Volume 49 Number 1 2015

Contents

Research Paper

Significance of bioinformatics in the detection of microbes 1-5Megha Kadam Bedekar and M. Makesh

Comparative effects of some botanicals and carbofuran in the managementof Meloidogyne incognita on okra 6-14A.A. Tanimola, D. A. Ukenwor and L. I. Akpheokhai

Evaluation of seven eggplant (Solanum species) accessions for resistance to root-knot 15-21nematode (Meloidogyne incognita)A.A. Tanimola, A.O. Asimiea and E.N. Ofoegbu

Effect of harvesting time and post harvest operations on seed yield and quality of niger 22-25[Guizotia abyssinica (L.f.) Cass]M.R. Deshmukh, Alok Jyotishi and A.R.G. Ranganatha

Genetic analysis of Indica-japonica derived rice NPT lines for yield and yield attributing 26-31traits under rainfed situationR.B. Yadav, D.K. Mishra, G.K. Koutu, S.K. Singh and Arpita Shrivastava

Efficacy of herbicides on productivity and economics of pigeonpea- 32-36based intercropping systemsChunni Lal Rai, R.K. Tiwari, Pawan Sirothia, Shailesh Pandey and Swati Jaiswal

Effect of sowing dates on growth, yield and economics of rice varieties under 37-40upland conditions of Rewa, Madhya PradeshPunit Tiwari, R.K. Tiwari, Amrita Tiwari, Vaishali Yadav and S.K. Tripathi

Effect of sowing date on weed infestation and yield of wheat (Triticum aestivum L.) 41-45varieties under different irrigation schedulesT.N.Thorat, Manish Bhan and K.K.Agrawal

Composition of macro and micro nutrients in leaves of shoot bearing healthy 46-51and malformed panicle in mango as influence by different source of nutrientsRajnee Sharma, P.K. Jain, S.K. Pandey and T.R. Sharma

Leaf spot and blight of soybean by Drechslera glycini sp. nov. in Madhya Pradesh: 52-53a new observationR.K. Varma, D.K. Pancheshwar and Satyendra Patel

Effect of culture media, pH and temperature on the mycelial growth and sporulation 54-60of Fusarium oxysporum f.sp ciceris isolates of chickpea from Central Zone of IndiaMinakshi Patil, Om Gupta, Maruti Pawar and Devashish Chobe

ISSN : 0021-3721 JNKVVVolume : 49 Research JournalNumber (1) : 2015 (January - April, 2015)

Issued : December 4, 2015

Available on website (www.jnkvv.org)

Pathogenic potential of Pratylenchus thornei and its management through commercial 61-65products in chickpeaJayant Bhatt, S.P. Tiwari and Arvind Jaware

Impact of district poverty initiative project (DPIP) in empowering the rural women 66-72M.Singh, M.K. Dubey and N.K. Khare

Profile of watershed beneficiaries in Jabalpur district of Madhya Pradesh, India 73-76Sonam Agrawal and Nalin Khare

Effect of moisture conditioning pretreatments on dehulling performance of soyabean dehuller 77-83Mohan Singh, D. Kumar and D.K. Verma

Effect of pre-milling treatments on the milling efficiency of CFTRI type dal mill 84-88A. Gupta, Mohan Singh, D.K. Verma and C.M. Abroal

Protease production by fungal isolate of vegetable waste 89-94Shiana Gaikwad, Roshni Choubey and Shikha Bansal

Production of amylase by Aspergillus flavus isolated from soil 95-99Shikha Gauri, Shikha Bansal and Roshini Choubey

Interactions between Agaricus campestris the edible mushroom and Aspergillus spp. 100-103the pathogenic fungus through dual-culture techniqueNeelima Raipuria and Femina Sobin

General zero inflated models with reference to poisson distribution 104-109H.L. Sharma, Arun Jhajharia and Siddarth Nayak

Fuzzy βββββ-continuous mappings 110-114M. Shukla

Maximum likelihood estimates for the parameters of an inflated poisson-lindley distribution 115-118H.L. Sharma, Arun Jhajharia and Siddarth Nayak

Use of Jack-knife technique in ratio estimator and circular systematic sampling scheme 119-123K.S. Kushwaha, A.K. Rai and Rajdeep Mishra

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Keywords: Bioinformatics, Gene, Gene Bank, BLAST,Homology, Diversity

Bioinformatics is the application of computer scienceand information technology to the field of biology andmedicine. It has become an important part of many areasof biology. The term bioinformatics was coined in 1978by Paulien Hogeweg and Ben Hesper. With theintroduction of human genome project in 1990's, thegenome revolution started. The genomic data frommanyliving species arebeing generated exponentially.This data is in the form of DNA/ RNA or amino acidsequences. Since a number of laboratories aregenerating genomic information on daily basis, genomedata repositories weredeveloped to give a commonplatform to deposit and access sequence data by allusers. With the introduction of a large number ofgenome sequencing projects of microbes, plants,invertebrates and higher vertebrates, molecular biologyhas now become a massively "data-oriented" scienceand wide spread, fast, and interdependent biologicalresearch data are being generated. This has createdthe problem of misleading results and inconclusiveinterpretations. Therefore, the re-introduction ofbiologically inspired computational methods in biologywas essentialto enhance the understanding of biologicalsystems as information processing systems (Hogeweg2011). Computational, statistical and informaticstechnologies along with mathematics developed parallelto the life science research and enabled scientists tointegrate, connect, and interpret the complexities of anybiological system eg. their genotypic, phenotypic andmetabolic character, cellular processes, development,regulatory pathway for metabolism, protein structure,function and spatial structure, changes and expressionsat the gene level, genome, proteome and metaboliteconstitution, post-transcriptional and post-translational

Significance of bioinformatics in the detection of microbes

Megha Kadam Bedekar and M. MakeshAquatic Environment and Animal Health Management DivisionCentral Institute of Fisheries Education, VersovaAndheri West, Mumbai 400 061Email: meghakadambedekar@gmail.com

modifications and their effect, responses of organisms tothe external stimuli like environment, stress, bioticstresses and interactions with other organisms(Singhet al. 2012).

The accessibility of microbial genomic andproteomics data and improved computational tools hasraised the expectation of the humanity to be able tocontrol the genetics by manipulating the existingmicrobes (Bansal 2005). The advantages are enormoussuch as better diagnosis of the diseases through theuse of protein biomarkers, molecular markers, protectionagainst diseases using correct candidate genesforeffective vaccines (Zagursky et al. 2003; Robinsonset al. 2003) and rational drug designand thedevelopment of techniques that help us visualize andunderstand the detailed microbial machine at thesystemic level. Since the sequencing of the firstmicrobial genome of Haemophilus influenzae in 1995(Fleischmann 1995), hundreds of microbial genomeshave been sequenced and archived for public researchin GenBank ftp://ftp.ncbi.nih.gov/genbank/ throughagencies such as DNA databank of Japan, NIH andDOE in USA, EMBL and EBI in Europe, nationallaboratories, universities, drug development companiessuch as Celera and organizations such as TIGR.

Following approaches have been used inbioinformatics: (i) use of computational search andalignment techniques to compare new genome againstknown genes to annotate the structure and function ofgenes in a new sequence, (ii) the use of mathematicalmodeling techniques such as statistical analysis, datamining, neural networks, and genetic algorithm,techniques to locate the common zones, features andhigh level functions and (iii) an integrated approach thatintegrates search techniques with mathematicalmodeling (Altschul et al. 1990; Altschul et al. 1997).

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The primary repositories for genetic informationare the NCBI GenBank, EMBL of Europe, and the DNAData Bank of Japan (DDBJ). All have almost identicalinformation due to international cooperativeagreements. Each entry or record in GenBank or itsmirror sites may contain identifying, descriptive, andgenetic information in ASCII-format files. Each recordis written in a specific standard format, organized sothat both humans and computer programs can extractthe desired information with reasonable ease.

Microbial disease diagnosis

Molecular diagnosis of microbial diseases (bacteria/virus/ fungi/ parasite) is basically dependent on theknowledge of nucleotide sequences, proteins and othersequences. Now-a-days PCR/ RT- PCR/ Realtime PCRhave become common molecular diagnostic tools fordisease detection. While developing a nucleic acidbased test, the most important criteria isthe identificationof the correct segment of nucleotide that should betargeted. This target is termed as candidate gene/candidate segment. The genome of an organism isclassified as segments like exons, introns, untranslattedregions (UTR), promoters, hypervariable regions,conserved regions etc. in DNA or RNA, and hydrophilicregion, hydrophobic region, transmembrane domains,signal peptide, hypervariable regions, antigenic regionsetc. in aminoacid sequences. During designing anucleotide based test all this information is needed tobe considered to develop a correct, unambiguous andreproducible assay.

For designing a nucleic acid based test certain pointsare needed to be considered

1) According to the central dogma of life, DNA istranscribed into RNA and RNA is translated intoprotein;all three forms are closely interconnected. Whiledesigning a nucleic acidbased test all three forms areneeded to be screened. The protein, which is translatedfrom a target DNA segment, its role in pathogenesis,and other pathways, should be thoroughly studiedbefore targeting a DNA/RNA segment. Many proteinshave isoforms,that are present at different stages ofdisease progression. Knowledge of these isoforms is amust.

2) A general diagnosis/ screening test needs to bedeveloped from a highly conserved segments of DNA,which is present is all the strains of the speciesconcerned. Differential diagnosis/ strain specific

diagnostic test should be developed from the DNAsegment that is variable or targeted hotspots formutations among strains.

3) Structural proteins/ hydrophilic proteins ofmicrobes are generally antigenic and mutation prone.

For all these points, knowledge of the sequenceof RNA/DNA/aminoacidis required. The nucleotidedatabases that are freely available on public serversare main platforms for getting all the requiredinformation.

Primary repository of DNA data for generating requiredinformation.

Sequence information

NCBI/ GENBANK is one of the most popular websitefor sequence information. Here each newly submittedsequence is given an accession number which isunique. The sequence information contains definitionof source, DNA/ amino acid sequence, coding region,classification of protein, type of tissue, country etc. Byusing key words/ accession number (if known),sequence of interest or protein can be retrieved.

Searching and alignment of sequence

After identifying the genes, the next step is to annotatethe genes with proper structure and function. Thefunction of the gene has been identified using popularsequence search and pair-wise gene alignmenttechniques. The four algorithms which are most popularfor functional annotation of the genes are BLAST,dynamic programming technique Smith-Watermanalignment and its variations (Waterman 1995), indexingbased scheme FASTA (Pearson and Lipman 1988 ) andits variations, and BLOCKS that uses multiple sequencealignment of conserved domains to identify motifs -characterizing patterns of proteins.

BLAST or Basic Local Alignment Search Tool, isone of the most common tool. This is an algorithm forcomparing primary biological sequence information,such as the amino-acid sequences of different proteinsor the nucleotides of DNA sequences. A BLAST searchenables a researcher to compare a query sequencewith a library or database of sequences, and identifylibrary sequences that resemble the query sequenceabove a certain threshold. Different types of BLASTsare available according to the query sequences. BLAST

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search is based upon expanding multiple probable seedpoints (longer than four nucleotides) that match (withthe help of scoring matrices such as BLOSUM or PAM(Moret et al. 2002) to identify the largest matchingnonrandom segment. Scoring matrices have positivematch-value for the amino acids that have commonbiochemical or biophysical properties and negativematch-values if the amino acids do not share biophysicalor biochemical properties. BLAST algorithm uses mostprobable combinations of nucleotide seeds to index thesequences in the database compromising someaccuracy.

BLAST algorithm has gone through manyimprovements in heuristics to improve the executionspeed, accuracy, and the dependence on predefinedscoring matrices. Two major improvements are: (i) theuse of two or more hits within a matching region beforeextending the high scoring segment and (ii) the use ofmultiple iteration of matching to derive a position specificscoring matrix to be used in place of predefinedbiochemical matrix PSI-BLAST.

Variations of BLAST

Nucleotide-nucleotide BLAST(blastn)

This program, given a DNA query, returns the mostsimilar DNA sequences from the DNA database thatthe user specifies.

Protein-protein BLAST (blastp)

This program, given a protein query, returns the mostsimilar protein sequences from the protein databasethat the user specifies.

Position-Specific Iterative BLAST (PSI-BLAST)

This program is used to find distant relatives of a protein.First, a list of all closely related proteins is created.These proteins are combined into a general "profile"sequence, which summaries significant features presentin these sequences. A query against the proteindatabase is then run using this profile, and a largergroup of proteins is found. This larger group is used toconstruct another profile, and the process is repeated.

BLAST programme gives an idea about the DNAfragment selected, how much this DNA is conservedamong different strains of microbes, location of

hypervariable region, type of protein it is associated withetc. Even without having any preliminary knowledge ofthe nucleotide sequence,a lot ofinformation can begenerated. This information is helpful in designingdiagnostics.

Other web based alignment tools are Clustal W, Multiplesequence alignment tool (MSA) Genetool etc.

Microbial diversity

Bioinformatics is used to comparemultiple genomesextensively to correlate and classify the genomes intofamilies and to study their evolutionary pattern. It isobserved by many scientists that evolution is acombination of point based mutation giving rise todifferent strains of pathogens based upon geneduplications, gene insertion, gene deletion, gene-fusion/fission, horizontal gene transfer, and domain levelrestructuring (Bansal 1999).

The evolutionary study efforts can be done usingthe following approaches: (1) construction of traditionalevolutionary tree using multiple sequence alignment of16SrRNA (Woese1998), (2) the study based upon wholegenome comparisons using sequence information oforthologous genes across multiple microbial genomes(Bansal and Meyer 2002).

16S ribosomal RNA is a component of the 30Ssmall subunit of prokaryotic ribosomes. The genescoding for it are referred to as 16S rDNA and are usedin reconstructing phylogenies.The 16SrRNA approachis rooted in the concept of point mutation of conservedgenes due to their slow mutation rate, uses 16SrRNAdatabase and multiple sequence alignment to build anevolutionary tree. Using 16SrRNA database threedistinct domains - bacteria, archaea, and eukaryotes -were identified. Archea domain is hyperthermophilic andits 16SrRNA is somewhat different from 16SrRNA ofbacteria (Bansal 2002).

Since 1998, after the availability of multiplemicrobial genomes, researchers have tried to build theevolutionary tree by comparing other highly conservedgenes. The results have shown that the evolutionarytree varies a lot depending upon the choice of theconserved genes.

The other approach is based on comparingoverall gene-content of functionally equivalent genesto identify the cumulative similarity of two genomes. Thedata is normalized to take care of different size ofgenomes. The major assumption in this scheme is that

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conserved genes are very few and do not give aconsensus, and slow mutation rate only contributes togood multiple sequence alignment. Whole genomecomparisons can balance out the error introduced bycomparing a single conserved gene.

Bioinformatics has enabled researchers to studymicrobial biodiversity because of its direct targeting ofsequences important in molecular identification, datastorage and retrieval system; something that was verydifficult inlab research. By means of bioinformatic toolsit is possible to work efficiently on microbial diversity,identification, characterization, molecular taxonomy andcommunity analysis patterns of both culturable andunculturable organisms (Rogozin et al. 2002).Cataloging and digitization of microbial diversity is oneof the most important tasks for bioinformaticians.

Till 2012 over 11,364 whole genome sequencesorganized in three major groups of organisms i.e.eukaryota, prokaryota (archaea and bacteria) andviruses were completed and submitted in Genomedatabase of NCBI, including complete chromosomes,organelles and plasmids as well as draft genomeassemblies (Singh et al. 2012).

Protein structure prediction

Earlier protein structures were being determined by X-ray crystallography, NMR, Mass spectroscopy etc, butwith the bioinformatic tools it is possible to predict theprotein structure by using homology modeling or ab initioapproach. In homologymodelling by using sequencesof available proteins a pairwise sequence alignment isdone to detect the relatively similar protein to determinethe protein structure and function. While in ab initiomethod structure of the newly determined sequence ispredicted on the basis of nature of the amino acids andthe state of least energy(Bansal 2005).

Identification of protein

Using protein tools information regarding protein/ ORFetcaregenerated. The UniProt Knowledgebase(UniProtKB) is the central access point for extensivecurated protein information, including function,classification, and cross-reference. It consists of twosections: UniProtKB/Swiss-Prot which is manuallyannotated and is reviewed and UniProtKB/TrEMBLwhich is automatically annotated and is not reviewed.The UniProt Reference Clusters (UniRef) databasesprovide clustered sets of sequences from the UniProtKBand selected UniProt Archive records to obtain complete

coverage of sequence space at several resolutionswhile hiding redundant sequences. The UniProt Archive(UniParc) is a comprehensive repository, used to keeptrack of sequences and their identif iers. TheUniProtMetagenomic and Environmental Sequences(UniMES) database is a repository specif icallydeveloped for metagenomic and environmental data.

Determination of antigenic peptides

Several methods based on various physio-chemicalproperties of aminoacids (flexibility, hydrophibility,accessibility) have been published for the prediction ofantigenic determinants of which the antigenic Index andPreditop are good examples.

The DNA sequence can be deposited to a Meta-server through internet, the metaserver is a commonplatform for many protein predictor algorithms. This willgenerate a collective data of protein structure, function,antigenicity, hydrophilic/ hydrophobic/ transmembranedomain etc.

The future of bioinformatics is dependent on theintegrated ability of the simulation, computationalmethods, and modeling to explore and extractinformation or predict what exactly is going on within acell in real time (Altman and Klein 2002). Integration ofa wide variety of data sources like genomic,metabolomic and proteomic, will allow us to use diseasesymptoms to predict genetic mutations and vice versa.Bioinformatics driven wet lab research can save a lotof energy, time and money in terms of pre determinationof structure and function of important genes. It is theduty of a bioinformatician to go through the datagenerated, critically before reaching any end points.Auditing of the algorithms should be of utmostimportance.

Acknowledgement

Author is thankful to Director W.S. Lakra of CentralInstitute of Fisheries Education, Mumbai, India, for hisguidance and support.

Reference

Altman RB, TE Klein (2002) Challenges for biomedicalinformatics and pharmacogenomics.Annual RevPharmacol Toxicol 42 : 113-133

Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990)Basic alignment search tools.Journal of Molecular

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Biology 215 : 403-410Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z,

Miller W, Lipman DJ (1997) Gapped BLAST and PSI-BLAST: A new generation of protein database searchprograms. Nucleic Acids Res 15(17) : 3389-3402

Bansal AK (1999) An automated comparative analysis ofseventeen complete microbial genomes.Bioinformatics 15(11) : 900-908

Bansal AK, Meyer TE (2002) Evolutionary analysis by wholegenome comparisons. J Bacteriol 184(8) : 2260-2272

Bansal A (2005) Bioinformatics in microbial biotechnology - amini review, Microbial Cell Factories 4:19

Fleischmann RD (1995) Whole-genome random sequencingand assembly of Haemophilus influenzae Rd.Science , 269:496-512

Hogeweg P (2011) The roots of bioinformatics intheoreticalbiology. PLoSComput. Biol 7:e1002021-e1002021

Moret BME, Bader DA, Warnow T (2002) High PerformanceAlgorithm Engineering for ComputationalPhylogenetics. J Supercomputing 22:99-111

Pearson WR, Lipman DJ (1988)Improved tools for biologicalsequence comparison.Proceed Natl Acad Sci USA85(8):2444-2448

Robinson WH, Fontoura P, Lee BJ (2003) Protein microarraysguide tolerizing DNA vaccine treatment ofautoimmune encephalomyelit is. NatureBiotechnology 21(9):1033-1039

Rogozin, IB, Makarova KS, Natale DA, Spiridonov AN, TatusovRL (2002)Congruent evolution of different classesofnon?coding DNA in prokaryotic genomes. NuclAcids Res 30: 4264-4271

Singh, DP, Prabha R, Rai A, Arora DK (2012) Bioinformatics-Assisted Microbiological Research: Tasks,Developments and Upcoming Challenges.American J Bioinformatics 1 (1): 10-19,

Waterman MS (1995) Introduction to Computational Biology.Maps, Sequence and Genomes.Chapman & Hall,London

Woese C (1998) The universal ancestor.PNAS 95(12):6854-6859

Zagursky RJ, Olmsted SB, Russell DP, Wooters JL (2003)Bioinformatics: how it is being used to identifybacterial vaccine candidates. Vaccines 2(3):417-436

(Manuscript Receivd : 30.05.2014; Accepted :30.04.2015)

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Abstract

The nematicidal efficacy of air-dried milled leaves of Ocimumgratissimum, Phyllanthus amarus, Vernonia amygdalina wascompared with carbofuran in the management of Meloidogyneincognita on okra in a pot experiment laid out in completelyrandomized design. Two-week old okra (Clemson spineless)seedlings were inoculated with 5,000 eggs of M. incognitaexcept uninoculated control seedlings. Milled leaves ofbotanicals at 100 kg/ha and carbofuran 5G at 2 kg a.i/ha wereapplied at one week after inoculation (WAI). Screening forphytochemicals in the botanicals was also carried out usingstandard procedures. Data were collected on vegetativegrowth (VG), fruit weight, gall index (GI) and nematodereproduction. Data were analyzed using ANOVA anddescriptive statistics. Carbofuran and the botanicalssignificantly improved VG and fruit weight of okra, but V.amygdalina was outstanding among the botanicals (P£0.05).Root damage was significantly reduced in carbofuran, V.amygdalina, O. gratissimum and P. amarus treated okra by70, 60, 58 and 54%, respectively. Nematode population wasreduced significantly in carbofuran, V. amygdalina, O.gratissimum and P. amarus treated okra and thus promotedyield. Phytochemicals in the botanicals are tannins, saponins,anthraquinones, cardenolides and alkaloids. The botanicalsespecially V. amygdalina at 100 kg/ha compared favourablywith carbofuran in the management of M. incognita on okraand therefore can be used to manage M. incognita on okra.

Keywords: Ocimum gratissimum, Phyllanthus amarus,Vernonia amygdalina, carbofuran, Meloidogyneincognita, okra, management.

Okra, Abelmoschus esculentus (L) Moench is one ofthe most important fruit vegetables in Nigeria where it

is consumed in different forms (Farinde et al. 2007).Okra is grown generally in Asia, East, Central and WestAfrica as well as the Caribbean (Udoh et al. 2005).Nigeria is the second highest producer of okra with aproduction statistics of 1.04 million metric tons after India(FAO 2010).

Plant-parasitic nematodes are one of the majorbio-constraint reported on okra (Adesiyan et al. 1990).Reports of studies on okra from most growing regionsworldwide showed that root-knot nematodes(Meloidogyne species) are the most important plant-parasitic nematodes of okra due to their pathogenicityand ubiquitous distribution in okra producing regions(Schippers 2000). In Nigeria, Meloidogyne incognita hasbeen reported as the major species of the root-knotnematodes infecting okra (Adesiyan et al. 1990). Yieldlosses due to Meloidogyne species on okra can be ashigh as 89% (Ogbuji and Okonkwo 1978). At present,the use of synthetic nematicides is employed in themanagement of PPNs to maintain their population beloweconomic threshold levels.

Effectiveness of carbofuran in the managementof M. incognita on okra has been acknowledged(Akinlade and Adesiyan 1982). However, managementof plant-parasitic nematodes with conventionalnematicides has declined internationally because ofenvironmental concerns and human safety (Whitehead1998, Adekunle and Fawole 2003). Many syntheticnematicides have been banned in many developedcountries and many chemical companies are graduallyphasing out their production. This makes nematicidesscarce and expensive where they are still being used

Comparative effects of some botanicals and carbofuran in themanagement of Meloidogyne incognita on okra

A.A. Tanimola, D.A. Ukenwor and L.I. Akpheokhai*Department of Crop and Soil ScienceFaculty of AgricultureUniversity of Port HarcourtPort Harcourt, Rivers State, Nigeria*Department of Crop ScienceFaculty of AgricultureUniversity of Uyo, Uyo, Akwa Ibom State, NigeriaEmail: tanimoladebo@yahoo.com

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in the developing countries (Adekunle and Fawole2003). There is increasing need to find more acceptablemanagement alternatives for plant-parasitic nematodes(Adesiyan et al. 1990, Eapen et al. 2005)

Plants are being explored in the management ofPPNs because they are considered environment-friendly and easy to apply (Adekunle and Fawole 2003).The nematicidal potentials of plants had been linked tothe presence of secondary metabolites(phytochemicals) such as alkaloids, phenolics, tanninsand fatty acids (Chitwood 2002). These phytochemicalshave been reported to show nematicidal properties andalso do enhance defense mechanisms in plants againstpests and pathogens (Chitwood 2002). Many plantshave been profitably screened based on theirphytochemical constituents and found relevant in themanagement of plant-parasitic nematodes (Chitwood2002).

Ocimum gratissimum (African basil) is of thefamily Fabaceae. A non-woody perennial shrub that isnative to Africa and other tropical and subtropicalregions of the world (Adeniji et al. 2010). Sulistiarini(1999) reported that O. gratissimum have antimicrobial,antibacterial and antifungal properties. The plant alsoshowed anthelmintic activity against Haemonchuscontortus and other strongylid nematodes of man(Pessoa et al. 2002). Phytochemically screened O.gratissimum were found to contain alkaloids, tannins,glycosides and flavonoids (Harve and Kamath 2004),but the types and quantities could vary from season toseason, and from geographical area to another(Sofowora 2006)

Phyllanthus amarus is an annual erect herb thatbelongs to the family Euphorbiaceae. The plant hadbeen reported to have nematicidal and fungicidalproperties which make it effective in control M. incognita(Chitwood 2002). Orech et al. (2005) posited thattannins, alkaloids, glycosides and flavonoids werephytochemicals present in Phyllanthus amarus.Vernonia amygdalina commonly known as bitter leaf isa dicot of the family Compositaceae. It is non-woodyperennial shrub that is native to Africa and other tropicalregions of the world. The organic crude extract from V.amygdalina was toxic to M. incognita in vitro (Iwalokunet al. 2004). It contains compound with nematicidal,antimicrobial and insecticidal activities (Adeniji et al.2010). The plant showed nematicidal activity both infresh and dried states (Harve and Kamath 2004).

The study was undertaken to compare thenematicidal effects of V. amgydalina, P. amarus and O.gratissimum with carbofuran in the management of M.incognita, the consequent of their effects on the yield of

okra, and to also identify the phytochemicals present ineach of the three botanicals.

Materials and methods

Experimental site/Laboratory

The experiment was conducted at the Research Farmand Nematology Research Laboratory, Department ofCrop and Soil Science, Faculty of Agriculture, Universityof Port Harcourt, Choba, Rivers State. The ResearchFarm lies within Lat. 0.40 538.3'N and Long. 0.06054.38'E in southern Nigeria. Other laboratory activitieswere carried out at the Nematology ResearchLaboratory, Department of Crop Protection andEnvironmental Biology, Pharmacognosy ResearchLaboratory and Multidisciplinary Central ResearchLaboratory, all in the University of Ibadan, Oyo State,Nigeria.

Sources and Preparation of Botanicals

Vernonia amygdalina (L.), Ocimum gratissimum (L.),Phyllanthus amarus (L.) were collected at Choba, RiversState and the identity of the plants were authenticatedby a botanist in the Department of Forestry and WildlifeManagement, University of Port Harcourt. Specimenvouchers were deposited at the herbarium ofDepartment of Forestry and Wildlife Management,University of Port Harcourt. The leaves of each botanicalwere spread separately on laboratory benches and air-dried in the laboratory for six weeks. The air-dried leaveswere milled into powder using Kenwood® WarringBlender. Each milled sample of botanical was collectedinto a glass bottle and properly labeled until when milledsamples are needed.

Preparation of Nematode Inoculum

Nematode eggs were extracted from galled roots usingthe sodium hypoclorite (NaOCl) method (Hussey andBaker 1973). Meloidogyne incognita infected-galledroots of Celosia argentea were collected from theinoculum nursery of the National Institute of Horticulture(NIHORT), Ibadan. The galled roots were chopped into1-2 cm pieces and placed in a two litre capacity conicalflask into which 500 ml of 0.5% sodium hypochlorite(NaOCl) was poured. The roots were shaken in thesolution for four minutes and poured into a stack of threesieves, with the one mm aperture size sieve on topfollowed by the 45 m aperture size sieve and finally

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the 25 m aperture size sieve in which the eggs wereretained. The retained eggs were then washed into abeaker after several rinses to remove all traces ofNaOCl. The number of eggs per millilitre of thesuspension was estimated by counting under astereomicroscope (Leica Wild M3C). This was thenadjusted to 2,000 eggs per ml by concentrating thesuspension. The nematode egg suspension was usedas inoculums for the pot studies.

Pot Experiments

Sandy-loam topsoil was steam-sterilized at 1200C for150 minutes using a soil sterilizer and allowed to restfor four weeks to regain stability. Twenty-four pots of30 cm diameter by 18 cm depth (five kg capacity) werefilled each with 5 kg of the steam-sterilized sandy-loamtopsoil. The pots were arranged in a completelyrandomized design (CRD) in the screenhouse with eachtreatment replicated four times. Two seeds of okra(Clemson spineless) were sown in each pot and laterthinned to one seedling per pot at one week after sowing(WAS). Two weeks after sowing, the okra seedlingswere each inoculated with 5,000 eggs of M. incognitawhile the control pots were left uninoculated. Inoculationwas done by dispensing the inoculums into holes madeas close to the plant roots as possible using anEppendorf pipette. A week after inoculation, carbofuranand the air-dried milled leaves of botanicals wereapplied to the soil. The treatments include inoculated-untreated, O. gratisimum at 100 kg/ha (5 g/pot), V.amygdalina at 100 kg/ha (5 g/pot), P. amarus at 100kg/ha (5 g/pot), carbofuran at 2 kg. a.i/ha (0.2 a.i. g/pot) and distilled water was added to the uninoculatedcontrol pots. The plants were watered daily and keptweed free. The experiment was terminated at 10 weeksafter inoculation (WAI). The experiment was repeatedto validate data.

Data collection

Plant height (cm) was collected at eight WAI using metrerule. Also at eight WAI, yield parameters such as weightof fruits/plant (g), fresh shoot and root weights (g) weredetermined using Mettler balance®. The plant roots aftereach harvest were carefully dug out after each pot wasturned upside down on a polyethylene sheet, soil wasdislodged carefully from roots and roots were rinsed inwater to ensure clarity. Each root system was then ratedfor galls on a scale of 0-5 as described by Taylor andSasser (1978); where 0=No galls or egg masses; 1=1-2 galls or egg masses; 2=3-10 galls or egg masses;

3=11-30 galls or egg masses, 4=31-100 galls or eggmasses and 5=more than 100 galls or egg masses.

The entire root system of each plant was thencut into 2 cm pieces and shaken vigorously in 0.5%sodium hypochlorite (NaOCl) solution to extract the eggs(Hussey and Barker 1973). The number of eggsextracted from each root system was estimated asdescribed in preparation of inoculum. The soil nematodepopulation was also estimated from 100 cm3 soil fromeach pot using the modified Baermann pie-pan method(Whitehead and Hemming 1965) described by Coyneet al. (2007); total number of nematodes in soil wascomputed by extrapolating the number in 100 cm3 tothe volume of soil The final nematode population perpot was then computed by adding the total number ofnematodes per plant root and the total number ofnematodes in soil per pot. The reproductive factor (RF)of the nematode was then computed by dividing thefinal nematode population (Pf) by the initial nematodepopulation (Pi) (5,000 eggs). The shoot dry weight ofthe plants was also determined using Mettler balance®.

Data analysis

Count data were transformed using Log10 (X+1) priorto analysis (Gomez and Gomez 1984). Data collectedin first trial were statistically similar to those of secondtrial; therefore, they were pooled before analysis usinganalysis of variance (ANOVA) with statistical analysissystem (SAS) 9.1 (2002) package and means werepartitioned using Least Significant Difference (LSD) at5% level of probability.

Qualitative analysis of phytochemicals

The phytochemical screening of leaves of each air-driedmilled botanicals was carried out to determine thepresence of tannins, saponins, anthraquinones,alkaloids and cardenolides.

Test for tannins

Weights of 0.5 g of air-dried milled leaves of eachbotanical was taken using a Mettler balance into testtubes with 5 ml distilled water and thereafter, the mixturewas properly shaken.. The test tube was heated in awater bath to 1000C after which it was left to cool andthen filtered with Whatmann No. 1 filter paper. Ferricchloride was added to the filtrate as a reagent to indicatethe presence of tannins. The presence of tannin was

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acknowledged when the solution turned dark blue, whilethe presence of saponins was acknowledged by frothing(Trease and Evans 1989).

Test for alkaloids

Milled leaves (0.5 g) of each botanical were weighedinto test tubes with 10 ml of distilled water. The set-upwas heated at 70o C for two minutes and filtered.Aqueous extracts of filtrate was spotted as Thin LayerChromatography plates and later sprayed withDragendorff's reagent. The presence of alkaloids wasconfirmed when there was orange-red colour (Treaseand Evans 1989).

Test for anthraquinones

The Borntrager test was used for this in which 2 ml ofthe test sample was shaken with 4 ml of hexane. Theupper lipophilic layer was separated and treated with 4ml dilute ammonia solution. If the lower layer changedfrom violet to pink it indicated the presence ofanthraquinones (Chhabra et al. 1984, Orech et al.2005).

Test for cardenolides

The powdered leaves were thoroughly mixed with 20ml distilled water and kept at room temperature for twohours. The suspension was filtered into separate testtubes (Acid B).To A, four drops of reagent was added.The appearance of a blue violet colour indicated thepresence of cardenolides. Test tube B was used tomonitor and compare colour changes (Chhabra et al.1984).

Infrared analyses (IR) of leaves of botanicals

The air-dried milled leaves (0.8 mg) of O. gratissimum,V. amygdalina and P. amarus were mixed with 80 mgof potassium bromide (KBr). The mixture was latercompressed into a transparent disc. The disc wasscanned in a Fourier Infrared Transform (FITR)spectrometer (Perkin Elmer spectrum BXII) in theMultidisciplinary Central Research Laboratory,University of Ibadan. The IR spectra were printed outwith the aid of the machine printer.

Results and discussion

Effects of Ocimum gratissimum, Vernonia amygdalina,Phyllanthus amarus and carbofuran on growth of M.incognita-infected okra

At eight weeks after inoculation (WAI), carbofuran-treated okra had the tallest plant of 30.6 cm but thiswas not significantly taller (P 0.05) than okra plantstreated with V. amygdalina (26.3 cm) as presented inTable 1. The mean height of uninoculated okra was notsignificantly different (P 0.05) from V. amygdalina andO. gratissimum treated okra. However, the M. incognita-infected okra without botanical or carbofuran treatmentwas significantly shorter (P 0.05) in height (12.4 cm)than the carbofuran and okra plants treated withbotanicals (Table 1).

Carbofuran-treated okra had the highest freshshoot weight (12.2 g) and this was significantly higher(P 0.05) than the fresh shoot weights of P. amarus (4.9g), O. gratissimum (5.2 g) and V. amygdalina (8.6 g).Inoculated-untreated okra had the lowest fresh shootweight of 3.5 g compared with the treated plants.Inoculated-untreated control okra showed the highestfresh root weight of 5.3 g and the lowest fresh root weightwas obtained in M. incognita-infected okra treated withP. amarus (2.9 g) (Table 1).

Okra plants treated with milled leaves ofbotanicals and carbofuran had significantly increased(P?0.05) fruit weight when compared with theinoculated-untreated okra (Table 1). Carbofuran and V.amygdalina treated okra had fruit weights of 8.7 g and7.1 g, respectively which was significantly higher thanother botanical treatments and the uninoculated controlokra (Table 1).

The galling index, second stage juveniles in soil,total egg population per root system, final nematodepopulation in pot and reproductive factor in inoculated-untreated control increased significantly (P£0.05) thanthe botanically treated and the uninoculated okra plants(Table 2). However, all the botanicals and carbofurantreated okra plants had fewer galls when compared withinoculated control plants (Table 2). Phytochemicalsidentified in milled leaves of O. gratissimum, V.amygdalina and P. amarus include alkaloids,cardenolides, saponins, and anthraquinones which wasabsent in P. amarus and V. amygdalina (Table 3).

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Table 1. Effects of botanicals and carbofuran on plant height (cm), fresh shoot, root and fruit weights (g) ofMeloidogyne incognita-infected okra

Treatments Plant height Fresh shoot weight Fresh root weight Fruit weight(cm) (g) (g) (g)

P. amarus (5 g/pot) 20.7 4.9 2.9 4.0

V. amygdalina (5 g/pot) 26.3 8.5 4.6 7.1

O. gratissimum (5 g/pot) 23.8 5.2 4.2 3.1

Carbofuran (0.2 a.i.g/pot) 30.6 12.2 4.6 8.7

Uninoculated control 25.9 7.2 4.1 3.8

Inoculated control 12.4 3.5 5.3 0.5

LSD (P 0.05) 4.5 1.9 1.7 2.7

Each value is a mean of four replicates. Inoculated control=inoculated-untreated okra. Uninoculated = M. incognitauninfected okra. a.i. g/pot=active ingredient per pot

Table 2. Effects of botanicals and carbofuran on gall index, Meloidogyne incognita population and reproduction onokra

Treatment Gall index J2 Total egg Final nematode Reproductivepopulation population/root population Factor

system

P. amarus (5 g/pot) 2.5 1400 13250 14650 2.9

V. amygdalina (5 g/pot) 2.1 1050 10000 11050 2.2

O. gratissimum (5g/pot) 2.3 1350 12000 13350 2.7

Carbofuran (0.2 a.i.g/pot) 1.5 700 5125 5825 1.2

Uninoculated control 0.0 0.0 0.0 0.0 0.0

Inoculated control 5 8350 67250 75600 15.1

LSD(P 0.05) 0.7 501.3 3407.3 3503 0.7

Each value is a mean of four replicates. Inoculated control=inoculated-untreated okra. Uninoculated = M. incognitauninfected okra. a.i. g/pot=active ingredient per pot

Table 3. Phytochemicals identified in the milled leaves of some selected botanicals

Phytochemicals O. gratissimum V. amygdalina P. amarus

Alkaloids + + +Tannins + + +Anthraquinones + - -Saponins + + +Cardenolides + + +

+ Means present; - means absent

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Fig. 1. Infrared spectrum showing functional groups in the leaves of Ocimum gratissimumT= Transmittance. W= Wavelength

Key:Wavelength (cm-1) group3500-4000 OH3300-3500 NH2900-3000 CH1650-1730 Carbonyl1600-1650 Aromatic1500-1400 Double bonds1200-1100 Carboxyl996-700 Phenol

Key:Wavelength (cm-1) group3500-4001 OH3300-3501 NH2900-3001 CH1650-1730 Carbonyl1600-1651 Aromatic1500-1401 Double bonds1200-1101 Carboxyl996-700 Phenol

Fig. 2. Infrared spectrum showing functional groups in the leaves of Phyllantus amarusT= Transmittance. W= Wavelength

Fig. 3. Infrared spectrum showing functional groups in the leaves Vernonia amygdalinaT= Transmittance. W= Wavelength

Key: Wavelength (cm-1) group3500-4002 OH3300-3502 NH2900-3002 CH1650-1730 Carbonyl1600-1652 Aromatic1500-1402 Double bonds1200-1102 Carboxyl996-700 Phenol

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Infrared analyses of leaves of Vernonia amygdalina,Ocimum gratissimum and Phyllanthus amarus

The spectra of the infrared analyses of milled leaves ofthe botanicals showed the functional groups present ineach botanical. Figure 1 showed that Ocimumgratissimum has hydroxyl (OH), amine (NH), carboxylicacid, carbonyl, phenol and aromatic groups. Figure 2showed that Phyllanthus amarus contains hydroxyl(OH), amine (NH), double bonds, phenol as thefunctional groups present. Figure 3 showed that V.amygdalina have hydroxyl (OH), amine (NH), carboxylicacid, carbonyl, double bonds, and phenol as thefunctional groups present.

Meloidogyne incognita-infected okra treated witheither botanicals or carbofuran showed improvementin growth when compared with the inoculated-untreatedokra plants. This was evident in the increase in plantheight than uninoculated-untreated okra. Theimprovement in growth observed in the O. gratissimum,V. amygdalina and P. amarus treated okra comparedfavourably with those of carbofuran-treated okra. Theobservations showed that the botanical treatmentapplied had nematicidal effects on M. incognita, thusmitigated adverse effects of the nematode on growth ofokra. This view was corroborated by Siji et al. (2010)when they reported increase in plant growth parametersof okra such as plant height and number of leaves whenCleome viscosa powder was applied at 50, 100 and250 g per plot on M. incognita-infected okra.

The good performance of carbofuran-treated okramight be due to the efficacy of the active ingredients inthe nematicide that had been attested to as effective inmanaging Meloidogyne incognita on okra, soybean andother crops (Adegbite et al. 2003, Tanimola and Godwin-Egein, 2009, Siji et al. 2010, Akpheokhai et al. 2012).The prophylactic effects of carbofuran in improvingvegetative growth of plants have also been attested toby various workers (Adegbite and Adesiyan 2001,Tanimola and Godwin-Egein 2009, Akpheokhai et al.2012). The poor growth observed in the inoculated-untreated okra was probably due to M. incognitainfection that impaired root efficiency in absorbingnutrients and water from the soil for good growth andyield (Adegbite et al. 2003, Tanimola and Adesiyan2006).

The improvement in growth and yield observedin M. incognita-infected okra plants treated with the threebotanicals could be due to increased nitrogen andorganic matter content of the soil as is achieved whenplants are mulched thereby making nutrients available

easily. It could also mean that the botanicals possesssome nematicidal properties that mitigated thedamaging effects of M. incognita on okra and thuspromoted crop growth (Onifade and Fawole, 1996,Harve and Kamath 2004, Orech et al. 2005, Tanimolaand Adesiyan 2006). The improvement in fruit weightin the carbofuran and botanicals-treated okra might bethat the treated okra had good growth, developmentand subsequently good yield due to mitigation ofdestructive impacts of M. incognita (Adegbite et al. 2003)

The treated M. incognita-infected okrasignificantly reduced galling (root damage), eggpopulation and second-stage juveniles than theinoculated-untreated okra. The better root health intreated okra compared to infected-untreated might haveencouraged better anchorage of plants, uptake ofnutrients and water from soil by treated plants(Akpheokhai et al. 2012). These botanicals showednematicidal activity by reducing galling just like someother previously reported plants and the nematicidalactivity could be linked to the presence ofphytochemicals in these botanicals. Siji et al. (2010)reported the efficacy of botanicals such as Cleomeviscosa in reducing root galling and nematodepopulation in M. incognita-infected okra when thepowder was applied. The okra plants were reported freeof galls when the botanical was applied at 250 g/plot.Ramakrishnan et al. (1999) reported that cassavaleaves applied as soil amendment at 50 g or 100 g/plotsignificantly reduced M. incognita population andimproved plant growth parameters of rice (var. jaya).,and a reduction in nematode (Meloidogyne graminicola)infestation was observed in plots amended with thechopped botanicals of Polygonum hydropipr, neemseed, Ageratum sp., Mikania sp., rice straw and waterhyacinth.

The large population of M. incognita in the rootsand soils of inoculated-untreated okra as compared toother treated plants indicated that M. incognitareproduced freely since there was neither theapplication of the botanicals nor carbofuran that mighthave hindered activities of M. incognita. Reproductiverate of M. incognita was least in carbofuran-treated okrabecause of the efficiency of the nematicide on M.incognita either by ensuring mortality or causingdysfunction in the reproductive ability of the nematode(Disanzo 1977). It may also be that carbofuran preventsor limits hatching of eggs and the movement of juvenilesinto roots where they ought to complete theirdevelopment (Adegbite and Adesiyan 2001).Meloidogyne incognita had lower rate of reproduction

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in okra treated with V. amygdalina, O. gratissimum andP. amarus as compared with inoculated-untreated okra.The nematicidal efficacy of the botanicals was evidentin the reduction of reproduction rate of M. incognita onokra and this might be due to the active principles inthem. This implies that the botanicals were quitenematicidal, but not as much as carbofuran.

This study showed the presence of somephytochemicals and identified functional groups whichhave been reported to impact nematicidal activity inplants (Harve and Kamath 2004, Orech et al. 2005).The view was supported by the findings of Adeniji et al.(2010) that reported that the pesticidal potentials of theethanolic extracts of V. amygdalina and O. gratissimummight be linked to the active ingredients in them.Iwalokun et al. (2004) have also reported that V.amygdalina possesses antibacterial, antifungal,antiplasmodial and nematicidal properties. Thus, theair-dried milled leaves of these botanicals can equallybe used in place of carbofuran since they comparedfavourably with carbofuran-treated okra.

The phytochemicals and functional groups inthese botanicals might be the basis of their nematicidalefficacy (Adeniji et al. 2010). These phytochemicalshave been identified as alkaloids, saponins, phenols,anthraquinones, tannins amongst others. Thesephytochemicals when present in plants have beenreported to confer pesticidal abilities (Adeniji et al. 2010,Harve and Kamath 2004, Orech et al. 2005). The resultsof the phytochemical screening of the botanicals in thisstudy are in support of those of other workers (Harveand Kamath 2004, Orech et al. 2005, Adamu et al. 2008,Adeniji et al. 2010). These phytochemicals andfunctional groups identified might be the reason behindthe nematicidal potentials of the botanicals used.

Conclusion

Air-dried milled leaves of V. amygdalina, O. gratissimumand P. amarus at 100 kg/ha (5 g/ 5 kg soil) were highlyeffective in suppressing the adverse effects of M.incognita on okra and equally promoted good growthand yield. Vernonia amygdalina showed more promisethan the rest of the botanicals applied. The botanicalsespecially V. amygdalina compared favourably withcarbofuran in the management of M. incognita on okra,and thus their application can be encouraged. Mostbotanicals are easily available, less expensive, non-toxic to humans and non- target organisms; these shouldencourage their use in the management of root-knot

nematodes in lieu of carbofuran.

References

Adamu M, Nwosu CO, Igbokwe IO (2008) Toxicity andphytochemical constituents of Aqueous extract ofOcimum gratissmum leaf. Nigeria Veter J 29(3): 48-57

Adekunle OK, Fawole B (2003) Chemical and non-chemicalcontrol of Meloidogyne incognita infecting Okra underfield conditions. Moor J Agric Res 4 (1): 94-99

Adeniji SA, Orjiekwe CL, Ehiagbonare JE, Arimah BD (2010)Preliminary phytochemical analysis and insecticidalactivity of ethanolic extracts of four tropical plants(Vernonia amygdalina, Sida acuta, Ocimumgratissmum and Telfaria occidentalis against beansweevil (Acanthscelides obtectus). International JPhysical Sci 5(6): 753-762

Adegbite AA, Adesiyan SO (2001) Effect of carbofuran(Furadan) on the performance of four nematodesusceptible varieties of soyabean (Glycine max (L.)Merr. Tropical Oilseeds J 6:11-23

Adegbite AA, Agbaje GO, Akinolosotu TA (2003) Performanceof new hybrid yam (D. rotundata Poir) varieties inthe forest zone of Nigeria. Tropicultura 21 (3): 149-152

Adesiyan SO, Caveness FE, Adeniji MO, Fawole B (1990)Nematode pests of tropical crops. HeinemannEducational Books (Nigeria) Ltd. 114 pp

Akinlade EA, Adesiyan SO (1982) The efficacy of carbofuranin controlling Meloidogyne incognita on Okra(Abelmoschus esculentus). Nigerian J Pesticides andAgric Chem 1:22-34

Akpheokhai LI, Claudius-Cole AO, Fawole B (2012) Evaluationof some plant extracts for the management ofMeloidogyne incognita on Soybean (Glycine max).World J Agric Sci 8 (4): 429-435

Chhabra SC, Uiso FC, Nshin EN (1984) Phytochemicalscreening of Tanzanian medicinal plants. JEthnopharma 11:157-179

Chitwood DJ (2002) Phytochemical based strategies fornematode control. Annual Rev Phytopathol 40: 221-249

Coyne DL, Nicol JM, Claudius-Cole B (2007) Practical plantnematology: A field and laboratory guide. SP-IPMSecretariat, International Institute of TropicalAgriculture (IITA), Cotonou, Benin pp 42-47

Disanzo, C. P. (1977). Nematode responses to carbofuran. JNematol 5(1): 22 -27

Eapen SJ, Beena B, Ramana KV 2005 Tropical soil microfloraof spice-based cropping

systems as potential antagonists of root-knot nematodes. JInvert Pathol 88: 218-225

FAO (2010) Okra production statistics retrieved online on 13thSeptember 2011 from http//faostat fao org/site/339//default

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Farinde AJ, Owolarafe, OK, Ogunbemi OI (2007) "An overviewof production, processing, marketing and utilizationof okra in of Osun State, Nigeria" AgriculturalEngineering International: the CIGRE Journal,Manuscript No. MES 07002 Vol. IX

Gomez KA, Gomez A A (1984) Statistical procedures forAgricultural research. Second edition. New York, NY,USA, John Wiley and Sons Inc: 680 pp

Harbourne JB (1973) Phytochemical methods: A guide tomodern technique of plant analysis. Chapman andHall Ltd. London pp 44-188

Harve G, Kamath V (2004) Larvicidal activity of plant extractused alone and in combination with known syntheticlarvicidal agent on Aedes aegypt. Indian J Exptl Biol42: 1216-1219

Hussey RS, Baker KR (1973) A comparison of methods ofcollecting inocula of Meloidogyne spp., Plant DiseaseRep 57: 1024-1028

Iwalokun AU, Bamiro BA, Durojaye SB (2004) An antimicrobialevaluation of V. amygdalina (compositae) againstgram positive and gram negative bacteria fromLagos, Nigeria. W Afr J Pharmacol Drug Res 19:9-15

Ogbuji RO, Okonkwo JC (1977) 'Influence of Soil pH onreproduction of Meloidogyne incognita'. Niger J PlantProt 3:136-138

Onifade AK, Fawole B (1996) Effect of some plant extractson the pathogenicity of M. incognita on cowpea.Global J Pure and Appl Sci 2: 9-15

Orech FO, Akenga T, Ochara J, Friis H, Aagard-Hansen J(2005) Potential toxicity of some traditional leafyvegetables consumed in Nyang Oma Division,Western Kenya. African J Food, Agric, Nutrition andDevel 5 No 1

Pessoa LM, Morais SM, Bevilaqua CML, Luciano JHS (2002)Antihelmintic activity of essential oil of Ocimumgratissimum Linn and eugenol against Haemoachuscontortus. Vet Parasitol 109: 59-63

Ramarkarishnan S, Sreeja P, Mohandas C (1999) Cassavawastes for management of root-knot nematode.Proceedings of National Symposium on RationalApproaches in Nematode Management forSustainable Agriculture, Anand India

Schippers RR (2000) African indigenous vegetables: anoverview of the cultivated species. NationalResource Institute (NRI) University of Greenwitch,London, United Kingdom, 214 pp

Siji JV, Jayaprakas CA, Sheela MS, Mohandas C (2010)Efficacy of Cleome viscosa L. against

Meloidogyne incognita infestation in okra (Abelmoschusesculentus L.) Thai J Agric Sci 43 (3): 151-156

Sofowora A (2006) Medicinal plants and traditional medicinein Africa. Spectrum Books Limited, Ibadan, 289 pp

Statistical Analysis System (2002). SAS User's Guide:Statistics, version 9.1e SAS Institute Inc., Cary. NC.USA

Sulistiarini D (1999) Ocimum gratissimum L. in oyen. L.P.A &Nguyen Xunan Dung (Eds.): Plant Resources ofSouth East Asia. No. 19: Essential-oil Plants. ProseaFoundation, Bogor, Indonesia pp.140-142

Tanimola AA, Adesiyan SO (2006) Effect of carbofuran(furadan) and Aloe vera (L.) Burm gel on the growthand yield of root-knot infected cowpea (Vignaunguiculata (L.) Walp.). Environ and Ecol. 24:792-795

Tanimola AA, Godwin-Egein MI (2009) Assessing the efficacyof carbofuran (furadan) as a standard nematicidefor protecting crops in the field. Environ and Ecol27(4): 1681-1684.

