saturation and normal concentration of CO
2
; a
Ф
is initial slope of the light curve of
photosynthesis, I
Ф
is PAR intensity.
Ross and Bihele’s formula combines the dependence of photosynthesis on
radiation, CO
2
concentration and on diffusion resistance
,
1
1
1
sc
L
А
m
ас
Ф
Ф
Ф
m
с
r
r
r
I
a
Ф
Ф
+
+
+
+
=
(7)
where Ф
m
is potential photosynthesis that is, lim Ф
L
= Ф
m
which depends on the
І
Ф
→∞
с
А
→∞
temperature and age of the leaf, аnd r
m
= r
md
+ r
mx
.
The influence of other environmental factors (temperature, water regime, wind
speed and air humidity) on photosynthesis is taken into account indirectly, mainly
through diffusion resistance.
Experimental. The input data to develop methods for determining the linear
dimensions of trees (diameter and height) using mathematical models are mensurational
indexes of the main forest-forming species (oaks) of CPE on the site of an area of 1 ha of
the railroad Lviv -Stryi, consisting of one row of oak trees. These indexes are presented
in two YTs, where the data on changes in heights and diameters of 100 tree plants that
have been growing for 88 years are recorded. We only give Table 1 which is a fragment
of the mapping of the growth course of the plantations.
Let us consider the site on which a CPE is formed, consisting of one row of oak
trees. Table 1 presents a fragment of YT for oak trees. Statistical analysis of oak growth
has shown that the probability distribution of oak growth is not described by normal
law. Therefore, we obtained estimates of growth based on the Kolmogorov-Smirnov
criterion [15, p. 359] (defined mathematical expectation and confidence interval for
each value
)
30
,
1
(=
i
t
i
, from Table 2). Whatever the true function of the distribution of
growth of a tree plant
()
x
F
mod
, we have:
()
()
()
()
()
d
x
F
x
F
d
x
F
P
n
n
+
−
mod
for all
,
1
−
=
x
where
d
is the critical value D
n
at a value level α.
Thus, confidence interval is a band of width ± d
α
around the sampling function
()
()
x
F
n
and with probability
−
1
true function
()
x
F
mod
lies entirely within this band.
Using this result, we can obtain estimates of the sample size required to approximate
the distribution function with the required accuracy. It is known that with
2
,
0
і
n
d
n
2
ln
2
1
,
80
−
[15]. For example, when α=0,05 we find that when the sample volume
n = 100 with probability 0,095 the empirical distribution function is distant from the
true one no more than Δ = 0,061.
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