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.

2020

Abstract

The term Circular Regulations (CR) is introduced to describe a broad regulatory framework, designed with a circular understanding of the economy. Central in this discussion is the transition towards bioeconomy, a term that is not always used consistently, and sometimes treated in the same way as circular economy (CE), although these terms are not necessarily equivalent. In this article we endorse a systemic interpretation of CE, where a continuum of approaches, extending from reusing/recycling/upcycling to refuse/rethink/reduce, gradually replace existing linear “end-of-life” concepts. CE is a key prerequisite for the bioeconomy shift, a transition that further builds on CE, where circular design and processes are further augmented with increased resource utilization and intensive applications of innovative science and technology. The prevailing regulatory arrangements in CE, however, remain either fragmented or largely based on pre-existing policies, drafted to address issues of the linear economy, thus presenting several limitations when dealing with the underlying paradigm shift: complex market relationships that go beyond the standard neoclassical model. CR adopts an encompassing approach to regulatory design; it is not meant to be a rigid set of rules, but rather a regulatory framework where institutions, market rules, and business practice explicitly account for environmental and socially responsible activities, while securing an enabling environment for innovation. CR directly reflects on CE, where bioeconomy growth is informed by science, enabled by technology, driven by business, and supported by relevant policies and institutional frameworks. The article presents a conceptual setting towards CR and a practical example for its development.

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Abstract

Wheat dwarf virus (WDV), a mastrevirus transmitted by the leafhopper Psammotettix alienus, causes a severe disease in cereal crops. Typical symptoms of wheat plants infected by WDV are yellowing and severe dwarfing. In this present study, RNA-Seq was used to perform gene expression analysis in wheat plants in response to WDV infection. Comparative transcriptome analysis indicated that a total of 1042 differentially expressed genes (DEGs) were identified in the comparison between mock and WDV-inoculated wheat plants. Genomes ontology (GO) annotation revealed a number of DEGs associated with different biological processes, such as phytohormone metabolism, photosynthesis, DNA metabolic process, response to biotic stimulus and defense response. Among these, DEGs involved in phytohormone and photosynthesis metabolism and response pathways were further enriched and analyzed, which indicated that hormone biosynthesis, signaling and chloroplast photosynthesis-related genes might play an important role in symptom development after WDV infection. These results illustrate the dynamic nature of the wheat-WDV interaction at the transcriptome level and confirm that symptom development is a complex process, providing a solid foundation to elucidate the pathogenesis of WDV.

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To support decision-makers considering adopting integrated pest management (IPM) cropping in Norway, we used stochastic efficiency analysis to compare the risk efficiency of IPM cropping and conventional cropping, using data from a long-term field experiment in southeastern Norway, along with data on recent prices, costs, and subsidies. Initial results were not definitive, so we applied stochastic efficiency with respect to a function, limiting the assumed risk aversion of farmers to a plausible range. We found that, for farmers who are risk-indifferent to moderately (hardly) risk averse, the conventional system was, compared to IPM, less (equally) preferred.

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Although it is well known that insects are sensitive to temperature, how they will be affected by ongoing global warming remains uncertain because these responses are multifaceted and ecologically complex. We reviewed the effects of climate warming on 31 globally important phytophagous (plant‐eating) insect pests to determine whether general trends in their responses to warming were detectable. We included four response categories (range expansion, life history, population dynamics, and trophic interactions) in this assessment. For the majority of these species, we identified at least one response to warming that affects the severity of the threat they pose as pests. Among these insect species, 41% showed responses expected to lead to increased pest damage, whereas only 4% exhibited responses consistent with reduced effects; notably, most of these species (55%) demonstrated mixed responses. This means that the severity of a given insect pest may both increase and decrease with ongoing climate warming. Overall, our analysis indicated that anticipating the effects of climate warming on phytophagous insect pests is far from straightforward. Rather, efforts to mitigate the undesirable effects of warming on insect pests must include a better understanding of how individual species will respond, and the complex ecological mechanisms underlying their responses.

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Abstract

Aim: Distribution modelling is a useful approach to obtain knowledge about the spatial distribution of biodiversity, required for, for example, red-list assessments. While distribution modelling methods have been applied mostly to single species, modelling of communities and ecosystems (EDM; ecosystem-level distribution modelling) produces results that are more directly relevant for management and decision-making. Although the choice of predictors is a pivotal part of the modelling process, few studies have compared the suitability of different sets of predictors for EDM. In this study, we compare the performance of 50 single environmental variables with that of 11 composite landscape gradients (CLGs) for prediction of ecosystem types. The CLGs represent gradients in landscape element composition derived from multivariate analyses, for example “inner-outer coast” and “land use intensity.” Location: Norway. Methods: We used data from field-based ecosystem-type mapping of nine ecosystem types, and environmental variables with a resolution of 100 × 100 m. We built nine models for each ecosystem type with variables from different predictor sets. Logistic regression with forward selection of variables was used for EDM. Models were evaluated with independently collected data. Results: Most ecosystem types could be predicted reliably, although model performance differed among ecosystem types. We identified significant differences in predictive power and model parsimony across models built from different predictor sets. Climatic variables alone performed poorly, indicating that the current climate alone is not sufficient to predict the current distribution of ecosystems. Used alone, the CLGs resulted in parsimonious models with relatively high predictive power. Used together with other variables, they consistently improved the models. Main conclusions: Our study highlights the importance of variable selection in EDM. We argue that the use of composite variables as proxies for complex environmental gradients has the potential to improve predictions from EDMs and thus to inform conservation planning as well as improve the precision and credibility of red lists and global change assessments.conservation planning, distribution modelling, ecosystem classification, ecosystem types, IUCN Red List of Ecosystems, landscape gradients, spatial prediction, species response curves

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Land-sea riverine carbon transfer (LSRCT) is one of the key processes in the global carbon cycle. Although natural factors (e.g. climate, soil) influence LSRCT, human water management strategies have also been identified as a critical component. However, few systematic approaches quantifying the contribution of coupled natural and anthropogenic factors on LSRCT have been published. This study presents an integrated framework coupling hydrological modeling, field sampling and stable isotope analysis for the quantitative assessment of the impact of human water management practices (e.g. irrigation, dam construction) on LSRCT under different hydrological conditions. By applying this approach to the case study of the Nandu River, China, we find that carbon (C) concentrations originating from different land-uses (e.g. forest, cropland) are relatively stable and outlet C variations are mainly dominated by controlled runoff volumes rather than by input C concentrations. These results indicate that human water management practices are responsible for a reduction of ∼60% of riverine C at seasonal timescales, with an even greater reduction during drought conditions. Annual C discharges have been significantly reduced (e.g. 77 ± 5% in 2015 and 39 ± 11% in 2016) due to changes in human water extraction coupled with climate variation. In addition, isotope analysis also shows that C fluxes influenced by human activities (e.g. agriculture, aquaculture) could contribute the dominant particulate organic carbon under typical climatic conditions, as well as drought conditions. This research demonstrates the substantial effect that human water management practices have on the seasonal and annual fluxes of LSRCT, especially in such small basins. This work also shows the applicability of this integrated approach, using multiple tools to quantify the contribution of coupled anthropogenic and natural factors on LSRCT, and the general framework is believed to be feasible with limited modifications for larger basins in future research.