Quantification of above- and belowground biomass carbonin agricultural landscapesThe significance ofempirically validated allometries
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Quantification of above- and belowground biomass carbonin agricultural landscapesThe significance ofempirically validated allometries
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Quantification of above- and belowground biomass carbonin agricultural landscapesThe significance ofempirically validated allometries
- 1. Quantification of above- and belowground biomass carbonin agricultural landscapesThe significance ofempirically validated allometries Kuyah Shem and Dietz J, Jamnadass R, Muthuri C, Mwangi P ICRAF Seminar Series - 03 May 2011
- 2. Measurement of Biomass Carbon Trees in agricultural landscapes are sinks for carbon Biomass carbon can be measured by direct or indirect methods (e.g. Allometric Equations) Allometric equations relate biomass to measureable parameters e.g. diameter at breast height (dbh) Power function was used: It has a more natural scaling than polynomials, quadratic and cubic
- 6. Where we worked In three 100 km2 Sentinel sites Elevation: 1200 – 2200 m a.s.l. In western Kenya A landscape approach Random sampling Stratified by size class; 6 dbh classes used 30 x 30 m plots LDSF (Walsh and Vȧgen, 2006)
- 8. 879 trees measured to estimate representative biomass
- 9. Also belowground biomass (BGB) Root collar diameter (RCD) Diameters of main roots Length of main roots Depth excavated Biomass of missing roots determined by extrapolation 2 m l1 = total root length; l2 = excavated section; l3 = missing portion
- 10. The equations: development and validation Diameter (dbh) as lone predictor for AGB AGB, dbh and RCD as lone predictor for BGB Height, wood density, crown area as additional explanatory variables Multiple sample holdouts for cross-validation Equations = Average of parameters in 12 holdouts Model fit and accuracy determined Suitability of using published models assessed
- 11. Cross validation
- 12. Published equations tested
- 15. Performance of equation depends on tree size H = height ρ = wood density
- 17. dbh = -4 %
- 18. AGB = 3 %
- 19. RCD = -1 %
- 26. Conclusions Diameter was confirmed as a robust proxy even complex agricultural landscape Management significantly affect biomass and contribute to the heterogeneity of the landscape Root:shoot ratios should be used with great care depending on soil and management conditions
- 27. Outlook Testing the performance of equations developed at national level Tested in Uganda on coffee trees Validation of Non-destructive approaches Fractal branch Analysis (van Noordwijk) Relate Root:Shoot ratios to soil properties
- 28. Potentials Guidelines for establishing regional allometric equations for biomass estimation through destructive sampling Validation of non-destructive methods Remote sensing Fractal branch analysis Up-scaling of biomass Use for national greenhouse national inventory
- 30. Acknowledgement ICRAF for the fellowship Supervisors Anja and Team (Research Methods) Kisumu Field crew
- 31. Thank you
Notes de l'éditeur
- The power function provide a more natural scaling than the polynomial as they don’t go off the track outside the calibration range, (an unpleasant habit of cubic & quadratic equations). The polynomial, quadratic & cubic functions, tends to have 3 or 4 parameters, which have no direct biological interpretation, while those of the power law have
- Follow-up measurements – because standardized parameters are used e.g. dbhLarge area – forest inventory; up-scaling landscape biomass
- Yes, for agricultural landscapes. We need reliable and practical approaches for assessing biomass in trees across such landscapes
- The Land Degradation Surveillance Framework (LDSF), Cluster level sampling: Sentinel sites (blocks) = 10x10 m; a cluster of 10 plots (30x30 m)
- GPS for trees and plots – geo-reference for recognition in satellite imagery; dbh - the main predictor of biomass; tree height - assess improvement on model fit or accuracy; crown area – develop model that can act as a link between ground measurements and remote-sensing based estimates; cores – for determination of wood density; aboveground fresh weights for components and subsamples – for determination of aboveground biomass
- RCD - a predictor of Biomass; diameters and lengths of main roots – determination of volume of excavated roots and extrapolation to determine missing root portion.
- Model fit assessed by R2 for equations with one explanatory variable and adjusted R2 for equations with two or more explanatory variables; Model accuracy inferred from the error
- Wood density improved model fit, crown area improved the fit marginally and height did not
- The need for empirically validated equations. One could easily classify forests in Kenya according to Brown/Chave’s guidelines (rainfall, evapotranspiration) but then miss out
- It is difficult to predict the biomass of small trees
- Diameter was conservative in biomass estimation. RCD is poor in predicting small trees which for 80 % of the population