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
Bjørn Egil FløSammendrag
Moderniteten har gjort jordbruket til eit instrument for stat og marknad, men somme bønder vel å gå sin eigen veg.
Sammendrag
Norsk institutt for bioøkonomi (Nibio) ville finne ut om krav til høy selvforsyningsgrad med fôr hindrer konvensjonelle melkeprodusenter i å legge om driften til økologisk.
Sammendrag
I en ny rapport har NIBIO sett på ulike strategier som brukes for å oppnå høy selvforsyning av fôr. Det kan se ut som om skjerpede krav til selvforsyning av fôr ikke er til hinder for økologisk melkeproduksjon.
Sammendrag
Nibio-rapport viser at økologiske melkeprodusenter fint kan håndtere økte krav til selvforsyning.
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Heikki Korpunen Yrjö Nuutinen Paula Jylhä Lars Eliasson Aksel Granhus Juha Laitila Stephan Hoffmann Timo MuhonenSammendrag
• Overall forest management objectives and stand properties set the requirements and possibilities for harvesting in continuous cover forestry (CCF). • Harvester and forwarder operators play a key role in successful CCF harvesting, as both productivity and quality of work are essential factors in harvesting operations. • Optimal stand conditions improve work productivity on selection harvesting sites; harvested stem volume correlates well with work productivity in cutting, and density of remaining trees does not significantly reduce work productivity in forwarding. • Carefully executed group cutting and shelterwood harvesting can reduce the number of damaged remaining trees, which is beneficial for future tree generations. • Research-based information is needed about work productivity in harvesting, damage caused by harvesting, and optimisation of strip road and forest road networks for CCF.
Sammendrag
Ole Green, farmer, Benfarm. The farm is regenerative organic, and crops are grown in stripcropping. Robots are used extensively, and in AllEcoSys the effect of diversification of bush-berries (black currant and gooseberries) grown in strips between annual crops is studied together with University of Copenhagen.
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
The AllEcoSys project aims to promote sustainable and resilient agricultural systems by implementing a network of living labs across Europe. November 26th is the first AllEcoSys Living Lab seminar. Main speaker was Ole Green from the Danish Living Lab. Ole Green is honorary professor at Aarhus University, founder of Agrointelli, producing innovative farm robotics, and a farmer, developing a novel stripcropping system of berry bushes and annual crops. Project co-funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency (REA). Neither the European Union nor the granting authority can be held responsible for them.
Forfattere
Mostafa Hoseini Helle Ross Gobakken Stephan Hoffmann Csongor Horvath Johannes Rahlf Jan Bjerketvedt Stefano Puliti Rasmus AstrupSammendrag
RoadSens is a platform designed to expedite the digitalization process of forest roads, a cornerstone of efficient forest operations and management. We incorporate stereo-vision spatial mapping and deep-learning image segmentation to extract, measure, and analyze various geometric features of the roads. The features are precisely georeferenced by fusing post-processing results of an integrated global navigation satellite system (GNSS) module and odometric localization data obtained from the stereo camera. The first version of RoadSens, RSv1, provides measurements of longitudinal slope, horizontal/vertical radius of curvature and various cross-sectional parameters, e.g., visible road width, centerline/midpoint positions, left and right sidefall slopes, and the depth and distance of visible ditches from the road’s edges. The potential of RSv1 is demonstrated and validated through its application to two road segments in southern Norway. The results highlight a promising performance. The trained image segmentation model detects the road surface with the precision and recall values of 96.8 and 81.9 , respectively. The measurements of visible road width indicate sub-decimeter level inter-consistency and 0.38 m median accuracy. The cross-section profiles over the road surface show 0.87 correlation and 9.8 cm root mean squared error (RMSE) against ground truth. The RSv1’s georeferenced road midpoints exhibit an overall accuracy of 21.6 cm in horizontal direction. The GNSS height measurements, which are used to derive longitudinal slope and vertical curvature exhibit an average error of 5.7 cm compared to ground truth. The study also identifies and discusses the limitations and issues of RSv1, which provide useful insights into the challenges in future versions.