Growth and yield modelling of Acacia mangium in Colombia

Vélez, Danny; Valle, Jorge I. del

doi:10.1007/s11056-007-9056-5

Cited by 23 publications

(13 citation statements)

References 10 publications

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“…(a) Aboveground and (b) root biomass (Mg C ha −1 ) of Acacia mangium trees as a function of the age of the plantation. The solid curve indicates the relationship based on measurements (solid diamonds) achieved by several authors ( Awang and Taylor [1993], Bernhard‐Reversat et al [1993], Ihwanudin [1994], Pudjiharta [1995], Hiratsuka et al [2004], Syahrinudin [2005], Wachrinrat et al [2005], Thanyapraneedkul and Susaki [2006], Heriansyah et al [2007], Torres Vélez and Del Valle [2007], and Laclau et al [2008] for Figure 1a; and Bernhard‐Reversat et al [1993], Högberg and Wester [1998], Syahrinudin [2005], and Heriansyah et al [2007] for Figure 1b. The equation of the relationships, the r 2 of the linear regression between observed and predicted trees aboveground C stocks (ABG C) (Figure 1a) and belowground C stocks (BG C) (Figure 1b), and the number of observations ( n ) are specified in the top left corner of each panel.…”

Section: Resultsmentioning

confidence: 99%

“… c Sources: 1, Brady [1997]; 2, Waldes and Page [2001]; 3, Ludang and Palangka Jaya [2007]; 4, Verwer and van der Meer [2010]; 5, Page et al [2002]; 6, Rieley and Page [2008]; 7, Halenda [1989]; 8, Jensen [1993]; 9, Hashimotio et al [2000]; 10, Hartemink [2001]; 11, Palm et al [2000]; 12, Swamy and Puri [2005]; 13, Jepsen [2006]; 14, Johnson et al [2006]; 15, Syahrinudin [2005]; 16, Matthews et al [2000]; 17, Pathak et al [2005]; 18, Huang et al [2009]; 19, Germer and Sauerborn [2008]; 20, Henson and Dolmat [2003]; 21, Awang and Taylor [1993]; 22, Bernhard‐Reversat et al [1993]; 23, Ihwanudin [1994]; 24, Pudjiharta [1995]; 25, Hiratsuka et al [2004]; 26, Wachrinrat et al [2005]; 27, Thanyapraneedkul and Susaki [2006]; 28, Heriansyah et al [2007]; 29, Torres Vélez and Del Valle [2007]; 30, Laclau et al [2008]; 31, Tsai [1988]; 32, Wibowo [1996]; 33, Nuruddin and Pangalin [2007]; 34, Högberg and Wester [1998]. …”

Section: Methodsmentioning

confidence: 99%

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Stocks and fluxes of carbon associated with land use change in Southeast Asian tropical peatlands: A review

Hergoualc’h

Verchot

2011

Global Biogeochem. Cycles

154

115

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[1] The increasing and alarming trend of degradation and deforestation of tropical peat swamp forests may contribute greatly to climate change. Estimates of carbon (C) losses associated with land use change in tropical peatlands are needed. To assess these losses we examined C stocks and peat C fluxes in virgin peat swamp forests and tropical peatlands affected by six common types of land use. Phytomass C loss from the conversion of virgin peat swamp forest to logged forest, fire-damaged forest, mixed croplands and shrublands, rice field, oil palm plantation, and Acacia plantation were calculated using the stock difference method and estimated at 116.9 ± 39.8, 151.6 ± 36.0, 204.1 ± 28.6, 214.9 ± 28.4, 188.1 ± 29.8, and 191.7 ± 28.5 Mg C ha −1 , respectively. Total C loss from uncontrolled fires ranged from 289.5 ± 68.1 Mg C ha −1 in rice fields to 436.2 ± 77.0 Mg C ha −1 in virgin peat swamp forest. We assessed the effects of land use change on C stocks in the peat by looking at how the change in vegetation cover altered the main C inputs (litterfall and root mortality) and outputs (heterotrophic respiration, CH 4 flux, fires, and soluble and physical removal) before and after conversion. The difference between the soil input-output balances in the virgin peat swamp forest and in the oil palm plantation gave an estimate of peat C loss of 10.8 ± 3.5 Mg C ha −1 yr −1 . Peat C loss from other land use conversions could not be assessed due to lack of data, principally on soil heterotrophic respiration rates. Over 25 years, the conversion of tropical virgin peat swamp forest into oil palm plantation represents a total C loss from both biomass and peat of 427.2 ± 90.7 Mg C ha −1 or 17.1 ± 3.6 Mg C ha. In all situations, peat C loss contributed more than 63% to total C loss, demonstrating the urgent need in terms of the atmospheric greenhouse gas burden to protect tropical virgin peat swamp forests from land use change and fires.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Stocks and fluxes of carbon associated with land use change in Southeast Asian tropical peatlands: A review

