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
Potato field management in Europe is already optimized for high production and tuber quality; however, numerous environmental challenges remain if the industry is to achieve “green economy” targets, such as less resources utilized, and less nitrate leached to the environment. Strategic co-scheduling irrigation and nitrogen (N) fertilization might increase resource use efficiency while minimizing reactive losses such as nitrate leaching. This study aimed to quantify the combined effect of irrigation and N fertilization on potato production, growth, and resource use efficiencies. A field experiment was conducted from 2017 to 2019 on a coarse sandy soil in Denmark, with a drought event occurring in 2018. Full (Ifull, maximized), deficit (Idef, 70–80 % of Ifull) and low irrigation treatments (Ilow, minimized amount to keep crop survival), each under full (Nfull, maximized) and variable (Nvar, variable amount according to the crops’ needs) N fertilization were applied. The analyses results show that Ilow limited potato growth under a drought-heat event; otherwise, potato growth was comparable between Ifull and Idef treatments, with 31–32 % higher irrigation efficiency (IE) under Idef than under Ifull. Nitrate leaching was variable and not significantly different among the treatments, being in general 9–13 % lower under Idef in absolute terms than under Ifull. Unexpectedly, outcomes from Nvar were statistically lower compared to those from Nfull. Radiation use efficiencies (RUEs) from Ilow and Nvar were significantly lower than from Ifull and Idef (14–19 %), and from Nfull (9–11 %). N use efficiencies (NUE) were comparable between N fertilization treatments but significantly different among different irrigation treatments. Overall, this study confirms that Idef is the best irrigation strategy. Future efforts should focus on developing improved approaches for detecting in-season crop N status and further quantifying N requirements, as well as promoting the co-scheduled management of irrigation and N fertilization. Remote sensing approaches have great potential to assist with this.
2025
Authors
Tuomas Yrttimaa Aapo Erkkilä Teemu Kamula Carolin Fischer Nicolas Cattaneo Mostafa Hoseini Juha Hyyppä Mikko VastarantaAbstract
Efficient use of forest resources requires identifying the origin of wood to determine its intended purpose before harvesting. This purpose depends on the wood’s quality, which is influenced by the tree’s growth process and only fully revealed during processing at the sawmill. Identifying which attributes of a standing tree align with the quality requirements of sawn timber necessitates linking forest-collected data to information obtained at the sawmill. However, a nondestructive approach for establishing this connection without artificial marking of logs is currently unavailable. We propose a potential solution employing “tree fingerprints”—biometric patterns that capture trees’ unique branching arrangements along the stem. The tree architecture reflects a hierarchical growth pattern shaped by the interplay between genetics and the environment. Environmental variation leads to unique resource availability between individuals, and thus we assume that each tree develops distinct architectural characteristics, akin to the uniqueness of a human fingerprint. To investigate whether this uniqueness can be captured using terrestrial laser scanning (TLS), we conducted an experiment with 65 Scots pine (Pinus sylvestris L.) trees in a managed boreal forest stand. We derived tree fingerprints from two independent TLS data acquisitions (September 2021, November 2022) and matched corresponding fingerprints. In total, 52 trees (80.0%) were identified based on their architectural characteristics. The results showed that identifying ≥10 branch origins from independent reconstructions was sufficient to establish architectural uniqueness, resulting in 100% identification accuracy (n = 20 trees). These findings suggest that tree fingerprints can be used to condense the complex three-dimensional tree architecture into a two-dimensional pattern of points representing unique branch arrangement. Further, we demonstrate how this tree fingerprinting concept could be expanded across laser scanning methods to enable operational-scale wood traceability if point cloud data of standing trees is collected during forest operations and corresponding sawlogs are scanned at sawmills using X-ray computed tomography. Existing incentives support this kind of development: laser scanners on harvesters can assist operators, and sawlog scanning is essential for optimising timber yield. Seamlessly integrating wood traceability into industry practices would enable automated recording of data that can be further used for linking architectural characteristics of standing trees, grown within specific site conditions, to sawlog properties. This integration would enhance understanding of how tree architecture, environmental factors, and forest management influence desired properties of processed wood, enabling more informed decision-making for the wood procurement process.
