Measurement error effects on estimates from linear and nonlinear regression whole‐stand yield models.

Systems of whole‐stand yield models facilitate projections of forest attributes, but their inputs may be difficult to measure accurately. This study conducted sensitivity analyses to examine the effect of systematic and stochastic measurement errors on outputs from a representative system of equations. Simulated error was added to explanatory variables stand age, site index, or both. Results showed that large systematic error in one variable tended to produce moderate to large percent changes in all models, particularly the height and volume equations (often >50% change). Systematic error in both variables amplified this effect, especially for young, less productive stands. Stochastic error dramatically increased estimate variability (some relative standard errors >50%), particularly in the height and volume models at young ages and low site indices. These results suggest that measurement error may considerably alter projections and increase uncertainty when using whole‐stand yield models, highlighting the need for careful crew training. Recommendations for Resource Managers: Measurement error in input variables has the potential to significantly affect estimates from systems of whole‐stand yield models.Effects can be over‐ or underestimation of volume yields and financial returns or other forest attributes of interest.Careful crew training is paramount to ensure forest measurements remain consistent with the quality of the data used to fit the models. [ABSTRACT FROM AUTHOR]