338 results on '"Arid-Land Agricultural Research Center"'
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2. Similar estimates of temperature impacts on global wheat yield by three independent methods
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Frank Ewert, Jakarat Anothai, P. V. Vara Prasad, Davide Cammarano, Curtis D. Jones, Elias Fereres, Margarita Garcia-Vila, Soora Naresh Kumar, Eckart Priesack, Phillip D. Alderman, Andrew J. Challinor, Reimund P. Rötter, Alex C. Ruane, Christian Folberth, Gerrit Hoogenboom, Pierre Martre, Roberto C. Izaurralde, Fulu Tao, Pramod K. Aggarwal, Mohamed Jabloun, Jordi Doltra, Joshua Elliott, Christoph Müller, Bing Liu, Iurii Shcherbak, Jeffrey W. White, Bruno Basso, Senthold Asseng, Pierre Stratonovitch, Peter J. Thorburn, Claas Nendel, Taru Palosuo, Joost Wolf, Ann-Kristin Koehler, Thilo Streck, Jørgen E. Olesen, David B. Lobell, Kurt Christian Kersebaum, Delphine Deryng, L. A. Hunt, Garry O'Leary, Katharina Waha, Giacomo De Sanctis, Daniel Wallach, Yan Zhu, James W. Jones, Elke Stehfest, Mikhail A. Semenov, Christian Biernath, Claudio O. Stöckle, Thomas A. M. Pugh, Matthew P. Reynolds, Enli Wang, Bruce A. Kimball, Erwin Schmid, Iwan Supit, Zhigan Zhao, Michael J. Ottman, Sebastian Gayler, Cynthia Rosenzweig, Ehsan Eyshi Rezaei, Gerard W. Wall, National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricutural University, Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Potsdam Institute for Climate Impact Research (PIK), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF), Leibniz Association, Center for Climate Systems Research [New York] (CCSR), Columbia University [New York], Computation Institute, Loyola University of Chicago, Department of Environmental Earth System Science and Center on Food Security and the Environment, Stanford University, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut National de la Recherche Agronomique (INRA), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, CGIAR Research Program on Climate Change, Agriculture and Food Security, Borlaug Institute for South Asia, CIMMYT, Consultative Group on International Agricultural Research (CGIAR), Department of Plant and Soil Sciences, Mississippi State University [Mississippi], Department of Plant Science, Faculty of Natural Resources, Prince of Songkla University (PSU), Department of Geological Sciences, University of Oregon [Eugene], W. K. Kellogg Biological Station (KBS), Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Institute of Soil Ecology [Neuherberg] (IBOE), Helmholtz-Zentrum München (HZM), The James Hutton Institute, Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, CGIAR-ESSP Program on Climate Change, Agriculture and Food Security, International Center for Tropical Agriculture, European Commission - Joint Research Centre [Ispra] (JRC), Cantabrian Agricultural Research and Training Centre, Department of Agronomy, Purdue University [West Lafayette], Department of Geography, University of Liverpool, Ecosystem Services and Management Program, International Institute for Applied Systems Analysis (IIASA), Institute of Soil Science and Land Evaluation, University of Hohenheim, AgWeatherNet Program, Washington State University (WSU), Department of Plant Agriculture, University of Guelph, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A and M AgriLife Research, Texas A&M University System, Department of Agroecology, Aarhus University [Aarhus], US Arid-Land Agricultural Research Center, United States Department of Agriculture, Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research, Landscape & Water Sciences, Department of Environment of Victoria, The School of Plant Sciences, University of Arizona, Natural resources institute Finland, Institute of Ecology, German Research Center for Environmental Health, Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung (IMK-IFU), Karlsruher Institut für Technologie (KIT), School of Geography, Earth and Environmental Sciences [Birmingham], University of Birmingham [Birmingham], Birmingham Institute of Forest Research (BIFoR), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Center for Development Research (ZEF), Environmental Impacts Group, Georg-August-University [Göttingen], Universität für Bodenkultur Wien [Vienne, Autriche] (BOKU), Computational and Systems Biology Department, Rothamsted Research, Biotechnology and Biological Sciences Research Council, Netherlands Environmental Assessment Agency, Department of Biological Systems Engineering, University of Wisconsin-Madison, PPS, WSG and CALM, Wageningen University and Research [Wageningen] (WUR), Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), USDA-ARS, Arid-Land Agricultural Research Center, China Agricultural University (CAU), National High-Tech Research and Development Program of China (2013AA100404), the National Natural Science Foundation of China (31271616, 31611130182, 41571088 and 31561143003), the National Research Foundation for the Doctoral Program of Higher Education of China (20120097110042), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), China Scholarship Council., IFPRI through the Global Futures and Strategic Foresight project, the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), the CGIAR Research Program on Wheat, the Agricultural Model Intercomparison and Improvement Project (AgMIP), Agricultural & Biological Engineering Department, University of Florida [Gainesville], Institute of Crop Science and Resource Conservation, University of Bonn-Division of Plant Nutrition, Stanford University [Stanford], Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Prince of Songkla University, Texas A&M AgriLife Research and Extension Center, Natural Resources Institute Finland, Georg-August-Universität Göttingen, Wageningen University and Research Center (WUR), China Agricultural University, Division of Plant Nutrition-University of Bonn, Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), University of Florida, Potsdam Institute for Climate Impact Research ( PIK ), Leibniz Centre for Agricultural Landscape Research, Institute for Landscape Biogeochemistry, Center for Climate Systems Research [New York] ( CCSR ), Écophysiologie des Plantes sous Stress environnementaux ( LEPSE ), Institut National de la Recherche Agronomique ( INRA ) -Centre international d'études supérieures en sciences agronomiques ( Montpellier SupAgro ) -Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), Consultative Group on International Agricultural Research ( CGIAR ), W.K. Kellogg Biological Station, Institute of Soil Ecology [Neuherberg] ( IBOE ), Helmholtz-Zentrum München ( HZM ), James Hutton Institute, Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, European Commission - Joint Research Centre [Ispra] ( JRC ), International Institute for Applied Systems Analysis ( IIASA ), Washington State University ( WSU ), Texas A and M University ( TAMU ), Leibniz Centre for Agricultural Landscape Research (ZALF), Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung ( IMK-IFU ), Karlsruher Institut für Technologie ( KIT ), School of Geography, Earth & Environmental Science and Birmingham Institute of Forest Research, University of Birmingham, International Maize and Wheat Improvement Center ( CIMMYT ), Bonn Universität [Bonn], University of Natural Resources and Life Sciences, University of Wisconsin-Madison [Madison], Wageningen University and Research Center ( WUR ), Chinese Academy of Sciences [Beijing] ( CAS ), Commonwealth Scientific and Industrial Research Organisation, Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Université de Toulouse (UT)-Université de Toulouse (UT), Helmholtz Zentrum München = German Research Center for Environmental Health, Natural Resources Institute Finland (LUKE), Georg-August-University = Georg-August-Universität Göttingen, Universität für Bodenkultur Wien = University of Natural Resources and Life [Vienne, Autriche] (BOKU), Biotechnology and Biological Sciences Research Council (BBSRC), and Institute of geographical sciences and natural resources research [CAS] (IGSNRR)
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0106 biological sciences ,[SDV.SA]Life Sciences [q-bio]/Agricultural sciences ,[ SDV.BV ] Life Sciences [q-bio]/Vegetal Biology ,régression statistique ,010504 meteorology & atmospheric sciences ,impact sur le rendement ,klim ,Atmospheric sciences ,01 natural sciences ,incertitude ,wheat ,uncertainty ,[ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences ,2. Zero hunger ,changement climatique ,Regression analysis ,statistical regression ,simulation ,PE&RC ,[ SDE.MCG ] Environmental Sciences/Global Changes ,sécurité alimentaire ,Plant Production Systems ,modèle de récolte ,Yield (finance) ,comparaison de modèles ,[SDE.MCG]Environmental Sciences/Global Changes ,Climate change ,Environmental Science (miscellaneous) ,Earth System Science ,blé ,température ,Life Science ,[SDV.BV]Life Sciences [q-bio]/Vegetal Biology ,réchauffement climatique ,global change ,0105 earth and related environmental sciences ,Hydrology ,WIMEK ,Global temperature ,business.industry ,Crop yield ,Global warming ,Climate Resilience ,13. Climate action ,Agriculture ,Klimaatbestendigheid ,Plantaardige Productiesystemen ,Environmental science ,Leerstoelgroep Aardsysteemkunde ,Climate model ,business ,Social Sciences (miscellaneous) ,010606 plant biology & botany - Abstract
The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify ‘method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security. The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify ‘method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.
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- 2016
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3. Engineering the production of conjugated fatty acids in Arabidopsis thaliana leaves
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Dyer, John [USDA-ARS, US Arid-Land Agricultural Research Center, Maricopa AZ USA]
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- 2017
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4. Prediction of Evapotranspiration and Yields of Maize: An Inter-comparison among 29 Maize Models and Future Plans
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Kimball, Bruce, Boote, Kenneth, Hatfield, Jerry, Ahuja, Laj, Stockle, Claudio, Archontoulis, Sotirios, Baron, Christian, Basso, Bruno, Bertuzzi, Patrick, Constantin, Julie, Deryng, Delphine, Benjamin, Dumont, Durand, Jean-Louis, Ewert, Frank, Gaiser, Thomas, Sebastian, Gayler, Hoffman, Munir, Jiang, Qianjing, Kim, Soo-Hyung, Lizaso, Jon, Moulin, Sophie, Nendel, Claas, Parker, Philip, Palosuo, Taru, Priesack, Eckart, Qi, Zhiming, Srivastava, Amit, Stella, Tommaso, Tao, Fulu, Thorp, Kelly, Timlin, Dennis, Webber, Heidi, Willaume, Magali, Williams, Karina, Suyker, Andrew, Evett, Steven, U.S. Arid Land Agricultural Research Center, University of Florida [Gainesville] (UF), and Pradal, Christophe
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[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation ,[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation - Abstract
International audience; Crop yield can be affected by crop water use (evapotranspiration, ET) and vice versa, so when trying to simulate one or the other, it can be important to simulate both well. Method: To determine how well 29 maize growth models can simulate ET, an inter-comparison study was initiated under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project) using eddy cova-riance data from Ames, IA as the standard for comparison (Kimball et al., 2019).
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- 2020
5. Photosynthesis in a changing global climate: Scaling up and scaling down in crops
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Marouane Baslam, Toshiaki Mitsui, Michael Hodges, Eckart Priesack, Matthew T. Herritt, Iker Aranjuelo, Álvaro Sanz-Sáez, Niigata University, Institut des Sciences des Plantes de Paris-Saclay (IPS2 (UMR_9213 / UMR_1403)), Université d'Évry-Val-d'Essonne (UEVE)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institute of Biochemical Plant Pathology (BIOP), German Research Center for Environmental Health - Helmholtz Center München (GmbH), USDA-ARS, Arid-Land Agricultural Research Center, United States Department of Agriculture, Centro de Investigaciones Biológicas (CSIC), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Auburn University (AU), ANR-14-CE19-0015,Regul3P,Régulation de la photorespiration par phosphorylation protéique(2014), University of Illinois at Urbana-Champaign [Urbana], University of Illinois System, Japan Science and Technology Agency, Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), Ministry of Education, Culture, Sports, Science and Technology (Japan), and Université d'Évry-Val-d'Essonne (UEVE)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
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0106 biological sciences ,0301 basic medicine ,phenotyping ,omics ,Omics ,Climate change ,Review ,Plant Science ,lcsh:Plant culture ,Photosynthesis ,01 natural sciences ,Metabolic engineering ,03 medical and health sciences ,[SDV.BV]Life Sciences [q-bio]/Vegetal Biology ,lcsh:SB1-1110 ,[SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology ,ComputingMilieux_MISCELLANEOUS ,Abiotic component ,photosynthesis ,Ecology ,Crop yield ,food and beverages ,Biosphere ,modeling ,Climate Change ,Crop Improvement ,Phenotyping ,Modeling ,crop improvement ,Salinity ,030104 developmental biology ,climate change ,13. Climate action ,Environmental science ,Adaptation ,010606 plant biology & botany - Abstract
Photosynthesis is the major process leading to primary production in the Biosphere. There is a total of 7000bn tons of CO in the atmosphere and photosynthesis fixes more than 100bn tons annually. The CO assimilated by the photosynthetic apparatus is the basis of crop production and, therefore, of animal and human food. This has led to a renewed interest in photosynthesis as a target to increase plant production and there is now increasing evidence showing that the strategy of improving photosynthetic traits can increase plant yield. However, photosynthesis and the photosynthetic apparatus are both conditioned by environmental variables such as water availability, temperature, [CO], salinity, and ozone. The “omics” revolution has allowed a better understanding of the genetic mechanisms regulating stress responses including the identification of genes and proteins involved in the regulation, acclimation, and adaptation of processes that impact photosynthesis. The development of novel non-destructive high-throughput phenotyping techniques has been important to monitor crop photosynthetic responses to changing environmental conditions. This wealth of data is being incorporated into new modeling algorithms to predict plant growth and development under specific environmental constraints. This review gives a multi-perspective description of the impact of changing environmental conditions on photosynthetic performance and consequently plant growth by briefly highlighting how major technological advances including omics, high-throughput photosynthetic measurements, metabolic engineering, and whole plant photosynthetic modeling have helped to improve our understanding of how the photosynthetic machinery can be modified by different abiotic stresses and thus impact crop production., This work was supported by IRUEC project funded by EIG CONCERT-Japan 3rd Joint Call on “Food Crops and Biomass Production Technologies” under the Strategic International Research Cooperative Program of the Japan Science and Technology Agency (JST) and the Spanish Innovation and Universities Ministry (Acciones de programación conjunta Internacional, PCIN-2017-007), and by the ANR-14-CE19-0015 grant REGUL3P. A Grant for Promotion of KAAB Projects (Niigata University) from the Ministry of Education, Culture, Sports, Science, and Technology-Japan is also acknowledged.
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- 2020
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6. The Hot Serial Cereal Experiment for modeling wheat response to temperature: Field experiments and AgMIP-Wheat multi-model simulations
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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
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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
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- 2018
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7. Wheat response to a wide range of temperatures, as determined from the Hot Serial Cereal (HSC) Experiment
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Gerard W. Wall, Pierre Martre, Michael J. Ottman, Jeffrey W. White, Bruce A. Kimball, Agricultural Research Service / US Arid Land Agricultural Research Center, United States Department of Agriculture, University of Arizona, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)
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0106 biological sciences ,0301 basic medicine ,Range (biology) ,01 natural sciences ,modèle de croissance ,Crop ,03 medical and health sciences ,date de plantation ,blé ,Yield (wine) ,température ,Planting date ,Climate change ,[SDV.BV]Life Sciences [q-bio]/Vegetal Biology ,triticum aestivum ,Agricultural productivity ,changement climatique ,Vegetal Biology ,Phenology ,Global warming ,Temperature ,Sowing ,food and beverages ,Wheat ,Infrared warming ,030104 developmental biology ,Agronomy ,Air temperature ,Soil water ,rendement agricole ,Environmental science ,écophysiologie végétale ,Biologie végétale ,010606 plant biology & botany - Abstract
Data access via doi 10.7910/DVN/AEFLHB.; Temperatures are warming on a global scale, a phenomenon that likely will affect future crop productivity. Crop growth models are useful tools to predict the likely effects of these global changes on agricultural productivity and to develop strategies to maximize the benefits and minimize the detriments of such changes. However, few such models have been tested at the higher temperatures expected in the future. Therefore, a “Hot Serial Cereal” experiment was conducted on wheat (Triticum aestivum L.), the world’s foremost food and feed crop, in order to obtain a dataset appropriate for testing the high temperature performance of wheat growth models. The wheat (Cereal) was planted serially (Serial) about every six weeks for over two years at Maricopa, Arizona, USA, which experiences the whole range of temperatures at which plants grow on Earth. In addition, on six planting dates infrared heaters in a T-FACE (temperature free-air controlled enhancement) system (Hot) were deployed over one-third of the plots to warm the wheat by additional target 1.5°C during daytime and 3.0°C at night. Achieved average degrees of warming were 1.3 and 2.7°C for day and night. Overall, a dataset covering 27 differently treated wheat crops with three replicates each was obtained covering an air temperature range from -2 to 42°C. Herein, the management, soils, weather, physiology, phenology, growth, yield, quality, and other data are presented.
