Stand age significantly influences the functioning of forest ecosystems by shaping structural and physiological plant traits, affecting water and carbon budgets. Forest age distribution is determined by the interplay of tree mortality and regeneration, influenced by both natural and anthropogenic disturbances. Unfortunately, human-driven alteration of tree age distribution presents an underexplored avenue for enhancing forest stability and resilience. In our study, we investigated how age impacts the stability and resilience of the forest carbon budget under both current and future climate conditions. We employed a state-of-the-science biogeochemical, biophysical, validated process-based model on historically managed forest stands, projecting their future as undisturbed systems, i.e., left at their natural evolution with no management interventions (i.e., forests are left to develop undisturbed). Such a model, forced by climate data from five Earth System Models under four representative climate scenarios and one baseline scenario to disentangle the effect of climate change, spanned several age classes as representative of the current European forests' context, for each stand. Our findings indicate that Net Primary Production (NPP) peaks in the young and middle-aged classes (16- to 50-year-old), aligning with longstanding ecological theories, regardless of the climate scenario. Under climate change, the beech forest exhibited an increase in NPP and maintained stability across all age classes, while resilience remained constant with rising atmospheric CO₂ and temperatures. However, NPP declined under climate change scenarios for the Norway spruce and Scots pine sites. In these coniferous forests, stability and resilience were more influenced. These results underscore the necessity of accounting for age class diversity -lacking in most, if not all, the current Global Vegetation Models - for reliable and robust assessments of the impacts of climate change on future forests' stability and resilience capacity. We, therefore, advocate for customized management strategies that enhance the adaptability of forests to changing climatic conditions, taking into account the diverse responses of different species and age groups to climate.

Lignocellulosic biomass is an exciting renewable resource for producing sustainable biofuels, thanks to its abundance and low environmental impact. However, its intricate structure makes it tough for enzymes to break it down effectively. Only efficient pretreatment methods can solve these problems. Among these, mechanical pretreatment methods are particularly good for industry because they are easy to use, do not require chemicals, and make it easier to achieve biomass. This systematic review adhered to the PRISMA protocols and used text analysis with VOSviewer to examine 33 academic articles published between 2005 and 2025. It highlighted two main types of mechanical pretreatment: size reduction (which includes grinding, crushing, and shredding) and densification (like pelletizing and briquetting). The results show that mechanical pretreatment can significantly boost biofuel yields by increasing surface area, lowering crystallinity, and allowing better enzyme penetration. Energy consumption remains a major hurdle for the overall sustainability of biomass conversion processes. This research provides a comprehensive review of current mechanical techniques, detailing their operational settings and performance metrics while also offering suggestions for optimizing biomass conversion processes. By promoting the use of mechanical pretreatment in biofuel production systems, the findings align with the principles of a circular economy and contribute to the development of greener energy sources.

Stand age significantly influences the functioning of forest ecosystems by shaping structural and physiological plant traits, affecting water and carbon budgets. Forest age distribution is determined by the interplay of tree mortality and regeneration, influenced by both natural and anthropogenic disturbances. Thus, human-driven alteration of tree age distribution presents an underexplored avenue for enhancing forest stability and resilience. In our study, we investigated how age distribution impacts the stability and resilience of the forest carbon budget under both current and future climate conditions. We employed a biogeochemical model on three historically managed forest stands, projecting their future as undisturbed systems, i.e., left at their natural evolution with no management interventions. The model, driven by climate data from five Earth System Models under four representative climate scenarios and one baseline scenario, spanned 11 age classes for each stand. Our findings indicate that Net Primary Production (NPP) peaks in the young and middle-aged classes (16- to 50-year-old), aligning with ecological theories, regardless of the climate scenario. Under climate change, the beech forest exhibited an increase in NPP and maintained stability across all age classes, while resilience remained constant with rising atmospheric CO ₂ and temperatures. However, NPP declined under climate change scenarios for the Norway spruce and Scots pine sites. In these coniferous forests, stability and resilience were more influenced. These results underscore the necessity of accounting for age classes and species-specific reactions in evaluating the impacts of climate change on forest stability and resilience. We, therefore, advocate for customized management strategies that enhance the adaptability of forests to changing climatic conditions, taking into account the diverse responses of different species and age groups to climate.

Stereo cameras, also known as depth cameras or RGBD cameras, are increasingly employed in a large variety of machinery for obstacle detection purposes and navigation planning. This also represents an opportunity in agricultural machinery for safety purposes to detect the presence of workers on foot and avoid collisions. However, their outdoor performance at medium and long range under operational light conditions remains weakly quantified: authors then fit a field protocol and a model to characterize the pipeline of stereo cameras, taking the Intel RealSense D455 as benchmark, across various distances from 4 meters to 16 meters in realistic farm settings. Tests have been conducted using a 1 square meter planar target in outdoor environments, under diverse illumination conditions and with the panel being located at 0°, 10°, 20° and 35° from the center of the camera's field of view (FoV). Built-in presets were also adjusted during tests, to generate a total of 128 samples. Authors then fit disparity surfaces to predict and correct systematic bias as a function of distance and radial FoV position, allowing to compute mean depth and estimate a model of systematic error that takes depth bias as a function of distance, light conditions and FoV position. Results showed that the model can predict depth errors achieving a good degree of precision in every tested scenario (RMSE: 0.46 – 0.64 m, MAE: 0.40 – 0.51 m), enabling the possibility of replication and benchmarking on other sensors and field contexts while supporting safety-critical perception systems in agriculture.

Stereo cameras, also known as depth cameras or RGB-D cameras, are increasingly employed in a large variety of machinery for obstacle detection purposes and navigation planning. This also represents an opportunity in agricultural machinery for safety purposes to detect the presence of workers on foot and avoid collisions. However, their outdoor performance at medium and long range under operational light conditions remains weakly quantified: the authors then fit a field protocol and a model to characterize the pipeline of stereo cameras, taking the Intel RealSense D455 as benchmark, across various distances from 4 m to 16 m in realistic farm settings. Tests have been conducted using a 1 square meter planar target in outdoor environments, under diverse illumination conditions and with the panel being located at 0°, 10°, 20° and 35° from the center of the camera’s field of view (FoV). Built-in presets were also adjusted during tests, to generate a total of 128 samples. The authors then fit disparity surfaces to predict and correct systematic bias as a function of distance and radial FoV position, allowing them to compute mean depth and estimate a model of systematic error that takes depth bias as a function of distance, light conditions and FoV position. The results showed that the model can predict depth errors achieving a good degree of precision in every tested scenario (RMSE: 0.46–0.64 m, MAE: 0.40–0.51 m), enabling the possibility of replication and benchmarking on other sensors and field contexts while supporting safety-critical perception systems in agriculture.