Hopp til hovedinnholdet

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.

2003

Til dokument

Sammendrag

EU-kommisjonen har prioritert jordsmonn på linje med luft og vann og har gitt ut en meddelelse med en beskrivelse av en strategi for beskyttelse av jordsmonn. Som et ledd i denne strategien planlagges det et felles europeisk system for jordsmonnovervåking. I denne rapporten er det gitt en oppsummering av meddelelsen fra EU-kommisjonen, med vekt på trusler mot jordsmonn i Europa, relevante forskrifter og direktiver samt krav til en europeisk jordsmonnovervåking. Det er også gitt en kort omtale av jordsmonntilstanden i Norge og de viktigste eksisterende overvåkingsprogrammene som kan bygges videre til en mer fullstendig jordsmonnovervåking.

Sammendrag

The rationale for stand growth modelling is often either grounded in a search for improved scientific understanding or in support for management decisions. The ultimate goal under the first task is seen in mechanistic models, i.e. models that represent the stand structure realistically and predict future growth as a function of the current status of the stand. Such mechanistic models tend to be over-parameterized with respect to the data actually available for a given stand. Calibration of these models may lead to non-unique representations and unreliable predictions. Empirical models, the second major line of growth modelling, typically match available data sets as well as do process-based models. They have less degrees of freedom, hence mitigate the problem of non-unique calibration results, but they employ often parameters without physiological or physical meaning. That is why empirical models cannot be extrapolated beyond the existing conditions of observations. Here we argue that this widespread dilemma can be overcome by using interactive models as an alternative approach to mechanistic (algorithmic) models. Interactive models can be used at two levels: a) the interactions among trees of a species or ecosystem and b) the interactions between forest management and a stand structure, e.g. in thinning trials. In such a model data from a range of sources (scientific, administrative, empirical) can be incorporated into consistent growth reconstructions. Interactive selection among such growth reconstructions may be theoretically more powerful than algorithmic automatic selection. We suggest a modelling approach in which this theoretical conjecture can be put to a practical test. To this end growth models need to be equipped with interactive visualization interfaces in order to be utilized as input devices for silvicultural expertise. Interactive models will not affect the difficulties of predicting forest growth, but may be at their best in documenting and disseminating silvicultural competence in forestry.