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.
2023
Forfattere
Inger HansenSammendrag
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Forfattere
Isabella BørjaSammendrag
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Forfattere
Trond HaraldsenSammendrag
Catastrophic floods have large effect on agricultural land both in short and long term. In this chapter, examples of impact of floods of different size in cold regions with glaziers have been presented. The largest floods occur as combination of heavy rainfall and melting and snow and ice in the mountainous areas. Periods of waterlogging by cold running water resulted in decreased yields, but N-fertilization after the soil no longer was water saturated could reduce the yield loss considerably. Although the floods cause severe erosion and sedimentation, results show that it is possible to find measures for reconstruction of the soils with the same productivity as undamaged soils, while the average result was about 85% of the original productivity.
Forfattere
Oskar PuschmannSammendrag
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Forfattere
Atle Wehn HegnesSammendrag
This book analyses the implementation and challenges of using Geographical Indications in Norway. Adapting the modern and global system of Geographical Indications (GIs) to food cultures is a recurring challenge. This text uses Norway as a case study to describe, understand, and explain the socio-cultural adaptation of GIs. The empirical analysis shows that administrators, producers, consultants, and others make a significant effort to adapt the scheme to Norwegian food culture and the food culture to the scheme. Through the development and use of a new conceptual framework, the book continues to show how adaptations occurred and their influence on the development of the Norwegian food culture. The author also reflects upon the status of Norwegian GIs in emerging food cultural contexts related to sustainable and technology change. In summary, this book exhibits the connection between modern global legislative arrangements and traditional local products, providing a springboard for further research on cultural adaptation work of GIs in established and future global food cultures. This book will be of interest to researchers, policymakers, and students in agri-food studies, sociology of food and agriculture, agricultural and rural development, and cultural studies.
Forfattere
Marianne Stenrød Kathinka Lang Marit Almvik Roger Holten Agnethe Christiansen Xingang Liu Qiu JingSammendrag
To ensure compliance with food safety regulations, monitoring programs and reliable analytical methods to detect relevant chemical pollutants in food and the environment are key instruments. Pesticides are an important part of pest management in agriculture to sustain and increase crop yields and control post-harvest decay, while pesticide residues in food may pose a risk to human health. Thus, the levels of pesticide residues in food must be controlled and should align with Maximum Residue Levels regulations to ensure food safety. Food safety monitoring programs and analytical methods for pesticide residues and metabolites are well developed. Future developments to ensure food safety must include the increased awareness and improved regulatory framework to meet the challenges with natural toxins, emerging contaminants, novel biopesticides, and antimicrobial resistance in food and the environment. The reality of a complex mixture of pollutants, natural toxins, and their metabolites potentially occurring in food and the environment implies the necessity to consider combined effects of chemicals in risk assessment. Here, we present challenges, monitoring efforts, and future perspectives for chemical food safety focused on the importance of current developments in high-resolution mass spectrometry (HRMS) technologies to meet the needs in food safety and environmental monitoring.
Forfattere
Divina Gracia P. RodriguezSammendrag
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Forfattere
Stefano Puliti Grant Pearse Peter Surovy Luke Wallace Markus Hollaus Maciej Wielgosz Rasmus AstrupSammendrag
The challenge of accurately segmenting individual trees from laser scanning data hinders the assessment of crucial tree parameters necessary for effective forest management, impacting many downstream applications. While dense laser scanning offers detailed 3D representations, automating the segmentation of trees and their structures from point clouds remains difficult. The lack of suitable benchmark datasets and reliance on small datasets have limited method development. The emergence of deep learning models exacerbates the need for standardized benchmarks. Addressing these gaps, the FOR-instance data represent a novel benchmarking dataset to enhance forest measurement using dense airborne laser scanning data, aiding researchers in advancing segmentation methods for forested 3D scenes. In this repository, users will find forest laser scanning point clouds from unamnned aerial vehicle (using Riegl sensors) that are manually segmented according to the individual trees (1130 trees) and semantic classes. The point clouds are subdivided into five data collections representing different forests in Norway, the Czech Republic, Austria, New Zealand, and Australia. These data are meant to be used either for developement of new methods (using the dev data) or for testing of exisitng methods (test data). The data splits are provided in the data_split_metadata.csv file. A full description of the FOR-instance data can be found at http://arxiv.org/abs/2309.01279
Forfattere
Stefano Puliti Grant Pearse Peter Surovy Luke Wallace Markus Hollaus Maciej Wielgosz Rasmus AstrupSammendrag
The FOR-instance dataset (available at this https URL) addresses the challenge of accurate individual tree segmentation from laser scanning data, crucial for understanding forest ecosystems and sustainable management. Despite the growing need for detailed tree data, automating segmentation and tracking scientific progress remains difficult. Existing methodologies often overfit small datasets and lack comparability, limiting their applicability. Amid the progress triggered by the emergence of deep learning methodologies, standardized benchmarking assumes paramount importance in these research domains. This data paper introduces a benchmarking dataset for dense airborne laser scanning data, aimed at advancing instance and semantic segmentation techniques and promoting progress in 3D forest scene segmentation. The FOR-instance dataset comprises five curated and ML-ready UAV-based laser scanning data collections from diverse global locations, representing various forest types. The laser scanning data were manually annotated into individual trees (instances) and different semantic classes (e.g. stem, woody branches, live branches, terrain, low vegetation). The dataset is divided into development and test subsets, enabling method advancement and evaluation, with specific guidelines for utilization. It supports instance and semantic segmentation, offering adaptability to deep learning frameworks and diverse segmentation strategies, while the inclusion of diameter at breast height data expands its utility to the measurement of a classic tree variable. In conclusion, the FOR-instance dataset contributes to filling a gap in the 3D forest research, enhancing the development and benchmarking of segmentation algorithms for dense airborne laser scanning data.
Forfattere
Danielle M. Duni Matthew M. McIntosh Shelemia Nyamuryekung'e Andres F. Cibils Michael C. Duniway Richard Estell Sheri Spiegal Alfredo L. Gonzalez Melakeneh G. Gedefaw Matthew Redd Robert Paulin Caitriana M. Steele Santiago A. Utsumi Andres PereaSammendrag
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