Comparing local calibration using random effects estimation and Bayesian calibrations: a case study with a mixed effect stem profile model

Abstract

Local site-level calibration of allometric models was scrutinized. Two Bayesian calibration methods were compared to local random effects estimations. The Bayesian calibration methods proved more effective than local estimation of random effects in reducing prediction bias. The simplest literature-based calibration can be recommended. The local calibration had minor effects on stem volume estimations. The spatial variability of trees allometry has long prompted the necessity for local, site- or stand-level calibrations. Mixed-effect models have enabled a quantitative progress through a local calibration where data are available. More recently, Bayesian statistics brought new alternatives owing to their formal definition of the random effects from prior information. To compare three local calibration methods: (i) a calibration based on the estimation of the local random effects, (ii) a Bayesian calibration where prevailing measurements are used to produce prior estimations, and (iii) a Bayesian calibration reproducing a calibration based on literature data only. The three calibrations were compared using a stem taper model developed for Norway spruce in Romania. The taper model was fitted to a large dataset, then applied locally to two high-elevation sites with contrasting growing conditions. The local calibration of mixed-effect models resulted in small gains and high biases. The Bayesian calibrations yielded better results, mostly because the Monte Carlo Markov Chain implementation permitted to tune of all the model’s parameters simultaneously. The differences in stem volume estimations were however always very small ranging from − 5.2 to 3.3% of the non-calibrated volume. The Bayesian literature-like calibration performed as well as the calibration using the large dataset (4–97% bias reduction according to the tree) and can be preferred for its ease of use.