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- 2018
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8. The uncertainty of crop yield projections is reduced by improved temperature response functions
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Alex C. Ruane, Peter J. Thorburn, Mikhail A. Semenov, Joost Wolf, Claudio O. Stöckle, Pramod K. Aggarwal, Gerard W. Wall, Margarita Garcia-Vila, Matthew P. Reynolds, Eckart Priesack, Jørgen E. Olesen, Enli Wang, Bruce A. Kimball, Jordi Doltra, Iurii Shcherbak, Ehsan Eyshi Rezaei, Jeffrey W. White, Leilei Liu, L. A. Hunt, Senthold Asseng, Frank Ewert, Yan Zhu, Fulu Tao, Christoph Müller, Daniel Wallach, Christian Biernath, Davide Cammarano, Mohamed Jabloun, Zhigan Zhao, Michael J. Ottman, Pierre Martre, Sebastian Gayler, Garry O'Leary, Zhimin Wang, Jakarat Anothai, Elias Fereres, Claas Nendel, Bruno Basso, Thilo Streck, Curtis D. Jones, Andrea Maiorano, Phillip D. Alderman, Andrew J. Challinor, Reimund P. Rötter, Taru Palosuo, Iwan Supit, Katharina Waha, Giacomo De Sanctis, Kurt Christian Kersebaum, Soora Naresh Kumar, Gerrit Hoogenboom, Dominique Ripoche, Pierre Stratonovitch, Ann-Kristin Koehler, Roberto C. Izaurralde, Commonwealth Scientific and Industrial Research Organisation (Australia), Chinese Academy of Sciences, China Scholarship Council, Ministry of Education of the People's Republic of China, Institut National de la Recherche Agronomique (France), European Commission, International Food Policy Research Institute (US), CGIAR (France), Department of Agriculture (US), Federal Ministry of Education and Research (Germany), Deutsche Gesellschaft für Internationale Zusammenarbeit, Danish Council for Strategic Research, Federal Ministry of Food and Agriculture (Germany), Finnish Ministry of Agriculture and Forestry, National Natural Science Foundation of China, Helmholtz Association, Grains Research and Development Corporation (Australia), Texas AgriLife Research, Texas A&M University, National Institute of Food and Agriculture (US), CSIRO, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), College of Agronomy and Biotechnology, Southwest University, Institute of Crop Science and Resource Conservation, Division of Plant Nutrition-University of Bonn, Institute of Landscape Systems Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Department of Crop Sciences, University of Goettingen, Centre of Biodiversity and Sustainable Land Use (CBL), United States Department of Agriculture - Agricultural Research Service, The School of Plant Sciences, University of Arizona, Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Washington State University (WSU), Department of Earth and Environmental Sciences and W.K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Institute of Biochemical Plant Pathology (BIOP), German Research Center for Environmental Health - Helmholtz Center München (GmbH), Agricultural and Biological Engineering Department, Purdue University [West Lafayette], Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, GMO Unit, European Food Safety Authority = Autorité européenne de sécurité des aliments, Cantabrian Agricultural Research and Training Centre, Dep. Agronomia, University of Córdoba [Córdoba], Spanish National Research Council (CSIC), Institute of Soil Science and Land Evaluation, University of Hohenheim, Department of Plant Agriculture, University of Guelph, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A&M AgriLife Research and Extension Center, Texas A&M University System, Department of Agroecology, Aarhus University [Aarhus], National Engineering and Technology Center for Information Agriculture, Nanjing Agricutural University, Potsdam Institute for Climate Impact Research (PIK), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Department of Economic Development, Department of Economic Development, Jobs, Transport and Resources (DEDJTR), Natural Resources Institute Finland, Institute of Crop Science and Resource Conservation (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Computational and Systems Biology Department, Rothamsted Research, Biological Systems Engineering, University of Wisconsin-Madison, PPS and WSG &CALM, Wageningen University and Research Center (WUR), Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), Agriculture and Food, Universidad de La Rioja (UR), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, China Agricultural University, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), China Agricultural University (CAU), Institute of Crop Science and Resource Conservation [Bonn], Georg-August-University [Göttingen], Arid-Land Agricultural Research Center, Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), University of Leeds, European Food Safety Authority (EFSA), Catabrian Agricultural Research and Training Center (CIFA), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), AgWeatherNet Program, Texas A and M AgriLife Research, Jiangsu Collaborative Innovation Center for Modern Crop Production, Landscape and Water Sciences, Natural Resources Institute Finland (LUKE), Agroclim (AGROCLIM), Wageningen University and Research [Wageningen] (WUR), Chinese Academy of Agricultural Sciences (CAAS), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, European Project: 267196,EC:FP7:PEOPLE,FP7-PEOPLE-2010-COFUND,AGREENSKILLS(2012), Écophysiologie des Plantes sous Stress environnementaux ( LEPSE ), Institut National de la Recherche Agronomique ( INRA ) -Centre international d'études supérieures en sciences agronomiques ( Montpellier SupAgro ) -Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), Leibniz Centre for Agricultural Landscape Research, International Maize and Wheat Improvement Center ( CIMMYT ), CGIAR Research Program on Climate Change, Agriculture and Food Security ( CCAFS ), Washington State University ( WSU ), Michigan State University, Institute of Biochemical Plant Pathology ( BIOP ), Institute for Climate and Atmospheric Science, School of Earth and Environment, European Food Safety Authority, Spanish National Research Council ( CSIC ), Texas A and M University ( TAMU ), Potsdam Institute for Climate Impact Research ( PIK ), Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Department of Economic Development, Jobs, Transport and Resources ( DEDJTR ), University of Bonn (Rheinische Friedrich-Wilhelms), UE Agroclim ( UE AGROCLIM ), Institut National de la Recherche Agronomique ( INRA ), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), University of Wisconsin-Madison [Madison], Wageningen University and Research Center ( WUR ), Chinese Academy of Sciences [Beijing] ( CAS ), Universidad de La Rioja ( UR ), University of Bonn-Division of Plant Nutrition, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), USDA-ARS : Agricultural Research Service, Consultative Group on International Agricultural Research [CGIAR]-Consultative Group on International Agricultural Research [CGIAR], Natural resources institute Finland, Georg-August-University = Georg-August-Universität Göttingen, Universidad de Córdoba = University of Córdoba [Córdoba], Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), and Université de Toulouse (UT)-Université de Toulouse (UT)
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Crops, Agricultural ,010504 meteorology & atmospheric sciences ,[SDV]Life Sciences [q-bio] ,Yield (finance) ,Water en Voedsel ,Growing season ,Climate change ,klim ,Plant Science ,Agricultural engineering ,Models, Biological ,01 natural sciences ,[SHS]Humanities and Social Sciences ,Crop ,Life Science ,Computer Simulation ,Productivity ,0105 earth and related environmental sciences ,2. Zero hunger ,[ SDE.BE ] Environmental Sciences/Biodiversity and Ecology ,WIMEK ,Water and Food ,Food security ,business.industry ,Crop yield ,Temperature ,Agriculture ,04 agricultural and veterinary sciences ,15. Life on land ,Climate Resilience ,Agronomy ,Klimaatbestendigheid ,13. Climate action ,[SDE]Environmental Sciences ,040103 agronomy & agriculture ,0401 agriculture, forestry, and fisheries ,Environmental science ,Water Systems and Global Change ,[SDE.BE]Environmental Sciences/Biodiversity and Ecology ,business - Abstract
Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections., E.W. acknowledges support from the CSIRO project ‘Enhanced modelling of genotype by environment interactions’ and the project ‘Advancing crop yield while reducing the use of water and nitrogen’ jointly funded by CSIRO and the Chinese Academy of Sciences (CAS). Z.Z. received a scholarship from the China Scholarship Council through the CSIRO and the Chinese Ministry of Education PhD Research Program. P.M., A.M. and D.R. acknowledge support from the FACCE JPI MACSUR project (031A103B) through the metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA). A.M. received the support of the EU in the framework of the Marie-Curie FP7 COFUND People Programme, through the award of an AgreenSkills fellowship under grant agreement No. PCOFUND-GA-2010-267196. S.A. and D.C. acknowledge support provided by the International Food Policy Research Institute (IFPRI), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), the CGIAR Research Program on Wheat and the Wheat Initiative. C.S. was funded through USDA National Institute for Food and Agriculture award 32011-68002-30191. C.M. received financial support from the KULUNDA project (01LL0905 L) and the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (BMBF). F.E. received support from the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (2812ERA115) and E.E.R. was funded through the German Federal Ministry of Economic Cooperation and Development (Project: PARI). M.J. and J.E.O. were funded through the FACCE MACSUR project by the Danish Strategic Research Council. K.C.K. and C.N. were funded by the FACCE MACSUR project through the German Federal Ministry of Food and Agriculture (BMEL). F.T., T.P. and R.P.R. received financial support from the FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry (MMM); F.T. was also funded through the National Natural Science Foundation of China (No. 41071030). C.B. was funded through the Helmholtz project ‘REKLIM-Regional Climate Change: Causes and Effects’ Topic 9: ‘Climate Change and Air Quality’. M.P.R. and PD.A. received funding from the CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS). G.O'L. was funded through the Australian Grains Research and Development Corporation and the Department of Economic Development, Jobs, Transport and Resources Victoria, Australia. R.C.I. was funded by Texas AgriLife Research, Texas A&M University. B.B. was funded by USDA-NIFA Grant No: 2015-68007-23133.
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- 2017
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9. Multi-wheat-model ensemble responses to interannual climate variability
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Carlos Angulo, Frank Ewert, Tom M. Osborne, Pramod K. Aggarwal, Senthold Asseng, Pasquale Steduto, Kurt Christian Kersebaum, Eckart Priesack, Patrick Bertuzzi, Roberto C. Izaurralde, Dominique Ripoche, Thilo Streck, Joost Wolf, Pierre Stratonovitch, Alex C. Ruane, Richard Goldberg, Robert F. Grant, Taru Palosuo, Iurii Shcherbak, Kenneth J. Boote, Christian Biernath, Garry O'Leary, J. Hooker, Peter J. Thorburn, Joachim Ingwersen, Soora Naresh Kumar, Lee Heng, Maria I. Travasso, Pierre Martre, Katharina Waha, Nicholas I. Hudson, Claas Nendel, Fulu Tao, Christoph Müller, Andrew J. Challinor, Jørgen E. Olesen, Reimund P. Rötter, Davide Camarrano, L. A. Hunt, Sebastian Gayler, Nadine Brisson, Daniel Wallach, Mikhail A. Semenov, Claudio O. Stöckle, Iwan Supit, Jordi Doltra, Jeffrey W. White, Bruno Basso, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Center for Climate Systems Research [New York] (CCSR), Columbia University [New York], Agricultural & Biological Engineering Department, University of Florida [Gainesville], The James Hutton Institute, Institute of Crop Science and Resource Conservation, Rheinische Friedrich-Wilhelms-Universität Bonn, Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF), Leibniz Association, 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), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Research Program on Climate Change, Agriculture and Food Security, International Water Management Institute, International Water Management Institute, Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), Institute of Biochemical Plant Pathology, German Research Center for Environmental Health - Helmholtz Center München (GmbH), Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, CGIAR-ESSP Program on Climate Change, Agriculture and Food Security, International Center for Tropical Agriculture, Cantabrian Agricultural Research and Training Centre, Institute of Soil Science and Land Evaluation, University of Hohenheim, Department of Renewable Resources, University of Alberta, International Atomic Energy Agency [Vienna] (IAEA), School of Agriculture, Policy and Development, University of Reading (UOR), Department of Plant Agriculture, University of Guelph, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Potsdam Institute for Climate Impact Research (PIK), Landscape & Water Sciences, Department of Environment of Victoria, Department of Agroecology, Aarhus University, National Centre for Atmospheric Science, Department of Meteorology, Environmental Impacts Group, Natural Resources Institute Finland, Georg-August-Universität Göttingen, Computational and Systems Biology Department, Rothamsted Research, Biotechnology and Biological Sciences Research Council, Institute for Future Environments, Queensland University of Technology, Food and Agriculture Organization, Biological Systems Engineering, Washington State University (WSU), Earth System Science-Climate Change and Adaptive Land-use and Water Management, Wageningen University and Research Center (WUR), Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Changchun Branch] (CAS), Institute for Climate and Water [Castelnar], Instituto Nacional de Tecnología Agropecuaria (INTA), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, USDA-ARS, Arid-Land Agricultural Research Center, United States Department of Agriculture, Plant Production Systems, Modelling European Agriculture with Climate Change for Food Security (MACSUR), JPI FACCE, 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], 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), Agroclim (AGROCLIM), Aarhus University [Aarhus], Natural resources institute Finland, Georg-August-University [Göttingen], Wageningen University and Research [Wageningen] (WUR), 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, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Natural Resources Institute Finland (LUKE), Georg-August-University = Georg-August-Universität Göttingen, Biotechnology and Biological Sciences Research Council (BBSRC), Queensland University of Technology [Brisbane] (QUT), Institute of geographical sciences and natural resources research [CAS] (IGSNRR), Chinese Academy of Sciences [Beijing] (CAS), Université de Toulouse (UT)-Université de Toulouse (UT), USDA Agricultural Research Service [Maricopa, AZ] (USDA), United States Department of Agriculture (USDA), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), Center for Climate Systems Research [New York] ( CCSR ), University of Florida, University of Bonn (Rheinische Friedrich-Wilhelms), Leibniz Centre for Agricultural Landscape Research (ZALF), 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 ), Génétique Diversité et Ecophysiologie des Céréales ( GDEC ), Institut National de la Recherche Agronomique ( INRA ) -Université Blaise Pascal - Clermont-Ferrand 2 ( UBP ), Commonwealth Scientific and Industrial Research Organisation, Department of Geological Sciences and Kellogg Biological Station, Michigan State University, UE Agroclim ( UE AGROCLIM ), Institut National de la Recherche Agronomique ( INRA ), Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, International Atomic Energy Agency [Vienna] ( IAEA ), University of Reading ( UOR ), University of Maryland, Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Potsdam Institute for Climate Impact Research ( PIK ), Washington State University ( WSU ), Wageningen University and Research Center ( WUR ), Chinese Academy of Sciences [Changchun Branch] ( CAS ), Institute for Climate and Water, and Instituto Nacional de Tecnología Agropecuaria
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[SDV.SA]Life Sciences [q-bio]/Agricultural sciences ,Temperature sensitivity ,010504 meteorology & atmospheric sciences ,Precipitation ,01 natural sciences ,modèle de croissance ,wheat ,uncertainty ,[ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences ,climate impacts ,2. Zero hunger ,Multi-model ensemble ,changement climatique ,Ecological Modeling ,Temperature ,Uncertainty ,04 agricultural and veterinary sciences ,PE&RC ,Plant Production Systems ,Climatology ,PRECIPITATION ,Climate impacts ,Environmental Engineering ,interannual variability ,Yield (finance) ,australie ,variation interannuelle ,Growing season ,Climate change ,multi-model ensemble ,Earth System Science ,Interannual variability ,blé ,température ,pays bas ,global change ,0105 earth and related environmental sciences ,[ SDE.BE ] Environmental Sciences/Biodiversity and Ecology ,WIMEK ,précipitation ,business.industry ,argentine ,Simulation modeling ,temperature ,Global change ,15. Life on land ,inde ,Climate Resilience ,13. Climate action ,Agriculture ,Klimaatbestendigheid ,Plantaardige Productiesystemen ,040103 agronomy & agriculture ,AgMIP ,0401 agriculture, forestry, and fisheries ,Environmental science ,Leerstoelgroep Aardsysteemkunde ,Crop modeling ,[SDE.BE]Environmental Sciences/Biodiversity and Ecology ,business ,Software - Abstract
We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981-2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2???0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts. Compares interannual climate response of 27 wheat models at four locations.Calculates the diminishing return of constructing multi-model ensembles for assessment.Identifies similarities and major differences of model responses.Differentiates between interannual temperature sensitivity and climate change response.
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- 2016
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10. Benchmark data set for wheat growth models: field experiments and AgMIP multi-model simulations
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Asseng, S, Ewert, F., Martre, P, Rosenzweig, C, Jones, J.W., Hatfield, J L, Ruane, A C, Boote, K J, Thorburn, P, Rötter, RP, Cammarano, D, Brisson, N, Basso, B, Aggarwal, PK, Angulo, C, Bertuzzi, P, Biernath, C, Challinor, AJ, Doltra, J, Gayler, S, Goldberg, R, Grant, R, Heng, L, Hooker, J, Hunt, L A, Ingwersen, J, Izaurralde, RC, Kersebaum, KC, Müller, C, Naresh Kumar, S, Nendel, C, O'Leary, G, Olesen, Jørgen Eivind, Osborne, T M, Palosuo, T, Priesack, E, Ripoche, D, Semenov, MA, Shcherbak, I, Steduto, P, Stöckle, C, Stratonovitch, P, Streck, T, Supit, I, Tao, F, Travasso, M, Waha, K, Wallach, D, White, JW, Williams, J R, Wolf, J., 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, 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), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), National laboratory for agriculture and the environment, Department of agronomy, University of Florida [Gainesville] (UF), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Plant Production Research, Agrifood Research Finland, Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, International Water Management Institute, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), German Research Center for Environmental Health, University of Leeds, Catabrian Agricultural Research and Training Center (CIFA), Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Department of Renewable Resources, University of Alberta, International Atomic Energy Agency [Vienna] (IAEA), Agriculture Department, University of Reading (UOR), Department of Plant Agriculture, University of Guelph, Institute of Soil Science and Land Evaluation, University of Hohenheim, Joint Global Change Research Institute, Institute of landscape systems analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Potsdam Institute for Climate Impact Research (PIK), Division of Environmental Sciences, University of Hertfordshire [Hatfield] (UH), Department of Primary Industries, Department of Agroecology, Aarhus University [Aarhus], NCAS-Climate, Walker Institute, Computational and Systems Biology Department, Rothamsted Research, Food and Agriculture Organization, Biological Systems Engineering, Washington State University (WSU), Wageningen University and Research Centre (WUR), Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), Institute for Climate and Water, Instituto Nacional de Tecnología Agropecuaria (INTA), 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, Arid-Land Agricultural Research Center, Texas A&M University System, Consultative Group on International Agricultural Research (CGIAR), 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), Helmholtz Zentrum München = German Research Center for Environmental Health, Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), Institute of geographical sciences and natural resources research [CAS] (IGSNRR), Chinese Academy of Sciences [Beijing] (CAS), and Université de Toulouse (UT)-Université de Toulouse (UT)
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010504 meteorology & atmospheric sciences ,[SDV]Life Sciences [q-bio] ,Climate change ,Atmospheric sciences ,01 natural sciences ,Earth System Science ,[SHS]Humanities and Social Sciences ,Anthesis ,sensitivity analysis ,wheat ,Life Science ,[SDV.BV]Life Sciences [q-bio]/Vegetal Biology ,Relative humidity ,Precipitation ,field experimental data ,0105 earth and related environmental sciences ,2. Zero hunger ,WIMEK ,Humidity ,04 agricultural and veterinary sciences ,15. Life on land ,Climate Resilience ,Dew point ,13. Climate action ,Klimaatbestendigheid ,climate change impact ,Soil water ,[SDE]Environmental Sciences ,040103 agronomy & agriculture ,0401 agriculture, forestry, and fisheries ,Environmental science ,Leerstoelgroep Aardsysteemkunde ,Climate model ,simulations ,Sensitivity analysis - Abstract
International audience; The data set includes a current representative management treatment from detailed, quality-tested sentinel field experiments with wheat from four contrasting environments including Australia, The Netherlands, India and Argentina. Measurements include local daily climate data (solar radiation, maximum and minimum temperature, precipitation, surface wind, dew point temperature, relative humidity, and vapor pressure), soil characteristics, frequent growth, nitrogen in crop and soil, crop and soil water and yield components. Simulations include results from 27 wheat models and a sensitivity analysis with 26 models and 30 years (1981-2010) for each location, for elevated atmospheric CO2 and temperature changes, a heat stress sensitivity analysis at anthesis, and a sensitivity analysis with soil and crop management variations and a Global Climate Model end-century scenario.
