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
No abstract has been registered
Authors
Christine Ribeiro-Kumara Jukka Pumpanen Jussi Heinonsalo Marek Metslaid Argo Orumaa Kalev Jogiste Frank Berninger Kajar KösterAbstract
Fire is the most important natural disturbance in boreal forests, and it has a major role regulating the carbon (C) budget of these systems. With the expected increase in fire frequency, the greenhouse gas (GHG) budget of boreal forest soils may change. In order to understand the long-term nature of the soil–atmosphere GHG exchange after fire, we established a fire chronosequence representing successional stages at 8, 19, 34, 65, 76 and 179 years following stand-replacing fires in hemiboreal Scots pine forests in Estonia. Changes in extracellular activity, litter decomposition, vegetation biomass, and soil physicochemical properties were assessed in relation to carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) emissions. Soil temperature was highest 8 years after fire, whereas soil moisture varied through the fire chronosequences without a consistent pattern. Litter decomposition and CO2 efflux were still lower 8 years after fire compared with pre-fire levels (179 years after fire). Both returned to pre-fire levels before vegetation re-established, and CO2 efflux was only strongly responsive to temperature from 19 years after fire onward. Recovery of CO2 efflux in the long term was associated with a moderate effect of fire on enzyme activity, the input of above- and below-ground litter carbon, and the re-establishment of vegetation. Soil acted as a CH4 sink and N2O source similarly in all successional stages. Compared with soil moisture and time after fire, soil temperature was the most important predictor for both GHGs. The re-establishment of overstorey and vegetation cover (mosses and lichens) might have caused an increase in CH4 and N2O effluxes in the studied areas, respectively.
Authors
Ning Wang Huan Peng Shi-ming Liu Wen-kun Huang Ricardo Holgado Jihong Liu Clarke De-liang PengAbstract
Soybean cyst nematode (SCN, Heterodera glycines (I.)) is one of the most important soil-borne pathogens for soybeans. In plant parasitic nematodes, including SCN, lysozyme plays important roles in the innate defense system. In this study, two new lysozyme genes (Hg-lys1 and Hg-lys2) from SCN were cloned and characterized. The in situ hybridization analyses indicated that the transcripts of both Hg-lys1 and Hg-lys2 accumulated in the intestine of SCN. The qRT-PCR analyses showed that both Hg-lys1 and Hg-lys2 were upregulated after SCN second stage juveniles (J2s) were exposed to the Gram-positive bacteria Bacillus thuringiensis, Bacillus subtilis or Staphylococcus aureus. Knockdown of the identified lysozyme genes by in vitro RNA interference caused a significant decrease in the survival rate of SCN. All of the obtained results indicate that lysozyme is very important in the defense system and survival of SCN.
Authors
Juliana PerminowAbstract
No abstract has been registered
Authors
Bjørn Hallvard Samset Camilla Weum Stjern Marianne Tronstad Lund Christian Wilhelm Mohr Maria Sand Anne Sophie DalozAbstract
The climate is an aggregate of the mean and variability of a range of meteorological variables, notably temperature (T) and precipitation (P). While the impacts of an increase in global mean surface temperature (GMST) are commonly quantified through changes in regional means and extreme value distributions, a concurrent shift in the shapes of the distributions of daily T and P is arguably equally important. Here, we employ a 30‐member ensemble of coupled climate model simulations (CESM1 LENS) to consistently quantify the changes of regionally and seasonally resolved probability density functions of daily T and P as function of GMST. Focusing on aggregate regions covering both populated and rural zones, we identify large regional and seasonal diversity in the probability density functions and quantify where CESM1 projects the most noticeable changes compared to the preindustrial era. As global temperature increases, Europe and the United States are projected to see a rapid reduction in wintertime cold days, and East Asia to experience a strong increase in intense summertime precipitation. Southern Africa may see a shift to a more intrinsically variable climate but with little change in mean properties. The sensitivities of Arctic and African intrinsic variability to GMST are found to be particularly high. Our results highlight the need to further quantify future changes to daily temperature and precipitation distributions as an integral part of preparing for the societal and ecological impacts of climate change and show how large ensemble simulations can be a useful tool for such research.
Abstract
No abstract has been registered
Authors
Tomáš Hlásny Rupert Seidl Kenneth F Raffa Jörg Müller Claire Montagné-Huck Andrew M. Liebhold Hua Qin Mart-Jan Schelhaas Paal Krokene Miroslav Svoboda Heli ViiriAbstract
No abstract has been registered
Abstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Patrick J. Drohan Marianne Bechmann Anthony Buda Faruk Djodjic Donnacha Doody Jonathon M. Duncan Antti Iho Phil Jordan Peter J. Kleinman Richard McDowell Per-Erik Mellander Ian A. Thomas Paul J. A. WithersAbstract
The evolution of phosphorus (P) management decision support tools (DSTs) and systems (DSS), in support of food and environmental security has been most strongly affected in developed regions by national strategies (i) to optimize levels of plant available P in agricultural soils, and (ii) to mitigate P runoff to water bodies. In the United States, Western Europe, and New Zealand, combinations of regulatory and voluntary strategies, sometimes backed by economic incentives, have often been driven by reactive legislation to protect water bodies. Farmer‐specific DSSs, either based on modeling of P transfer source and transport mechanisms, or when coupled with farm‐specific information or local knowledge, have typically guided best practices, education, and implementation, yet applying DSSs in data poor catchments and/or where user adoption is poor hampers the effectiveness of these systems. Recent developments focused on integrated digital mapping of hydrologically sensitive areas and critical source areas, sometimes using real‐time data and weather forecasting, have rapidly advanced runoff modeling and education. Advances in technology related to monitoring, imaging, sensors, remote sensing, and analytical instrumentation will facilitate the development of DSSs that can predict heterogeneity over wider geographical areas. However, significant challenges remain in developing DSSs that incorporate “big data” in a format that is acceptable to users, and that adequately accounts for catchment variability, farming systems, and farmer behavior. Future efforts will undoubtedly focus on improving efficiency and conserving phosphate rock reserves in the face of future scarcity or prohibitive cost. Most importantly, the principles reviewed here are critical for sustainable agriculture.