Biomass and mineralmass estimates in a "cerrado" ecosystem

Delitti, Welington Bráz Carvalho; Meguro, Marico; Pausas, Juli G.

doi:10.1590/s0100-84042006000400003

Cited by 36 publications

(31 citation statements)

References 33 publications

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“…These changes have generated intense interest in measuring and monitoring carbon (C) pools and fluxes to predict future changes in C concentrations and to develop C management strategies (IPCC 2003(IPCC , 2006Terakupisut et al 2007). Biomass estimates are useful for quantifying net primary productivity, energy pathways, nutrient and C cycles, and harvestable biomass yields, and in evaluating habitats and combustible fuel (Saint-André et al 2005;Delitti et al 2006;Saglan et al 2008). For strategic C management, auditing of national C stocks and fluxes, screening of tree species for C sequestration potential in agroforestry systems, and measurement and monitoring of C stocks in reforestation programmes, require collection of credible biomass data (IPCC 2003;Brown et al 2004).…”

Section: Introductionmentioning

confidence: 99%

Allometric models for estimation of aboveground carbon stocks in improved fallows in eastern Zambia

Kaonga¹,

Bayliss‐Smith

2009

Agroforest Syst

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This paper presents allometric functions for estimation of C stocks in aboveground tree biomass in 2-year-old improved fallows in eastern Zambia. A total of 222 individual trees representing 12 tree species were destructively harvested for C analysis by LECO CHN-1000 analyzer. Allometric models relating collar diameter (D 10 ) and total tree height (H) to stem and total aboveground C stocks were developed using data from tree fallows. Logarithmically transformed power functions displayed a good ability to stabilize variance of aboveground C stocks and showed a good fit (84 \ R 2 \ 99) with a bias of 0.7-3.6%. D 10 alone and in combination with H explained most of the variability in total aboveground C stocks. Validation of the species-specific and generalized models with field data indicated that they accurately predicted aboveground tree C stocks. Generalized C estimation functions were also validated and described 73-97% of variability in aboveground C stocks with an average unsigned deviation of 1.5-4.9%. The C functions will serve as a vital tool for predicting and monitoring C pool sizes in longterm studies and agroforestry projects, especially where destructive sampling is not possible.

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Section: Introductionmentioning

confidence: 99%

Allometric models for estimation of aboveground carbon stocks in improved fallows in eastern Zambia

Kaonga¹,

Bayliss‐Smith

2009

Agroforest Syst

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“…Various types of regression models and different combinations of variables have been used in the development of predictive models of phytomass for tropical ecosystems (Folster et al 1976, Golley et al 1978, Saldarriaga et al 1988, Brown et al 1989, Overman et al 1994, Moreira-Burger & Delitti 1999, Chave et al 2001, Chave et al 2005, Delitti et al 2006. These models have been applied to quantify nutrient stores, accumulated organic material and potential carbon reserves, to determine vegetation growth rate after perturbation, and to identify determinants of variation in biomass.…”

Section: Introductionmentioning

confidence: 99%

Allometric models for estimating the phytomass of a secondary Atlantic Forest area of southeastern Brazil

Burger

Delitti

2008

Biota Neotrop.

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models for estimating the phytomass of a secondary Atlantic Forest area of southeastern Brazil. Biota Neotrop., 8(4): http://www.biotaneotropica.org.br/v8n4/en/ abstract?article+bn03308042008Abstract: The purpose of this study was to develop and validate equations to estimate the aboveground phytomass of a 30 years old plot of Atlantic Forest. In two plots of 100 m 2 , a total of 82 trees were cut down at ground level. For each tree, height and diameter were measured. Leaves and woody material were separated in order to determine their fresh weights in field conditions. Samples of each fraction were oven dried at 80 °C to constant weight to determine their dry weight. Tree data were divided into two random samples. One sample was used for the development of the regression equations, and the other for validation. The models were developed using single linear regression analysis, where the dependent variable was the dry mass, and the independent variables were height (h), diameter (d) and d 2 h. The validation was carried out using Pearson correlation coefficient, paired t-Student test and standard error of estimation. The best equations to estimate aboveground phytomass were: lnDW = -3.068+2.522lnd (r 2 = 0.91; s y/x = 0.67) and lnDW = -3.676+0. Resumo: O objetivo deste estudo foi desenvolver e validar modelos preditores para a fitomassa epigéa de uma área de Floresta Atlântica secundária. Em duas parcelas de 100m 2 , 82 árvores foram cortadas, ao nível do solo, e anotadas suas medidas de altura e diâmetro. As folhas foram separadas dos ramos para determinação do massa fresca da porção foliar e lenhosa. Amostras de cada fração foram secas em estufa a 80 °C, até massa constante, para determinação do massa seca. As árvores foram distribuídas em duas amostras aleatórias, sendo uma utilizada para o desenvolvimento das equações de regressão, e a outra para validá-las. Os modelos foram desenvolvidos através da análise de regressão linear simples, tendo como variável dependente a massa seca (DW) e, como variáveis independentes a altura (h), o diâmetro (d) e o quadrado do diâmetro multiplicado pela altura (d 2 h). A validação foi analisada através da comparação entre os valores obtidos em campo e os estimados pelas equações, através da análise de correlação intraclasse de Pearson e teste t-Sudent pareado. As melhores equações para estimar o massa seca das árvores foram: lnDW = -3,068+2,522lnd (r 2 = 0,91; s y/x = 0,67) e lnDW = -3,676+0,951ln d 2 h (r 2 = 0,94; s y/x = 0,56). Palavras-chave: biomassa, alometria, regressão, Mata Atlântica, floresta secundária.

