Home⇛Journal of Interdisciplinary Perspectives⇛vol. 3 no. 10 (2025)

Time Series Analysis of Factors Affecting Coconut Price in the Philippines

Cristian T. Camañan | Joy L. Picar

Discipline: Agriculture

 

Abstract:

Coconut remains a fundamental agricultural product in the Philippines, widely used in food production, cosmetics, and bio-industries. Despite its importance, the price of copra, its dried kernel, has experienced substantial volatility due to environmental conditions, market dynamics, and production levels. This study aims to analyze and forecast the factors influencing copra prices in the country, utilizing time series data from 2013 to 2022. Specifically, it investigates the influence of rainfall, coconut production, and market demand. Utilizing a non-experimental quantitative approach, the study employed ARIMA and SARIMA models for trend analysis and forecasting. AIC, BIC, AICc, residual variance, and log-likelihood guided model selection. Results revealed that ARIMA(0,1,1) was optimal for forecasting coconut production, ARIMA(2,1,1) for copra prices, and SARIMA(2,0,1)(1,0,1)[12] for Philippine rainfall,SARIMA(0,0,1)(2,1,1)[12] was used for Luzon, SARIMA(1,0,0)(2,0,0)[12] for Visayas, ARIMA(1,1,1) for Mindanao. Regression results confirmed that rainfall did not significantly influence coconut production, and neither coconut production nor market demand had a statistically significant effect on copra prices. The forecast indicated a potential decline in coconut production and copra prices if current conditions persist. The study concludes that time series forecasting models are essential for agricultural stakeholders, enabling data-driven planning, risk management, and policy formulation. To improve predictive accuracy and policy relevance, future studies are encouraged to incorporate historical data points on market demand and coconut production, and to adopt multivariate time series models, such as ARIMAX or Vector Autoregression (VAR), which can account for external shocks and global economic indicators. These approaches could better capture the complex interactions within the Philippine coconut industry and support more effective decision-making.



References:

  1. Abdullah, A., Sarpong-Streetor, R., Sokkalingam, R., Othman, M., Azad, A., Syahrantau, G., & Arifin, Z. (2023). Intelligent hybrid ARIMA-NARX time series model for forecasting coconut prices. IEEE Access, 11, 48568-48577. https://doi.org/10.1109/access.2023.3275534
  2. Abeysekara, M. G. D., & Waidyarathne, K. (2020). The coconut industry: A review of price forecasting modelling in major coconut-producing countries. Cord, 36https:/S&Porg/10.37833/cord.v36i.422
  3. Acharya, R. (2024). Comparative analysis of stock bubbles in S&P 500 individual stocks: A study using SADF and GSADF models. Journal of Risk and Financial Management, 17(2), 59. doi:10.3390/jrfm17020059
  4. Adebiyi, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Stock price prediction using the ARIMA model. 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, 106–112. https://doi.org/10.1109/uksim.2014.67
  5. Aguilar, E. A., Montesur, J. G., & Lacsina, J. C. (2023). Capacitating strategies to promote climate-resilient coconut-based farming systems (CR-CBFS) in vulnerable coconut communities of the Philippines. IOP Conference Series: Earth and Environmental Science, 1235(1), 012002. https://doi.org/10.1088/1755-1315/1235/1/012002
  6. Ahalya, S. P., Murugananthi, D., Rohini, A., Devi, R. P., & Kalpana, M. (2023). Study on predicting the price of coconut in the Tamil Nadu market. Asian Journal of Agricultural Extension, Economics & Sociology, 41(10), 149–158. https://doi.org/10.9734/ajaees/2023/v41i102153
  7. Ahn, J., Park, C.-G., & Park, C. (2017). Pass-through of imported input prices to domestic producer prices: Evidence from sector-level data. The B.E. Journal of Macroeconomics, 17(2), Article 20160034. https://doi.org/10.1515/bejm-2016-0034
  8. Alouw, J. C., & Wulandari, S. (2020). Present status and outlook of coconut development in Indonesia. IOP Conference Series: Earth and Environmental Science (Vol. 418, No. 1, p. 012035). IOP Publishing. https://doi.org/10.1088/1755-1315/418/1/012035
  9. Antonio, R. J., Valera, H. G., Mishra, A. K., Pede, V. O., Yamano, T., & Vieira, B. O. (2025). Rice price inflation dynamics in the Philippines. Australian Journal of Agricultural and Resource Economics, 69(2), 440-452. https://doi.org/10.1111/1467-8489.70012
  10. Arapović, A. O., & Karkin, Z. (2015). The impact of the agricultural market information system in Bosnia & Herzegovina on market integration: Asymmetric information and market performance. Khazar Journal of Humanities and Social Sciences, 18(1), 56-67. https://doi.org/10.5782/2223-2621.2014.18.1.56
  11. ArunKumar, K., Kalaga, D., Kumar, C., Chilkoor, G., Kawaji, M., & Brenza, T. (2021). Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered, and deaths) for the top-16 countries using statistical machine learning models: Auto-regressive integrated moving average (ARIMA) and seasonal auto-regressive integrated moving average (SARIMA). Applied Soft Computing, 103, 107161. https://doi.org/10.1016/j.asoc.2021.107161
  12. Assa, H. (2016). Financial engineering in pricing agricultural derivatives based on demand and volatility. Agricultural Finance Review, 76(1), 42–53. https://doi.org/10.1108/afr-11-2015-0053
  13. Boudreau, P., Cajal-Grossi, J., & Macchiavello, R. (2023). Weather, productivity, and agricultural markets: Evidence from coconut farming. Agricultural Systems Journal, 222, 103938. https://doi.org/10.1016/j.agsy.2023.103938
  14. Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: A practical information-theoretic approach (2nd ed.). Springer.
  15. CEIC Data. (2025). Philippines: Copra prices (₱ per 100 kg). Retrieved from https://www.ceicdata.com/en/philippines/copra-price
  16. Dawe, D. (2008). Have recent increases in international cereal prices been transmitted to domestic economies? The experience in seven large Asian countries (Working Paper No. 08-03). Agricultural and Development Economics Division, Food and Agriculture Organization of the United Nations. https://www.fao.org/3/ai506e/ai506e.pdf
  17. Descals, A., Wich, S. A., Szantoi, Z., Struebig, M. J., Dennis, R., Hatton, Z., & Meijaard, E. (2023). High-resolution global map of closed-canopy coconut palm. Earth System Science Data, 15(9), 3991-4010. https://doi.org/10.5194/essd-15-3991-2023
  18. Duan, Q., Xia, H., & Huang, Y. (2024). High slope deformation prediction based on residual modified ARIMA-GA-BP modeling. In International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024) (Proc. SPIE 13281, 1328108). SPIE. https://doi.org/10.1117/12.3050788
