Open access peer-reviewed chapter

Biomass Accumulation and Carbon Storage in Pinus maximinoi, Quercus robur, Quercus rugosa, and Pinus patula from Village- Forests of Chiapas, Mexico

Written By

Francisco Guevara-Hernández, Luis Alfredo Rodríguez-Larramendi, Luis Reyes-Muro, José Nahed-Toral, Alejandro Ley-de Coss, René Pinto-Ruiz, Leopoldo Medina-Sanson, Julio Díaz-José, Fredy Delgado-Ruiz, Deb Raj Aryal, José Apolonio Venegas-Venegas, Jesús Ovando-Cruz, María de los Ángeles Rosales-Esquinca, Carlos Ernesto Aguilar-Jiménez, Miguel Angel Salas-Marina, Francisco Javier Medina-Jonapá, Adalberto Hernández-Lopez and Vidal Hernández-García

Submitted: May 1st, 2017 Reviewed: November 30th, 2017 Published: December 29th, 2017

DOI: 10.5772/intechopen.72838

From the Edited Volume

New Perspectives in Forest Science

Edited by Helder Filipe dos Santos Viana and Francisco Antonio García Morote

Chapter metrics overview

1,229 Chapter Downloads

View Full Metrics


The Frailesca region (Chiapas, Mexico) presents a lack of forest studies and its environmental contribution. This chapter displays a first case study with preliminary research information regarding the identification of main forest trees and rural villages with best potential for biomass production and carbon storage management. Twenty two plots of 500 m2 were selected in 11 villages of the region, in order to identify the main and dominant forest trees species and then to estimate the biomass production and carbon storage in pine (Pinus maximinoi), oak (Quercus robur), holm oak (Quercus rugosa) and Mexican weeping pine (Pinus patula) species. This study shows that the largest accumulation of both biomass and carbon occurred in the pine forests and the lowest in the oak forests. Pine trees showed carbon storage of 516.75 Mg ha−1, followed by holm oaks, with 297.21 Mg ha−1; the species with the lowest value was oak, with 75.02 Mg ha−1. The forests of the 24 de Febrero villages had the highest potential for carbon storage. Deep studies are being conducted in relation to the aboveground biomass, carbon contents in trees stem, branches and leaves, and the relation to biomass dynamics and carbon stocks and other ecological aspects of village-forests.


  • total biomass
  • carbon sequestration
  • allometric relationships
  • tropical forest

1. Introduction

The role and importance of forests in environmental issues like carbon sequestration has been evaluated by many researchers, e.g., Cook et al. [4]; de Jong et al. [10] and Dixon et al. [9]. These analyses suggest that forest conservation and their sustainable management can contribute to global carbon sequestration and conservation while providing goods and services for rural communities of many countries. De Jong et al. [10] stated that forestry and agroforestry can compensate for greenhouse gas emissions in two ways: (i) by the creation of new sinks for carbon dioxide by increasing the mass of woody material within growing trees; and (ii) through the safeguard of endangered natural forests and soils which are carbon stores.

In Mexico, conifer and broadleaf forests occupy 15.4% of the national territory; managed forests cover 7.3 million hectares, while protected forests cover 7.1 million hectares [32]. On the other hand, nearly 80% of the forest areas are communal property, and 95% of the forest exploitations originate mainly from the native temperate forests [22].

In the state of Chiapas, these forests cover 1,117,248 ha, and the rainforests occupy 2,175,948 ha [32]; therefore, the entity has the second place nationally for forest surface area and timber extraction from pine, cypress, sweetgum, holm oak, Rosy trumpet, amate ficus, cedar, and mahogany trees. In addition, its vast forest cover confers it a great potential for CO2 sequestration.

Forest ecosystems can capture significant amounts of greenhouse gases (GHG), particularly CO2. For this reason, in the recent decades, there is considerable interest in increasing the carbon content of the vegetation through the preservation of forests, reforestation, the creation of forest farms, and other land management methods. A great number of studies have demonstrated the ability of forest species to store carbon in their biomass [2, 26, 29, 31].

