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51. Uncertainty of wheat water use: Simulated patterns and sensitivity to temperature and CO₂

Author

Cammarano, D, Rötter, RP, Asseng, S, Ewert, F, Wallach, D, Martre, P, Hatfield, JL, Jones, JW, Rosenzweig, C, Ruane, AC, Boote, KJ, Thorburn, PJ, Kersebaum, KC, Aggarwal, PK, Angulo, C, Basso, B, Bertuzzi, P, Biernath, C, Brisson, N, Challinor, AJ, Doltra, J, Gayler, S, Goldberg, R, Heng, L, Hooker, JE, Hunt, LA, Ingwersen, J, Izaurralde, RC, Müller, C, Kumar, SN, Nendel, C, O'Leary, G, Olesen, JE, Osborne, TM, Palosuo, T, Priesack, E, Ripoche, D, Semenov, MA, Shcherbak, I, Steduto, P, Stöckle, CO, Stratonovitch, P, Streck, T, Supit, I, Tao, F, Travasso, M, Waha, K, White, JW, and Wolf, J

Abstract

Projected global warming and population growth will reduce future water availability for agriculture. Thus, it is essential to increase the efficiency in using water to ensure crop productivity. Quantifying crop water use (WU; i.e. actual evapotranspiration) is a critical step towards this goal. Here, sixteen wheat simulation models were used to quantify sources of model uncertainty and to estimate the relative changes and variability between models for simulated WU, water use efficiency (WUE, WU per unit of grain dry mass produced), transpiration efficiency (Teff, transpiration per kg of unit of grain yield dry mass produced), grain yield, crop transpiration and soil evaporation at increased temperatures and elevated atmospheric carbon dioxide concentrations ([CO2]). The greatest uncertainty in simulating water use, potential evapotranspiration, crop transpiration and soil evaporation was due to differences in how crop transpiration was modelled and accounted for 50% of the total variability among models. The simulation results for the sensitivity to temperature indicated that crop WU will decline with increasing temperature due to reduced growing seasons. The uncertainties in simulated crop WU, and in particularly due to uncertainties in simulating crop transpiration, were greater under conditions of increased temperatures and with high temperatures in combination with elevated atmospheric [CO2] concentrations. Hence the simulation of crop WU, and in particularly crop transpiration under higher temperature, needs to be improved and evaluated with field measurements before models can be used to simulate climate change impacts on future crop water demand.

Published
2016

54. How does inter-annual variability of attainable yield affect the magnitude of yield gaps for wheat and maize? An analysis at ten sites

Author

Hoffmann, M.P., primary, Haakana, M., additional, Asseng, S., additional, Höhn, J.G., additional, Palosuo, T., additional, Ruiz-Ramos, M., additional, Fronzek, S., additional, Ewert, F., additional, Gaiser, T., additional, Kassie, B.T., additional, Paff, K., additional, Rezaei, E.E., additional, Rodríguez, A., additional, Semenov, M., additional, Srivastava, A.K., additional, Stratonovitch, P., additional, Tao, F., additional, Chen, Y., additional, and Rötter, R.P., additional

Published
2018
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55. Adaptation response surfaces for managing wheat under perturbed climate and CO2 in a Mediterranean environment

Author

Ruiz-Ramos, M., primary, Ferrise, R., additional, Rodríguez, A., additional, Lorite, I.J., additional, Bindi, M., additional, Carter, T.R., additional, Fronzek, S., additional, Palosuo, T., additional, Pirttioja, N., additional, Baranowski, P., additional, Buis, S., additional, Cammarano, D., additional, Chen, Y., additional, Dumont, B., additional, Ewert, F., additional, Gaiser, T., additional, Hlavinka, P., additional, Hoffmann, H., additional, Höhn, J.G., additional, Jurecka, F., additional, Kersebaum, K.C., additional, Krzyszczak, J., additional, Lana, M., additional, Mechiche-Alami, A., additional, Minet, J., additional, Montesino, M., additional, Nendel, C., additional, Porter, J.R., additional, Ruget, F., additional, Semenov, M.A., additional, Steinmetz, Z., additional, Stratonovitch, P., additional, Supit, I., additional, Tao, F., additional, Trnka, M., additional, de Wit, A., additional, and Rötter, R.P., additional

Published
2018
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56. Collaborative meta-analysis of prospective studies of plasma fibrinogen and cardiovascular disease

Author

Kostis, JB, Wilson, AC, Folsom, AR, Chambless, L, Wu, K, Benderly, M, Goldbourt, U, Willeit, J, Kiechl, S, Yarnell, JWG, Sweetnam, PM, Elwood, PC, Cushman, M, Psaty, BM, Tybjaerg-Hansen, A, Haverkate, F, de Maat, MPM, Thompson, SG, Fowkes, FGR, Lee, AJ, Smith, FB, Salomaa, V, Rasi, V, Vahtera, E, Jousilahti, P, Pekkanen, J, D'Agostino, R, Kannel, WB, Levy, D, Wilson, PWF, Arocha-Pinango, CL, Rodriguez-Larralde, A, Nagy, E, Mijares, M, Espinosa, R, Roa, E, Ryder, E, Diez-Ewald, MP, Campos, G, Fernandez, V, Torres, E, Marchioli, R, Valagussa, F, Rosengren, A, Wilhelmsen, L, Lappas, G, Eriksson, H, Cremer, P, Nagel, D, Curb, JD, Rodriguez, B, Yano, K, Salonen, JT, Nyyssonen, K, Tuomainen, TP, Hedblad, B, Lind, P, Loewel, H, Hense, HW, Koenig, W, Meade, TW, Cooper, JA, De Stavola, B, Garrow, K, Knottenbelt, C, Miller, GJ, Bauer, KA, Rosenberg, RD, Sato, S, Kitamura, A, Naito, Y, Iso, H, Palosuo, T, Ducimetiere, P, Amouyel, P, Arveiler, D, Evans, AE, Ferrieres, J, Juhan-Vague, I, Schulte, H, Assmann, G, Cantin, B, Lamarche, B, Despres, JP, Dagenais, GR, Tunstall-Pedoe, H, Lowe, GDO, Collins, R, Danesh, J, Dickinson, A, Lewington, S, Memon, A, Thompson, S, Walker, M, Wheeler, J, Ben-Shlomo, Y, Smith, GD, Palmieri, V, Yeh, JL, Rudnicka, A, Brennan, P, Cooper, J, Rodeghiero, F, Tosetto, A, Shepherd, J, Ford, I, Norrie, J, Brunner, E, Shipley, M, Feskens, EJM, Kromhout, D, and Collaboration, FS

Subjects
medicine.medical_specialty, Epidemiology, business.industry, Regression dilution, Confounding, Fibrinogen, Regression analysis, Fibrinogen Measurement, Surgery, Meta-Analysis as Topic, Cardiovascular Diseases, Internal medicine, Meta-analysis, Data Interpretation, Statistical, Medicine, Humans, Prospective Studies, Risk factor, Cooperative Behavior, Cardiology and Cardiovascular Medicine, business, Prospective cohort study, Biomarkers, medicine.drug
Abstract

BACKGROUND: Many long-term studies have reported on associations of plasma fibrinogen concentration with cardiovascular disease, but few have been large enough to provide reliable estimates in different circumstances. Moreover, most published prospective studies have related disease risk only to baseline values of plasma fibrinogen (which can lead to substantial underestimation of any risk relationships) and have corrected only for baseline values of possible confounding factors (which can lead to residual biases). OBJECTIVES: By appropriate combination of data from individual participants from all relevant prospective studies in a systematic 'meta-analysis', with correction for regression dilution, the Fibrinogen Studies Collaboration will aim to characterize more precisely than has previously been possible the strength and shape of the age- and sex-specific associations of plasma fibrinogen with coronary heart disease (and, where data are sufficient, with other vascular diseases). It will also help to determine to what extent such associations are independent of possible confounding factors. METHODS: A central database has been established containing data on plasma fibrinogen, sex and other potential confounding factors, age at baseline fibrinogen measurement, age at event or at last follow-up, major vascular morbidity and cause-specific mortality. Information about any repeat measurements of fibrinogen and potential confounding factors is being sought to allow study-specific correction for regression dilution. The analyses will involve age-specific regression models. Synthesis of the available prospective studies of plasma fibrinogen will yield information on more than 10000 incident cardiovascular deaths and events among the approximately 200000 total participants who have been monitored, on average, for about 10 years.

