The spatially explicit assessment of land use and land-use change patterns can identify critical areas and provide insights to improve land management policies and associated decisions. This study mapped the land uses and land-use changes in Lithuanian municipalities since 1971. Additionally, an analysis was conducted of three shorter periods, corresponding to major national land-use policy epochs. Data on land uses, available from the Lithuanian National Forest Inventory (NFI) and collected on an annual basis with the primary objective of conducting greenhouse gas (GHG) accounting and reporting for the land use, land-use change, and forestry (LULUCF) sectors, were explored. The overall trend in Lithuania during the last five decades has been an increase in the area of forest and built-up land and decrease in the area of producing land, meadow/pasture, wetlands, and other land uses. Nevertheless, the development trends for the proportions of producing land and meadow/pasture changed trajectories several times, and the breakpoints were linked with important dates in Lithuanian history and associated with the reorganization of land management and land-use relations. Global Moran’s I statistic and Anselin Local Moran’s I were used to check for global and local patterns in the distribution of land use in Lithuanian municipalities. The proportions of producing land and pasture/meadow remained spatially autocorrelated during the whole period analysed. Local spatial clusters and outliers were identified for all land-use types used in GHG inventories in the LULUCF sector at all the time points analysed. Ordinary least squares (OLS) regression was used to explain the land-use change trends during several historical periods due to differing land management policies, utilizing data from freely available databases as the regressors. The percentage of variance explained by the models ranged from 37 to 65, depending on the land-use type and the period in question.

Lithuanian forestry has long been shaped by the classical normal forest theory, aiming for even long-term flow of timber, and the aspiration to preserve domestic forest resources, leading to very conservative forest management. With radically changing forest management conditions, climate change mitigation efforts suggest increasing timber demands in the future. The main research question asked in this study addresses whether current forest management principles in Lithuania can secure non-decreasing long-term flow of timber and carbon accumulation. The development of national forest resources and forestry was simulated for the next century using the Kupolis decision support system and assuming that current forest management is continued under the condition of three scenarios, differing by climate change mitigation efforts. Potential development trends of key forest attributes were analysed and compared with projected carbon stock changes over time, incorporating major forest carbon pools—biomass, harvested wood products and emission savings due to energy and product substitution. The key finding was that the total carbon balance should remain positive in Lithuania during the next one hundred years; however, it might start to decrease after several decades, with steadily increasing harvesting and a reduced increase of forest productivity. Additionally, incorporating the harvested wood and CO₂ emissions savings in carbon balance evaluations is essential.

Effective management decisions regarding greenhouse gas (GHG) emissions may be hampered by the lack of scientific tools for modeling future land use change. This study addresses methodological principles for land use development scenario modeling assumed for use in processes of GHG accounting and management. Associated land use policy implications in Lithuania are also discussed. Data on land uses, available from the National Forest Inventory (NFI) and collected for GHG accounting from the land use, land use change and forestry (LULUCF) sector in the country, as well as freely available geographic information, were tested as an input for modeling land use development in the country. The modeling was implemented using the TerrSet Land Change Modeler. Calibration of the modeling approach using historical land use data indicated that land use types important for GHG management in the LULUCF sector were predicted with an accuracy above 80% during a five-year period into the future, while the prediction accuracy for forest and built-up land was 96% or more. Based on several land management scenarios tested, it was predicted that the LULUCF sector in Lithuania will accumulate CO₂, with the forest land use type contributing most to CO₂ absorption. Key measures to improve the GHG balance and carbon stock changes were suggested to be the afforestation of abandoned or unused agricultural land and prevention of the conversion of grassland into producing land.

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.

This paper aims to demonstrate the use of qualitative research methods, specifically in-depth interviews, to explore the intangible and often difficult-to-quantify needs for forestry scenario modelling in Lithuania, which are frequently not adequately perceived. The study involved informants representing key actors in forest policy, forest management, research, and education. A total of 21 informants from 11 different institutions, which hold significant power and expertise in forest decision making, were interviewed. The purpose of these interviews was to gather their perspectives on the potential forest decision support system in the country, aiming to address most of their needs. The interview questions explored various aspects, including the requirements for forestry scenario modelling, the desired level of detail and information content for decision making, and both functional and nonfunctional requirements for the scenario modelling system. It is worth noting that the expected functionality of the planned forest DSSs aligns with modern international standards. Nevertheless, the diversity of perspectives, wishes, visions, and intentions of key Lithuanian forestry actors regarding the aims, objectives, and essential functionality of forestry scenario modelling tools were identified. The understanding of the requirements for modern forest DSSs was greatly influenced by the current forestry paradigms in the country and the professional experiences of individual informants. In conclusion, our findings demonstrate that the utilization of qualitative research, particularly through in-depth interviews, has proven to be a highly effective tool for accurately specifying the requirements of a modern forest DSS. It helped mitigate preconceived notions and address gaps in the envisioned product, specifically by developing a framework of core solutions for the national forestry and land-use scenario modelling system.