Role of DSS to support carbon farming at the level of private forest estate

Carbon farming is increasingly promoted as a climate mitigation instrument in agriculture and land use, requiring additional, measurable, and sustainable increases in carbon sequestration. In forestry, meeting these requirements is more challenging due to strict conditions related to additionality, permanence, sustainability, and regulatory compliance. In Lithuania, forest management is strongly governed by legislation, which limits the potential for generating additional carbon benefits without deviating from established management rules. Consequently, additional carbon sequestration in forestry is most commonly associated with afforestation and postponement of final fellings – measure that requires robust justification of additionality.

Demonstrating such additionality requires decision support systems (DSS) capable of modelling carbon stock changes under alternative forest management scenarios and comparing them with baseline management. This study aims to demonstrate the role of DSS in supporting carbon farming at the level of private forest estate by integrating forest inventory data with carbon accounting tools. It shows how enhanced forest inventory data, combined with carbon accounting, can support scenario modelling, improve the transparency of additionality claims, and inform both management decisions and policy design for carbon farming schemes in forestry.

More specifically, the study introduces newly developed DSS tools for predicting future carbon stock changes under conventional and alternative forest management models designed to increase carbon sequestration at the scale of relatively large private forest estates. In addition, it presents new inventory approaches based on the integration of optical remote sensing data with airborne and drone-based laser scanning, aimed at supporting carbon farming–oriented forest management planning.