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

Author

Wallach, Daniel, Palosuo, Taru, Thorburn, Peter, Hochman, Zvi, Gourdain, Emmanuelle, Andrianasolo, Fety, Asseng, Senthold, Basso, Bruno, Buis, Samuel, Crout, Neil, Dibari, Camilla, Dumont, Benjamin, Ferrise, Roberto, Gaiser, Thomas, Garcia, Cecile, Gayler, Sebastian, Ghahramani, Afshin, Hiremath, Santosh, Hoek, Steven, Horan, Heidi, Hoogenboom, Gerrit, Huang, Mingxia, Jabloun, Mohamed, Jansson, Per-Erik, Jing, Qi, Justes, Eric, Kersebaum, Kurt Christian, Klosterhalfen, Anne, Launay, Marie, Lewan, Elisabet, Luo, Qunying, Maestrini, Bernardo, Mielenz, Henrike, Moriondo, Marco, Nariman Zadeh, Hasti, Padovan, Gloria, Olesen, Jørgen Eivind, Poyda, Arne, Priesack, Eckart, Pullens, Johannes Wilhelmus Maria, Qian, Budong, Schütze, Niels, Shelia, Vakhtang, Souissi, Amir, Specka, Xenia, Srivastava, Amit Kumar, Stella, Tommaso, Streck, Thilo, Trombi, Giacomo, Wallor, Evelyn, Wang, Jing, Weber, Tobias K.D., Weihermüller, Lutz, de Wit, Allard, Wöhling, Thomas, Xiao, Liujun, Zhao, Chuang, Zhu, Yan, and Seidel, Sabine J.

Published
2021
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4. Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment

Author

Dueri, Sibylle, Brown, Hamish, Asseng, Senthold, Ewert, Frank, Webber, Heidi, George, Mike, Craigie, Rob, Guarin, Jose Rafael, Pequeno, Diego N.L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip D., Basso, Bruno, Berger, Andres G., Mujica, Gennady Bracho, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres, Elias, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, Garcia-Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Padovan, Gloria, Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ramos, Margarita Ruiz, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, Martre, Pierre, Rebetzke, Greg, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, 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), The New Zealand Institute for Plant & Food Research Limited [Auckland] (Plant & Food Research), Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Institut für Nutzpflanzenwissenschaften und Ressourcenschutz (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Brandenburg University of Technology [Cottbus – Senftenberg] (BTU), Foundation for Arable Research (FAR), University of Florida [Gainesville] (UF), Earth Institute at Columbia University, Columbia University [New York], International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Swedish University of Agricultural Sciences (SLU), Pir Mehr Ali Shah Arid Agriculture University = PMAS-Arid Agriculture University Rawalpindi (AAUR), Oklahoma State University [Stillwater] (OSU), Michigan State University [East Lansing], Michigan State University System, Instituto Nacional de Investigación Agropecuaria (INIA), Georg-August-University = Georg-August-Universität Göttingen, Aarhus University [Aarhus], Institute of geographical sciences and natural resources research [CAS] (IGSNRR), Chinese Academy of Sciences [Beijing] (CAS), Gembloux Agro-Bio Tech [Gembloux], Université de Liège, Instituto de Agricultura Sostenible - Institute for Sustainable Agriculture (IAS CSIC), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Universidad de Córdoba = University of Córdoba [Córdoba], Department of Agriculture, Food, Environment and Forestry (DAGRI), Università degli Studi di Firenze = University of Florence (UniFI), Institute of Crop Science and Resource Conservation [Bonn] (INRES), University of Hohenheim, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Global Change Research Centre (CzechGlobe), University of Potsdam = Universität Potsdam, Natural Resources Institute Finland (LUKE), Helmholtz Zentrum München = German Research Center for Environmental Health, German Research Center for Environmental Health - Helmholtz Center München (GmbH), Institute of Biochemical Plant Pathology (BIOP), Centro de Estudios e Investigación para la Gestión de Riesgos Agrarios y Medioambientales (CEIGRAM), Universidad Politécnica de Madrid (UPM), Universidad de Castilla-La Mancha = University of Castilla-La Mancha (UCLM), Centre for Biodiversity and Sustainable Land-use [University of Göttingen] (CBL), Rothamsted Research, Biotechnology and Biological Sciences Research Council (BBSRC), Washington State University (WSU), Wageningen University and Research [Wageningen] (WUR), Zhejiang University, Nanjing Agricultural University (NAU), China Agricultural University (CAU), Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4 and was supported by the French National Research Institute for Agriculture, Food (INRAE) and the International Maize and Wheat Improvement Center (CIMMYT) through the International Wheat Yield Partnership (IWYP, grant IWYP115)., metaprogram Agriculture and forestry in the face of climate change: adaptation and mitigation (CLIMAE) of INRAE, grant-aided support from the Biotechnology and Biological Sciences Research Council (BBSRC) through Designing Future Wheat [BB/P016855/1] and Achieving Sustainable Agricultural Systems [NE/N018125/1] jointly funded with NERC, DivCSA project funded by the Academy of Finland (decision no. 316215)., National Natural Science Foundation of China (No. 31761143006), financial support from BARISTA project (031B0811A) through ERA-NET SusCrop under EU-FACCE JPI, German Federal Ministry of Education and Research (BMBF) through the BonaRes project ’’I4S’’ (031B0513I), German Federal Ministry of Education and Research (BMBF) through the BonaRes Project 'Soil3' (FKZ 031B0026A), Ministry of Education, Youth and Sports of Czech Republic through SustES—Adaption strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/000797), Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC 2070 – 390732324', German Research Foundation (DFG, Grant Agreement SFB 1253/1 2017), European Project: 618105,EC:FP7:KBBE,FP7-ERANET-2013-RTD,FACCE ERA NET PLUS(2013), Institut National de la Recherche Agronomique (France), International Maize and Wheat Improvement Center, International Wheat Yield Partnership, National Natural Science Foundation of China, European Commission, Federal Ministry of Education and Research (Germany), Ministry of Education, Youth and Sports (Czech Republic), German Research Foundation, Biotechnology and Biological Sciences Research Council (UK), Natural Environment Research Council (UK), and Academy of Finland

