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
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
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Forfattere
Wendy Marie Waalen Anne Kjersti Uhlen Jon Arne Dieseth Vilde Gadderud Shirin Mohammadi Chloé GrieuSammendrag
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Forfattere
Wendy Marie WaalenSammendrag
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Forfattere
Daniel Liptzin Jens Boy John L. Campbell Nicholas Clarke Jean-Paul Laclau Roberto Godoy Sherri L. Johnson Klaus Kaiser Gene E. Likens Gunilla Pihl Karlsson Daniel Markewitz Michela Rogora Stephen D. Sebestyen James B. Shanley Elena Vanguelova Arne Verstraeten Wolfgang Wilcke Fred Worrall William H. McDowellSammendrag
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Forfattere
Abdelhameed Elameen Denis Tourvieille de Labrouhe Emmanuelle Bret-Mestries Francois DelmotteSammendrag
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Forfattere
Markus A. K. Sydenham Joseph Chipperfield Yoko L. Dupont Katrine Eldegard Stein Joar Hegland Henning Bang Madsen Anders Nielsen Jens M* Olesen Claus Rasmussen Trond Reitan Graciela Rusch Astrid Brekke Skrindo Zander VenterSammendrag
Det er ikke registrert sammendrag
Forfattere
Vilde Lytskjold Haukenes Mikael Ohlson Johan Asplund Line Nybakken Ken Olaf Storaunet Jørund Rolstad Lisa ÅsgårdSammendrag
Det er ikke registrert sammendrag
Forfattere
Vilde Lytskjold Haukenes Lisa Åsgård Johan Asplund Line Nybakken Jørund Rolstad Ken Olaf Storaunet Mikael OhlsonSammendrag
Knowledge about the spatial variation of boreal forest soil carbon (C) stocks is limited, but crucial for establishing management practices that prevent losses of soil C. Here, we quantified the surface soil C stocks across small spatial scales, and aim to contribute to an improved understanding of the drivers involved in boreal forest soil C accumulation. Our study is based on C analyses of 192 soil cores, positioned and recorded systematically within a forest area of 11 ha. The study area is a south-central Norwegian boreal forest landscape, where the fire history for the past 650 years has been reconstructed. Soil C stocks ranged from 1.3 to 96.7 kg m−2 and were related to fire frequency, ecosystem productivity, vegetation attributes, and hydro-topography. Soil C stocks increased with soil nitrogen concentration, soil water content, Sphagnum- and litter-dominated forest floor vegetation, and proportion of silt in the mineral soil, and decreased with fire frequency in site 1, feathermoss- and lichen-dominated forest floor vegetation and increasing slope. Our results emphasize that boreal forest surface soil C stocks are highly variable in size across fine spatial scales, shaped by an interplay between historical forest fires, ecosystem productivity, forest floor vegetation, and hydro-topography.
Forfattere
Marian Schönauer Robert Prinz Kari Väätäinen Rasmus Astrup Dariusz Pszenny Harri Lindeman Dirk JaegerSammendrag
Milder winters and extended wetter periods in spring and autumn limit the amount of time available for carrying out ground-based forest operations on soils with satisfactory bearing capacity. Thus, damage to soil in form of compaction and displacement is reported to be becoming more widespread. The prediction of trafficability has become one of the most central issues in planning of mechanized harvesting operations. The work presented looks at methods to model field measured spatio-temporal variations of soil moisture content (SMC, [%vol]) – a crucial factor for soil strength and thus trafficability. We incorporated large-scaled maps of soil characteristics, high-resolution topographic information – depth-to-water (DTW) and topographic wetness index – and openly available temporal soil moisture retrievals provided by the NASA Soil Moisture Active Passive mission. Time-series measurements of SMC were captured at six study sites across Europe. These data were then used to develop linear models, a generalized additive model, and the machine learning algorithms Random Forest (RF) and eXtreme Gradient Boosting (XGB). The models were trained on a randomly selected 10% subset of the dataset. Predictions of SMC made with RF and XGB attained the highest R2 values of 0.49 and 0.51, respectively, calculated on the remaining 90% test set. This corresponds to a major increase in predictive performance, compared to basic DTW maps (R2 = 0.022). Accordingly, the quality for predicting wet soils was increased by 49% when XGB was applied (Matthews correlation coefficient = 0.45). We demonstrated how open access data can be used to clearly improve the prediction of SMC and enable adequate trafficability mappings with high spatial and temporal resolution. Spatio-temporal modelling could contribute to sustainable forest management.