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1. There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and netecosystem exchange varied significantly according to the length of the modeler’s experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in 'trial-and-error' calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler’s assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details

Author: Fabrizio Albanito, David McBey, Matthew Harrison, Pete Smith, Fiona Ehrhardt, Arti Bhatia, Gianni Bellocchi, Lorenzo Brilli, Marco Carozzi, Karen Christie, Jordi Doltra, Christopher Dorich, Luca Doro, Peter Grace, Brian Grant, Joël Léonard, Mark Liebig, Cameron Ludemann, Raphael Martin, Elizabeth Meier, Rachelle Meyer, Massimiliano De Antoni Migliorati, Vasileios Myrgiotis, Sylvie Recous, Renáta Sándor, Val Snow, Jean-François Soussana, Ward N. Smith, Nuala Fitton, Producció Vegetal, Cultius Extensius Sostenibles, University of Aberdeen, School of Biological Sciences, University of Aberdeen, Aberdeen, UK, Tasmanian Institute of Agriculture, University of Tasmania [Hobart, Australia] (UTAS), RITTMO Agroenvironnement (RITTMO), Collège de Direction (CODIR), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Indian Council of Agricultural Research (ICAR), Unité Mixte de Recherche sur l'Ecosystème Prairial - UMR (UREP), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institute for BioEconomy [Sesto Fiorentino] (IBE | CNR), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut de Recerca i Tecnologia Agroalimentàries = Institute of Agrifood Research and Technology (IRTA), Natural Resource Ecology Laboratory [Fort Collins] (NREL), Colorado State University [Fort Collins] (CSU), Texas A and M AgriLife Research, Texas A&M University System, Università degli Studi di Sassari = University of Sassari [Sassari] (UNISS), Queensland University of Technology [Brisbane] (QUT), Ottawa Research and Development Center, Agriculture and Agri-Food (AAFC), Transfrontalière BioEcoAgro - UMR 1158 (BioEcoAgro), Université d'Artois (UA)-Université de Liège-Université de Picardie Jules Verne (UPJV)-Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), USDA-ARS, Northern Plains Agricultural Research Laboratory, Sidney, Montana, Cameron Ludemann Consulting, CSIRO Agriculture and Food (CSIRO), Faculty of Veterinary & Agricultural Sciences, University of Melbourne, Parkville, Victoria, School of Geosciences, University of Edimburgh, Fractionnement des AgroRessources et Environnement (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre for Agricultural Research [Budapest] (ATK), Hungarian Academy of Sciences (MTA), AgResearch Ltd, and Institute of Biological and Environmental Sciences, University of Aberdeen
Subjects: model ensembles, biogeochemical models, model calibration, Nitrogen, [SDE.MCG]Environmental Sciences/Global Changes, Uncertainty, General Chemistry, Carbon, Soil, model intercomparison, climate change, greenhouse gases, AgMIP, Environmental Chemistry, multi-criteria decision-making, Humans, soil carbon, Ecosystem
Abstract: There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler’s experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in “trial-and-error” calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler’s assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details. info:eu-repo/semantics/acceptedVersion
Published: 2022

2. Climate change impact and adaptation for wheat protein

Author: Mohamed Jabloun, Pierre Martre, Garry O'Leary, Dominique Ripoche, Bruno Basso, Pramod K. Aggarwal, Daniel Wallach, Matthew P. Reynolds, Marijn van der Velde, John R. Porter, Heidi Webber, Enli Wang, Frank Ewert, Joost Wolf, Christian Klein, Belay T. Kassie, Christian Biernath, Margarita Garcia-Vila, M. Ali Babar, Pierre Stratonovitch, Yujing Gao, Glenn J. Fitzgerald, Davide Cammarano, Bing Liu, Peter J. Thorburn, Fulu Tao, Andrew J. Challinor, Reimund P. Rötter, Christine Girousse, Zhigan Zhao, Christoph Müller, Ann-Kristin Koehler, Jørgen E. Olesen, Elias Fereres, Iwan Supit, Andrea Maiorano, Marco Bindi, Sebastian Gayler, Kurt Christian Kersebaum, Giacomo De Sanctis, Alex C. Ruane, Rosella Motzo, Juraj Balkovic, Manuel Montesino San Martin, Roberto Ferrise, Mikhail A. Semenov, Claudio O. Stöckle, Soora Naresh Kumar, Gerrit Hoogenboom, Benjamin Dumont, Ehsan Eyshi Rezaei, Mukhtar Ahmed, Senthold Asseng, Thilo Streck, Yan Zhu, R. Cesar Izaurralde, Katharina Waha, Ahmed M. S. Kheir, Taru Palosuo, Liujun Xiao, Sara Minoli, Eckart Priesack, Heidi Horan, Curtis D. Jones, Francesco Giunta, Zhao Zhang, Claas Nendel, International Food Policy Research Institute (US), CGIAR (France), European Commission, Institut National de la Recherche Agronomique (France), National Natural Science Foundation of China, Federal Ministry of Food and Agriculture (Germany), Biotechnology and Biological Sciences Research Council (UK), Innovation Fund Denmark, China Scholarship Council, Ministero delle Politiche Agricole Alimentari e Forestali, Academy of Finland, Finnish Ministry of Agriculture and Forestry, Federal Ministry of Education and Research (Germany), Department of Agriculture and Water Resources (Australia), University of Melbourne, Grains Research and Development Corporation (Australia), National Institute of Food and Agriculture (US), German Research Foundation, Gorgan 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), 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), European Food Safety Authority = Autorité européenne de sécurité des aliments, Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), Georg-August-University = Georg-August-Universität Göttingen, Centre for Biodiversity and Sustainable Land-use [University of Göttingen] (CBL), Department of Economic Drt and Resources, Grains Innovation Park, Agriculture Victoria Research, Department of Economic Development, Jobs, Transport and Resources, Faculty of Veterinary and Agricultural Science [Melbourne], Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Department of Agricultural Sciences, University of Naples Federico II = Università degli studi di Napoli Federico II, World Food Crops Breeding, Department of Agronomy, IFAS, University of Florida [Gainesville] (UF), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Soils, Water and Environment Research Institute, Agricultural Research Center (ARC), Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), International Maize and Wheat Improvement Centre [Inde] (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Biological Systems Engineering, University of Wisconsin-Madison, Department of Agronomy, University of El-Tarf, Ecosystem Services and Management Program, International Institute for Applied Systems Analysis (IIASA), Department of Soil Science, Faculty of Natural Sciences, Comenius University in Bratislava, W. K. Kellogg Biological Station (KBS), Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Department of Earth and Environmental Sciences [Ann Arbor], University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, Institute of Biochemical Plant Pathology, Research Center for Environmental Health, Helmholtz Zentrum München = German Research Center for Environmental Health, Department of Agri‐food Production and Environmental Sciences (DISPAA), Università degli Studi di Firenze = University of Florence (UniFI), 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, Collaborative Research Program from CGIAR and Future Earth on Climate Change, Agriculture and Food Security (CCAFS), International Center for Tropical Agriculture, GMO Unit, European Food Safety Authority, Department Terra & AgroBioChem, Gembloux Agro‐Bio Tech, Université de Liège, Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Department of Crop Sciences, Instituto de Agricultura Sostenible - Institute for Sustainable Agriculture (IAS CSIC), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Institute of Soil Science and Land Evaluation, University of Hohenheim, Food Systems Institute [Gainesville] (UF|IFAS), Department of Geographical Sciences, College Park, 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), Institute of Biochemical Plant Pathology [Neuherberg], German Research Center for Environmental Health - Helmholtz Center München (GmbH), National Engineering and Technology Center for Information Agriculture, 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, Nanjing Agricutural University, Member of the Leibniz Association, Potsdam Institute for Climate Impact Research (PIK), Department of Plant and Environmental Sciences [Copenhagen], Faculty of Science [Copenhagen], University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), Centre for Environment Science and Climate Resilient Agriculture [New Delhi], Indian Agricultural Research Institute (IARI), Natural Resources Institute Finland (LUKE), Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens (UMR SYSTEM), 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)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), University of Lincoln, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Rothamsted Research, Biotechnology and Biological Sciences Research Council (BBSRC), Water & Food and Water Systems & Global Change Group, Wageningen University and Research [Wageningen] (WUR), Institute of geographical sciences and natural resources research [CAS] (IGSNRR), Chinese Academy of Sciences [Beijing] (CAS), Joint Research Centre (IPTS), Commission Européenne, AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT), CSIRO Agriculture and Food (CSIRO), Plant Production Systems, State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University (BNU), Department of Agronomy and Biotechnology, China Agricultural University (CAU), National Research Foundation for the Doctoral Program of Higher Education of China, Grant/Award Number: 20120097110042, International Food Policy, European Project: 267196,EC:FP7:PEOPLE,FP7-PEOPLE-2010-COFUND,AGREENSKILLS(2012), 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), Georg-August-University [Göttingen], Centre of Biodiversity and Sustainable Land Use (CBL), University of Göttingen - Georg-August-Universität Göttingen, University of Naples Federico II, Helmholtz-Zentrum München (HZM), Universtiy of Florence, University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), Natural Resources Institute Finland, 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), Wageningen University, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Agricultural & Biological Engineering Department, University of Florida [Gainesville], Georg-August-Universität Göttingen, University of Goettingen, Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Research Program on Climate Change, Agriculture and Food Security, BISA‐CIMMYT, Consultative Group on International Agricultural Research (CGIAR), Comenius University [Bratislava], Helmholtz Zentrum München, Institute of Crop Science and Resource Conservation INRES, University of Bonn, IAS‐CSIC, Universidad de Cordoba, Institute for Sustainable Food Systems, Texas A&M AgriLife Research and Extension Center, Aarhus University, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), UE Agroclim (UE AGROCLIM), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Wageningen University and Research Center (WUR), Beijing Normal University, and China Agricultural University
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 0106 biological sciences, 010504 meteorology & atmospheric sciences, Water en Voedsel, 01 natural sciences, grain protein, adaptation au milieu, climate change adaptation, climate change impact, food security, wheat, Co2 concentration, adaptation to the environment, Triticum, General Environmental Science, 2. Zero hunger, changement climatique, Global and Planetary Change, Food security, Ecology, Temperature, food and beverages, Adaptation, Physiological, Droughts, Nitrogen, Climate Change, Climate change, 010603 evolutionary biology, blé, Crop production, Food Quality, Grain quality, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Environmental Chemistry, Grain Proteins, global change, 0105 earth and related environmental sciences, WIMEK, Water and Food, Global change, Carbon Dioxide, Models, Theoretical, 15. Life on land, Agronomy, 13. Climate action, Grain yield, Environmental science, Water Systems and Global Change, Protein concentration
Abstract: Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32‐multi‐model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low‐rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2. Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1 percentage points, representing a relative change of −8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production., B.L received support from the International Food Policy Research Institute (IFPRI) through the Global Futures and Strategic Foresight project, the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and the CGIAR Research Program on Wheat. A.M. received support from the EU Marie Curie FP7 COFUND People Programme, through an AgreenSkills fellowship under grant agreement no. PCOFUND‐GA‐2010‐267196. PM, A.M., D.R., and D.W. acknowledge support from the FACCE JPI MACSUR project (031A103B) through the metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA). L.X. and Y.Z. were supported by the National High‐Tech Research and Development Program of China (2013AA100404), the National Natural Science Foundation of China (31271616), the National Research Foundation for the Doctoral Program of Higher Education of China (20120097110042), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). F.T. and Z.Z. were supported by the National Natural Science Foundation of China (41571088, 41571493 and 31561143003). R.R. received support from the German Ministry for Research and Education (BMBF) through project SPACES‐LLL. Rothamsted Research receives support from the Biotechnology and Biological Sciences Research Council (BBSRC) Designing Future Wheat programme [BB/P016855/1]. M.J. and J.E.O. were supported by Innovation Fund Denmark through the MACSUR project. L.X. and Y.G. acknowledge support from the China Scholarship Council. M.B and R.F. were funded by JPI FACCE MACSUR2 through the Italian Ministry for Agricultural, Food and Forestry Policies and thank A. Soltani from Gorgan Univ. of Agric. Sci. & Natur. Resour. for his support. R.P.R., T.P., and F.T. received financial support from the FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry (MMM) and from the Academy of Finland through the projects NORFASYS (decision nos. 268277 and 292944) and PLUMES (decision nos. 277403 and 292836). K.C.K. and C.N. received support from the German Ministry for Research and Education (BMBF) within the FACCE JPI MACSUR project. S.M. and C.M. acknowledge financial support from the MACMIT project (01LN1317A) funded through BMBF. G.J.O. and G.J.F. acknowledge support from the Victorian Department of Economic Development, Jobs, Transport and Resources, the Australian Department of Agriculture and Water Resources, The University of Melbourne and the Grains Research Development Corporation, Australia. P.K.A.'s work was implemented as part of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), which is carried out with support from the CGIAR Trust Fund and through bilateral funding agreements. For details please visit https://ccafs.cgiar.org/donors. The views expressed in this document cannot be taken to reflect the official opinions of these organizations.. B.B. received financial support from USDA NIFA‐Water Cap Award 2015‐68007‐23133. F.E. acknowledges support from the FACCE JPI MACSUR project through the German Federal Ministry of Food and Agriculture (2815ERA01J) and from the German Science Foundation (project EW 119/5‐1).
Published: 2019

