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.
2016
Authors
N.A. Peres C. Fraisse Z. Guan G. Schnabel W. Turechek David Gadoury W. Wilcox R.C. Seem R Martin Arne StensvandAbstract
No abstract has been registered
Authors
Lise Dalsgaard Holger Lange Line Tau Strand Ingeborg Callesen Signe Kynding Borgen Jari Liski Rasmus AstrupAbstract
Soil organic carbon (C), accumulated over millennia, comprise more than half of the C stored in boreal and temperate forest landscapes. We used the Norwegian national forest inventory and soil survey network (n = 719, no deep organic soils) to explore the validity of a deterministic model representation of this pool (Yasso07). We statistically compared simulated and measured soil C stocks and related differences (measured – simulated) to site factors (drainage, topography, climate, vegetation, C-to-N ratio, and soil classification). Median C stocks were 5.0 kg C·m−2 (model) and 14.5 kg C·m−2 (measurements). Soil C differences related to site factors (r2 of 0.16 to 0.37). For Brunisols, Gleysols, and wet Organic soils, differences related primarily to topographic wetness. For Regosols, Podzols, and Dystric Eluviated Brunisols, they related to climate, profile depth, and, in some cases, drainage class and site index. We argue that soil moisture regimes in our study area overrule tree productivity effects in the determination of soil C stocks and present conditions for soil formation that the model cannot (and does not explicitly) account for. These are processes such as humification and podsolization that involve eluviation and illuviation of dissolved organic C (DOC) with sesquioxides to form spodic B horizons and carbon enrichment due to hampered decomposition in frequently anoxic conditions.
Authors
Melissa MagerøyAbstract
No abstract has been registered
Authors
Marcos Viejo Elena Carneros Hugh Cross YeonKyeong Lee Igor A. Yakovlev Carl Gunnar Fossdal Jorunn Elisabeth OlsenAbstract
No abstract has been registered
Authors
Marcos Viejo Elena Carneros Hugh Cross YeonKyeong Lee Igor A. Yakovlev Carl Gunnar Fossdal Jorunn Elisabeth OlsenAbstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Göran Ståhl Svetlana Saarela Sebastian Schnell Sören Holm Johannes Breidenbach Sean P. Healey Paul L. Patterson Steen Magnussen Erik Næsset Ronald E. McRoberts Timothy G. GregoireAbstract
This paper focuses on the use of models for increasing the precision of estimators in large-area forest surveys. It is motivated by the increasing availability of remotely sensed data, which facilitates the development of models predicting the variables of interest in forest surveys. We present, review and compare three different estimation frameworks where models play a core role: model-assisted, model-based, and hybrid estimation. The first two are well known, whereas the third has only recently been introduced in forest surveys. Hybrid inference mixes design-based and model-based inference, since it relies on a probability sample of auxiliary data and a model predicting the target variable from the auxiliary data..We review studies on large-area forest surveys based on model-assisted, model-based, and hybrid estimation, and discuss advantages and disadvantages of the approaches. We conclude that no general recommendations can be made about whether model-assisted, model-based, or hybrid estimation should be preferred. The choice depends on the objective of the survey and the possibilities to acquire appropriate field and remotely sensed data. We also conclude that modelling approaches can only be successfully applied for estimating target variables such as growing stock volume or biomass, which are adequately related to commonly available remotely sensed data, and thus purely field based surveys remain important for several important forest parameters.
Authors
Ari HietalaAbstract
No abstract has been registered
Abstract
Food production contributes considerably to global greenhouse gas (GHG) emissions. Animal products – particularly meat from ruminants – generally have higher GHG emissions than plant products. Over the last few decades the global per capita consumption of animal products has increased. This has a negative impact on climate change, land and water availability, and human health. We are faced with the two-fold challenge of reducing GHG emissions while still producing enough food for our growing population. Part of the solution could be for consumers to change towards a more sustainable diet. In this paper we take Norway as a case study for estimating optimal taxes and subsidies on different food items which can change consumption patterns in order to reduce the GHG emissions derived from the average Norwegian diet. In the estimate we ensure that the average calorie intake with the new diet remains the same as with the current diet, and factor in other health considerations. Our findings suggest that limited but useful emission reduction targets can be set with only a few changes in diets. The methodology presented in this paper may be used to estimate optimal climate taxes and subsidies under different emission, quantities, taxes, subsidies, and health constraints.
Authors
Bruce Talbot Marek Marian Pierzchala Jan Bjerketvedt Dag FjeldAbstract
No abstract has been registered