Assessing productivity and carbon sequestration capacity of Eucalyptus globulus plantations using the process model Forest-DNDC: Calibration and validation

• Published in: Ecological modelling 2006-02, Vol.192 (1), p.83-94
• Main Authors: Miehle, P., Livesley, S.J., Feikema, P.M., Li, C., Arndt, S.K.
• Format: Article
• Language: English
• Subjects: Afforestation; Animal, plant and microbial ecology; Biological and medical sciences; Carbon sequestration; Eucalyptus globulus; Forest-DNDC; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Methods and techniques (sampling, tagging, trapping, modelling...); Model validation; Plantation; Prediction error; Productivity
• ISSN: 0304-3800; 1872-7026
• DOI: 10.1016/j.ecolmodel.2005.07.021

The tree growth sub-module (PnET) of the mechanistic model Forest-DNDC was calibrated and validated for plantation grown Eucalyptus globulus. Forest-DNDC describes the biogeochemical cycles of C and N and can assist in estimating soil-borne greenhouse gas fluxes. For validation of the forest growth sub-module, data from commercial forest plantations in south-eastern Australia was used. Growth predictions agreed well with growth measurements taken at age 6 years from 28 permanent sample plots, with an average prediction error of −1.62 t C ha −1 (−3.19%). Differences between predicted and measured aboveground C stocks ranged between −23.5 and 12.6 t C ha −1, which amounted to a relative root mean square error in prediction of 17.9%. Correlation between modelled and measured C in standing biomass was good ( r 2 = 0.73), with a Nash–Sutcliffe coefficient of model efficiency, ME = 0.65. The results obtained from the validation test reveal that Forest-DNDC can predict growth of E. globulus to a high level of precision across a broad range of climatic conditions and soil types. Forest-DNDC performed satisfactorily in comparison to other growth and yield models that have already been calibrated for E. globulus (e.g. BIOMASS, 3-PG, PROMOD or CABALA). In contrast to these growth and yield models, Forest-DNDC can additionally estimate total greenhouse gas budgets. The slightly lower precision of Forest-DNDC in comparison with specific management models, such as CABALA, are compensated for by the simple input requirements and application to regional situations.