Taylor AL, Sasser JN (1978) Biology, identification and controlof root-knot nematodes (Meloidogyne spp). Coop.Publ. Dep. Plant Pathol, North Carolina State Univand U.S Agency Int. Dev, Raleigh, N.C. 111pp

Trease BT, Evans WA (1989) Preliminary screening of plantsfor their chemical constituents. CAB International 104pp

Udoh, DJ, Ndon BA, Asuquo PE, Ndaeyo NU (2005) Cropproduction techniques for the tropics. Conceptpublication Limited 247pp

Whitehead AG, Hemming JR (1965) A comparison of somequantitative methods of extracting small vermiformnematodes from soil. Ann Appl Bio 55: 25-28

Whitehead AG (1998) Plant nematode control. CABInternational, Walling Ford UK, 384 pp

(Manuscript Receivd :08-09-2014; Accepted :12-12-2014)

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Abstract

Seven eggplant accessions (Ngwa small, Ngwa large, Anaraocha, Anara ogi, African beauty, Yalo and Kotobi) werescreened for resistance against Meloidogyne incognitainfection in a pot experiment laid out in completely randomizeddesign. At three weeks after transplanting, each eggplantseedling was inoculated with 10,000 eggs of M. incognita.Data were collected on plant height, number of leaves, freshshoot and root weights, and dry shoot weight at 12 weeksafter inoculation (WAI). Data were also collected on freshweight of fruits (FWF), gall index (GI), nematode population(NP) and reproductive factor (RF) at 12 WAI. All the accessionsshowed variability in their response to M. incognita infectionwith respect to growth. Anara ogi had the highest FWF (13.7g) and showed the least GI, NP and RF of 1.2, 7,476 and 0.7,respectively. Maximum invasion and development ofnematodes was recorded in Kotobi with the highest GI, NPand RF of 3.2, 360,480 and 36, respectively. Anara ogi andNgwa large were resistant to M. incognita; whereas, Yalo istolerant. Anara ogi and Ngwa large showed more promise forresistance to M. incognita and should be grown to manageM. incognita on infested farmlands.

Keywords: Eggplant accessions, Screening,Resistance, Meloidogyne incognita, Inoculation

Eggplant, also known as Garden egg (Solanum spp.)is one of the most important vegetable crops in Nigeria(Owusu-Ansah et al. 2001, Grubben and Denton 2004).It is consumed on an almost daily basis by rural andurban families and represents the main source ofincome for many rural household in forest zones in Africa(Danquah-Jones 2000, Owusu-Ansah et al. 2001).Theaverage yield is very low due to various yield reducingagents such as pathogenic fungi, bacteria, viruses,plant-parasitic nematodes, insects amongst others

JNKVV Res J 49(1): 15-21 (2015)

(Baral et al. 2006, Tanimola and Godwin-Egein 2011).Yield reduction in eggplant could be as high as 70%(Islam and Karim 1991, Dhandapani et al. 2003).Plant-parasitic nematodes, especially root-knotnematodes (Meloidogyne species) are one of the majorpathogens on the eggplant (Stirling et al. 2002, Coyneet al. 2007, Tanimola and Godwin-Egein 2011).Meloidogyne species are considered as one of the mostimportant plant-parasitic nematodes on eggplant inNigeria (Tanimola and Godwin-Egein 2011). They givechances for opportunistic bacteria and fungi to infecteggplant (Hagag and Amin 2001, Haseeb et al. 2005).There is need to manage these nematodes with costeffective and environment-friendly options.The use of host resistance in some plants has beenreported as a viable option in the management of plant-parasitic nematodes (Starr et al. 2002, Davis 2007).Resistance to plant-parasitic nematodes refers to thesuppressive effect of the plant on the nematode's abilityto reproduce (Cook and Evans 1987, Mashela and Pofu2012). Poor growth, development and yield losses areexpected to decrease as level of resistance increasesin crops even though exposed to nematode infection.However, resistant cultivars have to be identified andgenes that conferred resistance in such cultivars shouldbe identified. The knowledge of the levels ofsusceptibility of available eggplant varieties will help tosave cost on the control of plant-parasitic nematodessince such varieties will not be cultivated. If resistantaccessions are identified and planted, it might reducethe damage caused by root-knot nematodes. A step inthe right direction then is to screen for resistance to M.incognita in some eggplant varieties available in Nigeriasince there is paucity of information on this.

Evaluation of seven eggplant (Solanum species) accessions forresistance to root-knot nematode (Meloidogyne incognita)

A.A. Tanimola, A.O. Asimiea and E.N. OfoegbuDepartment of Crop and Soil ScienceFaculty of AgricultureUniversity of Port HarcourtPort Harcourt, Rivers StateNigeriaEmail:tanimoladebo@yahoo.com

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Material and methods

Sources of eggplant accessions

The seven accessions of eggplant used for this projectwere obtained partly from collections of Mr. S. Atijegbeof Department of Crop and Soil Science, Faculty ofAgriculture, University of Port Harcourt. Other varietieswere purchased from Amens Agricultural Services inIbadan, Oyo State, Nigeria.

Soil sterilization

Top sandy-loamy soil was collected at the Crop and SoilScience Research Farm, University of Port Harcourt andsteam-sterilized using a 50 kg drum and allowed to restfor four weeks so that the soil can regain stability.

Culture of Meloidogyne incognita

Pots (diameter 22 cm and depth of 30 cm) were filledwith steam-sterilized sandy loam soil and sown withCelosia argentea. Meloidogyne incognita-infected rootswere collected from Meloidogyne incognita culture plotat National Horticultural Research Institute (NIHORT),Ibadan. Two weeks after sowing, celosia plants wereinoculated with infected roots of M. incognita and thenematode was allowed to reproduce in the roots of C.argentea plants for eight weeks before eggs of M.incognita were extracted using the hypochlorite method(Hussey and Barker 1973).

Experimental design

The 10-litre capacity black polyethylene bags were usedas pots in the study. Holes were made in the bags usinga perforator to allow for drainage. The experimentaldesign was completely randomized design and theseven accessions were sown and replicated five times.Eggplant accessions were raised in the nursery for threeweeks and later transplanted into the polyethylene bagswith diameter 22 cm and depth of 30 cm (one seedlingper pot) containing 10 kg of steam-sterilized soil. Eachplant was inoculated at three weeks after transplantingwith 10,000 eggs of M. incognita extracted (Hussey andBarker 1973). Necessary cultural practices, such asweeding, watering, amongst others were carried out.Three months after inoculation, the plants were carefullyuprooted and the roots were rinsed with water to removeadhering soil particles. The roots were later examined

and rated for nematode infection by gall index (GI) on ascale of 0-5 (Osunlola 2011);

Where,

0 = no gall; 1 = 1-20% of the root system galled; 2 = 21-40% of the root system galled; 3 = 41-60% of the rootsystem galled; 4 = 61-80% of the root system galled;and 5 = 81-100% of the root system galled.

The entire root system of each plant was cut into2 cm pieces and was placed in a conical flask, and 0.5%hypochlorite solution was poured into the conical flaskand shaken vigorously for 3-4 minutes (Hussey andBarker 1973). The suspension was poured into 200mesh sieve nested over 500 mesh sieve. The 200 meshsieve retained the roots and the debris, while the 500mesh sieve retained the eggs which were later rinsedinto a beaker using a wash bottle. The content was leftand allowed to settle down and later decanted. Analiquot of 2 ml of the egg suspension was taken intoDoncaster dish (Doncaster 1962) and counted understereomicroscope. Second-stage juveniles (J2) of M.incognita were extracted from infested soil using thepie-pan method (Whitehead and Hemming 1965). Soilwas thoroughly mixed together and sieved to removestones and debris. Two hundred millilitre of soil samplewas taken and placed on a facial tissue in a plasticsieve and then water was added to the extraction platesby the sides. The set-up was left for 48 hours and thesieves later removed. The suspension in the plates waspoured into a beaker. After settling, water was pouredoff gently and the J2 population was estimated using astereo-microscope. The total number of J2 in the soilwas extrapolated from the number of second-stagejuveniles counted from 200 ml soil sample. Thereafter,the number of nematodes in the soil was added to thenumber of eggs extracted from the roots to obtain thefinal nematode population (Pf).

The host efficiency was determined by the reproductivefactor: RF = Pf/ Pi

and then calculated; where Pf (final nematodepopulation) and Pi (initial nematode population=10,000eggs). A reproduction factor of >1 indicates an increasein nematode reproduction where an RF factor of <1implies no increase in reproduction. The finalassessment of resistance of various cultivars was basedon Canto-Saenz's host designation scheme (Sasser etal. 1984). Plants with GI (Gall index) > 2 are defined aseither susceptible (RF >1) or hypersusceptible (RF 1);plants with GI 2 are classified either resistant (RF 1)or tolerant (RF > 1).

1 7

Data analysis

Data were analysed using analysis of variance andmeans were separated using Fisher's Least SignificantDifference at 5% level of probability with the StatisticalAnalysis System (2009).

Results and discussion

Effects of Meloidogyne incognita on growth of sevenaccessions of eggplant

At the end of the experiment (12 WAI), African beautyhad the highest mean plant height (59.4 cm), but thiswas not higher (P£0.05) than mean height of Kotobi(53.2 cm). The lowest mean plant height of 33.6 cmwas recorded in Yalo (Table 1). At 12 WAI, Kotobi hadthe highest mean number of leaves (27.4), but this wasnot significantly higher than number of leaves of othereggplant accessions. The fewest number of leaves wasrecorded in Anara ogi (18.6) (Table 1).

Effects of Meloidogyne incognita on mean fresh shootand root weights (g) and dry shoot weight (g) of seveneggplant accessions

At 12 WAI, African beauty showed the highest meanfresh shoot weight of 38.8 g, but this was not significantlyhigher than mean fresh shoot weights of Ngwa small(37.8 g) and Kotobi (37.4g). The lowest mean freshshoot weight of 21.4 g was recorded in Yalo (21.4 g)(Table 1). Table 4 also showed effects of M. incognita onmean fresh root weight. Kotobi had the highest mean

fresh root weight of 30.9 g, but this was not significantlyhigher than mean fresh root weights of Anara ocha, Yaloand Ngwa small. The lowest mean fresh root weight of14.2 g was recorded in Anara ogi. Kotobi had the highestmean dry shoot weight of 29.2 g, followed by Ngwa small(24.4 g) and African beauty (22.4 g). The lowest meandry shoot weight of 13.6 g was recorded in Yalo (Table1).

Effects of Meloidogyne incognita on mean fresh weightof fruits (g) of seven accessions of egg plant

The highest mean fresh fruit weight of 13.7 g wasrecorded in Anara ogi and this was not significantlyhigher than fresh fruit weights of Ngwa large and Yalo(Table 2). The lowest fresh fruit weight was recorded inKotobi (3.8 g) (Table 2).

Effects of Meloidogyne incognita on mean gall index,egg population, second-stage juvenile (J2), finalnematode population (Pf) and reproductive factor (RF)of seven accessions of eggplant

The degrees of root damage measured with gall indexvaried among the eggplant accessions. Kotobi had themean highest gall index (3.2), but this was notsignificantly higher than GI of 3.0 recorded in Anaraocha. The mean lowest gall index was in Anara ogi (1.2).The highest egg population was also recorded in Kotobi(360,080) and the lowest mean egg population wasrecorded in Anara ogi (7,116) (Table 3). Ngwa small (560)had the highest second-stage juvenile population of M.incognita, but this was not significantly higher than theJ2 population in the soil of the other eggplant accessions.

Table 1. Effect of M. incognita on mean growth of seven accessions of eggplant

Eggplant accession Mean plant Number of leaves Fresh shoot Fresh root Dry shootheight (cm) at 12 WAI weight (g) weight (g) weight (g)at 12 WAI

Ngwa large 38.4 21.2 30.6 18.3 21.3Yalo 33.6 23.4 21.4 29.2 13.6Anara ocha 42.0 22.4 22.2 30.6 16.9African beauty 59.4 22.0 38.8 19.1 22.4Ngwa small 38.2 24.6 37.8 24.1 24.4Anara ogi 40.2 18.6 25.0 14.2 18.4Kotobi 53.2 27.4 37.4 30.9 29.2LSD (P 0.05) 8.8 6.4 15.9 12.0 12.1

Each value is mean of five replicates. WAI= weeks after inoculation

1 8

However, Yalo had the lowest second-stage juvenilepopulation (240). Kotobi had the highest mean finalnematode population (360,480) at 12 WAI; whereasAnara ogi showed the lowest final nematode population(7,476) (Table 3).

The values of the host efficiency obtained usingreproductive factor (RF) showed that eggplantaccessions showed differences in their abilities tosupport the reproduction of M. incognita. The highestrate of reproduction of M. incognita was recorded inKotobi (RF=36) among all the eggplant accessionsscreened and this was significantly higher than rates ofreproduction from other accessions. The RF valueswere less than one (RF<1) in Anara ogi and Ngwa large.The lowest rate of reproduction was recorded in Anaraogi (0.7) (Table 3).

Resistance designations of seven eggplant accessionsto Meloidogyne incognita

On the basis of GI and RF, Anara ocha, African beauty,Ngwa small and Kotobi were susceptible with highnematode reproduction (RF> 1) and high plant damage(GI>2); whereas, Yalo was classified as tolerant withhigh nematode reproduction (RF >1) but minimal rootdamage (GI 2). However, Anara ogi and Ngwa largewere classified as resistant with minimal root damage(GI 2) and did not support nematode reproduction(RF<1). The lowest values of 1.2 and 0.7 in Anara ogifor gall index and reproductive factor respectivelyshowed that the highest level of resistance among theeggplant accessions screened is in Anara ogi (Table 3).

All eggplant accessions showed variability ingrowth and nematode population in their response toM. incognita infection, although M. incognita multipliedon all accessions. The variability in pathogenicity acrossaccessions might be due to presence of nematoderesistance genes in some of the accessions(Hadisoeganda and Sasser 1982, Roberts andThomason 1986). Plants with resistant genes are lessattractive for attack by the nematode. The variabilityexperienced in the growth as reflected in number ofleaves and plant height might be an indicator thateggplant accessions are quite different based onphenotypic traits. This could also mean that they mighthave different genetic constituents that conferred thedifferent phenotypic traits.

The resistant accessions, Ngwa large and Anaraogi might have failed to produce functional feeding sites

Table 2. Effect of Meloidogyne incognita on mean numberof fruits and weight of fruits (g) of seven accessions ofeggplant

Eggplant accession Weight of fruits (g)

Ngwa large 10.8Yalo 10.6Anara ocha 7.8African beauty 4.2Ngwa small 7.5Anara ogi 13.7Kotobi 3.8LSD (P 0.05) 5.6

Table 3. Effect of Meloidogyne incognita on mean gall index, egg population, second-stage juvenile (J2), final nematodepopulation (Pf) and reproductive factor (RF) of seven accessions of eggplant

Eggplant accessions Gall index Egg popn J2 popn Pf RF Degree of resistancedesignation

Ngwa large 1.4 8,118 280.0 8,398 0.8 ResistantYalo 2.0 18,836 240.0 19,076 1.9 TolerantAnara ocha 3.0 130,320 400.0 130,720 13.1 SusceptibleAfrican beauty 2.6 114,762 520.0 115,282 11.5 SusceptibleNgwa small 2.8 146,220 560.0 146,780 14.6 SusceptibleAnara ogi 1.2 7,116 360.0 7,476 0.7 ResistantKotobi 3.2 360,080 400.0 360,480 36.0 SusceptibleLSD (P 0.05) 0.7 85,634 352.8 85,633 8.6

J2 popn = second-stage juvenile population; Egg popn= Egg population per root system; Pf = final nematodepopulation; RF= reproductive factor

1 9

in the host after invasion due to hypersensitiveresponses facilitated by resistant genes that might haveled to failure in nematode development (Davis et al2000, Williamson and Kumar 2006). Once feeding sitesare not produced in the host plant, M. incognita will notbe able to access nutrients and as such will have theirdevelopment and reproduction impaired. The resistanceto M. incognita observed in Anara ogi and Ngwa largemight also be due to pre-infection resistance linked topresence of toxic or antagonistic chemicals in roots ofeggplant that prevented the entry of some of thenematodes into roots (Bendezu and Starr 2003). Theresistance might also be as a result of post-infectionresistance in which the nematodes penetrated the roots,but failed to develop adequately and this is linked toearly hypersensitive reaction that might have led todeath of cells in root tissues around the nematodes(Shakeel et al. 2012). Thus, the formation of feedingsites for the nematodes that penetrated the roots isprevented thus leading to resistance (Williamson 1999,Shakeel et al. 2012). Holtzmann (1965) opined thateggplant cultivars with moderate to high resistance blocknematode reproduction significantly resulting indecreases in nematode population levels in rhizosphere.

There is the possibility of Yalo to grow andproduce significant yield without showing signs ofdamage because it is tolerant. However, it will facilitatemultiplication of M. incognita leading to large populationdensities of the nematode in the soil and this will bedetrimental to other crops that might be cultivated in arotation.

The study showed that susceptible eggplantaccessions had significant population of M. incognitathan the resistant accessions and this showed thatdevelopment and reproduction were not hindered insusceptible accessions (Bendezu and Starr 2003). Thisobservation was corroborated by Roberts and May

(1986) who found greater number of females, galls andeggs per plant in susceptible tomato accessionsinoculated with M. incognita as compared to resistantaccessions in their study.

Four of the seven eggplant accessions (Ngwasmall, Anara ocha, African beauty and Kotobi) werefound to be susceptible to M. incognita but the extent ofsusceptibility varied significantly among them. Itindicates that these accessions might vary in geneticcontents owing to their diverse responses to M. incognitainfection (Brian et al. 2010). These susceptibleaccessions should not be planted on M. incognita-infested farmlands or use in rotation schemes for themanagement of M. incognita to avoid yield losses orcrop failure. The study has demonstrated that eggplantcultivars are susceptible to M. incognita. The root-knotnematodes have become one of the most seriousproblems for sound production of eggplant (Anwar etal. 2007). This suggests that growers should avoidplanting of eggplants in previously nematode-infestedfields.

The fewer galls, lower nematode population andRF observed in Anara ogi and Ngwa large compared tothe other five accessions might be an indication thatthey are resistant to M. incognita since the magnitudeof infection was more significantly reduced than in theother accessions (Roberts and May 1986). The lowestfresh root weight observed in Anara ogi showed thatthis eggplant does not support development andreproduction of M. incognita. However the highest meanfresh root weight recorded for Kotobi confirmed its highsusceptibility and the increase in root weight could betraced to the presence of heavy galls on the roots(Bendezu and Starr 2003).

Reproductive factors showed that maximuminvasion, development and reproduction of M. incognitawas recorded in Kotobi followed by Ngwa small and

Table 4. Canto-Saenz's Resistance designations of seven eggplant accessions infected with M. incognita

Eggplant accession Gall index RF Degree of resistance designation

Ngwa large 1.4 0.8 ResistantYalo 2.0 1.9 TolerantAnara ocha 3.0 13.1 SusceptibleAfrican beauty 2.6 11.5 SusceptibleNgwa small 2.8 14.6 SusceptibleAnara ogi 1.2 0.7 ResistantKotobi 3.2 36.0 SusceptibleLSD (P 0.05) 0.7 8.6

2 0

African beauty which showed that they have little or noresistance to M. incognita. Since the reproductive factorsof M. incognita for Anara ogi and Ngwa large are lessthan unity, it suggests that the nematode might havefailed to feed and subsequently did not reproduce(Mashela and Pofu 2012). This indicates that Anara ogiand Ngwa large are non host to M. incognita, bothcultivars have significant level of resistance than theother accessions.

The better yield obtained in the resistant thansusceptible eggplant accessions might be becauseaccessions with resistance to M. incognita significantlyreduced galling (i.e. plant damage) and egg masses onthe roots and this resulted in increased yield since theplants were able to perform basic physiological functions.

Conclusion

The seven eggplant accessions evaluated showedvariability in their resistance to M. incognita. Anara ogiand Ngwa large were resistant, whereas Yalo wastolerant. Anara ogi was outstanding because it gavethe minimum support for M. incognita activity,development and reproduction. Anara ogi and Ngwalarge could be incorporated into crop rotation schemefor the management of M. incognita on the field withother crops.

References

Anwar S A, Javed N, Zia A, Kumran P, Mandjaved M (2007)Root-knot nematode reproduction and gallingseverity on vegetable crops. In: proc. Internationalsymposium on prospects of horticultural industry inPakistan, university of agriculture, Faisalabad March28-30

Baral K, Roy B C, Rahim K M B, Chatterjee H, Mondal P,Mondal D, Ghosh D, Talekar N S (2006) Socio-economic parameters of pesticide use andassessment of impact of an IPM strategy for thecontrol of eggplant fruit and shoot borer in WestBengal, India. Technical Bulletin No. 37. AVRDCpublication number 06-673. AVRDC - The WorldVegetable Center, Shanhua, Taiwan. 36 pp

Bendezu I F, Starr J (2003) Mechanism of resistance toMeloidogyne arenaria in the peanut cultivar. Coan JNematol 35: 115-118

Brian M S, Kevin E K, William T C, Jason A F, Grady L M,Kenneth H Q (2010) Variable responses of zoysiagrass genotypes to the sting nematode. Crop Sci 50: 723-729

Cook R, Evans K (1987) Resistance and tolerance. Pp. 179-231 In Brown, R. H and Kerry (ed) Principles and

practice of nematode control in crops. Marrickville,NSW, Australia, Academic Press

Coyne D L, Nicol J M, Claudius-Cole B (2007) Practical plantnematology: a field and laboratory guide: SP.IPMSecretariat, international institute of tropicalagriculture (IITA) Cottonou Benin

Danquah-Jones A (2000) Variation and correlation amongagronomic traits in Egg plant (Solanum gilo Radii).Department of Crop Science. Accra, University ofGhana, Legon pp 30

Davis E L, Hussey R S, Baum T J, Bakker J, Schots A, RossoM N, Abad P (2000) Nematode parasitism genes.Ann Rev Phytopath 38: 365-396

Davis R F (2007) Utilization of resistance and tolerance toroot-knot nematode in cotton. Retrieved online fromhttp://wcrc.confex.com/wcrc/2007/techprogram/P1318.HTM on 2nd April 2010

Dhandapani N, Shelkar U R, Murugan M (2003) Bio-intensivepest management in major vegetable crops: AnIndian perspective. J Food, Agric Environ 1(2): 330-339

Doncaster CC (1962) A counting dish for nematodes.Nematologica 6 : 33-36

Grubben GJH, Denton OA (2004) Plant resources of tropicalAfrica II: Vegetables, Leiden, Wageningen: BackhuysPublishers p 47

Hadisoeganda WW, Sasser JN (1982) Resistance of tomato,bean, southern pea, and garden pea cultivars to root-knot nematodes based on host suitability. P Dis 66:145-150

Haggag WM, Amin AW (2001) Efficiency of Trichodermaspecies in control of Fusarium-rot, root knot andreniform nematodes disease complex on sunflower.Pakistan J Bio Sci 4:314-318

Haseeb A, Sharma A, Prabhat KS (2005) Studies on themanagement of root-knot nematode, Meloidogyneincognita-wilt fungus, Fusarium oxysporum diseasecomplex of green gram, Vigna radiata cv ML-1108.J Zhejiang Univ Sci B 6: 736-742

Holtzmann OV (1965) Effect of soil temperature on resistanceof eggplant to root-knot nematode (Meloidogyneincognita). Phytopathology 55 : 990-992

Hussey RS, Baker KR (1973) A comparison of methods ofcollecting eggs of Meloidogyne species including anew technique. Plant Disease Reporter 57 : 1025-1028

Islam MN, Karim MA (1991) Management of the brinjal shootand fruit borer, Leucinodes orbonalis Guen,(Lepidoptera: Pyralidae) in field. In: Annual ResearchReport 1990-91. Entomology Division, BangladeshAgric Res Inst Joydebpur, Gazipur 44 -46 pp

Mashela P, Pofu K (2012) Host response of Capsicum frutenscultivar 'Capistrano' to Meloidogyne incognita race2, Acta Agriculturae Scandinavica, Section B - Soil& Plant Science, 62:8, 765-768, DOI:10.1080/09064710.2012.711355

Owusu-Ansah F, Afreh-Nuamah K, Obeng-Ofori D, Ofosu-

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Budu KG (2001) Managing infestation levels ofmajor insect pests of garden eggs (Solanumintegrifolium L.) with aqueous neem seed extracts.J Ghana Sci Assoc 3 (3): 70 - 84

Osunlola O S (2011) The biology, pathogenicity and controlof root- knot nematode (Meloidogyne incognitaKofoid and White, Chitwood) on Sweetpotato. PhDThesis, University of Ibadan, Nigeria: 164 pp

Roberts PA, May D (1986) Meloidogyne incognita resistancecharacteristics in tomato genotypes developed forprocessing. J Nematol 18: 173-178

Roberts P A, Thomason I J (1986) Variability in reproductionof isolates of Meloidogyne incognita and M. javanicain resistant tomato genotypes. Pl Dis 70: 547-557

SAS (2009) Statistical Analysis System User's Guide. SASInstitute Inc. Carry NC USA

Sasser JN, Carter CC, Hartman KM (1984) Standardizationof host suitability studies and reporting resistance toroot-knot Nematodes. A cooperative publication ofthe Department of Plant Pathology, North CarolinaState University and the United States Agency forInternational Development. North Carolina StateUniversity, USA. 18pp. ISBN 0-93190-03-01984Raleigh, N.C., USA

Shakeel Q, Javed N, Khan SA, Haq IU, Anwar SA, Khan NA,Amrao L (2012) Response of eggplant (Solanummelongena L.) cultivars to Meloidogyne incognita

infection. Pak J Nematol 30 (2): 169-178Starr JL, Morgan ER, Simpson CE (2010) Management of

the peanut root-not nematode, Meloidogyne arenaria,with host resistance. Online. Plant Health Progressdoi: 10.1094/PHP-2002-1121-01-HM

Stirling G, Nicol J, Reay F (2002) Advisory services fornematode pests- operational guidelines. RIRDCPublication No. 99/41

Tanimola AA, Godwin-Egein MI (2011) Plant-parasiticnematodes associated with garden egg (Solanumaethiopicum) in Obio-Akpor LGA, Rivers State,Nigeria. Pakistan J Nematol 29 (2): 139-146

Taylor AL, Sasser JN (1978) Biology, identification and controlof root-knot nematodes (Meloidogyne species). A co-operative publication of North Carolina University andUSAID. 111p

Whitehead A G, Hemming J R (1965) A comparison of somequantitative methods of extracting small vermiformnematodes from soil. Annals Appl Bio 55:25-38

Williamson VM (1999) Plant nematode resistance genes.Current Opinion in Pl Biol 2: 327-331

Williamson VM, Kumar A (2006) Nematode resistance inplants: the battle underground. Trends in Genetics22: 396-403

(Manuscript Receivd :08-09-2014; Accepted :12-12-2014)

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Abstract

Field experiments were carriedout on niger cv JNC-6 for 3consecutive years (2008-09 to 2010-11) at Jabalpur (MadhyaPradesh) to evaluate the losses in yields and quality of nigerseeds during harvesting and post harvesting operations.Results revealed that seed yields were maximum (559 kg/ha) by harvesting of crop at optimum maturity which reducedby 14.3% by harvesting the crop 7 days earlier to optimumtime. The seed yields could be saved 5.9% by drying theharvested produce directly in the field by putting bundlesvertically tied with khuntees over drying the bundleshorizontally on the surface. Physical purity, protein contentand oil contents were unaffected due to different harvestingtime, but drying of produce directly on the field by putting thebundles horizontally increased the impurity in the seeds.Moisture content in seeds was normal in late harvested crop(8.19%) and harvested produce dried keeping bundles invertical direction. Germination of seeds was poor in earlyharvested crop as well as dried harvested produce bykeeping bundles horizontally on the ground.

Keywords: Harvesting time, Post harvest operations,Seed yield, Quality and Niger

Niger [Guizotia abyssinica (L.f.) Cass] is one of the oilseedcrop. It contains a considerable quality of edible oil (38 to40%), protein (20%), sugar (12%) and minerals essentialfor human and animal means (Gentient and Teklewold1995). India is the prime producer of niger in the world. Itis grown in the country in an area of 3.0 Lakh ha with aproduction of 0.99 lakh tonnes and a productivity of 329kg seeds/ha (Kumar and Varaprasad 2013). Nigercultivation is confined mainly to the states of MadhyaPradesh, Chhattisgarh, Orissa and Maharashtra and alesser extent in Karnataka, Bihar, Jharkhand and AndhraPradesh in the country. Madhya Pradesh contributesnearly 0.87 lakh ha area under this crop with a production

JNKVV Res J 49(1): 22-25 (2015)

Effect of harvesting time and post harvest operations on seed yieldand quality of niger [Guizotia abyssinica (L.f.) Cass]

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

of 0.30 lakh tonnes and productivity of 343 kg seeds/ha.Niger in mostly grown on marginal and submarginal landshaving low organic matter and poor soil fertility underrainfed conditions. The main reasons for low productivityin the state are its cultivation confined on marginal andsubmarginal lands with the use of negligible agroinputs.These facts appear to be probable reasons for lowproductivity of this crop. Moreover, this crop has potentialto produce upto 800 kg seeds/ha on the research farmwith adoption of improved varieties and productiontechnologies including manipulation of improved agro-techniques. Niger crop faces a lot of problem during itsharvesting and post harvest operations viz., transportation,drying, threshing and winnowing which results in reductionin seed yield and quality of niger etc. Normally, earlyharvesting of niger is found to reduce seed yields due toshriveling of seeds, while its later harvesting results inpoor seed yields due to seed shattering. The seeds ofcrop harvested at pre-mature stage have poor germinationability. Agronomic research for minimizing the losses inseed yields of niger during the harvesting and postharvesting operations has not been given attention. Theinformation pertaining to assess the degree of losses andto overcome the possible losses through research arescanty. Hence, these fields deserve to undertake ansystematic investigation.

Material and methods

Field experiments were conducted on irrigated winterseason niger cv. JNC-6 for 3 consecutive years (2008-09to 2010-11). Twelve treatments consisted with 3harvesting time (harvesting at optimum time, 7 days beforeoptimum time and 7 days after optimum time), 2 dryingmethods of harvested produce (drying directly in field inhorizontal bundles and in upright/vertical bundles tied with

2 3

khuntees) and 2 threshing methods (single threshing 15days after harvesting, double threshing at 7 and 15 daysafter harvesting). The plots pertaining to these treatmentswere maintained in factorial randomized block design with3 replications. Sowing of crop was done in first week ofOctober every year of drilling seeds in rows 30 cm apartby using 5 kg seeds/ha. The crop received a uniform doseof 40 kg N + 30 kg P2O5 + 20 kg K2O/ha through urea,single superphosphate and muriate of potash respectivelyin all plots. Half dose of N and full dose of P and K fertilizerswere applied as basal dose and remaining half dose of Nwas top dressed at 2 days after first irrigation. One lightirrigation was given immediately after sowing forgermination of crop. Thereafter the 3 irrigations were givenafter germination at 20 days intervals from the date ofcomplete germination. Seed and stover yields wererecorded as per treatments. Seed samples were collectedfrom each treatment for various studies pertaining tophysical purity, moisture content, protein content, oilcontent and germination ability of seeds. Yearwise datapertaining to seed and stover yields were subjected tostatistically analysis, but other data, 3 years mean datawere allowed to statistical analysis.

Results and discussion

Seed and stover yields

Seed yields wer significantly varied due to different timingsof harvesting, but stover yields did not show remarkablevariations with them (Table 1). The seed yields of nigerwas maximum with timely harvesting at optimum maturityin every years and then seed yields reduced by harvestingthe crop 7 days earlier or 7 days late to optimum maturity.But rate of reduction in seed yield was significant onlywhen the crop was harvested 7 days before to optimumtime. The yield attributes viz., capitulae/plant, seeds/capitula and test weight of seeds did not differ due todifferent harvesting time. But poor development of seedson physiological maturity phase may be the reason forsuch reduction in seed yields. The reduction in seed yieldin late harvested crop may be due to shettering of someseeding during harvesting as well as losses of moisturefrom the seeds. Non-seed portion of plants did not faceany influence to deviate the stover yields due to varyingharvesting time.

Seed yields also significantly varied due to differentdrying methods of harvested produce in the field, butdifferent yield attributes were almost comparable. Dryingof the bundles of harvested produce by putting them

horizontally on the surface caused significant reductionin the seed yields than these of the bundles of harvestedproduce directly kept upright (vertically) by tying themwith khuntees for drying. Stover yields did not vary due todifferent drying methods. Both seed and stover yields didnot differ due to single threshing at 15 days after harvestand double threshing at 7 and 15 days after harvest. Theseresults also corroborated the findings of Ankita (2009),Anon (2011) and Rai et al. (2013).

Quality of seeds

Physical purity content of seeds did not differ due todifferent timings of harvesting and methods of threshing,but it was markedly superior when harvested produce weredried in the field by keeping the plants vertically with thehelp of khuntees than those kept horizontally on soilsurface for drying. Similarly, the seeds of the harvestedproduce obtained by drying the bundles in vertical directionhad significantly lesser moisture content than drying thebundles of harvested produce by putting them horizontalon the surface. The moisture content of seeds wassignificantly lesser by harvesting the crop 7 days lateover the optimum maturity than harvesting of crop atoptimum time and 7 days before optimum time. Proteincontent and oil content of seeds did not vary due todifferent harvesting time, drying methods of produce inthe field and threshing methods (Table 2).

Germination of seeds

The germination of seeds tested at 15, 30 and 45 daysafter harvesting (DAH) did not show any remarkablevariation by threshing of crop once or twice, but itsignificantly varied due to different harvesting time anddrying methods of harvested produce in the field (Table2). Seeds obtained by early harvesting of crop 7 daysbefore optimum maturity had significantly lessergermination of seeds in all observations than the otherseeds obtained by harvesting of crop at optimum timeand 7 days after optimum time. The later two werecomparable in this regard. Incomplete setting anddevelopment of seeds under early harvested crop may bethe reason for poor germination of seeds. The harvestedproduce allowed for drying by keeping the bundleshorizontally had remarkably lesser germination of seedsin all observations than the produce dried by putting thebundles vertically. The seeds contained moisture for longerperiod by drying the produce horizontally, which attributed

2 4

Tabl

e 1.

Effe

ct o

f har

vest

ing

time

and

post

har

vest

ope

ratio

ns o

n m

ean

seed

yie

ld a

nd y

ield

attr

ibut

es o

f nig

er

Trea

tmen

tS

eed

yiel

d (k

g/ha

)S

traw

yie

ld (k

g/ha

)M

ean

yiel

d at

tribu

tes

Cap

itula

e/S

eeds

/Te

st w

tH

arve

st20

0920

1020

11M

ean

2009

2010

2011

Mea

npl

ant

capi

tulu

min

dex

(#)

(#)

(g)

(%)

Har

vest

ing

time

(H)

H1 -

Har

vest

ing

at o

ptim

um ti

me

(OT)

634

564

480

559

2916

2480

2315

2570

29.3

29.6

4.63

17.8

6H

2 - H

arve

stin

g 7

days

bef

ore

OT

505

527

403

478

2823

2318

2290

2477

29.2

29.3

4.61

16.1

7H

3 - H

arve

stin

g 7

days

afte

r OT

620

563

468

550

2852

2478

2360

2563

29.1

29.4

4.68

17.6

6S

Em

±6.

425.

303.

855.

20-

--

0.35

0.17

0.44

0.02

0.40

CD

(P

=0.0

5)18

.85

16.6

011

.31

15.5

0N

SN

SN

SN

SN

SN

SN

S1.

18D

ryin

g m

etho

ds (

D)

D1 -

Dry

ing

dire

ctly

in fi

eld

in h

oriz

onta

l bun

dles

581

497

449

509

2672

2486

2375

2511

28.4

28.1

4.64

16.8

5D

2 - D

ryin

g di

rect

ly in

fiel

d in

upr

ight

/ ver

tical

bun

dles

597

557

463

539

2746

2450

2340

2512

28.4

28.6

4.65

17.6

6tie

d w

ithkh

unte

esS

Em

±5.

244.

484.

124.

38-

--

0.25

0.14

0.36

0.03

0.31

CD

(P

=0.0

5)15

.39

13.8

011

.80

13.2

0N

SN

SN

SN

SN

SN

SN

SN

STh

resh

ing

met

hods

(T)

T 1 - S

ingl

e th

resh

ing

15 d

ays

afte

r har

vest

559

522

435

505

2571

2297

2220

2363

28.3

28.2

4.66

17.6

0T 2 -

Dou

ble

thre

shin

g at

7 a

nd 1

5 da

ys a

fter h

arve

st57

053

145

051

726

2223

3622

8024

1328

.328

.54.

6517

.64

SE

m±

5.24

4.98

6.24

5.12

--

-0.

250.

140.

360.

030.

31C

D (

P=0

.05)

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

Tabl

e 2.

Effe

ct o

f har

vest

ing

time

and

post

har

vest

ope

ratio

ns o

n m

ean

phys

ical

pur

ity, m

oist

ure

cont

ent,

prot

ein

cont

ent,

mea

n ge

rmin

atio

n (%

) and

oil y

ield

of n

iger

Trea

tmen

tP

hysi

cal

Moi

stur

eP

rote

inM

ean

germ

inat

ion

(%)

purit

y (%

)co

nten

t (%

)co

nten

t (%

)15

DA

H30

DA

H45

DA

HO

il (%

)O

il yi

eld

(%)

Har

vest

ing

time

(H)

H1 -

Har

vest

ing

at o

ptim

um ti

me

(OT)

94.9

98.

4314

.38

88.3

291

.82

91.7

032

.30

181

H2 -

Har

vest

ing

7 da

ys b

efor

e O

T95

.15

8.39

14.4

487

.00

89.0

089

.50

31.3

615

0H

3 - H

arve

stin

g 7

days

afte

r OT

94.7

68.

1914

.32

87.9

091

.50

91.8

031

.83

175

SE

m±

0.34

0.04

0.13

0.33

0.96

0.77

0.44

4.1

CD

(P

=0.0

5)N

S0.

11N

S0.

992.

832.

27N

S12

.0D

ryin

g m

etho

ds (

D)

D1 -

Dry

ing

dire

ctly

in fi

eld

in h

oriz

onta

l bun

dles

94.2

68.

3914

.26

86.5

588

.60

88.7

431

.54

160

D2 -

Dry

ing

dire

ctly

in fi

eld

in u

prig

ht/ v

ertic

al b

undl

es95

.65

8.28

14.2

887

.60

92.9

093

.12

32.1

317

3tie

d w

ithkh

unte

esS

Em

±0.

280.

030.

110.

270.

790.

630.

363.

3C

D (

P=0

.05)

0.83

0.09

NS

0.81

2.31

1.85

NS

9.96

Thre

shin

g m

etho

ds (

T)T 1 -

Sin

gle

thre

shin

g 15

day

s af

ter h

arve

st94

.99

8.35

14.3

787

.40

90.8

090

.95

32.1

516

2T 2 -

Dou

ble

thre

shin

g at

7 a

nd 1

5 da

ys a

fter h

arve

st94

.93

8.32

14.3

786

.80

90.6

090

.80

31.5

216

7S

Em

±0.

280.

030.

110.

270.

790.

600.

363.

3C

D (

P=0

.05)

NS

NS

NS

NS

NS

NS

NS

NS

2 5

to deterioration of germination ability of seeds. Similarresults are also reported in niger crop by Ankita (2009)and Rai et al. (2013).

o"kZ 2008-09 ls 2010-11 ds 'khrdkyhu ekSle es ¼flafpr voLFkkds varZxr½ jkefry dh uofodflr fdLe ts-,u-lh & 6 ij yxkrkjrhu o"kksZrd ifj;kstuk leUo;u bZdkbZ] fry ,oa jkefry] t-us-—-fo-fo- tcyiqj ¼e/; izns'k½ ds vuqlaFkku iz{ks= ij ifj{k.k iz;ksx fd;s x;sA bl ifj{k.k iz;ksxdk m|s'; jkefry Qly dh dVkbZ dk le; ,oadVkbZ mijkUr Qly dh mit ,oa xq.koRrk esa gksus okyh {kfr dkvk¡dyu djuk Fkk A ifj{k.k ls izkIr ijh.kkeksa ds vuqlkj vf/kdremit ¼559 fd-xzk-@gsDVs;j½ Qly dh dVkbZ mfpr le;ij djus lsizkIr gqbZ rFkk Qly dks mfpr le; ls 7 fnu iwoZ dkVus ij 14.3%dh xhjkoV ntZ dh x;h A Qly dVkbZ i'pkr Qly ds xðksadks [ksresa{kSfrt lrgij lq[kkus ds ctk; [kqVh ls ck¡/kdj m/okZ/kj [kM+s djdslq[kkus ij Qly mit esa gksus okyh {kfr dks 5.9% rd de fd;k tkldrk gS A cht dh HkkSfrd 'kq)rk ,oa chtksa esa izksVhu ,oa rsyds fughrva'kij dVkbZ ds fHkUu&fHkUu le;ksadk dksbZ izHkko ugha ik;k x;k A fdUrqxðksa dks {kSfrt lrg ij lq[kkus ls chtksa esa gksus okyh v'kq)rk esa of̀)gksuk ik;k x;k A nsj ls dkVh x;h Qly ls izkIr mit ds chtksa esa rFkk (Manuscript Receivd :28.11.2014; Accepted :15.03.2015)

dVkbZ i'pkr Qly mRikn dks m/okZ/kj rjhds ls [kM+s djrs gq;s [kqVhls ck¡/kdj lq[kkus ls ueh dk va'k lkekU; ¼8.19%½ vkWdk x;k Avuq'kaflr le; ls 7 fnu iwoZ dkVh x;h Qly rFkk dkVs x;s QlymRikn ds xðksa dks tehu dh {kSfrt lgr ij j[krs gq;s lq[kkus okysmipkjksa ls izkIr chtksa esa vadqj.k dk izfr'kr de ntZ fd;k x;k A

References

Ankita Neekhara (2009) Effect of harvesting time and postharvest operations on the seed yield and quality ofniger [Guizotia abyssinica (L.f.) Cass]. M.Sc.(Ag.)Thesis JNKVV, Jabalpur p 64

Gentinet A, Teklewold (1995) An agronomic and seed qualityevaluation of niger [Guizotia abyssinica (L.f.) Cass]germplasm grown in Ethiopia. Plant Breed 14 : 375-376

Kumar GDS, Varaprasad KS (2013) Frontline demonstrationson oilseeds : Annual Report 2012-13. Directorateof Oilseeds Research, Hyderabad p 150

Rai GK, Thakur SK, Deshmukh MR, Rai AK (2013) Effect ofharvest time and post harvest operations on thequality of niger. JNKVV Res J 47(1) : 45-48

2 6

Abstract

One hundred twenty four rice genotypes including four checkentry i.e. Kranti, IR 64, Sahbhagi Dhan and JRH 8 while 120other NPT genotypes were grown at seed Breading Farm,JNKVV, Jabalpur during kharif 2012 to study the geneticvariability parameters, correlation coefficient and pathcoefficient for yield and its attributing traits. The estimate ofGCV were high for traits viz., number of filled grains per panicle,dry weight of roots per plant, fresh weight of roots per plant,weight of panicle per plant, grain yield per plant, biologicalyield per plant, tillers per plant, harvest index, culm lengthand 1000 seed weight. Remaining traits showed moderateGCV and PCV except for days to 50% flowering and days tomaturity with low PCV and GCV values. High heritabilitycoupled with high genetic advance as percent of mean wererecorded for all the traits except days to 50% flowering, daysto maturity and number of filled grains per panicle. Correlationstudies showed highly significant and positive associationbetween grain yield per plant with panicle weight per plant,biological yield per plant, plant height and panicle length.The path coefficient analysis indicated high positive directeffect of biological yield per plant, harvest index, plant heightand panicle weight per plant on grain yield per plant.

Keywords: NPT Lines, Rice, Variability, AssociationAnalysis

Rice (Oryza sativa L.) is the central to the lives of billionof people around the world. It is an important cereal cropacross the globe and India. Enhancing crop yield is oneof the most priorities in crop breeding programs. Resultshave indicated that effective way to develop super ricelines first in developing new plant type and storage vigourby crossing indica with japonica subspecies and thenconsolidating the two advantages by optimizing thecombination of desirable traits via multiple crossing andback crossing (Cheng et al. 2001).

JNKVV Res J 49(1): 26-31 (2015)

Genetic analysis of Indica-japonica derived rice NPT lines for yieldand yield attributing traits under rainfed situation

R.B. Yadav, D.K. Mishra, G.K. Koutu, S.K. Singh and Arpita ShrivastavaDepartment of Plant Breeding and GeneticsJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482 004 (MP)Email: gk_koutu@yahoo.co.in

In rice first generation new plant type (NPT) washaving low tillering capacity with few unproductive tillerssturdy stems, erect leaves, a vigorous root system andincreased harvest index (Peng et al. 1994). In 1997development of 2nd generation NPT lines was initiatedby crossing first generation tropical japonica NPT lineswith elite indica parents to increase tillering capacity andto improve biomass production genes from indica parentshave effectively reduced panicle size and increasedtillering capacity and also improved other NPT attributessuch as grain quality, disease and insect resistance(Peng et al. 2014).

Hence the knowledge about relationship betweenyield and its attributing traits is needed for an efficientselection strategy utilizing indica-japonica derivedpopulation for the plant breeders. The information aboutphenotypic and genotypic interactions of various economictraits is of immense importance for selection and breedingof different genotypes for increasing yield potentials. Inthe light of above information, the present study will beinitiated to obtain the precise information on the extent ofnatural variability in respect of various yield and itsattributing traits of NPT lines derived from indica X japonicasub- species crosses of rice .

Material and methods

The experiment was carried out during kharif 2012 at SeedBreeding Farm JNKVV Jabalpur. The genetic materialobtained from F12 generation seeds of NPT derived lineswere used for the present study comprising of 120 riceNPT lines with 4 cheek entries i.e. Kranti, IR 64 SahbhagiDhan and JRH 8 were grown in randomized completeblock design with three replications. Twenty one daysold seedlings were transplanted in separate strips with

2 7

spacing of 20 x 20cm. Necessary intercultural operationswere made during cropping period for proper growth anddevelopment of the plants. Data were collected from 10randomly selected plants for days to 50% flowering, daysto maturity, plant height, culm length, panicle length, flagleaf length, flag leaf width, total number of branches perpanicles, number of filled grains per panicle, number ofunfilled grains per panicle, 1000 seed weight, seed length,seed width, length breadth ratio, panicle weight per plant,biological yield per plant, harvest index, fresh weight ofroots per plant and dry weight of roots per plant. Meandata of each trait was subjected to genetic variability,association analysis and path analysis.

Results and discussion

Phenotypic coefficient of variation (PCV) and genotypiccoefficient of variation (GCV) were high (> 20%) for numberof filled grains per panicle, followed by dry weight of rootsper plant, fresh weight of roots per plant, panicle weight

per plant, grain yield per plant, biological yield per plant,total number of tillers per plant, harvest index, culm lengthand 1000 seed weight. This suggested that these traitswere under the influence of genetic control and lessinfluenced by the environment. These results were similarwith earlier reports of Debchoudhary and Das (1998) andMishra and Verma (2002). Moderate (10-20%) value ofPCV and GCV were observed for plant height, length andbreadth ratio, panicle length, flag leaf length, total numberof branches per panicle, flag leaf width, seed width andseed length indicated considerable amount of variabilityfor these traits. These results were in agreement with thefindings reported by Kaw et al. (1990) and Chaudhary etal. (2004). Low value of GCV and PCV recorded for daysto 50% flowering and days to maturity.

High heritability (> 60%) was recorded for all thetraits except panicle length and number of filled grainsper panicle. High heritability coupled with high geneticadvance as percentage of mean (>20%) were recordedfor all the traits in the study except days to 50% flowering,

Table 1. Parameter of genetic variability for yield and yield attributing traits under rainfed situation

Traits Mean Rang GCV PCV h2 (b)% GA at 5% G.A. at % ofMin. Max. mean at 5%

DTFF 117.89 92.67 140.67 8.581 8.587 99.9 20.824 17.664TNTPP 6.399 3.67 12.73 22.806 27.852 67.0 2.462 38.470CLPP (cm) 71.433 33.93 131.93 23.208 23.950 93.9 33.094 46.33PLPP (cm) 24.47 16.2 33.73 16.255 21.195 58.8 6.284 25.682PHPP (cm) 95.79 50.67 163.53 19.153 19.853 93.1 36.46 38.064FLL (cm) 27.204 16.13 38.067 16.277 19.089 72.7 7.778 28.593FLW (cm) 1.732 0.9133 2.4333 15.920 17.527 82.5 0.513 29.787TNBPPm 13.761 9.00 22.20 16.249 17.558 85.6 4.263 30.976NFGPP 248.85 16.67 352.40 20.454 451.459 0.2 4.751 1.909NUFGPP 58.85 7.33 236.93 59.70 67.148 79.0 64.308 109.3431000 s.w (g) 21.391 9.63 33.03 21.658 21.734 99.3 9.510 44.458L.S. (mm) 8.236 6.17 10.80 11.005 11.654 89.2 1.763 21.409B.S. (mm) 2.479 1.77 3.233 13.661 14.525 88.5 0.656 26.467L/BR 3.404 2.19 5.317 18.246 19.49 87.6 1.1198 35.189WPPP (q) 29.54 7.53 47.73 30.967 33.400 86.0 17.471 59.144BYPP (q) 61.05 27.60 112.00 27.126 30.048 81.5 30.824 50.445GYPP (q) 22.8874 0.667 39.00 29.681 32.153 85.2 12.917 56.441HI% 38.53 1.27 66.10 23.914 26.522 81.3 17.116 44.420FWRPP (q) 23.28 9.33 48.53 32.716 33.770 93.9 15.198 65.290DWRRPP (q) 10.46 3.36 22.40 38.513 39.595 94.6 8.147 77.167DTM 143.38 115.0 165.0 8.061 8.061 100.0 23.807 16.604

2 8

Tabl

e 2.

Phe

noty

pic

corr

elat

ion

coef

ficie

nts

for y

ield

and

yie

ld a

ttrib

utin

g tra

its u

nder

rain

fed

situ

atio

nTr

aits

DTF

FTN

TPP

CLP

PPL

PPPH

PPFL

LFL

WTN

BN

FGN

UFG

1000

L.S.

B.S.

L/BR

WPP

PBY

PPH

I%FW

RPP

DW

RPP

DT

M(c

m)

(cm

)(c

m)

(cm

)(c

m)

PPn

PPn

PPn

sq (

q)(m

m)

(mm

)(q

)(q

)(g

)

DTE

FF1.

