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1. Modeling the early phases of epidemics by Phakospora pachyrhizi in Brazilian soybean

Author: Belay T. Kassie, David W. Onstad, Lucimara Koga, Tim Hart, Randy Clark, and Gerie van der Heijden
Subjects: modeling, epidemiology, Asian soybean rust, Phakospora pachyrhizi, Brazil, soybean, Agriculture, Plant culture, SB1-1110
Abstract: Asian soybean rust, caused by the biotrophic basidiomycete Phakospora pachyrhizi, is a foliar disease that often causes considerable damage to soybean crops. The purpose of our work was to create a mechanistic model that can reliably represent epidemics of ASR in commercial soybean fields in Brazil. The most important inputs for the model are weather data (observations and forecast) and the initial observation of disease (or uredospore arrival). Our focus is on the first two or three cycles of infection after immigration into a soybean field. The model includes state variables for latent, infectious and senesced lesions, disease severity, uredospores, and soybean leaf area. Processes modeled include maturation through the latent and infectious periods, germination, sporulation, and processes affecting uredospores in the canopy. The model results were tested against field observations from trials at four locations in Brazil for the 2019/2020 growing season. The predictions generally matched the daily dynamics of disease progress in the field trials. The predictions reproduced the observed severity well with R2 value of 0.84. This high correlation indicates that our model is accurate enough to be used as a tool to predict the dynamics of ASR epidemics during the first few cycles after uredospore invasion into a soybean field. A sensitivity analysis was performed that showed that the model is sensitive to time and duration of the initial spore arrival. This indicates that spore traps or other observations should measure not only the first day of arrival but also subsequent days.
Published: 2023
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2. Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations.

Author: Holger Hoffmann, Gang Zhao, Senthold Asseng, Marco Bindi, Christian Biernath, Julie Constantin, Elsa Coucheney, Rene Dechow, Luca Doro, Henrik Eckersten, Thomas Gaiser, Balázs Grosz, Florian Heinlein, Belay T Kassie, Kurt-Christian Kersebaum, Christian Klein, Matthias Kuhnert, Elisabet Lewan, Marco Moriondo, Claas Nendel, Eckart Priesack, Helene Raynal, Pier P Roggero, Reimund P Rötter, Stefan Siebert, Xenia Specka, Fulu Tao, Edmar Teixeira, Giacomo Trombi, Daniel Wallach, Lutz Weihermüller, Jagadeesh Yeluripati, and Frank Ewert
Subjects: Medicine, Science
Abstract: We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by
Published: 2016
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3. 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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4. Climate change impact on Mexico wheat production

Author: Kai Sonder, Ali Babar, Wei Xiong, Matthew P. Reynolds, Diego Notelo Luz Pequeno, Gerrit Hoogenboom, Senthold Asseng, Belay T. Kassie, Ricky Robertson, Anabel Molero Milan, and Ixchel M. Hernandez-Ochoa
Subjects: Atmospheric Science, Global and Planetary Change, Irrigation, 010504 meteorology & atmospheric sciences, Crop yield, Climate change, Forestry, Representative Concentration Pathways, 04 agricultural and veterinary sciences, Atmospheric sciences, 01 natural sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Climate model, Spatial variability, Crop simulation model, Agronomy and Crop Science, Cropping, 0105 earth and related environmental sciences
Abstract: Wheat is one of the most important cereal crops in Mexico, but the impact of future climate change on production is not known. To quantify the impact of future climate change together with its uncertainty, two wheat crop models were executed in parallel, using two scaling methods, five Global Climate Models (GCMs) and two main Representative Concentration Pathways (RCPs) for the 2050s. Simulated outputs varied among crop models, scaling methods, GCMs, and RCPs; however, they all projected a general decline in wheat yields by the 2050s. Despite the growth-stimulating effect of elevated CO2 concentrations, consistent yield declines were simulated across most of the main wheat growing regions of Mexico due to the projected increase in temperature. Exceptions occurred in some cooler areas, where temperature improved sub-optimal conditions, and in a few areas where rainfall increased, but these increases only provided negligible contributions to national production. Larger and more variable yield declines were projected for rainfed wheat due to current and projected spatial variability of temperature and rainfall patterns. Rainfed wheat, however, only contributes about 6% of Mexico’s wheat production. When aggregating the simulated climate change impacts, considering temperature increase, rainfall change, and elevated atmospheric CO2 concentrations for irrigated and rainfed wheat cropping systems, national wheat production for Mexico is projected to decline between 6.9% for RCP 4.5 and 7.9% for RCP 8.5. Model uncertainty (combined for crop and climate models) in simulated yield changes, and across two scaling methods, was smaller than temporal and spatial variability in both RCPs. Spatial variability tends to be the largest in both future scenarios. To maintain or increase future wheat production in Mexico, adaptation strategies, particularly to increasing temperatures affecting irrigated wheat, or expanding the cropping area, will be necessary.
Published: 2018
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5. How does inter-annual variability of attainable yield affect the magnitude of yield gaps for wheat and maize? : An analysis at ten sites