Each year, these forest areas store significant amounts of biomass that contribute to reduce carbon levels in the atmosphere [8, 23], which acquire greater significance if we consider that the CO2 content in the atmosphere has augmented since the industrial revolution, and estimations indicate that in the twenty-first century this tendency will increase further [6, 28, 35]. All this indicates that anthropic activities have caused disturbances that contribute to the deterioration of the ecosystems [7].

Various studies performed in Mexico have proven the potential of forests to capture atmospheric carbon. Masera et al. [22] created a model simulating carbon capture during the 2000–2030 period, which divided land use into forests, rain forests, arid zones, and nonforest uses. These authors used two scenarios: the first, referred to as “baseline,” and the second, as “policies.” If the proposed options derived from their results were to be adopted, Mexico would be able to capture approximately 46 million tons of carbon during the 2000–2030 period. Part of this mitigation would be achieved as follows: (a) by preventing deforestation, (b) through sustainable management of the natural forests, and (c) by restoring the degraded forest areas.

CO2 is one of the main components of GHGs and is produced by human activities when fossil fuels are utilized to generate energy and to meet other demands of the society. Deforestation processes, land-use changes, and methane concentrations resulting from agricultural and stockbreeding activities also promote climate change [35].

The increase in GHG concentrations in the atmosphere has caused the “greenhouse effect” phenomenon, which has resulted in changes in the climatic scales of the Earth [21]. The increase of CO2 in the atmosphere produces extreme climatic events such as floods caused by hurricanes, which result in regrettable losses of human lives as well as economic losses [20].

According to Alberto and Elvir [2], carbon sequestration has been the object of study of forest research in various countries. Montero and Kaninnen [25] point out that, in southern Costa Rica, the production of aboveground biomass and carbon sequestration in managed Terminalia amazonia(J. F. Gmel.) plantations after 10 years was of 97.03 and 45.30 Mg ha−1, respectively. In central Honduras, the production of aboveground biomass was of 80.53 Mg ha−1 in natural Pinus oocarpaforests. Carbon storage in the aboveground biomass of the pine forests of the La Majadavillage in Michoacán, Mexico, amounted to 28.85 Mg ha−1, while in the conifer forests of Tancítaro, Michoacán, Mexico, the annual capture and storage of carbon in the aboveground biomass added up to 19.00 and 1.65 Mg ha−1, respectively [13]. Based on the lack of forests studies and their potential contribution to the mitigation of climate change, the purposes of this case study were (a) the generation of basic and preliminary information about the main forest trees species and (b) the identification of rural villages with best potential regarding biomass production and carbon storage. Thus, to estimate the pine, holm oak, Mexican weeping pine, and oak forests in the forest areas from rural villages of the Frailesca region, as well as to determine the relationship between the age of trees and biomass production, allometric relationships were used.


2. Methodology

2.1. Location

The Frailesca region is made up of valleys and plains dominated by monoculture maize fields with Green Revolution technologies, surrounded by vast mountainous zones. The region is composed of six municipalities: Villaflores, El Parral, la Concordia, Angel Albino Corzo, Montecristo de Guerrero, and Villa Corzo, where traditional farming systems are practiced in small villages located within the natural forests from two natural protected areas, La Frailescanaand La Sepulturareserves.

Climate is warm and semiwarm; the most predominant subclassifications are warm subhumid with occasional summer rain, followed by semiwarm humid with abundant summer rain. From May to October, the minimum average temperature ranges from 12 to 21°C (18 to 21°C in 54.9% of the region and 15 to 18°C in 37.8% of the region), while the maximum average temperature ranges from 21 to 34.5°C (30 to 33°C in 35.2% of the region and 27 to 30°C in 29.34% of the region). Average annual precipitation from May to October varies from 1000 to 2600 mm. From November to April, minimum average temperature ranges from 9 to 15°C, with averages of 12 to 15°C in 92.96% of the region, while the maximum ranges from 21 to 33°C, with averages of 27 to 30°C in 49.3% of the region and 33°C in 27.2% of the region.

