Search Results (7,115)

Oak is one of the most economically important hardwood tree species in Europe, and its prevalence will increase due to progressing global climate change, according to predictive models. With the increasing demand for timber and with the need for a balance between carbon emissions and sequestration, it is essential to address the afforestation of agricultural land. Therefore, this research aimed to investigate the physico-mechanical properties and anatomical structure of pendulate oak (Quercus robur L.) wood—specifically focusing on the trunk’s cross-section—in post-agricultural areas compared with the forest land in the western part of Poland. Wood density, bending strength, modulus of elasticity, and other parameters were analyzed from 1626 wood samples. The analysis of physico-mechanical properties reveals that, historically, agricultural land use has an almost negligible impact on wood quality. Despite significant differences in small vessel diameter and fiber length favoring trees from post-agricultural land, the physico-mechanical properties remain consistent. Large vessel measurements show comparable diameter and length in both land types. These findings suggest that post-agricultural land can serve as an effective alternative for high-quality pendulate oak wood production for industrial purposes. However, wood from post-agricultural land may exhibit a decrease in modulus of rupture by over 30% and potentially lower density above the trunk’s halfway point. This observation hints at the fact that oak trees in post-agricultural areas could be cultivated in shorter rotation periods compared to forest land. Full article

Phenology and photosynthetic capacity both regulate carbon uptake by vegetation. Previous research investigating the impact of phenology on vegetation productivity has focused predominantly on the start and end of growing seasons (SOS and EOS), leaving the influence of peak phenology metrics—particularly in typical climatic transition zones—relatively unexplored. Using a 24-year (2000–2023) enhanced vegetation index (EVI) dataset from the Moderate Resolution Imaging Spectroradiometer (MODIS), we extracted and examined the spatiotemporal variation for peak of season (POS) and peak growth (defined as EVI_max) of forest vegetation in the Funiu Mountain region, China. In addition to quantifying the factors influencing the peak phenology metrics, the relationship between vegetation productivity and peak phenological metrics (POS and EVI_max) was investigated. Our findings reveal that POS and EVI_max showed advancement and increase, respectively, negatively and positively correlated with vegetation productivity. This suggested that variations in EVI_max and peak phenology both increase vegetation productivity. Our analysis also showed that EVI_max was heavily impacted by precipitation, whereas SOS had the greatest effect on POS variation. Our findings highlighted the significance of considering climate variables as well as biological rhythms when examining the global carbon cycle and phenological shifts in response to climate change. Full article

Yield prediction is an important agriculture management for crop policy making. In recent years, unmanned aerial vehicles (UAVs) and spectral sensor technology have been widely used in crop production. This study aims to evaluate the ability of UAVs equipped with spectral sensors to predict oilseed rape yield. In an experiment, RGB and hyperspectral images were captured using a UAV at the seedling (S1), budding (S2), flowering (S3), and pod (S4) stages in oilseed rape plants. Canopy reflectance and spectral indices of oilseed rape were extracted and calculated from the hyperspectral images. After correlation analysis and principal component analysis (PCA), input spectral indices were screened to build yield prediction models using random forest regression (RF), multiple linear regression (MLR), and support vector machine regression (SVM). The results showed that UAVs equipped with spectral sensors have great potential in predicting crop yield at a large scale. Machine learning approaches such as RF can improve the accuracy of yield models in comparison with traditional methods (e.g., MLR). The RF-based training model had the highest determination coefficient (R²) (0.925) and lowest relative root mean square error (RRMSE) (5.91%). In testing, the MLR-based model had the highest R² (0.732) and lowest RRMSE (11.26%). Moreover, we found that S2 was the best stage for predicting oilseed rape yield compared with the other growth stages. This study demonstrates a relatively accurate prediction for crop yield and provides valuable insight for field crop management. Full article