3. Registration of tropical populations of maize selected in parallel for early flowering time across the United States

Author: Natalia de Leon, Wenwei Xu, Seth C. Murray, Teclemariam Weldekidan, Nicole Choquette, Randall J. Wisser, Nick Lauter, James B. Holland, Major M. Goodman, Sherry Flint-Garcia, Heather C. Manching, University of Delaware [Newark], North Carolina State University [Raleigh] (NC State), University of North Carolina System (UNC), University of Wisconsin-Madison, USDA-ARS : Agricultural Research Service, University of Missouri [Columbia] (Mizzou), University of Missouri System, Texas A&M University [College Station], Texas A and M AgriLife Research, Texas A&M University System, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), 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)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Subjects: 0106 biological sciences, 2. Zero hunger, 0303 health sciences, 03 medical and health sciences, [SDV.BV.AP]Life Sciences [q-bio]/Vegetal Biology/Plant breeding, Agronomy, Genetics, Biology, Flowering time, 01 natural sciences, Agronomy and Crop Science, 030304 developmental biology, 010606 plant biology & botany
Abstract: International audience; Tropical strains of maize (Zea mays subsp. mays L.) flower very late in temperate environments. This is a barrier to maize diversification and improvement in regions where a large share of the world's corn production takes place. For investigating early flowering time adaptation, a tightly controlled parallel selection experiment spanning a 28 degrees latitudinal range (similar to 3,100 km) across the United States was conducted. First, a tropical synthetic population (TropicS-G0) (Reg. no. GP-605, PI 698625) of maize was created from seven inbred parents. The molecular genetic diversity in TropicS-G0 is representative of tropical inbreds that are differentiated from the prevailing germplasm used for hybrid production in the United States. Admixture analysis and genome simulation showed that breeding of TropicS-G0 captured the parental genomes mostly at random, as intended prior to selection. With TropicS-G0 as a common base population, a standardized protocol was used to recurrently select for early flowering time at eight locations for two generations, giving rise to location-specific lineages (TropicS-G1-PR, Reg. no. GP-621, PI 698641; TropicS-G2-PR, Reg. no. GP-622, PI 698642; TropicS-G2-FL, Reg. no. GP-620, PI 698640; TropicS-G1-cTX, Reg. no. GP-618, PI 698638; TropicS-G2-cTX, Reg. no. GP-619, PI 698639; TropicS-G1-nTX, Reg. no. GP-616, PI 698636; TropicS-G2-nTX, Reg. no. GP-617, PI 698637; TropicS-G1-NC, Reg. no. GP-614, PI 698634; TropicS-G2-NC, Reg. no. GP-615, PI 698635; TropicS-G1-DE, Reg. no. GP-610, PI 698630; TropicS-G1-IA, Reg. no. GP-608, PI 698628; TropicS-G2-IA, Reg. no. GP-609, PI 698629; TropicS-G1-WI, Reg. no. GP-606, PI 698626; TropicS-G2-WI, Reg. no. GP-607, PI 698627). Additional generations of selection were performed for the DE lineage (TropicS-G3-DE, Reg. no. GP-611, PI 698631; TropicS-G4-DE, Reg. no. GP-612, PI 698632; TropicS-G5-DE, Reg. no. GP-613, PI 698633). The parallel-selected maize population is a novel resource for breeders and those seeking to investigate adaptation.
Published: 2021

