28 results on '"Ehrhardt F"'
Search Results
2. How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies.
- Author
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Albanito, F, McBey, D, Harrison, M, Smith, P, Ehrhardt, F, Bhatia, A, Bellocchi, G, Brilli, L, Carozzi, M, Christie, K, Doltra, J, Dorich, C, Doro, L, Grace, P, Grant, B, Léonard, J, Liebig, M, Ludemann, C, Martin, R, Meier, E, Meyer, R, De Antoni Migliorati, M, Myrgiotis, V, Recous, S, Sándor, R, Snow, V, Soussana, J-F, Smith, WN, Fitton, N, Albanito, F, McBey, D, Harrison, M, Smith, P, Ehrhardt, F, Bhatia, A, Bellocchi, G, Brilli, L, Carozzi, M, Christie, K, Doltra, J, Dorich, C, Doro, L, Grace, P, Grant, B, Léonard, J, Liebig, M, Ludemann, C, Martin, R, Meier, E, Meyer, R, De Antoni Migliorati, M, Myrgiotis, V, Recous, S, Sándor, R, Snow, V, Soussana, J-F, Smith, WN, and Fitton, N
- Abstract
There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler's experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in "trial-and-error" calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler's assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance
- Published
- 2022
3. Ensemble modelling, uncertainty and robust predictions of organic carbon in long‐term bare‐fallow soils
- Author
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Farina, R., Sándor, R., Abdalla, M., Álvaro‐Fuentes, J., Bechini, L., Bolinder, M.A., Brilli, L., Chenu, C., Clivot, H., De Antoni Migliorati, M., Di Bene, C., Dorich, C.D., Ehrhardt, F., Ferchaud, F., Fitton, N., Francaviglia, R., Franko, Uwe, Giltrap, D.L., Grant, B.B., Guenet, B., Harrison, M.T., Kirschbaum, M.U.F., Kuka, K., Kulmala, L., Liski, J., McGrath, M.J., Meier, E., Menichetti, L., Moyano, F., Nendel, C., Recous, S., Reibold, N., Shepherd, A., Smith, W.N., Smith, P., Soussana, J.-F., Stella, T., Taghizadeh‐Toosi, A., Tsutskikh, E., Bellocchi, G., Farina, R., Sándor, R., Abdalla, M., Álvaro‐Fuentes, J., Bechini, L., Bolinder, M.A., Brilli, L., Chenu, C., Clivot, H., De Antoni Migliorati, M., Di Bene, C., Dorich, C.D., Ehrhardt, F., Ferchaud, F., Fitton, N., Francaviglia, R., Franko, Uwe, Giltrap, D.L., Grant, B.B., Guenet, B., Harrison, M.T., Kirschbaum, M.U.F., Kuka, K., Kulmala, L., Liski, J., McGrath, M.J., Meier, E., Menichetti, L., Moyano, F., Nendel, C., Recous, S., Reibold, N., Shepherd, A., Smith, W.N., Smith, P., Soussana, J.-F., Stella, T., Taghizadeh‐Toosi, A., Tsutskikh, E., and Bellocchi, G.
- Abstract
Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate‐change studies. It is imperative to increase confidence in long‐term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process‐based C models by comparing simulations to experimental data from seven long‐term bare‐fallow (vegetation‐free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi‐year simulation periods (from 28 to 80 years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge‐based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin‐up initialization of SOC. Changes in the multi‐model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (±15.5) Mg C/ha compared to the observed mean of 36.0 (±19.7) Mg C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5 ± 16.7 Mg C/ha) and Spe (36.8 ± 19.8 Mg C/ha) provided only marginal gains in accuracy, but modellers would
- Published
- 2020
4. Matching policy and science: Rationale for the ‘4 per 1000 - soils for food security and climate’ initiative
- Author
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Soussana, J.-F., Lutfalla, S., Ehrhardt, F., Rosenstock, T., Lamanna, C., Havlik, P., Richards, M., Wollenberg, E., Chotte, J.-L., Torquebiau, E., Ciais, P., Smith, P., Lal, R., Soussana, J.-F., Lutfalla, S., Ehrhardt, F., Rosenstock, T., Lamanna, C., Havlik, P., Richards, M., Wollenberg, E., Chotte, J.-L., Torquebiau, E., Ciais, P., Smith, P., and Lal, R.
