the j. res. pjtsau vol. xlvi no. 4 pp 1- 73, oct.-dec., 2018

The J Res PJTSAU Vol XLVI No 4 pp 1- 73 Oct-Dec 2018

Th

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Res P

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Extension Education Institute

Controller of Examinations

RESEARCH EDITOR

Smt M PallaviAIampCC and PJTSAU PressRajendranagar Hyderabad

Professor amp Head

DDrsquos may be drawn in favour of Principal Agricultural Information Officer and sent to Agricultural Information amp

Communication Centreand PJTSAU Press ARI Campus Rajendranagar Hyderabad - 500 030

Dr S Sudheer Kumar

Dr KVS Meena Kumari

Dr R Jagadeeshwar

Principal Scientist amp HeadRice Research Centre ARI Rajendranagar

Dean of Agril Engineering amp Technologyand Home Science

PJTSAU Rajendranagar Hyderabad

Project Director

ic

CONTENTS

PART I REVIEW ARTICLE

Indian Seed Sector - Challenges and Opportunities 1M V NAGESH KUMAR Y BHARATHI and T PRADEEP

PART II PLANT SCIENCESEffect of Alternate Wetting and Drying Irrigation on Yield and Quality of Rabi Rice 13(Oryza sativa L) under varied Nitrogen levelsK SRIDHAR A SRINIVAS K AVIL KUMAR T RAMPRAKASH and P RAGHUVEER RAO

A Non-destructive seed Sampling Method for high throughput Genotyping in Groundnut 20DNYANESHWAR B DESHMUKH SUNIL CHAUDHARI BALRAM MARATHICH V DURGA RANI HARI KISHAN SUDINI MURALI T VARIATH SURENDRA S MANOHARand JANILA PASUPULETI

Yield and Yield Attributes of Rice (Oryza sativa L) as influenced by Foliar Application of 28Zinc Oxide Nanoparticles under different crop establishment MethodsBRIJBHOOSHAN A SRINIVAS R MAHENDRA KUMAR T RAM PRAKASH K AVIL KUMART N K V K PRASAD and S NARENDER REDDY

Effect of Integrated Nutrient Management Practices on Yield Attributes and Yield of Baby 36Corn in Baby Corn (Zea mays L) - Hyacinth Bean (Lablab purpureus Var typicus)Cropping SystemR PREETHAM K AVIL KUMAR A SRINIVAS A MANOHAR RAO and T RAM PRAKASH

PART III SOCIAL SCIENCEDeterminants of Key Entrepreneurial Characteristics A Study of Entrepreneurial Attitude of 44Agricultural Students of Telangana StateA MEENA D UTTEJ and E SREE CHARAN

PART IV VETERINARY SCIENCEPatterns of Chicken Meat and Egg Consumption in Rural House Holds of Jagitial District 49of Telangana StateSRINIVAS GURRAM V CHINNI PREETAM and SWATHI BORA

PART V HOME SCIENCE

In Vitro Protein Digestibility of Millet Based South Indian Breakfast Preparations 52JANE BRIDGET KANDEH UMA DEVI K K UMA MAHESWARI T SARAH KAMALAand V DURGA RANI

Green Synthesis of Nanoparticles from Plant Sources for Textile Application 59PUSHPALATHA KYATHAM and A PADMA

PART VI RESEARCH NOTES

Correlation and Path Coefficient analysis for Grain Yield in Aerobic rice (Oryza sativa l) 64GenotypesA SRIJAN KULDEEP SINGH DANGI P SENGUTTUVEL R M SUNDARAMD SRINIVASA CHARY and S SUDHEER KUMAR

Selection criterion based on trait linkages in African and Asian Pearl millet 69[Pennisetum glaucum (l) r Br] populations to enhance productivityK SUDARSHAN PATIL S K GUPTA KULDEEP SINGH DANGI D SHASHIBHUSHANM BALRAM and T RAMESH

2222

1

INDIAN SEED SECTOR - CHALLENGES AND OPPORTUNITIESM V NAGESH KUMAR Y BHARATHI and T PRADEEP

Seed Research and Technology CentreProfessor Jayashankar Telangana State Agricultural University

Rajendranagar Hyderabad - 500 030

Review ArticleThe J Res PJTSAU 46(4)1-12 2018

Date of Receipt 05-12-2018 Date of Acceptance 22-12-2018

ABSTRACT

Seed is a bridge of hope between present and future and is inevitable for lifersquos continuity India has a vibrant seedmarket Over the years the seed industry has evolved side by side with Indian agriculture From the practice of saving seeds fromthe previous crop Indian farmers have come a long way Today the Indian seed industry is the fifth largest seed market in theworld accounting for 44 of global seed market In terms of global trade India is almost self-sufficient in flower fruits andvegetables and field crops seeds Overall paddy maize and vegetables are expected to drive the growth of Indian hybrid seedindustry in the next five years The governmentrsquos policy support has also gone a long way in supporting the seed industryrsquosevolution and development From the Seedrsquos Act of 1966 to the Seedrsquos Bill 2004 Indiarsquos seed policy has been suggestive of Indiarsquosevolving needs and market dynamics Conducive policy reforms and government support has spurred the transformation of thisindustry in recent times Private seed sector has played a major role in changing Indian seed sector Investment and technicalexpertise garnered from different parts of the world has made this fete possible This review emphasizes on evolving of Indianseed sector its growth future challenges and expansion to export market

Key words Seed sector seed replacement rates seed policies public and private seed industry

Seeds in fact were responsible for manyrevolutions and transformations that our country hadwitnessed The famedrsquo Green Revolutionrsquo that tookIndiarsquos agriculture to new heights was also based uponthe seeds of High yielding varieties of wheat thatproduced good yields Similarly lsquoBt cottonrsquotransformed Indiarsquos cotton economics Invariably goodseeds produced good results But arriving upon a goodseed is a herculean task It involves years of researchand standardization procedures before it reaches thefarmers Today most of the farming in India dependsupon these seeds that are released either by researchstations or private companies These seeds havebecome the staple of Indian agriculture Seeds forma crucial and unavoidable component of agricultureThey convert the resources and labour into fruitfulharvest In fact the response of all other inputsdepends on the seed itself Studies have pointed thatquality seeds can alone contribute to about 15-20to the total production depending upon the crop andit can be further raised up to 45 with efficientmanagement of other inputs It is the fact that theincrease in availability of quality seed has a hugebearing on food grain production (Chauhan et al2013)

The growth of seed industry has beencommensurate with that of Indian agriculture At each

step the Indian agriculture was amply supported bythe seed sector Sixties were particularly a crucial timein Indian seed sector and several significant andimpactful events occurred during this time In 1963National Seed Corporation (NSC) was establishedThe objective was to undertake production of foundationand certified seeds Now it has grown into a SchedulelsquoBrsquo-Miniratna Category-I company wholly owned byGovernment of India under the administrative controlof Ministry of Agriculture and Farmers Welfare Atpresent NSC is undertaking production of certifiedseeds of nearly 600 varieties of 60 crops through itsregistered seed growers There are about 8000registered seed growers all over the country who areundertaking the seed production programmes in differentagro-climatic conditions (National Seed CorporationLimited wwwindiaseedscom 15012015) TheGovernment of India enacted the Seeds Act in 1966to regulate the growing seed industry The Seeds Actstipulated that seeds should conform to a minimumstipulated level of physical and genetic purity andassured percentage germination either by compulsorylabelling or voluntary certification Further the Actprovided a system for seed quality control throughindependent State Seed Certification Agencies whichwere placed under the control of State Departments ofAgriculture (Agriculture today 2016)

emailsrtcpjtsaugmailcom

2

The eighties were also impressive for seedsector as it witnessed two more important policydevelopments for the seed industry viz granting ofpermission to MRTP (Monopolies and Restrictive TradePractices)FERA(Foreign Exchange Regulation Act)companies for investment in the seed sector in 1987and the introduction of lsquoNew Policyrsquo on seeddevelopment in 1988 Besides this the time sawlaunching of the World Bank aided National SeedsProgramme (1975-85) in three phases leading to thecreation of State Seeds Corporations State SeedCerti f ication Agencies State Seed Test ingLaboratories Breeder Seed Programmes etc SeedControl Order (1983) Creation of the TechnologyMission on Oilseeds amp Pulses (TMOP) in 1986 nowcalled The Integrated Scheme of Oilseeds PulsesOil Palm and Maize (ISOPOM) Production andDistribution Subsidy Distribution of Seed Mini-kitsand Seed Transport Subsidy Scheme were alsoenacted during the same period Seed sector garneredfurther support in the beginning of nineties Under the1991 Industrial Policy seed production was identifiedas a lsquohigh priority industryrsquo In line with Indiarsquos largerliberalization and privatization policies during thesame period the new policy on Seed Developmentopened the doors for import of vegetable and flowerseeds in general and seeds of other commodities in arestricted manner and also encouraged multinationalseed companies to enter the seed business As aresult more than 24 companies initiated research anddevelopment activities Further to strengthen the seedsector and to address certain unattended areas inSeeds control order National Seed Policy 2002 wasinitiated The aim was to provide intellectual propertyprotection to new varieties usher this sector intoplanned development protect the interest of farmersand encourage conservation of agro-biodiversity This

national seed policy 2002 had 10 thrust areas - VarietalDevelopment and Plant Varieties Protection SeedProduction Quality Assurance Seed Distribution andMarketing Infrastructure facilities Transgenic PlantVarieties Import of seeds and planting materialsExport of seeds Promotion of Domestic Seed andStrengthening of monitoring system Under this policythe Protection of Plant Varieties amp Farmersrsquo RightsAuthority (PPVampFRA) was established to undertakeregistration of extant and new plant varieties ANat ional Gene Fund was also created forimplementation of the benefit sharing arrangement andpayment of compensation to village communities fortheir contribution to the development and conservationof plant genetic resources and also to promoteconservation and sustainable use of genetic resourcesThe National Seeds Board (NSB) was established inplace of existing Central Seed Committee and CentralSeed Certification Board which was entrusted with theresponsibility of executing and implementing theprovisions of the Seeds Act and advising theGovernment on all matters relating to seed planningand development The Seeds Bill 2004 seeks toregulate the production distribution and sale of seedsIt requires every seller of seeds (including farmers) tomeet certain minimum standards The StandingCommittee has recommended seed sales andexchange by farmers to exempt from this requirementThe Bill has been pending since December 2004(Agriculture today 2016)

Indian seed sector- Public and PrivateThe growth of seed industry can be considered

parallel to the growth of Indiarsquos agriculture productionToday the Indian seed industry is the fifth largest seedmarket in the world accounting for 44 of global seedmarket after the US (27) China (20) France (8)and Brazil (6) (Fig 1)

NAGESH KUMAR et al

Fig1 Market size-USD Bn

12

9

362625 2 155 117 075 071 0585 055 05

141210

86420

china

USA

Fran

ce

Brazil

India

Japa

nGer

many

Arge

ntin

a

Italy

Nethe

rland

s

Can

ada

Russia

3

In terms of global trade India is almost self-sufficient in flower fruits and vegetables and field cropsseeds According to Ken Researchrsquos report ldquoIndianSeed Industry Outlook to FYrsquo2018 - RapidHybridization in Vegetables Corn and Rice to ImpelGrowthrdquo the hybrid seed market has grown at astupendous CAGR of 361 per cent over the periodFYrsquo2007-FYrsquo2013 The contribution of varietal seedsto the overall commercial seed market in India haswitnessed a steep decline from 72 per cent in FYrsquo2007to 368 per cent in FYrsquo2013 The Indian seed marketis majorly contributed by non-vegetable seeds such

Fig2 Indian seed market size by value

as corn cotton paddy wheat sorghum sunflowerand millets In FYrsquo2013 the non-vegetable seedsaccounted for 822 per cent of the overall seed marketin India Non-vegetable seed market in India is largelyconcentrated in cotton contributing the largest shareof 408 per cent Overall paddy maize and vegetablesare expected to drive the growth of Indian hybrid seedindustry in the next five years It is expected thatbetter rice hybrids will be developed to give a yieldadvantage of at least 3-4 tonnes per hectare over theresearch varieties (Fig 2 amp3)

Corn 11Rice 6

Others 40Cotton 28

Fruits amp Veg-etables 14

Fig 3 Indian seed market by volume

INDIAN SEED SECTOR - CHALLENGES AND OPPORTUNITIES

Total Market 32 MT

Cotton 07Others 86

Wheat 312

Paddy 259

Soybean 106

Groundnut 87

Gram 58

Potato 55

Maize 55

Total Market INR 132 bn

4

With the arrival of newer forms of seedvarieties the traditional open pollinated varieties havetaken a back seat In 2010 they valued at 019 billionUSD which was around 12 per cent of total marketvalue The open pollinated seeds which are savedover years for their desirable trait has a varying SeedReplacement Rate (SRR) of 20-80 per cent(Agriculture today 2015) (Fig4)

Fig 4 Seed Replacement Rate of major crops in India

Seed Sector Seed Replacement Rate of MajorCrops () India 2006-2017

According to the report the Indian seedsmarket reached a value of US$ 36 Billion in 2017exhibiting a CAGR of around 17 during 2010-2017and is further expected to grow at a CAGR of 143during 2018-2023 reaching a value of more than US$8 Billion by 2023The Indian seed market haswitnessed a major restructuring as a result of the

implementation of some progressive policies by thegovernment Seed Development 1988 and NationalSeed Policy 2002 have helped in strengthening theIndian seed industry in the areas of RampD productdevelopment supply chain management and qualityassurance Owing to this India has emerged as thefifth largest seed market across the globe Moreoverthe active participation of both public and private

sectors has also played a vital role in laying a strongfoundation of the industry This includes launching ofinitiatives to promote the use of hybrid seeds amongthe farmers who had earlier used outmoded openpollinated varieties Some other growth-inducingforces such as growth in income levelscommercialization of agriculture patent protectionsystems and intellectual rights over plant varietieshave given a great push to the market (Agriculturetoday 2015) (Fig 5)

Fig 5 Indian Seed Industry growth over years (Value in USD Bn)

NAGESH KUMAR et al

Wheat Paddy Maize Groundnut Chickpea Ragi PigeonPea

Blackgram

Mustard Soybean Cotton0

20

40

60

80

100

See

d R

epla

cem

ent

Rat

e (

)

2006-07 2011-12 2016-17

3

25

2

15

1

05

0

013

05

17

27

1990 2000 2010 2013

5

At present only government agencies and fewsmall regional players are involved in the multiplicationand distribution of traditional and improved varietiesof crops Hybrid seeds on the other hand now occupythe highest share in terms of total market value Infact this is a promising sector for the private playersas this section of seed needs cent per cent SRRSyngenta Dupont Mahyco J K seeds BioseedRasi and Bayer are some of the players in thisbusiness The most controversial category of seedsis the one which are genetically modified and morecommonly referred to as GM seeds This category ofseeds were seen capturing a good share of marketswith heavy investments in RampD Unfortunately theunsteady policy measures and wavering governmentstand has dampened the spirits of this sectorMonsanto Dupont Syngenta Rasi and Pioneer aresome of the companies working on this line of seedsDespite the strong statistics in support of thecommercial seed sector Indian seed market isstrongly dominated by the farm saved seeds Traditionof saving seeds economic feasibility and ease ofusing the saved seeds have till now made them themost favoured category of seed varieties among theIndian farmers Around 75 per cent of seed used inthe country falls in this category Public sector has astrong representation in the countryrsquos seed marketThis includes 99 ICAR research institutes 65Agricultural Universities (SAUs amp DUs) 15 State SeedCorporation (SSC) National Seed Corporation (NSC)and State Farms Corporation of India (SFCI) Indianseed programme includes the participation of Centraland State governments Indian Council of AgriculturalResearch (ICAR) State Agricultural Universities (SAU)system and institutions in public private andcooperative seed sectors in India consists of twonational level corporations ie National SeedsCorporation (NSC) and State Farms Corporation ofIndia (SFCI) 15 State Seed Corporations (SSCs) andabout 100 major seed companies For quality controland certification there are 22 State Seed CertificationAgencies (SSCAs) and 104 state Seed TestingLaboratories (SSTLs) (Chauhan et al 2014b Dattaet al 2013 National Seed Corporation Limitedwwwindiaseedscom 15062015) Since latenineties the private sector has started to play asignificant role in the production and distribution ofseeds However the organized seed sector

particularly for food crops (cereals) continues to bedominated by the public sector The private sectorwhich in India mostly cashes in on high value lowvolume crop seeds such as vegetables (Hanchinal2012) Their research focus is on Pest amp diseaseresistance and most recently into developing varietiesthat can resist climatic extremities The private sectoris represented by 500 small amp medium players and50 large national amp Multinational players (Agrawal2012a) Among the strong contenders for growthhybrid seed sector is stealing the show The Indianhybrid seed market with over 300 companies hasbeen growing at 15-20 per cent annually over the pastseveral years and is projected to reach around Rs18000 crore by 2018 (Agriculture today 2015)

Indian Seed Industry compared to globalscenario

The seed industry market size is more thandoubled when compared between 2009-10 and 2014-15 Within a period of five years the market has grownfrom Rs 6000 Crores to Rs 13500 Crores Coupledwith increasing domestic demand and demand forquality seeds in various foreign countries mainly theSouth East Asian countries seed industry in India iswitnessing new paradigms of growth and developmentWhen compared to the global seed production Indiaacutesshare is very less India is way behind countries likeUSA and China in terms of total seed market size In2014 the total seed market size of USA was almostUSD 12 billion while that of China was USD 10 billionWhen compared to just these two countries Indiarsquostotal seeds market is currently very low atapproximately USD 22 billion According to a marketresearch report recently published by Sandlerresearchorg the Global Seeds Market size isexpected to grow at a cumulative annual growth rateof about 11 between 2015 and 2019 (AgricultureToday 2015) The growth during this period isexpected to be driven to a major extent by theincreased adoption of genetically modified seedsVegetable seed constitutes a major segment of thetotal seed market Currently the vegetable seedindustry is estimated to be around USD 350 millionin 2014 It is expected to grow at a cumulated averagegrowth rate of 146There is a steady demand forvegetable seeds in various foreign countries India isthe ninth major exporter of fruit and vegetable seeds


6

in the world there by earning good foreign exchangereserves Vegetable seeds consist of seeds forvegetables such as cauliflower tomato brinjalspinach lettuce watermelon onion pepperwatermelon carrot and others As a whole increaseddemand for food production all over the world and arise in various agricultural challenges has to a largeextent driven the way for the adoption of high-qualityhigh-performing genetically engineered and hybridseeds To meet this demand various seed companiesglobally have increased their investment in RampD toimprove the quality of seeds The export of seedsreceived a major boost when 38 varieties from Indiawere registered with the Organization for EconomicCooperation and Development (OECD) This is a groupof 34 countries which guarantees the quality of seedsthat can be imported by countries participating in theOECD Seed Schemes About 57 nations areregistered in such seed schemes (Agriculture Today2015)

Future Outlook of Indian Seed Industry

The seed industry in India has to align withthe changing food consumption scenario of thecountry There has been a decline in the proportion ofexpenditure on food items in last three decades Thechange has occurred in both urban and rural areasBetween 1990 and 2010 consumption of cereals andpulses has decreased considerably whileconsumption of fruits and vegetables has almostdoubled The future seed market of the country isexpected to witness more adoption of hybrid seedsThe country is going to witness an increase in thecultivation of vegetables and fruits driven by thegrowth and development of the food processing sectoralong with changing consumption habits of the peopleThe export sector is also set to witness growth Oneof the most important developments in future will bethe increased demand for quality vegetable and fruit

products because of the increased consumerawareness and increase in disposable income of thepopulation As a result of this trend protectedcultivation will increase in India The current seedindustry might also need to develop new products inorder to attune with the greater mechanization ofagriculture sector in India in the near future vastlydue to labor challenges One more expected changethat the seed industry is going to witness is in theform of an increased demand for fruits and vegetablevarieties with higher nutritional values Life stylerelated health problems are increasing in the countrywith diseases like obesity diabetes malnutrition etcon the rise and hence people are becoming morehealth conscious which will propel the use of fruitsand vegetables with more nutri tional values(Agriculture Today 2015)

Challenges for seed industry

On global front population will breach 9billion mark by 2050 with India leading the marchie each farmer must feed more people with decliningland and water resources On the verge of climatechange seed research is the key to unlock thepotential of technologies In the Indian context froma long period of time seed requirement of farmers ismainly met by farm saved seed ie still 65 of thefarmers are using their own saved seed or seeddistributed among them (Vision 2050 Directorate ofseed Research) Making the quality seed availableat right time is greater challenge rather than productionper se Seed being the principal input in determiningproductivity seed replacement should be given utmostpriority Amelioration of skewed SRR ie thepercentage of area sown out of total cropped area byusing certifiedquality seeds other than farm savedseed is the major challenge (Table 1)

Table 1 Seed Replacement Rate in different crops and its variation across states

Crop National Highest SRR () Lowest SRR ()AverageSRR ()

Paddy 375 820 Andhra Pradesh 90 Uttarakhand

Wheat 326 420 Maharashtra 110 Jammu amp Kashmir

Maize 541 1000 Karnataka 50 Odisha

Jowar 259 650 Andhra Pradesh 110 Tamil Nadu

Bajra 614 1000 Gujarat 290 Karnataka

NAGESH KUMAR et al

7


Bengal gram 184 780 Andhra Pradesh 40 Rajasthan

Blackgram 292 770 Haryana 30 Chattisgarh

Greengram 267 940 Uttar Pradesh 10 Odisha

Redgram 175 550 Andhra Pradesh 20 Odisha

Groundnut 245 500 Andhra Pradesh 05 Madhya Pradesh

Mustard 636 780 Rajasthan 130 Odisha

Soybean 359 1000 Andhra Pradesh 110 Rajasthan

Sunflower 612 1000 Andhra Pradesh 80 Madhya Pradesh

Cotton 104 1000 Andhra Pradesh 20 Odisha

Source Directorate of Economics amp Statistics Ministry of Agriculture GOI 2012 (httpdacnetnicineands)

Increasing Seed Replacement Rates in CriticalCrops

The growth of the seed industry in anycountry is dependent on how often farmers arereplacing their seeds The seed replacement ratesfor almost all the crops in India have considerablyimproved in the recent times Over a ten-year periodfrom 2002 to 2012 the seed replacement rates ofkey cereal crops have more than doubled with thatof rice experiencing a 111 jump wheat increasingby 154 and maize 238 Increasing seedreplacement rates are a result of increased farmerextension and marketing activ it ies of seedcompanies as well as the general perception amongfarmers about yield improvements that can beachieved through replacement of seeds every year

Even though there is slight improvementrecently still a long way ahead in making the qualityseed available at farmersrsquo doorsteps and for achieving100 SRR which will herald the growth driven byquality seed in agriculture sector Another majorproblem of seed chain is Varietal Replacement Rate(VRR) Even though with more than 4500 varietiesnotified and available indents for basic seed and itsfurther multiplication is restricted to a few varietiesMany improved varieties which are location specificresistant to biotic and abiotic stresses never sawthe light of the day may be due to below parextension activities Hence special focus need to begiven on improvement of VRR which certainly pavesthe way for improved productivity levels manifestedin the form of increased production Regarding seed

Fig6 Global Seed trade

0

2

4

6

8

10

12

14

16

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

USD Bn


8

Table 2 Quality seed requirement of major crops in the country (Assuming 100 SRR)

Crops Gross Seed Certified seed SMR Foundation Breedercultivated area Rate Requirement seed require- seed require-

(million ha) (kgha) (000 tonnes) ment ment(tonnes)(000 tonnes)

Rice 456 50 22800 100 228 2280

Wheat 272 125 34000 20 1700 85000

Sorghum 77 15 1155 160 07 45

Pearl millet 87 5 435 200 02 11

Maize 80 20 1600 80 20 250

Pigeon pea 34 15 510 100 05 51

Chickpea 82 80 6560 10 656 65600

Groundnut 62 100 6200 8 97 12109

RampM 63 5 315 200 02 08

Soybean 89 75 6675 16 417 26074

Sunflower 19 10 190 50 04 76

Cotton 95 12 1140 50 23 456

Jute 08 5 40 100 004 04

Total 1424 81620 3161 191964

technological research emphasis has to be givenon basic research relevant to floral biology pollinationseed development and maturation studies in a bid toattain better seed recovery Refinement of seedproduction technology in varied crops must be giventhe highest priority for enhanced profitability in seedproduction Globally the contemplated research workrelevant to fields of seed testing quality assuranceand seed quality enhancement is at par excellence(Table 2)

Groundnut foundation seed requirement is calculated on basis of two stage cs production

In order to match global requirement and to strengthenIndiarsquos position in international market there is needfor cutting edge infrastructure and world classlaboratories accredited to international seed agenciesinstitutes Collaborative research projects inpartnership with private seed sector to deliver desiredoutputs that benefit farming community of the countryshould be formulated (Vision 2050 2015)

New Opportunities for seed sectorThe nature gifted biodiversity and the diversity

in agro-climatic conditions offer exciting opportunitiesfor India to be globally competitive in producing andpromoting a number of seed crops for export We must

be proactive to explore export potential and createenabling environment This can be achieved througha well planned strategy and targeted implementationplan Future growth drivers of seed industry could behybrids in crops viz paddy maize mustard pigeonpeafollowed by vegetables and flowers India is a majorplayer in the global paddy market with about 430 millionha cropped area Although having largest acreageunder paddy cultivation productivity is stagnant atabout 35 tha Hybrid rice could be a game changer

for India as only 41 of area is under hybrid riceTotal paddy seed turnover is Rs 218 billion in 2011 Itcould be increased to Rs 42 billion over the next fouryears if 50 of the cultivated area is covered byimproved varieties and 10 by hybrids (ISTA News2012) Over the past few years world demand formaize is increasing due to its alternative usages likebiodiesel production Globally India ranks 4th inproduction as well as acreage with average productivityof 25 tha In future global warming is likely to havedetrimental effect on wheat and rice cultivation undersuch situation maize a C4 Plant may play decisiverole in mitigating harmful impact of climate change

NAGESH KUMAR et al

9

Therefore focus on quality seeds of hybrid maize willlargely contribute to growth of Indian seed market innear future Technological breakthroughs in relevantfields of seed science and technology are quitecommendable both at national as well as internationaldomain Seed testing and quality assurance is onearea where lots of policy infrastructure and humanresource support is needed Establishing world classfacilities for seed testing certification and to matchrequisites of international conventions viz UPOV ISTAetc so that seed from India can carve its niche ininternational trade Even though India is fifth in positionin terms of value share there is ample scope to amendthe figure mentioned and for excelling in internationalseed trade India with diverse agro-climatic zonesunlimited options for crops and probably with largestresearch man power has an enormous opportunity tostamp authority on world seed front Expanding theknow-how of conventional seed science andtechnology viz floral biology pollination seeddevelopment maturation and seed productiontechnology per se can create formidable opportunitiesin maximizing the productivity levels thereby food andsocio-economic security in the country Regarding seedquality enhancement whole spectrums of opportunitiesare awaiting Second and third generation seed qualityenhancement strategies will play a vital role in givingan altogether new dimension to seed Seed designingwill emerge as a futuristic technology where seedsare fabricated with all necessary additives that giveadequate planting value across diverse agro-climaticzones by insulating them against biotic and abioticstresses A new era of seed designing is going to beunraveled in near future where seeds are bought orsold on number basis making it as the most preciousinput of cropping system (Vision 2050 2015)

Opportunities under Seed Quality AugmentationDeployment of intelimer additives for coating

pelleting of seed Utilization of advanced polymersystems (thermohydro) for seed designing Smartdelivery systems (nanotechnology) for controlled releaseof analytes State of art disinfection techniques (thermoplasma treatments) for seed protection Deploymentof OMICS technologies in seed quality improvementDevelopment of Image Analysis Systems for seed ampseedlings quality assurance are a few techniques worthmentioning

Prospects of seed export

Indiarsquos share in global seed exports is about06 (ISF 2012) To give a boost to seed exportIndia decided to participate in OECD Seed Schemesin five categories viz Grasses and legumesCrucifers and other oil or fibre species CerealsMaize sorghum and Vegetables OECD SeedSchemes is one of the international frameworksavailable for certification of agricultural seeds movingin international trade Its objective is to encourageuse of seeds of consistently high quality inparticipating countries Complying to internationallyacceptable procedures of seed quality assuranceshall certainly boost our seed exports and enableIndia a force to reckon with in global seed realm (Vision2050 2015)

Opportunities for production of high quality seed

Seed Production Research

Future of agricultural production will largelydepend upon development of improved varietieshybrids in various crops supported by efficient costeffective seed production technologies Diversificationof areas for seed production and development ofappropriate seed production technology needs to befocused for expansion of seed production system inthe country Identification of alternativespecific areasfor quality seed production and mapping of diseasefree seed production zones may go a long way inpopularizing seed production technologies in non-traditional areas

Climate Resilient Seed Production

The reproduction success in plants isdetermined largely by the environmental conditionsprevailing during the growing season Among thevarious environmental factors moisture and temperaturehave direct influence on reproduction Early reproductiveprocesses like pollen viability stigma receptivityanthesis pollination fertilization and early embryodevelopment are all highly prone to moisture and ortemperature stresses Failure of any of these processesincreases early embryo abortion leading to poor seedset thus limiting the seed yield The physiologicalmechanisms of reproductive failure under stress arenot well understood Hence considerable efforts shouldbe made to study the effect of climate change on seedproduction of various crops to develop suitable crop


10

management technologies and mitigate the adverseeffects on the reproductive phase (Vision 2050 2015)

Development of crop and location specific organicseed production technologies and harmonizationof organic seed standards

Organic agriculture for sure shall carve itsjustified niche in future hence gearing up to the needsof development of crop and location specific organicseed production technologies deserve high prioritySince seed is starting point of production systemsorganic seed (production technology field and seedstandards) is inevitable for location specific producercommunity based organic agriculture

Application of GISGPS Remote Sensing forhigher seed productivity

GPSGIS applications includes guidance ofequipment viz micro irrigation facilities fertilizerpesticides applicators and tillage implementsmapping of pests and diseases to reduce excessoverlaps and skips and enable towards precision inseed production Quality seed production basicallyis the enterprise to make available the seeds ofhighest genetic purity and also possess qualityattributes such as high germinability vigour andfreedom from insect pests and diseases There aredifferent strategies well in place to ensure the highestgenetic integrity

Proprietary Seed Production technology (SPT)

Technology involves using a geneticallymodified (GM) line to propagate a male sterile linewhich is then used as one of the parents to producehybrid seed The genetic modification is not inheritedby the hybrid The principle of SPT could be appliedto other crops particularly cereals (wheat and rice)some of the pulses and oilseeds where there is aneed for better hybrid systems and where alternativemale sterility systems are yet to be developed

Setting up of effective standard operatingframework to tackle novel technologies vizGenetic use restriction technology (GURT)

GURT is the name given to proposedmethods for restricting the use of genetically modifiedplants by causing second generation seeds to besterile There are conceptually two types ofGURT Variety-level Genetic Use Restriction

Technologies (V-GURTs) This type of GURT producessterile seeds so the seed from this crop could notbe used for further propagation but only for sale asfood or fodder If technology ever gets approval thiswould have an immediate impact on the large numberof indigenous farmers who use their farm saved seedsand instead they would be forced to buy seeds fromseed production companies Consequentiallyresistance to the introduction of GURT technologyinto developing countries is strong bull T-GURT A secondtype of GURT modifies a crop in such a way that thegenetic enhancement engineered into the crop doesnot function until the crop plant is treated with achemical that is sold by the developer With thistechnology farmers can save seeds for use each yearHowever they do not get to use the enhanced trait inthe crop unless they purchase the activator compoundThe technology is restricted at the trait level hencethe term T-GURT

Seed Quality AssuranceVarietal Maintenanceand Testing

Innovation has different meaning to scientificcommunity industry and farmers As far as the farmeris concerned all the scientific innovations would beof little value unless he gets refined end product(seed) which is genetically pure (true to type) andposses other desired quali ties namely highgermination and vigour sound health etc Differentseed testing protocols currently used in India needto be upgraded on the lines of international standardsof seed testing such as ISTA AOSA and OECD forbetter seed quality assurance and easy access tointernational seed trade Use of biochemical andmolecular markers including electrophoresis ofproteins isoenzymes and DNA fingerprinting involvingfirst and second generation markers for establishingthe distinctiveness of varieties may supplement GrowOut Test in genetic purity testing Particular attentionneeds to be paid on distinguishing closely relatedand essentially derived varieties (EDVs) Further focusshould be on development of user friendly moleculardetection kits for fast and accurate identification ofvarieties hybrids pathogens and GMOs Withincreasing biotechnological intervention in differentcrops and development of GM crops research oncertification standards isolation distance from nonGM crops and cost effective kits for detection of

NAGESH KUMAR et al

11

transgenes by using micro array chips and proteomicapproaches needs to be carried out

Seed Biotechnology

Genomics should be undertaken to discovergenes governing dormancy germination and longevityand stress tolerant genes to produce superior qualityseeds Transcriptomics of seed development to unzipmolecular regulation of improved seed characters isan area where immense potential lies for qualityimprovement Development of national data base forDNA profiles of crop varieties Development of DNAbar coding system for tracking the breeder seedproduction and supply system Validationup-gradation of field and seed standardsprotocolsisolation distance sample size physical purity andODV Standardization of minimum weed seedstandards and development of interactive software forweed seed identification Developing mechanisms foruniformity in seed testing and reporting amp facilitationin establishment of ISTA accredited laboratories Highperformance phenotyping and second generationimaging technologies (Vision 2050 2015) are fewareas which can address quality related aspects

Seed Quality Enhancement

Intellicoat Technology

Polymer-based technology used forcontrolling the time of seed germination through seedcoating It is based on the Intelimer polymers whichdiffer from other polymers in that they can becustomized to abruptly change their physicalcharacteristics when heated or cooled through a pre-set temperature switch By coating the seeds withIntelimer polymers that have required pre-settemperature switch mechanism the time ofgermination of the coated seeds can be adjusted andthe synchronization problem of parental lines in hybridseed production could be prevailed over Besides thistechnology also helps in relay cropping system

Cold Plasma Coating

Seeds could be coated with different(hydrophobichydrophilic) gaseous polymers underhigh energy and low temperature Under suchconditions the gases attain the plasma state and getcoated on the seed surface Application of this

technology has been shown to control the speed ofgermination

Molecular Impulse Response (MIR)

It is a non-chemical energy-basedenhancement which is supposed to provide improvedtolerance to different types of abiotic stress effects oftenincreasing germination accelerating maturity and raisingyields MIR uses an extremely low energy electronshower to create a short-term rise in free radical levelsinside the cells of seed This causes the cells naturaldefense to produce more anti-oxidants which disablethe free radicals and leave the cell with less free radiclesand more anti-oxidants than before the process began

Bioprospecting

Application of biological agents to crop seedshave focused on root colonizing bacteria termedrhizobacteria PGPR (Plant Growth PromotingRhizobacteria) comprise those rhizobacteria that includebeneficial effects on plants during colonization Benefitsof PGPR include promotion of plant growth andbiological control of plant diseases In addition tocausing yield increase often induce early seasongrowth promotion that can be manifested in variousforms including enhanced seedling emergenceincreased biomass of roots andor foliage and earlierflowering

Nanotechnology for Seed Quality Enhancement

Seed treatment with carbon nano tubes(CNTs) array of nano particles (goldsilverborates) isa whole new field yet to be fully unraveled Applicationof Nanotechnology in seed science research is still atnascent stage and its full potential is yet to be tappedRight from designing smart delivery systems (CNTsnanofibres) loaded with nutrientsPGRspesticides forsustained release dormancy breakdown longevityenhancement vigour augmentation physiologicalprocess regulation and molecular modification thatmeans it is a research realm with infinite boundariesand shall reorient the entire concept of seedenhancement

Advances in seed quality (vigour) assessment

CF analyser Vigour (apart from germinationdesiccation tolerance and longevity) is developedduring the maturation or ripening stage chlorophyllpresence on seed has a direct link to maturity andcan be fast but precisely measured through itsfluorescence


12

Single seed oxygen consumption technologyOxygen consumption is directly related to energyproduction so this technology gives us a perfectview on the different quality aspects of seeds suchas imbibition time speed of germinationhomogeneity and energy availability during thegermination This system is thus revolutionary inseed testing for basic research and commercialoperations alike It provides fast and accuratemeasurements of the different germination aspectsof a seed lot In addition data is more robust anddefining than traditional germination tests

Under second generation imaging technologies 3Dimaging of seedlings with an array of cameras fromdifferent angles point cloud model is used to createa virtual plant model Based on model softwareclassifies seedling into different classes (egnormal abnormal)

Infrared Thermography can be used to predictwhether a quiescent seed will germinate or dieupon water uptake When a dry seed takes upwater the sugar within the seed dissolves andthis process cools the seed down For examplethe temperature of a single pea seed falls rapidlyby 2 to 3degC Viable seeds maintain cooltemperatures because they break down storagereserves into sugar Aged seeds also fail to breakdown their reserves or can only break them downafter a phase of repair delaying the thermal profileSuch thermal profiles of a seeds can be recordedand analyzed to construct a library of ldquothermalfingerprintsrdquo that allows to distinguish betweenviable and dead seeds

Seed sector has a twofold responsibility in thearea of seed health to deliver sufficiently healthyseed to farmers and seed producers and torespect international phytosanitary regulations

Development of ultramodern seed processing andstorage technologies like thermal seed processingfacilities (high precision seed protection highthroughput process)

REFERENCES

Agrawal P K 2012a Indian Seed Industry Today andits potential in next five years (In) National SeedCongress on Welfare and economic prosperityof the Indian farmers through seeds RaipurChhattisgarh 21-23 Dec 2012 p 60ndash9

Agriculture today year book 2016 Pp 202-205

Agriculture today Indiarsquos own Seed Story (2015) Pp20-31

Chauhan J S Rajendra Prasad S and Pal Satinder2013 Quality seed and productiv ityenhancement in major crops in India (In) CurrentTrends in Plant Biology Research Proceedingsof National Conference on Plant PhysiologyJAU Junagadh Gujarat 13-15 Dec 2013 pp160ndash8

Chauhan J S Rajendra Prasad S and Pal Satinder2014b An overview of seed trade and seedresearch in India Paper presented in 7thNational Seed Congress lsquoQuality Seeds forSuccessful Agriculturersquo Bhopal MadhyaPradesh 25-27 September 2014

Datta S K Chauhan J S and Rajendra Prasad S2013 Indian seed sector ndash emerging issuesPaper presented in FAI Annual Seminar-FertilizerSector at Crossroads New Delhi 11-13 Dec2013 p 104

Directorate of Economics amp Statistics Ministry ofAgriculture GOI 2012 (httpdacnetnicineands)

Directorate of Economics amp Statistics Ministry ofAgriculture GOI (http dacnetnicineands)

Future growth drivers for the Indian seed industry (2012)Seed Testing International ISTA News BulletinNo 144 Pp 4-9

Hanchinal R R 2012 An overview of developmentsin Indian seed sector and future challenges (In)National Seed Congress on Welfare andeconomic prosperity of the Indian farmers throughseeds Raipur Chhattisgarh 21-23 Dec 2012pp 1ndash 12 httpwwwworldseedorgisfseed_statisticshtml

India Seed Sector Analysis ndash Segmented by CropType - Growth Trends and Forecast (2018 -2023) Research report 2018

Nat ional Seeds Corporation Limi ted(wwwindiaseedscom) 15062015

Vision 2050 Directorate of Seed Research ICAR(2015) Pp 10-16

NAGESH KUMAR et al

13

EFFECT OF ALTERNATE WETTING AND DRYING IRRIGATION ON YIELD ANDQUALITY OF RABI RICE( Oryza sativa L) UNDER VARIED NITROGEN LEVELSK SRIDHAR A SRINIVAS K AVIL KUMAR T RAMPRAKASH and P RAGHUVEER RAO

Agricultural Research Institute Main Farm Rajendranagar Hyderabad - 500 030


ABSTRACT

A field experiment was conducted during rabi 2016-17 and 2017-18 at Agricultural Research Institute Main FarmRajendranagar Hyderabad on a clay loam soil to study the effect of alternate wetting and drying irrigation on rabi rice undervaried nitrogen levels The experiment consisted of three irrigation regimes (recommended submergence of 2-5 cm water level asper crop growth stage AWD irrigation of 5 cm when water level drops to 3 cm in water tube AWD irrigation of 5 cm when waterlevel drops to 5cm in water tube) as main plot treatments and three nitrogen levels (120 160 and 200 kg N ha-1) as sub plottreatments laid out in split plot design with three replications Significant improvement in yield and quality parameters of rice exceptprotein content amylose content was observed with recommended submergence of 2 to 5 cm water level as per crop growthstage which was on par with AW D irrigation of 5 cm when water level drops to 3 cm in water tube Among nitrogen levelsapplication of 200 kg N ha-1 resulted in significantly higher yield and quality parameters except hulling percent milling percent andhead rice recovery of rabi rice which was on par with application of 160 kg N ha-1

Rice (Oryza sativa (L)) is one of the mostimportant staple food crops in the world In Asia morethan two billion people are getting 60-70 per cent oftheir energy requirement from rice and its derivedproducts Among the rice growing countries India hasthe largest area (4350 million ha) and it is the secondlargest producer (16351 million tonnes) of rice nextto China (20314 million tonnes) with an averageproductiv ity of 376 t ha-1 though increasingmarginally but is still well below the worldrsquos averageyield of 451 t ha 1 (wwwricestatirriorg) In IndiaTelangana State is a key rice producing state with1046 lakh hectares with a production of 3047 milliontonnes (DoES 2017) A huge amount of water is usedfor the rice irrigation under the conventional watermanagement in lowland rice termed as lsquolsquocontinuousdeep flooding irrigationrsquorsquo consuming about 70 to 80per cent of the total irrigated fresh water resources inthe major part of the rice growing regions in Asiaincluding India (Bouman and Tuong 2001) Futurepredictions on water scarcity limiting agriculturalproduction have estimated that by 2025 about 15-20million ha of Asiarsquos irrigated rice fields will suffer fromwater shortage in the dry season especially sinceflood irrigated rice uses more than 45 of 90 oftotal freshwater used for agricultural purposesGenerally rice consumes about 3000-5000 litres ofwater to produce one kg of rice which is about two to

three times more than to produce one kilogram of othercereals such as wheat or maize Therefore there isneed to develop and adopt water saving methods inrice cultivation so that production and productivity levelsare elevated despite the looming water crisis

However rice is very sensitive to water stressAttempts to reduce water in rice production may resultin yield reduction and may threaten food securitySeveral water-efficient irrigation strategies had beentested advanced applied and spread in different ricegrowing regions Among these important water-saving technique is alternate wetting and drying(AWD) AWD is an irrigation technique where wateris applied to the field a number of days afterdisappearance of ponded water This means that therice fields are not kept continuously submerged butare allowed to dry intermittently during the rice growingstage The AWD irrigation aims in reducing water inputand increasing water productivity while maintaininggrain yield (Bouman and Tuong 2001) Singh et al(1996) reported that in India the AWD irrigationapproach can reduce water use by about 40ndash70per cent compared to the traditional practice ofcontinuous submergence without a significant yieldloss The water availability in Telangana is limitedduring the rabi season thereby paddy is subjected towater stress Alternate Wetting and Drying (AWD) isa suitable water saving irrigation technique

Research ArticleThe J Res PJTSAU 46(4)13-19 2018

emailsridharagrongmailcom

14

Among nutrients nitrogen is the most importantlimiting element in rice growth (Jayanthi et al 2007)Limitation of this nutrient in the growth period causesreduction of dry matter accumulation and prevents grainfilling and therefore increases the number of unfilledgrains Rice shows excellent response to nitrogenapplication but the recovery of applied nitrogen is quitelow approximately 31-40 (Cassman et al 2002)

Both water and nitrogen are most importantinputs in rice production The behaviour of soil nitrogenunder wet soil conditions of lowland rice is markedlydifferent from its behavior under dry soil conditionsUnder flooded conditions most nitrogen to be takenup by rice is in ammonium form The practice of AWDresults in periodic aerobic soil conditions stimulatingsequential nitrification and denitrification losses(Buresh and Haefele 2010) Growing rice under AWDcould consequently lead to a greater loss of appliedfertilizer and soil nitrogen compared with that undersubmergence conditions Water and nutrients mayinteract with each other to produce a coupling effectFurthermore if an interaction exists between watermanagement practice and nitrogen rate then the Ninput will have to be changed under AWD In thiscontext the present study is undertaken to studythe effect of alternate wetting and drying irrigation undervaried nitrogen levels on rabi Rice

MATERIALS AND METHODS

A field experiment was conducted atAgricultural Research Insti tute Main FarmRajendranagar Hyderabad situated in SouthernTelangana Zone of Telangana state at 17019rsquo NLatitude 78023rsquo E Longitude with an altitude of 5423m above mean sea level The soil of the experimentalfield was clay loam in texture moderately alkaline inreaction non-saline low in organic carbon contentlow in available nitrogen (N) medium in availablephosphorous (P2O5) and potassium (K2O) Theexperiment consisted of three irrigation regimes (I)[(recommended submergence of 2 to 5 cm water levelas per crop growth stage (I1) AWD irrigation of 5 cmwhen water level drops to 3cm in water tube (I2) AWDirrigation of 5cm when water level drops to 5 cm inwater tube (I3)) as main plot treatments and threenitrogen levels (N) [(120 kg N ha-1 (N1) 160 kgN ha-1 (N2) and 200 kg N ha-1 (N3) ] as sub plottreatments laid out in split plot design with threereplications Nitrogen was applied in the form of urea

in three equal splits viz 13rd as basal 13rd at activetillering stage and 13rd at panicle initiation stage Auniform dose of 60 kg P2O5 and 40 kg K2O ha-1 wasapplied where entire phosphorus was applied as basalin the form of single super phosphate whereaspotassium was applied in the form of muriate of potashin two equal splits viz as basal and top dressing atpanicle initiation stage The rice variety KNM-118 wastransplanted with 30 days old seedlings at a spacingof 15 cm X 15 cm 2 seedlings per hill-1 Theconventional flooding irrigation practice was followedin all the treatments till 15 days after transplantingfor proper establishment of the crop and after thatthe irrigation schedules were imposed as per thetreatment requirements with the help of field watertube The field water tube was made of plastic pipehaving 40 cm length and 15 cm in diameter so thatthe water table is easily visible The field tube alsocontains perforations of 05 cm in diameter and 2 cmapart so that water can flow readily in and out of thetube The field tube was hammered in to the soil ineach net plot such that 15 cm protrudes above thesoil surface After installation the soil from inside thefield tube was removed so that the bottom of the tubeis visible Irrigation was applied to re flood the field toa water depth of 5 cm when the water level in the fieldtube dropped to a threshold level of about 3 or 5 cmas per the treatment Irrigation was withheld 10 daysahead of harvest The size of the gross net plot sizeof 60 m times 40 m and net plot size of 54 m times 34 mwas adopted in field experiment The plants from eachnet plot were harvested separately sun driedthreshed winnowed and cleaned Thus obtainedgrain yield from each net plot area was weighed at14 moisture content and expressed as kg ha-1

Hulling percentage was determined usingSatake dehuller milling percentage with Satakepolisher head rice recovery through rice grader whilegrain protein content was determined according toAOAC (1994) and amylose content was calculatedas per method described by Juliano (1971) The weedswere managed using pre-emergence application of therecommended herbicide ie Oxadiargyl 875 g ha-1

dissolved in water and mixed with soil and broadcasteduniformly 3 days after transplanting maintaining a thinfilm of water in the field and followed by one handweeding at 35 days after transplanting The data onvarious parameters studied during the course of

SRIDHAR et al

15

investigation were statistically analyzed as suggestedby Gomez and Gomez (1984)

RESULTS AND DISCUSSION

Grain and Straw yield

Irrigation maintained at recommendedsubmergence of 2-5 cm water level as per crop growthstage (I1) registered significantly higher grain and strawyield during both the years However the grain yieldrecorded in AWD irrigation of 5 cm when water levelfalls below 3 cm from soil surface in perforated pipe(I2) was found to be statistically at par with thetreatment recommended submergence of 2-5 cmwater level as per crop growth stage (I1) and both ofthese treatments produced significantly superior grainand straw yield compared to AWD irrigation of 5 cmwhen water level falls below 5 cm from soil surface inperforated pipe(I3) This might be due to favorablevegetative growth and development as they receivedadequate and sufficient moisture at proper amountand critical stages during entire period of growth sincewater plays a vital role in the carbohydratemetabolism protein synthesis cell division cellenlargement and partitioning of photosynthates to sinkfor improved development of growth traits Waterstress soon after panicle initiation reduces the numberof spikelet primordia that develop Hence plants wereunable to extract more water and nutrients fromdeeper layers of soil under moisture deficit conditionswhich ultimately led to poor growth and yieldattributes Nitrogen levels had a profound influenceon the grain and straw yield of rice during both theyears Maximum grain and straw yield was registeredwith the application of 200 kg N ha-1 and was at parwith the application of 160 kg N ha-1 during both theyears Significantly less grain and straw yield wasobtained with the application of 120 kg N ha-1

compared to the application of 200 kg N ha-1 and 160kg N ha-1 during both the years These results are inconformity with the findings of Babu et al (2013)

Application of higher levels of nitrogen madenitrogen more available for plant uptake thus promotinggrowth such as plant height number of tillers and drymatter production Finally the adequate supply ofnitrogen according to crop needs might have resultedin to a more vigorous and extensive root system ofcrop leading to increased vegetative growth and

means of more efficient sink formation and greater sinksize greater carbohydrate translocation from vegetativeplant parts to the grains ultimately reflected in highergrain yield in 160 and 200 kg N ha-1 treatments asreported by Anil et al (2014)

Quality parameters

Irrigation maintained at recommendedsubmergence of 2-5 cm water level as per crop growthstage (I1) registered significantly higher millingpercentage head rice recovery of rice during boththe years However the milling percentage head ricerecovery of rice with AWD irrigation of 5 cm whenwater level falls below 3 cm from soil surface inperforated pipe (I2) was found to be statistically at parwith the treatment recommended submergence of2-5 cm water level as per crop growth stage (I1) andboth of these treatments were superior as comparedto AWD irrigation of 5 cm when water level falls below5 cm from soil surface in perforated pipe(I3) This mightbe due to favorable vegetative growth and developmentas they received adequate and sufficient moisture atproper amount and critical stages during entire periodof growth However hulling percentage protein andamylose content in rice were not significantlyinfluenced by irrigation regimes

Among the nitrogen levels significantly highergrain protein content and amylose contents wererecorded with application of 200 kg N ha-1 which washowever on par with 160 kg N ha-1 during both theyears Nitrogen is an integral part of proteins and itsincreased application resulted in increased proteincontent in milled rice Similar results were obtained byRay et al(2015) However nitrogen levels did notinfluence hulling and milling percentage and head ricerecovery of rice during both the years The interactioneffect of irrigation regimes and nitrogen levels on grainstraw yield and quality parameters of rice during boththe years and in pooled means was found to be nonsignificant

CONCLUSIONS

Based on the research results it can beconcluded that recommended submergence of 2-5 cmwater level as per crop growth stage (I1) recordedsignificantly higher grain and straw yields millingpercentage and head rice recovery of rice which wason par with AWD irrigation of 5 cm when water level

EFFECT OF ALTERNATE WETTING AND DRYING IRRIGATION ON YIELD

16

Tabl

e 1

Gra

in a

nd S

traw

yie

ld (k

g ha

-1) o

f ric

e as

influ

ence

d by

alte

rnat

e w

ettin

g an

d dr

ying

irrig

atio

n an

d ni

trog

en le

vels

dur

ing

rabi

201

620

17 a

nd p

oole

d m

eans

Trea

tmen

tsG

rain

yie

ld (k

g ha

-1)

Stra

w y

ield

(kg

ha-1)

2016

2017

Pool

ed20

1620

17Po

oled

Irrig

atio

n re

gim

es (I

)

I 1- 2-

5cm

sub

mer

genc

e as

per

cro

p st

age

7026

7221

7123

7936

8153

8045

I 2- AW

D ir

rigat

ion

of 5

cm

with

3 c

m fa

ll in

fiel

d69

2571

5070

3778

4480

5679

56w

ater

tube

I 3- AW

D ir

rigat

ion

of 5

cm

with

5 c

m fa

ll in

fiel

d64

1466

5265

3373

3375

4574

39w

ater

tube

SEm

plusmn54

060

455

252

852

152

4

CD

at 5

15

016

815

314

714

514

6

Nitr

ogen

lev

els

(N)

N1-1

20 k

g ha

-167

0669

2268

1476

0578

1777

11

N2-1

60 k

g ha

-168

0270

2169

1177

1079

2178

16

N3-2

00 k

g ha

-168

5770

8169

6977

9880

1679

07

SE

mplusmn

372

445

390

410

411

410

CD

at 5

81

9785

8989

89

Inte

ract

ions

NS

NS

NS

NS

NS

NS

SRIDHAR et al

17

Tabl

e 2

Hul

ling

and

mill

ing

perc

enta

ge (

) of r

ice

as in

fluen

ced

by a

ltern

ate

wet

ting

and

dryi

ng ir

rigat

ion

and

nitr

ogen

leve

ls d

urin

g ra

bi20

16 2

017

and

pool

ed m

eans

Hul

ling

()

Mill

ing

()

2016

2017

Pool

ed20

1620

17Po

oled

Irrig

atio

n re

gim

es (I

)

I 1- 2-

5cm

sub

mer

genc

e as

per

cro

p st

age

808

810

809

675

674

674

I 2- AW

D ir

rigat

ion

of 5

cm

with

3 c

m fa

ll in

fiel

d80

780

980

867

367

267

3w

ater

tube

I 3- AW

D ir

rigat

ion

of 5

cm

with

5 c

m fa

ll in

fiel

d80

580

780

667

066

866

9w

ater

tube

SEm

plusmn0

10

20

20

20

20

2

CD

at 5

N

SN

SN

S0

40

50

5

Nitr

ogen

lev

els

(N)

N1-1

20 k

g ha

-180

780

880

867

267

467

3

N2-1

60 k

g ha

-180

680

880

767

367

367

3

N3-2

00 k

g ha

-180

780

980

867

267

067

1

SE

mplusmn

01

01

01

06

02

04

CD

at 5

N

SN

SN

SN

SN

SN

S

Inte

ract

ions

( IxN

)N

SN

SN

SN

SN

SN

S


18

Tabl

e 3

Hea

d ric

e re

cove

ry (

) G

rain

pro

tein

con

tent

and

am

ylos

e (

) of r

ice

as in

fluen

ced

by a

ltern

ate

wet

ting

and

dryi

ng ir

rigat

ion

and

nitr

ogen

leve

ls d

urin

g ra

bi 2

016

201

7 an

d po

oled

mea

ns

2016

2017

Pool

ed20

1620

17Po

oled

2016

2017

Pool

ed

Hea

d ric

e re

cove

ry (

)G

rain

pro

tein

con

tent

()

Amyl

ose(

)

Irrig

atio

n re

gim

es (I

)

I 1- 2-

5cm

sub

mer

genc

e as

per

cro

p st

age

650

665

12

650

99

189

149

1623

925

024

5

I 2- AW

D ir

rigat

ion

of 5

cm

with

3 c

m fa

ll in

fiel

d63

95

638

763

91

907

904

906

238

247

242

wat

er tu

be

I 3- AW

D ir

rigat

ion

of 5

cm

with

5 c

m fa

ll in

fiel

d63

32

633

463

33

903

898

901

234

243

239

wat

er tu

be

SEm

plusmn0

200

230

210

040

050

040

440

450

44

CD

at 5

0

570

640

60N

SN

SN

SN

SN

SN

S

Nitr

ogen

lev

els

(N)

N1-1

20 k

g ha

-163

93

639

863

95

894

890

892

230

239

235

N2-1

60 k

g ha

-164

16

640

464

10

916

911

913

240

246

243

N3-2

00 k

g ha

-164

23

643

164

27

920

916

918

241

246

244

SE

mplusmn

014

016

015

004

004

004

023

019

021

CD

at 5

N

SN

SN

S0

100

090

090

750

50

63

Inte

ract

ions

( IxN

)N

SN

SN

SN

SN

SN

SN

SN

SN

S

SRIDHAR et al

19

falls below 3 cm from soil surface in perforated pipe(I2) This indicates that the water in AWD irrigationregimes can be allowed to drop to levels of 3 cm belowground level in field water tube by delaying irrigation for2 to 3 days before reflooding Among nitrogen levelsapplication of 200 kg N ha-1 recorded significantly highergrain and straw yields protein content and amylosecontents of grain which was however on par withapplication of 160 kg N ha-1

REFERENCES

Anil K Yakadri M and Jayasree G 2014 Influenceof nitrogen levels and times of application ongrowth parameters of aerobic rice InternationalJournal of Plant Animal and EnvironmentalSciences 4(3) 231-234

AOAC1994Association of Official AnalyticalChemists Official Methods of AnalysisWashington DC

Babu PVR Rao ChP Subbaiah G and Rani YA2013 Effect of different levels of nitrogen andphosphorus on growth and yield of kharif rice(Oryza sativa L) The Andhra AgriculturalJournal 60(3) 528-534

Bouman BAM and Tuong TP 2001 Field watermanagement to save water and increase itsproductivity in irrigated lowland rice AgriculturalWater Management 49 11-30

Buresh RJ and Haefele SM 2010 Changes inpaddy soils under transition to water savingand div ersif ied cropping systems

In Presented at World Congress of SoilScience Brisbane Australia 1ndash6 August 2010

Cassman KG Dobermann A and Walters DT2002 Agroecosystems nitrogen use efficiencyand nitrogen management Ambio Journal ofHuman Environment 31 132-140

DoES (Directorate of Economics and Statistics)Statistical Year Book Government ofTelangana 2017

Gomez KA and Gomez AA 1984 Statisticalprocedures for agricultural research A Wileyinter science publication John Wiley andSons New York p 680

Jayanthi T Gali SK Chimmad VP and AngadiVV 2007 Leaf colour chart based Nmanagement on yield harvest index and partialfactor productivity of rainfed rice KarnatakaJournal of Agricultural Sciences 20(2) 405-406

Juliano BO 1971 A simplified assay for milled riceamylase Cereal Science Today 16 334-330

Ray S Sarkar MAR Paul SK Islam AKMMand Yeasmin S 2015 Variation of growthyield and protein content of transplanted amanrice by three agronomic practices Agriculturaland Biological Sciences Journal 1(4) 167-176

Singh CB Aujla TS Sandhu BS and Khera KL1996 Effect of transplanting date and irrigationregime on growth yield and water use in rice(Oryza sativa) in northern India Indian Journalof Agricuture Sciences 66(3) 137-141


20

A NON-DESTRUCTIVE SEED SAMPLING METHOD FOR HIGH THROUGHPUTGENOTYPING IN GROUNDNUT

DNYANESHWAR B DESHMUKH SUNIL CHAUDHARI BALRAM MARATHI CH V DURGA RANI HARI KISHAN SUDINI MURALI T VARIATH SURENDRA S MANOHAR AND JANILA PASUPULETI

Institute of Biotechnology Professor Jayashankar Telangana State Agricultural UniversityRajendranagar Hyderabad - 500 030

emailbalumarathigmailcom


ABSTRACT

The routine isolation of plant genomic DNA uses leaf tissues for genotyping in Marker Assisted Selection (MAS) Thisrequires time and resources for planting the segregating material logistics of labeling and sampling in the field and harvesting theselected plants We describe here a simple non-destructive sampling method for genomic DNA isolation from a mature drygroundnut seed For the comparative study the leaf disc and seed chips of 46 genotypes were sent to Intertek Pvt Ltd for DNAisolation and genotyping The DNA isolation yielded a mean concentration of 168 ngmicrol and 318 ngmicrol from seed and leaf tissuesrespectively with A260A280 ratio of 19 (mean) for both the samples The isolated DNA was genotyped using the Kompetitiveallele-specific PCR assay with 10 SNPs associated with resistance to late leaf spot rust and high oleic acid The KASP assayresulted in a 97 allele calls in the DNA isolated from seed and leaf tissues The quality of DNA isolated from seed chips wasfunctionally comparable to DNA extracted from leaf disc which gave the similar results in KASP assay This method is non-destructive and useful for MAS to screen segregating population for targeted traits prior to planting which enables to minimizeresources

