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Estimation of above‐ground live biomass and carbon stocks in different plant formations and in the soil of dry forests of the Ecuadorian coast

Estimation of above‐ground live biomass and carbon stocks in different plant formations and in... IntroductionOne of the environmental problems arising from human development is the increase in greenhouse gas emissions. Specifically, CO2 emissions have prompted research related to climate change so as to contribute with knowledge that will help mitigate such emissions (Pérez et al. ; Fonseca et al. ; Aguilar‐Arias et al. ). According to the United Nations Framework Convention on Climate Change (UNFCCC), adopted in New York in May 1992, natural climate variability observed over comparable time periods are attributed directly or indirectly to human activity (land‐use change, use of fossil fuels, use of agrochemicals, among others), which cause an increase in “greenhouse gas” concentrations in the atmosphere, affecting the increase in the average global temperature.In view of this, the IPCC () warns that, in the future, gases such as nitrous oxide (N2O), carbon dioxide (CO2), methane (CH4), and ozone (O3) would produce a global temperature increase between 3°C and 5°C, which would affect current precipitation patterns due to its impact on the land‐ocean‐atmosphere system. Of these, carbon dioxide (CO2) is the most significant because of the large quantities produced as a result of human activity. In addition, about 20% of CO2 emissions result from degradation or removal of natural ecosystems such as forests (Schimel et al. ; Schlegel et al. ). However, and in order to gather information regarding mitigation and adaptation options, the premise is that it is possible to capture carbon dioxide from the atmosphere and store it in the ecosystems themselves, preventing them from accumulating in the atmosphere.In this context, several studies point out the potential of forests in terms of carbon storage, including studies carried out by Yatskov (), Jew et al. (), Fonseca et al. (, , ), De Britez (), Mutuo et al. (), Oelbermann et al. (), Schimel et al. (), Ávila et al. (), among others. This is how forest ecosystems appear as large carbon sinks containing more than 80% of all above‐ground carbon.Nevertheless, carbon storage capacity can vary markedly depending on the structure and composition of a forest. It could be therefore assumed that rainforests, because of their diversity and the size of the individuals living in them, have greater carbon storage capacity than dry forests. The latter are some of the least known and most threatened terrestrial ecosystems (Murphy and Lugo ); they are characterized by seasonal ecological and production processes, and, when compared to rainforests, are of lower stature and basal area (Gentry et al. ; Linares‐Palomino ,).In general, Ecuador's dry forests are scarcely known, highly threatened, and economically important for large segments of the rural population, as they provide timber and nontimber products for subsistence and sometimes for sale. Several researches have been carried out on this type of forest, but it was not until only a few years ago that, after an intense and successful project, Ecuador managed to obtain important data at the country level, thanks to the so‐called National Forest Assessment.This type of (dry) forest can be found along the Ecuadorian coastline, the most diverse of which are located in the province of Loja (219 species), Guayas (169 species), and Manabí (143 species; Aguirre et al. ). In Manabí, dry forests are little known and highly threatened because of the economic importance they represent for certain rural sectors for whom they provide timber and consumer products.In this sense, and as research progresses, a significant number of methodologies and guidelines have been established. Often, inventories use permanent plots of measurement to obtain statistically reliable data and reduce monitoring costs. In this regard, there are two commonly used methods to estimate biomass: the direct and the indirect methods.The direct or destructive method consists in cutting down trees and determining their biomass by directly weighing each component. However, in this case, an indirect method was used given that this research was carried out in a reserve area. First, the above‐ground biomass was estimated followed by the carbon stored in said biomass.This indirect method consists in the use of models based on mathematical equations that relate biomass to tree variables (DBH, total height, wood density, crown diameter, among others). Above‐ground biomass may be calculated using allometric equations provided that a statistically representative sampling is designed to measure the independent variables of the selected allometric equation.The purpose of this study is to estimate the carbon stocks of the above‐ground biomass expressed in megagrams (Mg) of oven‐dry weight/unit area, in addition to the carbon stored in the soils of three plant formations in dry forests along the Ecuadorian coastline.Materials and MethodsThe research was carried out in an area located along the center of the Ecuadorian coast, consisting of the eastern and western slopes of the Pacoche, Los Lugos, Agua Fria, and Monte Oscuro mountains, which form part of the discontinuous massif of the coastal mountain range in Manabí. Politically, the area is part of the parishes of San Lorenzo and Montecristi, belonging to the cantons of Manta and Montecristi, respectively, and within microregion 4 of the province of Manabí. Specifically, the area under study was the terrestrial part of the Refugio de Vida Silvestre Marino Costero Pacoche (Pacoche Marine and Coastal Wildlife Refuge; RVSMC‐Pacoche), which occupies around 5096.41 ha (MAE, ).The area stretches from sea level to 363 m above sea level. It is crossed longitudinally by the E15 or Marginal Way of the Coast, which connects Manabí with provinces Península de Santa Elena, to the South, and Esmeraldas, to the North. The southern boundary is located 30 km from Puerto Cayo, a small town with tourist importance in the area. To the north, and 25 km from the boundary, lies the city of Manta, the closest city with tourism potential.Even though the Ecuadorian State, through the National Forest Assessment developed by the Ministry of the Environment, has established a methodology for carbon studies, such methodology considers many other parameters that are not included in the scope of this research. However, both the methodology developed by the Ministry of the Environment and that of this paper use the same allometric equation to estimate carbon in above‐ground live biomass.Determining plot type and numberDetermining the type and number of plots was subject to the type of coverage, the precision required, the