Aboveground and Belowground Biomass and Carbon Estimates for Clonal Eucalyptus Trees in Southeast Brazil

Ribeiro, Sabina Cerruto; Soares, Carlos Pedro Boëchat; Fehrmann, Lutz; Jacovine, Laércio Antônio Gonçalves; Gadow, Klaus von

doi:10.1590/0100-67622015000200015

Cited by 42 publications

(34 citation statements)

References 21 publications

(18 reference statements)

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“…So, we regress the carbon stock by crown cover from predictors (DBH and height) to develop allometric equation to estimate carbon stock. This is consistent with the previous studies in which DBH and Height were used as predictor variables for the estimation of the total amount of carbon and biomass (Joshi et al 2015;Karki et al 2016;Luyssaert et al 2008;Ribeiro et al 2015). However, allometric equations developed for predicting biomass or carbon vary widely, but the easiest and most commonly used is a linear model in the form of y=a+bX where x is DBH and a and b are slopes.…”

Section: Discussionsupporting

confidence: 89%

“…However, allometric equations developed for predicting biomass or carbon vary widely, but the easiest and most commonly used is a linear model in the form of y=a+bX where x is DBH and a and b are slopes. Unlike other studies (Cole and Ewel 2006;Joshi et al 2015;Ribeiro et al 2015;Shah and Acharya 2010;Tashi et al 2017;Mohd Zaki et al 2016) where models are mostly developed from directly measured variable like DBH and/or Height, with categorization into different crown cover classes, linear model with natural logarithm of DBH 2 and height was best fitted for Chure Sal forest. These regression equations are highly precise (adjusted R 2 >0.99, residuals are normally distributed as seen in Figure 4).…”

Section: Discussionmentioning

confidence: 88%

“…However, even standard allometric equations which are reasonably accurate to predict the biomass mostly incorporate easily measured parameter like DBH and height only (Baral, Malla, and Ranabhat 2010;Cole and Ewel 2006;Karki et al 2016;Ni-Meister et al 2010;Pandey and Bhusal 2016;Parrotta, Wildburger, and Mansourian 2012;Ribeiro et al 2015;Shah and Acharya 2010;Sharma and Kakchapati 2018). A study by for REDD + piloting project that included crown cover strata (sparse < 70% crown cover and dense ≥ 70% crown cover) shows that there is a significant difference in stand-level carbon stock estimation by canopy coverage.…”

Section: Introductionmentioning

confidence: 99%

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Carbon Status and Regression Model for Tree Carbon by Crown Cover for Sal Forest of Nepal

Paudel¹,

Kalakheti

Maraseni

2019

Preprint

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Volume, biomass and carbon of forest ecosystem are generally estimated using lookup tables or allometric equations known as models. These general equation-based models are usually exclusively based on dimensional measurement such as diameter at breast height (DBH) and/or height, which sometimes makes it difficult to judge applicability of equation to given forest condition or types. It is therefore important to estimate carbon stock and develop models to predict biomass or carbon stock with stratification by categorical variables like crown cover, slope, forest types, etc. Stratification of forest by remote sensing approach while designing forest inventory not only improves the reliability of the estimation but also reduces the cost of measurement. Taking crown coverage (<25%, 25-50%,

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

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Carbon Status and Regression Model for Tree Carbon by Crown Cover for Sal Forest of Nepal

Paudel¹,

Kalakheti

Maraseni

2019

Preprint

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“…The same order of distribution was found in four-year-old Eucalyptus spp. in Vera Cruz, RS, Brazil (Schumacher et al, 2011); in 18-month-old E. urograndis in Piratini, RS, Brazil (Viera et al, 2012); in the biomass in a clone of E. urophylla × E. grandis, at 5.5 years of age, in the southeastern region of Brazil (Ribeiro et al, 2015); and in the biomass of Eucalyptus spp. in Kenya (Kuyah et al, 2013).…”

Section: Aboveground Biomassmentioning

confidence: 99%

Biomass production and nutrient content in different Eucalyptus genotypes in Pampa Gaúcho, Brazil