4. Global wheat production with 1.5 and 2.0°C above pre‐industrial warming

Author: Mónica Espadafor, Bing Liu, Frank Ewert, Mukhtar Ahmed, Liujun Xiao, Thilo Streck, Senthold Asseng, Soora Naresh Kumar, Gerrit Hoogenboom, John R. Porter, Sara Minoli, Margarita Garcia-Vila, Joost Wolf, Juraj Balkovic, Alex C. Ruane, Giacomo De Sanctis, Pierre Martre, Roberto C. Izaurralde, Marijn van der Velde, Dominique Ripoche, Roberto Ferrise, Davide Cammarano, Fulu Tao, Bruno Basso, Christoph Müller, Heidi Webber, Yujing Gao, Andrea Maiorano, Christian Klein, Ann-Kristin Koehler, Andrew J. Challinor, Reimund P. Rötter, Garry O'Leary, Manuel Montesino San Martin, Eckart Priesack, Peter J. Thorburn, Heidi Horan, Kurt Christian Kersebaum, Iwan Supit, Zhigan Zhao, Taru Palosuo, Belay T. Kassie, Christian Biernath, Pramod K. Aggarwal, Katharina Waha, Sebastian Gayler, Daniel Wallach, Yan Zhu, Marco Bindi, Zhao Zhang, Claas Nendel, Enli Wang, Curtis D. Jones, Ehsan Eyshi Rezaei, Mikhail A. Semenov, Claudio O. Stöckle, Benjamin Dumont, National Science Foundation (US), National Natural Science Foundation of China, International Food Policy Research Institute (US), CGIAR (France), Institut National de la Recherche Agronomique (France), Federal Ministry of Education and Research (Germany), Biotechnology and Biological Sciences Research Council (UK), China Scholarship Council, Department of Agriculture and Water Resources (Australia), Ministero delle Politiche Agricole Alimentari e Forestali, Gorgan University, Victoria State Government, National Institute of Food and Agriculture (US), Federal Ministry of Food and Agriculture (Germany), German Research Foundation, Academy of Finland, LabEx Agro, Natural Resources Institute Finland, National Engineering and Technology Center for Information Agriculture, 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, Nanjing Agricutural University, 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), Institut für Nutzpflanzenwissenschaften und Ressourcenschutz (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens (UMR SYSTEM), 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)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-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 Copenhagen = Københavns Universitet (KU), Lincoln University, University of Leeds, CGIAR-ESSP Program on Climate Change,Agriculture and Food Security, International Center for Tropical Agriculture, Potsdam Institute for Climate Impact Research (PIK), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), International Maize and Wheat Improvement Centre [Inde] (CIMMYT), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Biological Systems Engineering, Washington State University (WSU), Department of Agronomy, University of El-Tarf, International Institute for Applied Systems Analysis, Ecosystem Services and Management Program, affiliation inconnue, Comenius University in Bratislava, Department of Earth and Environmental Sciences [East Lansing], Michigan State University [East Lansing], Michigan State University System-Michigan State University System, W. K. Kellogg Biological Station (KBS), Institute of Biochemical Plant Pathology (BIOP), German Research Center for Environmental Health - Helmholtz Center München (GmbH), Department of Agrifood Production and Environmental Sciences (DISPAA), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), The James Hutton Institute, GMO Unit, European Food Safety Authority = Autorité européenne de sécurité des aliments, Université de Liège, University of Córdoba, Department of Crop Sciences, Georg-August-University [Göttingen], Institute of Soil Science and Land Evaluation, University of Hohenheim, 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), Food Systems Institute [Gainesville] (UF|IFAS), Department of Geographical Sciences, College Park, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A and M AgriLife Research, Texas A&M University System, European Food Safety Authority (EFSA), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Agriculture Victoria Research, Institute for Natural Resources, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), Centre of Biodiversity and Sustainable Land Use (CBL), University of Göttingen - Georg-August-Universität Göttingen, Rothamsted Research, Wageningen University and Research Centre (WUR), Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), European Commission - Joint Research Centre [Ispra] (JRC), 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, Plant Production Systems, Wageningen University and Research [Wageningen] (WUR), State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University (BNU), Department of Agronomy and Biotechnology, China Agricultural University (CAU), Agricultural Model Intercomparison and Improvement Project (AgMIP), and Biotechnology and Biological Sciences Research Council (BBSRC), Grant/Award Number: BB/, P016855/1
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 0106 biological sciences, 010504 meteorology & atmospheric sciences, Food prices, Water en Voedsel, Climate change, Atmospheric sciences, 010603 evolutionary biology, 01 natural sciences, Model ensemble, Extreme low yields, model ensemble, 1.5°C warming, Temperate climate, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Environmental Chemistry, 0105 earth and related environmental sciences, General Environmental Science, 2. Zero hunger, Global and Planetary Change, WIMEK, Water and Food, Food security, Ecology, business.industry, wheat production, Crop yield, Global warming, food security, 15. Life on land, PE&RC, climate change, Plant Production Systems, 13. Climate action, Agriculture, Plantaardige Productiesystemen, extreme low yields, 8. Economic growth, Environmental science, Wheat production, Water Systems and Global Change, business, Cropping
Abstract: Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by −2.3% to 7.0% under the 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming, We thank the Agricultural Model Intercomparison and Improvement Project (AgMIP) for support. B.L., L.X., and Y.Z. were supported by the National Science Foundation for Distinguished Young Scholars (31725020), the National Natural Science Foundation of China (31801260, 51711520319, and 31611130182), the Natural Science Foundation of Jiangsu province (BK20180523), the 111 Project (B16026), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). S.A. and B.K. received support from the International Food Policy Research Institute (IFPRI) through the Global Futures and Strategic Foresight project, the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), and the CGIAR Research Program on Wheat. P.M, D.R., and D.W. acknowledge support from the FACCE JPI MACSUR project (031A103B) through the metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA). F.T. and Z.Z. were supported by the National Natural Science Foundation of China (41571088, 41571493, 31761143006, and 31561143003). R.R. acknowledges support from the German Federal Ministry for Research and Education (BMBF) through project “Limpopo Living Landscapes” project (SPACES program; grant number 01LL1304A). Rothamsted Research receives grant‐aided support from the Biotechnology and Biological Sciences Research Council (BBSRC) Designing Future Wheat project [BB/P016855/1]. L.X. and Y.G. acknowledge support from the China Scholarship Council. M.B and R.F. were funded by JPI FACCE MACSUR2 through the Italian Ministry for Agricultural, Food and Forestry Policies and thank A. Soltani from Gorgan Univ. of Agric. Sci. & Natur. Resour for his support. K.C.K. and C.N. received support from the German Ministry for Research and Education (BMBF) within the FACCE JPI MACSUR project. S.M. and C.M. acknowledge financial support from the MACMIT project (01LN1317A) funded through BMBF. G.J.O. acknowledges support from the Victorian Department of Economic Development, Jobs, Transport and Resources, the Australian Department of Agriculture and Water Resources. P.K.A. was supported by the multiple donors contributing to the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). B.B. received financial support from USDA NIFA‐Water Cap Award 2015‐68007‐23133. F.E. acknowledges support from the FACCE JPI MACSUR project through the German Federal Ministry of Food and Agriculture (2815ERA01J) and from the German Science Foundation (project EW 119/5‐1). J.R.P. acknowledges the support of the Labex Agro (Agropolis no. 1501‐003). La. T.P. and F.T. received financial support from the Academy of Finland through the project PLUMES (decision nos. 277403 and 292836) and from Natural Resources Institute Finland through the project ClimSmartAgri.
Published: 2019

