Descriptor: "comparaison de modèles" / Language: english - Searchworks@Jio Institute Digital Library Search Results

Your search keyword '"comparaison de modèles"' showing total 23 results

Start Over Descriptor "comparaison de modèles" Language english

23 results on '"comparaison de modèles"'

1. Comparison and estimation of different linear and nonlinear lactation curve submodels in random regression analyses on dairy cattle.

Author: Zhou, Xiaojing and Zhang, Jingyan
Subjects: LACTATION, DAIRY cattle, REGRESSION analysis, LEGENDRE'S polynomials, CURVES, MILK yield, GOODNESS-of-fit tests, PHENOTYPES
Abstract: Copyright of Canadian Journal of Animal Science is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Published: 2021
Full Text: View/download PDF

2. A global evaluation of apple flowering phenology models for climate adaptation

Author: Gabriel Berenhauser Leite, Vincent Mathieu, Adnane El Yaacoubi, Rebecca Darbyshire, Jean-Michel Legave, Johann Martínez-Lüscher, Isabelle Farrera, New South Wales Department of Primary Industries (NSW DPI), Faculty of Veterinary and Agricultural Science [Melbourne], University of Melbourne, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut National de la Recherche Agronomique (INRA)-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)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Centre for Horticulture, School of Agriculture, Policy and Development, University of Reading (UOR), Genetics and Crop Improvement Programme, NIAB-EMR, National Institute of Agricultural Botany (NIAB), Empresa de Pesquisa Agropecuária e Extensão Rural de Santa Catarina, Centre de Ballandran, Centre Technique Interprofessionnel des Fruits et Légumes (CTIFL), Faculty of Science, Suez Canal University. Ismailia. Egypt, Architecture et Fonctionnement des Espèces Fruitières [AGAP] (AFEF), 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)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), CAPES/COFECUB cooperation program (Brazil/France) (project number 686/10-2010/2013), PRAD project (France/Morocco) (11/08 − 2011/2013), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-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), and 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)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-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)
Subjects: 0106 biological sciences, Atmospheric Science, 010504 meteorology & atmospheric sciences, comparaison de modèles, [SDE.MCG]Environmental Sciences/Global Changes, pommier, Climate change, Biology, 01 natural sciences, phenology, Heat requirement, adaptation au climat, Temperate climate, modèle phénologique, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, apple tree, date de floraison, Sequential model, modèle séquentiel, Chill overlap model, 0105 earth and related environmental sciences, Global and Planetary Change, Phenology, Chill requirement, Model selection, Global warming, Forestry, golden delicious, phénologie, acclimatization, Chilling requirement, 13. Climate action, Climatology, Adaptation, Agronomy and Crop Science, 010606 plant biology & botany
Abstract: UMR AGAP - équipe AFEF - Architecture et fonctionnement des espèces fruitières; This study presents the first evaluation of apple flowering phenology models using data from 14 sites across the globe. The dataset includes large variability in growing climates, a prerequisite to investigate phenology models for use in climate change applications. Two flowering stages, early and full, were investigated allowing for unique model evaluation based on both statistical performance and biological assumptions. Two overarching phenology models (Sequential and Chill Overlap) and two sub-models of chill (Dynamic and Triangular) and heat (GDH and Sigmoidal) were tested. Flowering times from the different sites illustrated the differing effects of contrasting winter and spring temperatures. Sites with similar springtime temperatures, but different winter temperatures, had different flowering patterns (warmer winter sites flowered later). Across all analyses, results from the Chill Overlap model were better than those from the Sequential model. Of the Chill Overlap models, those fitted with the Triangular or Dynamic chill model and the GDH heat sub-model performed well statistically and met the assumptions of the model across both flowering stages. The mild sites in the analysis were least well represented, regardless of model selection. This global evaluation demonstrated that flowering modelling in temperate fruit trees would progress through appropriate choices of overarching model, sub-models and parameters.
Published: 2017
Full Text: View/download PDF

3. Causes of variation among rice models in yield response to CO2 examined with Free-Air CO2 Enrichment and growth chamber experiments

Author: Yulong Wang, Hiroe Yoshida, Liang Tang, Manuel Marcaida, Yuji Masutomi, Donald S. Gaydon, Roberto Confalonieri, Kenneth J. Boote, Hiroshi Nakagawa, Fulu Tao, Philippe Oriol, Lloyd T. Wilson, Yan Zhu, Samuel Buis, Simone Bregaglio, Xinyou Yin, Jeffrey T. Baker, Soora Naresh Kumar, Françoise Ruget, Lianxin Yang, Jianguo Zhu, Job Fugice, Yubin Yang, Upendra Singh, Tao Li, Toshihiro Hasegawa, Zhao Zhang, Tanguy Lafarge, Hitomi Wakatsuki, Daniel Wallach, Tamon Fumoto, Tohoku Agricultural Research Center, National Agriculture and Food Research Organization (NARO), International Rice Research Institute [Philippines] (IRRI), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Centre for Crop Systems Analysis, Wageningen University and Research [Wageningen] (WUR), National Engineering and Technology Center for Information Agriculture, China Agricultural University (CAU), Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, University of Florida [Gainesville] (UF), USDA-ARS : Agricultural Research Service, Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria (CREA), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Cassandra laboratory, University of Milan, International Fertilizer Development Center (IFDC), Institute for Agro-Environmental Sciences, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Indian Agricultural Research Institute (IARI), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut National de la Recherche Agronomique (INRA)-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)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Institut National de la Recherche Agronomique (INRA), 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), College of Agriculture, Northeast Agricultural University [Harbin], Muscle Shoals, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS), Natural resources institute Finland, 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, Yangzhou University, Texas A&M University System, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University (BNU), State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Sciences, Chinese Academy of Sciences, National Agriculture and Food Research Organization, International Rice Research Institute, Wageningen University and Research Centre [Wageningen] (WUR), Chinese Agricultural University, University of Florida [Gainesville], United States Department of Agriculture - Agricultural Research Service, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Natural Resources Institute Finland, UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Beijing Normal University, Wageningen University and Research Centre [Wageningen] ( WUR ), University of Florida, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria ( CREA ), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes ( EMMAH ), Institut National de la Recherche Agronomique ( INRA ) -Université d'Avignon et des Pays de Vaucluse ( UAPV ), International Fertilizer Development Center ( IFDC ), Commonwealth Scientific and Industrial Research Organisation, Indian Agricultural Research Institute ( IARI ), UMR AGAP, Centre de Coopération Internationale en Recherche Agronomique pour le Développement, AGAP, Université de Montpellier ( UM ), Institut National de la Recherche Agronomique ( INRA ), Institut National de Recherche en Informatique et en Automatique ( Inria ), Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), Chinese Academy of Sciences ( CAS ), and Texas A and M AgriLIFE Research Center at Beaumont
Subjects: 010504 meteorology & atmospheric sciences, lcsh:Medicine, variabilité du rendement, diffusion de co2, 01 natural sciences, F50 - Anatomie et morphologie des plantes, Productivité, modèle de culture, F01 - Culture des plantes, Photosynthèse, lcsh:Science, Milieux et Changements globaux, riz, 2. Zero hunger, Multidisciplinary, élément fertilisant, Ecology, food and beverages, Feuille, Surface foliaire, 04 agricultural and veterinary sciences, PE&RC, [ SDE.MCG ] Environmental Sciences/Global Changes, Pratique culturale, Variation (linguistics), Rendement des cultures, Crops, Agricultural, Crop Physiology, Nitrogen, Climate Change, Yield (finance), comparaison de modèles, [SDE.MCG]Environmental Sciences/Global Changes, F60 - Physiologie et biochimie végétale, Climate change, Soil science, Teneur en azote, Models, Biological, Article, Fertilisation, growth chambers, Life Science, chambre de croissance, Management practices, 0105 earth and related environmental sciences, atmospheric carbon-dioxide, climate change, elevated CO2, environmental variation, leaf-area, oryza-sativa l, simulation model, seasonal change, crop production, biomass growth, Morphologie végétale, Méthode statistique, lcsh:R, Oryza, Modèle de simulation, Carbon Dioxide, Plant Leaves, F61 - Physiologie végétale - Nutrition, 13. Climate action, émission d'azote, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, lcsh:Q, adaptation au changement climatique, Cycle du carbone
Abstract: The CO2 fertilization effect is a major source of uncertainty in crop models for future yield forecasts, but coordinated efforts to determine the mechanisms of this uncertainty have been lacking. Here, we studied causes of uncertainty among 16 crop models in predicting rice yield in response to elevated [CO2] (E-[CO2]) by comparison to free-air CO2 enrichment (FACE) and chamber experiments. The model ensemble reproduced the experimental results well. However, yield prediction in response to E-[CO2] varied significantly among the rice models. The variation was not random: models that overestimated at one experiment simulated greater yield enhancements at the others. The variation was not associated with model structure or magnitude of photosynthetic response to E-[CO2] but was significantly associated with the predictions of leaf area. This suggests that modelled secondary effects of E-[CO2] on morphological development, primarily leaf area, are the sources of model uncertainty. Rice morphological development is conservative to carbon acquisition. Uncertainty will be reduced by incorporating this conservative nature of the morphological response to E-[CO2] into the models. Nitrogen levels, particularly under limited situations, make the prediction more uncertain. Improving models to account for [CO2] × N interactions is necessary to better evaluate management practices under climate change.
Published: 2017
Full Text: View/download PDF

