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The oil palm in Malaysia has seen significant progress. Started as an ornamental plant in Malaysia, it has turned into a huge industry. It is the fastest growing global demand as an input for food products, cosmetics, animal feed, bio-energy etc. Nonetheless, currently it is encountering a period of slow or less growing in terms of contributing to...
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... palm trees potentially produce economically viable volumes of FFB. With a lifespan of more than 30 years, palm trees are likely to yield an amount FFB which is capable of incurring profit when cultivated commercially. Peak yielding period for a palm tree is between the age of 9-18, and in the subsequent period, yielding capacity gradually decline (Fig. 2). It is estimated that currently a majority of Malaysia's total oil palm area is occupied by trees aged between 9-28+, while a significant 26 percent has crossed the pick yielding age. Government report shows that about 8 percent of the national crop area or palm trees in 365,000 hectares is currently 25-37 years old which is about 8 ...
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... Oil palm (Elaeis guineensis) is a native crop cultivated in West Africa which provides major vegetable oil produced worldwide [1]. It constitutes about 60% of the vegetable oil trading internationally and 25% of global consumption. ...
... Hence, it is crucial to assess the oil palm age in order to obtain high production of yield. The peak yield profile of mature oil palm is when the oil palm is at the age of 9 years and above [1]. As for young oil palm, their age is in the range of 3 to 8 years. ...
Currently, remote sensing has been used extensively in the agriculture industry for oil palm monitoring due to their large plantation area. Oil palm monitoring can be done by performing land cover classification using various classification methods and machine learning algorithms. This study was conducted to perform oil palm mapping using WorldView-2 satellite imagery and classify land cover features using machine learning algorithms such as Random Forest (RF) and Linear Support Vector Classifier (LSVC). A total of 58609 sampling points were classified into six classes which are water, built-up, bare soil, forest, mature oil palm (≥9 years) and young oil palm (3-8 years). The training and testing samples were split using 3-fold cross-validation. 67% of the total sampling points were used for training samples whereas the other 33% were used for testing samples. The methods used to validate the data in this study is by using spectral reflectance and Google Earth Pro. Accuracy assessment was conducted after obtaining the classification output such as Overall Accuracy (OA), Kappa Accuracy (KA), Precision, Recall and F1-score. As a result, the oil palm mapping using RF has a higher accuracy than LSVC which is 72.49% for OA and 62.98% for KA. The p -value obtained from the McNemar’s test conducted in this study is 0.683 (>0.05) which concludes that the predictive performance of the two models are equal.
... Focusing on employee well-being benefits individual employees and positively impacts the organization's performance and bottom line. The labor-intensive nature of the palm oil industry has led to contentious issues such as labor shortages, forced labor, and inadequate working conditions [8,9]. To prepare for its future, Malaysia requires a sustainable approach to well-being. ...
This paper examines the relationship between social and environmental sustainability and workers’ well-being as well as the mediation role of affective organizational commitment for those relationships through social exchange and stakeholder theories. A questionnaire was used to collect data from 112 workers employed in palm oil plantations in Malaysia. Structural equation modeling was then applied to examine the hypotheses. Findings show that social and environmental sustainability positively relates to workers’ well-being. We also found that affective commitment positively mediates social and environmental sustainability relationships with workers’ well-being. Our research findings have practical implications for various stakeholders, such as investors, policymakers, and managers in the palm oil industry. Our results highlight the importance of addressing the well-being of workers and the sustainability factors in palm oil plantations. This study suggests a widened perspective on sustainability factors and well-being which allows professionals to evaluate and enhance sustainability across their operations in a more comprehensive manner.
... In addition to the focus on environmental, social, and governance (ESG) factors, the rise of Islamic finance has also created new opportunities for commodity trade (Foglie & Keshminder, 2022;Langly & Giugale, 2000). This has further emphasized the significance of agricultural commodities as underlying assets, as exemplified by the Malaysian palm oil contracts, which have demonstrated their substantial economic impact and global relevance in the international market (Alam, Er, & Bagum, 2015;Carter, Finley, Fry, Jackson & Willis, 2007;Arouri, Ameur, & Nabila, 2013). Furthermore, the rise of Islamic finance has also created new opportunities for commodity trade. ...