availability of resources for the development of field activities, and laboratory analyses (Rügnitz et al. ), and the objectives of the research (IDEAM, ; Yepes and Duque ). Given the exploratory purpose, the number of plots and sampling intensity is based on minimum sampling for carbon estimation investigations in areas with low tree density and small diameters (Rügnitz et al. ). However, the calculations to determine the number of plots were verified using the Winrock Sample Plot Calculator Spreadsheet Tool, a tool developed within the framework of the Clean Development Mechanism (CDM; Walker et al. ).Thus, temporary plots, generally used in rapid exploratory sampling, were determined. The information gathered result from specific data that do not require delimiting the unit or marking individuals for a periodic evaluation (Melo and Vargas ). Each plot had an area of 250 m2 (10 m × 25 m), and five replications (MINAM, ) were installed in each of the previously determined plant formations. Sampling intensity was set at ±20% (Pearson et al. ) of the average carbon at a 95% confidence level. This means, for example, that in 95% of the cases in which a 100 Mg C per ha carbon value is identified, the actual amount will be between 80 and 120 Mg C per ha.Carbon poolsThe carbon pools of the various forest formations were selected based on logistic factors (ease to transport samples to laboratories, and technical aspects for REDD projects). In this context, two of the five pools that can be measured (Brown ; Rügnitz et al. ; IDEAM, ; Yepes and Duque ) were selected: above‐ground live biomass and soil carbon.Carbon stored in above‐ground biomassCompared to the destructive method, the indirect method is less expensive, and requires less time and less resources, which is why the latter was used to determine the carbon stores in above‐ground biomass. In any case, destructive methods would not have been possible given that the area of study is a protected area. Thus, the allometric equation for mixed dry forests proposed by Chave et al. () was used, requiring to measure variables in trees within the plots, to be entered into the following model: AGBest=exp(−2.187+0.916×ln(ρD2H))≡0.0112×(ρD2H)0.916,where AGBest = Estimated above‐ground biomass (kg DM./tree); ρ = Wood density (g/cm3); D = Diameter at chest height (cm); H = Total height of the tree (m).Measurement of dasometric variablesOnce the sampling plots were installed, the required measurements were taken to apply to the allometric model. Such model includes measurements of the total height (m), DBH (cm), and wood density (g/cm3). Only trees with diameter at chest height >5 cm were measured.With regard to wood density, species were identified in each sampling plot and the “Global Wood Density Database was used (Zanne et al. ). The objective set was to obtain the wood density for each species. However, in cases where there were no data in the database or in other bibliographic sources, the density of the genus or family was used. (Honorio and Baker ).Calculation of carbon stock in above‐ground biomass per hectareAfter calculating the above‐ground biomass (kg dry matter/tree), the total biomass is calculated in megagrams per hectare (Mg/ha), and this value is extrapolated to the hectare, as follows: AGB=∑AU/1000×10,000/plotarea,where AGB = Above‐ground tree biomass (Mg DM/ha); ∑ AU = Sum of the tree biomass of all trees in the plot (kg DM/plot area); Factor 1000 = Conversion of sample units of kg DM/Mg; DM Factor 10,000 = Conversion of the area (m2) to hectare.Above‐ground biomass to carbon conversions were performed pursuant to the guidelines established in the IPCC Good Practice Guidance for Land Use, Land‐Use Change and Forestry (Penman et al. ), which assumes carbon content to be 50% of the above‐ground biomass of each living tree (Barrett and Christensen ; Barrett ; Hetland et al. ; Jew et al. ; Rajput ; Tashi et al. ; Vijayakumar et al. ; Yatskov ). ΔAGB=(AGB×CF),where ΔAGB = Carbon amount in above‐ground biomass (Mg C/ha); AGB = Above‐ground tree biomass (Mg DM/ha); CF = Carbon Fraction (Mg C/t DM). The default value is 0.50.Soil carbonThe total amount of carbon in soil (%) was measured in each layer of the profile at depths of 0–10 cm, 10–20 cm, and 20–30 cm. Bulk density (g/cc) was measured at each depth using the cylinder method in undisturbed soils. Wet and dry soils were measured to calculate the dry soil in the cylinder and percentage of soil moisture using the following formula: Dap=PssVolc,where BD = Bulk Density; DSw = Dry Soil weight; WSw = Wet Soil Weight; Volc = Volume of the Sampling Cylinder.In the meantime, three soil samples were taken to determine organic carbon by applying the Walkley–Black method (wet oxidation method; Bazan ). The estimation of soil carbon stocks per unit area on a plot is calculated using the following formula (Eggleston et al. ; Rügnitz et al. ): COSt=∑horizon=ihorizon=nBDi×THi×1−CRi100×Ci×100,where COSt = Full profile organic carbon (Mg/ha); BDi = Bulk density of horizon i (g/cm3); THi = Thickness of horizon i (m); CRi = Volume of thick fragments of the horizon i (vol. %); Ci = % of organic carbon in i horizon (%).Carbon stored in each plant formationThe total carbon stored in each plant formation will be given by the sum of the components as follows: CT=CS+CB, where: CT = Total Carbon; CS = Carbon in soil; CBA = Total carbon stored biomass (Mg C/ha).Results and DiscussionThe variability in the biophysical characteristics in the different forest formations in the study area (microclimate, land cover, use or conservation status) causes differences in the carbon stored inside each formation (IDEAM, ; Phillips et al. ; Yepes and Duque ).Total carbon stored in the above‐ground biomass was higher in the Dry Semideciduous Forest (DSF), decreasing in the dry deciduous forest and dry scrubland (DS; Table ). This situation responds to the fact that in the DSF there are trees of larger size and diameter and more diversity. Moreover, MS forests are characterized by shrub vegetation and are more threatened by human activity.Above‐ground biomass carbon (Mg/ha) stored in the plant formations present in the study areaPlant formationsCarbon stored (Mg C/ha)SDDS33.4713.26DDF38.4924.43DSF59.7725.93DS, Dry Scrubland; DDF, Dry Deciduous Forest; DSF, Dry Semideciduous Forest; SD, standard deviation.The results obtained are in agreement with the research carried out on this type of plant formation where carbon storage in above‐ground biomass in dry forests could be between 25 and 60 Mg C/ha (Brown et al. ; Brown and Lugo ; Sánchez and Méndez ). Similarly, the Ministry of Environment of Ecuador (MAE), in its publication “Estadísticas de Patrimonio Natural” (Natural Heritage Statistics; MAE ) reports a mean dry forest carbon data of 37 Mg/ha which is in line with what was found in this