In groundnut the improvements for foliar fungaldisease resistance ie rust and late leaf spot (LLS)and oil quality are among the most priority traits forbreeders The availability of cost-effective and reliablemolecular marker facilitates the plant breeders to deploymarker-assisted selection (MAS) simultaneousimprovement of one or more target trait based ongenotype The earlier studies suggested that a majorquantitative trait locus (QTL) for rust and two majorQTLs for LLS resistance using the recombinant inbredline (RIL) population derived from a cross betweenTAG 24 times GPBD 4 (Khedikar et al 2010 Sujay et al2012 Kolekar et al 2016) These QTL mapping laidthe foundation for user-friendly SSR markers in marker-assisted selection (MAS) in groundnut for particularlyfoliar fungal disease resistance MAS can be used asa tool of choice when it comes to the complex traits likeLLS and rust as disease screening is seasondependent and breeding cycle often delayed to latergenerations to get reliable disease score Molecularmarkers are being widely used for identificationscreening and selection of LLS and rust resistanceQTLs in many groundnut cultivar improvementprograms viz ICGV 91114 JL 24 and TAG 24(Varshney et al 2014) TMV 2 and JL 24 (Yeri et al2014) TMV 2 (Kolekar et al 2017) SSR makers wereused to select resistance to LLS and rust to developbreeding lines that combine earliness and disease

resistance in the genetic background of TAG 24 ICGV91114 and JL 24 (Janila et al 2016a)

In the context of oil quality the higher oleicacid and lower linoleic acid content are desirable forconsumer preference and for the longer shelf life ofgroundnut kernels and its derived products Theidentification of natural mutant F435 (~80 oleic acid)(Norden et al 1987) which lacks functional fatty aciddesaturase (FAD) enzyme was breakthrough ingroundnut breeding to breed high oleic acid andreduced linoleic acid content in kernels Sequencingof these FAD alleles has led to the identification ofmutations (substitution from GC rsquo AT in the A-genome AT insertion in the B-genome) The allele-specif ic and Cleaved Amplif ied PolymorphicSequences (CAPS) markers were developeddifferentiating mutant and wild-type alleles of ahFAD2Aand ahFAD2B These markers are successfully usedin groundnut breeding programs across the globe toselect mutant ahFAD2A and ahFAD2B alleles todevelop high oleic lines (Chu et al 2011 Mienie andPretorius 2013 Xiuzhen et al 2016 Janila et al 2016bBera et al 2018) Even though gel-based SSRs allele-specific and CAPS markers are accurate and expeditemany breeding programs it holds certain limitationssuch as time-consuming tedious and laborious


21

The entire genome of groundnut progenitorsA duranensis and A ipaensis has been sequencedand analyzed (Bertioli et al 2016) This paved the wayfor development and utilization of sequence informationfor high throughput genotyping The development ofPCR array-based SNP genotyping protocols hasployed to analyze single nucleotide polymorphisms(SNP) markers quickly and accurately Additionallyimprovements in laboratory automation have increasedthe capacity of MAS enabling high-throughput samplescreening and thus save the time required to breednew cultivar (Desmae et al 2018) Recently QTLsequencing (QTL-seq) strategy was deployed to mapgenomic regions associated with resistance to LLS andrust in groundnut This study developed 26 diagnosticSNPs derived from earlier mapped QTLs on linkagegroup A03 for LLS and rust resistance (Pandey et al2017) Based on identified mutations in ahFAD2A andahFAD2B alleles the diagnostic SNPs wereconstructed at ICRISAT and shared with Intertek PvtLtd Hyderabad for initial testing and validation inKompetitive allele-specific PCR (KASP) The initialstandardization and wet-lab validation of KASP assaycome up with a diagnostic SNP marker for ahFAD2Ballele four SNPs for rust and five SNPs for LLSGroundnut breeding program at ICRISAT perated smallleaf disc (2 mm) samples for KASP assay to genotypelarge number of breeding population with 10 diagnosticSNP for high oleic acid rust and LLS resistance

The routine MAS program uses leaf tissuesfor the DNA isolation followed by genotyping on gel-based andor high throughput platforms Such MASprogram uses a relatively small number of markersacross relatively large numbers of individuals linesfamilies or populations DNA needs to be extractedfrom a large number of leaf samples for genotypingon gel-based andor high throughput platforms Theisolation of DNA from leaf tissues (Doyle and Doyle1987) is common in various crops and works well toutilize DNA for various genotyping platforms But theleaf tissue based sampling for genotyping demandslogistics to grow plants in field or glasshouse andagronomic management labeling and samplingUtilization of seed chips based sampling DNAextraction and genotyping holds the advantage ofbeing quick independent of growing season andsaves the resources and time to grow the plants Inthe present study an attempt has been made to utilizethe mature groundnut seed chip method to isolate

genomic DNA verification of seed DNA for yield andquality and its effectiveness for PCR analysis incomparison with the leaf isolation method


A total of 46 representative groundnutgenotypes (Table 1) comprising of advanced breedinglines and released cultivars were utilized in the presentstudy Thirty-nine entries represent high oleic acidlines (gt75 oleic acid content) and seven entries (SNo 40-46) represent normal oleic acid lines (lt50)(Table 1) Seeds of these genotypes were sown onexperimental plots of Alfisols at ICRISAT PatancheruIndia (at 1753 aeligN latitude and 7827 aeligE) duringthe rainy season of 2018 (July-October) Leaf tissuesobtained from three-week-old seedlings were usedfor DNA isolation The seeds samples of the same46 representative genotypes were used for seed chipDNA isolation A small piece (002 gseed) was cutfrom the distal end of the seed carefully withoutdisturbing the embryo The leaf and seed tissues wereplaced in 12 times 8-well strip tube with strip cap (MarshBiomarket USA) in a 96 well plate The DNA wasisolated using sbeadex TM (surface-coatedsuperparamagnetic beads) along with KleargeneTM

(spin columns) and steps were followed according tothe specifications of the manufacturer facility availableat Intertek Pvt Ltd Hyderabad The quality of DNAsample extracted from seed and leaf tissue wasverified by spectrophotometry using Nano Drop(Shimadzu UV160A Japan) The isolated DNA wasutilized to assay 10 SNPs (one SNP for ahFAD2Bfour SNPs for LLS and five SNPs for rust) usingKASPTM genotyping platform

Table 1 List of genotypes used for leaf andseed sampling

S No Genotype ID

1 ICGV 15016

2 ICGV 15064

3 ICGV 16013

4 ICGV 16017

5 ICGV 16030

6 ICGV 16035

7 ICGV 16045

8 ICGV 16668

A NON-DESTRUCTIVE SEED SAMPLING METHOD

22

S No Genotype ID

9 ICGV 16687

10 ICGV 16700

11 ICGV 16701

12 ICGV 171011

13 ICGV 16686

14 ICGV 171007

15 ICGV 171023

16 ICGV 171027

17 ICGV 171006

18 ICGV 16667

19 ICGV 16698

20 ICGV 171017

21 ICGV 171015

22 ICGV 16020

23 ICGV 171051

24 ICGV 171040

25 ICGV 171039

26 ICGV 171046

27 ICGV 171041

S No Genotype ID

28 ICGV 171048

29 ICGV 171026

30 ICGV 171004

31 ICGV 171021

32 ICGV 171019

33 ICGV 171024

34 ICGV 171016

35 ICGV 171009

36 ICGV 171018

37 ICGV 171025

38 ICGV 171020

39 ICGV 171002

40 ICGV 03043

41 ICGV 07222

42 ICGV 07220

43 ICGV 00350

44 ICGV 00351

45 ICGV 02266

46 ICGV 91114


The quality of each extracted DNA from seedand leaf tissue was verified spectrophotometricallyusing NanoDrop for the detection of contaminantssuch as protein salts and polysaccharides High-quality genomic DNA was comparable to the leaftissues could be isolated from groundnut seeds usingthe seed chip method (Table 2) The isolated DNAfrom 46 seed samples yielded a mean concentrationof 168 ngmicrol DNA per extraction with a mean A260A280 ratio of 19 The highest seed DNA yield wasobtained from the genotype ICGV 171039 with aconcentration of 35 ngmicrol The groundnut leaf yieldeda mean of 318 ngmicrol of DNA per extraction with anaverage A260A280 ratio of 19 The purity of theextracted DNA and minimal contamination bypolysaccharides and phenolic compounds wasconfirmed by A260A280 nm ratio of around 18 Theobtained quantity of seed DNA (mean 168 ngmicrol) issufficient to conduct about four PCR reactions

In order to evaluate the efficiency reliabilityand quality of the DNA extracted from the leaf andseed of groundnut genotypes KASP genotypingassay was utilized The KASP assay mix containsthe allele-specific primers for three traits in groundnut

viz LLS rust and high oleic acid content (AhFAD2B)allele The allele-specific primers each harbored aunique tail sequence that corresponds with a universalfluorescence resonant energy transfer (FRET)cassette The wild-type allele of AhFAD2B was labeledwith FAMtrade dye whereas the mutant allele ofAhFAD2B-gene was labeled with HEXtrade dye Bi-allelic discrimination was achieved through thecompetitive binding of these two allele-specificprimers If a genotype at a given SNP washomozygous for wild-type allele only FAM fluorescentsignal was generated However HEXtrade fluorescentsignals were generated if the respective genotypeshave mutant alleles of AhFAD2B gene The SNPmarker snpAH0002 showed AA and A_ basecombination for mutant allele in the homozygous andheterozygous states respectively (Table 3) The wild-type AhFAD2B allele was represented as null [--]Similarly the FAM and HEXtrade fluorescent dyes wereused to differentiate susceptible and resistance allelesfor LLS and rust respectively This assay considersthe quantitative metrics such as call rate andsensitivity to differentiate the tested genotypes amongthe population for zygosity and presenceabsence oftarget alleles

DNYANESHWAR et al

23

Table 2 DNA concentration and purity obtained from genomic DNA of groundnut leaf disc andseed chip (OD Optical density measured by NanoDropTM)

Concentration DNA Purity Concentration DNA Purity(ngmicrol) (A260A280) (ngmicrol) (A260A280)

S No Genotype Leaf disc Seed chips

1 ICGV 171051 336 20 145 20

2 ICGV 171048 435 20 173 17

3 ICGV 171046 923 17 284 15

4 ICGV 171041 499 20 137 19

5 ICGV 171040 365 18 240 19

6 ICGV 171039 262 17 350 21

7 ICGV 171027 252 20 97 20

8 ICGV 171026 273 20 62 23

9 ICGV 171025 297 19 295 21

10 ICGV 171024 321 20 216 21

11 ICGV 171023 367 20 71 21

12 ICGV 171021 251 19 135 19

13 ICGV 171020 302 21 222 19

14 ICGV 171019 699 16 140 20

15 ICGV 171018 504 20 173 19

16 ICGV 171017 188 19 150 19

17 ICGV 171016 158 21 156 19

18 ICGV 171015 250 20 183 21

19 ICGV 171011 413 19 281 18

20 ICGV 171009 231 19 205 20

21 ICGV 171007 269 19 106 21

22 ICGV 171006 203 18 141 21

23 ICGV 171004 176 22 178 20

24 ICGV 171002 293 20 217 18

25 ICGV 16701 337 19 104 21

26 ICGV 16700 256 21 59 22

27 ICGV 16698 243 21 182 19

28 ICGV 16687 417 19 76 24

29 ICGV 16686 245 17 166 16

30 ICGV 16668 438 19 191 18

31 ICGV 16667 215 21 132 17

32 ICGV 16045 348 21 113 18

33 ICGV 16035 186 20 48 22

34 ICGV 16030 251 18 62 21

35 ICGV 16020 06 14 248 21


24



36 ICGV 16017 284 19 125 17

37 ICGV 16013 157 20 138 14

38 ICGV 15064 347 19 267 17

39 ICGV 15016 395 19 133 21

40 ICGV 07222 286 20 276 18

41 ICGV 07220 381 19 110 18

42 ICGV 03043 360 20 107 17

43 ICGV 02266 448 20 230 18

44 ICGV 00351 275 20 270 20

45 ICGV 00350 309 21 176 17

46 ICGV 91114 174 22 279 19

The reactions produced for each genotypewith both seed and leaf DNA was compared and theresult indicated a good allele call between both setsexcept for three individuals (ICGVs 16700 171002and 171006) A summary of the calls is provided inTable 3 The SNPs studied in this experimentproduced polymorphic results among LLS and rustresistant and susceptible genotypes The KASPassay was performed with 10 SNP markers in 46genotypes The current assay (901 out of 940 SNPcalls) operated with good efficiency with a call rate of97 and includes 19 alleles that were scored withthe unknown 20 alleles as uncallable

In summary we report a simple samplingprocedure for non-destructive DNA extraction ofindividual groundnut seeds followed by comparative

study with leaf DNA by using KASP-basedgenotyping The extraction method is cost-effectiveand time-efficient to use on a large scale and arereliable enough for a large breeding population In thecurrent sampling set-up we used a small box of 94cases for a given seed lot and thereby maintaining aunique plate identity for the genotyping Once thegenotypic results are obtained the selection isoperated tracing back to seed box and seed basedselection rejection of individuals is done based ongenotypic results This reduces the efforts of labelingof individual seeds and maintains the fidelity throughthe process of sampling genotyping and selectionThese results could assist plant breeders in fine-tuning their tissue sampling procedure throughmolecular breeding

DNYANESHWAR et al

25

Tabl

e 3

Com

para

tive

SNP

prof

ile o

btai

ned

from

gen

omic

DN

A of

gro

undn

ut le

af d

isc

and

seed

chi

p

DN

A A

ssay

SNP

for

SNPs

for l

ate

leaf

spo

tSN

Ps fo

r rus

thi

gh o

leic

acid

snpA

H00

02sn

pAH

0004

snpA

H00

05sn

pAH

0010

snpA

H00

11sn

pAH

0015

snpA

H00

17sn

pAH

0018

snpA

H00

21sn

pAH

0026

ICG

V 15

064_

Leaf

AA

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

1506

4_Se

edA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

687_

Lea

fA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

687_

See

dA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

698_

Lea

fA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

698_

See

dA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

700_

Lea

fA

AG

GC

CA

ATT

AA

CC

AA

GT

CC

ICG

V 16

700_

See

dA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 00

351_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0035

1_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 02

266_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0226

6_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 03

043_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0304

3_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 07

222_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0722

2_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

701_

Lea

fA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 16

701_

See

dA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1002

_ Le

afA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1002

_ S

eed

AA

GG

CC

AA

TTA

AC

CA

TG

GC

CIC

GV

1710

04_

Leaf

AA

GG

CC

AA

TTA

AC

CA

AG

GC

CIC

GV

1710

04_

See

dA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1006

_ Le

afA

AG

GC

GA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1006

_ S

eed

AA

GG

CC

AA

TTA

AC

CA

AG

GC

C--

Nul

l alle

le


26

REFERENCES

Bera SK Kamdar JH Kasundra SV Dash PMaurya AK Jasani MD ChandrashekarAB Manivannan N Vasanthi RPDobariya KL Pandey MK Janila PRadhakrishnan T and Varshney RK 2018Improving oil quality by altering levels of fattyacids through marker-assisted selection ofahfad2 alleles in peanut (Arachis hypogaeaL) Euphytica 214 (9) 162

Bertioli DJ Cannon SB Froenicke L HuangG Farmer AD Cannon EK Liu X GaoD Clevenger J Dash S and Ren L 2016The genome sequences of Arachis duranensisand Arachis ipaensis the diploid ancestors ofcultivated peanut Nature Genetics 47 (3) 438-449

Chu Y Wu CL Holbrook CC Tillman BLPerson G and Ozias-Akins P 2011 Marker-assisted selection to pyramid nematoderesistance and the high oleic trait in peanut ThePlant Genome 4 (2) 110-117

Desmae H Janila P Okori P Pandey MKMotagi BN Monyo E Mponda O OkelloD Sako D Echeckwu C and OtengFrimpong R 2018 Genetics genomics andbreeding of groundnut (Arachis hypogaeaL) Plant Breeding 12645 1ndash20

Doyle JJ and Doyle JL 1987 A rapid DNA isolationprocedure for small quantities of fresh leaftissue Photochemistry Bulletin 19 11-15

Janila P Pandey MK Manohar SS Variath MTNallathambi P Nadaf HL Sudini H andVarshney RK 2016a Foliar fungal diseaseresistant introgression lines of groundnut(Arachis hypogaea L) record higher pod andhaulm yield in multilocation testing PlantBreeding 135 (3) 355-366

Janila P Pandey MK Shasidhar Y Variath MTSriswathi M Khera P Manohar SSNagesh P Vishwakarma MK Mishra GPand Radhakrishnan T 2016b Molecularbreeding for introgression of fatty aciddesaturase mutant alleles (ahFAD2A andahFAD2B) enhances oil quality in high and low

oil containing peanut genotypes PlantScience 242 203-213

Khedikar YP Gowda MVC Sarvamangala CPatgar KV Upadhyaya HD and VarshneyRK 2010 A QTL study on late leaf spot andrust revealed one major QTL for molecularbreeding for rust resistance in groundnut(Arachis hypogaea L) Theoretical and appliedgenetics 121 (5) 971-984

Kolekar RM Sujay V Shirasawa K Sukruth MKhedikar YP Gowda MVC Pandey MKVarshney RK and Bhat RS 2016 QTLmapping for late leaf spot and rust resistanceusing an improved genetic map and extensivephenotypic data on a recombinant inbred linepopulation in peanut (Arachis hypogaeaL) Euphytica 209 (1) 147-156

Kolekar RM Sukruth M Shirasawa K NadafHL Motagi BN Lingaraju S Patil PVand Bhat RS 2017 Marker assistedbackcrossing to develop foliar disease resistantgenotypes in TMV 2 variety of peanut (Arachishypogaea L) Plant Breeding 136 (6) 948-953

Mienie C M S and Pretorius A E 2013 Applicationof marker-assisted selection for ahFAD2A andahFAD2B genes governing the high-oleic acidtrait in South African groundnut cultivars(Arachis hypogaea L) African Journal ofBiotechnology 12 (27) 4283ndash4289

Norden AJ Gorbet DW Knauft DA and YoungCT 1987 Variability in oil quality amongpeanut genotypes in the Florida breedingprogram Peanut Science 14 (1) 7-11

Pandey MK Khan AW Singh VKVishwakarma MK Shasidhar Y Kumar VGarg V Bhat RS Chitikineni A Janila Pand Guo B 2017 QTL seq approach identifiedgenomic regions and diagnostic markers forrust and late leaf spot resistance in groundnut(Arachis hypogaea L) Plant BiotechnologyJournal 15 (8) 927-941

Sujay V Gowda MVC Pandey MK Bhat RSKhedikar YP Nadaf HL Gautami BSarvamangala C Lingaraju S

DNYANESHWAR et al

27

Radhakrishan T and Knapp SJ 2012Quantitative trait locus analysis andconstruction of consensus genetic map forfoliar disease resistance based on tworecombinant inbred line populations incultivated groundnut (Arachis hypogaea L)Molecular breeding 30 (2) 773-788

Varshney RK Pandey MK Janila P NigamSN Sudini H Gowda MVC SriswathiM Radhakrishnan T Manohar SS andNagesh P 2014 Marker-assistedintrogression of a QTL region to improve rustresistance in three elite and popular varietiesof peanut (Arachis hypogaea L) Theoreticaland Applied Genetics 127 (8) 1771-1781

Xiuzhen W Yueyi T Qi W Ligui Z Quanxi Sand Zhiwei W 2016Development of a newhigh-oleic peanut cultivar Huayu 662 by usingmolecular marker aided selection and NIRSJournal of Nuclear Agricultural Sciences 30(6)1054-1058

Yeri SB Kolekar RM Motagi BN Nadaf HLLingaraju S Gowda MVC and Bhat RS2014 Development of late leaf spot and rusttolerant genotypes from TMV 2 and JL 24 bymarker-assisted backcross breeding ingroundnut Electronic Journal of PlantBreeding 1059580975-928X2016000053


28

YIELD AND YIELD ATTRIBUTES OF RICE (Oryza sativa L) AS INFLUENCED BYFOLIAR APPLICATION OF ZINC OXIDE NANOPARTICLES UNDER DIFFERENT

CROP ESTABLISHMENT METHODS

BRIJBHOOSHAN A SRINIVAS R MAHENDRA KUMAR T RAM PRAKASHK AVIL KUMAR T NKVKPRASAD and S NARENDER REDDY

Department of Agronomy College of AgricultureProfessor Jayashankar Telangana State Agricultural University



ABSTRACT

A field experiment was conducted during rabi 2015-16 and 2016-17 at ICAR-Indian Institute of Rice Research RajendranagarHyderabad to study the effect of nano zinc oxide foliar application in rice under different crop establishment methods Theexperiment was laid out in split plot design with two establishment methods (direct seeding through dibbling under puddledcondition and normal transplanting) in main plots and six nutrient management practices (Absolute control-no fertilizer control-RDF NPK RDF + soil application of ZnSO47H2O 25 kg ha-1 RDF + two foliar sprays of ZnSO47H2O at 05 RDF + two foliarsprays of nano ZnO at 1000 ppm and RDF + two foliar sprays of nano ZnO at 1500 ppm) in sub-plots with four replications Theresults revealed that number of panicles m-2 were significantly higher in direct seeded rice as compared to normal transplantingbut other yield attributes like panicle length panicle weight and filled grains panicle-1 were significantly more in normal transplantedrice Direct seeded rice recorded significantly higher grain yield (984 885 and 945 during 2015 2016 and pooled meanrespectively) as compared to normal transplanting Among nutrient management practices RDF + two foliar sprays of nano ZnOat 1000 ppm being at par with RDF + two foliar sprays of ZnSO47H2O at 05 and RDF + two foliar sprays of nano ZnO at 1500ppm produced higher yield attributes ie number of panicles m-2 panicle length panicle weight grains panicle-1 test weight andgrain and straw yield compared to other nutrient management practices

Rice is the most consumed cereal grain inthe world constituting the dietary staple food for morethan half of human population of the planet Riceoccupies approximately 160 million ha area globallyof which 134 million ha is in Asia World riceproduction is 741 million tonnes with an averageproductivity of 4631 kg ha-1 (FAOSTAT 2018) Indiais the second largest producer after China and hasan area of about 4296 million ha and production of11291 million tonnes with the productivity of 2628 kgha-1 (DoES 2018) In Telangana state it is cultivatedin an area of 141 million ha with production andproductivity of 454 million tonnes and 3211 kg ha-1respectively (BoES 2015)

Traditionally rice is established by transplantingseedlings in puddled soils which demands a hugeamount of water and labour In the present loomingscenario of scarcity of water and labour due topopulation explosion and urbanization which posesa serious threat to sustainability of traditional methodsof rice production there is an urgent need to replaceconventional transplanting method of rice by moreresource saving crop establishment method of rice likedirect seeding of rice (DSR) Under the DSR technology

emailbrij35537gmailcom

sowing of rice seeds is done directly in the soil ratherthan transplanting seedlings

The productivity of rice soil depends upon theadequate and balanced nutrition of all essential nutrientsincluding the micronutrients In rice continuous use ofNPK fertilizer has remarkably increased production butsimultaneously brought about problems related tomicronutrient deficiencies particularly that of zinc (Zn)in soil Rice one of the worldrsquos most important cerealcrops is affected by Zn deficiency At least 70 of therice crop is produced in flooded conditions resulting inincrement in phosphorus and bicarbonate concentrationwhich reduces soil Zn availability to the crop About50 paddy soils in the world are Zn deficient with 35 in Asia alone (Cakmak 2008) In an Indian scenarioaround 49 soils from all the main agricultural areasare deficient in Zn (Naik and Das 2008)

Zinc is one of the essential nutrients requiredfor plant growth Its important role can be adjudgedas it controls the synthesis of Indole acetic acid (IAA)a phytohormone which dramatically regulates the plantgrowth It is also necessary for the chlorophyll synthesisand carbohydrate formation As Zn is the structural


29

component of phosphorous (P)-mobil iz ingphosphatase and phytase enzymes it can behypothesized that application of nano ZnO may helpin more secretion of P-mobilizing enzymes which isinvolved in native P mobilization for plant nutrition fromunavailable organic sources (Tarafdar and Claassen2003)

Hence Zinc (Zn) is one of the importantmicronutrients required for plant growth anddevelopment Soil Zn deficiency is a major problemand hence adequate Zn supply during cropdevelopmental stages are recommended to improvethe nutrient content in the rice grain and also improveproductivity of the soil Crops require only smallamount of Zn for their normal growth but its applicationrate is high due to very low fertilizer use efficiencywhich vary 1-3 and 5-8 in soil and foliar applicationrespectively Hence there is a need to haveformulations of Zn with improved use efficiency forthe better crop performance with less input

Nano fertilizers are synthesized in order toregulate the release of nutrients depending on therequirements of the crops and it is also reported thatnano fertilizers are more efficient than ordinary fertilizer(Suman et al 2010) In recent past the positive effectof nano fertilizers on germination photosynthesisgrowth nutrient absorption fertilizer use efficiencyand yield have been studied in various crops underpot culture and field studies Keeping these pointsin consideration the experiment was carried out tostudy the effect of zinc oxide nano particles foliarapplication on yield and yield attributes of rice (Oryzasativa L) under different crop establishment methodsMATERIALS AND METHODS

A field experiment was conducted during rabi2015-16 and 2016-17 at ICAR-Indian Institute of RiceResearch Rajendranagar Hyderabad The initial soilparameters were pH=81 EC= 028 dS m-1 organiccarbon = 046 available nitrogen = 2290 kg ha-1available P = 219 kg ha-1 available K= 3990 kg ha-

1 available zinc = 068 and available iron= 640 ppmThe analysis of soil sample revealed that soil wasclay loam in texture having low organic carbon andavailable nitrogen medium in available phosphorusand high in available potassium contents withmoderately alkaline in reaction The available zincand iron content were sufficient Rice variety varadhan(DRR Dhan 36) is tested in the experiment

The experiment was laid out in split plot designwith two crop establishment methods (DSR-directseeding through hand dibbling under puddled conditionand NTP- normal transplanting) in main plots and sixnutrient management practices (Absolute control-nofertilizer control-RDF NPK RDF + soil application ofZnSO47H2O 25 kg ha-1 RDF + two foliar spraysof ZnSO47H2O 05 RDF + two foliar sprays ofnano ZnO 1000 ppm and RDF + two foliar spraysof nano ZnO 1500 ppm) in sub-plots with fourreplications The recommended dose of fertilizer (RDF)for rice is 120 60 and 40 kg N P2O5 and K2O ha-1

respectively Nitrogen was applied in three equal splitdoses ie basal 45 DAS and 75 DAS in direct seededrice and basal 30 DAT and 60 DAT under normaltransplanting respectively Full dose of P K and SoilZn were applied as basal dose The nutrients N P2O5

and K2O were supplied through urea single superphosphate and muriate of potash respectively Thefoliar application of zinc shall be carried out at twostages ie 15 DAT and 25 DAT in transplanted riceand 40 DAS and 50 DAS in direct seeded rice Equalspacing of 20cm x 15cm was adopted in direct seedingand normal transplanting For transplanting 25 daysold seedlings were used The soil kept at saturationlevel throughout crop growth period For preparation ofparticle suspension ZnO-nanoparticulates weresuspended in the deionized water directly anddispersed by ultrasonic vibration for 30 min Theaggregation of particles was avoided by stirring thesuspensions with magnetic bars

At harvest plant samples from each plotswere harvested to record the yield-attributingcharacteristics such as the number of panicles m-2panicle length (cm) panicle weight (g) test weight(g) number of filled and unfilled grains panicle-1sterility percentage and grain yield The data recordedon various parameters of the crop during the courseof investigation was statistically analyzed followingthe analysis of variance for split plot design given byGomez and Gomez (1984)


Yield attributes

Direct seeded rice (DSR) recorded significantlyhigher number of panicles m-2 (29350 27529 and28442 panicles m-2) over normal transplanted rice(25942 24783 and 25361 panicles m-2) during 2015

YIELD AND YIELD ATTRIBUTES OF RICE

30

2016 and in pooled mean respectively (Table 1) Theper cent increase in panicles m-2 under direct seededrice was 131 111 and 122 over transplantationduring both the years and pooled mean respectivelyHigher no of panicles m-2 under DSR is mainlyattributed to significantly more number of seedlings hill-1 and tillers per unit area in DSR compared totransplanting The other yield parameters like paniclelength panicle weight and number of spikelets panicle-

1 were significantly higher under transplanted ricecompared to DSR due to intra plant competitionoccurred in DSR because of more no of tillers hill-1Differences in 1000-grain weight were not significantbetween transplanted and direct-seeded rice

The DSR has more panicles m-2 which wasattributed to higher number of tillers per hill andvigorous plant growth than the transplanted rice Therobust plants under DSR could be due to the deeperand vigorous root growth which enabled them toaccess nutrients from much greater volume of soilCrop establishment through direct seeding avoidstransplanting shock to the seedlings at initial stageunlike normal transplanting where growth disruptionoccur due to transplantation shock Direct seedingunder puddled condition provides better opportunityfor establishment deeper and vigorous root systemrobust plant growth more leaf area and LAI morechlorophyll content higher light harnessing capacitymore number of tillers and panicles per unit area(Peng et al 2006) While reviewing available literatureit was found that poor yield under DSR is mainlycaused due to poor or uneven plant population(Rickman et al 2001) heavy weed infestation (Kumaret al 2008) and improper water and nutrientmanagement practices (Sudhir Yadav et al 2011)But in present study DSR is sown through manualhand dibbling at optimum depth under puddled conditionwhich resulted in good germination and uniform plantpopulation at desired spacing which in turn facilitatedproper manual weed control

RDF+ 2 foliar sprays of nano Zn at 1000 ppm(N5) recorded significantly higher no of panicles m-2

compared to all other nutrient management practicesexcept RDF+ 2 foliar sprays of nano Zn at 1500 ppm(N6) (Table 1) Being on par with RDF+ 2 foliar spraysof ZnSO47H2O at 05 (N4) and RDF+ 2 foliar spraysof nano Zn at 1500 ppm (N6) treatments RDF+ 2

foliar sprays of nano Zn at 1000 ppm treatment (N5)recorded significantly more panicle length panicleweight and test weight compared to other nutrientmanagement practices Application of Zn stimulatedrice growth enhanced the tiller production which wasfinally manifested in superior yield attributes Theincrease in the number of panicles might be attributedto adequate Zn supply which may have increasedthe supply of other nutrients and stimulated the overallplant growth (Syed Talib et al 2016) All the yieldattributes like no of panicles m-2 panicle length (cm)panicle weight (g) test weight (g) number of filled grainspanicle-1 were significantly lowest and sterilitypercentage was highest under absolute control (N1)treatment

Grain and straw yield

The mean grain yield was 4965 kg ha-1 in2015 4688 kg ha-1 in 2016 and 4829 kg ha-1 in pooledmean In spite of the treatment differences highergrain yield was recorded during rabi 2015 than rabi2016 and it may be attributed to congenial weatherparameters (rainfall solar radiation and temperature)and yield attributes during first year (Table 3)

Direct seeded rice recorded significantlyhigher grain yield (5198 4886 and 5047 kg ha-1) overnormal transplanted rice (4733 4489 and 4611 kgha-1) during both the years and in pooled meanrespectively The per cent grain yield increase in DSRwas 984 885 and 945 over normal transplantingduring 2015 2016 and their pooled mean respectivelyHigher grain yield under DSR is mainly attributed tosignificantly more number of panicles per unit area underDSR compared to transplanting The other yieldparameters like panicle length panicle weight andnumber of spikelets panicle-1 were significantly higherunder transplanted rice compared to DSR due to intraplant competition in DSR because of more no of tillershill-1 But the benefit of this under transplanted rice couldhave diminished due to significantly more number ofpanicles m-2 under DSR Differences in 1000-grainweight were not significant between transplanted anddirect-seeded rice These findings indicate the existenceof several yield compensation mechanisms enablingrice to respond differently to various microclimaticconditions associated with different methods of cropestablishment (Rao et al 2007)

BRIJBHOOSHAN et al

31

Tabl

e 1

Yie

ld a

ttrib

utes

of r

ice

as in

fluen

ced

by c

rop

esta

blis

hmen

t met

hods

and

nut

rient

man

agem

ent p

ract

ices

(rab

i 20

15 a

nd 2

016)

Trea

tmen

tsN

umbe

r of p

anic

le m

-2Pa

nicl

e le

ngth

(cm

)Pa

nicl

e w

eigh

t (g)

Test

wei

ght (

g)

2015

2016

Pool

ed20

1520

16 P

oole

d20

1520

16Po

oled

2015

2016

Pool

edC

rop

esta

blis

hmen

t met

hods

(M)

M1-

Dire

ct S

eede

d R

ice

(DS

R)

293

527

53

284

423

18

227

922

99

314

308

311

222

322

15

221

9

M2-

Nor

mal

Tra

nspl

ante

d R

ice

(NTP

)25

94

247

825

36

244

924

08

242

93

323

283

3022

51

224

222

46

SEm

+4

104

014

230

270

270

270

040

040

030

260

230

24

CD

(p=0

05)

184

318

03

190

61

211

241

230

170

180

15N

SN

SN

S

Nut

rient

Man

agem

ent p

ract

ices

(N)

N1-

Abs

olut

e co

ntro

l (N

o fe

rtiliz

er)

148

413

59

142

122

03

216

021

82

274

271

273

203

520

25

203

0

N2-

Con

trol (

RD

F)27

95

262

027

09

231

522

77

229

63

153

113

1322

21

221

522

18

N3-R

DF+

SA

of

ZnS

O47

H2O

2

5 kg

ha-1

294

827

95

287

124

01

236

123

81

329

324

326

225

022

39

224

4

N4-R

DF+

2 F

S o

f ZnS

O47

H2O

0

5

301

528

60

293

724

16

237

423

95

335

328

331

228

922

78

228

3

N5-R

DF+

2 F

S o

f nan

o Zn

O

100

0 pp

m31

96

305

531

26

248

824

48

246

83

443

393

4123

21

231

323

17

N6-R

DF+

2 F

S o

f na

no Z

nO

150

0 pp

m31

50

300

530

78

248

224

42

246

23

423

363

3923

08

229

923

03

SEm

+4

024

034

300

290

280

290

040

040

040

190

200

19

CD

(p=0

05)

116

111

64

124

20

840

810

820

130

120

120

560

590

56

Inte

ract

ions

(MxN

)

Nut

rient

man

agem

ent a

t sam

e le

vel o

f cro

p es

tabl

ishm

ent

SEm

+5

695

706

080

410

390

400

060

060

060

270

290

27

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

Cro

p es

tabl

ishm

ent a

t sam

e or

diff

eren

t lev

el o

f nut

rient

man

agem

ent

SEm

+6

616

576

980

460

450

460

070

070

060

360

350

35

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

Gen

eral

Mea

n27

65

261

626

90

238

423

44

236

43

233

183

2022

37

222

822

33


32

Trea

tmen

tsFi

lled

grai

ns p

anic

le-1

Unf

illed

gra

ins

pani

cle-1

Ster

ility

per

cent

age

()

2015

2016

Pool

ed20

1520

16Po

oled

2015

2016

Pool

ed

Cro

p es

tabl

ishm

ent m

etho

ds (M

)M

1- D

irect

See

ded

Ric

e (D

SR

)12

466

116

0412

034

968

122

810

98

737

976

856

M2-

Nor

mal

Tra

nspl

ante

d R

ice

(NTP

)13

436

125

7613

007

881

112

510

03

630

838

734

SEm

+1

881

701

460

160

170

150

120

160

15

CD

(p=0

05)

847

765

658

073

077

069

056

073

065

Nut

rient

Man

agem

ent p

ract

ices

(N)

N1-

Abs

olut

e co

ntro

l (N

o fe

rtiliz

er)

109

3310

079

105

0518

37

208

819

63

144

217

23

158

2

N2-

Con

trol (

RD

F)12

531

116

6612

098

103

212

85

115

97

639

958

79

N3-R

DF+

SA

of

ZnS

O47

H2O

2

5 kg

ha-1

131

5312

289

127

208

2910

80

954

594

811

702

N4-R

DF+

2 F

S o

f ZnS

O47

H2O

0

5

132

7312

409

128

407

8710

38

913

562

775

668

N5-R

DF+

2 F

S o

f nan

o Zn

O

100

0 pp

m13

975

131

1313

546

546

798

672

377

576

477

N6-R

DF+

2 F

S o

f na

no Z

nO

150

0 pp

m13

844

129

8513

414

517

768

642

362

561

462

SEm

+1

451

431

280

200

180

200

150

170

17

CD

(p=0

05)

419

413

371

059

053

057

043

048

050

Inte

ract

ions

(MxN

)N

utrie

nt m

anag

emen

t at s

ame

leve

l of c

rop

esta

blis

hmen

tS

Em+

205

202

182

029

026

028

021

023

025

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Cro

p es

tabl

ishm

ent a

t sam

e or

diff

eren

t lev

el o

f nut

rient

man

agem

ent

SEm

+2

662

512

210

310

290

300

230

270

27

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Gen

eral

Mea

n12

951

120

9012

521

925

117

610

51

683

907

795

RD

F- R

ecom

men

ded

Dos

e of

Fer

tiliz

ers

(N

P2O

5amp K

2O

120

60

40 k

g ha

-1)

SA

- S

oil A

pplic

atio

n F

S-

Folia

r Sp

ray

NS

- N

on S

igni

fican

t

BRIJBHOOSHAN et al

33

Straw yield was also significantly higher underDSR (6675 6447 and 6561 kg ha-1 during 2015 2016and pooled mean respectively) compared to normaltransplanting Increase in straw yield was to the tuneof 113 109 and 111 under DSR over NTP duringboth the years and pooled mean respectively (Table3) This might be due to more number of tiller per unitarea and higher dry matter production in DSR Therewas no significant difference between direct seededrice and normal transplanting in respect of harvest index

RDF+ 2 FS of nano Zn at 1000 ppm (N5)was found to give significantly higher grain yield duringboth the years of study over other nutrient managementpractices except RDF+ 2 FS of nano Zn 1500ppm (N6) (Table 3) The grain yield of rice in RDF+ 2FS of nano Zn at 1000 ppm (6070 5659 and 5878 kgha-1) and RDF+ 2 FS of nano Zn 1500 ppm(5978 5574 and 5776 kg ha-1) were statistically atpar with each other but these nutrient managementpractices were significantly superior over absolutecontrol (2335 2274 and 2305 kg ha-1) RDF (47394469 and 4604 kg ha-1) RDF+ SA of ZnSO47H2O 25 kg ha-1 (5187 4993 and 5090 kg ha-1) and RDF+2 FS of ZnSO47H2O 05 ( 5483 5157 and 5320kg ha-1) during 2015 2016 and pooled meanrespectively The per cent increase in pooled grain yieldwas 1054 1554 2544 and 2765 in RDF+ SA ofZnSO47H2O 25 kg ha-1 (N3) RDF+ 2 FS ofZnSO47H2O 05 (N4) RDF+ 2 FS of nano Zn 1500 ppm (N6) and RDF+ 2 FS of nano Zn 1000 ppm (N5) compared to RDF application (N2)respectively

Significantly lowest grain yield (2335 2274 and2305 kg ha-1 in 2015 2016 and pooled meanrespectively) was recorded under absolute control (N1)Application of recommended dose of fertilizer NPK(N2) to the rice resulted in 10295 9282 and 9977grain yield increase during 2015 2016 and pooledmean respectively over absolute control treatment (nofertilizer) RDF along with soil application ofZnSO47H2O 25 kg ha-1 (N3) recorded significantlyhigher grain yield (5187 4993 and 5090 kg ha-1 in 20152016 and pooled mean respectively) compared to onlyRDF application (4739 4469 and 4604 kg ha-1 in 20152016 and pooled mean respectively) Soil applicationof ZnSO47H2O 25 kg ha-1 (N3) increase grain yield

in the tune of 944 1171 and 1054 during 20152016 and pooled mean respectively over NPK RDFapplication (N2) Two foliar application of ZnSO47H2O 05 (N4) was found better over soil application ofZnSO47H2O 25 kg ha-1 (N3) and resulted in 571329 and 452 grain yield increase during 2015 2016and pooled mean respectively over soil application ofZnSO47H2O 25 kg ha-1

Significant increase in grain yield due to nanozinc application is mainly attributed to significantly highernumber of panicle m-2 panicle length panicle weighttest weight number of spikelets per panicle

The increased yield attributes might be due to role ofZn in biosynthesis of Indole acetic acid (IAA) andespecially due to its role in initiation of primordialreproductive parts and partitioning of photosynthatestowards them It is evident that application of Znstimulated rice growth enhanced the tiller productionwhich was finally manifested in superior yield attributesThe increase in the number of panicles m-2 might beattributed to adequate Zn supply which may haveincreased the supply of other nutrients and stimulatedthe overall plant growth The increase in the numberof grains panicle-1 might have been owing to itsenhancing effect on the physiological activitiesphotosynthesis and translocation and assimilation ofphotosynthates and formation of higher number ofspikelets during the spikelet initiation process whichultimately resulted in higher number of grainspanicle(Syed Talib et al 2016)

Significantly higher straw yield was recordedin RDF+ 2 FS of nano Zn at 1000 ppm (N5) (75707224 and 7397 kg ha-1 during 2015 2016 and pooledmean respectively) compared to other nutrientmanagement practices except RDF+ 2 FS of nanoZn at 1500 ppm (N6) In respect to harvest index therewas no significant difference between nutrientmanagement practices except absolute control (N1)Significantly lowest harvest index (4167 4084 and4125 during both the year and pooled mean) wasrecorded in absolute control (N1)

CONCLUSION

The study revealed that direct seeding throughhand dibbling under puddled condition could be anoption for rice cultivation as normal transplanting


34

Tabl

e 2

Yie

ld a

nd h

arve

st in

dex

of ri

ce a

s in

fluen

ced

by c

rop

esta

blis

hmen

t met

hods

and

nut

rient

man

agem

ent p

ract

ices

(rab

i 20

15 a

nd 2

016)

Trea

tmen

tsG

rain

yie

ld (k

g ha

-1)

Stra

w y

ield

(kg

ha-1)

Har

vest

Inde

x(

)

2015

2016

Pool

ed20

1520

16Po

oled

2015

2016

Pool

ed

Cro

p es

tabl

ishm

ent m

etho

ds (M

)M

1- D

irect

See

ded

Ric

e (D

SR

)51

9848

8650

4766

7564

4765

6143

58

428

243

20

M2-

Nor

mal

Tra

nspl

ante

d R

ice

(NTP

)47

3344

8946

1160

0058

1359

0743

81

434

343

62

SEm

+98

8389

121

109

101

052

061

049

CD

(p=0

05)

441

373

401

545

492

455

NS

NS

NS

Nut

rient

Man

agem

ent p

ract

ices

(N)

N1-

Abs

olut

e co

ntro

l (N

o fe

rtiliz

er)

2335

2274

2305

3270

3320

3295

416

740

84

412

5

N2-

Con

trol (

RD

F)47

3944

6946

0461

5459

8160

6843

47

427

743

12

N3-R

DF+

SA

of

ZnS

O47

H2O

2

5 kg

ha-1

5187

4993

5090

6593

6462

6527

440

243

63

438

3

N4-R

DF+

2 F

S o

f ZnS

O47

H2O

0

5

5483

5157

5320

6902

6656

6779

442

543

66

439

6

N5-R

DF+

2 F

S o

f nan

o Zn

O

100

0 pp

m60

7056

5958

7875

7072

2473

9744

54

439

644

25

N6-R

DF+

2 F

S o

f na

no Z

nO

150

0 pp

m59

7855

7457

7675

3771

3773

3744

21

438

944

05

SEm

+13

410

911

114

711

412

10

400

550

51

CD

(p=0

05)

386

315

320

424

328

349

115

158

147

Inte

ract

ions

(MxN

)N

utrie

nt m

anag

emen

t at s

ame

leve

l of c

rop

esta

blis

hmen

tS

Em+

189

154

157

208

161

171

056

077

072

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Cro

p es

tabl

ishm

ent a

t sam

e or

diff

eren

t lev

el o

f nut

rient

man

agem

ent

SEm

+19

816

316

822

518

318

60

730

940

82

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Gen

eral

Mea

n49

6546

8848

2963

3761

3062

3443

70

431

343

41

Coe

ffici

ent o

f Var

iatio

n (

)9

78

79

09

48

77

95

97

05

5

RD

F- R

ecom

men

ded

Dos

e of

Fer

tiliz

ers

(N

P2O

5amp K

2O

120

60

40 k

g ha

-1)

SA

- S

oil A

pplic

atio

n F

S-

Folia

r Sp

ray

NS

- N

on S

igni

fican

t

BRIJBHOOSHAN et al

35

REFERENCES

BoES 2015 Agricultural situation in Telangana Bureauof economics and statistics Government ofTelangana Hyderabad

Cakmak I 2008 Enrichment of cereal grains withzinc agronomic or genetic biofortificationPlant and Soil 302(1-2) 1-17

DoES 2018 Department of Economics and Statistics(DoES) Department of Agriculture andCooperation Ministry of Agriculture Govt ofIndia

FAOSTAT 2018 Food and Agriculture OrganizationRome Italy

Gomez KA and Gomez AA 1984 StatisticalProcedures for Agriculture Research JohnWiley and Sons Publishers New York pp 357-423

Kumar GS Thavaprakash N Raja K Babu Cand Umashankar R 2008 Effect of systemsof cultivation with varied N levels on growthyield water productivity and economics of riceCrop Research 35 (3)157-164

Naik S K and Das D K 2008 Relative performanceof chelated zinc and zinc sulphate for lowlandrice (Oryza sativa L) Nutrient CyclingAgroecosystem (3) 219-227

Peng S Bouman BAM Visperas RMCastaneda A Nie L and Park H 2006Comparison between aerobic and flooded ricein the tropics agronomic performance in an eightseason experiment Field Crop Research 96252-259

Rao AN Johnson DE Sivaprasad B Ladha JKand Mortimer AM 2007 Weed managementin direct-seeded rice Advances in Agronomy93 153ndash255

Rickman J F Pyseth M Bunna S and Sinath P2001 Direct seeding of rice in CambodiaProceedings of an International Workshop ofACIAR Laos Vietnam 30 October-2November pp 101

Sudhir Yadav Humphreys E Gill G Kukal SSand Rangarajan R 2011 Effect of watermanagement on dry seeded and puddledtransplanted rice Field Crops Research120123-132

Suman PR Jain VK and Varma A 2010 Role ofnanomaterials in symbiotic fungus growthenhancement Current Science 991189-1191

Syed Talib Anwar Bhat M Ashaq Hussain ManzoorA G and Teli N A 2016 Effect of levels andsources of zinc on growth yield andeconomics of rice (Oryza sativa) undertemperate conditions Indian Journal ofAgronomy 61(2)186-190

Tarafdar JC and Claassen N 2003 Organicphosphorus utilization by wheat plants understerilized condition Biology and Fertility ofSoils 3925-29


required more labour and delayed maturity comparedto direct seeding Application of RDF + nano ZnO foliarapplication is new innovation to overcome Zn deficiencyin rice and enhancing grain yield over ZnSO47H2Oapplication

36

EFFECT OF INTEGRATED NUTRIENT MANAGEMENT PRACTICES ON YIELDATTRIBUTES AND YIELD OF BABY CORN IN BABY CORN (Zea mays L) -

HYACINTH BEAN (Lablab purpureus var typicus) CROPPING SYSTEMR PREETHAM K AVIL KUMAR A SRINIVAS A MANOHAR RAO and T RAM PRAKASH

Horticultural Research Station AdilabadSri Konda Laxman Telangana State Horticultural University


ABSTRACT

A field experiment was conducted at Horticultural Research Station Adilabad during kharif 2015 and 2016 and rabiseasons of 2015-16 and 2016-17 to study the effect of integrated nutrient management practices on yield attributes and yield ofbaby corn in baby corn-hyacinth bean cropping system The experiment was laid out in a randomized block design for baby cornduring kharif 2015 season with seven treatments comprised of T1 25 N through FYM + 75 RDF (Recommended dose offertilizer) T2 25 N through FYM + 75 RDF + Azospirillum and Bacillus megaterium 5 kg ha-1 each T3 25 N throughvermicompost + 75 RDF T4 25 N through vermicompost + 75 RDF + Azospirillum and Bacillus megaterium 5 kg ha-1 eachT5 100 RDF T6 100 RDF + Azospirillum and Bacillus megaterium 5 kg ha-1 each T7 Control (No fertilizer application)replicated thrice Each main treatment is sub divided into four sub plots and the treatments S1-100 RDF S2-75 RDF S3-100RDF + Bradyrhizobium 500 g ha-1 (seed treatment) and S4-75 RDF + Bradyrhizobium 500 g ha 1 (seed treatment) wereimposed for hyacinth bean in rabi season and data of kharif 2016 was analyzed in split plot design All the treatments (organicinorganic sources of nutrition in conjunction with bio-fertilizers) showed significantly higher number of cobs plant-1 cob lengthcob girth cob width cob weight with or without husk and yield over un-fertilized control Among the different treatmentsapplication of 75 RDF integrated with 25 RDN through vermicompost in conjunction with bio-fertilizers recorded significantlyhigher number of cobs plant -1 (223 and 222) cob length (1250 and 1224 cm) cob girth (574 and 565 cm) cob width (186 and183 cm) cob weight without husk (1346 and 1206 g) cob yield with husk (12290 and 11727 kg ha-1) cob yield without husk(2049 and 1890 kg ha-1) and stover yield (24020 and 21165 kg ha-1) during kharif 2015 and 2016 respectively Application of 100RDF along with seed treatment with Bradyrhizobium to hyacinth bean crop during rabi 2015-16 resulted in significantly higherbaby cob weight without husk (995 g) in succeeding kharif 2016 over 100 RDF 75 RDF and 75 RDF + seed treatment withBradyrhizobium

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Maize (Zea mays L) is the third most importantcereal crop next to rice and wheat and has the highestproduction potential among the cereals Fordiversification and value addition of maize as well asgrowth of food processing industries recentdevelopment is growing maize for vegetable purposewhich is commonly known as lsquobaby cornrsquo Baby cornis de-husked ear harvested two or three days aftersilk emergence Baby corn has high nutrient content agood source of foliate vitamin B6 riboflavin vitamin AC rich in potassium phosphorus and fiber content andlow in fat content free from saturated fat and cholesterolvery low in sodium It has nutritive value similar to thatof non-legume vegetable such as cauliflower tomatocucumber and cabbage (Paroda and Sashi 1994)Cultivation of baby corn to diversify cropping patternand to increase productivity of the cropping systemshas been considered important for improving thelivelihood and income of resource poor farmers in SouthAsia Baby corn is a profitable crop that allows adiversification of production aggregation of value andincreased income Being a very short duration crop it

can be grown throughout the year and it also fits wellin intensive cropping systems Use of organic manuresalong with inorganic fertilizers to meet the nutrientrequirement of crop would be an inevitable practice inthe years to come for sustainable agriculture Sinceorganic manures improve the soil physical chemicaland biological properties along with conserving soilmoisture Cultivation of baby corn by integrated nutrientmanagement (INM) approach might be the mostfeasible way to get the best quality produce forcompetitive international markets

Lablab bean or hyacinth bean is one of themost ancient among the cultivated legumes and isgrown throughout the tropical regions of Asia Africaand America The crop is indigenous to India andgrown all over the country The crop is put tomultipurpose uses such as pulse vegetable fodderThe crop is mainly grown for green pods while the dryseeds are used in the preparation of various vegetariandishes and is rich in protein The foliage of the cropprovides silage and green manure The dwarf bushytypes are determinate and are photo insensitive and


37

can be cultivated throughout the year Dwarf varieties(determinate bush type) have a potential for moreextensive cultivation of the crop because the plantsrequire no support system the pods mature uniformlyand the crop is amenable to mechanical harvestingwhich will reduce cost and labour

Erratic behavior of rainfall pattern especiallythe late onset of monsoon early cessation of rainfalllong duration cultivars are failing to yield good returnsin Telangana region Application of heavy doses ofchemical fertilizers without organic manures is causingdeterioration of soil health in terms of physical andchemical properties of soil declining soil microbialactivities reduction in soil humus increased pollutionof soil water and air

In view of the above baby corn of 55-60 daysduration is selected for testing during kharif and 130-140 days duration cultivar hyacinth bean a legumecrop for rabi The entire cropping system will becompleted in around 185-200 days premium price isthere in the market for baby corn and not muchfluctuation in the price of hyacinth bean round the yearand hence the system can be a profitable systemThough maize is an exhaustive crop baby corn is ashort duration crop and hyacinth bean is a legumeand the use of organic manures and microbial culturesas part of integrated nutrient management will sustainsoil health Hence a study was initiated to know theeffect of integrated nutrient management practices onyield attributes and yield of baby corn in baby corn-hyacinth bean cropping system and to evaluate theefficacy of FYM and vermicompost in conjunction withmicrobial culture and inorganic fertilizers


The experiment was conducted at HorticulturalResearch Station farm Adilabad during kharif 2015and 2016 The experimental site is at an altitude of264 m above mean sea level on 79o 56rsquo 03 E longitudeand 19o08rsquo 09 N latitude The experimental soil wassandy clay loam in texture neutral in reaction mediumin available nitrogen phosphorous and potassiumThe soil belongs to the order Alfisol of shallow to mediumdepth The experiment was laid out in a randomizedblock design for baby corn during kharif 2015 seasonwith seven treatments comprised of T1 25 N throughFYM + 75 RDF (Recommended dose of fertilizer)T2 25 N through FYM + 75 RDF + Azospirillum

and Bacillus megaterium 5 kg ha-1 each T3 25N through vermicompost + 75 RDF T4 25 Nthrough vermicompost + 75 RDF + Azospirillum andBacillus megaterium 5 kg ha-1 each T5 100 RDFT6 100 RDF + Azospir illum and Bacillusmegaterium 5 kg ha-1 each T7 Control (No fertilizerapplication) replicated thrice Each main treatment issub divided into four sub plots and the treatments S1-100 RDF S2-75 RDF S3-100 RDF +Bradyrhizobium 500 g ha-1 (seed treatment) andS4-75 RDF + Bradyrhizobium 500 g ha 1 (seedtreatment) were imposed for hyacinth bean in rabiseason and data of kharif 2016 was analyzed insplit plot design

Manures (FYM or vermicompost) and fertilizers(urea SSP and muriate of potash) were applied asper the treatment Manures were incorporated into thesoil before sowing Nitrogen was applied in three splitsin the form of urea at 10 25 and 40 DAS Entire P2O5

and K2O was applied as basal through single superphosphate and muriate of potash Azospirillum(nitrogen fixing bacterial formulation) and Bacillusmegaterium (phosphorus solubilizing bacterialformulation) 5 kg ha-1 each was applied to soil afterincubation with 50 kg FYM for baby corn as per thetreatments G-5414 variety of baby corn which growsto height of 180-200 cm and matures within 50-55 dayswas selected for testing The crop was sown on 22nd

and 3rd July in 2015 and 2016 respectively Two seedswere dibbled hill-1 at a depth of 3-4 cm with a spacingof 60 cm x 15 cm Gap filling was done on 7th dayafter sowing and thinning was done on 14th day aftersowing Atrazine 10 kg ai ha-1 was applied twodays after sowing to control the weeds The field wasmaintained weed free condition by hand weeding at15 and 30 DAS Biometric observations on baby cornwere recorded at harvest Five plants were selectedat random and ear marked in the net plot area in eachtreatment for recording the observations on yieldcomponents (cob length cob girth number of cobsplant-1 and cob weight) and yield

The data on observations were analyzedstatistically by applying the technique of analysis ofvariance as outlined by Panse and Sukhatme (1978)for Randomized Block Design and for Split plot designas suggested by Gomez and Gomez (1984)Statistical significance was tested by F test Criticaldifference for treatment means was evaluated at 5 percent level of probability (P=005)

EFFECT OF INTEGRATED NUTRIENT MANAGEMENT PRACTICES ON YIELD ATTRIBUTES

38

RESULTS AND DISCUSSIONYield attributesNumber of cobs plant-1

Organic and inorganic sources of nutrition withor without use of bio fertilizers to baby corn showedsignificantly higher number of cobs plant-1 during boththe years of study 2015 amp 2016 over un-fertilized controlCob number plant-1 ranged from 095 to 223 in 2015and 147 to 222 during 2016 in different treatmentsIntegration of 75 RDF with 25 N throughvermicompost along with the use of bio fertilizers(Azospirillum and Bacillus megaterium) showedsignificantly higher number of cobs plant-1 comparedto rest of the treatments of 75 RDF with or withoutbio-fertilizers integrated with 25 N through FYM orvermicompost and un-fertilized control Significantlylower cob number plant-1was recorded with un-fertilizedcontrol than rest of the treatments during both the yearsof study (Table 1) Aravinth et al(2011) reported thatuse of vermicompost in addition to inorganic sourcesof nutrition resulted in higher cobs plant-1 Similar resultsof higher cobs plant-1 were also noticed by Lone etal (2013) in baby corn by the use of FYM

Integration of 75 RDF with 25 RDNthrough FYM to baby corn showed significantly highernumber of cobs plant-1 over unfertilized control and wasat par with 100 RDF during both the years of studythough these were significantly lower than other organiccombination treatments Use of bio-fertilizer along with75 RDF with 25 RDN through FYM was at parwith 100 RDF in conjunction with the use of bio-fertilizers 75 RDF integrated with 25 RDN throughvermicompost and significantly higher number of cobsplant-1 than rest of the treatment except vermicompostwith bio-fertilizers in both the years

Residual effect of 100 RDF application alongwith seed treatment with Bradyrhizobium to hyacinthbean during rabi 2015-16 resulted in significantly highernumber of baby cobs plant-1 in the succeeding kharifseason over 75 RDF with or without use ofBradyrhizobium seed treatment and was at par with100 RDF application

Cob length girth and width (cm)

Organic (FYM and vermicompost) andinorganic sources of nutrition (75 RDF and 100 RDFwith chemical fertilizers) with or without use of bio

fertilizers have showed significantly higher cob lengthgirth and width over un-fertilized control (Table 1)Different treatments brought out significant variation incob length girth and width and the cob length girthand width varied from (750 to 1250 cm and 711 to1224 cm) (398 to 574 cm and 373 to 565 cm) and(127 to 186 cm and 127 to 183 cm) during 2015 and2016 respectively Roy et al (2015) reported significanthigher cob length with application of 75RDF + FYMthan 75 RDF and 100RDF alone

Conjunctive use of inorganic fertilizers ie 75RDF with 25 N through vermicompost along withbio-fertilizer (Azospirillum and Bacillus megaterium)resulted in significantly higher cob length girth and widthduring both the years of study than all the othertreatments Significantly lower cob length girth and widthwere recorded with un-fertilized control than rest of thetreatments

Integration of 75 RDF with 25 N throughvermicompost showed significantly higher cob lengthgirth and width over un fertilized control and was atpar with 100 RDF 100 RDF along with the use ofbio-fertilizers and 75 RDF + 25 N through FYM inconjunction with or without the use of bio fertilizers

Higher cob length was reported by Aravinthet al (2011) with use of vermicompost along withRDF in baby corn Similar results of increased cobgirth were noticed by Ravinchandran et al2016 Singhet al (2014) with integration of organic manure (FYM)along with inorganic fertilizers Jinjala et al (2016) withintegration of inorganic sources of nutrition in conjunctionwith bio-fertilizers

Application of 75 and 100 RDF with orwithout seed inoculation of Bradyrhizobium to hyacinthbean during rabi 2015-16 did not show any significantresidual effect on cob length girth and width of babycorn in the succeeding kharif 2016 season

Cob weight with or without husk (g)

Application of 25 N through organic manures(FYM or vermicompost) integrated with 75 RDF alongwith or without the use of bio-fertilizers (Azospirillumand Bacillus megaterium) and 100 RDF with orwithout the use of bio-fertilizers showed significantlyhigher cob weight with and without husk during boththe years of study and significantly higher cob weightwas with integration of 75 RDF with 25 RDN

PREETHAM et al

39

Tabl

e-1

Effe

ct o

f int

egra

ted

nutr

ient

man

agem

ent p

ract

ices

on

yiel

d at

trib

utes

of b

aby

corn

dur

ing

khar

if 2

015

and

2016

Trea

tmen

t giv

en to

kha

rif b

aby

corn

2015

2016

Cob

spl

ant-1

Cob

leng

th(c

m)

Cob

girth

(cm

)

