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
Authors
Janine Schweier Raffaele Spinelli Francesco Latterini Natascia Magagnotti Rodolfo Picchio Stelian A. Borz Csongor Horvath Leo G. Bont Stephan HoffmannAbstract
Purpose of review This review traces the development of mini forestry crawlers (MFCs) from earlier small-scale skidding machines to modern remote-controlled tool carriers, and evaluates their current applications, technological characteristics, operational performance, safety, soil impact, ergonomics, and automation potential. Recent findings Recent studies show that MFCs have evolved from simple extraction-oriented machines into multifunctional platforms whose suitability depends on machine class, task–machine matching, site conditions, and work organisation. Field and bench studies report productivity, soil impacts, operator workload, remote-controlled felling performance, non-harvesting applications, and early automation functions. Summary Mini forestry crawlers are most effective in constrained-access settings and in tasks that align with their limited payload while benefitting from high manoeuvrability, remote operation, and multifunctionality. Their advantages are therefore conditional on careful deployment, particularly with respect to soil moisture, turning intensity, traffic frequency, and operator workload. Future evaluations should adopt integrated performance metrics that jointly assess productivity, soil response, and human workload under realistic operating conditions.
Abstract
Neural Radiance Fields (NeRF) have been widely adopted for reconstructing high-quality 3D scenes from 2D RGB images. However, achieving accurate 3D object segmentation within these reconstructed scenes remains challenging. Existing NeRF-based segmentation methods either rely on post-processing (SA3D), which produces noisy point clouds due to the absence of density field optimization, or employ joint training with additional segmentation heads (FruitNeRF), which can lead to suboptimal performance due to conflicting learning objectives. In this work, we propose InvNeRF-Seg (Input-substitution NeRF for Segmentation), a two-stage fine-tuning strategy for 3D object segmentation that preserves the original NeRF architecture and loss function entirely. We first train a standard NeRF on RGB images and then fine-tune it using 2D segmentation masks formatted as RGB-like inputs, without introducing any architectural modifications or additional loss functions. This input-substitution approach reshapes the density field to align with object regions while suppressing background density. We validate InvNeRF-Seg through comprehensive ablation studies examining the roles of density and color MLPs, loss function choices, and training strategies. Field density analysis reveals consistent semantic refinement: densities of object regions increase while background densities are suppressed. Experiments on synthetic fruit datasets and real-world soybean imagery demonstrate that InvNeRF-Seg produces cleaner 3D segmented point clouds compared to both SA3D and FruitNeRF, enabling more accurate downstream object counting. The method is further validated on a self-collected soybean dataset to demonstrate its applicability in real-world agricultural scenarios.
Abstract
In Norway, agroclimatic zones (ACZs) are a valuable tool for national analyses in subject areas concerning the optimized management of agricultural land resources. However, current Norwegian ACZs have been criticized for having an outdated standard climate normal (1931–1960), a limited representation of the local climatic variation, a lack of important model parameters, and weak methodological documentation. Therefore, this paper presents new ACZs for Norway that address these weaknesses. The most significant methodological updates are the use of the standard climate normal of 1991–2020, additional weather data variables, the downscaling of weather data to 250 m hexagons, and the incorporation of phenological crop models for spring wheat, spring barley, and forage grass. The grass model was calibrated with the number of grass harvests at research stations, while the grain models were calibrated with subsidy claim data. The modeled zones for the three crops were combined into the general ACZs. Example maps of the crop zones and new ACZs for the selected regions and the whole country are presented. The new ACZs are more robust, agronomically relevant, and better aligned with the current climatic conditions in Norway. The deliberate exclusion of factors other than climate ensures the new ACZs’ national comparability and their applicability in policy development, land-use planning, climate adaptation, and agronomic assessments at the national scale.
