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
Sammendrag
Background Bioenergy plays a key role in the transition to a sustainable economy in Europe, but its own sustainability is being questioned. We study the experiences of Sweden, Finland, Denmark and Norway, to find out whether the forest-based bioenergy chains developed in the four countries have led to unsustainable outcomes and how the countries manage the sustainability risks. Data were collected from a diversity of sources including interviews, statistical databases, the scientific literature, government planning documents and legislation. Results Sustainability risks of deforestation, degradation of forests, reduced carbon pools in forests, expensive biopower and heat, resource competition, and lack of acceptance at the local level are considered. The experience of the four countries shows that the sustainability risks can to a high degree be managed with voluntary measures without resorting to prescriptive measures. It is possible to add to the carbon pools of forests along with higher harvest volumes if the risks are well managed. There is, however, a marginal trade-off between harvest volume and carbon pools. Economic sustainability risks may be more challenging than ecological risks because the competitiveness order of renewable energy technologies has been reversed in the last decade. The risk of resource competition harming other sectors in the economy was found to be small and manageable but requires continuous monitoring. Local communities acting as bioenergy communities have been agents of change behind the most expansive bioenergy chains. A fear of non-local actors reaping the economic gains involved in bioenergy chains was found to be one of the risks to the trust and acceptance necessary for local communities to act as bioenergy communities. Conclusions The Nordic experience shows that it has been possible to manage the sustainability risks examined in this paper to an extent avoiding unsustainable outcomes. Sustainability risks have been managed by developing an institutional framework involving laws, regulations, standards and community commitments. Particularly on the local level, bioenergy chains should be developed with stakeholder involvement in development and use, in order to safeguard the legitimacy of bioenergy development and reconcile tensions between the global quest for a climate neutral economy and the local quest for an economically viable community. Keywords: Bioenergy, Sustainability, Risk assessment, Risk management, Nordic countries
Forfattere
Matthew J. Kauffman Francesca Cagnacci Simon Chamaillé-Jammes Mark Hebblewhite J. Grant C. Hopcraft Jerod A. Merkle Thomas Mueller Atle Mysterud Wibke Erika Brigitta Peters Christiane Roettger Alethea Steingisser James E. Meacham Kasahun Abera Jan Adamczewski Ellen O. Aikens Hattie Bartlam-Brooks Emily Bennitt Joel Berger Charlotte Boyd Steeve D. Côté Lucie Isabelle Debeffe Andrea S. Dekrout Nandintsetseg Dejid Emiliano Donadio Luthando Dziba William F. Fagan Claude Fischer Stefano Focardi John M. Fryxell Richard W. S. Fynn Chris Geremia Benito A. González Anne Gunn Elie Gurarie Marco Dietmar Heurich Jodi Hilty Mark A. Hurley Aran Johnson Kyle Joly Petra Kaczensky Corinne J. Kendall Pavel Kochkarev Leonid Kolpaschikov Rafal Kowalczyk Frank van Langevelde Binbin V. Li Anne Loison Alex L. Lobora Tinaapi H. Madiri David Mallon Erling Meisingset Christer Moe Rolandsen Erling Johan Solberg Olav StrandSammendrag
Migration of ungulates (hooved mammals) is a fundamental ecological process that promotes abundant herds, whose effects cascade up and down terrestrial food webs. Migratory ungulates provide the prey base that maintains large carnivore and scavenger populations and underpins terrestrial biodiversity (fig. S1). When ungulates move in large aggregations, their hooves, feces, and urine create conditions that facilitate distinct biotic communities. The migrations of ungulates have sustained humans for thousands of years, forming tight cultural links among Indigenous people and local communities. Yet ungulate migrations are disappearing at an alarming rate (1). Efforts by wildlife managers and conservationists are thwarted by a singular challenge: Most ungulate migrations have never been mapped in sufficient detail to guide effective conservation. Without a strategic and collaborative effort, many of the world’s great migrations will continue to be truncated, severed, or lost in the coming decades. Fortunately, a combination of animal tracking datasets, historical records, and local and Indigenous knowledge can form the basis for a global atlas of migrations, designed to support conservation action and policy at local, national, and international levels.
