Allometric equations for tree species and carbon stocks for forests of northwestern Mexico

Návar, José

doi:10.1016/j.foreco.2008.09.028

Cited by 262 publications

(250 citation statements)

References 26 publications

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“…The biomass data of each component in different regions was summarized in Table 2. Allometric equations were widely used for estimating biomass [1,2,5,7]. The form of the equation provides a good balance between accurate predictions and low data requirements by using the most commonly and easily measured variable, which is DBH.…”

Section: Biomass Datamentioning

confidence: 99%

“…In recent years, many researchers have explored biomass models at national, regional, and global levels [1][2][3][4]. Allometric equations were often used to estimate forest biomass in these studies [1,[5][6][7][8]. The allometric equations usually had good fit performance and high values of R 2 , and the variables for prediction of aboveground biomass, such as tree diameter and height, are easy to measure in the field.…”

Section: Introductionmentioning

confidence: 99%

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Biomass Modeling of Larch (Larix spp.) Plantations in China Based on the Mixed Model, Dummy Variable Model, and Bayesian Hierarchical Model

Chen

Huang

Zhang

et al. 2017

Forests

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Abstract:With the development of national-scale forest biomass monitoring work, accurate estimation of forest biomass on a large scale is becoming an important research topic in forestry. In this study, the stem wood, branches, stem bark, needles, roots and total biomass models for larch were developed at the regional level, using a general allometric equation, a dummy variable model, a mixed effects model, and a Bayesian hierarchical model, to select the most effective method for predicting large-scale forest biomass. Results showed total biomass of trees with the same diameter gradually decreased from southern to northern regions in China, except in the Hebei province. We found that the stem wood, branch, stem bark, needle, root, and total biomass model relationships were statistically significant (p-values < 0.01) for the general allometric equation, linear mixed model, dummy variable model, and Bayesian hierarchical model, but the linear mixed, dummy variable, and Bayesian hierarchical models showed better performance than the general allometric equation. An F-test also showed significant differences between the models. The R 2 average values of the linear mixed model, dummy variable model, and Bayesian hierarchical model were higher than those of the general allometric equation by 0.007, 0.018, 0.015, 0.004, 0.09, and 0.117 for the total tree, root, stem wood, stem bark, branch, and needle models respectively. However, there were no significant differences between the linear mixed model, dummy variable model, and Bayesian hierarchical model. When the number of categories was increased, the linear mixed model and Bayesian hierarchical model were more flexible and applicable than the dummy variable model for the construction of regional biomass models.

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Section: Biomass Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Biomass Modeling of Larch (Larix spp.) Plantations in China Based on the Mixed Model, Dummy Variable Model, and Bayesian Hierarchical Model

Chen

Huang

Zhang

et al. 2017

Forests

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“…Se cuantificó la biomasa de residuos forestales por medio de un modelo alométrico desarrollado para varias especies de pinos de Durango y Chihuahua (Navar, 2009). La ecuación de tipo no linear utiliza dos pará-metros de ajuste y la variable de interés es el diámetro a la altura del pecho:…”

Section: Estimación De Residuos Forestalesunclassified

“…En este caso, β 1 = 0,0565 y β 2 = 2,2729 error estándar Sx = 84,32 kg y r 2 = 0,69 (Navar, 2009). Con esta ecuación, se calculó la biomasa equivalente (t) por árbol y posteriormente se infirió al Disponibilidad y costos de producción de biomasa forestal sitio, hectárea y rodal.…”

Section: Estimación De Residuos Forestalesunclassified

Disponibilidad y costos de producción de biomasa forestal como materia prima para la producción de bioetanol

