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Journal of Progressive Agriculture, Vol. 8 No. 1: April 2017---------------------------------------------------------------------------------------------------------------------------------------------------------------

A COMPARATIVE STUDY OF PLANT VARIETY PROTECTION (PVP) LEGISLATIONS OF DEVELOPED AND DEVELOPING COUNTRIES AND

FORMATION OF A REGIONAL IPR FORUM FOR SOUTH ASIA

D.S. Misra1, K. S. Shekhawat2, Seema Sharma3

1 Deputy Commissioner (Quality Control), Department of Agriculture, Cooperation & Farmers Welfare & Research Scholar, Department of Management Studies, IIT Delhi,

2Vice President Research, Dhanlaxmi Crop Science Private Limited, Himatnagar3Associate Professor, Department of Management Studies, IIT Delhi

Received: 14.01.17Accepted: 24.03.17

ABSTRACTThe Present paper provides comparative review of Plant Variety Protection (PVP) laws of developed and developing countries of UPOV1 in respect of various provisions with critical analysis and useful information for policy makers, legislatures, law makers and drafters of the PVP legislation of developing world who have not enacted their legislation. The paper gives in depth insight on some of the salient provisions and the approach being adopted on the PVP laws by countries located in all the geographical regions of the world. An innovative approach has been made for the formation of Intellectual Property Rights (IPR) regional platform for South Asia to give impetus for developing, strengthening and management of the IPR culture.

Key words: Plant Variety Protection, Plant Breeders Rights, Intellectual Property Rights

The Agreement on Trade Related Aspects of Intellectual Property Rights (TRIPS) requires that member states of the World Trade Organization (WTO) to comply with the guidelines on intellectual property rights(IPR). This Agreement is responsible for the “protection of plant varieties either by patents or an effective sui generis system or a combination of both” has forced many developing and least developing countries to take measures for the development of their protection systems for the plant varieties as made by most of the developed countries. India always believed in the philosophy of “Vasudhaiva Kutumbakam”2 and enjoyed the benefits of this concept in the era of green revolution. However, with the demise of concept of the ‘Common Heritage’ of the plant genetic resources was transformed to the sovereign rights of States on their natural resources with the advent of Convention on Biological Diversity (CBD) from 1994. World agriculture scenario has changed rapidly and a commercial approach to the genetic

variability was developed. A number of countries have enacted their PVP legislations on plant varieties but many developing countries are planning to have their own legislations. The policy makers and legislators often face challenges while drafting such law to make a balance between the right holders and the society at large. Besides, institutional options dealing with the placement of the PVP office either with the Ministry of Agriculture or any other Ministry or with the Patent Office, decisions have to be made the extent the Government has to be involved in the testing of applications, autonomy of the DUS centres & testing process, human and technical resources, international cooperation and the capacities of the breeders, applicants and of the courts systems are the key issues that have to be weighed3. Accordingly, a comparative study of laws of various countries helps other to customize the law according to their specific needs. Further, it is essential to analyze all kinds of provisions of existing laws in different geographical jurisdictions. The

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aim of this paper is to make a comparative analysis of Plant Variety Protection laws existing in different countries including India & Zimbabwe and to find out a viable solution for an ideal law suitable to fulfil the requirements of protection of plant varieties for developing countries in particular SAARC countries & other South Asian countries planning to draft their legislation on IPR. The paper examines the Plant Variety Protection (PVP) legislations of developed and developing countries of International Union for the Protection of New varieties

of Plants (UPOV). PVP legislations of both countries i.e., India and Zimbabwe are also part of the present study. The objectives of the present study include i) to study the key provisions of the legislations of the different countries; ii) to examine the unique provisions of PVP legislations in various countries; iii) to comprehend the techno–legal and institutional lessons learnt from the comparative analysis of PVP legislations of world over; and iv) to examine and design the feasibility of formation of a Regional IPR Forum for South Asian countries.

1http://www.upov.int/about/en/: There are 72 countries to the UPOV as on 10th June, 2014 and out of which, nineteen countries are members to UPOV as per 1978 Convention and the remaining are members to the UPOV as per 1991 Convention. There are nine countries from South Asia, including India and 12 countries including Zimbabwe from Africa having observer status in UPOV. 2Vasudhaiva Kutumbakam (Sanskrit: वसुधैव कुटुम्बकम. from “vasudha”, the earth; “iva”, is; and “kutumbakam”, family) is a Sanskrit phrase that means that the whole world is one single family. 3PVP course 2011 Naktuinbouw at Wageningen, Netherlands

Table 1: List of countries with year of enactment and the title of the legislationsCountry UPOV

ConventionYear(s) Title of the legislation

Australia 1991 1994/ 2003/2013

Plant Breeder’s Rights Act

Austria 1991 2001 Federal law on the protection of Plant Varieties (Plant Variety Protection Act)

Argentina 1978 1991 Regulatory Decree No. 2183/91Albania 1991 2002 Law No. 8880 on Plant Breeders Rights Azerbaijan 1991 2000/2004 Law on Selection AchievementsBrazil 1978 1997 Law No. 9456 on Plant Variety Protection LawBolivia 1978 1993/

1999/ 2001Decision No. 345-Establishing the regime on Common Protection of the Rights of Breeders of New Plant Varieties

Canada 1978 1990/1991 Plant Breeders’ Rights ActChile 1978 1994 Law No. 19.342 on the Rights of Breeders of New

Varieties of PlantsChina 1978 1999 The Protection of New Varieties of Plant Colombia 1978 1993/

1994Establishing the common regime on the Protection of the Rights of Breeders of New Plant Varieties

Czech Republic

1991 1996/2000/ 2006

Act on the Protection of Plant Variety Rights

Denmark 1991 1996 Consolidate Act of the Danish Plant Variety ProtectionEcuador 1978 1993 Common Provisions on the Protection of the Rights of

Breeders of New Plant VarietiesFinland 1991 1992 /1999 Act on Plant Breeder´s Right France 1991 1970/2011 Law on the Protection of New Plant VarietiesGermany 1991 1985/1997 The Plant Variety Protection LawHungary 1991 1995/2009 Act No. XXXIII. Protection of Inventions by PatentsIceland 1991 2000/2003 Breeder’s Right Act

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India (Observer) 2001/2006 Protection of Plant Varieties & Farmers’ Rights ActIreland 1991 1980/1998

/2000Plant Varieties (Proprietary Rights) Act

Italy 1978 1998 Legislative Decree No. 455 on Adaptation to the Provisions of the 1991 Act of the International Convention for the Protection of New Varieties of Plants

Japan 1991 1998/2007 Plant Variety Protection and Seed Act No.83Kenya 1978 1991/2002 The Seeds and Plant Varieties ActRepublic of Korea

1991 1995/2001 Seed Industry Law

Mexico 1978 1996/2000 Federal Law on Plant VarietiesNew Zealand 1978 1987/1999 Plant Variety Rights Act The Netherlands

1991 1986/1999/2005

Seeds and Planting Materials including the granting of Breeders Rights

Nicaragua 1978 1999/2001 Law No. 318 for the Protection of New Varieties of Plants

Norway 1978 1993/1997 Plant Breeder's RightParaguay 1978 1994/1998 Law No. 385 on Seeds and Protection of Plant

Varieties Poland 1991 2003/2007 Act on the Legal Protection of Plant Varieties Portugal 1978 1990 Decree Law No. 213 relating to Plant Breeders RightsRussian Federation

1991 1993 Law on the Protection of Selection Achievements

Singapore 1991 2004 Plant Variety Protection ActSlovakia 1991 1989 Act No. 132 Coll. on the Protection of Rights of New

Varieties and Animal BreedsSouth Africa 1978 1976/1996 Plant Breeders’ Rights Act No. 15Sweden 1991 1997 Act on the Protection of Plant Breeders' RightsSwitzerland 1991 1975/2008 Federal Law on the Protection of Plant Varieties

No.232Trinidad & Tobago

1978 1997 / 1999

The Protection of New Plant Varieties Act

United Kingdom

1991 1997/1999 Plant Varieties Act

Uruguay 1978 1997 Plant Variety protection Act No.82 & Law No. 16.811 on the Development, Production, Distribution and Internal and External Marketing of Seeds and Phylogenetic Creations

USA 1991 1970/2005 Plant Variety Protection ActUzbekistan 1991 2002 Law of the Republic of Uzbekistan on selection

achievements Zimbabwe (Observer) 1973/2001 Plant Breeders Rights Act

(Source: http://www.upov.int/portal/index.html.en & http://www.farmersrights.org/)i) PVP Legislation: A ComparisonAn ideal PVP legislation consists of a number of provisions starting with a suitable preamble in the beginning. Except India and Argentina, most of the legislations under study do not have the preamble, which highlight the very purpose and philosophy of the statue. However, most of the PVP legislations

in their opening clause explains the very purpose of the law i.e. to protect the rights of the plant breeders developing new plant varieties, who have created or discovered and developed a new plant variety by natural means or genetic engineering; hence shall be granted the title of breeder. The Law of Ecuador in its opening clause lucidly states the

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objective i.e. to recognize and ensure the protection of the rights of breeders of new plant varieties and promote research activities and technology transfer activities within and outside the sub-region. Sixteen important provisions have been selected to make a comparative analysis among legislations of 45 countries and UPOV Acts (Table 1 & 2). 1) Botanical genera and species covered- Most of the countries have covered all the botanical genera and species under the scope of protection in their legislations except eight countries namely Albania, Azerbaijan, Brazil, China, Singapore, South Africa, Trinidad & Tobago and Uzbekistan listed the specific botanical genera and species for the purpose of protection with maximum number of genera / species covered in South Africa. The number of genera / species protected by member states depends upon the cultivation of the popular choice of the majority of farmers and their communities in that country. As per UPOV Convention 1978, the member countries have to take all measures necessary for the coverage to the largest possible number of genera and species. However, the members shall apply the protection at least 10 genera or species within 3 years, at least 18 genera or species within 6 years and 24 genera or species within 8 years of joining the Convention. However, this condition of coverage of protection have become stringent in UPOV Convention 1991 wherein the member states will protect at least 15 genera or species immediately after joining and to all plant genera and species after the expiry of period 10 years. 2) Condition for Protection- All countries have uniformly followed DUS criteria of the UPOV model for the protection of plant varieties i.e., Novelty, Distinctness, Uniformity and Stability

(NDUS) with a denomination for a variety, fundamental principle for grant of protection of plant varieties in the form of Plant Breeders Rights irrespective of their status in UPOV. The said criteria of deciding the novelty of new plants in all the PVP legislations are quite similar and uniformly adopted by all the countries. A variety is considered novel, if on the date of filing of application for registration, the propagating or harvested material of such variety has not been sold or otherwise disposed of by or with the consent of its breeder or his successor for the purposes of exploitation of such variety earlier than one year in the country itself or outside the country earlier than six years for trees & vines and earlier than four years for other crops. The longer period of 4 & 6 years has been allowed to the breeders outside the country or to a foreign applicant in respect of registration of annual plants and for trees and wines respectively. UPOV has developed DUS guidelines for more than 300 genera/ species. Most of the countries rely on the DUS Testing guidelines for this purpose, and developed their own guidelines referring UPOV’s guidelines.3) Duration of the Protection- The duration of protection is generally 15-20 years in most of the countries (except UK which has more than 20 years protection period) which have joined UPOV as per 1978 Convention. Whereas the longer duration, above 20 years, has been chosen by countries who have adopted UPOV 1991 Convention. The rationale for choosing the shorter duration of protection is due to fact that these countries intend to bring the new varieties into the public domain early as compared to developed countries, who have opted longer duration of protection as applicable in patents due to their commercial interest. In fact, the duration of protection period also depends upon

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the agro-climatic conditions and national requirements. UPOV member countries who have adopted 1978 Convention probably do not support the monopoly of the breeders for longer period. 4) Plant Breeders’ Rights (PBR)- As per the 1978 Convention the Plant Breeders’ Rights (PBR) provide the rights to a breeder to produce seed for commercial purpose, offering for sale and marketing of the reproductive or vegetative propagating material which includes the whole plants, as such, of the variety. The scope of PBR was enhanced under 1991 Convention to other activities including production, reproduction, multiplication or propagation; conditioning for reproduction, multiplication or propagation purposes; offer for sale; sale or any other act which involves the introduction into the market of the reproduction, propagation or multiplication material for commercial purposes; export; import; possession for any of the purposes mentioned above, commercial use of ornamental and fruit plants, or parts thereof, as multiplication material in order to produce ornamental and fruit plants, or parts of ornamental or fruit plants, or cut flowers.5) Exceptions to Plant Breeders Rights – There are three exceptions available to plant breeders’ rights granted by all the PVP legislations of the countries viz. exemption for experimental purpose, ii) research or non-commercial purpose; and iii) farmers’ privileges. As far as the first two exemptions are concerned, all PVP legislations invariably provide both standard exemptions as above. Researcher’s privilege provides a person to propagate, grow or use a protected variety for non-commercial purposes or for experimental or research purpose. This provision provides scope for the plant breeders and researchers to develop new varieties with desirable and economic traits including Essentially

Derived Varieties (EDV), and to prevent cosmetic breeding. Repeated use of initial parent material for developing new variety is prohibited.As regards the provisions relating to farmers’ privilege, it is mentioned that the same finds explicit mention in the legislations of 15 countries namely Albania, Argentina, Brazil, Bolivia, Chile, China, Germany, Japan, Mexico, Nicaragua, Paraguay, United Kingdom, USA, Uruguay and Zimbabwe whereas Indian legislation has a exclusive chapter on Farmers’ Rights consisting of eight provisions with nine kinds of rights to farmers. It is also relevant to states that the UPOV 1991 Convention prohibits the farmers’ privileges and promote longer duration of protection as per the expectations of breeders of the developed countries where agriculture is a commercial activity for export earnings in sharp contrast to developing economies/Least developing countries (LDCs). There is a clear demarcation of productivity level between the developed and developing countries and most of them have to raise their productivity per hectare. 6) Essentially derived varieties (EDV)4–The EDV concept arise only in 1991 Convention of the UPOV and exists in all countries except in the legislations of Albania, Argentina, Canada, Chile, China, Kenya, Mexico and Trinidad & Tobago. The application of genetic engineering for the development of transferring alien gene to a plant variety for making it resistant to biotic and abiotic stresses has led to patenting of variety in the form of EDV. 7) Extant varieties5 - PVP legislations of all UPOV member countries protect only the new varieties of plants which have been developed by plant breeders using different conventional breeding methods including genetic modification for the purpose of

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developing transgenic plants. In addition to new, the existing varieties known as extant varieties are also eligible under the scope of protection in India & Zimbabwe. However, Indian legislation has specified a specific period for registration of an extant variety for protection, if it conforms to such criteria of DUS as specified under the regulations. 8) Right of priority - All countries under study provide rights of priority of filing of applications uniformly in their countries, in case the application has already been filed with the other member countries within a period of 12 months from the date of filing of the application in the first country. 9) Compulsory licensing - The provision exists in all the countries except a few to protect and safeguard the public interest. This provision enables the State to ensure availability of adequate quantities of the seeds and planting material of the protected varieties at reasonable prices to farmers. It has a great significance for the developing countries and this provision finds place in all the member states. 10) National register of protected varieties –The existence of National register of protected varieties is mandatory in all PVP legislations under study.The National register will be having the details of all the registered plant varieties with the names and addresses of their respective breeders, the rights of such breeders in respect of the registered varieties, the particulars of the denomination of each registered variety, its seed or other propagating material along with specification of salient features thereof and such other matters as may be prescribed. 11) Exclusion of certain plant varieties - The provision is made to protect public order or public morality or human, animal and plant life and health

or to avoid serious prejudice to the environment under the ambit of protection. This provision exists only in China, France, India & Zimbabwe. 12) Reciprocal treatment (Reciprocity) - Members of the International Conventions, Treaties and regional agreements grant rights to foreign plant breeders from other member countries too. The provision finds place in PVP legislation of all the Convention countries including India and Zimbabwe. It is an important provision of the PVP based on national treatment to the nationals of such other countries applying for registration of a variety or be entitled to get a variety registered under the Act in other countries. This is as per the basic philosophy UPOV Convention and WTO. 13) Infringements and penalties – It is an integral part of any legislation including PVP, but penalties are usually restricted to the monetary compensations in most of the developed countries. Barring a few PVP legislations of the developing countries, which have proposed fine in monetary terms or imprisonment, or both depends upon the gravity of charges established in the course of trials. Whereas most of the developed countries have the provision of compensation of damages on account of infringement of Plant Breeders’ Rights find explicit mention in their laws. 14) Title of legislation - The title of the legislation is very important and in general all the legislations under study emphasizes intention of their laws is to Protect the Plant Variety or Plant Breeder’s Rights or Rights of the Breeders of New Plant Varieties except the Republic of Korea where the title focuses on seed industry and to contribute to stability of agriculture, forestry and fishery by enacting provisions on protection of the breeders’ rights, management of the variety

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performance of the major crops, seed production certification, marketing etc.15) Annulment/nullity of plant breeders’ rights– It finds invariably

mentioned in all the PVP legislations of all the countries. 16) Annual/Renewal fees – It exists in all the countries except USA.

4 Under PPV&FR Act, 2001 “essentially derived variety”, in respect of a variety (the initial variety), shall be said to be essentially derived from such initial variety when it – (i) is predominantly derived from such initial variety, or from a variety that itself is predominantly derived from such initial variety, while retaining the expression of the essential characteristics that results from the genotype or combination of genotypes of such initial variety; (ii) is clearly distinguishable from such initial variety; and (iii) conforms (except for the differences which result from the act of derivation) to such initial variety in the expression of the essential characteristics that result from the genotype or combination of genotype of such initial variety;5 Under PPV&FR Act, 2001 “Extant variety” means a variety available in India which is - (i) notified under section 5 of the Seeds Act, 1966; or (ii) farmers; variety; or (iii) a variety about which there is common knowledge; or (iv) any other variety which is in public domain;

Table 2: Highlights of the comparisonHighlights / Particulars RemarksBotanical Genera & species All botanical genera & species in all 37 countries. But listed in

Albania (22), Azerbaijan (46), Brazil (180), China (230), Singapore (17), South Africa (517), Trinidad & Tobago (3 families and 5 genera) and Uzbekistan (95)

Duration (years) 15 – 18 (developing countries), 20-25 (developed countries) with additional 5-10 years protection period extension by Uzbekistan on the request of the applicant.

Conditions for Protection Novelty, Distinctiveness, Uniformity, Stability and a proper denomination (all countries) however in some of the countries the word uniformity has been replaced by homogeneous

Right of priority All countries Plant Breeders’ Rights All countries covered acts of production or reproduction, conditioning

for the purpose of propagation, offering for sale, selling of other forms of marketing, export and import, stocking for any of the purposes as stated above.

Exclusion of certain genera and species

Exists only in China, France, India and Zimbabwe

Researchers’ Privilege All countriesFarmers’ Privilege Explicitly mentioned in Albania, Argentina, Brazil, Bolivia, Chile,

China, Germany, Japan, Mexico, Nicaragua, Paraguay, United Kingdom, USA, Uruguay and Zimbabwe. Indian law has a separate chapter on Farmers’ Rights

Extant Varieties India and Zimbabwe onlyEssentially Derived Varieties (EDV)

All countries except in Albania, Argentina, Canada, Chile, China, , Kenya, Mexico, Trinidad & Tobago,

National Register of protected varieties

All countries

Annual Fee / Renewal Fee All countries except USAAnnulment / nullity of PBR All countries Compulsory Licensing All countries including Argentina, Portugal and Uruguay have

specified period ranging from 2-4 years with provision of further extension

Reciprocity/ National treatment

Based on general principle of Reciprocity

Infringement & Penalties Monetary and civil nature but in some countries it is monetary or imprisonment or both

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Source: http://www.upov.int/portal/index.html.en

ii) Unique Provisions The present paper reveals interesting and unique provisions in some of the PVP laws under study (Table 3). The Austria, Norway and South Africa legislations provide confidentiality of business and trade secrets including the details of parental lines of the hybrids while publishing in the journal for the purpose of inviting opposition from the public before grant of protection. Japan and Korea provide protection to the aquatic plants whereas algae and fungi are covered under the scope of protection in Australia, New Zealand, USA and Zimbabwe. Animal breeds get an explicit mention for the protection along with plants in the legislations of Azerbaijan, Czech Republic, Russian Federation and Slovakia, which is an interesting feature and call for further study to know how these countries have been implementing the same provisions for protecting the animal breeds as well. Similarly, the legislations of Azerbaijan and USA also made provision that the employees of their organization can’t have interest in plant variety protection during the periods of their employments to apply for plant variety protection to acquire directly or indirectly, except by inheritance of bequest. While studying the PVP legislations it has been observed that in contrast to most of the legislations only the Canadian and Indian PVP law provides laying the reports of progress of registration to their respective Parliament. Most of the countries provide farmers’ privilege to the small farmers of their countries to strike a balance between the rights of the plant breeders and the right to reuse farm saved seeds by farmers in the form of farmers’ privilege. The

farmers’ privileges for the purpose of sowing of seeds from the harvest of the crop for the next season find mention in 12 countries including Brazil wherein the farmers’ privilege are explained in detail and the farmers’ privilege is limited for a period of 2 years in Russia and Uzbekistan . Indian legislation is an unique example with regards to farmers’ rights which is a corner stone of the law. Indian legislation treats a farmer equivalent to a plant breeder and he is eligible for protection of his variety under the law, if it conforms to the criteria of Novelty, Distinctness, Uniformity and Stability. PVP legislations of Poland, Sweden, Albania, Trinidad and Tobacco, Finland, Slovakia and USA provide that a variety could be protected under their respective legislation even if it is discovered and developed by breeders and also making commercial use thereof. In France and European Union there is dual protection system one is centralized known as Community Plant Variety Office (CPVO) and other is the national system applicable to the respective member countries of the European Union. A variety can be protected under a single system at a time and dual protection is not allowed. In USA and in some other countries there are three kinds of protection i.e. patents, utility patents and plant variety protection whereas patents and plant variety protection are available in USA & Australia. Besides, patent protection also exists in Albania, Italy and Russian Federation and Uzbekistan. In Korea the legislation provides the grace period after the expiration of the paying period, on payment of double fee by the applicant and also exemption of varietal protection fee in case of Government or a district self-ruling entity as an applicant. The

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Minister of Agriculture has been vested (with enormous) power for approval and rejection of plant breeders rights to a variety in PVP legislations of Denmark and the Netherlands. Nicaragua legislation covers donation under the scope of plant breeders’ rights and renunciation of PBR by the owner at any point of time, consequently protected variety comes under public domain. The Polish legislation has devised a unique letters designations for individual plant groups for registration viz. R-Agriculture, W-Vegetables, S- Fruits, L-Forest plants, O-Ornamental, P- other plants. In South Africa, if the applicant is State then the plant breeders rights can be granted without any fee and the State may also takes over from any plant breeder rights by payment of certain amount of compensation. It was interesting to find that few countries like Japan, Kenya, Uruguay, Paraguay have comprehensive law wherein the IPR is integrated with seeds production,

quality control, certification and marketing. Having one single agency in place of multiple agencies in regulating various inter related activities of seed sector like National Center for Seeds and Seedlings (NCSS) of Japan is an interesting feature. Apart from the Governing Board/PVP Authority have, adequate representation and farmers (India, USA), women’s organization related to agricultural activities (India, Norway), Private seed industry(France, India, Poland and USA), other seed related associations (India, Portugal) have been ensured. Subordinate legislations of some of the States have also indicated the academic qualification of their officers dealing with the registration process (India, Paraguay). The PVP law of Albania is very simple, plain language and easy to read and understand; Australian law has been drafted with very clear definitions of terminologies whereas USA law is very exhaustive and articulated.

Table 3: Unique provisionsAustria, Norway, South Africa

Confidentiality of business and trade secrets including parental lines

Australia, USA,Zimbabwe

Algae & fungi are also covered for protection

Australia , Azerbaijan USA

Employees can’t have patents on their names whole life and only after 3 years leaving their services Azerbaijan – Stimulation of selection work by the state under Article 28-29

Australia Dual protection of plant varieties i.e. plant patent and PVP certificateCanada and India Laying the report of progress of the registration to the ParliamentAustria, Brazil, Chile, Finland, Iceland, Mexico, Nicaragua, Poland, Sweden, Slovakia, Uruguay

Farmers’ privileges

Switzerland Federation council determines the plant species applicable for farmers’ privilege purpose

Colombia Prescribed the timeframe of 3 years for short cycle varieties and 10 years for medium and long cycle varieties for grant of PBR Specified the manner for grant of funds for research initiative Novelty of the plant defined in many ways Varieties of Common Knowledge(VCK) under scope of protection Employer to transfer part of profit from plant breeding to its breeders in order to stimulate research activity

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France, EU Double system (Community Plant Variety Office & national) but one at a time. Dual protection not allowed

Chile, France, Kenya, USA, India

Fine and imprisonment both in case of infringement of PBR

Denmark & The Netherlands

Minister vested with enormous power for approval and rejection of PBR to a variety If testing material fails to comply the rules set out in Act, the material will be withdrawn the defective material from the market within a specified period (u/s 17) Designation of special breeders rights division in the District Court at Hague

Japan Aquatic plants are covered No registration fees where the holder of a breeder right is the national government

Japan, Kenya , Uruguay

Comprehensive law on seeds, quality control, certification, marketing and in Japan IPR law includes Production / Marketing

Korea Aquatic plants Grace period after the expiration of the pay period, but fees will be doubled Exemption of varietal protection fees in case of Government or a District Self-ruling entity.

Nicaragua,Sweden

Donation also comes under the scope of PBR Renunciation of PBR – and consequently comes under the public domain

New Zealand, Zimbabwe

Fungi are covered

Poland Letter designations for individual plant groups for registration i.e. R-Agriculture, W-Vegetables, S- Fruits, L-Forest plants, O-Ornamental, P- other plants

Albania, Australia, Azerbaijan, Russian Federation, Czech Republic and Slovakia, Uzbekistan

Animal breeds are under the scope of protection. Uzbekistan, the breed has been defined as a group of animals (including birds, insects and silkworms or their hybrids) which is defined by genetically determined biological and morphological characteristics and features. The period of protection is 20 / 25 years (tree vines, fruits and forest species) with a provision to extant the period of validity of a patent at the request of the patent owner, but by not more than 10 years. Russian and Ireland provide longest duration of protection i.e. 30-35 years. Patent protection

South Africa, Zimbabwe

State bound by PBR without any fees (u/s 30) and may take over by payment of compensation

UK Measure to prevent injurious cross pollination affecting crops of seeds [section 33 (5)(a)(b)]

USA, Australia Three systems of protection i.e. patents, utility patents & certificates of registration Library to the aid the examiners in the discharge of their duties Single certificate for varieties indistinguishable Confidential status of application Grandfather clause Section 112

Zimbabwe, India Extant varieties Uruguay National register of violators / offenders and its notification Albania, Australia, Bolivia, Finland, Hungary, Kenya, Nicaragua, Poland, Slovakia, Sweden,

Discovery of new plants are protected, if improved. Has discovered or developed that variety or that person’s legal successor UK – Measures to prevent injurious crops pollination affecting crops of seeds Section 33 (5)(a)(b)

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Trinidad & Tobago, UK

Source: On the basis of information gathered from the websites of UPOV and Farmers’ Rightsiii) Salient provisions of PPV&FR Act, 20016:- India has enacted the PPV&FR Act, 2001 by adopting sui generis system as per its international commitments under Article 27(3) b of the TRIPS agreement of the WTO. The Act provides protection for all the botanical genera and species as notified by the Central Government for a period of 15 and 18 years for annual and perennial plants subject to confirmation of the NDUS criteria and a distinct and valid denomination. All the other important provisions as discussed in foregoing paras are there including plant breeders’ rights, research rights, farmers’ rights, compulsory licensing, EDV and the provision of reciprocity on the basis of national legislations. The Indian legislation has two unique provisions i.e. protection of extant varieties and farmers’ rights. Farmers’ Rights include right to save, use, sow, re-sow, exchange, share or sell his farm produce including seed of a variety protected under the Act in the same manner, as he was entitled before the coming into force of this Act. However, a farmer shall not be entitled to sell branded seed of a variety protected under this Act. Besides, a farmer is also entitled for registration of a variety developed by him in the same manner as a breeder with relaxed standards for DUS testing and novelty. Farmers’ rights includes benefit sharing, protection of innocent infringement, right to compensation in case of losses for not meeting the claims made by the seed company and exemption from fees in any proceeding before the Authority or Registrar or the Tribunal or the High Court. Farmers have rights to recognition for their activities relating to conservation and preservation of genetic resources of the landraces and wild relatives of

economics plants and their improvement through selection and preservation out of a National Gene Fund, which have been established by the Central Government for the purpose. With regards to protection of the extant variety, it is stated that these are the varieties which have already been released for cultivation and notified under the Seeds Act, 1966. Varieties of common knowledge (VCK) and Farmers’ Varieties, which are presently under cultivation but not notified under Seeds Act and have been explained in, section 2(j) of the Act. The PPV&FR Authority was established by the Govt. of India to implement the provisions of the said Act. The Authority has so far received more than 9000 applications for different categories including farmers’ varieties for more than 60 genera/species out of 92 genera /species notified. Authority issued about 2000 certificates of registration over a period of a decade. India is quite mature with regards to PVP implementation and got rich experience at ground level.iv) Suggestions for improvement and fine-tuning of PPV&FR Act, 2001: While implementing the PVP legislation for a decade has brought to our knowledge some problems and real issues confronted by the Authority. In spite of passage of almost a decade after its enactment, the awareness about this law is scanty in farmers & equally in literate society and scientists. Because of large geographical size of the country it is not possible for Authority to reach every nook and corner of the country side. The other factor which is equally responsible for awareness is the illiteracy which is wide and rampant in farmers particularly. The need for availability of large databases of all kinds of varieties

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including’ varieties is of paramount importance and required prior to grant of registration of new and extant varieties. At present, the Authority takes financial grants from the nodal Ministry to sustain it, but for proper and smooth functioning, it should be self-sustaining rather than depends on Govt. grants. With the increase in the basket of registration of the Authority with additional new genera /species year after year, the issue relating to financial management and prudence of DUS centres has posed a big challenge. The Authority must frame DUS test guidelines for all existing crops/ species at one go and capture all the extant varieties before their possible extinction or misappropriation in respect of the said crop species at a single go to enrich database, encourage plant breeding and to provide incentive for research and development. The current practice of framing DUS test guidelines in a piecemeal manner after almost a time gap of one or two years is detrimental to plant breeding and IPR in plant varieties. Till now the Plant Varieties Registry has not been established like other well established registries and there is no facilities for search based system. Further, under the PPV&FR Act, 2001 the main function of the Authority is to publish a compendium on all farmers’ varieties and the contribution of farmers with respect to all varieties in India and till now this has not been done. To curtail the long registration period, Authority has to explore the possibility of curtailing of testing time period by using the testing reports from the applicants by way of affidavits and comparing with available databases of suitable reference varieties with necessary checks and balance. The registration of Farmers’ Varieties on quality testing and recording of passport data for ascertaining the claimed characteristics and comparing with proper match of reference varieties

in field trials is a real challenges before the Authority. Further, the utilization of National Gene Fund required framing suitable operational guidelines and fiscal management. Some of the issues which urgently requires attention has been looked into and may require amendments and refinements. The Authority has submitted a proposal to this effect to DAC, Ministry of Agriculture for consideration. Indian Law is in harmony with the other relevant national legislations and international Conventions and Treaties like CBD7 and ITPGRFA8. Our neighboring countries can make use of Indian experience for drafting their own legislation. It is relevant to mention that a SAARC platform is already available and may be widely used in establishing Plant Variety Protection Forum at SAARC level initially. Further for analyzing introspectively, India and other developing countries needs a comparative study of plant variety laws so that refinement of laws periodically could be undertaken so that law remains dynamic and not static to meet the future challenges.v) Regional IPR Forum for South-East Asia There are 17 countries in the South Asia, out of which three countries (China, Vietnam and Singapore) have already joined UPOV and the remaining countries so far have not joined the UPOV. However, nine countries of Asia i.e. Bangladesh, Cambodia, India, Indonesia, Malaysia, Pakistan, Philippines, Sri Lanka and Thailand are having the observer status in the UPOV. Agriculture is a commercial activity in developed world and only 2-5% of the total population is engaged in agriculture farming with large farm size and high subsidies and seed industry is primarily dominated by private sector. The situation of agriculture in developing

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countries is entirely different with majority of population sustaining on agriculture with large number of small and marginal farmers with fragmented piece of land for cultivation. Thus, the overall conditions of the agriculture system in the South Asian Countries and African continent neither suit to the UPOV for its membership nor conducive for supporting the livelihood of farming communities. Some other points in support of having an alternative regional IPR forum in place of UPOV are as under: Developing countries of South Asia

and Africa are one of the biodiversity hotspots having a rich genetic diversity of flora and fauna which is result of conservation and protection of farming communities from ancient period of human civilization.

Plant Genetic Resources for food and agriculture, landraces, wild relatives of cultivated plants and traditional varieties are the raw material for the development of new varieties.

Agriculture is the main profession of majority of the population and. therefore, the national food and nutritional security is a crucial issue for these countries at the time of natural calamities.

Consequent upon the enforcement of CBD, the States have sovereign rights over their natural resources including agro-biodiversity because of the contribution of the farmers to the conservation and preservation of their genetic variability and its knowledge & properties.

Developed economies have the twin objectives of exploiting the genetic variability of such gene rich countries and they foresee big markets for their finished products by applying their advanced technologies in these developing economies with cheaper human resource powers.

The African Regional Intellectual Property Organization (ARIPO)9

consisting of 19 member countries recently joined UPOV as per UPOV 1991 Convention on 10th July, 2014. The basic objective of the forum is to form a group / club of likeminded countries having similar agrarian economies, joins together with common objectives in the field of Protection of Plant Varieties and to facilitate international seed trade and business opportunities for mutual benefits. These countries may pool together their resources in the field of Intellectual Property Rights (IPR) and can move forward. It is expected that the joint collaborative programmes activities within the members of the forum will lead to creation of new varieties of plants and development of niche for global market, seed trade, transfer of best agricultural production technologies, utilization of the intellectual property rights to develop diversified businesses for producing seeds and planting material, and further development of the agriculture, forestry, fisheries, and other agro food industries of Asia. The formation of African Regional Intellectual Property Organization is a good lesson for Asia to establish a separate regional Intellectual Property Platform for Asia in their own interest which could serve nations better ways based on their similar agrarian economies. Again East Asia Plant Variety Protection Forum (EAPVPF)10, which is an official forum responsible for internationally harmonized Plant Variety Protection in East Asia. The forum is being used as a venue for the organizations in charge of protecting plant varieties in nations of East Asia (ASEAN +3) to exchange a wide range of ideas and information to facilitate the improvement of the implementation and the harmonization of the plant variety protection system in the

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Asian region through co-operative efforts and mutual understanding of each country’s systems and conditions. India has successfully implemented the PPV&FR Act and the Indian legislation is unique with regards to farmers’ rights and also hailed by the International Communities. The Indian PVP Act balances an equilibrium between the plant breeders and the farmers utilizing the flexibilities under the TRIPS agreement (Sahai S., 2001). The Indian PVP experience is now matured and almost completing a decade of its implementation may show a path to the other neighboring countries, to create an atmosphere of IRP as per their national requirements and join together to form a regional platform of IPR, alternative to the UPOV System. As already mentioned our neighboring countries including the countries of the South Asia will join the regional forum to have a mutual cooperation in seed sector and to facilitate International Seed Trade and to exchange the agriculture production technologies for mutual benefits. In view of the similar agriculture systems. The regional forum will give an opportunity to develop their economies and Plant Variety Protection System.Some efforts have been made in India to provide an alternative to the UPOV. The model law developed by the Organization of the African Unity and Gene Campaign’s Convention of Farmers and Breeders (CoFaB)11 are being discussed

in various fora. CoFaB has been described by the UNDP (United Nations Development Programme) as “a strong and coordinated international proposal which offers developing countries a far better alternative to European legislation, by focusing on the need to protect farmers’ rights and food and nutritional security goals of their people”12. Developing countries in Asia and Africa have the agrarian economies, which are more or less similar, therefore it is appropriate if they have their own platform to tackle effectively their common problems and needs. These countries of tropics are gene rich countries having wide agro- diversity, which can serve as the raw material for the development of new plant varieties. This platform can function as trustworthy source of supply of quality seeds to the majority of small and marginal farmers to achieve their national food and nutritional security with due importance to farmers’ rights of indigenous people and farming communities. This platform not only maintain the genetic diversity in the field but also promote the appropriate mechanism for the enforcement of the rights of the indigenous communities, including farming communities, and breeders and the congenial environment essential to biological resources, traditional knowledge, technologies and agri. farming practices of mutual interest.

6 www.plantauthority.gov.in7 https://www.cbd.int/ 8 http://www.planttreaty.org/ 9 African Regional IPR Organization: http://www.wipo.int/wipolex/en/profile.jsp?code=ARIPO 10The East Asia Plant Variety Protection Forum (http://eapvp.org/) consist of China, Republic of Korea, Japan, Indonesia, Malaysia, Philippines, Thailand, Brunei Darussalam, Vietnam, Singapore, Myanmar, Lao PDR and Cambodia are the participating countries.11 CoFaB: http://genecampaign.org/farmers-rights/12 Gene Campaign along with Centre for Environment and Agriculture Development (CEAD) developed Convention of Farmers and Breeders (CoFaB) as an alternative to UPOV to provide a forum for developing countries to implement their Farmers and Breeders Rights. CoFaB suggests an agenda appropriate for developing countries by reflecting their strengths and their

14

http://genecampaign.org/farmers-rights/

http://eapvp.org/

http://eapvp.org/

http://www.wipo.int/wipolex/en/profile.jsp?code=ARIPO

http://www.planttreaty.org/

https://www.cbd.int/

http://www.plantauthority.gov.in/


vulnerabilities, and it seeks to secure their interests in agriculture and fulfil the food and nutritional security goals of their people.

CONCLUSION

Keeping in mind the importance of agriculture in India’s economy, and successful implementation of the PVP legislation for about a decade India may play a catalytic role to strengthen the seed sector and take initiative to form a Regional IPR forum for mutual benefits.Currently, India is neither member to the UPOV nor notified list of convention countries. However, on the provision of reciprocity, India has developed bilateral cooperation with several countries including the Netherlands and Germany in the field of Protection of Plant Varieties. In other words, IPR in plant varieties in India is without international reciprocity whereas IPR in other field of industries like Trade Marks, Copyrights, Patents enjoy international reciprocity as to IPR protection. India is sometimes isolated as far as per International Conventions relating to plant variety protection is concerned. The situation must be addressed intelligently so as to make it positive with respect to plant breeders’ rights. The seed sector in India is quite matured and has taken a big leap with the development of private seed industry and the Indian plant breeders can have the same privilege to register their variety elsewhere in the world. This could only be possible when we would be

in a position to convince the International Community that the farmers’ rights in India as applicable for majority of the farmers belonging to small and marginal category are not detrimental to MNCs.To begin with, India must take initiative at SAARC level taking a cue from EAPVPF. SAARC13 countries have not yet PVP laws and other South Asian countries are not members of UPOV which is again an added advantage and none of the SAARC countries are having full-fledged plant variety protection law except India. India can take a lead in this regard and assist the other member countries technically and legally. Further, field DUS testing must be dispensed with by India in accordance with universal practices. Already checks and balances are there in our legislation. Though India has taken a small step in plant variety protection it can convert the same into a giant leap by bringing it under the aegis of SAARC.India must also take earnest efforts to convince other developed countries that the farmers’ rights enshrined in our PVP legislations are not against the economic interest of MNCs engaged in seed business as our farmers have been engaged in marginal and sustaining farming and protecting their rights cannot affect them in any way.

13SAARC: http://www.saarc-sec.org/ consist of Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, Sri Lanka

REFERENCESStephen B. Brush: The Demise of ‘Common

Heritage’ and Protection for Traditional Agricultural Knowledge

www.plantauthority.gov.in http:// www.upov.org/en/publications/npvlaws/

index.htmwww.saarc-sec.orghttp://www.upov.int/en/news/2003/pdf/

cbd_response_oct232003.pdf

(http://www.upov.int/export/sites/upov/es/documents/Symposium2003/wipo_upov_sym_10.pdf)

The East Asia Plant Variety Protection Forum (http://eapvp.org/)

African Regional IPR Organization Sahai S. (India’s Plant Variety Protection and

Farmers’ Rights Act, 2001) current science, Vol.84, No.3, 10 February, 2003

15

http://eapvp.org/

http://eapvp.org/

http://www.upov.int/export/sites/upov/es/documents/Symposium2003/wipo_upov_sym_10.pdf



http://www.upov.int/en/news/2003/pdf/cbd_response_oct232003.pdf

http://www.upov.int/en/news/2003/pdf/cbd_response_oct232003.pdf

http://www.upov.org/en/publications/npvlaws/index.htm



http://www.plantauthority.gov.in/

http://www.saarc-sec.org/


Sahai S., 2001, An Analysis of Plant Variety Protection & Farmers Rights Act, 2001, Gene Campaign, New Delhi

Ravishankar A., Sunil Archak, 1999, searching for Policy Options; In CoFaB a suitbale alternative to

UPOV, Economic & Political Weekly December 25, 1999.

www.farmersrights.org/database/index.html

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http://www.farmersrights.org/database/index.html


EFFECT OF LIQUID AND CARRIER BASED BIOINOCULANTS ON CONTENT AND UPTAKE OF MAJOR AND MICRO NUTRIENT IN

SUNFLOWER

S.B. Dahiphale, S.S. Mane and S.J.SupekarCollege of Agriculture, Badnapur,VNMKV,Parbhani-431402(M.S.)India.

Email: [email protected]: 29.12.16Accepted: 24.03.17

ABSTRACTLiquid biofertilizer formulation is the promising and updated technology of the conventional carrier based production technology. Comparative performance of liquid and carrier-based bio-inoculants were studied for content and uptake of NPK for sunflower. It was found that The combined application of the microbes enhanced the content and uptake of NPK better than individuals application. NPK content and uptake by sunflower was significantly the highest noted in dual inoculated plots (T8) with liquid form of Azotobacter and PSB over other treatments but was found statistically at par with carrier based source (T5). Both of these treatemnts i.e.T8 and T5 were superior over their single inoculations and control plots.

Key words : Liquid and carrier-based-bio-inoculants, Single and dual application.

Liquid biofertilizer formulation is the promising and updated technology of the conventional carrier based production technology which in spite of many advantages over the agrochemicals, left a considerable dispute among the farmer community in terms of several reasons major being the viability of the organism. Shelf life is the first and foremost problem of the carrier based biofertilizers which is up to 3 months and it does not retain throughout the crop cycle, liquid biofertilizers on the other hand facilitates the long survival of the organism by providing the suitable medium which is sufficient for the entire crop cycle. Carrier based bio fertilizers are not so tolerant to the temperature which is mostly unpredictable and uncertain in the crop fields while temperature tolerance is the other advantage of the liquid biofertilizers. The range of possible contamination is very high as bulk sterilization does not provide the desirable results in the case of carrier-based biofertilizers, where as the contamination can be controlled constructively by means of proper sterilization techniques and maintenance

of intensive hygiene conditions by appropriate quality control measures in the case of liquid biofertilizers. Moisture retaining capacity of the carrier based biofertilizers is very low which does not allow the organism viable for longer period and the liquid biofertilizers facilitates the enhanced viability of the organism. However, the administration of liquid biofertilizers in the fields is comparatively easier than carrier based biofertilizers. The other disadvantages of carrier based biofertilizers like poor cell protection, labour intensity, and dosage controversy, limited scope of export, expensive package and transport, very slow adaptation by the farmer community are some of the strongest problems which are being solved by the liquid biofertilizers very effectively. Therefore, liquid biofertilizers are believed to be the best alternative for the conventional carrier based biofertilizers in the modern agriculture research community witnessing the enhanced crop yields, regaining soil health and sustainable global food production (Pindi and Satyanarayana, 2012).

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Considering the above facts, the research work on “Comparative performance of liquid and carrier based bioinoculants in sunflower (Helianthus annus)” was conducted during 2015-16 with following treatments at Experimental farm of College of Agriculture, Badnapur:Treatments:T1: Un-inoculated and unfertilized (control)T2:Only RDF (60:40:30 kg N, P2O5 and K2O ha-1)Carrier based inoculants (with RDF)T3: RDF + AzotobacterT4: RDF + Bacillus megaterium (PSB)T5: RDF + Azotobacter + Bacillus megaterium (PSB)Liquid inoculants (with RDF)T6 : RDF + AzotobacterT7 : RDF + Bacillus megaterium (PSB)T8 : RDF + Azotobacter.+ Bacillus megaterium (PSB)

MATERIAL AND METHODS

Total eight treatments of bioinoculants were replicated three times in RBD. Seed treatment was done immediate before sowing with carrier based bioinoculants @ 250 g 10 kg-1 seed and liquid bioinoculants @ 100 ml 10 kg-1 seed. Soil pH : It was determined in (1:2.5) soil water suspension using digital pH meter (Jackson, 1973). Digital electronic conductivity: It was estimated in (1:2.5) soil : water ratio using conductivity meter (Jackson, 1973).Organic carbon: Organic carbon in soil was estimated by Walkley and Black’s rapid titration method as described by Jacksons (1973).Calicium carbonate: The free calcium carbonate was determined by rapid titration method as outlined by Piper (1966).Available nitrogen: It was estimated by using alkaline permanganate method as suggested by Subbiah and Asija (1956).Available phosphorus: It was determined by using 0.5 M sodium bicarbonate as an extractant as outlined by Olsen et al. (1954).Available potassium: It was determined by using normal ammonium acetate as an

extractant and measured on flame photometer Jacksons (1973).DTPA extractable zinc and iron:

DTPA extractable zinc and iron in soil was estimated as per procedure described by Lindsay and Norvell (1978).Plant analysis : Collection and preparation of plant sample : The treatment wise plant samples of sunflower were collected at various growth stages. Whole plants along with roots were cleaned with distilled water. The roots of the plants were discarded and the plant samples were dried in shade and oven dried at 700 c for 12 hours and they were ground in digital electronically operated stainless steel blades grinder up to maximum fineness. The ground samples were stored in polythene bags with proper labeling for chemical analysis (Bhargava and Raghupathi, 2001).Nitrogen content: The nitrogen content in dry seed and stalk was determined by Micro Kjeldhals method (AOAC, 2013).Phosphorus content: The phosphorus in stalk and seed was estimated spectrophotometrically by vanadomolybdate phosphoric acid yellow colour method (Jackson, 1973).Potassium content: Potassium content in plant and seed was determined from the diacid extract on flame photometer (Jackson, 1973).Uptake of nutrients: Uptake of nutrients i.e. N, P, K and S was calculated by considering biological yield (i.e. seed and whole plant) and concentration of the particular nutrient.Uptake (kg ha-1) = Nutrient content (%) x Yields of seed/Dry matter yield (kg ha-1)/ 100Total uptake (kg ha-1) = Seed uptake (kg ha-

1) + Dry matter uptake (kg ha-1)

RESULTS AND DISCUSSION

Effect of liquid and carrier based bioinoculants on content and uptake of nitrogen in sunflower The content and uptake of nitrogen by sunflower was also found to be enhanced

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significantly with seed inoculation of Azotobacter and PSB along with RDF (Table 1). The data shows increase in N content and its uptake by seed, stalk and total was maximum in dual inoculated treatments T5 & T8 over single inoculation and control. Further, the figure recorded (87.82 kg ha-1) in dual inoculation plots showed the requirement of N by sunflower for production. Dual inoculation of Mesorhizobium sp. + PGPR as carrier or liquid inoculants slightly improved the N uptake by seed and stalk compared to their inoculation alone. The results are in agreement with the findings of Gupta (2006) because of improvement in crop growth, nitrogen fixation and solubilization of insoluble P in soil by the inoculated microorganisms (Rodriguez and Fraga, 1999). Sarwar Muhameed and Tahir (2012) found seed and stover nitrogen content significantly affected by different

inoculation in sunflower. The increase total uptake of nutrients might due to the combined application of inorganic fertilizers and biofertilizer similar reports had been made byr Shillode and Tahir (2016). The increase in nitrogen content with phosphorus application might be ascribed to the positive effect of phosphorus on cell division and root elongation leading to higher nutrient concentration in plant and also increase in nitrogen content production of auxins and gibberellins by P-solubilzers which help in increasing root growth and nutrient absorption resulting higher nutrient content (Chesti and Ali 2008). Tanwar and Sakwat (2003) also revealed the highest N content in grain and straw in dual inoculation of Bradyrhizobium + PSB treatment in blackgram. Namdeo and Gupta (1999) found that seed inoculation with phosphate solublizing bacteria increased uptake of N.

Table 1. Effect of liquid and carrier based bioinoculants on content and uptake of NPK in sunflowerSr. No. Treatment N Content (%) N Uptake (kg ha-1)

P Content (%)

P Uptake (kg ha-1)

Seed Stalk Seed Stalk Total Seed Stalk Seed Stalk Total

T1 Uninoculated and unfertilized (Control) 2.51 0.51 25.45 15.71 41.16 0.61 0.20 6.18 6.07 12.26

T2 Only RDF (60:40:30 kg NPK ha-1) 2.72 0.59 43.63 22.58 66.21 0.68 0.24 10.90 8.46 19.36

Carrier based inoculantsT3 RDF + Azotobacter 2.89 0.68 47.72 25.71 73.43 0.69 0.26 11.40 9.34 20.74

T4 RDF + Bacillus megaterium ( PSB) 2.87 0.64 46.79 23.61 70.40 0.71 0.29 11.62 9.82 21.44

T5RDF + Azotobacter + Bacillus megaterium ( PSB)

3.05 0.76 51.62 29.71 81.33 0.73 0.30 12.41 11.85 24.26

Liquid inoculantsT6 RDF + Azotobacter 2.96 0.68 49.44 26.75 76.19 0.69 0.28 11.87 10.73 22.60

T7RDF + Bacillus megaterium ( PSB)

2.89 0.72 47.89 26.50 74.39 0.72 0.29 11.74 11.04 22.78


3.14 0.82 55.02 32.73 87.82 0.76 0.32 13.87 12.69 25.56

S.E.± 0.062 0.019 2.44 1.07 2.54 0.014 0.008 0.58 0.71 0.85C.D. at 5 % 0.189 0.059 7.38 3.23 7.67 0.044 0.024 1.75 2.14 2.58C.V. % 3.77 5.01 9.22 7.29 6.20 3.62 5.09 9.01 12.32 6.99

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Sr. No. Treatment K Content (%) K Uptake (kg ha-1)Seed Stalk Seed Stalk Total

T1 Uninoculated and unfertilized (Control) 1.14 0.31 11.56 9.33 20.89T2 Only RDF (60:40:30 kg NPK ha-1) 1.26 0.35 20.22 11.72 31.94

Carrier based inoculantsT3 RDF + Azotobacter 1.29 0.37 21.89 12.94 34.83T4 RDF + Bacillus megaterium ( PSB) 1.27 0.35 20.73 12.80 33.53

T5 RDF + Azotobacter + Bacillus megaterium ( PSB) 1.31 0.38 22.13 14.76 36.89

Liquid inoculantsT6 RDF + Azotobacter 1.30 0.36 21.73 13.84 35.57T7 RDF + Bacillus megaterium ( PSB) 1.28 0.35 21.09 13.79 34.88

T8 RDF + Azotobacter + Bacillus megaterium ( PSB) 1.39 0.40 24.37 15.79 40.16

S.E.± 0.032 0.006 0.87 0.61 0.95

C.D. at 5 % 0.098 0.019 2.64 1.87 2.88

C.V. % 4.41 3.03 7.43 8.17 4.94Effect of liquid and carrier based bioinoculants on content and uptake of phosphorus in sunflowerSimilar to that of Nitrogen, content and uptake of phosphorus by sunflower was also influenced significantly with the seed inoculation of Azotobacter and PSB along with RDF as compared to control T1 and T2. Significantly higher values of content and uptake of P by seed and stalk were recorded in dual inoculated plots (T8) with liquid source however carrier based dual inoculation was also found equally good (T5) showing at par results. Single inoculation also showed better phosphorus content and uptake over control (Table 1). The results are in agreement with the findings of Gupta (2006). The N content and P uptake was enhanced because of improvement in crop growth, nitrogen fixation and solubilization of insoluble P in soil by the inoculated microorganisms (Rodriguez and Fraga, 1999). Moreover, Zehra Ekin (2011) found that concentration of macronutrients i.e.P, K, Ca, Mg, Na were significantly increased by application of PSB and P fertilizers. Maximum increase in concentration of P in the seeds was observed 28.00% as compared to control. Further, Rasal et al.

(2004) observed that increased phosphorus uptake in soybean plants may be due to availability of more phosphorus in the soluble form from phosphatic fertilizer to the growing plant and to the microorganisms in the soil which might have influenced the symbiotic and non-symbiotic activities of the native and applied microflora. This might be helped to plant in fixation of atmospheric nitrogen and making more plant nutrients available which ultimately increased the plant growth, number of pods, final grain yield and nutrient uptake of the soybean crop. Effect of liquid and carrier based bioinoculants on content and uptake of potassium in sunflower: The data narrated in Table 1 shows that the content and uptake of K by sunflower crop was also increased with single as well as dual inoculation of Azotobacter and PSB along with recommended dose of fertilizers over control. Significantly the highest values of K content in seed (1.39 %) and stalk (0.40%) were noted in treatment received both the inoculants along with RDF in liquid form. However, in case of K uptake dual inoculation with carrier based

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source (T5) and liquid source (T8) were noted statistically at par with each other.Dhage and Kachhave (2008) reported that dual inoculation of Rhizobium +PSB enhanced the N, P, K contents in seed and found highest P (2.05%) contents recorded in Rhizobium +PSB treatment. This might due to ability of PSB to transform insoluble phosphate in soil into soluble forms by secreting organic acid.The dual inoculation of Azotobacter + PSB has showed higher concentration than single inoculation of Azotobacter or PSB it may be due to more K availability in soil as a result of disintegration of K minerals due to release of organic acids with the effect of microbes. Namdeo and Gupta (1999) found that seed inoculation with Rhizobium and PSB have higher N, P and K content in plant and seed in Pigeon pea. Similar findings were also reported by Purbey and Sen (2007). Further, Ram et al. (2008) found that

Rhizobium and Azotobacter increased the amount of K content in plant because of the solubilizing effect on K minerals responsible for stimulating effect in green gram.Effect of liquid and carrier based bioinoculants on content and uptake of Fe in sunflower The data presented in Table 2. revealed that the content and uptake of Fe by sunflower crop was also influenced significantly with the seed inoculation of Azotobacter and PSB along with RDF as compared to control. Significantly the highest values of Fe uptake in seed and stalk was noted in treatment receiving both the inoculants along with RDF in liquid form. However, in case of Fe content of seed the liquid source (T8) was found statistically at par with T6 and T7 treatments and content of stalk in liquid source (T8) was also noted statistically at par with (T7) treatment.

Table 2. Effect of liquid and carrier based bioinoculants on content and uptake of Fe and Zn in sunflower. Sr. No. Treatment

Fe Content (mg kg-1) Fe Uptake ( g ha-1) Zn Content

(mg kg-1) Zn Uptake ( g ha-1)

Seed Stalk Seed Stalk Total Seed Stalk Seed Stalk Total

T1 Uninoculated and unfertilized (Control) 61.08 24.85 61.98 75.10 137.08 61.51 25.18 62.42 69.67 132.08

T2 Only RDF (60:40:30 kg NPK ha-1) 67.51 29.34 108.23 108.58 216.82 66.85 28.05 107.03 103.85 210.88

Carrier based inoculantsT3 RDF + Azotobacter 70.20 31.74 115.80 119.83 235.63 69.80 30.00 115.23 113.98 229.21



72.33 33.14 124.30 129.34 253.91 71.25 32.54 120.68 127.02 247.70

Liquid inoculantsT6 RDF + Azotobacter 73.50 34.20 121.51 131.43 252.94 70.10 31.30 119.44 120.54 239.98



74.17 35.52 129.90 142.04 271.94 72.95 34.59 127.94 138.24 266.18

S.E.± 0.96 0.95 4.56 3.51 4.08 1.55 0.76 5.90 4.68 6.80C.D. at 5 % 2.89 2.86 13.75 10.63 12.34 4.68 2.29 17.82 14.15 20.53C.V. % 2.36 5.13 7.02 5.09 3.08 3.88 4.28 9.25 7.12 5.22

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Chand and Somani (2003) revealed that effective use of FYM, biofertilizers along with chemical fertilizers improved Fe and Zn content in mustard. Mekki et al. (1999) reported that organic manure either only or when it associated with biofertilizer increased Fe concentration in millet cuttings. This increase in Fe is mainly due to the action of biofertilizer that rendered phosphorus and most micronutrients in the available form. Therefore studies have been focused on the inoculation P-solubilizing bacteria (PSB) into the soil to increase the availability of native fixed P and applied phosphate as well as nutrients such as Zn, Fe through production of plant growth promoting substances (Adesemoye and Kloepper ,2009). Uptake of N, P, K, S, Fe, Zn and B to the application of micronutrients especially of Mo @ 1 kg ha-1 ammonium molybdate which recorded the highest values in case of uptake of all the nutrients. This might be attributed to increased growth of crop under this treatment due to enhanced N fixation by Mo with Rhizobium inoculation and also more P solubilization and uptake due to action of PSB resulting in higher uptake of all other complementary nutrients. Inoculation of biofertilizers (Rhizobium + PSB) also recorded significantly higher uptake of nutrients as against no inoculation. The findings were in agreement of those reported by Sarawgi et al. (1999) and Patel and Thakur (2003).Effect of liquid and carrier based bioinoculants on content and uptake of Zn in sunflowerPerusal of the results shown in Table 2 shows that the content and uptake of Zn by sunflower with bioinoculants. Both carrier based and liquid sources when applied in combination increased the uptake of Zn by crop as compared to single inoculation. Significantly highest

values of Zn uptake in seed and stalk were noted in treatment receiving both the inoculants along with RDF in liquid form. However, in case of Zn content of seed the liquid sources of bioinoculants (T8) were seen statistically at par with T5, T6 and T7 treatments and Zn content of stalk with liquid source was noted statistically at par with T5 and T7 treatments.Zehra Ekin (2011) found phosphorus fertilizer and PSB applications significantly increased the concentration of micronutrient Zn in the seed of sunflower.The PSB application alone significantly and relatively increased the micro nutrient element concentration of seed , and this is due to nutrient activity of PSB which had stimulated the availability of nutrient element .The concentration of N, P, S and Zn in grains were higher than those in stover. It indicated that these nutrients were translocated from plant parts to the grain during later growth stages. These findings are reported by Dhillion and Dev (1978). The concentration of Zn in plants at different growth stages increased significantly due to both the inoculants and Zn individually while decreased due to application of sulphur. Yaduvanshi (2002) reported that available Zn, after the harvest of wheat crop, ranged from 0.43 to 0.63 mg kg-1 in the different treatments. Ndakidemi and Bambara (2011) found that Rhiziobium strain significantly increases the uptake of Zn in different plant organ of common bean.