study.Furthermore, soil is an important carbon sink, containing more carbon than the sum in vegetation and the atmosphere (Swift ). This is why the IPCC recommends that it be considered as one of the compartments that should be evaluated in greenhouse gas inventories, for which the estimation is suggested to a depth of 30 cm (Eggleston et al. ; Solomon ). Accordingly, the results of the estimation of carbon stored in the study area are shown in Table .Carbon stored in soils of plant formations in the area of studyPlant formationOrganic matter (%)Bulk densityCarbon stored (Mg C/ha)Total carbon (Mg C/ha)Depth (cm)BD (g/cm3)DS1.500–101.0815.6626.830.9010–201.055.480.9020–301.095.69DDF3.300–100.9518.1831.131.0010–201.015.861.1020–301.117.08DSF5.400–101.1134.7763.282.4010–201.0915.172.0020–301.1513.34DS, Dry Scrubland; DDF, Dry Deciduous Forest; DSF, Dry Semideciduous Forest; BD, Bulk density.In general terms, the results show low bulk densities at different depths and were not significantly different; they fluctuated between 0.95 and 1.15 g/cm3, which indicate that DDF and DSF organic soils are rich in humus, approaching the characteristics of loam soils; these being a little more clayey. These values tend to increase with depth, due to the greater biological activity in the horizon A and in DS.The organic matter content for DS showed values ranging from 1.50% to 0.90%. Because many of these areas are intended for livestock, organic matter in the first horizon could increase. However, the values found do not show significant differences. Similarly, DDF results range from 1.00% to 3.30%, with the highest value corresponding to the first 10 cm of soil. In DSF, the highest organic matter value in the soil (2.00–5.40%) was found in the first 10 cm of the soil profile. This could be explained by the greater littering and biological activity in horizon A.Therefore, based on the recommendation issued by the United States Department of Agriculture (USDA)'s Soil Survey Laboratory (SSL), the Van Bemmelen correction factor (1.724) was used to calculate the total carbon stored, assuming that the organic matter has 58% of organic carbon, yielding the following values: 26.83, 31.13, and 63.28 Mg C/ha for DS, DDF, and DSF, respectively.Moreover, the estimated values of soil carbon storage in the study area are in agreement with Balesdent and Arrouays () and Trumbmore et al. () who reported stocks between 60 and 70 Mg C/ha in forest soils. Along with evidence of soil carbon storage, it should also be considered that the change in soil carbon content due to land use does not exceed 20 Mg C/ha (IPCC, ).Figure  shows the estimated data of carbon stored in biomass, carbon stored in soil, and total carbon for each of the plant formations under study. Thus, it can be observed that DSF contains more carbon in the above‐ground biomass (59.77 Mg/ha) than DDF and DS (38.49 and 33.47 Mg/ha, respectively). Carbon stored in soil followed this trend with 63.28 Mg/ha of carbon stock in DSF, followed by DDF with 31.13 and 26.83 Mg/ha for DS. The total carbon stored in each plant formation was represented by the sum of carbon in above‐ground live biomass and soil carbon, yielding values of 60.30, 69.62, and 123.05 Mg of Carbon per hectare for DS, DDF, and DSF, respectively.Carbon stocks (Mg/ha) for each of the plant formations under study. DS, Dry Scrubland; DDF, Dry Deciduous Forest; DSF, Dry Semideciduous Forest.ConclusionsThe carbon stored in live above‐ground biomass was higher in Dry Semideciduous Forest (59.77 Mg C/ha) followed by the formation of Dry Deciduous Forest (38.49 Mg C/ha) and Dry Scrubland (33.47 Mg C/ha).The soils of the Dry Semideciduous Forest formation have more stored carbon (63.28 Mg C/ha) than the Dry Deciduous Forest (31.13 Mg C/ha) and Dry Scrubland (26.83 Mg C/ha).The formation of dry semi‐deciduous forest contains more total carbon stocks (123.05 Mg C/ha) than the formations of Dry Deciduous Forest (69.62 Mg C/ha) and Dry Scrubland (60.30 Mg C/ha).For this case, the carbon stock was related to altitude; at higher altitude, higher carbon stocks.AcknowledgmentsThanks are extended to the Secretaría de Educación Superior, Ciencia, Tecnología e Innovación (SENESCYT); Instituto de Fomento al Talento Humano del Ecuador; Universidad Nacional Agraria La Molina (UNALM); Universidad Técnica de Manabí (UTM); Ministerio de Ambiente del Ecuador (MAE); Dirección del Refugio de Vida Silvestre Marino Costero Pacoche; Ing. Juan Manuel Moreira Castro (Jardín Botánico UTM).Conflict of InterestNone declared.NotesDeclared as a wildlife refuge by Ministerial Agreement No. 131, dated September 2, 2008.Usually, for forest projects, a precision level (sampling error) of ±10% of the average carbon value is used at a 95% confidence level. However, in the case of small‐scale CDM projects, a level of accuracy of ±20% is used (Pearson et al. ; Emmer ).In carbon projects, it is essential to include above‐ground biomass as a sink, as it is the pool that is most affected by deforestation/degradation of forests (BioCarbonFund, ).ReferencesAguilar‐Arias, H., E. Ortiz‐Malavasi, B. Vílchez‐Alvarado, and R. L. Chazdon. 2012. Biomasa sobre el suelo y carbono orgánico en el suelo en cuatro estadios de sucesión de bosques en la Península de Osa, Costa Rica. Rev. For. Mesoamericana Kurú 9:22–31.Aguirre, Z., L. P. Kvist, and O. Sánchez. 2006. Bosques secos en Ecuador y su diversidad. Bot. Econ. Andes Centrales 2:162–187.Ávila, G., F. Jiménez, J. Beer, M. Gómez, M. Ibrahim. 2001. Almacenamiento, fijación de carbono y valoración de servicios ambientales en sistemas agroforestales en Costa Rica. (CATIE) 8:32–35.Balesdent, J., and D. Arrouays. 1999. Usage des terres et stockage de carbone dans les sols du territoire francais. Une estimation des flux nets annuels pour la periode 1990‐1999. An estimate of the net annual carbon storage in French soils induced by land use change from 1900 to 1999 (note p). Comptes Rendus 85:265–277.Barrett, T. 2014. Storage and flux of carbon in live trees, snags, and logs in the Chugach and Tongass National Forests.Barrett, T. M., and G. A. Christensen. 2011. Forests of Southeast and South‐Central Alaska, 2004–2008: Five‐year forest inventory and analysis report.Bazan, R. 1996. Manual para análisis químico de suelos, aguas y plantas. Universidad Nacional Agraria la Molina. Ed. F Perú, Lima, Perú.BioCarbonFund. 2008. Methodology for estimating reductions in GHG emissions from mosaic deforestation. BioCarbon Fund, Washington, DC, USA.Brown, S. 2002. Measuring, monitoring, and verification of carbon benefits for forest–based projects. Philos. Trans. Roy. Soc. Lond. A Math. Phys. Eng. Sci. 360:1669–1683.Brown, S., and A. E. Lugo. 1992. Aboveground biomass estimates for tropical moist forests of the Brazilian Amazon. Interciencia 17:8–18.Brown, S., A. J. R. Gillespie, and A. E. Lugo. 1989. Biomass estimation methods for tropical forests with applications to forest inventory data. For. Sci. 35:881–902.Chave, J., C. Andalo, S. Brown, M. A. Cairns, J. Q. Chambers, D. Eamus, et al. 2005. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145:87–99.De Britez, R. M.. 2007. Estoque e incremento de carbono em florestas e povoamentos de espécies arbóreas com ênfase na Floresta Atlântica do sul do Brasil. Embrapa Florestas; Sociedade de Pesquisa em Vida Selvagem e Educação Ambiental, Curitiba.Eggleston, H. S., L. Buendia, K. Miwa, T. Ngara, and K. Tanabe. 2006. IPCC Guidelines for National Greenhouse Gas Inventories. Prepared by the National Greenhouse Gas Inventories Programme, vol. 4. IGES, Japan.Emmer, I. 2007. Manual de contabilidad de carbono y diseño de proyectos. Proyecto Encofor. Quito, Ecuador., Ecofor, p. 22.Fonseca, W., F. Alice, and J. M. Rey. 2009. Modelos para estimar la biomasa de especies nativas en plantaciones y bosques secundarios en la zona Caribe de Costa Rica. Bosque (Valdivia) 30:36–47.Fonseca, W., J. M. R. Benayas, and F. E. Alice. 2011. Carbon accumulation in the biomass and soil of different aged secondary forests in the humid tropics of Costa Rica. For. Ecol. Manage. 262:1400–1408.Fonseca, G., J. Galán, A. Enciso, R. Garduño, and E. Méndez. 2015. Cambio climático. s.l., CULCyT, 18.Gentry, A. H., S. H. Bullock, H. A. Mooney, and E. Medina. 1995. Seasonally dry tropical forests. Pp. 146–194.Hetland, J., P. Yowargana, S. Leduc, and F. Kraxner. 2016. Carbon‐negative emissions: systemic impacts of biomass conversion: a case study on CO2 capture and storage options. Int. J. Greenhouse Gas Control 49:330–342.Honorio, E. N., and T. R. Baker. 2010. Manual para el monitoreo del ciclo del carbono en bosques amazónicos. Instituto de Investigaciones de la Amazonia Peruana, Lima, Perú.IDEAM. 2010. Segunda comunicación nacional ante la Convención Marco de las Naciones Unidas sobre Cambio Climático. Instituto de Hidrología, Meteorología y Estudios Ambientales, Bogotá, Colombia.IPCC. 2007. IPCC Guidelines for national greenhouse gas inventories: Reference Manual. 1996. 2007.Jew, E. K. K., A. J. Dougill, S. M. Sallu, J. O'Connell, and T. G. Benton. 2016. Miombo woodland under threat: consequences for tree diversity and carbon storage. For. Ecol. Manage. 361:144–153.Linares‐Palomino, R. 2004a. Los bosques tropicales estacionalmente secos: I. El concepto de los bosques secos en el Perú. Arnoldia 11:85–102.Linares‐Palomino, R. 2004b. Los bosques tropicales estacionalmente secos: II. Fitogeografía y composición florística. Arnoldia 11:103–138.MAE. 2009. Plan de manejo. Refugio de vida silvestre marina y costera Pacoche.MAE. 2015. Estadísticas del Patrimonio Natural: Datos de bosques, ecosistemas, especies, carbono y deforestación del Ecuador continental. Pp. 1–20.Melo, O., and R. Vargas. 2003. Evaluación Ecológica y Silvicultural de Ecosistemas Boscosos.MINAM. 2009. Identificación de Metodologías existentes para determinar stock de carbono en ecosistemas forestales. Ministerio de Ambiente del Perú, Lima, Perú.Murphy, P. G., and A. E. Lugo. 1986. Ecology of tropical dry forest. Annu. Rev. Ecol. Syst. 1986:67–88.Mutuo, P. K., G. Cadisch, A. Albrecht, C. A. Palm, and L. Verchot. 2005. Potential of agroforestry for carbon sequestration and mitigation of greenhouse gas emissions from soils in the tropics. Nutr. Cycl. Agroecosyst. 71:43–54.Oelbermann, M., R. P. Voroney, and A. M. Gordon. 2004. Carbon sequestration in tropical and temperate agroforestry systems: a review with examples from Costa Rica and southern Canada. Agr. Ecosyst. Environ. 104:359–377.Pearson, T., S. Walker, and S. Brown. 2005. Sourcebook for Land use. Land‐use change and forestry projects. Development 21:64.Penman, J., M. Gytarsky, T. Hiraishi, T. Krug, D. Kruger, R. Pipatti, et al. 2003. Good practice guidance for land use, land‐use change and forestry. Institute for Global Environmental Strategies, Hayama, Japan.Pérez, A. R. Y., V. Pocomucha, and Y. Vargas. 2009. Carbono almacenado en diferentes sistemas de uso de la tierra del Distrito de José Crespo y Castillo, Huánuco, Perú. 48.Phillips, J. F., A. J. Duque, K. R. Cabrera, A. P. Yepes, D. A. Navarrete, M. C. García, et al. 2011. Estimación de las reservas potenciales de carbono almacenadas en la biomasa aérea en bosques naturales de Colombia. Instituto de Hidrología, Meteorología, y Estudios Ambientales‐IDEAM, Bogotá, CO.Rajput, P. 2016. Carbon storage, soil enrichment potential and bio‐economic appraisal of different land use systems in mid hill and sub‐humid zone‐II of Himachal Pradesh.Rügnitz, M., M. Chacón, and R. Porro. 2009. Guía para la determinación de carbono en pequeñas propiedades rurales, 1st ed. Centro Mundial Agroflorestal (ICRAF)/Consórcio Iniciativa Amazônica (IA), Lima, Perú.Sánchez, M. D., and M. R. Méndez. 2003. Estudio FAO producción y sanidad animal; 155. Agroforestería para la producción animal en América Latina‐II.Schimel, D. S., J. I. House, K. A. Hibbard, P. Bousquet, P. Ciais, P. Peylin, et al. 2001. Recent patterns and mechanisms of carbon exchange by terrestrial ecosystems. Nature 414:169–172.Schlegel, B., J. Gayoso, and J. Guerra. 2001. Manual de procedimientos para inventarios de carbono en ecosistemas forestales. Universidad Austral de Chile. Proyecto FONDEF D98I1076. Valdivia, Chile 18: 19–20.Solomon, S.. 2007. Climate change 2007‐the physical science basis: working group I contribution to the fourth assessment report of the IPCC, vol. 4. Cambridge University Press, New York, USA.Swift, R. S. 2001. Sequestration of carbon by soil. Soil Sci. 166:858–871.Tashi, S., B. Singh, C. Keitel, and M. Adams. 2016. Soil carbon and nitrogen stocks in forests along an altitudinal gradient in the eastern Himalayas and a meta‐analysis of global data. Glob. Change Biol. 22:2255–2268.Trumbore, S. E., E. A. Davidson, P. de Camargo, D. C. Nepstad, and L. A. Martinelli. 1995. Belowground cycling of carbon in forests and pastures of Eastern Amazonia. Global Biogeochem. Cycles 9:515–528.Vijayakumar, D. B. I. P., F. Raulier, P. Bernier, S. Gauthier, Y. Bergeron, and D. Pothier. 2016. Cover density recovery after fire disturbance controls landscape aboveground biomass carbon in the boreal forest of eastern Canada. For. Ecol. Manage. 360:170–180.Walker, S., T. Pearson, and S. Brown. 2014. Winrock Sample Plot Calculator Spreadsheet Tool.Yatskov, M. A. 2016. The impact of disturbance on carbon stores and dynamics in forests of coastal Alaska.Yepes, A. P., and Á. J. Duque. 2011. Protocolo para la estimación nacional y subnacional de biomasa‐carbono en Colombia. Bogot DC, Colombia, 162.Zanne, A., G. Lopez‐Gonzalez, D. Coomes, J. Ilic, S. Jansen, S. Lewis, et al. 2009. Global wood density database. Dryad Identifier. http://hdl.handle.net/10255/dryad. 235. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Food and Energy Security Wiley