Authors
Henrik Persson Stefano Puliti Tuomas Yrttimaa Vikash Ghildyal Rasmus Astrup Johan Holmgren Carolin Fischer Nicolas Cattaneo Mikko VastarantaAbstract
Tracing wood along the value chain is needed to preserve information about wood origin and understand associations between standing tree characteristics and the resulting wood quality. Linking timber products with standing trees without artificial marking remains a formidable challenge where detailed reconstruction of trees’ architectural characteristics could provide a solution. The objective is to develop an automated method for single-tree tracing using dense laser scanning from standing trees, leveraging branch patterns as unique fingerprints. A secondary objective is to explore how these fingerprints can be derived from computer tomography (CT) scans at sawmills, enabling the reconstruction of individual branch patterns. We use the AI algorithm BranchPoseNet to detect tree whorls and individual branch vectors from a terrestrial laser scanner-derived point cloud. A tree's unique fingerprint is derived by presenting the branch origins as a function of height and azimuth around the stem. This fingerprint is then reconstructed from a mobile laser scanner-derived point cloud collected from the same trees as well as from CT scans of knots and their positions in processed logs. By minimizing residuals between corresponding branch locations between the initial and reconstructed fingerprints, individual trees can be accurately linked, enabling full traceability from living trees to sawlogs. Preliminary results indicate that this approach is feasible for pine trees and that a limited number of unique branch connections may be sufficient for tree traceability in managed forest stands. More testing is needed to assess the performance of other species. We conclude that this method can be integrated into industry practices, being viable for automatically tracing trees from the harvested forest stands to the sawmill, thereby closing critical gaps in the value chain and enabling the attribution of additional information (e.g., origin, carbon sequestration potential) to wood products and other forest-based applications without artificial marking of logs.
Abstract
Efficient and objective measures of tree and stand structural complexity are essential to understanding the relationship between forest management, biodiversity, and ecosystem functioning, with laser scan-based structural complexity metrics playing an innovative role in monitoring and analysis.The objective is to develop an individual-tree crown complexity metric capable of distinguishing between structurally more or less complex trees while being scale-invariant. The second objective is to efficiently scale this metric to the stand level, enabling differentiation between forests of varying complexity and guiding precision silviculture.We developed a method to quantify crown complexity using individual-tree dense airborne LiDAR point clouds. Through optimization, we generate a 3D alpha shape crown model and calculate its volume and exposed surface area. This surface area is compared to that of a reference sphere with the same volume, as the sphere is the solid with the lowest surface-to-volume ratio, serving as a baseline for minimal complexity. This provides a scale-invariant measure of crown complexity. Summing this measure across all trees in a stand and applying a penalty for low vertical distribution yields a stand-level complexity metric that reflects structural heterogeneity.Applying our methodology to the FOR-instance dataset showed that the calculation of the 3D alpha shape crown model through optimization was successful, although sparse point clouds can present challenges. The crown complexity measure behaved as expected, ranking crowns according to their complexity, primarily determined by the roughness of the tree crowns, which increases the exposed surface area. When scaling the metric to the sample plot level, the measure effectively distinguishes between forests with structurally complex trees but low vertical stratification and those with less complex trees but high vertical stratification, identifying the latter as the more structurally complex forests.
Authors
Ryan BrightAbstract
Populærvitenskapelig Innlegg
Abstract
This paper explores the generation of “realistic” 3D representations of individual trees to enhance visualizations of forest simulation tool outcomes. By leveraging remote sensing data, we aim to capture individual tree features and characteristics accurately, linking them to dynamic simulations of forest structures and composition. Employing a deep learning approach, we train models on existing 3D scanned data to produce diverse and realistic visual representations of specific tree species. Our method addresses the limitations of existing synthetic tree generation techniques, which often overlook species-specific characteristics. Our approach emphasizes the generation of diverse tree forms, accounting for differences in trunk shape, canopy size, and branching structures. The resulting 3D data offers potential applications for realistic future forest visualizations and improved data augmentation in tree classification models, ultimately contributing to the creation of virtual forests that represent rich species diversity.
Authors
Thiago InagakiAbstract
No abstract has been registered
Authors
Lindner, Hanna NoemiAbstract
Plant Genetic Resources for Food and Agriculture (PGRFA) have declined drastically in recent decades, leaving the food system in a vulnerable state. In Norway, vegetable production relies heavily on imported seeds, which places the country in a fragile position regarding seed preparedness. To address this challenge, varieties adapted to Norway’s climatic and environmental conditions are needed, with landraces, characterized by their high genetic diversity, being particularly important. This case study applies a commoning approach to explore the role of KVANN, a Norwegian Community Seed Bank (CSB) together with the wider community of hobby gardeners, in facilitating access to seeds. The study combined 8 semi-structured interviews, two days participatory observation at a KVANN-seminar, and a questionnaire to hobby gardeners in Norway. Results indicate that although seed exchange is not the primary source of seeds and occurs only occasionally, these practices nonetheless impact management of plant genetic diversity, enable knowledge exchange and nurture trust in the community. By highlighting these dynamics, this case study contributes to the wider literature, revealing the important role hobby gardeners in Norway play in managing plant genetic diversity through seed and knowledge exchange. This is achieved by taking collective responsibility, collaboration with other actors in the seed system, and building trust among hobby gardeners. Ultimately, this thesis suggests that hobby gardeners, with KVANN as a central driver, can enhance access to diverse plant genetic resources and thereby contribute to seed-preparedness in Norway.
Authors
Mikarlsen, NoraAbstract
Pest control is a central part of modern strawberry farming. Spider mites are one of the most common pests in strawberries, and can cause significant reduction in yield. In order to properly manage and control spider mite populations, early detection is crucial. This thesis sets out to detect two-spotted spider mites (TSSM) in strawberries using hyperspectral imaging (HSI). A variety of methods have explored including visual inspection of the spectrum and its derivatives, as well as the use of vegetation indices (VIs). In addition, this thesis also explores machine learning (ML) and deep learning (DL) for early detection of TSSM. The mean spectrum from the images was used for classification in combination with Linear Discriminant Analysis (LDA) and Random Forest Classifier. Two separate Random Forest models were trained, one that distinguished between control, drought, and mite-infested strawberry plants, and one five-class with three different infestation levels, in addition to control and drought group. The three-class model achieved an F1-score of 0.86, while the five-class model had an F1-score of 0.845. The images themselves were used for classification by a ResNet18 model. The model was trained for each imaging day separately, and achieved accuracies in the range of 0.7-0.9 and F1-scores between 0.709-0.903. The work presented in this thesis highlights the capabilities of HSI in combination with ML and DL for early detection of TSSM in strawberries.
Authors
Hannah Rivedal Todd N. Temple Robert J. Starchvick Joseph P. Gallagher Dustin W. Herb Jason Crisp Nicole Anderson Tatiana Benedetti Amy B. PeetzAbstract
Oregon’s grass seed industry specialises in producing forage grasses including annual ryegrass (ARG, Lolium multiflorum), a host for the seed gall nematode (SGN, Anguina funesta). SGN causes yieldlimiting seed galls and are strictly regulated in international trade. From 2019 to 2020, over 500 metric tons of Oregon ARG seed were rejected from international ports due to SGN detection. A 2022 field survey of 22 ARG fields in the Willamette Valley of Oregon resulted in SGN detection in 50% of the fields throughout the growing season. Several approaches managing SGN are under evaluation. Previous reports indicate that there may be genetic resistance to SGN in other Lolium species. Therefore, a breeding population of 240 public accessions of L. multiflorum have been seeded with two seed galls and planted in the field. Seed were harvested to evaluate for galls in July 2025 and to identify potential resistant families for future study. To date, no nematicides are labelled for the control of SGN. Varied fluopyram timings and rates, as well as an untreated control, are being evaluated in the field with and without growth regulation for SGN control. Seed yield and galled seed data was collected showing limited differences between treatments. Cultural control methods are also being considered, including seed cleaning and utilizing high energy pulses on seed galls. Preliminary data suggests that these could be viable treatments to reduce SGN inoculum. Successful control options for the SGN in ARG seed production are important to reduce the spread of this nematode globally and maintain healthy forage production.