CONCLUSIONThe content and uptake of Nitrogen, Phosphorus, Potassium, Ferous and Zinc by sunflower was also found enhanced significantly with seed inoculation of Azotobacter and PSB along with RDF. The data shows increase in N, P,K,Fe and Zn content and its uptake by seed, stalk and total was maximum in dual

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inoculated treatments T5 & T8 over single inoculation and control.

REFERENCESKader, M.A. and Hoque, M.S. 2002 . Effect of

Azotobacter inoculants on the yield and nitrogen uptake by wheat. J.Biological Science., 4:259-261.

Ndakidemi,P.A. and Bambara, S. 2011. Micronutrient uptake in common beanas affected by Rhizobium inoculation and supply of molybdenum and lime. Plant Omics Journal.,4:40-52.

Namwar, A. and Teymur, K. 2012 .Effects of bio and chemical Nitrogen fertilizer on grain and oil yield of sunflower.(Helianthus annusL.) . Annals of Biological Research.3 (2):1125-1131.

Noumavo, P.A. and Kochoni,F. 2013. Effect of different plant growth promoting rhizobacteria on maize seed germination and seedling development. .American Journal of Plant Sciences., 4:1013-1021.

Pindi and Satyanarayana, S.D.V. 2012. Liquid Microbial Consortium- A Potential Tool for Sustainable Soil Health. Pindiand Satyanarayana. J. Biofertil. Biopestici.,pp 3:4.

Pramanik ,K. and. Bera, A.K 2013. Effect of biofertilizers and phytohormone on growth, productivity and quality of sunflower (Helianthusannus L)Journal of Crop sand Weed., 9(2):122-127.

Sharma, S.,Dharmwal, N.S. Sharma, C.R. and Upadhyaya, R.G. 2006. Response of Bradyrhizobium inoculation on microbial status, symbiotic parameters, nitroenase activity and yield of moong (Vignaradiata (L.) Wilczek) under mid hill conditions of Himachal Pradesh. Indian J. Agric. Res., 37(4): 291-294.

Shehata, M.M. and El-Khawas, S.A. 2003. Effect of two biofertilizer on growth parameters, yield characters, nitrogenous components, nucleic acid content, minerals oil content, protein profiles and DNA banding pattern of Sunflower (Helianthus annus L. cv. Vedock) yield. Pakistan J. Biological Sci., 6 (14):1257-1268.

Soleymanifard, A. 2011. Effect of inoculation with bio-fertilizer in different nitrogen levels on yield and yields components of safflower under dry land. American Eurasian Journal Agriculture & Environment Science.,11 (4): 473- 477.

Vessey, J.K. 2003. Plant growth promoting rhizobacteria as biofertilizer. Plant Soil, 255:571-586.

Yaduvanshi, N.P. 2002. Budgeting of P and K for a rice wheat cropping sequence on a sodic soil. Tropical Agriculture 79, 211- 216.

23


ELICITATION OF ISOFLAVONOIDS IN CELL CULTURES OF Pueraria tuberosa (L.) (INDIAN KUDZU)

Narendra Kumar1 and Raju Bhardwaj2

1Department of Botany, Collage of science, M.L.S. University, Udaipur, 2AU, Jodhpur.Email: [email protected]


ABSTRACTIn present study tissue culture experiments have been done for the enhancement of isoflavonoids via elicitation. The callus and suspension culture have been developed to observe the effect of various concentrations of Cuscuta (as biotic elicitor) and phenyl alanine and NAA (as abiotic elicitor). Cascuta elicitation is found much better elicitor for enhancement of production of studied isoflavonoids viz. Puerarin, Genistin, Daidzein and Genistein. These cultures were incubated on a rotary shaker at 100 rpm (with 2” displacement from the central axis) and 25±0.2 ºC in dark. The cell cultures were subcultured every 4th week with inoculums density being 125 mg dry weight/ 100 ml medium (10% v/v). HPLC with UV detector was performed at room temperature 25±0.2 ºC. The culture experiments and HPLC analysis were performed with triplicate and to be found reproducible and precise. Among the tested elicitors the cuscuta (50 mgl-1) has shown highest enhancement effect on total isoflavonoid content thus the biotic elicitors are found to be more effective triggering agent for isoflavonoid induction at late stationary phase cultures.

Key words: Elicitation, Isoflavonoids, Pueraria tuberosa.

In callus and cell cultures of angiosperms, the effect of nutrients and plant growth regulators on isoflavonoids production have been investigated. However, the productivity of such compounds obtained by in vitro techniques remained low (Ramawat and Mathur 2007; Verpoorte et al. 2002). The manipulation of the culture medium (e.g. carbon source, nitrogen and phosphate), plant growth regulators and culture conditions (e.g. temperature, light, growth period) have been used successfully to increase the production of secondary metabolites (Decendit et al., 1996; Zhong et al. 1999; Yamada et al., 2003; Trejo-Tapia et al., 2003, Tanwar et al., 2007; Mathur and Ramawat 2007). Recently, the use of various abiotic and biotic elicitors has been employed and one of the most interesting area of research all over the world (Edwards et. al., 1991; Harborne and Williams 2000; Humphreys and Chapple 2000; Shimada et. al.,2000; Ravi et. al., 2013; Nieves et.al., 2014).

Elicitors are compounds mainly of microbial origin or non biological origin, which upon contact with higher plant cells; trigger the increased production of pigments, flavones, phytoalexins and other defense related compounds. Elicitors from fungal origin have been widely employed to increase natural product formation in plant cell cultures and this strategy has been effective in stimulating the production of many chemical classes of SMs such as terpenoids, coumarin derivatives, alkaloids and flavonoids. While abiogenic elicitors like phenylalanine and NAA induces or increases the biosynthesis of many secondary metabolites.The cell cultures of lupine, Glycyrhiza echinata, Cicer arietinum and Pueraria lobata have been studied for elicitor-induced manipulation of isoflavonoids production. Fungal elicitor, Penicillium citrium, induced puerarin production in P. thomsonii and exogenous cork pieces, XAD-4 and methyl jasmonate induced 7-8 fold higher daidzein and genistien in P.

24

mailto:[email protected]


Montana (Kirakosyan et al., 2006; Luczkiewicz, 2008). Therefore, it is of interest to compare biotic and abiotic elicitors for isoflavonoids production. In the present communication we report the increased isoflavonoids production by elicitation effect in P. tuberosa cell cultures.

MATERIALS AND METHODSCulture and experimental set up Callus cultures were maintained on the modified Murashige and Skoog (MS) medium (KNO3 475 mg l-1, thiamine 1 mg l-1) containing biotin (1 mg l-1), calcium pantothenate (1 mg l-1), 2,4,5-T (0.1 mg l-1) and kinetin (0.1 mg l-1) with 3% sucrose (Goyal 2008).

The pH of the medium was adjusted to 5.8 and autoclaved at 1210C for 15 min. These cultures were incubated on a rotary shaker at 100 rpm (with 2” displacement from the central axis) and 25±0.2 ºC in dark. The cell cultures were sub-cultured every 4th week with inoculum density being 125 mg dry weight/ 100 ml medium (10% v/v).

Cell cultures were treated with methanolic extract of cuscuta, NAA and phenyl alanin. The elicitors were prepared as concentrated stock solutions and added to cultures after filter sterilization at the appropriate concentrations at 27 day of culture. HPLC sample preparation The cell cultures were harvested, washed with distilled water and filtered under mild vacuum. Further the sample was prepared as described before. For HPLC analysis all the extracts were prepared in HPLC grade methanol, filtered through nylon syringe filter and transferred in 300 µl autosampler vials. HPLC with UV detector was performed at room temperature 25±0.2 ºC. with method developed by Nurgiin et al., 2014.

RESULTS AND DISCUSSIONMarked increase in isoflavonoids accumulation in cell suspension cultures of P. tuberosa has been achieved by abiotic and biotic elicitors incorporated in the medium during late stationary phase.

Table 1 Effect of various concentration of Cuscuta on the isoflavonoids production in cell cultures on P. tuberosa in μg g-1DWCuscuta DWgl-1. Puerarin Genistin Daidzein Genistein Total Yieldμg l-1

control 12.7 4.4 ± 0.1 6.2± 0.2 1.6 ± 0.0 4.5± 0.1 16.9 21450 mgl-1 12.0 7.5±1.4 15.9±0.6 22.4±0.8 2.9±0.1 48.9 586100 mgl-1 9.05 9.2±0.1 8.8±0.0 11.8±0.1 7.3±0.1 37.2 336500 mgl-1 9.0 18.3±0.2 7.6±0.1 10.2±0.1 2.8±1.5 39.13 352

Table 2 Effect of various concentration of phenyl alanine on the isoflavonoids production in cell cultures on P. tuberosa in μg g-1DWphenyl alanine DWgl-1. Puerarin Genistin Daidzein Genistein Total Yieldμg l-1

control 10.5 4.4 ± 0.1 6.2± 0.2 1.6 ± 0.0 4.5± 0.1 16.9 177.45250 mgl-1 11.0 8.8±0.5 7.0±0.1 12.4±0.2 4.4±0.1 32.6 358.6500 mgl-1 10.05 16.7±0.1 - 4.3±0.1 5.5±0.0 26.5 266.31000 mgl-1 9.0 17.3±0.6 - 3.1±0.1 9±0.4 29.4 264.6

Table 3 Effect of various concentration of NAA on the isoflavonoids production in cell cultures on P. tuberosa in μg g-1DW

NAA DWgl-1. Puerarin Genistin Daidzein Genistein Total Yield μg l-1

control 11.5 6.3 ± 0.0 4.2± 0.1 3.6 ± 0.2 7.4± 0.3 21.6 248.4250 mgl-1 12.0 10.2±0.1 5.3±0.3 9.1±0.4 5.2±0.5 29.8 357.6500 mgl-1 10.00 14.3±0.3 6.3±00 5.0 ±0.2 7.0±0.2 22.6 2261000 mgl- 11.0 15.6 ±0.2 5.1±0.1 - 8±0.6 28.8 315.7

25


The cell suspension cultures of P.tuberosa were treated with varying concentrations of Cuscuta, Phenyl alanine and NAA at 27th

day (Tables 1-3).Among the tested elicitors the cuscuta (50 mgl-1) has shown highest enhancement effect on total isoflavonoid content (i.e. the sum of puerarin, genistin, daidzin and genistein) as well as individual content of different isoflavonoids (500mgl-1 For puerarin), (50 mgl-1 for genistin), (50 mgl-1

for daidzin) and (100 mgl-1 for genistein). Phenyl alanine and NAA has also shown the isoflavonoid elicitation effectively, but the concentration required (250-1000mgl-1) was much higher than cuscuta (50-500mgl-

1). On the same concentration the phenyl alanine has shown high elicitation effect on isoflavonoid production as compare to NAA.In comparison to chemical elicitor, biotic elicitor cuscuta was increasingly effective for puerarin with increased concentration in the medium, for other isoflavonoids the effect is not proportional to the increasing concentration but shows higher enhancement than the abiotic elicitors. The optimal concentration being 50mgl-1.The elicitation effect of MeJA, salicylic acid, yeast extract and ethrel on isoflavonoid content in cell suspension culture of P.tuberosa was previously recorded in our laboratory. The biotic elicitor yeast extract was increasingly effective with increased concentration in the medium as compare to abiotic elicitors (Goyal, 2008). Therefore, the biotic elicitors like cuscuta can be used as triggering agent for isoflavonoid induction at late stationary phase cultures.

Cuscuta as elicitor

18.315.9

22.4

7.3

0

5

10

15

20

25

control 50mgl-1 100mgl-1 500mgl-1

Cuscuta concn

μg g

-1D

W

PuerarinGenistinDaidzinGenistein

Pheny alanine as elicitor

17.3

7

12.4

9

02468

101214161820

control 250mgl-1 500mgl-1 1000mgl-1

Pheny alanine Concn.

μg g

-1DW


NAA as elicitor

15.6

6.3

9.1 8

02468

1012141618

control 250mgl-1 500mgl-1 1000mg-1

NAA concn.

μg g

-1D

W


Figure 1, 2 and 3: Effect of different concnentrations of Cuscuta, Phenyl alanine and NAA on production of puerarin, genistin, daidzin and genistein.

Highest optimatization of different isoflavonoids

18.3

15.9

22.4

9

0

5

10

15

20

25

Cuscuta Phenyl AL NAA

μg g

-1DW


Figure 4: Highest optimatization of puerarin, genistin, daidzin and genistein by elicitization with Cuscuta, Phenyl alanine and NAA.

Highest optimatization of total isoflavonoids

48.9

29.832.6

0

10

20

30

40

50

60

Cuscuta (50mgl-1) Phenyl AL (250mgl-1) NAA (250mgl-1)

μg g

-1DW

26


Figure 5: Highest optimatization of Total isoflavonoids (i.e. the sum of puerarin, genistin, daidzin and genistein) by

elicitization with Cuscuta, Phenyl alanine and NAA.

REFERENCESDecendit, A., Ramawat, K.G., Waffo, P.,

Deffieux, G., Badoc, A., Merillon, J.M. (1996). Anthocyanins, catechins, condensed tannins and piceid production in Vitis venifera cell bioreactor cultures. Biotechnology Letters. 18 : 659-662.

Edwards, R., Blount, J.W., and Dixon, R. A. (1991). Glutathione and elicitation of the phytoalexin response in legume cell-cultures. Planta 184: 403-409.

Harborne, J. B., and Williams, C. A. (2000). Advances in flavonoid research since 1992. Phytochemistry 55: 481-504.

Humphreys, J. M., and Chapple, C. (2000). Molecular 'pharming' with plant P450s. Trends Plant Sci. 5: 271-272.

Kirakosyan, A., Kaufman, P.B., Chang, S.C., Warber, S., Bolling, S., Vardapetyan, H. (2006 b) Regulation of isoflavone in hydroponically grown Pueraria montana (kudzu) by cork pieces, XAD-4, and methyl jasmonate. Plant Cell Rep. 25: 1387-1391.

Luczkiewicz, M. (2008). Research into isoflavonoids: phytoestrogens in plant cell cultures. In: Bioactive Molecules and Medicinal Plants. Ramawat, K.G., Merillon, J.M. (eds) Springer Verlag, Germany. (in press).

Nieves Baenas, Cristina García-Viguera and Diego A. Moreno (2014). Elicitation: A Tool for Enriching the Bioactive Composition of Foods. Molecules. 19: 13541-13563

Nurgiin, K., Özlem I., Hilal T., Uğur T., Nezaket A., Ban B., Aysegül K. (2014) Quantitative Determination of Isoflavones by HPLC-UV Method and Antioxidant Activity of Trifolium longidentatum. Turk J Pharm Sci 11(2): 185-194.

Mathur, M., Jain, A.K, Dass, S., Ramawat, K.G. (2007). Optimization of guggulsterone production in callus cultures of Commiphora wightii (arnott.). Indian J Biotech (in press).

Ramawat, K.G., Mathur, M. (2007). Factors affecting production of secondary metabolites. In: Biotechnology: Secondary Metabolites. Ramawat, K.G., Merillon, J.M. (eds). Sci. Pub., Inc., Enfield, USA. pp 59-102.

Ravi M., Palanisami E., Ajay K. P. (2013).Salyicylic acid elicitation on production of secondary metabolite by cell cultures of Jatropha curcus L. International Journal of Pharmacy and Pharmaceutical Sciences. 5: 0975-1491.

Shimada, N., Akashi, T., Aoki, T. and Ayabe, S. (2000). Induction of isoflavonoid pathway in the model legume Lotus japonicus: molecular characterization of enzymes involved in phytoalexin biosynthesis. Plant Sci. 160: 37-47.

Tanwar, Y.S., Mathur, M., Ramawat, K.G. (2007). Morphactin influences guggulsterone production in callus cultures of Commiphora wightii. Plant Growth Regul. 51: 93-98.

Trejo-Tapia, G., Arias-Castro, C., Rodriguez-Mendiola, M. (2003). Influence of culture medium constituents and inoculum size on the accumulation of blue pigment and cell growth of Lavandula spica. Plant Cell Tiss. Org. Cult. 72: 7-12.

Verpoorte, R., Contin, A., Memelink, J. (2002). Biotechnology for the production of plant secondary metabolites. Phytochemistry Reviews. 1:13-25.

Yamada, J., Fujita, K., Sakai, K. (2003). Effect of major inorganic nutrients on -thujaplicin production in a suspension culture of Cupressus lusitanica cells. J. Wood Sci. 49: 172-175.

Zhong, J.J., Chen, F., Hu, W.W. (1999). High density cultivation of Panax notoginseng cells in stirred bioreactors for the production of ginseng biomass and ginseng saponin. Process Biochemistry. 35: 491-498.

27


IDENTIFICATION OF SOIL NUTRIENT CONSTRAINTS BY GEOGRAPHIC INFORMATION SYSTEM (GIS) TECHNIQUE AND RESPONSE OF CROPS

TO IDENTIFIED NUTRIENT CONSTRAINTS IN SOUTHERN DRY ZONE OF KARNATAKA

Ravindra Naik M1, Anilkumar, K. S1, Natarajan1, A., Vasundhara2, R. Rajendra Hegde3

1Department of Soil Science and Agricultural Chemistry, UAS, Bangalore, Karnataka2ICAR-National Bureau of Soil Science and Land Use Planning, R.C.Bangalore, Karnataka


ABSTRACTThe selected micro watershed belongs to Kollegal taluk of Chamarajanagar district. Composite soil samples (0-30 cm) at random were collected during 21-25th July 2013. The exact sample location was recorded using GPS Collected and analyzed for physical and chemical properties.Upland soils was slightly acidic to neutral, lowlands soil reaction was neutral to slightly alkaline. The electrical conductivity of all the fertility points was negligible, The organic carbon content vary between low to medium, The cation exchange capacity of the soil varied from low to medium. The available nitrogen, phosphorus and potassium were low, low to medium and medium respectively.

Key words: Microwatershed, Remote Sensing (RS) tools, Toposheet

The ability of the land to produce crops is limited and the limits to produce crops are set by soil, climate and landform conditions. However, the capacity of a soil to produce crops is limited and the limits to production are set by intrinsic characteristics, agro-ecological settings, use and management (FAO, 1993a). Despite the significant growth in production, the sustainability of some

cropping systems has been showing signs of fatigue. Therefore, comprehensive account of our land resource and ascertain its potential and problems towards optimizing land use on sustainable basis is necessary. Keeping these considerations in view, an investigation was carried out for Kannur micro watershed in Kollegal taluk,Chamarajanagar.

MATERIALS AND METHODS

The kannuru microwatershed lies 20 km away from Kollegal taluk head quarter. GPS notations between 120 6' 34.5" and 120 7' 49.6" N latitude, 770 15' 30.9" and 770 14' 20.2" E longitude, with an average elevation of 775 m above Mean Sea Level (MSL). Structures like roads, settlements, and lakes were marked on the trace sheet mounted on the imagery . It occupies a part of the four villages viz., Kannuru, Anapura, Mangala, and Kamgare. Composite soil surface sample in strategic manner to cover all farming systems of the watershed. Remotely sensed data from IRS P6 was collected from Karnataka State Remote Sensing application Centre, Bangalore. Sensor

used in this satellite is LISS IV MX. The imagery scale is 1:12,500 scales, the imaginary collected on 5th July 2013. Toposheets used in this study are 57B/4,57C/4,57F/4. The imaginary was interpreted in conjunction with the toposheet based on the tonal variations, texture and pattern. Soil samples (0-30 cm) at random were collected, exact sample location was recorded using GPS Collected and analyzed for physical and chemical properties. Standard analytical methods as described by Richards (1954) and Jackson (1953) were followed for measuring various soils attributes like pH, ECe, soluble cations and anions, CEC and exchangeable cations, organic carbon content.FIGURE: Location Of Study Area

28



The pH values in the study area ranged from 5.04 to 8.60 with mean and SD values of 7.08 and 1.16, respectively. Most of the study area was under neutral to moderately alkaline range. The acidic nature of red soils was due to acidic nature of parent material of the study area. The reaction was alkaline neutral to alkaline nature of black soils which was mainly due to high exchangeable bases (Bhadrapur and Sheshagiri Rao 1979). The EC values in watershed area ranged from 0.01 to 1.15 dS m-1. The black soils which exhibited brown layers were relatively free from salts. The brown layer seems to be controlled salinity and exchangeablesodium (Dasog and Hadimani 1980 and Anon 1969). The organic carbon content in the study area ranged from 0.04 to 1.0 per cent of soil. Majorly soils of the study area fall under low to medium category. The low organic carbon content of the soils may be attributed to the prevalence of high

temperature (Table 1). The organic matter degradation and removal taken place at faster rate coupled with low vegetation cover, thereby leaving less chances of accumulation of organic matter in the soil (Govindarajan and Datta Biswas 1968). The available nitrogen status in study area ranged from 56.5 to 240 kg per ha with mean.All the soils of micro watershed fall under low category. All the study area was low in available nitrogen. Major portion of the nitrogen pool is contributed by organic matter (Table1). Low organic matter content in this area due to low rainfall and low vegetation cover facilitated faster degradation and removal of organic matter leading to nitrogen deficiency The available phosphorus content in the study area ranged from 7.3 to 63.0 kg per ha. The lowlands were medium in status, whereas uplands and midlands fall

under low category. The red soils shown low values of available phosphorus which may be due to low CEC, clay content and soil reaction of <6.5 The available potassium content in the study area ranged from 62.9 to 264.8 kg per ha. Majority of the area falls under low category. The lowland showed relatively high in available potassium than uplands and midlands (Table 1). Black soils shown high values due to predominance of K rich micaceous and feldspars minerals in parent material. The exchangeable Ca and Mg content in micro-watershed ranged from 30.3 to 197.6 ppm and 12.2 to 97.8 ppm. The upland soils are relatively less base saturated than lowlands and midlands Due to leaching of bases like Ca and Mg (Table 2). The available sulphur content of micro-watershed ranged from 0.6 to 18.0 ppm. The available sulphur content in lowlands was higher (Table 2) than that of uplands and midlands (Balanagoudar, 1989). The available

29


copper content of micro-watershed ranged from 0.42 to 4.32 ppm. The available Iron content of micro-watershed ranged from 2.1 to 26.1 ppm. The available Manganese content of micro-watershed ranged from 2.1 to 26.1 ppm. The available Manganese content in lowlands was higher than that of uplands and midlands, available Manganese was 7.2 and 4.7 ppm, respectively. The available Zinc content of micro-watershed ranged from 2.1 to 26.1 ppm. (Table 3) Soil available micronutrients showed sufficient presence in most of the soils studied (Anil Kumar et al 2010).

CONCLUSIONSoil reaction of upland soils was slightly acidic to neutral which is attributed to the presence of leaching of bases from the soil along with runoff and drainage water due to moderately high rainfall existing in the area. In lowlands, the soil reaction was neutral to slightly alkaline due to deposition of bases from the upland physiographic units. The electrical conductivity of all the samples was negligible, which indicates non-saline nature of soil and good leaching. Organic carbon content in all the samples to vary between low to medium due to low

vegetative/cropping cover. The soil erosion and warmer climate leading to lowaccumulation of organic carbon in the study area. Cation exchange capacity of the soil varied from low to medium. The upland physiographic units were low in cation exchange capacity values than midlands and lowlands owing to their low clay content, low organic matter and the predominance of 1:1 type of clay minerals, whereas lowlands exhibited moderate CEC values due to higher clay content. The available nitrogen, phosphorus and potassium were low, low to medium and medium respectively. The low nitrogen content is attributed to the low organic carbon due to warmer climate and low vegetative cover coupled with little nitrogen fertilization. Soil available micronutrients showed sufficient presence in most of the soils studied except in case of available zinc confirming the study of Anil Kumar et al. (2010). Present Study noticed that study area was adequate in iron, manganese, copper. Study area was low in available zinc status.

Acknowledgement

Authors thank the financial support from NBSS&LUP, ICAR

Table-1: Surface Samples Available NutrientsVillage Gps reading Soil

type Crop pH

(1:2.5) EC (1:2.5) (dSm1)

O.C (%)

AvailableMacronutrients

(kg ha-1)N P2O

5

K2O

1) Kannur 12°6'19.5" N 77°14'32.8" E

Red soil

Ragi 5.56 0.22 0.90 125.0 40.0 94.0

2) Mangala 12°7'0.5" N 77°15'8.6" E

Red soil

Ragi 7.05 0.18 0.42 96.0 31.2 79.1

3) Mangala 12°6'52.8" N 77°15'7.2" E

Red soil

Sugar cane (Harvested)

7.60 0.95 0.13 142.5 33.0 62.9

4) Kannur 12°6'9.6" N 77°14'41.8" E

Red soil

Maize 5.50 0.86 0.15 139.0 11.5 120.0

5) Kannur 12°6'20.5" N 77°15'11.0" E

Black soil

Cotton 8.23 0.34 0.11 106.0 37.3 110.9

6) Kannur 12°6'36.4" N 77°14'59.9" E

Black soil

Bengal gram 8.44 0.54 0.25 140.8 37.9 95.7

7) Kannur 12°6'9.6" N 77°14'41.8" E

Red soil

Maize 5.23 0.04 0.31 135.8 33.4 68.2

30


8) Kannur 12°6'7.6" N 77°14'49.9" E

Red soil

Maize-sunflower (inter crop)

5.72 0.05 0.38 128.1 72.7 84.4

9) Kannur 12°6'15.7" N 77°14'50.6" E

Red soil

Maize 7.40 0.04 0.16 119.0 7.5 98.8

10)Kannur 12°6'23.8" N 77°14'27.4" E

Red soil

Fallow 7.03 0.29 0.18 168.0 40.0 96.0

11)Kannur 12°6'38.6" N 77°14'10.5" E

Black soil


8.04 1.05 0.13 125.4 31.2 350.0

12)Kannur 12°6'46.1" N 77°14'8.5" E

Red soil

Bengal gram 8.60 0.32 0.53 136.7 21.5 246.3

13)Mangala 12°6'58.1" N 77°14'7.2" E

Black soil

Fallow 8.25 0.24 0.44 159.0 23.8 264.80

14)Kannur 12°6'52.4" N 77°14'9.0" E

Black soil

Fallow 8.64 0.36 0.25 240.9 29.3 170.5

15)Anapura 12°07'6.0" N 77°14'12.1" E

Red soil

Fallow 6.71 0.10 0.61 116.0 62.9 141.2

www.ijsrp.org 16)Anapura

12°6'55.6" N 77°14'36.8" E

Red soil

Maize 8.26 0.23 0.64 146.0 25.2 121.9

17)Anapura 12°6'55.6" N 77°14'36.8" E

Red soil

Maize 8.29 0.29 0.66 94.8 43.4 99.7

18)Mangala 12°7'11.0" N 77°14'26.7" E

Red soil

Maize 6.35 0.13 0.71 79.7 9.1 206.4

19)Mangala 12°7'4.08" N 77°14'52.0" E

Red soil

Ragi-Maize 5.04 0.14 0.82 89.4 18.3 184.5

20)Kamgare 12°7'39.1" N 77°14'17.0" E

Red soil

Maize 7.55 0.12 0.66 69.6 46.6 74.0

21)Anapura 12°7'06.1" N 77°14'12.2" E

Red soil

Fallow 6.13 0.02 0.81 150.8 36.0 117.0

22)Kamgare 12°7'28.5" N 77°14'35.5" E

Red soil

Fallow (maize harvested)

6.12 0.02 0.46 99.5 78.2 130.0

23)Kamgare 12°7'37.8" N 77°14'43.1" E

Red soil

Maize 5.29 0.02 0.45 79.5 39.0 112.6

24)Anapura 12°7'12.0" N 77°15'5.1" E

Red soil

Ragi 6.00 0.01 1.00 84.5 42.3 91.50

25)Ampere 12°7'00.1" N 77°15'00.8" E

Red soil

Fallow 8.47 0.87 0.43 97.8 36. 104.8

26)Ampere 12°7'15.1" N 77°14'3.7" E

Red soil

Maize 6.50 0.02 0.46 77.8 13.6 85.1

27)Anapura 12°7'14.9" N 77°14'3.7" E

Red soil

Coconut plantation

8.11 0.13 0.45 122.0 29.9 105.7

28)Mangala 12°7'18.2" N 77°14'35.2" E

Red soil

Ragi-Maize 7.60 0.1 0.91 98.5 19.1 145.2

29)Anapura 12°6'48.8" N 77°14'52.9" E

Red soil

Ragi 6.20 0.05 0.65 110.5 24.6 85.5

30)Kannur 12°6'41.3" N 77°14'25.5" E

Red soil

Ragi 8.37 0.06 0.62 56.5 20.4 16.0

Mean 7.07 0.25 0.48 119.95

33.60

129.19

S.D 1.16 0.29 0.25 35.19 19.93

21.40

Range (Min-Max) 5.04- 8.64

0.01- 1.05

0.11- 1.00

69.60-240.9

7.5-78.2

21.40-350

Village Gps reading Soil type Crop

AvailableSecondary Nutrients

(ppm)

Available MicroNutrients

(ppm)Ca Mg S Cu Fe Mn Zn

1) Kannur 12°6'19.5" N 77°14'32.8" E

Red soil

Ragi 160.4 64.8 4.1 2.34 7.56 11.7 0.04

2) Mangala 12°7'0.5" N 77°15'8.6" E

Red soil

Ragi 133.0 75.8 12.1 2.08 10.22 14.24 0.56

31


3) Mangala 12°6'52.8" N 77°15'7.2" E

Red soil


112.0 16.0 15.9 2.56 7.08 9.36 0.62

4) Kannur 12°6'9.6" N 77°14'41.8" E

Red soil

Maize 148.8 58.4 11.1 2.82 6.56 10.4 0.12

5) Kannur 12°6'20.5" N 77°15'11.0" E

Black soil

Cotton 152.2 97.8 0.6 2.00 2.76 5.60 0.04

6) Kannur 12°6'36.4" N 77°14'59.9" E

Black soil

Bengal gram

46.0 12.2 11.3 1.20 5.10 5.16 0.82

7) Kannur 12°6'9.6" N 77°14'41.8" E

Red soil

Maize 173.8 87.8 8.1 2.88 5.82 9.60 0.56

8) Kannur 12°6'7.6" N 77°14'49.9" E

Red soil

Maize-sunflower (inter crop)

102.2 66.4 12.4 2.96 26.18 8.20 0.48

9) Kannur 12°6'15.7" N 77°14'50.6" E

Red soil

Maize 197.6 63.2 11.4 2.70 7.34 8.40 2.14

10)Kannur 12°6'23.8" N 77°14'27.4" E

Red soil

Fallow 138.4 35.0 14.9 2.20 5.02 5.80 0.88

11)Kannur 12°6'38.6" N 77°14'10.5" E

Black soil


117.8 60.2 17.0 2.44 4.96 7.00 5.08

12)Kannur 12°6'46.1" N 77°14'8.5" E

Red soil

Bengal gram

165.0 69.2 14.9 2.70 7.5 7.20 0.22

13)Mangala 12°6'58.1" N 77°14'7.2" E

Black soil

Fallow 150.8 69.0 10.1 0.42 2.08 6.80 0.10

14)Kannur 12°6'52.4" N 77°14'9.0" E

Black soil

Fallow 108.4 60.6 15.6 3.12 13.08 8.60 0.30

15)Anapura 12°07'6.0" N 77°14'12.1" E

Red soil

Fallow 138.0 35.4 18.0 3.30 14.44 18.20 0.20

www.ijsrp.org 16)Anapura

12°6'55.6" N 77°14'36.8" E

Red soil

Maize 183.8 45.4 17.0 3.02 3.10 7.80 0.16

17)Anapura 12°6'55.6" N 77°14'36.8" E

Red soil

Maize 117.0 36.4 10.4 1.52 4.6 8.64 3.80

18)Mangala 12°7'11.0" N 77°14'26.7" E

Red soil

Maize 130.8 26.2 8.3 1.46 5.34 9.00 0.48

19)Mangala 12°7'4.08" N 77°14'52.0" E

Red soil

Ragi-Maize 179.2 40.0 17.3 2.84 5.16 8.60 0.96

20)Kamgare 12°7'39.1" N 77°14'17.0" E

Red soil

Maize 102.2 48.0 13.8 1.64 5.60 10.5 0.30

21)Anapura 12°7'06.1" N 77°14'12.2" E

Red soil

Fallow 160.4 44.8 12.9 3.38 7.08 11.5 0.22

22)Kamgare 12°7'28.5" N 77°14'35.5" E

Red soil

Fallow (maize harvested)

127.8 33.0 13.8 3.02 12.68 9.70 1.32

23)Kamgare 12°7'37.8" N 77°14'43.1" E

Red soil

Maize 128.2 28.8 8.3 2.76 6.08 5.70 0.16

24)Anapura 12°7'12.0" N 77°15'5.1" E

Red soil

Ragi 156.0 21.0 6.2 2.30 4.80 19.04 0.96

25)Ampere 12°7'00.1" N 77°15'00.8" E

Red soil

Fallow 165.0 28.8 7.6 4.32 13.62 5.16 0.88

26)Ampere 12°7'15.1" N 77°14'3.7" E

Red soil

Maize 134.4 15.0 17.3 2.60 3.16 9.50 0.08

27)Anapura 12°7'14.9" N 77°14'3.7" E

Red soil

Coconut plantation

140.0 31.0 10.8 3.68 6.06 9.30 0.12

28)Mangala 12°7'18.2" N 77°14'35.2" E

Red soil

Ragi-Maize 107.4 32.2 12.2 2.14 3.56 9.26 0.14

29)Anapura 12°6'48.8" N 77°14'52.9" E

Red soil

Ragi 106.0 36.4 6.9 2.90 3.56 4.96 0.22

30)Kannur 12°6'41.3" N 77°14'25.5" E

Red soil

Ragi 146.6 39.0 12.0 3.34 7.52 10.44 0.24

Mean 137.64 45.92667 11.74333 2.55 7.254 9.17 0.74S.D 30.35867 21.389 4.147022 0.77 4.74 3.29 1.09 Range (Min-Max) 30.35-

197.6 12.2-97.8 0.60-18 0.42-

4.32 2.08-26.18

4.96-19.04

0.04-5.08

32


REFERENCESAnil Kumar, K.S., Ramesh Kumar, S.C.,

Dhanorkar, B.A., Vaidivelu, S., Naidu, L.G.K. and Dipak Sarkar, 2010, Land resources of Kuppam mandal, Chitoor District, Andra Pradesh. Technical report NBSS Publ.No. (1030).

Balanagoudar, A.B., 1989, Investigation on status and forms of sulphur in soils of North Karnataka. M.Sc.(Agri.)Thesis, University of Agricultural Sciences, Dharwad.

Bhadrapur, T.G. Andseshagiri Rao,T., 1979,Theeffectofseepage and water logging on the development of saline and sodic soils in Tungabhadra Project Area of Karnataka.

Journal of the Indian Society of Soil Science. (27):408-413.

Dasog,G.S. and Hadimani, A.S., 1980 Genesis and chemical properties of some Vertisols. Journal of the Indian Society of Soil Science., (28):49-56.

Food And Agriculture Organization., 1993, Frame work for Land Evaluation. Soils Bulletin, (32), Rome.

Govindarajan, S.V. AND Datta Biswas, N.R., 1968, Characterization of certain soils in the sub tropical humid zone in the south eastern part of Indian soils of Muchkand basin. Journal of the Indian Society of Soil Science.,(16):117-186.

33

SOME BIOMETRIC PROPERTIES OF Cucumis sativus PLANT AND FRUIT THAT INFLUENCE MECHANICAL HARVESTING

M Ayisha, K P Shivaji and P NiyasKelappaji College of Agricultural Engineering and Technology, Kerala Agricultural

University, Tavanur, Malappuram -679573, Kerala, IndiaE mail: [email protected]


ABSTRACTHarvesting has been identified as one of the critical and resource consuming operation because of several reasons especially inside polyhouse structures. Knowledge of physical properties of crops and fruits like cucumber plays an important role in the design and optimization of its machinery. Evaluation of these properties like plant height, leaf numbers, leaf length, leaf width, fruit length and width were taken for observation. It was seen that, these properties had a direct impact on deciding the components of the harvester.

Key words: Cucumber, Polyhouse, Biometric properties, Leaf parameter, Fruit Parameter

Cucumber (Cucumis sativus) is a commonly used agricultural product which is considered as a summer vegetable. It belongs to the family cucurbitaceae [1]. The plant is a climber and the fruit is green in colour. The size of the fruit may vary depending upon the variety of seeds selected and various environmental factors. It is a nutritious product which mainly contains water as the main content and also contains minerals, vitamins and some organic acids. At present, cucumbers are grown in protected cultivated area like polyhouse. Evaluations of the biometric properties of the crops help in making harvesting smooth to certain extend. Knowledge of physical properties of crops like cucumber plays an important role in the design and optimization of its machinery. This help out to obtain quality products with reduced wastages [1]. Study of various biometric properties of plants and fruits which influence mechanical harvesting have been started since 1980s. Evaluation of physical properties of the crop and fruit may help in designing a harvester suitably. Despite these, it helps in grading, sorting and some processing

operations. The main objective of this study is to measure some physical properties of cucumber and to identify how it will affect the mechanical harvesting. The result can be used for designing a harvester or related equipment.

MATERIALS AND METHODSCucumber (Cucumis sativus) variety Hilton was used for the study. This variety is high yielding variety and is most suited for polyhouse cultivation. For analyzing the growth pattern of the crop, ten cucumber plants were selected. The main crop growth parameters like height of the plant, number of leaves, leaf length, leaf width, plant circumference, number of fruits, fruit length, and fruit circumference were measured. The readings were taken for about 70 days after transplanting, since the plants started to show yellowing of leaves, which indicates that the plants started to die and cannot be re-grown [2]. Vine Length: For cucumber plant, the length of vine was measured from the soil surface to the tip of the topmost leaf (youngest leaf). Readings were taken once in a week using a measuring tape.

34


The average was worked out for each plant.Number of Leaves: The total number of leaves on cucumber plant was counted. Readings were taken once in a week. Leaf Length and Leaf Width: A leaf was taken randomly for cucumber plant for measuring the length and width. Length was measured from petiole to leaf apex and width was measured between the sides. It was measured at weekly intervals using a ruler.Plant Circumference: Plant circumference of cucumber plant was taken at the axil where leaf parameters were measured. Reading was taken using a measuring tape. Measurements were taken at weekly intervals.Number of Fruits: Number of fruits per plant was counted at weekly intervals. Fruit Length: The fruit length was measured longitudinally from top to the bottom. Length of the fruits was measured using a measuring tape and it was taken once in a week. Fruit Circumference: The equatorial circumference of the fruit was measured using a measuring tape. The dimensions of the all fruits in each plant were taken once in a week. Peduncle parameters: The peduncle length and diameter of the selected samples of cucumbers were measured. The length was measures using a ruler and diameter was measured using digital vernier caliper.

RESULTS AND DISCUSSION The biometric properties of the crops that affect the mechanical harvesting are discussed below. Vine Length: Plant length (vine length) directly affects the design of the harvester. It determines the height of the mechanical harvester. Since cucumber is a climber, it will climb over a large distance. To certain extend the height can be controlled by adopting special plant training method like umbrella

system or tree trellis system. In polyhouse, the plant height can be controlled with respect to the height of structure. While in open field, the chances of spreading of the vine irrespective of the trainer are more. In both the cases, plant height influences the harvesting operation of the harvester. [3] reported that the size of the harvester should be foreseen while designing the harvester, depending upon the height of the crop. The data on vine length of cucumber plant and height of tomato plant are presented in Figure 1. Observations revealed considerable changes in the height of plant along with the crop growth. At the first stage of harvesting on 35th day after transplanting, the average value was observed to be 1887 mm. At 70 days after transplanting, the length of vine marked a mean value of 3965 mm. Number of Leaves: The number of leaves affects the performance of the harvester. Leaf density in the working area of the machine decides the presence of obstacles in the operation. A study by [4] reveals the importance of the working space of the machine. In spite of fruit related problems, locating the fruits that is occluded by the leaves were identified as a major issue that affects the efficiency of the harvester. An examination of the data presented in figure 2 demonstrates that during the first observation at 7 days after transplanting, the mean number of leaves was 6 and at 70 days after transplanting, it was observed as 124 numbers of leaves.

35

Figure 1. Vine length of cucumber plant on different DAT

Figure 2. Number of leaves of cucumber plant on different DATLeaf length and leaf width: Length of leaf and width of leaf affects the leaf canopy of a plant. This in turn affects the performance of the harvester. [4] suggest a thin leaf canopy for better result of the performance of the harvester. They find it difficult to harvest a fruit located in the interior of the leaf canopy. There was a substantial increase in leaf length of cucumber plant with plant growth. It was observed with a mean value of 100.4 mm at 7 days after transplanting and 70th day after transplanting, it was 231.4 mm. (Figure 3)

Figure 3. Average leaf length and leaf width of cucumber leaves on different DAT

Leaf width increased with increase in number of days in the initial days and had a slight decline in the final observations. The mean leaf width was 108 mm at 7 days after transplanting and it was recorded in the last observation as 246.6 mm. Plant circumference: [5] pointed out that the uniform shape and size of the plant in a nursery or orchard will help to

improve the efficiency of the harvester. Similar findings were explained by [4]. The fruit detaching mechanism will interact directly with the fruit and plant parts. The cutting tool can be designed by considering the circumferential area of the stem in order to reduce the damages occurring while harvesting.

Figure 4. Average circumference of cucumber vine on different DATBy analyzing the data given in the Figure 4, plant circumference of the cucumber vine increased gradually. The mean value varied from 15.1 mm to 38.4 mm in the first and final observation respectively. Fruit parameters: The fruit parameters like number of fruits per plant, mean fruit length and circumference of cucumber are discussed below. A study conducted by [4]reports that the number of uniform sets of fruits on a particular area helps to locate the fruit easily and also, the harvester can work with maximum productivity when the number of fruits in a specified work space is more.

Table 1. Number of fruits on cucumber and tomato plant at different days after plantingNumber of fruits

Days After Transplanting28 35 42 49 56 63 70

Cucumber 2 9 13 18 19 16 22

36

The length and circumference indicates the size of the fruit which will directly affect the harvesting operations. The weight holding capacity of the cutting unit is directly influenced by the size of the fruit. The volume of the fruit collector is also determined by the fruit size. [6] mentioned the relevance of fruit size while harvesting using mechanical harvester.

Figure 5. Fruit characteristics of cucumber on different DAT

The fruit parameters like average fruit length and equatorial fruit circumference of cucumber showed a modest change. A minimum length of 160 mm was observed on the harvest 42 days after transplanting and a maximum length of 185 mm was observed on the harvest of 49 days after transplanting. Regarding the equatorial circumference of the cucumber, a maximum value of 160 mm and a minimum value of 145 mm were noted for the harvested cucumbers. Table 2. Peduncle dimensions of cucumber

Sample No.

Cucumber

weight(gm)

Peduncle

length(mm)

Average peduncle

circumference(mm)

1 113.24 53 3.432 299.70 22 3.033 275.30 35 3.254 217.10 55 2.665 203.60 32 2.496 178.40 35 3.137 180.40 44 2.668 164.30 25 3.339 197.30 53 2.9110 159.20 22 2.48

The peduncle length and circumference have direct role in determining the cutting operation. Weight of the fruit, peduncle length and peduncle diameter are seen varying independently. Out of the selected samples, the highest peduncle length was noticed to be 55 mm and the lowest length was 22 mm. Likewise, the peduncle circumference showed up a maximum value of 3.43 mm and minimum value of 2.48 mm. Compared to the peduncle length of cucumber, tomato has shorter peduncles. This makes some inconvenience in the harvesting operation. The shortest value recorded for the peduncle length for the selected samples was 18 mm and the longest was 25 mm. Peduncle length is an unavoidable factor to determine the cutting portion. It decides where to make the cut, for detaching the fruit from the plant. [4] mentioned the importance of proper harvesting with and without the presence of peduncle after harvesting. If the peduncle length is too short, mechanical harvest will be a failure. Also, fruits harvested along with the peduncle have got more market value because of its appearance.

It was observed that, certain parameters affected the design of harvester directly and certain other parameters affected indirectly. Depending upon the height of the plant, the position at which fruits appeared also changes. So, the height of the harvester was decided based on the height of the plant. Similarly leaf parameters like number of leaves, leaf length and leaf width affects the performance of the harvester. These parameters have an impact on formation of leaf canopy and leaf density. And this in turn contributes to the presence of obstacles in the working area of the machine while in the operation. Another parameter that influences the harvesting performance is

37

the plant circumference. The chance of occurrence of damages to the plants and fruits while harvesting depends on this factor. The fruit detaching mechanism will interact directly with the fruit and plant parts. The cutting tool can be designed by considering the circumferential area of the stem in order to reduce the damages occurring while harvesting. Despite of all the plant parameters, fruit parameters also influence the harvesting operation. The number of uniform sets of fruits on a particular area helps to locate the fruit

easily. Along with this, the harvester can work with maximum productivity when the number of fruits in a specified work space is more. The operation of gathering fruits will be less difficult for harvesting equipment that concentrates on individual plant than collective plant operation. This was because, individual plants bear the fruits in a limited area, which makes the number of fruits per working area more. Apart from all these factors, harvesting was also influenced by the peduncle dimensions of the fruit

\

REFERENCES

Mousavizadehi, S.J., Mashayekhi, K., Garmakhany, A.D., Etheshamnia, A. & Jafari, S.M. 2010. Evaluation of some physical properties of cucumber (Cucumus Sativus L.). J.of Agric. Sci.Technol.4(4):107-114.

Food and Agriculture Organisation (FAO), 2015, Small-Scale Aquaponic Food Productio– Integrated Fish and Plant Farming.pp. 172- 177.Available online at: http://www.fao.org/3/i4021e/i4021e12.pdf

Gay, P., Piccarolo, P., Aimonino, R. D. and Deboli, R. 2008. Robotics for work and environment safety in green house. Proceedings of an International conference on innovation technology to empower safety,

health and welfare in agriculture and agro food systems, 15-17 September 2008. Italy.

Burks, T.,Villegas, F.,Hannan, M.,Flood, S.,Sivaraman, B.,Subramanian, V. and Sikes, J. 2005. Engineering and horticultural aspects of robotic fruit harvesting: opportunities and constraints. Hortechnology. 15(1): 79-87

Cargill, B. F. 1983. Harvesting high density red tart cherries. Proc. Intl. Symp. On Fruit, Nut and Veg. Harvesting Mechanization. Amer. Soc. Agr. Eng.,5: 195-200.

Coppock, G. E.,Hadden, S. L. and Lenker, D. H. 1969. Biophysical properties of citrus fruit related to mechanical harvesting. Amer. Soc. Agr. Eng. 12(4): 561-563

38

A STUDY OF TEMPERATURE, RELATIVE HUMIDITY AND RAINFALL TRENDS IN CRSS-CHETTALLI OF N-KODAGU, WESTERN GHAT, INDIA

Rudragouda1, Kishor Mote, Nagaraj Gokavi, Uma M.S and Manunath A.N. Central Coffee Research Institute, Coffee Research Station – 577 117


ABSTRACTThis study examines recent changes in monthly mean of maximum temperature (M-MAX), relative humidity (M-RH) and total rainfall (TMRF) of Coffee Research Sub Station (CRSS), Chettalli, N-Coorg, Karnataka, India over approximately last four decades stretching between years 1975 to 2012. The long–term change in temperature, relative humidity and rainfall has been assessed by linear trend analysis. The increasing trend in mean of maximum (M-MAX) temperature and relative humidity and decreasing trend in total mean annual rainfall (TMRF) is confirmed by Mann-Kendall trend test. It is observed that monthly mean of maximum (M-MAX) temperatures have increased significantly for all the months and for annual. While monthly mean of relative humidity (M-RH) has increased for the months from May to November and it is decreased from December to April months. The annual M-RH observed an increasing trend but is statistically insignificant. While monthly mean of TMRF have increased significantly for the months of March, April, July, September, October, November and December whereas it shows decreasing trend in January, February, May, June and August for the station. Similarly the mean of annual total rainfall observed a decreasing trend having an annual decrease of 2.992 mm per year.

Key words: CRSS, Rainfall, Relative Humidity, Temperature, Trends

The increase of greenhouse gas emissions (GHG) in the atmosphere is causing wide changes in atmospheric events, influencing climate change and variability with critical impacts on vegetations. Climatic variation has brought down unexpected changes not only in India but all over the regions across the world. One of the consequences of climate change is the alteration of rainfall patterns and increase in temperature. The latest fourth assessment report of the Intergovernmental Panel on Climate Change (IPCC 2007) has concluded that the global mean surface temperatures have raised by 0.74 ± 0.18 oC when estimated by a linear trend over the last 100 years (1906–2005). The rate of warming over the recent 50 years is almost double of that over the last 100 years (IPCC 2007). Further, surface air temperature could rise by between 1.1oC to 6.4oC over 21st century. In case of India, the climate change is expected to adversely affect its natural resources, forestry, agriculture, and change in

precipitation, temperature, monsoon timing and extreme events (Fulekar,M.H and Kale R.K, 2010). Due to global warming, precipitation amount, type and timing are changing or are expected to change because of increased evaporation, especially in the tropics (Ritter, 2006). Climate change can have a wide range of effects on agricultural systems and we must adapt to these changes to ensure that agricultural production is not only maintained but is increased to support a growing world population (Smith et al., 2008). Local adaptation practices and those practices introduced by national development, research and extension organizations need to be collected from the respective organizations and evaluated at different levels. Different agronomic, water and policy management adaptation strategies needed to be considered. The main objective of this study is to analyse the 1975 to 2012 rainfall, relative humidity and temperature data collected at Coffee Research Sub Station (CRSS) Chettalli as a basis for taking up the new experiments

39

on impacts of climate change on coffee pest and diseases and the coffee productivity. Study Area: Kodagu district lies on the eastern and western slopes of Western Ghats of Deccan plateau, south west Karnataka covering an area of 4102 km at an elevation of 830 to 17850 meters above MSL. The Coffee Research Sub Station, Chettalli ,a regional station of the research wing of Central Coffee Research Institute (CCRI), Coffee Board was established during 1947 to implement and demonstrate results of research, evolve region specific packages and to address zonal problems. The Coffee Research Sub Station (CRSS) is situated in Chettalli village linked by Madikeri-Virajpet state high way about 16 km south east of Madikeri town. The Research Station lies between 12o22’ 24.1” to 12o23’ 13.8”N latitude and 75o50’ 11.8” to 75o50’ 42.1” E longitude. The region is characterized by undulating topography with terraced slopes having narrow valleys. The climate is sub humid tropical with mean annual rainfall of about 1568.5 mm. More than 80 per cent of rainfall is received during the southwest monsoon from June to October and about 15 per cent during pre monsoon period (March to May). November to February is the least rainfall period. The mean annual air temperature is 27.20oC. The maximum temperature varies from 20.10oC to 34.2oC & the minimum temperature varies from 14.7 to 24.6oC.

METERIALS AND METHODSThe data used in this paper are the monthly average of maximum atmospheric temperatures and monthly mean relative humidity and monthly total rainfall from 1975 to 2012. The yearly average was calculated from the monthly readings which were collected at Coffee Research Sub Station (CRSS) Chettalli. The time series is made up of four

components known as seasonal, trend, cyclical and irregular (Patterson, 1987). Trend is defined as the general movement of a series over an extended period of time or it is the long term change in the dependent variable over a long period of time (Webber and Hawkins, 1980). Trend is determined by the relationship between the two variables as temperature and time, rainfall and time. The statistical methods such as regression analysis and coefficient of determination R2 (Murray R. Spiegel, and Larry J. Stephens, 2000) are used. The magnitudes of the trends of increasing or decreasing maximum temperatures, monthly mean relative humidity and total mean rainfall were derived and tested by the Mann-Kendall (M-K), trend test and slope of the regression line using the least squares method.

RESULTS AND DISCUSSIONStudy of temperature, relative humidity and rainfall trends of CRSS Chettali are presented here. The statistical summary on temperature revealed that the coefficient of variation for monthly mean of maximum temperature (M-MAX) is highest in the month of June (6.388%) , followed by October (6.365%) whereas it is lowest in the month of March (4.705%) , followed by September (4.903%), while for the annual mean it is 3.639 percent. This means that the M-MAX is more stable in the month of March and September and it is least stable in the month of June and October. Similarly the annual mean of M-MAX is also more stable (Table: 1). The trends of M-MAX over different years were obtained by using linear regression best fit lines. The linear regression trends with their linear regression equations and coefficient of determinations for all the months from January to December and for annual are represented in Figure 1 and 1a and summarized in Table: 2 below.

40

Table: 1. Statistical summary of monthly mean of maximum temperature(oC)Month Range Mean S.d SEm± C.V (%)

Jan 30.10 – 23.40 27.42 1.527 0.240 5.573Feb 32.70 – 24.90 29.62 1.554 0.253 5.250Mar 34.20 – 28.00 31.73 1.477 0.235 4.705Apr 34.20 – 28.80 31.47 1.448 0.192 3.821May 33.90 – 27.00 29.83 1.489 0.245 4.995Jun 28.50 – 22.60 25.31 1.615 0.260 6.388July 28.20 – 20.10 23.51 1.405 0.223 5.968Aug 27.20 – 21.70 23.80 1.602 0.204 5.327Sep 27.90 – 22.70 25.31 1.249 0.204 4.903Oct 29.50 – 20.60 26.32 1.678 0.278 6.365Nov 29.90 – 23.20 26.27 1.391 0.223 5.315Dec 30.30 – 23.60 26.35 1.512 0.244 5.764

A-Mean 29.50 – 24.90 27.19 0.985 0.162 3.639

41

Figure:1. Linear regression trends of monthly mean of maximum temprature.

Figure:1a. Linear regression trends of annual mean of monthly maximum temprature

Table:2. Annual and monthly linear regression equation of M-MAX temperratures.

Month Regression line R2

Jan y = 0.098x - 169.1 R² = 0.513Feb y = 0.083x - 135.9 R² = 0.352Mar y = 0.063x - 94.91 R² = 0.226April y = 0.048x - 64.47 R² = 0.199May y = 0.061x - 92.41 R² = 0.210June y = 0.081x - 136.1 R² = 0.310July y = 0.094x - 165.3 R² = 0.562

August y = 0.088x - 152.1 R² = 0.600Sept y = 0.074x - 122.2 R² = 0.439Oct y = 0.040x - 54.37 R² = 0.071Nov y = 0.079x - 131.8 R² = 0.402Dec y = 0.101x - 175.9 R² = 0.551

Annual y = 0.076x - 124.5 R² = 0.713

It is evident from the above figure 1 & table 2, that M-MAX has increased significantly for all the months. This implies that in CRSS, Chettalli the highest increase in M-MAX was observed in December (0.101 oC) and has increased by 3.838 oC during last 38 years (1975 to 2012). Similarly during January (0.098 oC) and July (0.094 oC) increased by 3.724 oC and 3.572 oC respectively. While the lowest increase was observed for the month of October (0.040 oC) by 1.520 oC during last 38 years. The annual M-MAX was observed an increasing trend having an annual increase of 0.0760C per year, as represented in figure 1a. This implies that at the station annual M- MAX has increased by 2.888 0C during the last 38 years. The increasing monthly and annual M-MAX results observed at CRSS Chettalli are in good agreement with the

findings of other studies on climate change (Omvir Singh et al., 2013; Deshmukh and Lunge., 2013). This rise in mean of monthly and annual maximum temperature may be due to increasing anthropogenic activities in the Western Ghats. The statistical summary on relative humidity revealed that the coefficient of variation for monthly mean of mean relative humidity (M-RH) is highest in the month of December (10.955 %), followed by February (9.136 %) whereas it is lowest in the month of June (4.400 %), followed by July (4.476 %). Similarly for the annual mean it is 4.491 percent. This means that the M-RH is more stable in the month of June and July and least stable in the month of December and February. Similarly the annual mean of M-RH is also more stable (Table: 3).

Table: 3.Statistical summary of monthly mean of mean relative humidity (M-RH %) Month Range Mean S.d SEm± C.V (%)

Jan 91.80 – 62.30 81.04 6.166 1.001 7.609Feb 90.50 – 62.60 79.58 7.262 1.172 9.136Mar 91.50 – 61.60 79.86 7.145 1.153 8.941Apr 94.00 – 61.70 83.98 7.131 1.159 8.498

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May 97.00 – 70.00 86.76 6.080 0.989 7.010Jun 98.40 – 83.00 91.43 4.022 0.656 4.400July 99.50 – 84.00 92.59 4.148 0.678 4.476Aug 98.80 – 82.40 91.80 4.833 0.784 5.263Sep 98.60 – 78.00 89.81 5.613 0.910 6.250Oct 99.30 – 70.40 87.27 6.411 1.041 7.357Nov 99.20 – 73.60 85.93 6.021 0.975 7.000Dec 98.80 – 49.50 82.10 8.992 1.457 10.953

A-Mean 92.70 – 71.90 86.02 3.869 0.626 4.491

The trends of monthly mean of relative humidity over different years were obtained by using linear regression best fit lines. The linear regression trends with their linear regression equations and coefficient of determinations for all the months from January to December and for annual are represented in Figure 2 and 2a and summarized in Table: 4.

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Figure:2. Linear regression trends of monthly mean of Relative Humidity.

Figure:2a. Linear regression trends of annual mean of monthly relative humidity

Table: 4. Linear regression equation of M-RH for all the months and annual.


Jan y = -0.193x + 466.2 R² = 0.121Feb y = -0.239x + 557.0 R² = 0.133Mar y = -0.222x + 523.4 R² = 0.120April y = -0.030x + 145.2 R² = 0.002May y = 0.084x - 80.72 R² = 0.023June y = 0.150x - 208.1 R² = 0.171July y = 0.154x - 216.2 R² = 0.172

August y = 0.184x - 275.5 R² = 0.179Sept y = 0.116x - 141.6 R² = 0.055Oct y = 0.119x - 150.4 R² = 0.042Nov y = 0.147x - 208.2 R² = 0.074Dec y = -0.193x + 468.2 R² = 0.057

Annual y = 0.007x + 70.51 R² = 0.000

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It is evident from the above figure 2 & table 4, that M-RH has increased significantly from May to November and it is decreased from December to April months. This implies that the highest increase in M-RH is observed in August (6.999 %) during last 38 years (1975 to 2012). While the highest decrease in M-RH in February (9.082%). The annual M-RH observed an increasing trend having an annual increase of 0.007 % per year, as represented in figure 2a. This shows that in CRSS Chettalli annual M- RH has increased by 0.266 % during the last 38 years. This result of marginal increase in annual M- RH confirms the findings of Ahmed et al (2007). The increase in relative humidity might be due to the increase of soil moisture evaporation.

