Plantation species-specific adjustment functions for the Forest Carbon Predictor in New Zealand

Abstract

Background: New Zealand’s planted forest area is dominated by radiata pine (90%), but also includes Douglas-fir (6%), and a range of minor species including eucalypts and cypress. Carbon sequestration in planted forest is currently estimated using yield tables from the Forest Carbon Predictor (FCP), which was designed to estimate dry matter in live biomass and dead organic matter pools in radiata pine stands. Stand variables required as model inputs include basal area, mean top height, stocking, and tending regime. In addition, wood density needs to be measured or estimated. When the FCP is applied to plantation species other than radiata pine, dry matter estimates may need to be adjusted to remove bias. Methods: To test for and remove bias, existing biomass studies were compiled and additional biomass data collected to fill gaps. Measured dry matter stock estimates were compared with predictions from the FCP model. Species assessed included Pseudotsuga menziesii (10 stands), Cupressus lusitanica (2), Eucalyptus regnans (7), E. nitens (5), E. saligna (7), E. botryoides (2), E. fastigata (2), and Acacia dealbata (2). Results: Stem bark and crown components were higher in Douglas-fir than predicted by the FCP, whereas crown components were appreciably lower in hardwood species than predicted. Root biomass estimates in Douglas-fir and hardwood species were similar to predictions from the FCP. Dead organic matter stock estimates were similar in Douglas-fir to predictions from the FCP. Dry matter predictions from the model were adjusted by species or species group, to help reduce model prediction error when the FCP is applied to these species. Model bias for Douglas-fir averaged 4.1% for aboveground live biomass, with a root mean square error of 12.9%. Model bias for hardwood species averaged − 0.11% for aboveground live biomass, with a root mean square error of 16.1%. Owing to data limitations, model bias was poorly estimated for dead organic matter pools. Conclusions: This study has shown that forest carbon models that have been developed for well-studied plantation species can be adapted with some degree of certainty and applied to other species with suitable, albeit limited, biomass data in order to improve the accuracy of predictions of stand biomass and carbon sequestration.