Oil Palm Trees Research Articles

Mapping smallholder plantation as a key to sustainable oil palm: A deep learning approach to high-resolution satellite imagery

The adverse impacts of oil palm plantations on socio-ecological conditions in Indonesia are a prominent issue. While farming improvement in large-scale plantations has shown good progress, accelerated expansion coupled with low productivity of smallholder plantations has raised concerns. Smallholder plantations are rarely monitored, as they are small, scattered, fragmented, often located in remote areas, with irregular shapes, susceptible to rapid change, and commonly mixed with other commodities. Accordingly, the present study aimed to (1) develop a new model for mapping smallholder oil palms, (2) describe the spatial distribution of smallholder oil palms, and (3) identify their potential impact on the environment. An object detection model based on a deep-learning approach was applied to high-resolution satellite imagery from Google Earth, Pleiades, and GeoEye to detect individual oil palm trees. The results showed that the derived model is highly accurate and can successfully map smallholder plantations. Village-level maps showed that the spatial distribution of smallholder oil palms was strongly related to local socio-ecological dynamics, whereas regency-level maps showed that smallholder oil palms have largely encroached on conservation zones and forested areas. These findings highlight the urgency of smallholder oil palm spatial data for reaching sustainable global palm oil industries.

Agroforestry orchards support greater butterfly diversity than monoculture plantations in the tropics

Large-scale deforestation in the tropics, triggered by logging and subsequent agricultural monoculture has a significant adverse impact on biodiversity due to habitat degradation. Here, we measured the diversity of butterfly species in three agricultural landscapes, agroforestry orchards, oil palm, and rubber tree plantations. Butterfly species were counted at 127 sampling points over the course of a year using the point count method. We found that agroforestry orchards supported a greater number of butterfly species (74 species) compared to rubber tree (61 species) and oil palm plantations (54 species) which were dominated by generalist (73%) followed by forest specialists (27%). We found no significant difference of butterfly species composition between agroforestry orchards and rubber tree plantation, with both habitats associated with more butterfly species compared to oil palm plantations. This indicates butterflies were able to persist better in certain agricultural landscapes. GLMMs suggested that tree height, undergrowth coverage and height, and elevation determined butterfly diversity. Butterfly species richness was also influenced by season and landscape-level variables such as proximity to forest, mean NDVI, and habitat. Understanding the factors that contributed to butterfly species richness in an agroecosystem, stakeholders should consider management practices to improve biodiversity conservation such as ground vegetation management and retaining adjacent forest areas to enhance butterfly species richness. Furthermore, our findings suggest that agroforestry system should be considered to enhance biodiversity in agricultural landscapes.

Leveraging on Advanced Remote Sensing- and Artificial Intelligence-Based Technologies to Manage Palm Oil Plantation for Current Global Scenario: A Review

Advanced remote sensing technologies have undoubtedly revolutionized palm oil industry management by bringing business and environmental benefits on a single platform. It is evident from the ongoing trend that remote sensing using satellite and aerial data is able to provide precise and quick information for huge palm oil plantation areas using high-resolution image processing, which is also recognized by the certification agencies, i.e., the Roundtable on Sustainable Palm Oil (RSPO) and ISCC (International Sustainability and Carbon Certification). A substantial improvement in the palm oil industry could be attained by utilizing the latest Geo-information tools and technologies equipped with AI (Artificial Intelligence) algorithms and image processing, which could help to identify illegal deforestation, tree count, tree height, and the early detection of diseased leaves. This paper reviews some of the latest technologies equipped with remote sensing, AI, and image processing for managing the palm oil plantation. This manuscript also highlights how the distress in the current palm oil industry could be handled by mentioning some of the improvised monitoring systems for palm oil plantation that could in turn increase the yield of palm oil. It is evident from the proposed review that the accuracy of AI algorithms for palm oil detection depends on various factors such as the quality of the training data, the design of the neural network, and the type of detection task. In general, AI models have achieved high accuracy in detecting palm oil tree images, with some studies reporting accuracy levels up to 91%. However, it is important to note that accuracy can still be affected by factors such as variations in lighting conditions and image resolution. Nonetheless, with any AI model, the accuracy of algorithms for palm oil tree detection can be improved by collecting more diverse training data and fine-tuning the model.