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Physiology, Climate Change, sowing date, Plant Science, CHINA, Multi-model Ensemble, New Zealand, Sowing Date, Sowing Density, Tiller Mortality, Tillering, Wheat, Yield Potential, tillering, wheat, USE EFFICIENCY, sowing density, Life Science, Biomass, ADAPTATION, PLANT-DENSITY, Triticum, METAANALYSIS, Multi-model ensemble, WIMEK, CLIMATE-CHANGE, tiller mortality, PRODUCTIVITY, Temperature, CROP MODELS, yield potential, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, ROTATION, GROWTH, Water Systems and Global Change, Seasons
Abstract

Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures., This study was a part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4 and was supported by the French National Research Institute for Agriculture, Food (INRAE) and the International Maize and Wheat Improvement Center (CIMMYT) through the International Wheat Yield Partnership (IWYP, grant IWYP115). SD and PM acknowledge support from the metaprogram Agriculture and forestry in the face of climate change: adaptation and mitigation (CLIMAE) of INRAE. YC and FT acknowledge support from the National Natural Science Foundation of China (No. 31761143006). RPR and GBM acknowledge financial support from BARISTA project (031B0811A) through ERA-NET SusCrop under EU-FACCE JPI. KCK was funded by the German Federal Ministry of Education and Research (BMBF) through the BonaRes project ’’I4S’’ (031B0513I). AS and TG acknowledge funding by the German Federal Ministry of Education and Research (BMBF) through the BonaRes Project “Soil3” (FKZ 031B0026A). KCK and JEO were supported by the Ministry of Education, Youth and Sports of Czech Republic through SustES—Adaption strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/000797). FE acknowledges support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC 2070 – 390732324”. TKDW was funded by the German Research Foundation (DFG, Grant Agreement SFB 1253/1 2017). MAS and NS at Rothamsted Research received grant-aided support from the Biotechnology and Biological Sciences Research Council (BBSRC) through Designing Future Wheat [BB/P016855/1] and Achieving Sustainable Agricultural Systems [NE/N018125/1] jointly funded with NERC. TP and FT are supported by the DivCSA project funded by the Academy of Finland (decision no. 316215).

Published
2022

5. Proposal and extensive test of a calibration protocol for crop phenology models

Author

Wallach, Daniel, Palosuo, Taru, Thorburn, Peter J., Mielenz, Henrike, Buis, Samuel, Hochman, Zvi, Gourdain, Emmanuelle, Andrianasolo, Fety, Dumont, Benjamin, Ferrise, Roberto, Gaiser, Thomas, Garcia, Cécile, Gayler, Sebastian, Harrison, Matthew, Hiremath, Santosh, Horan, Heidi, Hoogenboom, Gerrit, Jansson, Per-Erik, Jing, Qi, Justes, Eric, Kersebaum, Kurt Christian, Launay, Marie, Lewan, Elisabet, Liu, Ke, Mequanint, Fasil, Moriondo, Marco, Nendel, Claas, Padovan, Gloria, Qian, Budong, Schütze, Niels, Seserman, Diana‑Maria, Shelia, Vakhtang, Souissi, Amir, Specka, Xenia, Srivastava, Amit Kumar, Trombi, Giacomo, Weber, Tobias K. D., Weihermüller, Lutz, Wöhling, Thomas, Seidel, Sabine I., Wallach, Daniel, Palosuo, Taru, Thorburn, Peter J., Mielenz, Henrike, Buis, Samuel, Hochman, Zvi, Gourdain, Emmanuelle, Andrianasolo, Fety, Dumont, Benjamin, Ferrise, Roberto, Gaiser, Thomas, Garcia, Cécile, Gayler, Sebastian, Harrison, Matthew, Hiremath, Santosh, Horan, Heidi, Hoogenboom, Gerrit, Jansson, Per-Erik, Jing, Qi, Justes, Eric, Kersebaum, Kurt Christian, Launay, Marie, Lewan, Elisabet, Liu, Ke, Mequanint, Fasil, Moriondo, Marco, Nendel, Claas, Padovan, Gloria, Qian, Budong, Schütze, Niels, Seserman, Diana‑Maria, Shelia, Vakhtang, Souissi, Amir, Specka, Xenia, Srivastava, Amit Kumar, Trombi, Giacomo, Weber, Tobias K. D., Weihermüller, Lutz, Wöhling, Thomas, and Seidel, Sabine I.

Abstract

A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. We propose an original, detailed approach for calibration of such models, which we refer to as a calibration protocol. The protocol covers all the steps in the calibration workflow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values, and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are “obligatory” parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their “usual” calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and reduced prediction error by 11%.