4. Contribution of Crop Models to Adaptation in Wheat

Author: Karine Chenu, Marco Bindi, Senthold Asseng, Scott Chapman, Frank Ewert, Pierre Martre, Bruno Basso, John R. Porter, Queensland Alliance for Agriculture and Food Innovation (QAAFI), University of Queensland [Brisbane], Department of Plant and Environmental Sciences [Copenhagen], Faculty of Science [Copenhagen], University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), 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), Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Queensland Bioscience Precinct, Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Department of Agri-food Production and Environmental Sciences, Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Agricultural and Biological Engineering Department, Purdue University [West Lafayette], Wheat Initiative, Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institute of Crop Science and Resource Conservation, University of Bonn-Division of Plant Nutrition, University of Florence (UNIFI), and 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)
Subjects: Crops, Agricultural, 0106 biological sciences, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, comparaison de modèles, rendement des cultures, [SDE.MCG]Environmental Sciences/Global Changes, Climate change, simulation models, Context (language use), Plant Science, Agricultural engineering, Biology, modèle de simulation, 01 natural sciences, Crop, blé, wheat, Triticum, 2. Zero hunger, business.industry, Crop yield, Simulation modeling, fungi, food and beverages, Global change, 04 agricultural and veterinary sciences, World population, Models, Theoretical, 15. Life on land, crop yield, Adaptation, Physiological, Agronomy, 13. Climate action, Agriculture, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, adaptation au changement climatique, business, modèle de production, 010606 plant biology & botany, triticum sp
Abstract: With world population growing quickly, agriculture needs to produce more with fewer inputs while being environmentally friendly. In a context of changing environments, crop models are useful tools to simulate crop yields. Wheat (Triticum spp.) crop models have been evolving since the 1960s to translate processes related to crop growth and development into mathematical equations. These have been used over decades for agronomic purposes, and have more recently incorporated advances in the modeling of environmental footprints, biotic constraints, trait and gene effects, climate change impact, and the upscaling of global change impacts. This review outlines the potential and limitations of modern wheat crop models in assisting agronomists, breeders, and policymakers to address the current and future challenges facing agriculture.
Published: 2017
Full Text: View/download PDF

5. Similar estimates of temperature impacts on global wheat yield by three independent methods

Author: Frank Ewert, Jakarat Anothai, P. V. Vara Prasad, Davide Cammarano, Curtis D. Jones, Elias Fereres, Margarita Garcia-Vila, Soora Naresh Kumar, Eckart Priesack, Phillip D. Alderman, Andrew J. Challinor, Reimund P. Rötter, Alex C. Ruane, Christian Folberth, Gerrit Hoogenboom, Pierre Martre, Roberto C. Izaurralde, Fulu Tao, Pramod K. Aggarwal, Mohamed Jabloun, Jordi Doltra, Joshua Elliott, Christoph Müller, Bing Liu, Iurii Shcherbak, Jeffrey W. White, Bruno Basso, Senthold Asseng, Pierre Stratonovitch, Peter J. Thorburn, Claas Nendel, Taru Palosuo, Joost Wolf, Ann-Kristin Koehler, Thilo Streck, Jørgen E. Olesen, David B. Lobell, Kurt Christian Kersebaum, Delphine Deryng, L. A. Hunt, Garry O'Leary, Katharina Waha, Giacomo De Sanctis, Daniel Wallach, Yan Zhu, James W. Jones, Elke Stehfest, Mikhail A. Semenov, Christian Biernath, Claudio O. Stöckle, Thomas A. M. Pugh, Matthew P. Reynolds, Enli Wang, Bruce A. Kimball, Erwin Schmid, Iwan Supit, Zhigan Zhao, Michael J. Ottman, Sebastian Gayler, Cynthia Rosenzweig, Ehsan Eyshi Rezaei, Gerard W. Wall, National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricutural University, 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), Potsdam Institute for Climate Impact Research (PIK), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF), Leibniz Association, Center for Climate Systems Research [New York] (CCSR), Columbia University [New York], Computation Institute, Loyola University of Chicago, Department of Environmental Earth System Science and Center on Food Security and the Environment, Stanford University, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut National de la Recherche Agronomique (INRA), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), 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, CGIAR Research Program on Climate Change, Agriculture and Food Security, Borlaug Institute for South Asia, CIMMYT, Consultative Group on International Agricultural Research (CGIAR), Department of Plant and Soil Sciences, Mississippi State University [Mississippi], Department of Plant Science, Faculty of Natural Resources, Prince of Songkla University (PSU), Department of Geological Sciences, University of Oregon [Eugene], W. K. Kellogg Biological Station (KBS), Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Institute of Soil Ecology [Neuherberg] (IBOE), Helmholtz-Zentrum München (HZM), The James Hutton Institute, Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, CGIAR-ESSP Program on Climate Change, Agriculture and Food Security, International Center for Tropical Agriculture, European Commission - Joint Research Centre [Ispra] (JRC), Cantabrian Agricultural Research and Training Centre, Department of Agronomy, Purdue University [West Lafayette], Department of Geography, University of Liverpool, Ecosystem Services and Management Program, International Institute for Applied Systems Analysis (IIASA), Institute of Soil Science and Land Evaluation, University of Hohenheim, AgWeatherNet Program, Washington State University (WSU), 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], US Arid-Land Agricultural Research Center, United States Department of Agriculture, Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research, Landscape & Water Sciences, Department of Environment of Victoria, The School of Plant Sciences, University of Arizona, Natural resources institute Finland, Institute of Ecology, German Research Center for Environmental Health, Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung (IMK-IFU), Karlsruher Institut für Technologie (KIT), School of Geography, Earth and Environmental Sciences [Birmingham], University of Birmingham [Birmingham], Birmingham Institute of Forest Research (BIFoR), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Center for Development Research (ZEF), Environmental Impacts Group, Georg-August-University [Göttingen], Universität für Bodenkultur Wien [Vienne, Autriche] (BOKU), Computational and Systems Biology Department, Rothamsted Research, Biotechnology and Biological Sciences Research Council, Netherlands Environmental Assessment Agency, Department of Biological Systems Engineering, University of Wisconsin-Madison, PPS, WSG and CALM, Wageningen University and Research [Wageningen] (WUR), Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), USDA-ARS, Arid-Land Agricultural Research Center, China Agricultural University (CAU), National High-Tech Research and Development Program of China (2013AA100404), the National Natural Science Foundation of China (31271616, 31611130182, 41571088 and 31561143003), the National Research Foundation for the Doctoral Program of Higher Education of China (20120097110042), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), China Scholarship Council., IFPRI through the Global Futures and Strategic Foresight project, the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), the CGIAR Research Program on Wheat, the Agricultural Model Intercomparison and Improvement Project (AgMIP), Agricultural & Biological Engineering Department, University of Florida [Gainesville], Institute of Crop Science and Resource Conservation, University of Bonn-Division of Plant Nutrition, Stanford University [Stanford], 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), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Prince of Songkla University, Texas A&M AgriLife Research and Extension Center, Natural Resources Institute Finland, Georg-August-Universität Göttingen, Wageningen University and Research Center (WUR), China Agricultural University, Division of Plant Nutrition-University of Bonn, Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), University of Florida, Potsdam Institute for Climate Impact Research ( PIK ), Leibniz Centre for Agricultural Landscape Research, Institute for Landscape Biogeochemistry, Center for Climate Systems Research [New York] ( CCSR ), 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 ), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), Consultative Group on International Agricultural Research ( CGIAR ), W.K. Kellogg Biological Station, Institute of Soil Ecology [Neuherberg] ( IBOE ), Helmholtz-Zentrum München ( HZM ), James Hutton Institute, Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, European Commission - Joint Research Centre [Ispra] ( JRC ), International Institute for Applied Systems Analysis ( IIASA ), Washington State University ( WSU ), Texas A and M University ( TAMU ), Leibniz Centre for Agricultural Landscape Research (ZALF), Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung ( IMK-IFU ), Karlsruher Institut für Technologie ( KIT ), School of Geography, Earth & Environmental Science and Birmingham Institute of Forest Research, University of Birmingham, International Maize and Wheat Improvement Center ( CIMMYT ), Bonn Universität [Bonn], University of Natural Resources and Life Sciences, University of Wisconsin-Madison [Madison], Wageningen University and Research Center ( WUR ), Chinese Academy of Sciences [Beijing] ( CAS ), Commonwealth Scientific and Industrial Research Organisation, Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Université de Toulouse (UT)-Université de Toulouse (UT), Helmholtz Zentrum München = German Research Center for Environmental Health, Natural Resources Institute Finland (LUKE), Georg-August-University = Georg-August-Universität Göttingen, Universität für Bodenkultur Wien = University of Natural Resources and Life [Vienne, Autriche] (BOKU), Biotechnology and Biological Sciences Research Council (BBSRC), and Institute of geographical sciences and natural resources research [CAS] (IGSNRR)
Subjects: 0106 biological sciences, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, [ SDV.BV ] Life Sciences [q-bio]/Vegetal Biology, régression statistique, 010504 meteorology & atmospheric sciences, impact sur le rendement, klim, Atmospheric sciences, 01 natural sciences, incertitude, wheat, uncertainty, [ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences, 2. Zero hunger, changement climatique, Regression analysis, statistical regression, simulation, PE&RC, [ SDE.MCG ] Environmental Sciences/Global Changes, sécurité alimentaire, Plant Production Systems, modèle de récolte, Yield (finance), comparaison de modèles, [SDE.MCG]Environmental Sciences/Global Changes, Climate change, Environmental Science (miscellaneous), Earth System Science, blé, température, Life Science, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, réchauffement climatique, global change, 0105 earth and related environmental sciences, Hydrology, WIMEK, Global temperature, business.industry, Crop yield, Global warming, Climate Resilience, 13. Climate action, Agriculture, Klimaatbestendigheid, Plantaardige Productiesystemen, Environmental science, Leerstoelgroep Aardsysteemkunde, Climate model, business, Social Sciences (miscellaneous), 010606 plant biology & botany
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. 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
Full Text: View/download PDF