Smallholders Participation In Oil Palm Certification And Livelihood Strategy In The Santchou Sub-Division, West Region Cameroon

Palm oil is an edible vegetable oil that comes from the fruit of oil palm trees. The production of oil palm from its Fresh Fruit Punches (FFP) is commonly practice in the Santchou Sub-Divison west region of Cameroon. It is mostly done at a small scale by smallholders with at least 1-5 hectares of oil palm plantation. The productivity of oil palm depends on smallholders’ participation in certification and other livelihood strategy. The main objective of this study was to examine smallholders’ participation in oil palm certification and livelihood strategy in Santchou. This study was carried out using the case study approach which enable a field survey to be conducted in order to examine smallholders’ participation in certification and livelihood strategy. A total of 185 questionnaires were administered using a random sampling technique to three targeted communities within the Santchou subdivision. Ten interviews were conducted to complement the questionnaire in generating qualitative data for the study. Data collected was later analyse with the use of statistical data tool such as excel, software and spsss data tool and presented on maps, charts and tables. from the data collected, it was observed that very limited number of smallholder are fully involved in oil palm certification. A large number of smallholder oil palm production are engage in other sources of livelihood as livelihood strategy for survival. It was recommended that due to the enormous benefits of certification for the purpose of high productivity, authorities should make certification a reality amongst smallholders.

Effect of Topography on the Heavy Metal Levels of Raphia Palm Tree and Oil Palm Tree Wine Produced within Awka South and North Local Government Areas in Anambra State

Studies were carried out to evaluate the effect of topography on the heavy metal levels of raphia palm tree and oil palm tree wine produced within Awka South and North local government areas using standard analytical procedures and instrumentation. Soil and palm (oil palm tree and raphia palm tree) wine samples produced in the studied upland and lowland areas were wet digested and analyzed for the presence of Pb, Cu, Cd and Zn using atomic absorption spectrophotometer.The lowland soil samples had 0.38±0.08, 6.27±0.40, 0.18±0.05 and 9.55±0.80µg/g as mean levels for Pb, Cu, Cd and Zn respectively. The upland soil samples had 0.70±0.04, 4.89±0.15, 0.06±0.02 and 7.07±0.22 µg/g as the mean levels for Pb, Cu, Cd and Zn respectively. The studied heavy metals were present in the soil sample areas in the following decreasing order; Zn > Cu >Pb >Cd.The mean range of Pb, Cu, Cd and Zn levels in the palm (oil palm tree and raphia palm tree) wine samples produced in the upland areas were 0.04- 0.06, 1.06- 1.91, 0.03- 0.05 and 2.86- 4.04 µg/g respectively. Pb was at toxic level in the oil palm tree wine samples produced in the upland areas.The mean range of Pb, Cu, Cd and Zn levels in the palm (oil palm tree and raphia palm tree) wine samples produced in the lowland areas were 0.05- 0.09, 1.95- 3.23, 0.04- 0.07 and 4.66- 6.51µg/g respectively. Pb, Cu and Cd were at toxic levels in the oil palm tree wine samples produced in the lowland areas. The palm wine samples produced in both the lowland and upland areas contained the investigated heavy metals at statistically significant levels. Because of the near daily consumption of palm wine produced from the oil palm and raphia trees as a drink of leisure by all categories of the people in the society, especially with high volume of production taking place in the lowland areas, it is important that human activities that supports the contamination of the soil with heavy metals, where these wines are produced are checkmated by government and host communities so as to limit the exposure to the heavy metals by palm wine consumers.

Detecting Ganoderma basal stem rot disease on oil palm using artificial neural network method

The oil palm tree is one of the essential plants with a major contribution to the Indonesian economy but is also vulnerable to pathogen infection, such as Ganoderma. Ganoderma boninense is a group of polyporous fungi which is responsible for Basal Stem Rot disease. The disease is extremely serious and easily spreads, posing a significant threat to the economy, so early detection of the disease becomes vital. However, the current detection techniques for the disease are expensive and time-consuming; hence, they are not ideal for large plantation areas. The development of image processing technology could be utilized to predict Ganoderma infection, using the images that are captured by a drone. This research aims to predict the spread of Ganoderma infection, in the oil palm tree plantation area in North Sumatra, Indonesia, by utilizing image processing and Artificial Neural Network methods. Our model results showed the prediction accuracy (with Green color) was 73,8%. In addition, we also showed the distribution of Ganoderma infection in the area: score 0 was 229 trees, score 1 was 295 trees, score 2 was 112 trees, score 3 was 238 trees, and score 4 was 23 trees. Overall, our research provided a non-destructive method to detect Basal Stem Rot disease in the oil palm plantation sites.