Global warming and climate change cause large temperature oscillations and uneven annual rainfall patterns. The rainy cycles characterized by frequent high-intensity rainfall in the area of the Stubo–Rovni water reservoir, which in 2014 peaked at 129 mm of water in 24 h (the City of Valjevo, the Republic of Serbia), caused major floods in the wider area. Such extremes negatively affect erosion processes, sediment production, and the occurrence of flash floods. The erosion coefficient before the construction of the water reservoir was Z_m = 0.40, while the specific sediment production was about 916.49 m³∙km⁻²∙year⁻¹. A hydrological study at the profile near the confluence of the Jadar and Obnica rivers, i.e., the beginning of the Kolubara river, the right tributary of the Sava (in the Danube river basin), indicates that the natural riverbed can accommodate flows with a 20% to 50% probability of occurrence (about 94 m³/s), while centennial flows of about 218 m³/s exceed the capacities of the natural riverbed of the Jadar river, causing flooding of the terrain and increasing risks to the safety of the population and property. The paper presents the impacts of the man-made Stubo–Rovni water reservoir on the catchment area and land use as the primary condition for preventing erosion processes (specific sediment production has decreased by about 20%, the forest cover increased by about 25%, and barren land decreased by 90%). Moreover, planned and controlled management of the Stubo–Rovni reservoir has significantly influenced the downstream flow, reducing the risks of flash floods. Full article

Electrolytic hydrogen production from water is a promising approach for obtaining clean energy. The development of efficient oxygen evolution reaction (OER) electrocatalysts is crucial for the generation of hydrogen through water electrolysis. Transition metal oxides, such as Fe, Co, and Mn, have shown potential as efficient OER electrocatalysts for water splitting. However, accurately predicting their electrocatalytic performance in complex compositional spaces remains a challenge, impeding the precise design of compositions and processes for optimal performance. Herein, a machine learning-based method is proposed for predicting the OER activity of (FeCoMn)O_x catalysts across a wide range of compositions. Physical features that are highly relevant to the OER overpotential (OP) are identified and analyzed. The random forest algorithm is successfully used to establish the relationship between composition and overpotential. The model demonstrates good accuracy in predicting the outcomes of new experiments, with a mean relative error (MRE) of 9.3%. The features based on covalent radius (RC) and the number of electrons in the outermost d orbitals (DEs) are the primary factors. Their variances (δ_RC and δ_DE) exhibit a linearly decreasing relationship with the overpotential (OP), providing direct guidance for designing OP-oriented components. This work presents an effective and innovative approach for predicting and analyzing the physical factors of transition metal oxide electrocatalysts, which can enhance the design of highly catalytic materials for electrocatalysis. Full article

Black ginseng is the processed product of ginseng, and it has been found that the content and types of rare ginsenosides increased after processing. However, there is limited research on the ginsenoside differences between cultivated and forest ginseng before and after processing and among various plant parts. This study investigated the effects of processing on ginsenosides in different parts of cultivated and forest ginseng. After processing, the contents of Re, Rg1, S-Rg3, Rg5, R-Rh1, Rk1, Rk3, and F4 were significantly increased or decreased, the growth age of forest ginseng was not proportional to the content of ginsenosides, and the differences in ginsenoside content in ginseng from different cultivation methods were relatively small. Chemometric analysis identified processing biomarkers showing varying percentage changes in different parts. Network pharmacology predicted the EGFR/PI3K/Akt/mTOR pathway as a potential key pathway for the anti-cancer effect of black ginseng. Full article

The global proliferation of Pinus radiata, known for its rapid growth and wood density, has led to an environmental challenge—significant waste production, especially bark, without a clear valorization route. This waste poses ecological concerns, and despite the crucial role of forest resources in structural applications, their limited fire resistance requires the use of coatings. However, traditional coatings lack an eco-friendly footprint. Addressing this challenge, this study aims to develop an intumescent coating with tannins extracted from waste bark, offering a sustainable alternative. This not only repurposes waste on a global scale but also aligns with the imperative for environmentally friendly materials, contributing to sustainable practices in the construction and wood treatment industry. This study achieved an eco-friendly FRR15 (fire resistance ratio 15) fire resistance classification with a 15% equivalence of low-molecular-weight tannins, presenting a sustainable alternative to commercial products. Characterization showed low-molecular-weight tannins comparable to conventional charring agents, with high hydroxyl content and oil absorption, while high-molecular-weight tannins exhibited lower viability. A reference coating achieved FRR30 fire resistance, aligning with commercial strength. The mechanical properties of tannin-based coatings matched commercial standards, with increased abrasion resistance and adhesion and decreased flexibility. Intumescent coatings with higher tannin content significantly reduced wood substrate charring and mass loss in flame response assessments. Full article