Published
2016

59. An ensemble of projections of wheat adaptation to climate change in europe analyzed with impact response surfaces

Author

Ruiz-Ramos, Margarita, Ferrise, Roberto, Rodriguez, A., Lorite, Ignacio J., Tao, F., Pirttioja, N., Fronzek, S., Palosuo, T., Carter, Timothy R., Bindi, M., Höhn, Jukka G., Kersebaum, K. C., Trnka, M., Hoffmann, H., Baranowski, P., Buis, Samuel, Cammarano, D., Deligios, P., Havlinka, P., Minet, J., Montesino, M., Porter, J., Recio, J., Ruget, Francoise, Sanz, A, Steinmetz, Z., Stratonovitch, P., Supit, I., Ventrella, D., De Wit, A., Rotter, R. P., ETSI Agrónomos, Producción Vegetal: Fitotecnia, Universidad Politécnica de Madrid (UPM), University of Florence (UNIFI), Instituto de Investigación y Formación Agraria y Pesquera (IFAPA), Environmental Impacts Group, Natural Resources Institute Finland, Finnish Environment Institute (SYKE), Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of Agrosystems and Bioclimatology, Mendel University in Brno, Global Change Research Centre (CzechGlobe), Rheinische Friedrich-Wilhelms-Universität Bonn, Institute of Agrophysics Polish Academy of Sciences, Agricultural University of Lublin, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), The James Hutton Institute, University of Sassari, Université de Liège, University of Copenhagen = Københavns Universitet (KU), RIFCON GmbH, Rothamsted Research, Wageningen University, Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA‐SCA), Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria (CREA), MACSUR–, FACCE JPI, and by MULCLIVAR, from MINECO (CGL2012‐38923‐C02‐02), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Instituto Andaluz de Investigación y Formación Agraria y Pesquera (IFAPA), Natural resources institute Finland, Mendel University in Brno (MENDELU), and Ruiz-Ramos, Margarita

Subjects
changement climatique, ble tendre, sensitivity analysis, soft wheat, adaptation au changement, wheat, [SDE.MCG]Environmental Sciences/Global Changes, climate, surface de réponse, Milieux et Changements globaux, europe, global change
Abstract

IRS2 TEAM:Alfredo Rodríguez(1), Ignacio J. Lorite(3), Fulu Tao(4), Nina Pirttioja(5), Stefan Fronzek(5), Taru Palosuo(4), Timothy R. Carter(5), Marco Bindi(2), Jukka G Höhn(4), Kurt Christian Kersebaum(6), Miroslav Trnka(7,8),Holger Hoffmann(9), Piotr Baranowski(10), Samuel Buis(11), Davide Cammarano(12), Yi Chen(13,4), Paola Deligios(14), Petr Hlavinka(7,8), Frantisek Jurecka(7,8), Jaromir Krzyszczak(10), Marcos Lana(6), Julien Minet(15), Manuel Montesino(16), Claas Nendel(6), John Porter(16), Jaime Recio(1), Françoise Ruget(11), Alberto Sanz(1), Zacharias Steinmetz(17,18), Pierre Stratonovitch(19), Iwan Supit(20), Domenico Ventrella(21), Allard de Wit(20) and Reimund P. Rötter(4).; An ensemble of projections of wheat adaptation to climate change in europe analyzed with impact response surfaces . International Crop Modelling Symposium

Published
2016

60. Effect of different levels of calibration in rotation schemes simulated in five European sites in a multi-model approach

Author

Lana, M., Kersebaum, K. C., Kollas, C., Xiaogang Yin, Nendel, C., Kiril Manevski, Müller, C., Palosuo, T., Armas-Herrera, Ceclia M., Nicolas Beaudoin, Bindi, M., Monia Charfeddine, Tobias Conradt, Julie Constantin, Eitzinger, J., Ewert, F., Ferrise, R., Thomas Gaiser, Iñaki Garcia de Cortazar-Atauri, Luisa Giglio, Hlavinka, P., Hoffmann, H., Hoffmann, Munir P., Marie Launay, Remy Manderscheid, Bruno Mary, Mirschel, W., Moriondo, M., Olesen, Jørgen E., Isik Öztürk, Pacholski, A., Dominique Ripoche-Wachter, Pier Paolo Roggero, Svenja Roncossek, Rötter, R. P., Ruget, F., Behzad Sharif, Mirek Trnka, Domenico Ventrella, Waha, K., Martin Wegehenkel, Hans-Joachim Weigel, Wu, L., Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Wageningen University and Research Center (WUR), Department of Agroecology, Aarhus University [Aarhus], Molecular Ecology Lab, Department of Biological Sciences, Macquarie University, Natural Resources Institute Finland, Unité d'Agronomie de Laon-Reims-Mons (AGRO-LRM), Institut National de la Recherche Agronomique (INRA), University of Florence (UNIFI), Unità di ricerca per i sistemi colturali degli ambienti caldo-aridi, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA), Potsdam Institute for Climate Impact Research (PIK), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Institute of Meteorology, Universität Leipzig [Leipzig], Rheinische Friedrich-Wilhelms-Universität Bonn, UE Agroclim (UE AGROCLIM), Institute of Agrosystems and Bioclimatology, Mendel University in Brno, Global Change Research Institute CAS, Georg-August-Universität Göttingen, Thünen Institute of Biodiversity, Istituto di Biometeorologia [Firenze] (IBIMET), Consiglio Nazionale delle Ricerche (CNR), Leuphana University of Lüneburg, Nucleo di Ricerca sulla Desertificazione e Dipartimento di Agraria, University of Sassari, TROPAGS, Department of Crop Sciences, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institute of Landscape Systems Analysis, Rothamsted Research, Wageningen University and Research [Wageningen] (WUR), Natural resources institute Finland, Agroressources et Impacts environnementaux (AgroImpact), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria (CREA), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Agroclim (AGROCLIM), Mendel University in Brno (MENDELU), and Georg-August-University [Göttingen]

Subjects
[SDV]Life Sciences [q-bio]
Abstract

Effect of different levels of calibration in rotation schemes simulated in five European sites in a multi-model approach. iCROPM 2016 International Crop Modelling Symposium "Crop Modelling for Agriculture and Food Security under Global Change"

Published
2016

61. Using crop model ensembles to design future climate-resilient barley cultivars

Author

Tao, Fulu, P. Rotter, R., Palosuo, T., Hernandez C.G.,, Minguez M.I.,, Semenov, Mikhail, Kersebaum, K.C., Nendel, Claas, Cammarano, Davide, Hoffmann H.,, Ewert, Frank, DAMBREVILLE, Anaëlle, Martre, Pierre, Rodriguez, L., Ruiz-Ramos M.,, Gaiser T.,, Hohn J.G.,, Ferrise, Roberto, Bindi, Marco, Schulman A.,, Environmental Impacts Group, Natural Resources Institute Finland (LUKE), Universidad Politécnica de Madrid (UPM), Centro de Estudios e Investigación para la Gestión de Riesgos Agrarios y Medioambientales (CEIGRAM), Computational and Systems Biology Department, Rothamsted Research, Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), Institute of landscape systems analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), The James Hutton Institute, Rheinische Friedrich-Wilhelms-Universität Bonn, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Department of Agri-Food Production and Environmental Sciences, Università degli Studi di Firenze = University of Florence (UniFI), Green Technology, Helsingin yliopisto = Helsingfors universitet = University of Helsinki, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research, Leibniz Association (ZALF). DEU., Natural resources institute Finland, Technical University of Madrid, AgSystems–CEIGRAM Research Centre for Agricultural and Environmental Risk Management, Partenaires INRAE, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), University delgi Studi di Firenze, and University of Helsinki

Subjects
[SDV.BV]Life Sciences [q-bio]/Vegetal Biology, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2016

62. Classifying simulated wheat yield responses to changes in temperature and precipitation across a european transect

Author

Fronzek, Stefan, Pirttioja, N., Carter, Timothy R., Bindi, M., Hoffmann, H., Palosuo, T., Ruiz-Ramos, Margarita, Tao, F., Trnka, M., Acutis, M., Asseng, S., Baranowski, P., Basso, Benjamin, Bodin, P., Buis, Samuel, Cammarano, D., Deligios, P., Destain, Marie-France, Dumont, Bertrand, Ewert, Franck, Ferrise, R., FRANCOIS, Léa, Gaiser, T., Hlavinka, P., Jacquemin, Ingrid, Kersebaum, K. C., Kollas, C., Krzyszczak, J., Lorite, Ignacio J., Minet, Julien, Minguez, M. Ines, Montesino, M., Moriondo, Marco, MULLER, C, Nendel, C., Peregon, Anna, Rodríguez, A., Ruane, A. C., Ruget, Francoise, Sanna, Mattia, Semenov, M. A., Slawinski, Cezary, Stratonovitch, P., Supit, I., Waha, K., Wang, E., Wu, L., Zhao, Z., Rotter, R. P., Finnish Environment Institute (SYKE), Abeilles et Environnement (AE), Institut National de la Recherche Agronomique (INRA)-Avignon Université (AU), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Unité Mixte de Recherche sur les Herbivores - UMR 1213 (UMRH), 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), Reproduction et développement des plantes (RDP), École normale supérieure de Lyon (ENS de Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), FACCE-JPI Knowledge Hub MACSUR, Knowledge Hub FACCE MACSUR. INT. Agricultural Model Intercomparison and Improvement Project (AgMIP), USA., 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)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, and École normale supérieure - Lyon (ENS Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL)

Subjects
changement de température, sensitivity analysis, crop model, pluviomètre, [SDE.MCG]Environmental Sciences/Global Changes, région européenne, rendement du blé, transect, snow gauges
Abstract

Classifying simulated wheat yield responses to changes in temperature and precipitation across a european transect. International Crop Modelling Symposium

