Correlation and Path Analysis in Commercial Tenera Oil Palms Collected from Southern Thailand

Background: Tenera oil palm is widely planted as a commercial plantation crop throughout Southern Thailand. The purpose of this study was to evaluate the phenotypic correlation and the direct and indirect effects among bunch yields and vegetative characteristics in commercial tenera oil palms. Methods: The oil yield, fresh fruit bunch, bunch number, average bunch weight, leaf area, leaf dry weight and rachis length were recorded from six commercial tenera oil palm progenies. The data were recorded between January 2019 and June 2020 at The Chaipattana Foundation’s oil palm plantation in Trang Province, Thailand. Result: Results show that fresh fruit bunch, average bunch weight and rachis length positively correlated with oil yield (rp = 0.90**, 0.50* and 0.53, respectively), while bunch number and rachis length positively correlated with fresh fruit bunch (rp = 0.58* and 0.47*, respectively). The path analysis shows that bunch number significantly correlated with fresh fruit bunch (0.58*) and strongly directly affected it (1.11). The fresh fruit bunch significantly correlated with oil yield, (0.90**) and had a strong direct (2.08) and indirect effect (1.20). These results indicate that bunch number and fresh fruit bunch are useful variables for oil yield improvement in further breeding programs of oil palm.

Oil Palm (Elaeis Guineensis Jacq.) Genetic Differences in Mineral Nutrition (Part III): Specific Leaflet Mineral Concentrations of High-Yielding Oil Palm Progenies and their Implications for Managing K And Mg Nutrition

Background and aims: In oil palm, similar fertilization treatments can result in leaflet potassium and magnesium concentrations that vary significantly from one progeny to another. This hinders the development of standardized fertilizer recommendations for this crop, as they are usually calculated based on optimum leaflet nutrient concentrations.Methods: 4-high-yielding oil palm progenies with contrasting leaflet K and Mg concentrations (C1, C2, and C3 of Deli x La Mé origin and C4 of Deli x Yangambi origin) were treated with combinations of 3-levels of KCl and MgSO4, in a completely randomized split-plot factorial design with 6-replicates, where progenies were a sub-factor.Results: For a given level of KCl or MgSO4, different leaflet K and Mg concentrations were found between progenies (p < 0.0001). Leaflet K concentration and yield response to KCl applications revealed that the four oil palm progenies have different optimum leaflet K concentrations. In our study period (5-8 YAP), progenies C1 and C3 showed their highest fresh fruit bunch (FFB) yields (13.62 and 16.54 t ha-1 year-1, respectively) at K2, whereas progenies C2 and C4 showed their highest yields (14.62 and 12.39 t ha-1 year-1, respectively) at K1. Conclusion: Our study highlighted specific optimum leaflet K and Mg concentrations for different oil palm progenies in a given environment. It paves the way for adopting K and Mg fertilizer application rates adapted to specific requirements of each type of oil palm planting material.

Application of Optical Spectrometer to Determine Maturity Level of Oil Palm Fresh Fruit Bunches Based on Analysis of the Front Equatorial, Front Basil, Back Equatorial, Back Basil and Apical Parts of the Oil Palm Bunches

The quality of palm oil depends on the maturity level of the oil palm fresh fruit bunch (FFB). This research applied an optical spectrometer to collect the reflectance data of 96 FFB from unripe, ripe, and overripe classes for the maturity level classification. The spectrometer scanned the FFB from different parts, including apical, front equatorial, front basil, back equatorial, and back basil. Principal component analysis was carried out to extract principal components from the reflectance data of each of the parts. The extracted principal components were used in an ANOVA test, which found that the reflectance data of the front equatorial showed statistically significant differences between the three maturity groups. Then, the collected reflectance data was subjected to machine learning training and testing by using the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). The front equatorial achieved the highest accuracy, of 90.6%, by using SVM as classifiers; thus, it was proven to be the most optimal part of FFB that can be utilized for maturity classification. Next, the front equatorial dataset was divided into UV (180–400 nm), blue (450–490 nm), green (500–570 nm), red (630–700 nm), and NIR (800–1100 nm) regions for classification testing. The UV bands showed a 91.7% accuracy. After this, representative bands of 365, 460, 523, 590, 623, 660, 735, and 850 nm were extracted from the front equatorial dataset for further classification testing. The 660 nm band achieved an 89.6% accuracy using KNN as a classifier. Composite models were built from the representative bands. The combination of 365, 460, 735, and 850 nm had the highest accuracy in this research, which was 93.8% with the use of SVM. In conclusion, these research findings showed that the front equatorial has the better ability for maturity classification, whereas the composite model with only four bands has the best accuracy. These findings are useful to the industry for future oil palm FFB classification research.

