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.
2025
Authors
Kristian Hansen Håvard Steinshamn Sissel Hansen Matthias Koesling Tommy Dalgaard Bjørn Gunnar HansenAbstract
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.
Authors
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 ThamsborgAbstract
No abstract has been registered
Authors
Chala Adugna Kufa Afework Bekele Anagaw Atickem Desalegn Chala Diress Tsegaye Torbjørn Ergon Nils Christian Stenseth Dietmar ZinnerAbstract
No abstract has been registered
Authors
Eystein Skjerve Erik Georg Granquist Tone Kristin Bjordal Johansen Ingrid Olsen Truls Nesbakken Amin Sayyari Kristin Opdal Seljetun Morten Tryland Åsa Maria Olofsdotter Espmark Grete H. M. Jørgensen Janicke Nordgreen Ingrid Olesen Sonal Jayesh Patel Sokratis Ptochos Marco A. Vindas Tor Atle MoAbstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Erik J. JonerAbstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Cristina Micheloni Frank Willem Oudshoorn María Isabel Blanco Penedo Sari Autio Andrea Beste Jacopo Goracci Matthias Koesling Ursula Kretzschmar Eligio Malusá Maria Dolores Raigon Jimenez Bernhard Speiser Jan van der Blom Felix WäckersAbstract
No abstract has been registered
Report research – Final report on food X
Cristina Micheloni, Frank Willem Oudshoorn, Sari Autio, ...
Authors
Cristina Micheloni Frank Willem Oudshoorn Sari Autio Andrea Beste María Isabel Blanco Penedo Jacopo Goracci Matthias Koesling Eligio Malusá Bernhard Speiser Jan van der Blom Felix Wäckers Ursula KretzschmarAbstract
The Expert Group for Technical Advice on Organic Production (EGTOP) was requested to advise on the use of several substances in organic production. The Group discussed whether the use of these substances is in line with the objectives and principles of organic production and whether they should therefore be included in Annex V of Commission Implementing Regulation (EU) 2021/1165.
Authors
Monica Sanden Eirill Ager-Wick Johanna Eva Bodin Nur Duale Anne-Marthe Ganes Jevnaker Kristian Prydz Volha Shapaval Ville Erling Sipinen Tage ThorstensenAbstract
No abstract has been registered