Muestreo e incertidumbres Estimates of carbon sequestration from National Forest Inventories Fernando Casanoves Centro Agronómico Tropical de Investigación y Enseñanza, CATIE Fernando Casanoves Unidad de Bioestadística CATIE

Motivation REDD+ reduction of emissions from deforestation and forest degradation REDD+ requires the estimation of the amount of carbon accumulated in the ecosystem reservoirs.

Confidence intervals

Assessing uncertainties

REDD+ The payment considers the lower limit of the confidence interval The estimation is made every five years Uncertainty reduction is required

Carbon sequestration Above ground biomass Under ground biomass Fallen wood Litter Soil Cifuentes (2012)

Sampling design

Example: Ecuador Strata definition Sample size in each stratum Variance Importance of the strtaum in C content Sampling unit cost Cluster definition

Common problems Cluster size is not large enough to estimate the variation Different plot size according the reservoir Some plots cannot be sampled because of physical restrictions. Some plots are partially evaluated Correction for slope is necessary

Factors responsible for uncertainties Errors associated to the data collection Sampling Operator Measurement Protocol Data recording Temporal variation Measures every five years Species identification

Andrade y Segura 2015

Factors responsible for uncertainties Carbon above ground estimation Allometric equation error Selection of the allometric equations Range of allometric equations Use of general equations

Factors responsible for uncertainties Other factors Sample effort correction (expanding factors) Carbon fraction estimation (0.5 dry biomass) Carbon estimation in other reservoirs (0.90, 0.50, or 0.10 in fallen wood) Measures in time (plot location, change in land uses)

Data base quality control Identification of key variables Qualitative: scientific name, tree condition, etc. Quantitative: Maximum Minimum Logical values Outliers (standardization) Outliers in relations (graphs)

Allometric equations Diam30= Diameter at 30 cm Source: Andrade et al. (en prep.)

Above ground biomass estimation Direct method Allometric equations Specific General Indirect method Biomass expanding factors (FEB)

Chave´s allometric equations Dry forest Wet forest Mangrove Cloudy forest Chave et al. (2005)

Palm equations Ln(AGB)= -3,3488+2,7483*Ln(DAP) Genus Equation Astrocarym AGB= 21,302*Hc Attalea Ln(AGB)= 3,2579+1,1249*Ln(Hc+1) Euterpe AGB= -108,81+13,598*Hc Iriartea Ln(AGB)= -3,483+0,94371*Ln(dap2*Hc) Mauritia Ln(AGB)= 2,4647+1,3777*Ln(Hc) Mauritiella AGB= 2,8662*Hc Oenocarpus Ln(AGB)=4,5496+0,1387*Hc Ln(AGB)= -3,3488+2,7483*Ln(DAP) Ecuación Biomasa aérea para la Familia Arecaceae   Ln(BGB)= -0,3688+2,0106*Ln(Hc) Ecuación Biomasa en raíces de palmas Goodman et al. (2013)

Wood density FAO, Global wood density database Species: 23 658 individuals, 24.96% Genus: 54 397 individuals, 57.39% Family: 13 363 individuals, 14.10% General: 3 355 individuals, 3.5%

Death without branches Database Trees: 102 647 records, 1 639 plots Liter: 965 plots. Fallen wood: 577 plots. Understory: 1 185 plots. Condition Individuals Abundance Percentage Live trees 94.773 92.32% Stump 282 0.27% Death without leaves 744 0.72% Death without branches 6.848 6.67%

Sampling effort Stratum Cluster Plot Plot/Cluster 1 41 105 2,56 2 54 151 2,80 3 154 312 2,03 4 84 175 2,08 5 60 118 1,97 6 174 432 2,48 7 87 207 2,38 8 30 2,90 9 27 52 1,93 Total 711 1639 2,31

Above ground carbon (Mg/ha) Stratum Estimation S.E. n LL(95%) UL(95%) Uncertainty CV Estimated n 1 36,56 3,20 41 30,10 43,02 17,67 55,97 120 2 31,62 2,37 54 26,87 36,37 15,02 55,03 116 3 110,03 5,55 154 99,06 120,99 9,97 62,61 151 4 96,74 5,05 84 86,70 106,79 10,38 47,83 88 5 85,68 10,35 60 64,97 106,38 24,17 93,55 336 6 140,93 4,26 174 132,51 149,34 5,97 39,91 61 7 68,68 3,04 87 62,63 74,73 8,81 41,34 66 8 63,48 5,45 30 52,34 74,63 17,55 47,00 85 9 64,71 6,59 27 51,17 78,26 20,93 52,90 108

Conclusions Assessing uncertainty involves multiple factors Sampling design must reflect a compromise between uncertainty and practical restrictions Capacity building of personnel is necessary Biometricians must be involved throughout the whole process (planning to conclusions)

THANK YOU! ESKERRIK ASKO! GRACIAS!

References Brown, S; Gillespie, A; Lugo, A. 1989. Biomass Estimation Methods for Tropical Forests with Applications to Forest Inventory Data. Forest Science 35 (4): 881-902. Chave, J; Andalo, C; Brown, S. et al. 2005. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145: 87-99. Cifuentes, M. 2013. Introducción a la Guía de Buenas Prácticas para Uso de la Tierra, Cambio de Uso de la Tierra y Forestería.Materiales de curso: Herramientas para el Monitoreo del Secuestro de Carbono en Sistemas de Uso de la Tierra. Cochran, W. G. 1977. Técnicas de muestreo. Compañía Editorial Continental S.A. México. 513 p. Conti, G; Enrico, L; Casanoves, F; Díaz, S. 2013. Shrub biomass estimation in the semiarid Chaco forest: a contribution to the quantification of an underrated carbon stock. Annals of Forest Science. DOI: 10.1007/s13595-013-0285-9. Eguiguren, P. 2013. Los efectos de intervenciones forestales y la variabilidad climática sobre la dinámica a largo plazo de bosques tropicales en el noreste de Costa Rica. Tesis Mag. Sc. Turrialba, CR, CATIE. Myers, R.H. 1990. Classical and modern regression with applications. PWS-KENT publishing company. 484 p. Segura, M. 2013. Estimación de Biomasa y Carbono arriba del suelo en sistemas de uso de la tierra. Materiales de curso: Herramientas para el Monitoreo del Secuestro de Carbono en Sistemas de Uso de la Tierra.

Muestreo e incertidumbres Estimates of carbon sequestration from National Forest Inventories Fernando Casanoves Centro Agronómico Tropical de Investigación y Enseñanza, CATIE Fernando Casanoves casanoves@catie.ac.cr