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Livestock is both threatened by and contributing to climate change. The contribution of livestock to climate change and greenhouse gas (GHG) emission greatly vary under different management regimes. A number of mitigation options comprise livestock management, although there are a lot of uncertainties as to which management regime to use for a given pedoclimatic and farming system. Therefore, we 1) tested if an extensive cattle livestock farm is a net sink or a net source for GHG (carbon–dioxide, CO 2 ; methane, CH 4 ; nitrous oxide N 2 O) in Central–Eastern Europe, 2) compared the annual GHG balances between the grazed and mowed treatments of the farm 3) and investigated the role of climate variability in shaping these balances. Net ecosystem exchange of CO 2 ( NEE ) was measured with eddy covariance technique in both the grazed and mowed treatments. Estimations of lateral C fluxes were based on management data. Other GHG fluxes (CH 4 , N 2 O) were determined by chamber gas flux measurements technique (in case of soil) and IPCC guidelines (in case of manure decomposition and animal fermentation). Net greenhouse gas balance ( NGHG ) for the grazed treatment was 228 ± 283 g CO 2 equivalent m −2 year −1 (net sink) and −475 ± 144 g CO 2 equiv. m −2 year −1 (net source) for the mowed treatment. Net source activity at the mowed treatment was due to its higher herbage use intensity compared to the grazed treatment. At the farm scale the system was estimated to be a net sink for NGHG in a year with wet (135 g CO 2 equiv. m −2 year −1 ), while a net source in years with dry soil moisture conditions (−267 ± 214 g CO 2 equiv. m −2 year −1 ). We conclude that under a temperate continental climate extended extensive grazing could serve as a potential mitigation of GHG in contrast to mowing. Our study highlights the fact that livestock farming could create a net sink for GHG under proper management regimes. [ABSTRACT FROM AUTHOR]

• Vegetation composition and diversity did not change for six years after the conversion from grazing to mowing. • Even the detailed fine-scale analyses could not reveal differences. • Carbon uptake differed between grazed vs. mowed sites due to biomass removal intensity. • Returning from mowing to grazing would probably restore C uptake because the vegetation composition remained stable. Grasslands could play an important role in supporting livestock, in carbon sequestration and in biodiversity conservation. Different grassland managements may change the vegetation composition of grasslands, which could alter these ecosystem services. We studied the effects of grazing vs. mowing on the vegetation composition (2012–2016) and on the carbon uptake (2011–2013) of sandy grasslands in Hungary to study if the vegetation composition differs and if it does, whether it affects carbon uptake. To observe differences in vegetation composition between the two types of management regimes we performed fine-scale vegetation survey in adjacent grazed and mowed sites. We compared species compositions and vegetation diversity indices between the management regimes based on permutational ANOVA, mixed effect model and information theory models. We used fine-scale vegetation survey because it has the potential to indicate vegetation changes within six years as changes in species richness and diversity are usually detected after 10–20 years. Carbon uptake was measured in parallel with eddy covariance technique at both sites. In spite of the contrasting management regimes the vegetation composition was stable (no differences observed in the diversity indices), while carbon uptake was significantly greater in the grazed (sink: +1 ± 0.7 t C ha−1 year−1) compared to the mowed site (source: -0.3 ± 0.6 t C ha−1 year−1). The vegetation was heterogeneous (patchy) and had a high species richness at both sites (grazed: 91 species ha−1, mowed: 90 species ha−1) which might have contributed to the stability of the vegetation composition. We concluded that differences in carbon uptake existed between the grazed vs. mowed sites due to biomass removal intensity with no differences in the vegetation composition. [ABSTRACT FROM AUTHOR]

Grasslands, such as other managed fields, release large quantities of greenhouse gases (GHG). Fluxes of GHG are highly variable spatio-temporally, which hampers the quantification of the yearly sum. Although actual spatial patterns of ecosystem functions (EF) are determined by the actual spatial distribution and intensity of background or driving variables, a potential "optimal" or persistent state can also be detected on the basis of long-term climatic, surface relief and soil conditions. Changing climate could alter these instant and long-term patterns through the modification of the drivers. Field studies and spatial analysis can help to reveal such temporal dynamics of spatial patterns and to understand the shifts in EF.We used two ways of spatial analysis to study grassland EF. One way was the geostatistical analysis of low-resolution field-scale manual measurements on 10 × 10 m grids. Geostatistical data processing revealed that topographic differences, however small they might be, had primary importance in the generation of spatial patterns of both the soil nutrients and the microclimate due to the variability of the available soil moisture and fine shading effects of crests and depressions, both the patterns of the above-ground biomass, soil respiration, and nitrous oxide flux. We found that spatial patterns and spatial correlations between variable pairs were dynamically changing characteristics. A further controlling factor was the management of the grasslands, creating different levels of homogeneity/heterogeneity.The other way in spatial analysis can be the use of a high-resolution data. We used a 0.2 × 0.2 m resolution digital elevation model (DEM) in the geostatistical analysis for external drift kriging. In many cases kriging with DEM gave us better result than ordinary kriging with the variable itself. However, a more important use of DEM could be the calculation of different topographic attributes (e.g., slope, aspect, topographic position index, surface rugosity), because these data can reveal more about the neighboring effects within a landscape. We hypothesized that not just DEM, but the calculation of different indices along a spatial series can help to have a more detailed insight into the effects of topography on the spatial patterns of e.g. erosion, soil nutrient horizontal heterogeneity, and also on the EF. [ABSTRACT FROM AUTHOR]