The statistical summary on total monthly rainfall revealed that the coefficient of variation for total monthly rainfall (TMRF) is highest in the month of January (276.510 %), followed by February, December, March and it is 205.220, 166.078, 150.510 % respectively, whereas it is lowest in the months of August, July, June, September and it is 42.307, 47.975, 49.884, 51.372 % respectively. Similarly for the annual TMRF the coefficient of variation is 25.457 percent. This means that the TMRF is relatively more stable for August, July, June and September and it is least stable in the months of January, February, December and March. Similarly the annual TMRF is also more stable (Table: 5.).

Table: 5.Statistical summary of mean of total monthly rainfall (TMRF-mm) Month Range Mean S.d SEm± C.V (%)

Jan 46.50 – 0.00 3.09 8.528 1.382 276.510Feb 40.80 – 0.00 4.42 9.089 1.476 205.220Mar 148.60 – 0.00 24.41 36.759 5.961 150.510Apr 181.10 – 0.00 77.67 43.037 6.989 55.423May 309.70 – 7.40 104.09 66.803 10.837 64.174Jun 696.00 – 70.80 303.49 151.386 24.556 49.884July 877.50 – 110.70 398.05 190.989 30.982 47.975Aug 612.50 – 111.20 293.86 124.316 20.168 42.307Sep 306.50 – 27.50 132.12 67.870 11.017 51.372Oct 417.00 – 35.50 146.07 92.833 15.050 63.557Nov 267.00 – 0.00 68.69 63.122 10.234 91.886Dec 84.00 – 0.00 12.36 20.519 3.329 166.078

A-Mean 2445.10 – 946.20 1568.52 399.277 64.777 25.457The trends of TMRF over different years were obtained by using linear regression best fit lines. The linear regression trends with their linear regression equations and

coefficient of determinations for all the months from January to December and for annual are represented in Figure 3 and 3a and summarized in Table:

6. Figure:3. Linear regression trends of mean of monthly total rainfall.

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Figure:3a. Linear regression trends of mean of monttly total rainfall.

Table:6. Linear regression equation of TMRF for all the months and annual.


Jan y = -0.095x + 192.5 R² = 0.015Feb y = -0.205x + 413.0 R² = 0.062Mar y = 0.521x - 1015. R² = 0.024April y = 0.981x - 1879. R² = 0.064May y = -1.067x + 2233 R² = 0.031

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June y = -2.613x + 5513. R² = 0.036July y = 0.227x - 55.04 R² = 0.000

August y = -2.535x + 5347. R² = 0.051Sept y = 0.524x - 913.2 R² = 0.007Oct y = 0.893x - 1635. R² = 0.011Nov y = 0.250x - 430.7 R² = 0.001Dec y = 0.124x - 236.3 R² = 0.004

Annual y = -2.992x + 7533. R² = 0.006

It is evident from the above figure 3 & table 6, that monthly mean of TMRF have increased significantly for the months of March, April, July, September, October, November and December whereas it shows decreasing trend in January, February, May, June and August for the station. This implies that highest increase in TMRF occurs in April (37.278 mm), followed by October (33.934 mm) during the last 38 years. On the other hand the highest decrease in TMRF noticed in June and August and it is decreased by 99.294 mm & 96.33 mm respectively during last 38 years. Similarly the mean of annual TMRF observed a decreasing trend having an annual decrease of 2.992 mm per year, as represented in figure 3 a. This implies that in CRSS Chettalli annual TMRF has decreased by 113.696 mm during the last 38 years. The decreasing trends in annual and monsoon months June & August rainfall of the station are in lines of other studies on rainfall trends (Soman et al., 1988). The Mann-Kendall Test for Trend The Mann-Kendall test is a non-parametric test for identifying trends in time series data. The test was suggested by Mann (1945) and has been extensively used with environmental time series (Hipel and McLeod, 2005). The test compares the relative magnitudes of sample data rather than the data values

themselves. One benefit of this test is that the data need not conform to any particular distribution. Let X1, X2………. Xn represents n data points where Xj represents the data point at time j. Then the Mann-Kendall statistic (S) is given by S=Σ Σ sign (Xj- Xk), j=2, 3….n; k=1, 2…..j-1 Where: sign (Xj-Xk) = 1 if Xj-Xk >

= 0 if Xj-Xk =0 = -1 if Xj-Xk <0

A very high positive value of S is an indicator of an increasing trend, and a very low negative value indicates a decreasing trend. However, it is necessary to compute the probability associated with S and the sample size, n, to statistically quantify the significance of the trend. For a sample size>10, a normal approximations to the Mann-Kendall test may be used. For this, variance of S is obtained as, V(S) = [n (n-1) (2n+5) - Σtp(tp-1)(2tp+5)] /18, p=1,2…..q Where tp is the number of ties for the pth value and q is the number of tied values. Then standardized statistical test is computed by:

Z=S-1/√V(S) if S>0, =0 if S=0, =S+1/√V(S) if S<0

For annual M-MAX temperature, the value of S obtained as 426, a very high positive value indicating increasing trend

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and is statistically significant that there is enough evidence to determine an upward trend as shown in figure 1a which is confirmed by the M-K trend test at 5% level of significance. For annual M-RH the value of S obtained as 56 a positive value indicating increasing trend but is statistically insignificant that there is not enough evidence to determine there is an upward trend for M-RH shown in figure 2a and is confirmed by the M-K trend test at 5% level of significance. While for annual TMRF, the value of S obtained as -38, a negative value indicating decreasing trend and is statistically significant that there is enough evidence to determine an downward trend in TMRF shown in figure 3a and is confirmed by the M-K trend test at 5% level of significance.

CONCLUSION

It is observed that monthly mean of maximum (M-MAX) temperatures have increased significantly for all the months at CRSS, Chettalli station. The highest increase in MMAX temperature occurs in

December by 0.101 oC and has annually increased by 3.838 oC during last 38 years. Annual M-MAX temperature shows increasing trend by 0.076 oC per year and increased about 2.888 oC for last 38 year, which is statistically significant at 5% level of significance. While monthly mean of relative humidity (M-RH) has increased significantly for the months from May to November and it is decreased from December to April months. The annual M-RH observed an increasing trend where as statistically non significant. With regards to TMRF have increased significantly for the months of March, April, July, September, October, November and December whereas it shows decreasing trend in January, February, May, June and August for the station. The highest increase in TMRF occurs in April and has increased by 37.278 mm where as the highest decrease in TMRF occurs in June and decreased by 99.294 mm during the last 38 years. Similarly the mean of annual total rainfall observed a decreasing trend having an annual decrease of 2.992 mm per year.

REFERENCESAhmed A. Abu-Taleb, Ameen J. Alawneh and

Mahmoud M. Smadi., 2007: Statistical Analysis of Recent Changes in Relative Humidity in Jordan, American Journal of Environmental Sciences 3 (2): 75-77.

Deshmukh,D.T and Lunge, H.S, 2013: A Study Of Temperature And Rainfall Trends In Buldana District Of Vidarbha, India, International Journal Of

Scientific & Technology Research , 2 (2):67-73.

Fulekar, M.H. and Kale, R.K., 2010: Impact of Climate Change: Indian Scenario,

University News, 48 (24):15-23.

Intergovernmental Panel on Climate Change 2007: Summary for policymakers. In Climate Change: The Physical Science Basis (eds Solomon, S. D. et al.), Cambridge University Press, Cambridge, UK.

Lazaro R, Rodrigo FS, Gutierrez L, Domingo Fand Puigdefafregas J., 2001:Analysis of a

30-year rainfall record (1967-1997) in semi-arid SE Spain for implications on vegetation, J.Arid Environ. 48: 373-395.

Murray R. Spigel, Larry J. Stephens, 2000: SCHAUM’S outlines STATISTICS,Third Edition, TATAMcGRAW- HILL EDITION, 2000.

Omvir Singh, Poonam Arya and Bhagwan Singh Chaudhary, 2013: On rising temperature trends at Dehradun in Doon valley of Uttarakhand, India, J.Earth Syst. Sci. 122 (3) :613–622

Patterson, P.E.1987: Statistical Methods, Richard D. Irwin INC, Homewood, IL.

Ritter M E., 2006: The physical environment: an introduction to physical Geography, available online at:http://www.uwsp.edu/geo/faculty/ ritter/geog101/textbook/title_page.html.

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http://www.uwsp.edu/geo/faculty/%20ritter/geog101/textbook/title_page.html

http://www.uwsp.edu/geo/faculty/%20ritter/geog101/textbook/title_page.html

Sivakumar, M.V.K, Stefanski, R.,2008: Climate change mitigation, adaptation and sustainability in agriculture. In: Symposium on climate change and variability – agrometeorological monitoring and coping strategies for agriculture. Oscarsborg, Norway, Abstracts.WMO, p.44.

Smith, W, Desjardins, R and Grant, B., 2008: Some perspectives on agricultural GHG

mitigation and adaptation strategies with respect to the impact of climate

change- variability in vulnerable areas.In: Symposium on climate change and variability – agrometeorological

monitoring and coping strategies for agriculture. Oscarsborg, Norway, Abstracts. WMO, p.45.

Soman, M. K., Krishna Kumar, K. and Singh, N.,1988: Decreasing rend in the rainfall of Kerala. Current Science., 57:7–12.

Webber, J. and Hawkins, C., 1980: Statistical Analysis Applications to Business and Economics, Harper and Row, New York.

49

EFFECT OF DRIP IRRIGATION AND NITROGEN FERTIGATIO ON QUALITY, CHEMICAL PARAMETRS, YIELD AND ECONOMICS OF SEMI

RABI CASTOR (Ricinus communis L.)

K. M. Patel1, B. J. Patel2 and D.K. Patel2

2Dept. of Agro., C. P. College of Agriculture, S.D. Agri.Uni., Sardarkrushinagar 1Dr. K.M. Patel, Agriculture Officer, Centre for Research on IFS, SDAU,

Sardarkrushinagar (Gujarat)Email: [email protected]


ABSTRACTA field was conducted at Agronomy Instructional Farm, Sardarkrushinagar Dantiwada Agricultural University, Sardarkrushinagar during semi rabi 2013-14 and 2014-15 to find out a suitable irrigation scheduling and sources of nitrogen fertigation. The experiment comprised of four scheduling of irrigation (0.6 ADFPE, 0.8 ADFPE, 1.0 ADFPE through drip system and surface method and three nitrogen fertigation (N1: 50 % RDN, N2 : 75 % RDN and N3 : 100 % RDN (i.e. 80 kg/ha), thereby making twelve treatment combinations. The result revealed that to achieve profitable yield from semi rabi castor, follow irrigation at 1.0 ADFPE through drip in conjunction with fertigation of nitrogen @ 60 kg/ha through urea in four equal splits at 30, 60, 90 and 120 DAS, respectively. Additional 20 kg/N/ha along with 40 kg P2O5/ha need to be applied before sowing the crop.

Key words: IW: CPE ratio, recommended dose of nitrogen, water use efficiency nitrogen use efficiency

Castor (Ricinus communis L.) is non edible oil seed crop having high industrial importance due to presence of unique fatty acid and ricinoleic acid. The crop is grown mainly under irrigated condition. Castor is extensively cultivated in India, China, Brazil, Ethiopia, Thailand etc., in the world. Drip irrigation system optimizes the irrigation water and puts it uniformly and directly to the root zone of the plants at frequent interval based on crop water requirement through a closed net work of low pressure plastic pipes. Superiority of drip system in terms of water saving and increased in yield along with other benefits over surface method of irrigation is proved by many research evidences. Drip irrigation system improves the WUE by increasing yield of cotton with limited quantity of water. (Singh et al., 2005). Total area, production and productivity of castor crop in the world during 2011-12 were 20.06 lakh hectares, 18.77 lakh tonnes and 1070 kg/ha, respectively (SEA., 2013). In India total acreage, production and productivity of castor crop during 2011-12,were 11.20

lakh hectares, 15.80 lakh tonnes and 1240 kg/ha, respectively (SEA., 2013). The contribution of India in the world is 56 per cent in acreage and 84 per cent in production of castor. Thus, India is leading country in the world not only in acreage and production but in productivity of castor also. In India, during last decade area, production and productivity were increased to 37, 59 and 35 per cent, respectively (SEA., 2013). Further, Gujarat occupying area, production and productivity of castor crop were 4.22 lakh ha, 8.33 lakh MT and 1972 kg/ha, respectively (CICR., 2012-13).


An experiment was conducted at Agronomy Instructional Farm, Chimanbhai Patel College of Agriculture, Sardarkrushinagar Dantiwada Agricultural University, Sardarkrushinagar, District: Banaskantha (North Gujarat) to study “Effect of drip irrigation and nitrogen fertigation on quality, chemical parametrs, yield and economics of semi rabi castor

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(Ricinus communis L.) ” during 2013-14 and 2014-15.The experiment was comprised of four schedules of irrigation (0.6 ADFPE, 0.8 ADFPE, 1.0 ADFPE through drip system and surface method) and three levels of nitrogen fertigation (N1 : 50 % RDN, N2 : 75 % RDN and N3 : 100 % RDN (i.e. 80 kg/ha), thereby making twelve treatment combinations. The field was laid out in split plot design with four replications. Castor variety GCH 7 was used as a test crop. The soil of experimental field was loamy sand having good drainage capacity. It was low in organic carbon (0.27 %) and available nitrogen (161 kg/ha), medium in available phosphorus (36.7 kg/ha) but sulphur (10.51 ppm) but high in available potash (284 kg/ha). This zone is characterized by semi-arid climate with extreme cold winter and hot and dry windy summer. Generally, monsoon commences in the middle of June and retreats by the middle of September, most of the precipitation is received from the South-West monsoon, concentrating in the month of July and August. The annual average rainfall is about 638 mm in 26 rainy days (1981-2010). The winter season is fairly cold and dry starts from the end of October and continues till the end of February. The minimum temperature of the year reached in the months of December or January. The temperature starts rising from February and reaches the maximum in the months of April or May. The wind velocity is very high during summer. The annual rainfall was 1109 mm with 39 rainy days during 2013-14 and annual rainfall of 550 mm received in 23 rainy days during 2014-15.The field was laid out in split plot design with four replications. The experiment was comprised of four schedules of irrigation (0.6 ADFPE, 0.8 ADFPE, 1.0 ADFPE through drip system and surface method) and three levels of nitrogen fertigation (N1 : 50 % RDN, N2 : 75 % RDN and N3 : 100 % RDN (i.e. 80 kg/ha), thereby making twelve treatment combinations. Scheduling of irrigation for drip system was

maintained by considering the evapotranspiration. The quantity of water delivered per dripper at different places were measured and average volume was utilized calculating the quantity of water to be delivered and time of operation as per treatments at every alternate day for the drip system. In surface method irrigation was given at 0.8 IW: CPE ratio with 60 mm depth. Recommended dose of fertilizer was 80-25-00 kg NPK/ha. Application of 25 % RDN and full dose of phosphorus was given at basal and rest 75% RDN in four equal splits at 30, 60, 90 and 120 DAS as per treatments. Soil samples were collected prior to start of experiment and after completion of the experiment from o to 15 cm depth for analysis of available N. The soil was also analysed for pH and electrical conductivity for 0-15 cm depth. Treatment wise balance sheet of NPK indicating the available nutrients in the soil at initial stage, nutrients added for raising the crop, nutrient uptake by the crop, nutrient left in the soil after the harvesting, actual grain/loss over initial status and apparent nutrients balance in the soil were worked out. The economics of different treatments was calculated by taking into account the various inputs required and outputs realized as per the prevailing cost of inputs and outputs during the respective years. All the data obtained were statistically analysed using the F-test procedure for split plot design. The critical differences for comparing treatment means were worked out at 5 % level of significance. The trends of the results was similar during analysis for results and comparison.


Effect of irrigation schedulesEffect on growth and yield parameters Plant height was significantly increased under irrigation treatment I3 than I1 and I4 at harvest of the crop. Treatment I3 (1.0 ADFPE) of drip irrigation produced higher length of main spike. It might be due to drip irrigation at higher fraction improved

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soil moisture status around the root zone and increased the nutrients availability ultimately resulted in to growth and yield attributing characters. These findings are in accordance with those reported by Patel and Patel (2004), Nagabhushanam and Raghavaiah (2005) and Nagar and Patel (2012).Effect on yields A perusal of data indicated that seed yield of castor plant significantly increased with increase in levels of drip irrigation schedules from treatment I1 (0.6 ADFPE) to treatment I3 (1.0 ADFPE) during the years 2013-14, 2014-15 and on pooled basis. Significantly the highest seed yield of 273.3 g/plant was obtained with drip irrigation schedules treatment I3 (1.0 ADFPE) in pooled analysis. Treatment I4

(surface method) and I1 (0.6 ADFPE) recording lower values found equally effective.All the yield attributes studied during the course of investigation were closely associated with the growth attributes. Significantly the highest seed yield (2553 kg/ha) was obtained under drip irrigation treatment at 1.0 ADFPE (I3), while significantly the lowest seed yield was noticed under irrigation level I1 (1998 kg/ha) on pooled basis but it remained at par with irrigation level I4 (surface method). The magnitude of increase in seed yield under treatment I3 (1.0 ADFPE) was to the extent of 9.36 and 21.73 and 19.23 per cent over treatment I2 (0.8 ADFPE), I1

(0.6 ADFPE) and I4 (surface method) on pooled basis, respectively. The increase in seed yield might be due to the effect of timely and frequent irrigation under drip irrigation treatment I3 (1.0 ADFPE) provided constant wet root zone, increased the nutrients availability enhanced growth, yield attributes and ultimately higher seed yield. The simple correlation study also indicated that there was close association of almost all growth and yield attributes and chemical parameters with seed yield. These results are in close conformity with those reported by Patel and Patel (2004),

Nagabhushanam and Raghavaiah (2005), Saila and Reddy (2005), Reddy et al. (2006) and Nagar and Patel (2012) in castor.An appraisal of data revealed significant differences in stalk yield found due to irrigation schedules levels during the years 2013-14, 2014-15 and on pooled basis analysis. Treatment I3 (irrigation schedules at 1.0 ADFPE) ranked at top registering stalk yield of 2946 and 3102 during 2013-14 and 2014-15, while on pooled basis, I3

(irrigation schedules at 1.0 ADFPE) recorded significantly the highest stalk yield. Higher stalk yield obtained under I3

is contributed to increase in N uptake by stalk during both the years and on pooled basis.

Effect on quality parameter

The results in respect of oil content in seed indicated that there was non-significant difference observed among the irrigation treatments. But with regard to oil yield, drip irrigation treatment I3 (1.0 ADFPE) recorded significantly the highest oil yield as compared to rest of the treatments. These results are substantiated with Nagabhushanam and Raghavaiah (2005).

Effect on chemical parametersN-uptake in seed, stalk and total by crop was found significant due to different irrigation treatments. Drip irrigation treatment I3 (1.0 ADFPE) exhibited significant superiority with respect to N-uptake in seed, stalk and total by crop which was to the tune of 72.40, 36.08 and 108.48 kg/ha as compared to treatments I2

(0.8 ADFPE) and I1 (0.6 ADFPE), I4

(surface method), respectively. Balance of nutrients in the soil is the difference between the total nutrients initially available in the soil along with applied and the amount that has been removed by the crop, assuming the losses that was constant. The important factor affecting carryover of different nutrients are soil characteristics, weather conditions, amount and types of manures and fertilizers applied, time and

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method of their application, yield response and handling of crop. The pooled results pertaining to post harvest available soil nitrogen status differed non-significantly due to different levels of irrigation.Effect of nitrogen fertigationEffect on growth and yield parameters In the present investigation, application of 100 per cent recommended dose of nitrogen (RDN) recorded significantly higher values of plant height at harvest and length of main spike. Nitrogen is the primary essential element for growth, development and yield of castor crop. Superior vegetative growth due to increase in dose of nitrogen fertilizer as realized in the present investigation would obviously improve the yield attributes. These results are akin to those reported by Saila and Reddy (2005) and Patel and Patel (2004). Effect on yields Seed yield per plant was significantly influenced by different nitrogen fertigation treatments during the years 2013-14, 2014-15 and in pooled data. Significantly the highest seed yield of 263.74 g/plant was obtained with an application of 100 per cent RDN through fertigation (N3) in pooled results, respectively. While it was at par with N2 (75 % RD) during 2014-15. The results in respect of seed yield revealed that nitrogen level had pronounced effect on seed yield. Nitrogen application @ 100 per cent RD (N3) produced significantly the highest seed yield as compared to rest of the treatments. The increase in seed yield under treatment N3 (100 per cent RD) was to the extent of 8.54 and 17.97 per cent over treatment N2 (75 % RD) and N1 (50 % RD) in pooled analysis, respectively. Drip fertigation, under some condition can produce comparable or higher yields with substantial savings up to 50 per cent of N. The results obtained in the present study are in accordance with the findings reported by Patel and Patel (2004) and Patel et al. (2006) in castor crop. It was observed from the data that stalk yield was significantly influenced by different nitrogen fertigation treatments.

Significantly higher stalk yield of 2834 kg/ha was obtained with an application of 100 per cent RDN (N3) which was at par with an application of 75 per cent RDN (N2). Significantly the lowest stalk yield of 2595 kg/ha was observed under the treatment N1 (50 % RD) during pooled basis data. Effect on quality parametersData indicated that oil content in seed and oil yield was significantly the highest with 100 % RDN (N3) as compared to the rest of the treatments. This also might be due to marked increase in seed yield under higher levels of N i.e treatment N3 (100 per cent RD through fertigation) was directly responsible for higher oil yield. These results are substantiated with Saila and Reddy (2005).Effect on chemical parameters The results in respect of N-uptake in seed, stalk and total by the crop revealed that it was significantly influenced due to different nitrogen levels and maximum N-uptake in seed, stalk and by the crop were achieved under treatment N3 (100 % RD). The increase in N-uptake in seed and stalk under treatment N3 (100 % RD) might be due to production and conversion of more photosynthates needed for seed development. Besides, N fertilization increases the cation exchange capacity of plant root and thus makes them more efficient in absorbing nutrient ions. Higher N removal by castor under higher level of nitrogen i.e treatment N3 (100 per cent RD) was probably due to higher seed and stalk yields, improved the chemical properties of soil, better utilization of water, thereby enhanced seed yield led higher uptake of N. Moreover highly N uptake by the crop under N3 is contributed to increase in growth, development, yield attributing and high seed yield. The results corroborate the reports of Maan and Amin (2014).Interaction effect of I × NOn the basis of pooled data, the treatment combination I3N3 (1.0 ADFPE with 100 % RD through fertigation) showed superiority bearing the highest values of N-uptake by

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stalk and Total N- uptake by crop. Data outlined in Tables 3 showed that treatment combination I3N3 (1.0 ADFPE with 100 % RDN) registered significantly the highest N-uptake by stalk (43.52) during 2013-14 and in pooled results. Data showed that treatment combination I3N3 (1.0 ADFPE with 100 % RDN) registered significantly higher total N-uptake by crop (129.59 kg/ha) in pooled analysis, while lower total N-uptake by crop was recorded with treatment combination I1N1 (0.6 ADFPE with 50 % RDN, 59.01 kg/ha) in pooled analysis.ECONOMICSA perusal of data in Table 4 reflected the economics as influenced due to various irrigation schedules treatments. The drip

irrigation schedules treatment I3 (1.0 ADFPE) earned the highest net realization (

66153/ ha) along with BCR value of 2.97. This might be due to higher cost of inputs. Similar results were also reported by Saila and Reddy (2005) and Patel et al. (2003). Benefit cost analysis (Table 8 further showed that net realization/ ha and BCR values increased with graded level of nitrogen fertigation. Treatment 100 % RDN (N3) accrued the highest net return of 61871/ha. With the BCR value of (2.83). While the lowest BCR value (2.49) was observed under treatment N1 (50 per cent RDN). The results are in line with those reported by Patel et al. (2003), Saila and Reddy (2005) and Patel et al. (2006).

REFERENCESMaan, M. and Amin, A.U. 2014. Response of

Castor (Ricinus communis L.) to varying crop geometry and dates of sowing with levels of nitrogen under rabi season. Ph.D. (Agri.) thesis, Sardarkrushinagar Dantiwada Agricultural University, Sardarkrushinagar.

Nagabhushanam, U. and Raghavaiah, C.V. 2005. Seedling date and irrigation effects on the productivity and oil quality of post-monsoon grown castor, Ricinus communis L. in alfisols. Journal of Oilseeds Research 22(1): 206-208.

Nagar, S. and Patel, J.C. 2012. Evapotranspiration based scheduling of irrigation through drip system for castor crop (Ricinus communis L.). M.Sc. (Agri.) thesis, Sardarkrushinagar Dantiwada Agricultural University, Sardarkrushinagar.

Patel, J. C. and Patel, B. K. 2004. Maximizing castor yield through irrigation and nitrogen management strategies under different plant geometry. GAU Research Journal 29 (1-2):45-47.

Patel, K. S., Patel, M.K., Patel, G.N. and Pathak, H. C. 2006. Fertigation study in castor, Ricinus communis L. Journal of Oilseeds Research 23 (1): 122-123.

Reddy, A.P., Reddy, A.S. and Padmavathi, P. 2006. Effect of irrigation and integrated nutrient management on seed and oil yield of rabi castor, Ricinus communis L. Journal of Oilseeds Research 23 (2): 239-241.

Saila Sree, P. and Bhaskar Reddy, B. 2005. Effect of tillage and soil moisture regime on sedling emergence, growth and yield of castor, Ricinus communis L. Journal of Oilseeds Research 22 (2): 327-330.

S.K.Chauhan and Pramendra Singh, 2015. “Effect of sprinkler irrigation system over surface method of irrigation on growth and yield attributes of clusterbean” 4 (2): 12-15.

Singh, Y., Singh, C. S., Singh, A., Singh, A. K. and Singh, K. 2005, Fertigation a key for Hi-Tech Agriculture..Agriculture Water Mgt. 52 (7): 128-149.

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YIELD AND ECONOMICS OF SWEET SORGHUM VARIETIES UNDER VARIED FERTILIZATION GROWN AS MAIN AND RATOON CROPS

Ganga Devi M, Tirumala Reddy S, Naidu M.V.SScientist (Agro) KVK Lam Farm Guntur, Andhra Pradesh


ABSTACRTField experiments was conducted at S.V. Agricultural College farm, Tirupati campus of Acharya N.G. Ranga Agricultural University, Andhra Pradesh both during kharif and rabi seasons of 2010 and 2011 to investigate “ Nutrient management in direct sown and ratoon crops of sweet sorghum”. The results revealed that the yield parameters stalk yield, grain yield and net returns of sweet sorghum were recorded highest with variety SPV-422 followed by Madhura hybrid with application of 100-80-80 kg ha-1 N, P2O5 and K2O when grown as main crop. The ratoon crop of sweet sorghum also followed same trend and recorded highest yield and economics with application of 125 per cent of the nitrogen applied to main crop than the other treatments tested in this study.

Key words: Sweet sorghum, ratoon, economics, nutrient management.

Sweet sorghum (Sorghum bicolor (L) Moench) originated in tropical Africa and later spread to the near and far east. Sweet sorghum can be grown in regions ranging from 21oS to 47oN latitude and hence it is considered as the sugarcane of the temperate zone. The crop has a sweet and juicy stem because it can store considerable amount of sugar. Because of a faster growth rate, early maturity (90-120 days) and higher quantities of readily available fermentable sugar content, sweet sorghum has frequently been suggested as a good source of ethanol production (Prasad et al., 2006). The cost of cultivation of sweet sorghum is three times lesser compared to that of sugarcane (Dayakar Rao et al., 2004). Earlier, this crop was grown for grain purpose but now it is being grown as a multipurpose crop for grain, fodder, syrup and alcohol production. Because of the rapid increase in crude oil prices, sweet sorghum has been investigated as potential source of fermentable sugars for ethanol production. This is because of the crop’s high sugar content and biomass production as well as its considerable potential as an alternative to sugar cane. The fuel potential of the crop

can be exploited by following optimum agronomic practices. To maximize the yields of sweet sorghum using ratoon cropping systems, the role of management factors must be fully understood.


Field experiments was conducted at S.V. Agricultural College farm, Tirupati campus of Acharya N.G. Ranga Agricultural University, Andhra Pradesh both during kharif and rabi seasons of 2010 and 2011 to investigate “ nutrient management in direct sown and ratoon crops of sweet sorghum” which is geographically situated at 13.5oN latitude, 79.5oE longitude and at an altitude of 182.9 m above the mean sea level in the Southern Agroclimatic zone (Zone IV) of Andhra Pradesh. The soils were sandy clay loam in texture, low in organic carbon and available nitrogen and medium in available phosphorus and potassium with soil pH of 7.2. The experiment was laid out in a split-split plot design and replicated thrice. Three genotypes viz., SPV-422, ICSV-700 and Madhura hybrid were assigned to main plots and four fertilizer levels viz., 60-40-

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40, 80-60-60, 100-80-80 and 120-100-100 kg ha-1 of N, P2O5 and K2O were allotted to sub plots for main crop during kharif seasons, while three nitrogen levels viz.,100 per cent, 125 per cent and 150 per cent of nitrogen applied to main crop were allotted to sub-sub plots in the ratoon crop. The main plot comprising 13.5 m x 20.0 m, sub-plot with 13.5 m x 5.0 m and sub-sub plot 4.5 m x 5.0 m. Healthy sweet sorghum seeds, treated with carbendazim @ 3 g kg-1 of seed to prevent seed borne diseases were used for sowing. The seeds were dibbled @ 2 to 3 seeds hill-1 using a seed rate of 8-10 kg ha-1 with inter and intra row spacing of 45 cm x 20 cm respectively. Pre-emergence spray of atrazine @ 1 kg acre-1 was done on the next day after sowing for weed control. Four graded fertilizer levels of nitrogen, phosphorus and potassium viz., 60-40-40, 80-60-60, 100-80-80 and 120-100-100 kg ha-1 were applied to sub plots during kharif for the main crop while three levels viz., 100 per cent, 125 per cent and 150 per cent of the nitrogen applied to main crop were allotted to sub-sub plots during rabi season for the ratoon crop. In all the treatmental combinations, while the nitrogen was applied in 2 equal splits at sowing and at 25 DAS, entire quantity of phosphorus and potassium was applied as a basal dose at the time of sowing. Inter-cultivation with a blade harrow was also done along with the second hoeing at 45 DAS followed by earthing up of ridges on both sides of the crop. Sweet sorghum stalks were cut at 4-5 cm above the ground level at physiological maturity and later ear heads were cut and separated. The stripped stalks of sweet sorghum were weighed immediately after harvesting, after separating the leaves from stem, in each net plot and the stripped stalk yield was expressed in t_ha-

1. The panicles were air dried, threshed, cleaned and the grain was weighed and

expressed as grain yield in kg ha-1. The data recorded on various growth parameters, yield attributes, yield, quality, economics and nutrient uptake during the course of investigation were statistically analysed following the analysis of variance procedure as suggested by Panse and Sukhatme (1985).


Stripped Stalk Yield (t ha-1):Statistical analysis of pooled data of main crop of sweet sorghum (Table 1) revealed that the highest stripped stalk yield was recorded with the variety SPV-422 (V1) which was, however, comparable with Madhura hybrid (V3). Both these genotypes were significantly superior to the variety ICSV-700 (V2), which resulted in significantly lowest stripped stalk yield. The highest stripped stalk yield with SPV-422 (V1) could be attributed to an increase in the plant height and more importantly dry matter accumulation in stem. Among the fertilizer levels tried, the highest fertilizer level of 120-100-100 kg ha-1 N, P2O5 and K2O (F4) resulted in the highest stripped stalk yield followed by 100-80-80 kg ha-1

N, P2O5 and K2O (F3) which were on par with each other. Both these were significantly superior to 80-60-60 kg ha-1

N, P2O5 and K2O (F2) and 60-40-40 kg ha-

1 N, P2O5 and K2O (F1) with significant disparity between the later (F2 and F1) treatments and lowest was with F1. This could be attributed to the better vegetative growth which, in turn, enhanced the drymatter production Silliet al. (2001). The interaction effect of the variety SPV-422 with a fertilizer level of either 120-100-100 kg ha-1 N, P2O5 and K2O (V1F4) or 100-80-80 kg ha-1 N, P2O5

and K2O (V1F3) are comparable and recorded significantly higher stalk yield and Significantly the lowest stripped stalk yield was recorded with the genotype

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ICSV-700 coupled with the fertilizer level of 60-40-40 kg ha-1 N, P2O5 and K2O (V2F1).The pooled data analyses revealed that the highest stripped stalk yield of ratoon crop (Table 2) was recorded with the varietySPV-422 (V1) which, however, was comparable to Madhura hybrid (V3) and distinctly superior to the other variety ICSV-700 (V2). Stripped stalk yield increased marginally with application of fertilizers from F1 (60-40-40 kg ha-1 N, P2O5 and K2O) to F3 (100-80-80 kg ha-1

N, P2O5 and K2O) where after it decreased significantly with the highest fertilizer level of 120-100-100 kg ha-1 N, P2O5 and K2O (F4). Among the nitrogen management, application of 150% nitrogen (N3) resulted in the highest quantity of stripped stalk yield which, in turn, was at par with 125% nitrogen (N2). Both these were significantly superior to 100% nitrogen (N1) which resulted in the lowest stripped stalk yield. With respect to interaction effects, only the varieties and fertilizer levels exhibited discernible effects while the others failed to exert any significant interaction effects. The combination of the Madhura hybrid and the fertilizer level of 120-100-100 kg ha-1

N, P2O5 and K2O (V3F4) resulted in marginally higher stripped stalk yield. Grain Yield (Kg ha-1):The pooled data analysis revealed (Table 3) that the highest grain yield of main crop was recorded with the variety SPV-422 (V1) which was, however, at par with Madhura hybrid (V3) and significantly superior to ICSV-700 (V2). The variety SPV-422 (V1) was consistently superior to ICSV-700 (V2) in terms of plant height, leaf area and drymatter production. The better partitioning of the available photosynthates and efficiency of translocation towards sink resulting in the highest grain yield with the variety SPV-422 Latha and Sharanappa (2010). The highest grain yield was recorded with

100-80-80 kg ha-1 N, P2O5 and K2O (F3) which was at par with F4 but distinctly superior to other two lower fertilizer levels (F1 and F2) tried. Significantly lowest grain yield was recorded with the lowest level of 60-40-40 kg ha-1 N, P2O5 and K2O (F1). This might be due to sufficiency of nutrients at this level which resulted in efficient translocation of accumulated assimilates to reproductive organs resulting in higher grain yield. Above or below this level, most of the accumulated assimilates might have been utilized for drymatter production and less quantity of assimilates were translocated to reproductive organs.

With respect to interaction effects combination of either SPV-422 or Madhura genotypes along with the application of a fertilizer level of 100-80-80 kg ha-1 N, P2O5 and K2O (V1F3and V3F3) resulted in the highest grain yield. The pooled data analysis revealed (Table 4) that the highest grain yield of ratoon sweet sorghum crop was recorded with SPV-422 (V1) which in turn, was at par with Madhura hybrid (V3). Both these were significantly superior to the variety ICSV-700 (V2). With regard to fertilizer levels, 100-80-80 kg ha-1 N, P2O5 and K2O (F3) resulted in the highest grain yield after which a decreased grain yield was noticed with 120-100-100 kg ha-1 N, P2O5 and K2O (F4). Both these were significantly superior to the lower fertilizer levels of 80-60-60 kg ha-1 N, P2O5 and K2O (F2) and 60-40-40 kg ha-1

N, P2O5 and K2O (F1) with significant disparity between them and significantly least grain yield was recorded with the lowest fertilizer level (F1). With respect to nitrogen levels tried, the highest grain yield was recorded with 125% N (N2), after which, there was a declining trend with 150% N (N3). Both of them were on par with each other but significantly superior to 100% N i.e., 100 kg N ha-1

(N1) which registered the lowest grain yield. With respect to interaction the

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variety SPV-422 along with the application of a fertilizer level of 100-80-80 kg ha-1 N, P2O5 and K2O (V1F3) resulted in higher grain yield which was comparable to Madhura hybrid with the same fertilizer level of 100-80-80 kg ha-1

N, P2O5 and K2O (V3F3).Economics : Economics of sweet sorghum as main crop shown that (Table 5) highest net returns and benefit cost ratio was recorded with variety SPV-422 (V1) which was distinctly superior to all other varieties. This was closely followed by Madhura hybrid (V3) and significantly lowest economics were recorded with the variety ICSV-700 (V2). Among the fertilizer levels, application of 100-80-80 kg ha-1 N, P2O5 and K2O (F3) recorded highest net returns fb 120-100-100 kg ha-1

N, P2O5 and K2O (F4) and they were comparable to each other and lowest net returns were recorded with 60-40-40 (F2) kg ha-1 N, P2O5 and K2O. The fertilizer levels of 100-80-80 kg ha-1 N, P2O5 and K2O (F3) recorded significantly highest benefit cost ratio fb 80-60-60 (F3) and 60-40-40 (F2) kg ha-1 N, P2O5 and K2O. Significantly least benefit cost ratio was registered with 120-100-100 kg ha-1 N, P2O5 and K2O (F4). These results are in conformity with the findings of Ramesha et al. (2011). The interaction effect of varieties and fertilizer levels revealed that Madhura hybrid coupled with 100-80-80 kg ha-1 N, P2O5 and K2O (V3F3) recorded the highest net return which was comparable to SPV-422 in association with the same fertilizer level (V1F3). The benefit-cost ratio was maximum with the fertilizer level of 100-80-80 kg ha-1 N, P2O5 and K2O (F3) due to relatively less cost of fertilizers as well as higher yields under this level. On the other hand, the highest fertilizer level (F4), which

involved higher cost of input (fertilizer) compared to the former could not compensate the increased yield and ultimately resulted in the least B:C ratio. When the interaction effects were critically analyzed, this factor of higher cost on fertilizer application and inability to compensate seem to have a negative impact on B:C ratios.Economics of sweet sorghum as ratoon crop shown that (Table 6) highest net returns and benefit cost ratio was recorded with variety SPV-422 (V1) which was distinctly superior to all other varieties. This was closely followed by Madhura hybrid (V3) and significantly lowest returns were recorded with the variety ICSV-700 (V2). Among the fertilizer levels, application of 120-100-100 kg ha-1 N, P2O5 and K2O (F4) recorded highest net returns and benefit cost ratio which was however comparable with 100-80-80 kg ha-1 N, P2O5 and K2O (F3) and significantly superior to other fertilizer levels tried. With regard to nitrogen levels highest net returns was registered with 125% N (N2) which was, however, comparable to 150% N (N3). Both these were significantly superior to 100%N (N1) which recorded significantly the lowest net returns. He highest benefit cost ratio was registered with 125% N (N2) which significantly superior to all other nitrogen levels tried. The next best nitrogen level was 100% N (N1) which was however at par with the 150% nitrogen (N3).

CONCLUSIONSweet sorghum variety SPV-422 performing better as main and ratoon crop in terms of yield and net returns at fertilizer level of 100-80-80 kg ha-1 N, P2O5 and K2O and 125% nitrogen applied to main crop.

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Table: 1. Effect of varieties and fertilizer levels on stripped stalk yield (t ha-1) of main crop of sweet sorghum

Treatments V1- SPV-422 V2- ICSV-700 V3- Madhura MeanF1 - 60-40-40 46.3 39.0 46.9 44.1F2 - 80-60-60 49.7 40.4 49.5 46.5F3 - 100-80-80 52.6 45.6 53.8 50.7F4 - 120-100-100 53.5 47.4 52.8 51.2Mean 50.5 43.1 50.7

S.Em± CD (0.05)V 0.19 0.74F 0.23 0.69F at V 0.41 1.20V at F 0.38 1.22

Table: 2. Effect of varieties, fertilizer levels and nitrogen levels on stripped stalk yield (kg ha-1) of ratoon sweet sorghum at harvest.

TreatmentsPooled

V1-SPV-422 V2- ICSV-700 V3- Madhura Mean

F1 - 60-40-40

N1 – 100% N 2879.5 1130.5 2847.5 2285.8N2 – 125% N 3180.8 1150.8 2886.8 2406.2N3 – 150% N 3248.2 1160.2 3117.2 2508.5Mean 3102.8 1147.2 2950.5

F2 - 80-60-60

N1 – 100% N 3377.8 1170.2 3309.7 2619.2N2 – 125% N 3520.3 1176.8 3519.0 2738.7N3 – 150% N 3557.2 1184.7 3627.7 2789.8Mean 3485.1 1177.2 3485.4

F3 - 100-80-80

N1 – 100% N 3809.7 1190.3 3759.0 2919.7N2 – 125% N 3955.8 1192.5 3876.2 3008.2N3 – 150% N 4037.0 1194.2 3950.3 3060.5Mean 3934.2 1192.3 3861.8

F4- 120-100-100

N1 – 100% N 3961.3 964.5 3982.7 2969.5N2 – 125% N 4111.8 966.3 4158.5 3078.9N3 – 150% N 4183.3 969.8 4167.0 3106.7Mean 4085.5 966.9 4102.7

S.Em± CD (0.05)V 11.9 54.2F 17.8 61.2N 27.3 77.5V at F 29.3 97.0F at V 30.9 101.5

Table: 3. Effect of varieties and fertilizer levels on grain yield (kg ha-1) of main crop of sweet sorghum at harvest

Treatments V1- SPV-422 V2- ICSV-700 V3- Madhura MeanF1 - 60-40-40 764 375 675 605F2 - 80-60-60 789 402 756 649F3 - 100-80-80 852 482 870 735F4 - 120-100-100 845 482 834 721Mean 813 435 784

S.Em± CD (0.05)V 10 38F 6 17F at V 10 30V at F 17 33

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Table: 4 Effect of varieties, fertilizer levels and nitrogen levels on grain yield (kg ha-1) of ratoon sweet sorghum at harvest.

Treatments V1-

SPV-422V2-

ICSV-700V3-

Madhura Mean

F1 - 60-40-40

N1 – 100% N 221 206 237 221N2 – 125% N 246 223 250 240N3 – 150% N 240 214 245 233Mean 235 214 244

F2 - 80-60-60

N1 – 100% N 267 238 256 253N2 – 125% N 298 259 276 278N3 – 150% N 287 253 266 269Mean 284 250 266

F3 - 100-80-80

N1 – 100% N 333 275 326 311N2 – 125% N 350 290 342 327N3 – 150% N 347 288 336 324Mean 343 285 335

F4- 120-100-100

N1 – 100% N 325 271 297 297N2 – 125% N 352 285 334 324N3 – 150% N 344 315 323 327Mean 340 290 318

S.Em± CD (0.05)V 3 12F 2 6N 2 5V at F 4 12F at V 3 10

Treatments

Net returns (Rs/ha) B:C ratioV1-

(SPV-422)

V2-

(ICSV-700)

V3-

(Madhura) MeanV1-

(SPV-422)

V2-

(ICSV-700)

V3-

(Madhura) Mean

F1 - 60-40-40 27140 16813 24944 22965 3.21 2.32 3.02 2.85F2 - 80-60-60 28507 18036 26990 24511 3.17 2.37 3.05 2.86F3 - 100-80-80 30442 20705 30341 27163 3.17 2.48 3.16 2.93F4-120-100-100 30097 20726 28799 26540 3.02 2.39 2.93 2.78Mean 29046 19070 27769 3.14 2.39 3.04

S.Em± CD (0.05) S.Em± CD (0.05)V 94 369 0.014 0.055F 223 663 0.017 0.052F at V 386 NS 0.030 0.090V at F 253 NS 0.028 0.091

Treatments Net returns (Rs/ha) B:C ratioMain plots (Varieties)V1 - SPV-422 15041 2.44V2 - ICSV-700 4755 1.46V3 - Madhura 14556 2.40S.Em± 75 0.007CD (0.05) 338 0.03Sub-plots (Fertilizer levels – N, P2O5 and K2O kg ha-1)

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F1 - 60-40-40 8324 1.80F2 - 80-60-60 10716 2.03F3 - 100-80-80 13273 2.27F4 - 120-100-100 13490 2.29S.Em± 77 0.008CD (0.05) 265 0.02Sub-Sub plots (Nitrogen levels)N1 – 100% N 11019 2.09N2 – 125% N 11718 2.12N3 – 150% N 11616 2.08S.Em± 143 0.014CD (0.05) 406 0.03

REFERENCESDayakar Rao, B., Ratnavathi, C. C.,

Karthikeyan, Biswas P. K., Rao, S. S., Vijay Kumar, B.S. and Seetharama, N. 2004. Sweet sorghum cane for bio-fuel production: ASWAT analysis in Indian context. National Research Center for Sorghum, Hyderabad, AP. pp.20.

Panse, V.G and Sukhatme, P.U. 1985. Statistical methods for agricultural workers. Edn. 2. Indian Council of Agricultural Research, New Delhi. pp. 205-210.

Prasad, S., Singh, A and Joshi, H .C. 2006. Ethanol as an alternative fuel from agricultural, industrial and urban residues. Resources Conservation and Recycling doi: 10.1016/j. resconrec.2006.05.007. (Avail. online on www.Sciencedirect.com).

Ramesha,Y.M., Sharanappa,K.N., Kalyana Murthy and Sanna Thimmappa, H.G. 2011. Effect of phosphorus enriched vermicompost on juice quality, ethanol yield and nutrient balance in sweet sorghum (Sorghum bicolor) Indian Journal of Agronomy 56(2) : 138-144.

Silli, M.V., Hunshal, C.S and Patil, R.H. 2001. Fodder yield and quality of sweet sorghum genotypes as influenced by N and P levels. Journal of Maharashtra Agricultural Universities.26 (1): 65-67.

Latha, H.S and Sharanappa. 2010. Performance of sweet sorghum (Sorghumbicolor (L.) Moench) Cultivars as influenced by the plant population and fertility levels. . Mysore Journal of Agricultural Science.44 (4):766-769.

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http://www.Sciencedirect.com/

INCREASING PRODUCTIVITY OF HYBRID COTTON BY FOLIAR SPRAY OF DEFOLIATORS UNDER VARIOUS SOWING METHODS AND CROP GEOMETRIES

Harphool Meena, P.K.P. Meena and B.L. KumharAICRP on Irrigation Water Management, ARS, Ummedganj Farm

Agriculture University, Kota, RajasthanEmail: [email protected]


ABSTRACTAn experiment was conducted at Agricultural Research Station, Borwat Farm, Banswara during kharif -2011 and 2012 to find out the suitable sowing method, optimum plant geometry and defoliator for hybrid cotton under three sowing method ( normal sowing, transplanting and crowbar method), three plant geometries (90 x 45, 90 x 30 and 60 x 30 cm) and three defoliators (i.e. ABA, cycocel and ethrel 1000 ppm). Significantly higher seed cotton yield was recorded (2151 kg ha-1) under crow bar method as compared to transplanting and normal sowing methods. The maximum seed cotton yield was recorded with sowing of normal plant geometry of 90 x 45 cm (2059 kg ha-1) over closer plant geometry of 60 x 30 cm, but it was found at par with 90 x 30 cm plant geometry. Though, yield attributing parameters such as bolls plan-1 and boll weight were statically improved in normal as compared to closer plant geometry. Foliar spray of ethrel (1000 ppm) defoliator after boll development stage gave maximum seed cotton yield (2029 kg ha-1) over cycocel (1000 ppm) and ABA (1000 ppm). Foliage part of the cotton plant dropped after defoliator spray, which is ultimately help in increasing boll weight and early boll opening.

Key words: Hybrid cotton, plant geometry, sowing method, seed cotton yield and defoliator.

India ranks first in area in global scenario (about 33 % of the world cotton area) but with regard to production, it is ranked next to China (Anon., 2014). The production increased from a meagre 169 kg lint/ha in 1980-81 to a high of 517 kg lint/ha was recorded during 2013 (Anon., 2014). In India, the seed cotton yield per unit area is still far below than many other cotton growing countries in the world. Among the various factors responsible for low yield of cotton crop in the country low plant population and sowing methods are primary importance. Various agro techniques viz., maintaining optimum plant population, use of optimum dose of nutrition, growth regulators (defoliators) and sowing methods etc. are being used in enhancing commercial cotton productivity. Among the various cultural practices, spacing and plant population are crucial factors which influence the morphological traits and yield in cotton. Defoliants are chemicals

that either impact plant hormonal activity related to leaf loss or cause direct injury to leaves, both at a level that promotes leaf drop (abscission) and are often represents the final step in the production of a cotton crop. The defoliation process usually completes in 7 to 10 days, but in some situations, it may be delayed for as long as 30 days (Cathey, 1986, Gwathmey and Hayes, 1997, Kerby et al., 1984, Malik and Din, 1997). The success of defoliation process depends on the maturity of cotton crop and prevailing weather conditions at the time of application (Muhammad et al., 2002). With the emphasis on premium quality associated with cotton production, efficient defoliation is a matter of supreme concern in late season crop management (Silvertooth, 2001). Defoliants are therefore necessary to increase harvest efficiency, reduce lodging, reduce trash and lint staining, reduce cotton seed moisture and decrease

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insect populations and also to remove vegetative material to facilitate one time harvesting and to synchronize the opening of the bolls. There are a range of defoliants available in the market, but the work on comparison of their speed of action, its physiological impact in relation to leaf defoliation, yield and crop value (quality) is too little.The objective of the present study was to determine effect of chemical defoliant application on seed cotton yield, yield components, leaf defoliation under different planting geometry and sowing methods under Zone IV b of Rajasthan.

MATERIALS AND METHODSThe field experiment was conducted for two consecutive crop season kharif -2011 and 2012 at Agricultural Research Station, Banswara. The treatments comprised of three sowing method (normal sowing, transplanting and crowbar method) in main plot treatment, three plant geometries (90 x 45, 90 x 30 and 60 x 30 cm) in sub plot treatments and three defoliators (i.e. ABA, cycocel and ethrel 1000 ppm) in sub-sub plot treatments of split plot design with three replications. Experimental field was well prepared by two ploughing followed by harrowing & cultivator and one planking for uniform levelling were performed for sowing of cotton. The soil was medium in available nitrogen (247 and 254 kg/ha) and phosphorus (49.80 and 50.00 kg/ha) and high in available potassium (324 and 327 kg/ha) during both the years. The crops was sown in first week of June by dibbling 2-3 seeds per hills in normal sowing, crow bar and preparation of nursery for transplanting at onset of monsoon. The chemical defoliators were applied as a foliar spray as per treatments when cotton crop attained 75 per cent of boll opening. Observations were recorded before defoliator application and continued up to 18 days after defoliator

spray at every two days interval. Full dose of phosphorus and potash were applied before sowing, while nitrogen dose was given in two splits i.e. first half at the time of thinning and remaining half at flowering stage. All production and protection measures were applied as per package of the zone IV b.

RESULTS AND DISCUSSIONGrowth Parameters: Two years pooled data (Table.1) shows that the sowing of hybrid cotton by different methods significantly influence plant growth parameters. The maximum plant height (101.15 cm), monopodial branches plant-1

(1.58) and sympodial branches plant-1

(23.12) were observed by sowing of crow bar method over normal sowing plant height (88.16 cm), monopodial branches plant-1 (1.26) and sympodial branches plant-1 (18.54) and transplanting method plant height (91.30 cm), monopodial branches plant-1 (1.35) and sympodial branches plant-1 (19.53). Significantly higher plant height (98.50 cm), monopodial branches plant-1 (1.50) and sympodial branches plant-1 (18.51) were recorded at sowing of 90 x 45 cm normal plant geometry over closer plant geometry sowing of 60 x 30 cm plant height (87.44 cm), monopodial branches plant-1 (1.25) and sympodial branches plant-1 (15.15). However, it was found at par with 90 x 30 cm plant height (91.97 cm), monopodial branches plant-1 (1.39) and sympodial branches plant-1 (17.80). All the growth parameters were found statistically not significant with each other during both the years under foliar spray of defoliator chemicals. This increase in yield under normal plant geometry was attributed to significant improvement in number of bolls, monopodial and sympodial branches. Rekha, et al., (2008) also recorded significantly more seed cotton yield due to increase in number of sympodial

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branches /plant under etherl (1000 ppm). The increase in seed cotton yield with etherl (1000 ppm) has also been observed by Singh and Gill (2007) and Sunitha et al., (2010).Yield attributes: It is evident from pooled data (Table.1) the yield attributes of hybrid cotton were significantly influence by different sowing methods. The maximum bolls plant-1(27.73) and boll weight (3.90 g) were observed by sowing of crow bar method over normal sowing bolls plant-1(22.47) and boll weight (3.28 g) and transplanting method bolls plant-1(23.88) and boll weight (3.36 g).Yield attributes of hybrid cotton were significantly increase by sowing of normal plant geometry. Significantly higher bolls plant-1 (24.20) and boll weight (3.79 g) were recorded at sowing of 90 x 45 cm normal plant geometry over closer plant geometry sowing of 60 x 30 cm bolls plant-1 (19.45) and boll weight (3.26 g) in the pooled analysis. Significantly increase boll weight (3.86 g) with the foliar spray of etherl (1000 ppm) as compared to spray of cycocel (1000 ppm) and ABA (1000 ppm) during both the years as well as in pooled analysis. Under the foliar spray of defoliator chemicals bolls plant-1 was found statistically not significant in pooled analysis. Similar fact were reported by Cathey, 1986, Gwathmey and Hayes, 1997, Kerby et al., 1984, Malik and Din, 1997.

Seed cotton yield: Pooled data of two years shows that (Table.1) the seed cotton yield was significantly increasing by sowing of different methods. Significantly higher seed cotton yield was recorded (2151 kg ha-1) by sowing of crow bar method over normal sowing (1586 kg ha-1) and transplanting (1813 kg ha-1).Sowing of hybrid cotton at 90 x 45 cm normal plant geometry gave higher seed cotton yield (2059 kg ha-1) over closer spacing sowing of 60 x 30 cm (1652 kg ha-1) but it was found at par with 90 x 30 cm (2134 kg ha-1). Significantly increase the seed cotton yield with foliar spray of defoliator chemical, spray of ethrel (1000 ppm) gave maximum seed cotton yield (2029 kg ha-1) over foliar spray of cycocel (1692 kg ha-1) and ABA (1686 kg ha-1) in the pooled analysis. Higher seed cotton yield under normal plant geometry was observed due to optimum plant population. Similar effect of higher plant density has reported by Giri and Gore (2006) and Butter et al., (2010).

CONCLUSIONIt could be concluded that the maximum seed cotton yield (2151 kg ha-1) was obtain by sowing of crow bar method. The normal plant geometry of 90 x 45 cm gave higher seed cotton yield (2059 kg ha-1) than closer plant geometries and also increase seed cotton yield (2029 kg ha-1) with foliar spray of ethrel defoliator.

Table 1: Effect of sowing methods, crop geometries and defoliator chemicals on growth, yield attributes and yield of hybrid cotton Treatment Plant height (cm) Monopodial branches

/plantSympodial branches / plant

2011 2012 Pooled 2011 2012 Pooled 2011 2012 PooledSowing method Normal sowing 86.07 90.26 88.16 1.25 1.28 1.26 18.00 19.09 18.54Transplanting 88.62 93.98 91.30 1.34 1.36 1.35 19.01 20.05 19.53Crow bar method 99.24 103.0

6 101.15 1.59 1.56 1.58 23.04 23.20 23.12

SEm + 3.02 2.87 2.71 0.06 0.06 0.055 0.37 0.40 0.35CD (p=0.05) 10.00 8.70 8.13 0.19 0.19 0.17 1.11 1.23 1.07Crop geometry

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90 x 45 cm 92.80 104.20 98.50 1.49 1.50 1.50 17.03 20.00 18.5190 x 30 cm 86.44 97.14 91.79 1.35 1.42 1.39 16.10 19.51 17.8060 x 30 cm 82.55 92.33 87.44 1.24 1.25 1.25 14.08 16.22 15.15SEm + 2.60 2.52 2.35 0.05 0.05 0.046 0.41 0.43 0.38CD (p=0.05) 7.82 7.56 7.06 0.17 0.16 0.14 1.24 1.32 1.16DefoliatorABA (200 ppm) 87.01 96.69 91.85 1.42 1.53 1.47 18.40 19.90 19.15Cycocel (200 ppm) 88.06 99.80 93.90 1.44 1.52 1.48 19.00 19.56 19.28

Ethrel (200 ppm) 90.22 104.56 97.40 1.47 1.56 1.50 19.70 20.02 19.86SEm + 2.58 2.87 2.50 0.06 0.06 0.055 0.33 0.40 0.33CD (p=0.05) 7.80 8.64 7.56 0.17 0.18 0.16 1.00 1.22 1.08 Treatment Bolls/ plant Boll weight (g) Seed cotton yield(kg/ha)

2011 2012 Pooled 2011 2012 Pooled 2011 2012 PooledSowing method Normal sowing 22.90 22.04 22.47 3.27 3.29 3.28 1575 1597 1586Transplanting 24.61 23.15 23.88 3.32 3.41 3.36 1802 1823 1813Crow bar method 26.97 28.49 27.73 3.80 4.00 3.90 2157 2145 2151SEm + 0.70 0.60 0.59 0.13 0.11 0.11 115 105 101CD (p=0.05) 2.14 1.82 1.80 0.42 0.36 0.34 342 316 304Crop geometry90 x 45 cm 23.01 25.40 24.20 3.66 3.92 3.79 2094 2024 205990 x 30 cm 22.41 23.98 23.19 3.58 3.75 3.66 2159 2109 213460 x 30 cm 19.90 19.00 19.45 3.22 3.30 3.26 1697 1606 1652SEm + 0.69 0.90 0.73 0.12 0.14 0.12 110 107 100CD (p=0.05) 2.09 2.75 2.21 033 0.40 0.35 329 324 298DefoliatorABA (200 ppm) 24.03 27.44 25.73 3.27 3.25 3.26 1675 1697 1686Cycocel (200 ppm) 24.62 27.21 25.91 3.34 3.40 3.37 1681 1703 1692

Ethrel (200 ppm) 25.90 28.47 27.18 3.85 3.88 3.86 2017 2041 2029SEm + 0.65 0.72 0.63 0.14 0.13 0.12 109 105 98CD (p=0.05) 1.97 2.16 1.90 0.43 0.40 0.37 322 316 296

REFERENCESAnonymous, 2014: Foreign Agriculture Service,

United State Department of Agriculture (Cotton: World Markets and Trends, March 2014.

Buttar, G.S., Sekhon, K.S. and Singh, S. 2010. Effect of different spacing and nitrogen levels on growth and yield attributes of American cotton (Gossypium hirsutum L.) Bt hybrids under irrigated conditions. J. Cotton Res. Dev.24:73-75.

Cathey, G.W.1986. Physiology of defoliation in cotton production. In: J. R. Mauney and J. M. Stewart (Eds.), Cotton Physiology, pp: 143-154. The Cotton Foundation, Memphis, T.N. USA.

Giri, A.N. and Gore, S.B. 2006. Effect of plant densities and fertility levels on yield of newly released deshi varieties of cotton (Gossypium arboreum L.). J. Cotton Res. Dev.20: 77-79.

