11 0.41 8.38

Effect of micronutrients and biofertilizer on onion crop

197

Bulb yield

Significantly maximum 15.17 kg/plot was exhibited underthe micronutrient treatment (M2)- Fe 0.5%, B 0.1%, Zn0.5 %, Cu 0.2%, Mn 0.2% closely followed by M3 (Fe1.0%, B 0.2%, Zn 1.0 %, Cu 0.4%, Mn 0.4%) (14.83 kg/plot) and M4 - (Fe 1.5%, B 0.3%,Zn 1.5%, Cu 0.6 %, Mn0.6 %) (14.68 kg/plot) over to treatment M1 - (Fe 0%,B0%, Zn 0%, Cu 0%, Mn 0%) (11.88 kg). The similar resultswere observed by Sindhu and Tiwari (1996).

Azospirillum (5 kg/ha) (B2) soil application wasnoted significantly maximum (15.03 kg) bulb yield perplot followed by Azotobacter 5 kg/ha (B1) (14.28 kg) ascompared to (B0 - no biofertilizer) (13.16 kg). The similarresults were observed by Navale and Wani (2006) andKumar (2010). Biofertilizers application on increased theavailability of nitrogen to onion plant. The higher bulb yieldmay be due to greater root proliferation, more uptake ofnutrients and water, more photosynthesis area andenhance food accumulation.

The foliar application of micronutrients Fe 0.5%, B0.1%, Zn 0.5 %, Cu 0.2%, Mn 0.2% (M2) was recordedsignificantly maximum bulb yield (303.42 q/ha) followedby M3 (Fe 1.0%, B 0.2%, Zn 1.0 %, Cu 0.4%, Mn 0.4%)(296.66 q/ha) and M4 - (Fe 1.5%, B 0.3%, Zn 1.5%, Cu0.6 %, Mn 0.6 %) (293.60 q/ha) as compared to M1 - (Fe0%, B 0%, Zn 0%, Cu 0%, Mn 0%) (237.60 q/ha). Thismay be due to zinc, which is one of the most importantelements in the carbohydrates metabolism, mostenzymes that play a role in carbohydrates metabolismare activated by zinc. In addition to carbonic anhydrase,Fructose-1, 6-bisphosphate and Aldolase enzymes areactivated by zinc. These enzymes are active in thechloroplasts and cytoplasm, six-carbon sugar moleculeare separated between chloroplasts and cytoplasm byFructose-1, 6-bisphosphate and three-carbon sugarsmolecule in photosynthesis are transported fromcytoplasm to chloroplasts by Aldolase. The activity ofthese enzymes decreased in zinc deficiency condition,in resulting carbohydrate accumulation in plant leaves.Similar finding were reported by Meena and Singh (1998),Sliman et al. (1999), Gamelli et al. (2000), El-Shafie etal. (2002), El-Tohamy et al. (2009), Alam et al. (2010),Abd El-Samad et al. (2011), Ballabh et al. (2012), Manna(2013), Trivedi and Dhumal (2013) in onion. The similarresults were observed by Yadav et al. (2003), Malakouti(2008).

Soil application of Azospirillum (B2) 5 kg/ha wasnoted significantly maximum (300.71 q/ha) bulb yield perplot followed by Azotobacter (B1) (285.68 q/ha) ascompared to B0- no biofertilizer (263.33 q/ha). The similarresults were observed by Yadav et al. (2005), Aswani et

al. (2005), Ranjan et al. (2010), Jawadagi et al. (2012),Mengistu and singh (1999), Sarangthem and Singh (2014).

The application of micronutrients shows positiveeffect towards the growth, yield and yield parameters ofonion. Growth parameters such as plant height, numberof leaves were highly responsive to foliar spray ofmicronutrients. Yield parameters highly responded tomicronutrients so application of micronutrients mayprovide highest yield.

The increase in growth and yield parameters inthe inoculated treatments can be attributed to the multipleeffects of Azospirillum such as their ability to fixatmospheric nitrogen moreover, its role in solublizationof phosphate and general improvement in nutrient uptakeof the plant due to root proliferation might have alsoconsiderably contributed to enhanced growth and yieldsof the inoculated treatments in these findings.

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(Manuscript Receivd : 25-02-2015; Accepted : 15-05-2015)

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Abstract

An investigation was made to determine the effect ofmicronutrient complex and biofertilizers application on growthand yield in onion (Allium cepa L.) at Horticulture Complex,Maharajpur, Jawaharlal Nehru Krishi Vishwa Vidyalaya,Jabalpur (MP). Treatments were arranged in Factorial RBDduring Rabi season of 2013-14. Results revealed thatsignificantly maximum plant height (78.13 cm), number ofleaves /plant (17.83), neck thickness of bulb (5.60 mm),average weight of bulb (73.76 g), bulb diameter (5.35 cm)and bulb yield/ha (335.46 q/ha) was observed in treatmentT14 - (M2 + B2) (Fe 0.5 %, B 0.1%, Zn 0.5 %, Cu 0.2%, Mn 0.2%+ Azospirillum). However, minimum values of these traitswere found under the control treatment T1 - (M1 + B0 ) (Fe 0%,B 0%, Zn 0%, Cu 0%, Mn 0% + no biofertilizer).

Keywords: Onion, biofertilizer, micronutrients

Onion (Allium cepa L.) is a bulbous biennial herb of familyAlliaceae. It is commonly called as "Queen of kitchen"for its unique usage throughout the year in the form ofsalads, condiments or for cooking with other vegetables.The pungency in onion is due to sulphur compound "allypropyl disulphide" in the volatile oil and the outer skincolour is due to the presence of "querctin". Onion bulb isrich in minerals like phosphorus (50mg/100g), iron (0.7mg/100g), calcium (18mg/100g), carbohydrates (11.0g/100g),protein (1.2g/100g), vitamins 'C' (11mg/100g), fibers (0.6g/100g) and nicotinic acid (0.4mg/100g). The productivityof onion in India is very low (16 t /ha) in comparison toother countries. Micronutrients play an active role in theplant metabolic process from cell wall development torespiration, photosynthesis, chlorophyll formation,enzymes activity, nitrogen fixation etc. Ballabh and Rana(2012). Foliar application of micro-nutrients wassuccessfully used for correcting their deficits and

Effect of micronutrient complex and biofertilizer application on growthand yield in onion (Allium cepa L.)

Pratibha Singh, S.K. Sengupta, B.K. Verma and P.K. JainDepartment of HorticultureCollege of AgricultureJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482 004 (MP)E-mail:pratibha_atul08@yahoo.com

improving the mineral status of plants as well as increasingthe crop yield and quality. (Kolota and Osinska 2001).Producing of good quality onion bulbs is an importanttarget by onion growers whom have an inadequateknowledge about beneficial role of micronutrients inincreasing yield and quality of onion for local and foreignmarkets. Biofertilizer inoculation like Azotobacter,Azospirillum, PSB helps the plants to attain bettervegetative growth and increases yield by 10-30 percent(Mohondas 1999, Tilak and Annapurna 1993). Bio-fertilizers refer to living organisms, which augment plantnutrient supplies in symbiotic or asymbiotic way. Amongthe asymbiotic, nitrogen fixing-bacteria, Azotobacter andAzospirillum contribute to significant improvement in cropsyield by 15-20 per cent while reducing the depletion ofsoil nutrients (Motsara et al. 1995). In addition to thesebeneficial effects, biofertilizers allow the saving of at least20-30 kg/ha inorganic N fertilizers, as they possess atremendous potentiality in nitrogen fixation (Tilak 1991).

Material and methods

The experiment was conducted at Horticulture Complex,Department of Horticulture, JNKVV, Jabalpur (MP) during2013-14 to study the effect of micronutrient complex andbiofertilizer application on growth and yield in onion (Alliumcepa L.). The two facters were chosen as a treatment.Facter A consisted of micronutrient and Facter B relatedwith biofertilizers. The treatments consist of foliar sprayof micronutrients and soil application of biofertilizerscombination, T1 - (M1 + B0 ) (Fe 0%, B 0%, Zn 0%, Cu0%, Mn 0% + no biofertilizer, T2 - (M2 + B0) (Fe 0.5%, B0.1%, Zn 0.5 %, Cu 0.2%, Mn 0.2%+ no biofertilizer), T3- (M3 + B0) (Fe 1.0%, B 0.2%, Zn 1.0 %, Cu 0.4%, Mn0.4%+ no biofertilizer), T4-(M4 + B0) (Fe 1.5%, B 0.3%,Zn 1.5%, Cu 0.6 %, Mn 0.6 % + no biofertilizer), T5- (M5 +

JNKVV Res J 49(2): 200-204 (2015)

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B0 (Fe 2.0%, B 0.4%, Zn 2.0 %, Cu 0.8 %, Mn 0.8 % +no biofertilizer), T6- (M6 + B0) (Fe 2.5%, B 0.5%, Zn 2.5%,Cu 1.0 %, Mn 1.0 % + no biofertilizer), T7 - (M1 + B1) (Fe0 %, B 0%, Zn 0%, Cu 0%, Mn 0% + Azotobacter), T8 -(M2 + B1) (Fe 0.5%, B 0.1%, Zn 0.5 %, Cu 0.2%, Mn0.2% + Azotobacter), T9 - (M3 + B1) (Fe 1.0 %, B 0.2%,Zn 1.0 %, Cu0.4%, Mn 0.4% + Azotobacter), T10 - (M4 +B1) (Fe 1.5 %, B 0.3%, Zn 1.5 %, Cu0.6 %, Mn 0.6% +Azotobacter), T11 - (M5 + B1) (Fe 2.0 %, B 0.4%, Zn 2.0%, Cu 0.8%, Mn 0.8% + Azotobacter), T12 - (M6 + B1) (Fe2.5 %, B 0.5%, Zn 2.5 %, Cu 1.0%, Mn 1.0% +Azotobacter), T13 - M1 + B2 (Fe 0%, B 0%, Zn 0%, Cu0%, Mn 0% + Azospirillum), T14 - (M2 + B2) (Fe 0.5 %, B0.1%, Zn 0.5 %, Cu 0.2%, Mn 0.2%+ Azospirillum), T15 -(M3 + B2) (Fe 1.0 %, B 0.2%, Zn 1.0 %, Cu 0.4%, Mn 0.4%+ Azospirillum), T16 - (M4 + B2) (Fe 1.5 %, B 0.3%, Zn1.5 %, Cu 0.6%, Mn 0.6 % + Azospirillum), T17 - (M5 +B2) (Fe 2.0 %, B 0.4%, Zn 2.0 %, Cu 0.8 %, Mn 0.8 % +Azospirillum), T18 - (M6 + B2) (Fe 2.5 %, B 0.5 %, Zn 2.5%, Cu 1.0%, Mn 1.0 % + Azospirillum). Foliar sprays ofmicronutrients were done at 45 days after transplanting.The nursery sowing was done on 15 November 2013. Thecrop was transplanted (spacing of 15 cm × 10 cm),fertilized and irrigated as per the recommended practices.

The experiment was laid out in a Factorial randomizedblock design with three replications. The source ofmicronutrient for iron, boron, zinc, copper and maganesewere Ferrous sulphat, Borax, Zinc Sulphate, CupperSulphate and Maganese Sulphate respectively. Ten plantswere selected from each plot as a unit for all observationson growth and yield. Based on the net plot yield, yieldper hectare was calculated and expressed in q/ha.

Results and discussion

Plant growth parameters

Plant height

The significantly maximum plant height (78.13 cm) wererecorded in T14 - (M2 + B2) (Fe 0.5 %, B 0.1%, Zn 0.5 %,Cu 0.2%, Mn 0.2% + Azospirillum) followed by T15 - (M3 +B2) (Fe 1.0 %, B 0.2%, Zn 1.0 %, Cu 0.4%, Mn 0.4 % +Azospirillum) (76.73 cm), T16 - (M4 + B2) (Fe 1.5 %, B0.3%, Zn 1.5 %, Cu 0.6%, Mn 0.6 % + Azospirillum)(75.43) and T8 - (M2 + B1) (Fe 0.5%, B 0.1%, Zn 0.5 %,

Table 1. Effect of micronutrient complex and biofertilizer application on vegetative growth and Yield attributes parametersof onion

Treatment code Treatment Plant Number of Neck Average wt Bulb Blubsymbol height leaves/plant thickness of bulb diameter yield

(cm) (mm) (g) (cm) (q /ha)T1 M1 + B0 54.03 11.50 9.53 37.96 3.63 234.53T2 M2 + B0 66.46 14.20 6.76 45.96 4.90 270.40T3 M3 + B0 66.30 14.16 6.80 45.00 4.87 269.60T4 M4 + B0 65.83 14.16 7.00 41.26 4.87 269.06T5 M5 + B0 62.16 13.83 7.10 41.20 4.83 268.66T6 M6 + B0 60.77 13.66 7.36 40.26 4.65 267.73T7 M1 + B1 67.43 14.20 7.30 47.36 4.88 237.46T8 M2 + B1 74.16 14.70 6.00 55.96 5.27 304.40T9 M3 + B1 72.00 14.66 6.10 55.56 5.24 299.60T10 M4 + B1 71.53 14.60 6.20 55.33 5.22 296.93T11 M5 + B1 69.10 14.43 6.40 53.36 5.15 288.00T12 M6 + B1 68.06 14.40 6.40 51.76 5.09 287.73T13 M1 + B2 67.73 14.30 7.29 50.60 4.96 240.80T14 M2 + B2 78.13 17.83 5.60 73.76 5.35 335.46T15 M3 + B2 76.73 14.83 5.80 58.03 5.35 320.80T16 M4 + B2 75.43 14.76 5.80 56.33 5.33 314.80T17 M5 + B2 71.53 14.53 6.33 54.30 5.20 296.26T18 M6 + B2 70.76 14.53 6.36 54.23 5.16 296.13SEm± 0.76 0.23 0.22 1.38 0.09 7.11CD (P=0.05) 2.19 0.68 0.64 4.00 0.28 20.54

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Cu 0.2%, Mn 0.2% + Azotobacter) (74.16) as compareto T1- control (M1 + B0 ) (Fe 0%, B 0%, Zn 0%, Cu 0%,Mn 0% + no biofertilizer (54.03 cm). probable reason forthis may be zinc involves in auxin metabolism in vegetativegrowth that increases leaf length. Tisdale et al. (1985)reported that zinc is involved in auxin metabolism andother enzymatic reactions that increase leaf length. Itmay be due to collective effect of nitrogen and zinc thatstimulate plant growth and thus increases leaf length.Similar results were recorded by Choudhary et al. (2014).

Number of leaves /plant

Significantly maximum 17.83 and 14.83 leaves/plant wereobserved in treatment combination T14 - (M2 + B2) (Fe 0.5%, B 0.1%, Zn 0.5 %, Cu 0.2%, Mn 0.2%+ Azospirillum)and T15 - (M3 + B2) (Fe 1.0 %, B 0.2%, Zn 1.0 %, Cu0.4%, Mn 0.4 %+ Azospirillum) respectively and whichwere at par with each other. Therefore, the lowest 11.50leaves /plant was noted under treatment T1 - (M1 + B0 )(Fe 0%, B 0%, Zn 0%, Cu 0%, Mn 0% + no biofertilizer).Increase in number of leaves /plant this may be due tothe improved growth characters as a result of foliarapplication of micronutrient which would have enhancedphotosynthesis and other metabolic activities, which leadto increase in cell division and elongation. The enhancedplant growth characters might be due to higher nutrientavailability as well as better nutrient uptake by the crops.Similar results were recorded by Choudhary et al. (2014)

Neck thickness of bulb

Application of treatment T14 - (M2 + B2) (Fe 0.5 %, B 0.1%,Zn 0.5 %, Cu 0.2%, Mn 0.2%+ Azospirillum) (5.60 mm)

was recorded significantly minimum neck thickness ofbulb, followed by T15 - (M3 + B2) (Fe 1.0 %, B 0.2%, Zn1.0 %, Cu 0.4%, Mn 0.4 %+ Azospirillum) (5.80 mm), T16- (M4 + B2) (Fe 1.5 %, B 0.3%, Zn 1.5 %, Cu 0.6%, Mn0.6 % + Azospirillum) (5.80 mm) and T8 - (M2 + B1) (Fe0.5%, B 0.1%, Zn 0.5 %, Cu 0.2%, Mn 0.2% +Azotobacter) (6.00 mm) over the treatment combinationT1 control (M1 + B0 ) (Fe 0%, B 0%, Zn 0%, Cu 0%, Mn0% + no biofertilizer) (9.53 mm). Similar results wererecorded by Choudhary et al. (2014).

Yield attributing parameters

Average weight of bulb

Application of different combinations of micronutrients andbiofertilizer significantly influenced the average weight ofbulb of onion (Table 1). The highest fresh weight of bulb(73.76 gm) was found in T14 - (M2 + B2) (Fe 0.5 %, B0.1%, Zn 0.5 %, Cu 0.2%, Mn 0.2%+ Azospirillum) andthe second highest value was in T15 - (M3 + B2) (Fe 1.0 %,B 0.2%, Zn 1.0 %, Cu 0.4%, Mn 0.4 % + Azospirillum)(58.03). The lowest value was recorded in T1 - (M1 + B0 )(Fe 0%, B 0%, Zn 0%, Cu 0%, Mn 0% + no biofertilizer)(37.96). The results of fresh weight of bulbs are inagreement with the findings of Satbir et al. (1989) theystated that fresh weight of bulb significantly increased byfoliar application of Zn and B.

Bulb diameter

Significantly maximum 5.35, 5.35, 5.33, 5.27, 5.24, 5.22,5.20, 5.16 cm of onion bulb diameter were recorded under

Fig. 2. Effect of micronutrient complex and biofertilizerapplication on bulb diameter, neck thickness and numberof leaves of onion

Fig. 1. Effect of micronutrient complex and biofertilizerapplication on plant height and average weight of bulb ofonion

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the treatment combination of T14 - (M2 + B2) (Fe 0.5 %, B0.1%, Zn 0.5 %, Cu 0.2%, Mn 0.2%+ Azospirillum), T15 -(M3 + B2) (Fe 1.0 %, B 0.2%, Zn 1.0 %, Cu 0.4%, Mn 0.4%+ Azospirillum), T16 - (M4 + B2) (Fe 1.5 %, B 0.3%, Zn1.5 %, Cu 0.6%, Mn 0.6 % + Azospirillum), T8 - (M2 + B1)(Fe 0.5%, B 0.1%, Zn 0.5 %, Cu 0.2%, Mn 0.2% +Azotobacter), T9 - (M3 + B1) (Fe 1.0 %, B 0.2%, Zn 1.0%, Cu0.4%, Mn 0.4% + Azotobacter), T10 - (M4 + B1) (Fe1.5 %, B 0.3%, Zn 1.5 %, Cu0.6 %, Mn 0.6% +Azotobacter), T17 - (M5 + B2) (Fe 2.0 %, B 0.4%, Zn 2.0%, Cu 0.8 %, Mn 0.8 % + Azospirillum) and T18 - (M6 +B2) (Fe 2.5 %, B 0.5 %, Zn 2.5 %, Cu 1.0%, Mn 1.0 % +Azospirillum) respectively and which were at par with eachother. However, it was lowest (3.63 cm) in treatmentcombination T1 - (M1 + B0 ) (Fe 0%, B 0%, Zn 0%, Cu0%, Mn 0% + no biofertilizer). This may be due to themicronutrient application especially boron which enhancesthe enzyme activity which in turn trigger the physiologicalprocesses like protein and carbohydrate metabolism inplants. Similar results were reported by Singh and Tiwari(1995), Manna (2013) and Choudhary et al. (2014).

Bulb yield (q /ha)

Data (Table-1) revealed that the bulb yield of onionresponded significantly due to different combinations ofmicronutrients and biofertilizer. Foliar application oftreatment combination T14 - (M2 + B2) (Fe 0.5 %, B 0.1%,Zn 0.5 %, Cu 0.2%, Mn 0.2%+ Azospirillum) was recordedsignificantly superior and it was recorded 335.48 q /habulb yield followed by T15 - (M3 + B2) (Fe 1.0 %, B 0.2%,Zn 1.0 %, Cu 0.4%, Mn 0.4 % + Azospirillum) (320.80 q/ha), T16 - (M4 + B2) (Fe 1.5 %, B 0.3%, Zn 1.5 %, Cu0.6%, Mn 0.6 % + Azospirillum) (314.80 q /ha) and T8 -

(M2 + B1) (Fe 0.5%, B 0.1%, Zn 0.5 %, Cu 0.2%, Mn0.2% + Azotobacter) (304.40 q /ha) as compare totreatment combination T1 i.e. Control (M1 + B0 ) (Fe 0%,B 0%, Zn 0%, Cu 0%, Mn 0% + no biofertilizer) (234.53 q/ha). Onion yield increment could be due to the fact thatnitrogen supply to the plant increased carbohydratesynthesis with increase the rate of metabolism. Itincreases the bulb weight and thus increases total yield.Similar results were recorded by Choudhary et al. (2014),Ballabh and Rana (2012) they reported that onion plantsreceived Fe, Zn and/or Mn resulted in the heaviest bulbyield compared to the control plants. They also addedthat foliar application of Zn was found to give the besteffect. Moreover, many authors supporting the obtaineddata. All of them summarized that foliar application ofmicro-nutrients had a positive significant effect on onionbulb yield. This could be due to the increase in nutrientavailability and uptake of nutrients resulting in fastersynthesis and translocation of photosynthate from source(leaves) to sink (bulb).

References

Ballabh Khashti, Rana DK (2012) Response ofmicronutrients on qualitative and quantitativeparameters of onion (Allium cepa L.). ProgressiveHort 44(1): 40-46

Choudhary MK, Kavita A, Maurya IB, Singh B, Sharma MK,Hatwal PK (2014) Effect of biofertilizers andmicronutrients on growth and yield of garlic (Alliumsativum L.) var. 'G-282'. Progr Hort 46(2): 367-371

EI-Gamili AE, Hanna AB, EI-Hadi AHA (2000) The effect ofsome foliar fertilizers application on growth, bulbyield, quality and storageability of Giza 20 onioncultivar (Allium cepa L.). Annals of Agri Sci Moshtohor38: 1727-1737

Kolota E, Osinska M (2001) Efficiency of foliar nutrition offield vegetables grown at different nitrogen rates.Acta Hort 563: 87-91.

Manna D (2013) Growth, yield and bulb quality of onion (Alliumcepa L.) in response to foliar application of boronand zinc. SAARC J Agri 11: 149-153

Motsara MR, Bhattacharya P, Srivastaba B (1995) Biofertilisertechnology marketing and usage. FertilizerDevelopment Consultation Organization New Delhi,pp 183.

Mohandas S (1999) Biofertilizer for Horticulture crops. IndianHorticulture 43: 32-37

Singh MV (2005) Micronutrient deficiencies in Indian soilsand field usable practices for their correction. IndianInstitute of Soil Sciences Nabibagh Berasia RoadBhopal - 462058

Singh DP, Tiwari RS (1995) Effect of micronutrients on growthand yield of onion (Allium cepa L.) variety Pusa Red.Recent Hort 2: 70-77

Fig. 3. Effect of micronutrient complex and biofertilizerapplication on bulb yield of onion

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Satbir Singh-Sindhu, Tiwari RS, Sindhu SS (1989) Effect ofmicronutrients on the growth characters of onion(Allium cepa L.) cv. Pusa red. Harayana J Hortic Sci18(1-2): 146-149

Tisdale SL, Nelson WL, Beaton JD (1985) Micronutrientsand other beneficial elements in soils andfertilizers. In soil fertility and fertilizers, Zinc, pp.387-388.Macmillan Publishing Company 866 thirdavenue, New York, 10022

Tilak KVBR, Annapurna K (1993) Bacterial fertilizers ProceedIndian Nat Acad Sci 59 (3-4): 315-324

Tilak KVBR (1991) Bacterial fertilizers, Indian Council ofAgricultural Research New Delhi pp. 65

(Manuscript Receivd :2-03-2015; Accepted :15-06-2015)

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Abstract

The present investigation was carried out at Fruit ResearchStation, Imalia, College of Agriculture, JNKVV, Jabalpurduring 2012-14 on 40 years old pruned trees of mango cv.Langra. The experiment was comprised of 19 treatments ofdifferent levels of growth regulators (GA3 and NAA),micronutrients (ZnSO4 and Borax), bio-fertilizers (PSB andAzotobacter) and vermicompost. The results indicated thatthe application of vermicompost (30kg) + PSB (250g) +Azotobacter (250g) + GA3 40ppm significantly influenced thenumber of fruits at initial stage, at marble stage and fruits atpre-harvest stage. Fruit set per panicle and fruit drop alsoshowed significant difference over control.

