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.
2024
Authors
Emil Sandström Tove Ortman Christine A Watson Jan Bengtsson Clara Gustafsson Göran BergkvistAbstract
One of the major challenges facing agricultural and food systems today is the loss of agrobiodiversity. Considering the current impasse of preventing the worldwide loss of crop diversity, this paper highlights the possibility for a radical reorientation of current legal seed frameworks that could provide more space for alternative seed systems to evolve which centre on norms that support on-farm agrobiodiversity. Understanding the underlying norms that shape seed commons are important, since norms both delimit and contribute to what ultimately will constitute the seeds and who will ultimately have access to the seeds and thus to the extent to which agrobiodiversity is upheld and supported. This paper applies a commoning approach to explore the underpinning norms of a Swedish seed commons initiative and discusses the potential for furthering agrobiodiversity in the context of wider legal and authoritative discourses on seed enclosure. The paper shows how the seed commoning system is shaped and protected by a particular set of farming norms, which allows for sharing seeds among those who adhere to the norms but excludes those who will not. The paper further illustrates how farmers have been able to navigate fragile legal and economic pathways to collectively organize around landrace seeds, which function as an epistemic farming community, that maintain landraces from the past and shape new landraces for the present, adapted to diverse agro-ecological environments for low-input agriculture. The paper reveals how the ascribed norms to the seed commons in combination with the current seed laws set a certain limit to the extent to which agrobiodiversity is upheld and supported and discusses why prescriptions of “getting institutions right” for seed governance are difficult at best, when considering the shifting socio-nature of seeds. To further increase agrobiodiversity, the paper suggests future seed laws are redirected to the sustenance of a proliferation of protected seed commoning systems that can supply locally adapted plant material for diverse groups of farmers and farming systems.
Authors
Fernanda Leiva Florent Abdelghafour Muath K Alsheikh Nina Elisabeth Nagy Jahn Davik Aakash ChawadeAbstract
Common scab (CS) is a major bacterial disease causing lesions on potato tubers, degrading their appearance and reducing their market value. To accurately grade scab-infected potato tubers, this study introduces “ScabyNet”, an image processing approach combining color-morphology analysis with deep learning techniques. ScabyNet estimates tuber quality traits and accurately detects and quantifies CS severity levels from color images. It is presented as a standalone application with a graphical user interface comprising two main modules. One module identifies and separates tubers on images and estimates quality-related morphological features. In addition, it enables the extraction of tubers as standard tiles for the deep-learning module. The deep-learning module detects and quantifies the scab infection into five severity classes related to the relative infected area. The analysis was performed on a dataset of 7154 images of individual tiles collected from field and glasshouse experiments. Combining the two modules yields essential parameters for quality and disease inspection. The first module simplifies imaging by replacing the region proposal step of instance segmentation networks. Furthermore, the approach is an operational tool for an affordable phenotyping system that selects scab-resistant genotypes while maintaining their market standards.
Authors
Pia Heltoft Thomsen Karin Juul Hesselsøe Hannesson Bjarni Karin Normann Per Rasmussen Michelle DaCosta Eric Watkins Andrew Hollman Anne Friederike Borchert Trygve S. Aamlid Tatsiana Espevig Trond Olav Pettersen Jørgen HornslienAbstract
No abstract has been registered
Abstract
Scots pine (Pinus sylvestris L.) is a commercially important forest tree species in many Eurasian countries. Its wood has been commonly utilized for production of construction timber. In Sweden, a breeding program was launched in 1950s to improve Scots pine trees to better suit industrial requirements. The emphasis was mainly put on improving stem volume, vitality, stem straightness and branching characteristics whilst wood quality was neglected. However, since some of the important wood quality traits are negatively correlated with the prioritized volume production, the continuation of such an approach could in a long run lead to irreversible deterioration of wood quality. In our study, we focused on wood quality traits that are relevant for construction timber – wood density, stiffness, strength, grain angle and sawn-board shape stability (crook, bow and twist). We linked wood quality traits nondestructively assessed on standing trees with those measured on sawn boards. We estimated narrow-sense heritabilities, genetic correlations and correlated responses to selection with the aim of identifying reliable techniques for wood quality assessment on standing trees and proposing suitable strategies for incorporating wood quality traits into the breeding program. We have concluded that standing-tree drilling resistance, acoustic velocity and grain angle are good predictors of wood density, wood stiffness & strength, and sawn-board twisting, respectively. Taking into account the long-term development on wood market, we are proposing an inclusion of wood density in the breeding program, in the way that it will be retained at the current levels rather than increased, which would also positively affect wood stiffness and strength. Furthermore, we are suggesting to consider grain angle as a breeding trait although more research is needed to unravel its underlying biological mechanism.