This study introduces a new software, cLASpy_T, that helps design models for the automatic 3D point cloud classification of coastal environments. This software is based on machine learning algorithms from the scikit-learn library and can classify point clouds derived from LiDAR or photogrammetry. Input data can be imported via CSV or LAS files, providing a 3D point cloud, enhanced with geometric features or spectral information, such as colors from orthophotos or hyperspectral data. cLASpy_T lets the user run three supervised machine learning algorithms from the scikit-learn API to build automatic classification models: RandomForestClassifier, GradientBoostingClassifier and MLPClassifier. This work presents the general method for classification model design using cLASpy_T and the software’s complete workflow with an example of photogrammetry point cloud classification. Four photogrammetric models of a coastal dike were acquired on four different dates, in 2021. The aim is to classify each point according to whether it belongs to the ‘sand’ class of the beach, the ‘rock’ class of the riprap, or the ‘block’ class of the concrete blocks. This case study highlights the importance of adjusting algorithm parameters, selecting features, and the large number of tests necessary to design a classification model that can be generalized and used in production. Full article

Quercus ilex L., an evergreen oak species typical of the western and central Mediterranean basin, is facing decline and dieback episodes due to the increase in the severity and frequency of heat waves and drought events. Studying xylogenesis (the wood formation process) is crucial for understanding how trees respond with their secondary growth to environmental conditions and stress events. This study aimed to characterize the wood formation dynamics of Quercus ilex and their relationship with the meteorological conditions in an area experiencing prolonged drought periods. Cambial activity and xylem cell production were monitored during the 2019 and 2020 growing seasons in a Q. ilex forest located at the Vesuvius National Park (southern Italy). The results highlighted the significant roles of temperature and solar radiation in stimulating xylogenesis. Indeed, the correlation tests revealed that temperature and solar radiation positively influenced growth and cell development, while precipitation had an inhibitory effect on secondary wall formation. The earlier cell maturation in 2020 compared to 2019 underscored the impact of global warming trends. Overall, the trees studied demonstrated good health, growth and adaptability to local environmental fluctuations. This research provides novel insights into the intra-annual growth dynamics of this key Mediterranean species and its adaptation strategies to climatic variability, which will be crucial for forest management in the context of climate change. Full article

Amid unprecedented challenges to protect arable land, China’s permanent capital farmland (PCF) has played a crucial role in grain production. However, a clear legal and physical boundary between PCF and arable land seems to be unable to stop the spread of non-grain production. To address it, an analysis framework for PCF non-grain production was developed to examine the state and drivers of village-scale PCF non-grain production based on the logical relationship between PCF and arable land in the rapid urbanization of Liyang. The results suggested that PCF comprised approximately 70% arable land and 30% adjustable land. Meanwhile, forest land and aquaculture ponds occupied over 25% of PCF, while nearly 20% of PCF is unsuitable for the resumption of crop cultivation. The transition state (scenario SR) offered a realistic representation of PCF non-grain production, with an average non-grain production of 48.88%. This is 14.00% lower than the current state (scenario SD) and 9.65% higher than the future state (scenario ST). Furthermore, PCF area and agricultural income per capita significantly encouraged PCF non-grain production, with explanatory powers of 51.60% and 42.40%, respectively. In contrast, urbanization rate (with an explanatory power of 35.30%) significantly discouraged it. Therefore, this paper proposed PCF redefinition, flexible PCF, and diversified economic incentives to mitigate PCF non-grain production at the village scale. Full article