- Abstract
At the 21st session of the United Nations Framework Convention on Climate Change (UNFCCC, COP21), a voluntary action plan, the ‘4 per 1000 Initiative: Soils for Food Security and Climate’ was proposed under the Agenda for Action. The Initiative underlines the role of soil organic matter (SOM) in addressing the three-fold challenge of food and nutritional security, adaptation to climate change and mitigation of human-induced greenhouse gases (GHGs) emissions. It sets an ambitious aspirational target of a 4 per 1000 (i.e. 0.4%) rate of annual increase in global soil organic carbon (SOC) stocks, with a focus on agricultural lands where farmers would ensure the carbon stewardship of soils, like they manage day-to-day multipurpose production systems in a changing environment. In this paper, the opportunities and challenges for the 4 per 1000 initiative are discussed. We show that the 4 per 1000 target, calculated relative to global top soil SOC stocks, is consistent with literature estimates of the technical potential for SOC sequestration, though the achievable potential is likely to be substantially lower given socio-economic constraints. We calculate that land-based negative emissions from additional SOC sequestration could significantly contribute to reducing the anthropogenic CO2 equivalent emission gap identified from Nationally Determined Contributions pledged by countries to stabilize global warming levels below 2 °C or even 1.5 °C under the Paris agreement on climate. The 4 per 1000 target could be implemented by taking into account differentiated SOC stock baselines, reversing the current trend of huge soil CO2 losses, e.g. from agriculture encroaching peatland soils. We further discuss the potential benefits of SOC stewardship for both degraded and healthy soils along contrasting spatial scales (field, farm, landscape and country) and temporal (year to century) horizons. Last, we present some of the implications relative to non-CO2 GHGs emissions, water and nu
- Published
- 2019
5. Elektronisches Publizieren von Text und chemischen Strukturen am Beispiel des ChemInform
- Author
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Blücher, I., Christoph, B., Ehrhardt, F., Parlow, A., and Gasteiger, Johann, editor
- Published
- 1988
- Full Text
- View/download PDF
6. Global refining – game changing trends, response strategies, and the role of technology
- Author
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Ehrhardt, F
- Abstract
The petroleum refining industry has never been without substantial challenges, and it is appropriate from time to time to review the latest trends and challenges that will impact the industry. This article will address from a strategic impact point of view two of several prevailing and emerging trends that are of adequate significance to be considered game changers, and to review how technology can and will play a role in addressing effectively the related issues.
- Published
- 2016
7. Global Research Alliance on agricultural greenhouse gases - benchmark and ensemble crop and grassland model estimates
- Author
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Renata Sándor, Ehrhardt, F., Bruno Basso, Bathia, A., Gianni Bellocchi, Brilli, L., Laura Maritza Cardenas, Massimiliano de Antoni Migliorati, Bregon, J. D., Dorich, C., Lucas Doro, Fitton, N., Giacomini, S., Peter Grace, Grant, Brian B., Harrison, M., Stephanie Jones, Miko Kirschbaum, Katja Klumpp, Laville, P., Joël Léonard, Liebig, M., Lieffering, M., Raphaël Martin, Mcauliffe, R., Elizabeth Anne Meier, Lutz Merbold, Andrew Moore, Vasilis Myrgiotis, Elizabeth Pattey, Zhang, Q., Sylvie Recous, Suzanne Rolinski, Joanna Sharp, Raia Silvia Massad, Smith, P., Ward Smith, Val Snow, Soussana, J. F., Institute for Soil Sciences and Agricultural Chemistry (ATK TAKI), Centre for Agricultural Research [Budapest] (ATK), Hungarian Academy of Sciences (MTA)-Hungarian Academy of Sciences (MTA), Department of Earth and Environmental Sciences [East Lansing], Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Unité Mixte de Recherche sur l'Ecosystème Prairial - UMR (UREP), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Dipartimento