5. A taxonomy-based approach to shed light on the babel of mathematical models for rice simulation

Author: Simone Bregaglio, Hiroshi Nakagawa, Zhao Zhang, Toshihiro Hasegawa, Roberto Confalonieri, Yubin Yang, Fulu Tao, Samuel Buis, Tanguy Lafarge, Myriam Adam, Balwinder Singh, Tao Li, Hiroe Yoshida, Upendra Singh, Alex C. Ruane, Lloyd T. Wilson, Liang Tang, Kenneth J. Boote, Bas A. M. Bouman, Françoise Ruget, Manuel Marcaida, Yuji Masutomi, Job Fugice, Jeff Baker, Daniel Wallach, Yan Zhu, Marco Acutis, Tamon Fumoto, Xinyou Yin, Donald S. Gaydon, Cassandra Lab, University of Milan, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), 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), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), International Rice Research Institute [Philippines] (IRRI), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), National Institute of Agro-Environmental Sciences (NIAES), Centre for Crop Systems Analysis, Wageningen University and Research [Wageningen] (WUR), 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, University of Florida [Gainesville] (UF), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), National Agriculture and Food Research Organization (NARO), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), International Maize and Wheat Improvement Center (CIMMYT), International Fertilizer Development Center (IFDC), Nanjing Agricultural University, China Academy of Chinese Medicinal Sciences, Natural Resources Institute Finland (LUKE), State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University (BNU), Texas A, USDA-ARS : Agricultural Research Service, Texas A and M AgriLife Research, Texas A&M University System, College of Agriculture, Northeast Agricultural University [Harbin], 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, AgMIP project, Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), 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 international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)
Subjects: 010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], Analyse en composantes (statist), Degrees of freedom (statistics), Binary number, F62 - Physiologie végétale - Croissance et développement, Similarity measure, computer.software_genre, Model classification, Model ensemble, 01 natural sciences, [SHS]Humanities and Social Sciences, Model structure, Limit (mathematics), uncertainty, Mathematics, model classification, Mathematical model, U10 - Informatique, mathématiques et statistiques, Ecological Modeling, Uncertainty, 04 agricultural and veterinary sciences, PE&RC, Classification, model parameterisation, [SDE]Environmental Sciences, Principal component analysis, Modèle mathématique, Développement biologique, model structure, Crop Physiology, Environmental Engineering, Oryza sativa, Machine learning, model ensemble, Taxonomy (general), Model parameterisation, 0105 earth and related environmental sciences, Structure (mathematical logic), business.industry, rice, Modèle de simulation, Taxonomie, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Rice, Artificial intelligence, U30 - Méthodes de recherche, business, computer, Software
Abstract: For most biophysical domains, differences in model structures are seldom quantified. Here, we used a taxonomy-based approach to characterise thirteen rice models. Classification keys and binary attributes for each key were identified, and models were categorised into five clusters using a binary similarity measure and the unweighted pair-group method with arithmetic mean. Principal component analysis was performed on model outputs at four sites. Results indicated that (i) differences in structure often resulted in similar predictions and (ii) similar structures can lead to large differences in model outputs. User subjectivity during calibration may have hidden expected relationships between model structure and behaviour. This explanation, if confirmed, highlights the need for shared protocols to reduce the degrees of freedom during calibration, and to limit, in turn, the risk that user subjectivity influences model performance. A taxonomy-based approach was used to classify AgMIP rice simulation models.Different model structures often resulted in similar outputs.Similar structures often led to large differences in outputs.User subjectivity likely hides relationships between model structure and behaviour.Shared protocols are still needed to limit the risks during calibration.
Published: 2016
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6. The Hot Serial Cereal Experiment for modeling wheat response to temperature: Field experiments and AgMIP-Wheat multi-model simulations

Author: Roberto C. Izaurralde, Jakarat Anothai, Andrew J. Challinor, Reimund P. Rötter, Jørgen E. Olesen, Curtis D. Jones, Bing Liu, T. Palosuo, Peter J. Thorburn, Kurt Christian Kersebaum, Mohamed Jabloun, Iwan Supit, Frank Ewert, Mikhail A. Semenov, Margarita Garcia-Vila, Claudio O. Stöckle, Benjamin Dumont, Belay T. Kassie, Jordi Doltra, Christian Biernath, Dominique Ripoche, Giacomo De Sanctis, Enli Wang, Elias Fereres, Bruce A. Kimball, Thilo Streck, Gerard W. Wall, L. A. Hunt, Pierre Stratonovitch, Fulu Tao, Jeffrey W. White, Christoph Müller, Bruno Basso, Senthold Asseng, Katharina Waha, Ann-Kristin Koehler, Andrea Maiorano, Ehsan Eyshi Rezaei, Eckart Priesack, Soora Naresh Kumar, Claas Nendel, Gerrit Hoogenboom, Davide Cammarano, David B. Lobell, Joost Wolf, Pierre Martre, Pramod K. Aggarwal, Garry O'Leary, Zhigan Zhao, Michael J. Ottman, Sebastian Gayler, Yan Zhu, 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), Agricultural Research Service / US Arid Land Agricultural Research Center, United States Department of Agriculture, The School of Plant Sciences, University of Arizona, Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Consultative Group on International Agricultural Research (CGIAR), AgWeatherNet Program, Washington State University (WSU), Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, German Research Center for Environmental Health - Helmholtz Center München (GmbH), University of Leeds, GMO Unit, European Food Safety Authority = Autorité européenne de sécurité des aliments, Catabrian Agricultural Research and Training Center (CIFA), Universidad de Córdoba [Cordoba], Instituto de Agricultura Sostenible - Institute for Sustainable Agriculture (IAS CSIC), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Institute of Soil Science and Land Evaluation, University of Hohenheim, 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 Association, Potsdam Institute for Climate Impact Research (PIK), Indian Agricultural Research Institute (IARI), 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 Environmental Earth System Science and Center on Food Security and the Environment, Stanford University, Landscape & Water Sciences, Department of Environment of Victoria, Natural Resources Institute Finland (LUKE), Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Computational and Systems Biology Department, Rothamsted Research, Biological Systems Engineering, PPS and WSG &CALM, Wageningen University and Research [Wageningen] (WUR), Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), College of Agronomy and Biotechnology, and Southwest University
Subjects: Simulations, 0106 biological sciences, Irrigation, 010504 meteorology & atmospheric sciences, Water en Voedsel, Wheat, Field experimental data, Heat stress, Crop model simulations, AgMIP, Hot Serial Cereal, 01 natural sciences, donnée expérimentale, Crop, blé, température, Life Science, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Relative humidity, Cultivar, 0105 earth and related environmental sciences, 2. Zero hunger, WIMEK, Water and Food, Vegetal Biology, Global warming, Sowing, essai en plein champ, food and beverages, série climatique, Modélisation et simulation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Agronomy, Plant Production Systems, 13. Climate action, Plantaardige Productiesystemen, Modeling and Simulation, Frost, Weather data, Environmental science, Water Systems and Global Change, stress hydrique, Biologie végétale, modèle de production, 010606 plant biology & botany
Abstract: The data set reported here includes the part of a Hot Serial Cereal Experiment (HSC) experiment recently used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat models and quantify their response to temperature. The HSC experiment was conducted in an open-field in a semiarid environment in the southwest USA. The data reported herewith include one hard red spring wheat cultivar (Yecora Rojo) sown approximately every six weeks from December to August for a two-year period for a total of 11 planting dates out of the 15 of the entire HSC experiment. The treatments were chosen to avoid any effect of frost on grain yields. On late fall, winter and early spring plantings temperature free-air controlled enhancement (T-FACE) apparatus utilizing infrared heaters with supplemental irrigation were used to increase air temperature by 1.3°C/2.7°C (day/night) with conditions equivalent to raising air temperature at constant relative humidity (i.e. as expected with global warming) during the whole crop growth cycle. Experimental data include local daily weather data, soil characteristics and initial conditions, detailed crop measurements taken at three growth stages during the growth cycle, and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models. Data access via doi 10.7910/DVN/M9ZT0F
Published: 2018

