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
Volkmar TimmermannSammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Mladen Ognjenovic Ivan Seletkovic Mia Marušic Mathieu Jonard Pasi Rautio Volkmar Timmermann Melita Percec Tadic Miran Lanšcak Damir Ugarkovic Nenad PotocicSammendrag
Det er ikke registrert sammendrag
Sammendrag
In 2021, mean defoliation remained at approximately the same level as in 2020 with no change for broadleaves and only a very slight increase for conifers. Deciduous temperate oaks had the highest increase in mean defoliation (+1.4%), while common beech had the largest decrease (-1.7%). Based on the data of the past 20 years, trends show a considerable increase in defoliation of Austrian pine and evergreen oaks (7.1% and 6.7%, respectively). On the other hand, the increase in defoliation for deciduous temperate oaks (2.6%) and common beech (3.4%) has been relatively low and the trend for Scots pine and Norway spruce shows a moderate increase in defoliation of 4.3 and 3.8%, respectively. No trend was detected for deciduous (sub-) Mediterranean oaks. There was again a decrease in the number of observed damage symptoms compared to last year. As in previous years, the number of recorded damage symptoms per assessed tree was substantially higher for broadleaves than for conifers. Insects, abiotic causes, and fungi were the most common damage agent groups for all species, comprising altogether more than half of all damage records. Tree mortality increased again slightly in 2021, mainly due to abiotic factors.
Sammendrag
Bacterial diseases in woody plants are best characterized for ornamental and fruit trees and much less is known for forest trees. There are many diseases of forest trees whose etiology remains to be clarified and likely more bacterial diseases of forest trees will be discovered in the next years. An overview of the main bacterial pathogens that cause diseases in forest and ornamental trees is described in this chapter and the general differences between fungal and bacterial diseases are outlined. For bacteria pathogenic to trees, six types of diseases are described: Bacterial blight diseases, represented by Erwinia amylovora, the fireblight disease; Bacterial wilt disease, represented by Ralstonia solanacearum species complex; Root and stem galls of trees, represented by Agrobacterium tumefaciens; Wetwood disease, caused by several bacterial genera like Clostridium, Bacillus, Enterobacter, Klebsiella, and Pseudomonas, Xanthomonas and Pantoea; Bacterial scorch disease represented by Xylella fastidiosa with all its subspecies; Bacterial canker represented by Pseudomonas syringae with all its pathovars. Finally, the current diagnostic methods and specific issues related to bacteria detection, together with the main results of the scientific efforts and challenges in the genetic breeding to increase bacterial resistance of trees, are outlined.
Sammendrag
Pathogenic wood decay fungi such as species of Heterobasidion are some of the most serious forest pathogens in Europe, causing rot of tree boles and loss of growth, with estimated economic losses of eight hundred million euros per year. In conifers with low resinous heartwood such as species of Picea and Abies, these fungi are commonly confined to heartwood and thus external infection signs on the bark or foliage of trees are normally absent. Consequently, determining the extent of disease presence in a forest stand with field surveys is not practical for guiding forest management decisions such as optimal rotation time. Remote sensing technologies such as airborne laser scanning and aerial imagery are already used to reduce the reliance on fieldwork in forest inventories. This study aimed to use remote sensing to detect rot in spruce (Picea abies L. Karst.) forests in Norway. An airborne hyperspectral imager provided information for classifying the presence or absence of rot in a single-tree-based framework. Ground reference data showing the presence of rot were collected by harvest machine operators during the harvest of forest stands. Random forest and support vector machine algorithms were used to classify the presence and absence of rot. Results indicate a 64% overall classification accuracy for presence-absence classification of rot, although additional work remains to make the classifications usable for practical forest management.
Forfattere
Nina TrandemSammendrag
Det er ikke registrert sammendrag
Forfattere
Nina TrandemSammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag