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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
24/09/2018 |
Actualizado : |
12/02/2021 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Autor : |
AMORIM, S.T.; KLUSKA, S.; PIATTO BERTON, M.; ANTUNES DE LEMOS, M.V.; PERIPOLLI, E.; BONVINO STAFUZZA, N.; FERNÁNDEZ MARTÍN, J.; SAURA ÁLVAREZ, M.; VILLANUEVA GAVIÑA, B.; TORO, M.A.; BANCHERO, G.; SILVA OLIVEIRA, P.; GRIGOLETTO, L.; PEREIRA ELER, J.; BALDI, F.; STERMAN FERRAZ, J.B. |
Afiliación : |
SABRINA THAISE AMORIM, Universidade Federal de Santa Catarina; SABRINA KLUSKA, Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias; MARIANA PIATTO BERTON; MARCOS VINICIUS ANTUNES DE LEMOS, Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias; ELISA PERIPOLLI, Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias; NEDENIA BONVINO STAFUZZA, Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias; JESÚS FERNÁNDEZ MARTÍN, INIA España (Instituto Nacional de Investigación y Tecnología Agraría y Alimentaria); MARÍA SAURA ÁLVAREZ, INIA España (Instituto Nacional de Investigación y Tecnología Agraría y Alimentaria); BEATRIZ VILLANUEVA GAVIÑA, INIA España (Instituto Nacional de Investigación y Tecnología Agraría y Alimentaria); MIGUEL ÁNGEL TORO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GEORGGET ELIZABETH BANCHERO HUNZIKER, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; PRISCILA SILVA OLIVEIRA, Universidade de São Paulo, Faculdade de Zootecnia e Engenharia de Alimentos; LAÍS GRIGOLETTO, Universidade de São Paulo, Faculdade de Zootecnia e Engenharia de Alimentos; JOANIR PEREIRA ELER, Universidade de São Paulo, Faculdade de Zootecnia e Engenharia de Alimentos; FERNANDO BALDI, Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias; JOSÉ BENTO STERMAN FERRAZ, Universidade de São Paulo, Faculdade de Zootecnia e Engenharia de Alimentos. |
Título : |
Genomic study for maternal related traits in Santa Inês sheep breed. |
Fecha de publicación : |
2018 |
Fuente / Imprenta : |
Livestock Science, November 2018, Volume 217, Pages 76-84. Doi: https://doi.org/10.1016/j.livsci.2018.09.011 |
ISSN : |
1871-1413 |
DOI : |
10.1016/j.livsci.2018.09.011 |
Idioma : |
Inglés |
Notas : |
Article history: Received 23 January 2018 // Revised 6 September 2018 // Accepted 11 September 2018 // Available online 20 September 2018. |
Contenido : |
ABSTRACT.
The aim of this study was to estimate variance components and to identify genomic regions and pathways associated with maternal related traits in Santa Inês sheep breed adapted to tropical climate. Phenotypic records for maternal efficiency (ME), metabolic maternal efficiency (MME), twin lambing (TL), adult weight (AW), metabolic adult weight (MAW), and body condition score (BCS) from 1,333 ewes from Santa Inês breed were used. A total of 576 animals were genotyped with the Ovine SNP12k BeadChip (Illumina, Inc.), that contains 12,785 bialleleic SNP markers. The variance components were estimated using a single trait animal model by single step genomic BLUP procedure. For AW, MAW, BCS, ME, MME and TL the mean values were 50.30 (±9.76), 19.2 (±2.33), 2.76 (±0.72), 34.6 (±15.95), 91.8 (±42.52), and 1.27 (±0.44) respectively. The heritabilities estimated were moderate for AW (0.32) and MAW (0.33) and low for BCS (0.04), ME (0.07), MME (0.08), and TL (0.10). A total of 7, 8, 13, 16, 19, and 09 candidate regions for ME, MME, TL, AW, MAW and BCS traits were identified respectively. AW and MAW had a total of 15 regions in common, while AW and BCS had a common region on chromosome 21. ME and MME had six candidate regions in common, and TL had no common regions with any other features. The maternal indicator traits have genetic variability to respond to selection in Santa Inês breed, and it would be expected higher genetic gain for ewe adult weight when compared to the others studied traits. Several candidate regions related to growth, reproduction, lactation and enviromental adaptability were identified in this study. These candidate regions would give support to identify and select animals with higher maternal efficiency and fitness, and consequently, increase the productivity of Santa Inês sheep. Moreover, the results of this study should help to understand the genetic and physiologic mechanism associated with maternal related traits in Santa Inês breed.
