Oil Palm Trees Research Articles

Bioethanol Production from Elaeis Guineensis Oil Palm Trunk Sap using Repeated Batch Fermentation

Elaeis guineensis (E. guineensis) oil palm tree (OPT) presented a novel biomass source, with its OPT sap demonstrating the potential for bioethanol production due to its rich sugar content. The increasing waste of the OPT necessitated exploring ways to maximize its utilization and functionality, making bioethanol production a pertinent avenue for sustainable resource utilization. This study aimed to compare the bioethanol yield concentration production in optimized conditions and non–optimized conditions based on response surface methodology (RSM) data from Box–Behnken design (BBD). This study also investigated the viability of felled E. guineensis OPT sap for bioethanol production on the effect of sugar composition and fermentation conditions. Analysis revealed significant variations in fructose, glucose, and sucrose levels across different trunk segments, with sucrose notably higher in some areas. Using Saccharomyces cerevisiae (S. cerevisiae) Kyokai no. 7 in a 2L bioreactor, the study employed repeated batch fermentation to explore the efficiency of bioethanol yield production across 13 cycles under optimized and non–optimized conditions. The optimized fermentation conditions included a felled E. guineensis OPT sap medium with an initial pH of 6.50, supplemented with 6.80 g/L of peptone and 13.28 g/L of corn steep liquor (CSL) at 30°C. The non–optimized conditions were similar but conducted at room temperature. The maximum bioethanol yield concentration of 35.65 g/L, averaging 23–35 g/L per cycle, highlighted the effectiveness and stability of repeated fermentation under optimized conditions, with the bioethanol yield ranging from 3–4 volume/volume percentage (v/v %). The optimized condition significantly improved bioethanol concentration (38.42 %) and volume content (28.67 %), enhancing production efficiency. Bioethanol yield production was markedly improved under optimized conditions, as confirmed by statistical analysis, while non–optimized settings yielded unstable reported data. This research highlighted the importance of controlled environmental conditions and optimized fermentation processes. The study underscored the potential of felled E. guineensis OPT sap as a biomass source for bioethanol, suggesting promising avenues for the scale–up of bioethanol production in the future.

Investigation of gold nanoparticles for Ganoderma fungi treatment in oil palm trees

In recent years, oil palm has been one of the industries that contribute substantially to the gross domestic product of producing countries. As per Plantation Industries and Commodities, palm oil accounted for roughly 66.1% of the total export earnings, reaching RM 44.63 billion in 2022. However, several threats to palm trees, such as Ganoderma basal stem rot disease (BSR), pose risks to our country's gross domestic product. BSR, caused by the fungus Ganoderma boninense, leads to the breakdown and degradation of the lignin trunk of palm trees. Therefore, gold nanoparticles have been fabricated in this project because of their ability to treat fungi. Gold nanoparticles were synthesized in two forms: gold nanorods (GNRs) and gold nanobipyramids (GNBPs) utilizing a wet chemical technique called the Seed-Mediated Growth Method (SMGM). Two plasmon peaks were obtained for synthesized nanoparticles corresponding to transverse surface plasmon resonance (t-SPR) and longitudinal surface plasmon resonance (l-SPR). GNRs have a t-SPR peak at 536 nm and an l-SPR peak at 713 nm, while for GNBPs, a t-SPR peak was obtained at 563 nm and 809 nm for l-SPR peak. For structural properties, the intensity peak at the plane (111) for GNRs and GNBPs occurs at 38.19° and the plane (200) occurs at 44.39°. The surface density of GNRs is 65.26±3.44% and for GNBPs is 69.44±2.94%. For the Ganoderma treatment process, the observation was made for three samples; no control, GNRs and GNBPs. It was found that GNBPs have a high ability to inhibit fungal growth GNRs with no fungal found in their area. In the GNRs area, 20% of fungal growth was found, and there was 60% of fungal growth around no control. In conclusion, GNBPs successfully inhibited fungal growth due to their curvature and sharp edge tips that will interact with microbial cell membranes, resulting in membrane damage and leakage of cellular contents of fungi.

