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
Authors
Zhibo HamborgAbstract
No abstract has been registered
Authors
Åsgeir R. Almås Susanne Eich-Greatorex Trine Aulstad Sogn Tomasgaard Jan Mulder Manoj Kumar Pandey Vincent Dauby David Powlson Roberta Farina Jeroen Watté Daniel Rasse Nathalie BjørnebyAbstract
The soil organic carbon (SOC) Think Tank has identified and ranked the ten most critical knowledge gaps affecting SOC stocks, based on stakeholder input and iterative validation across multiple events. These prioritized gaps reflect new insights into land use impacts, policy influences, and methodological needs, forming a foundation for targeted research and innovation.
Authors
Junbin Zhao Cornelya Klutsch Hanna Marika Silvennoinen Carla Stadler David Kniha Runar Kjær Svein Wara Mikhail MastepanovAbstract
ABSTRACT Drained cultivated peatlands are recognized as substantial global carbon emission sources, prompting the exploration of water level elevation as a mitigation strategy. However, the efficacy of raised water table level (WTL) in Arctic/subarctic regions, characterized by continuous summer daylight, low temperatures and short growing seasons, remains poorly understood. This study presents a two‐year field experiment conducted at a northernmost cultivated peatland site in Norway. We used sub‐daily CO 2 , CH 4 , and N 2 O fluxes measured by automatic chambers to assess the impact of WTL, fertilization, and biomass harvesting on greenhouse gas (GHG) budgets and carbon balance. Well‐drained plots acted as GHG sources as substantial as those in temperate regions. Maintaining a WTL between −0.5 and −0.25 m effectively reduces CO 2 emissions, without significant CH 4 and N 2 O emissions, and can even result in a net GHG sink. Elevated temperatures, however, were found to increase CO 2 emissions, potentially attenuating the benefits of water level elevation. Notably, high WTL resulted in a greater suppression of maximum photosynthetic CO 2 uptake compared to respiration, and, yet caused lower net CO 2 emissions due to a low light compensation point that lengthens the net CO 2 uptake periods. Furthermore, the long summer photoperiod in the Arctic also enhanced net CO 2 uptake and, thus, the efficacy of CO 2 mitigation. Fertilization primarily enhanced biomass production without substantially affecting CO 2 or CH 4 emissions. Conversely, biomass harvesting led to a significant carbon depletion, even at a high WTL, indicating a risk of land degradation. These results suggest that while elevated WTL can effectively mitigate GHG emissions from cultivated peatlands, careful management of WTL, fertilization, and harvesting is crucial to balance GHG reduction with sustained agricultural productivity and long‐term carbon storage. The observed compatibility of GHG reduction and sustained grass productivity highlights the potential for future paludiculture implementation in the Arctic.
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