6. Model improvements reduce the uncertainty of wheat crop model ensembles under heat stress

Author: Maiorano, Andrea, Martre, Pierre, Asseng, S., Ewert, F., Müller, C., Rötter, R. P., Ruane, A. C., Semenov, M. A., Wallach, Daniel, Wang, E., Alderman, P. D., Kassie, B. T., Biernath, C., Basso, B., Cammarano, D., Challinor, A. J., Doltra, J., Dumont, B., Gayler, S., Kersebaum, Kimball, B. A., Koehler, A. K., Liu, L., O'Leary, G., Olesen, J. E., Ottman, Michael J., Priesack, E., Reynolds, M. P., Eyshi Rezaei, E., Stratonovitch, P., Streck, T., Thorburn, P., Waha, K., Wall, G. W., White, J. W., Zhao, Z., Zhu, Y., 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, Potsdam Institute for Climate Impact Research (PIK), Natural Resources Institute Finland (LUKE), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Computational and Systems Biology Department, Rothamsted Research, 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, Agriculture, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), 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), German Research Center for Environmental Health - Helmholtz Center München (GmbH), Michigan State University [East Lansing], Michigan State University System, University of Leeds, International Center for Tropical Agriculture, Catabrian Agricultural Research and Training Center (CIFA), Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University System-Michigan State University System, Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), ARS/ALARC, United States Department of Agriculture, Nanjing Agricultural University, Landscape & Water Sciences, Department of Environment of Victoria, Department of Agroecology, Aarhus University [Aarhus], The School of Plant Sciences, University of Arizona, Institute of Soil Science and Land Evaluation, University of Hohenheim, and Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research, Leibniz Association (ZALF). DEU.
Subjects: Vegetal Biology, comparaison de modèles, Modélisation et simulation, modèle de simulation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, incertitude, blé, Modeling and Simulation, température, modèle phénologique, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Biologie végétale, modèle de production, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2016

7. Crop yields, soil organic carbon and soil nitrogen content change under climate change

Author: Dumont, B., Basso, B., Shcherbak, I., Asseng, S., Bassu, Simona, Boote, K., Cammarano, D., De Sanctis, Giovanni, Durand, Jean-Louis, Ewert, F., Gayler, S., Grace, P., Grant, R., Kent, J., Martre, Pierre, Nendel, C., Paustian, K., Priesack, E., Ripoche, Dominique, Ruane, A., Thorburn, P., Hatfield, J., Jones, J., Rosenzweig, C., Department of geological sciences, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, 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), Agronomie, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Department of agronomy, University of Florida [Gainesville] (UF), The James Hutton Institute, Joint Research center, European Commission, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, WESS-Water and Earth System Science Competence Cluster, Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Institute for Future Environments, Queensland University of Technology, Natural Resource Ecology Laboratory [Fort Collins] (NREL), Colorado State University [Fort Collins] (CSU), 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), Institute of landscape systems analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of Soil Ecology, Helmholtz-Zentrum München (HZM), Agroclim (AGROCLIM), National Aeronautics and Space Administration, Partenaires INRAE, Ecosystem sciences, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), United States Department of Agriculture (USDA), and Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research, Leibniz Association (ZALF). DEU.
Subjects: blé, maïs, comparaison de modèles, [SDE.MCG]Environmental Sciences/Global Changes, température, conduite de la culture, modèle continu, interaction sol plante climat, Milieux et Changements globaux, co2 atmosphérique, modèle de production, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2016

8. Extraction of α-mangostin from Garcinia mangostana L. using alternative solvents: Computational predictive and experimental studies

Author: Kunnitee Bundeesomchok, Aurore Filly, Farid Chemat, Pharkphoom Panichayupakaranant, Njara Rakotomanomana, Prince of Songkla University (PSU), Sécurité et Qualité des Produits d'Origine Végétale (SQPOV), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Panichayupakaranant, Pharkphoom, and Chemat, Farid
Subjects: food.ingredient, comparaison de modèles, [SDV]Life Sciences [q-bio], Ethyl acetate, biotechnologie verte, Extraction, 01 natural sciences, composé aromatique, Garcinia mangostana, COSMO-RS, chemistry.chemical_compound, food, [SDV.IDA]Life Sciences [q-bio]/Food engineering, Ethyl lactate, [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, Solubility, Dichloromethane, Chromatography, 010405 organic chemistry, Chemistry, 010401 analytical chemistry, Extraction (chemistry), Green solvents, Hansen, extraction par solvant, comparaison de méthodes, fruit, 0104 chemical sciences, Solvent, solubilité, pericarpe, étude statistique, Food Science
Abstract: International audience; This study evaluated the performance of alternative green solvents, i.e. D-limonene, dimethylcarbonate (DMC), ethanol, ethyl acetate, ethyl lactate and methyltetrahydrofuran (MeTHF) compared to the petroleum based dichloromethane, for extraction of alpha-mangostin from Garcinia mangostana pericarps. The Hansen solubility parameters (HSPs) were used to explain the dissolution behavior of the solutes and solvents, and the conductor-like screening model for realistic solvation. The (COSMO-RS), a statistical thermodynamic approach based on the results of quantum chemical calculations for comprehending the dissolving mechanisms were used to predict the extraction prediction. On the basis of the Hansen analysis, dichloromethane was the most suitable solvent for extraction of alpha-mangostin. However, COSMO-RS analysis showed a higher solubility of alpha-mangostin in ethyl lactate, DMC, MeTHF, ethyl acetate and ethanol. Moreover, the experimental studies using a classical reflux extraction followed by a quantitative HPLC analysis of alpha-mangostin showed similar results to the predictive values from the COSMO-RS model. The alpha-mangostin levels extracted by ethyl lactate, DMC, MeTHF, ethyl acetate and ethanol were higher than those using dichloromethane and D-limonene. The results support the potential use of ethyl lactate, DMC, MeTHF, and ethanol as alternative green solvents for the preparation of alpha-mangostin extracts.
Published: 2016
Full Text: View/download PDF

