Site index for Chinese fir plantations varies with climatic and soil factors in southern China

Chinese fir[Cunninghamia lanceolata(Lamb.)Hook.]has a large native distribution range in southern China.Here,we tested differences in productivity of Chinese fir plantations in different climatic regions and screened the main environmental factors affecting site productivity in each region.Relationships of a Chinese fir site index with climatic factors and the soil physiochemical properties of five soil layers were examined in a long-term positioning observation trial comprising a total of 45 permanent plots in Fujian(eastern region in the middle subtropics),Guangxi(south subtropics)and Sichuan(central region in the middle subtropics)in southern China.Linear mixed effects models were developed to predict the site index for Chinese fir,which was found to vary significantly among different climatic regions.Available P,total N,bulk density and total K were dominant predictors of site index in three climatic regions.The regional linear mixed models built using these predictors in the three climatic regions fit well(R~2=0.86–0.97).For the whole study area,the available P in the 0–20-cm soil layer and total N in the 80–100-cm soil layer were the most indicative soil factors.MAP was the most important climatic variable influencing the site index.The model evaluation results showed that the fitting performance and prediction accuracy of the global site index model using the climatic region as the dummy variable and random parameters and the most important soil factors of the three climatic regions as predictors was higher than that of global site index model using the climatic variable and the most indicative soil variables of the whole study area.Our results will help with further evaluation of site quality of Chinese fir plantations and the selection of its appropriate sites in southern China as the climatic changes.

Stochastic frontiers or regression quantiles for estimating the self-thinning surface in higher dimensions?

Stochastic frontier analysis and quantile regression are the two econometric approaches that have been commonly adopted in the determination of the self-thinning boundary line or surface in two and higher dimensions since their introduction to the field some 20 years ago.However,the rational for using one method over the other has,in most cases,not been clearly explained perhaps due to a lack of adequate appreciation of differences between the two approaches for delineating the self-thinning surface.Without an adequate understanding of such differences,the most informative analysis may become a missed opportunity,leading to an inefficient use of data,weak statistical inferences and a failure to gain greater insight into the dynamics of plant populations and forest stands that would otherwise be obtained.Using data from 170 plot measurements in even-aged Larix olgensis(A.Henry) plantations across a wide range of site qualities and with different abundances of woody weeds,i.e.naturally regenerated non-crop species,in northeast China,this study compared the two methods in determining the self-thinning surface across eight sample sizes from 30 to 170 with an even interval of 20 observations and also over a range of quantiles through repeated random sampling and estimation.Across all sample sizes and over the quantile range of 0.90 ≤τ≤0.99,the normal-half normal stochastic frontier estimation proved to be superior to quantile regression in statistical efficiency.Its parameter estimates had lower degrees of variability and correspondingly narrower confidence intervals.This greater efficiency would naturally be conducive to making statistical inferences.The estimated self-thinning surface using all 170 observations enveloped about 96.5% of the data points,a degree of envelopment equivalent to a regression quantile estimation with aτ of 0.965.The stochastic frontier estimation was also more objective because it did not involve the subjective selection of a particular value of τ for the favoured self-thinning surface from several mutually intersecting surfaces as in quantile regression.However,quantile regression could still provide a valuable complement to stochastic frontier analysis in the estimation of the self-thinning surface as it allows the examination of the impact of variables other than stand density on different quantiles of stand biomass.