Data analysis
All measured data were subjected to the Shapiro-Wilk test for normality
before statistical analysis. Logarithmic transformation was carried out
when necessary. One- or two-way analysis of variation was conducted to
determine the effects of litter and clay mineral types. The means of
three replicates were compared using the least significant difference
test at P < 0.05. All statistical analyses were
conducted using SPSS 21.0 software.
We developed a new mineral-regulating decomposition model (equation (1))
that included a novel parameter (δ ), describing the feedback
effect of mineral-protection on cumulative soil respiration and the
relative contribution of decomposition of free litter pool and
mineral-protected litter pool. We proposed to use δ to quantify
the mineral-protection strength of soil or mineral type by fitting the
dynamics of cumulative soil respiration into the model using the least
square optimization method with SPSS 21 for Windows.
Linear regression was performed to predict the measured formation
efficiency of mineral-associated SOM from the measured
mineral-protection strength for all the soils mixed with either litter
type. Linear multivariable regression was performed to predict the
mineral-protection strength of the natural soil material from those of
its compositional all pure clay minerals and their relative abundances
in natural soil material for each litter type. As the natural soil
material contained a mixed layer mineral of vermiculite and illite, theδ value of this clay mineral type was set as the average of those
of vermiculite and illite. A simulated error item was added into the
multivariable regression model, and the root mean square error (RMSE)
between the measured and estimated δ values of the natural soil
material was calculated to estimate the quality of the regression
(equation (3)):