Published
2023

6. A high-yielding traits experiment for modeling potential production of wheat: field experiments and AgMIP-Wheat multi-model simulations

Author

Guarin, Jose, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeno, Diego, Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip, Basso, Bruno, Berger, Andres, Bindi, Marco, Bracho-Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Eyshi Rezaei, Ehsan, Fereres, Elias, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, Garcia-Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Hunt, Leslie, Kersebaum, Kurt, Nendel, Claas, Olesen, Jorgen, Palosuo, Taru, Priesack, Eckart, Pullens, Johannes, Rodriguez, Alfredo, Rotter, Reimund, Ruiz Ramos, Margarita, Semenov, Mikhail, Senapati, Nimai, Siebert, Stefan, Srivastava, Amit, Stockle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias, Xiao, Liujun, Zhang, Zhao, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, Asseng, Senthold, Guarin, Jose, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeno, Diego, Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip, Basso, Bruno, Berger, Andres, Bindi, Marco, Bracho-Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Eyshi Rezaei, Ehsan, Fereres, Elias, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, Garcia-Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Hunt, Leslie, Kersebaum, Kurt, Nendel, Claas, Olesen, Jorgen, Palosuo, Taru, Priesack, Eckart, Pullens, Johannes, Rodriguez, Alfredo, Rotter, Reimund, Ruiz Ramos, Margarita, Semenov, Mikhail, Senapati, Nimai, Siebert, Stefan, Srivastava, Amit, Stockle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias, Xiao, Liujun, Zhang, Zhao, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, and Asseng, Senthold

Abstract

Grain production must increase by 60% in the next four decades to keep up with the expected population growth and food demand. A significant part of this increase must come from the improvement of staple crop grain yield potential. Crop growth simulation models combined with field experiments and crop physiology are powerful tools to quantify the impact of traits and trait combinations on grain yield potential which helps to guide breeding towards the most effective traits and trait combinations for future wheat crosses. The dataset reported here was created to analyze the value of physiological traits identified by the International Wheat Yield Partnership (IWYP) to improve wheat potential in high-yielding environments. This dataset consists of 11 growing seasons at three high-yielding locations in Buenos Aires (Argentina), Ciudad Obregon (Mexico), and Valdivia (Chile) with the spring wheat cultivar Bacanora and a high-yielding genotype selected from a doubled haploid (DH) population developed from the cross between the Bacanora and Weebil cultivars from the International Maize and Wheat Improvement Center (CIMMYT). This dataset was used in the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4 to evaluate crop model performance when simulating high-yielding physiological traits and to determine the potential production of wheat using an ensemble of 29 wheat crop models. The field trials were managed for non-stress conditions with full irrigation, fertilizer application, and without biotic stress. Data include local daily weather, soil characteristics and initial soil conditions, cultivar information, and crop measurements (anthesis and maturity dates, total above-ground biomass, final grain yield, yield components, and photosynthetically active radiation interception). Simulations include both daily in-season and end-of-season results for 25 crop variables simulated by 29 wheat crop models.

Published
2023

8. Evidence for increasing global wheat yield potential

Author

Guarin, Jose Rafael, primary, Martre, Pierre, additional, Ewert, Frank, additional, Webber, Heidi, additional, Dueri, Sibylle, additional, Calderini, Daniel, additional, Reynolds, Matthew, additional, Molero, Gemma, additional, Miralles, Daniel, additional, Garcia, Guillermo, additional, Slafer, Gustavo, additional, Giunta, Francesco, additional, Pequeno, Diego N L, additional, Stella, Tommaso, additional, Ahmed, Mukhtar, additional, Alderman, Phillip D, additional, Basso, Bruno, additional, Berger, Andres G, additional, Bindi, Marco, additional, Bracho-Mujica, Gennady, additional, Cammarano, Davide, additional, Chen, Yi, additional, Dumont, Benjamin, additional, Rezaei, Ehsan Eyshi, additional, Fereres, Elias, additional, Ferrise, Roberto, additional, Gaiser, Thomas, additional, Gao, Yujing, additional, Garcia-Vila, Margarita, additional, Gayler, Sebastian, additional, Hochman, Zvi, additional, Hoogenboom, Gerrit, additional, Hunt, Leslie A, additional, Kersebaum, Kurt C, additional, Nendel, Claas, additional, Olesen, Jørgen E, additional, Palosuo, Taru, additional, Priesack, Eckart, additional, Pullens, Johannes W M, additional, Rodríguez, Alfredo, additional, Rötter, Reimund P, additional, Ramos, Margarita Ruiz, additional, Semenov, Mikhail A, additional, Senapati, Nimai, additional, Siebert, Stefan, additional, Srivastava, Amit Kumar, additional, Stöckle, Claudio, additional, Supit, Iwan, additional, Tao, Fulu, additional, Thorburn, Peter, additional, Wang, Enli, additional, Weber, Tobias Karl David, additional, Xiao, Liujun, additional, Zhang, Zhao, additional, Zhao, Chuang, additional, Zhao, Jin, additional, Zhao, Zhigan, additional, Zhu, Yan, additional, and Asseng, Senthold, additional

Published
2022
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9. APSIM – Evolution towards a new generation of agricultural systems simulation

Author

Holzworth, Dean P., Huth, Neil I., deVoil, Peter G., Zurcher, Eric J., Herrmann, Neville I., McLean, Greg, Chenu, Karine, van Oosterom, Erik J., Snow, Val, Murphy, Chris, Moore, Andrew D., Brown, Hamish, Whish, Jeremy P.M., Verrall, Shaun, Fainges, Justin, Bell, Lindsay W., Peake, Allan S., Poulton, Perry L., Hochman, Zvi, Thorburn, Peter J., Gaydon, Donald S., Dalgliesh, Neal P., Rodriguez, Daniel, Cox, Howard, Chapman, Scott, Doherty, Alastair, Teixeira, Edmar, Sharp, Joanna, Cichota, Rogerio, Vogeler, Iris, Li, Frank Y., Wang, Enli, Hammer, Graeme L., Robertson, Michael J., Dimes, John P., Whitbread, Anthony M., Hunt, James, van Rees, Harm, McClelland, Tim, Carberry, Peter S., Hargreaves, John N.G., MacLeod, Neil, McDonald, Cam, Harsdorf, Justin, Wedgwood, Sara, and Keating, Brian A.

