Site index for Prosopis alba plantations in the semi-arid chaco through mixed models
The classification of sites through curves of Site Index allows to predict the yield of the planted forests at a certain age of the stand and to plan cultural treatments. The goal of this research was to compare linear and non-linear models of fixed effects vs. mixed non-linear models to estimate th...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/106550 |
| Acceso en línea: | http://hdl.handle.net/11336/106550 |
| Access Level: | acceso abierto |
| Palabra clave: | Dominant height Forestry Site quality https://purl.org/becyt/ford/4.1 https://purl.org/becyt/ford/4 |
| Sumario: | The classification of sites through curves of Site Index allows to predict the yield of the planted forests at a certain age of the stand and to plan cultural treatments. The goal of this research was to compare linear and non-linear models of fixed effects vs. mixed non-linear models to estimate the site index in plantations of Prosopis alba var Griseb in the irrigated area of the province of Santiago del Estero, Argentina using the guide curve method. The data used comes from temporary plots, permanent plots and growth data from the stem analysis of selected individuals based on their greater growth in height within the sampled areas. The registered variable for the evaluation of the site was the dominant Height (HD), defined as the average height of the 100 thickest trees per hectare. Considering that the source of data from repeated measurements on the same subject implies the presence of correlation and/or heteroscedasticity, it was proposed to evaluate statistical models that allow to properly representing the structure of the variance-covariance matrix, improving the accuracy in the adjustment. From the analysis of the results, it appears that the models non-linear mixed models have had better performance in the adjustment of the Site Index than linear and non-linear models of fixed effects. The most accurate model (smallest AIC and BIC) in the site index estimation was the mixed non-linear regression model of 'Gompertz', with structure of composite symmetry correlation and exponential heteroscedasticity.v.25 n.2 2019 |
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