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
Belachew Asalf TadesseSammendrag
Det er ikke registrert sammendrag
Forfattere
Belachew Asalf TadesseSammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Belachew Asalf TadesseSammendrag
Det er ikke registrert sammendrag
Forfattere
Belachew Asalf TadesseSammendrag
Det er ikke registrert sammendrag
Forfattere
Björn Andersson Annika Djurle Jens Erik Ørum Marja Jalli Antanas Ronis Andrea Ficke Lise Nistrup JørgensenSammendrag
Validation of models for plant disease management is a crucial part in the development of decision support systems in plant protection. Bespoke field trials are usually conducted to determine the performance of a model under practical conditions. However, field trials are very resource-demanding, and the use of already existing field trial data could significantly reduce costs for model validation. In this study, we took this novel approach to verify the performance of models for determining the need of fungicide applications against leaf blotch diseases in wheat by utilising historical weather data and yield data available from fungicide efficacy field trials. Two models based on humidity factors were used in the study. To estimate how specific humidity settings in the two models affect the number of recommended fungicide treatments per season, historical weather data from a 5-year period from weather stations in Denmark, Sweden, Norway, Finland, and Lithuania was used. The model output shows major differences between seasons and regions, typically recommending between one and three treatments per season. To determine the prediction potential of the models, data on yield gains from either one or two fungicide applications in fungicide efficacy trials conducted in wheat over a 5-year period in the five countries was utilised. The yield responses from fungicide treatments in the efficacy trials varied considerably between years and countries, as did the proportion of predictions of profitable treatments. In general, there was a tendency for the models to overestimate the need to apply fungicides (low specificity), but they rarely failed to recommend an application that was needed (high sensitivity). Despite the importance of having specific trials across regions in order to adjust models to local cropping and weather conditions, our study shows that historical weather data and existing field trial data have the potential to be used in model validation.
Sammendrag
Septoria nodorum blotch (SNB), caused by the necrotrophic fungal pathogen Parastagonospora nodorum, is the dominant leaf blotch pathogen of wheat in Norway. Resistance/susceptibility to SNB is a quantitatively inherited trait, which can be partly explained by the interactions between wheat sensitivity loci (Snn) and corresponding P. nodorum necrotrophic effectors (NEs). Two Nordic wheat association mapping panels were assessed for SNB resistance in the field over three to four years: a spring wheat and a winter wheat panel (n = 296 and 102, respectively). Genome-wide association studies found consistent SNB resistance associated with quantitative trait loci (QTL) on eleven wheat chromosomes, and ten of those QTL were common in the spring and winter wheat panels. One robust QTL on the short arm of chromosome 2A, QSnb.nmbu-2AS, was significantly detected in both the winter and spring wheat panels. For winter wheat, using the four years of SNB field severity data in combination with five years of historical data, the effect of QSnb.nmbu-2AS was confirmed in seven of the nine years, while for spring wheat, the effect was confirmed for all tested years including the historical data from 2014 to 2015. However, lines containing the resistant haplotype are rare in both Nordic spring (4.0%) and winter wheat cultivars (15.7%), indicating the potential of integrating this QTL in SNB resistance breeding programs. In addition, clear and significant additive effects were observed by stacking resistant alleles of the detected QTL, suggesting that marker-assisted selection can greatly facilitate SNB resistance breeding.
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.