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1. The chaos in calibrating crop models:Lessons learned from a multi-model calibration exercise

Author: Eckart Priesack, Johannes Wilhelmus Maria Pullens, Heidi Horan, Anne Klosterhalfen, Elisabet Lewan, Marco Moriondo, Emmanuelle Gourdain, Roberto Ferrise, Tobias K. D. Weber, Camilla Dibari, Neil M.J. Crout, Daniel Wallach, Amir Souissi, Jing Wang, Eric Justes, Kurt Christian Kersebaum, Benjamin Dumont, Mohamed Jabloun, Niels Schütze, Qi Jing, G. Padovan, Bernardo Maestrini, Steven Hoek, Mingxia Huang, Sebastian Gayler, Giacomo Trombi, Gerrit Hoogenboom, Qunying Luo, Jørgen E. Olesen, Chuang Zhao, Evelyn Wallor, Per-Erik Jansson, Tommaso Stella, Peter J. Thorburn, Santosh Hiremath, Arne Poyda, Thomas Wöhling, Amit Kumar Srivastava, Thomas Gaiser, Sabine J. Seidel, Budong Qian, Vakhtang Shelia, Henrike Mielenz, Afshin Ghahramani, Allard de Wit, Senthold Asseng, Fety Andrianasolo, Bruno Basso, Liujun Xiao, Zvi Hochman, Taru Palosuo, Yan Zhu, Marie Launay, Cécile Garcia, Xenia Specka, Thilo Streck, Lutz Weihermüller, Hasti Nariman Zadeh, Samuel Buis, AGroécologie, Innovations, teRritoires (AGIR), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Natural Resources Institute Finland (LUKE), CSIRO Agriculture and Food (CSIRO), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), ARVALIS - Institut du végétal [Paris], ARVALIS - Institut du Végétal [Boigneville], University of Florida [Gainesville] (UF), Michigan State University [East Lansing], Michigan State University System, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of Nottingham, UK (UON), Department of Agriculture, Food, Environment and Forestry (DAGRI), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Gembloux Agro-Bio Tech [Gembloux], Université de Liège, Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, University of Hohenheim, University of Southern Queensland (USQ), Aalto University School of Science and Technology [Aalto, Finland], Wageningen University and Research [Wageningen] (WUR), China Agricultural University (CAU), Royal Institute of Technology [Stockholm] (KTH ), Agriculture and Agri-Food Canada, Saskatoon Research Centre, Agriculture and Agri-Food [Ottawa] (AAFC), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Global Change Research Institute (CAS), Institute of Bio- and Geosciences [Jülich] (IBG), Forschungszentrum Jülich GmbH | Centre de recherche de Juliers, Helmholtz-Gemeinschaft = Helmholtz Association-Helmholtz-Gemeinschaft = Helmholtz Association, Agroclim (AGROCLIM), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Swedish University of Agricultural Sciences (SLU), Hillridge Technology Pty Ltd, Julius Kühn-Institut - Federal Research Centre for Cultivated Plants (JKI), Institute of Bioeconomy (IBE), Consiglio Nazionale delle Ricerche (CNR), Aarhus University [Aarhus], Kiel University, Helmholtz-Zentrum München (HZM), Technische Universität Dresden = Dresden University of Technology (TU Dresden), Université de Carthage - University of Carthage, Helmholtz-Gemeinschaft = Helmholtz Association, Lincoln Agritech Ltd, Nanjing Agricultural University, Collaborative Research Center 1253 CAMPOS (Project 7: Stochastic Modelling Framework), funded by the German Research Foundation (DFG, Grant Agreement SFB 1253/1 2017), Academy of Finland through projects AICropPro (316172) and DivCSA (316215), National Science Foundation for Distinguished Young Scholars (31725020), Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), China Scholarship Council, Agriculture and Agri-Food Canada's Project 1387 under the Canadian Agricultural Partnership, DFG Research Unit FOR 1695 ‘Agricultural Landscapes under Global Climate Change – Processes and Feedbacks on a Regional Scale, U.S. Department of Agriculture National Institute of Food and Agriculture (award no. 2015-68007-23133) and USDA/NIFA HATCH grant N. MCL02368, National Key Research and Development Program of China (2016YFD0300105), Broadacre Agriculture Initiative, a research partnership between University of Southern Queensland and the Queensland Department of Agriculture and Fisheries, Academy of Finland through project AI-CropPro (315896), JPI FACCE MACSUR2 project, funded by the Italian Ministry for Agricultural, Food, and Forestry Policies (D.M. 24064/7303/15 of 6/Nov/2015), Natural Resources Institute Finland (Luke) through a strategic project BoostIA, BonaRes project 'Soil3' (BOMA 03037514) of the Federal Ministry of Education and Research (BMBF), Germany, Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2070 – 390732324 EXC (PhenoRob), Project BiomassWeb of the GlobeE programme (Grant number: FKZ031A258B) funded by the Federal Ministry of Education and Research (BMBF, Germany), INRA ACCAF meta-programme, German Federal Ministry of Education and Research (BMBF) in the framework of the funding measure 'Soil as a Sustainable Resource for the Bioeconomy – BonaRes', project 'BonaRes (Module B): BonaRes Centre for Soil Research, subproject B' (grant 031B0511B), and National Key Research and Development Program of China (2017YFD0300205)
Subjects: Earth Observation and Environmental Informatics, Environmental Engineering, 010504 meteorology & atmospheric sciences, Computer science, Process (engineering), Process-based models, Model parameters, Machine learning, computer.software_genre, 01 natural sciences, paramètre, Software, F01 - Culture des plantes, Component (UML), Aardobservatie en omgevingsinformatica, Calibration, Parameter estimation, Applied Ecology, 0105 earth and related environmental sciences, U10 - Informatique, mathématiques et statistiques, Modélisation des cultures, business.industry, Estimation theory, Ecological Modeling, Toegepaste Ecologie, Calibration recommendations, Experimental data, 04 agricultural and veterinary sciences, PE&RC, [STAT]Statistics [stat], CHAOS (operating system), Phenology, Calibration Recommendations, Process-based Models, Parameter Estimation, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Artificial intelligence, ddc:004, Phénologie, Modèle végétal, business, computer
Abstract: International audience; Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of process-based models and has an important impact on simulated values. We propose a novel method of developing guidelines for calibration of process-based models, based on development of recommendations for calibration of the phenology component of crop models. The approach was based on a multi-model study, where all teams were provided with the same data and asked to return simulations for the same conditions. All teams were asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices.
Published: 2021
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2. How well do crop modeling groups predict wheat phenology, given calibration data from the target population?

Author: Allard de Wit, Emmanuelle Gourdain, Chuang Zhao, Bruno Basso, Tommaso Stella, Sebastian Gayler, Qi Jing, Eric Justes, Marco Moriondo, Arne Poyda, Zvi Hochman, Kurt Christian Kersebaum, Neil M.J. Crout, Eckart Priesack, Niels Schütze, Sabine J. Seidel, T. Palosuo, Heidi Horan, Amit Kumar Srivastava, Amir Souissi, Anne Klosterhalfen, Giacomo Trombi, Gerrit Hoogenboom, Vakhtang Shelia, Tobias K. D. Weber, Evelyn Wallor, Daniel Wallach, Yan Zhu, Mohamed Jabloun, Budong Qian, Cécile Garcia, Johannes Wilhelmus Maria Pullens, Xenia Specka, Benjamin Dumont, Qunying Luo, Jing Wang, Camilla Dibari, Peter J. Thorburn, Roberto Ferrise, Bernardo Maestrini, Jørgen E. Olesen, Afshin Ghahramani, Senthold Asseng, Lutz Weihermüller, Marie Launay, Thomas Gaiser, Thilo Streck, Thomas Wöhling, Liujun Xiao, Henrike Mielenz, Steven Hoek, Mingxia Huang, Samuel Buis, Hasti Nariman Zadeh, AGroécologie, Innovations, teRritoires (AGIR), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Natural Resources Institute Finland (LUKE), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), ARVALIS - Institut du végétal [Paris], The University of Florida College of Medicine, Michigan State University [East Lansing], Michigan State University System, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of Nottingham, UK (UON), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Unité de recherche TERRA [Gembloux], Gembloux Agro-Bio Tech [Gembloux], Université de Liège-Université de Liège, University of Hohenheim, ARVALIS - Institut du Végétal [Ouzouer le Marché] (ARVALIS), University of Southern Queensland (USQ), CSIRO Agriculture and Food (CSIRO), University of North Florida [Jacksonville] (UNF), University of Florida [Gainesville] (UF), China Agricultural University Library, University of Nottingham Ningbo [China], Agriculture and Agri-Food Canada, Saskatoon Research Centre, Agriculture and Agri-Food [Ottawa] (AAFC), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Inst Landscape Biogeochem, Leibniz Ctr Agr Landscape Res, Muncheberg, Germany, Partenaires INRAE, ∗Agrosphere (IBG-3), Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, Jülich, Germany, Institute of Bio- and Geosciences [Jülich] (IBG), Forschungszentrum Jülich GmbH | Centre de recherche de Juliers, Helmholtz-Gemeinschaft = Helmholtz Association-Helmholtz-Gemeinschaft = Helmholtz Association-Forschungszentrum Jülich GmbH | Centre de recherche de Juliers, Helmholtz-Gemeinschaft = Helmholtz Association-Helmholtz-Gemeinschaft = Helmholtz Association, Agroclim (AGROCLIM), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Hillridge Technology Pty Ltd, Wageningen University and Research [Wageningen] (WUR), Julius Kühn-Institut - Federal Research Centre for Cultivated Plants (JKI), Aalto University School of Science and Technology [Aalto, Finland], Aarhus University [Aarhus], Kiel University, German Res Ctr Environm Hlth, Technische Universität Dresden = Dresden University of Technology (TU Dresden), Université de Carthage - University of Carthage, University of Bonn, Université de Florence, China Agricultural University (CAU), Helmholtz-Gemeinschaft = Helmholtz Association, Nanjing Agricultural University, Institut für Genetik - Universität Bonn / Institute of Genetics - University of Bonn, and German Research Foundation (DFG, Grant Agreement SFB 1253/1 2017), the Academy of Finland through projects AI-CropPro (316172 and 315896) and DivCSA (316215) BonaRes Center for Soil Research, subproject ‘Sustainable Subsoil Management – Soil3’ (grant 031B0151A), project BiomassWeb of the GlobeE programme (Grant number: FKZ031A258B)BonaRes Centre for Soil Research, subproject B' (grant 031B0511B), the National Key Research and Development Program of China (2017YFD0300205), the National Science Foundation for Distinguished Young Scholars (31725020),Program Development of Jiangsu Higher Education Institutions (PAPD), the 111 project (B16026)National Institute of Food and Agriculture (award no. 2015-68007-23133) USDA/NIFA HATCHgrant No. MCL02368, the National Key Research and Development Program of China (2016YFD0300105),Forestry Policies (D.M. 24064/7303/15 of 26/Nov/2015)
Subjects: 0106 biological sciences, Earth Observation and Environmental Informatics, 010504 meteorology & atmospheric sciences, Computer science, Calibration (statistics), Mean squared prediction error, Extrapolation, Climate change, Soil Science, Plant Science, Target population, Model evaluation Wheat, 01 natural sciences, Crop, Statistics, Aardobservatie en omgevingsinformatica, Crop Model, Phenology Prediction, Model Evaluation, Wheat, Crop model, Plant phenology, Crop management, Applied Ecology, Model evaluation, 0105 earth and related environmental sciences, Mathematics, 2. Zero hunger, Observational error, Phenology, Emphasis (telecommunications), Toegepaste Ecologie, Experimental data, 04 agricultural and veterinary sciences, 15. Life on land, PE&RC, Plant development, Agronomy, Current management, 13. Climate action, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, ddc:640, Agronomy and Crop Science, Phenology prediction, 010606 plant biology & botany
Abstract: Plant phenology, which describes the timing of plant development, is a major aspect of plant response to environment and for crops, a major determinant of yield. Since climate change is projected to alter crop phenology worldwide, there is a large effort to predict phenology as a function of environment. Many studies have focused on comparing model equations for describing how phenology responds to weather but the effect of crop model calibration, also expected to be important, has received much less attention. The objective here was to obtain a rigorous evaluation of prediction capability of wheat crop phenology models, and to analyze the role of calibration. The 27 participants in this multi-model study were provided experimental data for calibration and asked to submit predictions for sites and years not represented in those data. Participants were instructed to use and document their 99usual99 calibration approach. Overall, the models provided quite good predictions of phenology (median of mean absolute error of 6.1 days) and did much better than simply using the average of observed values as predictor. Calibration was found to compensate to some extent for differences between models, specifically for differences in simulated time to emergence and differences in the choice of input variables. Conversely, different calibration approaches led to major differences in prediction error between models with the same structure. Given the large diversity of calibration approaches and the importance of calibration, there is a clear need for guidelines and tools to aid with calibration. Arguably the most important and difficult choice for calibration is the choice of parameters to estimate. Several recommendations for calibration practices are proposed. Model applications, including model studies of climate change impact, should focus more on the data used for calibration and on the calibration methods employed.
Published: 2021
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3. Multi-model evaluation of phenology prediction for wheat in Australia

