Abstract Stakeholders from six European pilot sites engaged in participatory mapping and land suitability assessments to co-design climate-smart strategies for sustainable land management. The mixed methodology applied combined GIS based landscape vulnerability analysis, stakeholder knowledge, and assessments of ecosystem services. Key phases included preliminary assessment of environmental pressures, participatory SWOT analysis, and collaborative mapping exercises to identify suitable mixed farming (MF) and agroforestry (AF) practices. This approach empowered local communities, enhanced knowledge exchange, and integrated socio-ecological dimensions into land-use planning. Participatory mapping proved effective in capturing spatial perceptions, guiding context-specific transitions, and building consensus on landscape-scale interventions. Based on environmental pressure indicators, the scaling-up analysis showed that, depending on local conditions, the proportion of areas suitable for MF and AF ranged from 2 to 61% of the total area analysed across the six pilot sites. All the stakeholders agreed on the introduction of MF and AF and expressed differing views on their potential to reduce the environmental pressures of agricultural practices and enhance ecosystem services. Practitioners, such as farmers and advisors, emphasised the need for greater knowledge and stronger policy support to implement the transition toward more agroecological farming systems. While results highlight the large potential for MF and AF (up to 61% of the land use, in certain cases), it also showed the importance of participatory tools in bridging scientific research and practice, reinforcing the role of stakeholder engagement in designing resilient and multifunctional agricultural systems. While this might help to bridge the gap between planning and the implementation of agroecological practices across diverse European contexts, further research on the implementations and the socio-economic assessment of MF and AF at landscape scale is needed. Graphical abstract