  19. Faried, A. I., Syaula, M., Ananda, G. C., & Rahmadani, A. (2023). Future product intensification priorities for coconut plantation villages’ local conditions. International Journal of Management, Economic and Accounting, 1(2), 444-449. https://doi.org/10.61306/ijmea.v1i2.46
  20. Fernando, K. G. S., & Samarasinghe, S. A. A. T. (2019). Application of ARIMA models in agricultural production forecasting: A case study in coconut production in Sri Lanka. International Journal of Scientific and Research Publications, 9(2), 765–773. https://doi.org/10.29322/IJSRP.9.02.2019.p86100
  21. Fisher, A., Hodgdon, T., & Lewis, M. (2024). Time-series forecasting methods: A review. https://doi.org/10.21079/11681/49450
  22. Fujita, K. S. (2015). Estimating price elasticity using market-level appliance data. https://doi.org/10.2172/1236368
  23. Girsang, L., Sukiyono, K., & Asriani, P. S. (2018). Export demand for Indonesia’s crude palm oil (CPO) to Pakistan: Application of the error correction model. AGRITROPICA: Journal of Agricultural Sciences, 1(2), 68-77. https://doi.org/10.31186/j.agritropica.1.2.68-77
  24. Hadi, M. N. (2022). Implementation of traditional risk management as loss prevention in coconut production results. AKADEMIK: Jurnal Mahasiswa Ekonomi &Amp; Bisnis, 2(2), 92-102. https://doi.org/10.37481/jmeb.v2i2.554
  25. Kang, Y., Li, L., Guo, J., Guo, X., & Pu, W. (2023). Analysis of the demand pricing model in the cloud service market. Advances in Intelligent Systems Research, 47-51. https://doi.org/10.2991/978-94-6463-238-5_7
  26. Keintjem, K., Tulung, J. E., & Arie, F. V. (2023). Supply chain analysis of copra in Pakuure Village, Tenga of South Minahasa. Jurnal EMBA: Jurnal Riset Ekonomi, Manajemen, Bisnis dan Akuntansi, 11(1), 204-212. https://doi.org/10.35794/emba.v11i1.45000
  27. Lim, K. G. (2015). Model selection using information criteria for time series analysis. Journal of Applied Statistics, 42(6), 1231–1247. https://doi.org/10.1080/02664763.2014.980769
  28. M, S. S. (2025). A study on the effectiveness of multimodal transportation. International Journal of Scientific Research in Engineering and Management, 09(04), 1–9. https://doi.org/10.55041/ijsrem45954
  29. Mardiyati, S., & Natsir, M. (2023). Competitiveness and policy of soybean farming in Jeneponto Regency. Jurnal Penelitian Pertanian Terapan, 23(2). https://doi.org/10.25181/jppt.v23i2.272
  30. Mathlouthi, H., & Lebdi, M. (2021). Estimation of dry events duration in Northern Tunisia – Analysis of extreme trends. Proceedings of the International Association of Hydrological Sciences, 384, 195–201. doi:10.5194/piahs-384-195-2021
  31. Mialhe, F., Coudrain, A., & Viollaz, M. (2013). Rainfall variability and coconut production in the Philippines. Climate Risk Management, 2, 12–22. https://doi.org/10.1016/j.crm.2013.07.001
  32. Mu’min, H., Telaumbanua, E., Sya’rani, R., Basir, B., & Hasdiansa, I. W. (2024). Building competitive advantage: Copra marketing strategy with SWOT analysis approach. Journal of Economics, Entrepreneurship, Management, Business and Accounting, 2(1), 16–28. https://doi.org/10.61255/jeemba.v2i1.285
  33. Mulyadi, H., Nazamuddin, B., & Seftarita, C. (2019). What determines exports of coconut products? The case of Indonesia. International Journal of Academic Research in Economics and Management Sciences, 8(2). https://doi.org/10.6007/ijarems/v8-i2/5874
  34. Onkov, K., & Tegos, G. (2014). Forecasting algorithm adaptive automatically to time series length. In IFIP International Conference on Artificial Intelligence Applications and Innovations (pp. 537-545). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-44654-6_53
  35. Özdemir, E. (2022). Foreign exchange volatility and the bubble formation in financial markets: Evidence from the COVID-19 pandemic. Ekonomika, 101(1), 8. doi:10.15388/ekon.2022.101.1.8
  36. Panjaitan, B. R. (2024). Socioeconomic profile of coconut farmers in Sei Kepayang Tengah Village, Asahan Regency (analysis of education, housing, and income). Buletin Penelitian Sosial Ekonomi Pertanian Fakultas Pertanian Universitas Haluoleo, 26(1), 18-24. https://doi.org/10.37149/bpsosek.v26i1.1046
  37. Pathmeswaran, C., Lokupitiya, E., Waidyarathne, K. P., & Lokupitiya, R. S. (2018). Impact of extreme weather events on coconut productivity in three climatic zones of Sri Lanka. European Journal of Agronomy, 96, 47–53. https://doi.org/10.1016/j.eja.2018.03.001