9. 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

10. Assessment and comparison of leaf area modeling approaches for Maize

Author: Timlin, Dennis, Kim, S. -H., Dzotsi, K., Tollenaar, M., Kumudin, S., Yang, H., Maddonni, G., Lizaso, J., Fleisher, D., Tardieu, Francois, Kemanian, A., Quebedeaux, B., Boote, K., Stöckle, C., ARS, University of Washington [Seattle], University of Florida [Gainesville] (UF), Monsanto Company, University of Nebraska System, Universidad de Buenos Aires (UBA), Technical University of Madrid, 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), PennState University, University of Maryland [College Park], University of Maryland System, Washington State University (WSU), and Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research, Leibniz Association (ZALF). DEU.
Subjects: Vegetal Biology, maïs, croissance de la feuille, comparaison de modèles, température, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, surface foliaire, ComputingMilieux_MISCELLANEOUS, Biologie végétale, modélisation
Abstract: International audience
Published: 2016

11. Generic reactive transport codes as flexible tools to integrate soil organic matter degradation models with water, transport and geochemistry in soils

Author: Jacques, Diederik, Gérard, Frederic, Mayer, Uli, Simunek, Jirka, and Leterme, Bertrand
Subjects: géochimie, comparaison de modèles, dégradation de la matière organique, analyse de flux, Géologie appliquée, Applied geology, cycle biogéochimique, modélisation
Abstract: A large number of organic matter degradation, CO2 transport and dissolved organic matter models have been developed during the last decades. However, organic matter degradation models are in many cases strictly hard-coded in terms of organic pools, degradation kinetics and dependency on environmental variables. The scientific input of the model user is typically limited to the adjustment of input parameters. In addition, the coupling with geochemical soil processes including aqueous speciation, pH-dependent sorption and colloid-facilitated transport are not incorporated in many of these models, strongly limiting the scope of their application. Furthermore, the most comprehensive organic matter degradation models are combined with simplified representations of flow and transport processes in the soil system. We illustrate the capability of generic reactive transport codes to overcome these shortcomings. The formulations of reactive transport codes include a physics-based continuum representation of flow and transport processes, while biogeochemical reactions can be described as equilibrium processes constrained by thermodynamic principles and/or kinetic reaction networks. The flexibility of these type of codes allows for straight-forward extension of reaction networks, permits the inclusion of new model components (e.g.: organic matter pools, rate equations, parameter dependency on environmental conditions) and in such a way facilitates an application-tailored implementation of organic matter degradation models and related processes. A numerical benchmark involving two reactive transport codes (HPx and MIN3P) demonstrates how the process-based simulation of transient variably saturated water flow (Richards equation), solute transport (advection-dispersion equation), heat transfer and diffusion in the gas phase can be combined with a flexible implementation of a soil organic matter degradation model. The benchmark includes the production of leachable organic matter and inorganic carbon in the aqueous and gaseous phases, as well as different decomposition functions with first-order, linear dependence or nonlinear dependence on a biomass pool. In addition, we show how processes such as local bioturbation (bio-diffusion) can be included implicitly through a Fickian formulation of transport of soil organic matter. Coupling soil organic matter models with generic and flexible reactive transport codes offers a valuable tool to enhance insights into coupled physico-chemical processes at different scales within the scope of C-biogeochemical cycles, possibly linked with other chemical elements such as plant nutrients and pollutants.
Published: 2016

12. Seasonal and inter-annual dynamics in the stable oxygen isotope compositions of water pools in a temperate humid grassland ecosystem: results from MIBA sampling and MuSICA modelling

Author: Hirl, Regina, Schnyder, Hans, Auerswald, Karl, Vetter, Sylvia, Ostler, Ulrike, Schleip, Inga, Wingate, Lisa, Ogée, Jérôme, Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), School of Biological Sciences, University of Aberdeen, Interactions Sol Plante Atmosphère (UMR ISPA), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), and European Geosciences Union (EGU). DEU.
Subjects: continuum sol plante atmosphère, comparaison de modèles, [SDE.MCG]Environmental Sciences/Global Changes, musica, Milieux et Changements globaux, isotope de l'oxygène, ComputingMilieux_MISCELLANEOUS, prairie humide
Abstract: International audience
Published: 2015

13. Impacts des caractéristiques du peuplement et des cloisonnements sur la biodiversité floristique en forêt de plaine

Author: Wei, Liping, Laboratoire de Biologie des Ligneux et des Grandes Cultures (LBLGC), Institut National de la Recherche Agronomique (INRA)-Université d'Orléans (UO), Université d'Orléans, Frédéric Gosselin, and Frédéric Archaux
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Groupe écologique, Type de peuplement, Soil Moisture, Disturbance, Comparaison de modèles, Soil Compaction, Perturbation, Stand Type, Cloisonnement d’exploitation, Humidité du sol, Surface terrière, Compaction du sol, Equivalence Tests, Model Comparison, Basal Area, Ecological Group
Abstract: Maintaining or improving biodiversity is an important goal of sustainable forest management.Ground flora, which is responsible for most floristic diversity in temperate forests, plays multiple important roles in biodiversity but may be impacted by the increasing mechanisation of forest practices. At stand scale, we investigated in Montargis forest the individual and combined effects of tree stand attributes and skid trail area on ground flora diversity. Tree stand attributes (stand type or basal area) were the best indicators of ground flora diversity, depending on the successional traits or light preference of the species group. The effects of skid trail area were negligible. At finer scale, we studied plant response to skid trail disturbance (represented by subplot on and off skid trails), micro-environmental factors (soil moisture, soil compaction, light) and stand attribute (stand type, basal area). The best models for ecological groups included subplot location, soil moisture or soil compaction, depending on which ecological groups (classified by life form, seed bank persistence, light and moisture requirements) the species belonged to. Stand type as a covariate played a significantly important role in fine-scale diversity pattern. Subplot location was the dominant factor at species level. In conclusion, skid trails had either no impact or a positive impact on ground flora diversity. These results are dependent on the context of Montargis forest (ecological and historical), especially that mechanized harvesting is relatively recent. The employment of heavier machines and increased number of passages is likely to happen. This might induce greater soil compaction and negative effects on plant.; Le maintien ou l'amélioration de la biodiversité est un des objectifs importants de la gestion forestière durable. La flore du sous-bois, qui représente la partie la plus diversifiée de la flore dans les forêts tempérées, joue des rôles écologiques importants. Pourtant, elle pourrait être impactée par l'augmentation de la mécanisation de la gestion forestière. A l'échelle de la parcelle, nous avons étudié en forêt de Montargis les effets simples et combinés de caractéristiques du peuplement et de la surface en cloisonnement sur la diversité floristique du sous-bois (richesse et abondance). Les caractéristiques du peuplement (type de peuplement ou surface terrière des essences à étaient les meilleurs indicateurs de la diversité du sous-bois. La surface des cloisonnements avait un effet négligeable. A plus petite échelle – à l’intérieur du cloisonnement – nous avons étudié la réponse statistique de la diversité du sous-bois à la position dans ou hors du cloisonnement, à des facteurs micro-environnementaux (humidité du sol, compaction du sol, lumière) et aux caractéristiques du peuplement. A cette échelle, les meilleurs modèles incluaient pour les groupes écologiques la position par rapport au cloisonnement, l’humidité du sol et/ou la compaction du sol, selon le groupe écologique considéré. Au niveau espèce, la position par rapport au cloisonnement était le facteur dominant. Globalement, les cloisonnements avaient soit pas d’effet soit un impact positif sur la diversité floristique de sous-bois. Ces résultats ont dépendants du contexte écologique et historique de la forêt de Montargis. L’utilisation d’engins plus lourds ou des passages répétés sur une plus longue période pourraient changer ces conclusions.
Published: 2014