et al. 2017

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ResumenLa biomasa forestal es una alternativa ecológica y económicamente viable para la generación de bioetanol debido a que su fuente es abundante, renovable y contribuye a la reducción de gases efecto invernadero. En este estudio, se propone y analiza una metodología para la estimación de la disponibilidad y costos de producción del uso potencial de la biomasa forestal como materia prima para la producción de bioetanol en bosques de pinos del estado de Durango, México. Se usó información del Inventario Forestal Periódico, programas de manejo forestal y datos de empresas de aserraderos e industriales forestales para estimar la biomasa forestal que incluye los restos de aprovechamientos forestales (puntas, ramas) y desperdicios industriales (aserrín, costeras). Se utilizaron simulaciones Monte Carlo para estimar costos de producción de la recolección, extracción y transporte de la biomasa a centros de transformación. Los resultados indican que alrededor de 322.000 toneladas pueden utilizarse para la producción de 38 millones de litros de etanol por año. Las simulaciones Monte Carlo indican que el costo promedio de residuos forestales es de US $23,8 por tonelada (US $0,20 L -1 etanol) mientras que el de residuos industriales es de US $22,6 por tonelada (US $0,19 L -1 etanol). Los factores más importantes en el análi-sis de sensibilidad fueron el costo pagado a dueños del material, eficiencia tecnológica y distancia de transporte. En el corto plazo, el uso de la biomasa forestal para la generación de biocombustibles tiene varios retos entre los que se encuentran los costos de transporte y la competencia generada por industrias similares como pulpa, papel y tableros aglomerados. Como alternativa se encuentra el desarrollo de biorefinerías integradoras y el uso de medios de transporte más eficientes.Palabras clave: biocombustibles; costos de producción; Durango, México; residuos forestales; residuos industriales; simulaciones Monte Carlo. Abstract Availability and production costs of forest biomass as a feedstock for bioethanol productionForest biomass is a viable alternative to produce ethanol because is abundant, clean, renewable, and can help mitigate greenhouse gas emissions. In this study, a methodology to estimate availability and production costs of forest biomass in forest pines of the state of Durango, Mexico is presented. Forest periodic inventory, forest management plans, and sawmill information were used to estimate forest residues and mill residues, respectively. Since a market for bioethanol from forest biomass is still not well defined, Monte Carlo simulations were conducted to estimate procurement, transportation, and stumpage costs. Results show that about 322,000 tons can be used to produce up to 38 million of liters of ethanol per year. Of that amount, 66% is forest residues and the rest mill residues. Monte Carlo simulations indicated that the average cost of forest residues is US $23.8 per metric ton (US $0.20 L -1 ethanol) while the cost for mill residues is US $22.6 per metric ton (US $0.19 L ...

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“…Studies in different parts of the world and in Mexico often use a constant value for C concentration (usually 50%) to model C stocks in different ecosystems and taxa (Návar 2009, Vieilledent et al 2011, RojasGarcía et al 2015. Other authors, however, argue that such generalization can underor over-estimate real C stocks (Yerena-Yamallel et al 2011, Jiménez-Pérez et al 2013, causing uncertainty in the potential of forests as C sinks (Lamlom & Savidge 2003).…”

Section: Introductionmentioning

confidence: 99%

Tissue carbon concentration of 175 Mexican forest species

Pompa-García¹,

Rodríguez²,

Jurado³

et al. 2017

iForest

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Reliable calculations of carbon stocks in forest ecosystems are crucial for proper implementation of global warming mitigation policies. Accurate estimations depend upon applying the correct factor of carbon (C) concentration for different forest species and tissues instead of the often assumed 50% carbon content. Despite the high forest species richness in Mexico and the increasing CO2 emissions, data on carbon concentrations in forest plant tissues are scarce. In this study, we determined variation in C concentration of different tissues for 175 plant species common in Mexican forests. C contents were estimated and contrasted for plant distribution, taxa, and plant structure (main stems, branches, twigs, bark, leaves, buds, fruits, roots and root cuticles). The mean C concentration across species was 44.7%. Species significantly differed in C concentration by tissue, environment and taxa. These multi-species data contribute to improve precision on estimates of C balance in terrestrial ecosystems, reducing the uncertainty in C inventories in Mexico and elsewhere.

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Allometric equations for tree species and carbon stocks for forests of northwestern Mexico

Cited by 262 publications

References 26 publications

Biomass Modeling of Larch (Larix spp.) Plantations in China Based on the Mixed Model, Dummy Variable Model, and Bayesian Hierarchical Model

Biomass Modeling of Larch (Larix spp.) Plantations in China Based on the Mixed Model, Dummy Variable Model, and Bayesian Hierarchical Model

Disponibilidad y costos de producción de biomasa forestal como materia prima para la producción de bioetanol

Tissue carbon concentration of 175 Mexican forest species

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