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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Cerrados. |
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Data corrente: |
25/02/2022 |
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Data da última atualização: |
25/02/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
KOTHARI, K.; BATTISTI, R.; BOOTE, K. J.; ARCHONTOULIS, S. V.; CONFALONE, A.; CONSTANTIN, J.; CUADRA, S. V.; DEBAEKE, P.; FAYE, B.; GRANT, B.; HOOGENBOOM, G.; JING, Q.; VAN DER LAAN, M.; SILVA, F. A. M. da; MARIN, F. R.; NEHBANDANI, A.; NENDEL, C.; PURCELL, L. C.; QIAN, B.; RUANE, A. C.; SCHOVING, C.; SILVA, E. H. F. M.; SMITH, W.; SOLTANI, A.; SRIVASTAVA, A.; VIEIRA JÚNIOR, N. A.; SLONE, S.; SALMERÓN, M. |
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Afiliação: |
KRITIKA KOTHARI, UNIVERSITY OF KENTUCKY; RAFAEL BATTISTI, UFG; KENNETH J. BOOTE, UNIVERSITY OF FLORIDA; SOTIRIOS V. ARCHONTOULIS, IOWA STATE UNIVERSITY; ADRIANA CONFALONE, UNIVERSIDAD NACIONAL DEL CENTRO DE LA PROVINCIA DE BUENOS AIRES; JULIE CONSTANTIN, UNIVERSITÉ DE TOULOUSE; SANTIAGO VIANNA CUADRA, CNPTIA; PHILIPPE DEBAEKE, UNIVERSITÉ DE TOULOUSE; BABACAR FAYE, INSTITUT DE RECHERCHE POUR LE D ́EVELOPPEMENT (IRD) ESPACE-DEV; BRIAN GRANT, AGRICULTURE AND AGRI-FOOD CANADA; GERRIT HOOGENBOOM, UNIVERSITY OF FLORIDA; QI JING, AGRICULTURE AND AGRI-FOOD CANADA; MICHAEL VAN DER LAAN, UNIVERSITY OF PRETORIA; FERNANDO ANTONIO MACENA DA SILVA, CPAC; FÁBIO RICARDO MARIN, ESALQ/USP; ALIREZA NEHBANDANI, GORGAN UNIVERSITY OF AGRICULTURAL SCIENCES AND NATURAL RESOURCE; CLAAS NENDEL, University of PotsdaM, Leibniz Centre for Agricultural Landscape ResearcH; LARRY C. PURCELL, UNIVERSITY OF ARKANSAS; BUDONG QIAN, AGRICULTURE AND AGRI-FOOD CANADA; ALEX C. RUANE, NASA GODDARD INSTITUTE FOR SPACE STUDIES; CÉLINE SCHOVING, UNIVERSITÉ DE TOULOUSE, TERRES INOVIA; EVANDRO H. F. M. SILVA, ESALQ/USP; WARD SMITH, AGRICULTURE AND AGRI-FOOD CANADA; AFSHIN SOLTANI, GORGAN UNIVERSITY OF AGRICULTURAL SCIENCES AND NATURAL RE-SOURCES; AMIT SRIVASTAVA, UNIVERSITY OF BONN; NILSON A. VIEIRA JÚNIOR, ESALQ/USP; STACEY SLONE, UNIVERSITY OF KENTUCKY; MONTSERRAT SALMERÓN, UNIVERSITY OF KENTUCKY. |
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Título: |
Are soybean models ready for climate change food impact assessments? |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
European Journal of Agronomy, v. 135, 126482, Apr. 2022. |
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DOI: |
https://doi.org/10.1016/j.eja.2022.126482 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract. An accurate estimation of crop yield under climate change scenarios is essential to quantify our ability to feed a growing population and develop agronomic adaptations to meet future food demand. A coordinated evaluation of yield simulations from process-based eco-physiological models for climate change impact assessment is still missing for soybean, the most widely grown grain legume and the main source of protein in our food chain. In this first soybean multi-model study, we used ten prominent models capable of simulating soybean yield under varying temperature and atmospheric CO2 concentration [CO2] to quantify the uncertainty in soybean yield simulations in response to these factors. Models were first parametrized with high quality measured data from five contrasting environments. We found considerable variability among models in simulated yield responses to increasing temperature and [CO2]. For example, under a + 3 °C temperature rise in our coolest location in Argentina, some models simulated that yield would reduce as much as 24%, while others simulated yield increases up to 29%. In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 °C temperature rise to no effect on yield. Similarly, when increasing [CO2] from 360 to 540 ppm, the models simulated a yield increase that ranged from 6% to 31%. Model calibration did not reduce variability across models but had an unexpected effect on modifying yield responses to temperature for some of the models. The high uncertainty in model responses indicates the limited applicability of individual models for climate change food projections. However, the ensemble mean of simulations across models was an effective tool to reduce the high uncertainty in soybean yield simulations associated with individual models and their parametrization. Ensemble, ensemble mean yield responses to temperature and [CO2] were similar to those reported from the literature. Our study is the first demonstration of the benefits achieved from using an ensemble of grain legume models for climate change food projections, and highlights that further soybean model development with