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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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 | Acesso ao texto completo restrito à biblioteca da Embrapa Arroz e Feijão. Para informações adicionais entre em contato com cnpaf.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
28/05/2021 |
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Data da última atualização: |
04/10/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
TEIXEIRA, O. R.; BATISTA, C. de S.; COLUSSI, R.; MARTINO, H. S. D.; VANIER, N. L.; BASSINELLO, P. Z. |
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Afiliação: |
OLIVIA REIS TEIXEIRA, UNIVERSIDADE FEDERAL DE GOIÁS; CRISTIAN DE SOUZA BATISTA, UNIVERSIDADE FEDERAL DE PELOTAS; ROSANA COLUSSI, UNIVERSIDADE FEDERAL DE PELOTAS; HÉRCIA STAMPINI DUARTE MARTINO, UNIVERSIDADE FEDERAL DE VIÇOSA; NATHAN LEVIEN VANIER, UNIVERSIDADE FEDERAL DE PELOTAS; PRISCILA ZACZUK BASSINELLO, CNPAF. |
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Título: |
Impact of physicochemical properties on the digestibility of Brazilian whole and polished rice genotypes. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Cereal Chemistry, v. 98, n. 5, p. 1066-1080, Sept./Oct. 2021. |
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ISSN: |
1943-3638 |
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DOI: |
https://doi.org/10.1002/cche.10455 |
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Idioma: |
Inglês |
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Conteúdo: |
Background and Objetives: Some factors related to rice processing, composition, and grain microstructure may interfere on starch digestibility. The objective of this work was to evaluate the impact of physicochemical properties and rice processing on the starch digestibility of genotypes produced in Brazil. Five traditional white rice genotypes, one black rice, and one red rice genotype were studied. The traditional white rice genotypes were evaluated as brown rice as well as in the polished rice form, while both black and red rice were evaluated in the unpolished form. Findings: The digestibility ranged from 41.40% to 99.81%, being positively correlated with starch content and peak viscosity and negatively correlated with pasting temperature. The unpolished rice group exhibited lower digestibility value than the polished rice group as a result of the presence of bran. Low amylose cultivars had higher starch hydrolysis percentage. Conclusions: The type of processing influenced the starch digestibility profile, in which the presence of bran and pigments in whole rice reduce starch hydrolysis percentage. The extent of gelatinization of the starch, identified through the peak viscosity and the pasting temperature of RVA, also had a direct influence on digestibility. Significance and novelty: For the first time, different types and processing of Brazilian rice genotypes have been studied about the starch viscosity and digestibility profiles and related to other quality traits, what can drive consumption of a greater diversity of rice according to different market demands and energy needs. MenosBackground and Objetives: Some factors related to rice processing, composition, and grain microstructure may interfere on starch digestibility. The objective of this work was to evaluate the impact of physicochemical properties and rice processing on the starch digestibility of genotypes produced in Brazil. Five traditional white rice genotypes, one black rice, and one red rice genotype were studied. The traditional white rice genotypes were evaluated as brown rice as well as in the polished rice form, while both black and red rice were evaluated in the unpolished form. Findings: The digestibility ranged from 41.40% to 99.81%, being positively correlated with starch content and peak viscosity and negatively correlated with pasting temperature. The unpolished rice group exhibited lower digestibility value than the polished rice group as a result of the presence of bran. Low amylose cultivars had higher starch hydrolysis percentage. Conclusions: The type of processing influenced the starch digestibility profile, in which the presence of bran and pigments in whole rice reduce starch hydrolysis percentage. The extent of gelatinization of the starch, identified through the peak viscosity and the pasting temperature of RVA, also had a direct influence on digestibility. Significance and novelty: For the first time, different types and processing of Brazilian rice genotypes have been studied about the starch viscosity and digestibility profiles and related to other quality traits, w... Mostrar Tudo |
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Palavras-Chave: |
Pigmented rice; Starch hydrolysis. |
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Thesagro: |
Amilose; Arroz; Genótipo; Oryza Sativa; Pigmentação; Propriedade Físico-Química; Proteína Hidrolisada. |
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Thesaurus NAL: |
Amylopectin; Amylose; Hydrolysis; Pasting properties; Rice. |
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Categoria do assunto: |
Q Alimentos e Nutrição Humana |
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Marc: |
LEADER 02682naa a2200373 a 4500
001 2132084
005 2022-10-04
008 2021 bl uuuu u00u1 u #d
022 $a1943-3638
024 7 $ahttps://doi.org/10.1002/cche.10455$2DOI
100 1 $aTEIXEIRA, O. R.
245 $aImpact of physicochemical properties on the digestibility of Brazilian whole and polished rice genotypes.$h[electronic resource]
260 $c2021
520 $aBackground and Objetives: Some factors related to rice processing, composition, and grain microstructure may interfere on starch digestibility. The objective of this work was to evaluate the impact of physicochemical properties and rice processing on the starch digestibility of genotypes produced in Brazil. Five traditional white rice genotypes, one black rice, and one red rice genotype were studied. The traditional white rice genotypes were evaluated as brown rice as well as in the polished rice form, while both black and red rice were evaluated in the unpolished form. Findings: The digestibility ranged from 41.40% to 99.81%, being positively correlated with starch content and peak viscosity and negatively correlated with pasting temperature. The unpolished rice group exhibited lower digestibility value than the polished rice group as a result of the presence of bran. Low amylose cultivars had higher starch hydrolysis percentage. Conclusions: The type of processing influenced the starch digestibility profile, in which the presence of bran and pigments in whole rice reduce starch hydrolysis percentage. The extent of gelatinization of the starch, identified through the peak viscosity and the pasting temperature of RVA, also had a direct influence on digestibility. Significance and novelty: For the first time, different types and processing of Brazilian rice genotypes have been studied about the starch viscosity and digestibility profiles and related to other quality traits, what can drive consumption of a greater diversity of rice according to different market demands and energy needs.
650 $aAmylopectin
650 $aAmylose
650 $aHydrolysis
650 $aPasting properties
650 $aRice
650 $aAmilose
650 $aArroz
650 $aGenótipo
650 $aOryza Sativa
650 $aPigmentação
650 $aPropriedade Físico-Química
650 $aProteína Hidrolisada
653 $aPigmented rice
653 $aStarch hydrolysis
700 1 $aBATISTA, C. de S.
700 1 $aCOLUSSI, R.
700 1 $aMARTINO, H. S. D.
700 1 $aVANIER, N. L.
700 1 $aBASSINELLO, P. Z.
773 $tCereal Chemistry$gv. 98, n. 5, p. 1066-1080, Sept./Oct. 2021.
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