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Stand growth-and-yield models include whole-stand models, individual-tree models, and diameter distribution models. Based on the growth data of Chinese fir (Cunninghamia lanceolata [Lamb.] Hook.) in Fenyi County, Jiangxi Province, in southern China, Bayesian model averaging (BMA) was used to forecast stand basal areas by combining these three types of models into a single predictive model. BMA is a statistical method that infers consensus predictions by weighting individual predictions based on their posterior probabilities, with the better performing predictions getting higher weights than the poorer performing ones. Furthermore, BMA accounts for model uncertainty as reflected by the variance. The variance of BMA can be decomposed into a between-model variance that reflects the model’s consistency and a within-model variance that reflects the data variability. Results showed that the between-model variance was much greater than the within-model variance for all the stand basal area predictions. The resulting model produced accurate and reliable predictions, and the 95% confidence interval of BMA predictions encompassed the observations very well. The BMA method provided a consistent prediction of stand basal area from three types of models, thus improving compatibility among these models.

The Application of Bayesian Model Averaging in Compatibility of Stand Basal Area for Even-Aged Plantations in Southern China