The Environmental Consequence of Automobile and Motor-bike Traffic Noise Intensity on the Activity of African Grey Parrots (Psithacus erittacus) in Limbe Botanic Garden, Southwest Region, Cameroon

Highway traffic noise pollution is unavoidably generated by human activity, and has caused serious health problems to the human society. However, it’s also a challenge to many wildlife species, though, continous exposure to the noise stimuli has seemingly given them adaptation. Understanding the ability of wildlife species to cope with noise pollution is complex, thus, requires research. Therefore, the objective of this study was to explore automobile and motor bike traffic noise intensity on African grey parrots’ activity in Limbe Botanic Garden. The research data was collected for a period of three months from 8:00 am - 5:00 pm each day. Traffic noise pollution is difficult to avoid in urban settings because of huge human population and its activity, however, its interference with other sounds is common, and wildlife population living in cities, such as birds are affected. The study revealed a positive significance X2 = 25.796 df=2 P=0.000 and X2 =39.489 df=6 P=0.000 on photoperiod and meteorological conditions respectively. Moreover, meteorological changes in the study area recorded significance on sun 75%, cloud 15%, wind 6%, and rain 4% respectively. Furthermore, noise intensity of automobile and motor bike traffic around the botanic garden area showed a significance X2 = 5.657 df=8 P<0.05, and r = 0.209 P=0.000 on behavior and parrots' food availability respectively. The highest parrots’ activity recorded during this study was on contact calls 54%, and the least was roosting 3%. Though a comprehensive inventory is not carried out on the parrot population in Cameroon, there are signs, these birds are already facing population reduction; principally because of the pet-trade driven by the craving human love for the colourful plumage morphology and the talented sound-mimicking behavior of the parrots. There is a necessity for further study to be carried out on the birds’ population census in the country. The study found that traffic noise intensity at all levels have a negative influence on the aggregation activity of grey parrots, reason why they avoid the vegetation along the streets of the garden. Finally, oil palm fruit-feeding was a dominant activity recorded in this study, hence, we recommend more oil palm trees be planted in the garden far from major roads to enrich the feeding environment of the grey parrots.

RETRACTED: Geospatial assessment of potential land suitability for oil palm (Elaeis guineensis Jacq) cultivation in the western parts of Ethiopia

The editors have decided to retract this article for the following reasons: The authors classified the land suitability related to rainfall as follows: “Previous study by Abubakar et al. (2023b) classified rainfall into four classes: (1329–1420mm), (1420–1490mm), (1490–1560mm) and (>1560mm) and categorizes as highly suitable, moderately suitable, marginally suitable and not suitable, respectively for oil palm tree cultivation”. In oil palm literature (including the cited article by Abubakar et al.), however, the order is reversed. Ideally, oil palm requires >2000mm of well-distributed rainfall per year. The lower rainfall class is still not entirely unsuitable, but there will be a very strong seasonality in production and yields will not be large. As this misinterpretation affected their entire analysis, the authors have been asked to revise their article accordingly. In the meantime, an expression of concern was published. In the two successive revised versions received, the authors changed only a few paragraphs (about rainfall...), but they did not re-run their analysis accordingly, nor did they change the suitability assessment at all, leading to the same conclusion as before despite reversed precipitation assumptions.

Video based oil palm ripeness detection model using deep learning

Research on oil palm detection has been carried out for years, but there are only a few research that have conducted research using video datasets and only focus on development using non-sequential image. The use of the video dataset aims to adjust to the detection conditions carried out in real time so that it can automatically harvest directly from oil palm trees to increase efficiency in harvesting. To solve this problem, in this research, we develop an object detection model using a video dataset in training and testing. We used the 3 series YOLOv4 architecture to develop the model using video. Model development is done by means of hyperparameter tuning and frozen layer with data augmentation consisting of photometric and geometric augmentation experiment. To validate the outcomes of the YOLOv4 model development, a comparison of SSD-MobileNetV2 FPN and EfficientDet-D0 was performed. The results obtained show that YOLOv4-Tiny 3L is the most suitable architecture for use in real time object detection conditions with an mAP of 90.56% for single class category detection and 70.21% for multi class category detection with a detection speed of almost 4× faster than YOLOv4-CSPDarknet53, 5× faster than SSD-MobileNetV2 FPN, and 9× faster than EfficientDet-D0.

