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
Authors
Peter ZubkovAbstract
Nordic boreal forests deliver critical ecosystem services but are increasingly vulnerable to abiotic disturbances, particularly wind and snow damage, potentially intensified by climate change. Climate-resilient forest management requires reliable decision-support tools for proactive risk assessment and post-event damage mapping. This thesis contributes to advancing adaptive abiotic forest disturbance management by integrating high-resolution satellite imagery, numerical weather prediction, tree mechanics, and machine learning techniques. It is composed of three papers. The first paper demonstrated that very high-resolution stereo satellite imagery and photogrammetric digital surface model reconstruction effectively map windthrow, particularly in moderate-to-high-density conifer stands, even under challenging Nordic winter conditions. The second paper proposed a novel mechanistic modeling framework predicting snow-induced stem breakage at the single-tree level, leveraging numerical weather prediction-based snow accumulation data and mechanistic critical snow load computations. The model provides physically interpretable risk assessments using basic tree metrics and predicted snow loads and can be readily integrated into forest management scenario planning. The third paper applied interpretable machine learning to numerical weather prediction data to identify drivers of forest wind damage during catastrophic windstorms driven by atmospheric mountain waves in a complex terrain. The findings underline that it was atmospheric stratification, turbulence, and vertical airflow that primarily controlled forest damage during the investigated event. Forest structure played minimal role, emphasizing the importance of a landscape-scale risk management approach focused on topographic susceptibility to severe mountain wave occurrences. This work makes a small, yet important, contribution to an integrated decision-support framework strengthening forest damage risk prediction and post-event assessment capabilities under climate uncertainty. Improvement priorities include observational validation of the canopy snow accumulation model, generalizing the interpretation of mountain wave-induced damage to other landscapes, and exploring multi-sensor fusion for windthrow detection. Finally, future efforts should be aimed at scaling the framework to a national scope and integrating advanced neural network-driven models for holistic risk management in an uncertain future.
Abstract
Agricultural land abandonment is increasingly affecting rural and low-intensity farming regions across Europe, raising concerns about its impact on biodiversity. While some species may benefit from reduced human disturbance, many species in semi-natural ecosystem types depend on traditional agricultural management to maintain their ecological integrity. This study examines whether abandoned agricultural land in Norway contains semi-natural ecosystems that may hold important remnant populations of red-listed plant species and where continued cessation of farming may further threaten these biodiverse ecosystems. Using spatial data on abandoned farmland, semi-natural ecosystem types and species observations, we identify areas of conservation interest and assess the extent to which these areas support endangered species. In addition, we conducted a time-series analysis of vegetation change using NDVI data (2017–2024) to evaluate whether abandonment led to detectable ecological succession. We also analyzed the spatial distribution of abandonment and its correlation with proximity to active farms to understand regional patterns of abandonment. Our results show that only a small percentage (3.7 %) of the abandoned agricultural land considered in this study overlaps with known semi-natural ecosystem types, yet these areas support a significant number of red-listed plant species. The NDVI analysis revealed generally weak but positive greening trends, suggesting early successional changes that are not yet statistically significant across most habitat types. Our method thus suggests a potential approach to allocate limited management resources to key locations. At present, the amount of semi-natural ecosystems is probably underestimated, however, because of limited and time-consuming mapping activity. These findings emphasize the need for more extensive mapping and targeted conservation efforts and highlight the risks posed by abandonment in biodiversity rich semi-natural ecosystem types.
Authors
Habtamu AlemAbstract
Dairy farming significantly contributes to global greenhouse gas (GHG) emissions, particularly methane (CH4). This study evaluates the performance of Norwegian dairy farms and the socio-economic factors influencing emissions over 30 years (1991-2020). We assessed dairy farm performance by evaluating both efficiency and environmental impact, with a particular focus on reducing methane emissions. This is crucial for achieving sustainable and resource-efficient farming within a circular economy framework. Methane emissions were calculated using the Intergovernmental Panel on Climate Change (IPCC) Tier 2 methodology, incorporating country-specific data on dairy cattle diet and production. Utilizing a comprehensive panel dataset of 692 dairy farms, we employed a parametric model to analyze the intricate input-output relationships within dairy production. Our findings reveal an average eco-efficiency score of 0.95, suggesting a promising potential for a 5% reduction in resource use and CH4 emissions without compromising production levels. Socio-economic factors, such as land tenure, farm experience, and government subsidies, were found to exert a positive influence on both farm performance and GHG emissions. Conversely, higher debt-to-asset ratios were associated with lower performance. Our research underscores the necessity for policies that support improvements at the farm level, such as facilitating knowledge transfer among farmers and increasing access to subsidies for environmentally friendly technologies. Future research should delve into other environmental impacts, including nitrogen emissions and biodiversity, to establish a more comprehensive framework for sustainable agricultural practices. By identifying opportunities for reducing GHG emissions while maintaining productivity, this study offers valuable insights for policymakers and industry stakeholders seeking to enhance the sustainability of the dairy sector in Norway and beyond.
Authors
Siv Mari AurdalAbstract
This paper is a historical review of scientific progress on horticultural growing media, with particular attention to the role of peat and the recurring search for sustainable alternatives. It is well established that peat became the cornerstone of horticultural growing media because it offered a unique combination of nutrient control, pH buffering, water retention, absence of harmful microorganisms, and structural stability. Equally evident are the environmental concerns and sustainability goals that have driven the search for alternative materials since the 1980s. This historical review traces the evolution of growing media from the early 20th century to the mid-2020s, focusing on how peat came to dominate and why its substitution has proven so difficult. Drawing on a wide range of literature, including peer-reviewed experimental studies, historical sources, symposia proceedings, institutional reports, and synthesis articles, the historical development of growing media science and practice across each decade is outlined. Attention is given to various composts, coir, wood fiber, bark, and biochar and challenges with these materials related to product standardization for end-user reliability. While many alternatives show potential, particularly as partial components or as stand-alone media under certain conditions, no single material currently offers a fully viable replacement for peat. Instead, the most promising direction appears to be peat-reduced mixtures optimized for both functionality and sustainability. By understanding how growing media science has evolved and where it has struggled, this paper identifies lessons critical to navigating the ongoing transition toward more sustainable and functional systems.
Authors
Alouette van Hove Kristoffer Aalstad Vibeke Lind Claudia Arndt Vincent Odongo Rodolfo Ceriani Francesco Fava John Hulth Norbert PirkAbstract
Considerable uncertainties and unknowns remain in the regional mapping of methane sources, especially in the extensive agricultural areas of Africa. To address this issue, we developed an observing system that estimates methane emission rates by assimilating drone and flux tower observations into an atmospheric dispersion model. We used our novel Bayesian inference approach to estimate emissions from various ruminant livestock species in Kenya, including diverse herds of cattle, goats, and sheep, as well as camels, for which methane emission estimates are particularly sparse. Our Bayesian estimates aligned with Tier 2 emission values of the Intergovernmental Panel on Climate Change. In addition, we observed the hypothesized increase in methane emissions after feeding. Our findings suggest that the Bayesian inference method is more robust under non-stationary wind conditions compared to a conventional mass balance approach using drone observations. Furthermore, the Bayesian inference method performed better in quantifying emissions from weaker sources, estimating methane emission rates as low as 100 g h−1. We found a ± 50 % uncertainty in emission rate estimates for these weaker sources, such as sheep and goat herds, which reduced to ± 12 % for stronger sources, like cattle herds emitting 1000–1500 g h−1. Finally, we showed that radiance anomalies identified in hyperspectral satellite data can inform the planning of flight paths for targeted drone missions in areas where source locations are unknown, as these anomalies may serve as indicators of potential methane sources. These promising results demonstrate the efficacy of the Bayesian inference method for source term estimation. Future applications of drone-based Bayesian inference could extend to estimating methane emissions in Africa and other regions from various sources with complex spatiotemporal emission patterns, such as wetlands, landfills, and wastewater disposal sites. The Bayesian observing system could thereby contribute to the improvement of emission inventories and verification of other emission estimation methods.
Authors
Knut ØistadAbstract
No abstract has been registered
Authors
Mélanie SpedenerAbstract
Sammendrag på norsk I Norge beiter kjøttfe i store områder av boreal produksjonsskog preget av flatehogst på sommeren (mai-september). Vi studerte først mat- og habitatvalg av disse kyrne (Artikkel I– II), og deretter effektene av storfe på flora og fauna (Artikkel III-V). Datainnsamlingen foregikk i Sørost-Norge i 2015-2017 (Furnes/Vang og Stange/Romedal) og 2021-2023 (Steinvik og Deset). Vi studerte kyrnes ressursvalg ved å klassifisere deres adferd ved hjelp av GPS og akselerasjonsdata, ved å hente inn (fra kart) og måle (i felt) habitatvariabler, ved å samle inn møkkprøver til mikrohistologiske analyser og ved å modellere ressursseleksjonsfunksjoner. Vi fokuserte på unge granplantefelt for å studere effektene av kjøttfe på flora og fauna, siden kyrene selekterer for denne skogstypen. Dessuten har små grantrær høy økonomisk verdi og unge granplantefelt er rikere i blomster og pollinatorer enn det resterende skoglandskapet. På 24 unge granplantefelt satt vi opp parede prøveflater (20x20 m hver), hvorav en omgitt av et gjerde. Vi så på unge trær, vegetasjonen i feltsjiktet og blomsterbesøkende insekter. Siden halvparten av disse granplantefeltene lå innenfor, og den andre halvparten utenfor beiteområdene, kunne vi skille effektene av storfe fra effektene av hjortedyr, som lever vilt i disse skogene. Interaksjoner mellom storfe og hjortedyr studerte vi ved å sette opp viltkamera på de samme granplantefelt og ved å gjennomføre møkktellinger langs et rutemønster i ett av beiteområdene. Kyrne hadde en gressrik diett og selekterte for gressrike habitater, både på stor og på liten skala (Artikkel I). Storfe selekterte for forskjellige habitatvariabler (liten skala) avhengig av adferden: Når de beitet, selekterte de for gressrikt habitat, og når de hvilte, selekterte de for gressrikt habitat med lite helling og høy kronedekning (Artikkel II). Storfe førte til bittelitt høyere dødelighet av unge grantrær, men ikke til høyere risiko for tråkk- og beiteskader (Artikkel III). Storfe fjernet vegetasjon som konkurrerte med unge grantrær, det vil si unge løvtrær og vegetasjon i feltsjiktet (Artikkel III). Storfe påvirket plante-pollinatorsamfunnet på en annen måte enn hjortevilt: Utgjerding av klovdyr utenfor beiteområde (hjortedyr) førte til lavere abundans av blomster, mens utgjerding av klovdyr innenfor beiteområde (hjortedyr og storfe) førte til lavere abundans av blomster og lavere abundans av blomsterbesøkende insekter (Artikkel IV). Elg brukte andre habitattyper enn storfe (Artikkel V). Elgen sitt bruk av unge granplantefelt avtok med økende bruk av storfe (Artikkel V). Mulige beiteinnskrenkende tiltak, samt bevaring av artsmangfoldet i boreal produksjonsskog ble drøftet, og anbefalinger for videre forskning ble gitt.
Authors
Begum BilgicAbstract
The work was funded by the Research Council of Norway through grants 257622 (Bio4Fuels) and 319723 (BioSynGas)
Abstract
Environmental research is facing a drastic increase of available high-quality data, not the least due to the eLTER activities. Here simultaneous time series of numerous observables from the atmosphere, soil, streams, lakes and groundwater, etc., and comprising both abiotic and biotic variables will be made available from hundreds of sites. On the one hand quality control of these large data sets becomes a major challenge. On the other hand, though, it opens up completely new options for science as long as some key problems are solved:· How to differentiate between different effects?· How to deal with the filter effects of environmental systems?· How to identify unexpected relationships that a model would not depict?However, environmental sciences still lack a toolbox of approved integrated exploratory data analysis approaches to tackle these challenges in a systematic way. Here we suggest a combination of different methods that proved very efficient both in terms of data quality control and of exploratory data analysis for large sets of time series. Examples will be presented from the AgroScapeLab Quillow (LTER site DE-07-UM, Germany) and the Hurdal ICOS and ICP Forest Level II site (Norway). The Hurdal site is planned to be established as an elTER site as well.Any change of boundary conditions, of input fluxes, emerging invasive species etc. (termed “signal propagation” for short) in environmental systems is subject to filtering effects. A key feature thereof is low-pass filtering. Here we suggest the new Cumulative Periodogram Convexity (CPC) index to quantify the effect size for comparison of various time series. Principal Component Analysis of time series (termed Empirical Orthogonal Function approach in climatology) is suggested as another decisive step. Loadings on single components can be used for assessing the size of single effects on observed time series. Visualization of the communalities and of similarities between different observables and sites in a combination of Self-Organizing Maps and Sammon Mapping allows a rapid survey of some tens to hundreds of time series at a glance, e.g., for quality control. Additional consideration of the CPC index proved a powerful tool for identification of the respective key drivers and of the pathways of signal propagation through environmental systems, comprising both biotic and abiotic observables. Applying machine learning approaches to principal components rather than to the raw data facilitates developing a better understanding of complex interactions in environmental systems. To conclude, we see great potential in a systematic combination of existing approaches deserving to be explored further.