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Oil Palm (Elaeis guineensis) Biology, Productivity, Sustainability: From...
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Oil Palm (Elaeis guineensis) Biology, Productivity, Sustainability: From the Organism to the Ecosystem Scale

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Crop Physiology and Crop Production".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 39694

Special Issue Editor

Dr. Robert M. Beyer

E-Mail Website
Guest Editor
Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany
Interests: optimising global crop production; environmental impacts of agriculture; adaptation to climate change; plant growth modelling; forest science

Special Issue Information

Dear Colleagues,

One of the most world's major crops, oil palm (Elaeis guineensis) is the subject of a wide range of research disciplines, from plant genetics and soil science to agronomy and conservation biology. With a growing global demand for palm-based products, affecting industries and consumers around the world, state-of-the-art scientific insights into how to optimise oil palm production in environmentally sustainable ways are crucial. Recent advances in crop engineering, challenges posed by climate change, and debates about the ecological footprint of oil palm, amongst many other examples, highlight the topicality of this important crop.

This Special Issue brings together novel insights into the biological, technological, and ecological aspects of oil palm. It welcomes original research articles, perspectives, and reviews on areas including, but not limited to:

  • Oil palm physiology and ecophysiology
  • Genetic engineering and breeding technology
  • Farming practices, including soil, water, nutrient, and pest management
  • Processing of palm-based products for foodstuffs and biofuel
  • Ecology and biodiversity of oil palm plantations
  • Socioeconomic aspects of oil palm production for smallholders and agribusiness

Dr. Robert M. Beyer
Guest Editor

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • crop production
  • ecophysiology
  • crop breeding
  • framing practices
  • soil science
  • palm oil
  • biofuel
  • ecology
  • biodiversity

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Published Papers (6 papers)

