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.
2017
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Forskere mener potensialet for belgvekster er stort.
Forfattere
Tore SkrøppaSammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Emma Brunberg Ildri Kristine Bergslid Knut Egil Bøe Kristin SørheimSammendrag
Det er ikke registrert sammendrag
Forfattere
Levan Elbakidze Rodolfo M. Jr. NaygaSammendrag
In the context of testing the construct validity of stated preference studies, some researchers advocate the use of an “adding-up test” designed to gauge whether elicited values are sufficiently sensitive to a change in the scope (i.e. size) of a good. Crucial to the applicability of this test in practice, which relies on endowing a subsample of respondents with a good free of charge, is that the income effects due to endowment are negligible. In this study, we apply the adding-up test in an experimental value elicitation format to examine the potential effect of endowment as part of the test design on the adding-up property of elicited values. The results show that the adding-up property can be affected by free provision of part of the bundle.
Sammendrag
Estimates of stand averages are needed by forest management for planning purposes. In forest enterprise inventories supported by remotely sensed auxiliary data, these estimates are typically derived exclusively from a model that does not consider stand effects in the study variable. Variance estimators for these means may seriously underestimate uncertainty, and confidence intervals may be too narrow when a model used for computing a stand mean omits a nontrivial stand effect in one or more of the model parameters, a nontrivial spatial distance dependent autocorrelation in the model residuals, or both. In simulated sampling from 36 populations with stands of different sizes and differing with respect to (i) the correlation between a study variable (Y) and two auxiliary variables (X), (ii) the magnitude of stand effects in the intercept of a linear population model linking X to Y, and (iii) a first-order autoregression in Y and X, we learned that none of the tested designs provided reliable estimates of the within-stand autocorrelation among model residuals. More-reliable estimates were possible from stand-wide predictions of Y. The anticipated bias in an estimated autoregression parameter had a modest influence on estimates of variance and coverage of nominal 95% confidence intervals for a synthetic stand mean.