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
Abstract
No abstract has been registered
Authors
Kristian Nikolai Jæger 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.
Abstract
Small, forested catchments are prototypes of terrestrial ecosystems and have been studied in several disciplines of environmental science over several decades. Time series of water and matter fluxes and nutrient concentrations from these systems exhibit a bewildering diversity of spatiotemporal patterns, indicating the intricate nature of processes acting on a large range of time scales. Nonlinear dynamics is an obvious framework to investigate catchment time series. We analyzed selected long-term data from three headwater catchments in the Bramke valley, Harz mountains, Lower Saxony in Germany at common biweekly resolution for the period 1991 to 2023. For every time series, we performed gap filling, detrending, and removal of the annual cycle using singular system analysis (SSA), and then calculated metrics based on ordinal pattern statistics: the permutation entropy, permutation complexity, and Fisher information, as well as their generalized versions (q-entropy and α-entropy). Further, the position of each variable in Tarnopolski diagrams is displayed and compared to reference stochastic processes, like fractional Brownian motion, fractional Gaussian noise, and β noise. Still another way of distinguishing deterministic chaos and structured noise, and quantifying the latter, is provided by the complexity from ordinal pattern positioned slopes (COPPS). We also constructed horizontal visibility graphs and estimated the exponent of the decay of the degree distribution. Taken together, the analyses create a characterization of the dynamics of these systems which can be scrutinized for universality, either across variables or between the three geographically very close catchments.
Abstract
No abstract has been registered
Abstract
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.
Authors
Even Unsgård Erling Meisingset Inger Maren Rivrud Gunn Randi Fossland Pål Thorvaldsen Vebjørn Veiberg Atle MysterudAbstract
No abstract has been registered
Authors
Junbin Zhao Simon Weldon Alexandra Barthelmes Erin Swails Kristell Hergoualc’h Ülo Mander Chunjing Qiu John Connolly Whendee L. Silver David I. CampbellAbstract
No abstract has been registered
Authors
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 ThorstensenAbstract
No abstract has been registered
Authors
Carl-Fredrik Johannesson H. Ilvesniemi O. Janne Kjønaas K.S. Larsen A. Lehtonen Jenni Nordén D. Paré Hanna Marika Silvennoinen J. Stendahl I. Stupak L. Vesterdal Lise DalsgaardAbstract
No abstract has been registered
Abstract
No abstract has been registered