Published
2016

63. Inter-comparison of wheat models to identify knowledge gaps and improve process modeling

Author

Wang, E., Martre, Pierre, Asseng, S., Ewert, F., Zhao, Z., Maiorano, Andrea, Rotter, R. P., Kimball, B. A., Ottman, Michael J., Wall, G. W., White, J. W., Aggarwal, P. K., Alderman, P. D., Anothai, J., Basso, B., Biernath, C., Cammarano, D., Challinor, A. J., De Sanctis, Giacomo, Doltra, J., Fereres, E., Garcia-Vila, M., Gayler, S., Hoogenboom, G., Hunt, L. A., Izaurralde, R. C., Jabloun, M., Jones, C. D., Kersebaum, K.C., Koehler, A. K., Müller, C., Liu, L., Kumar Naresh, S., Nendel, C., O'Leary, G., Olesen, J. E., Palosuo, T., Priesack, E., Reynolds, M. P., Eyshi Rezaei, E., Ripoche, Dominique, Ruane, A. C., Semenov, M. A., Shcherbak, I., Stöckle, C., Stratonovitch, P., Streck, T., Supit, I., Tao, F., Thorburn, P., Waha, K., Wallach, Daniel, Wolf, J., Zhu, Y., Agriculture, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), University of Florida [Gainesville] (UF), INRES, Rheinische Friedrich-Wilhelms-Universität Bonn, Natural Resources Institute Finland (LUKE), ARS/ALARC, United States Department of Agriculture, The School of Plant Sciences, University of Arizona, CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), AgWeatherNet Program, Washington State University (WSU), Michigan State University [East Lansing], Michigan State University System, German Research Center for Environmental Health - Helmholtz Center München (GmbH), University of Leeds, International Center for Tropical Agriculture, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Catabrian Agricultural Research and Training Center (CIFA), Universidad de Córdoba [Cordoba], IAS, Princeton University, Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Department of Plant Agriculture, University of Guelph, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A and M AgriLife Research, Texas A&M University System, Department of Agroecology, Aarhus University [Aarhus], Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Potsdam Institute for Climate Impact Research (PIK), Nanjing Agricultural University, Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Landscape & Water Sciences, Department of Environment of Victoria, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Computational and Systems Biology Department, Rothamsted Research, Institute of Soil Science and Land Evaluation, University of Hohenheim, Wageningen University and Research Centre (WUR), Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, and Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research, Leibniz Association (ZALF). DEU.

Subjects
blé, Modeling and Simulation, comparaison de modèles, température, modèle phénologique, Modélisation et simulation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, ComputingMilieux_MISCELLANEOUS, modèle de production, incertitude
Abstract

International audience

Published
2016

64. Benchmark data set for wheat growth models: field experiments and AgMIP multi-model simulations

Author

Asseng, S, Ewert, F., Martre, P, Rosenzweig, C, Jones, J.W., Hatfield, J L, Ruane, A C, Boote, K J, Thorburn, P, Rötter, RP, Cammarano, D, Brisson, N, Basso, B, Aggarwal, PK, Angulo, C, Bertuzzi, P, Biernath, C, Challinor, AJ, Doltra, J, Gayler, S, Goldberg, R, Grant, R, Heng, L, Hooker, J, Hunt, L A, Ingwersen, J, Izaurralde, RC, Kersebaum, KC, Müller, C, Naresh Kumar, S, Nendel, C, O'Leary, G, Olesen, Jørgen Eivind, Osborne, T M, Palosuo, T, Priesack, E, Ripoche, D, Semenov, MA, Shcherbak, I, Steduto, P, Stöckle, C, Stratonovitch, P, Streck, T, Supit, I, Tao, F, Travasso, M, Waha, K, Wallach, D, White, JW, Williams, J R, Wolf, J., Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), National laboratory for agriculture and the environment, Department of agronomy, University of Florida [Gainesville] (UF), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Plant Production Research, Agrifood Research Finland, Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, International Water Management Institute, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), German Research Center for Environmental Health, University of Leeds, Catabrian Agricultural Research and Training Center (CIFA), Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Department of Renewable Resources, University of Alberta, International Atomic Energy Agency [Vienna] (IAEA), Agriculture Department, University of Reading (UOR), Department of Plant Agriculture, University of Guelph, Institute of Soil Science and Land Evaluation, University of Hohenheim, Joint Global Change Research Institute, Institute of landscape systems analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Potsdam Institute for Climate Impact Research (PIK), Division of Environmental Sciences, University of Hertfordshire [Hatfield] (UH), Department of Primary Industries, Department of Agroecology, Aarhus University [Aarhus], NCAS-Climate, Walker Institute, Computational and Systems Biology Department, Rothamsted Research, Food and Agriculture Organization, Biological Systems Engineering, Washington State University (WSU), Wageningen University and Research Centre (WUR), Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), Institute for Climate and Water, Instituto Nacional de Tecnología Agropecuaria (INTA), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Arid-Land Agricultural Research Center, Texas A&M University System, Consultative Group on International Agricultural Research (CGIAR), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Helmholtz Zentrum München = German Research Center for Environmental Health, Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), Institute of geographical sciences and natural resources research [CAS] (IGSNRR), Chinese Academy of Sciences [Beijing] (CAS), and Université de Toulouse (UT)-Université de Toulouse (UT)

Subjects
010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], Climate change, Atmospheric sciences, 01 natural sciences, Earth System Science, [SHS]Humanities and Social Sciences, Anthesis, sensitivity analysis, wheat, Life Science, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Relative humidity, Precipitation, field experimental data, 0105 earth and related environmental sciences, 2. Zero hunger, WIMEK, Humidity, 04 agricultural and veterinary sciences, 15. Life on land, Climate Resilience, Dew point, 13. Climate action, Klimaatbestendigheid, climate change impact, Soil water, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Leerstoelgroep Aardsysteemkunde, Climate model, simulations, Sensitivity analysis
Abstract

International audience; The data set includes a current representative management treatment from detailed, quality-tested sentinel field experiments with wheat from four contrasting environments including Australia, The Netherlands, India and Argentina. Measurements include local daily climate data (solar radiation, maximum and minimum temperature, precipitation, surface wind, dew point temperature, relative humidity, and vapor pressure), soil characteristics, frequent growth, nitrogen in crop and soil, crop and soil water and yield components. Simulations include results from 27 wheat models and a sensitivity analysis with 26 models and 30 years (1981-2010) for each location, for elevated atmospheric CO2 and temperature changes, a heat stress sensitivity analysis at anthesis, and a sensitivity analysis with soil and crop management variations and a Global Climate Model end-century scenario.

Published
2016

65. Uncertainty in simulating N uptake and N use efficiency in the crop rotation systems across Europe

Author

Xiaogang Yin, Kc, Kersebaum, Kollas, C., Armas-Herrera, Cecilia M., Sanmohan Baby, Nicolas Beaudoin, Bindi, M., Monica Charfeddine, Tobias Conradt, Iñaki Garcia de Cortazar-Atauri, Ewert, F., Roberto Ferrise, Hlavinka, P., Hoffmann, H., Lana, M., Marie Launay, Remy Manderscheid, Kiril Manevski, Bruno Mary, Mirschel, W., Moriondo, M., Müller, C., Nendel, C., Isik Öztürk, Palosuo, T., Dominique Ripoche-Wachter, Rp, Rötter, Ruget, F., Behzad Sharif, Trnka, M., Domenico Ventrella, Hans-Joachim Weigel, Wu, L., and Olesen, Jørgen E.

Published
2016

66. Similar negative impacts of temperature on global wheat yield estimated by three independent methods

Author

Liu, B., Asseng, S., Müller, C., Ewert, F., Elliott, J., Lobell, D.B., Martre, P., Ruane, A.C., Wallach, D., Jones, J.W., Rosenzweig, C., Aggarwal, P., Alderman, P.D., Anothai, J., Basso, B., Biernath, C.J., Cammarano, D., Challinor, A.J., Deryng, D., de Sanctis, G., Doltra, J., Fereres, E., Folberth, C., Garcia-Vila, M., Gayler, S., Hoogenboom, G., Hunt, L.A., Izaurralde, R.C., Jabloun, M., Jones, C.D., Kersebaum, K.C., Kimball, B.A., Koehler, A.-K., Kumar, S.N., Nendel, C., O´Leary, G., Olesen, J.E., Ottmann, M.J., Palosuo, T., Prasad, P.V.V., Priesack, E., Pugh, T.A., Reynolds, M., Rezaei, E.E., Rötter, R.P., Schmid, E., Semenov, M.A., Shcherbak, I., Stehfest, E., Stöckle, C.O., Stratonovitch, P., Streck,T., Supit, I., Tao, F., Thorburn, P.J., Waha, K., Wall, G.W., Wang, E., White, J.W., Wolf, J., Zhao, Z., and Zhu, Y.

Abstract

The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify ‘method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.

Published
2016

67. Letter : Rising temperatures reduce global wheat production

Author

Asseng, S., Ewert, F., Martre, P., Rötter, R.P., Cammarano, D., Kimball, B.A., Ottman, M.J., Wall, G.W., White, J.W., Reynolds, M.P., Alderman, P.D., Prasad, P.V.V., Lobell, D.B., Aggarwal, P.K., Anothai, J., Basso, B., Biernath, C., Challinor, A.J., De Sanctis, G., Doltra, J., Fereres, E., Garcia-Vila, M., Gayler, S., Hoogenboom, G., Hunt, L.A., Izaurralde, C., Jabloun, M., Jones, C.D., Kersebaum, K.C., Koehler, A.K., Müller, C., Naresh Kumar, S., Nendel, C., O’Leary, G., Olesen, J.E., Palosuo, T., Priesack, E., Eyshi Rezae, E., Ruane, A.C., Semenov, M.A., Shcherbak, I., Stöckle, C.O., Stratonovitch, P., Streck, T., Supit, I., Tao, T., Thorburn, P., Waha, K., Wang, E., Wallach, D., Wolf, J., Zhao, Z., and Zhu, Y.

