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.
2019
Abstract
Complex communities of microorganisms influence plant and agroecosystem health and productivity. Bacteria and fungi constitute a major part of the wheat head microbiome. A microorganism’s ability to colonize or infect a wheat seed is influenced by interacting microbiome. In Norway, wheat seed lots are routinely analysed for the infestation by Fusarium head blight and seedling blight diseases, such as Fusarium and Microdochium spp., and glume blotch caused by Parastagonospora nodorum using traditional methods (plating grain on PDA, recording presence or absence of fungal colonies) The purpose is to decide if the seed quality is suitable for sowing and whether seed treatment is needed. This method is time consuming, require knowledge within fungal morphology, and do not facilitate identification to species level in all cases. Molecular methods such as sequencing could allow detection and quantification of “all” microbial DNA, only limited by the specificity of the primers. Microbial profiling (metabarcoding) can be very time and cost-effective, since a mixture of many samples can be analysed simultaneously for both fungi and bacteria, and other microbes if required. In our project “Phytobiome” we used metabarcoding to analyse microbial communities in wheat heads and verify this information with results from qPCR and plate studies for a more complete study. Around 150 spring wheat seed lots from the years 2016-2017 (including two cultivars) were selected for analysis. One of the main objectives was to find microorganisms associated with seed germination. We will present findings from this work, but also some challenges when using PCR-based sequencing methods, especially regarding Fusarium head blight fungi.
Authors
Janneche Utne Skåre Jan Alexander Marte Haave Ignacy Jakubowicz Helle Katrine Knutsen Amy Lusher Martin Ogonowski Kirsten Eline Rakkestad Ida Skaar Line Emilie Sverdrup Martin Wagner Angelika Agdestein Johanna Eva Bodin Edel O. Elvevoll Gro Ingunn Hemre Dag Olav Hessen Merete Hofshagen Trine Husøy Åshild Krogdahl Asbjørn Magne Nilsen Trond Rafoss Olaug Taran Skjerdal Inger-Lise Steffensen Tor A Strand Vigdis Vandvik Yngvild WastesonAbstract
No abstract has been registered
Authors
Olalla Díaz-Yáñez Blas Mola-Yudego José Ramón González-OlabarriaAbstract
Snow and wind damages are one of the major abiotic disturbances playing a major role in forest ecosystems and affecting both stand dynamics and forest management decisions. This study analyses the occurrence of wind and snow damage on Norwegian forests, based on data from four consecutive forest inventories (1995–2014). The methodological approach is based on boosted regression trees, a machine learning method aiming to demonstrate the effects of different variables on damage probability and their interactions as well as to spatialize damage occurrence to make predictions. In total, 313 models are fitted to detect trends, interactions and effects among the variables. The main variables associated with damage occurrence are consistent across all the models and include: latitude, altitude and slope (related to site and location), and tree density, mean diameter and height (related to forest characteristics). The results show that stand dominant height is a key variable in explaining damage probability, whereas stand slenderness has a limited effect. More heterogeneous forest structures make birch dominated stands more resistant to damage. Finally, the models are translated into occurrence maps, to provide landscape-level information on snow and wind damage hazard. Further application of the models can be oriented towards assessing the probability of damage for alternate stand management scenarios.
Authors
Marleen Pallandt Bernhard Ahrens Sönke Zaehle Marion Schrumpf Holger Lange Markus ReichsteinAbstract
Soil organic carbon (SOC) is the largest terrestrial carbon pool. Changes in the hydrological cycle affect C-cycle turnover, with potential effects on the global C balance’s response to global change. However, large scale model representations of the sensitivity of soil carbon to soil moisture, through decomposition and interactions with nutrient cycles, are largely empirical to semi-empirical and uncertain. To better represent these dynamics, the aims of this PhD project* are to: • Investigate the role of soil moisture on SOC decomposition over a vertical profile; • Assess which moisture controls are (most) important in a multi-layered, mechanistic soil biogeochemistry model, the Jena Soil Model (JSM, Fig 2); • Update and improve the representations of soil moisture dynamics in JSM and evaluate this model for multiple sites along a moisture gradient and global scale.
Authors
Bernhard Ahrens Marleen Pallandt Markus Reichstein Holger Lange Marion Schrumpf Sonke ZaehleAbstract
The Jena Soil Model (JSM) is a multi-layer mechanistic soil biogeochemistry model with explicit representations of vertical transport, mineral sorption, and microbial control on decomposition rates. Reaction rates are further modified by temperature and moisture. While temperature determines the maximum reaction velocity (Vmax), moisture reduces this rate nonlinearly if either the diffusion of substrate is restricted (at low soil moisture) or oxygen availability for microbes is limited (at wet conditions). This moisture control on soil organic matter formation and decomposition is represented with the Dual Arrhenius Michaelis-Menten (DAMM) model concept (Davidson et al. 2012) and influences the reaction rates of microbial depolymerisation of litter and microbial residue pools as well as DOC (dissolved organic matter) uptake. Sorption of DOM and microbial residues to mineral surfaces is moisture dependent through a Langmuir sorption approach. We will validate the carbon cycle representation of moisture control on soil organic matter decomposition in JSM by comparing simulations with measured carbon stocks and respiration rates from different ecosystems ranging from boreal upland forests and wetlands to Mediterranean savannas. The modular structure of JSM will allow us to investigate the effect of moisture control on each decomposition step (depolymerisation, microbial uptake and growth, and OC sorption) separately.