experiments under elevated [CO2] and temperature is needed to reduce the uncertainty from the individual models. MenosAbstract. An accurate estimation of crop yield under climate change scenarios is essential to quantify our ability to feed a growing population and develop agronomic adaptations to meet future food demand. A coordinated evaluation of yield simulations from process-based eco-physiological models for climate change impact assessment is still missing for soybean, the most widely grown grain legume and the main source of protein in our food chain. In this first soybean multi-model study, we used ten prominent models capable of simulating soybean yield under varying temperature and atmospheric CO2 concentration [CO2] to quantify the uncertainty in soybean yield simulations in response to these factors. Models were first parametrized with high quality measured data from five contrasting environments. We found considerable variability among models in simulated yield responses to increasing temperature and [CO2]. For example, under a + 3 °C temperature rise in our coolest location in Argentina, some models simulated that yield would reduce as much as 24%, while others simulated yield increases up to 29%. In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 °C temperature rise to no effect on yield. Similarly, when increasing [CO2] from 360 to 540 ppm, the models simulated a yield increase that ranged from 6% to 31%. Model calibration did not reduce variability across models but had an unexpected effect on modifying yield res... Mostrar Tudo |
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Palavras-Chave: |
AgMIP; Agricultural Model Intercomparison and Improvement Project; Impacto das mudanças climáticas; Legume model; Model calibration; Model ensemble; Modelos de soja; Temperature Atmospheric CO2 concentration. |
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Thesagro: |
Glycine Max; Soja; Temperatura. |
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Thesaurus Nal: |
Models; Soybeans; Temperature. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/232002/1/AP-Soybean-models-2022.pdf
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Marc: |
LEADER 04032naa a2200625 a 4500
001 2140426
005 2022-02-25
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.eja.2022.126482$2DOI
100 1 $aKOTHARI, K.
245 $aAre soybean models ready for climate change food impact assessments?$h[electronic resource]
260 $c2022
520 $aAbstract. An accurate estimation of crop yield under climate change scenarios is essential to quantify our ability to feed a growing population and develop agronomic adaptations to meet future food demand. A coordinated evaluation of yield simulations from process-based eco-physiological models for climate change impact assessment is still missing for soybean, the most widely grown grain legume and the main source of protein in our food chain. In this first soybean multi-model study, we used ten prominent models capable of simulating soybean yield under varying temperature and atmospheric CO2 concentration [CO2] to quantify the uncertainty in soybean yield simulations in response to these factors. Models were first parametrized with high quality measured data from five contrasting environments. We found considerable variability among models in simulated yield responses to increasing temperature and [CO2]. For example, under a + 3 °C temperature rise in our coolest location in Argentina, some models simulated that yield would reduce as much as 24%, while others simulated yield increases up to 29%. In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 °C temperature rise to no effect on yield. Similarly, when increasing [CO2] from 360 to 540 ppm, the models simulated a yield increase that ranged from 6% to 31%. Model calibration did not reduce variability across models but had an unexpected effect on modifying yield responses to temperature for some of the models. The high uncertainty in model responses indicates the limited applicability of individual models for climate change food projections. However, the ensemble mean of simulations across models was an effective tool to reduce the high uncertainty in soybean yield simulations associated with individual models and their parametrization. Ensemble, ensemble mean yield responses to temperature and [CO2] were similar to those reported from the literature. Our study is the first demonstration of the benefits achieved from using an ensemble of grain legume models for climate change food projections, and highlights that further soybean model development with experiments under elevated [CO2] and temperature is needed to reduce the uncertainty from the individual models.
