Are soybean models ready for climate change food impact assessments?
Autor(a) principal: | |
---|---|
Data de Publicação: | 2022 |
Outros Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
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. |
id |
EMBR_7c335dd4c41f38a427a63a9dfa58d5a6 |
---|---|
oai_identifier_str |
oai:www.alice.cnptia.embrapa.br:doc/1140426 |
network_acronym_str |
EMBR |
network_name_str |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
repository_id_str |
2154 |
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 |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
instname_str |
Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
instacron_str |
EMBRAPA |
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 |
_version_ |
1794503518609997824 |