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)
Otimização de pontos amostrais ao longo do fuste para determinação da densidade básica da madeira ...
Nativa, Sinop, v. 11, n. 1, p. 128-133, 2023.
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influenciada por diferentes fatores, sendo um destes os
modelos distintos de espaçamento de plantio, pois o Local A
apresenta um espaçamento quadrático, que assegura um
adensamento proporcional de plantas em ambos os sentidos,
o que, segundo Ribaski (2003), acarreta em uma menor
desrama natural, que proporciona à árvore galhos mais
grossos e copas mais extensas. Além disso, é possível que este
fator tenha influenciado em uma maior densidade na região
superior do tronco, já que, conforme Sette Jr et al. (2012),
esta característica se deve a exigência mecânica de
sustentação da copa da árvore e, de acordo com Coelho et al.
(2020), este incremento da densidade da madeira de eucalipto
também pode estar ligado ao aumento da presença de desvios
de grã, os quais ocorrem em maior frequência em regiões
nodosas.
4.2. Ponto ótimo de amostragem
Bruder (2010) encontrou um resultado semelhante ao
estudar a determinação de densidade básica de Eucalyptus sp.,
onde concluiu que o ponto ideal de amostragem se daria a
37,5% de altura do fuste. Do mesmo modo, Benjamin;
Ballarin (2003) visualizaram que o ponto amostral que
apresenta maior correlação com a DBmp da madeira de
Eucalyptus saligna se encontra a aproximadamente 25% de
altura.
Além disso, ressalta-se que, mesmo havendo um apelo
financeiro e laboral pelo uso exclusivo do ponto amostral a
altura do peito, devido a facilidade de sua amostragem, este
experimento demonstrou que a densidade obtida na região
do DAP não apresenta boa correlação com a DBmp,
indicando que a possibilidade de realização de amostragem
não destrutiva no ponto de fácil acesso pode ocasionar em
erro de estimativa da densidade básica da árvore.
4.3. Otimização de pontos amostrais
Quanto a otimização utilizando dois pontos amostrais,
Benjamin; Ballarin (2003) observaram um comportamento
semelhante ao analisar a madeira de Eucalyptus saligna, onde
apontaram que a melhor estimativa da densidade média da
madeira desta espécie pelo uso de dois pontos amostrais se
daria amostrando o DAP e 25% da altura do tronco.
Em relação a otimização utilizando três pontos amostrais,
um resultado bastante semelhante foi observado por Bruder
(2010), que analisando a madeira de Eucalyptus sp. determinou
que, ao se optar por reduzir o número de amostras
longitudinais na determinação da densidade básica da
madeira, deve-se utilizar três amostras localizadas a 12,5%
(região próxima ao DAP), 37,5% e 62,5% da altura comercial
do tronco, ressaltando ainda que esta alternativa garantiria
alta precisão e pouquíssima interferência na DBmp da árvore.
5. CONCLUSÕES
De modo geral, não se pôde assumir com segurança
estatística que a densidade básica média aritmética representa
a densidade básica média ponderada da madeira de Eucalyptus
saligna. Em relação a variação longitudinal da densidade
básica dentro do fuste, a espécie em estudo
demonstrou uma redução da densidade da base do tronco em
relação ao topo do mesmo.
A definição de um único ponto ótimo de amostragem se
mostrou inviável, ressaltando ainda que a amostragem apenas
na altura do peito (DAP), apesar de ser desejada devido a
facilidade de amostragem, não é indicada, já que pode
subestimar a densidade básica média da madeira.
Referente a otimização de pontos amostrais, ao se utilizar
dois pontos amostrais para a estimativa da densidade da
madeira de Eucalyptus saligna é indicado que se faça a
amostragem nas regiões do DAP e 33% da altura comercial.
Ao se optar por utilizar três pontos amostrais, se pode obter
uma ótima precisão da estimativa da densidade básica da
madeira, desde que o fuste seja amostrado nas posições do
DAP, 33% e 66% da altura comercial.
6. REFERENCES
ABNT_Associação Brasileira de Normas Técnicas. NBR
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