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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

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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.

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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.

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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.