Authors
Tor MykingAbstract
No abstract has been registered
Authors
Elvira Castillo-Almansa Rubén G. Mateo Mercè Galbany-Casals Carme Blanco-Gavaldà Lucía D. Moreyra Cristina Roquet Christian Brochmann Abel Gizaw Seid Desalegn Chala Alfonso Susanna Juan A. CallejaAbstract
Climate change poses a significant threat to the Afrotemperate flora of the Eastern Afromontane Biodiversity Hotspot, particularly to species confined to high-elevation ecosystems such as those found on the African sky islands. This study evaluates the vulnerability of tropical Afroalpine and Afromontane Helichrysum taxa (Compositae) by assessing their climatic niches and predicting future shifts in distribution, range fragmentation, and altitudinal limits under climate change scenarios. Occurrence records for 14 taxa (eight Afroalpine and six Afromontane) were obtained from recent field campaigns, biodiversity databases and herbaria. Ensemble ecological niche models were developed combining Generalized Linear Models, Generalized Boosting Models, and Random Forest. Taxon-specific bioclimatic variables were selected after correlation analyses. The models were calibrated using current climate data and projected using the PSL-CM6A-LR and MRI-ESM2.0 climate models with both low- and high-emission scenarios. The results show that most Afrotemperate taxa currently occupy only a portion of their climatically suitable habitat, often in geographically distant areas. Future projections indicate significant range contractions and increased fragmentation. Afroalpine taxa could lose 50–66% of their suitable habitat, while Afromontane taxa could decline by 53–79%. Suitable areas were estimated to shift upwards in elevation, with limited potential for colonization of new areas, and with no significant latitudinal or longitudinal shifts. These findings represent the first continental-scale assessment of the impact of climate change on the Afrotemperate flora using ecological niche modelling. The projected climate-induced range losses and increased habitat fragmentation, in particular combined with increasing anthropogenic pressure in this region, highlight the urgent need for targeted conservation actions.
Abstract
This paper describes from a methodological point of view a recent attempt to test how Historic Landscape Characterisation (HLC) as developed with respect to the British landscape can be adapted and applied to the different and distinctive landscapes of a Norwegian upland territory, on the edges of the Hardangervidda plateau. This is an area characterised by mobility, close nature-culture interactions, and practices such as summer farming and short-distance transhumant practices. The research was carried out by two Norwegian agencies – NIKU and NIBIO – as part of a larger project known as PARKAS designed in the context of green transitions to promote better-integrated and publicly-responsible management and safeguarding of protected areas. We briefly describe the origins and principles of HLC in Britain, and then at greater length assess the suitability of HLC in Hardangervidda and key ways by which the approach would require modification and adaptation. A concrete method for a Hardangervidda HLC – and a suitable high-level classification – is identified and discussed.
Authors
Yi Wang Junjia Qi Meihui Wang Zhenke Zhu Yong Li Zhenghui Lv Jinyi Yu Haolin Yang Jian Liu Yuchen Lv Ying ZhaoAbstract
Ecological stoichiometry offers a mechanistic framework for linking soil biogeochemical processes with nitrogen (N) and phosphorus (P; collectively, NP) runoff losses in agroecosystems. This review synthesizes recent advances to elucidate how soil carbon: nitrogen: phosphorus (C:N:P) ratios regulate NP runoff losses through coupled soil–microbial processes. Crucially, we highlight that agricultural practices reshape soil stoichiometry primarily through labile nutrient pools and microbial biomass, whereas total soil nutrient ratios generally remain stable due to strong buffering capacity. Due to the relatively weak stoichiometric homeostasis of soil microorganisms, these shifts in labile C:N:P ratios strongly constrain microbial growth, community composition, and enzyme production. Consequently, stoichiometric imbalance acts as a primary regulator of key biochemical pathways—specifically soil organic carbon (SOC) mineralization, nitrification, and denitrification—that determine the mobilization of inorganic NP forms prone to runoff. Stoichiometric regulation exhibits dual characteristics of integration and independence. Soil C:N:P ratios simultaneously influence multiple biochemical processes, while each process responds to stoichiometric constraints with distinct threshold behaviors. These thresholds can be described by process-specific stoichiometric boundary limits, expressed as (C : N/P)max, beyond which reaction rates approach zero under element-limiting conditions. Despite growing empirical evidence, most existing models fail to explicitly incorporate these microbial-mediated stoichiometric constraints or (C : N/P)max thresholds, leading to predictive uncertainties. By framing NP runoff losses as emergent outcomes of stoichiometrically constrained processes, this review provides a robust theoretical basis for improving runoff modeling accuracy and developing stoichiometry-guided soil management strategies to mitigate agricultural non-point source pollution.
