Search Results (354)

In Cambodia, farmers construct artificial household bat roosts to collect and sell guano as fertilizer. We investigated farming practices and attendant spillover risks using (1) surveys on guano production; (2) an estimation of bat population size and species present using carcasses, visual identification, and audio recordings; (3) surveys of guano-producing and neighboring households on water, sanitation, and hygiene practices; and (4) the testing of guano and household food, water, and surfaces for coronaviruses using RT-qPCR. Bat roosts are constructed using dried palm leaves with coconut tree and/or steel/concrete supports. Roosting areas ranged from 42 to 327 m², bat abundance varied from 0 to 11,187, guano production was between 5 and 120 kg/week, guano yields were from 0.15 to 0.4 kg/m²/week, and farmers earned USD ~100–200/household/month. Higher guano production in the peak (normally wet) season was associated with greater bat abundance (p = 0.016). The lesser Asiatic yellow house bat (Scotophilus kuhlii) was the only bat species identified. Roosts were <20 m from guano-producing households. Neighbors and households’ hygiene risks included not having handwashing stations and not covering food in storage/while drying. Coronaviruses (Alphacoronaviruses or Infectious Bronchitis Virus) were detected in 14.6%, 17.3%, 2.9%, 1.4%, and 0.0% of guano, urine, household surface, food, and water samples, respectively. While guano farming offers economic benefits, spillover risks exist. Safe guano collection and storage, handwashing, and food covering in guano-producing communities are necessary to mitigate spillover risks. Full article

This paper presents a high-spatial-resolution 3D system to simulate air quality in urban environments by coupling the WRF/Chem regional model with the PALM4U computational fluid dynamics model, together with an emission model using the SUMO microscopic traffic model. The system has been applied to two experiments in the city of Madrid, Spain. The first study quantifies the impact of four high-rise buildings on pollutant dispersion. The second evaluates the effect of changing tree types (broad-leaf vs. needle-leaf) in the Retiro Park on NO₂ and O₃ concentrations. Both simulations adopt a multiscale approach, using detailed 3D urban morphology, traffic flow data and meteorological conditions. In the first experiment, high-rise buildings caused local variations in NO₂ and O₃ of up to 15% and 20%, respectively. In the second experiment, replacing broad-leaf trees with needle-leaf trees led to a mean NO₂ reduction of 1.69% across 90.67% of the study area. This research demonstrates the value of integrated CFD modeling for planning urban mitigation strategies and optimizing air quality in complex urban environments. Full article

The necrotrophic pathogen Sclerotinia sclerotiorum compromises the physiological and anatomical integrity of cotton, leading to substantial economic losses due to rapid tissue necrosis, stem blight, boll rot, and leaf wilting. In this context, the use of endophytic microorganisms emerges as a promising strategy for the biocontrol of white mold. This study tested the hypothesis that endophytic fungal strains isolated from the roots of Butia purpurascens, a palm tree endemic to the Cerrado biome, could mitigate disease symptoms in Gossypium hirsutum L. To evaluate this, cotton plants were subjected to biotic stress imposed by S. sclerotiorum to assess the effectiveness of seven fungal strains in attenuating disease. The impact of the pathogen was monitored through growth variables, gas exchange, leaf temperature, chlorophyll a fluorescence, antioxidant enzyme activity, proline and malondialdehyde (MDA) levels, and the incidence of rot in petioles, leaves, and flower buds. Overall, inoculation with endophytic fungi significantly alleviated the effects of the phytopathogen, promoting vegetative growth and optimizing physiological performance. Treated plants exhibited alleviated stress in primary photochemistry, reduced non-photochemical energy dissipation, and stable carbon fixation. Additionally, efficient modulation of the antioxidant system and preservation of anatomical structures were observed, minimizing the severe symptoms of white mold. Notably, the non-pathogenic strains BP10EF (Gibberella moniliformis), BP16EF (Penicillium purpurogenum), and BP33EF (Hamigera insecticola) acted as potent physiological modulators, yielding responses similar to those of healthy plants. These results highlight the biotechnological potential of these endophytic strains, which can be explored as both growth promoters and resistance inducers in cotton against white mold. Full article

