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      Factores de expansión de biomasa aérea para Pinus chiapensis (Mart.) Andresen Translated title: Aboveground biomass expansion factors for Pinus chiapensis (Mart.) Andresen

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          Abstract

          Los factores de expansión de biomasa (FEB) permiten estimar la cantidad de biomasa fijada por árbol con base en variables de inventario; información útil para proyectos sobre cambio climático. El objetivo fue estimar FEB variables por árbol a través de la generación de modelos de regresión para volumen y biomasa aérea en Pinus chiapensis (Mart.) Andresen, en una comunidad de la Sierra Norte de Oaxaca. Se empleó muestreo destructivo de 70 árboles realizado durante 2012, seleccionando aleatoriamente individuos de forma proporcional a la frecuencia diamétrica. El tallo se cubicó por el método de trozas traslapadas. El volumen de madera con corteza se transformó a biomasa mediante la gravedad específica y para el resto del componente aéreo se usaron factores de conversión de biomasa (peso seco/ peso verde). Se obtuvieron modelos no lineales de volumen total y comercial con y sin corteza y de biomasa total aérea. Se emplearon variables alométricas de inventario como diámetro normal (DN, cm) y altura total (AT, m). Para la elaboración de tarifas volumétricas, los modelos ajustados mostraron coeficientes de determinación entre 0.94 y 0.98. El modelo de Schumacher-Hall mostró el mejor ajuste para biomasa total aérea con R²= 0.95, utilizando las mismas variables independientes que en el volumen. El FEB se obtuvo mediante el cociente de los modelos de biomasa y de volumen total árbol con corteza (FEBvtcc= 1040.771 x DN0 15073 x AT-042946). Con ésta ecuación, es posible estimar confiablemente la biomasa total aérea (kg) en árboles de Pinus chiapensis a partir de variables de inventario o de volumen.

          Translated abstract

          The biomass expansion factors (BEF) allow estimating the amount of biomass set per tree based on inventory variables; useful information for climate change projects. The objective was to estimate BEF variables per tree, through the generation of regression models for volume and aboveground biomass in Pinus chiapensis (Mart.) Andresen, in a community in the northern highlands of Oaxaca. Destructive sampling of 70 trees was made during 2012, randomly selecting individuals in proportion to the diametric frequency. The stem was cubed by the overlapping bolt method. The volume of wood with bark was transformed to biomass by the specific gravity and for the rest of the aboveground component were used conversion factors of biomass (dry weight / fresh weight). Nonlinear models of total volume and trade volume with and without bark and total aboveground biomass were obtained. Allometric variables were used from inventory like normal diameter (ND, cm) and total height (TH, m). For the elaboration of volumetric rates, adjusted models showed determination coefficients between 0.94 and 0.98. The Schumacher-Hall model showed the best fit for total aboveground biomass R²= 0.95, using the same independent variables from volume. The BEF was obtained by the quotient of the model from biomass and total tree volume with bark (BEFvtcc= 1040.771 x ND015073 x TH042946). With this equation, it is possible to estimate reliably, the total aboveground biomass (kg) in trees of Pinus chiapensis from of inventory variables or volume.

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          Measuring carbon in forests: current status and future challenges.

          To accurately and precisely measure the carbon in forests is gaining global attention as countries seek to comply with agreements under the UN Framework Convention on Climate Change. Established methods for measuring carbon in forests exist, and are best based on permanent sample plots laid out in a statistically sound design. Measurements on trees in these plots can be readily converted to aboveground biomass using either biomass expansion factors or allometric regression equations. A compilation of existing root biomass data for upland forests of the world generated a significant regression equation that can be used to predict root biomass based on aboveground biomass only. Methods for measuring coarse dead wood have been tested in many forest types, but the methods could be improved if a non-destructive tool for measuring the density of dead wood was developed. Future measurements of carbon storage in forests may rely more on remote sensing data, and new remote data collection technologies are in development.
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            SAS/STAT 9.1 User's Guide

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              The role of sustainable agriculture and renewable-resource management in reducing greenhouse-gas emissions and increasing sinks in China and India

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                Author and article information

                Contributors
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Journal
                remexca
                Revista mexicana de ciencias agrícolas
                Rev. Mex. Cienc. Agríc
                Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias
                2007-0934
                September 2013
                : 4
                : spe6
                : 1273-1284
                Affiliations
                [1 ] Instituto Tecnológico del Valle de Oaxaca México
                [2 ] Centro Interdisciplinario de Investigación para el Desarrollo Integral Regional México
                Article
                S2007-09342013001000018
                d7b603a3-025f-4fe0-8af4-618df707ebf2

                This work is licensed under a Creative Commons Attribution-NonCommercial 3.0 International License.

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                Categories
                Agriculture, Multidisciplinary

                General agriculture
                modelos de regresión,muestreo destructivo,peso específico,regression models,destructive sampling,specific gravity

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