7. Multimodel ensembles improve predictions of crop–environment–management interactions

Author: Giacomo De Sanctis, Taru Palosuo, Davide Cammarano, Frank Ewert, Soora Naresh Kumar, Roberto C. Izaurralde, Gerrit Hoogenboom, Elias Fereres, Bing Liu, Thilo Streck, Mikhail A. Semenov, Ehsan Eyshi Rezaei, Peter J. Thorburn, Claudio O. Stöckle, Benjamin Dumont, Andrea Maiorano, Eckart Priesack, Iwan Supit, Heidi Horan, Kurt Christian Kersebaum, Pierre Martre, Margarita Garcia-Vila, Dominique Ripoche, Pierre Stratonovitch, Yujing Gao, Zhao Zhang, Ann-Kristin Koehler, Curtis D Jones, Fulu Tao, Claas Nendel, Christoph Müller, Andrew J. Challinor, Reimund P. Rötter, Mukhtar Ahmed, Senthold Asseng, Christine Girousse, Bruno Basso, Christian Klein, Pramod K. Aggarwal, Joost Wolf, Glenn J. Fitzgerald, Martin K. van Ittersum, Garry O'Leary, Belay T. Kassie, Christian Biernath, Sebastian Gayler, Daniel Wallach, Sara Minoli, 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, 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), Nanjing Agricultural 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), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Plant Production Systems Group, Wageningen University and Research [Wageningen] (WUR), Agriculture and Food Security (CCAFS), Biological Systems Engineering, Washington State University (WSU), Department of Agronomy, University of El-Tarf, Department of Earth and Environmental Sciences [East Lansing], Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Plant Pathology, The James Hutton Institute, University of Leeds, Consultative Group on International Agricultural Research (CGIAR), GMO Unit, European Food Safety Authority = Autorité européenne de sécurité des aliments, Department Terra & AgroBioChem, Gembloux Agro‐Bio Tech, Université de Liège, Center for Development Research, University of Córdoba, Agriculture Victoria Research, University of Melbourne, Institute of Soil Science and Land Evaluation, University of Hohenheim, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), University of Florida [Gainesville] (UF), 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, Institute of landscape systems analysis, School of Earth and Environment, European Food Safety Authority (EFSA), Potsdam Institute for Climate Impact Research (PIK), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Department of Economic Development, Jobs, Transport and Resources (DEDJTR), Natural Resources Institute Finland (LUKE), Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), University Medical Center Göttingen (UMG), Computational and Systems Biology Department, Rothamsted Research, Water & Food and Water Systems & Global Change Group, Wageningen University, Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), Plant Production Systems, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University (BNU), European Project: 267196,EC:FP7:PEOPLE,FP7-PEOPLE-2010-COFUND,AGREENSKILLS(2012), Université de Toulouse (UT)-Université de Toulouse (UT), 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), Nanjing Agricultural University (NAU), Universidad de Córdoba = University of Córdoba [Córdoba], Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), Institute of geographical sciences and natural resources research [CAS] (IGSNRR), and Chinese Academy of Sciences [Beijing] (CAS)
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 010504 meteorology & atmospheric sciences, Mean squared error, Climate Change, [SDE.MCG]Environmental Sciences/Global Changes, Inverse, Water en Voedsel, Monotonic function, multi-model ensemble, Environment, High skill, 01 natural sciences, crop models, ensemble mean, Statistics, Environmental Chemistry, Triticum, 0105 earth and related environmental sciences, General Environmental Science, Mathematics, 2. Zero hunger, ensemble median, Global and Planetary Change, WIMEK, Water and Food, Ecology, Ensemble average, Simulation modeling, Agriculture, 04 agricultural and veterinary sciences, Variance (accounting), prediction, Models, Theoretical, PE&RC, Plant Production Systems, multimodel ensemble, 13. Climate action, Plantaardige Productiesystemen, climate change impact, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Water Systems and Global Change, Environment management
Abstract: International audience; A recent innovation in assessment of climate change impact on agricultural production has been to use crop multi model ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e‐mean) and median (e‐median) often seem to predict quite well. However few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e‐mean and e‐median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e‐mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2‐6 models if best‐fit models are added first. Our theoretical results describe the ensemble using four parameters; average bias, model effect variance, environment effect variance and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e‐mean will always be smaller than MSEP averaged over models, and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e‐mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e‐mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.
Published: 2018

8. The uncertainty of crop yield projections is reduced by improved temperature response functions

Author: Alex C. Ruane, Peter J. Thorburn, Mikhail A. Semenov, Joost Wolf, Claudio O. Stöckle, Pramod K. Aggarwal, Gerard W. Wall, Margarita Garcia-Vila, Matthew P. Reynolds, Eckart Priesack, Jørgen E. Olesen, Enli Wang, Bruce A. Kimball, Jordi Doltra, Iurii Shcherbak, Ehsan Eyshi Rezaei, Jeffrey W. White, Leilei Liu, L. A. Hunt, Senthold Asseng, Frank Ewert, Yan Zhu, Fulu Tao, Christoph Müller, Daniel Wallach, Christian Biernath, Davide Cammarano, Mohamed Jabloun, Zhigan Zhao, Michael J. Ottman, Pierre Martre, Sebastian Gayler, Garry O'Leary, Zhimin Wang, Jakarat Anothai, Elias Fereres, Claas Nendel, Bruno Basso, Thilo Streck, Curtis D. Jones, Andrea Maiorano, Phillip D. Alderman, Andrew J. Challinor, Reimund P. Rötter, Taru Palosuo, Iwan Supit, Katharina Waha, Giacomo De Sanctis, Kurt Christian Kersebaum, Soora Naresh Kumar, Gerrit Hoogenboom, Dominique Ripoche, Pierre Stratonovitch, Ann-Kristin Koehler, Roberto C. Izaurralde, Commonwealth Scientific and Industrial Research Organisation (Australia), Chinese Academy of Sciences, China Scholarship Council, Ministry of Education of the People's Republic of China, Institut National de la Recherche Agronomique (France), European Commission, International Food Policy Research Institute (US), CGIAR (France), Department of Agriculture (US), Federal Ministry of Education and Research (Germany), Deutsche Gesellschaft für Internationale Zusammenarbeit, Danish Council for Strategic Research, Federal Ministry of Food and Agriculture (Germany), Finnish Ministry of Agriculture and Forestry, National Natural Science Foundation of China, Helmholtz Association, Grains Research and Development Corporation (Australia), Texas AgriLife Research, Texas A&M University, National Institute of Food and Agriculture (US), 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), College of Agronomy and Biotechnology, Southwest University, Institute of Crop Science and Resource Conservation, Division of Plant Nutrition-University of Bonn, Institute of Landscape Systems Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Department of Crop Sciences, University of Goettingen, Centre of Biodiversity and Sustainable Land Use (CBL), United States Department of Agriculture - Agricultural Research Service, The School of Plant Sciences, University of Arizona, Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Washington State University (WSU), Department of Earth and Environmental Sciences and W.K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Institute of Biochemical Plant Pathology (BIOP), German Research Center for Environmental Health - Helmholtz Center München (GmbH), Agricultural and Biological Engineering Department, Purdue University [West Lafayette], Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, GMO Unit, European Food Safety Authority = Autorité européenne de sécurité des aliments, Cantabrian Agricultural Research and Training Centre, Dep. Agronomia, University of Córdoba [Córdoba], Spanish National Research Council (CSIC), Institute of Soil Science and Land Evaluation, University of Hohenheim, 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&M AgriLife Research and Extension Center, Texas A&M University System, Department of Agroecology, Aarhus University [Aarhus], National Engineering and Technology Center for Information Agriculture, Nanjing Agricutural University, Potsdam Institute for Climate Impact Research (PIK), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Department of Economic Development, Department of Economic Development, Jobs, Transport and Resources (DEDJTR), Natural Resources Institute Finland, Institute of Crop Science and Resource Conservation (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Computational and Systems Biology Department, Rothamsted Research, Biological Systems Engineering, University of Wisconsin-Madison, PPS and WSG &CALM, Wageningen University and Research Center (WUR), Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), Agriculture and Food, Universidad de La Rioja (UR), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, China Agricultural University, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), 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), China Agricultural University (CAU), Institute of Crop Science and Resource Conservation [Bonn], Georg-August-University [Göttingen], Arid-Land Agricultural Research Center, Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), 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), University of Leeds, European Food Safety Authority (EFSA), Catabrian Agricultural Research and Training Center (CIFA), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), AgWeatherNet Program, Texas A and M AgriLife Research, Jiangsu Collaborative Innovation Center for Modern Crop Production, Landscape and Water Sciences, Natural Resources Institute Finland (LUKE), Agroclim (AGROCLIM), Wageningen University and Research [Wageningen] (WUR), Chinese Academy of Agricultural Sciences (CAAS), 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, European Project: 267196,EC:FP7:PEOPLE,FP7-PEOPLE-2010-COFUND,AGREENSKILLS(2012), 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 ), Leibniz Centre for Agricultural Landscape Research, International Maize and Wheat Improvement Center ( CIMMYT ), CGIAR Research Program on Climate Change, Agriculture and Food Security ( CCAFS ), Washington State University ( WSU ), Michigan State University, Institute of Biochemical Plant Pathology ( BIOP ), Institute for Climate and Atmospheric Science, School of Earth and Environment, European Food Safety Authority, Spanish National Research Council ( CSIC ), Texas A and M University ( TAMU ), Potsdam Institute for Climate Impact Research ( PIK ), Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Department of Economic Development, Jobs, Transport and Resources ( DEDJTR ), University of Bonn (Rheinische Friedrich-Wilhelms), UE Agroclim ( UE AGROCLIM ), Institut National de la Recherche Agronomique ( INRA ), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), University of Wisconsin-Madison [Madison], Wageningen University and Research Center ( WUR ), Chinese Academy of Sciences [Beijing] ( CAS ), Universidad de La Rioja ( UR ), University of Bonn-Division of Plant Nutrition, 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), USDA-ARS : Agricultural Research Service, Consultative Group on International Agricultural Research [CGIAR]-Consultative Group on International Agricultural Research [CGIAR], Natural resources institute Finland, Georg-August-University = Georg-August-Universität Göttingen, Universidad de Córdoba = University of Córdoba [Córdoba], Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), and Université de Toulouse (UT)-Université de Toulouse (UT)
Subjects: Crops, Agricultural, 010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], Yield (finance), Water en Voedsel, Growing season, Climate change, klim, Plant Science, Agricultural engineering, Models, Biological, 01 natural sciences, [SHS]Humanities and Social Sciences, Crop, Life Science, Computer Simulation, Productivity, 0105 earth and related environmental sciences, 2. Zero hunger, [ SDE.BE ] Environmental Sciences/Biodiversity and Ecology, WIMEK, Water and Food, Food security, business.industry, Crop yield, Temperature, Agriculture, 04 agricultural and veterinary sciences, 15. Life on land, Climate Resilience, Agronomy, Klimaatbestendigheid, 13. Climate action, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Water Systems and Global Change, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, business
Abstract: Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections., E.W. acknowledges support from the CSIRO project ‘Enhanced modelling of genotype by environment interactions’ and the project ‘Advancing crop yield while reducing the use of water and nitrogen’ jointly funded by CSIRO and the Chinese Academy of Sciences (CAS). Z.Z. received a scholarship from the China Scholarship Council through the CSIRO and the Chinese Ministry of Education PhD Research Program. P.M., A.M. and D.R. acknowledge support from the FACCE JPI MACSUR project (031A103B) through the metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA). A.M. received the support of the EU in the framework of the Marie-Curie FP7 COFUND People Programme, through the award of an AgreenSkills fellowship under grant agreement No. PCOFUND-GA-2010-267196. S.A. and D.C. acknowledge support provided by the International Food Policy Research Institute (IFPRI), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), the CGIAR Research Program on Wheat and the Wheat Initiative. C.S. was funded through USDA National Institute for Food and Agriculture award 32011-68002-30191. C.M. received financial support from the KULUNDA project (01LL0905 L) and the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (BMBF). F.E. received support from the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (2812ERA115) and E.E.R. was funded through the German Federal Ministry of Economic Cooperation and Development (Project: PARI). M.J. and J.E.O. were funded through the FACCE MACSUR project by the Danish Strategic Research Council. K.C.K. and C.N. were funded by the FACCE MACSUR project through the German Federal Ministry of Food and Agriculture (BMEL). F.T., T.P. and R.P.R. received financial support from the FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry (MMM); F.T. was also funded through the National Natural Science Foundation of China (No. 41071030). C.B. was funded through the Helmholtz project ‘REKLIM-Regional Climate Change: Causes and Effects’ Topic 9: ‘Climate Change and Air Quality’. M.P.R. and PD.A. received funding from the CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS). G.O'L. was funded through the Australian Grains Research and Development Corporation and the Department of Economic Development, Jobs, Transport and Resources Victoria, Australia. R.C.I. was funded by Texas AgriLife Research, Texas A&M University. B.B. was funded by USDA-NIFA Grant No: 2015-68007-23133.
Published: 2017