Colour Fastness of Silk Fabrics Dyed with Extracts from Oil Palm Tree Kernel Shell and Effect of Metal Ion Mordanting on Fabric Colour

Colour Fastness of Silk Fabrics Dyed with Extracts from Oil Palm Tree Kernel Shell and Effect of Metal Ion Mordanting on Fabric Colour

Automatic Detection of Oil Palm Growth Rate Status with YOLOv5

Oil palm plantations are essential for Indonesia as a source of foreign exchange and a provider of employment opportunities. However, large-scale land clearing is considered a cause of deforestation, which harms the environment and society. So, it is necessary to manage plantations that are sustainable and still maintain the preservation of forests and biodiversity. One solution is to apply remote sensing technology. The research was conducted to develop a multi-class detection method for the growth rate of oil palm trees, with five categories: healthy palm, dead palm, yellowish palm, mismanaged palm, and smallish palm. The deep learning-based object detection method, YOLO Version 5 (YOLOv5), is used. This study compares the YOLOv5 network models, namely YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. Parameter setting is also carried out in the BCE (Binary Cross Entropy) with Logits Loss Function to handle the problem of unbalanced data distribution in each class. The YOLOv5 model with the highest mAP value is the YOLOv5l and YOLOv5x, the YOLOv5x requires longer training time. In this study, hyperparameter optimization was also carried out using hyperparameter evolution techniques. However, it has yet to provide increased results because the experiments conducted in this study are still limited.

Study of Arbuscular Mycorrhizal Fungi population in the rhizosphere of oil palm planted on 4 different soil types in Central Kalimantan Indonesia

Arbuscular mycorrhizal fungi (AMF) are naturally found in the soil. The population of this fungus is influenced by environmental factors such as soil type. Different soil types have different characteristics and will affect the AMF population in the soil. Therefore, this study was carried out to determine the AMF population in the soil based on the number of spores in the rhizosphere of oil palm planted in 4 different soil types, namely Dystrudepts, Paleudults, Haplohumods, and Haplosaprists. Soil samples were taken from each soil type at 4 sample points. At each sample point, soil samples were taken from 5 adjacent oil palm trees. Rhizosphere soil samples were taken at 4 points inside the oil palm circle at a depth of up to 15 cm and 4 points from the outside and then composited to represent 1 sample point for inside and outside the circle. Extraction of AMF spores from soil samples was carried out by the wet sieving method. The results showed that the average number of AMF spores per 50 g of rhizosphere soil from inside and outside the oil palm circle were 66,7 and 53,5 spores for Dystrudepts, 115,5 and 91,5 spores for Paleudults, 27 and 71,8 spores for Haplohumods, and 83,3 and 55 spores for Haplosaprists respectively. In Dystrudepts, Paleudults, and Haplosaprists soil types, the number of spores in the rhizosphere of oil palm inside the circle was higher than outside. On the other hand, for Haplohumods soil types, the number of spores inside the circle was lower than outside the circle.

Automatic detection and counting of oil palm trees using remote sensing and object-based deep learning

Palm oil is one of the world's highest vegetable oil producers. Providing accurate oil palm plantation statistics is essential to supporting effective and efficient decision-making, especially since the oil palm industry has a strategic role in achieving several Sustainable Development Goals (SDGs). The existing conventional data collection has been focusing on field survey methods which require considerable human resources and costs, long collection and processing times, and have difficulty in reaching remote areas. Remote sensing with satellite imagery and Unmanned Aerial Vehicles (UAV) can be an alternative due to its advantages which include a more efficient workforce, shorter update times, comprehensive area coverage, and the ability to reach remote locations. In this study, we propose the utilization of Microsoft Bing Maps Very High Resolution (VHR) satellite imagery and UAV with image processing threshold and object-based deep learning methods to detect and count the number of oil palm trees. Experimental results in a case study in Rokan Hulu Regency, Riau, Indonesia show that the object-based deep learning model of the You Only Look Once (YOLO) architecture achieves excellent performance with an F1-Score of up to 91.05%. This model is superior to the widely adopted baseline model of the image processing threshold, which only reached an F1-Score of 40.59%. Our findings suggest the use of very high resolution images with object-based deep learning is promising for automatic detection and counting of oil palm trees to support the accurate and sustainable agricultural monitoring.

