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Background To assess the extent of belowground carbon transfer to ectomycorrhizal fungi in natural forests, we used δ13C and loge C/N measurements to calculate spatial dynamics of carbon movement into ectomycorrhizal sporocarps. Methods Fourteen broad-leaved trees were labeled with 13C-depleted CO2 from 2001–2005 in Switzerland and 13C traced into ectomycorrhizal sporocarps collected at different distances. We then used stepwise regressions on patterns of δ13C and loge C/N in ectomycorrhizal sporocarps as a function of distance (zone), solar radiation, fungal genus, and association type. Results CO2-labeled trees contributed 76 ± 5%, 36 ± 6%, and 19 ± 7% of sporocarp carbon at 0–6 m, 6–12 m, and 12–18 m from labeled trees, respectively. Literature estimates of hyphal development in different taxa did not correlate with carbon acquisition patterns. After drought in 2003, sporocarp loge C/N was low in 2004 and 13C-depleted carbon from elevated CO2 trees contributed less than in other years to sporocarps. In contrast, sporocarp loge C/N peaked in 2005 and contributions from elevated CO2 trees to the 6–12 m zone increased. Therefore, carbohydrate transport belowground decreased in 2004, reflecting plant allocation priorities, and increased in 2005. Sporocarp loge C/N varied less among years under elevated CO2 than elsewhere. Conclusions These patterns indicated that 1) belowground transport was influenced by climate and plant allocation, 2) root transport rather than ectomycorrhizal transport drove carbon spatial dynamics of ectomycorrhizal fungi, and 3) elevated CO2 decreased the sensitivity of belowground allocation to climatic fluctuations, suggesting improved drought resistance in a high-CO2 world.

Under the United Nations Framework Convention on Climate Change (UNFCCC), industrialized countries and countries with economies in transition (so called Annex 1 countries) are encouraged to move towards more sophisticated approaches for national greenhouse gas reporting. To develop a model-based approach for estimating nitrous oxide (N2O) emissions from agricultural soils, model calibration is one of the first important steps. Extensive multisite field observations are necessary for this purpose, as agricultural management in Western Europe is complex (e.g., diverse crop rotations, different types of fertilizer and soil tillage). In the present study, we used ca. 24,000 daily N2O flux observations from six cropland sites, two in France and four in Switzerland, to conduct an automatic data-driven calibration of the biogeochemical model DayCent. This model is planned to be used for greenhouse gas reporting in the entire European Union as well as in Switzerland. After a site-specific calibration, a leave-one-out (LOO) cross-evaluation was conducted to assess the model’s ability to predict N2O emissions for sites it was not calibrated for. Mean observed N2O fluxes for 54 interactions of crop cycles, field studies and treatments were used to evaluate the model. The LOO cross-evaluation resulted in a R2 of 0.63 for the prediction of mean N2O fluxes per crop cycle, compared to an R2 of 0.51 obtained with default parameterization. Our results showed that the improvement in N2O predictions was associated with the adjustment of only seven parameters controlling the N cycle in soil (e.g., the maximum daily nitrification amount and the inflection point for the effect of water-filled pore space on denitrification) out of several hundred parameters. These parameters showed a wide range of values between sites, revealing an important challenge for calibration-based improvement of N2O simulations. Despite the remaining uncertainty, our model-based estimates of N2O emission per crop cycle (2.64 kg N ha-1) were clearly closer to measurements (2.67 kg N ha-1) than commonly used emission factor approaches (1.60–1.71 kg N ha-1). Based on extensive field observations, our results suggest that, after data-driven calibration of only few N cycle parameters, DayCent simulations are useful for reporting N2O emissions of complex cropland management. These model based-estimates were more accurate, because they consider key drivers that are disregarded by simpler approaches. Moving towards more complex methods of N2O reporting, is therefore expected to improve the accuracy and additionally allows to assess mitigation options., European Journal of Agronomy, 141, ISSN:1161-0301, ISSN:1873-7331

Background. The spatial extent and timing of carbon fluxes from mature trees to ectomycorrhizal fungi associated with different hosts is challenging to assess in natural forests but could provide insights into carbon dynamics of fungi differing in exploration capabilities. Methods. We analyzed carbon movement into ectomycorrhizal sporocarps at the Swiss Forest Free-Air CO2 Enrichment (FACE) site in 2010 and 2011 during continuous labeling of five mature Picea abies (L.) H. Karst. with 13C-depleted CO2. Sporocarps were collected 0-6 m, 6-12 m, 12-18 m, and > 18 m from 13C-labeled trees. We then used stepwise regressions on patterns of 13C:12C ratios (δ13C) in ectomycorrhizal sporocarps as a function of distance (zone), solar radiation, fungal genus, and association type. Results. 18%, 10%, 40%, and 32% of ectomycorrhizal sporocarps were associated with conifers, broad-leaved trees, both, or of unknown association, respectively. Conifer-associated sporocarps derived 58 ± 7%, 41 ± 10%, and 20 ± 7% of their carbon from labeled trees in the 0-6 m, 6-12 m and 12-18 m zones, respectively, whereas other sporocarps in the 0-6 m zone only derived 34 ± 4% of their carbon from labeled trees. Sporocarp δ13C correlated positively with solar radiation for the 16-19 days prior to sporocarp harvest. Conclusions. These patterns indicated that (1) carbon spread further through conifer-associated mycorrhizal networks than through other fungal networks, and (2) carbon accumulation, storage, and transfer dynamics for sporocarp formation can take several weeks. Mature conifers supplied carbon to mycorrhizal fungi up to ~15 m from host trees.