0000

-0.1

227*

0.02

94-0

.392

7**

-0.0

407

-0.1

982*

0.29

27**

0.37

45**

-0.7

554*

*0.

5410

**-0

.171

3*-0

.333

1**

0.06

83-0

.245

6**

-0.2

631*

*0.

0039

-0.5

101*

*0.

0496

0.07

720.

9999

**

TNTP

P1.

0000

-0.2

46-0

.114

4*-0

.066

1-0

.346

0**

-0.3

873*

*-0

.578

8**

-1.7

152*

-0.3

443*

*0.

1032

*0.

0587

0.00

090.

0143

0.07

800.

0541

0.00

440.

1770

**0.

1833

**-0

.120

9*

CLP

P1.

0000

0.37

61**

0.98

25**

0.46

25**

0.22

97**

0.25

83**

-1.0

015*

-0.1

130*

0.49

47**

0.36

61**

0.34

00**

-0.0

514

0.57

77**

0.49

02**

0.16

66**

0.18

04**

0.09

520.

0334

PLPP

1.00

000.

5425

**0.

5892

**0.

1041

0*0.

2733

**1.

0473

*0.

0032

0.29

57**

0.61

76**

-0.0

735

0.41

27**

0.66

97**

0.57

47**

0.09

280.

2078

**0.

1654

**-0

.399

9**

PHPP

1.00

000.

5444

**0.

2426

**0.

3002

**-0

.664

6**

-0.0

984

0.50

00**

0.45

30**

0.28

89**

0.04

450.

6531

**0.

5596

**0.

1657

**0.

1973

**0.

1905

**-0

.038

8

FLU

(cm

)1.

0000

0.39

23**

0.34

30**

0.56

26**

0.14

69*

0.19

38**

0.31

34**

0.01

530.

1514

*0.

4846

**0.

5057

**-0

.074

30.

1304

*0.

1014

*-0

.200

2**

FLW

cm

1.00

000.

6642

**-0

.013

50.

4643

**-0

.034

9-0

.162

4**

0.17

10**

-0.2

251*

*0.

1990

**0.

3681

**-0

.250

50.

0620

0.08

670.

2933

**

TNB

PPn

1.00

001.

0827

**0.

4844

**0.

0186

0.02

520.

1235

*-0

.063

30.

3165

**0.

4907

**-0

.276

2**

0.29

20**

0.23

71**

0.37

53**

NFG

PPn

1.00

00-0

.638

6**

-1.0

055*

1.10

70*

-2.0

448*

*0.

6383

**-0

.156

0**

-1.0

068*

1.27

11*

-0.3

444*

*-0

.577

8**

-0.8

135*

*

NU

FGPP

n1.

0000

-0.2

963*

*-0

.215

0**

-0.1

580*

*0.

0046

-0.1

673*

*0.

0776

-0.6

179*

*0.

0340

0.03

870.

5381

**

1000

sq

(q)

1.00

000.

4264

**0.

7623

**-0

.296

1**

0.36

96**

0.18

97**

0.35

38**

-0.0

074

-0.0

761

-0.1

739*

*

L.S.

(mm

)1.

0000

-0.1

138*

0.69

08**

0.51

13**

0.37

65**

0.14

028

0.10

99*

-0.0

411

-0.3

377

B.S.

(mm

)1.

0000

-0.7

86**

0.05

73-0

.034

2-.2

628*

*0.

0779

-0.0

020

0.06

47

L/BR

1.00

000.

2534

0.23

35**

-0.1

043*

0.09

290.

0059

-0.2

468*

*

WPP

P (q

)1.

0000

0.82

17**

0.19

98**

0.25

59**

0.13

08*

-0.2

575*

*

BYPP

(g)

1.00

00-0

.282

5**

0.35

54**

0.28

06**

0.00

92

HI %

1.00

00-0

.181

2**

-0.1

884*

*-0

.510

3**

FWR

PP (

q)1.

0000

0.89

57**

0.04

96

DW

RPP

(q)

1.00

000.

0704

DT

M1.

0000

GYP

P (P

)-0

.370

7**

0.08

950.

5623

**0.

6002

**0.

6254

**0.

4254

**0.

1678

*0.

2235

**-0

.146

9*-0

.366

3*0.

3897

**0.

4343

**4.

1195

*0.

1615

**0.

9157

**0.

7125

**0.

4445

**0.

1992

**0.

1392

**-0

.365

9**

** s

igni

fican

t und

er 1

% le

vel o

f sig

nific

ance

* s

igni

fican

t und

er 5

% le

vel o

f sig

nific

ance

2 9

Tabl

e 3.

Gen

otyp

ic p

ath

coef

ficie

nts

for y

ield

and

yie

ld a

ttrib

utin

g tr

aits

on

grai

n yi

eld

per p

lant

und

er ra

infe

d si

tuat

ion

Trai

tsD

TFF

TNTP

PC

LPP

PLPP

PHPP

FLL

FLW

TNB

NFG

NU

FG10

00L.

S.B.

S.L/

BRW

PPP

BYPP

HI%

FWR

PPD

WR

PPD

TM

(cm

)(c

m)

(cm

)(c

m)

(cm

)PP

nPP

nPP

nsw

(g)

(mm

)(m

m)

(g)

(q)

(g)

(g)

DTF

F-0

.098

70.

0121

-0.0

029

0.03

880.

0040

0.01

96-0

.028

9-0

.037

00.

0745

-0.0

534

0.01

690.

0329

-0.0

067

0.02

420.

0260

-0.0

004

0.05

03-0

.004

90.

0071

-0.0

986

TNTP

-0.0

024

0.01

99-0

.000

5-0

.002

3-0

.001

3-0

.006

9-0

.007

7-0

.011

5-0

.034

2-0

.006

90.

0021

0.00

120.

0000

0.00

030.

0016

0.00

110.

0001

0.00

350.

0038

-0.0

024

CLP

P (c

m)

-0.0

111

0.00

93-0

.376

7-0

.141

6-0

.370

1-0

.174

2-0

.086

5-0

.097

30.

3772

0.04

26-0

.186

3-0

.134

1-0

.128

30.

0194

-0.2

176

-0.1

847

-1.0

628

-0.0

680

-0.0

358

-0.0

126

PLPP

(cm

)0.

0384

0.01

12-0

.036

8-0

.097

8-0

.053

-0.0

576

-0.0

102

-0.2

67-0

.102

4-0

.000

3-0

.028

9-0

.060

40.

0072

-0.0

404

-0.0

655

-0.0

562

-0.0

091

-0.0

23-0

.016

20.

0391

PHPP

(cm

)-0

.017

3-0

.0.2

810.

4168

0.23

01-0

.424

20.

2309

0.-1

029

0.12

74-0

.282

0-0

.041

80.

2121

0.19

220.

1226

0.01

890.

2771

0.23

740.

0703

0.08

370.

0465

-0.0

165

FLL

(cm

)-0

.002

3-0

.004

00.

0053

0-00

670.

0062

0.01

140.

0045

0.00

390.

0064

0.00

170.

0022

0.00

360.

0002

0.00

170.

0055

0.00

58-0

.000

80.

0015

0.00

12-0

.002

3

FLW

(cm

)-0

.012

9-0

.017

00.

0101

0.00

460.

0107

0.01

730.

0440

0.02

92-0

.000

60.

0204

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015

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071

0.00

75-0

.009

90.

0088

0.16

2-0

.011

00.

0027

0.00

380.

0129

TNBP

Pn-0

.027

50.

0426

-0.0

190

-0.0

201

-0.0

221

-0.0

252

-0.0

488

-0.0

735

-0.0

796

-0.0

356

-0.0

014

-0.0

019

-0.0

091

0.00

47-0

.023

3-0

.036

10.

0203

-0.0

215

-0.0

201

-0.0

276

NFG

PPn

-0.0

039

-0..0

088

-0.0

051

0.00

54-0

.003

40.

0029

-0.0

001

0.00

550.

0051

-0.0

033

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051

0.00

570.

0105

0.01

35-0

.000

8-0

.005

20.

0065

-0.0

018

-0.0

030

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042

NU

FGPP

n-0

.000

50.

0003

0.00

010.

0000

0.00

014

-0.0

001

-0.0

004

-0.0

004

0.00

060.

0009

0.00

030.

0002

0.00

010.

0000

0.00

02-0

.000

10.

006

0.00

000.

0000

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005

1000

SW

(g)

-0.0

1713

0.10

320.

4947

0.2*

570.

5000

0.19

38-0

.034

90.

0186

0.00

55-0

.296

3-0

.043

7-0

.018

2-0

.032

60.

0126

-0.0

158

0.00

81-0

.015

10.

0003

0.00

320.

0074

L.S.

(mm

)0.

3331

0.05

870.

3561

0.61

76-0

.453

00.

3134

-0.1

624

0.02

521.

1070

-0.2

150

-0.0

041

-0.0

095

0.00

110.

0066

-00.

0049

-0.0

036

-0.0

013

-0.0

010

0.00

040.

0032

B.S.

(mm

)0.

0683

0.00

090.

3406

-0.0

735

0.28

890.

0153

0.17

100.

1235

-2.0

448

-0.1

580

0.01

26-0

.001

90.

0165

-0.1

300.

0009

-0.0

006

0.00

43-0

.001

3-0

.001

20.

0011

L/BR

-0.0

2456

0.01

43-0

.051

40.

4127

0.04

450.

1514

-0.2

251

-0.0

633

2.63

830.

0048

0.00

06-0

.001

30.

0015

-0.0

019

-0.0

005

-0.0

040.

0002

-0.0

002

0.00

000.

0005

WPP

P (g

)-0

.263

10.

0780

0.57

770-

6697

0.65

310.

4846

0.19

900.

3165

-0.1

560

-0.1

673

0.11

030.

1526

0.01

710.

0757

-0.2

986

-0.2

453

0.05

960.

0764

0.03

90-0

.076

9

BYPP

(g)

0.00

390.

0541

0.49

020.

5747

0.55

960.

5057

0.36

810.

4907

-1.0

080

0.07

760.

1256

0.24

99-0

.022

70.

1550

0.54

540.

6637

-0.1

875

0.23

590.

1862

0.00

61

HI%

-0.0

5101

0.00

440.

1666

0.09

280.

1657

-0.0

743

-0.2

505

-0.2

762

1.27

11-0

.617

90.

2031

0.08

050.

1508

-0.0

599

0.11

47-0

.662

20.

5740

-0.1

040

-0.1

082

-0.2

929

FWR

PP (

g)0.

0496

0.17

700.

1806

0.20

780.

1973

0.13

040.

0620

0.29

20-0

.344

40.

0340

0.00

08-0

.012

40.

0088

-0.0

105

0.02

89-0

.040

20.

0205

-0.1

130

-0.1

012

-0.0

056

DW

RPP

(g)

0.72

20.

1883

0.09

520.

1654

0.10

950.

1014

0.08

670.

2731

-0.5

778

0.03

87-0

.010

7-0

.005

8-0

.010

20.

0008

0.01

850.

0396

-0.0

266

0.12

660.

1413

0.00

99

DT

M-0

.370

70.

0895

0.56

230.

6002

0.62

540.

4252

0.16

780.

2232

-0.1

469

-0.3

663

0.38

970.

4343

0.11

950.

1615

0.91

570.

7125

0.44

450.

1992

0.13

92-0

.365

9

** s

igni

fican

t und

er 1

% le

vel o

f sig

nific

ance

* s

igni

fican

t und

er 5

% le

vel o

f sig

nific

ance

3 0

days to maturity and number of filled grains per panicle,this showed that these traits were not much influencedby environmental factors. Hence these traits are mostlycontrolled by genetic factors and expected to respondthe direct selection for traits improvement. These findingshowed agreement with the findings of Murthy et al.(1999), Mishra and Verma (2002) and Abul Fiyaz et al.(2011). High heritability coupled with moderate geneticadvance as percentage of mean (11-20%) was recordedfor days to 50% flowering and days to maturity, whichshowed that these trait are controlled by the interactionof genetic and environmental components. The selectionbased on phenotypic observations alone may not be veryeffective for these traits similar findings were also reportedby Hasib et al. (2004) and Padmaja et al. (2008).

As presented on Table 2 grain yield showed positiveand significant correlation with panicle weight per plantfollowed by biological yield per plant, plant height, panicle

length, culm length, harvest index, seed length and flagleaf length. The high genetic correlations existing betweendifferent plant traits can be used to conduct indirectselection of a complex desired trait in a breedingprogramme. Genetic correlation between various plantscharacters arise because of linkage, pleiotrophy,developmental inter rrelations and functional relationship(Falconer 1960). Madhavilath et al. (2005b) reported similarresults. Negative significant correlation observed in daysto 50% flowering, number of filled spikelets per panicle,days to maturity and total number of grains per panicle.Thus, selection for enhancement of these traits could beexecuted separately but simultaneously.

The path analysis indicated high positive directeffect of biological yield per plant (0.6637), harvest index(0.5740), plant height (0.4242), panicle weight per plant(0.2986) on grain yield per plant. Culm length per plant(0.3767), fresh weight of roots per plant (0.1130), days to50% flowering. (0.0987) and panicle length per plant(0.0978) had negative direct effect on grain yield per plant.Path coefficient analysis provides the information for thedirect and indirect effects of different traits on grain yieldper plant. Mishra and Verma (2002) reported high positivedirect effects of biological yield, where as Gravois andMC new (1993) and Shanthala (2004) reported for panicleweight per plant. This is in conformity with our findings.This indicated that true relation ship between them anddirect selection for these traits well be rewarding for yieldimprovement.

Thick culm with 4-6 tillers/plant Panicle with 400-600 spikelets/panicle

3 1

/kku Hkkjro'kZ dh egRoiw.kZ Qly gSA /kku esa 124 tuunzO;@gsD ¼120,u-ih-Vh- ykbu ,oa 4 pSd½ dk v/;u 21 y{.kkas ds ewY;kadu fd;kx;kA izfr ikS/k mit ij izHkko Mkyus okys y{k.kksa dk v/;u la/kfo"kys'k.k i)fr }kjk fd;k x;k ,oa izR;{k ,oa vizR;{k vlj dk v/;u dj bu y{k.kks dks mudh egRrk ds vk/kkj ij oxhZÑr fd;k x;kA

orZeku v/;u esa lg lac/k xq.kkad ls O;Dr gksrk gS fd mit ijldkjkRed izHkko Mkyus okys y{k.kksa esa tSfod mit izfr ikS/k] ckyh ctuizfr ikS/k] Qly lwpdkad ¼gkjosLV baMsDl½ ,oa ikS/k yackbZ izeq[k gSA vr%/kku dh Qly ds mUufrdj.k ,oa mit c<kus gsrq bu y{k.kks dk mi;ksxQly vuqokaf"kd lq/kkj dk;ZØe esa fd;k tkuk pkfg,A

References

Abdul Fiyaz R, Ramya KT, Chikkalingaiah BC, Gireesh C,Kulkarni RS (2011) Genetic variability, correlationand path coefficient analysis studies in rice (Oryzasativa L.) under alkaline soil condition. Electronic JPlant Breed 2 (4) :531-537

Chaudhary M, Sarwgi AK, Matiramani NK (2004) Geneticvariability of quality yield and yield attributing traitsin aromatic rice (Oryza sativa L.) Adv Pl Sci 17(2):485-490

Cheng S, Mao CZ, Zhan XD, Sun ZX (2001) Construction ofdouble haploid and recombinant inbred populationof indica japonica hybrid and their differential inindica and japonica property. Chinese J Rice Sci15(4): 257-260

Debchoudhary PK, Das PK (1998) Genetic variability,correlation and path coefficient analysis in deepwater rice. Ann Agri Res 19:120-124

Falconer DS (1960) Introduction to quantitative genetics.Ronald Press. NewYork.

Gravois KA, MC New RW (1993) Combining ability andheterosis in US Southern lang grain rice. Crop Sci33:83-86

Hasib KM, Ganguli PK, Kole PC (2004) Evaluation lines ofmutant and Basmati crosses of scented rice. J InterAcademicia 8(1) 7-10

Kaw RN, Aquino RC, Moon HP, Yae JD, Haq N (1990)Variability and inter relations in rice under cold stressenvironment. Oryza 36:1-4

Madhavilatha L, Reddy Sekhar M, Suneetha Y, Srinivas T(2005b) Genetic variability, correlation and pathanalysis for yield and quality traits in rice (Oryzasativa L. ) Res on Crop. 6 (3) 527-534

Mishra LK, Verma RK (2002) Genetic variability for qualityand yield traits in non segregating population ofrice (Oryza sativa L.) Plant Archives 2(2)251-256

Murthy N, Kulkarni RS, Uday Kumar M, Muethy N (1999)Genetic variability, heritability and genetic advancefor morphological traits in rice. Oryza 36 (2) 159-160

Padmaja D, Radika K Rao, Subba LV, Padma V (2008) Studieson variability, heritability and genetic advance forquantitative characters in rice (Oryza sativa L.) CropRes 12(3) 355-357

Peng S., Khush GS, Cassman KG (1994) Evaluation of anew plant edeotype for in increase yield potential inbreaking the yield barrier. Proc. Of a work shop onrice yield potential in favorable environments (Ed.KG Cassman), 5-20 International Rice ResearchInstitute Manila Philippines

Peng S, cassman KG, Virmani SS, Sheehy J, Khush GS(2004) Yield potential trends of tropical rice sincethe release IR 8 and the chellans of increasing riceyield potential. Crop Sci 39: 1552-1559

Shanthala J (2004) Path coefficient analysis for grain yieldcomponents in hybrid rice. Enviro and Ecol 22(4)734-736

(Manuscript Receivd : 15.10.2014; Accepted :20.04.2015)

3 2

Abstract

A field experiment was conducted during 2012-13 and 2013-14 at the Rajola Farm of MGCGVV Chitrakoot, Satna (MP) tostudy the efficacy of herbicides on productivity and economicsof pigeonpea-based intercropping systems. Sole croppingof pigeonpea although gave the significantly higher growthand yield attributing parameters, but the LER,CEY and netincome were significantly higher in case of pigeonpea +blackgram (2:2) intercropping. The net income was up toRs. 62031 ha-1. Pigeonpea + greengram (2:2) stood thesecond best intercropping system. As regards with the weedcontrol methods, pendimethalin @ 1 kg ha-1 + imazethapyr@ 0.1 kg ha-1 resulted in significantly higher growth and yieldattributing parameters of pigeonpea, greengram andblackgram as well as LER, CEY and net income over theremaining weed control methods. However, the second bestweed control method was oxiflourfen @ 0.2 kg ha -1 +imazethapyr @ 0.1 kg ha-1 application. The net income wasup to Rs. 72088 and Rs. 62678 ha-1, respectively.

Keywords: Herbicides, intercropping systems,productivity, economics, Kharif pulses

Pulses like, pigeonpea, greengram, blackgram are oneof the important segments of Indian Agriculture aftercereals and oils. These are the excellent source of highquality protein, essential amino acids, fatty acids, fibers,minerals and vitamins. These crops improve soil healthby enriching long term fertility and meets up to 80% oftheir nitrogen requirement from symbiotic nitrogen fixationand leaves behind substantial amount of residual nitrogenand organic matter for subsequent crops. The water

Efficacy of herbicides on productivity and economics of pigeonpea-based intercropping systems

Chunni Lal Rai, R.K. Tiwari*, Pawan Sirothia, Shailesh Pandey and Swati JaiswalDepartment of Natural Resource ManagementMahatma Gandhi Chitrakoot Gramodaya Vishwa VidhyalayaChitrakoot 485 780Satna (MP)*Department of AgronomyCollege of AgricultureJawaharlal Nehru Krishi Vishwa VidyalayaRewa (MP)

JNKVV Res J 49(1): 32-36 (2015)

requirement of pulses is about one fifth of the requirementof cereals and thus effectively saves available preciousirrigation water.

The present production of pulses in the countryhovers around 13-15 million tones. As the result, per captaavailability of pulses in India has declined from 64 g/day(1951/52) to 34 g/day (2010) as against FAO/WHO'srecommendation of 80 g/day.

In view of dwindling area of cultivation in one handand increasing population on other hand, it has becomeimperative to harvest maximum from minimum per capitaland. So intercropping with short-duration pulses hasimmense potentiality of crop production in comparison tosole cropping of long duration pigeonpea. The work ondifferent intercropping systems is very limited and theresults obtained so for are not up to satisfaction. Theincreasing urbanization due to growing population hasaffected food production leading to irrevocable loss ofunable land. To achieve the target of additional productionof pulses the intercropping is the ultimate solution. Itovercomes the draw backs of mono cropping systemsand exploits the soil intensively for food production in viewof growing population at the alarming rate. The techniqueof intercropping helps in increasing the yield per unit areaper unit time. Looking to these facts, the present researchwas taken up.

Material and methods

A field experiment was conducted at the Rajola Farm,MGCGVV, Chitrakoot, Satna (M.P.) during 2012-13 and

3 3

2013-14. The soil of the experimental field was sandyclay loam with pH value 7.44 to 7.46, electrical conductivity0.32 to 30 dSm-1, organic carbon 2.9 to 2.4 g kg-1, availableN, 193.42 to 201.6 kg ha-1, available P2O5 16.72 to 20.11kg ha-1 and available K2O 207.28 to 201.5 kg ha-1. Therainfall received during the crop season was 1279 and1518 mm in first and second year, respectively. Thetreatments comprised 3 cropping systems [solepigeonpea, pigeonpea + greengram (2:2), pigeonpea +blackgram (2:2)] at 90/30 cm spacing in rows in mainplots and 6 weed control methods (weedy check,pendimethalin 1 kg a.i./ha PE, oxiflourfen 0.2 kg a.i./haPE and imazethapyr 0.1 kg a.i./ha POI, pendimethalin +imazethapyr and oxiflourfen + imazethapyr) as the sub-plot treatments. The experimental was laid out in splitplot design with three replications. Pigeonpea var "ICPL88039", greengram var. "Samrat" and blackgram var."Azad-1", were sown during 21 to 23 July @ 15, 12 and15 kg seed ha-1 respectively in both the years. Pigeonpeasole was sown in rows at 60 cm spacing. Fertilizers wereapplied @ 20-60-20 kg N, P2O5 and K2O ha-1 for each ofthe pulse crops. The inter crops greengram and blackgramwere harvested during 14-17 October and the main cropwas harvested during 19 to 24 December in 2013 and2014.

Results and discussion

Growth, yield and yield components

Amongst the intercropping systems, sole pigeonpeabrought about significantly higher population of monocotand dicot weeds as revealed from their density and drymatter/m2. Moreover the root and shoot growth, yieldattributes and thereby yield parameters were alsosignificantly higher in case of sole pigeonpea as comparedto that when pigeonpea was intercropped with shortduration pulses like greengram or blackgram. Themaximum yield attributes of pigeonpea were 107.17 pods/plant, 5.74 cm pod length, 4.66 seeds/pod, 37.84 g seedweight/plant and 94.48 g test weight. Consequently theseed and stover yields were recorded up to 7.18 and 29.51q ha-1, respectively (Table 1 and 2). The higher yieldparameters are exactly in accordance with the yieldattributes observed under sole pigeonpea. Such anincrease in growth, yield and yield attributing parametersmay be attributed to the fact that pigeonpea plants weregrowing without any competition with the intercrops forspace, light, soil moisture and nutrients. However, allthese parameters were statistically at par between boththe intercropping systems (pigeonpea + greengram or

blackgram). These findings corroborate with those ofRathod et al. (2004), Sharma et al. (2010) and Kumawatet al. (2012).

Amongst the herbicidal treatments, pendimethalin+ imazethapyr proved the most effective in controllingmaximum weeds/m2 (Table 1, 2 & 3) Consequently thegrowth parameters, yield and yield attributes of pigeonpeaas well as greengram and blackgram were recordedsignificantly higher from this dual herbicidal treatment overthe single herbicide applied treatments. However, secondbest weed control treatment, was oxiflourfen +imazethapyr which recorded significantly higher yield andyield attributes of pigeonpea, greengram and blackgramas compared to the single + herbicide applied treatments.The significantly lowest yield and yield attributes wereobtained from the weedy check treatment. These resultsare in consonance with those of Rao et al. (2003). Niralaand Dewangan (2012), Nirala et al. (2012).

Land equivalent ratio (LER)

Land equivalent ratio is the best parameter to identify thesuitability of an intercropping system. The yield advantagein terms of LER of pigeonpea + greengram and blackgramintercropping recorded higher (1.60 to 1.62) as indicatesin Table 4. This indicates that the row ratio was foundmore beneficial than their sole stands and there wasmutual compensation between both the crops havingdifferential life cycle, spatial arrangement, foliage cover,root development and nutrient absorption. Moreover betterplanting geometry might have avoided the coincidence ofthe peak period of growth of component crops. This mighthave helped for efficient crops under inter croppingsystems. Sharma et al. (2010) and Sharma and Guled(2012) have also reported higher LER when pigeonpeawas intercropped with greengram.

Crop equivalent yield (CEY)

The crop equivalent yield (Table 4) was enhancedsignificantly under pigeonpea + grengram (2:2) and PP +blackgram (2:2) intercropping systems (12.26 to 13.63 qha-1 as compared to sole pigeonpea (7.18 q ha-1). Thiswas owing to higher yield from the main plus intercropsas compared to main crop pigeonpea only. The totalincreased yields under intercropping systems fetchedincreased market price thereby increased the CEY. Thedouble herbicidal treatments recorded significantly higherCEY (13.00 to 14.27 g ha-1), while weedy check recordedless than half CEY (6.50 q ha-1).

3 4

Tabl

e 1.

Wee

d st

udie

s an

d gr

owth

par

amet

ers

of p

igeo

npea

as

influ

ence

d by

inte

rcro

ppin

g sy

stem

s an

d w

eed

man

agem

ent p

ract

ices

(poo

led

for 2

year

s)

Trea

tmen

tsW

eed

dens

ityD

ry m

atte

rW

CE

(%)

Pla

nt h

eigh

tPr

imar

yS

econ

dary

Roo

tR

oot

Roo

t dry

Roo

tN

odul

es/m

2 75

DA

Sof

wee

ds75

DA

S(c

m) a

tbr

ache

sbr

anch

esle

ngth

wid

thw

eigh

tno

dule

sdr

y w

eigh

t(g

/m2 )

75 D

AS

harv

est

/pla

nt/p

lant

(cm

)(c

m)

(g)

/pla

nt/p

lant

(g)

Inte

rcro

ppin

g sy

stem

sI 1:

Sole

pig

eonp

ea43

.43

28.9

469

.42

149.

1110

.66

9.88

20.9

927

.46

2.79

20.3

00.

69I 2 :

Pig

eonp

ea +

bla

ck g

ram

(2:2

)38

.42

25.9

669

.56

144.

719.

718.

8918

.76

24.7

62.

6318

.66

0.65

I 3 : P

igeo

npea

+ g

reen

gram

(2:2

)40

.33

26.9

069

.71

144.

4010

.38

9.26

18.9

725

.21

2.57

18.4

60.

64C

D (P

= 0

.05)

0.31

0.19

0.01

80.

980.

680.

600.

730.

640.

115

0.46

0.01

1W

eed

cont

rol m

etho

dsW

1: W

eedy

che

ck (c

ontro

l)16

1.16

89.6

20.

0012

2.49

8.46

6.78

15.3

320

.04

1.52

11.9

60.

36W

2:Pen

dim

etha

lin (1

kg

/ha)

18.8

113

.51

84.6

414

7.88

10.4

29.

7819

.10

26.4

22.

8520

.33

0.71

W3:

Oxy

fluor

fen

( 0.2

kg

/ha)

23.4

719

.74

78.5

414

5.75

9.76

9.13

19.4

025

.75

2.58

19.9

20.

69W

4: Im

azet

hapy

r ( 0

.1 k

g /h

a)19

.42

24.7

972

.09

147.

499.

688.

4719

.89

25.6

92.

7419

.18

0.69

W5:

Pend

imet

helin

+ Im

azet

hapy

r9.

377.

5591

.51

158.

8611

.90

11.3

022

.31

29.1

53.

2522

.09

0.78

W6:

Oxy

flurfe

n +

Imaz

etha

pyr

12.0

88.

3890

.58

153.

9711

.28

10.5

821

.43

27.8

33.

0521

.37

0.75

CD

(P=0

.05)

3.14

1.66

1.85

1.49

0.79

0.44

50.

701.

180.

070.

760.

015

Inte

ract

ion

Sig

.S

ig.

Sig

.S

ig.

NS

Sig

.S

ig.

NS

Sig

.N

SS

ig.

Sig

. = S

igni

fican

t NS

= N

on- s

igni

fican

t

Tabl

e 2.

Yie

ld a

ttrib

utes

and

yie

ld o

f pig

eonp

ea a

s in

fluen

ced

by in

terc

ropp

ing

syst

ems

and

wee

d m

anag

emen

t pra

ctic

es (p

oole

d fo

r 2 y

ears

)

Trea

tmen

tsPo

ds/p

lant

Pod

leng

thSe

eds/

pod

Seed

wei

ght

100

seed

Seed

yie

ldSt

over

yiel

dHI

(cm

)/p

lant

(g)

wei

ght (

g)(q

/ha)

(q/h

a)(%

)In

terc

ropp

ing

syst

ems

I 1: So

le p

igeo

npea

107.

175.

744.

6637

.84

94.4

87.

1828

.73

19.8

3I 2:

Pig

eonp

ea +

bla

ckgr

am (2

:2)

99.9

95.

524.

4035

.52

92.7

16.

4926

.86

19.1

5I 3 :

Pig

eonp

ea +

gre

engr

am (2

:2)

99.3

55.

484.

3835

.14

92.3

56.

2426

.54

18.6

5C

D (P

= 0

.05)

3.43

0.13

0.13

1.55

0.41

0.07

1.30

0.74

Wee

d co

ntro

l met

hods

W1:

Wee

dy c

heck

(co

ntro

l)66

.63

4.31

4.11

22.6

088

.44

3.69

18.3

016

.82

W2:P

endi

met

halin

(1 k

g /h

a)10

6.35

5.75

4.51

37.8

094

.23

7.04

28.7

319

.50

W3:

Oxy

fluor

fen

( 0.2

kg

/ha)

103.

895.

514.

3737

.31

93.3

86.

1826

.11

19.0

5W

4: Im

azet

hapy

r ( 0

.1 k

g /h

a)10

3.28

5.42

4.21

36.1

192

.38

6.37

27.2

319

.03

W5:

Pend

imet

helin

+ Im

azet

hapy

r12

0.55

6.41

5.04

42.6

396

.10

8.68

33.0

420

.64

W6:

Oxy

flurfe

n +

Imaz

etha

pyr

112.

326.

074.

6240

.53

94.5

77.

8530

.85

20.2

2C

D (P

=0.0

5)3.

130.

090.

155

1.47

1.23

0.11

41.

110.

56In

tera

ctio

nS

ig.

Sig

.S

ig.

Sig

.S

ig.

Sig

.S

ig.

Sig

. S

ig. =

Sig

nific

ant,

NS

= N

on- s

igni

fican

t

3 5

Tabl

e 3.

Yie

ld a

ttrib

utes

and

yie

ld o

f gre

engr

am a

nd b

lack

gram

as

influ

ence

d by

inte

rcro

ppin

g sy

stem

s an

d w

eed

man

agem

ent p

ract

ices

(poo

led

for

2 ye

ars)

Trea

tmen

tsPo

ds/p

lant

Pod

leng

thSe

eds/

pod

Seed

wei

ght

100

seed

Seed

yie

ldSt

over

yiel

dHI

(cm

)/p

lant

(g)

wei

ght (

g)(q

/ha)

(q/h

a)(%

)In

terc

ropp

ing

syst

ems

Gre

engr

amW

1: W

eedy

che

ck (

cont

rol)

9.50

5.31

7.98

2.73

32.4

03.

4814

.74

19.1

0W

2:Pen

dim

etha

lin (1

kg

/ha)

19.9

46.

058.

824.

8433

.94

5.38

17.1

123

.93

W3:

Oxy

fluor

fen

( 0.2

kg

/ha)

17.2

76.

317.

454.

5233

.90

4.69

16.3

522

.29

W4:

Imaz

etha

pyr (

0.1

kg

/ha)

17.5

55.

848.

034.

0533

.01

5.01

15.5

624

.39

W5:

Pend

imet

helin

+ Im

azet

hapy

r24

.89

6.73

10.4

95.

6936

.67

6.40

17.7

926

.45

W6:

Oxy

flurfe

n +

Imaz

etha

pyr

23.0

56.

539.

595.

4035

.79

5.82

17.1

225

.39

C D

(P=0

.05)

0.46

0.14

0.57

0.10

0.71

0.14

0.79

0.57

Wee

d co

ntro

l met

hods

Blac

kgra

mW

1: W

eedy

che

ck (

cont

rol)

9.89

2.29

7.01

2.75

38.1

43.

305.

8935

.90

W2:P

endi

met

halin

(1 k

g /h

a)19

.16

2.99

8.33

4.60

42.9

26.

399.

4240

.39

W3:

Oxy

fluor

fen

( 0.2

kg

/ha)

17.0

02.

788.

093.

9041

.83

5.20

8.66

37.5

1W

4: Im

azet

hapy

r ( 0

.1 k

g /h

a)16

.44

2.83

7.92

4.23

42.5

05.

719.

1738

.36

W5:

Pend

imet

helin

+ Im

azet

hapy

r23

.44

3.47

9.90

5.04

44.1

98.

5811

.55

42.6

4W

6: O

xyflu

rfen

+ Im

azet

hapy

r21

.66

3.16

9.18

4.98

43.6

87.

7110

.90

41.4

3C

D (P

=0.0

5)0.

250.

020.

340.

200.

100.

080.

330.

64

Tabl

e 4.

LER

, CEY

and

eco

nom

ical

gai

n fro

m p

igeo

npea

, gr

eeng

ram

and

bla

ckgr

am a

s in

fluen

ced

by in

terc

ropp

ing

syst

ems

and

wee

d m

anag

emen

tpr

actic

es (p

oole

d fo

r 2 y

ears

)

Trea

tmen

tsLa

nd e

quiv

alen

tC

rop

equi

vale

ntG

ross

inco

me

Net

inco

me

Incr

ease

inB:

C ra

tiora

tioyi

eld

(q/h

a)(R

s/ha

)(R

s/ha

)ne

t inc

ome

over

I 1

Inte

rcro

ppin

g sy

stem

sI 1:

Sole

pig

eonp

ea1.

007.

1854

698

3203

6-

2.39

I 2 : P

igeo

npea

+ b

lack

gra

m (2

:2)

1.60

12.2

682

425

5838

626

350

3.40

I 3 : P

igio

npea

+ g

reen

gram

(2:2

)1.

6213

.63

8607

062

031

2999

53.

54C

D (P

= 0

.05)

0.11

60.

6872

2272

22-

-W

eed

cont

rol m

etho

dsW

1: W

eedy

che

ck (

cont

rol)

1.20

6.50

4325

321

820

-2.

00W

2: Pe

ndim

etha

lin (1

kg

/ha)

1.43

11.5

978

430

5541

533

595

3.39

W3:

Oxy

fluor

fen

( 0.2

kg

/ha)

1.36

10.1

568

119

4490

423

084

2.92

W4:

Imaz

etha

pyr (

0.1

kg

/ha)

1.41

10.6

471

486

4800

226

182

3.03

W5:

Pend

imet

helin

+ Im

azet

hapy

r1.

5514

.27

9715

372

088

5026

83.

85W

6: O

xyflu

rfen

+ Im

azet

hapy

r1.

5013

.00

8794

362

678

4085

83.

46C

D (P

=0.0

5)0.

010.

155

7436

7436

--

Inte

ract

ion

Sig

.S

ig.

Sig

.S

ig.

--.

Sig

. = s

igni

fican

t, C

EY =

yie

ld e

quiv

alen

t to

pige

onpe

a

3 6

Economical gain

Among the cropping systems, pigeonpea + greengram(2:2) or pigeonpea + blackgram (2:2) gave the significantlyhigher net return extra by Rs 26350 to 29995 ha-1 ascompared to sole pigeonpea. B:C ratio ranged from 3.40to 3.54. The higher net return under both the intercroppingsystems was owing to more increase in gross returnsper unit area. In case of weed management treatments,the additional net return by Rs 50268 ha-1 was obtainedfrom pendimethalin + imazethapyr followed by Rs 40858ha-1 from oxiflourfen + imazethapyr as compared to weedycheck. The B:C ratio ranged from 3.46 to 3.85. Theeconomical gain from different treatments was inaccordance with the grain and stover yields obtained whichfetched higher market price giving increased gross returns.

References

Kumawat Narendra, Singh RP, Kumar Rakesh, KumariAnupama, Kumar Pramod (2012) Response ofintercropping and integrated nutrition on productionpotential and profitability on rainfed pigeonpea.Indian J Agric Sci 4 (7): 154-163

Nirala Hemlata, Dewangan DK (2012) Effect of weedmanagement on weeds, growth and yield of kharifblackgram. J Interacademicia 16 (4) : 835 - 844

Nirala Hemlata Choubey NK, Sandeep Bhoi (2012)Performance of post emergence herbicides andhand weeding with respect to their effects on weeddynamics and yields of blackgram. International JAgricl Stat Sci 8 (2) : 679-689

Rao MM, Ramalakshmi D, Khan MM, Sree SP, Reddy MV(2003) Effect of integrated weed management inpost rainy season pigeonpea + moong beenintercropping system in vertisols. Indian J PulseRes 16 (2) : 112-115

Rathod PS, Halikatti SI, Hiremath SM, Kajjidoni ST (2004)Comparative performance of pigeonpea basedintercropping systems in northern transitional zoneof Karnataka. Karnataka J Agricl Sci. 17 : 203-206

Sharma Arjun, Rathod PS, Chavan Mohan (2010) Cropresidue management in pigeonpea basedintercropping systems under rainfed condition.Indian J Dryland Agric Res Develop 25 (1): 47-52

Sharma Arjun, Guled MB (2012) Effect of setfurrow methodof cultivation in pigeonpea + greengramintercropping systems in medium deep black soilunder rainfed conditions. Karnataka J Agricl Sci 25(1) : 18-24

(Manuscript Receivd : 10.03.2015; Accepted : 25.03.2015)

3 7

Abstract

A field experiment was conducted during rainy season of2014 to study the effect of sowing dates on growth, yield andeconomics from rice varieties under upland conditions.Among the varieties, PS-3 and then PS-5 resulted inmaximum growth parameters , yield attributes and grain yieldup to 40.90 to 43.27 q/ha with net income up to Rs. 48652 to49778/ha. The most optimum date of sowing of upland ricewas 3rd July which gave maximum growth parameters, yieldattributes and grain yield unto 38.54 q/ha with net income upto Rs. 41446/ha. Thus, variety PS-3 or PS- 5 may be grownon 3rd July is suitable to obtain maximum productivity andeconomical grain from rice under upland conditions of thisregion.

Keywords : Upland rice, growth parameters, economics

In India, rice is being cultivated in 427.53 lakh ha with aproduction of about 105.24 million tons. Madhya Pradeshcovers 1.66 million ha and contributes a production of 1.7million tons. The productivity of rice in M.P. is 1103 kg/ha while irrigated area is 1273 kg/ha.

A large number of varieties have been released forcultivation by private and government sectors to enhanceits productivity. Hence, it is essential to compare theproductivity of drought tolerant yield varieties grown indifferent dates under upland conditions of rice growingbelt of the state.

Timely sowing of rice results in earlier harvest andallows timely planting of the next wheat or other crops.The rice-wheat system productivity was nearly 12 tonnesper hectare when about 25 days old rice seedlings weretransplanted before end of June. The total systemproductivity is reduced by more than 40 per cent whenfield were planted after 15 August (Rai 2006). Timely

Effect of sowing dates on growth, yield and economics of ricevarieties under upland conditions of Rewa, Madhya Pradesh

Punit Tiwari, R.K. Tiwari, Amrita Tiwari, Vaishali Yadav and S.K. TripathiDepartment of AgronomyJawaharlal Nehur Krishi Vishwa VidyalayaCollege of AgricultureRewa - 486 001 (MP)

JNKVV Res J 49(1): 37-40 (2015)

sowing of rice crop is also found to increase the rain wateruse efficiency as compared to the delayed planting. Theexact sowing date for direct seeding of rice also play avital role in improving its growth and increasing the yield.The sowing time of the rice crop is important for threemajor reasons. Firstly, it ensures that vegetative growthoccurs during a period of satisfactory temperatures andhigh levels of solar radiation. Secondly, the optimumsowing time for each cultivar ensures the cold sensitivestage occurs when the minimum night temperatures arehistorically the warmest. Thirdly, sowing on timeguarantees that grain filling occurs when milder autumntemperatures are more likely, hence good grain quality isachieved (Farrell et al. 2003). Sowing date also has adirect impact on the rate of establishment of rice seedling(Tashiro et al. 1999).

Rice cultivation under upland conditions is facinga great threat due to shortage of water as a result ofirregular rainfall. Looking to these facts in view, the presentresearch was taken up.

Material and methods

The field experiment was conducted in Kharif season of2014 at, College of Agriculture, Instructional Farm, Rewa(M.P.) under All India Coordinated Rice ImprovementProject. The experimental field was sandy clay loam intexture. It was just below neutral in reaction (pH 6.5) withnormal electrical conductivity (0.42 dS/m). The organiccarbon content was low (0.56-0.60%) while medium inavailable N.P.K. contents 294-337, 18-36 and 314-611kg/ha, respectively).

The experiment was laid out in split-plot designwith four replications. The treatments comprised of threesowing dates (3rd July, 8th July and 13th July) in main

3 8

plots and 5 varieties (Danteshwari, Vandana, IR 64, PS-3 and PS-5) in sub plots. The varieties were sown bydirect seeding on 3rd, 8th and 13th July.

The uniform dose of fertilizers (100 kg N, 60 kgP2O5 and 40 kg K2O/ha) were applied in all the treatments.The crops were grown under recommended package ofpractices. Various observations were recorded periodicallyin relation to growth and yield attributing characters andfinally, economics of the treatments was calculated.

Results and discussion

Growth parameters

As regards with the effect of different sowing dates, earliest3rd July sowing produced significantly higher growthcharacters as compared to the sowing of crop on thelater dates. The plant height was 91.40 cm, tillers 59.53/m row length, leaves 48.39/plant, leaf length 37.57 cm,leaf width 1.30 cm and LAI 4.94. On the other hand, 3rdJuly sowing date reduced all these parameters almostup to significant extent i.e. 89.60 cm plant height, 51.46tillers/m row length, 46.84 leaves/plant, 35.61 cm leaflength, 1.19 cm leaf with and 4.57 LAI.

Early or timely sowing of photo-sensitive cropvariety provided more photoperiod to complete itsvegetative phase which is responsible for production ofphotosynthates for increased crop growth. In the presentresearch, ten days late sowing adversely affect normalfunctions and maturity duration of actively growing plantsthere by resulted in reduced growth parameters. The resultcorroborate with those of Khalifa (2009), Kerketta et al.(2010), Singh et al. (2012) and Limochi and Eskandari(2013). Among the varieties, PS-3 resulted in significantlyhigher number of tillers (65.22/m row length) and leaf areaindex (5.31), whereas IR-64 recorded significantly higherplant height (100.79 cm). Vandana recorded significantlyhigher leaf length (39.85 cm) and leaf width (1.33 cm).The other characters viz. number of tillers/m row length(44.84 cm), leaves (41.62/plant) and leaf area index (4.37)were found significantly lower in case of Vandana. Theother varieties also showed significant differences in allthe growth characters under observation. The variation ingrowth parameters among the varieties might be attributedto the variation in their parental origin which causedvariation in their genetically inheritance for such traits. Infact, rice varieties vary in their seedling vigor, droughttolerance, maturity duration and differences in resourceutilization and productivity. These findings are closeconformity with those of Mukesh et al. (2008), Nawlakheet al. (2009) and Walia et al (2014).

Yield attributes

The yield attributes are the most important parameters ofany crop which directly influence the crop yield. Theseyield attributes are likely deviate upto considerable extentas a result of variability in the crop management practicesfor increased crop production. In the present research,the different sowing dates did not deviate the yieldattributes viz. panicle length, grain weight/panicle, numberof filled and unfilled grains/panicle as well as 1000 grainweight. This might be owing to shorter interval period (only5 days) of sowing dates. Such a short period of only 5days did not influence these parameters significantly.Among the varieties under test PS 3 brought aboutsignificantly higher yield attributes over all the remainingvarieties. The panicle length was 23.71 cm, grain weight2.47 g/panicle, filled grains 107.71/panicle, unfilled grains30.33/panicle. However, 1000 grain weight (25.78 g) wasfound significantly higher in case of IR - 64. The secondbest rice variety with respect to all these yield attributingcharacters was PS 5. This was followed by IR 64. On theother hand, Vandana rice recorded the significantly lowestall these characters under upland conditions. Suchvariations in yield attributes were exactly in accordancewith the growth parameters of varieties. Better the plantgrowth better would be the photosynthesis therebyincreased accumulation of photosynthates for theirtranslocation (partitioning) towards reproductive organs.The significant differences in yield attributes among ricevarieties have also been reported by Mukesh et al. (2008),Nawlakhe et al. (2009) and Walia et al (2014).

Productivity parameters

Grain yield (38.54 q/ha) straw yield (88.86 q/ha) and harvestindex (31.26%) was maximum in case of earliest 3rd Julysowing date. The significantly lowest grain yield (36.32q/ha) and straw yield (84.27 q/ha) was noted in case oflate sowing date of 13th July under upland conditions.The productivity parameters were found exactly inaccordance with the growth and yield attributes underthese sowing dates. The yield reduction under late sowncrop has been reported by Khalifa (2009), Kerketta et al.(2010), Singh et al. (2012) and Limochi and Erkandari(2013).

The variety PS 3 produced highest grain (43.27 q/ha) but lower straw (108.84 q/ha). However, the reversewas true in case of PS 5 variety of rice. The grain yieldwas lower (40.90 q/ha) and straw yield was highest(129.66 q/ha). However, the harvest index was foundmaximum (35.41%) from variety IR- 64, closely followedby Danteshwari (35.07%). The significantly lowest grain

3 9

Tabl

e 1.

Gro

wth

par

amet

ers

of u

plan

d ric

e as

influ

ence

d by

dat

es o

f sow

ing

and

varie

ties

Trea

tmen

tsPl

ant p

opul

atio

n/m

Plan

t hei

ght

Num

ber o

f tille

rsN

umbe

r of l

eave

sLe

af le

ngth

Leaf

wid

thLA

IPa

nicl

e ro

w le

ngth

(cm

) at

/m ro

w le

ngth

/pla

nt a

t(c

m) a

t(c

m) a

tat

90

DAS

leng

th(1

0 D

AS)

harv

est

at h

arve

st90

DAS

90 D

AS90

DAS

(cm

)D

ates

of s

owin

g:3r

d Ju

ly 8

.80

89.

60 5

9.53

48.

39 3

7.57

1.3

0 4

.94

22.

258t

h Ju

ly8.

6689

.23

56.6

747

.72

36.9

91.

214.

8322

.09

13th

Jul

y8.

6088

.14

51.4

646

.84

35.6

11.

194.

5722

.12

S.E

m+

0.08

0.52

0.33

0.68

0.38

0.02

0.02

10.

38C

.D. @

5%

NS

NS

1.13

NS

1.33

0.07

0.07

5N

SVa

rietie

s:D

ante

shw

ari

8.6

4 9

2.77

53.

10 4

4.08

36.

79 1

.27

4.5

2 2

1.46

Vand

ana

8.41

100.

7944

.84

41.6

239

.85

1.33

4.37

20.8

5IR

-64

8.61

98.6

356

.12

48.0

136

.60

1.21

4.64

21.9

4PS

-38.

9878

.70

65.2

251

.60

35.5

31.

165.

3123

.71

PS-5

8.80

79.4

960

.16

52.9

534

.83

1.20

5.06

22.8

0S

.Em

+0.

080.

570.

520.

550.

450.

010.

028

0.38

C.D

. @ 5

%0.

241.

641.

491.

561.

280.

050.

080

1.08

Inte

ract

ion

NS

NS

NS

NS

Sig

.N

SN

SN

S

Tabl

e 2.

Yie

ld a

ttrib

utes

, yie

ld a

nd e

cono

mic

al g

ain

from

upl

and

rice

as in

fluen

ced

by d

ates

of s

owin

g an

d va

rietie

s

Trea

tmen

tsW

eigh

t of g

rain

sN

umbe

r of

Num

ber o

fTe

st w

eigh

tG

rain

yie

ldSt

raw

yie

ldHI

Net

inco

me

B :C

ratio

/pan

icle

(g)

fille

d gr

ains

/un

fille

d gr

ains

/of

100

0(q

/ha)

(q/h

a)(%

)(R

s./h

a)pa

nicl

epa

nicl

egr

ain

(g)

Dat

es o

f sow

ing

:3r

d Ju

ly 2

.30

96.

31 2

6.24

23.

93 3

8.54

88.

86 3

1.26

414

46 3

.13

8th

July

2.25

95.0

926

.08

23.7

837

.52

87.6

531

.21

4037

93.

0813

th J

uly

2.19

92.9

023

.85

23.7

236

.32

84.2

731

.14

3802

42.

96S

.Em

+0.

023

0.84

0.84

0.14

0.17

0.46

0.08

--

C.D

. @ 5

%N

SN

SN

SN

S0.

581.

59N

S-

-Va

rietie

s:D

ante

shw

ari

2.2

3 9

0.57

17.

93 2

4.64

36.

29 6

7.18

35.

07 3

6330

2.8

7Va

ndan

a1.

9182

.52

22.9

323

.13

28.9

658

.53

33.1

425

504

2.32

IR-6

42.

2687

.58

22.8

325

.78

38.3

870

.42

35.4

139

485

3.04

PS-3

2.47

107.

7130

.33

23.1

443

.27

108.

8428

.35

4977

83.

55PS

-52.

3710

5.45

29.2

722

.37

40.9

012

9.66

24.0

548

652

3.49

S.E

m+

0.02

70.

861.

410.

170.

481.

750.

27-

-C

.D. @

5%

0.07

72.

474.

050.

491.

385.

010.

78-

-In

tera

ctio

nN

SN

SN

SN

SN

SN

SN

S-

-

4 0

yield (28.96 q/ha) and straw yield (58.53 q/ha) wasrecorded from Vandana variety. The variety IR 64 attainedthe third position with the productivity parameters. Thevariation in productivity in rice varieties was in accordancewith the growth and yield attributes responsible for suchdeviation. The similar results have also been reported byGill et al. (2009), Nawlakhe et al. (2009), Suresh (2013)and Walia et al. (2014).

Economical gain

Sowing on 3rd July of upland rice proved the most beneficialgiving maximum net income upto Rs. 41446/ha with B: Cratio 3.13. The crop sown five days late on 8th July reducedthe net income by Rs. 1067/ha, then when sown ten dayslate on 13th July, the net income reduced upto Rs. 3422/ha.

In case of rice varieties, PS 3 proved its superiorityby giving highest net income upto Rs. 49778/ha with B:C ratio 3.55. However the second equally best varietywas PS 5 giving net income upto Rs. 48652/ha with B:Cnearby ratio 3.49. The third best variety was IR 64 givingnet income upto Rs. 39.485/ha with B: C ratio 3.04. Thiswas followed by Danteshwari and then Vandana givinglowest net income upto Rs. 25504/ha in related to thecrop productivity and the gross income received. Thehigher net income from such treatments was due to higheryields which fetched higher market price.