Gwathmey, C.W. and Hayes, R. 1997: Harvest aid interactions under different temperature

regimes in field grown cotton. J. Cotton Sci., 1: 1-9.

Kerby, T.A., Johnson, S and Yamada, H. 1984: Efficacy of cotton defoliants. California Agric., 38: 24-27.

Malik, M.N. and Din. S. 1997.: Efficacy of Thidiazuron defoliant in cotton cultivars varying in maturity. The Pakistan Cottons, 41: 36-42.

Rekha, M. Sree, Dhurua, S. and Rao, Nageswara, G. 2008. Response of desi cotton (G. arboreum) to different plant densities and nitrogen levels under rainfed conditions. J. Cotton Res. Dev. 22:38-41.

Silvertooth. 2001: Defoliation of pima cotton, The University of Arizona Cooperative Extension, ag.arizona.edu/pubs/crops/az1241.pdf.

Singh, K. and Gill, J.S. 2007.Effect of different spacings and nitrogen levels on growth and yield attributes of desi cotton (Gossypium arboretum L.) hybrids. J. Cotton Res. Dev. 21:77-79.

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Sunitha, V., Chandrasekher, K. and Veeraraghavaiah, R. 2010. Performance of

Bt. cotton hybrids at different nitrogen levels. J. Cotton Res. Dev. 24:52-55.

AGRICULTURAL EXTENSION MANAGEMENT SYSTEM FOR SUSTAINABLE DRYLAND AGRICULTURE

S.L. Soni1, I.M. Khan2 and D.S. Bhati3

1Agriculture Officer, Commissionerate of Agriculture, Pant Krishi Bhawan, Jaipur Raj.2Professor, SKNAU, Jaipur and 3Asstt. Prof., Krishi Vigyan Kendra, Ajmer.


ABSTRACTIn the Rajasthan state the different systems are working almost individually or with the little coordination which are insufficient for the development of sustainable dry land agriculture. For the proper linkages and effective working suggest a single management system i.e. agricultural extension management system with common state agricultural policies for the development of agriculture resulting in the possibilities of the optimum utilization and effective management of resources provided to them and avoiding duplicacy of resources.

Key words : Sustainable, management, system, dryland, evolution

The concept of sustainable agriculture involves the evolution of new types of agriculture rich in technology and information, with much less intensive energy use and market purchased inputs. Thus, sustainability is the successful management of resources to satisfy the changing human needs, while maintaining or enhancing the quality of environment and conserving natural resources. But achieving this will not be as easy as said because of shrinking land holdings and reducing investment capacity of the farmers. For this the whole agriculture system in the country will have to be reoriented to make it sustainable and further to achieve sustainable growth. The system had to be made target oriented keeping in view far reaching effects on our environment. This is very important in Indian context that while achieving our agricultural targets we maintain the health of our soil, water and environment. Extension management refers to managing men, material, task and technology. More precisely the ways, the technology received is managed by the extension personnel and its dissemination to ultimate user-farmers, who in turn

accept and manage the new techniques while managing their farming systems are the important elements of sustainable agriculture. There is an urgent need to reorient and manage the agricultural extension systems to meet these new challenges. The Human Resource Development can play a very important role in this task. The Rajasthan state is confronting severe drought for last 4 – 5 years, due to that the production is decreased year by year, which is not sufficient to feed such a burgeoning population. So there is a need to sustain and increase the food production.In another side the different systems like state department of agriculture (Extension System), input support system, financial support system, research support system, marketing support system, information support system, SAUs, KVKs and NGOs are working almost individually with their own aim, without or with an insufficient coordination. Hence, the extension personnel as well as farmers do not get the proper advantages from them.

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METERIALS AND METHODS

The study was conducted in Rajasthan state. For study 3 agro-climatic zones, namely zone- IA, Zone IC and zone IIIA, 6 tehsils namely, Phalodi, Lunkaransar, Lalsot, Sambharlake, Chomu and Sarwar and a total of 20 villages were selected. From each selected village different categories of farmers (i.e. small, medium and large) were selected with the help of probability proportional to size technique. By this way a total sample of 200 farmers (82 small, 80 medium and 38 large) was drown for the present investigation. Besides that, a sample of 55 extension personnel working in the study area was also selected purposely from the State Department of Agriculture for the study purpose. To know the sustainability of existing extension system, it was considered essential to analyse the suitability of the existing extension system of state department of agriculture of Rajasthan state to sustainability. For this purpose the opinion of extension personnel about organizational structure, idealized situation needed in the extension organization, motivational climate, job satisfaction, extension teaching methods, extension trainings, extension programmes and infrastructure facilities in extension organization, from extension management point of view were analysed separately.Keeping in view the above concerns and on the basis of finding of the study, an ideal model of agricultural extension management system was suggested (Fig.1) in which the extension system (State department of agriculture), research support system, input support system, information support system, infrastructure support system, financial support system, marketing support system, SAUs, KVKs and NGOs are of crucial importance. When all the above

support systems operated according to the suggested agricultural extension management system will finally lead to the improvement in quality of life of the clientele.

1. Research support systemThe research support system is consisting of Agricultural Research Station (ARS), National Research Centres (NRC), State Agricultural Universities (SAUs), Central Research Institute (CAZRI) and different All India Coordinated Research Projects (AICRPs). These agencies are working to develop the new technology as well as for improving the existing one. These agencies should develop the technologies which are need based, location specific, suited to dry farming conditions, have low cost and also suitable to the farmers resources and their conditions. Efficiency of extension is almost dependent upon technology generation. The carrier of this technology must carry with him credibility, which will come only if the package he/she recommends, can be demonstrated, able to make an improvement in the existing practices, with increasing a very low cost. Hence, the need based and area specific technologies which the farmers and extension personnel perceived as economically feasible and viable as well as socially acceptable and sustainable should be taken on priority. In addition to it the research needs to be focussed on dryland horticulture, social forestry, apiculture and better farming systems.2. Input support systemThis system consisting of Government Agencies, Private Dealers, Primary Agricultural Credit Societies (PAC’s), National Seed Corporation (NSC) State Seed Corporation (SSC) etc. These agencies are working in supply of different agricultural inputs like seed, fertilizers, agro chemicals (pesticides, insecticides, fungicides, rodenticides,

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etc.) and agricultural implements. The important aspects in input supply include quality, adequacy, immediate accessibility, reasonable cost and the timeliness of supplies.

3. Information support systemInformation support system consisting of audio-visual aids, extension teaching methods, electronic and print media, computerization (Internet, e-mail etc.) and agri-clinic etc. This system work as the transfer of agricultural information to ultimate users i.e. farmers. If this information is fail to reach the farmers, it is waste for all. Communication of technology to farmers can not be successful if it is based on merely oral communication of extension and research functionaries. Demonstrations play an important role in the technology transfer process. The mass media approach in dissemination of technology will play an important role. Agricultural information and communication method included printed literatures, audio-visuals aids and the electronic media especially radio, T.V., computer etc. There is a need for strengthening mass media support for agricultural information communication.Information provided to the farmers should be simple, understandable, interesting and also is their local dialect. There should be regular feed back of the information provided to the clientele.4. Infrastructural support systemThis system provides the infrastructure facilities to the researcher and extension personnel needed in the development of sustainable dryland agriculture. Therefore, this system should be properly developed and provide all the necessary infrastructural facility to the researchers as well as extension personnel so that the development of sustainable agriculture can be achieved. Therefore, budgetary allocation need to be made in time, A.V. equipment should be made available

timely and adequately. The official facilities like buildings, furniture, writing material, computer, telephones, lecture halls etc. should be made available timely. It will improve the mobility, housing, effective supervision, employee’s effectiveness and performance. Training being an important input needs much emphasis.5. Financial support systemThis system is consisting of NABARD, Regional Rural Banks (RRBs), Land Development Bank (LDBs), District Cooperative Banks, Central Cooperative Banks, Primary Agricultural Credit Societies, AFC (Agrilcultural Finance Corporation), RFC (Rajasthan Finance Corporation), Land Lords and other agencies which provide the credit facilities to the farmers as well as different agricultural development projects. 6. Marketing support systemThis system related with FCI (Food corporation of India), CACP (Council for agricultural costs and price), NIAM (National institute of agricultural marketing), RAJFED (Rajasthan agricultural federation), NAFED (National agricultural federation), Mandi’s etc., which, are involved in the marketing of agricultural produce. The farmers aim that he get the adequate price of their produce. The Government should announce minimum support price well in advance, so that the, farmers can make their crop plan according to them. The supporting price announced by the Government should be according to the production costs of the produce. The government should also purchase the produce on that price. These agencies should also provide the marketing information to the farmers. With the help of extension personnel properly, adequately and regularly.There should be proper management in the mandi’s i.e. proper place to stay,

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storage facilities, avoiding of middle men, timely payment to the sellers, lesser quantity of Aadhats, Dharmada etc., therefore, the farmers can achieve their target and get a reasonable price of their produce.7. State agricultural universities (SAUs)The state agricultural universities are also working for education, research and extension. Therefore, SAUs can also help in the development of sustainable dryland agriculture by conducting research on dryland technology, proper transfer of technology from their own extension wing and development of the extension professionals. SAUs also conduct the trainings, kisan melas, demonstrations etc., which is helpful in development of the sustainable dryland agriculture.8. Extension system (State department of Agriculture)It is the main extension system of the state, which consists of administrative as well as advisory extension personnel. Administrative extension personnel (Director, Deputy Director, Joint Director, Additional Director, Subject Matter Specialists etc.) who work for making of agriculture policy, management, transfer of technology, implementation, monitoring and supervision etc. activities. The advisory extension personnel are field functionaries (Agriculture Officer, Assistant Agriculture Officer and Agriculture supervisor) who are working in the field to provide the agricultural informations, inputs, demonstrations, meetings, etc. activities with the farmers.9. Krishi Vigyan Kendras (KVKs) In the state there are three types of KVKs i.e.under the administrative control of ICAR, SAUs and NGOs. They are working as vocational training centres in the agriculture and other field activities. Their main aim is overall development of people (i.e. farmers). They are organizing and conducting

trainings, demonstrations, education, research and other social activities.10. Non Government Organizations (NGOs)Voluntary organizations are working in the field of agriculture and rural development but there is no integral linking of their functioning with other agricultural support systems. This have committed resource needs to be brought in to the manifold of governmental efforts. NGOs must be given clear roles and responsibilities in specified areas and should work under the overall management of agricultural extension management system.For the effective management of these systems under one umbrella i.e. agricultural extension management system there should be proper motivation, management culture, regular monitoring, evaluation and supervision of the entire system as follows.

CONCLUSIONFor proper management and development of sustainable dryland agriculture there should be a single agricultural extension management system in the state which include extension system of state department of agriculture, SAU’s, KVK’s and NGO’s etc., research support system, input support system, information support system, infrastructure support system, financial support system and marketing support system. There should be a management committee involving all the heads of these systems for proper coordination and linkage among these system, which should meet together regularly and discuss the progress, reporting, problems and their probable solutions and make a common yearly plan of work showing the responsibilities of each system. The responsibilities to each system should be allotted according to their working area and objectives, to avoid duplicacy of work, for optimum

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utilization of resources and effective extension management.

REFRENCESAkinbode, A. 1982. “A critical analysis of the

management of agricultural extension is Nigeria”. Agricultural Administration. 10 (1) : 45-60.

Arora, S.K. and Samanta, R.K. 1997. “Management of Agriculture extension in global perspective”. D.K. Publishers, New Delhi. F.F.

Bereton, P.R. 1972 “New model of effecting change.” Journal of Extension. Vol. 10, No. 1, Wisconsin.

Bhatia, M.S. 1979. “Changing pattern of resource structure and demand for input in Indian agriculture”. Agricultural situation in India, 34 (7) : 435-439.

Bhatnagar, O.P. and Desai, G.R. 1987. “Management of agricultural extension : concepts and constraints”. Journal of Rural Development, India : 6 (1) : 1-66.

Conway, G.R. and Barbier, E.B. 1990. “After the green revolution :Sustainable Agriculture for development.” London, U. K., Earthscan publications, pp 205

Denson, R.R. and Day, J.C. 1990. “Transfer of sustainable technology in dryland agriculture”. Agricultural Economics. 4 (3-4) : 255-266.

Desai, G.R. and Reddy, M.R. 1988. “Operational impact of agricultural extension management under T & V system”. Journal of Rural Development, India. 7 (2) : 119-135.

Gill, S.S. and Kaur, R. 1998. “Management of agricultural extension”. Ind. J. of Ext. Edu., Vol. 34 (1&2) 80 – 82

Gao, W.S. and Hu, H.J. 1993. “An approach to intensive and sustainable development of dryland agriculture in north China.” Research of Agricultural modernization, 14 (5) : 270-272.

Howell, J. 1984. “Conditions for the design and management of agricultural extension”. Discussion paper, Agricultural administration, Network, Agricultural administration-unit, Overseas development institute, U.K., No. 13, pp. 14.

Hossain, M.A. 1984. “Report on communication, extension and research management training programme.” Graduate training institute, Bangladesh Agricultural University. No. 40 (vi) pp.-28.

Kalra, R.K. and Hansra, B.S. 1998. “Management of extension strategies”. Ind. J. of Ext. Edu., vol. 34 (1&2) 87 – 89.

Koontz, H. 1971. “The management theory.” Jungle, in reading in management, Bombay.

Kooten, G.C. and Van, G.C. 1991. “Improving policy instrument for sustainable agriculture”. Canadian Journal of Agricultural Economics, 39 : 4 (1) ; 655-663.

Khumuk, T. and Oktay, E. 1993. “Agricultural extension policies towards 21st century “. Ege Universities – Zirrat Pakultesi Dergisi, Japan. 30 (3) : 199-206

Radhakrishna, R.B. and Bowen, B.E. 1991. “Agricultural extension problems”. Ind. J. of Ext. Edu., 27 : 7-12.

Ray, G.L. 1989. “Studies in agriculture extension and management”. Mittal Publications, Delhi. FF.

Samanta, R.K. 1989. “Management in agriculture and rural development”. pp. 147.

Samanta, R.K. and Arora, S.K. 1997. “Management of agricultural extension in global perspective.” D.K. Publishers, New Delhi.

Verma, O.S. 1998. “Management of extension system authority, responsibility and accountability.” Ind. J. of Ext. Edu., Vol. 34 (1 & 2).

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SOCIAL PROBLEMS AND WOMEN SCIENTISTS IN HISAR DISTRICT OF HARYANA

Rahul1 and Rashmi Tyagi2

1Ex Msc. Student, Sociology Dept.,CCSHAU,Hisar2Assistant Professor, Sociology Dept.,CCSHAU,Hisar


ABSTRACTWomen are increasingly and gradually seen marching into domains was previously reserved for males. However, the current statistics reveal that times are changing and Indian women are advancing but many barriers still remain. As a result women are still victims of gender disparity in families and at work places. The other factors that play a significant role in women’s academic careers include the constraints of dual careers; access to quality child care, individuals perception regarding professional recognition and career satisfaction and other quality of life issues. The study was conducted in Hisar district of Haryana state. A sample of 100 women scientists were selected randomly from CCSHAU, GJUS&T and LLRUVAS, Hisar purposively as per objectives of the study. It is revealed that various socio-economic factors were found affecting the social problem of women scientists. Age of the respondents was found highly significantly associated with the level of social problem of women scientists.

Key words: Quality, Women, Career

The rapid pace of economic development has increased the demand for educated female labour force almost in all fields. This new phenomena has also given economic power in the hands of women for which they were earlier totally dependents on males. Women’s presence as scientists is advantageous for the society at large. In addition to women bringing in a gentler and more humane perspective to scientific research necessary for sustainable development, loss of women power from science is a major loss of trained resource which can be difficult to compensate even in financial terms a point that the science and polity should remember (Women in Science, 2010). However, the current statistics reveal that times are changing and Indian women are advancing but many barriers still remain. As a result women are still victims of gender disparity in families and at work places. The other factors that play a significant role in women’s academic careers include the constraints of dual careers; access to quality child care, individuals perception

regarding professional recognition and career satisfaction and other quality of life issues. The women scientists are constantly confronted with the ideas and expectations of traditional society while conforming to a contemporary code of conduct at work.In view of this the present study was designed with following specific objective: To discern factors associated with the social problems of women scientist


The study was conducted in Hisar district of Haryana state. The district Hisar was selected purposively, because of more universities. The study has been conducted in CCS HAU, GJUS&T and LLRUVAS, Hisar, Haryana as maximum number of women scientists were available here. Purposive and random sampling technique has been followed for the present study. The sampling design is presented and sampling procedure is described in the following sub-head:The state Haryana comprises 21 districts.

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Among the districts, Hisar district was selected purposively for the present study. Hisar district has three Universities namely, CCS HAU, GJUS&T and LUVAS. A sample of 100 women scientists were selected randomly from CCSHAU, GJUS&T and LLRUVAS, Hisar purposively as per objectives of the study.


Different factors associated with the social problems of women scientistSocial problems: Social roles and role expectations which are experienced by men and women in concrete social situations as binding norms are dependent on the culture of the society. Similar is the case with family and sex roles of every human society.

Male and female roles are not defined by different cultures differently and are by no means determined by the physiological differentiation of sexes. Home and job are two different worlds separate not only spatially but in spirit and codes of conduct. In our Indian society women scientist in dual role has to face more difficulties as compared to her counterparts in many other countries. Child-care is considered generally only the duty of women irrespective of their job. Role expectation by in laws and other family members sometimes creates a problem of adjustment for women scientist. Employment of married women had certain ill-effects on the family like children are neglected, particularly so in the case of infants and primary school going.

Table 1.: Distribution of respondents according to social problems (N=100)Statements Yes Rank

Frequency PercentageWork beyond office hours 51 51.00 IInflexibility of working hours 38 38.00 IISuitable accommodation 27 27.00 IIILimited participation in social net work 22 22.00 IVLimited access to information sources 21 21.00 VConstraints in building professional context 19 19.00 VILimited access to willing colleagues with whom to collaborate. 14 14.00 VII

Commuting or mobility 13 13.00 VIIITransfer 12 12.00 IXTraditional ideas of family members about sex role 07 07.00 X

Table 1. revealed that work beyond office hours (51%) is the major social problem of women scientist followed by inflexibility of working hours (38%) ,suitable accommodation (27%), limited participation in social net work (22%), limited access to information sources (21%), constraints in building professional context (19%), limited

access to willing colleagues with whom to collaborate (14%), commuting or mobility (13%), transfer (12%) and traditional ideas of family members about sex role (7%) . These finding are in accordance with the findings of Vijaypariya ( 2001) , Sahoo and Pradhan, (2007) .

Table 2: Association between social factors and level of social problems regarding women scientists

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Age (in years) Level of Social Problems TotalLow Medium High

Young (<35) 10 (45.50) 3 (13.60) 9 (40.90) 22 (22.00)Middle (36-50) 7 (22.58) 6 (19.36) 18 (58.06) 31 (31.00)Old (>50) 7 (14.90) 24 (51.06) 16 (34.04) 47 (47.00)Total 24 (24.00) 33 (33.00) 43 (43.00) 100 (100.00) χ2 =17.301** C = 0.384

CasteGeneral 11 (25.00) 6 (13.60) 27 (61.40) 44 (44.00)S.B.C. 8 (61.50) -- 5 (38.50) 13 (13.00)Backward Classes 5 (20.00) 12 (48.00) 8 (32.00) 25 (25.00)Scheduled Castes -- 15 (83.30) 3 (16.70) 18 (18.00) χ2= 44.070 ** C = 0.553EducationPost graduate 7 (77.78) -- 2 (22.22) 9 (9.00)Doctorate 16 (20.00) 29 (36.25) 35 (43.75) 80 (80.00)Other professional qualification 1 (9.10) 4 (36.40) 6 (54.50) 11 (11.00)

χ2=16.916** C =0.380Family typeNuclear 16 (25.39) 21 (33.34) 26 (41.27) 63 (63.00)Joint 8 (21.62) 12 (32.43) 17 (45.95) 37 (37.00) χ2= 0.263 C = 0.051Family size (in member)Small (up to 4) 19 (54.30) 5 (14.28) 11 (31.42) 35 (35.00)Medium (5-6) 1 (3.57) 15 (53.57) 12 (42.86) 28 (28.00)Large (above 7) 4 (10.81) 13 (35.14) 20 (54.05) 37 (37.00) χ2=30.424 ** C = 0.483Marital statusUnmarried 1 (33.33) 1 (33.33) 1 (33.34) 3 (3.00)Married 21 (22.80) 32 (34.80) 39 (42.40) 92 (92.00)Divorced 1 (50.00) -- 1 (50.00) 2 (2.00)Widow 1 (33.30) -- 2 (66.70) 3 (3.00) χ2= 0.605 C = 0.078Mass Media ExposureLow 9 (40.90) 5 (22.73) 8 (36.37 ) 22 (22.00)Medium 9 (16.10) 26 (46.40) 21 (37.50) 56 (56.00)High 6 (27.30) 2 (9.10) 14 (63.60) 22 (22.00) χ2= 14.559 ** C = 0.356Social participationLow 7 (30.44) 9 (39.12) 7 (30.44) 23 (23.00)Medium 11 (20.00) 16 (29.10) 28 (50.90) 55 (55.00)High 6 (27.20) 8 (36.40) 8 (36.40) 22 (22.00) χ2=3.324 C = 0.179 Occupation (self)Assistant Professor 6 (18.80) 14 (43.80) 12 (37.40) 32 (32.00)Associate Professor 7 (25.90) 5 (18.50) 15 (55.60) 27 (27.00)Professor 11 (26.83) 14 (34.15) 16 (39.02) 41 (41.00) χ2= 4.764 C = 0.213Service Experience<10Year 5 (18.51) 13 (48.15) 9 (33.34) 27 (27.00)10-20 Year 5 (20.80) 9 (37.50) 10 (41.70) 24 (24.00)20-30Year 6 (20.69) 4 (13.79) 19 (65.52) 29 (29.00)>30Year 8 (40.00) 7 (35.00) 5 (25.00) 20 (20.00)

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χ2= 13.518 * C = 0.345Family incomeLow (<10L) 11 (39.29) 4 (14.28) 13 (46.43) 28 (28.0)Medium(11-15L) 6 (12.80) 19 (40.40) 22 (46.80) 47 (47.0)High (> 15L) 7 (28.00) 10 (40.00) 8 (32.00) 25 (25.0) χ2= 10.431 * C = 0.307Figure in the parenthesis denote percentage**indicate highly significant* indicate significant at1 percent level According to study various socio-economic factors were found affecting the social problem of women scientists. Age of the respondents was found highly significantly associated with the level of social problem of women scientists. Majority of middle aged women (58.06) and young respondents (40.90%) had high level of social problem on the other hand medium level of social problems (51.06%) were found among the respondent belonging to old age group. Highly significant association was again found between caste of the respondents and level of social problems. Analysis revealed majority of respondent hailed from general caste had high level of social problems (61.40%) and who hailed from Special Backward Class caste had low level of social problems (61.50%). On the other hand overwhelming majority of Schedule Caste respondents had medium level of social problems (83.30%). Educational level of respondent was also found highly significantly associated with level of social problems. Analysis revealed that majority of respondent were educated up to post graduate level (77.78%) had low level of social problems. On the other hand who had other professional qualification (43.75%) and who were educated up to doctorate (54.50%) had high level of social problems. Association between family type and level of social problems was found non-significantly. Respondents were facing high level of social problems irrespective of family type. Size of the family and

level of social problems were found highly significant associated. More than half of the respondents (54.30%) who belong to small sized family had low level of social problems, on the other hand more than half of the respondents (53.57%) who belonged to medium and large sized family (54.05%) were facing medium and high level of social problem respectively. Non-significant association was found between marital status and level of social problems. Analysis revealed that relatively more number of respondent who were widow (66.70%) and divorce (50%) were facing high level of social problem than their counterparts. Respondents with low level of mass media exposure (40.90%) had low level of social problems on the other hand respondents with high level of mass media exposure (63.60%) had medium and high level of social problems. Association between mass media exposure and social problems was found highly significant. Non-significant association was found between levels of social participation and level of social problems. Similar trend was found between occupational status of respondents and level of social problems. Medium (43.80%) and high (37.49%) level of social problems were faced by respondents who were working as assistant professor, on the other hand who were working as associated professor (55.60%) and professor (39.02%) were facing high level of social problems. Service experience of respondents was found significantly associated with level

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of social problems. Analysis revealed that respondents with less than 10 years of service faced medium (48.15%) and high (33.34%) level of social problems. On the other hand respondents with more than 30 years of experience were facing low (40%) and medium (35%) level of social problems. Significant association was again found between family income and level of social problems. Respondents with low level of income were facing high level of social problems (46.43%). On the other hand high (46.80%) and medium (40.40%) level of social problems were faced by the respondents who had medium level of income. Respondents with high income had medium (40%) and high (32%) level of social problems. These finding are in accordance with the findings of Pandya

(1999), Paul and Khan (1999), Rayaprol (2011) and Bhat A. Zameer (2014).

CONCLUSION

It is revealed that work beyond office hours (51%) is the major social problem of women scientist followed by inflexibility of working hours (38%), suitable accommodation (27%) and limited participation in social net work (22%). Various socio-economic factors were found affecting the social problem of women scientists. Age of the respondents was found highly significantly associated with the level of social problem of women scientists. Service experience of respondents was found significantly associated with level of social problems. Analysis revealed that respondents with less than 10 years of service faced medium (48.15%) and high (33.34%) level of social problems.

REFERENCESBhat A. Zameer 2014. Gender Bias and Socio-

economic Problems of Women in India. Abhinav National monthly refereed Journal of Research in Arts & Education, 3 (4): 8-13.

Pandya, R. 1999. Shifting Roles Redefine. Men’s work–women’s work. Social Welfare. 27(5): 17-20.

Paul, S.D. and Khan, G. 1999. Working Wives in Malda: Bearing a dual burden. Social-Welfare 18(12): 17-19.

Rayaprol, A. 2011. Teaching Gender in Indian Universities: Reflections on Feminist

Pedagogy. Sociological bulletin, 60(1): 65-78.

Sahoo, H. and Pradhan, M. R. 2007. Domestic Violence in India: An Empirical Analysis. Paper presented in National Seminar on Gender Issue and Empowerment of Women, Indian Social Institute, Kolkata, February.

Vijaypariya, S. 2001. Adolescents Attitudes of Youth towards Status of Women. Social Welfare,18(6): 8-15.

http://www.azadindia.org/social-issues/problem of working women. html.

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http://www.azadindia.org/social-issues/problem

OPTIMUM VELOCITY PROFILE OF WATER INSIDE A PORTABLE FRP CARP HATCHERY FOR ROHU (Labeo rohita Hamilton) SEED PRODUCTION

Bipin Bihari Mohanty1, B C Mal2, K K Sharma3 and B C Mohapatra4

1Ex. M.Tech. student, Agricultural and Food Engineering DepartmentIndian Institute of Technology, Kharagpur, 2Vice Chancellor, JIS University, Agarpara,

Kolkata, 3-4Scientists, CIFA, BhubaneswarEmail: [email protected], [email protected]


ABSTRACTIn this paper an effort has been made to optimize velocity profile of water inside a portable FRP carp hatchery for Rohu (Labeo rohita Hamilton) Seed Production at Central Institute of Freshwater Aquaculture (CIFA). Velocity Distribution at Different Depths and Locations in the Hatching Pool for a Discharge of 0.25 L/s during Hatching Operations were quantified. The variation in surface velocity is in the range of 54 to 74 mm/s from the outer wall towards the screen. But with depth the velocity increases upto the middle and then reduces towards the bottom. Velocity at surface of the tank is higher than bottom of the tank in the same vertical column. Minimum water requirements for breeding and hatching operations were quantified to be 6.946 and 82 cubic meter respectively. The minimum water requirement for the whole process was quantified to be around 90 cubic meters for a production of 1 Million spawn. Water qualities like temperature, dissolved oxygen (DO), NH4

+, pH, and NO3- were recorded during the process. All the

water quality parameters were well within the favorable range. Optimum velocity inside this FRP hatchery was also quantified. The velocity distribution inside the FRP hatchery followed a cono-volumetric pattern, with minimum at the top and bottom and maximum at the middle section.

Key words: Rohu, spawning and hatching operation, FRP hatchery, velocity distribution.

Indian Major Carp is the most important groups of fishes cultured in the Indian subcontinent and accounts for more than 95% of the world aquaculture production (Kalla et al.2004). Rohu (Labeo rohita L.) is a fish of the carp family Cyprinidae, found commonly in rivers and freshwater lakes in and around the South Asia and South-East Asia. To meet the gap between the ever increasing demands of seeds in the non-seasons, FRP hatchery plays a major role. Continuous seed supply has got some scientific loopholes like non-season mating, water availability etc. Quality fish feed is considered to be one of the major prerequisites for successful fish farming (Jhingaran, 1969). Bulk of India’s fish seed supply comes from riverine collections which comprises a mixture of undesirable species of fishes (includes uneconomical and predatory

forms). Also difficulties are encountered in transporting fish seeds to markets as these are available locally on specific river sites only. This causes heavy mortality and is also expensive. Again due to fast development in the field of aquaculture during the last decade, carp farming has moved to intensive culture.The developing egg needs oxygen continuously in high concentration. The oxygen consumption of eggs is negligible in the initial stages, but increases very considerably as development progresses. Maximum oxygen is required just before hatching. It has been found that reduced oxygen retards the development of fish embryo, while richly oxygenated water accelerates the process. (Kinne and Kinne, 1962). Oxygen level of 4.2-6.8 mg/litre is considered to be the best. According to Tapiador et al. (1977), D.O. concentration of more than 4 mg/litre is

76

good for development of eggs. Water must be properly oxygenated and should be free from poisonous gases (Waynarovick and Horvath, 1980). Clean and plankton free water is another basic requirement in hatcheries. Water for hatcheries should be filtered properly as filamentous algae can be a great nuisance in hatcheries. Deposition of silt on egg surfaces and gills of spawn prevents proper diffusion of oxygen through the gill membrane or egg cells. During their development, the eggs excrete certain harmful materials, such as CO2 and NH3

which, if allowed to accumulate, may poison the eggs. They must, therefore, be removed continuously as they are produced by maintaining a constant flow of water. A sudden flow of strong water current through the incubator may destroy all the eggs within a short period. Proper water flow velocity helps in even distribution of fertilized eggs in the water and keeps them moving slowly. According to Sengupta, et al. (1984), initially a flow rate of 2.5 L/s is maintained in the hatchery. At the early stage of development of eggs, the egg membrane is thinner. Therefore, to prevent the premature hatching, the flow is reduced to 2 L/s. After the embryos are hatched, the flow is again increased to 3.5 L/s to prevent the newly hatched hatchlings from sinking. Oxygen deficiency could be one of the reasons for mortality in certain parts of the incubator devices where the exchange of water is poor or nil. Unsuitable temperature also kills the eggs, usually during embryo development. According to Sahoo, et al. (2006), the capital expenditure incurred in detailed design, estimation and infrastructure development for different facilities including water supply of a carp seed production complex for 30 lakh eggs per cycle is about four million rupees with a recovery period of one year. In this paper an effort made to optimize the

quantity of water use during the hatching process of Rohu (Labeo rohita L.) is presented as water is an important factor during hatching process. Efforts have been made to quantify the minimum water requirement for the whole process; and to maintain a minimum flow rate, so that, neither the eggs should settle at the bottom of incubator nor should they collide with each other. As an engineering input it may help for judicious use of available water in the hatchery unit.The quantity of water is an important factor to be taken into consideration, since the hatchery water becomes polluted with dissolved organic matter and must be replaced. Recirculation of the already used water would be helpful only if a suitable cleaning mechanism is incorporated into the overall water circulation system. Such a system is usually too complicated and expensive. With the above rationale a scientific trial was undertaken with the following specific objectives. To find out the minimum flow rate required for hatching operation in the carp hatchery and to quantify the total water requirement in a portable FRP carp hatchery for rohu.


Description of the study area, available infrastructure, water quality monitoring, experimental set-up, methodology and procedures are briefly discussed in this section. The trial was undertaken at one of the portable FRP carp hatchery units of the Central Institute of Freshwater Aquaculture (CIFA), Bhubaneswar. The infrastructure such as breeding pool, hatching pool, spawn collection tank, water storage tank, water outlet etc (Fig.1) required for the experiment are available in the institute. The important water quality parameters such as temperature, dissolved oxygen (DO), pH, ammonia, nitrate etc. were monitored

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Hatching poolDiameter 1400 mm

Height 980 mmTotal volume 1200 L

Breeding poolDiameter 2150 mm

Height 900 mmTotal volume 3400 L

Overhead tank (Each

tank 1000 L capacity)

Collection tank1.0 x 0.5 x 0.5 m3

Total volume 250 L

during the hatching operation. The chronological operation during the experiment consisted of collection of proper brooders, arrangement of suitable water supply units for breeding and hatching pools, carrying out induced breeding in the breeding pool, transfer of spawns to the hatching pool and finally carrying out hatching operation through the continuous flow of water. Hatching took place within 14 to 18 hours. During hatching operation, velocity and discharge were measured with the help of a flow meter. Velocity distribution was

measured in nine points, at different radial and vertical locations. Survivability assessment was also done by manual counting with the help of a petri dish. Both spawning and hatching were carried out at different discharges to determine the minimum possible discharge for carrying out these operations. Attempt was made to determine the minimum discharge at which the eggs should not settle at the bottom and the water quality parameters in terms of DO, NH4-N, NO3-N, pH could be maintained.

Fig.1 Hatchery unit (front view)


The results obtained by conducting the experiments on spawning and hatching are briefly presented and discussed. Observation at breeding poolDifferent observations such as velocity, temperature and flow rate of water were taken in the breeding pool and the survival rate of fertilized eggs was assessed by using different discharges on different dates. At a discharge of less than 0.28 L/s, the eggs started settling at the bottom and thereby most of them died within a short period. Therefore, observations were recorded for the minimum discharge at which the eggs did not settle.

From

the observations it was found that a minimum flow rate of 0.28 L/s through the breeding pool is sufficient for spawning. For this discharge, the average velocity through the outlet pipe ranged between 359 and 371 mm/s and almost a constant survivable rate of 80-85% was obtained. A discharge higher than this leads to wastage of water and does not produce higher survival rate of fertilized eggs. A discharge lower than this causes many of the eggs to settle at the bottom of the tank and finally most of them died. Therefore, a discharge of 0.28 L/s can be regarded as the optimum discharge for release and fertilization of carp eggs for the specified size of the breeding pool.

Observation at hatching pool 78

Hatching of the eggs was carried out with several discharges on several days. Discharge, velocity of flow, different water quality parameters were measured and hatching efficiencies were assessed to arrive at the minimum water requirement for hatching operation. However, details of the first hatching operation with minimum discharge are only presented in this paper. First hatching operation with minimum discharge A minimum discharge of 0.25 L/s was used for hatching and observations were recorded at three different depths, viz. surface (at a depth of 32 mm), mid-depth (450 mm) and bottom (832 mm depth).This minimum discharge may be accepted as optimum as the eggs did not settle at the bottom and also maintained a hatching efficiency of 80- 85%. A total of 9 points at three depths and three radial distances inside the tank, namely, near the wall of the tank, at the middle of the tank and near the screen were selected for taking observations. Different observations such as velocity, temperature, depth and flow rate of water were recorded in the hatching pool and the survival rate of fertilized eggs was

assessed for three different hatching operations. The observed data on the surface, mid-depth and bottom for different locations are presented in Table-1.

It can be seen from Table-1 that for a flow rate of 0.25 L/s through the hatching pool, the average velocity gradually increases from wall of the tank towards the centre (screen of the tank). The variation in surface velocity is in the range of 54 to 74 mm/s from the outer wall towards the screen. But with depth the velocity increases upto the middle and then reduces towards the bottom. Velocity at surface of the tank is higher than bottom of the tank in the same vertical column. The measured velocity is actually the resultant of the tangential and the radial velocities. Water enters into the inner chamber through the screen for finally discharging out through the stand pipe. Therefore, radial velocity is the highest near the screen. This causes the stratification of velocity distribution along the radial distance from the centre and as a result the velocity distribution is not uniform in a horizontal plane. The velocity distribution in the hatching chamber is presented in Fig-2 and 3.

Fig.2. Velocity (mm/s) distribution for a discharge of 0.25L/s at different levels of the hatching pool

Fig.3. Three dimensional velocity distributions in a hatching pool

The vertical velocity distribution is also not uniform. The hatching chamber is of

conical shape and vortex formation takes place in the flow phenomena. Apart from

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that water enters into the inner chamber through the middle of the inner wall fitted with screen. As a result maximum velocity is obtained at the centre. Although as per the flow phenomenon in

an open channel, maximum velocity is expected at the top, it does not occur in this case as the top of the inner wall is closed to water flow.

Table-1. Velocity Distribution at Different Depths and Locations in the Hatching Pool for a Discharge of 0.25 L/s during First Hatching Operation.

Date and Time

Velocity, mm/sAt 32 mm depth At 450 mm depth At 832 mm depth

Near the outer wall of the tank05-08-08 07:00:00 52 68 4505-08-08 07:01:00 54 68 4505-08-08 07:02:00 52 67 4805-08-08 07:03:00 53 67 4805-08-08 07:04:00 54 66 47At the middle of the tank05-08-08 07:07:00 64 77 5405-08-08 07:08:00 65 78 5405-08-08 07:09:00 67 76 5505-08-08 07:10:00 66 78 5505-08-08 07:11:00 66 77 57Near the screen 05-08-08 07:15:00 74 85 6205-08-08 07:16:00 73 85 6005-08-08 07:17:00 73 84 6105-08-08 07:18:00 72 87 5905-08-08 07:19:00 73 86 62

Observations on water quality parameters Observations on changes in water quality parameters like dissolved oxygen, pH, ammonia (NH4-N), nitrate (NO3-N) due

to breeding and hatching were recorded. The above parameters of the incoming water as well as after breeding and hatching were recorded. They are presented in Table-2.

Table-2. Water Quality Parameters before and after Breeding and HatchingIncoming water quality ppm

Water quality after breeding, ppm

Water quality after hatching, ppm

DO pH NH4-N NO3-N DO pH NH4-N NO3-N DO pH NH4-N NO3-N4.8 7.03 0.3 0.6 4.7 7.03 0.4 0.7 4.4 7.03 0.6 0.84.7 7.03 0.3 0.5 4.6 7.03 0.4 0.6 4.3 7.03 0.6 0.74.8 7.03 0.2 0.6 4.7 7.03 0.3 0.7 4.4 7.03 0.5 0.8It can be seen from Table-2 that all the water quality parameters are within the tolerable range. The minimum DO requirement is about 4 ppm, whereas, the outflow water either from breeding or hatching pool has higher DO. The optimum pH requirement is between 6.5 and 7.5, whereas, the actual pH is 7.03 and it does not change during either breeding or hatching. Maximum tolerable limit of NH4-N or NO3-N is 1.0 ppm. The

actual values of 0.3 to 0.8 ppm after breeding or hatching are well within the tolerable limit. It can be concluded from the observation that the minimum velocity of water is the limiting factor. Once that is attained, water quality parameters are automatically maintained provided that the incoming water has qualities at least equal to that of the experimental water. From the above observations, the minimum water

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requirement for the FRP type of hatchery developed by CIFA, Bhubaneswar for one million Spawn production of Labeo

rohita L. was calculated and is presented below.

Water requirement in the breeding pool1. Initial filling = 2950 L2. For 5 hours showering = 1980 L3. Water requirement for 2 hours spawning = 0.28 x 7200= 2016 L4. Total water requirement in the breeding pool = 2950 +1980 + 2016 = 6946 L.

Water requirement in the hatching Pool1. Initial filling of hatching pool = 1275L2. Minimum average flow rate in hatching pool = (0.25+0.245+0.255)/3 = 0.25L/s3. Eggs hatch out at in 14-18 h and remain in the pool for 72 h.4. Total time of operation in the hatching pool = 72 +18 = 90 h5. Total water requirement in hatching pool = 1275 + (0.25 x 3600 x 90) = 82,275 LTherefore, total water requirement for breeding and hatching operation = 6946 + 82,275 = 89,221 L = 90 m3 (approx).

CONCLUSIONSFollowing conclusions can be drawn from the study: Optimum flow rate required for breeding operation in the carp hatchery is 0.28L/sOptimum flow rate required for hatching operation in the carp hatchery is 0.25L/sOptimum water requirement in hatchery for production of one million spawn = 90 m3.

Water quality parameters do not affect the hatching efficiency as long as there is continuous flow through the tank to keep the eggs floating. Therefore, there is a possibility of reuse of water for hatching operation.

ACKNOWLEDGEMENTThe authors express their gratitude to the Director, CIFA for providing the facilities for research.

REFERENCESJhingaran, V.G., 1969. Review of the present

status of knowledge on induced breeding of fishes and problems for future research, FAO/UNDP regional seminar on induced breeding of cultivated fishes. Calcutta. FIR/IBCF/27, 48.

Kalla, A., Bhatnagar, A. and Garg, S.K. (2004). Further studies on protein requirements of growing Indian major carps under field conditions. Asian Fisheries Society, Manila, Philippines. Asian Fisheries Science 17, 191-200.

Kinne, O. and Kinne. E.M. (1962). Rates of development in embryos of a cyprinodont fish exposed to different temperature-salinity-oxygen combination. Canadian J. of Zoology, 40, 231-253.

Sahoo, C.D. Bhakat, P.B. (2006). Design and construction of a low cost carp hatchery. Aquacultural Engineering Training Manual, CIFA.52-70.

Sengupta, R., Ray, R.N., and Basak, S.K. (1984). Development of carp hatcheries in West Bengal, National Workshop on fish seed production held at Calcutta.

Tapiador, D. D., Henderson, H. F., Delmendo, M. N. and Tsulsuni, H. (1977). Fresh water fisheries and aquaculture in China. FAO Fisheries Technical paper no.168.

Waynarovick E. and Horvath L. (1980). Artificial propagation of warm water finfishes—a manual for extension. FAO Technical Paper 201. FAO, Rome.

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SUMMER GROUNDNUT A LIVELIHOOD FOR TRIBALS OF CHHOTAUEPUR DISTRICT (SUKHI COMMAND AREA) OF MIDDLE

GUJARAT: A SOCIO-ECONOMIC APPRAISAL1H. C. Parmar, 1Vinod B. Mor, Asstt. Res. Scientist, ARS, AAU, Jabugam2M. Sajid, Asstt. Prof., R. R. Christian, Teaching Asso., COA, AAU, Jabugam


ABSTRACTGroundnut is an important oil seed crop provides significant sources of cash through the sales of seed, cakes, oil and haulms. It plays an important role in the diets of rural populations. 120 farmers involved in groundnut cultivation were randomly selected. Data was collected using primary and secondary sources. To examine the profitability of groundnut production, the gross margin and cost benefit analysis were carried out. The young farmers (31-50 years) were more involved in groundnut farming i.e. 61.67 per cent. The cost, availability and lack of technical knowledge of input requirement are responsible for poor use of the inputs. Maximum respondents holding marginal farm i.e. 41.66 per cent. On all farm size, average cost of cultivation was found Rs. 47965. BCR was found highest in marginal farm size group (1.11) followed by medium (1.08), large (1.07) and small farm size groups (1.05). Average net return over cost C2 was found Rs. 3714. A major problems faced in groundnut production are seed quality, less price of output and high price of seed, which accounted 100 per cent, 76.67 per cent and 71.67 per cent, respectively. It is suggested to strengthen extension services for increase awareness about improved technologies regarding seed material and to build proper channel, which can yield more prices to the farmers. Farmers are also advised to use quality seed materials to avoid cheating and also sale produce through farmers’ group, cooperatives and APMCs.

Key words: Groundnut, Production, BCR, ConstraintJEL classification: D24, D57, D61, Q12

India is one of the largest producers of oilseeds in the world and occupies an important position in the Indian agricultural economy. It is estimated that nine oilseeds namely groundnut, rapeseed-mustard, soybean, sunflower, safflower, sesame, Niger, castor and linseed, accounted for an area of 328.77 lakh tonnes of oilseeds with a productivity of 1153 kg/ha (Anonymous, 2014). Groundnut (Arachis Hypogaea) belongs to family Leguminoceae (Fabaceae) sub-family Papilionoideae. Groundnut is known as king of vegetable oilseed in India (Madhusudhana, 2013). It is regarded as poor man's almonds since it contains about 25 per cent protein, 45 per cent edible oil and 26 per cent carbohydrates besides other essential nutrients (Singh et al., 2014). In year 2013-14, India ranked first in groundnut

area (52.50 lakh ha), second in production (94.72 lakh tonne) and fourth in productivity (1804 kg/ha) in world. Gujarat ranked first in area (18.42 lakh ha) and production (49.18 lakh tonne) of groundnut in India, followed by Andhra Pradesh (Anonymous, 2014). However, average productivity is relatively low as groundnut is mostly grown under rain fed condition. Because of high productivity (almost double) under assured irrigation, in recent years, the area under summer groundnut has increased. In middle Gujarat summer groundnut mainly cultivated under Sukhi command area which falls in tribal region of newly declared Chhotaudepur district which is also known as hilarious district in Gujarat. The study has been carried out to study the costs and returns of summer groundnut production, to estimate cost of production per quintal and input-output

82

ratio and to identify the constraints faced by the summer groundnut growers in this district.

METERIALS AND METHODSChhotaudepur district of middle Gujarat was selected purposively for the study based on higher area under summer groundnut. Among six talukas of the district, Jetpur-Pavi taluka having highest area under summer groundnut in Sukhi command area was selected. Ten villages from the taluka were selected randomly. From each of the villages, twelve farmers selected randomly. Thus, total 120 groundnut growers were selected for the study. Personal interview method was followed to collect data from the farmers, relating to the agricultural year 2014-15. The collected data input-wise cost and output value were worked out according to CACP approach.

RESULTS AND DISCUSSION1. Socio-economic status

1.1 Age:The majority of the respondents (61.67 per cent) belonged to middle age group and equal numbers of the respondents (19.16 per cent) found to be in young and old age group (Table 1).

Table1: Distribution of Respondents According to Age groupsFarm size group Age of Head of the family (years)

Young(up to 30)

Middle(31 to 50)

Old(Above 50)

Total

Marginal 11 (22) 30 (60) 9 (18) 50 (100)Small 8 (20) 22 (55) 10 (25) 40 (100)Medium 3 (15.79) 14 (73.68) 2 (10.53) 19 (100)Large 1 (9.09) 8 (72.73) 2 (18.18) 11 (100)All 23 (19.17) 74 (61.67) 23 (19.16) 120 (100)Note: Figures within the parentheses indicate per cent to total.1.2 Educational level of the Respondents:Table 2: Distributions of Respondents According to their Level of EducationFarm size group Level of Education

Illiterate Primary(Up to VII Std.)

Secondary(VII to XII)

College Total

Marginal 6 (12) 24 (48) 20(40) -- 50 (100)Small 9 (22.5) 20 (50) 11 (27.5) -- 40 (100)Medium 2 (10.53) 6 (31.58) 10 (52.63) 1 (5.26) 19 (100)Large -- 1 (9.09) 8 (72.73) 2 18.18) 11 (100)All 17(14.17) 51 (42.50) 49 (40.83) 3 (2.50) 120 100)Note: Figures within the parentheses indicate per cent to total.More than two-fifth of the respondents had primary (42.50 per cent) and secondary (40.83 per cent) level of education followed by illiterate and college level of education (Table 2).

1.3 Family Size:It is clear from the data presented in Table-3 that more than half of the respondents (53.33 per cent) had medium size of family followed by large and

83

small size of family with 25.00 per cent and 21.67 per cent, respectively.Table 3: Distribution of Respondents According to the Family size

Family Size Farm CategoryMarginal Small Medium Large All

Small Family(Up to 4) 7 (14.0) 6 (15.0) 10 (52.64) 3 (27.27) 26 (21.67)Medium Family(5 to 8) 30(60.0) 22(55.0) 6 (31.57) 6(54.55) 64 (53.33)Large Family(Above 8) 13 (26.0) 12(30.0) 3 (15.79) 2(18.18) 30(25.00)Total 50(100) 40(100) 19(100) 11 (100) 120 (100)

Figures within the parentheses indicate per cent to total1.4 Land holding:More than two-fifth of the respondents (41.66 per cent ) had marginal farm size

followed by small, medium and large with 33.34 per cent,15.83 per cent and 9.17 per cent, respectively (Table 4).

Table 4: Distribution of Respondents According to their Size of Land holdingFarm size group Farms Average farm sizeNumber % to total farms

Marginal(Up to 1 hectare)

50 41.66 0.47

Small(1.01 to 2 hectare)

40 33.34 1.34

Medium(2.01 to 4 hectare)

19 15.83 2.62

Large(above 4 hectare)

11 9.17 5.02

All 120 100 2.36

1.5 Occupation:A perusal of Table-5 revealed that 100 per cent of the respondents had agriculture as main occupation along with live stock for their lively hood followed

by farming along with labour work and animal husbandry and farming with livestock with 58.33 per cent and 44.16 per cent, respectively.

Table 5: Distributions of Respondents According to their OccupationOccupation Farm Category

Marginal Small Medium Large AllFarming only 0 0 0 0 0Farming + Livestock 50(100) 40(100) 19(100) 11(100) 120(100)Farming + Labour work 25(50) 26(65) 2(10.52) 0 53(44.16)Farming + Service 0 0 0 2 (22.22) 02(1.66)Farming + Livestock + Labour work

41 (82)

29(72.50)

0 0 70 (58.33)

Figures within the parentheses indicate per cent to total

2. Cost Analysis:2.1 Factor wise cost:The details of per hectare cost and various factors cost for the production of groundnut on different size of farms are studied and the results are furnished in Table- 6. It is apparent from the table that the average total cost (Cost C2) per hectare was amounted to Rs.47965. It was highest (Rs.58282) on large farms

and the lowest (Rs.41276) on marginal farms. The share of operating cost in the total cost (Cost A) per hectare was 53.8 per cent in all farms. The break-up of cost components shows that the share of human labour in the total cost was 27.2 per cent followed by rental value of owned land (17 per cent), seeds (16.5), miscellaneous charges (8.7), bullock

84

labour (6.5), chemical fertilizers (5.7), etc.2.2 Yield, Price and BC Ratio:The result indicates that average yield obtained on sample farms was 15.87 quintal per hectare with a range from 14.84 to 17.07 quintal per hectare. The average farm harvest price ranged from Rs. 2540 to 3100 per quintal with Rs.2713 per quintal on overall farms. The gross returns inclusive of by-product were Rs.51678 per hectare on sample farms. It was highest (Rs.62367) in case of large farms followed by medium (Rs.52436), small (Rs.46444) and marginal (Rs.45899) farms. Thus the gross return increase with the increased in farm size.The average net return on the sample farms was worked out to be Rs.3714 per

hectare. It was highest in case of marginal farms (Rs.4623) followed by medium (Rs.4226), large (Rs.4085) and small (Rs.2353) farms. Benefit-Cost ratio indicates returns to per rupee investment in groundnut production. The overall BC ratio was 1.08 for the sample farms. It was highest (1.11) on marginal farms and lowest (1.05) on small farms. The overall per quintal cost was Rs.2516 and it was highest on large farms (Rs.2897) and lowest on marginal farms (Rs2293).Table-8 shows per hectare net returns over operational cost (Cost-A) was the highest (Rs.28025) on large farms and the lowest (Rs.24737) on small farms with average of Rs.25869 on sample farms as a whole.

Table: 6 Factor-wise Distribution of Total Cost on Summer Groundnut farms (Rs/ha)Item Farm size groups

Marginal Small Medium Large All Farms Human Labour

Family 10560(25.6)

10080(22.9) 7080(14.7) 7200(12.4) 8730(18.2)

Hired 240(0.6) 1440(3.3) 5640(11.7) 9960(17.1) 4320(9.0)

Total 10800(26.2)

11520(26.1)

12720(26.4)

17160(29.4)

13050(27.2)

Bullock Labour 2500(6.1) 3000(6.8) 3500(7.3) 3500(6.0) 3125(6.5)Seed (kg) 7650(18.5) 7500(17.0) 7650(15.9) 8775(15.1) 7894(16.5)Chemical fertilizer 2619(6.3) 2490(5.6) 2816(5.8) 3069(5.3) 2749(5.7)Irrigation 1875(4.5) 1875(4.3) 1875(3.9) 1875(3.2) 1875(3.9)Crop protection 303(0.7) 400(0.9) 423(0.9) 400(0.7) 382(0.8)Miscellaneous cost 3553(8.6) 3917(8.9) 4208(8.7) 5065(8.7) 4186(8.7)Depreciation 229(0.6) 250(0.6) 290(0.6) 378(0.6) 287(0.6)Int. on working capital 835(1.8) 835(1.9) 1056(2.2) 1320(2.3) 992(2.1)Rental value of owned land 6658(16.1) 7511(17.0) 8397(17.4) 10086(17.3

) 8163(17.0)

Int. on owned fixed capital 578(1.4) 785(1.8) 892(1.9) 1356(2.3) 903(1.9)

Managerial charges 3752(9.1) 4008(9.1) 4383(9.1) 5298(9.1) 4509(9.1)

Cost A 19728(47.8)

21707(49.2)

27458(57.0)

34342(58.9)

27298(53.8)

Cost B 26964(65.3)

30003(68.0)

36747(76.2)

45784(78.6)

36364(72.7)

Cost C1 37524(90.9)

40083(90.9)

43828(90.9)

52984(90.9)

45094(90.9)

Cost C2 41276(100) 44091(100) 48210(100) 58282(100) 47965(100)Figures within the parentheses indicate per cent to total

Table: 7 Yields and Value of Gross Output per Hectare85

Indicators Marginal Small Medium Large AllAverage Yield(q/ha) 14.98 14.84 16.18 17.07 15.87Price (Rs/q) 2550 2540 2700 3100 2713Gross Return (Rs/ha)Main 38199 37694 43689 52917 43015By-Product 7700 8750 8750 9450 8663Total 45899 46444 52439 62367 51678Total Cost (Rs/ha) 41276 44091 48210 58282 47965Gross ReturnsCost C1 (Rs/ha)

8375 6361 8609 9383 8074

Net Return (Rs/ha) 4623 2353 4226 4085 3714BC Ratio 1.11 1.05 1.08 1.07 1.08Cost of Production (Rs/q) 2293 2411 2482 2897 2516

2.3 Net Returns:Table: 8 Net Return over different cost Category of farms Net returns (Rs/ha)

Cost A Cost B Cost C1 Cost C2Marginal 26171 18935 8375 4623Small 24737 16441 6361 2353Medium 24978 15689 8609 4226Large 28025 16583 9383 4085All 25869 16804 8074 3714

ConstraintsMajor constraints in summer groundnut production as perceived by the farmers were listed and ranked in Table-9. The entire farmer faced problem of quality seed availability of groundnut which acts as major constraint in groundnut production. Majority of the farmers

reported price of groundnut and higher price of seed are major constraints in groundnut production. About 42 per cent farmers had the view that fertilizers cost was high followed by labour shortage (25 per cent), timely irrigation (20.83 per cent) and marketing of groundnut.

Table: 9 Constraints faced by Respondents in Groundnut ProductionConstraints No. of respondents % to total RankSeed quality 120 100 IPrice of output 92 76.67 IIHigher price of seed 86 71.67 IIICostly fertilizer 50 41.67 IVLabour shortage 30 25 VITimely canal Irrigation 25 20.83 VIIMarketing 21 17.5 VIII

CONCLUSIONSThe average total cost per hectare was amounted to Rs.47965. The share of operating cost in the total cost (Cost A) per hectare was 53.8 percent in all farms. Human labour and seeds cost was the major components in

operational cost. The net return was Rs.3714 per hectare. The overall BC ratio was 1.08 for the sample farms. The overall per quintal cost was Rs.2516. Quality seed availability and price of groundnut output was the major constraint in groundnut production.

REFERENCES

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Anonymous (2014) Status paper on oilseeds, Department of Agriculture and cooperation, Ministry of Agriculture, GOI.

Anonymous(2016)http://www.gktoday.in/blog/ current - data-on-oil-seeds-production-in-india/

Singh, H., Singh, N. and Bairwa, K. (2014): Resource-use efficiency in production of

groundnut in Rajasthan: an economic analysis. Ann. Agric. Res. New Series Vol. 35 (1): 92-97 (2014)

Madhusudhana, B. (2013): A Survey on Area, Production and Productivity of Groundnut Crop in India. IOSR Journal of Economics and Finance Vol. 1 (3): 01-07 (2013)

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http://www.gktoday.in/blog/%20current%20-%20data-on-oil-seeds-production-in-india/

http://www.gktoday.in/blog/%20current%20-%20data-on-oil-seeds-production-in-india/

CLASSICAL SELECTION INDICES IN SUGARCANE (Saccharum spp.)

G Vinay Kumar1 and V. Satya PriyaLalitha2

1Agicultural College, Bapatla, Guntur, Andhra Pradesh, 2Sugarcane Research Station, Vuyyuru, Andhra Pradesh, India

E-mail: [email protected]: 10.02.17Accepted: 18.03.17

ABSTRACTAn investigation was conducted at the Sugarcane Research Station, Vuyyuru, Krishna District, Andhra Pradesh, India to formulateSelection indicesconsidering cane yield and ten of its component characterswhich showed high correlation with cane yield. Selection indices were constructed comprising thirteen clones of sugarcane (Saccharumspp) at second clonal stage. Among eleven characters, cane yield (X1) was considered as dependent character while, other characters viz., number of germinants at 35 DAP (X2),number of millable canes (X3), length of millable cane (X4),diameter of millable cane(X5), single cane weight (X6), brix per cent(X7), sucrose per cent(X8), purity per cent(X9), CCS per cent(X10) and sugar yield (X11) were considered as independent variables. The selection indices constructed with the inclusion of more than one character gave higher genetic advance and relative efficiency over straight selection for cane yield

Key words: Selection indices, Genetic advance, Relative efficiency and Cane yield

Sugarcane is an important cash crop in India and one of the most important agro industrial crops of the world. It assumes an important position in the economy of country. Complex traits like cane yield and quality are influenced by a number of characters. These characters directly and indirectly contribute to the yield. An understanding of cane yield, sugar yield and inter relationships among other component traits with economic yield is also important to adopt an appropriate breeding strategy. Cane yield is a highly complex quantitative character influenced by several component characters and hence direct selection may not be reliable. Selection indices are useful in understanding the extent of improvement by a combination of characters so as to improve the selection efficiency through indirect selection for bringing genetic improvement of yield. The potential advantage of selection indices is that several traits are improved simultaneously (Falconer and Mackay, 1996).