Authors
Darius KviklysAbstract
No abstract has been registered
Authors
Dylan Grobler Juliana D. Klein Matthew L. Dicken Kolobe L, Mmonwa Michelle Soekoe Michaela van Staden Snorre Hagen Simo Maduna Aletta E. Bester-van der MerweAbstract
Globally, hammerhead sharks have experienced severe declines owing to continued overexploitation and anthropogenic change. The smooth hammerhead shark Sphyrna zygaena remains understudied compared to other members of the family Sphyrnidae. Despite its vulnerable status, a comprehensive understanding of its genetic landscape remains lacking in many regions worldwide. The present study aimed to conduct a fine-scale genomic assessment of Sphyrna zygaena within the highly dynamic marine environment of South Africa's coastline, using thousands of single nucleotide polymorphisms (SNPs) derived from restriction site-associated DNA sequencing (3RAD). A combination of differentiation-based outlier detection methods and genotype-environment association (GEA) analysis was employed in Sphyrna zygaena. Subsequent assessments of putatively adaptive loci revealed a distinctive south to east genetic cline. Among these, notable correlations between adaptive variation and sea-surface dissolved oxygen and salinity were evident. Conversely, analysis of 111,243 neutral SNP markers revealed a lack of regional population differentiation, a finding that remained consistent across various analytical approaches. These results provide evidence for the presence of differential selection pressures within a limited spatial range, despite high gene flow implied by the selectively neutral dataset. This study offers notable insights regarding the potential impacts of genomic variation in response to fluctuating environmental conditions in the circumglobally distributed Sphyrna zygaena.
Abstract
No abstract has been registered
Abstract
Aim Seedling recruitment is a vital process for forest regeneration and is influenced by various factors such as stand composition, climate, and soil disturbance. We conducted a long-term field experiment (18 years) to study the effects of these factors and their interactions on seedling recruitment. Location Our study focused on five main species in boreal mixed woods of eastern Canada: trembling aspen (Populus tremuloides), paper birch (Betula papyrifera), white spruce (Picea glauca), balsam fir (Abies balsamea), and white cedar (Thuja occidentalis). Methods Sixteen 1-m2 seedling monitoring subplots were set up in each of seven stands originating from different wildfires (fire years ranging from 1760 to 1944), with a soil scarification treatment applied to every other subplot. Annual new seedling counts were related to growing-season climate (mean temperature, growing degree days and drought code), scarification, and stand effects via a Bayesian generalized linear mixed model. Results Soil scarification had a large positive effect on seedling recruitment for three species (aspen, birch and spruce). As expected, high mean temperatures during the seed production period (two years prior to seedling emergence) increased seedling recruitment for all species but aspen. Contrary to other studies, we did not find a positive effect of dry conditions during the seed production period. Furthermore, high values of growing degree days suppressed conifer seedling recruitment. Except for white cedar, basal area was weakly correlated with seedling abundance, suggesting a small number of reproductive individuals is sufficient to saturate seedling recruitment. Conclusion Our findings underscore the importance of considering multiple factors, such as soil disturbance, climate, and stand composition, as well as their effects on different life stages when developing effective forest management strategies to promote regeneration in boreal mixed-wood ecosystems.
Abstract
This study focuses on advancing individual tree crown (ITC) segmentation in lidar data, developing a sensor- and platform-agnostic deep learning model transferable across a spectrum of dense laser scanning datasets from drone (ULS), to terrestrial (TLS), and mobile (MLS) laser scanning data. In a field where transferability across different data characteristics has been a longstanding challenge, this research marks a step towards versatile, efficient, and comprehensive 3D forest scene analysis. Central to this study is model performance evaluation based on platform type (ULS vs. MLS) and data density. This involved five distinct scenarios, each integrating different combinations of input training data, including ULS, MLS, and their augmented versions through random subsampling, to assess the model's transferability to varying resolutions and efficacy across different canopy layers. The core of the model, inspired by the PointGroup architecture, is a 3D convolutional neural network (CNN) with dedicated prediction heads for semantic and instance segmentation. The model underwent comprehensive validation on publicly available, machine learning-ready point cloud datasets. Additional analyses assessed model adaptability to different resolutions and performance across canopy layers. Our results reveal that point cloud random subsampling is an effective augmentation strategy and improves model performance and transferability. The model trained using the most aggressive augmentation, including point clouds as sparse as 10 points m−2, showed best performance and was found to be transferable to sparse lidar data and boosts detection and segmentation of codominant and dominated trees. Notably, the model showed consistent performance for point clouds with densities >50 points m−2 but exhibited a drop in performance at the sparsest level (10 points m−2), mainly due to increased omission rates. Benchmarking against current state-of-the-art methods revealed boosts of up to 20% in the detection rates, indicating the model's superior performance on multiple open benchmark datasets. Further, our experiments also set new performance baselines for the other public datasets. The comparison highlights the model's superior segmentation skill, mainly due to better detection and segmentation of understory trees below the canopy, with reduced computational demands compared to other recent methods. In conclusion, the present study demonstrates that it is indeed feasible to train a sensor-agnostic model that can handle diverse laser scanning data, going beyond current sensor-specific methodologies. Further, our study sets a new baseline for tree segmentation, especially in complex forest structures. By advancing the state-of-the-art in forest lidar analysis, our work also lays the foundation for future innovations in ecological modeling and forest management.
Authors
Nina SvartedalAbstract
No abstract has been registered