Oil Palm Yield Research Articles

Impact of COVID-19 on labour usage and the production of oil palm yield

Impact of COVID-19 on labour usage and the production of oil palm yield

Effect of slope, aspect, and position on soil properties at various depths in an oil palm plantation in Selangor, Malaysia

Abstract. Abdalrahem OMI, Ismail MH, Zaki PH, Singh DSAK. 2024. Effect of slope, aspect, and position on soil properties at various depths in an oil palm plantation in Selangor, Malaysia. Biodiversitas 25: 2507-2514. Limited information exists regarding the study of land and topographic characteristics and their influence on palm oil yield, as well as the effects on the physio-chemical properties of the soil. This study examined the impact of slope, aspect, and position at different soil depths on the physical and chemical properties of an oil palm (Elaeis guineensis Jacq.) plantation in an oil palm plantation in Selangor, Malaysia. Two sites with distinct north and south aspects were chosen for each slope site. Three equal positions were identified within each slope: Summit, Sideslope, and Toeslope. Random sampling determined soil physical and chemical properties at two depths (0-20 cm, 20-40 cm). This study analyzed soil chemical properties, including pH, EC, total N, P, Ext K, Ca, Mg, and soil texture, and the soil properties across different slope positions, depths, and aspects results indicated a significant difference (p<0.05). The soil properties' main factors contributing to the observed variations were slope position and soil depth. Slope position had a significant effect (p<0.05) on soil pH, EC, Ca, Mg, K, and N. Soil depth significantly influenced (p<0.05) all soil properties examined. The slope aspect also significantly affected (p<0.05) pH, Ca, Mg, and K. The soil texture classification ranged from sandy clay to sandy clay loam. Sand constituted the highest proportion of soil particles in both aspect slopes, followed by clay. The clay content increased with depth, specifically in the 20-40 cm range.

Oil Palm Production in the 20th Century and Beyond: The Impact of Climate Change in Malaysia

The widespread concern about the devastating impact of climate change, especially in the agricultural sector, has become severe. This paper aims to explore and predict the impact of climate change on oil palm production using future climate conditions projected by the National Water Research Institute of Malaysia (NAHRIM). An error correction model (ECM) and autoregressive distributed lags (ARDL) cointegration approach were applied to assess the short-run and long-run impact of climate change on oil palm production. The estimated short-run coefficients disclosed that the oil palm own price and fertilizer use positively affected oil palm production in the second lag period, and increased acreage benefited oil palm production in the long run. The rainfall variable negatively affected the second lag period but positively rose oil palm production in the long run. The results of forecasting analysis revealed that SN1 (minimum climate variability), SN2 (maximum climate variability), and SN3 (average climate variability) would increase oil palm production by 5%, 1%, and 2%, respectively. Meanwhile, the oil palm yield would rise from 16.73 t/ha in 2020 to 16.89 t/ha in 2030 under SN1. However, with maximum rainfall, the yield would drop to 16.41 t/ha in 2030. Overall, climate change would likely reduce oil palm yield. This research could serve as empirical guides for policymakers and oil palm stakeholders in terms of practical and policy implications to adapt to climate change-related risks and uncertainties. The practical cover investments in technologies, such as developing drought-tolerant and early-maturity crop varieties, boosting water saving, and reducing evapotranspiration.

Development and validation of an oil palm model for a wide range of planting densities and soil textures in Malaysian growing conditions

A semi-mechanistic oil palm growth and yield model called Sawit.jl was developed to account for a wide range of planting densities and soil textures under Malaysia's climate conditions. The model comprises components related to meteorology, photosynthesis, energy balance, soil water content, and crop growth. The model simulates instantaneous meteorological properties using daily weather data, calculates simultaneous evaporation from crop and soil with the Shuttleworth-Wallace model, determines soil water content through Darcy's law, and adapts a biochemical C3 model for photosynthesis. The model is also parameterized using updated measurements from the newer tenera oil palm, including temperature-dependent Rubisco kinetics, specific leaf area, and the partitioning of nutrients and dry matter between various tree parts. Sawit.jl was validated using historical field measurement data from seven Malaysian oil palm sites, encompassing palm ages spanning 1–23 years. These seven sites differed in soil type (Inceptisols and Ultisols), planting density (82–299 palms ha−1), soil texture (27–59 % clay and 7–67 % sand), and rainfall (1800–2800 mm yr−1). The model showed overall good accuracy in simulating oil palm parameters (except for trunk weight) across diverse conditions, with model agreement metrics ranging from 6 to 27 % for model absolute errors, −22 to +17 % for model bias, and 0.38 to 0.98 for the Kling-Gupta Efficiency index. The model also predicted the response of oil palm yield to abrupt rainfall changes, such as those during El Niño and La Niña events, while accounting for how soil texture, rainfall, and other meteorological factors influence water deficits and crop photosynthesis. However, model accuracy varied by site, planting density, and oil palm parameter. Model accuracy can be increased by more accurately representing the oil palm microclimate, incorporating fruiting activity, and refining the dry matter partitioning mechanism for the trunk.

