Accurate measurements of carbon pools and fluxes and of the related uncertainties are required to support
the estimation of regional and continental carbon budgets. For this purpose a rigorous statistical
method, known as Randomized Branching Sampling (RBS), has been applied for the direct assessment
of carbon pools, fluxes (Net Primary Productivity) and plant surface areas in three forests. RBS is an
unequal probability selection scheme that is design unbiased and efficient. Through its theory and design,
RBS provides an unbiased estimate of uncertainties both at single tree and ecosystem scales. RBS
designed samplings proved to be less time-consuming than traditional ones by lowering the number
of sample branches needed to achieve the target precision levels and by getting rid of fresh weight measurements
in the field. RBS estimates of C pools were compared and discussed to traditional estimates
achieved by allometric functions fitted using the power equation Y ¼ b Xa revealing good agreement;
differences between the RBS and allometric approaches were higher in older or more structured forests.
Optimal scaling exponents for foliage, branch and stem components, for pool, flux and surface parameters
in European beech, Scots pine and Norway spruce stands were estimated by analysis of the precision of
target aggregate estimators. In all stands, the scaling exponent for the stand-scale estimates proved to be
lower than the scaling exponent estimated from the allometric fitting and than analytically derived exponents.
This discrepancy could lead, should the latter scaling exponents be used, to over-estimate C pools