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.
2025
Authors
Ruochen Li Xin Pei Ming Zhang Xuhui Deng Chengyuan Tao Jiabao Wang Xueli Chen Nicholas Clarke Lidia Sas-Paszt Zongzhuang Shen Rong Li Qirong ShenAbstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Magne Nordang Skårn Chloé Grieu Anne-Grete Roer Hjelkrem Katherine Ann Gredvig Nielsen Silje Kvist Simonsen Nora Steinkopf Anne Kjersti Uhlen Guro BrodalAbstract
Chocolate spot (CS) is one of the most destructive diseases affecting faba beans worldwide, leading to yield reductions of up to 90% in susceptible cultivars under conducive environmental conditions. Traditionally, the disease has been attributed to the fungal pathogens Botrytis fabae and Botrytis cinerea, however recent studies have identified three additional Botrytis species capable of causing the disease. Fungicide applications during flowering are commonly used to control the disease and limit damage to pod set, but this approach is not always effective. The reasons for this lack of control are not fully understood. To increase our understanding of the CS species complex in Norway, we used species-specific PCR to identify different Botrytis species in symptomatic leaves collected at various locations and years. Some Botrytis species are known to be high-risk pathogens for fungicide resistance development, but resistance in Norwegian Botrytis populations in faba bean have not previously been studied. Therefore, we obtained Botrytis isolates from diseased leaves and used a mycelial growth assay to assess their response to the active ingredients (boscalid and pyraclostrobin) in the fungicide commonly used for CS control in Norway. Resistance to both boscalid and pyraclostrobin was detected among B. cinerea isolates, while only resistance to boscalid was detected among B. fabae isolates. To elucidate resistance mechanisms, we analyzed target gene sequences for the presence of mutations known to confer resistance to the two active ingredients. Field experiments were conducted to test the efficacy of various spray timings and fungicides in early and late faba bean varieties. Additionally, we are developing a disease risk model for CS to better understand the conditions that lead to disease and to improve the timing of fungicide applications.
Authors
Magne Nordang Skårn Chloé Grieu Anne-Grete Roer Hjelkrem Katherine Ann Gredvig Nielsen Silje Kvist Simonsen Nora Steinkopf Anne Kjersti Uhlen Guro BrodalAbstract
No abstract has been registered
Authors
Heidi Udnes Aamot Magne Nordang Skårn Chloé Grieu Anne-Grete Roer Hjelkrem Katherine Ann Gredvig Nielsen Silje Kvist Simonsen Nora Steinkopf Anne Kjersti Uhlen Guro BrodalAbstract
Chocolate spot (CS) is one of the most destructive diseases affecting faba beans worldwide, leading to yield reductions of up to 90% in susceptible cultivars under conducive environmental conditions. Traditionally, the disease has been attributed to the fungal pathogens Botrytis fabae and Botrytis cinerea, however recent studies have identified three additional Botrytis species capable of causing the disease. Fungicide applications during flowering are commonly used to control the disease and limit damage to pod set, but this approach is not always effective. The reasons for this lack of control are not fully understood. To increase our understanding of the CS species complex in Norway, we used species-specific PCR to identify different Botrytis species in symptomatic leaves collected at various locations and years. Some Botrytis species are known to be high-risk pathogens for fungicide resistance development, but resistance in Norwegian Botrytis populations in faba bean have not previously been studied. Therefore, we obtained Botrytis isolates from diseased leaves and used a mycelial growth assay to assess their response to the active ingredients (boscalid and pyraclostrobin) in the fungicide commonly used for CS control in Norway. Resistance to both boscalid and pyraclostrobin was detected among B. cinerea isolates, while only resistance to boscalid was detected among B. fabae isolates. To elucidate resistance mechanisms, we analyzed target gene sequences for the presence of mutations known to confer resistance to the two active ingredients. Field experiments were conducted to test the efficacy of various spray timings and fungicides in early and late faba bean varieties. Additionally, we are developing a disease risk model for CS to better understand the conditions that lead to disease and to improve the timing of fungicide applications.
Authors
Lone RossAbstract
No abstract has been registered
Abstract
Long-term monitoring of ecosystems is the only direct method to provide insights into the system dynamics on a range of timescales from the temporal resolution to the duration of the record. Time series of typical environmental variables reveal a striking diversity of trends, periodicities, and long-range correlations. Using several decades of observations of water chemistry in first-order streams of three adjacent catchments in the Harz mountains in Germany as example, we calculate metrics for these time series based on ordinal pattern statistics, e.g. permutation entropy and complexity, Fisher information, or q-complexity, and other indicators like Tarnopolski diagrams. The results are compared to those obtained for reference statistical processes, like fractional Brownian motion or ß noise. After detrending and removing significant periodicities from the time series, the distances of the residuals to the reference processes in this space of metrics serves as a classification of nonlinear dynamical behavior, and to judge whether inter-variable or rather inter-site differences are dominant. The classification can be combined with knowledge about the processes driving hydrochemistry, elucidating the connections between the variables. This can be the starting point for the next step, constructing causal networks from the multivariate dataset.
Abstract
No abstract has been registered
Abstract
No abstract has been registered
Abstract
No abstract has been registered