References

Farrell TC, Fox K, Williams RL, Fukai S, Lewin LG (2003)Avoiding low temperature damage in Australia's riceindustry with photoperiod sensitive cultivars.Proceedings of the 11th Australian AgronomyConference. Deakin University, Geelong (Feb. 2-6),Victoria, Australia

Kerketta NK, Dwivedi SK, Shrivastava GK, Saxena RR (2010)Rooting pattern and yield of rice under rained uplandsituation in Alfisol with different sowing dates and Pand K levels. Cur Adv Agril Sci 2(2):115 117.

Khalifa AABA (2009) Physiological evaluation of some hybridrice varieties under different sowing dates. AustralianJ Crop Sci 3(3): 178-183

Limochi K and Eskandari H (2013) Effect of planting date onperformance of flag leaf stomata and grain yield ofrice cultivar. International J Agron Pl Prod 4(4): 769-773

Mukesh, Singh I, Pannu RK, Prasad D, Ram A (2009) Effectsof different transplanting dates on yield and qualityof basmati rice (Oryza sativa) varieties. ChaudharyCharan Singh Haryana Agricultural University, Hisar

Nawlakhe SM, Mankar DD, Jiotode DJ (2009) Performanceof basmati type scented rice (Oryza sativa L.)cultivars under different dates of transplanting ineastern Vidarbha. Crop Res (Hisar) 37(1/3):158-160

Singh AK, Chandra N, Bharti RC (2012) Effects of genotypeand planting time on phonology and performanceof rice (Oryza sativa L.). Vegetos 25(1):151-156

Suresh K, Balaguravalah D, Ramalu V, Rao CSH (2013)Evaluation of rice varieties under differentmanagement practices for late planting situation ofNagarjune Sagar left canal command area AndhraPradesh, India. International J Plant Animal andEnviron Sci 3(2): 258-260

Rai HK, Kushwaha HS (2008) Effect of planting dates andsoil water regimes on growth and Yield of uplandrice. Oryza - An International J Rice 45(2):129-132

Tashiro T, Saigusa M, Shibuya K (1999) A Trial of No-tillageDirect Seeding of Rice (Oryza sativa L.) at EarlySpring in Cold Climate Region in Japan. JapaneseJ Crop Sci 68(1):146-150

Walia US, Walia SS, Sindhu, Nayyar S (2014) Production ofdirect-seeded rice in relation to different dates ofsowing and varieties in central Punjab. J Crop Weed10 (1):126-129

(Manuscript Receivd :10.03.2015; Accepted :25.03.2015)

4 1

Abstract

A field experiment was conducted during rabi season of 2014-15 at Jabalpur, to study the effect of sowing date on weedinfestation and yield of wheat varieties under differentirrigation schedules. The results indicated that the crop sownon 27th November exhibit significantly highest grain (4.85 t/ha) and straw (6.99 t/ha) yield as compared to delayedsowing. Similarly wheat cv. GW 366 under four irrigations atcritical stages i.e. CRI, flowering, late jointing and milkingrecorded highest grain and straw yield over rest of thetreatments under study. The lowest total weed density (29.11/m2) and biomass (3.46 g/m2) was obtained in delayed sowing(27th December). Among varieties MP 1202 recorded lowesttotal weed density (41.10/m2) and biomass (8.57 g/m2) ascompared to GW 366. While low frequency of irrigation i.e.two irrigation at CRI and flowering produced lowest weeddensity and biomass. Amongst the different weed speciesMelilotus indica and Chenopodium album are thepredominant broad leaved weed species observed throughout the crop growth of wheat crop. Weed density and biomassdid not reveal any significant difference irrespective of varietiesand irrigations schedules indicating sowing dates had primeimportance in respect of density and biomass.

Keywords: Weed density and biomass, sowing date,varieties, irrigation schedules, yield

Wheat is one of the most important cereal crops,occupying the prime position among food crops in theworld. India is the second largest producer of wheat afterChina in the world with maximum area of 31.19 m ha andannual production of 95.91 mt with average productivityof 3.08 t/ha. In Madhya Pradesh, it is cultivated in 5.79 mha of land with an annual production of 13.93 mt andproductivity of 2.48 t/ha (Agricultural Statistics at a Glance

Effect of sowing date on weed infestation and yield of wheat (Triticumaestivum L.) varieties under different irrigation schedules

T.N.Thorat, Manish Bhan and K.K.AgrawalDepartment of AgronomyJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482 004 (MP)Email: tnt161975@gmail.com

JNKVV Res J 49(1): 41-45 (2015)

2014). Weeds account for 37% of the total annual loss ofagricultural produce in India (Yaduraju 2006). Yieldreduction to the tune of 15-30% of wheat, 30-35% of riceand 18-85% of maize, sorghum, pulses and oilseeds hasbeen observed in many instances (Das 2008). Introductionof high yielding dwarf varieties coupled with increaseduse of fertilizer and irrigation have increased the weedproblems tremendously. Slow growth of wheat at earlystage and application of more fertilizer as well as irrigationright from sowing encourage the rapid growth of weeds,making the cultivation more problematic and if weeds arenot controlled in time, they cause substantial loss in yield.Uncontrolled weed growth may reduce wheat yield rangingfrom 15-40% depending upon magnitude, nature andduration of weed infestation (Jat et al. 2003). The criticalperiod for crop weed competition in dwarf wheat is 30-45DAS while in tall wheat 35-50 DAS (Gupta et al. 1968;Das et al. 2012). Among various factors that affect theyield of wheat, sowing date, irrigation and weedmanagement are of supreme importance. In currentscenario of changing climate, sowing time became crucialadaptation to decide the success of the crop. Hence,accurate knowledge of the sowing window of any particularvariety at a particular location is critical to achieve anoptimum yield. Water is a key input for all recommendedagronomic practices and therefore efficient utilization ofirrigation water is essential for wheat. Weeds competewith crops for water and conditions became severe underits scarcity. The yield increases significantly with increasein the level of irrigation. Appropriate weed managementis essential for improving water use efficiency and grainyield in wheat. The present study was therefore conductedto study the effect of sowing dates on weed infestationand yield of wheat varieties under different irrigationschedules.

4 2

Material and methods

A field experiment was conducted at Jawaharlal NehruKrishi Vishwa Vidyalaya, Jabalpur, Madhya Pradesh,during the rabi seaon of 2014-15 to study the effect ofsowing dates on weed incidence and yield of wheatvarieties under different irrigation schedules. Eighteentreatment combinations, consisting of three sowing dates(27th Nov., 12th Dec., 27th Dec.) and two varieties (GW366 and MP 1202) in main plot and three irrigationschedules (Crown root initiation + Flowering, Crown rootinitiation + Flowering + Milk, Crown root initiation + Latejointing + Flowering + Milk) in subplots were laid out insplit plot design with three replications. The soil of theexperimental field was sandy clay loam with 6.73 pH,medium in organic carbon (0.82%), medium in availableN (280 kg/ha), P (17.45 kg/ha) and K (260 kg/ha). Thecrop was sown in line 20 cm apart with seed rate of 100kg/ha. Fertilizers were applied uniformly to all the plotsthrough urea, single superphosphate and muriate ofpotash @ 120-60-40 kg N-P2O5-K2O/ha. Half of the nitrogenand full dose of phosphorus and potash were given basaland remaining nitrogen was given in two splits. Irrigationschedule was followed as per the treatments keeping 60mm depth of irrigation. However, a shallow come-upirrigation was given immediately after sowing of the wheatcrop to all the treatments. During the period of investigationtotal rainfall 175.1mm received in the months ofDecember, January, February and March was 4.8 mm,58.7 mm, 20.6 mm and 91.0 mm, respectively. Hence,the total water applied in the treatments was, twoirrigations (295.1 mm), three irrigations (355.1 mm) andfour irrigations (415.1 mm), respectively. Data on weedincidence under different treatments was recorded at 30days interval with the help of 0.25 m2 quadrate. The totalweed dry matter was also recorded at 30 days interval.The yield data was recorded and statistically analyzedby using SAS software.

Results and discussion

Weed infestation

The wheat crop was infested mainly with Chenopodiumalbum L., Melilotus indica L., Anagallis arvensis L.,Physalis minima L., Cichorium intybus L., Alternantherasessilis L., Medicago denticulata L. and Vicia sativa L.among the broad leaf weeds and Cyperus rotundus L. ofsedges group while, Phalaris minor and Echinochloacolona L. were the grassy weeds. However, few moreweeds were also recorded as other weeds. Broad-leavedweeds were predominant, followed by grassy weeds and

sedges. Among the broad-leaved weeds, Chenopodiumalbum L., Melilotus indica L. and Anagallis arvensis L.were the predominant weed species followed byCichoriumintybus L., Physalis minima L. and Alternanthera sessilisL. throughout the crop growth period. In case of narrowleaf weeds, Cyperus rotundus L. was recorded throughoutthe crop growth period while, Echinochloa colona L. wasobserved in the later stage due to the intermittent rainswhen the crop tends towards reproductive phase.

Grain yield of wheat

The crop sown on 27th November recorded the highestgrain (4.85 t/ha) and straw yield (6.99 t/ha) which wasstatistically on par with 12th December sowing, butsignificantly superior than the 27th December sown crop(Table 1). The reduction in grain and straw yield underlate sown wheat was 29.3 % and 7.75%, respectivelythan timely sown crop. There was significant differencein grain yield among the varieties. The highest grain yield(4.23 t/ha) was produced by cv. GW 366 as compared toMP 1202 (3.96 t/ha), whereas, the straw yield was notdiffered significantly. High yield of cv. GW 366 may beattributed to its higher biomass accumulation and geneticpotential difference. Among the irrigation schedules, thetreatment receiving irrigations at all the critical stagesi.e. CRI, flowering, late jointing and milking recordedhighest grain (4.28 t/ha) and straw yield (6.93 t/ha) whichwas statistically at par with the irrigations applied at CRI,Flowering and milking stage. This might be due to theappropriate moisture content in soil under thosetreatments. A similar finding related with higher frequencyof irrigation was reported by Rathod and Vadodaria (2004).

The data given in Table 1 with respect to periodicalincidence of weeds and their biomass indicated that theweed density and biomass was highest up to 60 DASwhen crop was sown on 27th Nov. while, after 60 DAS, itwas observed in 12th Dec. sowing. There was no significantdifference among these treatments while lowest weeddensity and biomass was observed in 27th Dec. sowing.This might be due to the reduced density of the dominantweeds as well as shift in weed flora in 27th Nov. sowncrop as compared to the 12th Dec. and 27th Dec. sowncrop. The varieties did not show any statistical differencesamong them at periodical interval from 30 DAS to harvest.Wheat cv.GW 366 recorded highest weed density andbiomass throughout growing period as compared to theMP 1202. This might be due to the larger ground covermade by the variety MP 1202, that restricts theinterception of light necessary for light demanding weedspecies which unable to develop under the closed canopy.The irrigation schedules have highest weed density and

4 3

Tabl

e 1.

Yie

ld a

nd p

erio

dica

l wee

d de

nsity

(No/

m2 )

and

biom

ass

(g /m

2 ) of

diff

eren

t spe

cies

as

influ

ence

d by

pla

ntin

g da

tes,

var

ietie

s an

d irr

igat

ion

sche

dule

s

Trea

tmen

tsYi

eld

Perio

dica

l wee

d de

nsity

Perio

dica

l wee

d bi

omas

s(t/

ha)

(No

/m2 )

(g /m

2 )G

rain

Stra

w30

DAS

60D

AS90

DAS

At h

arve

st30

DAS

60D

AS90

DAS

At h

arve

st

Sow

ing

date

s

D1-

27th N

ov.

4.85

6.99

9.51

6.88

6.74

5.23

2.17

3.50

2.87

4.32

(90.

61)*

(47.

33)

(29.

39)

(28.

33)

(3.8

6)(1

2.08

)(7

.63)

(18.

81)

D2-

12th D

ec.

4.01

6.79

9.02

6.26

5.40

5.54

2.32

2.50

4.60

4.69

(81.

39)

(38.

61)

(45.

28)

(31.

89)

(4.6

7)(5

.41)

(21.

34)

(21.

91)

D3-

27th D

ec.

3.43

6.45

5.92

5.29

4.99

5.13

1.45

1.91

2.68

2.09

(37.

17)

(28.

22)

(24.

61)

(28.

44)

(1.1

4)(2

.72)

(6.4

7)(3

.51)

LSD

(P=0

.05)

0.14

60.

557

0.92

00.

617

0.64

30.

924

0.25

30.

395

0.54

60.

593

Varie

ties

V 1- G

W 3

664.

236.

878.

196.

215.

895.

562.

032.

623.

453.

80(6

9.63

)(3

8.93

)(3

4.89

)(3

3.00

)(3

.49)

(6.4

4)(1

2.69

)(1

6.15

)

V 2- M

P 12

023.

966.

628.

106.

085.

535.

041.

932.

663.

323.

60(6

9.82

)(3

7.19

)(3

1.30

)(2

6.11

)(2

.96)

(7.0

4)(1

0.93

)(1

3.34

)

LSD

(P=0

.05)

0.11

90.

455

0.75

10.

504

0.52

50.

755

0.20

60.

322

0.44

60.

484

Irrig

atio

n sc

hedu

le

I 1- Irr

igat

ion

at C

RI +

FL

3.92

6.53

8.44

5.83

5.74

5.44

1.99

2.87

3.17

3.47

(73.

44)

(33.

78)

(33.

22)

(26.

00)

(3.3

7)(5

.53)

(9.8

6)(1

2.68

)

I 2- Irr

igat

ion

at C

RI +

FL

+ML

4.09

6.77

7.78

6.48

5.77

5.43

1.97

2.66

3.32

3.73

(64.

17)

(42.

61)

(34.

39)

(30.

83)

(3.1

7)(8

.04)

(11.

10)

(15.

28)

I 3- Irr

igat

ion

at C

RI +

LJ

+ FL

+ M

L4.

286.

938.

226.

125.

625.

031.

972.

383.

663.

90(1

4.98

)(3

7.78

)(3

1.67

)(3

1.83

)(3

.13)

(6.6

5)(1

4.49

)(1

6.26

)

LSD

(P=0

.05)

0.14

60.

557

0.92

00.

617

0.64

30.

924

0.25

30.

395

0.54

60.

593

*Val

ues

in p

aren

thes

is a

re o

rigin

al. D

ata

trans

form

ed to

squ

are

root

tran

sfor

mat

ion

CR

I- C

row

n ro

ot in

itiat

ion,

LJ-

Lat

e jo

intin

g, F

L- F

low

erin

g, M

L-M

ilk

4 4

Tabl

e 2.

Per

iodi

cal i

ncid

ence

of m

ajor

wee

d sp

ecie

s an

d to

tal w

eed

dens

ity (N

o./m

2 ) an

d bi

omas

s (g

/m2 )

as in

fluen

ced

by p

lant

ing

date

s, v

arie

ties

and

irrig

atio

n sc

hedu

les

Trea

tmen

tsTo

tal

Tota

lM

elilo

tus

indi

caL.

Che

nopo

dium

alb

umL.

wee

dw

eed

(No.

/m2 )

(No.

/m2 )

dens

itybi

omas

s30

DAS

60D

AS90

DAS

At h

arve

st30

DAS

60D

AS90

DAS

At h

arve

st(N

o./m

2 )(g

/m2 )

Sow

ing

date

s

D1-

27th N

ov.

6.89

3.21

6.86

4.73

2.39

2.19

5.00

3.95

2.06

1.86

(48.

92)*

(10.

59)

(51.

0)(2

3.7)

(6.4

)(5

.5)

(27.

3)(1

8.4)

(4.6

)(3

.9)

D2-

12th D

ec.

6.75

3.52

6.89

3.32

2.78

2.78

4.71

4.18

3.51

3.03

(49.

29)

(13.

33)

(49.

8)(1

2.4)

(8.4

)(8

.6)

(25.

4)(1

7.2)

(13.

3)(1

0.1)

D3-

27th D

ec.

5.33

2.03

4.43

2.12

1.94

1.37

2.23

2.68

2.25

1.38

(29.

11)

(3.4

6)(2

2.6)

(5.1

)(4

.6)

(2.6

)(6

.17)

(9.6

)(5

.8)

(2.2

)

LSD

(P=0

.05)

0.58

20.

363

1.09

00.

533

0.44

30.

518

0.84

70.

888

0.60

40.

444

Varie

ties

V1- G

W 3

666.

462.

976.

153.

292.

402.

034.

334.

062.

662.

03(4

4.11

)(9

.69)

(40.

7)(1

2.0)

(6.5

)(4

.9)

(21.

9)(1

9.0)

(8.0

)(5

.1)

V2- M

P 12

026.

192.

885.

793.

492.

343.

63.

693.

142.

552.

15(4

1.10

)(8

.57)

(41.

6)(1

5.5)

(6.6

)(5

.9)

(17.

4)(1

1.1)

(7.8

)(5

.7)

LSD

(P=0

.05)

0.47

50.

296

0.89

00.

435

0.36

20.

423

0.69

20.

725

0.49

30.

362

Irrig

atio

n sc

hedu

le

I 1- Irr

igat

ion

at C

RI +

FL

6.25

2.75

6.05

3.39

2.17

2.17

4.11

3.15

2.46

1.67

(41.

61)

(7.8

6)(4

0.4)

(13.

6)(5

.6)

(5.5

)(2

0.3)

(11.

9)(6

.7)

(3.2

)

I 2- Irr

igat

ion

at C

RI +

FL

+ML

6.36

2.97

5.93

3.58

2.66

2.04

3.74

3.89

2.85

2.41

(43.

00)

(9.4

0)(4

0.0)

(14.

9)(7

.7)

(4.8

)(1

6.7)

(18.

0)(9

.5)

(7.2

)

I 3- Irr

igat

ion

at C

RI +

LJ

+ FL

+ M

L6.

353.

046.

203.

192.

272.

134.

173.

762.

502.

18(4

3.21

)(1

0.13

)(4

3.0)

(12.

8)(6

.2)

(5.8

)(2

1.9)

(15.

3)(7

.6)

(5.7

)

LSD

(P=0

.05)

0.58

20.

363

1.09

00.

533

0.44

30.

518

0.84

70.

888

0.60

40.

444

*Val

ues

in p

aren

thes

is a

re o

rigin

al. D

ata

trans

form

ed to

squ

are

root

tran

sfor

mat

ion

4 5

biomass at later growth stages of the crop i.e. 60 DASup to the harvest of the crop. Irrigation applied at 3 and 4frequencies has highest weed density and biomass butthere were no significant differences among the treatmentsthroughout the growth period of crop. This might be dueto the intermittent rains received during the crop growingseason which resulted in better supply of moisture foreven growth of the weeds in all the treatments. Nadeemet al. (2007) reported non-significant differences betweenlow and high frequencies of irrigation for weed densityand biomass in wheat. Similar findings were also reportedby Yaduraju and Das (1999).

Weed density (m2) and dry biomass (g/m2)

Data pertaining to total weed density (no/m2) and biomass(g/m2) given in Table 2 indicated that, the highest totalweed density (49.29) and biomass (13.33) was notedwhen crop was sown on 12th Dec. which was closelyfollowed by 27th Nov. sowing while, lowest in 27th Decembersowing. Among the varieties, GW 366 recorded highestweed density (44.11) and biomass (9.69) as comparedto MP 1202, but with no significant differences amongthem. Irrigation schedules does not show significantdifferences in case of weed density but higher irrigationfrequency recorded highest weed biomass (10.13) ascompared to reduced number of irrigation frequencies.The increase in weed density and biomass in higherirrigation frequencies might be due to the sufficientmoisture present in the upper soil layers. Choubey et al.(1998) also reported significant increase in weedpopulation with the increase in irrigation frequency fromIW:CPE ratio 0.6 to 1.0.

During the period of investigation various weedspecies observed, among all that Melilotus indica andChenopodium album L. was the predominant broad leavedweed species infesting the wheat crop throughout thecrop growth period. It is evident from the data presentedin Table 2 that, both species had significantly lowest weeddensity at all the periodical intervals in delayed sown crop(27th December), which might be due to the inability ofprofusely germination of their seeds at elevatedtemperatures. There was no significant difference in weeddensity of both the weed species irrespective of thevariety, but lowest weed density was observed in MP1202,which might be due to the genetic makeup of maximumcanopy cover as compared to GW 366. Irrigationschedules did not reveal any significant difference betweenhigh and low frequency irrigation for both of the species.

Conclusion

It can be concluded from the experimentation that thesowing dates has profound effect on weed density,biomass and yield of wheat crop while varieties andirrigation schedules does not differ significantly throughoutthe growth period of the crop indicating less impactingfactors on density and biomass of weeds.

References

Agricultural Statistics at a Glance (2014) eands.dacnet.nic.inChoubey NK, Tripathi RS, Ghosh BC (1998) Influence of

irrigation scheduling and weed control methods ongrowth and yield of wheat (Triticum aestivum). IndianJournal of Weed Science 30 (1 & 2): 28-31

Das TK (2008) Weed science: Basics and applications. Publ.Jain Brothers New Delhi, First Edition p 91

Das TK, Tuti MD, Sharma R, Paul T, Mirjha PR (2012) Weedmanagement research in India: An overview. IndianJournal of Agronomy 57 (3rd IAC Special Issue):148-156

Gupta OP, Jain MM, Singh RM (1968) Indices of weed cropinterference in Zea mays L. Indian J Agron 13 (2):128-129

Jat RS, Nepalia V, Chaudhary PD (2003) Influence ofherbicides and method of sowing on weeddynamics in wheat (Triticum aestivum L.). Indian JWeed Sci 35: 18-20

Nadeem AM, Tanveer Asif, Ali Asghar, Ayub M, Tahir M (2007)Effect of weed control practice and irrigation levelson weeds and yield of wheat (Triticum aestivum).Indian J Agron 52 (1):60-63

Rathod IR, Vadodaria RP (2004) Response of irrigation andweed management on productivity of wheat(Triticum aestivum L.) under middle Gujaratcondition. Pakistan J Bio Sci 7 (3):346-349.

Yaduraju NT, Das TK (1999) Effect of weed competition ongrowth, nutrient uptake and yield of wheat asaffected by irrigation and fertilizers. J Agric Sci 113(1): 45-51

Yaduraju NT (2006) Herbicide resistant crops in weedmanagement. In: The Extended Summaries,Golden Jubilee National Symposium onConservation Agriculture and Environment. October,26-28, Banaras Hindu University, Varanasi, pp 297-298

(Manuscript Receivd :17.01.2015; Accepted :27.04.2015)

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Abstract

A field experiment was conducted to study the effect of nutrientsources on growth, nutrient composition of leaves of shootsbearing healthy and malformed panicles in cv. Amrapali ofmango under high density plantation at Horticulture Complex,Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur (MP)during 2012-13 and 2013-14. A total of twenty four treatmentcombinations of inorganic and organic sources of nutrientswere arranged in factorial randomized block design with threereplications. The results of study revealed that higher level ofnutrient either in the form of chemical fertilizer or organicsources enhanced the concentration of macro and micronutrient in leaves. The application 520: 160: 450 NPK g plant-

1 and Vermicompost (25 kg) + Oil cake (2.5 kg) + Azotobacter+ VAM + TV + PSB (100g each) registered higherconcentration of N (2.39 and 2.78%), K (62.90 and 77.82 mgkg-1), Zn (27.33 and 30.03 mg kg-1), Cu (9.53 and 10.51 mgkg-1), Fe (196.93 and 213.10 mg kg-1) and Mn (88.57 and98.03 mg kg-1) was in leaves of shoot bearing malformedpanicle then healthy once whereas, concentration of P (0.37and 0.34%) was higher in leaves of shoots bearing healthypanicle, respectively. Similarly, higher dry accumulation waswith malformed panicles over healthy one. The minimumseverity and intensity (1.8m2 and 9.42%) of malformed paniclewas noted when plant nourished with 100% RDF of chemicalfertilizer (415: 130: 360 NPK g plant-1) or (2.2m2 and 12.15%)organic sources of nutrient (Vermicompost (25 kg) + Oil cake(2.5 kg) + Azotobacter + VAM + TV + PSB (100g each) or itscombination registered (1.2m-2 and 5.56%).

Keywords: Malformation, Macro and Micro Nutrient,Trichoderma viridie

Malformation is one of the most serious disorders ofmango causing heavy losses in yield and fruit quality. Of

Composition of macro and micro nutrients in leaves of shoot bearinghealthy and malformed panicle in mango as influence by differentsource of nutrients

Rajnee Sharma, P.K. Jain, S.K. Pandey and T.R. SharmaDepartment of HorticultureCollege of AgricultureJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur - 482 004 (MP)Email: rajnisharma5886@gmail.com

JNKVV Res J 49(1): 46-51 (2015)

the two types of malformation i.e. vegetative and floralmalformation causes heavy losses in yield and ischaracterized by a condensed mass of flower bud (Singhand Dhillon 1990). However, the exact cause and controlof this malady are not clearly understood so far. Recently,Pandey et al. (2002) reported that multiple buds producedhigh proportion of malformation as compared to simplebud in mango cv. Amrapali and Dashehari. The deficiencyof nitrogen (Chattopadhya and Nandi 1978), potassium(Mishra 1976) and certain micronutrients has beenreported to be responsible for causing malformation.Nutrient refers to those entire compounds, which arerequired by the plant as a source of body building materialenergy, without which it will not be able to complete itslife cycle. The fruit tree nutrition is concerned with theprovision of plant with nutrients as well as nutrient uptakeand their distribution in the different organs of the plant.Therefore, a suitable approach needs to be identified. Thevarious sources and their interaction in order to get relieffrom the problems particularly malformation were studiedin high density orcharding of mango cv Amrapali.

Material and methods

A field experiment was conducted at HorticultureComplex, JNKVV during 2012-13 and 2013-14. Six yearold uniformed plant of cv. Amrapali spaced at 2.5 x 2.5 mand maintained under recommended practices except thetreatments were taken for the study. The soil ofexperimental site was clay in texture (58.4% clay, 21.5silt and 20.1% sand) having pH 7.4, electric conductivity(0.25 dSm-1), bulk density (1.48 Mg m-3) medium availableN(230.7 kg ha-1), low in P (12.6 kg ha-1), medium in K(340.2kg ha-1) and low in organic carbon (0.47%). The

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experiment consisted of four level of chemical fertilizers(F1:Without fertilizer, F2: 310: 100: 270 NPK g plant-1 (75%of RDF), F3: 415: 130: 360 NPK g plant-1 (100% of RDF)and F4: 520: 160: 450 NPK g plant-1 (125% of RDF) andsix organic sources O1: Oil cake (2.5 kg) ,O2:Azotobacter + VAM + TV + PSB (100g each),O3:Vermicompost (25 kg), O4: Vermicompost (25 kg) + Oilcake (2.5 kg), O5: Vermicompost (25 kg) + Azotobacter+ VAM + TV + PSB (100g plant -1 each), O6:Vermicompost (25 kg) + Oil cake (2.5 kg) + Azotobacter+ VAM + TV + PSB (100g each) were applied in 39thand 44th metrological week of 2012 and 2013, respectively.Leaves of ten shoots bearing healthy and malformedpanicles were detached separately from nodes for recordingfresh weight. To determine the dry weight, these leaveswere chopped and over dried at 60 ±2 0C till get constantweight. The content of nitrogen (Mc Donald 1978),potassium (Koenig and Jhomson 1942), potassium(Hanwey and Hindal 1952) and micronutrient weredetermined by Absorption Spectrometer (Lu and Chacko2000) in leaves of shoot bearing healthy and malformedpanicle separately during both the years.

Results and discussion

Severity, intensity and dry matter accumulation

Data reveal higher fresh and dry weight was observedwith malformed panicle over healthy one under applicationof nutrients either in the form of chemical fertilizer ororganic sources. The fresh and dry weight of malformedpanicles (102.1 and 41.5g) was observed respectively with125% of RDF (520:160:450g NPK plant-1). Whereas,recommended dose of chemical fertilizer minimized theintensity (1.8 m2) and severity (9.42%) of malformedpanicles. The nutrient deficiency may act as predisposingfactor for malformation incidence. The increases in freshand dry weight in malformed panicle might be due to factthat photosynthets accumulated higher rather itsutilization. Application of Vermicompost (25kg) + Oil cake(2.5kg) enriched with Azotobacter + VAM + TV + PSB(100g each) registered higher fresh weight (104.7g) anddry weight (41.0g) with minimum intensity (2.2 m2) andseverity (12.15%) of malformation. Nitrogen content inmalformed panicle was higher as it accumulates moreinstate of its utilization. The abnormality in nitrogenmetabolism might be a cause of malformation. Theinteraction of chemical fertilizer and organic sourcesregistered the minimum intensity (1.2 m2) and severity(5.56 %) of panicles malformation with recommended doseof chemical fertilizer. The maximum fresh (115.2g) anddry weight of malformed panicles (43.9g) was recorded

with application of 520:160:450g NPK (125% of RDF) incombination of Vermicompost (25 kg) + Oil cake (2.5 kg)+ Azotobacter + VAM + Trichoderma viridi + PSB (100geach). Similar findings were noted by. Muhammad at al.(2007)

Macro and micro nutrient content in leaves

Incremental dose of nutrient in the form of chemicalfertilizer increased the macro and micro nutrient contentin leaves Table 1 and 2. Higher concentration of P in theleaves of shoots bearing healthy panicle than malformedwhereas the reverse trend was observed in case of N, K,Cu, Zn, Fe, and Mn. Higher content of nitrogen (2.33 and2.58%), phosphorus (0.35 and 0.32%), potassium (58.39and 71.46mg kg-1), copper (8.77 and 9.75mg kg-1), zinc(25.85 and 27.15mg kg-1), iron (181.43 and 196.55mg kg-

1) and manganese (79.88 and 93.13 mg kg-1) wererecorded in the leaves of shoot bearing healthy andmalformed panicles, respectively, under the applicationof 125% of RDF (520:160:450g NPK plant-1) over theleaves of unfertilized plant. Higher content of thesenutrients might be due to fact that an application of NPKwith increasing dose increases dry matter accumulationand nutrient concentration. The increase in uptake of thesenutrients might be related to the development of root andplant canopy which accelerate the absorption of nutrients.These results are in close conformity with the findings ofSingh et al. (2012).

Organic sources also brought about the significantchange in the nutrient content of leaves of shoot bearinghealthy and malformed panicle. Maximum content ofnitrogen (2.52 and 2.73%), phosphorus (0.36 and 0.33%),potassium (61.31 and 75.89mg kg-1), copper (9.25 and10.29 mg kg-1), zinc (26.84 and 29.40 mg kg-1), iron(192.59 and 207.87 mg kg-1) and manganese (83.08 and96.13 mg kg-1) were recorded when plant nourished withVermicompost @ 25 kg and Oil cake (2.5 kg) enrichedwith Azotobacter + VAM + TV + PSB (each 100g). Thehigher concentration of macro and micro nutrient in leavesmight be due to fact that, steady available of nutrients invermicompost have resulted in increase uptake ofnutrients by plants Rajkhowa et al. (2000). Chaudhary etal. (2003) who reported increase to N, P and K contentwith application of vermicompost and farm yard manures.Hangarge et al. (2002) reported higher N, P and K contentwith the application of vermicompost and cow dung slurry.It was interesting of note that, when P was applied withphosphorous solubilizing bacteria increase, the uptakeof phosphorus and Mg both. This is possibly due to theimprovement in soil conditions as well as mineralization

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Table 1. Influence of nutrient sources on intensity, severity, dry weight accumulation and macro nutrient contents inhealthy and malformed bearing shoots of Amrapali verity of mango under high density

Treatments Intensity Severity Dry wt Nitrogen Phosphorous Potassium(%) (%) (mgkg-1)

(m2) (%) (g) H M H M H M

Without fertilizer+O C (2.5kg) 4.6 29.03 35.4 1.90 2.08 0.31 0.26 45.17 54.59

Without fertilizer+Azt+VAM+TV 5.2 34.44 34.9 1.89 2.00 0.25 0.24 43.74 47.43

Without fertilizer+VC (25 kg) 3.9 26.00 36.4 1.97 2.26 0.32 0.27 49.46 61.75

Without fertilizer+VC (25 kg)+OC(2.5 kg) 4.1 27.15 36.9 2.12 2.47 0.32 0.30 52.70 63.30

Without fertilizer+VC (25 kg)+Azt+VAM+TV 3.4 21.38 36.5 2.18 2.52 0.34 0.31 56.26 68.29

Without fertilizer+VC (25 kg)+OC(2.5 kg) 3.1 19.62 37.8 2.43 2.63 0.35 0.32 58.76 71.79

+ Azt+VAM+TV (100g each)

75% RDF+OC(2.5kg) 3.3 22.76 37.8 1.96 2.27 0.31 0.26 46.60 58.17

75% RDF+Azt+VAM+TV, (100g each) 3.8 27.34 36.4 1.91 2.02 0.27 0.24 44.81 51.01

75% RDF+VC (25 kg) 3.5 22.44 38.7 2.02 2.35 0.32 0.28 50.02 65.31

75%RDF+VC (25 kg)+OC (2.5 kg) 3.1 19.62 39.7 2.13 2.51 0.32 0.31 52.67 67.97

75% RDF+VC (25 kg)+Azt+VAM+TV 2.8 17.39 39.0 2.50 2.72 0.35 0.32 59.83 75.28

75% RDF+VC (25 kg)+OC (2.5 kg) 2.7 15.98 40.7 2.53 2.75 0.36 0.33 61.62 76.77

+ Azt+VAM+TV

100% RDF+OC (2.5kg) 1.9 10.56 37.3 2.08 2.32 0.32 0.26 47.30 61.74

100% RDF+Azt+VAM+TV 2.3 14.20 36.4 1.91 2.08 0.30 0.24 45.14 58.16

100% RDF+Vermicompost (25 kg) 2.1 11.35 38.1 2.28 2.51 0.34 0.32 57.18 66.69

100% RDF+VC(25 kg)+OC (2.5 kg) 1.8 8.91 38.9 2.36 2.55 0.34 0.32 59.11 68.60

100% RDF+VC (25 kg)+Azt+VAM+TV 1.6 7.96 40.9 2.52 2.76 0.36 0.33 60.54 74.24

100% RDF+VC (25 kg)+OC (2.5 kg) 1.9 5.56 41.8 2.53 2.78 0.37 0.33 61.97 77.18

+ Azt+VAM+TV

125% RDF+OC(2.5kg) 2.9 17.06 39.9 2.10 2.44 0.33 0.31 54.46 68.89

125% RDF+Azt+VAM+TV 3.3 20.37 39.5 2.05 2.35 0.32 0.28 52.67 63.90

125% RDF+VC (25 kg) 2.6 15.57 41.5 2.32 2.53 0.34 0.32 58.64 70.51

125%RDF+Vermicompost (25 kg)+Oil 2.6 15.29 42.2 2.39 2.63 0.34 0.33 60.54 72.73

cake (2.5 kg)

125% RDF+VC (25 kg)+Azt+VAM+TV 2.3 13.14 42.0 2.52 2.77 0.36 0.34 61.13 74.91

125% RDF+VC (25 kg)+OC (2.5 kg) 1.9 10.50 43.9 2.59 2.78 0.37 0.34 62.90 77.82

+Azt+VAM+TV

CD at 5%

Fertilizer 0.22 0.07 0.20 0.28 0.34 0.017 NS 1.14 1.11

Organic Sources 0.27 0.08 0.24 0.34 0.42 0.020 0.039 1.39 1.36

Interaction (FxO) 0.54 0.16 0.49 NS NS NS NS 2.78 2.72

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Table 2. Influence of nutrient sources on micro nutrient contents in healthy and malformed bearing shoots of Amrapaliverity of mango under high density

Treatments Copper Zinc Iron Manganese(mg kg-1) (mg kg-1) (mg kg-1)H M H M H M H M

Without fertilizer+O C (2.5kg) 7.39 8.38 21.89 23.26 152.03 169.10 67.87 69.83

Without fertilizer+Azt+VAM+TV 6.85 7.85 21.01 22.39 136.23 151.50 56.87 67.53

Without fertilizer+VC (25 kg) 7.79 8.87 22.84 23.81 163.23 179.30 69.10 83.93

Without fertilizer+VC (25 kg)+OC(2.5 kg) 8.39 9.38 24.49 25.38 170.53 187.60 69.40 84.23

Without fertilizer+VC (25 kg)+Azt+VAM+TV 8.50 9.49 25.72 26.31 179.53 195.90 71.30 86.13

Without fertilizer+VC (25 kg)+OC(2.5 kg) 9.01 10.00 25.95 27.54 181.70 198.10 76.10 90.93

+Azt+VAM+TV (100g each)

75% RDF+OC(2.5kg) 7.64 8.66 23.06 23.79 161.13 171.50 68.00 82.53

75% RDF+Azt+VAM+TV, (100g each) 6.72 8.04 21.38 22.99 142.03 153.30 59.37 71.37

75% RDF+VC (25 kg) 8.10 9.08 23.33 24.64 168.33 184.40 69.97 84.13

75%RDF+VC (25 kg)+OC (2.5 kg) 6.65 9.63 24.89 26.11 173.27 190.50 73.27 87.93

75% RDF+VC (25 kg)+Azt+VAM+TV 8.88 10.00 26.31 28.21 189.53 204.60 80.80 95.63

75% RDF+VC (25 kg)+OC (2.5 kg)+Azt 9.02 10.20 26.98 30.00 195.03 209.10 84.00 99.00

+VAM+TV

100% RDF+OC (2.5kg) 8.16 9.15 23.04 24.00 154.10 178.20 68.60 83.47

100% RDF+Azt+VAM+TV 7.45 8.24 22.33 23.05 134.10 159.10 60.83 72.33

100% RDF+Vermicompost (25 kg) 8.52 9.52 25.85 26.52 175.03 194.00 72.00 86.83

100% RDF+VC(25 kg)+OC (2.5 kg) 8.97 9.98 26.10 27.43 181.13 198.70 73.27 94.50

100% RDF+VC (25 kg)+Azt+VAM+TV 8.98 10.01 26.49 28.82 188.50 204.73 82.73 94.23

100% RDF+VC (25 kg)+OC (2.5 kg) 9.44 10.45 27.12 30.00 196.70 211.17 86.70 96.53

+Azt+VAM+TV

125% RDF+OC(2.5kg) 8.35 9.62 24.65 25.77 177.03 187.10 77.50 86.53

125% RDF+Azt+VAM+TV 7.72 9.33 23.15 23.74 169.53 176.60 71.70 92.30

125% RDF+VC (25 kg) 8.63 8.70 26.30 26.56 176.93 197.20 76.60 92.40

125%RDF+Vermicompost (25 kg)+Oil 9.16 10.14 26.39 27.72 184.63 200.70 79.10 93.90

cake (2.5 kg)

125% RDF+VC (25 kg)+Azt+VAM+TV 9.23 10.21 27.29 29.12 189.53 204.60 85.80 95.60

125% RDF+VC (25 kg)+OC (2.5 kg)+Azt 9.53 10.51 27.33 30.03 196.93 213.10 88.57 98.03

+VAM+TV

CD at 5%

Fertilizer 0.20 0.36 0.40 0.53 2.35 3.62 4.45 1.44

Organic Sources 0.24 0.44 0.49 0.64 2.87 4.68 5.45 1.76

Interaction (FxO) NS NS 0.98 NS 5.74 8.95 NS 3.52

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of salts, making them readily available to the plant system(Singh et al. (2012).

Application of organics bio-inoculants as well aschemical fertilizers positively increases the content ofvarious nutrients in the leaves. The content of potassium(62.90 and 77.82mg kg-1), zinc (27.33 and 30.03mg kg-1),iron (196.93 and 213.10 mg kg-1) and manganese (88.57and 98.03mg kg-1) were higher with 125 % recommendeddose of NPK while applied with Vermicompost (25 kg) +Oil cake (2.5 kg) + Azotobacter + VAM + Trichodermaand PSB (100g each) in leaves of shoot bearing healthyand malformed panicles, respectively. Higher N contentmay be attributed to an creation of favorable environmentin the rhizosphere with respect to proper aeration anddesired moisture level promote the microbial N fixationand mineralization of P due to active presence of. Theaddition of vermicompost and VAM promote rootcolonization through mycelial network of arbuscularmycorrhizal fungi, thus increased the surface area fornutrients and water absorption resulted in increased thenutrient contents of leaf (Morselli et al. 2004; Gupta et al.2005). Phosphorus is applied to soil through fertilizer getfix with the soil particles which is unavailable to the plant.The presence of phosphorus solubilizing micro-organismsreleased slowly and made available to the plant (Sundaraet al. 2002). The increase in micro nutrient concentrationin leaf might be due to the fact that vermicompost containmicronutrient in addition to macronutrients in availableform (Prakash et al. 2002; Sen 2003).

Acknowledgement

The first author is thankful to Jawaharlal Nehru KrishiVishwa Vidyalaya, Jabalpur authority for providing facilitiesand technical guidance during the course of investigationof Ph. D. research work.

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References

Chattopadhyay NC, Nandi B (1978) Changes in the Nitrogencontent in malformed inflorescence of mangocaused by Fusarium monili formae SheldonVar.subglutinans.Giomale Botanico Stalianop.112:343-46

Chaudhary RS, Das A, Pattnik US (2003) Organic farming forvegetable production using vermicompost and FYMin Kokriguda watershed of Orissa. Indian J SoilConservation 31:203-6

Gupta RK, Sharma KN, Singh B, Singh Y, Arora BR (2005)Effect of urea and manure addition on changes inmineral-N content in soil profile at various stagesof wheat. J Indian Soc Soil Sci 53: 74-80

Hangarge DS, Raut RS, Malewar GU, More SD, KeshbhatSS (2002) Yield attributes and nutrients uptake bychilli due to organic and inorganic on vertisol. JMaharashtra Agril Univ 27 (1): 109-110

Hanway JJ, Hindal H (1952) Soil analysis. Methods as usedin Iowa State College, Soil testing Laboratory IowaAgriculture 57: 43-45

Koenig RA, Jhonson CR.(1942). Colorimetric determinationof biological material. Indust. Engg Chem Anal Ed14: 155-156

Lu P , Chacko EK .(2000). Effect of water stress on mangoflowering in low latitude tropics of NorthernAustralia. Acta Horti 509:283-90

Mc Donald MS (1978) A simple and improved method for thedetermination of microgram quantities of N in plantmaterials. Ann Bot N S 42: 363-66

Mishra KA (1976) Studies on bearing of Mangifera indica L.and its malformation Ph.D dissertation,PunjabAgricultural University,Ludhiana,India

Morselli TBGA, Sallis MG, Terra S, Fernandes HS (2004)Response of lettuce to application of vermicompost.Revista Cientfica Rural 9:1-7

Muhammad Nafees, Muhammad AL, Muhammad A,Muhammad SY (2007) Effect of simple andcompounds fertilizers application on flushing andmalformation of inflorescence in mango.International Symposium on Prospects ofHorticulture Industry in Pakistan 28 to 30 March 220-225

Pandey RM, Pandey SN, Singh OP (2002) Bud morphology

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and Expression Floral Malformation in mango(Mangifera indica ). Indian J. Horti. 59:694-95

Prakash YS, Bhadoria PBS, Rakshit A. (2002). Comparativeefficacy of organic manures on the changes in soilproperties nutrient availability in Alfisol. J Indian SocSoil Sci 50:219-21

Rajkhowa DJ, Gogoi AK, Kaandali R, Rajkhowa KM (2000)Effect of vermicompost on green gram nutrition. JIndian Soc Soil Sci 48:207-8

Sen HS (2003) Problems soils in India and theirmanagement: Prospect and retrospect. J Indian SocSoil Sci 51: 388-408

Singh VJ, Shama SD, Kumar P, Bhardwaj SK (2012) Effect ofbio- organic and inorganic nutrient sources toimprove leaf nutrient status in apricot. Indian J Horti69 (1): 45-49

Singh Z, Dhillon BS (1990) In vivo role of indole-3-aceticacid,gibberellic acid, zeatin, abscisic acid andethylene in floral malformation of mango (Mangiferaindica L.). J Phytopathol 20:235-45

Sundara B, Natarajan V, Hari K (2002) Influence ofphosphorous solubilizing bacteria on change in soilavailable phosphorous and sugarcane yields. FieldCrop Res 77:43-49

(Manuscript Receivd :26.11.2014; Accepted :20.05.2015)

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During September 2014, disease on soybean [Glycinemax (L.) Merrill] was observed in Jabalpur and some partsof Malwa District (Hatod) with symptoms resemblingDrechslera blight. Circular (Fig. 1 a) to angular (Fig. 1 b)brown spots near the leaf margins were noticed thatenlarge and develop with gray centers having dark brownto black margins and occasionally surrounded by chlorotichalos. Isolation was made from the edge of lesion onpotato dextrose agar at 25 0C. The isolated fungusDrechslera glycini grow well on PDA, producing a brown,septate, profusely branched mycelium, conidiophores(Fig. 1 c) were erect, 6-7 µm wide, simple or branched,septate, brown, and geniculate. Conidia (9-13 x 31-94µm) were borne acrogenously and fusoid, (Fig.1 d) straightor slightly curved, septate, 3 to 8 celled (rarely upto 13)and a basal hilum Subramanian (1971). Spore germinateby producing germ tubes from both ends. Pathogenicitywas confirmed by inoculation of 6-week-old seedlings ofcv. JS 95-60 raised in earthen pots (32 x 20 cm). Fivesurface sterilized seeds of soybean were sown in eachpot at equi-distance and kept in net-house. The plants inpots were irrigated alternate day. After 40 days of sowingthe plants were inoculated by spraying of conidiasuspension (3 x 106 conidia per ml) with the help a ofglass atomizer @ of 10 ml per pot in evening. Afterinoculation the pots were covered with polyethylene bagsfor 8 hr. The symptoms of infection on lower leavesappeared after 10-15 days of inoculation as minute, circularpin tread spots (Fig. 1e, 1f) which enlarge with grey centrewith dark brown to black margin. The fungus was re-isolated and matched with original culture and measureof conidia. Symptoms, culture characteristic andmeasurement of conidia matched with Drechslera glycinisp. nov. as reported by Narayanasamy and Durairaj (1971)but does not matched with Drechslera halodes (Kulkarniand Patil 1976). Therefore the pathogen is identified asDrechslera glycini sp. nov. The pathogen may pose a

Leaf spot and blight of soybean by Drechslera glycini sp. nov. inMadhya Pradesh: a new observation

R.K. Varma, D.K. Pancheshwar and Satyendra PatelDepartment of Plant PathologyJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur - 482 004 (MP)Email: drrajeshkvarma@yahoo.co.in

JNKVV Res J 49(1): 52-53 (2015)

serious proportion in soybean cultivation. It has beenpreviously reported from Maharashtra and Coimbatore,and first time observed in Madhya Pradesh.

References

Kulkarni CS, Patil BC, Patil, PL (1976) Leaf spot of soybean[Glycine max (L.) Merr.] caused by Drechslerahalodes in India. J Maharashtra Agric Univ 1:167

Narayanasamy P, Durairaj P (1971) A new blight disease ofsoybean. Madras Agric J 58:711-712

Subramanian CV (1971) Hyphomycetes. Indian Council ofAgricultural Research New Delhi, 812

(Manuscript Receivd :25.11.2014; Accepted :17.01.2015)

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Fig. 1e Fig. 1f

Fig. 1b

Fig. 1d

Fig. 1a

Fig. 1c

5 4

Abstract

Laboratory studies were conducted to study the effect ofculture media, pH and temperature levels on mycelial growthand sporulation of different isolates of Fusarium oxysporumf.sp ciceris collected from Central Zone of India representingFOC isolates I- 19, I-28 (Race 2), I-4 (Race 3), I-20, I-13(Race 4), I-80 (Race 5) and I-1 (New variant). Among the sixculture media tested, Czapek's Dox agar medium and Potatodextrose agar medium were more favorable for the growth ofFOC than other medium. The isolates I-20 and I-13 fromIndore and Jabalpur, (MP) showed higher growth on Czapek'sDox agar medium (90.0 mm) followed by PDA ( 90 mm).Whereas, Ashby's agar was less favorable for the growth ofmany isolates. Isolates I-4, (Dongargaon, Narsingpur, MP)and I-19 (Ghootna, Jabalpur, MP) showed minimum growthi.e. 42.3 mm and 46.0 mm respectively on Ashby's agarmedium. Temperatures at 25 and 30 0C were most favourablefor the growth of these isolates. The highest growth ofpathogen was recorded at 300C i.e. 90.0 mm radial growthof colony in isolate I-13 and minimum radial growth i.e. 73.0mm was observed in isolate I-4 at 9 DAI. The most suitablepH level for growth of fungus was 6.0 and 6.5 which showedmaximum radial growth of colonies of all isolates. At 6.0 pHmaximum i.e. 89.6 mm radial growth was observed in isolateI-80 and minimum radial growth i.e. 68.0 mm was observedin Isolate I-19 at 9 DAI. In the study of effect of different mediumon sporulation maximum sporulation was observed in CZAmedium in isolate I-13 i.e. 28.4 conidia/µl followed by isolateI-80 i.e. 25.5 conidia/µl. Whereas, minimum sporulation wasfound in Richard's Agar medium i.e. 14.2 in isolate I-19followed by I-1 i.e. 16.1 conidia/µl. Regarding effect oftemperature on sporulation maximum sporulation wasobserved at 300C in isolates I-13 i.e. 28.8 conidia/µl followedby isolate I-80 i.e. 26.4 conidia/µl. Whereas, maximumsporulation was observed at 6.0 pH in isolate I-80 i.e. 25.7conidia/µl followed by isolate I-13 i.e. 25.1 conidia/µl.

Keywords: Fusarium oxysporum f.sp. ciceris, variousmedium, pH, temperature, mycelial growth, sporulation

Effect of culture media, pH and temperature on the mycelial growthand sporulation of Fusarium oxysporum f.sp ciceris isolates ofchickpea from Central Zone of India

Minakshi Patil, Om Gupta, Maruti Pawar and Devashish ChobeDepartment of Plant PathologyJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur - 482004 (MP)Email : *Minakshipatil013@gmail.com

JNKVV Res J 49(1): 54-60 (2015)

Chickpea (Cicer arietinum L.) is a major source of high-protein food and it is world's third important pulse cropremained virtually stagnant over recent decades becauseof it's biotic and abiotic stresses. Vascular wilt is a majorconstraint to chickpea production globally. India accountsfor approximately 75 percent of the world chickpeaproduction (Singh et al. 2006). F. oxysporum f.sp. cicerisis a soil borne root pathogen colonizing xylem vessels,blocking them and causing wilting (Bateman et al. 1996).Annual chickpea yield losses due to fusarium wilt varyfrom 10 to 15%, but the disease can destroy the cropcompletely under specific conditions (Navas-Cortes et al.2000). The disease is important in dry and warm season.Although actual yield loss is estimated to be 10 to 12percent globally (Nene and Reddy 1987). The objective ofthe present investigation was to determine the suitablemedia, temperature and pH requirement favouring themycelial growth and sporulation to understand ecologicalsurvival of pathogen which will be helpful in managementstrategy

Material and methods

Pure culture of FOC isolates representing different racesviz., I- 19, I-28 (Race 2), I-4 (Race 3), I-20, I-13 (Race 4),I-80 (Race 5) and I-1 (New variant) were transferred toPDA slants and kept at 40C for further use. Variation amongseven different isolates of F. oxysporum with respect tomycelial growth and sporulation on six different solid mediawas carried out in three replications. Twenty ml each ofthe media viz., Potato dextrose agar, Richard's agar,Czapek's Dox agar, Asthana & Hawker's, Martins RoseBengal, and Ashby's agar medium were poured into 90

5 5

mm diameter sterilized petriplates and allowed to solidify.Five mm discs from 7 days old culture of seven differentisolates of F. oxysporum f.sp. ciceris were used forinoculation. The inoculated plates were incubated at 28 ±10C. The colony diameter of all the isolates on differentmedia was measured at 9th days after inoculation andsporulation was calculated with the help of hemocytometerfive mm disc was cut from cultures and transferred in atube containing 10 ml of sterilized water. The suspensionwas prepared by shaking the tube gently. The spore countper µl of suspension was calculated with the help ofhemocytometer. Each treatment was replicated thrice.