Steroid hormones (SHs) have a high estrogenic potential, and urban wastewater is one of their main ways into the aquatic environment. Constructed wetlands (CWs) are considered one of the most sustainable alternatives for the treatment of wastewater from small communities. However, the use of gravel and sand implies a significant environmental impact associated with their extraction and transport. A more sustainable alternative is the use of plant residues, as they are abundant, inexpensive, and readily available, and they can improve the efficiency of hormone removal through sorption. Thus, the sorption of 15 SHs was studied on conventional, mineral substrates (gravel, sand, and volcanic ash) and alternative vegetal wastes, i.e., mulches from giant reed, palm tree, balsa wood, and pine needles. These materials were characterized by determining their Point of Zero Charge (pH_PZC), ash content, content of leachable polycyclic aromatic hydrocarbons (PAH) and heavy metals, total surface area (BET), and pore characteristics. Results indicated that SH sorption on the mineral substrates was quite low, in most cases less than 10–15%. However, in the mulches it reached between 50 and 95%, except for corticosteroids (11–43%). The pseudo-second-order kinetics provided the best fit in all cases, with R² values between 0.97 and 0.9999. Experiments with a contact time of 7 days showed that the palm tree was the only substrate that completely removed the three corticosteroids studied (cortisone, prednisone, and prednisolone). Additionally, a significant correlation was observed between removal due to sorption (%) and log octanol–water partition coefficient (log Kow). Freundlich isotherm provided a higher number of best fits than Langmuir. Lastly, to compare sand with palm mulch under more realistic experimental conditions, four lab-scale CWs (two with palm mulch and two with sand, with/without plants) were studied. The sand-based CWs achieved faster SH percentage removals, while after 24 h, SH mass removals were significantly higher in the palm mulch-based CWs. Full article

The carnauba palm trees in the Caatinga ecosystem, in Northeast Brazil, have been impacted by invasive species, particularly in areas subject to flooding. This study aimed to evaluate morphological, physiological, and nutritional responses of Copernicia prunifera (native) and Cryptostegia madagascariensis (invasive) seedlings exposed to flooding stress. The experiment was conducted in a randomized complete block design, with a split-plot arrangement and five replicates. The treatments were formed by two species and five periods of flood stress (0, 8, 12, 16, and 20 days). Flooding significantly reduced shoot dry mass in both species; however, the reduction was more pronounced in the invasive species (27%) compared to the native palm (20%). The invasive species showed strong use of resources, with higher values for leaf mineral nutrient, net photosynthesis, growth rate, and leaf area, regardless of the water regime. Under flooding, the invasive species produced adventitious roots, and the net photosynthetic rate was less impacted, despite greater sodium accumulation in the leaves. The results indicate that the characteristics of C. prunifera, such as slow growth rate, low specific leaf area, and morphological adaptations of the root system, may ensure greater stability in net carbon assimilation in the whole plant under flooding. However, the rapid growth and high absorption of soil resources of C. madagascariensis pose a significant threat to the establishment of C. prunifera seedlings, directly jeopardizing the long-term renewal of carnauba palm groves in the Caatinga ecosystem. Full article

Accurate real-time detection, geolocation, and counting of palm trees are essential for plantation management, yield estimation, and resource allocation in precision agriculture. Traditional approaches such as manual surveys or offline image processing are labor-intensive and unsuitable for large-scale applications. This study introduces a fully onboard real-time framework that integrates Unmanned Aerial Vehivle (UAV) imagery, the YOLOv12 deep learning model, and a camera projection technique to detect, geolocate, and count palm trees directly during flight. The lightweight YOLOv12n variant, deployed on an NVIDIA Jetson Nano edge device, achieved a detection precision of 92.4%, an average geolocation error of 2.14 m, and a counting error of only 0.2% across 915 trees. Unlike many existing methods that rely on offline processing or offboard computation, the proposed system performs all computations in real time, enabling immediate decision-making for tasks such as plantation density analysis, replanting planning, and yield forecasting. Experimental results demonstrate that the proposed approach provides a scalable, cost-effective, and autonomous solution for modern precision agriculture. Full article

Euterpe edulis, commonly known as juçara, is a palm tree native to the Brazilian Atlantic Forest whose purple fruits are rich in phenolic compounds associated with high antioxidant activity. Juçara pulp is traditionally produced under predominantly artisanal conditions, which limits its shelf life and commercial stability, making drying a relevant preservation strategy. This study investigated the drying of juçara pulp in a forced-air circulation oven at 45, 65, and 85 °C under different drying times. Classical drying models were fitted to the experimental moisture data. Higher temperatures accelerated moisture removal, with the sample dried at 85 °C reaching a powdered state within 60 min at approximately 10% moisture. Drying at 65 °C for 100 min reduced moisture to 5.30%, while drying at 45 °C for 180 min resulted in a moisture content of 6.62%. Total phenolic content decreased as a function of temperature and drying time. Among the evaluated conditions, drying at 65 °C for 100 min provided a favorable balance between efficient dehydration and phenolic retention, maintaining 12.38 mg gallic acid equivalents g⁻¹ (dry basis), corresponding to approximately 55% of the initial content. Full article

