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Aims: Soil respiration (Rs) is a complex process including a wide range of soil biota and pathways of carbon cycling, all being under the control of various drivers. The most important biotic driver is the photosynthetic activity of the vegetation providing supply mainly for the autotrophic component of Rs: roots and their symbiotic partners. The objective of this study was to describe the time-lagged relationship between gross primary production (GPP) and the mycorrhizal Rs component in order to determine the amount of carbon derived from GPP appearing as mycorrhizal respiration (Rmyc).Measurements of Rs were conducted in three treatments - (i) undisturbed, root and arbuscular mycorrhizal fungi (AMF)-included (Rs), (ii) root-excluded (Rhet+myc) and (iii) root- and AMF-excluded (Rhet) plots - for three consecutive years in a Central-Hungarian dry sandy grassland. GPP data were derived from eddy-covariance measurements, while an automated system was used for continuous measurements of Rs. We analysed the relationship between Rmyc and GPP by using cross-correlation and by fitting sine wave models on the diel datasets.GPP was found to be the main driver of Rmyc, responding with an average time lag of 18 h. The greatest lags were detected during periods characterized by minimal photosynthetic activity, while lags were the smallest during active periods.Based on the seasonal changes in the delay, we concluded that GPP and soil temperature had simultaneous effects on the diel pattern of CO2 emission of the different autotrophic components depending on the vegetation activity and environmental conditions.Methods: Soil respiration (Rs) is a complex process including a wide range of soil biota and pathways of carbon cycling, all being under the control of various drivers. The most important biotic driver is the photosynthetic activity of the vegetation providing supply mainly for the autotrophic component of Rs: roots and their symbiotic partners. The objective of this study was to describe the time-lagged relationship between gross primary production (GPP) and the mycorrhizal Rs component in order to determine the amount of carbon derived from GPP appearing as mycorrhizal respiration (Rmyc).Measurements of Rs were conducted in three treatments - (i) undisturbed, root and arbuscular mycorrhizal fungi (AMF)-included (Rs), (ii) root-excluded (Rhet+myc) and (iii) root- and AMF-excluded (Rhet) plots - for three consecutive years in a Central-Hungarian dry sandy grassland. GPP data were derived from eddy-covariance measurements, while an automated system was used for continuous measurements of Rs. We analysed the relationship between Rmyc and GPP by using cross-correlation and by fitting sine wave models on the diel datasets.GPP was found to be the main driver of Rmyc, responding with an average time lag of 18 h. The greatest lags were detected during periods characterized by minimal photosynthetic activity, while lags were the smallest during active periods.Based on the seasonal changes in the delay, we concluded that GPP and soil temperature had simultaneous effects on the diel pattern of CO2 emission of the different autotrophic components depending on the vegetation activity and environmental conditions.Results: Soil respiration (Rs) is a complex process including a wide range of soil biota and pathways of carbon cycling, all being under the control of various drivers. The most important biotic driver is the photosynthetic activity of the vegetation providing supply mainly for the autotrophic component of Rs: roots and their symbiotic partners. The objective of this study was to describe the time-lagged relationship between gross primary production (GPP) and the mycorrhizal Rs component in order to determine the amount of carbon derived from GPP appearing as mycorrhizal respiration (Rmyc).Measurements of Rs were conducted in three treatments - (i) undisturbed, root and arbuscular mycorrhizal fungi (AMF)-included (Rs), (ii) root-excluded (Rhet+myc) and (iii) root- and AMF-excluded (Rhet) plots - for three consecutive years in a Central-Hungarian dry sandy grassland. GPP data were derived from eddy-covariance measurements, while an automated system was used for continuous measurements of Rs. We analysed the relationship between Rmyc and GPP by using cross-correlation and by fitting sine wave models on the diel datasets.GPP was found to be the main driver of Rmyc, responding with an average time lag of 18 h. The greatest lags were detected during periods characterized by minimal photosynthetic activity, while lags were the smallest during active periods.Based on the seasonal changes in the delay, we concluded that GPP and soil temperature had simultaneous effects on the diel pattern of CO2 emission of the different autotrophic components depending on the vegetation activity and environmental conditions.Conclusion: Soil respiration (Rs) is a complex process including a wide range of soil biota and pathways of carbon cycling, all being under the control of various drivers. The most important biotic driver is the photosynthetic activity of the vegetation providing supply mainly for the autotrophic component of Rs: roots and their symbiotic partners. The objective of this study was to describe the time-lagged relationship between gross primary production (GPP) and the mycorrhizal Rs component in order to determine the amount of carbon derived from GPP appearing as mycorrhizal respiration (Rmyc).Measurements of Rs were conducted in three treatments - (i) undisturbed, root and arbuscular mycorrhizal fungi (AMF)-included (Rs), (ii) root-excluded (Rhet+myc) and (iii) root- and AMF-excluded (Rhet) plots - for three consecutive years in a Central-Hungarian dry sandy grassland. GPP data were derived from eddy-covariance measurements, while an automated system was used for continuous measurements of Rs. We analysed the relationship between Rmyc and GPP by using cross-correlation and by fitting sine wave models on the diel datasets.GPP was found to be the main driver of Rmyc, responding with an average time lag of 18 h. The greatest lags were detected during periods characterized by minimal photosynthetic activity, while lags were the smallest during active periods.Based on the seasonal changes in the delay, we concluded that GPP and soil temperature had simultaneous effects on the diel pattern of CO2 emission of the different autotrophic components depending on the vegetation activity and environmental conditions. [ABSTRACT FROM AUTHOR]

