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This paper presents a comprehensive forest mapping system using a customized drone payload equipped with Light Detection and Ranging (LiDAR), cameras, a Global Navigation Satellite System (GNSS), and Inertial Measurement Unit (IMU) sensors. The goal is to develop an efficient solution for collecting accurate forest data in dynamic environments and to highlight potential wildfire regions of interest to support precise forest management and conservation on the ground. Our paper provides a detailed description of the hardware and software components of the system, covering sensor synchronization, data acquisition, and processing. The overall system implements simultaneous localization and mapping (SLAM) techniques, particularly Fast LiDAR Inertial Odometry with Scan Context (FASTLIO-SC), and LiDAR Inertial Odometry Smoothing and Mapping (LIOSAM), for accurate odometry estimation and map generation. We also integrate a fuel mapping representation based on one of the models, used by the United States Secretary of Agriculture (USDA) to classify fire behavior, into the system using semantic segmentation, LiDAR camera registration, and odometry as inputs. Real-time representation of fuel properties is achieved through a lightweight map data structure at 4 Hz. The research results demonstrate the effectiveness and reliability of the proposed system and show that it can provide accurate forest data collection, accurate pose estimation, and comprehensive fuel mapping with precision values for the main segmented classes above 85%. Qualitative evaluations suggest the system’s capabilities and highlight its potential to improve forest management and conservation efforts. In summary, this study presents a versatile forest mapping system that provides accurate forest data for effective management.

The environmental crisis and the safeguarding of the population's health has led to research into different ways of mitigating harmful gases. Among the emissions that the wood industry has sought to reduce are those of formaldehyde, which is why new green adhesive methods for wood panels have been investigated in recent years. In this research, particleboard with two bio-based wood adhesive (PB-bbwa) formulations. The first PB-bbwa formulation, based on proteins obtained from compounds from the alcoholic beverage industry, and the second PB-bbwa formulation, based on proteins from a mixture of compounds from the alcoholic beverage and food industries, were manufactured and tested to evaluate the physical–mechanical, thermal and formaldehyde emission properties of untreated and UV-treated formulations at a laboratory scale. The results of the physical properties obtained in the PB-bbwa were similar or even better than those of the control PB. Additionally, PB-bbwas improve on the control PB sample’s Janka hardness by least 28%, and a decrease in thermal conductivity in the edgewise position and formaldehyde emissions by 12% and 88%, respectively, in comparison to the control PB. The tests performed evidenced that PB-bbwas showed comparable performance against the control PB made with urea-formaldehyde and satisfied international standard requirements.

Linear feature networks are the roads, trails, pipelines, and seismic lines developed throughout many commercial boreal forests. These linear features, while providing access for industrial, recreational, silvicultural, and fire management operations, also have environmental implications which involve both the active and non-active portions of the network. Management of the existing linear feature networks across boreal forests would lead to the optimization of maintenance and construction costs as well as the minimization of the cumulative environmental effects of the anthropogenic linear footprint. Remote sensing data and predictive modelling are valuable support tools for the multi-level management of this network by providing accurate and detailed quantitative information aiming to assess linear feature conditions (e.g., deterioration and vegetation characteristic dynamics). However, the potential of remote sensing datasets to improve knowledge of fine-scale vegetation characteristic dynamics within forest roads has not been fully explored. This study investigated the use of high-spatial resolution (1 m), airborne LiDAR, terrain, climatic, and field survey data, aiming to provide information on vegetation characteristic dynamics within forest roads by (i) developing a predictive model for the characterization of the LiDAR-CHM vegetation cover dynamic (response metric) and (ii) investigating causal factors driving the vegetation cover dynamic using LiDAR (topography: slope, TWI, hillshade, and orientation), Sentinel-2 optical imagery (NDVI), climate databases (sunlight and wind speed), and field inventory (clearing width and years post-clearing). For these purposes, we evaluated and compared the performance of ordinary least squares (OLS) and machine learning (ML) regression approaches commonly used in ecological modelling—multiple linear regression (mlr), multivariate adaptive regression splines (mars), generalized additive model (gam), k-nearest neighbors (knn), gradient boosting machines (gbm), and random forests (rf). We validated our models’ results using an error metric—root mean square error (RMSE)—and a goodness-of-fit metric—coefficient of determination (R2). The predictions were tested using stratified cross-validation and were validated against an independent dataset. Our findings revealed that the rf model showed the most accurate results (cross-validation: R2 = 0.69, RMSE = 18.69%, validation against an independent dataset: R2 = 0.62, RMSE = 20.29%). The most informative factors were clearing width, which had the strongest negative effect, suggesting the underlying influence of disturbance legacies, and years post-clearing, which had a positive effect on the vegetation cover dynamic. Our long-term predictions suggest that a timeframe of no less than 20 years is expected for both wide- and narrow-width roads to exhibit ~50% and ~80% vegetation cover, respectively. This study has improved our understanding of fine-scale vegetation dynamics around forest roads, both qualitatively and quantitatively. The information from the predictive model is useful for both the short- and long-term management of the existing network. Furthermore, the study demonstrates that spatially explicit models using LiDAR data are reliable tools for assessing vegetation dynamics around forest roads. It provides avenues for further research and the potential to integrate this quantitative approach with other linear feature studies. An improved knowledge of vegetation dynamic patterns on linear features can help support sustainable forest management.

