Among the largest and growing oil palm industries, Malaysia plays an important role in the world's oil market. The contribution of the palm plantation in absorbing carbon from the atmosphere is also considerable thought, it is rarely studied. The role of the plantation in balancing carbon dioxide is significant. However, the ability of palm tree in absorbing carbon may vary within the lifespan of the plant. Therefore, managing the plantation to reach the maximum carbon dioxide absorption along with maximum oil production is challenging. This study is aimed at analyzing the carbon absorption level of the palm oil plantation. A mathematical model is proposed by considering the characteristics of palm oil trees in absorbing carbon and producing oil. It is assumed that the rate of felling can be controlled, and a system of ordinary differential equations is developed to describe the behaviour of the plantation in terms of biomass and growth rate dynamics. The resulting parameter estimation problem is solved which leads to an optimal control problem. The objective of this problem was to maximize the oil production as well as carbon absorption. Numerical simulation is illustrated to highlight the application of the proposed model.

Abstract. In this paper, the problem of palm oil plantation management is considered. A non-linear mathematical model is proposed considering two state variables as the density of the young palm oil trees and the part of biomass that can produce oil. In the modelling process, it is assumed that the rate of planting new young trees and the rate of felling inefficient trees can be controlled. It is further assumed that the oil production rate is directly proportional to the biomass of palm oil plantation. A system of delay differential equations is developed to study the behaviour of palm oil plantation. The resulting optimal control problem is also solved to estimate the control variables while the objective is to maintain the biomass at a certain level and maximize the oil palm production in a long period. Numerical simulations are given to illustrate the results.

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COVID-19 is rapidly expanding across the globe. As a Southeast Asian region, Malaysia has also been affected by COVID-19. Since the COVID-19 outbreak first emerged in China at the end of 2019, Malaysia has taken precautionary measures to prevent entering the nation. However, since COVID-19 is more than undoubtedly unstoppable, Malaysia eventually received the first case in early January 2020. The increase in the epidemic scale has led to the (preface of non-pharmaceutical countermeasures). Hence, it is of utmost importance to analyze the trends of the cases to develop a forecasting model that could anticipate the number of confirmed COVID-19 cases in Malaysia and select the best forecasting model based on forecast measure accuracy to forecast the future course of outcomes. For this purpose, the number of daily cases from 15 March 2020 to 31 March 2021 was retrieved from the Ministry of Health (MOH) website and estimated using the Box-Jenkins approach. There were five models developed such ARIMA (1,1,1), ARIMA (1,1,2), ARIMA (1,1,3), ARIMA (2,1,1) and ARIMA (2,1,2). The models' effectiveness is evaluated based on AIC, BIC and RMSE criteria. The findings indicate that ARIMA (1,1,3) is the preferred model for forecasting since it has better performance regarding adopted criteria than compared models. The forecasted values showed an upward trend of COVID-19 cases until January 2022. In conclusion, subsequent studies would yield more discoveries and a more systematic approach to have better and more accurate forecasting. In the instance of the COVID-19, the recommended model appears to be correct. More complex modelling methodologies and extensive information on the disease are required to forecast the pandemic.

The increases of operational felling cost have prompted the oil palm industry to look at the current practices. The felling activity is considered as the main aspects to improve and maintain palm oil production through the provision of effective and agronomic practices. To support this success and achieve minimum cost of operation, this study aims to develop a time-invariant linear quadratic optimal control model for controlling the felling and harvest rate of the oil palm plantation. The proposed model involves two state variables which are biomass and crude oil. The optimal parameters for the model are estimated using a set of real data collected from Malaysian Palm Oil Board (MPOB). The study analyzes the solution of the resulting control problem within a limited time frame of 30 years and the results provide an optimal feedback control for the felling and harvest rates.