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values of p F calculated for the respective case are always lower than the assumed
error level (equal to 0.001).
The analysis of the soil texture significance in the regression (69), represented
by clay content was performed in the group of mineral soils. It was found that the
soil clay content texture significantly influences the regression equation
coefficients (Table 13, case no 2 in the group of mineral soils). The importance of
the soil clay content as an element in the regression is almost the same as the soil
density. The regression n=f (θ,clay) gave the values of R 2 and S yx the same as for
n=f (θ,ρ) regression.
Taking into account the regression analysis with all analyzed soil samples it
can be found that apart of the primary influence from the soil water content, θ, on
the value of the soil refractive index, n, also the soil solid phase modifies in the
statistically significant way the relation n(θ) by the soil density, ρ, and ρ s (or the
soil porosity, φ).
On the basis of the performed multiple regression analysis the following
empirical formula were derived:
Model I (Table 12, case no 1):
Model II (Table 12, case no 2):
n = 1 .491+
7.675θ
(70)
I
n = 0 .289 + 8.504θ + 0. 846ρ
(71)
II
Taking into account the significance of the soil density in the n(θ) relation, the
following modification of the equation (68) is proposed:
n = a + a ρ ) + ( b + ρ)
θ
(72)
( 1 2 1 b2
where the offset ( a 1 + a2
ρ)
and the slope ( b 1 + b2
ρ)
of the n(θ) line are linear
function of soil density.
The parameters a 1 , a 2 , b 1 and b 2 in the equation (72) were found with the use
of the least square method giving the formulae:
n = 0 .573 + 0.582ρ + (7.755 + 0.792ρ)
θ
(73)
III
referred to as the model III.
On the base of (66) and the set of measured data (n, ρ, ρ s , θ), using the least
square method for all analyzed data (663 soil samples), the values of the EM
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