di Scienze delle Produzioni Agroalimentari e dell'Ambiente (DISPAA), University of Florence (UNIFI), Department of Sustainable Soils and Grassland Systems, Rothamsted Research, Institute for Future Environments, Queensland University of Technology, Texas A and M AgriLife Research, Texas A&M University System, Institute of Biological and Environmental Sciences, (SFIRC), Universidade Federal de Santa Maria (UFSM), Science and Technology Branch, Environment and Climate Change Canada, Tasmanian Institute of Agriculture (TIA), University of Tasmania (UTAS), Soil Science and Systems Team, Scotland's Rural College (SCUR), Ecosystems and Global Change Team, Landscare Research, Unité d'Agronomie de Laon-Reims-Mons (AGRO-LRM), Institut National de la Recherche Agronomique (INRA), Agricultural Research Service (ARS), United States Department of Agriculture, CSIRO Ecosystem Sciences, Livestock Systems and Environment, International Livestock Research Institute, Agriculture & Food, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), School of GeoSciences, University of Edinburgh, Ottawa Research and Development Centre, Agriculture and Agri-Food [Ottawa] (AAFC), Fractionnement des AgroRessources et Environnement - UMR-A 614 (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de la Recherche Agronomique (INRA)-SFR Condorcet, Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS)-Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS), Climate Impacts and Vulnerabilities - Research Domain II, Potsdam Institute for Climate Impact Research (PIK), Modelling, Sustainable Production, New Zealand Institute for Plant and Food Research Limited, Farm Systems and Environment, AgResearch Ltd, UR 0874 Unité de recherche sur l'Ecosystème Prairial, Institut National de la Recherche Agronomique (INRA)-Unité de recherche sur l'Ecosystème Prairial (UREP)-Ecologie des Forêts, Prairies et milieux Aquatiques (EFPA), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Universidade Federal de Santa Maria = Federal University of Santa Maria [Santa Maria, RS, Brazil] (UFSM), University of Tasmania [Hobart, Australia] (UTAS), Scotland's Rural College (SRUC), Agroressources et Impacts environnementaux (AgroImpact), USDA-ARS : Agricultural Research Service, Fractionnement des AgroRessources et Environnement (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de la Recherche Agronomique (INRA), Plant & Food Research, and ProdInra, Archive Ouverte
- Subjects
[SDV] Life Sciences [q-bio] ,agricultural greenhouse gases ,grassland model ,[SDV]Life Sciences [q-bio] - Abstract
CT3 Biogéochimie, physique et écologie des solsEnjS4 Bouclage des cycles N et P et stockage de carboneTyp_Proj_Bourse de thèse/Post-DocTyp_Proj_Projet ANR; Uncertainties in the response of crop and grassland models to management and environmental drivers can be attributed to differences in the structure of different models. This has created an urgent need for international benchmarking of models, where uncertainties are estimated by running several models that simulate the same physical and management conditions (ensemble modelling) to generate expanded envelopes of uncertainty (e.g. Asseng et al., 2013). Simulations of the agricultural C and N fluxes, in particular, are inherently uncertain because they are driven by complex interactions (e.g. Sándor et al., 2016) and characterized by considerable spatial and temporal variability in the measurements. In this context, the Integrative Research Group of the Global Research Alliance (GRA) on Agricultural Greenhouse Gases promotes a coordinated activity across multiple international projects (e.g. C-N MIP and Models4Pastures of the FACCE-JPI, https://www.faccejpi.com) to benchmark and compare simulation models that estimate C-N related outputs (including greenhouse gas emissions) from arable crop and grassland systems (http://globalresearchalliance.org/e/model-intercomparison-on-agricultural-ghg-emissions). This study presents some preliminary results on the uncertainty of outputs from 12 grassland models while exploring model differences when models were calibrated with increasing data resources.