Subjects
dryland wheat, WIMEK, growth, Soil Science Centre, adaptation, drought, PE&RC, yield, Climate Resilience, spring wheat, Plant Production Systems, Klimaatbestendigheid, Plantaardige Productiesystemen, climate-change, co2, Alterra - Centrum Bodem, heat, agriculture
Abstract

Crop models are essential tools for assessing the threat of climate change to local and global food production(1). Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature(2). Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32 degrees C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each degrees C of further temperature increase and become more variable over space and time.

Published
2015

68. Plasma fibrinogen level and the risk of major cardiovascular diseases and nonvascular mortality : an individual participant Metaanalysis

Author

Danesh, J, Lewington, S, Thompson, SG, Lowe, GD, Collins, R, Kostis, JB, Wilson, AC, Folsom, AR, Wu, K, Benderly, M, Goldbourt, U, Willeit, J, Kiechl, S, Yarnell, JW, Sweetnam, PM, Elwood, PC, Cushman, M, Psaty, BM, Tracy, RP, Tybjaerg-Hansen, A, Haverkate, F, de Maat, MP, Fowkes, FG, Lee, AJ, Smith, FB, Salomaa, V, Harald, K, Rasi, R, Vahtera, E, Jousilahti, P, Pekkanen, J, D'Agostino, R, Kannel, WB, Wilson, PW, Tofler, G, Arocha-Piñango, CL, Rodriguez-Larralde, A, Nagy, E, Mijares, M, Espinosa, R, Rodriquez-Roa, E, Ryder, E, Diez-Ewald, MP, Campos, G, Fernandez, V, Torres, E, Marchioli, R, Valagussa, F, Rosengren, A, Wilhelmsen, L, Lappas, G, Eriksson, H, Cremer, P, Nagel, D, Curb, JD, Rodriguez, B, Yano, K, Salonen, JT, Nyyssönen, K, Tuomainen, TP, Hedblad, B, Lind, P, Loewel, H, Koenig, W, Meade, TW, Cooper, JA, De Stavola, B, Knottenbelt, C, Miller, GJ, Bauer, KA, Rosenberg, RD, Sato, S, Kitamura, A, Naito, Y, Palosuo, T, Ducimetiere, P, Amouyel, P, Arveiler, D, Evans, AE, Ferrieres, J, Juhan-Vague, I, Bingham, A, Schulte, H, Assmann, G, Cantin, B, Lamarche, B, Després, JP, Dagenais, GR, Tunstall-Pedoe, H, Woodward, M, Ben-Shlomo, Y, Davey Smith, G, Palmieri, V, Yeh, JL, Rudnicka, A, Ridker, P, Rodeghiero, F, Tosetto, A, Shepherd, J, Ford, I, Robertson, M, Brunner, E, Shipley, M, Feskens, EJ, Kromhout, D, Dickinson, A, Ireland, B, Juzwishin, K, Kaptoge, S, Memon, A, Sarwar, N, Walker, M, Wheeler, J, White, I, and Wood, A

Subjects
Adult, Risk, medicine.medical_specialty, base-line findings, blood-viscosity, Nutrition and Disease, Regression dilution, Blood viscosity, Myocardial Infarction, Context (language use), Coronary Disease, c-reactive protein, edinburgh artery, Internal medicine, Cause of Death, Voeding en Ziekte, medicine, follow-up, Humans, Vascular Diseases, Stroke, Aged, Proportional Hazards Models, VLAG, hemostatic factors, business.industry, Proportional hazards model, Confounding, Hazard ratio, Fibrinogen, General Medicine, Middle Aged, medicine.disease, myocardial-infarction, Surgery, relative risk, Relative risk, factor-vii, business, coronary-heart-disease
Abstract

CONTEXT: Plasma fibrinogen levels may be associated with the risk of coronary heart disease (CHD) and stroke. OBJECTIVE: To assess the relationships of fibrinogen levels with risk of major vascular and with risk of nonvascular outcomes based on individual participant data. DATA SOURCES: Relevant studies were identified by computer-assisted searches, hand searches of reference lists, and personal communication with relevant investigators. STUDY SELECTION: All identified prospective studies were included with information available on baseline fibrinogen levels and details of subsequent major vascular morbidity and/or cause-specific mortality during at least 1 year of follow-up. Studies were excluded if they recruited participants on the basis of having had a previous history of cardiovascular disease; participants with known preexisting CHD or stroke were excluded. DATA EXTRACTION: Individual records were provided on each of 154,211 participants in 31 prospective studies. During 1.38 million person-years of follow-up, there were 6944 first nonfatal myocardial infarctions or stroke events and 13,210 deaths. Cause-specific mortality was generally available. Analyses involved proportional hazards modeling with adjustment for confounding by known cardiovascular risk factors and for regression dilution bias. DATA SYNTHESIS: Within each age group considered (40-59, 60-69, and > or =70 years), there was an approximately log-linear association with usual fibrinogen level for the risk of any CHD, any stroke, other vascular (eg, non-CHD, nonstroke) mortality, and nonvascular mortality. There was no evidence of a threshold within the range of usual fibrinogen level studied at any age. The age- and sex- adjusted hazard ratio per 1-g/L increase in usual fibrinogen level for CHD was 2.42 (95% confidence interval [CI], 2.24-2.60); stroke, 2.06 (95% CI, 1.83-2.33); other vascular mortality, 2.76 (95% CI, 2.28-3.35); and nonvascular mortality, 2.03 (95% CI, 1.90-2.18). The hazard ratios for CHD and stroke were reduced to about 1.8 after further adjustment for measured values of several established vascular risk factors. In a subset of 7011 participants with available C-reactive protein values, the findings for CHD were essentially unchanged following additional adjustment for C-reactive protein. The associations of fibrinogen level with CHD or stroke did not differ substantially according to sex, smoking, blood pressure, blood lipid levels, or several features of study design. CONCLUSIONS: In this large individual participant meta-analysis, moderately strong associations were found between usual plasma fibrinogen level and the risks of CHD, stroke, other vascular mortality, and nonvascular mortality in a wide range of circumstances in healthy middle-aged adults. Assessment of any causal relevance of elevated fibrinogen levels to disease requires additional research.

Published
2005

70. Comparing the performance of 11 crop simulation models in predicting yield response to nitrogen fertilization

Author

SALO, T. J., primary, PALOSUO, T., additional, KERSEBAUM, K. C., additional, NENDEL, C., additional, ANGULO, C., additional, EWERT, F., additional, BINDI, M., additional, CALANCA, P., additional, KLEIN, T., additional, MORIONDO, M., additional, FERRISE, R., additional, OLESEN, J. E., additional, PATIL, R. H., additional, RUGET, F., additional, TAKÁČ, J., additional, HLAVINKA, P., additional, TRNKA, M., additional, and RÖTTER, R. P., additional

Published
2015
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71. Low-dose aspirin in prevention of miscarriage in women with unexplained or autoimmune related recurrent miscarriage: effect on prostacyclin and thromboxane A2 production

Author

M Marttunen, Olavi Ylikorkala, Tuija Foudila, Palosuo T, Maija Tulppala, Viveca Söderström-Anttila, and Ailus K

Subjects
Adult, Blood Platelets, Abortion, Habitual, medicine.medical_specialty, Prostacyclin, Autoimmune Diseases, Excretion, Thromboxane A2, chemistry.chemical_compound, Pregnancy, Internal medicine, Recurrent miscarriage, medicine, Humans, Creatinine, Aspirin, business.industry, Rehabilitation, Obstetrics and Gynecology, medicine.disease, Epoprostenol, Thromboxane B2, Endocrinology, Reproductive Medicine, chemistry, Female, lipids (amino acids, peptides, and proteins), business, medicine.drug
Abstract

Early pregnancies in women with a history of recurrent spontaneous abortion (RSA) are accompanied by a deficiency in vasodilatory and anti-aggregatory prostacyclin (PGI2) and/or overproduction of its endogenous antagonist thromboxane A2 (TXA2). We evaluated the effect of a low-dose aspirin (LDA) on PGI2 and TXA2 production and on pregnancy outcome in RSA women with and without detectable anticardiolipin antibodies (ACA). Of 82 RSA women studied, 66 became pregnant, and of them, 33 (six with elevated and 27 with normal ACA concentrations) were randomized to receive LDA (50 mg/day) and 33 (six with elevated and 27 with normal ACA concentrations) to receive placebo (PLA) from a mean of 6.6 days after the missed period to delivery. Treatment with LDA inhibited platelet TXA2 production similarly in RSA women with and without detectable ACA and with continuing pregnancies (7.0 +/- 0.7 ng/ml, LDA group versus 254.5 +/- 37.8 ng/ml, PLA group, mean +/- SEM, P < 0.0001) or miscarrying pregnancies (13.8 +/- 3.8 ng/ml compared with 233.6 +/- 59.8 ng/ml, P < 0.0001 respectively). Furthermore, LDA decreased urinary excretion of the TXA2 metabolite (2,3-dinor-TXB2) both in pregnancies which went to term (6.1 +/- 0.6 ng/mmol creatinine, LDA group versus 19.3 +/- 3.0 ng/mmol creatinine, PLA group, P < 0.0001) or again ended in miscarriage (4.7 +/- 0.8 ng/mmol creatinine versus 17.3 +/- 4.4 ng/mmol creatinine, P < 0.0001 respectively), but did not affect the excretion of the prostacyclin metabolite (2,3-dinor-6-keto-PGF1alpha). Early pregnancy ultrasound examination revealed a living fetus in 58 women. Of these, seven in the LDA group (23.3%, four with elevated and three with normal ACA concentrations) and five in the PLA group (17.9%, two with elevated and three with normal ACA concentrations; not significant) experienced a miscarriage. All infants were healthy, and the frequency of growth retardation was similar in both groups (13.0%). One woman in the LDA group (4.3%) and three women receiving PLA (13.0%) developed pre-eclampsia (not significant). Therefore, although treatment with LDA caused a desirable biochemical effect, it did not improve pregnancy outcome in RSA women with or without detectable ACA.

