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This study used the ECOSSE model (v. 5.0.1) to simulate soil respiration (Rs) fluxes estimated from ecosystem respiration (Reco) for eight European permanent grassland (PG) sites with varying grass species, soils, and management. The main aim was to evaluate the strengths and weaknesses of the model in estimating Rs from grasslands, and to gain a better understanding of the terrestrial carbon cycle and how Rs is affected by natural and anthropogenic drivers. Results revealed that the current version of the ECOSSE model might not be reliable for estimating daily Rs fluxes, particularly in dry sites. The daily estimated and simulated Rs ranged from 0.95 to 3.1 g CO2-C m−2, and from 0.72 to 1.58 g CO2-C m−2, respectively. However, ECOSSE could still be a valuable tool for predicting cumulative Rs from PG. The overall annual relative deviation (RD) value between the cumulative estimated and simulated annual Rs was 11.9%. Additionally, the model demonstrated accurate simulation of Rs in response to grass cutting and slurry application practices. The sensitivity analyses and attribution tests revealed that increased soil organic carbon (SOC), soil pH, temperature, reduced precipitation, and lower water table (WT) depth could lead to increased Rs from soils. The variability of Rs fluxes across sites and years was attributed to climate, weather, soil properties, and management practices. The study suggests the need for additional development and application of the ECOSSE model, specifically in dry and low input sites, to evaluate the impacts of various land management interventions on carbon sequestration and emissions in PG. [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]

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]