Investigating Long-Run and Short-Run Dynamics of Palm Oil Production with Key Factors Using the VECM Method

https://doi.org/10.47194/orics.v6i4.429

Authors

Keywords:

Palm oil production, rainfall, number of bunches, bunch weight, vector error correction model

Abstract

This study investigates the long-run and short-run relationships among palm oil production, rainfall, the number of bunches per palm (NOB), and average bunch weight (BTR) using the Vector Error Correction Model (VECM). Monthly data from 2015 to 2024 obtained from PT Perkebunan Nusantara IV (PTPN IV) Regional III, Sei Rokan Estate, were analyzed. Descriptive statistics indicate high variability in rainfall and relatively balanced distributions for production, NOB, and BTR. The Augmented Dickey-Fuller (ADF) test confirmed that all variables became stationary after first differencing, and the Johansen cointegration test identified three cointegrating relationships, suggesting both short-run and long-run linkages among variables. The VECM estimation results reveal positive long-run relationships for palm oil production (ECT = 0,052), rainfall (ECT = 0,090), and NOB (ECT = 0,042), indicating that these variables move toward long-run equilibrium in the same direction. In the short run, previous rainfall significantly affects both current palm oil production and NOB, with coefficients of 0,203 and 0,178, respectively, highlighting the critical role of rainfall fluctuations in influencing short-term productivity and fruit development. Model evaluation using the Root Mean Square Error (RMSE) shows low prediction errors across all variables, with rainfall having the highest RMSE (1,334) and NOB the lowest (0,962), confirming the model’s strong predictive performance. Overall, the findings demonstrate that the VECM approach effectively captures both long-run equilibrium and short-run dynamics among key determinants of palm oil productivity in the Sei Rokan plantation.

References

Ambala, R. N., & Anarfo, E. B. (2022). A Vector Autoregression (VAR) analysis of corruption, economic growth, and foreign direct investment in Ghana. Cogent Economics and Finance, 10(1).

BMKG. (2025). Curah hujan. BMKG. https://gaw-bariri.bmkg.go.id/index.php/karya-tulis-dan-artikel/gawsarium/262-curah-hujan

Granger, C. W. J., & Newbold, P. (1978). Spurious regressions in econometrics. Journal of Econometrics, 2(2), 111–120.

Hanck, C., Arnold, M., Gerber, A., & Schmelzer, M. (2025). Introduction to econometrics with R. Stata Press. https://www.econometrics-with-r.org/14.6-llsuic.html

Hasibuan, H. A. (2020). Penentuan rendemen, mutu dan komposisi kimia , minyak sawit dan minyak inti sawit tandan buah segar bervariasi kematangan sebagai dasar untuk penetapan standar kematangan panen. Jurnal Penelitian Kelapa Sawit, 28(3), 123–132.

Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2–3), 231–254.

Juselius, K. (2019). The Cointegrated VAR Model: Methodology and Applications. Oxford University Press.

Krisdiarto, A. W., Sutiarso, L., & Widodo, K. H. (2017). Optimasi kualitas tandan buah segar kelapa sawit dalam proses panen-angkut menggunakan model dinamis. Agritech, 37(1), 102.

Lubis, A. U. (2008). Kelapa sawit (Elaeis guineensis Jacq.) di Indonesia. Pusat Penelitian Marihat Bandar Kuala Pematang Siantar.

Lütkepohl, H. (2005). New introduction to multiple time series analysis. Springer.

Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1999). Forecasting method and applications (2nd ed.). Erlangga.

Morley, S. K., Brito, T. V., & Welling, D. T. (2018). Measures of model performance based on the log accuracy ratio. Space Weather, 16(1), 69–88.

PKT Group. (2024). Cara menghitung berat janjang rata-rata (BJR) kelapa Sawit. PKT Group. https://pkt-group.com/sawitnotif/ketahui-cara-menghitung-berat-janjang-rata-rata-bjr-kelapa-sawit/?utm_source=chatgpt.com

Prahutama, A., Ispriyanti, D., & Tiani Wahyu Utami, D. (2019). Pemodelan sektor-sektor inflasi di Indonesia menggunakan Vector Autorgressive (VAR). 20(1), 47–52.

PTPN IV. (2025). Company profile. PTPN IV. https://www.ptpn4.co.id/about

Qu, Z., & Perron, P. (2007). Estimating and testing structural changes in multivariate regressions. Econometrica, 75(2), 459–502.

Soekartawi. (2002). Prinsip dasar ekonomi pertanian: Teori dan aplikasi (2nd ed.). PT Raja Grafindo Persada.

Srinivas, T. A. S., Sravanthi, Y., Kumar, Y. V., & Srihith, I. V. D. (2024). Data standardization: Key to effective data integration. Advanced Innovations in Computer Programming Languages, 6(1), 1–4.

Van Dao, D., Ly, H. B., Trinh, S. H., Le, T. T., & Pham, B. T. (2019). Artificial intelligence approaches for prediction of compressive strength of Geopolymer Concrete. Materials, 12(6), 1–17.

Wei, W. W. (2006). Time series analysis: Univariate and multaivariate methods. Pearson Education.

Winarno, S., Usman, M., Warsono, Kurniasari, D., & Widiarti. (2021). Application of Vector Error Correction Model (VECM) and impulse response function for daily stock prices. Journal of Physics: Conference Series, 1751(1–17).

Published

2025-12-31

How to Cite

Lathifah Zahra, & Gustriza Erda. (2025). Investigating Long-Run and Short-Run Dynamics of Palm Oil Production with Key Factors Using the VECM Method. Operations Research: International Conference Series, 6(4), 169–182. https://doi.org/10.47194/orics.v6i4.429