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
Jarkko Hantula Malin Elfstrand Anne-Maarit Hekkala Ari Hietala Juha Honkaniemi Maartje Klapwijk Matti Koivula Juho Matala Jonas Rönnberg Juha Siitonen Fredrik WidemoSammendrag
Det er ikke registrert sammendrag
Forfattere
Alexey Mikaberidze Christian Cruz Ayalsew Zerihun Abel Barreto Pieter S. Beck Rocío Calderón Carlos Camino Rebecca Campbell Stephanie Delalieux Frederic Fabre Elin Falla Stuart Fraser Kaitlin Gold Carlos Gongora-Canul Frédéric Hamelin Dalphy Ondine Camira Harteveld Cheng-Fang Hong Melen Leclerc Da-Young Lee Murillo Lobo Jr Anne-Katrin Mahlein Emily McLay Paul Melloy Stephen Parnell Uwe Rascher Jack Rich Irene Sarlotti Samuel Soubeyrand Susie Sprague Antony Surano Sandhya Takooree Thomas Taylor Suzanne Touzeau Pablo Zarco-Tejada Nik CunniffeSammendrag
Plant diseases impair yield and quality of crops and threaten the health of natural plant communities. Epidemiological models can predict disease and inform management. However, data are scarce, since traditional methods to measure plant diseases are resource intensive and this often limits model performance. Optical sensing offers a methodology to acquire detailed data on plant diseases across various spatial and temporal scales. Key technologies include multispectral, hyperspectral and thermal imaging, and light detection and ranging; the associated sensors can be installed on ground-based platforms, uncrewed aerial vehicles, aeroplanes and satellites. However, despite enormous potential for synergy, optical sensing and epidemiological modelling have rarely been integrated. To address this gap, we first review the state-of-the-art to develop a common language accessible to both research communities. We then explore the opportunities and challenges in combining optical sensing with epidemiological modelling. We discuss how optical sensing can inform epidemiological modelling by improving model selection and parameterisation and providing accurate maps of host plants. Epidemiological modelling can inform optical sensing by boosting measurement accuracy, improving data interpretation and optimising sensor deployment. We consider outstanding challenges in: A) identifying particular diseases; B) data availability, quality and resolution, C) linking optical sensing and epidemiological modelling, and D) emerging diseases. We conclude with recommendations to motivate and shape research and practice in both fields. Among other suggestions, we propose to standardise methods and protocols for optical sensing of plant health and develop open access databases including both optical sensing data and epidemiological models to foster cross-disciplinary work.
Forfattere
Peter Waldner Katrin Meusburger Bruno de Vos Henning Meesenburg Kai Schwärzel Carmen Iacoban Zoran Galic Arne Verstraeten Andreas Schmitz Aldo Marchetto Nicholas Clarke Heleen Deroo Nathalie Cools Anne Thimonier Vera Fadrhonsovà Holger Sennhenn-Reulen Anna Andreetta Elena Vanguelova Antti-Jussi Lindroos Anita Zolles Tiina M. NieminenSammendrag
Det er ikke registrert sammendrag
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.
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.
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Samuel L. Zelinka Samuel V. Glass Natalia Farkas Emil E. Thybring Michael Altgen Lauri Rautkari Simon Curling Jinzhen Cao Yujiao Wang Tina Künniger Gustav Nyström Christopher Hubert Dreimol Ingo Burgert Mark G. Roper Darren P. Broom Matthew Schwarzkopf Arief Yudhanto Mohammad Subah Gilles Lubineau Maria Fredriksson Wiesław Olek Jerzy Majka Nanna Bjerregaard Pedersen Daniel J. Burnett Armando R. Garcia Frieder Dreisbach Louis Waguespack Jennifer Schott Luis G. Esteban Alberto García‑Iruela Thibaut Colinart Romain Rémond Brahim Mazian Patrick Perré Lukas EmmerichSammendrag
Det er ikke registrert sammendrag
Forfattere
Ingrid Marie Garfelt Paulsen Isabell Eischeid Åshild Ønvik Pedersen Jakob J. Assmann Nigel Yoccoz Jesper Bruun Mosbacher Eeva M Soininen Virve RavolainenSammendrag
The normalized difference vegetation index (NDVI) is a critical tool for studying Arctic vegetation patterns and changes, but more knowledge is needed about its links with plant biomass and disturbances, especially in sparsely vegetated habitats in the High Arctic. Here, we investigate the relationship between NDVI and vascular plant biomass, summer temperature, goose disturbance, and winter damage in Dryas ridge and moss tundra habitats on Svalbard, all recorded in the corresponding year across a 5-year time series. We test these relationships using mixed-effect models at two spatial resolutions (10 cm and 10 m) and two extents with data from drone and Sentinel-2 imagery. We found that in our plots, an increase in biomass of 100 g m−2 increased NDVI from drone imagery by 0.08 ± 0.03 (95% CI) for Dryas ridge and by 0.04 ± 0.03 for moss tundra. Despite record-warm summers, temperature of the same summer was not associated with NDVI in our time-series. In moss tundra, severe goose disturbance had a negative relationship with drone NDVI in plots, while in Dryas ridge habitat, winter damage had no clear correspondence with NDVI. Our study provides an example of context dependencies highlighted in remote-sensing literature in the Arctic, encouraging future studies to include effects of disturbance on NDVI and to establish habitat-specific relationships with NDVI.
Sammendrag
Context Dairy farming contributes approximately 2.5 % of annual global anthropogenic greenhouse gas (GHG) emissions, necessitating effective mitigation strategies. Two approaches are often discussed: low-intensity, low-cost production with minimal reliance on purchased inputs; and high-intensity production with higher-yielding cows to reduce land use and reduce methane emissions per unit of milk. Objective The objective was to identify management factors and farm characteristics that explain variations in GHG emissions, environmental, and economic performance. Indicators included were GHG emissions, land use occupation, energy intensity, nitrogen intensity, and gross margin. Methods Life Cycle Assessment (LCA) was used to calculate the environmental impacts for 200 commercial dairy farms in Central Norway based on farm activities, purchased inputs, machinery, and buildings from 2014 to 2016. A multiple regression analysis with backward elimination was conducted to highlight important variables for environmental impact and economic outcome. Results and conclusions A higher share of dairy cows was found to be the most important factor in reducing GHG emissions, energy and nitrogen intensity, and land use but also to decrease gross margin. Additional key factors for reducing environmental impact included less purchased nitrogen fertiliser, and higher forage yield. There were no statistical correlations between GHG emissions and gross margin per MJ of human-edible energy delivered. Significance Conducting LCA for many dairy farms allows to highlight important factors influencing environmental impact and economic outcome. Using the delivery of human-edible energy from milk and meat as a functional unit allows for a combined evaluation of milk and meat production on a farm.