Author: Thomas Gaiser, Yi Chen, Margarita Ruiz-Ramos, Jukka Höhn, Pierre Stratonovitch, Amit Kumar Srivastava, Kirsten Paff, Mikhail A. Semenov, Senthold Asseng, F. Ewert, Alfredo Rodríguez, Stefan Fronzek, Belay T. Kassie, Markus Haakana, Ehsan Eyshi Rezaei, Taru Palosuo, Fulu Tao, Reimund P. Rötter, and Munir P. Hoffmann
Subjects: Yield gap analysis, 010504 meteorology & atmospheric sciences, Yield (finance), ta1171, Magnitude (mathematics), Large range, engineering.material, 01 natural sciences, Crop, Economics, 0105 earth and related environmental sciences, 2. Zero hunger, Food security, business.industry, Inter-annual yield variability, Yield gap, food and beverages, 04 agricultural and veterinary sciences, 15. Life on land, ta4111, Climate-induced risk, Maize, Agronomy, 13. Climate action, Wheat, 040103 agronomy & agriculture, engineering, Food processing, 0401 agriculture, forestry, and fisheries, Animal Science and Zoology, Fertilizer, business, Agronomy and Crop Science
Abstract: Provision of food security in the face of increasing global food demand requires narrowing of the gap between actual farmer's yield and maximum attainable yield. So far, assessments of yield gaps have focused on average yield over 5–10 years, but yield gaps can vary substantially between crop seasons. In this study we hypothesized that climate-induced inter-annual yield variability and associated risk is a major barrier for farmers to invest, i.e. increase inputs to narrow the yield gap. We evaluated the importance of inter-annual attainable yield variability for the magnitude of the yield gap by utilizing data for wheat and maize at ten sites representing some major food production systems and a large range of climate and soil conditions across the world. Yield gaps were derived from the difference of simulated attainable yields and regional recorded farmer yields for 1981 to 2010. The size of the yield gap did not correlate with the amplitude of attainable yield variability at a site, but was rather associated with the level of available resources such as labor, fertilizer and plant protection inputs. For the sites in Africa, recorded yield reached only 20% of the attainable yield, while for European, Asian and North American sites it was 56–84%. Most sites showed that the higher the attainable yield of a specific season the larger was the yield gap. This significant relationship indicated that farmers were not able to take advantage of favorable seasonal weather conditions. To reduce yield gaps in the different environments, reliable seasonal weather forecasts would be required to allow farmers to manage each seasonal potential, i.e. overcoming season-specific yield limitations.
Published: 2018
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6. Field experimental data for crop modeling of wheat growth response to nitrogen fertilizer, elevated CO2, water stress, and high temperature

Author: Senthold Asseng, Peter D. Jamieson, Jeffrey W. White, J. W. Bowden, J. J. Groot, Ken D. Sayre, Steven W. Leavitt, Belay T. Kassie, Gerard W. Wall, Paul J. Pinter, Douglas J. Hunsaker, Bruce A. Kimball, and Robert L. LaMorte
Subjects: business.industry, chemistry.chemical_element, engineering.material, Nitrogen, Crop, chemistry.chemical_compound, chemistry, Agronomy, Agriculture, Yield (wine), Carbon dioxide, Soil water, engineering, Environmental science, Cultivar, Fertilizer, business
Abstract: Field experimental data of five experiments covering a wide range of growing conditions are assembled for wheat growth and cropping systems modeling. The data include an experiment on interactive effects of elevated CO2 by water and elevated CO2 by nitrogen fertilizer application from a Free-Air Carbon Dioxide Enrichment experiment (FACE) at Arizona in USA; a nitrogen rate fertilizer experiment from three locations and two years in The Netherlands; water deficit experiments at Lincoln in New Zealand and at Cunderdin in Australia; and a temperature sensitivity experiment at Obregon in Mexico. The data consist of 65 experimental treatments with more than 1000 detailed observations, with time series of development and growth, soil water and soil nitrogen dynamics, yield and yield components, daily weather, soil characteristics, and cultivar descriptions. These data have been used in various previous agronomic and crop modeling studies. Assembled data are quality checked and supplied in Agricultural Model Inter-comparison and Improvement (AgMIP) format. Data access via DOI 10.7910/DVN/V4P6PU.
Published: 2018
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7. Simulating the impact of source-sink manipulations in wheat

Author: Daniel F. Calderini, Senthold Asseng, C. Amador, Belay T. Kassie, and Marcelo H. Labra
Subjects: 0106 biological sciences, geography, geography.geographical_feature_category, Crop yield, Simulation modeling, Soil Science, 04 agricultural and veterinary sciences, Global dimming, 01 natural sciences, Grain size, Sink (geography), Crop, Agronomy, Anthesis, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Shading, Agronomy and Crop Science, 010606 plant biology & botany
Abstract: Grain yields in wheat can be limited by the assimilate supply (source) or by the carbohydrate demand of the grains (sink). Recently, there have been questions regarding the capability of crop models to simulate the physiology of source-sink interactions in crops; however, crop models have never been tested with source-sink manipulated data. We tested the Nwheat model with detailed measured field experimental data with treatments of manipulated source (i.e., assimilate supply), sink (i.e., kernel number), and their combinations. In general, the model could reproduce observed effects of shading before and after anthesis as well as the additional impact of halving the spikes. A 90% shading during grain filling reduced individual grain weights drastically, with the remaining yield mostly determined by carbohydrate remobilisation, which the model reproduced. The model also reproduced the decline of biomass accumulation due to shading, but was not sensitive enough to simulate the observed reduction of kernels per m2 from a 90% reduction in solar radiation between booting and the beginning of grain filling, resulting in an overestimated grain yield. The model reproduced the positive impact of a 7% genetically increased radiation use efficiency (RUE) on growth and yield. A sensitivity analysis indicated that the yield response to increased RUE can vary among environments. The yield impact can be positive in many environments, but negative in terminal drought environments. In these environments, stimulated early growth from higher RUE can cause accelerated water deficit during grain filling and reduced yields. The model adequately simulated source-sink interactions of most of the treatments, but there were obvious shortcomings in simulating kernel set and final grain size. Improving these will be critical for estimating crop-environmental interactions that affect assimilate supply, including breeding, industrialisation-induced or geo-engineered solar dimming, genetically and atmospheric CO2-related increased RUE, and source manipulations, such as pest and disease impacts.
Published: 2017
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8. 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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9. A SIMPLE crop model