© 2018 Elsevier B.V. All rights reserved. MenosABSTRACT.
The aim of this study was to estimate variance components and to identify genomic regions and pathways associated with maternal related traits in Santa Inês sheep breed adapted to tropical climate. Phenotypic records for maternal efficiency (ME), metabolic maternal efficiency (MME), twin lambing (TL), adult weight (AW), metabolic adult weight (MAW), and body condition score (BCS) from 1,333 ewes from Santa Inês breed were used. A total of 576 animals were genotyped with the Ovine SNP12k BeadChip (Illumina, Inc.), that contains 12,785 bialleleic SNP markers. The variance components were estimated using a single trait animal model by single step genomic BLUP procedure. For AW, MAW, BCS, ME, MME and TL the mean values were 50.30 (±9.76), 19.2 (±2.33), 2.76 (±0.72), 34.6 (±15.95), 91.8 (±42.52), and 1.27 (±0.44) respectively. The heritabilities estimated were moderate for AW (0.32) and MAW (0.33) and low for BCS (0.04), ME (0.07), MME (0.08), and TL (0.10). A total of 7, 8, 13, 16, 19, and 09 candidate regions for ME, MME, TL, AW, MAW and BCS traits were identified respectively. AW and MAW had a total of 15 regions in common, while AW and BCS had a common region on chromosome 21. ME and MME had six candidate regions in common, and TL had no common regions with any other features. The maternal indicator traits have genetic variability to respond to selection in Santa Inês breed, and it would be expected higher genetic gain for ewe adult weight when compared to the other... Presentar Todo |
Palabras claves : |
BODY WEIGHT; GWAS; MATTERNAL EFFICIENCY; OVIS ARIES. |
Asunto categoría : |
-- |
Marc : |
LEADER 03310naa a2200385 a 4500 001 1059073 005 2021-02-12 008 2018 bl uuuu u00u1 u #d 022 $a1871-1413 024 7 $a10.1016/j.livsci.2018.09.011$2DOI 100 1 $aAMORIM, S.T. 245 $aGenomic study for maternal related traits in Santa Inês sheep breed.$h[electronic resource] 260 $c2018 500 $aArticle history: Received 23 January 2018 // Revised 6 September 2018 // Accepted 11 September 2018 // Available online 20 September 2018. 520 $aABSTRACT. The aim of this study was to estimate variance components and to identify genomic regions and pathways associated with maternal related traits in Santa Inês sheep breed adapted to tropical climate. Phenotypic records for maternal efficiency (ME), metabolic maternal efficiency (MME), twin lambing (TL), adult weight (AW), metabolic adult weight (MAW), and body condition score (BCS) from 1,333 ewes from Santa Inês breed were used. A total of 576 animals were genotyped with the Ovine SNP12k BeadChip (Illumina, Inc.), that contains 12,785 bialleleic SNP markers. The variance components were estimated using a single trait animal model by single step genomic BLUP procedure. For AW, MAW, BCS, ME, MME and TL the mean values were 50.30 (±9.76), 19.2 (±2.33), 2.76 (±0.72), 34.6 (±15.95), 91.8 (±42.52), and 1.27 (±0.44) respectively. The heritabilities estimated were moderate for AW (0.32) and MAW (0.33) and low for BCS (0.04), ME (0.07), MME (0.08), and TL (0.10). A total of 7, 8, 13, 16, 19, and 09 candidate regions for ME, MME, TL, AW, MAW and BCS traits were identified respectively. AW and MAW had a total of 15 regions in common, while AW and BCS had a common region on chromosome 21. ME and MME had six candidate regions in common, and TL had no common regions with any other features. The maternal indicator traits have genetic variability to respond to selection in Santa Inês breed, and it would be expected higher genetic gain for ewe adult weight when compared to the others studied traits. Several candidate regions related to growth, reproduction, lactation and enviromental adaptability were identified in this study. These candidate regions would give support to identify and select animals with higher maternal efficiency and fitness, and consequently, increase the productivity of Santa Inês sheep. Moreover, the results of this study should help to understand the genetic and physiologic mechanism associated with maternal related traits in Santa Inês breed. © 2018 Elsevier B.V. All rights reserved. 