Author: Elisabet Lewan, Xenia Specka, Arne Poyda, Bernardo Maestrini, Liujun Xiao, Amir Souissi, Sabine J. Seidel, Roberto Ferrise, G. Padovan, Steven Hoek, Tobias K. D. Weber, Thilo Streck, Mingxia Huang, Qunying Luo, Niels Schütze, Jørgen E. Olesen, Samuel Buis, Qi Jing, Budong Qian, Yan Zhu, Marie Launay, Allard de Wit, Thomas Wöhling, Sebastian Gayler, Fety Andrianasolo, Eckart Priesack, Bruno Basso, Senthold Asseng, Benjamin Dumont, Heidi Horan, Eric Justes, Thomas Gaiser, Mohamed Jabloun, Giacomo Trombi, Santosh Hiremath, Lutz Weihermüller, Daniel Wallach, Jing Wang, Zvi Hochman, Taru Palosuo, Amit Kumar Srivastava, Marco Moriondo, Vakhtang Shelia, Peter J. Thorburn, Gerrit Hoogenboom, Evelyn Wallor, Kurt Christian Kersebaum, Johannes Wilhelmus Maria Pullens, Neil M.J. Crout, Chuang Zhao, Per-Erik Jansson, Tommaso Stella, AGroécologie, Innovations, teRritoires (AGIR), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Natural Resources Institute Finland (LUKE), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), ARVALIS - Institut du Végétal [Boigneville], ARVALIS - Institut du végétal [Paris], University of Florida [Gainesville] (UF), 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), Michigan State University [East Lansing], Michigan State University System, DEPARTMENT OF EARTH AND ENVIRONMENTAL SCIENCES MICHIGAN STATE UNIVERSITY USA, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of Nottingham, UK (UON), Université de Liège - Gembloux, Department Terra & AgroBioChem, Gembloux Agro‐Bio Tech, Université de Liège, Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Bonn, Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, University of Hohenheim, Institute of Soil Science and Land Evaluation, Soil Biology Section, Aalto University School of Science and Technology [Aalto, Finland], Wageningen University and Research [Wageningen] (WUR), CSIRO Agriculture and Food (CSIRO), Food Systems Institute [Gainesville] (UF|IFAS), China Agriculture University [Beijing], College of Resources and Environmental Sciences, China Agricultural University (CAU), Royal Institute of Technology [Stockholm] (KTH ), Agriculture and Agri-Food [Ottawa] (AAFC), Ottawa Research and Development Center, Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens (UMR SYSTEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-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)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Global Change Research Institute (CAS), Agroclim (AGROCLIM), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Swedish University of Agricultural Sciences (SLU), Hillridge Technology Pty Ltd, Institute of Bioeconomy (IBE), Consiglio Nazionale delle Ricerche (CNR), Aarhus University [Aarhus], Department of Agroecology, Aarhus University, Tjele, Denmark, Kiel University, Institute of Crop Science and Plant Breeding, Christian-Albrechts University of Kiel, Helmholtz-Zentrum München (HZM), German Res Ctr Environm Hlth, Partenaires INRAE, Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Technische Universität Dresden = Dresden University of Technology (TU Dresden), Université de Carthage - University of Carthage, Institut National de la Recherche Agronomique de Tunisie (INRAT), Forschungszentrum Jülich GmbH | Centre de recherche de Juliers, Helmholtz-Gemeinschaft = Helmholtz Association, Institute of Bio- and Geosciences [Jülich] (IBG), Helmholtz-Gemeinschaft = Helmholtz Association-Helmholtz-Gemeinschaft = Helmholtz Association, Lincoln Agritech Ltd, Nanjing Agricultural University, 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, This work was in part supported by the Collaborative Research Center 1253 CAMPOS (Project 7: Stochastic Modelling Framework), funded by the German Research Foundation (DFG, Grant Agreement SFB 1253/1 2017), the Academy of Finland through projects AI-CropPro (316172 and 315896) and DivCSA (316215) and Natural Resources Institute Finland (Luke) through a strategic project BoostIA, the BonaRes projects 'Soil3' (BOMA 03037514) and 'I4S' (031B0513I) of the Federal Ministry of Education and Research (BMBF), Germany, the German Research Foundation (DFG) under Germany's Excellence Strategy -EXC 2070 -390732324, the project BiomassWeb of the GlobeE programme (Grant number: FKZ031A258B) funded by the Federal Ministry of Education and Research (BMBF, Germany), the EU funded SustEs project (CZ.02.1.01/0.0/0.0/16_019/0000797), the INRAE ACCAF metaprogramme, the German Federal Ministry of Education and Research (BMBF) in the framework of the funding measure 'Soil as a Sustainable Resource for the Bioeconomy -BonaRes', project 'BonaRes (Module B): BonaRes Centre for Soil Research, subproject B' (grant 031B0511B), the National Key Research and Development Program of China (2017YFD0300205), the National Science Foundation for Distinguished Young Scholars (31725020), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), the 111 project (B16026), and China Scholarship Council, the Agriculture and AgriFood Canada's Project 1387 under the Canadian Agricultural Partnership, the DFG Research Unit FOR 1695 `Agricultural Landscapes under Global Climate Change -Processes and Feedbacks on a Regional Scale, the U.S. Department of Agriculture (USDA), National Institute of Food and Agriculture (award no. 2015-68007-23133) and USDA/NIFA HATCH grant N. MCL02368, the National Key Research and Development Program of China (2016YFD0300105), The Broadacre Agriculture Initiative, a research partnership between University of Southern Queensland and the Queensland Department of Agriculture and Fisheries, the JPI FACCE MACSUR2 project, funded by the Italian Ministry for Agricultural, Food, and Forestry Policies (D.M. 24064/7303/15 of 26/Nov/2015). The field work was jointly funded by CSIRO and the Grains Research and Development Corporation (GRDC) under the 'Adding Value to GRDC's National Variety Trial Network' project (CSA00027). The order in which the donors are listed is arbitrary., INRAE, Luke Natural Resources Institute Finland, CSIRO, Arvalis Institut du Végétal, University of Florida, Michigan State University, University of Nottingham, University of Liege, University of Florence, Department of Computer Science, Wageningen University and Research Centre, China Agricultural University, KTH Royal Institute of Technology, Ottawa Research and Development Centre, Leibniz Centre for Agricultural Landscape Research, Swedish University of Agricultural Sciences, National Research Council of Italy, Aarhus University, Helmholtz Zentrum München - German Research Center for Environmental Health, Technische Universität Dresden, University of Carthage, Forschungszentrum Jülich, Aalto-yliopisto, and Aalto University
Subjects: 0106 biological sciences, Atmospheric Science, 010504 meteorology & atmospheric sciences, Calibration (statistics), Structure uncertainty, 01 natural sciences, F01 - Culture des plantes, Aardobservatie en omgevingsinformatica, Statistics, Range (statistics), ddc:550, Evaluation, Applied Ecology, Triticum, Mathematics, 2. Zero hunger, Global and Planetary Change, U10 - Informatique, mathématiques et statistiques, Phenology, Toegepaste Ecologie, Forestry, 04 agricultural and veterinary sciences, Australia, Parameter Uncertainty, Structure Uncertainty, Wheat, technique de prévision, PE&RC, [SDE]Environmental Sciences, Incertitude, Phénologie, Earth Observation and Environmental Informatics, F40 - Écologie végétale, Parameter uncertainty, Benchmark (surveying), Baseline (configuration management), Selection (genetic algorithm), 0105 earth and related environmental sciences, Modélisation des cultures, cultivar selection [EN], Global change, 15. Life on land, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Stage (hydrology), Agronomy and Crop Science, 010606 plant biology & botany
Abstract: Predicting wheat phenology is important for cultivar selection, for effective crop management and provides a baseline for evaluating the effects of global change. Evaluating how well crop phenology can be predicted is therefore of major interest. Twenty-eight wheat modeling groups participated in this evaluation. Model predictions depend not only on model structure but also on the parameter values. This study is thus an evaluation of modeling groups, which choose the structure and fix or estimate the parameters, rather than an evaluation just of model structures. Our target population was wheat fields in the major wheat growing regions of Australia under current climatic conditions and with current local management practices. The environments used for calibration and for evaluation were both sampled from this same target population. The calibration and evaluation environments had neither sites nor years in common, so this is a rigorous evaluation of the ability of modeling groups to predict phenology for new sites and weather conditions. Mean absolute error (MAE) for the evaluation environments, averaged over predictions of three phenological stages and over modeling groups, was 9 days, with a range from 6 to 20 days. Predictions using the multi-modeling group mean and median had prediction errors nearly as small as the best modeling group. For a given modeling group, MAE for the evaluation environments was significantly correlated with MAE for the calibration environments, which suggests that it would be of interest to test ensemble predictors that weight individual modeling groups based on performance for the calibration data. About two thirds of the modeling groups performed better than a simple but relevant benchmark, which predicts phenology by assuming a constant temperature sum for each development stage. The added complexity of crop models beyond just the effect of temperature was thus justified in most cases. Finally, there was substantial variability between modeling groups using the same model structure, which implies that model improvement could be achieved not only by improving model structure, but also by improving parameter values, and in particular by improving calibration techniques.
Published: 2021
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4. 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
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5. Causes of wheat yield gaps and opportunities to advance the water-limited yield frontier in Australia

