Time-Series modeling of supply chain disruption drivers in ASEAN agri-food logistics networks
Article Sidebar
Main Article Content
Abstract
Disruptions in agri-food supply chains pose logistical and operational challenges for ASEAN member economies, whose agricultural sectors are simultaneously exposed to fluctuations in freight costs and climate-related production shocks. Prior time-series applications in shipping markets, agricultural commodity co-movement, and supply chain performance have generally examined these channels separately, and the joint transmission of these channels into ASEAN agri-food commodity logistics outcomes remains largely unexamined. This study applies a Vector Autoregression (VAR) time-series modeling framework to identify and quantify the principal drivers of disruption in ASEAN agri-food logistics networks, using 4,108 daily observations spanning January 2015 to March 2026. Seven variables are jointly modeled: Baltic Dry Index daily change (D_BDI), Brent crude oil log-return (R_Brent), Geopolitical Risk Index daily change (D_GPR), Oceanic Niño Index anomaly change (D_ONI), ASEAN aggregate agricultural production return (R_ASEAN_Production), and daily price changes of Robusta and Arabica coffee. Stationarity is verified by the Augmented Dickey-Fuller (ADF) tests. The lag order p = 2 is selected based on BIC and HQIC. GPR shocks exert statistically significant lagged effects on Arabica price (p = 0.007) and Robusta price (p = 0.021); ONI anomaly changes significantly propagate into Robusta prices (p = 0.021). A cross-domain pathway from ONI to GPR (p = 0.010) is consistent with climate disruption signals coinciding with subsequent geopolitical risk movements. BDI first-order persistence coefficient is 0.596 (p < 0.001). Forecast Error Variance Decomposition (FEVD) shows Robusta-to-Arabica cross-variance at 44.03% at horizon 10. Durbin-Watson statistics (1.958–2.061) confirm residual white noise across all equations. These results provide quantitative inputs for engineering early-warning systems for disruption and logistics resilience planning in ASEAN agri-food supply networks.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Guan L, Zhao Z, Sun Q. Understanding the Baltic Dry Index (BDI): an explainable decomposition approach. Marit Policy Manag. 2026;53(1):111-35. DOI: https://doi.org/10.1080/03088839.2024.2448446
Xue Y, Yan J, Mohsin M, Mehak A. Supply chain risks in agri-food systems: a comprehensive review of economic vulnerabilities and mitigation approaches. Front Sustain Food Syst. 2025;9:1649834. DOI: https://doi.org/10.3389/fsufs.2025.1649834
Bandyopadhyay A, Rajib P. The asymmetric relationship between Baltic Dry Index and commodity spot prices: evidence from nonparametric causality-in-quantiles test. Miner Econ. 2023;36:217-37. DOI: https://doi.org/10.1007/s13563-021-00287-y
Ubilava D. The role of El Niño Southern Oscillation in commodity price movement and predictability. Am J Agric Econ. 2018;100(1):239-63. DOI: https://doi.org/10.1093/ajae/aax060
Iizumi T, Luo JJ, Challinor AJ, Sakurai G, Yokozawa M, Sakuma H, et al. Impacts of El Niño Southern Oscillation on the global yields of major crops. Nat Commun. 2014;5:3712. DOI: https://doi.org/10.1038/ncomms4712
Cao J, Zhang Z, Tao F, Chen Y, Luo X, Xie J. Forecasting global crop yields based on El Niño Southern Oscillation signals. Agric Syst. 2023;205:103564. DOI: https://doi.org/10.1016/j.agsy.2022.103564
Ma C, Zhang L, You L, Tian W. A review of supply chain resilience: a network modeling perspective. Appl Sci. 2025;15(1):265. DOI: https://doi.org/10.3390/app15010265
Habibi F, Chakrabortty RK, Abbasi A, Ho W. Investigating disruption propagation and resilience of supply chain networks: interplay of tiers and connections. Int J Prod Res. 2025;63(17):6229-51. DOI: https://doi.org/10.1080/00207543.2025.2470348