Published
1997

73. IX Eular Workshop for Rheumatology Research: Molecular biology of autoantigens, autoantibodies and immunopeptides. Vienna, Austria, March 9–12, 1989

Author

Fournier, C., Texier, B., Chiocchia, G., Boissier, M. C., Herbage, D., Brown, C. M. S., Zyberk, C. Plater, Maini, R. N., Palacios, A., Sieper, J., Heinegard, D., Panayi, G. S., Gentric, A., Mackenzie, L., Lydyard, P. M., Youinou, P., Menzel, E. J., Kaik, B., Sykulev, Yu. K., Guschin, A. Je., Vasiljev, V. I., Ostreiko, K. K., Yeronina, T. V., Tumanova, I. A., Moynier, M., Abderrazik, M., Combe, B., Rucheton, M., Brochier, J., Tuomi, T., Palosuo, T., Heliövaara, M., Aho, K., McHugh, N. J., James, I. E., Kallenberg, C. G. M., Tervaert, J. W. Cohen, Goldschmeding, R., Von Dem Borne, A. E. G. K. R., Bouanani, M., Piechaczyk, M., Pau, B., Bastide, M., Le Page, S., Williams, W., Parkhouse, D., Cambridge, G., Isenberg, D. A., Nimmegeers, J., De Keyser, F., Verbruggen, G., Veys, E. M., Walravens M. J. F., Verdeyen I., Vandepol B., Cortens W., Schatteman, L., Goethals, K., Wu, D. -H., Tavoni, A., Neri, R., Garzelli, C., Vitali, C., Bombardieri, S., Logar, D., Kveder, T., Dobovisek, J., Rozman, B., Menard, H. A., Boire, G., Lopez-Longo, F. J., Masson, Ch., Lapointe, S., Clair, E. W. St., Zerva, L., Moutsopoulos, H. M., Keene, J. D., Pisetsky, D. S., Van Dam, A. P., Cuypers, H. T. M., Winkel, I., Smeenk, R. J. T., Taylor, D., Valente, E., Foster, J. P., Williams, D. G., Stocks, M. R., Caporali, R., De Gennaro, F., Cerino, A., Cobianchi, F., Astaldi-Ricotti, G. C. B., Montecucco, C., Habets, W., Sillekens, P. T. G., Hoet, M. H., McAllister, G., Lerner, M. R., Van Venrooij, W. J., Habets, W. J., Van Der Kemp, A., De Jong, B., Scarpa, R., Pucino, A., Di Girolamo, C., della Valle, G., Larizza, G., Casiere, D., Oriente, P., Paimela, L., Palvimo, J., Kurki, P., Hassfeld, W., Steiner, G., Graninger, W., Smolen, J. S., Lopez-Longo, E. J., Larose, A., Hoet, R., Zewald, R., Smeenk, R., Brinkman, K., Van Den Brink, H., Westgeest, A., Huss, R., Krapf, E. F., Herrmann, M., Leitmann, W., Kalden, J. R., Merétey, K., Cebecauer, L., Böhm, U., Kozakova, D., Brózik, M., Temesvári, P., Nagy, L., Bozic, B., Stegnar, M., Vene, N., Peternel, P., Giuggioli, C., Monti, P., Rossi, G., Ferri, C., Chiellini, S., Baboonian, C., Venables, P. J. W., Roffe, L., Booth, J., Krapf, F., Abuljadayel, I., Ebringer, A., Cox, N. L., Brand, S. R., McIntosh, D. P., Bernstein, R. M., Van Den Broek, M. F., Van Bruggen, M. C. J., Smetsers, T., Kuyer, P., Van De Putte, L., Van Den Berg, W. B., Toivanen, A., Jalkanen, S., Lahesmaa-Rantala, R., Isomäki, O., Pekkola-Heino, K., Merilahti-Palo Saario, R., Von Essen, R., Isomäki, H., Granfors, K., Gaston, J. S. H., Life, P. F., Bailey, L., Bacon, P. A., Khalafpour, S., Wilson, C., Awad, J., Toivanen, P., Saario, R., Skurnik, M., Van Der Straeten, C., Mielants, H., Gazic, M., Hartung, K., Riedel, T., Stannat, S., Specker, Ch., Röther, E., Pirner, K., Schendel, D., Baur, M., Corvetta, A., Peter, H. H., Lakomek, H. J., Deicher, H., Andonopoulos, A. P., Papasteriades, C. A., Drosos, A. A., Dimou, G. S., Shattles, W., Venables, P., Charles, P. J., Markwick, J. R., Venables, P. J., Galeazzi, M., Lulli, P., Tuzi, T., Cappellacci, S., Morellini, M., Trabace, S., Cutrupi, F., Sorrentino, R., Botti, S., Iannicola, C., Costanzi, S., Tosi, R., Gospodinoff, A., Eliaou, J. F., Humbert, H., Balaguer, P., Nicolas, J. C., Sany, J., Clot, J., Sakkas, L. I., Bird, H., Welsh, K. I., Pitzalis, C., Kingsley, G., Haskard, D., Vischer, T. L., Bas, S., Werner-Favre, C., Wohlwend, D., Zubler, R. H., Afeltra, A., De Pita, O., Basso, P., Pietrucci, A., Ferri, G. M., Bonomo, L., Gerli, R., Cernetti, C., Bertotto, A., Agea, E., Arcangeli, C., Lanfrancone, L., Rambotti, P., Crupi, S., Baglioni, A., Spinozzi, F., Papazoglou, S., Skoumi, D., Athanasiou, P., Iliopoulos, A., Stavropoulou, A., Kontomerkos, T., Hendrich, G., Kuipers, J. G., Hammer, M., Schmidt, R. E., Manoussakis, M. N., Germandis, G., Zerva, L. V., Siouna-Fatourou, H. J., Katsikis, P. D., Mavridis, A., Toubert, A., Sadouk, M., de la Tour, B., Vaquero, C., Amor, B., Miossec, P., Naviliat, M., Cretien, I., Banchereau, J., Graninger, P., Aschauer, B., Sinski, A., Smolen, J., Krutmann, J., Kirnbauer, R., Köck, A., Schwarz, T., May, L. T., Sehgal, P. B., Luger, T. A., Field, M., Chu, C. Q., Feldmann, M., Wilbrink, B., Nietfeld, J. J., Helle, M., Boeije, L. C. M., Van Roy, J. L. A. M., Den Otter, W., Aarden, L. A., Huber-Bruning, O., Malejczyk, J., Urbanski, A., Malejczyk, M., Karbowski, A., Völker, W., Feige, U., Otter, W. Den, Malfait, A. M., Wieme, N., Gyselbrecht, L., Van de Loo, A. A. J., Van Lent, P. L. E. M., Haskard, D. O., Wellicome, S., Lanchbury, J., Thornhill, M., Krutmann, K., Gschnait, F., Yaron, M., Yaron, I., Dayer, J. -M., Bleiberg, I., Meyer, F. -A., Maury, C. P. J., Teppo, A. -M., Salo, E., Pelkonen, P., Malfait, A., Cochez, Ph., Gruschwitz, M., Müller, P. U., Wick, G., Madhok, R., Wilson, R., Frame, M., Thompson, J., Sturrock, R. D., Partsch, G., Matucci-Cerinic, M., Marabini, S., Jantsch, S., Neumüller, J., Eberl, R., van Beuningen, H. M., Arntz, O. J., Zlabinger, G. J., Steffen, C., Brand, H. S., Van Kampen, G. P. J., De Koning, M. H. M. T., Kiljan, E., Van Der Korst, J. K., Gemmell, C. G., Swaak, A. J. G., Van Rooyen, A., Hall, N. D., Woolf, A. D., Kantharia, B., Maymo, J., Blake, D. R., Goulding, N. J., Maddison, P. J., Munthe, E., Berntzen, H. B., Fagerhol, M., Mathieu, A., Pala, R., Contu, L., Cirillo, R., Garau, P., Nurchis, P., Viberti, G. C., Meyer, O., Zenklusen, C., Le Thi Huong Du, Z., Gaudouen, C., Mery, J. Ph., Ronco, P., Kahn, M. F., Rasmussen, N., Szpirt, W., Thomsen, B., Humbel, R. L., Ter Borg, E. J., Horst, G., Hummel, E., Limburg, P. C., Aeschilmann, A., Bourgeois, P., De Rooij, D. J., Van de Putte, L. B. A., Verbeek, L., Farinaro, C., Infranzi, E., Couret, M., Ackerman, C., De Vlam, K., Carapic, V., Carapic, D., Annefeld, M., Erne, B., Rosenwasser, L. J., Pazoles, C. J., Otterness, I. G., Hanson, D. C., McDonald, B., Loose, L. D., Dougados, M., Machold, K. P., Wiesenberg-Böttcher, I., Wanner, K., Pignat, W., Altmann, H., Tuschl, H., Bröll, H., Balestrieri, G., Tincani, A., Cattaneo, R., Bertoli, M. T., Martinelli, M., Allegro, F., Meroni, P. L., Balesini, G., Aichinger, G., Schlögl, E., Huber, Ch., Shoenfeld, Y., Fleishmaker, E., Mendlovic, S., Mozes, E., Blank, M., Talal, N., Hogervorst, E. J. M., Van Eden, W., Van Der Zee, R., Psychos, D., Dimou, G., Stefanaki-Nikou, S., Papadimitriou, C. S., Settas, L., Alexiou, P., Dimitriadis, G., Mataftsi, E., Soliou, E., Tourkantonis, A., Babic, M., Jeurissen, M. E. C., and Boerbooms, A. MTh