Keywords: Azotobacter, PSB, GA3, vermicompost, fruitset

Mango (Mangifera indica L.) belongs to familyAnacardiaceae and is characterized by the presence ofresinous canal. It is one of the most important fruit cropof India and referred to as the "King of Fruits" because ofits taste, excellent flavour and attractive fragrance. Mangois a delicious fruit and holds a great degree of nutritivevalue. There is need to study the effect of integrated nutrientmanagement on fruiting behaviour of mango. Integratednutrient management refers to maintenance of soil fertilityand plant nutrient supply to an optimum level for sustainingthe desired crop productivity through optimization of thebenefits from all possible sources of plant nutrient in anintegrated manner.

Material and methods

The present investigation was conducted at the FruitResearch Station, Imalia, College of Agriculture, JNKVV,

Effect of growth regulators, micronutrients and bio-fertilizers onfruiting of mango cv Langra under Jabalpur condition

Akshata Tomar and S.K. PandeyDepartment of HorticultureJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)Email: akshatatomar20@gmail.com

JNKVV Res J 49(2): 205-207 (2015)

Jabalpur, during the year 2012-14 on 40-year-old prunedtrees of mango cv. Langra. The experiment was plannedwith 19 treatments combination with three replications inrandomized block design (one tree per treatment perreplication). Parameters like number of fruits at initialstage, marble stage and pre-harvest stage was recordedand fruit set per panicle and fruit drop was calculated byappropriate formula.

Results and discussion

The result of this study revealed that the maximum numberof fruits at initial stage (23.83), marble stage (4.15) andpre-harvest stage (0.63) and fruit set per panicle (2.62%)were recorded with the application of Vermicompost (30kg)+ PSB (250g) + Azotobacter (250g) + GA3 (40ppm)closely followed by the application of Vermicompost (30kg)+ PSB (250g) + Azotobacter (250g) + NAA (40ppm) whichmight be due to combined effect of vermicompost, bio-fertilizers and growth regulators. Application of bio-fertilizers along with vermicompost provides nitrogen fixerwhich is not only increased the availability of nitrogen tothe plant roots but also increased their translocation fromroot to flower through plant foliage. Growth regulatorsprovides better supplementation of deficient nutritionalcondition of plant in terms of better supply of water,balanced nutrients and other compounds vital for theirproper growth and development in plant which may provideequal opportunity to increase fruit set. Minimum fruit dropwas obtained with the positive effect of combinedapplication of growth regulators vermicompost and bio-fertilizers which might provide good availability and uptakeof macro and micronutrients, availability of photosynthatesand adequate hormonal balance which reduces the fruitdrop. Foliar application of NAA provides the optimumavailability of auxin and nutrients which delays the

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Table 1. Influence of growth regulators, micronutrients and bio-fertilizers on number of fruits at initial stage, marblestage and pre-harvest stage

Treatments Initial Marble Pre-harveststage stage stage

T1= Vermicompost (30 kg) + PSB (250g) + Azotobacter (250g) 17.88 2.92 0.27T2= Vermicompost (15 kg) + PSB (125g) + Azotobacter (125g) 20.60 2.77 0.23T3= Vermicompost (30 kg) + PSB (250g) + Azotobacter (250g) + NAA (20 ppm) 22.87 3.73 0.53T4= Vermicompost (30 kg) + PSB (250g) + Azotobacter (250g) + NAA (40 ppm) 23.06 3.97 0.60T5= Vermicompost (30 kg) + PSB (250g) + Azotobacter (250g) + GA3 (20 ppm) 23.58 3.88 0.58T6= Vermicompost (30 kg) + PSB (250g) + Azotobacter (250g) + GA3 (40 ppm) 23.83 4.15 0.63T7= Vermicompost (30 kg) + PSB (250g) + Azotobacter (250g) + ZnSO4 (0.5%) 15.03 3.25 0.33T8= Vermicompost (30 kg) + PSB (250g) + Azotobacter (250g) + ZnSO4 (1%) 15.35 3.25 0.30T9= Vermicompost (30 kg) + PSB (250g) + Azotobacter (250g) + Borax (0.25%) 19.22 3.35 0.38T10= Vermicompost (30 kg) + PSB (250g) + Azotobacter (250g) + Borax (0.5%) 21.07 3.43 0.42T11= Vermicompost (15 kg) + PSB (125g) + Azotobacter (125g) + NAA (20 ppm) 22.25 3.53 0.43T12= Vermicompost (15 kg) + PSB (125g) + Azotobacter (125g) + NAA (40 ppm) 21.30 3.55 0.47T13= Vermicompost (15 kg) + PSB (125g) + Azotobacter (125g) + GA3 (20 ppm) 21.91 3.70 0.48T14= Vermicompost (15 kg) + PSB (125g) + Azotobacter (125g) + GA3 (40 ppm) 22.20 3.75 0.53T15= Vermicompost (15 kg) + PSB (125g) + Azotobacter (125g) + ZnSO4 (0.5%) 14.88 2.97 0.30T16= Vermicompost (15 kg) + PSB (125g) + Azotobacter (125g) + ZnSO4 (1%) 14.90 3.25 0.35T17=Vermicompost (15 kg) + PSB (125g) + Azotobacter (125g) + Borax (0.25%) 16.38 3.18 0.35T18= Vermicompost (15 kg) + PSB (125g) + Azotobacter (125g) + Borax (0.5%) 16.02 2.98 0.30T19= Control 19.18 2.50 0.20CD at 5% 2.35 0.80 0.13

Table 2. Influence of growth regulators, micronutrients and bio-fertilizers on fruit set per panicle and fruit drop

Treatments Fruit set Fruit dropper panicle (%)

(%)T1= Vermicompost (30 kg) + PSB (250g) + Azotobacter (250g) 1.50 98.49T2= Vermicompost (15 kg) + PSB (125g) + Azotobacter (125g) 1.12 98.86T3= Vermicompost (30 kg) + PSB (250g) + Azotobacter (250g) + NAA (20 ppm) 2.33 97.65T4= Vermicompost (30 kg) + PSB (250g) + Azotobacter (250g) + NAA (40 ppm) 2.60 97.05T5= Vermicompost (30 kg) + PSB (250g) + Azotobacter (250g) + GA3 (20 ppm) 2.47 97.53T6= Vermicompost (30 kg) + PSB (250g) + Azotobacter (250g) + GA3 (40 ppm) 2.62 97.33T7= Vermicompost (30 kg) + PSB (250g) + Azotobacter (250g) + ZnSO4 (0.5%) 2.19 97.79T8= Vermicompost (30 kg) + PSB (250g) + Azotobacter (250g) + ZnSO4 (1%) 2.05 97.93T9= Vermicompost (30 kg) + PSB (250g) + Azotobacter (250g) + Borax (0.25%) 2.00 97.99T10= Vermicompost (30 kg) + PSB (250g) + Azotobacter (250g) + Borax (0.5%) 1.97 98.09T11= Vermicompost (15 kg) + PSB (125g) + Azotobacter (125g) + NAA (20 ppm) 1.95 98.12T12= Vermicompost (15 kg) + PSB (125g) + Azotobacter (125g) + NAA (40 ppm) 2.29 97.70T13= Vermicompost (15 kg) + PSB (125g) + Azotobacter (125g) + GA3 (20 ppm) 2.10 97.78T14= Vermicompost (15 kg) + PSB (125g) + Azotobacter (125g) + GA3 (40 ppm) 2.40 97.58T15= Vermicompost (15 kg) + PSB (125g) + Azotobacter (125g) + ZnSO4 (0.5%) 2.04 98.02T16= Vermicompost (15 kg) + PSB (125g) + Azotobacter (125g) + ZnSO4 (1%) 2.35 97.64T17= Vermicompost (15 kg) + PSB (125g) + Azotobacter (125g) + Borax (0.25%) 1.90 97.88T18= Vermicompost (15 kg) + PSB (125g) + Azotobacter (125g) + Borax (0.5%) 1.85 98.19T19= Control 0.96 98.95CD at 5% 0.77 0.84

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formation of abscission zone and ultimately reduces fruitdrop. Reduction in the fruit drop as a response of GA3might be due to an increase in initial growth of ovaries,ultimately reduced magnitude of the peak of abscission.The findings are in confirmatory with Kumar and Kumar(2013), Nkansah et al. (2012), Yadav et al. (2011) Rubyand Brahamachari (2004), Gofur et al. (1998), Haidry etal. (1997) and Rawash et al. (1983).

References

Gofur MA, Shafique MZ, Helali MOH, Ibrahim M, Rahman MM (1998) Effect of application of plant hormone onthe control of fruit drop, yield and qualitycharacteristics of mango (Mangifera indica L).Bangladesh J Sci and Indust Res 33(4): 493-498

Haidry GA, Jalal-Ud-Din B, Ghaffoor A, Munir M (1997)Effect of naphthalene acetic acid (NAA) on the fruitdrop, yield and quality of mango (Mangifera indicaL) cultivar Langra. Scientific Khyber (Pakistan).10(1): 13-20

Kumar M, Kumar R (2013) Response of organic manureson growth and yield of mango (Mangifera indica L)cv. Dashehari. Hortflora Res Spec 2(1): 64-67

Nkansah GO, Ofosu-Anim J, Mawuli A (2012) GibberellicAcid and Naphthalene Acetic Acid affect fruitretention, yield and quality of Keitt Mangoes in theCoastal Savanna Ecological Zone of Ghana.American J Pl Physiol 7: 243-251

Rawash MA, El-Hammady A., El-Nabawy S, Khalifa AS, El-Masry H (1983) Regulation of flowering and fruitingin mango trees by using some growth regulators.Annals Agri Sci, Ain-Shams Univ 28(1): 227-240

Ruby Rani, Brahmachari VS (2004) Effect of growthsubstances and calcium compounds on fruitretention, growth and yield of Amrapali mango.Orissa J Hort 32(1): 15-18

Yadav AK, Singh JK, Singh HK (2011) Studies on integratednutrient management in flowering, fruiting, yield andquality of mango cv. Amrapali under high densityorchard. Ind J Hort 68(4): 453-460

(Manuscript Receivd : 27-11-2015; Accepted : 30-06-2015)

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Abstract

Weekly field infestation of early shoot borer, Chilo infuscatellusSneller and its moth captures at pheromone traps andmeteorological parameters from 2009 to 2012 wereinvestigated at AICRP on Sugarcane, Zonal AgricultureResearch Station, Powarkheda, Hoshangabad, MadhyaPradesh to ascertain it's population dynamics. The pooleddata revealed that ESB infests the sugarcane from 2nd weekof February to 2nd week of July and its peak activity (1.3 to 2.10per cent/week) was observed from last week of March tosecond last week of May whereas the cumulative infestationranged from 17.20 to 25.20 per cent. For initiation of ESBinfestation, 26-270C maximum and 90C minimumtemperature, <93 per cent morning and <53 per cent eveningRH and scanty rains were appeared to be conducive. Whilefor built up of peak activity, 39 to 40 0C maximum and 13 to 170C minimum temperature, 69 to 72 per cent morning and 20to 24 per cent evening RH were seemed to be favourable.The moth captures at pheromone traps coincide with thefield infestation. Multiple regression analysis revealed thatthe moth captures at pheromone traps (positive) andminimum temperature (negative) strong significant and thenumber of rainy days (positive) showed significant influenceon pest build up. Whereas, the maximum temperature andamount of rain fall positive and relative humidity of morningand evening showed negative non-significant influence. Thedeterminant factor (R2=0.964) suggested a very good accountof variability.

Keywords: Early shoot borer, Infestation, Populationdynamics, Standard meteorological week

The Crambid moth borer, Chilo infuscatellus Snellen.,(Crambidae: Lepidoptera) is important pest in all oversugarcane growing areas in India and infests the crop attillering stage in spring. It is the internode borer in rainy

Population dynamics of sugarcane early shoot borer in MadhyaPradesh

A.K. Choudhary, P.K. Amrate and A. ChatterjeeZonal Agricultural Research StationPowarkheda 461 110 (MP)Jawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)Email: arun10091959@rediffmail.com

JNKVV Res J 49(2): 208-213 (2015)

season when continuous long spells (>12 days) arecommon. It is the most detrimental problem encountersto the spring planted and/or ratoon sugarcane. The springcrop receives high infestation as compared to autumncrop. Late planted or ratoon crop normally receives severeinfestation which causes gaps and drastically reducesthe economical returns. Maximum yield loss reaches upto 42 per cent when the incidence is at 60 day-old crop(Lakshminarayana 1983). It destroys 58 per cent of shootsin different states, causing reduction up to 33 per cent incane yield, 0.25-3.0 units in sugar recovery and 27 percent in jaggery (Chaudhary 1973; Khan andKrishnamurthy Rao 1956; Patil and Hapse 1981). Thepopulation dynamics of Chilo infuscatellus, moth capturesat pheromone trap and possible effects of meteorologicalparameters were studied so that effective managementstrategies can be formulated.

Material and methods

During 2009 to 2012 an investigation was made at ZonalAgricultural Research Station, Powarkheda,Hoshangabad, Madhya Pradesh on sugarcane variety,Co 86032 which was planted in 0.2 ha and allrecommended packages of practice were followed exceptapplication of insecticides. At third day of each Standardmeteorological week (SMW) observations on infestationof early shoot borer and their natural enemies wererecorded. The dead hearts were counted at randomlyselected five places on 100 tillers at each place. Afterrecording counts, the dead hearts were removed to avoidrecounting of the same in future. Three pheromone trapswere installed in the second fortnight of February till harvest

209

of crop in one acre of sugarcane crop (separate lureblock). The moth captures were recorded daily in themorning hours. The recorded data were subjected toasses the field infestation (per cent) and moth capturesper day per trap. Meteorological Standard Week (SMW)wise meteorological data on average temperature(minimum & maximum), average relative humidity (morning& evening), total rainfall and rainy days from was recordedfrom locally stationed meteorological observatory for theperiod under study. The relationship between the fieldinfestation with moth captures at pheromone trap andweather parameters were worked out by correlation andregression analysis.

Results and discussion

During 2009 to 2012, the cumulative infestation of earlyshoot borer (ESB) ranged from 17.20 to 25.20 per cent.The infestation initiated from 2nd week of February to 2nd

week of March. The peak activity (<1 per cent/week) ofborer was observed from 2nd week of March to 2nd week ofJune. The maximum infestation of ESB (2.20 to 3.4 percent /week) recorded from 4th week of April to 2nd week ofMay in different years (Table 1). Rao and Ramesh (2004)also found that the peak period of the sugarcane earlyshoot borer was May and September.

Early shoot borerChilo infuscatellus Snellen

Eggs Caterpillar Pupa Adult

Dead Heart Exit Hole Tunneling

Early shoot borer - Nature of Damage

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Table 1. Infestation of Early shoot borer and moth captures at pheromone trap during 2009 to 2012

Early Shoot borer 2009 2010 2011 2012Cumulative infestation 17.6 (-) 17.2 (-) 20.4 (-) 25.2 (-)Initiation 0.40 (10) 0.20 (9) 0.20 (10) 0.40 (6)Peak Activity 0.8-2.4 1.0-2.2 1.0-2.20 1.0-3.40

(13-23) (13-21) (14-23) (10-20)Peak infestation (per cent /week) 2.40 (19) 2.20 (19) 2.20 (17) 3.40 (16)Disappeared After 0.20 (25) 0.20 (26) 0.20 (28) 0.2 (24)Particulars Number Number Number NumberMoth captures - started 0.1 (13) 0.05 (10) 0.05 (9) 0.07 (7)Moth captures - Peak 0.57 (20) 0.48 (21) 0.52 (21) 0.79 (18)Moth captures - terminated 0.05 (23) 0.05 (25) 0.05 (28) 0.07 (24)Note - the values are for the per cent infestation of ESB / number of moths captured at pheromone trap, while thevalues in the parenthesis are of corresponding SMW

Table 2. Standard Meteorological Week wise Early shoot borer field infestation, moth captures at pheromone trapand Weather data, Zonal Agricultural Research Station, Powarkheda (Average 2009 to 2012)

SMW Temperature (0C) RH per cent Rainfall Rainy days ESB ESB Moth/day/Max Min Morn Even (mm) infestation cumulative trap

(%) (%)2 25.83 7.45 95.50 54.75 3.00 0.50 0.00 0.00 0.003 25.13 6.93 92.25 54.50 0.00 0.00 0.00 0.00 0.004 26.58 7.93 94.25 53.75 0.70 0.25 0.00 0.00 0.005 26.20 9.33 93.75 53.25 0.50 0.25 0.00 0.00 0.006 27.80 9.23 90.50 50.75 0.00 0.00 0.10 0.10 0.007 28.10 9.40 92.50 40.00 0.00 0.00 0.00 0.10 0.028 30.48 10.43 91.75 47.00 0.00 0.00 0.05 0.15 0.049 32.73 12.20 90.50 43.50 0.00 0.00 0.20 0.35 0.0910 33.23 12.23 94.25 38.75 0.00 0.00 0.55 0.90 0.0511 35.45 12.05 89.25 35.75 0.00 0.00 0.45 1.35 0.0612 37.68 14.53 78.00 27.25 0.00 0.00 0.70 2.05 0.0913 39.43 13.83 83.50 21.75 0.00 0.00 1.30 3.35 0.1714 39.43 17.38 72.75 24.25 1.38 0.50 1.50 4.85 0.2615 40.85 18.88 69.25 20.00 0.00 0.00 1.70 6.55 0.3116 42.23 20.68 65.50 17.25 0.00 0.00 2.00 8.55 0.3817 42.30 20.50 59.00 18.50 0.00 0.00 2.00 10.55 0.4418 42.43 24.38 65.75 18.25 1.55 0.50 1.85 12.40 0.4219 42.68 24.15 58.75 17.25 4.10 0.25 2.10 14.50 0.4120 44.40 25.25 63.25 18.00 3.50 0.50 1.75 16.25 0.4121 43.68 26.00 61.50 15.00 0.00 0.00 1.30 17.55 0.3922 43.00 27.63 64.50 20.00 2.85 0.50 0.90 18.45 0.2723 41.23 25.25 74.25 22.50 3.20 0.50 0.80 19.25 0.2324 42.45 24.25 86.00 25.75 25.20 1.25 0.35 19.60 0.1525 39.43 24.65 87.00 30.25 38.18 2.75 0.25 19.85 0.0526 37.98 24.75 93.25 40.50 44.90 3.25 0.15 20.00 0.0127 37.05 24.63 97.75 44.75 70.75 4.75 0.05 20.05 0.0028 35.70 24.10 97.00 51.75 43.55 3.50 0.05 20.10 0.0129 35.08 24.25 97.00 56.75 100.38 3.50 0.00 20.10 0.00n 28 28 28 28 28 28 28 28 28Max 44.40 27.63 97.75 56.75 100.38 4.75 2.10 20.10 0.44Min 25.13 6.93 58.75 15.00 0.00 0.00 0.00 0.00 0.00

1018.48 502.20 2298.50 961.75 343.73 22.75 20.10 256.95 4.24Av. 36.37 17.94 82.09 34.35 12.28 0.81 0.72 9.18 0.15

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The average data of 2009-12 (Table 3) reveals thatthe early shoot borer infests the sugarcane from 2nd weekof February to 2nd week of July. This is in agreement withthe finding of Raza Muhammand et al. (2012) who reportedthat adults of Chilo infuscatellus (Snellen) emerged fromover wintering larvae during fourth week of February. Thepeak activity (1.3 to 2.10 per cent/week) of ESB wasobserved from last week of March to second last week ofMay. The maximum ESB infestation (2.10 per cent/week)was recorded at 1st week of May. During this period noactivity of bio-agent was observed, except the negligiblepresence of a larval parasitoid, Sturmiopsis inferns. For

initiation of ESB infestation, 26-270C maximum and 90Cminimum temperature, <93 per cent morning and <53per cent evening RH and scanty rains were appeared tobe conducive. While, for built up of peak activity, 39 to 400C maximum and 13 to 17 0C minimum temperature, 69to 72 per cent morning and 20 to 24 per cent evening RHseems to be favourable. Present finding is in conformitywith the finding of Srivastava and Rai (2012) who alsoreported that the shoot borers (Chilo infuscatellus) attacksin the early phase during the months of plant growth i.e.April-June. Samsona and Kumara (1983) also reported

Table 3. Correlation and Regression statistics of ESB infestation, moth captures at pheromone trap and meteorologicalparameters (2009 -2012)

Particulars Correlation t values Coefficients t-values

Moth captures per day per trap 0.95419 16.261** 4.256599 4.9333**Mean maximum temperature (0C) 0.75578 5.885** 0.055113 1.7472Mean minimum temperature (0C) 0.43767 2.482** -0.09043 -4.9379**Mean RH per cent (morning) -0.92270 -12.204** -0.01244 -1.0652Mean RHper cent (evening) -0.87504 -9.218** -0.00499 -0.4247Total Rain fall (mm) -0.37082 -2.036* 0.002661 0.6859Total Rainy Days -0.37074 -2.035* 0.208542 2.5496*Intercept (Constant) - - 0.681616 0.4581** Significant at 1per cent , * Significant at 5per centRegression Multiple R R2 Adjusted R2 SE F calc.

0.9819 0.9641 0.9516 0.1678 76.7712

Y = 0.682 + 4.257*X1 + 0.055*X2 - 0.09*X3 - 0.012*X4 - 0.005*X5 +0.003*X6 + 0.209*X7

Note - X1 - Moth capture per day per trap, X2-Mean Max Temp 0C., X3-Mean Min Temp 0C, X4-Mean RH per cent(morning), X5-Mean RH per cent (evening), X6-Total Rain fall (mm), X7- Total Rainy Days

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212

that sugarcane borer (Eldana saccharina Walker, Chilozacconius Bleszynski and Sesamia spp.) populationincrease considerably during dry season and declinedgradually during rainy season.

The moth captures in pheromone trap started (0.02moths/day/trap) at 3rd week of February. Maximum mothscapture was recorded before two weeks of the maximumfield infestation in the last week of April. After 2nd week ofJuly no moths were captured. Similarly as present findingsRaza Muhammand et al. (2012) also find that moths ofChilo infuscatellus trapped in light traps coincided withthe larval infestations.

Multiple regression analysis indicated that mothcaptured at pheromone trap and minimum temperaturehad strong and significant influence on pest build up asthe t values (4.933 and - 4.938, respectively) suggestedstrong significant account of variability. The number ofrainy days had good influence on ESB field infestation (tvalue 2.550, statistically significant at 5 per cent). Themaximum temperature and amount of rain fall showed apositive, while the mean relative humidity of morning andevening indicated a negative, but non-significant impacton ESB infestation. The determinant factor (R2=0.964)suggested a very good account of variability due to thefactors on field infestation of ESB (Table 3).

The multiple regression equation obtained is asunder -

Y = 0.682 + 4.257*X1 + 0.055*X2 - 0.09*X3 - 0.012*X4 -0.005*X5 +0.003*X6 + 0.209*X7

where, X1 - Moth capture per day per trap, X2-Mean MaxTemp 0C., X3-Mean Min Temp 0C, X4-Mean RH per cent(morning), X5-Mean RHper cent (evening), X6-Total Rainfall (mm), X7- Total Rainy Days

Shahbaj Ahmad (2011) also recorded 70.4 per centcumulative effect of abiotic factors in fluctuating C.infuscatellus infestation and said that the regressionequation was fitted best. The findings are also inconformation of the Rao and Ramesh (2004) who reportedthat the ecological factors i.e., maximum and minimumtemperature had significant effect (positive correlation) onlight trap catches and relative humidity exerted a weaknegative correlation. But their finding that multipleregression analysis showed that the number of rainy daysexhibited significantly negative effect on light trap catchesis in contradiction with present findings.

