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metadata.artigo.dc.title: Growth and yield models for eucalyptus stands obtained by differential equations
metadata.artigo.dc.creator: Mendonça, Adriano Ribeiro de
Calegario, Natalino
Silva, Gilson Fernandes da
Carvalho, Samuel de Pádua Chaves e
metadata.artigo.dc.subject: Nonlinear models
Compatible models
Biological consistency
metadata.artigo.dc.publisher: Escola Superior de Agricultura "Luiz de Queiroz" 2017
metadata.artigo.dc.identifier.citation: MENDONÇA, A. R. de et al. Growth and yield models for eucalyptus stands obtained by differential equations. Scientia Agricola, Piracicaba, v. 74, n. 5, Sept.Oct. 2017.
metadata.artigo.dc.description.abstract: The main purpose of this study was to assess nonlinear models generated by integrating the basal area growth rate to estimate the growth and yield of forest stands. The database was collected from permanent sample units, in Paraopeba county, in the state of Minas Gerais, Brazil. The stands were represented by Eucalyptus camaldulensis × Eucalyptus urophylla hybrid trees, with 3 × 3 meters of spacing. The data were divided into two groups: fitting and validating databases. Two nonlinear models (Strategy A and Strategy B) were developed using differential equations to estimate the basal area growth and yield of the sample units. The logistic model was fitted to estimate the volumetric yield as a function of age, site index and basal area. The efficiency of the systems generated by logistic model and models obtained by differential equations (Strategy A and Strategy B) was also compared to the efficiency of the system estimated by the Clutter model (Strategy C). The projection models used to estimate basal area obtained by differential equations were compatible with forest growth and yield, and the logistic model with covariates was compatible with volumetric growth and yield. Strategy A and Strategy B generated different thinning and harvesting options for different site indices, which is biologically consistent.
metadata.artigo.dc.language: en_US
Appears in Collections:DCF - Artigos publicados em periódicos

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