The abandonment of agricultural land is now considered one of the primary land use changes driven by complex interactions between social, economic, and environmental factors. To understand and manage this process, a holistic approach that integrates multidimensional methodologies and interactions is essential. This study examines the key driving factors behind agricultural land abandonment in Lithuania using two methodological approaches. First, seventeen highly qualified land management experts were surveyed, and their insights were analysed using in-depth qualitative interviews, focusing on agricultural land abandonment and its underlying factors. Second, the development of agricultural land abandonment in a representative Lithuanian municipality was modelled using Markov chain models, incorporating freely available geographic data as factors influencing land use transformation. Actual areas of abandoned agricultural land were mapped using orthophotos from 2012, 2018, and 2021, for both model development and validation. The importance of predictors in the model was then assessed in relation to their significance as drivers of agricultural land abandonment. The findings indicate that natural factors, such as the proximity of forests and topographical constraints, play a significant role in explaining land abandonment processes. Additionally, agricultural land abandonment is influenced by social, economic, and legal factors, including land ownership structures, migration, and infrastructure accessibility. The importance of soil quality, productivity, and the presence of nearby arable land was found to vary depending on data accuracy and local environmental conditions, highlighting the complexity of agricultural land use patterns. The chosen mixed-method approach, combining qualitative surveys with numerical spatial modelling, demonstrates potential for identifying critical land use areas and providing insights to improve land management policies and decision making.

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.

The emergence of carbon trading mechanisms is increasing the need for transparent, reproducible, and policy-relevant tools to quantify carbon stock changes in the forest sector. In this context, this study presents an Excel-based carbon calculation tool developed in accordance with IPCC principles for estimating carbon stock changes within the LULUCF sector. The tool supports scenario-based assessments of land-use change and afforestation planning using readily available spatial and statistical inputs and enables evaluation of the potential impacts of legislative initiatives on carbon sequestration from afforestation of non-forest land, serving as an analytical instrument for policy formulation, legislative decision-making, and scientific analysis of forest cover expansion.To test the applicability of the tool, afforestation scenarios were developed for Jonava municipality (944 km²), Lithuania, using GIS-based identification of suitable areas. Twocontrasting cases were applied: afforestation limited by current land-use regulations and an extended scenario including drainage bund areas. The regulation-aligned case identified 2,862 ha suitable for afforestation, while the extended case increased the afforestable area to 20,189 ha, raising potential forest cover from 41.1% to 62.6%.When processed with the Excel-based IPCC-consistent tool, the extended scenario demonstrated a substantially higher carbon sequestration potential, with up to 14.1 million tons of additional CO₂ equivalent over a 50-year period – approximately six times the annual sequestration estimated under the regulation-aligned scenario. These results demonstrate the tool’s capacity to quantify long-term carbon stock changes under contrasting land-use assumptions, supporting its use for scenario testing, land-use planning, and carbon accounting.