14. Report on model-data comparison and improved model parameterisation

Author: Barioni, Luis Gustavo, Bellocchi, Gianni, Ben Touhami, Haythem, Conant, Rich, Chang, Jinfeng, Pereira Coltri, Priscilla, Hassen, Abubeker, Martin, Raphaël, Silvestri, Silvia, Sicerly, Jason, Tesfamariam, Eyob H., Viovy, Nicolas, Brazilian Agricultural Research Corporation (Embrapa), 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), Institut National de la Recherche Agronomique (INRA), International Livestock Research Institute, Commissariat à l'énergie atomique et aux énergies alternatives (CEA), CEPAGRI, UNICAMP, University of Pretoria (UPSpace), Absent, Contrat : FP7-266018, Financement : UE, Superviseur : Gianni Bellocchi, Type de commande : Commande avec contrat/convention/lettre de saisine, Date de signature : 2011-03-01, 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), Unité de recherche sur l'Ecosystème Prairial (UREP), Universidade Estadual de Campinas = University of Campinas (UNICAMP), and University of Pretoria [South Africa]
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, changement climatique, fourrage, comparaison de modèles, prairie, forage, parametrization, paramétrisation, productivité, data, sense organs, grassland, donnée, global change
Abstract: The goal of task 4.4 of AnimalChange project is to improve a range of models used to assess climate change impacts on the productivity of pastures, feeding crops and animals.
Published: 2014

15. Modeling nitrous oxide emissions from tile-drained winter wheat fields in Central France

Author: Philippe Rochette, Agnès Grossel, Catherine Hénault, Denis Loustau, Pierre Cellier, Guy Richard, Jiangxin Gu, Bernard Nicoullaud, Écologie fonctionnelle et physique de l'environnement (EPHYSE - UR1263), Institut National de la Recherche Agronomique (INRA), Northwest Agriculture and Forestry University, Unité de recherche Science du Sol (USS), Agriculture and Agri-Food [Ottawa] (AAFC), Environnement et Grandes Cultures (EGC), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Département Environnement et Agronomie (DPT_EA), Écologie fonctionnelle et physique de l'environnement (EPHYSE), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, and Département Environnement et Agronomie (DEPT EA)
Subjects: Denitrification, Ammonium nitrate, comparaison de modèles, [SDV]Life Sciences [q-bio], [SDE.MCG]Environmental Sciences/Global Changes, Soil Science, Soil science, engineering.material, Field capacity, chemistry.chemical_compound, biogéochimie, Nitrate, empirical model, process-based model, Milieux et Changements globaux, 2. Zero hunger, tile drainage, 15. Life on land, 6. Clean water, nitrification, Permanent wilting point, chemistry, 13. Climate action, agricultural landscape, Tile drainage, Soil water, engineering, culture d'hiver, Environmental science, N2O reduction, canopy interception, Fertilizer, Agronomy and Crop Science
Abstract: Modeling nitrous oxide (N2O) emissions from agricultural soils is still a challenge due to influences of artificial management practices on the complex interactions between soil factors and microbial activities. The aims of this study were to evaluate the process-based DeNitrification-DeComposition (DNDC, version 9.5) model and modified non-linear empirical Nitrous Oxide Emission (NOEV2) model with weekly N2O flux measurements at eight sites cropped with winter wheat across a tile-drained landscape (around 30-km(2)) in Central France. Adjustments of the model default field capacity and wilting point and the optimum crop production were necessary for DNDC95 to better match soil water content and crop biomass yields, respectively. Multiple effects of varying soil water and nitrate contents on the fraction of N2O emitted through denitrification were added in NOEV2. DNDC95 and NOEV2 successfully predicted background N2O emissions and fertilizer-induced emission peaks at all sites during the experimental period but overestimated the daily fluxes on the sampling dates by 54 and 25 % on average, respectively. Cumulative emissions were slightly overestimated by DNDC95 (4 %) and underestimated by NOEV2 (15 %). The differences between evaluations of both models for daily and cumulative emissions indicate that low frequency measurements induced uncertainty in model validation. Nonetheless, our validations for soil water content with daily resolution suggest that DNDC95 well represented the effect of tile drainage on soil hydrology. The model overestimated soil ammonium and nitrate contents mostly due to incorrect nitrogen partitioning when urea ammonium nitrate solution was applied. The performance of the model would be improved if DNDC included the canopy interception and foliar nitrogen uptake when liquid fertilizer was sprayed over the crops.
Published: 2014
Full Text: View/download PDF

16. GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: Validation and intercomparison with reference products

Author: Jesus Cernicharo, Frédéric Baret, Marie Weiss, Fernando Camacho, Roselyne Lacaze, Instituto de Ciencia de Materiales de Madrid (ICMM-CSIC), Consejo Superior de Investigaciones Científicas [Spain] (CSIC), HYGEOS (SARL), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Camacho, Fernando, Instituto de Ciencia de Materiales de Madrid (ICMM), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), and European Community: n218795
Subjects: Accuracy and precision, 010504 meteorology & atmospheric sciences, couvert végétal, comparaison de modèles, Biome, critère de performance, Soil Science, Magnitude (mathematics), Context (language use), 01 natural sciences, GEOV1, Vegetation variables, Validation, GMES, Land monitoring core service, validation scientifique, fraction of absorbed photosynthetically active radiation (fAPAR), fcover, Fraction (mathematics), Computers in Earth Sciences, Leaf area index, variable climatique, Milieux et Changements globaux, fraction de couvert, 0105 earth and related environmental sciences, Remote sensing, gmes, carte de référence, analyse statistique, fapar, Geology, 04 agricultural and veterinary sciences, resolution spatiale, 15. Life on land, Computer science, LAI, indice de surface foliaire, SeaWiFS, biome, 13. Climate action, Photosynthetically active radiation, Informatique (Sciences cognitives), surveillance de l'environnement, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science
Abstract: International audience; This paper describes the scientific validation of the first version of global biophysical products (i.e., leaf area index, fraction of absorbed photosynthetically active radiation and fraction of vegetation cover), namely GEOV1, developed in the framework of the geoland-2/BioPar core mapping service at 1 km spatial resolution and 10-days temporal frequency. The strategy follows the recommendations of the CEOS/WGCV Land Product Validation for LAI global products validation. Several criteria of performance were evaluated, including continuity, spatial and temporal consistency, dynamic range of retrievals, statistical analysis per biome type, precision and accuracy. The spatial and temporal consistencies of GEOV1 products were assessed by intercomparison with reference global products (MODIS c5, CYCLOPES v3.1, GLOBCARBON v2 LAI, and JRC SeaWIFS FAPAR) over a global network of homogeneous sites (BELMANIP-2) during the 2003–2005 period. The accuracy of GEOV1 was evaluated against a number of available ground reference maps. Our results show that GEOV1 products present reliable spatial distribution, smooth temporal profiles which are stable from year to year, good dynamic range with reliable magnitude for bare areas and dense forests, and optimal performances with ground-based maps. GEOV1 outperforms the quality of reference global products in most of the examined criteria, and constitutes a step forward in the development of consistent and accurate global biophysical variables within the context of the land monitoring core service of GMES.
Published: 2013
Full Text: View/download PDF

