Are soybean models ready for climate change food impact assessments?

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dc.contributorKRITIKA KOTHARI, UNIVERSITY OF KENTUCKY
dc.contributorRAFAEL BATTISTI, UFG
dc.contributorKENNETH J. BOOTE, UNIVERSITY OF FLORIDA
dc.contributorSOTIRIOS V. ARCHONTOULIS, IOWA STATE UNIVERSITY
dc.contributorADRIANA CONFALONE, UNIVERSIDAD NACIONAL DEL CENTRO DE LA PROVINCIA DE BUENOS AIRES
dc.contributorJULIE CONSTANTIN, UNIVERSITÉ DE TOULOUSE
dc.contributorSANTIAGO VIANNA CUADRA, CNPTIA
dc.contributorPHILIPPE DEBAEKE, UNIVERSITÉ DE TOULOUSE
dc.contributorBABACAR FAYE, INSTITUT DE RECHERCHE POUR LE D ́EVELOPPEMENT (IRD) ESPACE-DEV
dc.contributorBRIAN GRANT, AGRICULTURE AND AGRI-FOOD CANADA
dc.contributorGERRIT HOOGENBOOM, UNIVERSITY OF FLORIDA
dc.contributorQI JING, AGRICULTURE AND AGRI-FOOD CANADA
dc.contributorMICHAEL VAN DER LAAN, UNIVERSITY OF PRETORIA
dc.contributorFERNANDO ANTONIO MACENA DA SILVA, CPAC
dc.contributorFÁBIO RICARDO MARIN, ESALQ/USP
dc.contributorALIREZA NEHBANDANI, GORGAN UNIVERSITY OF AGRICULTURAL SCIENCES AND NATURAL RESOURCE
dc.contributorCLAAS NENDEL, University of PotsdaM, Leibniz Centre for Agricultural Landscape ResearcH
dc.contributorLARRY C. PURCELL, UNIVERSITY OF ARKANSAS
dc.contributorBUDONG QIAN, AGRICULTURE AND AGRI-FOOD CANADA
dc.contributorALEX C. RUANE, NASA GODDARD INSTITUTE FOR SPACE STUDIES
dc.contributorCÉLINE SCHOVING, UNIVERSITÉ DE TOULOUSE, TERRES INOVIA
dc.contributorEVANDRO H. F. M. SILVA, ESALQ/USP
dc.contributorWARD SMITH, AGRICULTURE AND AGRI-FOOD CANADA
dc.contributorAFSHIN SOLTANI, GORGAN UNIVERSITY OF AGRICULTURAL SCIENCES AND NATURAL RE-SOURCES
dc.contributorAMIT SRIVASTAVA, UNIVERSITY OF BONN
dc.contributorNILSON A. VIEIRA JÚNIOR, ESALQ/USP
dc.contributorSTACEY SLONE, UNIVERSITY OF KENTUCKY
dc.contributorMONTSERRAT SALMERÓN, UNIVERSITY OF KENTUCKY.
dc.creatorKOTHARI, K.
dc.creatorBATTISTI, R.
dc.creatorBOOTE, K. J.
dc.creatorARCHONTOULIS, S. V.
dc.creatorCONFALONE, A.
dc.creatorCONSTANTIN, J.
dc.creatorCUADRA, S. V.
dc.creatorDEBAEKE, P.
dc.creatorFAYE, B.
dc.creatorGRANT, B.
dc.creatorHOOGENBOOM, G.
dc.creatorJING, Q.
dc.creatorVAN DER LAAN, M.
dc.creatorSILVA, F. A. M. da
dc.creatorMARIN, F. R.
dc.creatorNEHBANDANI, A.
dc.creatorNENDEL, C.
dc.creatorPURCELL, L. C.
dc.creatorQIAN, B.
dc.creatorRUANE, A. C.
dc.creatorSCHOVING, C.
dc.creatorSILVA, E. H. F. M.
dc.creatorSMITH, W.
dc.creatorSOLTANI, A.
dc.creatorSRIVASTAVA, A.
dc.creatorVIEIRA JÚNIOR, N. A.
dc.creatorSLONE, S.
dc.creatorSALMERÓN, M.
dc.date2022-02-25T18:00:30Z
dc.date2022-02-25T18:00:30Z
dc.date2022-02-25
dc.date2022
dc.date.accessioned2026-07-07T04:16:43Z
dc.descriptionAbstract. 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.
dc.identifierEuropean Journal of Agronomy, v. 135, 126482, Apr. 2022.
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1140426
dc.identifierhttps://doi.org/10.1016/j.eja.2022.126482
dc.identifier.urihttp://hdl.handle.net/123456789/456246
dc.languageeng
dc.rightsopenAccess
dc.subjectImpacto das mudanças climáticas
dc.subjectModelos de soja
dc.subjectAgricultural Model Intercomparison and Improvement Project
dc.subjectAgMIP
dc.subjectModel ensemble
dc.subjectModel calibration
dc.subjectTemperature Atmospheric CO2 concentration
dc.subjectLegume model
dc.subjectSoja
dc.subjectGlycine Max
dc.subjectTemperatura
dc.subjectModels
dc.subjectSoybeans
dc.subjectTemperature
dc.titleAre soybean models ready for climate change food impact assessments?
dc.typeArtigo de periódico

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