Search Results (72)

The transition toward a sustainable bioeconomy is increasingly recognised as a key pathway for resource efficiency and climate resilience in emerging economies. However, system-level analyses integrating biomass flows, governance structures, and actor dynamics remain limited, particularly in Sub-Saharan Africa. This study develops a systems-oriented analytical framework combining material flow assessment, stakeholder mapping, governance assessment, and innovation systems analysis to evaluate the structure, performance, and circularity of biomass systems in Ghana. The analysis focuses on six major biomass sectors: cocoa, cassava, maize, plantain, oil palm, and shea. The results show that Ghana generates substantial biomass resources, yet significant inefficiencies persist, with major residue streams such as cocoa pod husks (~9 million tonnes (Mt) annually) and cassava peels (2.6–3.8 million tonnes annually) remaining largely underutilised. Across sectors, residue utilisation rates remain low, while biomass leakage is driven by fragmented governance, weak coordination among actors, spatially dispersed production systems, and limited processing and technological capacity. Compared with more integrated biomass-based economies, Ghana remains at an early stage of circular transition, despite considerable potential for value addition and resource recovery. The study contributes a transferable systems-based analytical framework for diagnosing circularity gaps and system inefficiencies in data-constrained bioeconomy contexts. Strengthening institutional coordination, decentralised processing infrastructure, and innovation systems is identified as critical for advancing a more circular and inclusive bioeconomy in Ghana. Full article

Angiogenesis is a spatially regulated hallmark of colorectal cancer (CRC) progression, yet current analytical frameworks fail to resolve how nutraceutical bioactive compounds interact with angiogenic signalling within the heterogeneous tumour microenvironment. This review advances a central hypothesis: that the spatial localisation of palm oil mill effluent (POME)-derived bioactive compounds within CRC tumour tissues is predictive of their functional anti-angiogenic activity. POME—the largest waste stream of palm oil processing—contains a chemically diverse array of bioactives, including tocotrienols, phenolics, carotenoids, and fatty acids, with reported antioxidant, anti-inflammatory, and anti-angiogenic properties. However, the existing evidence is predominantly derived from bulk in vitro analyses, limiting mechanistic conclusions about compound behaviour within spatially organised tumour architectures. To address this gap, we propose an integrated framework positioning mass spectrometry imaging (MSI)—across matrix-assisted laser desorption/ionisation (MALDI), desorption electrospray ionisation (DESI), and secondary ion mass spectrometry (SIMS) platforms—as the analytical bridge between compound localisation and angiogenic function. By enabling the label-free, spatially resolved co-localisation of POME-derived compounds with key angiogenic mediators, including VEGF, HIF-1α, and NF-κB, within intact CRC tissues, MSI provides a mechanistic platform that transcends the limitations of conventional molecular analyses. A four-component translational roadmap is outlined, encompassing POME bioactive profiling, spatial compound mapping, angiogenic co-localisation analysis, and functional validation. Critically, the existing evidence on oil palm-derived bioactives is appraised with respect to study quality, mechanistic depth, and translational limitations, identifying the most analytically tractable candidate compounds for spatial investigation. Collectively, this framework positions POME valorisation within a precision nutraceutical oncology paradigm, offering a spatially informed strategy for anti-angiogenic intervention in CRC while simultaneously addressing the environmental burden of palm oil processing waste. Full article

Second-generation bioethanol from food industry lignocellulosic residues offers a promising route toward low-carbon, circular bioenergy systems. However, the reported environmental impacts differ markedly across studies, challenging efforts to assess the true sustainability of these waste-derived bioethanol routes. This review synthesizes current knowledge on the production of bioethanol from key agro-industrial wastes including oil palm empty fruit bunches, sugarcane bagasse, brewers’ spent grain, spent coffee grounds, tea waste, citrus residues, and potato peel waste. We outline feedstock characteristics, availability, and prevailing management practices, and map the principal biochemical conversion routes to identify process steps that drive environmental performance. A systematic comparison of life cycle assessments reveals substantial methodological heterogeneity across functional units, system boundaries, allocation procedures, and impact assessment methods. Nonetheless, consistent hotspots emerge, particularly associated with pretreatment severity, enzyme production, thermal energy demand, and co-product handling. The review highlights robust cross-study trends, pinpoints methodological gaps, and proposes recommendations for harmonized LCA practice. By integrating technological and methodological perspectives, this work aims to support the development and policy uptake of sustainable, waste-based bioethanol within circular bioeconomies. Full article