Effect of temperature on the mycelial growth andsporulation of different isolates was studied at differentlevels of temperature. Various temperatures weremaintained in BOD incubator. The F. oxysporum wasinoculated on Czapek's Dox agar plates. About 20 ml ofCzapek's Dox agar medium was poured into sterilizedpetriplates under aseptic condition. Inoculation was madewith five mm disc of F. oxysporum f.sp. ciceris from 7days old culture. The inoculated petriplates wereincubated for a week at 20, 25, 30 and 350C in BODincubators with three replications and radial growth wasrecorded at 5th, 7th and 9th days after inoculation .

Studies on the mycelial growth and sporulation ofFOC isolates at different pH levels were done by usingCzapek's Dox agar medium. The pH of the medium wasadjusted to 5.5, 6.0, 6.5 and 7.0 in CZA medium by adding1 N HCL and 1N NaOH. The culture of F. oxysporum f.sp. ciceris was inoculated with five mm diameter disc ofthe isolate at the center of the plates. The plates wereincubated for a week at 28±10C in order to record thegrowth. The mycelial growth was recorded at 5th, 7th and9th days after inoculation.

Table 1. The location and race distribution of Fusariumoxysporum f. sp. ciceris isolates used in this study

Race/Variants Isolates Location

Race 2 I-19 Ghootna, Jabalpur, MP

Race 2 I-28 Sehore, MP

Race 3 I-4 Dongargaon,Narsingpur, MP

Race 4 I-20 Indore, MP

Race 4 I-13 Jabalpur, MP

Race 5 I-80 Dharampur, CG

New Variants I-1 Kawardha, CG Tabl

e 2.

Effe

ct o

f cul

ture

med

ia o

n m

ycel

ial g

row

th a

nd s

poru

latio

n of

Fusa

rium

oxy

spor

um f.

sp.

cice

ris fr

om d

iffer

ent l

ocat

ion

of C

entra

l Zon

e of

Indi

a

Isol

ate

Ash

by's

Aga

rA

stha

na &

Haw

ker's

CZA

Ric

hard

Aga

rPD

AM

artin

s R

ose

Ben

gal

Myc

elia

lS

poru

latio

nM

ycel

ial

Spo

rula

tion

Myc

elia

lS

poru

latio

nM

ycel

ial

Spo

rula

tion

Myc

elia

lS

poru

latio

nM

ycel

ial

Spo

rula

tion

grow

th o

f(c

onid

ia/

l)*gr

owth

of

(con

idia

/l)*

grow

th o

f(c

onid

ia/

l)*gr

owth

of

(con

idia

/l)*

grow

th o

f(c

onid

ia/

l)*gr

owth

of

(con

idia

/l)*

colo

nies

colo

nies

colo

nies

colo

nies

colo

nies

colo

nies

(mm

)*(m

m)*

(mm

)*(m

m)*

(mm

)*(m

m)*

I- 19

46.0

16.2

71.0

19.1

80.3

20.6

79.6

14.2

87.0

22.3

81.0

15.7

I-28

54.0

18.1

80.6

23.3

85.0

24.9

82.6

16.2

88.6

18.1

89.0

20.7

I-442

.318

.563

.619

.775

.022

.072

.016

.574

.022

.373

.016

.6I-2

049

.620

.161

.322

.169

.322

.868

.618

.190

.023

.371

.317

.1I-1

374

.020

.383

.624

.490

.028

.485

.019

.290

.025

.082

.620

.0I-8

053

.017

.172

.624

.164

.325

.564

.017

.280

.624

.363

.318

.1I-1

60.6

17.2

63.0

22.1

70.6

22.1

41.3

16.1

85.0

22.2

65.0

15.1

SEM

±0.

990.

710.

580.

900.

410.

720.

380.

600.

540.

740.

800.

31

CD

at 5

%2.

82.

051.

52.

611.

182.

081.

11.

751.

572.

132.

30.

91*A

vera

ge o

f thr

ee re

plic

atio

n

5 6

Results and discussion

Effect of culture media

A total of six media were used for studying the growth ofseven isolates. Among the different culture media tested,Czapek's Dox agar medium and Potato Dextrose agarmedium were more favorable for the growth of F.oxysporum f. sp. ciceris than other medium. The isolatesI-20 and I-13 from Indore, and Jabalpur, MP showed highergrowth in Czapek's Dox agar medium and on PDA i.e.(90.0 mm).Whereas, Ashby's agar was less favorable forthe growth of many isolates. Isolates I-4 (Dongargaon,Narsingpur, MP) and I-19 (Ghootna, Jabalpur, MP) showedminimum growth i.e.42.3 mm and 46.0 mm respectivelyon Ashby's agar medium (fig 1). On an average growthwere observed in such a manner, Ashby's agar (Maximumgrowth 74.0 mm with mean 54.2 mm), Asthana andHawker's (Maximum growth 83.6 mm with mean 70.8mm), CZA (90.0 mm with mean 76.4 mm), Richard agarmedium (Maximum growth 85.0 mm with mean 70.4 mm),PDA (90.0 mm with mean 85.04 mm) and Martins RoseBengal agar medium (Maximum growth 89.0 mm withmean 75.04 mm) (Fig 1).

In the study of effect of different medium onsporulation maximum sporulation was observed in CZAmedium in isolate I-13 i.e. 28.4 conidia/µl followed byisolate I-80 i.e. 25.5 conidia/µl. Whereas, minimumsporulation was found in Richard Agar medium i.e. 14.2in isolate I-19 followed by I-1 i.e. 16.1 conidia/µl (Fig 2).On an average it was noted that Czapek's Dox agar wasmore favorable for the sporulation in F. oxysporum f.sp.ciceris than Asthana and Hawker's and Ashby's agarmedium. Whereas, comparatively less sporulation wasobserved in Martins Rose Bengal agar. These results werein confirmation with Ingole (1995) who reported that PDAand Richard's agar supported best mycelial growth of F.udum. Jamaria (1972) also reported maximum growth andsporulation of F. oxysporum f. sp. niveum on potatodextrose agar, Richard's agar and Czapek's Dox agar.Khare et al. (1975) reported maximum growth of Fusariumoxysporum f. sp. lentis on PDA followed by lentil extractand Richard's agar. Anjaneya Reddy (2002) observedmaximum growth of F. udum on Richard's agar and potatodextrose agar. Gangadhara et al. (2010) studied effect oftemperature on growth of F. oxysporum f. sp. vanillaeisolates. The fungus showed best growth on Richard'sagar and potato dextrose agar media. Maximum growthwas at 250C after seven days of inoculation, which wasreduced drastically below 150C and showed zero growthat 400C. The most suitable pH level for growth of funguswas 5.0 and 6.0. Recently Imran Khan et al. (2011) studied

Tabl

e 3.

Effe

ct o

f diff

eren

t tem

pera

ture

on

myc

elia

l gro

wth

ofF

. oxy

spor

um f.

sp.c

icer

is fr

om d

iffer

ent l

ocat

ion

of C

entra

l Zon

e of

Indi

a

FOC

Isol

ate

Myc

elia

l gro

wth

of c

olon

ies(

mm

)*20

0 C25

0 C30

0 C35

0 C5

DAI

7 D

AI9

DAI

5 D

AI7

DAI

9 D

AI5

DAI

7 D

AI9

DAI

5 D

AI7

DAI

9 D

AI

I- 19

20.6

32.6

70.0

24.6

34.6

79.6

28.3

44.6

87.0

24.0

36.6

67.3

I-28

24.6

36.6

73.6

28.6

40.3

80.0

31.0

51.3

87.6

27.6

46.0

71.6

I-422

.634

.662

.326

.337

.664

.330

.645

.673

.027

.340

.058

.3

I-20

14.3

18.0

63.3

16.6

19.3

74.6

20.0

22.6

89.6

17.6

19.0

59.3

I-13

30.3

35.6

72.6

34.3

42.0

79.0

41.0

61.6

90.0

36.3

45.6

69.6

I-80

24.0

33.0

71.6

26.6

41.0

70.6

30.6

56.6

79.6

28.6

42.3

61.3

I-118

.032

.364

.620

.338

.371

.625

.058

.382

.322

.339

.663

.0

SEM

±0.

310.

940.

610.

470.

690.

680.

902.

500.

490.

510.

827.

92

CD

at 5

%0.

892.

731.

781.

371.

991.

982.

627.

231.

421.

502.

3022

.80

*Ave

rage

of t

hree

repl

icat

ion

DAI

-Day

s af

ter i

nocu

latio

n

5 7

effect of media on F. oxysporum f.sp. ciceris and foundthat PDA is best for the growth of different isolates ofFOC. The present study indicated that Czapek's Dox agarand Potato dextrose agar were best medium for growthof F. oxysporum f.sp. ciceris .

Effect of temperature

Growth of different isolates of F. oxysporum f. sp. ciceriswere studied at 20, 25, 30 and 350C. There was quite alarge variation in the growth of these isolate at differenttemperature after 9 days. Temperatures 25 and 350C weremost favorable for growth of these isolates. The maximumgrowth of pathogen was recorded at 300C i.e. (90.0 mm).It was observed that at 250C and 300C, the fungus attainedthe maximum growth 80.0 and 90.0 mm (fig.3) while at200C and 350C, it was 64.3 and 58.3 mm after nine daysof inoculation. Among the seven isolates, isolate I-13 andI-20 showed maximum radial growth of colonies i.e. 90.0mm and 89.6 mm respectively. Whereas, minimum radialgrowth of colonies i.e. 73.0 mm and 79.6 mm (fig.3) wasobserved in isolate I-4 and I-80 respectively at 300C.Isolates I-19 and I-28 were less varied at 300C showing87.0 and 87.6 mm radial growth of colonies respectively.Radial growth of the isolates I-4 and I-20 were reduced at200C and 350C temperature which showed minimum radialgrowth of colonies i.e. 62.3 and 58.3 mm in isolate I-4and 63.3 mm and 59.3 mm (Fig.3 ) in isolate I-80 at 250Cand 350C respectively. Studies conducted by Chi andHansen (1964) indicated that F. solani isolates grew wellat higher temperature of 280C the fungus grew at thetemperature range of 10-350C. However, growth of thefungus was drastically reduced below 150C and startedto decline above 300C and become zero at 400C, as thesetemperatures did not favor for growth of the fungus. Soiltemperature relationship indicated that suitabletemperature for development of chickpea wilt is 25-300C.Gupta et al. (1987) reported similar findings regardingtemperature requirements to this fungus. These studiesare in confirmation with Anjaneya Reddy (2002) whoreported that growth of 40 isolates of F. udum differed intheir temperature requirement which varied from 200C to350C. The effects of temperature of F. oxysporum f. sp.ciceris were studied by Landa et al. (2001). They foundthe disease development was greater at 25°C comparedwith 20 and 30°C. Scott et al. (2010) studied effect oftemperature on fusarium wilt of lettuce (Lactuca sativa),caused by F. oxysporum f. sp. lactucae, and observed toincrease from 10°C up to an apparent maximum near25°C. Results are in confirmation with Imran Khan et al.(2011) who reported that the F. oxysporum f.sp. cicerisgrew highest at 30OC. Ta

ble

4. E

ffect

of d

iffer

ent p

H o

n m

ycel

ial g

row

th o

fF. o

xysp

orum

f.sp

.cic

eris

from

diff

eren

t loc

atio

n of

Cen

tral Z

one

of In

dia

FOC

Isol

ate

Myc

elia

l gro

wth

of c

olon

ies(

mm

)*pH

5.5

pH 6

.0pH

6.5

pH 7

.05

DAI

7 D

AI9

DAI

5 D

AI7

DAI

9 D

AI5

DAI

7 D

AI9

DAI

5 D

AI7

DAI

9 D

AI

I- 19

29.6

49.3

72.3

39.3

57.0

68.0

39.6

50.6

63.3

25.3

47.0

61.0

I-28

48.0

70.3

72.3

65.0

75.0

83.6

53.3

65.0

75.0

43.6

54.3

65.3

I-434

.044

.065

.062

.669

.073

.049

.654

.667

.639

.047

.660

.6

I-20

43.6

68.0

70.0

62.0

71.0

77.3

52.3

63.0

73.3

40.6

51.0

62.6

I-13

60.0

71.0

74.6

66.6

76.0

88.0

55.3

67.3

78.0

46.6

56.3

67.6

I-80

63.0

79.0

80.3

68.6

75.0

89.6

58.3

69.0

80.0

49.0

58.6

72.3

I-132

.341

.364

.346

.060

.669

.647

.654

.065

.337

.045

.065

.6

SEM

±0.

790.

810.

650.

950.

700.

690.

360.

781.

310.

420.

801.

06

CD

at 5

%2.

302.

351.

872.

752.

022.

001.

052.

273.

701.

222.

313.

08* A

vera

ge o

f thr

ee re

plic

atio

ns,

DAI

- Day

s af

ter i

nocu

latio

n

5 8

Fig 1. Effect of culture media on mycelial growth ofFusarium oxysporum f. sp. ciceris from different locationof Central Zone at 9 DAI

Fig 2. Effect of culture media on sporulation of Fusariumoxysporum f. sp. ciceris from different location of CentralZone at 9 DAI

Fig 5. Effect of different pH on mycelial growth of F.oxysporum f.sp. ciceris from different location of CentralZone at 9 DAI

Fig 6. Effect of different pH on sporulation of F. oxysporumf.sp. ciceris from different location of Central Zone at 9DAI

Fig 4. Effect of different temperature on sporulation of F.oxysporum f.sp. ciceris from different location of CentralZone at 9 DAI

Fig 3. Effect of different temperature on mycelial growthof F. oxysporum f.sp. ciceris from different location ofCentral Zone at 9 DAI

5 9

Effect of different pH

The effect of pH on growth of FOC was highest at 6 with89.6 mm radial growth at 9 DAI (Table 4). However it wasfound that the range from 6.0 and 6.5 were best which issuitable for the growth of F. oxysporum f. sp. ciceris. Theforemost acidic and alkaline pH is not suitable for thegrowth of pathogen. Among the different isolates testedat pH 6.0, isolates I-80 and I-13 were showed the maximumradial growth of colonies i.e. 89.6 and 88.0 mm. Whereas,isolates I-19 and I-1 showed minimum radial growth ofcolonies i.e. 68.0 and 69.6 mm (Fig.5 ) at 9 DAI. Radialgrowth of the isolates were drastically reduced at 7.0 pHwhich showed minimum radial growth of colonies i.e. 60.6and 61.0 mm (Fig.5 ) in isolates I-4 and I-19 at 9 DAI.

The studies conducted by Jamaria (1972) on F.oxysporum f. sp. nivium indicated that, as the pHdecreases or increases from the optimum, the rate ofamount of growth gradually decreases. Gangadhara etal. (2010) studied effect of pH levels on growth of F.oxysporum f. sp. vanillae isolates. The fungus showedbest growth pH at 5.0, least growth of all the isolates wasrecorded at 9.0 pH. Imran Khan et al. (2011) showedoptimum pH for growth of F. oxysporum f.sp. cicerisranged from 6.5 to 7.0.

Spourlation of seven isolates of F. oxysporum f.sp.ciceris were studied at 20, 25, 30, and 350Ctemperature range and 5.5, 6.0, 6.5 and 7.0 pH range.The results are presented in table 5. It was seen thatthere was quite a variation in the sporulations of theseisolates were observed at 9 DAI. Maximum sporulation

was observed at 300C in isolates I-13 i.e. 28.8 conidia/µl(Fig.4), followed by isolate I-80 i.e. 26.4 conidia/µl.Whereas, minimum sporulation was observed at 200C inisolate I-19 i.e. 14.4 conidia/µl. Followed by I-1 i.e. 16.2conidia/µl (Fig.4 ). Studies conducted under effect of pHrange on sporulation indicate that maximum sporulationis observed at 6.0 pH in isolate I-80 i.e. 25.7 conidia/µl,followed by isolate I-13 i.e. 25.1 conidia/µl (fig.6). Whereas,minimum sporulation was observed in isolate I-19 i.e. 19.2conidia/µl (Fig.6) followed by isolate I-1 i.e. 19.3 conidia/µl respectively (Fig.6).

References

Anjaneya Reddy (2002) Variability of Fusarium udum andevaluation of pigeonpea (Cajanu cajan (L). Mills)genotypes. MSc (Agri) Thesis Univ Agril SciBangalore 115

Bateman G L, Kwasna H, Ward E (1996) Relationshipamong Fusarium sp. estimated by comparingrestriction fragment length polymorphism inpolymerase chain reaction amplified nuclear DNA.Canadian J Microbiol 42: 1232-1240

Gangadhara NB, Nagaraja R, Basavaraja M K, Krishna N R(2010) Variability studies of Fusarium oxysporum f.sp. vanillae isolates. International and Nature1(1):12-16

Gupta O, Kotasthane SR, Khare MN (1987) Factorsinfluencing epidemiology of vascular wilt ofchickpea. Proc Natl Acad Sci India 57:86-91

Imran Khan HS, Saifulla M, Mahesh SB, Pallavi MS (2011)Effect of different media and environmentalconditions on the growth of Fusarium oxysporum f.

Table 5. Effect of different temperature and pH on sporulation of F. oxysporum f.sp. ciceris from different Location ofCentral Zone of India

FOC Isolate Sporulation (conidial/ µl)*Temperature pH

20 0C 25 0C 30 0C 35 0C 20 0C 25 0C 30 0C 35 0C

I- 19 14.4 22.8 21.4 19.8 16.6 19.2 21.6 18.8I-28 16.4 18.2 24.3 23.0 21.0 23.8 22.9 19.3I-4 16.7 22.6 23.0 18.7 16.9 20.4 19.4 15.6I-20 18.4 23.0 24.1 22.1 17.8 21.4 20.1 16.8I-13 19.3 25.6 28.8 24.7 20.3 25.1 23.9 19.2I-80 17.9 24.6 26.4 24.0 19.1 25.7 25.5 20.5I-1 16.2 21.3 21.7 21.1 16.2 19.3 20.1 15.5SEM± 0.27 1.44 0.20 0.16 0.29 0.44 0.18 0.29CD at 5% 0.78 4.16 0.5 0.48 0.84 1.29 0.54 0.84*Average sporulation per microscopic field of three replications

6 0

sp. ciceri causing Fusarium wilt of chickpea.International J Sci & Nature 2(2):402-404

Ingole M N (1995) Estimation of losses, variability amongisolates and management of pigeon pea wiltcaused by Fusarium udum Butler. M Sc (Ag) ThesisDr. PDKV Akola 146p

Jamaria SL (1972) Nutritional requirement of Fusariumoxysporum f.sp. niveum. Indian Phytopath 25: 29-32

Khare MN, Agarwal SC, Dhingra OD, Kushwaha LS (1975)Variability in the growth of light strains of Fusariumoxysporum f.sp. lentis as different solid media.Indian Phytopath 28: 126-128

Khilare VC, Rafi Ahmed, Chavan SS, Kohire OD (2009)Management of Fusarium oxysporum f. sp. ciceriby different fungicides. Bioinfolet 6: 41 - 43

Landa BB, Navas-Cortés JA, Hervás A, Jimenez-Díaz RM(2001) Influence of temperature and inoculumdensity of Fusarium oxysporum f. sp. ciceris onsuppression of Fusarium wilt of chickpea byrhizosphere bacteria. Phytopath 91:807-816

Leslie J F, Summerell BA (2006) The Fusarium, LaboratoryManual, Blackwell Publishing 1 - 388

Moore E S (1924) The physiology of Fusarium coeruleum.Annals of Bot 149: 137-161

Navas-Cortés JA, Hau B Jiménez-Díaz, RM (2000) Yield lossin chickpeas in relation to development of Fusariumwilt epidemics. Phytopath 90:1269-1278

Nene Y L, Reddy M V (1987) Chickpea disease and theircontrol. In: Saxena MC, Singh KB (eds): TheChickpea. III Viral Diseases. Wallingford Oxon CABInternational, ICARDA: 233-270

Scott JC, Gordon TR, Shaw DV, Koike ST (2010) Effect oftemperature on severity of Fusarium wilt of lettucecaused by Fusarium oxysporum f. sp. lactucae.Plant Disease 94 (1): 13-17

Singh BP, Saikia R, Yadav M, Singh R, Chauhan VS, AroraDK (2006) Molecular characterization of Fusariumoxysporum f.sp. ciceri causing wilt of chickpea.African J of Biotechno 5 (6): 497-502

(Manuscript Receivd : 20.10.2014; Accepted :20.02.2015)

6 1

Abstract

An experiment, to determine pathogenic potential of root lesionnematode (Pratylenchus thornei) on chickpea and itsmanagement using organic neem and bio-agent basedcommercial products, was conducted in pots and under fieldconditions. All the plant growth parameters were declined at1000 J2/pot where as at highest inoculum level (10,000 J2/pot) the plant became stunted with significant reduction ingrowth parameters. The inoculum threshold level of P. Throneion chickpea was recorded 6 nematodes/g soil. The neembased product nemate and Paecilomyces lilacinus basedcompound samrat were found effective in managing lesionnematode in chickpea under field conditions.

Keywords: Pratylenchus thornei, Chickpea,Pathogenicty, Nemate, samrat

Chickpea (Cicer arietinum L.) is ranked third amongstthe important pulse crops growing countries of the world.India contributes more than 70 per cent to the globalchickpea production. The crop is being grown in an areaof 7.89 million hectares with 5.97 million tonesproduction. Madhya Pradesh alone contributes 2.84million hectares of land and 2.79 million tonesproduction and shares about 37 per cent in the Indianagriculture in (Anon 2010). Several species ofPratylenchus are ubiquitously distributed aroundchickpea plants. It is expedient to quality and their rolein causation of the diseases in field so as to develop anunderstanding towards appropriate viable and feasiblecontrol measures needed for reducing crop losses dueto this organism. In recent years, there has been asurging interest in phytoparasitic nematodes affectingcrop plants. Soil samples collected under AICRP onnematodes around the rhizosphere of the crop inMadhya Pradesh, revealed the prevalence of

Pathogenic potential of Pratylenchus thornei and its managementthrough commercial products in chickpea

Jayant Bhatt, S.P. Tiwari and Arvind JawareDepartment of Plant PathologyJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482 004 (MP)Email: jayant187@rediffmail.com

Pratylenchus sp. predominantly (Anon 1981). It hasbeen observed that the yield of chickpea seems to bedeclined and lesion nematode Pratylenchus thornei isone of the major constraints .The population of lesionnematode has crossed threshold level of damage dueto monocropping of chickpea and responsible to cause26 per cent losses in yield (Anon 2000.).

Material and methods

Effect of varied levels of inocula

The pre-tested culture of Pratylenchus thorneimaintained on chickpea was used as a source ofinoculum. Earthen pots containing 72 hr old singleseedlings were inoculated with surface disinfestedadults and juviniles by exposing the roots by removingthe soil with the help of a gentle jet of water. Calibratedpopulation of all the stages of nematodes, in logarithmicseries viz., 10, 100, 1000 and 10,000 centrifuged in 10ml sterile distilled water were then dispersed on theexposed root-zone and gently covered with sterilizedsoil. In control, ten ml of plain and sterilized water wasdispersed. The pots were randomized on theglasshouse bench. On termination of experiment,observations were recorded on shoot and root weight,number of nodules, and final nematode population ofPratylenchus thornei. For recording the dry weight, theplants were dried in oven at 60 °C till they achieved theconstant weight.

Efficacy of organic neem and bio-agent basedcommercial products against P. Thronei

The organic neem and bio-agent based commercial

JNKVV Res J 49(1): 61-65 (2015)

6 2

products viz., "Nemate" and "Samrat" at their variousdosages along with carbosulfan (Standard check) weretested against lesion nematode under field conditionswith initial population ranged from 275.95-315.75N/200cm3 soil.

The commercial products "Nemate" containingneem (@ 1, 1.5 and 2 kg ai./ha )and samrat acommercial formulation of P. lilacinus (@ 250, 500 and750 kg/ha) were used along with carbosulfan @1kg ai./ha (standard check). The compounds at their dosagesmentioned above were mixed with plot soil measuring2.75×3.50m following randomized block design. Theplots were sown with chickpea seed (var. JG 74)maintaining row to row distance 40cm and plant to plant30cm Adequate plant protection measures wereadopted to grow healthy crop.

Results and discussion

Effect of levels of inocula

There was a gradual decrease in plant height ofchickpea as the level of inocula of Pratylenchus thorneiincreased. Maximum (28.32 cm) plant height was notedin uninoculated control followed by 10, 100, 1000nematodes where plant heights were noted to be 27.35,24.99 and 23.56 cm respectively. Minimum (22.15 cm)plant height was noted in the treatment where 10,000nematodes were inoculated (Table 1).

Similar trend was noticed in fresh shoot weightwhere maximum (7.54 g) shoot weight was recorded inthe uninoculated control and minimum (6.12 g) in 10,000level of inoculum. Similarly there was a significantdecrease in fresh root weight as the level of inoculum.Minimum (4.12 g) root weight was recorded in 10,000level of inoculum and maximum (8.25g) in uninoculatedcontrol. There was gradual decrease in root weight inrest of the treatments viz., 10, 100 and 1000 level ofinocula where root weight were observed to be 7.52,6.67 and 5.81 g.

Shoot weights on dry basis, however gotsignificantly reduced when compared with uninoculatedcontrol but showed decreasing trend with increase inlevels of inocula. Minimum (0.78 g) shoot weight wasrecorded in 10,000 nematodes as maximum (1.78g) inuninoculated control.

There was a significant decrease in the numberof nodules. Drastic reduction at 10N level of nematodeinocula (28) was observed when compared touninoculated control (39) with slight stimulation in Ta

ble

1. E

ffect

of d

iffer

ent l

evel

s of

inoc

ula

of P

raty

lenc

hus

thor

nei o

n gr

owth

of c

hick

pea

(var

. JG

74)

Cro

p

Trea

tmen

tPl

ant g

row

th c

hara

cter

sN

emat

ode

popu

latio

nPl

ant h

eigh

tFr

esh

Shoo

t wt.

Fres

h R

oot w

t.D

ry S

hoot

wt.

Nod

ulat

ion

soil

(200

/cm

3 )(c

m)

(g)

(g)

(g)

/Pla

nt

Con

trol

28.3

27.

548.

251.

7839

0 (0

.70)

**

10N

27.2

57.

527.

521.

4228

52.5

(7.

28)

100N

24.9

97.

296.

671.

3530

.92

540

(21.

58)

1000

N23

.56

6.82

5.81

0.98

24.7

642

66 (

57.0

7)

10,0

00N

22.1

56.

124.

120.

7825

.92

9925

(99

.63)

S.Em

. (±)

0.39

0.03

0.26

0.66

1.22

(0.8

9)

CD

at 5

%1.

130.

100.

750.

193.

52(2

.57)

* M

ean

of fo

ur re

plic

atio

ns, *

* Fi

gure

s in

par

enth

eses

indi

cate

d sq

uare

root

tran

sfor

mat

ion

6 3

Tabl

e 2.

Effe

ct o

f org

anic

nee

m a

nd b

io-a

gent

bas

ed c

omm

erci

al p

rodu

cts

agai

nst P

raty

lenc

hus

thor

nei

Trea

tmen

tG

erm

inat

ion

% M

orta

lity

INP

stag

eN

.P. a

t flo

wer

ing

stag

eN

.P. a

t har

vest

ing

stag

eYi

eld

(%)

30 D

ays

60 D

ays

(soi

l/200

Soi

l/200

Roo

tS

oil/2

00R

oot

(q/h

a)cm

3 )(c

m3 )

popu

latio

n(c

m3 )

popu

latio

n/5

g/5

g

Nem

ate

10g

(1kg

ai./

ha)

89.4

67.

2510

.25

315.

7549

5.18

56.1

334

0.25

76.1

512

.55

(9.4

8)(2

.12)

(3.2

8)17

.78)

(22.

26)

(7.5

3)(1

8.46

)(8

.75)

Nem

ate

10g

(1.5

kg a

i./ha

)91

.05

5.00

9.50

290.

8248

0.11

52.2

031

0.75

69.0

512

.90

(9.5

6)(2

.55)

(3.1

6)(1

7.07

)(2

1.92

)(7

.26)

(17.

64)

(8.3

4)N

emat

e 10

g (2

kg a

i./ha

)91

.75

4.25

7.50

275.

9547

5.75

51.1

828

0.35

48.7

313

.16

(9.5

6)(1

.58)

(2.8

3)(1

6.63

)(2

1.82

)(7

.19)

(16.

76)

(7.0

2)Sa

mra

t 250

kg/

ha88

.96

7.50

9.00

290.

9849

0.86

71.8

638

5.65

86.2

212

.05

(9.4

6)(3

.08)

(3.0

8)(1

7.07

)(2

2.17

)(8

.52)

(19.

65)

(9.3

1)Sa

mra

t 500

kg/

ha90

.05

4.50

8.50

285.

1548

2.92

55.7

537

0.61

79.3

512

.70

(9.5

2)(1

.12)

(3.0

0)(1

6.90

)(2

1.99

)(7

.50)

(19.

26)

(8.9

4)Sa

mra

t 750

kg/

ha91

.35

3.75

5.35

305.

2546

0.65

47.7

232

0.32

72.2

512

.92

(9.5

8)(1

.58)

(2.4

2)(1

7.49

)(2

1.47

)(6

.94)

(17.

91)

(8.5

3)C

arbo

sulfa

n (1

kg a

i./ha

)92

.78

2.15

3.95

292.

3223

0.47

28.2

518

0.15

52.0

514

.78

(9.6

6)(2

.15)

(2.1

1)(1

7.11

)(1

5.20

)(5

.36)

(13.

44)

(7.2

5)C

ontro

l82

.95

12.7

519

.36

290.

3551

0.32

73.3

553

0.85

142.

1511

.09

(9.1

4)(3

.81)

(4.4

6)(1

7.05

)(2

2.60

)(8

.54)

(23.

05)

(11.

94)

S.Em

(±)

(0.4

4)0.

220.

240.

451.

060.

290.

760.

551.

76C

D (P

=0.0

5)(1

.39)

0.67

0.76

1.39

3.27

0.89

2.35

1.70

5.40

* M

ean

of fo

ur re

plic

atio

n**

Fig

ures

in p

aren

thes

es in

dica

ted

squa

re ro

ot tr

ansf

orm

atio

n

6 4

nodule number (30.92).

The nematode multiplication at highest level hadsuffered due to unavailability of additional sites for thefeeding which resulted in retardation of plant growth.Such a stress on plant growth at highest levels paredway for more competition amongst dispensed nematodepopulation for space of penetration. Thus, decrease infinal population of P. Thronei was noted to bedisproportional to the initial inoculum level andtherefore, led to the decline in rate of multiplication.Similar results have also been observed by Walia andSeshadri (1985) in chickpea, Tiyagi and Praveen (1992)reported significant reduction in growth parameters athigher level of inoculum. The population of P. Throneiremained confined near the cortical tissue during thegrowth and development of chickpea (Tiwari et al. 1992,Bhatt and Vadhera 1997). Threshold damage level ofP. Thronei on chickpea crop to the extent of 20N/g soilhas been reported by Bhatt (1994). The variance inresult in the above study and present investigation isattributed to varieties and / or the seedlings utilized..

Efficacy of organic neem and bio-agent basedcommercial products against Pratylenchus thornei

The effect of organic neem and bio-agent basedcommercial product against P. Thronei is presented inTable 2. There was no treatment wise significant effectof the products on emergence, when compared withcontrol. Least seedling mortality was recorded withcarbofuran (1 kg ai./ha) at 30 and 60 day after sowing.Which is followed by samrat (750 kg/ha) and nemate10g. Maximum mortality was recorded with control.

Reduced and minimum population of lesionnematode in soil at flowering stage was recorded withcarbofuran (230.47) followed by samrat (250 kg/ha) andnemate 10g (2 kg ai./ha) All the treatments reducednematode population, significant reduction (47.72) innematode population in roots at flowering stage wasrecorded with samrat (750 kg/ha) followed by nemate10g (2 kg/ha) and nemate (1.5 kg/ ai./ha) minimum(28.25) nematode population in roots was noted incarbosulfan (1 kg ai./ha) and maximum (73.35) inuntreated control. All the treatments were superior overcontrol in reducing the nematode population in soil and5g roots at flowering stage.

The soil population of nematode at harvest wasminimum (280.35) in nemate (2 kg ai./ha) followed bynemate (1.5 kg ai./ha) (310.75) and samrat (320.32)when applied @ 750 kg/ha. Lowest population (180.15)was recorded under carbosulfan (1 kg ai./ha). Rest of

the treatment were observed to be superior in reducingthe nematode population over control (530.85) at thetime of harvest.

Similar trend was also recorded when the rootpopulation was assessed at the time of harvest.Minimum nematode (52.05) were noted in carbosulfan(1 kg ai./ha) followed by nemate10g @ 1.5 kg ai/ha(69.05) and samrat @750 kg/ha (72.25).

Maximum yield (14.78 q/ha) was recorded withcarbosulfan followed by nemate 10g @ 2 kg/ha (13.16q/ha) and samrat (12.92 q/ha) when applied @ 750 kg/ha. Minimum yield (11.09 q/ha) was recorded withcontrol. All the treatments were observed to be superiorin increasing the yield when compared with control.Neem and egg parasitic fungus Paecilomyces lilacinusbased commercial products Nemate 10g and Samratrespectively were tried to manage lesion nematode inchickpea under field conditions. The data presented intable 5 revealed that both the products at their variousdosages are significantly superior over control in theirefficacies. However, samrat @ 750 kg/ha andnemate10g @ 2kg ai/ha were observed to be mosteffective in reducing the nematode populations. Theyield parameter was also noted to be increased in thesetreatments.

Nemate being a neem based formulation,compounds like Azadirachtin and triterpenoid inhibit thegrowth of the pathogen when used as soil treatment.Ali et al. (2004) and Govindachari et al., (1996)demonstrated the efficacy of Azadirachtin againstsheeth blight disease of winter rice and wilt of chickpearespectively.

Similarly samrat which is a commercialformulation based on an egg parasitic fungusPaecilomyces lilacinus affect growth and multiplicationof P. Thronei leading to its low population. The resultsare in accord with the findings of Anastasiadis et al.(1995) and Laffrangue et al., (2003) who reported P.lilacinus also attacks juveniles and females ofnematodes there by reducing their population.

tM+ fo{kr lw=d`fe ¼izsVhysadl Fkkjukb½ dh jksx tudrk dk pus ijizHkko ,oa jksx ds izca/ku ds fy;s ,d iz;ksx xeyksa esa dkap?kjokrkoj.k ,oa iz{ks= esa fd;k x;kA ikS/ks ds leLr o`f) xq.kksa esa 1000fMEHkd@xeyk ds ladze.k ds dkj.k deh ikbZ xbZ] tcfd 10000fMEed ij ikS/kksa esa ckSukiu ik;k x;k A pus ij tM+ fo{kr lw=d`fedh jksx tudrk 6 lw= d`fe @xzke e`nk vkadh xbZA uhe ,oaislhyksekblsl ds O;olkf;d mRikn uhesV ,oa lezkV lw=d`fe ds izca/ku es mi;ksxh ik;s x;sA

6 5

References

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

Anastasiadis IA, Giannakou IO, Prophetou DA,Athanasisadou, Gowen SR (1995) The combinedeffect of the application of a biocontrol agentPaecilomyces lilacinus with various practices for thecontrol of root-knot nematode. Crop Protec 27: 3/5,352-361

Anon (1981) Biennial Report. All India Co-ordinated ResearchProject on Nematodes, Center JNKVV, Jabalpur

Anon (2010) Agriculture statistics at a glance. Directorate ofEconomics and Statistics, Department of Agricultureand Cooperation.105

Bhatt J (1994) Effects of different levels of inoculum ofmigratory nematode Pratylenchus thornei (Filipjev,1936) Sher and Allen 1953 on growth of gram. Cicerarietinum (L.) Ad Plant Sci 7 :2: 239-243

Bhatt J, Vadhera I (1997) Histopathologyical studies oncohabitation of Pratylenchus thornei and Rhizoctoniabataticola on chickpea ( Cicer arietinum L.) Ad PlantSci 10:1: 33-37

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

Laffranque JP, Pi MO, Decroos Y, Aertens F (2003)Biological control of nematodes with the soil bornefungus Paecilomyces lilacinus strain 251. Colloqueinternational tomate sous abri, protection integree -agriculture biologique, Avignon, France, 17-18 et 19septembre 2003. 126-130

Tiwari SP, Vadhera I, Shukla BN, Bhatt J (1992) Studies onthe Pathogenicity and relative reactions of chickpealines to Pratylenchus thornei (Filipjev, 1936) Sherand Allen, 1953. Indian J Mycol and Plant Pathol 22:3: 255-259

Tiyagi SA, Parveen M (1992) Pathogenic effect of root lesionnematode. Pratylenchus thornei on plant growth.Water absorption capability and chlorophyll contentof chickpea. Intn chickpea News 26: 18-20

Walia RK, Seshadri AR (1985) Pathogenicity of the root lesionnematode Pratylenchus thornei on chickpea. Intenchickpea News12:31

(Manuscript Receivd :06.04.2014; Accepted : 17.01.2015)

6 6

Abstract

The present study was conducted in Tikamgarh ofbundelkhand region of Madhya state. A study on Impact ofDistrict Poverty Initiative Project (DPIP) in empowering therural women of Tikamgarh Block, District Tikamgarh (M.P)was conducted a sample of 120 respondents was selectedthrough proportionate random sampling method. The datawere collected with the help of well structured schedule. Theresults indicate that maximum number of the respondentshad illiterate (32.50%) and middle age group (50.00) majorityof women (62.50%) belonged to nuclear family. Agriculture +subsidiary occupation (33.34%) was the major occupationof the family 66.66 per cent of the family from low incomegroup. The study revealed that 46.66 per cent women familyhad landless. The study reports that 41.66 per cent ruralwomen had medium economic motivation and 45.84%belonged to medium mass media exposure. The studyreports that 50 per cent of rural women had medium extensioneducation contact and 41.66 per cent of rural women hadmedium training exposure. Maximum of the respondentshad medium level of income generation (45.84%). Age,education and caste had non-significant association andinfluenced the in empowering the rural women, whereasannual income, occupation, economic motivation, scientificorientation and mass media exposure, had significantassociation with income generation of rural womenrespectively.

Keywords: Women empowerment, DPIP, rural women.

Empowerment is defined as "giving power to creatingpower within and enabling is a relative concept whichdescribes a relationship between a powerful people haspower over others .empowerment entails power sharing achange in the balancing of power between people.Therefore, empowerment involves negotiation of thebalance of power between the more and less powerful"."Empowerment in an active, multi dimensional process,

Impact of district poverty initiative project (DPIP) in empowering therural women

M.Singh, M.K. Dubey and N.K. KhareDepartment of Extension EducationJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482 004 (MP)

JNKVV Res J 49(1): 66-72 (2015)

which should enable people to realize their full identifyand power in all spheres of life." It consist of greateraccess to knowledge and resource greater autonomy indecision making to enable them to have greater availabilityto plan their level or to have greater contribute over thecircumstances that influence their live and free from shocksimposed on them by custom, beliefs and practices. Ithas been said that "women hold up half the sky" aroundthe world, Women play a vital role in providing for families,sustaining communities and managing natural resources.Empowering women through better education, economicopportunity and healthcare including family planning ispivotal to world progress, with far-reaching benefits forfamilies, communities and the planet.

The DPIP being implemented in the States ofAndhra Pradesh, Madhya Pradesh and Rajasthan is apoverty alleviation program designed to empower men andwomen for self development so that the poor create andmanage their own development opportunities. The DPIPtargets socially and economically disadvantaged groups,particularly: the SC/ST households, households migratingout for wage employment, households without propershelters/ dwellings, women and women headedhouseholds. The DPIP programme was launched in theTikamgarh district since 2001 for the disadvantaged groupof people. For keeping the view the present study wascarrying out in Tikamgarh district of Madhya Pradesh. "Astudy on Impact of District Poverty Initiative Project (DPIP)in empowering the rural women of Tikamgarh Block ofTikamgarh District, Madhya Pradesh."

Methodology

The investigation was conducted in Tikamgarh district ofMadhya Pradesh, because the district has pioneer workunder DPIP Since 2001.The Tikamgarh district comprises

6 7

Table 1. Profile of rural women (N= 120)

Variables Categories Frequency PercentageIndependent variableAge Young age group (up to 35 years) 40 33.33

Middle age group (36 - 55 years) 60 50.00Old age group (56 and above) 20 16.67Total 120 100

Education Illiterate 39 32.50Up to Primary school 28 23.33Up to middle school 33 27.50High school and above 20 16.67Total 120 100

Type of family Nuclear family 75 62.50Joint family 45 37.50Total 120 100

land holding Landless 56 46.66Marginal (up to 1 ha) 44 36.67Small (1.01 to 2 ha) 20 16.67Total 120 100

Caste Scheduled caste 32 26.66Scheduled tribe 28 23.34Other backward caste 42 35.00General caste 18 15.00Total 120 100

Occupation Agriculture 25 20.83Labour 20 16.67Agriculture + Labour 35 29.16Agriculture + Subsidiary occupation 40 33.34Total 120 100

Annual income Low (Up to Rs.14,000) 80 66.66Medium (Rs.14,001 to 19,000) 25 20.84Large (Rs. 21,001 to 24,000) 15 12.50Total 120 100

Economic motivation Low (Score 6 to 18) 30 25.00Medium (Score 19 to 30) 50 41.66Large (Score 31 to 42) 40 33.34Total 120 100

Scientific orientation Low (Score 6 to 18) 40 33.34Medium (Score 19 to 30) 50 41.66Large (Score 31 to 42) 30 25.00Total 120 100

Mass media exposure Low ( Score up to 5 ) 35 29.16Medium (Score 6 to 10 ) 55 45.84High (score 11 to 14 ) 30 25.00Total 120 100

Extension contact Low (Score up to 10) 25 20.84Medium (Score 11 to 20) 60 50.00high (Score 21 to 30) 35 29.16Total 120 100

Training exposure Low (Score up to 2) 30 25.00Medium (Score 2 to 4) 50 41.66High (Score 5 to 6 ) 40 33.34Total 120 100

6 8

of 6 blocks namely Tikamgarh, Niwari, Jatara, Prathvipur,Baldevgarh, and Palera. Out of these Tikamgarh blockwas selected purposively due to maximum number ofwomen SHGs. carrying out agricultural activities like asvegetable, spices, poultry, seed production and dairyenterprises. Since 13 years under DPIP.The Tikamgarhblock consists of 72 villages in which 6798 beneficiarieswere benefited through 737 SHGs under DPIP. Out ofwhich 10 villages were selected on the basis of maximumnumber of beneficiaries has been benefited. The total 120women beneficiaries were selected as sample of the studythrough proportionate random sampling method. Asproposed by the data were collected with help ofprestructured interview schedule. These were analyzedby investigator using frequency, percentage, chi-squaretest and rank order. In order to ascertain relationshipbetween independent and dependent variables, the chi-square was worked out.

Result and discussion

The study revealed that 50 per cent of rural women weremiddle age in the study region followed by young (33.33%)age category. The probable reason for such distributionmight be that, majority of the middle age rural womenperceived agriculture as a profitable avenue and took upas a subsidiary occupation. The study revealed that 32.50per cent of respondents were illiterates and majority ofwomen (62.50%) belonged to nuclear family followed by

joint family (37.50%). About the occupation, the studyfound that agriculture + subsidiary occupation (33.34%)was the major occupation of the family followed bylabourers (29.16%). The present study exhibited that66.66 per cent of the families were low income followedby medium income category (20.84 %). The studyrevealed that 46.66 per cent women family had landlessfollowed by marginal farmers (36.67 %). The study reportedthat 41.66 per cent rural women had medium economicmotivation followed by low economic motivation (33.34%)and 45.84% had belonged to medium mass mediaexposure.

The study reported that 50 per cent of rural womenhad medium extension contact followed by 29.16 per centhigh extension contact and 41.66 per cent of rural womenhad medium training exposure. The distribution ofrespondents according to profile of rural women includedin the study is presented in Table 1.

Out of total respondents 120, 30 per cent belongedlow level of income generation, 45.84 per cent had mediumlevel of income generation and only 24.16 per cent hadhigh level of income generation (Table 2).

Therefore, it is concluded that the maximum 45.84per cent of respondents belonged medium level of incomegeneration.

The distribution of DPIP beneficiaries according toincrease in annual income before and after implementationof DPIP programme (Table 3).

Data revealed that before the implementation ofDPIP, out of total beneficiaries, 66.66 per cent had anannual income of up to Rs. 14,000/- , 20.84 per cent hadRs. 14,001 to 19,000/-, whereas, 12.50 per cent hadannual income of Rs. 19,001 to 24,000/- only. Afterimplementation of DPIP programme, out of totalbeneficiaries, 30.00 per cent had reported that their annualincome ranging from up to Rs. 14,000/-, while 45.83 percent had 14,001 to 19,000/- and 24.17 per cent hadincome in the range of Rs. 19,001 to 24,000/-. In case of

Table 3. Distribution of DPIP beneficiaries according to increase in their annual income before and after implementationof DPIP (N= 120)

Categories Before DPIP After DPIP Change in percentageNo. of beneficiaries No. of beneficiaries

Low (up to Rs. 14,000) 80 (66.66%) 36 (30.00%) -36.66Medium (Rs. 14,001 to 19,000) 25 (20.84%) 55 (45. 38%) +24.99High (Rs.19,001 to 24,000) 15 (12.50%) 29 (24.17%) +11.67Total 120 (100.00%) 120 (100.00%) 00.00

Table 2. [Distribution of rural women according to Incomegeneration (N= 120)]

Categories Frequency Percentage

Low (Up to Rs 14,000) 36 30.00Medium (Rs 14,001 to 19,000) 55 45.84Large (Rs 19,001 to 24,000) 29 24.16Total 120 100.00

6 9

Fig. 1. Distribution of rural women according to theirage

Fig. 2. Distribution of rural women according to theireducation level

Fig. 3. Distribution of rural women according to theirtype of family

Fig. 4. Distribution of rural women according to theirsize of landholding

Fig. 5. Distribution of rural women according to theircaste

Fig. 6. Distribution of rural women according to theiroccupation

7 0

Fig. 9. Distribution of rural women according to theirscientific orientation

Fig. 10. Distribution of rural women according to theirmass media exposure

Fig. 11. Distribution of rural women according to theirextension contact

Fig. 12. Distribution of rural women according to theirtraining exposure

Fig. 7. Distribution of rural women according to theirannual income

Fig. 8. Distribution of rural women according to theireconomic motivation

7 1

change in percentage the DPIP beneficiaries who belongto low income category had negative change inpercentage -36.36 and medium and high income categoryhad change in percentage 24.99 and 11.67 Thus, it canbe concluded that the annual income of DPIP beneficiarieshad increased as a result of implementation of DPIPprogrammed and higher percentage i.e. 24.17 per centbeneficiaries had crossed the poverty line.

Age, type of family and education had non-significant association and influenced the in empowering

the rural women, whereas annual income, education,occupation, economic motivation, scientific orientation,mass media exposure, extension contact, trainingexposure had significant association with incomegeneration of rural women.

Education, mass media exposure, trainingexposure, economic motivation, and extension contactshad significant. This finding is supported by Rana (2010)Rewani and Tochhawng (2014).

Conclusion

The findings of the study revealed that the majority of thebeneficiaries belonged to middle age group (50.00%),illiterate level of education (32.50%) and low income group(up to Rs 14, 000) (66.66%). After utilization the credit/loan, training for enterprise under the DPIP programme,a higher percentage of the beneficiaries belonged tomedium income generation (45.38%). It shows the goodimpact of DPIP programme among the beneficiaries.

orZeku esa v/;;u e/; jT; ds cqansy[kaM {ks= esa fd;k x;k] ftyk xjhchmUewyu dk;ZØe ds varxZr Vhdex<+ ftys ds Vhdex<+ fodk'k [kaM esaxzkeh.k efgykvksa ds l'kf'ädj.k ij v/;;u fd;k x;k Vhdex<+fodk'k [akM esa ;k/nzfPd uewuk fof/k ls 120 izfroknh p;u fd;s vkSjvkadM+s ljy ljapuk lwph dh enn ls bdV~Bs fd;s x;s] vr% ;gifj.kke izkIr gqvk fd vf/kdkj xzkeh.k efgyk;s e/;e vk;q oxZ ¼50-00%½] f'k{kk dk Lrj vf'kf{kr ¼32-40%½] vf/kDrj xzkeh.k efgyk;s¼62-40%½ ,sdkadh ifjokj esa jgrh gS] —f"k ,oa vU; O;olk; ¼33-

Table 4. The association between independent variableswith dependent variables beneficiaries of DPIP (N=120)

Independent variables 2 Value

Age 1.87NSEducation 14.57*Type of family 1.55NSLand holding 16.91*Caste 2.00NSOccupation 13.34*Annual income 12.65*Economic motivation 15.71*Scientific orientation 12.67*Mass media exposure 11.02*Extension contact 13.58*Training exposure 13.92*

NS - Non- significant*Significant at 0.05 level of probability

Fig. 13. Distribution of rural women according to theirincome generation

Fig. 14. Distribution of DPIP beneficiaries according toincrease in their annual income before and after

implementation of DPIP

7 2

34%½ vkSj ifjokj dh okf"kZd vk; ¼66-66%½ de vkenuh lewg ls gS]v/;;u ls irk pyk gS fd 46-66% xzkeh.k efgyk;s Hkwfeghu gS] e/;evkfFkZd vfHkizs.kk ¼41-66%½] e/;e tu lapkj lewg ¼45-94%½] e/;eizlkj lEidZ ¼40-00%½] vkSj vf/kDrj xzkeh.k efgyk;s ¼41-66%½izf'k{k.k izn'kZu] vk; e/;e Lrjh; dh izf'k{k.k izn'kZu Fkh] vr% vk;q]f'k{kk vkSj efgykvksa l'kf'ädj.k esa fujFkZd lkfcr jgha] tgk¡ tulapkj lewg] vkfFkZd vfHkizs.kk] okf"kd vk;] izf'k{k.k izn'kZu] O;olk;vkfn efgykvksa l'kf'ädj.k esa izHkkfo :i ls ykHknk;d lkfcr gq;s A

References

Choubey S (2007) A study on women empowerment throughagricultural entrepreneurial activities of Self HelpGroup (SHGs) in Jabalpur block of Jabalpur district(MP)

Bhagyalaxmi K, Rao GV, Sudarshanredd M (2003) Profile ofthe rural women micro-entrepreneurs.Journal ofResearch. ANGRU, Hyderabad 31 (4): 51-54

Shweta Kadu, Kotikhane RR and Nagawade DR (2013)Empowerment of Women's SHG through FoodProcessing and Dairy Management Practices. IndRes J Ext Edu13 (3):52-54

Namdeo S (2007) A study on vocational training programmeorganized by KVK on the income and employmentgeneration for the rural women of Seoni districtMadhya Pradesh Msc. (Ag) Thesis, JNKVV Jabalpur

Rana KK (2010) An analytical study of vocational trainingprogrammes conducted by Krishi Vigyan Kendrafor rural youth of Sohagpur block in Sahdol districtM.P. M.sc. (Ag.) Thesis JNKVV, Jabalpur

Rathod PK, Nikam TR, Sari put Landg (2011) Participation ofRural Women in Dairy Farming in Karnatak. Ind ResJ Ext Edu 11 (2) : 79

Rewani Sanjay Kumar, Lalhumliana Tochhawng (2014)Social Empowerment of Women Self Help GroupMembers Engaged in Livestock Rearing. Ind Res Jof Ext Edu14 (2):116-119

Sathapathy C, Mishra BP, Nayak N (2003) Women self helpgroup and Agribusiness Management. In the bookAgribusiness and Extension Management(Hansara, B S and VK ed). Concept PublishingCompany, New Delhi: 152-161

Satyanarayana, Rao BR (2012) Income Generation throughLivestock and Crop Enterprises as a means ofLivelihood. Ind Res J Ext Edu 1 : 132-137

Sharma Preeti, Verma SK (2007) Empowerment of womenthrough entrepreneurial activities of SHGs.Compendium 4th National Extension EducationCongress, Society of Extension Education Agra&JNKVV, Jabalpur : 30

(Manuscript Receivd : 15.01.2015; Accepted : 30.06.2015)

7 3

Abstract

This study was conducted during year 2014-15 in the Jabalpurdistrict of M.P. The profile analysis indicated that (57.2%)ofthe Watershed Development Programme beneficiariesbelonged to the medium age group, having education uptoprimary school (41.0%). The higher percentage of Watershedbeneficiaries (64.0%) had above 5 members in the familyand belonged to SC/ST (54.5%) category. Occupation wasfarming and cast occupation (33.5%) and had (44.5%) highsocial participation. They possessed marginal size of landholding (50.4%) and medium (42.0%) level of materialpossession. They had medium level of (42.0%)riskpreference and were medium level (42.0%) of economicmotivation. The research shows that watershed beneficiarieshadmedium level (46.5%) ofscientific orientationand had lowlevel (61.5)of extension participation. The respondent hadmedium level (42.5%)of mass media exposureand had lowlevel (42.5%) of contact with development agency. Theresidents possessed medium level (40.5%) ofcosmopoliteness.