... Furthermore, the rise of Islamic finance has also created new opportunities for commodity trade. The significance of agricultural trade can be exemplified by the Malaysian palm oil contracts, which demonstrate agricultural commodities' substantial economic impact and global relevance in the international market (Alam, et al., 2015;Carter et al., 2007). Investigating palm oil futures and derivatives in Malaysia and Indonesia underscores the profound significance of agricultural commodities as underlying assets, highlighting their pivotal role in shaping and influencing financial markets. ...
Traditional methods dominate agricultural commodity trade, leading to occasional supply surpluses due to inherent periodicity. The lack of adequate storage systems further compounds the challenge, resulting in price volatility and market instability. To address these issues, introducing licensed Warehousing and a Specialized Commodity Exchange system offers an opportunity to integrate agricultural commodity trade with global markets and establish a robust market structure. This integration involves transforming standardized agricultural products held in licensed warehouses into Electronic Warehouse Receipts (e-WhR), which can serve as essential tools for production, consumption, and investment instruments. Moreover, the innovative concept of "Yellow Sukuk" is introduced, representing a groundbreaking financial instrument that can be utilized within the framework of Islamic Capital Markets alongside conventional bank loan transactions. This study examines the evolution of agricultural commodity trade, spot transactions conducted in Commodity Exchanges, and the fundamental aspects of transactions. By embracing these advancements, the agricultural sector can enhance its efficiency, liquidity, and overall market depth while embracing the groundbreaking potential of Yellow Sukuk.
... About one-third of the vegetable oil and fat produced worldwide is made from oil palm trees, of which 85% is produced in Indonesia and Malaysia [2] [3]. About three-quarters of the global palm oil supply is used as food oil, while almost all of the rest are for industrial use [4]. Palm oil production contributes significantly to Malaysia's national economy and is essential to the nation's socioeconomic growth. ...
... Palm oil production contributes significantly to Malaysia's national economy and is essential to the nation's socioeconomic growth. In 2020, a total of more than 5 million hectares of oil palm plantations across the nation [4] [5]. However, the Malaysian oil palm business has seen a fall in yield since 2009. ...
... However, the Malaysian oil palm business has seen a fall in yield since 2009. Many causes, including biodiversity, plant diseases, the age of palm trees, etc., cause a drop in productivity [4]. One of the leading causes of a significant decrease in the annual palm oil yield was an infection, particularly basal stem rot (BSR) [1]. ...
... Malaysia is one of the largest exporters and producers of palm oil. The oil palm sector in Malaysia is a developing economic industry, and the production of palm oil results in high revenues [1]. Approximately 34.3% of global palm oil exports were contributed by Malaysia in 2020 [2]. ...
Biomass wastes originating from palm oil milling activities can be characterized by their high biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solid (TSS), and oil and grease content. The utilization of oil palm wastes such as palm oil mill effluent (POME) and empty fruit bunch (EFB) has great potential for a sustainable energy biomass pellet as it reduces the reliance on conventional materials for energy production. A mixture of POME and EFB in various ratios ranging between 7:3, 8:2, 9:1, and 10:0 was fermented in the presence of Lysinibacillus sp. LC 556247 for 120 h at 37 ± 2 °C and 180 rpm with the aim of elucidating the biodegradation of complex organic material in terms of BOD, COD, TSS, total organic carbon, inorganic carbon, and total carbon content. After fermentation, the mixtures were oven dried at 105 °C overnight and then subjected to calorific energy value (CEV) determination. The highest CEV of 20.26 MJ/kg was achieved for a ratio of 10:0 (slightly higher than the control experiment with the value of 18.67 MJ/kg), with maximum removal efficiencies of COD (12.19%), BOD (11.72%), TSS (93.94%), and oil reduction of 17.43%. The addition of EFB did not positively increase the CEV.
... However, a decline in growth has been observed in both Malaysia and Indonesia since year 2009, indicating several factors have negatively impacted the growth rate of the oil palm crop (Siddiqui et al., 2021). Alam, Er & Begum (2015) described that several factors have impeded the growth of the industry, i.e., labour shortage, competition with neighboring countries, ageing oil palm tree, biodiversity, plant disease and infections and other factors. The plant disease mentioned that has severely impacted the growth of the industry is called the basal stem rot (BSR). ...
... This manual method is also heavily dependent on manpower, which is prone to human error (Tee et al., 2021). Alam, Er & Begum (2015) also mentioned a labor shortage problem that can worsen the rate of BSR detection. The recent land labor ratio estimated that one worker covers about 10.9 ha plantation. ...
... Besides, visual inspection of oil palm tree for BSR infection often requires huge amount of experience and expertise, which the skillset is often not transferable. It will take very long period of time for the skillset to be passed down, therefore making manual inspection unfavorable (Siddiqui et al., 2021;Alam, Er & Begum, 2015;Tee et al., 2021;Ibrahim et al., 2020). ...
Oil palm is a key agricultural resource in Malaysia. However, palm disease, most prominently basal stem rot caused at least RM 255 million of annual economic loss. Basal stem rot is caused by a fungus known as Ganoderma boninense . An infected tree shows few symptoms during early stage of infection, while potentially suffers an 80% lifetime yield loss and the tree may be dead within 2 years. Early detection of basal stem rot is crucial since disease control efforts can be done. Laboratory BSR detection methods are effective, but the methods have accuracy, biosafety, and cost concerns. This review article consists of scientific articles related to the oil palm tree disease, basal stem rot, Ganoderma Boninense , remote sensors and deep learning that are listed in the Web of Science since year 2012. About 110 scientific articles were found that is related to the index terms mentioned and 60 research articles were found to be related to the objective of this research thus included in this review article. From the review, it was found that the potential use of deep learning methods were rarely explored. Some research showed unsatisfactory results due to limitations on dataset. However, based on studies related to other plant diseases, deep learning in combination with data augmentation techniques showed great potentials, showing remarkable detection accuracy. Therefore, the feasibility of analyzing oil palm remote sensor data using deep learning models together with data augmentation techniques should be studied. On a commercial scale, deep learning used together with remote sensors and unmanned aerial vehicle technologies showed great potential in the detection of basal stem rot disease.
... Furthermore, due to limited land availability, Malaysian palm oil production may soon reach its peak. It is expected that Malaysia's role as a leading crude palm oil exporter will decrease in the future due to land constraints, dependence on foreign workers, comparatively higher input costs and increasing rivalry from palm oil substitutes and other palm oil producing countries (e.g., Indonesia) (Alam et al., 2015). Figure 1 shows the palm oil value chain and the application from the upstream segment throughout the various activities to produce the downstream palm oil products. ...
This paper explore how export competitiveness in the Malaysian palm oil downstream industry is affected by European Union (EU) environmental regulation. Porter (1990) suggests that environmental policies may foster international competitiveness. To investigate the impact of EU environmental policies on trade competitiveness in the Malaysian palm oil downstream industry, the dynamic generalized method of moments (DGMM) is employed. The final results reveal that EU environmental regulations have a positive impact on palm oil industry competitiveness. This result is consistent with the Porter Hypothesis, which argues that a more stringent environmental regulation can trigger innovation to non-compliance cost. Palm oil downstream innovation is crucial to improve the overall competitiveness of the industry, including the smallholders’ sector. This implies that the Malaysian government may want to introduce certain measures, such as energy taxes to promote the use of renewable energy. This may lead to more sustainable palm oil production which may improve the overall competitiveness of the palm oil downstream industry.
... Nanocellulose, a material derived from plant fibers, has gained increasing attention for its advanced properties and potential for commercialization in various industries, particularly in biopolymers and biomedical fields [1,2]. Malaysia, being a major producer of oil palm, has significant potential to transform underutilized biomass into valuable products [3,4]. The extraction and purification of nanocellulose from oil palm empty fruit bunch (OPEFB) using green methods is an efficient way to minimize waste and keep production costs low [5]. ...
This study explores the potential of using nanocellulose extracted from oil palm empty fruit bunch (OPEFB) as a biomaterial ink for 3D printing. The research focuses on using nanocellulose hydrogels for the controlled uptake and release of proteins, with the specific protein solution being Bovine Serum Albumin (BSA). To provide a suitable material for the bioprinting process, the study examines the characteristics and properties of the printed hydrogels through various analyses, such as morphology, functional group, crystallinity, and compression test. Several parameters, such as initial concentration, temperature, and the presence of calcium chloride as an additional crosslinker, affect the protein uptake and release capabilities of the hydrogel. The study is important for biomedicine as it explores the behavior of protein uptake and release using nanocellulose and 3D printing and can serve as a preliminary study for using hydrogels in biological materials or living cells.
... Acknowledging the potential harm to the environment, the Malaysian government had banned open burning practices in 2000 under the Environmental Quality Act 1974 (EQA, 1974). In response, the guidelines on good agricultural practice for the palm oil industry was introduced (EQA, 1974), which set up certain replanting processes in its effort to lay the groundwork for sustainable oil palm cultivation (Alam et al., 2015). ...
Osteoarthritis (OA) is a chronic and irreversible degenerative joint disease that most commonly affects individuals in their forties and fifties worldwide due to the continuously increasing life expectancy. Although joint replacement is an effective remedy for severe end-stage OA, the functional outcomes could be unsatisfactory, while the implants might have a limited lifespan. Due to the drawbacks and limitations of the joint replacement approach, bone Tissue Engineering (TE) is one of the promising bone tissue regeneration technologies that aid in cartilage repair and regeneration and has attracted the attention of experts. The advanced development of biopolymers, in particular biopolymer derived from Oil Palm Empty Fruit Bunch (OPEFB), has been utilised in the fabrication of scaffolds that serve as a crucial component in bone TE. The abundant supply of OPEFB biomass and the increasing trend of converting waste into wealth for environmental sustainability have also provided the opportunity and interest to fully apply biopolymer-derived materials for bone scaffolding and other applications. Therefore, this paper aimed to provide a review of the biopolymers derived from OPEFB for the treatment of OA and other related applications. A brief overview of the biomass sources in Malaysia was presented, followed by a discussion on the chemical compositions and pre-treatment methods of OPEFB by using organosolv pre-treatment and enzymatic hydrolysis for maximum glucose recovery, monomer derived from cellulose OPEFB and synthesizing self-curing polymer scaffold. Additionally, a detailed review of the polymeric biomaterials in bone TE for the fabrication of scaffolds were included in this review. Most importantly, the paper described the potential use of injectable polymeric biomaterials that provide a significant benefit in orthopaedic applications. Overall, this paper provides a perspective on the potential of OPEFB-derived injectable scaffolds as an alternative OA treatment and future bone TE applications.
... Oil palm is commonly used as cooking oil. About three-quarters of the global palm oil supply is used as food oil while almost all of the rest are for industry use [4]. ...
... For Malaysia, palm oil is one of the largest contributors to the country's economy, and the industry plays a crucial role in elevating the socio-economic development of the country. The country as a whole has more than 5 million hectares of oil palm plantations in year 2020 [4] [5]. However, since year 2009, a yield decline was observed in the oil palm industry in Malaysia. ...
... However, since year 2009, a yield decline was observed in the oil palm industry in Malaysia. Several factors played a role in the decline of production, for example plant disease, biodiversity, age of palm tree and etc. [4]. Infection, especially basal stem rot (BSR) is one of the major factors that caused a significant reduction in the yearly yield of palm oil [1]. ...
Palm oil industry is an important economic resource for Malaysia. However, an oil palm tree disease called Basal Stem Rot has impeded the production of palm oil, which caused significant economic loss at the same time. The oil palm tree disease is caused by a fungus known as
Ganoderma Boninense
. Infected trees often have little to no symptoms during early stage of infection, which made early detection difficult. Early disease detection is necessary to allow early sanitization and disease control efforts. Using Terrestrial Laser Scanning technology, 88 grey-distribution canopy images of oil palm tree were obtained. The images were pre-processed and augmented before being used for training and testing of the deep learning models. The capabilities of the Convolution Neural Network deep learning models in the classification of dataset into healthy and non-healthy class were tested and the best performing model was identified based on the Macro-F1 score. Fine-tuned DenseNet121 model was the best performing model, recorded a Macro F1-score of 0.798. It was also noted that Baseline model showed a relatively remarkable macro-F1 score of 0.747, which was better than all feature extractor model and some fine-tuned models. However, fine-tuned models suffered from model overfitting due to the limitation on dataset. For future work, it is recommended to increase the sample size, utilize other CNN architectures and incorporate data augmentation for testing dataset to improve the model performance and progress towards detecting Basal Stem Rot at the early stage of infection by classifying sample images into multiple classes.