Cob

wid

th(c

m)

Cob

wei

ght

(g)

Cob

spl

ant-1

Cob

leng

th(c

m)

Cob

girth

(cm

)

Cob

wid

th(c

m

Cob

wei

ght

(g)

With

With

out

husk

husk

With

With

out

husk

husk

T 1- 25

N through FY

M + 75

RDF

178

106

95

251

6846

80

926

183

931

513

163

426

89

03T 2-

25

N th

roug

h FY

M +

75

RD

F +

200

108

35

601

7247

33

100

01

989

645

541

6843

16

908

Azo

spiri

llum

and

Bac

illus

meg

ater

ium

5

kg h

a-1 e

ach

T 3- 25

N

thr

ough

Ver

mic

ompo

st +

202

109

55

581

7649

17

100

02

029

655

461

7143

68

926

75

RD

FT 4-

T4 2

5 N

thro

ugh

Verm

icom

post

+2

2312

50

574

186

555

913

46

222

122

45

651

8348

73

120

675

R

DF

+ A

zosp

irillu

m a

nd B

acill

usm

egat

eriu

m

5 k

g ha

-1 e

ach

T 5- 10

0 R

DF

175

106

65

241

7146

29

920

178

989

512

161

423

69

01T 6-

100

RD

F +

Azo

spiri

llum

and

Bac

illus

200

109

65

551

8049

86

103

81

989

905

441

7443

10

936

meg

ater

ium

5

kg

ha-1 e

ach

T 7- C

ontro

l (N

o fe

rtiliz

er a

pplic

atio

n)0

957

503

981

2715

88

358

147

711

373

127

239

95

13S

Em

+0

060

410

150

051

550

400

060

500

150

050

970

13C

D (

P=0

05)

020

127

045

016

477

123

018

153

047

016

300

041

Trea

tmen

t giv

en to

rabi

cro

p (h

yaci

nth

bean

)S

1- 10

0 R

DF

191

987

520

161

424

99

25S

2- 75

R

DF

180

933

501

167

373

78

25S

3-10

0 R

DF

+ B

rady

rhiz

obiu

m

200

990

533

160

445

79

9550

0 g

ha-1 (

See

d tre

atm

ent)

S4-

75

RD

F +

Bra

dyrh

izob

ium

1

889

605

071

6639

98

850

500

g ha

-1 (

See

d tre

atm

ent)

SE

m+

003

024

017

005

086

019

CD

(P

=00

5)0

10N

SN

SN

S2

460

54In

tera

ctio

nB

ean

trea

tmen

t mea

ns a

t sam

e le

vel o

f bab

y co

rn IN

M tr

eatm

ents

SE

m+

009

064

044

014

228

050

CD

(P

=00

5)N

SN

SN

SN

SN

SN

SIN

M tr

eatm

ent m

eans

of b

aby

corn

at s

ame

leve

l of b

ean

trea

tmen

tsS

Em

+0

100

740

410

132

200

45C

D (

P=0

05)

NS

NS

NS

NS

NS

NS


40

through vermicompost in conjunction with bio-fertilizers(5559 g amp 4873 g and 1346 g amp 1206 g) with orwithout husk respectively and significantly lower (1588g and 2399 g respectively) was with unfertilized controlduring both the years of study kharif 2015 and 2016than rest of treatments

Residual effect with application of 100 RDF(chemical fertilizers) along with seed treatment withBradyrhizobium to hyacinth bean crop during rabi2015-16 resulted in significantly higher baby cob weightwith or without husk in succeeding baby corn cropover 75 RDF with or without seed treatment withBradyrhizobium and was at par with 100 RDF

Superior vegetative growth (plant height leafarea index dry matter production) due to integratedeffect of organic manures along with inorganic fertilizerand bio-fertilizers has realized in the increased babycob weight with or without husk Ashoka et al(2009)reported similar results with integration of RDF withvermicompost and Edwin Lukham et al (2003) withintegration of RDF with FYM

Significant improvement in yield attributes byintegrated nutrient management practices in baby cornmay be attributed to the combined effect of organicand inorganic sources which helped in maintain higherauxin level resulting in better plant height LAIpresumably chlorophyll content of the leaves resultedin better interception absorption and utilization of radiantenergy leading to higher photosynthesis rate and finallymore accumulation of dry matter by the plants Theoverall improvement of crop growth reflected into bettersource and sink relationship which in turn enhancedthe yield attributes as found by Ashoka et al (2009)

Cob Yield (kg ha-1)

In kharif 2015 and 2016 cob yield with huskranged from 4279 to 12290 kg ha-1 and 4393 to 11727kg ha-1 respectively Similarly cob yield without huskwas 823 to 2049 kg ha-1 and 749 to 1890 kg ha-1

respectively Integration of 75 RDF with 25 RDNthrough vermicompost in conjunction with the use ofbio-fertilizers (Azospirillum and Bacillus megaterium)showed significantly higher cob yields with or withouthusk during both the years of study (kharif2015 and2016) over integration of 75 RDF + 25 N throughvermicompost integration of 75 RDF + 25 N throughFYM with and without the use of bio-fertilizer 100RDF 100 RDF + bio fertilizer and un-fertilized control

(Table 2) Significantly lower yield (cob with or withouthusk) was recorded with un-fertilized control than restof the treatments

Ashish Shivran et al (2015) reported similarresults with application of N through chemical fertilizerand vermicompost in 7525 proportions over 100 soleN chemical fertilizer application and 5050 (chemicalfertilizer and vermicompost) Similar results were alsorecorded by Aravinth et al 2011 Ashoka et al 2008Dadarwal et al 2009 and Prasanna Kumar et al2007

Integration of 75 RDF with 25 N throughFYM showed on par cob yields with or without huskwith 100 RDF (chemical fertilizer) and significantlysuperior to unfertilized control and significantly lowerthan rest of the treatments during kharif 2015 and 2016

Integration of 75 RDF with 25 N throughFYM in conjugation with the use of bio-fertilizersintegration of 75 RDF with 25 N throughvermicompost and 100 RDF along with bio-fertilizersshowed on par yields of baby corn during both theyears of study (kharif 2015 and 2016) and weresignificantly superior to 100 RDF alone integrationof 75 RDF with 25 N through FYM and unfertilizedcontrol

Application of 100 RDF with or without seedtreatment with Bradyrhizobium to hyacinth bean duringrabi crop resulted in significantly higher cob yield withor without husk in succeeding kharif baby corn cropover 75 RDF alone or with Bradyrhizobium seedtreatment

Stover yield (kg ha-1)

Stover yield ranged from 13821 to 24020 kgha-1 in 2015 and 12168 to 21165 kg ha-1 during secondyear (Table-2) Application of 75 RDF along with25 N through vermicompost in conjunction with bio-fertilizers (Azospirillum and Bacillus megaterium)resulted in significantly higher stover yield of baby cornover rest of the treatments during both the years ofstudy (kharif 2015 and 2016) Significantly lower stoveryield was with control over rest of the treatments inboth the years Integration of 75 RDF with 25 Nthrough FYM resulted in significantly higher stover yieldsover un-fertilized control and was at par with 100RDF with or without the use of bio-fertilizer during theyear 2015 and significantly lower than rest of thetreatments except control in 2016 Integration of 75

PREETHAM et al

41

Tabl

e-2

Effe

ct o

f int

egra

ted

nutr

ient

man

agem

ent p

ract

ices

on

cob

yiel

d (k

g ha

-1) w

ith a

nd w

ithou

t hus

k an

d st

over

yie

ld o

f bab

y co

rn d

urin

gkh

arif

201

5 an

d 20

16

Trea

tmen

t giv

en to

kha

rif b

aby

corn

2015

2016

Cob

yie

ld (k

g ha

-1)

Stov

erC

ob y

ield

(kg

ha-1)

Stov

er y

ield

yiel

d(kg

ha-1

)(k

g ha

-1)

With

hus

kW

ithou

t hus

kW

ith h

usk

W

ithou

t hus

k

T 1- 25

N through FY

M + 75

RDF

8204

1602

1934

481

4714

9518

919

T 2- 25

N

thro

ugh

FYM

+ 7

5 R

DF

+ A

zosp

irillu

m90

3218

0320

865

8882

1596

1970

8an

d B

acill

us m

egat

eriu

m

5 k

g ha

-1 e

ach

T 3- 25

N

thro

ugh

Verm

icom

post

+ 7

5 R

DF

9400

1801

2147

589

9416

0119

762

T 4- T4

25

N th

roug

h Ve

rmic

ompo

st +

75

RD

F +

1229

020

4924

020

1172

718

9021

165

Azo

spiri

llum

and

Bac

illus

meg

ater

ium

5

kg

ha-1 e

ach

T 5- 10

0 R

DF

8049

1542

1906

081

9914

7119

364

T 6- 10

0 R

DF

+ A

zosp

irillu

m a

nd B

acill

us93

4418

0720

398

8824

1553

1963

1m

egat

eriu

m

5 k

g ha

-1 e

ach

T 7- C

ontro

l (N

o fe

rtiliz

er a

pplic

atio

n)42

7982

313

821

4393

749

1216

8S

Em

+26

764

429

200

1638

5C

D (

P=0

05)

824

198

1321

617

4911

87Tr

eatm

ent g

iven

to ra

bi c

rop

(hya

cint

h be

an)

S1-

100

RD

F88

2415

4619

086

S2-

75

RD

F78

8013

6917

833

S3-10

0 R

DF

+ B

rady

rhiz

obiu

m

500

g h

a-190

7315

9019

410

(See

d tre

atm

ent)

S4-

75

RD

F +

Bra

dyrh

izob

ium

5

00 g

ha-1

8033

1411

1836

5(S

eed

treat

men

t)S

Em

+20

216

280

CD

(P

=00

5)57

745

798

Inte

ract

ion

Bea

n tre

atm

ent m

eans

at s

ame

leve

l of b

aby

corn

INM

trea

tmen

tsS

Em

+53

541

740

CD

(P

=00

5)N

SN

SN

SIN

M tr

eatm

ent m

eans

of b

aby

corn

at s

ame

leve

l of b

ean

treat

men

tsS

Em

+50

439

748

CD

(P

=00

5)N

SN

SN

S


42

RDF with 25 N through vermicompost was on parwith integration of 75 RDF with 25 N through FYMand 100 RDF in conjunction with the use of bio-fertilizers

During 2016 integration of 75 RDF with 25RDN through vermicompost integration of 75 RDFwith 25 N through FYM in conjugation with or withoutthe use of bio-fertilizers and 100 RDF with or withoutthe use of bio-fertilizers showed on par stover yieldsand were significantly superior to un-fertilized controlSignificantly higher stover yields was recorded due toresidual effect of 100 RDF applied with or withoutBradyrhizobium seed treatment to hyacinth bean cropduring rabi 2015-16 in the succeeding kharif 2016 over75 RDF with or without seed treatment withBradyrhizobium to hyacinth bean

Organic manures along with inorganic fertilizersand seed inoculation with Azospirillum and Bacillusmegaterium had pronounced effect on green cob yieldand stover yield these might be due to fixation ofatmospheric nitrogen and secretion of growth promotingsubstances of Azospirillum and increased bacterialefficiency by phospho-bacteria combined together mighthave increased growth and yield parameters asreported by Somani et al(2005) Similar effect ofincreased cob yields was also observed byThavaprakash et al 2005 in baby corn due tosynergistic effect of INM Thavaprakash andVelayudham (2007) in sweet corn and Nath et al(2009)

Integrated use of fertilizers did bring aboutsignificant improvement in overall growth of crop byproviding need based nutrients from initial stage andincreasing supply of N P and K in more synchronizedway with the treatment receiving integrated supply ofnutrients from organic manures inorganic and bio-fertilizers which expressed in terms of cob plant-1 coblength cob girth cob weight with or without husk byvirtue of increased photosynthetic efficiency

Greater availability of photo-synthatesmetabolites and nutrients to develop reproductivestructures seems to have resulted in increased numberof cobs plant-1 cob girth cob length cob weight andyield with INM treatments

CONCLUSION

Based on the above results it can beconcluded that higher cob yield with or without huskand stover yield can be realized with combined use of75 RDF with 25 N through vermicompost in-conjunction with bio-fertilizers followed by 100 RDFwith bio-fertilizers integration of 25 N throughvermicompost and 75 RDF and combined use of25 N through FYM with 75 RDF in-conjunction withbio-fertilizers for kharif baby corn on Alfisols underNorthern Telangana Zone

REFERENCES

Aravinth V Kuppuswamy G and Ganapathy M2011 Growth and yield of baby corn (Zeamays) as influenced by inter cropping plantinggeometry and nutrient management IndianJournal of Agricultural Sciences 81(9) 875-877

Ashish Shivaran Mathukia RK Takar SSBijarniya AL and Kudi RK 2015Effect of fertilitylevels and fertilizer vermicompost proportions onyield and uptake of nutrients and economics ofbaby corn (Zea mays) Journal of Eco-friendlyAgriculture10(1) 36-38

Ashoka P Mudalagiriyappa Pujari BT Hugar PSand Desai BK 2008Effect of micronutrientswith or without organic manures on yield of babycorn (Zea mays L) ndash chickpea (CicerarietinumL) sequenceKarnataka Journal of AgriculturalSciences 21(4) 485-487

Ashoka P Anand S R Mudalagiriyappa andSmitha R 2009 Effect of macro andmicronutrients with organics on growth qualityyield and economics of baby corn (Zea maysL) in Tungabhadra command area CropResearch (Hisar) 37(1) 15-18

Dadarwal R S Jain NK and Singh D 2009Integrated nutrient management in baby corn(Zea mays)Indian Journal of AgriculturalSciences 79(12) 1023-1025

Edwin Luikham Krishna Rajan J Rajendran K andMariam Anal PS 2003Effect of organic andinorganic nitrogen on growth and yield of babycorn (Zea mays L)Agricultural Science Digest23(2) 119-121

PREETHAM et al

43

Gomez K A and Gomez A A 1984StatisticalProcedures for Agricultural Workers John-Wileyand Sons Inc New York258-259

JinjalaVR Virdia HM Saravaiya NN and Raj AD2016 Effect of integrated nutrient managementon baby corn (Zea mays L)Agricultural ScienceDigest 36(4) 291-294

Lone AA Allai BA and Nehvi FA 2013 Growthyield and economics of baby corn (Zea maysL) as influenced by integrated nutrientmanagement (INM) practices African Journalof Agricultural Research 8(37) 4537-4540

Nath K Nepalia V and Dilip Singh 2009 Effect ofintegrated nutrient management on growth andyield of sweet corn (Zea mays L sspSaccharata)Journal of Agricultural Research30(1-2) 73-76

Panse VG and Sukhatme PV1978 StatisticalMethods for Agricultural workers 2197 IndianCouncil for Agricultural Research New Delhi

Paroda and Sashi 1994 Thailand mei baby cornkisafalthakikahani Kheti48(1) 15-18

Prasanna Kumar Halepyati AS Desai B K andPujari BT 2007 Effect of integrated nutrientmanagement on economics of maizecultivationKarnataka Journal of AgricultureSciences 20(4)831-832

Rav ichandran S Suresh Kumar RandHariharasuthan V 2016 Growth yield andeconomics of baby corn (Zea mays L) asinfluenced by plant population and integratednutrient management International Journal ofAgriculture Sciences 8(53) 2774-2776

Roy S Sengupta A Barman M Puste AMandGunri SK 2015Effect of irrigation andnutrient management on growth yield qualityand water use of summer baby corn (Zea maysL) in new alluvial zone of West BengalJournalof Crop and Weed11(2) 111-116

Singh MV VedPrakasah Bhagwan Singh andShah HN 2014Response of maize hybridsto integrated nutrient management HaryanaJournal of Agronomy 30(1) 65-69

Somani LL Bhendari SC Saxena SN and VyasKC 2005Bio-fertilizers Kalyani PublishersLudhiana

Thavaprakash N Velayudham K and MuthukumarVB 2005 Effect of crop geometry intercroppingsystem and integrated nutrient managementpractices on productivity of baby corn (Zea maysL) based intercropping systems ResearchJournal of Agricultural and Biological Sciences1(4) 295-302

Thavaprakash N and Velayudham K 2007 Effectof crop geometry intercropping systems andINM practices on cob yield and nutrient uptakeof baby cornAsian Journal of AgriculturalResearch 1(1) 10-16


44



ABSTRACT

DETERMINANTS OF KEY ENTREPRENEURIAL CHARACTERISTICS - A STUDYOF ENTREPRENEURIAL ATTITUDE OF AGRICULTURAL STUDENTS OF

TELANGANA STATEA MEENA DUTTEJ and E SREE CHARAN

Department of Statistics amp Mathematics College of AgricultureProfessor Jayashankar Telangana State Agricultural University


The present study was carried out to study the Profile characteristics of Agricultural Students of Telangana State tostudy the agreement among several rankers with respect to the characteristics-Risk Orientation Achievement OrientationInnovativeness Market Orientation Decision making ability to identify the key Entrepreneurial characteristics The study concludedthat about 655 of the agricultural students were from the age group of 19-24 years 408 were first born child54 had firstclass marks amp 23 had distinction with low participation in extracurricular activities 883 of their fathers were literates andwere engaged in government service and business and 458 of them are having 26 to 5 acres of landFactor Analysis wasdone for extracting the key Entrepreneurial characteristics and Kendallrsquos Co-efficient of Concordance was used to test theagreement among several rankers The result showed that there is strong agreement among the respondents with respect toranking each of the attributes under Risk Orientation as Kendallrsquos W = 059 amp moderate agreement with respect to ranking theattributes under Market Orientation with W= 0292 There is no agreement among the respondents with respect to ranking eachof the attributes under Achievement Orientation Innovativeness and Decision making ability since Kendallrsquos w is equal to 01130182 and 0064 respectivelyThree key factors were extractedfinally ranking of the indicators has been made on the basis offactor scores Further from the Kendallrsquos Co-efficient value it is concluded there is complete disagreement between the ratersregarding each of the following - Agro based enterprise as the best option to earn money for landless rural people Agro basedenterprise can be a good source of income for low investors and establishment of Agro based enterprise is unproductive

The entrepreneurship is a very old conceptaccording to which anyone who runs business iscalled an entrepreneur The more precise meaning ofentrepreneur is one who perceives a need and thenbrings together manpower material and capitalrequired to meet that need Entrepreneur is one whounderstands the market dynamics and searches forchange respond to it and exploit it as an opportunitya person who organizes and manages any enterpriseespecially a business usually with considerableinitiative and risk

Agriculture sector plays a formidable role inthe sustainable growth and development of Indianeconomy It provides for the food and nutritionrequirements of 13 billion Indian people and createsemployment opportunities through forward andbackward linkages to support 60 of Indianpopulation

While it has achieved substantial progressover the years regarding food security accessibilityand affordability the agriculture sector is stillchallenged by low productivity low profitabilityincrease in input costs wastage of crops due to lack

of storage and supply chain management Thesechallenges present a host of opportunities for Agri-entrepreneurs

Agri-entrepreneurship employs entre-preneurial skills models and innovative ideas toeconomically solve problems in the agriculture sectorand increase the profitability of the farming business

Agri-entrepreneurship can play a significantrole in solving the challenges related to informationdissemination farm management capital availabilitymechanization of farm and the agriculture supplychain

As opposed to the increasing demand forhigher education unemployment of universitygraduates has been rapidly increasing Even thoughaccurate data on unemployment is not available it isbelieved that over 28 per cent of agricultural collegegraduates need to find job (Jalali 2003) This is whymany universities offer entrepreneurial coursesactiv ities and stimulate students to involve inentrepreneurial activities Universities are playing animportant role in entrepreneurship development(Menzies 2000) But the point is that could

emailmeenaadmala22gmailcom

45

entrepreneurship be made in universities and couldentrepreneurship be learned

Agricultural students are now better informedabout professional opportunities than in the past Theemployment opportunities the type of jobs and theearning potential influence student as well as theirparents to decide on the preferred course With theadvent of internet freedom and mobility for studentthey are making their choice in rapidly changing globaleconomy

The right choice of discipline and attitudetowards the education imparted through the instituteand vocational decisions have to occupy a centralplace in the life of the student community

The objectives of this study are delineated below

1 To study the Profile characteristics of AgriculturalStudents of Telangana State

2 To study the agreement among several rankerswith respect to the characterist ics-RiskOrientation Achievement OrientationInnovativeness Market Orientation Decisionmaking ability

3 To identify the key Entrepreneurial characteristics


The data was collected using a questionnairewhich was distributed randomly to 120 students ofAgriculture belonging to various Agricultural Collegesof Telangana StateThe questionnaire wasadministrated to a seven points Likert type summatedrating scales of questionnaire from strongly disagree(-3) to strongly agree (+3) were adopted to identifythe characteristics

SPSS was used to perform statisticalanalysis of the data collected through questionnaireswith an aim to study the Profile characteristics ofAgricultural Students of Telangana State to study theagreement among several rankers with respect to thecharacteristics-Risk Orientation AchievementOrientation Innovativeness Market OrientationDecision making ability and to identify the keyentrepreneurial characteristics

The independent variables undertaken in thisstudy are viz age birth order academic achievementparticipation in extra-curricular activities fatherrsquos

education fatherrsquos occupation family land holdingrisk orientat ion achievement mot ivationinnovativeness market orientation and decisionmaking ability were measured with the help of suitablescale and procedures

The dependent variables like attitude towardsagricultural entrepreneurship was measured with thehelp of scale developed by Meenaben Patel (2009)with due modification The methods used wereDescriptive statistics Factor analysis and Kendallrsquostest a Non-parametric technique

Tools of Data Analysis

The present study has used sophisticatedmethods of statistics ndash

1 KENDALLrsquoS COEFFICIENT OF CONCORDANCE

The concept of association between morethan two sets of ranking is measured non-parametrically by a statistic known as Kendallrsquoscoefficient of concordance This coefficient is usedto express the intensity of agreement among severalrankings and is given by

Where Ri is sum of all ranks received by option i

K= No of sets ranking ie no of rankers and

N= No of options being ranked

Kendallrsquos W ranges from 0 (no agreement) to 1(complete agreement)

If the test statistic W is 1 then all the surveyrespondents have been unanimous and eachrespondent has assigned the same order to the listof concerns If W is 0 then there is no overall trendof agreement among the respondents and theirresponses may be regarded as essentially randomIntermediate values of W indicate a greater or lesserdegree of unanimity among the various responses

2 FACTOR ANALYSIS

Data was analysed using Varimax rotationIn order to obtain interpretable characteristics andsimple structure solutions researchers havesubjected the initial factor matrices to Varimaxrotationprocedures (Kaiser 1958) Varimax rotated

DETERMINANTS OF KEY ENTREPRENEURIAL CHARACTERISTICS

2 2 212 R 3K N(N 1)i iW 2 2K N(N 1)

46

factors matrix provides orthogonal common factorsFinally ranking of the indicators has been made onthe basis of factor scores


Frequencies and percentages of variousindependent variables viz age birth order academicachievement participation in extra-curricularactivities fatherrsquos education fatherrsquos occupationfamily land holding were calculated and found thatabout 655 of the agricultural students were fromage group of 19-24 years 408 were first bornchild54 had first class marks amp 23 had distinctionfound with low participation in extracurricular activities883 of their fathers were literates and were engagedin government service and business and 458 ofthem are having 26 to 5 acres of land

Kendallrsquos test was used to test the agreementof different rankers or respondents with respect toeach of the attributes under Risk OrientationAchievement Orientation Innovativeness MarketOrientation and Decision making ability The resultshowed that there is strong agreement among therespondents with respect to ranking each of theattributes under Risk Orientation as KendallrsquosW = 059 amp moderate agreement with respect toranking the attributes under Market Orientation withW= 0292 There is no agreement among therespondents with respect to ranking each of theatt ributes under Achiev ement Orientation

Innovativeness and Decision making ability sinceKendallrsquos W is equal to 0113 0182 and 0064respectively

Reliability and Validity

The reliability value of our surveyed data was06 for the characteristics as given in Table 1 If wecompare our reliability value with the standard valuealpha of 07 advocated by Cronbach (1951) a moreaccurate recommendation (Nunnallyamp Bernsteinrsquos1994) or with the standard value of 06 asrecommendated by Bagozziamp Yirsquos (1988) we find thatthe scales used by us are sufficiently reliable for dataanalysis Regarding validity Kasier ndash Meyer ndashOlkin(KMO) measure of Sampling Adequacy is a measureof whether or not the distribution of value is adequatefor conducting FA As per KMO measure a measureof gt09 is marvellous gt08 is meritorious gt07 ismiddling gt06 is mediocre gt05 is miserable andlt05 is unacceptable

Table 1 KMO and Bartlettrsquos Test

Kaiser-Meyer- Measure of 0600Olkin Adequacy Sampling

Bartlettrsquos Test of Approx Chi-Square 125523Sphericity df 36

Sig 0000

The data returned a value sampling adequacyof 0600 indicating middling as presented in Table 1Bartlettrsquos test of Sphericity is a measure of the

Table 2 Total Variance Explained

Component

Initial Eigen values Extraction Sums of SquaredLoadings

Total ofVariance

Cumulative

Total ofVariance

Cumulative

Rotation Sums of SquaredLoadings

Total ofVariance

Cumulative

1 1965 21839 21839 1965 21839 21839 1894 21044 21044

2 1806 20070 41909 1806 20070 41909 1520 16887 37932

3 1086 12064 53973 1086 12064 53973 1444 16042 53973

4 927 10301 64274

5 887 9860 74134

6 738 8199 82333

7 630 6996 89329

8 529 5878 95207

9 431 4793 100000

Extraction Method Principal component Analysis

MEENA et al

47

multivariate normality of the set of distributions It alsotests whether the correlation matrix conducted withinthe FA is an identity matrix FA would be meaninglesswith an identity matrix A significance value lt005indicates that the data DO NOT produce an identitymatrix and are thus appropriately multivariate normaland acceptable for FA (George and Mallery 2003)The data within this study returned a significance valueof 0000 indicating that the data was acceptable forFA When the original 14 characteristics wereanalysed by the Principal Component Analysis (PCA)with Varimax rotation three characteristics extractedfrom the analysis with an Eigen value greater than orequal to 1 which explained 53973 percent of thetotal variance presented in Table 2

Component Number

Scree Plot

Eig

en v

alue

Figure 1 Plot of the Eigen valuesThe result of the Factor Analysis is presented in Table3 and figure 1

Table 3 Component Matrixa

Component

1 2 3

I feel that establishment of 0773agro based enterprise isunproductiveAgro based enterprise 0636is useful in solving un-employment problemI think that it is difficult to 0545follow legal procedure foragro based enterpriseI prefer to have service -0737but not own agro basedenterpriseAgro based enterprise is a 0308 0689best option to earn moneyfor landless rural peopleI prefer to be a part of agro 0383 -0307based enterprise than otherenterprise

Component1 2 3

I think that agro based 0522 0572enterprise can be a goodsource of income for lowinventors

Agro based enterprise 0491 0369 -0517is a best source of employment for irrespective level ofeducated rural youth

I donrsquot like to advice any one 0421 -0316 -0477to become entrepreneur

Extraction Method Principal Component Analysisa 3 components extracted

The study revealed (as presented in Table 3)that the factor loadings are ranged from 0773 to 0308The higher a factor loading the more would its testref lect or measure as characteristics Thecharacteristic getting highest loading becomes thetit le of each group of characteristics egEstablishment of agro based enterprise isunproductivendash title of characteristics group I andlikewise Further the present study has interpretedthe characteristics loaded by variables havingsignificant loadings of the magnitudes of 050 andabove (Pal 1986 Pal and Bagi 1987)

Characteristics group I One characteristic with0773 belonged to Establishment of agro basedenterprise is unproductive and another characteristicwith 0636 belonged to Agro based enterprise is usefulin solving unemployment problem

Characteristics group II One characteristic with0689 belonged to Agro based enterprise is a bestoption to earn money for landless rural people

Characteristics group III One characteristic with0572 belonged to agro based enterprise can be agood source of income for low investors

Ranking of the above characteristics in orderof their importance along with factor score is shownin Table 4 The importance of these characteristicsas perceived by the respondents has been rankedon the basis of factor score

As depicted in Table 4 the characteristicsAgro based enterprise is a best option to earn moneyfor landless rural people Agro based enterprise can


48

be a good source of income for low investorsestablishment of Agro based enterprise is unproductivegot the ranks of first second and third respectivelyand constitute the key characteristics of Agriculturetowards Agri Entrepreneurial ship

Table 4 Ranks

Mean RankI think that agro based enterprise 200can be a good source of income forlow investors

I feel that establishment of agro 195based enterprise is unproductive

Agro based enterprise is a best 205option to earn money for landlessrural people

Kendallrsquos W Test is referred to the normalizationof the Friedman statistic Kendallrsquos W is used toassess the t rend of agreement among therespondents To test the agreement of variousrespondents with respect to three key characteristicsKendallrsquos co-efficient is calculated and the result ofthe study is given in Table 5

Ho There is no difference in mean ranks for repeatedmeasures

H1 A difference exists in the mean ranks forrepeated measures

Table 5Test StatisticsN 120

Kendallrsquos Wa 0003

Chi-Square 0822

df 2

Asymp Sig 0663

a Kendallrsquos Coefficient of Concordance

Kendallrsquos W = 0003 from Table 5The studyrevealed that there is complete disagreement betweenthe raters regarding Agro based enterprise is a bestoption to earn money for landless rural people Agrobased enterprise can be a good source of income forlow investors establishment of Agro based enterpriseis unproductive

Further similar analysis was done to testwhether there is agreement among the respondentswith respect to ranking each of the attributes underRisk Orientation Market Orientation Achievement

Innovativeness and Decision making ability The studyrevealed that there is strong agreement among therespondents with respect to ranking each of theattributes under Risk Orientation as KendallrsquosW = 059 amp moderate agreement with respect toranking the attributes under Market Orientation withW = 0292 There is no agreement among therespondents with respect to ranking each of theatt ributes under Achiev ement OrientationInnovativeness Decision making ability sinceKendallrsquos W = 011301820064 respectivelyREFERENCES

Bagozzi RP and Yi Y 1988 ldquoOn the Evaluation ofStructural Equation Modelsrdquo Journal of theAcademy of Marketing Science 16(1)74-95

Cronbach LJ 1951 Coefficient Alpha and the InternalStructure of tests Psychometrika16 (3)297-334

George D and MalleryP 2003 SPSS for WindowsStep by Step A Simple Guide and Reference110 update (4th edition)

Jalali KhK 2003 ldquoAgricultural alumni rate ofunemployment (In Farsi)rdquo 31st Conference ofAgricultural Higher Education AdministersShiraz University Shiraz

KasierHF 1958 ldquoThe Varimax criterion for analyticrotation in Factor Analysisrdquo Psycometrica23187-200

Menzies T 2000 ldquoAn exploratory study of universityentrepreneurship centers in CanadaA first stepin model buildingrdquo Journal of Small Businessand Entrepreneurship 18 (3) Abstract

Nunnally J Cand Bernstein1994 IraPsychometrics Theory McGraw ndash Hill NewYork

Patel Meena 2009The scale ldquoScale to measureEntrepreneurial attituderdquo This was presentedin the meeting of 4th Agresco committee

Pal Y 1986 A Theoretical study of Some FactorAnalysis Problems

PalY and Bagai OP 1987 A Common FactorBettery Reliability Approach to Determine theNumber of Interpretable Factorsrdquo a paperpresented at the IX Annual Conference of theIndian Societyfor Probability and Statistics heldat Delhi University of Delhi India

MEENA et al

49

PATTERNS OF CHICKEN MEAT AND EGG CONSUMPTION IN RURAL HOUSEHOLDS OF JAGITIAL DISTRICT OF TELANGANA STATE

SRINIVAS GURRAM VCHINNI PREETAM and SWATHI BORAPV Narasimha Rao Telangana Veterinary University

College of Veterinary Science Korutla Jagitial-505 326

The consumption patterns of chicken meat and egg was studied in Jagitial district of Telangana on a sample size of 300households The results of the study concluded that most of the population is consuming chicken meat when compared to muttonpork and fish Majority of the respondents consuming 1 kg broiler meathouseholdtime and 3-5 eggs householdtime Majority ofthe respondents were spending 100-250 rupees for purchasing chicken meathouseholdtime Majority of the households preferredchicken curry as utilization product followed by fry biryani and kheema Most of the people do not know the nutritive value of meatand egg



ABSTRACT

emailgurramsrinivas4gmailcom

Many of the developing countries areexpected to undergo transformation in their economyand rapid urbanization over the next 30 years Indiarsquospoultry industry has transformed from a merebackyard activity into a major commercial activityIndia the worldrsquos second largest developing countryis contributing to the expansion of the poultry sectorthrough its rapid growth Meat consumption hasshifted over time from beef veal lamb mutton andchevon to a greater consumption of poultry and fishdue to changing tastes costs and income (Akbayand Boz 2005) This investigation focused on ruralhousehold chicken meat and egg consumption levelsand patterns of Jagitial district Even though manystudies existed on animal product consumption mostof them were focused on urban household animalproduct consumption in relatively small cities Thispaper is an attempt in that direction to address theissue of chicken meat and egg consumption in ruralhouseholds of Jagitial district of Telangana


The objective of this study is to know thechicken meat and egg consumption patterns in ruralhouseholds in Jagitial district of Telangana The mainsource of this study is data obtained over a period of4 months from August to November 2016 During thisperiod data was collected from 300 households of 20villages in 9 mandals of Jagitial district A single visitwas made to each household Using pretested

structured interview schedule the respondents werepersonally interviewed and the data was collectedThe data presented in tables was expressed infrequency and percentage

Table 1 Chicken meat and Egg Consumptionpatterns in Jagitial district of Telangana

Particulars Frequency Pern=300 centage

()Family sizelt5 members 192 640gt5 members 52 1735 members 56 187Meat of choiceChicken 251 837Mutton amp Chevon 41 137Pork 7 10Beef 1 1Frequency of chicken meat consumptionWeekly twice 15 50Weekly once 89 297Fortnightly 95 317Monthly 101 337Frequency of egg consumptionWeekly twice 95 317Weekly once 172 573Fortnightly 24 80Monthly 9 30

50

SRINIVAS et al


()Reasons for consumption of meatTaste 241 803Habituated 20 67Demand by children 12 40Because of guests 27 90Total Chicken consumption householdtime1 Kg 225 75015 Kg 52 1732 Kg 12 40gt2 Kg 11 37Total number of eggs consumption householdtime3-5 eggs 213 7105-7 eggs 45 1508-10 eggs 32 107gt10 eggs 10 33Reasons for less non consumption of meat andeggReligious sentiments 255 850Dislike 10 33Less availability 35 117Utilization of chickenCurry 236 787Fry 47 157Biryani 10 33Kheema 7 23Total expenditurehouseholdtime in rupees100-250 212 707250-350 72 240gt400 16 53Awareness about the nutritive value of meatand eggKnown 62 207Not Known 238 793Type of Meat RankBroiler meat 1Native birds meat 2Mutton 3Rajasri Giriraja and 4vanaraja etcFish 5


()

Type of Meat RankPork 6Beef 7Type of EggsCommercial eggs 1Native birds eggs 2Rajasri Giriraja and 3vanaraja etcDuck eggs 4SeasonWinter 1Monsoon 2Rainy season 3Summer 4


The meat and egg consumption patterns ofthe rural households of Jagitial district were presentedin Table 1 Majority of the farmers were marginal(605) followed by agriculture labour (255) smallfarmers (95) and big farmers (45) respectivelyThey had a family size of less than 5 members(640) more than 5 members (173) and 5members (187) From the study it was revealedthat most of the people preferred chicken meat(837) followed by mutton and chevon (137) andvery less percentage of people preferred to take pork(10) and beef (10) Similar type of results werereported by Srinivasa and Thammiraju (2010)Nagaraja kumari et al (2011) and Thammiraju andSurya Narayana (2005) The data revealed that mostof the rural people were consuming chicken meatmonthly once (337) followed by fortnightly (317)weekly once (297) and weekly twice (5) Thesefindings were partially in line with findings ofThammiraju and Suryanarayana (2005) who revealedthe respondents under their study were consumingchicken twice in a fortnight With respect to frequencyof egg consumption 573 of the respondentsconsumed eggs weekly once followed by weeklytwice (317) fortnightly (8) and Monthly (3) Thedifference in the frequency of consumption of chickenmeat and egg is mainly because of its cost and nonavailability in rural areas The major reasons for

51

consumption of meat were because of its taste(803) habituated (67) because of guests (9)and demand by children (40) Majority (75) ofthe respondents had an average consumption of 1 kgbroiler meat householdtime and 173 per cent of therespondents consumed 15 kg and 4 per cent of therespondents consumed 2 kg and very few respondentsconsumed more than 2 kg of chicken meathouseholdtime Majority (71) of the households consumed3-5 eggstime followed by 5-7 eggs (15) 8-10 eggs(107) and very few households consumed morethan 10 eggstime (33) Reasons for lessconsumption of meat and egg is due to religioussentiments (85) followed by less availability (117)and disl ike (33) Majority (707) of therespondents spent 100-250 rupees for purchasingchicken meathouseholdtime followed by 250-350rupeeshousehold (24) and more than 400 rupeeshousehold (53) In accordance with findings ofNagaraja kumari et al (2011) who reported that theconsumption was higher at households with aninvestment of 151-200 rupees per month preferenceowing towards festivals and occasions

Majority (787) of the households preferredcurry as utilization product followed by fry (157)biryani (33) and kheema (23) Most of the people(793) does not know the nutritive value of meatand egg where as 207 of the individuals expressedthat meat and egg in their diet will give them someenergy and protein Similar results were reported byJagadeesh Babu et al (2010) Analysing thepreference ranking of meat the respondents hadranked the broiler meat (rank 1) as highly preferredmeat followed by native bird meat (rank 2) Mutton(rank 3) colored bird meat (rank 4) Fish (rank 5)Pork (Rank 6) and Beef (Rank 7) Analysing thepreference ranking of type of eggs the respondentshad ranked the commercial eggs (rank 1) as highlypreferred eggs followed by native bird eggs (rank 2)colored bird eggs (rank 3) and Duck eggs (Rank 4)All the respondents preferred to eat meat in winterfollowed by monsoon rainy season and summer

CONCLUSION

The present study revealed that majority ofthe respondents preferred broiler meat and commercialeggs Majority of the respondents consuming 1 kgbroiler meathouseholdtime and 3-5 eggs householdtime Due to lack of proper education majority of thepeople do not know about the nutritive value of meatand egg The respondents had ranked the broiler meatas first and highly preferred meat followed by nativebird meat

Acknowledgments

The authors are grateful to the Departmentof Poultry science CVSc Korutla Jagitial for thefunding and technical support

REFERENCES

Akbay C and Boz I 2005 Turkeyrsquos Livestock sectorProduction consumption and policiesLivestock Research for Rural Development 17(09)

Jagadeesh Babu A Rupa Sundari A Triveni Gand Indumathi J 2010 Study on meatconsumption patterns in rural house holds ofchittoor district of Andhra Pradesh TamilnaduJournal of Veterinary amp Animal Sciences6 (4)183-187

Nagaraja Kumari K Mallika EN and Reddy PA2011 Consumption pattern of chicken meatand meat products in rural areas of coastalAndhra Pradesh districts Indian Journal ofPoultry Science 46 (1)132-134

Srinivasa RM and Thammiraju D 2010 Meatconsumption pattern in Hyderabad city IndianJournal of Animal research 44(4) 248 ndash 253

Thammiraju D and Suryanarayana MVAN 2005Meat consumption in Prakasam district ofAndhra Pradesh an analysis LivestockResearch for Rural Development 17(11) 130

PATTERNS OF CHICKEN MEAT AND EGG CONSUMPTION IN RURAL HOUSE HOLDS

52

IN VITRO PROTEIN DIGESTIBILITY OF MILLET BASED SOUTH INDIANBREAKFAST PREPARATIONS

JANE BRIDGET KANDEH UMA DEVI K K UMA MAHESWARI T SARAH KAMALAand V DURGA RANI

PhD Scholar Dept of Foods amp Nutrition PGRC College of Home Science Professor Jayashankar Telangana State Agricultural University




ABSTRACT

Millet grains are now receiving specific attention from the point of nutrition security and the importance of utilizationmillets as food is increasing in the 21st Century Six varieties of millets have been developed and standardized to serve as mostcommon breakfast dishes namely idly dosa and upma using traditional recipe The overall mean IVPD of idlys from 6 millets was7867plusmn436 and IVPD of millet idly was significantly high (Plt001) compared to the respective mean IVPD of millet dosa and milletupma in the decreasing order Mean IVPD of dosa breakfast from 6 millets was 7151plusmn658 and the in vitro protein digestibilityof millet dosa was significantly higher than IVPD of millet upma (Plt001) but significantly low compared to millet idly (Plt001)indicating that millet dosa stands next to millet idly in its protein digestibility The mean IVPD of upma from 6 millets was 6176 plusmn243 and it was significantly lower (Plt001) than that of millet idly and dosa Fermentation in idly and dosa batters improved IVPDto a great extent The in vitro protein digestibility of proso millet and foxtail millet products was high compared to pearl millet littlemillet sorghum and finger millet Higher in vitro protein digestibility of proso millet upma may be due to the low anti-nutrients andinhibitors like phyates and polyphenol content of proso millet

Millets have immense production potentialwith minimum inputs Millet is one of the mostimportant drought-resistant crops and the sixth cerealcrop in terms of world agriculture production Alsomillet has resistance to pests and diseases and hasshort growing season and better productivity underdrought conditions compared to major cereals Indiais the worldrsquos leading producer of millets Howeverin India the area under millets including sorghummillet pearl millet finger millet and small millets islesser compared to staple cereals namely rice (4553Mha) and wheat (2775 Mha) Similarly totalproduction of millets was 2039 Mt and much lowerthan rice and wheat which accounted to 12012 Mtand 9075 Mt respectively in 2012 (FAO 2012)Traditionally millets were important staple foods in thediets of Asians and Africans

With enormous cereal production milletsbecame the forgotten food leaving a wide gap innutrition security Realizing the excellent nutritionalquality and health benefits of millets people areshowing an inclination to incorporate millets into theirdiet Traditionally millets were used just for makingporridges or cooked as rice To improve milletconsumption there is a need to introduce milletsinto various meals

Millet grains are now receiving specific attentionfrom the developing countries in terms of utilization asfood (Li et al 2008) Millets are considered as crop offood security because of their sustainability in adverseagro-climatic conditions (Usha Kumari et al 2004)These crops have substantive potential in broadeningthe genetic diversity in the food basket and ensuringimproved food and nutrition security (Mal et al 2010)Along with nutrition millets offer health benefits in dailydiet and help in the management of disorders likediabetes mellitus obesity and hyperlipidemia (Veena2004)

It is remarkable that despite millet grainsbeing an ancient food research on millet and its foodvalue is in its infancy and its potential vastly untappedResearch results so far are promising showing thegrain to have great versatility and more and more usesfor millet are being discovered every year includingits potential benefits The requirement of people forhigh quality ready-to-eat foods of traditional naturewith modem technological application has become anecessity both from the economy point of view andto reduce the losses of seasonal agricultural produce(Ghosh et al 2001)

Digestibility of protein may be used as anindicator of protein availability It is essentially a

emailjanekandeh2014gmailcom

53

measure of the susceptibility of protein to proteolysisA protein with high digestibility is potentially of a betternutritional value than one of low digestibility becauseit would provide more amino acids for absorption onproteolysis Their function in nutrition is to supplyadequate amounts of needed amino acids The proteinnutritional quality of food depends on contentdigestion absorption and utilization of amino acidsHowever there is growing emphasis for improvementof protein quality and quantity in cereal cropsAttempts have been made to fortify these cereals withlegumes (FAO 1995)

Hima Bindu and Sumathi (2003) preparedcommon Indian traditional products namely murukuchegodi dosa chapathi laddu and payasam byincorporating Foxtail millet All the products wereacceptable It was suggested that nutritious Foxtailmillet could be exploited for the nutritional benefitsand value added nutritive health foods Barnyard andKodo millet based chapathi and dosa and twocommercial products viz noodles and rusk weredeveloped by Poongodi et al (2010) Millet along withwheat flour and defatted soy flour in varying proportionswere used The acceptable levels of incorporation ofmillet flour were reported to be 20 for noodles and30 for rusk chapathi and dosa

Idli and dosa are the most widely consumedfermented foods in India Other fermented foods arealso quiet popular and consumed all over the worldbecause of the importance as human food (Mugochaet al (2000) Gotcheva et al (2001) Fermentationmodifies the chemical composition of the grains ofmillet and thus the food products manufactured fromit This process also contributes to the reduction inthe level of anti-nutritional components and enhancesthe in vitro protein digestibility It has also beenobserved that pearl millet when processed and thenfermented shows the same significant reduction ofanti-nutritional components along with increase inIVPD (Hassan et al (2006) Pearl millet whenfermented leads to chemical changes in i tscomposition like ash content moisture content fatfibre and protein and also causes marked reductionin minerals like potassium sodium coppermagnesium zinc iron etc (Ahmed et al 2009) Highprotein digestibility is observed when pearl milletgerminated in ground is fermented which accountsfor 90

Fermentation not only improved the taste butat the same time enriches the food value in terms ofprotein calcium and fibre B vitamins in vitro proteindigestibility and decreased the levels of anti-nutrientsin food grain (Maha et al 2003 Verma and Patel2013)

Pepsin- pancreatin (two step) digestibility ofnative pearl millet was shown to be 46 whichreduced to 33 upon boiling and to 44 uponpressure cooking However another pearl milletvariety showed mixed results in the same studyTherefore millet variety cooking conditions and 68enzymatic digestion procedure were considered tobe some important factors that affect IVPD (Devi andSumathi 1997 Abdel-al 2008 Pushparaj and Asna2011)

The digestibility of cooked sorghum gruel wasreported to be lower than that of cooked gruels madewith wheat maize rice and millet The pepsin proteindigestibility of decorticated heat extruded sorghumwas higher than whole sorghum gruelsRamachandra(et al1977) The IVPD of cereals andmillets reported by Rajyalakshmi and Geervani (1990)indicated a reduction of 9 to 24 percent due to boiling


The selected six millets namely pearl millet(Pennisetum glaucum) finger millet (Eleusinecoracana) proso millet (Panicum miliaceum) foxtailmillet (Setaria italica) l ittle millet (Panicumsumatrense) and sorghum (Sorghum bicolor) wereprocured from Grameenmall Foundation ALEAPIndustrial Estate Gajularamaram HyderabadTelangana Pearl millet finger millet and sorghumgrains were obtained in whole form as they are ediblewith hull while foxtail millet little millet and prosomillet were obtained in dehulled form All six milletswere also obtained in the form of semolina to suitsome of the preparations

Three most common South Indian breakfastdishes namely idly dosa and upma were developedwith each of the six millets using the traditional basicrecipe To standardize the recipes with milletsvariations in proportions of main ingredients andsoaking time were tried and the products weresubjected to acceptability studies and highlyacceptable recipe was taken for preparation of finalidly dosa and upma Millet idly was prepared from

IN VITRO PROTEIN DIGESTIBILITY OF MILLET BASED SOUTH INDIAN BREAKFAST

54

JANE BRIDGET KANDEH et al

black gram dhal millet semolina in the ratio of 12respectively and dosa was prepared from black gramdhal edible whole millet grain in the ratio of 12 andUpma was made from millet semolina

The products were subjected to analysis ofprotein and in vitro protein digestibility on dry weightbasis

Analysis of protein

Dehydrated and finely powered idly dosaand upma samples of each of the six millets wereinitially analyzed for protein so as to take samplecontaining 675 plusmn 01mg of nitrogen for estimation ofprotein digestibility

The crude protein content of the sample wasestimated according to the Micro Kjeldhal Method(AOAC 2005) and calculated as percent nitrogen ofproduct and multiplied with 625 to obtain the proteincontent

For estimation of protein 05g of each samplewas weighed in to the digestion tube and five gramsof digestion mixture (98g of potassium sulphate + 2gcopper sulphate) plus 1000 ml of concentrated H2SO4

were carefully added and the samples were placed inthe digestion unit for 1frac12 hr at 375degC

In a 100 ml conical flask 4000 ml of 4 boricacid was added along with few drops of mixed indicatorcontaining (1ml of 02 bromocresol green + 3 ml of02 methyl red) Distillation was done for10 minutesin the Kjeldhal distillation apparatus adding 1500 mlof 40 NaOH and steaming for 10 seconds Thecontents collected in conical flask were blue in colorafter distillation was completed Titration was donewith standard 01N HCl till the contents of the flaskturned to pink color A blank was run simultaneously

An amount of sample containing 675plusmn01mgnitrogen was placed in a 50ml conical flask and 5mlof pepsin solution was added The flask was incubatedin a water bath shaker for 16hr at 370C Then 2ml ofpancreatin solution was added and the contents werefurther incubated for 24hrs at 370C Then 2 to 3 dropsof toluene was added during incubation and sampleswere stirred slowly on a mechanical shaker After24hrs the reaction was stopped by adding 20ml of10 of TCA and the suspension was centrifuged Theresidue was washed twice with 5 TCA An aliquotof 5ml was taken and evaporated to dryness at lowtemperature (80-900C) and nitrogen content wasdetermined by the Micro Kjeldal procedure Thendigestive of each sample was calculated in thefollowing way

Protein content (g) = Per cent nitrogen x 625

In Vitro Protein Digestibility

In vitro protein digestibility (IVPD) method wasdeveloped to simulate the conditions in the digestivetract of human gastro intestinal tract This method hasthe potential to give useful measures of in vivo aminoacid and protein digestibility for humans This wasestimated according to the procedure of Singh andJambunathan (1981)

IVPD =(N in sample supernatants - N in Blank)

N in starting materialx100


The in vitro protein digestibility (IVPD) of fingermillet foxtail millet little millet pearl millet prosomillet and sorghum prepared into breakfast foodsnamely idly dosa and upma is given in table1 alongwith the analysis of variance between the productsand between the millets

The in vitro protein digestibility of millet idlyranged between 7354 from little millet to 8500from foxtail millet The protein digestibility of milletidly in the decreasing order was 8500 from foxtailmillet 8134 from proso millet 7953 from fingermillet 7856 from sorghum 7407 from pearl milletand 7354 from little millet idly The overall meanIVPD of idlys from 6 millets was 7867plusmn436 andIVPD of millet idly was significantly high (Plt001)compared to the respective mean IVPD of millet dosaand millet upma in the decreasing order Proteindigestibility of millet idly was the best among the threemillet breakfast items followed by millet dosa andmillet upma (fig1) Higher protein digestibility of foxtailbased idly and proso millet idly may be attributed tothe lower antinutrient content of phyticacid andpolyphenols compared to pearl millet little milletfinger millet and sorghum Findings of the presentstudy are similar to the results reported byBalasubramanian et al (2012) on the nutritionalcomposition of pearl millet based idly

S=Sample titre valueB= Blank titre value

Nitrogen =(S-B)x Normality of HCL x14x100Weight of the sample taken x100

55

Table 1 In vitro protein digestibility of millet breakfast dishes

In vitro protein digestibility ()

Millets Idly Dosa Upma Millet wise Mean plusmn SDFinger millet 7953 6136 5899 6663 plusmn 1124Foxtail millet 85 807 6118 7563 plusmn 1269Little millet 7354 7277 6266 6965 plusmn 607Pearl millet 7407 7227 6136 6923 plusmn 687Proso millet 8134 7461 6605 7400 plusmn 767Sorghum 7856 6734 6029 6873 plusmn 921Product wise Mean plusmn SD 7867 plusmn 436c 7150 plusmn 658b 6175 plusmn 243a

Note From ANOVA CD of Millets 5 level is lsquoNot significantrsquo CD of Products 1 level = 522

The in vitro protein digestibility of differentmillet based dosas ranged between 6136 fromfinger millet to 8070 from foxtail millet dosa Theprotein digestibility of millet dosa in the decreasingorder was 8070 from foxtail millet 7461 fromproso millet 7277 from little millet 7227 frompearl millet 6734 from sorghum and 6136 fromfinger millet dosa The overall mean IVPD of dosabreakfast from 6 millets was 7151plusmn658 and thein vitro protein digestibility of millet dosa wassignificantly higher than IVPD of millet upma (Plt001)but significantly low compared to millet idly (Plt001)indicating that millet dosa stands next to millet idlyin its protein digestibility

Fig1 In vitro protein digestibility of breakfast preparations of millet

IVPD of Millet Breakfast items

0

10

20

30

40

50

6070

80

90

Finge

r Mille

t

Foxta

il Mille

t

Little M

illet

Pearl M

illet

Proso

Mille

t

Sorgh

um

Perc

enta

ge d

iges

tibili

ty

Dosa UpmaIdli

The IVPD of millet upma a popular breakfastpreparation ranged between 5899 from finger milletto 6605 proso millet The IVPD of upma fromdifferent millets in the decreasing order was 6605from proso millet 6266 from little millet 6136from pearl millet 6118 from foxtail millet 6029from sorghum and 5899 from finger millet upmaThe mean IVPD of upma from 6 millets was 6176 plusmn243 and it was significantly lower (Plt001) thanthat of millet idly and dosa The in vitro proteindigestibility of proso millet upma was significantlyhigher Comparatively in vitro protein digestibility ofproso millet and foxtail millet was high with leastdifference between the two Higher in vitro protein


56


digestibility of proso millet upma may be due to thelow antinutrients and inhibitors like phytates andpolyphenol content of proso millet

Comparison of IVPD of idly dosa and upmamade from each millet has shown that finger millethad slightly lower IVPD followed by sorghum pearlmillet and little millet with a relatively higher IVPDfrom proso millet and foxtail millet preparations Onan overall basis no significant difference wasobserved between the IVPD of different millets at 5level

The in vitro protein digestibility displayed inradar chart (Fig2) shows the performance of differentmillets in different preparations by displaying whichproduct and which millet was scoring high IVPD orlow IVPD

In vitro protein digestibilityFinger millet

Foxtail millet

Little millet

Pearl millet

Prosomillet

Sorghum

IdliDosaUpma

10080

6040

20 0

Fig2 Spread of IVPD of millet breakfastdishes

Digestibility of protein may be used as anindicator of protein availability It is essentially ameasure of the susceptibility of protein to proteolysisA protein with high digestibility is potentially of a betternutritional value than one of low digestibility becauseit would provide more amino acids for absorption onproteolysis (FAO1995)

The steps of soaking grinding andfermentation of batters as in idli and dosa preparationsgreatly enhanced in vitro protein digestibility whencompared to boiling method employed for makingupma Steaming subsequent to fermentation as inidli and open pan shallow frying as in dosa preparationhas not affected IVPD of the two preparations Severalresearch studies proved this fact

Idly and dosa are the most widely consumedfermented foods in India Other fermented foods arealso quiet popular and consumed all over the worldbecause of the importance as human food (Mugochaet al 2000) Gotcheva et al 2001) Fermentationmodifies the chemical composition of the grains ofmillet and thus the food products manufactured fromit This process also contributes to the reduction inthe level of anti-nutritional components and enhancesthe in vitro protein digestibility It has also beenobserved that pearl millet when processed and thenfermented shows the same significant reduction ofanti-nutritional components along with increase inIVPD (Hassan et al 2006) Pearl millet whenfermented leads to chemical changes in itscomposition like ash content moisture content fatfibre and protein and also causes marked reductionin minerals like potassium sodium coppermagnesium zinc iron etc (Ahmed et al 2009) Highprotein digestibility was observed when pearl milletgerminated in ground was fermented which accountsfor 90


Within the mil lets f inger mil let hadsignificantly lower IVPD than that of other five milletsstudied which is in agreement with the studies ofElshazali et al (2011) where the IVPD of the wholeraw flour was 4643 and 5123 percent while that ofthe dehulled raw flour 5054 and 5528 percent fortwo varieties of finger millet However the IVPD canbe higher depending upon the varieties as shown byHag et al (2002) where IVPD of two different pearlmillet cultivars were reported as 727 and 704percent With respect to treatment effect higher IVPDwas observed in the dehulled grains (5486)compared to whole grain This was due to theestablished fact that as the proportion of pericarpand germ material becomes less the IVPD improves(Duodu et al 2002 Chibber et al 1980) Dehullingdecreases the anti-nutrients that interfere with theIVPD Babiker and Eltinay (1993) also reported theimprovement in IVPD is likely due to reduction inantinutrients during traditional treatments High

57

molecular weight polyphenols are known to precipitateproteins reduce protein digestibility and produce off-coloured products (Hulse et al 1980)

Nutritional levels of food are of utmostimportance according to human health prospectsbecause nutritionally sufficient food will provide properdevelopment and increment in genetic potential inman Also for the solution of the large scale problemof insecurity of food and malnutrition dietary qualityis of major concern (Singh and Raghuvanshi 2012)

CONCLUSION

Protein digestibility of millet idly was the bestamong the three millet breakfast items followed bymillet dosa and millet upma Fermentation of milletidly and dosa batters has improved the proteindigestibility Proso millet and foxtail millet productshad better IVPD with other millets following closelyin their protein digestibility The protein digestibilityin vitro of the millet products proved millets to be apotential source of growth promoting protein with gooddigestibility Hence traditional rice based idly anddosa can be substituted with millet idly and dosasimilarly wheat upma with non glutinous millet upmafor better micronutrients and phytochemicals abundantin millets along with good protein digestibility

REFERENCES

Abdel-Aal ES 2008 Effects of baking on proteindigestibi l i ty of organic spelt productsdetermined by two in vitro digestion methodsLWT- Food Science and Technology 41(7)1282- 1288

Ahmed AI Abdulla AA and Tinay AH 2009 Effectof traditional processing on chemicalcomposition and mineral content of two cultivarsof pearl millet (Pennisetum glaucum) Journalof Applied Science Research 5(12)2271ndash2276

AOAC 2005 Official Methods of analysis for ash inflour Association of Official AnalyticalChemists 18th Ed Arlington VA 2209

Babiker EE and Tinay AH 1993 Effect of soakingin water or in sodium bicarbonate on tannincontent and in vitro protein digestibility ofsorghum cultivars Journal of Food Scienceand Technology 28 389-395

Balasubramanian S Singh KK Patil RT andKolhe KO 2012 Quality evaluation of millet-soy blended extrudates formulated throughlinear programming Journal Food ScienceTechnology 49(4) 450-458

Chibber BAK Mertz ET and Axtell JD 1980 Invitro digestibility of high-tannin sorghum atdifferent stages of dehulling Journal ofAgriculture and Food Chemistry 28160-161

Devi K Geervani P and Sumathi S 1997 Changesin solubility and in vitro digestibility of riceproteins subjected to heat processing Journalof Food Science and Technology 34(4)345-347

Duodu KG Nunes A Delgadillo I Parker MLMills ENC Belton PS and Taylor JRN2002 Effect of grain structure and cooking onsorghum and maize in vitro protein digestibilityJournal of Cereal Science 35 161ndash174

ElShazali AM Nahid AA Salma HA and ElfadilEB 2011 Effect of radiation process onantinutrients protein digestibility and sensoryquality of pearl millet flour during processingand storage International Food ResearchJournal 18(4)1401ndash1407

FAO 2012 Food and Agriculture organizationstatistical database (FAOSTAT)

FAO 1995 Sorghum and millets in human nutritionFood and Agriculture Organization Food andNutrition Series No 27 Rome Italy

Ghosh KG Krishnappa KG Srivatsava ANEapen KC and Vijayaraghavan PK 2001Inpack processing of ready-to-eat foods inindigenous flexible packaging materials PartIII Studies on newer packaging materialscapable of withstanding the processingtemperatures Journal of Food Science andTechnology 16 198-201

Gotcheva V Pandiella SS Angelov A RoshkovaZ and Webb C 2001 Monitoring thefermentation of the traditional Bulgarianbeverage boza International Journal of FoodScience and Technology 36129ndash134

Hag MEE Tinay AHE and Yousif NE 2002Effect of fermentation and dehulling on starch


58

total polyphenols phytic acid content and invitro protein digestibility of pearl millet Journalof Food Chemistry 77193-196

Hassan AB Ahmed IAM Osman NM EltayebMM Osman GA and Babiker EE 2006Effect of processing treatments followed byfermentation on protein content and digestibilityof pearl millet (Pennisetum typhoideum)cultivars Pakistan Journal of Nutrition 5 (1)86ndash89

Hima Bindu N and Sumathi S 2003 Organolepticevaluation and nutrient composition of foxtailmillet products Paper Presented at the 5thInternational Food Convention Mysore 5-8December p 121

