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The objective of this study was to identify the most effective system for predicting parameters of the Weibull function that characterize diameter distributions of black pine ( Pinus nigra Arn.) stands in Türkiye. We examined three Parameter Recovery methods: the Moment Recovery method, based on diameter moments (diameter variance and quadratic mean diameter), the Percentile Recovery method, relying on diameter percentiles (the 31st and 63rd percentiles), and the Hybrid method, which combines elements of both approaches. Within each of the three methods, we derived regression coefficients from four estimation approaches: Seemingly Unrelated Regression (SUR), Cumulative Distribution Function Regression (CDFR), Maximum Likelihood Estimator Regression (MLER), and Stand Table Regression (STR). Our findings demonstrated that the Moment Recovery method exhibited superior performance compared to the Percentile Recovery and Hybrid methods. Additionally, the MLER approach surpassed the other three estimation techniques. Notably, the Moment Recovery method, coupled with regression coefficients estimated through MLER, emerged as the top-performing combination overall. These results hold significant implications for the development of a diameter distribution growth and yield model tailored to black pine stands in Türkiye.
Alkan et al. (2024) studied this question.