Published
2014
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11. Data from the AgMIP-Wheat high-yielding traits experiment for modeling potential production of wheat: field experiments and multi-model simulations

Author

Guarin, Jose, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeno, Diego N.L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip D., Basso, Bruno, Berger, Andres G., Bindi, Marco, Bracho Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Eyshi Rezaei, Ehsan, Fereres, Elias, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, Garcia-Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Hunt, Leslie A., Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ruiz Ramos, Margarita, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhang, Zhao, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, Asseng, Senthold, Guarin, Jose, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeno, Diego N.L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip D., Basso, Bruno, Berger, Andres G., Bindi, Marco, Bracho Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Eyshi Rezaei, Ehsan, Fereres, Elias, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, Garcia-Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Hunt, Leslie A., Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ruiz Ramos, Margarita, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhang, Zhao, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, and Asseng, Senthold

Abstract

The dataset reported here was created to analyze the value of physiological traits identified by the International Wheat Yield Partnership (IWYP) to improve wheat potential in high-yielding environments. This dataset consists of 11 growing seasons at three high-yielding locations in Buenos Aires (Argentina), Ciudad Obregon (Mexico), and Valdivia (Chile) with the spring wheat cultivar Bacanora and a high-yielding genotype selected from a doubled haploid (DH) population developed from the cross between the Bacanora and Weebil cultivars from the International Maize and Wheat Improvement Center (CIMMYT). This dataset was used in the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4 to evaluate crop model performance when simulating high-yielding physiological traits and to determine the potential production of wheat using an ensemble of 29 wheat crop models. The field trials were managed for non-stress conditions with full irrigation, fertilizer application, and without biotic stress. Data include local daily weather, soil characteristics and initial soil conditions, cultivar information, and crop measurements (anthesis and maturity dates, total above-ground biomass, final grain yield, yield components, and photosynthetically active radiation interception). Simulations include both daily in-season and end-of-season results for 25 crop variables simulated by 29 wheat crop models. The R code and formatted data used for the statistical analyses are included.

Published
2022

12. Data from the AgMIP-Wheat high-yielding traits experiment for modeling potential production of wheat: field experiments and multi-model simulations

Author

Guarín, José Rafael, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeño, Diego N. L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip, Basso, Bruno, Berger, Andres G., Bindi, Marco, Bracho-Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres Castiel, Elías, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, García Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Hunt, Leslie A., Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ruiz Ramos, Margarita, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhang, Zhao, Zhao, Chuang, Zhao, Ji, Zhao, Zhigan, Asseng, Senthold, Guarín, José Rafael, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeño, Diego N. L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip, Basso, Bruno, Berger, Andres G., Bindi, Marco, Bracho-Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres Castiel, Elías, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, García Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Hunt, Leslie A., Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ruiz Ramos, Margarita, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhang, Zhao, Zhao, Chuang, Zhao, Ji, Zhao, Zhigan, and Asseng, Senthold

Abstract

The dataset reported here was created to analyze the value of physiological traits identified by the International Wheat Yield Partnership (IWYP) to improve wheat potential in high-yielding environments. This dataset consists of 11 growing seasons at three high-yielding locations in Buenos Aires (Argentina), Ciudad Obregon (Mexico), and Valdivia (Chile) with the spring wheat cultivar Bacanora and a high-yielding genotype selected from a doubled haploid (DH) population developed from the cross between the Bacanora and Weebil cultivars from the International Maize and Wheat Improvement Center (CIMMYT). This dataset was used in the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4 to evaluate crop model performance when simulating high-yielding physiological traits and to determine the potential production of wheat using an ensemble of 29 wheat crop models. The field trials were managed for non-stress conditions with full irrigation, fertilizer application, and without biotic stress. Data include local daily weather, soil characteristics and initial soil conditions, cultivar information, and crop measurements (anthesis and maturity dates, total above-ground biomass, final grain yield, yield components, and photosynthetically active radiation interception). Simulations include both daily in-season and end-of-season results for 25 crop variables simulated by 29 wheat crop models. The R code and formatted data used for the statistical analyses are included. (2022-02-11).

Published
2022

13. Evidence for increasing global wheat yield potential

Author

Guarin, Jose Rafael, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeno, Diego N.L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip D., Basso, Bruno, Berger, Andres G., Bindi, Marco, Bracho-Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres, Elias, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, Garcia-Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Hunt, Leslie A., Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ramos, Margarita Ruiz, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhang, Zhao, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, Asseng, Senthold, Guarin, Jose Rafael, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeno, Diego N.L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip D., Basso, Bruno, Berger, Andres G., Bindi, Marco, Bracho-Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres, Elias, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, Garcia-Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Hunt, Leslie A., Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ramos, Margarita Ruiz, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhang, Zhao, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, and Asseng, Senthold

Abstract

Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production. The DH field experiments were also used to determine a quantitative relationship between wheat production and solar radiation to estimate genetic yield potential. The multi-model ensemble projected a global annual wheat production of 1050 ± 145 Mt due to the improved canopy photosynthesis, a 37% increase, without expanding cropping area. Achieving this genetic yield potential would meet the lower estimate of the projected grain demand in 2050, albeit with considerable challenges.

