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Tesis - Maestría
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Aboveground biomass and carbon stock of a medium stature semievergreen tropical forest in the Intensive Carbon Monitoring Site of Calakmul-Campeche, Mexico / Sandra Lorena Santamaria Rojas
Santamaria Rojas, Sandra Lorena ; Lindner, André (first supervisor) ; Esparza Olguín, Ligia Guadalupe (second supervisor) ;
Dresden, Germany : Dresden University of Technology, Faculty of Environmental Sciences, Institute of International Forestry and Forest Products , 2014
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Índice | Resumen en: Inglés |
Resumen en inglés

The understanding of AGB and carbon stocks is important when trying to assess the role of tropical forest in the global carbon cycle as well as the performance of market-based mechanisms such as REDD+. The Peninsula of Yucatan has been described as a high biomass cluster among the tropical forest of South Mexico (De Jong, 2013). This study characterizes the living aboveground biomass and carbon stock of the Intensive Carbon Monitoring Site of Campeche-Mexico, by exploring AGB patterns across the different successional gradients and comparing the obtained estimates with previous studies in the region. The total AGB in the ICMS of Campeche was 163.59±3.63 Mg ha-1 while the estimated carbon stock was 81.79 Mg C ha-1. Successional gradients were strong predictors of biomass density showing an increasing pattern with age, with a difference of 84.41 Mg between the lowest biomass density for young secondary forest and the largest value for lowland mature forest. Diametric classes in turn were not a good predictor of AGB density. The biggest AGB density was for medium size trees (77.56±5.97 Mg ha-1) followed by large trees and the lowest density for small trees (37.36±5.66 Mg ha-1). Basal area by size classes was not a good predictor of AGB density as well. Even though the basal area of small trees (7.35 m2 ha-1) was significant larger (p < 0.0001) than the one of the large trees, AGB density was lower for small trees (37.36±5.66 Mg ha-1). In overall this study estimates were slightly lower than previous studies for the mature forest and significantly larger for secondary forest (Table 12). This analysis is novel in that it is the initial step to establish an intensive monitoring of carbon stocks and dynamics through the operation of an ICMS in the area of Calakmul, which ecologic conditions are of high interest.


List of Figures
List of Tables
1. Introduction
1.1. Research objectives
2. Theoretical Framework
2.1. Live Aboveground Biomass (AGB) and carbon stock
2.2. Tree Allometry and AGB estimation
2.3. Field measurements for AGB estimation
2.4. Intensive Carbon Monitoring Sites (ICMS)
3. Methods
3.1. Study site
3.2. Sampling design
3.3. Clusters and sampling units design
3.4. Field measurements
3.5. Establishment of monitoring site
3.6. Data processing and analysis
3.6.1. Descriptive statistics
3.6.2. Quality control for well-represented species
3.7. Biomass estimation
3.8. Ratio estimators
3.9. Uncertainity
4. Results
4.1. Forest structure and composition
4.2. Basal Area and tree height
4.3. Successional stages
4.4. Forest disturbance
4.5. Wood Specific Gravity
4.6. Estimates of AGB and carbon stock
5. Discussion
5.1. AGB and carbon stock estimates
5.2. Successional stages
5.3. Wood Specific Gravity
5.4. Comparison with previous estimates
6. Conclusions
7. References

