Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2025
Sammendrag
Understanding long-term effects of clear-cutting on current soil carbon (C) fluxes in boreal forests is important in the perspective of global C cycling and future forest management decisions. We studied twelve pairs of forest stands in South-Eastern Norway, each comprised of one previously clear-cut stand and one near-natural stand with similar macroclimate, topography and soil properties. We measured aboveground tree litterfall continuously during two consecutive years and soil respiration fluxes monthly during the snow-free period of one year. Ground vegetation litterfall was estimated from destructive biomass sampling. The previously clear-cut stands had on average 12 % higher annual soil respiration rates, 20 % greater tree litterfall, and tended to have greater total aboveground litterfall (12 %), while the near-natural stands had greater litterfall from ground vegetation (45 %). Litterfall from ground vegetation was strongly linked with below-canopy light transmission, but the contribution of this flux to the total aboveground litterfall was minor. Soil respiration rates were related to microclimate, nitrogen concentration in aboveground tree litter and tree basal area. Though, only basal area could be linked to management type differences in soil respiration, that likely has additional unidentified drivers. We found similar temperature sensitivities of soil respiration in the two management types. We emphasise that age of the dominating trees is an integrated part of the differences between these two types of forest stands. Jointly, our results suggest limited differences in the current net soil C balance of near-natural and previously clear-cut stands.
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Background Many studies show positive results of collegial trust in the workplace, e.g. performance, innovation and collaboration. However, no systematic review on collegial trust in hospital settings exists. This study aimed to provide the missing overview of factors that positively and negatively influence this trust relationship between healthcare providers. Methods Ten information sources (Web of Science, Embase, MEDLINE, APA PsycInfo, CINAHL, Scopus, EconLit, Taylor & Francis Online, SAGE Journals and Springer Link) were searched from database inception up until October 21st, 2022. Empirical studies included were written in English, undertaken in a hospital or similar setting, and addressed collegial trust relationships between healthcare professionals, without date restrictions. Studies were excluded if they only explored trust between healthcare professionals on different hierarchical levels. Theoretical studies, systematic reviews, conceptually unclear papers and anecdotal case studies were also excluded. Records were independently screened for eligibility by at least two researchers. A narrative synthesis technique was adopted to explore and discuss the influencing factors of trust between colleagues identified across both quantitative and qualitative studies. This method was chosen given the inclusion of studies with different research designs and the unsuitability of the data for a meta-analysis or meta-ethnography. Risk of bias was assessed independently by at least two researchers using four critical appraisal tools. Results Eight thousand two hundred sixty-eight studies were screened and 11 studies were included. Seven were qualitative and four were quantitative. Themes identified were professional competence, elements of communication, such as tacit knowledge sharing, and ethical conduct, such as honesty, confidentiality and accountability. Moreover, trust among colleagues was seen to thrive in work environments characterised by psychological safety. The results of the quality assessment show that most studies were of an acceptable quality, with some associated risk of bias. One of the limitations was represented by the lack of a definition for trust in some studies, and some inconsistency for those studies that did define trust. Conclusions Professionalism, communication and ethics were seen as the most important factors enhancing trust. However, these concepts were defined differently in the studies. Trial registration PROSPERO; CRD42023433021.
Forfattere
Margit Oami Kollstrøm Ulrike Böcker Anne Kjersti Uhlen Annbjørg Kristoffersen Jon Arne Dieseth Erik Tengstrand Shiori KogaSammendrag
The gluten-viscoelastic properties are essential for breadmaking quality and are affected by both genotypes and environments, such as weather conditions. However, it is still not clear how weather conditions cause variation in gluten quality and at which stage of the grain filling they are critical. The aim of the study was to explore the relationship between weather parameters during grain filling and the viscoelastic properties of gluten. The gluten of spring wheat varieties grown over 17 seasons, resulting in a total of 70 different environments, was analyzed with the Kieffer extensibility rig. The variation in viscoelastic properties of gluten was mainly explained by environment, followed by genotype, while the genotype*environment interaction was small. The results also indicated that the periods around heading and physical maturity were the most critical when weather conditions affected the gluten quality. Our results also revealed that factors other than weather conditions are responsible for the variation in gluten quality.
