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.
2026
Forfattere
Telmo Jorge Carneiro Amado Jackson Ernani Fiorin William Ramos da Silva Thiago Inagaki Ulfried Arns Tiago Wyzykowski Felipe José Cury Fracetto Ademir de Oliveira FerreiraSammendrag
Diversified no-tillage crop successions are key strategies for improving soil health, productivity, and sustainability in tropical environments. However, adoption of diversified cropping systems remains limited due to economic, cultural, and technical constraints. This study aimed to evaluate how long-term diversified crop rotations, differing in legume inclusion and mineral phosphorus (P) and potassium (K) fertilization, influence system nitrogen (N), P, and K budgets, biomass production, and crop productivity in mature tropical no-tillage systems (NTS). Using a 10-year field experiment established on a tropical Ferralsol in southern Brazil, we evaluated six long-term crop rotations that differed in crop diversification, inclusion of Fabaceae cover crops, and phosphorus and potassium mineral fertilization. Total system N, P, and K budgets were quantified by integrating nutrient inputs, crop uptake, grain nutrient export, biomass nutrient cycling, and net soil nutrient balances. Intensive legume-based rotations increased crop productivity compared with low-diversity, non-legume rotations (specifically, the two-crop black oat/common bean succession). Vetch-based rotations under optimal or suboptimal mineral fertilization, as well as unfertilized vetch rotations, produced the highest cumulative grain yields (∼52,000 kg ha -1 ), representing a 38% increase (a 1.38-fold increase) over the black oat/common bean rotation (∼38,000 kg ha -1 ). This response was associated with a 40% increase in aboveground biomass and grain yield when maize followed hairy vetch rather than black oat. Legume-intensive rotations under optimal mineral fertilization or without fertilization maintained a strongly positive N budget (+559 kg ha -1 ), whereas the low-diversity black oat/common bean rotation showed a net N deficit. Conversely, all unfertilized systems, including the highly productive unfertilized hairy vetch-based rotations, exhibited strongly negative P and K budgets, indicating a progressive risk of soil fertility depletion. Only suboptimal mineral-fertilized vetch rotations partially offset these nutrient deficits in highly crop-productive systems. The study highlights the role of legume-based crop rotations as a key strategy to sustain a positive N budget, thereby supporting nutrient cycling and crop productivity under tropical NTS. Maintaining nutrient balance remained dependent on coupling biological N fixation with balanced mineral P and K fertilization to offset nutrient export deficits in high-yield systems and to reduce the risk of long-term soil fertility depletion.
Forfattere
Andrea Ponzecchi Albin Lobo Claudia Cocozza Paolo Cherubini Matthias Saurer Erik Larnøy Lisbeth G. ThygesenSammendrag
Broadleaf afforestation increasingly requires planting material that combines valuable wood properties with resilience to climatic stress. High heartwood content is a desirable trait in pedunculate oak (Quercus robur), but it is unclear whether selecting for it could be a trade-off with resistance and resilience to drought. We addressed this question in a 24 year old progeny trial in southern Denmark where trees were ranked by heartwood production and the 2018 drought was used as a natural stress event. Trees with contrasting heartwood content were assessed using multiple indicators of drought response, including annual ring width, vessel anatomy, wood density, and dual stable isotopes (δ¹³C and δ¹⁸O) measured separately in earlywood and latewood over a five-year period. The 2018 summer drought was of moderate intensity at the trial site, with SPEI3 approximately −1.5, but it was clearly reflected in isotopic signals, indicating that trees perceived the water deficit. In contrast, radial growth did not decline, and ring widths remained above average in 2018. Across all metrics, trees with lower and higher heartwood content showed broadly similar physiological and wood-structural responses. Within the environmental conditions and drought intensity observed in this study, our results provide no evidence for a strong trade-off between heartwood content and response to a drought of moderate severity. They therefore support, with appropriate caution, the continued consideration of heartwood content as a selection trait in breeding and afforestation programs in comparable environments.
Forfattere
Stefano Zanotto Jon Arne Dieseth Wendy Marie Waalen Paolo Annicchiarico Anne Kjersti UhlenSammendrag
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.
Sammendrag
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.
Forfattere
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 StrauchSammendrag
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.
Forfattere
Adrian Straker Paul Magdon Marco Zullich Maximilian Freudenberg Christoph Kleinn Johannes Breidenbach Stefano Puliti Nils NölkeSammendrag
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.
Forfattere
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 KronvangSammendrag
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.
Sammendrag
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.
Forfattere
Babak Minofar Simon Weldon Alice Budai Beata Gavurova Lukáš Trakal Daniel Rasse Anna Maroušková Josef MarousekSammendrag
Det er ikke registrert sammendrag
Sammendrag
I forbindelse med reguleringsarbeid knyttet til Ravnsborg gård gnr./bnr. 28/54 i Hvalstad i Asker kommune er det behov for jordbruksfaglig utredning. Jordloven §§ 1 og 9 slår fast at ved omdisponering av god matjord bør jordressursene bevares. Rapporten vurderer jordas egenskaper, hydrologiske forhold, planteskadegjørere samt andre forhold. Det ble foretatt feltarbeid og befaringer 17. og 19. november 2025. Tiltaksarealet har to jorder hvor kun det ene (jorde 1) ble undersøkt i denne rapporten. Matjordlaget er moldholdig siltig mellomleire. Jordsmonnet er leirjord med leiranrikningsjikt. Det ble besluttet at deler av jorde 1 er forstyrret og ikke lenger kan regnes som jordbruksareal. Topplaget fra forstyrrede arealer kan brukes til grøntanlegg internt i prosjektet. Eiendommen er ikke registrert i floghavreregisteret. Jorde 1 er fri for PCN. I reguleringsprosessen ble det besluttet at jordbruksarealene på tiltaksarealet ikke skal nedbygges, og driften opprettholdes.