Yield Gaps and Factors Affecting Production Inefficiency in Smallholder Oil Palm Plantations in Muaro Jambi District Jambi Province

Abstract Oil palm plantations in Indonesia, including in Jambi Province, are categorized into large private plantation, state-owned plantation, and smallholder plantation. The productivity of smallholders is the lowest compared to the other 2 categories. At the district and sub-district level, the yield of smallholder oil palm is also vary, spreading from 1,675 to 6,680 kg CPO per ha per year. The large yield variation is inseparable from the plantation management among farmers. The objectives of this research are to study the yield gap and technical efficiency of production among smallholders, and to analyze the factors that affect the technical production inefficiency in smallholder oil palm plantation. The research was conducted in Muaro Jambi District as a main palm oil producing district in Jambi Province. Data was collected from a sampel of 120 independent smallholders. One way anova test was used to study the yield gap and double natural log (ln) of Cobb–Douglas stochastic frontier production function was used to analyze the technical efficiency and some factors affects its inefficiency. The results revealed that there were significant differences in yield among smallholders based on area size, level of education, and activeness in the farmer organizations. However, on average, the management of oil palm by independent smallholders is quite technically efficient. The factor that significantly affects the level of technical inefficiency are the size of the area and farmers’ level of education.

Block-scale Oil Palm Yield Prediction Using Machine Learning Approaches Based on Landsat and MODIS Satellite Data

Due to environmental threats and weather uncertainty concerns, oil palm yield prediction is crucial for sustaining crop production. This can be achieved through machine learning and utilising remotely sensed data to predict crop yield. However, the comparative studies on remotely sensed data in adopting the machine learning models are still limited due to the data accessibility. Therefore, we compare and evaluate the prediction accuracy between different satellites, namely MODIS and Landsat-7, using machine learning algorithms and the topology of deep neural networks. Random forest and stacking outperformed linear regression, ridge regression, and lasso regression for both Landsat-7 NDVI (R2= 0.78–0.80; RMSE=1.00- 1.26 tonnes per hectare; MAE=0.77- 0.79 tonnes per hectares; MAPE=0.03-0.04 tonnes per hectare) and MODIS NDVI (R2= 0.60–0.65 tonnes per hectares; RMSE= 2.72–2.81 tonne per hectares; MAE= 1.42-1.55, MAPE= 1.01- 1.02 tonnes per hectares). The Landsat-7 NDVI revealed that neural networks with a deeper network topology (R2= 0.85; RMSE= 1.42 tonnes per hectare; MAE=0.57 tonnes per hectares; MAPE=0.06 tonnes per hectare) outperformed neural networks with a baseline and broader network topologies in terms of performance. In contrast, MODIS-NDVI revealed that the neural network with a wider network topology had the highest overall prediction accuracy and the lowest prediction error (R2= 0.75; RMSE= 2.81 tonnes per hectare; MAE=2.27 tonnes per tonnes; MAPE= 0.13). Because of its higher spatial resolution in comparison to MODIS, landsat-7 NDVI used in neural networks with a deep network topology provided the best model performance. Although the use of NDVI as a single input factor may cause uncertainty in some extents, it is an efficient and reliable method for improving yield estimation with the use of medium-resolution satellites, which has important implications for early warning towards the reduction in yield production.

Root cutting on growth and yield of oil palm (Elaeis guenesis Jacq.)

