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.
2025
Abstract
No abstract has been registered
Abstract
Urban agriculture has the potential to contribute to more sustainable cities, but its impacts are complex and varied. By implementing robust monitoring systems, cities can better understand the true effects of urban farming initiatives. This evidence can then inform smarter policies and more effective urban planning strategies.
Abstract
Urban agriculture is often considered a tool to increase the economic, social and environmental sustainability of cities and city food systems. However, sustainability is difficult to measure, resulting in debate about how sustainable urban agriculture truly is. There is therefore a lack of incentive to promote urban agriculture or protect existing initiatives that are threatened by development pressure on urban land. Monitoring the sustainability impact of urban agriculture could provide evidence and enable politicians and decision makers to make informed decisions about whether and where to prioritise different forms of urban agriculture above competing interests. We used case examples from five European cities to identify the challenges involved in monitoring urban agriculture, from selecting indicators and gathering data, to using the results. We found large differences in approach in terms of what topics to monitor and who was responsible, who gathered the data and when, what data was recorded and how they were stored, and how findings were disseminated or published. Based on these experiences, we recommend stronger involvement of existing interest groups and educational institutions in monitoring urban agriculture, and promotion of convenient tools for data collection by citizen science and for long-term data storage.
Abstract
This study investigates food neophobia as a potential barrier to the use of unconventional fertilizers, such as fish sludge and human waste, in food production. Using data from Norway, the study estimates consumers’ willingness to pay (WTP) for lettuce grown with these fertilizers. Results from the random effect interval regression model show that, on average, consumers are willing to pay 8 % more for conventional lettuce compared to lettuce grown with fish sludge and 13 % more for lettuce grown with human waste. However, between 40 % and 50 % of respondents accepted lettuce produced with unconventional fertilizers and were not willing to pay more for conventional lettuce compared to these alternatives. Key factors influencing WTP include gender, the presence of children in the household, and food neophobia. These findings suggest that food neophobia and socio-demographic factors can significantly impact consumer acceptance of sustainable agricultural practices. Targeted communication strategies focusing on food safety, environmental sustainability, and the benefits of nutrient recycling are needed to foster broader public acceptance and support for recycled waste in agriculture.
Abstract
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.
2024
Authors
Nina SvartedalAbstract
No abstract has been registered
Authors
Nina SvartedalAbstract
No abstract has been registered
Authors
Nina SvartedalAbstract
No abstract has been registered
Authors
Nina SvartedalAbstract
No abstract has been registered
Authors
Nina SvartedalAbstract
No abstract has been registered