Current sustainability discourse promotes sustainable yard practices as a means for residents to contribute to urban environmental health and soil conservation. Social–ecological research suggests that yard practices are shaped by multiscale social drivers, including social contagion, whereby visible expressions of individuality in front yard design are copied by nearby neighbors. This study evaluated residential areas within the Río Piedras Watershed (RPWS) in the San Juan metropolitan area to assess evidence of social contagion in front yard configuration and vegetation structure, and to examine whether these variables were associated with socio-demographic and economic characteristics when spatial effects were considered. A total of 6858 front yards across six highly urbanized sites were analyzed using Google Earth Street View imagery. Housing lot sizes were quantified, and yards were classified into eight landscape configurations based on green and gray cover elements. Woody vegetation structures, including trees, shrubs, and palms, were also quantified to generate estimates of functional diversity and a front yard quality index. Significant differences in yard characteristics were observed among sites. Spatial analyses revealed significant clustering at distances of 65–80 m, particularly for front yard configuration, while clustering of woody vegetation density was weaker. Local clustering patterns and the distribution of outliers varied across sites. Spatial lag models indicated that lot area positively influenced yard configuration and quality, and the density and diversity of woody vegetation. While socio-economic variables were not significant predictors of yard quality, their effects cannot be discarded. Overall, results are consistent with social contagion processes but also highlight neighborhood design as a key driver of clustering, alongside widespread conversion of green to paved front yards, with implications for soil and green infrastructure loss as well as environmental and human health in the RPWS. Full article

Date palms, a vital Moroccan crop that typically flowers once a year, displayed a rare double flowering phenomenon in the summer of 2023. This study investigated the occurrence of this phenomenon across three small oases in the Zagora region of southeastern Morocco. Field surveys revealed that 60% of spring-blooming palms also produced a second bloom in July, affecting trees of all ages, sexes, and varieties. This secondary flowering cycle featured a compressed development period, leading to limited fruit enlargement and a failure of most fruit to reach maturity. Analysis suggests that climatic fluctuations, specifically a delayed temperature rise during the normal spring cycle and relatively cooler shifts in July 2023, likely disrupted the palms’ reproductive schedule, triggering the anomaly. Despite the failed second harvest, the phenomenon did not have a negative impact on the palms’ productivity for the subsequent year, confirming that double flowering is a complex, climate-influenced event that requires further research to understand its full implications for local date production. Full article

Phytoplasmas are obligate bacterial pathogens transmitted by phloem-feeding insects and responsible for severe diseases in numerous crops worldwide. In Iran, insect-associated phytoplasma transmission pathways remain poorly resolved, particularly at fine phylogenetic and vector-specific scales. Here, we investigated phytoplasma strains detected in four plant species, grapevine (Vitis vinifera), soybean (Glycine max), barberry (Berberis vulgaris), and the weed Conyza canadensis, and in three potential insect vectors (Tropidocephala prasina, Eysarcoris ventralis, and Nysius graminicola) collected from distinct agroecosystems across Iran. Phytoplasmas were characterized by using nearly full-length 16S rRNA gene sequences and a multilocus dataset of protein-coding genes obtained through a targeted next-generation sequencing approach. Five phytoplasma strains belonging to ribosomal groups 16SrI, 16SrVI, 16SrIX, and 16SrXII were identified, including two novel ribosomal subgroups, 16SrI-AS and 16SrIX-K. Several previously unreported plant–phytoplasma and insect–phytoplasma associations were documented. Comparative phylogenetic analyses revealed that ribosomal and multilocus markers capture complementary evolutionary signals, with protein-coding genes providing additional resolution beyond 16S-based classification. These findings highlight the potential role of diverse hosts and polyphagous insects, not yet confirmed as vectors, in phytoplasma circulation and underscore how high-throughput next-generation sequencing and multilocus approaches advance our understanding of phytoplasma diversity and evolution. Full article

The Acrocomia aculeata is a promising palm tree for biofuel production, but it faces challenges related to propagation, especially due to seed dormancy. This article presents an integrative review, supported by bibliometrics, of the sexual and asexual propagation methods of the species, conducted through searches in Scopus, SciELO, and Web of Science databases. The results indicate that sexual propagation is the predominant approach in the literature, although it faces significant challenges due to seed dormancy, such as the physical resistance to embryo protrusion imposed by the operculum. Asexual propagation demonstrates great potential through micropropagation techniques, which allow obtaining genetically uniform plants in relatively short periods. The non-deep physic dormancy exhibited by the seeds interferes with germination by constraining embryo growth potential and postponing the metabolic reactivation essential for successful germination. Despite the existence of promising methods for overcoming dormancy, additional studies are needed to understand the mechanisms involved in this process. This review maps the scientific literature to highlight areas of proven research success, identify critical gaps and underexplored topics, and indicate how future investigations can support the development of efficient propagation protocols and the establishment of commercial plantations. Full article