Published
2022

14. Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment

Author

Institut National de la Recherche Agronomique (France), International Maize and Wheat Improvement Center, International Wheat Yield Partnership, National Natural Science Foundation of China, European Commission, Federal Ministry of Education and Research (Germany), Ministry of Education, Youth and Sports (Czech Republic), German Research Foundation, Biotechnology and Biological Sciences Research Council (UK), Natural Environment Research Council (UK), Academy of Finland, Dueri, Sibylle, Brown, Hamish, Asseng, Senthold, Ewert, Frank, Webber, Heidi, George, Mike, Craigie, Rob, Guarin, Jose Rafael, Pequeño, Diego N. L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip, Basso, Bruno, Berger, Andres G., Mujica, Gennady Bracho, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres Castiel, Elías, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, García Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Padovan, Gloria, Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ruiz Ramos, Margarita, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, Martre, Pierre, Institut National de la Recherche Agronomique (France), International Maize and Wheat Improvement Center, International Wheat Yield Partnership, National Natural Science Foundation of China, European Commission, Federal Ministry of Education and Research (Germany), Ministry of Education, Youth and Sports (Czech Republic), German Research Foundation, Biotechnology and Biological Sciences Research Council (UK), Natural Environment Research Council (UK), Academy of Finland, Dueri, Sibylle, Brown, Hamish, Asseng, Senthold, Ewert, Frank, Webber, Heidi, George, Mike, Craigie, Rob, Guarin, Jose Rafael, Pequeño, Diego N. L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip, Basso, Bruno, Berger, Andres G., Mujica, Gennady Bracho, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres Castiel, Elías, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, García Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Padovan, Gloria, Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ruiz Ramos, Margarita, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, and Martre, Pierre

Abstract

Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures.

Published
2022

15. Evidence for increasing global wheat yield potential

Author

International Wheat Yield Partnership, International Maize and Wheat Improvement Center, Comisión Nacional de Investigación Científica y Tecnológica (Chile), Fondo Nacional de Desarrollo Científico y Tecnológico (Chile), National Natural Science Foundation of China, Ministry of Education, Youth and Sports (Czech Republic), Biotechnology and Biological Sciences Research Council (UK), Guarín, José Rafael, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeño, Diego N. L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip, Basso, Bruno, Berger, Andres G., Bindi, Marco, Bracho-Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres Castiel, Elías, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, García Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Hunt, Leslie A., Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ruiz Ramos, Margarita, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhang, Zhao, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, Asseng, Senthold, International Wheat Yield Partnership, International Maize and Wheat Improvement Center, Comisión Nacional de Investigación Científica y Tecnológica (Chile), Fondo Nacional de Desarrollo Científico y Tecnológico (Chile), National Natural Science Foundation of China, Ministry of Education, Youth and Sports (Czech Republic), Biotechnology and Biological Sciences Research Council (UK), Guarín, José Rafael, Martre, Pierre, Ewert, Frank, Webber, Heidi, Dueri, Sibylle, Calderini, Daniel, Reynolds, Matthew, Molero, Gemma, Miralles, Daniel, Garcia, Guillermo, Slafer, Gustavo, Giunta, Francesco, Pequeño, Diego N. L., Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip, Basso, Bruno, Berger, Andres G., Bindi, Marco, Bracho-Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres Castiel, Elías, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, García Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Hunt, Leslie A., Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ruiz Ramos, Margarita, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhang, Zhao, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, and Asseng, Senthold

Abstract

Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production. The DH field experiments were also used to determine a quantitative relationship between wheat production and solar radiation to estimate genetic yield potential. The multi-model ensemble projected a global annual wheat production of 1050 ± 145 Mt due to the improved canopy photosynthesis, a 37% increase, without expanding cropping area. Achieving this genetic yield potential would meet the lower estimate of the projected grain demand in 2050, albeit with considerable challenges.

Published
2022

16. Data from the winter wheat potential yield experiment in New Zealand and response to variable sowing dates and densities: field experiments and AgMIP-Wheat multi-model simulations

Author

Dueri, Sibylle, Brown, Hamish, Asseng, Senthold, Ewert, Frank, Webber, Heidi, George, Mike, Craigie, Rob, Guarin, Jose Rafael, Pequeno, Diego, Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip D., Basso, Bruno, Berger, Andres G., Bracho Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Eyshi Rezaei, Ehsan, Fereres, Elias, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, Garcia-Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Padovan, Gloria, Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ruiz Ramos, Margarita, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, Martre, Pierre, Dueri, Sibylle, Brown, Hamish, Asseng, Senthold, Ewert, Frank, Webber, Heidi, George, Mike, Craigie, Rob, Guarin, Jose Rafael, Pequeno, Diego, Stella, Tommaso, Ahmed, Mukhtar, Alderman, Phillip D., Basso, Bruno, Berger, Andres G., Bracho Mujica, Gennady, Cammarano, Davide, Chen, Yi, Dumont, Benjamin, Eyshi Rezaei, Ehsan, Fereres, Elias, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, Garcia-Vila, Margarita, Gayler, Sebastian, Hochman, Zvi, Hoogenboom, Gerrit, Kersebaum, Kurt C., Nendel, Claas, Olesen, Jørgen E., Padovan, Gloria, Palosuo, Taru, Priesack, Eckart, Pullens, Johannes W.M., Rodríguez, Alfredo, Rötter, Reimund P., Ruiz Ramos, Margarita, Semenov, Mikhail A., Senapati, Nimai, Siebert, Stefan, Srivastava, Amit Kumar, Stöckle, Claudio, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Wang, Enli, Weber, Tobias Karl David, Xiao, Liujun, Zhao, Chuang, Zhao, Jin, Zhao, Zhigan, Zhu, Yan, and Martre, Pierre

Abstract

The dataset contains 6 growing seasons of a local winter wheat cultivar ‘Wakanui’ at two farms located in the Canterbury Region of New Zealand. The data of the experiment was used in the AgMIP-Wheat Phase 4 project to evaluate the performance of an ensemble of 29 crop models to predict the effect of changing sowing dates and rates on yield and yield components, in a high-yielding environment. The treatments were managed for non-stress conditions. Data include local daily weather, soil characteristics and initial soil N conditions, crop measurements (anthesis and maturity dates, total above-ground biomass, final grain yield, and yield components), and cultivar information. Simulations include both daily in-season and end-of-season results from 29 wheat crop models.

Published
2022

17. Exploiting genotype × management interactions to increase rainfed crop production: a case study from south-eastern Australia

Author

Hunt, James R, primary, Kirkegaard, John A, additional, Harris, Felicity A, additional, Porker, Kenton D, additional, Rattey, Allan R, additional, Collins, Marisa J, additional, Celestina, Corinne, additional, Cann, David J, additional, Hochman, Zvi, additional, Lilley, Julianne M, additional, and Flohr, Bonnie M, additional

Published
2021
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19. The future of farming : Who will produce our food?

Author

Giller, Ken E., Delaune, Thomas, Silva, João Vasco, Descheemaeker, Katrien, van de Ven, Gerrie, Schut, Antonius G.T., van Wijk, Mark, Hammond, James, Hochman, Zvi, Taulya, Godfrey, Chikowo, Regis, Narayanan, Sudha, Kishore, Avinash, Bresciani, Fabrizio, Teixeira, Heitor Mancini, Andersson, Jens A., van Ittersum, Martin K., Environmental Sciences, and Environmental Sciences

Subjects
Safety net, Development, Agricultural economics, Population growth, Bosecologie en Bosbeheer, Yield gaps, Smallholder farms, Food security, Poverty, business.industry, Sustainable intensification, Farm Systems Ecology Group, Livelihood, PE&RC, Forest Ecology and Forest Management, Plant Production Systems, Agriculture, Plantaardige Productiesystemen, Technologie and Innovatie, Sustainability, Knowledge Technology and Innovation, Kennis, Food systems, Business, Agronomy and Crop Science, Kennis, Technologie and Innovatie, Food Science
Abstract

Achieving SDG2 (zero hunger) in a situation of rapid global population growth requires a continued focus on food production. Farming not merely needs to sustainably produce nutritious diets, but should also provide livelihoods for farmers, while retaining natural ecosystems and services. Rather than focusing on production principles, this article explores the interrelations between farms and farming systems in the global food system. Evaluating farming systems around the world, we reveal a bewildering diversity. While family farms predominate, these range in size from less than 0.1 ha to more than 10,000 ha, and from hand hoe use to machine-based cultivation, enabling one person to plant more than 500 ha in a day. Yet, farming in different parts of the world is highly interdependent, not least because prices paid for farm produce are largely determined by global markets. Furthermore, the economic viability of farming is a problem, globally. We highlight trends in major regions of the world and explore possible trajectories for the future and ask: Who are the farmers of the future? Changing patterns of land ownership, rental and exchange mean that the concept of ‘what is a farm’ becomes increasingly fluid. Next to declining employment and rural depopulation, we also foresee more environmentally-friendly, less external input dependent, regionalised production systems. This may require the reversal of a global trend towards increasing specialisation to a recoupling of arable and livestock farming, not least for the resilience it provides. It might also require a slow-down or reversal of the widespread trend of scale enlargement in agriculture. Next to this trend of scale enlargement, small farms persist in Asia: consolidation of farms proceeds at a snail’s pace in South-east Asia and 70% of farms in India are ‘ultra-small’ – less than 0.05 ha. Also in Africa, where we find smallholder farms are much smaller than often assumed (< 1 ha), farming households are often food insecure. A raft of pro-poor policies and investments are needed to stimulate small-scale agriculture as part of a broader focus on rural development to address persistent poverty and hunger. Smallholder farms will remain an important source of food and income, and a social safety net in absence of alternative livelihood security. But with limited possibilities for smallholders to ‘step-up’, the agricultural engine of growth appears to be broken. Smallholder agriculture cannot deliver the rate of economic growth currently assumed by many policy initiatives in Africa.

Published
2021

20. The future of farming: Who will produce our food?

Author

Environmental Sciences, Giller, Ken E., Delaune, Thomas, Silva, João Vasco, Descheemaeker, Katrien, van de Ven, Gerrie, Schut, Antonius G.T., van Wijk, Mark, Hammond, James, Hochman, Zvi, Taulya, Godfrey, Chikowo, Regis, Narayanan, Sudha, Kishore, Avinash, Bresciani, Fabrizio, Teixeira, Heitor Mancini, Andersson, Jens A., van Ittersum, Martin K., Environmental Sciences, Giller, Ken E., Delaune, Thomas, Silva, João Vasco, Descheemaeker, Katrien, van de Ven, Gerrie, Schut, Antonius G.T., van Wijk, Mark, Hammond, James, Hochman, Zvi, Taulya, Godfrey, Chikowo, Regis, Narayanan, Sudha, Kishore, Avinash, Bresciani, Fabrizio, Teixeira, Heitor Mancini, Andersson, Jens A., and van Ittersum, Martin K.

Published
2021

21. The chaos in calibrating crop models

Author

Wallach, Daniel, Palosuo, Taru, Thorburn, Peter J., Hochman, Zvi, Gourdain, Emmanuelle, Andrianasolo, Fety, Asseng, Senthold, Basso, Bruno, Buis, Samuel, Crout, Neil, de Wit, A.J.W., Seidel, Sabine J., Wallach, Daniel, Palosuo, Taru, Thorburn, Peter J., Hochman, Zvi, Gourdain, Emmanuelle, Andrianasolo, Fety, Asseng, Senthold, Basso, Bruno, Buis, Samuel, Crout, Neil, de Wit, A.J.W., and Seidel, Sabine J.

Abstract

Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in essentially every application of crop models and process models in other fields and has an important impact on simulated values. The goal of this study is to develop a comprehensive list of the decisions involved in calibration and to identify the range of choices made in practice, as groundwork for developing guidelines for crop model calibration starting with phenology. Three groups of decisions are identified; the criterion for choosing the parameter values, the choice of parameters to estimate and numerical aspects of parameter estimation. It is found that in practice there is a large diversity of choices for every decision, even among modeling groups using the same model structure. These findings are relevant to process models in other fields.