Keywords: Watershed, profile

A watershed is commonly defined as an area in which allrain water drains to a common point. From a hydrologicalperspective, a watershed is a useful unit of operation andanalysis because it facilitates a systems approach toland and water use in interconnected upstream anddownstream area (Kerr and Chung 2001). It is also definedas an independent hydrological unit based on the principleof proper management of all the precipitation by way ofcollection, storage and efficient utilisation of run-off water

Profile of watershed beneficiaries in Jabalpur district of MadhyaPradesh, India

Sonam Agrawal and Nalin KhareDepartment of Extension EducationCollege of AgricultureJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482 004 (MP)sonam.agri@gmail.com. nalin_khare@yahoo.co.in

JNKVV Res J 49(1): 73-76 (2015)

and use of groundwater that discharge to and receivedischarge from streams, wetlands, ponds, and lakes. Thearea of watershed may vary from a few hectares to severalthousands of hectares. Watershed is thus the land andwater area, which contributes runoff to a common point.

A Watershed is considered as the ideal unit forintegrated management of soil, water and vegetation in ageographical region, soil and vegetation can also bemanaged conveniently and efficiently in this unit.Watershed in this sense is the ideal unit for planning,implementation and monitoring of projects pertaining tointegrated natural resource management (Kishor Vimal2000).

Methodology

The present study was conducted in Jabalpur district ofMadhya Pradesh during 2014-15 Out of the 7 blocks inJabalpur district, Kundam block was selected for the study.A list of farmers (beneficiaries) from selected villages wasprepared with the help of Gram Panchayat 200beneficiaries were randomly selected for the study. Multistage random was used for the study.The study wasdesigned to know the personal, socio-economic,physiological, communicational and other character ofthe beneficiaries.The data were obtained through pre-tested structured schedule with the help of interview. Thecollected data were quantified, classified, tabulated andpresented on the basis of frequencies and percentages,average and standard deviation.

7 4

Result and discussion

Socio-economic Profile

In all 15 variables were studied and the study revealedthat majority of the beneficiaries belonged to middle(41.5%) and young age (30.0%) groups and this findingsupports the work of Mewara (2005), Paigvar (2006),Kulshrestha et al (2012). In case of education majority(45.0%) of the beneficiaries were up to high school passedand higher percentages (64.0%) belong to joint familyand the finding supported the work of Ali (2001) andMewara (2005), Paigvar (2006). Regarding the caste, thefindings indicate that higher percentage (64.0%) ofbeneficiaries belonged to SC/ST category and this findingis in agreement with the finding of Ali (2001), Paigvar(2006), Kulshrestha et al (2012). In case of occupationmajority of watershed beneficiaries (33.5%) had their mainoccupation as cultivation and cast occupation, this findingis supported by Ali (2001), Mewara (2005). Regardingthe social participation majority of watershed beneficiaries(44.5%) belonged to medium social participation, withregarding the size of land holdingmajority (41.0%) ofwatershed beneficiaries had marginal size of landholding.In case of material possession majority of watershedbeneficiaries (42.0%) belonged to medium category ofmaterial possession Ali (2001), Mishra (2012).

supported this finding. The higher percentage of watershedbeneficiaries (42.0%) had medium economic motivationand the work of Ali (2001) and Mewara (2005), Paigvar(2006) support this finding. In case of scientific orientationthe higher percentage(46.5%) of beneficiaries had mediumscientific orientation the work of Mewara (2005) confirmsthis finding.

Fig. 1. Socio economic characteristics of thebeneficiaries

Psychological Profile

As we concerned about the psychological variablesinregarding to the risk preference, the higher percentage(42.0%) of beneficiaries showed medium risk preferenceand the work of Ali (2001), Mewara (2005), Paigvar, (2006)

Fig. 2. Pyschlogical characteristics of beneficiaries

Communicational Profile

In case of communicational variable higher percentage(61.5%) of the beneficiaries belongs to low level ofextension participation. Regarding the mass mediaexposure the finding indicates that most of thebeneficiaries had moderate (42.50%) exposure. Thisfinding obtained support work of Mewara (2005), Paigvar(2006). As regard to contact with development agencies,the beneficiaries had low contact (42.5%). This might bedue to the low level of socio-economic conditions,cosmopoliteness and low mass media exposure. Thefindings of the present study agreement with the Ali (2001),Mewara (2005), Paigvar (2006). At last thecosmopoliteness higher percentage (40.5%) had mediumlevel of cosmopoliteness finding supported byKulshesrestha et al.

Fig. 3. Communicational characteristics ofbeneficiaries

7 5

Table 1. The profile of the beneficiaries

Variable Beneficiaries Mean S.DFrequency Percentage

Socio-economicAge 42.75 11.19Young age group(18-35 yr) 60 30Middle age group(36-55 yr) 83 41.5Old age group (above 55 yr) 57 28.5Education 1.63 1.14Illiterate 48 24Up to primary passed 62 31Up to High school 90 45Cast 1.48 0.70ST/SC 109 54.5OBC 61 30.5General 30 15Family 1.64 0.48Nuclear 72 36Joint 128 64Social participation 14.6 6.42Low 54 27Medium 57 28.5High 89 44.5Occupation 15.62 0.79Cultivation 37 18.50Cultivation and labour 28 14.00Cultivation and caste occupation 67 33.50Cultivation and business 48 24.00Cultivation and service 20 10.00Size of land holding 5.3 3.7Marginal (Up to one ha) 82 41Small (1.01 - 2 ha) 34 17Medium (2.01 - 5 ha) 72 36Large (Above 5 ha) 12 6Material possession 15.3 7.6Low 64 32Medium 84 42High 52 26PsychologicalRisk preference 25.09 8.74Low 50 25Medium 84 42High 66 33Economic motivation 24.85 8.66Low 58 29Medium 84 42High 58 29Scientific orientation 24.33 8.61Low 58 29Medium 93 46.5High 49 24.5CommunicationalExtension participation 9.44 6.60

7 6

Conclusion

It is concluded that majority of watershed beneficiariesbelongs to middle age group and up to primary schoolpassed, belong to SC\ST group and joint family. It canalso concluded that they had high level of socialparticipation, main occupation was cultivation andbusiness, having marginal size of land holding and mediumlevel of material possession and medium risk preference.It can also be concluded that maximum number ofbeneficiaries belongs to medium level of scientificorientation and economic motivation. It can also infer thatthey have high level extension participation and mediummass media exposure.

orZeku 'kks?k dk;Z e/; izns'k ds tcyiqj ftys esa 2014&15 esa fd;k x;kgS v/;;u ls izkIr fu"d"kZ ds vk/kkj ij vf/kdak'k ykHkkfUor —"kde/;e vk;q oxZ ds ¼57-2%½] ¼41-0%½ izkkFkfed d{kk rd f'k{kkizkIr] vf/kdre ¼64-5%½ 5 lnL;ksa ls vf/kd] ¼54-0%½ vuqlwfprtkfr rFkk vuqlwfpr tutkfr] ¼33-5%½ vf/kdrj —f"k ,oa tkfrxrO;lk; ,oa ¼44-5%½ mPp lkekftd Hkkxhnkjh] ¼50-4%½ —"kdksa dsikl lhekar tksr] ¼42-0%½ e/;e Lrj rd dh ?kjsyw lkexzh] ¼42-0%½ e/;e tksf[ke izkFkfedrk] ¼42-2%½ e/;e vkfFkZd vfHkizsj.kik;h xÃA tcfd 'kks/k dk;Z ;g Hkh n'kkZrk gS ty xzg.k ls ykHkkfUor—"kdksa esa ¼47-0%½ e/;e oSKkfud fnxn'kZu] fuEuLrj ¼61-0%½ ijizlkj dk;Z esa Hkkxhnkjh] e/;e Lrj ¼42-0%½ o`gr lEidZ fof/k;ksa dkmi;ksx djus okys rFkk ¼42-5%½ fuEu Lrj rd vU; fodkl laLFkkvksals laidZ j[kus okys ¼40-5%½ e/;e Lrj rd ckg; Lrj ij laidZj[kus okys ik;s x;sA

Low 123 61.5Medium 36 18High 41 20.5Mass media exposure 37.69 17.74Low 65 32.5Medium 85 42.5High 50 25Contact with extension agencies 7.59 3.64Low 85 42.5Medium 74 37High 41 20.5Cosmopolitness 6.12 2.77Low 55 27.5Medium 81 40.5High 64 32

Variable Beneficiaries Mean S.DFrequency Percentage

(Manuscript Receivd :20.11.2014; Accepted :30.03.2015)

References

AIi Lyaqat (2001) A study on the changes in cropping patternincome and employment status amongbeneficiaries of National Watershed DevelopmentProject of Panagar block, Jabalpur district (MP) MSc(Ag.) Thesis JNKVV Jabalpur:106

Kerr J, Chung K (2001) Evaluating Watershed ManagementProjects, International Food Policy ResearchInstitute, CAPRi Working paper, USA 17p

KishorVimal(2000) Problems and Prospects of WatershedDevelopment in India, Occasional Paper-12,NABARD

Kushrestha Anil, Kuswant TS, Singh YK, Rai DP (2010)Adoption of Watershed Technology by the Farmerin Morena District of Madhya Pradesh. Res J ExtnEdu 10 (2):58-60

Mewara YS (2005) Impact of watershed developmentprogramme (WSDP) on dynamics of farmerslifestyle in Sehore district (MP) MSc (Ag.) ThesisJNKVV Jabalpur:1-110.

Mishra B (2012) A Study on Impact of watershed developmentprogramme on productivity of major crops grown bythe beneficiaries of Katni block of Katni district,Madhya Pradesh MSc (Ag.) Thesis JNKVVJabalpur:1-118

Paigwar Vaibhav (2006) Impact of watershed developmentprogramme (WSDP) on tribal farmers in relation toemployment and income generation in Kundamblock of Jabalpur district (MP) MSc (Ag.) ThesisJNKVV Jabalpur: 110

7 7

Abstract

A soybean dehuller for such applications was developed byCIAE, its dehulling efficiency was evaluated under presentstudy. Soybean samples of 5 kg each was prepared atdifferent moisture content, the moisture level was increasedby addition of measured amount of water and conditioningfor 24 to 60 hr. The five moisture levels selected 9, 11, 13, 15,17 and 19% (w b). It was observed that dal recovery increasedwith increase in moisture content. The mass of soybeanpassing out unhusked through the dehuller increased withincrease in moisture content. This increase was very sharpafter 15%(w.b) moisture content. The dehulling efficiencydecreased with increase in moisture content. The rate ofdecrease becomes steeper after 15% moisture content (wb). The capacity of machine was found to be highest i.e. 96.2kg/hr at 9% moisture level, and it decreased with increase inmoisture content. The mathematical models were alsodeveloped to predict the correlation between differentvariables under study within the range of recordedobservations.

Keywords: Soybean dehuller, dehulling efficiency,moisture conditioning, broken grains, unhusked grains,medicinal benefits.

Soyabean offers number of reasons to be one of the mosteconomical and valuable agricultural products. It hasunique chemical composition of 40% protein and 20%oil, its protein has higher proportion of unsaturated fattyacids; it has several health benefits. Its hull althoughcontains very good quality dietary fibers but it needs tobe recovered for its use as soy dal or for preparation ofmilk and milk products from soybean. The machine usedto remove the hull is called soybean dehuller. Such a

JNKVV Res J 49(1): 77-83 (2015)

soybean dehuller was designed and developed by CIAEBhopal. Therefore, in the light of above facts it was decidedto test the performance of CIAE soybean dehuller.

Material and methods

The soybean was procured from research farm of Collegeof Agriculture, Jabalpur. Samples of 5 kg was preparedat different moisture content whenever necessary themoisture level was increased by addition measured amountof water and conditioning for 24 to 60 hrs. The five moisturelevels selected 9, 11, 13, 15, 17 and 19% (wb).

Experimental Design

The details of various parameters are given below

Effect of moisture conditioning pretreatments on dehullingperformance of soyabean dehuller

Mohan Singh, D. Kumar* and D.K. VermaDepartment of Post Harvest Process & Food EngineeringCollege of Agricultural EngineeringJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur-482 004 (MP)*PC, Krishi Vigyan Kendra, VaishaliRajendra Agriculture University, Pus

Parameters No. of Valuelevels

Independent Dependent

Moisture -- 6 9,11,13,15,content (wb) 17 & 19

Procedure

Every day a 5 kg sample of soybean was prepared andits moisture was determined. As per the requirementmeasuring amount of water was added and the soybeanwere conditioned to attain the design moisture levels atcertain intervals the moisture was sorted and as soon asthe designed moisture level was reached the soybeans

7 8

were subjected to dehulling and the data were recorded.The data so obtained were utilized for calculation of feedrate, capacity and dehulling efficiency. The dehullingefficiency was calculated using following equation

Mb Muh = ( 1 - ------- ) ( 1 - --------- ) x 100

Mt Mt

Where,

h = dehulling efficiency

Mt = Mass of total grains

Mb = Mass of broken grain

Muh = Mass of unhusked grains.

Result and discussion

Performance evaluation of soybean dehuller developed inCIAE Bhopal was the objective of this experiment. Toevaluate the performance the moisture content of theSoybean was taken as the independent variable. Basedupon preliminary observation it was decided to go fordehulling operations at six moisture levels of Soybeanmainly 9%, 11%, 13%, 15%, 17% and 19% wb. Alsobased upon the preliminary observations it was decidedto fix the sample size at 5 kg of whole Soybean.

After finalizing the experimental setup the requiredsample of 5 kg of conditioned Soybean was fed into thehopper of Soybean dehuller and the machine was started.The stopwatch used for calculation of feed rate was startedsimultaneously with the opening of feed shutter fixed atthe bottom of the hopper.

The various dehulled component were collectedfrom their respective outlets. Various data recorded wereanalyzed and discussed to obtain the different indicatorsof machine performance, results are as below:

Recovery of Soy Dal

Recovery of soy dal was found to be maximum at 9%,moisture content of whole soybean (i.e. 4059) and itdecreased with increase in moisture content of soybean(Fig. 1). This trend was same for all the five replicationsof observation. Lowest value of dal recovery (i.e. 2100.7)was observed at the highest moisture level (i.e. 19%moisture content w.b.). It can be seen from fig 8,9,10,11,and 12 that with increase in moisture content decreasein dal recovery is mainly due to short increase in themass of unhusked soybean coming out of the dehuller.The data obtained were analyzed statistically to give thecoefficient of correlation between the dal recovery andmoisture content; the data obtained are representative ofa very strong negative association between the moisturecontent and dal recovery i.e. with increase in moisture

Fig. 1. Variation inRecovery of Dal with Moisture Content of Soybean

7 9

content the dal recovery decreases. A second orderpolynomial was also developed based upon the methodof least sum of squares using MS Excel of MS Office2000. The equation and R2 values for the average value ofall the five replication is tabulated:

Recovery of Unhusked Soybean

The increase in amount of unhusked soybean follows theexponential growth in amount of unhusked mass withincrease in moisture content (Fig. 3). The unhuskedsoybean which was initially at 57 gm corresponding to9% moisture content of soybean finally increases toapproximately 1550 to 2200 gm. It can be seen from Fig.3 that after the 15% moisture content its growth shootsup very fast. However, the growth trend always followsthe exponential asocial and coefficient of correlation alsoshows are very strong positive associations. The dataobtained were analyzed based upon the method leastsum of square and the regression equation of unhuskedsoybean on moisture content were developed based uponthe method of least sum of square. The equations istabulated

Replication Second Polynomial R2 ValueNo.

Average Y= -22.109X2+444.32X+1807.4 0.9952

Recovery of Mealy Waste and Broken

Mealy waste consists of fine broken and germs where asthe broken consists of soy dal, broken in to size lessthan % of its original size. The recovery of mealy wasteand broken is although not very significant but it is one ofthe major parameter used in calculation of dehullingefficiency. The variation in mealy waste and broken has astrong positive association with moisture content of wholesoybean i.e. with increase in moisture content the amountof mealy waste and broken increases (Fig. 2). This isalso confirmed by the strong positive coefficient ofcorrelation obtained between the two variables. Thesecond order polynomial obtained for the average valueis tabulated as below:

Replication Second Polynomial R2 ValueNo.

Average Y=0.0263 X2 + 10.685X+159.54 0.9891

Fig. 2. Variation in Mealy Waste and Brokens with Moisture Content of Soybean

Replication Second Polynomial R2 ValueNo.

Average Y = 1.9529e 0.3751x 0.9886

Variation in Husk Recovery

Amount of husk coming out of the dehuller is a directfunction of mass of soybean dehulled. Because the huskconstitutes 10 to 12% of the mass of soybean, therefore,more the soybean is dehulled the higher will be huskrecovery and vice versa. The maximum value of huskrecovered is approximately 700 gm (replication 2) and

8 0

Fig. 4. Variation in Husk Recovery with Moisture Content of Soybean

Fig. 3. Variation in unhusked Soybean with Moisture Content % wb

8 1

Fig. 5. Variation in Dehulling Efficiency with Moisture Content of Soybean

Fig. 6. Variation in Feed Rate with Moisture Content

8 2

the minimum value of husk recovered is approximately287 gm (replication 1). The amount of husk recoverydecreases with increase in moisture content (Fig. 4).Although the husk recovery is not a function of moisturecontent but the decrease in husk recovery with increasein moisture content is mainly because of the fact thatwith increase in moisture content the amount of soybeanpassing out undehulled through the dehuller increasessharply and the amount of soybean dehulled decreaseswith increases in moisture content. Therefore the amountof husk recovery also decreases with increases inmoisture content.

Dehulling Efficiency

Dehulling efficiency is the parameter which is directly ameasure of performance of the machine.

It is calculating using the following equation.

Mb MuhN = ( 1 - --------- )( 1 - ---------) x 100

Mt. Mt

The dehulling efficiency has got reverse associationwith mass of broken and mass of unhusked soybeancoming out of the dehuller. Dehulling efficiency has thehighest value of 94% at 9% moisture content and thelowest value of 53% at 19% moisture content. Thedecrease in the dehulling efficiency is mainly due toincrease in the mass of unhusked soybean coming outof dehuller.

The dehulling efficiency has got a strong negativeassociation with the moisture content of soybean. Thestrong association is conform by a high correlationcoefficient between the dehulling efficiency and themoisture content of soybean (Fig. 5). A second orderpolynomial was also developed by the method of leastsum of square using MS Excel and MS office 2000. Thesecond order polynomial and R2 value for the average of 5replication is tabulated

based upon the time taken by the machine to dehull the2 kg of whole soybean. The feed rate of the machineobtain for 2 kg of soybean when express in terms ofamount of soybean dehulled per unit of time in kg/hr gavethe capacity of machine. The maximum capacity ofmachine was found approximately 96 kg/hr at 9%moisture content and minimum capacity was found to beabout 19 kg/hr at 19% moisture content. This sharpdecrease in capacity was mainly due to the fact that athigher moisture content. The feed rate was maintainedat very low level because even a slight increase in feedrate resulted in the choking of the machine. This chokingduring dehulling at higher moisture content was mainlydue to greater toughness, flexibility and fibrousness ofthe hull at high moisture content.

The feed rate has a strong negative associationwith the moisture content of soybean i.e. with the increasein moisture content feed rate decreases (Fig. 6). Thesecond order polynomial shows the regression equationof feed rate on moisture content was calculated basedon the method of least sum of squares using MS Exceland MS Office 2000. The Regression equation and R2

value for different replication are tabulated

Replication Second Polynomial R2 ValueNo.

Average Y=0.4464X2+8.5486X+52.595 0.9931

Capacity

The capacity of the soybean dehuller was calculated

Replication Second Polynomial R2 ValueNo.

Average Y = -0.7709X2+14.091X+26.766 0.9663

Soybean Samples of 5 kg each was prepared at differentmoisture content, the moisture level was increased byaddition of measured amount of water and conditioningfor 24 to 60 hrs. The five moisture levels selected 9, 11,13, 15, 17 and 19% (w b). It was found that dal recoveryincreased with increase in moisture content. The massof soybean passing out unhusked through the dehullerincreased with increase in moisture content. This increasewas very sharp after 15%(w.b) moisture content. Thedehulling efficiency decreased with increase in moisturecontent. The rate of decrease becomes steeper after15% moisture content (w b). The capacity of machinewas found to be highest i.e. 96.2 kg/hr at 9% moisturelevel, and it decreased with increase in moisture content.The mathematical models were also developed to predictthe correlation between different variables under studywithin the range of recorded observations.

lks;kchu esa izksVhu ,oa olk i;kZIr ek=k esa ik;h tkrh gS A blds fNydsesa ewyr% js'ks ik;s tkrs gS] vr% izksVhu cf/kZr [kkn~; mi;ksxsa ls igyslks;kchu dk fNydk mrkj ysuk okafNr gS A ls;kchu fMgyj ds-vfHk-

8 3

la Hkksiky n~kokjk fufeZr ,slk gh ,d ;a= gS ftlls lks;kchu ds nkuks lsfNydk mrkjk tkrk gS A izLrqr 'kks/k esa lks;kchu fMgyj dh fØ;k'khyrkdk fo'yks"k.k fMgfyax ls izkIr fofHkUu ?kVdksa ds vkadyu n~okjk fd;kx;k gS AfMgfyax ls izkIr fofHkuu vk¡dM+ksa dk fo'ys"k.k djus ij ;g ik;k x;kfd e'khu dh vf/kDre dk;Z {kerk 95 fd-xzk-@?kaVs] ,oa vf/kdrefefyax n{k ¼95%½ Hkh 9 izfr'kr ueh Lrj ij ik;h x;h A ifj.kkeksads vk/kkj ij ;g fu"d"kZ Hkh izkIr gqvk dh fefyax n{krk ,oa nky dhdqy ek=k esa] lks;kchu ds nkuksa esa ueh dh ek=k c<+us ds lkFk fxjkoVntZ dh x;h A

References

Ali Nawab, Deshpandey SD (1986) The pattern of magnitudeof soybean grain loss during post production phase.Paper presented at the national Seminar onSoybean Processing & Utilization in India, at C.I.A.E.Bhopal souvenir :134-144

Bal Satish, Mishra HN (1986) Engineering Properties ofSoybean. National Seminar on Soybean Processing& Utilization in India at C.I.A.E. Bhopal. Souvenir:146-165

Ojha TP (1986) Constraints of Producing Machines forSoybean Production and Processing. NationalSeminar on Soybean Processing & Utilization inIndia at C.I.A.E. Bhopal. Souvenir :395-400

Patil RT (1986) Equipment and Techniques for processingof Soybean at Rural level. National Seminar onSoybean Processing & Utilization in India at C.I.A.E.Bhopal. Souvenir :253-269

Anonymous (1986) The Soybean Solutions: Meeting WorldFood needs" Proc of INTSOY held at College ofAgriculture, University of Ill inois at Urbana-Champaign

(Manuscript Receivd : 26.11.2014; Accepted : 30.03.2015)

8 4

Abstract

Milling is the process of removing the fibrous seed coatadhering to the cotyledons and splitting the cotyledons toyield dal(split pulses). It can be classified as dry or wet milling.There are three types of pretreatment, viz. chemical, biologicaland enzymatic pretreatment. The soybean oil basedpretreatment was studied under natural drying conditionswith pitting times of 150, 180, 210, 240 and 270 seconds.Oilwas applied at the rate of 1, 1.5, 2, 2.5 and 3 %, water at therate of 8, 10, 12, 14 and 16 % with tempering timesof 8, 12,16, 20 and 24 hr respectively. The grains were conditionedfor 1 day and tempered for set periods. The highest millingefficiency of 67.49%. A decreasing trend of overall millingefficiency i.e. 50.13, 67.49, 63.46, 54.88 and 47.33% wasobserved.

Keywords: Pigeon pea, milling pretreatments, millingcharacteristics, quality parameters.

Pulses are the main source of proteins for vegetarians inIndia, where about 15 to 30% of daily protein needs aresupplied from edible legumes or pulses. About 80% ofthe total pulse production of the country is converted todal, making pulse milling industry the third largest foodindustry in the country (Chacko Jino et al. 2001). In India,about 80% of the pulse production is consumed in theform of dal or flour and the remaining 20% as the wholeseed and other forms (Chacko et al 2001; Mangaraj et al.2005). The availability of milled pulses has been reducedconsiderably because of population increase. The WorldHealth Organization has recommended 80 g per personper day considering the Indian food habits (SatyaSundarum 2010). Pigeon pea (Cajanus cajan) is one ofthe important pulse crops of India, contributing about 20%

Effect of pre-milling treatments on the milling efficiency of CFTRI typedal mill

A. Gupta, Mohan Singh, D K Verma and C.M. AbroalDepartment of Post-Harvest Process and Food EngineeringCollege of Agricultural EngineeringJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482 004 (MP)Email: mohansingh65@rediffmail.com

JNKVV Res J 49(1): 84-88 (2015)

to the total production of all pulses. India accounts for90% of the total world production of pigeon pea. It isconsumed as dehusked splits and is an important dietaryconstituent, especially for the vegetarian population ofIndia, as a source of protein.Pigeon Pea is rich in proteins,fat, carbohydrates, crude fibre and minerals.

Milling of edible pulses for the production of dal is an ageold process. There are more than 10,000 pulse mills withaverage processing capacity of 10 - 20 tonnes/day. Millingprocedures vary widely from place to place. The recoveryof dal varies from 60 to 75% depending upon the type ofpulses and techniques adopted by the millers incomparison to 88 to 89% maximum potential recovery ofsplits. There is excessive loss of pulse cotyledons andembryos in the form of brokens and powdered grains (5to 15%). There is no standard process for milling differentpulses where various parameters involved are optimized.The amount of oil mixed with pulses varies from 150 to500 g/q of grain. Similarly, addition of water also variesfrom region to region from 4 to 20 kg/100 kg of grain. Forloosening of husk and its complete removal, 3-8 passesthrough emery rollers are given. This action causesbreakage and powdering of kernel.

Materials and methods

The research work was carried out in Post HarvestProcess and Food Engineering laboratory of Departmentof Post Harvest Process and Food Engineering, Collegeof Agricultural Engineering, Jawaharlal Nehru KrishiVishwa Vidyalaya, Jabalpur (MP) during 2014.

8 5

Sample preparation

Pigeon Pea (Arhar) was procured from local market ofJabalpur and used in the study. The grains were weighedand samples each of 10 kg size were prepared. The grainswere cleaned to remove dust and grit and graded touniform size and shape in a Screen based Cleaner-cum-Grader and stored in polythene bags. The moisturecontent of grains was determined by standard AOACmethod (AOAC 1990). Thereafter, the grains were pitted.

Physical properties

Moisture content

The dry basis (%) moisture content (Haque and Langrish2005, Saeed et. al. 2006, Ceylon et al 2007, Upadhyayet al 2008) and on wet basis (%) moisture content (Hall1980, Simpson 1991, Rodrigues and Fernandes 2007)were calculated as follows

MCdb(%)= Ww/Ww x 100 …(1)

MCwb(%)= Ww/(Ww+ Wd) x 100 …(2)

Milling efficiency

The milling efficiency is the ratio of the mass of dalobtained to the whole grains fed to the dal mill. It iscalculated by Kupritz formula (1967) which can beexpressed as

milling= hulling x wk x 100

(n1 - n2) (k2- k1)hulling = ------------ and wk = ---------------------------------

n1 (k2-k1) + (d2-d1)+ (m2-m1)

Table 1. Specifications of machine

Screen Cleaner-cum-Grader CFTRI Dal MillParameter Value in Units Parameter Value in Units

Capacity 120 kg / hr Capacity 100 kg / hrPower Rating 1.5 hp Power Rating 1.5 hpCleaning 98% Dal Yield 78 %efficiency

Pretreatments

The grains were mixed with soybean oil in predefinedamount for a few minutes and then left to conditionovernight. The next day the grains were dried in sunshinefor a few hr. The grains were then tempered with water.After that, the grains were again sun dried for a few hr.The grains were then stored in sealed bags.

Milling

The grains were milled to requisite degree. The milledsamples were collected in one lot and separated indifferent fractions by sieving in a Screen based Cleaner-cum-Grader.The fractions obtained were husk, dal, gotaand broken kernels. All these fractions were weighedaccurately with the help of electronic balance and put intabular form. After that, the grains were graded into wholegrains, dal (splits), brokens, husk and powder. Thefractions were again weighed and kept in polythene bagsfor storage.

Parameters studied

The parameters studied were pitting time, oil applicationrate, water application rate and tempering time. The oilapplication rate was considered for conditioning whereasthe water application rate was considered for tempering.

Factors

Pitting time Oil Water Temperingapplication application time

rate rate(sec.) (%) (%) (hr.)

150 1.0 8 8180 1.5 10 12210 2.0 12 16240 2.5 14 20270 3.0 16 24

Results and discussion

Effect of pitting time on milling efficiency

The relationship between pitting time and milling efficiencyfollowed a pattern of second order polynomial whichincreases from more than 50.13% milling efficiency for

8 6

150 seconds pitting time and attains a maximum value ofa little less than 65% at about 200 seconds of pittingtime (Fig. 4.1). Thereafter milling efficiency startsdecreasing with increase in pitting time. The effect ofpitting time on milling efficiency is given by following asecond order polynomial equation

y = -0.04x2 + 1.718x-110

The significant relationship between the pitting timeand milling efficiency is endorsed by a good coefficient ofdetermination i.e., R2 = 0.81.

During the experiment, it was observed that above200 seconds of pitting time, the losses in terms of brokensand mealy waste starts increasing, which results indecrease in milling efficiency, after 200 seconds of pittingtime.

During the experiment, it was observed that beyond1.8% of oil application rate, the losses in terms of brokenspercentage and mealy waste starts increasing, whichresults in decrease in milling efficiency, after 1.8% of oilapplication rate.

Effect of Water Application Rate on Milling Efficiency

The relationship between water application rate and millingefficiency followed a pattern of second order polynomialwhich increases from 50.13%milling efficiency for 8%water application rate and attains a maximum value of alittle less than 65% at about 11.67% of water applicationrate (Fig. 4.3). Thereafter milling efficiency startsdecreasing with increase in water application rate. Theeffect of water application rate on milling efficiency is givenby following a second order polynomial equation

y = -0.951x2 + 21.97x - 62.33

Fig. 4.1. Pitting time vs. milling efficiency

Effect of oil application rate on milling efficiency

The relationship between oil application rate and millingefficiency followed a pattern of second order polynomialwhich increases from 50.13% milling efficiency for 1.0%oil application rate and attains a maximum value of alittle less than 65% at about 1.8% of oil application rate(Fig.2). Thereafter milling efficiency starts decreasing withincrease in oil application rate. The effect of oil applicationrate on milling efficiency is givenby following a secondorder polynomial equation

y = -15.22x2+ 57.45x+10.34

The significant relationship between the oilapplication rate and milling efficiency is endorsed by agood coefficient of determination, i.e., R2= 0.81.

Fig 4.2. Oil application rate vs. milling efficiency

Fig 4.3. Water application rate vs. milling efficiency

8 7

The significant relationship between the waterapplication rate and milling efficiency is endorsed by avery good coefficient of determination, i.e., R2 = 0.81.

During the experiment, it was observed that beyond11.67% of water application rate, the losses in terms ofbrokens percentage and mealy waste starts increasing,which results in decrease in milling efficiency, after 11.67%of water application rate.

Effect of tempering time on milling efficiency

The relationship between tempering time and millingefficiency followed a pattern of second order polynomialwhich increases from 50.13% milling efficiency for 8 hrtempering time and attains a maximum value of a littleless than 65% at about 15 hr of tempering time (Fig.4.4).Thereafter milling efficiency starts decreasing withincrease in tempering time. The effect of tempering timeon milling efficiency is given by following a second orderpolynomial equation

y = -0.237x2+ 7.182x+10.34

than traditional method of milling. A right mix ofpretreatments gives best results with minimum breakagein grains. In this case, it was found that at around 180seconds of pitting time, the milling efficiency is the highest.The milling is easily to carry out at this value. The increasein pitting time from 150 to 180 seconds greatly increasedthe milling efficiency and a peak was attained. The qualityof dal was affected by drying period and the amount of oiland waterapplied.

nky ds nkuksa ls fNydksa dks vyx&vyx djds nks Hkkxksa esa foHkkftrdjus dh izfØ;k dks njkbZ dgrs gSa A ;g nks izdkj dh gksrh gS] 'kq"d ,oaty vk/kkfjrA rhu rjg ls nkyksa dks izlaLdr̀ fd;k tkrk gS] jklk;fud]tSfod ,oa tSfod mRizsjdksa }kjk A izLrqr 'kks/k esa nky izlaLdj.k dkfofHkUu izfjLFkfr;ksa esa v/;;u fd;k x;k Fkk ftlesa Nhyu vof/k 150]180] 210] 240 ,oa 270 lsdsUM j[kh xbZ FkhA rsy ,oa ty ds iz;ksxdh nj 1-0] 1-5] 2-0] 2-5 ,oa 3-0 izfr'kr vkSj 8] 10] 12] 14,oa 16 izfr'kr dze'k% vkSj rsy ,oa ty ds vo'kks"ku ds vof/k dze'k%8]12]16]20 ,o 24 ?kaVs j[kh xbZ Fkh A nky dks rsy esa lks[kus ds fy;s1 fnu rd j[kk x;k A 67-49 izfr'kr dh mPpre nkjkbZ dh U;wurerqdM+s dk izfr'kr 180 lsdsUM dh Nhyu vof/k esa ik;k x;kA 50-13]67-49] 63-46] 54-88 ,oa 47-33 izfr'kr dh ?kVrh njkbZ dh njvkSj 23-50] 6-50] 11-50] 19-90] ,oa 21-98 izfr'kr dk c<rkVqdMSy dh nj ikbZ xbZA Nhyu vof/k ds c<us ij njkbZ dh xq.koRrkds ekudks] tSls nky] fNydksa ,oa VqdMSy esa fxjkoV ntZ dh xbZ A 150,oa 180 lsdsUM dh Nhyu vof/k esa VqdMs dh la[;k dze'k% vf/kdre,oa U;wure ikbZ xbZ A

References

Alam A, Jain DK, Yadav DS (1975) Study of dal milling lossesand possible ways to reduce them. The All IndiaCoordinated ICAR Scheme on Harvest and Post-Harvest Technology (Jabalpur Centre) 7-16

Anonymous (1976) Annual report on Post-HarvestTechnologyScheme, Jabalpur Centre, JawaharlalNehru Krishi Vishwa Vidyalaya, Jabalpur

Anonymous (1979)Annual report on Post-HarvestTechnology Scheme, Coimbatore Centre, T N A U,Coimbatore

AOAC (2000) In: Official methods of analysis of the associationof official analytical chemists (Horwitz W, Ed),17thedn. AOAC International, Maryland, USA

Chakravorty A (1987) Post Harvest Technology 175 -176Deshpande SD Enhancing dal recovery by application of soy

oil - water and sodium bicarbonatepremillingtreatments. Proc Int Agri Engg Conf,Bangkok Thailand IAEC 121: 1-6

The significant relationship between the temperingtime and milling efficiency is endorsed by a very goodcoefficient of determination, i.e., R2= 0.81.

During the experiment, it was observed that beyond15 hr of tempering time, the losses in terms of brokenspercentage and mealy waste starts increasing, whichresults in decrease in milling efficiency, after 15 hr oftempering time.

The purpose of milling is to convert the crop intouseful quality product which is easy to consume and canbe stored for longer periods.The milling when done withpretreatments in suitable combination gives better results

Fig 4.4.Tempering time vs. milling efficiency

8 8

Kurien PP(1971) Traditional and improved method of dal. Inproc of dhal milling conference Mysore, KarnatakaIndia Central Food Technological Research Institute

Kurien PP, Parpia HAB (1968) Pulse milling in India -Processing and Milling of Arhar. J Food Sci& Tech4: 203 - 207

Krishnamurthyet al(1972) A new process for the removal ofhusk of red gram using sirka. Bull. Grain Tech. 10(3):81

Karsolia RP (1978) Development of dry conditioning processfor milling of pulses, M. Tech. Thesis, IIT, Kharagpur

Kurien PP (1978) Pulse milling, Chapter in Food Industries.Chemical Engineering Education DevelopmentCentre, I I T Madras

Kurien PP, Parpia HAB (1968) Pulse milling in India -Procuring and Milling of Tur, Arhar (Cajanus cajanLinn.) J Food Sci & Tech 5:203

Opoku A (2003) Conditioning and dehulling of pigeon peasand moong beans. Can Soc for Agri Food &Biol Sys03 - 347: 1 - 18

Patil BG (1977) Engineering studies on conditioning ofpulses for milling, MTech Thesis, I I T, Kharagpur

Reddy BS (2006)Indigenous technical knowledge on pulsesstorage and processing practices in AndhraPradesh, Ind J Trad Knwl 5(1): 87

Reddy VPR (1981) Dehusking of arhar grains(CajanusCajan) M Techthesis, Govind Ballabh PantUniversity of Agriculture and Technology, UttarPradesh, India

Sagar D, Singh B (1978) Performance of different soy beandehulling mechanisms, J Agri Engg 4:1987

Saxena RP (1985a) Milling of pigeonpea grain (Cajanus cajanL.) and associated aspects. PhD thesis GovindBallabh Pant University of Agriculture and

Technology Pantnagar, Uttar Pradesh, IndiaSaxena RP, Singh BPN, Singh AK, Singh J K (1981b) Effect

of chemical treatment on husk removal of Arhar(Cajanus cajan) grain. ISAE Paper no. 81-PAS-156,New Delhi, India Indian Society of AgriculturalEngineers

Siripurapu SCB (1983) Instant Dal. AET 7(2):54Singh D, Sokhansanj S (1984) Cylinder concave mechanism

and chemical treatment for dehulling pigeon peaAMA 15(2):53

Shrivastava V, Mishra DP, Khare BP (1988a) Effect of insectinfestation on biochemical composition ofpigeonpea (Cajanus cajan (L.) Millsp.) seeds storedin mudbin. Bulletin of Grain Technology 26:120-125

Shrivastava V, Mishra DP, Gupta LC, RK, Singh BPN (1988b)Influence of soaking on various biochemicalchanges and dehulling efficiency in pigeonpea(Cajanus cajan L.) seeds. Journal of Food Scienceand Technology 21:54-62

Teckchandani CK, Jain SK (1982) Milling of rewetted BengalGram I S A E, XIX Annual Convention, Udaipur

Teckchandani CK (1988) Development of pearled grainseparator for improvement of milling performanceof Pigeon Pea. Unpublished Ph D Thesis, PHTC,IIT, Kharagpur

Teckchandani CK, Bal S (1990) Gota separation - A way toimprove Arhar milling recovery. Paper presented atNational Dal Mill Seminar held at P K V Akola I S A E,XIX Annual Convention, Udaipur 8-19

Teckchandani CK et al (1990) Survey of pulse millingindustries in India, Problems and Prospects

Vimla, Pushpamma P (1984) Dal Recovery of Pulses storedin three regions of Andhra Pradesh. J Food Sci Tech21(3):139

(Manuscript Receivd : 26.11.2014; Accepted :30.03.2015)

8 9

Abstract

Microbial proteases are one of the most important enzymesused in number of industries and their production by solidstate fermentation is found to be cost effective as it utilizesdifferent type of waste materials as substrate. The presentresearch work was done to obtain protease producing fungifrom different vegetable wastes. Out of eight vegetables(Potato, cauliflower, chilli, tomato, Brinjal, Pea, coriander andcapcicum) tested, the fungal isolate from chili was found togive positive result for protease production. The fungus wasidentified as Penicillium italicum. Solid state fermentationwas done by using rice broken. Highest zone of gelatinhydrolysis was found at pH 7, temperature 28°C, saltconcentration 1% and incubation period of 24 hrs and bestenzyme production was seen after 5 days of incubation atpH 7, temperature 28°C and salt concentration 1%. Theprotease enzyme was partially purified using ammoniumsulfate and immobilization was done using sodium alginateand chilled calcium chloride solution.

Keywords: Proteases, Solid State fermentation,optimization, immobilization

Protease is the most important industrial enzyme ofinterest accounting for about 60% of the total enzymeglobal market (Godfrey and West 1996, Chouyyok et al.2005). Microbial proteases are degradative enzymes, thatcatalyze the hydrolysis of proteins (Raju et al. 1994).The molecular weight of proteases ranges from 18 - 90kDa (Sidney and Lester 1972). They are generally usedin detergents (Barindra et al. 2006), food industries,leather, meat processing, cheese making, silver recoveryfrom photographic film, production of digestive and certainmedical treatments of inflammation and virulent wounds(Rao et al. 1998, Paranthaman et al. 2009). Theseenzymes are found in a wide diversity of sources such asplants, animals and microorganisms but they are mainly

Protease production by fungal isolate of vegetable waste

Shiana Gaikwad, Roshni Choubey and Shikha BansalDepartment of Botany & MicrobiologySt. Aloysius' College (Auto)Rani Durgawati UniverisityJabalpur 482 001 (MP)Email: roshnitiwari1987@gmail.com

JNKVV Res J 49(1): 89-94 (2015)

produced by bacteria and fungi. Microbial proteases arepredominantly extracellular and can be secreted in thefermentation medium.

A variety of microorganisms such as bacteria,fungi, yeast and actinomycetes are known to producethese enzymes. Molds of the genera Aspergillus,Penicillium and Rhizopus are especially useful forproducing proteases, as several species of these generaare generally regarded as safe (Sandhya et al. 2005).Fungi elaborate a wide variety of proteolytic enzymes thanbacteria. The filamentous fungi have a potential to growunder varying environmental conditions such as timecourse, pH and temperature, utilizing a wide variety ofsubstrates as nutrients.

Though microbial protease are the predominantsource of industrial enzyme due to their broad biochemicaldiversity, rapid growth of microorganisms and limited spacerequired for cell cultivation, still, it involves advancedtechnology in the field of biotechnology and microbiology( Rao et al. 1998). The cost of enzyme depends on itsproduction strategy and downstream processing. The twoimportant fermentation methodologies include submergedfermentation (SmF) and solid state fermentation (SSF).SSF has several advantages over submerged fermentationlike minimal water requirement, less complicateddownstream processing etc. Among the different microbialgroups attempted, filamentous fungi have scored highcommercial acceptance in SSF (Ghildyal 1985).

Several methods have been used in the past forestimation of enzyme activity. Most common methods ofquantitative estimation methods are agar plate assay,radial immune diffusion and thin layer enzyme assay.Few commonly used protein substrates for screeningprotease activity include casein, skim milk (Rajamani andHilda 1987), gelatin, BSA (Vermelho et al. 1996) andkeratin (Friedrich et al. 1999). Commonly employed

9 0

techniques for quantification of protease activity includespectrophotometry, f luorimetry enzyme-linkedimmunosorbent assay-based assays (ELISA) andradiometry. The collection of substrates that have beenused for conducting protease assays includes: naturallyoccurring insoluble substrates (collagen, elastin, fibrin,gelatin, keratin, etc) or those soluble substrates whichare rendered insoluble by cross-linking or entrapment orthermally modified or synthetic chromogenic substratessynthesized using 3,5-dinitro-salicylic acid (Safarik 1989,Gallegos et al. 1996).

The most widely used techniques for immobilizationof cell free enzymes are based on the binding of enzymemolecules to carriers by covalent bonds, or by adsorptiveinteractions, entrapment into gels/ beads/fibres, cross-linking or co-cross-linking with bifunctional reagents,encapsulation in microcapsules or membranes. Till dateseveral methods have been reported for immobilization ofproteolytic enzymes (Diaz and Balkus 1996, Pandaya etal. 2005, Jarzebski et al. 2007, Jang et al. 2006, Lee etal. 2002).

Keeping all these in view, the present work dealswith the isolation of protease producing fungi from differentvegetable wastes, production of protease from isolatedfungi by solid state fermentation and its optimization.

Material and methods

Sample Collection & processing

Degrading vegetables samples were collected in polybagsfrom domestic and market waste. Spoiled portion ofvegetables were chopped into small pieces and used asinoculums under laboratory conditions.

Isolation

The chopped pieces of samples were inoculated on pre

solidified Potato Dextrose Agar medium under sterilizedconditions. The plates were incubated at 28°C for 24hours in BOD incubator. Fungal growth obtained wasfurther point inoculated in new PDA plates for obtainingpure culture.

Preliminary estimation of protease production (QualitativeAnalysis)

Pure fungal cultures obtained were point inoculated onsterilized Gelatin agar medium and incubated at 28°C for24 hrs. The plates were then observed for zone ofhydrolysis by screening with 6.25% Mercuric Chloridesolution and kept undisturbed for 5-10 minutes. The zoneof clearance around the fungal colony was observed anddiameter of zone was recorded (mm).(Pic 1).

Identification

Zone forming fungal culture was identified on the basisof both macroscopic characters viz. colony color, shape,elevation and margin and microscopic features like shapeof spores , hyphae, presence or absence of septa etc.which were observed by using lactophenol cotton bluestain under high magnification i.e. 4 X (Alexopoulos &Mims 1996) (Pic 2).

Optimization for Zone development

Isolated fungal culture was inoculated on GAM platesand treated under different conditions like pH (5,7 & 9),temperature (28°, 37° & 45°C), Incubation period (24 hrs,48 hrs, 72 hrs) and Salt concentration (1%,2%,& 5%),for optimization. HgCl2 solution was applied for screening.Zone diameter under each condition was measuredaccurately.

At different pH

9 1

Mass Production

This was carried out through solid state fermentation in asubstrate of 5gms of broken rice in 250 ml Earlenmeyerflask, moistened with 10 ml of mineral salt solution(Ghildyal et al. 1985). After autoclaving and cooling it wasinoculated with 1 ml of fungal spore suspension andincubated at 28° C for 120 hrs.

Extraction of crude enzyme

0.1 % solution of Tween 80 was prepared. 10ml of thissolution was added to 2gm of fermented substrate whichwas then homogenized on rotary shaker at 180 rpm for1hr and centrifuged at 8000 X g at 4oC for 15 minutes.This resulted in the supernatant of crude enzyme extractfor further studies.

Assay for protease

200µl of crude enzyme extract, 500µl of casein (1%) and300 µl of 0.2 mol/l phosphate (pH 7.0) was prepared. Itwas centrifuged at 8000 X g for 15minutes. The resultantsupernatant was mixed with 5ml of 0.4mol/l Na2CO3, 1mlof 3 - fold diluted Folin & Ciocalteus phenol reagent.Incubation at room temperature for 30 min changed thecolor of reaction mixture into blue. The intensity of theblue color was measured at 660nm. Standard graph ofcasein was followed for estimation of protease activity.

Optimization was done for obtaining best environmentalcondition for protease production under various parametersof

1. Temperature- The inoculated substrate wereincubated at different temperature- 28ºC , 37°C, 45ºC tofind the effect of temperature on protease production.

2. pH- Protease production was evaluated at differentlevels of pH - 5, 7 & 9

3. Salt concentration- The effect of salt concentrationon protease was determined by preparing saltconcentrations (NaCl)- 1%, 2%, 5%.

4. Incubation period- The inoculated flasks were keptfor different period of incubation i.e 1 day, 2 days and 5days after which the amount of protease formation wasobserved.

Immobilization

Crude enzyme extract was precipitated by 40%ammonium sulfate saturation at 4ºC and the obtainedprecipitates were dissolved in Tris - HCl buffer (50 mM) atpH-7.5. Partially purified enzyme solution was mixed withSodium alginate solution (2%) in 1:1 ratio and the resultingsolution was added dropwise into prechilled Calciumchloride (0.2 M) solution with continous shaking at sametemperature for beads formation. Beads were washed 3-4 times with de-ionized water and finally with 50 mM Tris-HCl buffer (pH- 7.5). Beads were dried, weighed & enzymeactivity was again checked in the beads. (Pic 4)

Results and discussion

Rotten and infected vegetable samples inoculated onPotato Dextrose Agarmedium after incubationresulted into growth of fungalisolates. There were total 8isolates out of which only oneobtained from rotten chili wasfound to be protease producer.The lactophenol cotton bluestaining procedure identifiedthis isolate as Penicilliumitalicum.

Zone optimization was carried out for variousenvironmental parameters including temperature, pH, salt(NaCl) concentration and incubation time.

Out of 3 pH ranges tested i.e, 5, 7 and 9, the highestzone size of 29 mm was found at pH 7. A zone of 8 mmdiameter was obtained at pH 5 while no zone was thereat pH 9. Also the biggest zone of hydrolysis (14 mm)was found at 28°C out of 3 ranges of temperatures testedi.e, 28ºC, 37°C and 45. A Clear zone of 14.2 mm sizewas seen after 24 hrs of incubation while overgrowth wasobtained upon extending the incubation period up to 48and 72 hrs. 1 % salt concentration was found to beoptimum for maximum protease production with a zonediameter of 13mm as compared to 2% and 5%concentration of salt with zone size of 10 mm and 8mmrespectively.

