P.Agri.21-01-2016 450 AGRICULTURAL SITUATION IN INDIAJasdev Singh - Agro Economic Research Centre, Department of Economics and Sociology, Punjab Agricultural University, Ludhiana

P. Agri. 21-01-2016

450

FEBRUARY, 2016

FARM SECTOR NEWS

Determinates of RuralNon-Farm Employment inPunjab : A Logestic Analysis

Factors Determining EnergyConsumption in AgriculturalProduction: A Comparative EconomicAnalysis in Tamil Nadu

Production And Growth inPulse in India

Economietric Analysis ofArrivals AndPrices of black Gram inwestern Maharashtra

GENERAL SURVEY OF AGRICULTURE

ARTICLES

AGRICULTURAL

SITUATION IN INDIA

AGRICULTURAL

SITUATION IN INDIA

AGRO ECONOMIC RESEARCH

Possibilities And Constraints inAdoption of Alternative Crops toPaddy in Punjab

COMMODITY REVIEWSFoodgrainsCommercial Crops

TRENDS IN AGRICULTURE:Wages & Prices

AGRICULTURAL SITUATIONIN

INDIA

February, 2016

PUBLICATION DIVISIONDIRECTORATE OF ECONOMICS and STATISTICS

DEPARTMENT OF AGRICULTURE and CO-OPERATIONMINISTRY OF AGRICULTURE

GOVERNMENT OF INDIA

Editorial Board

ChairmanSangeeta Verma

EditorP. C. Bodh

Addl. Economic AdviserYogita Swaroop

Economic OfficerProsenjit Das

Officials Associated in Preparation of thePublication

D.K. Gaur — Tech. Asstt.S.K. Kaushal — Tech. Asstt. (Printing)

Uma Rani — Tech. Asstt. (Printing)V.M. Shobhana — P.A.

Cover Design By:Yogeshwari Tailor— Asstt. Graph

Publication DivisionDIRECTORATE OF ECONOMICS

AND STATISTICS

DEPARTMENT OF AGRICULTURE, COOPERATION & FARMERS WELFARE

MINISTRY OF AGRICULTURE & FARMERS

WELFARE

GOVERNMENT OF INDIA

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©Articles Published in the Journal cannot bereproduced in any form without the permissionof Economic and Statistical Adviser.

FARM SECTOR NEWS

GENERAL SURVEY OF AGRICULTURE

ARTICLES

Determinants of Rural Non-farm Employment in Punjab: A LogisticAnalysis - Dr. Neeraj Sharma & Harinder Mohan

Factors Determining Energy Consumption in AgriculturalProduction: A Comparative Economic Analysis in Tamil Nadu” -D. Murugan and E. Kanagaraj

Production and Growth in Pulses in India– Vishnu ShankerMeena, Shirish Sharma and Vishal Dagar

Econometric Analysis of Arrivals and Prices of Black Gram inWestern Maharashtra – D.S. Navadkar, A.J. Amale and R.B.Naik

AGRO-ECONOMIC RESEARCH

Possibilities and Constraints in Adoption of Alternative Cropsto Paddy in Punjab - D.K. Grover, Sanjay Kumar, J.M. Singh andJasdev Singh - Agro Economic Research Centre, Department ofEconomics and Sociology, Punjab Agricultural University,Ludhiana.

COMMODITY REVIEWS

(i) Foodgrains

(ii) COMMERCIAL CROPS :

Oilseeds and Edible Oils

Fruits and Vegetables

Potato

Onion

Condiments and Spices

Raw Cotton

Raw Jute

Agricultural Situationin India

VOL. LXXII February, 2016 No.11

CONTENTS

PAGES

1

7

11

17

27

32

39

54

56

56

56

56

5656

56

STATISTICAL TABLESPAGES

Wages

1. Daily Agricultural Wages in Some States—Category-wise.

1.1. DailyAgricultural Wages in Some States—Operation-wise.

Prices

2. Wholesale Prices of Certain Important Agricultural Commodities and Animal Husbandry Products at Selected Centres in India.

3. Month-end Wholesale Prices of Some Important Agricultural Commodities in International Market

during the year 2015.

Crop Production

4. Sowing and Harvesting Operations Normally in Progress during March, 2016.

Abbreviations used

N.A. — Not Available.N.Q. — Not Quoted.N.T. — No Transactions.N.S. — No Supply/No Stock.R. — Revised.M.C. — Market Closed.N.R. — Not Reported.Neg. — Negligible.Kg. — Kilogram.Q. — Quintal.(P) — Provisional.Plus (+) indicates surplus or increase.Minus (–) indicates deficit or decrease.

We are pleased to inform that our monthly journal AgriculturalSituation in India has been accredited by National Acedemy ofAgricultural Sciences (NAAS) and it has given a score of 2.76out of 6. The score is effective from January 1, 2016 onwards.The score may be seen in PDF at the following website:naasindia.org

Soft copy of the journal may be seen in PDF at the followingURL : eands.dacnet.nic.in/publication.htm

The Journal is brought out by the Directorateof Economics and Statistics, Ministry ofAgriculture & Farmers Welfare, it aims atpresenting a factual and integrated pictureof the food and agricultural situation in indiaon month to month basis. The viewsexpressed, are not necessarily those of theGovernment of India.

NOTE TO CONTRIBUTORS

Articles on the State of Indian Agriculture and alliedsectors are accepted for publication in theDirectorate of Economics & Statistics,Department of Agriculture, Cooperation &Farmers Welfare’s monthly Journal “AgriculturalSituation in India”. The Journal intends to providea forum for scholarly work and also to promotetechnical competence for research in agriculturaland allied subjects. Good articles in Hard Copyas well as Soft Copy ([email protected])in MS Word, not exceeding five thounsand words,may be sent in duplicate, typed in double spaceon one side of foolscap paper in Times NewRoman font size 12, addressed to the Editor,Publication Division, Directorate of Economicsand Statistics, M/o Agriculture & Farmers Welfare,C-1, Hutments Dalhousie Road, New Delhi-110011 along with a declaration by the author(s) thatthe article has neither been published norsubmitted for publication elsewhere. The author(s) should furnish their e-mail address, Phone No.and their permanent address only on theforwarding letter so as to maintain anonymity ofthe author while seeking comments of the refereeson the suitability of the article for publication.

Although authors are solely responsible forthe factual accuracy and the opinion expressed intheir articles, the Editorial Board of the Journal,reserves the right to edit, amend and delete anyportion of the article with a view to making itmore presentable or to reject any article, if notfound suitable. Articles which are not foundsuitable will not be returned unless accompaniedby a self-addressed and stamped envelope. Nocorrespondence will be entertained on the articlesrejected by the Editorial Board.

An honorarium of Rs. 2000/- per article ofatleast 2000 words for the regular issue andRs. 2500/- per article of at least 2500 words forthe Special/Annual issue is paid by the Directorateof Economics & Statistics to the authors of thearticles accepted for the Journal.

Disclaimer: Views expressed in the articles andstudies are of the authors only and may notnecessarily represent those of Government ofIndia.

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February, 2016 1

The Climatic Conditions of the Assam Coupled withRich Water Resources are Highly Conducive for theGrowth of Horticulture- Said By Shri Radha MohanSingh, Hon'ble Union Minister of Agriculture &Farmers Welfare

Union Agriculture and Farmers Welfare Minister, ShriRadha Mohan Singh, on the occasion of "RongoliMahotsava" at Guwahati, Assam, said that "the climaticconditions of the state coupled with rich water resourcesare highly conducive for the growth of horticulture in theState". In this festival, an "Exhibition on Agriculture andHorticulture" was organized to promote agriculture andHorticulture in North- East region including Assam.

Shri Singh further added that he wants to fulfill thedreams of the farmers by linking them with the schemesbeing operated by the Ministry of Agriculture and FarmersWelfare and to increase income of the farmers along withimprovement in the quality of the produce by promotingMega Seed Village Programme.

The Minister said that "the National HorticultureBoard is playing key role in development of CommercialHorticulture. Recently , we have reduced the area ceilingfor open field cultivation from 5 acre to 2.5 acre and forPoly House/ Net House from 2500 Sq Meter to 1000 SqMeter exclusively for North Eastern States under theschemes of the Board."

Excerpt from the speech of the Union Agriculture andFarmers Welfare Minister is as follows:

"It is a matter of great honour and privilege that Iam present with you on the occasion of "RongoliMahotsava". In this festival, an "Exhibition on Agricultureand Horticulture" is also being organized to promoteagriculture and Horticulture in North- East regionincluding Assam. This exhibition will provide a commonplatform to various stake holders and entrepreneursassociated with economic development of North EasternStates, Food Processing, Marketing, Govt. Institutions andAgencies.

State of Assam, expanded in an area of 78,440 km,is known of its premium tea, silk, one horned rhino andvarious species of Asian Birds. Kaziranga and ManasNational parks of the State have been given recognitionof world class wild life tourism spot. Bramhaputra River,

Farm Sector News*

older than the Himalaya, is lifeline of the State. State is abig reservoir of Oil, natural Gas and Tea and Tourismindustry is playing significant role in the economy of theState. Use of many languages in the state is a glaringexample of unity among diversity.

The climatic conditions of the state coupled withrich water resources are highly conducive for the growthof horticulture in the State. The state receive averagerainfall of 2200 Ml. Farmers of the Assam state have keeninterest in growing Cash Crops. Considering the problemof floods in several areas of the State, Present Govt intendsto motivate the farmers to adopt Horticulture farming overtraditional farming. We want to fulfill the dreams of thefarmers by linking them with the schemes being operatedby the Ministry of Agriculture and Farmers Welfare. Wealso want to increase income of the farmers along withimprovement in the quality of the produce by promotingMega Seed Village Programme.

National Horticulture Board is playing key role inthe development of Commercial Horticulture. Recently,we have reduced the area ceiling for open field cultivationfrom 5 acre to 2.5 acre and for Poly House/ Net Housefrom 2500 Sq Meter to 1000 Sq Meter exclusively forNorth Eastern States under the schemes of the Board. Thevery purpose of these changes is to ensure that hardworking farmers of NE Region including Assam State takemaximum benefit of the schemes of Govt of India andcontribute in the growth of the State.

Attempts have been made by National HorticultureBoard to bring you the famous horticulture produce ofvarious states through "Rongoli". From time to time Boardis also promoting Horticulture produce of NE Regions andalso creates awareness among the farmers about newtechnologies in various parts of the country through itsHorti Fair known as "Hortisangam".

Our Agriculture and Farmers Welfare Ministry isworking with the sole objective to make farmersprosperous by linking them with various schemes andprogrammes for Agriculture and HorticultureDevelopment.

I appeal to all Farmers to avail various schemes ofthe Govt of India and contribute significantly in thedevelopment of the State and the country."

Source:www.pib.nic.in

2 Agricultural Situation in India

Education is not Merely getting Good Grades orAcademic Awards but to provide value basedEducation which will lead to Good Society- Said byShri Radha Mohan Singh

Union Agriculture and Farmers Welfare Minister, ShriRadha Mohan Singh addressed the Ist Convocation of LalaLajpat Rai University of Veterinary and Animal Sciences(LUVAS), Hisar, Haryana. In his address to students ShriRadha Mohan Singh said to students that "remember thereis no room for complacency and for mediocrity, life is notbed of roses, you are likely to encounter setbacks at variousstages, do not let these discourage you. Experiencesdecrease our mistakes and mistakes increase ourexperience. Learning is a lifelong process. I would like toemphasize that education is not merely getting good gradesor academic awards. A good education system has toprovide value based education which will lead to goodsociety." Shri Singh also said that "Taittriya Upanishadspeaks about the moral and ethical ways to live for thestudents" and Shri Singh quote one of the shloka inTraittriya Upanishad which means "with knowledge, I amthe mover of tree of the universe, my fame rises high likemountain peak, I am the brightest treasure, I am the shiningwisdom, I am immortal and un-decaying. Indeedknowledge is the source of energy for wellbeing ofhumanity at large."


"All the dignitaries on the dais, guests, parents, faculty,students, brothers, sisters and friends from the Media. Itake pride to be here to deliver Ist Convocation address ofLala Lajpat Rai University of Veterinary and AnimalSciences, Hisar. It is a memorable day as we are celebrating151st birthday of great freedom fighter, Lala Lajpat RaiJi. First of all, I convey my heartiest congratulations to allthe graduates, post-graduates and doctorates forsuccessfully completing their degree, programmes and alsoto their learned teachers who have worked hard to enrichthe young minds with quality education, knowledge, skillsand moral values. I also greet all the parents who haveinspired their children towards veterinary studies andprovided them the resources and environment for theirgrowth.

The inception and growth of this University isclosely weaved with the narrative of rural empowermentthrough animal husbandry services. For us, the sketch ofempowerment is not just economical but social andeducational upliftment of people across the state ofHaryana. The Veterinary College has a glorious history asit is an off-shoot of the first veterinary college of undividedIndia established in 1882 in Lahore. It is backed by anenviable tract record of academic excellence, cutting edgeresearch and rendering quality services to the livestock of

the state. An extensive network of alumni occupyingimportant positions throughout the world is a source ofinspiration, enthusiasm and commitment to hard work forthe faculty and students.

LUVAS has several firsts to its credit and it has leftits mark in research, education and extension. I am veryproud and congratulate you all for continuing the legacyunder the able leadership of Maj. Gen. (Dr.) Shri Kant,SM VSM (Retd.), the first regular Vice Chancellor ofLUVAS. Befitting the occasion, I would like to speak onanimal wealth and how to leverage it for improving theprosperity of small farmers. I would like to highlight therole of animal farming in the lives of the small farmersand weaker sections, its contribution to the economy andthe ecology. I would like to pay my respect to the teachersof this university for their untiring efforts in bringing thisuniversity on world map.

The last few decades have seen the Indian livestocksector emerging as one of the fastest growing sub-sectorsof agriculture. However, the poor, both as producers andconsumers, have been largely by-passed by the benefitsof this growth. Both central and state governments haveshown the will to reshape livestock sector policies andprogrammes, so as to promote a more inclusive form ofgrowth and tap into the poverty reduction potential oflivestock sector development. India has world's largestanimal wealth. As per the 19th All India Livestock Census,total livestock population in the country in 2012 was over512 million, largest in the world, despite decline of 3.33per cent over 2007. In addition to this, poultry birds andfish further appreciate the wealth we have.

The Government of Haryana has recently passed theHaryana Gauvansh Sanrakshan and Gausamvardhan Act20, 2015 in which it is stated that the government shallestablish laboratories for differentiation of beef from thatof the meat of other species of animals, testing andidentification of various constituents of milk and milkproducts and testing and differentiation of A1 and A2 milkfor providing incentives for wholesome production of milkand milk products. I am delighted to know that LUVAShas already taken the lead role in differentiating cow meatfrom buffalo meat besides testing milk and milk products.

I am happy to know that LUVAS has signed eightMoUs with National and International public and privateinstitutions during the period 2011-14 and hope the MoUswould be functional and yield productive results. Signingof MoU with world class Pirbright Institute, U.K. istestimony of LUVAS's excellence in scientific capabilitiesand is first of its kind. The commercialization oftechnologies for their faster transfer to the stake-holdersand also to realize return on investments has become partof technology transfer pursuits. I have been told thatLUVAS has also established "Business Planning &

February, 2016 3

Development Unit and IPR Cell" for patenting andlicensing of technologies which will help not only thefarmers of Haryana but of other states as well.

I am happy to say that Haryana has taken the lead inwhole country in moving towards containment of Foot andMouth Disease (FMD) by the combined efforts of LUVASand State Animal Husbandry Department. The role ofRegional Research Centre on FMD in LUVAS is reallypraiseworthy in achieving this target.

We have an enormous and diverse animal wealththat generates primary as well as supplementary livelihoodopportunities for millions of people. Are we able to tapthis wealth for the benefit of the people involved in rearingand tending them as also the consumers who demand theproduce from animal farming activities? I am happy toknow that after putting years of research, the universityhas contributed in development of a new breed of dairycattle 'Hardhenu', which is a crossbred of Holstein Friesian,Hariana and Sahiwal. The beneficiary of such researchwork will be our poor farmers. I have been told thatLUVAS organizes trainings on Dairy Farming at differentplaces in Haryana. Hundreds of livestock owners/ un-employed youths, both men and women participate in thesetrainings and get direct benefit from our scientists".

Rabi Crops Sowing Crosses 590 Lakh Hectares

As per preliminary reports received from the States, thetotal area sown under Rabi crops as on 28th January,2015 stands at 591.51 lakh hectares.

Wheat has been sown/transplanted in 292.52 lakhhectares, pulses in 139.08 lakh hectares, coarse cereals in60.08 lakh hectares. Area sown under oilseeds is 77.43lakh hectares and Rice is 22.41 lakh hectares.

The area sown so far and that sown during last yearthis time is as follows:

(In Lakh hectare)

Crop Area sown Area sownin 2015-16 in 2014-15

Wheat 292.52 305.94

Pulses 139.08 143.01

Coarse Cereals 60.08 56.76

Oilseeds 77.43 79.51

Rice 22.41 24.08

Total 591.51 609.31

Department of Animal Husbandry Dairying andFisheries (DADF) issued the Post OperationSurveillance Plan

Avian Influenza in Tripura

The Director, Animal Resources DevelopmentDepartment, Government of Tripura informed thisDepartment about the unusual mortality in State PoultryFarm, Gandhigram, and Tripura West district of TripuraState. The samples were sent to National Institute of HighSecurity Animal Diseases (NIHSAD) on 14-01-2016which was found positive in RT-PCR and Real time RT-PCR for H5N1 AIV.

Keeping in view the revised action plan, theintimation of the result was given to the State by NIHSAD,Bhopal for starting the control and containment operationsso as to avoid the further spread of the disease. TheDirector, Animal Resources Development Department,Government of Tripura was requested to send a report onthe matter and take necessary measures as per Action Planon Avian Influenza.

The intimation was given to the Ministry of Health& Family Welfare by this Department.

The State government carried out control andcontainment operation at the epicenter which was over on25-01-2016 and the State issued the sanitization certificatefor the said epicenter on 25-01-2016. During the operation,7687 birds were culled while 1760 had died out of apopulation of 9447 birds in 1 km. radius. The Departmenthad issued the Post Operation Surveillance Plan (POSP)on 27.01.2016 which would be continued for a period ofthree months from the date of issue of sanitizationcertificate.

"India has been transformed from a food-deficientcountry to a food exporting country about the last sixdecades" - Said by Shri Radha Mohan Singh

Union Minister for Agriculture, Shri Radha MohanSingh addressed the Conference of Vice Chancellors ofAgricultural Universities and Interface with ICARDirectors on 22-23 January, 2016 at A. P. ShindeAuditorium, NASC Complex. Shri Singh said that overthe years, the network of Agricultural Universities hasresulted in development of skilled and trained humanresources which have been instrumental in generating newtechnologies, their assessment, refinement & disseminationto farming community. He also said that we (in India) havea very large network of public funded agriculture researchand education system, including 73 agriculturaluniversities, 101 ICAR institutes and 642 KVKs. I feelwe can justify organising such meetings only if we areable to harness the synergies of our institutions and


optimise the use of our limited resources to pursue thespecified goals efficiently with a focus on the farmingcommunity that is our ultimate client


Shri Sanjeev Kumar Balyan Ji, Shri Mohanbhai Kundariyaji, Dr S. Ayyappan, Secy. DARE & DG, ICAR, ShriChabbilendra Roul, Addl. Secy. DARE & Secy. ICAR,Shri S. K. Singh, Addl. Secy. & FA, DARE, Prof. M.C.Varshney, President, IAUA, Dr N. S. Rathor, DDG (Edn).,Vice Chancellors of Agricultural Universities, Directorsof ICAR Institutes, Senior Officers from ICAR, SpecialInvitees, Representatives of Press and Media, ladies andgentlemen.

Over the years, the network of AgriculturalUniversities has resulted in development of skilled andtrained human resources which have been instrumental ingenerating new technologies, their assessment, refinement& dissemination to farming community. Thanks to theconcerted efforts of our agricultural scientists, farmers,policy planners and other stakeholders that India has beentransformed from a food-deficient country to a foodexporting country in about last six decades.

ICAR celebrated 'Jai Kisan Jai Vigyan' Week from23 December to 29 December, 2015 on the birthanniversary of former Prime Ministers Shri Atal BihariVajpayee and Late Shri Chaudhary Charan Singh. Thecelebration was organized keeping in view their immensecontribution for promoting use of science for the welfareof farmers. ICAR Institutes and KVKs across the countryorganized various farmer awareness programs, showcasingand demonstration of new technological advances forenhancing productivity and profitability of agriculture.Progressive farmers, who have successfully adopted latesttechnologies and reaped the benefits, were felicitated.

In keeping with UN declaration of 2015 as the Yearof Soils, and understanding the need to conserve the mostprimary source of production - soil, the initiative oflaunching new measures to provide Soil Health Cards toall farmers in the country in a time bound manner will goa long way in further increasing the production andproductivity of Indian agriculture. To create awarenessamongst the farmers about the importance of the soil, ICARcelebrated World Soil Day on 5th December, 2015 at 607KVK and 80 Research Institute/Agricultural Universitiesacross the country. Notable thing is that the Hon'ble UnionMinisters, Governor, MP, MLA also graced the functions.In these programmes, about 2 - 5 lakh Soil Health Cardswere distributed to the farmers.

Recognising the importance of pulses as a cheapsource protein in human diet, especially for the vegetarianpopulation and the soil nitrogen fixing property of the

leguminous plants, the UN General Assembly has declaredthe year 2016 as the international Year of Pulses. It is goodto see that in the very beginning of the new year-2016,one of our leading institutions, IARI, New Delhi hasreleased PADT-16, a variety of arhar that yields about 20quintal/ha and matures in 120 days against 160-180 daystaken by the presently used varieties. We must ensure thatthe seeds are made available to the farmers so that thenew variety in brought into the production chain at theearliest. I am sure as the year rolls by, we shall see moreimproved varieties in the field, not only of pulses but theoilseeds, as well.

Research must be undertaken on priority basis incutting edge areas with objective of increasing factorproductivity, resource conservation and thwarting thechallenges of climate change. This presents a major riskto long term food security as it may have multidimensionaldebilitating effects on agriculture. There is an urgent needto address issues concerning rainfed agriculture whichaccounts for about 56% of the total cropped area to giveboost to national food production. For addressing vagariesof nature ICAR's National Initiative on Climate ResilientAgriculture (NICRA) is a step forward in right direction.The National Agriculture Science Fund created for solvingproblems in cutting edge areas and capacity building is astep in right direction. These have to be carried forwardin the Mission mode.

Livestock feed, healthcare and management are tobe addressed in an integrated manner for ensuringintensification in the animal sector. In the past we haveencountered several diseases outbreaks, especially in ourfast growing poultry sector resulting loss of precioushuman and livestock life. Changes in livestock productionincrease the potential for new pathogens to emerge, growand spread from animals to humans on a global scale.Effective phytosanitary and animal health regimes aidedby new vaccines, diagnostic products and tools, andepidemiological information are necessary to survive inthe emerging regulatory regime. What we need here is asound mechanism of surveillance at ground level so thatwith appearance of first sign of the disease causingorganism, control measures are taken at the earliest.

Strengthening of our institutions of research andeducation for competent human must always be a priorityissue for the Council. Considering the fact that globally,the state of agriculture is in a flux, the ICAR institutesneed to be geared up to respond to new and rapidlychanging economic, ecological and technologicalenvironment. Realizing the importance of agriculture andhigher agricultural education in Indian economy, theguidelines for the establishment of Central AgriculturalUniversity has been approved by the Parliament. The MOUhas been signed with Bihar government to convert theexisting Rajendra agricultural university to Rajendra

February, 2016 5

Central Agricultural University and Bill is introduced inthe Parliament. This step will promote excellence in humanresource development and strengthen research andextension in the State.

It is heartening to inform that Government has decidedto strengthen agricultural education, research and extensionactivities, more so in NE region. Accordingly, a new CentralAgricultural University, is to be established at Barapani,Meghalaya. Six new colleges related to agriculture,horticulture, fisheries and food science are also beingestablished under the existing CAU, Imphal. The numberof colleges in the North Eastern region will go up from 7 to13 and four of the six colleges are already established. InBundelkhand region, Rani Laxmibai Central AgriculturalUniversity was established in 2014. To strengthen the CAU,four new colleges were approved from which one collegehas already been established and the establishment processof rest three colleges are in progress.

Ladies and gentlemen, we, in India, have a verylarge network of public funded agriculture research andeducation system, including 73 agricultural universities,101 ICAR institutes and 642 KVKs. I feel we can justifyorganising such meetings only if we are able to harnessthe synergies of our institutions and optimise the use ofour limited resources to pursue the specified goalsefficiently with a focus on the farming community that isour ultimate client.

National Conference on Sustainable Agriculture andFarmers Welfare at Chintan Bhawan, Gangtok -UnionAgriculture Minister said lots to learn from Sikkim

There are a lot that the rest of the country could learnfrom Sikkim, says the Union Minister for Agriculture ShriRadha Mohan Singh. Speaking at the inaugural of theNational Conference on Sustainable Agriculture andFarmers Welfare at Chintan Bhawan, Gangtok, the ministersaid that Sikkim has taken steps for organic farming whichcan take a lead for others to learn. The minister said thatunder the leadership of Prime Minister Shri NarendraModi, several steps have been taken for improvingagriculture and also focusing on the welfare of farmers.The minister informed that there were several cropinsurance schemes running in the country that were fraughtwith multiple lacunae. The Hon'ble Prime Minister showedspecial interest in this and now, the new scheme namedPrime Minister Fasal Bima Yojna has been launched. Therewill be freedom from crop-wise and district-wise ratesunder this scheme and there is a uniform premium rate ofonly 2% for rabi and 1.5% for kharif for farmers. There isno capping and therefore, there will be no reduction inclaim payments. For the first time, post harvest losses havealso been included as well as extensive use of technologywould be made for accurate estimation and speedydisbursal of claims.

Shri Radha Mohan Singh highlighted the PrimeMinister Krishi Sinchayee Yojana (PMKSY) which hasbeen initiated with an outlay of Rs. 50,000 crore for 5years (2015-16 to 2019-2020) with Department ofAgriculture, Cooperation & Farmers Welfare (DAC&FW)as designated Nodal Department for implementation ofPMKSY. The scheme will be implemented in areadevelopment mode adopting 'decentralized State levelplanning and projectised execution' allowing States to drawup their own irrigation development plans. The UnionMinister informed that in order to ensure the targetedannual growth of 4% in the agriculture and allied sectors,various schemes of the Ministry of Agriculture & FarmersWelfare have been restructured into specific missions,schemes & programmes from the year 2014-15. Some newinitiatives such as issue of Soil Health Cards to all farmersin all 14 crore farm holdings regularly in a cycle of 2 years,introduction of the Neem Coated Urea scheme,implementation of the Paramparagat Krishi Vikas Yojanafor promoting organic farming in the country, efficiencyand implementation of the National Agriculture Marketfor setting up of a common e-market platform, have beenintroduced for the long term benefit of the farmers.