Santos

Ludvichak

Queiroz

et al. 2019

Agraria

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The objective of this study was to estimate the biomass, nutrient stocks, and simulate the export of nutrients, in different genotypes of Eucalyptus. The experiment was conducted in São Gabriel, RS, Brazil, in 43-month-old stands. The selected trees were fractionated into leaves, branches, stembark and stemwood. An estimate of nutrient exports by biomass was calculated for three scenarios. The amount of total biomass ranged from 33.68 to 84.00 Mg ha -1 , with the highest production being E. uroglobulus, and E. dunnii the lowest. The tree canopy accumulated between 47% and 65% of the total macronutrients and 46% to 67% of the total micronutrients (E. benthamii (P2) and E. urograndis). The highest amount of nutrients exported with biomass harvesting, for the first and second scenario (harvesting the stemwood and harvesting the stemwood with stembark), occurred in E. uroglobulus (N, K, and S), E. urograndis (P, Mg, and Cu) and E. saligna (Fe, Zn, and B). For the third scenario (stem and canopy), the highest nutrient exportation occurred in E. urograndis, except for S, where the highest removal occurred in E. uroglobulus. The harvesting of only the stemwood resulted in the removal of the least nutrients from the system, independent of the genotypes.Produção de biomassa e conteúdo de nutrientes em diferentes genótipos de Eucalyptus no Pampa gaúcho RESUMO: O objetivo deste trabalho foi estimar a biomassa, o estoque de nutrientes e simular a exportação de nutrientes em diferentes genótipos de Eucalyptus. O experimento foi conduzido em São Gabriel, RS, em povoamentos com 43 meses de idade. As árvores selecionadas foram fracionadas em folhas, galhos, casca do fuste e madeira do fuste. A estimativa da exportação de nutrientes por biomassa foi calculada para três cenários. A quantidade de biomassa total variou de 33,68 a 84,00 Mg ha -1 , destacando-se o E. uroglobulus com a maior produção e o E. dunnii com a menor. A copa das árvores acumulou entre 47% e 65% do total de macronutrientes e 46% a 67% do total de micronutrientes (E. benthamii (P2) e E. urograndis). Para o primeiro e segundo cenário (colheita da madeira do fuste e colheita da madeira do fuste com casca), a maior quantidade de nutrientes exportados ocorreu no E. uroglobulus (N, K e S), E. urograndis (P, Mg e Cu) e E. saligna (Fe, Zn e B). Para o terceiro cenário (fuste e copa), a maior exportação de nutrientes ocorreu no E. urograndis, com exceção de S, onde a maior remoção ocorreu no E. uroglobulus. A colheita apenas da madeira do fuste apresentou a menor remoção de nutrientes do sistema, independente do genótipo.Palavras-chave: clones de Eucalyptus; nutrição de florestas; silvicultura; sustentabilidade Biomass production and nutrient content in different Eucalyptus genotypes in Pampa Gaúcho, Brazil Rev. Bras.

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“…FEB: fator de expansão de biomassa SANQUETTA et al, 2011b;ALMEIDA et al, 2010;SANQUETTA et al, 2014). R: razão de raízes (RIBEIRO et al, 2015;SANQUETTA et al, 2011b;SETTE JR. et al, 2005;DALLAGNOL et al, 2011). TC: teor de carbono: (RIBEIRO et al, 2015;DALLAGNOL et al, 2011;ALMEIDA et al, 2010;SANQUETTA et al, 2014).…”

Section: Estimativa Da Biomassaunclassified

Dinâmica Em Superfície, Volume, Biomassa E Carbono Nas Florestas Plantadas Brasileiras: 1990-2016

Sanquetta¹,

Corte²,

Pelissari³

et al. 2018

BIOFIX

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O objetivo do trabalho foi quantificar a dinâmica em superfície e dos estoques de volume de madeira, biomassa e carbono nas plantações florestais brasileiras. Dados sobre a extensão dessas florestas por diferentes instituições embasaram as estimativas para o período 1990-2016 para os principais gêneros utilizados em plantações florestais no Brasil. As produtividades foram simuladas com programas computacionais da EMBRAPA. Fatores de expansão de biomassa, razão de raízes, massa específica e teores de carbono de literatura foram aplicados. A área de florestas plantadas aumentou para 10,212 milhões ha (ano de 2016) em relação a área de 4,934 milhões ha (para o ano de 1990). O volume de madeira comercial cresceu de 774 milhões m3 em 1990 para 1,999 bilhão de m3 em 2016. A biomassa apresentou mudança de 457 milhões t em 1990 a 1,246 bilhão t em 2016. Os estoques de carbono apontaram 210 milhões t em 1990 e 575 milhões t em 2016. Eucalyptus contribuía com 60% da área plantada em 1990, passando a 74% em 2016 e sua participação em volume, biomassa e carbono aumentou de 52, 57 e 58%, em 1990, para 67, 71 e 71% em 2016, respectivamente. O gênero Pinus pouco cresceu em área plantada, perdendo participação relativa. Outros gêneros têm participação modesta, mas vêm observando-se uma tendência de crescimento nos últimos anos. Concluiu-se que houve grande crescimento em área, volume, biomassa e carbono nas plantações florestais brasileiras nos 26 anos, em que as remoções de CO2 atmosférico equivalente nesse período foram da ordem de 1,338 bilhão t, as quais se devem pelo aumento da área plantada e pela maior produtividade dos povoamentos.

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Aboveground and Belowground Biomass and Carbon Estimates for Clonal Eucalyptus Trees in Southeast Brazil

Cited by 42 publications

References 21 publications

Carbon Status and Regression Model for Tree Carbon by Crown Cover for Sal Forest of Nepal

Carbon Status and Regression Model for Tree Carbon by Crown Cover for Sal Forest of Nepal

Biomass production and nutrient content in different Eucalyptus genotypes in Pampa Gaúcho, Brazil

Dinâmica Em Superfície, Volume, Biomassa E Carbono Nas Florestas Plantadas Brasileiras: 1990-2016

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