650 $aModels
650 $aSoybeans
650 $aTemperature
650 $aGlycine Max
650 $aSoja
650 $aTemperatura
653 $aAgMIP
653 $aAgricultural Model Intercomparison and Improvement Project
653 $aImpacto das mudanças climáticas
653 $aLegume model
653 $aModel calibration
653 $aModel ensemble
653 $aModelos de soja
653 $aTemperature Atmospheric CO2 concentration
700 1 $aBATTISTI, R.
700 1 $aBOOTE, K. J.
700 1 $aARCHONTOULIS, S. V.
700 1 $aCONFALONE, A.
700 1 $aCONSTANTIN, J.
700 1 $aCUADRA, S. V.
700 1 $aDEBAEKE, P.
700 1 $aFAYE, B.
700 1 $aGRANT, B.
700 1 $aHOOGENBOOM, G.
700 1 $aJING, Q.
700 1 $aVAN DER LAAN, M.
700 1 $aSILVA, F. A. M. da
700 1 $aMARIN, F. R.
700 1 $aNEHBANDANI, A.
700 1 $aNENDEL, C.
700 1 $aPURCELL, L. C.
700 1 $aQIAN, B.
700 1 $aRUANE, A. C.
700 1 $aSCHOVING, C.
700 1 $aSILVA, E. H. F. M.
700 1 $aSMITH, W.
700 1 $aSOLTANI, A.
700 1 $aSRIVASTAVA, A.
700 1 $aVIEIRA JÚNIOR, N. A.
700 1 $aSLONE, S.
700 1 $aSALMERÓN, M.
773 $tEuropean Journal of Agronomy$gv. 135, 126482, Apr. 2022.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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| Registros recuperados : 22 | |
| 1. |  | JUSTINO, L. F.; BATTISTI, R.; STONE, L. F.; HEINEMANN, A. B. Avaliação de doses e datas de aplicação de nitrogênio no feijão-comum por meio de modelo de simulação. In: CONGRESSO NACIONAL DE PESQUISA DE FEIJÃO, 13., 2021, Goiânia. Conectividade tecnológica, intensificação sustentável: resumos. Brasília, DF: Embrapa; Santo Antônio de Goiás: Embrapa Arroz e Feijão, 2021. p. 172. Evento online.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 2. | | JUSTINO, L. F.; BATTISTI, R.; STONE, L. F.; HEINEMANN, A. B. Avaliação econômica da aplicação de doses de nitrogênio na cultura do feijão-comum. In: CONGRESSO NACIONAL DE PESQUISA DE FEIJÃO, 13., 2021, Goiânia. Conectividade tecnológica, intensificação sustentável: resumos. Brasília, DF: Embrapa; Santo Antônio de Goiás: Embrapa Arroz e Feijão, 2021. p. 31. Evento online.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 5. | | MUNHOZ, J. S. B.; OLIVEIRA, S. F. de; MARIN, F.; BATTISTI, R. Estimativa da evapotranspiração de referência no Brasil: uma análise crítica. In: CONGRESSO BRASILEIRO DE METEOROLOGIA, 17.; ENCONTRO DE METEOROLOGIA DOS PAÍSES DO MERCOSUL E ASSOCIADOS, 1.; ENCONTRO SUL AMERICANO DE APLICAÇÕES DO SISTEMA EUMETCast PARA O MONITORAMENTO METEOROLÓGICO E AMBIENTAL, 4.; ENCONTRO DE METEOROLOGIA OPERACIONAL, 2., 2012, Gramado. Anais: programa. Gramado: UFRGS, 2012. 1 CD-ROM.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 6. | | BATTISTI, R.; PILAU, F. G.; SCHWERZ, L.; SOMAVILLA, L.; TOMM, G. O. Dinâmica floral e abortamento de flores em híbridos de canola e mostarda castanha. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 48, n. 2, p. 174-181, fev. 2013. Título em inglês: Floral dynamics and flower abortion in hybrids of canola and Indian mustard.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 10. | | JUSTINO, L. F.; BATTISTI, R.; STONE, L. F.; SILVA, S. C. da; HEINEMANN, A. B. Viabilidade econômica da aplicação de nitrogênio na cultura do feijão-comum, em três épocas de cultivo. In: SEMINÁRIO JOVENS TALENTOS, 15., 2021, Santo Antônio de Goiás. Resumos... Brasília, DF: Embrapa; Santo Antônio de Goiás: Embrapa Arroz e Feijão, 2021. p. 74. Evento online.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 14. | | BATTISTI, R.; SENTELHAS, P. C.; BOOTE, K. J.; CÂMARA, G. M. de S.; FARIAS, J. R. B.; BASSO, C. J. Assessment of yield with altered soybean traits for drought tolerance in southern Brazil. In: CONGRESSO BRASILEIRO DE SOJA, 7.; MERCOSOJA, 2015, Florianópolis. Tecnologia e mercado global: perspectivas para soja: anais. Londrina: Embrapa Soja, 2015. 3 p. 1 CD-ROM.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Soja. |