Resumen en: Inglés |
Resumen en inglés

This paper presents new equations for estimating above-ground biomass (AGB) and biomass components of seventeen forest species in the temperate forests of northwestern Mexico. A data set corresponding to 1336 destructively sampled oak and pine trees was used to fit the models. The generalized method of moments was used to simultaneously fit systems of equations for biomass components and AGB, to ensure additivity. In addition, the carbon content of each tree component was calculated by the dry combustion method, in a TOC analyser. The results of crossvalidation indicated that the fitted equations accounted for on average 91%, 82%, 83% and 76% of the observed variance in stem wood and stem bark, branch and foliage biomass, respectively, whereas the total AGB equations explained on average 93% of the total observed variance in AGB. The inclusion of total height (h) or diameter at breast height² × total height (d²h) as a predictor in the d-only based equations systems slightly improved estimates for stem wood, stem bark and total above-ground biomass, and greatly improved the estimates produced by the branch and foliage biomass equations. The predictive power of the proposed equations is higher than that of existing models for the study area. The fitted equations were used to estimate stand level AGB stocks from data on growing stock in 429 permanent sampling plots. Three machine-learning techniques were used to model the estimated stand level AGB and carbon contents; the selected models were used to map the AGB and carbon distributions in the study area, for which mean values of respectively 129.84 Mg ha-¹ and 63.80 Mg ha-¹ were obtained.

Base de datos de la biomasa de los sitios del inventario nacional forestal periódico, ciclo 1992-1994
De Jong, Bernardus Hendricus Jozeph (autor) ; Olguín, Marcela (autora) ; Rojas, Fabiola (autora) ; Maldonado Montero, Vanessa (autora) ; Paz Pellat, Fernando (autor) ;
Disponible en línea
Contenido en: Elementos para Políticas Públicas Volumen 3, número 1 (enero-abril 2019), p. 57-69 ISSN: 2448-5578
Resumen en: Español | Inglés |
Resumen en español

Los inventarios nacionales forestales permiten estimar los almacenes de los ecosistemas terrestres y generar inventarios nacionales de gases efecto invernadero. Aun cuando México había realizado un primer inventario nacional forestal en el periodo 1961-1985, la base de datos se desconoce. Un segundo esfuerzo es el Inventario Nacional Forestal Periódico (INFP) realizado en el periodo 1992-1994. Este inventario se documenta en el presente trabajo y la base de datos asociada se utiliza para estimar la biomasa de las clases de uso del suelo y vegetación sensu INEGI, utilizando para esta tarea ecuaciones alométricas, en función de las clases de precipitación del país. Los resultados se presentan en una base datos abierta al público para su consulta (http://pmcarbono.org/pmc/bases_datos/)

Resumen en inglés

National forest inventories allow the estimation of the stocks of terrestrial ecosystems, and the generation of national inventories of greenhouse gases. Although Mexico has carried out a first national forest inventory in the period 1961-1985, the database is unknown. A second effort made by Mexico is the Periodic National Forest Inventory (PNFI) carried out in the period 1992-1994. This inventory is documented and the associated database is used to estimate the biomass of the land use and vegetation classes sensu INEGI, using allometric equations for this task according to the country’s precipitation classes. The results are presented in a database open to the public for consultation (http://pmcarbono.org/pmc/bases_datos/)

Base de datos de la biomasa de los sitios del inventario nacional forestal y de suelos del ciclo 2004-2007
De Jong, Bernardus Hendricus Jozeph (autor) ; Olguín, Marcela (autora) ; Rojas, Fabiola (autora) ; Maldonado Montero, Vanessa (autora) ; Paz Pellat, Fernando (autor) ;
Disponible en línea
Contenido en: Elementos para Políticas Públicas Volumen 2, número 2 (mayo-agosto 2018), p. 69-84 ISSN: 2448-5578
Resumen en: Español | Inglés |
Resumen en español

El contenido de carbono en la biomasa aérea y subterránea de los ecosistemas terrestres es un almacén importante que requiere ser cuantificado para el establecimiento de mecanismos para evitar su pérdida o degradación (i.e. REDD+). En México, a partir del 2004, se estableció un Inventario Nacional Forestal y de Suelos (INFyS) por parte de la CONAFOR para mediciones permanentes por ciclos de muestreo de cinco años, con remediciones cada cinco años, también. En el primer ciclo (2004-2007) se establecieron alrededor de 22 000 conglomerados con cuatro sitios de muestreo de 400 m² en una malla sistemática con espaciamiento variable en función del tipo de ecosistema o grupo de vegetación (sensu INEGI). En este trabajo se documenta el uso de una base de ecuaciones alométricas específicas y generales, usada para la estimación de la materia seca (biomasa) aérea y subterránea, viva y muerta, en distintos tipos de vegetación sensu INEGI con información en el INFyS, ciclo 2004-2007. Se describe la base de datos generada y los procedimientos usados para las estimaciones.

Resumen en inglés

The carbon content in the above and belowground biomass of the terrestrial ecosystems is an important stock that needs to be quantified for the establishment of mechanisms to avoid its loss or degradation (i.e. REDD +). In Mexico, in 2004, a National Forest and Soil Inventory (INFyS) was established by CONAFOR for permanent measurements in five-year sampling cycles, with re-measurements every five years, as well. In the first cycle (2004-2007) about 22 000 conglomerates were established with four sampling sites of 400 m² in a systematic grid with variable spacing according to the type of ecosystem or vegetation group (sensu INEGI). In this work, we document a base of specific and general alometric equations used for the estimation of the above and belowground dry matter (biomass), alive and dead, in different types of vegetation sensu INEGI with information in the INFyS, cycle 2004 -2007. The database generated is described, as well as the procedures used in the estimations.

A beginner's guide to GLM and GLMM with R: a frequentist and bayesian perspective for ecologists / Elena N. Ieno, Joseph M. Hilbe, Alain F. Zuur
Zuur, Alain F. (coaut.) ; Hilbe, Joseph M. (coaut.) ; Ieno, Elena N. (coaut.) ;
Newburgt, United Kingdom : Highland Statistics Ltd , 2013
Clasificación: 519.50285 / Z99
Bibliotecas: San Cristóbal
SIBE San Cristóbal
ECO010014800 (Disponible)
Disponibles para prestamo: 1
Índice | Resumen en: Inglés |
Resumen en inglés

his book presents Generalized Linear Models (GLM) and Generalized Linear Mixed Models (GLMM) based on both frequency-based and Bayesian concepts. Using ecological data from real-world studies, the text introduces the reader to the basics of GLM and mixed effects models, with demonstrations of binomial, gamma, Poisson, negative binomial regression, and beta and beta-binomial GLMs and GLMMs. The book uses the functions glm, lmer, glmer, glmmADMB, and also JAGS from within R. JAGS results are compared with frequentist results. R code to construct, fit, interpret, and comparatively evaluate models is provided at every stage. Otherwise challenging procedures are presented in a clear and comprehensible manner with each step of the modelling process explained in detail, and all code is provided so that it can be reproduced by the reader.


Datasets and R Code Used in This Book
Chapter 1 of Zuur et al. (2012a) and Zuur (2012b)
Cover Art
1 Introduction to Generalized Linear Models
1.1 Linear Regression Applied On Fisheries Data
1.2 Poisson GLM
1.2.1 Poisson distribution for count data
1.2.2 Predictor function
1.2.3 Linking the mean and the predictor function
1.2.4 Maximum likelihood to estimate the parameters
1.2.5 Application of Poisson GLM on the fisheries data
1.2.6 Overdispersion
1.2.7 Adding covariates
1.2.8 Using the offset
1.3 Negative Binomial GLM
1.3.1 Negative binomial distribution for count data
1.3.2 Example of Negative binomial regression
1.3.3 Heterogeneous Negative binomial regression
1.3.4 A note on modelling under-dispersion
1.4 Binomial GLM For Binary Data
1.4.1 Parasites in honeybee larvae 1.4.2 Visualizing the data
1.4.3 Defining the three steps of a binomial GLM
1.4.4 Results for the bee data
1.4.5 Likelihood function for a binomial GLM
1.4.6 Other link functions
1.5 Binomial GLM for Proportional Data
1.5.1 Binomial distribution
1.5.2 Predictor function
1.5.3 Link function
1.5.4 Fitting the model in R
1.6 Other Distributions
2 Generalized Linear Modelling Applied to Red Squirrel Data
2.1 Red Squirrels
2.2 Importing the Data
2.3 Data Exploration
2.3.1 Outliers
2.3.2 Collinearity
2.3.3 Relationships
2.4 Fitting The Poisson GLM in R
2.4.1 Specifying the model
2.4.2 Execute the Poisson GLM in R
2.4.3 Model validation
2.5 Fitting the Negative Binomial GLM in R
2.5.1 Using the glm.nb function
2.5.2 Heterogeneous negative binomial GLM
2.6 Bayesian Approach – Running the Poisson GLM
2.6.1 Obtaining and installing JAGS
2.6.2 Specifying the data for JAGS 2.6.3 Specifying the model for JAGS
2.6.4 Specifying the initial values
2.6.5 Parameters to store
2.6.6 Running JAGS via R

2.6.7 Generalizing the JAGS modelling code
2.7 Assessing Mixing Of Chains
2.7.1 Assess mixing of chains if R2jags is used
2.8 Model Validation
2.8.1 Checking for overdispersion
2.8.2 Obtaining Pearson residuals
2.9 Applying a Negative Binomial GLM in Jags
2.10 Mixing of Chains
2.11 Model Validation
2.12 Model Interpretation
2.13 Discussion
2.14 What to Present in A Paper
3 GLM Applied to Presence-Absence Polychaeta Data
3.1 Marine Benthic Data
3.2 Importing the Data and Housekeeping
3.3 Data Exploration
3.4 Binary GLM; A Frequentist Approach
3.4.1 Specifying the distribution and link function
3.4.2 Specifying the predictor function
3.4.3 Running the glm function
3.4.4 Results of the glm function
3.4.5 Model selection
3.4.6 Results of the optimal model
3.4.7 Model validation
3.4.8 Visualizing the model
3.5 Fitting A Bernoulli GLM in JAGS
3.5.1 Specifying the data for JAGS
3.5.2 JAGS modelling code
3.5.3 Initial values and parameters to save
3.5.4 Running JAGS from R
3.5.5 JAGS results presented within R
3.6 Model Selection Using AIC, DIC AND BIC in JAGS
3.7 Model Interpretation
3.8 Discussion
3.9 What to Present in A Paper
4 Introduction to Mixed Effects Models
4.1 Spiders . 4.2 Linear Regression Applied on the Spider Data
4.3 Linear Mixed Effects Models 4.3.1 Model formulation and interpretation
4.3.2 Fitting a linear mixed effects model using lmer
4.3.3 Analysis using lmer
4.4 Fitting a Linear Mixed Effects Model in Jags
4.5 Using a Variable As a Fixed or Random Term?
4.6 Random Intercept and Slope Model
4.7 Generalized Linear Mixed Effects Models
5 Glmm Applied on Honeybee Pollination Data
5.1 Honeybees and Dandelion Pollen
5.2 Data Description And Importing The Data
5.3 Data Exploration
5.4 Building Up a Model
5.5 Poisson GLMM Using Glmer
5.6 poisson GLMM Using JAGS
5.6.1 Data for JAGS
5.6.2 JAGS modelling code

5.6.3 Likelihood
5.6.4 Priors
5.6.5 Initial values
5.6.6 Parameters to save
5.6.7 Executing JAGS and obtaining results
5.7 Negative Binomial GLMM Using GlmmADMB
5.8 Negative Binomial GLMM Using JAGS
5.8.1 Data for JAGS
5.8.2 JAGS modelling code
5.8.3 Initial values
5.8.4 Parameters to save
5.8.5 Executing JAGS and obtaining results
5.8.6 Mixing of chains
5.8.7 Model validation
5.8.8 Model interpretation
5.9 GLMM With Auto-Regressive Correlation
5.9.1 Simulate temporal correlated counts
5.9.2 JAGS to estimate the Poisson GLM with AR correlation
5.9.3 Multiple Poisson time series
5.9.4 Poisson GLMM with AR correlation
5.10 What to Present in a Paper
6 GLMM for Strictly Positive Data: Biomass of Rainforest Trees
6.1 Rainforest Tree Species
6.2 Importing the Data and Housekeeping
6.3 Data Exploration
6.3.1 Outliers
6.3.2 Collinearity
6.3.3 Relationships
6.4 Multiple Linear Regression: A Frequentist Approach
6.5 Gamma GLM Using A Frequentist Approach
6.5.1. Formulating the gamma GLM
6.5.2 Scale and shape
6.5.3 Visualizing the gamma distribution
6.5.4 Different link functions
6.5.5 Running the Gamma GLM using the GLM function
6.5.6 Scale confusion
6.5.7 Identity link and inverse link function
6.6 Fitting A GAMMA GLM using JAGS
6.6.1 Specifying the data for JAGS
6.6.2 JAGS modelling code
6.6.3 Priors
6.6.4 Likelihood function
6.6.5 Initial values and parameters to save
6.6.6 Running JAGS from R
6.6.7 JAGS results presented within R
6.6.8 Model interpretation
6.6.9 Model validation
6.7 ADDING More Covariates to the GAMMA GLM in JAGS
6.8 gamma GLMM
6.8.1 R code for a gamma GLMM in JAGS
6.8.2 Results from JAGS for the gamma GLMM
6.9 Truncated Gaussian Linear Regression
6.9.1 Zero trick to fit any statistical distribution in JAGS
6.9.2 Multiple linear regression in JAGS with the zero trick
6.9.3 Tobit model in JAGS

6.9.4 Tobit model with random effects in JAGS
6.10 Discussion
6.11 What to Present in a Paper
7 Binomial, Beta-Binomial, and Beta GLMM Applied to Cheetah Data
7.1 Stereotypic Behaviours in Captive Cheetahs
7.2 Importing the Data
7.3 Data Exploration
7.3.1 Outliers
7.3.2 Collinearity
7.4 Binomial GLMM Using A Frequentist Approach
7.4.1 Standardizing covariates
7.4.2 Binomial GLMM with random intercept zoo
7.4.3 Executing the GLMM using the GLMER function
7.4.4 Overdispersion
7.4.5 Binomial GLMM with observation level random intercept
7.4.6 Visualization of results
7.5 Binomial GLMM With Random Intercept Zoo in JAGS
7.5.1 Data for JAGS
7.5.2 JAGS modelling code for a binomial GLMM
7.5.3 Results for the binomial GLMM 7.5.4 Overdispersion
7.6 Beta-Binomial GLMM in JAGS
7.6.1 The Beta distribution
7.6.2 From beta to beta-binomial distribution
7.6.3 JAGS code for beta-binomial GLMM
7.6.4 Beta-binomial GLMM results
7.6.5 Model validation of the beta-binomial GLMM
7.7 Using A Beta GLMM For Proportions
7.8 Comparing Estimated Parameters From All Models
7.9 Model Selection From a Frequentist Point of View 7.10 Model Selection From a Bayesian Point of View
7.10.1 Using the DIC, AIC and BIC
7.10.2 Inclusion probabilities
7.11 What To Present In A Paper
Books By Highland Statistics
Upcoming Books In 2013 And 2014

- Artículo con arbitraje
Biomasa en acahuales de tres unidades ecogeográficas del estado de Tabasco
García-Domínguez, Antonio ; Cámara Cabrales, Luisa del Carmen (coaut.) ; Van Der Wal, Hans (coaut.) ; Martínez Zurimendi, Pablo (coaut.) ;
Contenido en: Revista Mexicana de Ciencias Forestales Vol. 9, no. 48 (julio-agosto 2018) p. 69-91 ISSN: 1405-3586
Resumen en: Español | Inglés |
Resumen en español

La escasa información sobre los atributos de los acahuales en Tabasco contribuye a que no se les dé la debida importancia a esos ecosistemas en programas de conservación y como reservorios de carbono. El presente trabajo contribuye a subsanar el poco conocimiento que se tiene de la magnitud de la biomasa existente en acahuales. En tres unidades ecogeográficas, se establecieron 18 conglomerados de cuatro parcelas (10 × 40 m), en un arreglo de Y invertida, con un total de 28 800 m². Los años de abandono en las áreas de estudio fueron de 15, 20, 30 y 100; en cada uno se tomaron parámetros dasonómicos para determinar rasgos básicos estructurales y su biomasa. Los acahuales con menor tiempo de abandono mantuvieron mayor densidad de individuos, distribuidos principalmente en dos clases diamétricas (2-10 y 10-18 cm, DN) y tres estratos de altura (2-7, 7-12 y 12-17 m). El área basal y la biomasa no cambiaron, proporcionalmente, con respecto a la etapa sucesional. A pesar de la variabilidad en su estructura, estos ecosistemas mostraron tener un potencial de captura y reservorio de carbono importante, en relación a la vegetación primaria en menores lapsos de tiempo; por lo que, si se siguen manteniendo hacia etapas avanzadas de sucesión pueden ser una alternativa de vegetación estable que genere servicios ambientales de captura de carbono y biodiversidad.

Resumen en inglés

The lack of sufficient information on the attributes of the secondary vegetation in Tabasco contributes to obscure the importance of these ecosystems in conservation programs and as carbon reservoirs. The present study contributes to fill in the knowledge gaps regarding the magnitude of the existing biomass in fallow lands. In three eco-geographic units, 18 clumps —each consisting of four 10 × 40 m plots— with an inverted Y arrangement were established in secondary forests aged 15, 20, 30, and 100 years, covering a total surface area of 28 800 m². In each study area, mensuration parameters were taken to determine the plant biomass and the basic structural characteristics. Results showed that younger secondary forests support a higher density of individuals, which are mainly distributed in two diameter classes (2-10 and 10-18 cm, ND) and three height levels (2-7, 7 -12 and 12- 17 m). The basal area and biomass did not change proportionally to the age or successional stage. In spite of their variability, these ecosystems have been shown to have a high potential for carbon capture and accumulation in a shorter period compared to the primary vegetation. Therefore, if these abandoned areas continue to grow to advanced successional stages, they will be an alternative strategy for such environmental services as carbon sequestration and biodiversity.

Tesis - Maestría
*En proceso técnico. Solicítelo con la bibliotecaria de SIBE-Tapachula
Cambios en la estructura del manglar y reservorio de carbono en la zona núcleo de la Reserva La Encrucijada / Carolina Velázquez Pérez
Velázquez Pérez, Carolina ; Tovilla Hernández, Cristian (director) ; De Jesús Navarrete, Alberto (asesor) ; Romero Berny, Emilio Ismael (asesor) ;
Tapachula, Chiapas, México : El Colegio de la Frontera Sur , 2018
Clasificación: TE/583.42097275 / V4
Bibliotecas: Tapachula
SIBE Tapachula
ECO020011113 (Disponible)
Disponibles para prestamo: 1
Nota: En proceso técnico. Solicítelo con la bibliotecaria de SIBE-Tapachula

*En hemeroteca, SIBE-Villahermosa
Captura de carbono arbórea en un pastizal adyacente al río Carrizal en la ciudad de Villahermosa, Tabasco
López López, Evireny ; Guzmán Hernández, Jesús Manuel (coaut.) ; Martínez Sánchez, José Luis (coaut.) ;
Contenido en: Kuxulkab’. Revista de Divulgación Vol. XIII, no. 25 (julio-diciembre 2007), p. 41-48 ISSN: 1665-0514
Bibliotecas: Villahermosa
SIBE Villahermosa
52363-10 (Disponible)
Disponibles para prestamo: 1
Nota: En hemeroteca, SIBE-Villahermosa

*En hemeroteca, SIBE-Villahermosa
Captura de carbono en un remanente de Selva Alta Perennifolia en el Ejido Niños Héroes, Tenosique, Tabasco
Montero Gordillo, Nayme ; Castillo Acosta, Ofelia (coaut.) ; Martínez Sánchez, José Luis (coaut.) ;
Contenido en: Kuxulkab’, Revista de Divulgación Vol. XIV, no. 26 (enero-junio 2008), p. 45-49 ISSN: 1665-0514
Bibliotecas: Villahermosa
SIBE Villahermosa
52538-10 (Disponible)
Disponibles para prestamo: 1
Nota: En hemeroteca, SIBE-Villahermosa

- Libro con arbitraje
Carbon sequestration potential of agroforestry systems: opportunities and challenges / B. Mohad Kumar, P. K. Ramachandran Nair, editors
Kumar, Mohan B. (editor) ; Nair, Ramachandran P. K. (editor) ;
London, England, United Kingdom : Springer Press , 2011
Clasificación: 363.738747 / C37
Bibliotecas: Campeche , San Cristóbal
SIBE Campeche
ECO040005090 (Disponible)
Disponibles para prestamo: 1
SIBE San Cristóbal
ECO010008468 (Prestado)
Disponibles para prestamo: 0
Índice | Resumen en: Inglés |
Resumen en inglés

This multi-authored volume contains peer-reviewed chapters from the world’s leading researchers and professionals in this topic. It is a compendium of original research articles, case studies, and regional overviews and summarizes the current state of knowledge on carbon sequestration potential of agroforestry systems. The main hypothesis of the book is that the farmers since time immemorial have integrated an array of tree and crop species in their land use systems as a means to achieve higher productivity, risk avoidance, product diversification, and sustainability. These multispecies production systems also impact the ecosystem processes favorably. Yet, our understanding of the diversity attributes and carbon dynamics under agroforestry is not adequate. Although carbon sequestration is a focal theme of discussion in most agroforestry and climate conferences, publications on carbon sequestration in agroforestry are scattered. This book, with 16 chapters organized into three broad sections titled: Measurement and Estimation, Agrobiodiversity and Tree Management, and Policy and Socioeconomic Aspects, represent a cross section of the opportunities and challenges in current research and emerging issues in harnessing carbon sequestration potential of agroforestry systems. The book is unique in its exclusive and global coverage of the subject, and constitutes a valuable reference material for students and researchers in the field of agroforestry and climate change mitigation.


Part I Measurement and Estimation
Methodological Challenges in Estimating Carbon Sequestration Potential of Agroforestry Systems
Carbon Sequestration Potential of Agroforestry Practices in Temperate North America
Carbon Sequestration in European Agroforestry Systems
Carbon Sequestration Potential of Agroforestry Systems in Africa
Soil Carbon Sequestration in Cacao Agroforestry Systems: A Case Study from Bahia, Brazil
Carbon Sequestration Potential of Silvopastoral and Other Land Use Systems in the Chilean Patagonia
Carbon Pools in Tree Biomass and Soils Under Rotational Woodlot Systems in Eastern Tanzania
Silvopasture and Carbon Sequestration with Special Reference to the Brazilian Savanna (Cerrado)
Biomass and Carbon Accumulation in Land Use Systems of Claveria, the Philippines
Part II Agrobiodiversity and Tree Management Linking Carbon, Biodiversity and Livelihoods Near Forest Margins: The Role of Agroforestry
Assessing the Carbon Sequestration in Short Rotation Coppices of Robinia pseudoacacia L. on Marginal Sites in Northeast Germany
Does Tree Management Affect Biomass and Soil Carbon Stocks of Acacia mangium Willd. Stands in Kerala, India?
Part III Policy and Socioeconomic Aspects Can Forest Carbon Finance Influence Land Tenure Security in Project Areas? Preliminary Lessons from Projects in Niger and Kenya
Constructing Public Policy in a Participatory Manner: From Local Carbon Sequestration Projects to Network Governance in Chiapas, Mexico
Inpang Carbon Bank in Northeast Thailand: A Community Effort in Carbon Trading from Agroforestry Projects
The Socioeconomic Context of Carbon Sequestration in Agroforestry: A Case Study from Homegardens of Kerala, India