Forecasting Import Demand for Soybean Meal in Thailand Using Box-Jenkins Method

Korawat Tanong, Chalermpon Jatuporn, Vas Suvanvihok, Nareerut Seerasarn


The objective of this research was to forecast the import demand for soybean meal, which is used in animal feeds, in Thailand using a monthly time series from January 2011 to December 2020 (a total of 120 months). Econometric analysis was employed, which was comprised of: (1) stationary test of the time series using the ADF unit root method; and (2) forecast of the import demand for soybean meal in Thailand using the Box-Jenkins method or SARIMA(p,d,q)(P,D,Q)s model, respectively. The empirical results revealed that: (1) The time series of import demand for soybean meal in Thailand contained non-seasonal and seasonal stationarities at level stage and first differencing order, respectively. (2) The most suitable model to forecast the import demand for soybean meal in Thailand was SARIMA(0,0,1)(0,1,1)12 based on the lowest value of AC and SC statistics. (3) The forecasting import demand for soybean meal in Thailand predicted that the volume in 2021 should increase by 1.71% compared to 2020. The government and related stakeholders should further promote and support domestic soybean production to replace imports from abroad.




Keywords: livestock, forage crop, animal feed, time series forecasting, SARIMA.





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OFFICE OF AGRICULTURAL ECONOMICS. Agricultural economic outlook in 2020 and trends in 2021. Agricultural Development Policy and Planning Division, Office of Agricultural Economics, Bangkok, 2020.

THAI FEED MILL ASSOCIATION. The table of estimated animal population, feed quantity, and the use of raw materials in 2019, 2019.

OFFICE OF PERMANENT SECRETARY MINISTRY OF COMMERCE. International trade of Thailand: Soybean, 2021.

OFFICE OF AGRICULTURAL ECONOMICS. Import statistics: Soybean meal, 2021.

OFFICE OF AGRICULTURAL ECONOMICS. Major agricultural commodity situation and trends in 2021. Bureau of Agricultural Economic Research, Office of Agricultural Economics, Bangkok, 2021.

JATUPORN C., SUKPRASERT P., TONGCHURE S., SUVANVIHOK V., and THONGKAEW S. Forecasting import demand of table grapes: Empirical evidence from Thailand. Asian Journal of Agriculture and Rural Development, 2020, 10(2): 578-586.

RATSAMINET P. Forecasting import quantity of coffee of Thailand: The empirical study using Box-Jenkins approach. School of Economics, Sukhothai Thammathirat Open University, Nonthaburi, 2021.

RUEANGRIT P., JATUPORN C., SUVANVIHOK V., and WANASET A. Forecasting production and export of Thailand’s durian fruit: An empirical study using the Box–Jenkins approach. Humanities and Social Sciences Letters, 2020, 8(4): 430-437.

CO H. C., & BOOSARAWONGSE R. Forecasting Thailand’s rice export: Statistical techniques vs. artificial neural networks. Computers & Industrial Engineering, 2007, 53(4): 610-627.

BADMUS M. A., & ARIYO O. S. Forecasting cultivated areas and production of maize in Nigerian using ARIMA model. Asian Journal of Agricultural Sciences, 2011, 3(3): 171-176.

PAUL R. K., PANWAR S., SARKAR S. K., KUMAR A., SINGH K. N., FAROOQI S., and CHOUDHARY V. K. Modelling and forecasting of meat exports from India. Agricultural Economics Research Review, 2013, 26(2): 249-255.

UPADHYAY V. K. Modelling and forecasting export and import of Indian wood based panel using ARIMA models. Elixir Statistics, 2013, 63: 18145-18148.

JATUPORN C., & SUKPRASERT P. Forecasting models for rubber production and export quantity of Thailand. Khon Kaen Agriculture Journal, 2016, 44(2): 219-228.

PANNAKKONG W., HUYNH V. N., and SRIBOONCHITTA S. ARIMA versus artificial neural network for Thailand’s cassava starch export forecasting. In: HUYNH V. N., KREINOVICH V., and SRIBOONCHITTA S. (eds.) Causal Inference in Econometrics. Studies in Computational Intelligence, Vol. 622. Springer, Cham, 2016: 255-277.

BAŞER U., BOZOĞLU M., EROĞLU N. A., and TOPUZ B. K. Forecasting chestnut production and export of Turkey using ARIMA model. Turkish Journal of Forecasting, 2018, 2(2): 27-33.

ISLAM M. A., SUMY M. S. A., UDDIN M. A., and HOSSAIN M. S. Fitting ARIMA model and forecasting for the tea production, and internal consumption of tea (per year) and export of tea. International Journal of Material and Mathematical Sciences, 2020, 2(1): 8-15.

BOX G. E. P., JENKINS G. M., and REINSEL G. C. Time series analysis: Forecasting and control. 3rd ed. Prentice-Hall, Englewood Cliffs, New Jersey, 1994.

GUJARATI D. N., & PORTER D. C. Basic econometrics. 5th ed. McGraw Hill, New York, 2009.

DICKEY D. A., & FULLER W. A. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 1979, 74(366a): 427-431.

DICKEY D. A., & FULLER W. A. Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 1981, 49(4): 1057-1072.<1057:LRSFAT>2.0.CO;2-4

THANSETTAKIJ. Update “emergency decree”, “curfew” and “order of centre for covid-19 situation administration”, 2021.

THAIRATH ONLINE. Full announcement extended declaration of an emergency situation pursuant to the emergency decrees on public administration in emergency situation due to people do not pay attention to social distancing measures, 2020.


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