Search Results (210)

Reliable estimation of oil palm carbon stock is essential for climate mitigation, concession management, and sustainability certification. While satellite-based approaches offer scalable solutions, redundancy among spectral indices and inter-sensor variability complicate model development. This study evaluates machine learning regressors for predicting oil palm carbon stock at tree (CO_tree, kg C tree⁻¹) and hectare (CO_ha, Mg C ha⁻¹) scales using spectral indices derived from Landsat-8, Landsat-9, and Sentinel-2. Fourteen vegetation indices were screened for multicollinearity, resulting in a lean feature set dominated by NDMI, EVI, MSI, NDWI, and sensor-specific indices such as NBR2 and ARVI. Ten regression algorithms were benchmarked through cross-validation. Ensemble models, particularly Random Forest, Gradient Boosting, and XGBoost, outperformed linear and kernel methods, achieving R² values of 0.86–0.88 and RMSE of 59–64 kg tree⁻¹ or 8–9 Mg ha⁻¹. Feature importance analysis consistently identified NDMI as the strongest predictor of standing carbon. Spatial predictions showed stable carbon patterns across sensors, with CO_tree ranging from 200–500 kg C tree⁻¹ and CO_ha from 20–70 Mg C ha⁻¹, consistent with published values for mature plantations. The study demonstrates that ensemble learning with sensor-specific index sets provides accurate, dual-scale carbon monitoring for oil palm. Limitations include geographic scope, dependence on allometric equations, and omission of belowground carbon. Future work should integrate age dynamics, multi-year composites, and deep learning approaches for operational carbon accounting. Full article

Oil palm is an important economic crop that is widely cultivated, especially in Southeast Asia. Thailand is one of the world’s largest producers and exporters of palm oil. Efficient management of oil palm plantations is crucial for increasing yields and minimizing agricultural losses. This study aimed to develop a smart oil palm plantation and production management system. This system utilizes Internet of Things (IoT) technology and an integrated supervised machine learning model utilizing regression analysis to monitor and control agricultural equipment within the plantation. MySQL database was used for management of sensor data. Python (version 3.9.6) programming and Google Map API were used for data analysis, spatial analysis and data visualization suite in the system. The results showed that the data from the sensors are displayed in real-time, allowing plantation managers to monitor conditions remotely and make informed adjustments as needed. The system also includes data analysis and data visualization tools for decision-making regarding production management. The model attained an accuracy of over 95%, which reflects its reliability in performing the specified prediction task. The system serves as a support tool for automating soil quality monitoring, fertilization, and field maintenance in oil palm plantations. This enhances productivity, reduces operational costs, and improves yield planning. Full article

Palm oil is a significant product, predominantly from Indonesia and Malaysia, and is included in many products. However, oil palm (OP) plantations have been associated with deforestation and destruction of peat soil, tending to increase CO₂ in the atmosphere and contribute to climate change. The growth of OP may be affected detrimentally by climate change. Also, OP is susceptible to basal stem rot (BSR) caused by the fungus Ganoderma boninense. Previous CLIMEX-modelled scenarios have indicated decreases in suitable climate for growing OP in the future, and narrative models suggest increases in BSR. However, the climate maps show regions in Malaysia and Indonesia that were previously unsuitable, which have become highly suitable climate (HSC) areas and were previously unreported. These areas include the higher altitudes of (a) the west coast of Sumatra, (b) areas between Sarawak, Sabah, and Kalimantan, (c) the central region of Sulawesi, (d) northern West Papua, (e) and the Titiwangsa Mountains of Peninsular Malaysia. These trends are remarkable per se. The incidence of BSR will likely be low because the palms would experience HSC, making them more resistant to infection. For example, HSC is projected to increase from 0% at present to 95% by 2100, while BSR is projected to increase from 0% at present to 30% over the same time period in Sumatra. In Borneo, HSC is projected to increase from 0% at present to 95% by 2100, while BSR is projected to increase from 0% to 7% over the same time period. Higher CO₂ fertilisation may occur which would increase OP vigour again leading to greater resistance to BSR. However, many of the regions may be biodiverse and it would be unreasonable to replace them with plantations and whether these areas would be suitable for growing OP requires careful consideration. This report of increasing areas of HSC for growing OP is unique. Full article