The experimental material for the present study consisted of 13 clones of sugarcane including 11 early maturing clones and two standards viz., Co 6907 and 87 A 298. The experiment was laid out during 2013-14 cropping season in randomized block design with three replications, each genotype planted in eight rows( 80 cm apart) and each row having of eight-meter length with a net plot size of 38.4m2 (6 rows×8m×0.8 m). The setts having three eye buds each were planted with four sets per meter. The crop received 168 kg N, 75kg P2O5 and 100 kg K2O/ hectare. All the recommended package of practices was adopted during the entire crop season. Observations were recorded on net plot basis viz., number of Germinants at 35 DAP, Number of millable canesand Cane yield (t/ha). Sugar yield (t/ha) was estimated based on cane yield and CCS per cent. Data on Length of millable cane, diameter of millable cane and single cane weight were recorded on 10 randomly selected canes in each plot and replication at harvest. Juice quality parameters such are brix per cent, sucrose per cent, purity per

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cent and CCS per cent were also estimated.Selection index or score proposed by Smith (1936) based on the discriminant function of Fisher (1936) was used to combine the component characters and arrive at a selection index which indicate the genetic worth of the plant. Application of discriminant function as a basis for making selection on several characters simultaneously was aimed at discriminating the desirable genotypes from undesirable ones on the basis of their phenotypic performance. A number of different selection indices are constructed using combination of characters. The expected genetic advance based on the composition of characters that was included for formulation of the various selection indices was calculated as per the formula of Robinson et al., (1951). The relative efficiency of each selection index formulated was evaluated by comparing with yield alone which is considered as 100 per cent efficient as given by Brim et al., (1959).


Initially Cane yield alone was taken as component in selection index (direct selection) and genetic advance was estimated. Later all other combinations of yield and yield components were used and selection indices were constructed. Different constructs which are showing high value of genetic advance and relative gain over cane yield were presented in Table1.The selection indices were constructed by assigning equal economic weights to all the characters. Those indices which gave higher estimates of genetic advance compared to the direct selection were discussed here under.When independent characters were considered, number of millable canes(X3) showed higher relative efficiency coupled with high genetic advance after cane yield(X1).Among two

character combinations, combination involving cane yield(X1)and number of millable canes(X3) recorded maximum values of genetic advance of 74.85 with relative efficiency of 145.43. In case of selection indices constructed using three character viz., Cane yield(X1), number of millable cane(X3) and length of millablecane(X4) recorded maximum values of genetic advance of 86.14 with relative efficiency of 167.35.Among four character combinations, cane yield(X1), Number of germinants at 35 DAP(X2),number of millable cane(X3) and lengthof millable cane(X4) exhibited high relative efficiency of 190.77 coupled with high genetic advance of 98.20, whereas among five character combinations, maximum relative efficiency of 203.12 was observed for the combination of cane yield(X1), Number of germinants at 35 DAP(X2), number of millable cane(X3), length of millable cane(X4) and sugar yield(X11)with high genetic advance of 104.56. High relative efficiency of 204.57 coupled with high genetic advance 105.30 was observed in six character combinations viz., cane yield(X1), Number of germinants at 35 DAP(X2), number of millable cane (X3), length of millable cane(X4), purity per cent(X9) and sugar yield(X11), where as in seven character combinations of cane yield (X1), Number of germinants at 35 DAP(X2), number of millable cane (X3), length of millable cane(X4), purity per cent(X9), CCS per cent(X10) and sugar yield(X11), high relative efficiency of 205.42 was found with high genetic advance of 105.73. Among eight character combination such as cane yield(X1), Number of germinants at 35 DAP(X2), number of millable cane (X3), length of millable cane(X4), brix per cent(X7), sucrose per cent(X8), purity per cent(X9) and sugar yield(X11) high relative efficiency of 205.97 was found with high genetic advance of 106.02,

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where as in nine character combinations, cane yield(X1), Number of germinants at 35 DAP(X2), number of millable cane (X3), length of millable cane(X4), brix per cent(X7), sucrose per cent(X8), purity per cent(X9), CCS per cent(X10) and sugar yield(X11) recorded high relative efficiency of 206.46 with high genetic advance of 106.27. Among ten character combinations characters of cane yield (X1), Number of germinants at 35 DAP(X2), number of millable cane (X3), length of millable cane(X4), diameter of millable cane(X5), single cane weight(X6), sucrose per cent(X8), purity per cent(X9), CCS per cent(X10) and sugar yield(X11) recorded high relative efficiency of 206.92 with high genetic advance of 106.50, whereas when all eleven character in this studytaken into the consideration, a high relative efficiency of 206.31 with high genetic advance of 106.70 was recorded.In the eleven character combination, a slight decrease of relative efficiency but not genetic advance was found compared to the ten character combination as it includes the character brix per cent which had negative correlation with cane yield. These results clearly indicate that selection based on index value is efficient than direct selection on yield alone. Similar conclusions were drawn by

Kumar and Singh (2005), Doule and Balasundaram (2006), Kumar et al. (2007), Sabitha (2007),Sahuet al. (2008), Sirishaet al. (2010) and Charumathi (2015).The ‘simultaneous selection index values/scores’ considering all the eleven component characters considered in present study were calculated for different genotypes (table 2), it is found that the genotypes 2008 V 98, 2008 V 312 recorded higher selection index values when compared with standard viz., Co 6907 and 87 A 298(figure 1).These results indicate that these two genotypes are superior compared to all other genotypes when simultaneous selection for all the characters is carried out.

CONCLUSION

From the above result it is evident that, the genetic advance and relative efficiency were increased progressively with inclusion of more characters in the index along with cane yield and the best possible construct in case of sugarcane for selecting superior genotypes should include all the eleven characters which are considered for this investigation, and the genotype showing high selection index score by considering all eleven characters can be regarded as best genotype under investigation.

Table 1: Genetic advance estimates and relative efficiency over cane yield of best possible indices of eleven characters under study in sugarcane (Saccharumofficinarum L.)Selection index Expected genetic

advanceRelative efficiency over cane yield

Cane yield (X1) 51.47 100.00Number of germinanatsat 35DAP(X2) 22.18 43.10Number of millable canes (X3) 35.08 68.16Length of millable cane(X4) 13.11 25.48Diameter of millable cane(X5) 0.06 0.13Single cane weight(X6) 0.15 0.28Brix per cent(X7) 0.86 1.68Sucrose per cent(X8) 1.08 2.10Purity per cent(X9) 2.79 5.41CCS per cent(X10) 0.92 1.78Sugar yield(X11) 5.86 11.39

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X1+X3 74.85 145.43X1+X3+X4 86.14 167.35X1+X2+X3+X4 98.20 190.78X1+X2+X3+X4+X11 104.55 203.12X1+X2+X3+X4+X9+X11 105.30 204.57X1+X2+X3+X4+X9+X10+X11 105.73 205.42X1+X2+X3+X4+X7+X8+X9+X11 106.02 205.97X1+X2+X3+X4+X7+X8+X9+X10+X11 106.27 206.46X1+X2+X3+X4+X5+X6+X8+X9+X10+X11 106.50 206.92X1+X2+X3+X4+X5+X6+X7+X8+X9+X10+X11 106.70 206.31

Table 2: Selection index values/scores of 13 sugarcane genotypes in sugarcane (Saccharumofficinarum L.)

Genotype Selection index score2008 V 40 1571.1542008 V 52 1832.2002008 V 55 1828.4682008 V 78 1857.1572008 V 98 2000.5652008 V 109 1944.9402008 V 188 1865.5942008 V 229 1852.1022008 V 240 1895.5672008 V 312 1974.5612008 V 367 1887.32487 A 298 1834.496Co 6907 1929.711

Fig.1 Selection index scores of sugarcane genotypes for cane yield in sugarcane (Saccharumofficinarum L.)

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Genotypes

REFERENCESBrim, C.A., Johnson, H.W and Cockerham,

C.C. 1959.Multiple selection criteria in soybeans.Agronomy Journal 51 42-46.

Charumathi, Mand Naidu, N.V. 2015.Sequential selection indices for improvement of cane and sugar yieldsin seedling and succeding clonal stages of selection in sugarcane.International Journal of Current Agricultural Research, Vol. 3, No. 12, PP. 173-176.,

Doule, R. B. and Balasundaram, N. 2006. Relative efficiency of canecharacters in selection for cane yield in sugarcane (Saccharumspp. hybrid). Indian Journal of Genetics66 (4): 347-348.

Falconer, D.S and Mackay, T.F.C. 1996.Introduction to Quantitative Genetics.4th ed., Longman, London Chap.

Fisher R.A. 1936.The use of multiple measurements in taxonomic problem. Annals of Eugenics 7 179-88.

Kumar, K. and Singh, P. K. 2005.Selection indices in mid latematuring clones of sugarcane (Saccharum complex hybrid). CropImprovement 32 (2): 173-177

Kumar, A., Pandey, D.D., Singh, S.B., Ganesh Prasad, Singh, S.K.,Singh, D.N. and

Singh, R.R. 2007. Correlations among the somaclones created through tissue culture in sugarcane (Saccharumspp.) Co-operative Sugar 38 (10): 29-33

Robinson, H.F., Comstock, R.E and Harvey, P.H.1951. Genotypic and phenotypic correlation in corn and their implications in selection.Agronomy Journal 43 282-287.

Sabitha, N. 2007.Genetic parameters and selection indices insugarcane (SaccharumofficinarumL.).M.Sc (Ag.) Thesissubmitted to the Acharya N. G. Ranga Agricultural UniversityHyderabad

Sahu, R.S., Pandey, S.S. and Kamat, D.N. 2008.Correlation studies between seedling and their settlings in inter varietal crosses of sugarcane for yield and quality traits. Indian Sugar LVIII(2): 27-30.

Sirisha, M., Prasad Rao, K., Panduranga Rao, C. and Srinivas Rao,V. 2010.Classical selection indices in sugarcane (SaccharumefficinarumL.) The Andhra Agricultural Journal 57 (3) :223 – 225.

Smith, H. F. 1936.A discriminant function for plant selection. Annals ofEugenics 7: 240-250.

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EXTENT USE OF MOBILE PHONE IN AGRICULTURE

Rajneesh1, N.K. Sharma2, S.S. Sisodia3

1 Ph.D. Scholar, Department of Extension Education, RCA, Udaipur, 2 Professor, Dept. Ext. Edu., SKNCOA, Jobner, 3 Professor, Dept.Ext. Edu., RCA, Udaipur


ABSTRACTInformation and communication technology is a new approach for development of agriculture through dissemination and obtain new technology and agriculture information, timely and in appropriate format to each and every farmer of country. Mobile phone is a tool of ICT with high potential of dissemination and acquisition of information.This study was conducted to assess the extent of use of mobile phone by farmers for their crop management, agriculture related queries and other agriculture advisory services. The results shows that 73.75 per cent respondents had medium use followed by high extent of use of mobile phone (22.50% respondents) and majority of respondents made use of mobile phone for gaining information about crop production practices with 51.81 MPS followed by contact with peoples (i.e.38.37MPS).

Key words: - Information and Communication Technology, Mobile Phone Services.

Development of agriculture is possible by fulfil the needs of “Information Hungry” of farmers, information is also a critical input as important as other key inputs such as credit, seeds, fertilizers and water in agriculture. By providing information extension system can enable farmers to understand and solve their own location-specific problems, which is possible through continuous two-way interaction among the farmers and agricultural scientists.The use of Information and Communication Technology (ICT) is the only way to provide on-demand, customized, relevant, understandable, timely and need based information in local language and easily accessible services with active communication to enhance the farm productivity. In agricultural sector use of ICT especially mobile phone services which is an important tool of ICT has provided information on market, weather, transport and agricultural techniques. Mobile phone plays an important and potential role in increasing the reach of agricultural extension system and farmers both. In

terms of India where farmers explore the use of mobile phone through voice call, voice message, multimedia messages (MMS), short message service (SMS) and internet to access agricultural knowledge and information as well as weather and disaster from experts.(Masuki, K.F.G. 2010).Mobile phone penetration, especially in developing countries, which had more than 85 percent of the world’s mobile phones, is because of easy access, easy appliances, easy applications and affordability. Mobile phones significantly reduce communication and information costs for the rural poor and serve both as direct information channels to farmers and as indirect channels improving extension agents, agribusinesses and other intermediaries’ access to information resources. In India a skilful synthesis between traditional form of communication on the one hand and the modern audio-visual media including satellite communication and mobile telephony on the other, is being attempted. The level of usage of mobile phone is spreading rapidly for the

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purpose of business, education and agriculture development. This study is an attempt to assess the extent of use of mobile phone by farmers in agriculture.


This study was conducted to assess the extent of use of mobile phone for agriculture by farmers who were using mobile phone in the Bundi district of Rajasthan. An interview schedule specially designed to measure the extent of use of mobile phone services for agriculture by the respondents including five different areas of use of mobile phone and each one include a set of questions was used for data collection. The collected data were tabulated, analyzed and inferences were drawn after subjecting the data to statistical analysis.To measure the extent of use of mobile phone the three point continuum scale developed by Chaturvedi (2000) was applied. Extent of use of mobile phone is directly related to the frequency of use of mobile phone which is calculated by the interview schedule, One score was given to every correct answer and zero for wrong answer for questions of interview schedule that give total use score obtained by each respondent. Finally the use index was calculated by the following formula:

Use index = × 100

The responses obtained from the respondents were counted and converted into mean, S.D. (standard deviation) and M.P.S. (mean per cent score). The mean and S.D. of all the respondents’ scores were computed for classifying the extent of use of mobile phone into different categories. Based on the mean score and standard deviation the farmers were categorized under three categories, namely low, medium and high extent of use of mobile phone services as follows:

Category Scores obtainedLow extent of use = < (Mean Score – S.D.)Medium extent of use = (Mean – S.D) to (Mean + S.D.)High extent of use = > (Mean Score + S.D.)


To get an overall view of extent of use of mobile phone services, the mean and standard deviation were calculated. Based on calculated mean ( = 14.91) and standard deviation ( =9.76) the score of extent of use were calculated and based on score of extent of use the farmers were categorized into three categories of extent of use of mobile phone services i.e. low extent of use, medium extent of use, high extent of use of mobile phone services and most of the respondents (73.75 %) shows high extent of use of mobile phone followed by high extent of use (22.50 %). Only 3.75% respondents show low extent of use of mobile phone for agriculture purposes

Table 1 - Distribution of farmers based on their score of extent of use of mobile phone UNDER DIFFERENT CATEGORIES OF EXTENT OF USE. N=80

Adoption category Number of respondents Percentage (%)High extent of use 18 22.50Medium extent of use 59 73.75Low extent of use 3 3.75Total 80 100

= 14.91 =9.76.

Use of mobile phone services is directly or indirectly related to crop management. Hence, it was necessary to assess the

extent of use of mobile phone by farmers which influenced the decision making in crop management.

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From the findings, it was apparent that majority of the farmers (73.75 %) had medium extent of use of mobile phone services, because most of the farmers were facing technical constraints in the use of mobile phone services.Out of all the respondents with high extent of use majority were under thirty to forty year age group those are well educated and resource full individuals.

Furthermore, the extent of use of mobile phone was also analyzed separately. The relative importance of all the five areas related to use of mobile phone services was highlighted by ranking their extent of use on the basis of mean per cent scores (MPS) of use and data have been presented in table 2.

Table 2 - Extent of use of mobile phone services by the farmers in different arean = 80Elements related to use MPS Ran

kUse of mobile phone for gaining information about crop production practices. 51.81 IUse of mobile phone for gaining information about marketing 38.25 IIIUse of mobile phone for gaining information about KVK activities 24.38 VUse of mobile phone to contact with other peoples 38.37 IIMode of use of mobile phone 26.39 IVAverage 35.84MPS- Mean Percent Score

The data were analyzed with regard to the use of mobile phone in five areas by respondents and results shows that majority of respondents made use of mobile phone for gaining information about crop production practices with MPS 51.81 and was ranked first because farmers using mobile phones to know the latest important production practices. Use of mobile phone to contact with other peoples was at second rank (38.37MPS) because farmers using mobile phone to contact with different people and experts for getting information about agriculture followed by Use of mobile phone for gaining information about marketing (38.25 MPS)for making themselves update with the marketing activities related to inputs required for crop production and market price of crop produce, Mode of use of mobile phone (26.39 MPS), Use of mobile phone for gaining information about KVK activities (24.38 MPS) because farmers use mobile phone for voice call and less for SMS,

MMS, internet etc. and accorded ranks III, IV and V, respectively.

CONCLUSIONFindings clearly indicated that about three fourth (73.75 %) respondents had medium extent of use followed by high extent of use of mobile in agriculture (22.50%). Whereas, only 3.75 per cent respondents had low extent of use of mobile phone in agriculture. The Extent of use of mobile phone in each of the five areas of use of mobile phone was also measured. It was observed that majority of respondents use mobile phone for gaining information about crop production practices with MPS 51.81 and was ranked first. Use of mobile phone to contact with peoples was at second rank (38.37MPS) followed by Use of mobile phone for gaining information about marketing (38.25 MPS), mode of use of mobile phone (26.39 MPS) and use of mobile phone for gaining information about KVK activities(24.38 MPS) .

REFERENCESAnimashaun, J.O., Fakayode, S.B., Idris, K. A.

and Adedokun, K. F.2014. Patterns and Drivers of Mobile Telephony for Sustainable Livelihood among Farming Households

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in Kwara State, Nigeria.Journal of Agricultural Informatics, 5(2):34‐44.

Chauhan, M.N. 2010.Farmers' Perception about ICT Application: A Case study of Gujarat state.Indian Research Journal of Extension Education, 10(3):42-47.

Chhachhar, A.H.R. 2013.The Use of Mobile Phone among Farmers for Agriculture

Development. International Journal of Scientific Research, 2:96-98.

Das, A.,Basu, D. and Goswami, R. 2012. Accessing Agricultural Information through Mobile phone: Lessons of IKSL Services in West Bengal.Indian Research Journal of Extension Education,12(3): 37-41.

Devi, U. and Verma, S. 2011. Farm Women Preferences of Communication Sources for Farm Information.Indian Research Journal Extension Education, 11:2.

Dhaka, B.L. and Chayal, K. 2010. Farmers’ Experience with ICTs on Transfer of Technology in Changing Agri-rural Environment. Indian Research Journal of Extension Education, 10(3):20-24.

Gupta, V.,Mankar, D.M. and Chandargyi, D.M. 2003. Communication Channel and

methods used by extension personnel for establishing linkage with scientist.

Maharastra Journal of Extension Education, 22:190-193.

Jehan, N., Aujla, K.M., Muhammad, S., Hussain, A., Muhammad,Z., Khan, M. and Bilal, A. 2014. Use of Mobile

phones by Farming Community and its Impact on Vegetable Productivity. Pakistan Journal of Agriculture Research, 27(1)10-14.

Meera, S.N. and Jhamtani, A. 2004. Future projections of use of information technology in agricultural development in India. Indian journal of Extension Education, 40:1-7.

Parihar, S.S., Mishra, B. and Rai, D.P. 2010. Sustainable Models of Information Technology for Agriculture and Rural Development.Indian Research Journal

of Extension Education.10(1)1-5.

Saxena, A.,Tomar, D.S. and Dixit, A.K. 2011. Cyber extension-an effective linkage between scientist and farmers.Rajasthan Journal of Extension Education,19:25-29.

Shankaraiah, N., Swamy, B.K.N., Shashekala, S.G. and Kumar, P.R. 2012. Dissemination of Agricultural Technologies through Mobile Message Service in Karnataka. Journal of agricultural extension management, 13(2)11-14.

Singh, A.K., Singh, L. and Riyajuddeen.2008. Role of Helpline Services in Technology

Dissemination. Indian Research Journal of Extension Education, 8(1):9-11.

Tenywa, M., Zizinga, K.B., Muyingo, A., Kasule, R., James, N. and Kaliisa, R. 2009. Exploring the Usage of Mobile Technologies and Introducing Innovations for Improved Incomes and Livelihoods. A Case of L3F Initiative inSouth Western Uganda. Open Distance Learning Network (ODLN)-Makerere University Agricultural Research Institute, Kabanyolo (MUARIK).

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TO STUDY THE GROWTH RATE ANALYSIS OF POTATO PRODUCTION IN SRIGANGANAGAR DISTRICT AND RAJASTHAN

Maya Rahar, Garbar Singh and Prem Singh ShekhawatDepartment of Agril. Economics, COA, Bikaner S.K.R.A.U. Bikaner Rajasthan


ABSTRACTThe study was conducted in Sriganganagar district of Rajasthan. The analysis of data revealed that the growth rates of area, production and productivity of potato in Sriganganagar district and Rajasthan were positive and non-significant. The growth rates were 8.00, 4.10 and _3.20 per cent per annum in Sriganganagar and 8.20, 8.30 and _0.1 in Rajasthan state for area, production and productivity, respectively. The CGR of area and production of potato were positive and significant in Rajasthan. However, CGR in area for Sriganganager district was found non-significant. The productivity for both state and for district was recorded negative.

In India, the major potato producing states are Punjab, Haryana, Uttar Pradesh, Bihar, West Bengal, Gujarat and Madhya Pradesh. The total area under potato cultivation in India was 19.73 Lakh ha and production 480.09 million tonnes in year 2014-2015. According to www.indiastate.org In Rajasthan, major potato producing district are Dholpur, Bharatpur, Sriganganagar, Kota. The total area under potato cultivation in Rajasthan was 12514 ha and production 150869 tonnes in year2014-2015. According to www.rajasthankrishi.com Pandit at all growth rates of area, production and yield of potato in India including its major producing states were estimated. Growth rates of potato area, production and yield in major potato producing states.In Sriganganagar district, area under potato cultivation was 647ha and production 17145 tonnes in 2012-13 which was increased 670ha and 17225 tonnes in 2014-15.In Sriganganagar district potato is cultivated in Sriganganagar, Sadulshahar, Padampur, Vijaynagar, karanpur, Raisinghar, Anoopgarh, Surtgarh, Gharsana tehsils. Area and production of potato during 2014 -15 in these tehsils have been shown in table no.3.1.2.Sriganganagar

tehsil alone contribute total 23.43% area in Sriganganagar of total area and about 24% of total production of total potato in Sriganganagar district. It rank first in area and production among all potato producing tehsil of Srigangangar district. In view of the importance of potato crop, the proposed study were analyse growth of potato in respect of area, production and productivity over period of time in Sriganganagar district and Rajasthan state as whole, economics of potato crop, resource use efficiency and constraint confronted by Potato growers in the study area. Study of these aspect may focus the benefits of producers as well as of consumers of the study area. The study will also useful to the policy makers, researchers and all those who are interested in the cultivation of potato crop. Objective: To study the growth in area, production and productivity of potato in Sriganganagar district and Rajasthan.

METERIAL AND METHODS

Compound Growth RatesTo study growth in area, production and productivity of potato in Sriganganagar district and Rajasthan state, compound growth rates were worked out by using

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http://www.rajasthankrishi.com/

the following formula: Exponential equation:Yt = abt Ut ……………… (I) Where,Yt is area/production/productivity of potato in time period tt is time element which takes the values 1, 2,3…..na and b are parameters to be estimatedWhere b= (1 + g); g is the rate at which y grows every year in relation to its value in preceding years.Ut is the error term On logarithmic transformation of Equation (i) we get:Log yt = Log a + t Log b + Log Ut...... (ii) Equation (ii) can be rewritten asY*t = a* + b*t + U*t

Where, Y*t = log Yt, a* = log a; b*=log b and U*t = log Ut

The compound growth rate was obtained as g = (Antilog b*-1)*100The ‘F’ value was used for testing the significance of compound growth rates.


Growth in area, production and productivity of potatoThe analysis of year wise area under potato in Sriganganagar district and its share in Rajasthan (Table No. 1) reveals that the percentage share of area under potato during the year 2005-06 to 2014-15 varied between 5.35 per cent and 14.41 per cent. It was highest in 2005-06 and lowest in 2014-15. It is clear frame the table that percentage of the average area of in Sriganganagar in Rajasthan widely fluctuation during the period from 2005-06 to 2009-10. However, it is continued decrease from 2010-2011 to 2014-15.In case of production, the share of Sriganganagar district in Rajasthan was highest in 2005-06 and lowest in 2011-12. No trend was observed in production of potato in Sriganganagar district and it was fluctuated every year. Similarly, the same trend was seen in productivity of potato. The productivity of potato in Srigangangar district varied between 37.73 kg per hectare to 50.42 kg per hectare.

Table No. 1 Area, production and productivity of potato in Rajasthan and Sriganganagar during 2005-06 to 2014-15

Years

Area ( ha) Production (tonnes) Productivity (kg ha-1)

Raj. SGNGR % Share Raj. SGNGR % Share

Raj. SGNGR

2005-06 4100 591 14.41 55300 11720 21.19 74.14 50.422006-07 6000 608 10.13 79200 12768 16.14 75.75 47.612007-08 11130 671 6.02 112270 13796 12.28 99.13 48.632008-09 9100 683 7.48 92240 13749 14.90 98.65 49.672009-10 7180 736 10.25 90627 14720 16.24 79.22 502010-11 10621 785 7.39 76019 15700 20.65 139.71 502011-12 11859 800 6.74 178024 16803 9.43 66.61 47.612012-13 9388 647 6.89 116710 17145 14.69 80.43 37.732013-14 9448 595 6.29 116580 15767 13.52 81.04 37.732014-15 12514 670 5.35 150869 17226 11.41 82.94 38.89Source: www.rajasthankrishi.com

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http://www.rajasthankrishi.com/

Fig. 1 Area of potato in Rajasthan and SGNR during 2005-06 to 2014-15

Fig. 2 Production of potato in Rajasthan and SGNR during 2005-06 to 2014-15

Fig. 3 Productivity of potato in Rajasthan and SGNR during 2005-06 to 2014-15

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Compound growth rate for area, production and productivity of potato in Sriganganagar district and Rajasthan stateIt was observed that there was positive and significant increase in area and production in Rajasthan state. The compound growth rate of area and production of state increased significantly at 8.20 percent and 8.30 percent respectively. The compound growth rate in area and production in Sriganganagar district was also recorded positive. It was 8 and 4.10 percent respectively. But

compound growth rate of area was found non-significant in Sriganganagar, however, compound growth was seen significant in production. In case of productivity of potato, the compound growth rate was worked out in negative. It was 0.1 percent for state and 3.20 percent for Sriganganagar district. The significant decrease in productivity of potato was recorded for Sriganganagar district. However, in decrease the productivity in state was noted non-significant.

Table No. 2 Compound growth rates of area, production and productivity of Rajasthan and Sriganganagar district Rajasthan SriganganagarA. AreaCGR 8.20 8.00F- Value 8.44 0.431Significance * NSB. ProductionCGR 8.30 4.10F- Value 9.39 57.121Significance * *C. ProductivityCGR _ 0.1 _ 3.20F- Value 0.002 13.34Significance NS *N.S.: Non significant at 5% level of significance

CONCLUSION

The analysis of data revealed that area under potato in the Rajasthan state was 12514 hectares and Sriganganagar district had 647 hectares which was 5.17 per cent of total area under potato in the state for the year 2014-15. The compound growth rate of area and production of state increased significantly at 8.20 per cent and 8.30 per cent respectively. The compound growth rate in area and production in Sriganganagar district was also recorded positive. It was

8 and 4.10 per cent respectively. But compound growth rate of area was found non-significant in Sriganganagar. however, compound growth was seen significant in production. In case of productivity of potato, the compound growth rate was worked out in negative. It was 0.1 per cent for state and 3.20 per cent for Sriganganagar district. The significant decrease in productivity of potato was recorded for Sriganganagar district. However, in decrease the productivity in state was noted non-significant

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REFERENCESPrasad, Jagdish. 2001. Vegetable Production and

Marketing in Bihar: A Farm Level Study. The Bihar Journal of Agricultural Marketing, 9 (3): 225-44.

Pandit A. and Chandran K.P. 2011. Growth of Potato Production in India a Non–parametric Analysis of Time Series Data, 38 (1): 32-40, 2011.

Pandey, N.K.; Kumar Nalini, Dahiya, P.S. and Srinivas, K. 2004. Economics Analysis of potato Cultivation in District (Himachal Pradesh). Potato Journal, 31(3-4): 171-175.

Pandit, Arun, Pandey N.K., Rajesh, K. Rana, Kumar N.R. and Deka, C.K. 2006. Production of potato in Barpeta District of Assam State. Journal of Agricultural Marketing, 20 (1): 100-110.

Sachin Kumar, Dan Singh and R.P.Singh, 2016. “Assessment of knowledge level of potato growers and their constraints related to potato production technology”. 5 (2): 107-111.

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IMPACT OF FARMERS FIELD SCHOOL (FFS) ON SOYBEAN CROP MANAGEMENT PRACTICES IN TIKAMGARH DISTRICT OF MADHYA PRADESH

Lokendra Parmar1, S.P. Singh2, Sheela Raghuwanshi3 and Kamini Bisht4

1 M.Sc Student, 2,3&4Assistant Professor , Department of Extension Education, JNKVV College of Agriculture, Tikamgarh, M.P.


ABSTRACTA farmer field school is a school without walls. It is a form of adult education, which evolved from the concept that farmers learn optimally from field observation and experimentation. A group of farmers gets together in one of their own fields to learn about their crops and things that affect the system of working. They learn how to farm better by observing, analyzing and trying out new ideas on their own fields. Farmer Field School Approach (FFS) is based on the concepts and principles of people centered learning, and were the answers for themselves. That means the farmers can develop solutions to their own problems and developed as an alternative to the conventional top-down test and verification of the old extension approaches. The present study was conducted in Tikamgarh district of Madhya Pradesh.Tikamgarh block was selected purposively because in the block maximum Farmer Field School were implemented by the Department of Agriculture as compared to other blocks.The statistical sample of the research was 168 (84 participants and 84 non participants). Study revealed that, 51.19 per cent of FFS participants and 44.05 per cent of non FFS participants had ‘knowledge’ about soybean management practices. Regarding attitude towards soybean management practices, 83.33 per cent of FFS farmers belonged to ‘favourable’ attitude category. Whereas, higher percentage of 36.90 percent non FFS farmers have favourable attitude towards improved soybean management practices.

Key words:-Farmer Field School, Knowledge, Attitude, Soybean management practices

These days, the emergence of new paradigms and approaches of extension are shifting towards to the empowerment of farmers.Department of Agriculture in India has recognized the need for innovation in its extension institutions and since the early 1980s has been supporting experimental approaches to meet farmer’s needs.The use of broadcasting media,group approaches and the privatization of services are some of the ongoing initiatives (RasheedSulaiman, 2003 ). A notable innovation introduced in the extension system on a large scale is the Farmer’s Field School (FFS) model.A farmer field school is a school without walls. It is a form of adult education, which evolved from the concept that farmers learn optimally from field observation and experimentation. A group of farmers gets together in one of their own fields to

learn about their crops and things that affect the system of working. They learn how to farm better by observing, analyzing and trying out new ideas on their own fields.Farmer’s Field School (FFS) were implemented in Madhya Pradesh also to motivate the farmers for adoption of improved crop production technology and enhance sustainability of crop production systems.In Madhya Pradesh soybean cultivated in about 62.605 lakh hectare area with the production of 59.475 lakh metric tonnes while, in Tikamgarh district soybean is cultivated in 0.737 lakh hectare area with the production of 0.792 lakh metric tonnes. In soybean cultivation one main short coming is observed that soybean yield in M.P. is low (10.1q/ha. a decade average ) compared to other major soybean growing states (more than 20q/ha) (Source – www.sopa.org

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2013).Farmers Field School (FFS) were implemented for soybean management practices in Tikamgarh district of Madhya Pradesh The present study was undertaken with the objective toassess the impact of knowledge and attitude of FFS members and non members regarding soybean management practices.


The study was conducted in Tikamgarh district of Madhya Pradesh. Tikamgarh district comprises of six blocks, out of six blocks, Tikamgarh block was selected purposively because in the block maximum number of Farmer Field School were implemented by the Department of Agriculture as compared

to other blocks. Three villages namely Kathi, Jamdhar and Manduarwere selected purposively as the Farmer Field School on soybean management practices were conducted in the villages. Out of these three villages,84 FSSparticipants and 84 non participants were selected randomly as respondents. Thus, total sample size comprised of 168respondents. The data were collected and recorded using pre tested and well structured interview schedule.Secondary data was collected from source of reports and documents.Frequencies, percentage and meanpercent score, standard deviation, t- test and correlation analysis were used for analyzing the data statistically.

RESULT AND DISCUSSION

Table 1.Distribution of the soybean growers according to their socio economic& personal variables:-

Characteristics CategoriesFrequency of soybean growers

Participant % Non participant %

Age

Young 23 27.38 26 30.95Middle 35 41.67 27 32.15Old 26 30.95 31 36.90Total 84 100.00 84 100.00

Education

Illiterate and formal 21 25.00 38 45.24

Medium education 30 35.71 25 29.76Higher education 33 39.29 21 25.00Total 84 100.00 84 100.00

Family size

Small 25 29.76 32 38.10Medium 31 36.90 30 35.71Large 28 33.34 22 26.19Total 84 100.00 84 100.00

Annual income

Low 24 28.57 33 39.29Medium 26 30.95 30 35.71High 34 40.48 21 25.00Total 84 100.00 84 100.00

Land holding

Small 22 26.19 35 41.67Medium 30 35.71 29 34.52Large 32 38.10 20 23.81Total 84 100.00 84 100.00

Farm power

Low 21 25.00 39 46.43Medium 32 38.10 26 30.95High 31 36.90 19 22.62Total 84 100.00 84 100.00Low 24 28.57 41 48.81

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Farming experience

Medium 26 30.95 26 30.95High 34 40.48 17 20.24Total 84 100.00 84 100.00

Socio-economic and personal characteristics of the participants and non-participants in FSS :-The study revealed that in case of participant of FFS the higher numbers of the soybean growers (41.67%) were of middle age group followed by old age group (30.95%) and young age group (27.38%) respectively. In case of non participant (36.90%) found to old age group followed by middle age group (32.15%) and young age group (30.95%) respectively. In addition, the results show that the most of the participants were belongs to(39.29%) higher education group followed by medium education group (35.71%) than illiterate and formal education group (25.00%) respectively.In case of non participant, higher numbers of the soybean growers (45.24%) were found to illiterate and formal education group followed by medium education group (29.76%) and higher education group (25.00%) respectively.Theperusal of data indicates thatthe participant of FFSwere belongs to (36.90%) medium family size group followed by large family size group (33.34%) and small family size group (29.76%) respectively. In case of non participant, higher numbers of the soybean growers (38.10%) found to small family size group followed by medium family size group (35.71%) and large family size group (26.19%) respectively. The results shows thatthe most of the participants (40.48%) were of high level of income followed by medium level of income

(30.95%) and low level of income (28.57%) respectively. In case of non participantt(39.29%) were of low level of income followed by medium level of income (35.71%) and high level of income (25.00%).Data shows that land holding of participant, the higher number of soybean growers (38.10%) belongs to large size of land holding followed by medium size of land holding (35.71%) and small size of holding (26.19%) respectively. In non participant, the higher number of respondents (41.67%) belongs to small size of land holding followed by medium size of land holding (34.52%) and large size of land holding (23.81%) .The data regardingfarm powershows thatparticipant of FFS (38.10%) possess medium number of farm power followed by high number of farm power (36.90%) and low number of farm power (25.00%) .In non participant, the higher number of soybean growers (46.43%) possess low number of farm power followed by medium number of farm power (30.95%) and high number of farm power (22.62%) .The data revealed that in case of participant, the higher number of soybean growers (40.48%) possess high farming experience followed by medium farming experience (30.95%) and low farming experience (28.57%) respectively. In case of non participant, the higher number of soybean growers (48.81%) possess low farming experience followed by medium farming experience (30.95%) and high farming experience (20.24%)

Table 2.Distribution of the soybean growers according to their psychological variables.



Information seeking behaviour

Low 25 29.76 34 40.48Medium 27 32.14 29 34.52High 32 38.10 21 25.00

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Total 84 100.00 84 100.00

Information sharingbehaviour


Achievement motivation


Level of aspiration


Psychological variables of FSS participants and non-participants :-The data presented in Table 2 revealed that in case of participant (38.10%) have high information seeking behaviour followed by medium information seeking behaviour (32.14%) and low information seeking behaviour (29.76%) respectively. In case of non participant (40.48%) have low information seeking behaviour followed by medium information seeking behaviour (34.52%) and high information seeking behaviour (25.00%). Data showed that the higher number ofparticipant (38.10%) have high information sharing behaviour followed by medium information sharing behaviour (35.71%) and low information sharing behaviour (26.19%) In case of non participant(44.05%) have low

information sharing behaviour followed by medium information sharing behaviour (30.95%) and high information sharing behaviour (25.00%).Data revealed that the higher number of participant(36.90%) have medium achievement motivation followed by high achievement motivation (34.53%) and low achievement motivation (28.57%).In case of non participant(38.10%) have low achievement motivation followed by medium achievement motivation (34.52%) and high achievement motivation (27.38%)that in case of (participant) soybean growers, the higher number of soybean growers (40.48%) have medium level of aspiration followed by high level of aspiration (32.14%) and low level of aspiration (27.38%).

Table 3: Communicational variablesof FSS participants and non-participants :-



Mass media exposure


Extension Participation


Social Participation


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The data presented in Table 3 revealed that in case of participant (47.62%) have medium mass media exposure followed by low mass media exposure (32.14%) and high mass media exposure (20.24%) In case of non participant (40.48%) have low mass media exposure followed by medium mass media exposure (34.52%) and high mass media exposure (25.00%).Data showed that the higher number ofparticipant(36.90%) were of medium level of extension participation followed by high level of extension participation (35.72%) and low level of extension participation (27.38%) In case of non participant (36.90%) were of low level of extension participation followed by medium (34.53%) and high level of extension participation (28.57%)The data interpreted thatthe higher number ofparticipant (41.67%) were of high level of social participation followed by medium level of social participation (29.76%) and low level of social

participation (28.57%) In case of non participant (38.10%) were of low level of social participation followed by medium level of social participation (36.90%) and high level of social participation (25.00%)A) Impact of FFS on knowledge of soybean management practices.The data regarding knowledge of participant and non-participant soybean growers under Farmer Field School (FFS) programme has been analyzed and presented in Table1.

Table 4. Knowledge level of FFS participants and non-participants about soybean production practices:

Soybean Production Practices

Participant(n=84)(Frequency) (%)

Non- Participant (n=84)(Frequency) (%)

No Knowledge Knowledge No

Knowledge Knowledge

Varieties of soybean 34(40.48) 50*(59.52) 39(46.43) 45(53.57)Sowing time 49(58.33) 35(41.67) 54(64.29) 30(35.71)Spacing in the field 37(44.05) 47*(55.95) 41(48.81) 43(51.19)Seed rate (/ha.) 39(46.43) 45*(53.57) 41(48.81) 43(51.19)Seed treatment 45(53.57) 39(46.43) 49(58.33) 35(41.67)Seed inoculation 36(42.86) 48*(57.14) 37(44.05) 47(55.95)FYM to be applied (/ha.) 48(57.14) 36(42.86) 55(65.48) 29(34.52)Chemical fertilizer (/ha.) 49(58.33) 35(41.67) 50(59.52) 34(40.48)Time of application of fertilizer 45(53.57) 39(46.43) 48(57.14) 36(42.86)

Integrated water management 30(35.71) 52*(61.90) 37(44.05) 47(55.95)Integrated weed management 34(40.48) 50*(59.52) 40(47.62) 44(52.38)Integrated pest management 37(44.05) 47*(55.95) 54(64.29) 30(35.71)Integrated disease management 51(60.71) 33(39.29) 61(72.62) 23(27.38)

41(48.81)

43**(51.19)

47(55.95)

37(44.05)

Calculated t’ value= 2.82 and table t’ value= 2.17 * Significant at 5% level of significance with 12 d.f.;** More than average value figure in parentheses shows per cent to total

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Thirteen items of improved soybean management practices were considered to examine the knowledge level of participants and non-participants under Farmer Field School. Data shows that about half of the (51.19%) FFS participants have knowledge whereas near about half (48.81%) have no knowledge about overall improved soybean management practices. In case of non-participants55.95 per cent of the respondents had no knowledge whereas 44.05 per cent of soybean growers had knowledge about overall improved soybean management practices. The data also shows that the higher number of participant soybean growers have higher than average value of knowledge regarding “integrated water management” (61.91%) followed by “varieties of soybean” and “integrated weed management” (59.52% each), “seed inoculation” (57.14%), “spacing in the field” and “integrated pest management” (55.95% each), “seed rate” (53.57%).On the other hand, the data shows that the higher number of participant soybean growers were having average knowledge regarding “seed treatment” and “time of application of fertilizer” (46.43% each) followed by “FYM to be applied (/ha.)” (42.86%), “sowing time” and “chemical fertilizer (/ha.)” (41.67% each) and “integrated disease management” (39.29%).The calculated ‘t’ value 2.82 at 5 per cent level with 12 d.f. was higher than the table value of ‘t’ 2.17. This was declared to be significant. Therefore it may be concluded that, there was a significant difference between knowledge level of FFS participants and non-participants.

Tomar and Sharma (2002), Ortiz et al. (2004), Kadamet al. (2005) and George and Hegde (2009) also in their studiesconcluded that training given by

Farmer Field School personnel created a positive impact on knowledge of participating soybean growers in making them knowledgeable..B) Impact of FFS on attitude of soybean management practices.Thirteen statements were considered to examine the attitude of soybean growers of FFS participants and non-participants. Data shows that the higher number of participants (70 out of 84) hadfavourable attitude whereas 14 FFS participants hadunfavourable attitude towards overall improved soybean management practices. In case of non-participant data shows that the higher number of respondents (53) hadunfavourable attitude and 31 respondents were having favourable attitude towards overall improved soybean management practices. This implies that less percentage ofnon-participants(36.90%) hadfavourable attitude towards improved soybean management practices.The data shows that the higher number of participant soybean growers have higher than average value of attitude as favourable regarding “FFS helps establishing farmers to farmers information network” (90.48%) followed by “The information provided through FFS are dependable, practical and can be trusted to be accurate as possible” (88.10%), “FFS supply information about agriculture service” (86.90%), “in FFS the emphasis is on demonstrating improved agriculture technology to farmers“(86.90%), “FFS help farmers in effective decision making” (86.90%), “member participating in FFS have better economic condition than non member” (86.90%), “FFS are more efficient as compared to other information services provided by agriculture Institution” (85.71%).

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Table 5.Impact of FFS on attitude of soybean management practices.

Statements of Attitude

Participant(n=84)(Frequency) (%)

Non- Participant (n=84)(Frequency) (%)

Unfavourable Favourable

Unfavourable Favourable

FFS motivate farmers to adopt new technology

21(25.00)

63(75.00)

40(47.62)

44(52.38)

FFS help farmers to acquire needed knowledge

15(17.86)

69(82.14)

55(65.48)

29(34.52)

FFS supply information about agriculture Service

11(13.10)

73*(86.90)

46(54.76)

38(45.24)

FFS serve as a link between govt. and farmers

18(21.43)

66(78.57)

55(65.48)

29(34.52)

In FFS the emphasis is on demonstrating improved agriculture technology to farmers

11(13.10)

73*(86.90)

45(53.57)

39(46.43)

FFS help farmers in effective decision making

11(13.10)

73*(86.90)

57(67.86)

27(32.14)

FFS provide possible solution to the farmers problems

19(22.62)

65(77.38)

54(64.29)

30(35.71)

Member participating in FFS have better economic condition than non member

11(13.10)

73*(86.90)

49(58.33)

35(41.67)

FFS does not creates self employment to the farmers

14(16.67)

70(83.33)

53(63.10)

31(36.90)

The latest agriculture Information can be transferred to farmers through FFS programme

20(23.81)

64(76.19)

61(72.62)

23(27.38)

FFS are more efficient as compared to other information services provided by agriculture Institution

12(14.29)

72*(85.71)

54(64.29)

30(35.71)

The information provided through FFS are dependable, practical and can be trusted to be accurate as possible

10(11.90)

74*(88.10)

60(71.43)

24(28.57)

FFS helps establishing farmers to farmers information network

8(9.52)

76*(90.48)

66(78.57)

18(21.43)

14(16.58)

70*(83.33)

53(63.10)

31(36.90)

Calculated ‘t’ value= 3.95 and table ‘t’ value= 2.17 * Significant at 5% level of significance with 12 d.f.;** More than average valueOn the other hand, the data shows that the higher number of participant soybean growers had less than average value of attitude as favourable regarding “FFS does not creates self employment to the farmers” (83.33%) followed by “FFS help farmers to acquire needed knowledge” (82.14%), “FFS serve as a link between govt. and farmers” (78.57%), “FFS provide possible solution to the farmers problems” (77.38%), “the latest agriculture information can be transferred to farmers through FFS programme” (76.19%) and “FFS

motivate farmers to adopt new technology” (75.00%).The calculated‘t’ value 3.95 at 5 per cent level with 12 d.f. was higher than the table value of‘t’ 2.17.This was declared to be significant. Therefore it may be concluded that, there was a significant difference between attitude of participant and non participant regarding soybean management practices under Farmer Field School. Sharma and Khan (1997), Velusamy and Manoharam (1999) and Prasad (2002) also concluded in their studies that, the

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higher attitude towards improved soybean management practices of participant was due to training programme, higher education, high extent of participation in extension and social activities and mass media utilization which might have contributed indicated their overall attitude.

CONCLUSION

The study concluded that all the participants of FSS had high information seeking and information sharing behaviour and medium level of achievement motivation and aspiration level while non participants had low level of information seeking

behaviour,information sharing behaviour ,achievement motivation level and aspiration level . Amongst the communicationl variablesparticipants of FSS had medium level of extension participation and mass media media exposure and high level of social participation, while non participants of FSS had low level of extension participaton, mass media exposure and social participation. There was a significant difference between knowledge andattitude of participant and non participant regarding soybean management practices under Farmer Field School.

REFERENCESGeorge S and Hegde MR. 2009. Impact of

farmers filed school in Tomato. Asian J.Extn.Educ. 27(2):67.72.

Gupta AK and Shrivastava JP. 2002. Technological gap in soybean cultivation. Bioved. 13(1&2):145-146.

KadamRP ,Wangikar SD , Pawar GS and Bhosale PB. 2005. Knowledge level of farmers about improved soybean production technology. J. Soil Crops. 15(1):210-212.

Kumar NaikYeshwanth LG. 2008. A study on knowledge and adoption of integrated crop management practices by the participants of farmers filed school in Bellary district. M.Sc. (Agri.) Thesis, University of Agriculture, Science, Dharwad, Karnataka, India.

M.P.Singh, Ajay Kumar, R.P.Sahu, J.P.Srivastava, 2016. “Impact of frontline demonstrations on farmers adoption behaviour”. 5 (1): 131-133.

PanwarMP ,Pande AK and Sanoria YC. 2000. Knowledge and adoption of soybean production technology among farmers. Madhya Journal of Extension Education. 2 &3:44-47.

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PRODUCTIVITY ENHANCEMENT OF BLACKGRAM (Vigna mungo l.) THROUGH IMPROVED PRODUCTION TECHNOLOGIES AT FARMERS’

FIELD IN TRIBAL BELT OF RAJASTHAN

Bheroo Singh Bhati*, Ramawtar, R.L. Soni and Ranjeet SinghKrishi Vigyan Kendra, MPUAT, Banswara-327001, India

*E-mail: [email protected]: 20.12.16Accepted: 12.02.17

ABSTRACTBlackgram (Vigna mungo L.) is an important pulse crop in Rajasthan. One of the major constraints of its low productivity is non-adoption of improved technologies. Front line demonstrations were conducted at 200 farmers’ fields, to demonstrate production potential and economic benefits of improved technologies comprising yellow vein mosaic resistant varieties (PU-31, PU-1, Azad Urd-3), line sowing (30x10 cm), integrated nutrient management (20 : 40 : 20, NPK kg/ha + Rhizobium + PSB@20 g/kg seed) and weed removal (at 25 days after sowing) at Banswara district of Rajasthan which comes under southern sub-humid plain zone IV B during kharif seasons 2013 to 2016 in rainfed conditions . The improved technologies recorded a mean yield of 6.15 q ha-1 which was 102 per cent higher than that obtained with farmer’s practice yield of 3.08 q ha-1. The improved technologies resulted higher mean net income of Rs. 15116 per ha with a benefit cost ratio of 2.03 as compared to local practice (Rs. 1625 per ha and 1.10)

Key words: Blackgram, frontline demonstrations, improved technologies, net return, productivity Blackgram (Vigna mungo L.) is important pulse crop of India. Being of short duration, this crop fits well in different cropping systems and is grown under mono, mixed and multiple cropping system during rainy, spring season and summer seasons under wide range of agro climatic conditions, contributing 10.86 % ( 3.06 m ha) and 9.28% (1.70 m tonnes) to the total pulses area and production, respectively of the country with a productivity of 555 kg/ha during 2013-14. Rajasthan is among six major blackgram growing states viz. Maharashta, Madhya Pradesh, Andhra Pradesh, Orissa, Tamil Nadu and Rajasthan. In Rajasthan it is grown on 196 thousand ha area with production of 70.6 thousand tonnes and productivity of 360 kg/ha. In Rajasthan its cultivation is mainly confined to Ajmer, Chittorgarh, Kota, Tonk, Sawai Madhopur, Jhalawar, Banswara and Dungarpur districts.In Banswara blackgram is cultivated in 11 thousand ha with production of 3.85 thousand tonnes. The average

productivity of blackgram in the district continues to be lower (350 kg /ha) than expected from improved technology, mainly due to its cultivation on marginal lands, under poor management and without inputs except seed. The major constraints responsible for lower yield are inappropriate production technologies viz, broadcast method of sowing, mungbean yellow vein mosaic virus (MYMV) susceptible varieties, no use of fertilizers and untimely weed management (45 DAS). Shrivastava and Shrivastava (1995) and Das et al. (1998) reported that yield of blackgram can be increased by improved variety, recommended dose of fertilizer, weed management and plant protection. Keeping this in view, front line demonstrations on blackgram were conducted to demonstrate the production potential and economic benefits of latest improved technologies on farmer’s fields.


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In the present study performance of improved technologies of Blackgram against local check was evaluated through front line demonstration at farmers’ field during kharif season 2013, 2014, 2015 and 2016 in rainfed conditions. A total of 200 demonstrations were laid on 80 hectare area in 22 villages across 4 blocks (Bagidaura, Ghatol, Anandpuri & Sajjangarh) of Banswara district. The district has sub humid agro climatic condition with average temperature of the district varies from 21.3 – 40.60C in summer and 9.5 – 34.90C in winter and annual rainfall is about 900 mm Soil of the area is medium red loamy soil.Each demonstration was conducted on an area of 0.4 ha and the same area adjacent to the demonstration plot was kept as farmer’s practices. The package of improved technologies included improved varieties, line sowing, integrated nutrient management and timely weed removal. The varieties of blackgram PU-31, PU-1 and Azad Urd-3 were included in the demonstrations. The spacing was 30 x 10 cm sown between July 6 – July 10 in 2013, July 17 – July 22 in 2014, June 21 – June 30 in 2015 and June 29 to July 4 in 2016 with a seed rate of 20 kg ha-1. Entire dose of N and P through urea and single super phosphate and K through muriate of potash @ 20 : 40 : 20 kg/ha, respectively, was applied as basal dose before sowing. The seeds were treated with Trichoderma viridae @ 5 g/kg seed than inoculated with Rhizobium and Phosphate – solubilizing bacteria each @ 20 g/kg of seeds. Hand weeding was done once at 25 days after sowing. The crop was harvested during September 9 to October 18, every year as per the maturity.Results and DiscussionEffect of season A total rainfall of 1053.5, 629, 674.6 and 1329 mm was received in 66, 33, 25 and 42 days during

2013, 2014, 2015 and 2016, respectively. The blackgram crop received 263.1 m.m. rains in 2013 at grain filling to maturity stages (18 August to 24 September). This caused seed sprouting in the pod itself in standing crop condition which lowered the productivity in 2013. The farmer also faced difficulty in harvesting the mature crop due to inter mittent rains.Grain yield The productivity of blackgram in Banswara district of Rajasthan under improved production technologies ranged between 3.70 and 10.10 with a mean yield of 6.15 q/ha. The productivity under improved technologies varies from 3.75 to 8.75, 4.25 to 8.70, 3.70 to 8.10 and 4.40 to 10.10 q/ha with a mean yield of 5.40, 6.50, 5.80 and 6.90 q/ha during 2013, 2014, 2015 and 2016, respectively (Table 1) as against a yield range between 2.47 and 3.70 with a mean of 3.08 q/ha under farmer’s practices (local check). The additional yield under improved technologies over local practice ranged from 2.60 to 3.55 q/ha with a mean of 3.07 q/ha. In comparison to local practice, there was an increase of 119, 120, 81 and 86% in productivity of blackgram under improved technologies in respective years. The increased grain yield with improved technologies was mainly because of line sowing, use of MYMV resistant varieties, integrated nutrient management and timely weed management. Nazrul Islam et al (2004) reported that adoption of improved variety increased productivity by 35 per cent than local variety of blackgram. Singh et al (1999) obtained increased (9%) yield of blackgram due to line sowing (30 x 10 cm) over broadcasting method of sowing. Tomar (1998) reported that the application of balanced fertilizers (20 : 60 : 20 NPK kg/ha) along with PSB increased yield of blackgram by 97% over no fertilizer application. Hand weeding once at 25 days after

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sowing produced 57% more yield over no weeding (Yadav and Shrivastava 1998).Net return The economic viability of improved technologies over traditional farmer’s practices was calculated depending on prevailing prices of inputs and output costs (Table 2). It was found that cost of production of blackgram under improved technologies varied from Rs. 12,000 to 17,300 / ha with an average of Rs. 14,450/- ha as against Rs. 10,500 to 16,000 / ha with an average of Rs. 13,275/ ha in local practice. The improved production technologies registered an additional cost of production ranging from 900 to 1500 with a mean of Rs. 1,175 / ha over local check. The additional cost incurred in the improved technologies was mainly due to more costs involved in balanced fertilization, improved seed and weed management practices. Cultivation of blackgram under improved technologies gave higher net return which ranged from

Rs. 11,220 to 22,720 / ha with a mean value of Rs. 15,116 / ha as compared to local check which recorded Rs. 121 to 5,460 with a mean value of Rs. 1,625 / ha. There was an additional net return of Rs. 11,099 in 2013, 14,897 in 2014, 10,700 in 2015 and 17,260 in 2016 under demonstration plots. The improved technologies also gave higher benefit cost ratio of 1.94 , 2.08, 1.79 and 2.31 compared to 1.01, 1.01, 1.06 and 1.34 under local check in the corresponding seasons. This may be due to higher yields obtained under improved technologies compared to local check (farmer’s practice). This finding is corroboration with the finding of Mokidue et al. (2011), Tomar (2010).The results from the current study clearly indicates the potential of improved production technologies in enhancing blackgram production and economic gains in tribal belt of Rajasthan.

Table 1. Seed yield of blackgram as affected by improved and local practices in farmer’s fieldsYear Area

(ha)

Dem

onst

ratio

n (N

o.)

Yield (q/ha)Improved Technology

Local check (q/ha)

Additional yield (q/ha) over Local check

Per cent increase in yield over local check

Maximum Minimum Average

2013 20 50 8.75 3.75 5.40 2.47 2.93 1192014 20 50 8.70 4.25 6.50 2.95 3.55 1202015 20 50 8.10 3.70 5.80 3.20 2.60 812016 20 50 10.10 4.40 6.90 3.70 3.20 86Average 200 8.91 4.02 6.15 3.08 3.07 102

Table 2. Cost of cultivation (Rs/ha), net return (Rs/ha) and benefit : cost ratio of blackgram as affected by improved and local practicesYear Total cost of

Cultivation (Rs/ha)

Gross return (Rs/ha)

Net return (Rs/ha)

B:C ratio Additional cost of cultivation (Rs/ha)

Additional net return (Rs/ha)Improved

technology

Local check

Improved technology

Local check

Improved technology

Local check

Improved technology

Local check

2013 12000 10500 23220 10621 11220 121 1.94 1.01 1500 110992014 13900 13000 28925 13128 15025 128 2.08 1.01 900 148972015 14600 13600 26100 14400 11500 800 1.79 1.06 1000 107002016 17300 16000 40020 21460 22720 5460 2.31 1.34 1300 17260

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Average 14450 13275 29566 14900 15116 1625 2.03 1.10 1175 13439Sale rate of blackgram seed in 2013 = Rs. 4300/q., 2014 = Rs. 4450/q., 2015 = Rs. 4500/q. and 2016 = Rs. 5800/q.

REFERENCESDas P, Das SK, Mishra PK, Mishra A and

Tripathi AK. 1998. Farming system analysis of results of front line demonstration on pulse crops conducted in different agro-climatic zone of M.P. and Orissa ZUC for TOT Projects Zone VII, Jabalpur pp-37.

Mokidue I, Mohanty A K and Sanjay K. 2011. Correlating growth, yield and adoption of urdbean technologies. Indian Journal of Extension Education 11 (2): 20-24

Nazrul Islam M, Rezual Karim Md and Safigual Islam QM. 2004. Economic performance of BARI Mash 1 (Improved variety of blackgram) with traditional variety at farmer’s field of Bangladesh Asian Journal of Plant Sciences 3 (2): 247-250.

Shrivastava GP and Shrivastava VC. 1995. Contribution of production factors in

growth, yield and economics of blackgram. Indian Journal of Agronomy 40 (1): 64-69.

Singh NK, Singh NP, Sharma BB and Sahu JP. 1999. Effect of non-monetary inputs on sustained productivity of urdbean (Phaseolus mungo). Indian Journal of Agronomy 44 (4): 773-777.

Tomar RKS. 1998. Effect of phosphate- solubilizing bacteria and farm yard manure on the yield of blackgram (Phaseolus mungo). Indian Journal of Agricultural Sciences 68 (2): 81-83.

Tomar RKS. 2010. Maximization of productivity for chickpea (Cicer arietinum Linn.) through improved technologies in farmer’s fields. Indian Journal of National Products and Resources 1 (4): 515-517

Yadav RP and Shrivastava UK. 1998. Integrated weed management in blackgram (Phaseolus mungo). Indian Journal of Agronomy 43 (1): 106-109.

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IMPACT OF POTASSIUM NITRATE FOLIAR SPRAY ON PRODUCTIVITY OF Bt COTTON

Harphool Meena, P.K.P. Meena and B.L. Kumhar AICRP on Irrigation Water Management, ARS, Ummedganj Farm

Agriculture University, Kota-324001, Rajasthan Email: [email protected]


ABSTRACTAn experiment was conducted at Agricultural Research Station, Borwat Farm, Banswara during kharif -2010, 2011 and 2012 to find out the optimum quantity of foliar spray of KNO 3 in Bt cotton under various KNO3 foliar spray treatments. Significantly higher seed cotton yield (2761 kg ha -1) was recorded under three foliar sprays of 3 per cent KNO3 over control, application of MOP in four splits RD-K, full dose of MOP at sowing and remaining foliar spray of KNO 3 treatments. However, it was found at par with four foliar sprays of 3 per cent KNO3 seed cotton yield (2816 kg ha-1) and four foliar sprays of 2 per cent KNO3 seed cotton yield (2612 kg ha-1), respectively.

Key words: Bt cotton, foliar spray, KNO3 and seed cotton yield.