Predicting Heritability of Oil Palm Breeding Using Phenotypic Traits and Machine Learning

Oil palm is one of the main crops grown to help achieve sustainability in Malaysia. The selection of the best breeds will produce quality crops and increase crop yields. This study aimed to examine machine learning (ML) in oil palm breeding (OPB) using factors other than genetic data. A new conceptual framework to adopt the ML in OPB will be presented at the end of this paper. At first, data types, phenotype traits, current ML models, and evaluation technique will be identified through a literature survey. This study found that the phenotype and genotype data are widely used in oil palm breeding programs. The average bunch weight, bunch number, and fresh fruit bunch are the most important characteristics that can influence the genetic improvement of progenies. Although machine learning approaches have been applied to increase the productivity of the crop, most studies focus on molecular markers or genotypes for plant breeding, rather than on phenotype. Theoretically, the use of phenotypic data related to offspring should predict high breeding values by using ML. Therefore, a new ML conceptual framework to study the phenotype and progeny data of oil palm breeds will be discussed in relation to achieving the Sustainable Development Goals (SDGs).

Impact of El Niño on Oil Palm Yield in Malaysia

Oil palm crop yield is sensitive to heat and drought. Therefore, El Niño events affect oil palm production, resulting in price fluctuations of crude palm oil due to global supply shortage. This study developed a new Fresh Fruit Bunch Index (FFBI) model based on the monthly oil palm fresh fruit bunch (FFB) yield data, which correlates directly with the Oceanic Niño Index (ONI) to model the impact of past El Niño events in Malaysia in terms of production and economic losses. FFBI is derived from Malaysian monthly FFB yields from January 1986 to July 2021 in the same way ONI is derived from monthly sea surface temperatures (SST). With FFBI model, the Malaysian oil palm yields are better correlated with ONI and have higher predictive ability. The descriptive and inferential statistical assessments show that the newly proposed FFBI time series model (adjusted R-squared = 0.9312 and residual median = 0.0051) has a better monthly oil palm yield predictive ability than the FFB model (adjusted R-squared = 0.8274 and residual median = 0.0077). The FFBI model also revealed an oil palm under yield concern of the Malaysian oil palm industry in the next thirty-month forecasted period from July 2021 to December 2023.

Quantification of muscles activations and joints range of motions during oil palm fresh fruit bunch harvesting and loose fruit collection

Although global demand for palm oil has been increasing, most activities in the oil palm plantations still rely heavily on manual labour, which includes fresh fruit bunch (FFB) harvesting and loose fruit (LF) collection. As a result, harvesters and/or collectors face ergonomic risks resulting in musculoskeletal disorder (MSD) due to awkward, extreme and repetitive posture during their daily work routines. Traditionally, indirect approaches were adopted to assess these risks using a survey or manual visual observations. In this study, a direct measurement approach was performed using Inertial Measurement Units, and surface Electromyography sensors. The instruments were attached to different body parts of the plantation workers to quantify their muscle activities and assess the ergonomics risks during FFB harvesting and LF collection. The results revealed that the workers generally displayed poor and discomfort posture in both activities. Biceps, multifidus and longissimus muscles were found to be heavily used during FFB harvesting. Longissimus, iliocostalis, and multifidus muscles were the most used muscles during LF collection. These findings can be beneficial in the design of various assistive tools which could improve workers' posture, reduce the risk of injury and MSD, and potentially improve their overall productivity and quality of life.

PATRON-CLIENT SOCIAL RELATIONSHIP BETWEEN PALM OIL FARMERS AND TAUKE IN KREUNG ITAM VILLAGE TADU RAYA DISTRICT NAGAN RAYA DISTRICT

Krueng Itam village is one of the villages where the majority of the residents work as palm oil farmers and depend on palm oil plantations. In marketing to sell FFB (Fresh Fruit Bunch) palm oil farmers still rely on collector traders (agents) which in this case is referred to by the term "Tauke Sawit" (toke sawit). In the beginning, the relationship between palm oil farmers and the Tauke was limited to economic relations, such as the sale and purchase relationship between sellers and buyers. But in its development, the relationship turns into a relationship of dependence and interest that leads to a patron-client relationship. This study used a descriptive qualitative method with data collection techniques through observation and interview process that is expected to be able to provide an overview of the social relationship between tauke and farmers. Based on the results of the study, the relationship between tauke and palm oil farmers is a patron- client relationship where palm oil tauke as the patron and palm oil farmers as clients. Palm oil farmers need tauke to accomodate the harvest of palm oil FFB and tauke needs palm oil FFB from farmers to be sold to Palm oil mills. In this cooperative relationship, farmers will usually borrow money for capital needs and others to the tauke, this makes the farmers' dependence on the tauke become greater so that each of them will maintain the relationship by respecting the existing norms so that the relationship has been established will not be broken easily. The relationship of patron-clients is more visible in the relationship between tauke and small farmers, this is because there is a clear difference in socio-economic status between the two, so that the tauke as a patron play a big role.