Plant diseases are mostly multicausal with several factors influencing the health status of affected hosts. Common ash (Fraxinus excelsior), a significant tree species of European forests, is currently mostly endangered by ash dieback, caused by the invasive fungus Hymenoscyphus fraxineus. However, contributing factors, including pathogenic viruses, are poorly understood. Here, we report the results of a virus screening conducted on selected special stands of F. excelsior. Over three consecutive years, ash trees from different origins were tested, including leaf material from mature seed trees, young trees and ash seedlings from the natural regeneration. Using RT-PCR, we screened for five viruses, including the generalist species ArMV (Nepovirus arabis) and CLRV (Nepovirus avii), as well as newly discovered viruses in ash, including the emaravirus ASaV (Emaravirus fraxini), the idaeovirus PrLBaV (Idaeovirus ligustri), and cytorhabdoviruses. The results revealed a high virus diversity in common ash. An association of ASaV detection with specific leaf symptoms, including shoestring, chlorotic ringspots, and vein yellowing, was documented. An analyses of relevant gene products of cytorhabdoviruses obtained from ashes of different sites revealed sequence diversities and two distinct phylogenetic groups present in ash populations. Signatures of novel viruses from different families have been identified by high-throughput sequencing. Together, our results provide insights into the virus diversity and distribution of viruses in ash and expand our knowledge about the virome of this endangered tree species. Full article

Solar-induced chlorophyll fluorescence (SIF) has been widely utilized to track the dynamics of gross primary productivity (GPP). It has been shown that the photochemical reflectance index (PRI), which may be utilized as an indicator of non-photochemical quenching (NPQ), improves SIF-based GPP estimation. However, the influence of weather conditions on GPP estimation using SIF and PRI has not been well explored. In this study, using an open-access dataset, we examined the impact of the clearness index (CI), which is associated with the proportional intensity of solar incident radiation and can represent weather conditions, on soybean GPP estimation using SIF and PRI. The midday PRI (xanthophyll de-epoxidation state) minus the early morning PRI (xanthophyll epoxidation state) yielded the corrected PRI (ΔPRI), which described the amplitude of xanthophyll pigment interconversion during the day. The observed canopy SIF at 760 nm (${\mathrm{S}\mathrm{I}\mathrm{F}}_{\mathrm{T}\mathrm{O}\mathrm{C}\_760}$) was downscaled to the broadband photosystem-level SIF for photosystem II (${\mathrm{S}\mathrm{I}\mathrm{F}}_{\mathrm{T}\mathrm{O}\mathrm{T}\_\mathrm{F}\mathrm{U}\mathrm{L}\mathrm{L}\_\mathrm{P}\mathrm{S}\mathrm{I}\mathrm{I}}$). Our results show that GPP can be accurately estimated using a multi-linear model with ${\mathrm{S}\mathrm{I}\mathrm{F}}_{\mathrm{T}\mathrm{O}\mathrm{T}\_\mathrm{F}\mathrm{U}\mathrm{L}\mathrm{L}\_\mathrm{P}\mathrm{S}\mathrm{I}\mathrm{I}}$ and ΔPRI. The ratio of GPP measured using the eddy covariance (EC) method (${\mathrm{G}\mathrm{P}\mathrm{P}}_{\mathrm{E}\mathrm{C}}$) to GPP estimated using ${\mathrm{S}\mathrm{I}\mathrm{F}}_{\mathrm{T}\mathrm{O}\mathrm{T}\_\mathrm{F}\mathrm{U}\mathrm{L}\mathrm{L}\_\mathrm{P}\mathrm{S}\mathrm{I}\mathrm{I}}$ and ΔPRI exhibited a non-linear correlation with the CI along both the half-hourly (R² = 0.21) and daily scales (R² = 0.25). The GPP estimates using ${\mathrm{S}\mathrm{I}\mathrm{F}}_{\mathrm{T}\mathrm{O}\mathrm{T}\_\mathrm{F}\mathrm{U}\mathrm{L}\mathrm{L}\_\mathrm{P}\mathrm{S}\mathrm{I}\mathrm{I}}$ and ΔPRI were significantly improved by the addition of the CI (for the half-hourly data, R² improved from 0.64 to 0.71 and the RMSE decreased from 8.28 to 7.42 $\mathsf{\mu }$mol•m⁻²•s⁻¹; for the daily data, R² improved from 0.71 to 0.81 and the RMSE decreased from 6.69 to 5.34 $\mathsf{\mu }$mol•m⁻²•s⁻¹). This was confirmed by the validation results. In addition, the GPP estimated using the Random Forest method was also largely improved by considering the influences of the CI. Therefore, our findings demonstrate that GPP can be well estimated using ${\mathrm{S}\mathrm{I}\mathrm{F}}_{\mathrm{T}\mathrm{O}\mathrm{T}\_\mathrm{F}\mathrm{U}\mathrm{L}\mathrm{L}\_\mathrm{P}\mathrm{S}\mathrm{I}\mathrm{I}}$ and ΔPRI, and it can be significantly enhanced by accounting for the CI. These results will be beneficial to vegetation GPP estimation using different remote sensing platforms, especially under various weather conditions. Full article