Mycobiome analysis of leaf, root, and soil of symptomatic oil palm trees (Elaeis guineensis Jacq.) affected by leaf spot disease.

Recently, attention has been shifting toward the perspective of the existence of plants and microbes as a functioning ecological unit. However, studies highlighting the impacts of the microbial community on plant health are still limited. In this study, fungal community (mycobiome) of leaf, root, and soil of symptomatic leaf-spot diseased (SS) oil palm were compared against asymptomatic (AS) trees using ITS2 rRNA gene metabarcoding. A total of 3,435,417 high-quality sequences were obtained from 29 samples investigated. Out of the 14 phyla identified, Ascomycota and Basidiomycota were the most dominant accounting for 94.2 and 4.7% of the total counts in AS, and 75 and 21.2% in SS, respectively. Neopestalotiopsis is the most abundant genus for AS representing 8.0% of the identified amplicons compared to 2.0% in SS while Peniophora is the most abundant with 8.6% of the identified amplicons for SS compared to 0.1% in AS. The biomarker discovery algorithm LEfSe revealed different taxa signatures for the sample categories, particularly soil samples from asymptomatic trees, which were the most enriched. Network analysis revealed high modularity across all groups, except in root samples. Additionally, a large proportion of the identified keystone species consisted of rare taxa, suggesting potential role in ecosystem functions. Surprisingly both AS and SS leaf samples shared taxa previously associated with oil palm leaf spot disease. The significant abundance of Trichoderma asperellum in the asymptomatic root samples could be further explored as a potential biocontrol agent against oil palm disease.

Palm Oil Tree Canopy Identification Using Deep Learning Approach (Case Study: Tanjung Gusta District, North Sumatera)

Abstract The palm oil plantation industry in Indonesia has growing rapidly as demand for palm oil increases globally. This needs to be supported by technological innovation to increase palm oil production. One of them is to integrate the power of artificial intelligence technology. This research aims to develop a robust and accurate method for segmenting oil palm trees in plantation areas. Leveraging deep learning algorithms techniques, the research explores the potential of SAM in accurately delineating individual oil palm trees derived from aerial imagery data. The study also involves the development of a comprehensive and versatile labelled dataset to support the training and validation of the deep learning models for oil palm tree counting and segmentation. The performance of the proposed approach is evaluated and discussed critically. This research demonstrates the potential of deep learning algorithms for large-scale mapping and accurate counting of oil palm trees in plantation areas. The author hopes that the result and analysis of this research will give insight and improvement in detecting oil palm trees using automatic method.

Deep Learning Mask R-CNN and Template Matching Algorithm For Tree Counting Analysis of Oil Palm Trees (Case Study: East Tanjung Jabung District, Jambi Province)

Abstract Indonesia is one of the largest producers and exporters of palm oil in the world. According to the Central Bureau of Statistics (BPS) in 2022 palm oil production has increased to 46.82 million tons. Palm oil has great potential; thus, technology is needed to analyze its productivity. Therefore, calculating palm tree percentages is an important aspect of land monitoring, plant maintenance, and efficient production planning. However, manual palm tree counting is time-consuming and labor-intensive and tends to be less efficient in large plantations. In addition, the lack of accuracy in estimating the number of trees and the age of crops can affect land planning and management. Algorithm development in geomatics science can automatically calculate oil palm trees using aerial photo data combined with template matching and deep learning methods, which are expected to provide efficient and accurate solutions. In this study, 620 samples of oil palm trees were trained. From the visual interpretation results, 6212 trees were obtained; from the processing results using the deep learning method, 6359 trees were obtained, and using the template matching method, 6756 trees were obtained. Through the validation test using the confusion matrix, the overall accuracy of the deep learning method was 95.15%, and the overall accuracy obtained using the template matching method was 87.17%.