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- 2016
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11. Multimodel ensembles of wheat growth: many models are better than one
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Pierre Martre, Bruno Basso, James W. Jones, Jordi Doltra, Garry O'Leary, Pramod K. Aggarwal, Christian Biernath, Jeffrey W. White, Sebastian Gayler, R. Goldberg, Eckart Priesack, Robert F. Grant, Nadine Brisson, Patrick Bertuzzi, Thilo Streck, Daniel Wallach, Joachim Ingwersen, Davide Cammarano, J. Hooker, Fulu Tao, Christoph Müller, Carlos Angulo, Soora Naresh Kumar, Claas Nendel, Jørgen E. Olesen, Lee Heng, Maria I. Travasso, Iurii Shcherbak, Mikhail A. Semenov, Claudio O. Stöckle, Tom M. Osborne, L. A. Hunt, Alex C. Ruane, Frank Ewert, Kenneth J. Boote, Andrew J. Challinor, Reimund P. Rötter, Iwan Supit, Jerry L. Hatfield, Roberto C. Izaurralde, Senthold Asseng, Cynthia Rosenzweig, Pasquale Steduto, Kurt Christian Kersebaum, Dominique Ripoche, Peter J. Thorburn, Pierre Stratonovitch, Joost Wolf, Katharina Waha, Taru Palosuo, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Agrosystèmes Cultivés et Herbagers (ARCHE), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure Agronomique de Toulouse-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Agricultural & Biological Engineering Department, University of Florida [Gainesville], Institute of Crop Science and Resource Conservation, Rheinische Friedrich-Wilhelms-Universität Bonn, Plant Production Research, Agrifood Research Finland, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Ecosystem Sciences, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), National Laboratory for Agriculture and Environment, Consultative Group on International Agricultural Research, Research Program on ClimateChange, Agriculture and Food Security, International Water Management Institute, Department of Geological Sciences, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, W. K. Kellogg Biological Station (KBS), UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), Institute of Soil Ecology, German Research Center for Environmental Health, Helmholtz-Zentrum München (HZM), Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), CGIAR-ESSP Program on Climate Change,Agriculture and Food Security, International Center for Tropical Agriculture, School of Earth and Environment [Leeds] (SEE), University of Leeds, Cantabrian Agricultural Research and Training Centre, Water & Earth System Science Competence Cluster, Eberhard Karls Universität Tübingen, Department of Renewable Resources, University of Alberta, International Atomic Energy Agency [Vienna] (IAEA), School of Agriculture, Policy and Development, University of Reading (UOR), Department of Plant Agriculture, University of Guelph, Institute of Soil Science and Land Evaluation, Universität Stuttgart, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Institute of Landscape Systems Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Potsdam Institute for Climate Impact Research (PIK), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Department of Primary Industries, Landscape & Water Sciences, Université Paris Diderot - Paris 7 (UPD7), Department of Agroecology, Aarhus University [Aarhus], National Centre for Atmospheric Science, Department of Meteorology, Institute of Soil Ecology German Research Center for Environmental Health, Computational and Systems Biology Department, Rothamsted Research, Food and Agriculture Organization of the United Nations, Washington State University (WSU), University of Hohenheim, Wageningen University and Research Centre [Wageningen] (WUR), Institute Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), CIRN, Institute forClimate and Water, Instituto Nacional de Tecnología Agropecuaria (INTA), Arid-Land Agricultural Research Center, United States Department of Agriculture, Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), 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], Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Wageningen University and Research [Wageningen] (WUR), Génétique Diversité et Ecophysiologie des Céréales ( GDEC ), Université Blaise Pascal - Clermont-Ferrand 2 ( UBP ) -Institut National de la Recherche Agronomique ( INRA ), Agrosystèmes Cultivés et Herbagers ( ARCHE ), Institut National Polytechnique [Toulouse] ( INP ) -Institut National de la Recherche Agronomique ( INRA ) -Ecole Nationale Supérieure Agronomique de Toulouse, University of Bonn (Rheinische Friedrich-Wilhelms), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), Commonwealth Scientific and Industrial Research Organisation, Kellogg Biological Station, UE Agroclim ( UE AGROCLIM ), Institut National de la Recherche Agronomique ( INRA ), Helmholtz-Zentrum München ( HZM ), Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, School of Earth and Environment [Leeds] ( SEE ), University of Alberta [Edmonton], International Atomic Energy Agency [Vienna] ( IAEA ), University of Reading ( UOR ), Leibniz Centre for Agricultural Landscape Research, Potsdam Institute for Climate Impact Research ( PIK ), Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Washington State University ( WSU ), Wageningen University and Research Centre [Wageningen] ( WUR ), Chinese Academy of Sciences [Beijing] ( CAS ), Instituto Nacional de Tecnología Agropecuaria, Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse (ENSAT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT), Helmholtz Zentrum München = German Research Center for Environmental Health, and Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC)
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Calibration (statistics) ,Climate ,Statistics ,process-based model ,grain ,uncertainty ,Triticum ,General Environmental Science ,Mathematics ,2. Zero hunger ,Global and Planetary Change ,Ecology ,Mathematical model ,Estimator ,PE&RC ,simulation ,ecophysiological model ,Europe ,[ SDE.MCG ] Environmental Sciences/Global Changes ,model intercomparison ,Plant Production Systems ,Wheat (Triticum aestivum L.) ,climate-change ,wheat (Triticum aestivum L.) ,Centre for Crop Systems Analysis ,impact ,Seasons ,simulations ,europe ,ensemble modeling ,Climate Change ,[SDE.MCG]Environmental Sciences/Global Changes ,australia ,crop production ,Environment ,Models, Biological ,Consistency (statistics) ,Approximation error ,Environmental Chemistry ,Alterra - Centrum Bodem ,impacts ,Hydrology ,[ SDE.BE ] Environmental Sciences/Biodiversity and Ecology ,WIMEK ,Ensemble forecasting ,Crop yield ,Simulation modeling ,Soil Science Centre ,Australia ,yield ,calibration ,Climate Resilience ,13. Climate action ,Klimaatbestendigheid ,Plantaardige Productiesystemen ,biodiversity conservation ,[SDE.BE]Environmental Sciences/Biodiversity and Ecology ,Environmental Sciences ,billion - Abstract
Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.
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- 2015
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12. Statistical analysis of large simulated yield datasets for studying climate change effects
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David Makowski, Senthold Asseng, Frank Ewert, Simona Bassu, Jean-Louis Durand, Pierre Martre, Myriam Adam, Pramod K. Aggarwal, Carlos Angulo, Christian Baron, Bruno Basso, Patrick Bertuzzi, Christian Biernath, Hendrik Boogaard, Kenneth J. Boote, Nadine Brisson, Davide Cammarano, Andrew J. Challinor, Sjakk J. G. Conijn, Marc Corbeels, Delphine Deryng, Giacomo De Sanctis, Jordi Doltra, Sebastian Gayler, Richard Goldberg, Patricio Grassini, Jerry L. Hatfield, Lee Heng, Steven Hoek, Josh Hooker, Tony L. A. Hunt, Joachim Ingwersen, Cesar Izaurralde, Raymond E. E. Jongschaap, James W. Jones, Armen R. Kemanian, Christian Kersebaum, Soo-Hyung Kim, Jon Lizaso, Christoph Müller, Naresh S. Kumar, Claas Nendel, Garry J. O'Leary, Jorgen E. Olesen, Tom M. Osborne, Taru Palosuo, Maria V. Pravia, Eckart Priesack, Dominique Ripoche, Cynthia Rosenzweig, Alexander C. Ruane, Fredirico Sau, Mickhail A. Semenov, Iurii Shcherbak, Pasquale Steduto, Claudio Stöckle, Pierre Stratonovitch, Thilo Streck, Iwan Supit, Fulu Tao, Edmar I. Teixeira, Peter Thorburn, Denis Timlin, Maria Travasso, Reimund Rötter, Katharina Waha, Daniel Wallach, Jeffrey W. White, Jimmy R. Williams, Joost Wolf, Agronomie, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, University of Florida [Gainesville] (UF), Rheinische Friedrich-Wilhelms-Universität Bonn, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Michigan State University [East Lansing], Michigan State University System, Agroclim (AGROCLIM), German Research Center for Environmental Health, Centre for Geo-Information, University of Leeds, International Center for Tropical Agriculture, Wageningen University and Research [Wageningen] (WUR), Chinese Academy of Sciences [Changchun Branch] (CAS), University of East Anglia, Catabrian Agricultural Research and Training Center (CIFA), University of Tübingen, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), University of Nebraska [Lincoln], University of Nebraska System, National Laboratory for Agriculture and Environment, International Atomic Energy Agency [Vienna] (IAEA), University of Reading (UOR), University of Guelph, University of Hohenheim, Joint Global Change Research Institute, Instituto Nacional de Investigación Agropecuaria (INIA), Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), University of Washington, Universidad Politécnica de Madrid (UPM), Potsdam Institute for Climate Impact Research (PIK), Indian Agricultural Research Institute (IARI), Department of Environment and Primary Industries, Landscape and Water Sciences, Aarhus University [Aarhus], Agrifood Research Finland, Pennsylvania State University (Penn State), Penn State System, Rothamsted Research, FAO Sub-regional Office for Eastern Africa [Addis Ababa, Ethiopie] (FAO), Food and Agriculture Organization of the United Nations [Rome, Italie] (FAO), Washington State University (WSU), Plant & Food Research, Ecosystem Sciences, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), USDA-ARS : Agricultural Research Service, Institute for Climate and Water, 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, Arid-Land Agricultural Research Center, Texas A&M University System, Hillel, D., Rosenzweig, C., Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, University of Florida, University of Bonn (Rheinische Friedrich-Wilhelms), Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères ( P3F ), Institut National de la Recherche Agronomique ( INRA ), Génétique Diversité et Ecophysiologie des Céréales ( GDEC ), Université Clermont Auvergne ( UCA ), Université Blaise Pascal (Clermont Ferrand 2) ( UBP ), Centre de Coopération Internationale en Recherche Agronomique pour le Développement, CGIAR Research Program on Climate Change, Agriculture and Food Security ( CCAFS ), Michigan State University, UE Agroclim ( UE AGROCLIM ), Wageningen University and Research Center ( WUR ), Chinese Academy of Sciences [Changchun Branch] ( CAS ), Catabrian Agricultural Research and Training Center ( CIFA ), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), University of Nebraska Lincoln ( UNL ), International Atomic Energy Agency [Vienna] ( IAEA ), University of Reading ( UOR ), Instituto Nacional de Investigación Agropecuaria, Leibniz Centre for Agricultural Landscape Research, Universidad Politécnica de Madrid ( UPM ), Potsdam Institute for Climate Impact Research ( PIK ), Indian Agricultural Research Institute ( IARI ), Aarhus University, PennState University [Pennsylvania] ( PSU ), Food and Agricultural Organization ( FAO ), Washington State University ( WSU ), New Zealand Institute for Plant and Food Research Limited, Commonwealth Scientific and Industrial Research Organisation, United States Department of Agriculture - Agricultural Research Service, UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Texas A and M University ( TAMU ), AgroParisTech-Institut National de la Recherche Agronomique (INRA), University of Florida [Gainesville], Génétique Diversité et Ecophysiologie des Céréales - Clermont Auvergne (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Université Blaise Pascal (Clermont Ferrand 2) (UBP), UE Agroclim (UE AGROCLIM), Wageningen University and Research Center (WUR), Food and Agricultural Organization (FAO), Helmholtz Zentrum München = German Research Center for Environmental Health, University of East Anglia [Norwich] (UEA), University of Nebraska–Lincoln, Biotechnology and Biological Sciences Research Council (BBSRC), and Université de Toulouse (UT)-Université de Toulouse (UT)
- Subjects
analyse de données ,[SDV.SA]Life Sciences [q-bio]/Agricultural sciences ,Earth Observation and Environmental Informatics ,010504 meteorology & atmospheric sciences ,Yield (finance) ,data analysis ,Climate change ,01 natural sciences ,Agro Water- en Biobased Economy ,statistical analysis ,Effects of global warming ,Aardobservatie en omgevingsinformatica ,Life Science ,Alterra - Centrum Bodem ,[ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences ,global change ,0105 earth and related environmental sciences ,2. Zero hunger ,changement climatique ,WIMEK ,Mathematical model ,analyse statistique ,Crop yield ,Soil Science Centre ,Global change ,Statistical model ,04 agricultural and veterinary sciences ,15. Life on land ,PE&RC ,Climate resilience ,Climate Resilience ,Plant Production Systems ,Klimaatbestendigheid ,13. Climate action ,Plantaardige Productiesystemen ,Climatology ,040103 agronomy & agriculture ,0401 agriculture, forestry, and fisheries ,Environmental science - Abstract
Many studies have been carried out during the last decade to study the effect of climate change on crop yields and other key crop characteristics. In these studies, one or several crop models were used to simulate crop growth and development for different climate scenarios that correspond to different projections of atmospheric CO2 concentration, temperature, and rainfall changes (Semenov et al., 1996; Tubiello and Ewert, 2002; White et al., 2011). The Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013) builds on these studies with the goal of using an ensemble of multiple crop models in order to assess effects of climate change scenarios for several crops in contrasting environments. These studies generate large datasets, including thousands of simulated crop yield data. They include series of yield values obtained by combining several crop models with different climate scenarios that are defined by several climatic variables (temperature, CO2, rainfall, etc.). Such datasets potentially provide useful information on the possible effects of different climate change scenarios on crop yields. However, it is sometimes difficult to analyze these datasets and to summarize them in a useful way due to their structural complexity; simulated yield data can differ among contrasting climate scenarios, sites, and crop models. Another issue is that it is not straightforward to extrapolate the results obtained for the scenarios to alternative climate change scenarios not initially included in the simulation protocols. Additional dynamic crop model simulations for new climate change scenarios are an option but this approach is costly, especially when a large number of crop models are used to generate the simulated data, as in AgMIP. Statistical models have been used to analyze responses of measured yield data to climate variables in past studies (Lobell et al., 2011), but the use of a statistical model to analyze yields simulated by complex process-based crop models is a rather new idea. We demonstrate herewith that statistical methods can play an important role in analyzing simulated yield data sets obtained from the ensembles of process-based crop models. Formal statistical analysis is helpful to estimate the effects of different climatic variables on yield, and to describe the between-model variability of these effects.