Speaking on the occasion, the Minister of State forAgriculture & Farmers' Welfare Dr Sanjeev Kumar Balyansaid the green revolution which increased production bymanifold brought along side effects which we are nowfacing. The increase in death rate due to use of chemicalfertilizer is turning people back to organic and sustainablefarming. The Minister of State for Agriculture ShriMohanbhai Kalyanjibhai Kundariya said the variousschemes and programmes meant for the welfare of farmersshould reach the intended targets and not remain only onpaper. He expressed the hope the two day conferencewould work towards enhancing the productivity in theagriculture field and at the same time help the farmers.

The Chief Minister of Sikkim Shri Pawan Chamlingexpressed his gratitude to the Prime Minister oncomplimenting the efforts of the State towards organicmission within and outside the country which furtherstrengthened Sikkim's commitment. He said that while thebenefits of organic farming is widely known to no statesand country in the world before, Sikkim has implementedit as statewide mission and wide policy solution tounsustainable and harmful technique of agricultureproduction. Shri Chamling informed that a historicdeclaration has made through a resolution in the SikkimLegislative Assembly on February 24th 2003. Since then,Sikkim has marched ahead with an uncompromising visionof transforming the state into a total organic mission.


"Today's Agriculture is not only a source of livelihoodbut also leading to job or entrepreneurshipdevelopment" : Said Shri Radha Mohan Singh

Union Agriculture and Farmers Welfare Minister, ShriRadha Mohan Singh inaugurated the AMATHON, 2016-A Three Days International Agriculture Trade Summit atIIM, Ahmadabad, Gujarat. Shri Singh said that "As youall know that today's Agriculture is not only a source oflivelihood but leading to job or entrepreneurshipdevelopment. Today's farmers cannot be self reliant onlyby using old farm techniques because Agri inputs are costlyand in order to use new Agriculture technologies, there isnecessity of more investment. That is why, it is necessarythat our farmers should use low cost based Agriculturetechnology to get more crop yield and increase theirincome, so that the dreams of our Prime minister " JAIKISAN JAI VIGYAN" can be fulfilled".

Cabinet approves New Crop Insurance Scheme -Pradhan Mantri Fasal Bima Yojana - A boost to thefarming sector

The Union Cabinet chaired by the Prime Minister ShriNarendra Modi has approved the 'Pradhan Mantri FasalBima Yojana' - a path breaking scheme for farmers'welfare.

The highlights of this scheme are as under:

i) There will be a uniform premium of only 2%to be paid by farmers for all Kharif crops and1.5% for all Rabi crops. In case of annualcommercial and horticultural crops, thepremium to be paid by farmers will be only5%. The premium rates to be paid by farmersare very low and balance premium will be paidby the Government to provide full insuredamount to the farmers against crop loss onaccount of natural calamities.

ii) There is no upper limit on Governmentsubsidy. Even if balance premium is 90%, itwill be borne by the Government.

iii) Earlier, there was a provision of capping thepremium rate which resulted in low claimsbeing paid to farmers. This capping was doneto limit Government outgo on the premiumsubsidy. This capping has now been removedand farmers will get claim against full suminsured without any reduction.

iv) The use of technology will be encouraged toa great extent. Smart phones will be used tocapture and upload data of crop cutting toreduce the delays in claim payment to farmers.Remote sensing will be used to reduce thenumber of crop cutting experiments.

The new Crop Insurance Scheme is in line with OneNation - One Scheme theme. It incorporates the bestfeatures of all previous schemes and at the same time, allprevious shortcomings/weaknesses have been removed.

New Software for E-Connectivity of AgriculturalMarkets: Shri Radha Mohan Singh

Shri Radha Mohan Singh, Union Minister of Agriculture& Farmers Welfare said that the Centre will soon launchnew software to enable E-connectivity of Agriculturalmarkets in the country. Speaking at the foundationceremony of Shri Konda Laxman Telangana StateHorticulture University, at Mulugu village (MedakDistrict), Telangana State , Shri Singh said that the newsoftware could break the virtual and connectivity barriersof Agricultural Markets and will benefit the farmers ingetting fair prices.

Shri Radha Mohan Singh added that Karnataka wasable to bring 50- Agricultural Markets on one platformfor the benefit of farmers. "12 states including Telanganahave sent proposals regarding this and the centre willsanction Rs. 30 lakhs to each Agricultural market", headded. Shri Singh complimented the State Govt for naminghorticultural University after Sri Konda Lakshman Ji whois popularly known as Telangana Gandhi. Theestablishment of this Horticultural University comes inresponse to the Budget announcement made by Hon'bleFinance Minister to establish Horticultural University inthe State of Telangana along with one more HorticulturalUniversity in Haryana and two Agricultural Universitiesin Andhra Pradesh and Rajasthan.

An amount of Rs. 10.00 crores have been released to thisHorticultural University in the financial year 2014-15. Inthe current financial year 2015-16, an amount of Rs. 75crores has been allocated and out of which Rs 37.5 croreshas already been released as first installment. Withestablishment of this university, we are confident that theagricultural education and agriculture in general andhorticulture in particular will get much needed boost, saidMinister.

February, 2016 7

Trends in Foodgrain Prices

During the month of November, 2015, the All India IndexNumber of Wholesale Price (2004-05=100) of Food grainsincreased by 1.51 percent from 257.5 in October, 2015 to261.4 in November, 2015.

The Wholesale Price Index (WPI) Number ofCereals increased by 0.55 percent from 234.8 to 236.1and WPI of Pulses increased by 4.39 percent from 364.4to 380.4 during the same period.

The Wholesale Price Index Number of Wheatincreased by 1.00 percent from 219.3 to 221.5 while thatof Rice increased by 0.46 percent from 238.6 to 237.5during the same period.

Rainfall Situation

The cumulative rainfall in the country during the winterseason, i.e., 01st January to 20th January 2016 has beenlower by 55% than Long Period Average (LPA). Rainfallin the four broad geographical divisions of the countryduring the above period has been lower than LPA by 78%in South Peninsula, 73% in North West India, 26% inCentral India and 6% in East & North East India.

Out of 36 met sub-divisions, 12 met sub-divisionshave received excess/normal rainfall, 19 met sub-divisions

received deficient/scanty rainfall and 05 met sub-divisionshave received no rain.

Water Storage in Major Reservoirs

Central Water Commission monitors 91 major reservoirsin the country which have total live capacity of 157.80Billion Cubic Meter (BCM) at Full Reservoir Level (FRL).Out of these, 76 reservoirs with FRL capacity of 116.08BCM have irrigation potential. Live storage in the 91major reservoirs as on 21st January, 2016 has been 64.50BCM as against 83.11 BCM on 21.01.2015 (last year)and 85.10 BCM of normal (average of the last 10 years)storage. Current year's storage is lower than the last year'sstorage by 22% and the normal storage by 24%.

Sowing Position during Rabi 2016

As per latest information available on sowing of crops,around 95% of the normal area under Rabi crops has beensown upto 22.01.2016. Area sown under all Rabi cropstaken together has been reported to be 589.95 lakh hectaresat All India level as compared to 607.90 lakh hectares inthe corresponding period of last year i.e. lower by 17.9lakh ha. than the last year.

A statement indicating comparative position of areacoverage under major crops as on 22.01.2016 duringcurrent Rabi season vis-a-vis the coverage during thecorresponding period of last year is given below:

ALL INDIA CROP SITUTATION—RABI (2015-16) AS ON 22.01.2016(Area in lakh hectares)

Crop Name Normal Average Area sown Absolute Change over (+)/(-) % Change overArea Area as 22.01.16 % of 22.01.16 Average as Last Year Average as Last Year

on date Normal on date on date

Wheat 295.74 299.83 291.97 98.7 305.60 -7.9 -13.6 -2.6 -4.5

Rice 43.11 24.15 22.28 51.7 23.99 -1.9 -1.7 -7.7 -7.1

Jowar 39.57 36.72 37.19 94.0 33.22 0.5 4.0 1.3 12.0

Maize 14.17 13.24 14.01 98.9 14.74 0.8 -0.7 5.8 -5.0

Barley 6.68 7.24 7.20 107.8 7.59 0.0 -0.4 -0.6 -5.1

Total Coarse Cereals 60.43 58.04 59.30 98.1 56.56 1.3 2.7 2.2 4.8

Total Cereals 399.18 382.02 373.55 93.6 386.15 -8.5 -12.6 -2.2 -3.3

Gram 88.21 90.97 85.48 96.9 85.74 -5.5 -0.3 -6.0 -0.3

Lentil 14.81 15.27 13.45 90.8 15.01 -1.8 -1.6 -11.9 -10.4

Peas 7.93 10.09 9.79 123.4 10.37 -0.3 -0.6 -3.0 -5.6

Kulthi (House Gram) 2.13 4.82 4.16 195.1 5.07 -0.7 -0.9 -13.7 -17.9

Urad 7.47 6.81 8.16 109.2 8.08 1.4 0.1 19.8 1.0

General Survey of Agriculture


1 2 3 4 5 6 7 8 9 10

Moong 7.92 5.33 5.59 70.6 6.45 0.3 -0.9 4.9 -13.3Lathyrus 4.86 4.22 3.91 80.5 4.02 -0.3 -0.1 -7.3 -2.7Others 3.16 7.80 8.53 270.1 8.27 0.7 0.3 9.4 3.1Total Pulses 136.48 145.31 139.08 101.9 143.01 -6.2 -3.9 -4.3 -2.7Total Foodgrains 535.66 527.33 512.63 95.7 529.16 -14.7 -16.5 -2.8 -3.1Rapeseed & Mustard 62.78 68.25 64.48 102.7 65.14 -3.8 -0.7 -5.5 -1.0Groundnut 8.68 6.73 4.39 50.6 5.30 -2.3 -0.9 -34.8 -17.2Safflower 2.29 1.76 1.10 48.1 0.96 -0.7 0.1 -37.5 14.6Sunflower 5.89 4.21 2.95 50.1 2.83 -1.3 -0.1 -29.9 4.2Seasamum 2.43 0.72 0.55 22.6 0.88 -0.2 -0.3 -23.6 -37.5Linseed 3.23 3.83 2.92 90.5 3.12 -0.9 -0.2 -23.8 -6.4Total Oilseeds (Nine) 85.29 86.15 77.32 90.7 78.73 -8.8 -1.4 -10.2 -1.8

All-Crops 620.95 613.48 589.95 95.0 607.90 -23.5 -17.9 -3.8 -3.0

Normal Area—Average of 5 years area (2009-10 to 2013-14) for whole Rabi Season.Average Area as on date—Average of last 5 years area (2010-11 to 2014-15) of corresponding period.Source: Crops & TMOP Divisions, DAC&FW.

ALL INDIA CROP SITUTATION—RABI (2015-16) AS ON 22.01.2016—CONTD.(Area in lakh hectares)

Economic Growth*As per the quarterly estimates of Gross Domestic Product(GDP) released by the Central Statistics Office (CSO) on30th November 2015, the growth rate of GDP at constant(2011-12) market prices for the second quarter (Q2) (July-September) of 2015-16 is estimated at 7.4 per cent ascompared to the growth of 7.0 per cent in Q1 of 2015-16.Growth in the first half (H1) of 2015-16 works out to 7.2per cent. The growth rate of Gross Domestic Product(GDP) at constant (2011-12) market prices was estimatedat 7.3 per cent in 2014-15 (Provisional estimates), ascompared to 6.9 per cent and 5.1 per cent in 2013-14 and2012-13 respectively (Table 1).

The growth of Gross Value Added (GVA) at constant(2011-12) basic prices for agriculture & allied sectors,industry and services sectors are estimated at 2.2 per cent,6.8 per cent and 8.8 per cent respectively in Q2 of2015-16 as compared to the growth rates of 2.1 per cent, 7.6per cent and 10.4 per cent respectively for these sectors inQ2 of 2014-15 (Table 2).

The growth in private final consumption expenditurestood a little lower at 6.8 per cent in Q2 of 2015-16, asopposed to 7.1 per cent in Q2 of 2014-15. The growth ofGross Fixed Capital Formation growth was much higherat 6.8 per cent in Q2 of 2015-16 in comparsion with 3.8per cent in Q2 of 2014-15.

There was a decline in the rate of gross domesticsaving from 33.9 per cent of the GDP in 2011-12 to 31.8per cent in 2012-13 and further to 30.6 per cent in2013-14. This was primarily due to the sharp decline inthe rate of household physical savings.Agriculture and Food ManagementRainfall: The cumulative rainfall received for the countryas a whole, during the period 1st October, 2015—

16th December 2015, has been 20 per cent below normal.The actual rainfall received during this period has been96.6 mm as against the normal of 120.0 mm. Out of thetotal 36 meteorological sub-divisions, 6 sub-divisionsreceived excess season rainfall, 4 sub-divisions recivednormal season rainfall and the remaining 26 sub-divisionsreceived deficient/scanty season rainfall.

All India Production of Foodgrains: As per the 1stadvane estimates released by Ministry of Agriculture on16th September, 2015, production of kharif foodgrainsduring 2015-16 is estimated at 124.1 million tonnescompared to 120.3 million tonnes in 2014-15 (Table 3).Procurement: Procurement of rice as on 18.12.2015 was16.4 million tonnes during Kharif Marketing Season 2015-16 and procurement of wheat as on 18th December, 2015was 28.1 million tonnes during Rabi Marketing Season2015-16 (Table 4).Off-take: Off-take of rice during the month of October,2015 was 24.7 lakh tonnes. This comprises 22.8 lakhtonnes under TPDS/NFSA (off-take against the allocationfor the month of November, 2015) and 1.9 lakh tonnesunder other schemes. In respect of wheat, the total off-take was 26.1 lakh tonnes comprising of 18.5 lakh tonnesunder TPDS/NFSA (off-take against the allocation for themonth of November, 2015) and 7.6 lakh tonnes under otherschemes. Cumulative Off-take of foodgrains during 2015-16 (till October, 2015) is 381.1 lakh tonnes (Table 5).Stocks: Stocks of foodgrains (rice and wheat) held by FCIas on December, 1, 2015 were 50.5 million tonnes,compared to 49.5 million tonnes as on December 1, 2014(Table 6).

*Source : www.finmin.nic.in

February, 2016 9

TABLES

TABLE 1 : GROWTH OF GVA AT BASIC PRICES BY ECONOMIC ACTIVITY (AT 2011-12 PRICES) (IN PER CENT)

Growth Share in GVASector 2012-13 2013-14 2014-15 2012-13 2013-14 2014-15

(PE) (PE)

Agriculture, forestry & fishing 1.2 3.7 0.2 17.7 17.2 16.1Industry 2.4 4.5 6.1 32.3 31.7 31.7

Mininh & quarrying -0.2 5.4 2.4 3.0 3.0 2.9Manufacturing 6.2 5.3 7.1 18.3 18.1 18.1Electricity, gas, water supply & other 4.0 4.8 7.9 2.4 2.3 2.3utility servicesConstruction -4.3 2.5 4.8 8.6 8.3 8.1

Services 8.0 9.1 10.2 50.0 51.1 52.5Trade, hotels, transport, communication 9.6 11.1 10.7 18 18.8 19.4and services related to broadcastingFinancial, real estate & professional 8.8 7.9 11.5 19.5 19.7 20.5servicesPublic administration, defence and Other 4.7 7.9 7.2 12.5 12.6 12.6services

GVA at basic prices 4.9 6.6 7.2 100.0 100.0 100.0GDP at market prices 5.1 6.9 7.3 - - -

Source : Central Statistics Office (CSO). PE: Provisional Estimates.

TABLE 2 : QUARTER-WISE GROWTH OF GVA AT CONSTANT (2011-12) BASIC PRICES (PER CENT)

2013-14 2014-15 2015-16

Sectors Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2

Agriculture, forestry &

fishing 2.7 3.6 3.8 4.4 2.6 2.1 -1.1 -1.4 1.9 2.2

Industry 4.8 4.0 5.0 4.3 7.7 7.6 3.6 5.6 6.5 6.8

Mining & quarrying 0.8 4.5 4.2 11.5 4.3 1.4 1.5 2.3 4.0 3.2

Manufacturing 7.2 3.8 5.9 4.4 8.4 7.9 3.6 8.4 7.2 9.3Electricity, gas, watersupply & other utilityservices 2.8 6.5 3.9 5.9 10.1 8.7 8.7 4.2 3.2 6.7

Construction 1.5 3.5 3.8 1.2 6.5 8.7 3.1 1.4 6.9 2.6

Services 10.2 10.6 9.1 6.4 8.7 10.4 12.5 9.2 8.9 8.8

Trade, hotels, transport,communication andservices related tobroadcasting 10.3 11.9 12.4 9.9 12.1 8.9 7.4 14.1 12.8 10.6

Financial, real estate &professional services 7.7 11.9 5.7 5.5 9.3 13.5 13.3 10.2 8.9 9.7Public administration,defence and Otherservices 14.4 6.9 9.1 2.4 2.8 7.1 19.7 0.1 2.7 4.7

GVA at basic prices 7.2 7.5 6.6 5.3 7.4 8.4 6.8 6.1 7.1 7.4

GDP at market prices 7.0 7.5 6.4 6.7 6.7 8.4 6.6 7.5 7.0 7.4

Source : Central Statistics Office (CSO).


TABLE 3: PRODUCTION OF MAJOR AGRICULTURAL CROPS (1ST ADV. EST)

Crops Production (in Million Tonnes)

2012-13 2013-14 2014-15 2015-16$(Final) (Final) (4th AE) (1st AE)

Total Foodgrains 257.1 265.0 252.7 124.1

Rice 105.2 106.7 104.8 90.6

Wheat 93.5 95.9 88.9 -

Total Coarse Cereals 40.0 43.3 41.8 27.9

Total Pulses 18.3 19.3 17.2 5.6

Total Oilseeds 30.9 32.8 26.7 19.9

Sugarcane 341.2 352.1 359.3 341.4

Cotton 34.2 35.9 35.5 33.5

$ : Covers only Kharif Crops.Source: DES, DAC&FW, M/o Agriculture & Farmers Welfare

TABLE 4 : PROCUREMENT OF CROPS IN MILLION TONNES

Crops 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16

Rice# 34.2 35.0 34.0 31.8 32.2 16.4�

Wheat@ 22.5 28.3 38.2 25.1 28.0 28.1�

Total 56.7 63.4 72.2 56.9 60.2 41.0

#Kharif Marketing Season (October-September), @ Rabi Marketing Season (April-March), �Position as on 18.12.2015.Source: DFPD, M/oConsumer Affairs and Public Distribution

TABLE 5: OFF-TAKE OF FOODGRAINS (MILLION TONNES)

Crops 2012-13 2013-14 2014-15 2015-16(Till October)

Rice 32.6 29.2 30.7 20.9

Wheat 33.2 30.6 25.2 17.2

Total (Rice & Wheat) 65.9 59.8 55.9 38.1

Source : DFPD, M/o Consumer Affairs and Public Distribution

TABLE 6: STOCKS OF FOODGRAINS (MILLION TONNES)

Crops December 1, 2014 December 1, 2015

1. Rice 10.6 9.9

2. Unmilled Paddy# 16.9 20.4

3. Converted Unmilled Paddy in terms of Rice 11.3 13.7

4. Wheat 27.6 26.9

Total (Rice & Wheat) (1+3+4) 49.5 50.5

#Since September, 2013, FCI gives separate figures for rice and unmilled paddy lying with FCI & state agencies in terms of rice.Source: FCI

February, 2016 11

Abstract

The present study evaluates the determinants of rural non-farm employment in the state of Punjab by utilizingsecondary data sources pertaining to the period between1983 to 2005. A multinomial logit model has been utilizedto assess the determinants of rural non-farm employmentin the state of Punjab for the year 2011. The analysis ofthe rural non-farm employment in Punjab showed that theshare of male non-farm workers has registered an increaseof 23 percentage points during 1983 to 2004-05, whereasfor females, rural non-farm workforce has registered anincrease of only 3.1 percentage points during the sameperiod. In addition, the analysis showed that in pre-reformperiod in rural and urban areas of Punjab, workparticipation was shifting from primary to tertiary sector.Secondary sector work participation was also increasingwith little change. During post-reform years, the workparticipation in Punjab has been moving from primary andtertiary sectors to secondary sector at both the levels.Workers are shifting from agriculture to other sectors butthe shift has been very sluggish. Among the majordeterminants of rural non-farm sector, the study notedsignificant positive impact of level of higher education,age, urbanization and length of PWD pucca road on theprobability of work participation in the rural areas ofPunjab. In order to encourage foreign investment in thestate, special incentives are need to be given to the foreignplayers. To make Punjab agriculture more conducive tothe current competitive environment, it is suggested thatgovernment's investment in skill and technology as wellas technical and general skills should be encouraged.

Key Words: Infrastructural Development, Rural Non-farmEmployment, Skill Development.

Introduction

Rural non-farm employment has made significantcontribution in providing diverse employmentopportunities particularly to the rural poor in the variousparts of the rural world. In India, the share of rural non-farm activities has increased manifold during the variousNSSO years. However, the movement towards non-farmemployment was seen to be more pronounced for the males

than that of the females (Vaidyanathan, 1986;Hazell andHaggblade, 1991; Unni, 1991).The state of Punjab is alsono exception towards this end. An engagement of 36.2per cent of rural male workers in various non-farmactivities has placed the state of Punjab in the category ofstates of India having a relatively high share of rural non-farm employment. Census 2001 data also shows theincreasing tendency of rural workers in the state to seekemployment outside agriculture. By adding livestock andother agricultural related activities as a component of ruralnon-farm sector, then the dependence of rural mainworkers on non-farm endeavours increased from 26 percent in 1991 to 46 per cent in 2001 in Punjab. Such a hugeincrease in a period of 10 years has been a matter of muchdiscussion and debate (Ghuman, 2005). The state ofPunjab has gone through the process of transformation inthe era of 'Green Revolution' which impacted the socio-economic lives of both the rural and urban people in thestate. In addition, different micro and macro level policiesinitiated by central and state government have changedthe living standards of the people in the state. However,Green Revolution was limited in its impact only to thelarger farmers. Over the years, the growth of agriculturalsector started declining. Till recently, farm sector waslooked as the panacea for solving all the rural problemsof the rural masses, since large proportion of the ruralpopulation have been deriving their source of livelihoodfrom this sector. In the meantime, agricultural productionand productivity both had decelerated over the years.Therefore, it is now being realized that the farm sector islimited source of rural employment and economicdevelopment in future. Slowing down of agriculturalgrowth, decline in the number of operational holdings from11.17 lakh during 1990-91 to 9.97 lakh during 2000-07indicated that large portion of the rural masses is nowseeking job opportunities outside the agricultural sector(Kamal Vatta and B.R. Garg, 2008).

The present study is an attempt to access the majordeterminants of non-farm activities in the rural areas ofPunjab. Specifically, the broad objectives of the study are:i) to examine the major determinants affecting non-farmemployment in the rural areas of Punjab; ii) to providepolicy options on the basis of empirical findings of theproposed study.

Articles

Determinants of Rural Non-farm Employment in Punjab: A Logistic Analysis

DR. NEERAJ SHARMA* AND HARINDER MOHAN**

*Associate Professor, Department of Evening Studies, Punjab University, Chandigarh**Assistant professor in Economics, Guru Nanak College, Moga.


The study is divided into five sections, keeping thebroad objectives of the study. Section I gives theintroduction along with the broad objectives of the study.Section II discusses the data base and methodology.Section III studies the broad review of literature based onthe theme of the present paper. Section IV gives theempirical findings of the study. Section V concludes thestudy and brings out the policy options.

Methodology and Data (Section II)

Data for the trends in rural non-farm employment in Punjabhas been obtained from NSSO reports for the years 1983,1993-94, 1999-2000 and 2004-05. In order to estimatethe determinants of RNFE in the rural areas of Punjab,data on the selected indicators has been obtained fromvarious reports of Punjab Statistical Abstract for the year2011. A multinomial logit model has been utilized toidentify the major factors affecting the possibility ofparticipating in the rural non-farm activities in the ruralareas of Punjab. The major variables included have beendiscussed below. It has been hypothesized that femaleheaded household has less access to rural non-farmactivities compared to males. Level of education putpositive impact on the participation in rural non-farmactivities. Urbanization creates employment opportunitiesfor the rural educated, skilled and semi-skilled workerswho generally commute to towns on daily basis. Thehousehold size has a positive relationship with the ruralnon-farm employment. Whereas, it was expected that sizeof land holding has negative relationship with the ruralnon-farm employment.

Review of Literature (Section III)

Many researchers and scholars have tried to examine thedeterminants of rural non-farm sector both at aggregatedand disaggregated level. However, there are few studiesavailable on the theme of the paper in the state. Some ofthe studies and their findings have been discussed below.R.S. Ghuman and et al. (2005) noticed that most of thenon-farm sector activities emerged due to the distressconditions in agricultural sector rather than 'pull' factors.In aggregate terms, the results of the study argued thatrural non-farm sector did not make any significant impactin diversifying rural economies in the studied villages. Incontrast to this, Jagdeep Singh and H.S. Sidhu (2001) on

the basis of primary data collected for the reference year2001 through a survey conducted in 17 villages of Punjab;empirically identified the most potent factors that influencethe incomes in rural non-farm activities in the rural areasof Punjab. The results of the regression analysis foundthat working capital, investment, working expenses,amount of loan taken and value of assets of these ruralnon-farm activities, educational attainments, length of timea person has been in the RNFA, working hours, workingdays and age of the rural non-farm activity worker had asignificant impact on the net income per household in therural non-farm activities. Similarly, Kamal Vatta and R.S.Sidhu (2007), on the basis of the empirical analysis,it isnoted that caste and size of operational area has negativerelationship with the rural non-farm employment.Relatively poor rural households were found to be largelyincome diversified as compared to the medium and largecultivator households. Moreover, the study found that ruralnon-farm income sources failed to make any significantdent into rural poverty among the rural households. In theirlater work, Kamal Vatta and B.R. Garg (2008) foundgender, age, education, caste, family size and nearness tothe urban settlement as the major variables significantlyinfluencing the incidence of rural non-farm employmentand income in the rural areas of Punjab. Educational accessappeared to be the most significant factor in improvingaccess to rural non-farm employment and income. On thebasis of village level data collected through primary samplesurvey, J.S. Toor and H.S. Sidhu (2006) argued that betterinfrastructural development, education facilities andcloseness to urban centers have significant impact on non-farm activities in rural areas of Punjab. Nomita P. Kumar(2007) focused her study on various issues pertaining tothe growth trends of rural non-farm employment and itsmajor determinants in Uttar Pradesh by using populationcensus data for the period 1991-2001. The findings of thestudy noted both pull and push factors influencing the ruralnon-farm activities. The results of the study noted thaturbanization, use of electric pumps, average size of landholdings and literacy rate explained 91.6 percent ofvariations in rural non-farm employment. Finally, the studyproposed that different policies are required for specificproblems such as growth-oriented sectors are neededfocused attention and policies relating to training, financialfacilities along with the provision of better infrastructuralfacilities to improve the process of rural industrialization.