Author: Zvi Hochman and Heidi Horan
Subjects: 0106 biological sciences, Conventional tillage, Yield (finance), Yield gap, Soil Science, Sowing, 04 agricultural and veterinary sciences, Agricultural engineering, Weed control, 01 natural sciences, Summer fallow, Extreme weather, Frost, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Agronomy and Crop Science, 010606 plant biology & botany, Mathematics
Abstract: Closing the yield gap is essential for global food security and for farmers who face increasing costs of production. Recent work showed that Australia’s wheat growers are achieving about half their water-limited yield. While quantifying the yield gap is a necessary first step towards closing them, the next step is to understand which factors constrain rainfed grain growers from achieving their water-limited yields. Here we conducted in silico experiments over 15 years at 50 weather stations to ascertain the impact on grain yield of suboptimal practices against the ‘best management practice’ rules that were used to calculate the benchmark water-limited yields. Average national losses per suboptimal practice were: the average N fertiliser application rate – 40%; conventional tillage – 33%; suboptimal weed control during the summer fallow – 26%; low seedling density – 12%; and a two week delay in sowing – 7%. Combining two suboptimal practices does not necessarily lead to an additive effect on yield. Other factors that contribute to the yield gap include biotic stresses such as plant diseases, insects and other pests, in-crop weeds and extreme weather events (e.g. floods, strong winds and hail). In addition to calculating the impact of causes of the yield gap we investigated the opportunity to lift the water-limited yield by adopting an emergent new management practice of sowing on an optimised site specific date that is earlier than the traditional sowing window as described for the currently accepted best practice. We found that this emergent practice, matched with slower maturing varieties and additional N inputs as required, has the potential to increase wheat yields nationally by 30%. Frost and heat stress accounted for losses of 16% to 26% depending on the stress function used. Allowing for the impact of frost and heat stress reduced the yield potential of both the current and emergent water-limited yields yet it did not reduce the advantage of the emergent practice.
Published: 2018
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6. Seasonal climate forecasts provide more definitive and accurate crop yield predictions

Author: Dean Holzworth, Zvi Hochman, Heidi Horan, and Jaclyn N. Brown
Subjects: Atmospheric Science, Global and Planetary Change, 010504 meteorology & atmospheric sciences, Yield (finance), Forestry, 04 agricultural and veterinary sciences, 01 natural sciences, Decile, Climatology, 040103 agronomy & agriculture, Range (statistics), 0401 agriculture, forestry, and fisheries, Environmental science, Climate model, Baseline (configuration management), Agronomy and Crop Science, Cropping, Lead time, 0105 earth and related environmental sciences, Downscaling
Abstract: As a cropping season progresses yield forecasts become more reliable. Optimal management strategies however rely on early estimates of climate and yield. These estimates are usually derived from the historic range of climate variations applied to current crop conditions. Early in the season these predictions are wide ranging as they incorporate all past historic climate variability and hence a large range of possible yields. Dynamical seasonal climate models offer the opportunity to narrow this range contingent on the models having adequate predictive skill. This study explores the benefits of using a climate model over historical climate to predict wheat yield in the Australian cropping zone throughout the cropping season. We take an ensemble of daily outputs of temperature, radiation and rainfall from a seasonal climate model (POAMA) and apply a simple downscaling and calibration to align with 57 stations across the Australian cropping zone. These data are then used as an input to a crop model (APSIM) to translate seasonal conditions into a yield prediction. Simulations deploy historic weather data up to a date on which forecast data replace measured data. Here we used a range of dates (April to October) through the cropping season for the period 1981 to 2015, to determine where and when the forecast is skilful compared to using the full weather record up to harvest. The forecasts are categorised in three yield categories low (decile 1–3), average (4–7) and high (8–10) and determined to be ‘misleading’ if they predict low instead of high or vice versa. In the west and south of Australia less than 3 years in 20 give a misleading forecast in April, and less than 1 in 20 years by August. The forecast for east of Australia has less skill primarily due to a strong rainfall bias with the climate model not being able to simulate the correct daily rainfall patterns. Compared to the predictions gained from using the full range of historical climate, POAMA derived forecasts have a narrower prediction range than the climatology driven ones, however this comes at the expense of a higher number of misleading forecasts. Nevertheless, in June (August) the POAMA driven simulations have a greater than 65% (80%) chance of being in the correct or one category out, which was higher than using climatology in each region at the same lead time. The baseline set by this study demonstrates the potential utility of dynamic climate models to predict yield, which should only improve with on-going advances in climate modelling and techniques in downscaling.
Published: 2018
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7. Evaluation of the APSIM model in cropping systems of Asia