Published
2020

23. How well do crop models predict phenology, with emphasis on the effect of calibration?

Author

Wallach, Daniel, Palosuo, Taru, Thorburn, Peter, Seidel, Sabine J., Gourdain, Emmanuelle, Asseng, Senthold, Basso, Bruno, Buis, Samuel, Crout, Neil, Dibari, Camilla, Dumont, Benjamin, Ferrise, Roberto, Gaiser, Thomas, Garcia, Cécile, Gayler, Sebastian, Ghahramani, Afshin, Hochman, Zvi, Hoek, Steven, Horan, Heidi, Hoogenboom, Gerrit, Huang, Mingxia, Jabloun, Mohamed, Jing, Qi, Justes, Eric, Kersebaum, Kurt Christian, Klosterhalfen, Anne, Launay, Marie, Luo, Qunying, Maestrini, Bernardo, Moriondo, Marco, Nariman Zadeh, Hasti, Olesen, Jørgen Eivind, Poyda, Arne, Priesack, Eckart, Pullens, Johannes Wilhelmus Maria, Qian, Budong, Schütze, Niels, Shelia, Vakhtang, Souissi, Amir, Specka, Xenia, Srivastava, Amit Kumar, Stella, Tommaso, Streck, Thilo, Trombi, Giacomo, Wallor, Evelyn, Wang, Jing, Weber, Tobias K.D., Weihermüller, Lutz, de Wit, Allard, Wöhling, Thomas, Xiao, Liujun, Zhao, Chuang, and Zhu, Yan

Subjects
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
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. Many studies have focused on comparing model equations for describing how phenology responds to climate but the effect of crop model calibration, also important for determining model performance, has received much less attention. The objectives here were to obtain a rigorous evaluation of prediction capability of wheat phenology models, to analyze the role of calibration and to document the various calibration approaches. 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 “usual” 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. The results suggest that calibration can compensate to some extent for different model formulations, specifically for differences in simulated time to emergence and differences in the choice of input variables. Conversely, different calibration approaches were associated with major differences in prediction error between the same models used by different groups. 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
2019
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24. Roadside collection of training data for cropland mapping is viable when environmental and management gradients are surveyed

Author

UCL - SST/ELI/ELIE - Environmental Sciences, Waldner, François, Bellemans, Nicolas, Hochman, Zvi, Newby, Terence, de Abelleyra, Diego, Verón, Santiago R., Bartalev, Sergey, Lavreniuk, Mykola, Kussul, Nataliia, Le Maire, Guerric, Simoes, Margareth, Skakun, Sergii, Defourny, Pierre, UCL - SST/ELI/ELIE - Environmental Sciences, Waldner, François, Bellemans, Nicolas, Hochman, Zvi, Newby, Terence, de Abelleyra, Diego, Verón, Santiago R., Bartalev, Sergey, Lavreniuk, Mykola, Kussul, Nataliia, Le Maire, Guerric, Simoes, Margareth, Skakun, Sergii, and Defourny, Pierre

Abstract

Cropland maps derived from satellite imagery have become a common source of information to estimate food production, support land use policies, and measure the environmental impacts of agriculture. Cropland classification models are typically calibrated with data collected from roadside surveys which enable the sampling of large areas at a relatively low cost. However, there is a risk of providing biased data as environmental and management gradients may not be fully captured from road networks, thereby violating the assumption of representativeness of calibration data. Despite being widely adopted, the potential biases of roadside sampling have so far not been thoroughly addressed. In this study, we looked for evidence of these biases by comparing three sampling strategies: Random sampling, Roadside sampling, and Transect sampling – a spatially constrained variant of Roadside sampling. In these three strategies, non-cropland data are randomly distributed as they can be photo-interpreted. Based on reference maps at 30m in four study sites, we followed a Monte Carlo approach to generate multiple realizations of each sampling strategy for ten sample sizes. The effect of the sampling strategy was then assessed in terms of representativeness of the data set collected and accuracy of the resulting maps. Results showed that data sets obtained from Roadside sampling were significantly less representative than those obtained from Random sampling but the resulting maps were only marginally less accurate (2% difference). Transect sampling captured systematically less variability than Random or Roadside sampling which led to differences in accuracy as large as 15%. The effect of sample size on accuracy varied across sites but generally leveled off after reaching 3000 pixels. Augmenting the size of Transect samples improved the classification accuracy but not sufficiently to match the performance of the other sampling strategies. Finally, we found that Random and Roadside train

Published
2019

25. Beyond the plot: technology extrapolation domains for scaling out agronomic science

Author

Rattalino Edreira, Juan I., Cassman, Kenneth G., Hochman, Zvi, van Ittersum, Martin K., van Bussel, Lenny, Claessens, Lieven, and Grassini, Patricio

Subjects
impact assessment, Letter, WIMEK, technology extrapolation, PE&RC, geospatial analysis, Bodemgeografie en Landschap, Environmental Systems Analysis, Plant Production Systems, Plantaardige Productiesystemen, Milieusysteemanalyse, Soil Geography and Landscape, research prioritization
Abstract

Ensuring an adequate food supply in systems that protect environmental quality and conserve natural resources requires productive and resource-efficient cropping systems on existing farmland. Meeting this challenge will be difficult without a robust spatial framework that facilitates rapid evaluation and scaling-out of currently available and emerging technologies. Here we develop a global spatial framework to delineate 'technology extrapolation domains' based on key climate and soil factors that govern crop yields and yield stability in rainfed crop production. The proposed framework adequately represents the spatial pattern of crop yields and stability when evaluated over the data-rich US Corn Belt. It also facilitates evaluation of cropping system performance across continents, which can improve efficiency of agricultural research that seeks to intensify production on existing farmland. Populating this biophysical spatial framework with appropriate socio-economic attributes provides the potential to amplify the return on investments in agricultural research and development by improving the effectiveness of research prioritization and impact assessment.