February, 2016 13

Results and Analysis (Section IV)

Size and Composition of Rural Non-farm Sector in Punjab

TABLE 1 : PERCENTAGE DISTRIBUTION OF NET STATE DOMESTIC PRODUCT AT FACTOR COST BY SECTORS IN

PUNJAB AT CONSTANT (2004-05) PRICES

Sector 2004-05 2005-06 2008-09 2009-10 Q 2010-11 2011-12 2012-12(R) (P) (Q)

Agriculture and Livestock 32.95 31.56 26.93 25.00 23.73 22.78 21.66

Agriculture 21.81 20.21 17.42 16.15 15.19 14.54 13.51

Livestock 11.14 11.35 9.51 8.85 8.54 8.24 8.15

Forestry and Logging 1.37 1.35 1.16 1.13 1.09 1.08 1.08

Fishing 0.28 0.30 0.23 0.24 0.22 0.21 0.20

Agril. & Allied 34.60 33.21 28.32 26.37 25.04 24.07 22.94

Mining and Quarrying 0.02 0.02 0.02 0.02 0.02 0.01 0.00

Sub-total (Primary) 34.62 33.23 28.34 26.39 25.06 24.08 22.94

Manufacturing 14.02 14.55 17.87 19.26 19.79 19.01 18.78

Registered 5.88 5.97 9.11 10.57 11.09 10.50 10.42

Un-registered 8.14 8.58 8.76 8.69 8.70 8.51 8.36

Construction 7.02 7.69 8.47 8.28 7.85 7.39 7.20

Electricity, Gas and Water Supply 1.94 1.72 2.40 2.26 2.13 1.96 1.88

Sub-Total (Secondary) 22.98 23.96 28.74 29.80 29.77 28.36 27.86

Total Industry (Secondary) 23.00 23.99 28.76 29.82 29.79 28.37 27.86

Transport, Storage and Communication 5.88 6.11 6.22 6.31 6.19 6.31 6.31

Railways 0.78 0.92 0.87 0.82 0.68 0.77 0.76

Transport by other means 3.14 3.12 2.92 2.92 2.83 2.89 2.93

Storage 0.42 0.40 0.32 0.34 0.32 0.33 0.36

Communication 1.54 1.67 2.11 2.23 2.36 2.32 2.26

Trade, Hotels & Restaurant 13.20 13.05 12.26 11.89 12.19 12.36 12.48

Banking and Insurance 5.00 5.57 7.08 7.74 8.62 10.54 11.48

Real Estate, Ownership of dwellings & 4.65 4.56 3.94 3.72 3.56 3.46 3.37Business Services

Public Administration 4.43 4.26 4.45 4.69 4.65 4.72 4.92

Other Services 9.24 9.26 8.97 9.46 9.96 10.17 10.64

Sub-Total of Tertiary 42.40 42.81 42.92 43.81 45.17 47.56 49.20

Source : Statistical Abstract of Punjab, 2013NOTE : R: Revised P: Provisional Q: Quick Estimates

The contribution in GDP can be viewed as an indicator ofperformance of different sectors of the economy. Thefigures in the above table clearly highlight the growingimportance of non-farm activities in the state during thegiven period. The share of primary activities which was32.94 percent in 2004-05 of the total GDP, declined to22.78 percent during 2011-12 and further to 21.66 percent during 2012-13. On the other hand, the corresponding

share of manufacturing and tertiary sector has been foundto be increasing during the same period. Among the majornon-farm activities transport by other means, trade, hotels&restaurant and banking and insurance registeredsignificant increase in net state domestic product in theyear 2012-13. This fact shows the emerging importanceof non-farm activities in the rural parts of the state.


TABLE 2 : GENDER-WISE DISTRIBUTION OF USUAL STATUS (PS+SS) RURAL WORKERS IN PUNJAB DURING 1983 TO 2004-05

Year Male Female PersonPs Ss All Ps Ss All Ps Ss All

1983 54.9 3.2 58.1 41.0 -9.1 31.9 37.8 7.2 45.01993-94 54.2 0.4 54.6 3.7 18.3 22.0 30.3 8.9 39.21999-2000 52.6 0.4 53.0 4.0 24.0 28.0 29.2 11.8 41.02004-2005 54.2 0.7 54.9 4.2 28.0 32.2 30.3 13.7 44.0

Source : NSSO Reports of 38th, 50th, 55th and 61st Rounds

Table 2 gives the gender-wise distribution of usual

status rural workers in Punjab during 1983 to 2004-05.

The analysis of the table 2 shows that during the period

1993-94 to 2004-05, rural Female workforce participation

rate has increased by more than percentage points i.e. from

22.0 percent in 1993-94 to 32.2 percent in 2004-05. But

such trend can be viewed as illusionary because the

increase has occurred only in female Subsidiary status

workers. For males, on the other hand, workforce

participation rate continued declining till 1999-2000 but

during 2004-05 it again increased to 54.9 per cent. In net

terms, workforce participation rate has shown slight

improvement during various NSS rounds. Such trend

shows the dismal performance of employment scenario in

the state of Punjab.

TABLE 3 : DISTRIBUTION OF RURAL NON-FARM WORKERS

(USUAL STATUS: PRINCIPAL + SUBSIDIARY STATUS) IN

PUNJAB DURING 1983-2004-05

NSS Round Year Male Female

38th 1983 22.3 7.243rd 1987-88 30.3 8.350th 1993-94 31.9 7.355th 1999-2000 36.0 9.361st 2004-05 45.3 10.3

Source : Kamal Vatta and B.R. Garg (2008)

Table 3 gives the percentage distribution of workersin the rural areas of Punjab during various NSS rounds.The analysis of the table revealed that the share of maleworkforce in Punjab has increased from 22.3 per cent in1983 to 36.0 per cent in 1999-2000 and further to 45.3per cent in 2004-05. For females the respective share, onthe other hand, has increase from 7.2 per cent in 1983 to9.3 per cent in 1999-2000 and further to 10.3 per cent in2004-05. In net terms, the share of male non-farm workershas registered an increase of 23 percentage points during1983 to 2004-05, whereas for females, rural non-farmworkforce has registered an increase of only 3.1 percentagepoints during the same period.

TABLE 4 : SECTORAL DISTRIBUTION OF USUAL STATUS WORKERS IN PUNJAB BY REGION DURING 1983 TO 2004-05

Year Rural UrbanRound Primary Secondary Tertiary Primary Secondary Tertiary

1983 38th 82.0 7.9 9.4 13.6 31.2 52.8

1993-94 50th 74.7 9.6 15.8 9.3 30.8 59.9

1999-00 55th 72.6 12.0 15.5 8.7 30.1 61.3

2004-05 61st 67.0 16.9 13.3 5.9 34.6 59.6

Source : NSSO, Various Rounds

Table - 4 depicts that rural workforce in primarysector was maximum (82.0) per cent, whereas it was atthe lowest (7.9 per cent) in the secondary sector in theyear 1983. This may due to the over dependence of rural

workforce on rural techniques. In urban primary sector,the work participation in 1983 was less than half of theurban secondary sector work participation rate. Tertiarysector was found employing large chunk of urban

February, 2016 15

workforce in the year 1983. Primary, secondary and tertiarysector work participation rates were 13.6 percent, 31.2percent and 52.8 per cent respectively in 1983.In the year1993-94, rural primary sector work participation declinedto 74.7 percent from 82.0 percent in 1983. These personsshifted to secondary and tertiary sectors but the shift ofworkers was more towards tertiary sector. Thus workparticipation in tertiary sector increased to 15.8 per centat rural level and 59.9 percent at urban level in 1993-94.At rural level secondary sector WPR increased from 7.9percent in 1983 to 9.6 per cent in 1993-94; for urban 31.2percent to 30.8 percent. The New Economic Reforms wereimplemented during 1990-91 and a number of changeswere introduced in industrial policy. Due to new industrialpolicy, work participation in industrial sectors started toincrease. In 2004-05, similar trend intended in ruralprimary sector i.e. WPR decreased to 67.0 percent in 2004-05 and tertiary sector shown a decline from15.8 percentto 13.3 percent in corresponding periods. On the otherside, rural secondary sector WP increased to almost doubleduring 1993-94 to 2004-05. Due to adoption of LPG in1991-92 in secondary sector, employment opportunitiesincreased that led people from primary and tertiary sectorsto secondary sector. On the other side, in urban Punjab,primary sector work participation declined from 9.3percent (1993-94) to 5.9 percent (2004-05), secondarysector work participation increased from 30.8 percent(1993-94) to 34.6 percent (2004-05) and tertiary sectordid not show any considerable change in WPR during1993-94 to 2004-05. This reveals that there have beensignificant changes in sectoral distribution of workforcein Punjab during pre and post- reform periods. Dataindicates that in pre-reform period in rural and urban areasof Punjab, work participation was turning from primaryto tertiary sector. Secondary sector work participation wasalso increasing with little change. In post-reform years,the work participation in Punjab has been moving fromprimary and tertiary sectors towards secondary sector atboth the levels. Workers are shifting from agriculture toother sectors but the shift has been very slow.

Determinants of Rural Non-farm Employment inPunjab (Section V)

In order to assess the major determinants of rural non-farm employment, we have applied multinomial logisticanalysis. Table 5gives the estimates of Logit analysis. Thelogit estimates revealed that level of higher education, agehas significant impact on the probability of participatingin the rural non-farm activities. Whereas caste, noeducational level put negative impact on the rural non-farm employment in the rural areas of Punjab.

TABLE 5 : LOGIT ESTIMATES FOR RURAL NON-FARM

EMPLOYMENT IN PUNJAB IN 2011

Variable Coefficient Prob Value

Age 0.247*** 0.000

Age Squared -0.001*** 0.000

Non educated -0.713*** 0.081

Primary Education -0.154*** 0.524

Secondary Education 4.572*** 0

Higher education 24.60*** 0

ST/ST Population -0.414*** 0.120

Population Density -0.242*** 0.017

Urbanisation (percentage 0.194* 0.050of urban population)

Length of PWD pucca road 0.339* 0.118per lakh population (kms.)

Source : Author's Calculations***Significant at 10 per cent level of significance*significant at 1 per cent level of significance

Table 5 gives the logit estimates for rural non-farmemployment in the rural areas of Punjab. The logitestimates revealed that level of higher education,household size and age has significant impact on theincidence of rural non-farm employment in the rural areasof Punjab. The rise in the age increases the probability ofparticipation in the rural non-farm activities by 24.7 percent. Similarly the higher the level of education, higher isthe probability of participating in rural non-farm activities.On the other hand, variables like age squared, not educated,primary education and SC/ST population put negativeimpact on the participation in rural non-farm activities inthe rural areas of Punjab. Special efforts are required tobe given on many sectoral policy constraints creatinghurdles in the way of gainful employment opportunitiesin rural areas. In the era of globalization and liberalization,government should promote human capital formation inthe form of improving skill formation and health measuresto improve the efficiency and productivity of rural workers.Above all, female labour participation must be encouragedin the process of economic growth so they can enjoy thefruits of economic growth. Towards this end, activitieslike animal husbandry, fishing should be encouraged toenhance employment opportunities in the state.

Conclusions and Policy Implications (Section V)

The economy of Punjab has played a significant rolein making the country self-sufficient in food grainproduction. Now production and productivity ofagriculture has reached a plateau. Over emphasis of wheat-paddy, cultivation has put adverse impact on the bio-diversity of state in the form of degrading soil health, water


table depletion etc. High use of chemical fertilizers andpesticides resulted into the deterioration of bio diversityin the state. In a nutshell, the peasant economy of Punjabis facing the real burnt of crisis in the form of overutilization of natural resource base, water table depletion,environmental pollution, rising debt burden etc. The shareof primary activities which was 32.94 percent in 2004-05of the total GDP, declined to 22.78 percent during 2011-12 and further to 21.66 per cent during 2012-13. On theother hand, the corresponding share of manufacturing andtertiary sector has been found to be increasing during thesame period. Among the major non-farm activitiestransport by other means, trade, hotels & restaurant andbanking and insurance registered significant increase innet state domestic product in the year 2012-13. Theanalysis of the rural non-farm employment in Punjabshowed that the share of male non-farm workers hasregistered an increase of 23 percentage points during 1983to 2004-05, whereas for females, rural non-farm workforcehas registered an increase of only 3.1 percentage pointsduring the same period. In addition, the analysis showedthat in pre-reform period in rural and urban areas ofPunjab, work participation was turning from primary totertiary sector. Secondary sector work participation wasalso increasing with little change. In post-reform years,the work participation in Punjab has been moving fromprimary and tertiary sectors towards secondary sector atboth the levels. Workers are shifting from agriculture toother sectors but the shift has been very slow. Among themajor determinants of rural non-farm sector, the studynoted significant positive impact of level of highereducation, age, urbanization and length of PWD puccaroad on the probability of participation in the rural areasof Punjab. Government and private efforts are required toaugment investment in rural infrastructure in general andagricultural infrastructure (power, irrigation, roads, ruralcooperatives) in particular in the state of Punjab. In orderto encourage foreign investment in the state, specialincentives are need to be given to the foreign players. Tomake Punjab agriculture more conducive to the currentcompetitive environment, it is suggested that government'sinvestment in skill and technology as well as technicaland general skills should be encouraged. Over a longperiod of time, agricultural sector in Punjab as well as inIndia has been kept outside the purview of taxation. Inorder to accumulate large amount of resources to meetincreasing need of infrastructure services for agriculture,it is advisable to tax big capitalist farmers. Hence effortsare required to find alternative options along with specialemphasis on R&D to put the economy of Punjab on thepath of growth and prosperity. The above suggested policyoption may be helpful to policy makers to bring theeconomy of Punjab out of severe socio-economic crisiswith long run sustainability.

REFERENCES

Bhaumik, S.K., 2002, "Employment Diversificationin Rural India: A State Level Analysis", The Indian Journalof Labour Economics, Vol. 45, No. 4, pp.719-769.

Ghuman, R.S. (2001), "WTO and IndianAgriculture: Crisis and Challenges - A Case Study ofPunjab", Man and Development, Vol. 23, No. 2, pp. 67-98.

Ghuman, R.S., 2005, "Rural Non-Farm EmploymentScenario- Reflections From Recent Data in Punjab",Economic and Political Weekly, Vol. 40, Oct. 8, pp.4473-4480.

Hazell, Peter B.R and S.Haggblade (1991).Rural-Urban Growth Linkages in India.Indian Journal ofAgricultural Economics, 46(4), 515-529.

Kumar, N.P. (2007), "Trends in Rural Diversificationand its Determinants in Uttar Pradesh", The Indian Journalof Labour Economics, Vol. 50, No. 4, pp.703-714.

Lanjouw, Jean O. and Peter Lanjouw (2001), "'TheRural Non-farm Sector: Issues and Evidences fromDeveloping Countries", Agricultural Economics, Vol.26,No. 1-23, pp.1-34

NSSO, 2011, "Report on Employment andUnemployment in India", 2011-12: Key Indicators", NSS68th Round July2011-June 2012, Report, No.NSS KI (68/10). June, National Sample Survey Organization, NewDelhi.

Sidhu, H.S. and Toor, J.S. (2002),"Nature andDeterminants of Rural Non-farm SActivities inPunjab",The Indian Journal of Labour Economics, Vol.,45, No. 4, pp. 626-640.

Statistical Abstract of Punjab, 1992, 1994, 2009 and2011, Government of Punjab, Economic and StatisticalOrganisation, Publication No. 938, www.punjab.gov.in.

Toor, J.S. and Sidhu, H.S. (2006), "Determinants ofIncome in Rural Non-Farm Activities: Empirical Evidencefrom Punjab", Indian Journal of Labour Economics, Vol.49, No.3, pp. 527-538.

Unni, J. (1991). Regional Variations in Rural non-agricultural Employment: An Exploratory Analysis.Economic and Political Weekly, Vol. 26, No.2, 109-122.

Vaidyanathan, A. (1986). Labor Use in Rural India-A Study of Spatial And Temporal Variations. Economicand Political Weekly, 21(52), A130-A146.

Vatta, Kamal and Garg, B.R. (2008) , " Rural Non-Farm Sector in Punjab: Pattren and Access to Employmentand Income, Volume 63, No. 2, pp.224-242.

Vatta, Kamal and Sidhu, R.S. (2007), "IncomeDiversification among Rural Households in Punjab:Dynamics, Impacts and Policy Implications", The IndianJournal of Labour Economics, Vol. 50, No. 4, pp.723-736.

February, 2016 17

Factors Determining Energy Consumption in Agricultural Production: A ComparativeEconomic Analysis in Tamil Nadu

D. MURUGAN* AND E. KANAGARAJ**

Abstract

The present study is concerned with the factors determiningenergy consumption in agricultural production. To analysethe factors determining per acre value of output energy,various energy inputs used in agricultural production havebeen taken into account under irrigated and unirrigatedland by taking different sizes of land holding viz., Marginalfarms, Small farms, Medium farms and large farms.Further, two districts have been selected on the basisagricultural performance index framed for the presentstudy. Finally, the results of the study exhibit that the energyinputs such as diesel, pesticide, nitrogen and phosphorusare found to be significant under pooled farms. Overall,the higher consumption of these inputs have led to increasein per acre value of output energy under pooled irrigatedfarms than the unirrigated pooled farms.

Key Words : Energy Input, Energy Output, ChemicalEnergy

Introduction and Background

Indian agriculture has undergone a drastic change in energyconsumption in a span of four decades and a half. It isnoticed that a heap of energy sources are employed toperform agricultural activities. Despite the introductionof new agricultural technology in the mid sixties, animallabour and human labour play a vital role in agriculturalproduction. In crop cultivation energy sources are used inthe form of human labour/animal labour, diesel engine,electric motor, power tiller, tractor, seed, fertilizer, farmyard manure, pesticide, agricultural waste, solar powerand wind energy. It is perceived that agriculture and energyhave strong linkage with each other and the relationshipshave become more and more acute with the intensificationof agriculture and it is considered to be the only means ofraising output. The intensification has led the growth inagriculture and would obviously demand more of energy.

It could be seen that energy use in agriculture isgoverned by a host of factors like agro-climatic conditions,nature of farming, governmental support through subsidies,precipitation, farm size, credit availability, price support,extent of area under irrigation and high-yielding varieties,agricultural investments, cropping pattern, level of input

use, commercialization and availability of naturalresources. Like-wise, the energy inputs used in cropproduction exhibit the variation in influencing the outputenergy among the different sizes of land holdings underirrigated and unirrigated farming conditions. Yet, anindepth analysis regarding energy inputs which determinethe per acre value of output energy is not only crucial, butalso reveals the level of determinants that vary betweenirrigated and unirrigated lands for an indepth analysis.

In view of the above factors observed, this studyproposes to make an analysis of the determinants of peracre output energy under different sizes of land holdingsin Cuddalore and Thanjavur Districts of Tamil Nadu.

Objectives

i. To identify factors determining per acre value ofoutput energy among the different sizes of landholdings under irrigated, unirrigated and the pooledfarms

ii. To analyse whether there exists any structuralvariation in energy use between irrigated andunirrigated lands under different sizes of landholdings, and

iii. To suggest suitable policies in order to motivate thefarmers to cope up energy efficiency method.

Methodology

The survey design of the study is based on a four-stagesampling incorporated at four different stages, so as toelicit adequate and accurate information by the field ofenquiry in Tamil Nadu.

In the first stage, two districts are selected in thestate on the basis of "agro - economic conditions." Theimportant factors included in the agricultural conditionsare cropping intensity, irrigation potential, percent age ofarea under HYV, etc. Detailed information on theseindicators and related aspects of secondary data arecollected from "Tamil Nadu - An Economic Appraisal,"published by the Evaluation and Research Department,Government of Tamilnadu, Chennai. By keeping this data,the "Agricultural Performance Index" (API) is prepared

* Assistant Professor, Department of Economics, Annamalai University, Annamalai Nagar.**Associate Professor and Head, Department of Social Work, Mizoram University, Mizoram.


for each of the districts in Tamil Nadu state from the year2009-2010. Thanjavur district is selected to represent "thehighest agricultural performance index" and Cuddaloredistrict is selected on the basis of "the lowest agriculturalperformance index", by using non-proportionate purposiverandom sampling method.

In the second stage of the survey design, two blocksare selected in each of the selected districts i.e., Thanjavurand Cuddalore. In this context, the "agriculturalperformance index" is prepared for each of the blocks inthe selected districts. Kumbakonam and Buthaloore blocksare selected to represent "the above and the below averageagricultural performance index" respectively in Thanjavurdistrict. Similarly, Cuddalore and Mangalore blocks areselected on the basis of the "above average and belowaverage agricultural performance index," respectively forCuddalore District. These blocks are selected by usingrandom sampling method.

The next stage is to select a few villages in each ofthe identified blocks. A similar exercise is carried out onthe basis of available secondary data for each of thevillages in the selected blocks. The secondary data arecollected from the Block Development Office and TalukOffice in selected areas. Again the performance index onthe basis of above indicators is prepared for the villagesin each of the selected blocks. Another merit of thesampling is that the usage of energy consumption by thefarmers varies due to the level of accessibility of villagesto the block head quarters and other infrastructure facilitiesavailable. Therefore, in addition to API, consideration isalso given to rural roads, infrastructure facilities availablein the selection of villages in each of the blocks.

The crucial stage in the sampling process is theselection of farm households in selected villages. About200 farm households are interviewed for the present study,in which, 100 farm households are selected in each of theselected districts, by using stratified random samplingmethod. Out of the two hundred samples, about 84, 47,42 and 27 samples are covered in the form of marginal,small, medium and large farmers, respectively in the studydistricts. The primary data are collected from the selectedfarm households through a well structured interviewschedule method. The reference period of the study isconfined to one agricultural year starting from 1st July,2014 to 30th June, 2015. The conversion table of physicalinputs into energy for the present study is based on thevalues given by Saini, et al.

Framework of Analysis

In order to examine the energy inputs, which causevariation in per acre value of output energy among thedifferent sizes of land holding classes under irrigated,unirrigated and pooled farms, the log-linear multiple

regression model has been employed. The present methodis highly appropriate to verify the objective stated.

where,

y = Per acre value of output energy in MJ

X1 = Human energy in MJ

X2 = Animal energy in MJ

X3 = Diesel energy in MJ

X4 = Electrical energy in MJ

X5 = Seed energy in MJ

X6 = Farm yard manure energy in MJ

X7 = Pesticide energy in MJ

X8 = Mechanical energy in MJ

X9 = Nitrogen energy in MJ

X10 = Phosphorous energy in MJ

X11 = Potash energy in MJ

�’s are normally, identically and independently distributedwith mean ’0’ (zero) and variance ‘�2’.

�0, �1, �3, . . . ,�11 are the parameters to be estimated.

The above model has been estimated separately formarginal, small, medium, large and the pooled farms bythe method of least squares.

Chow's Test

To test whether there exists any structural differences inthe input-output energy relationship among the differentsizes of land holdings under irrigated, unirrigated andpooled farms, the Chow's test has been employed.

The formula used for Chow's test is as follows:

where

k = Number of parameters including the interceptterm

�e2 = Unexplained sum of squares for pooled farms

�e12 = Unexplained sum of squares for marginal farms

Log y = �0 +π

1��i

log Xi + �

(�e12 + �e2

2 + �e32 + �e4

2) / kF* = - ��

(�e12 + �e2

2 + �e32 + �e4

2) / 1 + 2 + 3 + 4 – 4k

February, 2016 19

�e22 = Unexplained sum of squares for small farms

�e32 = Unexplained sum of squares for medium farms

�e42 = Unexplained sum of squares for large farms

1= Number of observations for marginal farms

2= Number of observations for small farms

3= Number of observations for medium farms

4 = Number of observations for large farms

To find out the combined effect of the givenindependent input energies to the total output energy,theadjusted co-efficient of multiple determination (R–2) has been used. The suitability of the model for the givenanalysis is tested through 'F' at 5 per cent level.

Results and Discussion

In order to determine the relative contributions of differentsources of energy inputs to the per acre value of output,the log-linear model is estimated. The results of theirrigated, unirrigated and the pooled farms are exhibitedin Tables 1, 2 and 3.

a. Irrigated Farms

The results of log-linear model on irrigated farms areshown in Table - 1. The value of co-efficient ofdetermination (R–2) indicates that all the energy inputs havejointly explained about 94 per cent of the variation in theper acre value of output energy for medium farms. It isfollowed by marginal farms at 59 per cent, small farms at46 per cent, and it is the least in the case of large farms at33 per cent. The value of (R–2) for the pooled irrigatedfarms is indicated at 53 per cent. It is implied that out of100 per cent variation of per acre value of output energy,the given 11 independent energy inputs contribute themaximum to the tune of 94 per cent in the case of mediumfarms, 59 per cent for marginal farms, 46 per cent for smallfarms and it is the least in the case of large farms at 33 percent. Similarly, the pooled farms attribute about 53 percent co-efficient of determination. It means that out of100 per cent per acre value of output energy variation,about 53 per cent is attributed due to the given 11independent energy inputs, the balance 47 per cent isexplained by the factors 'other than' the specified one. The'F' ratios are found to be statistically significant, whichare based on (R–2) in each size of land holding classes aswell as the pooled farms. Therefore, all the models aremore suitable irrespective of the size of holding classes aswell as the pooled farms.

