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.
2021
Authors
Mekjell Meland Milica Fotiric-Aksic Fuad Gasi Radoslav CerovicAbstract
No abstract has been registered
Authors
Iwona Dembicz Jürgen Dengler Manuel J. Steinbauer Thomas J. Matthews Sándor Bartha Sabina Burrascano Alessandro Chiarucci Goffredo Filibeck François Gillet Monika Janišová Salza Palpurina David Storch Werner Ulrich Svetlana Aćić Steffen Boch Juan Antonio Campos Laura Cancellieri Marta Carboni Giampiero Ciaschetti Timo Conradi Pieter De Frenne Jiri Dolezal Christian Dolnik Franz Essl Edy Fantinato Itziar García-Mijangos Gian Pietro Giusso del Galdo John Arvid Grytnes Riccardo Guarino Behlul Güler Jutta Kapfer Ewelina Klichowska Łukasz Kozub Anna Kuzemko Swantje Löbel Michael Manthey Corrado Marcenó Anne Mimet Alireza Naqinezhad Jalil Noroozi Arkadiusz Nowak Harald Pauli Robert K. Peet Vincent Pellissier Remigiusz Pielech Massimo Terzi Emin Uğurlu Orsolya Valko Iuliia Vasheniak Kiril Vassilev Denys Vynokurov Hannah J. White Wolfgang Willner Manuela Winkler Sebastian Wolfrum Jinghui Zhang Idoia BiurrunAbstract
Questions Which environmental factors influence fine-grain beta diversity of vegetation and do they vary among taxonomic groups? Location Palaearctic biogeographic realm. Methods We extracted 4,654 nested-plot series with at least four different grain sizes between 0.0001 m² and 1,024 m² from the GrassPlot database, covering a wide range of different grassland and other open habitat types. We derived extensive environmental and structural information for these series. For each series and four taxonomic groups (vascular plants, bryophytes, lichens, all), we calculated the slope parameter (z-value) of the power law species–area relationship (SAR), as a beta diversity measure. We tested whether z-values differed among taxonomic groups and with respect to biogeographic gradients (latitude, elevation, macroclimate), ecological (site) characteristics (several stress–productivity, disturbance and heterogeneity measures, including land use) and alpha diversity (c-value of the power law SAR). Results Mean z-values were highest for lichens, intermediate for vascular plants and lowest for bryophytes. Bivariate regressions of z-values against environmental variables had rather low predictive power (mean R² = 0.07 for vascular plants, less for other taxa). For vascular plants, the strongest predictors of z-values were herb layer cover (negative), elevation (positive), rock and stone cover (positive) and the c-value (U-shaped). All tested metrics related to land use (fertilization, livestock grazing, mowing, burning, decrease in naturalness) led to a decrease in z-values. Other predictors had little or no impact on z-values. The patterns for bryophytes, lichens and all taxa combined were similar but weaker than those for vascular plants. Conclusions We conclude that productivity has negative and heterogeneity positive effects on z-values, while the effect of disturbance varies depending on type and intensity. These patterns and the differences among taxonomic groups can be explained via the effects of these drivers on the mean occupancy of species, which is mathematically linked to beta diversity.
Abstract
No abstract has been registered
Authors
Jorunn Elisabeth Olsen Camilo Chiang Hazel Aynaga Navidad Oda Toresdatter Aas Inger Sundheim Fløistad Sissel TorreAbstract
No abstract has been registered
Authors
Cornelya Klutsch Simo Maduna Natalia Polikarpova Kristin Forfang Benedicte Beddari Karl Øystein Gjelland Paul Eric Aspholm Per-Arne Amundsen Snorre HagenAbstract
No abstract has been registered
Abstract
Abandonment of agricultural land is a process described from different regions of many industrialized countries. Given the current focus on land use, land use change and food security, it appears highly relevant to develop improved tools to identify and monitor the dynamics of agricultural land abandonment. In particular, the temporal aspect of abandonment needs to be assessed and discussed. In this study, we used the detailed information available through the Norwegian subsidy claim database and analyzed the history of use of unique land parcels through a fourteen-year period. We developed and tested five different statistics identifying these land parcels, their temporal dynamics and the extent of occurrence. What became apparent was that a large number of land parcels existing in the database as agricultural land were taken out of production, but then entered into production again at a later stage. We believe that this approach to describe the temporal dynamics of land abandonment, including how it can be measured and mapped, may contribute to the understanding of the dynamics in land abandonment, and thus also contribute to an improved understanding of the food production system.
Authors
Erika Krüger B. Brauksiepe P. Chartier M.-N. Demene B. Denoye K. Eimert I. Sowik A. Masny G. Savini Anita SønstebyAbstract
No abstract has been registered
Authors
Anita SønstebyAbstract
No abstract has been registered
Abstract
This study investigated the potential of in-season airborne hyperspectral imaging for the calibration of robust forage yield and quality estimation models. An unmanned aerial vehicle (UAV) and a hyperspectral imager were used to capture canopy reflections of a grass-legume mixture in the range of 450 nm to 800 nm. Measurements were performed over two years at two locations in Southeast and Central Norway. All images were subject to radiometric and geometric corrections before being processed to ortho-images, carrying canopy reflectance information. The data (n = 707) was split in two, using half the data for model calibration and the remaining half for validation. Several powered partial least squares regression (PPLSR) models were fitted to the reflectance data to estimate fresh (FM) and dry matter (DM) yields, as well as crude protein (CP), dry matter digestibility (DMD), neutral detergent fibre (NDF), and indigestible neutral detergent fibre (iNDF) content. Prediction performance of these models was compared with the prediction performance of simple linear regression (SLR) models, which were based on selected vegetation indices and plant height. The highest prediction accuracies for general models, based on the pooled data, were achieved by means of PPLSR, with relative root-mean-square errors of validation of 14.2% (2550 kg FM ha−1), 15.2% (555 kg DM ha−1), 11.7% (1.32 g CP 100 g−1 DM), 2.4% (1.71 g DMD 100 g−1 DM), 4.8% (2.72 g NDF 100 g−1 DM), and 12.8% (1.32 g iNDF 100 g−1 DM) for the prediction of FM, DM, CP, DMD, NDF, and iNDF content, respectively. None of the tested SLR models achieved acceptable prediction accuracies.
Authors
Shelemia Nyamuryekung'e Andres F. Cibils Richard Estell Matthew M. McIntosh Dawn VanLeeuwen Caitriana M. Steele Alfredo L. Gonzalez Sheri Spiegal Leonel Avendano Reyes Felipe A. Rodriguez Almeida Martha AndersonAbstract
No abstract has been registered