Author: A.K. Chaki, Jing Wang, Lalith Suriyagoda, Heidi Horan, R. Darai, Balwinder-Singh, Zvi Hochman, M.A.P.W.K. Malaviachichi, B. Power, E.Y. Hosang, Md. Moklesur Rahman Sarker, Arun K. Das, M.A. Muttaleb, Guocheng Wang, Burhan U. Choudhury, Alison M. Laing, Azhar Khan, R.K. Yadav, N. Subash, Ibne Karim M. Ali, Chao Chen, Faheem Ahmed, Christian Roth, Enli Wang, Donald S. Gaydon, Muzamil Rashid, K.P. Mohapatra, S. Akhter, P. Vijaya Kumar, A. Suriadi, M. Shamim, Gaurav Rai, Lily Y. Liu, Ando M. Radanielson, Basim Ahmad, P. L. Poulton, W.M.U.K. Rathanayake, D.R. Sena, and R.P.R.K. Amarasingha
Subjects: 0106 biological sciences, Agroforestry, business.industry, Conservation agriculture, Simulation modeling, Soil Science, 04 agricultural and veterinary sciences, Agricultural engineering, Multiple cropping, 01 natural sciences, Agriculture, Sustainability, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Agricultural productivity, business, Agronomy and Crop Science, Cropping, Water use, 010606 plant biology & botany
Abstract: Resource shortages, driven by climatic, institutional and social changes in many regions of Asia, combined with growing imperatives to increase food production whilst ensuring environmental sustainability, are driving research into modified agricultural practices. Well-tested cropping systems models that capture interactions between soil water and nutrient dynamics, crop growth, climate and farmer management can assist in the evaluation of such new agricultural practices. One such cropping systems model is the Agricultural Production Systems Simulator (APSIM). We evaluated APSIM’s ability to simulate the performance of cropping systems in Asia from several perspectives: crop phenology, production, water use, soil dynamics (water and organic carbon) and crop CO 2 response, as well as its ability to simulate cropping sequences without reset of soil variables. The evaluation was conducted over a diverse range of environments (12 countries, numerous soils), crops and management practices throughout the region. APSIM’s performance was statistically assessed against assembled replicated experimental datasets. Once properly parameterised, the model performed well in simulating the diversity of cropping systems to which it was applied with RMSEs generally less than observed experimental standard deviations (indicating robust model performance), and with particular strength in simulation of multi-crop sequences. Input parameter estimation challenges were encountered, and although ‘work-arounds’ were developed and described, in some cases these actually represent model deficiencies which need to be addressed. Desirable future improvements have been identified to better position APSIM as a useful tool for Asian cropping systems research into the future. These include aspects related to harsh environments (high temperatures, diffuse light conditions, salinity, and submergence), conservation agriculture, greenhouse gas emissions, as well as aspects more specific to Southern Asia and low input systems (such as deficiencies in soil micro-nutrients).
Published: 2017
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8. Smallholder farmers managing climate risk in India: 2. Is it climate-smart?

Author: Alison M. Laing, Kamalesh K. Singh, Heidi Horan, Donald S. Gaydon, Philip Kokic, D. Raji Reddy, Christian Roth, Ravindra Adusumilli, Zvi Hochman, G. Sreenivas, and Chiranjeevi Tallapragada
Subjects: 010504 meteorology & atmospheric sciences, business.industry, Climate risk, Global warming, Environmental resource management, Climate change, 04 agricultural and veterinary sciences, 01 natural sciences, Gross margin, Greenhouse gas, Sustainability, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Animal Science and Zoology, business, Agronomy and Crop Science, Cropping, 0105 earth and related environmental sciences, Downscaling
Abstract: Research about adaptation of crops to climate change at a regional scale is based on simplifying assumptions about current and future weather and about farmer management practices. Additionally, the impacts of adaptations are usually measured only in production terms and the feasibility of implementing proposed adaptations is rarely tested. In this study into adaptations of rice based cropping systems to future climate scenarios in Telangana, India, all adaptations were generated through participatory engagement, and were field-tested with local smallholder households in three villages as well as by cropping system simulation analysis. Adaptation options were first evaluated for historical climate variability, with outcomes assessed in terms of production, profitability and environmental consequences before they were evaluated as climate-smart adaptations to medium term climate change. In an earlier study, participatory intervention at household level was used to identify and evaluate new practices. These adaptations to climate variability were then tested with the cropping systems simulator APSIM on local historical weather data. Here we test the applicability of these adaptations to likely climate scenarios in 2021–2040 by using and statistically downscaling two contrasting global circulation models to generate contrasting climate change scenarios for each location. Adaptations were simulated with these future climate data sets and evaluated in terms of their gross margin, yield, yield stability, gross margin stability, global warming potential, greenhouse gas emissions intensity and, where irrigation treatments were varied, net water use, irrigation water productivity, contribution to the recharge of aquifers and nitrogen leached from the root zone. Compared with variability in historic yields the simulated yield changes in 2021–2040 climate scenarios were modest and their direction was dependent on the global circulation model used. Sustainability polygons were used to compare historic and future climate scenarios. These polygons clearly showed that adaptation options mostly resulted in trade-offs between productivity and environmental outcomes and between competing environmental outcomes. Results that were simulated for historic weather were strongly reflected in the two future weather scenarios, leading to the conclusion that participatory action research with smallholder farmers, coupled with field testing and simulation analysis can produce practical, sustainable and productive adaptations to climate variability that are also climate smart in that they are robust for future climate scenarios to 2021–2040. We propose that sustainability polygons may be a useful quantitative tool for analysis of the degree to which adaptations may be regarded as climate smart.
Published: 2017
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9. High temporal resolution of leaf area data improves empirical estimation of grain yield