Sammendrag
The size and location of agricultural fields that are in active use and the type of use during the growing season are among the vital information that is needed for the careful planning and forecasting of agricultural production at national and regional scales. In areas where such data are not readily available, an independent seasonal monitoring method is needed. Remote sensing is a widely used tool to map land use types, although there are some limitations that can partly be circumvented by using, among others, multiple observations, careful feature selection and appropriate analysis methods. Here, we used Sentinel-2 satellite image time series (SITS) over the land area of Norway to map three agricultural land use classes: cereal crops, fodder crops (grass) and unused areas. The Multilayer Perceptron (MLP) and two variants of the Convolutional Neural Network (CNN), are implemented on SITS data of four different temporal resolutions. These enabled us to compare twelve model-dataset combinations to identify the model-dataset combination that results in the most accurate predictions. The CNN is implemented in the spectral and temporal dimensions instead of the conventional spatial dimension. Rather than using existing deep learning architectures, an autotuning procedure is implemented so that the model hyperparameters are empirically optimized during the training. The results obtained on held-out test data show that up to 94% overall accuracy and 90% Cohen’s Kappa can be obtained when the 2D CNN is applied on the SITS data with a temporal resolution of 7 days. This is closely followed by the 1D CNN on the same dataset. However, the latter performs better than the former in predicting data outside the training set. It is further observed that cereal is predicted with the highest accuracy, followed by grass. Predicting the unused areas has been found to be difficult as there is no distinct surface condition that is common for all unused areas.
Forfattere
Habtamu AlemSammendrag
Purpose The study measures the technology gap and performance of the Norwegian dairy farms accounting for farm heterogeneity. Design/methodology/approach The analysis was based on a meta-frontier and unbalanced farm-level panel data for 1991–2014 from 417 Norwegian farms specialized in dairy production in five regions of Norway. Findings The result of the analysis provides empirical evidence of regional differences in technical efficiencies, technological gap ratios (TGRs) and input use. Consequently, the paper provides some insights into policies to increase the efficiency of dairy production in the country across all regions. Research limitations/implications The author used a meta-frontier approach for modeling regional differences based on a single-output production function specification. This approach has commonly been used in the economics literature since Battese et al. (2004). To get more informative and useful results, it would be necessary to repeat the analysis within terms of multiple input-output frameworks using, for instance, the input distance function approach. Moreover, the author estimated the meta-frontier using the non-parametric approach, thus it is also a need for further analysis if the values are different by estimating using a parametric approach. Practical implications One implication for farmers (and their advisers) is that dairy farms in all regions used available technology in the area sub-optimally. Thus, those lagging the best-performing farms need to look at the way the best-performing farmers are operating. Policymakers might reduce the gap is through training, including sharing information about relevant technologies from one area to another, provided that the technologies being shared fit the working environment of the lagging area. Moreover, some of the dairy technologies they use may not fit other regions, suggesting that agricultural policies that aim to encourage efficient dairy production, such as innovation of improved technology (like breeding, bull selection and improved feed varieties) through research and development, need to account the environmental differences between regions. Social implications For both taxpayers and consumers, one implication is that the contributions they pay that go to subsidize dairy farmers appear to bring some benefits in terms of more efficient milk production that, in turn, increases the supply of some foods so possibly making food prices more affordable. Originality/value The paper contributes to the literature in several ways. In contrast to Battese et al. (2004), the author accounts for farm-level performance differences by applying the model devised by Greene (2005), thus may serve as a model for future studies at more local levels or of other industries. Moreover, the author is fortunate to able to use a large level farm-level panel data from 1991 to 2014.
Forfattere
Matthias Vanmaercke Panos Panagos Tom Vanwalleghem Antonio Hayas Saskia Foerster Pasquale Borrelli Mauro Rossi Dino Torri Javier Casali Lorenzo Borselli Olga Vigiak Michael Maerker Nigussie Haregeweyn Sofie De Geeter Wojciech Zglobicki Charles Bielders Artemi Cerdà Christian Conoscenti Tomas de Figueiredo Bob Evans Valentin Golosov Ion Ionita Christos Karydas Adam Kertesz Josef Krasa Caroline Le Bouteiller Maria Radoane Ratko Ristic Svetla Rousseva Milos Stankoviansky Jannes Stolte Christian Stolz Rebecca Bartley Scott Wilkinson Ben Jarihani Jean PoesenSammendrag
Soil erosion is generally recognized as the dominant process of land degradation. The formation and expansion of gullies is often a highly significant process of soil erosion. However, our ability to assess and simulate gully erosion and its impacts remains very limited. This is especially so at regional to continental scales. As a result, gullying is often overlooked in policies and land and catchment management strategies. Nevertheless, significant progress has been made over the past decades. Based on a review of >590 scientific articles and policy documents, we provide a state-of-the-art on our ability to monitor, model and manage gully erosion at regional to continental scales. In this review we discuss the relevance and need of assessing gully erosion at regional to continental scales (Section 1); current methods to monitor gully erosion as well as pitfalls and opportunities to apply them at larger scales (section 2); field-based gully erosion research conducted in Europe and European Russia (section 3); model approaches to simulate gully erosion and its contribution to catchment sediment yields at large scales (section 4); data products that can be used for such simulations (section 5); and currently existing policy tools and needs to address the problem of gully erosion (section 6). Section 7 formulates a series of recommendations for further research and policy development, based on this review. While several of these sections have a strong focus on Europe, most of our findings and recommendations are of global significance.
