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.
2019
Abstract
No abstract has been registered
Authors
Jana Spulerova Alexandra Kruse Paola Branduini Csaba Centeri Sebastian Eiter Viviana Ferrario Bénédicte Gaillard Fausto Gusmeroli Suzan Jurgens Drago Kladnik Hans Renes Michael Roth Giovanni Sala Hanne Sickel Maurizia Sigura Dagmar Štefunková Kari Stensgaard Peter Strasser Cosmin Marius Ivascu Kinga ÖllererAbstract
Hay-making structures are part of the agricultural landscape of meadows and pastures. Hay meadows are still used and found all over Europe, but their distribution patterns as well as their characteristics and regional features depend on geographical area, climate, culture, and intensity of agriculture. Intensively used hay meadows are the most dominant, using heavy machinery to store hay mostly as rounded or square bales. Traditional hay-making structures represent structures or constructions, used to quickly dry freshly cut fodder and to protect it from humidity. The ‘ancient’ forms of traditional hay-making structures are becoming a relic, due to mechanisation and the use of new technologies. Both the need for drying hay and the traditional methods for doing so were similar across Europe. Our study of hay-making structures focuses on their current state, their development and history, current use and cultural values in various European countries. Regarding the construction and use of hay-making structures, we have distinguished three different types, which correlate to natural and regional conditions: (1) temporary hay racks of various shapes; (2) hay barracks, a special type of shelters for storing hay and (3) different types of permanent construction and buildings for drying and storing hay. Hay-making structures have been mostly preserved in connection with traditional agricultural landscapes, and particularly in the more remote regions or where associated with strong cultural identity.
Abstract
Repeat photography is an efficient method for documenting long-term landscape changes. So far, the usage of repeat photographs for quantitative analyses is limited to approaches based on manual classification. In this paper, we demonstrate the application of a convolutional neural network (CNN) for the automatic detection and classification of woody regrowth vegetation in repeat landscape photographs. We also tested if the classification results based on the automatic approach can be used for quantifying changes in woody vegetation cover between image pairs. The CNN was trained with 50 × 50 pixel tiles of woody vegetation and non-woody vegetation. We then tested the classifier on 17 pairs of repeat photographs to assess the model performance on unseen data. Results show that the CNN performed well in differentiating woody vegetation from non-woody vegetation (accuracy = 87.7%), but accuracy varied strongly between individual images. The very similar appearance of woody vegetation and herbaceous species in photographs made this a much more challenging task compared to the classification of vegetation as a single class (accuracy = 95.2%). In this regard, image quality was identified as one important factor influencing classification accuracy. Although the automatic classification provided good individual results on most of the 34 test photographs, change statistics based on the automatic approach deviated from actual changes. Nevertheless, the automatic approach was capable of identifying clear trends in increasing or decreasing woody vegetation in repeat photographs. Generally, the use of repeat photography in landscape monitoring represents a significant added value to other quantitative data retrieved from remote sensing and field measurements. Moreover, these photographs are able to raise awareness on landscape change among policy makers and public as well as they provide clear feedback on the effects of land management.
Abstract
No abstract has been registered
Abstract
No abstract has been registered
Abstract
No abstract has been registered
Abstract
No abstract has been registered
Abstract
The extent of land lease is increasing in many countries, including Norway. This paper develops a von Thünen type model of optimal land plots to lease from a farm’s center. For a single farm setting, the optimality principle is that land is leased as long as the expected marginal value of leasing a tract of land is greater than or equal to the expected marginal costs of leasing the land. The single farm model setting captures land lease at the extensive margin, i.e., under absence of competition for leasing land. Land lease at the intensive margin, i.e., when there is competition for leasing farm fields, is more interesting. We distinguish between two cases. In the first case, continued farm operations do not depend on being able to lease more land. Then we show that optimal land lease results when the expected profits for each farm of leasing its least profitable field is equal among farms competing for the same farm field. This also corresponds to an economically efficient allocation of leased land. Our second case at the intensive margin is more complicated. Here, farm survival depends on attracting acreage of leased land to allow for investment in cost saving technology. We show that the resulting allocation of leased land corresponds to the solution of a game involving bidding for land in order to prevent other farmers from getting land, which in turn leads to farmer exit and therefore increases the future supply of land available at the land lease market. In the first round of the game, winners of the land lease auction pay more for the leased land than they would have done in the absence of preventive bidding. The model framework is applicable for other settings where locking out competitors are parts of agents’ strategy space.
Authors
Christian PedersenAbstract
No abstract has been registered
Authors
Jürgen Dengler Thomas J. Matthews Manuel J. Steinbauer Sebastian Wolfrum Steffen Boch Alessandro Chiarucci Timo Conradi Iwona Dembicz Corrado Marcenó Itziar García-Mijangos Arkadiusz Nowak David Storch Werner Ulrich Juan Antonio Campos Laura Cancellieri Marta Carboni Giampiero Ciaschetti Pieter De Frenne Jiří Doležal Christian Dolnik Franz Essl Edy Fantinato Goffredo Filibeck John-Arvid Grytnes Riccardo Guarino Behlül Güler Monika Janišová Ewelina Klichowska Łukasz Kozub Anna Kuzemko Michael Manthey Anne Mimet Alireza Naqinezhad Christian Pedersen Robert K. Peet Vincent Pellissier Remigiusz Pielech Giovanna Potenza Leonardo Rosati Massimo Terzi Orsolya Valkó Denys Vynokurov Hannah White Manuela Winkler Idoia BiurrunAbstract
Aim Species–area relationships (SARs) are fundamental scaling laws in ecology although their shape is still disputed. At larger areas, power laws best represent SARs. Yet, it remains unclear whether SARs follow other shapes at finer spatial grains in continuous vegetation. We asked which function describes SARs best at small grains and explored how sampling methodology or the environment influence SAR shape. Location Palaearctic grasslands and other non‐forested habitats. Taxa Vascular plants, bryophytes and lichens. Methods We used the GrassPlot database, containing standardized vegetation‐plot data from vascular plants, bryophytes and lichens spanning a wide range of grassland types throughout the Palaearctic and including 2,057 nested‐plot series with at least seven grain sizes ranging from 1 cm2 to 1,024 m2. Using nonlinear regression, we assessed the appropriateness of different SAR functions (power, power quadratic, power breakpoint, logarithmic, Michaelis–Menten). Based on AICc, we tested whether the ranking of functions differed among taxonomic groups, methodological settings, biomes or vegetation types. Results The power function was the most suitable function across the studied taxonomic groups. The superiority of this function increased from lichens to bryophytes to vascular plants to all three taxonomic groups together. The sampling method was highly influential as rooted presence sampling decreased the performance of the power function. By contrast, biome and vegetation type had practically no influence on the superiority of the power law. Main conclusions We conclude that SARs of sessile organisms at smaller spatial grains are best approximated by a power function. This coincides with several other comprehensive studies of SARs at different grain sizes and for different taxa, thus supporting the general appropriateness of the power function for modelling species diversity over a wide range of grain sizes. The poor performance of the Michaelis–Menten function demonstrates that richness within plant communities generally does not approach any saturation, thus calling into question the concept of minimal area.