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“…Altura e diâmetro são informações facilmente obtidas em campo. Todas as variáveis têm sido amplamente utilizadas em modelos preditores de biomassa de vegetação arbórea de ecossistemas tropicais (Folster et al 1976, Jordan & Uhl 1978, Golley et al 1978, Tanner 1980, Saldarriaga et al 1988, Brown et al 1989, Martinez-Yrizar et al 1992, Scatena et al 1993, Overman et al 1994, Santos 1996, Moreira-Burger & Delitti 1999, Chave et al 2001, Chave et al 2005, Delitti et al 2006.…”

Section: Methodsunclassified

“…No Brasil os modelos preditores de fitomassa florestal, a partir de métodos destrutivos, concentraramse nas Florestas Amazônicas (Klinge et al 1975, Uhl & Jordan 1984, McWillian et al 1993, Kauffman et al 1994, Santos 1996, Higuchi et al 1998, Nelson et al 1999, Nogueira et al 2007, Nogueira et al 2008. Em menor número, encontram-se os modelos desenvolvidos para outros ecossistemas, tais como: Cerrados (Delitti & Meguro 1984, Castro & Kauffman 1998, Abdala et al 1998, Delitti et al 2006, Caatinga (Sampaio & Silva 2005), Mata Mesófila Semidecídua Ciliar (Moreira- Burger & Delitti 1999), Manguezal (Soares & Novelli 2005) e Mata Atlântica (Burger & Delitti, 2008).…”

Section: Introductionunclassified

Modelos preditores da fitomassa aérea da Floresta Baixa de Restinga

Moreira-Burger¹,

Delitti²

2010

Rev. bras. Bot.

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-(Regression models to estimate aboveground phytomass of the Floresta Baixa de Restinga). The purpose of this study was to develop equations to estimate the aboveground phytomass of the Floresta Baixa de Restinga. We selected 102 trees of 29 species occurring in the study area and 102 trees of Syagrus romanzoffiana (Cham.) Glassman, distributed proportionately among the diameter classes of trees. The trees were cut at ground level and the total height and diameter at breast height (DBH) of each tree were measured. Leaves and woody material were separated in order to determine their fresh weights under field conditions. Samples of each fraction were oven dried at 70 °C to constant weight to determine their dry weight. The models were developed by using single linear regression analysis, where the dependent variable was the dry mass (DW), and the independent variables were height (h), diameter (d), the product of diameter and height (d 2 h) and the product of diameter, height and woody density (r d 2 h). The models developed indicate that the diameter explains much of the variability of the trees phytomass and the tree height is the explanatory variable of the specific equation for Syagrus romanzoffiana. The best equations to estimate aboveground phytomass were: ln DW = -1. Autor para correspondência: deborahmburger@yahoo.com.br IntroduçãoA biomassa é considerada um importante parâmetro de caracterização dos ecossistemas, indicando sua capacidade de acúmulo de matéria orgânica ao longo do tempo. Em condições ideais, um ecossistema maduro atinge seu potencial máximo de armazenamento de material e os valores de biomassa podem ser usados como um ponto de referência.A matéria orgânica acumulada pela vegetação, a fitomassa, pode ser determinada através do corte e medida da massa das plantas de uma área amostral relativamente pequena, cujo valor médio encontrado é extrapolado para uma área maior. Ecossistemas florestais raramente têm sua fitomassa medida por métodos destrutivos devido às dificuldades inerentes a este tipo de estudo, como o elevado custo e as restrições legais referentes ao corte da vegetação. A fitomassa de florestas, em geral, é estimada de maneira indireta, através da aplicação de modelos preditores desenvolvidos a partir das relações alométricas encontradas nos componentes da vegetação. Porém, são necessários modelos específicos para os diferentes ecossistemas para que o erro das estimativas seja minimizado (Rochow 1974, McWillian et al. 1993.Os modelos preditores são elaborados a partir de análise de regressão, onde se estudam as relações entre a massa seca das árvores e seus dados facilmente mensuráveis, como altura e diâmetro. Diversos tipos

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Biomass and mineralmass estimates in a "cerrado" ecosystem

Cited by 36 publications

References 33 publications

Allometric models for estimation of aboveground carbon stocks in improved fallows in eastern Zambia

Allometric models for estimation of aboveground carbon stocks in improved fallows in eastern Zambia

Allometric models for estimating the phytomass of a secondary Atlantic Forest area of southeastern Brazil

Modelos preditores da fitomassa aérea da Floresta Baixa de Restinga

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