ESTIMATE OF STAND DENSITY INDEX FOR EUCALYPTUS UROPHYLLA USING DIFFERENT FIT METHODS
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Universidade Federal de Viçosa
2016
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| author | Ernani Lopes Possato |
| author_facet | Ernani Lopes Possato |
| contents | ESTIMATE OF STAND DENSITY INDEX FOR EUCALYPTUS UROPHYLLA USING DIFFERENT FIT METHODS Ernani Lopes Possato Natalino Calegario Gilciano Saraiva Nogueira Elliezer de Almeida Melo Joyce de Almeida Alves Agrociencias Reineke Index Linear Regression Stochastic Frontier Analysis The Reineke stand density index (SDI) was created on 1933 and remains as target of researches due to its importance on helping decision making regarding the management of population density. Part of such works is focused on the manner by which plots were selected and methods for the fit of Reineke model parameters in order to improve the definition of SDI value for the genetic material evaluated. The present study aimed to estimate the SDI value for Eucalyptus urophylla using the Reineke model fitted by the method of linear regression (LR) and stochastic frontier analysis (SFA). The database containing pairs of data number of stems per hectare (N) and mean quadratic diameter (Dq) was selected in three intensities, containing the 8, 30 and 43 plots of greatest density, and models were fitted by LR and SFA on each selected intensities. The intensity of data selection altered slightly the estimates of parameters and SDI when comparing the fits of each method. On the other hand, the adjust method influenced the mean estimated values of slope and SDI, which corresponded to -1.863 and 740 for LR and -1.582 and 810 for SFA. 2016 artículo científico 0100-6762 https://www.redalyc.org/articulo.oa?id=48848756016 en http://www.redalyc.org/revista.oa?id=488 Revista Árvore application/pdf Universidade Federal de Viçosa Revista Árvore (Brasil) Num.5 Vol.40 |
| format | Artículo científico |
| id | redalyc_48848756016 |
| institution | Redalyc |
| language | en |
| publishDate | 2016 |
| publisher | Universidade Federal de Viçosa |
| spellingShingle | ESTIMATE OF STAND DENSITY INDEX FOR EUCALYPTUS UROPHYLLA USING DIFFERENT FIT METHODS Ernani Lopes Possato Agrociencias Reineke Index Linear Regression Stochastic Frontier Analysis ESTIMATE OF STAND DENSITY INDEX FOR EUCALYPTUS UROPHYLLA USING DIFFERENT FIT METHODS Ernani Lopes Possato Natalino Calegario Gilciano Saraiva Nogueira Elliezer de Almeida Melo Joyce de Almeida Alves Agrociencias Reineke Index Linear Regression Stochastic Frontier Analysis The Reineke stand density index (SDI) was created on 1933 and remains as target of researches due to its importance on helping decision making regarding the management of population density. Part of such works is focused on the manner by which plots were selected and methods for the fit of Reineke model parameters in order to improve the definition of SDI value for the genetic material evaluated. The present study aimed to estimate the SDI value for Eucalyptus urophylla using the Reineke model fitted by the method of linear regression (LR) and stochastic frontier analysis (SFA). The database containing pairs of data number of stems per hectare (N) and mean quadratic diameter (Dq) was selected in three intensities, containing the 8, 30 and 43 plots of greatest density, and models were fitted by LR and SFA on each selected intensities. The intensity of data selection altered slightly the estimates of parameters and SDI when comparing the fits of each method. On the other hand, the adjust method influenced the mean estimated values of slope and SDI, which corresponded to -1.863 and 740 for LR and -1.582 and 810 for SFA. 2016 artículo científico 0100-6762 https://www.redalyc.org/articulo.oa?id=48848756016 en http://www.redalyc.org/revista.oa?id=488 Revista Árvore application/pdf Universidade Federal de Viçosa Revista Árvore (Brasil) Num.5 Vol.40 |
| title | ESTIMATE OF STAND DENSITY INDEX FOR EUCALYPTUS UROPHYLLA USING DIFFERENT FIT METHODS |
| topic | Agrociencias Reineke Index Linear Regression Stochastic Frontier Analysis |
| url | https://www.redalyc.org/articulo.oa?id=48848756016 |