When the input and output energies are comparedamong the different sizes of land holding classes, theenergy inputs like seed, potash, pesticide and mechanical

energies are found to be statistically significant, 't' valueat 5 per cent level in the case of marginal farmers. It maybe inferred from the results that these energy inputs arepositively related to the per acre value of output energy inthe case of marginal farmers. It is also inferred from theresults that one per cent increase in the seed, pesticide,mechanical and potash energies, would increase the peracre value of output energy by 0.36 per cent, 1.56 percent, 0.33 per cent and 0.21 per cent respectively. Incontrast to these results, the human, animal and farm yardmanure energies are found to be negatively related to theper acre value of output energy and they are found to bestatistically significant. They denote that an additionalone per cent increase in these energy inputs leads to adecrease in the per acre value of output energy by 0.12per cent, 0.08 per cent, and 0.07 per cent, respectively. Itis argued from the results that the negative co-efficientsof these energy inputs are due to excessive consumptionof human, animal and farm yard manure energy inputs,since these inputs are available in plenty at their householdlevel, because the excess application of these inputenergies leads to a decreasing trend in the per acre outputenergy for marginal farmers in the irrigated farms.

It may be noted from the results of small farmersthat inputs like diesel energy, electrical energy, seed energy,mechanical energy have shown significance at 1 per centlevel. Further, nitrogen energy and potash energy havealso been significant at 5 per cent level. It can be deducedfrom the results that energy inputs are positively relatedto the per acre value of output energy in the case of smallfarms. In addition, it is interesting to note that one percent increase in the energy inputs like diesel, electrical,seed, mechanical, nitrogen and potash could increase theper acre value of output energy by 0.08 per cent, 0.14 percent, 0.89 per cent, 0.55 per cent, 0.22 per cent and 0.34per cent respectively. As against this, the significantnegative co-efficient of animal energy and farm yardmanure energy indicate that an additional one per centincrease in the above mentioned inputs' resultant reductionin the volume of output energy per acre by 0.17 per centand 0.24 per cent respectively. It is argued that thesenegative co-efficients of these energy inputs are due toexcessive use of animal energy and farm yard manureenergy, since the farmers posses their own animal labour.

With regard to medium farms, it may be noted thatthe human, electrical, seed and phosphorous energy inputshave been noticed to be significant at 5 per cent level.Similarly, animal energy is also significant at 5 per centlevel out of the total selected variables. In view of theabove, it may be pointed out that the human, animal,electrical, seed and phosphorous energy are positivelyrelated to per acre value of output energy and these energyinputs, could increase the per acre value of output energyby 0.39 per cent, 0.21 per cent, 0.15 per cent, 0.33 per


cent and 1.24 per cent respectively. It is also observedfrom the results that energy inputs like diesel andmechanical are also found to be statistically significant.However, the energy inputs are related to the output energynegatively. It indicates the fact that an additional increasein these energy inputs lead to a decreasing trend in the peracre value of output energy by 0.21 per cent and 0.86 percent respectively for diesel and mechanical powers.Therefore, it is evident to note that the negative co-efficientof diesel and mechanical energies are due to the excessuse of these input energies, particularly, oil engine, tractors,power tillers and sprayers in the cultivation process.However, this is possible due to sound financial positionand status of this type of farmers.

It could be perceived from the results of largefarmers that out of the eleven independent variables ofinput energies, human energy, diesel energy, electrical andphosphorous energies are found to be statisticallysignificant at 1 per cent level. It shows that one per centincrease in human, diesel, electrical and phosphorus energyinputs could increase the per acre value of output energyby 0.58 per cent, 0.18 per cent, 0.44 per cent and 0.61 percent respectively. Similarly, seed energy is also found tobe significant and this energy leads to an increase in theper acre value of output energy by 0.36 per cent.

Out of the eleven independent energy inputs, dieselenergy, seed energy, mechanical energy and phosphorousenergy have been statistically significant at 5 per cent levelfor the pooled irrigated farms. Among them, diesel energy,seed energy and mechanical energy have been positivelyrelated to the per acre value of output energy. It showsthat one per cent increase in these variables could increasethe per acre value of output energy by 0.06 per cent fordiesel energy, 0.11 per cent for seed energy and 0.14 percent for mechanical energy. It is generally believed thatonce the irrigated farm increases, the application ofmechanical powers like tractors, power tillers, sprayers,oil engine enhances, thereby the use of diesel power alsoincreases. The higher consumption of seed energies aredue to the application of required quantity of seed at theright time which is possible due to favourable irrigation.In the case of phosphorous energy, it is negatively relatedto the per acre value of output energy and it is found to bestatistically significant 't' value. It is implied that anadditional one per cent increase in this variable has broughtreduction in the per acre value of output energy by 0.06per cent. It is observed from the negative co-efficient ofphosphorous energy that the excess utilization of this inputis due to the lack of adequate knowledge in the applicationof recommended dose of this input, particularly, by themedium and large farms in the irrigated farms. It may alsobe observed that the dilatory practices in using thephosphorous energy is due to non-availability of moneyin time, conventional attitude and absences of skills on

the part of most of the farmers. Further, the low level ofthe dose of organic manure by the medium farms and largefarms tended the soil condition unfavourable to theincrease of phosphorous, nitrogen and pesticides. Amongthe significant variables, diesel energy, seed energy,mechanical energy and phosphorous energy have a greaterinfluence on the determination of per acre value of outputenergy in the irrigated pooled farms.

b. Unirrigated farms

Table - 2 delineates the factors determining per acre valueof output energy under four categories of farm sizes in theunirrigated farming conditions. It could be perceived fromthe results of the marginal farms that diesel energy, seedenergy, farm yard manure, mechanical energy, nitrogenenergy and potash energy are found to be significant at 1per cent level. It denotes the fact that these energy sourcesare positively related to the per acre value of output energyin the marginal farms. Further, it means that one per centincrease in diesel energy, seed energy, farm yard manureenergy, mechanical energy, nitrogen energy and potashenergy tend to increase the output energy by 0.10 per cent,0.34 per cent, 0.65 per cent, 0.22 per cent, 0.53 per centand 0.35 per cent respectively. Similar results could beseen in the case of small farms.

As far as the medium farms are concerned, theenergy inputs namely, human and electricity have beensignificant at 1 per cent level. Similarly, phosphorus andseed energy inputs are significant at 5 per cent level amongthe eleven energy inputs used. It is noticed that these energyinputs are positively related to the per acre value of outputenergy in the medium farmer category. In addition, it isnoticed that one per cent increase in these energy inputshas tended to increase the output energy by 0.28 per centfor human energy, 0.11 per cent for electrical energy, 0.19per cent for seed energy and 0.21 per cent for phosphorousenergy.

It may be observed from the results of large farmsthat human energy and animal energy are significant at 1per cent level. These energy input sources are positivelyrelated to the per acre value of output energy. Further, it isworth noting that one per cent increase in the volume ofthese energy inputs has helped increase in the volume ofoutput energy at 0.77 per cent and 0.50 per centrespectively.

It could be seen from the results of the pooled farmsthat inputs such as diesel, pesticides, nitrogen andphosphorous are found to be significant at 5 per cent level.Moreover, the relationship between these energy inputsand the outputs is positive. It means that 1 per cent increasein inputs of diesel energy, pesticide energy, nitrogen energyand phosphorous energy, increases the output energy by0.06 per cent, 0.80 per cent, 0.42 per cent and 0.61 per

February, 2016 21

cent respectively. The higher consumption of diesel energyis mainly because of the use of this input for oil enginewhen the land is an unirrigated one. Similarly, the higherconsumption of pesticides, nitrogen and phosphorous aremainly attributed due to applications of these inputs forcommercial crops like cotton and sugarcane, which arethe major crops cultivated in unirrigated farms.

The co-efficient of determination (R–2) is found to behigher for MDF at 0.59 per cent, followed by 0.41 percent for large farms, 0.37 per cent for small farms and0.15 per cent for marginal farms. The pooled unirrigatedfarm has shown the coefficient of determination to theextent of 0.32. It means that out 100 per cent total variationof energy, about 32 per cent is explained by the givenenergy inputs. The balance 68 per cent is explained byinputs other than the above. The suitability of the modelis indicated by 'F' ratios, which are found to be significant,irrespective of the size holding classes and the pooledfarms.

c. Pooled Farms

It may be observed from the marginal farms at the pooledlevel (Table 3) that out of the eleven independent variables,the diesel energy, electrical energy, farm yard manureenergy, pesticide energy, mechanical energy, nitrogenenergy and potash energy, have been significant at 5 percent level. It refers to the fact that these energy sourcesare positively related to the per acre value of output energy.It could be inferred from the results that the higherconsumption of diesel energy is mainly due to applicationof mechanical power like oil engine by these farmers.Further, high use of inputs like pesticide, nitrogen andpotash are done owing to the cultivation of commercialcrops like cotton and sugarcane. The use of FYM energyis also significant for them as it is attributed to using theirown stock.

In contrast to the above, the animal energy and seedenergy are negatively correlated to the per acre value ofoutput energy, and they are found to be significant at 5 percent level. It is important to note that the negativecoefficients of these inputs are due to over use of animalenergy and lack of adequate knowledge in the applicationof seed energy which result in decreasing the per acre valueof output energy by the marginal farms.

Out of the eleven independent variables consideredfor small farms, it has been noted that diesel energy,electrical energy, seed energy, farm yard manure energy,pesticide energy and nitrogen energy are found to besignificant at 5 per cent level. Among these variables,electrical energy, seed energy, pesticide energy andnitrogen energy are positively related to the per acre valueof output energy in the small farms. The reasons indicatedfor marginal farms are also suitable in this context. In the

case of farm yard manure energy, it is negatively correlatedto the per acre value of output energy. The negative co-efficient of the input is due to the fact when FYM is appliedmore and more to the crops that will remain unyieldingdue to higher fertility.

Among the eleven energy inputs of the mediumfarms at pooled level the diesel energy, electrical energy,pesticides, phosphorous and potash energy are found tobe significant at 5 per cent level. It may be observed thatamong these variables diesel and electrical are found tobe positively correlated to the per acre value of outputenergy, whereas, in the case of pesticide energy,phosphorous energy and potash energy, they are negativelycorrelated to the per acre value of output energy. It isattributed to the fact that the negative co-efficient of theseinputs are due to these energy inputs that require heavycost to the medium farmer category.

As far as the large farms are concerned, it might benoticed that, among the eleven variables used, dieselenergy, pesticide energy and phosphorous energy are foundto be significant at 5 per cent level. Among these variables,the diesel energy and pesticide energy are found to bepositively correlated to the per acre value of output energy.But, the phosphorous energy is found to be negativelycorrelated to the per acre value of output energy. It revealsthe fact that the large farmers have utilized phosphorousenergy abundantly to increase the per acre value of outputenergy.

On the whole in the pooled farms, it may be broughtto light that among the eleven independent variables used,diesel energy, pesticide energy, nitrogen energy and potashenergy have been found to be significant at 1 per centlevel. It could be stated that these energy sources arepositively correlated to the per acre value of output energyin the pooled farms. It implies that diesel energy, pesticideenergy, nitrogen energy and potash energy have influencedthe per acre volume of output energy in the pooled farmsi.e., irrigated and unirrigated as a whole. Since, the mediumand large farms are having strong economic background,they use excessive suitable inputs like pesticide,phosphorous and potash energy. In turn, there is a lowlevel of output for these energy inputs. When the co-efficient determinations (R–2) are compared among the sizeholding classes, it is interesting to note that the (R–2) valueis the highest for medium farms at 0.72, followed by smallfarms at 0.61, marginal farms at 0.57 and it is the least forlarge farms at 0.30. However, the co-efficient ofdetermination for the pooled farms is at 0.47. It meansthat out of 100 per cent variation in the total energy output,about 47 per cent is attributed to the influence of the elevenindependent variable input energies analysed in the presentmodel. The balance 53 per cent is explained due to thefactors other than the models considered. Moreover, the


present model is highly appropriate irrespective of farmsize holding classes and the pooled farms are testifiedthrough 'F' ratios.

It is observed from the results of the chow's testpresented in Table 4 that the 'F' ratios for irrigated areaare found to be 32.84 and those of unirrigated and pooled

farms are 84.92 and 56.41 respectively. Further, thesevalues have been found to be significant at 1 per cent level.It is implied that there exists structural differences amongthe energy inputs used and the per acre value of outputamong the four categories of farms in the irrigated,unirrigated and the pooled farms.

TABLE 1 : LOG LINEAR MULTIPLE REGRESSION MODEL FOR IRRIGATED FARMS

Sl. Sources of energy Marginal Small Medium Large TotalNo.

1. Constant 6.64* 4.72* 6.45* 12.31* 7.70*(4.86) (4.01) (3.50) (8.83) (-0.68)

2. Human energy -0.12 ** 0.62 0.39* 0.58* -0.04(-2.73) (0.26) (4.48) (5.93) (0.83)

3. Animal energy -0.08 ** -0.17 ** 0.21** 0.28 0.03(-2.13) (-3.13) (2.84) (0.11) (1.14)

4. Diesel energy 0.10 0.08* - 0.21** 0.18* 0.06 **(0.28) (2.86) (2.38) (6.18) (2.92)

5. Electrical energy 0.06 ** 0.14* 0.15* 0.44** 0.03(2.31) (5.31) (4.08) (13.19) (2.04)

6. Seed energy 0.36** 0.89* 0.33** 0.36** 0.11**(2.89) (9.40) (3.55) (2.89) (1.51)

7. Farmyard manure -0.07** -0.24** 0.03 0.03 0.06*(-2.03) (0.53) (0.28) (0.39) (3.65)

8. Pesticides 1.56* 0.16 -0.13 -0.10 0.18(14.29) (2.06) (-0.99) (-1.14) (1.76)

9. Mechanical energy 0.33* 0.55* -0.86** -0.01 0.14**(4.52) (4.43) (-3.55) (-0.11) (2.27)

10. Nitrogen energy 0.19 0.22** - 0.05 - 0.09 0.13(1.08) (2.80) (-0.36) (- 1.06) (1.00)

11. Phosphorous energy 0.05 0.09 1.24* 0.61* -0.06**(0.36) (0.63) (7.82) (6.02) (2.73)

12. Potash energy 0.21** 0.34** -0.13 -0.18 9.94(2.64) (3.33) (-1.45) (-2.18) (0.41)

13. Adj R square 0.59 0.46 0.94 0.33 0.53

14. F 5.49* 10.05* 107.22* 6.05* 4.67*

Source : Computed* Significant at 1%** Significant at 5%Figures in parentheses indicate 't' ratios

February, 2016 23

TABLE 2 : LOG LINEAR MULTIPLE REGRESSION MODEL FOR UNIRRIGATED FARMS


1. Constant 5.43* 7.16* 7.78* 14.76* 4.07*(6.74) (6.79) (5.21) (5.21) (7.62)

2. Human energy -0.05 -0.23 0.28* 0.77* 0.03(0.60) (-2.29) (4.23) (4.68) (0.76)

3. Animal energy -0.09 -0.05 0.09 0.50* 0.04(-2.03) (-0.42) (1.75) (5.75) (1.65)

4. Diesel energy 0.10* 0.11* -0.01 -0.02 0.06 *(4.17) (4.06) (-0.30) (-0.36) (4.27)

5. Electrical energy 0.00 0.02 0.11* -0.06 0.03(0.19) (0.72) (4.19) (-1.00) (0.82)

6. Seed energy 0.34* 0.44* 0.19** -0.29 0.05(4.20) (6.21) (2.49) (-1.62) (1.15)

7. Farmyard manure 0.65* 0.32* 0.02 0.08 0.07(4.04) (5.07) (0.43) (1.68) (2.23)

8. Pesticides 0.31 0.15 -0.09 0.14 0.80**(2.09) (2.00) (-1.02) (0.29) (2.04)

9. Mechanical energy 0.22* 0.25 0.02 0.20 0.03(4.61) (1.27) (0.15) (0.29) (0.91)

10. Nitrogen energy 0.53* 0.43 * -0.05 0.36 0.42**(14.46) (2.59) (-0.61) (0.12) (13.31)

11. Phosphorous energy 6.05 0.01 0.21** 0.27 0.61**(0.57) (0.08) (2.86) (0.26) (2.25)

12. Potash energy 0.35* 0.49* 0.07 0.15 0.25*(8.72) (3.30) (0.83) (1.69) (7.11)

13. Adj R square 0.15 0.37 0.59 0.41 0.32

14. F 4.36** 13.47* 56.17** 8.52* 44.58*


TABLE : 3 LOG LINEAR MULTIPLE REGRESSION MODEL FOR POOLED FARMS


1. Constant 6.41* 6.98* 12.21* 12.03* 3.06*(6.37) (5.39) (3.75) (2.44) (4.27)

2. Human energy 0.11 0.13 0.22 0.19 0.08(1.20) (1.23) (0.53) (0.56) (1.15)


3. Animal energy -0.61* 0.05 0.13 0.14 0.02(-2.00) (1.00) (0.91) (1.22) (0.5)

4. Diesel energy 0.08* 0.06** 0.23* 0.25** 0.10*(2.51) (-2.45) (8.35) (2.97) (5.34)

5. Electrical energy 0.07** 0.12 * 0.13* 0.22 0.03(2.86) (4.39) (3.10) (0.56) (1.73)

6. Seed energy 0.47* 0.53* 0.20 0.09 -0.05(-4.91) (5.09) (1.97) (0.31) (-0.91)

7. Farmyard manure 0.70* -0.24* 0.01 0.05 0.09(2.57) (-2.81) (0.07) (0.79) (0.88)

8. Pesticides 0.70* 0.72** -0.17 * 0.34* 0.13*(2.68) (5.98) (-2.27) (4.06) (2.28)

9. Mechanical energy 0.22* 0.35 -0.15 -0.18 -0.04(3.38) (3.10) (-1.43) (-0.97) (-0.79)

10. Nitrogen energy 0.52* 0.41* -0.22 -0.33 0.42*(13.66) (3.90) (-1.93) (-1.22) (11.19)

11. Phosphorous energy 0.13 0.23 -0.29** -0.68** 0.04(0.86) (1.73) (-2.65) (-2.82) (0.53)

12. Potash energy 0.33* 0.50 -1.26* 0.06 0.31*(7.38) (1.38) (-4.03) (0.37) (6.74)

13. Adj R square 0.57 0.61 0.72 0.30 0.47

14. F 12.98** 17.45* 56.28** 3.28* 40.68*


TABLE 4 CHOW'S TEST BETWEEN UNIRRIGATED AS WELL AS IRRIGATED FARMS IN EACH SIZE OF

LAND HOLDINGS AND THE POOLED FARMS

Residual Sum of Square Unirrigated Irrigated Pooled

All Farms �e2 61147761.3 76928355.3 970151321.67

Marginal �e12 8739111.325 9817124.232 91674321.39

Small �e22 5979818.74 2696974.393 992472231.2

Medium �e32 54048411.90 20715127.84 87094401.66

Large �e42 1897410.942 3308541.384 963376911.3

F* 32.84352162* 84.92879882* 56.41501923*

Source : Computed*Significant at 1% level


TABLE : 3 LOG LINEAR MULTIPLE REGRESSION MODEL FOR POOLED FARMS—CONTD.

February, 2016 25

Conclusions

The following conclusions emerge from the findings ofthe present study. It could be seen from the results of thepooled irrigated farms that energy inputs like diesel, seed,mechanical and phosphorous are found to be statisticallysignificant. The higher use of diesel energy by the farmersis mainly due to the application of mechanical methods.Further, the farmers excessively apply of phosphorousinput energy due to lack of adequate knowledge in theapplication of recommended dose of phosphorous by thefarmers and hence it is negatively correlated to the peracre value of output energy. It is also interesting to notethat the farmers use more mechanical power in the placeof human and bullock labour, as the size of land holdingincreases. This is possible due to wealth and social statusof the medium and large land holders. The human andanimal labour input energies are not found to bestatistically significant at the pooled irrigated farms,because of the influence in insignificant co-efficient ofhuman energy in the small farms and animal energy in thelarge farms.

From the results of pooled unirrigated farms theinputs such as diesel, pesticide, nitrogen and phosphorousare found to be statistically significant with positive signs.The higher consumption of diesel energy is mainly due tousage of this input for oil engine when the land is anunirrigated one. Similarly, high consumption of pesticides,nitrogen and phosphorous is made mainly because of theapplication of these inputs for commercial crops likecotton, sugarcane, groundnut, which are the major cropscultivated in the study area. The insignificance of seed,farm yard manure, and mechanical power, is mainly dueto the absence of assured irrigation and hence the farmersdo not want to take high risk towards the investment ofthese inputs. The same type of results could be observedfor pooled farms of irrigated and unirrigated farms in thestudy area. As a whole, it could be emphasized that anincrease in the use of energy inputs will increase the peracre value of output energy. Similarly, the use of energyinputs varies according to irrigation facilities.

Policy Implications

i. Short duration trainings on farm managementextension by the Agricultural universities should bearranged for the farmers, particularly, for marginaland small farmers so that they may be able tounderstand the importance of judicious allocationand use of scarce energy inputs to have more energyefficient, economically viable and sustainablefarming systems

ii. Among the different sources of energy, electricityis the major source of total energy used in cropproduction on all the farms since it is fully subsidized

by the Government. Measures to conserve electricenergy and use it efficiently should be adopted bythe agencies concerned with its generation anddistribution systems.

iii. Since mechanical energy is the main determinant ofenergy output, smaller horse power tractors shouldbe developed to meet the needs of comparativelysmall and marginal land holdings.

iv. There is a need to replenish the soil fertilizer byadopting modern technologies, like manuring,organic cycling and using bio-fertilizers in order toreduce the dependence on chemical fertilizers whichheavily depend upon imported energy resources suchas petroleum products, etc.

v. Alternative sources of energy like wind power, solarenergy, should be encouraged for energyconservation.

vi. The government may instruct banks, agriculturalcredit societies and other financial institutions toextend financial assistance at a nominal rate ofinterest at the appropriate of need.

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February, 2016 27

Abstract

Growth in the agriculture sector may well be judged bythe increase in agricultural production over time. The areaunder pulse has been fluctuating between 22 and 24 millionha however; there was improvement in production, whichis mainly on account of increase in productivity as resultof generation of new varieties or technology of theircultivation. The production was 2243.02 in Maharashtrawhich is less than the Madhya Pradesh's by 1200 milliontonnes, while in the 1980s Madhya Pradesh produced2561.00 million tonnes followed by the Uttar Pradesh andRajasthan which produced 2119.07 and 1633.55 milliontonnes respectively. The area had increased up to 3546.94lakh hectares in Maharashtra which is less than the MadhyaPradesh by 1000 lakh hectares while in the 1980s MadhyaPradesh area was 4841.22 lakh hectares followed by theUttar Pardesh and Rajasthan which had an area 2981.33and 3240.89 respectively. There was huge decline in thearea of pulses in Orissa, Punjab, Haryana and Bihar.Madhya Pradesh shows an increasing rate of growth interms of area and production in comparison to the otherstates. Orissa shows a negative growth rate in area in allthree decades while the production shows a negative levelof significance along with a decreasing growth rate ofproduction. In 1981-90, it was 7.77 per cent level ofsignificance decreased to -8.58 per cent in 1991-2000further it was decreased. Punjab shows a declining growthrates for area and production in all three decades. Rajasthanshow a positive growth rate for area and production in allthree decades, in 2000-14 it was 6.24 per cent and 5.90per cent for area and production respectively while it was1.44 per cent and 3.33 per cent for area and production in1991-2000 which indicates a good level of significance.

Key Words : Area, Growth rate, Pluses, production andtrend

Introduction

Growth in the agriculture sector may well be judged bythe increase in agricultural production over time. In theIndian context, rice, wheat, maize, millets and pulses arethe major food crops. Oilseeds, sugarcane, cotton, jute,

mesta and potatoes are the major cash crops. Tobacco,chilies, ginger, onion, turmeric, tapioca, sweats potatoes,etc are minor cash crops. Among plantation crops, tea,coffee and rubber are important.

The cereals and pulses occupy about 3/4th of thegross cropped area under cultivation. Millets and pulsesregistered an increase in productivity to the extent of 30-35 percent increase (Agricultural Statistics at a Glance,2014). The pulses are unique crops as they have in-builtmechanism to fix atmospheric nitrogen in their rootnodules. They are also rich in protein and fit well in variouscropping systems. India is a rare country which grows sucha variety of pulse crops which none of other countries inthe world grow. India is the largest producer, largestconsumer and the largest importer of pulses in the world.In India, the pulses are grown in around 24-26 millionhectares of area producing about 17-19 million tonnes ofpulses annually (India Pulses & Grains Association,Mumbai). India accounts for over one third of the totalworld area and over 20 per cent of total world production(Agricultural Statistics at a Glance, 2014). India primarilyproduces Bengal gram (chickpeas), red gram (tur), lentil(masur), green gram (mung) and black gram (urad). Formajority of vegetarian population in India, pulses are themajor source of protein. Pulses and pulse crop residuesare also major sources of high quality livestock feed inIndia.

The area under pulses has been fluctuating between24 and 26 million ha. However, there was improvementin production, which is mainly on account of introductionof new varieties and improved technology of cultivation.Pulses improvement work before Independence wasconfined to collection and evaluation of land races indifferent regions. Farmers were growing local cultivarswhich lacked genetic purity. Pure line selections from localcultivars were evaluated for yield potential. The promisingtype was further tested in multi-vocational trials. On thebasis of performance in multi location trials, the bestgenotypes were identified as varieties. Such improvedvarieties contributed to productivity of pulses in earlyphase of pulse improvement. The present study is aimedat exploring pulses scenario of India since 1980s.

Production and Growth in Pulses in India

VISHNU SHANKER MEENA* SHIRISH SHARMA** AND VISHAL DAGAR***

* R I, AER Center Delhi University, Delhi E-mail: [email protected]** Senior Research Fellow, Department of Agricultural Economics, College of Agriculture, SKRAU, Bikaner -334001, Rajasthan, E-mail:

[email protected]**** Research Fellow AER Center Delhi University, Delhi E-mail: [email protected]


Methodology

Data Sources

The study relies on secondary data compiled from variouspublished sources. Data on area, production and yield werecollected from the Directorate of Economics and Statistics(DES), Ministry of Agriculture Government of India. Datawere collected for all pulses for major growing states inIndia for the period 1981-82 to 2013-14. The study periodwas divided into three periods decade wise; i.e. 1981-90,1991-00 and 2001-2013, Compound annual growth rateswere worked out using exponential analysis.