Root cutting is speculated able to increase nutrient uptake by oil palm. The objective of this study was to investigate the effect of root trimming on the growth, flowering, and yield of oil palm. The research focused on oil palm plants aged five years old in the field. The experiment used a nested design with two key factors: root-cutting depth as the main plot and root-pruning intensity as the subplot. Results showed that root cutting at a depth of 0 to 20 cm from the soil surface followed by cutting intensities of 50% and 75% enhanced plant height at 0, 3, and 9 months after treatment. The treatments did not affect other variables including flowering and yield of oil palm trees. Keywords: bunch weight; cutting intensity; root trimming; sex ratio

Palm Oil Yield Prediction using Machine Learning

This project aims to revolutionize palm oil production by leveraging machine learning techniques to develop a predictive model for palm oil yield. By incorporating a wide range of variables such as climate conditions, soil characteristics, and cultivation techniques, the model seeks to provide accurate predictions to stakeholders in the palm oil industry. The ultimate goal is to empower farmers and decision-makers with a tool that can enhance decision-making processes and contribute to the overall sustainability of palm oil production. With the global demand for palm oil on the rise, the need for precision and efficiency in cultivation practices has never been more critical, making this project timely and impactful.

Antifungal potential of volatiles produced by Aspergillus neoniger F14 against Ganoderma boninense

Ganoderma boninense Pat. is a fungal pathogen that primarily afflicts oil palm, leading to decreased palm oil yield and significant economic losses. The recent focus on biocontrol agents producing acids and volatile compounds has gained momentum due to their straightforward synthesis and ease of interaction with the phytopathogen. This research is a component of a project aimed at exploring the local biodiversity of antagonistic fungi in the oil palm plantation environment, with a specific focus on volatile organic compounds (VOCs) detected using Gas Chromatography-Mass Spectrometry (GC-MS). Isolate F14, a collection in the Laboratory of Applied Microbiology, BRIN, Indonesia was identified as Aspergillus neoniger, a novel report as a soil-originating fungal community in Indonesian oil palm plantations. VOC profiling using GC-MS revealed five dominant compounds namely (Z)-9-Tricosene, 1,2,3,4,5-Pentamethylcyclopentane, Dihexyl carbonate, Pentadecane, and Tridecyl laurate. Based on aromatogram disc tests against G. boninense colonies, it was determined that VOCs from A. neoniger F14 inhibited growth by 48% on the fifth day. The findings of this study contribute supportive information for further investigations into VOCs produced by other local antagonistic isolates against G. boninense.

Predicting oil palm yield using a comprehensive agronomy dataset and 17 machine learning and deep learning models

The rising global demand for oil palm emphasizes the importance of accurate oil palm yield predictions. This predictive capability is critical for effective crop management, supply chain optimization, and sustainable farming practices. However, the oil palm sector faces challenges in yield projection, stressing a noteworthy gap in the application and evaluation of modern machine learning and deep learning technologies. Our study addressed this gap by systematically evaluating 17 machine and deep learning models in predicting oil palm yield, incorporating various agronomic parameters, e.g., soil composition, climatic conditions, plant age, and farming techniques. This holistic approach enhances the application of machine and deep learning in agriculture. Using the feature selection technique and a maximum depth of 32 and 1000 estimators, the Extra Trees Regressor exhibited positive performance, i.e., MSE = 860.36 and an R2 = 0.65, and stands out among the 17 models evaluated. Our findings also showed that incorporating a comprehensive agronomic dataset is critical to accurate yield prediction. Hence, this model and approach have the potential to be a robust decision-making tool for agronomists and farmers in the oil palm industry, setting the stage for future innovations in sustainable agriculture practices.

Agronomic Suitability for Oil Palm Growing in Uganda

The ever-increasing demand for vegetable oils with its products in Uganda, calls for new areas to be put under oil palm cultivation. A study was conducted to investigate the agronomic suitability of oil palm cultivation in six areas located within a 30 km radius off-shore, on the island and more than 30 km radius off-shore on the mainland. The experiment was superimposed on 45 × 45 m plots in oil palm adaptive trial plantations established in 2008 in Bugiri, Mayuge, Buvuma, Mukono, Kibaale and Masaka districts. The experiment was laid out in a randomized complete block design with three replications. Data on number of oil palm bunches, bunch weight and yield was collected while climatic data (rainfall, temperature, relative humidity and radiation) was acquired from meteorological stations near the experimental locations. Rainfall data analyzed and dry season duration suggested that Mayuge, Masaka, Mukono, Buvuma and Kibaale were moderately favorable for oil palm growing, while temperature and radiation data indicated that all study locations were suitable for oil palm growing. Furthermore, based on relative humidity, Buvuma, Mayuge and Bugiri were suitable for oil palm cultivation but Kibaale, Masaka and Mukono were moderately suitable. Oil palm yield varied significantly (P < 0.05) across locations. The yield was much greater in Mukono (17.7 t/ha/yr) followed by Buvuma (13.8 t/ha/yr) and Kibaale (12.9 t/ha/yr) then Mayuge (10.7 t/ha/yr), Bugiri (10.2 t/ha/yr), and Masaka (10.3 t/ha/yr). The significantly high yield of oil palm observed in Mukono was attributed to the high rainfall received in this location. Based on research findings, Mukono, Masaka, Bugiri, Buvuma, Kibaale and areas within the 30 km radius of Lake Victoria are agronomically suitable for oil palm cultivation.