9. Quantitative models of Rhipicephalus (Boophilus) ticks: historical review and synthesis

Author: Pete D. Teel, Hsiao-Hsuan Wang, William E. Grant, Michael S. Corson, Texas A&M University System, Sol Agro et hydrosystème Spatialisation (SAS), AGROCAMPUS OUEST, 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), Texas A and M AgriLife Research, Texas A&M AgriLife Research Project [TEX08911], Texas A&M University Open Access to Knowledge Fund (OAKFund) - University Libraries, Office of the Vice President for Research, Wang, Hsiao-Hsuan, and Corson, Michael S.
Subjects: 0301 basic medicine, Range (biology), Rhipicephalus (Boophilus) microplus, 030231 tropical medicine, Population, Wildlife, Tick, 03 medical and health sciences, 0302 clinical medicine, Ecological relationship, education, Ecology, Evolution, Behavior and Systematics, 2. Zero hunger, Rhipicephalus (Boophilus) decoloratus, education.field_of_study, Ecology, biology, business.industry, Acaricide, cattle tick, simulation model, Rhipicephalus (Boophilus) annulatus, Microbiology and Parasitology, analytical model, Rhipicephalus (Boophilus) australis, biology.organism_classification, Microbiologie et Parasitologie, Rhipicephalus, 030104 developmental biology, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, 13. Climate action, Livestock, business
Abstract: Synthesis & Integration; International audience; Several tick species, in what is now known as the subgenus Boophilus in the genus Rhipicephalus, are economically important ectoparasites of livestock and other ungulates; as vectors of pathogens that kill cattle, they remain among the most studied ticks in the world. Researchers have developed quantitative computer models of Rhipicephalus ticks since the early 1970s to study complex biological and ecological relationships that influence management or eradication of ticks and tick-borne diseases. We review the 45-yr history of Rhipicephalus (Boophilus) models, which were developed and applied first in Australia, 10 yr later in North and South America, then soon after in Africa. Models progressed from analytical models of a portion of tick life cycles, to simulation models of complete life cycles or ecoclimatic indices, to the current emphasis on GIS-based bioclimatic envelope models estimated from remotely sensed data and tick presence records. Earlier models were used primarily to predict effects of management techniques, such as use of sterile hybrid ticks, pasture rotation, acaricides, vaccines, and resistant cattle, while more recent models have been used to predict the potential for range expansion, especially due to global climate change and wildlife hosts, as well as in the face of competition with other tick species. We summarize characteristics of these models and compare those of population dynamics and bioclimatic envelope models. We discuss the past and present utility of these models and provide a perspective on future Rhipicephalus (Boophilus) modeling efforts.
Published: 2017

10. 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

11. 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

12. Global Research Alliance on agricultural greenhouse gases - benchmark and ensemble crop and grassland model estimates

Author: Renata Sándor, Ehrhardt, F., Bruno Basso, Bathia, A., Gianni Bellocchi, Brilli, L., Laura Maritza Cardenas, Massimiliano de Antoni Migliorati, Bregon, J. D., Dorich, C., Lucas Doro, Fitton, N., Giacomini, S., Peter Grace, Grant, Brian B., Harrison, M., Stephanie Jones, Miko Kirschbaum, Katja Klumpp, Laville, P., Joël Léonard, Liebig, M., Lieffering, M., Raphaël Martin, Mcauliffe, R., Elizabeth Anne Meier, Lutz Merbold, Andrew Moore, Vasilis Myrgiotis, Elizabeth Pattey, Zhang, Q., Sylvie Recous, Suzanne Rolinski, Joanna Sharp, Raia Silvia Massad, Smith, P., Ward Smith, Val Snow, Soussana, J. F., Institute for Soil Sciences and Agricultural Chemistry (ATK TAKI), Centre for Agricultural Research [Budapest] (ATK), Hungarian Academy of Sciences (MTA)-Hungarian Academy of Sciences (MTA), Department of Earth and Environmental Sciences [East Lansing], Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Unité Mixte de Recherche sur l'Ecosystème Prairial - UMR (UREP), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Dipartimento di Scienze delle Produzioni Agroalimentari e dell'Ambiente (DISPAA), University of Florence (UNIFI), Department of Sustainable Soils and Grassland Systems, Rothamsted Research, Institute for Future Environments, Queensland University of Technology, Texas A and M AgriLife Research, Texas A&M University System, Institute of Biological and Environmental Sciences, (SFIRC), Universidade Federal de Santa Maria (UFSM), Science and Technology Branch, Environment and Climate Change Canada, Tasmanian Institute of Agriculture (TIA), University of Tasmania (UTAS), Soil Science and Systems Team, Scotland's Rural College (SCUR), Ecosystems and Global Change Team, Landscare Research, Unité d'Agronomie de Laon-Reims-Mons (AGRO-LRM), Institut National de la Recherche Agronomique (INRA), Agricultural Research Service (ARS), United States Department of Agriculture, CSIRO Ecosystem Sciences, Livestock Systems and Environment, International Livestock Research Institute, Agriculture & Food, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), School of GeoSciences, University of Edinburgh, Ottawa Research and Development Centre, Agriculture and Agri-Food [Ottawa] (AAFC), Fractionnement des AgroRessources et Environnement - UMR-A 614 (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de la Recherche Agronomique (INRA)-SFR Condorcet, Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS)-Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS), Climate Impacts and Vulnerabilities - Research Domain II, Potsdam Institute for Climate Impact Research (PIK), Modelling, Sustainable Production, New Zealand Institute for Plant and Food Research Limited, Farm Systems and Environment, AgResearch Ltd, UR 0874 Unité de recherche sur l'Ecosystème Prairial, Institut National de la Recherche Agronomique (INRA)-Unité de recherche sur l'Ecosystème Prairial (UREP)-Ecologie des Forêts, Prairies et milieux Aquatiques (EFPA), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Universidade Federal de Santa Maria = Federal University of Santa Maria [Santa Maria, RS, Brazil] (UFSM), University of Tasmania [Hobart, Australia] (UTAS), Scotland's Rural College (SRUC), Agroressources et Impacts environnementaux (AgroImpact), USDA-ARS : Agricultural Research Service, Fractionnement des AgroRessources et Environnement (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de la Recherche Agronomique (INRA), Plant & Food Research, and ProdInra, Archive Ouverte
Subjects: [SDV] Life Sciences [q-bio], agricultural greenhouse gases, grassland model, [SDV]Life Sciences [q-bio]
Abstract: CT3 Biogéochimie, physique et écologie des solsEnjS4 Bouclage des cycles N et P et stockage de carboneTyp_Proj_Bourse de thèse/Post-DocTyp_Proj_Projet ANR; Uncertainties in the response of crop and grassland models to management and environmental drivers can be attributed to differences in the structure of different models. This has created an urgent need for international benchmarking of models, where uncertainties are estimated by running several models that simulate the same physical and management conditions (ensemble modelling) to generate expanded envelopes of uncertainty (e.g. Asseng et al., 2013). Simulations of the agricultural C and N fluxes, in particular, are inherently uncertain because they are driven by complex interactions (e.g. Sándor et al., 2016) and characterized by considerable spatial and temporal variability in the measurements. In this context, the Integrative Research Group of the Global Research Alliance (GRA) on Agricultural Greenhouse Gases promotes a coordinated activity across multiple international projects (e.g. C-N MIP and Models4Pastures of the FACCE-JPI, https://www.faccejpi.com) to benchmark and compare simulation models that estimate C-N related outputs (including greenhouse gas emissions) from arable crop and grassland systems (http://globalresearchalliance.org/e/model-intercomparison-on-agricultural-ghg-emissions). This study presents some preliminary results on the uncertainty of outputs from 12 grassland models while exploring model differences when models were calibrated with increasing data resources.
Published: 2016

