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
Authors
Kim Viggo Paulsen Weiby Sophie Julie Krizsan Margrete Eknæs Angela Dagmar Schwarm Anne Cathrine Whist Ingunn Schei Håvard Steinshamn Peter Lund Karen A. Beauchemin Ingjerd DønnemAbstract
Grass-clover silage constitutes a large part of ruminant diets in Northern and Western Europe, but the impact of silage quality on methane (CH4) production is largely unknown. This study was conducted to identify the quality attributes of grass silage associated with variation in CH4 yield. We expected that silage nutrient concentrations and silage fermentation products would affect CH4 yield, and that these factors could be used to predict the methanogenic potential of the silages. Round bales (n = 78) of grass and grass-clover silage from 37 farms in Norway were sampled, incubated, and screened for in vitro CH4 yield, i.e. CH4 production expressed on the basis of incubated organic matter (CH4-OM) and digestible OM (CH4-dOM) using sheep. Concentration of indigestible neutral detergent fiber (iNDF) was quantified using the in situ technique. The data were subjected to correlation and principal component analyses. Stepwise multiple regression was used to model methanogenic potential of silages. Among all investigated silage composition variables, neutral detergent fiber (aNDFom) and water-soluble carbohydrate (WSC) concentrations obtained the greatest correlations to CH4-OM (r = −0.63 and r = 0.57, respectively, P < 0.001), while concentration of iNDF negatively correlated with CH4-OM (r = −0.48, P < 0.001). In vivo organic matter digestibility (OMD) and concentration of ammonia-N (NH3-N) in silages were also correlated to CH4-OM (r = 0.44 and r = −0.32, P < 0.001 and P < 0.01, respectively). The stepwise regression using CH4-OM as response variable included aNDFom, WSC, iNDF, silage propionic acid and pH in descending order. The stepwise regression using CH4-dOM as response variable included WSC, aNDFom and iNDF in descending order. Among in vitro rumen short chain fatty acids (SCFA), molar proportion of butyrate was the most prominent in increasing CH4-OM and CH4-dOM (r = 0.23 and r = 0.36, P < 0.05 and P < 0.01, respectively), while molar proportion of propionate was the most prominent SCFA in reducing CH4-OM and CH4-dOM (r = −0.23 and r = −0.26, respectively, P < 0.05). Regression models that account for silage quality attributes can be used to predict CH4 yield from silages with a coefficient of determination (R2) between 0.33 (CH4-dOM) and 0.65 (CH4-OM). In conclusion, concentration of WSC increased in vitro CH4-OM and CH4-dOM, while concentration of aNDFom and iNDF decreased CH4-OM and CH4-dOM in grass silages.
Authors
P.H. Templer J.L. Harrison Francesca Pilotto A. Flores-Díaz P. Haase W.H. McDowell R. Sharif H. Shibata D. Blankman A. Avila U. Baatar H.R. Bogena I. Bourgeois J. Campbell T. Dirnböck W.K. Dodds Marit Hauken I. Kokorite K. Lajtha I.-L. Lai H. Laudon T.C. Lin S.R.M. Lins H. Meesenburg P. Pinho A. Robison M. Rogora B. Scheler P. Schleppi R. Sommaruga T. Staszewski M. TakaAbstract
Previous studies have evaluated how changes in atmospheric nitrogen (N) inputs and climate affect stream N concentrations and fluxes, but none have synthesized data from sites around the globe. We identified variables controlling stream inorganic N concentrations and fluxes, and how they have changed, by synthesizing 20 time series ranging from 5 to 51 years of data collected from forest and grassland dominated watersheds across Europe, North America, and East Asia and across four climate types (tropical, temperate, Mediterranean, and boreal) using the International Long-Term Ecological Research Network. We hypothesized that sites with greater atmospheric N deposition have greater stream N export rates, but that climate has taken a stronger role as atmospheric deposition declines in many regions of the globe. We found declining trends in bulk ammonium and nitrate deposition, especially in the longest time-series, with ammonium contributing relatively more to atmospheric N deposition over time. Among sites, there were statistically significant positive relationships between (1) annual rates of precipitation and stream ammonium and nitrate fluxes and (2) annual rates of atmospheric N inputs and stream nitrate concentrations and fluxes. There were no significant relationships between air temperature and stream N export. Our long-term data shows that although N deposition is declining over time, atmospheric N inputs and precipitation remain important predictors for inorganic N exported from forested and grassland watersheds. Overall, we also demonstrate that long-term monitoring provides understanding of ecosystems and biogeochemical cycling that would not be possible with short-term studies alone.
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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Authors
Frederik BøeAbstract
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Authors
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.