India is important grower of cotton on a global scale. Cotton is an important commercial crop in India and plays a vital role in our economy. It is known as white gold and queen of fibres. It is an important cash crop of global significance which plays a dominant role in world agriculture and industrial economy. Cotton enjoys a predominant position amongst all of India’s cash crops; its production in 2013-14 increased 3.5 times from the previous decade, reaching a peak of 39 million bales with a productivity of 565.4 kg ha–1

(Anon., 2014), which indicates that, there is an ample scope to boost its productivity by adopting improved agronomic practices. The nutrient management is the key of agronomic management practice in irrigated condition. Generally, nutrients are supplied to the plant through soil application. But, plants are also showing more response to foliar application of nutrients with better absorption through foliage. Since, cotton is being a long duration crop and due to its indeterminate type of growth habit, it requires nutrients throughout the crop growth period. Besides, Bt cotton is retaining more

number of square and bolls due to its inherent resistance capacity against the bollworm, which could leads to imbalance in the source and sink relationship. For which, plant needs more and continuous supply of nutrients at peak flowering and boll development stages. So, foliar nutrition would helps in better retention of squares, flowers and boll development would results in higher yield. But, meager information on foliar application of KNO3 is available in Rajasthan area. The factors which have a direct influence on seed cotton yield are yield components viz., total number of bolls per plant, number of good opened bolls per plant and mean boll weight. In the present investigation, all the yield attributing characters increased significantly due to foliar application of KNO3 treatments, which are directly related to physiological processes in plant and helped in increasing the production by improving the reproductive phase and might have contributed for increased yield, noticed with the foliar spray of KNO3 treatments. In this context, the present investigation was carried out at Agricultural Research Station, Borwat Farm, Banswara “Impact of potassium

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nitrate foliar spray on productivity of Bt cotton”.


The field experiment was conducted for three consecutive crop season kharif -2010, 2011 and 2012 at Agricultural Research Station, Banswara. Nine foliar spray treatments comprised i. e. (two foliar sprays of 2 per cent KNO3, three foliar sprays of 2 per cent KNO3, four foliar sprays of 2 per cent KNO3, two foliar sprays of 3 per cent KNO3, three foliar sprays of 3 per cent KNO3, four foliar sprays of 3 per cent KNO3, MOP in four splits RD-K, full dose of MOP at sowing and control) in Randomized Block Design with three replications. Experimental field was well prepared by two ploughing followed by harrowing & cultivator and one planking for uniform levelling were performed for sowing of cotton. The soil was medium in available nitrogen (245 and 253 kg/ha) and phosphorus (48.98 and 50.10 kg/ha) and high in available potassium (326 and 329 kg/ha) during both the years. The crop was sown in first week of June by dibbling 2-3 seeds per hills. Sprays of KNO3 were done at 30, 45, 60 and 75 DAS of cotton. Full dose of phosphorus was applied before sowing, while nitrogen dose was given in two splits i.e. first half at the time of thinning and remaining half at flowering stage. Spray of KNO3 was done at thinning, square formation, flowering and boll development stage as per treatments and production and protection measures were applied as per package of the zone IV b.


Growth Parameters: Three years pooled data (Table.1) shows that the foliar sprays of KNO3 significantly influences plant growth parameters. The maximum plant height (96.57 cm) and sympodial branches plant-1 (20.85) were observed under three

sprays of 3 per cent KNO3 over control, application of MOP in four splits RD-K, full dose of MOP at sowing and remaining spray of KNO3 treatments. However, it was found at par with four sprays of 3 per cent KNO3 plant height (98.52 cm) and sympodial branches plant-1 (21.33) and four sprays of 2 per cent KNO3 plant height (92.97 cm) and sympodial branches plant-1 (20.28), respectively. Significantly higher monopodial branches plant-1 was recorded in all the treatments as compared to control in the pooled analysis. Similarly, Kumar et al., (2011), reported highest seed cotton and lint yield were observed with four foliar spray of KNO3 2 per cent, whereas, in an another study conducted at Guntur by Narayana et al., (2011), revealed that application of 100 per cent RDF based on soil test values plus two sprays of 2 per cent KNO3 each at flowering and boll development stage recorded the highest plant height and sympodial branches plant-1 this might be due to better absorption and utilization of foliar applied nutrients at critical stages of cotton growth.Yield attributes: It is evident from pooled data (Table.2) the yield attributes of Bt cotton were significantly influence by foliar spray of KNO3. The maximum bolls plant-1(34.21) and boll weight (3.93 g) were observed under three spray of 3 per cent KNO3 over control, application of MOP in four splits RD-K, full dose of MOP at sowing and remaining spray of KNO3 treatments. However, it was found at par with four spray of 3 per cent KNO3

bolls plant-1(35.49) and boll weight (4.03 g) and four spray of 2 per cent KNO3 bolls plant-1(33.13) and boll weight (3.84 g), respectively during both the years as well as in pooled analysis. Higher growth and growth attributes were reported in Bt cotton with three foliar application of micronutrient along with RDF by Ravikiran et al. (2012), Rajendran et al. (2011) and Hosmath (2011). The reason

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for increase in growth components might be due to which might have increased the photosynthetic activity, enzyme activity and other biochemical process.Seed cotton yield: The results of the experiment indicated that foliar spray of potassium nitrate has greatly influenced the seed cotton yield in all the 3 years. The pooled results (Table 2) over 3 years indicated that, higher yield of three spray of 3 per cent KNO3 seed cotton yield (2816 kg ha-1), four spray of 3 per cent KNO3 (2761 kg ha-1) and four spray of 2 per cent KNO3 (2612 kg ha-1) were found at par with each other but these treatment gave significantly higher seed cotton yield over control (1786 kg ha-1) , application of MOP in four splits RD-K

(2315 kg ha-1) , full dose of MOP at sowing (2364 kg ha-1) and two spray of 3 per cent KNO3 (2309 kg ha-1), three spray of 2 per cent KNO3 (2136 kg ha-1) and two spray of 2 per cent KNO3 (2085 kg ha-1) during both the years as well as in pooled analysis. Similar work related to the present investigation was also carried out by Deshmukh et al., (2011), Durgude et al., (2014), Gadhiya et al., (2009) and Sakarvadia et al., (2012).

CONCLUSION

It can be concluded that the three foliar sprays of 3 per cent KNO3 gave higher seed cotton yield (2816 kg ha-1) but it was found at par with four spray of 3 per cent KNO3 and four spray of 2 per cent KNO3.

Table: 1 Effect of foliar spray of potassium nitrate on growth parameters of Bt cottonTreatment Plant height (cm) Monopodial branches / plant Sympodial branches /

plant

2010 2011 2012 Pooled 2010 2011 2012 Pooled 2010 2011 2012 Pooled

2 % KNO3 (2 sprays) 84.32 87.30

87.01 86.21 1.27 1.36 1.30 1.31 14.0

316.9

216.9

0 15.95

2 % KNO3 (3 sprays) 85.60 88.02

87.89 87.17 1.30 1.39 1.33 1.34 14.6

017.8

817.2

2 16.56

2 % KNO3 (4 sprays) 91.56 95.24

92.13 92.97 1.35 1.45 1.40 1.40 17.7

321.1

221.9

9 20.28

3 % KNO3 (2 sprays) 87.40 90.50

90.78 89.56 1.34 1.42 1.38 1.38 15.6

718.9

518.6

2 17.74

3 % KNO3 (3 sprays) 94.24 97.45

98.02 96.57 1.39 1.49 1.48 1.45 18.2

322.40

21.94 20.85

3 % KNO3 (4 sprays) 96.70 99.02

99.85 98.52 1.47 1.52 1.50 1.49 19.0

022.8

922.1

1 21.33

MOP in four splits RD-K 86.05 89.22

89.84 88.37 1.36 1.37 1.38 1.37 15.1

118.0

318.4

6 17.20

Full dose of MOP at sowing 85.90 87.6

488.2

0 87.24 1.35 1.37 1.39 1.37 15.29

18.63

18.89 17.60

Control 78.60 81.00

80.45 80.01 1.20 1.24 1.26 1.23 11.4

514.3

114.2

5 13.33

SEm + 1.82 1.91 1.65 1.65 0.05 0.07 0.04 0.05 0.82 0.80 0.85 0.76CD (p=0.05) 5.58 5.80 5.10 4.98 0.16 0.20 0.14 0.15 2.46 2.39 2.52 2.30

Table: 2 Effect of foliar spray of potassium nitrate on yield attributes and seed cotton yield of Bt cottonTreatment Boll plant-1 Boll weight (g) Seed cotton yield (kg ha-1)

2010 2011 2012 Pooled 2010 2011 2012 Poole

d 2010 2011 2012

Pooled

2 % KNO3 (2 sprays) 20.09 30.01 31.05 27.05 3.29 3.52 3.48 3.43 1398 2439 241

8 2085

2 % KNO3 (3 20.89 30.67 31.16 27.57 3.30 3.67 3.56 3.51 1442 2474 249 2136

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sprays) 12 % KNO3 (4 sprays) 25.86 37.33 36.21 33.13 3.59 3.98 3.96 3.84 1725 3069 304

2 2612

3 % KNO3 (2 sprays) 22.41 34.06 34.67 30.38 3.37 3.76 3.62 3.58 1503 2705 272

0 2309

3 % KNO3 (3 sprays) 26.38 38.33 37.93 34.21 3.70 4.09 4.00 3.93 1858 3278 314

8 2761

3 % KNO3 (4 sprays) 28.35 39.24 38.90 35.49 3.73 4.17 4.19 4.03 1901 3342 320

4 2816

MOP in four splits RD-K 22.38 34.00 34.24 30.20 3.36 3.74 3.64 3.58 1498 2708 273

9 2315

Full dose of MOP at sowing 22.42 34.13 34.33 30.29 3.38 3.79 3.66 3.61 1520 2789 278

3 2364

Control 16.22 24.08 24.60 21.63 3.20 3.32 3.25 3.26 1251 2053 2055 1786

SEm + 1.08 1.22 1.17 1.07 0.09 0.08 0.11 0.09 109 116 112 104CD (p=0.05) 3.34 3.70 3.54 3.25 0.29 0.26 0.32 0.28 326 355 340 310

REFERENCESAnonymous. 2014. Action Plan of NFSM -

Commercial Crops Cotton (2014-15). Ministry of Agriculture, Department of Agriculture and Cooperation, India. p. 1-10.

Deshmukh, S.V., Kudtarkar, U.S., Gaikwad, S.P. and Patil, K.B. 2011. Yield and nutrient uptake of kharif Bt cotton as influenced by conjoint use of FYM and chemical fertilizers. Adv. Res. J. Crop Improv., 2 (1) : 115-120.

Durgude, A.G., Kadam, S.R. and Pharande, A.L. 2014. Effect of soil and foliar application of ferrous sulphate and zinc sulphate on nutrient availability in soil and yield of Bt cotton. Asian J. Soil Sci., 9(1): 82-86.

Gadhiya, S.S., Patel, B.B., Jadav, N.J., Pavaya, R.P., Patel, M.V. and Patel, V.R. 2009. Effect of different levels of nitrogen, phosphorus and potassium on growth, yield and quality of Bt cotton. Asian J. Soil Sci., 4 (1) : 37-42.

Hosmath, J. A., 2011. Evaluation of Bt cotton genotypes and nutrient management to control leaf reddening. Ph.D. Thesis, Univ. Agric. Sci., Dharwad (India).

Narayana, E., Aparna, D. and George, Mridula 2011. Response of Bt cotton (Gossypium hirsutum L) for integrated rain water and nutrient management. J. Cotton Res. Dev. 25: 68-70.

Rajendran, K., Palchamy, A., Sankaranarayanan, K., Prabakaran, K. and Bhararhi, K., 2011. Enhancing productivity of summer irrigated cotton through plant growth regulator and foliar nutrition. J. Madras Agric., 98(7-9): 248-250.

Ravikiran, S., Halepyati, A. S., Pujari, B. T., Koppalakar, B. G. And Narayanarao, K., 2012. Effect of macro and soluble micronutrients on yield, uptake of nutrients, quality and economics of Bt cotton (Gossypium hirsutum L.) under irrigation. Karnataka J. Agric. Sci., 25(4): 418-422.

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IMPACT OF FRONTLINE DEMONSTRATIONS ON RAPESEED MUSTARD GROWERS

Ramakant Sharma, D.S. Bhati and S K SharmaSMS, Krishi Vigyan Kendra Ajmer, SKN Agriculture University, Jobner (Rajasthan)


ABSTRACTThe KVK Ajmer has carried out FLDs on rapeseed-mustard at 65 farmers’ field from Rabi 2013-14 to 2015-16 to assess the productivity of frontline demonstration as well as farmer’s practice. The results revealed that under demonstration plot, the yield of Rapeseed-mustard was found to be remarkably higher than that under farmers’ practice during all the three years. The yield of Rapeseed-mustard in FLD fields was ranging from 12.9 to 22.2 quintals per ha while it was 10.6 to 19.4 quintals per ha in non FLD fields during the study period. It was found that majority of FLD farmers (84 percent) had medium to high knowledge level whereas only 48 percent non FLD farmers have medium to high knowledge level about improved technologies of Rapeseed-mustard. The study also highlighted that there was a significant difference in knowledge level about different aspects of improved technologies of Rapeseed-mustard cultivation between FLD and non-FLD farmers.

Key words: Knowledge, oilseeds, technology, demonstration.

Rapeseed mustard crop is the major oilseed crop of India. Area, production and productivity of Rapeseed mustard crop during 2015-16 in Ajmer district was 39309 ha., 47433 lakh ton and 1207 kg per ha, respectively. However, its productivity is very low as compared to other districts viz Hanumangarh (1555), Ganganagar (1524), Bharatpur (1521), Alwar (1452) etc . The low productivity of this crop is due to inadequate knowledge and low adoption of improved technologies of rapeseed-mustard by the farmers. So, efforts were made to increase the knowledge and adoption of improved technologies through frontline demonstration (FLD). Frontline demonstration is one of the most powerful tools for transfer of technology among farming community. Realising the importance of FLD in transfer of technology, it was thought appropriate to undertake to study the impact of FLD on gain in knowledge about improved technology of rapeseed-mustard with the following specific objectives.

1. To assess the recommended technology and farmers practice of Rapeseed-mustard production.

2. To study the productivity of improved technologies of Rapeseed-mustard under frontline demonstrations on farmers field.

3. To find out the extent of knowledge of FLD and non-FLD farmers about improved technologies of Rapeseed-mustard.METERIALS AND METHODS

The KVK Ajmer has carried out FLDs on Rapeseed-mustard covering an area of 30 ha area at 65 farmers’ field from Rabi 2013-14 to 2015-16 to exhibit latest production technologies. An attempt was made to assess the productivity of frontline demonstration as well as farmer’s practice.These were compared with prevailing production technologies of rapeseed-mustard crop (check plots). The performances were evaluated by organising seasonal trainings, method of demonstrations, field days and by taking crop-cut experiments, regular diagnostic visit. Production and economic data for FLDs and local practice were collected

119

and analyzed. A total of 100 farmers were selected as respondents (50 FLD & 50 Non FLD). To test the knowledge of the respondents, a knowledge schedule was developed with the help of SMS of KVK. One mark was given to every right answer and zero for wrong answer.

RESULTS AND DISCUSSIONAssessment of production technology in FLD plots and local check plots

The records of recommended production technology of rapeseed-mustard in frontline demonstration plots were compared with the farmers practices adopted in local check plots. Table 1 revealed that there is slight gap between the farmers and demonstration practices, still farmers not treated their seed and used lower doze of fertilizers in comparison to demonstration plot.

Table 1.Comparison between adoption of demonstration and farmers practices of Rapeseed-mustard

Particular practice Demonstration Farmers practiceImproved variety Variety RH-749 / NRCDR-2 Variety Bio-902 /Pusa Bold Optimum seed rate 4-5 kg 4-5 kgSeed treatment Seed treatment with mancozeb @ 2

gm/kg seed No seed treatment

Sowing method Line sowing line sowingBasal application of fertilizer

60 kg N + 35 kg P2O5 & 25 kg Sulphur dust /ha

Low doze of fertilizer 30Kg N+25KgP2O5 /ha

PP measures Adopted for the control of aphids Adopted for the control of aphids

Performance of frontline demonstrations on rapeseed-mustard cropThe grain yield data (Table 2) indicated that variety NRCDR-2 and RH-749 with improved technologies of wheat in frontline demonstration were superior to local check.Results showed that average yield of demonstration plots were 19.85, 17.80, 14.36 q/ha during 2013-14, 2014-15 and 2015-16 while in local check it was 17.09, 14.45 and 12.21 q/ha

respectively. Table also indicated that yield increased over the local cultivars were 16.14 %, 23.25 % and 17.60%, respectively during 2013-14, 2014-15 and 2015-16. The finding is in line with Rao et al (2011) who found that the variety TCGS-320 with improved package of practices in groundnut gave 22.80% to 44.39% increase in yield over local variety with an average improvement of 35.03%. Similar results were also reported by Pradhan et al (2011).

Table 2. Performance of frontline demonstrations on rapeseed-mustard cropYear Variety Avg. Yield (q/ha) % increase in yield Demo farmer’s practice2013-14 NRCDR-2 19.85 17.09 16.14 2014-15 NRCDR-2 17.80 14.45 23.252015-16 RH-749 14.36 12.21 17.60Overall average 17.33 14.58 18.86

Knowledge of farmers about improved technology of Rapeseed-mustardThe data in table 3 stated that 50 per cent of FLD farmers were in High knowledge category (67 to 100 % knowledge level).Whereas, 16 percent FLD and 52 percent non-FLD farmers were having

low knowledge level about improved technology of rapeseed-mustard. It leads to the conclusion that FLD farmers in general had higher knowledge than non-FLD farmers about the improved technology of rapeseed-mustard. Similar finding was also reported by Sharma

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et.al.(2011) who concluded that there was gap in knowledge between respondents of beneficiary and non-beneficiary.Likewise Raghuvanshi et.al.(2016) reported in his

study that majority of FLD farmers had medium to high knowledge level about the improved technology of wheat cultivation.

Table 3. Distribution of respondents on the basis of knowledge level about improved technologies of Rapeseed-mustard n = 100

Knowledge Category FLD farmers Non-FLD farmersFrequency %age Frequency %age

Low knowledge (0-33%) 08 16.00 26 52.00Medium Knowledge (34 to 66%) 17 34.00 15 30.00High Knowledge (67-100%) 25 50.00 09 18.00

Further, the percentage of farmers having knowledge about different aspects of improved technology rapeseed-mustard were analyzed separately (table 4). The data in table 4 indicated that all the FLD farmers had knowledge about use of high yielding variety seeds, sowing method and sowing time and hence these were ranked first. The second highest percentage of FLD farmers (96 percent) had knowledge about recommended seed rate followed by sowing distance (92 percent). Further, 4th, 5th and 6th rank were awarded to basal application of fertilizers, seed treatment and plant protection measures, respectively by the FLD farmers. Only 78 percent FLD farmers had knowledge about plant protection measures hence, it was awarded lowest rank. It was also evident that all the non-FLD farmers had knowledge about sowing time and ranked first. Like-wise, Kumawat (2008) reported that plant protection

management, use of balanced fertilizer and manures and seed treatment were least adopted in caster protection technology by both demonstrator and non-demonstrator farmers

Table 4 also stated that there was a wide gap in other aspects such as recommended seed rate, sowing distance, plant protection measures, basal application of fertilizers and seed treatment in FLD and non FLD farmers. This might be due to the reason that the FLD farmers have gained knowledge about improved technology of rapeseed-mustard through trainings, field days, scientists visit and demonstrations conducted by KVK. The results were similar to the findings of Asiwal et al (2008) who concluded that there was a significant difference in knowledge level with regards to different improved cultivation practices of mustard crop in beneficiary and non-beneficiary respondents under FLD programme.

Table 4. Extent of knowledge about improved technologies of Rapeseed-mustard crop by the FLD farmers and non-FLD farmers

n=100Improved practices FLD farmers Non-FLD farmers

MPS Rank MPS RankUse of HYV seeds 100.00 I 70.00 IVRecommended seed rate 96.00 II 72.00 IIISeed treatment 86.00 V 44.00 VIIISowing time 100.00 I 100.00 ISowing method 100.00 I 90.00 IISowing distance 92.00 III 50.00 VIBasal application of fertilizer 88.00 IV 48.00 VIIPP measures 78.00 VI 58.00 V

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CONCLUSION

Improved technology of rapeseed- mustard in FLD enhanced yield from 14.36 qt/ha to 19.85 qt/ha with an average of 17.33 qt/ha. The percentage of increase in yield of FLD ranges from 16.14 to 23.25 with an average increase of 18.86 percent over the farmers practice. This created greater motivation among other farmers who do not adopt improved technology of rapeseed- mustard. Moreover, knowledge about improved technology of rapeseed-

mustard was higher among FLD farmers than the non-FLD farmers. This might be due to the fact that the FLD programme was effective in changing attitude towards knowledge and adoption of improved technologies of rapeseed-mustard. During FLD programme, KVK also organised on and off campus trainings, scientist visits to farmers’ fields, field day and these activities improved the rapport between the farmers and scientists and built confidence between them

REFERENCESAsiwal,B.L.,Singh,S. and Khan,I.M.(2008).

Knowledge level of beneficiary farmers and non-beneficiary farmers of FLD regarding improved technologies of mustard in Sikar district of Rajasthan. Rajasthan Journal of Extn. Edu. Vol. 16: 119-123

Kumawat,S.R.(2008).Impact of FLD on adoption of improved castor production technology. Rajasthan Journal of Extn. Edu. Vol. 16: 144-147

Pradhan, A., Tomar N S, Sharma, R L and Nag, S K (2011). Enhancing productivity of finger millet. Indian Farming Vol 60,No.11 : 33-34

Raghuvanshi, N. Bisht, K.Singh, S. P. and Raghuvanshi S. (2016). Impact of Frontline demonstration on scientific temperament of wheat growers. Journal of Progressive Agriculture Vol 7,No.1 : 27-37

Rao, D M, Chandrashekhar, P and Neeraiah, R (2011). Productivity enhancement in groundnut. Indian Farming Vol 60 No.11 : 39-40.

Sharma, A. K., Kumar, V., Jha, S.K. and Sachan, R.C. (2011) Frontline demonstration on Indian Mustard: An impact assessment. Indian Res. J. Ext. Edu. 11(3) 27-31.

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KNOWLEDGE OF FARM WOMEN ABOUT IMPROVED BLACK GRAM CULTIVATION PRACTICES

Seema Jat1, K.L. Dangi2 and Bheru Lal Kumhar3

1P.G. Student, Maharana Pratap University of Agriculture and Technology, Udaipur.2Professor (Extn. Edu.), Rajasthan College of Agriculture, MPUAT Udaipur.

3SRF, Agriculture University, Kota.Email: [email protected]


ABSTRACTBlack gram is a versatile crop that is grown in almost every part of the globe today. It is the most largely produced pulse crop. India produces 70 per cent of worlds’ black gram production and accounts for 10 per cent of country’s total pulse production. The present study was confined to the black gram growers of Mandalgarh and Jahazpur panchayat samiti of Bhilwara district, Rajasthan. Majority of the respondents belonged to middle age group were illiterate, had big and joint families with family of up to 8 or more members (68%). Majority of them had semi-pucca houses, had media ownership T.V. (60%), farming as main occupation and most of them were small farmers having 1-2 hectare land. Most of them possessed medium level of knowledge (89%) about the improved cultivation practices of black gram. Majority of the respondents had knowledge about the soil and land preparation (100%) followed by harvesting, irrigation management and agro climatic condition. They had poor knowledge about the improved cultivation practices like weed management, sowing, improved seed varieties, and plant protection measures.

Key words: Black gram growers; Knowledge, Black gram cultivation practices

India grows a variety of pulse crop under a wide range of agro-climatic conditions and has a pride of being the world’s largest producer of pulses. It is important source of protein especially for vegetarian and is also referred as poor man’s meat. The major pulse crops grown in India are black gram, green gram, chickpea, pigeonpea, lentil and fieldpea, in which India produces 70 per cent of worlds’ black gram production and accounts for 10 per cent of country’s total pulse production (Gowda et al, 2013). Black gram is also known as Urd or Black lentil. It is one of the most important pulse crops grown throughout the country in very diverse agro-climatic conditions. According to annual report of Ministry of Agriculture, 2014 black gram produces 22.10 Kg of Nitrogen/ha, which is equivalent to 59 thousand tons of urea annually. Furthermore, it helps in fixing atmospheric nitrogen in symbiotic association with the rhizobium bacteria that is present on the root nodules and

hence maintains the soil fertility. Black gram supplements the cereal-based diet and contains about 26 per cent vegetable protein, which is three times that of cereals. It is well known that a diet deficient in protein intake can cause Protein Energy Malnutrition. The leading states producing black gram in India are Maharashtra, Uttar Pradesh, Andhra Pradesh, Rajasthan, Madhya Pradesh and Karnataka. These states contribute 80 per cent of total pulse production as reported by the Directorate of Economics and Statistics, Department of Agriculture and Cooperation, 2010. In Rajasthan State black gram is grown in 1, 96 lakh/ ha. area with a production of 70,561 tonnes, with average yield of 360 kg/ha. Bhilwara occupies first position with respect to area 50,089 ha with annual production of 17,111 tonnes and an average yield of 342 kg/ha. Agriculture is main occupation of majority of the population in the rural area of Bhilwara district. According to

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the Commissionerate of Agriculture, Govt. of Rajasthan, Jaipur (2013-14) the average yield of black gram is only 360 kg/ha. as against the recommended average yield of the crop is 15-20 quintals/ha. (Panda, 2012). The low production of black gram may be due to the poor knowledge of improved cultivation practices of black gram for the farm womens. Hence this is a challenging task for the scientist and farmers. Under such condition it is quite imperative that reasons for the technological gap in black gram should be identified and studied critically in order to face the existing challenge of low productivity. In this context the present study was undertaken to study the knowledge of improved cultivation practices of black gram. The Government of India initiated several developmental programmes to accelerate growth in pulse production. However, technological advancements have not stretched out to this important segment of the farming population. They are still using traditional practices in crop cultivation and there exists a spacious technological gap with respect to adoption of different improved practices.

Therefore, it is imperative was undertaken to know whether the farm women are aware about improved black gram cultivation practices or whether these technologies have been adopted by them. Therefore, find out the knowledge of farm women regarding cultivation practices of improved black gram.


The present study was undertaken in Mandalgarh and Jahazpur panchayat samiti of Bhilwara district was purposively selected on the basis of maximum sown area of black gram. A list of black gram growers was prepared for each selected village. In all 100 respondents were selected from the lists through proportionate random sampling technique. The data were calculated with the help of well structured interview schedule. Respondents were categorized as poor, average and good knowledge. The practice wise knowledge of improved cultivation practices of black gram was ranked based on mean percent score (MPS) values. The mean percent scores were calculated with the help of following formula.

MPS = ×100


Background information of the respondents: The study depicted that the respondents (57%) were found in middle age group belonging to OBC (75%) and literate (80%). Joint families were observed (68%) in their families. The land holding size below 1-2 ha was observed with majority of the respondents (73%). Agriculture was

observed as main occupation (92%) and none of the respondents associate to any organization. Media ownership was portrays that 60 per cent respondents had T.V. and 25 per cent respondents used T.V. as a Source of information. Beneficiaries of developmental programme (13%) respondent were beneficiaries of MGNREGA and none of them attended any training program related to pulse cultivation.

Table 1. Overall knowledge of improved black gram cultivation practices124

Sum of scores obtained by respondents in an item

Maximum obtainable scores

n= 100Categories f/%Poor (>33.33) 0Average (33.34 to 66.67) 89Good (<66.67) 11

Overall knowledge of improved black gram cultivation practices: Table-1 shows that majority of the respondents (89%) were in the category of average knowledge, only 11 per cent respondents

were in the category of good knowledge and none of them were found in the category of poor knowledge. Almost similar findings were obtained by Meena (2010) and Chandawat et al. (2014).

Table 2. Distribution of respondents according to their knowledge of various improved black gram cultivation practices

n=100Aspects MPS RANKSoil and land preparation 100 IHarvesting 78.25 IIIrrigation management 75.75 IIIAgro climatic condition 64 IVIntercropping 57.87 VManure and fertilizer application 57.85 VIWeed management 47.23 VIISowing 39.8 VIIIImproved seed variety 34.62 IXPlant protection measures 15.80 X

Overall Mean Per Cent knowledge Score – 57.11It is observed from the Table-2 that among all the 10 agricultural practices of black gram cultivation, soil and land preparation was ranked at 1st (100 %) The practices like harvesting was put at rank 2nd (78.25 %), irrigation management ranked at 3rd (75.75 %), agro climatic condition at 4th (64%), intercropping at 5th

(57.87%), manure and fertilizer application at 6th (57.85%), weed management at 7th (47.23%) respectively. The other practices viz. sowing, improved seed variety and plant protection measures were ranked at 8th

(39.8%), 9th (34.62%) and 10th (15.80%) respectively. The overall knowledge index was calculated to be 57.11 per cent.Findings are supported by the findings of Meena (2010) in a study on “Knowledge and adoption of improved cluster bean production technology” that majority of the cluster bean growers were in the category of medium level of knowledge

and 16.66 per cent were in the category of high level of knowledge about improved cluster bean cultivation technology. The findings are also supported that the findings of Chandawat et al. (2014) reported that most of the respondents possessed medium level of knowledge (72%) about the improved cultivation practices of gram.

CONCLUSIONIt may be concluded that the overall knowledge, which was 57.11 per cent, seems to be average knowledge about black gram cultivation practices. The study also indicated that most of the respondents had knowledge about the soil and land preparation (100%) followed by harvesting, irrigation management and agro climatic condition. The respondents had poor knowledge about the improved cultivation practices like weed management, sowing, improved seed varieties, and plant protection measures. So there is need to emphasize specially

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on less known improved practices of black gram crop cultivation through trainings and effective extension programmes to impart knowledge among the black gram growers of the area.

Hence, their knowledge could be increased and the adoption of improved cultivation practices would ultimately be enhanced.

REFERENCESChandawat, M.S., Parmar, A.B., Sharma, P.K.

and Singh, Bhupender. 2014. Knowledge of Improved Cultivation Practices of Gram among the Farmers of Kheda District of Gujarat. International Journal of Farm Sciences. 4: 215-220.

Commissionerate of Agriculture, Rajasthan -Jaipur 2013-14. Rajasthan Krishi retrieved from articles.www.krishi.rajasthan.gov.in Crop Production. Pdf on July 23rd, 2014.Pp 4-5.

Gowda, C. L. Laxmipathi, Srinivasan, S., Gaur, P. M. and Saxena, K. B 2013. Enhancing the Productivity and Production of Pulses in India. International Journal of Scientific and Research Publications. 19: 11-13.

Khare, A. L., Wakle, P. K. and Mankar D. M. 2013. Indian Journal of Applied Research. 10: 1-5.

Meena, N.R. 2010. Knowledge and Adoption of Improved Cluster bean Production Technology by the Farmers in Jaipur District of Rajasthan. M.Sc. thesis, Maharana Pratap University of Agriculture and Technology, Udaipur.

Ministry of Agriculture. 2014. As per the latest reports of sowing of kharif pulses, sowing area has reached 44.47 lakh hectares. International Journal of Innovative Research in Science. 3: 2319-8753.

Panda S.C 2012. Crop Production and Management. The Hand Book of Agriculture. Pp 349-350.

Raj, A. D., Yadav, V. and Rathod, J. H 2013. Impact of Front Line Demonstrations (FLD) On the Yield of Pulses. International Journal of Scientific and Research Publications. 3: 1-3.

Ramakant Sharma, et. al., 2014. “Knowledge empowerment of green gram growers through frontline demonstrations”. Jr. of Progressive Agriculutre, 5 (2): 121-123.

Ramakant Sharma and B.D.Tyagi, 2015. “A study of small farmers knowledge for potato production technology”. Jr. of Technofame, 4 (2): 104-106.

Sachin Kumar, Dan Singh and R.P.Singh, 2016. “Assessment of knowledge level of potato growers and their constraints related to potato production technology”. Jr. of Technofame, 5 (2): 107-111.

Singh R.P., 2011. Status report on pulses. Directorate of Pulses Development, Bhopal, Madhya Pradesh, pp 66-78.

Thombre A.P., Ghulghule J.N. and More S.S. 2009. Economics of production of Black gram in Marathwada region of Maharasthra. Agriculture Update 4(1-2): 167-171.

Yogita Ranawat, et.al, 2014. “Association between knowledge and adoption level of improved maize cultivation practices with selected variables of maize growers” Jr. of Progressive Agriculture, 5 (1) : 24-27.

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FARMERS’ KNOWLEDGE RELATED TO ORGANIC FARMING: A STUDY IN TIKAMGARH DISTRICT OF MADHYA PRADESH

Anjali Sihare1, Kamini Bisht2, S.P. Singh2 and Sheela Raghuwanshi2

1M.Sc. student, 2Assistant Professor, JNKVV COA, Tikamgarh, M.P.Received: 10.02.17Accepted: 16.03.17

ABSTRACTOrganic farming is now a promising option due to the low external input cost for cultivation such as low fertilizer and low pesticide amounts by increasing the efficient use of farm resources. Knowledge has been found to be an important factor contributing to adoption of recommended practices by the farmers.The study was focused to know the knowledge of farmers about organic farming in relation to socio-personal, psychological and communicational variables of the farmers i.e. age, education qualification, size of family, farm size, annual income, livestock possession, organic farming experience, innovative proneness, attitude towards organic farming, mass media exposure, participation in social organization and knowledge towards improved agriculture practices. The present study was conducted in Tikamgarh block of Tikamgarh district of Madhya Pradesh state. As per the findings, majority of farmers possessed medium knowledge whereas in case of inorganic farmers majority of the farmers were having low knowledge on organic farming. In case of organic farmers, only one variable showed positive and significant relationship i.e. knowledge about improved agricultural practices with knowledge about organic farming, whereas in case of knowledge level of inorganic farmers, no relationship was found between independent and dependent variables. Regarding in adoption of organic cultivation practices, the major constraints reported was that organic farming is a slow process and time consuming.

Key words: Organic, chemical, pesticide, resistance, bio-fertilizer.

In modern agriculture indiscriminate application of pesticides has resulted in pesticide resistance in insects that compelled to use different molecules and higher dosages. These practices not only increase the cost of production but also quality of food is being affected and environment is polluted. A range of alternative eco-friendly methods of pest management practices and for substituting the chemical fertilizers, various forms of organic manures and bio-fertilizers are used.The global concerns of safe foods have introduced the concept of organic farming. Badgley et al. (2006) showed organic food can fulfil the demands for food and sustain the environment. National Programme for Organic Production (NPOP) was launched in May 2000 with the objective of promoting organic farming in India leading to development of a movement among the farmers, agriculture experts and scientists

in favour of organic farming. By March 2010 India has brought more than 4.48 million ha area under organic certification process. Organic agriculture is a unique production management system which promotes and enhances agro-ecosystem health, including biodiversity, biological cycles and soil biological activities. With the increase in population need not only to stabilize agricultural production but to increase it further in sustainable manner. Organic farming is now a promising option due to the low external input cost for cultivation such as low fertilizer and low pesticide amounts by increasing the efficient use of farm resources (Ramesh, Singh & Subba, 2005). Knowledge has been found to be an important factor contributing to adoption of recommended practices by the farmers. Hence, assessment of farmer’s knowledge related to organic farming has become an

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important issue which needs to be explored.


The study was conducted in Tikamgarh block of Tikamgarh district of Madhya Pradesh state. Tikamgarh block was selected purposively because the Krishi Vigyan Kendra, Tikamgarh had adopted 15 villages from Tikamgarh block to impart knowledge on organic cultivation practices to 50 respondents from each village. Out of these 15 adopted villages, four villages were selected randomly. Further, four villages where no knowledge on organic cultivation practices was imparted were selected on random basis to compare the extent of knowledge and practices of organic farmers with that of inorganic farmers. From each selected village, 15 respondents were selected randomly for the present study. Thus a total of 120 farmers comprising 60 organic farmers and 60 inorganic farmers were selected from eight villages.


1. Profile of the respondentsTable 1 reveals that out of total organic farmers, 65 per cent were of middle age

group, education up to middle school and graduation (23.33%), medium family size (55%), were having small size of land holding (43.33%), medium level of annual income (90%), had low livestock possession (51.67%), majority of the respondents (56.67%) were having 1-5 years of experience of organic farming, high mass media exposure (45%), medium level of social participation (85%), medium level of innovative proneness (70%) and highly favourable attitude towards organic farming (61.67%). In case of inorganic farmers, 58.33 per cent were of middle age group, educated up to high school (35.00%), medium family size (63.33), small size of land holding (53.33%), belonged to medium income group (85%), were having medium level of livestock possession (48.33%), majority (91.67%) of the respondents were not having experience of organic farming, low mass media exposure (68.33%), medium level of social participation (58.33%), medium innovative proneness (60%), and favourable attitude towards organic farming (61.67%).

Table 1: Profile of the respondents

Response categoryOrganic farmers (N=60)

Inorganic farmers (N=60)

Age Young (up to 43 years) 11 (18.33 10 (16.67)Middle (43 to 59 years) 39 (65.00) 35 (58.33)Old (above 59 years) 10 (16.67) 15 (25.00)

Education Illiterate 05 (8.33) 01 (1.67)Primary 06 (10.00) 09 (15.00)Middle 14 (23.33) 19 (31.67)High school 10 (16.67) 21 (35.00)Higher secondary 11 (18.33) 06 (10.00)Graduation/More 14 (23.33) 04 (6.67)

Family size Small family size (up to 7 members) 13 (21.67) 12 (20.00)Medium family size (7 to 11 members) 33 (55.00) 38 (63.33)Large family size (above 11 members) 14 (23.33) 10 (16.67)

Farm size Marginal farmers (up to 2 ha) 04 (6.67) 15 (25.00)Small farmers (2 to 5 ha) 26 (43.33) 32 (53.33)Medium farmers (5 to 8 ha) 22 (36.67) 09 (15.00)

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Large farmers (above 8 ha) 08 (13.33) 04 (6.67)Social participation

Low (up to 2) 0 23 (38.33)Medium (3 to 5) 51 (85.00) 35 (58.33)High (above 5) 09 (15.00) 02 (3.33)

Annual income Low income (Below 31120) 05 (8.33) 07 (11.7)Medium income ( 31120 to 55646) 54 (90.00) 51 (85.00)High income (Above 55646) 01 (1.7) 02 (3.33)

Livestock possession

Low (up to 2 Score) 31 (51.67) 22 (36.67)Medium (2 to 4 Score) 22 (36.67) 29 (48.33)High (above 4 Score) 7 (11.67) 09 (15.00)

Innovative proneness

Low (up to 23 Score) 0 24 (40.00)Medium (23 to 29 Score) 42 (70.00) 36 (60.00)High (above 29 Score) 18 (30.00) 0

Organic farming experience

No experience 0 55 (91.67)1-5 years of experience 34 (56.67) 05 (8.33)More than five years of experience 26 (43.33) 0

Mass media exposure

Low (up to 5 Score) 27 (28.33) 08 (68.33)Medium (5 to 7 Score) 16 (26.67) 11 (18.33)High (above 7 Score) 17 (45.00) 41 (13.33)

Attitude towards organic farming

Not favourable (up to 19) 01 (1.66) 22 (36.67)Medium (19 to 23 Score) 22 (36.67) 37 (61.67)High (above 23 Score) 37 (61.67) 01 (1.66)

2. Knowledge about improved agriculture practicesFrom Table 2 it could be clearly observed that equal percentage of organic farmers i.e. 91.67 per cent had knowledge about soil type and seed treatment. About 80 per cent of them knew about improved varieties and hybrids, whereas 75.00 per cent of the organic farmers had knowledge about important characteristics of soil and main purpose of seed treatment. Nearly 66 per cent of the organic farmers could suggest suitable crops for different soil. Further, 58.33 per cent of the organic farmers were having knowledge about soil testing and fifty per cent of them knew the varieties of which seeds to be changed every year.

Regarding inorganic villages, 75.00 per cent of the farmers had knowledge about soil type and 70.00 per cent had knowledge about seed treatment. Near about 66 per cent of the respondents had knowledge of improved varieties and hybrids and 63.33 per cent of the respondents were having knowledge about suitable crops for different soil. Equal percentage i.e. 58.33 per cent of the respondents had knowledge about important characteristics of soil type and main purpose of seed treatment. Half of the respondents from inorganic village knew about soil testing and 30 per cent of the inorganic farmers had knowledge of varieties of which seeds to be changed every year.

Table 2: Knowledge level about improved agriculture practicesCategories Organic farmers (N=60) Inorganic farmers (N=60)

KnowF (%)

Don’t knowF (%)

KnowF (%)

Don’t knowF (%)

Knowledge about soil type 55(91.67) 05(8.33) 45(75.00) 15(25.00)Important characteristics of soil type 45(75.00) 15(25.00) 35(58.33) 25(41.67)Suitable crops for different soil 40(66.67) 20(33.33) 38(63.33) 22(36.67)Knowledge about soil testing 35(58.33) 25(41.67) 30(50.00) 30(50.00)

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Knowledge about improved varieties and hybrids

50(83.33)

10(16.67)

40(66.67)

20(33.33)

Varieties of which seeds to be changed every year

30(50.00)

30(50.00)

18(30.00)

42(70.00)

Knowledge about seed treatment 55(91.67) 05(8.33) 42(70.00) 18(30.00)Main purpose of seed treatment 45(75.00) 15(25.00) 35(58.33) 25(41.67)

Fig.1 indicates that out of total organic farmers, 55.00 per cent were having medium knowledge about improved agricultural practices followed by high knowledge (45.00%) and none of the respondents came in the category of low knowledge about improved agriculture practices.In case of inorganic farmers, 71.67 per cent were having medium knowledge about improved agriculture practices followed by low knowledge (28.33%). However, none of the respondents were in the category of high knowledge level about improved agriculture practices.Hence, on the basis of the data it can be concluded that maximum percentages of

the organic farmers (55.00%) and inorganic farmers (71.67%) were having medium knowledge about improved agriculture practices.In case of inorganic farmers, 71.67 per cent were having medium knowledge about improved agriculture practices followed by low knowledge (28.33%). However, none of the respondents were in the category of high knowledge level about improved agriculture practices.Hence, on the basis of the data it can be concluded that maximum percentages of the organic farmers (55.00%) and inorganic farmers (71.67%) were having medium knowledge about improved agriculture practices.

Fig.1 Knowledge about improved agricultural practices

3. Knowledge about organic cultivation practicesThe results in Table 3 indicate the knowledge about organic cultivation practices. From the table it could be seen that 83.33 per cent of the farmers knew about application of organic manure, 75 per cent had knowledge about organic farming, 66.67 per cent knew about the advantages of organic farming, 58.33 per cent had knowledge about the preparation

of organic compost. Half of the organic farmers had knowledge of FYM, compost, vermi-compost and green manure as organic inputs. Near about 40 per cent knew about the method of seed treatment, 33.33 per cent had knowledge of crop rotation, 25 per cent had knowledge of the weeding practices used to control weed, 16.67 per cent were having knowledge about vermi-compost, 13.33 per cent knew about green manuring crops and only 5 per cent knew about the methods used to control pest.Regarding inorganic farmers, more than half of the respondents (58.33%) were having knowledge of organic farming and its advantages. Fifty per cent of inorganic farmers knew about organic input used and 41.67 per cent had knowledge of preparation of organic compost. One-fourth of farmers had knowledge of method of seed treatment and 16.67 per cent had knowledge of application of organic manure, crop rotation and weeding practices used to control weed.

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Only 5.00 per cent of respondents knew about vermi-compost and green manuring crops. However, majority of the

percentage of inorganic farmers did not have knowledge about organic cultivation practices.

Table 3: Knowledge about organic cultivation practicesCategories Organic farmers (N=60) Inorganic farmers (N=60)

KnowF (%)

Don’t knowF (%)

KnowF (%)

Don’t knowF (%)

Knowledge about organic farming

45(75.00)

15(25.00)

35(58.33)

25(41.67)

Application of organic manure 50(83.33) 10(16.67) 10(16.67) 50(83.33)Knowledge about vermi-compost

10(16.67)

50(83.33)

03(5.00)

57(95.00)

Organic input used.FYM /Compost /Vermicompost /Green manure /Bio-fertilizer /Bio-pesticides.

30(50.00)

30(50.00)

30(50.00)

30(50.00)

Green manuring crops 08(13.33) 52(86.67) 03(5.00) 57(95.00)Advantages of organic farming 40(66.67) 20(33.33) 35(58.33) 25(41.66)Preparation of organic compost 35

(58.33)25(41.67)

25(41.67)

35(58.33)

Crop rotation 20(33.33) 40(66.67) 10(16.67) 50(83.33)Method of seed treatment 25(41.67) 35(58.33) 15(25.00) 45(75.00)Method used to control pest 3(5.00) 57(95.00) 0(0.00) 60(100)Weeding practices used to control weed

15(25.00)

45(75.00)

10(16.67)

50(83.33)

Fig. 2 indicates that out of total organic farmers, 81.67 per cent possessed medium knowledge followed by high (18.33%) knowledge about organic farming. However, none of the respondent falls in the category of low knowledge level.

In case of inorganic farmers, majority of the farmers i.e. 81.67 per cent were having low knowledge followed by 18.33 per cent were having medium knowledge. No respondent was in the category of high knowledge on organic farming.

Fig.2 Knowledge about organic cultivation practices

Hence, on the basis of the data it can be concluded that maximum percentages of the organic farmers (81.67%) were

having medium knowledge and 81.67 per cent of the inorganic farmers were having low knowledge about organic farming.

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4. Association of selected independent variables with knowledge about organic cultivation practicesTable 4: Relationship of selected independent variables with knowledge about organic farming

Variabler value

Organic farmers(N=60)

Inorganic farmers(N=60)

Age -0.032 NS 0.034 NS

Education qualification 0.137 NS 0.118 NS

Size of family -0.081 NS -0.091 NS

Farm size -0.105 NS 0.084 NS

Annual income -0.151 NS -0.058 NS

Livestock possession 0.062 NS 0.050 NS

Organic farming experience 0.123 NS -0.112 NS

Innovativeness 0.198 NS -0.033 NS

Attitude towards organic farming 0.254 NS 0.164 NS

Mass media exposure -0.267 NS 0.256 NS

Participation in social organization 0.073 NS 0.251 NS

Knowledge about improved agricultural practices 0.284* 0.139 NS

* = Significant at 0.05 probability levelNS = Non-significantThe results in Table 4 indicate the level of knowledge about organic cultivation practices among organic and inorganic farmers. It was seen that in organic farmers, the variable knowledge about improved agricultural practices showed positive and significant relationship with the level of knowledge about organic farming. Whereas, other selected independent variables viz., age, education, size of family, farm size,

annual income, livestock possession, organic farming experience, innovativeness, attitude towards organic farming, mass media exposure and participation in social organization were not found to have any association with the knowledge about organic farming. Regarding the knowledge level of inorganic farmers, no relationship was found between independent and dependent variables.

5. Constraints faced by farmers in adoption of organic cultivation practicesTable 5: Constraints faced by farmers in adoption of organic cultivation practices Constraints Frequency

(N= 120)% Rank order

Lack of knowledge about organic manure 38 31.67 IIILow production 29 24.17 IVOrganic farming is a slow process and time consuming 54 45.00 ILack of market facilities for organic produce 40 33.33 IILack of awareness about certification process 08 6.67 VIIIMalpractices in organic farming by others 12 10.00 VIILow availability of organic manure 18 15.00 VILow price for organic produce 20 16.67 V

Table 5 shows the constraints reported by organic as well as inorganic farmers in

adoption of organic cultivation practices. It is evident from the data that the major

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constraints reported were that organic farming is a slow process and time consuming (45.00%) followed by lack of market facilities for organic produce (33.33%), lack of knowledge about organic manure (31.67), low production (24.17%), low price for organic produce (16.67%), low availability of organic manure (15.00%), malpractices in organic farming by others (10.00%), and lack of awareness about certification process (6.67%).

CONCLUSION

The findings of the study concluded that adopted villages where knowledge on organic farming was imparted to the farmers by the KVK scientists had comparatively good knowledge about organic farming practices as compared to the inorganic villages. In case of organic farmers, only one variable showed positive and significant relationship i.e. knowledge about improved agricultural practices with knowledge about organic

farming, whereas in case of knowledge level of inorganic farmers, no relationship was found between independent and dependent variables. Regarding in adoption of organic cultivation practices, the major constraints reported was that organic farming is a slow process and time consuming. Extension work will have to be intensified through frequent visits by extension workers and conducting extension activities at village level for achieving better knowledge about organic farming. Also, training should be imparted to the farmers on organic farming and livestock maintenance, so that it is possible for them to utilize the scarce resources, save the environment and protect their health. Farmers should be encouraged by the government in using organic method with institutional support and providing financial services such as loans and subsidies for farmers and certification of organic produce.

REFERENCESBadgley C, Moghtader J, Quintero E, Zakem

E, Chappeli K, Avilés- Vázquez MJ, Samulon A and Perfecto I, 2006. Organic agriculture and the global food supply. Renewable Agricultural Food System. 22(2): 86–108.

Narendra Prasad, S.K.Verma and S.P.Singh, 2016. “Organic farming: Livelihoods for small farmers”. 5 (1): 131-133.

Ramesh P, Singh MA and Subba R 2005. Organic farming: Its relevance to the Indian context. Current Science. 88: 561–568.

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PERFORMANCE OF NEEM (Azadirachta indica) SEEDS COLLECTED FROM DIFFERENT DISTRICTS OF HARYANA

M.K.Singh, K.S.Ahlawat, Bimlendra Kumari and K.K.BhardwajDepartment of Forestry, CCS Haryana Agricultural University, Hisar-125004

Email:[email protected]: 17.01.17Accepted: 31.03.17

ABSTRACT In order to study the performance of Neem (Azadirachta indica) seeds under semi-arid condition. Seed samples were collected from plus trees of Neem from nineteen districts of Haryana state. Fresh seeds of equal maturity were collected by shaking the branches on a clean floor. The basic characters of mother trees such as height, forking height, dbh were recorded which varied from 5.6 to 16.5 m, 1.5 to 9.5 m, 45.7 to 195.4 cm respectively. Crown spreads was measured in four directions (North, East, West, South) and its mean size varied from 2.8 to 9.8m. Seed bearing habit of selected plus tree of different districts varied from moderate to heavy and their stem was straight in nature. Seed pulp was removed by washing with water and dried in shade. These seeds were sown in polyethylene bags filled with mixture of sand and FYM in 2:1 ratio in nursery area of Forestry department, CCS Haryana Agricultural University, Hisar. On the basis of percent Germination and other growth parameters viz., survival (%), plant height (cm) and collar diameter (mm) recorded at 180 days of sowing. It was revealed that there was high variability among the seed lots. The seed lots collected from Yamunanagar district performed significantly better followed by Faridabad and Jind districts than the seeds lots collected from other districts of Haryana.

Key words: Azadirachta indica, germination percent, plant height and collar diameter

Azadirachta indica A. Juss (Neem), belonging to family Meliaceae, is one of the most useful tree species for both rural and urban population. In India neem is found upto 1200m altitude in hills, in regions of dry deciduous, thorn forest and in tropical evergreen forest (Schmutterer., 1995). It is largely found in the states of Utter Pradesh, Uttarakhand, Haryana, Punjab, Himachal Pradesh, Rajasthan, Delhi, Gujarat, Madhya Pradesh, Bihar, Maharashtra, Andhra Pradesh, Karnataka, Tamil Nadu, Kerala, Orissa, Wes Bengal and Assam. Neem trees are generally found in farmer’s field, meadows, along roads, canals and railways tracts etc. Neem grows in a variety of soil and climatic conditions.Neem is generally propagated by seeds; however, the seed have a short storage life and loose viability rapidly which is a major problem for tree planting programmes. The longevity of neem seeds appears very uncertain. Neem seed of Asian origin have been shown more or

less recalcitrant (Gamene et al., 1994) while those of African provenances as orthodox (Bellofontaine and Audinct., 1993). However, behavior of neem seed has been described as short lived (Ezumah., 1986) and (Maithani et al., 1989).However, elsewhere, the tree is reported for numerous uses and values including medicinal, insecticidal, fertilization and cosmetic industry in addition to other complementary uses making double attractive for incorporation in scale rural development effort (Ahmed and Graing., 1986).Neem tree is described as drought tolerant with no specific site requirement (Elteraifi et al., 2001). Thus it is suitable for the improvement of degraded lands and therefore, considered important in desertification control and reforestation programs in many parts of the country.

MATERIAL AND METHODSSeed sources of Neem (Azadirachta indica) trees were selected from Ambala,

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Karnal, Kurukshetra, Panipat and Yamunanagar, Jhajjar, Jind, Kaithal, Rohtak, and Sonipat districts, Bhiwani, Fatehabad, Hisar and Sirsa, Faridabad,

Gurgoan, Mahandergarh and Rewari districts of Haryana.. The details of study site have been given in Table-1.

Table-1. List of selected provenances collected from nineteen districts of HaryanaDistrict Name Longitude (N) Latitude (E) Altitude (m)Hisar 29008 75042 217Bhiwani 28049 76007 220Mahendagarh 28016 76009 270Rewari 28010 76037 247Gurgoan 28027 77001 229Jhajjar 28036 76038 215Rohtak 28052 76031 222Sonipat 28055 77006 219Panipat 29027 76058 235Karnal 29040 77000 248Kurukshetra 29058 76052 258Yamunanagar 30007 77016 275Ambala 30022 76046 274Bhadurgarh 28040 76055 216Kaithal 29048 76022 237Jind 29018 76019 226Fathebad 29031 75026 219Sirsa 29032 75000 205Faridabad 28024 77018 211

The survey was done during flowering season. Ten phenotypically superior mother trees from each provenance were selected on the basis of morphological characteristics viz., height (m), girth at dbh (cm), forking height(m), seed frequency, bole straightness and crown spread from four directions (viz., East, West, North and South) were presented in Table-2. The basic characters of mother

trees such as height, forking height, dbh were recorded which varied from 5.6 to 16.5 m, 1.5 to 9.5 m, 45.7 to 195.4 cm respectively. Crown spreads was measured in four directions (North, East, West, South) and its mean size varied from 2.8 to 9.8m. Seed bearing habit of selected plus tree of different districts varied from moderate to heavy and their stem was straight in nature.

Table-2. Characteristics of Neem trees selected from nineteen districts Haryana

DistrictsTree height (m)

Forking

height (m)

DBH(cm)

Seed frequenc

y

Bole straightnes

s

Crown spread (m)

North

West

South

East

Hisar 7.5 3.2 69.3 Heavy Straight 4.6 3.9 3.7 5.1Bhiwani 12.6 4.7 175.

8Heavy Straight 6.2 4.6 5.9 6.4

Mahandragarh

15.8 4.9 185.3

Moderate Straight 7.2 6.8 5.2 7.0

Rewari 9.6 3.4 63.2 Moderate Straight 4.2 5.6 5.0 4.8Gurgaun 5.6 1.5 55.3 Moderate Straight 3.4 1.9 2.1 3.8Jhajjar 9.6 3.0 81.5 Heavy Straight 3.8 2.5 2.1 3.4Rohtak 10.5 2.9 57.0 Heavy Straight 6.0 4.5 5.5 6.4Sonipat 10.5 5.1 47.5 Moderate Straight 3.9 3.2 3.5 4.8Panipat 6.5 2.7 52.3 Heavy Straight 4.9 3.1 2.6 5.4Karnal 12.2 5.3 87.9 Moderate Straight 6.1 5.6 4.9 6.8Kukrashtra 14.2 6.1 165. Heavy Straight 6.5 5.5 6.2 6.9

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5Yamunanagar 15.3 7.2 184.

5Heavy Straight 8.6 7.5 8.0 9.2

Ambala 13.6 6.7 154.3

Heavy Straight 7.0 6.7 6.2 7.2

Bhadurgarh 14.7 8.2 134.5


Kaithal 12.9 5.1 111.4

Moderate Straight 6.3 4.6 6.1 7.0

Jind 12.3 7.1 110.2


Fathaibad 9.9 5.6 45.7 Heavy Straight 6.7 3.8 4.2 6.1Sirsa 15.3 8.1 99.9 Heavy Straight 5.4 4.5 4.8 6.2Faridabad 16.5 9.5 195.

4Heavy Straight 10.0 9.0 9.4 10.8

Since, Neem trees were found either on road, canal side or agriculture field bunds, hence it become difficult to find out the actual age of the trees. Fresh seeds of equal maturity were collected by shaking the branches on a clean floor. Seeds from the selected superior trees in different seed sources were collected and make one composite sample from each provenance, depulped with the help of hand, washed and remove the pulp and foreign material from the seeds, dry them separately of each selected seed source from different district of Haryana and these seeds were sown in polyethylene bags filled with mixture of sand and FYM in 2:1 ratio in nursery area of Forestry department, CCS Haryana Agricultural University, Hisar. The growth of seedlings after 180 days of seed sowing was measured including parameters like survival percent, seedling height and collar diameter.

RESULTS AND DISCUSSIONGermination percent The results of the assessment of germination pattern of nineteen seed sources of Azadirachta indica showed significant outcomes. The germination percentages of the seeds across the

nineteen locations range between 54.0-91.0 percent (Fig.1). Out of nineteen provenances nine provenances namely Bhawani (55.7), Jhajjar (61.0), Sonipat (63.0), Panipat (54.3), Karnal (61.7), Ambala (54.0), Kaithal (61.0), Jind (66.3) and Faridabad (69.7) having lower than 75 percent germination was recorded and ten provenances namely Mahandragarh (91.0), Hisar (83.3), Rewari (80.0), Gurgaun (76.7), Rohtak (75.0), Kukrashtra (75.0), Yamunanagar (86.7), Punchkula (75.7), Fathaibad (78.3) and Sirsa (75.3) having more than 75 percent permination of neem seeds were recorded. Among the seed sources, seeds collected from Mahandragarh (91.0) districts recorded significantly higher germination percentage followed by Yamunanagar (86.7) and Hisar (83.3) districts whereas, seeds collected from Ambala (54.0) followed by Panipat (54.3) Bhawani (55.7) districts showed poor germination percentage. (Nayak et al., 2005) also reported that the seed germination of Neem seeds collected from different agro-climatic zones of Karnataka have shown significant differences.

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Fig-1: Seeds germination (%) of different seed sources

Survival percentage Azadirachta indica seedlings survival rate in nursery condition indicated that the survival percentage of the seedlings among the seed sources were different (Fig.2). Generally, the highest survival percentage of seedlings was recorded in Mahandragarh (80.7) followed by Hisar (74) and Rewari (70.7). On the other

hand, Panipat (49.7) seed source was characterized by the highest mortality rate followed by Bhawani (51.3) and Ambala (53.3) of seedlings from all the rest. (Bahru et al.,2014) reported that the survival rate of Tamarindus indica seedlings recorded at the age of four varied from 81 to 94 percent among the provenances.