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| 15. | | SENTELHAS, P. C.; BATTISTI, R.; CÂMARA, G. M. S.; FARIAS, J. R. B.; HAMPF, A. C.; NENDEL, C. The soybean yield gap in Brazil - magnitude, causes and possible solutions for sustainable production. Journal of Agricultural Science, v. 153, n. 8. p. 1394-1411, Nov. 2015.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Soja. |
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| 16. | | BATTISTI, R.; SENTELHAS, P. C.; PARKER, P. S.; NENDEL, C.; CÂMARA, G. M. de S.; FARIAS, J. R. B.; BASSO, C. J. Assessment of crop-management strategies to improve soybean resilience to climate change in Southern Brazil. Crop & pasture science, v. 69, n. 2, p. 154-162, 2018.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Soja. |
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| 17. | | SILVA, Y. F.; VALADARES, R. V.; DIAS, H. B.; CUADRA, S. V.; CAMPBELL, E. E.; LAMPARELLI, R. A. C.; MORO, E.; BATTISTI, R.; ALVES, M. R.; MAGALHÃES, P. S. G.; FIGUEIREDO, G. K. D. A. Intense pasture management in Brazil in an integrated crop-livestock system simulated by the DayCent model. Sustainability, v. 14, n. 6, p. 1-24, Mar. 2022. Article 3517.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 18. | | MARIN, F. R.; ZANON, A. J.; MONZON, J. P.; ANDRADE, J. F.; SILVA, E. H. F. M.; RITCHER, G.; SAN MARTIN, B.; BATTISTI, R.; HEINEMANN, A. B.; GRASSINI, P. Intensificação agrícola pode ajudar a proteger a Floresta Amazônica e reduzir o aquecimento global. In: CONGRESSO BRASILEIRO DE AGROMETEOROLOGIA, 22., 2023, Natal. A agrometeorologia e a agropecuária: adaptação às mudanças climáticas: anais... Natal: Sociedade Brasileira de Agrometeorologia, 2023. p. 2631-2641. CBAGRO 2023.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 19. | | MARIN, F. R.; ZANON, A. J.; MONZON, J. P.; ANDRADE, J. F.; SILVA, E. H. F. M.; RICHTER, G. L.; ANTOLIN, L. A. S.; RIBEIRO, B. S. M. R.; RIBAS, G. G.; BATTISTI, R.; HEINEMANN, A. B.; GRASSINI, P. Protecting the Amazon forest and reducing global warming via agricultural intensification. Nature Sustainability, v. 5, p. 1018-1026, Dec. 2022.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 20. | | ALVES JÚNIOR, J.; NARCISO, M. G.; SILVEIRA, P. M. da; HEINEMANN, A. B.; BEZERRA, R. de S.; BATTISTI, R.; EVANGELISTA, A. W. P.; CASAROLI, D.; SANTOS, J. C. C. dos; KNAPP, F. M. Software para manejo da irrigação na cultura do tomate para processamento industrial em Goiás. In: MELO, J. O. F. (org.). Ciências agrárias: o avanço da ciência no Brasil. São Paulo: Editora Científica Digital, 2022. v. 5. Cap. 34, p. 486-505.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Arroz e Feijão. |
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| Registros recuperados : 22 | |
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