Production, Activation and CO2 Uptake Capacity of a Carbonaceous Microporous Material from Palm Oil Residues

While Malaysia produces about half of the world’s palm oil and is the largest producer and exporter worldwide, oil palm industries generate large amounts of lignocellulosic biomass waste as a sub-product with no economic market value other than feedstock for energy valorisation. With the aim to increase the sustainability of the sector, in this work we prepare new materials for CO2 capture from palm oil residues (empty fruit bunches and kernels). The biochar is obtained through the carbonisation of the residues and is physically and chemically activated to produce porous materials. The resulting microporous samples have similar properties to other commercial activated carbons, with BET surfaces in the 320–880 m2/g range and pore volumes of 0.1–0.3 cm3·g−1. The CO2 uptake at room temperature for physically activated biochar (AC) was 2.4–3.6 mmolCO2/gAC, whereas the average CO2 uptake for chemically activated biochar was 3.36–3.80 mmolCO2/gAC. The amount of CO2 adsorbed decreased at the highest temperature, as expected due to the exothermic nature of adsorption. These findings confirm the high potential of palm oil tree residues as sustainable materials for CO2 capture.

Best management practices and its effect on soil properties in smallholder oil palm plantations, Jambi Province, Indonesia

The Best Management Practices (BMP) could improve oil palm production in smallholder plantations. This study aims to identify the effect of BMP and farmers’ practice (REF) on bulk density (BD), total N, available P, and organic C. The research was conducted in the smallholder oil palm plantations in Jambi Province, Indonesia. BMP applied pruned fronds stacks in inter-rows and within rows with fertilizer application above it except nitrogen fertilizer applied in the weeded circle. Besides, REF practice applied prune fronds stacks in inter-rows with all type of fertilizer application in the weeded circle. The result based on transect analysis showed that organic C, total N, and available P significantly higher near oil palm trees. In contrast, BD did not show a significantly different. Comparing BMP and REF practice revealed that BD, organic C, and total N did not differ significantly in all point observations. However, available P showed significantly different in point 2, 4, and 6. As this study was conducted in the second year of BMP treatment, the soil properties of BD, organic C, and total N were not influenced by BMP treatment. BMP study give land management information to improve soil properties that support oil palm production.

Penentuan Waktu Panen Berdasarkan Variasi Hari Berondolan Pada Perkebunan Kelapa Sawit Rakyat Studi Kasus Di Kabupaten Tanah Laut, Kalimantan Selatan

The aim of this research was to determine the optimal harvest time based on variations in brondolan days through parameters of free fatty acid content (ALB) and water content in smallholder oil palm plantations. This study applied a factorial randomized block design (RAK) method consisting of 2 factors, namely variations in treatment days and variations in oil palm trees with a planting age of 10 years with 2 replications. Observation parameters include free fatty acid content and water content referring to the quality standard of CPO (Crud Palm Oil) in SNI. Determining the harvest time of oil palm fruit in the garden is carried out based on the level of maturity of the fruit. Determining the maturity of oil palm fruit is often referred as a fraction, but harvesters still use the method of fruit maturity degree by brondolan on the land before the bunch is cut. This brondolan is an indicator of suitable palm fruit for harvesting. The ALB content of the brondolan on the first day to the sixth day was analyzed with the standard method. The results of the free fatty acid content (ALB) according to the SNI standard are a maximum of 5% and the maximum factory standard of 3%. The effective or appropriate harvest time for harvesting oil palm fruit in smallholder plantations is on the first day <3%, resulting when the palm fruit arrives at the factory, it is still with ALB content that is in accordance with predetermined quality standards.

