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
Giacomo Nicolini David Durden Luca Di Fiore Christopher Florian Simone Sabbatini Bert Gielen Arne Iserbyt Benjamin Loubet Ivan Mammarella Adriana Mariotti Maarten Op de Beeck Caleb Slemmons Carlo Trotta Adam Young Abad Chabbi Iris Feigenwinter Bernard Heinesch Natalia Kowalska Matthias Mauder Ladislav Šigut Michiel van der Molen Flavio Bastos Campos Daniel Berveiller Christian Brümmer Matthias Cuntz Jean‐Christophe Domec Benjamin Dumont Silvano Fares Damiano Gianelle Rasmus Jensen Carmen Kalalian Natascha Kljun Holger Lange Jean‐Marc Limousin Erik Lundin Antonio Manco Leonardo Montagnani Eiko Nemitz Matthias Peichl Erkka Rinne Marilyn Roland Marius Schmidt Guillaume Simioni Abin Thomas Caroline Vincke Dario PapaleSammendrag
The lack of energy balance closure in Eddy‐Covariance (EC) measurements is a well‐known, still unresolved challenge in micrometeorology, with energy balance closure (EBC) rates typically ranging between 60% and 80%. While numerous hypotheses have been proposed to explain this imbalance, the relative contributions of neglected energy storage terms, data quality and flux processing options remain insufficiently disentangled. Using standardized ICOS and NEON datasets, we show that a significant portion of the observed energy imbalance can be attributed to overlooked or inconsistently handled energy components and turbulent flux quality control. Using data drawn from 84 sites, we show that comprehensive energy accounting—including soil heat flux, storage terms (soil, air, biomass), photosynthetic energy demand, and strict quality filtering of turbulent fluxes—improved EBC by 16% on average, with site‐specific gains up to 40%. However, we also identify a persistent residual imbalance that is unlikely to be resolved through methodological refinements or additional measurements alone, pointing to fundamental physical processes that are not accounted for in the standard measurement and processing. We argue that this unresolved imbalance should be explicitly acknowledged and bounded, rather than implicitly absorbed into correction schemes, and we outline practical guidance for diagnosing and interpreting EBC in standardized flux networks. This perspective evaluates methodological advances and residual uncertainties, providing an actionable framework for the appropriate use of EC energy fluxes in carbon, water, and climate research.
Forfattere
Hanna Huitu Tor-Einar Skog Christophe Pradal Antonio Calatayud Tor Skaslien Brita Linnestad Ari Ronkainen Christian Fournier Marc Labadie Dave Skirvin Matti Pastell David Melchior Johannes Tobiassen Langvatn Berit NordskogSammendrag
Decision support systems (DSS) in crop protection provide valuable support for pest risk prognosis and recommendations for pest control, enabling farmers to make better-informed decisions. As a part of the European Union’s strategy for the sustainable use of plant protection products, the “IPM Decisions” project developed an online platform that gives farmers and advisors access to a wide range of DSS for major pests, weeds, and diseases in a variety of crops across Europe. Multiple DSS models relevant for different crops and geographical regions of Europe were selected for integration in the platform. Information on the models is compiled into a model catalogue, which serves as a core component of the IPM Decisions platform. To facilitate the use of these models, two application programming interfaces (APIs) were developed. In line with the FAIR (findable, accessible, interoperable, and reusable) principles, the DSS API provides access to models and their metadata, including descriptions of input and output parameters. The weather API enables access to European online weather data sources and adapts this data to meet the requirements of DSS models. While these APIs are integrated into the IPM decisions platform, they are also open source, allowing other crop protection and farm management software to inspect, download, modify, install, run, and use them. In this article, we describe the development of the DSS and weather APIs, outline their structure and definitions, and present the services that DSS API and weather API provide. Finally, we demonstrate their application through three practical use cases.
