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.
2020
Forfattere
Nanna Bjerregaard Pedersen Henning Matthiesen Robert A. Blanchette Gry Alfredsen Benjamin W. Held Andreas Westergaard‑Nielsen Jørgen HollesenSammendrag
Climate change is expected to accelerate the microbial degradation of the many extraordinary well-preserved organic archaeological deposits found in the Arctic. This could potentially lead to a major loss of wooden artefacts that are still buried within the region. Here, we carry out the first large-scale investigation of wood degradation within archaeological deposits in the Arctic. This is done based on wooden samples from 11 archaeological sites that are located along a climatic gradient in Western Greenland. Our results show that Ascomycota fungi are causing extensive soft rot decay at all sites regardless of climate and local environment, but the group is diverse and many of the species were only found once. Cadophora species known to cause soft rot in polar environments were the most abundant Ascomycota found and their occurrence in native wood samples underlines that they are present locally. Basidiomycota fungi were also present at all sites. In the majority of samples, however, these aggressive and potentially very damaging wood degraders have caused limited decay so far, probably due to unfavorable growth conditions. The presence of these wood degrading fungi suggests that archaeological wooden artefacts may become further endangered if climate change leads to more favorable growth conditions.
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Ingerd Skow Hofgaard Heidi Udnes Aamot Till Seehusen Hugh Riley Ruth Dill-Macky Børge Holen Guro BrodalSammendrag
To mitigate the risk of erosion and nutrient runoff, reduced tillage has become more prevalent in Norway. Within within recent decades, there have been some years with relatively high occurrence of Fusarium head blight and mycotoxins in Norwegian cereal grain. This is thought to have been caused by an increased inoculum potential (IP) of Fusarium spp. due to larger amount of crop residues remaining on the soil surface, in combination with weather conditions promoting fungal growth and infection of cereal plants. The objective of this work was to elucidate the influence of different tillage practices on the IP of Fusarium spp. and the subsequent Fusarium-infection and mycotoxin contamination of spring wheat grain at harvest. Tillage trials were conducted at two locations in southeast Norway (Solør and Toten) over three years, 2010-2012. Residues of wheat from the previous year were collected in spring. Fusarium avenaceum and Fusarium graminearum were the most common Fusarium species recorded on wheat straw residues. IP was calculated as the percentage of the residues infested with Fusarium spp. multiplied by the proportion of the soil surface covered with residues. The IP of Fusarium spp. was lower in ploughed plots compared to those tilled with harrowing only. Ploughing in spring resulted in a similarly low IP as autumn ploughing. In contrast, harrowing in autumn generally reduced IP more than did spring harrowing. The mycotoxin levels in the harvested wheat were generally low, except for deoxynivalenol at high levels in Solør 2011. Despite a lower IP of ploughed versus harrowed plots, this was not reflected in the content of Fusarium and mycotoxins in harvested grain. The Fusarium species that dominated in the residues examined in this study were the same as those detected in the harvested grain, supporting the finding that residues are an important source of inoculum.
Sammendrag
For bare ti år siden var Fusarium-sopper et stort problem i norske kornavlinger. Noen viktige grep, og hell med været, har resultert i svært lave konsentrasjoner av mykotoksinet DON de siste årene.
Forfattere
Han Zhang Sabine Andert Lars Olav Brandsæter Jesper Rasmussen M-H. Robin Jukka Salonen Kirsten Tørresen Muriel Valantin-Morison Bärbel GerowittSammendrag
Det er ikke registrert sammendrag
Sammendrag
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Forfattere
Synnøve RivedalSammendrag
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Forfattere
Oskar PuschmannSammendrag
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Sammendrag
Soil respiration is an important ecosystem process that releases carbon dioxide into the atmosphere. While soil respiration can be measured continuously at high temporal resolutions, gaps in the dataset are inevitable, leading to uncertainties in carbon budget estimations. Therefore, robust methods used to fill the gaps are needed. The process-based non-linear least squares (NLS) regression is the most widely used gap-filling method, which utilizes the established relationship between the soil respiration and temperature. In addition to NLS, we also implemented three other methods based on: 1) artificial neural networks (ANN), driven by temperature and moisture measurements, 2) singular spectrum analysis (SSA), relying only on the time series itself, and 3) the expectation-maximization (EM) approach, referencing to parallel flux measurements in the spatial vicinity. Six soil respiration datasets (2017–2019) from two boreal forests were used for benchmarking. Artificial gaps were randomly introduced into the datasets and then filled using the four methods. The time-series-based methods, SSA and EM, showed higher accuracies than NLS and ANN in small gaps (<1 day). In larger gaps (15 days), the performance was similar among NLS, SSA and EM; however, ANN showed large errors in gaps that coincided with precipitation events. Compared to the observations, gap-filled data by SSA showed similar degree of variances and those filled by EM were associated with similar first-order autocorrelation coefficients. In contrast, data filled by both NLS and ANN exhibited lower variance and higher autocorrelation than the observations. For estimations of the annual soil respiration budget, NLS, SSA and EM resulted in errors between −3.7% and 5.8% given the budgets ranged from 463 to 1152 g C m−2 year−1, while ANN exhibited larger errors from −11.3 to 16.0%. Our study highlights the two time-series-based methods which showed great potential in gap-filling carbon flux data, especially when environmental variables are unavailable.
Sammendrag
Det er ikke registrert sammendrag