Generalized Height-Diameter Models for Pinus montezumae Lamb. and Pinus pseudostrobus Lindl. Plantations in Michoacan, Mexico  

作　　者：Jonathan Hernández-Ramos Valentín José Reyes-Hernández Héctor Manuel De los Santos-Posadas Aurelio Manuel Fierros-González Enrique Buendía-Rodríguez Gerónimo Quiñonez-Barraza Jonathan Hernández-Ramos;Valentín José Reyes-Hernández;Héctor Manuel De los Santos-Posadas;Aurelio Manuel Fierros-González;Enrique Buendía-Rodríguez;Gerónimo Quiñonez-Barraza(Instituto Nacional de Investigaciones Forestales, Agrcolas y Pecuarias (INIFAP)-Campo Experimental Bajo, Celaya, Guanajuato, Mxico;Colegio de Postgraduados (COLPOS), Campus Montecillo, Mxico;INIFAP, Campo Experimental Valle de Mxico, Texcoco, Mxico;INIFAP, Campo Experimental Valle del Guardiana, Durango, Mxico)

机构地区：[1]Instituto Nacional de Investigaciones Forestales, Agrcolas y Pecuarias (INIFAP)-Campo Experimental Bajo, Celaya, Guanajuato, Mxico [2]Colegio de Postgraduados (COLPOS), Campus Montecillo, Mxico [3]INIFAP, Campo Experimental Valle de Mxico, Texcoco, Mxico [4]INIFAP, Campo Experimental Valle del Guardiana, Durango, Mxico

出　　处：《Open Journal of Forestry》2024年第3期214-232,共19页林学期刊（英文）

摘　　要：Tree height (H) in a natural stand or forest plantation is a fundamental variable in management, and the use of mathematical expressions that estimate H as a function of diameter at breast height (d) or variables at the stand level is a valuable support tool in forest inventories. The objective was to fit and propose a generalized H-d model for Pinus montezumae and Pinus pseudostrobus established in forest plantations of Nuevo San Juan Parangaricutiro, Michoacan, Mexico. Using nonlinear least squares (NLS), 10 generalized H-d models were fitted to 883 and 1226 pairs of H-d data from Pinus montezumae and Pinus pseudostrobus, respectively. The best model was refitted with the maximum likelihood mixed effects model (MEM) approach by including the site as a classification variable and a known variance structure. The Wang and Tang equation was selected as the best model with NLS;the MEM with an additive effect on two of its parameters and an exponential variance function improved the fit statistics for Pinus montezumae and Pinus pseudostrobus, respectively. The model validation showed equality of means among the estimates for both species and an independent subsample. The calibration of the MEM at the plot level was efficient and might increase the applicability of these results. The inclusion of dominant height in the MEM approach helped to reduce bias in the estimates and also to better explain the variability among plots.Tree height (H) in a natural stand or forest plantation is a fundamental variable in management, and the use of mathematical expressions that estimate H as a function of diameter at breast height (d) or variables at the stand level is a valuable support tool in forest inventories. The objective was to fit and propose a generalized H-d model for Pinus montezumae and Pinus pseudostrobus established in forest plantations of Nuevo San Juan Parangaricutiro, Michoacan, Mexico. Using nonlinear least squares (NLS), 10 generalized H-d models were fitted to 883 and 1226 pairs of H-d data from Pinus montezumae and Pinus pseudostrobus, respectively. The best model was refitted with the maximum likelihood mixed effects model (MEM) approach by including the site as a classification variable and a known variance structure. The Wang and Tang equation was selected as the best model with NLS;the MEM with an additive effect on two of its parameters and an exponential variance function improved the fit statistics for Pinus montezumae and Pinus pseudostrobus, respectively. The model validation showed equality of means among the estimates for both species and an independent subsample. The calibration of the MEM at the plot level was efficient and might increase the applicability of these results. The inclusion of dominant height in the MEM approach helped to reduce bias in the estimates and also to better explain the variability among plots.

关 键 词：Random Covariate Random Effects Variance Structure Forest Inventories Forest Management Mixed Models 

分 类 号：O17[理学—数学]