Revisiting foliar diagnosis for oil palm potassium nutrition

Oil palm is a highly potassium (K)-demanding tree requiring fertilisation throughout its life cycle. One of the most common indices to monitor K fertilisation is K content (in % of DW, further noted as %K) in leaflets, typically used in foliar diagnosis (FD). However, uncertainty remains as to whether leaflets are always the most K-responsive organs and furthermore, implementing FD may require separate response curves for different soil and climatic conditions, and newly also i.e. genetic materials (progenies). As such, a universal K indicator ideally responsive to K availability and valid across many progenies could simplify FD. We investigated K responses of oil palm trees in four different progenies grown in North Sumatra, from planting to the maturation stage. Elemental analyses were performed along with biometrical measurements at tree scale, completed by data from agronomic databases. Uni- and multi-variate analyses were performed to identify the best indicators of K response. Tree K balance (difference between K availability and K needs) was computed to determine directly if (i) K supply met tree K demand and (ii) %K in leaflets, rachis or trunk were linked to the K budget. We also interrogate the accuracy of %K expression for K leaf content estimation and answer to it. Standard effects of K availability on height, leaf area, and yield were observed and varied mostly with the progeny. K balance calculations showed a systematic K imbalance when bunch production started, suggesting a non-negligible contribution of exchangeable soil K. %K in leaflets, rachis and trunk does not appear to be correlated with K balance. Our work brings out the important result that unlike %K in leaflets, phosphorus (P) and %K in rachis (more precisely %K + 3 %P) appeared to be tightly correlated with yield (FFB: fresh fruit bunches) two years later, suggesting a possible interdependence between fruits sink force and K-P elemental content in leaves. Since this indicator is under important genetic effect, we propose to use the combination of mineral contents generated by multivariate analysis, which successfully predicts FFB after two years, independently of the progeny.

An Ecological Context That Scars: Reflections on Three Nature Documentaries of 2020

An Ecological Context That Scars: Reflections on Three Nature Documentaries of 2020

Potential of Biomass from Felled Trees in the Oil Palm Replanting Program as a Raw Material for Ready-to-Eat Cattle Feed

This study aims to estimate the potential and develop strategies for using the biomass of old oil palm tree cuttings in the oil palm replanting program in smallholders as a source of feed raw materials for producing ready-to-eat animal feed. This observation research was carried out for three months in the people's oil palm plantation center in the Purnama Kempas area of Jambi Province, Indonesia. Selection of five samples of old oil palm trees that have entered the replanting period using a systematic sampling technique and data analysis of field measurements using a simple mathematical approach carried out in stages. The estimation results show that the potential for fresh biomass of animal feed ingredients for each hectare of old oil palm plantations in the replanting program reaches 56.76 tons of biomass consisting of 41.52 tons of fresh pith (73.15%) and 15.24 tons of green palm fronds (28,85%). The potential of this biomass is more than 4.23 times compared to the ability of a hectare for one year of productive oil palm plantations to provide fresh forage between plants, namely, 13.37 tons/ha, which means that tree biomass because of the replanting program can substitute for the availability of forage between coconut trees, producing palm oil for more than four years. However, because of the simultaneous harvesting process, it must be done under collectively planned management, such as in the form of a village corporation.

Assessing the changes in river water quality across a land-use change (forest to oil palm plantation) in peninsular Malaysia using the stable isotopes of water and nitrate

Land conversion from natural forests to plantations (e.g., oil palm) in Southeast Asia is one of the most intensive land-use changes occurring worldwide. To clarify the effects of oil palm plantations on water quality, we conducted multipoint river and stream water sampling in peninsular Malaysia at the end of the rainy season over a 3-year period (2013–2015). We measured the major dissolved ions and stable isotope ratios of water (δ2H-H2O and δ18O-H2O) and nitrate (δ15N-NO3− and δ18O-NO3−) in water from the upper streams in mountainous forests to the midstream areas of two major rivers in peninsular Malaysia. The electrical conductivity increased, and the d-excess value (as an index of the degree of evaporation) decreased with increasing distance from the headwaters, suggesting the effect of evaporative enrichment and the addition of pollutants. We separated the sampling points into four groups (G1–G4) through cluster analysis of the water quality data. From the land use/land cover (LULC) classification maps developed from satellite images and local information, we found that G1 and G2 mainly consisted of sampling points in forested areas, while G3 and G4 were located in oil-palm-affected areas. The concentrations of major ions were higher in the oil palm areas, indicating the effects of fertilizer and limestone (i.e., pH adjustment) applications. The dissolved inorganic nitrogen concentration did not differ among the groups, but the dissolved organic carbon, total dissolved nitrogen, and δ15N-NO3− were higher in the oil palm area than in the forested area. Although the nitrogen concentration was low, even in the oil palm area, the significantly higher δ15N-NO3− in the oil palm area indicated substantial denitrification. This implies that denitrification contributed to the lowering of the NO3− concentration in rivers in the oil palm area, in addition to nutrient uptake by oil palm trees.