Author: Vakhtang Shelia, Ixchel M. Hernandez-Ochoa, Chuang Zhao, Cheryl Porter, Bing Liu, Kwang Soo Kim, Gerrit Hoogenboom, Kenneth J. Boote, Claudio O. Stöckle, Willingthon Pavan, Daniel Wallach, Senthold Asseng, Belay T. Kassie, Liujun Xiao, Yan Zhu, University of Florida [Gainesville] (UF), Nanjing Agricultural University, Seoul National University [Seoul] (SNU), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, and Washington State University (WSU)
Subjects: 0106 biological sciences, [SDV]Life Sciences [q-bio], Soil Science, Plant Science, 01 natural sciences, [SHS]Humanities and Social Sciences, Crop, Yield (wine), Cultivar, climate impact, 2. Zero hunger, Biomass (ecology), Phenology, crop model, Sowing, 04 agricultural and veterinary sciences, 15. Life on land, simulation, calibration, Agronomy, 13. Climate action, simple, Soil water, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, DSSAT, Agronomy and Crop Science, 010606 plant biology & botany
Abstract: International audience; Crop models are important tools for assessing the impact of climate change on crop production. While multiple models have been developed over the past decades for the major food and fiber crops such as wheat, maize, soybean, rice, and cotton, there are few or none for many other crops. The goal of this study was to develop a simple generic crop model (SIMPLE) that could be easily modified for any crop to simulate development, crop growth and yield. The crop model SIMPLE includes 13 parameters to specify a crop type, with four of these for cultivar characteristics. Commonly available inputs that are required for the crop model SIMPLE include daily weather data, crop management, and soil water holding parameters. The initial SIMPLE model was calibrated and evaluated for 14 different annual crops using observations for biomass growth, solar radiation interception, and yield from 25 detailed field experiments for a total of 70 treatments from 17 sites, resulting in a RRMSE of 25.4% for final yield. A sensitivity analysis comparing a C3, C4 and a legume crop showed an expected response to a gradual increase in temperature and atmospheric CO2 concentrations. A regional gridded simulation for US potatoes reproduced the general observed patterns of spatial yield variability. Because the model is simple, it has several limitations, including the lack of response to vernalization and photoperiod effect on phenology. The model includes water, but no nutrient dynamics. However, an advantage of the model simplicity is that it can be easily adapted and evaluated for any new crop, based on literature data and field experiments, or general crop data such as sowing and harvest dates and yield statistics. The model is available in several simulation frame-works including a stand-alone version in R, Excel and as part of DSSAT.
Published: 2019
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10. Performance of DSSAT-Nwheat across a wide range of current and future growing conditions

Author: Senthold Asseng, Frederick S. Royce, Cheryl Porter, and Belay T. Kassie
Subjects: Biomass (ecology), Irrigation, 010504 meteorology & atmospheric sciences, Soil Science, Sowing, Growing season, 04 agricultural and veterinary sciences, Plant Science, Rainout, 01 natural sciences, Agronomy, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, DSSAT, Environmental science, Interception, Leaf area index, Agronomy and Crop Science, 0105 earth and related environmental sciences
Abstract: Crop models are widely used in agricultural impact studies. However, many studies have reported large uncertainties from single-model-based simulation analyses, suggesting the need for multi-model simulation capabilities. In this study, the APSIM-Nwheat model was integrated into the Decision Support System for Agro-technology (DSSAT), which already includes two wheat models, to create multi-model simulation capabilities for wheat cropping systems analysis. The new model in DSSAT (DSSAT-Nwheat) was evaluated using more than 1000 observations from field experiments of 65 treatments, which included a wide range of nitrogen fertilizer applications, water supply (irrigation and rainout shelter), planting dates, elevated atmospheric CO2 concentrations, temperature variations, cultivars, and soil types in diverse climatic regions that represented the main wheat growing areas of the world. DSSAT-Nwheat reproduced the observed grain yields well with an overall root mean square deviation (RMSD) of 0.89 t/ha (13%). Nitrogen applications, water supply, and planting dates had large effects on observed biomass and grain yields, and the model reproduced these crop responses well. Crop total biomass and nitrogen uptake were reproduced well despite relatively poor simulations of observed leaf area measurements during the growing season. The low sensitivity of biomass simulations to poor simulations of leaf area index (LAI) were due to little changes in intercepted solar radiation at LAI >3 and water and nitrogen stress often limiting photosynthesis and growth rather than light interception at low LAI. The responses of DSSAT-Nwheat to temperature variations and elevated atmospheric CO2 concentrations were close to observed responses. When compared with the two other DSSAT-wheat models (CERES and CROPSIM), these responses were similar, except for the responses to hot environments, due to different approaches in modeling heat stress effects. The comprehensive evaluation of the DSSAT-Nwheat model with field measurements, including a comparison with two other DSSAT-wheat models, created a multi-model simulation platform that allows the quantification of model uncertainties in wheat impact assessments.
Published: 2016
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11. Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops

Author: Elisabet Lewan, Eckart Priesack, Edmar Teixeira, Davide Cammarano, Balázs Grosz, Julie Constantin, Elsa Coucheney, Senthold Asseng, Jagadeesh Yeluripati, Pier Paolo Roggero, Giacomo Trombi, Henrik Eckersten, Stefan Siebert, Specka Xenia, Reimund P. Rötter, Frank Ewert, Thomas Gaiser, Marco Bindi, Marco Moriondo, Matthias Kuhnert, Luca Doro, Christian Klein, Fulu Tao, Gang Zhao, Helene Raynal, Ralf Kiese, Kurt Christian Kersebaum, Claas Nendel, Belay T. Kassie, Christian Biernath, Holger Hoffmann, Edwin Haas, Florian Heinlein, Daniel Wallach, Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, The James Hutton Institute, Departement of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of landscape systems analysis, INSTITUTE OF BIOLOGICAL AND ENVIRONMENTAL SCIENCES, University of Aberdeen, Environmental Impacts Group, Natural resources institute Finland, AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Systems Modelling Team (Sustainable Production Group), Plant & Food Research, Johann Heinrich von Thünen Institut, Università degli Studi di Sassari, Dipartimento di Agraria, University of Sassari, Karlsruhe Institute of Technology (KIT), University of Agricultural Sciences (UAS), 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), Consiglio Nazionale delle Ricerche (CNR), Department of Agri-Food Production and Environmental Sciences, University delgi Studi di Firenze, Institute of Biochemical Plant Pathology (BIOP), German Research Center for Environmental Health - Helmholtz Center München (GmbH), German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE) [2851ERA01J], Joint Programme Initiative FACCE, MACSUR Knowledge Hub, German Federal Ministry of Education and Research (BMBF) through the SPACES project 'Living Landscapes Limpopo', WASCAL (West African Science Service Center on Climate Change and Adapted Land Use) project, NOAA RISA grant, FACCE MACSUR project by the Finnish Ministry of Agriculture and Forestry (MMM), Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning [220-2007-1218], strategic funding 'Soil-Water-Landscape' from the faculty of Natural Resources and Agricultural Sciences (Swedish University of Agricultural Sciences), ACCAF INRA meta-program, and Università degli Studi di Sassari = University of Sassari [Sassari] (UNISS)
Subjects: Environmental Engineering, 010504 meteorology & atmospheric sciences, Simple random sampling, [SDV]Life Sciences [q-bio], Stratified random sampling, 01 natural sciences, [SHS]Humanities and Social Sciences, Environment variable, Ecosystem model, Statistics, [INFO]Computer Science [cs], Crop model, [MATH]Mathematics [math], 0105 earth and related environmental sciences, Mathematics, 2. Zero hunger, Ecological Modeling, Crop yield, fungi, food and beverages, Sampling (statistics), Model comparison, 04 agricultural and veterinary sciences, 15. Life on land, Simple random sample, Stratified sampling, Sample size determination, [SDE]Environmental Sciences, Soil water, Up-scaling, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Clustering, Crop Model, Model Comparison, Precision Gain, Simple Random Sampling, Stratified Random Sampling, Precision gain, Software
Abstract: We compared the precision of simple random sampling (SimRS) and seven types of stratified random sampling (StrRS) schemes in estimating regional mean of water-limited yields for two crops (winter wheat and silage maize) that were simulated by fourteen crop models. We found that the precision gains of StrRS varied considerably across stratification methods and crop models. Precision gains for compact geographical stratification were positive, stable and consistent across crop models. Stratification with soil water holding capacity had very high precision gains for twelve models, but resulted in negative gains for two models. Increasing the sample size monotonously decreased the sampling errors for all the sampling schemes. We conclude that compact geographical stratification can modestly but consistently improve the precision in estimating regional mean yields. Using the most influential environmental variable for stratification can notably improve the sampling precision, especially when the sensitivity behavior of a crop model is known. We compare eight sampling schemes for estimating regional mean crop yield.Precision of eight schemes is compared across fourteen crop models.Compact geographical stratification can always improve the precision.Stratification with soil had very high gains of precision for twelve crop models.Our findings can improve the precision of site-based regional crop modeling.
Published: 2016
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12. The Hot Serial Cereal Experiment for modeling wheat response to temperature: Field experiments and AgMIP-Wheat multi-model simulations

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

Author: Marco Bindi, Behnam Ababaei, Senthold Asseng, Matthew P. Reynolds, Ehsan Eyshi Rezaei, Douglas J. Hunsaker, Heidi Webber, Bruce A. Kimball, Roberto Ferrise, Pierre Martre, Kurt Christian Kersebaum, Paul J. Pinter, Arne M. Ratjen, Adam Luig, Jerry L. Hatfield, Mikhail A. Semenov, Gerard W. Wall, Pierre Stratonovitch, Jordi Doltra, Steven W. Leavitt, Jeffrey W. White, Belay T. Kassie, Frank Ewert, Jørgen E. Olesen, Robert L. LaMorte, Henning Kage, Rheinische Friedrich-Wilhelms-Universität Bonn, USDA-ARS : Agricultural Research Service, 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), Center for Development Research (ZEF), European Biological Control Laboratory, USDA-ARS (EBCL - USDA ARS), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), DISPAA, Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Catabrian Agricultural Research and Training Center (CIFA), Institute of Crop Science and Plant Breeding, Christian-Albrechts University of Kiel, Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF), Leibniz Association, Department of Agroecology, Aarhus University [Aarhus], Computational and Systems Biology Department, Rothamsted Research, Laboratory of Tree-Ring Research, University of Arizona, German Science Foundation (project EW 119/5-1), FACCE JPI MACSUR project (2812ERA115), Food Policy Research Institute (IFPRI) through the Global Futures and Strategic Foresight project, CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), CGIAR Research Program on Wheat, FACCE JPI MACSUR project (031A103B), metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) INRA, JPI FACCE MACSUR, JPI FACCE MACSUR2, and German Science Foundation (project KA 3046/8-1)
Subjects: Canopy, 010504 meteorology & atmospheric sciences, STOMATAL CONDUCTANCE, AIR CO2 ENRICHMENT, Energy balance, Soil Science, Climate change, klim, Canopy temperature, Atmospheric sciences, 01 natural sciences, Heat stress, climate change impact assessment, SPRING WHEAT, Atmospheric instability, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, FARMING SYSTEMS, Robustness (economics), Heat and drought interactions, Crop model improvement, WHEAT EVAPOTRANSPIRATION, 0105 earth and related environmental sciences, 2. Zero hunger, CLIMATE-CHANGE, MULTIMODEL ENSEMBLES, Simulation modeling, Climate change impact assessments, 04 agricultural and veterinary sciences, 15. Life on land, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, ELEVATED CARBON-DIOXIDE, Stability conditions, Agronomy, heat and drought interaction, 13. Climate action, Soil water, Wheat, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, WATER-USE EFFICIENCY, DROUGHT-STRESS, Agronomy and Crop Science
Abstract: International audience; Despite widespread application in studying climate change impacts, most crop models ignore complex interactions among air temperature, crop and soil water status, CO2 concentration and atmospheric conditions that influence crop canopy temperature. The current study extended previous studies by evaluating Tc simulations from nine crop models at six locations across environmental and production conditions. Each crop model implemented one of an empirical (EMP), an energy balance assuming neutral stability (EBN) or an energy balance correcting for atmospheric stability conditions (EBSC) approach to simulate Tc. Model performance in predicting Tc was evaluated for two experiments in continental North America with various water, nitrogen and CO2 treatments. An empirical model fit to one dataset had the best performance, followed by the EBSC models. Stability conditions explained much of the differences between modeling approaches. More accurate simulation of heat stress will likely require use of energy balance approaches that consider atmospheric stability conditions.
Published: 2018
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14. 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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15. The International Heat Stress Genotype Experiment for modeling wheat response to heat: field experiments and AgMIP-Wheat multi-model simulations