Fig-2: Seedlings survival percentage of different seed sources

Collar diameterThe collar diameter (mm) of the seeds across the nineteen locations range between 2.0 to 3.5mm (Fig. 3). Collar diameter of Jind district followed by Faridabad and Punchkula districts was significantly higher than the Gurgaun and Rewari districts. (Jain and Dhar.,2008) also reported that collar diameter of

Azadirachta indica seedlings exhibited a range of 0.36cm to 0.58cm from Madhya Pradesh, 0.37cm to 0.57cm in Orissa and 0.35cm to 0.44cm in Chhattisgarh. statistical analysis revealed that there were 8 groups of provenances in which the means were not significantly different from one another.

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Fig-3: Seedlings collar diameter of different seed sources

Plant heightPlant height (cm) of neem seedlings collected from nineteen seed sources varies from 11.8 to 24.8cm (Fig.4) plant height of neem was found significantly taller of Faridabad district followed by Jind (24.7cm) and Yamunanagar (23.6cm) as compared to seeds collected from other districts of Haryana. Minimum plant height was recorded in Panipat district followed by Rewari and Mahandragarh districts. Neem growing in

different parts of India has adapted to its edapho-climatic conditions and has developed genetic variations. (Fredrick et al.,2015) revealed that existence of considerable variation among provenances with respect to seedling growth of F. albida. Highly significant variation in seed morphological characteristics among and within the provenances of F. albida may be due to both environmental and genetic variation and their interaction.

Fig-4: Seedlings height of different seed sources

CONCLUSIONIt may be concluded that there was high variability among the seed lots of Azadirachta indica. Seedling height and collar diameter of seed lots collected from nineteen districts of Haryana were taken as criteria for assessment of

superiority of seedlings. Seedlings having highest collar diameter and height will consider as superior. The seed lots collected from Yamunanagar district performed significantly better followed by Faridabad and Jind districts than the

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seeds lots collected from other districts of Haryana.

ACKNOWLEDGMENTThe authors are thankful to Dr. J.C.Kaushik, Professor, Department of

Forestry, CCS HAU, Hisar for providing valuable guidance and suggestions during experimentation.

REFERENCESAhmed, Salem and Graing, Michael 1986.

Potential for the neem tree (Azadirachta indica) for pest control and rural development. Economic Botany. 40(2): 201-209.

Bahru, T., Eshete, A., Mulatu, Y., Kebede, Y., Tadesse, W., Mohammed, O and Dejene, T 2014. Effect of provenances on seed germination, early survival and growth performance of Tamarindus indica L. in Ethiopia: A key multipurpose species. Advance in Materials Science and Engineering. 1(1): 1-8.

Bellefontaine, R and M. Audinet 1993. La conservation de grains de neem (Azadirachta indica A. Juss.). Tree seed problems with special reference to Africa (Some, L.M and M.de Kam eds.) Barkhuya Publishers, Leiden. The Netherlands. pp 268-274.

Elteraifi, I.E., Elnour, M.M and Mahjoub, S 2001. Effect of storage temperature, maturity stage and fruit pulp on viability of neem (Azadirachta indica) seed in Sudan. Proc. of the IUFRO Symposium of 2001, Philippines. pp 31-41.

Ezumah, B.S 1986. Germinaiton of storage of neem (Azadirachta indica A. Juss.). Seed Sci. and Technol. 14:593-600.

Fredrick,C., Muthuri, C., Ngaman, K and Sinclair, F 2015. Provenance variation in seed morphological characteristics,

germination and early seedling growth of Faidherbia albida. Journal of Horticulture and Forestry. 7(5): 127-140.

Gamene, C.S., H.L.Kraak and J.G.Pijlen 1994. Azadirachta indica seeds from Burkina Faso: intermediate storage behavior. Proc. Intl. Workshop on Desiccation Tolerance and Sensitivity of seeds and vegetative plant tissue, Kruger National Park. South Africa. pp 23.

Jain, Avinash and Dhar, Pranav 2008. Evaluation of provenances for seedling growth and biomass attributes in Azadirachta indica. A. Juss. Indian Forester. 907-915.

Maithani, G.P., V.K.Bahuguna, M.M.S.Rawat and O.P. Sood 1989. Fruit maturity and interrelated effects of temperatures and container on longevity of neem (Azadirachta indica) seed. Indian Forester. 115(2): 89-97.

Nayak, B.G; Janagiri, Parasurama; Kyatappanavar, Sanjeev and Shivanna, H 2005. Studies on seed quality and germination of Azadirachta indica in relation to provenances of different Agroclimatic zones of Karnataka. Karnataka Journal of Agricultural Sciences. 18(3): 706-709.

Schmutterer, H 1995. The Neem Tree. Sources of Unique National Products for Integrated Pest Management, Medicinal, Industry and other purpose. Weinhein, New York.

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IMPACT OF KRISHI VIGYAN KENDRA TRAINING PROGRAMMES ON KNOWLEDGE AND ADOPTION OF HOME SCIENCE AND AGRICULTURAL TECHNOLOGIES BY RURAL WOMEN

Seema Chawla1 and Chander Bhan2 1Assistant Professor (Home Sc.), KVK (SKRAU), Sriganganagar

2Assistant Professor (Horti.), ARS (SKRAU), Sriganganagar, RajasthanEmail: [email protected]


ABSTRACTKrishi Vigyan Kendra (KVK), Sriganganagar organise trainings to empower trainees in a more skilled and educated workforce. For this purpose KVK has to develop and adopt on-campus and off-campus training programmes. The training programmes are multipurpose to cover not only the varied needs of the rural women but also the entire needs of farming community. The term training refers to the acquisition of knowledge, skills, and competencies as a result of the teaching of vocational or practical skills. A study was conducted to find out the impact of training programmes on knowledge and adoption of selected home science and agricultural technologies imparted by Krishi Vigyan Kendra Sriganganagar to rural women. Total 250 trained rural women were selected for the study. The data revealed that majority of trained women had high level of knowledge with respect to soap making, papad & vadi making, processing and value addition and medium level of knowledge about seed treatment, vermi composting and dairy management. With respect to adoption, majority of trained women belonged to high level of adoption in soap making and papad & vadi making and medium level in processing and value addition. Dairy management technology adopted by more number of trained rural women where as low adoption was found in seed treatment and vermi composting technology. Major constraints faced by trained women in adoption of the technologies were financial assistance, non availability of raw materials, market facility, non co-operation and lack of family encouragement.

Key words: Management, knowledge, training, vermin compost, processing. Training is the process of improving the knowledge and skills, changing the attitude of an individual for doing a specific job. Along with the changing situation, the people also need to acquire new knowledge, skills and attitude to keep up with the changing environment. Rural women spend much of their time in unpaid activities like working in the family, farm and other domestic work (Sharma et al., 2013). Therefore training has continued to be considered as the most important device for developing an individual and improving his/her work efficiency. One of the main tasks of Krishi Vigyan Kendra is to provide and improve the level of knowledge of the trainees about the improved farm practices (Gupta and Verma, 2013). Krishi Vigyan Kendra Sriganganagar conducts many training

programmes exclusively for rural women with the aim to make them competent in performing various activities related to home science and agricultural sciences. Hence, the present study was designed to know the impact of KVK training programmes on knowledge and adoption of home science and agricultural technologies by the rural women with the specific objectives - to find out the knowledge level of trained rural women with regard to home science and agricultural technologies, to analyze the extent of adoption of technologies by trained rural women and to identify the problems in adoption of imparted technologies.

MATERIAL AND METHODSThe study was conducted in Sriganganagar district of Rajasthan state during 2015-16. A list of rural women of

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prepared by KVK Sriganganagar, who were imparted training on home science and agricultural technologies during this time period. Out of Nine Blocks/Tehsils of Sriganganagar district, highest number of trainees were observed in Sadulsahar, Karanpur, Padampur and Ganganagar blocks. From each Block/Tehsil name of villages were listed and final selection of villages were made based on availability of women trained from KVK. Total Two hundred and fifty rural women were selected for the selected six technologies. Out of the total sample, equal number of trained rural women were considered for each selected technology from the selected villages.To assess the impact of trainings on rural women, six home science and agricultural technologies viz., soap making, papad & vadi making, processing and value addition, seed treatment, vermi composting and dairy management were selected for the study. The data was collected from trained rural women with the help of pre tested schedule by personal interview technique in an informal atmosphere. Mean and standard deviation were used for classification of respondents into various categories.

RESULTS AND DISCUSSIONKnowledge level of rural women about home science and agricultural technologiesIt could be observed from the Table 1 and Figure 1 that, regarding soap making technology majority of the trained women (60.00%) belonged to high level of knowledge followed by medium (24.00%) and low (16.00%). This showed that when educational efforts by way of training were made, it might be possible to increase their knowledge. It was also showed highly significant association between training and knowledge level about soap making. The findings of the

study are similar with the findings of the Nazir et al., (2012) in which after training majority of the women (65.5%) were belonged to high level of knowledge category. In case of preparation of papad & vadi making, it was found that more number of trained women (52.00%) were belonged to high level of knowledge followed by low (27.20%) and medium (20.80%) level of knowledge. Similar findings were reported by Dubey et al., (2008) that 74.67 per cent of trained women had high level of knowledge followed by medium (24.00%) and low level of knowledge (1.33%). Training also created awareness and knowledge about the yearlong products such as papad & vadi and made them to participate in the demonstration attentively and actively. These may be the possible reasons to have good knowledge about this technology. Chi-square test indicated high significant association between training and knowledge about papad and vadi making. The findings of the present study are in agree with the findings of the Bala et al., (2006). He reported that majority (68.67%) of the women belonged to self-help groups (SHGs) were in ‘average knowledge’, about 10 per cent were in ‘above average knowledge’, and rest of participants in ‘below average knowledge’ groups. The data related to knowledge level of rural women about preparation of processed products indicated that 42.40 per cent of trained women had high level of knowledge followed by 36.00 per cent and 21.60 per cent who possessed medium and low level of knowledge respectively. Similar findings were reported by Chauhan (2012) wherein, majority of women (85.00%) were belonged to low level of knowledge category whereas, after training 80.00 per cent of women belonged to high level of

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knowledge category. Possession of good knowledge might be due to appropriate training received by the women. This trend is in confirmation with the research findings conducted by Kumari et al., (2010). She reported that majority of the respondents had low level of knowledge about nutrition practices before training and after training there was a significant gain in knowledge of all the components of nutrition domain included in the training programme. The data presented in the Table 1 and Figure 1 further revealed the knowledge level of trained and untrained women about seed treatment, which indicate that more than sixty per cent of trained women (46.40%) had medium level of knowledge followed by high (30.00%) and low (24.40%) knowledge level. This might be because of participation in training and also trained women knew the importance of seed treatment. It was also showed highly significant association between training and knowledge about seed treatment. The findings are in consistent with the findings of Deo et al., (2010) wherein, before training majority of the women (80.00%) had low level of knowledge whereas after the training, majority of the women (66.60%) had medium level of knowledge. With regard to vermi composting, majority of the trained women (49.20%) were from medium knowledge category followed by high (26.00%) and low (24.80%) The findings of the study were in conformity with the findings of Gupta and Verma (2013) that majority of farm women (55.55%) were belonged to medium level of knowledge followed by high (23.33%) and low level of knowledge (21.11%) regarding home science and agricultural technologies. Less number of trained women were found in high knowledge category.Chi-square test indicated high significant association between knowledge level and

training. Similar findings were reported by Binkadakatti (2008) that majority (68.67%) of the women belonging to self-help groups (SHGs) were in ‘average knowledge’ groups.In case of dairy management, more number of trained women (55.20%) had high level of knowledge followed by medium (44.80%) and low (27.60%). In rural areas almost all the families had buffalos and cows. Participation of rural women is more in dairy management. She takes the responsibility of caring the animals like her family members. It also exhibited highly significant association between knowledge about dairy management and training (Table 2). Similar findings were reported by Ajrawat and Kumar (2012) that majority of the respondents belonged to high knowledge category (73.00%) followed by medium (26.00%) and low (1.00%) category.Adoption level of trained rural women about home science and agricultural technologiesA close perusal of Table 2 and Figure 2 elicits that, more number of trained women (52.00%) belonged to high level of adoption followed by low (27.60%) and medium (20.41%) level category with respect to soap making. Similar study reported by Kharatmol (2006) that majority of the trained respondents (45.00%) had high level of adoption with respect to vermi compost technique followed by medium (40.00%) and low level of adoption (15.00%). The probable reason for high adoption of this technology may be the methodology of preparation of soap making was found to be easy to understand and useful because of proper guidance given to the trainees. In case of preparation of papad and vadi making, more than fifty per cent of trained women (62.4%) were belonged to high level of adoption followed by medium (25.20%) and low level

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(12.40%) adoption. Availability of raw material in their farm and local market, easy procedure of the recipe, taste of products liked by the family members, attending up to last stage of training programme, consuming less time for preparing, giving much importance to learn new things were the possible reasons for high adoption of papad and vadi making. The findings of the study were in conformity with the findings of Panwar et al., (2006). The data projected in the Figure 2 indicated that 50.40 per cent of trained women had medium level of adoption followed by 33.20 per cent and 16.40 per cent who possessed high and low level of adoption respectively with regard to preparation of processed products. The findings of the present study agree with the findings of the Borua and Brahma (2012) that majority of the trained youth (53.75%) had medium level of adoption on selected technology practices while, 26.25 per cent and 20.00 per cent of them had high and low level of adoption on technology practices respectively. The reason for medium adoption of processed and value added products is that the trained women perceive it as difficult to prepare in proper consistency and hence showed less interest in preparation of processed products.The data presented in table 2 with respect to adoption level about seed treatment revealed that, more than fifty per cent of trained women (52.40%) had low level of adoption followed by medium (31.20%) and high (16.40%) adoption level. Remembering the chemicals name by rural women and non availability of chemicals in the nearby market may be other probable reasons for low adoption. The findings of the study were in conformity with the findings of Borua and Brahma (2012) that majority of the trained youth (53.75%) had medium level of adoption on selected technology

practices while, 26.25 per cent and 20.00 per cent of them had high and low level of adoption on technology practices, respectively. Table 2 further depicts that more than two third of respondents (79.2%) had low level of adoption for vermi composting and 11.2% rural women had medium level of adoption followed by 9.6% respondents with high level of adoption.In case of dairy management, about fifty per cent of women (51.20%) had high level of adoption followed by medium (39.60 %) and low (09.20 %) adoption level. Training and guidance given to the trainees have played prime role in influencing the adoption of technology and money saving also permits house wives to earn some money may be the reason for high adoption of dairy management. The findings are in consistent with the findings of Chauhan (2012) that majority of the farm women (88.00%) had high level of adoption regarding scientific cultivation of kitchen gardening followed by low level of adoption (07.00%) and medium level of adoption (5.00%). The study showed less adoption of vermi compost making technology. The reason for this could be that, whatever the rural women had learnt under the training situation, they must have found it difficult to practice under actual field condition, due to several practical problems. This could be explained by the fact that acceptance might be there, for the new technology in a training situation, but behavioral adoption would not have taken due to several situational constraints like financial assistance, marketing channels, less land, protection from enemies like termites, ants, flat worms, poultry birds etc. Lack of guidance and less no. of follow up visits to encourage rural women to adopt vermi composting may be reasons for less adoption.

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Constraints in adoption of home science and agricultural technologies by trained rural women. Table 3 indicated that, majority of them stated lack of adequate time (37.20 %) as their main constraint as their main occupation of the family is agriculture and fully engaged in farm, they may not get time to practice non-farm activities followed by lack of financial assistance (30.40%). Lack of guidance (09.60%), non-availability of raw materials (08.40%), non co-operation and lack of family encouragement (06.40%), lack of market facility (04.80%) and high cost of raw materials (03.20%) were other reasons in adoption of home science and agricultural technologies. The findings of the present study are in confirmation with the findings of the Santhi et al., (2013) wherein, 38.67 per cent of women were unable to give sufficient time for carrying out the activity and marketing of the products, lack of financial assistance and non co-operation at home were other reasons for not adopting technologies. Santhi et al., (2013) studied employment generating technology transfer by Krishi Vigyan Kendra as a means for empowering rural women in

Kancheepuram district and reported that 38.67 per cent of women were unable to give sufficient time for carrying out the activity and marketing of products, lack of financial assistance and non co-operation at home were other reasons for not adopting technologies.

CONCLUSIONThe efforts put by KVK staff had a good impact on knowledge gain and adoption of home science and agricultural technologies of rural women. Training programme helped in capacity building of rural women by creating awareness, increasing the knowledge about innovative technologies and practicing improved skills which will help in the empowerment of rural women. The adoption of home science technologies found to be good compare to agricultural technologies. The adoption level of some technologies found to be less mainly because of lack of financial assistance, non co-operation and lack of family encouragement, lack of market facility, non availability of raw materials etc. Hence the KVK Sriganganagar should conduct feedback and follow up visits to get still better impact on imparted trainings.

Table 1. Knowledge level of rural women about home science and agricultural technologies.

Technology Knowledge Level (N=250)Low Medium High

Soap Making 40 (16%) 60 (24%) 150 (60%)Papad & vadi making 68 (27.2%) 52 (20.8%) 130 (52%)Processing and value addition 54 (21.6%) 90 (36%) 106 (42.4%)Seed Treatment 61 (24.4%) 116 (46.4%) 73 (29.2%)Vermi composting 62 (24.8%) 123 (49.2%) 65 (26%)Dairy management 69 (27.6%) 112 (44.8%) 69 (27.6%)Table 2. Adoption level of rural women about home science and agricultural technologies.

Technology Adoption level (N=250)Low Medium High

Soap Making 69 (27.6%) 51(20.4%) 130 (52%)Papad & vadi making 31 (12.4%) 63 (25.2%) 156 (62.4%)Processing and value addition 41 (16.4%) 126 (50.4%) 83 (33.2%)Seed Treatment 131 (52.4%) 78 (31.2%) 41 (16.4%)Vermi composting 198 (79.2%) 28 (11.2%) 24 (9.6%)Dairy management 23 (9.2%) 119 (39.6%) 128 (51.2%)

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Table 3. Constraints in adoption of home science and agricultural technologies by trained rural women. (N=250)Constraints Home Science and Agricultural Technology

F %Non availability of raw materials 21 8.4High cost of raw materials 8 3.2Lack of financial assistance 76 30.4Lack of market facility 12 4.8Lack of guidance 25 9.6Lack of adequate time 93 37.2Non co-operation and lack of family encouragement

16 6.4

Figure 1. Knowledge level of rural women about home science and agricultural technologies.

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Figure 2. Adoption level of rural women about home science and agricultural technologies.

REFERENCESAjrawat, B. and Kumar, A. 2012. Impact of

KVK training programme on socio-economic status and knowledge of trainees in Kathua District. J. Krishi Vigyan, 1(1): 31-34.

Bala, B., Sharma, S.D. and Sharma, R.K. 2006. Knowledge and adoption level of improved technology among rural women owing to extension programmes. Agric. Econ. Res. Rev., 19: 301-310.

Binkadakatti, J.S. 2008. Impact of Krishi Vigyana Kendra trainings on use of bio-fertilizers and bio-pesticides by tur farmers in Gulbarga district. M.Sc. (Agri.) Thesis, Univ. Agric. Sci., Dharwad, Karnataka (India).

Borua, S. and Brahma, A.K. 2012. A study on the knowledge level and extent of adoption of selected technology by rural youth trained in KVKs of AAU in Aassam. J. Acad. Indus. Res., 1(7): 374-378.

Chauhan, N.M., 2012. Knowledge level of farmers regarding package of practices for gram crop. J. Krishi Vigyan, 1(1): 46-48.

Deo, S., Sarkar and S.R. and Sil, A. 2010. Analysis of training effectiveness of handloom weaving and value addition. Indian J. Extn. Edu., 46(3&4): 62-66.

Dubey, A.K., Srivastva, J.P., Singh, R.P. and Sharma, V.K. 2008. Impact of KVK training programme on socio-economic status and knowledge of trainees in Allahabad district. Indian Res. J. Ext. Edu., 8(2&3): 60-61.

Gupta, S. and Verma, S. 2013. Impact of KVK on knowledge level of farm women. J. Rural Agric. Res., 13(2): 87-89.

Kharatmol, S.N. 2006. Impact of trainings conducted on vermicompost by Krishi Vigyan Kendra, Bijapur. M. Sc. (Agri.) Thesis, Univ. Agric. Sci., Dharwad, Karnataka (India).

Kumari, M., Srivastava, A.K. and Sinha, N. 2010. Extent of knowledge of farm women

on nutrition. Indian Res. J. Ext. Edu., 10(1): 65-68.

Malabasari, R.T. and Hiremath, U.S. 2016. Effect of krishi vigyan kendra training programmes on knowledge and adoption of home science and agricultural technologies. J. Farm Sci., 29(2): 251-256.

Meti, S.K. 2013. Social and economic empowerment of farm women in agro based entrepreneurship for sustainable income. Proc. Int. Conference on Social Sci. Res., 4-5 June 2013, Penang, Malaysia, p.1031.

Nazir, T., Vaida, N. and Dar, M.A. 2012. Impact of Krishi Vigyana Kendras in empowerment of rural women. Researcher, 4(12): 30-33.

Panwar, P., Gupta, P. and Rathore, N.S. 2006, Smokeless-chulha an important drudgery reducing technology for farm women in Bhilwar district of Rajasthan. Indian Res. J. Extn. Edu., 6(3): 1-4.

Raghuprasad, K.P., Madhu Prasad, U.L. and Gangadharappa, N.R. 2008. Constraints faced and suggestions expressed by resource poor SC or ST women in oyster mushroom cultivation. Asian J. Extn. Edu., 37(1 &2): 176-180.

Santhi, P., Sathyavathimuthu and Bhuvaneswari, K. 2013. Employment generating technology transfer by Krishi Vigyan Kendra as a means for empowering rural women. American Int. J. Res. Sci. Technol., Engineering Mathematics, 2(2): 213-219.

Shankara, M.H., Mamatha, H.S., Srinivasa Reddy K.M. and Desai, N. 2014. An evaluation of training programmes conducted by Krishi Vigyan Kendra, Tumkur, Karnataka. International Journal of Farm Sciences, 4(2): 240-248.

Sharma, P., Singh, G.P. and Jha, S.K. 2013. Impact of training programme on knowledge and adoption of preservation technologies among farm women- A comparative study. Indian Res. J. Ext. Edu., 13(1): 96-100.

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KNOWLEDGE LEVEL OF DIFFERENT CATEGORIES OF FARMERS ABOUT IMPROVED MUSTARD PRODUCTION TECHNOLOGY IN

DISTRICT AJMER, RAJASTHAN

D. S. Bhati, Ramakant Sharma, R. Porwal, S.K.Sharma and Dinesh AroraSMS, KVK, AJMER, SKN Agriculture University, Jobner


ABSTRACTOn the whole majority (about 63 per cent) of the farmers had medium to high knowledge level about the recommended mustard production technology. Majority (about 78 per cent) of big farmers were also found to have medium to high knowledge level about recommended practices of mustard cultivation. While 74.60 per cent and 75.50 per cent of small and marginal farmers, respectively showed low to medium knowledge level about recommended mustard production technology. There was a significant difference between the big & small and big & marginal farmers as 'far as their knowledge about improved production technology of mustard was concerned. Whereas there was non-significant difference between the small & marginal farmers knowledge about recommended mustard production technology.

Key words: Category, marginal, technology, awareness, improved.

Mustard is one of the main oilseed crops in Rabi season. Our vegetable oil requirement by the end of 2020 AD was estimated around 35 million tones of oil seeds against the present production of 25 million tones. This situation, in fact, is very alarming. Obviously, there is an urgent need for increasing the production of oilseeds in the country. To cope up with this situation, our research scientists, extension workers and farmers have great responsibility to maximize the production of oilseeds which is only possible, if farmers have a knowledge and awareness about the new technology which is recommended by the Agriculture Research Scientists specially for the rapeseed-rnustard crop,' then they may adopt the recommended practices.Because in the direction of adoption process of any practice, knowledge and awareness about such technology and practice must be necessary. Therefore first. we go for the study of knowledge level of .different categories of farmers about improved mustard production technology. For this purpose State Agriculture Department, Krishi Vigyan Kendra organize various activities to

create awareness and increase the knowledge of farmers about improved mustard production practices. A large numbers of yield maximization trials laid out at the research stations as well as at the farmers fields have shown the potentiality of the new technology to, be highly effective in concern of knowledge improvement about new technology and by the improvement in knowledge level of farmers about mustard cultivation the gap of recommended and adopted practices may reduce by adopting the recommended practices. The Objective was the empirical measures and quantification of the knowledge level of different categories of farmers about improved technology of mustard cultivation.


The Ajmer district of Rajasthan was purposely selected for the study. There were nine Panchayat Sarnities in Ajmer district. Out of these, three Panchayat Sarnities namely Pisangan, Vijaynagar and Baiwar were selected by simple random sampling technique. The Panchayat samities Pisangan, Kekri and

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Bhinay comprise of 35, 31 and 35 Gram Panchayats, respectively. Out of which 2 Gram Panchayat were selected randomly from each of the three selected Panchayat Samities making a total of Gram Panchayats. Two villages from each of the above six selected Gram Panchayats were selected randomly comprising a total of 12 villages. A list of all big, small and marginal farmers, who have been cultivating mustard, was prepared for each selected village with the help of Patwari and VLW/Agriculture supervisor. Out of those 10 respondents consisting of big, small and marginal farmers from each village were selected with the help of probability proportional. By this way the total sample for the present investigation was 120 respondents. The present study aims at finding out the level of knowledge of the farmers with regard to recommended package of practices for mustard cultivation.


Knowledge level of farmers about the recommended mustard production technology:On the basis of knowledge scores, the knowledge level of respondents was classified into four categories viz. low, medium, high and extremely high. The farmers were then classified into four groups accordingly as presented below: (i) The farmer who obtained a score between 0 to 25 per cent were categorized as having low knowledge level;(ii) The fanner who got a score between 26 to 50 per cent were categorized as having medium knowledge level; (iii) The farmer who secured a score between 51 to 75 per cent were classified under high knowledge level; and (iv The farmers who obtained. a score between 76 to 100 per cent were categorized as having extremely high

knowledge level. Statistical data regarding the knowledge level of different categories of farmers about the recommended practices of mustard cultivation have been presented in Table 1.

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Table 1: Knowledge level of different categories mustard production technology N=120Farmers categories Percentage of farmers under different knowledge levels based on

scoresLow(0-25%) Medium (26-50%) High (51-75 %) Extremely high (76-100%)

Big farmers (N=12) 19.7 38.19 40.12 1.99Small Farmers (N=45) 39.4 35.2 21.16 4.24Marginal farmers (N=63) 43.96 31.54 23.04 1.46Over all knowledge level 34.35 34.97 28.10 2.56It is obvious from the data in Table-l that 34.35 percent, 34.97 per cent, 28.10 per cent and 2.56 per cent fam1ers fell under the knowledge categories of low, medium, high and extremely high, respectively as far as their overall knowledge about the recommended production technology of mustard was concerned. It may be indicated that more than half of the farmers were having medium to high knowledge level about the recommended production technology of mustard.Further, farmers' category-wise analysis revealed that there were 19.7 per cent 38.19 per cent, 40.12 per cent and 1.99 per cent of big farmers who could be categorized under low, medium, high knowledge and extremely high knowledge levels respectively. Whereas, 39.40 per cent, 35.20 per cent) 21.16 per cent and 4.24 per cent of the small farmers were having low, medium, high and extremely high knowledge about mustard 'production technology, respectively. However, there were 43.96 per cent, 31.54 per cent, 23.04 per cent and 1.46 per cent marginal farmers who could be categorized as having low, medium, high and extremely high knowledge about mustard production technology, respectively. It may be deduced from the above description that more than 3/4 of the big farmers were having medium to high knowledge regarding mustard production

technology. Whereas about 3/4 of the small as well as marginal farmers were having low to medium knowledge about improved practices of mustard cultivation. Sharma Ramakant, et. al. 2014 and D.P. Rai1 , et. al. 2012, also, reveals that the majority of the respondents were observed in medium category of knowledge.

RECOMMENDATIONS

1. Majority of farmers comes under the small & marginal categories and they had low to medium knowledge level about Rapeseed-Mustard cultivation. Thus we must concentrate on these categories.

2. Although the farmers in general possessed medium to high knowledge level about mustard production technology but there is a still scope to convert medium knowledge into high knowledge and high into extremely high knowledge level.

3. For above said purposes, practical training courses should be introduced for the farmers.

4. While preparing practical training courses farmers profile, namely knowledge, size of family, education, age, caste, occupation, social participation, farm power, farm implements, source of information utilized, irrigation potentiality, credit behaviour and cropping intensity be kept in mind.

REFERENCEBana, S.S., Sharma, N.K., Badhala, B.S. and

Ghaswa, Ramdhan, 2013. Knowledge of beneficiary farmers as compared to non-beneficiary farmers about recommended

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bajra production technology. Agric. Update, 8(4): 632- 634.

Bishnoi Manmeet, Sisodia S.S., Ranawat Yogita, 2016. “Knowledge of farmers about improved Bt cotton production technology”, Jr. Of Progressive Agriculture, 7 (2): 64-66.

D.P. Rai1 , Santosh Kumar Singh and Sachindra Kumar Pandey, 2012. “Extent of Knowledge and Adoption of Mustard Production Technology by the Farmers”, Indian Res. J. Ext. Edu. 12 (3): 108-111.

Kalla, P .N., Mathur, P. and Panjabi, N.K., 2001. "Knowledge of fanners about chemical fertilizers". Rural India, Vol. 64 (7-8), 146-147.

Kamlesh Kumar, 2011, “Assessment of information need of farmers about mustard production technology in Gohad block of Bhind District Madhya Pradesh”, PhD Thesis unpublished Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya Gwalior.

Kumar Vikas and Sisodia S.S. 2016. “Knowledge of farmers about improved ginger (Zingiber officinale L.) production

technology in UDAIPUR district of Rajasthan”, Jr. Of Progressive Agriculture, 7 (2): 98-100.

Rathore Sachin, Raghuwanshi Sheela, Bisht Kamini, Singh S.P., 2016. “Knowledge level of farmers on fish production technology in Tikamgarh district of Madhya Pradesh”, Jr. Of Progressive Agriculture, 7 (1): 50-53.

Sharma Ramakant, Arora D., Porwal R., Bhati D.S., 2014. “Knowledge empowerment of green gram growers through frontline demonstrations”, Jr. Of Progressive Agriculture, 7 (1): 50-53.

151

STATISTICAL ANALYSIS OF SOCIO-ECONOMIC AND CULTURAL DETERMINANTS AFFECTING POPULATION GROWTH IN

RAJASTHAN

Mana Ram Jakhar1 and Pankaj Nagar2

1Research scholar and 2Asso. Professor, Department of Statistics, University of Rajasthan Jaipur


ABSTRACTThe main goal of population policy 2000 is control the population growth rate. Population growth rate depends upon various socio economic and cultural variables. In this paper, a statistical analysis of population growth rate of desert area of Rajasthan (total and rural) those are factorized on various socio-economic variables referred to as factor here. The major finding revealed that literacy rate has influence over population growth rate in desert area of Rajasthan.If population policy is implemented effectively than the literacy rate is increase than the population growth rate will certainly decrease to great extent in desert area of Rajasthan. The data for literacy rate (rural and total) across desert area of Rajasthan for census year 2011 are considered.

Key words: population growth rate(PGR),population growth raterural(PGRR), literacy rate(lit), regression, literacy rate rural(lit),

The importance of population studies in India has been recognized, since very ancient times the Arthashastra, of Kautilya gives a detailed description of how to conduct a population economic and agriculture census. During the region of Akabar, Abu Fatal compiled the Ain-A-Akbar containing comprehensive data on population industry and other characteristics of population. During the British period, system of decennial census started with the first census in 1872. The first census of independent India was conducted in in 1951, which was seventh census in its continuous series. The population growth of a region and its literacy rate are closely linked .India has been a victim of population growth. According to C.P. Blacker four level of population growth. In first stage, the birth rate and death rate high and population growth is low. In second stage birth rate remain high but death rate is low and population growth rate very high. In third stage birth rate comparative second stage is low and death rate in normal. And population growth rate comparative second stage low. In fourth stage birth

and death rate is very low and population growth also low. There are many reseason of high birth rate or population growth. The illiteracy rate is most reason of high birth rate. In educated families fertility rate are not only less compared to uneducated families rather with the l academic level would decrease. The low female literacy rate has had a dramatically negative impact on family planning and population stabilisation efforts in India. Studies have indicated that female literacy is strong predictor of the use of contraception among married Indian couples, even when woman do not otherwise have economic independence.Government of India introduced population policy for population control in 2000, In which literacy rate increase has been taken as an important component. Rajasthan is the biggest state in India covering the area of 342239sqkm. Or 10.4% of India’s total area, Desert of Rajasthan is the biggest part covering 61% of Rajasthan’s total area. The desert area is spread over 12 district of the state, In which of 40% of Rajasthan’s population resides.

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Sourcesofdata:-Secondary data used in this study. The literacy rate and population growth rate are used as secondary data. Data were drawn from primary census abstract, census of India 2011. District wise literacy rate and district wise rural literacy rate are taken as explanatory variable. Literacy rate is a social factor.Objective of study:-Low literacy rate and high population growth rate is an open challenge for Rajasthan govt. This studyprovides answer to the following research questions:-What is the magnitude of effect of literacy rate on population growth rate; What is the magnitude of effect of rural literacy rate on rural population growth rate. The main objective of this research paper is to examine the effect of literacy rate on population growth rate of desert areaof Rajasthan. H0:β1=0 (literacy rate has no significant effect on population growth rate)H01: β1=0 (Rural literacy rate has no significant effect on rural population growth rate.)

RESULT AND DISCUSSION

The research instrument employed in the course of this analysis is the econometric method because it facilitates model speciation, parameter estimation and the conduct of appropriate statistical and econometric tests.The techniques for evaluation of the result will be based on economic a priori expectations, statistical tests of significance and econometric tests.

The model is specified Linear regression model: Pop =β0+β1*lit+µi

Power function model: Pop=β0* β1

**lit;Exponential model: Pop=β0exp (β1*lit+µi)

Evaluation based on economic criteria:-Under these criteria, the a priori expectation (size and signs) of the parameter estimates of the variables in the model will be evaluated to check whether they conform to economic theory.Evaluation based on statistical criteria:-R2: this major or explain the total variation in the dependent variable (population growth (total and rural) rate) caused by variation in the explanatory variable (literacy rate).t-test this test is used to test whether the variables included in the model are individually statistically significant or not. F-test this test is used to determine the overall significance of the regression model.We reject H0 if the P-value of F obtained is sufficiently low. From the table-4, we conclude that the constant conformed to a priori expectation.PGR represents Population growth rate and it is positive. It stipulates that PGR will increase by 102.8 when literacy rate is not operational.From the table-4, we conclude that the result of our study supports the hypothesis of a negative relationship between literacy rate and Population growth rate. This implies that a 1% increase in literacy rate will decrease population growth rate by 1.24%.From the table-1, the adjusted R2 is 0.77. The value of R2 shows that the model explains variations in PGR to the tuneof 77%.t-test:- this used to test the significance of each of the parameter estimates with (n-k) df at 5% level of significance. The following hypothesis is tested: H0: β1=0 (The parameter estimated are not statistically significant), At α= 5% with n-k dfDecision rule:Reject H0 if tcal>ttabor tcal<-ttal; Accept it otherwise.

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Where:-N=number of observation; K= number of parameter estimates; α= level of significanceFrom the table-5, we observe that the tcalis greater than the ttab hence we reject null hypothesis at 5% level significance.We can say that the variables (literacy rate) are significance at 5% level. Thus they have significant impact on population growth rate.F-test, the f-test measure the overall significance of the model, the null hypothesis is stated thus: H0: β1=0(the model is not significant), At α=5% with (k-1) and (n-k) df; Decision rule: reject H0 if Fcal>Ftab(v1,v2)df, Accept it otherwise.From the table-1,Since Fcal(37.49)>Ftab(5.99). We reject H0 and conclude that the overall regression is statistically significant at α=5% level of significance it implies that the model has a good fit. This means that individual independent (literacy rate) variable significantly impact on population growth. That is, there exist a significant relationship between the Population growth rate and literacy rate.Result and DiscussionruralFrom the table-7, we conclude that the constant conformed to a priori expectation .PGRR represents Population growth rate rural and it is positive. It stipulates that PGRR will increase by 108.28 when literacy rate is not operational.From the table-7, we conclude that the result of our study supports the hypothesis of a negative relationship between literacy rate rural and Population growth rate rural. This implies that a 1% increase in literacy rate rural will decrease population growth rate rural by 1.41%.From the table-2, the adjusted R2 is 0.934. The value of R2 shows that the model explains variations in PGRR to the tune of 93.4%.

t–test:-this is used to test the significance of each of the parameter estimates with (n-k) df at 5% level of significance. The following hypothesis is tested.H0: β1=0 (The parameter estimated are not statistically significant); At α= 5% with n-k df (degree of freedom); Decision rule: Reject H0 if tcal>ttabor tcal<-ttab, Accept it otherwise.Where:-N=number of observation; K= number of parameter estimates; α= level of significanceFrom the table-8, we observe that the tcalis greater than the ttab hence we reject null hypothesis at 5% level significance.We can say that the variables (literacy rate rural) are significance at 5% level. Thus they have significant impact on population growth rate rural. F-test:- the f-test measure the overall significance of the model, The null hypothesis is stated thus: H0 : β1=0(the model is not significant), At α=5% with (k-1) and (n-k) df, Decision rule : reject H0 if Fcal>Ftab(v1,v2)df,Accept it otherwise.From the table-2, we observed that the,Fcal(90.32)>Ftab(5.99). We reject H0

and conclude that the overall regression is statistically significant at α=5% level of significance it implies that the model has a good fit. This means that individual independent (literacy rate rural) variable significantly impact on population growth rate rural. That is, there exist a significant relationship between the Population growth rate rural and literacy rate rural.Evaluation of working hypothesisResearch hypothesis of this study includeH0:β1=0(literacy rate has no significant effect on population growth rate)H01:β1=0 (Rural literacy rate has no significant effect on rural population growth rate)These hypotheses can be evaluated from the result of our model. From the t-test that were carried out on the explanatory

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variable (literacy rate total and literacy rate rural). We found literacy rate (rural and total) to be statistically significant. This means that literacy rate (total and rural) scientifically impact on population growth rate(total and rural).We there for draw the following conclusions based on the following above:1. For the first hypothesis, we reject the

null hypothesis that literacy rate has no significant effect on population growth rate and accept the alternative hypothesis.

2. For the second hypothesis, we reject the null hypothesis that literacy rate rural has no significant effect on population growth rate rural and accept the alternative hypothesis.

CONCLUSION

This study examines the impact of literacy rate (total and rural) on

population growth rate (total and rural). Our conclusions are that population growth rate (rural and total) formed a significant relationship with literacy rate (rural and total). It can be concluded by results that literacy rate (total and rural) have influence over the population growth rate (total and rural) in desert area of Rajasthan. The literacy is low in desert area of Rajasthan and the population growth rate is high. Government conduct any programme for literacy. If these acts are implemented effectively than the literacy rate is increase than the population growth will certainly decreased to a great extent in desert area of Rajasthan. By increasing literacy rate cent present population growth rate cannot be decreased because there are some socio-economic and cultural variables which are also responsible for population growth rate in desert area of Rajasthan (total and rural ).

Table -1 ANOVA(a) (Pgr total)Source Sum of Squares df Mean SquaresRegression 4327.478 4 1081.869Residual 115.412 4 28.853Uncorrected Total 4442.890 8Corrected Total 504.609 7Dependent variable: PGR2011a R squared = 1 - (Residual Sum of Squares) / (Corrected Sum of Squares) = .771.

Table -2 ANOVA(a) (Pgrr)Source Sum of Squares df Mean SquaresRegression 4382.359 4 1095.590Residual 48.551 4 12.138Uncorrected Total 4430.910 8Corrected Total 737.209 7Dependent variable: GRR2011a R squared = 1 - (Residual Sum of Squares) / (Corrected Sum of Squares) = .934.

Table-3 (regression result pgr)Variable coefficient Std. error t-statistics Probe valuePGR 102.8 18.83 5.46 0.001Literacy rate -1.24 0.28 -4.29 0.005

Table -4 (Evaluation of Regression Result)Variable Sign Interpretation

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PGR Positive Conformed to a priori expectationLiteracy rate Negative Conformed to a priori expectation

Table-5 (t-test value)Variable t-value t-tab decision ConclusionPgr 5.46 2.365 reject SignificanceLiteracy -4.292 -2.365 reject SignificanceTable-6 (regression result pgrr)Variable coefficient Std. error t-statistics Prob valuePgr 108.28 17.35 6.24 0.0007Literacy rate -1.41 0.28 -5.02 0.002

Table-7 (Evaluation of Regression Result)Variable Sign InterpretationPgrr Positive Conformed to a priori expectationLiteracy rate rural Negative Conformed to a priori expectation

Table-8 (t-test value)Variable t-value t-tab decision ConclusionPgr 6.24 2.365 reject SignificanceLiteracy -5.02 -2.365 reject Significance

REFERENCESMukherjee P. Reducing out-of-school children in

India, national university of education planning and administration, Delhi (2011).

Damoder, N. Gujarati (2009) “Basic Economics” Tata McGraw – Hill Book company limited, New York.

Nwosu C. , Dike A.O. “The effect of population growth on economic growth in Nigeria” Department of statistics, federal university of agriculture Makurdi.

http://www.censusindia.gov.in

Census of India (2011) Final Population totals, series 1: India, Registrar general and census commissioner, India.

Agrawal S.N. (1983) India population problem. Tata McGraw Hill Publishing New Delhi.

Gupta M.K, Prasad J. (2013) “statistical analysis of socio-economic variable responsible for wastage in elementary education in Rajasthan, India” department of statistics university of Rajasthan.

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ASSESSMENT OF LOSSES IN BRINJAL INFESTING MITE, Tetranychus neocalidonicus andre (Acari tetranychialae)

Sharma, S.L. and Sharma, A.Department of Entomology, SKN College of Agriculture, Jobner (Jaipur)

Email:- [email protected]: 18.10.16Accepted: 17.02.17

ABSTRACTBrinjal (Solanum melongena L.,) is an important vegetable crop grown through out the India. A large number of pest and non-insect pest are found to attack the crop during its growing period which result loss of yield. A cage experiment was conducted to estimate the avoidable losses specially against infestation due to the mite, Tetranychus neocalidonicus. The mite caused an avoidable loss of 35.10 per cent in first year and 25.40 per cent in second year was estimated during the course of investigation..

Key words: Redspider mite, Avoidable loss, Brinjal.

Brinjal or eggplant, Solanum melongena L., native of India, is a widely grown vegetable crop in India, Asia and Southern Europe. It is an important vegetable of high nutritive value, consisting of minerals like Iron, phosphorous, calcium and vitamin A, B and C.A large number of factors have been reported responsible for limiting the yield of brinjal, out of which insect and non-insect pests are major ones ( Prasad and Singh, 2011). Though, many species of tetranychid mites viz., Tetranychus urticae Koch, T. ludeni Zacher. and Tetranychus neocalidonicus Andre have been reported to attack the brinjal crop (Nair, 1970) but red spider mite, Tetranychus neocalidonicus Andre is one of the serious mite pest infesting brinjal crop (Prasad, 1974 and Singh, 2001) and has also attained pest status in Rajasthan (Sharma and Kuswaha, 1984).It sucks the cell sap from the leaves and produces white spots which later get covered by thick web. In windy weather, these webs are filled with soil particles. The photosynthetic activity is retarted, affected leaves to lose green colour, dry

and drop before, finally resulting in poor fruit setting (Puttaswamy and Reddy, 1980).A considerable work has been done in India as well as abroad on the biology and management of red spider mite but meagre work have been done on estimation of avoidable losses in brinjal due to the infestation of mite, T. neocalidonicus in semi-arid region of Rajasthan. As such the present investigation has been under taken to determine the avoidable losses.

MATERIAL AND METHODS

To find out the per cent loss, the experiments were conducted under cage house conditions for two years. Ten individual pots in protected as well as unprotected sets were maintained for this purpose. To keep the plant free from mites, recommended acaricide i.e. ethion (0.05 %) was sprayed at 15 days interval. Unprotected pots were exposed for natural infestation of mite. The observation on the mite population was recorded. From the yield of protected and unprotected pots, the per cent avoidable loss was calculated using the formula given by Powell and Landis (1955).

Yield in protected pots- yield unprotected potsPer cent avoidable loss = ------------------------------------------------------------- x 100

Yield in protected pots

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https://ovidsp.tx.ovid.com/sp-3.11.0a/ovidweb.cgi?&S=EKHGFPPMEKDDAAJGNCNKAEOBCHDMAA00&Search+Link=%22Janardan+Singh%22.au.

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To estimate the extent of damage, the per cent avoidable loss due to infestation of mite, T. neocalidonicus was assessed through pot experiments conducted for two consecutive years. The observations recorded and presented in Table 1 and 2 clearly indicated significant higher yield in protected pots as compared to unprotected pots at each pickings in both the years. The average fruit yield in protected pots during first year was found to be 2.56 kg/pot, whereas it was only

1.66 kg/pot in unprotected pots. Similarly, the yield during second year was recorded to be 2.63 kg and 1.96 kg/pot in protected and unprotected pots, respectively. The data further, indicated a net loss of 35.10 per cent during first year, however, such loss was 25.40 per cent in the second year. Likewise Jaydeb et al., (1996) reported a cumulative yield loss of 17.46 per cent in okra crop under field conditions in west Bengal due to infestation of four mite species including T. neocalidonicus partial support the present investigation.

Table 1. Effect of mite infestation of Tetranychus neocalidonicus on fruit yield of brinjal during first year No. of

setFruit yield (kg) at different pickings

I II III IV V VI VII Total Prot. Upr. Prot. Upr. Prot. Upr. Prot. Upr. Prot Upr. Prot. Upr. Prot. Upr. Prot. Upr.

1 0.25 0.23 0.32 0.24 0.35 0.24 0.54 0.42 0.55 0.43 0.31 0.25 0.15 0.13 2.47 1.942 0.30 0.27 0.40 0.31 0.41 0.28 0.51 0.43 0.52 0.44 0.40 0.32 0.20 0.16 2.74 2.213 0.20 0.16 0.42 0.30 0.54 0.34 0.48 0.33 0.49 0.31 0.26 0.20 0.10 0.08 2.49 1.72

4 0.33 0.33 0.48 0.36 0.39 0.27 0.40 0.28 0.42 0.27 0.23 0.19 0.11 0.07 2.38 1.77

5 0.28 0.26 0.27 0.20 0.48 0.35 0.59 0.42 0.60 0.45 0.31 0.24 0.16 0.13 2.69 2.05

6 0.24 0.21 0.38 0.28 0.42 0.32 0.61 0.43 0.58 0.43 0.32 0.23 0.14 0.12 2.69 2.02

7 0.30 0.25 0.45 0.31 0.50 0.35 0.59 0.43 0.57 0.42 0.28 0.22 0.13 0.10 2.82 2.088 0.34 0.32 0.50 0.37 0.29 0.12 0.54 0.42 0.60 0.44 0.31 0.24 0.15 0.12 2.73 2.039 0.32 0.31 0.29 0.22 0.31 0.13 0.52 0.41 0.53 0.40 0.27 0.22 0.13 0.10 2.37 1.79

10 0.28 0.27 0.34 0.26 0.29 0.12 0.48 0.32 0.51 0.42 0.26 0.21 0.10 0.08 2.26 1.66Averag

e 0.28 0.26 0.42 0.31 0.44 0.28 0.56 0.42 0.58 0.43 0.31 0.25 0.14 0.11 2.56 1.66

SEm+ 0.00365 0.00744 0.00954 0.00905 0.00905 0.00428 0.00250 0.0336

t val. 6.708* 13.561* 15.432* 15.161* 15.161* 14.644* 11.608* 19.645*Average fruit yield in protected pots = 2.56 kg Average fruit yield in unprotected pots = 1.66 kg Per cent avoidable loss = 35.10 * Significant at 5% level

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Table 2. Effect of mite infestation of Tetranychus neocalidonicus on fruit yield of brinjal during second year

No. of set

Fruit yield (kg) at different pickings

I II III IV V VI VII Total Prot. Upr. Prot. Upr. Prot. Upr. Prot. Upr. Prot Upr. Prot. Upr. Prot. Upr. Prot. Upr.

1 0.26 0.24 0.34 0.25 0.38 0.27 0.55 0.41 0.57 0.44 0.32 0.23 0.14 0.11 2.56 1.952 0.32 0.28 0.42 0.33 0.43 0.29 0.52 0.42 0.54 0.46 0.42 0.33 0.22 0.18 2.87 2.293 0.22 0.18 0.44 0.31 0.52 0.32 0.50 0.32 0.49 0.30 0.28 0.19 0.22 0.06 2.57 1.68

4 0.37 0.32 0.46 0.34 0.40 0.26 0.42 0.30 0.44 0.28 0.25 0.20 0.11 0.05 2.45 1.75

5 0.30 0.28 0.29 0.22 0.50 0.37 0.60 0.44 0.62 0.44 0.34 0.22 0.14 0.12 2.89 2.09

6 0.26 0.23 0.40 0.30 0.44 0.34 0.62 0.45 0.60 0.43 0.33 0.23 0.15 0.13 2.80 2.11

7 0.30 0.26 0.46 0.32 0.52 0.35 0.60 0.44 0.58 0.42 0.28 0.22 0.13 0.12 2.87 2.138 0.36 0.34 0.31 0.24 0.33 0.15 0.54 0.43 0.55 0.44 0.27 0.24 0.12 0.10 2.48 1.949 0.34 0.32 0.36 0.28 0.31 0.14 0.50 0.34 0.53 0.43 0.26 0.20 0.10 0.08 2.40 1.79

10 0.30 0.29 0.41 0.29 0.51 0.34 0.45 0.33 0.46 0.32 0.32 0.25 0.08 0.06 2.53 1.88Averag

e 0.30 0.27 0.38 0.28 0.43 0.28 0.53 0.38 0.53 0.39 0.30 0.23 0.13 0.10 2.63 1.96

SEm+ 0.00368 0.00719 0.00919 0.00798 0.0104 0.00767 0.00504 0.0283

t val. 7.902* 14.029* 16.420* 17.869* 13.599* 9.836* 5.946* 23.708*Average fruit yield in protected pots = 2.63 kg Average fruit yield in unprotected pots = 1.96 kg Per cent avoidable loss = 25.4 * Significant at 5% level

REFERENCEJaydeb, G., Mukherji AB, and Sarkar PK

(1996) Assessment of losses of bhindi against red spider mite. Environment and Ecology, 14 (2) : 480-81.

Nair MGK (1970) Insect and mites of crops in India. ICAR, New Delhi, pp. 155.

Powell DM and Landis BJ (1955) A comparison of two sampling methods for estimating population trends of thrips and mites in potato. Can. Ent., 58 : 1141-1144.

Prasad, R. and Singh, J. (2011) Status of mite pest fauna prevailing in brinjal agro-ecosystem. Uttar Pradesh Journal of Zoology, 31(1):15-23.

Prasad V (1974) A catalogave of mites of India. Indira Publishing House, Ludhiana, pp. 1-320.

Puttaswamy and Reddy DNR (1980) Tetranychus neocalidonicus Andre (Acarina : Tetrachidae) a new pest on cardamom. Acarology News Letter, 10 : 6-7.

Sharma A and Kushwaha KS (1984) Susceptibility of different varieties of brinjal to Tetranychus neocalidonicus (Acari : Tetranychidae). Indian J. Acarol., 8 : 100-3.

Singh J (2001) Injurious plant feeding mites. In : Mites their identification and management, Ed. P.R. Yadav, R. Chauhan, B.N., Putatunda and B.S., CCS Haryana Agriculture University, Hissar, pp, 82-89.

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https://ovidsp.tx.ovid.com/sp-3.11.0a/ovidweb.cgi?&S=EKHGFPPMEKDDAAJGNCNKAEOBCHDMAA00&Search+Link=%22Janardan+Singh%22.au.

https://ovidsp.tx.ovid.com/sp-3.11.0a/ovidweb.cgi?&S=EKHGFPPMEKDDAAJGNCNKAEOBCHDMAA00&Search+Link=%22Rabindra+Prasad%22.au.

INTERACTIVE EFFECT OF DIFFERENT ORGANIC MANURES AND FERTILITY LEVELS ON YIELD AND ECONOMICS OF OKRA [Abelmoschus

esculentus (L.) MOENCH]

Bhawani Singh, S. P. Singh and B.L. YadavS.K.N. College of Agriculture, Jobner-303329, India

Email: [email protected] Received: 28.12.16Accepted: 29.01.17

ABSTRACTThe field experiment was conducted during kharif season to study the influence of different organic manures and fertility levels on yield attributes and economics of okra [Abelmoschus esculentus (L.) Moench] under semi-arid conditions of Jobner, Rajasthan. Results revealed that application of poultry manure @ 5 t ha-1, significantly showed increased days to first flowering, number of fruits per plant, fruit length, fruit weight, fruit yield per plot, fruit yield per hectare and net returns as compared to control, FYM @ 15 t ha-1 and remained at par with vermicompost @ 5 t ha -1, while the application of 75 % RDF was significantly at par with 100 % RDF and showed enhanced growth and higher B:C ratio over rest of the lower levels. Application of poultry manure @ 5 t ha -1 with 75 % RDF proved the best treatment combination in comparison to other treatment combinations.

Keywords: Okra, Poultry manure, vermicompost, fertility levels, Economics

Okra [Abelmoschus esculentus (L.) Moench] is one of the important vegetable crop in the tropical and subtropical parts of the world. The fruits are iodine rich and help to control goitre. The dry seed contains 13-22 % good quality edible oil and 20-24% protein. Okra produces fruit for a long time and needs balanced and sufficient supply of nutrients for higher yield and better quality. It is well proved that growth, yield and quality of plant are greatly influenced by a wide range of nutrients. But indiscriminate use of inorganic fertilizers has resulted in decreased nutrient uptake, poor quality of vegetables and deterioration of soil health (Agrawal, 2003). It has been observed that sole application of organic manures or inorganic fertilizers are not able to sustain the soil fertility and crop productivity. However, their integration has proved superior to individual components with respect to yield, quality and nutrient uptake (Abusaleha and Shanmugavelu, 1988). Organic manure increases CEC, water holding capacity and phosphate availability of the soil besides improving the fertilizers use

efficiency and microbial population of soil; it reduces nitrogen losses due to slow release of nutrients. Hence the experiment was conducted to find out effect of different organic manures and fertility levels on yield aspects and economics of okra in semi arid conditions.


The experiment was conducted at Agronomy farm, S.K.N. College of Agriculture, Jobner (Rajasthan) during kharif season. The soil of experimental field was alkaline loamy sand in texture at pH 8.1, poor in organic carbon (0.135 %), available N (135 kg ha-1), P (16.85 kg ha-1) and K (152 kg ha-1). The experiment was laid out in randomized block design (RBD) and consisted of 16 treatment combinations with four organic manures (control, FYM @ 15 t ha-1, vermicompost @ 5 t ha-1, poultry manure @ 5 t ha-1) and four fertility levels (control, 50% RDF, 75% RDF and 100% RDF). The treatments were randomly allotted to different plots using random number table of Fisher and Yates (1963). The okra seed of variety Arka Anamika, were

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sown in plot of size was 2.4 x 1.8 m2 with 45 cm × 60 cm spacing between rows and plants. Organic manures i.e., FYM, vermicompost and poultry manure were applied at their respective rates and spread uniformly in the bed size of 2.4 x 1.8 m2 with the appropriate quantity calculated and applied before sowing. For each fertilizer treatment combination, the NPK dose were calculated and applied timely. Full dose of phosphorus and potassium and half dose of nitrogen are applied as basal dose just before sowing and rest half dose of nitrogen was applied in two splits i.e. 30 and 45 days after sowing. The recommended dose of NPK taken was 120:60:60 kg ha-1, respectively and were applied through urea, DAP and MOP, respectively. Yield attributes like days to first flowering, number of fruits per plant, fruit length, fruit weight, fruit yield per plot and fruit yield per hectare were calculated following standard procedures. The net returns were calculated by subtracting the cost of cultivation for each treatment from gross returns gained from the economic yield. Benefit: Cost ratio was computed by dividing net returns by cost of cultivation for each treatment. The ‘F-test’ at 5 per cent level of significance and critical difference (CD) were calculated to test significance of difference among the treatments using the technique of analysis of variance suggested by Fisher (1950), wherever the results were significant.

RESULTS AND DISCUSSIONS

Effect of organic manures and fertility levels Yield attributes and yield Results show that the application of vermicompost @ 5 t ha-1 enhanced all the above parameters significantly over control and FYM @ 15 t ha-1 but statistically at par to poultry manure @ 5 t ha-1. The beneficial effect of vermicompost and poultry manure on

yield and yield attributes might be attributed to its ability of sustain availability of nutrient throughout the growing season and increased balanced C: N ratio (Yadav and Yadav, 2010 and Islam et al., 2012). The application of 100 % RDF resulted in the highest and significantly more values of yield and yield attributes as compared to control and 50 % RDF, whereas as, plant height remained at par with 75 % RDF (Table 1). The application of NPK favoured the metabolic and auxin activities in plant and ultimately resulted in increased fruit size, number of fruits per plant, fruit weight and yield. These findings are similar of that Sharma et al. (2009), Dar et al. (2010) and Sajid et al. (2012) in okra crop.Effect of organic manures and fertility levels EconomicsAmong different organic manure treatments significantly higher net returns (Rs. 214368 ha-1) and B: C ratios (4.35) were obtained with application of poultry manure @ 5 t ha-1 which was at par with vermicompost @ 5 t ha-1. Similarly, application of 75 % RDF also fetched significantly higher net returns (Rs. 227380 ha-1) and B: C ratio (4.52) as compared to preceding treatments but remained at par with 100 % RDF.Interactive effect of different organic manures and fertility levels The yield obtained in plot receiving poultry manure @ 5 t ha-1 + 75 % RDF was statically at par with yield obtained under plot receiving poultry manure @ 5 t ha-1 + 100 % RDF, vermicompost @ 5 t ha-1 + 100 % RDF and vermicompost @ 5 t ha-1 + 75 % RDF (Tables 2, 3, 4, 5). Thus, it is interesting to note that 25 % RDF can be saved by application of 5 t vermicompost ha-1 along with 75 % RDF. The treatment combination poultry manure @ 5 t ha-1 + 75 % RDF proved most effective and remained statistically

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at par to poultry manure @ 5 t ha-1 + 100 % RDF in enhancing the net return and B:C ratio (Tables 6,7).

Table 1 Influence of different organic manures and fertility levels on yield aspects and economics of okra

Treatments Day to first flower appearanc

e

Number of fruitper

plant

Fruit length (cm)

Fruit weight

(g)

Fruit yield

(kg/plot)

Fruit yield

(q/ha)

Net return (Rs.)