Forfattere
Markus A. K. Sydenham Zander Venter Trond Reitan Claus Rasmussen Astrid Brekke Skrindo Daniel Skoog Kaj-Andreas Hanevik Stein Joar Hegland Yoko Dupont Anders Nielsen Joseph Chipperfield Graciela Monica RuschSammendrag
1. Predicting plant-pollinator interaction networks over space and time will improve our understanding of how environmental change is likely to impact the functioning of ecosystems. Here we propose a framework for producing spatially explicit predictions of the occurrence and number of pairwise plant-pollinator interactions and of the species richness, diversity, and abundance of pollinators visiting flowers. We call the framework ‘MetaComNet’ because it aims to link metacommunity dynamics to the assembly of ecological networks. 2. To illustrate the MetaComNet functionality, we used a dataset on bee-flower networks sampled at 16 sites in southeast Norway along with random forest models to predict bee-flower interactions. We included variables associated with climatic conditions (elevation) and habitat availability within a 250m radius of each site. Regional commonness, site-specific distance to conspecifics, social guild, and floral preference were included as bee traits. Each plant species was assigned a score reflecting its site-specific abundance, and four scores reflecting the bee species that the plant family is known to attract. We used leave-one-out cross-validations to assess the models’ ability to predict pairwise plant-bee interactions across the landscape. 3. The relationship between observed occurrence or absence of interactions and the predicted probability of interactions was nearly proportional (GLMlogistic regression slope = 1.09), matching the data well (AUC = 0.88), and explained 30% of the variation. Predicted probability of interactions was also correlated with the number of observed pairwise interactions (r = 0.32). The sum of predicted probabilities of bee-flower interactions were positively correlated with observed species richness (r = 0.50), diversity (r = 0.48), and abundance (r = 0.42) of wild bees interacting with plant species within sites. 4. Our findings show that the MetaComNet framework can be a useful approach for making spatially explicit predictions and mapping plant-pollinator interactions. Such predictions have the potential to identify areas where the pollination potential for wild plants is particularly high, and where conservation action should be directed to preserve this ecosystem function.
Forfattere
Markus A. K. Sydenham Zander Venter Trond Reitan Claus Rasmussen Astrid Brekke Skrindo Daniel Ingvar Jeuderan Skoog Kaj-Andreas Hanevik Stein Joar Hegland Yoko L. Dupont Anders Nielsen Joseph Chipperfield Graciela RuschSammendrag
1. Predicting plant–pollinator interaction networks over space and time will improve our understanding of how environmental change is likely to impact the functioning of ecosystems. Here we propose a framework for producing spatially explicit predictions of the occurrence and number of pairwise plant–pollinator interactions and of the species richness, diversity and abundance of pollinators visiting flowers. We call the framework ‘MetaComNet’ because it aims to link metacommunity dynamics to the assembly of ecological networks. 2. To illustrate the MetaComNet functionality, we used a dataset on bee–flower networks sampled at 16 sites in southeast Norway along with random forest models to predict bee–flower interactions. We included variables associated with climatic conditions (elevation) and habitat availability within a 250 m radius of each site. Regional commonness, site-specific distance to conspecifics, social guild and floral preference were included as bee traits. Each plant species was assigned a score reflecting its site-specific abundance, and four scores reflecting the bee species that the plant family is known to attract. We used leave-one-out cross-validations to assess the models' ability to predict pairwise plant–bee interactions across the landscape. 3. The relationship between observed occurrence or absence of interactions and the predicted probability of interactions was nearly proportional (GLMlogistic regression slope = 1.09), matching the data well (AUC = 0.88), and explained 30% of the variation. Predicted probability of interactions was also correlated with the number of observed pairwise interactions (r = 0.32). The sum of predicted probabilities of bee–flower interactions were positively correlated with observed species richness (r = 0.50), diversity (r = 0.48) and abundance (r = 0.42) of wild bees interacting with plant species within sites. 4. Our findings show that the MetaComNet framework can be a useful approach for making spatially explicit predictions and mapping plant–pollinator interactions. Such predictions have the potential to identify areas where the pollination potential for wild plants is particularly high, and where conservation action should be directed to preserve this ecosystem function. interactions, network, plants, pollinators, predict, random forest
Sammendrag
The ascomycete Hymenoscyphus fraxineus has spread across most of the host range of European ash with a high level of mortality, causing important economic, cultural and environmental effects. We present a novel method combining a Monte-Carlo approach with a generalised additive model that confirms the importance of meteorology to the magnitude and timing of H. fraxineus spore emissions. The variability in model selection and the relative degree to which our models are over- or under-fitting the data has been quantified. We find that both the daily magnitude and timing of spore emissions are affected by meteorology during and prior to the spore emission diurnal peak. We found the daily emission magnitude has the strongest associations to weekly average net radiation and leaf moisture before the emission, soil temperature during the day before emission and net radiation during the spore emission. The timing of the daily peak in spore emissions has the strongest associations to net radiation both during spore emission and in the day preceding the emission. The seasonal peak in spore emissions has a near-exponential increase/decrease, and the mean daily emission peak is approximately Gaussian.