17. How errors on meteorological variables impact simulated ecosystem fluxes: a case study for six French sites

Author: Jean-Marc Bonnefond, Fabienne Maignan, Philippe Ciais, Serge Rambal, Nicolas Viovy, Albert Olioso, Jean-Christophe Calvet, Philippe Peylin, Pierre Cellier, Bernard Longdoz, Thomas Eglin, Yue Zhao, Katja Klumpp, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS), 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), Ecologie et Ecophysiologie Forestières [devient SILVA en 2018] (EEF), Institut National de la Recherche Agronomique (INRA)-Université de Lorraine (UL), Écologie fonctionnelle et physique de l'environnement (EPHYSE - UR1263), Institut National de la Recherche Agronomique (INRA), Unité de recherche sur l'Ecosystème Prairial, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Environnement et Grandes Cultures (EGC), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Groupe d'étude de l'atmosphère météorologique (CNRM-GAME), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), ICOS-ATC (ICOS-ATC), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Recherche Agronomique (INRA)-Université Pierre et Marie Curie - Paris 6 (UPMC)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS), Modélisation des Surfaces et Interfaces Continentales (MOSAIC), Écologie fonctionnelle et physique de l'environnement (EPHYSE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), École normale supérieure - Paris (ENS-PSL), 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), Unité de recherche sur l'Ecosystème Prairial (UREP), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), and Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)
Subjects: 010504 meteorology & atmospheric sciences, comparaison de modèles, [SDE.MCG]Environmental Sciences/Global Changes, lcsh:Life, Magnitude (mathematics), Forcing (mathematics), modèle, Atmospheric sciences, 01 natural sciences, Altitude, Ecosystem model, lcsh:QH540-549.5, eddy covariance, Milieux et Changements globaux, Ecology, Evolution, Behavior and Systematics, Earth-Surface Processes, 0105 earth and related environmental sciences, flux de co2, lcsh:QE1-996.5, Vegetation, 04 agricultural and veterinary sciences, 15. Life on land, donnée météorologique, lcsh:Geology, lcsh:QH501-531, 13. Climate action, Climatology, Data analysis, 040103 agronomy & agriculture, Environmental science, Errors-in-variables models, 0401 agriculture, forestry, and fisheries, lcsh:Ecology, Shortwave
Abstract: Times Cited: 1Zhao, Y. Ciais, P. Peylin, P. Viovy, N. Longdoz, B. Bonnefond, J. M. Rambal, S. Klumpp, K. Olioso, A. Cellier, P. Maignan, F. Eglin, T. Calvet, J. C.; We analyze how biases of meteorological drivers impact the calculation of ecosystem CO2, water and energy fluxes by models. To do so, we drive the same ecosystem model by meteorology from gridded products and by meteorology from local observation at eddy-covariance flux sites. The study is focused on six flux tower sites in France spanning across a climate gradient of 7-14 A degrees C annual mean surface air temperature and 600-1040 mm mean annual rainfall, with forest, grassland and cropland ecosystems. We evaluate the results of the ORCHIDEE process-based model driven by meteorology from four different analysis data sets against the same model driven by site-observed meteorology. The evaluation is decomposed into characteristic time scales. The main result is that there are significant differences in meteorology between analysis data sets and local observation. The phase of seasonal cycle of air temperature, humidity and shortwave downward radiation is reproduced correctly by all meteorological models (average R-2 = 0.90). At sites located in altitude, the misfit of meteorological drivers from analysis data sets and tower meteorology is the largest. We show that day-to-day variations in weather are not completely well reproduced by meteorological models, with R-2 between analysis data sets and measured local meteorology going from 0.35 to 0.70. The bias of meteorological driver impacts the flux simulation by ORCHIDEE, and thus would have an effect on regional and global budgets. The forcing error, defined by the simulated flux difference resulting from prescribing modeled instead of observed local meteorology drivers to ORCHIDEE, is quantified for the six studied sites at different time scales. The magnitude of this forcing error is compared to that of the model error defined as the modeled-minus-observed flux, thus containing uncertain parameterizations, parameter values, and initialization. The forcing error is on average smaller than but still comparable to model error, with the ratio of forcing error to model error being the largest on daily time scale (86%) and annual time scales (80%). The forcing error incurred from using a gridded meteorological data set to drive vegetation models is therefore an important component of the uncertainty budget of regional CO2, water and energy fluxes simulations, and should be taken into consideration in up-scaling studies.
Published: 2012
Full Text: View/download PDF

18. Improving biodiversity indicators of sustainable forest management: Tree genus abundance rather than tree genus richness and dominance for understory vegetation in French lowland oak hornbeam forests

Author: Laurent Bergès, Frédéric Gosselin, Richard Chevalier, Stéphane Barbier, Philippe Loussot, Ecosystèmes forestiers (UR EFNO), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Chambre d'agriculture de Seine et Marne, Chambre d'agriculture, Écosystèmes forestiers (UR EFNO), and Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF)
Subjects: 0106 biological sciences, FORET, FORET TEMPEREE, Biodiversity, PEUPLEMENT PUR, Management, Monitoring, Policy and Law, 010603 evolutionary biology, 01 natural sciences, Basal area, ESSENCE FORESTIERE, MODELE, RICHESSE SPECIFIQUE, PEUPLEMENT FORESTIER, PEUPLEMENT MELANGE, INDICATEUR DE DEVELOPPEMENT DURABLE, ABONDANCE D'ESPECE, Dominance (ecology), DIVERSITE FLORISTIQUE, FORET FEUILLUE, DONNEES ORDINALES, SOUS ETAGE FORESTIER, Relative species abundance, Nature and Landscape Conservation, RICHESSE FLORISTIQUE, MODELES BAYESIENS SUR DONNEE DE COMPTAGE, SOUS BOIS, TILIA CORDATA, Ecology, CARPINUS, BIODIVERSITE, TESTS D'EQUIVALENCE, Species diversity, Forestry, Understory, ZONE TEMPEREE, 15. Life on land, BRIE FRANCILIENNE, FEUILLU, QUERCUS, COMPARAISON DE MODELES, Geography, [SDE]Environmental Sciences, Rank abundance curve, Species richness, VEGETATION, 010606 plant biology & botany, FLORE
Abstract: Deux indicateurs de biodiversité associés à la diversité du peuplement arboré sont utilisés en France et en Europe, en l'absence de validation scientifique forte: (1) la richesse en essences ou en genres d'essences, comme un indicateur positif; et (2) l'abondance relative de l'essence dominante ("dominance") comme un indicateur négatif. Nous avons testé le caractère indicateur de ces indicateurs vis-à-vis de la biodiversité floristique du sous-bois, en les comparant à d'autres modèles écologiques, principalement associés à la composition et l'abondance des essences. Nous avons utilisé des modèles statistiques Bayésiens des données d'abondance et de richesse de groupes écologiques, définis sur la base du statut successionnel des espèces ou de leur tolérance à l'ombrage. Les distributions de probabilités utilisées dans les modèles sont nouvelles en écologie. Nous avons étudié la magnitude des effets avec des tests d'équivalence. 49 placettes adultes situées dans la région IFN de la Brie Francilienne ont été étudiées. Sur notre jeu de données, les indicateurs basés sur la richesse en genres ou la dominance étaient plus mauvais que les indicateurs basés sur l'abondance des genres d'essences. La magnitude des effets et l'identité du meilleur modèle indicateur variaient d'un groupe écologique à l'autre. Nos résultats montrent par ailleurs l'influence négative de la surface terrière des essences de demi-ombre sur le recouvrement de tous les groupes écologiques d'herbacées et de ligneux et sur la richesse des groupes non-forestiers et péri-forestiers de ligneux et d'herbacées. Comparé aux études du même genre, notre échantillonnage contrôlait fortement le type de station, éliminant ainsi partiellement la confusion potentielle entre les influences de la gestion et du type de station sur la biodiversité. / Two different biodiversity indicators based on tree species diversity are being used, in Europe and France respectively, without strong prior scientific validation: (1) tree species or genus richness as a positive indicator, and (2) relative abundance of the main species (dominance') as a negative indicator. We tested the relevance of these ecological models as indicators of understory vegetation biodiversity by comparing them to other ecological models, mainly related to tree species composition and abundance. We developed Bayesian statistical models for richness and abundance of ecological groups of understory vegetation species, classified according to successional status or shade tolerance. The count data probability distributions in the models were new to ecology. These models were fitted using data from 49 plots in mature lowland forests in the center of France (Bassin Parisien) with similar site conditions. We used equivalence and inequivalence tests to detect negligible and non-negligible effects. Tree genus richness and dominance resulted in models that were worse than ones based on the abundance of tree genus groups. Furthermore, the only significant results for dominance and tree genus richness were opposite to the ones implicitly assumed in the indicator system. However, the magnitude of the effects and which indicator provided the best statistical model varied among ecological groups of plants. Our results show the negative non-negligible effect of the basal area of undergrowth tree species on the cover of all ecological groups of herbaceous and woody species, and on the species richness of non-forest and peri-forest herbaceous and woody species. Compared to the literature, our sampling design strongly controlled forest and site type, thus removing to some degree the potential confusion between influences on biodiversity of management specific variables and other ecological variables. We discuss our results from both an ecological perspective and in terms of the value of these models as indicators of sustainable management. For example, the best-performing model was a multivariate model, which may be more difficult to explain to forest managers or policy-makers than an indicator simply based on tree genus richness.
Published: 2009
Full Text: View/download PDF

19. La diversité et l'équitabilité des espèces d'arbres sont-elles de bons indicateurs de la diversité floristique ? Etude de cas en chênaies françaises

