Are soybean models ready for climate change food impact assessments?

Detalhes bibliográficos
Autor(a) principal: KOTHARI, K.
Data de Publicação: 2022
Outros Autores: 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.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
Texto Completo: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1140426
https://doi.org/10.1016/j.eja.2022.126482
Resumo: 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.
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spelling Are soybean models ready for climate change food impact assessments?Impacto das mudanças climáticasModelos de sojaAgricultural Model Intercomparison and Improvement ProjectAgMIPModel ensembleModel calibrationTemperature Atmospheric CO2 concentrationLegume modelSojaGlycine MaxTemperaturaModelsSoybeansTemperatureAbstract. 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.KRITIKA KOTHARI, UNIVERSITY OF KENTUCKYRAFAEL BATTISTI, UFGKENNETH J. BOOTE, UNIVERSITY OF FLORIDASOTIRIOS V. ARCHONTOULIS, IOWA STATE UNIVERSITYADRIANA CONFALONE, UNIVERSIDAD NACIONAL DEL CENTRO DE LA PROVINCIA DE BUENOS AIRESJULIE CONSTANTIN, UNIVERSITÉ DE TOULOUSESANTIAGO VIANNA CUADRA, CNPTIAPHILIPPE DEBAEKE, UNIVERSITÉ DE TOULOUSEBABACAR FAYE, INSTITUT DE RECHERCHE POUR LE D ́EVELOPPEMENT (IRD) ESPACE-DEVBRIAN GRANT, AGRICULTURE AND AGRI-FOOD CANADAGERRIT HOOGENBOOM, UNIVERSITY OF FLORIDAQI JING, AGRICULTURE AND AGRI-FOOD CANADAMICHAEL VAN DER LAAN, UNIVERSITY OF PRETORIAFERNANDO ANTONIO MACENA DA SILVA, CPACFÁBIO RICARDO MARIN, ESALQ/USPALIREZA NEHBANDANI, GORGAN UNIVERSITY OF AGRICULTURAL SCIENCES AND NATURAL RESOURCECLAAS NENDEL, University of PotsdaM, Leibniz Centre for Agricultural Landscape ResearcHLARRY C. PURCELL, UNIVERSITY OF ARKANSASBUDONG QIAN, AGRICULTURE AND AGRI-FOOD CANADAALEX C. RUANE, NASA GODDARD INSTITUTE FOR SPACE STUDIESCÉLINE SCHOVING, UNIVERSITÉ DE TOULOUSE, TERRES INOVIAEVANDRO H. F. M. SILVA, ESALQ/USPWARD SMITH, AGRICULTURE AND AGRI-FOOD CANADAAFSHIN SOLTANI, GORGAN UNIVERSITY OF AGRICULTURAL SCIENCES AND NATURAL RE-SOURCESAMIT SRIVASTAVA, UNIVERSITY OF BONNNILSON A. VIEIRA JÚNIOR, ESALQ/USPSTACEY SLONE, UNIVERSITY OF KENTUCKYMONTSERRAT SALMERÓN, UNIVERSITY OF KENTUCKY.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. daMARIN, 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.2022-02-25T18:00:30Z2022-02-25T18:00:30Z2022-02-252022info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleEuropean Journal of Agronomy, v. 135, 126482, Apr. 2022.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1140426https://doi.org/10.1016/j.eja.2022.126482enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2022-02-25T18:00:41Zoai:www.alice.cnptia.embrapa.br:doc/1140426Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542022-02-25T18:00:41falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542022-02-25T18:00:41Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false
dc.title.none.fl_str_mv Are soybean models ready for climate change food impact assessments?
title Are soybean models ready for climate change food impact assessments?
spellingShingle Are soybean models ready for climate change food impact assessments?
KOTHARI, K.
Impacto das mudanças climáticas
Modelos de soja
Agricultural Model Intercomparison and Improvement Project
AgMIP
Model ensemble
Model calibration
Temperature Atmospheric CO2 concentration
Legume model
Soja
Glycine Max
Temperatura
Models
Soybeans
Temperature
title_short Are soybean models ready for climate change food impact assessments?
title_full Are soybean models ready for climate change food impact assessments?
title_fullStr Are soybean models ready for climate change food impact assessments?
title_full_unstemmed Are soybean models ready for climate change food impact assessments?
title_sort Are soybean models ready for climate change food impact assessments?
author KOTHARI, K.
author_facet 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.
author_role author
author2 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.
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv 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.
dc.contributor.author.fl_str_mv 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.
dc.subject.por.fl_str_mv Impacto das mudanças climáticas
Modelos de soja
Agricultural Model Intercomparison and Improvement Project
AgMIP
Model ensemble
Model calibration
Temperature Atmospheric CO2 concentration
Legume model
Soja
Glycine Max
Temperatura
Models
Soybeans
Temperature
topic Impacto das mudanças climáticas
Modelos de soja
Agricultural Model Intercomparison and Improvement Project
AgMIP
Model ensemble
Model calibration
Temperature Atmospheric CO2 concentration
Legume model
Soja
Glycine Max
Temperatura
Models
Soybeans
Temperature
description 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.
publishDate 2022
dc.date.none.fl_str_mv 2022-02-25T18:00:30Z
2022-02-25T18:00:30Z
2022-02-25
2022
dc.type.driver.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv European Journal of Agronomy, v. 135, 126482, Apr. 2022.
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1140426
https://doi.org/10.1016/j.eja.2022.126482
identifier_str_mv European Journal of Agronomy, v. 135, 126482, Apr. 2022.
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/1140426
https://doi.org/10.1016/j.eja.2022.126482
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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institution EMBRAPA
reponame_str Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
collection Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
repository.name.fl_str_mv Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
repository.mail.fl_str_mv cg-riaa@embrapa.br
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