Genotyping by sequencing for the construction of oil palm (Elaeis guineensis Jacq.) genetic linkage map and mapping of yield related quantitative trait loci.

Oil palm (Elaeis guineensis Jacq.) is one of the major oil-producing crops. Improving the quality and increasing the production yield of oil palm have been the primary focuses of both conventional and modern breeding approaches. However, the conventional breeding approach for oil palm is very challenging due to its longevity, which results in a long breeding cycle. Thus, the establishment of marker assisted selection (MAS) for oil palm breeding programs would speed up the breeding pipeline by generating new oil palm varieties that possess high commercial traits. With the decreasing cost of sequencing, Genotyping-by-sequencing (GBS) is currently feasible to many researchers and it provides a platform to accelerate the discovery of single nucleotide polymorphism (SNP) as well as insertion and deletion (InDel) markers for the construction of a genetic linkage map. A genetic linkage map facilitates the identification of significant DNA regions associated with the trait of interest via quantitative trait loci (QTL) analysis. A mapping population of 112 F1 individuals from a cross of Deli dura and Serdang pisifera was used in this study. GBS libraries were constructed using the double digestion method with HindIII and TaqI enzymes. Reduced representation libraries (RRL) of 112 F1 progeny and their parents were sequenced and the reads were mapped against the E. guineensis reference genome. To construct the oil palm genetic linkage map, informative SNP and InDel markers were used to discover significant DNA regions associated with the traits of interest. The nine traits of interest in this study were fresh fruit bunch (FFB) yield, oil yield (OY), oil to bunch ratio (O/B), oil to dry mesocarp ratio (O/DM) ratio, oil to wet mesocarp ratio (O/WM), mesocarp to fruit ratio (M/F), kernel to fruit ratio (K/F), shell to fruit ratio (S/F), and fruit to bunch ratio (F/B). A total of 2.5 million SNP and 153,547 InDel markers were identified. However, only a subset of 5,278 markers comprising of 4,838 SNPs and 440 InDels were informative for the construction of a genetic linkage map. Sixteen linkage groups were produced, spanning 2,737.6 cM for the maternal map and 4,571.6 cM for the paternal map, with average marker densities of one marker per 2.9 cM and one per 2.0 cM respectively, were produced. A QTL analysis was performed on nine traits; however, only QTL regions linked to M/F, K/F and S/F were declared to be significant. Of those QTLs were detected: two for M/F, four for K/F and one for S/F. These QTLs explained 18.1-25.6% of the phenotypic variance and were located near putative genes, such as casein kinase II and the zinc finger CCCH domain, which are involved in seed germination and growth. The identified QTL regions for M/F, K/F and S/F from this study could be applied in an oil palm breeding program and used to screen palms with desired traits via marker assisted selection (MAS).

Utilization Of Soil Ameliorant To Control Emission In Oil Palm Plantation On Tropical Peat Soil

Development of oil palm plantation areas today is increasingly leading to marginal areas, particularly on peatlands. However, development of peatland as agricultural land or as oil palm plantation has the potential to cause peatland to change from a carbon sink into a source of carbon emission. This objective of this research to determine the effect of utilization soil ameliorant to oil palm yield production and in controlling carbon emission and its impact to soil peat chemical properties. The research was conducted in Rokan Hilir district, Riau. The study was carried out in a randomized block design with three replications and three levels of soil ameliorant. The results show that application of high rate soil ameliorant significantly increase FFB yield relatively from control plot. Based on observational data for two months after first application of boiler ash, the CO2 emission was tends to decrease in plot that get the application of soil ameliorant compared to the control plot. Application of 1.5 tonne/ha/yr of ameliorant give the lowest CO2 emission value. Improvement of soil peat chemical properties by application of boiler ash as soil ameliorant also observed in this study, where the high rate of soil ameliorant significantly improves soil pH and also tends to increase total Potassium and Cation Exchange Capacity (CEC).