653 $aBODY WEIGHT 653 $aGWAS 653 $aMATTERNAL EFFICIENCY 653 $aOVIS ARIES 700 1 $aKLUSKA, S. 700 1 $aPIATTO BERTON, M. 700 1 $aANTUNES DE LEMOS, M.V. 700 1 $aPERIPOLLI, E. 700 1 $aBONVINO STAFUZZA, N. 700 1 $aFERNÁNDEZ MARTÍN, J. 700 1 $aSAURA ÁLVAREZ, M. 700 1 $aVILLANUEVA GAVIÑA, B. 700 1 $aTORO, M.A. 700 1 $aBANCHERO, G. 700 1 $aSILVA OLIVEIRA, P. 700 1 $aGRIGOLETTO, L. 700 1 $aPEREIRA ELER, J. 700 1 $aBALDI, F. 700 1 $aSTERMAN FERRAZ, J.B. 773 $tLivestock Science, November 2018, Volume 217, Pages 76-84. Doi: https://doi.org/10.1016/j.livsci.2018.09.011
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Biblioteca (s) : |
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Fecha actual : |
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Actualizado : |
10/01/2023 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Internacional - -- |
Autor : |
GUARIN, J. R.; MARTRE, P; EWERT, F.; WEBBER, H.; DUERI, S.; CALDERINI, D.; REYNOLDS, M.; MOLERO, G.; MIRALLES, D.; GARCIA, G.; SLAFER, G.; GIUNTA, F.; PEQUENO, D. N. L.; STELLA, T.; AHMED, M.; ALDERMAN, P. D.; BASSO, B.; BERGER, A.; BINDI, M.; BRACHO-MUJICA, G.; CAMMARANO, D.; CHEN, Y.; DUMONT, B.; REZAEI, E. E.; FERERES, E.; FERRISE, R.; GAISER, T.; GAO, Y.; GARCIA-VILA, M.; GAYLER, S.; HOCHMAN, Z.; HOOGENBOOM, G.; HUNT, L. A.; KERSEBAUM, K. C.; NENDEL, C.; OLESEN, J. E.; PALOSUO, T.; PRIESACK, E.; PULLENS, J. W. M.; RODRÍGUEZ, A.; RÖTTER, R. P.; RUIZ RAMOS, M.; SEMENOV, M. A.; SENAPATI, N.; SIEBERT, S.; SRIVASTAVA, A. M.; STÖCKLE, C.; SUPIT, I.; TAO, F.; THORBURN, P.; WANG, E.; WEBER, T. K. D.; XIAO, L.; ZHANG, Z.; ZHAO, C.; ZHAO, J.; ZHAO, Z.; ZHU, Y.; ASSENG, S. |
Afiliación : |
JOSE RAFAEL GUARIN, Agricultural & Biological Engineering Dpt., Univ. of Florida, FL, USA; Center for Climate Systems Research, Columbia Univ., NY, USA; NASA Goddard Institute for Space Studies, NY, USA.; PIERRE MARTRE, LEPSE, Univ Montpellier, INRAE, Institut Agro Montpellier SupAgro, Montpellier, France; FRANK EWERT, Institute of Crop Science and Resource Conservation INRES, University of Bonn, Bonn, Germany; Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany; HEIDI WEBBER, Institute of Crop Science and Resource Conservation INRES, University of Bonn, Bonn, Germany; Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany; SIBYLLE DUERI, LEPSE, Univ Montpellier, INRAE, Institut Agro Montpellier SupAgro, Montpellier, France; DANIEL CALDERINI, Institute of Plant Production and Protection, Austral University of Chile, Valdivia, Chile; MATTHEW REYNOLDS, International Maize and Wheat Improvement Center (CIMMYT), Mexico DF, Mexico; GEMMA MOLERO, KWS, Lille, France; DANIEL MIRALLES, Department of Plant Production, University of Buenos Aires, IFEVA-CONICET, Buenos Aires, Argentina; GUILLERMO GARCIA, Department of Plant Production, University of Buenos Aires, IFEVA-CONICET, Buenos Aires, Argentina; GUSTAVO SLAFER, Department of Crop and Forest Sciences, University of Lleida—AGROTECNIO-CERCA Center, Lleida, Spain; and ICREA, Catalonian Institution for Research and Advanced Studies, Barcelona, Spain; FRANCESCO GIUNTA, Department of Agricultural Sciences, University of Sassari, Sassari, Ital; DIEGO N L PEQUENO, International Maize and Wheat