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An improved monthly oil palm yield predictive model in Malaysia

Khor, Jen Feng and Yusop, Zulkifli and Ling, Lloyd (2023) An improved monthly oil palm yield predictive model in Malaysia. In: 6th International Conference on Architecture and Civil Engineering, ICACE 2022, 18 August 2022-18 August 2022, Kuala Lumpur, Malaysia.

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Official URL: http://dx.doi.org/10.1007/978-981-19-8024-4_15

Abstract

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.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:El Niño, Oil palm, Yield modelling and prediction
Subjects:T Technology > TA Engineering (General). Civil engineering (General)
Divisions:Civil Engineering
ID Code:107895
Deposited By: Widya Wahid
Deposited On:08 Oct 2024 06:56
Last Modified:08 Oct 2024 06:56

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