- Published
- 2016
8. Global Research Alliance on Greenhouse Gases - benchmark and ensemble crop and grassland model estimates
- Author
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Laville, P., Myrgiotis, V., Bellocchi, Gianni, Martin, R., Zhang, Q., Brilli, Lorenzo, Liebig, M., Doltra, J., DORO, Luca, Kirschbaum, M.U.F., Bhatia, A., Smith, P., Newton, P., Sharp, J., Pattey, E., Rolinski, S., Soussana, J.F., Massad, R.S., Sándor, Renáta, Basso, Bruno, De Antoni Migliorati, M., Lieffering, M., Meier, E., Snow, V., Moore, A., Fitton, N., Jones, S., Klumpp, K., Dorich, C., Ehrhardt, F., Léonard, J., McAuliffe, R., Giacomini, S.J., Wu, L., Harrison, M. T., Smith, W., Merbold, L., Recous, S., Grant, B., and Grace, P.
- Published
- 2016
9. MAGGnet: An international network to foster mitigation of agricultural greenhouse gases
- Author
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Liebig, M.A., primary, Franzluebbers, A.J., additional, Alvarez, C., additional, Chiesa, T.D., additional, Lewczuk, N., additional, Piñeiro, G., additional, Posse, G., additional, Yahdjian, L., additional, Grace, P., additional, Cabral, O. Machado Rodrigues, additional, Martin-Neto, L., additional, de Aragão Ribeiro Rodrigues, R., additional, Amiro, B., additional, Angers, D., additional, Hao, X., additional, Oelbermann, M., additional, Tenuta, M., additional, Munkholm, L.J., additional, Regina, K., additional, Cellier, P., additional, Ehrhardt, F., additional, Richard, G., additional, Dechow, R., additional, Agus, F., additional, Widiarta, N., additional, Spink, J., additional, Berti, A., additional, Grignani, C., additional, Mazzoncini, M., additional, Orsini, R., additional, Roggero, P.P., additional, Seddaiu, G., additional, Tei, F., additional, Ventrella, D., additional, Vitali, G., additional, Kishimoto-Mo, A., additional, Shirato, Y., additional, Sudo, S., additional, Shin, J., additional, Schipper, L., additional, Savé, R., additional, Leifeld, J., additional, Spadavecchia, L., additional, Yeluripati, J., additional, Grosso, S. Del, additional, Rice, C., additional, and Sawchik, J., additional
- Published
- 2016
- Full Text
- View/download PDF
10. C and N models Intercomparison – benchmark and ensemble model estimates for grassland production
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Sándor, R., primary, Ehrhardt, F., additional, Basso, B., additional, Bellocchi, G., additional, Bhatia, A., additional, Brilli, L., additional, Migliorati, M.DeAntoni, additional, Doltra, J., additional, Dorich, C., additional, Doro, L., additional, Fitton, N., additional, Giacomini, S.J., additional, Grace, P., additional, Grant, B., additional, Harrison, M.T., additional, Jones, S., additional, Kirschbaum, M.U.F., additional, Klumpp, K., additional, Laville, P., additional, Léonard, J., additional, Liebig, M., additional, Lieffering, M., additional, Martin, R., additional, McAuliffe, R., additional, Meier, E., additional, Merbold, L., additional, Moore, A., additional, Myrgiotis, V., additional, Newton, P., additional, Pattey, E., additional, Recous, S., additional, Rolinski, S., additional, Sharp, J., additional, Massad, R.S., additional, Smith, P., additional, Smith, W., additional, Snow, V., additional, Wu, L., additional, Zhang, Q., additional, and Soussana, J.F., additional
- Published
- 2016
- Full Text
- View/download PDF
11. Untersuchungen über den Einfluß des Klimas auf die Stickstoffnachlieferung von Waldhumus in verschiedenen Höhenlagen der Tiroler Alpen
- Author
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Ehrhardt, F.
