Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2022
Abstract
The assessment of forest abiotic damages such as snow breakage is important to ensure compensation to forest owners. Currently, information on the extent of snow breakage is gathered through time-consuming and potentially biased field surveys. In such situations where field surveys are still common practice, unmanned aerial vehicles (UAVs) are increasingly being used to provide a more cost-efficient and objective methods to answer forest information needs. Further, the advent of sophisticated computer vision techniques such as convolutional neural networks (CNNs) offers new ways to analyze image data more efficiently and accurately. We proposed an object detection method to automatically identify trees and classify them according to the damage by snow based on a YOLO CNN architecture. UAV imagery collected across 89 study areas and over the course of the entire year were manually annotated into a total of >55 K single trees classified as healthy, damaged, or dead. The annotated trees, along with the corresponding UAV imagery were used to train a YOLOv5 object detection model. Furthermore, we tested the effect of seasonality, and varying atmospheric and lighting conditions on the model’s performance. Based on an independent test set of data we found that the general model including all of the data (i.e. any seasons, atmospheric conditions, and time of the day) outperformed all other tested scenarios (i.e. precision = 62 %; recall = 61 %). Furthermore, we found that despite the fact that the snow damaged trees represented a minority class (i.e. 16 % of the annotated trees), they were detected with the largest precision (76 %) and recall (78 %). Finally, the general model transferred well across the variation in seasons, atmospheric and illumination conditions, making it suitable for usage for any new UAV image acquisition.
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Bacterial diseases in woody plants are best characterized for ornamental and fruit trees and much less is known for forest trees. There are many diseases of forest trees whose etiology remains to be clarified and likely more bacterial diseases of forest trees will be discovered in the next years. An overview of the main bacterial pathogens that cause diseases in forest and ornamental trees is described in this chapter and the general differences between fungal and bacterial diseases are outlined. For bacteria pathogenic to trees, six types of diseases are described: Bacterial blight diseases, represented by Erwinia amylovora, the fireblight disease; Bacterial wilt disease, represented by Ralstonia solanacearum species complex; Root and stem galls of trees, represented by Agrobacterium tumefaciens; Wetwood disease, caused by several bacterial genera like Clostridium, Bacillus, Enterobacter, Klebsiella, and Pseudomonas, Xanthomonas and Pantoea; Bacterial scorch disease represented by Xylella fastidiosa with all its subspecies; Bacterial canker represented by Pseudomonas syringae with all its pathovars. Finally, the current diagnostic methods and specific issues related to bacteria detection, together with the main results of the scientific efforts and challenges in the genetic breeding to increase bacterial resistance of trees, are outlined.
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Authors
Dalphy Ondine Camira Harteveld Marcel Wenneker Engelien Kerkhof Peter Frans de Jong Bart van der SluisAbstract
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Frederik BøeAbstract
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Silvia Vanino Tiziana Pirelli Claudia Di Bene Frederik Bøe Nádia Castanheira Claire Chenu Sophie Cornu Virginijus Feiza Dario Fornara Olivier Heller Raimonds Kasparinskis Saskia Keesstra Maria Valentina Lasorella Sevinç Madenoglu Katharina H. E. Meurer Lilian O'Sullivan Noemi Peter Chiara Piccini Grzegorz Siebielec Bozena Smreczak Martin Hvarregaard Thorsøe Roberta FarinaAbstract
Climate-smart sustainable management of agricultural soil is critical to improve soil health, enhance food and water security, contribute to climate change mitigation and adaptation, biodiversity preservation, and improve human health and wellbeing. The European Joint Programme for Soil (EJP SOIL) started in 2020 with the aim to significantly improve soil management knowledge and create a sustainable and integrated European soil research system. EJP SOIL involves more than 350 scientists across 24 Countries and has been addressing multiple aspects associated with soil management across different European agroecosystems. This study summarizes the key findings of stakeholder consultations conducted at the national level across 20 countries with the aim to identify important barriers and challenges currently affecting soil knowledge but also assess opportunities to overcome these obstacles. Our findings demonstrate that there is significant room for improvement in terms of knowledge production, dissemination and adoption. Among the most important barriers identified by consulted stakeholders are technical, political, social and economic obstacles, which strongly limit the development and full exploitation of the outcomes of soil research. The main soil challenge across consulted member states remains to improve soil organic matter and peat soil conservation while soil water storage capacity is a key challenge in Southern Europe. Findings from this study clearly suggest that going forward climate-smart sustainable soil management will benefit from (1) increases in research funding, (2) the maintenance and valorisation of long-term (field) experiments, (3) the creation of knowledge sharing networks and interlinked national and European infrastructures, and (4) the development of regionally-tailored soil management strategies. All the above-mentioned interventions can contribute to the creation of healthy, resilient and sustainable soil ecosystems across Europe.
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No abstract has been registered
Authors
Maria Papale Carmen Rizzo Stefania Giannarelli Gabriella Caruso Stefano Amalfitano Paul Eric Aspholm Giovanna Maimone Stefano Miserocchi Alessandro Ciro Rappazzo Angelina Lo Giudice Maurizio AzzaroAbstract
The Pasvik River experiences chemical, physical, and biological stressors due to the direct discharges of domestic sewage from settlements located within the catchment and runoff from smelter and mine wastes. Sediments, as a natural repository of organic matter and associated contaminants, are of global concern for the possible release of pollutants in the water column, with detrimental effects on aquatic organisms. The present study was aimed at characterizing the riverine benthic microbial community and evaluating its ecological role in relation to the contamination level. Sediments were sampled along the river during two contrasting environmental periods (i.e., beginning and ongoing phases of ice melting). Microbial enzymatic activities, cell abundance, and morphological traits were evaluated, along with the phylogenetic community composition. Amplified 16S rRNA genes from bacteria were sequenced using a next-generation approach. Sediments were also analyzed for a variety of chemical features, namely particulate material characteristics and concentration of polychlorobiphenyls, polycyclic aromatic hydrocarbons, and pesticides. Riverine and brackish sites did not affect the microbial community in terms of main phylogenetic diversity (at phylum level), morphometry, enzymatic activities, and abundance. Instead, bacterial diversity in the river sediments appeared to be influenced by the micro-niche conditions, with differences in the relative abundance of selected taxa. In particular, our results highlighted the occurrence of bacterial taxa directly involved in the C, Fe, and N cycles, as well as in the degradation of organic pollutants and toxic compounds.