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213

References

Chaudhary JP (1973) Some important insect pests ofSugarcane. Haryana Agric Uni Magazine 78-83

Khan MQ, Krishnamurthy R (1956) Assessment of loss dueto Chilotraea infuscatellus Snellen in sugarcane.Proc Int Soc Sugarcane Techno 9 : 870-879

Lakshminarayana K (1983) Pest Management in AndhraPradesh. pp. 63-70. In: Balasubramanyan, M andSolayappan AR (Eds). Sugarcane Pest Managementin India. Tamil Nadu Cooperative Sugar Federation,Madras

Patil AS, Hapase DG (1981) Research on sugarcane borersin Maharashtra State. Proceedings of NationalSymposium on Stalk Borer 165-175

Rao N Venugopala, Ramesh T Babu (2004) Monitoring ofthe sugarcane early shoot borer, Chilo infuscatellusSnellen population by using light traps. J EntomolRes 28 (3): 233-239

Raza Muhammad, Maqsood Anwar Rustamani, Nazir Ahmad,Qadeer Ahmad (2012) Effect of Different InfestationLevels of Chilo infuscatellus (Snellen) on Quantityand Quality Parameters of Sugarcane. J Basic andApplied Sci 8 (2): 702-705

Sampsona MA, Kumara R (1983) Population dynamics ofthe stem-borer complex on sugar-cane in SouthernGhana. International J Tropical Insect Sci 4 (special1-2): 25-31

Shahbaj Ahmad (2011) Development and implementingstrategic IPM module of sugarcane stem borer,Chilo infuscatellus (Pyralidae: Lepidoptera), PhDthesis, Deptt. Of Entomology, University ofAgriculture, Faislabad, Pakistan, pp 49

Srivastava Ashok K, Rai Mahendra K (2012) Review:Sugarcane production: Impact of climate changeand its mitigation. Biodiversitas 13 (4): 214-227

(Manuscript Receivd :11-01-2015; Accepted :20-04-2015)

214

Abstract

Soybean crop was grown with recommended package ofpractices so as to raise good crop. The crop was harvestedat 85, 90, 95 and 100 days after sowing (DAS). Beforeharvesting the maturity symptoms were recorded at thesestages. Seeds after drying were subject to storage in gunnybags under ambient conditions. The bimonthly observationswere recorded for germination, moisture content, vigour indexI & II and electric conductivity of seeds harvested at differentstages. There was significant difference in germinationpercent age of seed harvested at different stages. Thesignificantly highest initial germination was observed in theseeds when the crop was harvested at physiological maturityand remains highest at subsequent storage period up to 12months. The minimum seed certification standards (MSCS)for germination were maintained for 12, 10, 8 and 6 monthsof storage. The vigour index decreased whereas, electricalconductivity was increased with delay in harvesting stages.The maturity symptoms recorded at 85 DAS were yellowingof leaves, stem and basal 2-3 leaves were dried however,upper 2-3 leaves were still green. These symptomsresemble the symptoms of physiological maturity. Hence itis concluded that soybean for seed production should beharvested at physiological maturity stage which retain itsviability during storage above MSCS up to 12 months insoybean cv. JS 335.

Keywords: Seed quality, storage, soybean

Soybean [Glycine max (L.) Merril] occupies second placeamong the oilseed in India (Rasad et al. 2007). Soybeancontains about 20 per cent oil and 40 per cent high qualityprotein. Soybean protein is rich in valuable amino acidlycine (5%) in which most of the cereals are deficient. Inaddition, it contains a good amount of minerals, saltsand vitamins (thiamine and riboflavin) and its sproutinggrains contain a considerable amount of Vitamin C,Vitamin A is present in the form of precursor carotene,

Impact of harvesting stages on seed quality of soybean duringstorage

V.R. Shelar, A.P. Karjule and K.C. GagareSeed Technology Research UnitMahatma Phule Krishi VidyapeethRahuri 413 722 (Maharashtra)

JNKVV Res J 49(2): 214-218 (2015)

which is converted into vitamin A in the intestine. Thepoor seed storability is major problem in soybean. Theharvesting time is one of the critical steps in soybeanseeds production. The changes associated with seeddeterioration are manifested in various seed and seedlingcharacters among different stages (Dadlani and Agrawal1983). Loss of Seed viability during storage and resultantpoor stand are the major constraints in soybean seedproduction and it is common phenomenon in many cropseeds but it is well marked in soybean. Production ofhigh quality seed, which retains its viability· through astorage season, is a major challenge.

Non-availability of short duration high yielding, goodquality seed on adequate scale are the major constraintin achieving higher productivity (Pawar et al. 2011).Soybean seeds were used for next sowing by variousfarmers. It is important to seed have good germinationand vigour at the time of sowing. In view of this anexperiment was conducted to see the effect of differentharvesting stages on seed quality of soybean duringstorage.

Material and methods

The soybean variety JS 335 was sown at Seed TechnologyResearch Unit farm in the second fort night of June during2004-05 to 2006-07. The crop was sown in four replicationswith spacing 45 X 15 cm. The crop was grown withrecommended package of practices so as to raise goodcrop. The soybean crop was harvested at 85, 90, 95 and100 DAS. Before harvesting the maturity symptoms wasrecorded at these stages. Crop was allowed to dry in thefield itself before threshing. After proper drying the seedwas stored in gunny bags and stored in godown at ambientconditions. The bimonthly observations for seed qualityparameters viz. germination, moisture content, vigor indexand electrical conductivity from different harvesting stages

215

was recorded separately as per ISTA (Anonymous 1999).The data generated from the experiments were subjectedto statistical analysis in Factorial Completely RandomizedDesign (FCRD) whenever, necessary as prescribed byPanse and Sukhatme (1985). Transformation of data wascarried out prior to statistical analysis as suggested bySteel and Torrie (1981).

Results and discussion

The maturity symptoms of the crop harvested at differentstages are presented in Table 1. From the table it is seenthat the symptoms recorded at 85 days after sowing wassimilar to physiological maturity. At this stage there wasyellowing of leaves and plant along with peduncle wasobserved. Though the basal 2-3 leaves were dried, upper2-3 leaves were still green. At 90 days after sowing i.e. 5days after physiological maturity yellowing of all leaves.Stems and pod was observed. The drying of lower leavesand few pod was also noticed and only 2-3 yellow leavesand few pod was also noticed and 2-3 yellow leaves wereon same plant and defoliation of remaining plant wasobserved. At 95 days after sowing drying whole plantincluding leaves and pod was observed. Only few leavesand pods of same plants were yellowish. Some plantswere completely dried and shattering of few pods wasobserved. At 100 days after sowing, all plants had 2-3yellow leaves at the tip. Shattering of seeds from samepods was observed. As soon as the plant starts changingcolour of its leaves, there are no more photosynthetically

active and function for supplying food. Hence there is noany harm in harvesting the crop when they start changingtheir colour from green to yellow. Similar types ofsymptoms and observations were recorded by Fehr et al.(1971) at physiological maturity of soybean and statedthat change of pod colour or leaves colour to yellow canbe used as an index for harvesting of soybean.

The pooled data of three years studies effect ofdifferent harvesting stages on seed quality parameters ofsoybean during storage are presented in Table 2. Thereis significant difference in germination due to differentharvesting stages and storage periods. It is significantlyhigher in seed harvested at physiological maturity,however at par with the crop harvested at five days afterphysiological maturity, during most of the storage period.Highest initial germination (93%) observed when the cropwas harvested at 85 days after sowing and remains highestat subsequent storage period up to 12 months. The MSCSfor germination was maintained up to 12 months of storagewhen the crop was harvested at 85 DAS. The MSCS forgermination was maintained up to 10 months when thecrop was harvested at 90 days after sowing. The lowestinitial germination (86%) was recorded when the crop washarvested at 100 days after sowing. The MSCS forgermination was maintained only for 8 and 6 months at95 and 100 DAS, respectively. The per cent germinationof soybean seed decreased with advancement of storageperiod (Rasad et al. 2007). The difference in a per centgermination of seed in the two cultivar was non-significantduring early period of storage but become significant at

Table 1. Symptoms of maturity at the time of harvesting

Stage of Harvesting SymptomsPhysiological maturity Maturity period is 85 days

Yellowing of peduncle of third leaf from topYellowing of lower leaves, stem and basal 2-3 leaves are dried. Two-threeupper leaves are still green

5 days after physiological maturity Maturity period is 90 daysYellowing of all leaves, stem and podsDrying of lower leaves, peduncle and few podsOnly 2-3 yellow leaves are remain on plants. Some plants are completelydefoliated

10 days after physiological maturity Maturity period is 95 daysDrying of whole plants, including leaves and pods. Only few pods and leavesof some plants are yellowishSome plants are completely dried, defoliated and shattering of seed of fewpods

15 days after physiological maturity Maturity period is 100 daysAll plants were completely dried and defoliatedOnly few plants have 2-3 yellow leaves at the tipShattering of seed of some pods

216

Tabl

e 2.

Year

wis

e an

d po

oled

ana

lysi

s of

thre

e ye

ars

data

of e

ffect

of d

iffer

ent h

arve

stin

g st

ages

on

stor

abilit

y an

d se

ed q

ualit

y of

soy

bean

dur

ing

stor

age

(A1

- Phy

siol

ogic

al m

atur

ity, A

2 - 4

day

s af

ter p

hysi

olog

ical

mat

urity

, A3

- 10

days

afte

r phy

siol

ogic

al m

atur

ity, A

4 - 1

5 da

ys a

fter p

hysi

olog

ical

mat

urity

, B1

- Ini

tial,

B2 -

2 m

onth

of s

tora

ge, B

3 - 4

mon

th o

f sto

rage

, B4

- 6 m

onth

of s

tora

ge, B

5 - 8

mon

th o

f sto

rage

, B6

- 10

mon

th o

f sto

rage

, B7

- 12

mon

th o

f sto

rage

Trea

tmen

tG

erm

inat

ion

(%)

Viro

ur in

dex-

IVi

gour

Inde

x-II

Moi

stur

e co

nten

tEl

ectri

cal c

ondu

ctiv

ity o

f(G

xSD

W)

(GxR

S)(%

)se

ed le

acha

te(m

m h

os/c

m/g

)20

0420

0520

06M

ean

2004

2005

2006

Mea

n20

0420

0520

06M

ean

2004

2005

2006

Mea

n20

0420

0520

06M

ean

-05

-06

-07

-05

-06

-07

-05

-06

-07

-05

-06

-07

-05

-06

-07

A1B1

9292

9490

9410

810

010

129

5531

2831

4630

769.

4310

.18.

929.

470.

758

0.69

80.

697

0.71

5A1

B290

9092

9190

104

106

100

2798

2880

2875

2851

9.13

9.98

9.05

9.39

0.79

10.

715

0.72

60.

742

A1B3

8788

9089

8710

110

297

2617

2824

2812

2751

8.88

9.58

89.

009.

150.

825

0.78

10.

785

0.79

5A1

B485

8285

8484

8686

8524

5023

7024

0324

089.

758.

859.

229.

270.

826

0.80

80.

806

0.81

2A1

B588

7576

8078

7374

7522

8722

5022

7722

7110

.49.

958.

959.

770.

839

0.81

80.

828

0.82

8A1

B676

7373

7473

6968

7020

3920

3021

2620

6510

.910

.19.

0510

.00.

860

0.70

60.

855

0.81

6A1

B771

7170

7068

6767

6718

4019

7419

6419

269.

959.

888.

979.

600.

885

0.83

90.

879

0.87

0A2

B192

9191

9293

106

106

102

2849

3011

3012

2957

9.05

9.95

9.00

9.33

0.75

40.

700

0.71

20.

721

A2B2

9089

9090

9010

410

499

2693

2766

2755

2738

9.06

9.98

9.15

9.40

0.79

00.

730

0.73

70.

750

A2B3

8588

8887

8410

110

296

2558

2720

2720

2666

8.93

9.45

8.95

9.11

0.83

00.

783

0.82

00.

813

A2B4

8288

8282

8184

8282

2296

2282

2321

2300

9.83

8.80

9.17

9.27

0.83

40.

815

0.82

50.

825

A2B5

7874

7576

7670

7172

2152

2138

2163

2151

10.4

9.98

8.87

9.74

0.86

20.

823

0.83

60.

840

A2B6

7272

7272

7068

6768

1902

2037

2098

2012

10.9

10.2

9.07

10.0

0.88

20.

847

0.86

50.

865

A2B7

6970

6969

6866

6767

1766

1890

1882

1846

10.1

9.93

9.60

9.86

0.91

00.

843

0.88

80.

882

A3B1

8589

8988

8602

104

9725

4327

5027

6026

879.

1510

.010

.19.

730.

790

0.72

60.

781

0.76

9A3

B284

8888

8782

101

101

9524

2927

2026

8226

109.

109.

8810

.29.

710.

798

0.76

80.

805

0.79

3A3

B381

8585

8479

9799

9222

6126

4326

1725

078.

989.

539.

769.

420.

837

0.80

50.

850

0.83

4A3

B478

7375

7575

7376

7521

4519

7820

2220

489.

908.

759.

309.

320.

841

0.82

80.

912

0.86

9A3

B571

7170

7168

6767

6718

6819

1719

1619

0010

.39.

959.

9510

.10.

860

0.83

90.

948

0.89

4A3

B667

6967

6864

6561

6316

8118

5618

0417

8010

.910

.09.

8010

.30.

895

0.85

60.

981

0.92

3A3

B762

6762

6458

6257

5915

4017

4815

9916

2910

.19.

939.

929.

970.

931

0.86

10.

995

0.94

1A4

B185

8887

8685

100

100

9524

9327

2026

8526

339.

159.

9810

.49.

850.

823

0.73

60.

811

0.79

4A4

B283

8885

8579

101

9993

2342

2713

2635

2563

9.18

9.88

10.1

9.73

0.84

90.

770

0.83

10.

820

A4B3

7985

8483

7596

9689

2219

2535

2547

2434

9.05

9.53

10.4

9.66

0.87

90.

810

0.86

40.

854

A4B4

7572

7474

7170

7271

1988

1915

1988

1964

10.0

8.75

10.5

9.78

0.88

40.

844

0.91

20.

886

A4B5

7070

6568

6766

6064

1791

1890

1740

1807

10.5

9.95

10.2

10.2

0.90

00.

860

0.96

70.

919

A4B6

6268

6364

5863

5759

1550

1823

1683

1685

12.0

9.98

10.2

10.7

0.93

00.

884

0.99

00.

944

A4B7

6067

6062

5462

5557

1374

1729

1569

1557

10.2

9.93

10.8

10.3

0.93

90.

886

1.00

30.

953

SEm

±1.

373.

1946

.50

0345

0.01

CD3.

909.

0213

1.55

0.97

60.

03

217

nine month of storage (Gupta and Aneja 2004).

The vigour index I and II showed significantdifferences due to harvesting stages and storage periods.The VI-I and VI-II decreased with progressive harvestingstages and storage periods. Vigour index-I wassignificantly decreased with increase in the storage periodof seed. It was significantly higher in the crop harvestedat 5 days after physiological maturity at initial storageperiod (102) followed by crop harvested at physiologicalmaturity at initial storage period (101). The vigour index-IIwas significantly highest (3076) when the crop washarvested at 85 days after sowing however it was at parwith vigour index of crop harvested at 90 DAS (2957). Thesignificantly lower (2633) vigour index was observed whenthe crop was harvested at 100 days after sowing. Decreaseof the vigour index-II in BARl Masur-2 might be due tolower germination percentage and seedling length (Khareand Satpute 1999). Similar varietal difference was reportedby Matthews (1973) in peas. Borate et al. (1993) observedthat vigour index-T1 was higher (2568) in large size seedsand lower (2111) in small size seeds of groundnut.Varieties of BARl Masur responded significantly toharvesting stage, producing higher seed vigour index-II in2005 (Khatun et al. 2009).

No significant differences observed in moisturecontent due to different harvesting stages. But cropharvest at physiological maturity content numerically lessmoisture content as compared to 15 days afterphysiological maturity accompanied with the goodgermination in all the storage stages. The moisture contentof the seed increased with storage period however, theincrease was lower in polythene gunny bag of 400 gaugeas compared to gunny bag (Rasad et al. 2007). Electricalconductivity of seed leachate differed significantly due toharvesting stages and storage periods. Electricalconductivity was increased with progressive storageperiods in all the harvesting stages. The initial electricalconductivity was significantly lower (0.715) when the cropwas harvested at 85 days after sowing, than the seeds ofcrop harvested at 95 and 100 days after sowing. However,it was at par with electrical conductivity of the seeds ofcrop harvested at 90 days after sowing. Similar trend wasmaintained during all the periods of storage.

In electrical conductivity test which evaluate seedvigor and viability, the damaged seed coat allows seedmatters to exit and damaged seed has more exudationand higher rate of electrical conductivity. Verasilpa et al.(2001) and Rahman et al. (2004) also verify this subject.At accelerated aging test when seeds undergo hightemperatures and moisture, the seeds with intact seedcoat and without internal damage tolerate this sever

condition better and show better germination result incomparison with damaged seeds. Rahman et al. (2004)and Francisco et al. (2001) also found similar results. Asseed deterioration progresses, the cell membranesbecome less rigid and become more water permeable. Itallows the cell contents to leakage into solution with thewater and increasing electrical conductivity. It provides arapid indication of seed viability for seed lots (Jyoti andMalik 2013).

Conclusion

In consequence, based on the results of this experiment,Seed quality, germination, vigor index I, vigor index II andelectrical conductivity are highly influenced by harvestingtime of crop and storage period of seed. There was adistinct reduction in capacity to germinate, vigour index I& II and increased electrical conductivity when cropharvest after physiological maturity which causenumerous harmful effect on seed quality during the storageperiod of seed. Harvesting of soybean at physiologicalmaturity stage found to be superior for laboratorygermination and storability of seed than the crop harvestedafter physiological maturity stage.

References

Anonymous (1999) International rules for seed testing. SeedSci and Tech 13(2) : 299-513

Borate DN, Dumbre AD, Bhingarde MT (1993). Effect of seedsize on growth, yield and seed quality of groundnut(Arachis hypogaea L.) under summer conditions.Seed res 21(2) : 107-109

Dadlani M, Agrawal PK (1983) Mechanism of seeddeterioration. Pl Physiol & Biochem 10 : 23-30

Fehr WR, Caviness CE, Burmood DT, Pennington JS (1971).Stage of development description for soybeans[Glycine max (L.) Merrill]. Crop Sci 11(6) : 929-931.

Francisco Dubbern H De Souza, Marcos-Filho J (2001) Theseed coat as a modulator of seed-environmentrelationship in Fabaceae. Rev Bras Bot Sao PauloDec 24(4)

Gupta Anuja, Aneja KR (2004). Seed deterioration in soybeanvarieties during storage-physiological attributes.Seed Res 32(1) : 26-32

Jyoti, Malik CP (2013). Seed deterioration : A Review. Int JLife Sci Bot & Pharm Res 2(3) : 374-385

Khare D, Satpute RG (1999) Influence of days to maturityand seed size on germination and seedling vigourin pigeonpea. Seed Res 27(2) : 170-173

Khatun A, Kabir G, Bhuiyan MAH (2009) Effect of harvestingstages on the seed quality of lentil (Lens culinarisL.) during storage. Bangladesh J Agril Res 34(4) :

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565-576Marcos-filho J, Chamma HMCP, Casagrande RRR, Marcos

EA (1994) Effect of harvesting time on seedphysiological quality, chemical composition andstorability of soybean. Sci Agric Piracicaba 91(2) :298-304

Matthews S (1973) The effect of time of harvest on viabilityand pr-emergence mortality in soil of pea (Pisumsativum L.) seeds. Ann Appi Biol 73(2) : 211-219.

Panse, VG, Sukhatme PV (1985). Statistical methods foragricultural workers. 4th Ed. ICAR, New Delhi 131-143

Pawar RS, Wagh VM, Panaskar DB, Adaskar VA, Pawar PR(2011). A case study of soybean crop production,installed capacity and utilized capacity of oil plantsin Nanded district, Maharashtra, India. Adv Appl SciRes 2(2) : 342-350

Rahman MM, Hampton JG, Hill MJ (2004) Effect of seedmoisture content following hand harvest andmachine threshing on seed quality of cool tolerantsoybean. Seed Sci & Tech 32(10) : 149-158

Rasad EP, Mate SN, Shelar VR (2007) Effect of invigorationtreatment on storability of soybean seed. Seed Res35(2) : 248-251

Steel RGD, Torrie JH (1981) In : principles and proceduresof statistics. McGraw Hill International BookCompany, London (2nd Edition) : 254-238

Vearasilpa S, Somchai P, Nattasak K, Sanguansak Th,Sangtiwa S, Elike P (2001) Assessment of PostHarvest Soybean Seed Quality Loss. Conferenceon International Agricultural Research forDevelopment Institute for Agricultural Chemistry,Georg-August University, Gottingen, 37075Germany

(Manuscript Receivd : 01-03-2015; Accepted : 30-06-2015)

219

Abstract

Investigation were made to determine effect of mechanicaldamage due to different threshing and processing methodson seed quality of soybean during storage. The mechanicaldamage to the soybean seed due to threshing andprocessing were detected by sodium hypochlorite testshowed that the lowest mechanical damage (10. 28 %) wasreported in variety JS -9305 (V2). Among the threshingmethods, the lowest mechanical damage (8.62 %) wasrecorded in stick beating threshing method (T1). The seedsample collected before processing (P1) showed minimummechanical damage (10.22 %). The variety JS-9305 (V2)recorded the highest germination (88.14%) at 0 days ofstorage irrespective of threshing methods and processing.The highest germination (89.42%) was recorded when seedthreshed with stick beating (T1) 0 days of storage irrespectiveof varieties and processing and the seeds processed uptoseed grader (P2 location) records highest initial germination(87.67%)0 days of storage. The variety JS-9305 (V2) recordedhighest vigour index (2729.99) at 0 days of storageirrespective of threshing methods and processing. Thethreshing method (T1: threshed with stick beating) recordedthe highest vigour index (2806.78) at 0 days of storageirrespective of varieties and processing and the seedsprocessed upto seed grader (P2 location) records highestvigour index (2856.29) at 0 days of storage. The same trendwas observed up to 360 days during storage. The variety JS-9305 (V2) recorded the lowest electrical conductivity (518.25µs/cm) at 0 days of storage irrespective of threshing methodsand processing. The threshing method (T1: threshed withstick beating) recorded the lowest electrical conductivity(485.25 µs/cm) at 0 days of storage irrespective of varietiesand processing and the seed sample collected beforeprocessing (P1) recorded the lowest electrical conductivity(454.11 µs/cm) at 0 days of storage. The lowest seedmycoflora (3.54%) was recorded in the variety JS-9305 (V2)at 0 days of storage irrespective of threshing methods andprocessing. The threshing method (T1: threshed with stickbeating) recorded the lowest seed mycoflora (3.38%) at 0days of storage irrespective of varieties and processing and

Mechanical damage due to threshing and processing methods andits effect on seed quality of soybean seed

K.C. Gagare, R.W. Bharud, V.R. Shelar, A.P. Karjule and S.N. MateSeed Technology Research UnitMahatma Phule Krishi VidyapeethRahuri 413 722 (Maharashtra)

JNKVV Res J 49(2): 219-227 (2015)

the seed sample collected before processing (P1) recordedlowest seed mycoflora (3.11 %) at 0 days of storage. Thesame trend was observed up to 360 days during storage.

Keywords: Mechanical damage, threshing methods,processing locations, processing plant sequence,germination vigour index, electrical conductivity ,seedmycoflora

Soybean [Glycine max (L.) Merril] has become a miraclecrop of the twentieth century and is designated as a'Golden bean'. It is a triple beneficiary crop, a unique food,a valuable feed and an industrial raw material withconsiderable potential. Among the various pulses,soybean is recognized as an excellent source of highquality protein and oil. The problems in soybean productionare increasing because of its seed quality, which isdependent on handling of seeds during harvesting,processing and storage. One of the major problemsencountered in soybean production in India is lack ofavailability of good quality seeds at the time of plantingas many of the seeds lots produced may loss their viabilityquickly because of improper handling during post harvestoperations. Soybean seed loses its viability faster thanother crops (Priestley et al. 1985) especially under tropicalconditions (Delouche et al. 1973).

The soybean seed coat is very thin and low in lignincontent provides little protection to the fragile radical whichlies in a vulnerable position directly beneath the seedcoat. Carbonell and Krzyzanowski observed theoccurrence of genetic variability in seed resistance to(ICHH) mechanical damage among different soybeancultivars. Franca Neto and Henning (1984) reported thatthe mechanical damage is one of the causes of greatloss in soybean seed quality during harvest poured intograders from the top. Therefore experiment was

220

conducted to find out the extent of mechanical damagedue to different threshing, processing methods on seedquality of soybean varieties during storage.

Material and methods

The soybean varieties viz. JS 335(V1), JS 93-05 (V2) andJS 95-60 (V3) were sown in Kharif 2012. The soybeanwas threshed by different threshing methods viz. Handbeating with stick (T1), threshing with the help of multi-crop thresher (T2) at 400 rpm : [Model - Anand make]RPM 400, concave clearance 2.5 cm, puly size - 4 inch(small) and 12 inch (big), total no. of bitters 16 andthreshing with the help of combine harvester (T3)specification : [Make - Class - Crop Tiger 30-wheel]. Duringprocessing the seed samples were collected from twodifferent processing plants sequences viz. from firstprocessing plant sequence the seed sample was collectedfrom unprocessed seed sample (P1), seed grader (P2),bucket elevator (P3), specific gravity separator (P4) andfrom second processing plant sequence the seed samplewas collected from inclined flight belt conveyor-I (P5), seedgrader (P6), inclined flight belt conveyor - II (P7) and specificgravity separator (P8).

