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Estimating aboveground biomass and its components requires sound statistical formulation and evaluation. Using data collected from 55 destructively sampled trees in different parts of Oregon, we evaluated the performance of three groups of methods to estimate total aboveground biomass and (or) its components based on the bias and root mean squared error (RMSE) that they produced. The first group of methods used an analytical approach to estimate total and component biomass using existing equations and produced biased estimates for our dataset. The second group of methods used a system of equations fitted with seemingly unrelated regression (SUR) and were superior to the first group of methods in terms of bias and RMSE. The third group of methods predicted the proportion of biomass in each component using beta regression, Dirichlet regression, and multinomial log-linear regression. The predicted proportions were then applied to the total aboveground biomass to obtain the amount of biomass in each component. The multinomial log-linear regression approach consistently produced smaller RMSEs compared with both SUR methods. The beta and Dirichlet regressions were superior to both SUR methods except for Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) branch biomass, for which the simple SUR method produced smaller RMSE compared with the beta and Dirichlet regressions.

Despite the maturing entrepreneurial orientation literature, limited advance has occurred in the theoretical aspect of entrepreneurial orientation-firm performance relationship, and wideranging empirical investigations are few. The authors address these concerns by testing a comprehensive model and by extending the theoretical boundary. Specifically, drawing on dynamic capability theory, core competence perspective, and consumer theories, we argue that technological capability is central for growth and financial performance of an entrepreneurially oriented firm. We introduce a construct called consumer attitude dynamism. We reason that this variable influences the growth of entrepreneurial firms. Our theory and empirical results primarily contribute to the entrepreneurship literature.

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Abstract:Using lidar for large-scale forest management can improve operational and management decisions. Using multi-temporal lidar sampling and remeasured field inventory data collected from 78 plots in the Panther Creek Watershed, Oregon, USA, we evaluated the performance of different fixed and mixed models in estimating change in aboveground biomass (∆AGB) and cubic volume including top and stump (∆CVTS) over a five-year period. Actual values of CVTS and AGB were obtained using newly fitted volume and biomass equations or the equations used by the Pacific Northwest unit of the Forest Inventory and Analysis program. Estimates of change based on fixed and mixed-effect linear models were more accurate than change estimates based on differences in LIDAR-based estimates. This may have been due to the compounding of errors in LIDAR-based estimates over the two time periods. Models used to predict volume and biomass at a given time were, however, more precise than the models used to predict change. Models used to estimate ∆CVTS were not as accurate as the models employed to estimate ∆AGB. Final models had cross-validation root mean squared errors as low as 40.90% for ∆AGB and 54.36% for ∆CVTS.

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