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
Abstract
Honey can be contaminated by various natural and anthropogenic substances, posing a health risk to consumers. Pyrrolizidine alkaloids (PAs) are naturally toxic compounds many plant species produce to protect against herbivores. Honey may become contaminated if bees collect nectar and pollen from PA-producing plants. Clopyralid is the active ingredient in some herbicides, including Matrigon 72 SG, approved for weed control in oilseed rape in several countries. As a systemic substance, its application before flowering may contaminate nectar, pollen, and honey. In 2023, 30 Norwegian honey samples were tested for the content of PAs and 22 other honey samples for clopyralid. Pyrrolizidine alkaloids were detected in 20 per cent of the samples, but predominantly at low levels (<12 μg kg−1). One sample contained a higher level (27.8 μg kg−1). Clopyralid was detected at levels exceeding the EU Maximum Residue Level (MRL) at the time (0.05 mg kg−1) and the current EU MRL (2024) (0.15 mg kg−1) in seven of 22 honey samples, including five honey samples produced close to clopyralid treated oilseed rape fields, one honey sample collected next to unsprayed fields, and in one sample received from a beekeeper. It was later clarified that beehives in proximity to unsprayed cropping areas with honey with a high clopyralid content also were close to conventional clopyralid-treated oilseed rape fields. The results indicate that a more extensive survey would be appropriate to evaluate whether PAs and clopyralid are a common problem in Norwegian honeybee products.
Authors
Stefano Puliti Binbin Xiang Maciej Wielgosz Eivind Handegard Nicolas Cattaneo Marta Vergarechea Terje Gobakken Juha Hyyppä Erik Næsset Mikko Vastaranta Tuomas Yrttimaa Rasmus AstrupAbstract
Accurately determining the age of individual trees is important for understanding forest dynamics, tree growth, site productivity and describing ecological processes. Traditional methods, such as dendrochronological coring, are invasive, labor-intensive, and costly. This study investigates the use of deep learning (DL) to predict tree age from high-density laser scanning data as a scalable, non-invasive alternative. The dataset includes approximately 1700 tree point clouds from approx. 1 K trees across Norway, Sweden, and Finland, encompassing Norway spruce (Picea abies) and Scots pine (Pinus sylvestris) and a broad range of tree age and developmental stages, from young seedlings (1 year) to old trees (∼350 years). Data were collected using terrestrial, mobile, and high-density airborne laser scanning platforms, enabling the development of sensor-agnostic models. We evaluated multiple modelling approaches, from linear regression to transformer architectures, using both training-from-scratch and fine-tuning strategies. Models fine-tuned starting from pre-trained weights from ForestFormer3D's U-Net as well as the transformer architecture (PointTransformerV3) trained from scratch, proved effective for age regression (RMSE ≤23 years). Although our analysis was limited to two tree species, we demonstrated that a single joint age-estimation model can be successfully trained for both species. We demonstrate that models trained on high-resolution data can generalize to lower-resolution, less costly inputs, provided that data augmentations that mimic reduced resolutions are included during training. This study presents a data-driven framework for estimating tree age without destructive sampling. The findings support the potential for AI-based methods to complement or replace traditional age estimation techniques in forest inventory and monitoring.
Authors
Josef Taher Eric Hyyppä Matti Hyyppä Klaara Salolahti Xiaowei Yu Leena Matikainen Antero Kukko Matti Lehtomäki Harri Kaartinen Sopitta Thurachen Paula Litkey Ville Luoma Markus Holopainen Gefei Kong Hongchao Fan Petri Rönnholm Matti Vaaja Antti Polvivaara Samuli Junttila Mikko Vastaranta Stefano Puliti Rasmus Astrup Joel Kostensalo Mari Myllymäki Maksymilian Kulicki Krzysztof Stereńczak Raul de Paula Pires Ruben Valbuena Juan Pedro Carbonell-Rivera Jesús Torralba Yi-Chen Chen Lukas Winiwarter Markus Hollaus Gottfried Mandlburger Narges Takhtkeshha Fabio Remondino Maciej Lisiewicz Bartłomiej Kraszewski Xinlian Liang Jianchang Chen Eero Ahokas Kirsi Karila Eugeniu Vezeteu Petri Manninen Roope Näsi Heikki Hyyti Siiri Pyykkönen Peilun Hu Juha HyyppäAbstract
No abstract has been registered
Abstract
Farm animal welfare is assessed for compliance with legislation and labelling schemes, to evaluate the impacts of management change and for animal welfare research. Here, we ask: how can we assess how the animal experiences its life on farm? Animal welfare is what the animal experiences and spans from very negative to very positive emotional states. Welfare is influenced by nutrition, the environment, health and the behavioural interactions that animals have, integrated through the impacts that these have on the mental state of the animal. Whilst we can often readily measure and describe the conditions under which the animal is housed on farm (inputs), and the physical state of the animal within these conditions, these are not sufficient to tell us what the animal is experiencing. The Gold Standard for animal welfare, therefore, would be a measure that tells us something about the mental state of the animal. Currently, this is not considered a directly measurable entity, and it may remain a hypothetical rather than a realistic construct in welfare assessment. Thus, welfare assessment relies on a series of measurable outputs, or indicators, that serve as proxies for the welfare state. Emotional state and welfare are then inferred from these measures. Animal-based measures are considered the most relevant to understanding animal experience, although these require rigorous assessment of the validity and reliability of measurement. Many animal-based measures rely on the assessment of behaviour. These may be ongoing changes in response, such as approach or avoidance, facial expression, Qualitative Behaviour Assessment and circadian rhythms or complex patterns of behaviour, such as preferences, motivational priorities, or perceptions, which allow deeper insight into the likely mental state of the animal. Many measures are only suitable for research purposes at present and can be challenging to assess on farm, but some may be amenable to the use of sensors or automated methods in the future. These have technological and ethical challenges to overcome and require an understanding of what is being measured and how this relates to the animal’s mental state but may offer opportunities for continual assessment of animal behaviour and welfare in the future. Currently, however, theoretical understanding of animal welfare, particularly positive welfare and cumulative ‘Quality of Life’, is not well-captured in existing on−farm welfare assessment. We conclude that there is a need for a more comprehensive and integrated approach to the development of methods that can truly address the animal’s experience on farm.
Authors
Darius KviklysAbstract
No abstract has been registered
Authors
Darius KviklysAbstract
No abstract has been registered
Authors
Darius KviklysAbstract
No abstract has been registered
Authors
Darius KviklysAbstract
No abstract has been registered
Authors
Darius KviklysAbstract
No abstract has been registered
Authors
Darius KviklysAbstract
No abstract has been registered