Improvement Center (CIMMYT), Mexico DF, Mexico; TOMMASO STELLA, Institute of Crop Science and Resource Conservation INRES, University of Bonn, Bonn, Germany; Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany; MUKHTAR AHMED, Department of Agronomy, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, Pakistan; Department of Agricultural Research for Northern Sweden, Swedish University of Agricultural Sciences, Umeå, Sweden; PHILLIP D ALDERMAN, Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, OK, United States of America; BRUNO BASSO, Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI, United States of America; W.K. Kellogg Biological Station, Michigan State University, East Lansing, MI, United States of America; ANDRES GUSTAVO BERGER RICCA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARCO BINDI, Department of AGRIculture, food, environment and forestry (DAGRI), Department of Agri-food Production and Environmental Sciences (DISPAA), University of Florence, Florence, Italy; GENNADY BRACHO-MUJICA, Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), University of Göttingen, Göttingen, Germany; DAVIDE CAMMARANO, Department of Agronomy, Purdue University, West Lafayette, IN, United States of America; YI CHEN, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Science, Beijing, People’s Republic of China; BENJAMIN DUMONT, Department Terra & AgroBioChem, Gembloux Agro-Bio Tech, University of Liege, Gembloux, Belgium; EHSAN EYSHI REZAEI, Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany; ELIAS FERERES, IAS-CSIC DAUCO, University of Cordoba, Cordoba, Spain; ROBERTO FERRISE, Department of AGRIculture, food, environment and forestry (DAGRI), Department of Agri-food Production and Environmental Sciences (DISPAA), University of Florence, Florence, Italy; THOMAS GAISER, Institute of Crop Science and Resource Conservation INRES, University of Bonn, Bonn, Germany; YUJING GAO, Agricultural & Biological Engineering Department, University of Florida, Gainesville, FL, United States of America; MARGARITA GARCIA-VILA, IAS-CSIC DAUCO, University of Cordoba, Cordoba, Spain; SEBASTIAN GAYLER, Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, Germany; ZVI HOCHMAN, CSIRO Agriculture and Food, Brisbane, Queensland, Australia; GERRIT HOOGENBOOM, Agricultural & Biological Engineering Department, University of Florida, Gainesville, FL, United States of America; Institute for Sustainable Food Systems, University of Florida, Gainesville, FL, United States of America; LESLIE A HUNT, Department of Plant Agriculture, University of Guelph, Guelph, Ontario, Canada; KURT C KERSEBAUM, Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany; Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), Univ. of Göttingen, Göttingen, Germany; Global Change Research Institute Academy of Sciences of the Czech Rep; CLAAS NENDEL, Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany; Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), Univ. of Göttingen, Göttingen, Germany; Global Change Research Institute Academy of Sciences of the Czech Repu; JØRGEN E OLESEN, Department of Agroecology, Aarhus University, Tjele, Denmark; TARU PALOSUO, Natural Resources Institute Finland (Luke), Helsinki, Finland; ECKART PRIESACK, Institute of Biochemical Plant Pathology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; JOHANNES W M PULLENS, Department of Agroecology, Aarhus University, Tjele, Denmark; ALFREDO RODRÍGUEZ, CEIGRAM, Technic University of Madrid, Madrid, Spain; Department of Economic Analysis and Finances, University of Castilla-La Mancha, Toledo, Spain; REIMUND P RÖTTER, Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), University