Modelling carbon and water balance of Eucalyptus plantations at regional scale: Effect of climate, soil and genotypes

FOREST ECOLOGY AND MANAGEMENT

卷:449

Article

Attia, Ahmed¹  Nouvellon, Yann^2,3,4  Cuadra, Santiago⁵  Cabral, Osvaldo⁶  Laclau, Jean-Paul^2,3,4  Guillemot, Joannes^2,3,4  Campoe, Otavio^7,8  Stape, Jose-Luiz⁸  Galdos, Marcelo⁹  Lamparelli, Rubens¹  le Maire, Guerric^1,2,3 

[1] Univ Estadual Campinas, NIPE, BR-13083860 Campinas, SP, Brazil

[2] CIRAD, UMR Eco & Sols, F-34398 Montpellier, France

[3] Univ Montpellier, CIRAD, Eco & Sols, INRA,IRD,SupAgro, Montpellier, France

[4] Univ Sao Paulo, ESALQ, BR-13418900 Piracicaba, SP, Brazil

[5] EMBRAPA CNPTIA, BR-13083886 Campinas, SP, Brazil

[6] EMBRAPA Meio Ambiente, BR-13820000 Jaguariuna, SP, Brazil

[7] Fed Univ Lavras UFLA, BR-37200000 Lavras, MG, Brazil

[8] UNESP FCA, BR-18610300 Botucatu, SP, Brazil

[9] Univ Leeds, Sch Earth & Environm, Inst Climate & Atmospher Sci, Leeds LS2 9JT, W Yorkshire, England

关键词: Eucalyptus plantations;    Ecophysiological model;    G'DAY;    Optimization;    Productivity;   

DOI  :  10.1016/j.foreco.2019.117460

来源: Elsevier

【 摘 要 】

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 Sao 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.

【 授权许可】

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