Spatial distribution of soil organic carbon stocks in France

Soil organic carbon plays a major role in the global carbon budget, and can act as a source or a sink of atmospheric carbon, thereby possibly influencing the course of climate change. Changes in soil organic carbon (SOC) stocks are now taken into account in international negotiations regarding climate change. Consequently, developing sampling schemes and models for estimating the spatial distribution of SOC stocks is a priority. The French soil monitoring network has been established on a 16 km × 16 km grid and the first sampling campaign has recently been completed, providing around 2200 measurements of stocks of soil organic carbon, obtained through an in situ composite sampling, uniformly distributed over the French territory. <br><br> We calibrated a boosted regression tree model on the observed stocks, modelling SOC stocks as a function of other variables such as climatic parameters, vegetation net primary productivity, soil properties and land use. The calibrated model was evaluated through cross-validation and eventually used for estimating SOC stocks for mainland France. Two other models were calibrated on forest and agricultural soils separately, in order to assess more precisely the influence of pedo-climatic variables on SOC for such soils. <br><br> The boosted regression tree model showed good predictive ability, and enabled quantification of relationships between SOC stocks and pedo-climatic variables (plus their interactions) over the French territory. These relationships strongly depended on the land use, and more specifically, differed between forest soils and cultivated soil. The total estimate of SOC stocks in France was 3.260 ± 0.872 PgC for the first 30 cm. It was compared to another estimate, based on the previously published European soil organic carbon and bulk density maps, of 5.303 PgC. We demonstrate that the present estimate might better represent the actual SOC stock distributions of France, and consequently that the previously published approach at the European level greatly overestimates SOC stocks

Cool Farm Tool Water : A global on-line tool to assess water use in crop production

© 2018 The Authors.Peer reviewedPublisher PD

Effects of climate extremes on the terrestrial carbon cycle : concepts, processes and potential future impacts

This article is protected by copyright. All rights reserved. Acknowledgements This work emerged from the CARBO-Extreme project, funded by the European Community’s 7th framework programme under grant agreement (FP7-ENV-2008-1-226701). We are grateful to the Reviewers and the Subject Editor for helpful guidance. We thank to Silvana Schott for graphic support. Mirco Miglivacca provided helpful comments on the manuscript. Michael Bahn acknowledges support from the Austrian Science Fund (FWF; P22214-B17). Sara Vicca is a postdoctoral research associate of the Fund for Scientific Research – Flanders. Wolfgang Cramer contributes to the Labex OT-Med (n° ANR-11- LABX-0061) funded by the French government through the A*MIDEX project (n° ANR-11-IDEX-0001-02). Flurin Babst acknowledges support from the Swiss National Science Foundation (P300P2_154543).Peer reviewedPublisher PD

Sources and Sinks of Greenhouse Gases from European Grasslands and Mitigation Options: The ‘GreenGrass’ Project

Adapting the management of grasslands may be used to enhance carbon sequestration into soil, but could also increase N2O and CH4 emissions. In support of the European post-Kyoto policy, the European \u27GreenGrass\u27 project (EC FP5, EVK2-CT2001-00105) has three main objectives: i) to reduce the large uncertainties concerning the estimates of CO2, N2O and CH4 fluxes to and from grassland plots under different climatic conditions and assess their global warming potential, ii) to measure net greenhouse gas (GHG) fluxes for different management which reflect potential mitigation options, iii) to construct a model of the controlling processes to quantify the net fluxes and to evaluate mitigation scenarios by up-scaling to a European level

Supporting decision-making in agricultural water management under data scarcity using global datasets – chances, limits and potential improvements

Assessing alternative agricultural water management strategies requires long-term field trials or vast data collection for model calibration and simulation.This work aims to assess whether an uncalibrated agro-hydrological model using global input datasets for climate, soil and crop information can serve as a decision support tool for crop water management under data scarcity.This study employs the Cool Farm Tool Water (CFTW) at eight eddy covariance sites of the FLUXNET2015 dataset. CFTW is tested using global (CFTWglobal) and local (CFTWlocal) input datasets under current and alternative management scenarios.Results show that the use of global datasets for estimating daily evapotranspiration had little effect on the median Root Mean Square Error (RMSE) (CFTWglobal: 1.70 mm, CFTWlocal: 1.79 mm), while, however, the median model bias is much greater (CFTWglobal: − 18.6%, CFTWlocal: − 4.3%). Furthermore, the periods of water stress were little affected by the use of local or global data (median accuracy: 0.84), whereas the use of global data inputs led to a significant overestimation of irrigation water requirements (median difference:110 mm). The model performance improves predominantly through the use of more representative local precipitation data, followed by local reference evapotranspiration and soil for some European growing seasons.We identify model outputs that can support decision-making when relying on global data, such as periods of water stress and the daily dynamics of water use. However, our findings also emphasize the difficulty of overcoming data scarcity in decision-making in agricultural water management. Furthermore, we provide recommendations for enhancing model performance and thus may increase the accessibility of reliable decision support tools in the futur

Unterstützung der Entscheidungsfindung in der landwirtschaftlichen Wasserbewirtschaftung bei Datenknappheit unter Verwendung globaler Datensätze : Chancen, Grenzen und mögliche Verbesserungen

Assessing alternative agricultural water management strategies requires long-term field trials or vast data collection for model calibration and simulation. This work aims to assess whether an uncalibrated agro-hydrological model using global input datasets for climate, soil and crop information can serve as a decision support tool for crop water management under data scarcity. This study employs the Cool Farm Tool Water (CFTW) at eight eddy covariance sites of the FLUXNET2015 dataset. CFTW is tested using global (CFTWglobal) and local (CFTWlocal) input datasets under current and alternative management scenarios. Results show that the use of global datasets for estimating daily evapotranspiration had little effect on the median Root Mean Square Error (RMSE) (CFTWglobal: 1.70 mm, CFTWlocal: 1.79 mm), while, however, the median model bias is much greater (CFTWglobal: - 18.6%, CFTWlocal: - 4.3%). Furthermore, the periods of water stress were little affected by the use of local or global data (median accuracy: 0.84), whereas the use of global data inputs led to a significant overestimation of irrigation water requirements (median difference: 110 mm). The model performance improves predominantly through the use of more representative local precipitation data, followed by local reference evapotranspiration and soil for some European growing seasons. We identify model outputs that can support decision-making when relying on global data, such as periods of water stress and the daily dynamics of water use. However, our findings also emphasize the difficulty of overcoming data scarcity in decision-making in agricultural water management. Furthermore, we provide recommendations for enhancing model performance and thus may increase the accessibility of reliable decision support tools in the future.Die Bewertung alternativer Wasserbewirtschaftungsstrategien in der Landwirtschaft erfordert langfristige Feldversuche oder umfangreiche Datenerhebungen Datenerhebung für die Modellkalibrierung und Simulation. Ziel dieser Arbeit ist es, zu beurteilen, ob ein unkalibriertes agrarhydrologisches Modell, das globale Eingabedatensätze für Klima-, Boden- und Kulturpflanzeninformationen als Entscheidungshilfe für das in der Landwirtschaft bei Datenknappheit Knappheit dienen kann. Die Ergebnisse zeigen, dass die Verwendung globaler Datensätze für die Schätzung der täglichen Evapotranspiration kaum Auswirkungen auf den mittleren quadratischen Fehler (RMSE) hat (CFTWglobal: 1,70 mm, CFTWlocal: 1,79 mm), während jedoch die die mediane Modellverzerrung jedoch viel größer ist (CFTWglobal: - 18,6 %, CFTWlocal: - 4,3 %). Außerdem wurden die Perioden des Außerdem wurden die Perioden des Wasserstresses durch die Verwendung lokaler oder globaler Daten nur wenig beeinflusst (mediane Genauigkeit: 0,84), während die Verwendung globaler globalen Dateneingaben zu einer signifikanten Überschätzung des Bewässerungswasserbedarfs führte (medianer Unterschied: 110 mm). Die Leistung des Modells verbessert sich in erster Linie durch die Verwendung repräsentativerer lokaler Niederschlagsdaten Daten, gefolgt von lokalen Referenzwerten für Evapotranspiration und Boden für einige europäische Wachstumsperioden. Wir ermitteln Modelloutputs, die die Entscheidungsfindung unterstützen können, wenn man sich auf globale Daten stützt, z. B. Perioden von Wasserstress und die tägliche Dynamik der Wassernutzung. Unsere Ergebnisse verdeutlichen jedoch auch die Schwierigkeit, die Datenknappheit bei der Entscheidungsfindung in der landwirtschaftlichen Wasserwirtschaft. Darüber hinaus geben wir Empfehlungen Empfehlungen zur Verbesserung der Modellleistung und können so die Verfügbarkeit von zuverlässigen Entscheidungshilfen in der Zukunft verbessern

The potential distribution of bioenergy crops in the UK under present and future climate

We have predicted the potential distribution of 26 bioenergy crops in the UK, based on the simple model described by Tuck et al. [1]. The model has been applied at a 5 km resolution using the UKCIP02 model for scenarios at Low, Medium-Low, Medium-High and High emissions. In the analysis of the results the limitations for crop growth are assigned to elevation, temperature, high and low rainfall. Most of the crops currently grown are predicted to remain prevalent in the UK. A number of crops are suitable for introduction to the UK under a changing climate, whereas others retreat to northern parts of the UK. The greatest changes are expected in England. The simplicity of the model means that it has a relatively high uncertainty, with minor modifications to the model leading to quite different results. Nevertheless, it is well suited for identifying areas and crops that are most likely to be affected by the greatest changes. It has been noted that Miscanthus and Short Rotation Coppice (SRC) willow and poplar, which are currently regarded as highly suitable for UK conditions, may be less suited to southern areas in the future, where, for example, kenaf could have a greater potential. Further investigations are required to reduce uncertainty associated with the projections based on this simple model and to make conclusions more firmly

Projected changes in mineral soil carbon of European forests, 1990–2100

Forests are a major land use in Europe, and European forest soils contain about the same amount of carbon as is found in tree biomass. Changes in the size of the forest soil carbon pool could have significant impacts on the European carbon budget. We present the first assessment of future changes in European forest soil organic carbon (SOC) stocks using a dedicated process-based SOC model and state-of-the-art databases of driving variables. Soil carbon change was calculated for Europe using the Rothamsted Carbon model using climate data from four climate models, forced by four Intergovernmental Panel on Climate Change (IPCC) emissions scenarios (SRES). Changes in litter input to the soil due to forest management, projected changes in net primary production (NPP), forest age-class structure, and changes in forest area were taken into account. Results are presented for mineral soil only. Under some climate scenarios carbon in forest soils will increase slightly (0.1 to 4.6 Pg) in Europe over the 21st Century, whilst for one scenario, forest SOC stocks are predicted to decrease by 0.3 Pg. Different trends are seen in different regions. Climate change will tend to speed decomposition, whereas increases in litter input due to increasing NPP and changing age-class structure will slow the loss of SOC. Increases in forest area could further enhance the total soil carbon stock of European forests. Whilst climate change will be a key driver of change in forest soil carbon, changes in ageclass structure and land-use change are estimated to have greater effects