A generalized algebraic difference approach allows an improved estimation of aboveground biomass dynamics of Cunninghamia lanceolata and Castanopsis sclerophylla forests

Abstract

Key message A generalized algebraic difference approach (GADA) developed in this study improved the estimation of aboveground biomass dynamics of Cunninghamia lanceolata (Lamb.) Hook and Castanopsis sclerophylla (Lindl.) Schott forests. This could significantly improve the fieldwork efficiency for dynamic biomass estimation without repeated measurements.

Context The estimation of biomass growth dynamics and stocks is a fundamental requirement for evaluating both the capability and potential of forest carbon sequestration. However, the biomass dynamics of Cunninghamia lanceolata and Castanopsis sclerophylla using the generalized algebraic difference approach (GADA) model has not been made to date.

Aims This study aimed to quantify aboveground biomass (AGB, including stem, branch and leaf biomass) dynamics and AGB increment in C. lanceolata and C. sclerophylla forests by combining a GADA for diameter prediction with allometric biomass models.

Methods A total of 12 plots for a C. lanceolata plantation and 11 plots for a C. sclerophylla forest were selected randomly from a 100 m × 100 m systematic grid placed over the study area. GADA model was developed based on tree ring data for each stand.

Results GADA models performed well for diameter prediction and successfully predicted AGB dynamics for both stands. The mean AGB of the C. lanceolata stand ranged from 69.4 ± 7.7 Mg ha−1 in 2010 to 102.5 ± 11.4 Mg ha−1 in 2013, compared to 136.9 ± 7.0 Mg ha−1 in 2010 to 154.8 ± 8.0 Mg ha−1 in 2013 for C. sclerophylla. The stem was the main component of AGB stocks and production. Significantly higher production efficiency (stem production/leaf area index) and AGB increment was observed for C. lancolata compared to C. sclerophylla.

Conclusion Dynamic GADA models could overcome the limitations posed by within-stand competition and limited biometric data, can be applied to study AGB dynamics and AGB increment, and contribute to improving our understanding of net primary production and carbon sequestration dynamics in forest ecosystems.