Estimation of above‐ground live biomass and carbon stocks in different plant formations and in the soil of dry forests of the Ecuadorian coast

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Abstract

IntroductionOne of the environmental problems arising from human development is the increase in greenhouse gas emissions. Specifically, CO2 emissions have prompted research related to climate change so as to contribute with knowledge that will help mitigate such emissions (Pérez et al. ; Fonseca et al. ; Aguilar‐Arias et al. ). According to the United Nations Framework Convention on Climate Change (UNFCCC), adopted in New York in May 1992, natural climate variability observed over comparable time periods are attributed directly or indirectly to human activity (land‐use change, use of fossil fuels, use of agrochemicals, among others), which cause an increase in “greenhouse gas” concentrations in the atmosphere, affecting the increase in the average global temperature.In view of this, the IPCC () warns that, in the future, gases such as nitrous oxide (N2O), carbon dioxide (CO2), methane (CH4), and ozone (O3) would produce a global temperature increase between 3°C and 5°C, which would affect current precipitation patterns due to its impact on the land‐ocean‐atmosphere system. Of these, carbon dioxide (CO2) is the most significant because of the large quantities produced as a result of human activity. In addition, about 20% of CO2 emissions result from degradation or removal of natural ecosystems such as forests (Schimel et al. ; Schlegel et al. ). However, and in order to gather information regarding mitigation and adaptation options, the premise is that it is possible to capture carbon dioxide from the atmosphere and store it in the ecosystems themselves, preventing them from accumulating in the atmosphere.In this context, several studies point out the potential of forests in terms of carbon storage, including studies carried out by Yatskov (), Jew et al. (), Fonseca et al. (, , ), De Britez (), Mutuo et al. (), Oelbermann et al. (), Schimel et al. (), Ávila et al. (), among others. This is how forest ecosystems appear as large carbon sinks containing more than 80% of all above‐ground carbon.Nevertheless, carbon storage capacity can vary markedly depending on the structure and composition of a forest. It could be therefore assumed that rainforests, because of their diversity and the size of the individuals living in them, have greater carbon storage capacity than dry forests. The latter are some of the least known and most threatened terrestrial ecosystems (Murphy and Lugo ); they are characterized by seasonal ecological and production processes, and, when compared to rainforests, are of lower stature and basal area (Gentry et al. ; Linares‐Palomino ,).In general, Ecuador's dry forests are scarcely known, highly threatened, and economically important for large segments of the rural population, as they provide timber and nontimber products for subsistence and sometimes for sale. Several researches have been carried out on this type of forest, but it was not until only a few years ago that, after an intense and successful project, Ecuador managed to obtain important data at the country level, thanks to the so‐called National Forest Assessment.This type of (dry) forest can be found along the Ecuadorian coastline, the most diverse of which are located in the province of Loja (219 species), Guayas (169 species), and Manabí (143 species; Aguirre et al. ). In Manabí, dry forests are little known and highly threatened because of the economic importance they represent for certain rural sectors for whom they provide timber and consumer products.In this sense, and as research progresses, a significant number of methodologies and guidelines have been established. Often, inventories use permanent plots of measurement to obtain statistically reliable data and reduce monitoring costs. In this regard, there are two commonly used methods to estimate biomass: the direct and the indirect methods.The direct or destructive method consists in cutting down trees and determining their biomass by directly weighing each component. However, in this case, an indirect method was used given that this research was carried out in a reserve area. First, the above‐ground biomass was estimated followed by the carbon stored in said biomass.This indirect method consists in the use of models based on mathematical equations that relate biomass to tree variables (DBH, total height, wood density, crown diameter, among others). Above‐ground biomass may be calculated using allometric equations provided that a statistically representative sampling is designed to measure the independent variables of the selected allometric equation.The purpose of this study is to estimate the carbon stocks of the above‐ground biomass expressed in megagrams (Mg) of oven‐dry weight/unit area, in addition to the carbon stored in the soils of three plant formations in dry forests along the Ecuadorian coastline.Materials and MethodsThe research was carried out in an area located along the center of the Ecuadorian coast, consisting of the eastern and western slopes of the Pacoche, Los Lugos, Agua Fria, and Monte Oscuro mountains, which form part of the discontinuous massif of the coastal mountain range in Manabí. Politically, the area is part of the parishes of San Lorenzo and Montecristi, belonging to the cantons of Manta and Montecristi, respectively, and within microregion 4 of the province of Manabí. Specifically, the area under study was the terrestrial part of the Refugio de Vida Silvestre Marino Costero Pacoche (Pacoche Marine and Coastal Wildlife Refuge; RVSMC‐Pacoche), which occupies around 5096.41 ha (MAE, ).The area stretches from sea level to 363 m above sea level. It is crossed longitudinally by the E15 or Marginal Way of the Coast, which connects Manabí with provinces Península de Santa Elena, to the South, and Esmeraldas, to the North. The southern boundary is located 30 km from Puerto Cayo, a small town with tourist importance in the area. To the north, and 25 km from the boundary, lies the city of Manta, the closest city with tourism potential.Even though the Ecuadorian State, through the National Forest Assessment developed by the Ministry of the Environment, has established a methodology for carbon studies, such methodology considers many other parameters that are not included in the scope of this research. However, both the methodology developed by the Ministry of the Environment and that of this paper use the same allometric equation to estimate carbon in above‐ground live biomass.Determining plot type and numberDetermining the type and number of plots was subject to the type of coverage, the precision required, the availability of resources for the development of field activities, and laboratory