Author: François Waldner, Heidi Horan, Yang Chen, and Zvi Hochman
Subjects: Crops, Agricultural, 010504 meteorology & atmospheric sciences, Yield (finance), lcsh:Medicine, Empirical Research, 01 natural sciences, Article, bepress|Life Sciences, Statistics, EarthArXiv|Life Sciences, Range (statistics), Relevance (information retrieval), Satellite imagery, Leaf area index, Dimension (data warehouse), lcsh:Science, Weather, Triticum, 0105 earth and related environmental sciences, Mathematics, Multidisciplinary, Crop yield, EarthArXiv|Life Sciences|Agriculture, lcsh:R, Australia, Empirical modelling, 04 agricultural and veterinary sciences, Models, Theoretical, Plant Leaves, Environmental sciences, bepress|Life Sciences|Agriculture, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, lcsh:Q, Edible Grain, Plant sciences
Abstract: Empirical yield estimation from satellite data has long lacked suitable combinations of spatial and temporal resolutions. Consequently, the selection of metrics, i.e., temporal descriptors that predict grain yield, has likely been driven by practicality and data availability rather than by systematic targetting of critically sensitive periods as suggested by knowledge of crop physiology. The current trend towards hyper-temporal data raises two questions: How does temporality affect the accuracy of empirical models? Which metrics achieve optimal performance? We followed an in silico approach based on crop modelling which can generate any observation frequency, explore a range of growing conditions and reduce the cost of measuring yields in situ. We simulated wheat crops across Australia and regressed six types of metrics derived from the resulting time series of Leaf Area Index (LAI) against wheat yields. Empirical models using advanced LAI metrics achieved national relevance and, contrary to simple metrics, did not benefit from the addition of weather information. This suggests that they already integrate most climatic effects on yield. Simple metrics remained the best choice when LAI data are sparse. As we progress into a data-rich era, our results support a shift towards metrics that truly harness the temporal dimension of LAI data.
Published: 2019
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10. 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
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11. Climate trends account for stalled wheat yields in Australia since 1990

Author: Zvi Hochman, David Gobbett, and Heidi Horan
Subjects: Crops, Agricultural, 0106 biological sciences, Climate, Climate Change, Yield (finance), Climate change, Body weight, 01 natural sciences, Environmental Chemistry, Triticum, General Environmental Science, Global and Planetary Change, Maximum temperature, Food security, Ecology, Crop yield, Australia, Sowing, Soil classification, 04 agricultural and veterinary sciences, Agronomy, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Edible Grain, 010606 plant biology & botany
Abstract: Global food security requires that grain yields continue to increase to 2050, yet yields have stalled in many developed countries. This disturbing trend has so far been only partially explained. Here, we show that wheat yields in Australia have stalled since 1990 and investigate the extent to which climate trends account for this observation. Based on simulation of 50 sites with quality weather data, that are representative of the agro-ecological zones and of soil types in the grain zone, we show that water-limited yield potential declined by 27% over a 26 year period from 1990 to 2015. We attribute this decline to reduced rainfall and to rising temperatures while the positive effect of elevated atmospheric CO2 concentrations prevented a further 4% loss relative to 1990 yields. Closer investigation of three sites revealed the nature of the simulated response of water-limited yield to water availability, water stress and maximum temperatures. At all three sites, maximum temperature hastened time from sowing to flowering and to maturity and reduced grain number per m2 and average weight per grain. This 27% climate-driven decline in water-limited yield is not fully expressed in actual national yields. This is due to an unprecedented rate of technology-driven gains closing the gap between actual and water-limited potential yields by 25 kg ha−1 yr−1 enabling relative yields to increase from 39% in 1990 to 55% in 2015. It remains to be seen whether technology can continue to maintain current yields, let alone increase them to those required by 2050.
Published: 2017
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12. Smallholder farmers managing climate risk in India: 1. Adapting to a variable climate

Author: Ravindra Adusumilli, Zvi Hochman, Donald S. Gaydon, D. Raji Reddy, Kamalesh K. Singh, Heidi Horan, Christian Roth, Chiranjeevi Tallapragada, and Gade Sreenivas
Subjects: 010504 meteorology & atmospheric sciences, business.industry, Agroforestry, Climate risk, Climate change, 04 agricultural and veterinary sciences, 01 natural sciences, Gross margin, Water resources, Geography, Agriculture, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Animal Science and Zoology, Cropping system, Agricultural productivity, business, Agronomy and Crop Science, Cropping, 0105 earth and related environmental sciences
Abstract: This paper describes an investigation of various adaptations of rice based cropping systems to climate variability in India's Telangana State. All adaptations were generated through participatory engagement and were field-tested with local smallholder households before being evaluated through cropping system simulation analysis. This approach contrasts with most research about adaptation of cropping systems to climate variability and climate change that is mostly based on simplifying assumptions about current farmer management practices and where the feasibility of implementing proposed adaptations is rarely tested. In this study, the investigation commenced with discussions about climate related issues in rice based farming systems between researchers, farmers and NGOs in three villages in three Mandals of the state of Telangana. Participatory intervention was used to identify new practices that could provide more adaptive and robust responses to climate variability. Suggested adaptations were implemented in on-farm experimentation. Fields demonstrating these adaptations were monitored and results were discussed with participating farmers at regular ‘Climate Club’ village meetings. Crop and soil data from these fields were used to locally parameterise the cropping systems simulator APSIM. Local adaptations that were trialled in the villages were simulated using local soil and long term historical weather data. In each of the case studies, a number of adaptations that were developed and implemented in the villages were shown through simulation to be successful in terms of agricultural production, stability of yields and resource use efficiency. Of the adaptations investigated, sowing rules to reduce the chance of crop failure due to early dry spells were most readily adopted and are also relatively easy to extend to other villages. Strategic irrigation of rainfed crops such as maize and cotton resulted in significant gains to profitability and stability of these crops but cannot be considered in isolation where access to water is limited. Reduced irrigation of rice resulted in over 60 mm/ha/yr. savings in water and some improvements in gross margins but this adaptation was not popular with farmers due to its burden on labour and added risks associated with unreliable supply of electricity for pumping at critical times. The reduced rice area for strategic irrigation of rainfed crops adaptation resulted in improved gross margins per hectare per year and higher net water productivity. This adaptation is most promising but will require institutional change around water use policy and more equitable allocation of limited water resources within villages. These results led us to the proposition that participatory action research with smallholder farmers, coupled with field-testing and simulation analysis can produce practical and productive adaptations to climate variability.
Published: 2017
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13. Data rich yield gap analysis of wheat in Australia