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- 2015
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13. A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration
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Joost Wolf, Xinyou Yin, Pierre Martre, Zhengtao Zhang, H. K. Soo, Manuel Marcaida, Nadine Brisson, Patrick Bertuzzi, Soo-Hyung Kim, Yan Zhu, Roberto C. Izaurralde, L. A. Hunt, Maria I. Travasso, Christian Baron, James W. Jones, R.E.E. Jongschaap, T. Palosuo, Daniel Wallach, Jerry L. Hatfield, Christian Biernath, G. De Sanctis, Senthold Asseng, H. Yoshida, Donald S. Gaydon, Edmar Teixeira, Davide Cammarano, Alex C. Ruane, C. Nendel, T. Hasegawa, Thilo Streck, Garry O'Leary, Upendra Singh, Frank Ewert, Delphine Deryng, R. Goldberg, Bas A. M. Bouman, Peter J. Thorburn, Tao Li, Roberto Confalonieri, Myriam Adam, Jes Olesen, Reimund P. Rötter, Tamon Fumoto, Patricio Grassini, Joachim Ingwersen, Robert F. Grant, Katharina Waha, James Williams, Fulu Tao, Eckart Priesack, Pramod K. Aggarwal, Liang Tang, Sebastian Gayler, Jordi Doltra, L. Heng, Christoph Müller, J.G. Conijn, Iwan Supit, S. Naresh Kumar, Iurii Shcherbak, Jeffrey W. White, Hendrik Boogaard, Kenneth J. Boote, David Makowski, Federico Sau, Jean-Louis Durand, Mikhail A. Semenov, Claudio O. Stöckle, Marc Corbeels, Steven Hoek, Simone Bregaglio, Hiroshi Nakagawa, Philippe Oriol, Anthony Challinor, R. A. Kemanian, Carlos Angulo, Pasquale Steduto, Bruno Basso, Kurt Christian Kersebaum, Cynthia Rosenzweig, Dennis Timlin, J. Hooker, Samuel Buis, Maria Virginia Pravia, Françoise Ruget, Dominique Ripoche, Simona Bassu, Pierre Stratonovitch, Jon I. Lizaso, Balwinder Singh, Tom M. Osborne, Paul W. Wilkens, Agronomie, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, 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, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), International Rice Research Institute [Philippines] (IRRI), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Int Rice Res Inst, Los Banos, Philippines, Université Paris Diderot - Paris 7 (UPD7), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), International Water Management Institute, Research Program on Climate Change, Agriculture and Food Security, CGIAR, Institute of Crops Science and Resource Conservation INRES, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Department of Geological Sciences [East Lansing], Agroclim (AGROCLIM), German Research Center for Environmental Health, Institute of Soil Ecololgy, Helmholtz-Zentrum München (HZM), Center for Geo-information, Alterra, Department of Agronomy, University of Florida [Gainesville] (UF), Cassandra Lab, University of Milan, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), The James Hutton Institute, CGIAR ESSP Program on Climate Change, Agriculture and Food Security, International Center for Tropical Agriculture, School of Earth and Environment [Leeds] (SEE), University of Leeds, Plant Research International, Wageningen University and Research [Wageningen] (WUR), Embrapa Cerrados, Agroécologie et Intensification Durables des cultures annuelles (UPR AIDA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Tyndall Centre for Climate Change Research, School of Environmental Science, University of East Anglia [Norwich] (UEA), European Commission - Joint Research Centre [Ispra] (JRC), Cantabrian Agricultural Research and Training Centre, Tsukuba, National Institute of Agro-Environmental Sciences (NIAES), Agriculture Flagship, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), WESS Water and Earth System Science Competence Cluster, Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Departement of Renewable Resources, University of Alberta, Department of Agronomy and Horticulture, University of Nebraska [Lincoln], University of Nebraska System-University of Nebraska System, National Laboratory for Agriculture and Environment, International Atomic Energy Agency [Vienna] (IAEA), Centre for Geo-Information, Agriculture Department, University of Reading (UOR), Department of Plant Agriculture, University of Guelph, Institute of Soil Science and Land Evaluation, University of Hohenheim, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, 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, Instituto Nacional de Investigación Agropecuaria (INIA), Institute of Landscape System Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), College of the Environment, School of Environmental and Forest Sciences, University of Washington, Department Produccion Vegetal, Fitotecnia, Universidad Politécnica de Madrid (UPM), Potsdam Institute for Climate Impact Research (PIK), National Agriculture and Food Research Organization (NARO), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Institute of Landscape Systems Analysis, Department of Economic Development Jobs, Transport and Resources, Grains Innovation Park, Department of Agroecology, Aarhus University [Aarhus], Walker Institute, NCAS Climate, Natural Resources Institute Finland, Department of Plant Science, Pennsylvania State University (Penn State), Penn State System-Penn State System, German Research Center for Environmental Health, Institute of Soil Ecology, Department Biologia Vegetal, Computational and Systems Biology Department, Rothamsted Research, Department of Geological Sciences and W.K. Kellogg Biological Station, 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), International Fertilizer Development Center (IFDC), College of the Environment, School of Environmental and Forest Science, University of Washington [Seattle], FAO Sub-regional Office for Eastern Africa [Addis Ababa, Ethiopie] (FAO), Food and Agriculture Organization of the United Nations [Rome, Italie] (FAO), Biological Systems Engineering, Washington State University (WSU), Plant Production Systems and Earth System Science, National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Sustainable Production, Plant & Food Research, ARS Crop Systems and Global Change Laboratory, United States Department of Agriculture, CIRN, Institute for Climate and Water, Instituto Nacional de Tecnología Agropecuaria (INTA), Agriculture, Agrosystèmes Cultivés et Herbagers (ARCHE), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Arid-Land Agricultural Research Center, Texas AgriLife Research and Extension, Texas A&M University System, Centre for Crop Systems Analysis, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University (BNU), Metaprogramme ACCAF, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Helmholtz Zentrum München = German Research Center for Environmental Health, Università degli Studi di Milano = University of Milan (UNIMI), University of Nebraska–Lincoln, Université de Toulouse (UT)-Université de Toulouse (UT), Natural Resources Institute Finland (LUKE), Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), Nanjing Agricultural University (NAU), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse (ENSAT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Agricultural and Biological Engineering Department, University of Florida [Gainesville], Institute of Crop Science and Resource Conservation INRES, International Rice Research Institute, Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), UE Agroclim (UE AGROCLIM), Wageningen University and Research Centre [Wageningen] (WUR), Agroécologie et Intensification Durables des cultures annuelles (Cirad-Persyst-UPR 115 AIDA), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Eberhard Karls Universität Tübingen, Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), National Agriculture and Food Research Organization, International Maize and Wheat Improvement Centre (CIMMYT), Food and Agricultural Organization (FAO), New Zealand Institute for Plant and Food Research Limited, Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure Agronomique de Toulouse-Institut National Polytechnique (Toulouse) (Toulouse INP), Beijing Normal University, Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, University of Bonn (Rheinische Friedrich-Wilhelms), Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères ( P3F ), Institut National de la Recherche Agronomique ( INRA ), Génétique Diversité et Ecophysiologie des Céréales ( GDEC ), Institut National de la Recherche Agronomique ( INRA ) -Université Blaise Pascal - Clermont-Ferrand 2 ( UBP ), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales ( UMR AGAP ), Institut national de la recherche agronomique [Montpellier] ( INRA Montpellier ) -Centre international d'études supérieures en sciences agronomiques ( Montpellier SupAgro ) -Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ) -Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), Territoires, Environnement, Télédétection et Information Spatiale ( UMR TETIS ), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture ( IRSTEA ) -AgroParisTech-Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ), Department of Geological Sciences, W.K. Kellogg Biological Station, Michigan State Univ, Dept Geol Sci, E Lansing, MI 48823 USA, UE Agroclim ( UE AGROCLIM ), Helmholtz-Zentrum München ( HZM ), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes ( EMMAH ), Université d'Avignon et des Pays de Vaucluse ( UAPV ) -Institut National de la Recherche Agronomique ( INRA ), Invergowrie, School of Earth and Environment [Leeds] ( SEE ), Wageningen University and Research Centre [Wageningen] ( WUR ), Agro-ecologyand Sustainable Intensification of Annual Crops, Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ), University of East Anglia [Norwich] ( UEA ), European Commission - Joint Research Centre [Ispra] ( JRC ), National Institute for Agro-Environmental Sciences, Commonwealth Scientific and Industrial Research Organisation, NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), University of Nebraska-Lincoln, International Atomic Energy Agency [Vienna] ( IAEA ), University of Reading ( UOR ), UMR 1248 Agrosystèmes et Développement Territorial (AGIR), Agro-ecology and Sustainable Intensification of Annual Crops, Instituto Nacional de Investigación Agropecuaria, Leibniz Centre for Agricultural Landscape Research, Universidad Politécnica de Madrid ( UPM ), Potsdam Institute for Climate Impact Research ( PIK ), Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), PennState University [Pennsylvania] ( PSU ), W.K. Kellogg Biological Station, Department of Geological Sciences, International Maize and Wheat Improvement Centre ( CIMMYT ), International Fertilizer Development Center ( IFDC ), Food and Agricultural Organization ( FAO ), Washington State University ( WSU ), Instituto Nacional de Tecnología Agropecuaria, Agrosystèmes Cultivés et Herbagers ( ARCHE ), Institut National Polytechnique [Toulouse] ( INP ) -Institut National de la Recherche Agronomique ( INRA ) -Ecole Nationale Supérieure Agronomique de Toulouse, and Texas A and M University ( TAMU )
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[SDV.SA]Life Sciences [q-bio]/Agricultural sciences ,Atmospheric Science ,010504 meteorology & atmospheric sciences ,F62 - Physiologie végétale - Croissance et développement ,01 natural sciences ,Statistics ,Aardobservatie en omgevingsinformatica ,Climate change ,Crop model ,[ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences ,Triticum ,Mathematics ,2. Zero hunger ,Global and Planetary Change ,Mathematical model ,Air ,Forestry ,Regression analysis ,04 agricultural and veterinary sciences ,PE&RC ,[ SDE.MCG ] Environmental Sciences/Global Changes ,Rendement des cultures ,Plant Production Systems ,Statistical model ,Modèle mathématique ,Atmosphère ,Earth Observation and Environmental Informatics ,Yield ,Crop Physiology ,P40 - Météorologie et climatologie ,[SDE.MCG]Environmental Sciences/Global Changes ,Oryza sativa ,Zea mays ,Earth System Science ,Emulator ,Agro Water- en Biobased Economy ,Alterra - Centrum Bodem ,Precipitation ,Croissance ,0105 earth and related environmental sciences ,Meta-model ,Changement climatique ,Hydrology ,Modélisation des cultures ,Crop yield ,Simulation modeling ,Soil Science Centre ,15. Life on land ,Température ,Laboratorium voor Phytopathologie ,Climate Resilience ,13. Climate action ,Klimaatbestendigheid ,Yield (chemistry) ,Plantaardige Productiesystemen ,Laboratory of Phytopathology ,040103 agronomy & agriculture ,0401 agriculture, forestry, and fisheries ,Leerstoelgroep Aardsysteemkunde ,Plante de culture ,Agronomy and Crop Science ,Dioxyde de carbone - Abstract
Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield data. Such datasets potentially provide new information but it is difficult to summarize them in a useful way due to their structural complexities. An associated issue is that it is not straightforward to compare crops and to interpolate the results to alternative climate scenarios not initially included in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2 concentration levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without rerunning the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to these datasets, and are then used to analyze the variability of the yield response to [CO2] and temperature. Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effect of a temperature increase of +2 degrees C in the considered sites. Compared to wheat, required levels of [CO2] increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulating climate change impacts increase more with temperature than with elevated [CO2]. (C) 2015 Elsevier B.V. All rights reserved.
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- 2015
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14. Uncertainty in simulating wheat yields under climate change
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J. Hooker, Pramod K. Aggarwal, Joost Wolf, Pierre Martre, Iurii Shcherbak, L. A. Hunt, Kenneth J. Boote, L. Heng, James W. Jones, Jerry L. Hatfield, Katharina Waha, Christian Biernath, Iwan Supit, Eckart Priesack, Pasquale Steduto, S. Naresh Kumar, Davide Cammarano, Joachim Ingwersen, Kurt Christian Kersebaum, Fulu Tao, Christoph Müller, Jordi Doltra, Thilo Streck, Senthold Asseng, Alex C. Ruane, Jeffrey W. White, Roberto C. Izaurralde, Tom M. Osborne, Patrick Bertuzzi, Sebastian Gayler, Andrew J. Challinor, Taru Palosuo, Reimund P. Rötter, Jørgen E. Olesen, Peter J. Thorburn, Nadine Brisson, Mikhail A. Semenov, Claudio O. Stöckle, Maria I. Travasso, Daniel Wallach, James Williams, Garry O'Leary, Cynthia Rosenzweig, Carlos Angulo, Bruno Basso, R. Goldberg, Robert F. Grant, Frank Ewert, Dominique Ripoche, Pierre Stratonovitch, Claas Nendel, Agricultural and Biological Engineering Department, University of Florida [Gainesville], Institut für Nutzpflanzenwissenschaften und Ressourcenschutz (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), National Laboratory for Agriculture and Environment, Department of Agronomy, Ecosystem Sciences, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Plant Production Research, Agrifood Research Finland, Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), CCAFS, IWMI, NASC Complex, DPS Marg, UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), Institute of Soil Ecology, Helmholtz-Zentrum München (HZM), Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, CGIAR-ESSP Program on Climate Change, Agriculture and Food Security, International Center for Tropical Agriculture, Centro de Investigaciòn y Formenta Agrario (CIFA), WESS-Water and Earth System Science Competence Cluster, Eberhard Karls Universität Tübingen, Department of Renewable Resources, University of Alberta, International Atomic Energy Agency [Vienna] (IAEA), Agriculture Department, University of Reading (UOR), Department of Plant Agriculture, University of Guelph, nstitute of Soil Science and Land Evaluation, University of Hohenheim, Joint Global Change Research Institute, University of Maryland [College Park], University of Maryland System-University of Maryland System, Institute of Landscape Systems Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Potsdam Institute for Climate Impact Research (PIK), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Landscape and Water Sciences, Department of Primary Industries, Department of Agroecology, Aarhus University [Aarhus], NCAS-Climate, Walker Institute, Computational and Systems Biology Department, Rothamsted Research, Food and Agriculture Organization, Biological Systems Engineering, Washington State University (WSU), Institute of Soil Science and Land Evaluation, Plant Production Systems and Earth System Science-Climate Change, Wageningen University and Research Centre [Wageningen] (WUR), Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), Institute for Climate and Water, Instituto Nacional de Tecnología Agropecuaria (INTA), Agrosystèmes Cultivés et Herbagers (ARCHE), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure Agronomique de Toulouse-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Arid-Land Agricultural Research Center, Texas A&M University System, Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), University of Florida [Gainesville] (UF), Agroclim (AGROCLIM), Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Wageningen University and Research [Wageningen] (WUR), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Institut für Nutzpflanzenwissenschaften und Ressourcenschutz ( INRES ), University of Bonn (Rheinische Friedrich-Wilhelms), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), Commonwealth Scientific and Industrial Research Organisation, Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, Génétique Diversité et Ecophysiologie des Céréales ( GDEC ), Institut National de la Recherche Agronomique ( INRA ) -Université Blaise Pascal - Clermont-Ferrand 2 ( UBP ), UE Agroclim ( UE AGROCLIM ), Institut National de la Recherche Agronomique ( INRA ), Helmholtz-Zentrum München ( HZM ), Institute for Climate and Atmospheric Science [Leeds] ( ICAS ), University of Leeds, Centro de Investigaciòn y Formenta Agrario ( CIFA ), University of Alberta [Edmonton], International Atomic Energy Agency [Vienna] ( IAEA ), University of Reading ( UOR ), Leibniz Centre for Agricultural Landscape Research, Potsdam Institute for Climate Impact Research ( PIK ), Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Washington State University ( WSU ), Wageningen University and Research Centre [Wageningen] ( WUR ), Chinese Academy of Sciences [Beijing] ( CAS ), Instituto Nacional de Tecnología Agropecuaria, Agrosystèmes Cultivés et Herbagers ( ARCHE ), Institut National Polytechnique [Toulouse] ( INP ) -Institut National de la Recherche Agronomique ( INRA ) -Ecole Nationale Supérieure Agronomique de Toulouse, Texas A and M University ( TAMU ), Helmholtz Zentrum München = German Research Center for Environmental Health, Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), Institute of geographical sciences and natural resources research [CAS] (IGSNRR), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse (ENSAT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), and Université de Toulouse (UT)-Université de Toulouse (UT)
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010504 meteorology & atmospheric sciences ,[SDV]Life Sciences [q-bio] ,Climate change ,projection ,crop production ,adaptation ,Environmental Science (miscellaneous) ,01 natural sciences ,Greenhouse effect ,Uncertainty analysis ,0105 earth and related environmental sciences ,2. Zero hunger ,model ,[ SDV ] Life Sciences [q-bio] ,food ,Simulation modeling ,ensemble ,temperature ,04 agricultural and veterinary sciences ,15. Life on land ,Transient climate simulation ,scenario ,13. Climate action ,Greenhouse gas ,Climatology ,040103 agronomy & agriculture ,impact ,0401 agriculture, forestry, and fisheries ,Environmental science ,co2 ,Climate model ,Crop simulation model ,Social Sciences (miscellaneous) - Abstract
Projections of climate change impacts on crop yields are inherently uncertain1. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate2. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models1, 3 are difficult4. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development andpolicymaking. Projections of climate change impacts on crop yields are inherently uncertain1. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate2. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models1, 3 are difficult4. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking.
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- 2013
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15. Estimating canal pool resonance with auto tune variation
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X. Litrico, Albert J. Clemmens, P. J. van Overloop, R. J. Strand, Center Director, U.S. Arid Land Agricultural Research Center, Gestion de l'Eau, Acteurs, Usages (UMR G-EAU), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut de Recherche pour le Développement (IRD [France-Sud]), and Delft University of Technology (TU Delft)
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Engineering ,Flow (psychology) ,0207 environmental engineering ,02 engineering and technology ,Inflow ,Variation (game tree) ,Pseudorandom binary sequence ,Upstream (networking) ,020701 environmental engineering ,Water Science and Technology ,Civil and Structural Engineering ,Hydrology ,business.industry ,CANAL D'IRRIGATION ,Resonance ,04 agricultural and veterinary sciences ,Agricultural and Biological Sciences (miscellaneous) ,MODELISATION ,6. Clean water ,Water depth ,Control system ,[SDE]Environmental Sciences ,040103 agronomy & agriculture ,0401 agriculture, forestry, and fisheries ,sense organs ,business ,AUTOMATISATION ,CONTROLE ,Marine engineering - Abstract
The integrator-delay (ID) model is commonly used to model canal pools that do not exhibit resonance behavior. Simple step tests are often used to estimate ID model parameters; namely, delay time and backwater surface area. These step tests change the canal inflow at the upstream end of the pool and observe water depth variations at the downstream end. Some knowledge of the canal pool characteristics are needed to determine the amount of flow change and its duration. Auto tune variation (ATV) is one method for determining the duration of these step tests. Pools that are under backwater over their entire length tend to exhibit oscillations attributable to resonance waves. Random Binary Sequence (RBS) tests have been used to determine the resonance frequency of such pools, for which step tests with different durations are used. RBS tests are difficult to implement in practice and may not provide the resonance frequency. The intent of this paper is to dem- onstrate on a real canal that the ATV method can determine both the resonance frequency and the resonance-peak height for canal pools whose water levels oscillate. DOI: 10.1061/(ASCE)IR.1943-4774.0000384. © 2012 American Society of Civil Engineers. CE Database subject headings: Irrigation; Canals; Automation; Control systems. Author keywords: Irrigation districts; Canals; Automation; Control systems.
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- 2012
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16. Detecting Landcover Change at Jornada, New Mexico with ASTER Emissivities
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French, A.N., Schmugge, T.J., Ritchie, J.C., Hsu, A., Jacob, Frédéric, Ogawa, K., US Arid-Land Agricultural Research Center, United States Department of Agriculture, New Mexico State University, United States Department of Agriculture (USDA), Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and The University of Tokyo (UTokyo)
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[SPI.OTHER]Engineering Sciences [physics]/Other ,TELE-DETECTION INFRAROUGE ,NOUVEAU MEXIQUE - Abstract
E-mail address: Andrew.French@ARS.USDA.GOV (A.N. French).; International audience; Multispectral thermal infrared remote sensing of surface emissivities can detect and monitor long term land vegetation cover changes over arid regions. The technique is based on the link between spectral emissivities within the 8.5–9.5 μm interval and density of sparsely covered terrains. The link exists regardless of plant color, which means that it is often possible to distinguish bare soils from senescent and non-green vegetation. This capability is typically not feasible with vegetation indices. The method is demonstrated and verified using ASTER remote sensing observations between 2001 and 2003 over the Jornada Experimental Range, a semi-arid site in southern New Mexico, USA. A compilation of 27 nearly cloud-free, multispectral thermal infrared scenes revealed spatially coherent patterns of spectral emissivities decreasing at rates on the order of 3% per year with R2 values of ∼0.82. These patterns are interpreted as regions of decreased vegetation densities, a view supported by groundbased leaf area index transect data. The multi-year trend revealed by ASTER's 90-m resolution data are independently confirmed by 1-km data from Terra MODIS. Comparable NDVI images do not detect the long-term spatially coherent changes in vegetation. These results show that multispectral thermal infrared data, used in conjunction with visible and near infrared data, could be particularly valuable for monitoring land cover changes..