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Research

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19 pages, 3996 KiB  
Article
Impact Comparison of El Niño and Ageing Crops on Malaysian Oil Palm Yield
by Jen Feng Khor, Lloyd Ling, Zulkifli Yusop, Ren Jie Chin, Sai Hin Lai, Ban Hoe Kwan and Danny Wee Kiat Ng
Plants 2023, 12(3), 424; https://doi.org/10.3390/plants12030424 - 17 Jan 2023
Cited by 7 | Viewed by 5293
Abstract
Ageing oil palm crops show a significant correlation with the declining oil palm yield in Malaysia. Not only do aged crops result in lower production, but they are also more costly and difficult to harvest. The Malaysian oil palm yield recovered to the [...] Read more.
Ageing oil palm crops show a significant correlation with the declining oil palm yield in Malaysia. Not only do aged crops result in lower production, but they are also more costly and difficult to harvest. The Malaysian oil palm yield recovered to the pre-El Niño level after the 1997/98 El Niño event. However, the oil palm yield failed to recover after the recent 2015/16 El Niño. Due to the accumulation of aged oil palm plantations in Malaysia, the financial losses from different magnitudes of El Niño events are increasing. Thirty-four years of monthly oil palm yield trends in Malaysia were compared with the El Niño–free yield dataset to show that the oil palm yield downtrend pattern is the same with or without El Niño events in Malaysia for the most recent 15 years (2005 to 2019). The performance of oil palm yield did not show any significant difference from 2000 to 2019. This study estimates that ageing oil palms would lead to a minimum opportunity loss of USD 431 million by December 2022. Without a proper replanting program, the total combined loss attributable to the ageing crops from 2009 to 2022 is estimated to be USD 3.94 billion, which is more profound than losses due to El Niño events within the same period. This study also concluded that a continuous 7-year replanting scheme of at least 115,000 hectares per year is needed to address the adverse impact of ageing crops on the Malaysian oil palm yield, which accounts for nearly 30% of the global palm oil production. Full article
(This article belongs to the Special Issue Oil Palm (Elaeis guineensis) Biology, Productivity, Sustainability: From the Organism to the Ecosystem Scale)
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13 pages, 2097 KiB  
Article
Electrical Impedance Spectroscopy for Moisture and Oil Content Prediction in Oil Palm (Elaeis guineensis Jacq.) Fruitlets
by Nur Fizura Chin-Hashim, Alfadhl Yahya Khaled, Diyana Jamaludin and Samsuzana Abd Aziz
Plants 2022, 11(23), 3373; https://doi.org/10.3390/plants11233373 - 5 Dec 2022
Cited by 5 | Viewed by 2350
Abstract
The global palm oil industry is targeting an increased oil extraction rate in oil palm milling to meet global demand. This can be achieved through the certification of mills and adherence to bunch grading as part of ensuring that only high-quality and ripe [...] Read more.
The global palm oil industry is targeting an increased oil extraction rate in oil palm milling to meet global demand. This can be achieved through the certification of mills and adherence to bunch grading as part of ensuring that only high-quality and ripe fresh fruit bunches are accepted and processed at all mills. However, the current grading process requires the analysis of oil palm bunches, which is laborious and tedious or prone to error due to human subjectivity. This paper introduces a non-destructive technique to predict the moisture and oil content in oil palm fruitlets using electrical impedance spectroscopy. In total, 90 samples of oil palm fruitlets at different stages of ripeness were acquired. Electrical impedance measurement of each fruitlet was done using electrocardiogram (ECG) electrodes connected to an LCR meter at frequencies of 1 kHz, 10 kHz, 20 kHz, and 100 kHz. The actual oil content in the fruitlets was determined using the Soxhlet extraction method, while the actual moisture content was determined using a standard oven-drying method. The variation of electrical impedance values at each frequency was analyzed. At 100 kHz, the correlation coefficients relating the electrical impedance to the moisture and oil content were around −0.84 and 0.80, respectively. Predictions of the moisture and oil content using linear regression of the impedance measurements at 100 kHz gave RMSE values of 5.85% and 5.71%, respectively. This information is useful for oil palm fruit grading and oil yield production estimation in the palm oil industry. Full article
(This article belongs to the Special Issue Oil Palm (Elaeis guineensis) Biology, Productivity, Sustainability: From the Organism to the Ecosystem Scale)
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16 pages, 2626 KiB  
Article
Oil Palm Fruits Ripeness Classification Based on the Characteristics of Protein, Lipid, Carotene, and Guanine/Cytosine from the Raman Spectra
by Gabriel Tan Hong Tzuan, Fazida Hanim Hashim, Thinal Raj, Aqilah Baseri Huddin and Mohd Shaiful Sajab
Plants 2022, 11(15), 1936; https://doi.org/10.3390/plants11151936 - 26 Jul 2022
Cited by 12 | Viewed by 3176
Abstract
The capacity of palm oil production is directly affected by the ripeness of the fresh fruit bunches (FFB) upon harvesting. Conventional harvesting standards rely on rigid harvesting scheduling as well as the number of fruitlets that have loosened from the bunch. Harvesting is [...] Read more.
The capacity of palm oil production is directly affected by the ripeness of the fresh fruit bunches (FFB) upon harvesting. Conventional harvesting standards rely on rigid harvesting scheduling as well as the number of fruitlets that have loosened from the bunch. Harvesting is usually done every 10 to 14 days, and an FFB is deemed ready to be harvested if there are around 5 to 10 empty sockets on the fruit bunch. Technology aided by imaging techniques relies heavily on the color of the fruit bunch, which is highly dependent on the surrounding light intensities. In this study, Raman spectroscopy is used for ripeness classification of oil palm fruits, based on the molecular assignments extracted from the Raman bands between 1240 cm−1 and 1360 cm−1. The Raman spectra of 52 oil palm fruit samples which contain the fingerprints of different organic compounds were collected. Signal processing was applied to perform baseline correction and to reduce background noises. Characteristic data of the organic compounds were extracted through deconvolution and curve fitting processes. Subsequently, a correlation study between organic compounds was developed and eight hidden Raman peaks including protein, beta carotene, carotene, lipid, guanine/cytosine, chlorophyll-a, and tryptophan were successfully located. Through ANOVA statistical analysis, a total of six peak intensities from proteins through Amide III (β-sheet), beta-carotene, carotene, lipid, guanine/cytosine, and carotene and one peak location from lipid were found to be significant. An automated oil palm fruit ripeness classification system deployed with artificial neural network (ANN) using the seven signification features showed an overall performance of 97.9% accuracy. An efficient and accurate ripeness classification model which uses seven significant Raman peak features from the correlation analysis between organic compounds was successfully developed. Full article
(This article belongs to the Special Issue Oil Palm (Elaeis guineensis) Biology, Productivity, Sustainability: From the Organism to the Ecosystem Scale)
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19 pages, 5354 KiB  
Article
Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow
by Nuzhat Khan, Mohamad Anuar Kamaruddin, Usman Ullah Sheikh, Mohd Hafiz Zawawi, Yusri Yusup, Muhammed Paend Bakht and Norazian Mohamed Noor
Plants 2022, 11(13), 1697; https://doi.org/10.3390/plants11131697 - 27 Jun 2022
Cited by 21 | Viewed by 6276
Abstract
Current development in precision agriculture has underscored the role of machine learning in crop yield prediction. Machine learning algorithms are capable of learning linear and nonlinear patterns in complex agro-meteorological data. However, the application of machine learning methods for predictive analysis is lacking [...] Read more.
Current development in precision agriculture has underscored the role of machine learning in crop yield prediction. Machine learning algorithms are capable of learning linear and nonlinear patterns in complex agro-meteorological data. However, the application of machine learning methods for predictive analysis is lacking in the oil palm industry. This work evaluated a supervised machine learning approach to develop an explainable and reusable oil palm yield prediction workflow. The input data included 12 weather and three soil moisture parameters along with 420 months of actual yield records of the study site. Multisource data and conventional machine learning techniques were coupled with an automated model selection process. The performance of two top regression models, namely Extra Tree and AdaBoost was evaluated using six statistical evaluation metrics. The prediction was followed by data preprocessing and feature selection. Selected regression models were compared with Random Forest, Gradient Boosting, Decision Tree, and other non-tree algorithms to prove the R2 driven performance superiority of tree-based ensemble models. In addition, the learning process of the models was examined using model-based feature importance, learning curve, validation curve, residual analysis, and prediction error. Results indicated that rainfall frequency, root-zone soil moisture, and temperature could make a significant impact on oil palm yield. Most influential features that contributed to the prediction process are rainfall, cloud amount, number of rain days, wind speed, and root zone soil wetness. It is concluded that the means of machine learning have great potential for the application to predict oil palm yield using weather and soil moisture data. Full article
(This article belongs to the Special Issue Oil Palm (Elaeis guineensis) Biology, Productivity, Sustainability: From the Organism to the Ecosystem Scale)
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Review

