Analysis and Prediction of Eucalyptus urophylla×E.grandis in Response to Thinning and Pruning
[Objective]To explore the effects of thinning and pruning with different intensities on the growth of Eucalyptus urophylla×E.grandis,the BP neural network model was developed to predict the growth of E.urophylla×E.grandis under thinning and pruning treatment,for providing theoretical guidance for effi-cient management technology of E.urophylla×E.grandis.[Method]Taking 20%,40%and 60%thinning and 38.18%,42.39%and 48.18%Based on the E.urophylla×E.grandis in the Southern National Forest Seedling Demonstration Base with treatments of 20%,40%and 60%thinning and 38.18%,42.39%and 48.18%pruning,the stand growth indexes were investigated for 7 years after treatment,and the effects of different thinning and pruning treatments on the growth increment of the stands were analyzed.Further-more,BP neural network was used to predict the response of cumulative increment to thinning and prun-ing.Root-mean-square error,Kappa and Pearson correlation coefficient were used to compare the predic-tion effect of models,and the optimal model was determined.[Results]Thinning treatment promoted the growth of DBH,crown width and tree volume,but did not promote the growth of height and wood produc-tion.The increment of DBH and tree volume was the highest in the stands with 60%thinning intensity,the increment of crown width was the highest in the stands with 20%thinning intensity,and the increment of height was the highest in the control stands.Pruning promoted the growth of DBH,but did not promote the growth of height and wood production.DBH increase was the highest in the stands with 38.18%pruning in-tensity.The treatment with 60%thinning and 48.18%pruning was conducive to the growth of E.urophylla×E.grandis.Both thinning and pruning could promote the right-sided distribution of diameter class,but the effect of pruning was not significant.In summary,the BP neural network model with 4 nodes in the hidden layer had the lowest root-mean-square error and the highest Kappa value and r value.[Conclusion]Thin-ning and pruning can significantly promote the growth of E.urophylla×E.grandis and the right-sided distri-bution of diameter class.The combination of high-intensity thinning and pruning is more beneficial to the growth of E.urophylla×E.grandis plantation and the cultivation of large diameter wood.The reasonable BP neural network model can accurately predict the promotion effect of thinning and pruning on stand growth and is an excellent model for predicting stand growth.
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