Refine your search
Collections
Co-Authors
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Sreenivas, A.
- Analysis of Trend in Area, Production and Productivity of Cotton Crop in Three Districts of Northern Telangana Zone
Abstract Views :567 |
PDF Views:0
Authors
Affiliations
1 Department of Statistics and Mathematics, College of Agriculture, Professor Jayashankar Telangana State Agricultural University, Rajendranagar, Hyderabad (Telangana), IN
2 Department of Statistics and Mathematics, Professor Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), IN
1 Department of Statistics and Mathematics, College of Agriculture, Professor Jayashankar Telangana State Agricultural University, Rajendranagar, Hyderabad (Telangana), IN
2 Department of Statistics and Mathematics, Professor Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 10, No 2 (2019), Pagination: 175-185Abstract
Attempts have been made to examine the trends and forecasting in area, production and productivity of cotton crop in three districts of Northern Telangana Zone. Linear and compound growth rates were calculated for this purpose. Ten growth models were fitted to the area, production and productivity of cotton crop and best- fitted model for future projection was chosen based upon least residual mean square (RMS) and significant Adj R2 besides, the important assumption of randomness of residuals was tested using one sample run test. The reference period of study was from 1979-80 to 2012-13 and it was carried out in three districts of Northern Telangana zone.Keywords
Production, Productivity, Cotton Crop, Best- Fitted Model.References
- Aparna, B., Shareef, S.M., Raju,V.T . and Srinivasa Rao, V. (2008). Growth trends of major vegetables in Visakhapatnam. Andhra Agric. J., 55 (1): 68-69.
- Chattopadhyay, A.K. and Das, P.S. (2000). Estimation of growth rate: A critical analysis with reference to West Bengal Agriculture. Indian J.Agric. Econ., 55 (2): 116-135.
- Deka, N., Sarmah, A.K. and Raja, M.S. (2002). Trend in area, production and productivity of Areca nut in Assam. J. Agric. Sci. Soc.North East India., 15 (2) : 137-140.
- Navadkar, D.S., Pagire, B.V. and Patole, S.D. (2004). Production processing and trade of fruits and vegetables in Himachal Pradesh vis-à-vis other states. Indian J. Agric. Mktg., 18 (3) : 262-270.
- Parthasarathy, P.B. and Suryanarayana, K.S. (1976). Regional growth rates of area, production and productivity of major food grain crops in the selected districts of Andhra Pradesh. Occasional Paper I. Agro-Economic Research PL 480 Project, Department of Agricultural Economics, Andhra Pradesh Agricultural University, College of Agriculture, Hyderabad, 4-8.
- Ravichandran, S. and Banumathy, V. (2011). A study on trend in area, production and price movement and marketing strategies of chilly in Guntur district, Andhra Pradesh. Indian J. Agric. Mktg., 25 (3): 205-214.
- Sreekanth, P.D. and Rao, E.V.V.B. (2003). Model for forecasting the yield in cashew. Cashew, 17(4): 23-31.
- Tuteja, U. (2006). Growth programme and acreage response of pulse crops: A state-level analysis. Indian J. Agric. Econ., 61 (2): 218-237.
- WEBLIOGRAPHY
- www.indianstat.com.
- ARIMA Model for Forecasting of Greengram Prices in Telangana by using SAS
Abstract Views :495 |
PDF Views:0
Authors
Affiliations
1 Department of Agricultural Economics (A.M.I.C.), College of Agriculture, Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), IN
2 Department of Agricultural Economics (A.M.I.C.), College of Agriculture, Professor Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), IN
1 Department of Agricultural Economics (A.M.I.C.), College of Agriculture, Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), IN
2 Department of Agricultural Economics (A.M.I.C.), College of Agriculture, Professor Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 10, No 2 (2019), Pagination: 210-214Abstract
Autoregressive integrated moving average (ARIMA) approach has been applied for modeling and forecasting of greengram prices in Telangana. Autocorrelation (AC) and partial autocorrelation (PAC) functions were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting the future production. To this end, evaluation of forecasting is carried out with Akaike’s information criterion (AIC) and Schwarz’s Bayesian information criterion ( BIC). The best identified model for the data under consideration was used for out-of-sample forecasting upto November 2019.Keywords
ARIMA Model, Forecasting, Greengram, SAS.References
- Box, G.E.P., Jenkins, G.M. and Reinsel, G.C. (2007). Time series analysis: forecasting and control, 3rd Ed. Dorling Kindersley, Pvt. Ltd., New Delhi (India).
- Contreras, J., Espinola, R., Nogales, F.J. and Conejo, A.J. (2003). ARIMA models to predict nextday electricity prices, IEEE Transactions on Power Systems, 18 (3): 1014- 1020.
- Meyler, Aidan, Kenny, Geoff and Quinn, Terry (1998). Forecasting Irish inflation using ARIMA models, Central Bank and Financial Services Authority of Ireland Technical Paper Series, Vol. 1998, No. 3/RT/98, pp. 1-48.
- Prajneshu and Venugopalan, R. (1998). On nonlinear procedure for obtaining length - weight relationship. Indian J. Anim. Sci., 68 (1) : 452-456.
- Singh, S., Ramasubramanian,V. and Mehta, S.C. (2007). Statistical models for forecasting rice production of India. J. Indian Society of Agric. Statist., 61(2): 80- 83.
- Stergiou, K. I. (1989), Modeling and forecasting the fishery for pilchard (Sardina pilchardus) in Greek waters using ARIMA time-series models, ICES J. Marine Sci., 46 (1):16-23.
- Agricultural Market Intelligence Center–A Case Study of Chilli Crop Price Forecasting Intelangana
Abstract Views :537 |
PDF Views:0
Authors
Affiliations
1 Department of Agricultural Economics (A.M.I.C.), College of Agriculture, Professor Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), IN
1 Department of Agricultural Economics (A.M.I.C.), College of Agriculture, Professor Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 10, No 2 (2019), Pagination: 257-261Abstract
Indian is an agriculture based country, where more than 50 per cent of population is depend on agriculture. This structures the main source of income. The commitment of agribusiness in the national income in India is all the more, subsequently, it is said that agriculture in India is a backbone for Indian Economy. The majority of the rural producers are unable to understand and interpret the market and price behaviour to their advantages. Hence, market information and intelligence are crucial to enable farmers and traders to make informed decisions about what to grow, when to sell, and where to sell. The price forecasts are made by analyzing the prices of agricultural commodities concerned over 17 years using advanced statistical tools like ARIMA, ARCH, GARCH models, comparing the same with prices of futures markets and national and international reports of trade surveys besides conducting state level trade surveys. Under the project price forecasts were made for chilli twice once during Kharif season for 2 years fromKharif 2017-18 and 2018-19. Thus, out of total 4 price forecasts 3 price forecasts with more than 90 per cent precision were developed and disseminated through various means like university website, university magazine “Vyavasayam”, SMS to contact farmers, All India radio, farmers’ trainings and meetings, etc.Keywords
Agricultural Market, Intelligence Center, Chilli Crop.References
- Bharathi, R., Havaldhar, Y.N., Megeri, S.N. and Patil, G.M. (2009). Forecasting of arrivals and prices in Ramnagar and Siddlagatta market. J. Indian Society Agric. Stat., 63(3). 247-257.
- Box, G.E.P. and Jenkin, G.M. (1976). Time series of analysis. Forecasting and Control, Sam Franscico, Holden Day, California, USA.
- Haridev Singh, E. (2013). Forecasting tourist Inflow in Bhutan using seasonal ARIMA. Internat. J. Sci. & Res., 9 (2) : 242 - 245.
- Meyler, Aidan, Kenny, Geoff and Quinn, Terry (1998). Forecasting Irish inflation using ARIMA models, Central Bank and Financial Services Authority of Ireland Technical Paper Series, Vol. 1998, No. 3/RT/98 (December 1998), pp.1-48.
- Paul, R. K., Alam, Wasi and Paul, A.K. (2014). Prospects of livestock and dairy production in India under time series framework. Indian J.Anim. Sci., 84 (4): 462–466.
- Prajneshu and Venugopalan, R. (1998). On non-linear procedure for obtaining length - weight relationship. Indian J. Anim. Sci., 68 (1) : 452-456.
- Singh, S., Ramasubramanian,V. and Mehta, S.C. (2007). Statistical models for forecasting rice production of India. J. Indian Soc. Agric. Statist., 61(2) : 80- 83.
- WEBLIOGRAPHY
- http://agrimarketing.telangana.gov.in/indexnew.jsp.
- Chilli Price Forecasting using Auto Regressive Integrated Moving Average (ARIMA)
Abstract Views :472 |
PDF Views:0
Authors
Affiliations
1 Department of Agricultural Economics (A.M.I.C.), College of Agriculture Professor Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), IN
2 Department of Agricultural Economics (A.M.I.C.), College of Agriculture, Professor Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), IN
1 Department of Agricultural Economics (A.M.I.C.), College of Agriculture Professor Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), IN
2 Department of Agricultural Economics (A.M.I.C.), College of Agriculture, Professor Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 10, No 2 (2019), Pagination: 290-295Abstract
Chilli is considered as one of the commercial spice crops. It is the most widely used universal spice, named as wonder spice. Indian chilli is considered to be world famous for two important commercial qualities namely, its colour and pungency levels. India is the world leader in chilli production followed by China, Mexico, Turkey, Indonesia, Spain and United States. Farmer’s decision making on acreage under chilli depends on the future prices to be realized during harvest period (January- March). Hence, this paper presents a methodology to forecast prices during harvest period and applied the method to forecast for the Kharif 2019-20. This price forecast is based on the monthly modal price of chilli obtained for 17 years from Khammam regulated market using econometric models like ARIMA, SARIMA, ARIMAX, ARCH and GARCH and also the market survey.Model parameters were estimated using the SAS 9.3 software. The performance of fitted model was examined by computing various measures of goodness of fit viz., low AIC, BIC and MAPE values. The ARIMA (212) model was the best model for the price forecast of chilli.Chilli price per quintal will be around Rs. 8500 – 9100 at the time of harvesting (January to March 2020).Keywords
Stationary, Differencing, ARIMA, SARIMA, ARCH, GARCH, Price Forecast, MAPE.References
- Bharathi, R., Havaldhar,Y.N., Megeri, S.N. and Patil, G.M. (2009). Forecasting of arrivals and prices in Ramnagar and Siddlagatta market. J. Indian Soc. Agric. Statist., 63 (3). 247-257.
- Havaldhar, Y.N., Rajashekhar, K.R. and Banakar, Basavaraj (2006). Behaviour of arrivals and prices of vegetables. J. Indian Society of Agric. Statist., 60 : 47.
- Khem Chand, Jangid, B.L., Roy, P.K. and Rao, L.S.S. (2010). Price spread and efficiency ofHenna marketing in Western Raasthan. Indian J. Agric.Mktg., 24(2): 136-144.
- Prajneshu, Ravichandran, S. and Savita (2002). Structural time series models for describing cyclical fluctuations. J. Indian Soc. Agric. Statist., 55(1) : 70-78.
- Terry, L. Kastens, Rodney Jones and Ted, C. Schroeder ( 1998). Futures-based price forecasts for agricultural producers and businesses. J. Agric. & Resource Econ., 23 : 294-307.
- WEBLIOGRAPHY
- http://tsmarketing.in/