Research On Branch Pruning Technology Based On Knot Analysis Of Planted Korean Pine Trees

Pruning plays an important role in controlling the stand growth,to a certain degree,affecting the forest growth and yield simulation.Therefore,reasonable predicted pruning time and intensity for forest stand growth is very important.Based on 1534 knot data from 60 sample trees in a Korean pine plantation in Mengjiagang Forest Farm,Heilongjiang Province,China,mixed effect model of knot attribute factors(knot diameter,sound knot length,year of death of knot and knot angle)of Korean pine plantation were established using NLMIXED and GLIMMIX procedures of SAS software.The prediction accuracy of models were compared using evaluation statistics,such as Akaike information criterion(AIC),Bayesian information criterion(BIC),-2Log likelihood,and likelihood ratio test(LRT).Results showed that all of the mixed effect models that considered tree effect performed better than conventional fixed effect models.For knot diameter models,the model with random parameter combination of bi,b2 had the best performance.For sound knot length models,the model with random parameter combination of b1,b3 had the best performance.For the models of year of death of knot,the model with random variables of knot diameter was proved to be the optimal generalized linear mixed model.For the models of knot angle,the model with random variables of intercept,knot diameter,sound knot length was proved to be the optimal generalized linear mixed model.Mixed effect model is more effective than conventional fixed-effect model for describing knot attributes.Through the screening of 349 knots as establishing height of tree,effective crown and dynamic growth model for the height of lowest branches were developed.Richards equation was selected as foundation model and the effective crown and branches under the lowest crown mixed-effects model was developed based on Richards equation.Considering the difference between individual trees,the random effects of individual tree was added into the mixed-models.Using NLMIXED procedures of SAS software,a simulation and prediction accuracy of models were compared based on the evaluation statistics,such as Akaike information criterion(AIC),Bayesian information criterion(BIC),-2Log likelihood,coefficient of determination(R2),root mean square error(RMSE)and bias(Bias).Using independent sample test data,the fitting precision was proved to be best.Results showed that all of the mixed effects models that considered tree effect performed better than conventional fixed effect models and the model with two random effects parameters performed better than the model with only one random effects parameter.Based on the establishment of the model,the pruning time and intensity was calculated.According to the calculated results and combined with simulation diagram,it was concluded that the first training time for 15 years and pruning intensity is 1.01 m.There are 5 times pruning in the young forest stand,the average intensity of pruning is 1.46 m.After entering the mid-maturation forest stand,pruning time interval is similar to the young forest stand and pruning intensity decreased obviously which the last one is just 0.34m.The combination of knot attributes models and reasonable pruned schemes could improve timber quality of Korean pine which is one of the main commercial tree species in northeast of China.