Measurement and modeling of N balance between atmosphere and biosphere over a grazed grassland (Bugacpuszta) in Hungary

This work is a synthesis of a 5-year estimation of nitrogen balance at a semi-arid, semi-natural, undisturbed grassland site (Bugac). We measured the N input of atmospheric pollutants by wet and dry deposition of gases and aerosols, while we considered N output as NO and N2O gases volatilized from soil. Besides measurements of soil fluxes, the denitrification-decomposition (DNDC) ecological model was also used and simulations were compared to and validated against the measured values. The daily flux simulations generally did not match well the measured data for N2O and NO. In most cases, the mean fluxes were underestimated, though results of the comparison of monthly values suggest that model data, together with observed deposition data, are applicable to estimate the net N balance for grasslands. The calculated yearly N balance (net flux) between atmosphere and surface, without biological fixation and effect of grazing, ranged between −9.4 and −14 kg N ha−1 year−1 as the sum of the measured deposition and emission terms, −11 to −15 and 0.9 to 2.9 kg N ha−1 year−1, respectively, between 2006 and 2010. Observed and modeled soil emissions were lower by one order of magnitude than atmospheric deposition. Considering the biological nitrogen fixation and the effect of grazing (effects of both grazed plant and excreta), the net nitrogen balance varies within −6.6 and −11 kg N ha−1 year−1. It seems — taking into account the high uncertainty in calculation due to the effect of grazing — that sources of nitrogen exceed the sinks; the surplus is probably mineralized in the soil

Innovációbarát kormányzás Magyarországon: a regionális innovációs fejlesztéspolitika kihívásai = Innovation-friendly Governance in Hungary: Challenges of Regional Innovation Policy

A kutatás célterülete az innováció központi és regionális irányításának magyarországi rendszere. Kutatás az innovációs kormányzás intézményi megközelítései mellett a kormányzástudományi és tudásszociológiai aspektusait is feltárta, amit empirikus adatbázisunkon (interjúk, kérdőívek) teszteltük. Igazoltuk, hogy az 1 innovációs teljesítmény szorosan összefügg a kormányzás teljesítményével, a kormányzási rendszer nyújtotta feltételekkel és az innovációs szakpolitika mozgásterével, 2 TTI rendszer működése intézményszervezési és irányítási problémákra vezethetők vissza. A nemzeti innovációs rendszert jellemző szervezeti centralizáltság az irányítás, a tervezés és a stratégiaalkotás esetében is domináns, ennélfogva erősen központi irányítás függő maradt a regionális innovációpolitikai irányítás is, 3 az egyetemek fejlesztő szerepe és térségi elkötelezettsége nemcsak a képzés és a kutatás területein valósulhat meg, hanem például a regionális intézményekben és a helyi kormányzásban is, 4 az innovációs irányítás rendszere a finanszírozás, intézményi aspektusok mellett a társadalmi korlátok kontextusában is vizsgálandó. Fontos eredmények születtek az innovációs szereplők vonatkozásában a siker, a kapcsolati tőke és az állam szerepéről vallott felfogás vonatkozásában, 5 nincs kedvező változás az innovációs szereplők közötti kapcsolatok, a bizalom tekintetében, a növekvő centralizációval a kezdeti regionális intézményfejlesztési és hálózatépítési törekvések is visszaszorultak. | The objective of the research is the investigation of the Hungarian system of the central & regional management of innovation. The novelty of the research is that apart from institutional approaches, innovation was studied from the aspect of the governance science & knowledge sociology as well. The theory was tested through empirical data collection. The research has proved the following 1 A strong correlation exists between innovation performance and governance performance, the conditions provided by the system of governance and the scope of action of innovation policy 2 The central management of the national innovation system is dominant in planning & strategy-making alike, therefore regional innovation policy management has also remained subordinate to central control 3 The innovative role played by universities and commitment to their region can be detected not only in the area of RTD, but within regional institutions and local governance 4 The analysis of the innovation governance cannot be restricted to financing & institutional aspects, it needs to be examined in the context of social constraints as well. Results have been achieved concerning innovation stakeholders’ views on success, social capital and the role of the state 5 No favorable changes have occurred in the area of innovation stakeholders’ relationships, trust, and the increased centralisation led to the weakening of the initial efforts directed at regional institutional development and network building