The mechanical damage was detected by sodiumhypochlorite test as per Henning et al. (2006) and VanUtrecht et al. (2000).

= ------------------------------------------------ x 100

The germination (%) was worked out by fourreplications of 100 seed from different treatmentcombinations were germinated using between papermethod (BP) at 25± 2°C in germinator for 8 days(Anonymous 1999). The germination percentage wasworked out on the basis of normal seedlings only.

Germination (%) = ----------------------------------------------- x 100

The vigour index was worked out by those seedlingswhich were used to record the seedling length. The tennormal seedlings from each replication was selected forcalculation of vigour index (Abdul-Baki and Anderson(1973) and was calculated as under.

Vigour index = x

The electrical conductivity (µs/cm) of the soybeanseed was worked out by four replications of 50 seedswas randomly counted from different treatmentcombinations and soaked in distilled water at 25°C for 24hours. The solution and seeds was gently stir/swirled for10 to 15 seconds prior to evaluation. Then the electricalconductivity of the solute was measured in µs/cm (Loeffleret al. 1988).

Seed mycoflora was determined by blotter test(Anonymous 1099). Three layers of blotter papers soakedin sterilized distilled water and was placed in petridish.Ten seed in four replications of each treatmentcombination was placed in each petridish at equidistanceand the pertridishes was kept in incubator at 20± 2°C for7 days beneath near ultraviolet (NUV) light with a cycle of12 hour light and 12 hour darkness. The seeds was thenexamined on 8th day under stereoscopic binocularmicroscope. The fungi was identified on the basis ofsporulation and their fruiting structures.

Results and discussion

There was significant difference in mechanical damageto soybean seed due to different methods of threshingand processing (Table 1). The data revealed that the lowestdamage (10.28%) was recorded in variety JS-9305 (V2).The highest mechanical damage was observed in varietyJS-9560 (V3) (14.06%). Among the threshing methods,the lowest mechanical damage (8.62%) was recordeddue to threshing with stick beating (T1). Threshing withcombine harvester (T3) recorded the highest mechanicaldamage (15.87%). The mechanical damage to soybeanseeds collected at different processing locations showedsignificant difference. The seed sample collected beforeprocessing (P1) showed the minimum damage (10.22%)which was at par with mechanical damage of soybeanseed drawn in inclined flight belt conveyor-I (P5) (10.26%).The highest mechanical damage was recorded in soybeanseed collected at specific gravity separator (P8) (14.44%) from second processing plant sequence which wasat par with the soybean seed collected at specific gravityseparator (P4) (14.18%) of first processing plant.

There was significant difference on germination ofsoybean seed due to variety, different methods of threshingand processing (Table 2). The data revealed that the varietyJS-9305 (V2) recorded the highest initial germination(88.14%) and maintains the germination (70.42%) above.Minimum Seed Certification Standards (70%) up to 330

Mechanicaldamage (%)

Total no. swollen seedscounted after conduct of test

Total no. of seeds kept forconduct of test

Total no. of normal seedlingscounted after germination test

Total no. of seeds kept forgermination test

Average seedlinglength (cm)

Average germinationpercentage

221

days of storage irrespective of threshing and processingmethods. The threshing method (T1: threshed with stickbeating) recorded the highest initial germination (89.42%)and maintains the germination (71.49%) above MinimumSeed Certification Standards (70%) up to 330 days ofstorage irrespective of varieties and processing methodsand the seeds processed upto seed grader (P2 location)infirst processing plant sequence records highest initialgermination (87.67%) and maintains the germination(74.37%) above Minimum Seed Certification Standards(70%) up to 300 days of storage.

The variety JS-9305 (V2) recorded the highest vigourindex (2729.99) at 0 days of storage and maintains thevigour index (1409.84) up to 360 days of storage which

was significantly higher than the varieties (V1: JS-335)and (V3: JS-9560) irrespective of threshing methods andprocessing (Table 3). The threshing method (T1: threshedwith stick beating) recorded the highest vigour index(2806.78) at 0 days of storage and maintains the vigourindex (1468.39) up to 360 days of storage which wassignificantly higher than the other threshing methods viz.,multi-crop thresher at 400 rpm (T2) and combine harvester(T3) irrespective of varieties and processing and the seedsprocessed upto seed grader (P2 location) records highestvigour index (2856.29) at 0 days of storage and maintainsthe vigour index (1388.88) up to 360 days of storage whichwas significantly higher than the seed samples collectedfrom other processing locations of processing plantsequences irrespective of varieties and threshing methods.

The variety JS 93-05 (V2) recorded the lowestelectrical conductivity (518.25 µs/cm) at 0 days of storageand maintains the lowest electrical conductivity (1884.29µs/cm) up to 360 days of storage which was significantlylower than the varieties (V1: JS-335) and (V3: JS 95-60)irrespective of threshing methods and processing (Table4). The threshing method (T1: threshed with stick beating)recorded the lowest electrical conductivity (485.25 µs/cm) at 0 days of storage and maintains lowest (1670.79µs/cm) up to 360 days of storage which was significantlylower that the other threshing methods viz., multi-cropthresher at 400 ppm (T2) and combine harvester (T3)irrespective of varieties and processing and the seedsthe seed sample collected before processing (P1) recordslowest electrical conductivity (451.11 µs/cm) at 0 days ofstorage and maintains lowest (1633.88) up to 360 daysof storage which was significantly lowest than the seedsample collected from other processing locations ofprocessing plant sequences irrespective of varieties andthreshing methods.

The variety JS 93-05 (V2) recorded the lowest seedmycoflora (3.54%) at 0 days of storage and maintainsthe lowest mycoflora (17%) up to 360 days of storagewhich was significantly lower than the varieties (V1 : JS-335) and (V3 : JS-9560) irrespective of threshing methodsand processing (Table 5). The threshing method (T1 :threshed with stick beating) recorded the lowest seedmycoflora (3.38%) at 0 days of storage and maintainslowest (17.87%) up to 360 days of storage which wassignificantly lower than the other threshing methods viz.,multi-crop thresher at 400 rpm (T2) and combined harvester(T5) irrespective of varieties and processing and the seedsthe seed sample collected before processing (P1)irrespective of varieties and processing and the seedsthe seed sample collected before processing (P1) recordslowest mycoflora (3.11%) at 0 days of storage andmaintains lowest (15.67%) up to 360 days of storage which

Table 1. Effect of varieties, threshing methods andprocessing locations on mechanical damage of soybeanseed detected by sodium hypochlorite test

Treatment Mechanicaldamage (%)

VarietiesV1 - JS 335 12.40V2 - JS 93-05 10.28V3 - JS 95-06 14.06SEm± 0.108CD at 5% 0.302Threshing methodsT1 - Stick beating 8.62T2 - Multi crop thresher 12.24T3 - Combine harvester 15.87SEm± 0.108CD at 5% 0.302Processing locationsFirst processing plantP1 - Seed collected before processing 10.22P2 - Seed grader 11.30P3 - Bucket elevator 13.78P4 - Specific gravity separator 14.18P5 - Second processing plantP6 - Inclined flight belt conveyor-I 10.26P6 - Seed grader 11.78P7 - Inclined flight belt conveyor-II 12.00P8 - Specific gravity separator 14.44SEm± 0.176CD at 5% 0.493

222

Tabl

e 2.

Effe

ct o

f var

ietie

s, th

resh

ing

met

hods

and

pro

cess

ing

on g

erm

inat

ion

perc

enta

ge o

f soy

bean

see

d

Trea

tmen

tsG

erm

inat

ion

per c

ent (

days

afte

r sto

rage

)0

3060

9012

015

018

021

024

027

030

033

036

0V 1 -

JS

335

85.9

284

.62

83.0

481

.72

80.5

479

.42

78.1

776

.21

74.7

972

.62

70.9

267

.50

63.6

2(6

8.37

67.1

665

.90

64.8

663

.97

63.1

962

.28

60.9

559

.97

58.5

557

.44

55.3

152

.96

V 2 - J

S 93

-05

88.1

486

.46

84.5

883

.35

82.6

181

.21

80.4

679

.12

77.6

576

.21

74.0

370

.42

65.2

9(7

0.13

68.7

067

.16

66.2

065

.59

64.5

564

.02

62.9

861

.95

60.9

959

.51

57.1

953

.99

V 3 - 9

5-60

81.8

380

.72

79.2

478

.39

77.3

376

.54

76.1

074

.57

73.3

971

.25

69.0

078

.78

58.2

9(6

4.91

64.1

063

.04

62.4

261

.68

61.1

460

.81

59.7

859

.03

57.6

556

.25

50.1

049

.81

SEm

±0.

220.

210.

190.

180.

150.

150.

170.

140.

130.

140.

140.

120.

12C

D a

t 5%

0.63

0.60

0.54

0.52

0.43

0.41

0.47

0.39

0.37

0.38

0.38

0.35

0.35

Thre

shin

g m

etho

dsT 1 -

Stic

k be

atin

g89

.42

87.9

386

.83

85.7

284

.83

83.9

782

.82

80.9

979

.78

78.3

776

.32

71.4

968

.58

(71.

0069

.15

67.9

364

.20

66.5

265

.50

64.7

363

.67

62.5

261

.47

60.0

357

.85

54.7

6T 2 -

Mul

ti cr

op th

resh

er85

.50

84.6

182

.58

81.2

980

.04

79.6

278

.82

77.5

076

.18

74.2

972

.21

65.5

462

.21

(67.

7967

.07

65.4

364

.45

63.5

263

.22

62.6

561

.72

60.8

259

.57

58.2

254

.11

52.0

9T 3 -

Com

bine

har

vest

er80

.97

79.2

677

.44

76.4

475

.61

73.5

773

.08

71.4

269

.87

37.4

265

.42

59.6

756

.42

(64.

7163

.87

62.8

761

.98

61.3

260

.32

59.9

058

.49

57.8

156

.42

55.2

150

.75

50.0

3SE

m±

0.22

0.21

0.19

0.18

0.15

0.15

0.17

0.14

0.13

0.14

0.14

0.12

0.12

CD

at 5

%0.

630.

600.

540.

520.

430.

410.

470.

390.

370.

380.

380.

350.

35Pr

oces

sing

loca

tions

P 1 - S

eed

colle

cted

bef

ore

proc

essi

ng83

.22

81.7

880

.44

79.5

678

.67

77.7

876

.67

75.5

674

.33

72.0

069

.89

64.6

360

.78

(66.

0364

.89

63.8

963

.25

62.6

462

.01

61.2

460

.47

59.6

558

.15

56.8

153

.58

51.2

8P 2 -

See

d gr

ader

87.6

786

.30

84.9

683

.78

83.1

181

.56

80.8

979

.22

78.1

176

.11

74.3

769

.33

66.6

7(6

9.80

68.5

967

.50

66.4

965

.97

64.7

864

.25

63.0

462

.26

60.8

959

.70

56.5

254

.81

P 3 - B

ucke

t ele

vato

r82

.26

81.7

079

.52

77.9

676

.89

75.3

375

.18

72.8

971

.56

69.7

867

.11

61.4

458

.56

(65.

3265

.03

63.2

762

.15

61.3

860

.34

60.3

058

.72

57.8

656

.75

55.0

651

.70

49.9

7P 4 -

Spe

cific

gra

vity

sep

arat

or85

.33

84.2

282

.67

81.9

380

.56

79.7

878

.67

77.4

475

.89

74.1

172

.00

66.8

963

.78

(67.

7866

.84

65.6

165

.02

63.9

863

.44

62.6

561

.78

60.7

059

.55

58.1

755

.01

53.0

6P 5 -

Incl

ined

flig

ht b

elt c

onve

yor-

I83

.22

81.6

779

.89

79.3

778

.07

77.2

676

.33

74.7

873

.89

71.4

469

.33

62.4

459

.22

(66.

0364

.82

63.5

063

.23

62.2

361

.68

61.0

059

.95

59.3

657

.79

56.4

752

.29

50.3

7P 6 -

See

d gr

ader

87.6

786

.00

84.6

783

.33

82.4

481

.33

80.2

278

.18

77.0

075

.44

73.7

067

.78

64.8

9(6

9.81

68.3

267

.21

66.1

565

.44

64.6

163

.78

62.3

161

.46

60.4

359

.27

55.5

553

.74

P 7 - In

clin

ed fl

ight

bel

t con

veyo

r-II

87.6

785

.78

84.2

682

.18

81.6

780

.63

79.7

878

.22

76.5

674

.78

73.1

166

.44

63.1

1(6

9.85

68.0

666

.91

65.2

564

.86

64.0

963

.43

62.3

161

.18

59.9

958

.88

54.7

352

.67

P 8 - S

peci

fic g

ravi

ty s

epar

ator

85.3

384

.04

81.8

981

.11

79.8

978

.78

78.1

876

.78

74.8

973

.22

71.0

065

.56

62.2

2(6

7.75

66.6

965

.03

64.4

063

.48

62.7

262

.30

61.3

160

.03

58.9

657

.52

54.2

052

.13

SEm

±0.

370.

350.

320.

300.

250.

240.

270.

230.

220.

220.

220.

200.

20C

D a

t 5%

1.02

0.98

0.89

0.84

0.70

0.67

0.76

0.64

0.61

0.62

0.63

0.57

0.57

223

Tabl

e 3.

Effe

ct o

f var

ietie

s, th

resh

ing

met

hods

and

pro

cess

ing

on v

igou

r ind

ex o

f soy

bean

see

d

Trea

tmen

tsVi

gour

inde

x I (

days

afte

r sto

rage

)

030

6090

120

150

180

210

240

270

300

330

360

V 1 - J

S 33

526

91.9

125

22.8

524

11.5

023

24.8

322

52.7

821

73.4

421

00.0

619

81.4

818

93.2

617

96.9

316

98.1

415

56.6

812

88.0

7

V 2 - J

S 93

-05

2729

.99

2548

.83

2441

.75

2381

.14

2281

.14

2186

.44

2119

.06

2030

.95

1961

.52

1875

.63

1727

.08

1590

.80

1409

.84

V 3 - 9

5-60

2465

.62

2337

.82

2232

.66

2128

.86

2128

.86

2074

.49

2032

.81

1959

.66

1890

.15

1788

.49

1666

.07

1246

.43

1129

.19

SEm

±39

.732

7.89

923

.783

6.41

46.

414

6.20

96.

353

5.67

46.

178

6.18

55.

723

6.98

54.

742

CD

at 5

%11

1.07

522

.082

66.7

3917

.930

17.9

3017

.359

17.7

6115

.862

17.2

7217

.290

16.0

0019

.529

13.2

57

Thre

shin

g m

etho

ds

T 1 - S

tick

beat

ing

2806

.78

2463

.91

2551

.13

2460

.87

2400

.16

2323

.50

2259

.75

2139

.60

2068

.67

1988

.29

1864

.98

1651

.79

1468

.39

T 2 - M

ulti

crop

thre

sher

2631

.63

2486

.88

2371

.25

2284

.39

2209

.32

2153

.57

2090

.04

2014

.55

1938

.17

1840

.55

1710

.72

1451

.83

1254

.50

T 3 - C

ombi

ne h

arve

ster

2449

.11

2278

.72

2163

.53

2115

.14

2053

.31

1957

.30

1902

.14

1817

.94

1738

.09

1632

.22

1515

.60

1290

.28

110.

21

SEm

±39

.732

7.89

923

.873

7.32

56.

414

6.20

96.

353

5.67

46.

178

6.18

55.

723

6.98

54.

742

CD

at 5

%11

1.07

522

.082

66.7

3920

.477

17.9

3017

.359

17.7

6115

.862

17.2

7217

.290

16.0

0019

.529

13.2

57

Proc

essi

ng lo

catio

ns

P 1 - S

eed

colle

cted

bef

ore

proc

essi

ng25

38.2

824

13.2

523

26.0

822

53.4

321

73.0

520

99.6

220

22.2

219

56.1

018

5.25

1766

.11

1611

.73

1400

.52

1221

.01

P 2 - S

eed

grad

er28

56.2

926

49.5

425

38.4

124

24.9

023

69.3

422

75.2

522

11.2

121

07.4

520

16.2

819

16.1

117

71.4

015

71.0

813

88.4

6

P 3 - B

ucke

t ele

vato

r25

14.0

023

39.0

921

45.5

721

19.2

120

56.9

819

83.1

119

25.6

718

12.3

317

43.2

616

64.7

815

31.1

012

96.5

311

39.6

3

P 4 - S

peci

fic g

ravi

ty s

epar

ator

2849

.98

2521

.29

2413

.11

2349

.10

2266

.77

2200

.94

2128

.99

2043

.34

1956

.27

1871

.06

1769

.23

1553

.66

1351

.63

P 5 - In

clin

ed fl

ight

bel

t con

veyo

r-I24

71.9

823

79.2

522

71.9

822

10.5

321

49.0

820

79.3

320

10.5

719

22.8

318

73.7

517

51.3

716

38.7

713

51.8

611

65.3

3

P 6 - S

eed

grad

er26

64.7

425

58.1

124

60.6

823

59.6

923

06.8

822

25.0

221

55.2

420

41.2

619

74.9

718

94.2

817

76.7

615

36.3

713

54.4

2

P 7 - In

clin

ed fl

ight

bel

t con

veyo

r-II

2561

.52

2430

.49

2357

.58

2265

.04

2210

.61

2120

.61

2089

.80

1989

.79

1922

.27

1823

.81

1723

.96

1485

.77

1284

.17

P 8 - S

peci

fic g

ravi

ty s

epar

ator

2576

.59

2467

.65

2382

.36

2312

.51

2234

.72

2174

.45

2128

.11

2052

.47

1947

.76

1875

.29

1753

.83

1521

.30

1300

.95

SEm

±64

.882

12.8

9938

.984

11.9

6110

.473

10.1

4010

.375

9.26

610

.089

10.1

009.

346

11.4

077.

744

CD

at 5

%18

1.38

436

.061

108.

985

33.4

3929

.280

28.3

4729

.004

25.9

0328

.205

28.2

3526

.128

31.8

9021

.649

224

Tabl

e 4.

Effe

ct o

f var

ietie

s, th

resh

ing

met

hods

and

pro

cess

ing

on e

lect

rical

con

duct

ivity

of s

oybe

an s

eed

Trea

tmen

tsEl

ectri

cal c

ondu

ctiv

ity (

s/cm

)I (d

ays

afte

r sto

rage

)

030

6090

120

150

180

210

240

270

300

330

360

V1 -

JS

335

540.

6265

2.83

798.

7994

1.08

1044

.58

1135

.12

1345

.45

1371

.201

528.

8716

11.4

116

78.8

318

36.2

519

29.7

9

V2 -

JS

93-

0551

8.25

633.

6276

9.95

898.

1697

4.08

1100

.54

1322

.12

1419

.501

495.

2015

73.9

116

51.6

617

87.3

718

84.2

9

V3 -

95-

6057

3.95

684.

7582

6.29

1032

.48

1108

.16

1200

.12

1443

.83

1581

.661

671.

2517

71.7

518

83.1

620

18.2

921

29.4

0

SE

m±

0.32

20.

277

0.31

00.

335

0.33

50.

261

0.80

40.

380

0.28

10.

3330

0.57

80.

312

0.28

3

CD

at 5

%0.

900

0.77

60.

867

0.93

60.

936

0.73

02.

247

1.06

20.

785

0.92

21.

650.

873

0.79

1

Thre

shin

g m

etho

ds

T 1 - S

tick

beat

ing

485.

2557

1.41

684.

4181

6.62

816.

6295

9.04

1172

.08

1212

.121

325.

4513

99.7

714

83.0

015

84.0

016

70.7

9

T 2 - M

ulti

crop

thre

sher

531.

4566

1.45

814.

2591

8.87

918.

8711

03.8

713

43.9

113

83.8

7148

5.25

1570

.79

1654

.95

1821

.75

1933

.50

T 3 -

Com

bine

har

vest

er61

6.12

738.

3389

6.37

1136

.23

1136

.23

1372

.87

1595

.41

1776

.371

884.

6219

86.5

820

75.7

022

36.1

623

39.2

0

SE

m±

0.32

20.

277

0.31

00.

335

0.33

50.

261

0.80

40.

380

0.28

10.

330

0.57

80.

312

0.28

3

CD

at 5

%0.

900

0.77

60.

867

0.93

60.

936

0.73

02.

247

1.06

20.

785

0.92

21.

615

0.87

30.

791

Pro

cess

ing

loca

tions

P1

- S

eed

colle

cted

bef

ore

proc

essi

ng45

4.11

548.

1166

8.88

844.

3392

1.66

939.

2210

73.8

811

55.4

4123

1.88

1268

.33

1356

.33

1540

.22

1633

.88

P2 -

See

d gr

ader

517.

6662

4.5

768.

1193

3.33

985.

5510

88.8

813

64.2

212

48.0

0154

2.22

1669

.66

1774

.55

1902

.11

1999

.11

P3 -

Buc

ket e

leva

tor

569.

5568

8.33

821.

0095

7.00

1024

.66

1190

.88

1452

.88

1630

.551

732.

0017

95.5

518

38.8

820

15.7

721

09.3

3

P4 -

Spe

cific

gra

vity

sep

arat

or61

9.44

741.

0087

6.11

1020

.63

1077

.22

1237

.11

1562

.77

1677

.221

792.

2219

16.2

219

96.8

820

83.0

021

79.1

1

P5 -

Incl

ined

flig

ht b

elt c

onve

yor-

I47

4.77

595.

1175

3.55

901.

596

3.11

1005

.33

1105

.88

1195

.551

264.

0013

17.5

513

98.5

515

8.33

1693

.66

P6 -

See

d gr

ader

533.

8864

4.88

785.

6695

8.00

1029

.88

1136

.44

1404

.55

1489

.441

545.

2216

61.4

417

55.4

419

00.3

320

05.0

0

P7 -

Incl

ined

flig

ht b

elt c

onve

yor-

II55

7.11

674.

6682

9.22

988.

8810

93.5

512

24.3

314

52.3

315

39.7

7160

0.55

1693

.55

1818

.66

1943

.55

2050

.44

P8 -

Spe

cific

gra

vity

sep

arat

or62

7.66

739.

8888

4.22

1054

.22

1162

.213

39.8

815

47.2

217

23.6

6181

2.77

1896

.55

1963

.77

2071

.77

2178

.77

SE

m±

0.52

60.

453

0.50

70.

547

0.49

40.

426

1.31

20.

620

0.45

90.

593

0.94

30.

510

0.46

2

CD

at 5

%1.

469

1.26

71.

416

1.52

91.

382

1.19

13.

669

1.73

41.

282

1.50

62.

637

1.42

61.

292

225

Tabl

e 5.

Effe

ct o

f var

ietie

s, th

resh

ing

met

hods

and

pro

cess

ing

on g

erm

inat

ion

perc

enta

ge o

f soy

bean

see

d

Trea

tmen

tsG

erm

inat

ion

per c

ent (

days

afte

r sto

rage

)0

3060

9012

015

018

021

024

027

030

033

036

0V 1 -

JS

335

4.46

4.87

5.62

6.21

6.71

7.54

9.75

11.2

912

.79

14.2

114

.87

17.1

718

.92

12.0

112

.64

13.6

014

.32

14.9

015

.84

18.0

319

.54

20.8

22.0

822

.59

24.3

425

.68

V 2 - J

S 93

-05

3.54

4.08

5.12

5.21

6.04

6.42

7.17

8.87

10.4

212

.83

13.2

515

.04

17.0

010

.63

11.5

012

.97

13.0

914

.10

14.5

515

.42

17.2

618

.77

15.1

221

.10

22.7

624

.27

V 3 - 9

5-60

4.54

5.25

6.46

6.75

7.83

9.21

11.5

412

.62

13.3

322

.80

16.7

118

.12

21.2

512

.12

13.1

014

.60

14.9

416

.13

17.5

419

.76

20.7

321

.36

0.11

24.0

225

.08

27.3

3SE

m±

0.19

0.17

0.17

0.15

0.14

0.14

0.12

0.12

0.11

0.29

0.12

0.18

0.11

CD

at 5

%0.

520.

460.

480.

420.

380.

390.

340.

330.

320.

340.

510.

30Th

resh

ing

met

hods

T 1 - S

tick

beat

ing

3.38

3.96

5.29

5.67

5.92

6.67

8.12

9.79

11.2

913

.29

13.5

415

.71

17.8

710

.37

11.3

713

.12

13.4

514

.02

14.3

915

.63

17.6

619

.26

21.1

421

.18

22.8

824

.4T 2 -

Mul

ti cr

op th

resh

er4.

545.

045.

626.

007.

178.

259.

9211

.17

12.4

614

.25

15.2

916

.79

19.5

412

.15

12.8

513

.59

14.0

415

.41

16.5

618

.16

19.3

820

.56

22.1

122

.93

24.1

126

.12

T 3 - C

ombi

ne h

arve

ster

4.62

5.21

6.29

6.50

7.50

8.25

10.4

211

.83

12.8

014

.62

16.0

017

.83

19.7

512

.24

12.9

714

.40

14.8

715

.70

16.9

819

.41

20.5

221

.16

22.4

823

.68

25.1

426

.80

SEm

±0.

190.

170.

170.

150.

140.

140.

120.

120.

110.

110.

120.

180.

11C

D a

t 5%

0.52

0.46

0.48

0.42

0.38

0.39

0.34

0.33

0.32

0.29

0.34

0.51

0.30

Proc

essi

ng lo

catio

nsP 1 -

See

d co

llect

ed b

efor

e pr

oces

sing

3.11

4.11

5.44

5.56

6.22

7.00

7.89

9.67

10.4

412

.00

13.1

114

.33

15.6

79.

9511

.49

13.3

913

.43

14.2

815

.20

16.1

918

.03

18.8

120

.21

21.1

722

.19

23.2

3P 2 -

See

d gr

ader

3.67

4.33

5.44

5.78

6.44

7.67

9.22

10.7

811

.56

13.5

614

.67

16.1

118

.67

10.8

611

.89

13.3

813

.81

14.5

115

.92

17.5

219

.09

19.8

421

.58

22.4

923

.64

25.5

5P 3 -

Buc

ket e

leva

tor

3.22

3.89

4.56

5.00

5.56

5.78

6.56

8.56

9.78

11.3

311

.22

14.2

214

.44

10.2

211

.30

12.2

012

.82

13.5

613

.82

14.6

616

.94

18.1

719

.65

19.3

621

.86

22.2

7P 4 -

Spe

cific

gra

vity

sep

arat

or4.

224.

335.

335.

676.

337.

569.

4410

.33

11.8

914

.11

15.1

116

.56

18.6

711

.64

11.8

113

.14

13.6

914

.45

15.8

017

.70

18.6

220

.09

22.0

322

.83

23.9

725

.54

P 5 - In

clin

ed fl

ight

bel

t con

veyo

r-I

4.00

5.22

5.67

5.67

6.56

7.44

10.5

611

.33

12.3

314

.11

14.6

716

.11

18.8

911

.43

13.1

113

.67

13.6

814

.74

15.6

718

.71

19.4

620

.46

22.0

022

.44

23.6

025

.70

P 6 - S

eed

grad

er4.

675.

336.

116.

337.

117.

8910

.22

11.5

612

.89

14.7

816

.22

17.5

620

.67

12.2

613

.20

14.2

114

.51

15.4

016

.22

18.5

319

.76

20.9

622

.56

23.6

924

.71

26.9

9P 7 -

Incl

ined

flig

ht b

elt c

onve

yor-I

I5.

005.

006.

116.

898.

118.

7810

.78

12.5

613

.44

15.1

116

.22

17.6

720

.78

12.8

212

.81

14.2

415

.14

16.4

817

.15

19.0

820

.69

21.4

622

.84

23.7

024

.81

27.0

7P 8 -

Spe

cific

gra

vity

sep

arat

or5.

565.

677.

227.

568.

569.

6711

.22

12.6

715

.11

17.4

418

.33

21.6

724

.67

13.5

213

.69

15.5

415

.88

16.9

418

.04

19.4

720

.80

22.8

824

.66

25.1

827

.70

29.7

4SE

m±

0.30

0.27

0.28

0.25

0.22

0.23

0.20

0.19

0.19

0.17

0.20

0.30

0.17

CD

at 5

%0.

820.

760.

780.

690.

620.

640.

560.

540.

520.

480.

550.

830.

48

226

was significantly lowest than the seed samples collectedfrom other processing locations of processing plantsequences irrespective of varieties and threshing methods.

The highest mechanical damage was recorded insoybean variety JS 65-60 (V3) when seed was collectedat specific gravity separator (P8) (24%) in second seedprocessing plant sequence. The results are in conformitywith Sonawski and Kuzniar (1999) who reported that thesoybean seed is very susceptible to mechanical damagethat occurs during handling and processing after harvestand it reduces seed quality. Weathering, fungi, insects,artificial drying and mechanical damage during harvest,handling, threshing and storage are the causes of thesoybean seed damage/injuries.

The mechanical damage due to different methodsof threshing and processing showed minimum damagein variety JS 93-05 (V2). This might be due to small seedsize of variety JS 30-05. The highest mechanical damagewas observed in variety JS 95-60 (V3) which might beattributed to its bold seed size. Bhatia et al. (1996)observed that the soybean seed is highly susceptible tofield weathering and mechanical damage which adverselyaffect its longevity.

The significant difference in mechanical damageand broken seeds after processing and handling wasobserved due to soybean cultivars. The cultivar which hadlarger seed size and thinner seed coat had moremechanical damage. The similar observations was madeby Verasilpa et al. (2001). The threshing with stick beating(T1) recorded the lowest mechanical damage and havinghigher germination and vigour with lower electricalconductivity and seed mycoflora infection. These resultsare in conformity with those of Ujjinaiah and Shreedhara(1998) who found significantly higher mechanical damagewas recorded in multi crop thresher as compared tobeating with stick. The injuries directly affected seedgermination and thus reduces seed vigour and storagepotential. Delouche (1974) observed the mechanicalinjuries can occur at any time during harvesting, dryingand conditioning of seeds and results cracks or breaksin the seed coat or cotyledon where it would no longer beclassified as a part of pure seed fraction which causeslower germination.

Conclusion

The mechanical damage to the soybean seed duringthreshing and processing revealed that the lowest damagewas observed in variety V2 : JS 93-05 threshed with stickbeating with higher germination and vigour with lowerelectrical conductivity and seed mycoflora infection. The

seed sample collected before processing showedminimum damage and lower electrical conductivity andseed mycoflora infection.

From the present study it was revealed that thesoybean seed is highly susceptible to field weatheringand mechanical damages during threshing and processingwhich adversely affect is longevity. Management duringharvesting and seed processing frequently increasesmechanical seed injuries and this problem has been provedto be one of the most important causes of low seed quality.The soybean seed threshed with steak beating couldreduce the mechanical damage to the seed at the time ofharvesting. Due to delicate seed coat, the minimum seedcoat damage during post harvest handling of the soybeanseed resulted in to higher quality and longevity duringstorage. From the above investigation, it can be concludedthat for best quality of soybean seed during storage it isrecommended to thresh the seed with stick beating andprocessed upto seed grader (P2 and P6 location) in firstand second processing plant sequence which maintainedthe germination above MSCS (70%) upto 330 days ofstorage and higher vigour with lower electrical conductivityand seed mycoflora infection could be achieved.

References

Abdual-Baki AA, Anderson JD (1973) Vigour determinationin soybean seed by multiple criteria. Crop Sci 13 :630-632

Anonymous (1999) International rules for seed testing. SeedSci and Tech 13(2) : 299-513

Bhatia VS, Bhatnagar PS, Joshi OP (1996) Screening of Indiansoybean genotypes for seed longevity as affectedby field weathering. Soybean Genetics Newsletter23 : 102-104

Carbonell SAM, Krzyzanowsk FC (1995) The pendulum testfor screening soybean genotypes for seed resistantto mechanical damage. Seed Sci Technol 23 : 331-339

Delouche JC (1974) Maintaining soybean seed quality. Insoybean : production, marketing and use, Y-69 :46-62. TVA Bull V-19, Alabama : Muscle Sholas

Delouche JC, Matthews RK, Dougherty GM and Boyd AH(1973) Storage of seeds in subtropical and tropicalregions. Seed Sci and Tech 1 : 663-692

Franca Neto JB, Henning AA (1984) Qualidades fisiologicae sanitaria de sementes de soja (Physiological andPathological qualities of soybean seeds) EMBRAPA- National Soybean Research Centre, CircularTechnica 09 : 39 Londrina, Parana, Brazil

Henning A, Krzyzanowski, Fransico C, Franca Neto, Jose B,Costa Nilton P (2006) Technologies that add valueto soybean seed news. The International SeedMagazine www.Seed News

227

Loeffler TM, Tekrony DM, Egli DB (1988) The bulk conductivitytest as an indicator of soybean seed quality. J SeedTech 12 : 37-53

Priestley DA, Cullinan VJ, Wolfe J (1985) Difference in seedlongevity at species level. Plant Cell andEnvironment 8 : 557-562

Sonawski S, Kuzniar P (1999) Effect of dynamic loading onthe quality of soybean. Department of AgriculturalProduct. International Agro Physics 13 : 125-133.

Ujjinaiah US, Shreedhara MV (1998) Effect of threshingmethod on mechanical seed damage and qualityof soybean seed during storage. Seed Tech News28)4) : 34

Van Utrecht D, Bern CJ, Rukunudin IH (2000) Soybeanmechanical damage detection. Applied Engineeringin Agriculture 137-141

Vearasilpa S, Somchai P, Nattasak K, Sa-nguansak Th,Sangitwa S, Elke P (2001) Assessment of PostHarvest Soybean Seed Quality Loss. Conferenceon International Agricultural Research forDevelopment, Institute for Agricultural Chemistry,Georg-August University, Gottingen, 37075Germany

(Manuscript Receivd : 01-03-2015; Accepted : 30-06-2015)

228

Abstract

The influence of family environment of girl students on extentof participation in sports and games at different educationallevel (middle, high and higher secondary) was assessedthrough survey of 520 girls students ( from rural and urbanarea) of Rewa Division of Madhya Pradesh State. Thestatistical analysis confirmed the significant relationshipbetween the educational level and rate of participation on allthe factors of family environment. The extent of participationof girl students in sports and games was increased with theincrease in their level of education. The girl students havingvaried family environment had interest and eager toparticipate in the sports and games. Thus it is suggested toprovide the congenial atmosphere for sports and gamesand increase level of education that would help in increasingthe participation.

Keywords : Educational level, Sports and Games, Femalestudents, family environment and rate of participate.

Education is the process of instruction aimed at the allround development of boys and girls. It is the only wealththat can not be robbed. Nelson Mandela once said"Education is the most powerful weaper which you canuse to change the world". It is a powerful instrument forreducing inequity and poverty and for laying a foundationof sustained economic growth for individuals and theircommunities learning includes the moral values, physicalstatus, improvement of character and the methods toincrease the strength of mind. Education is very importantin the present day life and only a literate person can adjustwith the development of society. Especially, girls or womenmust be educated, as they play vital role in the familyand society. When a girl is educated she attainsknowledge and gains power, which helps in her selfdevelopment. When she has family it gives a positive

Family environment of girl students and its effect on extent ofparticipation in sports and games at different educational level inRewa Division of Madhya Pradesh

Rachna MishraSt. Lawrence, College of Higher EducationGeeta colony, New Delhi, Pin - 110 031Email : rac321978@gmail.com

JNKVV Res J 49(2): 228-233 (2015)

thinking and influences that family in various ways. Asher children grow, she educate them, which helps thesociety to progress in all aspects. It is a fact that everybody must get education but when girl get educated itbenefit future generations of our country. Sports andgames are integral component of education and it bringsrecognition in the society. A healthy person is always anasset to the society due to his ability to contribute towardsdevelopment. Sports and games activity for girls is one ofthe important process through which the healthy pillarsof the nation can be built-up in future. The present trendstowards more or less unlimited participation for women inwholesome (Gendel 1960) competitive activities isgainining momentum. As society become even moreaware of the fact that such activity is well within thecapabilities of the female, there will be completeacceptance of woman in sports activities (Synder andElmer 1973). Over the years efforts have been made tointroduce sports and games as an integral part of theschool education, thus, level of education may effect theextent of participation of girl students in sports and games.Thus, the present study on factors affecting participationas different school level for rural and urban girl studentsin sports and games was undertaken.

Material and methods

The Rewa division comprises of three district (Rewa, Sidhiand Satna) were selected for study. From each districtthree rural and three urban schools were selectedrandomly. In all 18 school were selected for drawingsamples. From each selected schools 10 girls from eachstrata (middle, high and higher secondary schools) wereselected. Thus in all, the sample frame will be comprisesof 540 girls students were interviewed for collecting ofprimary data through pre-tested interview schedule and

229

Tabl

e 1.

Effe

ct o

f fam

ily e

nviro

nmen

t of g

irl s

tude

nts

on e

xten

t of p

artic

ipat

ion

in s

ports

and

gam

es a

t diff

eren

t edu

catio

nal le

vel

Rew

a di

stric

tS

atna

dis

trict

Fact

orLe

vel

Rur

alU

rban

Rur

alU

rban

Mid

dle

Hig

hH

SM

iddl

eH

igh

HS

Mid

dle

Hig

hH

SM

iddl

eH

igh

HS

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Fam

ily T

ypes

Join

t10

1013

917

57

69

59

313

1115

717

38

69

38

2N

ucle

ar6

45

36

29

810

615

33

35

38

29

712

617

3Fa

mily

siz

eS

mal

l10

812

518

311

1012

615

29

811

518

212

1015

514

2B

ig7

58

57

25

49

312

17

610

48

24

47

312

2Fo

od h

abit

Veg.

66

135

112

109

53

91

88

155

122

99

73

102

Non

.Veg

.9

99

315

26

517

518

27

77

314

26

616

415

3E

xpos

ure

toE

xpos

ure

87

1010

1212

1211

1514

255

54

93

204

1312

208

182

cine

ma

Not

Exp

osur

e8

75

53

34

31

00

011

1013

55

13

22

09

1E

xpos

ure

toE

xpos

ure

1413

187

243

1515

209

255

43

155

122

76

82

212

Tele

visi

onN

ot E

xpos

ure

21

32

21

00

10

00

1310

73

142

98

164

61

Exp

osur

e to

Exp

osur

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1118

323

214

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319

113

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112

87

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112

44

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112

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53

232

new

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122

96

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Effe

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No

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No

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No

Yes

No

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Mix

ing

with

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sP

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99

126

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1313

184

192

119

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1513

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202

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411

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15

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55

93

1017

00

31

51

66

104

132

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1422

523

212

814

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215

1522

422

29

911

514

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12

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1110

207

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1513

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272

128

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241

1613

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282

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87

63

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30

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88

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All

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230

Tabl

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tude

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on e

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ctor

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tor

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Mid

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Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Fam

ily T

ypes

Pub

lic12

1015

520

47

510

417

3Pr

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47

35

110

812

48

2Fa

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eH

indi

98

145

193

88

93

193

Eng

lish

76

83

71

77

144

71

Food

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itM

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814

415

212

1012

415

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311

24

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211

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114

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, the

refo

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entio

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Tabl

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Effe

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HS

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Mix

ing

with

boy

sP

erm

itted

1111

125

172

133

155

222

Not

per

mitt

ed4

49

410

112

27

35

1U

se o

f spo

rt ki

tsP

erm

itted

00

20

30

77

93

121

Not

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1420

825

29

714

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222

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208

242

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414

310

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128

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109

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66

83

918

55

104

153

Not

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915

41

210

1012

410

2G

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ted

88

194

202

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52

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Not

res

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75

27

15

418

57

1Ta

king

mea

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1211

205

242

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176

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13

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15

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231

survey method.

The collected data were tabulated, classified andstatistically analysed using frequency distribution,arithmetical mean and chi-square test to evaluate theeffect of educational back ground of girl students on extentof participation in sports and games at differenteducational level.

Results and discussion

Different factors of family environment at various level wereassumed to influence the participation of girl students insports and games. The relationship of these factors wasexamined by statistical test (Chi-Square test) and resultsobtained are presented in Table1.

The results of statistical analysis indicate that inrural areas of Rewa division joint and big family arepredominant with vegetarian food habit. The girl's studentseven from rural areas have good exposer to cinema andTelevision but poorly exposed to news papers. In ruralareas the maximum girl students not allowed to mixedup with boys. However there is no family restriction toparticipate in sports and games and having meal out side,but there is family restriction for going out side for longerperiod and fallower of parda system in high and highersecondary classes. It was also observed that in generalsociety has not shown interest in sports and games inrural areas.

It is evident from the result that factors like familytypes, family size, food habit, exposure to cinema,television, radio and news papers, residential facilities,mixing with boys, use of sports kits, family restriction,

Table 2. Effect of family environment of girl students on participation in sports and games

Factor Level ResponseYes No 2

Family Types Middle 98 82 30.63**High 122 58Higher Secondary 147 33

Family size Middle 96 84 48.30**High 129 51Higher Secondary 156 24

Food habit Middle 92 88 57.99**High 136 44Higher Secondary 156 24

Exposure to cinema Middle 94 86 30.90**High 117 63Higher Secondary 144 36

Exposure to Television Middle 94 86 57.52**High 135 45Higher Secondary 158 22

Exposure to radio Middle 94 86 69.19**High 141 39Higher Secondary 162 18

Exposure to news paper Middle 94 86 61.90**High 139 41Higher Secondary 159 21

Residential facility Middle 94 86 67.30**High 130 50Higher Secondary 164 16

** Significant at 1% level

232

purda system, traveling for participation, going out sidefor longer period, taking meal out side and society interestdid not have any effect on the extent of participation ofgirl students in sports and games.

These result leads to conclude that whether thegirl students belongs to small or big, joint or nuclear family,their food habit may be vegetarian or non-vegetarian theirexposer may be of mass media, they may be day scholaror hosteller, they may be allowed or not allowed to mixwith boys, may be allowed or not allowed to use sportkits, they may or may not have family restrictions, theymay or may not allowed to travel for participation in sportsand games for longer period, they may or may not haverestriction for taking meal out side and their society mayor may not have interest in sports and games they haveeager to participate in the sports and games in rural andurban areas of Rewa division. Thus there is a need to

Table 2. Effect of family environment of girl students on participation in sports and games

Factor Level ResponseYes No 2

Mixing with boys Middle 93 87 59.56**High 132 48Higher Secondary 159 21

Use of sport kits Middle 95 85 67.21**High 138 42Higher Secondary 163 17

Family restriction Middle 98 82 62.63**High 128 52Higher Secondary 165 15

Purda system Middle 97 83 56.20**High 135 45Higher Secondary 154 20

Traveling for participation Middle 92 88 53.96**High 135 45Higher Secondary 154 26

Going out side for longer period Middle 98 82 53.74**High 137 43Higher Secondary 159 21

Taking meal out side Middle 95 85 68.13**High 136 44Higher Secondary 164 16

Society interest Middle 93 87 70.64**High 134 46Higher Secondary 164 16

** Significant at 1% level

provide the congenial environment which help for increasingthe participation of girl students in sports and games.

Different factors of educational level (middle, highand higher secondary) were also assumed to influencethe participation of girl students in sports and games.The relationship of these factors was examined bystatistical test (Chi-square test) and results obtained arepresented in Table 2.

It is observed from the result that there is significantrelationship between the educational level of girl studentsand rate of participation on all the factors of educationalbackground. These results lead to conclude that theextent of participation of girl students in sports and gameswas increased with the increase in their level of education.This might be due to increased interest and under standingof girl students towards sports and games by the increase

233

in their level of education. This is also evident from thestudy by Eitle (2002) Stevenson (2003) , Halawal (2006,)Blackwell and MC laughlin (2009), Lutz et al. (2009) andNandekar et al. (2010).

Thus it is concluded from the present finding thatthe girl students having different educational backgroundshave interest and eager to participate in the sports andgames. Although the extent of participation in sports andgames was increased with the increase in their level ofeducation. Thus, there is a need to provide the congenialatmosphere for sports and games and increase their levelof educational which help for increasing the participationof girl students in sports and games.

References

Blackwell DL, McLaughlin DK (2009) Do rural youth attaintheir educational goals. Rural DevelopmentProspective 13(3) : 37-40

Eitle J David (2002) Race cultural capital and the educationaleffects of participation in sports. Sociology ofEducation 75 : 123-146

Gendel E (1960) Women and the Medical Aspects of Sports,Hopper and Row Publishers New York

Halawah Ibtesam (2006) The effect of motivation, familyenvironment and student characteristics onacademic achievement. Instructional Psychol. 3 :91-97

Klint A Maureens (1998) Received competence and motivesfor participating in youth sports. A tests of hartericompetence motivation theory. Sports Psychol. 9 :55-65

Lutz GM, Cornish DL, Gonnerman ME, Jr. Ralston M (2009)Impact of participation in high school extra curricularactivities on early adult life experience : A study ofIowa Graduates. Instructional Psychol. 6 : 15-20

Nandurkar PB, Nandarkar PP, Petkar HJ (2010) Role ofincentive marks in women's sports participation.Brjsports Med. 44 : 163

Stevenson Betsey (2003) Evidence on the effects of sportsparticipation examining the impact of title IX. http://bppk:-wharton,upenn.edu/betsegs/ oldsite/title ix(2)pdf. Synder Eldon Sprtzer Elmer (1973) Familyinfluence and involvement in sports Res. Quarterly44:249

(Manuscript Receivd : 28-04-2015; Accepted :30-05-2015)

234

Abstract

The present paper contributes to the quantitativeassessment of Universal Soil Loss Equation's RainfallErosivity (R) from average annual rainfall depth in a datascarce region of Shakkar River Watershed. Rainfall erosivity,considering rainfall amount and intensity, is an importantparameter for soil erosion risk assessment under futureland use and climate change. Despite its importance, rainfallerosivity is usually implemented in models with a low spatialand temporal resolution. This study is concentrated onShakkar River watershed which lies in Narmada Basin inNarsighapur and Chhindwara districts of Madhya Pradesh,India. It covers a total geographical area of 2223 km² withannual average rainfall of 1245 mm. Rainfall depth from threeraingauge locations in the watershed was collected and asimple model was employed for rainfall erosivity estimation.The main objective of this study was to estimate rainfallerosivity factor (R) values for the study area. The R map ofthe area was prepared with the help of thiessen polygontechnique by using ArcGIS software. The final rainfall erosivityfactor (R) map was generated using the empirical equationin Spatial Analyst tool of ArcGIS 9.3 software. It was foundthat R factor value of the study area varies between 308.36 to830.63.

Keywords : Universal soil loss equation, Rainfall erosivity,Shakkar River, AroGIS

Rainfall drives the process of soil erosion by water(Petrovsek and Mikos 2004). The parameter that is oftenused to describe rainfall in the process of soil erosion isthe USLE's R factor. Climate change may lead to changesin rainfall characteristics and is thus a major concern tosoil conservation. The relation between rainfall andsediment yield is given by the rainfall erosivity, which

Estimation of rainfall erosivity factor (R) of universal soil loss equationfor soil erosion modelling using GIS techniques in Shakkar Riverwatershed

A.P.M.Sharma, S.K.Sharma and R.J.PatilDepartment of Soil and Water EngineeringCollege of Agricultural EngineeringJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482 004 (MP)Email.: sharmashailesh501@gmail.com

JNKVV Res J 49(2): 234-238 (2015)

quantifies the kinetic energy of raindrop impact. The rainfallerosivity factor (R) in the Universal Soil Loss Equation(USLE) is generally recognized as one of the governingparameters for the prediction of the erosive potential ofraindrop impact (Loureiro and Coutinho 2001).The originalmethod to calculate the erosivity values for a storm eventrequires pluviographic records (Wischmeier and Smith1978). Due to limited availability of long precipitation time-series with a high temporal resolution, several alternativestrategies have been deployed based on the rainfall volume(instead of intensity) for R-factor estimation (Meusburgeret al. 2012). Hence it is important to accurately determinespatial distribution of rainfall erosivity for quantitativeestimation of soil erosion.

