Age and Growth Affect Oleoresin Yield From Copaiba Trees in the Cerrado-Amazonia Ecotone

Roquette, José Guilherme; Drescher, Ronaldo; Brondani, Gilvano Ebling; Souza, Édila Cristina de; Neto, Rubens Marques Rondon; Ebert, A Genovesi; Teixeira, Leandro Ribeiro; Dias, André Pereira; Gava, Fernando Henrique

doi:10.1590/01047760201824022527

Cited by 4 publications

(10 citation statements)

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“…was made during the months of July, August, and September of 2012, corresponding to the region's dry season. The extraction procedures were similar to methods described in Roquette et al (2018).…”

Section: Study Areamentioning

confidence: 99%

“…The Dc was calculated using the measurement of largest crown diameter observed from the ground, and the perpendicular to this measurement. The age and mean annual increment (MAI) (cm yr -1 ) of DBH were obtained as described in Roquette et al (2018).…”

Section: Mensuration Of Dendrometric Variablesmentioning

confidence: 99%

“…Many researchers have tried to identify the factors that influence oleoresin production from Copaifera spp., relating differences to species of the genus, the size of the trees, the seasonality of extraction, the position of trees in the landscape, plus other environmental and genetic characteristics (Alencar, 1982;Baima et al, 1999;Barbosa et al, 2009;Ferreira & Braz, 2001;Martins et al, 2013;Medeiros & Vieira, 2008;Medeiros, Vieira, Almeida, & Tomazello Filho, 2018;Newton, Watkinson, & Peres, 2011;Oliveira, Lameira, & Zoghbi, 2006;Plowden, 2001;Rigamonte-Azevedo, Wadt, & Wadt, 2006;Roquette et al, 2018;Wadtet al, 2007). Although diverse studies have attempted to identify the factors that interfered in the yield of oleoresin, no consensus has been reached about the factors that determine the oleoresin yield from Copaifera spp.…”

Section: Introductionmentioning

confidence: 99%

“…Research conducted to date has raised the hypothesis that genetic and physiological factors are related to the production of copaiba oleoresin (Alencar, 1982;Martins et al, 2013;Medeiros & Vieira, 2008). However, tree physiological processes may affect yield, which could vary by forest type, hydrology, soil fertility, growth, interspecific competition, and intensity of exploitation (Alencar, 1982;Medeiros & Vieira, 2008;Medeiros et al, 2018;Newton et al, 2011;Plowden, 2003;Roquette et al, 2018). In addition, tree size has also been highlighted as a determinant factor of yield, due to the relationships between physiological activity and tree growth with the production of copaiba oleoresin (Medeiros & Vieira, 2008;Medeiros et al, 2018;Roquette et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…However, tree physiological processes may affect yield, which could vary by forest type, hydrology, soil fertility, growth, interspecific competition, and intensity of exploitation (Alencar, 1982;Medeiros & Vieira, 2008;Medeiros et al, 2018;Newton et al, 2011;Plowden, 2003;Roquette et al, 2018). In addition, tree size has also been highlighted as a determinant factor of yield, due to the relationships between physiological activity and tree growth with the production of copaiba oleoresin (Medeiros & Vieira, 2008;Medeiros et al, 2018;Roquette et al, 2018). Newton et al (2011) states that understanding the relationships between genetic, morphological, and environmental factors, with the production of non-timber products, is key to creating sustainable extraction protocols for resources.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of oleoresin yield from Copaifera spp. in primary forest in meridional Amazon

Roquette

Drescher

Brondani

et al. 2019

MYB

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El objetivo de este estudio fue investigar la relación entre las variables dendrométricas y del suelo con el rendimiento de aceite de Copaifera spp. y ajustar una ecuación para predecir la productividad en bosque primario. En el área de la investigación, 30 árboles de Copaifera spp. fueron seleccionados como muestras para la extracción del aceite por un período de 24 horas, con el uso de la barrena con 1,91 cm (3/4"). Además, fueron colectadas informaciones sobre el tamaño de los árboles y las características del suelo superficial en los alrededores de cada árbol. Se aplicó el análisis de componentes principales para verificar la relación de las variables con el rendimiento de aceite y, posteriormente, fue ajustada una ecuación para predecir este rendimiento mediante análisis de regresión. La única variable relacionada con el rendimiento de aceite fue la altura del fuste, y fue selecionada como el modelo que presentó mejor coeficiente de determinácion (0,84) y error típico de estimación (13,11%). Por lo tanto, se concluyó que el rendimiento del aceite de Copaifera spp. en bosque primario tiene una correlación positiva y significativa con la altura del fuste, mientras que las otras variables dendrométricas y del suelo superficial no presentaron correlación significativa con el rendimiento de aceite.

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Section: Study Areamentioning

confidence: 99%

Section: Mensuration Of Dendrometric Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of oleoresin yield from Copaifera spp. in primary forest in meridional Amazon

Roquette

Drescher

Brondani

et al. 2019

MYB

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Potential of Copaifera spp. oleoresin for sustainable extraction in the Eastern Amazon

Benathar

Almeida

Rodrigues

et al. 2020

Environ Dev Sustain

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Oleoresin prospection in Copaifera sp. trees by using impulse tomography

Martins

Santos

Latorraca

2021

CERNE

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Background: Copaiba oil (Copaifera L.) is a raw material used by pharmaceutical, cosmetic, and energy industries. However, the difficulty in locating the oleoresin reservoirs, is an obstacle to its continued supply, affecting the sustainable commercialization of the product. So, the potential of impulse tomography for prospecting oleoresin reservoirs in the trunk of 18 Copaifera sp. trees was tested, in cross-sections at heights levels 0% (DBH), 25%, 50%, 75%, and 100% (1st fork). The impulse tomography prospections (ITP) were performed only at the 0%, in others 12 trees, because of the risk associated with climbing hollow trees. In total 30 trees were tested. Altogether 102 tomograms were analyzed obtaining: average mechanical wave propagation speed (aMPS), minimum mechanical wave propagation speed (minS) and maximum mechanical wave propagation speed (maxS), prospecting height (Hp%) and total tree height (Ht), diameter at the Hp% (Dhp), and low speed mechanical wave propagation percentage areas (LSa%). These variables were analyzed using multivariate analysis. Results:The reservoirs were located exclusively at DBH and confirmed by borer prospection increment in 26.7% of the trees. ITP resulted in 37.3% of correct answers and 62.7% of errors, considering the 99 tomograms. However, it was found that the ITP is efficient to indicate sections for which no significant reservoir or hollow presence is expected to be found. The analysis of the main components showed that, except for Ht, the components are good indicators for the location of the reservoirs. Conclusion:We were able to use tomography to search reservoirs with a significant amount of oleoresin, identify hollow trees, and indicate the exclusion of trees that do not have reservoirs or other alterations.

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Age and Growth Affect Oleoresin Yield From Copaiba Trees in the Cerrado-Amazonia Ecotone

Abstract: HIGHLIGHTS Oleoresin production of Copaifera spp. under natural environment is dependent of the age and growth. The methodology can be applied for the individual selection in natural environment. Oleoresin production is variable in natural environment.

Cited by 4 publications

References 20 publications

Estimation of oleoresin yield from Copaifera spp. in primary forest in meridional Amazon

Estimation of oleoresin yield from Copaifera spp. in primary forest in meridional Amazon

Potential of Copaifera spp. oleoresin for sustainable extraction in the Eastern Amazon

Oleoresin prospection in Copaifera sp. trees by using impulse tomography

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