Author: L. A. Hunt, David B. Lobell, Phillip D. Alderman, Alex C. Ruane, Zhigan Zhao, T. Palosuo, Mohamed Jabloun, Margarita Garcia-Vila, Andrew J. Challinor, Reimund P. Rötter, Jordi Doltra, Dominique Ripoche, Jeffrey W. White, Bing Liu, Jakarat Anothai, Fulu Tao, Katharina Waha, Eckart Priesack, Sebastian Gayler, Pierre Stratonovitch, Andrea Maiorano, Davide Cammarano, Christoph Müller, Bruno Basso, Ehsan Eyshi Rezaei, Senthold Asseng, Claas Nendel, Joost Wolf, Curtis D. Jones, Ann-Kristin Koehler, Matthew P. Reynolds, Enli Wang, Belay T. Kassie, Christian Biernath, Soora Naresh Kumar, Pierre Martre, Frank Ewert, Iwan Supit, Jørgen E. Olesen, Gerrit Hoogenboom, Giacomo De Sanctis, Thilo Streck, Elias Fereres, Yan Zhu, Kurt Christian Kersebaum, Mikhail A. Semenov, Claudio O. Stöckle, Benjamin Dumont, Roberto C. Izaurralde, Peter J. Thorburn, Garry O'Leary, and Pramod K. Aggarwal
Subjects: Simulations, 0106 biological sciences, Water en Voedsel, klim, 01 natural sciences, Heat stress, Crop, heat stress, Anthesis, Yield (wine), wheat, Life Science, Cultivar, field experimental data, Biomass (ecology), WIMEK, Water and Food, Field experimental data, Sowing, 04 agricultural and veterinary sciences, PE&RC, Productivity (ecology), Agronomy, Plant Production Systems, Plantaardige Productiesystemen, Wheat, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Water Systems and Global Change, simulations, Cropping, 010606 plant biology & botany
Abstract: All data are available via DOI http://doi.org/10.7910/DVN/ECSFZG, he data set contains a portion of the International Heat Stress Genotype Experiment (IHSGE) data used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat crop models and quantify the impact of heat on global wheat yield productivity. It includes two spring wheat cultivars grown during two consecutive winter cropping cycles at hot, irrigated, and low latitude sites in Mexico (Ciudad Obregon and Tlaltizapan), Egypt (Aswan), India (Dharwar), the Sudan (Wad Medani), and Bangladesh (Dinajpur). Experiments in Mexico included normal (November-December) and late (January-March) sowing dates. Data include local daily weather data, soil characteristics and initial soil conditions, crop measurements (anthesis and maturity dates, anthesis and final total above ground biomass, final grain yields and yields components), and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models.
Published: 2017
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16. The implication of input data aggregation on up-scaling soil organic carbon changes