Universal Soil Loss Equation (USLE) was designedto predict longtime average soil losses by runoff fromspecific field areas in specified cropping and managementsystems. The USLE (Wischmeier and Smith 1978)estimates the average annual soil loss from:

A = R.K.LS.C.P

Where, A is the estimated soil loss per year, R is therunoff factor, K is the soil erodibility factor, LS is the slopelength and steepness factor, C is the cover andmanagement factor and P is the support practice factor(Wischmeier and Smith, 1978). The R factor expressesthe erosive power of rainfall corresponding to the amountand intensity of rainfall over the year (erosivity index unit)at a particular location. An increase in the intensity andamount of rainfall results in an increase in the value of R.The K factor expresses inherent erodibility of the soil orsurface material. The value of "K" is defined as a function

235

of the particle-size distribution, organic-matter content,structure, and permeability of the soil or surfacetopography, specifically hill-slope length and steepness,on soil erosion. An increase in hill-slope length andsteepness results in an increase in the LS factor. The Ccover-management factor is used to express the effect ofplants and soil cover. Plants can reduce the runoff velocityand protect surface pores. The C-factor measures thecombined effect of all interrelated cover and managementvariables, and it is the factor that is most readily changedby human activities. The P factor is the support practicefactor. It expresses the effects of supporting conservationpractices, such as contouring, buffer strips of close-growing vegetation, and terracing on soil loss at a particularsite. A good conservation practice will result in reducedrunoff volume, velocity and less soil erosion. The USLEconcept has more recently been modified and adaptedby a large number of researchers by including additionaldata and incorporating research results. One of the mostimportant parameters in USLE is the rainfall erosivity factor(R) that represents/describes the potential for soil to bewashed off from disturbed, unvegetated areas into surfacewaters during a storm. The R factor reflects the effect ofintensity of the rainfall event on the soil erosion rate. TheR factor indicates how rainfall distribution affects theaverage annual soil loss and how that soil-loss potentialwill be distributed in space during different seasons andcover conditions (Van der Knijff et al. 2000). Vegetationcover protects the soil by dissipating the raindrop energybefore reaching the soil surface. As such, soil erosioncan be effectively limited with proper management ofvegetation, plant residue, and tillage (Lee 2004). In USLEthe R factor is computed using an empirical equation thatrequires intensity data (Wischmeier and Smith 1978;Renard et al. 1997).

However, the erosivity estimation relationships forspecific locations remain still difficult to be developed onhomogeneous bases of data over broad areas wherespatial-time variability of climatic variables is high. Therehave been few attempts to assess rainfall erosivity at largespatial scales. At global scale, Yang et al. (2003)identified alternate power and polynomial relationshipsbetween annual values of rainfall erosivity and precipitation.In Indian context, Babu et al. (2004) established linearrelationships between average annual and seasonal rainfallto compute rainfall erosivity. This study aimed to evaluatethe spatial distribution of rainfall and to produce rainfallerosivity map for the considered watershed. In presentstudy estimation of rainfall erosivity was carried out for10 years (2001-2011) and with 20 m × 20 m cell size forthe preparation of the R-thematic maps of the area.

Study area

The Shakkar River rises in the Satpura range, east of theChhindi village, Chhindwara district, Madhya Pradesh,India. The watershed area lies between 22°20'N to 23°00'Nlatitudes and 78°40'E to 79°20'E longitudes with anelevation ranges from 314 to 1154 m above MSL (meansea level). The watershed covers 2223 km2 of totalgeographical area up to the gauging point. The climate ofthe basin is generally dry except the southwest monsoonseason. May is the driest month of the year. The normalmaximum temperature during the month of May is 42.50C and minimum during the month of January is 8.2 0C.Soils are mainly clayey to loamy in texture with calcareousconcretions invariably present. Soils are mainly clayeyto loamy in texture with calcareous concretions invariablypresent. They are sticky and in summer, due to shrinkage,develop deep cracks. They generally predominate inmontmorillonite and beidellite type of clays. The averageannual rainfall of the area is 1245 mm and normal annualrainfall is 1192.1mm. The location map of the study areais presented in Fig 1.

Fig 1. Location map of study area

Materials and Methods

Rainfall erosivity factor (R) is the basic and important factorin the assessment of soil erosion in the mathematicalmodel, Universal Soil Loss Equation (USLE) and itsrevised form RUSLE (Elangovan and Seetharaman 2011).Erosivity is the potential capacity of the raindrops to causedetachment of the soil particles from its location and itdepends on rainfall intensity its recurrence. Therefore, itis important to accurately estimate the erosivity for

236

quantitative estimation of soil erosion. The R-factor isdefined as the mean annual sum of individual storm erosionindex values, EI30, where E is the total storm kineticenergy and I30 is the maximum rainfall intensity in 30minutes.

Mathematically,

...1

where,

KE = Kinetic energy of the storm. The KE in metric tones/ha-cm and expressed as

KE = 210.3 + 89 logI ...2

where,

I = rainfall intensity in cm/h and

I30 = maximum 30 minutes rainfall intensity of the storm

...3

where,

KE = Kinetic energy of the storm (MJ/ha)

Computation of R-Factor

Vegetation cover, soil infiltration, erodibility and rainfallerosivity are the major factors governing soil erosion. Ofall, rainfall erosivity is particularly difficult to predict andcontrol. However, is considered one of the determiningparameters in the universal soil loss equation. Rainfallerosivity is the potential ability for rainfall to cause soilloss (Silva 2004). Numerous studies have assessed therelationship between conventional rainfall characteristicsand soil detachment (Arnoldus 1977; Hudson 1971;Wischmeier and Smith 1978). The original methodrequires high resolution rainfall data with continuouspluviographic records; however, these are rarely availablein many parts of the world (Aronica and Ferro 1997;Diodato 2005; Silva 2004). Thus several models, basedon correlations between the measured R-factor and theavailable rainfall, have been developed to estimate therainfall R-factor from daily, monthly, or yearly rainfall (Ferroet al. 1999; Wang et al. 1995; Zhang et al. 2002).

To compute storm KI30, continuous rainfall data isneeded. For Indian conditions, a linear relationship wasdeveloped by Babu et al. (2004) between average annual

& seasonal (June - September) rainfall and rainfall erosivityfactor (R). They used 123 rain gauge stations situated invarious parts of India. Derived relationships are as follow:

Annual relationship:

R = 81.5 + 0.38 Rn (340 Rn 3500 mm) ...4

Seasonal relationship:

R = 71.9 + 0.361 Rs (293 Rs 3190 mm) ...5

where,

R is the average annual / seasonal erosion index,

Rn is the average annual rainfall (mm) and

Rs is the average seasonal rainfall (mm).

In the present study, equation 4 was used tocalculate annual values of R factor by replacing Rs withactual observed rainfall in a year.

The thematic map of rainfall erosivity factor (R) wasdeveloped in the GIS platform. The thiessen polygon ofthe study area was prepared using spatial analyst toolboxof the ArcGIS 9.3 considering three rain gauge stations

Fig 2. Thiessen polygon map of study area

EI30

KE I30100

R Erosionindex KE Ii

n ( )301

237

namely, Gadarwara, Ammarwar and Harrai and ispresented in Fig.2. Thiessen polygon gives fair distributionof rainfall in the surrounding area of the rain gauge station(Aggarwal et al. 2000). After attributing estimated R factorvalues to the Thiessen polygons, raster maps of R factorfor individual years were prepared using Conversion toolbox of ArcGIS 9.3.

Result and discussion

Shakkar River watershed has R factor values ranges from308.36 to 830.63. The R map of the area was prepared inArcGIS 9.3 software using thessien polygon techniqueand is presented in Fig 3. The estimated values of R factorare presented in Table 1.

Fig 3. R-map of study area

Table 1. Estimated R factor values

Stations Gardarwara Harra AmmarwaraYear Rainfall R-Factor Rainfall R-Factor Rainfall R-Factor

2001 993.0 458.84 1149.0 518.12 625.0 319.002002 1031.0 473.28 965.0 448.20 961.0 446.682003 1033.0 474.04 1357.0 597.16 938.0 437.942004 889.0 419.32 973.0 451.24 601.0 309.882005 1279.0 567.52 1202.0 538.26 1392.0 610.462006 684.0 341.42 1101.0 499.88 1351.0 594.882007 597.0 308.36 1078.4 491.29 1017.4 468.112008 619.7 316.99 916.4 429.73 847.6 403.592009 1348.0 593.74 1384.4 607.57 1971.4 830.632010 907.0 426.16 1194.4 535.37 769.0 373.72Average 938.1 438 1132.1 511.7 1047.3 479.5

The basic relation between rainfall and R factor is presentedin Fig. 4. This shows higher the value of rainfall higher thevalue of R factor.

Conclusion

An attempt has been made to estimate R factor valueswith empirical equation for modelling soil erosion using

Fig 4. Relation between rainfall depth and R-factor

238

ArcGIS 9.3 software. R factor values were assigned topixels of different polygons created through ArcGIS withinthe study area. Based on the assumption, thiessenpolygon gives fair distribution of rainfall in the surroundingarea of the rain gauge station, the R factor map of Shakkarriver watershed was produced to use in soil erosionmethods such as USLE. It should be noted that R factorvalues can be precisely estimated using high resolutiontemporal rainfall data. In this particular study GIStechniques are used as it offers an optimal method toestimate Rainfall erosivity factor (R) values of large areasin a short time.

orZeku 'kks/k i= 'kDdj unh okVj'ksM+ ,d tgkW ij gkbMªksyksftdy MsVkdh deh gS esa ;wfuolZy lk;y bD;sos'ku ds jsuQky bZjksftfoVh QsDVj¼vkj½ tks fd vkSlr okf"kZd jsuQky MsV ls fudkyk x;k gSA jsuQkybZjksftfoVh] jsuQky dk ,ekmaV vkSj rhozrk dks /;ku esa j[krs gq;s ,degRoiw.kZ isjkehVj gS] tks fd lk;y bZjkstu fjLd dks ,lsl djrk gS ,oaHkfo"; esa ysM ;wt ,oa DykbZesV psat ij vlj M+kyrk gSA blds egRods ckctwn jsuQky bZjksftfoVh ds de Lif'k;y ,oa Vsaiksjy fjtky;wluekWMy esa mi;ksx fd;k tk ldrk gSA ;g LVM+h {ks= ujflagiqj ,oafNanokMk ftys e/;izns'k] Hkkjr esa fLFkr gSA blds HkkSxksfyd {ks= 2223oxZ fd-eh- ,oa okf"kZd vkSlr o"kkZ 1245 fe-eh- gSA bl okVj'ksM dsrhu jsuxkst LVs'ku dh o"kkZ ,oa flaiy ekMy dk mi;ksx djrs gq;sjsuQky bZjksftfoVh fudkyh xbZ gSA bl dk;Z ds eq[; mi;ksx bl {ks=ds fy;s jsuQky bZjksftfoVh QSDVj ¼vkj½ osY;w dks fudkyuk gSAArcGIS 9.3 lkVos;j dk mi;ksx djrs gq;s fFklu iksyhxksu }kjk vkjeSi cuk;k x;k gSA ArcGIS 9.3 lkVos;j ds Lisf'k;y ,susfyLV Vwy}kjk Qkbuy jsuQky bZjksftfofyVh QsDVj ¼vkj½ eSi cuk;k x;k gSAbl v/;;u }kjk vkj QSDVj dh osY;w 308.36 ls 830.63 ds chpvkrh gSA

References

Aggarwal SP, Desilva RP, Mohamed Rinos MH (2000)Application of Remote Sensing and GIS on soilerosion assessment at Bata River Basin, India.Natural Hazard Management Booklet

Arnoldus HMJ (1977) Methodology used to determine themaximum potential average annual soil loss dueto sheet and rill erosion in Morocco, FAO SoilsBulletin 34:39-51

Aronica G, Ferro V (1997) Rainfall erosivity over the Calabrianregion. Hydrol Sci J 42(1):35-48

Babu R, Dhyani BL, Kumar N (2004) Assessment of erodibilitystatus and refined Iso-erodent map of India. IndianJ Soil Cons 32(3):171-177

Diodato N (2005) Predicting RUSLE (Revised Universal SoilLoss Equation) monthly erosivity index from readilyavailable rainfall data in Mediterranean area.Environmentalist 25:63-70

Ferro V, Porto P, Yu B (1999) A comparative study of rainfallerosivity estimation for southern Italy andsoutheastern Australia. Hydrol Sci J 44:3-24

Hudson N (1971) Soil Conservation.Batsford Ltd., London.pp388

Lee S (2004) Soil erosion assessment and its verificationusing the universal soil loss equation andgeographic information system: A case study atBoun Korea. Environmental Geology 45: 457-465

Loureiro N, Coutinho M (2001) A new procedure to estimatethe RUSLE EI30 index based on monthly rainfalldata and applied to the Algarve region, Portugal. JHydrol 250:12-18

Meusburger K, Steel A, Panagos P, Montanarella L, AlewellC (2012) Spatial and temporal variability of rainfallerosivity factor for Switzerland. Hydrol Earth Syst Sci16:167-177

Petrovšek G, Mikoš M (2004) Estimating the R factor fromdaily rainfall data in the Sub Mediterranean climateof Southwest Solvenia, Hydrological SciencesJournal 49(5):869-877

Renard KG, Foster GR, Weesies GA, McCool DK, Yoder DC(1997) Predicting soil erosion by water: a guide toconservation planning with the revised UniversalSoil Loss Equation (RUSLE), Washington DC:Agricultural Handbook, United States Departmentof Agriculture

Silva AM (2004) Rainfall erosivity map for Brazil. Catena57:251-259

Van der Knijff JM, Jones RJA, Montanarella L (2000) SoilErosion risk Assessment in Europe, EuropeanCommission, European Soil Bureau

Wischmeier WH, Smith DD (1978) Predicting rainfall-erosionlosses - a guide to conservation planning. USDAAgricultural Handbook 537, Washington DC pp 57

Yang D, Kanae S, Oki T, Koike T, Musiake K (2003) Globalpotential soil erosion with reference to land useand climate change. Hydrol Proc 17:2913-2928

Zhang WB, Xie Y, Liu BY (2002) Rainfall erosivity estimationusing daily rainfall amounts. Sci Geogr Sin 22:705-711

(Manuscript Receivd :30-03-2015; Accepted :27-06-2015)

239

Abstract

Extruded product of rice as base material and brokens ofpigeon pea and chickpea was developed by using Brabendersingle screw laboratory extruder. Effect of feed variables likemoisture content (10-18% w.b) and blend ratio of rice (70%),pigeon pea (5-25%) and chickpea (5-25%); operationalvariables like barrel temperature (130-170 0C), die headtemperature (180-220 0C) and screw speed (60-100 rpm)on textural properties viz., hardness, crispness and cuttingstrength of extrudates were investigated by using responsesurface methodology. Minimum hardness (1.25 kg) ofextrudates was obtained at 12% moisture content, 140 0Cbarrel temperature, 190 0C die head temperature, 90 rpmscrew speed and 10% blending of chickpea. Maximumcrispness (7 peaks) was obtained at 12% and 14% moisturecontent, 140 0C and 150 0C barrel temperature, 200 0C and210 0C die head temperature, 70 rpm and 100 rpm screwspeed and 10% and 15% blending of chickpea. Minimumcutting strength (1.07 kg) was obtained at 14% moisturecontent, 150 0C barrel temperature, 200 0C die headtemperature, 80 rpm screw speed and 15% blending ratio ofchickpea.

Keywords: Extrusion cooking, Textural properties, Pigeonpea, Chickpea, Response surface methodology

Extrusion cooking is a high temperature short time (HTST)process that is widely popular in the food industries. It isadopted to make products having better flavor, digestibility,storage life, and safety (Mercier et al. 1989). Severalready-to-eat snack foods have been prepared by usingcereals or their blends with legume protein sources

Textural properties of extruded product prepared by using by-products of dhal milling industry

Thongam Sunita Devi*, A. K. Gupta*, Sheela Pandey* and A.P. Mahanta Sharma***Department of Post Harvest Process and Food EngineeringCollege of Agricultural EngineeringJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 428004 (MP)**College of Agricultural EngineeringJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 428004 (MP)Email: sunita0thongam@gmail.com

JNKVV Res J 49(2): 239-248 (2015)

(Camire et al. 1990; Cheman et al. 1992; Adesina et al.1998). Cereal flours are usually low in protein content,but high in sulfur containing amino acids, while legumesare rich in protein with a high proportion of lysine andonly small quantity of sulfur containing amino acids.

Inadequate intake of protein in developing countrieshas led to various forms of malnutrition in children andadults (Okpala et al. 2011). Therefore, the need toinvestigate inexpensive sources of protein food of goodquality cannot be ignored. The protein calorie sources ofvegetable origin have been proposed as an approach toresolve this problem (Abioye et al. 2011). Blending ofpigeon pea, chickpea and rice would provide a wide rangeof high protein, calories, and micronutrients, if properlyprocessed.

The aim of the study was to develop extrusioncooked food product using by-products of dhal millingindustry and to evaluate the textural properties of extrudedsnacks.

Material and Methods

Brokens of pigeon pea and chickpea were procured fromthe local dhal milling industry. Rice grains were procuredfrom the local market. The rice, pigeon pea and chickpeagrains were ground with the help of a hammer mill andthe flours were mixed to prepare samples of differentselected blend ratio. The proportion of rice flour was keptconstant (70%) in all the samples and the proportion ofpigeon pea and chickpea flour varied (5% to 25%). The

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blends in different proportion were made as per theexperimental plan. Analysis of the results, obtained byusing Central Composite Rotatable Design of ResponseSurface Methodology, was done by developing suitableempirical model and testing their correlation andsignificance of variables. The experimental rangeconsisted of 5 independent variables each having 5 levels.

The effect of feed and operational parameters on hardness,crispness and cutting strength was measured usingResponse Surface Methodology.

Rice, pigeon pea and chickpea flours were mixedto prepare samples of different selected blend ratio.

Moisture content of flour of different blend ratio wasmeasured separately for each 32 samples. To achievethe desired level of moisture content in different blendratio the moisture was added by sprinkling with acalculated amount of distilled water to the samples. Thewater added samples were then mixed at medium speedin a domestic blender for uniform distribution of water andto avoid formation of lumps. The samples were kept forconditioning for 24 hours.

The texture properties of extruded snacks in termsof hardness, crispness and cutting strength weremeasured using a Stable Micro System TAXT2i texture

Table 1. Independent variables and their levels

Independent variables Coded Levels-2 -1 0 +1 +2

Blend ratio (rice:pigeonpea:chickpea) 70:25:5 70:20:10 70:15:15 70:10:20 70:5:25Moisture content of feed (% wb) 10 12 14 16 18Screw speed (rpm) 60 70 80 90 100Barrel temperature zone-III (0C) 130 140 150 160 170Die Head Temperature (0C) 180 190 200 210 220

RICE FLOUR PIGEON PEA FLOUR CHICKPEA FLOUR

SAMPLE PREPARATION WITH DIFFERENT BLEND RATIO

MOISTURE ADJUSTMENT(10, 12, 14, 16, 18% wb)

CONDITIONING (24 HR)

EXTRUSION COOKINGDHT=180, 190, 200, 210, 2200 C

SS= 60, 70, 80, 90, 100 rpm

EXTRUDED PRODUCT

TEXTURALPROPERTIES

Fig 1. Process flow chart for preparation of extrusioncooked food product of rice, pigeon pea and chickpea

analyzer. Cylindrical probe, needle probe and Warner-Bratzler shear blade were used to measure the texturalattributes. Test speed of 5 mm/s and compression of 50%of the sample height was used. The necessary force tocompress 50% of the sample height, in kg, was taken tobe the result for hardness and cutting strength test andnumber of peaks for crispness test.

Results and Discussion

The observations for hardness, crispness and cuttingstrength with different combinations of the processparameters are presented in Table 2. Response surfaceanalysis was applied to the experimental data using acommercial statistical package Design Expert 9.

HardnessThe hardness of extrudates were measured and foundmaximum (9.391 kg) at 14% moisture content (MC),1500C barrel temperature (BT), 2000C die head temperature(DHT), 80 rpm screw speed and 70:15:15 (Rice:Pigeonpea: Chickpea) blending ratio (BR) and wasminimum (1.25 kg) at 12% MC, 1400C barrel temperature(BT), 1900C die head temperature (DHT), 90 rpm screwspeed and 70:20:10 blending ratio (BR). The regression

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equation describing the effect of the process andoperational variables on hardness of extrudates in termsof actual level of the variables is given as:

Hardness = -81.99241 + 2.55269 * MC -0.15063 * BR -0.011090 * BT + 0.71028 * DHT -0.065426 * SS -0.028225* MC * BR -0.017956 * MC * BT -6.11250E-003 * MC *DHT -2.98125E-003 * MC * SS -1.8E-004 * BR * BT +2.29250E-003 * BR * DHT + 3.18500E-003 * BR * SS +1.01250E-003 * BT * DHT + 1.16250E-004 * BT * SS +3.71750E-003 * DHT * SS + 0.0729924 * MC2 -4.28122E-003 * BR2 + 2.62256E-004 * BT2 -2.80155E-003 * DHT2

-4.42780E-003 * SS2 … (Eq. 1)

Hardness of extrudates is the resistance offered forbreaking when subjected to a compressive load.Regression analysis shows that moisture content; blendratio and barrel temperature had positive effect on hardnessof extrudates. Hardness increases with increase inmoisture content of feed. Samples extruded at high doughmoisture resulted harder than the other ones (Fig. 3.1 toFig 3.4). These data are in accordance with the resultsobtained by Faubion and Hoseney (1982). These authorsattributed this behaviour to the air bubble collapse of doughat the die exit caused by high quantity of water vapourproduced in these conditions. Increased in blend ofchickpea in feed increases hardness of extrudates (Fig.3.1, 3.5, 3.6 and 3.7). This may be attributed because ofincrease in amount of chickpea flour resulted in increasethe strength of outer crust of extrudates. With increasein barrel temperature, hardness of extrudates increases(Fig. 3.2, 3.5, 3.8 and 3.9).

However die head temperature and screw speedhad negative effect on hardness of extrudtes. Hardnessof extrudates increases with decrease in die headtemperature (Fig. 3.3, 3.6, 3.8 and 3.10) and also increasein screw speed decreases the hardness (Fig. 3.4, 3.7,3.9 and 3.10) similar trend was observed in the extrudatesprepared from brewers-spent-grain, maize, corn andchickpea flour (Ainsworth 2007; Meng et al. 2010).

Crispness

The crispness of extrudates were measured and foundmaximum (7 peaks) at 12% to 14% moisture content(MC), 140 to 150 0C barrel temperature (BT), 200 0C to210 0C die head temperature (DHT), 70 rpm to 100 rpmscrew speed and 70:15:15 and 70:20:10 (Rice: Pigeonpea:Chickpea) blending ratio (BR) and was minimum (3 peaks)at 10% MC, 150 0C barrel temperature (BT), 200 0C diehead temperature (DHT), 80 rpm screw speed and

70:15:15 blending ratio (BR). The regression equationdescribing the effect of process and operational variableson crispness of extrudates in terms of actual level of thevariables is given as:

Crispness = -63.26728 + 0.191057 * MC -2.07439 * BR +0.86677 * BT + 0.31245 * DHT -0.37627 * SS + 0.03125* MC * BR -3.125E-003 * MC * BT + 3.125E-003 * MC *DHT -3.125E-003 * MC * SS + 8.75E-003 * BR * BT +1.25E-003 * BR * DHT -1.25E-003* BR * SS -3.125E-003 * BT * DHT + 6.25E-004 * BT * SS -6.25E-004 * DHT* SS -0.020960 * MC2 + 6.64634E-003 * BR2 -1.29573E-003 * BT2 + 4.11585E-004 * DHT2 + 2.91159E-003 * SS2

…(Eq. 2)

Crispness is measured by counting the number of peaksformed on textural profile analysis curve when subjectedto uniaxial compressive loading by a needle probe.Regression analysis shows that moisture content of feed,barrel temperature and screw speed had negative effecton crispness of the extrudates. Crispness increases withdecrease in moisture content of feed which is an indicationof formation of more porous structure of extrudate whichleads in increasing crispness (Fig. 3.11, 3.12, 3.13 and3.14). Increase in barrel temperature decreases thecrispness of the extrudates (Fig. 3.12, 3.15, 3.18 and3.19), respectively. Also decreased in crispness withincreased in screw speed is observed in Fig. 3.14, 3.17,3.19 and 3.20.

However, increment in proportion of chickpea flourin blend and die head temperature increases thecrispness. Crispness of extrudate was increased with theincrement of chickpea flour in blend (from Fig. 3.11, 3.15,3.16 and 3.17). The effect of die head temperature oncrispness of the extrudate and it reveals that crispnessincreases with respect to die head temperature (Fig. 3.13,3.16, 3.18 and 3.20). This is because of high die headtemperature is mainly responsible for harder outer surfacewhich increase the crispness of the extrudates.

Cutting Strength

The cutting strength of extrudates were measured andfound maximum (6.298 kg) at 10% moisture content (MC),150 0C barrel temperature (BT), 200 0C die headtemperature (DHT), 80 rpm screw speed and 70:15:15(Rice: Pigeonpea: Chickpea) blending ratio (BR) and wasminimum (1.07 kg) at 14% MC, 150 0C barrel temperature(BT), 200 0C die head temperature (DHT), 80 rpm screwspeed and 70:15:15 blending ratio (BR). The regressionequation describing the effect of process and operationalvariables on cutting strength of extrudates in terms of

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Table 2. Treatment combinations for extrudate with 3 variable 2nd-order RSM designs

RUN MC BR BT DHT SS Hardness Crispness Cutting(%wb) (0C) (0C) (rpm) (kg) strength (kg)

1 16 70:10:20 140 190 90 1.554 4 1.4052 14 70:15:15 150 200 80 1.786 3 1.2683 14 70:25:5 150 200 80 1.535 5 1.3754 14 70:15:15 150 200 100 1.633 7 1.9725 16 70:20:10 160 210 70 2.292 4 1.7816 14 70:15:15 150 180 80 2.985 4 2.0147 12 70:10:20 160 190 90 2.191 6 1.4398 12 70:20:10 160 210 90 3.039 5 1.6559 18 70:15:15 150 200 80 5.011 6 1.86910 16 70:10:20 140 210 70 1.362 6 1.22711 12 70:20:10 140 190 90 1.25 6 1.468412 12 70:10:20 140 210 90 2.392 5 6.06913 16 70:20:10 160 190 90 1.947 4 3.74714 14 70:5:25 150 200 80 4.394 6 1.82515 14 70:15:15 150 200 80 1.4 5 1.4916 14 70:15:15 150 200 80 1.928 5 1.87417 14 70:15:15 150 200 80 9.391 4 1.1318 16 70:20:10 140 190 70 3.962 5 2.9319 10 70:15:15 150 200 80 4.11 3 6.29820 14 70:15:15 150 200 80 1.923 6 1.95621 16 70:20:10 140 210 90 2.601 5 1.33622 14 70:15:15 150 200 60 1.61 5 1.21323 12 70:20:10 160 190 70 3.221 6 3.47224 12 70:10:20 140 190 70 1.93 5 1.91725 14 70:15:15 150 220 80 1.559 6 3.93626 16 70:10:20 160 190 70 1.654 6 1.40727 14 70:15:15 170 200 80 4.452 4 7.6328 12 70:20:10 140 210 70 1.487 7 3.06729 16 70:10:20 160 210 90 1.84 5 1.96930 14 70:15:15 150 200 80 1.895 7 1.0731 14 70:15:15 150 200 80 1.389 5 1.11932 12 70:10:20 160 210 70 2.608 6 1.825

actual level of the variables is given as:

Cutting Strength = 188.9503 -0.904478* MC -1.1011 *BR -2.32273 * BT + 0.13013 * DHT -0.31452 * SS -0.033585 * MC * BR + 0.0191762 * MC * BT -0.023424 *MC * DHT + 2.38E-003 * MC * SS -7.2895E-003 * BR *BT + 0.010876 * BR * DHT + 9.43700E-003 * BR * SS -4.25850E-003 * BT * DHT -5.07750E-004 * BT * SS +2.99725E-003 * DHT * SS + 0.097684 * MC2 -9.20554E-003 * BR2 + 0.010214 * BT2 + 1.13612E-003 * DHT2 -2.32013E-003 * SS2 …(Eq. 3)

Regression analysis shows that moisture content of feedand blend ratio of chickpea had negative effect on cuttingstrength of the exrudates. Cutting strength increases withdecrease in moisture content of feed (Fig. 3.21, 3.22,3.23 and 3.24). Increase in amount of chickpea flourresulted in decrease in cutting strength of outer crust ofextrudates making less force required to cut theextrudates (Fig. 3.21, 3.25, 3.26 and 3.27). However thebarrel temperature, die head temperature and screw speedhad positive effects on cutting strength of extrudates. Thecutting strength increases with increase barrel

243

Fig 3.1. Response surface graph of blend ratio andmoisture content on hardness of extrudates

Fig 3.2. Response surface graph of blend ratio andmoisture content on hardness of extrudates

Fig 3.3. Response surface graph of die head temperatureand moisture content on hardness of extrudates

Fig 3.4. Response surface graph of screw speed andmoisture content on hardness of extrudates

Fig 3.5. Response surface graph of barrel temperatureand blend ratio on hardness of extrudates

Fig 3.6. Response surface graph of die head temperatureand blend ratio on hardness of extrudates

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Fig 3.7. Response surface graph of screw speed andblend ratio on hardness of extrudates

Fig 3.8. Response surface graph of die head temperatureand barrel temperature on hardness of extrudates

Fig 3.9. Response surface graph of screw speed andbarrel temperature on hardness of extrudates

Fig 3.10. Response surface graph of screw speed anddie head temperature on hardness of extrudates

Fig 3.11. Response surface graph of blend ratio andmoisture content on crispness of extrudates

Fig 3.12. Response surface graph of barrel temperatureand moisture content on crispness of extrudates

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Fig 3.13. Response surface graph of die head temperatureand moisture content on crispness of extrudates

Fig 3.14. Response surface graph of screw speed andmoisture content on crispness of extrudates

Fig 3.15. Response surface graph of barrel temperatureand blend ratio on crispness of extrudates

Fig 3.16. Response surface graph of die head temperatureand blend ratio on crispness of extrudates

Fig 3.17. Response surface graph of screw speed andblend ratio on crispness of extrudates

Fig 3.18. Response surface graph of die head temperaturead barrel temperature on crispness of extrudates

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Fig 3.19. Response surface graph of screw speed andbarrel temperature on crispness of extrudates

Fig 3.20. Response surface graph of screw speed anddie head temperature crispness of extrudates

Fig 3.21. Response surface graph of blend ratio andmoisture content on cutting strength of extrudates

Fig 3.22. Response surface graph of barrel temperatureand moisture content on cutting strength of extrudates

Fig 3.23. Response surface graph of die head temperatureand moisture content on cutting strength of extrudates

Fig 3.24. Response surface graph of screw speed andmoisture content on cutting strength of extrudates

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Fig 3.25. Response surface graph of barrel temperatureand blend ratio on cutting strength of extrudates

Fig 3.26. Response surface graph of die head temperatureand blend ratio on cutting strength of extrudates

Fig 3.27. Response surface graph of screw speed andblend ratio on cutting strength of extrudates

Fig 3.28. Response surface graph of die head temperatureand barrel temperature on cutting strength of extrudates

Fig 3.29. Response surface graph of screw speed andbarrel temperature on cutting strength of extrudates

Fig 3.30. Response surface graph of screw speed anddie head temperature on cutting strength of extrudates

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temperature (Fig. 3.22, 3.25, 3.28 and 3.29). Increase indie head temperature increases the cutting strength (Fig.3.23, 3.26, 3.28 and 3.30). Higher extrusion temperatureresulted in faster water evaporation and cell walls wereinstantly fixed. In this way, time for air bubble coalescencewas not sufficient. Products obtained at high extrusiontemperature showed a thicker structure and were harderthan the others (higher cutting strength). At high screwspeed higher value cutting strength is attained (Fig. 3.24,3.27, 3.29 and 3.30), respectively. This may be attributedto the reduction of the residence time that determined adecrease in the hydration of pre-gelatinized flour, makingthe product texture hard.

Conclusion

Textural properties viz. hardness, crispness and cuttingstrength of the extrudates were affected by both feedvariables (moisture content of feed and blend ratio ofchickpea) and operational variables (barrel temperature,die head temperature and screw speed). Minimumhardness (1.25 kg) of extrudates was obtained at 12%moisture content, 140 0C barrel temperature, 190 0C diehead temperature, 90 rpm screw speed and 10% blendingof chickpea. Maximum crispness (7 peaks) was obtainedat 12% and 14% moisture content, 140 0C and 150 0Cbarrel temperature, 200 0C and 210 0C die headtemperature, 70 rpm and 100 rpm screw speed and 10%and 15% blending of chickpea. Minimum cutting strength(1.07 kg) was obtained at 14% moisture content, 150 0Cbarrel temperature, 200 0C die head temperature, 80 rpmscrew speed and 15% blending ratio of chickpea.

References

Abioye VF, Ade-Omowaye BIO, Babarinde GO, Adesigbin MK(2011) Chemical, Physico Chemical and SensoryProperties of Soy Plantain Flour. Afri J Food Sc5(4):176-180

Adesina AA, Sowbhagya CM, Bhattacharya S, Zakiuddin AS(1998) Maize- soy based ready-to-eat extrudedsnack food. J Food Sci Technol 35:40-43

Ainsworth P, Ibanoglu S, Plunkett A, Ibanoglu E, Stojceska V(2007) Effect of brewers spent grain addition andscrew speed on the selected physical andnutritional properties of an extruded snack. J FoodEng 81:702-709

Camire ME, Camire A, Krumhar K (1990) Chemical andnutritional changes in foods during extrusion. Crist.Rev. Food Sci Nutr 29:35-36

Cheman YB, Mohamad NB, Abdul Karim, Tan TK (1992)Evaluation of four high protein ricesoy snackformulations. J Food Sci Technol 27:715-719

Faubion JM, Hoseney RC (1982) High-temperature short-time extrusion cooking of wheat starch and flour. I.effect of moisture and flour type on extrudateproperties. Cereal Chemistry 59(6):529-533

Meng X, Threinen D, Hansen M, Driedger D (2010) Effects ofextrusion conditions on system parameters andphysical properties of a chickpea flour-based snack.Food Res Int 43(2):650-658

Mercier C, Linko P, Harper JM (1989) Extrusion cooking;American Association of Cereal Chemists Inc StPaul MN 461

Okpala LC, Okoli EC (2011) Nutritional Evaluation of cookiesproduced from pigeon pea, cocoyam and sorghumflour blends. Afr J Biotechnol 10(3):433-438

(Manuscript Receivd : 30-03-2015; Accepted : 27-06-2015)

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Rice is the staple food for people of Asia. In India 27insects have been recorded as important pests on riceknown to cause damage from the seedling to ear headstage (Regupathy et al. 1996). Various insect pests knownto da~age paddy crop right from nursery to harvest, amongthem gall midge is one pest which is assuming importanceand reducing yields substantially in different parts ofAndhra Pradesh. Farmers are applying the recommendedinsecticides to control gall midge, however many farmersreporting ineffectiveness of insecticides like phorate andcarbofuran granules against gall midge in and aroundJagtial and Kampasagar. Hence an observational trial wasconducted at Jagtial centre where gall midge is endemicand at Kampasagar centre where it is problematic underlate transplanted conditions. The experiment was carriedout during Kharif, 2008 to re-test the efficacy recommendedchemicals against rice gall midge. Treatments wereimposed in nursery taking one sq mt nursery was raisedper treatment and in nursery granules were applied on28-08-2008 and in main field taking plot size of 100 sq.mtper treatment was taken with a spacing of 20x15 em withtaking Swarna and BPT-5204 as test varieties at Jagtialand Kampasagar centres. Nursery was transplanted atboth locations on 12.09.2008. Treatments were imposedin nursery as well as in main field. Gall midge incidencewas recorded by taking silver shoots from 25 random hills/treatment at 30 and 50 OAT. Grain yield was also recordedat harvest. All the treatment recorded good control of gallmidge compared with untreated control. Phorate lOG @1.25 kg a.i/ha in nursery and main field recorded lowestgall midge (3.6% silver shoots) followed by Phorate lOG@ 1.25 kg a.ilha in nursery + 1.00 kg a.i/ha in main field(4.3% silver shoots) and carbofuran 3G @ 1.25 kg a.i/hain nursery + 1.00 kg a.ilha in main field (6.4% silvershoots) compared with untreated control (13.8% silvershoots). In grain yield carbofuran recorded higher yield(7160kg/ha) followed by phorate lOG @ 1.25 kg a.i/ha(6882 kg/ha) and phorate lOG @ 1.00 kg a.i/ha (6771 kg/

ha) compared with untreated (5772 kg/h). Overallconclusions showed that all the three treatments i.ephorate @ 1.25 kg a.i/ha and 1.00 kg a.ilha and carbofuran@ 1.00 kg a.i/ha were found to be effective in controllinggall midge damage and recording higher grain yields. Theeffectiveness of nursery and transplanted field applicationof various granules applied in paddy in present studiesare more or less similar to those of Kumar et al. (2011)who reported the nursery application of isazofos 3G @0.75Kg a.i./ha recorded lowest gall midge infestation (4.98percent silver shoots), higher grain yield (60.02 q/ha) andbiomass yield (61.91/ha). Also Tripathy et al. (1999)reported that 0.06 kg isazofos/ha (MiraI3G) was best forcontrolling gall midge of paddy (2.28 percent silver shoots)at 15 OAT compared with 6.93 percent in the control.

Reassessing the efficacy of recommended insecticides against ricegall midge in different agro-climate zones of Andhra Pradesh

R. Bala Muralidhar Naik, D. Seshagiri Rao, L. Krishna and Md. Lathee PashaAgricultural Research Station, KampasagarNalgonda, ANGRAU (AP)Regional Agricultural Research Station, JagtialKarimnagar, ANGRAU (AP)

JNKVV Res J 49(2): 249 (2015)

Treatment Dosage Damage Grain(kg a.i/ha) (%) (kg/ha)

T1 Phorare lOGNursery 1.25 4.3 6771Main Field 1.00

T2 Phorate 10 GNursery 1.25 3.6 6882Main Field 1.00

T3 Phorate 10 GNursery 1.25 6.4 7160Main Field 1.00

T4 Untreated Control - 13.8 5772

Regupathy A, Palaniswamy S, Chandrashan N,Ganathilagaraj K (1996) A guide on crop pests.Sooriya Desk Publisher Coimbatore : 1-10

Tripathy MK, Setapati B, Acharya S, Patnaik HP (1999) J AppliZoolog Res 10(2) : 123-125

Kumar IV, Patil SU, Prasanna Kumar MK, Chakravarthy AK(2011) Current Biotica 5(3) : 323-329

(Manuscript Receivd :20-03-2015; Accepted : 30-06-2015)

250

Study on the pest complex and their succession revealedthat nine species of insect pests were recorded at variousstages of crop growth in an overlapping manner on Rabigroundnut crop under Kampasagar conditions of Nalgondadistrict of Andhra Pradesh. Groundnut variety TMV -2 wassown on November 15, 20 and 22 during the three yearswith a gross plot size of 48x28sq.mt. A distance of 30x IOcm was maintained between row to row and plant toplant. The experimental plot was kept free frominsecticidal spray throughout the crop season. The cropwas observed weekly from seedling to harvest for theincidence of insect pests. Observations were taken tenrandomly pre determined tagged plants.

Absolute population per plant (Pradhan 1964) wasrecorded in case of jassids, thrips, white fly, aphids andlarvae were counted per plant. Various insect pestscausing damage to TMV -2 variety of groundnut at differentstages of crop growth are shown below in table. Of all the

Population dynamics of pest on rabi groundnut crop at Kampasagarof Nalgonda district, Andhra Pradesh

R. Bala Muralidhar Naik, L. Krishna, D. Bhadru and Md. Lathee PashaAgricultural Research Station, KampasagarNalgonda, ANGRAU (AP)

JNKVV Res J 49(2): 250-251 (2015)

pests, the pests like groundnut jassid, Empoasca kerriPruthi, chilly thrips, Scirtothrips dorsalis Hood, cottonwhite fly, Bemisia tabaci Gennadius and groundnut aphid,Aphis craccivora Koch were present in considerablenumber from seedling stage till harvest.

Out of these above four pests, Bemisia tabaciGennadius caused minor damage whereas the otherpests like Empoasca kerri Pruthi, Scirtothrips dorsalisHood and Aphis craccivora Koch reached to a maximumnumber during active vegetative to flowering stages andcaused major economic damage to the crop. White grub,Holotricha consanguinea Blanchard caused minor damagein isolated patches which was seen in active vegetativestage. Groundnut leaf miner, Aproaerema modicellaDeventer, gram caterpillar, Helicoverpa armigera Hubnerand tobacco caterpillar Spodoptera litura(F) were presentfrom active vegetative stage to peg formation stages. Outof all these above three pests Aproaerema modicella

Table 1. Pest complex of Rabi groundnut at Agricultural Research Station, Kampasagar

CN SN Crop stage Status

Groundnut jassid Empoasca kerri Pruthi Seedling to harvest Major

Chilly thrips Scirtothrips dorsalis Hood Seedling to Peg penetration Major

Cotton white fly Bemisia tabaci Gennadius Seedling to harvest Minor

Gram caterpillar Helicoverpa armigera Hubner Active vegetative stage Minor

Tobacco caterpillar Spodoptera litura (F) Active vegetative stage to peg formation stage Major

Groundnut leaf miner Aproaerema modicella Deventer Active vegetative stage Major

Groundnut aphid Aphis craccivora Koch Active vegetative stage to flowering stage Minor

White grub Holotricha consanguinea Blanchard Active vegetative stage Minor

Flower thrips Megalurothrips usitatus Schautz Flowering stage Minor

251

Deventer and Spodoptera litura (F) caused major economicdamage to crop whereas Helicoverpa armigera Hubnercaused minor damage .The succession and pest complexobserved in present studies are more or less similar toSingh et al. (1990) and Jayanthi et al. (1993) who reportedfifty two and eighteen insect pests at different stages ofcrop growth at Delhi conditions.

References

Pradhan S (1964) Assessment of losses caused by insectpests of crops and estimation of insect population.In : Entomology in India (Ed) NC Pant Silver JubileeNumber of Entomological Society of India pp 17-58

Singh TVK, Singh KM, Singh RN (1990) Groundnut pestcomplex. II Succession of pests. Indian J Ent 52(3): 493-498

Jayanti M, Singh KM, Singh RN (1993) Pest complex of ahigh yielding groundnut variety MH4 under Delhiconditions. Indian J Ent 55(1) : 30-33

(Manuscript Receivd : 20-03-2015; Accepted :30-06-2015)

252

Light trap is an important tool in integrated pestmanagement for monitoring pest build up in an area. Achinsurah model light trap with 200 W incandescent bulbwas set up at a height of 1.5 mt from the ground in thepaddy field of Agricultural Research Station, Kampasagaron 20th October 2009 located in the Nagarjuna SagarProject area. Regular light trap catches in respect ofBrown Plant Hopper (BPH), Green Leaf Hopper (GLH),White Backed Plant Hopper (WBPH) and stem borer werecounted and recorded by operating the light trap fromdusk to dawn every day. When the catches were in largenumbers the insects were divided into convenient equalpaIis and computed to fortnights for convenience andpresented graphically after log transformation.

In the project area the crop is usually transplantedfrom 2nd week of August for kharif season and last weekof December for rabi season with a delay in one or twoweeks depending upon the release of water in the canal.The prominent varieties grown in this region are BPT5204during kharif season and MTU-1010 during rabi season.

The results of three successive years revealed thatBPH, GLH and WBPH were the prominent pestsoccurring during kharif season whereas stem borer wasconsidered to be the prominent pest occurring during rabiseason.

The brown plant hopper, Nilaparvata lugens (Stal)occurrence in trap (Table below) for the rabi crop startedfrom first fortnight of January for the years 2010 and 2011.Further the peak catches were observed during 1st or 2nd

fortnight of March for the rabi crop and 1st or 2nd fort nightsof October for the kharif crop. The mean of observationsrevealed that peak catches of BPH during kharifwas the2nd fortnight of October (13,226) where as for the rabi cropit was 2nd fortnight of March (884.3) which coincided withear head emergence of the crop. The data clearly revealsthat the pest occurs definitely after 60 days of transplanting

Light trap catches of major pests of rice in Nagarjuna Sagar ProjectArea of Nalgonda district of Andhra Pradesh

R.Bala Muralidhar Naik, Md.Latheef Pasha, L.Krishna, D.Bhadru and P.Rajani KanthAgricultural Research Station, KampasagarAcharya N.G.Ranga Agricultural UniversityNalgonda District 508207 (AP)

JNKVV Res J 49(2): 252-254 (2015)

in both the seasons as planting is completed during 2nd

fortnight of August and 1st fortnight of January respectivelyfor the kharif and rabi crop. The results of the presentinvestigation are in close agreement with the light trapstudies of Kelietta et al. (1990) who reported peaks ofBPH during September to December but disagree withthe peat catches reported by Qadeer et al. (1990) whohas reported peak of the paddy delphacid during August- September from Kamal and Kaushik (1985) who hasreported peak during November 2nd fortnight. This variationis mainly attributed due to the difference in planting timeof the crop in different agro climatic regions.

The white backed plant hopper, Sogatella furcifera(Horvath.) population for the rabi crop was not observedindicating its catches were not noticed in light trap duringrabi season. However during the three years 2009, 10and 11 it catches coincided with BPH mean peak catch(October 2nd fortnight) i.e. (2936.6) catches indicatingoccurrence of both BPH & WBPH as a mixed populationduring 2nd fortnight of October during kharif season. Thepresent results are in close conformity with the findingsof Kerketta et al. (1990).

The green leaf hopper, Nephotettix virescence(Distant) for the rabi season was started from 1 st fortnightof January for the years 2010& 2011. Further the peakcatches were observed during 1 st or 2nd fortnights of Marchfor the rabi crop and 1 st or 2nd fortnights of October to thekharif crop. The mean of observations revealed that peakcatches of GLH during kharif was the 2nd fortnight ofOctober (11,627.6) whereas for the rabi crop it was 2nd

fortnight of March (2062) which coincided active tilleringto ear head emergence stage of the crop. The data clearlyreveals that the pest occurs definitely after 35-40 days oftransplanting in both the seasons as the planting iscompleted during 2nd fortnight of August and 1 st fortnightof January respectively for the kharif and rabi crop. Theresults of the present investigation are in close agreement

253

Tabl

e 1.

Lig

ht tr

ap c

atch

es o

f key

pes

ts o

f pad

dy in

Nag

arju

na S

agar

Pro

ject

are

a of

Nal

gond

a di

stric

t

BPH

GLH

WBP

HSt

em b

orer

Fortn

igh

2009

2010

2011

Mea

n20

0920

1020

11M

ean

2009

2010

2011

Mea

n20

0920

1020

11M

ean

Jan

I-

511

107

206.

0-

572

133

235.

0-

--

--

399

16.0

Jan

II-

281

127

13.0

-74

117

230

4.3

--

--

-38

4126

.3

Feb

I-

669

165

278.

0-

508

187

231.

6-

--

--

109

8966

.0

Feb

II-

529

310

279.

6-

1130

359

496.

3-

--

--

8211

967

.0

Mar

ch I

-17

8066

081

3.3

-20

9829

179

6.3

--

--

-25

4924

.6

Mar

ch II

-19

2672

788

4.3

-56

9249

420

62.0

--

--

-17

119.

3

April

I-

1122

490

406.

6-

1294

9446

2.6

--

--

-59

6039

.6

April

II-

122

8569

.0-

190

7588

.3-

--

--

-21

7.0

May

I-

--

--

213

203

138.

6-

--

--

--

-

May

II-

912

.0-

269

245

171.

3-

--

--

--

-

June

I-

4535

26.6

-75

6546

.7-

--

--

--

-

June

II-

--

--

2725

17.3

--

--

--

--

July

I-

--

--

4350

31.0

--

--

--

--

July

II-

--

--

3632

22.6

--

--

--

--

Aug

I-

301

326

209.

0-

293

372

222.

0-

--

--

172

6.3

Aug

II-

498

578

358.

6-

572

512

361.

3-

--

--

--

-

Sept

I-

305

799

368.

0-

296

462

252.

6-

2512

550

.0-

16-

5.3

Sept

II-

545

788

444.

3-

555

532

362.

3-

3075

35.0

-13

-4.

3

Oct

I-

1233

2282

580

19.3

-10

9720

070

7055

.6-

9045

018

0.0

-9

189.

0

Oct

II67

9233

4129

545

1322

6.0

1137

018

4321

670

1162

7.6

4320

8544

0529

36.6

109

99.

3

Nov

I25

6645

053

6727

94.3

2966

291

470

2653

.046

475

145

228.

045

4221

36.0

Nov

II21

5032

819

6514

81.0

4144

324

1906

2124

.651

029

3519

1.3

5212

4436

.0

Dec

I84

249

274

1165

.035

0723

515

712

99.6

3428

4836

.625

2030

25.0

Dec

II11

02-

107

403.

012

2814

178

482.

3-

2025

58.0

3520

1523

.3

254

with the light trap studies of Sharma et al. (2004) whoreported peaks of GLH during 2nd week of October duringkharif but disagree with the peak catches reported by Raiand Khan (2002) who reported peak catches of GLH during1st fortnight of August in the kharif season of rice crop.This variation is mainly attributed due to the difference inplanting time of the crop.