Author: Gosselin, Frédéric, Barbier, S., Chevalier, Richard, Bergès, Laurent, Balandier, Philippe, Écosystèmes forestiers (UR EFNO), and Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF)
Subjects: [SDE]Environmental Sciences, INDICATEURS, GROUPES ECOLOGIQUES, COMPARAISON DE MODELES
Abstract: International audience; Tree species is one of the main attributes of forest ecosystems, that modifies resources levels (light, nutrients, water), and so, indirectly, understory vegetation composition and diversity. Furthermore, forest managers often apprehend biodiversity through the diversity of tree species. This may explain why two biodiversity indicators based on tree species diversity are used, without strong prior scientific validation, in Europe and France respectively: (1) tree species richness as a positive indicator, and (2) relative abundance of the main species ("dominance") as a negative indicator. We developed Bayesian statistical models to test these indicators for the richness of ecological groups of understory vegetation species, classified according to their successional status and/or their shade tolerance. We compared these models with other ones, based on tree species composition and abundance. We worked in 49 stands of adult lowland forests in the center of France (Bassin Parisien), in a controlled site type. Tree species richness and dominance of the main tree species were not good indicators of understory vegetation species richness. There were better indicators than tree species richness or dominance, such as the abundance of tree species successional groups (pioneers, oaks, shade tolerant trees). The magnitude of the effects however varied among ecological groups of vascular plants. We discuss the meaning and limitation of our results in the light of possible extensions of this approach to bigger data sets. First, we discuss the magnitude and significance of the effects and their relation to the sampling scheme. Second, the best model is a multivariate model which is more difficult to communicate to forest managers or policy-makers than an indicator simply based on tree species richness. Lastly, our approach deals only with plant biodiversity; other taxa should also be considered.; La diversification des essences arborées en forêt est préconisée pour plusieurs raisons, parmi lesquelles l'augmentation de la diversité végétale. Cet effet a priori positif sur la flore du sous-bois n'a cependant pas été beaucoup étudié. La diversité en essences a plusieurs composantes. Parmi les quelques études disponibles qui se sont intéressées à l'effet de la diversité (richesse ou indice de shannon) en essences sur la diversité des strates herbacées ou muscinales, certaines montrent un effet positif. L'effet négatif d'essences de sous-étage telles que le charme a été montré, mais on manque de données sur l'effet du degré de mélange d'essences arborées, en particulier en France. Les principaux indicateurs de biodiversité basés sur la diversité en essences sont la richesse en essences en elle-même et le niveau de pureté de l'essence dominante (indicateurs de gestion durable 4.1 et 4.1.1, édition 2006, Ministère de l'Agriculture et de la Pêche). Nous avons testé dans des forêts de Brie (peuplements adultes à base de chêne, charme, tilleul, bouleau et tremble) l'effet de la richesse en essences et l'effet de la pureté. Nous distinguons les espèces de la flore (vasculaires et bryophytes) en plusieurs groupes selon leur caractère forestier. Nous avons comparé ces deux indicateurs à d'autres indicateurs potentiels de biodiversité. Nos résultats montrent que ces deux indicateurs ont des effets non significatifs, souvent faibles. Un modèle indicateur, basé sur l'abondance de différents groupes d'essences, apparaît être un meilleur indicateur de biodiversité floristique. Attention, ces résultats ne sont pas forcément généralisables ; des études doivent être menées sur d'autres forêts françaises. Pour le moment, trop peu d'études se sont intéressées à l'effet de la diversité en essences sur la flore. Enfin, d'autres groupes que la flore devraient être étudiés.
Published: 2008

20. How realistic should knowledge diffusion models be?

Author: jean-philippe cointet, Roth, C., Centre de recherche en épistémologie appliquée (CREA), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), École polytechnique (X), and Università degli Studi di Modena e Reggio Emilia (UNIMORE)
Subjects: [SHS.HISPHILSO]Humanities and Social Sciences/History, Philosophy and Sociology of Sciences, Agent-Based Simulation, Complex Systems, Empirical Calibration and Validation, Knowledge Diffusion, Model Comparison, Social Networks, [INFO.INFO-CY]Computer Science [cs]/Computers and Society [cs.CY], réseau social, [SDV]Life Sciences [q-bio], comparaison de modèles, diffusion, diffusion des connaissances, méthode empirique, ComputingMilieux_MISCELLANEOUS, méthode de simulation
Abstract: Knowledge diffusion models typically involve two main features: an underlying social network topology on one side, and a particular design of interaction rules driving knowledge transmission on the other side. Acknowledging the need for realistic topologies and adoption behaviors backed by empirical measurements, it becomes unclear how accurately existing models render real-world phenomena: if indeed both topology and transmission mechanisms have a key impact on these phenomena, to which extent does the use of more or less stylized assumptions affect modeling results? In order to evaluate various classical topologies and mechanisms, we push the comparison to more empirical benchmarks: real-world network structures and empirically measured mechanisms. Special attention is paid to appraising the discrepancy between diffusion phenomena (i) on some real network topologies vs. various kinds of scale-free networks, and (ii) using an empirically-measured transmission mechanism, compared with canonical appropriate models such as threshold models. We find very sensible differences between the more realistic settings and their traditional stylized counterparts. On the whole, our point is thus also epistemological by insisting that models should be tested against simulation-based empirical benchmarks.
Published: 2007

21. Intercomparison of Flow and Transport Models Applied to Vertical Drainage in Cropped Lysimeters

Author: Tao Chen, Georges Vachaud, Wiesław Fiałkiewicz, Dominique Thiéry, Thomas Pütz, Harry Vereecken, Christophe Mouvet, Michael Herbst, Agrosphere Institute, Bureau de Recherches Géologiques et Minières (BRGM) (BRGM), CEA Cadarache, Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Laboratoire d'étude des transferts en hydrologie et environnement (LTHE), Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Bureau de Recherches Géologiques et Minières (BRGM) ( BRGM ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ), Laboratoire d'étude des transferts en hydrologie et environnement ( LTHE ), Centre National de la Recherche Scientifique ( CNRS ) -Institut National Polytechnique de Grenoble ( INPG ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Université Joseph Fourier - Grenoble 1 ( UJF ) -Observatoire des Sciences de l'Univers de Grenoble ( OSUG ), and Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA )
Subjects: Water flow, Modèle MARTHE, [SDE.MCG]Environmental Sciences/Global Changes, 0207 environmental engineering, Soil Science, 02 engineering and technology, Water balance, Evapotranspiration, Vadose zone, Lysimètre, Drainage, Macro, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, 020701 environmental engineering, Water content, Hydrologie, Hydrology, Infiltration, 04 agricultural and veterinary sciences, 15. Life on land, 6. Clean water, Comparaison de modèles, [ SDE.MCG ] Environmental Sciences/Global Changes, Lysimeter, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, [ SDU.STU.HY ] Sciences of the Universe [physics]/Earth Sciences/Hydrology
Abstract: International audience; The vertical water flow, heat flow and transport of the herbicide methabenzthiazuron were monitored for 627 days in lysimeters sampled at a field site close to the research centre Jülich, Germany. During this period the lysimeters were cropped with winter wheat, winter barley and oat. The models TRACE, MARTHE, ANSWERS and MACRO were applied to the lysimeter data with the scope of upscaling local scale process understanding for regional scale. MARTHE and TRACE solve the 3-d Richards' equation for variably saturated water flow. MACRO is a 1-d model based on the Richards' Equation and accounting for preferential flow in the unsaturated zone, while ANSWERS is a regional scale capacity based watershed model. Measurements of soil moisture, evapotranspiration, drainage, soil temperature, pesticide residues and leaching are used for comparison with model results. Although the adopted models differ in terms of model concepts, the use of model performance indices proved a proper simulation of water flow for all models. The heat flow is also well described with ANSWERS, MARTHE and MACRO. Larger deviations were found between model results and measured pesticide transport. An inadequate reproduction of the measured MBT degradation was found for the available model input parameters. A very small amount of MBT leaching, observed in the measurements, was only reproduced with MACRO after strong calibration. In other respects only plant parameters were calibrated. Calibration of the crop conversion factor used for scaling of the potential evapotranspiration was found to be a crucial parameter for the adequate description of the water balance by the models.
Published: 2005
Full Text: View/download PDF

22. Indicateurs, gradients écologiques et sélection de modèles. Cas de la composition et de la richesse en essences comme indicateurs de biodiversité floristique