analyses (Rügnitz et al. ), and the objectives of the research (IDEAM, ; Yepes and Duque ). Given the exploratory purpose, the number of plots and sampling intensity is based on minimum sampling for carbon estimation investigations in areas with low tree density and small diameters (Rügnitz et al. ). However, the calculations to determine the number of plots were verified using the Winrock Sample Plot Calculator Spreadsheet Tool, a tool developed within the framework of the Clean Development Mechanism (CDM; Walker et al. ).Thus, temporary plots, generally used in rapid exploratory sampling, were determined. The information gathered result from specific data that do not require delimiting the unit or marking individuals for a periodic evaluation (Melo and Vargas ). Each plot had an area of 250 m2 (10 m × 25 m), and five replications (MINAM, ) were installed in each of the previously determined plant formations. Sampling intensity was set at ±20% (Pearson et al. ) of the average carbon at a 95% confidence level. This means, for example, that in 95% of the cases in which a 100 Mg C per ha carbon value is identified, the actual amount will be between 80 and 120 Mg C per ha.Carbon poolsThe carbon pools of the various forest formations were selected based on logistic factors (ease to transport samples to laboratories, and technical aspects for REDD projects). In this context, two of the five pools that can be measured (Brown ; Rügnitz et al. ; IDEAM, ; Yepes and Duque ) were selected: above‐ground live biomass and soil carbon.Carbon stored in above‐ground biomassCompared to the destructive method, the indirect method is less expensive, and requires less time and less resources, which is why the latter was used to determine the carbon stores in above‐ground biomass. In any case, destructive methods would not have been possible given that the area of study is a protected area. Thus, the allometric equation for mixed dry forests proposed by Chave et al. () was used, requiring to measure variables in trees within the plots, to be entered into the following model: AGBest=exp(−2.187+0.916×ln(ρD2H))≡0.0112×(ρD2H)0.916,where AGBest = Estimated above‐ground biomass (kg DM./tree); ρ = Wood density (g/cm3); D = Diameter at chest height (cm); H = Total height of the tree (m).Measurement of dasometric variablesOnce the sampling plots were installed, the required measurements were taken to apply to the allometric model. Such model includes measurements of the total height (m), DBH (cm), and wood density (g/cm3). Only trees with diameter at chest height >5 cm were measured.With regard to wood density, species were identified in each sampling plot and the “Global Wood Density Database was used (Zanne et al. ). The objective set was to obtain the wood density for each species. However, in cases where there were no data in the database or in other bibliographic sources, the density of the genus or family was used. (Honorio and Baker ).Calculation of carbon stock in above‐ground biomass per hectareAfter calculating the above‐ground biomass (kg dry matter/tree), the total biomass is calculated in megagrams per hectare (Mg/ha), and this value is extrapolated to the hectare, as follows: AGB=∑AU/1000×10,000/plotarea,where AGB = Above‐ground tree biomass (Mg DM/ha); ∑ AU = Sum of the tree biomass of all trees in the plot (kg DM/plot area); Factor 1000 = Conversion of sample units of kg DM/Mg; DM Factor 10,000 = Conversion of the area (m2) to hectare.Above‐ground biomass to carbon conversions were performed pursuant to the guidelines established in the IPCC Good Practice Guidance for Land Use, Land‐Use Change and Forestry (Penman et al. ), which assumes carbon content to be 50% of the above‐ground biomass of each living tree (Barrett and Christensen ; Barrett ; Hetland et al. ; Jew et al. ; Rajput ; Tashi et al. ; Vijayakumar et al. ; Yatskov ). ΔAGB=(AGB×CF),where ΔAGB = Carbon amount in above‐ground biomass (Mg C/ha); AGB = Above‐ground tree biomass (Mg DM/ha); CF = Carbon Fraction (Mg C/t DM). The default value is 0.50.Soil carbonThe total amount of carbon in soil (%) was measured in each layer of the profile at depths of 0–10 cm, 10–20 cm, and 20–30 cm. Bulk density (g/cc) was measured at each depth using the cylinder method in undisturbed soils. Wet and dry soils were measured to calculate the dry soil in the cylinder and percentage of soil moisture using the following formula: Dap=PssVolc,where BD = Bulk Density; DSw = Dry Soil weight; WSw = Wet Soil Weight; Volc = Volume of the Sampling Cylinder.In the meantime, three soil samples were taken to determine organic carbon by applying the Walkley–Black method (wet oxidation method; Bazan ). The estimation of soil carbon stocks per unit area on a plot is calculated using the following formula (Eggleston et al. ; Rügnitz et al. ): COSt=∑horizon=ihorizon=nBDi×THi×1−CRi100×Ci×100,where COSt = Full profile organic carbon (Mg/ha); BDi = Bulk density of horizon i (g/cm3); THi = Thickness of horizon i (m); CRi = Volume of thick fragments of the horizon i (vol. %); Ci = % of organic carbon in i horizon (%).Carbon stored in each plant formationThe total carbon stored in each plant formation will be given by the sum of the components as follows: CT=CS+CB, where: CT = Total Carbon; CS = Carbon in soil; CBA = Total carbon stored biomass (Mg C/ha).Results and DiscussionThe variability in the biophysical characteristics in the different forest formations in the study area (microclimate, land cover, use or conservation status) causes differences in the carbon stored inside each formation (IDEAM, ; Phillips et al. ; Yepes and Duque ).Total carbon stored in the above‐ground biomass was higher in the Dry Semideciduous Forest (DSF), decreasing in the dry deciduous forest and dry scrubland (DS; Table ). This situation responds to the fact that in the DSF there are trees of larger size and diameter and more diversity. Moreover, MS forests are characterized by shrub vegetation and are more threatened by human activity.Above‐ground biomass carbon (Mg/ha) stored in the plant formations present in the study areaPlant formationsCarbon stored (Mg C/ha)SDDS33.4713.26DDF38.4924.43DSF59.7725.93DS, Dry Scrubland; DDF, Dry Deciduous Forest; DSF, Dry Semideciduous Forest; SD, standard deviation.The results obtained are in agreement with the research carried out on this type of plant formation where carbon storage in above‐ground biomass in dry forests could be between 25 and 60 Mg C/ha (Brown et al. ; Brown and Lugo ; Sánchez and Méndez ). Similarly, the Ministry of Environment of Ecuador (MAE), in its publication “Estadísticas de Patrimonio Natural” (Natural Heritage Statistics; MAE ) reports a mean dry forest carbon data of 37 Mg/ha which is in line with what was found in this study.Furthermore, soil is an important carbon sink, containing more carbon than the sum in