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
Dafni Foti Stephen Amiandamhen Eleni Voulgaridou Elias Voulgaridis Costas Passialis Stergios AdamopoulosAbstract
This study investigated the incorporation of various waste materials including wastepaper, Tetra Pak, wood chips and scrap tire fluff into flue gas desulfurization (FGD) gypsum and cement mortar matrices to produce sustainable composite materials. Four distinct composite types based on the waste materials were developed and evaluated for selected properties including thermal and acoustic insulation. The proportion of the waste materials was varied between 10 and 40 vol% of the base matrix. The compressive strength of the filled gypsum composites was in the range of 4.17–10.39 N/mm² while the pure gypsum was 11.38 N/mm². The addition of the wastes in gypsum composites reduced compressive strength by about 10% for the best recipe and as large as 60% for the worst combination. However, the measured strength still exceeds the strength of typical gypsum wallboard with a compressive strength of about 3–4 N/mm² for whole-board crushing tests and it is much lower for point loads. The normal-incidence sound absorption coefficient indicated that the waste-filled samples absorbed around 80% of the incident sound energy between 2000 and 3000 Hz, comparable to some commercial acoustic foams. The results highlight the potential of utilising these waste-based composites in environmentally friendly construction applications. Depending on the waste type and matrix used, the results revealed trade-offs between multi-functional performance and sustainability benefits.
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
Shimelis Gizachew Raji Bimrew Asmare Yohannes Ewunetu Tesema Dangura Marit Jørgensen Yonas BerhanuAbstract
Prosjektbanken NFR
Abstract
No abstract has been registered
Authors
Heikki Korpunen Yrjö Nuutinen Paula Jylhä Lars Eliasson Aksel Granhus Juha Laitila Stephan Hoffmann Timo MuhonenAbstract
• Overall forest management objectives and stand properties set the requirements and possibilities for harvesting in continuous cover forestry (CCF). • Harvester and forwarder operators play a key role in successful CCF harvesting, as both productivity and quality of work are essential factors in harvesting operations. • Optimal stand conditions improve work productivity on selection harvesting sites; harvested stem volume correlates well with work productivity in cutting, and density of remaining trees does not significantly reduce work productivity in forwarding. • Carefully executed group cutting and shelterwood harvesting can reduce the number of damaged remaining trees, which is beneficial for future tree generations. • Research-based information is needed about work productivity in harvesting, damage caused by harvesting, and optimisation of strip road and forest road networks for CCF.
Abstract
The AllEcoSys project aims to promote sustainable and resilient agricultural systems by implementing a network of living labs across Europe. November 26th is the first AllEcoSys Living Lab seminar. Main speaker was Ole Green from the Danish Living Lab. Ole Green is honorary professor at Aarhus University, founder of Agrointelli, producing innovative farm robotics, and a farmer, developing a novel stripcropping system of berry bushes and annual crops. Project co-funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency (REA). Neither the European Union nor the granting authority can be held responsible for them.
Authors
Mostafa Hoseini Helle Ross Gobakken Stephan Hoffmann Csongor Horvath Johannes Rahlf Jan Bjerketvedt Stefano Puliti Rasmus AstrupAbstract
RoadSens is a platform designed to expedite the digitalization process of forest roads, a cornerstone of efficient forest operations and management. We incorporate stereo-vision spatial mapping and deep-learning image segmentation to extract, measure, and analyze various geometric features of the roads. The features are precisely georeferenced by fusing post-processing results of an integrated global navigation satellite system (GNSS) module and odometric localization data obtained from the stereo camera. The first version of RoadSens, RSv1, provides measurements of longitudinal slope, horizontal/vertical radius of curvature and various cross-sectional parameters, e.g., visible road width, centerline/midpoint positions, left and right sidefall slopes, and the depth and distance of visible ditches from the road’s edges. The potential of RSv1 is demonstrated and validated through its application to two road segments in southern Norway. The results highlight a promising performance. The trained image segmentation model detects the road surface with the precision and recall values of 96.8 and 81.9 , respectively. The measurements of visible road width indicate sub-decimeter level inter-consistency and 0.38 m median accuracy. The cross-section profiles over the road surface show 0.87 correlation and 9.8 cm root mean squared error (RMSE) against ground truth. The RSv1’s georeferenced road midpoints exhibit an overall accuracy of 21.6 cm in horizontal direction. The GNSS height measurements, which are used to derive longitudinal slope and vertical curvature exhibit an average error of 5.7 cm compared to ground truth. The study also identifies and discusses the limitations and issues of RSv1, which provide useful insights into the challenges in future versions.
Abstract
No abstract has been registered
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.