Characterization of basal stem rot pathogen in oil palm tree in Adolina Seed Center, North Sumatra, to support sustainable food security

Abstract The productivity of oil palm plants is influenced by several factors, one of which is basal stem rot disease. The aim of this research was to morphologically and molecularly characterized the causal pathogen of basal stem rot disease in oil palm plants in the Adolina Seed Center, Serdang Bedagai Regency, North Sumatra. The study, conducted from September 2023 to March 2024. Sampling was done using a purposive random method based on visible symptoms of the disease. The research involved isolating and purifying the fungus, followed by macroscopic and microscopic observations, molecular characterization, and phylogenetic analysis. Seven isolates were identified as Ganoderma boninense and Ganoderma sp. The study found significant diversity in the fruit body morphology in Block AD-02 and Block AD-04, while microscopic observations showed similarities across all samples. Molecular and phylogenetic analyses revealed a close relationship between the isolates from Indonesia, G. boninense from Malaysia, and Ganoderma sp. from Singapore. The incidence of basal stem rotdisease was higher in Block AD-02 (54%), compared to Block AD-04 (48%). These findings contribute to the understanding of the pathogen diversity and spread in oil palm plantations.

Variation in Morphological Characteristic and Growth Rate of Isolated Dikaryotic Ganoderma boninense Mycelia on Nutrient Medium and Rubberwood Block: A Cue for Their Virulency

Undeniably, the formation of new Ganoderma variants within the plantation rapidly declines the life expectancy of oil palm trees. The produced variants induce severe infection on the palm, largely impeding disease management approaches to prevent infection. Selection of the high virulent of Ganoderma spp. affiliated with stem rot disease is crucial for assessing the planting material to identify either resistant or tolerant oil palm cultivars and optimise treatment strategies in line with species varieties. So far, the genetic diversity and virulency of these Ganoderma species in the IOI Group estate have not yet been explored. About seven basidocarps were collected from Ganoderma-infected palms and identified as Ganoderma boninense. The cultural traits of these nine isolates, including P4.1E and PER71 showed distinct phenotypical variations on potato dextrose agar (PDA). Though sequence analysis revealed four PM09C isolates were 100 per cent identical, incompatible reactions were observed among isolates, similar to other isolates from different fields. The mycelia rate expansion on the nutrient medium had a significantly correlated relationship to the disease incidence (DI) (r = 0.750), disease severity index (DSI) (r = 0.690) and death rate (DR) (r = 0.732). Overall, these findings contribute to the understanding of Ganoderma species in the estate and provide valuable insights for developing targeted management strategies in evaluating tolerant planting material for future replanting. Keywords: Ganoderma, oil palm, basal stem rot, genetic diversity, virulency.

Relationship analysis between Ganoderma boninense-derived Basal Stem Rot disease severity with multiple leaf physiology parameters in mature oil palm tree responses toward water use efficiency (WUE)

Relationship analysis between Ganoderma boninense-derived Basal Stem Rot disease severity with multiple leaf physiology parameters in mature oil palm tree responses toward water use efficiency (WUE)

Middle-Level Fusion YOLO on Multispectral Image to Detect Unhealthy Oil Palm Trees

Abstract Locating the unhealthy oil palm trees is one of the essential things in precision agriculture. In several countries such as Indonesia, Malaysia, and Thailand, Ganoderma disease often attacks oil palm trees. Detection techniques using leaf sample pieces, the possibility of biological changes in the leaf, and the collection process are too tricky. Detection techniques using image samples captured by the drone can be more accessible, but they need to provide complete information related to plant vegetation. This research proposes detecting unhealthy oil palms by capturing top-view images using drone. The drone has dual cameras (RGB and OCN bands) to get more information on plant vegetation. Middle-level fusion YOLO is used to recognize the unhealthy oil palm trees. The data was collected at the Oil Palm Plantation in Bogor, which contains over 100 unhealthy objects. Using multispectral images can improve performance compared to using only RGB images. The proposed method provides better performance than using only RGB images and OCN images with mAP (mean Average Precision) is 0.919. The proposed method provides better performance in detecting unhealthy oil palm trees.