vegetation and the atmosphere (Swift ). This is why the IPCC recommends that it be considered as one of the compartments that should be evaluated in greenhouse gas inventories, for which the estimation is suggested to a depth of 30 cm (Eggleston et al. ; Solomon ). Accordingly, the results of the estimation of carbon stored in the study area are shown in Table .Carbon stored in soils of plant formations in the area of studyPlant formationOrganic matter (%)Bulk densityCarbon stored (Mg C/ha)Total carbon (Mg C/ha)Depth (cm)BD (g/cm3)DS1.500–101.0815.6626.830.9010–201.055.480.9020–301.095.69DDF3.300–100.9518.1831.131.0010–201.015.861.1020–301.117.08DSF5.400–101.1134.7763.282.4010–201.0915.172.0020–301.1513.34DS, Dry Scrubland; DDF, Dry Deciduous Forest; DSF, Dry Semideciduous Forest; BD, Bulk density.In general terms, the results show low bulk densities at different depths and were not significantly different; they fluctuated between 0.95 and 1.15 g/cm3, which indicate that DDF and DSF organic soils are rich in humus, approaching the characteristics of loam soils; these being a little more clayey. These values tend to increase with depth, due to the greater biological activity in the horizon A and in DS.The organic matter content for DS showed values ranging from 1.50% to 0.90%. Because many of these areas are intended for livestock, organic matter in the first horizon could increase. However, the values found do not show significant differences. Similarly, DDF results range from 1.00% to 3.30%, with the highest value corresponding to the first 10 cm of soil. In DSF, the highest organic matter value in the soil (2.00–5.40%) was found in the first 10 cm of the soil profile. This could be explained by the greater littering and biological activity in horizon A.Therefore, based on the recommendation issued by the United States Department of Agriculture (USDA)'s Soil Survey Laboratory (SSL), the Van Bemmelen correction factor (1.724) was used to calculate the total carbon stored, assuming that the organic matter has 58% of organic carbon, yielding the following values: 26.83, 31.13, and 63.28 Mg C/ha for DS, DDF, and DSF, respectively.Moreover, the estimated values of soil carbon storage in the study area are in agreement with Balesdent and Arrouays () and Trumbmore et al. () who reported stocks between 60 and 70 Mg C/ha in forest soils. Along with evidence of soil carbon storage, it should also be considered that the change in soil carbon content due to land use does not exceed 20 Mg C/ha (IPCC, ).Figure  shows the estimated data of carbon stored in biomass, carbon stored in soil, and total carbon for each of the plant formations under study. Thus, it can be observed that DSF contains more carbon in the above‐ground biomass (59.77 Mg/ha) than DDF and DS (38.49 and 33.47 Mg/ha, respectively). Carbon stored in soil followed this trend with 63.28 Mg/ha of carbon stock in DSF, followed by DDF with 31.13 and 26.83 Mg/ha for DS. The total carbon stored in each plant formation was represented by the sum of carbon in above‐ground live biomass and soil carbon, yielding values of 60.30, 69.62, and 123.05 Mg of Carbon per hectare for DS, DDF, and DSF, respectively.Carbon stocks (Mg/ha) for each of the plant formations under study. DS, Dry Scrubland; DDF, Dry Deciduous Forest; DSF, Dry Semideciduous Forest.ConclusionsThe carbon stored in live above‐ground biomass was higher in Dry Semideciduous Forest (59.77 Mg C/ha) followed by the formation of Dry Deciduous Forest (38.49 Mg C/ha) and Dry Scrubland (33.47 Mg C/ha).The soils of the Dry Semideciduous Forest formation have more stored carbon (63.28 Mg C/ha) than the Dry Deciduous Forest (31.13 Mg C/ha) and Dry Scrubland (26.83 Mg C/ha).The formation of dry semi‐deciduous forest contains more total carbon stocks (123.05 Mg C/ha) than the formations of Dry Deciduous Forest (69.62 Mg C/ha) and Dry Scrubland (60.30 Mg C/ha).For this case, the carbon stock was related to altitude; at higher altitude, higher carbon stocks.AcknowledgmentsThanks are extended to the Secretaría de Educación Superior, Ciencia, Tecnología e Innovación (SENESCYT); Instituto de Fomento al Talento Humano del Ecuador; Universidad Nacional Agraria La Molina (UNALM); Universidad Técnica de Manabí (UTM); Ministerio de Ambiente del Ecuador (MAE); Dirección del Refugio de Vida Silvestre Marino Costero Pacoche; Ing. Juan Manuel Moreira Castro (Jardín Botánico UTM).Conflict of InterestNone declared.NotesDeclared as a wildlife refuge by Ministerial Agreement No. 131, dated September 2, 2008.Usually, for forest projects, a precision level (sampling error) of ±10% of the average carbon value is used at a 95% confidence level. However, in the case of small‐scale CDM projects, a level of accuracy of ±20% is used (Pearson et al. ; Emmer ).In carbon projects, it is essential to include above‐ground biomass as a sink, as it is the pool that is most affected by deforestation/degradation of forests (BioCarbonFund, ).ReferencesAguilar‐Arias, H., E. Ortiz‐Malavasi, B. Vílchez‐Alvarado, and R. L. Chazdon. 2012. Biomasa sobre el suelo y carbono orgánico en el suelo en cuatro estadios de sucesión de bosques en la Península de Osa, Costa Rica. Rev. For. Mesoamericana Kurú 9:22–31.Aguirre, Z., L. P. Kvist, and O. Sánchez. 2006. Bosques secos en Ecuador y su diversidad. Bot. Econ. Andes Centrales 2:162–187.Ávila, G., F. Jiménez, J. Beer, M. Gómez, M. Ibrahim. 2001. Almacenamiento, fijación de carbono y valoración de servicios ambientales en sistemas agroforestales en Costa Rica. (CATIE) 8:32–35.Balesdent, J., and D. Arrouays. 1999. Usage des terres et stockage de carbone dans les sols du territoire francais. Une estimation des flux nets annuels pour la periode 1990‐1999. An estimate of the net annual carbon storage in French soils induced by land use change from 1900 to 1999 (note p). Comptes Rendus 85:265–277.Barrett, T. 2014. Storage and flux of carbon in live trees, snags, and logs in the Chugach and Tongass National Forests.Barrett, T. M., and G. A. Christensen. 2011. Forests of Southeast and South‐Central Alaska, 2004–2008: Five‐year forest inventory and analysis report.Bazan, R. 1996. Manual para análisis químico de suelos, aguas y plantas. Universidad Nacional Agraria la Molina. Ed. F Perú, Lima, Perú.BioCarbonFund. 2008. Methodology for estimating reductions in GHG emissions from mosaic deforestation. BioCarbon Fund, Washington, DC, USA.Brown, S. 2002. Measuring, monitoring, and verification of carbon benefits for forest–based projects. Philos. Trans. Roy. Soc. Lond. A Math. Phys. Eng. Sci. 360:1669–1683.Brown, S., and A. E. Lugo. 1992. Aboveground biomass estimates for tropical moist forests of the Brazilian Amazon. Interciencia 17:8–18.Brown, S., A. J. R. Gillespie, and A. E. Lugo. 1989. Biomass estimation methods for tropical forests with applications to forest inventory data. For. Sci. 35:881–902.Chave, J., C. Andalo, S. Brown, M. A. Cairns, J. Q. Chambers, D. Eamus, et al. 2005. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145:87–99.De Britez, R. M.. 2007. Estoque e incremento de carbono em florestas e povoamentos de espécies arbóreas com ênfase na Floresta Atlântica do sul do Brasil. Embrapa Florestas; Sociedade de Pesquisa em Vida Selvagem e Educação Ambiental, Curitiba.Eggleston, H. S., L. Buendia, K. Miwa, T. Ngara, and K. Tanabe. 2006. IPCC Guidelines for National Greenhouse Gas Inventories. Prepared by the National Greenhouse Gas Inventories Programme, vol. 4. IGES, Japan.Emmer, I. 2007. Manual de contabilidad de carbono y diseño de proyectos. Proyecto Encofor. Quito, Ecuador., Ecofor, p. 22.Fonseca, W., F. Alice, and J. M. Rey. 2009. Modelos para estimar la biomasa de especies nativas en plantaciones y bosques secundarios en la zona Caribe de Costa Rica. Bosque (Valdivia) 30:36–47.Fonseca, W., J. M. R. Benayas, and F. E. Alice. 2011. Carbon accumulation in the biomass and soil of different aged secondary forests in the humid tropics of Costa Rica. For. Ecol. Manage. 262:1400–1408.Fonseca, G., J. Galán, A. Enciso, R. Garduño, and E. Méndez. 2015. Cambio climático. s.l., CULCyT, 18.Gentry, A. H., S. H. Bullock, H. A. Mooney, and E. Medina. 1995. Seasonally dry tropical forests. Pp. 146–194.Hetland, J., P. Yowargana, S. Leduc, and F. Kraxner. 2016. Carbon‐negative emissions: systemic impacts of biomass conversion: a case study on CO2 capture and storage options. Int. J. Greenhouse Gas Control 49:330–342.Honorio, E. N., and T. R. Baker. 2010. Manual para el monitoreo del ciclo del carbono en bosques amazónicos. Instituto de Investigaciones de la Amazonia Peruana, Lima, Perú.IDEAM. 2010. Segunda comunicación nacional ante la Convención Marco de las Naciones Unidas sobre Cambio Climático. Instituto de Hidrología, Meteorología y Estudios Ambientales, Bogotá, Colombia.IPCC. 2007. IPCC Guidelines for national greenhouse gas inventories: Reference Manual. 1996. 2007.Jew, E. K. K., A. J. Dougill, S. M. Sallu, J. O'Connell, and T. G. Benton. 2016. Miombo woodland under threat: consequences for tree diversity and carbon storage. For. Ecol. Manage. 361:144–153.Linares‐Palomino, R. 2004a. Los bosques tropicales estacionalmente secos: I. El concepto de los bosques secos en el Perú. Arnoldia 11:85–102.Linares‐Palomino, R. 2004b. Los bosques tropicales estacionalmente secos: II. Fitogeografía y composición florística. Arnoldia 11:103–138.MAE. 2009. Plan de manejo. Refugio de vida silvestre marina y costera Pacoche.MAE. 2015. Estadísticas del Patrimonio Natural: Datos de bosques, ecosistemas, especies, carbono y deforestación del Ecuador continental. Pp. 1–20.Melo, O., and R. Vargas. 2003. Evaluación Ecológica y Silvicultural de Ecosistemas Boscosos.MINAM. 2009. Identificación de Metodologías existentes para determinar stock de carbono en ecosistemas forestales. Ministerio de Ambiente del Perú, Lima, Perú.Murphy, P. G., and A. E. Lugo. 1986. Ecology of tropical dry forest. Annu. Rev. Ecol. Syst. 1986:67–88.Mutuo, P. K., G. Cadisch, A. Albrecht, C. A. Palm, and L. Verchot. 2005. Potential of agroforestry for carbon sequestration and mitigation of greenhouse gas emissions from soils in the tropics. Nutr. Cycl. Agroecosyst. 71:43–54.Oelbermann, M., R. P. Voroney, and A. M. Gordon. 2004. Carbon sequestration in tropical and temperate agroforestry systems: a review with examples from Costa Rica and southern Canada. Agr. Ecosyst. Environ. 104:359–377.Pearson, T., S. Walker, and S. Brown. 2005. Sourcebook for Land use. Land‐use change and forestry projects. Development 21:64.Penman, J., M. Gytarsky, T. Hiraishi, T. Krug, D. Kruger, R. Pipatti, et al. 2003. Good practice guidance for land use, land‐use change and forestry. Institute for Global Environmental Strategies, Hayama, Japan.Pérez, A. R. Y., V. Pocomucha, and Y. Vargas. 2009. Carbono almacenado en diferentes sistemas de uso de la tierra del Distrito de José Crespo y Castillo, Huánuco, Perú. 48.Phillips, J. F., A. J. Duque, K. R. Cabrera, A. P. Yepes, D. A. Navarrete, M. C. García, et al. 2011. Estimación de las reservas potenciales de carbono almacenadas en la biomasa aérea en bosques naturales de Colombia. Instituto de Hidrología, Meteorología, y Estudios Ambientales‐IDEAM, Bogotá, CO.Rajput, P. 2016. Carbon storage, soil enrichment potential and bio‐economic appraisal of different land use systems in mid hill and sub‐humid zone‐II of Himachal Pradesh.Rügnitz, M., M. Chacón, and R. Porro. 2009. Guía para la determinación de carbono en pequeñas propiedades rurales, 1st ed. Centro Mundial Agroflorestal (ICRAF)/Consórcio Iniciativa Amazônica (IA), Lima, Perú.Sánchez, M. D., and M. R. Méndez. 2003. Estudio FAO producción y sanidad animal; 155. Agroforestería para la producción animal en América Latina‐II.Schimel, D. S., J. I. House, K. A. Hibbard, P. Bousquet, P. Ciais, P. Peylin, et al. 2001. Recent patterns and mechanisms of carbon exchange by terrestrial ecosystems. Nature 414:169–172.Schlegel, B., J. Gayoso, and J. Guerra. 2001. Manual de procedimientos para inventarios de carbono en ecosistemas forestales. Universidad Austral de Chile. Proyecto FONDEF D98I1076. Valdivia, Chile 18: 19–20.Solomon, S.. 2007. Climate change 2007‐the physical science basis: working group I contribution to the fourth assessment report of the IPCC, vol. 4. Cambridge University Press, New York, USA.Swift, R. S. 2001. Sequestration of carbon by soil. Soil Sci. 166:858–871.Tashi, S., B. Singh, C. Keitel, and M. Adams. 2016. Soil carbon and nitrogen stocks in forests along an altitudinal gradient in the eastern Himalayas and a meta‐analysis of global data. Glob. Change Biol. 22:2255–2268.Trumbore, S. E., E. A. Davidson, P. de Camargo, D. C. Nepstad, and L. A. Martinelli. 1995. Belowground cycling of carbon in forests and pastures of Eastern Amazonia. Global Biogeochem. Cycles 9:515–528.Vijayakumar, D. B. I. P., F. Raulier, P. Bernier, S. Gauthier, Y. Bergeron, and D. Pothier. 2016. Cover density recovery after fire disturbance controls landscape aboveground biomass carbon in the boreal forest of eastern Canada. For. Ecol. Manage. 360:170–180.Walker, S., T. Pearson, and S. Brown. 2014. Winrock Sample Plot Calculator Spreadsheet Tool.Yatskov, M. A. 2016. The impact of disturbance on carbon stores and dynamics in forests of coastal Alaska.Yepes, A. P., and Á. J. Duque. 2011. Protocolo para la estimación nacional y subnacional de biomasa‐carbono en Colombia. Bogot DC, Colombia, 162.Zanne, A., G. Lopez‐Gonzalez, D. Coomes, J. Ilic, S. Jansen, S. Lewis, et al. 2009. Global wood density database. Dryad Identifier. http://hdl.handle.net/10255/dryad. 235.

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Food and Energy SecurityWiley

Published: Jan 1, 2017

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