Ordered and unordered multinomial response models: an application to assess loblolly pine merchantability

Qualitative response models constitute a class of regression models used to predict one of a discrete number of mutually exclusive outcomes. These models differ from continuous regression models in that the response variable takes only discrete values. In forestry applications, the use of such models has been largely confined to mortality studies where the dependent variable is dichotomous. However, it is common in forestry to deal with variables that are either naturally discrete or continuous but recorded discretely. Consequently, there is a need for models that are appropriate for polychotomous dependent variables. Two models that appear to be suitable for forestry applications are presented, namely the ordered and unordered multinomial models, with emphasis on their theoretical justification, statistical inference, and model selection criteria. Using permanent plot data from loblolly pine (Pinustaeda L.) plantations on cutover, site-prepared areas throughout the southern United States, these models were fitted to assess the merchantability of loblolly pine trees. The results demonstrate the potential of qualitative response models for meaningful implementation in a variety of forestry applications and, also, for suggested topics for future research work.