Interactive effects of elevated CO2 and nitrogen deposition on fatty acid molecular and isotope composition of above‐ and belowground tree biomass and forest soil fractions

Atmospheric carbon dioxide (CO₂) and reactive nitrogen (N) concentrations have been increasing due to human activities and impact the global carbon (C) cycle by affecting plant photosynthesis and decomposition processes in soil. Large amounts of C are stored in plants and soils, but the mechanisms behind the stabilization of plant‐ and microbial‐derived organic matter (OM) in soils are still under debate and it is not clear how N deposition affects soil OM dynamics. Here, we studied the effects of 4 years of elevated (¹³C‐depleted) CO₂ and N deposition in forest ecosystems established in open‐top chambers on composition and turnover of fatty acids (FAs) in plants and soils. FAs served as biomarkers for plant‐ and microbial‐derived OM in soil density fractions. We analyzed above‐ and belowground plant biomass of beech and spruce trees as well as soil density fractions for the total organic C and FA molecular and isotope (δ¹³C) composition. FAs did not accumulate relative to total organic C in fine mineral fractions, showing that FAs are not effectively stabilized by association with soil minerals. The δ¹³C values of FAs in plant biomass increased under high N deposition. However, the N effect was only apparent under elevated CO₂ suggesting a N limitation of the system. In soil fractions, only isotope compositions of short‐chain FAs (C₁₆₊₁₈) were affected. Fractions of ‘new’ (experimental‐derived) FAs were calculated using isotope depletion in elevated CO₂ plots and decreased from free light to fine mineral fractions. ‘New’ FAs were higher in short‐chain compared to long‐chain FAs (C_20?30), indicating a faster turnover of short‐chain compared to long‐chain FAs. Increased N deposition did not significantly affect the quantity of ‘new’ FAs in soil fractions, but showed a tendency of increased amounts of ‘old’ (pre‐experimental) C suggesting that decomposition of ‘old’ C is retarded by high N inputs.     

A multi‐criteria based review of models that predict environmental impacts of land use‐change for perennial energy crops on water,carbon and nitrogen cycling

Reduction in energy sector greenhouse gas GHG emissions is a key aim of European Commission plans to expand cultivation of bioenergy crops. Since agriculture makes up 10–12% of anthropogenic GHG emissions, impacts of land‐use change must be considered, which requires detailed understanding of specific changes to agroecosystems. The greenhouse gas (GHG) balance of perennials may differ significantly from the previous ecosystem. Net change in GHG emissions with land‐use change for bioenergy may exceed avoided fossil fuel emissions, meaning that actual GHG mitigation benefits are variable. Carbon (C) and nitrogen (N) cycling are complex interlinked systems, and a change in land management may affect both differently at different sites, depending on other variables. Change in evapotranspiration with land‐use change may also have significant environmental or water resource impacts at some locations. This article derives a multi‐criteria based decision analysis approach to objectively identify the most appropriate assessment method of the environmental impacts of land‐use change for perennial energy crops. Based on a literature review and conceptual model in support of this approach, the potential impacts of land‐use change for perennial energy crops on GHG emissions and evapotranspiration were identified, as well as likely controlling variables. These findings were used to structure the decision problem and to outline model requirements. A process‐based model representing the complete agroecosystem was identified as the best predictive tool, where adequate data are available. Nineteen models were assessed according to suitability criteria, to identify current model capability, based on the conceptual model, and explicit representation of processes at appropriate resolution. FASSET, ECOSSE, ANIMO, DNDC, DayCent, Expert‐N, Ecosys, WNMM and CERES‐NOE were identified as appropriate models, with factors such as crop, location and data availability dictating the final decision for a given project. A database to inform such decisions is included.