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
Rapport – Bruk av biorest til produksjon av plengras
Arne Sæbø, Joan Homet Salvans, Anne Falk Øgaard
Sammendrag
Digestate from the biogas facility of IVAR at Grødaland, Rogaland County was tested for fertilizer effects in the production of turf gras in a pot experiment at NIBIO Særheim. Digestate was applied to the pot soil, with quantities equivalent to 0, 5, 10 and 20 kg N/daa and compared to mineral fertilizers with the same N-quantities. Germination of the gras seeds was not affected by neither digestate nor mineral fertilizers. The biomass production was largest when fertilized with mineral fertilizer, which increased the gras growth also when 5 kg N/daa was applied, with maximal yield reached at 10 kg N/daa. Digestate increased biomass production significantly, with approximately the same biomass increase from levels of 5 to 10 and to 20 kg N/daa. The digestate had a lower nitrogen use efficiency than mineral fertilizers, due to lack of complete mineralization, or delayed mineralization compared to the time of the plant’s needs for N.
Forfattere
Anne Kjersti BakkenSammendrag
Det er ikke registrert sammendrag
Forfattere
Anne Kjersti BakkenSammendrag
Det er ikke registrert sammendrag
Rapport – Bærekraftig forvaltning av jordbrukets arealbehov
Frøydis Gillund, Marianne Vileid Uleberg
Sammendrag
Rapporten presenterer en analysere av offentlige styringsdokumenter som har betydning for forvaltning av jordbrukets arealbehov, inkludert dyrket, dyrkbart og utmarksareal. Målet var å kartlegge målsettinger og virkemidler for bærekraftig forvaltning på nasjonalt, regionalt (Nord-Norge) og kommunalt nivå (Alta, Tromsø, Vestvågøy). Analysen viser at alle myndighetsnivåer har utarbeidet mål og virkemidler for bærekraftig arealforvaltning. Alta, Tromsø og Vestvågøy kommuner har godt planverk og følger nasjonale og regionale føringer. Vi fant en rekke målsettinger for bærekraftig forvaltning av jordbrukets arealbehov som dekker fire tema: bærekraft, vern av dyrka og dyrkbar jord, aktiv drift av jordbruksareal og bærekraftig forvaltning av beiteareal i utmark. Målsettinger om vern av dyrka jord er mest fremhevet. De viktigste virkemidlene for bærekraftig arealforvaltning inkluderer tiltak for kunnskapsbasert arealforvaltning og økt bevissthet om jordbrukets arealbehov, samt juridiske og økonomiske virkemidler.
Forfattere
Luiz C. Garcia Carlos H. Rocha Nátali M. de Souza Pedro H. Weirich Neto Jaime A. Gomes Thiago InagakiSammendrag
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Sammendrag
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Forfattere
Lone RossSammendrag
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Sammendrag
Long-term monitoring of ecosystems is the only direct method to provide insights into the system dynamics on a range of timescales from the temporal resolution to the duration of the record. Time series of typical environmental variables reveal a striking diversity of trends, periodicities, and long-range correlations. Using several decades of observations of water chemistry in first-order streams of three adjacent catchments in the Harz mountains in Germany as example, we calculate metrics for these time series based on ordinal pattern statistics, e.g. permutation entropy and complexity, Fisher information, or q-complexity, and other indicators like Tarnopolski diagrams. The results are compared to those obtained for reference statistical processes, like fractional Brownian motion or ß noise. After detrending and removing significant periodicities from the time series, the distances of the residuals to the reference processes in this space of metrics serves as a classification of nonlinear dynamical behavior, and to judge whether inter-variable or rather inter-site differences are dominant. The classification can be combined with knowledge about the processes driving hydrochemistry, elucidating the connections between the variables. This can be the starting point for the next step, constructing causal networks from the multivariate dataset.
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.