Oil palm (Elaeis guineensis), a rapidly expanding crop in northeastern Pará, first emerged in the 1970s as a crucial response to the global oil crisis. However, its swift expansion has subsequently generated significant socio-environmental conflicts, profoundly altering local socioecological dynamics. Therefore, we aimed to investigate land-use and land-cover changes within the headwater sub-basin of the Caeté River, focusing specifically on the municipality of Bonito, Pará. To achieve this, we employed remote sensing and geospatial analysis to accurately delineate the study area and perform supervised classifications. Specifically, we used the Random Forest algorithm to map five distinct periods: 1985, 1995, 2004, 2015, and 2023. In addition, we calculate an Anthropogenic Transformation Index (ATI) in order to observe the human influence in the landscape. Our classification models exhibited high accuracy, with overall accuracy values ranging from 0.63 to 0.87 and Kappa coefficients between 0.53 and 0.76, demonstrating consistent discrimination among LULC classes. The results revealed a marked transformation of the landscape, with oil palm monocultures progressively expanding at the expense of dense forest and human-modified vegetation. For instance, the ATI increased from 3.14 in 1985 to 5.56 in 2004, followed by a slight decline to 4.90 in 2023, suggesting a potential stabilisation—but not a reversal—of anthropogenic pressures. Nonetheless, the negative socioecological impacts of the oil palm monocultures in this Amazonian landscape remain severe, encompassing issues such as water pollution and ongoing socio-environmental conflicts. In conclusion, this research highlights the importance of understanding these dynamics to support sustainable management of the Caeté River basin. Furthermore, we underscore the urgent need for further research to rigorously evaluate effective mitigation strategies and foster genuinely sustainable development within the region. 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

Palm trees, referred to here as vegetation cover (VC), provide essential ecosystem services in an arid Oasis. However, because of socioeconomic transformation, the rapid urban expansion of major cities and villages at the expense of agricultural lands of the Al-Ahsa Oasis, Saudi Arabia, has placed enormous pressure on the palm-growing area and led to the loss of productive land. These challenges highlight the need for robust, integrative methods to assess their impact on the agroecosystem. Here, we analyze spatiotemporal fluctuations in vegetation cover and its effect on the agroecosystem to determine the potential influencing factors. Data from Landsat satellites, including TM (Thematic mapper of Landsat 5), ETM+ (Enhanced Thematic mapper plus of Landsat 7), and OIL (Landsat 8) and Sentinel-2A imageries were used for analysis, while GeoEye-1 satellite images as well as socioeconomic data were applied for result validation. Principal Component Analysis (PCA) was applied to extract pure endmembers, facilitating Spectral Mixture Analysis (SMA) for mapping vegetation and urban fractions. The spatiotemporal change patterns were analyzed using time- and space-oriented detection algorithms. Results indicated that vegetation fraction patterns differed significantly; pixels with high fraction values declined significantly from 1990 to 2020. The mean vegetation fraction value varied from 0.79 to 0.37. This indicates that a reduction in palm trees was quickly occurring at a decreasing rate of −14.24%. Results also suggest that vegetation fractions decreased significantly between 1990 and 2020, and this decrease had the greatest effect on the agroecosystem situation of the Oasis. We assessed urban sprawl, and our results indicated substantial variability in average urban fractions: 0.208%, 0.247%, 0.699%, and 0.807% in 1990, 2000, 2010, and 2020, respectively. Overall, the data revealed an association between changes in palm tree fractions and urban ones, supporting strategic vegetation and/or agricultural management to enhance the agroecosystem in an arid Oasis. Full article

Deforestation monitoring is critical to support compliance with regulatory frameworks such as the EU Deforestation Regulation (EUDR), which requires that products containing or derived from beef, cocoa, coffee, palm oil, rubber, soy, and timber are deforestation-free after 31 December 2020. Earth observation (EO) offers a means to assess deforestation, yet map-based verification remains technically limited and uncertain. This study addresses the lack of a systematic assessment of global Forest/Non-Forest (FNF), Tree Cover/Non-Tree Cover (TC/NTC) and Land Use/Land Cover (LULC) datasets by identifying and evaluating 21 publicly available global forest/tree cover reference maps for their alignment with EUDR criteria. This goes beyond merely treating these datasets as simply “fit” or “not fit” for the purpose of the EUDR, but rather aims to assess how well each dataset meets the needs compared to others, acknowledging strengths, weaknesses, and trade-offs. The 21 datasets are reviewed based on EUDR-related parameters (temporal proximity, spatial resolution, and forest definition) as well as accuracy metrics. From this broader review, eight datasets are shortlisted based on their alignment with key regulatory requirements. However, most datasets fail to fully meet all EUDR requirements, particularly forest definitions, with only two datasets satisfying all indicators. Notably, all datasets are unable to distinguish forests from other non-forest, tree-based systems. Reported accuracy metrics reveal a general overestimation of forest areas, while canopy height-based maps tend to underestimate tree cover, potentially excluding forested regions. Regional comparisons show more consistent estimates in South America, while Europe and North America display greater variability. These findings support informed decision-making by companies and policymakers for selecting suitable datasets, while also highlighting conflicts and challenges associated with the use of global forest/tree cover maps for regulatory compliance. Full article

As a globally significant oil crop, precise ripeness identification of palm fruits directly impacts harvesting efficiency and oil quality. However, the progress and application of identifying the ripeness of palm fruits have been impeded by the computational limitations of agricultural hardware and the insufficient robustness in accurately identifying palm fruits in complex on-tree environments. To address these challenges, this paper proposes an efficient recognition network tailored for complex canopy-level palm fruit ripeness assessment. Progressive combination optimization enhances the baseline network, which utilizes the YOLOv8 architecture. This study has individually enhanced the backbone network, neck, detection head, and loss function. Specifically, the backbone integrates the StarNet framework, while the detection head incorporates the lightweight LSCD structure. To enhance recognition precision, StarNet-derived Star Blocks replace standard bottleneck modules in the neck, forming optimized C2F-Star components, complemented by DIoU loss implementation to accelerate convergence. The resultant on-tree model for recognizing palm fruit ripeness achieves substantial efficiency gains. While simultaneously elevating detection precision to 76.0% mAP@0.5, our method’s GFLOPs, parameters, and model size are only 4.5 G, 1.37 M, and 2.85 MB, which are 56.0%, 46.0%, and 48.0% of the original model. The effectiveness of the model in recognizing palm fruit ripeness in complex environments, such as uneven lighting, motion blur, and occlusion, validates its robustness. 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

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

The remote sensing community benefits from new sensors and easier access to Earth Observation data to frequently released new land-cover maps. The propagation of such independent and heterogeneous products offers promising perspectives for various scientific domains and for the implementation and monitoring of land-use policies. Yet, it may also confuse the end-users when it comes to identifying the most appropriate product to address their requirements. Data fusion methods can help to combine competing and/or complementary maps in order to capitalize on their strengths while overcoming their limitations. We assessed the potential of the Dempster–Shafer Theory (DST) to enhance oil palm mapping in Sumatra (Indonesia) by combining four land-cover maps, hereafter named DESCALS, IIASA, XU, and MAPBIOMAS, according to the first author’s name or the research group that published it. The application of DST relied on four steps: (1) a discernment framework, (2) the assignment of mass functions, (3) the DST fusion rule, and (4) the DST decision rule. Our results showed that the DST decision map achieved significantly higher accuracy (Kappa = 0.78) than the most accurate input product (Kappa = 0.724). The best result was reached by considering the probabilities of pixels to belong to the OP class associated with DESCALS map. In addition, the belief (i.e., confidence) and conflict (i.e., uncertainty) maps produced by DST evidenced that industrial plantations were detected with higher confidence than smallholder plantations. Consequently, Kappa values computed locally were lower in areas dominated by smallholder plantations. Combining land-use products with DST contributes to producing state-of-the-art maps and continuous information for enhanced land-cover analysis. Full article

