Quantification of above- and belowground biomass carbonin agricultural landscapesThe significance ofempirically validated ...
Measurement of Biomass Carbon<br />Trees in agricultural landscapes are sinks for carbon<br />Biomass carbon can be measur...
Allometric equations have advantages<br />Once developed:<br /><ul><li>Are non-destructive, less laborious
Allow ‘follow-up measurements’
Can be applied on a large area e.g. forest inventories</li></li></ul><li>Do we need new allometries?<br />What exists:<br ...
Where we worked<br />In three 100 km2 Sentinel sites<br />Elevation: 1200 – 2200 m a.s.l.<br />In western Kenya<br />A lan...
What was measured<br />GPS coordinates <br />Diameters<br />Tree height<br />Crown dimensions<br />Crown conditions<br />T...
879 trees measured to</li></ul>	estimate representative biomass<br />
Also belowground biomass (BGB) <br />Root collar diameter (RCD)<br />Diameters of main roots<br />Length of main roots<br ...
The equations: development and validation<br />Diameter (dbh) as lone predictor for AGB<br />AGB, dbh and RCD as lone pred...
Cross validation<br />
Published equations  tested<br />
Diameter is a reliable proxy for estimation of aboveground biomass<br /><ul><li>Error = 5 %
Strong correlation with AGB, R2 = 0.98</li></li></ul><li>Global equations overestimated AGB<br />Agricultural landscapes r...
Performance of equation depends on tree size<br />H = height<br />ρ = wood density<br />
Diameter best predictor of BGB<br /><ul><li>Error for BGB models
dbh = -4 %
AGB =  3 %
RCD = -1 %
dbh, AGB and RCD showed strong correlation with BGB, R2 >0.90</li></li></ul><li>Root:Shoot ratios  (RS)<br />Decreased wit...
Global equations underestimated BGB<br />Performance of RS was inconsistent:<br /><ul><li>Overall error (3 blocks) = 1  %;
Lower Yala = -35 %, Mid. Yala = 11 % Upper Yala = 17 %</li></li></ul><li>It is also possible to estimate whole tree biomas...
Crown area models can be a useful link between ground data and remotely sensed imagery<br /><ul><li>Greater variability ex...
Management and interplant competition have a significant influence</li></li></ul><li>Representative landscape biomass<br /...
5 % largest trees = 60 % biomass</li></li></ul><li>The potential of agricultural mosaics<br />Average carbon content was 0...
Conclusions<br />Diameter was confirmed as a robust proxy even complex agricultural landscape <br />Management significant...
Upcoming SlideShare
Loading in...5
×

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

1,326
-1

Published on

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

Published in: Technology
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
1,326
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
25
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide
  • 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 &amp; quadratic equations). The polynomial, quadratic &amp; 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

