Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States

ABSTRACT Pinus palustris Mill. ecosystem is considered one of the most threatened of North America. In this context, studies on biomass quantification are fundamental for forest management plans. Thus, the objective of this study was to develop a set of allometric equations to predict total P. palus...

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Main Authors: Aline Araújo Farias, Salvador A. Gezan, Melissa Pisaroglo de Carvalho, Antonio Carlos Ferraz Filho, Carlos Pedro Boechat Soares
Format: Article
Language:English
Published: Universidade Federal Rural do Rio de Janeiro
Series:Floresta e Ambiente
Subjects:
Online Access:http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872019005000106&lng=en&tlng=en
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spelling doaj-d53ad1ee1d1342f58da6296242b96cf32020-11-24T22:10:51ZengUniversidade Federal Rural do Rio de JaneiroFloresta e Ambiente2179-808726spe110.1590/2179-8087.040318S2179-80872019005000106Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United StatesAline Araújo FariasSalvador A. GezanMelissa Pisaroglo de CarvalhoAntonio Carlos Ferraz FilhoCarlos Pedro Boechat SoaresABSTRACT Pinus palustris Mill. ecosystem is considered one of the most threatened of North America. In this context, studies on biomass quantification are fundamental for forest management plans. Thus, the objective of this study was to develop a set of allometric equations to predict total P. palustris stump-biomass. Biomass data were collected at different locations in the southeastern United States. A total of 119 allometric equations were fitted from the combination of explanatory variables: diameter at breast height (DBH), height (H), age (I), basal area (G), number of trees per hectare (N), site index (S) and quadratic diameter (Dq). One of the models that presented the lowest residual standard error (Sy.x) and root mean square error (RMSE) was ln(W) = -0.9978+0.7082.(H)+0.1009.ln(H.DBH)-0.5310.(N)-0.0003.ln(Dq). Therefore, the insertion of dendrometric variables characteristic of forest stands has great efficacy in biomass prediction for trees from different sites.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872019005000106&lng=en&tlng=enforest managementmodelingregression
collection DOAJ
language English
format Article
sources DOAJ
author Aline Araújo Farias
Salvador A. Gezan
Melissa Pisaroglo de Carvalho
Antonio Carlos Ferraz Filho
Carlos Pedro Boechat Soares
spellingShingle Aline Araújo Farias
Salvador A. Gezan
Melissa Pisaroglo de Carvalho
Antonio Carlos Ferraz Filho
Carlos Pedro Boechat Soares
Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States
Floresta e Ambiente
forest management
modeling
regression
author_facet Aline Araújo Farias
Salvador A. Gezan
Melissa Pisaroglo de Carvalho
Antonio Carlos Ferraz Filho
Carlos Pedro Boechat Soares
author_sort Aline Araújo Farias
title Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States
title_short Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States
title_full Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States
title_fullStr Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States
title_full_unstemmed Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States
title_sort allometric equations to predict pinus palustris biomass in the southeastern united states
publisher Universidade Federal Rural do Rio de Janeiro
series Floresta e Ambiente
issn 2179-8087
description ABSTRACT Pinus palustris Mill. ecosystem is considered one of the most threatened of North America. In this context, studies on biomass quantification are fundamental for forest management plans. Thus, the objective of this study was to develop a set of allometric equations to predict total P. palustris stump-biomass. Biomass data were collected at different locations in the southeastern United States. A total of 119 allometric equations were fitted from the combination of explanatory variables: diameter at breast height (DBH), height (H), age (I), basal area (G), number of trees per hectare (N), site index (S) and quadratic diameter (Dq). One of the models that presented the lowest residual standard error (Sy.x) and root mean square error (RMSE) was ln(W) = -0.9978+0.7082.(H)+0.1009.ln(H.DBH)-0.5310.(N)-0.0003.ln(Dq). Therefore, the insertion of dendrometric variables characteristic of forest stands has great efficacy in biomass prediction for trees from different sites.
topic forest management
modeling
regression
url http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872019005000106&lng=en&tlng=en
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