Abstract
Introduction Leaf area index (LAI) estimation is sensitive to sensor field of view (FOV), within-plot spatial heterogeneity, and sampling layout. Because LAI influences canopy radiation transmission, microclimate and vegetation–atmosphere exchange, robust field estimation is important for biometeorological and ecosystem research. Methods We evaluated these effects in mature Norway spruce [ Picea abies (L.) H. Karst] stands in the Czech Republic across 15 sites at elevations of 407–1,019 m a.s.l., using a combined gap-fraction approach based on LAI-2200 PCA measurements and digital hemispherical photography. At each site, a measured 9 × 9 grid of 81 below-canopy measurement points with 2-m spacing was used as an operational within-plot benchmark for mean optically derived LAI and spatial structure. Monte Carlo subsampling was then used to compare how reduced layouts, including random, row-wise, column-wise, block-wise, and spatially balanced block–row–column layouts, reproduced the full-grid benchmark mean. Results Narrower FOVs produced higher stand-level optically derived LAI estimates and greater within-plot variability. The full grids also showed directional spatial structure, with stronger autocorrelation along the north–south column direction than along the west–east row direction, although this contrast weakened under the narrowest FOV. Reduced layouts with more even spatial coverage outperformed random sampling. The spatially balanced block–row–column layout performed most consistently, whereas the row-wise layout provided little improvement. Approximately 25–36 well-distributed below-canopy measurements were sufficient to keep reduced-layout LAI deviations within 0.15 m 2 m −2 of the full-grid benchmark, whereas 7–9 measurements were enough to remain within 5% of this benchmark in most stands. Discussion Spatially balanced sampling can therefore improve the robustness and efficiency of stand-level optically derived LAI estimation, with relevance to forest biometeorology, ecosystem monitoring, and the validation of satellite-derived LAI products.
Abstract
Semi‐natural grasslands are valuable habitat for bumblebees ( Bombus spp.) in Fennoscandia. These grasslands are often maintained by livestock such as free‐ranging sheep, but it is unclear if sheep grazing impacts bumblebee communities, and if so, which grazing intensity is best. We compared bumblebee species richness and abundance on semi‐natural grasslands in southeastern Norway at varying levels of disturbance by sheep (0–1680 initially released). We used Bayesian hierarchical models to test the intermediate disturbance hypothesis, that the highest bumblebee abundance and richness would occur at moderate grazing intensity, while considering other important habitat variables: distance to forest, meadow size, sward height, and availability of flowers, litter, and bare soil. We found no support for the intermediate disturbance hypothesis but rather a negative relationship between the number of sheep released and measures of bumblebee species richness and abundance from targeted netting. Effects varied among the most common species. B. pratorum and B. wurflenii were more sensitive to the number of sheep released than were B. jonellus . Bumblebee abundance estimated by two capture methods (blue vane trapping and targeted netting) was only weakly correlated and bumblebee species richness estimated from traps was unrelated to any of our predictor variables. Practical implication . Our results suggest a weak negative influence of any amount of sheep grazing on bumblebee abundance and richness. Targeted netting was a more reliable method than blue vane traps to assess effects on the bumblebee community.
Authors
Simone Bianchi Cornelia Roberge Johannes Schumacher Johannes Breidenbach Kari T. Korhonen Harri MäkinenAbstract
Sustainable forest management needs growth models. Few studies have explored regional models in Fennoscandia despite similar conditions and challenges. We examined the feasibility of regional models for basal area increment of Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), and birch (Betula pendula Roth. and Betula pubescens Ehrh.). We compiled over 880,000 growth observations and estimated competition indices, climate variables, and site fertility classes by integrating data from recent National Forest Inventories (2004–2023) in Finland, Norway, and Sweden. Using Random Forest models, we identified the main growth drivers across countries (tree size, accumulated temperature sum, latitude, competition, and site fertility), with minor differences in their responses across countries. However, periodic NFI measurements could not capture the effect of additional climate variables. Using species-specific nonlinear mixed models, we demonstrated that predictive regional models can be fitted using those main drivers. Although we achieved only moderate predictive performance (Weighted Absolute Percentage Error of 45–71%, depending on the species and country), there were no residual geographical biases. The results confirm the potential of Fennoscandian growth models to address shared challenges. Future work should better account for site fertility, integrate process-based approaches for climate responses, and carry out independent validation.