Forfattere
Milica Fotirić Akšić Dragana Dabić Zagorac Marko Kitanović Kristina Đorđević Maja Natić Oddmund Frøynes Mekjell MelandSammendrag
Commercial production of sweet cherries is possible up to approximately 60° N latitude in Norway and is among the most economically important fruit crops in the country. The harvest is late, but yields are very high, and the fruit is intended solely for the fresh market. The objective of this study was to assess whether sweet cherry can be grown in pots and to determine fruit quality (sugar, acid, polyphenol, and mineral content) of three sweet cherry cultivars (‘Van’, ‘Lapins’, and ‘Regina’) grown in high tunnels with varying levels of fertigation (F) and the application of slow-release (SR) fertilisers. Trees were planted in 35 L plastic bags, trained as spindle trees, with a spacing of 1 × 2.5 m (4000 trees/ha). The tunnel was covered with polythene from flowering until harvest. Fruit produced in pots had low levels of sugars and acids and high levels of phenolic acids and flavonoids, while the mineral content depended on treatment and cultivar. The main sugar components (glucose and fructose), the sweetness index, phenolic acids (chlorogenic acid and ferulic acid), flavanols (catechin, rutin, quercetin, and hyperoside), and minerals (P, K, Mg, Ca, and Na) were much higher in the F treatment. SR treatments were more effective in increasing the content of acids (shikimic, malic, and quinic) and total phenolic content (TPC). Radical scavenging activity (RSA) and total sugars showed no statistically significant differences between the treatments studied. ‘Lapins’ fruit obtained from the fertigation regimes (when Kristalon brown + Calcinit + Magnesium-sulphate were added from mid-April to 1 September and plain water for the rest of the season, up to an electric conductivity (EC) of 0.5 and 1.0) contained the highest levels of minerals (P, K, Mg, Ca). The ‘Van’ cultivar from F treatments, especially VF2 (when Kristalon brown + Calcinit + Magnesium-sulphate were added from mid-April to 1 September and plain water for the rest of the season, with EC 1.0) and VF3 (when Kristalon brown is added in July, Kristalon brown + Calcinit + Magnesium-sulphate in August, and plain water for the rest of the season) had the highest sweetness index, glucose, fructose, chlorogenic acid, ferulic acid, and hyperoside in sweet cherry fruit. ‘Regina’ under the RSR1 (50 g Multicote and 30 g chalk lime per tree) and RSR2 regimes (100 g Multicote and 30 g chalk lime per tree) produced fruit with the highest acid components, RSA and TPC. This suggests that sweet cherry trees can be grown in pots under high tunnels, but nutrition should be adjusted for each cultivar according to its physiological responses to specific microclimate conditions.
Forfattere
Mikhail MastepanovSammendrag
The closed chamber method is widely used for measuring greenhouse gas fluxes (CO2, CH4, N2O) in natural and agricultural ecosystems. Automatic chambers are essential for long-term monitoring with high temporal resolution, but their production typically demands significant time, labor and expertise. While ready-to-use commercial solutions are available, many projects avoid them because of their high prices. We present a cost-effective and scalable alternative: modular automatic chambers built from off-the-shelf components. These chambers feature integrated valves and wireless controllers, enabling flexible deployment without the need for multiplexers. Systems can be easily expanded by adding more units. Our modular chambers have been successfully deployed in Arctic and subarctic field studies: north-eastern Greenland, natural wet tundra, two sites, 5 + 5 chambers, three summer seasons, CO2 and CH4 flux monitoring; northern Finland, natural boreal fen, 2–12 chambers, year-round measurements over four years, CO2 and CH4 fluxes; northern Norway, cultivated drained peatland, 30 chambers along a 300 m transect, four growing seasons (May–November), CO2, CH4, and N2O fluxes. Across all sites, the chambers demonstrated reliability, ease of construction, operation and maintenance. While further improvements are always possible, the current design offers a practical and accessible solution for the broader scientific community.
Sammendrag
The top‐of‐atmosphere (TOA) albedo controls the amount of solar energy absorbed by Earth and is influenced by the reflectivity of both the atmosphere and surface. With considerable changes in land use over the past few decades it is reasonable to question whether a perturbed surface albedo has influenced TOA albedo over the corresponding period. Here, we identify regions for which surface albedo changes have been the dominant driver of TOA albedo trends from 2001 to 2020 and examine the degree to which this relates to changes in snow cover, surface soil moisture, and vegetation density and greenness. We show that land surface albedo changes have been the dominant driver of TOA albedo trends in 10.0% of the global land area, within which surface albedo decreases have led to increases in absorbed solar radiation of 0.737 ± 4.984 Wm −2 from 2001 to 2020. This corresponds to global change in absorbed solar radiation of 0.019 ± 0.812 Wm −2 , which is equivalent to approximately 7.0% of the radiative forcing from anthropogenic CO 2 emissions from 2011 to 2019 (IPCC, 2021, https://doi.org/10.1017/9781009157896.009 ). Net TOA darkening above tundra and deserts constitutes 38.6% and 21.4%, respectively, to the radiative feedback identified, whereas temperate biomes induced net TOA brightening, corresponding to 22.3%. Collectively, changes in snow cover, vegetation density and greenness, and surface soil moisture drive 68.5% of the surface albedo changes. The importance of surface albedo in explaining TOA albedo trends for parts of the globe highlights the relevance of land surface changes in understanding Earth's energy imbalance.
