Joint International Conference on Long-term Experiments ...

Value of c1’ is 0,274262498 while mean of Vi value is 0,04 the minimum 0,08 the
maximum 0,11. When taking into consideration the all regularity these values mean the
precision of the determination of biomass production. The random component depends
on the climate and cultivation condition mainly. But only that part of it which is not
affected by the periodicity and the trend. Higher random factor can be occur anywhere
(e.g. extreme year) but it is more likely on irregular increasing interval of biomass-curve
(e.g. indeterminate sow conditions).
SUMMARY
We have introduced a math treatment first time in Hungarian biomass research which is
made by dissociation of 6 years time series from earth satellite multispectral remote
sensing data in region of Nyírlugos. This model consists of linear trend, periodic,
autoregressive and random components which were successfully transformed forward.
Result of trend analyze filtered out long term effect. This means that the affects of
biomass increase and decrease like wet or drought years were detached.
With detaching periodic component sinus changing was separated from data source.
Autoregressive analyses quantify statistical rules which resulted from dependence of
sequential years.
Random component demonstrated the uncertainty of model. This is the factor which
makes biomass production characteristics undetermined in an added point or in
investigational space. This means by taking into consideration the all well-known
influential factor divergent value can be appeared according to the expected value.
After adjusting model component successfully backward transformation was carried out
which procedure proved the applicability clearly. This confirms the fact that significant
regularity can be assumed in biomass growth in such a dynamic model was set in this
study.
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