Srusti Management Review

A Journal of Management & IT

ISSN NO: 0974-4274(PRINT), ISSN NO: 2582-1148(ONLINE)Listed in Ulrich's Periodicals Directory, INDEXED IN J-GATE E-JOURNAL GATEWAY, EBSCOHOST, PROQUEST, U.S.A. & GOOGLE SCHOLAR A Peer Reviewed and Refereed Journal

Importance of Market Intelligence, Price Forecasting and Time Series Analysis in Agriculture

Year 2016
Volume/Issue/Review Month Vol. - IX | Issue I | January - June
Title Importance of Market Intelligence, Price Forecasting and Time Series Analysis in Agriculture
Authors Bibhu Santosh Behera , Anama Charan Behera , Rudra Ashish Behera , Jishnu
Broad area Importance of Market Intelligence, Price Forecasting and Time Series Analysis in Agriculture
Abstract
Agriculture is the backbone of Indian economy. Agriculture, with its allied sectors, is unquestionably the largest
livelihood provider in India. The Indian agriculture sector accounts for 14 per cent of India’s gross domestic product
(GDP) and employs just more than 50 per cent of the country’s workforce. It has to support almost 17 per cent of
world population from 2.3 per cent of world geographical area and 4.2 per cent of world’s water resources .In 2013-
14 India achieved a record food grain production of 264 million tonnes , beating the previous year’s (2012-13) 257
MT, according to data provided by Department of Economics and Statistics .Amidst in these high potentiality, we
are facing lots of challenges in the marketing aspects of agriculture. Better marketing with increased and assured
remuneration is the need of the hour to foster and sustain the tempo of rural economic development.
For bettering marketing prospects in agriculture, market intelligence needs to be bettered.
Description Market Intelligence (MI) is knowledge based management system which may be defined as a process primarily based on market information collected over period of time. An analysis based on past information helps to take decision about the future. MI synthesi
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Referenceses
1. Advian, D. (2012) “A model based approach to
forecasting corn and soybean yield”, Proceeding of
the fourth international conference on establishment
survey (ICES) , Montreal, Canada, available through
the American Statistical association, Betnesda
Maiyland, June 2012.
2. Johansen S (1988). “ statistical Analysis of co
integrating vectors”, Journal of Economics Dynamic
and Control, iz, pp.231-254.
3. Kailash Chandra Pradhan and K.Sham
Bhat(2009)An Empirical Analysis of price Discovery,
casualty and forecasting in the nifty future markets.
International Research Journal of finance and
Economics. Euro Journals publishing, Inc. ISSN
1450-2887 issue 26pp83-92
4. Johansen, S. (1988), “Statistical Analysis of Co
integrating Vectors”, Journal of Economics Dynamic
and control, 12, pp. 231-254.
5. Johansen, Soren (1991). “Estimation and Hypothesis
Testing of Cointegration Vectors in Gaussian Vector
Autoregressive Models,” Econometrica, 59, 1551-
1580
6. Johansen, Soren (1995). Likelihood-based Inference
in Cointegrated Vector Autoregressive Models,
Oxford: Oxford University Press.
7. Erik Hjalmarsson and Par Osterholm (2007) Testing
for Co integration Using the Johansen Methodology
when Variables are Near-Integrated. International
Finance Discussion Papers Number 915, Board of
Governors of the Federal Reserve System.
8. Megha Mukim, Karan Singh, A Kanakaraj (2009).
Market Integration, Transaction Costs and the Indian
Wheat Market: A Systematic Study. Economic &
Political Weekly EPW may 30, vol xliv no 22.
9. op.cit.
10. Madhusudan Ghosh(2003) Spatial Integration of
Wheat Markets in India: Evidence from Co
integration Tests, Oxford Development Studies, Vol.
31, No. 2,