Author: David Gobbett, Heidi Horan, Zvi Hochman, and Javier Navarro Garcia
Subjects: 010504 meteorology & atmospheric sciences, Ecology, Yield (finance), Crop yield, Yield gap, Soil Science, High density, 04 agricultural and veterinary sciences, 01 natural sciences, Relative yield, Agronomy, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Current technology, Agronomy and Crop Science, Analysis method, 0105 earth and related environmental sciences, Mathematics
Abstract: Closing the gap between yields currently achieved on farms and those that can be achieved with best practice and current technology in a given environment is a key strategy for increasing grain production on existing cropland. In this study, the yield gap of rainfed wheat in Australia was derived by a data rich (DR) analysis method made possible by the high density of data available in the Australian grain zone. At the national scale, the analysis revealed that Australia’s 15 year (1996–2010) average actual wheat yield (Ya) was 1.7 t/ha, while the simulated water-limited yield potential (Yw) was 3.5 t/ha; thus the yield gap (Yg = Yw-Ya) was 1.8 t/ha; and the relative yield (Y % = 100 x Ya/Yw) was 49%. The fifteen year average Ya and Yw values for each of the 245 statistical local areas (SLAs) in Australia’s wheat zone were strongly and positively correlated (Ya = 0.456 x Yw + 0.15; R 2 = 0.65; RMSE = 0.291; p 2 = 0.95; RMSE = 0.087; p
Published: 2016
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14. Yield gap analysis of rainfed wheat demonstrates local to global relevance

Author: Kenneth G. Cassman, Zvi Hochman, J. Navarro Garcia, Heidi Horan, Patricio Grassini, and David Gobbett
Subjects: 0106 biological sciences, Winter cereal, business.industry, Yield gap, Distribution (economics), Sowing, Context (language use), 04 agricultural and veterinary sciences, 01 natural sciences, Agricultural science, Agronomy, Agriculture, 040103 agronomy & agriculture, Genetics, 0401 agriculture, forestry, and fisheries, Animal Science and Zoology, Agricultural productivity, business, Agronomy and Crop Science, Cropping, 010606 plant biology & botany, Mathematics
Abstract: SUMMARYAustralia has a role to play in future global food security as it contributes 0·12 of global wheat exports. How much more can it contribute with current technology and varieties? The present paper seeks to quantify the gap between water-limited yield potential (Yw) and farmer yields (Ya) for wheat in Australia by implementing a new protocol developed by the Global Yield Gap and Water Productivity Atlas (GYGA) project. Results of past Australian yield gap studies are difficult to compare with studies in other countries because they were conducted using a variety of methods and at a range of scales. The GYGA project protocols were designed to facilitate comparisons among countries through the application of a consistent yet flexible methodology. This is the first implementation of GYGA protocols in a country with the high spatial and temporal climatic variability that exists in Australia.The present paper describes the application of the GYGA protocol to the whole Australian grain zone to derive estimates of rainfed wheat yield gap. The Australian grain zone was partitioned into six key agro-climatic zones (CZs) defined by the GYGA Extrapolation Domain (GYGA-ED) zonation scheme. A total of 22 Reference Weather Stations (RWS) were selected, distributed among the CZs to represent the entire Australian grain zone. The Agricultural Production Systems sIMulator (APSIM) Wheat crop model was used to simulate Yw of wheat crops for major soil types at each RWS from 1996 to 2010. Wheat varieties, agronomy and distribution of wheat cropping were held constant over the 15-year period. Locally representative dominant soils were selected for each RWS and generic sowing rules were specified based on local expertise. Actual yield (Ya) data were sourced from national agricultural data sets. To upscale Ya and Yw values from RWS to CZs and then to national scale, values were weighted according to the area of winter cereal cropping within RWS buffer zones. The national yield gap (Yg = Yw–Ya) and relative yield (Y% = 100 × Ya/Yw) were then calculated from the weighted values.The present study found that the national Yg was 2·0 tonnes (t)/ha and Y% was 47%. The analysis was extended to consider factors contributing to the yield gap. It was revealed that the RWS 15-year average Ya and Yw were strongly correlated (R2 = 0·76) and that RWS with higher Yw had higher Yg. Despite variable seasonal conditions, Y% was relatively stable over the 15 years. For the 22 RWS, average Yg correlated positively and strongly with average annual rainfall amount, but surprisingly it correlated poorly with RWS rainfall variability. Similarly, Y% correlated negatively but less strongly (R2 = 0·33) with RWS average annual rainfall, and correlated poorly with RWS rainfall variability, which raises questions about how Australian farmers manage climate risk. Interestingly a negative relationship was found between Yg and variability of Yw for the 22 RWS (R2 = 0·66), and a positive relationship between Y% and Yw variability (R2 = 0·23), which suggests that farmers in lower yielding, more variable sites are achieving yields closer to Yw. The Yg estimates appear to be quite robust in the context of estimates from other Australian studies, adding confidence to the validity of the GYGA protocol. Closing the national yield gap so that Ya is 0·80 of Yw, which is the level of Yg closure achieved consistently by the most progressive Australian farmers, would increase the average annual wheat production (20·9 million t in 1996/07 to 2010/11) by an estimated 15·3 million t, which is a 72% increase. This indicates substantial potential for Australia to increase wheat production on existing farmland areas using currently available crop varieties and farming practices and thus make a substantial contribution to achieving future global food security.
Published: 2016
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15. Climate drivers provide valuable insights into late season prediction of Australian wheat yield

Author: François Waldner, Heidi Horan, Zvi Hochman, Jaclyn N. Brown, Yang Chen, Roger Lawes, Kavina Dayal, and Randall J. Donohue
Subjects: 0106 biological sciences, Atmospheric Science, Global and Planetary Change, 010504 meteorology & atmospheric sciences, Yield (finance), Southern oscillation, Forestry, 01 natural sciences, General Circulation Model, Climatology, Late season, Environmental science, Indian Ocean Dipole, Data input, Agronomy and Crop Science, Cropping, 010606 plant biology & botany, 0105 earth and related environmental sciences
Abstract: Foresight of grain yields prior to harvest would be empowering for many stakeholders along the supply chain from farmers through to bulk handlers, banks and insurance companies. Estimating Australian grain production ahead of harvest is difficult for many reasons including the highly variable year to year rainfall. The rainfall in the final months prior to harvest, can be crucial to final harvest totals. Here we explore the importance of rainfall from September 1, which broadly corresponds to the close of the top-dressing fertilizer application window, for the remaining cropping season in determining final yield. This is assessed via sensitivity analysis of water-limited wheat potential yield totals from historical climate in the APSIM crop model. At locations where the rainfall influences wheat yield, we compare three methods to forecast wheat yields that differ based on the climate data input: 1) climatology approach, which uses 30 years of observed climate data, 2) analogue climatology, which uses information from climate drivers (El-Nino Southern Oscillation and Indian Ocean Dipole) to create analogue years; and 3) dynamical climate forecasts from a general circulation model (ACCESS-S). We find that potential yields strongly depend on in-season plant available water (PAW) where years with high PAW are unaffected by the late season rainfall. Predicting the potential yield from analogue climatology (climate drivers) had the greatest skill, with smallest Root Mean Squared Error of 0.45 t/ha. This approach ranked first for 42% of the study locations compared to the climatology and ACCESS-S forecasting methods. This knowledge can help inform decision makers about the need to incorporate seasonal climate forecasts and the most appropriate climate forecasting method.
Published: 2020
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16. Yield potential determines Australian wheat growers’ capacity to close yield gaps while mitigating economic risk