The fungal culture of Penicillium italicum was masscultivated by Solid state fermentation. SSF method waschosen because it has been reported previously to givegreater productivity than submerged fermentation SSFoffers many advantages, including superior volumetricproductivity, use of simpler machinery, use of inexpensivesubstrates, simpler downstream processing and lower

9 2

energy requirements with submerged fermentation(Ghildyal et al. 1985). The use of broken rice proved as agood solid substrate for growing fungi as enough growthwas obtained after 5 days of inoculation.

Quantitative assay

Quantitative assay for protease activity was performedusing Lowry's Method and optimization of environmentalconditions was carried out for obtaining better productionof protease enzyme.

Effect of pH

Protease production by microbial strain depends on theextra-cellular pH because culture pH is strongly influencesmany enzymatic processes and transport of variouscomponents across the cell membrane, which in turnsupport the cell growth and metabolite production. Theoptimum pH for production of protease was recorded aspH 7 (Graph 1) with an OD of 1.681 and proteinconcentration 4.0µg/µl. Similar results were found in theproduction of protease from rice mill waste by Aspergillusniger in solid state fermentation for which the optimumpH for production of protease was recorded at 7.0.in allvarieties of rice broken (Paranthaman R et al. 2009). Lessprotease production was seen at pH 9 with OD 1.103 andprotein concentration 2.4 µg/µl. No fungal growth wasobtained at pH 5 therefore no enzyme production obtained.

optical density 1.814 and protein concentration 4.25 µg/µl Preliminary studies on growth and enzyme productionat 28°C, 37ºC and 45°C indicated that although littlegrowth occurred at 37°C and 45ºC temperature butproductivity was high at 28ºC. The research done byKhosravi and Jalali (2008) also indicates that a pH of range7 to 11 and temperature ranging between 20 - 50°C resultsin highest enzyme production. An OD of 1.097 and proteinconcentration 2.56 µg/µl was obtained upon incubationat 37º C while no fungal growth took place at temperature45º C.

Effect of Temperature

The optimum temperature for protease production wasdetermined by incubation of the reaction mixture atdifferent temperatures i.e 28ºC, 37 ºC and 45ºC for 24hrs. Fermentation carried out at 28ºC was best suited forenzyme production (Graph 2) as it resulted in highest

Effect of Salt Concentration

Three concentrations of salt (NaCl) i.e, 1%, 2% & 5%were used to check the effect of salt on proteaseproduction. out of 3 concentrations tested 1% saltconcentration showed maximum enzyme production withan OD of1.833 and protein concentration 4.3 µg/µlindicating 1% as the optimum salt concentration forproduction of proteases from Penicillium italicum (Graph3). 2 % salt concentration resulted in an OD of 1.466 andconcentration of 3.5 µg/µl. the concentration of protein

9 3

further decreased with increasing salt concentration to 5% which gave 0.92 OD and 2.2 µg/µl proteinconcentration. The graph clearly indicates that proteaseproduction decreases with increasing salt concentration.

Effect of Incubation time

The incubation period greatly affect the enzymeproduction. It was seen that with increasing time ofincubation, the enzyme production was found to beincreased. Maximum OD of 1.691 was obtained at anincubation of five days indicating 4.1 µg/µl proteinconcentrations. Comparatively lesser OD 1.238 and 0.892were obtained at 1 and 2 days of incubation with 3.213and2.02 µg/µl protein concentration.

Acknowledgement

The authors are thankful to the Principal of St. Aloysius'College (Auto.) Jabalpur for providing necessary facilitiesfor the conduct of this research work.

lw{ethoksa }kjk mRiUu izksfV,t ,Utkbe loskZRre ,utkbEl esa ls ,d gSatks vkS/kksfxd Lrj ij cgqvi;ksxh lkfcr gq, gSa A fd.ou ¼SSF½ }kjkvuksi;ksxh oLrqvksa dks HkksT; inkFkZ ds :i esa iz;ksx djus ls budkmRiknu de ykxr esa fd;k tk ldrk gS A izLrqr 'kks/k dk;Z dk eq[;mnn~s'k [kjkc ¼lM+h&xyh½ lfCt;ksa ls izksfV,t+ ,Ut+kbe mRiUu djus okysdod ¼QatkbZ½ dks izkIr djuk Fkk A bl dk;Z gsrq vkB lfCt;ksa dk iz;ksxfd;k x;k Fkk ftuls gjh fepZ ls izkIr dod }kjk lQy izksfV,t+ mRiknuns[kk x;k A bl dod dh igpku isfulhfy;e bVsfyde ds :i esa dhxbZ A fd.ou ¼SSF½ gsrq pkoy dh dudh ¼[kaMk½ dk iz;ksx fd;k x;kA ih-,p- 7] rkieku 280C] yo.k dh lkanzrk 1% ,oa 24 ?kaVksa dsm"ek;u ij loksZRre ftysfVu gkbMªksfyfll ¼tyh; vi?kVu½ ns[kh xbZA ,Utkbe dk mRre mRiknu Hkh mi;qZDr ifjfLFkfr;ksa ij ik¡p fnu dsm"ek;u ij ik;k x;k A izksfV,t ,Utkbe dk vkaf'kd 'kqf/dj.k ¼partialpurification½ vek s fu;e lYQ sV }kjk ,o a f LF kjhdj.k¼immobilization½ lksfM;e ,fYtusV o 'khry dSfY'k;e DyksjkbM dsfofy;u }kjk fd;k x;k A

References

Barindra S, Debashish G, Malay S and Joydeep M ( 2006)Purification and characterization of a salt, solvent,detergent and bleach tolerant protease from a newgamma Proteobacterium isolated from the marineenvironment of the Sundarbans. Process Biochem41: 208-215

Chouyyok W, Wongmongkol N, Siwarungson N, PrichnontS (2005) Extraction of alkaline protease using anaqueous two-phase system from cell free Bacillussubtilis TISTR 25 fermentation broth. ProcessBiochem 40: 3514-3518

Diaz JF, Balkus KJ Jr (1996) Enzyme immobilization in MCM-41 molecular sieve. J Mol Catalysis B: Enzymatic2:115-126.

Friedrich J, Gradisar H, Mandin D, Chaumont JP (1999)Screening fungi for synthesis of keratinolyticenzymes. Letters in Appl Microbiol 28:127-130

Gallegos NG, Jun J, Hageman JH (1996)Preparation ofgeneral proteinase substrates using 3,5-dinitrosalicylaldehyde. J Biochem Biophys Methods33:31-41

Ghildyal WP, Lonsane BK, Sreekantish KR,Sreeanivasamurthy V (1985) Economics of

Immobilization

Partial purification of Protease was done by using 40%Ammonium sulfate at 4° C Obtained precipitates weredissolved in Tris - HCl buffer(50 mM , pH-7.5). Beadswere dried, weighed &found to be 2.001gms.Immobilized enzyme wasagain used to check itsactivity. The optical densityat 650 nm was recorded as0.820 for control while 1.550in case of enzyme used.Increase in OD is a positiveindication of enzyme activity even after immobilization.

The fungal culture isolated from chili waste gavethe best result and was identified as Penecillium italicum.The optimum conditions for enzyme production were foundto be pH 7, temperature 28ºC, salt concentration 1% andincubation period of 120hrs within that culture.

9 4

submerged and solid state fermentation for theproduction of amyloglucosidases. J Food SciTechnol 22: 171-176

Godfrey T, West S (1996) Industrial Enzymology, 2nd ed,Macmillan Publishers Inc New York.

Jang S, Kim D, Choi J, Row K, Ahn W(2006)Trypsinimmobilization on mesoporous silica with or withoutthiol functionalization. J Porous Materials 13(3):385-391

Jarzebski AB, Szymanska K, Bryjak J, Mrowiec-Bialon J(2007) Covalent immobilization of trypsin on tosiliceous mesostructured cellular foams to obtaineffective biocatalysts. Catalysis Today 124 (1):2-10

Lee H, Suh DB, Hwang JH, Suh HJ (2002) Characterizationof a keratinolytic metalloprotease from Bacillus sp.SCB-3. Appl Microbiol and Biotechnol 97:123-133

Pandya PH, Raksh VJ, Bharat LN, Prashant NB (2005)Studies on the activity and stability of immobilized?-amylase in ordered mesoporous si licas,Microporous and Mesoporous Material 77 (1):L67-77

Paranthaman R, Alagusundaram K, Indhumathi J(2009)Production of protease from rice mill wastes byAspergillus niger in solid state fermentation. WorldJ Agric Sci 5(3): 308-312

Rajamani S, Hilda A (1988) Plate assay to screen fungi forproteolytic activity.Curr Sci 56:1179-1181

Raju K, Jaya R, Ayyanna C (1994) Hydrolysis of casein byBajara protease importance. Biotechnol ComingDecadea 181: 55-70

Rao MB, Aparna, M, Tanksale M, Ghatge S, Deshpande V(1998) Molecular and biotechnological aspects ofmicrobial proteases. Microbiol Mol Biol Rev 62: 597-635

Safarik I (1989) Spectrophotometric determination ofproteolytic activity in coloured solutions. J Biochemand Biophy Methods 19:201-206

Sandhya C, Sumantha A, Szakacs G, Pandey A(2005)Comparative evaluation of neutral proteaseproduction by Aspergillus oryzae in submerged andsolid state fermentation. Biochemcial Process 40:2689-2694

Sidney F, Lester P (1972) Methods in Enzymology. AcademicPress Inc, New York

Vermelho AB, Meirelles MNL, Andréa Lopes, Petinate SDG,Chaia AA, Branquinha MH (1996) Detection ofextracellular proteases from microorganisms onagar plates. Mem Inst Oswaldo Cruz, Rio de Janeiro91(6):755-760

(Manuscript Receivd : 20.01.2015; Accepted :25.03.015)

9 5

Abstract

Aspergillus flavus was isolated from soil sample collectedfrom botanical garden of St. Aloysius College (Auto. Thefungus was found as a starch hydrolysing with a inhibitionzone of 40 mm. Screening of crude enzyme extract was doneto check the antimicrobial activity against bacterial pathogens(Bacillus subtilis, Staphlococcus aureus, Staphylococcusepidermis, Pseudomonas aeroginosa, Escherichia coli) andno inhibition zone was observed. The influence of pH,temperature, starch concentration and nitrogen source ofthe medium utilizing different substrate (wheat flour, steamedrice and tea waste) was investigated and maximum amylaseproduction was recorded by using wheat flour as substrate.The maximum production of amylase was observed at 80°Cwith a concentration of 2.489mg/ml/sec whereas optimumpH was 3 with a concentration of 0.8859mg/ml/sec. Amongnitrogen source peptone had maximum concentration of 2.5mg/ml/sec . Rf value was found to be 1.71.

Keywords: Amylase production, Antimicrobial activity,Aspergillus flavus

Amylases are hydrolytic enzymes that stand out as aclass of enzymes which are of useful applications in thebrewing, textile, detergent and pharmaceutical industries[Asghar et al. 2000].Many chemical transformationprocesses used in various industries have inherendrawbacks from a commercial and environmental point ofview. In particular, a greater awareness of conservationissues has forced industries to consider alternative,cleaner methods (Rao et al. 1998) With this regard, theuse of enzymes as industrial catalyst is becoming thebest option, and enzymes are gradually replacing chemicalcatalysts in many areas of industry (Smith 1996).Microbial enzymes are becoming increasingly importantfor their technical and economic advantages. With annual

Production of amylase by Aspergillus flavus isolated from soil

Shikha Gauri, Shikha Bansal and Roshini ChoubeyDepartment of Botany and MicrobiologySt. Aloysius' College (Auto)Jabalpur 482 002 (MP)E-mail- shikha877@yahoo.com

JNKVV Res J 49(1): 95-99 (2015)

growth rate of about 3.3 %, the global market for enzymesreached about $ 2 billion in 2004 (Sivaramakrishnan etal. 2006).

Up to the early 1970's it was considered that plantand animal materials were the best sources of enzymes.Nowadays, however microbial enzymes are becomingincreasingly important for their technical and economicadvantages, (Kelly and Fogarthy 1976; Mohammed et al.2007). In recent years the capability of some fungi todegrade starch has aroused the interest of severalresearchers who recognized the potential values of variousfungi for certain biotechnological applications such assingle cell proteins, or ethanol from starchy biomass(Demot and Verachtert 1987; Ettalibi and Baratti 1988).A. flavus appears to spend most of its life growing as asaprophyte in the soil, where it plays an important roleas nutrient recycler, supported by plant and animal debris(Scheidegger and Payne 2003). The ability of A. flavus tosurvive in harsh conditions allows it to easily out-competeother organisms for substrates in the soil or in the plant(Bhatnagar et al. 2000). The fungus overwinters either asmycelium or as resistant structures known as sclerotia.The sclerotia either germinate to produce additional hyphaeor they produce conidia (asexual spores), which can befurther dispersed in the soil and air.

Enzyme activity is controlled by many factors,including environment, enzyme inhibitors and few more.The present study focus on the extracellular amylaseproduced by the fungi Aspergillus flavus at different pH,temperature, nitrogen source and substrate. The resultsindicate the maximum enzyme production at pH 6.8. Sothis study was further extended to find optimum conditionsunder which the fungal amylase is at its best in terms ofstability to yield the desired results or products.

9 6

Material and methods

Isolation

Soil sample was collected from Botanical Garden ofSt.Aloysius College (Auto) Jabalpur (MP). Serial dilutionof soil sample was made and 1 ml of 10 5 dilution wasspread over PDA media. Plates were incubated at 28 0Cfor 48 hrs.

Identification : The isolated micro-organisms weretentatively identified on the basis of its microscopic andmorphological characteristics. For cultural characteristics,fungal isolates were point inoculated on PDA media insterile petri-plates and incubated at 25 0C for 6 days.

Screening for amylolytic activity of Aspergillusflavus-Amylolytic activity of the test isolate wasdetermined by starch agar plate method (Bertland et al.2004). Accordingly the test organism was inoculated intoPDA media supplemented with 1 g of starch .The plateswere then incubated at 50°C for 5 days. After incubationperiod, Lugol's iodine solution was added to the cultureplate to identify the zone around the cultures which wasmeasured that represent the amylolytic activity.

Preparation of the medium to determine amylaseproduction

The production medium was prepared (composition ingrams/litre :KH2PO4 - 0.14g, NH4NO3 -0.1g, KCl- 0.05g,MgSO4.7H2O - 0.01g, FeSO4.7H2O -0.001g, starch - 2g)and was autoclaved at 121°C for 45 mins. The media wasthen inoculated with isolated fungal culture and incubatedat 28° C for 48 hr.

Extraction of Amylase from Aspergillus flavus-Extraction of crude enzyme was done by centrifugationof the fermented media at 2000 rpm for 5 mins,supernatant collected and filtered by using whattman no1 filter paper. The filtrate was used as crude enzymeextract (Ali et al. 1998; Oyeleke et al. 2009).

Antimicrobial potential of crude enzyme extract-Bacterial pathogens were chosen to evaluate theantibacterial activity of crude enzyme extract by agar welldiffusion method, firstly the lawn was prepared byswabbing the surface of nutrient agar media bacteriapathogens procured from MTCC Chandigarh (Listeriamonocytogenus MTCC (657), Bacillus subtilis MTCC(121), Staphylococcus aureus, MTCC (3160),Staphylococcus epidermis, MTCC (30886), Pseudomonasaeruginosa MTCC (424) and Escherichia.coli MTCC (40)

and incubate for 15 minutes. Equally spaced wells weremade in the solidified agar with the help of a pre-sterilizedcork borer. Crude enzyme extract was poured in the well.The plates were incubated at 37°C for 2-3 days forbacterial growth.

Amylase enzyme Assay-Amylase activity wasassayed by pipetting 0.5 ml of culture extract enzymeinto test tube and 1ml of 1% soluble starch in citratephosphate buffer having a pH of 6.4 .The reducing sugarliberated were estimated by the 3, 5 Dinitrosalicycilc acid(DNSA) method (Bertland et al. 2004) The reaction mixturewas incubated in a water bath at 40°C for 30 mins . Ablank consisting of 1ml of soluble starch in citrate -phosphate buffer pH (6.4) was also incubated in a waterbath at the same temperature and time with the othertest - tubes. The reaction was terminated by adding 1mlof DNSA reagent in each test -tubes and then immersingthe tubes in a boiling water bath for 5 mins after whichthey were allowed to cool and 5ml of distilled water wasadded. The absorbance was measured at 540 nm.

Determination of reducing sugar-The reducing sugarliberated was estimated by using the 3, 5 Dinitrosalicyclicacid method as advocated by ( Bertland et al. 2004 ) .Thereaction mixed was incubated in a water bath at 40°C for15 mins and the reaction was terminated by adding 1mlof prepared DNSA reagent in the reaction tubes and thenimmersing the tubes in a boiling water bath (100° C) for 5mins after which they were allowed to cool under runningtap water. The reducing sugar content was determinedby referring the standard curve of known concentration ofglucose.

Effect of different parameters on amylase production

Effect of different parameters such as pH, temperature,nitrogen sources and substrate on amylase productionwas studied.

pH - The effect of pH on amylase production wasdetermined using pH values of 3,6,8 and 10 after whichan assay was also carried out based on Dinitrosalicyclicacid method (DNSA) ( Bertland et al. 2004).

Temperature -The effect of temperature on amylaseproduction was carried out using the following temperaturevalues of 30°C, 40°C,60°C and 80°C after which an assaywas also carried out based on Dinitrosalicyclic acidmethod (DNSA) (Bertland et al. 2004).

Nitrogen source-Different nitrogen sources (peptone, beefextract, tryptone) were used for the production of amylase

9 7

after which an assay was also carried out based on(DNSA) (Bertland et al.2004).

Substrates -The basal media was supplemented with 2 gof various substrates viz., tea waste, wheat flour, steamedrice. After inoculation of the fungus, the flasks wereincubated at 30°C for 3 days. To determine the effect ofsubstrates on fungal amylases a mixture of crude enzyme,with 1% soluble starch and citrate phosphate buffer pH6.4 was first incubated for 30 min. Then the residual activityof the enzyme was determined by assaying the incubatedcrude enzyme while after which an assay was also carriedout based on DNSA method.

Thin layer Chromatography

The type of amylase from the fungal isolates based onthe starch hydrolysates TLC system (Kimura andHorikosh (1989) cited in Gashaw Mamo and AmareGessesse 1999) was followed. First 0.9 ml 2% solublestarch mixed with 0.3 ml crude enzyme from therespective fungal sources was incubated for 30 minutesat 65°C respectively in the water bath. Thereafter eachhydrolysate was spotted on TLC plate along with standardknown sugar (glucose and maltose) solutions. A onedimensional ascend was done using a solvent system (v/v) of butanol: ethanol: water (5:3:2). After a total of 4ascends air-dry TLC plates were sprayed with 50% (v/v)Methanol- H2SO4 mixture and heated for 10 min. at about100°C. The dark brown sugar spots appeared wasidentified by comparing with the standards.

Statistical analysis

Effect of each parameter was studied in triplicate and thedata have been statically analysed and represented bythe procedure suggested by Panse and Sukhatme (1967).

Result and discussion

Isolation and Identification

The fungal culture was isolated by serial dilution methodand was tentatively identified as Aspergillus flavus onthe bases of habit characters microscopic andmacroscopic characterstics observation.

Screening for amylolytic activity of Aspergillusflavus-Amylolytic activity of Aspergillus flavus wasdetermined by using starch agar plate method (Bertand

et al. 2004).The clearing zone with diameter 42 mm forAspergillus flavus was observed.

Antimicrobial potential of crude enzyme extract-Crude enzyme extract of Aspergillus flavus was studiedfor its antimicrobial potential against bacterial pathogensprocured from MTCC Chandigarh (Listeria monocytogenusMTCC(657) ,Bacillus subtilis MTCC (121),Staphylococcus aureus, MTCC(3160), Staphylococcusepidermis, MTCC (30886), Pseudomonas aeruginosaMTCC(424) and Escherichia. coli MTCC(40) and noinhibition zone was observed.

Fig. 1. Effect of pH on amylase production

Fig. 2. Effect of temperature on amylase production

Fig. 3. Effect of nitrogen source on amylase production

9 8

Effect of pH amylase production

The optimum pH for Aspergillus flavus for this study wasrecorded at pH 3, with a concentration of 0.859 mg/ml/sec (Fig 1).

Effect of temperature on amylase production

The optimum temperature for the activity of Aspergillusflavus was recorded at temperature 80 0C with aconcentration of 2.489 mg/ml/sec (Fig 2).

Effect of nitrogen source on amylase production

Figure 3 shows the effect of nitrogen supplementation onamylase production under SSF showed that peptonesupported the highest production of amylase byAspergillus flavus (4.189mg/ml/sec).

Effect of substrates on amylase production

The effect of different substrate on the activity of enzymeproduced by Aspergillus flavus. Maximum amylaseactivity was found by using wheat bran with concentrationof 2.591mg/ml/sec.

Thin layer Chromatography

The crude amylase enzyme produced by selected fungalisolates Aspergillus flavus was spotted on pre coatedsilica plates with solvent system butanol:ethanol:water(5:3:2). TLC plates were sprayed with 50% (v/v) MethanolH2SO4 and heated for 10 min at about 100 0C.The darkbrown sugar spots appeared was identified by comparingwith the standard with Rf value 1.77.

Acknowledgement

Authors are thankful to the Principal, St. Aloysius' College(Auto), Jabalpur for encouragement and providing researchfacilities.

References

Ali A, Ogbonna C, Rahman A (1998) Hydrolysis of certainNigerian starches using crude fungal amylase.Niger J Biotechnl 9 : 24-36

Asgher M, Asad M, Rehman S, Legge R (2007) Journal ofFood Engineering 79: 950-955

Bertland T, Frederic T, Robert N (2004) Production and partialcharacterization of a thermostable amylase fromAscomycetes yeast strain isolated from StrachySail.Mc Graw Hill Inc Newyork USA 20-152

Bhatnagar D, Cleveland T, Payne G (2000) Encyclopedia ofFood Microbiology, 72-79. London: Academic Press

Demot R, Verachtert H (1987) Purification andcharacterization of extracellular ? - amylase andglucoamylase from yeast Candida antarctica. CBS.6678. European J Biochem 164 : 643- 654

Ettalibi M, Baratti J (1988) Isolation and characterization ofan amylolytic yeast Candida edax. Mircen Jo ofApplied Microbiol and Biotechnology 4 : 193-202

Gashaw Mamo and Amare Gessesse (1999) Production andcharacterization of two raw starch digestingthermostable alpha amylases from thermophilicBacillus sp. Enz Microbial Technol 25 : 433- 438

Kelly C, Fogarthy W (1976) Microbial Alkaline enzymes.Process Biochemistry 11 : 3- 9

Mohamed, Lagzouli, Mennane Zakaria, Artounejjar Ali, WafaeSenhaji, Ouhssine Mohamed, Elyachioui Mohamed,Berny El-Hassan, Jadal Mohamed (2007)Optimization of growth and extracellularglucoamylase production by candida famataFig. 4. Starch hodrolysis by Aspergillus flavus

Fig. 5. Thin layer chromatography of amylaseproduction by Aspergillus flavus

1.77

9 9

isolate. African J pp. 2590-2595Oyeleke S, Oduwole A (2009) Production of amylase by

bacteria isolated from a cassava dumpsite inminna, Nigerstate, Nigeria. African J MicrobiologyRes 3(4): 143-146

Panse V, Sukhatme PK (1967) Statistical methods foragriculture workers.II ed.ICAR publication, NewDelhi 2 : 103-107

Rao MB, Tanksale AM, Gathe MS, Deshpande VV (1998)Molecular and biotechnological aspects ofmicrobial proteases. Micrbiol Mol Biol Rev62(3):597-635

Scheidegger KA, Payne GA (2003) Unlocking the secretsbehind secondary metabolism: a review ofAspergillus flavus from pathogenicity to functionalgenomics. J Toxicol 22 423-459

Sivaramakrishnan S, Gangadharan D, Nampoothiri KM,Soccol CR, Pandey A (2006) Alpha amylase frommicrobial sources: an over view on recentdevelopments. Food Technol Biotechnol 44 (2): 173-184

Smith BW, Roe JH (1949) A photometric method for thedetermination of alpha amylase in blood and urinewith the use of starch iodine color. J Biol Chem179:53-59

(Manuscript Receivd : 20.01.2015; Accepted : 25.03.2015)

100

Abstract

In India majority of the mushroom holdings are lackingadequate compost preparation, pasteurization and properenvironmental control facilit ies, which lead to thedevelopment of various diseases and pests sufficiently to alevel to cause considerable yield loss. It is therefore veryimportant for the mushroom growers that they should knowthe importance of diseases and microbial competitors togrow mushrooms successfully and profitably.

In the present study, pathogenic fungi, Aspergillus niger,Aspergillus flavus and Aspergillus fumigatus were pairedwith edible mushroom, Agaricus campestris, in all possiblecombination in dual culture experiments. In the total dualculture experimental set up 33.33% pairing shows deadlockon mycelial contact and 66.67% pairing shows deadlock ata distance.

Keywords:Aspergillus niger, Aspergillus flavus,Aspergillus fumigatus, Agaricus campestris, dual cultureexperiment

The major species of mushrooms grown in India are whitebutton mushroom (Agaricus bisporus), oyster mushroom(Pleurotus species) and paddy straw mushroom(Volvariella volvacae) of these, white button mushroomcontributes about 90% of the total product (Lidhoo andAgarwal 2006; Khader 2001).

In India majority of the mushroom holdings are

Interactions between Agaricus campestris the edible mushroomand Aspergillus spp. the pathogenic fungus through dual-culturetechnique

Neelima Raipuria and Femina Sobin*Department of Botany and MicrobiologyGovt. MH College of Home Science and Science for WomenRani Durgavati VishwavidyalayaJabalpur 482 004 (MP)*Department of Botany and MicrobiologySt. Aloysius' College (Auto.)Rani Durgavati VishwavidyalayaJabalpur 482 004 (MP)Email: j.m.femina@gmail.com

JNKVV Res J 49(1): 100-103 (2015)

lacking adequate compost preparation, pasteurizationand proper environmental control facilities, which lead tothe development of various diseases and pests sufficientlyto a level to cause considerable yield loss. It is thereforevery important for the mushroom growers that they shouldknow the importance of diseases and competitors andshould understand the importance of hygiene to growmushrooms successfully and profitably.

One of the most common and destructive diseasesin mushroom cultivation is the green mould which is mainlycaused by different species of Trichoderma, Penicilliumand Aspergillus (Sharma et al. 2007).

In the present study, pathogenic fungi, species ofAspergillus and edible mushrooms (Agaricus campestris)were paired in all possible combination in dual cultureexperiments to understand the antagonistic interactionsof these fungi have on each other in vitro.

Materials and methods

Collection of natural competitor

During the survey in the month of January (2012), it wasobserved that white button mushroom is widely prevalentin Jabalpur district under natural environmental conditions.Fungal infected mushroom compost from variousmushroom cultivation units of Jabalpur district, M P, Indiawere collected in sterile polythene bags and carried tothe laboratory for further studies.

101

Isolation and identification of the fungal pathogens

Pieces of mycelium taken from green mold affected areaof each sample were aseptically placed on 2% maltextract agar media (MEA) using a sterilized needle. Theplates were incubated at 27°C, until the fungal growthwas visible. The fungi were then sub cultured on freshPotato Dextrose Agar medium with streptomycin (Jayalaland Adikaram 2007).

Fungal isolates were identified on the basis of itscultural and morphological characteristics. The identityof the pathogens were also confirmed from National FungalCulture Collection of India, Agharkar Research Institute,Pune, Maharashtra, India.

Maintenance of pure culture of edible mushroom -Agaricus campestris

Pure cultures of the edible mushroom Agaricus campestrisin Lyophilized form was brought from the culture bankMicrobial Type Culture Collection, Institute of MicrobialTechnology, Chandigarh, India and was maintained in PDAslants and petriplates at 25°C until the growth was visible.

Dual culture experiment

Competitive interactions between edible mushroomAgaricus campestris and fungal pathogens namely -Aspergillus fumigatus, Aspergillus niger and Aspergillus

Table 1. Macroscopic characters of the isolated pathogenic fungi

Pathogenic Fungus Macroscopic charactersColour Texture Shape/ margin

A9 Aspergillus niger Black colour colony Powdery CircularA13 Aspergillus flavus White mycelium with green spore Powdery Circular with white borderA19 Aspergillus fumigatus White mycelium, greenish blue spore Cottony/ rough Circular narrow white

border

Fig 1. Pure culture of fungal pathogens

Fig 2. Micro photographs of isolated fungal pathogens

102

flavus were studied in dual-culture experiments on PDAin Petri dishes. In each dish, two 2- mm diameter ofmycelial disks, one from the mushroom colony and onefrom the fungal pathogen, were placed on the agar surface30 mm apart. Mushroom host was inoculated 3 daysbefore to the inoculation of pathogens. Three replicateswere prepared for each pairing. A rating scale with 3 types(A, B and C) and 4 sub-types (CA1, CB1, CA2 and CB2) ofreactions was used for each fungus, where: A, deadlock,mutual inhibition, in which neither organism was able toovergrow the other after mycelial contact; B, deadlock ata distance i.e. without mycelia contact; C, replacement,overgrowth without initial deadlock; CA1, partialreplacement after initial deadlock; CA2, completereplacement after initial deadlock; CB1, partial replacementafter initial deadlock at a distance; CB2, completereplacement after initial deadlock at a distance. Thefollowing score was assigned to each type or sub-type ofreaction: A=1; B=2; C=3; CA1=3.5; CB1=4; CA2=4.5; CB2=5.The antagonism index (AI) was calculated for each fungalspecies using the formula: AI = n × i where n= number

(frequency) of each type or sub-type of reaction; i=corresponding score (Badalyan et al. 2002; Badalyan etal. 2004).

Result and discussion

Three pathogenic fungi were isolated and named as A9,A13 and A19 from green mold infected areas in mushroomcompost (Fig 1) which were identified as Aspergillus niger,Aspergillus flavus and Aspergillus fumigatus, respectively(Fig 2 and Table 1).

Pure culture of edible mushroom host Agaricuscampestris (MTCC No. 972) was prepared in Petri platesand slants of PDA medium (Fig 3).

On performing competitive interaction studiesbetween host Agaricus campestris and fungal pathogenAspergillus niger, Aspergillus flavus and Aspergillusfumigatus in dual-culture experiments, following resultswere obtained (Fig 4 and Table 2).

Dual culture experiments of Aspergillus niger,Aspergillus flavus and Aspergillus fumigatus (A9, A13and A19) with the host edible mushroom Agaricuscampestris revealed that all the Aspergillus isolates andthe test host mushrooms were strongly antagonistic. Inthe total dual culture experimental set up 33.33% pairingshows deadlock on mycelial contact. Dense myceliumin the interaction zone was produced by both, mushroomand Aspergillus niger, when there was deadlock,suggesting that some form of recognition response wasinvolved. Such result was also obtained by Rayner andWebber (1984) in their interaction studies with differenthost and mycoparasite. 66.67% pairing shows deadlock

Fig 4. Interaction between the host Agaricus campestris and the fungal pathogens namely Aspergillus niger,Aspergillus flavus and Aspergillus fumigatus in dual culture experiment.

Fig 3. Pure culture of edible mushroom Agaricuscampestris

103

at a distance which suggests that fungal pathogensproduce volatile/non-volatile metabolite(s) active againstthe mushroom (Rayner and Webber 1984).

On the basis of calculated AI values Agaricuscampestris was found to be less active against testedAspergillus isolates. AI is relatively constant for eachspecies and can be used for bio-ecologicalcharacterization. Establishing the AI is the first step inscreening for physiological and biological activity(Badalyan 1998).

This study reveals the growing interest ofmushroom cultivation and its scope in Jabalpur district ofMadhya Pradesh, India. Cases of green mould werereported, isolation and identification of the fungal pathogenindicates presence and prevalence of Aspergillus sp. inJabalpur area.

Acknowledgement

Second author (Femina Sobin) is thankful to UGC, CentralRegional Office, Bhopal for financial assistance receivedfor the minor research project. The authors are alsothankful to National Fungal Culture Collection of India(NFCCI), Agharkar Research Institute, Pune, AutonomousGrant-in-Aid Institute under the Department of Scienceand Technology, Govt. of India for identification of isolatesof fungal pathogen and Microbial Type Culture Collection,Chandigarh for providing pure cultures of mushroom.

References

Badalyan SM (1998) Biological properties of certainmacroscopic basidiomycetes (Morphology, ecologyand physiological activities). PhD Thesis, inBiological Sciences, Yerevan University Armenia

Badalyan SM, Innocenti G, Garibyan NG (2002) Antagonisticactivity of xylotrophic mushrooms againstpathogenic fungi of cereals in dual culture.Phytopathol Mediterr 41: 200-225

Badalyan SM, Innocenti G, Garibyan NG (2004) Interactionsbetween xylotrophic mushrooms and mycoparasiticfungi in dual-culture experiments. PhytopatholMediterr 43: 44-48

Jayalal RGU, Adikaram NKB (2007) Influence of Trichodermaharzianum metabolites on the development ofgreen mold disease in the oyster mushroom. Cey JSci (Bio Sci) 36 (1): 53-60

Khader V (2001) Text Book of Food Science and Technology,Directorate of Information and Publication, ICAR,Krishi Anusandhan Bhavan, Pusa, New Delhi

Lidhoo CK, Agrawal YC (2006) Hot oven drying characteristicsof button mushroom safe drying temperature.Mushroom Res 15 (1): 59-62

Rayner ADM, Webber JF (1984) Interspecific mycelialinteractions: an overview. In: The Ecology andPhysiology of the Fungal Mycelium (D. H. Jennings& A.D.M. Rayner, eds.), pp. 383-417, CambridgeUniversity Press: Cambridge U K

Sharma SR, Kumar S, Sharma VP (2007) Diseases andcompetitor moulds of mushrooms and theirmanagement, National Research Center forMushroom (Indian Council of Agricultural Research)Chambaghat, Solan 173-213 (HP)

Table 2. Results of dual culture experiment of ediblemushroom Agaricus campestris with fungalpathogensAspergillus niger, Aspergillus flavus andAspergillus fumigatus (A9, A13 and A19)

Pathogenic fungi Edible MushroomAgaricus campestris

[5]A9 A

(Aspergillus niger gr.)[1]

A13 B(Aspergillus flavus gr.)

[2]A19 B

(Aspergillus fumigatus Fresen.) [2]1antagonism index values are being represented in squarebrackets.

(Manuscript Receivd :16.01.2015; Accepted :20.02.2015)

104

General zero inflated models with reference to poisson distribution H.L. Sharma, Arun Jhajharia and Siddarth Nayak College of Agriculture Jawaharlal Nehru Krishi Vishwavidyalaya, Jabalpur 482 004 (MP) Email drhlsharma_jnkvv@rediffmail.com Abstract

In the present paper, a study of the general zero inflated models has been provided. A general expressions for the estimation of their parameters i.e., proportion of zero’th cell, method of moments, and maximum likelihood has also been derived. A zero inflated Poisson distribution with three examples has been added as an application of the paper.

Keyword: Poisson distribution, zero inflated models

Poisson distribution is often used as a standard probability model for data having counts. However, such data sets quite in general are not well fitted by a Poisson model, because they consist of more zero counts than are compatible with the Poisson model. For these situations, a zero inflated Poisson model is generally proposed. Ghosh et.al. (2006) has rightly pointed out that when some production processes are in a neat perfect state, zero defects will occur with a high probability. However, random changes in the manufacturing environment can lead the process to an imperfect state, producing items with defects. The production process can move randomly. For this type of production process many items will be produced with zero defects, and this excess might be better attributed by a zero inflated Poisson model than a Poisson model. Most of the current researchers assume that the imperfect state is Poisson distributed, which excludes some other useful distributions, such as negative binomial distribution. The aim of the present paper is to develop the expressions in relation to general zero-inflated models by assuming that the imperfect state is distributed among a larger class of distributions. An interesting class of distributions consists of continuous distributions with the common support [0; +∞]. Another class of distributions is with the support of the non-negative integers.

Assume that the perfect state 0X is 0 with

probability 1 and the imperfect state 1X is a random

variable taking non-negative integers with the following probability density function

P ( 1X = k) = g (k, λ) … (1.1)

For k = 0, 1..., where λ = ).........,( 821 ′λλλ is

an unknown parameter vector in an open subset D of s-

dimensional Euclidean space sR . The 1X 's distribution

is called the distribution of the imperfect state. Consider

the mixture of 0X and 1X with the distribution

Bernoulli (ω) where 0 < ω ≤ 1. Assume that the probability mass function of the mixture is ;

⎟⎠⎞⎜

⎝⎛

⎟⎠⎞⎜

⎝⎛ ω

λ,kf .

Note that we exclude the case that ω = 0 since that is a trivial case and usually is not interesting. However, we would like to include the case that ω = 1, in which only the imperfect state exists. For example, we might be interested in testing that ω = 1, which tests whether the data are from a zero-inflated model or from the distribution of the imperfect state.

Define

⎟⎠⎞⎜

⎝⎛=

ω

λθ … (1.2)

and

.)1,0( D×=Θ

JNKVV Res J 49(1): 104-109 (2015) 

105

( ) ( )[ ]( ) ωλωω

λω

k

g

gknl ++−

−−−=∂

∂,01

,01log

( )( )

( )( )

( )∑= ∂

∂+∂

∂+−−−=

∂∂ t

i

I

I

Yg

Yg

g

g

knl

1

,

,

1,0

,01

log

λλ

λλλ

λωωω

λ

( ) ( )[ ] ( ) ( )[ ]( ) 222

2

],01[

,011,01log

p

t

pgp

ggtn

p

l −+−

+−×−×−−−=∂

∂λ

λλ

We write ⎟⎠⎞⎜

⎝⎛

⎟⎠⎞⎜

⎝⎛ ω

λ,kf

as f (k, θ) where Θ∈θ .

It is easy to propose that the general zero-inflated models are

( )( )

( ) ( )

⎪⎩

⎪⎨⎧

==+−

=

0,1

,....2,1,

,kforog

kforkg

kfλωω

λω

θ

... (1.3) & (1.4)

It is obvious that zero-inflated Poisson distributions in Lambert (1992) and zero-altered models in Heilbron (1994) are generalized zero-inflated models.

Methods of Estimation

Estimation of parameters by method of proportion of the zeroth cell In this method we equate the observed proportion

of thzero cell and observed mean to their corresponding theoretical values in order to obtain the estimates of the parameters which are given below.

),0(100 λωω g

N

nP +−== … (2.1)

),(/

1 λωμ iyg= … (2.2)

where 0n is the number of observation of thzero cell

and N is the total number of observations, ω and λ are the parameters to be estimated. Method of moments In this method we equate the observed mean and variance to their corresponding theoretical values in order to estimate the parameters which are given below. )],([/

1 λωμ iygE= … (2.3)

2/

2 )}],({}),{([ λλωμ ii ygEygV += … (2.4)

The two parameters can be estimated with the help of above equations. Method of Maximum-Likelihood Consider a sample consisting of N observations of the random variable X whose probability function can be given by (1.3) & (1.4) above. The Likelihood function can be written as:

( ) ( )[ ] ( ).,),(1,1

λωλωωλω I

k

i

kkn Ygogl ∏=− ××+−= … (2.5)

Then the log-likelihood is

( ) [ ] ( )λωλωω ,loglog),(1loglog1

i

k

i

Ygkogknl ∑=

+++−−= .

It is easy to derive the following

… (2.6)

… (2.7)

…(2.8)

( ) ( )[ ]( ) 22

2

],01[

,01

ωλωωλ k

g

gkn −+−

−−−=

⎥⎦⎤

⎢⎣⎡

′∂∂

∂∂=

′∂∂∂

λωλωll loglog2

( )( )

( )λ

λλωω

ωω ′∂

∂×⎥⎦

⎤⎢⎣

⎡+−−

∂∂= ,

,1

og

og

kn

( ) ( )( )λ

λλωω

ωω ′∂

∂×⎥⎦

⎤⎢⎣

⎡+−∂

∂×−= ,

,1

og

ogkn

( )

( )λ

λλωω ′∂

∂+−

−= ,

],1[ 2

og

og

kn

… (2.9)

( ) ( )[ ]( ) ⎥

⎦

⎤⎢⎣

⎡+−

−−−∂

=⎥⎦⎤

⎢⎣⎡

∂∂

∂∂=

∂∂∂

λωωλ

λλ

ωλωλ ,01

,01loglog2

g

gknll

( ) ( )( )⎥⎦

⎤⎢⎣

⎡+−

−∂∂−=

λωωλ

λ ,01

,01

g

gkn

( )[ ]( )λ

λλωω ∂

∂+−

−= ,0

,01 2

g

g

kn

106

( )( )

( )( )

( ) ( )

( )( )

( )( ) ( )∑

= ⎥⎥⎦

⎤

⎢⎢⎣

⎡′∂

∂∂

∂−′∂∂

∂+

⎥⎦

⎤⎢⎣

⎡′∂

∂∂

∂+−

−′∂

∂+−−=

t

i

ii

i

i

i

Ygyg

Yg

Yg

Yg

gg

g

pg

g

kn

1 ,

2 ,,1,

,

1

.,0,0

,01

,0

,01

λλ

λλ

λλλλ

λ

λλ

λλ

λωωλλ

λωωω

∏=

−−−

⎟⎟⎠

⎞⎜⎜⎝

⎛+−=

R

k

nNkn

k

eeL

1

0

0

!)1(

λωωωλ

λ

( ) ( )∑=

−−

⎟⎟⎠

⎞⎜⎜⎝

⎛−++−=

R

k

k

k

enNenLogL

100 !

log1logλωωω

λλ

( ) ( )∑ ∑=

−−

⎟⎟⎠

⎞⎜⎜⎝

⎛−−++−=

R

k

kR

k

enNenLogL

100 !

log1logλωωωω

λλ

( )∑=

−

− −++−−−=

∂∂ R

k

nN

e

enLogL

1

00

1

)1(

ωωωω λ

λ

)1(ˆ 0

λω −−−=

eN

nN

( ) 0)(

1 1 1

00

0 =−+−−+−×−=

∂∂ ∑ ∑

= =−

− R

k

R

k

nNknN

e

enLogL

λωωω

λ λ

λ

( )[ ]( )λ

λλωω

ω∂

∂+−

−= ,0

,01

)(2

g

g

kn

⎥⎦⎤

⎢⎣⎡

′∂∂

∂∂=

∂∂

λλλll loglog

2

2

( )( )

( )( )

( )∑=

⎥⎦

⎤⎢⎣

⎡′∂

∂∂∂+⎥

⎦

⎤⎢⎣

⎡′∂

∂+−−

∂∂=

t

i

i

i

Yg

Yg

g

gi

kn

1

,

,

1,0

,0 λλ

λλλλ

λωωω

λ

.

( ) ( )( )

( )( )∑

=⎥⎦

⎤⎢⎣

⎡′∂

∂∂∂+⎥

⎦

⎤⎢⎣

⎡′∂

∂+−∂

∂×−=t

i

i

i

Yg

Yg

g

gkn

1

,

,

1,0

,01

1

λλ

λλλλ

λωωλω

…(2.10)

It is obvious to take

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

∂∂

∂∂∂

′∂∂∂

∂∂

2

22

2

2

2

loglog

loglog

λωλ

λωωll

ll

<0 … (2.11)

Is equivalent to

0loglogloglog 2

2

22

2

2

<′∂∂

∂×∂

∂×∂∂

∂−∂

∂λωλωλω

llll … (2.12)

Zero inflated Poisson model

λωω −+−= eP 10

!k

eP

k

k

λω λ−

=

Method of proportion of zeroth cell

λωω −+−= eP 10 ...(2.13)

ωλ=X

Method of moments

ωλμ =/1 … (2.14)

222/2 )( λωλλωμ −+=

Method of maximum likelihood

...(2.15) ...(2.16) From the equation (2.15) and (2.16) the estimated values of ω and λ can be obtained in few iterations.

Examples Here we provide some illustrative examples considering zero inflated Poisson model based on three sets of data.

107

Table 1. Distribution of observed and expected number of leaves according to the number of insects Zero inflated Poisson Model No. of insects

(x) Observed frequency

(f) MPZC MM MLE 0 1 2 3 4 5

33 12 6 3 1 1

33.02 11.06 7.30

62.4⎪⎭

⎪⎬⎫

32.98 11.21 7.29

52.4⎪⎭

⎪⎬⎫

33.00 11.40 7.24

36.4⎪⎭

⎪⎬⎫

56 56.0 56.0 56.0 Estimation of parameters ω̂ = 0.56

λ̂ = 1.32

ω̂ = 0.565

λ̂ =1.30

ω̂ = 0.571

λ̂ = 1.27 2χ

d. f.

0.342 2

0.335 2

0.334 2

In this distribution the values of ω̂ and λ̂ were found to be 0.56, 0.565 & 0.571 and 1.32, 1.30 & 1.27 respectively. It reveals that the numbers of plants are exposed to the insects per leaf. The MLE provides a good fitting of the zero inflated Poisson distribution. In order to apply a chi-square test of goodness of fit, some last cells were grouped together. The values of chi-square for this distribution were found to be 0.342,

0.335 and 0.334. Looking to the value of minimum 2χ ,

MLE provides a good fitting. It indicates that almost 44% plants are such who are not exposed to the risk of insects (Table 1). For visual displayed the graphical representation of zero inflated Poisson distribution using these methods was given in Fig. 1.

Fig. 1. Distribution of observed and expected number of leaves according to the number of insects

Table 2. Distribution of observed and expected number of accidents per woman (Shankaran 1970)

Zero inflated Poisson distribution No. of accidents (x)

Observed frequency (f) MPZC MM MLE

0 1 2 3 4 5

447 132 42 21 3 2

449.85 120.87 54.99 16.68

61.4⎭⎬⎫

448.72 122.96 54.71 16.24

37.4⎭⎬⎫

447.08 124.69 54.86 16.09

28.4⎭⎬⎫

647 647.0 647.0 647.0 Estimation of parameters ω̂ = 0.51

λ̂ = 0.91

ω̂ = 0.52

λ̂ = 0.89

ω̂ = 0.528

λ̂ = 0.88 2χ

d. f.

5.26 2

5.11 2

5.062 2

108

In this distribution the values of ω̂ and λ̂ are found to be 0.51, 0.52 & 0.528 and 0.91, 0.89, 0.88 respectively. It reveals that the numbers of accidents are exposed to women. The MLE provides a good fitting of the zero-inflated Poisson distribution. In order to apply a chi-square test of goodness of fit, some last cells are grouped together. The values of chi-square for this distribution are found to be 5.26, 5.11 and 5.062.

Looking to the value of the 2χ of MLE provides a good

fitting. It indicates that almost 50 % women are such who are not exposed to the risk of accidents (Table 2). For visual displayed the graphical representation of zero-inflated Poisson distribution using these methods is given in Figure 2.

Fig. 2. Distribution of observed and expected number of accidents per woman (Shankaran 1970)

Table 3. Distribution of observed and expected number of errors per group

Zero inflated Poisson distribution No. of errors per group (x)

Observed frequency (f) MPZC MM MLE

0 1 2 3 4

35 11 8 4 2

37.69 8.21 7.19

91.6⎭⎬⎫

35.42 10.26 7.95

37.6⎭⎬⎫

35.02 10.89 8.06

03.6⎭⎬⎫

60 60.0 60.0 60.0 Estimation of parameters ω̂ = 0.45

λ̂ =1.75

ω̂ = 0.52

λ̂ = 1.55

ω̂ = 0.539

λ̂ = 1.48 2χ

d. f.

1.35127 1

0.08016 1

0.0017 1

In this distribution the values of ω̂ and λ̂ are found to

be 0.45, 0.52 & 0.539 and 1.75, 1.55 & 1.48

respectively. It reveals that the numbers of errors per

group. The MLE provides a good fitting of the zero-

inflated Poisson distribution. In order to apply a chi-

square test of goodness of fit, some last cells are

grouped together. The values of chi-square for this

distribution are found to be 5.26, 5.11 and 5.062.

Looking to the value of the 2χ of MLE provides a good

fitting. It indicates that almost 50 % groups are such

who are not exposed to the risk of errors (Table 3). For

visual displayed the graphical representation of zero-

inflated Poisson distribution using these methods was

given in Figure 3.

Fig 3. Distribution of observed and expected number of errors per group

110

bl orZeku isij esa] ,d lkekU; 'kwU; Qwyk gqvk izfr:iksa dks v/;;u iznku fd;k x;k gSS A muds izkpykdksa ds vkdyu ds fy, ,d tsls fd ’kwU; dksf’kdk dk vuqikr] vk/kw.kZ fof/k ,oa vf/kdre laHkkfork dk ,d lkekU; O;atd O;qriUu fd;k x;k gS A ,d 'kwU; Qqyk gqvk Ioklksa cVu dks isij ds vuqiz;ksx ds fy,] rhu mnkgj.kksa ds lkFk tksM+k x;k gS A

References

Ghosh SK, Mukhopadhyay P, Lu JC (2006) Bayesian analysis of zero-inflated regression models. J Stat Plan Infer 136 : 1360-1375

Heilbron, D. (1994) Zero-altered and other regression models for count data with added zeros. Biometrical J 36: 531-547

Lambert D (1992) Zero-inflated Poisson regression with an application to defects in manufacturing Techno-metrics 34: 1-14

Shankaran M (1970) The discrete Poisson Lindley distribution, Biometrics 26:145-146

(Receivd : 20.01.2014; Accepted : 30.03.2015)

110

Abstract

In the concept of fuzzy β-continuous maps on fuzzy topologicalspaces, I have established equivalent conditions for a mapfrom one fuzzy topological space to another to be fuzzy β-continuous. Some significant properties of fuzzy β-continuousmaps have also been established.

Keywords: Fuzzy topology, Fuzzy α-open, Fuzzy semi-open, Fuzzy pre-open, Fuzzy semi-continuous, Fuzzypre-continuous, Fuzzy α-continuous, fuzzy β-continuousmaps.

The concept of Fuzzy sets was introduced by Zadeh(1965). Fuzzy sets are the generalization of abstractsets or crisp sets. The concepts in General topologyhas been generalized in fuzzy settings and the theoryof Fuzzy topology has been developed. The notion ofFuzzy topological space was introduced by Chang(1968). Thakur and Singh (1998) have defined fuzzysemi-precontinuous maps. In this paper I have studiedfuzzy β-continuous maps also known as fuzzy semi-pre continuous maps and investigated necessary andsufficient conditions for a map to be fuzzy β-continuous.Further I have established some important propertiesof fuzzy β-continuous maps.

Material and methods

Let X be a universal crisp set. A fuzzy set on X is amap λ:X→I, where I = [0,1]. Suppose {λj: X→I}j∈J

, J isan index set, is any family of fuzzy sets on X. Thenunion of fuzzy sets λj, j∈J

is a fuzzy set Uj∈J

λj:X→I,defined as (Uj∈J

λj) (x) = sup {λj(x) : j∈J }, ∀ x ∈ X. Theintersection of the fuzzy sets λj, j∈J

is a fuzzy j∈J

λj:

Fuzzy βββββ-continuous mappings

M. ShuklaDepartment of Applied MathematicsGyan Ganga Institute of Technology & SciencesJabalpur 482 011 (MP)Email:shukla.madhulika07@gmail.com

X→I, defined as ( j∈J λj) (x) = inf {λj(x) : j∈J}, ∀ x ∈ X. If

λ, μ:X→I are two fuzzy sets on X then λ is said to besubset of μ if λ(x)≤μ(x), ∀ x∈X. The complement of afuzzy set λ is denoted by λc and is defined as λc (x) =1-λ(x), ∀ x ∈ X.