- Published
- 1961
- Full Text
- View/download PDF
12. Die waldbauliche Auswertung pflanzensoziologischer und bodenkundlicher Untersuchungen auf Buntsandstein (Forstamt Mittelsinn, Nordspessart)
- Author
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Ehrhardt, F. and Klöck, W.
- Published
- 1951
- Full Text
- View/download PDF
13. Biological soil crusts (BSC) in the Sahelian zone. Can they impact soil C and N cycles ? [abstract]
- Author
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Ehrhardt, F., Bertrand, I., Joulian, C., Valentin, Christian, Alavoine, G., Malam Issa, Oumarou, Biogéochimie et écologie des milieux continentaux (Bioemco), Centre National de la Recherche Scientifique (CNRS)-AgroParisTech-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Recherche Agronomique (INRA)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), École normale supérieure - Paris (ENS-PSL), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Université Pierre et Marie Curie - Paris 6 (UPMC)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)
- Subjects
ZONE SAHELIENNE ,[SDU.STU]Sciences of the Universe [physics]/Earth Sciences ,NIGER - Abstract
EGU.European Geosciences Union General Assembly, Vienne, AUT, 22-/04/2012 - 27/04/2012
- Published
- 2012
14. Biological soil crusts (BSC) in the Sahelian zone. Can they impact soil C and N cycles ? [poster abstract]
- Author
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Ehrhardt, F., Bertrand, I., Joulian, C., Valentin, Christian, Alavoine, G., and Malam Issa, Oumarou
- Published
- 2012
15. Silicon nanoparticles in silicon oxynitride layer for PV application
- Author
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Ehrhardt, F., Ferblantier, G., Muller, D., Slaoui, A., and Jung, Marie-Anne
- Subjects
ComputingMilieux_MISCELLANEOUS - Published
- 2012
16. Effect of the silicon excess on the nanoparticles embedded in a SiOxNy matrix fabricated by plasma enhanced chemical vapour deposition technique
- Author
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Ferblantier, G., Ehrhardt, F., Delachat, F., Slaoui, A., and Jung, Marie-Anne
- Published
- 2011
17. Analyses structurales de couches d'oxynitrure de silicium contenant des nano-particules de silicium fabriquées par la technique de PECVD
- Author
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Ehrhardt, F., Muller, D., Ferblantier, G., Delachat, F., Slaoui, A., and Jung, Marie-Anne
- Published
- 2010
18. Intro 'Content is King'
- Author
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Ehrhardt F. Heinold
- Subjects
media_common.quotation_subject ,Art ,Humanities ,media_common - Abstract
„Every information on your fingertips — everywhere“: Diese Vision der modernen Informationsgesellschaft kann durch integriertes Content Management Wirklichkeit werden. Ob Mitarbeiter, Lieferanten, Kunden oder Interessenten: Jeder erhalt per PC Zugriff auf die fur ihn relevanten Informationen. Ob langes Suchen in Archiven, Blattern in inhaltlich uberholten Broschuren oder aufwendige Recherchen auf Servern: All dies gehort der Vergangenheit an, wenn Inhalte digital zuganglich gemacht werden.
- Published
- 2001
19. Creep fatigue crack development in dissimilar metal welded joints between steels and nickel based alloy
- Author
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Ehrhardt, F., primary, Holdsworth, S. R., additional, Kühn, I., additional, and Mazza, E., additional
- Published
- 2013
- Full Text
- View/download PDF
20. Control of silicon nanoparticle size embedded in silicon oxynitride dielectric matrix
- Author
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Ehrhardt, F., primary, Ferblantier, G., additional, Muller, D., additional, Ulhaq-Bouillet, C., additional, Rinnert, H., additional, and Slaoui, A., additional
- Published
- 2013
- Full Text
- View/download PDF
21. Creep fatigue crack development in dissimilar metal welded joints between steels and nickel based alloy.
- Author
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Ehrhardt, F., Holdsworth, S. R., Kühn, I., and Mazza, E.
- Subjects
CREEP (Materials) ,WELDED joints ,MATERIAL fatigue ,FATIGUE crack growth ,FATIGUE cracks ,TRANSITION metals - Abstract
Creep and strain controlled cyclic/hold creep fatigue tests have been performed at temperatures in the range of 550-575°C on specimens extracted from dissimilar metal welded (DMW) joints between two classes of steel and a nickel based alloy. The details and results of the tests are described. While crack development in the cyclic/hold creep fatigue test specimens tends to be creep dominated, the microstructural paths followed in the steels in the vicinity of their heat affected zones are not identical to those observed in creep rupture testpieces taken from the same DMW joint. In pure creep tests, cracking may occur adjacent to the fusion line and/or in the fine grain heat affected zone (FGHAZ), with rupture location being dependent on temperature stress and microstructural condition. In contrast, creep dominated creep fatigue cracking typically occurs in the intercritical heat affected zone/FGHAZ or the overtempered parent material on the steel side of such weldments, depending on the composition of the joint. [ABSTRACT FROM AUTHOR]
- Published
- 2013
- Full Text
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22. Evaluating the Potential of Legumes to Mitigate N$_{2}$O Emissions From Permanent Grassland Using Process-Based Models
- Author
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Fuchs, Kathrin, Merbold, L., Buchmann, N., Bellocchi, G., Bindi, M., Brilli, L., Conant, R. T., Dorich, C. D., Ehrhardt, F., Fitton, N., Grace, P., Klumpp, K., Liebig, M., Lieffering, M., Martin, R., McAuliffe, R., Newton, P. C. D., Rees, R. M., Recous, S., Smith, P., Soussana, J.-F., Topp, C. F. E., and Snow, V.
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2. Zero hunger ,13. Climate action ,15. Life on land - Abstract
A potential strategy for mitigating nitrous oxide (N$_{2}$O) emissions from permanent grasslands is the partial substitution of fertilizer nitrogen (N$_{fert}$) with symbiotically fixed nitrogen (N$_{symb}$) from legumes. The input of N$_{symb}$ reduces the energy costs of producing fertilizer and provides a supply of nitrogen (N) for plants that is more synchronous to plant demand than occasional fertilizer applications. Legumes have been promoted as a potential N$_{2}$O mitigation strategy for grasslands, but evidence to support their efficacy is limited, partly due to the difficulty in conducting experiments across the large range of potential combinations of legume proportions and fertilizer N inputs. These experimental constraints can be overcome by biogeochemical models that can vary legume‐fertilizer combinations and subsequently aid the design of targeted experiments. Using two variants each of two biogeochemical models (APSIM and DayCent), we tested the N$_{2}$O mitigation potential and productivity of full factorial combinations of legume proportions and fertilizer rates for five temperate grassland sites across the globe. Both models showed that replacing fertilizer with legumes reduced N$_{2}$O emissions without reducing productivity across a broad range of legume‐fertilizer combinations. Although the models were consistent with the relative changes of N$_{2}$O emissions compared to the baseline scenario (200 kg N ha$^{-1}$ yr$^{-1}$; no legumes), they predicted different levels of absolute N$_{2}$O emissions and thus also of absolute N$_{2}$O emission reductions; both were greater in DayCent than in APSIM. We recommend confirming these results with experimental studies assessing the effect of clover proportions in the range 30–50% and ≤150 kg N ha$^{-1}$ yr$^{-1}$ input as these were identified as best‐bet climate smart agricultural practices.
23. Structural properties of SiOxNy containing silicon nanoparticles deposited by ECR-PECVD
- Author
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Ferblantier, G., Ehrhardt, F., Delachat, F., Marzia Carrada, Slaoui, A., Jung, Marie-Anne, Institut d'Electronique du Solide et des Systèmes (InESS), and Centre National de la Recherche Scientifique (CNRS)
24. How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies.
- Author
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Albanito F, McBey D, Harrison M, Smith P, Ehrhardt F, Bhatia A, Bellocchi G, Brilli L, Carozzi M, Christie K, Doltra J, Dorich C, Doro L, Grace P, Grant B, Léonard J, Liebig M, Ludemann C, Martin R, Meier E, Meyer R, De Antoni Migliorati M, Myrgiotis V, Recous S, Sándor R, Snow V, Soussana JF, Smith WN, and Fitton N
- Subjects
- Ecosystem, Humans, Nitrogen, Uncertainty, Carbon, Soil
- Abstract
There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler's experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in "trial-and-error" calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler's assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details.