The global energy transition is pushing the development of advanced biofuels to reduce greenhouse gas (GHG) emissions in the aviation industry. This study thoroughly evaluates the potential of the Colombian crude palm oil (CPO) sector to support sustainable aviation fuel (SAF) production. Extensive primary data from 53 palm oil mills and 269 palm plantations were examined. The methodology included a carbon footprint analysis of SAF produced from Colombian CPO through the HEFA pathway, an economic aspects analysis, a review of renewable fuel standards, and an assessment of market access for low-CO₂-emitting feedstocks. The results show that the carbon footprint of the Colombian palm oil-SAF is 16.11 g CO₂eq MJ⁻¹ SAF, which is significantly lower than the 89.2 g CO₂eq MJ⁻¹ reference value for traditional jet fuel. This figure considers current direct Land Use-Change (DLUC) emissions and existing methane capture practices within the Colombian palm oil agro-industry. A sensitivity analysis indicated that this SAF’s carbon footprint could decrease to negative values of −4.58 g CO₂eq MJ⁻¹ if all surveyed palm oil mills implement methane capture. Conversely, excluding DLUC emissions from the assessment raised the values to 47.46 g CO₂eq MJ⁻¹, highlighting Colombia’s favorable DLUC profile as a major factor in its low overall CPO carbon footprint. These findings also emphasize that methane capture is a key low-carbon practice for reducing the environmental impact of sustainable fuel production, as outlined by the CORSIA methodology. Based on the economic analysis, investing in Colombian CPO-based SAF production is a financially sound decision. However, the project’s profitability is highly susceptible to the volatility of SAF sales prices and raw material costs, underscoring the need for meticulous risk management. Overall, these results demonstrate the strong potential of Colombian palm oil for producing sustainable aviation fuels that comply with CORSIA requirements. Full article

Tropical peatlands in Indonesia are increasingly degraded by conventional oil palm practices involving drainage and chemical fertilizers. This study evaluates AeroHydro Culture (AHC), a method applying microbe-enriched organic media aboveground, as a sustainable alternative that maintains high groundwater levels while supporting plant productivity. Field trials were conducted at two sites: a managed plantation in Siak and a degraded, abandoned plantation in Pulang Pisau. Ten months after treatment, AHC plots showed development of aerial-like lateral roots, improved chlorophyll levels, and increased arbuscular mycorrhizae colonization (from 0–46% to 22–73% in Siak, and 1.7–20% to 16–60% in Pulang Pisau). In Siak, AHC significantly increased IAA-producing and proteolytic bacteria in the 0–25 cm soil layer and raised oil palm yield by 36% over controls. This yield benefit was sustained in 2025, five years after the initial application. In Pulang Pisau, AHC also enhanced microbial abundance and promoted growth in the native species Shorea balangeran, suggesting its potential for reforestation. Drone imagery confirmed visible long-term differences in canopy color, supporting lasting physiological improvement. These results demonstrate that AHC promotes plant–microbe symbiosis, enhances nutrient acquisition, and sustains oil palm yield under saturated conditions. AHC offers a promising strategy for peatland rehabilitation where ecological recovery and agricultural productivity must be achieved in parallel. Full article

In the forests of Jambi Province, Indonesia, the Indigenous Suku Anak Dalam have encountered rapid alterations to the environment upon which they previously depended. Their culinary traditions—and the knowledge that accompanies them—are placed at a greater risk as palm oil plantations expand and forest areas diminish. This research is based on extensive interviews with customary leaders (called Tumenggung, who guide communal life and cultural practices), elders, and women in five settlements in Merangin District. Rather than regarding participants as research subjects, we engaged with their narratives. The image that emerged was not merely one of food scarcity but also one of cultural loss. Instead of forest tubers, untamed fruits, or fish, families now depend on instant noodles or cassava. The rivers are no longer clean, and the trees that were once a source of both sustenance and medicine are largely extinct. Nevertheless, individuals devise strategies to adapt, including cultivating small crops in the vicinity of their dwellings, collecting what is left along the plantation’s perimeter, and distributing their meager possessions to their neighbors. This research demonstrates that food security for Indigenous peoples is not solely dependent on agriculture or nutrition. It is about the right to have a voice in one’s own land, dignity, and memory. Genuine solutions must transcend technical fixes and nutritional aid. The first step is to respect Indigenous voices, protect their territories, and support their methods of knowing and living before they are also lost. Full article

