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

Quantification of above- and belowground biomass carbonin agricultural landscapesThe significance ofempirically validated allometries

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  • 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

Quantification of above- and belowground biomass carbonin agricultural landscapesThe significance ofempirically validated allometries Quantification of above- and belowground biomass carbonin agricultural landscapesThe significance ofempirically validated allometries Presentation Transcript

  • 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
  • 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
  • Allometric equations have advantages
    Once developed:
    • Are non-destructive, less laborious
    • Allow ‘follow-up measurements’
    • Can be applied on a large area e.g. forest inventories
  • Do we need new allometries?
    What exists:
    Species specific equations
    Global equations (e.g. Chave et al. 2005)
    Their limitations:
    Agricultural mosaics are heterogeneous
    Global equations have not been validated
    Diverse species
    Varied management
  • 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)
  • What was measured
    GPS coordinates
    Diameters
    Tree height
    Crown dimensions
    Crown conditions
    Tree species name
    Cores for wood density
    Aboveground biomass (AGB)
    • 72 trees sampled
    • 879 trees measured to
    estimate representative biomass
  • 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
  • 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
  • Cross validation
  • Published equations tested
  • Diameter is a reliable proxy for estimation of aboveground biomass
    • Error = 5 %
    • Strong correlation with AGB, R2 = 0.98
  • Global equations overestimated AGB
    Agricultural landscapes resemble a hybrid of dry and wet forest type
    Henry et al. 2009 underestimated AGB
  • Performance of equation depends on tree size
    H = height
    ρ = wood density
  • Diameter best predictor of BGB
    • Error for BGB models
    • dbh = -4 %
    • AGB = 3 %
    • RCD = -1 %
    • dbh, AGB and RCD showed strong correlation with BGB, R2 >0.90
  • Root:Shoot ratios (RS)
    Decreased with increase in dbh, and AGB
    Was greatly influenced by management (black)
    Varied across the three sites investigated
    Mean = 0.33; Median = 0.29
  • Global equations underestimated BGB
    Performance of RS was inconsistent:
    • Overall error (3 blocks) = 1 %;
    • Lower Yala = -35 %, Mid. Yala = 11 % Upper Yala = 17 %
  • It is also possible to estimate whole tree biomass using diameter
  • Crown area models can be a useful link between ground data and remotely sensed imagery
    • Greater variability exists compared to dbh-biomass relationship
    • Management and interplant competition have a significant influence
  • Representative landscape biomass
    Size does matter
    • <20 cm diameter = 20 % biomass
    • 5 % largest trees = 60 % biomass
  • The potential of agricultural mosaics
    Average carbon content was 0.48
    Aboveground biomass carbon = 17.36 Mg C ha-1
    Foliage = 4 %; branches = 39 %; stem = 57 %
    Belowground biomass carbon = 5.27 Mg C ha-1
    BGB account for 23 % of the total tree biomass
    Biomass of roots not excavated was 23 % of the total BGB
  • 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
  • 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
  • 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
  • Acknowledgement
    ICRAF for the fellowship
    Supervisors
    Anja and Team (Research Methods)
    Kisumu Field crew
  • Thank you