Forfattere
Jan-Peter George Wei Yang Hideki Kobayashi Tobias Biermann Arnaud Carrara Edoardo Cremonese Matthias Cuntz Silvano Fares Giacomo Gerosa Thomas Grünwald Niklas Hase Michael Heliasz Andreas Ibrom Alexander Knohl Bart Kruijt Holger Lange Jean-Marc Limousin Denis Loustau Petr Lukeš Riccardo Marzuoli Meelis Mölder Leonardo Montagnani Johan Neirynck Matthias Peichl Corinna Rebmann Marius Schmidt Francisco Ramon Lopez Serrano Kamel Soudani Caroline Vincke Jan PisekSammendrag
Leaf area index (LAI) is a key ecological indicator for describing the structure of canopies and for modelling energy exchange between atmosphere and biosphere. While LAI of the forest overstory can be accurately assessed over large spatial scales via remote sensing, LAI of the forest understory (LAIu) is still largely ignored in ecological studies and ecosystem modelling due to the fact that it is often too complex to be destructively sampled or approximated by other site parameters. Additionally, so far only few attempts have been made to retrieve understory LAI via remote sensing, because dense canopies with high LAI are often hindering retrieval algorithms to produce meaningful estimates for understory LAI. Consequently, the forest understory still constitutes a poorly investigated research realm impeding ecological studies to properly account for its contribution to the energy absorption capacity of forest stands. This study aims to compare three conceptually different indirect retrieval methodologies for LAIu over a diverse panel of forest understory types distributed across Europe. For this we carried out near-to-surface measurements of understory reflectance spectra as well as digital surface photography over the extended network of Integrated Carbon Observation System (ICOS) forest ecosystem sites. LAIu was assessed by exploiting the empirical relationship between vegetation cover and light absorption (Beer-Lambert- Bouguer law) as well as by utilizing proposed relationships with two prominent vegetation indices: normalized difference vegetation index (NDVI) and simple ratio (SR). Retrievals from the three methods were significantly correlated with each other (r = 0.63–0.99, RMSE = 0.53–0.72), but exhibited also significant bias depending on the LAI scale. The NDVI based retrieval approach most likely overestimates LAI at productive sites when LAIu > 2, while the simple ratio algorithm overestimates LAIu at sites with sparse understory vegetation and presence of litter or bare soil. The purely empirical method based on the Beer-Lambert law of light absorption seems to offer a good compromise, since it provides reasonable LAIu values at both low and higher LAI ranges. Surprisingly, LAIu variation among sites seems to be largely decoupled from differences in climate and light permeability of the overstory, but significantly increased with vegetation diversity (expressed as species richness) and hence proposes new applications of LAIu in ecological modelling.
Forfattere
Michal Sposob Hee-Sung Moon Dongjin Lee Yeo-Myeong YunSammendrag
In this study, the microbiomes linked with the operational parameters in seven mesophilic full-scale AD plants mainly treating food waste (four plants) and sewage sludge (three plants) were analyzed. The results obtained indicated lower diversity and evenness of the microbial population in sludge digestion (SD) plants compared to food digestion (FD) plants. Candidatus Accumulibacter dominated (up to 42.1%) in SD plants due to microbial immigration from fed secondary sludge (up to 89%). Its potential activity in SD plants was correlated to H2 production, which was related to the dominance of hydrogenotrophic methanogens (Methanococcus). In FD plants, a balance between the hydrogenotrophic and methylotrophic pathways was found, while Flavobacterium and Levilinea played an important role during acidogenesis. Levilinea also expressed sensitivity to ammonia in FD plants. The substantial differences in hydraulic retention time (HRT), organic loading rate (OLR), and total ammonium nitrogen (TAN) among the studied FD plants did not influence the archaeal methane production pathway. In addition, the bacterial genera responsible for acetate production through syntrophy and homoacetogenesis (Smithella, Treponema) were present in all the plants studied.