Comprehensive propagation of errors for the prediction of woody biomass

Abstract Managing vegetation to sequester carbon in biomass requires estimates to meet standards for accuracy, with methods that are transparent, verifiable and cost‐effective. Allometric models are commonly used to predict biomass from non‐destructive field inventory data. Although a number of studies have addressed biomass error propagation, none have provided a general set of methods for linking errors all the way from initial allometric model development through to the final site‐based biomass prediction, for both above‐ and below‐ground biomass. Error sources in total biomass (above‐ + below‐ground) were quantified using a combination of analytical and Monte Carlo methods, illustrated with four contrasting case studies using either site‐ and‐species‐specific, species‐specific or generalised allometric models. Sampling error was found to be the most important contributor to site‐level biomass uncertainty, arising from the interaction between spatial variability and the field sampling design. The contribution of allometric model covariance to total error was also quantified, with errors in the determination of moisture content during allometric model development identified as a potentially important yet often overlooked error source. Application of different allometric models to the same inventory data suggested the error from generalised models was no greater than that from site‐ or species‐specific models, with increases in the generalised model prediction error balanced by decreases in other error sources associated with the increased sample size on which generalised models are based. Recommendations for reducing errors in predicted biomass include increasing field survey sample size, adopting field survey designs that ensure spatial representativeness and improving moisture content measurement protocols and increasing the moisture content sample size during allometric model development. To reduce costs while maintaining acceptable accuracy, the use of generalised allometric models is recommended, with the caveat that additional biomass sampling for model validation may be required to limit the potential for biased predictions.