Author: Gosselin, Frédéric, Barbier, S., Écosystèmes forestiers et paysages (UR EFNO), Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), and Irstea Publications, Migration
Subjects: [SDE] Environmental Sciences, GESTION FORESTIERE, MODELE NON LINEAIRE A EFFET MIXTE, [SDE]Environmental Sciences, GRADIENT ECOLOGIQUE, MODELE STATISTIQUE, MODELE NON-LINEAIRE A EFFET MIXTE, INDICATEUR DE BIODIVERSITE, COMPARAISON DE MODELES
Abstract: Biodiversity is a complex matter related to a developing scientific field. In these conditions, we think both simple and more complex, research related indicators should be tested. We propose that both sets of indicators be dynamically related, and especially insist here on the way complicated indicators, found to be more accurate than currently preferred simple indicators, could be incorporated to a list of simple indicators. We apply the method to a case study. We find that models of species abundance of the herbaceous layer vegetation based on ecological gradients of tree species richness and tree species evenness behave poorer than models based on gradients of tree abundance. We propose two ways to implement these more complicated models into new lists of simple indicators., Dans le cadre de la stratégie pour diminuer l'érosion de biodiversité d'ici 2010, des approches sectorielles sont mises en oeuvre. L'un des outils de ces approches sont des indicateurs de biodiversité, assez souvent basés sur des concepts scientifiques ou des idées généraux. Nous proposons de soumettre ces indicateurs de biodiversité sectoriels à des tests de qualité sur des données réelles. Nous présentons un tel test sur des indicateurs de richesse et de mélange d'essences en milieu forestier pour la biodiversité floristique. Nous trouvons que ces indicateurs expliquent moins bien la biodiversité floristique que des indicateurs d'abondance des essences. Nous discutons comment de telles approches peuvent être intégrées dans les dispositifs d'indicateurs sectoriels.
Published: 2005

23. Modeling study of the interannual variability in global tropospheric hydroxyl radical and methane concentrations over the last two decades

Author: Drevet, Jérôme and Bey, Isabelle
Subjects: vapeur d'eau, interannual variation, methane, model intercomparisons, modélisation globale, comparaison de modèles, terres inondées, hydroxy radical, variation interannuelle, global modeling, radical hydroxyle, tropospheric chemistry, water vapor, méthane, chimie troposphérique
Abstract: Methane (CH4) is a major greenhouse gas whose global warming potential is 23 times more important than carbon dioxide (CO2). CH4 concentrations have steadily increased since the beginning of the industrial era, reaching an unprecedent level (almost 1800 ppb at present times). CH4 is currently considered as a one of the major driver of the climate change and its warming potential has leaded the Kyoto protocol to plan a significative reduction of its emissions. The objective of this thesis is to examine the factors that contribute to the CH4 global budget as well as its year-to-year variations. To that purpose, we have implemented a methane simulation in a global model of chemistry and transport. Conducting a CH4 simulation requires a comprehensive set of interannual varying CH4 emission inventories as well as a year-to-year varying 3-D fields of concentrations of hydroxyl radicals (OH) that is the main sink for CH4. The first part of this thesis examines the OH interannual variation, using results from a "full-chemistry" simulation that accounts for interannual variations in emissions of the main O3 precursors (including carbon monoxide (CO), nitrogen oxides (NOx), and hydrocarbons) as well as the year-to-year variation in meteorology (e.g. atmospheric water vapor content), lightning emissions, and overhead O3 column. We calculated a global OH concentration of 1.22×106 molec cm-3 averaged over the period. We find that global OH concentrations steadily increased during the early 90's by 8% and reached a maximum in 1997. Afterward, concentrations decrease by 5%. We find that changes in water vapor concentrations, anthropogenic emissions of CO and NOx, lightning NOx emissions, and overhead ozone column all play a role in driving the OH year-to-year variations. The relative contributions of these parameters depend on the latitudinal and altitudinal regions of the troposphere. We examine in particular the influence of the 1997-1998 ENSO on the global OH concentrations. We find that the 1997-1998 ENSO resulted in a large increase in OH in the tropical areas and in the extra-tropical areas of the northern hemisphere (largely driven by an increase in water vapor) while it leads to a decrease in OH in the extra-tropical region of the southern hemisphere (that results from changes in the transport pathways that bring CO in the most southern latitudes and deplete OH). Our results (in terms of OH variability) are in contradiction to those found by other methods, especially those using inverse modeling approaches that are based on methyl chloroform (CH3CCl3) observations. This may results from poorly constrained sources. We then seek to implement a comprehensive set of CH4 emissions in our global model. We used anthropogenic CH4 emission data set of anthropogenic emissions from the International Institute of Technology (IIASA) whose emissions vary between 250 and 290 Tg/year between 1990 and 2000. We estimated biomass burning emissions, derived from an inventory of the total annual biomass burned area and emission factors that we constrained with measurements of the isotopic composition of atmospheric methane. This results in a global emissions rate of 60 Tg/year, which is much stronger than most of previous studies. We also developed a wetland scheme that accounts for soil carbon content, wetland fraction areas, soil temperature and humidity (with the three latter parameters varying interannually) and we evaluated the wetland areas with satellite-based estimates. Our global wetland emissions amount to about 150 Tg/year. We find a global CH4 lifetime of 10.6 years, which is in the range of values reported in previous studies. Comparing our results with different set of measurements gives promising results. The model reproduces well the year-to-year variation even if it slightly overestimates concentrations after 2000. By conducting different set of sensitivity simulations, we investigated the role of different parameters on the CH4 variations. We find that the long-term trend in CH4 is driven by a competition between anthropogenic emissions and tropospheric decay. Peaks of growth rate are driven by biomass burning emissions and to a lesser extend, wetland emissions. We find that even if we used increasing anthropogenic emissions during the 90s (that is in contradiction with some previous studies), reconstructing the observed year-to-year variation of CH4 concentrations was possible if one considers an increasing in OH during the 90s.

Catalog

Books, media, physical & digital resources

See catalog results

Searchworks

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources

Refine your results

23 results on '"comparaison de modèles"'

1. Comparison and estimation of different linear and nonlinear lactation curve submodels in random regression analyses on dairy cattle.

2. A global evaluation of apple flowering phenology models for climate adaptation

3. Causes of variation among rice models in yield response to CO2 examined with Free-Air CO2 Enrichment and growth chamber experiments

4. Contribution of Crop Models to Adaptation in Wheat

5. Similar estimates of temperature impacts on global wheat yield by three independent methods

6. Model improvements reduce the uncertainty of wheat crop model ensembles under heat stress

7. Crop yields, soil organic carbon and soil nitrogen content change under climate change

8. Extraction of α-mangostin from Garcinia mangostana L. using alternative solvents: Computational predictive and experimental studies

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

10. Assessment and comparison of leaf area modeling approaches for Maize

11. Generic reactive transport codes as flexible tools to integrate soil organic matter degradation models with water, transport and geochemistry in soils

12. Seasonal and inter-annual dynamics in the stable oxygen isotope compositions of water pools in a temperate humid grassland ecosystem: results from MIBA sampling and MuSICA modelling

13. Impacts des caractéristiques du peuplement et des cloisonnements sur la biodiversité floristique en forêt de plaine

14. Report on model-data comparison and improved model parameterisation

15. Modeling nitrous oxide emissions from tile-drained winter wheat fields in Central France

16. GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: Validation and intercomparison with reference products

17. How errors on meteorological variables impact simulated ecosystem fluxes: a case study for six French sites

18. Improving biodiversity indicators of sustainable forest management: Tree genus abundance rather than tree genus richness and dominance for understory vegetation in French lowland oak hornbeam forests

19. La diversité et l'équitabilité des espèces d'arbres sont-elles de bons indicateurs de la diversité floristique ? Etude de cas en chênaies françaises

20. How realistic should knowledge diffusion models be?

21. Intercomparison of Flow and Transport Models Applied to Vertical Drainage in Cropped Lysimeters

22. Indicateurs, gradients écologiques et sélection de modèles. Cas de la composition et de la richesse en essences comme indicateurs de biodiversité floristique

23. Modeling study of the interannual variability in global tropospheric hydroxyl radical and methane concentrations over the last two decades

Catalog

Searchworks

Select search scope, currently: Articles Catalog books, media & more in Jio Institute collections Articles journal articles & other e-resources

Search

Search Constraints

Refine your results

Search Limiters

Topic

Publication Year Range

Publication Type

Database

Publisher

23 results on '"comparaison de modèles"'

Search Results

Catalog

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources