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Registro Completo
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Biblioteca(s): |
Epagri-Sede. |
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Data corrente: |
30/11/2018 |
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Data da última atualização: |
30/11/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
Internacional - A |
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Autoria: |
SOUFIZADEH, S.; MUNARO, E.; MCLEAN, G.; MASSIGNAM, A. M.; OOSTEROM, E. V.; CHAPMAN, S.; MESSINA, C.; COOPER, M.; HAMMER, G. |
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Título: |
Modelling the nitrogen dynamics of maize crops - Enhancing the APSIM maize model. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
European Journal of Agronomy, Agroscope, Suiça, v. 100, p. 118-131, 2018. |
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Idioma: |
Inglês |
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Conteúdo: |
Crop growth simulation models require robust ecophysiological functionality to support credible simulation of diverse genotype×management×environment (G×M×E) combinations. Most efforts on modeling the nitrogen (N) dynamics of crops use a minimum, critical, and maximum N concentration per unit biomass based empirically on experimental observations. Here we present a physiologically more robust approach, originally implemented in sorghum, which uses the N content per unit leaf area as a key driver of N demand. The objective was to implement the conceptual framework of the APSIM sorghum nitrogen dynamics model in APSIM maize and to validate the robustness of the model across a range of G×M×E combinations. The N modelling framework is described and its parameterisation for maize is developed based on three previously reported detailed field experiments, conducted at Gatton (27°34′S, 152°20′), Queensland, Australia, supplemented by literature data. There was considerable correspondence with parameterisation results found for sorghum, suggesting potential for generality of this framework for modelling crop N dynamics in cereals. Comprehensive model testing indicated accurate predictions at organ and crop scale across a diverse range of experiments and demonstrated that observed responses to a range of management factors were reproduced credibly. This supports the use of the model to extrapolate and predict performance and adaptation under new G×M×E combinations. Capturing this advance with reduced complexity compared to the N concentration approach provides a firm basis to progress the role of modelling in exploring the genetic underpinning of complex traits and in plant breeding and crop improvement generally. MenosCrop growth simulation models require robust ecophysiological functionality to support credible simulation of diverse genotype×management×environment (G×M×E) combinations. Most efforts on modeling the nitrogen (N) dynamics of crops use a minimum, critical, and maximum N concentration per unit biomass based empirically on experimental observations. Here we present a physiologically more robust approach, originally implemented in sorghum, which uses the N content per unit leaf area as a key driver of N demand. The objective was to implement the conceptual framework of the APSIM sorghum nitrogen dynamics model in APSIM maize and to validate the robustness of the model across a range of G×M×E combinations. The N modelling framework is described and its parameterisation for maize is developed based on three previously reported detailed field experiments, conducted at Gatton (27°34′S, 152°20′), Queensland, Australia, supplemented by literature data. There was considerable correspondence with parameterisation results found for sorghum, suggesting potential for generality of this framework for modelling crop N dynamics in cereals. Comprehensive model testing indicated accurate predictions at organ and crop scale across a diverse range of experiments and demonstrated that observed responses to a range of management factors were reproduced credibly. This supports the use of the model to extrapolate and predict performance and adaptation under new G×M×E combinations. Captur... Mostrar Tudo |
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Palavras-Chave: |
Ecophysiology; Maize; Modelling; Nitrogen; Simulation. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02518naa a2200277 a 4500
001 1128099
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008 2018 bl uuuu u00u1 u #d
100 1 $aSOUFIZADEH, S.
245 $aModelling the nitrogen dynamics of maize crops - Enhancing the APSIM maize model.$h[electronic resource]
260 $c2018
520 $aCrop growth simulation models require robust ecophysiological functionality to support credible simulation of diverse genotype×management×environment (G×M×E) combinations. Most efforts on modeling the nitrogen (N) dynamics of crops use a minimum, critical, and maximum N concentration per unit biomass based empirically on experimental observations. Here we present a physiologically more robust approach, originally implemented in sorghum, which uses the N content per unit leaf area as a key driver of N demand. The objective was to implement the conceptual framework of the APSIM sorghum nitrogen dynamics model in APSIM maize and to validate the robustness of the model across a range of G×M×E combinations. The N modelling framework is described and its parameterisation for maize is developed based on three previously reported detailed field experiments, conducted at Gatton (27°34′S, 152°20′), Queensland, Australia, supplemented by literature data. There was considerable correspondence with parameterisation results found for sorghum, suggesting potential for generality of this framework for modelling crop N dynamics in cereals. Comprehensive model testing indicated accurate predictions at organ and crop scale across a diverse range of experiments and demonstrated that observed responses to a range of management factors were reproduced credibly. This supports the use of the model to extrapolate and predict performance and adaptation under new G×M×E combinations. Capturing this advance with reduced complexity compared to the N concentration approach provides a firm basis to progress the role of modelling in exploring the genetic underpinning of complex traits and in plant breeding and crop improvement generally.
653 $aEcophysiology
653 $aMaize
653 $aModelling
653 $aNitrogen
653 $aSimulation
700 1 $aMUNARO, E.
700 1 $aMCLEAN, G.
700 1 $aMASSIGNAM, A. M.
700 1 $aOOSTEROM, E. V.
700 1 $aCHAPMAN, S.
700 1 $aMESSINA, C.
700 1 $aCOOPER, M.
700 1 $aHAMMER, G.
773 $tEuropean Journal of Agronomy, Agroscope, Suiça$gv. 100, p. 118-131, 2018.
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