Characterizing insect communities in pollinator-dependent crops helps determine the potential pollinator effectiveness and their effects on crop yield. Few studies have examined pollinator communities and their services to crops in South America. Furthermore, optimal sampling methods for these communities in the crop habitat have received little attention. Pan traps are one of the simplest and most widely used sampling methods to assess insect diversity. We compared different pan trap arrangements to describe potential pollinator communities in two commercial crops (blueberry and canola) in Southern Chile. We compared communities in the crops and assessed how sampling position (border or center) and pan trap color (blue, white, or yellow) affected sample composition. Species composition was significantly different between crops. Furthermore, trap color affected sample composition in blueberry, but trap position did not, whereas color had no significant effect on canola, but trap position did. In all cases, yellow pans captured the largest number of species. Hymenoptera explained most of the differences in sampling efficiency because of the differential responses across species. We suggest that pan trap assessments of the diversity in potential pollinator insects depend on crop characteristics, including planting configuration and floral morphology. Therefore, comparative studies should include pans of different colors positioned at various locations within the crop.

Drought and fire reduce productivity and increase tree mortality in tropical forests. Fires also produce pyrogenic carbon (PyC), which persists in situ for centuries to millennia, and represents a legacy of past fires, potentially improving soil fertility and water holding capacity and selecting for the survival and recruitment of certain tree life-history (or successional) strategies. We investigated whether PyC is correlated with physicochemical soil properties, wood density, aboveground carbon (AGC) dynamics and forest resistance to severe drought. To achieve our aim, we used an Amazon-wide, long-term plot network, in forests without known recent fires, integrating site-specific measures of forest dynamics, soil properties and a unique soil PyC concentration database. We found that forests with higher concentrations of soil PyC had both higher soil fertility and lower wood density. Soil PyC was not associated with AGC dynamics in non-drought years. However, during extreme drought events (10% driest years), forests with higher concentrations of soil PyC experienced lower reductions in AGC gains (woody growth and recruitment), with this drought-immunizing effect increasing with drought severity. Forests with a legacy of ancient fires are therefore more likely to continue to grow and recruit under increased drought severity. Forests with high soil PyC concentrations (third quartile) had 3.8% greater AGC gains under mean drought, but 33.7% greater under the most extreme drought than forests with low soil PyC concentrations (first quartile), offsetting losses of up to 0.68 Mg C ha–1yr–1 of AGC under extreme drought events. This suggests that ancient fires have legacy effects on current forest dynamics, by altering soil fertility and favoring tree species capable of continued growth and recruitment during droughts. Therefore, mature forest that experienced fires centuries or millennia ago may have greater resistance to current short-term droughts.

Nanoparticles are recognized due to their particular physical and chemical properties, which are conferred due to their size, in the range of nanometers. Nanoparticles are recognized for their application in medicine, electronics, and the textile industry, among others, but also in agriculture. The application of nanoparticles as nanofertilizers and biostimulants can help improve growth and crop productivity, and it has therefore been mentioned as an essential tool to control the adverse effects of abiotic stress. However, nanoparticles have also been noted for their exceptional antimicrobial properties. Therefore, this work reviews the state of the art of different nanoparticles that have shown the capacity to control biotic stress in plants. In this regard, metal and metal oxide nanoparticles, polymeric nanoparticles, and others, such as silica nanoparticles, have been described. Moreover, uptake and translocation are covered. Finally, future remarks about the studies on nanoparticles and their beneficial role in biotic stress management are made.

This study aimed to investigate the multivariate relationship between the glycolytic potential, and meat quality traits from grass-fed steers carcasses with variations in early postmortem pH. From a contemporary group of steers (n=70) from the same production unit and slaughtered under similar conditions, thirty carcasses (10/group) were selected based on pH values measured at 3h (pH3h): Low (< 6.2), Intermediate pH3h (6.2–6.5), and High (> 6.5). Carcasses segregated by pH3h groups were different (p < 0.05) in muscular glycogen (MCG), glucose (G+G6P) content, glycolytic potential (GP) and GPstrict (GP without lactate content). The interaction pH3h groups × sampling time was significant only for lactate content (LC) (p < 0.05). Quality traits, except redness, did not vary (p > 0.05) among pH3h groups. Color variables had a positive and moderate correlation with MGC, G+G6P, LC, and GP. Results does not allow to recommend early carcass segregation by very early pH (3h postmortem); however, important bivariate and multivariate relationships between G+G6P, lactate content and instrumental color parameters in LL muscle from grass-fed cattle were demonstrated.

Urea present in cattle urine contributes large amounts of nitrogen (N) to grazed pastures, which can be the equivalent to approximately 1000 kg N ha−1. However, there are no studies in volcanic soils of southern Chile on the effect of different concentrations of urinary N deposited in the soil, nor of the effect different wetting and drying conditions mimicking the variation in weather conditions on the nitrification process from urea to NH4+ and total oxidized nitrogen (TON) over time. In addition, the inhibition of nitrification driven by the accumulation of NH3 at high rates of N applied to Andisol has not been evaluated. Fresh cattle urine was applied at three different rates of N equivalent to 247 kg N ha−1 (Low N), 461 kg N ha−1 (Medium N), and 921 kg N ha−1 (High N), as well as deionized water as a control. Further, three moisture conditions were imposed: constant moisture (CM), drying–rewetting (DRW) cycles at 7-day intervals, and soil drying (SD). Destructive soil core samples were evaluated for top and bottom halves individually every 7 days over a 36-day period to measure changes on inorganic N and pH. There were no interaction effects for N rates and soil moisture. The main effect of the different rates of N on mineralization was significant throughout the incubation period, while the effect of the different moisture conditions was variable over time. High N was associated with elevated NH3 concentrations and could explain why total N mineralization was partially inhibited. These results suggest that the presence of different nitrifying microorganisms in soil under different chemical and physical conditions determines nitrification, and thus, the oxidation of ammonia should be studied in more detail as the first step of nitrification, specifically in volcanic soils.