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- 2008
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17. Advances in land remote sensign : system, modeling, inversion and application
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Franc̨ois Petitcolin, Andrew N. French, Ghani Chehbouni, Dominique Courault, Kenta Ogawa, Ana Pinheiro, Jeffrey L. Privette, Thomas J. Schmugge, Frédéric Jacob, Albert Olioso, Liang, S. (ed.), Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-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), Ecole supérieure d'agriculture de Purpan (ESAP), New Mexico State University, Unité Climat, Sol et Environnement (CSE), Institut National de la Recherche Agronomique (INRA), USDA Agricultural Research Service [Maricopa, AZ] (USDA), United States Department of Agriculture (USDA), The University of Tokyo (UTokyo), Hitachi, Ltd, Analytic and Computational Research, Inc. - Earth Sciences (ACRI-ST), Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), GSFC Hydrospheric and Biospheric Sciences Laboratory, NASA Goddard Space Flight Center (GSFC), NOAA National Climatic Data Center (NCDC), National Oceanic and Atmospheric Administration (NOAA), S. Liang, Shunlin Liang, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut de Recherche pour le Développement (IRD [ Madagascar])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), College of Agriculture, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Agricultural Research Service / US Arid Land Agricultural Research Center, United States Department of Agriculture, Department of Geo-system Engineering, The University of Tokyo, Centre National d'Études Spatiales [Toulouse] (CNES)-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), Biospheric Sciences Laboratory, Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), and Institut de Recherche pour le Développement (IRD)-Institut de Recherche pour le Développement (IRD [ Madagascar])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)
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[SDV.SA]Life Sciences [q-bio]/Agricultural sciences ,Brightness ,TEMPORAL SCALE ,sol ,010504 meteorology & atmospheric sciences ,télédétection ,modélisation spatiale ,02 engineering and technology ,01 natural sciences ,Sciences de la Terre ,CANOPY ,MODELING TOOL ,Radiative transfer ,Cryosphere ,INVERSION ,020701 environmental engineering ,Milieux et Changements globaux ,TRANSFERT DE CHALEUR ,METHODE D'ANALYSE ,SPECTROMETRIE INFRAROUGE ,température de surface ,MULTIANGULAR MEASUREMENT ,Agricultural sciences ,Geography ,échelle spatiale ,RADIOMETRIC TEMPERATURE ,Brightness temperature ,remote sensing ,radiative transfert équation ,thermal infrared équation ,modeling tool ,inversion ,simulation model ,land surface temperature ,canopy ,radiometric temperature ,multiangular measurement ,spatial scale ,temporal scale ,COUVERT VEGETAL ,Planetary boundary layer ,couvert végétal ,[SDE.MCG]Environmental Sciences/Global Changes ,RAYONNEMENT IR THERMIQUE ,0207 environmental engineering ,[SDU.STU]Sciences of the Universe [physics]/Earth Sciences ,SIMULATION MODEL ,RADIATIVE TRANSFERT EQUATION ,REMOTE SENSING ,Physics::Geophysics ,mesure multiangulaire ,[SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems ,Thermal ,échelle temporelle ,transfert radiatif ,TELEDETECTION ,0105 earth and related environmental sciences ,Remote sensing ,modélisation ,TEMPERATURE DE SURFACE ,SURFACE DU SOL ,Simulation modeling ,Inversion (meteorology) ,TRANSFERT RADIATIF ,15. Life on land ,MODELISATION ,13. Climate action ,Earth Sciences ,SPATIAL SCALE ,LAND SURFACE TEMPERATURE ,Sciences agricoles ,THERMAL INFRARED EQUATION - Abstract
Proceedings paper from the 9th International Symposium on Physical Measurements and Signatures in Remote SensingChinese Acad Sci, Inst Geog Sci & Nat Resource Res, Beijing, China, OCT, 2005; Thermal Infra Red (TIR) Remote sensing allow spatializing various land surface temperatures: ensemble brightness, radiometric and aerodynamic temperatures, soil and vegetation temperatures optionally sunlit and shaded, and canopy temperature profile. These are of interest for monitoring vegetated land surface processes: heat and mass exchanges, soil respiration and vegetation physiological activity. TIR remote sensors collect information according to spectral, directional, temporal and spatial dimensions. Inferring temperatures from measurements relies on developing and inverting modeling tools. Simple radiative transfer equations directly link measurements and variables of interest, and can be analytically inverted. Simulation models allow linking radiative regime to measurements. They require indirect inversions by minimizing differences between simulations and observations, or by calibrating simple equations and inductive learning methods. In both cases, inversion consists of solving an ill posed problem, with several parameters to be constrained from few information. Brightness and radiometric temperatures have been inferred by inverting simulation models and simple radiative transfer equations, designed for atmosphere and land surfaces. Obtained accuracies suggest refining the use of spectral and temporal information, rather than innovative approaches. Forthcoming challenge is recovering more elaborated temperatures. Soil and vegetation components can replace aerodynamic temperature, which retrieval seems almost impossible. They can be inferred using multiangular measurements, via simple radiative transfer equations previously parameterized from simulation models. Retrieving sunlit and shaded components or canopy temperature profile requires inverting simulation models. Then, additional difficulties are the influence of thermal regime, and the limitations of spaceborne observations which have to be along track due to the temperature fluctuations. Finally, forefront investigations focus on adequately using TIR information with various spatial resolutions and temporal samplings, to monitor the considered processes with adequate spatial and temporal scales. 10.1 Introduction Using TIR remote sensing for environmental issues have been investigated the last three decades. This is motivated by the potential of the spatialized information for documenting the considered processes within and between the Earth system components: cryosphere [1–2], atmosphere [3–6], oceans [7–9], and land surfaces [10]. For the latter, TIR remote sensing is used to monitor forested areas [11–14], urban areas [15–17], and vegetated areas. We focus here on vegetated areas, natural and cultivated. The monitored processes are related to climatology, meteorology, hydrology and agronomy: (1) radiation, heat and water transfers at the soil–vegetation–atmosphere interface [18–24]; (2) interactions between land surface and atmospheric boundary layer [25]; (3) vegetation physiological processes such as transpiration and water consumption, photosynthetic activity and CO2 uptake, vegetation growth and
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- 2008
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18. Canopy temperature for simulation of heat stress in irrigated wheat in a semi-arid environment: A multi-model comparison
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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)
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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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19. Rising temperatures reduce global wheat production
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Margarita Garcia-Vila, L. A. Hunt, Jørgen E. Olesen, P. V. V. Prasad, Pierre Martre, Katharina Waha, Curtis D. Jones, Pramod K. Aggarwal, Yan Zhu, Phillip D. Alderman, Christian Biernath, Fulu Tao, Bruno Basso, Davide Cammarano, Christoph Müller, Eckart Priesack, Taru Palosuo, Mohamed Jabloun, Thilo Streck, Zhigan Zhao, Jordi Doltra, Roberto C. Izaurralde, Alex C. Ruane, Andrew J. Challinor, Michael J. Ottman, Jeffrey W. White, Garry O'Leary, Reimund P. Rötter, David B. Lobell, Sebastian Gayler, Gerard W. Wall, G. De Sanctis, Matthew P. Reynolds, Senthold Asseng, Iurii Shcherbak, Peter J. Thorburn, Enli Wang, Bruce A. Kimball, Daniel Wallach, Mikhail A. Semenov, Claudio O. Stöckle, Claas Nendel, Jakarat Anothai, Ehsan Eyshi Rezaei, Pierre Stratonovitch, Ann-Kristin Koehler, Joost Wolf, Kurt Christian Kersebaum, Gerrit Hoogenboom, Frank Ewert, Iwan Supit, S. Naresh Kumar, Elias Fereres, NASA's Goddard Space Flight Center, Columbia University, University of Florida, Department of Agriculture (US), Oregon State University, International Maize and Wheat Improvement Center, University of Agriculture Faisalabad, Shahid Beheshti University, ARVALIS, International Food Policy Research Institute (US), Federal Ministry of Education and Research (Germany), German Research Foundation, Danish Council for Strategic Research, Federal Ministry of Food and Agriculture (Germany), Finnish Ministry of Agriculture and Forestry, National Natural Science Foundation of China, CGIAR (France), Grains Research and Development Corporation (Australia), Department of Environment and Primary Industries (Australia), Texas AgriLife Research, Texas A&M University, Commonwealth Scientific and Industrial Research Organisation (Australia), Chinese Academy of Sciences, 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, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Plant Production Research, Agrifood Research Finland, Stanford University, Agricultural Research Service / US Arid Land Agricultural Research Center, United States Department of Agriculture, The School of Plant Sciences, University of Arizona, International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Department of Agronomy, Purdue University [West Lafayette], CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Washington State University (WSU), Department of geological sciences, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, W. K. Kellogg Biological Station (KBS), Institute of Soil Ecology, Helmholtz-Zentrum München (HZM), CGIAR-ESSP Program on Climate Change,Agriculture and Food Security, International Center for Tropical Agriculture, University of Leeds, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Catabrian Agricultural Research and Training Center (CIFA), Consejo Superior de Investigaciones Científicas (CSIC), Universidad de Córdoba [Cordoba], WESS-Water and Earth System Science Competence Cluster, Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Biological Systems Engineering, Department of Plant Agriculture, University of Guelph, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A&M University System, Department of Agroecology, Aarhus University [Aarhus], Institute of Landscape System Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Potsdam Institute for Climate Impact Research (PIK), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Institute of landscape systems analysis, Department of Environment and Primary Industries, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Computational and Systems Biology Department, Rothamsted Research, Department of Geological Sciences, University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, Computational and Systems Biology, John Innes Centre [Norwich], Institute of Soil Science and Land Evaluation, University of Hohenheim, Plant Production Systems and Earth System Science, Wageningen University and Research [Wageningen] (WUR), Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), Agriculture Flagship, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), 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, Department of Agronomy and Biotechnology, China Agricultural University (CAU), Nanjing Agricultural University, International Food Policy Research Institute (IFPRI), USDA National Institute for Food and Agriculture [32011-68002-30191], KULUNDA [01LL0905L], FACCE MACSUR project through the German FederalMinistry of Education and Research (BMBF) [031A103B, 2812ERA115], German Science Foundation [EW119/5-1], FACCEMACSUR project by the Danish Strategic Research Council, FACCE MACSUR project through the German Federal Ministry of Food and Agriculture (BMEL), FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry, National Natural Science Foundation of China [41071030], Helmholtz project 'REKLIM-Regional Climate Change: Causes and Effects' Topic 9: 'Climate Change and Air Quality', CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS), Australian Grains Research and Development Corporation, Department of Environment and Primary Industries Victoria, Australia, Texas AgriLife Research, Texas AM University, CSIRO, Chinese Academy of Sciences (CAS), Helmholtz Zentrum München = German Research Center for Environmental Health, 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), Chinese Academy of Sciences [Beijing] (CAS), Université de Toulouse (UT)-Université de Toulouse (UT), and Nanjing Agricultural University (NAU)
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0106 biological sciences ,010504 meteorology & atmospheric sciences ,[SDV]Life Sciences [q-bio] ,Yield (finance) ,Growing season ,Climate change ,Environmental Science (miscellaneous) ,Atmospheric sciences ,01 natural sciences ,[SHS]Humanities and Social Sciences ,Crop ,[SDV.BV]Life Sciences [q-bio]/Vegetal Biology ,Production (economics) ,0105 earth and related environmental sciences ,2. Zero hunger ,business.industry ,Global warming ,Simulation modeling ,15. Life on land ,Agronomy ,13. Climate action ,Agriculture ,[SDE]Environmental Sciences ,Environmental science ,business ,Social Sciences (miscellaneous) ,010606 plant biology & botany - Abstract
Asseng, S. et al., Crop models are essential tools for assessing the threat of climate change to local and global food production1. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature2. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 °C to 32 °C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each °C of further temperature increase and become more variable over space and time., We thank the Agricultural Model Intercomparison and Improvement Project and its leaders C. Rosenzweig from NASA Goddard Institute for Space Studies and Columbia University (USA), J. Jones from University of Florida (USA), J. Hatfield from United States Department of Agriculture (USA) and J. Antle from Oregon State University (USA) for support. We also thank M. Lopez from CIMMYT (Turkey), M. Usman Bashir from University of Agriculture, Faisalabad (Pakistan), S. Soufizadeh from Shahid Beheshti University (Iran), and J. Lorgeou and J-C. Deswarte from ARVALIS—Institut du Végétal (France) for assistance with selecting key locations and quantifying regional crop cultivars, anthesis and maturity dates and R. Raymundo for assistance with GIS. S.A. and D.C. received financial support from the International Food Policy Research Institute (IFPRI). C.S. was funded through USDA National Institute for Food and Agriculture award 32011-68002-30191. C.M. received financial support from the KULUNDA project (01LL0905L) and the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (BMBF). F.E. received support from the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (2812ERA115) and E.E.R. was funded through the German Science Foundation (project EW 119/5-1). M.J. and J.E.O. were funded through the FACCE MACSUR project by the Danish Strategic Research Council. K.C.K. and C.N. were funded by the FACCE MACSUR project through the German Federal Ministry of Food and Agriculture (BMEL). F.T., T.P. and R.P.R. received financial support from FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry (MMM); F.T. was also funded through National Natural Science Foundation of China (No. 41071030). C.B. was funded through the Helmholtz project ‘REKLIM—Regional Climate Change: Causes and Effects’ Topic 9: ‘Climate Change and Air Quality’. M.P.R. and P.D.A. received funding from the CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS). G.O’L. was funded through the Australian Grains Research and Development Corporation and the Department of Environment and Primary Industries Victoria, Australia. R.C.I. was funded by Texas AgriLife Research, Texas A&M University. E.W. and Z.Z. were funded by CSIRO and the Chinese Academy of Sciences (CAS) through the research project ‘Advancing crop yield while reducing the use of water and nitrogen’ and by the CSIRO-MoE PhD Research Program.
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20. Using syringe filtration after lab-scale adsorption processes potentially overestimates PFAS adsorption removal efficiency from non-conventional irrigation water.
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Zheng YH, Carter E, Zou S, Williams CF, Chow AT, and Chen H
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- Adsorption, Syringes, Agricultural Irrigation, Caprylates chemistry, Alkanesulfonic Acids chemistry, Alkanesulfonic Acids analysis, Fluorocarbons chemistry, Filtration methods, Water Pollutants, Chemical chemistry, Water Pollutants, Chemical analysis, Water Purification methods
- Abstract
The adsorption process, known for its cost-effectiveness and high efficiency, has been extensively investigated at the laboratory scale for removing per- and polyfluoroalkyl substances (PFAS) from non-conventional irrigation water. However, a syringe filtration step is commonly used when quantifying PFAS removal during this adsorption process, potentially leading to PFAS retention onto the filters and an overestimate of adsorption removal efficiency. Here, we assessed the retention of three prevalent PFAS (i.e., perfluorooctanoic acid [PFOA], perfluorooctane sulfonic acid [PFOS], and perfluorobutanoic acid [PFBA]) on six syringe filters. When filtering distilled deionized water spiked with 1 µg/L and 100 µg/L of each PFAS, we observed the highest and lowest PFAS recovery percentages by mixed cellulose ester (MCE) (0.20 µm, 25 mm; 97 ± 11%, 101 ± 4.8%) and polytetrafluoroethylene (0.45 µm, 13 mm; 61 ± 37%, 80 ± 28%), respectively. Under the initial concentration of 1 µg/L and 100 µg/L, PFOS had recovery percentages of 55 ± 25% and 68 ± 24%, significantly lower than 96 ± 12% and 99 ± 5% for PFOA and 95 ± 8% and 97 ± 4% for PFBA, highlighting the importance of PFAS functional groups. PFAS recovery percentage increased with filtration volume in the order of 80 ± 28% (1 mL) < 85 ± 21% (5 mL) < 90 ± 18% (10 mL). Using MCE to filter treated municipal wastewater spiked with 1 µg/L and 100 µg/L of each PFAS, we found recovery percentages >90% for all three PFAS. Our study underscores the significance of syringe filter selection and potential overestimate of PFAS removal efficacy by the lab-scale adsorption processes., (© 2024 The Author(s). Journal of Environmental Quality published by Wiley Periodicals LLC on behalf of American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America.)
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- 2025
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21. Enhancing the Resilience of Agroecosystems Through Improved Rhizosphere Processes: A Strategic Review.
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Asghar W, Craven KD, Swenson JR, Kataoka R, Mahmood A, and Farias JG
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- Crops, Agricultural microbiology, Crops, Agricultural growth & development, Ecosystem, Plant Roots microbiology, Microbiota, Rhizosphere, Soil Microbiology, Agriculture methods
- Abstract
As farming practices evolve and climate conditions shift, achieving sustainable food production for a growing global population requires innovative strategies to optimize environmentally friendly practices and minimize ecological impacts. Agroecosystems, which integrate agricultural practices with the surrounding environment, play a vital role in maintaining ecological balance and ensuring food security. Rhizosphere management has emerged as a pivotal approach to enhancing crop yields, reducing reliance on synthetic fertilizers, and supporting sustainable agriculture. The rhizosphere, a dynamic zone surrounding plant roots, hosts intense microbial activity fueled by root exudates. These exudates, along with practices such as green manure application and intercropping, significantly influence the soil's microbial community structure. Beneficial plant-associated microbes, including Trichoderma spp., Penicillium spp., Aspergillus spp., and Bacillus spp., play a crucial role in improving nutrient cycling and promoting plant health, yet their interactions within the rhizosphere remain inadequately understood. This review explores how integrating beneficial microbes, green manures, and intercropping enhances rhizosphere processes to rebuild microbial communities, sequester carbon, and reduce greenhouse gas emissions. These practices not only contribute to maintaining soil health but also foster positive plant-microbe-rhizosphere interactions that benefit entire ecosystems. By implementing such strategies alongside sound policy measures, sustainable cropping systems can be developed to address predicted climate challenges. Strengthening agroecosystem resilience through improved rhizosphere processes is essential for ensuring food security and environmental sustainability in the future. In conclusion, using these rhizosphere-driven processes, we could develop more sustainable and resilient agricultural systems that ensure food security and environmental preservation amidst changing climate situations.
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- 2024
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22. Determinants of antimicrobial resistance in biosolids: A systematic review, database, and meta-analysis.
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Harrison JC, Morgan GV, Kuppravalli A, Novak N, Farrell M, Bircher S, Garner E, Ashbolt NJ, Pruden A, Muenich RL, Boyer TH, Williams C, Ahmed W, Maal-Bared R, and Hamilton KA
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- Anti-Bacterial Agents pharmacology, Bacteria drug effects, Bacteria genetics, Bacteria isolation & purification, Drug Resistance, Bacterial genetics, Drug Resistance, Microbial genetics, Sewage analysis, Sewage microbiology
- Abstract
Biosolids can provide a nutrient rich soil amendment, particularly for poor soils and semi-arid or drought-prone areas. However, there are concerns that sludge and biosolids could be a source of propagation and exposure to AMR determinants such as antibiotic resistant bacteria (ARB), and antibiotic resistance genes (ARGs). To inform risk assessment efforts, a systematic literature review was performed to build a comprehensive spreadsheet database of ARB and ARG concentrations in biosolids (and some sludges specified as intended for land application), along with 69 other quantitative and qualitative meta-data fields from 68 published studies describing sampling information and processing methods that can be used for modeling purposes. Mean ARG concentrations per gram in positive samples of biosolids ranged from -5.7 log
10 (gene copies [gc]/g) to 12.92 log10 (gc/g) (with these range values reported per dry weight), and aqueous concentrations ranged from 0.9 log10 (gc/L) to 14.6 log10 (gc/L). Mean ARB concentrations per gram of biosolids ranged from 2.02 log10 (colony forming units [CFU]/g) to 9.00 log10 (CFU/g) (dry weight), and aqueous concentrations ranged from 3.23 log10 (CFU/L) to 12.0 log10 (CFU/L). ARG log removal values (LRVs) during sewage sludge stabilization were calculated from a meta-analysis of mean concentrations before and after stabilization from 31 studies, ranging from -2.05 to 5.52 logs. The classes of resistance most relevant for a risk assessment corresponded to sulfonamide (sul1 and sul2), tetracycline (tetZ, tetX, tetA and tetG), beta-lactam (blaTEM ), macrolide (ermB and ermF), aminoglycoside (strA and aac(6')-Ib-cr), and integron-associated (intI1). The resistance classes most relevant for ARB risk assessment included sulfonamides (sulfamethoxazole and sulfamethazine), cephalosporin (cephalothin and cefoxitin), penicillin (ampicillin), and ciprofloxin (ciprofloxacin). Considerations for exposure assessment are discussed to highlight risk assessment needs relating to antimicrobial resistance (AMR) associated with biosolids application. This study aids in prioritization of resources for reducing the spread of AMR within a One Health framework., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Kerry Hamilton reports financial support was provided by Water Research Foundation. Kerry Hamilton reports financial support was provided by U.S. Department of Agriculture. Nicholas Ashbolt reports administrative support was provided by The Cooperative Research Centre for Solving Antimicrobial Resistance in Agribusiness, Food and Environments (CRC SAAFE). If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)- Published
- 2024
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23. Fusarium boothii , Fusarium meridionale , and Fusarium temperatum are emerging preharvest maize ear rot pathogens in Ethiopia.