The research was carried out in Villa Corzo municipality, in forest areas of 11 rural villages of La Frailesca region (Figure 1, Table 1), located between the coordinates 16°11′05″ N and 93°16′03″ W, at a mean altitude of 584 m above the sea level [3]. This municipality has a subhumid warm climate with abundant summer rains that prevails in the study area. The minimum annual precipitation is 1200 mm, and the maximum is 3000 mm, distributed among 100 and 200 days a year. The soils are affected by silt erosion precipitated by the action of the wind and by river floods; its fertility is variable, with its agricultural use conditioned by its depth and stoniness [3]. The localities have a vegetal cover that consists mainly of conifer forest secondary vegetation; montane cloud and holm oak forests; and deciduous, subdeciduous, and evergreen rainforests.

Figure 1.

Location of Villa Corzo municipality in La Frailesca region (Chiapas, Mexico). Source: RED AC [36].

Villages500 m2 plots
PineHolm oakOakMexican weeping pine
24 de Febrero++
Patria Chica+
La Frailesca+
Juan Sabines Gutiérrez+
La Libertad+
Tierra Santa+
La Unión+
Nuevo Refugio+
Unión del Carmen+

Table 1.

Distribution of plots in the rural villages and by the identified main forest species.

2.2. Plot selection and estimation of total biomass production and carbon storage

Dasometric data were recorded for the twenty-two 500 m2 sampling plots (two in each village). This information was entered into a database which included data of 358 individuals of the following species: Pinus maximinoiH. E. Moore (pine), Quercus roburL. (oak), Quercus rugosa Neé(holm oak), and Pinus patulaSchiede ex Schltdl. & Cham. (Mexican weeping pine) (Table 1).

The age of the trees was determined using a Pressler drill, as well as based on information provided by the common land holders; the normalized diameter formerly known as diameter at breast height (ND) was measured with a diameter measuring tape and the height (H) of the main stem with a clinometer [15].

The stem volume (V; m3) was calculated using the following equation:


where H= tree height (m); SC= shape coefficient (Table 2); ND= normalized diameter (m).

SpeciesSCr (kg m−3)BEF
Pinus maximinoiH. E. Moore0.525071.25
Quercus rugosaNeé0.396501.27
Quercus roburL.0.396501.27
Pinus patulaSchiede ex Schltdl. & Cham0.255071.25

Table 2.

Biomass expansion factors (BEF), shape coefficients (SC), and density (r) of the studied species.

In order to estimate the stem biomass, the total volume was multiplied by the density of each of the species (Table 2) [16]. The carbon sequestration was estimated by multiplying the biomass by 0.50, a value that represents the mean concentration of carbon for conifers [18] and a value cited in the Green House Gas Inventories of the forestry sector for Mexico [20].

The value of the biomass production in the various parts of the tree was determined based on the biomass expansion factors (BEF) and shape coefficients (SC) published by González [16] (Table 2), using the following expressions:

Stem biomass (SBM)


where SBM= stem biomass (kg); V= stem volume (m3); r= wood density (kg m−3) (Table 2).

Tree aboveground biomass (AGBM)


where AGBM= tree aboveground biomass (Mg); SBM= stem biomass (Mg); BEF= biomass expansion factor.

Root biomass (RBM)


where RBM= root biomass (Mg); AGBM= tree aboveground biomass (Mg).

2.3. Statistical analyses

The differences in the total biomass produced among species and between tissues were determined by processing the data through an ANOVA. Frequency histograms were created for the age and the normalized diameter (ND), and nonlinear regression analyses were carried out between the tree height and normalized diameter (ND) variables, as well as between the total biomass (TBM) and the tree’s age by species. The mean comparison was carried out using Tukey’s test for p ≤ 0.05 [33]. Previously to all the statistical analyses, the assumptions of normality and of variance homogeneity were also verified using the STATISTICA®, version 8.0 software [33].


3. Results and discussion

The histogram of tree age by species shows that the pine plots reflected a rather heterogeneous distribution, with 12% of the trees aged 40–50 years (Figure 2), followed by 10% of trees aged 90–100 years. The age of holm oaks ranged between 20 and 70 years, with 10% of the individuals in an age interval of 30–50 years. The oak forests had 95 trees aged 20–40 years, while 18% of the trees of Mexican weeping pine forests aged 30–60 years (Figure 2). The largest number of trees with ND values of 0.1–0.4 m were oaks, followed by pines and Mexican weeping pines (Figure 3). In every case, like for the tree age, the highest degree of heterogeneity was found in the pine plots, with ND values ranging between 0.13 and 1.02 m (Figure 3). The interval between maximums, minimums, and ND (Table 3) made it possible to determine that the species with the highest variability were pine, holm oak, oak, and Mexican weeping pine, in this order (Table 3).

Figure 2.

Frequency histograms of tree ages sampled by species in La Frailesca region (Chiapas, Mexico). A: Pine; B: Holm oak; C: Oak; D: Mexican weeping pine.

Figure 3.

Frequency histograms of the ND of the sampled trees by species in La Frailesca region (Chiapas, Mexico). A: Pine; B: Holm oak; C: Oak; D: Mexican weeping pine.

Pinus maximinoiH. E. Moore0.401.020.130.89
Quercus rugosaNeé0.330.750.150.60
Quercus roburL.0.260.460.050.41
Pinus patulaSchiede ex Schltdl. & Cham0.270.400.100.30

Table 3.

Intervals between the maximums, the minimums, and the normalized diameters (NDs) of the studied species.

Given the close relationship observed between the ND, the age of the trees, and biomass production and carbon sequestration in various forest species [11, 12, 15], it is possible to understand the capacity of these village-forests to capture carbon from the atmosphere, particularly if it can be proven that the total biomass produced and the captured carbon increase with the age of the trees. The relationship between the height and the ND of the species was adjusted to an exponential model (Figure 4) called allometric, previously cited by Acosta et al. [1] and Gómez-Castro et al. [15]. The highest determination coefficients (r2) were observed in pines and holm oaks, which also had the broadest interval between tree ages and NDs. For all the species, the mathematical adjustment produced a statistical significance of the model parameters, according to the Student’s t-test (Table 4), a fact that corroborates the selection of the exponential model to estimate tree heights through the normalized diameter, although it suggests the need to delve into the effect of the normalized diameter interval, for which the best estimates were obtained.

Figure 4.

Exponential regression models of adjustment between the height and the ND of the studied species of La Frailesca region (Chiapas, Mexico). A: Pine; B: Holm oak; C: Oak; D: Mexican weeping pine.

SpeciesModel parametersEstimateStandard errortpConfidence limits 95%
Pinus maximinoiH. E. Moorea8.710.52416.7010.017.7079.792
Quercus rugosaNeéa6.640.31421.1460.016.0187.261
Quercus roburL.a6.830.41416.4750.016.0087.647
Pinus patulaSchiede ex Schltdl. & Chama11.781.2519.4170.019.28314.277

Table 4.

Parameters and statistical significance of the model of mathematical fit between the height and the normalized diameter.

t = Student’s t; p = probability of error.

For all species, the ratio of the total biomass of the tree (BMt) and the age was significantly fitted according to an exponential model (Figure 5, Table 5), with determination coefficients ranging between 0.56 and 0.85. Similar results were obtained by Rodríguez et al. [30] for the allometric relationships between the biomass production and the age of the pine trees. This result corroborates the biomass and carbon estimation studies based on the ND and ratifies the relationship between tree age and the photosynthetic processes that trigger biomass and carbon storage [27].

Figure 5.

Regression curves of fit between the total biomass (BMt) and age of trees of (A) pine, (B) holm oak, (C) oak, and (D) Mexican weeping pine.

SpeciesModel parametersEstimateStandard errortpConfidence limits 95%
Pinus maximinoiH. E. Moorea0.−0.0550.23
Quercus rugosaNeéa0.0340.0162.180.050.0020.07
Quercus roburL.a0.130.0363.720.010.0630.21
Pinus patulaSchiede ex Schltdl. & Chama0.0060.0242.480.010.0120.1

Table 5.

Parameters and statistical significance of the model of mathematical fit between the biomass and the age of the trees.

t = Student’s t; p = probability of error.

According to Fonseca et al. [12], both the aboveground biomass and the root biomass increase with the age in secondary forests and forest plantations. Hughes et al. [19] register an average biomass of 272.1 Mg ha−1 at 16 years of age. Corrales [5] registers a biomass of 162.1 Mg ha−1 in secondary forests aged 15 years and of 324.1 Mg ha−1 in primary forests in humid and very humid climates in Costa Rica.

The trees that produced the largest amount of biomass in the stem and other aboveground parts and in the roots were pines, with values above 506.92, 882.05, and 264.61 Mg ha−1, respectively (Figure 6). Oak trees produced the least biomass. Monroy and Navar [24] cite similar results for Hevea brasiliensis(Willd. ex A. Juss.) Müll. Arg., with values of 73.9% stem biomass and 27.1% branch biomass, both of which increased with the age of trees.

Figure 6.

Total biomass production in the stem, branches, and roots of pine, oak, holm oak, and Mexican weeping pine trees in villages of La Frailesca region (Chiapas, Mexico). Note: Vertical lines in the columns represent the mean standard error.

As for the relationship between the carbon content and the total biomass production in the various components of the plant, the results suggest that the high rates registered in pine forests for both growth and aboveground carbon fixation, may be due, as Pacheco et al. [27] proved, to a good combination between the production of wood and cellulose compared to other species; this is helpful for the implementation of reforestation and CO2 sequestration projects [14]. However, other factors, such as the site and the tree mass, also determine biomass production and carbon storage. Furthermore, this author cites a direct relationship between the sequestered carbon and growth, in both the normal diameter (ND) and the total height of the trees [34]. Analysis of the information from the various villages proved that the total biomass produced in pine trees was highest at 24 de Febreroand Juan Sabinesvillages (Figure 7), with values above 2606.67 and 1045.61 Mg ha−1, respectively. Therefore, these villages also had the highest values for carbon capture.

Figure 7.

Total biomass (A) and carbon (B) storage inPinus maximinoiH. E. Moore forests of theJuan Sabines, Tierra Santa, La Uniónand24 de Febrerovillages of La Frailesca region (Chiapas, México). Note: Vertical lines in the columns represent the mean standard error.

Holm oak forests, which are prevalent in the Bonanzaand 24 de Febrerovillages, produced up to 120.87 and 988.82 ha−1 Mg of total biomass, respectively (Figure 8), whereas the carbon sequestered was higher at 24 de Febrero, with 433.98 Mg of C ha−1, which proves the potential of this village for CO2 capture and its contribution to the abatement of GHGs.

Figure 8.

Total biomass (A) and carbon (B) storage inQuercus rugosaNeé forests in theJuan Sabines, Tierra Santa, La Unión, and24 de Febrerovillages of La Frailesca region (Chiapas, Mexico). Note: Vertical lines in the columns represent the mean standard error.

The total biomass production in oak trees was 154.03–176.21 Mg ha−1, the highest record in the forest areas of the La Libertadvillage, followed by Monterrey(Figure 9). The lowest value was found at Patria Chica. As for carbon storage, in all the villages, it was above 50 Mg ha−1; the highest was obtained in the forests of the La Libertadvillage, with 88.11 Mg of C ha−1 (Figure 9).

Figure 9.

Total biomass (A) and carbon (B) storage inQuercus roburL. forests of theJuan Sabines, Tierra Santa, La Unión, and24 de Febrerovillages of La Frailesca region (Chiapas, Mexico). Note: Vertical lines in the columns represent the mean standard error.

The total biomass produced by Mexican weeping pines (Pinus oocarpaSchiede) ranged between 281.78 and 450.52 Mg ha−1 (Figure 10). This species occurred only in the La Frailescaand Nuevo Refugiovillages; the forests of the latter contributed a larger amount to this production. The highest value for carbon storage (225.26 Mg of C ha−1) was found in the forests of Nuevo Refugio, while the La Frailescavillage stored only 140.89 Mg ha−1. As for the production of biomass per tree components (Figures 610), in all the taxa and localities, the highest accumulation occurred in the aboveground biomass, as indicated by Gower et al. [17], who proved that approximately 75% of the biomass of a tree is produced in the aboveground parts, while only 25% is accumulated in the roots.

Figure 10.

Biomass (A) and carbon (B) storage inPinus oocarpaSchiede forests of theLa FrailescaandNuevo Refugiovillages in La Frailesca region (Chiapas, Mexico). Note: Vertical lines in the columns represent the mean standard error.

Comparison between biomass production and carbon storage by species showed that Pinusspp. and Quercusspp. had the highest values, while Quercus roburhad the lowest (Table 6). This agrees with the findings of González [16], who, after comparing three tree species, determined that P. maximinoihad the highest biomass accumulation, followed by Quercusspp. In regard to carbon, pine and holm oak had a storage potential of 516.75–247.02 Mg of C ha−1 (Table 6).

SpeciesTotal biomass (Mg ha−1)Carbon storage (Mg ha−1)
MeanStandard errorMeanStandard error
Pinus maximinoiH. E. Moore1033.49557.65516.75278.82
Quercus rugosaNeé494.41373.54247.21186.77
Quercus roburL.150.0310.6075.025.30
Pinus patulaSchiede ex Schltdl. & Cham366.1584.37183.0842.19

Table 6.

Total biomass and carbon storage in pine, holm oak, oak, and Mexican weeping pine forests of La Frailesca region, Chiapas.


4. Conclusions

The forests of the 24 de Febrerovillages registered the highest growth, based on their height and stem diameter, as well as on the total biomass, rendering this village one of the localities with the highest potential for carbon storage of all the studied sites. Pine, holm oak, oak, and Mexican weeping pine trees in villages of Frailescaregion in Chiapas, Mexico, accumulate between 150.03 and 1033.49 Mg ha−1 of vegetal biomass; therefore, they are considered to have a high potential for carbon sequestration. Pine trees reached a value of 516.75 Mg ha−1 of C; due to its high degree of development, this species has the highest capture potential of all.

Since the present study was an initial effort to begin with data collection for preliminary insights and understandings about the village-forests in la Frailesca region, it is important to keep in the research track with deeper and longer term investigations on forest dynamics in this geographical area, especially due to its importance for biodiversity conservation, sustainable management of natural resources, and the environmental services provision for the rural and urban areas of the Chiapas state.