Summary The application of dynamic models to report changes in soil organic carbon (SOC) stocks, for example as part of greenhouse gas inventories, is becoming increasingly important. Most of these models rely on input data from harvest residues or decaying plant parts and also organic fertilizer, together referred to as soil carbon inputs (C). The soil C inputs from plants are derived from measured agricultural yields using allometric equations. Here we compared the results of five previously published equations. Our goal was to test whether the choice of method is critical for modelling soil C and if so, which of these equations is most suitable for Swiss conditions. For this purpose we used the five equations to calculate soil C inputs based on yield data from a Swiss long-term cropping experiment. Estimated annual soil C inputs from various crops were averaged from 28 years and four fertilizer treatments; the average was 3.6 Mg C ha−1 and covered a surprisingly large range from 2.1 to 5.3 Mg C ha−1 year−1 among the five equations. For single crop species, differences reached 6.6 Mg C ha−1 year−1. The variation between estimated soil C inputs depended on crop type and increased with yield. Simulations with the model C-TOOL showed that calculated SOC stocks were affected strongly by the choice of the allometric equation. With four equations, a decrease in SOC stocks was simulated, whereas with one equation there was no change. This considerable uncertainty in modelled soil C is attributable solely to the allometric equation used to estimate the soil C input. We identify the evaluation and selection of allometric equations and associated coefficients as critical steps when setting up a model-based soil C inventory for agricultural systems. Highlights Uncertainty analyses in soil C modelling have focused mainly on model parameters and structures. The uncertainty related to estimation of soil C inputs from crop residues was calculated. Simulated soil C stocks and stock changes depend strongly on the method of estimating C input. The method to derive soil C inputs is critical for model-based soil C inventories.

Models predicting ecosystem carbon dioxide (CO2 ) exchange under future climate change rely on relatively few real-world tests of their assumptions and outputs. Here, we demonstrate a rapid and cost-effective method to estimate CO2 exchange from intact vegetation patches under varying atmospheric CO2 concentrations. We find that net ecosystem CO2 uptake (NEE) in a boreal forest rose linearly by 4.7 ± 0.2% of the current ambient rate for every 10 ppm CO2 increase, with no detectable influence of foliar biomass, season, or nitrogen (N) fertilization. The lack of any clear short-term NEE response to fertilization in such an N-limited system is inconsistent with the instantaneous downregulation of photosynthesis formalized in many global models. Incorporating an alternative mechanism with considerable empirical support - diversion of excess carbon to storage compounds - into an existing earth system model brings the model output into closer agreement with our field measurements. A global simulation incorporating this modified model reduces a long-standing mismatch between the modeled and observed seasonal amplitude of atmospheric CO2 . Wider application of this chamber approach would provide critical data needed to further improve modeled projections of biosphere-atmosphere CO2 exchange in a changing climate.

Records of stable oxygen isotope ratios in tree rings are valuable tools to reconstruct past climatic conditions and investigate the response of trees to those conditions. So far the use of stable oxygen isotope signatures of tree rings has not been systematically evaluated in dynamic global vegetation models (DGVMs). DGVMs integrate many hydrological and physiological processes and their application could improve proxy-model comparisons and the interpretation of oxygen isotope records. Here we present an approach to simulate leaf water and stem cellulose δ18O of trees using the LPX-Bern DGVM (LPX-Bern). Our results lie within a few per mil of measured tree ring δ18O of 31 different forest stands mainly located in Europe. Temporal means over the last 5 decades as well as interannual variations for a subset of sites in Switzerland are captured. A sensitivity analysis reveals that relative humidity, temperature, and the water isotope boundary conditions have the largest influence on simulated stem cellulose δ18O, followed by all climatic factors combined, whereas increasing atmospheric CO2 and nitrogen deposition exert no impact. We conclude that simulations with LPX-Bern are useful for investigating large-scale oxygen isotope patterns of tree ring cellulose to elucidate the importance of different environmental factors on isotope variations and therefore help to reduce uncertainties in the interpretation of δ18O of tree rings.

Soil carbon sequestration (SCS) is one of the cheapest and technically least demanding carbon dioxide (CO2) removal (CDR) or negative CO2 emission technologies. For a realistic assessment of SCS, it is critical to evaluate how much carbon (C) can be stored in soil organic matter under actual agricultural practices. This includes typical crop rotations and fertilization strategies, depends on resources that are available (e.g. farmyard manure (FYM)) and are affordable for farmers. Furthermore, it is important to assess SCS based on given climatic and soil conditions. Here, we evaluate changes in soil C storage for Switzerland using data from eleven long-term field experiments on cropland and permanent grassland that include common local practices. At all sites, changes in soil organic carbon (SOC) stocks were measured in topsoil (∼0-0.2 m) in response to a total of 80 different treatments including different types of mineral or organic fertilization (e.g. FYM, slurry, peat, compost) or soil management (tillage vs. no-till). The treatments were applied to different, diverse crop rotations or grass mixtures that are representative for Switzerland. We found that topsoils lost C at an average rate of 0.29 Mg C ha−1 yr−1, although many of the investigated treatments were expected to lead to SOC increases. Based on a linear mixed effects model we showed that SOC change rates (ΔSOC) were driven by C inputs to soil (harvest residues and organic fertilizer), soil cover and initial SOC stocks. The type of land use or soil tillage had no significant effect. Our analysis suggests that current efforts to manage soils sustainably need to be intensified and complemented with further techniques if Switzerland wants to achieve the goal of the 4 per 1000 initiative.