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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
07/01/2020 |
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Data da última atualização: |
07/01/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
ATTIA, A.; NOUVELLON, Y.; CUADRA, S. V.; CABRAL, O. M. R.; LACLAU, J. P.; GUILLEMOT, J.; CAMPOE, O.; STAPE, J.; GALDOS, M.; LAMPARELLI, R.; LE MARIE, G. |
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Afiliação: |
AHMED ATTIA, Unicamp; YANN NOUVELLON, CIRAD, ESALQ/USP; SANTIAGO VIANNA CUADRA, CNPTIA; OSVALDO MACHADO RODRIGUES CABRAL, CNPMA; JEAN-PAUL LACLAU, CIRAD, ESALQ/USP; JOANNÈS GUILLEMOT, CIRAD, ESALQ/USP; OTAVIO CAMPOE, UFLA, FCA/UNESP; JOSÉ-LUIZ STAPE, FCA/UNESP; MARCELO GALDOS, University of Leeds; RUBENS LAMPARELLI, Unicamp; GUERRIC LE MAIRE, Unicamp, CIRAD. |
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Título: |
Modelling carbon and water balance of Eucalyptus plantations at regional scale: effect of climate, soil and genotypes. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Forest Ecology and Management, v. 449, p. 1-13, Oct. 2019. |
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DOI: |
https://doi.org/10.1016/j.foreco.2019.117460 |
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Idioma: |
Inglês |
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Notas: |
Article 117460. |
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Conteúdo: |
Carbon and water budgets of forest plantations are spatially and temporally variable and hardly empirically predictable. We applied G?DAY, a process-based ecophysiological model, to simulate carbon and water budgets and stem biomass production of Eucalyptus plantations in São Paulo State, Brazil. Our main objective was to assess the drivers of spatial variability in plantation production at regional scale. We followed a multi-site calibration approach: the model was first parameterized using a detailed experimental dataset. Then a subset of the parameters were re-calibrated on two independent experimental datasets. An additional genotype-specific calibration of a subset of parameters was performed. Model predictions of key carbon-related variables (e.g., gross primary production, leaf area index and stem biomass) and key water-related variables (e.g., plant available water and evapotranspiration) agreed closely with measurements. Application of the model across ca. 27,500 ha of forests planted with different genotypes of Eucalyptus indicated that the model was able to capture 89% of stem biomass variability measured at different ages. Several factors controlling Eucalyptus production variability in time and space were grouped in three categories: soil, climate, and the planted genotype. Modelling analysis showed that calibrating the model for genotypic differences was critical for stem biomass prediction at regional scale, but that taking into account climate and soil variability significantly improved the results. We conclude that application of process-based models at regional scale can be used for accurate predictions of Eucalyptus production, provided that an accurate calibration of the model for key genotype-specific parameters is conducted. MenosCarbon and water budgets of forest plantations are spatially and temporally variable and hardly empirically predictable. We applied G?DAY, a process-based ecophysiological model, to simulate carbon and water budgets and stem biomass production of Eucalyptus plantations in São Paulo State, Brazil. Our main objective was to assess the drivers of spatial variability in plantation production at regional scale. We followed a multi-site calibration approach: the model was first parameterized using a detailed experimental dataset. Then a subset of the parameters were re-calibrated on two independent experimental datasets. An additional genotype-specific calibration of a subset of parameters was performed. Model predictions of key carbon-related variables (e.g., gross primary production, leaf area index and stem biomass) and key water-related variables (e.g., plant available water and evapotranspiration) agreed closely with measurements. Application of the model across ca. 27,500 ha of forests planted with different genotypes of Eucalyptus indicated that the model was able to capture 89% of stem biomass variability measured at different ages. Several factors controlling Eucalyptus production variability in time and space were grouped in three categories: soil, climate, and the planted genotype. Modelling analysis showed that calibrating the model for genotypic differences was critical for stem biomass prediction at regional scale, but that taking into account climate and soil variab... Mostrar Tudo |
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Palavras-Chave: |
Ecophysiological model; Eucalyptus plantations; G'DAY; Modelo ecofisiológico; Optimization; Plantação de eucalipto; Productivity. |
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Thesagro: |
Eucalipto. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02851naa a2200361 a 4500
001 2118284
005 2020-01-07
008 2019 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.foreco.2019.117460$2DOI
100 1 $aATTIA, A.
245 $aModelling carbon and water balance of Eucalyptus plantations at regional scale$beffect of climate, soil and genotypes.$h[electronic resource]
260 $c2019
500 $aArticle 117460.
520 $aCarbon and water budgets of forest plantations are spatially and temporally variable and hardly empirically predictable. We applied G?DAY, a process-based ecophysiological model, to simulate carbon and water budgets and stem biomass production of Eucalyptus plantations in São Paulo State, Brazil. Our main objective was to assess the drivers of spatial variability in plantation production at regional scale. We followed a multi-site calibration approach: the model was first parameterized using a detailed experimental dataset. Then a subset of the parameters were re-calibrated on two independent experimental datasets. An additional genotype-specific calibration of a subset of parameters was performed. Model predictions of key carbon-related variables (e.g., gross primary production, leaf area index and stem biomass) and key water-related variables (e.g., plant available water and evapotranspiration) agreed closely with measurements. Application of the model across ca. 27,500 ha of forests planted with different genotypes of Eucalyptus indicated that the model was able to capture 89% of stem biomass variability measured at different ages. Several factors controlling Eucalyptus production variability in time and space were grouped in three categories: soil, climate, and the planted genotype. Modelling analysis showed that calibrating the model for genotypic differences was critical for stem biomass prediction at regional scale, but that taking into account climate and soil variability significantly improved the results. We conclude that application of process-based models at regional scale can be used for accurate predictions of Eucalyptus production, provided that an accurate calibration of the model for key genotype-specific parameters is conducted.
650 $aEucalipto
653 $aEcophysiological model
653 $aEucalyptus plantations
653 $aG'DAY
653 $aModelo ecofisiológico
653 $aOptimization
653 $aPlantação de eucalipto
653 $aProductivity
700 1 $aNOUVELLON, Y.
700 1 $aCUADRA, S. V.
700 1 $aCABRAL, O. M. R.
700 1 $aLACLAU, J. P.
700 1 $aGUILLEMOT, J.
700 1 $aCAMPOE, O.
700 1 $aSTAPE, J.
700 1 $aGALDOS, M.
700 1 $aLAMPARELLI, R.
700 1 $aLE MARIE, G.
773 $tForest Ecology and Management$gv. 449, p. 1-13, Oct. 2019.
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