The probability that female bagworms (Lepidoptera: Psychidae) are in mating time-in (live pheromone calling) was recorded in three bagworm species: Oiketicus kirbyi in a Costa Rican oil palm plantation in 1993–1994; Metisa plana in Malaysian oil palm plantations during five consecutive generations of bagworms in 1996; and Thyridopteryx ephemeraeformis on ornamental trees in the Midwest United States. Because females entirely reproduce within their bag (mate attraction, copulation, and oviposition), it is possible to assess the mating success of time-out females (dead individuals from an ongoing generation that either mated or died as a lifelong virgin) and incidence of calling females that may or may not mate before death. Synchronous larval development and discrete (non-overlapping) generations imply a declining proportion of live calling females over time in all three bagworm species: ‘young’ calling females prevail in the early season as opposed to a majority of time-out (post-reproductive) females in the late season. Calling females are long-lived relative to males (one-day lifespan) and thus expected to mate as adults when abundance of males is high and/or female longevity exceeds three days. A low mating success of calling females is associated with extreme protogyny (early season male shortage; O. kirbyi in 1994) or late adult emergence in populations at the edge of the distribution range (T. ephemeraeformis at latitudes > 41° N in 2019). Full article

This research aimed to conduct a bibliometric review on Acrocomia aculeata (Jacq.) Lodd. ex Mart., popularly known as “macaúba”, a palm tree of the Arecaceae family with great potential to promote sustainable practices. The review focused on the applications associated with the oil, pulp, and almonds of the fruit, products that can be used in industries such as food, cosmetics, and bioenergy, contributing to the development of more ecological production chains with less environmental impact. Data were collected from the Scopus, Web of Science, and ScienceDirect databases for publications related to phytochemical and bioactive aspects, while only Web of Science was used for data on energy aspects. The documents found were analyzed in the VOSviewer software (version 1.6.20), allowing the creation of bibliometric networks (clusters) and tables on scientific production. The analyses included authors, co-authors, countries, institutions, journal sources, and keywords. For phytochemical and bioactive aspects, the search resulted in 1026 articles, of which 261 were selected after applying the exclusion criteria. For energy aspects, 99 publications were found. Based on the data, it was possible to analyze the existing research on A. aculeata, identifying the state of the research and possible gaps in studies related to this oilseed. The results highlight the importance of macaúba as a sustainable alternative for diversifying agricultural and bioindustrial products, promoting the bioeconomy and contributing to the mitigation of environmental impacts. In addition, the research allowed us to identify the universities and researchers most dedicated to this species, their main results and the areas that still require investment to advance research. Thus, A. aculeata emerges as a relevant option to strengthen sustainable practices in key sectors, integrating economic, social, and environmental benefits. Full article

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

Deep learning has become a powerful tool for diagnosing pests and plant diseases, although conventional convolutional neural networks (CNNs) generally suffer from limited interpretability and suboptimal focus on important image features. This study examines the integration of attention mechanisms into two prevalent CNN architectures—ResNet50 and MobileNetV2—to improve the interpretability and classification of diseases impacting date palm trees. Four attention modules—Squeeze-and-Excitation (SE), Efficient Channel Attention (ECA), Soft Attention, and the Convolutional Block Attention Module (CBAM)—were systematically integrated into ResNet50 and MobileNetV2 and assessed on the Palm Leaves dataset. Using transfer learning, the models were trained and evaluated through accuracy, F1-score, Grad-CAM visualizations, and quantitative metrics such as entropy and Attention Focus Scores. Analysis was also performed on the model’s complexity, including parameters and FLOPs. To confirm generalization, we tested the improved models on field data that was not part of the dataset used for learning. The experimental results demonstrated that the integration of attention mechanisms substantially improved both predictive accuracy and interpretability across all evaluated architectures. For MobileNetV2, the best performance and the most compact attention maps were obtained with SE and ECA (reaching 91%), while Soft Attention improved accuracy but produced broader, less concentrated activation patterns. For ResNet50, SE achieved the most focused and symptom-specific heatmaps, whereas CBAM reached the highest classification accuracy (up to 90.4%) but generated more spatially diffuse Grad-CAM activations. Overall, these findings demonstrate that attention-enhanced CNNs can provide accurate, interpretable, and robust detection of palm tree diseases and pests under real-world agricultural conditions. Full article