%0 Special section
%T Stratification of regional sampling by model-predicted changes of carbon stocks in forested mineral soils
%A Peltoniemi, Mikko
%A Heikkinen, Juha
%A Mäkipää, Raisa
%D 2007
%J Silva Fennica
%V 41
%N 3
%R doi:10.14214/sf.287
%U https://silvafennica.fi/article/287
%X Monitoring changes in soil C has recently received interest due to  reporting under the Kyoto Protocol. Model-based approaches to estimate  changes in soil C stocks exist, but they cannot fully replace repeated  measurements. Measuring changes in soil C is laborious due to small  expected changes and large spatial variation. Stratification of soil  sampling allows the reduction of sample size without reducing precision.  If there are no previous measurements, the stratification can be made  with model-predictions of target variable. Our aim was to present a  simulation-based stratification method, and to estimate how much  stratification of inventory plots could improve the efficiency of the  sampling. The effect of large uncertainties related to soil C change  measurements and simulated predictions was targeted since they may  considerably decrease the efficiency of stratification. According to our  simulations, stratification can be useful with a feasible soil sample  number if other uncertainties (simulated predictions and forecasted  forest management) can be controlled. For example, the optimal (Neyman)  allocation of plots to 4 strata with 10 soil samples from each plot  (unpaired repeated sampling) reduced the standard error (SE) of the  stratified mean by 9–34% from that of simple random sampling, depending  on the assumptions of uncertainties. When the uncertainties of  measurements and simulations were not accounted for in the division to  strata, the decreases of SEs were 2–9 units less. Stratified sampling  scheme that accounts for the uncertainties in measured material and in  the correlates (simulated predictions) is recommended for the sampling  design of soil C stock changes.