  1. 1. Acosta M, Vargas JH, Velázquez AM, Etchevers JB. Estimación de la biomasa aérea mediante el uso de relaciones alométricas en seis especies arbóreas en Oaxaca, México. Agrociencia. 2002;36(6):725-736
  2. 2. Alberto DM, Elvir JA. Acumulación y fijación de carbono en biomasa aérea de Pinus oocarpa en bosques naturales de Cabañas, La Paz. Tatascán. 2005;17(2):1-12
  3. 3. Comité Estatal de Información Estadística y Geográfica. Gobierno del Estado de Chiapas (CEIEG). Comité Estatal de Información Estadística y Geográfica. Gobierno del Estado de Chiapas. 2011. (10 de agosto de 2012)
  4. 4. Cook AG, Janetos AC, Hinds WT. Global effects of tropical deforestation: Towards an integrated perspective. Environmental Conservation. 1990;17:201-212
  5. 5. Corrales L. Estimación de la cantidad de carbono almacenado y captado (masa aérea) en el Corredor Biológico Mesoamericano de Costa Rica. San José, Costa Rica: PROARCA/CAPAS/CCAD/USAID; 1998. 15 p
  6. 6. Crowley TJ. Causes of climate change over the past 1000 years. Science. 2000;289(5477):270-277
  7. 7. Dhillon RS, Von Wuehlisch G. Mitigation of global warming through renewable biomass. Biomass Bioenergy. 2013;48:75-89
  8. 8. Dixon RK, Brown S, Houghton RA, Solomon AM, Trexler MC, Wisniewski J. Carbon pools and flux of global forest ecosystems. Science. 1994;263:185-190
  9. 9. Dixon RK, Andrasko KJ, Sussman FG, Lavinson MA, Trexler MC, Vinson TS. Forest sector carbon offset projects: Near-term opportunities to mitigate greenhouse gas emissions. Water, Air and Soil Pollution. 1993;70:561-577
  10. 10. de Jong B, Montoya G, Nelson KC, Soto-Pinto L, Taylor J, Tipper R. Community forest management and carbon sequestration: A feasibility study from Chiapas, Mexico. Interciencia. 1995;20(60):409-416
  11. 11. Domínguez CG, Aguirre AO, Jiménezy PM, Villalón H. Evaluación del contenido de carbono en bosques del sur de Nuevo León. Madera y Bosques. 2007;13(1):35-46
  12. 12. Fonseca W, Federico AGE, Montero J, Leblanc HA. Acumulación de biomasa y carbono en bosques secundarios y plantaciones forestales de Vochysia guatemalensis e Hieronyma alchorneoides en el Caribe de Costa Rica. Agroforestería en Las Américas. 2008;46:47-64
  13. 13. Fragoso P. Estimación del contenido y captura de carbono en biomasa aérea del predio Cerro Grande municipio de Tancítaro. Morelia, Mich., México: Universidad Michoacana de San Nicolás de Hidalgo; 2003. 66 p
  14. 14. Gamara J. Estimación de carbono en plantaciones deEucalyptus globulusLABILL, en Junin, Perú. In: Galloso J, Jandl R, editors. Simposio Internacional “Medición y Monitoreo de la Captura de Carbono en Ecosistemas Forestales”. Valdivia, Chile: International Union of Forest Research Organizations (IUFRO); 18-20 de octubre; 2001. 21 p
  15. 15. Gómez CH, PintoRuiz RPintoRuiz R, Guevara-Hernandez F, González-Reyna A. Esti-maciones de biomasa aérea y carbono almacenado en Gliricidia sepium (lam.) y Leucaena leucocephala (Jacq.) y su aplicación en sistemas silvopastoriles. ITEA; 2010;106(4): 256-270
  16. 16. González MZ, Estimación de la biomasa aérea y la captura de carbono en regeneración natural de Pinus maximinoi H. E. Moore, Pinus oocarpa var. ochoterenai Mtz. y Quercus sp. en el norte del Estado de Chiapas, México. Tesis de Maestría. Manejo y Conservación de Bosques Naturales y Biodiversidad. Costa Rica: CATIE; 2008. 97 p
  17. 17. Gower S, Reich B, Son Y. Canopy dynamics and aboveground production for five tree species with different leaf longevities. Tree Physiology. 1993;12(4):327-345
  18. 18. Hamburg SP. Simple rules for measuring changes in ecosystem carbon in forestry-offset projects. Mitigation and Adaptation Strategies for Global Change. 2000;5(1):25-37
  19. 19. Hughes RF, Kauffman JB, Jaramillo VJ. Biomass, carbon and nutrient dynamics of secondary forest in a humid tropical region of Mexico. Ecology. 1999;80(6):1882-1907
  20. 20. Intergovernmental Panel on Climate Change (IPCC). Special report on Carbon Dioxide Capture and Storage (Prepared by working group III of the Intergovernmental Panel On Climate Change—IPCC). Cambridge, UK and New York, USA: Cambridge University Press; 2005. 442 p