The Use of the Normalized Difference Red Edge (NDRE) Vegetation Index from Multispectral Cameras Mounted on Unmanned Aerial Vehicle to Estimate the Nutrient Content in Oil Palm Leaves

This study aimed to develop a prediction model for the nutrient content of N, P, K, Mg, and Ca in oil palm leaves using the Normalized Difference RedEdge (NDRE) vegetation index derived from multispectral camera data. Data acquisition was carried out by an unmanned aerial vehicle (UAV), which was correlated to leaf sample analysis of the 17th frond number. Results showed that simple regression analysis successfully represented nutrient content (N, P, K, Mg, and Ca) based on NDRE values. Based on the MAPE and Correctness values, the nutrient content prediction model for N and P yields reliable results, while for K, Mg, and Ca, they are considered good, with Correctness values of 95.5%, 96.6%, 88.8%, 87.3%, and 90.0% for N, P, K, Mg, and Ca, respectively. The study found that the NDRE vegetation index can be used to predict the nutrient content of oil palm leaves with reliable results in accuracy for N and P, and good accuracy for K, Mg, and Ca. This is a promising finding, as it could lead to the development of a non-destructive and rapid method for monitoring the nutrient status of oil palm trees, with the validation models for N, P, K, Mg, and Ca are yN = 1.1089x - 0.2497, yP = 0.99x + 0.002, yK = 1.204x - 0.1576, yMg = 0.9149x + 0.0183, and yCa = 1.0418x - 0.0218. Keywords: Multispectral Cameras, Oil Palm, Leaf Nutrient Contents, Prediction.

Optimization of Supply Superior Oil Palm Seeds Using Artificial Pollination to Support Sustainable Palm Plantations

Seeds as planting material play an important role in agricultural development. The length of seed germination is an obstacle to meeting the high demand for oil palm seeds. This research determines the effect of pollen variety and dosage on the production of superior oil palm seeds. The study used a factorial Randomized Block Design (RAK) with two factors, namely pollen source (V1: Yangambi; V2: PPKS 540) and pollen dose (gr) (D1: 0.25; D2: 0.12; D3: 0.06; D4: 0.04; and D5: talk/blank (control). There were 10 treatment combinations (V1D1, V1D2, V1D3, V1D4, V1D5, V2D1, V2D2, V2D3, V2D4 and V2D5) so that there were 30 experimental units (oil palm trees); if they are significantly different, a further 5% DMRT test (Duncan Multiple Range Test) is carried out. The parameters observed were the number of seeds (grains), number of good and rejected seeds (grains), bunch weight (kg), and seed germination. The results showed that the Yangambi variety produced superior oil palm seeds. The V1D4 treatment showed the best results with the highest number of good seeds produced (2,302 seeds/bundle. The best results were 2,302 items. Treatments V2D2, V2D3, and V2D4 showed the best viability with the same results (81.65%. The treatments did not significantly affect bunch weight.

Optimal Hybrid Renewable Energy System to Accelerate a Sustainable Energy Transition in Johor, Malaysia

As the world’s second-largest palm oil producer, Malaysia heavily depends on its extensive oil palm cultivation, which accounts for nearly 90% of the country’s lignocellulosic biomass waste. Approximately 20–22 tonnes of empty fruit bunches (EFBs) can be derived from an initial yield of 100 tonnes of fresh fruit bunches (FFBs) from oil palm trees. The average annual amount of EFBs produced in Johor is 3233 tonnes per day. Recognising that urban areas contribute significantly to anthropogenic greenhouse gas emissions, and to support Malaysia’s transition from fossil fuel-based energy to a low-carbon energy system, this research employed HOMER Pro software 3.18.3 to develop an optimal hybrid renewable energy system integrating solar and biomass (EFB) energy sources in Johor, Malaysia. The most cost-effective system (solar–biomass) consists of 4075 kW solar photovoltaics, a 2100 kW biomass gasifier, 9363 battery units and 1939 kW converters. This configuration results in a total net present cost (NPC) of USD 44,596,990 and a levelised cost of energy (LCOE) of USD 0.2364/kWh. This system satisfies the residential load demand via 6,020,427 kWh (64.7%) of solar-based and 3,286,257 kWh (35.3%) of biomass-based electricity production, with an annual surplus of 2,613,329 kWh (28.1%). The minimal percentages of unmet electric load and capacity shortage, both <0.1%, indicate that all systems can meet the power demand. In conclusion, this research provides valuable insights into the economic viability and technical feasibility of powering the Kulai district with a solar–biomass system.