Compound Growth Rate

The compound growth rates were estimated by exponentialanalysis using the following model:

Compound growth model

yt = abt ut ...........................................................(1)

Where :

yt = is area/production of pulses in time period t

t = Time element which takes the value 1, 2, 3, 4................. n

a and b are parameters to be estimated and

b = (1+g), where g is the rate at which y grows every yearin relation to value in preceding year.

ut = Disturbance term

On logarithmic transformation of equation (1) we get

Log yt = log a + t log b + log ut

This can be expressed as:

yt* = a* + bt* +ut*

where yt* = log yt; a* = log a; b*= log b and u* t = log ut

The estimate of compound growth rate can be obtained as

g = (antilog b*-1) X 100

The F test was used for testing significance of theCGR

Results and Discussion

Table 1 shows decadal average of area under pulse in India.The table revealed that Madhya Pradesh had mention toprank in area in all the three periods. The state of Rajasthanwhich ranked second had slipped to third rank in the periodof 2001-2013. However, Maharashtra state madesignificant gain in terms of area under pulses. It had fourthrank in first period and climbed to second rank in thesecond period. The state of Orissa was significant looserand had slipped eighth rank in third period (2001-13) fromthe fifth period and the first period. Uttar Pradesh alsoslipped to fourth rank in period three as compared to thirdrank in the period one. Himachal Pradesh and Punjab hadlowest area under pulse crop. Area under pulses in leadingfive states is shown in figure 1.

TABLE : 1 DECADAL AVERAGES OF AREA UNDER PULSE CROPS IN INDIA

(In 000'ha)

States/ Years Decadal Averages1981-90 Rank 1991-2000 Rank 2001-2013 Rank

Madhya Pradesh 4841.22 I 4953.10 I 4567.46 I

Rajasthan 3240.89 II 3572.47 II 3523.17 III

Uttar Pradesh 2981.33 III 2845.65 IV 2535.07 IV

Maharashtra 2941.00 IV 3363.14 III 3546.94 II

Orissa 1839.33 V 1063.30 VII 755.11 VIII

Karnataka 1631.67 VI 1692.42 V 2224.41 V

Andhra Pradesh 1449.67 VII 1604.85 VI 1941.17 VI

Bihar 1213.56 VIII 971.57 VIII 612.35 IX

Tamil Nadu 759.33 IX 702.70 X 604.11 X

Gujarat 736.44 X 875.34 IX 792.93 VII

Haryana 685.00 XI 435.28 XI 169.41 XI

Punjab 205.00 XII 98.69 XII 37.97 XII

Himachal Pradesh 44.22 XIII 36.91 XIII 31.37 XIII

Source: www.indiastat.com

February, 2016 29

Table 2 shows decadal averages of production underpulse crops in India. It reveals that Madhya Pradesh gotfirst rank in all the three periods. Rajasthan slipped tofourth rank in the period three. Uttar Pradesh also slippedto third rank in period three. Maharashtra gain significantlyin production of pulses and achieved second rank in the

period three as compared to period one. The state of Orissastate was a significant loser in terms of production of pulsesand slipped to ninth rank from fifth rank in the first period.Himachal Pradesh and Punjab occupied the lowest rank inthe production. The production in leading five states isshown graphically in figure 2.

TABLE : 2 DECADAL AVERAGES OF PRODUCTION UNDER PULSES CROPS IN INDIA

(In 000'ha)

States/ Years Decadal Averages1981-90 Rank 1991-2000 Rank 2001-2013 Rank

Madhya Pradesh 2561.00 I 3029.04 I 3502.00 I

Uttar Pradesh 2542.21 II 2483.84 II 2119.07 III

Rajasthan 1347.46 III 1640.08 IV 1633.55 IV

Maharashtra 1265.69 IV 1743.36 III 2243.02 II

Orissa 1016.11 V 494.75 IX 334.71 IX

Bihar 811.67 VI 749.58 V 513.97 VIII

Andhra Pradesh 598.22 VII 732.84 VI 1332.32 V

Karnataka 568.96 VIII 653.36 VII 1038.10 VI

Haryana 416.22 IX 366.51 X 128.46 XI

Gujrat 412.33 X 549.85 VIII 558.04 VII

Tamil Nadu 290.76 XI 302.37 XI 265.39 X

Punjab 142.17 XII 75.40 XII 30.59 XII

Himachal Pradesh 9.33 XIII 12.32 XIII 26.63 XIII

Source: www.indiastat.com

RajasthanMadhya Pradesh Uttar Pradesh Maharastra Orissa

Figure 1: Area in leading five states in India


The Table 3 shows compound growth rate of areaand production of all pulses in different state in India. TheTable revealed that in Madhya Pradesh, area andproduction registered positive and significant growth ratesin Period III. Rajasthan too registered positive andsignificant growth rates in Period III. The area andproduction under pulse crop in Andhra Pradesh shows levelof significance in 1981-90 and 2000-2014 while shows anon significance in 1991-2000. In Bihar the area andproduction shows a non significance level. Gujrat showsan increase level of significance in 2000-2014 comparisonto 1981-90 and 1991-2000. Haryana shows a huge declinein the growth rate of area and production of pulses in allthree decades. Himachal Pradesh was a small contributorbut there was a good level of significance found in thegrowth rate of pulses. Karnataka shows a high growthrate in terms of area as well as production as the table no3 shows the significance level improved in 2000-14 incomparison to 1980-90 and 1991-2000. The largecontributor of pulses in India shown in Madhya Pradesh

in 1981-90, table indicates that there was a negativesignificance in the area but the production shows a positivetrend. Madhya Pradesh shows an increasing rate of growthin terms of area and production in comparison to the otherstates. Orissa shows a negative growth rate in area in allthree decades while the production shows a negative levelof significance along with a decreasing growth rate ofproduction. In 1981-90, the level of significance was 7.77and this decreased to -8.58 in 1991-2000. Punjab showsa declining growth rates for area and production in allthree decades. Rajasthan show a positive growth rate forarea and production in all three decades, in 2000-14 itwas 6.24 and 5.90 for area and production respectively,while it was 1.44 and 3.33 for area and production in 1991-2000 which indicates a good level of significance.Tamilnadu shows declining growth rate for area andproduction in all three states. One of the major contributingstates in terms of area and production for pulses is UttarPradesh which shows a continuous declining growth ratein area and production in all three decades.

TABLE : 3 COMPOUND GROWTH RATES OF AREA AND PRODUCTION OF ALL PULSES IN INDIA

(In Per cent)

1981-82 to 1989-90 1991-92 to 1999-2000 2000-01 to 2013-14Area Production Area Production Area Production

Madhya Pradesh -7.45* 1.57 4.29 -2.43 8.58* 7.71*

Uttar Pradesh 1.20 1.92 -8.93* -4.28 -6.79* -3.73

Rajasthan -5.91 -4.29 1.44 3.33 6.24* 5.90*

Maharashtra 8.49* 7.88* 5.60 4.75 1.13 6.68*

Orissa 8.42* 7.77* -8.25* -8.58* 6.80* 8.59*

Bihar -3.73 4.48 -9.28* -3.83 -9.50* -2.89

Madhya Pradesh Uttar Pradesh Rajasthan Maharastra Orissa

Figure 2: Production in leading five states in India

February, 2016 31

Andhra Pradesh 4.34 8.50* -1.37 0.68 -2.40 7.47*

Karnataka 5.83 -2.26 5.45 5.26 7.41* 8.05*

Haryana -4.25 0.96 -6.67* -5.35 -1.08 2.24

Gujrat 0.52 -1.50 -5.65 -0.34 3.69 7.58*

Tamil Nadu 8.91* 9.49* -4.03 -2.69 0.68 2.69

Punjab -8.14* -3.99 -8.76* -8.34* -4.28 -3.25

Himachal Pradesh -4.60 -3.57 -8.82* -1.13 0.70 8.18*

India -2.56 3.34 -4.53 2.89 7.77* 9.20*

*Significant at 1 per cent level of Significance

Policy Implication

One of the prime reasons for the failure of the country'spulses production to keep pace with the rise in demand isthe lack of adequate returns to producers. Low pulse yieldin India compared to other counties is attributed to poorspread of improved varieties and technologies, abruptclimatic changes, vulnerability to pests and diseases, andgenerally declining growth rate of total factor productivity.Though the government routinely announces MSPs forall major pulses, these are of little value to the growers inthe absence of adequate arrangements for procurement atthe recommended prices. Distress sales of pulsesimmediately after the harvest at prices lower than the MSPare quite common. Even the latest move to raise the MSPsof pulses by a hefty margin and offering a bonus on topmay fall flat, unless firm arrangements can be put in placeto ensure that the farmers actually get these prices.

REFERENCE

A Component Analysis of the Growth of AgriculturalProductivity in Rajasthan: 1956-61 to 1969-74. IndianJournal of Agricultural Economics, 32 (1).

Agricultural Statistics at a Glance, 2014, Directorateof Economics and Statistics, Ministry of Agriculture.Directorate of Economics and Statistics - http://eands.dacnet.nic.in

Chand, Ramesh and Sonia Chauhan (1999). AreDisparities in Indian Agriculture Growing?. PolicyBriefNo. 8. New Delhi: National Centre for AgriculturalEconomics and Policy Research.

Department of Agriculture and Cooperation -www.agricoop.nic.in

India Pulses & Grains Association, Mumbai -www.ipga.co.in

Reddy, D Narasimha and Srijit Mishra (2009).Agriculture in the Reforms Regime. In D Narasimha Reddyand Srijit Mishra (ed), Agrarian Crisis in India. New Delhi:Oxford University Press. Sagar, Vidya (1977).

1981-82 to 1989-90 1991-92 to 1999-2000 2000-01 to 2013-14Area Production Area Production Area Production

TABLE : 3 COMPOUND GROWTH RATES OF AREA AND PRODUCTION OF ALL PULSES IN INDIA—CONTD.

(In Per cent)


Abstract

In this paper, an attempt has been made to study the arrivalsand prices of black gram in Barshi tahsil of Solapur district.The time series data on monthly arrivals and prices ofblack gram were collected from the purposively selectedAPMC, Barshi for the years from 2001-02 to 2011-12 inorder to compute the trends, growth rates and relationshipbetween arrivals and prices.

The arrivals of black gram in the post-harvest periodwere the maximum and during the lean period were theleast. Whereas, in the case of prices, opposite trend wasobserved. At the Barshi market, the prices of black gramincreased rapidly over the period of 11 years ending 2011-12 and at a faster rate. The arrivals could not increase atthe similar rate of prices during this period, where theyotherwise showed a casual increase which may be due todecline in production and productivity of black gram inthe study area. This situation could be improved bygrowing black gram on irrigated land, taking timely andappropriate plant protection measures against insectinfestation like pod borers and doing sufficient efforts togrow high yielding, pest resistant varieties, etc. The farmersshould stop to consider the black gram as the crop of dryland agriculture and grow them on irrigated land to apossible extent. Subsidies, incentives and quick servicesshould be given to the cultivators to encourage higher cropproduction. For this purpose, HYV seeds, fertilizers,pesticides and irrigation facilities should be provided tocultivators.

Introduction

In Maharashtra, the area under the total pulses was 3.38million ha (2009-10), production 2.37 million tonnes andproductivity 702 Kg ha-1, whereas, in India, in 2009-10,23.28 million ha area was under these pulses, with 14.66million tonnes production and 630 kg ha-/roductivity. TheMaharashtra state contributes to the 14.50 per cent of thetotal area and 14.66 per cent of the total production ofIndia.

In Solapur district, the area under Tur (Pigeonpea)was 12 thousand ha with production of 4.2 thousand tonnesin 2011-12. The area under mung (Green chickpea) was 8hundred ha and production 5 hundred tonnes. Area under

Econometric Analysis of Arrivals and Prices of Black Gram in Western Maharashtra

D. S. NAVADKAR *A. J. AMALE** AND R. B. NAIK***

udid (Black chickpea) was 3.8 thousand ha (2011-12) andproduction 2.8 thousand tonnes and the area underchickpea (Chickpea) was 24.6 thousand ha and production15.9 thousand tonnes. In view of this, there is a very vastscope in the processing industries of pulses in Solapurdistrict. Therefore, attempts have made to study the arrivalsand prices of selected pulses.

Methodology

Selection of Study Area

The choice of Solapur district (Barshi tahsil) was purposivebecause of the fact that the dal mills of different capacitieshave been established in this area. The pulse processingactivities have been carried out on commercial basis by alarge number of dal mills in this area.

The Barshi town from Barshi tahsil was purposivelyselected since the majority of dal mills have been locatedand centered at the same. The market has good absorptioncapacity for the produce that is reflected in the installedcapacity of dal mills. For purchase of raw material theproduced from Solapur district is shipped to other partsof the country.

Data Requirements and Sources of Data

The data on general features and selected indicators ofthe agricultural economy of the study area was obtainedfrom the official records of the district statistical officer,Solapur and the Tahsildar, Barshi.

Collection of Data

The market level secondary data on monthly arrivals andprices of black gram were collected from the officialrecords of the Agricultural Produce Market Committee,Barshi for the years 2001-02 to 2011-12.

Analytical Framework

The analytical procedure adopted for the presentinvestigation has been described below.

The time series data on monthly arrivals and pricesof black gram collected from the sample market Barshifor the 11 years from 2001-2002 to 2011-2012 were

* Associate Professor, ** Junior Research Assistant, and *** M.Sc. Scholar, Department of Agricultural Economics, Mahatma Phule Krishi Vidyapeeth,Rahuri- M.S., 413 722.

February, 2016 33

analyzed with a view to compute the trends, growth ratesand relationship held therein between. The differentestimates were obtained by arranging the data separatelyat every point of analysis. Some basic measures of statisticswere used to interpret the results more effectively. Themethod adopted for the analysis of data is given belowwith further more explanation.

i) Co-efficient of variation in arrival and prices wascomputed for black gram, so as to see the variationand predict the behavior of prices whether they werestable involving less risk and uncertainty and vice-versa. The formula for Co-efficient of variation isas bellow:

ii) Seasonal variations are those periodic movementsin business activity which occur every year and havetheir origin in the year itself. Since such variationsrepeat during a period of twelve months that canpredicted fairly accurately. Seasonal indices can becalculated in index form as a measure of seasonalvariations. Seasonal index for each month wascalculated. Thus specific seasonal index refers tothe seasonal changes during a particular year.Seasonal indices are given as percentage of theiraverage.

Seasonal variations in arrival and prices werecalculated by simple average method.

iii) The linear and compound growth rates of annualarrivals and annual mean prices of black gram wereworked out by fitting linear and exponential formsof equations given below, respectively.

Linear form-

Y = a+ bT

Linear growth rate =

Exponential form-

Y = a bT

b = 1+ r

Compound growth rate 'r' (%) = (Anti log of b-1) x 100

Where,

Y = Annual arrivals of black gram in Qtls. (or)

Annual mean prices of black gram in rupees perQtls.(or)

Monthly arrivals of black gram in Qtls. (or)

Monthly prices of black gram in rupees per quintal

T = Time (s) in years

a = Constant (s)

b = Trend coefficient (s)

iv) To examine the relationship between annual arrivalsand annual mean prices as well as monthly arrivalsand monthly prices of black gram, correlationcoefficient 'r', as measure of marketing efficiency,was calculated with the help of following formula.

Where,

X = Annual mean prices of black gram in rupee perquintal (or)

Monthly prices of black gram in rupees per quintal

Y = Annual arrivals of black gram in Qtls. (or)

Monthly arrivals of black gram in Qtls.

N = Number of observations (or)

Time in number of years

Results

1. National Scenario of Pulses:

The Madhya Pradesh produces 28 % of India's total pulsesproduction; other top producing states includeMaharashtra, Rajasthan, Uttar Pradesh, Andhra Pradesh,Karnataka and Gujarat. The per hectare total pulsesproduction is highest in Bihar, followed by Jharkhand andUttar Pradesh. (Table 1)

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TABLE : 1 STATE WISE AREA, PRODUCTION AND YIELD OF TOTAL PULSES IN INDIA (2012-13)(AREA- MILLION HA., PRODUCTION- MILLION TONES AND YIELD- KG./HA.)

State Area % to All-India Production % to All India Yield

Madhya Pradesh 5.31 22.83 5.17 28.19 974

Maharashtra 3.27 14.06 2.31 12.60 706

Rajasthan 3.25 13.97 1.96 10.69 603

Uttar Pradesh 2.37 10.19 2.33 12.70 983

Andhra Pradesh 1.95 8.38 1.62 8.83 831

Karnataka 2.27 9.76 1.26 6.87 555

Gujarat 0.66 2.84 0.57 3.11 864

Jharkhand 0.59 2.54 0.61 3.33 1034

Bihar 0.52 2.24 0.54 2.94 1038

Chattisgarh 0.93 4.00 0.65 3.54 6.99

Tamilnadu 0.51 2.19 0.21 1.15 412

Odisha 0.83 3.57 0.42 2.29 506

West Bengal 0.20 0.86 0.19 1.04 950

Haryana 0.16 0.69 0.13 0.71 813

Others 0.44 1.89 0.37 2.02 @

All India 23.26 100.00 18.34 100.00 788

@- Since area/production is low in individual states, yield rates are not worked out(Source : Agricultural Statistics at a glance, GOI, 2014)

2 Variability in Arrivals and Prices of Black Gramin APMC, Barshi

2.1 Inter Year Variability in Arrivals of Black Gramin APMC, Barshi

The inter year variability in arrivals of black gram inAPMC, Barshi were estimated over eleven years from2001-02 to 2011-12 and presented in Table 2.

TABLE 2 YEARLY VARIABILITY IN ARRIVALS OF BLACK GRAM

IN APMC, BARSHI DURING THE PERIOD

FROM 2001-02 TO 2011-12

Years Mean Arrivals (q) C.V. (%)

2001-02 1407.17 212.272002-03 3133.50 183.132003-04 1554.00 150.232004-05 1113.75 148.712005-06 1992.71 206.942006-07 846.10 179.832007-08 692.17 139.772008-09 308.82 142.752009-10 516.50 268.422010-11 789.75 121.332011-12 2251.58 199.66

The market arrivals of black gram in APMC, Barshirevealed that the maximum variability in arrivals of blackgram was during the year 2009-10 (268.42 per cent) andit was found to be lowest in the year 2010-11(121.33 percent).

The similar results were reported by Brahmprakashand Shrivastava (1995) while conducting the study oneffect of market arrivals on price of field pea in UttarPradesh.

2.2 Inter Year Variability in Prices of Black Gramin APMC, Barshi

The inter year variability in prices of black gram in APMC,Barshi were estimated over eleven years from 2001-02 to2011-12 and presented in Table 3.

In case of price variability of black gram, it wasrevealed that the maximum variability was found duringthe year 2005-06 (49.39 per cent) and it was minimum inthe year 2001-02 (10.92 per cent).

February, 2016 35

TABLE : 3 YEARLY VARIABILITY IN PRICES OF BLACK GRAM

IN APMC, BARSHI DURING THE PERIOD

FROM 2001-02 TO 2011-12

Years Mean Arrivals (q) C.V. (%)

2001-02 1467.00 10.92

2002-03 1063.83 15.61

2003-04 1277.92 17.08

2004-05 1461.08 17.18

2005-06 2652.50 49.39

2006-07 2341.30 42.66

2007-08 2052.25 31.15

2008-09 2869.18 40.77

2009-10 3912.50 21.56

2010-11 3074.08 23.91

2011-12 3152.17 19.25

2.3 Inter Year variability in Arrivals of Black Gramin APMC, Barshi

The intra year variability in arrivals of black gram inAPMC, Barshi were estimated over eleven years from2001-02 to 2011-12 and presented in Table 4.

TABLE : 4 MONTHLY VARIABILITY IN ARRIVALS OF BLACK

GRAM IN APMC, BARSHI DURING THE PERIOD

FROM 2001-02 TO 2011-12

Month Mean Arrivals (q) C.V. (%)

Oct. 5290.36 113.34

Nov. 1552.54 77.16

Dec 920.73 97.55

Jan. 388.82 93.67

Feb. 149.45 107.14

Mar. 37.10 122.09

Apr. 38.62 76.11

May 14.90 62.98

June 82.36 57.19

July 99.09 67.74

Aug. 1358.54 137.26

Sept. 5472.36 85.28

The information depicted in Table revealed thatvariability in black gram was maximum during the monthof August (137.26 per cent) while it was minimum duringthe month of June (57.19 per cent).

2.4 Intra Year Variability in Prices of Black Gramin APMC, Barshi

The intra year variability in prices of black gram in APMC,Barshi were estimated over eleven years from 2001-02 to2011-12 and presented in Table 5.

Price variability in black gram was maximum in themonth of April (70.48 per cent) while it was lowest in themonth of February (29.30 per cent) followed by October(36.46 per cent).

Similar results were reported by Waykar (1997)while conducting the study on economics of tur (pigeonpea) processing by mills in Barshi district Solapur.

TABLE : 5 MONTHLY VARIABILITY IN PRICES OF BLACK


FROM 2001-02 TO 2011-12

Month Mean Arrivals (q) C.V. (%)

Oct. 1969.45 26.01

Nov. 1895.54 23.51

Dec 1864.00 22.09

Jan. 1997.00 33.67

Feb. 2097.82 27.15

Mar. 2106.54 31.14

Apr. 2026.09 27.46

May 2139.82 30.46

June 1883.45 36.56

July 2264.27 38.26

Aug. 2263.91 43.34

Sept. 2320.27 44.25

2.5 Seasonal indices of Aarrivals and Prices ofBlack Gram

The seasonal indices of arrivals and prices of black gramin APMC, Barshi were estimated over eleven years from2001-02 to 2011-12 and presented in Table 6.


TABLE : 6 SEASONAL INDICES OF ARRIVALS AND PRICES OF

BLACK GRAM IN APMC, BARSHI DURING THE PERIOD

FROM 2001-02 TO 2011-12

(Per cent)

Month Arrivals Prices

Oct. 412.51 107.81

Nov. 121.06 106.83

Dec. 71.79 97.06

Jan. 30.32 94.94

Feb. 11.65 85.47

Mar. 2.89 78.18

Apr. 3.01 88.89

May 1.16 92.74

June 6.42 121.14

July 7.73 103.79

Aug. 105.93 128.55

Sept. 426.70 134.43

It was seen that the seasonal indices for black gramarrival was more during the month of September (426.70)followed by October (412.51), while it was lowest duringthe month of May (1.16 followed by March (2.89). In caseof prices, the maximum seasonal indices were noticed inthe month of September (134.43) followed by August(128.55) and minimum in the month of March (78.18)followed by February (85.47), respectively.

Similar results were reported by Ravi kumar et al.(2001) in the case of arrivals and prices of selectedcommodities in Anakapalle regulated market of AndhraPradesh and by Andhalkar et al. (2010) in the case ofarrivals and prices of selected major pulses in APMC,Amaravati, respectively.

2.6 Linear and Compound Growth Rates in AnnualArrivals and Prices of Black Gram

The linear and compound growth rates of annual arrivalsand prices of black gram were estimated by fitting linearand exponential forms of equations, respectively. Thesignificance of both the growth rates was examined withthe help of student's 't'- test. The results have beenpresented in Table 7.

It is revealed from the table that the linear andcompound growth rates of prices of black gram were to

the extent of 22.10 and 12.05 per cent per annum,respectively and were observed to be positive and highlysignificant at 1 per cent level of probability during thisperiod. While, in annual growth rates in arrivals of blackgram were -5.53 and -8.76 per cent, respectively, in thecase of linear and compound type, they were found to benegative and non significant. The coefficients ofdeterminations (R2) estimated on account of linear andcompound growth rates explained variations in prices ofblack gram to 76 and 78 per cent, respectively. In case ofthe arrivals of black gram, the coefficients of determination(R2) were seen to be less in both the types of growth rates,i.e., 16 and 20 per cent, respectively.

It can be observed from the above results that theprices of black gram at Barshi market had been increasingat rapidly over the time span of 11 years and at a fasterrate. This might be on account of general rise in pricesand failure of supply to keep pace with the increaseddemand due to human consumption and animal feed.

It is noteworthy that the market arrivals of blackgram could not increase at similar rate by which the pricesof black gram increased during this period, where theyotherwise could show a casual decrease of about 5.53 and8.76 per cent respectively, by the linear and compoundgrowth rates.

Similar results were reported by Tuteja (2006) inthe case of state level analysis to study the growthperformance of pulse crops in India, similarly, alsoreported by Salunkhe (2010) in the case of price behaviourof selected crops in Akola district.

TABLE : 7 LINEAR AND COMPOUND GROWTH RATES (R) OF

ANNUAL ARRIVALS AND ANNUAL MEAN PRICES OF BLACK


FROM 2001-02 TO 2011-12.

Growth rates (r)Items Linear Compound

R2 r (%) 't' calcul- R2 r (%) 't' calcul-ated ated

Arrivals 0.16 -5.53 -1.29 0.20 -8.76 -1.48NS NS

Price 0.76 22.10 5.40 0.78 12.05 5.63** **

** = Significant at 1 per cent level of probability.NS = Non significant.

2.7 Relationship between Arrivals and Prices ofBlack gram

To examine the relationship held between annual arrivalsand annual mean prices as well as monthly arrivals and

February, 2016 37

monthly prices of black gram during 11 years, thecorrelation coefficients 'r' as measures of marketingefficiency were calculated and are given in table-8.

TABLE : 8 COEFFICIENTS OF CORRELATION BETWEEN

ARRIVALS AND PRICES OF BLACK GRAM IN APMC, BARSHI

DURING THE PERIOD FROM 2001-02 TO 2011-2012.

(N=11)

Sr. no. Month Correlation coefficients (r)

1 Oct. -0.178NS

2 Nov. -0.402 NS

3 Dec. -0.681*

4 Jan. -0.645*

5 Feb. 0.011NS

6 Mar. -0.299 NS

7 Apr. 0.258 NS

8 May 0.18 NS

9 June -0.148 NS

10 July -0.46 NS

11 Aug. -0.411 NS

12 Sept. -0.413 NS

13 Annual -0.447 NS

* = Significant at 10 per cent level.NS = Non significant.

It was observed from the above table that thecorrelation coefficients were negative for the marketarrivals and prices of black gram in the months of March,June to January. Similarly, the negative correlationcoefficient was also observed in the case of annual arrivaland annual mean prices. This indicated that there existsan inverse relationship between arrivals and prices. Thearrivals and prices of black gram moved in oppositedirection. The correlation coefficients were noticedpositive for the market arrivals and prices of black gramin the months of February, April and May. It showed thatthere lies direct relationship between arrivals and prices.The arrivals are directly correlated to prices. The arrivalsand prices of black gram moved in the same direction,thus, hold a straight way relationship between them. The't'- test indicated that the correlation coefficients for themarket arrivals and prices in the months of February toNovember and the coefficient of correlation betweenannual arrivals and annual mean prices of black gramwere statistically non significant except the months ofDecember and January. The coefficients of correlationbetween arrivals and prices for the months of Decemberand January were noted to be significant at 10 per centlevel of probability. Moreover, these correlation

coefficients including the coefficient of correlation in thecase of December and January were negative.

The hypothesis proposed in the chapter entitled'Introduction' that the arrivals and prices of pulses areinversely correlated has been accepted and proved by suchtype of above cases. The coefficients of correlationbetween market arrivals and prices of black gram for themonths of February, April and May were positive, but werestatistically non significant except May in the case of greenblack gram which are statistically significant. Such typeof cases rejected and disproved to some extent thehypothesis stated earlier and stood as an exception.

The studies confined to correlation analyses betweenmarket arrivals and prices of black gram by Gangawar etal. (1983), and Waykar (1997) resemble with the analysisbrought out in the case of pulses and put it here under thissub head.

Conclusions

1. The study pointed out that the seasonal fluctuationsin monthly arrivals and prices of black gram werenot uniform over a year and throughout the timeseries.

2. It can be concluded from the seasonal indicesanalysis of arrivals and prices of black gram that"when bulk of the produce reaches in the market,prices reach at their lowest level".

3. It is apparent that the prices of black gram at Barshimarket have been increasing rapidly over the timespan of the years ending 2011-12 and at a faster rate.The market arrivals of black gram in Barshi couldnot increase at the similar rate by which the pricesof black gram increased during this period, wherethey otherwise could show a casual increase.

4. The arrivals in Barshi market are inversely correlatedto prices. The fluctuations in prices were unrelatedwith the arrivals.

R E F E R E N C E S

Andhalkar, G.K., D.H. Ulemale, N.P. Tayde and S.U.Mokhale (2010). Arrival and Prices of major pulses inselected A.P.M.C'S. in Amravati District. International Res.Journal of Agril. Econ. And Stat. 2(1)126.

Basavaraja, H. (1993). Behaviour of price andmarket arrivals of major crops in Bijapur. Indian Journalof Agricultural Marketing 7(2): 149-155.

Chahal, S.S., R. Sinala and P. Kataria (2004).Marketing efficiency and price behaviour of green peasin Punjab. Indian Journal of Agricultural Marketing18(1):115-128.


Gangawar, A.C. and Ajit Yadav.(1986). Economicanalysis of pulses (Black gram) in Haryana state.Publication of Deptt. of Agril.Econ. HAU, Hissar.Research Bulletin: 223-225.

Goswami, S. N., A. N. Choudhary and B. K. Sharma.(1996). Growth trend of oilseed and pulses in India.Agricultural Situation in India. 52(6):191-193.

Kumar, H., Devaraj and Shivkumar, (2005). Trendsand decomposition analysis of pigeon pea in India.Agricultural Situation in India, LXII (8):563-566.

Mamledesai, N.R. (1987). An economic analysis ofmarketing of tur in Gulbarga district of Karnataka state.Mysore J. Agril. Sci. 21(1):1-134.

Ravikumar, K. N., K. Sreelakshmi and V. T. Raju(2001): Trends in arrivals and prices of selectedcommodities in Anakapalle regulated market of A.P.Agricultural Marketing, 43(4): 26-34.

Waykar, K.R. (1997). Economics of tur processingby mills in Barshi, District Solapur. AgriculturalEconomics Research Review 11(1):88-90.

February, 2016 39

Backdrop: Cereals, particularly rice and wheat dominatethe cropping pattern in Punjab. This has been madepossible through the technological breakthrough in wheatand rice crops along with higher use of fertilizers and HYVseeds and assured price policy for these crops in the state.There has been continuous increase in area under paddyand wheat in the state which is also causing a seriousdamage to the natural resource base. Paddy, in particular,a water-intensive crop, is blamed for water-table depletionin tube-well irrigated areas and water-logging in canalirrigated areas. Despite huge contribution of ground waterin agricultural growth, it is heading for crisis and needsurgent attention. Due to unregulated use and heavysubsidies on power, there has been a tendency of excesswithdrawal of this precious resource. The monoculture ofpaddy-wheat has also resulted into increasing incidenceof nutrient deficiency in the soils, including micronutrientsand insect-pest attacks on the crops, are also posing majorthreats to productivity, food grain production andsustainability of agriculture in the long run. The PunjabGovernment realized the problems of the rice and wheatmonoculture, as early as in early eighties, and thisprompted the state government to appoint variouscommittees/initiatives for diversification of agriculture inthe state. But the area under paddy in Punjab increasedcontinuously from 228 thousand hectare in 1960-61 to2845 thousand hectare in the year 2012-13. This increasewas at the expense of area under maize, other coarsecereals, oilseed and pulses.

Objectives of the Study

(a) To examine the production and procurement patternof paddy in Punjab

(b) To workout the relative economics of cultivation ofpaddy vis-a-vis competing/alternative crops

(c) To bring out the constraints in adoption of alternativecrops

(d) To suggest policy measures to overcome theconstraint in adoption of alternative crops of paddyin Punjab

Methodology

The study was conducted in the Punjab state. In the presentstudy, the important crops competing with paddy during

kharif season viz., basmati-paddy, maize, cotton, guara andsugarcane were selected for the in-depth analysis. Amongstdifferent districts of the state, six districts with the highestarea/production of crops/highest diversification in the state(depending upon the coverage of the crops to be selected)were taken purposively. Amongst the selected district, oneblock was selected randomly from each district. From eachblock, a cluster of 3 to 5 villages was randomly chosen.Finally, a sample of 35 farmers was selected randomlyfrom each selected cluster spreading over various farmsize categories, viz. small (less than two hectares), medium(2-10 hectares) and large (more than 10 hectares) basedon the size of the operational holding, making a totalsample of 210 farmers. The primary data collection wasdone by the personal interview method for the referenceyear 2012-13. The secondary data on the area, production,productivity and procurement of the important crops ofPunjab state was collected from the various publishedsources.

Production Status for Major Kharif Corps in the State

The area under paddy has increased ten folds during lastfive decades by replacing crops like cotton, kharif pulses,maize, jowar,bajra and kharif oilseeds. Similarly, theproduction of rice during this period increased by abouttwelve times from 0.92 million tonnes to 10.5 milliontonnes i.e., at CAGR 5.94 percent per annum (Table 1).The productivity of paddy was consistently improving overthe years but the growth has slowed down since 1980s(Table 2).The area under cotton in 1970-71 was about 7percent of gross cropped area went down to 5.97 percentin 2011-12 (Table 3). The proportionate area under maizekept on declining since 1970-71 from 9.77 percent to 1.69percent in 2011-12. Area under sugarcane and potato hasnot remained stable. Respective share of pulses andoilseeds in GCA has recorded a sharp decline from 7.29and 5.20 percent in 1971-72 to 0.20 and 0.63 percent in2011-12. The production of pulses and oilseeds also wentdown drastically over this period and that of sugarcanewith some variations remained almost same. The reasonof decline of production of these crops was the drasticdecline of area under crops due to encroachment by paddyand wheat. It can be concluded that imbalance in favourof two main cereals viz. rice and wheat in the croppingpattern has further sharpened despite all efforts ofdiversification in the state agriculture.

Agro-Economic Research

Possibilities and Constraints in Adoption of Alternative Crops to Paddy in Punjab

D.K. GROVER, SANJAY KUMAR, J M SINGH AND JASDEV SINGH*

*A.E.R.C., Department of Economics and Sociology,Punjab Agricultural University, Ludhiana.


TABLE : 1 TRENDS IN AREA, PRODUCTION AND YIELD FOR MAJOR KHARIF CROPS IN PUNJAB, 1970-71 TO 2011-12

Crop TE-1970-71 TE 1985-86 TE 2000-01 TE 2011-12

Rice Area 365 1613 2578 2815

Production 564 5024 8604 10866

Yield 1540 3112 3355 3860

Maize Area 500 286 161 133

Production 755 509 411 489

Yield 1512 1772 2552 3696

Bajra Area 204 45 5 3

Production 228 48 5 3

Yield 1116 1044 894 1148

Total kharif Area 1069 1944 2744 2951

cereals Production 1547 5581 9020 11358

Cotton Area 399 560 504 503

Production 135 190 156 309

Yield 337 353 317 615

Sugarcane Area 145 80 111 70

Production 554 517 689 418

Yield 3851 6426 6214 5986

Mash Area 26 13 4 3

Production 12 7 2 1

Yield 466 561 475 481

Arhar Area - 41 9 4

Production - 42 7 4

Yield - 1022 780 935

Moong Area 5 38 37 7


Yield 512 840 608 867

Total Kharif Area 30 92 50 14

Pulses Production 14 81 31 12

Groundnut Area 194 49 5 2


Yield 916 893 874 1594

Sesamum Area 12 14 16 6

Production 5 5 6 2

Yield 373 381 365 364

Total Kharif Area 206 63 21 8

oilseeds Production 182 49 10 6

Total kharif Area 1099 2036 2794 2965

foodgrains Production 1562 5662 9051 11370

Note: TE indicates Triennium Ending AverageArea: 000'000' ha; Production: metric tonnes and Yield: Kg/ha

February, 2016 41

TABLE : 2 PER CENT CHANGE IN AREA, PRODUCTION AND YIELD OF MAJOR KHARIF CROPS IN PUNJAB, 1970, 1970-71 TO

2011-12

Crop Period I Period II Period III Overall1970-71 to 1985-96 to 2000-01 to 1970-71 to

1985-86 2000-01 2011-12 2011-12

Rice Area 441.92 159.83 109.19 771.23

Production 890.78 171.26 126.29 1926.60

Yield 202.08 107.81 115.05 250.65

Maize Area 57.20 56.29 82.61 26.60

Production 67.42 80.75 118.98 64.77

Yield 117.20 144.02 144.83 244.44

Bajra Area 22.06 11.11 60.00 1.47

Production 21.05 10.42 60.00 1.32

Yield 93.55 85.63 128.41 102.87

Total kharif Area 181.85 141.15 107.54 276.05

cereals Production 360.76 161.62 125.92 734.20

Cotton Area 140.35 90.00 99.80 126.07

Production 140.74 82.11 198.08 228.89

Yield 104.75 89.80 194.01 182.49

Sugarcane Area 55.17 138.75 63.06 48.28

Production 93.32 133.27 60.67 75.45

Yield 166.87 96.70 96.33 155.44

Mash Area 50.00 30.77 75.00 11.54

Production 58.33 28.57 50.00 8.33

Yield 120.39 84.67 101.26 103.22

Arhar Area NA 21.95 44.44 NA

Production NA 16.67 57.14 NA

Yield NA 76.32 119.87 NA

Moong Area 760.00 97.37 18.92 140.00

Production 1600.00 68.75 27.27 300.00

Yield 164.06 72.38 142.60 169.34


pulses Production 578.57 38.27 38.71 85.71

Groundnut Area 25.26 10.20 40.00 1.03

Production 24.16 11.63 60.00 1.69

Yield 97.49 97.87 182.38 174.02

Sesamum Area 116.67 114.29 37.50 50.00

Production 100.00 120.00 33.33 40.00

Yield 102.14 95.80 99.73 97.59


oilseeds Production 26.92 20.41 60.00 3.30


foodgrains Production 362.48 159.86 125.62 727.91


TAB

LE :

3C

OM

POU

ND

AN

NU

AL

GR

OW

TH R

ATES

(CA

GR) F

OR

AR

EA, P

RO

DU

CTI

ON

AN

DY

IELD

FO

R M

AJO

R K

HA

RIF

CR

OPS

IN P

UN

JAB, 1

970-

71TO

201

1-12 (Per

cent

/ann

um)

Perio

d I

Perio

d II

Perio

d III

Ove

rall

Cro

p19

70-7

1 to

198

4-85

1985

-86

to 1

999-

2000

2000

-01

to 2

011-

1219

70-7

1 to

201

1-12

AP

YA

PY

AP

YA

PY

Ric

e11

.36*

**15

.65*

**3.

84**

*2.

85**

*3.

25**

*0.

381.

01**

*2.

01**

*0.

99**

4.53

***

6.07

***

1.48

***

Mai

ze-5

.11*

**-3

.43*

**1.

77**

-3.6

5***

-0.9

32.

82**

*-1

.96*

**2.

14*

4.18

***

-3.8

1***

-1.6

3***

2.27

***

Baj

ra-1

0.31

***

-9.6

0***

0.79

-10.

37**

*-1

0.40

***

-0.4

0-8

.94*

**-6

.91*

**2.

10*

-9.9

6***

-10.

08**

*-0

.11

Tota

l kha

rif4.

10**

*9.

22**

*-

2.17

***

2.99

***

-0.

84**

*2.

01**

*-

2.45

***

4.58

***

-ce

real

s

Cot

ton

2.45

***

0.62

-1.7

8*-0

.35

-4.9

4*-4

.61*

*0.

404.

75*

4.34

*-0

.10

1.41

***

1.52

***

Suga

rcan

e-2

.54*

**0.

302.

91**

*2.

11*

1.94

*-0

.15

-6.3

1***

-6.6

8***

-0.3

9-0

280.

240.

52**

*

Mas

h-5

.42*

**-5

.21*

**0.

22-7

.17*

**-8

.69*

**-0

1.64

-4.9

1***

-4.7

4***

0.17

-6.1

1***

-6.1

4***

-0.0

4

Arh

ar25

.23*

**33

.83*

**6.

86**

*-9

.63

-10.

69**

*-1

.18

-8.0

8***

-8.1

1***

0.77

*0.

211.

651.

43**

*

Moo

ng16

.05*

**19

.97*

**3.

36**

*0.

15-1

.09

1.24

-11.

77**

*-8

.47*

**3.

75**

*2.

22*

2.98

*0.

74**

*

Tota

l kha

rif6.

87**

*11

.87*

**-

-3.1

4***

-4.6

1***

--9

.90*

**-8

.25*

**-

-1.6

9**

-0.6

5-

puls

es

Gro

undn

ut-8

.63*

**-9

.30*

**-0

.76-

-13.

02**

*-1

2.92

***

0.12

-7.2

7***

-2.6

0*6.

51**

*11

.63*

**-1

1.27

***

0.28

Sesa

mum

-1.6

4-2

.49*

-0.8

71.

020.

06-0

.96

-10.

99**

*-1

1.37

-0.4

4-1

.62*

**-1

.87

-0.2

5***

Tota

l kha

rif-7

.70*

**-8

.90*

**-

-6.3

8***

-9.5

1***

--9

.87*

**-6

.79*

**-

-7.7

9***

-9.3

0***

-oi

lsee

ds

Tota

l kha

rif4.

19**

*9.

25**

*-

2.00

***

2.93

***

-0.

74**

*1.

99**

*-

2.38

***

4.55

***

-fo

odgr

ains

***,

**

and

* si

gnifi

cant

at o

ne,fi

ve a

nd te

n pe

rcen

t lev

el o

f sig

nific

ance

Not

e:A

: Are

a, P

: Pro

duct

ion

and

Y: Y

ield

February, 2016 43

TABLE 4: PROCUREMENT OF MAJOR CROPS BY DIFFERENT AGENCIES IN PUNJAB, 1971-72 TO 2011-12)

(Thousand tonnes)

Agency 1981-82 1991-92 2001-02 2011-12

Paddy

State government 301.70 458.0 1254.0 3375.0(5.84) (5.80) (11.33) (28.30)

FCI 1688.50 1507.0 2387.0 182.0(32.68) 19.07) (21.57) (1.53)

Markfed 258.50 575.0 1798.0 2597.0(5.0) ( 7.28) ( 16.25) (21.78)

PUNSUP 161.90 585.0 1821.0 2837.0(3.13) (7.40) (16.46) (23.79)

PSWC - - 1544.0 1303.0(13.95) (10.93)

Traders 2755.40 4777.0 1632.0 483.0(53.35) (60.45) (14.75) (4.05)

PAIC - - 630.0 1149.0(5.69) (9.62)

Total 5166.00 7902.0 11066.0 11926.0(100.0) (100.0) (100.0) (100.0)

Wheat

State government 1362.0 790.0 1075.0 1760.0(36.16) (12.41) (10.16) 15.87)

FCI 952.0 1981.0 3069.0 1664.0(25.28) (31.11) (29.01) (15.0)

Markfed 747.0 1357.0 2210.0 2623.0(19.84) (21.31) (20.89) (23.65)

PUNSUP 705.0 1415.0 1919.0 2492.0(18.72) (22.22) (18.14) (22.46)

PSWC - - 1250.0 1284.0(11.82) (11.57)

Traders - 825.0 12.0 37.0(12.95) (0.11) 0.33)

PAIC - - 1044.0 1234.0(9.87) (11.12)

Total 3766.0 6368.0 10579.0 11094.0(100.0) (100.0) (100.0) (100.0)

Note: Figures in parentheses indicate percentage to total in each column.


In the initial years, traders were found to procurelarge volumes of paddy but their share in total procurementin the state declined over the years, from 53 per cent in1981-82 to 4 per cent in 2011-12 (Table 4). FoodCorporation of India (FCI) had also a large share in totalquantity procured but the volume of procurement variedfrom year to year and came at the lowest ebb in 2011-12(around 2 per cent of the total paddy procured in the state).In case of wheat, the quantity procured by StateGovernment has declined from 36 per cent in 1981-82 to16 per cent in 2011-12. The Food Corporation of India(FCI) procured as high as 31 per cent in 1991-92. TheMarkfed, Punsup and Punjab State WarehousingCorporation were also found to procure good proportionsof wheat in the Punjab State, which were 24, 22 and 12per cent respectively in 2011-12.

Socio-Economic Characteristics of Growers

Most of the heads of the household were in the age groupof 31 to 50 years in different farm size categories exceptfor the small farm size group, where most of the heads ofthe household were in the age group of above 50 years.Literacy rate in case of head of the family was about 78per cent to 84 per cent across various farm size categories.The family size of the sample households was found to bethe lowest for the small farms (6), as compared to 7 forthe other farm size categories. Most of the family members(about 35%) were in the age group of 35 to 60 years.Agriculture was found to be the main occupation of about99 per cent of the sample heads of the family. The customof keeping permanent farm labour was found to increasewith the increasing farm size. Only one of the small farmwas found to keep permanent farm labour, while most ofthe large farms were keeping permanent farm labour.Almost all the area had the irrigation facilities highlightingwell developed irrigation infrastructure in the study region.The average operational holding size of sample householdswas 7.97 hectares. The level of leased in land (3.73 hectares)was much higher than the leased out land (0.05 hectares)among the sample respondents. The average operationalland was observed to vary from 1.66 hectares for smallfarms to 16.72 hectares for large farm categories. Theaverage sample household was found to possess assetsworth Rs. 3 lakh and the asset value was found to increasewith the increasing farm size. The average samplehousehold was found to possess 0.79 tractors and less thanone electric motor (0.80).Paddy and wheat were the major,kharif and rabi crops in the study area grown on about 47and 79 per cent area respectively. Sugarcane, basmati,maize, cotton and guara were the other important cropsgrown during the kharif Season occupying about 15,15,8,7and 4 percent of the net cropped area during the KharifSeason.The farmers revealed that basmati-paddy,sugarcane, maize,cotton, groundnut and guara were thepotential alternative crops to paddy during the kharifseason. Fodder is grown in the kharif and rabi seasons in

the state and occupied about 4 per cent of the net croppedarea during these seasons. Wheat was the major rabi seasoncrop in the study area. Potato,field pea and rapeseed andmustard were the other important crops of the season whichoccupied about 4,3 and one per cent area of the net croppedarea, respectively. Maize was the summer fodder cropgrown on less than one per cent of the net cropped area.The proportion of net quantity sold to the total productionfor paddy, basmati- paddy,maize and sugarcane was 99.79,96.89, 96.41 and 88.92 Per cent respectively (Table 5).The whole quantity of guara and sugarcane produced wasfound to be disposed of by the sample farmers as nothingwas kept for home consumption, seed for next year andfor feed purposes. As there is assured marketing of paddyin the state, the government agency (FCI) was purchasingthe paddy at minimum support price (MSP) from thefarmers in the state. Unlike the fine varieties of paddy,there is no provision of procurement by the governmentagency in case of other competing crops.

Economics of Production for Paddy vis-a-vis CompetingCrops

The input use pattern for the cultivation of paddy cropshowed that on per hectare basis, about 40 man days wererequired for carrying out the various operations likesowing, transplanting, fertiliser/insecticide application,irrigation, harvesting etc. Transplanting is the labourintensive operation in paddy crop (Table 6). The paddycrop also required about 13 machine labour hoursparticularly for field preparation and harvesting of cropthrough combine harvesters. The paddy growers werefound to use about 15 Kg of seed per hectare.Being highlywater intensive crop, paddy required about 155 hours forthe irrigation at different stages of paddy production. Forbasmati-paddy, the labour requirement was more thanpaddy as manual harvesting of crop was more popular forbasmati-paddy. The basmati-paddy crop also requiredabout 10 machine labour hours particularly for fieldpreparation. The basmati-paddy growers were found touse about 13 Kg of seed per hectare,which is lower ascompared to the recommended level of 20 Kg/hectare.Basmati-paddy required about 112 hours for theirrigation at different stages of its production,which werelower as compared to the requirement for fine varieties ofpaddy. For maize,on per hectare basis,about 55 man daysand about 12 machine labour hours were required forcarrying out different inter culture operations. The maizegrowers were found to use about 24 Kg of seed per hectare.The maize crop required about 34 hours for the irrigationat different stages of its production,which was only onefifth as required for paddy cultivation. The labourrequirement for cotton was more than other competingcrops during kharif season as more labour was requiredfor harvesting of crop which is done manually. The cottoncorp required about 78 hours for the irrigation at differentstages of its production, which were lower as comparedto the requirement for paddy. Guara, on per hectare basis,

February, 2016 45

TABLE 5: PRODUCTION, CROP RETENTION AND DISPOSAL PATTEREN OF PADDY VIS-A-VIS COMPETING CROPS, SAMPLE HOUSEHOLDS

(Quantity in Qtl/farm)

Crop Paddy Basmatipaddy Maize Cotton Guara Sugarcane

Production 255.10 66.76 66.86 46.85 17.23 2231.63(100.00) (100.00) (100.00) (100.00) (100.00) (100.00)

Retention for

Self consumption 0.06 2.0 1.44 - - 3.92(0.02) (3.00) (2.15) (0.18)

Seed 0.07 0.05 - - - 243.35(0.03) (0.07) (10.90)

Feed 0.16 0.02 0.94 - - -(0.06) (0.03) (1.41)

Payments in kind 0.02 0.01 0.02 - - -(0.01) (0.01) (0.03)

Total retention 0.31 2.08 2.40 - - 247.27(0.12) (3.11) (3.59) (11.08)

Total qty. sold 254.79 64.68 64.46 46.85 17.23 1984.36(99.88) (96.89) (96.41) (100.0) (100.0) (88.92)

Disposal pattern:sold to govt. agency

Quantity 254.79 64.68 64.46 46.46 17.23 1984.36

Price (Rs/quintal) 1280.0 3159.0 860.0 4150.0 5525.0 270.0

Note: figures in parentheses indicate the percentages to total production.

TABLE 6 : INPUT USE PATTERN FOR CULTIVATION OF PADDY VIS-A-VIS COMPETING CROPS, SAMPLE HOUSEHOLDS

(Per hectare)

Crop Unit Unit Paddy Basmati- Maize Cotton Guara Sugarcanepaddy

1. Human Labour Man days

(i) Hired 24.02 41.90 44.42 64.58 14.02 124.10

(ii) Family 14.70 12.55 10.32 17.24 6.19 17.76

2. Machine labour Hours 13.25 10.51 12.29 15.54 9.78 21.41

3. Seed Kg. 15.23 12.82 23.99 4.06 14.32 74.01

4. FYMQuintals - 7.08 4.28 12.35 - 12.20

5. Fertilizer

i. Urea 283.05 166.68 294.09 248.48 12.94 488.60

ii. CAN - - - - - -

iii. DAP KG. 116.03 76.64 135.26 118.56 1.47 227.70

iv. MOP 5.00 9.16 11.97 4.45 - 59.43

v. Zinc sulphate 9.27 7.98 3.07 - - 8.65

vi. Ferrous sulphate 3.15 3.94 - - - 3.06

6. Plant protection Rs. 2915.00 3287.00 1769.00 5147.00 360 4239.00measures

7. Irrigation Hours 155.17 112.47 34.35 77.51 6.35 115.29


required about 20 man days and about 10 machine labourhours. The guara crop required only about 6 hours for theirrigation at different stages of its production, which wasless than 5 per cent as required for paddy cultivation andthe crop can even be grown in rainfed areas. The inputuse pattern for the cultivation of sugarcane shows thatabout 142 man days per hectare were required for carryingout the various operations which was more than othercompeting crops during kharif season,as more labour wasrequired for harvesting of crop which is done manually.The sugarcane crop required about 115 hours for theirrigation at different stages of its production which werelower as compared to the requirement for paddy.

The total variable cost on per hectare basis for paddycrop was found to vary between Rs. 26475 for mediumfarms to Rs. 29590 for the small farms. On overall basis,the total variable cost on per hectare basis was found tobe Rs. 27465. Amongst variable cost components, the shareof human labour was about 45 per cent. Expenses onmachine labour, fertilisers and seed were the otherimportant components of the variable cost. The totalvariable cost on per hectare basis for basmati-paddy cropwas found to vary between Rs. 29707 for small farms toRs. 25615 for the medium farms which is due to the highlevel of cost of machine labour incurred by small farmswhich were mostly using the hired machinery for carryingout the harvesting and other operations during Basmati-paddy production. On overall basis, the total variable coston per hectare basis was found to be Rs. 26704. Humanlabour was found to take larger proportion of the cost asits share was about 56 per cent. Most of the labour isrequired during the transplantation and harvesting of thecrop. Expenses on machine labour, fertilisers and plantprotection measures were the other important componentsof the variable cost and the expenses on these were 12 to13 per cent of the total variable cost. The total variablecost on per hectare basis for maize was found to varybetween Rs .33186 for small farms to Rs. 31602 for thelarge farms which is due to the highest level of costmachine labour incurred by small farms. On overall basis,the total variable cost on per hectare basis was found tobe Rs. 32179. About 50 per cent of the operational costwas incurred on human labour, most of which is requiredduring the inter culture and harvesting of the crop.Expenses on fertilisers, seed and machine labour were theother important components of the variable cost and theexpenses on these were about 16,14 and 12 per cent of thetotal variable cost respectively. The total variable cost onper hectare basis for cotton crop was found to vary betweenRs. 37766 for small farms to Rs. 36132 for the mediumfarms. On overall basis, the total variable cost on perhectare basis was found to be Rs. 36523. Amongst variablecost components, the share of human labour was about 41per cent. Expenses on seed, plant protection measures,fertilizers and machine labor were the other importantcomponents of the variable cost. The total variable coston per hectare basis for guara crop was found to vary

between Rs. 9101 for small farms to Rs. 10801 for themedium farms which is due to the high level of costmachine labour incurred by small farms which were mostlyusing the hired machinery for carrying out the harvestingand other operations during guara production. On overallbasis, the total variable cost on per hectare basis was foundto be Rs. 9943. Human labour was found to take largerproportion of the cost as its share was about 54 per cent.Expenses on machine labour, seed and plant protectionmeasures were the other important components of thevariable cost and the expenses on these were about 24, 16and 4 per cent of the total variable cost respectively. Thetotal variable cost on per hectare basis for sugarcane wasfound to vary between Rs. 72214 for small farms to Rs81050 for the large farms which is due to the highest levelof machine labour incurred by small farms. On overallbasis, the total variable cost on per hectare basis was foundto be Rs. 80890. About 48 per cent of the operational costwas incurred on human labour, most of which is requiredduring the inter culture and harvesting of the crop.Expenses on seed, fertilizers and machine labour were theother important components of the variable cost and theexpenses on there were about 24, 12 and 7 per cent of thetotal variable cost respectively.

The results showed that the returns over variablecost fetched from basmati-paddy were the highest on perhectare basis (Rs. 122276) even more than the fine variableof paddy (Rs. 60113), which was mainly due the higheraverage price of basmati-paddy (Rs. 3673/q) fetchedduring the reference year under the study (Table 7).Otherwise, the last year average price received by thefarmers was about Rs. 2800/q. The production scenarioof basmati-paddy in the state has significantly improvedin the recent years due to the adoption of Pusa 1509 andPusa 1121 varietise in the state, which have becomepopular in the state due to their better yield. It was followedby guara, cotton and maize as the returns over variablecost on per hectare basis for these crop were Rs. 57075,Rs. 50407 and Rs. 13602 respectively . The returns overvariable cost on per hertare basis were the least for maizecrop as the average price realized by the farmers was onlyRs. 860/q, which was only about 70 per cent as comparedto the MSP announced by the Government (Rs.1310/q).The returns over variable cost for maize could be increasedthrough the Government procurment at MSP.The MSPcoupled with effective procurement can go a long way inmaking maize cultivation remunerative in the state. Theguara growers got handsome returns over variable costdue to remunerative prices in the market. In the long runthe yield of the crop would have to be increased to makeguara crop remunerative. Cotton crop requires specificagro-climatic conditions, which are prevalent only in theSouth western region of the state. In terms of benefitcost analysis, the ratio was the highest for guara (6.73),which may be due to the lowest variable be cost incurredduring production of the crop.

February, 2016 47

TABLE 7: ECONOMICS OF PADDY VIS-A-VIS COMPETING CROPS, SAMPEL HOUSEHOLDS

(Rs./ha.)

Crop Small Medium Large Overall

PaddyYield (qtl/ha) 66.42 68.84 68.45 68.42Price(Rs/qtl) 1280 1280 1280 1280Gross returns 85018 88115 87616 87578(main product+by-product)total Variable cost 29590 26475 26898 27465Returns over variable cost 55428 61640 60718 60113Benefits cost ratio 2.87 3.33 3.25 3.19Basmati-paddyYield (qtl/ha) 42.36 40.15 40.52 40.56Price(Rs/qtl) 3565 3669 3737 3673Gross returns 151013 147310 151423 148980(main product+by-product)total Variable cost 29707 25615 26189 26704Returns over variable cost 121306 121695 125234 122276Benefits cost ratio 5.08 5.75 5.78 5.58MaizeYield (qtl/ha) 46.62 50.97 50.33 50.08Price(Rs/qtl) 865 864 856 860Gross returns 42908 46926 45728 45781(main product+by-product)total Variable cost 33186 31766 31602 32179Returns over variable cost 9722 15160 14126 13602Benefits cost ratio 1.25 1.47 1.45 1.33CottonYield (qtl/ha) 17.52 20.49 20.78 20.46Price(Rs/qtl) 4232 4060 4200 4150Gross returns 76132 85244 89294 86930(main product+by-product)total Variable cost 37766 36132 36813 36523Returns over variable cost 38366 49112 52481 50407Benefits cost ratio 2.01 2.36 2.43 2.38GuaraYield (qtl/ha) 12.40 11.45 12.53 12.13Price(Rs/qtl) 5157 5274 5755 5525Gross returns 63947 60387 72110 67018(main product+by-product)total Variable cost 9101 10801 9561 9943Returns over variable cost 54846 49586 62549 57075Benefits cost ratio 6.93 5.60 7.52 6.73SugarcaneYield (qtl/ha) 617.50 759.59 693.85 706.26Price(Rs/qtl) 270 270 270 270Gross returns 170430 211583 192811 196303(main product+by-product)total Variable cost 72214 80943 81050 80891Returns over variable cost 98216 130648 111761 115412Benefits cost ratio 2.36 2.61 2.38 2.43

Note: The economics of sugarcane, being the annual crops, cannot be compared with other kharif crops and only the benefit cost analysis has beenutilised for the purpose.


Human labour was found be significant variableaffecting the productivity of cotton and sugarcane crops,which are still dependent upon the manual labour for carryingout the various operations, particularly during harvesting ofthe crop and the use of machine labour is the least forthese crops (Table 8). The coefficient of machine labourwas observed to be negative and significant in basmati-paddy. It shows that there was excessive use of machinelabour and its use needs to be curtailed. The use of DAPFertilizer was found to be significant variable affectingthe productivity of maize, cotton and sugarcane crops,which was negative for cotton indicating excessive use in

the crops. The coefficient of expenses on Ferrous Sulphatewas negative and significant for paddy productionindicating excessive use of the input. The regressioncoefficient for expenditure on plant protection measureswas positive and significant for paddy showing that thevariable play an important role in paddy production. Thecoefficient of irrigation hours was found to be positiveand significant for basmati-paddy while it was observedto be negative and significant for fine varieties for paddy.Positive significant sign for irrigation coefficient indictedthat irrigation hours can still be increased to increase theproductivity of basmati-paddy, while it needs to be curtailed

TABLE 8: ESTIMATED YIELD FUNCTION FOR PADDY VIS-A-VIS COMPETING CROPS, SAMPLE HOUSEHOLDS

Particular Paddy Basmatipaddy Maize Cotton Guara Sugarcane

Intercept 3.46** 3.52** 3.15** 1.31 2.96** 4.17**(0.32) (0.42) (0.63) (1.90) (0.71) (1.05)

Human labour 0.095 –0.11 –0.075 0.55** –0.22 0.26*(mandays/ha) (0.041) (0.080) (0.178) (0.16) (0.20) (0.13)

Machine 0.16** –0.067* 0.22 –0.40 –0.027 –0.0063labour (hrs/ha) (0.04) (0.042) (0.12) (0.18) (0.160) (0.0917)

Seed (kg/ha) –0.046 0.0060 0.29 –0.14 –0.015 0.020(0.044) (0.0069) (0.20) (0.19) (0.130) (0.107)

Urea (kg/ha) 0.038 0.0038 -0.019 0.22 0.0016 -0.031*(0.040) (0.0086) (0.012) (0.14) (0.0085) (0.171)

DAP (kg/ha) –0.00048 –0.00050 0.014* –0.018* –0.021 0.13*(0.00178) (0.00173) (0.006) (0.0085) (0.016) (0.08)

MOP (kg/ha) 0.0043 0.0031 0.013** –0.0085 — 0.0021(0.0019) (0.00235) (0.004) (0.0065) (0.0036)

Zinc –0.0011 –0.000062 –0.00032 — — 0.0028sulphate (kg/ha) (0.00123) (0.00194) (0.0047) (0.0042)

Ferrous –0.0047* 0.00085 — — — –0.0085sulphate (kg/ha) (0.0018) (0.00202) (0.0076)

Plant protection 0.060** 0.030 –0.031 –0.025 –0.00092 –0.0069expenses (Rs/ha) (0.018) (0.034) (0.073) (0.077) (0.00457) (0.0862)

Irrigations –0.12* 0.12* –0.0078 0.068 0.00092 0.12(hrs/ha) (0.043) (0.058) (0.0064) (0.133) (0.0067) (0.12)

R2 0.23* 0.13* 0.24* 0.74* 0.11 0.27(5.95) (2.06) (3.13) (14.21) (0.62) (1.19)

Note: Figures in parentheses indicate standard error** and * shows significant at one and five percent level of probability, respectively

for five varieties of paddy (Table 9). The regression analysisfor guara crop showed that none of the independent variablewas found to be significantly affecting the yield. As thecrop is neglected in terms of the research and extensioninitiatives, therefore the high yielding varieties responsiveto the input use were not available. The value of R2 variedbetween 0.74 to 0.11 which was the highest for cotton andthe lowest for guara.

Constraint Analysis for Various Alternative Crops

Adaptation to the soil/climate and the attractive price ofthe basmati-paddy,. cotton and guara has been the majorreasons for attraction of the farmers to the crop. Some ofthe maize, guara and sugarcane growers preferred the cropas it fits well in the cropping pattern.

February, 2016 49

TABLE 9: THE MARGINAL VALUE PRODUCT (MVP) AND MARGINAL FACTOR COST (MFC) OF IMPORTANT INPUTS FOR PADDY

VIS-A-VIS COMPETING CROPS, SAMPLE HOUSEHOLDS

Particular Paddy Basmatipaddy Maize Cotton Guara Sugarcane

Human labour

MVP (Rs.) 214.87 –258.85 –59.10 570.76** –729.54 349.50*

MFC (Rs.) 250.0 250.0 250.0 250.0 250.0 250.0

MVP : MFC 0.86 –1.03 –0.24 2.28 –2.92 1.40

Machine labour

MVP (Rs.) 1057.54** –816.07* 770.96 –218.56 –185.02 –56.11

MFC (Rs.) 415.0 313.0 320.0 220.0 248.0 256.0

MVP : MFC 2.55 –2.61 2.41 –1.00 –0.75 –0.22

Seed

MVP (Rs.) –264.52 59.97 529.63* –2927.90 –70.20 51.33

MFC (Rs.) 37.50 75.0 150.0 2000.0 110.0 275.0

MVP : MFC –7.05 0.80 3.47 –1.46 –0.64 0.19

Urea

MVP (Rs.) 11.76 29.92 –2.78 75.18 8.29 –12.10

MFC (Rs.) 5.40 5.40 5.40 5.40 5.40 5.40

MVP : MFC 2.18 5.54 –0.51 13.92 1.54 –2.24

DAP

MVP (Rs.) –0.36 –0.84 4.46* –12.89* –957.40 108.87*

MFC (Rs.) 24.0 24.0 24.0 24.0 24.0 24.0

MVP : MFC –0.15 0.035 0.19 –0.54 –39.89 4.54

MOP

MVP (Rs.) 75.32 43.36 46.77** –162.19 — 6.74

MFC (Rs.) 15.30 15.30 15.30 15.30 — 15.30

MVP : MFC 4.92 2.83 3.06 –10.60 — 0.44

Zinc sulphate

MVP (Rs.) –10.39 –0.99 –4.49 — — 61.73

MFC (Rs.) 25.0 25.0 25.0 25.0

MVP : MFC –0.42 –0.040 –0.180 — — 2.47

Ferrous sulphate

MVP (Rs.) –130.67* 27.64 — — — –529.69

MFC (Rs.) 70.0 70.0 — — — 70.0

MVP : MFC –1.87 0.39 — — — –7.57

Plant protection expenses

MVP (Rs.) 1.80** 1.17 0.75 –0.41 –0.17 –0.31

MFC (Rs.) 1 1 1 1 1 1

MVP : MFC 1.80 1.17 0.75 –0.41 –0.17 –0.31

Irrigation

MVP (Rs.) –24.27* 136.71* –9.78 74.49 9.71 198.48

MFC (Rs.) 31.25 31.25 31.25 31.25 31.25 3125

MVP : MFC –0.78 4.37 –0.31 2.38 0.31 6.35

Note: ** and * shows significant at one and five percent level of probability, respectively


The prevalence of insect pest and diseases and shortage oflabour for performing various operations were the mostprevalent problems during production of the various kharifseason crops (Table 10). Low price in the market wasreported as the major marketing problem confronted bygrowers of basmati-paddy and maize in the study area. Formaize, the price in the market for most of the time remainslower as compared to MSP and as the prices comes downthe farmers find it very difficult to dispose of the produceat the remunerative prices in the market due to its shortshelf life. The price in market abruptly changes with thearrivals in the market. Whenever there is glut in the market,the prices comes down and farmers find it very difficult todispose of the produce at the remunerative prices in themarket. Delay in payment by the sugarcane mills was alsoobserved to be the problem by sugarcane growers. Thefarmers reported that their payments were not made evenafter six months of sale to the firm. More than 33 per centof the maize growers also reported the problem ofdestruction by the stray animals and monkeys when the cropis ready for the harvest. Cotton and guara crops growers insouth western districts of the state confronted the problemof water logging on their farms.

None of the diseases, insects/pests and weeds was tocause severe damage to the crop showing that the farmerswere able to manage them in time and the losses werereduced to the minimum. The attack of insects/pests wasfound to be more prevalent in the kharif crops as comparedto diseases and weeds. Amongst various diseases, bacterialleaf blight was observed by about 2 per cent of the paddygrowers in study area which reduced the yield by about 2per cent. Amongst the insects/pests, stem borer and leaffolder caused maximum damage and reduced the yield byabout 2 per cent and one per cent, respectively. Amongstvarious diseases in basmati-paddy production, foot rot andblast were prevalent in the study area, which reduced theyield by about 2 per cent. Amongst the insects/pests, leaffolder and stem borer were the major insects which reducedthe yield by about 2 per cent. Bacterial stalk rot was theonly disease infesting maize crop in the study area but theyield loss to the diseases was moderate (about 5 per cent).Amongst the insects/pests, maize borer and Jassid, thrips,pyrilla, grey weevil and leaf-feeding insects were found tocause slight damage (less than 2 per cent) and high (about 4per cent), respectively. Madhana, Itsit, sonfa and jhatta werethe important weeds but madhana observed by about oneper cent of maize growers on their fields and caused slightdamages (less than 2 per cent). In cotton, the sucking pests,jassid, aphid, whitefly and mealy bug are most serious andreduced the yield by about 2 per cent. The attack of insects/pests, diseases and weeds was the least prevalent for guarain the study area as compared to the other competing cropsduring the kharif season. The incidence of aphids and leafcurl disease was observed by the guara growers in studyarea respectively and were found to reduce the yield by

about 2 per cent. Red rot and wilt were the the diseasesinfesting sugarcane crops in the study area and its attackwas observed by the growers and the yield loss to thediseases was less than 3 per cent. Amongst the insects/pests, top borer and pyrilla were prevalent in the studyarea and caused yield loss by about 2 per cent each. Dilawas observed by sugarcane growers on their fields andcaused slight damage (about 2 per cent).

In Punjab, the irrigation coverage has increased tothe level of about 97 per cent. Therefore, the problemsof environment stress particularly during the kharifseason is absent in the study area. The productivity ofcotton and guara crops were found to be affected due towater logging problems in some areas. sugarcane wasfound to suffer from drought especially during hot monthswhen proper supply to irrigation was was not available.

Amongst the problems regarding inputs, theshortage of labour for performing various cultivationoperations, supply of poor quality and un-recommendedvarieties of seed, non availability of disease resistantvarities were the major impediments in cultivation ofkharif crops in the state. The acquisition of credit wasalso one of the problems confronted by guara, maize andcotton growers. The growers were also facing theproblem of lack of assured irrigation facilities as thefarmers were still dependent upon rains or are notsupplied adequate canal water. The problems comfrontedby the guara growers regarding the inputs were the leastamongst all the selected kharif crops.

Low price/variability of price in the market wasreported as the major marketing problem confronted bygrowers of the study area. The price in the marketabruptly changes with the arrivals in the market. Themaize farmers were not even able to get the priceequivalent to 75 per cent MSP of the crop and had tosell their output price at lower rate. whenever there isghut in the market, the prices comes down and farmersfind it very difficult to dispose of the produce at theremunerative prices in the market. Lack of marketinformation was reported as the other bottleneck facedin the marketing of these crops. The sharp fluctuationsin prices in wake of even small changes in production/supply are another serious concern impacting thecultivation of these crop choices as prices are dependentupon the demand by traders in the particular year. Maize,cotton and sugarcane farmers reported the shortage oflabour required for sorting of the produce. About 45 percent of the sugarcane growers reported the delayedpayment of the produce by the millers.

The farmers put forward the suggestions topromote various alternative crops to paddy (Table 11).For basmati-paddy, application of the irrigation at theright time, timely sowing and transplanting schedule,

February, 2016 51

TAB

LE 1

0:M

AIN

PR

OB

LEM

S FA

CED

BY

GR

OW

ERS

OF C

OM

PETI

NG

CR

OPS

WIT

H P

AD

DY

, SA

MPL

E H

OU

SEH

OLD

S

(% m

ultip

le re

spon

se)

Rea

sons

Bas

mat

i-pad

dyM

aize

Cot

ton

Gua

raSu

garc

ane

12

31

23

12

31

23

12

3

Prod

uctio

n pr

oble

ms

Dis

ease

13.9

18.

610.

664.

081.

099.

1830

.024

.016

.50

19.0

——

17.5

012

.50

2.50

Inse

cts/

pest

s47

.02

7.95

—3.

063.

0616

.33

96.0

——

16.6

7—

—35

.015

.07.

50

Wee

ds0.

660.

66—

——

——

——

——

—11

.25

20.0

—

Env

iron

men

tal p

robl

ems

Dra

ught

——

——

——

——

——

——

——

—

Wat

erlo

ggin

g—

——

——

——

—10

.023

.81

2.38

——

——

Hig

h te

mpe

ratu

re—

——

——

——

——

——

——

——

Non

ava

ilabl

e of

inpu

ts

Seed

0.66

—1.

991.

02—

——

——

——

——

—1.

25

Ferti

lizer

——

——

——

——

——

——

——

—

Che

mic

als

——

——

——

——

——

——

——

—— La

bour

29.1

324

.50

5.30

31.6

327

.55

4.08

32.0

28.0

2.0

19.0

519

.05

15.0

31.2

521

.25

16.2

5

Cre

dit

——

——

1.02

1.02

——

——

——

——

—

Mar

ketin

g pr

oble

ms

Stor

age

——

——

——

——

——

——

——

—— Pr

ices

17.8

822

.51

—39

.80

22.4

515

.10

22.0

32.0

2.0

23.8

133

.0—

6.25

125

—

Info

rmat

ion

65.5

615

.89

13.9

133

.67

38.0

—32

.042

.0—

39.9

535

.71

15.0

——

—

Tran

spor

t—

——

—1.

02—

14.0

22.0

——

——

20.0

16.2

5—

Dem

and

——

——

——

—2,

0—

2.38

——

6.25

6.25

—

Del

ay in

pay

men

t—

——

——

——

——

——

—37

.50

27.5

07.

50

Not

e: R

anks

are

in o

rder

of i

mpo

rtanc

e fr

om 1

(mos

t im

porta

nt),

2 (im

porta

nt),

to 3

(lea

st im

porta

nt).


monitoring of the insect-pest population/damage and useof recommended control measures and seed treatment toavoid seed borne diseases were the secret of success.Timely plantation, irrigation and weeding and use ofrecommended plant protection measures were the mostimportant suggestions forwarded by maize growers in thestudy area. As cotton crop is highly sensitive to the use ofinsect, pests and diseases, therefore the use of proper plantprotection measures, timely sowing and irrigation is thekey to success of the crop. Farmers were suggesting fortimely irrigation and use of proper protection measures toimprove the yield of guara in the study area. The sugarcamegrowers advocated for the timely irrigation and plantinguse the recommended chemical and cultural controlmeasures and to plant seed from a healthy and absolutelydisease free seed crop of recommended varieties only. Toimprove the yield of the kharif crops, the farmers suggestedthat the researcher should develop disease resistant, excessmoisture/drought tolerant varieties of the crop. The farmersalso feld the need for effective procurement of produceby government agencies at MSP and better marketintelligence. The sugarcame growers were advocating thatthe Government should work as regulatory in ensuring thetimely availability of the payments of the produce by themill owners. Most of the growers felt the need to improvethe extension activities through increase in number oftraining camps or field vists by the experts and providingthe information parrticularly regarding the high yieldingrecommended varieties of the crop particulary for hithertoneglected guara crop.

Policy Implications

Remunerative price of the produce is the main force tomotivate the farmers to grow the alternative crops to paddy.The farmers are selling fine varieties of paddy at minimumsupport prices to the government agencies. The resultsshowed that the returns over variable cost fetched fromthe most of the competing crops, were less than fine

varieties of paddy except for basmati-paddy, which wasmainly due the higher average price of Basmati-Paddy(Rs. 3673/q) during the year. The returns over variablecost on per hectare basis were the least for maize crop asthe average price realized by the farmers was only Rs.860/q, which was only about 70 per cent as compared tothe MSP announced by the government (Rs. 1310/q).Although, the Government announces MSP for maize andcotton, there is procurement of cotton by the governmentagencies, but only negligible quantity is procured. For thecompeting crops, the farmers are dependent upon theprivate traders for disposal of their produce. The marketprices for these crops decrease tremendously, when thereis gult in the market. The farmers left the need for effectiveprocurement of produce by government agencies at MSPand better market intelligence so that the farmers may getthe remunerative prices for their produce. The sugarcanegrowers were advocating that the Government should workas regulatory authority in ensuring the timely availabilityof the payments of the produce by the mill owners. Thefarmers observed that the yield losses was due toinfestation of various insect, pests, diseases and weedsand water logging in the south western districts of the state.To improve the yield of these alternative crops, theresearcher should develop disease resistant, excessmoisture tolerant varieties for guara and cotton and droughttolerant varieties for sugarcame. On the production front,application of the irrigation at the right time, timely sowingand transplaning schedule, monitoring of the insect-pestpopulation/damage and use of recommended controlmeasures and seed treatment to avoid seed borne diseaseswere the secret of success for these alternative crops. Mostof the growers felt the need to improve the extensionactivities through the increase in the number of trainingcamps or field visits by the experts and providing theinformation particularly regarding the high yieldingrecommended varieties of the crop particularly hithertoneglected guara crop.

February, 2016 53

TAB

LE 1

1: S

ugge

stio

n to

Pro

mot

e C

ompe

ting

Cro

ps w

ith P

addy

as R

evea

led

by F

arm

ers,

Sam

ple

Hou

seho

ld

(% m

ultip

le re

spon

se)

Sugg

estio

nB

asm

ati-p

addy

Mai

zeC

otto

nG

uara

Suga

rcan

e1

23

12

31

23

12

31

23

To in

crea

se y

ield

Incr

ease

the

plan

t pop

ulat

ion

—0.

663.

31—

——

——

——

——

——

—U

se m

ore

ferti

lizer

——

—1.

02—

3.06

——

——

——

——

—U

se m

ore

chem

ical

s—

——

——

1.02

12.0

2.0

2.0

——

——

——

Tim

ely

plan

ning

13.9

17.

2811

.26

7.14

3.06

—4.

0—

——

——

1.25

1.25

1.25

Tim

ely

wee

ding

——

—1.

02—

3.06

——

——

——

——

—Pr

ovid

e irr

igat

ion

at ri

ght t

ime

27.8

113

.91

4.64

1.02

—4.

044.

04.

04.

014

.28

——

18.7

55.

00—

Use

of p

rope

r pla

nt p

rote

ctio

n11

.26

21.1

910

.60

2.04

12.2

43.

0618

.00

16.0

—4.

767.

14—

17.5

013

.75

2.50

mea

sure

sTo

rese

arch

ers

Incr

ease

the

seed

siz

e—

——

——

——

——

——

——

——

Incr

ease

num

ber o

f pod

s pe

r pla

nt—

——

——

——

——

——

——

——

Dev

elop

ferti

lizer

s re

spon

sive

——

—3.

062.

041.

02—

——

44.9

014

.28

—33

.49

——

varie

ties

Dev

elop

sho

rt du

ratio

n va

rietie

s—

——

4.08

5.10

—43

.032

.02.

018

.95

——

48.5

226

.25

1.25

Dev

elop

dro

ught

tole

rant

var

ietie

s—

9.27

——

——

—2.

0—

——

——

——

Dev

elop

dis

ease

resi

stan

t var

ities

——

—.1

06.

1217

.35

29.0

6.0

—47

.14

11.9

0—

26.2

5—

—D

evel

op e

xces

s m

oist

ure

tole

rant

29.1

42.

650.

66—

—4.

0844

.014

.02.

029

.52

20.6

3—

3.75

5.00

—va

rietie

sTo

impr

ove

mar

ketin

gIn

crea

se th

e ac

cess

to in

form

atio

n on

58.5

27.

95—

34.0

856

.12

4.08

52.0

24.5

6—

58.9

628

.65

——

——

pric

eD

ecre

ase

Loss

es d

urin

g sh

orta

ge—

——

—1.

023.

06—

——

——

——

——

Impr

ove

the

grad

ing

faci

litie

s—

——

——

——

——

——

——

——

Effe

ctiv

e pr

ocur

emen

t by

Gov

t.38

.24

6.62

—87

.75

5.10

—8.

0—

—16

.67

——

——

—ag

enci

esTo

impr

ove

exte

nsio

n ac

tiviti

esIn

crea

se th

e nu

mbe

r of t

rain

ing

23.1

811

.92

—45

.92

42.8

61.

0228

.06.

0—

9.56

2.38

—28

.75

5.0

—pr

ogra

mm

esIn

form

atio

n re

gard

ing

HY

Vs

17.2

28.

61—

46.9

440

.82

—14

.022

.0—

16.6

7—

2.38

22.5

06.

25—

Fiel

d vi

sits

by

expe

rts16

.56

1.32

19.2

044

.83

1.02

47.9

06.

08.

0—

2.38

2.38

—5.

007.

5011

.25

Not

e: R

anks

are

in o

rder

of i

mpo

rtanc

e fr

om 1

(mos

t im

porta

nt),

2 (im

porta

nt),

to 3

(lea

st im

porta

nt)


ALL INDIA INDEX NUMBER OF WHOLESALE PRICES

(Base: 2004-2005=100)

Commodity Weight WPI for the WPI for the WPI Percentage changeMonth of Month of during

(%) December 2015 November 2015 A year ago A month A year

Rice 1.793 237.3 237.5 240.3 -0.08 -1.25

Wheat 1.116 222.7 221.5 214.7 0.54 3.73

Jowar 0.096 289.1 282.3 290.7 2.41 -0.55

Bajra 0.115 270.3 261.0 237.4 3.56 13.86

Maize 0.217 260.6 257.0 235.0 1.40 10.89

Barley 0.017 240.3 236.8 235.0 1.48 2.26

Ragi 0.019 328.3 326.7 324.5 0.49 1.17

Cereals 3.373 237.1 236.1 233.3 0.42 1.63

Pulses 0.717 378.2 380.4 243.0 -0.58 55.64

Foodgrains 4.09 261.8 261.4 235.0 0.15 11.40

Source : Office of the Economic Adviser, M/O Commerce and Industry.

Commodity Main Trend Rising Falling Mixed Steady

Rice Rising Haryana Karnataka

Jharkhand U.P.

Kerala

Wheat Rising Gujarat Rajasthan Karnataka

Haryana

Madhya Pradesh

Maharashtra

U.P.

Jowar Rising Gujarat Maharashtra Karnataka A.P.

Rajasthan

Bajra Rising Gujarat Karnataka

Haryana

Maharashtra

Rajasthan

Maize Rising & Falling M.P. Gujarat Karnataka

U.P. Rajasthan

Commodity Reviews

Foodgrains

During the month of December,2015 the Wholesale PriceIndex (Base 2004-05=100) of pulses decreased by 0.58%,

cereals increased by 0.42% & foodgrains increased by0.15% respectively over the previous month.

The following Table indicates the State wise trend of Wholesale Prices of Cereals during the month of December, 2015.

February, 2016 55

Procurement of Rice

5.00 million tonnes of Rice(including paddy convertedinto rice) was procured during December 2015 as against4.10 million tonnes of rice (including paddy convertedinto rice) procured during December 2014. The total

procurement of Rice in the current marketing season i.e2015-2016, up to 31.12.2015 stood at 18.89 milliontonnes, as against 14.75 million tonnes of rice procured,during the corresponding period of last year. The detailsare given in the following table :

PROCUREMENT OF RICE

(In Thousand Tonnes)

State Marketing Season Corresponding Marketing Year2015-16 Period of last Year (October-September)

(upto 31.12.2015) 2014-15 2014-15 2013-14

Procurement %age to Procurement %age to Procurement %age to Procurement %age toTotal Total Total Total

1 2 3 4 5 6 7 8 9

Andhra Pradesh 938 4.97 386 2.62 3591 11.17 3722 11.76Chhatisgarh 2311 12.23 1629 11.05 3423 10.64 4290 13.56Haryana 2854 15.11 2015 13.66 2015 6.27 2406 7.60Maharashtra 51 0.27 45 0.31 199 0.62 161 0.51Punjab 9349 49.49 7781 52.77 7786 24.21 8106 25.62Tamil Nadu 41 0.22 5 0.03 1049 3.26 684 2.16Uttar Pradesh 839 4.44 489 3.32 1698 5.28 1127 3.56Uttarakhand 274 1.45 151 1.02 465 1.45 463 1.46Others 2232 11.82 2245 15.22 11936 37.11 10678 33.75

Total 18889 100.00 14746 100.00 32162 100.00 31637 100.00

Source : Department of Food & Public Distribution.

Procurement of Wheat

The total procurement of wheat in the current marketingseason i.e 2015-2016 up to July, 2015 is 28.09 million

tonnes against a total of 27.17 million tonnes of wheatprocured during last year. The details are given in thefollowing table :

PROCUREMENT OF WHEAT

(In Thousand Tonnes)

State Marketing Season Corresponding Marketing Year2015-16 Period of last Year (October-September)

(upto 13.7.2015) 2014-15 2014-15 2013-14

Procurement %age to Procurement %age to Procurement %age to Procurement %age toTotal Total Total Total

1 2 3 4 5 6 7 8 9

Haryana 6778 24.13 6414 23.61 6495 23.20 5873 23.41Madhya Pradesh 7309 26.02 7188 26.46 7094 25.34 6355 25.33Punjab 10344 36.83 10775 39.66 11641 41.58 10897 43.43Rajasthan 1300 4.63 2155 7.93 2159 7.71 1268 5.06Uttar Pradesh 2267 8.07 628 2.31 599 2.14 683 2.72Others 90 0.32 6 0.02 6 0.02 16 0.06

Total 28088 100.00 27166 100.00 27994 100.00 25092 100.00

Source: Department of Food & Public Distribution.


Commodity Latest Month Year % Variation Over

December, 15 November, 15 December,2014 Month Year

OIL SEEDS 217.9 217.8 201.7 0.0 8.0

Groundnut Seed 242.2 232.0 205.8 4.4 17.7

Rape & Mustard Seed 246.9 244.1 198.3 1.1 24.5

Cotton Seed 203.1 205.8 165.6 -1.3 22.6

Copra (Coconut) 139.1 140.9 173.9 -1.3 -20.0

Gingelly Seed (Sesamum) 291.4 301.1 411.5 -3.2 -29.2

Niger Seed 402.4 390.3 201.9 3.1 99.3

Safflower (Kardi Seed) 148.4 148.4 121.8 0.0 21.8

Sunflower 200.0 198.3 181.3 0.9 10.3

Soyabean 212.7 220.0 196.6 -3.3 8.2

EDIBLE OILS 151.5 150.3 143.2 0.8 5.8

Groundnut Oil 193.8 192.2 168.3 0.8 15.2

Cotton Seed Oil 194.2 186.3 167.2 4.2 16.1

Oilseeds and Edible OilsThe Wholesale Price Index (WPI) of nine major oilseedsas a group stood at 217.9 in December, 2015 showingmarginal increase over the previous month. However, it ishigher by 8 % over the previous year. The WPI ofgroundnut seed increased by 4.4 %, niger seed by 3.1%,rape & mustard seed by 1.1 % and sunflower seed by 0.9% over the previous month. However, the WPI ofsoyabean decreased by 3.3%, gingelly seed by 3.2%, copraby 1.3% and cotton seed by 1.3 % over the previous month.The WPI of safflower seed remained unchanged over themonth. The Wholesale Price Index (WPI) of edible oilsas a group stood at 151.5 in December, 2015 showing anincrease of 0.8% and 5.8% over the previous month andyear, respectively. The WPI of cotton seed oil increasedby 4.2 %, soyabean oil by 1.4 %, mustard & rapeseed oilby 1.1 % and groundnut oil by 0.8 % over the previousmonth. However, the WPI of sunflower oil decreased by1.7 %, gingelly oil by 1.1 % and copra oil by 0.5 % overthe previous month.

Fruits & Vegetable

The Wholesale Price Index (WPI) of fruits & vegetableas a group stood at 273.8 in December, 2015 showing adecrease of 2.4 % over the previous month. However, itshows an increase of 10.3 % over the previous year.

PotatoThe Wholesale Price Index (WPI) of potato stood at 174.8in December, 2015 showing a decrease of 11.4% and35.0% over the previous month and year, respectively.OnionThe Wholesale Price Index (WPI) of onion stood at 435.4in December, 2015 showing a decrease of 18.5 % overthe previous month. However, it shows an increase 26.0% over the previous year.Condiments & SpicesThe Wholesale Price Index (WPI) of condiments & spices(group) stood at 372.3 in December, 2015 showing anincrease of 2.5 % and 21.7 % over the previous monthand year, respectively. The WPI of chillies (dry) (1.6 %)and turmeric (1.8 %) increased over the previous month.black pepper shows decrease of 2.8 % over the previousmonth.Raw CottonThe Wholesale Price Index (WPI) of raw cotton stood at187.7 in December, 2015 showing an increase of 2.3 %over the previous month. However, it shows a decrease of2.1 % over the previous year.Raw JuteThe Wholesale Price Index (WPI) of raw jute stood at453.4 in December, 2015 showing an increase of 4.7 %and 51.6 % over the previous month and year, respectively.

Commercial Crops

WHOLESALE PRICE INDEX OF COMMERCIAL CROPS

February, 2016 57

Mustard & Rapeseed Oil 193.0 190.9 159.4 1.1 21.1

Soyabean Oil 150.4 148.3 151.2 1.4 -0.5

Copra Oil 147.8 148.6 143.7 -0.5 2.9

Sunflower Oil 132.4 134.7 122.6 -1.7 8.0

Gingelly Oil 158.8 160.6 176.0 -1.1 -9.8

FRUITS & VEGETABLES 273.8 280.5 248.3 -2.4 10.3

Potato 174.8 197.4 268.9 -11.4 -35.0

Onion 435.4 534.1 345.6 -18.5 26.0

CONDIMENTS & SPICES 372.3 363.3 306.0 2.5 21.7

Black Pepper 740.7 762.1 765.4 -2.8 -3.2

Chillies(Dry) 396.7 390.4 323.6 1.6 22.6

Turmeric 267.1 262.4 235.8 1.8 13.3

Raw Cotton 187.7 183.4 191.8 2.3 -2.1

Raw Jute 453.4 433.0 299.1 4.7 51.6

WHOLESALE PRICE INDEX OF COMMERCIAL CROPS—CONTD.

Commodity Latest Month Year % Variation Over

December, 15 November, 15 December,2014 Month Year


STATISTICAL TABLESWages

1. AVERAGE DAILY AGRICULTURAL WAGES IN SOME STATES (CATEGORY-WISE)(In Rs.)

State District Centre Month & Daily Field Labour Other Agri. Herdsman Skilled LabourYear Normal Labour Car- Black Cobbler

Working penter SmithHours

M W M W M W M M M

Andhra Pradesh Krishna Ghantasala Aug,15 8 267 175 300 NA 250 200 NA NA NAGuntur Tadikonda Aug,15 8 275 200 275 NA 225 NA NA NA NA

Telangana Ranga Reddy Arutala March,15 8 260 190 300 NA NA NA NA NA NAKarnataka Bangalore Harisandra Aug,15 8 NA NA NA NA NA NA NA NA NA

Tumkur Gidlahali Aug,15 8 168 160 180 180 180 180 180 180 180Maharashtra Nagpur Mauda Sep, 14 8 100 80 NA NA NA NA NA NA NA

Ahmednagar Akole Sep, 14 8 NA NA NA NA NA NA NA NA NA

Jharkhand Ranchi Gaitalsood March,14 8 120 120 100 100 75 75 200 200 NA

1.1 AVERAGE DAILY AGRICULTURAL WAGES IN SOME STATES (OPERATION-WISE)(In Rs.)

State District Centre Month Type of Normal Plou- Sowing Weed- Harves- Other Herds- Skilled Labours& Year Labour Daily ghing ing ting Agri man Car- Black Cobbler

Working Labour penter SmithHours

Assam Barpeta Laharapara June,15 M 8 250 250 250 250 250 200 300 300 250

W 8 NA NA NA NA NA NA NA NA NA

Bihar Muzaffarpur Bhalui Rasul June,14 M 8 310 210 210 260 250 210 350 360 310

W 8 NA NA NA 250 210 NA NA NA NA

Shekhpura Kutaut June,14 M 8 220 NA NA NA 220 NA 280 NA NA


Chhattisgarh Dhamtari Sihava Sep,15 M 8 NA NA 120 NA 150 100 250 150 100

W 8 NA NA 100 NA 100 100 200 100 100

Gujarat* Rajkot Rajkot July,15 M 8 221 213 160 183 150 190 442 442 350

W 8 NA 169 150 180 138 125 NA NA NA

Dahod Dahod July,15 M 8 186 157 157 157 129 NA 257 207 207

W 8 NA 157 157 157 129 NA NA NA NA

Haryana Panipat Ugarakheri Oct,15 M 8 400 400 400 400 400 NA NA NA NA

W 8 NA NA 300 300 300 NA NA NA NA

Himachal Mandi Mandi Dec,13 M 8 NA 162 162 162 162 NA 260 240 240

Pradesh W 8 NA 162 162 162 162 NA 650 NA NA

Kerala Kozhikode Koduvally July,15 M 4-8 1230 660 NA 660 957 NA 760 NA NA

W 4-8 NA NA 460 510 510 NA NA NA NA

Palakkad Elappally July,15 M 4-8 500 500 NA NA 467 NA 600 NA NA

W 4-8 NA NA 300 NA 300 NA NA NA NA

February, 2016 59

Madhya Hoshangabad Sangarkhera Sep,15 M 8 100 130 130 NA 100 100 500 500 NA

Pradesh W 8 NA 130 130 NA 100 NA NA NA NA

Satna Kotar Sep,15 M 8 NA NA NA NA NA NA NA NA NA


Shyopurkala Vijaypur Sep,15 M 8 NA 300 NA 300 NA 250 300 NA NA


Odisha Bhadrak Chandbali Sep,15 M 8 250 150 250 200 250 250 350 200 200

W 8 NA NA 200 150 200 200 NA NA NA

Ganjam Aska Sep,15 M 8 300 200 200 250 200 200 400 400 300

W 8 NA 100 100 150 100 100 NA NA NA

Punjab Ludhiyana Pakhowal July,14 M 8 300 300 300 NA 365 NA 395 395 NA


Rajasthan Barmer Kuseep Aug,15 M 8 NA NA 300 NA NA 300 700 500 NA

W 8 NA NA 200 NA NA 200 NA NA NA

Jalore Sarnau Aug,15 M 8 NA NA NA NA NA NA NA NA NA


Tamil Nadu* Thanjavur Pulvarnatham Oct,15 M 8 NA 339 120 257 342 NA NA NA NA

W 8 NA NA NA 120 121 NA NA NA NA

Tirunelveli Malayakulam Oct, 15 M 8 NA 362 NA 375 490 NA NA NA NA

W 8 NA 200 160 175 358 NA NA NA NA

Tripura State Average Apr, 14 M 8 287 262 264 277 261 270 305 212 285

W 8 NA 197 201 209 197 200 NA NA NA

Uttar Meerut Ganeshpur Aug,15 M 8 280 267 269 NA 267 NA 381 NA NAPradesh*

W 8 NA 204 208 NA 250 NA NA NA NA

Aurraiya Aurraiya Aug,15 M 8 NA 150 150 NA 160 NA 336 NA .NA

W 8 NA NA NA NA 162 NA NA NA NA

Chandauli Chandauli Aug,15 M 8 200 200 200 NA 200 NA 350 NA NA


M-ManW-WomanNA- Not Available * States reported district average daily wages

1.1 AVERAGE DAILY AGRICULTURAL WAGES IN SOME STATES (OPERATION-WISE)—CONTD.

(In Rs.)

State District Centre Month Type of Normal Plou- Sowing Weed- Harves- Other Herds- Skilled Labours& Year Labour Daily ghing ing ting Agri man Car- Black Cobbler

Working Labour penter SmithHours


Prices2. WHOLESALE PRICES OF CERTAIN AGRICULTURAL COMMODITIES AND ANIMAL HUSBANDRY PRODUCTS AT

SELECTED CENTRES IN INDIA

(Month end Prices in Rupees)

Commodity Variety Unit State Centre Dec-15 Nov-15 Dec-14

Wheat PBW 343 Quintal Punjab Amritsar 1600 1600 1500Wheat Dara Quintal Uttar Pradesh Chandausi 1590 1560 1550

Wheat Lokvan Quintal Madhya Pradesh Bhopal 1580 1530 1660

Jowar - Quintal Maharashtra Mumbai 2300 2300 2200

Gram No III Quintal Madhya Pradesh Sehore 4286 4300 2850

Maize Yellow Quintal Uttar Pradesh Kanpur 1355 1360 1325

Gram Split - Quintal Bihar Patna 6040 6150 4420

Gram Split - Quintal Maharashtra Mumbai 6150 6150 3900

Arhar Split - Quintal Bihar Patna 14800 15000 6900

Arhar Split - Quintal Maharashtra Mumbai 13250 12600 6800

Arhar Split - Quintal NCT of Delhi Delhi 13350 12800 6075

Arhar Split Sort II Quintal Tamil Nadu Chennai 12800 12300 7700

Gur - Quintal Maharashtra Mumbai 3100 3000 3400

Gur Sort II Quintal Tamil Nadu Coimbatore 4000 4650

Gur Balti Quintal Uttar Pradesh Hapur 2200 2130 2250

Mustard Seed Black (S) Quintal Uttar Pradesh Kanpur 4385 4450 3340

Mustard Seed Black Quintal West Bengal Raniganj 5000 3850

Mustard Seed - Quintal West Bengal Kolkata 5300 4800 4400

Linseed Bada Dana Quintal Uttar Pradesh Kanpur 4425 4415 4260

Linseed Small Quintal Uttar Pradesh Varanasi 4250 4100 3880

Cotton Seed Mixed Quintal Tamil Nadu Virudhunagar 1900 1900 1200

Cotton Seed MCU 5 Quintal Tamil Nadu Coimbatore 2300 2000

Castor Seed - Quintal Telangana Hyderabad 3500 3650 3900

Sesamum Seed White Quintal Uttar Pradesh Varanasi 13550 13400

Copra FAQ Quintal Kerala Alleppey 6650 6950 9150

Groundnut Pods Quintal Tamil Nadu Coimbatore 4500 4500

Groundnut - Quintal Maharashtra Mumbai 5900 5900 5100

Mustard Oil - 15 Kg. Uttar Pradesh Kanpur 1515 1470 1200

Mustard Oil Ordinary 15 Kg. West Bengal Kolkata 1710 1590 1380

Groundnut Oil - 15 Kg. Maharashtra Mumbai 1335 1335 1275

Groundnut Oil Ordinary 15 Kg. Tamil Nadu Chennai 1725 1725 1290

Linseed Oil - 15 Kg. Uttar Pradesh Kanpur 1455 1462 1493

Castor Oil - 15 Kg. Telangana Hyderabad 1125 1185 1305

Sesamum Oil - 15 Kg. NCT of Delhi Delhi 1385 1380 1900

Sesamum Oil Ordinary 15 Kg. Tamil Nadu Chennai 1725 1725 2805

Coconut Oil - 15 Kg. Kerala Cochin 1350 1485 1965

Mustard Cake - Quintal Uttar Pradesh Kanpur 2450 2250 1780

Groundnut Cake - Quintal Telangana Hyderabad 3429 3500 3000

Cotton/Kapas NH 44 Quintal Andhra Pradesh Nandyal 4100 3900 3800

Cotton/Kapas LRA Quintal Tamil Nadu Virudhunagar 3300 3000 3066

February, 2016 61

Jute Raw TD 5 Quintal West Bengal Kolkata 5290 4910 3125

Jute Raw W 5 Quintal West Bengal Kolkata 5230 4860 3075

Oranges - 100 No NCT of Delhi Delhi 650 600 417

Oranges Big 100 No Tamil Nadu Chennai 480 500 360

Oranges Nagpuri 100 No West Bengal Kolkata 400

Banana - 100 No. NCT of Delhi Delhi 267 333 292

Banana Medium 100 No. Tamil Nadu Kodaikkanal 495 501 484

Cashewnuts Raw Quintal Maharashtra Mumbai 82000 82000 60000

Almonds - Quintal Maharashtra Mumbai 95000 95000 67000

Walnuts - Quintal Maharashtra Mumbai 82000 82000 67000

Kishmish - Quintal Maharashtra Mumbai 23000 23000 22000

Peas Green - Quintal Maharashtra Mumbai 4200 4200 4500

Tomato Ripe Quintal Uttar Pradesh Kanpur 1400 2200 1025

Ladyfinger - Quintal Tamil Nadu Chennai 4000 4000 3000

Cauliflower - 100 No. Tamil Nadu Chennai 2000 3500 1500

Potato Red Quintal Bihar Patna 820 970 1000

Potato Desi Quintal West Bengal Kolkata 860 1000 1440

Onion Pole Quintal Maharashtra Nashik 1250 1500 1400

Turmeric Nadan Quintal Kerala Cochin 13000 12500 11000

Turmeric Salam Quintal Tamil Nadu Chennai 9000 8800 8400

Chillies - Quintal Bihar Patna 10250 10000 9900

Black Pepper Nadan Quintal Kerala Kozhikode 64000 67000 67000

Ginger Dry Quintal Kerala Cochin 19500 20500 20000

Cardamom Major Quintal NCT of Delhi Delhi 131500 131000 130000

Cardamom Small Quintal West Bengal Kolkata 100000 105000 115000

Milk Buffalo 100 Liters West Bengal Kolkata 3600 3600 3600

Ghee Deshi Deshi No 1 Quintal NCT of Delhi Delhi 34017 34684 28681

Ghee Deshi - Quintal Maharashtra Mumbai 46000 46000 40000

Ghee Deshi Desi Quintal Uttar Pradesh Kanpur 35650 35600 35700

Fish Rohu Quintal NCT of Delhi Delhi 10000 9000 9200

Fish Pomphrets Quintal Tamil Nadu Chennai 32000 32000 29000

Eggs Madras 1000 No. West Bengal Kolkata 4250 4100 4500

Tea - Quintal Bihar Patna 21100 21100 21000

Tea Atti Kunna Quintal Tamil Nadu Coimbatore 33000 34000

Coffee Plant-A Quintal Tamil Nadu Coimbatore 31000 30000

Coffee Rubusta Quintal Tamil Nadu Coimbatore 13500 15500

Tobacco Kampila Quintal Uttar Pradesh Farukhabad 4600 4700

Tobacco Raisa Quintal Uttar Pradesh Farukhabad 3550 3600

Tobacco Bidi Tobacco Quintal West Bengal Kolkata 3900

Rubber - Quintal Kerala Kottayam 9100 9400 10700

Arecanut Pheton Quintal Tamil Nadu Chennai 31500 31500 29900

2. WHOLESALE PRICES OF CERTAIN AGRICULTURAL COMMODITIES AND ANIMAL HUSBANDRY PRODUCTS AT

SELECTED CENTRES IN INDIA — CONTD.

Commodity Variety Unit State Centre Dec-15 Nov-15 Dec-14


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February, 2016 63

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Crop Production4. SOWING AND HARVESTING OPERATIONS NORMALLY IN PROGRESS DURING MARCH, 2016

State Sowing Harvesting1 2 3Andhra Pradesh Summer Winter rice, Summer rice, Jowar (R), Maize (R), Ragi

(R), Wheat, Barley, Small Millets (R), Gram, Tur (K)other Kharif Pulses Urad (R), Mung (R), Other RabiPulses, Sugarcane, Chilies (Dry), Castorseed,Linseed, Cotton, Turneric, Onion (2nd crop), Tapioca

Assam Small Millets (R), Summer Potato Wheat Gram,Tur(K), Urad (R), Tobacco, Rapeseed(Hills), Sugarcane, jute, Mesta and Mustard, Linseed

Bihar Jute Wheat, Barley, Gram, Tur(K), Winter Patato (Plains),Sugarcane, Rapeseed and Mustard, Linseed Wheat,Barley, Gram, Tur (K), Winter Potato.

Gujarat Sugarcane Sugarcane, Chillies (Dry), Castorseed, Rapeseed andMustard, Cotton, Onion

Himachal Pradesh Sugarcane, Cotton Rapeseed and Mustard, Linseed Winter Rice, Jowar(R), Wheat, Gram, Urad (R), Mung (R), WinterPotato (Plains), Summer Potato

Karnataka Sugarcane (Plains), Sugarcane, Linseed, Cotton, Turmeric,Cardiseed, Onion

Kerala Sugarcane, Sesamum (1st crop), Summer Rice, Sesamum (3rd crop), Cotton, SweetTapioca (2nd crop) Potato Jowar (R), Wheat, Barley Small Millets (R),

Gram, Tur, Urad (R), Mung (R), Other RabiMadhya Pradesh Sugarcane Pulses, Winter Potato, Sugarcane, Chillies (Dry),

Tobacco, Castorseed, Rapeseed & Mustard, Linseed,sannhemp Cardiseed, Onion Jowar (R), Maize (r),Wheat Barley, Gram, Tur (K), Other Rabi Pulses,Chillies (Dry), Tobacco, Catorssed,

Maharashtra Sugarcane Rapeseed and Mustard, Linseed, Cotton, Cardiseed,Onion,

Manipur Maize, Jute Wheat, Gram, Castorseed, Rapeseed and Mustard,Linseed,

Orissa Sugarcane Bajra, Ragi, Wheat, Barley, Urad (R), Mung (R),Rapeseed and Mustard,

Punjab and Haryana Winter Potato (Hills), Summer Gram, Tur(K), Summer Potato, Sugarcane,Potato (Hills), Sugarcane, Ginger, Castorseed, Rapeseed and Mustard, Linseed,Chillies (Dry), Tobacco, Turmeric, Onion Turmeric

Rajasthan Small Millets (R), Sugarcane Wheat, Barley, Gram, Tur (K), Urad (R), Mung (R),Other Rabi Pulses, winter Potato (Plains), Castorseed,Rapeseed and Mustard, Linseed

Tamil Nadu Summer Rice, Jowar (R), Sugarcane, Winter Rice, Jowar (R), Bajra, Ragi, Small MilletsGroundnut (Early), Sesamum, Onion (K), Mung (K), Other Rabi Pulses (Kulthi), Winter

Potato, Sugarcane, Tobacco, Castorseed, Sesamum(Late), Cotton, Onion

February, 2016 65

Tripura Autumn Rice, Sugarcane, Summer Rice, Urad (R), Mung (R), Other RabiSesamum, Cotton Jute Pulses, Winter Potato (Plains), Sugarcane, Chillies

(Dry), Rapeseed and Mustard, Wheat Barley, SmallMillets (R) Gram, Tur (K),

Uttar Pradesh Small Millets(R), Sugarcane, Winter Potato (Hills), Ginger, Tobacco, Castersood,Ginger, Jute, Mesta, Tapioca Rapeseed and Mustard, Linseed, Sweet Potato,

Onion, Tapioca Wheat, Barlery, Gram, Tur (K), Urad(R), Other Rabi

West Bengal Autumn Rice, Sugarcane, Ginger, Pulses, Winter Potato (Plains, Sugarcane, Ginger,Sesamum, Jute Tobacco, Sesamum, Rapeseed and Mustard, Chillies

(Dry)

Delhi Sugarcane, Tobacco, Jute Barley, Gram, Sugarcane, Tobacco

4. SOWING AND HARVESTING OPERATIONS NORMALLY IN PROGRESS DURING MARCH, 2016—CONTD.

1 2 3

Documents

P.Agri.21-01-2016 450 AGRICULTURAL SITUATION IN INDIAJasdev Singh - Agro Economic Research Centre, Department of Economics and Sociology, Punjab Agricultural University, Ludhiana