The main objective of the present study was to determine the contributions of autotrophic and heterotrophic components to the total soil CO 2 efflux over three years with high-frequency data acquisition by means of automated measurements. Soil CO 2 efflux was measured continuously by using an automated open system of 10 soil respiration chambers in a sandy grassland in Hungary. Mesh-collar technique was applied to separate the components of the total respiration. Data were collected (1) in root-exclusion (Exr), (2) in root- and mycorrhiza exclusion (Exrm) and (3) in control plots (Exc, roots and mycorrhiza included). We fitted three different models to describe the dependence of total soil CO 2 efflux measured on the Exc (Rs), CO 2 efflux measured on Exr (R TR ) and CO 2 efflux measured on Exrm (R TRM ) on abiotic and biotic drivers. The best fitted model (based on AIC) was later on used in a simulation process. The contribution by rhizospheric respiration (simulated, R rhizo* ) was 36 ± 21%, the contribution by mycorrhizal respiration (simulated, R myc* ) to the total soil respiration was 9 ± 9% while the contribution by heterotrophic respiration (simulated, R het* ) was 55 ± 21% on average. Measured mycorrhizal respiration (R M ) responded to GPP with a time lag of 0–2 days in active period. Drought affected the autotrophic component of soil respiration the most intensively: raise of soil water content resulted in increase of R S by 175% while R TR and R TRM increased by 127% and 93%, respectively. The results highlight the fact that it would be useful to establish and apply separate models for each component. [ABSTRACT FROM AUTHOR]

Diel variability of soil respiration is influenced by several factors including temperature and carbon allocation as the most significant ones, co-varying on multiple time scales. In an attempt to disentangle their effects we analyzed the dynamics of soil respiration components using data from a three-year soil respiration study. We measured CO2 efflux in intact, root-excluded and root- and mycorrhizal fungi excluded plots and analyzed the diel variability in different phenological stages. We used sine wave models to describe the diel pattern of soil respiration and to disentangle the effects of temperature from belowground carbon allocation based on the differences between component dynamics inferred from the fitted models. Rhizospheric respiration peaked 8–12 hours after GPP peak, while mycorrhizal fungi respiration had a longer time lag of 13–20 hours. Results of δ13CO2 isotopic signals from the respiration components showed similar patterns. It was found that drought affected the component respiration rates differently. Also, the speed and the amount of carbon allocation to the roots as well as to the mycorrhizal fungi was reduced under drought. We conclude that the diel variability of soil respiration is the result of the integrated patterns of temperature- and carbon allocation-driven components in dry grasslands and their share depends on their phenological stages and stress state. [ABSTRACT FROM AUTHOR]

Spatial patterns of ecosystem processes constitute significant sources of uncertainty in greenhouse gas flux estimations partly because the patterns are temporally dynamic. The aim of this study was to describe temporal variability in the spatial patterns of grassland CO2 and N2O flux under varying environmental conditions and to assess effects of the grassland management (grazing and mowing) on flux patterns. We made spatially explicit measurements of variables including soil respiration, aboveground biomass, N2O flux, soil water content, and soil temperature during a 4-year study in the vegetation periods at grazed and mowed grasslands. Sampling was conducted in 80 × 60 m grids of 10 m resolution with 78 sampling points in both study plots. Soil respiration was monitored nine times, and N2O flux was monitored twice during the study period. Altitude, soil organic carbon, and total soil nitrogen were used as background factors at each sampling position, while aboveground biomass, soil water content, and soil temperature were considered as covariates in the spatial analysis. Data were analyzed using variography and kriging. Altitude was autocorrelated over distances of 40–50 m in both plots and influenced spatial patterns of soil organic carbon, total soil nitrogen, and the covariates. Altitude was inversely related to soil water content and aboveground biomass and positively related to soil temperature. Autocorrelation lengths for soil respiration were similar on both plots (about 30 m), whereas autocorrelation lengths of N2O flux differed between plots (39 m in the grazed plot vs. 18 m in the mowed plot). Grazing appeared to increase heterogeneity and linkage of the spatial patterns, whereas mowing had a homogenizing effect. Spatial patterns of soil water content, soil respiration, and aboveground biomass were temporally variable especially in the first 2 years of the experiment, whereas spatial patterns were more persistent (mostly significant correlation at p < 0.05 between location ranks) in the second 2 years, following a wet year. Increased persistence of spatial patterns after a wet year indicated the recovery potential of grasslands following drought and suggested that adequate water supply could have a homogenizing effect on CO2 and N2O fluxes. [ABSTRACT FROM AUTHOR]

