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Palm oil plantations cover millions of hectares worldwide, which encompass a significant portion of global trade. Palm oil trees, or Arecaceae, are a genus of stemless, tree-like monocot plants that thrive in the tropics and are extremely valuable to humans and the ecosystem [1]. The African oil palm, or Elaeis guineensis, is the most prominent palm species native to West Africa, cultivated for its oil-rich fruit as a semiwild food source for over 7000 years. The tree produces a profusion of fruit bunches yearly with each containing between 1000 and 3000 fruits.
- palm oil
- palm oil breeding
- agriculture
1. Palm Oil Background
The Arecaceae family includes palm oil (Elaeis spp.), encompassing two species: African palm oil (E. guineensis) and American palm oil (E. occidentalis) (E. oleifera). The male and female inflorescences can be detected on the same palm, and on rare occasions, inflorescences of hermaphrodites can be found as well. A cross-pollinated crop is created when the male and female inflorescences are produced alternately. However, artificial pollination is necessary to produce specific hybrids [47,48]. The drupe, or the oil palm’s fruit, matures approximately six months after pollination. Within the drupe, a pericarp is formed from the exocarp (the outer layer) and the husk. The outer layer protects the kernel containing the endosperm and embryo located within the endocarp (the inner layers).
The E. guineensis species are classified into three oil palm fruit forms (pisifera, dura, and tenera) based on the thickness of their shell, an essential factor that will be used for breeding [49]. The pisifera genotype of the recessive homozygous (Sh−Sh−) alleles is shell-less. It is believed that most palms are sterile; however, several were reported to bear fruit and have varying degrees of sterility. The dura genotype comprises a thick shell consisting of the dominant (Sh+Sh+) alleles. Meanwhile, the tenera of the heterozygous (Sh+Sh+) alleles exhibit a mesocarp with a thinner outer shell and a thicker inner ring of fibres. Notably, the tenera genotype is the only form of oil palm fruit used for commercial planting because of its higher mesocarp content. The three types of oil palm fruits are the dura (D), pisifera (P) and tenera (T), identified based on the thickness of their shells. Alleles Sh+ and Sh− are codominantly expressed at a single locus that controls shell thickness [50]. The thick-shell dura is controlled by a dominant homozygote gene (Sh+Sh+), whereas the shell-less pisifera is controlled by a recessive homozygote gene (Sh−Sh−). The cross between the dura and pisifera would result in a homozygote tenera hybrid (Sh+Sh−) with a thin shell. Despite having a high mesocarp content of 95 per cent, pisifera is usually female sterile or semifertile and does not produce bunches. Hence, pisifera is only used as the male parent in the tenera hybrid. Notably, the tenera genotype is the only form of oil palm fruit used for commercial planting due to its higher mesocarp content.
2. Breeding Programmes
Tenera is a commercial term for palm oil, a cross between dura × pisifera, responsible for various new varieties. The germplasm of these materials is used to produce hybrid seeds. Vegetative characteristics and the bunch performance of four seedlings introduced to Indonesia in 1848 are consistent [51]. In 1848, Indonesia introduced the vegetative characteristics and consistent performance of four oil palm seedlings [51,52]. Private companies proposed various independent, breed-specific variations. Deli subpopulations and breeding materials are currently available for purchase or trade worldwide. Accordingly, the following populations were used in effective breeding programmes [2,53].
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Deli: The thick-shelled dura is a descendant of the original Bogor palms from Java. The subsequent progeny and local selection distribution to other countries resulted in the development of subpopulations in Malaysia. The regions include Elmina, Serdang, Avenue, and Ulu Remis Deli Dura, followed by the Ivory Coast of Dabou and Le Mé Dura. This idea led to speculation that all four Bogor palms were descended from the same ancestor. All major commercial hybrid seed production programmes utilise the mother Deli (dura) palm. The Dumpy and Gunung Melayu palms are short variants of the longer Deli palms.
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AVROS: AVROS seeds were collected from the Eala Botanical Garden (Jardin Botanique d’Eala) in Zaire (now the Democratic Republic of Congo) in 1923. SP540 is a common name for this pisifera, known for its vigorous growth, precocious bearing, thin shell, thick mesocarp, and high-yielding traits. Notably, the Deli (dura) × AVROS (pisifera) is the basis for effective seed production programmes in Indonesia, Malaysia, Colombia, Papua New Guinea, and Costa Rica.
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Yangambi: The seeds are acquired from the INEAC in Yangambi, Democratic Republic of the Congo. The population of the Dejongo palm and Yawenda tenera was developed using open-pollinated seeds, distinguished from their large fruits and high oil content.
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La Mé: Twenty-one tenera palm seeds were collected from the wild groves of the Ivory Coast by IRHO, creating the La Mé populations. The tenera palms are used in the seed production industry in West Africa and Indonesia. The La Mé progenies (pisifera) are smaller and bear fewer fruits per bunch but are resilient in less-ideal growing conditions.
