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.
2022
Forfattere
Anna Birgitte MilfordSammendrag
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Forfattere
Wendy Marie Waalen Anne Kjersti Uhlen Jon Arne Dieseth Vilde Gadderud Shirin Mohammadi Chloé GrieuSammendrag
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Forfattere
Synnøve Rivedal Samson Øpstad Åsmund Mikalsen Kvifte Berit Nordskog Brita Linnestad Liv ØstremSammendrag
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Forfattere
Annbjørg KristoffersenSammendrag
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Forfattere
Maja Turnšek Siv Skar Marit Piirman Ragnheiður I. Thorarinsdottir Martina Bavec Ranka JungeSammendrag
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Forfattere
Milica Fotirić Akšić Milica Nešović Ivanka Ćirić Živoslav Tešić Lato Pezo Tomislav Tosti Uroš Gašić Biljana Dojčinović Biljana Lončar Mekjell MelandSammendrag
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Sammendrag
Tree diameter increment (ΔDBH) and total tree height increment (ΔHT) are key components of a forest growth and yield model. A problem in complex, multi-species forests is that individual tree attributes such as ΔDBH and ΔHT need to be characterized for a large number of distinct woody species of highly varying levels of occurrence. Based on more than 2.5 million ΔDBH observations and over 1 million ΔHT records from up to 60 tree species and genera, respectively, this study aimed to improve existing ΔDBH and ΔHT equations of the Acadian Variant of the Forest Vegetation Simulator (FVS-ACD) using a revised method that utilize tree species as a random effect. Our study clearly highlighted the efficiency and flexibility of this method for predicting ΔDBH and ΔHT. However, results also highlighted shortcomings of this approach, e.g., reversal of plausible parameter signs as a result of combining fixed and random effects parameter estimates after extending the random effect structure by incorporating North American ecoregions. Despite these potential shortcomings, the newly developed ΔDBH and ΔHT equations outperformed the ones currently used in FVS-ACD by reducing prediction bias quantified as mean absolute bias and root mean square error by at least 11% for an independent dataset and up to 41% for the model development dataset. Using the revised ΔDBH and ΔHT estimates, greater prediction accuracy in individual tree aboveground live carbon mass estimation was also found in general but performance varied with dataset and accuracy metric examined. Overall, this analysis highlights the importance and challenges of developing robust ΔDBH and ΔHT equations across broad regions dominated by mixed-species, managed forests.