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Publications

NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.

2022

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Abstract

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.

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Abstract

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.

Abstract

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.

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Abstract

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.

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Abstract

European beech (Fagus sylvatica L.) forests provide multiple essential ecosystem goods and services. The projected climatic conditions for the current century will significantly affect the vitality of European beech. The expected impact of climate change on forest ecosystems will be potentially stronger in southeast Europe than on the rest of the continent. Therefore, our aim was to use the long-term monitoring data of crown vitality indicators in Croatia to identify long-term trends, and to investigate the influence of current and previous year climate conditions and available site factors using defoliation (DEF) and defoliation change (DDEF) as response variables. The results reveal an increasing trend of DEF during the study period from 1996 to 2017. In contrast, no significant trend in annual DDEF was observed. The applied linear mixed effects models indicate a very strong influence of previous year drought on DDEF, while climate conditions have a weak or insignificant effect on DEF. The results suggest that site factors explain 25 to 30% DEF variance, while similar values of conditional and marginal R2 show a uniform influence of drought on DDEF. These results suggest that DEF represents the accumulated impact of location-specific stressful environmental conditions on tree vitality, while DDEF reflects intense stress and represents the current or recent status of tree vitality that could be more appropriate for analysing the effect of climate conditions on forest trees.

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Abstract

European ash (Fraxinus excelsior) and narrow-leafed ash (F. angustifolia) are keystone forest tree species with a broad ecological amplitude and significant economic importance. Besides global warming both species are currently under significant threat by an invasive fungal pathogen that has been spreading progressively throughout the continent for almost three decades. Ash dieback caused by the ascomycete Hymenoscyphus fraxineus is capable of damaging ash trees of all age classes and often ultimately leads to the death of a tree after years of progressively developing crown defoliation. While studies at national and regional level already suggested rapid decline of ash populations as a result of ash dieback, a comprehensive survey at European level with harmonized crown assessment data across countries could shed more light into the population decline from a pan-European perspective and could also pave the way for a new conservation strategy beyond national boarders. Here we present data from the ICP Forests Level I crown condition monitoring from 27 countries resulting in > 36,000 observations. We found a substantial increase in defoliation and mortality over time indicating that crown defoliation has almost doubled during the last three decades. Hotspots of mortality are currently situated in southern Scandinavia and north-eastern Europe. Overall survival probability after nearly 30 years of infection has already reached a critical value of 0.51, but with large differences among regions (0.20–0.86). Both a Cox proportional hazard model as well as an Aalen additive regression model strongly suggest that survival of ash is significantly lower in locations with excessive water regime and which experienced more extreme precipitation events during the last two decades. Our results underpin the necessity for fast governmental action and joint rescue efforts beyond national borders since overall mean defoliation will likely reach 50% as early as 2030 as suggested by time series forecasting.