- Published
- 2022
- Full Text
- View/download PDF
25. Ensemble modelling, uncertainty and robust predictions of organic carbon in long-term bare-fallow soils.
- Author
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Farina R, Sándor R, Abdalla M, Álvaro-Fuentes J, Bechini L, Bolinder MA, Brilli L, Chenu C, Clivot H, De Antoni Migliorati M, Di Bene C, Dorich CD, Ehrhardt F, Ferchaud F, Fitton N, Francaviglia R, Franko U, Giltrap DL, Grant BB, Guenet B, Harrison MT, Kirschbaum MUF, Kuka K, Kulmala L, Liski J, McGrath MJ, Meier E, Menichetti L, Moyano F, Nendel C, Recous S, Reibold N, Shepherd A, Smith WN, Smith P, Soussana JF, Stella T, Taghizadeh-Toosi A, Tsutskikh E, and Bellocchi G
- Subjects
- Agriculture, France, Russia, Sweden, Uncertainty, United Kingdom, Carbon analysis, Soil
- Abstract
Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate-change studies. It is imperative to increase confidence in long-term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process-based C models by comparing simulations to experimental data from seven long-term bare-fallow (vegetation-free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi-year simulation periods (from 28 to 80 years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge-based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin-up initialization of SOC. Changes in the multi-model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (±15.5) Mg C/ha compared to the observed mean of 36.0 (±19.7) Mg C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5 ± 16.7 Mg C/ha) and Spe (36.8 ± 19.8 Mg C/ha) provided only marginal gains in accuracy, but modellers would need to apply more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models., (© 2020 John Wiley & Sons Ltd.)
- Published
- 2021
- Full Text
- View/download PDF
26. The use of biogeochemical models to evaluate mitigation of greenhouse gas emissions from managed grasslands.
- Author
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Sándor R, Ehrhardt F, Brilli L, Carozzi M, Recous S, Smith P, Snow V, Soussana JF, Dorich CD, Fuchs K, Fitton N, Gongadze K, Klumpp K, Liebig M, Martin R, Merbold L, Newton PCD, Rees RM, Rolinski S, and Bellocchi G
- Abstract
Simulation models quantify the impacts on carbon (C) and nitrogen (N) cycling in grassland systems caused by changes in management practices. To support agricultural policies, it is however important to contrast the responses of alternative models, which can differ greatly in their treatment of key processes and in their response to management. We applied eight biogeochemical models at five grassland sites (in France, New Zealand, Switzerland, United Kingdom and United States) to compare the sensitivity of modelled C and N fluxes to changes in the density of grazing animals (from 100% to 50% of the original livestock densities), also in combination with decreasing N fertilization levels (reduced to zero from the initial levels). Simulated multi-model median values indicated that input reduction would lead to an increase in the C sink strength (negative net ecosystem C exchange) in intensive grazing systems: -64 ± 74 g C m
-2 yr-1 (animal density reduction) and -81 ± 74 g C m-2 yr-1 (N and animal density reduction), against the baseline of -30.5 ± 69.5 g C m-2 yr-1 (LSU [livestock units] ≥ 0.76 ha-1 yr-1 ). Simulations also indicated a strong effect of N fertilizer reduction on N fluxes, e.g. N2 O-N emissions decreased from 0.34 ± 0.22 (baseline) to 0.1 ± 0.05 g N m-2 yr-1 (no N fertilization). Simulated decline in grazing intensity had only limited impact on the N balance. The simulated pattern of enteric methane emissions was dominated by high model-to-model variability. The reduction in simulated offtake (animal intake + cut biomass) led to a doubling in net primary production per animal (increased by 11.6 ± 8.1 t C LSU-1 yr-1 across sites). The highest N2 O-N intensities (N2 O-N/offtake) were simulated at mown and extensively grazed arid sites. We show the possibility of using grassland models to determine sound mitigation practices while quantifying the uncertainties associated with the simulated outputs., (Copyright © 2018 Elsevier B.V. All rights reserved.)- Published
- 2018
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27. Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N 2 O emissions.