Sammendrag
Accurate species identification is essential for conserving and managing plants that provide important ecosystem services and have ethnobotanical value. The Greyia tree genus ( G. sutherlandii , G. radlkoferi and G. flanaganii ) is endemic to South Africa and Eswatini, and certain genotypes have medicinal value for treating skin hyper‐pigmentation. However, distinguishing among species is difficult because of overlapping phenotypes and the limited resolution of standard DNA barcodes. To overcome these limitations, a robust molecular identification assay was developed using a two‐phase strategy. First, de novo SNP discovery using 3RAD sequencing identified 47,726 genome‐wide SNPs from two to three plants sampled from each species' core geographic range: G. radlkoferi in northern Limpopo, G. sutherlandii in eastern KwaZulu‐Natal, and G. flanaganii in the south‐eastern Eastern Cape. Principal component analysis and coancestry matrices revealed three discrete genetic clusters, supporting the recognition of the three species. Selecting a set of 200 SNPs with intermediate Fst values (0.2–0.5) resulted in optimal separation of the three clusters. This led to the final selection of a 23‐SNP panel that included five informative barcoding loci (ITS, trnL‐F , matK ). Second, the 23 SNPs were converted into allele‐specific fluorescent PCR assays (SNP Type) for genotyping on the BioMark HD platform. The panel was validated using genomic DNA from 17 individuals from the 3RAD population groups and successfully differentiated all three species. It was then applied to 73 trees sampled across a 1000‐km transect from the Eastern Cape to Limpopo. Genetic clustering (PCA, UPGMA and ADMIXTURE) assigned each tree to one of three species‐level groups matching their expected ranges. In a practical case study, the assay also identified the species origin of 33 Greyia trees of unknown provenance from production orchards. This study provides an efficient SNP‐based tool for accurate species identification, supporting conservation planning and the sustainable management of Greyia populations.
Sammendrag
Maximizing genetic response to selection while constraining inbreeding is a central challenge in breeding and conservation. Classic optimal contribution selection methods address this by managing average population coancestry. However, this often results in complex, nonlinear optimization problems that cannot be guaranteed to reach a global optimum. Furthermore, many applications require a stricter pairwise constraint to avoid immediate inbreeding in offspring. Here, we present a binary integer linear programming formulation to select an optimal subset of individuals under a strict maximum tolerable pairwise genomic relationship threshold. We construct a binary matrix indicating whether each pair exceeds this threshold. This reformulation transforms the problem from a complex nonlinear program into a binary integer linear program. While this formulation remains NP-hard, the linearity allows modern solvers to efficiently navigate the solution space and, when convergence is achieved within the imposed runtime and tolerance settings, certify global optimality, a key advantage over heuristic approaches. We demonstrate the method using two distinct datasets: a large Norway spruce breeding population and a conservation population of German Black Pied cattle. We explore the trade-offs between the selection response, the relationship threshold, and the maximum number of individuals that can be selected under the threshold. Although large, dense problem instances remain computationally demanding, our results show that typical applications can often be solved to proven global optimality in seconds, whereas denser instances may terminate with a remaining optimality gap. This method is a practical solution for breeders and conservation geneticists to select optimal subsets under a strict relationship threshold, enabling applications from maximizing gain in breeding populations to establishing genetic reserves for endangered species.
Sammendrag
Time and motion studies in forest operations benefit from video-based analysis, but manual annotation is time consuming. This pilot study aims to reduce analysis time by developing a deep-learning framework that classifies dashcam video into four work elements: crane out, cutting and processing, driving, and processing. Using a 3D ResNet-50 (PyTorchVideo) trained on manually annotated clips, the model achieved validation F1 = 0.88 and precision = 0.90, showing that spatiotemporal CNNs can capture rele-vant motion and appearance cues in forest environments. Overfitting indicates that more diverse data and better class balance are needed, but the approach shows clear potential to scale automated work-element monitoring and efficiency analysis.
Sammendrag
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Forfattere
Arne StensvandSammendrag
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