Hulse JH Laing EM and Pearson OE 1980Sorghum and the millets Their compositionand nutritive value London Academic PressIndian Council of Medical Science ResearchNew York Academic Press 1-997

Li J Chen Z Guan X Liu J Zhang M and XuB 2008 Optimizat ion of germinat ionconditions to enhance hydroxyl radicalinhibition by water soluble protein from stressmillet Journal of Cereal Science 48 619ndash24

Mal B Padulosi S and Ravi SB 2010 Minormillets in South Asia Learnings from IFAD-NUS Project in India and Nepal BiodiversityInternational MS Swaminathan ResearchFoundation Chennai India 1ndash185

Maha AMA Tinay AH and Abdalla AH 2003Effect of fermentation on the in vitro proteindigestibility of pearl millet Food Chemistry80(1) 51-54

Mugocha PT Taylor JRN and Bester BH 2000Fermentation of a composite finger millet-dairybeverage World Journal of Microbiology andBiotechnology 16341ndash4

Poongodi Vijayakumar Jemima Beryl Mohankumarand T Srinivasan 2010 Quality evaluation of

noodles from millet flour blend incorporatedcomposite flour Journal of Scientific andIndustrial Research vol 69 pp- 48- 54

Pushparaj SF and Asna U 2011 Influence ofprocessing on dietary fiber tannin and in vitroprotein digestibility of pearl millet Food andNutrition Sciences 2(8) 101-106

Rajyalakshmi P Geervani P 1990 Stuidies on tribalfoods of south India effect of processingmethods on the vitamin and in vitro proteindigestibility of cereals millets and legumesJournal of Food Science Technology 27 260-263

Ramachandra G Virupaksha TK and ShandakshaR M 1977 Relatioship between Tannin levelsand in vitro protein digestibility in finger millet(Eleusine coracana) Journal of AgriculturalFood Chemistry 2 1101-1104

Singh U and Jambunathan R 1981 Studies on Desiand Kabuli chickpea (Cicer arietinum) cultivarsLevels of protease inhibitor level of polyphenoliccompounds and in vitro digestibility Journalof Food Science Technology 46 1364-1367

Singh P and Raghuvanshi RS 2012 Finger milletfor food and nutritional security African Journalof Food Science 6(4) 77ndash84

Ushakumari SR Latha S and Malleshi NG 2004The functional properties of popped flakedextruded and roller-dried foxtail millet (Setariaitalica) International Journal of Food Scienceand Technology 39907ndash15

Veena B Chimmad BV Naik RK andShantakumar G 2004 Development ofbarnyard millet based traditional foodsKarnataka Journal of Agricultural Sciences17 (3) 522-527)

Verma V and Patel S 2013 Value added productsfrom nutri-cereals Finger millet (Eleusinecoracana) Emirates Journal of Food andAgriculture 25(3) 169176


59

GREEN SYNTHESIS OF NANOPARTICLES FROM PLANT SOURCES FORTEXTILE APPLICATION

PUSHPALATHA KYATHAM and A PADMA Department of Apparel and Textiles

College of Home Science Saifabad Hyderabad - 500 004

Research ArticleThe J Res PJTSAU 46(4) 59-63 2018


ABSTRACT

Textiles are infected very easily by microbes which create health problems to the consumers To avoid and control theinfection and infestation from micro organisms antimicrobial finishes are given to them There are a number of natural andsynthetic antimicrobial agents that can be applied on textiles In the present study an attempt was made to finish cotton textileswith nanoparticles from plant sources to impart the antimicrobial finish Finish was applied to cotton knitted textiles using pad-dry-cure method with curing at 35deg to 40degC The antibacterial activity was evaluated by a modified qualitative test method AATCC-1472004 against both Staphylococcus aureus and Escherichia coli It was found that all the treated samples with all combinationsindicated better antibacterial resistance Fabric treated with Cassia and silver nanoparticles were found to possess high antibacterialresistance

emailpushuulatha97gmailcom

In the present world most of us are veryconscious about our hygiene and cleanlinessClothing and textile materials are not only the carriersof microorganisms such as pathogenic bacteriaodour generating bacteria and mould fungi but alsogood media for the growth of the microorganismsMicrobial infestation poses danger to both living andnon living matters Though the use of antimicrobialshave been known for the decades it is only in therecent couple of years several attempts have beenmade on finishing texti les with antimicrobialcompounds Antimicrobial finishing of textiles protectsusers f rom pathogenic or odor-generat ingmicroorganisms which can cause medical andhygienic problems and protects textiles fromundesirable aesthetic changes or damage caused byrotting which can result in reduced functionality(Gedanken et al 2012) As a consequence of theirimportance the number of different antimicrobialagents suitable for textile application on the markethas increased dramatically These antimicrobialagents differ in their chemical structure effectivenessmethod of application and influence on people andthe environment (Vyas et al 2010)

The majority of antimicrobial agents in thetex ti le industry uti l ize a control led releasemechanism These agents which are also calledleaching antimicrobials are not chemically bound tothe textile fibers and their antimicrobial activitydecreases after several washes and gradually fallsunder the limit of effectiveness or even expire (Hariniet al 2007)

Nanotechnology has created a landmark intextile finishing The combination of nanotechnologyand textile surface modification provides a way toimpart new and diverse properties to textiles whileretaining comfort and mechanical strength Currentlyfunctional finishes on textile fabrics are of criticalimportance to improve texti le products withmultifunctional properties


Four plant sources Pomegranate CassiaPeriwinkle and Marigold leaves possessingantibacterial property were synthesized using titaniumdioxide Zinc Oxide and Silver nitrate as a precursorwith green synthesis method The finish was appliedto the fabric by pad- dry - cure method Theantibacterial activity of all treated fabric samples wasevaluated by a modified qualitative test methodAATCC-147 2004 against Staphylococcus aureusand Escherichia coli Zone of inhibition (Zoi) wascalculated in mm

RESULTS AND DISCUSSIONBased on the secondary data an exhaustive

list of plants having bacterial efficacy was preparedOut of the prepared list four plants having goodbacterial resistance and availability in abundancewere selected for the study The antimicrobial efficacywas tested for twenty four nanoparticles which wereprepared with different precursors with aqueous andethanol extractions The prepared nanoparticles areshown in the table 1 and figure 1

60

PUSHPALATHA and PADMA

Pomegranate Cassia Periwinkle Marigold

Table 1 Nanoparticles prepared with different precursors

Aqueous Ethanol Aqueous Ethanol Aqueous Ethanol Aqueous Ethanol

TiO2 TiO2 TiO2 TiO2 TiO2 TiO2 TiO2 TiO2

Zno Zno Zno Zno Zno Zno Zno Zno

AgNO3 AgNO3 AgNO3 AgNO3 AgNO3 AgNO3 AgNO3 AgNO3

T- Titanium Z- Zinc AgNO3- Silver nitrate

1 Antibacterial efficacy of prepared nanoparticles from selected plant sources with differentextraction media

Nanoparticles were synthesized usingaqueous and ethanol extraction with four precursorswere tested for antibacterial resistance against

Table 2 Antibacterial efficacy of the nanoparticles

Fig 1 Green synthesized nanoparticles with aqueous and ethanolic extracts

Staphylococcus aureus and E Coli (Shown in figure2 to 4)

Zoi in mmAgNO3 TiO2 Zno

Aqueous Ethanol Aqueous Ethanol Aqueous EthanolSA Ecoli SA Ecoli SA Ecoli SA Ecoli SA Ecoli SA Ecoli

Pomegranate 21 23 17 16 18 19 13 12 17 18 12 14

Cassia 31 33 24 24 26 28 20 21 22 23 21 22

Periwinkle 29 31 23 25 25 24 19 20 21 21 22 21

Marigold 21 23 18 19 16 18 12 13 16 17 14 16

61

The results in the table 2 revealed that theaqueous and ethanol extracts of various plantsexhibited varied antimicrobial activity The zone ofinhibition observed with aqueous extracts of all plantsources was high compared to the ethanol extractsagainst to the both gram positive and gram negativecultures

It was observed that the highest zone ofinhibition was registered for all the plant sources withboth the extraction methods with silver nanoparticlesamong all precursors and extraction methods

It was also noticed that among the all plantsources with different extraction methods andprecursors Cassia and Periwinkle showed high zoneof inhibition followed by Pomegranate and MarigoldIt was noticed that silver nanoparticles of cassia withaqueous extraction registered high zone of inhibitionagainst Ecoli 33 mm and 31 mm for S aureus andleast was observed for titanium nanoparticles ofpomegranate with ethanol extract against both thebacteria

The aqueous extract of cassia exhibitedstrong activity ie 28 mm zone of inhibition for Ecoliand 26 mm for Staphylococcus aureus while zoneof inhibition observed with its ethanol extract was 21and 20 mm for Ecoli and Staphylococcus aureus

It was evident from the results that eventhough the antibacterial activity of all the plant sourceswith both the extraction methods were observed tobe strong with slight variations It was also observedfrom the results that amongst all the extracts testedfor antibacterial efficacy zone of inhibition of silvernanoparticles registered high followed by titanium andzinc nanoparticles

These finding corroborated with the findingsby (Kavimani etal 2015) In their study antibacterialactivity of Cassia ariculata linn they found that theantibacterial activity of C auriculata extract showeda clear zone of inhibition against five microorganismssuch as Ecoli Bacillus subtilis Pseudomonasauregenosa Streptococcus aureus and Kpneumonia

Fig 2 Zoi of nano treated fabrics with different media and extraction

GREEN SYNTHESIS OF NANOPARTICLES FROM PLANT SOURCES

62


Fig 4 Antibacterial efficacy of the nanoparticles with ethanolic extracts

0

5

10

15

20

25

30

S aureus Ecoli

Pomegran

teCas

siaPeri

winkleMari

gold

Pomegran

te

Cassia

Periwinkle

Marigold

SilverTitaniumzinc

Fig 3 Antibacterial efficacy of the nanoparticles with aqueous extracts

0

5

10

30

Pomegran

teCas

siaPeri

winkleMari

gold

Pomegran

teCas

siaPeri

winkleMari

gold

35

15

20

S aureus Ecoli

SilverTitaniumzinc

25

63

CONCLUSION

There is a demand for eco friendly textilesbecause of growing needs of people in aspects ofhealth and hygiene This study indicated that greensynthesized nanoparticles can impart to cotton knittedfabrics to achieve the better bacterial resistance Itwas found that all the treated samples with allcombinat ions indicated bet ter antibacter ialresistance Fabric treated with Cassia and silvernanoparticles were found to possess high antibacterialresistance

REFERENCESGedanken A Perelshtein N and Perkas N 2012

Ultrasonic Coating of Textiles by Antibacterialand Antibiofilm Nanoparticles Research gate

Harini K Sivapriya S and Giridev VR2007Checkmate for Microbes Synthetic Fiber 358-12

Kavimani V Ramadevi A Kannan K GnanavelS and Sivaperumal G 2015 Antibacterialactivity of Cassia ariculata linn Journal ofChemical and Pharmaceutical Research7(9)479-485

Vyas SK Gulve AL and Kandekar TS2010Antimicrobial finishing agents for textiles AsianTextile Journal 57-64


64

CORRELATION AND PATH COEFFICIENT ANALYSIS FOR GRAIN YIELD INAEROBIC RICE (Oryza sativa L) GENOTYPES

A SRIJAN KULDEEP SINGH DANGI P SENGUTTUVEL R M SUNDARAM D SRINIVASA CHARYand S SUDHEER KUMAR

Department of Genetics and Plant Breeding College of AgricultureProfessor Jayashankar Telangana State Agricultural University

Rajendranagar Hyderabad-500 030

Research NoteThe J Res PJTSAU 46(4) 64-68 2018


emailsrijanambati1gmailcom

As rice is the staple food in most parts of Indiato feed the increasing population there is a great needto increase production of rice and productivity of landunder rice cultivation In Asia about 17 million ha ofirrigated rice area may experience ldquoPhysical waterscarcityrdquo and 22 million ha may have ldquoEconomic waterscarcityrdquo by 2025 (Tuong and Bouman 2003)Therefore efficient use of water is required in riceproduction Aerobic rice is one such new concept todecrease water requirement in rice production(Vijayakumar 2006) However crop yield is the endproduct of the interaction of a number of otherinterrelated attributes The efficiency of selection foryield mainly depends on the direction and magnitudeof association between yield and its componentcharacters and also among themselves Correlationgives only the relation between two variables whereaspath coefficient analysis allows separation of thedirect effect and their indirect effects through otherattributes by partitioning the correlations (Wright1921) In this study an attempt was made to identifythe major contributing characters for grain yield theirdirect and indirect effects

The material for this study consisted of 52 ricegenotypes and were evaluated at Indian Institute ofRice Research (ICAR-IIRR) Rajendranagar duringKharif 2016 The experiment was laid out inRandomized Block Design (RBD) with threereplications The crop was cultivated purely as aerobicrice which does not require any puddling and floodingThe soil condition (moisture status) was maintained atbelow saturation level and throughout the crop growthit was maintained as irrigated dry crop A plot size oftwo rows of 2m length for each entry with a spacing of20 x 15 cm was maintained and later thinning wasdone retaining one seedling per hill after one week

The recommended package of practices and plantprotection measures of IIRR was followed for raisinga healthy crop Data was recorded on elevencharacters viz days to fifty per cent flowering plantheight panicle length number of productive tillers perplant number of filled grains per panicle spikelet fertilitypercentage 1000 grain weight productivity per daybiomass per plant harvest index and grain yield perplant from each replication and the mean data weresubjected to statistical analysis as per Singh andChaudhary (1985) for Correlation coefficients andDewey and Lu (1959) for path analysis

In the present study phenotypic andgenotypic correlations between yield and yieldcomponents were estimated In general genotypiccorrelations were found to be higher than phenotypiccorrelations which indicated that though there isstrong inherent association between charactersstudied its expression is lessened due to influenceof environment on the association of characters atgenic level

Phenotypic and genotypic correlationcoefficients between yield and yield components arepresented in Table 1 Days to 50 per cent floweringhad a significant positive phenotypic correlation withgrain yield per plant panicle length number ofproductive tillers per plant number of filled grains perpanicle and harvest index The plant height showed asignificant positive phenotypic correlation with grainyield per plant number of filled grains per panicle1000 seed weight and biomass per plant whilenegative and significant correlation of plant height wasobserved with number of productive tillers plant andharvest index Panicle length exhibited a significantpositive phenotypic correlation with number of filledgrains per panicle spikelet fertility and 1000 seed

65

Tabl

e 1

Est

imat

es o

f phe

noty

pic

and

geno

typi

c co

rrel

atio

n co

effic

ient

s fo

r yie

ld a

nd it

s at

trib

utin

g ch

arac

ters

Cha

ract

erDa

ys to

Plan

tPa

nicl

eN

o o

fN

o o

fSp

ikel

et10

00-s

eed

Prod

uctiv

ityBi

omas

sHa

rves

tG

rain

50

heig

htle

ngth

Prod

uctiv

efil

led

fert

ility

wei

ght

per d

aype

r pl

ant

Inde

xyi

eld

flow

erin

gtil

lers

grai

nspe

r pl

ant

per

plan

tpe

rpa

nicl

e

Day

s to

50

P1

0000

012

230

3431

0

1594

0

5331

0

1150

-00

195

000

49-0

053

60

2191

0

2472

flow

erin

gG

100

000

1181

037

30

016

91

054

21

011

49-0

022

90

0073

-00

657

022

80

025

70

Plan

t hei

ght

P

100

000

5653

-0

441

4

037

95

012

720

5292

014

700

4604

-0

253

6

016

30

G

1

0000

061

45

-05

199

0

3892

0

1301

054

12

014

540

4798

-02

621

0

1605

Pani

cle

leng

thP

100

00-0

025

80

3811

0

2527

0

1930

0

0041

-00

205

006

370

0774

G

100

000

0038

040

71

027

06

020

60

002

420

0073

006

540

1048

No

of P

rodu

ctiv

eP

1

0000

-00

043

020

21

-0

3806

023

08

-03

316

0

4903

0

2561

tille

rs p

er p

lant

G

100

00-0

002

90

2298

-0

434

00

2296

-0

453

0

057

49

025

85

No

of f

illed

gra

ins

P

1

0000

050

67

008

680

5112

0

3200

0

2694

0

6261

per p

anic

leG

100

000

5088

0

0874

053

48

034

06

027

36

065

35

Spik

elet

fert

ility

()

P

100

000

0613

049

97

004

900

4247

0

5043

G

1

0000

006

150

5270

0

0519

043

59

053

01

1000

see

d w

eigh

tP

100

000

1600

0

2852

-0

068

90

1470

G

100

000

1656

0

2999

-00

697

015

13

Prod

uctiv

ity p

er d

ayP

1

0000

029

02

057

85

096

81

G

1

0000

023

91

061

47

096

62

Bio

mas

s pe

r pla

ntP

100

00-0

561

9

025

85

G

100

00-0

571

1

020

47

Har

vest

inde

x (

)P

1

0000

061

74

G

1

0000

065

49

P- P

heno

typi

c co

rrel

atio

n co

effic

ient

G-

Gen

otyp

ic c

orre

latio

n co

effic

ient

Si

gnifi

cant

at

1

Si

gnifi

cant

at

5

CORRELATION AND PATH COEFFICIENT ANALYSIS FOR GRAIN YIELD

66

SRIJAN et al

weight Number of productive tillers per plant exhibitedsignificant positive phenotypic correlation with grain yieldper plant spikelet fertility productivity day and harvestindex while significant negative correlation with 1000seed weight and biomass per plant The significantpositive phenotypic correlation of days to 50 percentflowering plant height and number of productive tillersper plant with grain yield per plant was also reportedearlier by Patel et al (2014) and Srijan et al (2016)

Number of filled grains per panicle and spikelet fertility exhibited a significant positivephenotypic correlation with grain yield per plantproductivity day and harvest index 1000 grain weighthas positive significant correlation with productivity dayand biomass plant Productivity per day exhibited asignificant positive phenotypic correlation with grain yieldper plant biomass plant and harvest index Bhadruet al (2011) and Srijan et al (2016) also reportedsignificant positive phenotypic correlation of productivityper day with grain yield per plant Biomass per plantand harvest index showed significant positivecorrelation with grain yield per plant whereas biomassper plant showed significant negative correlation withharvest index

As simple correlation cannot provide the truecontribution of the characters towards the yield thesegenotypic correlations were partitioned into direct andindirect effects through path coefficient analysis Theestimates of path coefficient analysis are provided foryield and yield component characters in Table 2 Amongall the characters productivity day was the majorcontributor for grain yield followed by days to 50 percent flowering number of filled grains per panicle and1000 seed weight These characters showed directpositive effects on grain yield per plant On the otherhand characters that had negative direct effect includespikelet fertility panicle length number of productivetillers per plant plant height harvest index and biomassper plant The positive indirect effects of variouscharacters with grain yield include Days to 50 per centflowering through number of filled grains per panicleproductivity day and biomass per plant plant heightthrough days to 50 per cent flowering number ofproductive tillers plant number of filled grains perpanicle 1000 seed weight productivity day andharvest index Panicle length through days to 50 per

cent flowering number of productive tillers per plantnumber of filled grains per panicle productivity day1000 grain weight and biomass per plant Number ofproductive tillers per plant through days to 50 per centflowering plant height panicle length productivity dayand biomass per plant Number of filled grains perpanicle through days to 50 per cent flowering numberof productive tillers per plant 1000 grain weight andproductivity day Spikelet fertility through days to 50per cent flowering number of filled grains per panicleproductivity day and 1000 grain weight 1000 grainweight through number of productive tillers per plantnumber of filled grains per panicle productivity dayand harvest index Productivity per day through daysto 50 per cent flowering panicle length number of filledgrains per panicle and 1000 - grain weight Biomassper plant through panicle length number of productivetillers per plant number of filled grains per panicle and1000 grain weight and Harvest index through days to50 per cent flowering plant height number of filled grainsper panicle productivity day and biomass per plantBhadru et al (2011) and Srijan et al (2016) reportedthe positive direct effects of productivity day days to50 per cent flowering number of productive tillers perplant number of filled grains per panicle 1000 grainweight and negative direct effects of spikelet fertilitypercent on grain yield per plant

The study of phenotypic correlation studiesrevealed that selection of plants with more number ofproductive tillers per plant higher number of filled grainsper panicle more 1000 grain weight high productivityper day more biomass per plant tall plants with longduration would result in improvement of yield Pathanalysis revealed that productivity day days to 50per cent flowering number of filled grains per panicleand 1000 grain weight are the most important characterswhich could be used as selection criteria for effectiveimprovement in grain yield Therefore it is suggestedthat preference should be given to these characters inselection programmes so as to isolate superior lineswith high genetic potential in rice

Acknowledgement

Authors are highly thankful to DST-INSPIRE forproviding financial support for carrying out research

67

Tabl

e 2

Dire

ct a

nd in

dire

ct e

ffect

s of

var

ious

com

pone

nt c

hara

cter

s on

yie

ld in

rice

Cha

ract

erDa

ys to

Plan

tPa

nicl

eN

o o

fN

o o

fSp

ikel

et10

00-s

eed

Prod

uctiv

ityBi

omas

sHa

rves

tG

rain

50

heig

htle

ngth

Prod

uctiv

efil

led

fert

ility

wei

ght

per d

aype

r pl

ant

Inde

xyi

eld

flow

erin

gtil

lers

grai

nspe

r pl

ant

per

plan

tpe

rpa

nicl

e

Day

s to

50

P0

2451

-00

015

-00

035

-00

018

001

32-0

001

2-0

000

10

0049

000

30-0

011

00

2472

flow

erin

gG

025

22-0

001

8-0

004

0-0

002

30

0157

-00

011

-00

002

000

730

0041

-00

128

025

70

Plan

t hei

ght

P0

0300

-00

123

-00

058

000

480

0094

-00

013

000

310

1482

-00

260

001

280

1630

G

002

98-0

015

3-0

006

60

0072

001

12-0

001

20

0041

014

65-0

029

80

0147

016

05

Pani

cle

leng

thP

008

41-0

006

9-0

010

20

0003

000

94-0

002

50

0011

000

420

0012

-00

032

007

74

G

009

41-0

009

4-0

010

8-0

000

10

0118

-00

025

000

150

0243

-00

005

-00

037

010

48

No

of P

rodu

ctiv

eP

003

910

0054

000

03-0

011

0-0

000

1-0

002

0-0

002

20

2327

001

87-0

024

70

2561

tille

rs p

er p

lant

G0

0426

000

800

0000

-00

138

-00

001

-00

022

-00

033

023

140

0282

-00

323

025

85

No

of f

illed

gra

ins

P0

1307

-00

047

-00

039

000

000

0247

-00

051

000

050

5154

-00

181

-00

136

062

61

per p

anic

leG

013

67-0

006

0-0

004

40

0000

002

89-0

004

80

0007

053

89-0

021

2-0

015

40

6535

Spik

elet

fert

ility

()

P0

0282

-00

016

-00

026

-00

022

001

25-0

010

00

0004

050

38-0

002

8-0

021

40

5043

G

002

90-0

002

0-0

002

9-0

003

20

0147

-00

094

000

050

5311

-00

032

-00

245

053

01

1000

see

d w

eigh

tP

-00

048

-00

065

-00

020

000

420

0021

-00

006

000

580

1613

-00

161

000

350

1470

G

-00

058

-00

083

-00

022

000

600

0025

-00

006

000

750

1669

-00

186

000

390

1513

Prod

uctiv

ity p

er d

ayP

000

12-0

001

80

0000

-00

025

001

26-0

005

00

0009

100

82-0

016

4-0

029

10

9681

G

000

18-0

002

2-0

000

3-0

003

20

0154

-00

049

000

121

0077

-00

149

-00

345

096

62

Bio

mas

s pe

r pla

ntP

-00

131

-00

056

000

020

0036

000

79-0

000

50

0017

029

26-0

056

40

0283

025

85

G

-00

166

-00

074

-00

001

000

620

0098

-00

005

000

220

2410

-00

621

003

210

2047

Har

vest

inde

x (

)P

005

370

0031

-00

006

-00

054

000

66-0

004

3-0

000

40

5832

003

17-0

050

30

6174

G

005

750

0040

-00

007

-00

079

000

79-0

004

1-0

000

50

6194

003

55-0

056

10

6549

Phen

otyp

ic r

esid

ual e

ffec

t =

005

78

Gen

otyp

ic r

esid

ual

effe

ct =

00

560

P-

Phe

noty

pic

leve

l G

- G

enot

ypic

leve

l

Bol

d va

lues

- D

irec

t ef

fect

s N

orm

al V

alue

s -

Indi

rect

effe

cts

Sign

ifica

nt a

t 1

S

igni

fican

t at

5


68

REFERENCES

Bhadru D Lokanadha Reddy D and Ramesha MS2011 Correlation and path coefficient analysisof yield and yield contributing traits in ricehybrids and their parental lines ElectronicJournal of Plant Breeding 2(1) 112-116

Dewey JR and Lu KH 1959 Correlation and pathcoefficient analysis of components of crestedwheat grass seed production AgronomyJournal 51 515-518

Patel JR Saiyad MR Prajapati KN Patel RAand Bhavani RT 2014 Genetic variability andcharacter association studies in rainfed uplandrice (Oryza sativa L) Electronic Journal ofPlant Breeding 5(3) 531-537

Singh RK and Chaudhary BD 1985 Biometricalmethods in quantitative genetic analysisKalyani Publishers New Delhi LudhianaIndia 205-215

Srijan A Sudheer Kumar S Damodar Raju Chand Jagadeeshwar R 2016 Characterassociation and path coefficient analysis forgrain yield of parents and hybrids in rice (Oryzasativa L) Journal of Applied and NaturalScience 8 (1) 167 ndash172

Tuong TP and Bouman BAM 2003 Waterproductiv ity in Agriculture Limits andopportunities for improvement CABIPublishing pp 55-67

Vijaykumar CHM Shobha RN Subbarao LVMahender KR Voleti SR Viraktamath BCand Mishra B 2006 Breeding for high yieldingrice (Oryza sativa L) varieties and hybridsadapted to aerobic (non-flooded irrigated)conditions II Evaluation of released varietiesIndian Journal of Genetics 66(3) 182-186

Wright S 1921 Correlation and causation Journalof Agricultural Research 20 57-585

SRIJAN et al

69

SELECTION CRITERION BASED ON TRAIT LINKAGES IN AFRICAN AND ASIANPEARL MILLET [Pennisetum glaucum (L) R Br] POPULATIONS TO

ENHANCE PRODUCTIVITYK SUDARSHAN PATIL S K GUPTA KULDEEP SINGH DANGI D SHASHIBHUSHAN

M BALRAM and T RAMESHDepartment of Genetics and Plant Breeding College of Agriculture

Professor Jayashankar Telangana State Agricultural University Rajendranagar Hyderabad - 500 030


Pearl millet [Pennisetum glaucum (L) R Br]is commonly grown in the arid and semi-arid regionsof Africa and Asia It serves as staple food for thepeople living in relatively dry tracts of the India andSub-Sahelian Africa and an important source of fodderfeed for livestock and poultry It can be cultivated evenin the poor infertile soils and drought proneenvironments where no other cereal crop can surviveIn India currently pearl milletis cultivated on ~75 mha area with grain production of 97 Mt with an averageproductivity of 1305 kg ha-1(wwwindiastatcom) Theultimate aim in most plant breeding programs is theimprovement in the productiv ity of grains asmeasured in terms of the yield per unit area Thepossibilities of achieving this goal through geneticimprovement have been elucidated by evolving highyielding hybrids and varieties of pearl millet in Asiaand West Africa

Pearl millet germplasm such as landracesopen pollinated varieties (OPVs) and populationsexhibits a wide range of valuable genetic variabilityfor agronomic traits tolerance to biotic and abioticstresses and are often well adapted to varying climaticcondition These landraces and OPVs are majorlycultivated in African countries where as Indian pearlmillet cultivation is dominated by hybrids and an areaof about 2 m ha is under OPVs and landraces Theexisting wide variability in the pearl millet germplasmis less utilized in the breeding program (Yadav et al2009) Therefore assessing the extent of variabilityfor the economically important traits and identificationof promising germplasm is essential to generateknowledge useful for efficient breeding programsRecently few studies have been carried out to assessvariability among pearl millet germplasms of Asia(Yadav et al 2004 Yadav et al 2009 Jyothi Kumari

et al 2016) and Africa (Bashir et al 2014 Pucheret al 2015 Sattler et al 2017) They reportedexistence of wide variability among the germplasmfor various yield-contributing traits Nevertheless untilnow there is no increase in productivity (or inimprovement in yield of OPVs) even though there issubstantial increase in area and production in Africa(ICRISAT and ICARDA 2012) This necessitatesutilization of germplasms in current breeding programsby studying the relationship and effects of variousyield-contributing traits on grain yield to derive properselection criteria for enhancing productivity in pearlmillet crop

Correlation analyses relationship among thecharacters has great value in the evaluation of themost effective procedures for selection of superiorgenotypes Positive association between major yieldcontributing characters would be desirable and iteases the selection process in breeding programCorrelation analyses provides relationship atphenotypic genotypic and environment level Thegenotypic correlation is the heritable associationamong the traits and the environmental correlationis environmental deviations together with non-additivegenetic deviations (Allard 1960 Falconer andMackay 1996) However Correlation measures onlymutual association between traits and does not sayanything about the cause and effect relationship (Roy2000) Hence Path coefficient analysis is animportant statistical tool that indicates which variables(causes) exert influence on other variables (effects)It measures the direct influence of one variable uponanother and permits the separation of correlationcoefficient into components of direct and indirecteffects as well as specifies the relative importance ofthe characters (Dewey and Lu 1959) In any breeding


emailsudarshangpbgmailcom

70

SUDARSHAN PATIL et al

program where target trait is grain yield improvementfor which direct selection is not effective it becomesessential to measure the contribution of each of thecomponent variables towards higher grain yield andto partition the correlation into components of directand indirect effect Therefore the present study wasconducted to determine trait association and directand indirect effects of yield contributing traits on grainyield of pearl millet populations

Forty-five pearl millet populations of Asian andAfrican genetic backgrounds were evaluated in thisstudy The experiment was conducted during rainyseason of 2015 at two locations International CropsResearch Insti tute for the Semi-Arid Tropics(ICRISAT) Patancheru (18degN 78degE) and RegionalAgricultural Research station (RARS) Palem (17degN78degE) Professor Jayashankar Telangana StateAgricultural University (PJTSAU)

The experimental t rial involv ing 45populations (10 Asian origin and bred in Asia 7 Africanorigin and bred in Africa and 28 African origin andbred in Asia) laid out in alpha-lattice design with threereplications In each replication the size of the plotconsisted of 6 rows with a length of 4 meters Thespacing between the rows and within the plants wasmaintained at 75 cm and 12-15 cm respectively Abasal dose of 100 kg of di-ammonium phosphate (18N and 46 P) was applied at the time of fieldpreparation and 100 kg of urea (46 N) was appliedas top dressing in two-split dose at the stage of threeweeks and five weeks after sowing Trials wereregularly irrigated to avoid any moisture stress Allthe recommended agronomic practices were followedfor raising good crop

Data collection was done for the grain yieldand six yield component characters The observationswere taken on 20 random plants from each populationfor the traits like plant height (cm) number ofproductive tillersplant panicle length (cm) andpanicle girth (cm) Other traits such as days to 50flowering 1000-grain weight (g) and grain yield (g)data were recorded on plot basis Further data ofgrain yield was converted to kg ha-1

Phenotypic and genotypic linear correlationcoefficients were calculated for all the possiblecomparisons using the formula suggested byFalconer and Mackay (1996) The correlation

coefficients were partitioned into direct and indirecteffects using the path coefficient analysis accordingto Dewey and Lu (1959) Data analysis was carriedout using SAS v 94 software (SAS Inc 2017)

The genotypic and phenotypic correlationcoefficients between all possible combinations oftraits and the direct and indirect effects are presentedin Tables 1 and 2

Phenotypic and genotypic correlationbetween grain yield and all of the component traitswas significant and positive for all the traits exceptfor number of productive tiller per plant for both

Table 1 Analysis of genotypic (above diagonal)and phenotypic (below diagonal) correlation

coefficients for grain yield and yieldcomponent traits in pearl millet across

locationsTrait DFF PHT NPT PL PG TGW GY

DFF 086 -057 063 019 -029 033

PHT 072 -061 072 022 002 050

NPT -035 -045 -047 -070 -042 -041

PL 054 068 -030 -005 000 023

PG 013 018 -045 -005 065 059

TGW -024 -003 -016 000 043 028

GY 019 038 -022 002 039 018

Significance at 005 001 levels of probability respectively

DFF Days to 50 flowering PHT Plant height (cm) NPT Numberof productive tillersplant PL Panicle length (cm) PG Paniclegirth (cm) TGW 1000-grain weight(g) GY-Grain yield (kg ha-1)

Table 2 Path coefficient analysis of grainyield [direct effects (diagonal) and indirecteffects] with other yield component traits in

pearl millet across locations

Trait DFF PHT NPT PL PG TGW GY

DFF -127 128 -017 002 023 024 033

PHT -109 149 -019 003 028 -002 050

NPT 072 -091 031 001 -088 034 -041

PL -080 107 -014 016 -006 000 023

PG -024 033 -022 -001 125 -053 059

TGW 037 003 -013 001 081 -081 028


DFF Days to 50 flowering PHT Plant height (cm) NPT Numberof productive tillersplant PL Panicle length (cm) PG Paniclegirth (cm) TGW 1000-grain weight (g) GY-Grain yield(kg ha-1)

71

phenotypic correlation coefficients for all thecharacters under study This indicated strong inherentassociation between the different characters studiedbut the phenotypic values was lessened by thesignificant interaction of environment on the traitsunder study Previous studies in pearl millet alsoreported high genotypic correlation than phenotypiccorrelation (Chaudry et al 2003 Atif et al 2012Yahaya et al 2015 and Dehinwal et al 2017)

Days to 50 flowering had significantpositive correlation with most of the traits exceptnumber of productive tillers per plant and 1000-grainweight The plant height had high positive genotypicand phenotypic correlation with all yield componenttraits except with number of productive tillersplant(Table1) It indicated that selection for plant heightwill be rewarding to improve the grain yield Thenumber of productive tillersplant had highly significantnegative genotypic and phenotypic correlations withall the component traits (Table 1) Panicle lengthexhibited highly significant positive correlation withdays to 50 flowering (r = 063 P drdquo 001) and plantheight (r = 072 P drdquo 001) while it had negative

phenotypic and genotypic level (Table 1) Highestsignificant positive correlation for grain yield was foundwith panicle girth (r = 059 P drdquo 001) followed byplant height (r = 050 P drdquo 001) days to 50flowering (r = 033 P drdquo 001) 1000-grain weight (r =027 P drdquo 001) and panicle length (r = 023 P drdquo001) Whereas number of productive tillers per planthad negative correlation with grain yield However hightillering and asynchrony of tillering is attributed toadaptation to drought stress during the vegetativegrowth phase rather than its positive association withgrain yield under optimum environmental condition(Yadav and Rai 2013) This indicated that grain yieldincreases with increase in panicle girth plant heightbold seed size and late flowering Previous studiesby Kulkarni et al (2000) Borkhataria et al (2005)and Yahaya et al (2015) also reported the strongcorrelation between grain yield with plant heightpanicle girth panicle length and 1000-grain weightThe correlation coefficient for most of the pairs ofcharacters revealed the presence of strong positivegenotypic association between yield and itscomponent traits In addition genotypic correlationcoefficients were higher than their respective

Fig1 Path diagram for grain yield (kg ha-1) and its component traits for pearl millet[DFF Days to50 flowering PHT Plant height (cm) NPT Number of productive tillersplant PL Panicle length (cm) PG Paniclegirth (cm) TGW 1000-grain weight (g) GY-Grain yield (kg ha-1)]

Residual GYPx7=029

Px2=149 Px3=031 Px4=0 Px5=125

DFF PHT NPT PL PG TKW

rx12=004 rx23=-061 rx34=-047 rx45=-005 rx54= 065

rx13=-057 rx24=072 rx35=-07 rx44=0

rx14=083 rx25=022 rx30=-042

rx15=019 rx26=002

rx16=-029

Px6=-001Px1=127

SELECTION CRITERION BASED ON TRAIT LINKAGES

72


correlation with rest of the traits Panicle girth hadsignificant but low positive correlation with days to50 flowering and plant height Thousand-grain weighthas significant positive correlation with panicle girth(r = 065 P lt 001) and it had no correlation withplant height and panicle length (Table 1)

Path coefficient used to study the cause andeffect relationship delineates the correlationcoefficient into direct and indirect effect Direct andindirect effects of all the characters are presented inTable 2 and Fig1 Plant height had strong positivedirect effect (149) on grain yield followed by paniclegirth (125) whereas number of productive tillers perplant (031) and panicle length (016) had low directeffects On the other hand grain yield had directnegative effect by days to 50 flowering (-127) and1000-seed weight (-081) Residual effect was low(029) indicated that most of the yield contributingtraits in pearl millet were considered in the presentstudy The high positive direct effects of plant heightand panicle girth were in agreement with previousreports of Kumar et al (2014) Ezeaku et al (2015)Talwar et al (2017) and Dehinwal et al (2017)Therelatively high positive indirect effects on grain yieldwas caused by days to 50 flowering via plant height(128) followed by panicle length via plant height (107)and1000-grain weight via panicle girth (081)Thisindicated that selection for days to 50 flowering andpanicle length traits would contribute to high grainyield via plant height Talwar et al (2017) andUnnikrishnan et al(2004) reported high positiveindirect effect of these traits on grain yield The highestnegative indirect effects on grain yield was causedby plant height via days to 50 flowering (-109)followed by number of productive tillersplant via plantheight (-091)

The results indicated that days to 50flowering plant height and panicle girth had highpositive significant correlation and these traits hadhigh direct effects with grain yield In addition plantheight and panicle girth have high positive indirecteffect each other on grain yield This shows thatselection based on plant height and panicle girth aremore effective to improve grain yield Panicle lengthalso an important trait to be considered since it hadpositive direct effect and high indirect effect on grainyield via plant height However number of productive

tillers had high direct effects it cannot be consideredfor selection since it had negative indirect effects viaplant height and panicle girth as well as negativecorrelation with yield and its component traits Henceselection should be carried out based on plant heightpanicle girth and days to 50 flowering to developsuperior breeding material

REFERENCES

Allard RW 1960 Principles of Plant Breeding JohnWiley and Sons Inc New York pp 48

Atif IA Awadalla AA and Atif EI 2012 CharacterAssociation and Path Analysis in Pearl Millet(Pennisetum glaucum L) American Journal ofExperimental Agriculture 2(3) 370-381

Bashir EMAAbdelbagi MA AdamMAMelchingerAEParzies HK andHaussmannBIG 2014 Characterization ofSudanese pearl millet germplasm for agro-morphological traits and grain nutritional valuesPlant Genetic ResourcesCharacterization andUtilization 12(1) 35ndash47

Borkhataria PR Bhatiya VJ Pandya HM andValu MG 2005 Variability and correlationstudies in pearl millet National Journal of PlantImprovement7 21-23

Chaudry MH Subhani GM Shaheen MS andSalman U 2003 Correlation and pathanalysis in pearl millet Pakistan Journal ofBiological Sciences 6(6) 597-600

Dehinwal AK Yadav YP Anil Kumar and SiviaSS2017 Correlation and path coefficient analysisfor different biometrical and harvest plus traitsin pearl millet Pennisetum glaucum (L) RBrResearch in Environment and LifeSciences 10(5) 407-410

Dewey DR and Lu KH 1959 A correlation andpath coefficient analysis of components ofcrested wheat grass seed productionAgronomy Journal51 515-518

Ezeaku IE Angarawai II Aladele SE andMohammed SG 2015 Correlation pathcoefficient analysis and heritability of grainyield components in pearl millet [Pennisetumglaucum (L) R Br]parental lines Journal of

73

Plant Breeding and Crop Science 7(2) 254-259

Falconer DS and Mackay FCT 1996 Introductionto Quantitative Genetics (4th eds) LongmanGroup Ltd England 122-125

ICRISAT and ICARDA 2012 CGIAR Researchprogram on Dryland cereals Dryland cereals -Program focus and justification pp17

Jyoti KumariManas Bag K Pandey S Jha SKChauhan SS Girish Jha K Gautam NKand Dutta M 2016 Assessment of phenotypicdiversity in pearl millet [Pennisetum glaucum(L) R Br] germplasm of Indian origin andidentification of trait-specific germplasm Cropamp Pasture Science67(12) 1223-1234

Kulkarni VM Navale PA and Harinarayana G2000 Variability and path analysis in whitegrain pearl millet [Pennisetum glaucum (L) RBr] Tropical Agriculture77 130-132

Kumar R HarishSDalal MS MalikVDevvartChughLK Garg P and RajK 2014 Studieson variability correlation and path analysis inpearl millet [Pennisetum glaucum (L) R BR]Forage Research 40 (3) 163-167

Pucher A Ousmane SIAngarawai I Jada GRoger Z Mahamadi O Moussa DSBoureimaS Hash CT Haussmann BIG2015 Agro-morphological characterization ofwest and central African pearl mil letaccessions Crop Science55 737-748

Roy D 2000 Plant Breeding Analysis andExploitation of Variation Narosa PublishingHouse New Delhi India

SAS Institute Inc 2017 SASSTATreg 141Userrsquos GuideCary NC

Sattler FT Sanogo MD Kassari IA AngarawaiII Gwadi KW Dodo H and HaussmannBIG 2017 Characterization of West andCentral African accessions from a pearl milletreference collection for agro- morphologicaltraits and Striga resistance Plant GeneticResources 16(3) 260-272

Talawar AM Girish GChannabasavanna AS andKitturmath MS 2017 Studies on geneticvariability correlation and path analysis in pearlmillet (Pennisetum glaucum L) germplasmlines Agriculture Science Digest 37(1) 75-77

Unnikrishnan KV Balzor Singh Ramesh SinghVerma APS and Singh KP 2004Correlation and path analysis of hybrids andparents in pearl millet Annals of AgriculturalResearch25 110-112wwwindiastatcomagriculture-data2agricultural-production225bajra-spiked-millet17198statsaspxas seen onNovember 15 2018

Yadav OPWeItzien-Rattunde E and Bidinger FR2004 Diversity Among Pearl Millet LandracesCollected in North-western India Annals of Aridzone 43(1) 45-53

YadavOP Bidinger FR and Singh DV 2009Utility of pearl millet landraces in breeding dual-purpose hybrids for arid zone environments ofIndia Euphytica 166 239ndash247

Yadav OP and Rai KN 2013 Genetic Improvementof Pearl Millet in India Agricultural Research2(4) 275ndash292

Yahaya Y Echekwu CA and Mohammed SG2015 Genetic variability and path coefficientanalysis in pearl millet (Pennisetum glaucum(L) RBr) African Journal of Agronomy 3(1)224-227


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7 RESULTS AND DISCUSSION Great care should be taken to highlight the important findings withsupport of the data well distinguished by statistical measures like CD r Z test etc Too descriptiveexplanation for the whole data is not desirable The treatments should be briefly expressed instead ofabbreviations like T1 T 2 etc The discussion should be crisp and relate to the limitations or advantagesof the findings in comparison with the work of others

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Journals and Bulletins

Abdul Salam M and Mazrooe SA 2007 Water requirement of maize (Zea mays L) as influenced byplanting dates in Kuwait Journal of Agrometeorology 9 (1) 34-41

Hu J Yue B and Vick BA 2007 Integration of trap makers onto a sunflower SSR marker linkage mapconstructed from 92 recombinant inbred lines Helia 30 (46) 25-36

Books

AOAC 1990 Official methods of analysis Association of official analytical chemists 15th Ed WashingtonDC USA pp 256

Federer WT 1993 Statistical design and analysis for intercropping experiments Volume I two cropsSpringer ndash Verlag Cornell University Ithaca New York USA pp 298-305

Thesis

Ibrahim F 2007 Genetic variability for resistance to sorghum aphid (Melanaphis sacchari Zentner) insorghum PhD Thesis submitted to Acharya NG Ranga Agricultural University Hyderabad

i

Seminars Symposia Workshops

Naveen Kumar PG and Shaik Mohammad 2007 Farming Systems approach ndash A way towards organicfarming Paper presented at the National symposium on integrated farming systems and its roletowards livelihood improvement Jaipur 26 ndash 28 October 2007 pp43-46

Proceedings of Seminars Symposia

Bind M and Howden M 2004 Challenges and opportunities for cropping systems in a changing climateProceedings of International crop science congress Brisbane ndashAustralia 26 September ndash 1 October2004 pp 52-54

(wwwcropscience 2004com 03-11-2004)

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ii

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1

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30AJPJTSAU2018

2pdf

Page 2

3pdf

Page 3

Extension Education Institute

Controller of Examinations

RESEARCH EDITOR

Smt M PallaviAIampCC and PJTSAU PressRajendranagar Hyderabad

Professor amp Head

DDrsquos may be drawn in favour of Principal Agricultural Information Officer and sent to Agricultural Information amp

Communication Centreand PJTSAU Press ARI Campus Rajendranagar Hyderabad - 500 030

Dr S Sudheer Kumar

Dr KVS Meena Kumari

Dr R Jagadeeshwar

Principal Scientist amp HeadRice Research Centre ARI Rajendranagar

Dean of Agril Engineering amp Technologyand Home Science

PJTSAU Rajendranagar Hyderabad

Project Director

ic

CONTENTS









PART V HOME SCIENCE






2222

1






ABSTRACT







2





NAGESH KUMAR et al


12

9

362625 2 155 117 075 071 0585 055 05

141210

86420

china

USA

Fran

ce

Brazil

India

Japa

nGer

many

Arge

ntin

a

Italy

Nethe

rland

s

Can

ada

Russia

3




Corn 11Rice 6

Others 40Cotton 28




Total Market 32 MT

Cotton 07Others 86

Wheat 312

Paddy 259

Soybean 106

Groundnut 87

Gram 58

Potato 55

Maize 55


4








NAGESH KUMAR et al


Blackgram


20

40

60

80

100

See

d R

epla

cem

ent

Rat

e (

)

2006-07 2011-12 2016-17

3

25

2

15

1

05

0

013

05

17

27

1990 2000 2010 2013

5






6














NAGESH KUMAR et al

7
















0

2

4

6

8

10

12

14

16

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

USD Bn


8




Rice 456 50 22800 100 228 2280

Wheat 272 125 34000 20 1700 85000

Sorghum 77 15 1155 160 07 45

Pearl millet 87 5 435 200 02 11

Maize 80 20 1600 80 20 250

Pigeon pea 34 15 510 100 05 51

Chickpea 82 80 6560 10 656 65600

Groundnut 62 100 6200 8 97 12109

RampM 63 5 315 200 02 08

Soybean 89 75 6675 16 417 26074

Sunflower 19 10 190 50 04 76

Cotton 95 12 1140 50 23 456

Jute 08 5 40 100 004 04

Total 1424 81620 3161 191964








NAGESH KUMAR et al

9












10













NAGESH KUMAR et al

11


Seed Biotechnology





Cold Plasma Coating





Bioprospecting







12






REFERENCES














NAGESH KUMAR et al

13




ABSTRACT







14








SRIDHAR et al

15








Quality parameters



CONCLUSIONS



16

Tabl

e 1

Gra

in a

nd S

traw

yie

ld (k

g ha

-1) o

f ric

e as

influ

ence

d by

alte

rnat

e w

ettin

g an

d dr

ying

irrig

atio

n an

d ni

trog

en le

vels

dur

ing

rabi

201

620

17 a

nd p

oole

d m

eans

Trea

tmen

tsG

rain

yie

ld (k

g ha

-1)

Stra

w y

ield

(kg

ha-1)

2016

2017

Pool

ed20

1620

17Po

oled

Irrig

atio

n re

gim

es (I

)

I 1- 2-

5cm

sub

mer

genc

e as

per

cro

p st

age

7026

7221

7123

7936

8153

8045

I 2- AW

D ir

rigat

ion

of 5

cm

with

3 c

m fa

ll in

fiel

d69

2571

5070

3778

4480

5679

56w

ater

tube

I 3- AW

D ir

rigat

ion

of 5

cm

with

5 c

m fa

ll in

fiel

d64

1466

5265

3373

3375

4574

39w

ater

tube

SEm

plusmn54

060

455

252

852

152

4

CD

at 5

15

016

815

314

714

514

6

Nitr

ogen

lev

els

(N)

N1-1

20 k

g ha

-167

0669

2268

1476

0578

1777

11

N2-1

60 k

g ha

-168

0270

2169

1177

1079

2178

16

N3-2

00 k

g ha

-168

5770

8169

6977

9880

1679

07

SE

mplusmn

372

445

390

410

411

410

CD

at 5

81

9785

8989

89

Inte

ract

ions

NS

NS

NS

NS

NS

NS

SRIDHAR et al

17

Tabl

e 2

Hul

ling

and

mill

ing

perc

enta

ge (

) of r

ice

as in

fluen

ced

by a

ltern

ate

wet

ting

and

dryi

ng ir

rigat

ion

and

nitr

ogen

leve

ls d

urin

g ra

bi20

16 2

017

and

pool

ed m

eans

Hul

ling

()

Mill

ing

()

2016

2017

Pool

ed20

1620

17Po

oled

Irrig

atio

n re

gim

es (I

)

I 1- 2-

5cm

sub

mer

genc

e as

per

cro

p st

age

808

810

809

675

674

674

I 2- AW

D ir

rigat

ion

of 5

cm

with

3 c

m fa

ll in

fiel

d80

780

980

867

367

267

3w

ater

tube

I 3- AW

D ir

rigat

ion

of 5

cm

with

5 c

m fa

ll in

fiel

d80

580

780

667

066

866

9w

ater

tube

SEm

plusmn0

10

20

20

20

20

2

CD

at 5

N

SN

SN

S0

40

50

5

Nitr

ogen

lev

els

(N)

N1-1

20 k

g ha

-180

780

880

867

267

467

3

N2-1

60 k

g ha

-180

680

880

767

367

367

3

N3-2

00 k

g ha

-180

780

980

867

267

067

1

SE

mplusmn

01

01

01

06

02

04

CD

at 5

N

SN

SN

SN

SN

SN

S

Inte

ract

ions

( IxN

)N

SN

SN

SN

SN

SN

S


18

Tabl

e 3

Hea

d ric

e re

cove

ry (

) G

rain

pro

tein

con

tent

and

am

ylos

e (

) of r

ice

as in

fluen

ced

by a

ltern

ate

wet

ting

and

dryi

ng ir

rigat

ion

and

nitr

ogen

leve

ls d

urin

g ra

bi 2

016

201

7 an

d po

oled

mea

ns

2016

2017

Pool

ed20

1620

17Po

oled

2016

2017

Pool

ed

Hea

d ric

e re

cove

ry (

)G

rain

pro

tein

con

tent

()

Amyl

ose(

)

Irrig

atio

n re

gim

es (I

)

I 1- 2-

5cm

sub

mer

genc

e as

per

cro

p st

age

650

665

12

650

99

189

149

1623

925

024

5

I 2- AW

D ir

rigat

ion

of 5

cm

with

3 c

m fa

ll in

fiel

d63

95

638

763

91

907

904

906

238

247

242

wat

er tu

be

I 3- AW

D ir

rigat

ion

of 5

cm

with

5 c

m fa

ll in

fiel

d63

32

633

463

33

903

898

901

234

243

239

wat

er tu

be

SEm

plusmn0

200

230

210

040

050

040

440

450

44

CD

at 5

0

570

640

60N

SN

SN

SN

SN

SN

S

Nitr

ogen

lev

els

(N)

N1-1

20 k

g ha

-163

93

639

863

95

894

890

892

230

239

235

N2-1

60 k

g ha

-164

16

640

464

10

916

911

913

240

246

243

N3-2

00 k

g ha

-164

23

643

164

27

920

916

918

241

246

244

SE

mplusmn

014

016

015

004

004

004

023

019

021

CD

at 5

N

SN

SN

S0

100

090

090

750

50

63

Inte

ract

ions

( IxN

)N

SN

SN

SN

SN

SN

SN

SN

SN

S

SRIDHAR et al

19


REFERENCES















20






ABSTRACT






21








S No Genotype ID

1 ICGV 15016

2 ICGV 15064

3 ICGV 16013

4 ICGV 16017

5 ICGV 16030

6 ICGV 16035

7 ICGV 16045

8 ICGV 16668


22

S No Genotype ID

9 ICGV 16687

10 ICGV 16700

11 ICGV 16701

12 ICGV 171011

13 ICGV 16686

14 ICGV 171007

15 ICGV 171023

16 ICGV 171027

17 ICGV 171006

18 ICGV 16667

19 ICGV 16698

20 ICGV 171017

21 ICGV 171015

22 ICGV 16020

23 ICGV 171051

24 ICGV 171040

25 ICGV 171039

26 ICGV 171046

27 ICGV 171041

S No Genotype ID

28 ICGV 171048

29 ICGV 171026

30 ICGV 171004

31 ICGV 171021

32 ICGV 171019

33 ICGV 171024

34 ICGV 171016

35 ICGV 171009

36 ICGV 171018

37 ICGV 171025

38 ICGV 171020

39 ICGV 171002

40 ICGV 03043

41 ICGV 07222

42 ICGV 07220

43 ICGV 00350

44 ICGV 00351

45 ICGV 02266

46 ICGV 91114





DNYANESHWAR et al

23




1 ICGV 171051 336 20 145 20

2 ICGV 171048 435 20 173 17

3 ICGV 171046 923 17 284 15

4 ICGV 171041 499 20 137 19

5 ICGV 171040 365 18 240 19

6 ICGV 171039 262 17 350 21

7 ICGV 171027 252 20 97 20

8 ICGV 171026 273 20 62 23

9 ICGV 171025 297 19 295 21

10 ICGV 171024 321 20 216 21

11 ICGV 171023 367 20 71 21

12 ICGV 171021 251 19 135 19

13 ICGV 171020 302 21 222 19

14 ICGV 171019 699 16 140 20

15 ICGV 171018 504 20 173 19

16 ICGV 171017 188 19 150 19

17 ICGV 171016 158 21 156 19

18 ICGV 171015 250 20 183 21

19 ICGV 171011 413 19 281 18

20 ICGV 171009 231 19 205 20

21 ICGV 171007 269 19 106 21

22 ICGV 171006 203 18 141 21

23 ICGV 171004 176 22 178 20

24 ICGV 171002 293 20 217 18

25 ICGV 16701 337 19 104 21

26 ICGV 16700 256 21 59 22

27 ICGV 16698 243 21 182 19

28 ICGV 16687 417 19 76 24

29 ICGV 16686 245 17 166 16

30 ICGV 16668 438 19 191 18

31 ICGV 16667 215 21 132 17

32 ICGV 16045 348 21 113 18

33 ICGV 16035 186 20 48 22

34 ICGV 16030 251 18 62 21

35 ICGV 16020 06 14 248 21


24



36 ICGV 16017 284 19 125 17

37 ICGV 16013 157 20 138 14

38 ICGV 15064 347 19 267 17

39 ICGV 15016 395 19 133 21

40 ICGV 07222 286 20 276 18

41 ICGV 07220 381 19 110 18

42 ICGV 03043 360 20 107 17

43 ICGV 02266 448 20 230 18

44 ICGV 00351 275 20 270 20

45 ICGV 00350 309 21 176 17

46 ICGV 91114 174 22 279 19




DNYANESHWAR et al

25

Tabl

e 3

Com

para

tive

SNP

prof

ile o

btai

ned

from

gen

omic

DN

A of

gro

undn

ut le

af d

isc

and

seed

chi

p

DN

A A

ssay

SNP

for

SNPs

for l

ate

leaf

spo

tSN

Ps fo

r rus

thi

gh o

leic

acid

snpA

H00

02sn

pAH

0004

snpA

H00

05sn

pAH

0010

snpA

H00

11sn

pAH

0015

snpA

H00

17sn

pAH

0018

snpA

H00

21sn

pAH

0026

ICG

V 15

064_

Leaf

AA

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

1506

4_Se

edA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

687_

Lea

fA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

687_

See

dA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

698_

Lea

fA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

698_

See

dA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

700_

Lea

fA

AG

GC

CA

ATT

AA

CC

AA

GT

CC

ICG

V 16

700_

See

dA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 00

351_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0035

1_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 02

266_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0226

6_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 03

043_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0304

3_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 07

222_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0722

2_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

701_

Lea

fA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 16

701_

See

dA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1002

_ Le

afA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1002

_ S

eed

AA

GG

CC

AA

TTA

AC

CA

TG

GC

CIC

GV

1710

04_

Leaf

AA

GG

CC

AA

TTA

AC

CA

AG

GC

CIC

GV

1710

04_

See

dA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1006

_ Le

afA

AG

GC

GA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1006

_ S

eed

AA

GG

CC

AA

TTA

AC

CA

AG

GC

C--

Nul

l alle

le


26

REFERENCES
















DNYANESHWAR et al

27






28







ABSTRACT









29











Yield attributes



30










BRIJBHOOSHAN et al

31

Tabl

e 1

Yie

ld a

ttrib

utes

of r

ice

as in

fluen

ced

by c

rop

esta

blis

hmen

t met

hods

and

nut

rient

man

agem

ent p

ract

ices

(rab

i 20

15 a

nd 2

016)

Trea

tmen

tsN

umbe

r of p

anic

le m

-2Pa

nicl

e le

ngth

(cm

)Pa

nicl

e w

eigh

t (g)

Test

wei

ght (

g)

2015

2016

Pool

ed20

1520

16 P

oole

d20

1520

16Po

oled

2015

2016

Pool

edC

rop

esta

blis

hmen

t met

hods

(M)

M1-

Dire

ct S

eede

d R

ice

(DS

R)

293

527

53

284

423

18

227

922

99

314

308

311

222

322

15

221

9

M2-

Nor

mal

Tra

nspl

ante

d R

ice

(NTP

)25

94

247

825

36

244

924

08

242

93

323

283

3022

51

224

222

46

SEm

+4

104

014

230

270

270

270

040

040

030

260

230

24

CD

(p=0

05)

184

318

03

190

61

211

241

230

170

180

15N

SN

SN

S

Nut

rient

Man

agem

ent p

ract

ices

(N)

N1-

Abs

olut

e co

ntro

l (N

o fe

rtiliz

er)

148

413

59

142

122

03

216

021

82

274

271

273

203

520

25

203

0

N2-

Con

trol (

RD

F)27

95

262

027

09

231

522

77

229

63

153

113

1322

21

221

522

18

N3-R

DF+

SA

of

ZnS

O47

H2O

2

5 kg

ha-1

294

827

95

287

124

01

236

123

81

329

324

326

225

022

39

224

4

N4-R

DF+

2 F

S o

f ZnS

O47

H2O

0

5

301

528

60

293

724

16

237

423

95

335

328

331

228

922

78

228

3

N5-R

DF+

2 F

S o

f nan

o Zn

O

100

0 pp

m31

96

305

531

26

248

824

48

246

83

443

393

4123

21

231

323

17

N6-R

DF+

2 F

S o

f na

no Z

nO

150

0 pp

m31

50

300

530

78

248

224

42

246

23

423

363

3923

08

229

923

03

SEm

+4

024

034

300

290

280

290

040

040

040

190

200

19

CD

(p=0

05)

116

111

64

124

20

840

810

820

130

120

120

560

590

56

Inte

ract

ions

(MxN

)

Nut

rient

man

agem

ent a

t sam

e le

vel o

f cro

p es

tabl

ishm

ent

SEm

+5

695

706

080

410

390

400

060

060

060

270

290

27

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

Cro

p es

tabl

ishm

ent a

t sam

e or

diff

eren

t lev

el o

f nut

rient

man

agem

ent

SEm

+6

616

576

980

460

450

460

070

070

060

360

350

35

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

Gen

eral

Mea

n27

65

261

626

90

238

423

44

236

43

233

183

2022

37

222

822

33


32

Trea

tmen

tsFi

lled

grai

ns p

anic

le-1

Unf

illed

gra

ins

pani

cle-1

Ster

ility

per

cent

age

()

2015

2016

Pool

ed20

1520

16Po

oled

2015

2016

Pool

ed

Cro

p es

tabl

ishm

ent m

etho

ds (M

)M

1- D

irect

See

ded

Ric

e (D

SR

)12

466

116

0412

034

968

122

810

98

737

976

856

M2-

Nor

mal

Tra

nspl

ante

d R

ice

(NTP

)13

436

125

7613

007

881

112

510

03

630

838

734

SEm

+1

881

701

460

160

170

150

120

160

15

CD

(p=0

05)