- Author
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Ehrhardt F, Soussana JF, Bellocchi G, Grace P, McAuliffe R, Recous S, Sándor R, Smith P, Snow V, de Antoni Migliorati M, Basso B, Bhatia A, Brilli L, Doltra J, Dorich CD, Doro L, Fitton N, Giacomini SJ, Grant B, Harrison MT, Jones SK, Kirschbaum MUF, Klumpp K, Laville P, Léonard J, Liebig M, Lieffering M, Martin R, Massad RS, Meier E, Merbold L, Moore AD, Myrgiotis V, Newton P, Pattey E, Rolinski S, Sharp J, Smith WN, Wu L, and Zhang Q
- Subjects
- Computer Simulation, Food Supply, Uncertainty, Agriculture methods, Crops, Agricultural physiology, Models, Biological, Nitrous Oxide metabolism
- Abstract
Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N
2 O) emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage modelling protocol, from blind simulations (stage 1) to partial (stages 2-4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N2 O emissions. Results showed that across sites and crop/grassland types, 23%-40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N2 O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2 O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2-4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N2 O emissions. Yield-scaled N2 O emissions (N2 O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N2 O emissions at field scale is discussed., (© 2017 John Wiley & Sons Ltd.)- Published
- 2018
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28. Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes.
- Author
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Brilli L, Bechini L, Bindi M, Carozzi M, Cavalli D, Conant R, Dorich CD, Doro L, Ehrhardt F, Farina R, Ferrise R, Fitton N, Francaviglia R, Grace P, Iocola I, Klumpp K, Léonard J, Martin R, Massad RS, Recous S, Seddaiu G, Sharp J, Smith P, Smith WN, Soussana JF, and Bellocchi G
- Abstract
Biogeochemical simulation models are important tools for describing and quantifying the contribution of agricultural systems to C sequestration and GHG source/sink status. The abundance of simulation tools developed over recent decades, however, creates a difficulty because predictions from different models show large variability. Discrepancies between the conclusions of different modelling studies are often ascribed to differences in the physical and biogeochemical processes incorporated in equations of C and N cycles and their interactions. Here we review the literature to determine the state-of-the-art in modelling agricultural (crop and grassland) systems. In order to carry out this study, we selected the range of biogeochemical models used by the CN-MIP consortium of FACCE-JPI (http://www.faccejpi.com): APSIM, CERES-EGC, DayCent, DNDC, DSSAT, EPIC, PaSim, RothC and STICS. In our analysis, these models were assessed for the quality and comprehensiveness of underlying processes related to pedo-climatic conditions and management practices, but also with respect to time and space of application, and for their accuracy in multiple contexts. Overall, it emerged that there is a possible impact of ill-defined pedo-climatic conditions in the unsatisfactory performance of the models (46.2%), followed by limitations in the algorithms simulating the effects of management practices (33.1%). The multiplicity of scales in both time and space is a fundamental feature, which explains the remaining weaknesses (i.e. 20.7%). Innovative aspects have been identified for future development of C and N models. They include the explicit representation of soil microbial biomass to drive soil organic matter turnover, the effect of N shortage on SOM decomposition, the improvements related to the production and consumption of gases and an adequate simulations of gas transport in soil. On these bases, the assessment of trends and gaps in the modelling approaches currently employed to represent biogeochemical cycles in crop and grassland systems appears an essential step for future research., (Copyright © 2017 Elsevier B.V. All rights reserved.)
- Published
- 2017
- Full Text
- View/download PDF
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