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.
2025
Forfattere
Alexander Oliver Jüterbock Hin Hoarau Heemstra Karin Andrea Wigger Bernardo Duarte Christian Guido Bruckner Annelise Chapman Delin Duan Aschwin Engelen Clement Gauci Griffin Goldstein Hill Zi-Min Hu Prabhat Khanal Ananya Khatei Amy Leigh Mackintosh Heidi Meland Ricardo Melo Anne Margrete Leiros Nilsen Leonore Olsen Ralf Rautenberger Henning Reiss Jie ZhangSammendrag
How to build a sustainable seaweed industry is important in Europe’s quest to produce 8 million tons of seaweed by 2030. Based on interviews with industry representatives and an expert-workshop, we developed an interdisciplinary roadmap that addresses sustainable development holistically. We argue that sustainable practices must leverage synergies with existing industries (e.g. IMTA systems, offshore wind farms), as the industry develops beyond experimental cultivation towards economic viability.
Forfattere
Tatsiana Espevig Kristine Sundsdal Victoria Stornes Moen Kate Entwistle Marina Usoltseva Sabine Braitmaier Daniel Hunt Carlos Guerrero Monica Skogen Erik LysøeSammendrag
Det er ikke registrert sammendrag
Sammendrag
This article presents a novel, ultralight tree planting mechanism for use on an aerial vehicle. Current tree planting operations are typically performed manually, and existing automated solutions use large land-based vehicles or excavators which cause significant site damage and are limited to open, clear-cut plots. Our device uses a high-pressure compressed air power system and a novel double-telescoping design to achieve a weight of only 8 kg: well within the payload capacity of medium to large drones. This article describes the functionality and key components of the device and validates its feasibility through experimental testing. We propose this mechanism as a cost-effective, highly scalable solution that avoids ground damage, produces minimal emissions, and can operate equally well on open clear-cut sites as in denser, selectively-harvested forests.
Forfattere
Ellen Johanne Svalheim Bolette Bele Bjørn Egil Flø Elin Blütecher Synnøve Grenne Marie Uhlen Maurset Pål ThorvaldsenSammendrag
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Forfattere
Anne MuolaSammendrag
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Sammendrag
Det er ikke registrert sammendrag
Forfattere
Anne MuolaSammendrag
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Forfattere
Lucas K. Johnson Zhiqiang Yang Angela Erb Ryan Bright Grant M. Domke Tracey S. Frescino Crystal B. Schaaf Sean P. HealeySammendrag
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Forfattere
Jari Hynynen Narayanan Subramanian Clara Antón Fernandéz Soili Haikarainen Emma Holmström Micky Allen Saija Huuskonen Jouni Siipilehto Hannu Salminen Mika Lehtonen Kjell Andreassen Urban NilssonSammendrag
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Forfattere
Andreas Hagenbo Lise Dalsgaard Marius Hauglin Stephanie Eisner Line Tau Strand O. Janne KjønaasSammendrag
Boreal forest soils are a critical terrestrial carbon (C) reservoir, with soil organic carbon (SOC) stocks playing a key role in global C cycling. In this study, we generated high-resolution (16 m) spatial predictions of SOC stocks in Norwegian forests for three depth intervals: (1) soil surface down to 100 cm depth, (2) forest floor (LFH layer), and (3) 0–30 cm into the mineral soil. Our predictions were based on legacy soil data collected between 1988 and 1992 from a subset (n = 1014) of National Forest Inventory plots. We used boosted regression tree models to generate SOC estimates, incorporating environmental predictors such as land cover, site moisture, climate, and remote sensing data. Based on the resulting maps, we estimate total SOC stocks of 1.57–1.87 Pg C down to 100 cm, with 0.55–0.66 Pg C stored in the LFH layer and 0.68–0.80 Pg C in the upper mineral soil. These correspond to average SOC densities of 15.3, 5.4, and 6.6 kg C m−2, respectively. We compared the predictive performance of these models with another set, supplemented by soil chemistry variables. These models showed higher predictive performance (R2 = 0.65–0.71) than those used for mapping (R2 = 0.44–0.58), suggesting that the mapping models did not fully capture environmental variability influencing SOC stock distributions. Within the spatial predictive models, Sentinel-2 Normalized Difference Vegetation Index, depth to water table, and slope contributed strongly, while soil nitrogen and manganese concentrations had major roles in models incorporating soil chemistry. Prediction uncertainties were related to soil depth, soil types, and geographical regions, and we compared the spatial prediction against external SOC data. The generated maps of this offer a valuable starting point for identifying forest areas in Norway where SOC may be vulnerable to climate warming and management-related disturbances, with implications for soil CO2 emissions.