Published
1989
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76. Simulation of spring barley yield variability in different climatic zones of Northern and Central Europe:A comparison of nine crop growth models

Author

Rötter, R P, Palosuo, T, Kersebaum, K C, Angulo, C, Bindi, M, Ewert, F, Ferrise, R, Hlavinka, P, Moriondo, M, Nendel, C, Olesen, Jørgen E, Patil, R, Ruget, F, Takác, J, and Trnka, M

Abstract

In this study, the performance of nine widely used and accessible crop growth simulation models (APES-ACE, CROPSYST, DAISY, DSSAT-CERES, FASSET, HERMES, MONICA, STICS and WOFOST) was compared during 44 growing seasons of spring barley (Hordeum vulgare L.) at seven sites in Northern and Central Europe. The aims of this model comparison were to examine how different process-based crop models perform at multiple sites across Europe when applied with minimal information for model calibration of spring barley at field scale, whether individual models perform better than the multi-model mean, and what the uncertainty ranges are in simulated grain yields. The reasons for differences among the models and how results for barley compare to winter wheat are discussed. Regarding yield estimation, best performing based on the root mean square error (RMSE) were models HERMES, MONICA and WOFOST with lowest values of 1124, 1282 and 1325 (kg ha−1), respectively. Applying the index of agreement (IA), models WOFOST, DAISY and HERMES scored best having highest values (0.632, 0.631 and 0.585, respectively). Most models systematically underestimated yields, whereby CROPSYST showed the highest deviation as indicated by the mean bias error (MBE) (−1159 kg ha−1). While the wide range of simulated yields across all sites and years shows the high uncertainties in model estimates with only restricted calibration, mean predictions from the nine models agreed well with observations. Results of this paper also show that models that were more accurate in predicting phenology were not necessarily the ones better estimating grain yields. Total above-ground biomass estimates often did not follow the patterns of grain yield estimates and, thus, harvest indices were also different. Estimates of soil moisture dynamics varied greatly. In comparison, even though the growing cycle for winter wheat is several months longer than for spring barley, using RMSE and IA as indicators, models performed slightly, but not significantly, better in predicting wheat yields. Errors in reproducing crop phenology were similar, which in conjunction with the shorter growth cycle of barley has higher effects on accuracy in yield prediction. -------------------------------------------------------------------------------- In this study, the performance of nine widely used and accessible crop growth simulation models (APES-ACE, CROPSYST, DAISY, DSSAT-CERES, FASSET, HERMES, MONICA, STICS and WOFOST) was compared during 44 growing seasons of spring barley (Hordeum vulgare L.) at seven sites in Northern and Central Europe. The aims of this model comparison were to examine how different process-based crop models perform at multiple sites across Europe when applied with minimal information for model calibration of spring barley at field scale, whether individual models perform better than the multi-model mean, and what the uncertainty ranges are in simulated grain yields. The reasons for differences among the models and how results for barley compare to winter wheat are discussed. Regarding yield estimation, best performing based on the root mean square error (RMSE) were models HERMES, MONICA and WOFOST with lowest values of 1124, 1282 and 1325 (kg ha−1), respectively. Applying the index of agreement (IA), models WOFOST, DAISY and HERMES scored best having highest values (0.632, 0.631 and 0.585, respectively). Most models systematically underestimated yields, whereby CROPSYST showed the highest deviation as indicated by the mean bias error (MBE) (−1159 kg ha−1). While the wide range of simulated yields across all sites and years shows the high uncertainties in model estimates with only restricted calibration, mean predictions from the nine models agreed well with observations. Results of this paper also show that models that were more accurate in predicting phenology were not necessarily the ones better estimating grain yields. Total above-ground biomass estimates often did not follow the patterns of grain yield estimates and, thus, harvest indices were also different. Estimates of soil moisture dynamics varied greatly. In comparison, even though the growing cycle for winter wheat is several months longer than for spring barley, using RMSE and IA as indicators, models performed slightly, but not significantly, better in predicting wheat yields. Errors in reproducing crop phenology were similar, which in conjunction with the shorter growth cycle of barley has higher effects on accuracy in yield prediction.

Published
2012

79. Assessing adaptive management options to cope with climate change at the farm level

Author

Rötter, R.P., Lehtonen, H., Kahiluoto, J., Helin, J., Palosuo, T., Salo, T., Pavlova, Y., Wolf, J., Carter, T.R., and Ewert, F.

Subjects
climatic change, bedrijfssystemen, landbouw, klimaatadaptatie, Soil Science Centre, climate adaptation, farming systems, Alterra - Centrum Bodem, klimaatverandering, Wageningen Environmental Research, PE&RC, agriculture
Abstract

In recent years, considerable achievements have been made by several European climate research groups in gaining a better understanding of and in developing methodologies and tools for integrated, multiscale analyses of how to adapt agricultural systems to climate change. Efforts are under way to link these advancements with the CGIAR-led program on “Climate Change, Agriculture, and Food Security” (CCAFS) for the developing world. In this context some methodological advancements and findings are presented from Finnish and European collaborative research on integrated assessment and modeling (IAM) of agrifood systems in the context of climate change.

Published
2010

80. Rising temperatures reduce global wheat production

Author

Asseng, S., primary, Ewert, F., additional, Martre, P., additional, Rötter, R. P., additional, Lobell, D. B., additional, Cammarano, D., additional, Kimball, B. A., additional, Ottman, M. J., additional, Wall, G. W., additional, White, J. W., additional, Reynolds, M. P., additional, Alderman, P. D., additional, Prasad, P. V. V., additional, Aggarwal, P. K., additional, Anothai, J., additional, Basso, B., additional, Biernath, C., additional, Challinor, A. J., additional, De Sanctis, G., additional, Doltra, J., additional, Fereres, E., additional, Garcia-Vila, M., additional, Gayler, S., additional, Hoogenboom, G., additional, Hunt, L. A., additional, Izaurralde, R. C., additional, Jabloun, M., additional, Jones, C. D., additional, Kersebaum, K. C., additional, Koehler, A-K., additional, Müller, C., additional, Naresh Kumar, S., additional, Nendel, C., additional, O’Leary, G., additional, Olesen, J. E., additional, Palosuo, T., additional, Priesack, E., additional, Eyshi Rezaei, E., additional, Ruane, A. C., additional, Semenov, M. A., additional, Shcherbak, I., additional, Stöckle, C., additional, Stratonovitch, P., additional, Streck, T., additional, Supit, I., additional, Tao, F., additional, Thorburn, P. J., additional, Waha, K., additional, Wang, E., additional, Wallach, D., additional, Wolf, J., additional, Zhao, Z., additional, and Zhu, Y., additional

Published
2014
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81. Systematically missing confounders in individual participant data meta-analysis of observational cohort studies

Author

Fibrinogen Studies Collaboration, Jackson, D, White, I, Kostis, JB, Wilson, AC, Folsom, AR, Wu, K, Chambless, L, Benderly, M, Goldbourt, U, Willeit, J, Kiechl, S, Yarnell, JW, Sweetnam, PM, Elwood, PC, Cushman, M, Psaty, BM, Tracy, RP, Tybjaerg-Hansen, A, Haverkate, F, de Maat, MP, Thompson, SG, Fowkes, FG, Lee, AJ, Smith, FB, Salomaa, V, Harald, K, Rasi, V, Vahtera, E, Jousilahti, P, D'Agostino, R, Kannel, WB, Wilson, PW, Tofler, G, Levy, D, Marchioli, R, Valagussa, F, Rosengren, A, Wilhelmsen, L, Lappas, G, Eriksson, H, Cremer, P, Nagel, D, Curb, JD, Rodriguez, B, Yano, K, Salonen, J, Nyyssönen, K, Tuomainen, TP, Hedblad, B, Engström, G, Berglund, G, Loewel, H, Koenig, W, Hense, HW, Meade, TW, Cooper, JA, De Stavola, B, Knottenbelt, C, Miller, GJ, Bauer, KA, Rosenberg, RD, Sato, S, Kitamura, A, Naito, Y, Iso, H, Palosuo, T, Ducimetiere, P, Amouyel, P, Arveiler, D, Evans, AE, Ferrieres, J, Juhan-Vague, I, Bingham, A, Schulte, H, Assmann, G, Cantin, B, Lamarche, B, Despres, JP, Dagenais, GR, Tunstall-Pedoe, H, Lowe, GD, Woodward, M, Ben-Shlomo, Y, Davey Smith, G, Palmieri, V, Yeh, JL, Rudnicka, A, Brennan, P, Ridker, P, Rodeghiero, F, Tosetto, A, Shepherd, J, Ford, I, Robertson, M, Brunner, E, Shipley, M, Feskens, EJ, Di Angelantonio, E, Kaptoge, S, Lewington, S, Sarwar, N, Walker, M, Watson, S, White, IR, Wood, AM, and Danesh, J