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Binga: The pisifera subpopulation was derived from Yangambi progenies from F2 and F3 generations. They are planted in the Binga plantation in Yangambi, Democratic Republic of Congo. The Bg 312/3 and Bg 312/3 are two-parent palm varieties of interest for breeding purposes.
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Ekona: Wild palms were used from the Ekona region to create the Ekona population. The regions include Unilever’s Crown Estate, Ndian Estate, and Lobe Estate plantations in Cameroon. Its high bunch yield, excellent oil content, and wilt resistance make it a sought-after crop.
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Calabar: Aba, Calabar, Ufuma, and Umuabi are all represented in Nigeria Institute for Oil Palm Research’s (NIFOR) breeders, which are more diverse than their predecessors. Hence, many seed-production programmes make use of this pisifera.
3. Factors Affecting Palm Oil Growth and Quality
Palm oil forecasting has been critically shown in studies, particularly in early warning of potential problems. The current state of palm oil modelling indicates a lack of knowledge on palm oil growth and the ability to improve the fruit quality [54,55]; thus, an in-depth analysis is required. It is necessary to examine the heterogeneity of its production to better comprehend and exert control over the emergence of palm oil. An initial step can be taken before establishing predictive modelling to gain insight and understand the factors (environmental factors, phenotypes, and genotypes).
This step includes determining the relationships between palm oil and the method used in the growth analysis. An analysis method is essential to gain insight into the factors and inter-relationships in palm oil production. Palm oil analysis provides a comprehensive understanding of the uncertainty and nonlinearity of its forecast. The most common method of analysing the palm oil data is through, for example, using a preprocessing algorithm or feature selection algorithm. However, previous studies only needed the analysis with no attempt at making predictions.
Oil palms require light and nutrients such as nitrogen and phosphorus as a starting point for photosynthesis. Temperature and water turbidity are other environmental factors that affect palm oil production. Five environmental parameters facilitate the oil palm stress tolerance [56]: rainfall, temperature, relative humidity, light intensity, and wind speed. The inefficient production of palm oil may occur in low humidity levels, especially in the dry seasons when the watering holes and rivers have dried up. Oil palms may be more stressed during this season, affecting their yields.
Humidity concentration in palm oil cultivation can be predicted [57], for example, a study recognised several environmental effects influencing inflorescence abortion and sex determination. These factors include rainfall, monthly rain, sunshine hour, and evapotranspiration, followed by the minimum and maximum temperature [58]. Notably, in various studies, rainfall is positively correlated with crop output but is weakly associated with crop prices [59]. However, a cointegration analysis from 2018 to 2050 reported that rainfall changes affect future and spot prices with different time lags [60]. Furthermore, a study utilised monthly temperature anomalies to successfully predict palm oil yields [61].
Soil type and texture potentially improve the accuracy of forecasts and climate. Malaysian yields of over 30T fruit bunches per hectare were reported on all soil types, excluding shallow ones. This type exhibits issues such as reduced root proliferation, increased sensitivity to drought and flooding, and a higher risk of palms toppling. The most common soil type based on the Asian soil taxonomy is ultisols and oxiols [62,63]. One study used 14 different soil textures and chemical variables on six palm oil plantations to measure the effects on palm oil and the physicochemical properties of soil [64]. Anaba et al. [65] mentioned that palm oil physiological and behavioural adaptations to survive were defined as early tenera hybrid seedling stages. Sandy soils with macropores exhibit low resistance to penetration, producing excellent root growth in length and ramification. Meanwhile, the variability of soil in the water column is recognised as a more effective measurement approach than conventional techniques.
Phenotyping is possible at all levels of an organism’s organisation, including the subcellular, cellular, tissue, organismic, and agrophytocenosis levels. This approach is used to identify productivity determinants, abiotic stressors, and plantation planning, especially on lands. This list can be extended to include the determining of the critical mechanisms of oil palm’s resistance to pathogens [66]. In oil palm phenotyping, the fruit’s size, shape, and physiological and biochemical characteristics are considered. These factors are then evaluated under specific environmental conditions and the oil palm genome [67,68]. Essentially, modern phenotyping methods enable the collection of real-time data and information analysis on the entirety of the phenotypic features. Hence, palm oil growth, development, and reproduction processes can now be comprehensively investigated [69,70]. One common phenotype in palm oil is the identification of ripe fruit. One of the prevalent phenotypes used in research is identifying the types of fruits, whether ripe or unripe.