Esteso A, Alemany MME, Ottati F, Ortiz A. System dynamics model for improving the robustness of a fresh agri-food supply chain to disruptions. Oper Res. 2023;23(2):28. DOI: https://doi.org/10.1007/s12351-023-00769-7
Kettele M, Lechner G, Baumgartner RJ, Füllsack M. Assessing operational resilience in food supply chains: a discrete-event simulation approach based on a framework for integrated decision-making. J Simul. 2025;19(4):381-410. DOI: https://doi.org/10.1080/17477778.2024.2399076
Imbiri S, Rameezdeen R, Chileshe N, Statsenko L. Stakeholder perspectives on supply chain risks: the case of Indonesian palm oil industry in West Papua. Sustainability. 2023;15(12):9605. DOI: https://doi.org/10.3390/su15129605
Yadav VS, Singh AR, Gunasekaran A, Raut RD, Narkhede BE. A systematic literature review of the agro-food supply chain: Challenges, network design, and performance measurement perspectives. Sustain Prod Consum. 2022;29:685-704. DOI: https://doi.org/10.1016/j.spc.2021.11.019
Behzadi G, O’Sullivan MJ, Olsen TL, Zhang A. Agribusiness supply chain risk management: a review of quantitative decision models. Omega. 2018;79:21–42. DOI: https://doi.org/10.1016/j.omega.2017.07.005
Yang J, Zhang X, Ge YE. Measuring risk spillover effects on dry bulk shipping market: a value-at-risk approach. Marit Policy Manag. 2022;49(4):558-76. DOI: https://doi.org/10.1080/03088839.2021.1889064
Guo K, Li Y, Zhang Y, Ji Q, Zhao W. How are climate risk shocks connected to agricultural markets?. J Commod Mark. 2023;32:100367. DOI: https://doi.org/10.1016/j.jcomm.2023.100367
Wang H, Lu L, Liu Z, Sun Y. Study on measurement and prediction of agricultural product supply chain resilience based on improved EW-TOPSIS and GM (1,1)-Markov models under public emergencies. PLoS One. 2025;20(5):e0321248. DOI: https://doi.org/10.1371/journal.pone.0321248
Caldara D, Iacoviello M. Measuring geopolitical risk. Am Econ Rev. 2022;112(4):1194-225. DOI: https://doi.org/10.1257/aer.20191823
Sundram P. Food security in ASEAN: progress, challenges and future. Front Sustain Food Syst. 2023;7:1260619. DOI: https://doi.org/10.3389/fsufs.2023.1260619
Chuyen TT, Rajagopal P. An overview of logistics network through bibliometric analysis using VOSviewer. Eng Appl Sci Res. 2026;53(1):47-60. DOI: https://doi.org/10.64960/easr.2026.261451
Wiangkam N, Jamrus T, Sureeyatanapas P. The decision-making for selecting cold chain logistics providers in the food industry. Eng Appl Sci Res. 2022;49(6):811-8.
Saengsathien A, Nontapa D, Jantep W, Namchimplee K. The cold chain of golden apple snail: a case study. Eng Appl Sci Res. 2023;50(1):1-9.
Massrie KD. Why is the price of coffee rising globally? future prospects for Ethiopian coffee. Front Sustain Food Syst. 2025;9:1545168. DOI: https://doi.org/10.3389/fsufs.2025.1545168
Wanzala RW, Obokoh LO. Global competitiveness of Kenyan coffee: the price volatility perspective. Cogent Econ Finance. 2025;13(1):2499019. DOI: https://doi.org/10.1080/23322039.2025.2499019
Kilian L. Small-sample confidence intervals for impulse response functions. Rev Econ Stat. 1998;80(2):218-30. DOI: https://doi.org/10.1162/003465398557465
Shi Y. A new unique impulse response function in linear vector autoregressive models. Int Rev Finance. 2023;23(2):460-8. DOI: https://doi.org/10.1111/irfi.12396
Grosche SC. What does Granger causality prove? a critical examination of the interpretation of Granger causality results on price effects of index trading in agricultural commodity markets. J Agric Econ. 2014;65(2):279-302. DOI: https://doi.org/10.1111/1477-9552.12058
Bai J, Perron P. Estimating and testing linear models with multiple structural changes. Econometrica. 1998;66(1):47-78. DOI: https://doi.org/10.2307/2998540
Yongsheng L, Li J. Design of early warning indicator system of enterprise logistics risk based on supply chain management. 2009 Second International Conference on Intelligent Computation Technology and Automation; 2009 Oct 10-11; Changsha, China. USA: IEEE; 2009. p. 918-23. DOI: https://doi.org/10.1109/ICICTA.2009.687
