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.
2025
Forfattere
Inger HansenSammendrag
Det er ikke registrert sammendrag
Sammendrag
• Conventional forest operations can exert significant impacts on the hydrology and water quality of downstream aquatic environments. • Few research results have been published on the impacts of continuous cover forestry (CCF) on water quality. • CCF could be useful for reducing nutrient, carbon, and suspended solid exports in waterways. • CCF may be a better alternative to rotation forestry (RF) on mineral soils and drained peatlands. • Further research is needed on the many processes controlling nutrient and carbon exports in CCF and RF.
Populærvitenskapelig artikkel – Vegetasjon og beite fra Vigga til Bøsetre i Lesja
Magnus Stenbrenden
Forfattere
Magnus StenbrendenSammendrag
Det er ikke registrert sammendrag
Forfattere
Svein EilertsenSammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Tove Vaaje-KolstadSammendrag
Det er ikke registrert sammendrag
Forfattere
Kristian Hansen Håvard Steinshamn Sissel Hansen Matthias Koesling Tommy Dalgaard Bjørn Gunnar HansenSammendrag
To evaluate the environmental impact across multiple dairy farms cost-effectively, the methodological frame- work for environmental assessments may be redefined. This article aims to assess the ability of various statistical tools to predict impact assessment made from a Life Cyle Assessment (LCA). The different models predicted estimates of Greenhouse Gas (GHG) emissions, Energy (E) and Nitrogen (N) intensity. The functional unit in the study was defined as 2.78 MJMM human-edible energy from milk and meat. This amount is equivalent to the edible energy in one kg of energy-corrected milk but includes energy from milk and meat. The GHG emissions (GWP100) were calculated as kg CO2-eq per number of FU delivered, E intensity as fossil and renewable energy used divided by number of FU delivered, and N intensity as kg N imported and produced divided by kg N delivered in milk or meat (kg N/kg N). These predictions were based on 24 independent variables describing farm characteristics, management, use of external inputs, and dairy herd characteristics. All models were able to moderately estimate the results from the LCA calculations. However, their precision was low. Artificial Neural Network (ANN) was best for predicting GHG emissions on the test dataset, (RMSE = 0.50, R2 = 0.86), followed by Multiple Linear Regression (MLR) (RMSE = 0.68, R2 = 0.74). For E intensity, the Supported Vector Machine (SVM) model was performing best, (RMSE = 0.68, R2 = 0.73), followed by ANN (RMSE = 0.55, R2 = 0.71,) and Gradient Boosting Machine (GBM) (RMSE = 0.55, R2 = 0.71). For N intensity predictions the Multiple Linear Regression (MLR) (RMSE = 0.36, R2 = 0.89) and Lasso regression (RMSE = 0.36, R2 = 0.88), followed by the ANN (RMSE = 0.41, R2 = 0.86,). In this study, machine learning provided some benefits in prediction of GHG emission, over simpler models like Multiple Linear Regressions with backward selection. This benefit was limited for N and E intensity. The precision of predictions improved most when including the variables “fertiliser import nitrogen” (kg N/ha) and “proportion of milking cows” (number of dairy cows/number of all cattle) for predicting GHG emission across the different models. The inclusion of “fertiliser import nitrogen” was also important across the different models and prediction of E and N intensity.
Forfattere
Atle Wibe Berit Marie Blomstrand Lisa Deiana Davide Bochicchio Tommy Ruud Richard Helliwell Matthias Koesling Anne Grete Kongsted Marina Štukelj Marina Spinu A Vasiu Andrew Richard Williams Amalie Camilla Pedersen Helena Meijer Stig Milan ThamsborgSammendrag
Det er ikke registrert sammendrag
Forfattere
Chala Adugna Kufa Afework Bekele Anagaw Meshesha Atickem Desalegn Chala Diress Tsegaye Alemu Torbjørn Ergon Nils Christian Stenseth Dietmar ZinnerSammendrag
Ethiopia is home to two subspecies of Colobus guereza, C. g. guereza and C. g. gallarum. Whereas C. g. guereza is listed as Least Concern by IUCN, the conservation status of C. g. gallarum is unclear, but according to a recent assessment, it will most likely be listed as Vulnerable, because of habitat loss due to agricultural expansion. We used climate data to model the habitat suitability for both taxa in a comparative study to identify suitable habitats within and outside of protected areas that may serve as Anthropocene refugia. Our ensemble models estimated 168,731 km2 as climatically suitable habitat for C. g. guereza and 69,542 km2 for C. g. gallarum with an overlap between the two taxa of 17.2 %. Areas that qualified as refugia, i.e., areas covered by forest, were 47,101 km2 (only 27.9 % of the total suitable habitat) and 8430 km2 (12.1 % of the suitable habitat) for C. g. guereza and C. g. gallarum, respectively. Of these, 39.8 % (C. g. guereza) and 53.7 % (C. g. gallarum) are within Ethiopia’s current protected area network. Given that potential Anthropocene refugia are found only partly within protected areas, conservation management should include this information when developing conservation strategies for both taxa. As the majority of suitable habitats for the two colobus taxa exist in non-forested regions, afforestation in these areas would be highly beneficial and is strongly recommended.