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.
2026
Abstract
Background Faba bean cultivation in northern Europe is challenged by high variability in yield performance due to strong genotype × environment interaction (GEI). Appropriate GEI modeling and interpretation is essential for both cultivar recommendation and breeding for Nordic conditions. Methods Grain yield data from fifteen spring faba bean cultivars evaluated in eleven Norwegian environments during four growing seasons (2022–2025) were analyzed using the Additive Main Effects and Multiplicative Interaction (AMMI) model. Climatic drivers of GEI were investigated using factorial regression, while genome-wide SNP markers were used to assess the relationship between molecular diversity and adaptive differentiation. Results Environment, genotype, and GEI significantly influenced grain yield. The AMMI model with three GEI principal components (AMMI3) represented the biologically meaningful GEI signal and explained 83.4% of the GEI variation. The predictive accuracy of this model closely approached that of BLUP and GBLUP. Factorial regression showed that maximum and mean September temperatures explained 38.5% of the GEI variation, indicating that late-season thermal conditions are the principal environmental drivers of cultivar adaptation. We identified three top-yielding genotypes (Ketu, Birgit, and Vire) that can be object of site-specific recommendation. Two sub-regions emerged as possibly distinct breeding targets, namely, a major one, and a smaller one featuring cooler late-cycle temperatures and specific adaptation of very early material. A significant Mantel correlation between genomic and adaptive dissimilarity (r = 0.40, P = 0.029) suggested that differences in environmental adaptation have a measurable genetic basis. Conclusions This study identified elite cultivars for recommendation and generated crucial information for future crop breeding and variety testing in Norway. Breeding for wide adaptation across climatically diversified environments is justified by the modest size of the smaller sub-region and its expected decreasing importance due to climate change. The relationship between genomic and adaptive dissimilarity, if confirmed for a larger genotype set, could be exploited for a preliminary screening of regional breeding material and novel plant introductions.
Abstract
As forests face increasing anthropogenic pressure, the robust, large-scale remote sensing of key biophysical parameters, such as forest height and above-ground biomass density, has become essential to guide conservation efforts and climatological analyses. While deep learning (DL) applied to interferometric synthetic aperture radar (InSAR) data has achieved state-of-the-art performance, its operational potential for long-term forest monitoring remains limited by a lack of uncertainty reporting, which is essential for error propagation, and for evaluating prediction reliability and temporal stability. This study introduces a novel Bayesian DL framework for the uncertainty-aware estimation of forest height from single-pass TanDEM-X InSAR data, marking its first application to synthetic aperture radar interferometry. The approach is validated through a case study in Norway, using national airborne laser scanning data to assess the calibration of the self-reported uncertainties, and to examine the effects of tree species variability and temporal mismatches in training pairs on model generalization. To capture total predictive uncertainty, the framework explicitly models the aleatoric component during estimation while integrating epistemic uncertainty through the comparison of different intra- and interbasin approximation strategies. In addition, the robustness of the model is evaluated under out-of-distribution (OOD) conditions (i.e., data domains absent from the training set), reflecting the challenges encountered in operational remote sensing. The results demonstrate robust generalization performance and the generation of well-calibrated uncertainty maps under in-distribution conditions, while highlighting the critical necessity of complementing data-driven models with an ad hoc OOD detector to reliably manage OOD scenarios. The resulting framework paves the way for real-world prediction of uncertainty-aware forest canopy height products from InSAR data.