Hergoualc’h

Verchot

2011

Global Biogeochem. Cycles

154

115

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“…Finally, parameters A and k represent potential environmental effects on growth and yield since they are related to metabolic outcomes of resource availability (Brown and Lugo, 1982;González, 1994;Lugo et al, 1988;Pienaar, 1979;Restrepo and Alviar, 2010). The mixed effect models are often one of the statistical methods used to estimate forest yield (Bravo-Oviedo et al, 2007;Calegario et al, 2005;Diéguez-Aranda et al, 2006;Hall and Bailey, 2001;Hall and Clutter, 2004;Torres, 2004;Torres and del Valle, 2007;Torres et al, 2012;Wang et al, 2007;Zapata, 2007), and can be written as:…”

Section: Model Estimationmentioning

confidence: 99%

“…An empirical and well-known method is the von Bertalanffy model (Fekedulegn et al, 1999;Moser and Hall, 1969;Pienaar and Turnbull, 1973;Pienaar, 1979;Torres, 2004;Torres and del Valle, 2007;Zapata, 2007;Zeide, 1993). The von Bertalanffy model is based on a biological theory which conceives that the growth of any organism is the result of two main and opposite forces known as anabolism and catabolism (Pienaar and Turnbull, 1973).…”

Section: Introductionmentioning

confidence: 99%

A comprehensive analysis of teak plantation investment in Colombia

Restrepo

Orrego

2015

Forest Policy and Economics

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“…Monitoring of forest biomass and carbon stocks and establishment of biomass models suitable for larger areas are therefore increasingly important (Case and Hall, 2008;Gurdak et al, 2014;Jenkins et al, 2003;Muukkonen, 2007;Návar, 2009;Ritchie et al, 2013;Snorrason and Einarsson, 2006;Vallet et al, 2006). Both linear and nonlinear models are commonly used for biomass modelling (Durkaya et al, 2009;Sajdak et al, 2014;Schmidt et al, 2009;Torres Vélez and Del Valle, 2007;Zeng et al, 2011;Zianis et al, 2004). Moreover, compatible biomass modelling systems have been proposed and applied to ensure compatibility between total biomass models and component biomass models.…”

Section: Introductionmentioning

confidence: 99%

A single-tree additive biomass model of Quercus variabilis Blume forests in North China

Zheng

Mason

Jia

et al. 2015

Trees

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Key message Fitting and comparing three sets of compatible biomass models for prediction of biomass or carbon stocks of natural and planted Quercus variabilis Blume forests.Abstract To make the sum of estimated values from biomass models of various components of a tree equal to estimated tree total biomass for Quercus variabilis Blume (cork oak) forests in North China, single-tree compatible biomass models were developed. 100 trees from 100 plots in North China were felled to obtain biomass of aboveground components, and roots of 19 of those trees were extracted for measurement of root biomass. After Box-Cox transformations of variables, two sets of independent component biomass models with a dummy variable in models to define stand origin were separately built using linear mixed effects analyses (one set of models with site as a random factor; the other set without any random factor). Then three methods were compared to force compatibility of the biomass models: sums of linear mixed effects models, sums of linear models, and simultaneous equation fits based on linear models. Model parameters were estimated by ordinary least squares (OLS) or seeming unrelated regression procedures (SUR). Coefficients of determination (r 2 ), root mean square error (Rmse), residuals plots and histograms of residuals indicated that models fitted with sums of linear mixed effects models were the least biased and most precise at estimating total above-ground biomass.Further testing for the linear mixed effects models with jackknife validation and prediction sum of squares (PRESS) statistics indicated that the compatible biomass models can be used to predict biomass or carbon stocks of cork oak forests in North China within specific tree diameter at breast height and height ranges.

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Growth and yield modelling of Acacia mangium in Colombia

Cited by 23 publications

References 10 publications

Stocks and fluxes of carbon associated with land use change in Southeast Asian tropical peatlands: A review

Stocks and fluxes of carbon associated with land use change in Southeast Asian tropical peatlands: A review

A comprehensive analysis of teak plantation investment in Colombia

A single-tree additive biomass model of Quercus variabilis Blume forests in North China

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