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Deressa T, Adugna G, Suresh LM, Bekeko Z, Iriarte-Broders G, Vaughan MM, Proctor R, Mehl HL, Prasanna BM, and Opoku J
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Fusarium ear rot (FER) and Gibberella ear rot (GER) caused by Fusarium species are major diseases affecting maize production in Ethiopia. In addition to reducing quality and yield, these fungi can produce mycotoxins that contaminate maize kernels and, thereby, pose health hazards to humans and livestock. A survey was conducted in 10 administrative zones of Ethiopia within the major maize-growing regions of the country to identify the species of Fusarium associated with ear rot. Twenty kernels were sampled from ears randomly collected from each zone (12 ears per field, 24 fields per zone). Ninety-two fungal isolates recovered from the kernels were tentatively identified as Fusarium based on morphological traits. Subsequently, the species identity of each isolate was determined by DNA sequence analysis of a portion of the translation elongation factor 1-α (TEF1) gene and two non-contiguous fragments of the RNA polymerase II subunit gene (RPB2). Based on phylogenetic analysis of the data, 37.3% of the isolates recovered from maize kernels were from three species that have not been reported previously in Ethiopia: Fusarium boothii (4.3%), Fusarium meridionale (10.2%), and Fusarium temperatum (22.8%). Koch's postulates of selected isolates confirmed that these three species can cause maize ear rot. Information on causal agents of maize ear rots in Ethiopia should be taken into consideration when developing disease management strategies, including breeding for resistance.
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- 2024
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24. Combined Treatment Methods for Removal of Antibiotics from Beef Wastewater.
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Stromer BS, Woodbury BL, Williams CF, and Spiehs MJ
- Abstract
Use of antibiotics is common practice in agriculture; however, they can be released into the environment, potentially causing antimicrobial resistance. Naturally mined diatomaceous earth with bentonite was tested as a remediation material for tylosin, chlortetracycline, and ceftiofur in wastewater from a beef cattle feedlot. Langmuir binding affinity in 10 mM sodium phosphate buffer at pH 6.7 was established prior to testing wastewater to determine binding potential. Chlortetracycline was found to have a binding affinity of 15.2 mM
-1 and a binding capacity of 123 mg per g of diatomaceous earth while ceftiofur showed a much lower binding affinity and loading at 7.8 mM-1 and 3 mg per g of diatomaceous earth, respectively. From spiked wastewater, tylosin (50 μg mL-1 , pH 8) and chlortetracycline (300 μg mL-1 , pH 6) were removed (100 and 80%, respectively) when treated with 20 mg of diatomaceous earth while ceftiofur (300 μg mL-1 , pH 8) remained in solution. When the spiked wastewater was flocculated with aluminum sulfate, a change in pH from 8 to 4 was observed, and chlortetracycline was removed from the wastewater; tylosin and ceftiofur remained in solution. When subsequently treated with diatomaceous earth, ceftiofur and tylosin were completely removed by diatomaceous earth from the flocculated wastewater., Competing Interests: The authors declare no competing financial interest., (Not subject to U.S. Copyright. Published 2024 by American Chemical Society.)- Published
- 2024
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25. Phenotyping cotton leaf chlorophyll via in situ hyperspectral reflectance sensing, spectral vegetation indices, and machine learning.
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Thorp KR, Thompson AL, and Herritt MT
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Cotton ( Gossypium hirsutum L.) leaf chlorophyll (Chl) has been targeted as a phenotype for breeding selection to improve cotton tolerance to environmental stress. However, high-throughput phenotyping methods based on hyperspectral reflectance sensing are needed to rapidly screen cultivars for chlorophyll in the field. The objectives of this study were to deploy a cart-based field spectroradiometer to measure cotton leaf reflectance in two field experiments over four growing seasons at Maricopa, Arizona and to evaluate 148 spectral vegetation indices (SVI's) and 14 machine learning methods (MLM's) for estimating leaf chlorophyll from spectral information. Leaf tissue was sampled concurrently with reflectance measurements, and laboratory processing provided leaf Chl a , Chl b , and Chl a+b as both areas-basis (µg cm
-2 ) and mass-basis (mg g-1 ) measurements. Leaf reflectance along with several data transformations involving spectral derivatives, log-inverse reflectance, and SVI's were evaluated as MLM input. Models trained with 2019-2020 data performed poorly in tests with 2021-2022 data (e.g., RMSE=23.7% and r2 = 0.46 for area-basis Chl a+b ), indicating difficulty transferring models between experiments. Performance was more satisfactory when training and testing data were based on a random split of all data from both experiments (e.g., RMSE=10.5% and r2 = 0.88 for area basis Chl a+b ), but performance beyond the conditions of the present study cannot be guaranteed. Performance of SVI's was in the middle (e.g., RMSE=16.2% and r2 = 0.69 for area-basis Chl a+b ), and SVI's provided more consistent error metrics compared to MLM's. Ensemble MLM's which combined estimates from several base estimators (e.g., random forest, gradient booting, and AdaBoost regressors) and a multi-layer perceptron neural network method performed best among MLM's. Input features based on spectral derivatives or SVI's improved MLM's performance compared to inputting reflectance data. Spectral reflectance data and SVI's involving red edge radiation were the most important inputs to MLM's for estimation of cotton leaf chlorophyll. Because MLM's struggled to perform beyond the constraints of their training data, SVI's should not be overlooked as practical plant trait estimators for high-throughput phenotyping, whereas MLM's offer great opportunity for data mining to develop more robust indices., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Thorp, Thompson and Herritt.)- Published
- 2024
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26. Combined effects of polyamide microplastic and sulfamethoxazole in modulating the growth and transcriptome profile of hydroponically grown rice (Oryza sativa L.).
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Ullah R, Farias J, Feyissa BA, Tsui MT, Chow A, Williams C, Karanfil T, and Ligaba-Osena A
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- Hydroponics, Sulfamethoxazole toxicity, Oryza genetics, Oryza drug effects, Oryza growth & development, Water Pollutants, Chemical toxicity, Microplastics toxicity, Transcriptome drug effects
- Abstract
The use of reclaimed water from wastewater treatment plants for irrigation has a risk of introducing micropollutants such as microplastics (MPs) and antimicrobials (AMs) into the agroecosystem. This study was conducted to investigate the effects of single and combined treatment of 0.1 % polyamide (PA ∼15 μm), and varying sulfamethoxazole (SMX) levels 0, 10, 50, and 150 mg/L on rice seedlings (Oryza sativa L.) for 12 days. The study aimed to assess the impact of these contaminants on the morphological, physiological, and biochemical parameters of the rice plants. The findings revealed that rice seedlings were not sensitive to PA alone. However, SMX alone or in combination with PA, significantly inhibited shoot and root growth, total biomass, and affected photosynthetic pigments. Higher concentrations of SMX increased antioxidant enzyme activity, indicating oxidative stress. The roots had a higher SMX content than the shoots, and the concentration of minerals such as iron, copper, and magnesium were reduced in roots treated with SMX. RNA-seq analysis showed changes in the expression of genes related to stress, metabolism, and transport in response to the micropollutants. Overall, this study provides valuable insights on the combined impacts of MPs and AMs on food crops, the environment, and human health in future risk assessments and management strategies in using reclaimed water., Competing Interests: Declaration of competing interest All the authors declare that there is no conflict of interest., (Copyright © 2024. Published by Elsevier B.V.)
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- 2024
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27. Mismatch between lab-generated and field-evolved resistance to transgenic Bt crops in Helicoverpa zea .
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Legan AW, Allan CW, Jensen ZN, Degain BA, Yang F, Kerns DL, Benowitz KM, Fabrick JA, Li X, Carrière Y, Matzkin LM, and Tabashnik BE
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- Animals, Mutation, Pest Control, Biological methods, Gene Flow, Plants, Genetically Modified genetics, Plants, Genetically Modified parasitology, Insecticide Resistance genetics, Moths genetics, Bacillus thuringiensis Toxins, Bacterial Proteins genetics, Bacterial Proteins metabolism, Endotoxins genetics, Endotoxins metabolism, Hemolysin Proteins genetics, Hemolysin Proteins metabolism, Bacillus thuringiensis genetics, Crops, Agricultural genetics, Crops, Agricultural parasitology
- Abstract
Transgenic crops producing crystalline (Cry) proteins from the bacterium Bacillus thuringiensis (Bt) have been used extensively to control some major crop pests. However, many populations of the noctuid moth Helicoverpa zea , one of the most important crop pests in the United States, have evolved practical resistance to several Cry proteins including Cry1Ac. Although mutations in single genes that confer resistance to Cry proteins have been identified in lab-selected and gene-edited strains of H. zea and other lepidopteran pests, the genetic basis of field-evolved resistance to Cry proteins in H. zea has remained elusive. We used a genomic approach to analyze the genetic basis of field-evolved resistance to Cry1Ac in 937 H. zea derived from 17 sites in seven states of the southern United States. We found evidence for extensive gene flow among all populations studied. Field-evolved resistance was not associated with mutations in 20 single candidate genes previously implicated in resistance or susceptibility to Cry proteins in H. zea or other lepidopterans. Instead, resistance in field samples was associated with increased copy number of a cluster of nine trypsin genes. However, trypsin gene amplification occurred in a susceptible sample and not in all resistant samples, implying that this amplification does not always confer resistance and mutations in other genes also contribute to field-evolved resistance to Cry1Ac in H. zea . The mismatch between lab-generated and field-evolved resistance in H. zea is unlike other cases of Bt resistance and reflects challenges for managing this pest., Competing Interests: Competing interests statement:J.A.F. and B.E.T. are coauthors of patents on engineering Bacillus thuringiensis (Bt) toxins to counter resistance (US10704059) and potentiating Bt toxins (US20090175974A1), respectively. Badische Anilin und Soda Fabrik (BASF), Corteva Agriscience, Cotton Incorporated, Syngenta, and the Agricultural Biotechnology Stewardship Technical Committee (representing a consortium of agricultural biotechnology companies) did not provide funding to support this work but have funded other work by some of the authors.
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- 2024
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28. Female contact sex pheromone recognition in the German cockroach (Blattella germanica) is mediated by two male antennae-enriched sensory neuron membrane proteins.
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Feng HY, Zhao YQ, Yang T, Zhou YY, Gong LL, Zhang MQ, Ma YF, Hull JJ, Dewer Y, Zhang F, Smagghe G, He M, and He P
- Abstract
Background: The German cockroach Blattella germanica is a notorious urban health pest that has developed resistance to multiple pesticides. Thus, novel non-lethal pest control agents are urgently needed. Olfaction interference via disruption of sex pheromone recognition-related genes offers a promising approach. The German cockroach has a unique courtship behavior in which female adults emit contact sex pheromones (CSPs) in response to antennal touching, which subsequently triggers distinctive male sex behavioral responses. Due to the limited volatility of CSPs, the molecular mechanisms underlying their recognition and the specific olfactory pathways activated remain poorly defined. Although the odorant receptor coreceptor (Orco) is critical for most insect olfaction, sensory neuron membrane proteins (SNMPs), in particular SNMP1, also play crucial roles in sex pheromone recognition in moths and flies. While multiple SNMP1 homologs have been identified in multiple insect species, they have yet to be fully functionally characterized in cockroaches., Results: In this study, RNA-interference (RNAi)-mediated knockdown of BgerOrco reduced both the electrophysiology responses and courtship behaviors of males, indicating CSP perception proceeds via an olfaction pathway. Similar RNAi knockdown of BgerSNMP1e and BgerSNMP1d, which are predominantly expressed in male antennae, revealed critical roles in perceiving the major component of the Blattella germanica CSP blend. Unlike BgerSNMP1e, BgerSNMP1d was also found to function in the perception of the minor CSP component. Molecular docking analyses revealed no differences in the binding affinities of BgerSNMP1d for the major and minor CSP components, whereas the binding affinities of BgerSNMP1e displayed clear selectivity for the major component., Conclusion: Our results show that the olfactory pathway is critical for CSP recognition and that two male-enriched SNMP genes, BgerSNMP1e and BgerSNMP1d, are crucial factors mediating the male response to CSP stimulation in German cockroaches. This study lays a foundation for studying the mechanisms of CSP recognition and provides novel molecular targets with potential to be exploited as disruptors of courtship behavior. © 2024 Society of Chemical Industry., (© 2024 Society of Chemical Industry.)
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- 2024
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29. A spray-induced gene silencing strategy for Spodoptera frugiperda oviposition inhibition using nanomaterial-encapsulated dsEcR.
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Li N, Xu X, Li J, Hull JJ, Chen L, and Liang G
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- Animals, Female, RNA Interference, Insect Proteins genetics, Receptors, Steroid genetics, Receptors, Steroid metabolism, RNA, Double-Stranded genetics, RNA, Double-Stranded pharmacology, Oviposition drug effects, Spodoptera drug effects, Spodoptera physiology, Nanostructures chemistry, Gene Silencing
- Abstract
Although RNAi-based pest management holds great potential as an alternative to traditional chemical control, its efficiency is restricted by dsRNA instability and limited cellular uptake. Using nanomaterials to facilitate dsRNA delivery has shown promise in solving these challenges. In this study, we firstly used RNAi to investigate the role of the juvenile hormone and ecdysteroid signaling pathways genes in reproduction of Spodoptera frugiperda, the fall armyworm. Females in knocked-down treatments of any of the Met, EcR, and USP genes had greatly reduced fertility with the most pronounced inhibitory effects on oviposition observed following EcR knockdown, and thus the dsEcR could be a candidate target for RNAi-based oviposition inhibitory agency. Then a combinatorial spray-induced and nanocarrier-delivered gene silencing (SI-NDGS) approach that targeted EcR was conducted. At 72 h post-spay, the transcript levels of EcR and the oviposition were successfully reduced and inhibited. These findings support the groundwork for further developing novel RNAi-based pest management strategies for S. frugiperda., Competing Interests: Declaration of competing interest The authors have declared no conflict of interest., (Copyright © 2024. Published by Elsevier B.V.)
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- 2024
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30. Research gaps and priorities for quantitative microbial risk assessment (QMRA).
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Hamilton KA, Ciol Harrison J, Mitchell J, Weir M, Verhougstraete M, Haas CN, Nejadhashemi AP, Libarkin J, Gim Aw T, Bibby K, Bivins A, Brown J, Dean K, Dunbar G, Eisenberg JNS, Emelko M, Gerrity D, Gurian PL, Hartnett E, Jahne M, Jones RM, Julian TR, Li H, Li Y, Gibson JM, Medema G, Meschke JS, Mraz A, Murphy H, Oryang D, Owusu-Ansah EDJ, Pasek E, Pradhan AK, Razzolini MTP, Ryan MO, Schoen M, Smeets PWMH, Soller J, Solo-Gabriele H, Williams C, Wilson AM, Zimmer-Faust A, Alja'fari J, and Rose JB
- Subjects
- Risk Assessment methods, Humans, Public Health, SARS-CoV-2, Pandemics, Research, Evidence Gaps, COVID-19 prevention & control
- Abstract
The coronavirus disease 2019 pandemic highlighted the need for more rapid and routine application of modeling approaches such as quantitative microbial risk assessment (QMRA) for protecting public health. QMRA is a transdisciplinary science dedicated to understanding, predicting, and mitigating infectious disease risks. To better equip QMRA researchers to inform policy and public health management, an Advances in Research for QMRA workshop was held to synthesize a path forward for QMRA research. We summarize insights from 41 QMRA researchers and experts to clarify the role of QMRA in risk analysis by (1) identifying key research needs, (2) highlighting emerging applications of QMRA; and (3) describing data needs and key scientific efforts to improve the science of QMRA. Key identified research priorities included using molecular tools in QMRA, advancing dose-response methodology, addressing needed exposure assessments, harmonizing environmental monitoring for QMRA, unifying a divide between disease transmission and QMRA models, calibrating and/or validating QMRA models, modeling co-exposures and mixtures, and standardizing practices for incorporating variability and uncertainty throughout the source-to-outcome continuum. Cross-cutting needs identified were to: develop a community of research and practice, integrate QMRA with other scientific approaches, increase QMRA translation and impacts, build communication strategies, and encourage sustainable funding mechanisms. Ultimately, a vision for advancing the science of QMRA is outlined for informing national to global health assessments, controls, and policies., (© 2024 The Author(s). Risk Analysis published by Wiley Periodicals LLC on behalf of Society for Risk Analysis.)
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- 2024
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31. Determining selectivity of isocycloseram and afidopyropen and their compatibility with conservation biological control in Arizona cotton.
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Bordini I, Naranjo SE, Fournier A, and Ellsworth PC
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- Animals, Arizona, Weevils, Gossypium, Insecticides, Pest Control, Biological
- Abstract
Background: Selective tools, including selective insecticides and transgenic cotton, have been crucial in reducing insecticide usage within the integrated pest management (IPM) plan for Arizona cotton. To guide growers effectively, cotton field trials evaluated the effects of the novel insecticides, isocycloseram and afidopyropen against our primary pests, Bemisia argentifolii and Lygus hesperus, and their impacts on nontarget arthropods, including key predators: Collops spp., Orius tristicolor, Geocoris spp., Misumenops celer, Drapetis nr. divergens and Chrysoperla carnea s.l. Assessments involved over 27 arthropod taxa through community analyses, individual predator abundance, and biological control function via predator to prey ratios and a sentinel prey method. Comparisons were made with an untreated check, a proven fully selective insecticide (flonicamid) and acephate-treated positive controls., Results: Overall, relative to the untreated check, afidopyropen showed no significant differences, whereas isocycloseram exhibited some negative impacts, primarily reducing M. celer and Geocoris spp. nymphs, yet it was less detrimental compared with the positive control, acephate, and it did not affect four of the six key predators and most nontarget arthropods., Conclusion: Afidopyropen was classified as a fully selective insecticide and isocycloseram as a partially selective insecticide. Their fit for conservation biological control in Arizona cotton IPM and similar systems is discussed. © 2024 Society of Chemical Industry., (© 2024 Society of Chemical Industry.)
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- 2025
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32. Proteomic and Targeted Lipidomic Analyses of Fluid and Rigid Rubber Particle Membrane Domains in Guayule.
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Blakeslee JJ, Han EH, Lin Y, Lin J, Nath S, Zhang L, Li Z, and Cornish K
- Abstract
Rubber ( cis -1,4-polyisoprene) is produced in cytosolic unilamellar vesicles called rubber particles (RPs), and the protein complex responsible for this synthesis, the rubber transferase (RTase), is embedded in, or tethered to, the membranes of these RPs. Solubilized enzyme activity is very difficult to achieve because the polymerization of highly hydrophilic substrates into hydrophobic polymers requires a polar/non-polar interface and a hydrophobic compartment. Using guayule ( Parthenium argentatum ) as a model rubber-producing species, we optimized methods to isolate RP unilamellear membranes and then a subset of membrane microdomains (detergent-resistant membranes) likely to contain protein complexes such as RTase. The phospholipid and sterol composition of these membranes and microdomains were analyzed using thin-layer chromatography (TLC) and liquid chromatography tandem mass spectroscopy (LC-MS/MS). Our data indicate that RP membranes consist predominantly of phosphatidic acid-containing membrane microdomains (DRMs or "lipid rafts"). Proteomic analyses of guayule RP membranes and membrane microdomains identified 80 putative membrane proteins covering 30 functional categories. From this population, we have tentatively identified several proteins in multiple functional domains associated with membrane microdomains which may be critical to RTase function. Definition of the mechanisms underlying rubber synthesis will provide targets for both metabolic engineering and breeding strategies designed to increase natural rubber production in latex-producing species.
- Published
- 2024
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33. Visualizing Plant Responses: Novel Insights Possible Through Affordable Imaging Techniques in the Greenhouse.
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Conley MM, Hejl RW, Serba DD, and Williams CF
- Subjects
- Image Processing, Computer-Assisted methods, Humans, Phenotype, Color, Plants, Photography methods
- Abstract
Efficient and affordable plant phenotyping methods are an essential response to global climatic pressures. This study demonstrates the continued potential of consumer-grade photography to capture plant phenotypic traits in turfgrass and derive new calculations. Yet the effects of image corrections on individual calculations are often unreported. Turfgrass lysimeters were photographed over 8 weeks using a custom lightbox and consumer-grade camera. Subsequent imagery was analyzed for area of cover, color metrics, and sensitivity to image corrections. Findings were compared to active spectral reflectance data and previously reported measurements of visual quality, productivity, and water use. Results confirm that Red-Green-Blue imagery effectively measures plant treatment effects. Notable correlations were observed for corrected imagery, including between yellow fractional area with human visual quality ratings (r = -0.89), dark green color index with clipping productivity (r = 0.61), and an index combination term with water use (r = -0.60). The calculation of green fractional area correlated with Normalized Difference Vegetation Index (r = 0.91), and its RED reflectance spectra (r = -0.87). A new chromatic ratio correlated with Normalized Difference Red-Edge index (r = 0.90) and its Red-Edge reflectance spectra (r = -0.74), while a new calculation correlated strongest to Near-Infrared (r = 0.90). Additionally, the combined index term significantly differentiated between the treatment effects of date, mowing height, deficit irrigation, and their interactions ( p < 0.001). Sensitivity and statistical analyses of typical image file formats and corrections that included JPEG, TIFF, geometric lens distortion correction, and color correction were conducted. Findings highlight the need for more standardization in image corrections and to determine the biological relevance of the new image data calculations.
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- 2024
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34. Crucial role of a takeout protein in white-backed planthopper Sogatella furcifera (Horváth) orientation towards its host rice plants.
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He M, Long GJ, Feng HY, Zhao YQ, Zhou YY, Zhang MQ, Ma YF, Gong LL, Hull JJ, Zotti MJ, Dewer Y, He P, and Smagghe G
- Abstract
The takeout (TO) gene family impacts diverse physiological and behavioral functions in insects, yet specific olfactory-associated roles for the family have yet to be fully elucidated. To provide insights into TO function in rice planthoppers, the genomes of three rice planthoppers (white-backed planthopper, brown planthopper and small brown planthopper) were searched for TO homologs and their degree of conservation assessed via chromosomal localization, exon-intron boundaries, phylogenetic relationships and protein domains/motifs. We performed a tissue-specific expression analysis of the 20 TO genes in the white-backed planthopper (WBPH, Sogatella furcifera) and found that SfTO17 is enriched in adult antennae. RNAi-mediated knockdown of SfTO17 impaired WBPH olfaction and reduced host-seeking responses following exposure to rice plants. The binding profile of β-ionone, hexyl benzoate and benzyl benzoate with recombinant SfTO17 was evaluated via competitive fluorescence binding assays. Conformational prediction of SfTO17 coupled with molecular docking analyses revealed several amino acid residues potentially critical for odorant binding. This study demonstrates the olfactory function of SfTO17 in WBPH and highlights its potential as a target for controlling this rice pest. © 2024 Society of Chemical Industry., (© 2024 Society of Chemical Industry.)
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- 2024
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35. Regulation of a single inositol 1-phosphate synthase homeologue by HSFA6B contributes to fibre yield maintenance under drought conditions in upland cotton.
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Yu L, Dittrich ACN, Zhang X, Brock JR, Thirumalaikumar VP, Melandri G, Skirycz A, Edger PP, Thorp KR, Hinze L, Pauli D, and Nelson ADL
- Subjects
- Stress, Physiological genetics, Genome-Wide Association Study, Gossypium genetics, Gossypium metabolism, Droughts, Plant Proteins genetics, Plant Proteins metabolism, Cotton Fiber, Transcription Factors genetics, Transcription Factors metabolism, Gene Expression Regulation, Plant
- Abstract
Drought stress substantially impacts crop physiology resulting in alteration of growth and productivity. Understanding the genetic and molecular crosstalk between stress responses and agronomically important traits such as fibre yield is particularly complicated in the allopolyploid species, upland cotton (Gossypium hirsutum), due to reduced sequence variability between A and D subgenomes. To better understand how drought stress impacts yield, the transcriptomes of 22 genetically and phenotypically diverse upland cotton accessions grown under well-watered and water-limited conditions in the Arizona low desert were sequenced. Gene co-expression analyses were performed, uncovering a group of stress response genes, in particular transcription factors GhDREB2A-A and GhHSFA6B-D, associated with improved yield under water-limited conditions in an ABA-independent manner. DNA affinity purification sequencing (DAP-seq), as well as public cistrome data from Arabidopsis, were used to identify targets of these two TFs. Among these targets were two lint yield-associated genes previously identified through genome-wide association studies (GWAS)-based approaches, GhABP-D and GhIPS1-A. Biochemical and phylogenetic approaches were used to determine that GhIPS1-A is positively regulated by GhHSFA6B-D, and that this regulatory mechanism is specific to Gossypium spp. containing the A (old world) genome. Finally, an SNP was identified within the GhHSFA6B-D binding site in GhIPS1-A that is positively associated with yield under water-limiting conditions. These data lay out a regulatory connection between abiotic stress and fibre yield in cotton that appears conserved in other systems such as Arabidopsis., (© 2024 The Author(s). Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.)
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- 2024
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36. Graphene-coated sand for enhanced water reuse: Impact on water quality and chemicals of emerging concern.
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Nusair A, Barber M, Pramanik A, Ethridge C, William C, Alkhateb H, Ucak-Astarlioglu M, Ray PC, and D'Alessio M
- Abstract
This paper investigates the potential of graphene-coated sand (GCS) as an advanced filtration medium for improving water quality and mitigating chemicals of emerging concern (CECs) in treated municipal wastewater, aiming to enhance water reuse. The study utilizes three types of sand (Ottawa, masonry, and concrete) coated with graphene to assess the impact of surface morphology, particle shape, and chemical composition on coating and filtration efficiency. Additionally, sand coated with graphene and activated graphene coated sand were both tested to understand the effect of coating and activation on the filtration process. The materials were characterized using digital microscopy, Raman spectroscopy, scanning electron microscopy (SEM), and X-ray diffraction analysis. The material's efficiency in removing turbidity, nutrients, chemical oxygen demand (COD), bacteria, and specific CECs (Aciclovir, Diatrizoic acid, Levodopa, Miconazole, Carbamazepine, Diphenhydramine, Irbesartan, Lidocaine, Losartan, and Sulfamethoxazole) was studied. Our findings indicate that GCS significantly improves water quality parameters, with notable efficiency in removing turbidity, COD (14.1 % and 69.1 % removal), and bacterial contaminants (64.9 % and 99.9 % removal). The study also highlights the material's capacity to remove challenging CECs like Sulfamethoxazole (up to 80 % removal) and Diphenhydramine (up to 90 % removal), showcasing its potential as a sustainable solution for water reuse applications. This research contributes to the field by providing a comprehensive evaluation of GCS in water treatment, suggesting its potential for removing CECs from treated municipal wastewater., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)
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- 2024
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37. The role of aquaporins in osmotic cell lysis induced by Bacillus thuringiensis Cry1Ac toxin in Helicoverpa armigera.
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Cai Y, Hou B, Fabrick JA, Yang Y, and Wu Y
- Subjects
- Animals, Insect Proteins metabolism, Insect Proteins genetics, Bacillus thuringiensis metabolism, Bacillus thuringiensis genetics, Xenopus laevis, Oocytes metabolism, Oocytes drug effects, Insecticides toxicity, Insecticides pharmacology, Osmosis, Helicoverpa armigera, Bacillus thuringiensis Toxins toxicity, Hemolysin Proteins toxicity, Hemolysin Proteins pharmacology, Hemolysin Proteins metabolism, Endotoxins toxicity, Endotoxins pharmacology, Bacterial Proteins metabolism, Bacterial Proteins genetics, Moths drug effects, Moths metabolism, Moths genetics, Larva drug effects, Larva metabolism, Aquaporins metabolism, Aquaporins genetics
- Abstract
The insecticidal crystalline (Cry) and vegetative insecticidal (Vip) proteins derived from Bacillus thuringiensis (Bt) are used globally to manage insect pests, including the cotton bollworm, Helicoverpa armigera, one of the world's most damaging agricultural pests. Cry proteins bind to the ATP-binding cassette transporter C2 (ABCC2) receptor on the membrane surface of larval midgut cells, resulting in Cry toxin pores, and ultimately leading to cell swelling and/or lysis. Insect aquaporin (AQP) proteins within the membranes of larval midgut cells are proposed to allow the rapid influx of water into enterocytes following the osmotic imbalance triggered by the formation of Cry toxin pores. Here, we examined the involvement of H. armigera AQPs in Cry1Ac-induced osmotic cell swelling. We identified and characterized eight H. armigera AQPs and demonstrated that five are functional water channel proteins. Three of these (HaDrip1, HaPrip, and HaEglp1) were found to be expressed in the larval midgut. Xenopus laevis oocytes co-expressing the known Cry1Ac receptor HaABCC2 and each of the three HaAQPs displayed abnormal morphology and were lysed following exposure to Cry1Ac, suggesting a rapid influx of water was induced after Cry1Ac pore formation. In contrast, oocytes producing either HaABCC2 or HaAQP alone failed to swell or lyse after treatment with Cry1Ac, implying that both Cry1Ac pore formation and HaAQP function are needed for osmotic cell swelling. However, CRISPR/Cas9-mediated knockout of any one of the three HaAQP genes failed to cause significant changes in susceptibility to the Bt toxins Cry1Ac, Cry2Ab, or Vip3Aa. Our findings suggest that the multiple HaAQPs produced in larval midgut cells compensate for each other in allowing for the rapid influx of water in H. armigera midgut cells following Cry toxin pore formation, and that mutations affecting a single HaAQP are unlikely to confer resistance to Bt proteins., (Copyright © 2024 Elsevier Inc. All rights reserved.)
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- 2024
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38. Spray-induced and nanocarrier-delivered gene silencing system targeting juvenile hormone receptor components: potential application as fertility inhibitors for Adelphocoris suturalis management.
- Author
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Zheng W, Xu X, Huang X, Peng J, Ma W, Hull JJ, Hua H, and Chen L
- Subjects
- Animals, Female, RNA, Double-Stranded genetics, RNA, Double-Stranded pharmacology, Fertility drug effects, Insect Control methods, Juvenile Hormones pharmacology, Heteroptera genetics, Heteroptera drug effects, Heteroptera growth & development, Insect Proteins genetics, Insect Proteins metabolism, Gene Silencing, RNA Interference
- Abstract
Background: Adelphocoris suturalis is a destructive pest that attacks > 270 plants, including cotton, maize, soybean, and fruit trees. Adelphocoris suturalis can cause tremendous crop losses when the density exceeds economic thresholds, but because it can be both phytophagous and zoophytophagous it can serve as a natural enemy of other pests when the density is below the economic threshold. Effective control of its population is beneficial for maximizing yield and profits. RNA interference (RNAi) has potential to be a viable alternative to conventional pesticide-based pest management, but the lack of efficient double-stranded RNA (dsRNA) delivery systems and candidate genes are currently limiting factors for field applications., Results: In this study, RNAi of juvenile hormone (JH) receptor components methoprene-tolerant (Met)/Taiman (Tai) in Adelphocoris suturalis reduced fertility. Based on this reproductive role, we targeted Adelphocoris suturalis Met and Tai for knockdown by coupling nanomaterial-dsRNA complexes with a transdermal spray delivery system. Within 12 h of adult emergence, females were sprayed with star polycation (SPc)-dsRNA formulations and the RNAi effects were assessed over time. RNAi knockdown efficiencies of 39-58% were observed at 5 days post-treatment and abnormal ovarian development was apparent by 10 days post-treatment., Conclusion: Our results show that spray-induced and nanocarrier-delivered gene silencing (SI-NDGS) system targeting JH signal genes effectively impaired oviposition, thus developing a novel RNA fertility inhibitor to control Adelphocoris suturalis populations. These results give new perspective on pest management and suggest broad prospects for field applications. © 2024 Society of Chemical Industry., (© 2024 Society of Chemical Industry.)
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- 2024
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39. Efficient DIPA-CRISPR-mediated knockout of an eye pigment gene in the white-backed planthopper, Sogatella furcifera.
- Author
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Zhang MQ, Gong LL, Zhao YQ, Ma YF, Long GJ, Guo H, Liu XZ, Hull JJ, Dewer Y, Yang C, Zhang NN, He M, and He P
- Subjects
- Animals, Female, Gene Knockout Techniques, Hemiptera genetics, CRISPR-Cas Systems, Gene Editing methods
- Abstract
Although CRISPR/Cas9 has been widely used in insect gene editing, the need for the microinjection of preblastoderm embryos can preclude the technique being used in insect species with eggs that are small, have hard shells, and/or are difficult to collect and maintain outside of their normal environment. Such is the case with Sogatella furcifera, the white-backed planthopper (WBPH), a significant pest of Oryza sativa (rice) that oviposits inside rice stems. Egg extraction from the stem runs the risk of mechanical damage and hatching is heavily influenced by the micro-environment of the rice stem. To bypass these issues, we targeted embryos prior to oviposition via direct parental (DIPA)-CRISPR, in which Cas9 and single-guide RNAs (sgRNAs) for the WBPH eye pigment gene tryptophan 2,3-dioxygenase were injected into the hemocoel of adult females. Females at varying numbers of days posteclosion were evaluated to determine at what stage their oocyte might be most capable of taking up the gene-editing components. An evaluation of the offspring indicated that the highest G0 gene-edited efficacy (56.7%) occurred in females injected 2 d posteclosion, and that those mutations were heritably transmitted to the G1 generation. This study demonstrates the potential utility of DIPA-CRISPR for future gene-editing studies in non-model insect species and can facilitate the development of novel pest management applications., (© 2023 Institute of Zoology, Chinese Academy of Sciences.)
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- 2024
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40. An Effective Fluorescent Marker for Tracking the Dispersal of Small Insects with Field Evidence of Mark-Release-Recapture of Trissolcus japonicus .
- Author
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Paul RL, Hagler JR, Janasov EG, McDonald NS, Voyvot S, and Lee JC
- Abstract
Understanding insect dispersal helps us predict the spread of insect pests and their natural enemies. Dispersal can be studied by marking, releasing, and recapturing insects, known as mark-release-recapture (MRR). MRR techniques should be convenient, economical, and persistent. Currently, there are limited options for marking small parasitoids that do not impact their fitness and dispersal ability. We evaluated commercially available fluorescent markers used in forensics. These fluorophores can easily be detected by ultraviolet (UV) light, requiring minimal costs and labor to process the marked specimens. This fluorophore marking technique was evaluated with the pest Drosophila suzukii and three parasitoids: Trissolcus japonicus , Pachycrepoideus vindemiae , Ganaspis brasiliensis (= G. kimorum ). We evaluated the persistence of the marks on all the insects over time and examined the parasitoids for impacts on longevity, parasitism, locomotor activity, and flight take-off. The green fluorophore marker persisted for over 20 days on all four species. Marking generally did not consistently reduce the survival, parasitism rate, locomotor activity, or take-off of the parasitoids tested. Marked T. japonicus were recaptured in the field up to 100 m away from the release point and three weeks after release, indicating that this technique is a viable method for studying parasitoid dispersal.
- Published
- 2024
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41. Genetic Diversity and Population Structure of a Large USDA Sesame Collection.