  21. 21. Intergovernmental Panel on Climate Change (IPCC). Resumen Técnico en Cambio Climático 2007: Mitigación. In: Metz B, Davidson OR, Bosch PR, Dave R, Meyer LA, editors. Contribución del Grupo de Trabajo III al Cuarto Informe de Evaluación del Panel Intergubernamental de Expertos sobre Cambio Climático. Cambridge, UK y New York, USA: Cambridge University Press; 2007. 72 p
  22. 22. Masera O, Cerón A, Ordóñez A. Forestry mitigation options for Mexico: Finding synergies between national sustainable development priorities and global concerns. Mitigation and Adaptation Strategies for Global Change. 2001;6(3):291-312
  23. 23. Melillo JM, McGuire AD, Kickligther DW, Moore B, Vorosmarty CJ, Schloss AL. Global climate change and terrestrial net primary production. Nature. 1993;363:234-240
  24. 24. Monroy RC, Navar JJC. Ecuaciones de aditividad para estimar componentes de biomasa de Hevea brasilensis Mull. Arg. en Veracruz México. Madera y Bosques. 2004;10(2):29-43
  25. 25. Montero M, Kanninen M. Biomasa y Carbono en plantaciones de Terminalia amazonia (Gmel.) Excell en la zona Sur de Costa Rica. Revista Forestal Centroamericana. 2002;39-40:50-55
  26. 26. Nájera LJ, Hernández H. Acumulación de biomasa aérea en un bosque coetáneo de la región de El Salto, Durango. Ra Ximhai. 2009;52(2):225-230
  27. 27. Pacheco E, Clemencia F, Alderete A, Gómez G, Fierros G, Cetina A, Vaquera H. Almacenamiento de carbono en la biomasa aérea de una plantación joven de Pinus greggii Engelm. Revista Fitotecnia Mexicana. 2007;30(3):251-254
  28. 28. Petit JR, Jouzel J, Raynaud D, Barkov NI, Barnola JM, Basile I, Benders M, Chappellaz J, Davis M, Delayque G, Delmotte M, Kotlyakov VM, Legrand M, Lipenkov VY, Lorius C, Pépin L, Ritz C, Saltzman E, Stievenard M. Climate and atmospheric history of the past 420 000 years from the Vostok ice core, Antarctica. Nature. 1999;399:429-436
  29. 29. Pimienta T, Domínguez GC, Aguirre C, Hernández FJ, Jiménez P. Estimación de biomasa y contenido de carbono de Pinus cooperi Blanco, en Pueblo Nuevo, Durango. Madera y Bosques. 2007;13(1):35-46
  30. 30. Rodríguez F, Prieto R, Návar JC. Producción de biomasa en plantaciones forestales del norte de México. Revista Ciencia Forestal en México. 2004;29(96):67-90
  31. 31. Roncal-García S, Soto-Pinto L, Castellanos-Albores J, Ramírez-Marcial N, De Jong B. Sistemas agroforestales y almacenamiento de carbono en comunidades indígenas de Chiapas, México. Interciencia. 2008;33(3):200-206
  32. 32. Secretaría de Agricultura y Recursos Hidráulicos- Subsecretaría Forestal y Fauna (SARH-SFF). Inventario Nacional Forestal Periódico 1992-1994. Mexico: Secretaría de Agricultura y Recursos Hidráulicos- Subsecretaría Forestal y Fauna (SARH-SFF); 1994. 122 p
  33. 33. StatSoft. 2007. STATISTICA. Data analysis software system. Version 8.0. Tulsa, OK, USA. n/p. In: Steel RG, Torrie JH, editors. Principles and Procedures of Statistics: A Biometrical Approach. 2nd ed. New York: McGraw-Hill Book; 1980. 672 p
  34. 34. Ugalde A. Resultados de 10 años de investigación silvicultural del proyecto MADELEÑA en Costa Rica. CATIE. Turrialba, Costa Rica. 1997; Serie Técnica, Informe Técnico Núm. 287. 162 p
  35. 35. United Nations Environment (UNEP). Climate Change. Geneva, Switzerland: Information sheets Switzerland, UNEP, UNFCCC. Information Kit; 2001. 30 p
  36. 36. RED AC. Cartografia de la región Frailesca de Chiapas. Archivos Digitalizados. Villa Corzo: RED AC; 2017

Written By

Francisco Guevara-Hernández, Luis Alfredo Rodríguez-Larramendi, Luis Reyes-Muro, José Nahed-Toral, Alejandro Ley-de Coss, René Pinto-Ruiz, Leopoldo Medina-Sanson, Julio Díaz-José, Fredy Delgado-Ruiz, Deb Raj Aryal, José Apolonio Venegas-Venegas, Jesús Ovando-Cruz, María de los Ángeles Rosales-Esquinca, Carlos Ernesto Aguilar-Jiménez, Miguel Angel Salas-Marina, Francisco Javier Medina-Jonapá, Adalberto Hernández-Lopez and Vidal Hernández-García

Submitted: May 1st, 2017 Reviewed: November 30th, 2017 Published: December 29th, 2017