Mapping the oil palm (Elaeis guineensis), the globally leading oil-bearing crop and a crucial industrial commodity, is of vital importance for food security and raw material supply. However, existing remote sensing approaches for oil palm mapping present several methodological challenges including temporal resolution constraints, suboptimal feature parameterization, and limitations in age structure assessment. This study addresses these gaps by systematically optimizing temporal, spatial, and textural parameters for enhanced oil palm mapping and age structure analysis through integration of Landsat 4/5/7/8/9, Sentinel-2 multispectral, and Sentinel-1 radar data (LSMR). Analysis of oil palm distribution and dynamics in Malaysia revealed several key insights: (1) Methodological optimization: The integrated LSMR approach achieved 94% classification accuracy through optimal parameter configuration (3-month temporal interval, 3-pixel median filter, and 3 × 3 GLCM window), significantly outperforming conventional single-sensor approaches. (2) Age estimation capabilities: The adapted LandTrendr algorithm enabled precise estimation of the plantation establishment year with an RMSE of 1.14 years, effectively overcoming saturation effects that limit traditional regression-based methods. (3) Regional expansion patterns: West Malaysia exhibits continued plantation expansion, particularly in Johor and Pahang states, while East Malaysia shows significant contraction in Sarawak (3.34 × 10⁵ hectares decline from 2019–2023), with both regions now converging toward similar topographic preferences (100–120 m elevation, 6–7° slopes). (4) Age structure concerns: Analysis identified a critical “replanting gap” with 13.3% of plantations exceeding their 25-year optimal lifespan and declining proportions of young plantations (from 60% to 47%) over the past five years. These findings provide crucial insights for sustainable land management strategies, offering policymakers an evidence-based framework to balance economic productivity with environmental conservation while addressing the identified replanting gap in one of the world’s most important agricultural commodities. Full article

The growing global demand for bioplastics highlights the need for sustainable starch sources, and Indonesia has considerable potential for cultivating such feedstock. While cassava has been widely promoted, there is limited scientific justification for prioritizing it over alternatives such as sago. An important distinction is that cassava is grown on mineral soils, where many alternative crops are viable, whereas sago is cultivated on peatlands, where relatively few crops can be grown sustainably. This study compares the socio-economic benefits of cassava and sago, considering their competitiveness against their main competing crops (i.e., corn on mineral soils and oil palm on peatlands). For new plantations, sago generated lower farm-level benefits than cassava, with net present values of 1534 EUR/ha and 5719 EUR/ha, respectively. However, when integrating starch processing and environmental impacts, sago provided greater benefits than cassava (4166 EUR/ha vs. 3555 EUR/ha). In the long term, sago may become more profitable than cassava due to its low maintenance and lack of replanting needs. Additionally, sago offers broader societal and environmental advantages, as it thrives on undrained peatlands, for which few alternatives exist. This study concludes that sago, as a paludiculture crop, is a sustainable option for bioplastic feedstock and can support peatland restoration. Full article

Oil palm cultivation plays a critical role in rural livelihoods in Indonesia, yet previous research has often overlooked systematic institutional differences among smallholders. This study aims to analyze disparities in assets, strategies, and livelihood outcomes among three oil palm smallholder typologies—ex-Perkebunan Inti Rakyat (PIR) transmigrant smallholders who received land through government transmigration programs, independent smallholders who cultivate oil palm without formal partnerships, and plasma smallholders operating under corporate partnership schemes—in Central Mamuju Regency, West Sulawesi. A descriptive quantitative approach based on the sustainable livelihoods framework was employed, using chi-square analysis of data collected from 90 respondents through structured interviews and field observations. The results show that ex-PIR smallholders possess higher physical, financial, and social capital and achieve better income and welfare outcomes compared to independent and plasma smallholders. Independent smallholders exhibit resilience through diversified livelihood strategies, whereas plasma smallholders face asset limitations and structural dependency on partner companies, increasing their economic vulnerability. The study concludes that differentiated policy approaches are necessary to enhance the resilience of each group, including improving capital access, promoting income diversification, and strengthening institutions for plasma smallholders. Future research should expand geographical scope and explore factors such as technology adoption, gender dynamics, and intergenerational knowledge transfer to deepen understanding of sustainable smallholder livelihoods in tropical plantation contexts. Full article