Abstract

One difficulty in performing meta-analyses of observational cohort studies is that the availability of confounders may vary between cohorts, so that some cohorts provide fully adjusted analyses while others only provide partially adjusted analyses. Commonly, analyses of the association between an exposure and disease either are restricted to cohorts with full confounder information, or use all cohorts but do not fully adjust for confounding. We propose using a bivariate random-effects meta-analysis model to use information from all available cohorts while still adjusting for all the potential confounders. Our method uses both the fully adjusted and the partially adjusted estimated effects in the cohorts with full confounder information, together with an estimate of their within-cohort correlation. The method is applied to estimate the association between fibrinogen level and coronary heart disease incidence using data from 154,012 participants in 31 cohorts

Published
2009

82. A modelling framework for assessing adaptive management options of Finnish agrifood systems to global change

Author

Rötter, R.P., Lehtonen, H., Kahiluoto, H., Palosuo, T., Aakkula, J., Salo, T., Helenius, J., Helin, J., Granlund, K., Rankinen, K., and Carter, T.

Subjects
Environmental aspects
Abstract

The AGRISIMU-project represents a novel approach to integrate data and output from several existing models. While sector models linking agricultural policy, economics and land use have long existed, a spatially explicit treatment of the interactions between land use and impacts on nutrient flows, biodiversity and climate change has not yet been attempted.

Published
2009

83. Systematically missing confounders in individual participant data meta-analysis of observational cohort studies

Author

Jackson, D, White, I, Kostis, JB, Wilson, AC, Folsom, AR, Wu, K, Chambless, L, Benderly, M, Goldbourt, U, Willeit, J, Kiechl, S, Yarnell, JW, Sweetnam, PM, Elwood, PC, Cushman, M, Psaty, BM, Tracy, RP, Tybjaerg-Hansen, A, Haverkate, F, de Maat, MP, Thompson, SG, Fowkes, FG, Lee, AJ, Smith, FB, Salomaa, V, Harald, K, Rasi, V, Vahtera, E, Jousilahti, P, D'Agostino, R, Kannel, WB, Wilson, PW, Tofler, G, Levy, D, Marchioli, R, Valagussa, F, Rosengren, A, Wilhelmsen, L, Lappas, G, Eriksson, H, Cremer, P, Nagel, D, Curb, JD, Rodriguez, B, Yano, K, Salonen, J, Nyyssönen, K, Tuomainen, TP, Hedblad, B, Engström, G, Berglund, G, Loewel, H, Koenig, W, Hense, HW, Meade, TW, Cooper, JA, De Stavola, B, Knottenbelt, C, Miller, GJ, Bauer, KA, Rosenberg, RD, Sato, S, Kitamura, A, Naito, Y, Iso, H, Palosuo, T, Ducimetiere, P, Amouyel, P, Arveiler, D, Evans, AE, Ferrieres, J, Juhan-Vague, I, Bingham, A, Schulte, H, Assmann, G, Cantin, B, Lamarche, B, Despres, JP, Dagenais, GR, Tunstall-Pedoe, H, Lowe, GD, Woodward, M, Ben-Shlomo, Y, Davey Smith, G, Palmieri, V, Yeh, JL, Rudnicka, A, Brennan, P, Ridker, P, Rodeghiero, F, Tosetto, A, Shepherd, J, Ford, I, Robertson, M, Brunner, E, Shipley, M, Feskens, EJ, Di Angelantonio, E, Kaptoge, S, Lewington, S, Sarwar, N, Walker, M, Watson, S, White, IR, Wood, AM, and Danesh, J

Subjects
Statistics and Probability, Male, Nutrition and Disease, Epidemiology, Coronary Disease, Bivariate analysis, Logistic regression, 01 natural sciences, survival analysis, Cohort Studies, 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, Meta-Analysis as Topic, Voeding en Ziekte, Statistics, Medicine, Humans, Computer Simulation, 030212 general & internal medicine, 0101 mathematics, observational studies, patient data, Survival analysis, time, VLAG, Models, Statistical, aggregate data, model, business.industry, Incidence (epidemiology), Confounding, Fibrinogen, confounders, event outcomes, meta-analysis, Meta-analysis, Data Interpretation, Statistical, missing covariates, logistic-regression analysis, Observational study, Female, heterogeneity, business, Demography, Cohort study, Research Article
Abstract

One difficulty in performing meta-analyses of observational cohort studies is that the availability of confounders may vary between cohorts, so that some cohorts provide fully adjusted analyses while others only provide partially adjusted analyses. Commonly, analyses of the association between an exposure and disease either are restricted to cohorts with full confounder information, or use all cohorts but do not fully adjust for confounding. We propose using a bivariate random-effects meta-analysis model to use information from all available cohorts while still adjusting for all the potential confounders. Our method uses both the fully adjusted and the partially adjusted estimated effects in the cohorts with full confounder information, together with an estimate of their within-cohort correlation. The method is applied to estimate the association between fibrinogen level and coronary heart disease incidence using data from 154 012 participants in 31 cohorts.† Copyright © 2009 John Wiley & Sons, Ltd.

Published
2009

84. Reference futures and scenarios for the European forestry wood chain

Author

Arets, E.J.M.M., Palosuo, T., Moiseyev, A., Nabuurs, G.J., Slimani, D., Olsmat, C., Laurijssen, J., Mason, B., McGowan, D., and Vötter, D.

Subjects
future, supply chain management, scenario planning, ketenmanagement, economic development, bos- en houtproductenindustrieën, hout, wood products, Centrum Ecosystemen, Centre for Ecosystem Studies, europa, houtproducten, reference standards, forest products industries, toekomst, Wageningen Environmental Research, europe, economische ontwikkeling, wood, referentienormen
Abstract

The aim of this document is to define, quantify and downscale the reference futures A1 and B2 to European forest wood chain specific input material. The quantified material serves as consistent input to the model runs in the Modules. The full input material is available on the Eforwood portal. Furthermore this document describes the actual scenarios to be used in Eforwood. Scenarios are neither predictions nor forecasts, but are used to create a consistent image of a future. Each storyline assumes a distinctly different direction for future developments, and does not necessarily aim to be realistic. Conclusions should not be drawn from these storylines; nor do they represent an expressed view of the Eforwood consortium on the future of European forests and the European forest industry.

Published
2008

86. Plasma fibrinogen level and the risk of major cardiovascular diseases and nonvascular mortality: an individual participant meta-analysis

Author

Fibrinogen Studies Collaboration, Danesh, John, Lewington, Sarah, Thompson, Simon G, Lowe, Gordon DO, Collins, Rory, Kostis, JB, Wilson, AC, Folsom, AR, Wu, K, Benderly, M, Goldbourt, U, Willeit, J, Kiechl, S, Yarnell, JWG, Sweetnam, PM, Elwood, PC, Cushman, M, Psaty, BM, Tracy, RP, Tybjaerg-Hansen, A, Haverkate, F, de Maat, MPM, Fowkes, FGR, Lee, AJ, Smith, FB, Salomaa, V, Harald, K, Rasi, R, Vahtera, E, Jousilahti, P, Pekkanen, J, D'Agostino, R, Kannel, WB, Wilson, PWF, Tofler, G, Arocha-Piñango, CL, Rodriguez-Larralde, A, Nagy, E, Mijares, M, Espinosa, R, Rodriquez-Roa, E, Ryder, E, Diez-Ewald, MP, Campos, G, Fernandez, V, Torres, E, Marchioli, R, Valagussa, F, Rosengren, A, Wilhelmsen, L, Lappas, G, Eriksson, H, Cremer, P, Nagel, D, Curb, JD, Rodriguez, B, Yano, K, Salonen, JT, Nyyssönen, K, Tuomainen, T-P, Hedblad, B, Lind, P, Loewel, H, Koenig, W, Meade, TW, Cooper, JA, De Stavola, B, Knottenbelt, C, Miller, GJ, Bauer, KA, Rosenberg, RD, Sato, S, Kitamura, A, Naito, Y, Palosuo, T, Ducimetiere, P, Amouyel, P, Arveiler, D, Evans, AE, Ferrieres, J, Juhan-Vague, I, Bingham, A, Schulte, H, Assmann, G, Cantin, B, Lamarche, B, Després, J-P, Dagenais, GR, Tunstall-Pedoe, H, Woodward, M, Ben-Shlomo, Y, Davey Smith, G, Palmieri, V, Yeh, JL, Rudnicka, A, Ridker, P, Rodeghiero, F, Tosetto, A, Shepherd, J, Ford, I, Robertson, M, Brunner, E, Shipley, M, Feskens, EJM, Kromhout, D, Dickinson, A, Ireland, B, Juzwishin, K, Kaptoge, S, Lewington, S, Memon, A, Sarwar, N, Walker, M, Wheeler, J, White, I, and Wood, A