Author: Zvi Hochman, Marta Monjardino, Heidi Horan, and CSIRO Agriculture and Food (CSIRO)
Subjects: 0106 biological sciences, Matching (statistics), Environmental Engineering, Yield gap analysis, Best practice, Yield (finance), [SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy, Agricultural engineering, Decision analysis, 01 natural sciences, Agronomic management, Socio-economic yield gap, Mathematics, 2. Zero hunger, Sustainable development, Risk aversion, business.industry, Yield gap, [SDV.SA.AEP]Life Sciences [q-bio]/Agricultural sciences/Agriculture, economy and politics, 04 agricultural and veterinary sciences, 15. Life on land, Dryland cropping, Agronomic efficiency yield gap, Agriculture, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Allocative efficiency, Innovation yield gap, business, Agronomy and Crop Science, 010606 plant biology & botany
Abstract: International audience; Australia’s farmers are among the most efficient in the world, despite a relatively large gap between potential and achieved water-limited grain yield. With wheat yield gaps typically > 1.7 t/ha or 50% of the water-limited yield, it is important to investigate the degree to which this gap may be attributable to (rational) subprofit-maximising input levels in response to risk and risk aversion in many major grain-growing regions, particularly those with lower and more variable rainfall. Here, we use a set of 14 case study sites across the Australian wheatbelt to examine the risk-return profile of several agronomic management practices and show the extent to which the farmers’ risk attitude determines their decision-making. Using a novel profit-risk-utility framework that incorporates crop simulation, probability theory, finance techniques and risk-aversion analysis, we were able to better demonstrate how farmers might select practices that manage economic risk across sites ranging from low to high rainfall. Results varied with risk preference and yield potential. However, there are real opportunities to close the yield gap by adopting non-limiting or near non-limiting nitrogen fertiliser practices and controlling fallow weeds. We show for the first time that yields associated with current best practice can be surpassed for most levels of risk aversion by adopting an emergent practice of optimising the site-specific time of sowing and matching variety to time of sowing. For some sites and risk profiles, the emerging best practice package which includes additional N fertiliser is also profitable under risk. We also propose a modified integrated framework for yield gaps. Here, we distinguish allocative input constrains due to risk aversion from those due to access to resources, and we account for an innovation gap where the current agronomic frontier is shifted upwards by growers successfully, implementing new technologies that are not yet part of current best practice.
Published: 2019
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17. Socio-psychological and management drivers explain farm level wheat yield gaps in Australia

Author: Zvi Hochman, Airong Zhang, Javier Navarro, Heidi Horan, François Waldner, and Bianca Das
Subjects: 0106 biological sciences, Environmental Engineering, [SDV]Life Sciences [q-bio], Soil type, 01 natural sciences, Agricultural economics, Crop rotation, Consulting services, Path analysis (statistics), 2. Zero hunger, Sustainable development, Food security, business.industry, Crop yield, 04 agricultural and veterinary sciences, World population, 15. Life on land, Nitrogen fertiliser, Geography, Crop diversity, Agriculture, Sustainability, Wheat, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, business, Agronomy and Crop Science, 010606 plant biology & botany, Crop management, Technology adoption
Abstract: International audience; AbstractAchieving sustainable global food security for a rapidly growing world population is one of the greatest challenges of our time. Producing more food efficiently by closing the yield gaps is regarded as a promising solution to address this challenge without further expanding farming land. However, there is limited understanding of the causes contributing to yield gaps. The present study aimed to comprehensively examine three dimensions of the causes for the wheat yield gaps in Australia: farm management practices, farm characteristics and grower characteristics. Computer-assisted telephone interviews of 232 wheat producers from 14 contrasting local areas were conducted. The data collected on these three dimensions were used to develop a comprehensive framework to understand causes of yield gaps. Results reveal significant differences between farms with smaller yield gaps and those with greater yield gaps in relation to farming management as well as farm and grower characteristics. Findings further underline that farms with smaller yield gaps are likely to be smaller holdings growing less wheat on more favourable soil types, are more likely to apply more N fertiliser, to have a greater crop diversity, to soil-test a greater proportion of their fields, to have fewer resistant weeds, to adopt new technologies, and are less likely to grow wheat following either cereal crops or a pasture. They are more likely to use and trust a fee-for-service agronomist, and have a university education. The dynamic relationships between grower characteristics and farm management practices in causing yield gaps are further highlighted through a path analysis. This study is the first to demonstrate that yield gaps are the result of the intertwined dynamics between biophysical factors, grower socio-psychological characteristics and farm management practices. Socio-psychological factors not only directly contribute to yield gaps, but they also influence farm management practices that in turn contribute to yield gaps. Our findings suggest that, to close wheat yield gaps, it is important to develop integrated strategies that address both socio-psychological and farm management dimensions.
Published: 2019
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18. 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
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18 results on '"Heidi Horan"'

1. The chaos in calibrating crop models:Lessons learned from a multi-model calibration exercise

2. How well do crop modeling groups predict wheat phenology, given calibration data from the target population?

3. Multi-model evaluation of phenology prediction for wheat in Australia

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8. Smallholder farmers managing climate risk in India: 2. Is it climate-smart?

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

11. Climate trends account for stalled wheat yields in Australia since 1990

12. Smallholder farmers managing climate risk in India: 1. Adapting to a variable climate

13. Data rich yield gap analysis of wheat in Australia

14. Yield gap analysis of rainfed wheat demonstrates local to global relevance

15. Climate drivers provide valuable insights into late season prediction of Australian wheat yield

16. Yield potential determines Australian wheat growers’ capacity to close yield gaps while mitigating economic risk

17. Socio-psychological and management drivers explain farm level wheat yield gaps in Australia

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

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