Our research examines the dynamics of policy implementation in forest fire management and how local governments in Indonesia can successfully implement these policies. There are two main issues: first, the extent to which forest fire management practices are collaborative, which we assess by examining whether government implementation has focused on developing integrated forest fire management policies represented through collaborative networks. Second, we consider whether and how governments and other competing stakeholders move from conflict to collaboration to enable policy implementation. This research explores whether and how collaborative management can provide a foundation for successful forest fire management, particularly in Riau Province, Sumatra, Indonesia, an area that has experienced significant forest fires and expansion of plantations and oil palm industries. Data were collected through in-depth interviews and observations. We revealed a lack of coordination among local, central, and other stakeholders, which might result in policy “tyranny”. In order to effectively reduce the number of fires, the government needs to empower those responsible for fire prevention through law and policy. However, because forest fire management is inherently top-down and often excludes lower levels of bureaucracy, collaborative management remains challenging. Full article

While the role of expanding agriculture in deforestation and the loss of other natural ecosystems is well known, the specific drivers in the context of small- and large-scale agriculture remain poorly understood. In this study, we employed satellite data and a deep learning algorithm to map the agricultural landscape of Central Africa (Cameroon, Central Africa Republic, Congo, Democratic Republic of Congo, Equatorial Guinea, and Gabon) into large- (including for plantations and intensively cultivated areas) and small-scale tree crops and non-tree crop cover. This permits the assessment of forest loss between the years 2000 and 2022 as a result of small- and large-scale agriculture. Thematic [user’s] accuracy ranged between 91.2 ± 2.5 percent (large-scale oil palm) and 17.8 ± 3.9 percent (large-scale non-tree crops). Small-scale tree crops achieved relatively low accuracy (63.5 ± 5.9 percent), highlighting the difficulties of reliably mapping crop types at a regional scale. In general, we observed that small-scale agriculture is fifteen times the size of large-scale agriculture, as area estimates of small-scale non-tree crops and small-scale tree crops ranged between 164,823 ± 4224 km² and 293,249 ± 12,695 km², respectively. Large-scale non-tree crops and large-scale tree crops ranged between 20,153 ± 1195 km² and 7436 ± 280 km², respectively. Small-scale cropping activities represent 12 percent of the total land cover and have led to dramatic encroachment into tropical moist forests in the past two decades in all six countries. We summarized key recommendations to help the forest conservation effort of existing policy frameworks. Full article

This study aimed to identify the pathogen of oil palm (Elaeis guineensis) leaf spot disease in Hainan Province, China and examine the effects of environmental factors and fungicide sensitivity on the pathogen. The research confirmed that the pathogen responsible for this novel leaf spot disease was Colletotrichum siamense, marking the first report of this pathogen on oil palm in China. Field observations revealed summer-onset disease symptoms with concomitant leaf damage. The pathogen demonstrated optimal growth at a temperature of 30 °C and pH of 7.0, indicating its adaptability to prevailing climatic conditions in the region. Laboratory tests assessed the effects of various environmental factors on mycelial growth, revealing a marked decline in growth at temperatures below 20 °C and above 35 °C, as well as at acidic pH levels. Fungicide sensitivity assays identified pyraclostrobin, tebuconazole, prochloraz, and carbendazim as the most effective compounds, significantly inhibiting the growth of C. siamense with low EC₅₀ values. These findings provide essential information for developing effective disease management strategies to combat leaf spot disease in oil palm plantations. Full article