of Göttingen, Göttingen, Germany; Centre of Biodiversity and Sustainable Land Use (CBL), University of Göttingen, Göttingen, Germany; MARGARITA RUIZ RAMOS, CEIGRAM, Technic University of Madrid, Madrid, Spain; MIKHAIL A SEMENOV, Rothamsted Research, Harpenden AL5 2JQ, United Kingdom; NIMAI SENAPATI, Rothamsted Research, Harpenden AL5 2JQ, United Kingdom; STEFAN SIEBERT, Department of Crop Sciences, University of Göttingen, Göttingen, Germany; AMIT KUMAR SRIVASTAVA, Institute of Crop Science and Resource Conservation INRES, University of Bonn, Bonn, Germany; CLAUDIO STÖCKLE, Biological Systems Engineering, Washington State University, Pullman, WA, United States of America; IWAN SUPIT, Water & Food and Water Systems & Global Change Group, Wageningen University, Wageningen, The Netherlands; FULU TAO, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Science, Beijing, People’s Republic of China; Natural Resources Institute Finland (Luke), Helsinki, Finland; PETER THORBURN, CSIRO Agriculture and Food, Brisbane, Queensland, Australia; ENLI WANG, CSIRO Agriculture and Food, Canberra, Australian Capital Territory, Australia; TOBIAS KARL DAVID WEBER, Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, Germany; Current affiliation: Department of Soil Science, Faculty of Organic Soil Sciences, University of Kassel, Kassel, Germany; LIUJUN XIAO, College of Environmental and Resource Sciences, Zhejiang Univ., Hangzhou, Zhejiang, China; National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiang; ZHAO ZHANG, State Key Laboratory for Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, People’s Republic of China; CHUANG ZHAO, College of Resources and Environmental Sciences, China Agricultural University, Beijing, People’s Republic of China; JIN ZHAO, College of Resources and Environmental Sciences, China Agricultural University, Beijing, People’s Republic of China; Department of Agroecology, Aarhus University, Tjele, Denmark; ZHIGAN ZHAO, CSIRO Agriculture and Food, Canberra, Australian Capital Territory, Australia; Department of Agronomy and Biotechnology, China Agricultural University, Beijing, People’s Republic of China; YAN ZHU, National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for M; SENTHOLD ASSENG, 8 Department of Life Science Engineering, Digital Agriculture, Technical University of Munich, Freising, Germany. |
Título : |
Evidence for increasing global wheat yield potential. [Letter]. |
Fecha de publicación : |
2022 |
Fuente / Imprenta : |
Environmental Research Letters, 12 December 2022, Volume 17, 124045. OPEN ACCESS. doi: https://doi.org/10.1088/1748-9326/aca77c |
ISSN : |
1748-9326 |
DOI : |
10.1088/1748-9326/aca77c |
Idioma : |
Inglés |
Notas : |
Article history: Received 13 June 2022; Accepted 30 November 2022; Published 12 December 2022. -- Corresponding author: Jose Rafael Guarin, E-mail: j.guarin@columbia.edu -- LICENSE: Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence (https://creativecommons.org/licenses/by/4.0/ ) -- Supplementary material for this article is available online (http://doi.org/10.1088/1748-9326/aca77c ) -- |
Contenido : |
Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production. The DH field experiments were also used to determine a quantitative relationship between wheat production and solar radiation to estimate genetic yield potential. The multi-model ensemble projected a global annual wheat production of 1050 ± 145 Mt due to the improved canopy photosynthesis, a 37% increase, without expanding cropping area. Achieving this genetic yield potential would meet the lower estimate of the projected grain demand in 2050, albeit with considerable challenges.