Author: Gang Zhao, Elisabet Lewan, Sren Gebbert, Claas Nendel, Zhigan Zhao, Thomas Gaiser, Edmar Teixeira, Edwin Haas, Elsa Coucheney, Kurt Christian Kersebaum, Reimund Rtter, Giacomo Trombi, Frank Ewert, Enli Wang, Jagadeesh Yeluripati, Davide Cammarano, Marco Moriondo, Stefan Siebert, Matthias Kuhnert, Julie Constantin, Luca Doro, Daniel Wallach, Xenia Specka, Marco Bindi, Belay T. Kassie, Holger Hoffmann, Fulu Tao, Helene Raynal, Henrik Eckersten, Balzs Grosz, Rene Dechow, Senthold Asseng, Pier Paolo Roggero, Lutz Weihermller, Thünen Institute of Climate Smart Agriculture, Crop Science Group, Rheinische Friedrich-Wilhelms-Universität Bonn, UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Department of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Department of Crop Production Ecology, Institute of Landscape Systems Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Biological and Environmental Sciences, School of Biological Sciences, University of Aberdeen, The James Hutton Institute, Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung (IMK-IFU), Karlsruher Institut für Technologie (KIT), Systems Modeling Team (Sustainable Production Group), New Zealand Institute for Plant and Food Research Limited, Department of Agri-food Production and Environmental Sciences, University of Florence (UNIFI), Istituto di Biometeorologia [Firenze] (IBIMET), Consiglio Nazionale delle Ricerche (CNR), Desertification Research Centre, University of Sassari, Desertification Research Centre - Department of Agricultural Sciences, CSIRO, Climate Impacts Group, Department of Crop Sciences, Georg-August-Universität Göttingen, Agricultural & Biological Engineering Department, University of Florida [Gainesville], Institute of Bio- and Geosciences Agrosphere (IBG-3), 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, Departement of Soil and Environment, Plant & Food Research, Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Georg-August-University [Göttingen], Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), and University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF)
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Biogeochemical cycle, Environmental Engineering, 010504 meteorology & atmospheric sciences, data aggregation, biogeochemical model, up-scaling error, Ecological Modeling, Up scaling, Soil science, 04 agricultural and veterinary sciences, Soil carbon, Common method, 15. Life on land, 01 natural sciences, Data availability, Data aggregator, soil organic carbon, North west, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Limit (mathematics), Software, 0105 earth and related environmental sciences
Abstract: In up-scaling studies, model input data aggregation is a common method to cope with deficient data availability and limit the computational effort. We analyzed model errors due to soil data aggregation for modeled SOC trends. For a region in North West Germany, gridded soil data of spatial resolutions between 1km and 100km has been derived by majority selection. This data was used to simulate changes in SOC for a period of 30 years by 7 biogeochemical models. Soil data aggregation strongly affected modeled SOC trends. Prediction errors of simulated SOC changes decreased with increasing spatial resolution of model output. Output data aggregation only marginally reduced differences of model outputs between models indicating that errors caused by deficient model structure are likely to persist even if requirements on the spatial resolution of model outputs are low. Analysis of soil data aggregation on model errors of up-scaled SOC trends.Determination of factors controlling aggregation effects (AE) on modeled SOC trends.Comparison of variability between 7 biogeochemical models and AE.Development of ex ante methods to approximate AE for SOC simulation studies.
Published: 2017
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17. Climate-induced yield variability and yield gaps of maize (Zea mays L.) in the Central Rift Valley of Ethiopia

Author: Joost Wolf, Huib Hengsdijk, Belay T. Kassie, M.K. van Ittersum, Reimund P. Rötter, and Senthold Asseng
Subjects: simulation-model, ceres, Yield (finance), crop growth-models, Soil Science, Growing season, adaptation, Crop, Agro Water- en Biobased Economy, ecological intensification, Cultivar, agriculture, Mathematics, Crop yield, Yield gap, use systems-analysis, food and beverages, fertilizer application, PE&RC, Plant Production Systems, Agronomy, sub-saharan africa, Plantaardige Productiesystemen, DSSAT, water-uptake, Agronomy and Crop Science, Water use
Abstract: There is a high demand for quantitative information on impacts of climate on crop yields, yield gaps and their variability in Ethiopia, yet, quantitative studies that include an indication of uncertainties in the estimates are rare. A multi-model crop growth simulation approach using the two crop models, i.e. Decision Support System for Agro-Technology (DSSAT) and WOrld FOod STudies (WOFOST) was applied to characterize climate-induced variability and yield gaps of maize. The models were calibrated and evaluated with experimental data from the Central Rift Valley (CRV) in Ethiopia. Subsequently, a simulation experiment was carried out with an early maturing (Melkassa1) and a late maturing (BH540) cultivar using historical weather data (1984-2009) of three locations in the CRV. Yield gaps were computed as differences among simulated water-limited yield, on-farm trial yields and average actual farmers' yields. The simulation experiment revealed that the potential yield (average across three sites and 1984-2009) is 8.2-9.2 and 6.8-7.1 Mg/ha for the late maturing and early maturing cultivars, respectively; ranges indicate mean differences between the two models. The simulated water-limited yield (averaged across three sites and 1984-2009) is 7.2-7.9 Mg/ha for the late maturing and 6.1-6.7 Mg/ha for the early maturing cultivar. The water-limited yield shows high inter-annual variability (CV 36%) and about 60% of this variability in yield is explained by the variation in growing season rainfall. The gap between average farmers yield and simulated water-limited yield ranges from 4.7 to 6.0 Mg/ha. The average farmers' yields were 2.0-2.3 Mg/ha, which is about 1.1-3.1 Mg/ha lower than on-farm trial yields. In relative terms, average farmers' yields are 28-30% of the water-limited yield and 44-65% of on-farm trial yields. Analysis of yield gaps for different number of years to drive average yields indicates that yield gap estimation on the basis of few years may result in misleading conclusions. Approximately ten years of data are required to be able to estimate yield gaps for the Central Rift Valley in a robust manner. Existing yield gaps indicate that there is scope for significantly increasing maize yield in the CRV and other, similar agro-ecological zones in Africa, through improved crop and climate risk management strategies. As crop models differ in detail of describing the complex, dynamic processes of crop growth, water use and soil water balances, the multi-model approach provides information on the uncertainty in simulating crop-climate interactions. (C) 2014 Elsevier B.V. All rights reserved.
Published: 2014
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18. Climate variability and change in the Central Rift Valley of Ethiopia: challenges for rainfed crop production