Trees’ structural defects are responsible for the reduction in forest product quality and the accident of tree collapse under extreme environmental conditions. Although the manual view inspection for assessing tree health condition is reliable, it is inefficient in discriminating, locating, and quantifying the defects with various features (i.e., crack and hole). There is a general need for investigation of efficient ways to assess these defects to enhance the sustainability of trees. In this study, the deep learning algorithms of lightweight You Only Look Once (YOLO) and encoder-decoder network named DeepLabv3+ are combined in unmanned aerial vehicle (UAV) observations to evaluate trees’ structural defects. Experimentally, we found that the state-of-the-art detector YOLOv7-tiny offers real-time (i.e., 50–60 fps) and long-range sensing (i.e., 5 m) of tree defects but has limited capacity to acquire the patterns of defects at the millimeter scale. To address this limitation, we further utilized DeepLabv3+ cascaded with different network architectures of ResNet18, ResNet50, Xception, and MobileNetv2 to obtain the actual morphology of defects through close-range and pixel-wise image semantic segmentation. Moreover, the proposed hybrid scheme YOLOv7-tiny_DeepLabv3+_UAV assesses tree’s defect size with an averaged accuracy of 92.62% (±6%). Full article

In many countries, such as Norway, there are vast quantities of sea urchins that have formed barrens over large areas of the coastline. Research has shown that removal of sufficient quantities of sea urchins from these barrens can lead to them reverting to a macroalgae forest. Identifying the chemical composition of sea urchins for various uses, such as agricultural fertiliser, would incentivise this sea urchin removal. This study investigates the composition of sea urchins and whether the composition varies when sea urchin collection sites vary both geographically and temporally. Sea urchins were collected from three sites within 10 km of each other in northern Norway at three times through the year. The sea urchins were dried, crushed, powdered, and analysed for nutrient content. An elemental analysis from the sea urchin samples showed high calcium and relatively high magnesium levels; smaller relative quantities of nitrogen, phosphorous, and potassium were also found. Micronutrients such as iron (Fe), zinc (Zn), manganese (Mn), and copper (Cu) were found. More importantly, both primary, macro-, and micronutrients showed high variability when collected from different sites and at different times of the year. This will be a critical consideration when investigating the use of this product as a plant fertiliser or for any other use. Full article

The production of oak seedlings in intensive crops involves the modification of natural conditions, namely the degree of humidity, through artificial irrigation, which favors the appearance of the pathogen Erysiphe alphitoides, responsible for the Oak Powdery Mildew (OPM) disease. Thus, it is necessary to identify new substances and technologies to control OPM. In this sense, new products approved by the European Union (EU) and Forest Stewardship Council (FSC) were identified, both synthetic and, a great novelty, biological (based on chito-oligosaccharides-oligogalacturonans: COS-OGA). In order to quantify the results, a correlation was made with climatic factors, by sampling data related to temperature and relative humidity with Data Logger devices. The obtained results suggest that OPM has a high virulence in the temperature range of 20 to 30 °C; at a relative humidity above 75%. The data obtained from the field experiments show that the synthetic products controlled OPM with an effectiveness between 70% and 95%, and the biological product behaved almost similarly, between 60% and 90%, which creates high opportunities for environmentally friendly control of forest pathogens. Full article