Let τ be a collection of fuzzy sets on X. Then τ is said tobe a Fuzzy Topology on X if it satisfies followingconditions:

• The fuzzy sets 0 and 1 are in τ , where 0,1: X→I,are defined as 0(x)=0 and 1(x)=1, ∀ x ∈ X.

• If λ, μ∈τ then λ μ∈τ.

• If {λj},j∈J is any family of fuzzy sets on X and λj,∈ τ, ∀ j∈J then j∈J λj∈ τ.

The pair (X,τ ) is called a fuzzy topological space. Weusually denote fuzzy topological space (X,τ ) by X.

The members of the collection τ are called fuzzyopen sets in the space X. A fuzzy set λ:X→I is called afuzzy closed set in X provided its complement fuzzy setλc is a fuzzy open set in X. In view of the definition offuzzy topological space we note that arbitrary union offuzzy open sets is a fuzzy open set and finite intersectionof fuzzy open sets is a fuzzy open set. As fuzzy closedsets are complement of fuzzy open sets it follows thatarbitrary intersection of fuzzy closed sets is a fuzzyclosed set and finite union of fuzzy closed sets is afuzzy closed set. In a fuzzy topological space X, closureof a fuzzy set λ is denoted by cl(λ) and is defined to bethe intersection of all fuzzy closed sets in X containingλ. The interior of a fuzzy set λ is denoted by int(λ) andis defined to be the union of all fuzzy open sets in X

JNKVV Res J 49(1): 110-114 (2015)

111

contained in λ.

It follows from the property of fuzzy topology thatcl(λ) is a fuzzy closed set in X such that int(λ) is a fuzzyopen set in X and int(λ)≤ λ ≤ cl(λ). We observe that afuzzy set λ in X is fuzzy open iff int(λ)=λ and λ is fuzzyclosed iff cl(λ)=λ. Further, int(1-λ)=1-cl(λ) and cl(1-λ)=1-int(λ). If λj:X→I,j∈J is any arbitrary collection of fuzzysets in X, then int( j∈J λj) ≥ j∈J int(λj) and cl( j∈J λj) ≥

j∈J cl(λj).

Definition 2.1: Let X be a fuzzy topological space. Afuzzy set T in the space X is called:

• Fuzzy semi-open [1] if λ≤ cl(int(λ)), (Azad 1981).

• Fuzzy pre-open [3] if λ≤ int(cl(λ)), (Shahana, 1991).

• Fuzzy α-open [3] if λ≤ int(cl(int(λ))), (Shahana, 1991).

• Fuzzy β-open if λ≤ cl(int(cl(λ))) (Thakur & Singh, 1998).

Remark

• Every fuzzy open set is fuzzy α-open.

• Every fuzzy α-open set is fuzzy semi open and fuzzypre-open both.

• Every fuzzy semi open or fuzzy pre-open set is fuzzyβ-open. But the separation converses may not betrue.

The complement of a fuzzy semi-open (resp.fuzzy pre-open, fuzzy α-open, fuzzy β-open) set is calledfuzzy semi-closed (resp. fuzzy pre-closed, fuzzy α-closed, fuzzy β-closed). The β-closure and β-interior ofa fuzzy set λ are defined as follows:

βcl(λ)=inf{μ:μ≤λ and μ is fuzzy β-closed set in X},

and βint(λ)=sup{μ:μ≤λ and μ is fuzzy β-open set in X}.

Definition 2.2: Let X and Y be fuzzy topological spacesand f: X→Y be a map. Then the map ƒ is:

• Fuzzy continuous if ƒ -1(λ) is fuzzy open set in X foreach fuzzy open set λ of Y (Chang, 1968).

• Fuzzy semi-continuous if ƒ -1(λ) is fuzzy semi-openin X for every fuzzy open set λ of Y(Chang, 1968).

• Fuzzy pre-continuous if ƒ -1 (λ) is fuzzy pre-open inX for every fuzzy open set λ of Y (Shahana, 1991).

• Fuzzy α-continuous if ƒ -1 (λ) is fuzzy α-open in Xfor every fuzzy open set λ of Y (Shahana, 1991).

Remark:

• Every fuzzy continuous map is fuzzy α-continuousmap is fuzzy α-continuous.

• Every fuzzy α-continuous map is fuzzy semi-continuous and fuzzy pre-continuous both.

Results and discussion

Definition 3.1: Let X and Y be fuzzy topological spacesand ƒ : X→Y be a map. Then the map ƒ is said to befuzzy β-continuous if for each fuzzy open set λ in Y, ƒ -

1(λ) is fuzzy β-open set in X.

Since each fuzzy semi-open or each fuzzy pre-open set is fuzzy β-open, it follows that each fuzzy semi-continuous or each fuzzy pre-continuous map is fuzzyβ-continuous. Hence every fuzzy continuous (fuzzy α-continuous) map is also fuzzy β-continuous. Howeverseparate converse may not be true. We have followingexample.

Example 3.2: Let X = {x1,x2}, Y = {y1,y2} and μ, λ befuzzy sets in X and Y, defined as μ(x1) = 0.5, μ(x2) =0.2, λ(y1) = 0.4, and λ(y2) = 0.3. Let τ = {0, μ, 1} and τ ' ={0,λ,1} be fuzzy topologies on sets X and Y respectively.We see that map ƒ : X→Y defined as ƒ (xi)=yi, =1,2 isfuzzy β-continuous. Further we see that the map ƒ isneither fuzzy semi-continuous nor fuzzy pre-continuous.

In the following result we have obtained variousequivalent conditions for a map ƒ : X→Y, where X andY are fuzzy topological spaces to be fuzzy β-continuous.

Theorem 3.3: Let X and Y be fuzzy topological spacesand f : X→Y be a map. Then following conditions areequivalent:

• ƒ is fuzzy β-continuous.

• For each fuzzy point pβx in X and each fuzzy open

set λ in Y containing ƒ (pβx), there exists a fuzzy

β-open set μ in X containing pβx such that ƒ (μ)≤λ.

• For each fuzzy closed set λ in Y, ƒ −1(λ) is fuzzyβ-closed set in X.

112

• For each fuzzy set μ in X, ƒ (β- cl(μ)) ≤ cl(ƒ (μ)).

• For each fuzzy set λ inY, β-cl(ƒ -1(λ)) ≤ ƒ-1 (cl(λ)).

• For each fuzzy set λ inY, ƒ -1 (int(λ)) ≤ β-int(ƒ -1(λ)).

• For each fuzzy set λ inY int (cl(int(ƒ -1(λ)))) ≤ ƒ-1

(cl(λ)).

• For each fuzzy set μ in X, ƒ (int(cl(int(μ))))≤cl(ƒ (μ)).

Proof: (i)⇒(ii). Let ƒ : X→Y be a fuzzy β-continuous map.Let px

r, where x ∈ X and 0<r ≤ 1, be a fuzzy point in Xand let λ be a fuzzy open set in Y containing the fuzzypoint ƒ (px

r). Since, ƒ (pxr)(ƒ (x))=r≤λ(ƒ (x)), we have β≤(ƒ -1

(λ))(x), i.e. μ = ƒ -1(λ) contains the fuzzy point pxr.

Moreover, since ƒ is fuzzy β-continuous map, so μ isfuzzy β-open set in X, containing the fuzzy point px

r andƒ (μ)≤λ.

(ii)⇒(i). Let λ be a fuzzy open set in Y. For x∈X and 0<r≤1, let px

r be a fuzzy point in ƒ -1(λ). Then λ containsƒ (px

r) and so by given condition (ii), there exists a fuzzyβ-open set μ in X containing the fuzzy point px

r and ƒ (μ)≤λ.This implies, μ≤cl(int(clμ))) and hence cl(int(cl(ƒ -1(λ))))contains the fuzzy point px

r. Thus each fuzzy point of ƒ -

1(λ) is also a fuzzy point of cl(int(cl(ƒ -1 (λ)))). This showsthat ƒ -1(λ)≤cl(int(cl(ƒ -1(λ)))), i.e., ƒ -1(λ) is a fuzzy β-openset in X. Thus ƒ :X→Y is fuzzy β-continuous.

(i)⇒(iii). Let λ be a fuzzy closed set in Y. Then λc= 1-λ isfuzzy open set in Y. Since ƒ :X→Y is fuzzy β-continuous,ƒ -1(λc) = ƒ -1(1−λ) is fuzzy β-open set in X. This implies,ƒ -1(λ) = ƒ -1(1−λc) =1- ƒ -1(λc) is fuzzy β-closed set in X.

(iii)⇒(i). Let λ be a fuzzy open set in Y. Then λc=1-λ isfuzzy closed set in Y. Therefore by given condition (iii),ƒ -1(λc) = ƒ -1(1−λ) is fuzzy β-closed set in X.

Hence ƒ -1(λ) = ƒ -1(1−λc) = 1- ƒ -1(λc) is fuzzy β-open set inX. Thus ƒ :X→Y is fuzzy β-continuous.

(iii)⇒(iv). Let μ be a fuzzy set in X. Since μ≤ƒ-1 (cl(ƒ(μ)) wehave μ≤ƒ-1 (cl(ƒ(μ))). Now cl(ƒ (μ)) is a fuzzy closed set inY. Hence by given condition (iii), ƒ -1 (cl(ƒ(μ))) is fuzzy β-closed set in X containing μ. Since β-cl(μ) is the smallestfuzzy β-closed set containing μ, it follows that β-cl(μ)≤ƒ-1

(cl(ƒ(μ)). This implies ƒ (β-cl(μ))≤ƒ-1 cl(ƒ(μ)).

(iv)⇒(iii). Let λ be a fuzzy closed set in Y. Then by givencondition (iv), we have ƒ (β-cl(ƒ -1(λ))≤ cl(ƒ (ƒ -1(λ))) ≤ cl(λ)=λ. This implies, β-cl(ƒ -1(λ))≤ ƒ-1(λ). Since ƒ -1(λ) ≤ β−cl(λ) -cl (ƒ -1(λ)), we deduce that ƒ -1(λ)= β−cl(ƒ -1(λ)). As β−cl(ƒ -

1(λ)) is fuzzy β-closed set in X, it follows that ƒ -1(λ) isfuzzy β-closed set in X.

(iv)⇒(v). Let λ be a fuzzy set in Y. Then by given condition(iv), ƒ (β-cl(ƒ -1(λ))) ≤ cl(ƒ (ƒ -1(λ)))≤ cl(λ). This implies β-cl(ƒ -

1(λ)) ≤ ƒ -1 (cl(λ)).

(v)⇒(iv). Let μ be a fuzzy set in X. Then by given condition(v), β-cl(ƒ −1(ƒ (μ))) ≤ ƒ -1(cl(ƒ(μ))). This implies, β-cl(μ) ≤ ƒ -

1(cl(ƒ(μ))), and hence ƒ (β-cl(μ)) ≤ cl(ƒ(μ)).

(i)⇒(vi). Let λ be a fuzzy set in Y. Since int(λ) is a fuzzyopen set in Y, by given condition (i), ƒ -1(int(λ)) is fuzzyβ-open set in X. Hence we have ƒ -1(int(λ)) = β−int(ƒ -1

(int(λ))). Since, β-int(ƒ -1 (int(λ))) ≤ β-int(ƒ -1 (λ)), we findthat ƒ -1 (int(λ)) ≤ β-int(ƒ -1 (λ)).

(vi)⇒(i). Let λ be a fuzzy open set in Y. Then we haveint(λ) = λ. Therefore by given condition (vi), ƒ -1 (λ)) = ƒ -1

(int(λ)) ≤ β-int(ƒ -1 (λ)), i.e. ƒ -1 (λ)) ≤ β-int(ƒ -1 (λ)). Since β-int(ƒ -1 (λ)) ≤ ƒ-1 (λ), we get that ƒ -1 (int(λ) = β-int(ƒ -1 (λ)).Hence ƒ -1 (λ) is fuzzy β-open set in X. Thus f:X→Y is afuzzy β-continuous map.

(iii)⇒(vii). Let λ be a fuzzy set in Y. Then cl(λ) is fuzzyclosed set in Y. From given condition (iii), ƒ -1 (cl(λ)) isfuzzy β-closed set in X. This implies, ƒ -1 (cl(λ)) ≥ int(cl(int(ƒ -1 (cl(λ))))) ≥ int(cl(int (ƒ -1 (λ)))), i.e., int(cl(int (ƒ -1 (λ))))≤ ƒ-1 (cl(λ)).

(vii)⇒(viii). Let μ be a fuzzy set in X. Then f(μ) is afuzzy set in Y. From given condition (vii) we have, ƒ -1

(cl(ƒ (μ)) ≥ int(cl(int (ƒ -1 (ƒ (μ))))) ≥ int(cl(int (μ))).Thisimplies, cl(ƒ (μ)) ≥ ƒ (ƒ -1 (cl(ƒ (μ)))) ≥ ƒ(int(cl(int (μ)))). Thuswe have, ƒ (int(cl(int(μ)))) ≤ cl(ƒ (μ)).

(viii)⇒(iii). Let λ be a fuzzy closed set in Y. Then ƒ -1 (λ)is a fuzzy set in X. From given condition (viii) we have,ƒ (int(cl(int(ƒ −1(λ))))) ≤ cl(ƒ (ƒ −1(λ))) ≤ cl (λ) = λ. Thus ƒ −1(λ)is a fuzzy β-closed set in X.

Theorem 3.4: Let X and Y be fuzzy topological spacesand ƒ : X→Y be a bijective map. Then f is fuzzy β-continuous iff for each fuzzy set μ in X, int(ƒ (μ) ) ≤ ƒ (β-int(μ)).

Proof: Let ƒ : X→Y be a bijective map. Suppose f is fuzzyβ-continuous. If μ is a fuzzy set in X then ƒ (μ) is fuzzyset in Y. Since f is fuzzy β-continuous, from Theorem3.3, we have, ƒ -1 (int(ƒ (μ))) ≤ β-int(ƒ -1 (int(ƒ (μ))). Since ƒis one-one, β-int(ƒ -1 (ƒ (μ)) = β-int(μ). This shows that ƒ -

1 (int(ƒ (μ))) ≤ β-int(μ). Further since ƒ is onto we have,int(ƒ (μ)) = ƒ (ƒ -1(int(ƒ (μ)))) ≤ ƒ (β-int(μ)). Thus int(ƒ (μ)) ≤ƒ (β-int(μ)).

Conversely let λ be a fuzzy open set in Y. Then int(λ)=λ. Now ƒ -1 (λ) is a fuzzy set in X, from hypothesis, ƒ (β-int(ƒ -1 (μ)) ≥ int(ƒ (ƒ -1(λ)). Since ƒ is onto, int(ƒ (ƒ -1(λ)) = int(λ)

113

= λ. Therefore ƒ (β-int(f-1 (λ))) ≥ λ. Further since ƒ is one-one, β-int(ƒ -1(λ)) = ƒ -1(ƒ(β-int(ƒ -1(λ))) ≥ ƒ-1(λ). As β-int(ƒ -1(λ))≤ ƒ-1(λ), we deduce that ƒ -1(λ) = β-int(ƒ -1(λ)). Thus ƒ -1(λ) isa fuzzy β-open set in X. Hence ƒ :X→Y is fuzzy β-continuous.

Theorem 3.5: Let X and Y be fuzzy topological spacesand ƒ :X→Y be a map. Let B be a basis for the fuzzytopological space Y. Then ƒ is fuzzy β-continuous ifffor each fuzzy basic open set μ in B, ƒ -1(μ) is fuzzy β-open set in X.

Proof: Let ƒ :X→Y be a fuzzy β-continuous map. Sinceevery basis element in B is a fuzzy open set in Y, itfollows that for each fuzzy basic open set λ in B,ƒ -1(λ)is fuzzy β-open set in X. Conversely let μ be a fuzzyopen set in Y. Then there exist μj ∈ B, j∈J such that μ =μ = μj ∈ μj, where μj's are fuzzy basic open sets in Y.We have ƒ -1 (μ)= ƒ -1(∪j∈J μj)= ∪j∈Jƒ

-1(μj). Now byhypothesis, ƒ -1(μj) is fuzzy β-open set in X for each j∈J.As arbitrary union of fuzzy β-open sets is fuzzy β-open,it follows that ƒ -1(μ) is fuzzy β-open set in X. Henceƒ :X→Y is fuzzy β-continuous.

Properties of Fuzzy continuous mapings

Theorem 4.1: Let X,Y and Z be fuzzy topological spaces.If ƒ :X→Y is fuzzy β-continuous and g:Y→Z is fuzzycontinuous then gof:X→Z is fuzzy β-continuous.

Proof: Let λ be a fuzzy open set in Z. Since g:Y→Z isfuzzy continuous, g-1(λ) is fuzzy open set in Y. Further,since ƒ :X→Y is fuzzy β-continuous map, ƒ -1(g-1(λ)) =(goƒ )-1 (λ) is fuzzy β-open set in X. Hence (goƒ )-1 (λ) isfuzzy β-open set in X, for each fuzzy open set λ in Z.Thus goƒ :X→Z is fuzzy β-continuous.

Corollary 4.2: Let X,Y and Z be fuzzy topological spaces.Let p:Y×Z→Y and q:Y×Z→Z be projection maps onspaces Y and Z respectively. If ƒ :X→Y×Z is fuzzy β-continuous then pof:X→Y and qoƒ :X→Z are fuzzy β-continuous maps.

Proof: Since the projection maps p:Y×Z→Y, q:Y×Z→Zare fuzzy continuous maps, and ƒ :X→Y×Z is fuzzy β-continuous, it follows from Theorem 4.1, that thecomposite maps poƒ :X→Y and qoƒ :X→Z are fuzzy β-continuous maps.

Theorem 4.3: Let X and Y be fuzzy topological spacesand ƒ : X→Y be a map. Let g:X→X×Y be the graph ofmap ƒ . If g is fuzzy β-continuous then ƒ is fuzzy β-continuous.

Proof: Let λ be a fuzzy open set in Y. Then 1 × λ is a

fuzzy basic open set in X × Y. Since g: X→X × Y is fuzzyβ-continuous, g-1(1× λ) = ƒ -1 (λ) is a fuzzy β-open set in X.This implies ƒ -1 (λ) is a fuzzy β-open set in X, for eachfuzzy open set λ in Y. Hence ƒ : X→Y is fuzzy β-continuous.

Theorem 4.4: Let Xi and Yi,i=1,2 be fuzzy topologicalspaces, and ƒ i:Xi→Yi, i=1,2 be maps. Suppose X1 isproduct related to X2. Then ƒ 1× ƒ 2 : X1 × X2→Y1 × Y2 isfuzzy β-continuous iff ƒ 1 : X1→Y1 and ƒ 2 : X2→Y2 bothare fuzzy β-continuous.

Proof: Let ƒ i:Xi→Yi, i=1,2 be fuzzy β-continuous maps.Let λ1× λ2 be a fuzzy basic open set in Y1 × Y2, where λ1be fuzzy open set in Y1 and λ2 be fuzzy open set in Y2.Then we have, (ƒ 1 × ƒ 2)

-1 (λ1 × λ2) = ƒ 1-1 (λ1) × ƒ 2

-1 (λ2).Since ƒ i : Xi → Yi, i = 1,2, is fuzzy continuous, ƒ i

-1 (λi) isfuzzy β-open set in Xi (i=1,2). Again since X1 is productrelated to X2, ƒ 1

-1 (λ1) × ƒ 2-1 (λ2) is a fuzzy β-open set in

X1 × X2. It follows that ƒ 1 × ƒ 2 :X1 x X2 →Y1 × Y2 is fuzzy β-continuous map.

Conversely suppose ƒ 1 × ƒ 2 : X1 × X2→Y1 × Y2 is fuzzy β-continuous. Let λ be a fuzzy open set in Y1. Then λ×1is a fuzzy basic open set in Y1 × Y2. Since ƒ 1 × ƒ 2 : X1 ×X2→Y1 × Y2 is fuzzy β-continuous, we have (ƒ 1 × ƒ 2)

-1

(λ×1)= ƒ 1-1(λ)×1 is a fuzzy β-open set in X1 × X2. Further

since X1 is product related to X2, we have cl(int(cl(ƒ 1-1

(λ)×1))) = cl(int(cl(ƒ 1-1(λ)))) × 1 ≥ ƒ 1

-1 (λ) × 1. This implies,cl(int(cl(ƒ 1

-1(λ)))) ≥ ƒ 1-1 (λ). Thus ƒ 1

-1 (λ) is a fuzzy β-open set in X1. Hence ƒ 1 : X1→Y1 is fuzzy β-continuous.Similarly we can show that, ƒ 2 : X2→Y2 is fuzzy β-continuous.

References

Azad KK (1981) On fuzzy semi continuity, fuzzy almostcontinuity and fuzzy weak continuity. J Math AnalAppl 82: 14-32

Beceren Y, Nori T (2001) Strongly β-preirresolute mappingsand strongly α-preirresolute mappings. J Pure Math18: 1-7

Bin Shahana AS (1991) On fuzzy strong semi-continuity andfuzzy pre continuity. Fuzzy Sets and Systems 44:303-308

Chang CL (1968) Fuzzy topological spaces. J Math Anal Appl24: 182-190

Mashhour AS, Hasanein IA, El-Deeb SN (1983) α-continuousand α-open mappings. Acta Math Hungar 41: 213-218

Shukla M (2013) On fuzzy strongly β-preirresolute. Acta SinceaIndica 41: 423-429

Shukla M, Shukla P (2012) On Fuzzy semi α-irresolute maps.Fuzzy Sets Rough sets and Multi valued operationsand Applications 4: 7-13

114

Singh S (1998) Generalization of certain fuzzy topologicalconcepts Ph D Dissertation. Rani DurgavatiVishwavidyalaya Jabalpur (MP), India

Thakur SS, Singh S (1998) On fuzzy semi preopen sets andfuzzy semi precontinuity Fuzzy Sets and Systems98: 383-391

Yalvac TH (1988) Semi interior and semiinterior of fuzzy sets.J Math Anal Appl 132: 365-364

Zadeh LA (1965) Fuzzy Sets. Inform and control 8: 338-353

(Manuscript Receivd : 08.08.2012; Accepted :20.01.2014)

115  

( ) ( ) ( ) λθβλλ λθλ

dek

ekXP

k

X 1.!0

+== −∞ −

∫

Maximum likelihood estimates for the parameters of an inflated poisson-lindley distribution H.L. Sharma, Arun Jhajharia and Siddarth Nayak College of Agriculture Jawaharlal Nehru Krishi Vishwavidyalaya, Jabalpur 482 004 (MP) Email drhlsharma_jnkvv@rediffmail.com

Abstract

In the present paper, maximum likelihood estimates for the parameters of an inflated Poisson-Lindley distribution have been derived. The elements of the information matrix are given for the determination of asymptotic variances and covariance of the estimates. The suitability of the distribution is tested using the data of number of insects per leaf (Beall, 1940) and number of accidents per woman (Shankaran, 1970). The fit is also compared with inflated Poisson distribution.

Keywords: Inflated Poisson-Lindley distribution, Maximum likelihood, Asymptotic variance-covariance, Parameter.

In recent years, the applied distributions involving probability have attracted increasing attention of researchers, scientists who are associated in the experiments with count data having excess of zeroes. For example to improve electronics manufacturing quality, in medical research of HIV patients with high risk behaviours and in agricultural study of the number of aphids on mustard and safflower crops, the number of leaf affected due to virus of potato, the number of insects per leaf, the number of larvae on castor and paddy crops, the number of little leaf of brinjal due to bacteria etc.

As a matter of fact, probability distributions are a type of abstract of simplified representation of the essentially important aspects of the real phenomena. A major motivating force was the empirical observations that many distributions obtained in the course of experimental investigation often had an excess of zeroes as compared with a Poisson distribution with the same mean. This phenomenon is to be expected when some kind of clustering is present, and, indeed, many of the distributions possess the property that the proportion in the zero class is greater than exp [-(expected value)], which is the value which would be

predicted on the basis of a Poisson distribution. The simplest way of increasing the proportion of zeroes is just to add an arbitrary proportion of zeroes decreasing the remaining proportions in an appropriate constant ratio (Johnson, Kotz and Kemp 1992).

The aim of the present paper is to estimate the parameters of an inflated Poisson-Lindley distribution by maximum likelihood method. For completeness, Section 2 deals with inflated Poisson-Lindley distribution. Section 3 devotes the estimation procedure. Illustrative examples are included at the end to investigate the suitability of the distribution.

Inflated Poisson-Lindley Distribution

The derivation of Poison-Lindley distribution is given below, which is based on two strong assumptions:

1. The objects which are counted occur in groups, the number of groups follows a Poisson distribution with parameter λ i.e.,

P [Y = k] = !k

e kλλ−

λ >0 k=0, 1, 2, 3…

2. The number of individuals per group , has the continuous Lindley-distribution with parameter θ,

xg [λ,θ] = ( ) ( )θβλλθ 1+−e where β (θ) =1

2

+θθ

λ,θ>0

Using the method of compounding, the distribution becomes compound Poisson-Lindley distribution.

JNKVV Res J 49(1):115-118 (2015)

116  

( )( )

( )( )

knR

kK

nkw

wwL ∏=

+ ⎟⎟⎠

⎞⎜⎜⎝

⎛+

++⎟⎟⎠

⎞⎜⎜⎝

⎛+++−≅

13

2

32

1

2

1

21

0

θθθ

θθθ

( )( )

( )( )∑

++ ⎟⎟⎠

⎞⎜⎜⎝

⎛+

+++

⎟⎟⎠

⎞⎜⎜⎝

⎛+

++−=

R

kkk

kLogn

LognLogL

13

2

3

2

0

1

2

1

21

θθθω

θθθωω

( ) ( )( )

( )

( ) ( )13

22

1

311

1

1 1

3

23

0

++−

+++++

⎟⎟⎠

⎞⎜⎜⎝

⎛+

++−+=

∑

∑ ∑ ∑

=

+ =

θ

θθω

θθθωθ

Lognk

kLognLognLogn

LognLogL

k

R

k

R

k

R

Kkkk

( ) ( )[ ]( ) ( )( ) ∑

+

=++++−+

++−×+=∂

∂ R

k

knnLogL

1323

230 0

1311

311

ωθθθωθθθθ

ω

( ) ( )( )

( )

( )( )( )∑ ∑ ∑

= = =

=+

+−++

++

++×

++−+=

∂∂

R

k

R

k

R

k

kkk nk

k

nn

nL

1 1 1

230

01

3

2

2

1

4

311

log

θθθ

θθωθ

θθωθθ

( )( )

( )( )∑

++ ⎟⎟⎠

⎞⎜⎜⎝

⎛+

+++

⎟⎟⎠

⎞⎜⎜⎝

⎛+

++−=

R

kkk

kLogn

LognLogL

13

2

3

2

0

1

2

1

21

θθθω

θθθωω

( ) ( )( )

( ) ( ) ( )∑ ∑ ∑ ∑+ = =

++−+++++

⎟⎟⎠

⎞⎜⎜⎝

⎛+

++−+=

R

k

R

Kk

R

kkkk LognkkLognLognLogn

LognLogL

1 1 1

3

23

0

1322

1

311

θθθω

θθθωθ

( )( )2

30

31

1

θθθω

+++−=

N

nN

( )( )2

30

31

1.ˆ

θθθω

+++−=

N

nN

( )

( )( )

!

1

1 kk −′

−=

θμθβ

θβ k = 0, 1, 2, 3…

where ( )1−′ θμk is the raw moment of order k of

Lindley distribution with ( )1−θ as the parameter. After

simplification the distribution becomes:

( ) ( )( ) 3

2

1

2++++=

kk

kP

θθθθ k=0, 1, 2, 3.....

Let X be the random variable denoting the number of insects /leaf and the number of accidents per woman and ω be the proportion of leaves and women which are exposed to the risk of bearing insects & accidents and (l –ω) not exposed to the risk, then the inflated Poisson-Lindley distribution is as follows

P[X=0]( )

( )3

2

1

21

+++−=

θθθωω …(2.1)

P[x=k] = ( )

( ) 3

2

1

2++++

k

k

θθθω for k > 0 ... (2.2)

Estimation of Parameters by Maximum Likelihood Method Let us consider a sample consisting of N observations of the random variable X with probability mass function given by eq. (2.1) & (2.2). The likelihood function can be written as;

… (3.1)

where kn is the sample frequency of k, n is the number

of non-zero sample observations n=N- 0n and

Π denotes the products over n non-zero observations. R is the largest numbers of observations. Taking logarithm of L, differentiating with respect to ω and θ in turn and setting the derivatives equal to zero gives the estimating equations. We have,

... (3.2)

…(3.3)

Maximum likelihood estimates, when they exist may be determined by solving the system of equations (3.1) and (3.2) by employing a trial and error technique by which convergence is greatly accelerated ( Sampford 1955).

Let us assume a specified value of ω. From equation (3.2), θ can be obtained. We can check whether the equation (3.3) converges to zero or not, if it does not converge into zero, then a slight change in the

117  

( )( ) ( )[ ] 2

0223

220

2

2

311

31log

ωθθωθθθ

ωnNnL −−

++−+

++−=∂

∂

( )( )( )∑ ∑ ∑

= = =

=+

+−++

−−R

k

R

k

R

k

kkk nk

k

nn

1 1 1222

01

3

2

2...

θθθ

( ) ( )( ) ( ) ⎥

⎦

⎤⎢⎣

⎡=⎟⎟⎠

⎞⎜⎜⎝

⎛θωθ

θωωθω

ˆˆˆcov

ˆˆcovˆˆˆ

v

vv

( ) ( ) ( )( ){ } ( ) ( ) ( ){ }[ ]( ) ( )( )[ ]224

2324

02

1311

483144131142

+++−+++−++−+++−++

=∂

∂

θθθωθθθωθθθθθθωθθ

ωθ

nLogL

( )[ ]( ) ( ){ } ( ) ( ) ( )[ ]( ) ( )[ ]223

220

2230

22

311

23133131123

loglog

θθωθ

θωθθθθθωθθωθθω

++−+

+−+++−++−++

=∂∂

∂=∂∂

∂

nn

LL

( )

( ) ( ) ( )( ){ } ( )( ) ( )( )[ ]( ) ( )( )[ ]

( )( )( )∑ ∑ ∑

= = = ++

−++

−−

+++−+

++++++−+++−++

=⎟⎟⎠

⎞⎜⎜⎝

⎛

∂∂−

R

k

R

k

R

k

kkk nk

k

nn

nEL

E

1 1 1222

224

2324

02

2

1

3

22......

1311

4834144131142

log

θθθ

θθθωθ

θθθωθθθθθθθωθθ

ωθ

( )( )( ) ( )[ ]

( )2

0223

220

2

2

311

31log

ωθθωθ

θθω

nNEnELE

−+

++−+

++=

⎥⎥⎦

⎤

⎢⎢⎣

⎡

∂∂−

( )( ) [ ]( ) ( )[ ]23

20

22

311

41loglog

θθωθθθθ

ωθθω ++−+

++−=

⎥⎥⎦

⎤

⎢⎢⎣

⎡∂∂

∂−=

⎥⎥⎦

⎤

⎢⎢⎣

⎡∂∂

∂−

nELE

LE

original value of ω may be taken and repeated all the steps accordingly. This trial and error process is repeated until, a true set of estimation of the parameters are not obtained.

The asymptotic variance-covariance matrix is obtained by inverting the information matrix whose elements are negatives of expected values of the second order derivatives of logarithms of likelihood function. These derivatives are given below:

Thus, we can have the variance–covariance matrix of

the parameter ω and θ as follows

Illustrative Examples

The suitability of the Poisson-Lindley distribution is tested using the data of Beall (1940) and Shankaran (1970). the distribution of observed and expected number of leaves according to number of insects is depicted in Table 1. It is important to note that

( )( )1

2

++

θθθw

gives the average number of insects per

leaf. Once the estimates of ω and θ are obtained, expected frequencies can easily be computed. The estimated values of ω and θ are found to be 0.85 and 1.55 respectively which indicates that 15% of the leaves are not exposed to the risk of attracting insects on them.

Table 1. Distribution of observed and expected number of leaves according to the number of insects

Expected frequency No. of Insects

Observed Frequency Poisson

Lindley Inflated Poisson

0 1 2 3 4 5

33 12 6 3 1 1

32.89 12.31 5.89

⎪⎭

⎪⎬⎫

4.91

32.86 11.01 7.36

⎪⎭

⎪⎬⎫

4.77

Total 56 56.00 56.00

θ̂ =1.55 =λ̂ 1.34

Parameter estimates ω̂ = 0.85 ω̂ = 0.56

=2χ 0.01 2χ = 0.35 d.f.= 1 d.f.=1

118  

For applying a chi-square test, some last cells are grouped together. The value of chi-square does not seem to be significant in the case of Poisson-Lindley and Poisson distribution. But the value of chi-square in the case of inflated Poisson is found to be larger rather than that of inflated Poisson-Lindley. Thus we conclude that inflated Poisson-Lindley distribution provides excellent fitting for the data having excess of zeroes. Hence this distribution is recommended for the kinds of data where we do have the excess of zeroes specially for the number of insects/leaf.

Table 2 provides the distribution of observed and expected number of accidents per woman. It is to

be noted that ( )( )1

2

++

θθθw

gives the average number of

accidents. Once the estimates of ω and θ are obtained, expected frequencies can easily be computed. The estimated values of ω and θ are found to be 0.88 and 2.45 respectively which indicates that 12% of the women are not exposed to the risk of accidents .

Table 2. Distribution of observed and expected number of accidents per woman (Shankaran 1970)

Expected frequency No. of accidents

Observed Frequency Poisson-

Lindley Inflated Poisson

0 1 2 3 4 5

447 132 42 21 3 2

446.68 133.39 45.04 14.85

04.7⎭⎬⎫

447.00 124.70 54.90 16.11

29.4⎭⎬⎫

Total 647 647.00 647.00

θ̂ =2.45 =λ̂ .8805

Parameter estimates ω̂ = 0.88 ω̂ = .528

2χ =3.358 .. 2χ = 5.06

d.f. = 2 d.f.=2

For applying a chi-square test, some last cells are grouped together. The value of chi-square does not occur to be significant in the case of Poisson-Lindley and Poisson distribution. But the value of chi-square in the case of inflated Poisson-Lindley is found to be smaller rather than that of inflated Poisson. Thus we conclude that inflated Poisson-Lindley distribution provides excellent fitting for the data having excess of zeroes. Hence this distribution is recommended for the

types of data where the excess of zeroes specially for the number of accidents per woman is happened.

bl orZeku isij esa] ,d Qqyk gqvk Ioklksa&fy.Mys cVu ds izkpyd dsfy, vf/kdre] laHkkfork vkdyu O;qriUu fd;k x;k gS A vkdyu ds mixkeh izlkj.k ,oa lg izlkj.k ds fu/kkZj.k ds fy, voxe vkO;wg ds vYika’k fn;s x;s gSa A cVu dh mi;qDrrk dk ijh{k.k izfr iRrk dhM+ksa dh la[;k (Beall 1940) ,oa izfr efgyk nq?kZVukvksa dh la[;k (Shankran, 1970) ls fd;k x;k gS A bl vklatu dk Qwyk gqvk Ioklksa cVu ls Hkh rqyuk dh x;h gS A References Beall G (1940) The fit and significance of contagious

distributions when applied to Observations on larval insects Ecology, 21 : 460-474

Johnson NL, Kotz S Kemp AW (1992) Univariate Discrete Distributions : The distributions in statistics, IInd Edn, John Wiley & Sons Inc

Sampford MR (1955) The truncated negative-binomial distribution Biometrika 42 : 58-69

Sankaran M (1970) The discrete Poisson Lindley distribution Biometrics 26 : 145-146

(Received : 05.11.2014; Accepted : 01.01.2015)

119

Abstract

In the present paper, a class of ratio type estimators hasbeen proposed to estimate the population mean of the studyvariate y, using Jack-knife technique by Quenouille (1949))to make the class unbiased. The explicit expression for thesampling variance of the class is derived to the first order ofapproximation. Minimum variance unbiased estimator in theclass is investigated. An empirical illustration is provided toexamine the applicability of the results derived in the study.

Keywords: Ratio estimator, Jack-knife technique, Circularsystematic sampling, MVUE estimator

The classical ratio estimator under linear systematicsampling scheme was proposed by Swan (1964) andits properties were studied. In general, the ratioestimator is biased. A weighted class of ratio typeestimators was proposed and made almost unbiasedby Kushwaha and Singh (1989) using Jack-knifetechnique.

A serious demerit of linear systematic samplingscheme is that it is not possible to estimate the samplingvariance of the estimator understudy but using thetechnique of interpenetrating systematic sampling withindependent random start, the sampling variance of theestimator can be estimated unbiasedly.

A simple modification of linear systematicsampling (LSS) makes it possible to ensure a fixedsample of size n. and to make the sample meanunbiased for the population mean YN, even in case of Nbeing not multiple of n. This sampling scheme is knownas circular systematic sampling (CSS) scheme.

Murthy (1977) and Sukhatme and Sukhatme

Use of Jack-knife technique in ratio estimator and circularsystematic sampling scheme

K.S. Kushwaha, A.K. Rai* and Rajdeep MishraDepartment of Mathematics and Statistics*Department of Head Instrument Development and Service CentreJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482 004 (MP)Email: akrai_jnau@yahoo.co.in

(1970) have suggested to use CSS in situation when Nis not a multiple of n.

The main steps involved in selecting a sample usingCSS are

(1) Select a random number from 1 to N and name itas 'random start'.

(2) Choose some integer value of k = N/n, or numbernearest to N/n and name it as skip.

(3) Select all units in the sample with serial numbers

r+jk, if r+jk≤N, {j=0, 1, 2… (n-1)}, 1≤r≤N

r+jk-N, if r+jk >N, {j=0, 1, 2… (n-1)}, 1≤r≤N ...(1)

Sudhakar (1978) pointed that the use of skip orspan of sampling as an integer nearest to (N/n), in CSSdose not always draws a sample of desired size.Sudhakar (1978) has also mentioned that if the span ofsampling is the nearest integer k£(N/n), then we do notencounter the above cited difficulty although this choicedepends upon n.

Jack-knife technique has been profitablyemployed in several estimation and testing problems.In this study, we have utilized Jack-knife technique toget rid of bias in usual ratio estimator proposed in CSSscheme and have derived a general class of almostunbiased ratio type estimators.

The expression for the sampling variance of theproposed class of estimators is derived. An empiricalillustration is provided to see the performance of thederived estimators (class) with respect to efficiency pointof views over other estimators existing in the literature.

JNKVV Res J 49(1): 119-123 (2015)

120

Class of Almost Unbiased Ratio Type Estimators

Suppose the population consists of N units U = (U1,U2…UN) labeled from 1 to N in circular order. We assumeN?nk, where k is an integer nearest to N/n. We selectone sample at random and observe both the variates(y, x) for each and every unit included in the sample.Let (yr+jk, xr+jk, where j = 0, 1, 2…n-1 and r = 1, 2,…N)be the sample units.

The circular systematic sample means ( , ) are definedas

The expressions for bias of ( yR , xR•) to the terms oforder O (n-1) are respectively written as

y 1n j 0

n 1yr jk=

−−

+Σ

x 1n j

n 1xr jk=

−

−+Σ

1

j = 0,1,2...n-1r = 1,2...Nk = sampling interval ...(2)

Here sample mean (

y

, x ) are respectively

unbiased estimators of the population means ( y , x).

The population mean x of auxiliary variate x is assumedto be known in prior.

The classical ratio estimator yR of x based ona circular systematic sample is defined as,

...(3)

To reduce the bias of yR, we use jack knifetechnique. Here, we take n=gm and split the selectedcircular systematic sample of size n into g subsampleseach of size m in a systematic manner as this avoidsthe need for selecting the sample in the form ofsubsamples of smaller size and thereby retaining theefficiency generally obtained by taking a large circularsystematic sample. Let ( yt, xt , t = 1,2,…,g) be unbiased

estimators of ( y , x) based on circular systematicsubsamples each of size m. With this background, wedefine Jack Knife version of ratio estimator written as

y3yx

.X=

yR1g t=

gyRt• = Σ

1...(4)

B1 yRYn 1+(n 1) c (1 k*)Cx2F

HGIKJ

RSTUVW= − −ρ

B1 yRYn g+(n g) c (1 k*)Cx2• = − −F

HGIKJ

RSTUVWρ

and ...(5)

where,

B 1 y RYn

v ( x )

X 2c o v ( y . x )

Y XFHG

IKJ

L

N

MMMM

O

Q

PPPP= −

V(y)css 1N r 1

Nyr Y

2=

=−

FHGG

IKJJΣ

  2

1

1)( ⎟

⎠⎞⎜

⎝⎛ −Σ=

−−

=

−Yy

NyV r

N

rcss

  21

01

11

⎭⎬⎫

⎩⎨⎧ −ΣΣ=

−

+

−

==Yy

nNjkr

n

j

N

r

  21

02

1

⎭⎬⎫

⎩⎨⎧ −Σ=

−

+

−

=YnY

Nnjkr

n

j

 { }yc

yn

nρ

σ)1(1

2

−+=

 

css

xV ⎟⎠⎞⎜

⎝⎛ −  

{ }xcx nn

ρσ)1(1

2

−+=

and

By definition, the intra-class correlation coefficient withinthe sample of the same variate (say y) is defined asfollows:

 

2

'

⎟⎠⎞⎜

⎝⎛ −

⎟⎠⎞⎜

⎝⎛ −⎟

⎠⎞⎜

⎝⎛ −

==−

+

−

+

−

+

YYE

YYYYE

jkr

kjrjkr

ycyy ρρ

121

Here ryx being the correlation coefficient between twovariates (x, y) in the population, and rc is the intraclasscorrelation coefficient for both the variates and has beenassumed to be the same (see Murthy 1977, pp. 374-375), (Cx,Cy)are the coefficient of variation for (x, y)respectively.

Now, taking the linear combination of y , yR and

xR•, we propose a weighted class of estimators TRfollows as.

where and are constants to be chosen suitably. Thuswe obtain a general class TRU of almost unbiased ratiotype estimators in css scheme written as

Properties of the class TR

The expression for sampling variance of the proposedclass TR can be written as

To the terms of order 0(n-1), the variance and covariancesexpre-ssions for various estimators in (3.1) are cited inthe lemma (3.1)

Lemma

 

22

⎟⎠⎞⎜

⎝⎛ −⎟

⎠⎞⎜

⎝⎛ −

⎟⎠⎞⎜

⎝⎛ −⎟

⎠⎞⎜

⎝⎛ −

=−

+

−

+

−

+

−

+

XxEYyE

XxYyE

jkrjkr

jkrjkr

yxρ

 

yyxx

yxyxnn

nxy ρρ

σσρ)1(1)1(1,cov −+−+=⎟

⎠⎞⎜

⎝⎛ −−

 ( ) ,,,, *

x

y

yxz

ZC

CKyxz

Z

SC ρ=== −

where α'1s (i = 1,2,3) are suitably chosen weightsattached to different estimators. Now we state thefollowing theorem,

Theorem (2.1): The weighted class TR. of the estimatorsdefined in (2.5) would we unbiased if

 1,

3

1321 =++= ∑

=

−

•

−−

iiRRR yyyT αααα

...(6)

  { }c

c

n

gngh

ρρ

)1(1

)(

−+−+

=

  032 =+ ααh

...(7)

If we take α1=α, α2= β and α3 = (1−α−β) the unbiasednesscondition (2.6) reduces to

  ( )1

1

−−−=

h

αβ

  ( )( )1

1

1

1RRRU yh

hy

hyT

−

•

−−

−−+⎟

⎠⎞⎜

⎝⎛

−−−= ααα ...(8)

 ),cov(2)()()()( 21

2

3

2

2

21 •

−−

•

−

+++= RRRR yyyVyVyVTV ααααα

 ),cov(2),cov(2 1332

−−−−

• ++ yyyy RRR αααα

 

{ } 22

)1(1 yc Cnn

YyV ρ−+=⎟

⎠⎞⎜

⎝⎛

−−

 ⎟⎠⎞⎜

⎝⎛=⎟

⎠⎞⎜

⎝⎛=⎟

⎠⎞⎜

⎝⎛ −−−

•

−

RRRR yyCovyVyV ,.

 

{ } ( ))21()1(1 2*22

xyc CkCnn

Y−−−+=

−

ρ ...(10)

 ⎟⎠⎞⎜

⎝⎛=⎟

⎠⎞⎜

⎝⎛ −−

•

−−

RR yyCovyyCov ,,

 

{ } ( )2*22

)1(1 xyc CkCnn

Y−−+=

−

ρ

Substituting the results (3.2) and α1=α, α2= β and α3 =(1−α−β) in (3.1) and simplifying we get the expressionV(TRU) given as

122

V(TRU)

The variance of V(TRU) in (3.3) will be minimum for

Thus the resulting minimum variance of TRU is

which is equivalent to the approximate variance of usualbiased linear regression estimator in circular systematicsampling written as

where byx is the sample regression coefficient of y onx. under C.S.S. scheme. Substituting α1 =α* = (1-k*),α2 = α* = -k*/(h-1) and α3 = (1- α*- β*) = k*h/(h-1) in(3.1), we obtain an optimum estimator in the class TRUgiven as

with minm V(TRO) written in (3.5)

It is to be pointed out that the class TRU in (2.7) wouldbe more efficient than the conventional unbiasedestimator

y

and ratio estimator yR under CSS schemeaccording if.

 

{ } { }[ ] )11(2)1()1()1(1 2*22

xyc CkCnn

Y −−−+−+=

−

ααρ

  ( ) )(1 ** sayk αα =−= ...(12)

 

{ }( ))1(1)(minm 22*22

xycRU CkCnn

YTV −−+=

−

ρ ...(13)

 )14(⎟

⎠⎞⎜

⎝⎛ −+=

−−−−

xXbyy yxlr

Numerical Illustration

To see the performance of the class TRU of the ratiotype estimators we consider the data on y: the timbervolume and x: the strip length in strip wise completeenumeration. The 25 observations are considered fromMurthy (1977) and is treated as a population of sizeN=25. The summarized data is provided as below:

N =25 C2y = 0.04 ρxy = 0.08072

Y N =459.84 C2x = 0.0153 k* = 1.306

X N = 10.44 Sxy = 104.19 c = 0.0135

n = 10, k = 2.5 3 (sampling space)

The intraclass correlation coefficient c has been workedout as

where s2 and sw2 are the population and within sample

variance respectively defined under C.S.S. scheme as

We have the calculated the values of

And P.R.E. (• , y ) = V(y)V(TRU) , the percent relative

efficiency (P.R.E.) of the proposed class with respectto y for different values of and are provided in thetable 1. Analysis work has been done using SAS Version9.1 by Mervyn and kennedy (2008)

  ( )RRRO y

h

hky

h

kykT

−

•

−−

⎟⎟⎠

⎞⎜⎜⎝

⎛−

+⎟⎟⎠

⎞⎜⎜⎝

⎛−

−−=11

1**

* ...(15)

  ( )

( )

)1(2

)1(2

121

211

*

*

*

*

ok

ko

k

k

<<−

−<<

<<−

−<<

α

α

α

αeither

or

and either

or

...(16)

...(17)

 

  

2

2

.)1(

1σσρ ω

−−=

n

nc

 

∑∑=

−

=

−

+ −=N

r

n

jjkr Yy

N 1

1

1

2 )(1σ

 

∑ ∑∑=

−

=

−

+=

⎟⎟⎠

⎞⎜⎜⎝

⎛−==

N

r

n

j

rjkr

N

r

rww yynNN 1

1

0

2

1

22 )(111 σσ

 

{ }{ }[ ]2*2

1 2)1()1(

)1(1

)( xy

c

RU

CkC

nn

Y

TV

V +−−+=

−+

⎟⎠⎞⎜

⎝⎛

=• − αα

ρ

123

The relative efficiency of the estimator TRO forthe choice of α=α*= -0.306 is maximum among theestimators considered here which shows that TRO is themost efficient (optimum) estimator in the class TRU(Table 1) In practice, one may substitute the estimatedvalues of the variances and covariances in order toobtain a 'near optimum' values of α. For the choice ofin the interval (-1.612 <α<1), the class TRU is alwaysefficient than the sample mean y . It is also evident fromthe table that for the values of α in the subinterval (0<α<-0.612), the class TRU is not only efficient than y but

also more efficient than the ratio estimator yR .

orZeku v/;;u izfd;k esa v/;;u pj ds tula[;k ek/; dksvkdfyd djus ds fy, vuqikfrr izdkj ds yxHkx iw.kZ:i lsvufHkur vkdydksa ds o`gRr lewg dks izLrkfor fd;k x;k gS ADohukSyh ¼1949]1956½ }kjk lq>k;s x;s tSdukbd izfd;k dk o`gRrlewg dks vufHkur cukus ds fy;s mi;ksx esa yk;k x;k gSA lewg dsizfrp;u izlj.k ds fuf'pr lw++= dks o (n-1) ds in rd izkIr fd;kx;k gSA lewg esas de ls de izlj.k okys vufHkr vkdyd dk irkyxk;k x;k gSA izkIr fd;s x;s lw+=ksa ¼Qyuksa½ ds mi;ksfxrk dh tkapdjus ds fy;s ,d vuqHkkfod mnkgj.k dk izLrqr fd;k x;k gS A

References

Kushwaha KS, Singh HP (1989) Class of almost unbiasedratio and product type estimator in systematicsampling. Indian Soc Agric Stat. 193-205 New Delhi

Mervyn G Marasinghe, Kennedy William J (2008)SAS for DataAnalysis, Intermediate Statistical Methods with 100SAS program, SAS InstituteInc Cary, NC USA

Murthy MN (1977) Sampling Theory and Methods. StatisticalPublishing Society Calcutta

Quenouille MH (1949) Approximation test of correction in timeseries. Roy Stat Soc(11) 68-84 London

Sudhakar K (1978) A note on Circular Systematic SamplingDesign. Sankhya. Indian J Stat 40(C1) 72-73Kolkatta

Sukhatame PV, Sukhatame BV (1970) Sampling Theory ofSurveys with Applications. Iowa State Unive PressAmes Iowa USA

Swan AKPC (1964) The use of systematic sampling in ratioestimate, Ind Stat Assoc 2 (213): 160-164

Table 1. The valuesof V1(.) and P.R.E. (., y )

Values of α Estimators V'1 P.R.E. (., y )

- y 29.9 x 10-3 100α*= -0.306 TRU0 8.6 x 10-3 347.67(1-2k*)<α<1or TRU < V1 ( y ) >100-1.612 <α<1-1.400 TRU 47.99 x 10-3 62.31-1.900 TRU 41.74 x 10-3 71.63- y R or y R. 78.3 x 10-3 38.182(1-k*)<α<0or TRU <V1 ( y ) >71.63-0.612<α<0-0.500 TRU 12.6 x 10-3 237.31-0.950 TRU 24.8 x 10-3 120.56

(Manuscript Receivd : 06.11.2013; Accepted :08.08.2014)