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
Aim: Many countries lack informative, high‐resolution, wall‐to‐wall vegetation or land cover maps. Such maps are useful for land use and nature management, and for input to regional climate and hydrological models. Land cover maps based on remote sensing data typically lack the required ecological information, whereas traditional field‐based mapping is too expensive to be carried out over large areas. In this study, we therefore explore the extent to which distribution modelling (DM) methods are useful for predicting the current distribution of vegetation types (VT) on a national scale. Location: Mainland Norway, covering ca. 324,000 km2. Methods: We used presence/absence data for 31 different VTs, mapped wall‐to‐wall in an area frame survey with 1081 rectangular plots of 0.9 km2. Distribution models for each VT were obtained by logistic generalised linear modelling, using stepwise forward selection with an F‐ratio test. A total of 116 explanatory variables, recorded in 100 m × 100 m grid cells, were used. The 31 models were evaluated by applying the AUC criterion to an independent evaluation dataset. Results: Twenty‐one of the 31 models had AUC values higher than 0.8. The highest AUC value (0.989) was obtained for Poor/rich broadleaf deciduous forest, whereas the lowest AUC (0.671) was obtained for Lichen and heather spruce forest. Overall, we found that rare VTs are predicted better than common ones, and coastal VTs are predicted better than inland ones. Conclusions: Our study establishes DM as a viable tool for spatial prediction of aggregated species‐based entities such as VTs on a regional scale and at a fine (100 m) spatial resolution, provided relevant predictor variables are available. We discuss the potential uses of distribution models in utilizing large‐scale international vegetation surveys. We also argue that predictions from such models may improve parameterisation of vegetation distribution in earth system models.
Abstract
In many areas where spring is wet, fungicides are applied in relation to rain events that trigger ejection of ascospores of Venturia inaequalis, which cause primary infections of apple scab. Past studies established the rate of ejection during rain in relation to light and temperature, and determined the wetting time required for infection. Simulation software uses this information to calculate risk and help time sprays accordingly. However, the distribution of the infection time required by a population of spores landed on leaves was never studied, and assumptions were used. To estimate this, we inoculated ascospores of V. inaequalis on potted trees at different temperatures for specific wetting times. Lesions were enumerated after incubation. Lesions increased with wetness time and leveled off once the slowest spores infected the host, closely matching the monomolecular model. Wetness hours were best adjusted for temperature using the Yin equation. The minimum infection time on the youngest leaves was about 5 h, matching results from previous studies, whereas half the lesions appeared after 7 h of infection. Infection times for leaves with ontogenic resistance were longer. Our results improve current software estimates and may improve spraying decisions.
Authors
Sylwia Wierzcholska Amy Elizabeth Eycott Fride Høistad Schei John-Arvid Grytnes Patryk Czortex Olga Cholewinska Bogdan JaroszewiczAbstract
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Anna Birgitte MilfordAbstract
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Increasing global levels of meat consumption are a threat to the environment and to human health. To identify measures that may change consumption patterns towards more plant-based foods, it is necessary to improve our understanding of the causes behind the demand for meat. In this paper we use data from 137 different countries to identify and assess factors that influence meat consumption at the national level using a cross-country multivariate regression analysis. We specify either total meat or ruminant meat as the dependent variable and we consider a broad range of potential drivers of meat consumption. The combination of explanatory variables we use is new for this type of analysis. In addition, we estimate the relative importance of the different drivers. We find that income per capita followed by rate of urbanisation are the two most important drivers of total meat consumption per capita. Income per capita and natural endowment factors are major drivers of ruminant meat consumption per capita. Other drivers are Western culture, Muslim religion, female labour participation, economic and social globalisation and meat prices. The main identified drivers of meat demand are difficult to influence through direct policy intervention. Thus, acting indirectly on consumers’ preferences and consumption habits (for instance through information, education policy and increased availability of ready-made plant based products) could be of key importance for mitigating the rise of meat consumption per capita all over the world.