Exogenous Application of Calcium, α-Naphthaleneacetic Acid and 1Methylcyclopropene Improved Fruit Growth and Oil Yield of Oil Palm (Elaeis guineensis Jacq.) Grown on Ultisol

<span>Soil acidity is one of the main factors limiting the growth and sustainable production of oil palm in Indonesia. The aim of this study was to evaluate the effect of various exogenous compounds on improving fruit growth and oil yield of palm oil grown on Ultisol. The experiment employed three treatments which were arranged in a Randomized Complete Block Design (RCBD) with three replications. The treatments were: 1-MCP (0, 100, 200, and 300 ppm), CaCl</span><span>2 </span><span>(0 and 50 ppm) and NAA (0 and 200 ppm). Application of Ca</span><span>2+</span><span>, NAA and 1-MCP either single or in combination improved fruit growth and oil yield by increasing almost all variables, except the number of bunches, bunch weight and fruit set. The 100 ppm 1-MCP + 50 ppm Ca</span><span>2+ </span><span>+ 200 ppm NAA treatments showed the highest oil-to-dry mesocarp content. To get the highest oil to bunch (OB), the combination of 100 ppm 1-MCP + 200 ppm NAA; 50 ppm Ca</span><span>2+ </span><span>+ 200 ppm NAA, and a single treatment of 50 ppm Ca</span><span>2+ </span><span>were very promising to be utilized.</span>

Effect of NBPT-DCD (Urease and Nitrification Inhibitor) on Nitrogen Loss, N Uptake and Oil Palm Yields (Elaeis guineensis Jacq.) bn Typic Dystrudepts Soil

Incorporating N-(n-butyl) thiophosphoric triamide (NBPT) as a urease inhibitor and Dicyandiamide (DCD) as a nitrification inhibitor can enhance the efficiency of nitrogen (N) fertilization. The main aim of the study was to evaluate the impact of urease and nitrification inhibitors (NBPT and DCD) on N loss in urea fertilizer, N absorption, and oil palm (Elaeis guineensis Jacq.) yield. Researchers conducted the field trial using a single-factor, completely randomized design (CRD) with three replications. The experimental treatments included nitrogen (N) fertilization with 5 different levels. The levels were: 100% urea (2.75 kg/plant/year: 1.50 kg/plant in rotation 1 and 1.25 kg/plant in rotation 2) (N0), 80% urea + NBPT-DCD (2.20 kg/plant/year: 1.20 kg/plant in rotation 1 and 1.0 kg/plant in rotation 2) (N1), 60% urea + NBPT-DCD (1.65 kg/plant/year: 0.90 kg/plant in rotation 1 and 0.75 kg/plant in rotation 2) (N2), Urea dosage of 80% (2.20 kg/year: 1.20 kg/plant in rotation 1 and 1.00 kg/plant in rotation 2) (N3), and Urea dosage of 60% (1.65 kg/plant/year: 0.90 kg/plant in rotation 1 and 0.75 kg/plant in rotation 2) (N4). The findings revealed that the application of Urea in combination with NBPT-DCD (inhibitors of urease and nitrification) led to a reduction in N loss by 30.33-37.48% and an increase in N absorption compared to Urea without NBPT-DCD. Oil palm trees grew on Typic Dystrudept soil. The plants received 2.20 kg of urea treatment per plant. The researchers combined Urea with NBPT-DCD. These trees had better outcomes than others. They had less N volatilization and better N absorption. They also had higher fresh fruit bunch productivity. The others received N fertilization but not NBPT-DCD.

Analysis of Palm Oil Mill Effluent Quality

Palm oil plantations are currently growing rapidly in Indonesia. The area of palm oil plantations continues to increase from year to year. The total area of palm oil in 2022 will reach 14.99 million hectares, both privately owned plantations and PT. Pola Kahuripan Inti Sawit. To manage the yield of palm oil, assistance from the palm oil plantation industry is needed. However, the palm oil industry also produces wastewater. Therefore, researchers conducted an analysis of the quality of palm oil mill effluent. The method used in this research is exploratory method and data collection and testing is done by grab sampling. The results of the analysis contained in this palm oil mill effluent, namely the TSS content ranges from 30-40 mg/L, the pH content ranges from 7.5-8.9, BOD levels ranged from 20-300 mg/L, COD levels ranged from 30-200 mg/L, Oil and Fat levels ranged from -0.5 mg/L - 5.5 mg/L, Nitrogen (N) levels ranged from 1 mg/L - 18 mg/L. Based on AMDAL and the standard provisions of the Republic of Indonesia Minister of Environment Regulation Number 5 of 2014, all parameters tested as a whole have met national quality standards.