847

765

658

073

077

069

056

073

065

Nut

rient

Man

agem

ent p

ract

ices

(N)

N1-

Abs

olut

e co

ntro

l (N

o fe

rtiliz

er)

109

3310

079

105

0518

37

208

819

63

144

217

23

158

2

N2-

Con

trol (

RD

F)12

531

116

6612

098

103

212

85

115

97

639

958

79

N3-R

DF+

SA

of

ZnS

O47

H2O

2

5 kg

ha-1

131

5312

289

127

208

2910

80

954

594

811

702

N4-R

DF+

2 F

S o

f ZnS

O47

H2O

0

5

132

7312

409

128

407

8710

38

913

562

775

668

N5-R

DF+

2 F

S o

f nan

o Zn

O

100

0 pp

m13

975

131

1313

546

546

798

672

377

576

477

N6-R

DF+

2 F

S o

f na

no Z

nO

150

0 pp

m13

844

129

8513

414

517

768

642

362

561

462

SEm

+1

451

431

280

200

180

200

150

170

17

CD

(p=0

05)

419

413

371

059

053

057

043

048

050

Inte

ract

ions

(MxN

)N

utrie

nt m

anag

emen

t at s

ame

leve

l of c

rop

esta

blis

hmen

tS

Em+

205

202

182

029

026

028

021

023

025

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Cro

p es

tabl

ishm

ent a

t sam

e or

diff

eren

t lev

el o

f nut

rient

man

agem

ent

SEm

+2

662

512

210

310

290

300

230

270

27

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Gen

eral

Mea

n12

951

120

9012

521

925

117

610

51

683

907

795

RD

F- R

ecom

men

ded

Dos

e of

Fer

tiliz

ers

(N

P2O

5amp K

2O

120

60

40 k

g ha

-1)

SA

- S

oil A

pplic

atio

n F

S-

Folia

r Sp

ray

NS

- N

on S

igni

fican

t

BRIJBHOOSHAN et al

33








CONCLUSION



34

Tabl

e 2

Yie

ld a

nd h

arve

st in

dex

of ri

ce a

s in

fluen

ced

by c

rop

esta

blis

hmen

t met

hods

and

nut

rient

man

agem

ent p

ract

ices

(rab

i 20

15 a

nd 2

016)

Trea

tmen

tsG

rain

yie

ld (k

g ha

-1)

Stra

w y

ield

(kg

ha-1)

Har

vest

Inde

x(

)

2015

2016

Pool

ed20

1520

16Po

oled

2015

2016

Pool

ed

Cro

p es

tabl

ishm

ent m

etho

ds (M

)M

1- D

irect

See

ded

Ric

e (D

SR

)51

9848

8650

4766

7564

4765

6143

58

428

243

20

M2-

Nor

mal

Tra

nspl

ante

d R

ice

(NTP

)47

3344

8946

1160

0058

1359

0743

81

434

343

62

SEm

+98

8389

121

109

101

052

061

049

CD

(p=0

05)

441

373

401

545

492

455

NS

NS

NS

Nut

rient

Man

agem

ent p

ract

ices

(N)

N1-

Abs

olut

e co

ntro

l (N

o fe

rtiliz

er)

2335

2274

2305

3270

3320

3295

416

740

84

412

5

N2-

Con

trol (

RD

F)47

3944

6946

0461

5459

8160

6843

47

427

743

12

N3-R

DF+

SA

of

ZnS

O47

H2O

2

5 kg

ha-1

5187

4993

5090

6593

6462

6527

440

243

63

438

3

N4-R

DF+

2 F

S o

f ZnS

O47

H2O

0

5

5483

5157

5320

6902

6656

6779

442

543

66

439

6

N5-R

DF+

2 F

S o

f nan

o Zn

O

100

0 pp

m60

7056

5958

7875

7072

2473

9744

54

439

644

25

N6-R

DF+

2 F

S o

f na

no Z

nO

150

0 pp

m59

7855

7457

7675

3771

3773

3744

21

438

944

05

SEm

+13

410

911

114

711

412

10

400

550

51

CD

(p=0

05)

386

315

320

424

328

349

115

158

147

Inte

ract

ions

(MxN

)N

utrie

nt m

anag

emen

t at s

ame

leve

l of c

rop

esta

blis

hmen

tS

Em+

189

154

157

208

161

171

056

077

072

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Cro

p es

tabl

ishm

ent a

t sam

e or

diff

eren

t lev

el o

f nut

rient

man

agem

ent

SEm

+19

816

316

822

518

318

60

730

940

82

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Gen

eral

Mea

n49

6546

8848

2963

3761

3062

3443

70

431

343

41

Coe

ffici

ent o

f Var

iatio

n (

)9

78

79

09

48

77

95

97

05

5

RD

F- R

ecom

men

ded

Dos

e of

Fer

tiliz

ers

(N

P2O

5amp K

2O

120

60

40 k

g ha

-1)

SA

- S

oil A

pplic

atio

n F

S-

Folia

r Sp

ray

NS

- N

on S

igni

fican

t

BRIJBHOOSHAN et al

35

REFERENCES

















36





ABSTRACT







37












38














PREETHAM et al

39

Tabl

e-1

Effe

ct o

f int

egra

ted

nutr

ient

man

agem

ent p

ract

ices

on

yiel

d at

trib

utes

of b

aby

corn

dur

ing

khar

if 2

015

and

2016

Trea

tmen

t giv

en to

kha

rif b

aby

corn

2015

2016

Cob

spl

ant-1

Cob

leng

th(c

m)

Cob

girth

(cm

)

Cob

wid

th(c

m)

Cob

wei

ght

(g)

Cob

spl

ant-1

Cob

leng

th(c

m)

Cob

girth

(cm

)

Cob

wid

th(c

m

Cob

wei

ght

(g)

With

With

out

husk

husk

With

With

out

husk

husk

T 1- 25

N through FY

M + 75

RDF

178

106

95

251

6846

80

926

183

931

513

163

426

89

03T 2-

25

N th

roug

h FY

M +

75

RD

F +

200

108

35

601

7247

33

100

01

989

645

541

6843

16

908

Azo

spiri

llum

and

Bac

illus

meg

ater

ium

5

kg h

a-1 e

ach

T 3- 25

N

thr

ough

Ver

mic

ompo

st +

202

109

55

581

7649

17

100

02

029

655

461

7143

68

926

75

RD

FT 4-

T4 2

5 N

thro

ugh

Verm

icom

post

+2

2312

50

574

186

555

913

46

222

122

45

651

8348

73

120

675

R

DF

+ A

zosp

irillu

m a

nd B

acill

usm

egat

eriu

m

5 k

g ha

-1 e

ach

T 5- 10

0 R

DF

175

106

65

241

7146

29

920

178

989

512

161

423

69

01T 6-

100

RD

F +

Azo

spiri

llum

and

Bac

illus

200

109

65

551

8049

86

103

81

989

905

441

7443

10

936

meg

ater

ium

5

kg

ha-1 e

ach

T 7- C

ontro

l (N

o fe

rtiliz

er a

pplic

atio

n)0

957

503

981

2715

88

358

147

711

373

127

239

95

13S

Em

+0

060

410

150

051

550

400

060

500

150

050

970

13C

D (

P=0

05)

020

127

045

016

477

123

018

153

047

016

300

041

Trea

tmen

t giv

en to

rabi

cro

p (h

yaci

nth

bean

)S

1- 10

0 R

DF

191

987

520

161

424

99

25S

2- 75

R

DF

180

933

501

167

373

78

25S

3-10

0 R

DF

+ B

rady

rhiz

obiu

m

200

990

533

160

445

79

9550

0 g

ha-1 (

See

d tre

atm

ent)

S4-

75

RD

F +

Bra

dyrh

izob

ium

1

889

605

071

6639

98

850

500

g ha

-1 (

See

d tre

atm

ent)

SE

m+

003

024

017

005

086

019

CD

(P

=00

5)0

10N

SN

SN

S2

460

54In

tera

ctio

nB

ean

trea

tmen

t mea

ns a

t sam

e le

vel o

f bab

y co

rn IN

M tr

eatm

ents

SE

m+

009

064

044

014

228

050

CD

(P

=00

5)N

SN

SN

SN

SN

SN

SIN

M tr

eatm

ent m

eans

of b

aby

corn

at s

ame

leve

l of b

ean

trea

tmen

tsS

Em

+0

100

740

410

132

200

45C

D (

P=0

05)

NS

NS

NS

NS

NS

NS


40





Cob Yield (kg ha-1)










PREETHAM et al

41

Tabl

e-2

Effe

ct o

f int

egra

ted

nutr

ient

man

agem

ent p

ract

ices

on

cob

yiel

d (k

g ha

-1) w

ith a

nd w

ithou

t hus

k an

d st

over

yie

ld o

f bab

y co

rn d

urin

gkh

arif

201

5 an

d 20

16

Trea

tmen

t giv

en to

kha

rif b

aby

corn

2015

2016

Cob

yie

ld (k

g ha

-1)

Stov

erC

ob y

ield

(kg

ha-1)

Stov

er y

ield

yiel

d(kg

ha-1

)(k

g ha

-1)

With

hus

kW

ithou

t hus

kW

ith h

usk

W

ithou

t hus

k

T 1- 25

N through FY

M + 75

RDF

8204

1602

1934

481

4714

9518

919

T 2- 25

N

thro

ugh

FYM

+ 7

5 R

DF

+ A

zosp

irillu

m90

3218

0320

865

8882

1596

1970

8an

d B

acill

us m

egat

eriu

m

5 k

g ha

-1 e

ach

T 3- 25

N

thro

ugh

Verm

icom

post

+ 7

5 R

DF

9400

1801

2147

589

9416

0119

762

T 4- T4

25

N th

roug

h Ve

rmic

ompo

st +

75

RD

F +

1229

020

4924

020

1172

718

9021

165

Azo

spiri

llum

and

Bac

illus

meg

ater

ium

5

kg

ha-1 e

ach

T 5- 10

0 R

DF

8049

1542

1906

081

9914

7119

364

T 6- 10

0 R

DF

+ A

zosp

irillu

m a

nd B

acill

us93

4418

0720

398

8824

1553

1963

1m

egat

eriu

m

5 k

g ha

-1 e

ach

T 7- C

ontro

l (N

o fe

rtiliz

er a

pplic

atio

n)42

7982

313

821

4393

749

1216

8S

Em

+26

764

429

200

1638

5C

D (

P=0

05)

824

198

1321

617

4911

87Tr

eatm

ent g

iven

to ra

bi c

rop

(hya

cint

h be

an)

S1-

100

RD

F88

2415

4619

086

S2-

75

RD

F78

8013

6917

833

S3-10

0 R

DF

+ B

rady

rhiz

obiu

m

500

g h

a-190

7315

9019

410

(See

d tre

atm

ent)

S4-

75

RD

F +

Bra

dyrh

izob

ium

5

00 g

ha-1

8033

1411

1836

5(S

eed

treat

men

t)S

Em

+20

216

280

CD

(P

=00

5)57

745

798

Inte

ract

ion

Bea

n tre

atm

ent m

eans

at s

ame

leve

l of b

aby

corn

INM

trea

tmen

tsS

Em

+53

541

740

CD

(P

=00

5)N

SN

SN

SIN

M tr

eatm

ent m

eans

of b

aby

corn

at s

ame

leve

l of b

ean

treat

men

tsS

Em

+50

439

748

CD

(P

=00

5)N

SN

SN

S


42






CONCLUSION


REFERENCES







PREETHAM et al

43















44



ABSTRACT














45























2 FACTOR ANALYSIS



2 2 212 R 3K N(N 1)i iW 2 2K N(N 1)

46











Sig 0000



Component


Total ofVariance

Cumulative

Total ofVariance

Cumulative


Total ofVariance

Cumulative

1 1965 21839 21839 1965 21839 21839 1894 21044 21044

2 1806 20070 41909 1806 20070 41909 1520 16887 37932

3 1086 12064 53973 1086 12064 53973 1444 16042 53973

4 927 10301 64274

5 887 9860 74134

6 738 8199 82333

7 630 6996 89329

8 529 5878 95207

9 431 4793 100000


MEENA et al

47


Component Number

Scree Plot

Eig

en v

alue



Component

1 2 3


Component1 2 3












48


Table 4 Ranks









Chi-Square 0822

df 2

Asymp Sig 0663















MEENA et al

49







ABSTRACT









50

SRINIVAS et al




()




51



CONCLUSION


Acknowledgments


REFERENCES







52







ABSTRACT








53











54




Analysis of protein

















55








0

10

20

30

40

50

6070

80

90

Finge

r Mille

t

Foxta

il Mille

t

Little M

illet

Pearl M

illet

Proso

Mille

t

Sorgh

um

Perc

enta

ge d

iges

tibili

ty

Dosa UpmaIdli



56






Foxtail millet

Little millet

Pearl millet

Prosomillet

Sorghum

IdliDosaUpma

10080

6040

20 0







57



CONCLUSION


REFERENCES
















58




















59






ABSTRACT










60
















Pomegranate 21 23 17 16 18 19 13 12 17 18 12 14

Cassia 31 33 24 24 26 28 20 21 22 23 21 22

Periwinkle 29 31 23 25 25 24 19 20 21 21 22 21

Marigold 21 23 18 19 16 18 12 13 16 17 14 16

61









62



0

5

10

15

20

25

30

S aureus Ecoli

Pomegran

teCas

siaPeri

winkleMari

gold

Pomegran

te

Cassia

Periwinkle

Marigold

SilverTitaniumzinc


0

5

10

30

Pomegran

teCas

siaPeri

winkleMari

gold

Pomegran

teCas

siaPeri

winkleMari

gold

35

15

20

S aureus Ecoli

SilverTitaniumzinc

25

63

CONCLUSION








64













65

Tabl

e 1

Est

imat

es o

f phe

noty

pic

and

geno

typi

c co

rrel

atio

n co

effic

ient

s fo

r yie

ld a

nd it

s at

trib

utin

g ch

arac

ters

Cha

ract

erDa

ys to

Plan

tPa

nicl

eN

o o

fN

o o

fSp

ikel

et10

00-s

eed

Prod

uctiv

ityBi

omas

sHa

rves

tG

rain

50

heig

htle

ngth

Prod

uctiv

efil

led

fert

ility

wei

ght

per d

aype

r pl

ant

Inde

xyi

eld

flow

erin

gtil

lers

grai

nspe

r pl

ant

per

plan

tpe

rpa

nicl

e

Day

s to

50

P1

0000

012

230

3431

0

1594

0

5331

0

1150

-00

195

000

49-0

053

60

2191

0

2472

flow

erin

gG

100

000

1181

037

30

016

91

054

21

011

49-0

022

90

0073

-00

657

022

80

025

70

Plan

t hei

ght

P

100

000

5653

-0

441

4

037

95

012

720

5292

014

700

4604

-0

253

6

016

30

G

1

0000

061

45

-05

199

0

3892

0

1301

054

12

014

540

4798

-02

621

0

1605

Pani

cle

leng

thP

100

00-0

025

80

3811

0

2527

0

1930

0

0041

-00

205

006

370

0774

G

100

000

0038

040

71

027

06

020

60

002

420

0073

006

540

1048

No

of P

rodu

ctiv

eP

1

0000

-00

043

020

21

-0

3806

023

08

-03

316

0

4903

0

2561

tille

rs p

er p

lant

G

100

00-0

002

90

2298

-0

434

00

2296

-0

453

0

057

49

025

85

No

of f

illed

gra

ins

P

1

0000

050

67

008

680

5112

0

3200

0

2694

0

6261

per p

anic

leG

100

000

5088

0

0874

053

48

034

06

027

36

065

35

Spik

elet

fert

ility

()

P

100

000

0613

049

97

004

900

4247

0

5043

G

1

0000

006

150

5270

0

0519

043

59

053

01

1000

see

d w

eigh

tP

100

000

1600

0

2852

-0

068

90

1470

G

100

000

1656

0

2999

-00

697

015

13

Prod

uctiv

ity p

er d

ayP

1

0000

029

02

057

85

096

81

G

1

0000

023

91

061

47

096

62

Bio

mas

s pe

r pla

ntP

100

00-0

561

9

025

85

G

100

00-0

571

1

020

47

Har

vest

inde

x (

)P

1

0000

061

74

G

1

0000

065

49

P- P

heno

typi

c co

rrel

atio

n co

effic

ient

G-

Gen

otyp

ic c

orre

latio

n co

effic

ient

Si

gnifi

cant

at

1

Si

gnifi

cant

at

5


66

SRIJAN et al






Acknowledgement


67

Tabl

e 2

Dire

ct a

nd in

dire

ct e

ffect

s of

var

ious

com

pone

nt c

hara

cter

s on

yie

ld in

rice

Cha

ract

erDa

ys to

Plan

tPa

nicl

eN

o o

fN

o o

fSp

ikel

et10

00-s

eed

Prod

uctiv

ityBi

omas

sHa

rves

tG

rain

50

heig

htle

ngth

Prod

uctiv

efil

led

fert

ility

wei

ght

per d

aype

r pl

ant

Inde

xyi

eld

flow

erin

gtil

lers

grai

nspe

r pl

ant

per

plan

tpe

rpa

nicl

e

Day

s to

50

P0

2451

-00

015

-00

035

-00

018

001

32-0

001

2-0

000

10

0049

000

30-0

011

00

2472

flow

erin

gG

025

22-0

001

8-0

004

0-0

002

30

0157

-00

011

-00

002

000

730

0041

-00

128

025

70

Plan

t hei

ght

P0

0300

-00

123

-00

058

000

480

0094

-00

013

000

310

1482

-00

260

001

280

1630

G

002

98-0

015

3-0

006

60

0072

001

12-0

001

20

0041

014

65-0

029

80

0147

016

05

Pani

cle

leng

thP

008

41-0

006

9-0

010

20

0003

000

94-0

002

50

0011

000

420

0012

-00

032

007

74

G

009

41-0

009

4-0

010

8-0

000

10

0118

-00

025

000

150

0243

-00

005

-00

037

010

48

No

of P

rodu

ctiv

eP

003

910

0054

000

03-0

011

0-0

000

1-0

002

0-0

002

20

2327

001

87-0

024

70

2561

tille

rs p

er p

lant

G0

0426

000

800

0000

-00

138

-00

001

-00

022

-00

033

023

140

0282

-00

323

025

85

No

of f

illed

gra

ins

P0

1307

-00

047

-00

039

000

000

0247

-00

051

000

050

5154

-00

181

-00

136

062

61

per p

anic

leG

013

67-0

006

0-0

004

40

0000

002

89-0

004

80

0007

053

89-0

021

2-0

015

40

6535

Spik

elet

fert

ility

()

P0

0282

-00

016

-00

026

-00

022

001

25-0

010

00

0004

050

38-0

002

8-0

021

40

5043

G

002

90-0

002

0-0

002

9-0

003

20

0147

-00

094

000

050

5311

-00

032

-00

245

053

01

1000

see

d w

eigh

tP

-00

048

-00

065

-00

020

000

420

0021

-00

006

000

580

1613

-00

161

000

350

1470

G

-00

058

-00

083

-00

022

000

600

0025

-00

006

000

750

1669

-00

186

000

390

1513

Prod

uctiv

ity p

er d

ayP

000

12-0

001

80

0000

-00

025

001

26-0

005

00

0009

100

82-0

016

4-0

029

10

9681

G

000

18-0

002

2-0

000

3-0

003

20

0154

-00

049

000

121

0077

-00

149

-00

345

096

62

Bio

mas

s pe

r pla

ntP

-00

131

-00

056

000

020

0036

000

79-0

000

50

0017

029

26-0

056

40

0283

025

85

G

-00

166

-00

074

-00

001

000

620

0098

-00

005

000

220

2410

-00

621

003

210

2047

Har

vest

inde

x (

)P

005

370

0031

-00

006

-00

054

000

66-0

004

3-0

000

40

5832

003

17-0

050

30

6174

G

005

750

0040

-00

007

-00

079

000

79-0

004

1-0

000

50

6194

003

55-0

056

10

6549

Phen

otyp

ic r

esid

ual e

ffec

t =

005

78

Gen

otyp

ic r

esid

ual

effe

ct =

00

560

P-

Phe

noty

pic

leve

l G

- G

enot

ypic

leve

l

Bol

d va

lues

- D

irec

t ef

fect

s N

orm

al V

alue

s -

Indi

rect

effe

cts

Sign

ifica

nt a

t 1

S

igni

fican

t at

5


68

REFERENCES









SRIJAN et al

69












70













DFF 086 -057 063 019 -029 033

PHT 072 -061 072 022 002 050

NPT -035 -045 -047 -070 -042 -041

PL 054 068 -030 -005 000 023

PG 013 018 -045 -005 065 059

TGW -024 -003 -016 000 043 028

GY 019 038 -022 002 039 018






DFF -127 128 -017 002 023 024 033

PHT -109 149 -019 003 028 -002 050

NPT 072 -091 031 001 -088 034 -041

PL -080 107 -014 016 -006 000 023

PG -024 033 -022 -001 125 -053 059

TGW 037 003 -013 001 081 -081 028



71





Residual GYPx7=029

Px2=149 Px3=031 Px4=0 Px5=125


rx12=004 rx23=-061 rx34=-047 rx45=-005 rx54= 065

rx13=-057 rx24=072 rx35=-07 rx44=0

rx14=083 rx25=022 rx30=-042

rx15=019 rx26=002

rx16=-029

Px6=-001Px1=127


72






REFERENCES









73






























Books



Thesis


i









REVIEW PROCESS




ii



____________________________________________________________________________________







1

2

3








Name

Signature

Office seal


iii






Nationality Indian



Nationality Indian



Nationality Indian





iv














v




30AJPJTSAU2018

Page 1

2pdf

3pdf

Page 3

CONTENTS









PART V HOME SCIENCE






2222

1






ABSTRACT







2





NAGESH KUMAR et al


12

9

362625 2 155 117 075 071 0585 055 05

141210

86420

china

USA

Fran

ce

Brazil

India

Japa

nGer

many

Arge

ntin

a

Italy

Nethe

rland

s

Can

ada

Russia

3




Corn 11Rice 6

Others 40Cotton 28




Total Market 32 MT

Cotton 07Others 86

Wheat 312

Paddy 259

Soybean 106

Groundnut 87

Gram 58

Potato 55

Maize 55


4








NAGESH KUMAR et al


Blackgram


20

40

60

80

100

See

d R

epla

cem

ent

Rat

e (

)

2006-07 2011-12 2016-17

3

25

2

15

1

05

0

013

05

17

27

1990 2000 2010 2013

5






6














NAGESH KUMAR et al

7
















0

2

4

6

8

10

12

14

16

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

USD Bn


8




Rice 456 50 22800 100 228 2280

Wheat 272 125 34000 20 1700 85000

Sorghum 77 15 1155 160 07 45

Pearl millet 87 5 435 200 02 11

Maize 80 20 1600 80 20 250

Pigeon pea 34 15 510 100 05 51

Chickpea 82 80 6560 10 656 65600

Groundnut 62 100 6200 8 97 12109

RampM 63 5 315 200 02 08

Soybean 89 75 6675 16 417 26074

Sunflower 19 10 190 50 04 76

Cotton 95 12 1140 50 23 456

Jute 08 5 40 100 004 04

Total 1424 81620 3161 191964








NAGESH KUMAR et al

9












10













NAGESH KUMAR et al

11


Seed Biotechnology





Cold Plasma Coating





Bioprospecting







12






REFERENCES














NAGESH KUMAR et al

13




ABSTRACT







14








SRIDHAR et al

15








Quality parameters



CONCLUSIONS



16

Tabl

e 1

Gra

in a

nd S

traw

yie

ld (k

g ha

-1) o

f ric

e as

influ

ence

d by

alte

rnat

e w

ettin

g an

d dr

ying

irrig

atio

n an

d ni

trog

en le

vels

dur

ing

rabi

201

620

17 a

nd p

oole

d m

eans

Trea

tmen

tsG

rain

yie

ld (k

g ha

-1)

Stra

w y

ield

(kg

ha-1)

2016

2017

Pool

ed20

1620

17Po

oled

Irrig

atio

n re

gim

es (I

)

I 1- 2-

5cm

sub

mer

genc

e as

per

cro

p st

age

7026

7221

7123

7936

8153

8045

I 2- AW

D ir

rigat

ion

of 5

cm

with

3 c

m fa

ll in

fiel

d69

2571

5070

3778

4480

5679

56w

ater

tube

I 3- AW

D ir

rigat

ion

of 5

cm

with

5 c

m fa

ll in

fiel

d64

1466

5265

3373

3375

4574

39w

ater

tube

SEm

plusmn54

060

455

252

852

152

4

CD

at 5

15

016

815

314

714

514

6

Nitr

ogen

lev

els

(N)

N1-1

20 k

g ha

-167

0669

2268

1476

0578

1777

11

N2-1

60 k

g ha

-168

0270

2169

1177

1079

2178

16

N3-2

00 k

g ha

-168

5770

8169

6977

9880

1679

07

SE

mplusmn

372

445

390

410

411

410

CD

at 5

81

9785

8989

89

Inte

ract

ions

NS

NS

NS

NS

NS

NS

SRIDHAR et al

17

Tabl

e 2

Hul

ling

and

mill

ing

perc

enta

ge (

) of r

ice

as in

fluen

ced

by a

ltern

ate

wet

ting

and

dryi

ng ir

rigat

ion

and

nitr

ogen

leve

ls d

urin

g ra

bi20

16 2

017

and

pool

ed m

eans

Hul

ling

()

Mill

ing

()

2016

2017

Pool

ed20

1620

17Po

oled

Irrig

atio

n re

gim

es (I

)

I 1- 2-

5cm

sub

mer

genc

e as

per

cro

p st

age

808

810

809

675

674

674

I 2- AW

D ir

rigat

ion

of 5

cm

with

3 c

m fa

ll in

fiel

d80

780

980

867

367

267

3w

ater

tube

I 3- AW

D ir

rigat

ion

of 5

cm

with

5 c

m fa

ll in

fiel

d80

580

780

667

066

866

9w

ater

tube

SEm

plusmn0

10

20

20

20

20

2

CD

at 5

N

SN

SN

S0

40

50

5

Nitr

ogen

lev

els

(N)

N1-1

20 k

g ha

-180

780

880

867

267

467

3

N2-1

60 k

g ha

-180

680

880

767

367

367

3

N3-2

00 k

g ha

-180

780

980

867

267

067

1

SE

mplusmn

01

01

01

06

02

04

CD

at 5

N

SN

SN

SN

SN

SN

S

Inte

ract

ions

( IxN

)N

SN

SN

SN

SN

SN

S


18

Tabl

e 3

Hea

d ric

e re

cove

ry (

) G

rain

pro

tein

con

tent

and

am

ylos

e (

) of r

ice

as in

fluen

ced

by a

ltern

ate

wet

ting

and

dryi

ng ir

rigat

ion

and

nitr

ogen

leve

ls d

urin

g ra

bi 2

016

201

7 an

d po

oled

mea

ns

2016

2017

Pool

ed20

1620

17Po

oled

2016

2017

Pool

ed

Hea

d ric

e re

cove

ry (

)G

rain

pro

tein

con

tent

()

Amyl

ose(

)

Irrig

atio

n re

gim

es (I

)

I 1- 2-

5cm

sub

mer

genc

e as

per

cro

p st

age

650

665

12

650

99

189

149

1623

925

024

5

I 2- AW

D ir

rigat

ion

of 5

cm

with

3 c

m fa

ll in

fiel

d63

95

638

763

91

907

904

906

238

247

242

wat

er tu

be

I 3- AW

D ir

rigat

ion

of 5

cm

with

5 c

m fa

ll in

fiel

d63

32

633

463

33

903

898

901

234

243

239

wat

er tu

be

SEm

plusmn0

200

230

210

040

050

040

440

450

44

CD

at 5

0

570

640

60N

SN

SN

SN

SN

SN

S

Nitr

ogen

lev

els

(N)

N1-1

20 k

g ha

-163

93

639

863

95

894

890

892

230

239

235

N2-1

60 k

g ha

-164

16

640

464

10

916

911

913

240

246

243

N3-2

00 k

g ha

-164

23

643

164

27

920

916

918

241

246

244

SE

mplusmn

014

016

015

004

004

004

023

019

021

CD

at 5

N

SN

SN

S0

100

090

090

750

50

63

Inte

ract

ions

( IxN

)N

SN

SN

SN

SN

SN

SN

SN

SN

S

SRIDHAR et al

19


REFERENCES















20






ABSTRACT






21








S No Genotype ID

1 ICGV 15016

2 ICGV 15064

3 ICGV 16013

4 ICGV 16017

5 ICGV 16030

6 ICGV 16035

7 ICGV 16045

8 ICGV 16668


22

S No Genotype ID

9 ICGV 16687

10 ICGV 16700

11 ICGV 16701

12 ICGV 171011

13 ICGV 16686

14 ICGV 171007

15 ICGV 171023

16 ICGV 171027

17 ICGV 171006

18 ICGV 16667

19 ICGV 16698

20 ICGV 171017

21 ICGV 171015

22 ICGV 16020

23 ICGV 171051

24 ICGV 171040

25 ICGV 171039

26 ICGV 171046

27 ICGV 171041

S No Genotype ID

28 ICGV 171048

29 ICGV 171026

30 ICGV 171004

31 ICGV 171021

32 ICGV 171019

33 ICGV 171024

34 ICGV 171016

35 ICGV 171009

36 ICGV 171018

37 ICGV 171025

38 ICGV 171020

39 ICGV 171002

40 ICGV 03043

41 ICGV 07222

42 ICGV 07220

43 ICGV 00350

44 ICGV 00351

45 ICGV 02266

46 ICGV 91114





DNYANESHWAR et al

23




1 ICGV 171051 336 20 145 20

2 ICGV 171048 435 20 173 17

3 ICGV 171046 923 17 284 15

4 ICGV 171041 499 20 137 19

5 ICGV 171040 365 18 240 19

6 ICGV 171039 262 17 350 21

7 ICGV 171027 252 20 97 20

8 ICGV 171026 273 20 62 23

9 ICGV 171025 297 19 295 21

10 ICGV 171024 321 20 216 21

11 ICGV 171023 367 20 71 21

12 ICGV 171021 251 19 135 19

13 ICGV 171020 302 21 222 19

14 ICGV 171019 699 16 140 20

15 ICGV 171018 504 20 173 19

16 ICGV 171017 188 19 150 19

17 ICGV 171016 158 21 156 19

18 ICGV 171015 250 20 183 21

19 ICGV 171011 413 19 281 18

20 ICGV 171009 231 19 205 20

21 ICGV 171007 269 19 106 21

22 ICGV 171006 203 18 141 21

23 ICGV 171004 176 22 178 20

24 ICGV 171002 293 20 217 18

25 ICGV 16701 337 19 104 21

26 ICGV 16700 256 21 59 22

27 ICGV 16698 243 21 182 19

28 ICGV 16687 417 19 76 24

29 ICGV 16686 245 17 166 16

30 ICGV 16668 438 19 191 18

31 ICGV 16667 215 21 132 17

32 ICGV 16045 348 21 113 18

33 ICGV 16035 186 20 48 22

34 ICGV 16030 251 18 62 21

35 ICGV 16020 06 14 248 21


24



36 ICGV 16017 284 19 125 17

37 ICGV 16013 157 20 138 14

38 ICGV 15064 347 19 267 17

39 ICGV 15016 395 19 133 21

40 ICGV 07222 286 20 276 18

41 ICGV 07220 381 19 110 18

42 ICGV 03043 360 20 107 17

43 ICGV 02266 448 20 230 18

44 ICGV 00351 275 20 270 20

45 ICGV 00350 309 21 176 17

46 ICGV 91114 174 22 279 19




DNYANESHWAR et al

25

Tabl

e 3

Com

para

tive

SNP

prof

ile o

btai

ned

from

gen

omic

DN

A of

gro

undn

ut le

af d

isc

and

seed

chi

p

DN

A A

ssay

SNP

for

SNPs

for l

ate

leaf

spo

tSN

Ps fo

r rus

thi

gh o

leic

acid

snpA

H00

02sn

pAH

0004

snpA

H00

05sn

pAH

0010

snpA

H00

11sn

pAH

0015

snpA

H00

17sn

pAH

0018

snpA

H00

21sn

pAH

0026

ICG

V 15

064_

Leaf

AA

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

1506

4_Se

edA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

687_

Lea

fA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

687_

See

dA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

698_

Lea

fA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

698_

See

dA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

700_

Lea

fA

AG

GC

CA

ATT

AA

CC

AA

GT

CC

ICG

V 16

700_

See

dA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 00

351_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0035

1_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 02

266_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0226

6_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 03

043_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0304

3_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 07

222_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0722

2_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

701_

Lea

fA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 16

701_

See

dA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1002

_ Le

afA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1002

_ S

eed

AA

GG

CC

AA

TTA

AC

CA

TG

GC

CIC

GV

1710

04_

Leaf

AA

GG

CC

AA

TTA

AC

CA

AG

GC

CIC

GV

1710

04_

See

dA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1006

_ Le

afA

AG

GC

GA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1006

_ S

eed

AA

GG

CC

AA

TTA

AC

CA

AG

GC

C--

Nul

l alle

le


26

REFERENCES
















DNYANESHWAR et al

27






28







ABSTRACT









29











Yield attributes



30










BRIJBHOOSHAN et al

31

Tabl

e 1

Yie

ld a

ttrib

utes

of r

ice

as in

fluen

ced

by c

rop

esta

blis

hmen

t met

hods

and

nut

rient

man

agem

ent p

ract

ices

(rab

i 20

15 a

nd 2

016)

Trea

tmen

tsN

umbe

r of p

anic

le m

-2Pa

nicl

e le

ngth

(cm

)Pa

nicl

e w

eigh

t (g)

Test

wei

ght (

g)

2015

2016

Pool

ed20

1520

16 P

oole

d20

1520

16Po

oled

2015

2016

Pool

edC

rop

esta

blis

hmen

t met

hods

(M)

M1-

Dire

ct S

eede

d R

ice

(DS

R)

293

527

53

284

423

18

227

922

99

314

308

311

222

322

15

221

9

M2-

Nor

mal

Tra

nspl

ante

d R

ice

(NTP

)25

94

247

825

36

244

924

08

242

93

323

283

3022

51

224

222

46

SEm

+4

104

014

230

270

270

270

040

040

030

260

230

24

CD

(p=0

05)

184

318

03

190

61

211

241

230

170

180

15N

SN

SN

S

Nut

rient

Man

agem

ent p

ract

ices

(N)

N1-

Abs

olut

e co

ntro

l (N

o fe

rtiliz

er)

148

413

59

142

122

03

216

021

82

274

271

273

203

520

25

203

0

N2-

Con

trol (

RD

F)27

95

262

027

09

231

522

77

229

63

153

113

1322

21

221

522

18

N3-R

DF+

SA

of

ZnS

O47

H2O

2

5 kg

ha-1

294

827

95

287

124

01

236

123

81

329

324

326

225

022

39

224

4

N4-R

DF+

2 F

S o

f ZnS

O47

H2O

0

5

301

528

60

293

724

16

237

423

95

335

328

331

228

922

78

228

3

N5-R

DF+

2 F

S o

f nan

o Zn

O

100

0 pp

m31

96

305

531

26

248

824

48

246

83

443

393

4123

21

231

323

17

N6-R

DF+

2 F

S o

f na

no Z

nO

150

0 pp

m31

50

300

530

78

248

224

42

246

23

423

363

3923

08

229

923

03

SEm

+4

024

034

300

290

280

290

040

040

040

190

200

19

CD

(p=0

05)

116

111

64

124

20

840

810

820

130

120

120

560

590

56

Inte

ract

ions

(MxN

)

Nut

rient

man

agem

ent a

t sam

e le

vel o

f cro

p es

tabl

ishm

ent

SEm

+5

695

706

080

410

390

400

060

060

060

270

290

27

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

Cro

p es

tabl

ishm

ent a

t sam

e or

diff

eren

t lev

el o

f nut

rient

man

agem

ent

SEm

+6

616

576

980

460

450

460

070

070

060

360

350

35

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

Gen

eral

Mea

n27

65

261

626

90

238

423

44

236

43

233

183

2022

37

222

822

33


32

Trea

tmen

tsFi

lled

grai

ns p

anic

le-1

Unf

illed

gra

ins

pani

cle-1

Ster

ility

per

cent

age

()

2015

2016

Pool

ed20

1520

16Po

oled

2015

2016

Pool

ed

Cro

p es

tabl

ishm

ent m

etho

ds (M

)M

1- D

irect

See

ded

Ric

e (D

SR

)12

466

116

0412

034

968

122

810

98

737

976

856

M2-

Nor

mal

Tra

nspl

ante

d R

ice

(NTP

)13

436

125

7613

007

881

112

510

03

630

838

734

SEm

+1

881

701

460

160

170

150

120

160

15

CD

(p=0

05)

847

765

658

073

077

069

056

073

065

Nut

rient

Man

agem

ent p

ract

ices

(N)

N1-

Abs

olut

e co

ntro

l (N

o fe

rtiliz

er)

109

3310

079

105

0518

37

208

819

63

144

217

23

158

2

N2-

Con

trol (

RD

F)12

531

116

6612

098

103

212

85

115

97

639

958

79

N3-R

DF+

SA

of

ZnS

O47

H2O

2

5 kg

ha-1

131

5312

289

127

208

2910

80

954

594

811

702

N4-R

DF+

2 F

S o

f ZnS

O47

H2O

0

5

132

7312

409

128

407

8710

38

913

562

775

668

N5-R

DF+

2 F

S o

f nan

o Zn

O

100

0 pp

m13

975

131

1313

546

546

798

672

377

576

477

N6-R

DF+

2 F

S o

f na

no Z

nO

150

0 pp

m13

844

129

8513

414

517

768

642

362

561

462

SEm

+1

451

431

280

200

180

200

150

170

17

CD

(p=0

05)

419

413

371

059

053

057

043

048

050

Inte

ract

ions

(MxN

)N

utrie

nt m

anag

emen

t at s

ame

leve

l of c

rop

esta

blis

hmen

tS

Em+

205

202

182

029

026

028

021

023

025

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Cro

p es

tabl

ishm

ent a

t sam

e or

diff

eren

t lev

el o

f nut

rient

man

agem

ent

SEm

+2

662

512

210

310

290

300

230

270

27

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Gen

eral

Mea

n12

951

120

9012

521

925

117

610

51

683

907

795

RD

F- R

ecom

men

ded

Dos

e of

Fer

tiliz

ers

(N

P2O

5amp K

2O

120

60

40 k

g ha

-1)

SA

- S

oil A

pplic

atio

n F

S-

Folia

r Sp

ray

NS

- N

on S

igni

fican

t

BRIJBHOOSHAN et al

33








CONCLUSION



34

Tabl

e 2

Yie

ld a

nd h

arve

st in

dex

of ri

ce a

s in

fluen

ced

by c

rop

esta

blis

hmen

t met

hods

and

nut

rient

man

agem

ent p

ract

ices

(rab

i 20

15 a

nd 2

016)

Trea

tmen

tsG

rain

yie

ld (k

g ha

-1)

Stra

w y

ield

(kg

ha-1)

Har

vest

Inde

x(

)

2015

2016

Pool

ed20

1520

16Po

oled

2015

2016

Pool

ed

Cro

p es

tabl

ishm

ent m

etho

ds (M

)M

1- D

irect

See

ded

Ric

e (D

SR

)51

9848

8650

4766

7564

4765

6143

58

428

243

20

M2-

Nor

mal

Tra

nspl

ante

d R

ice

(NTP

)47

3344

8946

1160

0058

1359

0743

81

434

343

62

SEm

+98

8389

121

109

101

052

061

049

CD

(p=0

05)

441

373

401

545

492

455

NS

NS

NS

Nut

rient

Man

agem

ent p

ract

ices

(N)

N1-

Abs

olut

e co

ntro

l (N

o fe

rtiliz

er)

2335

2274

2305

3270

3320

3295

416

740

84

412

5

N2-

Con

trol (

RD

F)47

3944

6946

0461

5459

8160

6843

47

427

743

12

N3-R

DF+

SA

of

ZnS

O47

H2O

2

5 kg

ha-1

5187

4993

5090

6593

6462

6527

440

243

63

438

3

N4-R

DF+

2 F

S o

f ZnS

O47

H2O

0

5

5483

5157

5320

6902

6656

6779

442

543

66

439

6

N5-R

DF+

2 F

S o

f nan

o Zn

O

100

0 pp

m60

7056

5958

7875

7072

2473

9744

54

439

644

25

N6-R

DF+

2 F

S o

f na

no Z

nO

150

0 pp

m59

7855

7457

7675

3771

3773

3744

21

438

944

05

SEm

+13

410

911

114

711

412

10

400

550

51

CD

(p=0

05)

386

315

320

424

328

349

115

158

147

Inte

ract

ions

(MxN

)N

utrie

nt m

anag

emen

t at s

ame

leve

l of c

rop

esta

blis

hmen

tS

Em+

189

154

157

208

161

171

056

077

072

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Cro

p es

tabl

ishm

ent a

t sam

e or

diff

eren

t lev

el o

f nut

rient

man

agem

ent

SEm

+19

816

316

822

518

318

60

730

940

82

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Gen

eral

Mea

n49

6546

8848

2963

3761

3062

3443

70

431

343

41

Coe

ffici

ent o

f Var

iatio

n (

)9

78

79

09

48

77

95

97

05

5

RD

F- R

ecom

men

ded

Dos

e of

Fer

tiliz

ers

(N

P2O

5amp K

2O

120

60

40 k

g ha

-1)

SA

- S

oil A

pplic

atio

n F

S-

Folia

r Sp

ray

NS

- N

on S

igni

fican

t

BRIJBHOOSHAN et al

35

REFERENCES

















36





ABSTRACT







37












38














PREETHAM et al

39

Tabl

e-1

Effe

ct o

f int

egra

ted

nutr

ient

man

agem

ent p

ract

ices

on

yiel

d at

trib

utes

of b

aby

corn

dur

ing

khar

if 2

015

and

2016

Trea

tmen

t giv

en to

kha

rif b

aby

corn

2015

2016

Cob

spl

ant-1

Cob

leng

th(c

m)

Cob

girth

(cm

)

Cob

wid

th(c

m)

Cob

wei

ght

(g)

Cob

spl

ant-1

Cob

leng

th(c

m)

Cob

girth

(cm

)

Cob

wid

th(c

m

Cob

wei

ght

(g)

With

With

out

husk

husk

With

With

out

husk

husk

T 1- 25

N through FY

M + 75

RDF

178

106

95

251

6846

80

926

183

931

513

163

426

89

03T 2-

25

N th

roug

h FY

M +

75

RD

F +

200

108

35

601

7247

33

100

01

989

645

541

6843

16

908

Azo

spiri

llum

and

Bac

illus

meg

ater

ium

5

kg h

a-1 e

ach

T 3- 25

N

thr

ough

Ver

mic

ompo

st +

202

109

55

581

7649

17

100

02

029

655

461

7143

68

926

75

RD

FT 4-

T4 2

5 N

thro

ugh

Verm

icom

post

+2

2312

50

574

186

555

913

46

222

122

45

651

8348

73

120

675

R

DF

+ A

zosp

irillu

m a

nd B

acill

usm

egat

eriu

m

5 k

g ha

-1 e

ach

T 5- 10

0 R

DF

175

106

65

241

7146

29

920

178

989

512

161

423

69

01T 6-

100

RD

F +

Azo

spiri

llum

and

Bac

illus

200

109

65

551

8049

86

103

81

989

905

441

7443

10

936

meg

ater

ium

5

kg

ha-1 e

ach

T 7- C

ontro

l (N

o fe

rtiliz

er a

pplic

atio

n)0

957

503

981

2715

88

358

147

711

373

127

239

95

13S

Em

+0

060

410

150

051

550

400

060

500

150

050

970

13C

D (

P=0

05)

020

127

045

016

477

123

018

153

047

016

300

041

Trea

tmen

t giv

en to

rabi

cro

p (h

yaci

nth

bean

)S

1- 10

0 R

DF

191

987

520

161

424

99

25S

2- 75

R

DF

180

933

501

167

373

78

25S

3-10

0 R

DF

+ B

rady

rhiz

obiu

m

200

990

533

160

445

79

9550

0 g

ha-1 (

See

d tre

atm

ent)

S4-

75

RD

F +

Bra

dyrh

izob

ium

1

889

605

071

6639

98

850

500

g ha

-1 (

See

d tre

atm

ent)

SE

m+

003

024

017

005

086

019

CD

(P

=00

5)0

10N

SN

SN

S2

460

54In

tera

ctio

nB

ean

trea

tmen

t mea

ns a

t sam

e le

vel o

f bab

y co

rn IN

M tr

eatm

ents

SE

m+

009

064

044

014

228

050

CD

(P

=00

5)N

SN

SN

SN

SN

SN

SIN

M tr

eatm

ent m

eans

of b

aby

corn

at s

ame

leve

l of b

ean

trea

tmen

tsS

Em

+0

100

740

410

132

200

45C

D (

P=0

05)

NS

NS

NS

NS

NS

NS


40





Cob Yield (kg ha-1)










PREETHAM et al

41

Tabl

e-2

Effe

ct o

f int

egra

ted

nutr

ient

man

agem

ent p

ract

ices

on

cob

yiel

d (k

g ha

-1) w

ith a

nd w

ithou

t hus

k an

d st

over

yie

ld o

f bab

y co

rn d

urin

gkh

arif

201

5 an

d 20

16

Trea

tmen

t giv

en to

kha

rif b

aby

corn

2015

2016

Cob

yie

ld (k

g ha

-1)

Stov

erC

ob y

ield

(kg

ha-1)

Stov

er y

ield

yiel

d(kg

ha-1

)(k

g ha

-1)

With

hus

kW

ithou

t hus

kW

ith h

usk

W

ithou

t hus

k

T 1- 25

N through FY

M + 75

RDF

8204

1602

1934

481

4714

9518

919

T 2- 25

N

thro

ugh

FYM

+ 7

5 R

DF

+ A

zosp

irillu

m90

3218

0320

865

8882

1596

1970

8an

d B

acill

us m

egat

eriu

m

5 k

g ha

-1 e

ach

T 3- 25

N

thro

ugh

Verm

icom

post

+ 7

5 R

DF

9400

1801

2147

589

9416

0119

762

T 4- T4

25

N th

roug

h Ve

rmic

ompo

st +

75

RD

F +

1229

020

4924

020

1172

718

9021

165

Azo

spiri

llum

and

Bac

illus

meg

ater

ium

5

kg

ha-1 e

ach

T 5- 10

0 R

DF

8049

1542

1906

081

9914

7119

364

T 6- 10

0 R

DF

+ A

zosp

irillu

m a

nd B

acill

us93

4418

0720

398

8824

1553

1963

1m

egat

eriu

m

5 k

g ha

-1 e

ach

T 7- C

ontro

l (N

o fe

rtiliz

er a

pplic

atio

n)42

7982

313

821

4393

749

1216

8S

Em

+26

764

429

200

1638

5C

D (

P=0

05)

824

198

1321

617

4911

87Tr

eatm

ent g

iven

to ra

bi c

rop

(hya

cint

h be

an)

S1-

100

RD

F88

2415

4619

086

S2-

75

RD

F78

8013

6917

833

S3-10

0 R

DF

+ B

rady

rhiz

obiu

m

500

g h

a-190

7315

9019

410

(See

d tre

atm

ent)

S4-

75

RD

F +

Bra

dyrh

izob

ium

5

00 g

ha-1

8033

1411

1836

5(S

eed

treat

men

t)S

Em

+20

216

280

CD

(P

=00

5)57

745

798

Inte

ract

ion

Bea

n tre

atm

ent m

eans

at s

ame

leve

l of b

aby

corn

INM

trea

tmen

tsS

Em

+53

541

740

CD

(P

=00

5)N

SN

SN

SIN

M tr

eatm

ent m

eans

of b

aby

corn

at s

ame

leve

l of b

ean

treat

men

tsS

Em

+50

439

748

CD

(P

=00

5)N

SN

SN

S


42






CONCLUSION


REFERENCES







PREETHAM et al

43















44



ABSTRACT














45























2 FACTOR ANALYSIS



2 2 212 R 3K N(N 1)i iW 2 2K N(N 1)

46











Sig 0000



Component


Total ofVariance

Cumulative

Total ofVariance

Cumulative


Total ofVariance

Cumulative

1 1965 21839 21839 1965 21839 21839 1894 21044 21044

2 1806 20070 41909 1806 20070 41909 1520 16887 37932

3 1086 12064 53973 1086 12064 53973 1444 16042 53973

4 927 10301 64274

5 887 9860 74134

6 738 8199 82333

7 630 6996 89329

8 529 5878 95207

9 431 4793 100000


MEENA et al

47


Component Number

Scree Plot

Eig

en v

alue



Component

1 2 3


Component1 2 3












48


Table 4 Ranks









Chi-Square 0822

df 2

Asymp Sig 0663















MEENA et al

49







ABSTRACT









50

SRINIVAS et al




()




51



CONCLUSION


Acknowledgments


REFERENCES







52







ABSTRACT








53











54




Analysis of protein

















55








0

10

20

30

40

50

6070

80

90

Finge

r Mille

t

Foxta

il Mille

t

Little M

illet

Pearl M

illet

Proso

Mille

t

Sorgh

um

Perc

enta

ge d

iges

tibili

ty

Dosa UpmaIdli



56






Foxtail millet

Little millet

Pearl millet

Prosomillet

Sorghum

IdliDosaUpma

10080

6040

20 0







57



CONCLUSION


REFERENCES
















58




















59






ABSTRACT










60
















Pomegranate 21 23 17 16 18 19 13 12 17 18 12 14

Cassia 31 33 24 24 26 28 20 21 22 23 21 22

Periwinkle 29 31 23 25 25 24 19 20 21 21 22 21

Marigold 21 23 18 19 16 18 12 13 16 17 14 16

61









62



0

5

10

15

20

25

30

S aureus Ecoli

Pomegran

teCas

siaPeri

winkleMari

gold

Pomegran

te

Cassia

Periwinkle

Marigold

SilverTitaniumzinc


0

5

10

30

Pomegran

teCas

siaPeri

winkleMari

gold

Pomegran

teCas

siaPeri

winkleMari

gold

35

15

20

S aureus Ecoli

SilverTitaniumzinc

25

63

CONCLUSION








64













65

Tabl

e 1

Est

imat

es o

f phe

noty

pic

and

geno

typi

c co

rrel

atio

n co

effic

ient

s fo

r yie

ld a

nd it

s at

trib

utin

g ch

arac

ters

Cha

ract

erDa

ys to

Plan

tPa

nicl

eN

o o

fN

o o

fSp

ikel

et10

00-s

eed

Prod

uctiv

ityBi

omas

sHa

rves

tG

rain

50

heig

htle

ngth

Prod

uctiv

efil

led

fert

ility

wei

ght

per d

aype

r pl

ant

Inde

xyi

eld

flow

erin

gtil

lers

grai

nspe

r pl

ant

per

plan

tpe

rpa

nicl

e

Day

s to

50

P1

0000

012

230

3431

0

1594

0

5331

0

1150

-00

195

000

49-0

053

60

2191

0

2472

flow

erin

gG

100

000

1181

037

30

016

91

054

21

011

49-0

022

90

0073

-00

657

022

80

025

70

Plan

t hei

ght

P

100

000

5653

-0

441

4

037

95

012

720

5292

014

700

4604

-0

253

6

016

30

G

1

0000

061

45

-05

199

0

3892

0

1301

054

12

014

540

4798

-02

621

0

1605

Pani

cle

leng

thP

100

00-0

025

80

3811

0

2527

0

1930

0

0041

-00

205

006

370

0774

G

100

000

0038

040

71

027

06

020

60

002

420

0073

006

540

1048

No

of P

rodu

ctiv

eP

1

0000

-00

043

020

21

-0

3806

023

08

-03

316

0

4903

0

2561

tille

rs p

er p

lant

G

100

00-0

002

90

2298

-0

434

00

2296

-0

453

0

057

49

025

85

No

of f

illed

gra

ins

P

1

0000

050

67

008

680

5112

0

3200

0

2694

0

6261

per p

anic

leG

100

000

5088

0

0874

053

48

034

06

027

36

065

35

Spik

elet

fert

ility

()

P

100

000

0613

049

97

004

900

4247

0

5043

G

1

0000

006

150

5270

0

0519

043

59

053

01

1000

see

d w

eigh

tP

100

000

1600

0

2852

-0

068

90

1470

G

100

000

1656

0

2999

-00

697

015

13

Prod

uctiv

ity p

er d

ayP

1

0000

029

02

057

85

096

81

G

1

0000

023

91

061

47

096

62

Bio

mas

s pe

r pla

ntP

100

00-0

561

9

025

85

G

100

00-0

571

1

020

47

Har

vest

inde

x (

)P

1

0000

061

74

G

1

0000

065

49

P- P

heno

typi

c co

rrel

atio

n co

effic

ient

G-

Gen

otyp

ic c

orre

latio

n co

effic

ient

Si

gnifi

cant

at

1

Si

gnifi

cant

at

5


66

SRIJAN et al






Acknowledgement


67

Tabl

e 2

Dire

ct a

nd in

dire

ct e

ffect

s of

var

ious

com

pone

nt c

hara

cter

s on

yie

ld in

rice

Cha

ract

erDa

ys to

Plan

tPa

nicl

eN

o o

fN

o o

fSp

ikel

et10

00-s

eed

Prod

uctiv

ityBi

omas

sHa

rves

tG

rain

50

heig

htle

ngth

Prod

uctiv

efil

led

fert

ility

wei

ght

per d

aype

r pl

ant

Inde

xyi

eld

flow

erin

gtil

lers

grai

nspe

r pl

ant

per

plan

tpe

rpa

nicl

e

Day

s to

50

P0

2451

-00

015

-00

035

-00

018

001

32-0

001

2-0

000

10

0049

000

30-0

011

00

2472

flow

erin

gG

025

22-0

001

8-0

004

0-0

002

30

0157

-00

011

-00

002

000

730

0041

-00

128

025

70

Plan

t hei

ght

P0

0300

-00

123

-00

058

000

480

0094

-00

013

000

310

1482

-00

260

001

280

1630

G

002

98-0

015

3-0

006

60

0072

001

12-0

001

20

0041

014

65-0

029

80

0147

016

05

Pani

cle

leng

thP

008

41-0

006

9-0

010

20

0003

000

94-0

002

50

0011

000

420

0012

-00

032

007

74

G

009

41-0

009

4-0

010

8-0

000

10

0118

-00

025

000

150

0243

-00

005

-00

037

010

48

No

of P

rodu

ctiv

eP

003

910

0054

000

03-0

011

0-0

000

1-0

002

0-0

002

20

2327

001

87-0

024

70

2561

tille

rs p

er p

lant

G0

0426

000

800

0000

-00

138

-00

001

-00

022

-00

033

023

140

0282

-00

323

025

85

No

of f

illed

gra

ins

P0

1307

-00

047

-00

039

000

000

0247

-00

051

000

050

5154

-00

181

-00

136

062

61

per p

anic

leG

013

67-0

006

0-0

004

40

0000

002

89-0

004

80

0007

053

89-0

021

2-0

015

40

6535

Spik

elet

fert

ility

()

P0

0282

-00

016

-00

026

-00

022

001

25-0

010

00

0004

050

38-0

002

8-0

021

40

5043

G

002

90-0

002

0-0

002

9-0

003

20

0147

-00

094

000

050

5311

-00

032

-00

245

053

01

1000

see

d w

eigh

tP

-00

048

-00

065

-00

020

000

420

0021

-00

006

000

580

1613

-00

161

000

350

1470

G

-00

058

-00

083

-00

022

000

600

0025

-00

006

000

750

1669

-00

186

000

390

1513

Prod

uctiv

ity p

er d

ayP

000

12-0

001

80

0000

-00

025

001

26-0

005

00

0009

100

82-0

016

4-0

029

10

9681

G

000

18-0

002

2-0

000

3-0

003

20

0154

-00

049

000

121

0077

-00

149

-00

345

096

62

Bio

mas

s pe

r pla

ntP

-00

131

-00

056

000

020

0036

000

79-0

000

50

0017

029

26-0

056

40

0283

025

85

G

-00

166

-00

074

-00

001

000

620

0098

-00

005

000

220

2410

-00

621

003

210

2047

Har

vest

inde

x (

)P

005

370

0031

-00

006

-00

054

000

66-0

004

3-0

000

40

5832

003

17-0

050

30

6174

G

005

750

0040

-00

007

-00

079

000

79-0

004

1-0

000

50

6194

003

55-0

056

10

6549

Phen

otyp

ic r

esid

ual e

ffec

t =

005

78

Gen

otyp

ic r

esid

ual

effe

ct =

00

560

P-

Phe

noty

pic

leve

l G

- G

enot

ypic

leve

l

Bol

d va

lues

- D

irec

t ef

fect

s N

orm

al V

alue

s -

Indi

rect

effe

cts

Sign

ifica

nt a

t 1

S

igni

fican

t at

5


68

REFERENCES









SRIJAN et al

69












70













DFF 086 -057 063 019 -029 033

PHT 072 -061 072 022 002 050

NPT -035 -045 -047 -070 -042 -041

PL 054 068 -030 -005 000 023

PG 013 018 -045 -005 065 059

TGW -024 -003 -016 000 043 028

GY 019 038 -022 002 039 018






DFF -127 128 -017 002 023 024 033

PHT -109 149 -019 003 028 -002 050

NPT 072 -091 031 001 -088 034 -041

PL -080 107 -014 016 -006 000 023

PG -024 033 -022 -001 125 -053 059

TGW 037 003 -013 001 081 -081 028



71





Residual GYPx7=029

Px2=149 Px3=031 Px4=0 Px5=125


rx12=004 rx23=-061 rx34=-047 rx45=-005 rx54= 065

rx13=-057 rx24=072 rx35=-07 rx44=0

rx14=083 rx25=022 rx30=-042

rx15=019 rx26=002

rx16=-029

Px6=-001Px1=127


72






REFERENCES









73






























Books



Thesis


i









REVIEW PROCESS




ii



____________________________________________________________________________________







1

2

3








Name

Signature

Office seal


iii






Nationality Indian



Nationality Indian



Nationality Indian





iv














v




30AJPJTSAU2018

Page 1

2pdf

Page 2

3pdf

2222

1






ABSTRACT







2





NAGESH KUMAR et al


12

9

362625 2 155 117 075 071 0585 055 05

141210

86420

china

USA

Fran

ce

Brazil

India

Japa

nGer

many

Arge

ntin

a

Italy

Nethe

rland

s

Can

ada

Russia

3




Corn 11Rice 6

Others 40Cotton 28




Total Market 32 MT

Cotton 07Others 86

Wheat 312

Paddy 259

Soybean 106

Groundnut 87

Gram 58

Potato 55

Maize 55


4








NAGESH KUMAR et al


Blackgram


20

40

60

80

100

See

d R

epla

cem

ent

Rat

e (

)

2006-07 2011-12 2016-17

3

25

2

15

1

05

0

013

05

17

27

1990 2000 2010 2013

5






6














NAGESH KUMAR et al

7
















0

2

4

6

8

10

12

14

16

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

USD Bn


8




Rice 456 50 22800 100 228 2280

Wheat 272 125 34000 20 1700 85000

Sorghum 77 15 1155 160 07 45

Pearl millet 87 5 435 200 02 11

Maize 80 20 1600 80 20 250

Pigeon pea 34 15 510 100 05 51

Chickpea 82 80 6560 10 656 65600

Groundnut 62 100 6200 8 97 12109

RampM 63 5 315 200 02 08

Soybean 89 75 6675 16 417 26074

Sunflower 19 10 190 50 04 76

Cotton 95 12 1140 50 23 456

Jute 08 5 40 100 004 04

Total 1424 81620 3161 191964








NAGESH KUMAR et al

9












10













NAGESH KUMAR et al

11


Seed Biotechnology





Cold Plasma Coating





Bioprospecting







12






REFERENCES














NAGESH KUMAR et al

13




ABSTRACT







14








SRIDHAR et al

15








Quality parameters



CONCLUSIONS



16

Tabl

e 1

Gra

in a

nd S

traw

yie

ld (k

g ha

-1) o

f ric

e as

influ

ence

d by

alte

rnat

e w

ettin

g an

d dr

ying

irrig

atio

n an

d ni

trog

en le

vels

dur

ing

rabi

201

620

17 a

nd p

oole

d m

eans

Trea

tmen

tsG

rain

yie

ld (k

g ha

-1)