Evaluation of the Effect of Soil Water Conditions on the Development and Water Requirements of Adult Oil Palm (Elaeis guineensis Jacq.) in the Northern Region of Colombia

Sustainable water management is a key approach for enhancing the productivity of oil palm trees while addressing the impacts of climate change and variability. Determining the water needs of a crop is crucial for the appropriate application of water. This research was carried out in two plantations in Agustín Codazzi, Colombia, using a completely randomized design. This study involved examining the impacts of five different water conditions (50, 150, 300, 450, and 600 L per day) on the growth and yields of mature oil palm trees (aged 10–17 years), and their water consumption was calculated using the water balance method. The results indicated that the crop was negatively affected by daily water applications of 300, 150, and 50 L per day, showing statistically significant differences (p < 0.05) when compared to the 450 and 600 L per day treatments, particularly in terms of leaf emergence, leaf area index, and yield (tons per hectare). The 50 L per day treatment resulted in the most substantial decrease in yield (around 26%), primarily attributed to a reduction in the number of bunches. The most favorable crop responses were observed with water applications of 450 and 600 L per day, aligning with the crop’s potential evapotranspiration values (ranging between 5.4 and 5.7 mm per day) and yielding crop coefficients of 0.88 and 0.9, respectively. Notably, these values varied between dry and rainy seasons, peaking between December and March.

Sound Classification for Non-Destructive Diagnosis of Basal Stem Rot Disease based on Stem Density in Oil Palm Trunks

Oil palm trees play a crucial role in the economies of Southeast Asian countries, providing palm oil that is essential for various products and consumption. However, the Basal Stem Rot (BSR) disease caused by the white-rot fungus Ganoderma spp. poses a significant challenge, leading to wilting and death of the trees. This study proposes a practical and accessible approach for the routine detection of Basal Stem Rot (BSR) disease in oil palm trees caused by Ganoderma spp. A prototype system for initial disease diagnosis is developed by utilizing the local wisdom technique of tapping and assessing knocking sounds based on stem density in oil palm trunks. Three algorithms, Convolutional Neural Network (CNN), Support Vector Machine Classifier (SVC), and Multi-Layer Perceptron (MLP) models are compared for accuracy in two sound recognition experiments. The first experiment involves a three-class classification model (healthy, boundary, and infected areas), while the second excludes the boundary area. Results demonstrate that the CNN model outperforms SVC and MLP, achieving the highest accuracy of 90.73% in the two-class scenario and 84.97% in the three-class scenario, along with superior precision, recall, and F1 score. The CNN model proves to be the most effective in accurately classifying the data in both scenarios compared to the other models. This prototype system offers promising implications for the early detection and proactive management of Ganoderma spp. disease, contributing to the preservation and sustainability of oil palm plantations in Southeast Asia.

Enhancing sustainability in the production of palm oil: creative monitoring methods using YOLOv7 and YOLOv8 for effective plantation management

The You Only Look Once (YOLO) deep learning model iterations—YOLOv7–YOLOv8—were put through a rigorous evaluation process to see how well they could recognize oil palm plants. Precision, recall, F1-score, and detection time metrics are analyzed for a variety of configurations, including YOLOv7x, YOLOv7-W6, YOLOv7-D6, YOLOv8s, YOLOv8n, YOLOv8m, YOLOv8l, and YOLOv8x. YOLO label v1.2.1 was used to label a dataset of 80,486 images for training, and 482 drone-captured images, including 5,233 images of oil palms, were used for testing the models. The YOLOv8 series showed notable advancements; with 99.31 %, YOLOv8m obtained the greatest F1-score, signifying the highest detection accuracy. Furthermore, YOLOv8s showed a notable decrease in detection times, improving its suitability for comprehensive environmental surveys and in-the-moment monitoring. Precise identification of oil palm trees is beneficial for improved resource management and less environmental effect; this supports the use of these models in conjunction with drone and satellite imaging technologies for agricultural economic sustainability and optimal crop management.

