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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.

2025

Sammendrag

Biochar and pesticides are likely to be increasingly used in combination in agricultural soils, yet their combined effects on climate change mitigation remain unexplored. This study presents an 8-month incubation experiment with different soil types (silt loam and sandy loam), biochars (corncob and corn stem), and pesticides (with and without a pesticide mixture), during which CO2 production from soil organic matter (SOM) and biochar mineralisation was monitored using isotopic methods. A comprehensive modelling approach, describing all mineralisation results over the entire incubation with a reduced set of parameters, was employed to isolate the effects of biochar, pesticides, and their interactions across soil types and carbon pools, and captured the dynamic effect of biochar on SOM mineralisation. Over 99.5% of biochars remained inert after 8 months, confirming the role of biochar as a carbon sequestration technology. Biochar addition showed higher SOM stabilisation potential in soil with high clay content compared to soil with low clay content. This suggests that biochar amendment should be considered carefully in clay-depleted soils, as it could result in a loss of native SOM. Corn stem biochar, characterised by high surface area and low C/N ratio, demonstrated higher SOM stabilisation potential than corncob biochar with low surface area and high C/N ratio. Pesticide application reduced SOM mineralisation by 10% regardless of soil and biochar types. Finally, the interaction between corncob biochar and pesticides further reduced SOM mineralisation by 5%, while no interactive effect was observed with corn stem biochar. These findings highlight the importance of considering biochar-pesticide interactions when evaluating the impact of biochar amendments on native SOM stability.

Sammendrag

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.

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Sammendrag

Empirical field data and simulation models are often used separately to monitor and analyse the dynamics of insect pest populations over time. Greater insight may be achieved when field data are used directly to parametrize population dynamic models. In this paper, we use a differential evolution algorithm to integrate mechanistic physiological-based population models and monitoring data to estimate the population density and the physiological age of the first cohort at the start of the field monitoring. We introduce an ad hoc temperature-driven life-cycle model of Bemisia tabaci in conjunction with field monitoring data. The likely date of local whitefly invasion is estimated, with a subsequent improvement of the model’s predictive accuracy. The method allows computation of the likely date of the first field incursion by the pest and demonstrates that the initial physiological age somewhat neglected in prior studies can improve the accuracy of model simulations. Given the increasing availability of monitoring data and models describing terrestrial arthropods, the integration of monitoring data and simulation models to improve model prediction and pioneer invasion date estimate will lead to better decision-making in pest management.