© 2022 The Author(s). Published by IOP Publishing Ltd |
Palabras claves : |
Crop model ensemble; Global food security; Radiation use efficiency; Wheat potential yield; Yield increase. |
Asunto categoría : |
F01 Cultivo |
URL : |
https://iopscience.iop.org/article/10.1088/1748-9326/aca77c/pdf
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Marc : |
LEADER 03876naa a2200913 a 4500 001 1063941 005 2023-01-10 008 2022 bl uuuu u00u1 u #d 022 $a1748-9326 024 7 $a10.1088/1748-9326/aca77c$2DOI 100 1 $aGUARIN, J. R. 245 $aEvidence for increasing global wheat yield potential. [Letter].$h[electronic resource] 260 $c2022 500 $aArticle history: Received 13 June 2022; Accepted 30 November 2022; Published 12 December 2022. -- Corresponding author: Jose Rafael Guarin, E-mail: j.guarin@columbia.edu -- LICENSE: Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence (https://creativecommons.org/licenses/by/4.0/ ) -- Supplementary material for this article is available online (http://doi.org/10.1088/1748-9326/aca77c ) -- 520 $aWheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production. The DH field experiments were also used to determine a quantitative relationship between wheat production and solar radiation to estimate genetic yield potential. The multi-model ensemble projected a global annual wheat production of 1050 ± 145 Mt due to the improved canopy photosynthesis, a 37% increase, without expanding cropping area. Achieving this genetic yield potential would meet the lower estimate of the projected grain demand in 2050, albeit with considerable challenges. © 2022 The Author(s). Published by IOP Publishing Ltd 653 $aCrop model ensemble 653 $aGlobal food security 653 $aRadiation use efficiency 653 $aWheat potential yield 653 $aYield increase 700 1 $aMARTRE, P 700 1 $aEWERT, F. 700 1 $aWEBBER, H. 700 1 $aDUERI, S. 700 1 $aCALDERINI, D. 700 1 $aREYNOLDS, M. 700 1 $aMOLERO, G. 700 1 $aMIRALLES, D. 700 1 $aGARCIA, G. 700 1 $aSLAFER, G. 700 1 $aGIUNTA, F. 700 1 $aPEQUENO, D. N. L. 700 1 $aSTELLA, T. 700 1 $aAHMED, M. 700 1 $aALDERMAN, P. D. 700 1 $aBASSO, B. 700 1 $aBERGER, A. 700 1 $aBINDI, M. 700 1 $aBRACHO-MUJICA, G. 700 1 $aCAMMARANO, D. 700 1 $aCHEN, Y. 700 1 $aDUMONT, B. 700 1 $aREZAEI, E. E. 700 1 $aFERERES, E. 700 1 $aFERRISE, R. 700 1 $aGAISER, T. 700 1 $aGAO, Y. 700 1 $aGARCIA-VILA, M. 700 1 $aGAYLER, S. 700 1 $aHOCHMAN, Z. 700 1 $aHOOGENBOOM, G. 700 1 $aHUNT, L. A. 700 1 $aKERSEBAUM, K. C. 700 1 $aNENDEL, C. 700 1 $aOLESEN, J. E. 700 1 $aPALOSUO, T. 700 1 $aPRIESACK, E. 700 1 $aPULLENS, J. W. M. 700 1 $aRODRÍGUEZ, A. 700 1 $aRÖTTER, R. P. 700 1 $aRUIZ RAMOS, M. 700 1 $aSEMENOV, M. A. 700 1 $aSENAPATI, N. 700 1 $aSIEBERT, S. 700 1 $aSRIVASTAVA, A. M. 700 1 $aSTÖCKLE, C. 700 1 $aSUPIT, I. 700 1 $aTAO, F. 700 1 $aTHORBURN, P. 700 1 $aWANG, E. 700 1 $aWEBER, T. K. D. 700 1 $aXIAO, L. 700 1 $aZHANG, Z. 700 1 $aZHAO, C. 700 1 $aZHAO, J. 700 1 $aZHAO, Z. 700 1 $aZHU, Y. 700 1 $aASSENG, S. 773 $tEnvironmental Research Letters, 12 December 2022, Volume 17, 124045. OPEN ACCESS. doi: https://doi.org/10.1088/1748-9326/aca77c
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