Analisis Perlakuan Akuntansi Aktivitas Perkebunan Berdasarkan Implementasi PSAK 69: Studi pada PT Salim Ivomas Pratama TBK

This case study focuses on a company with a single object in the form of biological assets. This study seeks to evaluate the implementation of PSAK 69 at PT. Salim Ivomas Pratama Tbk, focusing on the recognition, measurement, and disclosure of biological assets, including oil palm and rubber trees, and assessing the extent to which PT Salim Ivomas Pratama Tbk applies PSAK 69 in accounting practices. This research method is descriptive qualitative analysis, with secondary data obtained through documentation methods and literature studies. The analysis involves explaining the reality of an object in narrative form based on data from the 2021-2022 financial statements of PT. Tunas Baru Lampung Tbk. The results of the study indicate that the SIMP Group has adopted PSAK 69 effectively in terms of recognition, measurement, and disclosure of agricultural products. However, there are differences in the application of entities to PSAK 69 relating to measurement and disclosure.

Proximate Analysis and Occurrence of Antibiotic Resistant Escherichia coli in Street Vended Ready to Eat Edible Larvae of Rhynchophorus phoenicis in Port Harcourt, Nigeria

Rhynchophorus phoenicis, known as the African palm tree larva weevil or edible worm, is a popular edible insect in southern Nigeria and other African countries. These larvae, by-products of oil palm and palm trees, are consumed roasted or boiled and are known for their nutritional and medicinal properties. In Nigeria, they are widely sold along highways in states like Bayelsa, Delta, Edo, Imo, Rivers, and the upper Cross River basin. Preparation methods include consuming them raw, boiled, fried, smoked, or in stews and soups. Typically, the larvae are washed, skewered, sprinkled with salt and pepper, and grilled. They are juicy and have a taste reminiscent of Torzo or cow intestines. Despite their popularity, edible worms pose safety and health concerns due to contamination and the emergence of antimicrobial-resistant bacteria. The indiscriminate use of antimicrobials in animals and humans has led to the development of resistant microorganisms, including multidrug-resistant Escherichia coli, which has been increasingly reported in food poisoning outbreaks. There is limited data on the incidence and antibiotic resistance of Escherichia coli in ready-to-eat foods in Nigeria, prompting this study. The study aimed to determine the proximate composition, occurrence, and antibiotic-resistant pattern of Escherichia coli from edible larvae (Rhynchophorus phoenicis). Objectives included determining the proximate composition of the larvae, the occurrence of Escherichia coli in them, and their antibiotic susceptibility pattern. Fifty ready-to-eat food samples of edible larvae were randomly collected from hawkers. These samples, often partially exposed and poorly packaged, were transported to the microbiology laboratory for analysis. Sterilized glassware and media were used, including Nutrient Agar, Eosin Methylene Blue (EMB) Agar, and Mueller Hinton Agar. Samples were diluted using peptone water, and bacteriological determination involved homogenizing 25 grams of the sample in 225 ml of peptone water, followed by serial dilutions. Aliquots were inoculated on EMB Agar plates, and incubated at 37°C for 24 hours, and colonies were counted and recorded as colony-forming units per gram (cfu/g). Isolates were identified using standard microbiological methods and biochemical tests. Proximate analysis revealed moisture (35.49%), carbohydrate (6.01%), protein (20.73%), ash (1.25%), lipid (22.3%), and fiber (14.23%) content. Bacterial counts ranged from 8.1 x 10^7 to 6.34 x 10^8 cfu/g, with the highest count at the Checkpoint sample and the lowest at Elele-Alumini. Of the 27 isolates, 15 (55.56%) were confirmed as E. coli, with 18.52% of samples exceeding the acceptable microbiological limit (≥10^7 CFU/g). Antibiotic susceptibility tests showed all isolates resistant to augmentin, penicillin, cephalosporin, and ofloxacin, with 46.67% resistance to sulfamethoxazole and streptomycin.