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations

This work was financially supported by the German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE), (2851ERA01J). FT and RPR were supported by FACCE MACSUR (3200009600) through the Finnish Ministry of Agriculture and Forestry (MMM). EC, HE and EL were supported by The Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (220-2007-1218) and by the strategic funding ‘Soil-Water-Landscape’ from the faculty of Natural Resources and Agricultural Sciences (Swedish University of Agricultural Sciences) and thank professor P-E Jansson (Royal Institute of Technology, Stockholm) for support. JC, HR and DW thank the INRA ACCAF metaprogramm for funding and Eric Casellas from UR MIAT INRA for support. CB was funded by the Helmholtz project “REKLIM—Regional Climate Change”. CK was funded by the HGF Alliance “Remote Sensing and Earth System Dynamics” (EDA). FH was funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) under the Grant FOR1695. FE and SS acknowledge support by the German Science Foundation (project EW 119/5-1). HH, GZ, SS, TG and FE thank Andreas Enders and Gunther Krauss (INRES, University of Bonn) for support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Understanding the Impact of Liquid Organic Fertilisation and Associated Application Techniques on N₂, N₂O and CO₂ Fluxes from Agricultural Soils

The prediction of liquid manure effects on N transformations in soils and the associated N2O and N2 fluxes is poor because previous investigations have mostly excluded N2. The objectives of this study were thus to quantify N2, N2O and CO2 fluxes, the source processes of N2O, N2O reduction and the depth distribution of moisture, NO3−, NH4+, water-extractable organic carbon concentration and pH in a laboratory incubation study with sandy arable soil using 15N tracing to quantify N processes and gaseous fluxes. The soil was amended with and without artificial slurry in various manure treatments (control, surface and injected) and incubated for 10 days at varying moisture levels, where the depth distribution of control parameters was determined twice during the experiment. Manure application was found to increase N2 and N2O fluxes from denitrification, with the highest fluxes occurring in the wet manure injection treatment (33 ± 32 mg N m−2 d−1 and 36.1 ± 39.1 mg N m−2 d−1, respectively), confirming that manure injection under wet conditions enhances denitrification and possibly also N2O fluxes. This study concluded that the current dataset is suitable as a first step towards improving the capability of biogeochemical models to predict manure application effects, but further studies with more soils and refined experiments are needed

Model-Based Biospheric Greenhouse Gas Balance of Hungary

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Effects of climate data aggregation on regional NPP modelling

In ecosystem modelling studies, data are often collected at a small scale but are used in models to predict ecosystem responses e.g. net primary production (NPP) at coarser scale, using data aggregation. The data aggregation causes errors that are difficult to predict, because of model complexity and our limited knowledge of the spatial heterogeneity underlying our aggregated input values. Input aggregation thus adds to the uncertainty associated with model predictions. One way of reducing uncertainty related to input aggregation error is to evaluate models at different resolutions for areas where high-resolution input data are available. The objective of this study was the quantification of the aggregation error on modelled NPP introduced by climate data aggregation. Therefore, climate data at 1 km x 1 km resolution (>30 000 grid cells) were used as baseline to simulate NPP with 11 different crop and biogeochemical models for the federal state North Rhine-Westphalia (Germany). These results, for 29 year monocultures of wheat and maize cropping systems, respectively, were compared with simulation results using aggregated climate data for four resolutions (10, 25, 50, 100 km grid cell side length).The aggregation effect is represented by the maximum differences between the NPP, averaged for wheat and maize cropping systems over the growing season, simulated for the five different resolutions. Input data aggregation had little impact on NPP of 29 year averages (0.5 – 7.8 % for wheat and 0.3 – 10 % for maize), while the climate data aggregation effect was higher for single years; up to 9 % and 13 % for wheat and maize, respectively, gradually decreasing to low effects for averages over 10 year periods or longer. The scale effect differed among models and shows only a minor impact (2%) for an ensemble run