Published
2018

27. Vertical farms bear fruit.

Author

O'Sullivan, Cathryn A., McIntyre, C. Lynne, Dry, Ian B., Hani, Susan M., Hochman, Zvi, and Bonnett, Graham D.

Abstract

Engineering perishable crops for use in indoor farms promises to expand the adoption of this high-yielding, efficient means of food production. [ABSTRACT FROM AUTHOR]

Published
2020
Full Text
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28. Farmscape online : participatory design of internet meetings with farmers

Author

Hargreaves, Dean, Kethers, Stefanie, Brereton, Margot, McCown, Bob, Hochman, Zvi, Carbery, Peter, Antoniades, Mark, and Bond, Adrian

Subjects
Internet, 080602 Computer-Human Interaction, 120304 Digital and Interaction Design, video conferencing, Agriculture, Participatory design, Microsoft NetMeeting
Abstract

This paper presents aspects of a longitudinal study in the design and practice of Internet meetings between farmer their advisors and researchers in rural Australia. It reports on the use of Microsoft NetMeeting (NM) by a group of agricultural researchers from Australia's CSIRO (Commonwealth Scientific and Industrial Research Organisation) for regular meetings, over nine years, with farmers and the commercial advisers. It describes lessons drawn from this experience about the conditions under which telecollaborative tools, such as NM and video conferencing, are likely to be both useful and used.

Published
2004

29. Participative research on use of enhanced climate variability information within agribusiness

Author

Brennan, Lisa E., Carberry, Peter S., and Hochman, Zvi

Subjects
Agribusiness, Risk and Uncertainty
Abstract

Climate variability impacts significantly on the agricultural service sector, affecting the operations and policies of agribusiness suppliers, banking and insurance companies. For example, bank lending policy and agribusiness advice was likely affected by recent El Niño drought events. Through consultation with agribusiness suppliers, banks and insurance companies, it is clear that their business operations and policies could benefit substantially from access to enhanced processes for dealing with climate variability. The Agricultural Production Systems Research Unit (APSRU) has demonstrated that farmers can utilise information derived from climate forecasts and simulation models in interpreting past experience, planning and decision making. Variability in production also poses challenges for both input suppliers and firms involved in the value chain from farm to consumer. Bank lending policies, crop insurance policies, product inventories and marketing advice may all be positively influenced through better dealing with climate variability. For example, insurance policies based on the Agricultural Production Systems Simulator (APSIM) model’s objective prediction of yields may potentially reduce claimant disputes and cut legal costs, representing significant savings to industry. Likewise, better prediction of seasonal outlooks using the APSIM model, climate forecasts and fallow water reserves would allow farmers and lenders, such as produce suppliers and banks, to negotiate individually-tailored financial packages. APSRU has recently extended risk management tools developed primarily for farmers to agribusiness to determine whether better targeted and costeffective agribusiness services can be provided for the benefit of agribusiness organisations and Australian farmers. In this paper we report on our experiences and learnings from our action research approach, where APSRU researchers are working alongside agribusiness staff on relevant case studies to identify the opportunities for and to resolve constraints against implementation of improved agribusiness operations based on climate forecasts and use of the APSIM model. Our collaborators represent a mix of agribusiness organisations, including input suppliers, marketers, banks and insurance companies. The case studies involving our collaborators are described and discussed in the paper, along with results of our initial evaluation. The use of climate forecasts and APSIM has generated interest amongst the agribusiness sector. While the costs of conducting this research are high, we conclude that there are good opportunities for these tools to assist agribusiness operations in providing better services to Australian farmers.

Published
2001

30. FARMSCAPE Online: participatory design of Internet meetings with farmers

Author

Bond, A, Hargreaves, Dean, Kethers, Stefanie, Brereton, Margot, McCown, Bob, Hochman, Zvi, Carbery, Peter, Antoniades, Mark, Bond, A, Hargreaves, Dean, Kethers, Stefanie, Brereton, Margot, McCown, Bob, Hochman, Zvi, Carbery, Peter, and Antoniades, Mark

Abstract

This paper presents aspects of a longitudinal study in the design and practice of Internet meetings between farmer their advisors and researchers in rural Australia. It reports on the use of Microsoft NetMeeting (NM) by a group of agricultural researchers from Australia's CSIRO (Commonwealth Scientific and Industrial Research Organisation) for regular meetings, over nine years, with farmers and the commercial advisers. It describes lessons drawn from this experience about the conditions under which telecollaborative tools, such as NM and video conferencing, are likely to be both useful and used.

Published
2004

31. Beyond the plot: technology extrapolation domains for scaling out agronomic science.

Author

Edreira JIR, Cassman KG, Hochman Z, van Ittersum MK, van Bussel L, Claessens L, and Grassini P

Abstract

Ensuring an adequate food supply in systems that protect environmental quality and conserve natural resources requires productive and resource-efficient cropping systems on existing farmland. Meeting this challenge will be difficult without a robust spatial framework that facilitates rapid evaluation and scaling-out of currently available and emerging technologies. Here we develop a global spatial framework to delineate 'technology extrapolation domains' based on key climate and soil factors that govern crop yields and yield stability in rainfed crop production. The proposed framework adequately represents the spatial pattern of crop yields and stability when evaluated over the data-rich US Corn Belt. It also facilitates evaluation of cropping system performance across continents, which can improve efficiency of agricultural research that seeks to intensify production on existing farmland. Populating this biophysical spatial framework with appropriate socio-economic attributes provides the potential to amplify the return on investments in agricultural research and development by improving the effectiveness of research prioritization and impact assessment., (© 2018 The Author(s).)

Published
2018
Full Text
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