NON-LINEAR EFFECT OF TEMPERATURE ON OIL PALM YIELD: A NOTE BASED ON PANEL THRESHOLD ANALYSIS

The effect of climate change on agriculture and food production has emerged as one of the major concerns of major global economies, especially given that the global population is projected to reach 10.9 billion people by the end of the 21st century (United Nations, 2019). Crops react differently to climate change and hence it is crucial to understand its effect on specific agricultural plants. Specifically, the impact of climate change on agriculture yield may not be linear. As such, this study investigated the impact of climate factors on oil palm yield using state level data from Malaysia. The results suggested that temperature and oil palm yield have a non-linear inverted U-shaped relationship. In addition, rainfall has a positive impact on oil palm yield.

Deciphering the SNP variations linked to female sterility in Dura fruit form of oil palm

Among the vegetable oils, palm oil which is obtained from African oil palm (Elaeisguineensis Jacq.) contributes a major share at the global oil trading centres. Taxonomically, oil palm belongs to the family Aricaceae (formerly known as Palmaceae), sub-family Arecoideae and the genus Elaeis. One breeding cycle of oil palm takes 15-18 years with lot of techniques involved in achieving good yield and oil quality of oil palm in a sustainable manner. Oil palm is a highly heterozygous and heterogeneous crop with high diversity in the germplasm (Babu et al. 2020). Oil palm can be divided into mainly three forms based on the shell thickness of the fruits i.e., dura, pisifera, and tenera, whereas another type of fruit form called macrocarya also exists but not common. Based on fruit color, oil palm is classified into two types i.e., Virescence and Nigrescence type. Nigrescence is the commonly found fruitcolor among the commercially grown oil palm farms. In terms of quality, palm oil has approximately equal distribution of saturated to unsaturated fatty acids (UFAs). Most of the oil palm breeding programmes in generating high yielding varieties were restricted to dura variety which is having thick shelled fruit form till the 1950s. Later, research efforts at some countries like Africa, Malaysia and Indonesia resulted in generation of various breeding populations originated from the four Bogorpalms and their progenies.

Kajian Pengaruh Perbedaan Kerapatan Tanam dan Tahun Tanam terhadap Produktivitas Kelapa Sawit

The oil palm yield depends on its genetic and the interaction with environmental factors is affected by spatial arrangement of palm trees. The research objectives are to determine the effect of population density and planting year on oil palm productivity and vegetative growth. This research uses a factorial design arranged with a randomized complete design. Secondary data are palm productivity, fresh bunch number and bunch weight that were collected for three years (2019—2022). Primary data are stem height, frond length, and stem gird which were measured on 90 trees for each treatment combination. There are nine combinations with 810 palm trees that were sampled from 27 estate blocks (30 hectares for one block). The results showed that productivity and amount of oil palm FFB at densities ≥ 142, 136-141, and ≤ 135 SPH and planting years 2007, 2009 and 2011 (mature crop 12-15, 10-13, and 8-11 years) did not show significant differences. The heavier bunch weight was produced palm tree planted in 2007 with density ± 135 trees.ha-1.

Gibberellic acid (GA) role in acetyl-coA carboxylase enzyme regulation and in improving oil palm yield

Seaweed specifically, Sargassum sp. is known to contains a boosting hormone growth that has been promoted plant growth and yield due to the containing of auxin, gibberelic acid (GA) and cytokinine, and also some amino acids especially glutamic acid. Those composition could be used as an booster of palm oil production which related to acetyl co-A carboxylase activity (ACC). ACC is the rate determination step in fatty acid accumulation, and becomes active through dephosphorylation of some serine residues that induced by magnesium and glutamate. Moreover, ACC was regulated by AtWRI1 and AtWRI1-TCP4 interaction, a mechanism that allow fine-tuning of the oil biosynthetic pathway. In this research we conducted gene expression experiments, and molecular docking analyses to study the possible mechanism of seaweed composition stimulating oil accumulation in the oil palm. Further analysis was conducted to ensure whether the interaction between TCP4 and candidate inhibitors were able to phosphorylate TCP4 and decrease its activity. GA application resulted in the increase of oil accumulation in 1 month after application, although in the second month the oil accumulation showed decreasing. Increase of oil accumulation in the first month in line with the increase of the expression of ACC in 3rd and 5th weeks. Meanwhile, TCP4 showed decrease expression that resulted in the increase of the WRI1 in 5th week. From this result, it was indicated that GA application could block the TCP4, so it could not interact with WRI1, resulted in the expression of WRI1 and ACC. This interaction stimulates the oil accumulation in oil palm.