Abstract
Temperature during seed maturation can induce an epigenetic memory effect in growth phenology of Norway spruce (Picea abies (L.) Karst.) that lasts for several years. To quantify the epigenetic modifications induced by natural climatic variation, common garden experiments with plants originating from different provenances and seed years were performed. Plants from warmer seed years showed delayed phenology with later bud flush, bud set and growth cessation. This effect was quantified by linear models of phenology traits as a function of climate indices for the origin and seed year of the plants. Significant effects of the temperature during seed production (seed year) was found for the bud set in seedlings in their first growing season and for bud flush and growth cessation in the 7th-8th growing season from seed. The models suggest that growth start and growth cessation are delayed 0.7–1.8 days per 100 additional degree days experienced by the seed during embryo development and seed maturation. Models that include factors that are known to induce epigenetic effects could be used to better predict future performance of forest reproductive material.
Abstract
No abstract has been registered
Abstract
A negative impact of multiple anthropogenic stressors on surface waters can be observed worldwide threatening fresh- and marine water ecosystem functioning, integrity and services. Water pollution may result from point or diffuse sources. An important difference between a point and a diffuse source is that a point source may be collected, treated or controlled. Agricultural activities related to crop production are considered as diffuse sources and are among the main contributors of nutrient loads to open water courses, being to a large degree responsible for the eutrophication of inland and coastal waters. Knowledge of hydrological and biogeochemical processes are needed for climate adaptive water management as well as for introducing mitigation measures aiming to improve surface water quality. Mathematical models have the potential to estimate changes in hydrological and biogeochemical processes under changing climatic or land use conditions. These models, indeed, need careful calibration and testing before being applied in decision making. The aim of this study was to evaluate the efficiency of various water protective adaptation strategies and mitigation measures in reducing the soil particle and nutrient losses towards surface water courses from agricultural dominated catchments. We applied the INCA-N and INCA-P models to a well-studied Norwegian watershed belonging to the Norwegian Agricultural Environmental Monitoring Program. Available measurements on water discharge, TN and TP concentration of stream water and local expert knowledge were used as reference data on land-use specific sediment, N and P losses. The calibration and the validation of both the models was successful; the Nash-Sutcliffe statistics indicated good agreement between the measured and simulated discharge and nutrient loads data. Further, we created a scenario matrix consisting of land use and soil management scenarios combined with different climate change scenarios. Our results indicate that land use change can lead to more significant reduction in particle and nutrient losses than changes in agricultural practices. The most favourable scenario for freshwater ecosystems would be afforestation: changing half of the agricultural areas to forest would reduce sediment, total N and total P losses by approximately 44, 35 and 40%, respectively. Changes in agricultural practices could also improve the situation, especially by reducing areas with autumn tillage to a minimum. We concluded, that the implementation of realistic land use and soil management scenarios still would not lead to satisfactory reduction in freshwater pollution. Hence, mitigation measures, enhancing water and particle retention in the landscape – as sedimentation ponds, constructed wetlands etc. – are important in facing the upcoming pressures on water quality in the future.
Authors
Juraj Parajka Nejc Bezak John Burkhart Bjarki Hauksson Ladislav Holko Yeshewa Hundecha Michal Jenicek Pavel Krajčí Walter Mangini Peter Molnar Philippe Riboust Jonathan Rizzi Aynur Sensoy Guillaume Thirel Alberto ViglioneAbstract
This study evaluates MODIS snow cover characteristics for large number of snowmelt runoff events in 145 catchments from 9 countries in Europe. The analysis is based on open discharge daily time series from the Global Runoff Data Center database and daily MODIS snow cover data. Runoff events are identified by a base flow separation approach. The MODIS snow cover characteristics are derived from Terra 500 m observations (MOD10A1 dataset, V005) in the period 2000–2015 and include snow cover area, cloud coverage, regional snowline elevation (RSLE) and its changes during the snowmelt runoff events. The snowmelt events are identified by using estimated RSLE changes during a runoff event. The results indicate that in the majority of catchments there are between 3 and 6 snowmelt runoff events per year. The mean duration between the start and peak of snowmelt runoff events is about 3 days and the proportion of snowmelt events in all runoff events tends to increase with the maximum elevation of catchments. Clouds limit the estimation of snow cover area and RSLE, particularly for dates of runoff peaks. In most of the catchments, the median of cloud coverage during runoff peaks is larger than 80%. The mean minimum RSLE, which represents the conditions at the beginning of snowmelt events, is situated approximately at the mean catchment elevation. It means that snowmelt events do not start only during maximum snow cover conditions, but also after this maximum. The mean RSLE during snowmelt peaks is on average 170 m lower than at the start of the snowmelt events, but there is a large regional variability.
Abstract
No abstract has been registered