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.
2025
Abstract
No abstract has been registered
Authors
Paul Eric Aspholm Carmen Rizzo Gabriella Caruso Giovanna Maimone Luisa Patrolecco Marco Termine Marco Bertolino Stefania Giannarelli Alessandro Ciro Rappazzo Josef Elster Alessio Lena Maria Papale Tanita Pescatore Jasmin Rauseo Rosamaria Soldano Francesca Spataro Maurizio Azzaro Angelina Lo GiudiceAbstract
No abstract has been registered
Authors
Meriel McClatchie Véronique Matterne Núria Rovira Buendia Mila Andonova Ulrike Lohwasser Wendy Marie Waalen Filippos Bantis Marija Knez Jelena Milešević Amil Orahovac Paolo Prosperi Aparajita Banerjee Ivana Radić Aldona Mueller-Bieniek Meline Beglaryan Donal Murphy-Bokern David Gil Bálint Balázs Sónia NegrãoAbstract
No abstract has been registered
Authors
Ramūnas Digaitis Greeley Beck Sune Tjalfe Thomsen Maria Fredriksson Emil Engelund ThybringAbstract
No abstract has been registered
Authors
Lathika Y. Hitige Rashmi N.J.K. Arachchi Nimal Ratnayake Miyuru Gunathilake Upaka RathnayakeAbstract
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Authors
Udara Senatilleke Ravindu Panditharathne Ruchiru D. Herath Dushyantha M. Aththanayake Randika K. Makubura Sajana Hemakumara Miyuru Gunathilake Hazi Md. Azamathulla Komali Kantamaneni Upaka RathnayakeAbstract
No abstract has been registered
Abstract
Reliable estimates of the size and composition of harvested populations over time are key to designing adequate population management plans, regardless of management objectives. In Norway, a national system for collecting and analysing hunter-reported data on red deer (Cervus elaphus) has been operational for about 20 years. The system was expected to provide population metrics that would substantially improve deer population management routines at the municipal level. This has proven to be challenging when using existing state-of-the-art estimation methodology. The main reasons are that the variation in the observation data is generally much larger than population abundance variability, and that one does not have a clear understanding of the stochastic process generating the observation data. Here, using hunter-reported observation data and harvest data from six Norwegian municipalities collected in the period 2007–2023, we show that a straightforward estimation methodology based on population modelling can produce robust abundance estimates despite frequent low quality of the observation data. Its major assets are that it does not involve strong assumptions about the stochastic processes underlying the observation process and that it does not involve assumptions about initial population size and structure in terms of prior statistical distributions. We anticipate that the method can be applied in several other population management contexts, and we think that the results offer fresh perspectives on to what extent noisy citizen-collected time series data can be used to inform management decisions.
Authors
Mohammad Tirgariseraji A. Pouyan Nejadhashemi Mahmood Sabouhi Sabouni Yaghoob Jafari Tomas Persson Alisher Mirzabaev Alireza Nikouei Kieron Moller Naser Shahnoushi ForoushaniAbstract
Food production is the primary source of nitrogen pollution, which has significantly impacted the nitrogen cycle and exceeded the nitrogen-safe operation space of the planet. The objective of this study is to assess the effectiveness of the Nitrogen Regulatory Policy (NRP) in reducing nitrogen fertilizer use under population pressure for meat, dairy, wheat, and potatoes in the Zayandeh-Rud River basin, Iran. The methodology of this study involves two main components. First, an elasticity criterion was formulated to assess the trade-off between nitrogen fertilizer use and food production capacity. This criterion integrates optimized cropland, the Block of Distributed Calories (BDC), and nitrogen fertilizer use, with food production capacity quantified in terms of the BDC at its optimal level. Second, the simulated distribution of the elasticity criterion was analyzed using Simulation and Econometrics to Analyze Risk (Simetar), defining elastic and inelastic zones to capture the variability in the trade-off under different conditions. The results of this study identified key factors influencing the elastic and inelastic ranges of the elasticity criterion, including technological change, the weight of diet components in dietary preferences, and the diminishing returns of the NRP. The NRP solution aims to reduce nitrogen fertilizer use by targeting a lower application range. It addresses the challenges of fertilizer management under population pressure, specifically for farming systems in the Zayandeh-Rud River basin operating at the ‘diminishing marginal production’ stage. The trade-off between livestock and non-livestock diet components enhances nitrogen fertilizer efficiency under population pressure as long as livestock components remain within the elastic zone and non-livestock components stay within the inelastic zone. The novelty of this study lies in the introduction of the elasticity criterion for nitrogen fertilizer use under population pressure. This innovative metric highlights the risk of ineffective trade-offs between food production capacity and nitrogen fertilizer adjustments, offering a crucial tool to guide sustainable agricultural practices within the defined criterion ranges.
Authors
Linn Fenna Groeneveld Oksana Bekkevold Trond Bergskås Martin Linkogel Cord Luellmann Marit AlmvikAbstract
Regulatory bodies aim to protect consumers from harmful substances. The use of certain antibiotics is prohibited in food-producing animals in the EU due to their potential detrimental effects on humans. Among these are nitrofuran antibiotics, which degrade rapidly so that their metabolites are used as markers in screening for their illegal use. The use of one metabolite, semicarbazide (SEM), as a marker for detecting the antibiotic nitrofurazone, has been criticized due to the many pathways it can be formed by and its natural occurrence in some food items. A recent change in the reference point of action (RPA) for SEM, as stated in Commission Regulation (EU) 2019/1871, due to a reassessment of sensitivity of the analyses,poses a problem for the export of heather honey in Norway. Norwegian heather honey seems to exceed the lowered RPA in numerous cases. Here we show that Norwegian heather honey samples, but not polyfloral ‘summer’ honey samples from the same hives, contain SEM. The simplest explanation for the demonstrated pattern is a natural source of SEM in heather honey, not the use of a banned antibiotic. Based on our results, we propose that an exception to the EU regulation should be added, exempting heather honey derived from Calluna vulgaris unless other nitrofurans or their metabolites are found together with SEM.
Abstract
The precise spatially explicit data on land cover and land use changes is one of the essential variables for enhancing the quantification of greenhouse gas emissions and removals, which is relevant for meeting the goal of the European economy and society to become climate-neutral by 2050. The accuracy of the machine learning models trained on remote-sensed data suffers from a lack of reliable training datasets and they are often site-specific. Therefore, in this study, we proposed a method that integrates the bi-temporal analysis of the combination of spectral indices that detects the potential changes, which then serve as reference data for the Random Forest classifier. In addition, we examined the transferability of the pre-trained model over time, which is an important aspect from the operational point of view and may significantly reduce the time required for the preparation of reliable and accurate training data. Two types of vegetation losses were identified: woody coverage converted to non-woody vegetation, and vegetated areas converted to sealed surfaces or bare soil. The vegetation losses were detected annually over the period 2018–2021 with an overall accuracy (OA) above 0.97 and a Kappa coefficient of 0.95 for all time intervals in the study regions in Poland and Norway. Additionally, the pre-trained model’s temporal transferability revealed an improvement of the OA by 5 percentage points and the macroF1-Score value by 12 percentage points compared to the original model.