Forecasting rice area, yield and production of Vietnam: ARIMA model approach
Vol. 132 No. 5C (2023), Original Article
Vol. 132 No. 5C (2023)

Forecasting rice area, yield and production of Vietnam: ARIMA model approach

Original Article
https://doi.org/10.26459/hueunijed.v132i5C.7179
Published 2023-11-28
Ho Trong Phuc
University of Economics, Hue University, 99 Ho Dac Di St., Hue, Vietnam
Pham Xuan Hung
University of Economics, Hue University, 99 Ho Dac Di St., Hue, Vietnam
PDF (Vietnamese)

Keywords

mô hình ARIMA
dự báo
diện tích lúa
năng suất lúa
sản lượng lúa
Việt Nam ARIMA model
forecasting
rice area
rice yield
rice production
Vietnam

Abstract

This paper analyzes and forecasts rice area, yield, and production of Vietnam by 2030 using                  Box–Jenkins ARIMA model. The study uses time series data over the 1990–2021 period compiled from the General Statistics Office of Vietnam. The statistical summary shows that in 2021, the total rice-cultivated area is 7.24 million ha, with an average yield of 6.06 tonnes/ha and a total rice production of 43.85 million tonnes. Over the 1990–2021 period, rice yield and production slightly increase, with an average annual increase of 2.10% and 2.70%, respectively; the rice-cultivated area also increases, but at a lower rate (0.58%) and it tends to decrease from 2013 onwards. The forecasted results show that by 2030, the rice-cultivated area will continue to decrease by approximately 0.8 million ha, to 6.42 (4.17; 8.67) million ha. While, rice yield and production will increase and reach 6.90 (6.26; 7.53) tonnes/ha and 46.60 (36.02; 57.19) million tonnes, respectively. This study suggests that in the next decade, policies related to rice production should focus on promoting the improvements in rice yield and profits instead of expanding the rice-cultivated area. The improvement of production efficiency and the adoption of advanced technologies would be appropriate directions.

https://doi.org/10.26459/hueunijed.v132i5C.7179
PDF (Vietnamese)

References

  1. Phuc, H. T., Burton, M., Ma, C., and Hailu, A. (2022), Quantifying heterogeneity, heteroscedasticity and publication bias effects on technical efficiency estimates of rice farming: A meta‐regression analysis, Journal of Agricultural Economics, 73(2), 580–597.
  2. GSO (2023), Diện tích, sản lượng và năng suất lúa cả năm của Việt Nam. Available: https://www.gso.gov.vn/px-web-2/?pxid=V0612&theme=N%C3%B4ng%2C%20l%C3%A2 m%20nghi%E1%BB%87p%20v%C3%A0%20th%E1%BB%A7y%20s%E1%BA%A3n.
  3. FAOSTAT (2023), Area harvested, yield, and production quantity of global rice, paddy. Available: http://www.fao.org/faostat/en/#data/QC.
  4. GSO (2023), Một số mặt hàng xuất khẩu chủ yếu. Available: https://www.gso.gov.vn/px-web-2/?pxid=V0812&theme=Th%C6%B0%C6%A1ng%20m%E1%BA%A1i%2C%20gi%C3 %A1%20c%E1%BA%A3.
  5. Hien, N. T. M., Kawaguchi, T., and Suzuki, N. (2003), A study on technical efficiency of rice production in the Mekong Delta-Vietnam by stochastic frontier analysis, J. Fac. Agr., Kyushu Univ., 48(1/2), 325–57.
  6. Huy, H. T. (2009), Technical efficiency of rice-producing households in the Mekong Delta of Vietnam, Asian Journal of Agriculture and Development, 6(2), 35–50.
  7. Khai, H. V. and Yabe, M. (2011), Technical efficiency analysis of rice production in Vietnam, Journal of ISSAAS, 17(1), 135–146.
  8. Linh, V. H. (2012), Efficiency of rice farming households in Vietnam, International Journal of Development Issues, 11(1), 60–73.
  9. Tung, D. T. (2013), Changes in the technical and scale efficiency of rice production activities in the Mekong Delta, Vietnam, Agricultural and Food Economics, 1(1), 1–11.
  10. Linh, T. T., Nanseki, T., and Chomei, Y. (2015), Productive efficiency of crop farms in Viet Nam: A DEA with a smooth bootstrap application, Journal of Agricultural Science, 7(5), 37.
  11. Phuc, H. T. and Napasintuwong, O. (2015), Profit inefficiency among hybrid rice farmers in Central Vietnam, Agriculture and Agricultural Science Procedia, 5, 89–95.
  12. Phuc, H. T. (2021), Profit Efficiency and Rice Variety Choice in Rice Farming in Vietnam: A Stochastic Frontier Analysis Approach, Doctoral Thesis, UWA School of Agriculture and Environment, The University of Western Australia, Australia.
  13. Hyndman, R. J. and Athanasopoulos, G. (2018), Forecasting: Principles and Practice, 2nd ed., Monash University, Australia.
  14. Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., Taieb, S. B., Bergmeir, C., Bessa, R. J., Bijak, J. and Boylan, J. E. (2022), Forecasting: Theory and practice, International Journal of Forecasting.
  15. Dương Văn Khảm (2000), Áp dụng phương pháp tích phân hồi quy bội và mô hình ARIMA trong việc dự báo năng suất lúa ở Hà Nội, Tạp chí Khí tượng Thủy Văn, 1–10.
  16. Rahman, N. (2010), Forecasting of boro rice production in Bangladesh: An ARIMA approach, Journal of the Bangladesh Agricultural University, 8(1), 103–112.
  17. Võ Văn Tài (2012), Dự báo sản lượng lúa Việt Nam bằng các mô hình toán học, Tạp chí Khoa học Trường Đại học Cần Thơ, 23b, 125–134.
  18. Biswas, R. and Bhattacharyya, B. (2013), ARIMA modeling to forecast area and production of rice in West Bengal, Journal of crop and weed, 9(2), 26–31.
  19. Hamjah, M. A. (2014), Rice production forecasting in Bangladesh: An application of Box-Jenkins ARIMA model, Mathematical theory and modeling, 4(4), 1–11.
  20. Sahu, P., Mishra, P., Dhekale, B., Vishwajith, K., and Padmanaban, K. (2015), Modelling and forecasting of area, production, yield and total seeds of rice and wheat in SAARC countries and the world towards food security, American Journal of Applied Mathematics and Statistics, 3(1), 34–48.
  21. Hemavathi, M. and Prabakaran, K. (2018), ARIMA model for forecasting of area, production and productivity of rice and its growth status in Thanjavur District of Tamil Nadu, India, Int. J. Curr. Microbiol. App. Sci, 7(2), 149–156.
  22. Phạm Thị Thảo Hiền, Nguyễn Ngọc Ánh, Tôn Nữ Tuyết Trinh và Nguyễn Đức Hồng (2020), Ứng dụng mô hình ARIMA dự báo sản lượng lúa Tỉnh Thừa Thiên Huế đến năm 2025, Tạp chí Khoa học và công nghệ nông nghiệp Trường Đại học Nông Lâm Huế, 4(2), 1915–1921.
  23. Noorunnahar, M., Chowdhury, A. H., and Mila, F. A. (2023), A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh, PloS one, 18(3), e0283452.
  24. Đinh Phi Hổ và Nguyễn Văn Phương (2015), Kinh tế phát triển: Cơ bản và nâng cao, Nxb. Kinh tế TP. Hồ Chí Minh.
  25. Box, G. E. P. and Jenkins, G. M. (1970), Time series analysis: Forecasting and control, San Francisco: Holden-Day.
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