Genotype has received significant attention, exhibiting the potential to improve the environment, phenotype, and forecast accuracy. The individual genotype can be determined using genotypic assaying, a method used in genotyping technology. Previous research used molecular markers (known DNA markers) breeding by deciphering genetics based on deoxyribonucleic acid (DNA) to enhance palm oil fruits. These markers include restriction fragment length polymorphism markers (RFLPs), amplified fragment length polymorphism markers (AFLPs), and short tandem repeat or simple sequence repeat markers (SSRs). Furthermore, the markers are used in the early stages of oil palm genetic mapping. For instance, the SNP’s marker entails linkage and linkage disequilibrium (LD) mapping. This marker is favoured because of its abundance, low mutation rates, and amenability to high-throughput analysis.
Automated and high-throughput genotyping is well-suited to the binary SNPs at the genomewide scale. This genotyping technique is currently possible using the array [71,72] or sequencing-based technologies [73,74]. The SNP arrays serve as an alternative to the laborious cloning and primer design, though it lacks the discovery process and favours genotyping new populations. Hence, new sequencing techniques have emerged, including next-generation sequencing, i.e., restriction-site-associated DNA sequencing (RAD-seq) [75] and genotyping by sequencing (GBS) [76]. Currently, genome-wide markers can be discovered in a model of palm oil. Table 2 presents several categories in which the variables listed above, and others, can be sorted, and additional variables present a list of categories that influence oil palm growth along with input features used in previous studies.
Table 2. Categorical variables.
| Type of Components | Features Data | Factor Category |
|---|---|---|
| Soil and Fertilisation | Levels of potassium (K), nitrogen (N), phosphorus (P), magnesium (Mg), calcium (Ca), nutrient supplements of groundwater, topography and slope, consumption of chemical fertilisers, consumption of agricultural pesticide, type and texture of the soil, and planting density. | Environment (E) |
| Climatic | Precipitation, humidity, maximum temperature, minimum temperature, mean temperature, rainfall, irrigation carbon dioxide concentration, solar radiation, water measurements, and waterlogging. | |
| UAV for Canopy Surveillance | Tree crown, stems length, tree colours, crown size, bare land, and water. | Phenotype (P) |
| Yield | Low lipase, high stearic and high oleic, low free fatty acids content and high iodine value, oil quality, quality, yield, oil to-dry mesocarp (O/DM), total oil yield per palm (O/P), oil content in mesocarp, oil-to-wet mesocarp (O/WM), palm oil yield (PO), FFB, yield, height, and high carotene. | |
| Fruit | Mesocarp-to-Fruit (M/F), Shell-to-Fruit (S/F), Kernel-to-Fruit (K/F), oil-bearing mesocarp, shell thickness, reasonable stalk length, good fruit set, and low parthenocarpy, fruit form, fruit quality, fruit colour virescent (VIR), and fruit ripeness. | |
| Bunch | Annual cumulative bunch number (BN), annual average bunch weight (ABW), annual cumulative bunch production (FFB), bunch production, bunch index, pulp-to-fruit ratio (PF), fruit-to-bunch (F/B), oil to bunch (O/B) oil extraction rate (OER), and oil-to-pulp ratio (OP), average bunch number per hectare (BUNCH_HA), and bunch weight (BW). | |
| Crop Samples | Flavonoid, anthocyanin content, fruit colour, fruit size, hue, saturation, intensity, contour lines, and blue-to-red fluorescence ratio (BRR_FRF). | |
| Tree Detection | Positive/negative histogram of oriented gradients (HOG), crown size, images of palm oil, built-up, bare land, water, and forest. | |
| Vegetative | Total dry matter, plant architecture, leaf length, number of leaves per plant, plant height, height increment (HT), and frond length (FL). | |
| Estimation | Nutrient content and plant height, shell thickness, mantled somaclonal variation together with gene networks for oil biosynthesis, drought and cold tolerance, in vitro regeneration potential, lipase activity, carotene and vitamin E contents, FA composition and iodine value, fruit shell thickness, fatty acid (FA) profile, fruit age, plant life, age factor, average value, daily CPO prices, monthly closing prices of oils, benefits, plant scale index, sex determination, inflorescence abortion, foliar nutrient composition, FFB yield, growth, and respiration. | |
| Disease detection | Bud and spear rot, sudden wilt, red ring disease and basal stem rot disease spectral reflectance, leaves and stem colours, freckle (Cercospora elaeidis), blast (Pythium splendens and Rhizoctonia lamellifera), vascular wilt (Fusarium oxysporum f. sp. elaeidis), and ganoderma trunk rot (Ganoderma spp.). | |
| Others | Tree crowns and categorical features, thermal images, quantitative features, oil per palm, chlorophyll-sensitive wavelengths, and electrical properties of leaves. | |
| Breeding | DNA and tissues of the dura, pisifera, tenera; breeding-Deli, AVROS, Yangambi, Calabar, Ekona, Binga, and La Mé. | Genotype |
The review results based on this review will be highly significant given that essential factors are incorporated into the prediction process via big data. However, more research is required to determine if big data can improve the prediction of palm oil performance.
This entry is adapted from the peer-reviewed paper 10.3390/a15060218
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