An improved monthly oil palm yield predictive model in Malaysia

• Main Authors: Khor, Jen Feng, Yusop, Zulkifli, Ling, Lloyd
• Format: Conference or Workshop Item
• Published: 2023
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: http://eprints.utm.my/107895/; http://dx.doi.org/10.1007/978-981-19-8024-4_15

Oil palm crop is sensitive to the heat stress. A new model is developed with 36 years of national monthly yield data to quantify the impact of past El Niño events on the Malaysian palm oil industry, namely Fresh Fruit Bunch Index (FFBI) model. The FFBI model shows significant correlation with the National Oceanic and Atmospheric Administration (NOAA), Oceanic Niño Index (ONI) and higher predictive accuracy (adjusted R-squared = 0.9312) than the conventional FFB model (adjusted R-squared = 0.8274). The FFBI model suggests that oil palm yields in Malaysia could be affected after 2–16 months of the occurrence of El Niño events. The FFBI model also forecasts an oil palm under yield concern in Malaysia from July 2021 to December 2023 and matches with the actual national oil palm under yield trend to date (July 2021–April 2022). Malaysian oil palm yields failed to recover from the 2015/16 very strong El Niño and showed a production downtrend pattern even before the pandemic market lock down. This strongly suggests that there are other hidden threats that have plagued the Malaysian palm oil industry for years, other than the climatic factor.