  38. Prades, A., Salum, U. N., & Pioch, D. (2016). New era for the coconut sector. What prospects for research? Ocl, 23(6), D607. https://doi.org/10.1051/ocl/2016048
  39. Pujiharto, P., & Wahyuni, S. (2023). Formulation of SCP-based coconut sugar marketing model through analysis of marketing patterns in Central Java Province, Indonesia. Tekirdağ Ziraat Fakültesi Dergisi, 20(4), 765-772. https://doi.org/10.33462/jotaf.1150532
  40. Pulhin, F. B., Peras, R. J. J., Pulhin, J. M., & Gevaña, D. T. (2016). Influence of rainfall variability on coconut yield in selected provinces in the Philippines. Environmental Science and Management, 19(2), 54–61. https://doi.org/10.47125/jesam/2016_sp1/01
  41. Punzalan, J. K. M., & Rosentrater, K. A. (2024). Copra meal: A review of its production, properties, and prospects. Animals, 14(11), 1689. https://doi.org/10.3390/ani14111689
  42. Rahmawati, E., Nuraini, C., & Mutolib, A. (2023). Export competitiveness of Indonesian copra in international trade 2017-2021. East Asian Journal of Multidisciplinary Research, 2(9), 3621-3630. https://doi.org/10.55927/eajmr.v2i9.6134
  43. Reddy, S. M. W., Groves, T., & Nagavarapu, S. (2014). Consequences of a government-controlled agricultural price increase on fishing and the coral reef ecosystem in the Republic of Kiribati. PLoS O,NE, 9(5), e96817. https://doi.org/10.1371/journal.pone.0096817
  44. Reio, T. G. (2016). Nonexperimental research: Strengths, weaknesses, and issues of precision. European Journal of Training and Development, 40(8/9), 676–690. https://doi.org/10.1108/ejtd-07-2015-0058
  45. Rodríguez, A. C., Daudt, R. C., D’Aronco, S., Schindler, K., & Wegner, J. D. (2021). Robust damage estimation of typhoon Goni on coconut crops with sentinel-2 imagery. Remote Sensing, 13(21), 4302. https://doi.org/10.3390/rs13214302
  46. Senadheera, S. D. N. M., Bandara, A. M. K. R., & Lankapura, A. I. Y. (2020). “Factors influencing fertilizer usage by medium and large-scale coconut farmers in Gampaha District, Sri Lanka”. Asian Journal of Research in Agriculture and Forestry, 6 (4), 41-47. https://doi.org/10.9734/ajraf/2020/v6i430113
  47. Sharma, V. K., & Nigam, U. (2020). Modeling and forecasting of the COVID-19 growth curve in India. https://doi.org/10.1101/2020.05.20.20107540
  48. Sirisha, U., Belavagi, M., & Attigeri, G. (2022). Profit prediction using ARIMA, SARIMA, and LSTM models in time series forecasting: A comparison. IEEE Access, 10, 124715–124727. https://doi.org/10.1109/access.2022.3224938
  49. Sportel, T., & Véron, R. (2016). Coconut crisis in Kerala? Mainstream narrative and alternative perspectives. Development and Change, 47(5), 1051–1077. https://doi.org/10.1111/dech.12260
  50. United Nations. (2015). Transforming our world: The 2030 agenda for sustainable development. Retrieved from: https://sdgs.un.org/2030agenda
  51. Utomo, Y., Jumali, M., & Rohman, F. (2024). Autoregressive integrated moving average (ARIMA) simulation methods in product inventory 9969b printable splicing tape. Tibuana, 7(2), 137-143. https://doi.org/10.36456/tibuana.7.2.9304.137-143
  52. Wanjuki, T., Wagala, A., & Muriithi, D. (2021). https://doi.the org/10.36456/tibuana.7.2.9304.137-143
  53. ages in Kenya using seasonal autoregressive integrated moving average (SARIMA) models. European Journal of Mathematics and Statistics, 2(6), 50-63. https://doi.org/10.24018/ejmath.2021.2.6.80
  54. Yan, X., Zhang, H., Zhi-gang, W., & Miao, Q. (2024). Probabilistic time series forecasting based on similar segment importance in the process industry. Processes, 12(12), 2700. https://doi.org/10.3390/pr12122700
  55. Zainol, F. A., Arumugam, N., Daud, W. N. W., Suhaimi, N. A. M., Ishola, B. D., Ishak, A. Z., & Afthanorhan, A. (2023). Coconut value chain analysis: A systematic review. Agriculture, 13(7), 1379. https://doi.org/10.3390/agriculture13071379
  56. Zakia, Z. F., & Marifatullah, A. (2023). Analysis of the causes of coconut production decline and its impact on farmers’ economy in Durian Payung Village. Al-Hijrah: Journal of Islamic Economics and Banking, 1(1), 1. https://doi.org/10.55062/al-hijrah.v1i1.297
  57. Zheng, Y., Zhang, L., Wang, C., Wang, K., Guo, G., Zhang, X., & Wang, J. (2021). Predictive analysis of the number of human brucellosis cases in Xinjiang, China. Scientific Reports, 11(1). https://doi.org/10.1038/s41598-021-91176-5

Tools


Copyright © 2025  KITE Digital Educational Solutions |  Exclusively distributed by CE-Logic