The impact of intraspecific competition on tree growth  in planted Korean pine forest

Nguyen Thanh Tuan; Vu Dinh Duy; Shen H-L

doi:10.15625/0866-7160/v42n1.14435

Author affiliations

Authors

Nguyen Thanh Tuan Department of Forestry, Vietnam Forestry University-Dongnai Campus, Vietnam
Vu Dinh Duy Institute of Tropical Ecology , Vietnam - Russia Tropical Centre, 63 Nguyen Van Huyen, Nghia Do, Cau Giay, Hanoi, Vietnam
Shen H-L School of Forestry, Northeast Forestry University, Harbin, China

DOI:

https://doi.org/10.15625/0866-7160/v42n1.14435

Keywords:

Pinus koraiensis, competition index, individual tree growth.

Abstract

The aim of this study was to explore the correlation of competition indices (CIs) on individual tree growth for Korean pine (Pinus koraiensis) plantation using partial correlation analysis and generalized linear models. The data were collected from 15 permanent plots in Mengjiagang forestry farm, Northeast China. The results showed that the distance dependent CIs have a higher predictive capacity for individual growth of pine trees. The control index of competitive trees number (CI₁) combined with the selection fixed competitor trees (M2) was found to be the most well correlated competition measure with five - years diameter increment. Thus, the competition index (CI_1-M2) was recommended for developing individual tree growth models. The subject tree diameter at breast height, crown width, height to crown base, tree volume and basal area all showed a significantly linear correlation with tree competition intensity (P < 0,05). Diameter at breast height, crown width, tree volume and basal area all decreased with increasing competition intensity, whereas the height to crown base increased. There was no significant relationship between competition intensity and tree height (P > 0,05). The optimal model of predicting individual growth with logarithm of diameter at breast height and CIs as the independent variables due to the best fitting performance. This results also showed considerable improvement in predicting individual tree periodic growth when including competition indices that the mean absolute error is reduced in the range of 22−25%.

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