13. 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

14. Land-atmosphere coupling in EURO-CORDEX evaluation experiments

Author: Knist , Sebastian, Goergen , Klaus, Buonomo , Erasmo, Christensen , Ole Bøssing, Colette , Augustin, Cardoso , Rita M., Fealy , Rowan, Fernández , Jesús, García-Díez , Markel, Jacob , Daniela, Kartsios , Stergios, Katragkou , Eleni, Keuler , Klaus, Mayer , Stephanie, Van Meijgaard , Erik, Nikulin , Grigory, Soares , Pedro M. M., Sobolowski , Stefan, Szepszo , Gabriella, Teichmann , Claas, Vautard , Robert, Warrach-Sagi , Kirsten, Wulfmeyer , Volker, Simmer , Clemens, Centre de recherche public Gabriel Lippmann, Centre de Recherche Public Gabriel Lippmann, Met Office Hadley Centre ( MOHC ), United Kingdom Met Office [Exeter], Danish Climate Centre, Danish Meteorological Institute ( DMI ), Institut National de l'Environnement Industriel et des Risques ( INERIS ), Texas A and M University ( TAMU ), Texas A and M AgriLife Research, Rural Economy and Development Programme, Irish Agriculture and Food Development Authority, Spanish National Institute for Agriculture and Food Research and Technology ( INIA ), Instituto de Fisica de Cantabria, Instituto de Física de Cantabria, Macquarie Univ, Dept Earth & Planetary Sci, N Ryde, NSW 2009, Australia, Department of Meteorology and Climatology [Thessaloniki], Aristotle University of Thessaloniki, Brandenburg University of Technology, ARVALIS - Institut du Végétal, Royal Netherlands Meteorological Institute ( KNMI ), Rossby Centre, Swedish Meteorological and Hydrological Institute ( SMHI ), Centro de Computação Gráfica ( CCG ), Bjerknes Centre for Climate Research ( BCCR ), University of Bergen ( UIB ), Climate Service Center [Hambourg] ( GERICS ), Helmholtz-Zentrum Geesthacht ( GKSS ), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] ( LSCE ), Université de Versailles Saint-Quentin-en-Yvelines ( UVSQ ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Centre National de la Recherche Scientifique ( CNRS ), Institut für Physik und Meteorologie [Stuttgart] ( IPM ), Universität Hohenheim, Meteorological Institute University Bonn, Centre de Recherche Public - Gabriel Lippmann (LUXEMBOURG), Met Office Hadley Centre for Climate Change (MOHC), Danish Meteorological Institute (DMI), Institut National de l'Environnement Industriel et des Risques (INERIS), Texas A&M University System, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria = National Institute for Agricultural and Food Research and Technology (INIA), Climate Service Center [Hambourg] (GERICS), Helmholtz-Zentrum Geesthacht (GKSS), ARVALIS - Institut du végétal [Paris], Royal Netherlands Meteorological Institute (KNMI), Rossby Centre, SMHI, Norrköping, 601 76, Sweden, Swedish Meteorological and Hydrological Institute (SMHI), Centro de Computação Gráfica (CCG), Bjerknes Centre for Climate Research (BCCR), Department of Biological Sciences [Bergen] (BIO / UiB), University of Bergen (UiB)-University of Bergen (UiB), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), 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), Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR), 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), Institut für Physik und Meteorologie [Stuttgart] (IPM), Meteorological Institute [Bonn], Rheinische Friedrich-Wilhelms-Universität Bonn, Brandenburgische Technische Universität = Brandenburg Technical University (BTU), 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), and 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)
Subjects: [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, [ SDU.OCEAN ] Sciences of the Universe [physics]/Ocean, Atmosphere, ddc:550, [ SDU.ENVI ] Sciences of the Universe [physics]/Continental interfaces, environment, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, ComputingMilieux_MISCELLANEOUS
Abstract: International audience; Interactions between the land surface and the atmosphere play a fundamental role in the weather and climate system. Here we present a comparison of summertime land-atmosphere coupling strength found in a subset of the ERA-Interim-driven European domain Coordinated Regional Climate Downscaling Experiment (EURO-CORDEX) model ensemble (1989-2008). Most of the regional climate models (RCMs) reproduce the overall soil moisture interannual variability, spatial patterns, and annual cycles of surface exchange fluxes for the different European climate zones suggested by the observational Global Land Evaporation Amsterdam Model (GLEAM) and FLUXNET data sets. However, some RCMs differ substantially from FLUXNET observations for some regions. The coupling strength is quantified by the correlation between the surface sensible and the latent heat flux, and by the correlation between the latent heat flux and 2 m temperature. The first correlation is compared to its estimate from the few available long-term European high-quality FLUXNET observations, and the latter to results from gridded GLEAM data. The RCM simulations agree with both observational datasets in the large-scale pattern characterized by strong coupling in southern Europe and weak coupling in northern Europe. However, in the transition zone from strong to weak coupling covering large parts of central Europe many of the RCMs tend to overestimate the coupling strength in comparison to both FLUXNET and GLEAM. The RCM ensemble spread is caused primarily by the different land surface models applied, and by the model-specific weather conditions resulting from different atmospheric parameterizations.
Published: 2016