Authors
Cordula I.H. Wittekind Felix Witing Sydney E. White Christoph Schürz Marie Anne Eurie Forio Moritz Shore Csilla Farkas Martin Futter Dennis Collentine Lorenzo Sanguanini Ronald A. Kolcsar Brigitta Szabo Piroska Kassai Martin Volk Michael StrauchAbstract
This study presents ParetoPick-R, an R Shiny application for post-processing multi-objective optimisation (MOO) results in environmental modelling. Although MOO is widely used in environmental management, its multi-dimensional and abstract outputs are often hard to interpret and apply directly. ParetoPick-R addresses this challenge by enhancing the interpretability and usability of MOO results for decision support. The tool integrates three key strategies. First, interactive visualisation techniques enable users to explore synergies and trade-offs among objectives and link objective and decision spaces. Second, a clustering-based data reduction approach condenses large Pareto sets into representative subsets while preserving essential trade-off structures. Third, an Analytical Hierarchy Process facilitates the systematic incorporation of stakeholder preferences across objectives and decision variables. This paper describes the design and implementation of ParetoPick-R and demonstrates its functionality and procedural application through two distinct case studies.
Authors
Adrian Straker Paul Magdon Marco Zullich Maximilian Freudenberg Christoph Kleinn Johannes Breidenbach Stefano Puliti Nils NölkeAbstract
Deep learning-based tree species classification models applied to terrestrial laser scanning data achieve high accuracies, yet their classification decisions remain poorly understood. Addressing this gap, we present a novel explanation framework that systematically links salient regions of Finer-CAM saliency maps—which highlight image features contributing to the classification of a target species while suppressing features shared with similar species—to structural tree features in 2D side-view images of terrestrial laser scanning (TLS) point clouds. We applied this framework using YOLOv8-based tree species classification models trained on a subset of the FOR-species20K benchmark data set. Faithfulness analysis confirmed that Finer-CAM reliably identifies the most discriminative image regions for model predictions, validating its suitability for enhancing model interpretability. Analysis of 630 saliency maps indicate that the models primarily rely on image regions associated with tree crowns across most species, particularly for Silver Birch, European Beech, English Oak, and Norway Spruce, while stem regions contribute more strongly to the differentiation of European Ash, Scots Pine, and Douglas-fir. Image perturbation experiments further demonstrated that the visibility of tree structure—such as branching patterns—in the 2D side-view images enhances classification performance, indicating YOLOv8’s capabilities to leverage detailed structural information encoded in high-density TLS point clouds. Using a cross-validation approach our trained models achieved a mean overall accuracy of 96% (SD = 0.24%) on a test data set unseen during training. Beyond assessing the contribution of image regions associated with structural tree features to tree species differentiation, our methodological framework can facilitate a better understanding of model limitations, the detection of data set biases and shortcut learning, and contributes in building confidence in deep learning-based tree species classifiers.
Authors
Martyn Futter Katri Rankinen Joachim Audet Heleen de Wit Csilla Farkas Martin Forsius Jens Fölster Anastasija Isidorova Øyvind Kaste Dominika Krzeminska Katarina Kyllmar Emma Lannergård James Edward Sample Eva Skarbøvik Lars Sonesten Brian KronvangAbstract
River mouth nitrogen: phosphorus: silicon (N:P:Si) stoichiometry can predict marine eutrophication potential. Furthermore, deviations from Redfield molar N: P:Si = 16:1:20 ratios can offer insight into limiting nutrients and risks for harmful algal blooms (HAB). Here, we present N:P:Si stoichiometry based on total P, dissolved inorganic N and dissolved Si for 88 coastal river mouths in Norway, Sweden and Finland between 2017 and 2024. Rivers ranged from ultra-oligotrophic to hyper-eutrophic. N and P concentrations increased from north to south, with no latitudinal Si gradient. Most rivers were either P or jointly NP depleted relative to Si, with no overall evidence of Si depletion. However, there was some evidence of seasonal Si depletion. We show how using multi-element approaches, e.g., the Index of Coastal Eutrophication Potential (ICEP) can help to guide management actions. Specifically, Using Si depletion relative to N and P concentrations as an indicator of HAB potential may offer a means to identify catchments where nutrient load reductions can have the largest benefit on marine ecosystem health. Such multi-element approaches can complement single nutrient assessments based on, e.g., the Carlson Trophic Status Index or Water Framework Directive status class assessments.
Abstract
In this paper, we propose a fishery control scheme that happens to be first best in the context of a Gordon‐Schaefer model with asymmetric information about effort/harvesting costs. It is widely acknowledged that in the absence of regulations, competition between vessels leads to a competitive game wherein the outcome is inefficient. We introduce a management scheme that regulates the fishing industry through an individual convex tax on effort or on catches. The shares of this tax can be traded in a market, thus solving problems related to asymmetric information. This can be achieved since heterogeneous fishing firms are individually stimulated to solve the same optimality problem as a social planner would. We apply this model to the Northwest Mediterranean demersal fishery as a case study.
Authors
Babak Minofar Simon Weldon Alice Budai Beata Gavurova Lukáš Trakal Daniel Rasse Anna Maroušková Josef MarousekAbstract
No abstract has been registered
Authors
Eirik Haugstvedt Henriksen Simo Njabulo Maduna Snorre B. Hagen Guttorm ChristensenAbstract
Pink salmon ( Oncorhynchus gorbuscha ) has rapidly expanded throughout northern Europe, raising concern that continued warming may enable establishment even in high Arctic ecosystems. Svalbard has experienced increasing occurrences of pink salmon in recent years, yet it remains unclear whether these individuals reflect successful local reproduction or straying from mainland populations. We combined field surveys and population genomics to assess the origin and establishment potential of pink salmon on Svalbard. Five rivers around Isfjorden were surveyed in 2024 following observations of unusually high pink salmon abundance and suspected spawning in 2023. Despite extensive sampling of spawning substrates, no eggs or fry were detected. Because most Svalbard rivers freeze to the bed or dry out during winter, current environmental conditions are likely insufficient to support successful egg incubation and emergence . Genomic analyses of 24 adults captured in 2023, integrated with a 3RAD SNP reference dataset (6174 SNPs), revealed that Svalbard individuals retained genetic diversity within the observed range of the mainland populations but formed no distinct genetic cluster. Instead, they exhibited admixture and greatest affinity to sampling populations in the Varanger Fjord region, indicating recurrent long-distance dispersal from established invasion hotspots. Together, the absence of detected recruitment and the lack of a distinct Svalbard genetic cluster indicate that pink salmon in Svalbard currently represent a demographic and genetic sink sustained by recurrent immigration from mainland populations rather than a self-sustaining population. However, the genetic diversity of incoming strays suggests that, if future environmental change weakens current barriers to egg incubation and juvenile emergence, establishment may become increasingly possible.
Abstract
Plastic mulch films are widely used in agriculture, often in combination with pesticide application. While biodegradable plastic mulch films (BDM) are designed for soil incorporation, their influence on pesticide fate remains poorly understood. We investigated the influence of BDM on the degradation kinetics of four widely used pesticides (boscalid, pyraclostrobin, fluopyram and pyraflufen ethyl) in two agricultural soils under controlled conditions. Pesticides were applied at agronomically relevant doses either directly to soil or adsorbed onto a commercially available PBAT-starch BDM prior to soil incorporation, and pesticides and their relevant metabolites were monitored for 112 days. Adsorption to BDM significantly slowed pesticide degradation for all compounds, increasing degradation half-life DT50 values by factors of 2-26 compared with direct soil application. While degradation pathways were not fundamentally altered, the presence of mulch delayed metabolite formation and prolonged pesticide persistence. Rapid degradation of pyraflufen ethyl led to the accumulation of pyraflufen-ethyl metabolite E 3, a highly persistent multi halogenated metabolite that remains analytically and regulatorily overlooked. BDM itself showed limited mass loss (12 %) over the experimental period, suggesting that pesticide-plastic interactions can persist over environmentally relevant timescales. Overall, this study identifies plastic mulch residues as overlooked modifiers of pesticide persistence and metabolite accumulation in soil, suggesting that plastic-pesticide interactions warrant further consideration in environmental exposure and risk assessment frameworks.
Authors
Celayir, IremAbstract
--- Abstract and files will be published: 2027-05-15