- Author
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Seay D, Szczepanek A, De La Fuente GN, Votava E, and Abdel-Haleem H
- Abstract
Sesame, Sesamum indicum L., is one of the oldest domesticated crops used for its oil and protein in many parts of the world. To build genomic resources for sesame that could be used to improve sesame productivity and responses to stresses, a USDA sesame germplasm collection of 501 accessions originating from 36 countries was used in this study. The panel was genotyped using genotyping-by-sequencing (GBS) technology to explore its genetic diversity and population structure and the relatedness among its accessions. A total of 24,735 high-quality single-nucleotide polymorphism (SNP) markers were identified over the 13 chromosomes. The marker density was 1900 SNP per chromosome, with an average polymorphism information content (PIC) value of 0.267. The marker polymorphisms and heterozygosity estimators indicated the usefulness of the identified SNPs to be used in future genetic studies and breeding activities. The population structure, principal components analysis (PCA), and unrooted neighbor-joining phylogenetic tree analyses classified two distinct subpopulations, indicating a wide genetic diversity within the USDA sesame collection. Analysis of molecular variance (AMOVA) revealed that 29.5% of the variation in this population was due to subpopulations, while 57.5% of the variation was due to variation among the accessions within the subpopulations. These results showed the degree of differentiation between the two subpopulations as well as within each subpopulation. The high fixation index ( F
ST ) between the distinguished subpopulations indicates a wide genetic diversity and high genetic differentiation among and within the identified subpopulations. The linkage disequilibrium (LD) pattern averaged 161 Kbp for the whole sesame genome, while the LD decay ranged from 168 Kbp at chromosome LG09 to 123 Kbp in chromosome LG05. These findings could explain the complications of linkage drag among the traits during selections. The selected accessions and genotyped SNPs provide tools to enhance genetic gain in sesame breeding programs through molecular approaches.- Published
- 2024
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42. An analysis of culture-based methods used for the detection and isolation of Salmonella spp., Escherichia coli, and Enterococcus spp. from surface water: A systematic review.
- Author
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McConn BR, Kraft AL, Durso LM, Ibekwe AM, Frye JG, Wells JE, Tobey EM, Ritchie S, Williams CF, Cook KL, and Sharma M
- Subjects
- Water Microbiology, Enterococcus isolation & purification, Salmonella isolation & purification, Environmental Monitoring methods, Escherichia coli isolation & purification
- Abstract
Identification of methods for the standardized assessment of bacterial pathogens and antimicrobial resistance (AMR) in environmental water can improve the quality of monitoring and data collected, support global surveillance efforts, and enhance the understanding of environmental water sources. We conducted a systematic review to assemble and synthesize available literature that identified methods for assessment of prevalence and abundance of bacterial fecal indicators and pathogens in water for the purposes of monitoring bacterial pathogens and AMR. After screening for quality, 175 unique publications were identified from 15 databases, and data were extracted for analysis. This review identifies the most common and robust methods, and media used to isolate target organisms from surface water sources, summarizes methodological trends, and recognizes knowledge gaps. The information presented in this review will be useful when establishing standardized methods for monitoring bacterial pathogens and AMR in water in the United States and globally., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier B.V.)
- Published
- 2024
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43. Multi-locus genome-wide association study reveal genomic regions underlying root system architecture traits in Ethiopian sorghum germplasm.
- Author
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Elias M, Chere D, Lule D, Serba D, Tirfessa A, Gelmesa D, Tesso T, Bantte K, and Menamo TM
- Subjects
- Ethiopia, Genome, Plant, Phenotype, Sorghum genetics, Genome-Wide Association Study, Plant Roots genetics, Quantitative Trait Loci, Polymorphism, Single Nucleotide
- Abstract
The identification of genomic regions underlying the root system architecture (RSA) is vital for improving crop abiotic stress tolerance. To improve sorghum (Sorghum bicolor L. Moench) for environmental stress tolerance, information on genetic variability and genomic regions linked to RSA traits is paramount. The aim of this study was, therefore, to investigate common quantitative trait nucleotides (QTNs) via multiple methodologies and identify genomic regions linked to RSA traits in a panel of 274 Ethiopian sorghum accessions. Multi-locus genome-wide association study was conducted using 265,944 high-quality single nucleotide polymorphism markers. Considering the QTN detected by at least three different methods, a total of 17 reliable QTNs were found to be significantly associated with root angle, number, length, and dry weight. Four QTNs were detected on chromosome SBI-05, followed by SBI-01 and SBI-02 with three QTNs each. Among the 17 QTNs, 11 are colocated with previously identified root traits quantitative trait loci and the remaining six are genome regions with novel genes. A total of 118 genes are colocated with these up- and down-streams of the QTNs. Moreover, five QTNs were found intragenic. These QTNs are S5_8994835 (number of nodal roots), S10_55702393 (number of nodal roots), S1_56872999 (nodal root angle), S9_1212069 (nodal root angle), and S5_5667192 (root dry weight) intragenic regions of Sobic.005G073101, Sobic.010G198000, Sobic.001G273000, Sobic.009G013600, and Sobic.005G054700, respectively. Particularly, Sobic.005G073101, Sobic.010G198000, and Sobic.009G013600 were found responsible for the plant growth hormone-induced RSA. These genes may regulate root development in the seedling stage. Further analysis on these genes might be important to explore the genetic structure of RSA of sorghum., (© 2024 The Authors. The Plant Genome published by Wiley Periodicals LLC on behalf of Crop Science Society of America.)
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- 2024
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44. Nanoparticle-delivered RNAi-based pesticide target screening for the rice pest white-backed planthopper and risk assessment for a natural predator.
- Author
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Ma YF, Zhao YQ, Zhou YY, Feng HY, Gong LL, Zhang MQ, Hull JJ, Dewer Y, Roy A, Smagghe G, He M, and He P
- Subjects
- Animals, RNA Interference, Risk Assessment, Adenosine Triphosphate, Oryza genetics, Hemiptera, Heteroptera, Pesticides
- Abstract
Vacuolar-type (H
+ )-ATPase (vATPase) is a conserved multi-subunit eukaryotic enzyme composed of 14 subunits that form a functional complex consisting of an ATP-hydrolytic domain (V1) and a proton-translocation domain (V0). ATP hydrolysis and subsequent H+ translocation rely heavily on a fully assembled V1/V0 complex. Since vATPase is crucial for insect survival, it is a viable molecular target for pest control. However, detailed functional analyses of the 14 subunits and their suitability for pest control have not been fully explored in a single insect species. In this study, we identified 22 vATPase subunit transcripts that correspond to 13 subunits (A1, A2, B, C, D, E, F, G, H, a1, a2, c and d) in the white-backed planthopper (WBPH), Sogatella furcifera, a major hemipteran pest of rice. RNAi screens using microinjection and spray-based methods revealed that the SfVHA-F, SfVHA-a2 and SfVHA-c2 subunits are critical. Furthermore, star polymer (SPc) nanoparticles were utilized to conduct spray-induced and nanoparticle-delivered gene silencing (SI-NDGS) to evaluate the pest control efficacy of RNAi targeting the SfVHA-F, SfVHA-a2 and SfVHA-c2 transcripts. Target mRNA levels and vATPase enzymatic activity were both reduced. Honeydew excreta was likewise reduced in WBPH treated with dsRNAs targeting SfVHA-F, SfVHA-a2 and SfVHA-c2. To assess the environmental safety of the nanoparticle-wrapped dsRNAs, Cyrtorhinus lividipennis Reuter, a major natural enemy of planthoppers, was also sprayed with dsRNAs targeting SfVHA-F, SfVHA-a2 and SfVHA-c2. Post-spray effects of dsSfVHA-a2 and dsSfVHA-c2 on C. lividipennis were innocuous. This study identifies SfVHA-a2 and SfVHA-c2 as promising targets for biorational control of WBPH and lays the foundation for developing environment-friendly RNAi biopesticides., Competing Interests: Declaration of competing interest Peng He reports financial support was provided by Guizhou University. Peng He reports a relationship with Guizhou University that includes: employment. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)- Published
- 2024
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45. A one health approach for monitoring antimicrobial resistance: developing a national freshwater pilot effort.
- Author
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Franklin AM, Weller DL, Durso LM, Bagley M, Davis BC, Frye JG, Grim CJ, Ibekwe AM, Jahne MA, Keely SP, Kraft AL, McConn BR, Mitchell RM, Ottesen AR, Sharma M, Strain EA, Tadesse DA, Tate H, Wells JE, Williams CF, Cook KL, Kabera C, McDermott PF, and Garland JL
- Abstract
Antimicrobial resistance (AMR) is a world-wide public health threat that is projected to lead to 10 million annual deaths globally by 2050. The AMR public health issue has led to the development of action plans to combat AMR, including improved antimicrobial stewardship, development of new antimicrobials, and advanced monitoring. The National Antimicrobial Resistance Monitoring System (NARMS) led by the United States (U.S) Food and Drug Administration along with the U.S. Centers for Disease Control and U.S. Department of Agriculture has monitored antimicrobial resistant bacteria in retail meats, humans, and food animals since the mid 1990's. NARMS is currently exploring an integrated One Health monitoring model recognizing that human, animal, plant, and environmental systems are linked to public health. Since 2020, the U.S. Environmental Protection Agency has led an interagency NARMS environmental working group (EWG) to implement a surface water AMR monitoring program (SWAM) at watershed and national scales. The NARMS EWG divided the development of the environmental monitoring effort into five areas: (i) defining objectives and questions, (ii) designing study/sampling design, (iii) selecting AMR indicators, (iv) establishing analytical methods, and (v) developing data management/analytics/metadata plans. For each of these areas, the consensus among the scientific community and literature was reviewed and carefully considered prior to the development of this environmental monitoring program. The data produced from the SWAM effort will help develop robust surface water monitoring programs with the goal of assessing public health risks associated with AMR pathogens in surface water (e.g., recreational water exposures), provide a comprehensive picture of how resistant strains are related spatially and temporally within a watershed, and help assess how anthropogenic drivers and intervention strategies impact the transmission of AMR within human, animal, and environmental systems., Competing Interests: Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
- Published
- 2024
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46. Knockdown of MAPK p38-linked genes increases the susceptibility of Chilo suppressalis larvae to various transgenic Bt rice lines.
- Author
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Niu X, Jiang J, Sun Y, Hull JJ, Ma W, Hua H, and Lin Y
- Subjects
- Animals, Bacillus thuringiensis genetics, Bacillus thuringiensis Toxins, Endotoxins genetics, Moths genetics, Hemolysin Proteins genetics, Oryza genetics, Oryza parasitology, Plants, Genetically Modified genetics, Larva genetics, p38 Mitogen-Activated Protein Kinases metabolism, p38 Mitogen-Activated Protein Kinases genetics, Gene Knockdown Techniques
- Abstract
Bacillus thuringiensis (Bt) toxins have provided exceptional control of agricultural insect pests, however, over reliance on the proteins would potentially contribute to the development of field tolerance. Developing new sustainable insect pest control methods that target the mechanisms underlying Bt tolerance can potentially support the Bt control paradigm while also providing insights into basic insect physiology. The MAPK p38 pathway is strongly associated with Bt tolerance in Chilo suppressalis, a major pest of rice. To gain insights into how this pathway impacts tolerance, high-throughput screening of C. suppressalis larval midguts initially identified eight novel target genes. Increased larval sensitivity to the transgenic cry1Ca rice strain T1C-19 was observed following RNA interference-mediated knockdown of four of the genes, Cscnc, Csgcp, Cszfp26 and CsZMYM1. Similar enhanced sensitivity to the TT51 (expressing Cry1Ab/1Ac) and T2A-1 (expressing Cry2Aa) transgenic rice lines occurred when Cszfp26 and CsZMYM1 were knocked down. All four target genes are downstream of the MAPK p38 pathway but do not participate in negative feedback loop of the pathway. These results implicate Cscnc, Csgcp, Cszfp and CsZMYM1 in the C. suppressalis transgenic cry1Ca rice tolerance mechanism regulated by MAPK p38. These findings further enhance our understanding of the MAPK p38-dependent molecular mechanisms underlying Bt tolerance in C. suppressalis and open new avenues of tolerance management to develop., Competing Interests: Declaration of competing interest The authors have declared no conflict of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)
- Published
- 2024
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47. The melanin pigment gene black mediates body pigmentation and courtship behaviour in the German cockroach Blattella germanica .
- Author
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Gong LL, Ma YF, Zhang MQ, Feng HY, Zhou YY, Zhao YQ, Hull JJ, Dewer Y, He M, and He P
- Subjects
- Animals, Male, Female, Insect Proteins genetics, Insect Proteins metabolism, Sexual Behavior, Animal, RNA Interference, Blattellidae genetics, Blattellidae physiology, Pigmentation genetics, Melanins metabolism
- Abstract
Genes involved in melanin production directly impact insect pigmentation and can affect diverse physiology and behaviours. The role these genes have on sex behaviour, however, is unclear. In the present study, the crucial melanin pigment gene black was functionally characterised in an urban pest, the German cockroach, Blattella germanica . RNAi knockdown of B. germanica black ( Bgblack ) had no effect on survival, but did result in black pigmentation of the thoraxes, abdomens, heads, wings, legs, antennae, and cerci due to cuticular accumulation of melanin. Sex-specific variation in the pigmentation pattern was apparent, with females exhibiting darker coloration on the abdomen and thorax than males. Bgblack knockdown also resulted in wing deformation and negatively impacted the contact sex pheromone-based courtship behaviour of males. This study provides evidence for black function in multiple aspects of B. germanica biology and opens new avenues of exploration for novel pest control strategies.
- Published
- 2024
- Full Text
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48. Telomere-to-telomere genome assembly of the aflatoxin biocontrol agent Aspergillus flavus isolate La3279 isolated from maize in Nigeria.
- Author
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Legan AW, Mehl HL, Wissotski M, Adhikari BN, and Callicott KA
- Abstract
Here, we report the complete genome of the non-aflatoxigenic Aspergillus flavus isolate La3279, which is an active ingredient of the aflatoxin biocontrol product Aflasafe. The chromosome-scale assembly clarifies the deletion pattern in the aflatoxin biosynthesis gene cluster and corrects a misidentified assembly previously published for this isolate., Competing Interests: The authors declare no conflict of interest.
- Published
- 2024
- Full Text
- View/download PDF
49. Doublesex is essential for masculinization but not feminization in Lygus hesperus.
- Author
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Hull JJ, Heu CC, Gross RJ, LeRoy DM, Schutze IX, Langhorst D, Fabrick JA, and Brent CS
- Subjects
- Female, Male, Animals, Sex Differentiation, Sexual Development, Heteroptera genetics, Coleoptera
- Abstract
In most holometabolous insects, sex differentiation occurs via a hierarchical cascade of transcription factors, with doublesex (dsx) regulating genes that control sex-specific traits. Although less is known in hemimetabolous insects, early evidence suggests that substantial differences exist from more evolutionarily advanced insects. Here, we identified and characterized dsx in Lygus hesperus (western tarnished plant bug), a hemipteran pest of many agricultural crops in western North America. The full-length transcript for L. hesperus dsx (Lhdsx) and several variants encode proteins with conserved DNA binding and oligomerization domains. Transcript profiling revealed that Lhdsx is ubiquitously expressed, likely undergoes alternative pre-mRNA splicing, and, unlike several model insects, is sex-biased rather than sex-specific. Embryonic RNA interference (RNAi) of Lhdsx only impacted sex development in adult males, which lacked both internal reproductive organs and external genitalia. No discernible impacts on adult female development or reproductivity were observed. RNAi knockdown of Lhdsx in nymphs likewise only affected adult males, which lacked the characteristic dimorphic coloration but had dramatically elevated vitellogenin transcripts. Gene knockout of Lhdsx by CRISPR/Cas9 editing yielded only females in G
0 and strongly biased heterozygous G1 offspring to females with the few surviving males showing severely impaired genital development. These results indicate that L. hesperus male development requires Lhdsx, whereas female development proceeds via a basal pathway that functions independently of dsx. A fundamental understanding of sex differentiation in L. hesperus could be important for future gene-based management strategies of this important agricultural pest., (Published by Elsevier Ltd.)- Published
- 2024
- Full Text
- View/download PDF
50. Genome-wide profiling of histone (H3) lysine 4 (K4) tri-methylation (me3) under drought, heat, and combined stresses in switchgrass.
- Author
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Ayyappan V, Sripathi VR, Xie S, Saha MC, Hayford R, Serba DD, Subramani M, Thimmapuram J, Todd A, and Kalavacharla VK
- Subjects
- Hot Temperature, Lysine metabolism, Histones metabolism, Droughts, Stress, Physiological genetics, Methylation, Gene Expression Regulation, Plant, Gene Expression Profiling, Panicum metabolism
- Abstract
Background: Switchgrass (Panicum virgatum L.) is a warm-season perennial (C4) grass identified as an important biofuel crop in the United States. It is well adapted to the marginal environment where heat and moisture stresses predominantly affect crop growth. However, the underlying molecular mechanisms associated with heat and drought stress tolerance still need to be fully understood in switchgrass. The methylation of H3K4 is often associated with transcriptional activation of genes, including stress-responsive. Therefore, this study aimed to analyze genome-wide histone H3K4-tri-methylation in switchgrass under heat, drought, and combined stress., Results: In total, ~ 1.3 million H3K4me3 peaks were identified in this study using SICER. Among them, 7,342; 6,510; and 8,536 peaks responded under drought (DT), drought and heat (DTHT), and heat (HT) stresses, respectively. Most DT and DTHT peaks spanned 0 to + 2000 bases from the transcription start site [TSS]. By comparing differentially marked peaks with RNA-Seq data, we identified peaks associated with genes: 155 DT-responsive peaks with 118 DT-responsive genes, 121 DTHT-responsive peaks with 110 DTHT-responsive genes, and 175 HT-responsive peaks with 136 HT-responsive genes. We have identified various transcription factors involved in DT, DTHT, and HT stresses. Gene Ontology analysis using the AgriGO revealed that most genes belonged to biological processes. Most annotated peaks belonged to metabolite interconversion, RNA metabolism, transporter, protein modifying, defense/immunity, membrane traffic protein, transmembrane signal receptor, and transcriptional regulator protein families. Further, we identified significant peaks associated with TFs, hormones, signaling, fatty acid and carbohydrate metabolism, and secondary metabolites. qRT-PCR analysis revealed the relative expressions of six abiotic stress-responsive genes (transketolase, chromatin remodeling factor-CDH3, fatty-acid desaturase A, transmembrane protein 14C, beta-amylase 1, and integrase-type DNA binding protein genes) that were significantly (P < 0.05) marked during drought, heat, and combined stresses by comparing stress-induced against un-stressed and input controls., Conclusion: Our study provides a comprehensive and reproducible epigenomic analysis of drought, heat, and combined stress responses in switchgrass. Significant enrichment of H3K4me3 peaks downstream of the TSS of protein-coding genes was observed. In addition, the cost-effective experimental design, modified ChIP-Seq approach, and analyses presented here can serve as a prototype for other non-model plant species for conducting stress studies., (© 2024. The Author(s).)
- Published
- 2024
- Full Text
- View/download PDF
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