The conversion of tropical forests into urban and agriculture landscapes may alter insect populations through habitat disturbance and impact the diets of aerial insectivores. Most dietary studies on aerial insectivores have limitation on identifying prey at higher taxonomic levels in broad landscapes, restricting species-level identification and thus making a detailed dietary comparison impossible. This study examines the dietary changes through adaptation of house-farm swiftlets (Aerodramus sp.) and Pacific swallows (Hirundo tahitica) across three distinct habitats in Peninsular Malaysia: mixed-use landscapes, oil palm plantations, and paddy fields. High-throughput DNA metabarcoding with ANML primers targeting mitochondrial CO1 gene, identified 245 arthropod prey species, with six dominant orders: Coleoptera, Diptera, Blattodea, Hemiptera, Hymenoptera, and Lepidoptera. Mixed-use landscapes supported the highest dietary diversity and niche breadth, reflecting their ecological complexity. Paddy fields exhibited moderate diversity, while oil palm plantations demonstrated the lowest diversity, influenced by simplified vegetation structures and limited prey availability. The consumption of agricultural pests and vector species highlights the critical ecological role of aerial insectivorous birds in natural pest management and mitigating vector-borne disease risks. This research emphasizes the importance of conserving habitat heterogeneity to sustain the ecological services provided by these birds, benefiting both agricultural productivity and public health. Full article

Floods, as a form of disaster, significantly affect individuals and farmers in impacted areas, particularly through crop damage and the inability to harvest due to prolonged and extensive flooding. Among the most severely affected agricultural sectors are oil palm plantations, which regularly experience such disruptions annually. Current methods of assistance and relief during flooding rely on field surveys conducted manually by personnel, a process constrained by its time-intensive nature. Moreover, existing applications or platforms do not support the classification and inspection of oil palm plantations affected by floods during harvesting. This research aims to develop a method and application for inspecting oil palm plantations impacted by floods during harvesting. The approach utilizes deep learning and geographic information systems (GIS) to classify and analyze flood-affected areas and determine the ripeness of oil palm bunches on trees, enabling accurate and rapid identification of flood-affected areas. The study results demonstrate that the proposed method achieves a flood classification accuracy ranging from 96.80% to 98.29% and ripeness classification accuracy for oil palm bunches on trees ranging from 97.60% to 99.75%. These findings indicate that the proposed model effectively and efficiently monitors flood-affected areas. Additionally, the developed application serves as a valuable tool for flood management, facilitating timely assistance and relief for farmers impacted by flooding. Full article

Palm oil is being criticized as an unsustainable product by the EU due to its association with deforestation and high carbon emissions. However, the producers in Indonesia do not acknowledge the criticisms. Therefore, this study aimed to compare the carbon footprint of a representative EU-produced vegetable oil, rapeseed oil, with Indonesian palm oil. The analysis is divided into two stages, namely (a) land use conversion (LUC) as well as (b) plantation and oil processing. LUC entailed the conversion of native vegetation, such as forest areas and grassland, to vegetable oil crops. The carbon opportunity cost was used to account for the LUC contribution to the carbon footprint. For plantation and oil processing stages, the LCA SIMAPRO was adopted. The results showed that when vegetable oil replaced high-carbon-storage vegetation such as forests, the LUC carbon footprint of rapeseed oil and palm oil production were 2.09 and 1.49 t CO₂eq t⁻¹ oil, respectively. Replacing low-carbon-storage vegetation, namely shrub/grassland, led to 0.05 and −0.43 t CO₂eq t⁻¹ of repressed and palm oils, respectively. Based on the LCA SIMAPRO, the carbon footprints of plantation and oil processing stages were 1.05 and 0.88 t CO₂eq t⁻¹ oil for rapeseed and palm oils, respectively. The cultivation of oil palm in peatland generated a higher total carbon footprint (i.e., combined LUC and plantation and oil processing stages) than rapeseed oil (13.8 to 3.14 t CO₂eq t⁻¹ oil). However, in non-peatland areas, the total carbon footprint of palm oil was lower than rapeseed oil (2.37 to 3.14 t CO₂eq t⁻¹ oil) when replacing tropical forest and temperate forest vegetation, respectively. The total footprint was 1.2 to 0.45 t CO₂eq t⁻¹ oil when both replaced shrub/grassland. The higher productivity of oil palm and lower fertilizer requirement contributed primarily to the lower carbon footprint in non-peatland areas. Full article