Abstract

CONTEXT: Plasma fibrinogen levels may be associated with the risk of coronary heart disease (CHD) and stroke. OBJECTIVE: To assess the relationships of fibrinogen levels with risk of major vascular and with risk of nonvascular outcomes based on individual participant data. DATA SOURCES: Relevant studies were identified by computer-assisted searches, hand searches of reference lists, and personal communication with relevant investigators. STUDY SELECTION: All identified prospective studies were included with information available on baseline fibrinogen levels and details of subsequent major vascular morbidity and/or cause-specific mortality during at least 1 year of follow-up. Studies were excluded if they recruited participants on the basis of having had a previous history of cardiovascular disease; participants with known preexisting CHD or stroke were excluded. DATA EXTRACTION: Individual records were provided on each of 154,211 participants in 31 prospective studies. During 1.38 million person-years of follow-up, there were 6944 first nonfatal myocardial infarctions or stroke events and 13,210 deaths. Cause-specific mortality was generally available. Analyses involved proportional hazards modeling with adjustment for confounding by known cardiovascular risk factors and for regression dilution bias. DATA SYNTHESIS: Within each age group considered (40-59, 60-69, and > or =70 years), there was an approximately log-linear association with usual fibrinogen level for the risk of any CHD, any stroke, other vascular (eg, non-CHD, nonstroke) mortality, and nonvascular mortality. There was no evidence of a threshold within the range of usual fibrinogen level studied at any age. The age- and sex- adjusted hazard ratio per 1-g/L increase in usual fibrinogen level for CHD was 2.42 (95% confidence interval [CI], 2.24-2.60); stroke, 2.06 (95% CI, 1.83-2.33); other vascular mortality, 2.76 (95% CI, 2.28-3.35); and nonvascular mortality, 2.03 (95% CI, 1.90-2.18). The hazard ratios for CHD and stroke were reduced to about 1.8 after further adjustment for measured values of several established vascular risk factors. In a subset of 7011 participants with available C-reactive protein values, the findings for CHD were essentially unchanged following additional adjustment for C-reactive protein. The associations of fibrinogen level with CHD or stroke did not differ substantially according to sex, smoking, blood pressure, blood lipid levels, or several features of study design. CONCLUSIONS: In this large individual participant meta-analysis, moderately strong associations were found between usual plasma fibrinogen level and the risks of CHD, stroke, other vascular mortality, and nonvascular mortality in a wide range of circumstances in healthy middle-aged adults. Assessment of any causal relevance of elevated fibrinogen levels to disease requires additional research.

Published
2005

88. CO2FIX V 3.1 A modelling framework for quantifying carbon sequestration in forest ecosystems

Author

Schelhaas, M.J., van Esch, P.W., Groen, T.A., de Jong, B.H.J., Kanninen, M., Liski, J., Masera, O., Mohren, G.M.J., Nabuurs, G.J., Palosuo, T., Pedroni, L., Vallejo, A., and Vilén, T.

Subjects
koolstofcyclus, accounting, forest management, simulation models, voorraden, bioenergy, stocks, bio-energie, agroforestry, afforestation, biomass production, carbon cycle, biomassa productie, Bosecologie en Bosbeheer, Wageningen Environmental Research, bebossing, boekhouding, bosbedrijfsvoering, forests, carbon, biobased economy, PE&RC, simulatiemodellen, Forest Ecology and Forest Management, Centrum Ecosystemen, Centre for Ecosystem Studies, Wildlife Ecology and Conservation, koolstof, bossen
Abstract

This report describes the conceptual approach of the CO2FIX V 3.1 model, as well as its implementation and numerous examples. This stand level simulation model is a tool which quantifies the C stocks and fluxes in the forest biomass, the soil organic matter and the wood products chain. Included are also a bioenergy module, a financial module and a carbon accounting module. The model is applicable to many different situations: afforestation projects, agroforestry systems, and selective logging systems. The model is freely available from the web, together with numerous examples. The model has many users. The two earlier versions of the model have been downloaded already almost 2000 times

Published
2004

89. Uncertainty in simulating wheat yields under climate change

Author

Asseng, S., primary, Ewert, F., additional, Rosenzweig, C., additional, Jones, J. W., additional, Hatfield, J. L., additional, Ruane, A. C., additional, Boote, K. J., additional, Thorburn, P. J., additional, Rötter, R. P., additional, Cammarano, D., additional, Brisson, N., additional, Basso, B., additional, Martre, P., additional, Aggarwal, P. K., additional, Angulo, C., additional, Bertuzzi, P., additional, Biernath, C., additional, Challinor, A. J., additional, Doltra, J., additional, Gayler, S., additional, Goldberg, R., additional, Grant, R., additional, Heng, L., additional, Hooker, J., additional, Hunt, L. A., additional, Ingwersen, J., additional, Izaurralde, R. C., additional, Kersebaum, K. C., additional, Müller, C., additional, Naresh Kumar, S., additional, Nendel, C., additional, O’Leary, G., additional, Olesen, J. E., additional, Osborne, T. M., additional, Palosuo, T., additional, Priesack, E., additional, Ripoche, D., additional, Semenov, M. A., additional, Shcherbak, I., additional, Steduto, P., additional, Stöckle, C., additional, Stratonovitch, P., additional, Streck, T., additional, Supit, I., additional, Tao, F., additional, Travasso, M., additional, Waha, K., additional, Wallach, D., additional, White, J. W., additional, Williams, J. R., additional, and Wolf, J., additional

Published
2013
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90. Sensitivities of crop models to extreme weather conditions during flowering period demonstrated for maize and winter wheat in Austria

Author

EITZINGER, J., primary, THALER, S., additional, SCHMID, E., additional, STRAUSS, F., additional, FERRISE, R., additional, MORIONDO, M., additional, BINDI, M., additional, PALOSUO, T., additional, RÖTTER, R., additional, KERSEBAUM, K. C., additional, OLESEN, J. E., additional, PATIL, R. H., additional, ŞAYLAN, L., additional, ÇALDAĞ, B., additional, and ÇAYLAK, O., additional

Published
2012
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91. Changes in time of sowing, flowering and maturity of cereals in Europe under climate change

Author

Olesen, J.E., primary, Børgesen, C.D., additional, Elsgaard, L., additional, Palosuo, T., additional, Rötter, R.P., additional, Skjelvåg, A.O., additional, Peltonen-Sainio, P., additional, Börjesson, T., additional, Trnka, M., additional, Ewert, F., additional, Siebert, S., additional, Brisson, N., additional, Eitzinger, J., additional, van Asselt, E.D., additional, Oberforster, M., additional, and van der Fels-Klerx, H.J., additional

Published
2012
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92. Affinity-purified cardiolipin-binding antibodies show heterogeneity in their binding to oxidized low-density lipoprotein

Author

Aho K, Marjatta Leirisalo-Repo, M. Lukka, Puurunen M, Outi Vaarala, Georg Alfthan, and Palosuo T

Subjects
Adult, Immunology, Phospholipid, Antibody Affinity, 030204 cardiovascular system & hematology, Cross Reactions, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Antigen, Cardiolipin, medicine, Immunology and Allergy, Humans, Aged, Glycoproteins, 030203 arthritis & rheumatology, biology, medicine.diagnostic_test, Original Articles, Middle Aged, 3. Good health, Lipoproteins, LDL, Apolipoproteins, chemistry, beta 2-Glycoprotein I, Immunoassay, Antibodies, Anticardiolipin, biology.protein, Cardiolipin binding, lipids (amino acids, peptides, and proteins), Antibody, Apolipoprotein H, Oxidation-Reduction, Lipoprotein
Abstract

SUMMARY Antiphospholipid antibodies in autoimmune sera have been shown to react with a complex of phospholipids (cardiolipin) and a plasma phospholipid-binding protein, β2-glycoprotein I (apolipoprotein H). The binding of these antibodies was inhibited by oxidized low-density lipoprotein (LDL) in sera from patients with systemic lupus erythematosus (SLE), suggesting cross-reactivity between antiphospholipid antibodies and antibodies binding to oxidized LDL. We purified antiphospholipid antibodies by cardiolipin-polyacrylamide column from seven SLE sera and studied the reactivity of eluted fractions with cardiolipin–β2-glycoprotein I complex and oxidized LDL (malondialdehyde-conjugated LDL) in solid-phase enzyme immunoassay. In four sera the binding of IgG antibodies to cardiolipin–β2-glycoprotein I complex and to oxidized LDL appeared in the same fractions, whereas in three sera reactivities against cardiolipin and oxidized LDL were observed, at least in part, in separate fractions. The binding to solid-phase cardiolipin was dependent on the presence of exogenous β2-glycoprotein I in all fractions. Our findings show that antiphospholipid antibodies are heterogeneous in their binding to oxidized LDL, indicating that these two antibodies may have different subspecificities. Some eluted fractions reacted only with oxidized LDL, and did not show binding to cardiolipin–β2-glycoprotein I complex, suggesting that the lipid part in the antigenic complex might be responsible for the cross-reactivity of these antibodies. Accordingly, the biological functions of antibodies against phospholipid–β2-glycoprotein I complex and antibodies against oxidized LDL may also be different.

Published
1996

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