Forfattere
Alexander N. Hristov André Bannink Marco Battelli Alejandro Belanche M. Cecilia Cajarville Sanz Gonzalo Fernandez-Turren Florencia Garcia Arjan Jonker David A. Kenny Vibeke Lind Sarah J. Meale David Meo Zilio Camila Muñoz David Pacheco Nico Peiren Mohammad Ramin Luca Rapetti Angela Schwarm Sokratis Stergiadis Katerina Theodoridou Emilio M. Ungerfeld Sanne van Gastelen David R. Yáñez-Ruiz Sinead M. Waters Peter LundSammendrag
There is a need for rigorous and scientifically-based testing standards for existing and new enteric methane mitigation technologies, including antimethanogenic feed additives (AMFA). The current review provides guidelines for conducting and analyzing data from experiments with ruminants intended to test the antimethanogenic and production effects of feed additives. Recommendations include study design and statistical analysis of the data, dietary effects, associative effect of AMFA with other mitigation strategies, appropriate methods for measuring methane emissions, production and physiological responses to AMFA, and their effects on animal health and product quality. Animal experiments should be planned based on clear hypotheses, and experimental designs must be chosen to best answer the scientific questions asked, with pre-experimental power analysis and robust post-experimental statistical analyses being important requisites. Long-term studies for evaluating AMFA are currently lacking and are highly needed. Experimental conditions should be representative of the production system of interest, so results and conclusions are applicable and practical. Methane-mitigating effects of AMFA may be combined with other mitigation strategies to explore additivity and synergism, as well as trade-offs, including relevant manure emissions, and these need to be studied in appropriately designed experiments. Methane emissions can be successfully measured, and efficacy of AMFA determined, using respiration chambers, the sulfur hexafluoride method, and the GreenFeed system. Other techniques, such as hood and face masks, can also be used in short-term studies, ensuring they do not significantly affect feed intake, feeding behavior, and animal production. For the success of an AMFA, it is critically important that representative animal production data are collected, analyzed, and reported. In addition, evaluating the effects of AMFA on nutrient digestibility, animal physiology, animal health and reproduction, product quality, and how AMFA interact with nutrient composition of the diet is necessary and should be conducted at various stages of the evaluation process. The authors emphasize that enteric methane mitigation claims should not be made until the efficacy of AMFA is confirmed in animal studies designed and conducted considering the guidelines provided herein.
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Urban green structures (UGS) play important roles in enhancing urban ecosystems by providing benefits such as mitigating the urban heat island effect, improving air quality, supporting biodiversity, and aiding in stormwater management. Accurately mapping UGS is important for sustainable urban planning and management. Traditional methods of mapping such as manual mapping, aerial photography interpretation and pixel-based classification have limitations in terms of coverage, accuracy, and efficiency. Object-based image analysis (OBIA) has gained prominence due to its ability to incorporate both spectral and spatial information making it particularly effective for classification of high-resolution satellite data. This paper reviews the application of OBIA on satellite images for UGS mapping, focusing on various data sources, popular segmentation methods, and classification techniques, highlighting their respective advantages and limitations. Key segmentation methodologies discussed include multi-resolution segmentation and watershed segmentation. For classification, the review covers machine learning techniques such as random forests, support vector machines, and convolutional neural networks, among others. Several case studies highlight the successful implementation of OBIA in diverse urban environments by demonstrating improvements in classification accuracy and detail. The review also addresses the challenges associated with OBIA, such as dealing with heterogenous urban landscapes, data sources and with OBIA methods itself. Future directions for UGS mapping include the integration of deep learning algorithms, advancements in satellite data technologies, and the development of standardized classification frameworks. By providing a detailed analysis of the current state-of-the-art in object-based UGS mapping, this review aims to guide future research and practical applications in UGS management.
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Anders Lorentzen Kolstad Joachim Paul Töpper Marianne Evju Line Johansen Magni Olsen Kyrkjeeide Anders Lyngstad Jan Ketil Rød Lise Tingstad Liv Guri VelleSammendrag
Det er ikke registrert sammendrag
Forfattere
Mostafa Hoseini Helle Ross Gobakken Stephan Hoffmann Csongor Horvath Johannes Rahlf Jan Bjerketvedt Stefano Puliti Rasmus AstrupSammendrag
RoadSens is a platform designed to expedite the digitalization process of forest roads, a cornerstone of efficient forest operations and management. We incorporate stereo-vision spatial mapping and deep-learning image segmentation to extract, measure, and analyze various geometric features of the roads. The features are precisely georeferenced by fusing post-processing results of an integrated global navigation satellite system (GNSS) module and odometric localization data obtained from the stereo camera. The first version of RoadSens, RSv1, provides measurements of longitudinal slope, horizontal/vertical radius of curvature and various cross-sectional parameters, e.g., visible road width, centerline/midpoint positions, left and right sidefall slopes, and the depth and distance of visible ditches from the road’s edges. The potential of RSv1 is demonstrated and validated through its application to two road segments in southern Norway. The results highlight a promising performance. The trained image segmentation model detects the road surface with the precision and recall values of 96.8 and 81.9 , respectively. The measurements of visible road width indicate sub-decimeter level inter-consistency and 0.38 m median accuracy. The cross-section profiles over the road surface show 0.87 correlation and 9.8 cm root mean squared error (RMSE) against ground truth. The RSv1’s georeferenced road midpoints exhibit an overall accuracy of 21.6 cm in horizontal direction. The GNSS height measurements, which are used to derive longitudinal slope and vertical curvature exhibit an average error of 5.7 cm compared to ground truth. The study also identifies and discusses the limitations and issues of RSv1, which provide useful insights into the challenges in future versions.