Quang V. Cao

A Tree Survival Equation and Diameter Growth Model for Loblolly Pine Based on the Self-Thinning Rule

1. A system of equations was developed to describe the reciprocal effects of stand density and quadratic mean diameter through time, by use of data from a loblolly pine (Pinus taeda) plantation. 2. These equations were based on the self-thinning rule, which regulates an overcrowded population by imposing a maximum mean tree size for a given stand density. 3. The system explained 98% of the variations in surviving tree number and quadratic mean diameter, and should provide reasonable extrapolation.

Predicted leaf area growth and foliage efficiency of loblolly pine plantations

Abstract Foliage dynamics research is helpful for better understanding the process of forest production and improving silvicultural practice. However, the difficulty of measuring foliage amount has slowed down the research progress. Since leaf area of an individual tree can be reliably predicted from its diameter, growth and yield models that provide detailed information for each diameter class can be used to benefit foliage dynamics research. Simulation results from a growth and yield system for unthinned loblolly pine plantations indicated that foliage area increased with stand age, peaked between ages 36 and 51, and decreased after that. Volume growth increased with leaf area for young stands and decreased for older stands, whereas foliage efficiency consistently decreased with age. Better sites supported higher levels of leaf area index, volume growth, and foliage efficiency. Higher planting densities led to higher maximum leaf area indices and shorter time to reach that level. Initial density had no effect on foliage efficiency through time.

Linking individual-tree and whole-stand models for forest growth and yield prediction

BackgroundDifferent types of growth and yield models provide essential information for making informed decisions on how to manage forests. Whole-stand models often provide well-behaved outputs at the stand level, but lack information on stand structures. Detailed information from individual-tree models and size-class models typically suffers from accumulation of errors. The disaggregation method, in assuming that predictions from a whole-stand model are reliable, partitions these outputs to individual trees. On the other hand, the combination method seeks to improve stand-level predictions from both whole-stand and individual-tree models by combining them.MethodsData from 100 plots randomly selected from the Southwide Seed Source Study of loblolly pine (Pinus taeda L.) were used to evaluate the unadjusted individual-tree model against the disaggregation and combination methods.ResultsCompared to the whole-stand model, the combination method did not show improvements in predicting stand attributes in this study. The combination method also did not perform as well as the disaggregation method in tree-level predictions. The disaggregation method provided the best predictions of tree- and stand-level survival and growth.ConclusionsThe disaggregation approach provides a link between individual-tree models and whole-stand models, and should be considered as a better alternative to the unadjusted tree model.

Characterizing wood fiber and particle length with a mixture distribution and a segmented distribution

Abstract The length data from 12 samples of wood fibers and particles were described using lognormal and Weibull distributions. While both distributions fitted the middle range of the data well, the lognormal distribution provided a closer fit for short fibers and particles and the Weibull distribution was more appropriate for long ones. A mixture of the lognormal and Weibull distributions was developed using a variable weight to allow the new distribution to take the lognormal form for short fibers and gradually change to the Weibull form for long fibers. In the segmented distribution approach, a left segment of the lognormal distribution was joined to a right segment from the Weibull form. The Anderson-Darling goodness-of-fit test at the 5% level failed to reject the hypothesis that the mixture distribution and the segmented distribution fitted the data. Q-Q plots showed that both the mixture and segmented distributions provided an excellent fit to the fiber and particle length data, combining the best features of the lognormal and the Weibull distributions. These two new distributions are therefore better alternatives than the single lognormal and Weibull distributions for this data set.