Diel variability of soil respiration could be driven by several factors, but temperature and carbon allocation have the most significant effect on that. However, these two factors covariate on multiple time scales, therefore it is not easy to disentangle their effect.We measured CO2 efflux in undisturbed, root-excluded and root- and mycorrhizal fungi excluded plots and analyzed the diel variability and the responses to GPP in different phenological stages. We used sine wave models to describe the characteristics of diel variability and to estimate the temperature and GPP driven part of total soil respiration.Rhizospheric respiration peaked 8-12 hours after GPP peak, while mycorrhizal fungi respiration had a longer time lag of 13-20 hours. Results of isotopic measurements also showed similar pattern. Drought affected respiration rates as well as carbon allocation pattern; both the amount and the speed of carbon allocated to mycorrhizal fungi were reduced under drought.Based on these results we assume that the commonly used Q10 values does not always reflect the temperature response of soil CO2 efflux, but the propagated effect of the temperature and belowground carbon allocation and those share in soil respiration vary with phenological stages. [ABSTRACT FROM AUTHOR]

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]

In this study eight temperate grassland sites were monitored for soil CO 2 efflux (R s ) and the spatial covariate soil water content (SWC) and soil temperature (T s ) at fine scale in over 77 measurement campaigns. The goals of this multisite study were to explore the correlations between environmental gradients and spatial patterns of R s , SWC and T s , which are not site-specific and to quantify the relevance of biotic and abiotic controls over spatial patterns along increasing vegetation structural complexity. These patterns in water-limited ecosystems in East-Central Europe are likely to be influenced by summer droughts caused by the changing climate. A consistent experimental setup was applied at the study sites including 75 sampling locations along 15 m circular transects. Spatial data processing was mainly based on variography. Two proxy variables were introduced to relate the site characteristics in terms of soils, water status and vegetation. Normalised SWC (SWCn) reconciled site-specific soil water regimes while normalised day of year integrated temperature and vegetation phenology. A principal component analysis revealed that the progressing closure of vegetation in combination with large R s and SWCn values, as well as low T s and R s variability support the detectability of spatial patterns found in both the abiotic and biotic variables. Our results showed that apart from SWC the pattern of soil temperature also had an effect on spatial structures. We detected that when the spatially structured variability of T s was low, a strong negative correlation existed between SWCn and the spatial autocorrelation length of R s with r = 0.66 (p < 0.001). However, for high spatially structured variability of T s , occurring presumably at low T s in spring and autumn, the correlation did not exist and it was difficult to quantify the spatial autocorrelation of R s . Our results are indicative of a potential shift from homogeneity and dominance of biotic processes to an increased heterogeneity and abiotic regulation in drought prone ecosystems under conditions of decreasing soil moisture. [ABSTRACT FROM AUTHOR]

Aims: Our aim was to study the effect of potential biotic drivers, including evapotranspiration (ET) and gross primary production (GPP), on the soil CO production and efflux on the diel time scale. Methods: Eddy covariance, soil respiration and soil CO gradient systems were used to measure the CO and HO fluxes in a dry, sandy grassland in Hungary. The contribution of CO production from three soil layers to plot-scale soil respiration was quantified. CO production and efflux residuals after subtracting the effects of the main abiotic and biotic drivers were analysed. Results: Soil CO production showed a strong negative correlation with ET rates with a time lag of 0.5 h in the two upper layers, whereas less strong, but still significant time-lagged and positive correlations were found between GPP and soil CO production. Our results suggest a rapid negative response of soil CO production rates to transpiration changes, and a delayed positive response to GPP. Conclusions: We found evidence for a combined effect of soil temperature and transpiration that influenced the diel changes in soil CO production. A possible explanation for this pattern could be that a significant part of CO produced in the soil may be transported across soil layers via the xylem. [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]

Soil respiration of grasslands is spatio-temporally variable reflecting the changing biological activities of the soil. In our study we analysed how the long-term soil respiration activities of dry grasslands would perform in terms of resistance and resilience. We also investigated how terrain features are responsible for response stability. We conducted a 7-year-long spatial study in a Hungarian dry grassland, measuring soil respiration (Rs), soil temperature (Ts) and soil water content (SWC) along 15 measuring campaigns in 80 × 60 m grids and soil organic carbon content in 6 of the occasions. Two proxy variables were introduced to grasp the overall Rs activity, as well as its temporal stability: average rankRs, the temporal average Rs rank of a measuring position from the campaigns revealed the persistent spatial pattern of Rs, while rangeRs, the range of ranks of the positions from the campaigns described the amplitude of the Rs response in time, referring to the response stability in terms of resistance or resilience. We formulated a hypothetic concept of a two-state equilibrium to describe the performance of the long-term Rs activity: Rs activity with smaller rangeRs, that is both the lower elevation positions with larger rankRs ("state I") and the higher elevation positions with smaller rankRs ("state II") correspond to an equilibrium state with several terrain attributes being responsible for the equilibrium responses. Majority of the measuring positions was belonging to none of these equilibrium states. These positions showed higher rangeRs for medium rankRs, suggesting resilience (not resistance) as a major strategy for this ecosystem. [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]