Autoregressive Integration Moving Average (ARIMA) Model for Prices of Selected Grains in the South West Nigeria

DOI: 10.18805/ag.D-239    | Article Id: D-239 | Page : 35-41
Citation :- Autoregressive Integration Moving Average (ARIMA) Model for Prices of Selected Grains in the South West Nigeria.Agricultural Science Digest.2021.(41):35-41
Taofik O. Dauda, S. Tiamiyu-Ibrahim
Address : Institute of Agricultural Research and Training, Obafemi Awolowo University, Moor Plantation, Ibadan, Nigeria.
Submitted Date : 17-02-2020
Accepted Date : 9-06-2020


Background: Price serves as signals of relative scarcity as well as abundance of a given agricultural product. Prices of agricultural products vary from month to month and even from day to day and this constitutes a source of risk to farmers whose livelihood depends on good pricing of their products.
Method: This study was conducted to identify pattern of price dynamics and to develop model for price dynamics of beans, yellow maize and local rice using monthly price data of grains from the National Bureau of Statistics (2006-2015). 
Result: The rk of the beans followed a regular decline ranging from 0.4035 to 1 while those (rk) of yellow maize and local rice follow non regular decline with the range 0.1448 – 1(yellow maize) and 0.2945 -1 (local rice) thus indicating that the data are non-stationary. The corrected Akaike information criteria (AICC) and other information obtained for the ARIMA1,2,2 were higher than those of ARIMA2,2,2. The AICC obtained for the ARIMA1,2,2 are 958.422 (beans), 1178.169 (yellow Maize) and 1211.778 (local rice). The ARIMA2,2,2 was thence favored above ARIMA1,2,2.


Correlogram Droop Heteroscedastic Hop Signal


  1. Abu, O., Olaide, A.R. and Okwoche, V.A. O. (2015). Acreage Response of Soybeans to Price in Nigeria. European Journal of Physical and Agricultural Sciences. 3: 22-31
  2. Aina, I.V., Ayinde, O.E. and Falola, A. (2015) Effect of Price Variation on Rice Production in Nigeria (1970 – 2011). Proceedings of the 29th International Conference of Agricultural Economist (ICAE) held between 8th – 14th August, 2015 at Milan, Italy, 15pp.
  3. Akanni, K.A. (2014) Agricultural Price Policy, Consumer Demand and Implications for Household Food Security in Nigeria. International Journal of Food and Agricultural Economics. 2: 121-132.
  4. Akintunde O.K., Yusuf S.A., Bolarinwa A.O. and Ibe R.B. (2012). Price Formation and Transmission of Staple Food Stuffs in Osun State, Nigeria. ARPN Journal of Agricultural and Biological Science. 7: 699 - 708
  5. Akpan , S.B., Udoh, E.J. and Udo, U.J. (2014). Monthly Price Analysis of Cowpea (Beans) and Maize in Akwa Ibom State, Southern Nigeria. International Journal of Food and Agricultural Economics. 2: 65-86
  6. Assis, K., Amran, A., Remali, Y. and Affendy, H. (2010). A Comparison of Univariate Time Series Methods for Forecasting Cocoa Bean Prices. Trends in Agricultural Economics. 3: 207 - 215.
  7. Box, G.E.P. and Jenkins, G.M. (1976) Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day. 328pp.
  8. Chatfield, C., 2003, The analysis of time series, an introduction, sixth edition: New York, Chapman and Hall/CRC Press, sixth edition.
  9. Dickey, D.A. and Pantula, S.G. (2002). “Determining the Order of Differencing in AR Processes.” Journal of Business Economics Statistics. 20: 18-24.
  10. Economic Times (2019). Definition of Pricing Strategies. An http documents available at and assesed on 20th November, 2019, 6pp.
  11. Gieri, A.A, Salihu M. and Salamatu, U. (2015). Determination of Conduct, Performance and Structure of Cowpea Marketing in Yola North and South Local Government Areas of Adamawa State, Nigeria. American Research Journal of Agriculture. 1(2): 23-31.
  12. Huka, H., Ruoja, C. and Mchopa, A. (2014). Price Fluctuation of Agricultural Products and its Impact on Small Scale Farmers Development: Case Analysis from Kilimanjaro Tanzania. European Journal of Business and Management. 6: 155-161.
  13. Ikeokwu N. 2019. The Global Food Crisis and the Challenge to Nigeria. The Nigerian Village Square. An http document available at Assessed on Wednessday, 20th November, 2019, 21pp
  14. Kassim, A.A. (2012). Economics of Marketing of Food Grains in South Western Nigeria. economía Mexicana nuevaépoca. 21(2): 373-390.
  15. Karthika, M., Krishnaveni, V. and Thirunavukkarasu, (2017). Forecasting of meteorological drought using ARIMA model. Indian Journal of Agricultural Research. 51(2): 103-111
  16. Kirimi, J. (2016). Modelling the Volatility of Maize Prices Using Autoregressive Integrated Moving Average Model. Mathematical Theory and Modeling. 6(6): 143-149
  17. Lasker, E.A., Masudul, Islam, Md. Rashed K. and Faruque, A. (2013). Forecasting production of food grain using ARIMA model and its requirement in Bangladesh. J.Mech.Cont. and Math. Sci. 7: 1056-1066
  18. Montgomery, D.C. (1990). Forecasting and Time Series Analysis”, 2nd edition, New York: McGRAW-Hill, Inc, 569pp.
  19. Mustapha, U.M. and Culas, R.J. (2019). Causes, magnitude and consequences of price variability, JEL Codes: Q15, Q16, Q18. Australian Conference of Economists. An http document assessed at November, 2019.
  20. Pani, R, Biswal S.K. and Mishra, U.S. (2019) Green gram weekly price forecasting using time series model. Espacios. 40(7): 15 - 19
  21. Tahir, H.M. (2014). Hectrage Response of Some Selected Cereal Crops to Price and Non-Price Factors in Nigeria (1983-2008). Research on Humanities and Social Sciences. Vol.4: 44-52

Global Footprints