International Research Journal of Agricultural Economics and Statistics

R. Vijaya Kumari ¹, G. Ramakrishna ², Panasa Venkatesh ², A. Sreenivas ²

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

Abstract

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.

Full Text

     

R. Vijaya Kumari ¹, Gundu Ramakrishna ¹, Panasa Venkatesh ¹, Akula Sreenivas ¹

Affiliations
1 Department of Agricultural Economics (A.M.I.C.), College of Agriculture, Professor Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), IN

Abstract

The main objective of the study is to assess cointegration of major redgram markets and price movement in major markets in India using important econometric tools like Augmented dickey-fuller (ADF), Johansen’s cointegration test, granger causality test and vector error correction model (VECM). The results of the study indicated that in the long-run there was a two direction relationship between market prices. There is bidirectional causality affected on redgram prices redgram prices of Mumbai – Indore and Mumbai –Amravati. There is unidirectional causality affected on redgram prices of Amravati –Vijayawada, Gulbarga – Tandur, Gulbarga –Vijayawada, Indore -Vijayawada, Mumbai –Vijayawada. Results of vector error correction model (VECM) showed Mumbai market one month lag price is affecting current prices of Gulbarga market. Amravati market one month lag price is affecting current prices of Vijayawada market. Tandur market two months lag price is affecting current prices of Gulbarga and Amravati markets.

Keywords

Price Movements, Redgram Major Markets, using Cointegration Analysis.

Full Text

     

R. Vijaya Kumari ¹, Panasa Venkatesh ¹, G. Ramakrishna ¹, A. Sreenivas ¹

Affiliations
1 Department of Agricultural Economics (A.M.I.C.), College of Agriculture, Professor Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), IN

Abstract

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.

Full Text

     

R. Vijaya Kumari ¹, P. Venkatesh ², G. Ramakrishna ², A. Sreenivas ²

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

Abstract

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.

Full Text