Growth and yield drivers of loblolly pine in the southeastern U.S.: A meta-analysis
Publication date: 1 March 2019
Source: Forest Ecology and Management, Volume 435
Author(s): Héctor I. Restrepo, Bronson P. Bullock, Cristian R. Montes
An abundant amount of information has been accumulated over the past century on loblolly pine. However, few studies have been aimed at assembling this information. Three possible approaches can be used to synthesize available information on loblolly pine: a review paper in the form of a narrative discussion, systematic review compiling data in tables, and meta-analysis to statistically summarize data. The purpose of this research is to statistically synthesize suitable loblolly pine yield data in the southeastern United States using meta-analysis. There were 18 studies selected out of approximately 500 peer-reviewed papers, and three high-quality studies (one proceeding, one M.S. thesis, and one Ph.D. dissertation) evaluated, from which a database was compiled. Since forest growth has several drivers (i.e. age, site quality, genetics, density, and management) the use of meta-regression, a meta-analysis technique to account for variability associated with covariates, was used. Thus, meta-regression linear mixed effects yield models using the log-transformed Schumacher form, at the whole-stand level, were estimated as a function of the mentioned forest growth factors for diameter at breast height (DBH), height (Ht), basal area (BA), and volume (V). Overall, the estimated models suggest that these forest growth factors successfully explain yield variability. The Raudenbush’s pseudo-R2, which measures the amount of variation explained by the covariates, were 97, 94, 97, and 91%, for DBH, Ht, BA, and V models, respectively. However, the 95% confidence intervals (CI) of yield curves associated with some growth factor levels overlapped their corresponding reference level, suggesting no statistical differences at certain ages. In this sense, the CI’s width is driven mainly by the number of studies, and their number of replicates, available for factor levels. Thus, the lack of information of factor levels, and their combinations, was identified and suggested to be investigated in future research in order to achieve narrower CIs. Meta-analysis and meta-regression are promising techniques to be applied in forestry research to give insight into the effect of growth factors on forest yield.
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