15. Residual correlation and ensemble modelling to improve crop and grassland models

Author: Renáta Sándor, Fiona Ehrhardt, Peter Grace, Sylvie Recous, Pete Smith, Val Snow, Jean-François Soussana, Bruno Basso, Arti Bhatia, Lorenzo Brilli, Jordi Doltra, Christopher D. Dorich, Luca Doro, Nuala Fitton, Brian Grant, Matthew Tom Harrison, Ute Skiba, Miko U.F. Kirschbaum, Katja Klumpp, Patricia Laville, Joel Léonard, Raphaël Martin, Raia Silvia Massad, Andrew D. Moore, Vasileios Myrgiotis, Elizabeth Pattey, Susanne Rolinski, Joanna Sharp, Ward Smith, Lianhai Wu, Qing Zhang, Gianni Bellocchi, Producció Vegetal, Cultius Extensius Sostenibles, Unité Mixte de Recherche sur l'Ecosystème Prairial - UMR (UREP), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Agricultural Institute (ELKH CAR), Collège de Direction (CODIR), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), RITTMO Agroenvironnement (RITTMO), Queensland University of Technology [Brisbane] (QUT), Fractionnement des AgroRessources et Environnement (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), INSTITUTE OF BIOLOGICAL AND ENVIRONMENTAL SCIENCES, University of Aberdeen, AgResearch, Lincoln Res Ctr, PB 4749, Christchurch 8140, New Zealand, Partenaires INRAE, Indian Agricultural Research Institute (IARI), Department of Agriculture, Food, Environment and Forestry (DAGRI), Università degli Studi di Firenze = University of Florence (UniFI), Institute for BioEconomy [Sesto Fiorentino] (IBE | CNR), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Institut de Recerca i Tecnologia Agroalimentàries = Institute of Agrifood Research and Technology (IRTA), Natural Resource Ecology Laboratory [Fort Collins] (NREL), Colorado State University [Fort Collins] (CSU), Desertification Research Group, Università degli Studi di Sassari = University of Sassari [Sassari] (UNISS), Texas A and M AgriLife Research, Texas A&M University System, Canadian National Collection of Insects, Arachnids, and Nematodes, Science & Technology Branch, Agriculture and Agri-Food Canada, Ottawa, ON, K1A 0C6, Agriculture and Agri-Food (AAFC), University of Tasmania [Launceston] (UTAS), NERC Centre for Ecology and Hydrology, Penicuik, UK, Manaaki Whenua – Landcare Research [New Zealand], Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Transfrontalière BioEcoAgro - UMR 1158 (BioEcoAgro), Université d'Artois (UA)-Université de Liège-Université de Picardie Jules Verne (UPJV)-Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), CSIRO Agriculture and Food (CSIRO), School of Geosciences [Edinburgh], University of Edinburgh, Potsdam Institute for Climate Impact Research (PIK), The New Zealand Institute for Plant and Food Research Limited (PFR), Mt. Albert Research Centre, Sustainable Agriculture Systems (Rothamsted Research), Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics [Beijing] (IAP), and Chinese Academy of Sciences [Beijing] (CAS)-Chinese Academy of Sciences [Beijing] (CAS)
Subjects: Environmental Engineering, Correlation matrices, Ecological Modeling, [SDE]Environmental Sciences, Ensemble modelling, Software, Biogeochemical models
Abstract: Multi-model ensembles are becoming increasingly accepted for the estimation of agricultural carbon-nitrogen fluxes, productivity and sustainability. There is mounting evidence that with some site-specific observations available for model calibration (with vegetation data as a minimum requirement), median outputs assimilated from biogeochemical models (multi-model medians) provide more accurate simulations than individual models. Here, we evaluate potential deficiencies in how model ensembles represent (in relation to climatic factors) the processes underlying biogeochemical outputs in complex agricultural systems such as grassland and crop rotations including fallow periods. We do that by exploring the correlation of model residuals. We restricted the distinction between partial and full calibration to the two most relevant calibration stages, i.e. with plant data only (partial) and with a combination of plant, soil physical and biogeochemical data (full). It introduces and evaluates the trade-off between (1) what is practical to apply for model users and beneficiaries, and (2) what constitutes best modelling practice. The lower correlations obtained overall with fully calibrated models highlight the centrality of the full calibration scenario for identifying areas of model structures that require further development. info:eu-repo/semantics/acceptedVersion

16. Improving rice models for more reliable prediction of responses of rice yield to CO2 and temperature elevation

Author: Li, Tao, Yin, Xinyou, Hasegawa, Toshihiro, Boote, Ken, Zhu, Yan, Myriam ADAM, Baker, Jeff, Bouman, Bas, Bregaglio, Simone, Buis, Samuel, Confalonieri, Roberto, Fugice, Job, Fumoto, Tamon, Gaydon, Donald, Kumar, S. N., Lafarge, Tanguy, Marcaida, Manuel, Masutomi, Y., Nakagawa, Hitochi, Pequeno, D. N. L., Ruane, Alex C., Ruget, Françoise, Singh, Upendra, Tang, Liang, Tao, Fulu, Wallach, Daniel, Wilson, Lloyd Ted, Yang, Yubin, Yoshida, Hiroe, Zhang, Zhao, Zhu, Jinyu, International Rice Research Institute, Centre for Crop Systems Analysis, Wageningen University and Research Centre [Wageningen] (WUR), National Institute of Agro-Environmental Sciences (NIAES), University of Florida [Gainesville], National Engineering and Technology Center for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricutural University, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), 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), WCA-Resilient Dryland Systems, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Agricultural Research Service, United States Department of Agriculture, Cassandra lab, University of Milan, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Muscle Shoals, International Fertilizer Development Center (IFDC), CSIRO, Indian Agricultural Research Institute (IARI), College of Agriculture, Northeast Agricultural University [Harbin], National Agriculture and Food Research Organization, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Texas A&M University System, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Sciences, Chinese Academy of Sciences [Changchun Branch] (CAS), International Rice Research Institute [Philippines] (IRRI), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Wageningen University and Research [Wageningen] (WUR), University of Florida [Gainesville] (UF), 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), International Crops Research Institute for the Semi-Arid Tropics [Inde] (ICRISAT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), USDA-ARS : Agricultural Research Service, National Agriculture and Food Research Organization (NARO), 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, Beijing Normal University (BNU), ProdInra, Archive Ouverte, Wageningen University and Research Centre [Wageningen] ( WUR ), National Institute for Agro-Environmental Sciences, University of Florida, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales ( UMR AGAP ), Institut national de la recherche agronomique [Montpellier] ( INRA Montpellier ) -Centre international d'études supérieures en sciences agronomiques ( Montpellier SupAgro ) -Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ) -Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), International Crops Research Institute for the Semi-Arid Tropics ( ICRISAT ), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes ( EMMAH ), Université d'Avignon et des Pays de Vaucluse ( UAPV ) -Institut National de la Recherche Agronomique ( INRA ), International Fertilizer Development Center ( IFDC ), Indian Agricultural Research Institute ( IARI ), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), Chinese Academy of Sciences [Beijing] ( CAS ), Texas A and M AgriLIFE Research Center at Beaumont, and Chinese Academy of Sciences [Changchun Branch] ( CAS )
Subjects: production rizicole, [ SDV ] Life Sciences [q-bio], U10 - Informatique, mathématiques et statistiques, P40 - Météorologie et climatologie, F60 - Physiologie et biochimie végétale, crop model, rice, flux de co2, [SDV]Life Sciences [q-bio], education, food and beverages, F62 - Physiologie végétale - Croissance et développement, humanities, fertilisation, [SDV] Life Sciences [q-bio], high temperature, croissance des graines, calibrage du modèle, F01 - Culture des plantes, fertilization, haute température, mesure par chambre, biomasse aérienne, reproductive and urinary physiology, health care economics and organizations
Abstract: Improving rice models for more reliable prediction of responses of rice yield to CO2 and temperature elevation . International Crop Modelling Symposium

17. Functional gene categories differentiate maize leaf drought-related microbial epiphytic communities.

Author: Methe BA, Hiltbrand D, Roach J, Xu W, Gordon SG, Goodner BW, and Stapleton AE
Subjects: Droughts, Gene Regulatory Networks, Genes, Plant, Metagenomics, Microbiota, Molecular Sequence Annotation, Plant Leaves physiology, Stress, Physiological, Water metabolism, Zea mays physiology, Plant Leaves genetics, Plant Leaves microbiology, Zea mays genetics, Zea mays microbiology
Abstract: The phyllosphere epiphytic microbiome is composed of microorganisms that colonize the external aerial portions of plants. Relationships of plant responses to specific microorganisms-both pathogenic and beneficial-have been examined, but the phyllosphere microbiome functional and metabolic profile responses are not well described. Changing crop growth conditions, such as increased drought, can have profound impacts on crop productivity. Also, epiphytic microbial communities provide a new target for crop yield optimization. We compared Zea mays leaf microbiomes collected under drought and well-watered conditions by examining functional gene annotation patterns across three physically disparate locations each with and without drought treatment, through the application of short read metagenomic sequencing. Drought samples exhibited different functional sequence compositions at each of the three field sites. Maize phyllosphere functional profiles revealed a wide variety of metabolic and regulatory processes that differed in drought and normal water conditions and provide key baseline information for future selective breeding., Competing Interests: The authors have declared that no competing interests exist.
Published: 2020
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2. Climate change impact and adaptation for wheat protein

3. Registration of tropical populations of maize selected in parallel for early flowering time across the United States

4. Global wheat production with 1.5 and 2.0°C above pre‐industrial warming

5. A taxonomy-based approach to shed light on the babel of mathematical models for rice simulation

6. The Hot Serial Cereal Experiment for modeling wheat response to temperature: Field experiments and AgMIP-Wheat multi-model simulations

7. Multimodel ensembles improve predictions of crop–environment–management interactions

8. The uncertainty of crop yield projections is reduced by improved temperature response functions

9. Quantitative models of Rhipicephalus (Boophilus) ticks: historical review and synthesis

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

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

12. Global Research Alliance on agricultural greenhouse gases - benchmark and ensemble crop and grassland model estimates

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

14. Land-atmosphere coupling in EURO-CORDEX evaluation experiments

15. Residual correlation and ensemble modelling to improve crop and grassland models

16. Improving rice models for more reliable prediction of responses of rice yield to CO2 and temperature elevation

17. Functional gene categories differentiate maize leaf drought-related microbial epiphytic communities.

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