Stra

w y

ield

(kg

ha-1)

2016

2017

Pool

ed20

1620

17Po

oled

Irrig

atio

n re

gim

es (I

)

I 1- 2-

5cm

sub

mer

genc

e as

per

cro

p st

age

7026

7221

7123

7936

8153

8045

I 2- AW

D ir

rigat

ion

of 5

cm

with

3 c

m fa

ll in

fiel

d69

2571

5070

3778

4480

5679

56w

ater

tube

I 3- AW

D ir

rigat

ion

of 5

cm

with

5 c

m fa

ll in

fiel

d64

1466

5265

3373

3375

4574

39w

ater

tube

SEm

plusmn54

060

455

252

852

152

4

CD

at 5

15

016

815

314

714

514

6

Nitr

ogen

lev

els

(N)

N1-1

20 k

g ha

-167

0669

2268

1476

0578

1777

11

N2-1

60 k

g ha

-168

0270

2169

1177

1079

2178

16

N3-2

00 k

g ha

-168

5770

8169

6977

9880

1679

07

SE

mplusmn

372

445

390

410

411

410

CD

at 5

81

9785

8989

89

Inte

ract

ions

NS

NS

NS

NS

NS

NS

SRIDHAR et al

17

Tabl

e 2

Hul

ling

and

mill

ing

perc

enta

ge (

) of r

ice

as in

fluen

ced

by a

ltern

ate

wet

ting

and

dryi

ng ir

rigat

ion

and

nitr

ogen

leve

ls d

urin

g ra

bi20

16 2

017

and

pool

ed m

eans

Hul

ling

()

Mill

ing

()

2016

2017

Pool

ed20

1620

17Po

oled

Irrig

atio

n re

gim

es (I

)

I 1- 2-

5cm

sub

mer

genc

e as

per

cro

p st

age

808

810

809

675

674

674

I 2- AW

D ir

rigat

ion

of 5

cm

with

3 c

m fa

ll in

fiel

d80

780

980

867

367

267

3w

ater

tube

I 3- AW

D ir

rigat

ion

of 5

cm

with

5 c

m fa

ll in

fiel

d80

580

780

667

066

866

9w

ater

tube

SEm

plusmn0

10

20

20

20

20

2

CD

at 5

N

SN

SN

S0

40

50

5

Nitr

ogen

lev

els

(N)

N1-1

20 k

g ha

-180

780

880

867

267

467

3

N2-1

60 k

g ha

-180

680

880

767

367

367

3

N3-2

00 k

g ha

-180

780

980

867

267

067

1

SE

mplusmn

01

01

01

06

02

04

CD

at 5

N

SN

SN

SN

SN

SN

S

Inte

ract

ions

( IxN

)N

SN

SN

SN

SN

SN

S


18

Tabl

e 3

Hea

d ric

e re

cove

ry (

) G

rain

pro

tein

con

tent

and

am

ylos

e (

) of r

ice

as in

fluen

ced

by a

ltern

ate

wet

ting

and

dryi

ng ir

rigat

ion

and

nitr

ogen

leve

ls d

urin

g ra

bi 2

016

201

7 an

d po

oled

mea

ns

2016

2017

Pool

ed20

1620

17Po

oled

2016

2017

Pool

ed

Hea

d ric

e re

cove

ry (

)G

rain

pro

tein

con

tent

()

Amyl

ose(

)

Irrig

atio

n re

gim

es (I

)

I 1- 2-

5cm

sub

mer

genc

e as

per

cro

p st

age

650

665

12

650

99

189

149

1623

925

024

5

I 2- AW

D ir

rigat

ion

of 5

cm

with

3 c

m fa

ll in

fiel

d63

95

638

763

91

907

904

906

238

247

242

wat

er tu

be

I 3- AW

D ir

rigat

ion

of 5

cm

with

5 c

m fa

ll in

fiel

d63

32

633

463

33

903

898

901

234

243

239

wat

er tu

be

SEm

plusmn0

200

230

210

040

050

040

440

450

44

CD

at 5

0

570

640

60N

SN

SN

SN

SN

SN

S

Nitr

ogen

lev

els

(N)

N1-1

20 k

g ha

-163

93

639

863

95

894

890

892

230

239

235

N2-1

60 k

g ha

-164

16

640

464

10

916

911

913

240

246

243

N3-2

00 k

g ha

-164

23

643

164

27

920

916

918

241

246

244

SE

mplusmn

014

016

015

004

004

004

023

019

021

CD

at 5

N

SN

SN

S0

100

090

090

750

50

63

Inte

ract

ions

( IxN

)N

SN

SN

SN

SN

SN

SN

SN

SN

S

SRIDHAR et al

19


REFERENCES















20






ABSTRACT






21








S No Genotype ID

1 ICGV 15016

2 ICGV 15064

3 ICGV 16013

4 ICGV 16017

5 ICGV 16030

6 ICGV 16035

7 ICGV 16045

8 ICGV 16668


22

S No Genotype ID

9 ICGV 16687

10 ICGV 16700

11 ICGV 16701

12 ICGV 171011

13 ICGV 16686

14 ICGV 171007

15 ICGV 171023

16 ICGV 171027

17 ICGV 171006

18 ICGV 16667

19 ICGV 16698

20 ICGV 171017

21 ICGV 171015

22 ICGV 16020

23 ICGV 171051

24 ICGV 171040

25 ICGV 171039

26 ICGV 171046

27 ICGV 171041

S No Genotype ID

28 ICGV 171048

29 ICGV 171026

30 ICGV 171004

31 ICGV 171021

32 ICGV 171019

33 ICGV 171024

34 ICGV 171016

35 ICGV 171009

36 ICGV 171018

37 ICGV 171025

38 ICGV 171020

39 ICGV 171002

40 ICGV 03043

41 ICGV 07222

42 ICGV 07220

43 ICGV 00350

44 ICGV 00351

45 ICGV 02266

46 ICGV 91114





DNYANESHWAR et al

23




1 ICGV 171051 336 20 145 20

2 ICGV 171048 435 20 173 17

3 ICGV 171046 923 17 284 15

4 ICGV 171041 499 20 137 19

5 ICGV 171040 365 18 240 19

6 ICGV 171039 262 17 350 21

7 ICGV 171027 252 20 97 20

8 ICGV 171026 273 20 62 23

9 ICGV 171025 297 19 295 21

10 ICGV 171024 321 20 216 21

11 ICGV 171023 367 20 71 21

12 ICGV 171021 251 19 135 19

13 ICGV 171020 302 21 222 19

14 ICGV 171019 699 16 140 20

15 ICGV 171018 504 20 173 19

16 ICGV 171017 188 19 150 19

17 ICGV 171016 158 21 156 19

18 ICGV 171015 250 20 183 21

19 ICGV 171011 413 19 281 18

20 ICGV 171009 231 19 205 20

21 ICGV 171007 269 19 106 21

22 ICGV 171006 203 18 141 21

23 ICGV 171004 176 22 178 20

24 ICGV 171002 293 20 217 18

25 ICGV 16701 337 19 104 21

26 ICGV 16700 256 21 59 22

27 ICGV 16698 243 21 182 19

28 ICGV 16687 417 19 76 24

29 ICGV 16686 245 17 166 16

30 ICGV 16668 438 19 191 18

31 ICGV 16667 215 21 132 17

32 ICGV 16045 348 21 113 18

33 ICGV 16035 186 20 48 22

34 ICGV 16030 251 18 62 21

35 ICGV 16020 06 14 248 21


24



36 ICGV 16017 284 19 125 17

37 ICGV 16013 157 20 138 14

38 ICGV 15064 347 19 267 17

39 ICGV 15016 395 19 133 21

40 ICGV 07222 286 20 276 18

41 ICGV 07220 381 19 110 18

42 ICGV 03043 360 20 107 17

43 ICGV 02266 448 20 230 18

44 ICGV 00351 275 20 270 20

45 ICGV 00350 309 21 176 17

46 ICGV 91114 174 22 279 19




DNYANESHWAR et al

25

Tabl

e 3

Com

para

tive

SNP

prof

ile o

btai

ned

from

gen

omic

DN

A of

gro

undn

ut le

af d

isc

and

seed

chi

p

DN

A A

ssay

SNP

for

SNPs

for l

ate

leaf

spo

tSN

Ps fo

r rus

thi

gh o

leic

acid

snpA

H00

02sn

pAH

0004

snpA

H00

05sn

pAH

0010

snpA

H00

11sn

pAH

0015

snpA

H00

17sn

pAH

0018

snpA

H00

21sn

pAH

0026

ICG

V 15

064_

Leaf

AA

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

1506

4_Se

edA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

687_

Lea

fA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

687_

See

dA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

698_

Lea

fA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

698_

See

dA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

700_

Lea

fA

AG

GC

CA

ATT

AA

CC

AA

GT

CC

ICG

V 16

700_

See

dA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 00

351_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0035

1_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 02

266_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0226

6_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 03

043_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0304

3_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 07

222_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0722

2_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

701_

Lea

fA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 16

701_

See

dA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1002

_ Le

afA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1002

_ S

eed

AA

GG

CC

AA

TTA

AC

CA

TG

GC

CIC

GV

1710

04_

Leaf

AA

GG

CC

AA

TTA

AC

CA

AG

GC

CIC

GV

1710

04_

See

dA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1006

_ Le

afA

AG

GC

GA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1006

_ S

eed

AA

GG

CC

AA

TTA

AC

CA

AG

GC

C--

Nul

l alle

le


26

REFERENCES
















DNYANESHWAR et al

27






28







ABSTRACT









29











Yield attributes



30










BRIJBHOOSHAN et al

31

Tabl

e 1

Yie

ld a

ttrib

utes

of r

ice

as in

fluen

ced

by c

rop

esta

blis

hmen

t met

hods

and

nut

rient

man

agem

ent p

ract

ices

(rab

i 20

15 a

nd 2

016)

Trea

tmen

tsN

umbe

r of p

anic

le m

-2Pa

nicl

e le

ngth

(cm

)Pa

nicl

e w

eigh

t (g)

Test

wei

ght (

g)

2015

2016

Pool

ed20

1520

16 P

oole

d20

1520

16Po

oled

2015

2016

Pool

edC

rop

esta

blis

hmen

t met

hods

(M)

M1-

Dire

ct S

eede

d R

ice

(DS

R)

293

527

53

284

423

18

227

922

99

314

308

311

222

322

15

221

9

M2-

Nor

mal

Tra

nspl

ante

d R

ice

(NTP

)25

94

247

825

36

244

924

08

242

93

323

283

3022

51

224

222

46

SEm

+4

104

014

230

270

270

270

040

040

030

260

230

24

CD

(p=0

05)

184

318

03

190

61

211

241

230

170

180

15N

SN

SN

S

Nut

rient

Man

agem

ent p

ract

ices

(N)

N1-

Abs

olut

e co

ntro

l (N

o fe

rtiliz

er)

148

413

59

142

122

03

216

021

82

274

271

273

203

520

25

203

0

N2-

Con

trol (

RD

F)27

95

262

027

09

231

522

77

229

63

153

113

1322

21

221

522

18

N3-R

DF+

SA

of

ZnS

O47

H2O

2

5 kg

ha-1

294

827

95

287

124

01

236

123

81

329

324

326

225

022

39

224

4

N4-R

DF+

2 F

S o

f ZnS

O47

H2O

0

5

301

528

60

293

724

16

237

423

95

335

328

331

228

922

78

228

3

N5-R

DF+

2 F

S o

f nan

o Zn

O

100

0 pp

m31

96

305

531

26

248

824

48

246

83

443

393

4123

21

231

323

17

N6-R

DF+

2 F

S o

f na

no Z

nO

150

0 pp

m31

50

300

530

78

248

224

42

246

23

423

363

3923

08

229

923

03

SEm

+4

024

034

300

290

280

290

040

040

040

190

200

19

CD

(p=0

05)

116

111

64

124

20

840

810

820

130

120

120

560

590

56

Inte

ract

ions

(MxN

)

Nut

rient

man

agem

ent a

t sam

e le

vel o

f cro

p es

tabl

ishm

ent

SEm

+5

695

706

080

410

390

400

060

060

060

270

290

27

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

Cro

p es

tabl

ishm

ent a

t sam

e or

diff

eren

t lev

el o

f nut

rient

man

agem

ent

SEm

+6

616

576

980

460

450

460

070

070

060

360

350

35

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

Gen

eral

Mea

n27

65

261

626

90

238

423

44

236

43

233

183

2022

37

222

822

33


32

Trea

tmen

tsFi

lled

grai

ns p

anic

le-1

Unf

illed

gra

ins

pani

cle-1

Ster

ility

per

cent

age

()

2015

2016

Pool

ed20

1520

16Po

oled

2015

2016

Pool

ed

Cro

p es

tabl

ishm

ent m

etho

ds (M

)M

1- D

irect

See

ded

Ric

e (D

SR

)12

466

116

0412

034

968

122

810

98

737

976

856

M2-

Nor

mal

Tra

nspl

ante

d R

ice

(NTP

)13

436

125

7613

007

881

112

510

03

630

838

734

SEm

+1

881

701

460

160

170

150

120

160

15

CD

(p=0

05)

847

765

658

073

077

069

056

073

065

Nut

rient

Man

agem

ent p

ract

ices

(N)

N1-

Abs

olut

e co

ntro

l (N

o fe

rtiliz

er)

109

3310

079

105

0518

37

208

819

63

144

217

23

158

2

N2-

Con

trol (

RD

F)12

531

116

6612

098

103

212

85

115

97

639

958

79

N3-R

DF+

SA

of

ZnS

O47

H2O

2

5 kg

ha-1

131

5312

289

127

208

2910

80

954

594

811

702

N4-R

DF+

2 F

S o

f ZnS

O47

H2O

0

5

132

7312

409

128

407

8710

38

913

562

775

668

N5-R

DF+

2 F

S o

f nan

o Zn

O

100

0 pp

m13

975

131

1313

546

546

798

672

377

576

477

N6-R

DF+

2 F

S o

f na

no Z

nO

150

0 pp

m13

844

129

8513

414

517

768

642

362

561

462

SEm

+1

451

431

280

200

180

200

150

170

17

CD

(p=0

05)

419

413

371

059

053

057

043

048

050

Inte

ract

ions

(MxN

)N

utrie

nt m

anag

emen

t at s

ame

leve

l of c

rop

esta

blis

hmen

tS

Em+

205

202

182

029

026

028

021

023

025

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Cro

p es

tabl

ishm

ent a

t sam

e or

diff

eren

t lev

el o

f nut

rient

man

agem

ent

SEm

+2

662

512

210

310

290

300

230

270

27

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Gen

eral

Mea

n12

951

120

9012

521

925

117

610

51

683

907

795

RD

F- R

ecom

men

ded

Dos

e of

Fer

tiliz

ers

(N

P2O

5amp K

2O

120

60

40 k

g ha

-1)

SA

- S

oil A

pplic

atio

n F

S-

Folia

r Sp

ray

NS

- N

on S

igni

fican

t

BRIJBHOOSHAN et al

33








CONCLUSION



34

Tabl

e 2

Yie

ld a

nd h

arve

st in

dex

of ri

ce a

s in

fluen

ced

by c

rop

esta

blis

hmen

t met

hods

and

nut

rient

man

agem

ent p

ract

ices

(rab

i 20

15 a

nd 2

016)

Trea

tmen

tsG

rain

yie

ld (k

g ha

-1)

Stra

w y

ield

(kg

ha-1)

Har

vest

Inde

x(

)

2015

2016

Pool

ed20

1520

16Po

oled

2015

2016

Pool

ed

Cro

p es

tabl

ishm

ent m

etho

ds (M

)M

1- D

irect

See

ded

Ric

e (D

SR

)51

9848

8650

4766

7564

4765

6143

58

428

243

20

M2-

Nor

mal

Tra

nspl

ante

d R

ice

(NTP

)47

3344

8946

1160

0058

1359

0743

81

434

343

62

SEm

+98

8389

121

109

101

052

061

049

CD

(p=0

05)

441

373

401

545

492

455

NS

NS

NS

Nut

rient

Man

agem

ent p

ract

ices

(N)

N1-

Abs

olut

e co

ntro

l (N

o fe

rtiliz

er)

2335

2274

2305

3270

3320

3295

416

740

84

412

5

N2-

Con

trol (

RD

F)47

3944

6946

0461

5459

8160

6843

47

427

743

12

N3-R

DF+

SA

of

ZnS

O47

H2O

2

5 kg

ha-1

5187

4993

5090

6593

6462

6527

440

243

63

438

3

N4-R

DF+

2 F

S o

f ZnS

O47

H2O

0

5

5483

5157

5320

6902

6656

6779

442

543

66

439

6

N5-R

DF+

2 F

S o

f nan

o Zn

O

100

0 pp

m60

7056

5958

7875

7072

2473

9744

54

439

644

25

N6-R

DF+

2 F

S o

f na

no Z

nO

150

0 pp

m59

7855

7457

7675

3771

3773

3744

21

438

944

05

SEm

+13

410

911

114

711

412

10

400

550

51

CD

(p=0

05)

386

315

320

424

328

349

115

158

147

Inte

ract

ions

(MxN

)N

utrie

nt m

anag

emen

t at s

ame

leve

l of c

rop

esta

blis

hmen

tS

Em+

189

154

157

208

161

171

056

077

072

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Cro

p es

tabl

ishm

ent a

t sam

e or

diff

eren

t lev

el o

f nut

rient

man

agem

ent

SEm

+19

816

316

822

518

318

60

730

940

82

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Gen

eral

Mea

n49

6546

8848

2963

3761

3062

3443

70

431

343

41

Coe

ffici

ent o

f Var

iatio

n (

)9

78

79

09

48

77

95

97

05

5

RD

F- R

ecom

men

ded

Dos

e of

Fer

tiliz

ers

(N

P2O

5amp K

2O

120

60

40 k

g ha

-1)

SA

- S

oil A

pplic

atio

n F

S-

Folia

r Sp

ray

NS

- N

on S

igni

fican

t

BRIJBHOOSHAN et al

35

REFERENCES

















36





ABSTRACT







37












38














PREETHAM et al

39

Tabl

e-1

Effe

ct o

f int

egra

ted

nutr

ient

man

agem

ent p

ract

ices

on

yiel

d at

trib

utes

of b

aby

corn

dur

ing

khar

if 2

015

and

2016

Trea

tmen

t giv

en to

kha

rif b

aby

corn

2015

2016

Cob

spl

ant-1

Cob

leng

th(c

m)

Cob

girth

(cm

)

Cob

wid

th(c

m)

Cob

wei

ght

(g)

Cob

spl

ant-1

Cob

leng

th(c

m)

Cob

girth

(cm

)

Cob

wid

th(c

m

Cob

wei

ght

(g)

With

With

out

husk

husk

With

With

out

husk

husk

T 1- 25

N through FY

M + 75

RDF

178

106

95

251

6846

80

926

183

931

513

163

426

89

03T 2-

25

N th

roug

h FY

M +

75

RD

F +

200

108

35

601

7247

33

100

01

989

645

541

6843

16

908

Azo

spiri

llum

and

Bac

illus

meg

ater

ium

5

kg h

a-1 e

ach

T 3- 25

N

thr

ough

Ver

mic

ompo

st +

202

109

55

581

7649

17

100

02

029

655

461

7143

68

926

75

RD

FT 4-

T4 2

5 N

thro

ugh

Verm

icom

post

+2

2312

50

574

186

555

913

46

222

122

45

651

8348

73

120

675

R

DF

+ A

zosp

irillu

m a

nd B

acill

usm

egat

eriu

m

5 k

g ha

-1 e

ach

T 5- 10

0 R

DF

175

106

65

241

7146

29

920

178

989

512

161

423

69

01T 6-

100

RD

F +

Azo

spiri

llum

and

Bac

illus

200

109

65

551

8049

86

103

81

989

905

441

7443

10

936

meg

ater

ium

5

kg

ha-1 e

ach

T 7- C

ontro

l (N

o fe

rtiliz

er a

pplic

atio

n)0

957

503

981

2715

88

358

147

711

373

127

239

95

13S

Em

+0

060

410

150

051

550

400

060

500

150

050

970

13C

D (

P=0

05)

020

127

045

016

477

123

018

153

047

016

300

041

Trea

tmen

t giv

en to

rabi

cro

p (h

yaci

nth

bean

)S

1- 10

0 R

DF

191

987

520

161

424

99

25S

2- 75

R

DF

180

933

501

167

373

78

25S

3-10

0 R

DF

+ B

rady

rhiz

obiu

m

200

990

533

160

445

79

9550

0 g

ha-1 (

See

d tre

atm

ent)

S4-

75

RD

F +

Bra

dyrh

izob

ium

1

889

605

071

6639

98

850

500

g ha

-1 (

See

d tre

atm

ent)

SE

m+

003

024

017

005

086

019

CD

(P

=00

5)0

10N

SN

SN

S2

460

54In

tera

ctio

nB

ean

trea

tmen

t mea

ns a

t sam

e le

vel o

f bab

y co

rn IN

M tr

eatm

ents

SE

m+

009

064

044

014

228

050

CD

(P

=00

5)N

SN

SN

SN

SN

SN

SIN

M tr

eatm

ent m

eans

of b

aby

corn

at s

ame

leve

l of b

ean

trea

tmen

tsS

Em

+0

100

740

410

132

200

45C

D (

P=0

05)

NS

NS

NS

NS

NS

NS


40





Cob Yield (kg ha-1)










PREETHAM et al

41

Tabl

e-2

Effe

ct o

f int

egra

ted

nutr

ient

man

agem

ent p

ract

ices

on

cob

yiel

d (k

g ha

-1) w

ith a

nd w

ithou

t hus

k an

d st

over

yie

ld o

f bab

y co

rn d

urin

gkh

arif

201

5 an

d 20

16

Trea

tmen

t giv

en to

kha

rif b

aby

corn

2015

2016

Cob

yie

ld (k

g ha

-1)

Stov

erC

ob y

ield

(kg

ha-1)

Stov

er y

ield

yiel

d(kg

ha-1

)(k

g ha

-1)

With

hus

kW

ithou

t hus

kW

ith h

usk

W

ithou

t hus

k

T 1- 25

N through FY

M + 75

RDF

8204

1602

1934

481

4714

9518

919

T 2- 25

N

thro

ugh

FYM

+ 7

5 R

DF

+ A

zosp

irillu

m90

3218

0320

865

8882

1596

1970

8an

d B

acill

us m

egat

eriu

m

5 k

g ha

-1 e

ach

T 3- 25

N

thro

ugh

Verm

icom

post

+ 7

5 R

DF

9400

1801

2147

589

9416

0119

762

T 4- T4

25

N th

roug

h Ve

rmic

ompo

st +

75

RD

F +

1229

020

4924

020

1172

718

9021

165

Azo

spiri

llum

and

Bac

illus

meg

ater

ium

5

kg

ha-1 e

ach

T 5- 10

0 R

DF

8049

1542

1906

081

9914

7119

364

T 6- 10

0 R

DF

+ A

zosp

irillu

m a

nd B

acill

us93

4418

0720

398

8824

1553

1963

1m

egat

eriu

m

5 k

g ha

-1 e

ach

T 7- C

ontro

l (N

o fe

rtiliz

er a

pplic

atio

n)42

7982

313

821

4393

749

1216

8S

Em

+26

764

429

200

1638

5C

D (

P=0

05)

824

198

1321

617

4911

87Tr

eatm

ent g

iven

to ra

bi c

rop

(hya

cint

h be

an)

S1-

100

RD

F88

2415

4619

086

S2-

75

RD

F78

8013

6917

833

S3-10

0 R

DF

+ B

rady

rhiz

obiu

m

500

g h

a-190

7315

9019

410

(See

d tre

atm

ent)

S4-

75

RD

F +

Bra

dyrh

izob

ium

5

00 g

ha-1

8033

1411

1836

5(S

eed

treat

men

t)S

Em

+20

216

280

CD

(P

=00

5)57

745

798

Inte

ract

ion

Bea

n tre

atm

ent m

eans

at s

ame

leve

l of b

aby

corn

INM

trea

tmen

tsS

Em

+53

541

740

CD

(P

=00

5)N

SN

SN

SIN

M tr

eatm

ent m

eans

of b

aby

corn

at s

ame

leve

l of b

ean

treat

men

tsS

Em

+50

439

748

CD

(P

=00

5)N

SN

SN

S


42






CONCLUSION


REFERENCES







PREETHAM et al

43















44



ABSTRACT














45























2 FACTOR ANALYSIS



2 2 212 R 3K N(N 1)i iW 2 2K N(N 1)

46











Sig 0000



Component


Total ofVariance

Cumulative

Total ofVariance

Cumulative


Total ofVariance

Cumulative

1 1965 21839 21839 1965 21839 21839 1894 21044 21044

2 1806 20070 41909 1806 20070 41909 1520 16887 37932

3 1086 12064 53973 1086 12064 53973 1444 16042 53973

4 927 10301 64274

5 887 9860 74134

6 738 8199 82333

7 630 6996 89329

8 529 5878 95207

9 431 4793 100000


MEENA et al

47


Component Number

Scree Plot

Eig

en v

alue



Component

1 2 3


Component1 2 3












48


Table 4 Ranks









Chi-Square 0822

df 2

Asymp Sig 0663















MEENA et al

49







ABSTRACT









50

SRINIVAS et al




()




51



CONCLUSION


Acknowledgments


REFERENCES







52







ABSTRACT








53











54




Analysis of protein

















55








0

10

20

30

40

50

6070

80

90

Finge

r Mille

t

Foxta

il Mille

t

Little M

illet

Pearl M

illet

Proso

Mille

t

Sorgh

um

Perc

enta

ge d

iges

tibili

ty

Dosa UpmaIdli



56






Foxtail millet

Little millet

Pearl millet

Prosomillet

Sorghum

IdliDosaUpma

10080

6040

20 0







57



CONCLUSION


REFERENCES
















58




















59






ABSTRACT










60
















Pomegranate 21 23 17 16 18 19 13 12 17 18 12 14

Cassia 31 33 24 24 26 28 20 21 22 23 21 22

Periwinkle 29 31 23 25 25 24 19 20 21 21 22 21

Marigold 21 23 18 19 16 18 12 13 16 17 14 16

61









62



0

5

10

15

20

25

30

S aureus Ecoli

Pomegran

teCas

siaPeri

winkleMari

gold

Pomegran

te

Cassia

Periwinkle

Marigold

SilverTitaniumzinc


0

5

10

30

Pomegran

teCas

siaPeri

winkleMari

gold

Pomegran

teCas

siaPeri

winkleMari

gold

35

15

20

S aureus Ecoli

SilverTitaniumzinc

25

63

CONCLUSION








64













65

Tabl

e 1

Est

imat

es o

f phe

noty

pic

and

geno

typi

c co

rrel

atio

n co

effic

ient

s fo

r yie

ld a

nd it

s at

trib

utin

g ch

arac

ters

Cha

ract

erDa

ys to

Plan

tPa

nicl

eN

o o

fN

o o

fSp

ikel

et10

00-s

eed

Prod

uctiv

ityBi

omas

sHa

rves

tG

rain

50

heig

htle

ngth

Prod

uctiv

efil

led

fert

ility

wei

ght

per d

aype

r pl

ant

Inde

xyi

eld

flow

erin

gtil

lers

grai

nspe

r pl

ant

per

plan

tpe

rpa

nicl

e

Day

s to

50

P1

0000

012

230

3431

0

1594

0

5331

0

1150

-00

195

000

49-0

053

60

2191

0

2472

flow

erin

gG

100

000

1181

037

30

016

91

054

21

011

49-0

022

90

0073

-00

657

022

80

025

70

Plan

t hei

ght

P

100

000

5653

-0

441

4

037

95

012

720

5292

014

700

4604

-0

253

6

016

30

G

1

0000

061

45

-05

199

0

3892

0

1301

054

12

014

540

4798

-02

621

0

1605

Pani

cle

leng

thP

100

00-0

025

80

3811

0

2527

0

1930

0

0041

-00

205

006

370

0774

G

100

000

0038

040

71

027

06

020

60

002

420

0073

006

540

1048

No

of P

rodu

ctiv

eP

1

0000

-00

043

020

21

-0

3806

023

08

-03

316

0

4903

0

2561

tille

rs p

er p

lant

G

100

00-0

002

90

2298

-0

434

00

2296

-0

453

0

057

49

025

85

No

of f

illed

gra

ins

P

1

0000

050

67

008

680

5112

0

3200

0

2694

0

6261

per p

anic

leG

100

000

5088

0

0874

053

48

034

06

027

36

065

35

Spik

elet

fert

ility

()

P

100

000

0613

049

97

004

900

4247

0

5043

G

1

0000

006

150

5270

0

0519

043

59

053

01

1000

see

d w

eigh

tP

100

000

1600

0

2852

-0

068

90

1470

G

100

000

1656

0

2999

-00

697

015

13

Prod

uctiv

ity p

er d

ayP

1

0000

029

02

057

85

096

81

G

1

0000

023

91

061

47

096

62

Bio

mas

s pe

r pla

ntP

100

00-0

561

9

025

85

G

100

00-0

571

1

020

47

Har

vest

inde

x (

)P

1

0000

061

74

G

1

0000

065

49

P- P

heno

typi

c co

rrel

atio

n co

effic

ient

G-

Gen

otyp

ic c

orre

latio

n co

effic

ient

Si

gnifi

cant

at

1

Si

gnifi

cant

at

5


66

SRIJAN et al






Acknowledgement


67

Tabl

e 2

Dire

ct a

nd in

dire

ct e

ffect

s of

var

ious

com

pone

nt c

hara

cter

s on

yie

ld in

rice

Cha

ract

erDa

ys to

Plan

tPa

nicl

eN

o o

fN

o o

fSp

ikel

et10

00-s

eed

Prod

uctiv

ityBi

omas

sHa

rves

tG

rain

50

heig

htle

ngth

Prod

uctiv

efil

led

fert

ility

wei

ght

per d

aype

r pl

ant

Inde

xyi

eld

flow

erin

gtil

lers

grai

nspe

r pl

ant

per

plan

tpe

rpa

nicl

e

Day

s to

50

P0

2451

-00

015

-00

035

-00

018

001

32-0

001

2-0

000

10

0049

000

30-0

011

00

2472

flow

erin

gG

025

22-0

001

8-0

004

0-0

002

30

0157

-00

011

-00

002

000

730

0041

-00

128

025

70

Plan

t hei

ght

P0

0300

-00

123

-00

058

000

480

0094

-00

013

000

310

1482

-00

260

001

280

1630

G

002

98-0

015

3-0

006

60

0072

001

12-0

001

20

0041

014

65-0

029

80

0147

016

05

Pani

cle

leng

thP

008

41-0

006

9-0

010

20

0003

000

94-0

002

50

0011

000

420

0012

-00

032

007

74

G

009

41-0

009

4-0

010

8-0

000

10

0118

-00

025

000

150

0243

-00

005

-00

037

010

48

No

of P

rodu

ctiv

eP

003

910

0054

000

03-0

011

0-0

000

1-0

002

0-0

002

20

2327

001

87-0

024

70

2561

tille

rs p

er p

lant

G0

0426

000

800

0000

-00

138

-00

001

-00

022

-00

033

023

140

0282

-00

323

025

85

No

of f

illed

gra

ins

P0

1307

-00

047

-00

039

000

000

0247

-00

051

000

050

5154

-00

181

-00

136

062

61

per p

anic

leG

013

67-0

006

0-0

004

40

0000

002

89-0

004

80

0007

053

89-0

021

2-0

015

40

6535

Spik

elet

fert

ility

()

P0

0282

-00

016

-00

026

-00

022

001

25-0

010

00

0004

050

38-0

002

8-0

021

40

5043

G

002

90-0

002

0-0

002

9-0

003

20

0147

-00

094

000

050

5311

-00

032

-00

245

053

01

1000

see

d w

eigh

tP

-00

048

-00

065

-00

020

000

420

0021

-00

006

000

580

1613

-00

161

000

350

1470

G

-00

058

-00

083

-00

022

000

600

0025

-00

006

000

750

1669

-00

186

000

390

1513

Prod

uctiv

ity p

er d

ayP

000

12-0

001

80

0000

-00

025

001

26-0

005

00

0009

100

82-0

016

4-0

029

10

9681

G

000

18-0

002

2-0

000

3-0

003

20

0154

-00

049

000

121

0077

-00

149

-00

345

096

62

Bio

mas

s pe

r pla

ntP

-00

131

-00

056

000

020

0036

000

79-0

000

50

0017

029

26-0

056

40

0283

025

85

G

-00

166

-00

074

-00

001

000

620

0098

-00

005

000

220

2410

-00

621

003

210

2047

Har

vest

inde

x (

)P

005

370

0031

-00

006

-00

054

000

66-0

004

3-0

000

40

5832

003

17-0

050

30

6174

G

005

750

0040

-00

007

-00

079

000

79-0

004

1-0

000

50

6194

003

55-0

056

10

6549

Phen

otyp

ic r

esid

ual e

ffec

t =

005

78

Gen

otyp

ic r

esid

ual

effe

ct =

00

560

P-

Phe

noty

pic

leve

l G

- G

enot

ypic

leve

l

Bol

d va

lues

- D

irec

t ef

fect

s N

orm

al V

alue

s -

Indi

rect

effe

cts

Sign

ifica

nt a

t 1

S

igni

fican

t at

5


68

REFERENCES









SRIJAN et al

69












70













DFF 086 -057 063 019 -029 033

PHT 072 -061 072 022 002 050

NPT -035 -045 -047 -070 -042 -041

PL 054 068 -030 -005 000 023

PG 013 018 -045 -005 065 059

TGW -024 -003 -016 000 043 028

GY 019 038 -022 002 039 018






DFF -127 128 -017 002 023 024 033

PHT -109 149 -019 003 028 -002 050

NPT 072 -091 031 001 -088 034 -041

PL -080 107 -014 016 -006 000 023

PG -024 033 -022 -001 125 -053 059

TGW 037 003 -013 001 081 -081 028



71





Residual GYPx7=029

Px2=149 Px3=031 Px4=0 Px5=125


rx12=004 rx23=-061 rx34=-047 rx45=-005 rx54= 065

rx13=-057 rx24=072 rx35=-07 rx44=0

rx14=083 rx25=022 rx30=-042

rx15=019 rx26=002

rx16=-029

Px6=-001Px1=127


72






REFERENCES









73






























Books



Thesis


i









REVIEW PROCESS




ii



____________________________________________________________________________________







1

2

3








Name

Signature

Office seal


iii






Nationality Indian



Nationality Indian



Nationality Indian





iv














v




30AJPJTSAU2018

Page 1

2pdf

Page 2

3pdf

Page 3

1






ABSTRACT







2





NAGESH KUMAR et al


12

9

362625 2 155 117 075 071 0585 055 05

141210

86420

china

USA

Fran

ce

Brazil

India

Japa

nGer

many

Arge

ntin

a

Italy

Nethe

rland

s

Can

ada

Russia

3




Corn 11Rice 6

Others 40Cotton 28




Total Market 32 MT

Cotton 07Others 86

Wheat 312

Paddy 259

Soybean 106

Groundnut 87

Gram 58

Potato 55

Maize 55


4








NAGESH KUMAR et al


Blackgram


20

40

60

80

100

See

d R

epla

cem

ent

Rat

e (

)

2006-07 2011-12 2016-17

3

25

2

15

1

05

0

013

05

17

27

1990 2000 2010 2013

5






6














NAGESH KUMAR et al

7
















0

2

4

6

8

10

12

14

16

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

USD Bn


8




Rice 456 50 22800 100 228 2280

Wheat 272 125 34000 20 1700 85000

Sorghum 77 15 1155 160 07 45

Pearl millet 87 5 435 200 02 11

Maize 80 20 1600 80 20 250

Pigeon pea 34 15 510 100 05 51

Chickpea 82 80 6560 10 656 65600

Groundnut 62 100 6200 8 97 12109

RampM 63 5 315 200 02 08

Soybean 89 75 6675 16 417 26074

Sunflower 19 10 190 50 04 76

Cotton 95 12 1140 50 23 456

Jute 08 5 40 100 004 04

Total 1424 81620 3161 191964








NAGESH KUMAR et al

9












10













NAGESH KUMAR et al

11


Seed Biotechnology





Cold Plasma Coating





Bioprospecting







12






REFERENCES














NAGESH KUMAR et al

13




ABSTRACT







14








SRIDHAR et al

15








Quality parameters



CONCLUSIONS



16

Tabl

e 1

Gra

in a

nd S

traw

yie

ld (k

g ha

-1) o

f ric

e as

influ

ence

d by

alte

rnat

e w

ettin

g an

d dr

ying

irrig

atio

n an

d ni

trog

en le

vels

dur

ing

rabi

201

620

17 a

nd p

oole

d m

eans

Trea

tmen

tsG

rain

yie

ld (k

g ha

-1)

Stra

w y

ield

(kg

ha-1)

2016

2017

Pool

ed20

1620

17Po

oled

Irrig

atio

n re

gim

es (I

)

I 1- 2-

5cm

sub

mer

genc

e as

per

cro

p st

age

7026

7221

7123

7936

8153

8045

I 2- AW

D ir

rigat

ion

of 5

cm

with

3 c

m fa

ll in

fiel

d69

2571

5070

3778

4480

5679

56w

ater

tube

I 3- AW

D ir

rigat

ion

of 5

cm

with

5 c

m fa

ll in

fiel

d64

1466

5265

3373

3375

4574

39w

ater

tube

SEm

plusmn54

060

455

252

852

152

4

CD

at 5

15

016

815

314

714

514

6

Nitr

ogen

lev

els

(N)

N1-1

20 k

g ha

-167

0669

2268

1476

0578

1777

11

N2-1

60 k

g ha

-168

0270

2169

1177

1079

2178

16

N3-2

00 k

g ha

-168

5770

8169

6977

9880

1679

07

SE

mplusmn

372

445

390

410

411

410

CD

at 5

81

9785

8989

89

Inte

ract

ions

NS

NS

NS

NS

NS

NS

SRIDHAR et al

17

Tabl

e 2

Hul

ling

and

mill

ing

perc

enta

ge (

) of r

ice

as in

fluen

ced

by a

ltern

ate

wet

ting

and

dryi

ng ir

rigat

ion

and

nitr

ogen

leve

ls d

urin

g ra

bi20

16 2

017

and

pool

ed m

eans

Hul

ling

()

Mill

ing

()

2016

2017

Pool

ed20

1620

17Po

oled

Irrig

atio

n re

gim

es (I

)

I 1- 2-

5cm

sub

mer

genc

e as

per

cro

p st

age

808

810

809

675

674

674

I 2- AW

D ir

rigat

ion

of 5

cm

with

3 c

m fa

ll in

fiel

d80

780

980

867

367

267

3w

ater

tube

I 3- AW

D ir

rigat

ion

of 5

cm

with

5 c

m fa

ll in

fiel

d80

580

780

667

066

866

9w

ater

tube

SEm

plusmn0

10

20

20

20

20

2

CD

at 5

N

SN

SN

S0

40

50

5

Nitr

ogen

lev

els

(N)

N1-1

20 k

g ha

-180

780

880

867

267

467

3

N2-1

60 k

g ha

-180

680

880

767

367

367

3

N3-2

00 k

g ha

-180

780

980

867

267

067

1

SE

mplusmn

01

01

01

06

02

04

CD

at 5

N

SN

SN

SN

SN

SN

S

Inte

ract

ions

( IxN

)N

SN

SN

SN

SN

SN

S


18

Tabl

e 3

Hea

d ric

e re

cove

ry (

) G

rain

pro

tein

con

tent

and

am

ylos

e (

) of r

ice

as in

fluen

ced

by a

ltern

ate

wet

ting

and

dryi

ng ir

rigat

ion

and

nitr

ogen

leve

ls d

urin

g ra

bi 2

016

201

7 an

d po

oled

mea

ns

2016

2017

Pool

ed20

1620

17Po

oled

2016

2017

Pool

ed

Hea

d ric

e re

cove

ry (

)G

rain

pro

tein

con

tent

()

Amyl

ose(

)

Irrig

atio

n re

gim

es (I

)

I 1- 2-

5cm

sub

mer

genc

e as

per

cro

p st

age

650

665

12

650

99

189

149

1623

925

024

5

I 2- AW

D ir

rigat

ion

of 5

cm

with

3 c

m fa

ll in

fiel

d63

95

638

763

91

907

904

906

238

247

242

wat

er tu

be

I 3- AW

D ir

rigat

ion

of 5

cm

with

5 c

m fa

ll in

fiel

d63

32

633

463

33

903

898

901

234

243

239

wat

er tu

be

SEm

plusmn0

200

230

210

040

050

040

440

450

44

CD

at 5

0

570

640

60N

SN

SN

SN

SN

SN

S

Nitr

ogen

lev

els

(N)

N1-1

20 k

g ha

-163

93

639

863

95

894

890

892

230

239

235

N2-1

60 k

g ha

-164

16

640

464

10

916

911

913

240

246

243

N3-2

00 k

g ha

-164

23

643

164

27

920

916

918

241

246

244

SE

mplusmn

014

016

015

004

004

004

023

019

021

CD

at 5

N

SN

SN

S0

100

090

090

750

50

63

Inte

ract

ions

( IxN

)N

SN

SN

SN

SN

SN

SN

SN

SN

S

SRIDHAR et al

19


REFERENCES















20






ABSTRACT






21








S No Genotype ID

1 ICGV 15016

2 ICGV 15064

3 ICGV 16013

4 ICGV 16017

5 ICGV 16030

6 ICGV 16035

7 ICGV 16045

8 ICGV 16668


22

S No Genotype ID

9 ICGV 16687

10 ICGV 16700

11 ICGV 16701

12 ICGV 171011

13 ICGV 16686

14 ICGV 171007

15 ICGV 171023

16 ICGV 171027

17 ICGV 171006

18 ICGV 16667

19 ICGV 16698

20 ICGV 171017

21 ICGV 171015

22 ICGV 16020

23 ICGV 171051

24 ICGV 171040

25 ICGV 171039

26 ICGV 171046

27 ICGV 171041

S No Genotype ID

28 ICGV 171048

29 ICGV 171026

30 ICGV 171004

31 ICGV 171021

32 ICGV 171019

33 ICGV 171024

34 ICGV 171016

35 ICGV 171009

36 ICGV 171018

37 ICGV 171025

38 ICGV 171020

39 ICGV 171002

40 ICGV 03043

41 ICGV 07222

42 ICGV 07220

43 ICGV 00350

44 ICGV 00351

45 ICGV 02266

46 ICGV 91114





DNYANESHWAR et al

23




1 ICGV 171051 336 20 145 20

2 ICGV 171048 435 20 173 17

3 ICGV 171046 923 17 284 15

4 ICGV 171041 499 20 137 19

5 ICGV 171040 365 18 240 19

6 ICGV 171039 262 17 350 21

7 ICGV 171027 252 20 97 20

8 ICGV 171026 273 20 62 23

9 ICGV 171025 297 19 295 21

10 ICGV 171024 321 20 216 21

11 ICGV 171023 367 20 71 21

12 ICGV 171021 251 19 135 19

13 ICGV 171020 302 21 222 19

14 ICGV 171019 699 16 140 20

15 ICGV 171018 504 20 173 19

16 ICGV 171017 188 19 150 19

17 ICGV 171016 158 21 156 19

18 ICGV 171015 250 20 183 21

19 ICGV 171011 413 19 281 18

20 ICGV 171009 231 19 205 20

21 ICGV 171007 269 19 106 21

22 ICGV 171006 203 18 141 21

23 ICGV 171004 176 22 178 20

24 ICGV 171002 293 20 217 18

25 ICGV 16701 337 19 104 21

26 ICGV 16700 256 21 59 22

27 ICGV 16698 243 21 182 19

28 ICGV 16687 417 19 76 24

29 ICGV 16686 245 17 166 16

30 ICGV 16668 438 19 191 18

31 ICGV 16667 215 21 132 17

32 ICGV 16045 348 21 113 18

33 ICGV 16035 186 20 48 22

34 ICGV 16030 251 18 62 21

35 ICGV 16020 06 14 248 21


24



36 ICGV 16017 284 19 125 17

37 ICGV 16013 157 20 138 14

38 ICGV 15064 347 19 267 17

39 ICGV 15016 395 19 133 21

40 ICGV 07222 286 20 276 18

41 ICGV 07220 381 19 110 18

42 ICGV 03043 360 20 107 17

43 ICGV 02266 448 20 230 18

44 ICGV 00351 275 20 270 20

45 ICGV 00350 309 21 176 17

46 ICGV 91114 174 22 279 19




DNYANESHWAR et al

25

Tabl

e 3

Com

para

tive

SNP

prof

ile o

btai

ned

from

gen

omic

DN

A of

gro

undn

ut le

af d

isc

and

seed

chi

p

DN

A A

ssay

SNP

for

SNPs

for l

ate

leaf

spo

tSN

Ps fo

r rus

thi

gh o

leic

acid

snpA

H00

02sn

pAH

0004

snpA

H00

05sn

pAH

0010

snpA

H00

11sn

pAH

0015

snpA

H00

17sn

pAH

0018

snpA

H00

21sn

pAH

0026

ICG

V 15

064_

Leaf

AA

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

1506

4_Se

edA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

687_

Lea

fA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

687_

See

dA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

698_

Lea

fA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

698_

See

dA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

700_

Lea

fA

AG

GC

CA

ATT

AA

CC

AA

GT

CC

ICG

V 16

700_

See

dA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 00

351_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0035

1_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 02

266_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0226

6_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 03

043_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0304

3_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 07

222_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0722

2_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

701_

Lea

fA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 16

701_

See

dA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1002

_ Le

afA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1002

_ S

eed

AA

GG

CC

AA

TTA

AC

CA

TG

GC

CIC

GV

1710

04_

Leaf

AA

GG

CC

AA

TTA

AC

CA

AG

GC

CIC

GV

1710

04_

See

dA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1006

_ Le

afA

AG

GC

GA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1006

_ S

eed

AA

GG

CC

AA

TTA

AC

CA

AG

GC

C--

Nul

l alle

le


26

REFERENCES
















DNYANESHWAR et al

27






28







ABSTRACT









29











Yield attributes



30










BRIJBHOOSHAN et al

31

Tabl

e 1

Yie

ld a

ttrib

utes

of r

ice

as in

fluen

ced

by c

rop

esta

blis

hmen

t met

hods

and

nut

rient

man

agem

ent p

ract

ices

(rab

i 20

15 a

nd 2

016)

Trea

tmen

tsN

umbe

r of p

anic

le m

-2Pa

nicl

e le

ngth

(cm

)Pa

nicl

e w

eigh

t (g)

Test

wei

ght (

g)

2015

2016

Pool

ed20

1520

16 P

oole

d20

1520

16Po

oled

2015

2016

Pool

edC

rop

esta

blis

hmen

t met

hods

(M)

M1-

Dire

ct S

eede

d R

ice

(DS

R)

293

527

53

284

423

18

227

922

99

314

308

311

222

322

15

221

9

M2-

Nor

mal

Tra

nspl

ante

d R

ice

(NTP

)25

94

247

825

36

244

924

08

242

93

323

283

3022

51

224

222

46

SEm

+4

104

014

230

270

270

270

040

040

030

260

230

24

CD

(p=0

05)

184

318

03

190

61

211

241

230

170

180

15N

SN

SN

S

Nut

rient

Man

agem

ent p

ract

ices

(N)

N1-

Abs

olut

e co

ntro

l (N

o fe

rtiliz

er)

148

413

59

142

122

03

216

021

82

274

271

273

203

520

25

203

0

N2-

Con

trol (

RD

F)27

95

262

027

09

231

522

77

229

63

153

113

1322

21

221

522

18

N3-R

DF+

SA

of

ZnS

O47

H2O

2

5 kg

ha-1

294

827

95

287

124

01

236

123

81

329

324

326

225

022

39

224

4

N4-R

DF+

2 F

S o

f ZnS

O47

H2O

0

5

301

528

60

293

724

16

237

423

95

335

328

331

228

922

78

228

3

N5-R

DF+

2 F

S o

f nan

o Zn

O

100

0 pp

m31

96

305

531

26

248

824

48

246

83

443

393

4123

21

231

323

17

N6-R

DF+

2 F

S o

f na

no Z

nO

150

0 pp

m31

50

300

530

78

248

224

42

246

23

423

363

3923

08

229

923

03

SEm

+4

024

034

300

290

280

290

040

040

040

190

200

19

CD

(p=0

05)

116

111

64

124

20

840

810

820

130

120

120

560

590

56

Inte

ract

ions

(MxN

)

Nut

rient

man

agem

ent a

t sam

e le

vel o

f cro

p es

tabl

ishm

ent

SEm

+5

695

706

080

410

390

400

060

060

060

270

290

27

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

Cro

p es

tabl

ishm

ent a

t sam

e or

diff

eren

t lev

el o

f nut

rient

man

agem

ent

SEm

+6

616

576

980

460

450

460

070

070

060

360

350

35

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

Gen

eral

Mea

n27

65

261

626

90

238

423

44

236

43

233

183

2022

37

222

822

33


32

Trea

tmen

tsFi

lled

grai

ns p

anic

le-1

Unf

illed

gra

ins

pani

cle-1

Ster

ility

per

cent

age

()

2015

2016

Pool

ed20

1520

16Po

oled

2015

2016

Pool

ed

Cro

p es

tabl

ishm

ent m

etho

ds (M

)M

1- D

irect

See

ded

Ric

e (D

SR

)12

466

116

0412

034

968

122

810

98

737

976

856

M2-

Nor

mal

Tra

nspl

ante

d R

ice

(NTP

)13

436

125

7613

007

881

112

510

03

630

838

734

SEm

+1

881

701

460

160

170

150

120

160

15

CD

(p=0

05)

847

765

658

073

077

069

056

073

065

Nut

rient

Man

agem

ent p

ract

ices

(N)

N1-

Abs

olut

e co

ntro

l (N

o fe

rtiliz

er)

109

3310

079

105

0518

37

208

819

63

144

217

23

158

2

N2-

Con

trol (

RD

F)12

531

116

6612

098

103

212

85

115

97

639

958

79

N3-R

DF+

SA

of

ZnS

O47

H2O

2

5 kg

ha-1

131

5312

289

127

208

2910

80

954

594

811

702

N4-R

DF+

2 F

S o

f ZnS

O47

H2O

0

5

132

7312

409

128

407

8710

38

913

562

775

668

N5-R

DF+

2 F

S o

f nan

o Zn

O

100

0 pp

m13

975

131

1313

546

546

798

672

377

576

477

N6-R

DF+

2 F

S o

f na

no Z

nO

150

0 pp

m13

844

129

8513

414

517

768

642

362

561

462

SEm

+1

451

431

280

200

180

200

150

170

17

CD

(p=0

05)

419

413

371

059

053

057

043

048

050

Inte

ract

ions

(MxN

)N

utrie

nt m

anag

emen

t at s

ame

leve

l of c

rop

esta

blis

hmen

tS

Em+

205

202

182

029

026

028

021

023

025

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Cro

p es

tabl

ishm

ent a

t sam

e or

diff

eren

t lev

el o

f nut

rient

man

agem

ent

SEm

+2

662

512

210

310

290

300

230

270

27

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Gen

eral

Mea

n12

951

120

9012

521

925

117

610

51

683

907

795

RD

F- R

ecom

men

ded

Dos

e of

Fer

tiliz

ers

(N

P2O

5amp K

2O

120

60

40 k

g ha

-1)

SA

- S

oil A

pplic

atio

n F

S-

Folia

r Sp

ray

NS

- N

on S

igni

fican

t

BRIJBHOOSHAN et al

33








CONCLUSION



34

Tabl

e 2

Yie

ld a

nd h

arve

st in

dex

of ri

ce a

s in

fluen

ced

by c

rop

esta

blis

hmen

t met

hods

and

nut

rient

man

agem

ent p

ract

ices

(rab

i 20

15 a

nd 2

016)

Trea

tmen

tsG

rain

yie

ld (k

g ha

-1)

Stra

w y

ield

(kg

ha-1)

Har

vest

Inde

x(

)

2015

2016

Pool

ed20

1520

16Po

oled

2015

2016

Pool

ed

Cro

p es

tabl

ishm

ent m

etho

ds (M

)M

1- D

irect

See

ded

Ric

e (D

SR

)51

9848

8650

4766

7564

4765

6143

58

428

243

20

M2-

Nor

mal

Tra

nspl

ante

d R

ice

(NTP

)47

3344

8946

1160

0058

1359

0743

81

434

343

62

SEm

+98

8389

121

109

101

052

061

049

CD

(p=0

05)

441

373

401

545

492

455

NS

NS

NS

Nut

rient

Man

agem

ent p

ract

ices

(N)

N1-

Abs

olut

e co

ntro

l (N

o fe

rtiliz

er)

2335

2274

2305

3270

3320

3295

416

740

84

412

5

N2-

Con

trol (

RD

F)47

3944

6946

0461

5459

8160

6843

47

427

743

12

N3-R

DF+

SA

of

ZnS

O47

H2O

2

5 kg

ha-1

5187

4993

5090

6593

6462

6527

440

243

63

438

3

N4-R

DF+

2 F

S o

f ZnS

O47

H2O

0

5

5483

5157

5320

6902

6656

6779

442

543

66

439

6

N5-R

DF+

2 F

S o

f nan

o Zn

O

100

0 pp

m60

7056

5958

7875

7072

2473

9744

54

439

644

25

N6-R

DF+

2 F

S o

f na

no Z

nO

150

0 pp

m59

7855

7457

7675

3771

3773

3744

21

438

944

05

SEm

+13

410

911

114

711

412

10

400

550

51

CD

(p=0

05)

386

315

320

424

328

349

115

158

147

Inte

ract

ions

(MxN

)N

utrie

nt m

anag

emen

t at s

ame

leve

l of c

rop

esta

blis

hmen

tS

Em+

189

154

157

208

161

171

056

077

072

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Cro

p es

tabl

ishm

ent a

t sam

e or

diff

eren

t lev

el o

f nut

rient

man

agem

ent

SEm

+19

816

316

822

518

318

60

730

940

82

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Gen

eral

Mea

n49

6546

8848

2963

3761

3062

3443

70

431

343

41

Coe

ffici

ent o

f Var

iatio

n (

)9

78

79

09

48

77

95

97

05

5

RD

F- R

ecom

men

ded

Dos

e of

Fer

tiliz

ers

(N

P2O

5amp K

2O

120

60

40 k

g ha

-1)

SA

- S

oil A

pplic

atio

n F

S-

Folia

r Sp

ray

NS

- N

on S

igni

fican

t

BRIJBHOOSHAN et al

35

REFERENCES

















36





ABSTRACT







37












38














PREETHAM et al

39

Tabl

e-1

Effe

ct o

f int

egra

ted

nutr

ient

man

agem

ent p

ract

ices

on

yiel

d at

trib

utes

of b

aby

corn

dur

ing

khar

if 2

015

and

2016

Trea

tmen

t giv

en to

kha

rif b

aby

corn

2015

2016

Cob

spl

ant-1

Cob

leng

th(c

m)

Cob

girth

(cm

)

Cob

wid

th(c

m)

Cob

wei

ght

(g)

Cob

spl

ant-1

Cob

leng

th(c

m)

Cob

girth

(cm

)

Cob

wid

th(c

m

Cob

wei

ght

(g)

With

With

out

husk

husk

With

With

out

husk

husk

T 1- 25

N through FY

M + 75

RDF

178

106

95

251

6846

80

926

183

931

513

163

426

89

03T 2-

25

N th

roug

h FY

M +

75

RD

F +

200

108

35

601

7247

33

100

01

989

645

541

6843

16

908

Azo

spiri

llum

and

Bac

illus

meg

ater

ium

5

kg h

a-1 e

ach

T 3- 25

N

thr

ough

Ver

mic

ompo

st +

202

109

55

581

7649

17

100

02

029

655

461

7143

68

926

75

RD

FT 4-

T4 2

5 N

thro

ugh

Verm

icom

post

+2

2312

50

574

186

555

913

46

222

122

45

651

8348

73

120

675

R

DF

+ A

zosp

irillu

m a

nd B

acill

usm

egat

eriu

m

5 k

g ha

-1 e

ach

T 5- 10

0 R

DF

175

106

65

241

7146

29

920

178

989

512

161

423

69

01T 6-

100

RD

F +

Azo

spiri

llum

and

Bac

illus

200

109

65

551

8049

86

103

81

989

905

441

7443

10

936

meg

ater

ium

5

kg

ha-1 e

ach

T 7- C

ontro

l (N

o fe

rtiliz

er a

pplic

atio

n)0

957

503

981

2715

88

358

147

711

373

127

239

95

13S

Em

+0

060

410

150

051

550

400

060

500

150

050

970

13C

D (

P=0

05)

020

127

045

016

477

123

018

153

047

016

300

041

Trea

tmen

t giv

en to

rabi

cro

p (h

yaci

nth

bean

)S

1- 10

0 R

DF

191

987

520

161

424

99

25S

2- 75

R

DF

180

933

501

167

373

78

25S

3-10

0 R

DF

+ B

rady

rhiz

obiu

m

200

990

533

160

445

79

9550

0 g

ha-1 (

See

d tre

atm

ent)

S4-

75

RD

F +

Bra

dyrh

izob

ium

1

889

605

071

6639

98

850

500

g ha

-1 (

See

d tre

atm

ent)

SE

m+

003

024

017

005

086

019

CD

(P

=00

5)0

10N

SN

SN

S2

460

54In

tera

ctio

nB

ean

trea

tmen

t mea

ns a

t sam

e le

vel o

f bab

y co

rn IN

M tr

eatm

ents

SE

m+

009

064

044

014

228

050

CD

(P

=00

5)N

SN

SN

SN

SN

SN

SIN

M tr

eatm

ent m

eans

of b

aby

corn

at s

ame

leve

l of b

ean

trea

tmen

tsS

Em

+0

100

740

410

132

200

45C

D (

P=0

05)

NS

NS

NS

NS

NS

NS


40





Cob Yield (kg ha-1)










PREETHAM et al

41

Tabl

e-2

Effe

ct o

f int

egra

ted

nutr

ient

man

agem

ent p

ract

ices

on

cob

yiel

d (k

g ha

-1) w

ith a

nd w

ithou

t hus

k an

d st

over

yie

ld o

f bab

y co

rn d

urin

gkh

arif

201

5 an

d 20

16

Trea

tmen

t giv

en to

kha

rif b

aby

corn

2015

2016

Cob

yie

ld (k

g ha

-1)

Stov

erC

ob y

ield

(kg

ha-1)

Stov

er y

ield

yiel

d(kg

ha-1

)(k

g ha

-1)

With

hus

kW

ithou

t hus

kW

ith h

usk

W

ithou

t hus

k

T 1- 25

N through FY

M + 75

RDF

8204

1602

1934

481

4714

9518

919

T 2- 25

N

thro

ugh

FYM

+ 7

5 R

DF

+ A

zosp

irillu

m90

3218

0320

865

8882

1596

1970

8an

d B

acill

us m

egat

eriu

m

5 k

g ha

-1 e

ach

T 3- 25

N

thro

ugh

Verm

icom

post

+ 7

5 R

DF

9400

1801

2147

589

9416

0119

762

T 4- T4

25

N th

roug

h Ve

rmic

ompo

st +

75

RD

F +

1229

020

4924

020

1172

718

9021

165

Azo

spiri

llum

and

Bac

illus

meg

ater

ium

5

kg

ha-1 e

ach

T 5- 10

0 R

DF

8049

1542

1906

081

9914

7119

364

T 6- 10

0 R

DF

+ A

zosp

irillu

m a

nd B

acill

us93

4418

0720

398

8824

1553

1963

1m

egat

eriu

m

5 k

g ha

-1 e

ach

T 7- C

ontro

l (N

o fe

rtiliz

er a

pplic

atio

n)42

7982

313

821

4393

749

1216

8S

Em

+26

764

429

200

1638

5C

D (

P=0

05)

824

198

1321

617

4911

87Tr

eatm

ent g

iven

to ra

bi c

rop

(hya

cint

h be

an)

S1-

100

RD

F88

2415

4619

086

S2-

75

RD

F78

8013

6917

833

S3-10

0 R

DF

+ B

rady

rhiz

obiu

m

500

g h

a-190

7315

9019

410

(See

d tre

atm

ent)

S4-

75

RD

F +

Bra

dyrh

izob

ium

5

00 g

ha-1

8033

1411

1836

5(S

eed

treat

men

t)S

Em

+20

216

280

CD

(P

=00

5)57

745

798

Inte

ract

ion

Bea

n tre

atm

ent m

eans

at s

ame

leve

l of b

aby

corn

INM

trea

tmen

tsS

Em

+53

541

740

CD

(P

=00

5)N

SN

SN

SIN

M tr

eatm

ent m

eans

of b

aby

corn

at s

ame

leve

l of b

ean

treat

men

tsS

Em

+50

439

748

CD

(P

=00

5)N

SN

SN

S


42






CONCLUSION


REFERENCES







PREETHAM et al

43















44



ABSTRACT














45























2 FACTOR ANALYSIS



2 2 212 R 3K N(N 1)i iW 2 2K N(N 1)

46











Sig 0000



Component


Total ofVariance

Cumulative

Total ofVariance

Cumulative


Total ofVariance

Cumulative

1 1965 21839 21839 1965 21839 21839 1894 21044 21044

2 1806 20070 41909 1806 20070 41909 1520 16887 37932

3 1086 12064 53973 1086 12064 53973 1444 16042 53973

4 927 10301 64274

5 887 9860 74134

6 738 8199 82333

7 630 6996 89329

8 529 5878 95207

9 431 4793 100000


MEENA et al

47


Component Number

Scree Plot

Eig

en v

alue



Component

1 2 3


Component1 2 3












48


Table 4 Ranks









Chi-Square 0822

df 2

Asymp Sig 0663















MEENA et al

49







ABSTRACT









50

SRINIVAS et al




()




51



CONCLUSION


Acknowledgments


REFERENCES







52







ABSTRACT








53











54




Analysis of protein

















55








0

10

20

30

40

50

6070

80

90

Finge

r Mille

t

Foxta

il Mille

t

Little M

illet

Pearl M

illet

Proso

Mille

t

Sorgh

um

Perc

enta

ge d

iges

tibili

ty

Dosa UpmaIdli



56






Foxtail millet

Little millet

Pearl millet

Prosomillet

Sorghum

IdliDosaUpma

10080

6040

20 0







57



CONCLUSION


REFERENCES
















58




















59






ABSTRACT










60
















Pomegranate 21 23 17 16 18 19 13 12 17 18 12 14

Cassia 31 33 24 24 26 28 20 21 22 23 21 22

Periwinkle 29 31 23 25 25 24 19 20 21 21 22 21

Marigold 21 23 18 19 16 18 12 13 16 17 14 16

61









62



0

5

10

15

20

25

30

S aureus Ecoli

Pomegran

teCas

siaPeri

winkleMari

gold

Pomegran

te

Cassia

Periwinkle

Marigold

SilverTitaniumzinc


0

5

10

30

Pomegran

teCas

siaPeri

winkleMari

gold

Pomegran

teCas

siaPeri

winkleMari

gold

35

15

20

S aureus Ecoli

SilverTitaniumzinc

25

63

CONCLUSION








64













65

Tabl

e 1

Est

imat

es o

f phe

noty

pic

and

geno

typi

c co

rrel

atio

n co

effic

ient

s fo

r yie

ld a

nd it

s at

trib

utin

g ch

arac

ters

Cha

ract

erDa

ys to

Plan

tPa

nicl

eN

o o

fN

o o

fSp

ikel

et10

00-s

eed

Prod

uctiv

ityBi

omas

sHa

rves

tG

rain

50

heig

htle

ngth

Prod

uctiv

efil

led

fert

ility

wei

ght

per d

aype

r pl

ant

Inde

xyi

eld

flow

erin

gtil

lers

grai

nspe

r pl

ant

per

plan

tpe

rpa

nicl

e

Day

s to

50

P1

0000

012

230

3431

0

1594

0

5331

0

1150

-00

195

000

49-0

053

60

2191

0

2472

flow

erin

gG

100

000

1181

037

30

016

91

054

21

011

49-0

022

90

0073

-00

657

022

80

025

70

Plan

t hei

ght

P

100

000

5653

-0

441

4

037

95

012

720

5292

014

700

4604

-0

253

6

016

30

G

1

0000

061

45

-05

199

0

3892

0

1301

054

12

014

540

4798

-02

621

0

1605

Pani

cle

leng

thP

100

00-0

025

80

3811

0

2527

0

1930

0

0041

-00

205

006

370

0774

G

100

000

0038

040

71

027

06

020

60

002

420

0073

006

540

1048

No

of P

rodu

ctiv

eP

1

0000

-00

043

020

21

-0

3806

023

08

-03

316

0

4903

0

2561

tille

rs p

er p

lant

G

100

00-0

002

90

2298

-0

434

00

2296

-0

453

0

057

49

025

85

No

of f

illed

gra

ins

P

1

0000

050

67

008

680

5112

0

3200

0

2694

0

6261

per p

anic

leG

100

000

5088

0

0874

053

48

034

06

027

36

065

35

Spik

elet

fert

ility

()

P

100

000

0613

049

97

004

900

4247

0

5043

G

1

0000

006

150

5270

0

0519

043

59

053

01

1000

see

d w

eigh

tP

100

000

1600

0

2852

-0

068

90

1470

G

100

000

1656

0

2999

-00

697

015

13

Prod

uctiv

ity p

er d

ayP

1

0000

029

02

057

85

096

81

G

1

0000

023

91

061

47

096

62

Bio

mas

s pe

r pla

ntP

100

00-0

561

9

025

85

G

100

00-0

571

1

020

47

Har

vest

inde

x (

)P

1

0000

061

74

G

1

0000

065

49

P- P

heno

typi

c co

rrel

atio

n co

effic

ient

G-

Gen

otyp

ic c

orre

latio

n co

effic

ient

Si

gnifi

cant

at

1

Si

gnifi

cant

at

5


66

SRIJAN et al






Acknowledgement


67

Tabl

e 2

Dire

ct a

nd in

dire

ct e

ffect

s of

var

ious

com

pone

nt c

hara

cter

s on

yie

ld in

rice

Cha

ract

erDa

ys to

Plan

tPa

nicl

eN

o o

fN

o o

fSp

ikel

et10

00-s

eed

Prod

uctiv

ityBi

omas

sHa

rves

tG

rain

50

heig

htle

ngth

Prod

uctiv

efil

led

fert

ility

wei

ght

per d

aype

r pl

ant

Inde

xyi

eld

flow

erin

gtil

lers

grai

nspe

r pl

ant

per

plan

tpe

rpa

nicl

e

Day

s to

50

P0

2451

-00

015

-00

035

-00

018

001

32-0

001

2-0

000

10

0049

000

30-0

011

00

2472

flow

erin

gG

025

22-0

001

8-0

004

0-0

002

30

0157

-00

011

-00

002

000

730

0041

-00

128

025

70

Plan

t hei

ght

P0

0300

-00

123

-00

058

000

480

0094

-00

013

000

310

1482

-00

260

001

280

1630

G

002

98-0

015

3-0

006

60

0072

001

12-0

001

20

0041

014

65-0

029

80

0147

016

05

Pani

cle

leng

thP

008

41-0

006

9-0

010

20

0003

000

94-0

002

50

0011

000

420

0012

-00

032

007

74

G

009

41-0

009

4-0

010

8-0

000

10

0118

-00

025

000

150

0243

-00

005

-00

037

010

48

No

of P

rodu

ctiv

eP

003

910

0054

000

03-0

011

0-0

000

1-0

002

0-0

002

20

2327

001

87-0

024

70

2561

tille

rs p

er p

lant

G0

0426

000

800

0000

-00

138

-00

001

-00

022

-00

033

023

140

0282

-00

323

025

85

No

of f

illed

gra

ins

P0

1307

-00

047

-00

039

000

000

0247

-00

051

000

050

5154

-00

181

-00

136

062

61

per p

anic

leG

013

67-0

006

0-0

004

40

0000

002

89-0

004

80

0007

053

89-0

021

2-0

015

40

6535

Spik

elet

fert

ility

()

P0

0282

-00

016

-00

026

-00

022

001

25-0

010

00

0004

050

38-0

002

8-0

021

40

5043

G

002

90-0

002

0-0

002

9-0

003

20

0147

-00

094

000

050

5311

-00

032

-00

245

053

01

1000

see

d w

eigh

tP

-00

048

-00

065

-00

020

000

420

0021

-00

006

000

580

1613

-00

161

000

350

1470

G

-00

058

-00

083

-00

022

000

600

0025

-00

006

000

750

1669

-00

186

000

390

1513

Prod

uctiv

ity p

er d

ayP

000

12-0

001

80

0000

-00

025

001

26-0

005

00

0009

100

82-0

016

4-0

029

10

9681

G

000

18-0

002

2-0

000

3-0

003

20

0154

-00

049

000

121

0077

-00

149

-00

345

096

62

Bio

mas

s pe

r pla

ntP

-00

131

-00

056

000

020

0036

000

79-0

000

50

0017

029

26-0

056

40

0283

025

85

G

-00

166

-00

074

-00

001

000

620

0098

-00

005

000

220

2410

-00

621

003

210

2047

Har

vest

inde

x (

)P

005

370

0031

-00

006

-00

054

000

66-0

004

3-0

000

40

5832

003

17-0

050

30

6174

G

005

750

0040

-00

007

-00

079

000

79-0

004

1-0

000

50

6194

003

55-0

056

10

6549

Phen

otyp

ic r

esid

ual e

ffec

t =

005

78

Gen

otyp

ic r

esid

ual

effe

ct =

00

560

P-

Phe

noty

pic

leve

l G

- G

enot

ypic

leve

l

Bol

d va

lues

- D

irec

t ef

fect

s N

orm

al V

alue

s -

Indi

rect

effe

cts

Sign

ifica

nt a

t 1

S

igni

fican

t at

5


68

REFERENCES









SRIJAN et al

69












70













DFF 086 -057 063 019 -029 033

PHT 072 -061 072 022 002 050

NPT -035 -045 -047 -070 -042 -041

PL 054 068 -030 -005 000 023

PG 013 018 -045 -005 065 059

TGW -024 -003 -016 000 043 028

GY 019 038 -022 002 039 018






DFF -127 128 -017 002 023 024 033

PHT -109 149 -019 003 028 -002 050

NPT 072 -091 031 001 -088 034 -041

PL -080 107 -014 016 -006 000 023

PG -024 033 -022 -001 125 -053 059

TGW 037 003 -013 001 081 -081 028



71





Residual GYPx7=029

Px2=149 Px3=031 Px4=0 Px5=125


rx12=004 rx23=-061 rx34=-047 rx45=-005 rx54= 065

rx13=-057 rx24=072 rx35=-07 rx44=0

rx14=083 rx25=022 rx30=-042

rx15=019 rx26=002

rx16=-029

Px6=-001Px1=127


72






REFERENCES









73






























Books



Thesis


i









REVIEW PROCESS




ii



____________________________________________________________________________________







1

2

3








Name

Signature

Office seal


iii






Nationality Indian



Nationality Indian



Nationality Indian





iv














v




30AJPJTSAU2018

Page 1

2pdf

Page 2

3pdf

Page 3

2





NAGESH KUMAR et al


12

9

362625 2 155 117 075 071 0585 055 05

141210

86420

china

USA

Fran

ce

Brazil

India

Japa

nGer

many

Arge

ntin

a

Italy

Nethe

rland

s

Can

ada

Russia

3




Corn 11Rice 6

Others 40Cotton 28




Total Market 32 MT

Cotton 07Others 86

Wheat 312

Paddy 259

Soybean 106

Groundnut 87

Gram 58

Potato 55

Maize 55


4








NAGESH KUMAR et al


Blackgram


20

40

60

80

100

See

d R

epla

cem

ent

Rat

e (

)

2006-07 2011-12 2016-17

3

25

2

15

1

05

0

013

05

17

27

1990 2000 2010 2013

5






6














NAGESH KUMAR et al

7
















0

2

4

6

8

10

12

14

16

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

USD Bn


8




Rice 456 50 22800 100 228 2280

Wheat 272 125 34000 20 1700 85000

Sorghum 77 15 1155 160 07 45

Pearl millet 87 5 435 200 02 11

Maize 80 20 1600 80 20 250

Pigeon pea 34 15 510 100 05 51

Chickpea 82 80 6560 10 656 65600

Groundnut 62 100 6200 8 97 12109

RampM 63 5 315 200 02 08

Soybean 89 75 6675 16 417 26074

Sunflower 19 10 190 50 04 76

Cotton 95 12 1140 50 23 456

Jute 08 5 40 100 004 04

Total 1424 81620 3161 191964








NAGESH KUMAR et al

9












10













NAGESH KUMAR et al

11


Seed Biotechnology





Cold Plasma Coating





Bioprospecting







12






REFERENCES














NAGESH KUMAR et al

13




ABSTRACT







14








SRIDHAR et al

15








Quality parameters



CONCLUSIONS



16

Tabl

e 1

Gra

in a

nd S

traw

yie

ld (k

g ha

-1) o

f ric

e as

influ

ence

d by

alte

rnat

e w

ettin

g an

d dr

ying

irrig

atio

n an

d ni

trog

en le

vels

dur

ing

rabi

201

620

17 a

nd p

oole

d m

eans

Trea

tmen

tsG

rain

yie

ld (k

g ha

-1)

Stra

w y

ield

(kg

ha-1)

2016

2017

Pool

ed20

1620

17Po

oled

Irrig

atio

n re

gim

es (I

)

I 1- 2-

5cm

sub

mer

genc

e as

per

cro

p st

age

7026

7221

7123

7936

8153

8045

I 2- AW

D ir

rigat

ion

of 5

cm

with

3 c

m fa

ll in

fiel

d69

2571

5070

3778

4480

5679

56w

ater

tube

I 3- AW

D ir

rigat

ion

of 5

cm

with

5 c

m fa

ll in

fiel

d64

1466

5265

3373

3375

4574

39w

ater

tube

SEm

plusmn54

060

455

252

852

152

4

CD

at 5

15

016

815

314

714

514

6

Nitr

ogen

lev

els

(N)

N1-1

20 k

g ha

-167

0669

2268

1476

0578

1777

11

N2-1

60 k

g ha

-168

0270

2169

1177

1079

2178

16

N3-2

00 k

g ha

-168

5770

8169

6977

9880

1679

07

SE

mplusmn

372

445

390

410

411

410

CD

at 5

81

9785

8989

89

Inte

ract

ions

NS

NS

NS

NS

NS

NS

SRIDHAR et al

17

Tabl

e 2

Hul

ling

and

mill

ing

perc

enta

ge (

) of r

ice

as in

fluen

ced

by a

ltern

ate

wet

ting

and

dryi

ng ir

rigat

ion

and

nitr

ogen

leve

ls d

urin

g ra

bi20

16 2

017

and

pool

ed m

eans

Hul

ling

()

Mill

ing

()

2016

2017

Pool

ed20

1620

17Po

oled

Irrig

atio

n re

gim

es (I

)

I 1- 2-

5cm

sub

mer

genc

e as

per

cro

p st

age

808

810

809

675

674

674

I 2- AW

D ir

rigat

ion

of 5

cm

with

3 c

m fa

ll in

fiel

d80

780

980

867

367

267

3w

ater

tube

I 3- AW

D ir

rigat

ion

of 5

cm

with

5 c

m fa

ll in

fiel

d80

580

780

667

066

866

9w

ater

tube

SEm

plusmn0

10

20

20

20

20

2

CD

at 5

N

SN

SN

S0

40

50

5

Nitr

ogen

lev

els

(N)

N1-1

20 k

g ha

-180

780

880

867

267

467

3

N2-1

60 k

g ha

-180

680

880

767

367

367

3

N3-2

00 k

g ha

-180

780

980

867

267

067

1

SE

mplusmn

01

01

01

06

02

04

CD

at 5

N

SN

SN

SN

SN

SN

S

Inte

ract

ions

( IxN

)N

SN

SN

SN

SN

SN

S


18

Tabl

e 3

Hea

d ric

e re

cove

ry (

) G

rain

pro

tein

con

tent

and

am

ylos

e (

) of r

ice

as in

fluen

ced

by a

ltern

ate

wet

ting

and

dryi

ng ir

rigat

ion

and

nitr

ogen

leve

ls d

urin

g ra

bi 2

016

201

7 an

d po

oled

mea

ns

2016

2017

Pool

ed20

1620

17Po

oled

2016

2017

Pool

ed

Hea

d ric

e re

cove

ry (

)G

rain

pro

tein

con

tent

()

Amyl

ose(

)

Irrig

atio

n re

gim

es (I

)

I 1- 2-

5cm

sub

mer

genc

e as

per

cro

p st

age

650

665

12

650

99

189

149

1623

925

024

5

I 2- AW

D ir

rigat

ion

of 5

cm

with

3 c

m fa

ll in

fiel

d63

95

638

763

91

907

904

906

238

247

242

wat

er tu

be

I 3- AW

D ir

rigat

ion

of 5

cm

with

5 c

m fa

ll in

fiel

d63

32

633

463

33

903

898

901

234

243

239

wat

er tu

be

SEm

plusmn0

200

230

210

040

050

040

440

450

44

CD

at 5

0

570

640

60N

SN

SN

SN

SN

SN

S

Nitr

ogen

lev

els

(N)

N1-1

20 k

g ha

-163

93

639

863

95

894

890

892

230

239

235

N2-1

60 k

g ha

-164

16

640

464

10

916

911

913

240

246

243

N3-2

00 k

g ha

-164

23

643

164

27

920

916

918

241

246

244

SE

mplusmn

014

016

015

004

004

004

023

019

021

CD

at 5

N

SN

SN

S0

100

090

090

750

50

63

Inte

ract

ions

( IxN

)N

SN

SN

SN

SN

SN

SN

SN

SN

S

SRIDHAR et al

19


REFERENCES















20






ABSTRACT






21








S No Genotype ID

1 ICGV 15016

2 ICGV 15064

3 ICGV 16013

4 ICGV 16017

5 ICGV 16030

6 ICGV 16035

7 ICGV 16045

8 ICGV 16668


22

S No Genotype ID

9 ICGV 16687

10 ICGV 16700

11 ICGV 16701

12 ICGV 171011

13 ICGV 16686

14 ICGV 171007

15 ICGV 171023

16 ICGV 171027

17 ICGV 171006

18 ICGV 16667

19 ICGV 16698

20 ICGV 171017

21 ICGV 171015

22 ICGV 16020

23 ICGV 171051

24 ICGV 171040

25 ICGV 171039

26 ICGV 171046

27 ICGV 171041

S No Genotype ID

28 ICGV 171048

29 ICGV 171026

30 ICGV 171004

31 ICGV 171021

32 ICGV 171019

33 ICGV 171024

34 ICGV 171016

35 ICGV 171009

36 ICGV 171018

37 ICGV 171025

38 ICGV 171020

39 ICGV 171002

40 ICGV 03043

41 ICGV 07222

42 ICGV 07220

43 ICGV 00350

44 ICGV 00351

45 ICGV 02266

46 ICGV 91114





DNYANESHWAR et al

23




1 ICGV 171051 336 20 145 20

2 ICGV 171048 435 20 173 17

3 ICGV 171046 923 17 284 15

4 ICGV 171041 499 20 137 19

5 ICGV 171040 365 18 240 19

6 ICGV 171039 262 17 350 21

7 ICGV 171027 252 20 97 20

8 ICGV 171026 273 20 62 23

9 ICGV 171025 297 19 295 21

10 ICGV 171024 321 20 216 21

11 ICGV 171023 367 20 71 21

12 ICGV 171021 251 19 135 19

13 ICGV 171020 302 21 222 19

14 ICGV 171019 699 16 140 20

15 ICGV 171018 504 20 173 19

16 ICGV 171017 188 19 150 19

17 ICGV 171016 158 21 156 19

18 ICGV 171015 250 20 183 21

19 ICGV 171011 413 19 281 18

20 ICGV 171009 231 19 205 20

21 ICGV 171007 269 19 106 21

22 ICGV 171006 203 18 141 21

23 ICGV 171004 176 22 178 20

24 ICGV 171002 293 20 217 18

25 ICGV 16701 337 19 104 21

26 ICGV 16700 256 21 59 22

27 ICGV 16698 243 21 182 19

28 ICGV 16687 417 19 76 24

29 ICGV 16686 245 17 166 16

30 ICGV 16668 438 19 191 18

31 ICGV 16667 215 21 132 17

32 ICGV 16045 348 21 113 18

33 ICGV 16035 186 20 48 22

34 ICGV 16030 251 18 62 21

35 ICGV 16020 06 14 248 21


24



36 ICGV 16017 284 19 125 17

37 ICGV 16013 157 20 138 14

38 ICGV 15064 347 19 267 17

39 ICGV 15016 395 19 133 21

40 ICGV 07222 286 20 276 18

41 ICGV 07220 381 19 110 18

42 ICGV 03043 360 20 107 17

43 ICGV 02266 448 20 230 18

44 ICGV 00351 275 20 270 20

45 ICGV 00350 309 21 176 17

46 ICGV 91114 174 22 279 19




DNYANESHWAR et al

25

Tabl

e 3

Com

para

tive

SNP

prof

ile o

btai

ned

from

gen

omic

DN

A of

gro

undn

ut le

af d

isc

and

seed

chi

p

DN

A A

ssay

SNP

for

SNPs

for l

ate

leaf

spo

tSN

Ps fo

r rus

thi

gh o

leic

acid

snpA

H00

02sn

pAH

0004

snpA

H00

05sn

pAH

0010

snpA

H00

11sn

pAH

0015

snpA

H00

17sn

pAH

0018

snpA

H00

21sn

pAH

0026

ICG

V 15

064_

Leaf

AA

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

1506

4_Se

edA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

687_

Lea

fA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

687_

See

dA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

698_

Lea

fA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

698_

See

dA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

700_

Lea

fA

AG

GC

CA

ATT

AA

CC

AA

GT

CC

ICG

V 16

700_

See

dA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 00

351_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0035

1_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 02

266_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0226

6_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 03

043_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0304

3_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 07

222_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0722

2_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

701_

Lea

fA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 16

701_

See

dA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1002

_ Le

afA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1002

_ S

eed

AA

GG

CC

AA

TTA

AC

CA

TG

GC

CIC

GV

1710

04_

Leaf

AA

GG

CC

AA

TTA

AC

CA

AG

GC

CIC

GV

1710

04_

See

dA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1006

_ Le

afA

AG

GC

GA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1006

_ S

eed

AA

GG

CC

AA

TTA

AC

CA

AG

GC

C--

Nul

l alle

le


26

REFERENCES
















DNYANESHWAR et al

27






28







ABSTRACT









29











Yield attributes



30










BRIJBHOOSHAN et al

31

Tabl

e 1

Yie

ld a

ttrib

utes

of r

ice

as in

fluen

ced

by c

rop

esta

blis

hmen

t met

hods

and

nut

rient

man

agem

ent p

ract

ices

(rab

i 20

15 a

nd 2

016)

Trea

tmen

tsN

umbe

r of p

anic

le m

-2Pa

nicl

e le

ngth

(cm

)Pa

nicl

e w

eigh

t (g)

Test

wei

ght (

g)

2015

2016

Pool

ed20

1520

16 P

oole

d20

1520

16Po

oled

2015

2016

Pool

edC

rop

esta

blis

hmen

t met

hods

(M)

M1-

Dire

ct S

eede

d R

ice

(DS

R)

293

527

53

284

423

18

227

922

99

314

308

311

222

322

15

221

9

M2-

Nor

mal

Tra

nspl

ante

d R

ice

(NTP

)25

94

247

825

36

244

924

08

242

93

323

283

3022

51

224

222

46

SEm

+4

104

014

230

270

270

270

040

040

030

260

230

24

CD

(p=0

05)

184

318

03

190

61

211

241

230

170

180

15N

SN

SN

S

Nut

rient

Man

agem

ent p

ract

ices

(N)

N1-

Abs

olut

e co

ntro

l (N

o fe

rtiliz

er)

148

413

59

142

122

03

216

021

82

274

271

273

203

520

25

203

0

N2-

Con

trol (

RD

F)27

95

262

027

09

231

522

77

229

63

153

113

1322

21

221

522

18

N3-R

DF+

SA

of

ZnS

O47

H2O

2

5 kg

ha-1

294

827

95

287

124

01

236

123

81

329

324

326

225

022

39

224

4

N4-R

DF+

2 F

S o

f ZnS

O47

H2O

0

5

301

528

60

293

724

16

237

423

95

335

328

331

228

922

78

228

3

N5-R

DF+

2 F

S o

f nan

o Zn

O

100

0 pp

m31

96

305

531

26

248

824

48

246

83

443

393

4123

21

231

323

17

N6-R

DF+

2 F

S o

f na

no Z

nO

150

0 pp

m31

50

300

530

78

248

224

42

246

23

423

363

3923

08

229

923

03

SEm

+4

024

034

300

290

280

290

040

040

040

190

200

19

CD

(p=0

05)

116

111

64

124

20

840

810

820

130

120

120

560

590

56

Inte

ract

ions

(MxN

)

Nut

rient

man

agem

ent a

t sam

e le

vel o

f cro

p es

tabl

ishm

ent

SEm

+5

695

706

080

410

390

400

060

060

060

270

290

27

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

Cro

p es

tabl

ishm

ent a

t sam

e or

diff

eren

t lev

el o

f nut

rient

man

agem

ent

SEm

+6

616

576

980

460

450

460

070

070

060

360

350

35

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

Gen

eral

Mea

n27

65

261

626

90

238

423

44

236

43

233

183

2022

37

222

822

33


32

Trea

tmen

tsFi

lled

grai

ns p

anic

le-1

Unf

illed

gra

ins

pani

cle-1

Ster

ility

per

cent

age

()

2015

2016

Pool

ed20

1520

16Po

oled

2015

2016

Pool

ed

Cro

p es

tabl

ishm

ent m

etho

ds (M

)M

1- D

irect

See

ded

Ric

e (D

SR

)12

466

116

0412

034

968

122

810

98

737

976

856

M2-

Nor

mal

Tra

nspl

ante

d R

ice

(NTP

)13

436

125

7613

007

881

112

510

03

630

838

734

SEm

+1

881

701

460

160

170

150

120

160

15

CD

(p=0

05)

847

765

658

073

077

069

056

073

065

Nut

rient

Man

agem

ent p

ract

ices

(N)

N1-

Abs

olut

e co

ntro

l (N

o fe

rtiliz

er)

109

3310

079

105

0518

37

208

819

63

144

217

23

158

2

N2-

Con

trol (

RD

F)12

531

116

6612

098

103

212

85

115

97

639

958

79

N3-R

DF+

SA

of

ZnS

O47

H2O

2

5 kg

ha-1

131

5312

289

127

208

2910

80

954

594

811

702

N4-R

DF+

2 F

S o

f ZnS

O47

H2O

0

5

132

7312

409

128

407

8710

38

913

562

775

668

N5-R

DF+

2 F

S o

f nan

o Zn

O

100

0 pp

m13

975

131

1313

546

546

798

672

377

576

477

N6-R

DF+

2 F

S o

f na

no Z

nO

150

0 pp

m13

844

129

8513

414

517

768

642

362

561

462

SEm

+1

451

431

280

200

180

200

150

170

17

CD

(p=0

05)

419

413

371

059

053

057

043

048

050

Inte

ract

ions

(MxN

)N

utrie

nt m

anag

emen

t at s

ame

leve

l of c

rop

esta

blis

hmen

tS

Em+

205

202

182

029

026

028

021

023

025

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Cro

p es

tabl

ishm

ent a

t sam

e or

diff

eren

t lev

el o

f nut

rient

man

agem

ent

SEm

+2

662

512

210

310

290

300

230

270

27

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Gen

eral

Mea

n12

951

120

9012

521

925

117

610

51

683

907

795

RD

F- R

ecom

men

ded

Dos

e of

Fer

tiliz

ers

(N

P2O

5amp K

2O

120

60

40 k

g ha

-1)

SA

- S

oil A

pplic

atio

n F

S-

Folia

r Sp

ray

NS

- N

on S

igni

fican

t

BRIJBHOOSHAN et al

33








CONCLUSION



34

Tabl

e 2

Yie

ld a

nd h

arve

st in

dex

of ri

ce a

s in

fluen

ced

by c

rop

esta

blis

hmen

t met

hods

and

nut

rient

man

agem

ent p

ract

ices

(rab

i 20

15 a

nd 2

016)

Trea

tmen

tsG

rain

yie

ld (k

g ha

-1)

Stra

w y

ield

(kg

ha-1)

Har

vest

Inde

x(

)

2015

2016

Pool

ed20

1520

16Po

oled

2015

2016

Pool

ed

Cro

p es

tabl

ishm

ent m

etho

ds (M

)M

1- D

irect

See

ded

Ric

e (D

SR

)51

9848

8650

4766

7564

4765

6143

58

428

243

20

M2-

Nor

mal

Tra

nspl

ante

d R

ice

(NTP

)47

3344

8946

1160

0058

1359

0743

81

434

343

62

SEm

+98

8389

121

109

101

052

061

049

CD

(p=0

05)

441

373

401

545

492

455

NS

NS

NS

Nut

rient

Man

agem

ent p

ract

ices

(N)

N1-

Abs

olut

e co

ntro

l (N

o fe

rtiliz

er)

2335

2274

2305

3270

3320

3295

416

740

84

412

5

N2-

Con

trol (

RD

F)47

3944

6946

0461

5459

8160

6843

47

427

743

12

N3-R

DF+

SA

of

ZnS

O47

H2O

2

5 kg

ha-1

5187

4993

5090

6593

6462

6527

440

243

63

438

3

N4-R

DF+

2 F

S o

f ZnS

O47

H2O

0

5

5483

5157

5320

6902

6656

6779

442

543

66

439

6

N5-R

DF+

2 F

S o

f nan

o Zn

O

100

0 pp

m60

7056

5958

7875

7072

2473

9744

54

439

644

25

N6-R

DF+

2 F

S o

f na

no Z

nO

150

0 pp

m59

7855

7457

7675

3771

3773

3744

21

438

944

05

SEm

+13

410

911

114

711

412

10

400

550

51

CD

(p=0

05)

386

315

320

424

328

349

115

158

147

Inte

ract

ions

(MxN

)N

utrie

nt m

anag

emen

t at s

ame

leve

l of c

rop

esta

blis

hmen

tS

Em+

189

154

157

208

161

171

056

077

072

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Cro

p es

tabl

ishm

ent a

t sam

e or

diff

eren

t lev

el o

f nut

rient

man

agem

ent

SEm

+19

816

316

822

518

318

60

730

940

82

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Gen

eral

Mea

n49

6546

8848

2963

3761

3062

3443

70

431

343

41

Coe

ffici

ent o

f Var

iatio

n (

)9

78

79

09

48

77

95

97

05

5

RD

F- R

ecom

men

ded

Dos

e of

Fer

tiliz

ers

(N

P2O

5amp K

2O

120

60

40 k

g ha

-1)

SA

- S

oil A

pplic

atio

n F

S-

Folia

r Sp

ray

NS

- N

on S

igni

fican

t

BRIJBHOOSHAN et al

35

REFERENCES

















36





ABSTRACT







37












38














PREETHAM et al

39

Tabl

e-1

Effe

ct o

f int

egra

ted

nutr

ient

man

agem

ent p

ract

ices

on

yiel

d at

trib

utes

of b

aby

corn

dur

ing

khar

if 2

015

and

2016

Trea

tmen

t giv

en to

kha

rif b

aby

corn

2015

2016

Cob

spl

ant-1

Cob

leng

th(c

m)

Cob

girth

(cm

)

Cob

wid

th(c

m)

Cob

wei

ght

(g)

Cob

spl

ant-1

Cob

leng

th(c

m)

Cob

girth

(cm

)

Cob

wid

th(c

m

Cob

wei

ght

(g)

With

With

out

husk

husk

With

With

out

husk

husk

T 1- 25

N through FY

M + 75

RDF

178

106

95

251

6846

80

926

183

931

513

163

426

89

03T 2-

25

N th

roug

h FY

M +

75

RD

F +

200

108

35

601

7247

33

100

01

989

645

541

6843

16

908

Azo

spiri

llum

and

Bac

illus

meg

ater

ium

5

kg h

a-1 e

ach

T 3- 25

N

thr

ough

Ver

mic

ompo

st +

202

109

55

581

7649

17

100

02

029

655

461

7143

68

926

75

RD

FT 4-

T4 2

5 N

thro

ugh

Verm

icom

post

+2

2312

50

574

186

555

913

46

222

122

45

651

8348

73

120

675

R

DF

+ A

zosp

irillu

m a

nd B

acill

usm

egat

eriu

m

5 k

g ha

-1 e

ach

T 5- 10

0 R

DF

175

106

65

241

7146

29

920

178

989

512

161

423

69

01T 6-

100

RD

F +

Azo

spiri

llum

and

Bac

illus

200

109

65

551

8049

86

103

81

989

905

441

7443

10

936

meg

ater

ium

5

kg

ha-1 e

ach

T 7- C

ontro

l (N

o fe

rtiliz

er a

pplic

atio

n)0

957

503

981

2715

88

358

147

711

373

127

239

95

13S

Em

+0

060

410

150

051

550

400

060

500

150

050

970

13C

D (

P=0

05)

020

127

045

016

477

123

018

153

047

016

300

041

Trea

tmen

t giv

en to

rabi

cro

p (h

yaci

nth

bean

)S

1- 10

0 R

DF

191

987

520

161

424

99

25S

2- 75

R

DF

180

933

501

167

373

78

25S

3-10

0 R

DF

+ B

rady

rhiz

obiu

m

200

990

533

160

445

79

9550

0 g

ha-1 (

See

d tre

atm

ent)

S4-

75

RD

F +

Bra

dyrh

izob

ium

1

889

605

071

6639

98

850

500

g ha

-1 (

See

d tre

atm

ent)

SE

m+

003

024

017

005

086

019

CD

(P

=00

5)0

10N

SN

SN

S2

460

54In

tera

ctio

nB

ean

trea

tmen

t mea

ns a

t sam

e le

vel o

f bab

y co

rn IN

M tr

eatm

ents

SE

m+

009

064

044

014

228

050

CD

(P

=00

5)N

SN

SN

SN

SN

SN

SIN

M tr

eatm

ent m

eans

of b

aby

corn

at s

ame

leve

l of b

ean

trea

tmen

tsS

Em

+0

100

740

410

132

200

45C

D (

P=0

05)

NS

NS

NS

NS

NS

NS


40





Cob Yield (kg ha-1)










PREETHAM et al

41

Tabl

e-2

Effe

ct o

f int

egra

ted

nutr

ient

man

agem

ent p

ract

ices

on

cob

yiel

d (k

g ha

-1) w

ith a

nd w

ithou

t hus

k an

d st

over

yie

ld o

f bab

y co

rn d

urin

gkh

arif

201

5 an

d 20

16

Trea

tmen

t giv

en to

kha

rif b

aby

corn

2015

2016

Cob

yie

ld (k

g ha

-1)

Stov

erC

ob y

ield

(kg

ha-1)

Stov

er y

ield

yiel

d(kg

ha-1

)(k

g ha

-1)

With

hus

kW

ithou

t hus

kW

ith h

usk

W

ithou

t hus

k

T 1- 25

N through FY

M + 75

RDF

8204

1602

1934

481

4714

9518

919

T 2- 25

N

thro

ugh

FYM

+ 7

5 R

DF

+ A

zosp

irillu

m90

3218

0320

865

8882

1596

1970

8an

d B

acill

us m

egat

eriu

m

5 k

g ha

-1 e

ach

T 3- 25

N

thro

ugh

Verm

icom

post

+ 7

5 R

DF

9400

1801

2147

589

9416

0119

762

T 4- T4

25

N th

roug

h Ve

rmic

ompo

st +

75

RD

F +

1229

020

4924

020

1172

718

9021

165

Azo

spiri

llum

and

Bac

illus

meg

ater

ium

5

kg

ha-1 e

ach

T 5- 10

0 R

DF

8049

1542

1906

081

9914

7119

364

T 6- 10

0 R

DF

+ A

zosp

irillu

m a

nd B

acill

us93

4418

0720

398

8824

1553

1963

1m

egat

eriu

m

5 k

g ha

-1 e

ach

T 7- C

ontro

l (N

o fe

rtiliz

er a

pplic

atio

n)42

7982

313

821

4393

749

1216

8S

Em

+26

764

429

200

1638

5C

D (

P=0

05)

824

198

1321

617

4911

87Tr

eatm

ent g

iven

to ra

bi c

rop

(hya

cint

h be

an)

S1-

100

RD

F88

2415

4619

086

S2-

75

RD

F78

8013

6917

833

S3-10

0 R

DF

+ B

rady

rhiz

obiu

m

500

g h

a-190

7315

9019

410

(See

d tre

atm

ent)

S4-

75

RD

F +

Bra

dyrh

izob

ium

5

00 g

ha-1

8033

1411

1836

5(S

eed

treat

men

t)S

Em

+20

216

280

CD

(P

=00

5)57

745

798

Inte

ract

ion

Bea

n tre

atm

ent m

eans

at s

ame

leve

l of b

aby

corn

INM

trea

tmen

tsS

Em

+53

541

740

CD

(P

=00

5)N

SN

SN

SIN

M tr

eatm

ent m

eans

of b

aby

corn

at s

ame

leve

l of b

ean

treat

men

tsS

Em

+50

439

748

CD

(P

=00

5)N

SN

SN

S


42






CONCLUSION


REFERENCES







PREETHAM et al

43















44



ABSTRACT














45























2 FACTOR ANALYSIS



2 2 212 R 3K N(N 1)i iW 2 2K N(N 1)

46











Sig 0000



Component


Total ofVariance

Cumulative

Total ofVariance

Cumulative


Total ofVariance

Cumulative

1 1965 21839 21839 1965 21839 21839 1894 21044 21044

2 1806 20070 41909 1806 20070 41909 1520 16887 37932

3 1086 12064 53973 1086 12064 53973 1444 16042 53973

4 927 10301 64274

5 887 9860 74134

6 738 8199 82333

7 630 6996 89329

8 529 5878 95207

9 431 4793 100000


MEENA et al

47


Component Number

Scree Plot

Eig

en v

alue



Component

1 2 3


Component1 2 3












48


Table 4 Ranks









Chi-Square 0822

df 2

Asymp Sig 0663















MEENA et al

49







ABSTRACT









50

SRINIVAS et al




()




51



CONCLUSION


Acknowledgments


REFERENCES







52







ABSTRACT








53











54




Analysis of protein

















55








0

10

20

30

40

50

6070

80

90

Finge

r Mille

t

Foxta

il Mille

t

Little M

illet

Pearl M

illet

Proso

Mille

t

Sorgh

um

Perc

enta

ge d

iges

tibili

ty

Dosa UpmaIdli



56






Foxtail millet

Little millet

Pearl millet

Prosomillet

Sorghum

IdliDosaUpma

10080

6040

20 0







57



CONCLUSION


REFERENCES
















58




















59






ABSTRACT










60
















Pomegranate 21 23 17 16 18 19 13 12 17 18 12 14

Cassia 31 33 24 24 26 28 20 21 22 23 21 22

Periwinkle 29 31 23 25 25 24 19 20 21 21 22 21

Marigold 21 23 18 19 16 18 12 13 16 17 14 16

61









62



0

5

10

15

20

25

30

S aureus Ecoli

Pomegran

teCas

siaPeri

winkleMari

gold

Pomegran

te

Cassia

Periwinkle

Marigold

SilverTitaniumzinc


0

5

10

30

Pomegran

teCas

siaPeri

winkleMari

gold

Pomegran

teCas

siaPeri

winkleMari

gold

35

15

20

S aureus Ecoli

SilverTitaniumzinc

25

63

CONCLUSION








64













65

Tabl

e 1

Est

imat

es o

f phe

noty

pic

and

geno

typi

c co

rrel

atio

n co

effic

ient

s fo

r yie

ld a

nd it

s at

trib

utin

g ch

arac

ters

Cha

ract

erDa

ys to

Plan

tPa

nicl

eN

o o

fN

o o

fSp

ikel

et10

00-s

eed

Prod

uctiv

ityBi

omas

sHa

rves

tG

rain

50

heig

htle

ngth

Prod

uctiv

efil

led

fert

ility

wei

ght

per d

aype

r pl

ant

Inde

xyi

eld

flow

erin

gtil

lers

grai

nspe

r pl

ant

per

plan

tpe

rpa

nicl

e

Day

s to

50

P1

0000

012

230

3431

0

1594

0

5331

0

1150

-00

195

000

49-0

053

60

2191

0

2472

flow

erin

gG

100

000

1181

037

30

016

91

054

21

011

49-0

022

90

0073

-00

657

022

80

025

70

Plan

t hei

ght

P

100

000

5653

-0

441

4

037

95

012

720

5292

014

700

4604

-0

253

6

016

30

G

1

0000

061

45

-05

199

0

3892

0

1301

054

12

014

540

4798

-02

621

0

1605

Pani

cle

leng

thP

100

00-0

025

80

3811

0

2527

0

1930

0

0041

-00

205

006

370

0774

G

100

000

0038

040

71

027

06

020

60

002

420

0073

006

540

1048

No

of P

rodu

ctiv

eP

1

0000

-00

043

020

21

-0

3806

023

08

-03

316

0

4903

0

2561

tille

rs p

er p

lant

G

100

00-0

002

90

2298

-0

434

00

2296

-0

453

0

057

49

025

85

No

of f

illed

gra

ins

P

1

0000

050

67

008

680

5112

0

3200

0

2694

0

6261

per p

anic

leG

100

000

5088

0

0874

053

48

034

06

027

36

065

35

Spik

elet

fert

ility

()

P

100

000

0613

049

97

004

900

4247

0

5043

G

1

0000

006

150

5270

0

0519

043

59

053

01

1000

see

d w

eigh

tP

100

000

1600

0

2852

-0

068

90

1470

G

100

000

1656

0

2999

-00

697

015

13

Prod

uctiv

ity p

er d

ayP

1

0000

029

02

057

85

096

81

G

1

0000

023

91

061

47

096

62

Bio

mas

s pe

r pla

ntP

100

00-0

561

9

025

85

G

100

00-0

571

1

020

47

Har

vest

inde

x (

)P

1

0000

061

74

G

1

0000

065

49

P- P

heno

typi

c co

rrel

atio

n co

effic

ient

G-

Gen

otyp

ic c

orre

latio

n co

effic

ient

Si

gnifi

cant

at

1

Si

gnifi

cant

at

5


66

SRIJAN et al






Acknowledgement


67

Tabl

e 2

Dire

ct a

nd in

dire

ct e

ffect

s of

var

ious

com

pone

nt c

hara

cter

s on

yie

ld in

rice

Cha

ract

erDa

ys to

Plan

tPa

nicl

eN

o o

fN

o o

fSp

ikel

et10

00-s

eed

Prod

uctiv

ityBi

omas

sHa

rves

tG

rain

50

heig

htle

ngth

Prod

uctiv

efil

led

fert

ility

wei

ght

per d

aype

r pl

ant

Inde

xyi

eld

flow

erin

gtil

lers

grai

nspe

r pl

ant

per

plan

tpe

rpa

nicl

e

Day

s to

50

P0

2451

-00

015

-00

035

-00

018

001

32-0

001

2-0

000

10

0049

000

30-0

011

00

2472

flow

erin

gG

025

22-0

001

8-0

004

0-0

002

30

0157

-00

011

-00

002

000

730

0041

-00

128

025

70

Plan

t hei

ght

P0

0300

-00

123

-00

058

000

480

0094

-00

013

000

310

1482

-00

260

001

280

1630

G

002

98-0

015

3-0

006

60

0072

001

12-0

001

20

0041

014

65-0

029

80

0147

016

05

Pani

cle

leng

thP

008

41-0

006

9-0

010

20

0003

000

94-0

002

50

0011

000

420

0012

-00

032

007

74

G

009

41-0

009

4-0

010

8-0

000

10

0118

-00

025

000

150

0243

-00

005

-00

037

010

48

No

of P

rodu

ctiv

eP

003

910

0054

000

03-0

011

0-0

000

1-0

002

0-0

002

20

2327

001

87-0

024

70

2561

tille

rs p

er p

lant

G0

0426

000

800

0000

-00

138

-00

001

-00

022

-00

033

023

140

0282

-00

323

025

85

No

of f

illed

gra

ins

P0

1307

-00

047

-00

039

000

000

0247

-00

051

000

050

5154

-00

181

-00

136

062

61

per p

anic

leG

013

67-0

006

0-0

004

40

0000

002

89-0

004

80

0007

053

89-0

021

2-0

015

40

6535

Spik

elet

fert

ility

()

P0

0282

-00

016

-00

026

-00

022

001

25-0

010

00

0004

050

38-0

002

8-0

021

40

5043

G

002

90-0

002

0-0

002

9-0

003

20

0147

-00

094

000

050

5311

-00

032

-00

245

053

01

1000

see

d w

eigh

tP

-00

048

-00

065

-00

020

000

420

0021

-00

006

000

580

1613

-00

161

000

350

1470

G

-00

058

-00

083

-00

022

000

600

0025

-00

006

000

750

1669

-00

186

000

390

1513

Prod

uctiv

ity p

er d

ayP

000

12-0

001

80

0000

-00

025

001

26-0

005

00

0009

100

82-0

016

4-0

029

10

9681

G

000

18-0

002

2-0

000

3-0

003

20

0154

-00

049

000

121

0077

-00

149

-00

345

096

62

Bio

mas

s pe

r pla

ntP

-00

131

-00

056

000

020

0036

000

79-0

000

50

0017

029

26-0

056

40

0283

025

85

G

-00

166

-00

074

-00

001

000

620

0098

-00

005

000

220

2410

-00

621

003

210

2047

Har

vest

inde

x (

)P

005

370

0031

-00

006

-00

054

000

66-0

004

3-0

000

40

5832

003

17-0

050

30

6174

G

005

750

0040

-00

007

-00

079

000

79-0

004

1-0

000

50

6194

003

55-0

056

10

6549

Phen

otyp

ic r

esid

ual e

ffec

t =

005

78

Gen

otyp

ic r

esid

ual

effe

ct =

00

560

P-

Phe

noty

pic

leve

l G

- G

enot

ypic

leve

l

Bol

d va

lues

- D

irec

t ef

fect

s N

orm

al V

alue

s -

Indi

rect

effe

cts

Sign

ifica

nt a

t 1

S

igni

fican

t at

5


68

REFERENCES









SRIJAN et al

69












70













DFF 086 -057 063 019 -029 033

PHT 072 -061 072 022 002 050

NPT -035 -045 -047 -070 -042 -041

PL 054 068 -030 -005 000 023

PG 013 018 -045 -005 065 059

TGW -024 -003 -016 000 043 028

GY 019 038 -022 002 039 018






DFF -127 128 -017 002 023 024 033

PHT -109 149 -019 003 028 -002 050

NPT 072 -091 031 001 -088 034 -041

PL -080 107 -014 016 -006 000 023

PG -024 033 -022 -001 125 -053 059

TGW 037 003 -013 001 081 -081 028



71





Residual GYPx7=029

Px2=149 Px3=031 Px4=0 Px5=125


rx12=004 rx23=-061 rx34=-047 rx45=-005 rx54= 065

rx13=-057 rx24=072 rx35=-07 rx44=0

rx14=083 rx25=022 rx30=-042

rx15=019 rx26=002

rx16=-029

Px6=-001Px1=127


72






REFERENCES









73






























Books



Thesis


i









REVIEW PROCESS




ii



____________________________________________________________________________________







1

2

3








Name

Signature

Office seal


iii






Nationality Indian



Nationality Indian



Nationality Indian





iv














v




30AJPJTSAU2018

Page 1

2pdf

Page 2

3pdf

Page 3

3




Corn 11Rice 6

Others 40Cotton 28




Total Market 32 MT

Cotton 07Others 86

Wheat 312

Paddy 259

Soybean 106

Groundnut 87

Gram 58

Potato 55

Maize 55


4








NAGESH KUMAR et al


Blackgram


20

40

60

80

100

See

d R

epla

cem

ent

Rat

e (

)

2006-07 2011-12 2016-17

3

25

2

15

1

05

0

013

05

17

27

1990 2000 2010 2013

5






6














NAGESH KUMAR et al

7
















0

2

4

6

8

10

12

14

16

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

USD Bn


8




Rice 456 50 22800 100 228 2280

Wheat 272 125 34000 20 1700 85000

Sorghum 77 15 1155 160 07 45

Pearl millet 87 5 435 200 02 11

Maize 80 20 1600 80 20 250

Pigeon pea 34 15 510 100 05 51

Chickpea 82 80 6560 10 656 65600

Groundnut 62 100 6200 8 97 12109

RampM 63 5 315 200 02 08

Soybean 89 75 6675 16 417 26074

Sunflower 19 10 190 50 04 76

Cotton 95 12 1140 50 23 456

Jute 08 5 40 100 004 04

Total 1424 81620 3161 191964








NAGESH KUMAR et al

9












10













NAGESH KUMAR et al

11


Seed Biotechnology





Cold Plasma Coating





Bioprospecting







12






REFERENCES














NAGESH KUMAR et al

13




ABSTRACT







14








SRIDHAR et al

15








Quality parameters



CONCLUSIONS



16

Tabl

e 1

Gra

in a

nd S

traw

yie

ld (k

g ha

-1) o

f ric

e as

influ

ence

d by

alte

rnat

e w

ettin

g an

d dr

ying

irrig

atio

n an

d ni

trog

en le

vels

dur

ing

rabi

201

620

17 a

nd p

oole

d m

eans

Trea

tmen

tsG

rain

yie

ld (k

g ha

-1)

Stra

w y

ield

(kg

ha-1)

2016

2017

Pool

ed20

1620

17Po

oled

Irrig

atio

n re

gim

es (I

)

I 1- 2-

5cm

sub

mer

genc

e as

per

cro

p st

age

7026

7221

7123

7936

8153

8045

I 2- AW

D ir

rigat

ion

of 5

cm

with

3 c

m fa

ll in

fiel

d69

2571

5070

3778

4480

5679

56w

ater

tube

I 3- AW

D ir

rigat

ion

of 5

cm

with

5 c

m fa

ll in

fiel

d64

1466

5265

3373

3375

4574

39w

ater

tube

SEm

plusmn54

060

455

252

852

152

4

CD

at 5

15

016

815

314

714

514

6

Nitr

ogen

lev

els

(N)

N1-1

20 k

g ha

-167

0669

2268

1476

0578

1777

11

N2-1

60 k

g ha

-168

0270

2169

1177

1079

2178

16

N3-2

00 k

g ha

-168

5770

8169

6977

9880

1679

07

SE

mplusmn

372

445

390

410

411

410

CD

at 5

81

9785

8989

89

Inte

ract

ions

NS

NS

NS

NS

NS

NS

SRIDHAR et al

17

Tabl

e 2

Hul

ling

and

mill

ing

perc

enta

ge (

) of r

ice

as in

fluen

ced

by a

ltern

ate

wet

ting

and

dryi

ng ir

rigat

ion

and

nitr

ogen

leve

ls d

urin

g ra

bi20

16 2

017

and

pool

ed m

eans

Hul

ling

()

Mill

ing

()

2016

2017

Pool

ed20

1620

17Po

oled

Irrig

atio

n re

gim

es (I

)

I 1- 2-

5cm

sub

mer

genc

e as

per

cro

p st

age

808

810

809

675

674

674

I 2- AW

D ir

rigat

ion

of 5

cm

with

3 c

m fa

ll in

fiel

d80

780

980

867

367

267

3w

ater

tube

I 3- AW

D ir

rigat

ion

of 5

cm

with

5 c

m fa

ll in

fiel

d80

580

780

667

066

866

9w

ater

tube

SEm

plusmn0

10

20

20

20

20

2

CD

at 5

N

SN

SN

S0

40

50

5

Nitr

ogen

lev

els

(N)

N1-1

20 k

g ha

-180

780

880

867

267

467

3

N2-1

60 k

g ha

-180

680

880

767

367

367

3

N3-2

00 k

g ha

-180

780

980

867

267

067

1

SE

mplusmn

01

01

01

06

02

04

CD

at 5

N

SN

SN

SN

SN

SN

S

Inte

ract

ions

( IxN

)N

SN

SN

SN

SN

SN

S


18

Tabl

e 3

Hea

d ric

e re

cove

ry (

) G

rain

pro

tein

con

tent

and

am

ylos

e (

) of r

ice

as in

fluen

ced

by a

ltern

ate

wet

ting

and

dryi

ng ir

rigat

ion

and

nitr

ogen

leve

ls d

urin

g ra

bi 2

016

201

7 an

d po

oled

mea

ns

2016

2017

Pool

ed20

1620

17Po

oled

2016

2017

Pool

ed

Hea

d ric

e re

cove

ry (

)G

rain

pro

tein

con

tent

()

Amyl

ose(

)

Irrig

atio

n re

gim

es (I

)

I 1- 2-

5cm

sub

mer

genc

e as

per

cro

p st

age

650

665

12

650

99

189

149

1623

925

024

5

I 2- AW

D ir

rigat

ion

of 5

cm

with

3 c

m fa

ll in

fiel

d63

95

638

763

91

907

904

906

238

247

242

wat

er tu

be

I 3- AW

D ir

rigat

ion

of 5

cm

with

5 c

m fa

ll in

fiel

d63

32

633

463

33

903

898

901

234

243

239

wat

er tu

be

SEm

plusmn0

200

230

210

040

050

040

440

450

44

CD

at 5

0

570

640

60N

SN

SN

SN

SN

SN

S

Nitr

ogen

lev

els

(N)

N1-1

20 k

g ha

-163

93

639

863

95

894

890

892

230

239

235

N2-1

60 k

g ha

-164

16

640

464

10

916

911

913

240

246

243

N3-2

00 k

g ha

-164

23

643

164

27

920

916

918

241

246

244

SE

mplusmn

014

016

015

004

004

004

023

019

021

CD

at 5

N

SN

SN

S0

100

090

090

750

50

63

Inte

ract

ions

( IxN

)N

SN

SN

SN

SN

SN

SN

SN

SN

S

SRIDHAR et al

19


REFERENCES















20






ABSTRACT






21








S No Genotype ID

1 ICGV 15016

2 ICGV 15064

3 ICGV 16013

4 ICGV 16017

5 ICGV 16030

6 ICGV 16035

7 ICGV 16045

8 ICGV 16668


22

S No Genotype ID

9 ICGV 16687

10 ICGV 16700

11 ICGV 16701

12 ICGV 171011

13 ICGV 16686

14 ICGV 171007

15 ICGV 171023

16 ICGV 171027

17 ICGV 171006

18 ICGV 16667

19 ICGV 16698

20 ICGV 171017

21 ICGV 171015

22 ICGV 16020

23 ICGV 171051

24 ICGV 171040

25 ICGV 171039

26 ICGV 171046

27 ICGV 171041

S No Genotype ID

28 ICGV 171048

29 ICGV 171026

30 ICGV 171004

31 ICGV 171021

32 ICGV 171019

33 ICGV 171024

34 ICGV 171016

35 ICGV 171009

36 ICGV 171018

37 ICGV 171025

38 ICGV 171020

39 ICGV 171002

40 ICGV 03043

41 ICGV 07222

42 ICGV 07220

43 ICGV 00350

44 ICGV 00351

45 ICGV 02266

46 ICGV 91114





DNYANESHWAR et al

23




1 ICGV 171051 336 20 145 20

2 ICGV 171048 435 20 173 17

3 ICGV 171046 923 17 284 15

4 ICGV 171041 499 20 137 19

5 ICGV 171040 365 18 240 19

6 ICGV 171039 262 17 350 21

7 ICGV 171027 252 20 97 20

8 ICGV 171026 273 20 62 23

9 ICGV 171025 297 19 295 21

10 ICGV 171024 321 20 216 21

11 ICGV 171023 367 20 71 21

12 ICGV 171021 251 19 135 19

13 ICGV 171020 302 21 222 19

14 ICGV 171019 699 16 140 20

15 ICGV 171018 504 20 173 19

16 ICGV 171017 188 19 150 19

17 ICGV 171016 158 21 156 19

18 ICGV 171015 250 20 183 21

19 ICGV 171011 413 19 281 18

20 ICGV 171009 231 19 205 20

21 ICGV 171007 269 19 106 21

22 ICGV 171006 203 18 141 21

23 ICGV 171004 176 22 178 20

24 ICGV 171002 293 20 217 18

25 ICGV 16701 337 19 104 21

26 ICGV 16700 256 21 59 22

27 ICGV 16698 243 21 182 19

28 ICGV 16687 417 19 76 24

29 ICGV 16686 245 17 166 16

30 ICGV 16668 438 19 191 18

31 ICGV 16667 215 21 132 17

32 ICGV 16045 348 21 113 18

33 ICGV 16035 186 20 48 22

34 ICGV 16030 251 18 62 21

35 ICGV 16020 06 14 248 21


24



36 ICGV 16017 284 19 125 17

37 ICGV 16013 157 20 138 14

38 ICGV 15064 347 19 267 17

39 ICGV 15016 395 19 133 21

40 ICGV 07222 286 20 276 18

41 ICGV 07220 381 19 110 18

42 ICGV 03043 360 20 107 17

43 ICGV 02266 448 20 230 18

44 ICGV 00351 275 20 270 20

45 ICGV 00350 309 21 176 17

46 ICGV 91114 174 22 279 19




DNYANESHWAR et al

25

Tabl

e 3

Com

para

tive

SNP

prof

ile o

btai

ned

from

gen

omic

DN

A of

gro

undn

ut le

af d

isc

and

seed

chi

p

DN

A A

ssay

SNP

for

SNPs

for l

ate

leaf

spo

tSN

Ps fo

r rus

thi

gh o

leic

acid

snpA

H00

02sn

pAH

0004

snpA

H00

05sn

pAH

0010

snpA

H00

11sn

pAH

0015

snpA

H00

17sn

pAH

0018

snpA

H00

21sn

pAH

0026

ICG

V 15

064_

Leaf

AA

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

1506

4_Se

edA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

687_

Lea

fA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

687_

See

dA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

698_

Lea

fA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

698_

See

dA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

700_

Lea

fA

AG

GC

CA

ATT

AA

CC

AA

GT

CC

ICG

V 16

700_

See

dA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 00

351_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0035

1_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 02

266_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0226

6_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 03

043_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0304

3_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 07

222_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0722

2_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

701_

Lea

fA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 16

701_

See

dA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1002

_ Le

afA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1002

_ S

eed

AA

GG

CC

AA

TTA

AC

CA

TG

GC

CIC

GV

1710

04_

Leaf

AA

GG

CC

AA

TTA

AC

CA

AG

GC

CIC

GV

1710

04_

See

dA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1006

_ Le

afA

AG

GC

GA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1006

_ S

eed

AA

GG

CC

AA

TTA

AC

CA

AG

GC

C--

Nul

l alle

le


26

REFERENCES
















DNYANESHWAR et al

27






28







ABSTRACT









29











Yield attributes



30










BRIJBHOOSHAN et al

31

Tabl

e 1

Yie

ld a

ttrib

utes

of r

ice

as in

fluen

ced

by c

rop

esta

blis

hmen

t met

hods

and

nut

rient

man

agem

ent p

ract

ices

(rab

i 20

15 a

nd 2

016)

Trea

tmen

tsN

umbe

r of p

anic

le m

-2Pa

nicl

e le

ngth

(cm

)Pa

nicl

e w

eigh

t (g)

Test

wei

ght (

g)

2015

2016

Pool

ed20

1520

16 P

oole

d20

1520

16Po

oled

2015

2016

Pool

edC

rop

esta

blis

hmen

t met

hods

(M)

M1-

Dire

ct S

eede

d R

ice

(DS

R)

293

527

53

284

423

18

227

922

99

314

308

311

222

322

15

221

9

M2-

Nor

mal

Tra

nspl

ante

d R

ice

(NTP

)25

94

247

825

36

244

924

08

242

93

323

283

3022

51

224

222

46

SEm

+4

104

014

230

270

270

270

040

040

030

260

230

24

CD

(p=0

05)

184

318

03

190

61

211

241

230

170

180

15N

SN

SN

S

Nut

rient

Man

agem

ent p

ract

ices

(N)

N1-

Abs

olut

e co

ntro

l (N

o fe

rtiliz

er)

148

413

59

142

122

03

216

021

82

274

271

273

203

520

25

203

0

N2-

Con

trol (

RD

F)27

95

262

027

09

231

522

77

229

63

153

113

1322

21

221

522

18

N3-R

DF+

SA

of

ZnS

O47

H2O

2

5 kg

ha-1

294

827

95

287

124

01

236

123

81

329

324

326

225

022

39

224

4

N4-R

DF+

2 F

S o

f ZnS

O47

H2O

0

5

301

528

60

293

724

16

237

423

95

335

328

331

228

922

78

228

3

N5-R

DF+

2 F

S o

f nan

o Zn

O

100

0 pp

m31

96

305

531

26

248

824

48

246

83

443

393

4123

21

231

323

17

N6-R

DF+

2 F

S o

f na

no Z

nO

150

0 pp

m31

50

300

530

78

248

224

42

246

23

423

363

3923

08

229

923

03

SEm

+4

024

034

300

290

280

290

040

040

040

190

200

19

CD

(p=0

05)

116

111

64

124

20

840

810

820

130

120

120

560

590

56

Inte

ract

ions

(MxN

)

Nut

rient

man

agem

ent a

t sam

e le

vel o

f cro

p es

tabl

ishm

ent

SEm

+5

695

706

080

410

390

400

060

060

060

270

290

27

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

Cro

p es

tabl

ishm

ent a

t sam

e or

diff

eren

t lev

el o

f nut

rient

man

agem

ent

SEm

+6

616

576

980

460

450

460

070

070

060

360

350

35

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

Gen

eral

Mea

n27

65

261

626

90

238

423

44

236

43

233

183

2022

37

222

822

33


32

Trea

tmen

tsFi

lled

grai

ns p

anic

le-1

Unf

illed

gra

ins

pani

cle-1

Ster

ility

per

cent

age

()

2015

2016

Pool

ed20

1520

16Po

oled

2015

2016

Pool

ed

Cro

p es

tabl

ishm

ent m

etho

ds (M

)M

1- D

irect

See

ded

Ric

e (D

SR

)12

466

116

0412

034

968

122

810

98

737

976

856

M2-

Nor

mal

Tra

nspl

ante

d R

ice

(NTP

)13

436

125

7613

007

881

112

510

03

630

838

734

SEm

+1

881

701

460

160

170

150

120

160

15

CD

(p=0

05)

847

765

658

073

077

069

056

073

065

Nut

rient

Man

agem

ent p

ract

ices

(N)

N1-

Abs

olut

e co

ntro

l (N

o fe

rtiliz

er)

109

3310

079

105

0518

37

208

819

63

144

217

23

158

2

N2-

Con

trol (

RD

F)12

531

116

6612

098

103

212

85

115

97

639

958

79

N3-R

DF+

SA

of

ZnS

O47

H2O

2

5 kg

ha-1

131

5312

289

127

208

2910

80

954

594

811

702

N4-R

DF+

2 F

S o

f ZnS

O47

H2O

0

5

132

7312

409

128

407

8710

38

913

562

775

668

N5-R

DF+

2 F

S o

f nan

o Zn

O

100

0 pp

m13

975

131

1313

546

546

798

672

377

576

477

N6-R

DF+

2 F

S o

f na

no Z

nO

150

0 pp

m13

844

129

8513

414

517

768

642

362

561

462

SEm

+1

451

431

280

200

180

200

150

170

17

CD

(p=0

05)

419

413

371

059

053

057

043

048

050

Inte

ract

ions

(MxN

)N

utrie

nt m

anag

emen

t at s

ame

leve

l of c

rop

esta

blis

hmen

tS

Em+

205

202

182

029

026

028

021

023

025

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Cro

p es

tabl

ishm

ent a

t sam

e or

diff

eren

t lev

el o

f nut

rient

man

agem

ent

SEm

+2

662

512

210

310

290

300

230

270

27

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Gen

eral

Mea

n12

951

120

9012

521

925

117

610

51

683

907

795

RD

F- R

ecom

men

ded

Dos

e of

Fer

tiliz

ers

(N

P2O

5amp K

2O

120

60

40 k

g ha

-1)

SA

- S

oil A

pplic

atio

n F

S-

Folia

r Sp

ray

NS

- N

on S

igni

fican

t

BRIJBHOOSHAN et al

33








CONCLUSION



34

Tabl

e 2

Yie

ld a

nd h

arve

st in

dex

of ri

ce a

s in

fluen

ced

by c

rop

esta

blis

hmen

t met

hods

and

nut

rient

man

agem

ent p

ract

ices

(rab

i 20

15 a

nd 2

016)

Trea

tmen

tsG

rain

yie

ld (k

g ha

-1)

Stra

w y

ield

(kg

ha-1)

Har

vest

Inde

x(

)

2015

2016

Pool

ed20

1520

16Po

oled

2015

2016

Pool

ed

Cro

p es

tabl

ishm

ent m

etho

ds (M

)M

1- D

irect

See

ded

Ric

e (D

SR

)51

9848

8650

4766

7564

4765

6143

58

428

243

20

M2-

Nor

mal

Tra

nspl

ante

d R

ice

(NTP

)47

3344

8946

1160

0058

1359

0743

81

434

343

62

SEm

+98

8389

121

109

101

052

061

049

CD

(p=0

05)

441

373

401

545

492

455

NS

NS

NS

Nut

rient

Man

agem

ent p

ract

ices

(N)

N1-

Abs

olut

e co

ntro

l (N

o fe

rtiliz

er)

2335

2274

2305

3270

3320

3295

416

740

84

412

5

N2-

Con

trol (

RD

F)47

3944

6946

0461

5459

8160

6843

47

427

743

12

N3-R

DF+

SA

of

ZnS

O47

H2O

2

5 kg

ha-1

5187

4993

5090

6593

6462

6527

440

243

63

438

3

N4-R

DF+

2 F

S o

f ZnS

O47

H2O

0

5

5483

5157

5320

6902

6656

6779

442

543

66

439

6

N5-R

DF+

2 F

S o

f nan

o Zn

O

100

0 pp

m60

7056

5958

7875

7072

2473

9744

54

439

644

25

N6-R

DF+

2 F

S o

f na

no Z

nO

150

0 pp

m59

7855

7457

7675

3771

3773

3744

21

438

944

05

SEm

+13

410

911

114

711

412

10

400

550

51

CD

(p=0

05)

386

315

320

424

328

349

115

158

147

Inte

ract

ions

(MxN

)N

utrie

nt m

anag

emen

t at s

ame

leve

l of c

rop

esta

blis

hmen

tS

Em+

189

154

157

208

161

171

056

077

072

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Cro

p es

tabl

ishm

ent a

t sam

e or

diff

eren

t lev

el o

f nut

rient

man

agem

ent

SEm

+19

816

316

822

518

318

60

730

940

82

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Gen

eral

Mea

n49

6546

8848

2963

3761

3062

3443

70

431

343

41

Coe

ffici

ent o

f Var

iatio

n (

)9

78

79

09

48

77

95

97

05

5

RD

F- R

ecom

men

ded

Dos

e of

Fer

tiliz

ers

(N

P2O

5amp K

2O

120

60

40 k

g ha

-1)

SA

- S

oil A

pplic

atio

n F

S-

Folia

r Sp

ray

NS

- N

on S

igni

fican

t

BRIJBHOOSHAN et al

35

REFERENCES

















36





ABSTRACT







37












38














PREETHAM et al

39

Tabl

e-1

Effe

ct o

f int

egra

ted

nutr

ient

man

agem

ent p

ract

ices

on

yiel

d at

trib

utes

of b

aby

corn

dur

ing

khar

if 2

015

and

2016

Trea

tmen

t giv

en to

kha

rif b

aby

corn

2015

2016

Cob

spl

ant-1

Cob

leng

th(c

m)

Cob

girth

(cm

)

Cob

wid

th(c

m)

Cob

wei

ght

(g)

Cob

spl

ant-1

Cob

leng

th(c

m)

Cob

girth

(cm

)

Cob

wid

th(c

m

Cob

wei

ght

(g)

With

With

out

husk

husk

With

With

out

husk

husk

T 1- 25

N through FY

M + 75

RDF

178

106

95

251

6846

80

926

183

931

513

163

426

89

03T 2-

25

N th

roug

h FY

M +

75

RD

F +

200

108

35

601

7247

33

100

01

989

645

541

6843

16

908

Azo

spiri

llum

and

Bac

illus

meg

ater

ium

5

kg h

a-1 e

ach

T 3- 25

N

thr

ough

Ver

mic

ompo

st +

202

109

55

581

7649

17

100

02

029

655

461

7143

68

926

75

RD

FT 4-

T4 2

5 N

thro

ugh

Verm

icom

post

+2

2312

50

574

186

555

913

46

222

122

45

651

8348

73

120

675

R

DF

+ A

zosp

irillu

m a

nd B

acill

usm

egat

eriu

m

5 k

g ha

-1 e

ach

T 5- 10

0 R

DF

175

106

65

241

7146

29

920

178

989

512

161

423

69

01T 6-

100

RD

F +

Azo

spiri

llum

and

Bac

illus

200

109

65

551

8049

86

103

81

989

905

441

7443

10

936

meg

ater

ium

5

kg

ha-1 e

ach

T 7- C

ontro

l (N

o fe

rtiliz

er a

pplic

atio

n)0

957

503

981

2715

88

358

147

711

373

127

239

95

13S

Em

+0

060

410

150

051

550

400

060

500

150

050

970

13C

D (

P=0

05)

020

127

045

016

477

123

018

153

047

016

300

041

Trea

tmen

t giv

en to

rabi

cro

p (h

yaci

nth

bean

)S

1- 10

0 R

DF

191

987

520

161

424

99

25S

2- 75

R

DF

180

933

501

167

373

78

25S

3-10

0 R

DF

+ B

rady

rhiz

obiu

m

200

990

533

160

445

79

9550

0 g

ha-1 (

See

d tre

atm

ent)

S4-

75

RD

F +

Bra

dyrh

izob

ium

1

889

605

071

6639

98

850

500

g ha

-1 (

See

d tre

atm

ent)

SE

m+

003

024

017

005

086

019

CD

(P

=00

5)0

10N

SN

SN

S2

460

54In

tera

ctio

nB

ean

trea

tmen

t mea

ns a

t sam

e le

vel o

f bab

y co

rn IN

M tr

eatm

ents

SE

m+

009

064

044

014

228

050

CD

(P

=00

5)N

SN

SN

SN

SN

SN

SIN

M tr

eatm

ent m

eans

of b

aby

corn

at s

ame

leve

l of b

ean

trea

tmen

tsS

Em

+0

100

740

410

132

200

45C

D (

P=0

05)

NS

NS

NS

NS

NS

NS


40





Cob Yield (kg ha-1)










PREETHAM et al

41

Tabl

e-2

Effe

ct o

f int

egra

ted

nutr

ient

man

agem

ent p

ract

ices

on

cob

yiel

d (k

g ha

-1) w

ith a

nd w

ithou

t hus

k an

d st

over

yie

ld o

f bab

y co

rn d

urin

gkh

arif

201

5 an

d 20

16

Trea

tmen

t giv

en to

kha

rif b

aby

corn

2015

2016

Cob

yie

ld (k

g ha

-1)

Stov

erC

ob y

ield

(kg

ha-1)

Stov

er y

ield

yiel

d(kg

ha-1

)(k

g ha

-1)

With

hus

kW

ithou

t hus

kW

ith h

usk

W

ithou

t hus

k

T 1- 25

N through FY

M + 75

RDF

8204

1602

1934

481

4714

9518

919

T 2- 25

N

thro

ugh

FYM

+ 7

5 R

DF

+ A

zosp

irillu

m90

3218

0320

865

8882

1596

1970

8an

d B

acill

us m

egat

eriu

m

5 k

g ha

-1 e

ach

T 3- 25

N

thro

ugh

Verm

icom

post

+ 7

5 R

DF

9400

1801

2147

589

9416

0119

762

T 4- T4

25

N th

roug

h Ve

rmic

ompo

st +

75

RD

F +

1229

020

4924

020

1172

718

9021

165

Azo

spiri

llum

and

Bac

illus

meg

ater

ium

5

kg

ha-1 e

ach

T 5- 10

0 R

DF

8049

1542

1906

081

9914

7119

364

T 6- 10

0 R

DF

+ A

zosp

irillu

m a

nd B

acill

us93

4418

0720

398

8824

1553

1963

1m

egat

eriu

m

5 k

g ha

-1 e

ach

T 7- C

ontro

l (N

o fe

rtiliz

er a

pplic

atio

n)42

7982

313

821

4393

749

1216

8S

Em

+26

764

429

200

1638

5C

D (

P=0

05)

824

198

1321

617

4911

87Tr

eatm

ent g

iven

to ra

bi c

rop

(hya

cint

h be

an)

S1-

100

RD

F88

2415

4619

086

S2-

75

RD

F78

8013

6917

833

S3-10

0 R

DF

+ B

rady

rhiz

obiu

m

500

g h

a-190

7315

9019

410

(See

d tre

atm

ent)

S4-

75

RD

F +

Bra

dyrh

izob

ium

5

00 g

ha-1

8033

1411

1836

5(S

eed

treat

men

t)S

Em

+20

216

280

CD

(P

=00

5)57

745

798

Inte

ract

ion

Bea

n tre

atm

ent m

eans

at s

ame

leve

l of b

aby

corn

INM

trea

tmen

tsS

Em

+53

541

740

CD

(P

=00

5)N

SN

SN

SIN

M tr

eatm

ent m

eans

of b

aby

corn

at s

ame

leve

l of b

ean

treat

men

tsS

Em

+50

439

748

CD

(P

=00

5)N

SN

SN

S


42






CONCLUSION


REFERENCES







PREETHAM et al

43















44



ABSTRACT














45























2 FACTOR ANALYSIS



2 2 212 R 3K N(N 1)i iW 2 2K N(N 1)

46











Sig 0000



Component


Total ofVariance

Cumulative

Total ofVariance

Cumulative


Total ofVariance

Cumulative

1 1965 21839 21839 1965 21839 21839 1894 21044 21044

2 1806 20070 41909 1806 20070 41909 1520 16887 37932

3 1086 12064 53973 1086 12064 53973 1444 16042 53973

4 927 10301 64274

5 887 9860 74134

6 738 8199 82333

7 630 6996 89329

8 529 5878 95207

9 431 4793 100000


MEENA et al

47


Component Number

Scree Plot

Eig

en v

alue



Component

1 2 3


Component1 2 3












48


Table 4 Ranks









Chi-Square 0822

df 2

Asymp Sig 0663















MEENA et al

49







ABSTRACT









50

SRINIVAS et al




()




51



CONCLUSION


Acknowledgments


REFERENCES







52







ABSTRACT








53











54




Analysis of protein

















55








0

10

20

30

40

50

6070

80

90

Finge

r Mille

t

Foxta

il Mille

t

Little M

illet

Pearl M

illet

Proso

Mille

t

Sorgh

um

Perc

enta

ge d

iges

tibili

ty

Dosa UpmaIdli



56






Foxtail millet

Little millet

Pearl millet

Prosomillet

Sorghum

IdliDosaUpma

10080

6040

20 0







57



CONCLUSION


REFERENCES
















58




















59






ABSTRACT










60
















Pomegranate 21 23 17 16 18 19 13 12 17 18 12 14

Cassia 31 33 24 24 26 28 20 21 22 23 21 22

Periwinkle 29 31 23 25 25 24 19 20 21 21 22 21

Marigold 21 23 18 19 16 18 12 13 16 17 14 16

61









62



0

5

10

15

20

25

30

S aureus Ecoli

Pomegran

teCas

siaPeri

winkleMari

gold

Pomegran

te

Cassia

Periwinkle

Marigold

SilverTitaniumzinc


0

5

10

30

Pomegran

teCas

siaPeri

winkleMari

gold

Pomegran

teCas

siaPeri

winkleMari

gold

35

15

20

S aureus Ecoli

SilverTitaniumzinc

25

63

CONCLUSION








64













65

Tabl

e 1

Est

imat

es o

f phe

noty

pic

and

geno

typi

c co

rrel

atio

n co

effic

ient

s fo

r yie

ld a

nd it

s at

trib

utin

g ch

arac

ters

Cha

ract

erDa

ys to

Plan

tPa

nicl

eN

o o

fN

o o

fSp

ikel

et10

00-s

eed

Prod

uctiv

ityBi

omas

sHa

rves

tG

rain

50

heig

htle

ngth

Prod

uctiv

efil

led

fert

ility

wei

ght

per d

aype

r pl

ant

Inde

xyi

eld

flow

erin

gtil

lers

grai

nspe

r pl

ant

per

plan

tpe

rpa

nicl

e

Day

s to

50

P1

0000

012

230

3431

0

1594

0

5331

0

1150

-00

195

000

49-0

053

60

2191

0

2472

flow

erin

gG

100

000

1181

037

30

016

91

054

21

011

49-0

022

90

0073

-00

657

022

80

025

70

Plan

t hei

ght

P

100

000

5653

-0

441

4

037

95

012

720

5292

014

700

4604

-0

253

6

016

30

G

1

0000

061

45

-05

199

0

3892

0

1301

054

12

014

540

4798

-02

621

0

1605

Pani

cle

leng

thP

100

00-0

025

80

3811

0

2527

0

1930

0

0041

-00

205

006

370

0774

G

100

000

0038

040

71

027

06

020

60

002

420

0073

006

540

1048

No

of P

rodu

ctiv

eP

1

0000

-00

043

020

21

-0

3806

023

08

-03

316

0

4903

0

2561

tille

rs p

er p

lant

G

100

00-0

002

90

2298

-0

434

00

2296

-0

453

0

057

49

025

85

No

of f

illed

gra

ins

P

1

0000

050

67

008

680

5112

0

3200

0

2694

0

6261

per p

anic

leG

100

000

5088

0

0874

053

48

034

06

027

36

065

35

Spik

elet

fert

ility

()

P

100

000

0613

049

97

004

900

4247

0

5043

G

1

0000

006

150

5270

0

0519

043

59

053

01

1000

see

d w

eigh

tP

100

000

1600

0

2852

-0

068

90

1470

G

100

000

1656

0

2999

-00

697

015

13

Prod

uctiv

ity p

er d

ayP

1

0000

029

02

057

85

096

81

G

1

0000

023

91

061

47

096

62

Bio

mas

s pe

r pla

ntP

100

00-0

561

9

025

85

G

100

00-0

571

1

020

47

Har

vest

inde

x (

)P

1

0000

061

74

G

1

0000

065

49

P- P

heno

typi

c co

rrel

atio

n co

effic

ient

G-

Gen

otyp

ic c

orre

latio

n co

effic

ient

Si

gnifi

cant

at

1

Si

gnifi

cant

at

5


66

SRIJAN et al






Acknowledgement


67

Tabl

e 2

Dire

ct a

nd in

dire

ct e

ffect

s of

var

ious

com

pone

nt c

hara

cter

s on

yie

ld in

rice

Cha

ract

erDa

ys to

Plan

tPa

nicl

eN

o o

fN

o o

fSp

ikel

et10

00-s

eed

Prod

uctiv

ityBi

omas

sHa

rves

tG

rain

50

heig

htle

ngth

Prod

uctiv

efil

led

fert

ility

wei

ght

per d

aype

r pl

ant

Inde

xyi

eld

flow

erin

gtil

lers

grai

nspe

r pl

ant

per

plan

tpe

rpa

nicl

e

Day

s to

50

P0

2451

-00

015

-00

035

-00

018

001

32-0

001

2-0

000

10

0049

000

30-0

011

00

2472

flow

erin

gG

025

22-0

001

8-0

004

0-0

002

30

0157

-00

011

-00

002

000

730

0041

-00

128

025

70

Plan

t hei

ght

P0

0300

-00

123

-00

058

000

480

0094

-00

013

000

310

1482

-00

260

001

280

1630

G

002

98-0

015

3-0

006

60

0072

001

12-0

001

20

0041

014

65-0

029

80

0147

016

05

Pani

cle

leng

thP

008

41-0

006

9-0

010

20

0003

000

94-0

002

50

0011

000

420

0012

-00

032

007

74

G

009

41-0

009

4-0

010

8-0

000

10

0118

-00

025

000

150

0243

-00

005

-00

037

010

48

No

of P

rodu

ctiv

eP

003

910

0054

000

03-0

011

0-0

000

1-0

002

0-0

002

20

2327

001

87-0

024

70

2561

tille

rs p

er p

lant

G0

0426

000

800

0000

-00

138

-00

001

-00

022

-00

033

023

140

0282

-00

323

025

85

No

of f

illed

gra

ins

P0

1307

-00

047

-00

039

000

000

0247

-00

051

000

050

5154

-00

181

-00

136

062

61

per p

anic

leG

013

67-0

006

0-0

004

40

0000

002

89-0

004

80

0007

053

89-0

021

2-0

015

40

6535

Spik

elet

fert

ility

()

P0

0282

-00

016

-00

026

-00

022

001

25-0

010

00

0004

050

38-0

002

8-0

021

40

5043

G

002

90-0

002

0-0

002

9-0

003

20

0147

-00

094

000

050

5311

-00

032

-00

245

053

01

1000

see

d w

eigh

tP

-00

048

-00

065

-00

020

000

420

0021

-00

006

000

580

1613

-00

161

000

350

1470

G

-00

058

-00

083

-00

022

000

600

0025

-00

006

000

750

1669

-00

186

000

390

1513

Prod

uctiv

ity p

er d

ayP

000

12-0

001

80

0000

-00

025

001

26-0

005

00

0009

100

82-0

016

4-0

029

10

9681

G

000

18-0

002

2-0

000

3-0

003

20

0154

-00

049

000

121

0077

-00

149

-00

345

096

62

Bio

mas

s pe

r pla

ntP

-00

131

-00

056

000

020

0036

000

79-0

000

50

0017

029

26-0

056

40

0283

025

85

G

-00

166

-00

074

-00

001

000

620

0098

-00

005

000

220

2410

-00

621

003

210

2047

Har

vest

inde

x (

)P

005

370

0031

-00

006

-00

054

000

66-0

004

3-0

000

40

5832

003

17-0

050

30

6174

G

005

750

0040

-00

007

-00

079

000

79-0

004

1-0

000

50

6194

003

55-0

056

10

6549

Phen

otyp

ic r

esid

ual e

ffec

t =

005

78

Gen

otyp

ic r

esid

ual

effe

ct =

00

560

P-

Phe

noty

pic

leve

l G

- G

enot

ypic

leve

l

Bol

d va

lues

- D

irec

t ef

fect

s N

orm

al V

alue

s -

Indi

rect

effe

cts

Sign

ifica

nt a

t 1

S

igni

fican

t at

5


68

REFERENCES









SRIJAN et al

69












70













DFF 086 -057 063 019 -029 033

PHT 072 -061 072 022 002 050

NPT -035 -045 -047 -070 -042 -041

PL 054 068 -030 -005 000 023

PG 013 018 -045 -005 065 059

TGW -024 -003 -016 000 043 028

GY 019 038 -022 002 039 018






DFF -127 128 -017 002 023 024 033

PHT -109 149 -019 003 028 -002 050

NPT 072 -091 031 001 -088 034 -041

PL -080 107 -014 016 -006 000 023

PG -024 033 -022 -001 125 -053 059

TGW 037 003 -013 001 081 -081 028



71





Residual GYPx7=029

Px2=149 Px3=031 Px4=0 Px5=125


rx12=004 rx23=-061 rx34=-047 rx45=-005 rx54= 065

rx13=-057 rx24=072 rx35=-07 rx44=0

rx14=083 rx25=022 rx30=-042

rx15=019 rx26=002

rx16=-029

Px6=-001Px1=127


72






REFERENCES









73






























Books



Thesis


i









REVIEW PROCESS




ii



____________________________________________________________________________________







1

2

3








Name

Signature

Office seal


iii






Nationality Indian



Nationality Indian



Nationality Indian





iv














v




30AJPJTSAU2018

Page 1

2pdf

Page 2

3pdf

Page 3

4








NAGESH KUMAR et al


Blackgram


20

40

60

80

100

See

d R

epla

cem

ent

Rat

e (

)

2006-07 2011-12 2016-17

3

25

2

15

1

05

0

013

05

17

27

1990 2000 2010 2013

5






6














NAGESH KUMAR et al

7
















0

2

4

6

8

10

12

14

16

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

USD Bn


8




Rice 456 50 22800 100 228 2280

Wheat 272 125 34000 20 1700 85000

Sorghum 77 15 1155 160 07 45

Pearl millet 87 5 435 200 02 11

Maize 80 20 1600 80 20 250

Pigeon pea 34 15 510 100 05 51

Chickpea 82 80 6560 10 656 65600

Groundnut 62 100 6200 8 97 12109

RampM 63 5 315 200 02 08

Soybean 89 75 6675 16 417 26074

Sunflower 19 10 190 50 04 76

Cotton 95 12 1140 50 23 456

Jute 08 5 40 100 004 04

Total 1424 81620 3161 191964








NAGESH KUMAR et al

9












10













NAGESH KUMAR et al

11


Seed Biotechnology





Cold Plasma Coating





Bioprospecting







12






REFERENCES














NAGESH KUMAR et al

13




ABSTRACT







14








SRIDHAR et al

15








Quality parameters



CONCLUSIONS



16

Tabl

e 1

Gra

in a

nd S

traw

yie

ld (k

g ha

-1) o

f ric

e as

influ

ence

d by

alte

rnat

e w

ettin

g an

d dr

ying

irrig

atio

n an

d ni

trog

en le

vels

dur

ing

rabi

201

620

17 a

nd p

oole

d m

eans

Trea

tmen

tsG

rain

yie

ld (k

g ha

-1)

Stra

w y

ield

(kg

ha-1)

2016

2017

Pool

ed20

1620

17Po

oled

Irrig

atio

n re

gim

es (I

)

I 1- 2-

5cm

sub

mer

genc

e as

per

cro

p st

age

7026

7221

7123

7936

8153

8045

I 2- AW

D ir

rigat

ion

of 5

cm

with

3 c

m fa

ll in

fiel

d69

2571

5070

3778

4480

5679

56w

ater

tube

I 3- AW

D ir

rigat

ion

of 5

cm

with

5 c

m fa

ll in

fiel

d64

1466

5265

3373

3375

4574

39w

ater

tube

SEm

plusmn54

060

455

252

852

152

4

CD

at 5

15

016

815

314

714

514

6

Nitr

ogen

lev

els

(N)

N1-1

20 k

g ha

-167

0669

2268

1476

0578

1777

11

N2-1

60 k

g ha

-168

0270

2169

1177

1079

2178

16

N3-2

00 k

g ha

-168

5770

8169

6977

9880

1679

07

SE

mplusmn

372

445

390

410

411

410

CD

at 5

81

9785

8989

89

Inte

ract

ions

NS

NS

NS

NS

NS

NS

SRIDHAR et al

17

Tabl

e 2

Hul

ling

and

mill

ing

perc

enta

ge (

) of r

ice

as in

fluen

ced

by a

ltern

ate

wet

ting

and

dryi

ng ir

rigat

ion

and

nitr

ogen

leve

ls d

urin

g ra

bi20

16 2

017

and

pool

ed m

eans

Hul

ling

()

Mill

ing

()

2016

2017

Pool

ed20

1620

17Po

oled

Irrig

atio

n re

gim

es (I

)

I 1- 2-

5cm

sub

mer

genc

e as

per

cro

p st

age

808

810

809

675

674

674

I 2- AW

D ir

rigat

ion

of 5

cm

with

3 c

m fa

ll in

fiel

d80

780

980

867

367

267

3w

ater

tube

I 3- AW

D ir

rigat

ion

of 5

cm

with

5 c

m fa

ll in

fiel

d80

580

780

667

066

866

9w

ater

tube

SEm

plusmn0

10

20

20

20

20

2

CD

at 5

N

SN

SN

S0

40

50

5

Nitr

ogen

lev

els

(N)

N1-1

20 k

g ha

-180

780

880

867

267

467

3

N2-1

60 k

g ha

-180

680

880

767

367

367

3

N3-2

00 k

g ha

-180

780

980

867

267

067

1

SE

mplusmn

01

01

01

06

02

04

CD

at 5

N

SN

SN

SN

SN

SN

S

Inte

ract

ions

( IxN

)N

SN

SN

SN

SN

SN

S


18

Tabl

e 3

Hea

d ric

e re

cove

ry (

) G

rain

pro

tein

con

tent

and

am

ylos

e (

) of r

ice

as in

fluen

ced

by a

ltern

ate

wet

ting

and

dryi

ng ir

rigat

ion

and

nitr

ogen

leve

ls d

urin

g ra

bi 2

016

201

7 an

d po

oled

mea

ns

2016

2017

Pool

ed20

1620

17Po

oled

2016

2017

Pool

ed

Hea

d ric

e re

cove

ry (

)G

rain

pro

tein

con

tent

()

Amyl

ose(

)

Irrig

atio

n re

gim

es (I

)

I 1- 2-

5cm

sub

mer

genc

e as

per

cro

p st

age

650

665

12

650

99

189

149

1623

925

024

5

I 2- AW

D ir

rigat

ion

of 5

cm

with

3 c

m fa

ll in

fiel

d63

95

638

763

91

907

904

906

238

247

242

wat

er tu

be

I 3- AW

D ir

rigat

ion

of 5

cm

with

5 c

m fa

ll in

fiel

d63

32

633

463

33

903

898

901

234

243

239

wat

er tu

be

SEm

plusmn0

200

230

210

040

050

040

440

450

44

CD

at 5

0

570

640

60N

SN

SN

SN

SN

SN

S

Nitr

ogen

lev

els

(N)

N1-1

20 k

g ha

-163

93

639

863

95

894

890

892

230

239

235

N2-1

60 k

g ha

-164

16

640

464

10

916

911

913

240

246

243

N3-2

00 k

g ha

-164

23

643

164

27

920

916

918

241

246

244

SE

mplusmn

014

016

015

004

004

004

023

019

021

CD

at 5

N

SN

SN

S0

100

090

090

750

50

63

Inte

ract

ions

( IxN

)N

SN

SN

SN

SN

SN

SN

SN

SN

S

SRIDHAR et al

19


REFERENCES















20






ABSTRACT






21








S No Genotype ID

1 ICGV 15016

2 ICGV 15064

3 ICGV 16013

4 ICGV 16017

5 ICGV 16030

6 ICGV 16035

7 ICGV 16045

8 ICGV 16668


22

S No Genotype ID

9 ICGV 16687

10 ICGV 16700

11 ICGV 16701

12 ICGV 171011

13 ICGV 16686

14 ICGV 171007

15 ICGV 171023

16 ICGV 171027

17 ICGV 171006

18 ICGV 16667

19 ICGV 16698

20 ICGV 171017

21 ICGV 171015

22 ICGV 16020

23 ICGV 171051

24 ICGV 171040

25 ICGV 171039

26 ICGV 171046

27 ICGV 171041

S No Genotype ID

28 ICGV 171048

29 ICGV 171026

30 ICGV 171004

31 ICGV 171021

32 ICGV 171019

33 ICGV 171024

34 ICGV 171016

35 ICGV 171009

36 ICGV 171018

37 ICGV 171025

38 ICGV 171020

39 ICGV 171002

40 ICGV 03043

41 ICGV 07222

42 ICGV 07220

43 ICGV 00350

44 ICGV 00351

45 ICGV 02266

46 ICGV 91114





DNYANESHWAR et al

23




1 ICGV 171051 336 20 145 20

2 ICGV 171048 435 20 173 17

3 ICGV 171046 923 17 284 15

4 ICGV 171041 499 20 137 19

5 ICGV 171040 365 18 240 19

6 ICGV 171039 262 17 350 21

7 ICGV 171027 252 20 97 20

8 ICGV 171026 273 20 62 23

9 ICGV 171025 297 19 295 21

10 ICGV 171024 321 20 216 21

11 ICGV 171023 367 20 71 21

12 ICGV 171021 251 19 135 19

13 ICGV 171020 302 21 222 19

14 ICGV 171019 699 16 140 20

15 ICGV 171018 504 20 173 19

16 ICGV 171017 188 19 150 19

17 ICGV 171016 158 21 156 19

18 ICGV 171015 250 20 183 21

19 ICGV 171011 413 19 281 18

20 ICGV 171009 231 19 205 20

21 ICGV 171007 269 19 106 21

22 ICGV 171006 203 18 141 21

23 ICGV 171004 176 22 178 20

24 ICGV 171002 293 20 217 18

25 ICGV 16701 337 19 104 21

26 ICGV 16700 256 21 59 22

27 ICGV 16698 243 21 182 19

28 ICGV 16687 417 19 76 24

29 ICGV 16686 245 17 166 16

30 ICGV 16668 438 19 191 18

31 ICGV 16667 215 21 132 17

32 ICGV 16045 348 21 113 18

33 ICGV 16035 186 20 48 22

34 ICGV 16030 251 18 62 21

35 ICGV 16020 06 14 248 21


24



36 ICGV 16017 284 19 125 17

37 ICGV 16013 157 20 138 14

38 ICGV 15064 347 19 267 17

39 ICGV 15016 395 19 133 21

40 ICGV 07222 286 20 276 18

41 ICGV 07220 381 19 110 18

42 ICGV 03043 360 20 107 17

43 ICGV 02266 448 20 230 18

44 ICGV 00351 275 20 270 20

45 ICGV 00350 309 21 176 17

46 ICGV 91114 174 22 279 19




DNYANESHWAR et al

25

Tabl

e 3

Com

para

tive

SNP

prof

ile o

btai

ned

from

gen

omic

DN

A of

gro

undn

ut le

af d

isc

and

seed

chi

p

DN

A A

ssay

SNP

for

SNPs

for l

ate

leaf

spo

tSN

Ps fo

r rus

thi

gh o

leic

acid

snpA

H00

02sn

pAH

0004

snpA

H00

05sn

pAH

0010

snpA

H00

11sn

pAH

0015

snpA

H00

17sn

pAH

0018

snpA

H00

21sn

pAH

0026

ICG

V 15

064_

Leaf

AA

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

1506

4_Se

edA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

687_

Lea

fA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

687_

See

dA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

698_

Lea

fA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

698_

See

dA

AC

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

700_

Lea

fA

AG

GC

CA

ATT

AA

CC

AA

GT

CC

ICG

V 16

700_

See

dA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 00

351_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0035

1_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 02

266_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0226

6_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 03

043_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0304

3_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 07

222_

Lea

f--

CC

GG

CC

CC

TTA

AG

GC

CG

GIC

GV

0722

2_ S

eed

--C

CG

GC

CC

CTT

AA

GG

CC

GG

ICG

V 16

701_

Lea

fA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 16

701_

See

dA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1002

_ Le

afA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1002

_ S

eed

AA

GG

CC

AA

TTA

AC

CA

TG

GC

CIC

GV

1710

04_

Leaf

AA

GG

CC

AA

TTA

AC

CA

AG

GC

CIC

GV

1710

04_

See

dA

AG

GC

CA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1006

_ Le

afA

AG

GC

GA

ATT

AA

CC

AA

GG

CC

ICG

V 17

1006

_ S

eed

AA

GG

CC

AA

TTA

AC

CA

AG

GC

C--

Nul

l alle

le


26

REFERENCES
















DNYANESHWAR et al

27






28







ABSTRACT









29











Yield attributes



30










BRIJBHOOSHAN et al

31

Tabl

e 1

Yie

ld a

ttrib

utes

of r

ice

as in

fluen

ced

by c

rop

esta

blis

hmen

t met

hods

and

nut

rient

man

agem

ent p

ract

ices

(rab

i 20

15 a

nd 2

016)

Trea

tmen

tsN

umbe

r of p

anic

le m

-2Pa

nicl

e le

ngth

(cm

)Pa

nicl

e w

eigh

t (g)

Test

wei

ght (

g)

2015

2016

Pool

ed20

1520

16 P

oole

d20

1520

16Po

oled

2015

2016

Pool

edC

rop

esta

blis

hmen

t met

hods

(M)

M1-

Dire

ct S

eede

d R

ice

(DS

R)

293

527

53

284

423

18

227

922

99

314

308

311

222

322

15

221

9

M2-

Nor

mal

Tra

nspl

ante

d R

ice

(NTP

)25

94

247

825

36

244

924

08

242

93

323

283

3022

51

224

222

46

SEm

+4

104

014

230

270

270

270

040

040

030

260

230

24

CD

(p=0

05)

184

318

03

190

61

211

241

230

170

180

15N

SN

SN

S

Nut

rient

Man

agem

ent p

ract

ices

(N)

N1-

Abs

olut

e co

ntro

l (N

o fe

rtiliz

er)

148

413

59

142

122

03

216

021

82

274

271

273

203

520

25

203

0

N2-

Con

trol (

RD

F)27

95

262

027

09

231

522

77

229

63

153

113

1322

21

221

522

18

N3-R

DF+

SA

of

ZnS

O47

H2O

2

5 kg

ha-1

294

827

95

287

124

01

236

123

81

329

324

326

225

022

39

224

4

N4-R

DF+

2 F

S o

f ZnS

O47

H2O

0

5

301

528

60

293

724

16

237

423

95

335

328

331

228

922

78

228

3

N5-R

DF+

2 F

S o

f nan

o Zn

O

100

0 pp

m31

96

305

531

26

248

824

48

246

83

443

393

4123

21

231

323

17

N6-R

DF+

2 F

S o

f na

no Z

nO

150

0 pp

m31

50

300

530

78

248

224

42

246

23

423

363

3923

08

229

923

03

SEm

+4

024

034

300

290

280

290

040

040

040

190

200

19

CD

(p=0

05)

116

111

64

124

20

840

810

820

130

120

120

560

590

56

Inte

ract

ions

(MxN

)

Nut

rient

man

agem

ent a

t sam

e le

vel o

f cro

p es

tabl

ishm

ent

SEm

+5

695

706

080

410

390

400

060

060

060

270

290

27

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

Cro

p es

tabl

ishm

ent a

t sam

e or

diff

eren

t lev

el o

f nut

rient

man

agem

ent

SEm

+6

616

576

980

460

450

460

070

070

060

360

350

35

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

Gen

eral

Mea

n27

65

261

626

90

238

423

44

236

43

233

183

2022

37

222

822

33


32

Trea

tmen

tsFi

lled

grai

ns p

anic

le-1

Unf

illed

gra

ins

pani

cle-1

Ster

ility

per

cent

age

()

2015

2016

Pool

ed20

1520

16Po

oled

2015

2016

Pool

ed

Cro

p es

tabl

ishm

ent m

etho

ds (M

)M

1- D

irect

See

ded

Ric

e (D

SR

)12

466

116

0412

034

968

122

810

98

737

976

856

M2-

Nor

mal

Tra

nspl

ante

d R

ice

(NTP

)13

436

125

7613

007

881

112

510

03

630

838

734

SEm

+1

881

701

460

160

170

150

120

160

15

CD

(p=0

05)

847

765

658

073

077

069

056

073

065

Nut

rient

Man

agem

ent p

ract

ices

(N)

N1-

Abs

olut

e co

ntro

l (N

o fe

rtiliz

er)

109

3310

079

105

0518

37

208

819

63

144

217

23

158

2

N2-

Con

trol (

RD

F)12

531

116

6612

098

103

212

85

115

97

639

958

79

N3-R

DF+

SA

of

ZnS

O47

H2O

2

5 kg

ha-1

131

5312

289

127

208

2910

80

954

594

811

702

N4-R

DF+

2 F

S o

f ZnS

O47

H2O

0

5

132

7312

409

128

407

8710

38

913

562

775

668

N5-R

DF+

2 F

S o

f nan

o Zn

O

100

0 pp

m13

975

131

1313

546

546

798

672

377

576

477

N6-R

DF+

2 F

S o

f na

no Z

nO

150

0 pp

m13

844

129

8513

414

517

768

642

362

561

462

SEm

+1

451

431

280

200

180

200

150

170

17

CD

(p=0

05)

419

413

371

059

053

057

043

048

050

Inte

ract

ions

(MxN

)N

utrie

nt m

anag

emen

t at s

ame

leve

l of c

rop

esta

blis

hmen

tS

Em+

205

202

182

029

026

028

021

023

025

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Cro

p es

tabl

ishm

ent a

t sam

e or

diff

eren

t lev

el o

f nut

rient

man

agem

ent

SEm

+2

662

512

210

310

290

300

230

270

27

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Gen

eral

Mea

n12

951

120

9012

521

925

117

610

51

683

907

795

RD

F- R

ecom

men

ded

Dos

e of

Fer

tiliz

ers

(N

P2O

5amp K

2O

120

60

40 k

g ha

-1)

SA

- S

oil A

pplic

atio

n F

S-

Folia

r Sp

ray

NS

- N

on S

igni

fican

t

BRIJBHOOSHAN et al

33








CONCLUSION



34

Tabl

e 2

Yie

ld a

nd h

arve

st in

dex

of ri

ce a

s in

fluen

ced

by c

rop

esta

blis

hmen

t met

hods

and

nut

rient

man

agem

ent p

ract

ices

(rab

i 20

15 a

nd 2

016)

Trea

tmen

tsG

rain

yie

ld (k

g ha

-1)

Stra

w y

ield

(kg

ha-1)

Har

vest

Inde

x(

)

2015

2016

Pool

ed20

1520

16Po

oled

2015

2016

Pool

ed

Cro

p es

tabl

ishm

ent m

etho

ds (M

)M

1- D

irect

See

ded

Ric

e (D

SR

)51

9848

8650

4766

7564

4765

6143

58

428

243

20

M2-

Nor

mal

Tra

nspl

ante

d R

ice

(NTP

)47

3344

8946

1160

0058

1359

0743

81

434

343

62

SEm

+98

8389

121

109

101

052

061

049

CD

(p=0

05)

441

373

401

545

492

455

NS

NS

NS

Nut

rient

Man

agem

ent p

ract

ices

(N)

N1-

Abs

olut

e co

ntro

l (N

o fe

rtiliz

er)

2335

2274

2305

3270

3320

3295

416

740

84

412

5

N2-

Con

trol (

RD

F)47

3944

6946

0461

5459

8160

6843

47

427

743

12

N3-R

DF+

SA

of

ZnS

O47

H2O

2

5 kg

ha-1

5187

4993

5090

6593

6462

6527

440

243

63

438

3

N4-R

DF+

2 F

S o

f ZnS

O47

H2O

0

5

5483

5157

5320

6902

6656

6779

442

543

66

439

6

N5-R

DF+

2 F

S o

f nan

o Zn

O

100

0 pp

m60

7056

5958

7875

7072

2473

9744

54

439

644

25

N6-R

DF+

2 F

S o

f na

no Z

nO

150

0 pp

m59

7855

7457

7675

3771

3773

3744

21

438

944

05

SEm

+13

410

911

114

711

412

10

400

550

51

CD

(p=0

05)

386

315

320

424

328

349

115

158

147

Inte

ract

ions

(MxN

)N

utrie

nt m

anag

emen

t at s

ame

leve

l of c

rop

esta

blis

hmen

tS

Em+

189

154

157

208

161

171

056

077

072

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Cro

p es

tabl

ishm

ent a

t sam

e or

diff

eren

t lev

el o

f nut

rient

man

agem

ent

SEm

+19

816

316

822

518

318

60

730

940

82

CD

(p=0

05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

Gen

eral

Mea

n49

6546

8848

2963

3761

3062

3443

70

431

343

41

Coe

ffici

ent o

f Var

iatio

n (

)9

78

79

09

48

77

95

97

05

5

RD

F- R

ecom

men

ded

Dos

e of

Fer

tiliz

ers

(N

P2O

5amp K

2O

120

60

40 k

g ha

-1)

SA

- S

oil A

pplic

atio

n F

S-

Folia

r Sp

ray

NS

- N

on S

igni

fican

t

BRIJBHOOSHAN et al

35

REFERENCES

















36





ABSTRACT







37












38














PREETHAM et al

39

Tabl

e-1

Effe

ct o

f int

egra

ted

nutr

ient

man

agem

ent p

ract

ices

on

yiel

d at

trib

utes

of b

aby

corn

dur

ing

khar

if 2

015

and

2016

Trea

tmen

t giv

en to

kha

rif b

aby

corn

2015

2016

Cob

spl

ant-1

Cob

leng

th(c

m)

Cob

girth

(cm

)

Cob

wid

th(c

m)

Cob

wei

ght

(g)

Cob

spl

ant-1

Cob

leng

th(c

m)

Cob

girth

(cm

)

Cob

wid

th(c

m

Cob

wei

ght

(g)

With

With

out

husk

husk

With

With

out

husk

husk

T 1- 25

N through FY

M + 75

RDF

178

106

95

251

6846

80

926

183

931

513

163

426

89

03T 2-

25

N th

roug

h FY

M +

75

RD

F +

200

108

35

601

7247

33

100

01

989

645

541

6843

16

908

Azo

spiri

llum

and

Bac

illus

meg

ater

ium

5

kg h

a-1 e

ach

T 3- 25

N

thr

ough

Ver

mic

ompo

st +

202

109

55

581

7649

17

100

02

029

655

461

7143

68

926

75

RD

FT 4-

T4 2

5 N

thro

ugh

Verm

icom

post

+2

2312

50

574

186

555

913

46

222

122

45

651

8348

73

120

675

R

DF

+ A

zosp

irillu

m a

nd B

acill

usm

egat

eriu

m

5 k

g ha

-1 e

ach

T 5- 10

0 R

DF

175

106

65

241

7146

29

920

178

989

512

161

423

69

01T 6-

100

RD

F +

Azo

spiri

llum

and

Bac

illus

200

109

65

551

8049

86

103

81

989

905

441

7443

10

936

meg

ater

ium

5

kg

ha-1 e

ach

T 7- C

ontro

l (N

o fe

rtiliz

er a

pplic

atio

n)0

957

503

981

2715

88

358

147

711

373

127

239

95

13S

Em

+0

060

410

150

051

550

400

060

500

150

050

970

13C

D (

P=0

05)

020

127

045

016

477

123

018

153

047

016

300

041

Trea

tmen

t giv

en to

rabi

cro

p (h

yaci

nth

bean

)S

1- 10

0 R

DF

191

987

520

161

424

99

25S

2- 75

R

DF

180

933

501

167

373

78

25S

3-10

0 R

DF

+ B

rady

rhiz

obiu

m

200

990

533

160

445

79

9550

0 g

ha-1 (

See

d tre

atm

ent)

S4-

75

RD

F +

Bra

dyrh

izob

ium

1

889

605

071

6639

98

850

500

g ha

-1 (

See

d tre

atm

ent)

SE

m+

003

024

017

005

086

019

CD

(P

=00

5)0

10N

SN

SN

S2

460

54In

tera

ctio

nB

ean

trea

tmen

t mea

ns a

t sam

e le

vel o

f bab

y co

rn IN

M tr

eatm

ents

SE

m+

009

064

044

014

228

050

CD

(P

=00

5)N

SN

SN

SN

SN

SN

SIN

M tr

eatm

ent m

eans

of b

aby

corn

at s

ame

leve

l of b

ean

trea

tmen

tsS

Em

+0

100

740

410

132

200

45C

D (

P=0

05)

NS

NS

NS

NS

NS

NS


40





Cob Yield (kg ha-1)










PREETHAM et al

41

Tabl

e-2

Effe

ct o

f int

egra

ted

nutr

ient

man

agem

ent p

ract

ices

on

cob

yiel

d (k

g ha

-1) w

ith a

nd w

ithou

t hus

k an

d st

over

yie

ld o

f bab

y co

rn d

urin

gkh

arif

201

5 an

d 20

16

Trea

tmen

t giv

en to

kha

rif b

aby

corn

2015

2016

Cob

yie

ld (k

g ha

-1)

Stov

erC

ob y

ield

(kg

ha-1)

Stov

er y

ield

yiel

d(kg

ha-1

)(k

g ha

-1)

With

hus

kW

ithou

t hus

kW

ith h

usk

W

ithou

t hus

k

T 1- 25

N through FY

M + 75

RDF

8204

1602

1934

481

4714

9518

919

T 2- 25

N

thro

ugh

FYM

+ 7

5 R

DF

+ A

zosp

irillu

m90

3218

0320

865

8882

1596

1970

8an

d B

acill

us m

egat

eriu

m

5 k

g ha

-1 e

ach

T 3- 25

N

thro

ugh

Verm

icom

post

+ 7

5 R

DF

9400

1801

2147

589

9416

0119

762

T 4- T4

25

N th

roug

h Ve

rmic

ompo

st +

75

RD

F +

1229

020

4924

020

1172

718

9021

165

Azo

spiri

llum

and

Bac

illus

meg

ater

ium

5

kg

ha-1 e

ach

T 5- 10

0 R

DF

8049

1542

1906

081

9914

7119

364

T 6- 10

0 R

DF

+ A

zosp

irillu

m a

nd B

acill

us93

4418

0720

398

8824

1553

1963

1m

egat

eriu

m

5 k

g ha

-1 e

ach

T 7- C

ontro

l (N

o fe

rtiliz

er a

pplic

atio

n)42

7982

313

821

4393

749

1216

8S

Em

+26

764

429

200

1638

5C

D (

P=0

05)

824

198

1321

617

4911

87Tr

eatm

ent g

iven

to ra

bi c

rop

(hya

cint

h be

an)

S1-

100

RD

F88

2415

4619

086

S2-

75

RD

F78

8013

6917

833

S3-10

0 R

DF

+ B

rady

rhiz

obiu

m

500

g h

a-190

7315

9019

410

(See

d tre

atm

ent)

S4-

75

RD

F +

Bra

dyrh

izob

ium

5

00 g

ha-1

8033

1411

1836

5(S

eed

treat

men

t)S

Em

+20

216

280

CD

(P

=00

5)57

745

798

Inte

ract

ion

Bea

n tre

atm

ent m

eans

at s

ame

leve

l of b

aby

corn

INM

trea

tmen

tsS

Em

+53

541

740

CD

(P

=00

5)N

SN

SN

SIN

M tr

eatm

ent m

eans

of b

aby

corn

at s

ame

leve

l of b

ean

treat

men

tsS

Em

+50

439

748

CD

(P

=00

5)N

SN

SN

S


42






CONCLUSION


REFERENCES







PREETHAM et al

43















44



ABSTRACT














45























2 FACTOR ANALYSIS



2 2 212 R 3K N(N 1)i iW 2 2K N(N 1)

46











Sig 0000



Component


Total ofVariance

Cumulative

Total ofVariance

Cumulative


Total ofVariance

Cumulative

1 1965 21839 21839 1965 21839 21839 1894 21044 21044

2 1806 20070 41909 1806 20070 41909 1520 16887 37932

3 1086 12064 53973 1086 12064 53973 1444 16042 53973

4 927 10301 64274

5 887 9860 74134

6 738 8199 82333

7 630 6996 89329

8 529 5878 95207

9 431 4793 100000


MEENA et al

47


Component Number

Scree Plot

Eig

en v

alue



Component

1 2 3


Component1 2 3












48


Table 4 Ranks









Chi-Square 0822

df 2

Asymp Sig 0663















MEENA et al

49







ABSTRACT









50

SRINIVAS et al




()




51



CONCLUSION


Acknowledgments


REFERENCES







52







ABSTRACT








53











54




Analysis of protein

















55








0

10

20

30

40

50

6070

80

90

Finge

r Mille

t

Foxta

il Mille

t

Little M

illet

Pearl M

illet

Proso

Mille

t

Sorgh

um

Perc

enta

ge d

iges

tibili

ty

Dosa UpmaIdli



56






Foxtail millet

Little millet

Pearl millet

Prosomillet

Sorghum

IdliDosaUpma

10080

6040

20 0







57



CONCLUSION


REFERENCES
















58




















59






ABSTRACT










60
















Pomegranate 21 23 17 16 18 19 13 12 17 18 12 14

Cassia 31 33 24 24 26 28 20 21 22 23 21 22

Periwinkle 29 31 23 25 25 24 19 20 21 21 22 21

Marigold 21 23 18 19 16 18 12 13 16 17 14 16

61









62



0

5

10

15

20

25

30

S aureus Ecoli

Pomegran

teCas

siaPeri

winkleMari

gold

Pomegran

te

Cassia

Periwinkle

Marigold

SilverTitaniumzinc


0

5

10

30

Pomegran

teCas

siaPeri

winkleMari

gold

Pomegran

teCas

siaPeri

winkleMari

gold

35

15

20

S aureus Ecoli

SilverTitaniumzinc

25

63

CONCLUSION








64













65

Tabl

e 1

Est

imat

es o

f phe

noty

pic

and

geno

typi

c co

rrel

atio

n co

effic

ient

s fo

r yie

ld a

nd it

s at

trib

utin

g ch

arac

ters

Cha

ract

erDa

ys to

Plan

tPa

nicl

eN

o o

fN

o o

fSp

ikel

et10

00-s

eed

Prod

uctiv

ityBi

omas

sHa

rves

tG

rain

50

heig

htle

ngth

Prod

uctiv

efil

led

fert

ility

wei

ght

per d

aype

r pl

ant

Inde

xyi

eld

flow

erin

gtil

lers

grai

nspe

r pl

ant

per

plan

tpe

rpa

nicl

e

Day

s to

50

P1

0000

012

230

3431

0

1594

0

5331

0

1150

-00

195

000

49-0

053

60

2191

0

2472

flow

erin

gG

100

000

1181

037

30

016

91

054

21

011

49-0

022

90

0073

-00

657

022

80

025

70

Plan

t hei

ght

P

100

000

5653

-0

441

4

037

95

012

720

5292

014

700

4604

-0

253

6

016

30

G

1

0000

061

45

-05

199

0

3892

0

1301

054

12

014

540

4798

-02

621

0

1605

Pani

cle

leng

thP

100

00-0

025

80

3811

0

2527

0

1930

0

0041

-00

205

006

370

0774

G

100

000

0038

040

71

027

06

020

60

002

420

0073

006

540

1048

No

of P

rodu

ctiv

eP

1

0000

-00

043

020

21

-0

3806

023

08

-03

316

0

4903

0

2561

tille

rs p

er p

lant

G

100

00-0

002

90

2298

-0

434

00

2296

-0

453

0

057

49

025

85

No

of f

illed

gra

ins

P

1

0000

050

67

008

680

5112

0

3200

0

2694

0

6261

per p

anic

leG

100

000

5088

0

0874

053

48

034

06

027

36

065

35

Spik

elet

fert

ility

()

P

100

000

0613

049

97

004

900

4247

0

5043

G

1

0000

006

150

5270

0

0519

043

59

053

01

1000

see

d w

eigh

tP

100

000

1600

0

2852

-0

068

90

1470

G

100

000

1656

0

2999

-00

697

015

13

Prod

uctiv

ity p

er d

ayP

1

0000

029

02

057

85

096

81

G

1

0000

023

91

061

47

096

62

Bio

mas

s pe

r pla

ntP

100

00-0

561

9

025

85

G

100

00-0

571

1

020

47

Har

vest

inde

x (

)P

1

0000

061

74

G

1

0000

065

49

P- P

heno

typi

c co

rrel

atio

n co

effic

ient

G-

Gen

otyp

ic c

orre

latio

n co

effic

ient

Si

gnifi

cant

at

1

Si

gnifi

cant

at

5


66

SRIJAN et al






Acknowledgement


67

Tabl

e 2

Dire

ct a

nd in

dire

ct e

ffect

s of

var

ious

com

pone

nt c

hara

cter

s on

yie

ld in

rice

Cha

ract

erDa

ys to

Plan

tPa

nicl

eN

o o

fN

o o

fSp

ikel

et10

00-s

eed

Prod

uctiv

ityBi

omas

sHa

rves

tG

rain

50

heig

htle

ngth

Prod

uctiv

efil

led

fert

ility

wei

ght

per d

aype

r pl

ant

Inde

xyi

eld

flow

erin

gtil

lers

grai

nspe

r pl

ant

per

plan

tpe

rpa

nicl

e

Day

s to

50

P0

2451

-00

015

-00

035

-00

018

001

32-0

001

2-0

000

10

0049

000

30-0

011

00

2472

flow

erin

gG

025

22-0

001

8-0

004

0-0

002

30

0157

-00

011

-00

002

000

730

0041

-00

128

025

70

Plan

t hei

ght

P0

0300

-00

123

-00

058

000

480

0094

-00

013

000

310

1482

-00

260

001

280

1630

G

002

98-0

015

3-0

006

60

0072

001

12-0

001

20

0041

014

65-0

029

80

0147

016

05

Pani

cle

leng

thP

008

41-0

006

9-0

010

20

0003

000

94-0

002

50

0011

000

420

0012

-00

032

007

74

G

009

41-0

009

4-0

010

8-0

000

10

0118

-00

025

000

150

0243

-00

005

-00

037

010

48

No

of P

rodu

ctiv

eP

003

910

0054

000

03-0

011

0-0

000

1-0

002

0-0

002

20

2327

001

87-0

024

70

2561

tille

rs p

er p

lant

G0

0426

000

800

0000

-00

138

-00

001

-00

022

-00

033

023

140

0282

-00

323

025

85

No

of f

illed

gra

ins

P0

1307

-00

047

-00

039

000

000

0247

-00

051

000

050

5154

-00

181

-00

136

062

61

per p

anic

leG

013

67-0

006

0-0

004

40

0000

002

89-0

004

80

0007

053

89-0

021

2-0

015

40

6535

Spik

elet

fert

ility

()

P0

0282

-00

016

-00

026

-00

022

001

25-0

010

00

0004

050

38-0

002

8-0

021

40

5043

G

002

90-0

002

0-0

002

9-0

003

20

0147

-00

094

000

050

5311

-00

032

-00

245

053

01

1000

see

d w

eigh

tP

-00

048

-00

065

-00

020

000

420

0021

-00

006

000

580

1613

-00

161

000

350

1470

G

-00

058

-00

083

-00

022

000

600

0025

-00

006

000

750

1669

-00

186

000

390

1513

Prod

uctiv

ity p

er d

ayP

000

12-0

001

80

0000

-00

025

001

26-0

005

00

0009

100

82-0

016

4-0

029

10

9681

G

000

18-0

002

2-0

000

3-0

003

20

0154

-00

049

000

121

0077

-00

149

-00

345

096

62

Bio

mas

s pe

r pla

ntP

-00

131

-00

056

000

020

0036

000

79-0

000

50

0017

029

26-0

056

40

0283

025

85

G

-00

166

-00

074

-00

001

000

620

0098

-00

005

000

220

2410

-00

621

003

210

2047

Har

vest

inde

x (

)P

005

370

0031

-00

006

-00

054

000

66-0

004

3-0

000

40

5832

003

17-0

050

30

6174

G

005

750

0040

-00

007

-00

079

000

79-0

004

1-0

000

50

6194

003

55-0

056

10

6549

Phen

otyp

ic r

esid

ual e

ffec

t =

005

78

Gen

otyp

ic r

esid

ual

effe

ct =

00

560

P-

Phe

noty

pic

leve

l G

- G

enot

ypic

leve

l

Bol

d va

lues

- D

irec

t ef

fect

s N

orm

al V

alue

s -

Indi

rect

effe

cts

Sign

ifica

nt a

t 1

S

igni

fican

t at

5


68

REFERENCES









SRIJAN et al

69












70













DFF 086 -057 063 019 -029 033

PHT 072 -061 072 022 002 050

NPT -035 -045 -047 -070 -042 -041

PL 054 068 -030 -005 000 023

PG 013 018 -045 -005 065 059

TGW -024 -003 -016 000 043 028

GY 019 038 -022 002 039 018






DFF -127 128 -017 002 023 024 033

PHT -109 149 -019 003 028 -002 050

NPT 072 -091 031 001 -088 034 -041

PL -080 107 -014 016 -006 000 023

PG -024 033 -022 -001 125 -053 059

TGW 037 003 -013 001 081 -081 028



71





Residual GYPx7=029

Px2=149 Px3=031 Px4=0 Px5=125


rx12=004 rx23=-061 rx34=-047 rx45=-005 rx54= 065

rx13=-057 rx24=072 rx35=-07 rx44=0

rx14=083 rx25=022 rx30=-042

rx15=019 rx26=002

rx16=-029

Px6=-001Px1=127


72






REFERENCES









73






























Books



Thesis


i









REVIEW PROCESS




ii



____________________________________________________________________________________







1

2

3








Name

Signature

Office seal


iii






Nationality Indian



Nationality Indian



Nationality Indian





iv














v




30AJPJTSAU2018

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the j. res. pjtsau vol. xlvi no. 4 pp 1- 73, oct.-dec., 2018

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