B:C ratio

Organic manures Control 47.00 19.00 13.90 14.84 4.70 108.80 171953 3.75FYM @ 15t/ha 51.00 20.17 16.20 16.10 5.23 121.05 193455 3.96Vermicompost @5t/ha

54.00 24.33 18.00 18.79 5.80 134.26 212880 3.81

Poultry manure @5t/ha

53.00 23.92 17.65 18.63 5.69 131.75 214368 4.35

SEm+ 0.63 0.48 0.21 0.32 0.10 2.22 4441 0.09CD 1.81 1.39 0.60 0.94 0.28 6.41 12828 0.27

Fertility levels (NPK)

Control 48.00 18.58 13.80 14.28 4.21 97.51 147165 3.0850% RDF 50.00 20.67 16.28 16.37 5.15 119.21 188877 3.7975% RDF 53.00 23.67 17.68 18.67 6.00 138.89 227380 4.52100% RDF 54.00 24.50 18.00 19.05 6.06 140.24 229235 4.48SEm+ 0.63 0.48 0.21 0.32 0.10 2.22 4441 0.09CD 1.81 1.39 0.60 0.94 0.28 6.41 12828 0.27

Table 2: Interactive effect of number of fruits per plant Treatments Control FYM @

15t/haVermicompost @5t/ha


Control 15.67 18.33 20.33 20.0050% RDF 19.00 20.33 22.00 21.3375% RDF 20.33 20.00 27.33 27.00100% RDF 21.00 22.00 27.67 27.33

SEm+ 0.96CD 2.78

Table 3: Interactive effect of fruit weight Treatments Control FYM @

15t/haVermicompost @5t/ha


Control 12.70 14.73 14.67 15.0050% RDF 14.30 15.33 17.50 18.3375% RDF 16.00 17.00 21.33 20.33100% RDF 16.37 17.33 21.67 20.83

SEm+ 0.65CD 1.87

Table 4: Interactive effect of fruit yield per plot Treatments Treatments Control FYM @ 15t/ha Vermicompost

@5t/haControl 3.87 4.02 4.50 4.47

50% RDF 3.87 5.27 5.87 5.6075% RDF 5.47 5.80 6.40 6.33100% RDF 5.60 5.83 6.43 6.37

SEm+ 0.19 CD 0.55

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Table 5: Interactive effect of fruit yieldTreatments Treatments Control FYM @ 15t/ha Vermicompost

@5t/haControl 89.51 92.98 104.17 103.40

50% RDF 89.51 121.91 135.80 129.6375% RDF 126.54 134.26 148.15 146.60100% RDF 129.63 135.03 148.92 147.38

SEm+ 4.44 CD 12.83Table 6: Interactive effect of net return (Rs. ha-1)

Treatments M0 M1 M2 M3

F0 135274 139227 154600 159560F1 133588 195408 216175 210335F2 206815 219241 240021 243441F3 212136 219943 240723 244136

SEm+ 8883CD 25656

Table 7: Interactive effect of B: C ratio Treatments M0 M1 M2 M3

F0 3.09 2.98 2.88 3.38F1 2.94 4.04 3.90 4.30F2 4.47 4.45 4.27 4.89F3 4.50 4.39 4.21 4.82

SEm+ 0.18CD 0.53

REFERENCESAbusaleha and Shanmugavelu KG. 1988.

Studies on the effect of organic v/s inorganic source of nitrogen on growth yield and quality of okra (Abelmoschus esculentus (L.) Monech). Indian Journal of Horticulture, 45(3-5): 312-318.

Agrawal AK. 2003. Role of organic enriches in management of soil salinity. Agrobios 2: 21-23.

Dar, R., Gupta AK, Chopra S and Samnotra RK. 2010. Effect of integrated nutrient management on seed yield of okra [Abelmoschus esculentus (L). Moench]. Journal of Research, SKUAST–J. 9(1): 70-78.

Fisher RA.1950. Statistical method for research workers, Oliver and Boyd, Edinburg, London.

Fishers RA and Yates F. 1963. Statistical tables, Oliver and Boyd. Edinburgh, London.

Islam MM, Majid NM, Karim AJMS, Jahiruddin M, Islam MS and Hakim, MA. 2012. Integrated nutrient management for tomato-okra-stem amaranth cropping pattern in homestead area. Journal of Food, Agriculture & Environment. 9(2): 438-445.

Sharma RP, Datt N, Chander G. 2009. Effect of vermicompost, FYM and chemical fertilizers on yield, nutrient uptake and soil fertility in okra [Abelmoschus esculentus (L.) Moench] – onion (Allium cepa) sequence in wet temperate zone of Himachal Pradesh. Journal of Indian Society of soil Science. 57(3): 357-361.

Yadav SS and Yadav N. 2010. Effect of integrated nutrient management on yield of okra in zaid crop. Bhartiya Krishi Anusandhan Patrika. 25: 2-4.

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Short Communication

Economic Feasibility and Identification of Suitable Crop Okra Under different types of plastic mulch in Udaipur District of Rajasthan- A Case Study

S.S. Lakhawat1, P.S. Rao2 and S.R. Bhakar2

1Assistant Professor, Deptt. of SWE, 2Professor, Deptt. of SWE, College of Technology and Engineering, MPUAT, Udaipur.


Plastic mulching is a way of soil moisture conservation and improvement of water use efficiency of the crop. Plastic mulch also helps in better nutrient management and weed management. Different colour of plastic mulch has its individual effects on soil and plant health also on some micro-climatic factors of the growing plants. The okra and tomato crops were taken under black poly-mulch of plastic mulch. Crops will be sown on raised beds in the two rows. The water was supplied through gravity fed drip irrigation with one lateral for two rows of the crop. Economics of the crops were worked out.Drip irrigation is the best available technology for the judicious use of water for growing horticultural crops in the large scale on sustainable basis. Drip irrigation is a low labour intensive and highly efficient system of irrigation, which is also amenable to use in difficult situations and problematic soils, even with poor quality water. Irrigation water saving ranging from 36-79% can be obtained by adopting a suitable drip irrigation system. Drip irrigation is designed to supply filtered water directly to the root zone of the plant so as to maintain the moisture near to field capacity level for most of the time, which is found ideal for efficient growing of vegetable crops. The specific objectives of the study were -To study the effect of different coloured poly-mulch on the micro-climate near the growing plants. To study the impacts of microclimate on the growth and yield characters of okra and okra.

To study the ideal colour of poly-mulch and their economic feasibility with regards to okra and tomato.


Standardization of the height stand for water tank, length of main supply pipe line and the laterals and diameters of laterals is required before the adoption of the gravity fed drip irrigation system. The vegetable crop such as okra has been selected because of their easier adaptability to all type of climate & soil, high production capacity, good nutritional value of the fruit, popularity among the farmers and easy marketing. Hence, this study is to standardize and evaluate the performance of silver colour poly- mulch on the okra crops under gravity fed drip irrigation system under agro-climatic conditions of Udaipur region. Silver colour poly-mulch were used to investigate its effect on the growth and yield of the okra. The t okra cv. “Mhyce Bhindi no.64” was taken for cultivation during the year 2013. The total growing area of 0.10 ha were planted with using spacing of 50 x 50 cm for tomato crop.To study the economic feasibility of okra crop under black,silver, yellow and milky-white poly-mulch, the cost of cultivation, yield, gross income and net income (profit) were calculated. The statistical analysis was made on the data of one season.Calculation of cost of cultivationA separate cost of cultivation, yield, gross income and net income of okra and tomato was calculated considering the one sqm area and it was multiplied for 1.0 ha area

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for silver colour poly-mulch. On the basis of calculated data input-output ratio or income on per rupee investment has been calculated.Two components of cost of cultivation namely fixed and variable cost has been considered for this analysis.Items for fixed costs are-(i) Rental value of land- Assumed as it is prevailing in the locality.(ii) Depreciation of drip irrigation system was considered @ 10% of the value of drip irrigation system i.e.on Rs. 1,35,000/- per ha (half amount of total depreciation value for one crop has been taken into consideration).(iii) Depreciation on farm buildings @ 2 % of the value of the farm buildings i.e. on Rs. 8,00,000/- (Half amount of total depreciation value for one crop was considered)(iv) Depreciation on pacca well @ 2 % of the value of well, i.e. on Rs.2,00,000/-(half amount of total depreciation value for one crop was used for calculation)(v) Depreciation on pump house & electric pump set @ 2 % of the value of pump house & electric pump i.e. on Rs.1,50,000/-(half amt. of total depreciation value for one crop was considered).(vi) Fixed electricity meter charges@ Rs.320/-for every 2 months (three times)(vii) Interest on fixed capital was taken as 12 % of the total fixed capital.(viii) Rent paid for land (for one crop of 6 months @ Rs.250/- per ha.)(ix) Interest on fixed capital (i.e. @ 12% on Rs.19,335 per ha) Half amount for one crop of 6 months was taken into consideration.(x) Salary for permanent labour– These are the data of AICRP (All India Coordinated Research Project on Application of Plastics in Agriculture in which no permanent labour was employed in the scheme.Items/ operations for variable cost are-(i) Field preparation @ Rs.800/-hr. for 10 hrs.

(ii) Seeds- Okra @3 kg per ha, @Rs.1800 / per kg.(iii) Fertilizers, FYM, insecticides and pesticides were used as per recommended doses as suggested in the package of practices for the zone by department of agriculture, Udaipur(iv) Electricity Charges are considered as the bimonthly bills generated by the AVVNL, Ajmer.(v) Casual labour charges - Labour for cultural operations,watch and wards, marketing charges, harvesting charges are included in casual labour charges, @ 2 Labour/day/ hectare @ Rs.147/ per day as per govt. norms (half amount of total charge/crop)(vi) Poly-mulch material charges were used as per market price i.e. charges were same for two colours of poly-mulch namely black poly-mulch, silver poly-mulch and observed different from earlier two colours but remained same for two other colours namely yellow and milky-white.(vii) Half amount per crop of interest on working capital @12% per annum.(viii) Working capital- total working capital for 6 months is added. It included the charges for field preparation, high yielding variety seeds, farm yard manure, fertilizers, insecticides and pesticides, land revenue, electricity charges and casual labour charges for okra crops under black, silver, yellow and milky-white colour poly-mulch.(ix) Interest on working capital is calculated only for 6 months for okra crop (as per its life span).(x) Marketing cost, labour for watch and ward and harvesting charges are included in casual labour charges


Fixed Cost for cultivation of okra crops under black, silver, yellow and milky white poly mulch during the year 2013 has been shown in the table-1. The fixed costs for all four colored namely black, silver, yellow and milky white poly mulch poly mulches have same and found to be Rs. 39,835.00

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for the period of six months for okra crop. The components of fixed cost covers depreciation on drip irrigation system @ 10% of the value of drip irrigation system, i.e. on Rs.1,35,000/per ha (half amount of total depreciation value for one crop) has been calculated as Rs. 6,750.00 for okra crop. Depreciation on farm buildings @ 2% of the value of farm buildings, i.e. on Rs.8,00,000/per ha) half amount of total depreciation value for one season crop) has been calculated as Rs. 8,000.00. Depreciation on pacca well @ 2% of the value of well, i.e. on Rs.2,00,000/(half amt. of total depreciation value for one season

crop) found as Rs. 2,000.00/. Depreciation on pump house & electric pump set @ 2% of the value of pump house & electric pump i.e. on Rs.1,50,000/(half amt. of total depreciation value for one season crop) and was Rs.1500.00. Fixed electricity meter charges@ Rs.320/for every 2 months (three times) was found to be Rs. 960.00/. Interest on fixed capital was taken as 12 % of the total fixed capital was Rs. 2320.00/.Rent paid for land (for one crop of 6 months @ Rs.250/ per ha.) was Rs. 125.00. and Rental value of land (1.0 ha. area) half amount for one crop for 6 months was found to be Rs.17,500/.

Table 1. Per hectare fixed cost for okra crops under different types of plastic mulch for the years 2013. (in Rupees)Particulars Colours of Poly-Mulch

Black Silver Yellow Milky-WhiteFixed CostsDepreciation on drip irrigation system @10% of the value of drip irrigation system, i.e. on Rs.1,35,000/ per ha) (half amount of total depreciation value for one crop)

6750.00 6750.00 6750.00 6750.00

Depreciation on farm buildings @2% of the value of farm buildings, i.e. on Rs.8,00,000/ per ha) half amount of total depreciation value for one crop)

8000.00 8000.00 8000.00 8000.00

Depreciation on pacca well @2% of the value of well, i.e. on Rs.2,00,000/(half amt. of total depreciation value for one crop)

2000.00 2000.00 2000.00 2000.00

Depreciation on pump house & electric pump set @2% of the value, i.e. on Rs.1,50,000/(half amt. of total depreciation value for one crop)

1500.00 1500.00 1500.00 1500.00

Fixed electricity meter charges@ Rs.320/for every 2 months (three times)

960.00 960.00 960.00 960.00

Interest on fixed capital (i.e. on Rs.19305 per ha @ 12%) Half amount for 1 crop of 6 months

2320.00 2320.00 2320.00 2320.00

Rent paid for land(for one crop of 6 months @ Rs.250/ per ha.)

125.00 125.00 125.00 125.00

Rental value of land (0.10 ha. area) 17,500.00 17,500.00

17,500.00 17,500.00

Total Fixed Costs (A) 39,835.00 39,835.00

39,835.00 39,835.00

The variable and total cost for cultivation of tomato crop under black, silver, yellow and milky white colored poly mulch for the year 2013 has been given in the table-2. Same cost was observed for preparation of field. Cost on Okra hybrid seed was found

to be very high i.e.@ Rs.1800/ per kg. needs 3kg. seeds for one ha costing Rs.5400.00. The requirement of FYM was 200 qt per ha of Rs. 20,000.00 for okra crop and it was purchased at the rate of @ Rs.100 per qt from the market.

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Variable cost on fertilizer for this crop was observed to be Rs.7500.00 per hectare including urea,DAP,and Murate of potash. Cost for pesticide was observed to be Rs. 5000.00 for okra including Chloropyrophos, Prophanophos, Cypermethrin, Imedachloprid,MP Dust. Casual labour Charges were observed @2 Labour/day/hectare @Rs.147/ per day as per govt. norms (half amount of total charge/crop) was observed to be Rs. 35,280.00. They were also performed the work of Watchman, Marketing, Harvesting of crop etc. Electricity Charges was paid as Rs. 2,000.00 per ha for the season.

Expenditure on black and silver colored poly mulch charges was observed to be as Rs 46,875.00 per ha while it was Rs. 39,375.0 for yellow and milky white poly mulch. Half amount per season crop of interest on working capital was observed @12% per annum was found to be Rs. 5,050.00 for black and silver colored mulch and Rs. 4,750.20 for yellow and milky white mulch on tomato crop per hectare. Total working capital was calculated as Rs. 126,255.00 for black and silver colored poly mulch and Rs. 1,18,755.00 for tomato crop.

Table-2. Per hectare variable cost for okra crop under different types of plastic mulch during the year. 2013 (in Rupees)Particulars Colours of Poly-Mulch

Black Silver Yellow Milky-WhiteVariable CostsField preparation @ Rs.800/hr. for 10 hrs. 8,00.00 8,000.00 8,000.00 8,000.00Seeds-Tomato @ 125gm per ha @ Rs.49,000/ kg

5,400.00 5,400.00 5,400.00 5,400.00

FYM @ 250qt/ha @ Rs.1.0 per kg 20,000.0 20,000.0 20,000.0 20,000.0Fertilizers-(i)Nitrogen-Urea @ Rs.5.5/kg(ii)Phosphorus-DAP @ Rs.12 /kg(iii)Potash-MoP @ Rs.6/kg(iv)Micronutrients- Agromin and Calcium Nitrate

3700.001000.001100.00800.00800.00

3700.001000.001100.00800.00800.00

3700.001000.001100.00800.00800.00

3700.001000.001100.00800.00800.00

Insecticide and pesticides- -Chloropyrophos- Imedachloprid-Cypermethrin -Thiram -Mancozeb

5,000.001000.001000.001000.001000.001000.00

5,000.001000.001000.001000.001000.001000.00

5,000.001000.001000.001000.001000.001000.00

5,000.001000.001000.001000.001000.001000.00

Electricity Charges 2,000.00 2,000.00 2,000.00 2,000.00Casual labour Charges @ 2 Labour/day/hectare @ Rs.147/per day (half amount of total charge/crop)

35,280.00 35,280.00 35,280.00 35,280.00

Poly-mulch Charges (Rs./ha) - - - -Labour for watchman (included in casual labour charges)

- - - -

Marketing charges (included in casual labour charges)

- - - -

Harvesting charges (included in casual labour charges)

46,875.00 46,875.00 39,375.00 39,375.00

Half amount per crop of interest on working capital @ 12% per annum.

5,050.00 5,050.00 4,750.20 4,750.20

Total working capital (B) 1,26,255.00 126,255.00 1,18,755.00

1,18,755.00

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Total variable cost (A) 1,31,305.00 131,305.00 1,23,505.20

1,23,505.20

Cost of cultivation (A+B) 1,71,140.00 1,71,140.00 1,63,340.20

1,63,340.20

Cost of cultivation per sqm area 17.11 17.11 16.33 16.33The total value of the total variable cost (B) was found to be different for two types of poly mulches under black and silver poly mulches it was Rs. 131,305.00 and for yellow and milky white colored poly mulch was observed to be Rs. 1,23,505.20 for per ha tomato crop. Further, Cost of cultivation (A+B) for black and silver mulch was found to be Rs. 1,71,140.00 while it was Rs.1,63,340.20 for yellow and milky white poly mulch for per ha tomato. Cost of cultivation per sq. mt. area was found to be Rs. Rs. 17.11 for black and silver poly mulch and for yellow and milky white poly mulch was found to

be Rs. 16.33. Thus, it can be said that use of black poly mulch was observed as more profitable as compared to any other colored poly mulch for okra cultivation.The yield and income from different kinds of poly mulch for tomato cultivation in one ha has been presented in the table-3. Average yield of okra per hectare from black, silver, yellow and milky white poly mulch was found to be 15216.3, 16954.5, 11555.8 and 10938.7 kg respectively. While it was recorded 1.52, 1.69, 1.16 and 1.09 kg. per sqm area in black, silver, yellow and milky white colored poly mulch respectively for okra cultivation.

Table 3. Per hectare yield and income of okra crop grown under different types of plastic mulch during the year 2013. (in Rupees)Particulars Okra

black poly- mulch

silver poly- mulch

milky-white poly- mulch

yellow poly-mulch

Yield per sqm area (kg) 1.52163 1.69545 1.15558 1.09387Yield per ha(kg.) 15,216.3 16,954.5 11,555.8 10,938.7Gross Income per ha (Price of okra @ Rs.15.00 /kg

2,28,244.5 2,54,317.5 1,73,337.0 1,64,080.5

Cost of cultivation (Rs./ha)Gross income /sqm area (Rs.) 22.82 25.44 17.33 16.41 Net income per ha (Rs) 57,104.50 83,177.50 9,996.80 740.30 Net income per sqm area(Rs) 5.71 8.31 1.00 0.07Income on variable cost (Rs.)Input-output ratio 1.33 1.49 1.06 1.01

The average gross income of okra crop from 1.0 ha area by using black, silver, yellow and milky white poly mulch was found to be Rs. 2,28,244.5, 2,54,317.5, 1,73,337.0 and Rs. 1,64,080.5 respectively during the year 2013. The net income from okra by growing 1.0 ha area and using black, silver, yellow and milky white poly mulch was found to be Rs. 57,104.50, 83,177.50, 9,996.80 and Rs. 740.30 respectively during the year 2013.The variation in gross income and net income of black, silver, yellow and milky white poly mulch was mainly due to difference in their mulching prices as well as their power

of absorption of radiation which checks the evapotranspiration. The net income from per sq. mt. area of okra crop was found to be Rs. 5.71, 8.31, 1.00 and Rs. 0.07 for the year 2013 form black, silver, yellow and milky white poly-mulch respectively.The input out-put ratio or per rupee returns from 1.0 ha area form black, silver, yellow and milky white poly-mulch was obtained as 1.33, 1.49, 1.06 and 1.01 respectively. Thus, it can be concluded that black colored poly mulch have more input-output ratio provides more profit as compared to any other type of poly mulch for growing of okra followed by silver, milky white and

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yellow colored poly mulch in Udaipur district of Rajasthan.

vtesj ds tSu efUnjksa esa LFkkiR; dyk lqeu jkBkSM+

O;k[;krk&bfrgkl] jktfd; dU;k egkfo|ky;] [kSjokMkReceived: 06.12.16Accepted: 04.02.17

lkjka'k/keZ fo’ks"k dh lkaLd`frd fof’k"Vrk vius vki esa ,d cgqr cM+s bfrgkl dks lesVs gq;s gksrh gSA blh Øe esa vtesj esa tSu /keZ dh lkaLd`frd foospuk esa tSu efUnjksa dh LFkkiR; dyk dk o.kZu fd;k x;k gSA vtesj es tSu dyk o /keZ dks laj{k.k izkIr Fkk] mlh ds ifj.kkeLo:i tSu /keZ] dyk ,oa laLd`fr le; ds FkisM+ks dks lgrs gq, Hkh bfrgkl ds iUuksa] vfHkys[kksa] eafnjksa o ewfrZ;ksa ds :i esa vFkkZr f’kYi o LFkkiR; ds :i esa vkt Hkh fo|eku gSA eafnj LFkkiR;dyk dk fodk izR;{kr% ewfrZ iwtk ds ifj.kkeLo:i gqvkA tSu ewfrZ;ksa eas rhFkZadjksa dh ewfrZ;k¡ lokZf/kd gS] ftu ij mRdh.kZ fpUg~ ls budh igpku cgqr vklkuh ls dh tk ldrh gSA X;kjgoha&ckjgoha 'krkCnh vFkkZr~ pkSgku 'kkludky dykRed] lkfgR;d vkSj /kkfeZd fØ;kdykiksa dh n`f"V ls cgqr gh egRoiw.kZ jgkA bl le; esa fufeZr tSu eafnj dkQh fofo/krk ;qDr gSA bu eafnjksa esa nsodqfydk,sa] lgL=dwV] eku LrEHk] eB o fuflf/kdk;sa vkfn gSA eafnjksa dk lkekU; :i czkã.k eafnjksa ds leku gh gS] ysfdu ewfrZ;kW fHkUu gSA bl le; xqtZj 'kSyh lokZf/kd izHkko’kkyh FkhA eafnjksa esa iapjFk] f’k[kj ;qDr xHkZx`g] }kj e.Mi] izkdkj ;qDr layXu e.Mi] LrEHkksa ls ;qDr vUr% Hkkx] izos’k e.Mi o dgha&dgha rksj.k ;k vyad`r pks[kVsa Hkh gksrh FkhA ckckth dh ufl;k¡] ik’oZukFk fnxEcj tSu efUnj] xks/kk xokM+h] nknkckM+h] c?ksjk] ljokM+] ujsuk o eksjk>M+h esa bl izdkj ds eafnj ik;s x;s gSA eafnjksa esa vf/kdka’kr% laxejej dk iz;ksx fd;k x;k FkkA ckn ds eafnjksa fl)dwV pSR;ky;] egkiwr ftuky; dk LFkkiR; vkcw ds tSu eafnjksa ls lekurk j[krk gS ;s djksyh ds yky iRFkj ls fufeZr LrEHk o esgjkcnkj gSA eafnjksa esa tkyhnkj >jks[ks o dqjkbZ dk dk;Z cM+k gh

169

vuqie gSA laxejej ds LrEHkksa ij csy&cwVs] Qwy&ifRr;k¡ rjk’kh gqbZ gSA fl)dwV pSR;ky; esa [kM+k eku LrEHk leo’kj.k ftu eafnj dk izrhd gSA bu eafnjksa esa lksus ds ikuh ls fp=dkjh vkSj dkap dk dke fd;k gqvk gS vkSj cgqewY; jRu ls cuh izfrek,¡ Hkh fojkteku gS tks fd eafnjksa dh 'kksHkk esa pkj pkan yxk nsrh gS ,oa tSu lekt dh vkfFkZd le`f) dks n’kkZrsa gSAeq[; 'kCn %& vtesj] tSu laLd`fr] tSu efUnj] LFkkiR; dykLFkkiR; dyk] bekjr fo’ks"k dks euksgkjh rks cukrh gh gS] lkFk gh mls ,sfrgkfld o lkaLd`fr /kjksgj Lo:i Hkh izLrqr djrh gSA tSu /kekZoyfEc;ksa us ns’k ds vFkkg lkaLd`frd Hk.Mkj dks LFkkiR; o f’kYi dh vla[; cstksM+ d`fr;ksa ls ljkcksj fd;k gSA buesa ls vusdksa dh HkO;rk vkSj dyk burh mRd`"V gS fd mudh miek ugha feyrhA tSu LFkkiR; us dyk& Hkkouk] /kekZjk/kuk] lsok vkSj ru&eu&/ku dh mRlxZ Hkkouk dk vuqie mnkgj.k izLrqr fd;k gSaA LFkkiR;] f’kYi] fp=dyk] lkfgR; vkfn ls gh Hkkjrh; laLd`fr dk J`a[kyk&c) bfrgkl cuk gSA tSu /keZ ,oa laLd`fr ds vUrxZr vusd rhFkZ LFky ,oa efUnj gSAdyk,¡ /keZ dh vuqxkfeuh gh u jg dj] lk/kuk dh dBksjrk dks ljy cukus eas Hkh lgk;d jgh gSA vr% /keZ ds HkkokRed] HkfDr ijd ,oa yksdfiz; :iksa dks iYyfor djus ds fy, dyk vkSj LFkkiR; ds fofo/k d`fr;ksa ds fuekZ.k dh vko’;drk gqbZ FkhA efUnjksa dks lqUnj cukus es Je vkSj /ku dh dksbZ

deh ugha dh xbZ FkhA tSu /keZ dh vkRek] mldh dyk esa Li"V :i ls >ydrh gSA tSu dyk fofo/krk iw.kZ o /keksZeq[k jgh gSA tu&thou ds izR;sd igyw dh Hkk¡fr dyk vkSj LFkkiR; ds {ks= esa Hkh mudh fo’kys"k.kkRed n`f"V bruh vf/kd ijhyf{kr gS fd ijEijkxr tSu dyk esa fo’kq) lkSUn;Z dks mHkkjus okys rRoksa dk vHkko gSA tSl ekulkj vkfn xzUFkksa esas ,slh lw{e O;k[;k,¡ feyrh gS] ftuesa ewfrZ f’kYi vkSj Hkou fuekZ.k dh :<+ i)fr fn[kkbZ iM+rh gSA

tSu lk/kqvksa us vf/kdrj vkcknh ls nwj ioZrksa ds ik’oZHkkx ;k pksfV;ksa ij fLFkr izkd`frd xqQkvksa dks viuk vkJ; cuk;k FkkA tks tuinksa vkSj HkkSfrdrk ls xzflr lkalkfjd thou dh vkik/kkih ls nwj gjs&Hkjs izkd`frd n`’;ksa rFkk 'kakr eSnkuksa ds e/; fLFkr FksA tks /;ku dh ,dkxzrk vkSj vkRe fpUru esa lgk;d jgs FksA ogk¡ okLrq Lekjdksa ¼efUnj ,oa nsoky;ksa½ dh LFkkiR; dyk vkSj lcls vf/kd rhFkZadj izfrek,¡] viuh vaur

170

'kakfr] ohrjkx vkSj ,dkxzrk ls J)koku HkDr dks ijekRek ds lkfu/; dh vuqHkwfr djk nsrh gSAeafnj LFkkiR; dyk dk fodkl izR;{kr% ewfrZ iwtk ds ifj.kke Lo:i gqvk] tks tSuksa esa izkjEHk ls izpfyr FkhA eafnjksa ds fuekZ.k esa tSuksa us fofHkUu {ks=ksa vkSj dkyksa dh 'kSfy;ksa dks viuk;k FkkA lkFk gh viuh Lo;a dh laLd`fr vkSj fl)kUrksa fd n`f"V ls dqN yk{kf.kd fo’ks"krkvksa dks Hkh izLrqr fd;k] ftlds dkj.k tSu dyk dks ,d vyx Lo:i feyh gSAtSu ewfrZ;ksa esa rhFkZadjksa dh ewfrZ;ka fu%lUnsg lokZf/kd gSA lHkh ewfrZ;ka izk;% ,d tSlh gksus ds dkj.k dykdkjksa dks viuh izfrHkk iznf’kZr djus ds de gh volj izkIr gq, FksA fQj Hkh buesa vusd ewfrZ;k¡ cM+h vf}rh; gSA mnkgj.k ds :i esa Jo.k csy xksyk dh fo’o fo[;kr fo’kkydk; xksEeV izfrek gSA tSu rhFkZadj ewfrZ;k¡ mu egkiq:"kksa dh gS tks loksZPp LFkku ij fLFkj jgs gSA nwljh rjQ bUnz&bUnzk.kh] rhFkZadjksa ds vuqpj ;{k&;{k.kh] nsoh ljLorh] uoxzg] {ks=iky] lkekU; uj&ukjh vofLFkr gSA tSu nso fudk; ds vis{kkd`r de egRo ds nsorkvksa ;k nsorqY; euq";ksa ds ewrZu esa rhFkZadjksa vkSj vrhr ds vU; lqfo[;kr iq:"kksa ds thou pfj= ds n`’;kaduksa esas vkSj

fofo/k vyadj.k ds iz;ksx esa dykdkj dks iw.kZ :i ls LorU=rk FkhA blds vfrfjDr Hkh dykdkj dks viuh izfrHkk dk izn’kZu djus ds volj FksA izkd`frd n`’;ksa] ledkyhu thou dh /keZ fujis{k xfrfof/k;ksa ds f’kYikadu eaas dykRed lkSUn;Z lek;k gqvkA ekuoh; ewfrZ;ksa o vyadkfjd ewfrZ;ksa ds fuekZ.k esa Hkh tSuksa us viuh gh 'kSyh viuk;h FkhA LFkkiR; ds {ks= esa viuh fo’ks"k :fp ds vuq:i LrEHk/kkfjr Hkouksa ds fuekZ.k esa mPp dksfV dk dkS’ky iznf’kZr fd;k FkkA buesa ls dqN dyk&le`) Hkouksa dh fo[;kr dyk eeZKksaa us izkphu vkSj e/;dkyhu Hkkjrh; LFkkiR; dh lqUnjre d`fr;ksa esa x.kuk dh FkhA dykd`fr;ksa dh vf/kdrk vkSj fofo/krk ds dkj.k mRrjdkyhu tSu dyk us bl /keZ dh HkkoRedrk dks vfHkO;Dr fd;k gSALFkkiR; dk vuwBk :i Hkwfet efUnjksa esa ij feyrk gSA budk fuekZ.k lery Hkwfe ij bZaV&iRFkjksa }kjk fd;k tkrk FkkA mRrjizns’k] jktLFkku] xqtjkr] caxky] e/; izns’k vkfn esa Hkwfet efUnj cM+h la[;k eas fufeZr gq,A dHkh&dHkkj ;s tSu Lrwiksa ds lkFk gh cuk;s tkrs FksA fofo/k izfrekvksa ls izfrf"Br tSu efUnjksa ds fuekZ.k dk;Z esa fofHkUu jktoa’k ,oa tSu /kekZoyfEc;ksa ¼O;kikjh ,oa

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tu lk/kkj.k½ us eqDr ân; ls ;ksxnku fn;k FkkAjktLFkku ds tSu efUnj viuh mRd`"Vre LFkkiR; ,oa f’kYi dyk] le`f)iwoZ Hkwfedk] 'kkUr o ifo= Hkkoukvksa dks txkus okys vUrj&ckg~; okrkoj.k] xzUFk lkfgR; vkfn ds laj{k.k vkSj lk/kuk ds dsUnz gksus ds dkj.k Hkkjrh; laLd`fr ds bfrgkl esa loksZijh LFkku j[krs gSA Hkkjro"kZ ij ckj&ckj fons’kh vkØe.kksa ds ckotwn buds ;Fkkor vfLrRo esa jgus ls iqf"V gksrh gS fd rRdkyhu tSu /keZ o dyk dks jktdh; laj{k.k izkIr FkkA budk LFkkiR;] vf/kdka’kr% vk;Z ;k ukxj o dgha&dgha nzkfoM+ 'kSyh ;qDr gSA vyad`r rksj.k }kj] f’k[kj] xqEct] /ot vkfn buds eq[; vo;o gSA tSu rhFkZ vkSj efUnjksa dh LFkkiR; o f’kYidyk dh ,sfrgkfld fo’ks"krk ,oa mRd`"Vrk dh n`f"V ls] vtesj dk izeq[k LFkku gSAnloha 'krkCnh eas [;kfr izkIr pkSgku 'kkld oS".ko o 'kSo erkoyEch gksrs gq, Hkh vtesj o vU; {ks=ksa esa tSu /keZ dks i;kZIr vkJ; iznku dj] efUnj fuekZ.k dh vuqefr ,oa Hkwfe nku eas nh FkhA i`Fohjkt izFke ds iq= vt;jkt us] u dsoy viuh ubZ jkt/kkuh vt;es: ¼vtesj½ dks tSu efUnjksa ls lq’kksfHkr gksus dk volj fn;k] vfirq ogk¡ ij fufeZr Hkxoku ik’oZukFk] tSu efUnj dks Lo.kZdy’k Hkh HksaV fd;k FkkA 1 blh dky eas vtesj ds

'kkld] tulk/kkj.k ,oa Jsf"B;ksa ¼O;kikfj;ksa½ }kjk vusd efUnjksa dk fuekZ.k djok;k x;k FkkA ftuls dyk ,oa LFkkiR; ds egRoiw.kZ i{kksa dk irk pyrk gSA ysfdu ml le; ds tks lk{; miyC/k gksrs gS] os nqHkkZX; ls ns’k vkS dky dh n`f"V ls cgqr gh lhfer gSA bl le; jktLFkku esa vtesj] vkesj] ukxkSj] iYyw] lkaxkusj] j.kFkEHkksj ,oa fnYyh ¼fnYyh&esgjksyh {ks=½ vkSj gfj;k.kk ds vkfldk ¼gkalh½] fiatksj rFkk dqN vU; LFkkuksa ds pkSgku dkyhu HkO;] efUnjksa ds vo’ks"k brus [kf.Mr voLFkk eas gS fd muls efUnj vkSj mudh LFkkiR; fo’ks"krkvksa dks le>uk eqf’dy gSA 2 ysfdu vtesj {ks= esa orZeku esa Hkh X;kjgoha&ckjgoha 'krkCnh ds vf/kdka’k efUnj ewy vfLrRo esa gSApkSgku dky esa fufeZr tSu /kkfeZd Hkou dkQh fofo/krk iw.kZ gSA tSls efUnj&izklkn] nsodqfydk,¡] lgL=dwV ¼lkekU;r% fijkfeM ds vkdkj dh Bksl lajpuk ftl ij lgL=kf/kd rhFkZadj ewfrZ;k¡ gSA½ eku LrHk~ fuf"kf/kdk,as ¼Lekjd LrEHk½] eB vkfn gSA pkSgku jkT; izHkksRiknd prq’kkykvksa ls ;qDr lqLi"V y{k.kksa okys tSu Hkouksa ls ifjiw.kZ FkkA tSu efUnjksa dk lkekU; :i czkg~e; efUnjksa ds leku gh gSA ysfdu budh ewfrZ;ka vo’; fHkUu gSA D;ksafd mudk fuekZ.k tSu

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/keZ ds ikSjkf.kd vk[;kuksa] nk’kZfud fl)kUrksa rFkk laLdkj laca/kh dYiukvksa ds vuqlkj fd;k x;k FkkA okLrqdkj] f’kYih mlh oxZ ds Fks] tks fofHkUu izns’kksa esa czkg~e.k ;k vU; Hkouksa dk fuekZ.k dk;Z djrs FksA 3 bl dky ds tSu efUnj czkg~e.k Hkouksa ds leku gh {ksf=; fofHkUurkvksa rFkk 'kSyhxr fo’ks"krkvksa dks iznf’kZr djrs gSA pkSgku dky eas ftu eq[; Hkou&fuekZ.k 'kSfy;ksa us eafnj fuekZ.k eas Hkwfedk fuHkk;h os ml ewy Hkou fuekZ.k ijEijk ls tqM+h Fkh] ftldk lh/kk lEcU/k ,d vkSj izfrgkj okLrq Lekjdksa rFkk nwljh vksj xqtZj nsodks"Bksa dh 'kSyh ls FkkA lkFk gh ;gh Hkh lEHko gS fd ekyok&nf{k.k ds LFkkiR; dh ijEijk ls lEcfU/kr dqN efUnjksa dk izHkko fdlh lhek rd jktLFkku ij Hkh iM+k gksA ewy jktLFkkuh 'kSyh HkO; ,oa Js"Bdyk dkS’ky ls lEiUu gSA vusd LFkkuksa ij e/;dkyhu efUnjksa esa fuekZ.k] 'kSfy;ksa dk eksgd :i fn[kkbZ nsrk gSA efUnjksa esa 'kSfy;ksa ds v/;;u ls irk pyk gS fd xqtZj 'kSyh dk izHkko lokZf/kd FkkA 4

pkSgku dkyhu eafnjksa dh fo’ks"krk,sa bl izdkj gS %& iapjFk f’[kj ;qDr xHkZ x`g ¼ewy izklkn½ }kje.Mi] izkdkj ;qDr layXu e.Mi ¼lkekU;r% can½] LrEHkksa ls ;qDr vra%Hkkx] vyad`r Nr] izos’k e.Mi] rksj.k] vyadr

pkS[kVsa vkfnA pkSgku ;qx ds tSu rksj.k dk mRd`"V mnkgj.k vksfl;k¡ fLFkr izfl) egkohj efUnj esa fn[kkbZ nsrk gSA tgk¡ f’k[kjksa dh lTtk ds NksVs&NksVs J`xksa ds lewg ls dh xbZ gsA tks d.kZ] Hknz ¼dsUnzh;½ rFkk Hkou ds vU; Lrjksa ls Åij mBs gq, gSA xHkZ x`g ds Åij vad f’k[kjksa ls jfgr ,dkadh f’k[kjksa dk vyadj.k vf/kdka’kr% Hkwfe foHkktu rFkk varZx`fFkr vaduksa }kjk fd;k x;k gSA fo’ks"kdj mu f’k[kjksa dk ftu ij xqtZj&fuekZ.k dyk dh Nki gSApkSgku ;qx esa fo’kq) jktLFkkuh 'kSyh ds uewus viuh la;r ewfrZ lTtk ds lkFk dnkfpr vf/kd izHkko’kkyh cus jgsA Hkou rFkk e.Miks dh Nrksa ij lqUnj ldsfUnzr Lrjksa oky mjs[ku ;qDr vkd"kZd foU;kl gksrk FkkA bl le; ds efUnjksa ds nks oxksZ eas ckaVk tk ldrk gS ,d os ftuesa vayd`r rFkk l?ku ewR;kZdau gSA nwljs os ftudk :i ikjEifjd gS] ftuesa LrEHkkoyh ij o mlls Åij J`[kayk;qDr ?kf"Vdk] prqHkZaqth fVfd;k vkfn yksdfiz; dyk izrhdksa dk fufHkZd fdUrq lhfer vyadj.k gSA dqN LrEHk rks likV Hqktkvksa okys o lkns gSA 5

jktLFkku vkSj fnYyh ds tSu efUnjksa ds dqN vo’ks"kksa esa ijorhZ 'kSyh ds mnkgj.k fn[kkbZ nsrs gSA vtesj esas X;kjgoha ls ysdj 1956 bZLoh

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rd ds fufeZr tSu efUnjksa dh LFkkiR; dyk dk fo’ys"k.k fuEu izdkj gS %&ckckth dh ufl;k¡ bl ufl;ka dk izos’k }kj fo’kky gSA efUnj ifjlj Hkh dkQh foLr`r gS o efUnj dh ckgjh nhokj nkfguh rjQ ls cgqr lqUnj] dykRed ,oa LFkkiR; dyk dk vuwBk mnkgj.k izLrqr djrh gSA vtesj dh ;g ;g lcls izkphure~ ufl;k¡ foØe laor~ 1100 dh vuqekfur gSA efUnj esa osnh f’k[kj can gS o ewy osnh ds lkFk vU; osfn;ka Hkh cuh gqbZ gSAik’oZukFk fnxEcj tSu efUnj] xks/kk xokM+h;g efUnj 1057 bZLoh esa fufeZr Hkhrj ls HkO; ,oe~ vkd"kZd gSA ewyuk;d Hkxoku ik’oZukFk dh izkphu osnh vHkh rd lqjf{kr gSA 'kakfryky cM+tkR;k ds vuqlkj bl efUnj dk fuekZ.k pkSgku 'kkld nqyZHkjk; r`rh; dh lgerh o lg;ksx ls gqvk Fkk vkSj vt;jkt ds }kjk ;gka bl efUnj esa LoZ.k dy’k Hkh HksaV fd;k x;k FkkA 6 fczfV’kdky esa bl efUnj dk th.kksZ}kj fd;k x;k FkkA nknk ckM+hLFkkiR; dyk dh n`f"V ls mPp dksVh dk o fof’k"V ekWMy laxejej ls fufeZr o lksus ls ysfir nknk ckM+h eq[; vkd"kZ.k dk dsUnz gSA nknkckM+h fLFkr ys[k ds vuqlkj 1154 bZLoh esa

[kjrxPN 'kk[kk ds pkj vkpk;Z ¼ftunRr lwfj] ftudq’ky lwfj ef.k/kkjh ftupUn lwfj vkSj vdcj izcksa/kd ftupan lwfj½ esa ls ftunRr lwfj ¼nknkxq:½ dh fuokZ.k LFkyh nknkckM+h dk fuekZ.k gqvk FkkA bl efUnj ds fuekZ.k eas pkSgku 'kkld v.kksZjkt dk lg;ksx jgk FkkA 7

nknkckM+h esa rhFkZadj ik’oZukFk ds pj.k fpUgksa ds vykok rhFkZadj foeyukFk ds pj.k fpUg dh Hkh izfrek izfrf"Br gSA nknkxq: ds Lrwi ij dkykUrj esa HkO; efUnj dk fuekZ.k gqvk tks lajejej ls fufeZr] NksVs&NksVs dkap ds VqdM+ksa dh dkjhxjh ls lfTtr vkt Hkh u;kukfHkjke gSA efUnj dh ckgjh nhokjksa ij nknk xq: ds thou dh peRdkfjd ?kVukvksa dk fp=kadu gSA laxejej ds LrEHkksa ij mRdh.kZ lqUnj dk;Z rhFkZ ;kf=;ksa dk eu eksg ysrk gSA nknk ckM+h ifjlj dkQh cM+k gSA efUnj ds mijh Hkkx esa yky iRFkj ls vusd Nrfj;k¡ Hkh fufeZr gSA iRFkj ij ckfjdh dk dk;Z cgqr gh lqUnj o LFkkiR; dh n`f"V ls egRoiw.kZ gSA'kkfUrukFk efUnj] c?ksjkc?ksjk dk 'kakfrukFk efUnj nloha 'krkCnh eas fufeZr gqvk FkkA ;gk¡ ls [kqnkbZ esa izkIr izfrek,¡ X;kjgoha ls rsjgoha 'krkCnh dh gSA 8

ik’oZukFk fnxEcj tSu efUnj] eksjk>M+h

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eksjk>M+h esa Jh 1008 ik’oZukFk fnxEcj tSu efUnj Hkh uoha ;k nloha 'krkCnh esa fufeZr gqvk FkkA ;g lEiw.kZ efUnj ik"kk.k LrEHkksa ij [kM+k gSA bldh LFkkiR; dyk ds vk/kkj ij Hkh ;g nloha 'krkCnh iwoZ dk gh vuqekfur gksrk gSA bl efUnj eas nks osfn;ka gSA blesa 1190] 1151] 1204] 1654 bZLoh dh vusd izfrek,¡ fojkteku gSA 9

o/kZeku Lokeh tSu efUnj] ujsukujsuk eas o/kZeku Lokeh dk tSu efUnj 1079 bZLoh dk gSA ;gka ls X;kjgoha&ckjgoha 'krkCnh dh ewfrZ;k¡ HkwxHkZ ls izkIr gqbZ gSA[kqnkbZ esa izkIr ewfrZ;k¡ o Hkou dyk&d`frd ds vo’ks"k vtesj laxzgk; esa lqjf{kr gSA buesa nsyokM+k ¼ekmUVvkcw½ ds tSu efUnjksa ds leku lw{e dyk ds n’kZu gksrs gSAJh lEHkoukFk 'osrkEcj tSu efUnj] yka[ku dksBjhbl efUnj dk fuekZ.k 1755 bZLoh es gqvk FkkA ;g eafnj nks eaftyk vkSj cgqr gh euksjke gSA ;gk¡ ewy izfreka Hkxoku lEHkoukFk dh gSA eafnj esa izos’k djrs gh loZizFke vf/k"BkbZ nso lEHkoukFk ds n’kZu gksrs gSA vkjEHk esa dsoy lEHkoukFk dh izfrek LFkkfir dh xbZ FkhA ckn eas nks vkSj

osfn;ksa dk fuekZ.k fd;k x;k FkkA Hkxoku _"kHknso vkSj ik’oZukFk dh lHkh izfrek,¡ in~eklu eqnzk esas gSA 10 blh efUnj esa ck¡bZ rjQ lEesn f’k[kj dh u;ukfHkjke jpuk gSA tSu lekt ds lcls cM+s rhFkZ dh lEiw.kZ jpuk laxejej ds ,d gh f’kyk&iV~V ij mRdh.kZ dh xbZ gSA jpuk l`tu eas dyk dh ckjhdh ,oe~ lQkbZ dh rjQ fo’ks"k /;ku fn;k x;k gSA bl lajpuk ds e/; ds lkaofj;k¡ ik’oZukFk th dh izfrek gSaA efUnj esa ck¡bZ rjQ leo’kj.k dk frxM+k j[kk gqvk gSA ikl eas gh in~ekorh ekrk dk NksVk lk efUnj gSA blds ikl ,d vU; NksVs efUnj esa Hkxoku _"kHknso ,oe~ egkohj Lokeh ds pj.k fpUg ¼ixY;k½ fojkteku gSA efUnj ds lkeus ckabZ rjQ esjkcnkj LrEHk gSA bu esgjkcksa ij pkjksa rjQ dh nhokjksa ij Lof.kZe fp=dkjh gSA buesa flf)pØ o tSu /keZ ds vU; izrhdksa dk euksgkjh fp=.k fd;k x;k gSA yxHkx <kbZ lkS o"kZ chr tkus ij Hkh bl Lof.kZe vkSj jaxhu fp=dkjh dh rktxh esa Hkh U;wukf/kd Hkh vUrj ugha vk;kA efUnj dh Nr] ry ls yxHkx lkS QhV Åaph gS vkSj bruh Åaph Nr ij bruh lqUnj ,oe~ ckjhd fp=dkjh vk’;pZtud gSAJh fnxEcj tSu iapk;rh ufl;k¡] NksVk ?kM+k1853 bZLoh esa fufeZr] fnxEcj tSu chliaFkh] ukxkSjh

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vkeukFk dk NksVk /kM+k ufl;k¡ efUnj gSA ckgj ls ,d fo’kky bekjr tSlh fn[kkbZ nsus okyh bl ufl;k¡ dk rksj.k}kj jktlh egyksa tSlk fo’kky gS vkSj ydM+h ds fo’kky njoktksa ij LVhy dh pedrh gqbZ pknj p<+h gSA bl ufl;k¡ esa dqy ikap osfn;ka gSA eq[; osnh ij dkys ik"kk.k dh ewyuk;d Hkxoku vkfnukFk dh izfrek ineklu eqnzk esa gSA bl osnh ds nksuksa rjQ paoj <qykrs gq, bUnz dh izfrek,¡ gSA bl ufl;k¡ esa Lok/;k; Hkou ,oa o`rh lnu xqykch laxejej ls fufeZr gSA bl Hkou esa ,d gkWy] ,d xSyjh o vusd dejs gSA vk/ks Hkkx esa nks eaftyh /keZ’kkyk Hkh gSAegkiwr ftuky; ljkoxh eksgYys esa uqDdM+ ij fLFkr djksyh ds yky iRFkj ls fufeZr gksus ds dkj.k yky efUnj ds uke ls fo[;kr efUnj ds ckgj pkjksa rjQ yky irFkj esa ckjhd tkyhnkj dqjkbZ dh xbZ gSA blesa fofHkUu izdkj ds Qwy&ifRr;k¡] csy&cwVs vkfn mdsjs x;s gSA blds }kj ij mPp dksVh dh LFkkiR; dyk dk mnkgj.k izLrqr fd;k x;k gSA efUnj ds izos’k }kj ds nksuks arjQ nks xks[ks cus gSA ftuesa efUnj ds fuekZ.k lEcU/kh ys[k fy[ks x;s gSSAefUnj dh lhf<+;ksa esa lQsn ikjn’khZ laxejej yxk gqvk gSA eq[; njoktsa ij LVhy dh pednkj pn~nj yxh gSA bl efUnj ds Hkhrj mRd`"V LFkkiR; dyk

rFkk Lo.kZ o dk¡p dh ckjhd iPphdkjh dk cstksM+ dk;Z fd;k x;k gSA ftukY; dh nwljh eafty dh ewy osnh eas Hkxoku lqik’oZukFk dh izfrek fojkteku gSA ;gk¡ rhu osfn;k¡ gSA eq[; osnh ij dk¡p dk vf}rh; dykRed dk;Z u;;kfHkjke gSA dk¡p ds lkFk&lkFK lqugjs jax dk vkd"kZd fp=kadu Hkh eqX/kdkjh gSAefUnj esa rhuksa rjQ dkap ds uks }kj lqlfTtr gSA nhokjksa ij pkjksa rjQ tkyhuqek dk¡p dk vuqie dke fd;k gqvk gSA ftlesa izfrek ds gtkjksa vDl fn[kkbZ nsrs gSA vkd"kZd LrEHkksa ij dykRed esgjkcksa dk lkSUn;Z Hkh fprkd"kZd gSA prqfnZd nhokjksa ij Lo.kZ L;kgh ls /kkfeZd 'yksd o vU; dFkuksa dk vadu gSA yxHkx Ms<+ lkS o"kZ iqjkus efUnj d{k dh ped vkt Hkh ;Fkkor fo/keku gSA Q’kZ ij laxejej yxk gSA ckabZ rjQ Lok/;k; d{k eas ujd ,oa lalkj dh n’kkvksa dk fp=.k fd;k x;k gSA rhljh eafty ij lhf<+;ksa ds nkfguh rjQ pSR;ky; esa LQfVd ef.k ls fufeZr Hkxoku pUnzizHkq dh HkO; o euksje izfrek gSA blds vfrfjDr pkanh ,oa loZ/kkrq dh vusd cgqewY; izfrek,¡ gSA Nr o nhokjksa ij tSu /keZ xzUFkksa ds mnkgj.k lqUnj fy[kkoV esa vafdr gSA nhokjksa ij leo’kj.k dk ifjp;] pkSchl xzUFkksa dh ppkZ dk uD’kk] Hkk"kke; Lrqfr;ka] Ng <kyk pØorhZ

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foHkwfr o.kZu] bUnz foHkwfr o.kZu ,oa Jqr LdU/k dk uD’kk vkfn eksVs&eksVs v{kjksa esa fy[ks gSA lkFk gh 14 xq.k 14 QhV ds oxkZdkj 'kh’ks ds gky esa lekso’k.k jpuk gSA blesa Q’kZ ls Nr rd dh ÅapkbZ okyh niZ.k ifV~Vdkvksa ds vfrfjDr chp&chp eas Hkh cM+s&cM+s niZ.k yxs gSA blds vUnj ,d xU/kdqVh gS] ftlesa cgqewY; jRu] ek.kd ,oa LQfVd dh X;kjg rhFkZadj izfrek,¡ fojkteku gSAJh xksM+h ik’oZukFk eafnjbl efUnj dh LFkkiuk 1855 bZLoh es gqbZA ;g yk[ku dksVM+h efUnj uke ls fo[;kr gSA blesa izos’k djrs gh lkeus rhu cM+h&cM+h u;kfHkjke osfn;k¡ n`f"Vxkspj gksrh gSA ;gk¡ ewyuk;d ik’oZukFk dh izfrek ds vfrfjDr nksuksa rjQ vU; NksVh&NksVh izfrek,¡ Hkh fojkteku gSA izos’k }kj ds Hkhrj nksuksa rjQ j{kd nso dh izfrek,¡ o nk¡bZ rjQ fl)kf;dk nsoh 'ksj ij lokj vkSj vfEcdk nsoh gSA bl nks eaftys eafnj eas Hkxoku egkohj dh pkSeq[kh izfrek fojkteku gSA eafnj ds fupys Hkkx esa nhokj o esgjkcksa ij eueksgd fp=dkjh dh xbZ gSA bl efUnj esa ,d LQfVd dh izfrek Hkh gSA 11

Jh fl)dwV pSR;ky;djkSyh ds yky iRFkj ls fufeZr efUnj] vius f’kYi lkSUn;Z ,oa dykRed fp=dkjh ,oa iPphdkjh

ds fy, izfl) gSA efUnj vkSj ukfl;ka blds nks Hkkx gSA efUnj dk izos’k }kj mRrj fn’kk esa fdlh fdys ds rksj.k }kj tSlk fo’kky] Å¡pk o dkyRed gSA bleas cgqr gh ckjhd tkyhnkj dVkbZ rFkk csy&cwaVsa] Qwy ifRr;ka vkfn mRdh.kZ gSA nksuksa rjQ cM+s >jks[ks gSA }kj dh Nr ij esgjkcnkj xqEcn ,oa tkyhnkj Nrfj;ka cuh gSA efUnj izkax.k esa ,d pkSjklh QhV Å¡pk eku&LrEHk gSA ftu efUnj Lo;a leks’kj.k dk izrhd gSA LFkkiR; ij okrkoj.k ds izHkko dk ;Fkksfpr egRo le>rs gq, tSfu;ksa us efUnjksa ds fuekZ.k ds fy, lnSo izrhd LFkkuksa dks gh pquk ,oa vU; yfyr dykvksa dk Hkh l`tu fd;kA fl)dwV pSR;ky; esa fLFkr laxejej ls fufeZr ekuLrEHk ds pkjksa rjQ ,sjkor gkFkh cus gSA eku&LrEHk ds mijh Hkkx esa pkjksa rjQ Hkxoku vkfnukFk] pUnzizHkq] 'kakfrukFk ,oa egkohj Lokeh dh pkj izfrek,¡ gSA bUnzpUn tSu ds vuqlkj bl eku&LrEHk ij 'osr laxyk fd;k x;k gSA 12 ftlds dkj.k ;g ckâ; izHkko ls lqjf{kr gSA eku&LrEHk ds lkeus gh eafnj dk fo’kky izos’k }kj gSA }kj ds mij dykRed esgjkc] ckyduh] dy’k o >jks[ksa gSA ;g }kj lQsn laxejej ls fufeZr gSA lh<+h;ka p<+rs gh fl)dwV pSR;ky; dk izkxa.k vk tkrk gS ftlesa Åaps&Åaps dykRed LrEHk gSA LrEHkksa

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ds vk/kkj ij ckjhd Qwy&ifRr;ka] csy&cwVs vkfn cus gq, gSA eafnj ds rhuksa izos’k }kj dkap tfM+r dykRed dk;Z ls lqlfTtr gSAfl)dwV pSR;ky; ds ihNys fgLls esa fo’o&fo’;kr lksuh th dh ufl;k¡ gSA bldk ckgjh Hkou djkSyh ds yky iRFkj ls cuk gqvk gSA Hkhrj laxejej dk iz;ksx fd;k x;k gSA ;g ufl;k¡ oLrqr% uoklh QhV yEck] pkSlB QhV pkSM+k vkSj cjk.kosa QhV Å¡pk HkO; Hkou gSA ftlesa pkjksa rjQ dk¡p ds 'kh’ks yxs gSA ;g nks eaftyk Hkou ckgj ls pkj eaftyk fn[kkbZ nsrk gSA ;g blds f’kfYi;ksa dh dykdkjh gSA blds Åij pkjksa rjQ dykRed Nrfj;ka o muesa >jks[ksa cus gSA ftu ij Lo.kZ dy’k Hkh

p<+s gSA ufl;k¡ ds Hkhrj lekso’kj.k jpuk dh xbZ gSA tks ydM+h dh gS o bl ij lqugjs jax dh ikWfy’k dh xbZ gSAJh ik’oZukFk fnxEcj tSu ¼tSloky½ eafnj] dsljxat;g eafnj 1906 bZLoh esa fufeZr gqvkA bl efUnj ds nks Hkkx gSA cM+s efUnj esa dkys ik"kk.k dh ewyuk;d izfrek Hkxoku ik’oZukFk o NksVs efUnj esa Hkxoku vkfnukFk dh gSA ;g fo’kky f’k[kjcan efUnj laxejej ls fufeZr gSA Åij Lo.kZ dy’k Hkh yxk gqvk gSA eq[; osnh ij Lof.kZe dykRed dk;Z fd;k x;k gSA osnh ds ckgj laxejej ds esgjko ij lksyg LoIu mRdh.kZ gSA nkfguh rjQ lalkj n’kZu vkSj ck¡bZ rjQ ik’oZukFk dh riL;kjr >kadh mRdh.kZ gSA

ikn&fVIif.k;k¡'kekZ] n’kjFk & vyh pkSgku Mk;usLVht] i`"B la[;k 38] 62-?kks"k] veykuUn & tSu dyk ,oe~ LFkkiR; ¼[k.M 2½] i`"B la[;k 243-?kks"k] veykuUn & ogh] i`"B la[;k 244-<+kdh] ,e-,- & egkohj tSu fo|ky; xksYMu tqcyh okWY;we] i`"B la[;k 306] 311-?kks"k] veykuUn & tSu dyk ,ao LFkkiR;] i`"B la[;k 246-cM+tkR;k] 'kkfUryky & lfefr lnL;] ik’oZukFk fnxEcj tSu efUnj xks/kk xokM+h] vtesj-ys[k & nknkckM+h fLFkr nknk xq: ftunRr lwfj dk ys[k vtesj-izfrek ys[k & 'kkfUryky efUnj] c?ksjk-izfrek ys[k & lu~ 1190] 1151] 1204] 1654 'kakfrukFk efUnj c?ksjk-izcU/k lfefr & Jh lEHkoukFk tSu 'osrkEcj efUnj] yk[ku dksVM+h] vtesj-izcU/k lfefr & Jh xkSM+h ik’oZukFk eafnj] yk[ku dksVM+h] vtesj-ikVuh] bUnzpUn & O;oLFkkid] Jh fl)dwV pSR;ky;] vtesj

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web viewa comparative study of plant variety protection (pvp) legislations of developed and...

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