Evaluation of individual-tree growth models for Picea abies based on a case study of an uneven-sized stand in southern Sweden [Elektronisk resurs]

• To develop recommendations for tree selection in Continuous Cover Forestry (CCF), access to valid tools for simulating growth at individual tree-level is necessary. To assist efforts to develop such tools, in this study, long-term observation data from two uneven-sized Norway spruce plots in southern Sweden are used to evaluate old and new individual-tree growth models (two established Swedish models, two new preliminary models and included as a reference, a Finnish model). The plots' historical management records and site conditions are the same, but their last thinning treatment differs. Observed diameter increment at tree-level is investigated in relation to treatment. Individual tree growth residuals of tested models are evaluated in relation to tree diameter, treatment, projection length and sensitivity to the predictor mean stand age. Furthermore, the relations between displayed residuals and basal area local competition are analysed. The analyses indicate that active thinning made annual diameter increment independent of tree diameter above a threshold level, while the absence of thinning supported a concave relationship. All tested models displayed a significant linear bias leading to overestimation of small trees' growth and increasing underestimations of larger trees' growth with tree diameter. All distance-independent models displayed residual trends related to local competition. 

Ämnesord

Agricultural and Veterinary sciences  (hsv)

Agricultural Science, Forestry and Fisheries  (hsv)

Forest Science  (hsv)

Lantbruksvetenskap och veterinärmedicin  (hsv)

Lantbruksvetenskap, skogsbruk och fiske  (hsv)

Skogsvetenskap  (hsv)

Forestry and Wood Technology  (lnu)

Skog och träteknik  (lnu)

Genre

government publication  (marcgt)

Indexterm och SAB-rubrik

Continuous Cover Forestry

single-tree growth models

selective cutting

uneven-aged

single-tree selection

growth prediction