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
Sammendrag
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Forfattere
Kristian Nikolai Jæger 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
Christian Kuehne Emma Holmström Johanna Routa Saija Huuskonen Jonas Cedergren Pasi RautioSammendrag
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Forfattere
Isabell EischeidSammendrag
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Redaktører
Pasi Rautio Johanna Routa Saija Huuskonen Emma Holmström Jonas Cedergren Christian KuehneSammendrag
This open access book compiles the latest research on continuous cover forestry in boreal forests, highlighting both the need for additional information and the exciting possibilities that this method presents. Experts in the field explore topics such as forest regeneration, genetic effects, wood production and yield, wood harvesting, forest damage agents, biodiversity, water effects, carbon cycles of forests, economics, forest planning methods, multiple uses of forests, and forest owners' attitudes. As the world faces increasing pressure to balance the multiple goals of forest management, including raw material production, carbon sequestration, biodiversity, and climate change adaptation, it is becoming clear that different forest management methods are required. Even-aged forest management is well-researched, but continuous forest management is a newer and rapidly evolving approach that is gaining popularity in boreal forests. While an overall synthesis of the subject is not yet possible, this book provides an essential foundation for understanding the current state of continuous cover forestry in boreal forests. With the new research data being accumulated all the time, this book is an invaluable resource for researchers, policymakers, and forest managers who want to stay up-to-date on this important topic.
Forfattere
Even Unsgård Erling Meisingset Inger Maren Rivrud Gunn Randi Fossland Pål Thorvaldsen Vebjørn Veiberg Atle MysterudSammendrag
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Forfattere
Arti Rai Magne Nordang Skårn Abdelhameed Elameen Torstein Tengs Mathias Rudolf Amundsen Oskar S. Bjorå Lisa Karine Haugland Igor A. Yakovlev May Bente Brurberg Tage ThorstensenSammendrag
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Forfattere
Grete H. M. Jørgensen Ellen Elverland Bjørn Egil Flø Habtamu Alem Divina Gracia P. Rodriguez Anette Tjomsland Spilling Ragnhild BorchseniusSammendrag
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Sammendrag
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Sammendrag
Rapporten er en konseptanalyse knyttet til behovet for å definere presise arealer og dele informasjon om arealers tilstand og bruk i landbruket. Det er lagt bekt på behov, problemformuleringer og mulige overordnede tekniske løsninger for det vi ser på som et digitaliseringstiltak. Rapporten foreslår en videreføring gjennom å utrede en framtidig forvaltnings- og finansieringsmodell i regi av OPS-Landbruk og å teste ut anbefalt konsept gjennom ett eller to Proof of Concept i regi av parter i OPS-Landbruk.