Centre of Disaster Risk Reduction (CDRR), Lee Kong Chian Faculty of Engineering & Science, Civil Engineering Department, Universiti Tunku Abdul Rahman, Jalan Sungai Long, Kajang 43000, Malaysia
Lloyd Ling
Centre of Disaster Risk Reduction (CDRR), Lee Kong Chian Faculty of Engineering & Science, Civil Engineering Department, Universiti Tunku Abdul Rahman, Jalan Sungai Long, Kajang 43000, Malaysia
Zulkifli Yusop
Centre for Environmental Sustainability and Water Security, Universiti Teknologi Malaysia, Skudai 81310, Malaysia
Wei Lun Tan
Centre for Mathematical Sciences (CMS), Lee Kong Chian Faculty of Engineering & Science, Department of Mathematical and Actuarial Sciences, Universiti Tunku Abdul Rahman, Jalan Sungai Long, Kajang 43000, Malaysia
Joan Lucille Ling
Department of Liberal Arts and Sciences, American Degree Programme, Taylor’s University, No. 1, Jalan Taylors, Subang Jaya 47500, Malaysia
Eugene Zhen Xiang Soo
Centre of Disaster Risk Reduction (CDRR), Lee Kong Chian Faculty of Engineering & Science, Civil Engineering Department, Universiti Tunku Abdul Rahman, Jalan Sungai Long, Kajang 43000, Malaysia
Oil palm crop yield is sensitive to heat and drought. Therefore, El Niño events affect oil palm production, resulting in price fluctuations of crude palm oil due to global supply shortage. This study developed a new Fresh Fruit Bunch Index (FFBI) model based on the monthly oil palm fresh fruit bunch (FFB) yield data, which correlates directly with the Oceanic Niño Index (ONI) to model the impact of past El Niño events in Malaysia in terms of production and economic losses. FFBI is derived from Malaysian monthly FFB yields from January 1986 to July 2021 in the same way ONI is derived from monthly sea surface temperatures (SST). With FFBI model, the Malaysian oil palm yields are better correlated with ONI and have higher predictive ability. The descriptive and inferential statistical assessments show that the newly proposed FFBI time series model (adjusted R-squared = 0.9312 and residual median = 0.0051) has a better monthly oil palm yield predictive ability than the FFB model (adjusted R-squared = 0.8274 and residual median = 0.0077). The FFBI model also revealed an oil palm under yield concern of the Malaysian oil palm industry in the next thirty-month forecasted period from July 2021 to December 2023.
