Título:	Are soybean models ready for climate change food impact assessments?
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.
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.
Ano de publicação:	2022
Referência:	European Journal of Agronomy, v. 135, 126482, Apr. 2022.
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.
Thesagro:	Soja Glycine Max Temperatura
NAL Thesaurus:	Models Soybeans Temperature
Palavras-chave:	Impacto das mudanças climáticas Modelos de soja Agricultural Model Intercomparison and Improvement Project AgMIP Model ensemble Model calibration Temperature Atmospheric CO2 concentration Legume model
Digital Object Identifier:	https://doi.org/10.1016/j.eja.2022.126482
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (CNPTIA)