RUSSIAN JOURNAL OF FOREST SCIENCE. 2017, No. 1, pp. 17-23


REGRESSION MODELING OF THE SCOTS PINE RADIAL INCREMENT DYNAMICS IN FORESTS CONTAMINATED WITH RADIONUCLIDES IN BRYANSK OBLAST
A. A. Belov
All-Russian Research Institute of Silviculture and Mechanization of Forestry
Institutskaya st. 15, Pushkino, Moscow Oblast, 141202, Russia
Е-mail: belov@roslesrad.ru


Received 25 December 2015
We studied the dynamics of radial increment of the Scots pine in forests in Bryansk Oblast contaminated with radionuclides after Chernobyl disaster. We developed regression models of the relationship between specific activity of soil and radial increment of trees for the transition stage of the disaster in 1987-89 and four 5 yrs long periods since 1990 to 2009 characterizing the 20 yrs of the recovery. We found strong positive correlation of radial increment and specific activity of soil. The correlation coefficients vary from 0.695±0.198 to 0.895±0.124 for early increment, and from 0.549±0.231 to 0.937±0.097 for late increment at different time periods. Optimum statistic description of the relationship between radial increment and concentration of radiocesium in the soil was the cubic parabolic equation. We show that minimal effective specific activity for stimulating influence of radioactivity decreased from 25-26 kBq kg-1 in 1987-89 to 9-10 kBq kg-1 in 2004-09. For late increment the analogous indicator varied from 20-21 to 16-17 kBq kg-1 in respective periods. Maximal effective specific activity of soil indicative for the highest effect of radiation decreased consequently from 110-120 kBq kg-1 in 1987-89 to 60-70 kBq kg-1 in 2004-09 for early increment. For late increment analogous indicator varied within the same range with no tendencies. As the result of modeling of radial increment dynamics we assessed the role of radiation in the growing stock in a common pine forest in the study area during 25 yrs after Chernobyl disaster.


Keywords: Scots pine, radial increment, radiocesium contamination of soil, exposure – effect relationship, regression modeling.


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