Geospatial mapping and biophysical analysis of factors that affect oil palm (Elaeis guineensis) yields in Peninsular Malaysia

Authors

  • A. ABUBAKAR Faculty of Forestry and Environment, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
  • M. Y. ISHAK Faculty of Forestry and Environment, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
  • A. ABU BAKAR Faculty of Forestry and Environment, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
  • MD. K. UDDIN Faculty of Agriculture, University Putra Malaysia, 43400 Serdang, Selangor, Malaysia
  • N. K. UMAR Department of Geography, University of Maiduguri, Nigeria

DOI:

https://doi.org/10.54386/jam.v25i1.1871

Keywords:

Oil Palm, Modelling, Yield, Plantation, Peninsular Malaysia

Abstract

The objective of this study is to model oil palm yield distributions and investigate the factors that influence oil palm yields in Peninsular Malaysia using remote sensing and geographical information system (GIS) techniques. Herein, we investigate six factors that influence oil palm yield in Peninsular Malaysia, including mean annual minimum and maximum temperatures, mean annual rainfall, average number of rainy days per year, average annual relative humidity, and elevation. In order to model oil palm yield in Peninsular Malaysia, a large yield dataset covering Peninsular Malaysia for 37 years (1983 to 2020), as well as related explanatory variables, were collected. Areal interpolation was used to model the average yield distribution across the study area. The findings of this study show that oil palm yields vary across Peninsular Malaysia. Due to favourable climate and elevation, southern and southwestern Peninsular Malaysia, including Johor, Pahang, Melaka, and Selangor, recorded the highest amount of yield.

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Published

17-02-2023

How to Cite

A. ABUBAKAR, M. Y. ISHAK, A. ABU BAKAR, MD. K. UDDIN, & N. K. UMAR. (2023). Geospatial mapping and biophysical analysis of factors that affect oil palm (Elaeis guineensis) yields in Peninsular Malaysia. Journal of Agrometeorology, 25(1), 128–133. https://doi.org/10.54386/jam.v25i1.1871