    1. 1. Quantification of above- and belowground biomass carbonin agricultural landscapesThe significance ofempirically validated allometries<br />Kuyah Shem<br />and<br />Dietz J, Jamnadass R, Muthuri C, Mwangi P<br />ICRAF Seminar Series - 03 May 2011<br />
    2. 2. Measurement of Biomass Carbon<br />Trees in agricultural landscapes are sinks for carbon<br />Biomass carbon can be measured by direct or indirect methods (e.g. Allometric Equations)<br />Allometric equations relate biomass to measureable parameters <br /> e.g. diameter at breast height (dbh)<br />Power function was used:<br />It has a more natural scaling than polynomials, quadratic and cubic <br />
    3. 3. Allometric equations have advantages<br />Once developed:<br /><ul><li>Are non-destructive, less laborious
    4. 4. Allow ‘follow-up measurements’
    5. 5. Can be applied on a large area e.g. forest inventories</li></li></ul><li>Do we need new allometries?<br />What exists:<br />Species specific equations<br />Global equations (e.g. Chave et al. 2005)<br />Their limitations:<br />Agricultural mosaics are heterogeneous <br />Global equations have not been validated <br />Diverse species<br />Varied management<br />
    6. 6. Where we worked<br />In three 100 km2 Sentinel sites<br />Elevation: 1200 – 2200 m a.s.l.<br />In western Kenya<br />A landscape approach<br />Random sampling<br />Stratified by size class;<br />6 dbh classes used<br />30 x 30 m plots<br />LDSF (Walsh and Vȧgen, 2006)<br />
    7. 7. What was measured<br />GPS coordinates <br />Diameters<br />Tree height<br />Crown dimensions<br />Crown conditions<br />Tree species name<br />Cores for wood density<br />Aboveground biomass (AGB)<br /><ul><li>72 trees sampled
    8. 8. 879 trees measured to</li></ul> estimate representative biomass<br />
    9. 9. Also belowground biomass (BGB) <br />Root collar diameter (RCD)<br />Diameters of main roots<br />Length of main roots<br />Depth excavated<br />Biomass of missing roots determined by extrapolation <br />2 m<br />l1 = total root length; l2 = excavated section; l3 = missing portion<br />
    10. 10. The equations: development and validation<br />Diameter (dbh) as lone predictor for AGB<br />AGB, dbh and RCD as lone predictor for BGB<br /> Height, wood density, crown area as additional explanatory variables<br />Multiple sample holdouts for cross-validation <br />Equations = Average of parameters in 12 holdouts<br />Model fit and accuracy determined<br />Suitability of using published models assessed<br />
    11. 11. Cross validation<br />
    12. 12. Published equations tested<br />
    13. 13. Diameter is a reliable proxy for estimation of aboveground biomass<br /><ul><li>Error = 5 %
    14. 14. Strong correlation with AGB, R2 = 0.98</li></li></ul><li>Global equations overestimated AGB<br />Agricultural landscapes resemble a hybrid of dry and wet forest type<br />Henry et al. 2009 underestimated AGB<br />
    15. 15. Performance of equation depends on tree size<br />H = height<br />ρ = wood density<br />
    16. 16. Diameter best predictor of BGB<br /><ul><li>Error for BGB models
    17. 17. dbh = -4 %
    18. 18. AGB = 3 %
    19. 19. RCD = -1 %
    20. 20. dbh, AGB and RCD showed strong correlation with BGB, R2 >0.90</li></li></ul><li>Root:Shoot ratios (RS)<br />Decreased with increase in dbh, and AGB<br />Was greatly influenced by management (black)<br />Varied across the three sites investigated<br />Mean = 0.33; Median = 0.29<br />
    21. 21. Global equations underestimated BGB<br />Performance of RS was inconsistent:<br /><ul><li>Overall error (3 blocks) = 1 %;
    22. 22. Lower Yala = -35 %, Mid. Yala = 11 % Upper Yala = 17 %</li></li></ul><li>It is also possible to estimate whole tree biomass using diameter<br />
    23. 23. Crown area models can be a useful link between ground data and remotely sensed imagery<br /><ul><li>Greater variability exists compared to dbh-biomass relationship
    24. 24. Management and interplant competition have a significant influence</li></li></ul><li>Representative landscape biomass<br />Size does matter<br /><ul><li><20 cm diameter = 20 % biomass
    25. 25. 5 % largest trees = 60 % biomass</li></li></ul><li>The potential of agricultural mosaics<br />Average carbon content was 0.48<br />Aboveground biomass carbon = 17.36 Mg C ha-1<br />Foliage = 4 %; branches = 39 %; stem = 57 %<br />Belowground biomass carbon = 5.27 Mg C ha-1<br />BGB account for 23 % of the total tree biomass <br />Biomass of roots not excavated was 23 % of the total BGB<br />
    26. 26. Conclusions<br />Diameter was confirmed as a robust proxy even complex agricultural landscape <br />Management significantly affect biomass and contribute to the heterogeneity of the landscape <br />Root:shoot ratios should be used with great care depending on soil and management conditions<br />
    27. 27. Outlook <br />Testing the performance of equations developed at national level <br />Tested in Uganda on coffee trees <br />Validation of Non-destructive approaches<br />Fractal branch Analysis (van Noordwijk)<br />Relate Root:Shoot ratios to soil properties<br />
    28. 28. Potentials <br />Guidelines for establishing regional allometric equations for biomass estimation through destructive sampling <br />Validation of non-destructive methods<br />Remote sensing<br />Fractal branch analysis<br />Up-scaling of biomass <br />Use for national greenhouse national inventory<br />
    29. 29.
    30. 30. Acknowledgement <br />ICRAF for the fellowship<br />Supervisors<br />Anja and Team (Research Methods)<br />Kisumu Field crew<br />
    31. 31. Thank you<br />
    1. Gostou de algum slide específico?

      Recortar slides é uma maneira fácil de colecionar informações para acessar mais tarde.

    ×