Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2018
Forfattere
Erling MeisingsetSammendrag
Det er ikke registrert sammendrag
Forfattere
Erling MeisingsetSammendrag
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Forfattere
Erling MeisingsetSammendrag
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Sammendrag
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Forfattere
Lone RossSammendrag
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Sammendrag
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Forfattere
Bjørn Egil FløSammendrag
Det er ikke registrert sammendrag
Sammendrag
Horizontal Visibility Graphs (HVGs) are a recently developed method to construct networks based on time series. Values (the nodes of the network) of the time series are linked to each other if there is no value higher between them. The network properties reflect the nonlinear dynamics of the time series. For some classes of stochastic processes and for periodic time series, analytic results can be obtained for the degree distribution, the local clustering coefficient distribution, the mean path length, and others. HVGs have the potential to discern between deterministic-chaotic and correlated-stochastic time series. We investigate a set of around 150 river runoff time series at daily resolution from Brazil with an average length of 65 years. Most of the rivers are exploited for power generation and thus represent heavily managed basins. We investigate both long-term trends and human influence (e.g. the effect of dam construction) in the runoff regimes (disregarding direct upstream operations). HVGs are used to determine the degree and distance distributions. Statistical and information-theoretic properties of these distributions are calculated: robust estimators of skewness and kurtosis, the maximum degree occurring in the time series, the Shannon entropy, permutation complexity and Fisher Information. For the latter, we also compare the information measures obtained from the degree distributions to those using the original time series directly, to investigate the impact of graph construction on the dynamical properties as reflected in these measures. We also show that a specific pretreatment of the time series conventional in hydrology, the elimination of seasonality by a separate z-transformation for each calendar day, changes long-term correlations and the overall dynamics substantially and towards more random behaviour. Moreover, hydrological time series are typically limited in length and may contain ties, and we present empirical consequences and extensive simulations to investigate these issues from a HVG methodological perspective. Focus is on one hand on universal properties of the HVG, common to all runoff series, and on site-specific aspects on the other.
Forfattere
Åshild Ergon Giovanna Seddaiu Panu Korhonen Perttu Virkajärvi Gianni Bellocchi Marit Jørgensen Liv Østrem Dirk Reheul F. VolaireSammendrag
Climate change and its effects on grassland productivity vary across Europe. The Mediterranean and Nordic regions represent the opposite ends of a gradient of changes in temperature and precipitation patterns, with increasingly warmer and wetter winters in the north and increasingly warmer and drier summers in the south. Warming and elevated concentration of atmospheric CO2 may boost forage production in the Nordic region. Production in many Mediterranean areas is likely to become even more challenged by drought in the future, but elevated CO2 can to some extent alleviate drought limitation on photosynthesis and growth. In both regions, climate change will affect forage quality and lead to modifications of the annual productivity cycles, with an extended growing season in the Nordic region and a shift towards winter in the Mediterranean region. This will require adaptations in defoliation and fertilization strategies. The identity of species and mixtures with optimal performance is likely to shift somewhat in response to altered climate and management systems. It is argued that breeding of grassland species should aim to (i) improve plant strategies to cope with relevant abiotic stresses and (ii) optimize growth and phenology to new seasonal variation, and that plant diversity at all levels is a good adaptation strategy.
Forfattere
Nick Hutchings Seyda Özkan-Gülzari Michel de Haan Daniel L. SandarsSammendrag
The European Union Effort Sharing Regulation (ESR) will require a 30% reduction in greenhouse gas (GHG) emissions by 2030 compared with 2005 from the sectors not included in the European Emissions Trading Scheme, including agriculture. This will require the estimation of current and future emissions from agriculture, including dairy cattle production systems. Using a farm-scale model as part of a Tier 3 method for farm to national scales provides a more holistic and informative approach than IPCC (2006) Tier 2 but requires independent quality control. Comparing the results of using models to simulate a range of scenarios that explore an appropriate range of biophysical and management situations can support this process by providing a framework for placing model results in context. To assess the variation between models and the process of understanding differences, estimates of GHG emissions from four farm-scale models (DairyWise, FarmAC, HolosNor and SFARMMOD) were calculated for eight dairy farming scenarios within a factorial design consisting of two climates (cool/dry and warm/wet)×two soil types (sandy and clayey)×two feeding systems (grass only and grass/maize). The milk yield per cow, follower:cow ratio, manure management system, nitrogen (N) fertilisation and land area were standardised for all scenarios in order to associate the differences in the results with the model structure and function. Potential yield and application of available N in fertiliser and manure were specified separately for grass and maize. Significant differences between models were found in GHG emissions at the farm-scale and for most contributory sources, although there was no difference in the ranking of source magnitudes. The farm-scale GHG emissions, averaged over the four models, was 10.6 t carbon dioxide equivalents (CO2e)/ha per year, with a range of 1.9 t CO2e/ha per year. Even though key production characteristics were specified in the scenarios, there were still significant differences between models in the annual milk production per ha and the amounts of N fertiliser and concentrate feed imported. This was because the models differed in their description of biophysical responses and feedback mechanisms, and in the extent to which management functions were internalised. We conclude that comparing the results of different farm-scale models when applied to a range of scenarios would build confidence in their use in achieving ESR targets, justifying further investment in the development of a wider range of scenarios and software tools.