Agriculture and forestry remain vital sources of rural employment; yet both sectors face challenges of low labour productivity, demographic change, and structural inefficiencies. Modernisation improves productivity but often reduces labour demand, creating a policy dilemma between innovation and job preservation. Therefore, this study aims to quantify labour input across different land use types related to agriculture and forestry in Latvia. This study develops a multi-stage framework to quantify labour inputs across agricultural and forestry land uses in Latvia. The research results indicate that labour intensity decreases with farm size but varies more strongly by production type. Perennial plantations, vegetable and potato cultivation, and dairy and pig farming show the highest labour demands, whereas energy crops and grass-based systems require the least. In forestry, establishment and tending dominate labour needs, while mechanised harvesting reduces input requirements. These findings highlight the strategic role of labour-intensive, high-value activities in sustaining rural employment. Policy should prioritise targeted support for labour-intensive, high-value farming and forestry activities to maximise rural jobs and income growth.

Understanding when and where to shift land from agriculture to forestry is essential for designing sustainable land use strategies that align with climate, biodiversity, and rural development goals. However, traditional profitability comparisons rely on long-term discounting, which is highly sensitive to assumptions and often misaligned with the shorter-term decision-making horizons that are relevant for policymakers. This study presents a deposit-based framework that interprets annual timber biomass growth as accumulating economic value, enabling direct, per-hectare comparisons with yearly agricultural profits. The framework integrates parcel-level spatial data, land quality indicators, national statistics, and expert inputs to produce high-resolution maps of annual profitability for both agriculture and forestry. Applied to the case of Latvia, the results show strong spatial variation in agricultural returns, particularly in low-quality areas where profits are marginal or negative. By contrast, forestry provides more stable, though modest, economic gains across a wide range of biophysical conditions. These insights help identify where afforestation becomes a financially viable land use alternative. The framework is designed to be transferable to other regions by substituting local data on land quality, prices and growth. It complements policy instruments such as performance-based CAP payments and afforestation support, offering a future-oriented tool for spatially explicit and economically grounded land use planning.

Agriculture and forestry remain vital sources of rural employment; yet, both sectors face challenges of low labour productivity, demographic change, and structural inefficiencies. Modernisation improves productivity but often reduces labour demand, creating a policy dilemma between innovation and job preservation. Therefore, this study aims to quantify labour input across different land use types and farm sizes in agriculture and forestry. Latvia was used as a case region representing a sparsely populated territory suitable for both agricultural activities and forestry. This study develops a multi-stage framework to quantify labour inputs across agricultural and forestry land uses. The research findings suggest that labour use intensity decreases as farm size increases; however, it exhibits greater variation across agricultural production types. Perennial plantations, vegetable and potato cultivation, and dairy farming show the highest labour demands, whereas energy crops and grass-based systems require the least. In forestry, establishment and tending dominate labour needs, while mechanised harvesting reduces input requirements. These findings highlight the strategic role of labour-intensive, high-value activities in sustaining rural employment and the need for targeted rural development policies that recognise this pattern, supporting employment in rural areas without discouraging improvements in labour productivity.

Sustainable land use policy planning requires integrated approaches that account for environmental and socio-economic trade-offs of greenhouse gas (GHG) mitigation measures. This study presents a spatial decision-support tool developed to support the evaluation of policy scenarios in non-urban land-use sectors, with application to the land use, land-use change, and forestry (LULUCF) sector in Latvia. The tool enables users to select predefined mitigation measures, apply spatial selection criteria, and generate quantitative and spatially explicit outputs. In addition to estimating GHG mitigation potential, it evaluates impacts on profitability, employment, and habitat quality, allowing the assessment of trade-offs and synergies across multiple dimensions. Scenario results are reported as both absolute and relative impacts, improving transparency and comparability. Developed in Python 3.10 and supported by a PostgreSQL 17/PostGIS 3.5 database, the tool operates through a web-based interface and supports efficient scenario construction and evaluation. While results depend on underlying data and assumptions, the tool provides a transparent framework for exploring policy options and supports evidence-based decision-making in land-use and climate policy planning.

Understanding when and where to shift land from agriculture to forestry is essential for developing sustainable land-use strategies that balance climate, biodiversity, and rural development goals. Traditional profitability comparisons rely on long-term discounting, which is sensitive to assumptions and misaligned with the decision-making horizons of landowners and policymakers. This study introduces a deposit-based framework that treats annual timber biomass growth as accumulating economic value, enabling direct comparison with yearly agricultural profits on a per-hectare basis. By integrating parcel-level spatial data, land quality indicators, national statistics, and expert input, the framework generates high-resolution maps of annual profitability for both land uses. Applied in Latvia, the analysis reveals significant regional variation in agricultural returns, with many low-quality areas showing marginal or negative profits, while forestry offers stable, modest gains across diverse biophysical conditions. The results highlight where afforestation becomes a financially rational alternative and suggest transition pathways that enhance overall land-use profitability while supporting climate and biodiversity objectives. The framework is transferable to other contexts by substituting context-specific data on land quality, prices and growth, and can complement policy instruments such as performance-based CAP payments and afforestation support. The approach supports future-oriented differentiated land-use planning using annually updated spatial economic signals.