Author: Huib Hengsdijk, Belay T. Kassie, R.P. Roetter, M.K. van Ittersum, H. van Keulen, J. Kahiluoto, and Senthold Asseng
Subjects: Wet season, trends, Growing season, Agro Water- en Biobased Economy, Genetics, Rainfed agriculture, Alterra - Centrum Bodem, agriculture, risk, dry spell, projections, model, business.industry, Soil Science Centre, Moisture stress, PE&RC, Trend analysis, PPO/PRI AGRO Duurzame Bedrijfssystemen, Agronomy, Plant Production Systems, africa, Agriculture, Plantaardige Productiesystemen, Erosion, Environmental science, systems, Animal Science and Zoology, business, Agronomy and Crop Science, Rift valley
Abstract: SUMMARYEthiopia is one of the countries most vulnerable to the impacts of climate variability and change on agriculture. The present study aims to understand and characterize agro-climatic variability and changes and associated risks with respect to implications for rainfed crop production in the Central Rift Valley (CRV). Temporal variability and extreme values of selected rainfall and temperature indices were analysed and trends were evaluated using Sen's slope estimator and Mann–Kendall trend test methods. Projected future changes in rainfall and temperature for the 2080s relative to the 1971–90 baseline period were determined based on four General Circulation Models (GCMs) and two emission scenarios (SRES, A2 and B1). The analysis for current climate showed that in the short rainy season (March–May), total mean rainfall varies spatially from 178 to 358 mm with a coefficient of variation (CV) of 32–50%. In the main (long) rainy season (June–September), total mean rainfall ranges between 420 and 680 mm with a CV of 15–40%. During the period 1977–2007, total rainfall decreased but not significantly. Also, there was a decrease in the number of rainy days associated with an increase (statistically not significant) in the intensity per rainfall event for the main rainy season, which can have implications for soil and nutrient losses through erosion and run-off. The reduced number of rainy days increased the length of intermediate dry spells by 0·8 days per decade, leading to crop moisture stress during the growing season. There was also a large inter-annual variability in the length of growing season, ranging from 76 to 239 days. The mean annual temperature exhibited a significant warming trend of 0·12–0·54 °C per decade. Projections from GCMs suggest that future annual rainfall will change by +10 to −40% by 2080. Rainfall will increase during November–December (outside the growing season), but will decline during the growing seasons. Also, the length of the growing season is expected to be reduced by 12–35%. The annual mean temperature is expected to increase in the range of 1·4–4·1 °C by 2080. The past and future climate trends, especially in terms of rainfall and its variability, pose major risks to rainfed agriculture. Specific adaptation strategies are needed for the CRV to cope with the risks, sustain farming and improve food security.
Published: 2014
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19. Can Egypt become self-sufficient in wheat?

Author: Ahmed M. S. Kheir, Aly I. N. Abdelaal, Dorota Z. Haman, Gerrit Hoogenboom, Alex C. Ruane, Belay T. Kassie, and Senthold Asseng
Subjects: education.field_of_study, Irrigation, 010504 meteorology & atmospheric sciences, Renewable Energy, Sustainability and the Environment, Yield (finance), Population, Public Health, Environmental and Occupational Health, Climate change, 04 agricultural and veterinary sciences, 01 natural sciences, Agricultural economics, Crop, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Population growth, Climate model, Leaching (agriculture), education, 0105 earth and related environmental sciences, General Environmental Science
Abstract: Egypt produces half of the 20 million tons of wheat that it consumes with irrigation and imports the other half. Egypt is also the world's largest importer of wheat. The population of Egypt is currently growing at 2.2% annually, and projections indicate that the demand for wheat will triple by the end of the century. Combining multi-crop and -climate models for different climate change scenarios with recent trends in technology, we estimated that future wheat yield will decline mostly from climate change, despite some yield improvements from new technologies. The growth stimulus from elevated atmospheric CO2 will be overtaken by the negative impact of rising temperatures on crop growth and yield. An ongoing program to double the irrigated land area by 2035 in parallel with crop intensification could increase wheat production and make Egypt self-sufficient in the near future, but would be insufficient after 2040s, even with modest population growth. Additionally, the demand for irrigation will increase from 6 to 20 billion m3 for the expanded wheat production, but even more water is needed to account for irrigation efficiency and salt leaching (to a total of up to 29 billion m3). Supplying water for future irrigation and producing sufficient grain will remain challenges for Egypt.
Published: 2018
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20. Exploring climate change impacts and adaptation options for maize production in the Central Rift Valley of Ethiopia using different climate change scenarios and crop models

Author: Huib Hengsdijk, M.K. van Ittersum, Senthold Asseng, Reimund P. Rötter, Alex C. Ruane, and Belay T. Kassie
Subjects: Atmospheric Science, Yield (finance), vulnerability, Climate change, Agro Water- en Biobased Economy, opportunities, Agricultural productivity, Baseline (configuration management), uncertainty, agriculture, Global and Planetary Change, projections, Food security, Agroforestry, business.industry, variability, Crop yield, Representative Concentration Pathways, PE&RC, yield, Plant Production Systems, africa, Agriculture, Plantaardige Productiesystemen, temperatures, Environmental science, Water resource management, business, risks
Abstract: Exploring adaptation strategies for different climate change scenarios to support agricultural production and food security is a major concern to vulnerable regions, including Ethiopia. This study assesses the potential impacts of climate change on maize yield and explores specific adaptation options under climate change scenarios for the Central Rift Valley of Ethiopia by mid-century. Impacts and adaptation options were evaluated using three General Circulation Models (GCMs) in combination with two Representative Concentration Pathways (RCPs) and two crop models. Results indicate that maize yield decreases on average by 20 % in 2050s relative to the baseline (1980–2009) due to climate change. A negative impact on yield is very likely, while the extent of impact is more uncertain. The share in uncertainties of impact projections was higher for the three GCMs than it was for the two RCPs and two crop models used in this study. Increasing nitrogen fertilization and use of irrigation were assessed as potentially effective adaptation options, which would offset negative impacts. However, the response of yields to increased fertilizer and irrigation will be less for climate change scenarios than under the baseline. Changes in planting dates also reduced negative impacts, while changing the maturity type of maize cultivars was not effective in most scenarios. The multi-model based analysis allowed estimating climate change impact and adaptation uncertainties, which can provide valuable insights and guidance for adaptation planning.
Published: 2015

21. Canopy temperature for simulation of heat stress in irrigated wheat in a semi-arid environment: A multi-model comparison

Author: Michael J. Ottman, Jørgen E. Olesen, Ehsan Eyshi Rezaei, Mikhail A. Semenov, Giacomo De Sanctis, Bruce A. Kimball, Frank Ewert, Pierre Martre, Gerard W. Wall, Jordi Doltra, Jeffrey W. White, Heidi Webber, Belay T. Kassie, Senthold Asseng, Andrea Maiorano, Dominique Ripoche, Pierre Stratonovitch, Robert F. Grant, Rheinische Friedrich-Wilhelms-Universität Bonn, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), 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), Arid-Land Agricultural Research Center, School of Plant Sciences, University of Arizona, JRC Institute for Energy and Transport (IET), European Commission - Joint Research Centre [Petten], Cantabrian Agricultural Research and Training Centre, University of Alberta, Department of Agroecology, Aarhus University [Aarhus], Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Computational and Systems Biology Department, Rothamsted Research, German Science Foundation EW 119/5-1 /FACCE JPI MACSUR 031A103B, European Project: 267196, Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), Agricultural & Biological Engineering Department, University of Florida [Gainesville], and UE Agroclim (UE AGROCLIM)
Subjects: Canopy, stress thermique, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Yield (engineering), ble tendre, 010504 meteorology & atmospheric sciences, comparaison de modèles, Energy balance, Soil Science, Grain filling, Canopy temperature, Atmospheric sciences, 01 natural sciences, heat stress, high temperature, Crop model comparison, crop model comparison, Atmospheric instability, climat semi aride, condition environnementale, 0105 earth and related environmental sciences, semi arid climate, 2. Zero hunger, 04 agricultural and veterinary sciences, Arid, canopy temperature, Heat stress, Agronomy, soft wheat, 13. Climate action, Semi-arid climate, Wheat, 040103 agronomy & agriculture, rendement agricole, 0401 agriculture, forestry, and fisheries, Environmental science, haute température, Agronomy and Crop Science, modèle multifactoriel
Abstract: Even brief periods of high temperatures occurring around flowering and during grain filling can severely reduce grain yield in cereals. Recently, ecophysiological and crop models have begun to represent such phenomena. Most models use air temperature (T-air) in their heat stress responses despite evidence that crop canopy temperature (T-c) better explains grain yield losses. T-c can deviate significantly from T-air based on climatic factors and the crop water status. The broad objective of this study was to evaluate whether simulation of T-c improves the ability of crop models to simulate heat stress impacts on wheat under irrigated conditions. Nine process-based models, each using one of three broad approaches (empirical, EMP; energy balance assuming neutral atmospheric stability, EBN; and energy balance correcting for the atmospheric stability conditions, EBSC) to simulate To simulated grain yield under a range of temperature conditions. The models varied widely in their ability to reproduce the measured T-c with the commonly used EBN models performing much worse than either EMP or EBSC. Use of T-c to account for heat stress effects did improve simulations compared to using only T-air to a relatively minor extent, but the models that additionally use T-c on various other processes as well did not have better yield simulations. Models that simulated yield well under heat stress had varying skill in simulating T-c For example, the EBN models had very poor simulations of T-c but performed very well in simulating grain yield. These results highlight the need to more systematically understand and model heat stress events in wheat.
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22. Can Egypt become self-sufficient in wheat?

Author: Senthold Asseng, Ahmed M S Kheir, Belay T Kassie, Gerrit Hoogenboom, Aly I N Abdelaal, Dorota Z Haman, and Alex C Ruane
Subjects: wheat, climate change impact, food security, water demand, adaptation, Environmental technology. Sanitary engineering, TD1-1066, Environmental sciences, GE1-350, Science, Physics, QC1-999
Abstract: Egypt produces half of the 20 million tons of wheat that it consumes with irrigation and imports the other half. Egypt is also the world’s largest importer of wheat. The population of Egypt is currently growing at 2.2% annually, and projections indicate that the demand for wheat will triple by the end of the century. Combining multi-crop and -climate models for different climate change scenarios with recent trends in technology, we estimated that future wheat yield will decline mostly from climate change, despite some yield improvements from new technologies. The growth stimulus from elevated atmospheric CO _2 will be overtaken by the negative impact of rising temperatures on crop growth and yield. An ongoing program to double the irrigated land area by 2035 in parallel with crop intensification could increase wheat production and make Egypt self-sufficient in the near future, but would be insufficient after 2040s, even with modest population growth. Additionally, the demand for irrigation will increase from 6 to 20 billion m ^3 for the expanded wheat production, but even more water is needed to account for irrigation efficiency and salt leaching (to a total of up to 29 billion m ^3 ). Supplying water for future irrigation and producing sufficient grain will remain challenges for Egypt.
Published: 2018
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22 results on '"Belay T. Kassie"'

1. Modeling the early phases of epidemics by Phakospora pachyrhizi in Brazilian soybean

2. Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations.

3. Climate change impact and adaptation for wheat protein

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

13. Physical robustness of canopy temperature models for crop heat stress simulation across environments and production conditions

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

15. The International Heat Stress Genotype Experiment for modeling wheat response to heat: field experiments and AgMIP-Wheat multi-model simulations

16. The implication of input data aggregation on up-scaling soil organic carbon changes

17. Climate-induced yield variability and yield gaps of maize (Zea mays L.) in the Central Rift Valley of Ethiopia

18. Climate variability and change in the Central Rift Valley of Ethiopia: challenges for rainfed crop production

19. Can Egypt become self-sufficient in wheat?

20. Exploring climate change impacts and adaptation options for maize production in the Central Rift Valley of Ethiopia using different climate change scenarios and crop models

21. Canopy temperature for simulation of heat stress in irrigated wheat in a semi-arid environment: A multi-model comparison

22. Can Egypt become self-sufficient in wheat?

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