Today, the world is increasingly concerned about energy and environmental challenges, and the search for renewable energy sources has become an unavoidable priority. In this context, Elaeis guineensis (better known as the African oil palm) has been placed in the spotlight due to its great potential and specific characteristics for the production of alternative fuels in the search for sustainable energy solutions. In the present study, bibliometric and co-occurrence analyses are proposed to identify trends, gaps, future directions, and challenges related to the production of bioethanol and hydrogen from oil palm rachis, using VOSviewer v.1.6.20 as a tool to analyze data obtained from SCOPUS. A mapping of several topics related to bioethanol and hydrogen production from oil palm bagasse or rachis is provided, resulting in contributions to the topic under review. It is shown that research is trending towards the use of oil palm rachis as a raw material for hydrogen production, consolidating its position as a promising renewable energy source. The field of hydrogen production from renewable sources has undergone constant evolution, and it is expected to continue growing and playing a significant role in the transition towards cleaner and more sustainable energy sources, potentially involving the adoption of innovative technologies such as solar-powered steam generation. From an economic point of view, developing a circular economy approach to bioethanol and hydrogen production from oil palm rachis and waste management will require innovations in material design, recycling technologies, and the development of effective life cycle strategies that can be evaluated through computer-assisted process simulation. Additionally, the extraction and purification of other gases during the dark fermentation method contribute to reducing greenhouse gas emissions and minimizing energy consumption. Ultimately, the sustainability assessment of bioethanol production processes is crucial, employing various methodologies such as life cycle assessment (LCA), techno-economic analysis, techno-economic resilience, and environmental risk assessment (ERA). This research is original in that it evaluates not only the behavior of the scientific community on these topics over the past 20 years but also examines a less-studied biofuel, namely bioethanol. Full article

Oil palm is a versatile oil crop with numerous applications. Significant progress has been made in applying histological techniques in oil palm research in recent years. Whole genome sequencing of oil palm has been carried out to explain the function and structure of the order genome, facilitating the development of molecular markers and the construction of genetic maps, which are crucial for studying important traits and genetic resources in oil palm. Transcriptomics provides a powerful tool for studying various aspects of plant biology, including abiotic and biotic stresses, fatty acid composition and accumulation, and sexual reproduction, while proteomics and metabolomics provide opportunities to study lipid synthesis and stress responses, regulate fatty acid composition based on different gene and metabolite levels, elucidate the physiological mechanisms in response to abiotic stresses, and explain intriguing biological processes in oil palm. This paper summarizes the current status of oil palm research from a multi-omics perspective and hopes to provide a reference for further in-depth research on oil palm. Full article

This study presents a GIS-based Multi-Criteria Decision Analysis (MCDA) spatial model to assess land suitability for oil palm (OP) cultivation in rainfed conditions. Initially, twelve parameters, viz., rainfall, number of rainy days, mean temperature, RH, ground water level, soil pH, salinity, soil depth, surface texture, stoniness, slope, and drainage, were selected for assessing OP suitability in one of the states (Kerala). However, subsequent ground verification revealed significant discrepancies, which prompted refining the model by focusing on key parameters with greater accuracy and relevance. Accordingly, only five the most critical parameters affecting OP cultivation under rainfed conditions were selected through the rank sum method, and weights were assigned ac-cording to their significance. This study was aimed at creating a comprehensive tool for informed decision making in agricultural planning. District-level spatial data from reliable sources were utilized for Multi-Criteria Decision Analysis. Thematic rasters, representing key factors influencing land suitability, were created in a GIS. Utilizing MCDA techniques, a digital suitability map was generated in ArcGIS 10.3, delineating three distinct classes over an extensive area of 10.5 million hectares. Further, with an aim to focus on actual locations that can be readily planted with oil palm, the suitable locations identified were restricted to eight selected land use/land cover (LULC) classes. This strategic limitation aimed to facilitate the expansion of OP cultivation exclusively to areas deemed most suitable based on the identified criteria. The validation of this developed model involved comparing the suitability map generated with the performance of existing oil palm plantations across diverse locations. The reasonable similarity between the model’s predictions and real-world plantation outcomes validated the effectiveness of this MCDA spatial model. This model not only helps identify suitable locations for rainfed oil palm cultivation but also serves as a valuable tool for strategic decision making in agricultural land use planning. Full article