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14 pages, 818 KiB  
Review
A Review of Factors Affecting Ganoderma Basal Stem Rot Disease Progress in Oil Palm
by Nur Aliyah Jazuli, Assis Kamu, Khim Phin Chong, Darmesah Gabda, Affendy Hassan, Idris Abu Seman and Chong Mun Ho
Plants 2022, 11(19), 2462; https://doi.org/10.3390/plants11192462 - 21 Sep 2022
Cited by 22 | Viewed by 11049
Abstract
In recent years, oil palm has grown on a major scale as it is a prominent commodity crop that contributes the most to almost every producing country’s gross domestic product (GDP). Nonetheless, existing threats such as the Ganoderma basal stem rot (BSR) disease [...] Read more.
In recent years, oil palm has grown on a major scale as it is a prominent commodity crop that contributes the most to almost every producing country’s gross domestic product (GDP). Nonetheless, existing threats such as the Ganoderma basal stem rot (BSR) disease have been deteriorating the oil palm plantations and suitable actions to overcome the issue are still being investigated. The BSR disease progression in oil palm is being studied using the disease progression through the plant disease triangle idea. This concept looks at all potential elements that could affect the transmission and development of the disease. The elements include pathogenic, with their mode of infection in each studied factor. Full article
(This article belongs to the Special Issue Oil Palm (Elaeis guineensis) Biology, Productivity, Sustainability: From the Organism to the Ecosystem Scale)
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15 pages, 762 KiB  
Review
Oil Palm Breeding in the Modern Era: Challenges and Opportunities
by Jerome Jeyakumar John Martin, Rajesh Yarra, Lu Wei and Hongxing Cao
Plants 2022, 11(11), 1395; https://doi.org/10.3390/plants11111395 - 24 May 2022
Cited by 24 | Viewed by 9121
Abstract
Oil palm, a cross-pollinated crop with long generation time, poses a lot of challenges in achieving sustainable oil palm with high yield and quality. The African oil palm (Elaeis guineensis Jacq.) is the most productive and versatile oil-yielding crop in the world, [...] Read more.
Oil palm, a cross-pollinated crop with long generation time, poses a lot of challenges in achieving sustainable oil palm with high yield and quality. The African oil palm (Elaeis guineensis Jacq.) is the most productive and versatile oil-yielding crop in the world, producing more than any other oil-yielding crop. Despite recent challenges, such as stress tolerance, superior oil quality, disease tolerance, and the need for new market niches, there is a growing need to explore and develop new varieties with high yield potential and the genetic diversity required to maintain oil palm yield stability. Breeding is an indispensable part of producing high-quality planting materials to increase oil palm yield. Biotechnological technologies have transformed conventional plant breeding approaches by introducing novel genotypes for breeding. Innovative pre-breeding and breeding approaches, such as identifying candidate genes in wild or land races using genomics tools, can pave the way for genetic improvement in oil palm. In this review, we highlighted the modern breeding tools, including genomics, marker-assisted breeding, genetic engineering, and genome editing techniques in oil palm crops, and we explored certain concerns connected to the techniques and their applications in practical breeding. Full article
(This article belongs to the Special Issue Oil Palm (Elaeis guineensis) Biology, Productivity, Sustainability: From the Organism to the Ecosystem Scale)
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