The stem borer, Tryporyza incertulas (Wlk.) meanpeak patches (67) appeared in 2nd fortnight of Februaryfor the year 2010 and 2011 for the rabi crop indicating thecrop is at active tillering stage causing dead hearts andgradually again mean peak catches (39.6) appeared inthe 1 st fortnight of April for the year 2010 and 2011indicating the crop is at grain hardening stage causingwhite ear head to crop. The mean peak catches (36) forthe kharif crop was 1 st fortnight of November for all theyears i.e. for 2009, 10 and 11 indicating the crop is atgrain hardening stage resulting in white ear emergence.When the peak catches for both kharif and rabi wereobserved, it was observed the catches were comparativelylow in kharif when compared to rabi. However the peakcatches reported by Banerjee and Mookarjee (1976) andRaghupathy and Chamy (1989) are contradictory,reporting the peak catch of stem borers during Februarywhich may be due to difference in planting of the cropand availability of suitable age for the pest during themonth.

The present study clearly revealed that almost allthe key pests viz. BPH, WBPH and GLH appeared inlight trap catches in the peak during the mid croppingseason, particularly between 1st and 2nd fortnight of Marchfor rabi and between 1st and 2nd fortnight of October forkharif for sucking pests like BPH and GLH. WBPH activitywas same as above for kharif crop but its activity wascomparatively low in rabi crop. When the peak catchesfor all the three years were observed for all the suckingpests for kharif crop, their was comparatively lessincidence of these pests during the year 2010. The lesssusceptibility of these pests during the year 2010 wasmay be due to early transplanting of susceptible varietyBPT -5204 as early transplanted crop escapes BPH attackand also due to biotic factors like less relative humidityand intermittent rains which resulted in wash out of pestfrom crop canopy during that year leading to low pestload.

When the peak catches for all the three years wereobserved for all the sucking pests for rabi crop there werereports of light trap catches of sucking pests like BPH &GLH for the pest tolerant MTU-l 010 paddy variety whichwas due to application of high dose of nitrogenousfertilizers and also due to excessive spraying of syntheticpyrethroids. Also their might be breaking of resistance to

BPH in pest tolerant MTU-1010 paddy variety.Therefore,a keen vigilance on the crop during the peak catchesperiod may save a lot to protect the crop from these keypests.

References

Banerjee SN, Mookerjee AL (1976) Studies on the change inpopulation of paddy stem borer Tryporyza incertulas(Walker) in relation to preceeding weatherconditions. Indian J Plant Prot 4: 130-134

Kaushik UK (1985) Ecological studies of rice hopper(Nephotettix virescens Distant; Sogatella furciferaHorvath. Nilaparvata lugens Stal.) in Chattisgarh,Ph.D. Thesis submitted to Jawaharlal Krishi VishwaVidyalaya, Jabalapur.

Kerketta MS, Dubey AK, Kaushi UK (1990) Light trap studiesof two rice plant hoppers, Nilaparvata lugens andSogatella furcifera in relation to field population.Oryza 27(4): 503-506

Raghupathy A, Chamy A (1989) Light trap catches of riceyellow stem borer moths in Rice-summer cottonarea. 168-173. In: Chellaiah and Balasubramaniah(ed). Pest Management in Rice, Tamil NaduAgricultural University, Coimbatore

Rai AK, Khan MA (2002) Light trap catches of rice insectpest, Nephotettix virescens (Distant) and its relationwith climatic factors. Annal Plant Prot Sci 27(1): 17-22

Sharma MK, Pandey V, Singh RS, Singh RA (2004) A studyon light trap catches of some rice pests in relationto meterological factors. Ethiopian J Sci 27(2): 165-170

(Manuscript Receivd :20-03-2015; Accepted : 30-06-2015)

255

Parthenium hysterophorus L (Family Asteraceae) is anannual weeds exhibiting high competitiveness andadaptability to different climatic and soil conditions, it isthought to originate from middle America and is nowwidely spread in tropical regions, It was introduced toIndia in the mid 1956 and is now concerned as one of themost player noxious weeds species (Rao 1956). The aimsof the present work was to investigate etiology ofparthenium phyllody. Taye et al. (2004) reports of phyllodydisease of P. hysterophorus weeds in Ethiopia, Phyllodyand withches' broom previously reported on Partheniumhysterophorus from Uttar Pradesh was worked out Raj etal. (2008).

A field survey was carried out during Kharif seasonat Jawaharlal Nehru Krishi Vishvidyalay Jabalpur, nearSesame, Niger and other side field to find out the naturaloccurrence of virescence and wiches' broom pathogen ofParthenium hysterophorus.

The natural occurrence of virescence and witchesbroom was observed of P. hysterophorus, plant growingwidely along the sesame field and near threshing floorfield. The infected plants showed excessive greenbranches, tiny narrow leaves, shorting, of internodes,reduced plant height and leaf size as well as modificationpetals in to leaf like structure that lead to study (phyllody)witches broom like symptoms (Fig 1). Phyllody andwitches broom caused by Phytoplasma, "Candidatusphytoplasma asters"(16SrI) have been previously reportedon Parthenumn hysterophorus from Uttar Pradesh (Rajet al. 2008). The present finding iis in accordance withTaye et al. (2004). The observed symptomatology hasclearly revealed the phyllody disease in parthenium iscaused by phytoplasma.

Based on the symptoms recorded in the presentinvestigation match with the symptoms described in

A new record on association of phytoplasma with phyllody diseasein Parthenium hysterophorus from Madhya Pradesh, India

K. N. GuptaPC Unit, AICRP on Sesame & NigerJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482 004 (MP)Email: kngupta1@rediffmail.com

JNKVV Res J 49(2): 255 (2015)

Ethopia by Taye et. al. (2004) and Raj et.al. (2008) fromIndia. It was concluded that the disease identified as eitherphyllody or witches' broom disease witch caused byPhytoplasma.

References

Raj SK, Khan MS, Snehis K, Kumar S, Mall S, Rao GP (2008)First report of Phytoplasma "Candidatusphytoplasma astris" (16SrI) from Partheniumhysterporus L. Showing symptoms of virescenceand witches' broom in India. Australian Pl DisNotes. 3:44-45

Rao RS (1956) Parthenium. A new record for India J of theBombay Natural History Society. 54:218- 228

Taye T, Obermeier C, Einhorn G, Seemiiller E, Buttner C.(2004) Phyllody Disease of Parthenium weed inEthiopia. Pest management J of Ethiopia 8:39-50

Phyllody (witches' broom) affected parthenium plant

(Manuscript Receivd : 29-03-2015; Accepted :15-07-2015)

256

JNKVV Res J 49(2): 256-261 (2015)

Abstract

Study was conducted to find out the factors responsible forspread and outbreak of yellow mosaic in soybean crop.During survey a weed plant, Paracalyx scubious, was foundto be an alternate host of the disease which was transmittedby white fly (Bemisia tabaci). The other host Corchrousolitorious, Ageratum conyzoides , Alternanthera sessilis andPhaseolus trilobus may also play an important role forsurvival of the pathogen as these weeds were found infectedin kharif, rabi and summer seasons. Susceptible variety JS335 was found to show early and high disease incidence ascompared to other varieties. Field observations also showedthat the maximum disease was found in both the type ofcultivar at 61-67 days of sowing. It was found that spread ofyellow mosaic was very fast (30-45 days) after sowing. Therate of disease development was high when maximumtemperature and relative humidity ranged between 29.9 0Cto 36.2 0C and 62 to 75 per cent, respectively. The earliestincidence of yellow mosaic observed in July-August onsoybean was at 26-54 days after sowing (DAS). The diseaseincidence was more or less same i.e., 30-40 days aftersowing. The period of disease spread was 7 to 32 days afterfirst incidence of the disease in soybean JS -335 cultivar.

Keywords: Epidemiology, soybean yellow mosaic virus

In India, soybean [Glycine max (L.) Merrill] is grown inthe states of Madhya Pradesh, Maharashtra, Rajasthan,Karnataka, Andhra Pradesh, Chhattisgarh, Nagaland andGujarat as a rainfed crop during the rainy (kharif) season.However, its continuous cultivation with simultaneousincrease in area has led to increase in disease, insectand weed incidence. Currently soybean is severelyattacked about half a dozen major diseases, a dozen ofinsect-pests and several major weeds. Yield losses dueto individual disease/insect/weed species ranges from 20to 100 per cent. The crop suffers to a greater extent from

Ecological traits of yellow mosaic disease in relation to epidemics insoybean

K.N. Gupta and R.K. VarmaDepartment of Plant PathologyJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482 004 (M P)Email: kngupta1@rediffmail.com

many diseases such as charcoal rot (Rhizoctoniabataticola), collar rot (Sclerotium rolfsii) and yellow mosaicdisease (Mung bean yellow mosaic virus) causing losses,viz 25-50 and 15-75 per cent, respectively (Chenulu etal.1979). Studies were conducted to find out the factorsresponsible for spread and outbreak of yellow mosaic insoybean crop.

A Begomovirus under Geminivirus, has become amajor limiting factor for production of soybean crop. Thedisease is transmitted by white fly (Bemisia tabaci).Yellow mosaic disease causes a heavy losses atvegetative growth stage till pod formation, The infectionnot only drastically reduce seed yield but also.responsible for development of epidemic condition inMadhya Pradesh, during kharif seasons have been workout.

Material and methods

The pot culture and field trial were conducted at Collegeof Agriculture, JNKVV, Jabalpur, Madhya Pradesh as wellas in farmers field in low risk (irrigated field), medium risk(rain-fed), and high risk (un-irrigated field) areas. Soybeancultivar JS 335 and weeds Ageratum conyzoidesCorchorus olitorius, Phesolus trilobus, Sida spp., Ecliptaalba, affected by yellow mosaic disease were tagged fromthe field and the white fly culture was maintained on brinjal(Solanum melogena ), French bean (Phaseolus vulgaris)and tobacco (Nicotiana tabacum) plants. All the testplants were grown in polythene bags and transmissiontests using viruliferous white fly reared under controlledcondition were done in muslin cloth cages (micro cage).In field studies, the crop cultivars as well as the weedhosts and white fly population (Tables 1, 2) was observedat fortnight intervals. Weather factor was recorded of

257

meteorology department JNKVV, Jabalpur.

Result and discussion

Incidence of yellow mosaic disease of soybean revealedthat field near irrigation canal, water points, low-lying areasand foot hills were found to have high disease incidenceas compared to field located in unirrigated areas. Plantspecies Paracalyx scubious (Roxb) were found to act asreservoir host of yellow mosaic inoculums was observedcontinuously, being perennial in habit. Simultaneously,weeds like Alternanthera sessilis L. and Corchorusolitorious L. were found to help the multiplication andspread of inoculum. The white fly population was recordedon different crop and weed hosts in different seasons(Table 1). The weather parameter with yellow mosaic virusand white fly population revealed that the rate of diseasedevelopment was high when maximum temperature andrelative humidity ranged between 29.9 0C to 36.2 0C and62 to 75 per cent, respectively. The earliest incidence ofyellow mosaic was observed in July-August on soybeanwas at 26-54 days after sowing (DAS). The diseaseincidence was more or less same i.e., 30-40 days aftersowing. The period of disease spread was 7 to 32 daysafter first incidence of the disease in soybean JS 335.Thresh (1974) has indicated that viruses spread both into and within crops and newly infected plants soon becamefoci for secondary spread. The dispersal pattern of yellowmosaic also suggested that the vector efficiency might

have been affected by the crops grown on the surrounding,favored vector as well as yellow mosaic transmission andincidence. This phenomenon assumes importance withregards to survival of perennial as reported by Kranz(1974); Duffs 1971 and Verma et al. (1989) in perennialhost of the previous crop host as evidenced by a fewdiseased plants observed at 26 DAS. The dispersal patternand trap plot study showed that the disease was spreadingslowly in soybean when grown alone as mixed or alternatecrop with mung bean and urd bean. Higher disease occurswere also observed in soybean alone as compared tosoybean grown in alternation with mung bean, Thisindicated that the vector population and its movementwas favored by soybean as suggested by Costa (1976)and Dhingra and Chenulu (1985). The losses in grain yieldwere more when the plants were infected after 20 days ofsowing than 30 and 40 DAS. Chenulu et al. (1979)observed that the mung bean plants infected at early stageor crop growth reduce very few pod and the yield lossesup to 88.5 per cent. The late incidence of disease in mixedcrop indicated that vector migration from one to anotherhost was reduced in the present study by Thresh (1974)and Costa (1976). Crop to weed and weed to cropinoculation tests using viruliferous, B. tabaci as describedabove were made by in glass house and insectory underinsect proof condition. The data indicated that Corcorus

Table 1. Incidence of yellow mosaic disease in soybeanfields of different situations

Situation studied Incidence of yellow mosaicDAS* Incidence (%)

Single crop 40 20

Multi-crop 30 15Off type plants 20 10Un-irrigated field 40 30Irrigated field 30 60Near threshing floores 25 35Near canal 25 30Near hills 30 20Alternate cropping 40 40continuous cropping 25 50One rotation 30 12SEm± 1.93 1.09CD (P= 0.05) 5.71 3.27

*DAS= Days after sowing

Table 2. Population of Bemacia tabaci in different cropand seasons

Host Whitefly population(No/plant)

Kharif Rabi Summer

Acalyta Indica Linn 1 0 1Ageratum conizoides 0 0 0Alternanthera sessillis L 1 1 0Cajanus cajan L. 0 0 0Capscum annum 2 1 1Corchorus olitorious 2 0 1Carica papaya 1 1 1Datura stromonium 0 0 0Eclipta alba 1 1 0Glycine max 3 3 3Gossipium spp. 2 1 2Lycopersicon escultenum 1 1 1Malvastrum coromandelianum 0 0 0Nicotiana tabacum 1 3 3Phaseolus trilobus 2 2 1Phaseolus vulgaris 0 2 0

258

Tabl

e 3.

Dis

ease

pro

gres

s in

tim

e on

soy

bean

Dis

ease

obs

erve

dIn

cide

nce

of y

ello

w m

osai

c in

fiel

dat

DAS

Low

risk

(un-

irrig

ated

)M

ediu

m (a

ltern

ativ

e)H

igh

risk

(Irrig

ated

)AF

Ifie

ld %

(R1)

risk

field

% (R

2)fie

ld %

(R3)

dx/d

t per

Rat

e/Fi

nal

dx/d

t per

Rat

e/Fi

nal

dx/d

t per

Rat

e/Fi

nal

R1+

R2+

R3

unit

/ day

wee

kin

cide

nce

unit/

day

wee

kin

cide

nce

unit/

day

wee

kin

cide

nce

(ave

rage

of

thre

e fie

lds)

300.

332.

3310

.00.

563.

9617

.00

0.68

4.76

2500

17.3

360

1.80

12.2

36.0

01.

8512

.937

.00

3.05

21.3

061

.00

44.6

610

00.

654.

5513

.00.

654.

2012

.00

0.10

0.70

2.00

9.0

Mea

n0.

926.

3819

.66

1.00

6.97

22.0

01.

278.

758.

9323

.55

Sour

ceS.

Em

(±)

C.D

. (P

= 0.

05)

DAS

1.04

23.

096

Ris

k1.

042

3.09

6In

tera

ctio

n1.

805

5.36

4*D

AS=D

ays

afte

r sow

ing

**AF

I: Af

ter f

irst i

nitia

tion,

dx

(X2 -

X1 ),

dt (

t2 - t1 )

Con

vers

ion

of th

e m

easu

rem

ent i

s X

1 (as

sess

ed a

t tim

e t 1)

and

the

seco

nd X

2 (as

sess

ed a

t tim

e t 2)

Tabl

e 4.

Dis

ease

spr

ead

and

prog

ress

of y

ello

w m

osai

c vi

rus

in s

oybe

an

Dis

tanc

e fro

m lo

ciSp

read

of y

ello

w m

osai

c ob

serv

ed in

follo

win

g fie

ld(in

Met

er)

Low

risk

(irri

gate

d)M

ediu

m (a

ltern

ativ

e cr

op)

Hig

h ris

k (ir

rigat

ed)

Aver

age

of R

1, R

2, R

3fie

ld %

RI

risk

field

R2

field

R3

Inci

denc

eR

ate/

mIn

cide

nce

Rat

e/m

Inci

denc

eR

ate/

mIn

cide

nce

Rat

e/m

5 M

12.2

42.

4514

.13

2.82

18.2

636

514

.98

297

50 M

13.9

60.

2815

.94

0.31

22.2

00.

4417

.33

0.34

100

M8.

970.

0811

.80

0.12

15.1

10.

1512

.78

0.12

Mea

n14

.75

1.29

16.5

01.

4122

.00

1.87

17.9

71.

52So

urce

S. E

m ±

C.D

. (P

= 0.

05)

Dis

tanc

e0.

5879

1.71

60R

isk

0.50

911.

4861

Inte

ract

ion

1.01

832.

9724

259

Tabl

e 5.

Cor

rela

tion

co-e

ffici

ent b

etw

een

wea

ther

par

amet

er a

nd d

isea

se in

cide

nce

in s

oybe

an c

rop

Cha

ract

ers

Tem

pera

ture

(0 C)

Vapo

r Pre

ssur

eR

elat

ive

hum

idity

Suns

hine

Win

dTo

tal

Rai

nyD

isea

se(%

)ho

urs

velo

city

rain

fall

days

inci

denc

eM

axM

inM

orn

Even

Mor

nEv

en(k

m/h

)(m

m)

(No)

Max

imum

tem

pera

ture

(o C

)1.

00.

410.

78*

0.54

-0.3

5- 0

.53

- 0.6

8*- 0

.62*

- 0.3

1- 0

.63*

-0.7

4*M

inim

um te

mpe

ratu

re (o

C)

1.00

0.76

*0.

610.

430.

090.

33- 0

.15

- 0.3

8- 0

.53

0.24

Vapo

r pre

ssur

e m

orni

ng1.

000.

78*

0.02

- 0.1

40.

54- 0

.40

0.66

- 0.7

0*0.

52Va

por p

ress

ure

even

ing

1.00

0.28

0.23

0.19

- 0.1

2- 0

.43

- 0.6

1*0.

14R

elat

ive

hum

idity

(%),

mor

ning

1.00

0.44

- 0.7

6*0.

120.

170.

140.

24R

elat

ive

hum

idity

(%),

even

ing

1.00

- 0.5

70.

100.

170.

220.

87**

Sun

shin

e ho

urs

1.00

0.88

**- 0

.43

- 0.6

8*0.

67**

Win

d ve

loci

ty (k

m/h

)0.

520.

480.

530.

96**

Tota

l Rai

nfal

l (m

m)

1.00

1.00

0.99

**-0

.55*

Rai

ny d

ays

(No)

1.00

-0.6

1D

isea

se In

cide

nce

1.00

*Sig

nific

ant a

t 5%

, ** s

igni

fican

t at 1

%

olitorious acted as bridge host in bringing the virulence tosoybean from mungbean. Positive transmission frommung bean to Corcorus olitorious and Paracacalyxscubisus was observed but back mung bean inoculationdirectly from mung bean to soybean was not successful.Eraivan et al. (1998) was also reported that the role ofweeds as source of mungbean yellow mosaic virus. Itwas also observed the Paracalyx scubisus was found toserve as a source of inoculums for very long period helpingin over summering during May-June.

Disease progress in time

The rate of diseases progress in time was calculated asper the method given by Van der Plank (1963). Theparameter low risk, (irrigated field), medium risk (rainfed),and high risk (un-irrigated field), (Table 3) respectably,diseases incidence age wise were found to affect the rateof disease development and the total incidence of yellowmosaic in soybean. The disease development was foundto be highest between 40-60 DAS of the age of soybean.but significantly reduced afterwards days after sowing.The disease development was found to be highest betweenthe 30 days after sowing both crops.

Disease progress in space

The rate of disease progress in space was calculated asper the method given by Vander Plank (1963) datapresented in (Table 4). The linear spread of disease wasfound to be 32-43 per cent as observed at 10 meter

Fig 1. Weather parameter coinciding with diseaseincidence during study period

260

distance from the loci. The parameter was observed, lowrisk (irrigated field), medium risk (rainfed) and high risk(un-irrigated field), respectably the data also showed thatthe distance wise spread do not vary in high, low andmedium risk fields. It was more or less the same in allthe fields studied beyond 50 meter distance of the spreadof the disease in soybean in soybean crop.

Correlation study

Relative humidity during morning hours had negativecorrelation with disease incidence. When the relativehumidity decreases below 92.5 percent, every unitdecrease in humidity by one percent enhances the diseaseincidence by 3.75 %. Sharma et al. was also reportedthat the effect of temperature, relative humidity and rainfallon the incidence of natural infection of Vigna radiata bymungbean yellow mosaic. Duration of sunshine in hourshad positive correlation with disease incidence (Table 5).When the duration of sunshine increases more than 2.24hr per day, every increase in sunshine by one hourenhances the disease incidence by 3.78 %. Wind velocityshowed negative correlation with disease incidence whichindicates that when the wind velocity decreases below4.75 kmph, the every unit decreases in wind velocity,increases disease incidence by 4.94 %.

Therefore it is concluded that the year under studyhad received initiation of yellow mosaic disease of soybeanmore than 42-59 per cent incidence. Therefore, it wascalled for epidemic of yellow mosaic in soybean

lks;kohu esa ihyk ekstsd dh egkekjh dk i;kZoj.k ls lac/k ,oa jksx dksizHkkfor djus okys dkjdksa dk losZ{k.k lgv/;;u fd;k x;k A losZ{k.k

ds nkSjku iSjkdsfyDl LØqfo;l uked [kjirokj jksx dk ckgd ik;kx;k A lQsn e[[kh ¼csehfl;k VscslkbZ½ }kjk jksx dk lapj.k gksrk gS AvU; [kjirokj tSls dkjdks jl vfyVkSfj;l] ,tsfjVe dksusTokbbl]vYVjusFkzk fllfyl ,oa iSjkdSfydl vkfn Hkh jksxtud ds QSykc esaegkRoiw.kZ Hkwfedk fuHkkrs gS A ;g lHkh [kjirokj [kjhQ] jch] ,oa xehZds ekSleksa esa bl jksx ls laØfer ik;s x;s A lks;kchu dh fdLe ts-,l-335 esa vU; fdLeksa dh rqyuk esa jksx tYnh vkrk gS ,oa jksx dh rhozrkHkh vf/kd ik;h tkrh gS A iz{ks= losZ{k.k ds nkSjku ,df=r vkdM+ksa dsvk/kkj ij bl chekjh dk izdksi 61-67 fnuksa i'pkr vf/kd ik;k x;kfdUrq ihyk ekstSd jksx dh rhozrk cqokbZ ds 30 ls 45 fnuksa cknvf/kd ns[kh xbZ A bl chekjh dk QSyko ,oa izdksi 29.9 0C ls 36.60C rkieku ,oa 62-75 izfr'kr ij mPpre ntZ fd;k x;k A bl jksxdk izknqZHkko igyh okj gksus ds ckn bl jksx ds QSyko dh vof/k 7 ls32 fnuksa rd lks;kchu dh fdLe ts-,l- 335 esa izHkko'khy ns[kh x;hA

References

Chenulu VV, Venkateswarlu V, Rangaraju R (1979) Studieson yellow mosaic disease of mung bean. IndianPhytopath 32: 230-5

Costa A S (1976) White fly-transmitted plant diseases, AnnRev Phytopath 10: 429-48

Dhingra KL and Chenulu VV (1985) Effect of yellow mosaicon yield and nodulation of soybean. IndianPhytopath 38(2) : 248-51

Duffs JE. (1971) Role of weeds in the incidence of virusdisease. Ann Rev of Phytopath 9: 319-40

Eraivan Arutkani A, Chandraskaran K (1998) Studies onweed host of mungbean yellow mosaic virus.

Soybean yellow mosaicinfected soybean plants

Mungbean yellow mosaic infectedmung bean field

Vector (Bemisia tabaci) of yellowmosaic virus

261

Indian J Virol 14 (2):125-126Kranz J (1974) Comparison of epidemic. Annual Rev of

Phytopath 12: 355-74Sharma SP, Yadave RK, Kaushik JC (1993) Nature and extent

of losses due to mungbean yellow mosaiv virusand its epidemiology. Haryana AgriculturalUniversity Journal of Research 23 (1):51-53

Thresh JM (1974) Temporal pattern of virus spread. AnnualRev of Phytopath 12: 11-128

Van der Plank JE (1963) Plant diseases epidemic andcontrol. New York Academic p 349

Verma AD, Basu PS, Das SS, Mukhopadhyay, S (1989) Someecological consideration of white fly transmitted virusdisease of vegetables in West Bengal. Indian JVirol 5(1-2): 79-89

(Manuscript Receivd : 29-03-2015; Accepted : 15-07-2015)

262