Perencanaan Tahapan Persiapan Dan Perhitungan Reklamasi Di Lahan Bekas Tambang Air Rikai PT Timah Tbk Kabupaten Bangka

The research location chosen was the Air Rikai ex-mining land, which is an alluvial ex-mining land within the IUP of PT Timah Tbk. The negative impact that arises from mining is in the form of land damage. This research aims to overcome damage and also ensure that reclamation activities can be carried out well. The method used in this research is a quantitative method. The data taken is in the form of soil samples, soil pH and water pH data, topographic data (x,y,z coordinates) and bathymetric data for the depth of the pit. The results of data processing and analysis show that the condition of the land to be reclaimed has an area of 13 Ha and is divided into 2, namely voids with a total area of 1.57 Ha and land area of 11.43 Ha. Land use is carried out by stockpiling overburden with a required volume of overburden of 35,511.54 m³. Land leveling with a volume of pushed soil of 35,280 m³. Arrangement of the edge of the pit with a volume of soil moved of 230.29 m³. Construction of drainage channels with an excavation volume of 214.59 m³. Installation of concrete buis type culverts with dimensions of 50 x 50 cm and a length of 50 cm. In the revegetation, 1,517 oil palm trees were planted, then 463 sea cypress trees were planted along the underside and LCC seeds to help loosen the soil. Treating acid mine drainage with 350 water hyacinths. The costs required for reclamation are divided into 2, namely, Direct Costs and Indirect Costs. The direct costs required are IDR 679,286,160 and the indirect costs required are IDR 213,975,140. The total costs required for reclamation of land with an area of 13 Ha are IDR 893,261,300.

Effectiveness of using the trap barrier system to control rats in palm oil plantations

Abstract. Efriansyah, Priyambodo S, Hindayana D. 2024. Effectiveness of using the trap barrier system to control rats in palm oil plantations. Biodiversitas 25: 2993-2998. Oil palm is one of the plantation crops that is the largest contributor to the country's income; increased market demand affects palm oil production, which continues to increase. Rats are among the most important pests in oil palm plantations because of their diverse habitats, large populations, and easy breeding. One of the mechanical control concepts that can be implemented is the Trap Barrier System (TBS) technique. This research method was implemented by installing a trap barrier between oil palm trees. This TBS research is divided into two methods. The first method used three treatments with three replications in the main plot, and there were three main plots. The second method used nine treatment sub-plots with three replications. The Ciliwung variety of rice was planted inside the TBS in a 3 m x 3 m plot. Field observations included the number of rats caught and the percentage of damage done at the trial's beginning and end. Species of rats found in oil palm plantations are Malaysian field rat (Rattus tiomanicus Miller, 1900), oriental house rat (Rattus tanezumi Temminck, 1845), and dan müller's giant sunda rat (Sundamys muelleri Jentink, 1879). The S. muelleri was the most frequently caught species in each block and method. TBS placement does not need to pay attention to the initial severity of the block. Providing additional bait in traps can increase the attraction of rats to TBS. The extent and intensity of attacks and yield losses in each block with TBS treatment showed a greater reduction than in blocks without TBS.

The Impact of Tree Density on Automated Oil Palm Tree Counting Accuracy

The rapid development of oil palm plantations in Kualuh Leidong Subdistrict, Labuhanbatu Utara District, North Sumatra Province has led to an increased need for effective and efficient monitoring and supervision of oil palm trees. One method that supports such monitoring and supervision is the use of an automatic counting method using orthophoto data. This orthophoto data was used for automatic tree counting using a deep learning method with the Faster R-CNN algorithm. The study considered two planting patterns: regular planting patterns with spacing of 4 to 9 meters, and random planting patterns with varying spacing. Data processing involved an epoch value of 80 and a batch size value of 4. The accuracy of the automatic oil palm tree counting was evaluated based on the density of the spacing between trees with reference to the ground truth. The findings indicated that the deep learning Faster R-CNN algorithm achieved higher accuracy in automatic calculations for regular planting patterns.