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.
2023
Sammendrag
Arealet av vernet skog i Norge har økt med over tjue prosent, og omfatter i dag nesten 600.000 hektar, nær fem prosent av det totale skogarealet.
Sammendrag
Det er mye penger å spare på å lage sin egen ved.
Sammendrag
Askeskuddsjuken tar livet av asketrærne i Norge og i Europa. Nå ber forskerne publikum om hjelp til å registrere friske asketrær.
Sammendrag
Researchers report that the more time reindeer spend ruminating, the less sleepy they become. Chewing cud reduces the animals' need for sleep in the summer, which gives them more time to find food.
Sammendrag
Ny forskning viser at jo mer tid reinen bruker på å tygge drøv, desto mindre søvnige blir de. Drøvtyggingen reduserer dyras søvnbehov om sommeren, noe som gir mer tid til å finne mat.
Sammendrag
Hva det er som får folk til å bygge noe så smått, selge alle sine saker og flytte inn i et mikrohus?
Sammendrag
Bjørk brukes til møbler, panel og knivemner og er en viktig del av nordisk design. En ny rapport viser hvordan den kan brukes enda mer – til mye annet enn bare ved.
Sammendrag
Det viser resultater fra NIBIOs 25-årige overvåkingsprogram
Forfattere
Özgün Candan Onarman UmuSammendrag
Det er ikke registrert sammendrag
Forfattere
Christoph Schürz Svajunas Plunge Michael Strauch Natalja Cerkasova Brigitta Szabó Csilla Farkas Attila Nemes Mikołaj PiniewskiSammendrag
SWAT+ is a data intensive eco-hydrological model. The SWAT+ model setup procedure requires comprehensive data on topography, soils, vegetation cover, land management, or the hydrological connectivity in the landscape. Although SWAT+ allows a detailed representation of the landscape and its management, input data handling and processing can be challenging and computationally intensive. Eventually, the input files of a detailed model setup can easily comprise hundreds of thousands of lines which limits model verification and calibration. Script programming languages such as R and python are popular for data processing and analysis in many scientific disciplines. For SWAT2012 and SWAT+ several R packages and tools, such as SWATplusR and SWATfarmR have been developed over the last years which proved to be useful tools in SWAT modeling workflows. Yet, they have not been connected into consecutive workflows, which would add benefits of automation, transparency, reproducibility and flexibility. The EU funded H2020 project OPTAIN aims to create harmonized workflows for various stages of the SWAT+ model development. To achieve this, the project team improved and expanded upon existing SWAT model related R packages, and also created new R packages and scripted workflows. The developed workflows cover a wide range of modeling tasks from preparing and generating SWAT+ model input data, to setting up the model, model parametrization, creating land management schedules, verifying the model, and calibrating it. Although some of the workflows are specifically tailored to the needs of the project’s modeling case studies, most of the developed functions and workflows are generalized and can be implemented in other SWAT+ model applications. This presentation will give an overview of the available R packages and R workflows which were developed and extended within the OPTAIN framework, such as deriving soil properties , preparing weather data and the weather generator inputs with scripted workflows and the R package SWATprepR, the development of field scale connected SWAT+ model setups with SWATbuildR, scripted workflows to generate farm management crop rotation inputs employing the SWATfarmR package, or comprehensive model verification using the SWATdoctR. Further resources are presented which were compiled in the ongoing project and which can be valuable for other SWAT modelers. The presented work is a result of activities in the EU Horizon 2020 project OPTAIN (grant agreement No. 862756).