We will accompany and evaluate existing experiments and support an efficient redesign of landscape systems following the 5 steps of agroecological transition. We will actively cooperate and engage in dialogue with the EU Agroecology Partnership. To achieve the AGRECO4CAST goals we will: i) define agroecological measures tailored to local ecological, and socio-economic conditions in 6 LLs; ii) study their effects compared to conventional perennial cropping at the landscape level, iii) assess options for synergies from optimizing spatial configurations of landscape elements across farms and formulate scenarios that account for different climatic and socio-economic future conditions; iv) assess pathways of agroecological transitions; and v) develop recommendations for Common Agricultural Policy. Our assessment framework toolbox aims to measure multiple impact parameters to evaluate the velocity, success, potential synergies, and trade-offs of agroecological transition options at landscape level, and to design tailored monitoring systems that support steering the transformation process covering environmental, social, and economic parameters comprehensively. Out of this toolbox, we select case-specific parameters and matching indicators in cooperation with SHs, depending on local needs and preferences. Monitoring and simulating key parameters at both farm and landscape scale support co-development taking into account cross-scale feedbacks and outscaling. The sum of local actions at the farm level allows for novel patterns to emerge at the landscape level, in turn, affecting local processes and decision-making. Currently, process-based modeling, AI-driven analytics, and SH-based impact assessments are mostly used independently, albeit to achieve the same goal for agroecological transition. We aim to support SH-based impact assessments with different modeling approaches to quantitatively project outcomes of codesigned transition pathways covering soil-vegetation feedback affecting water, biodiversity and climate. On the other hand, SH-based assessments complement modeling approaches by tapping into the knowledge and expertise of local stakeholders to assess impacts that are difficult to model, such as farmers’ satisfaction or landscape aesthetics. To support communication and involvement of SHs, we develop an AI-Chatbot and a digital round table that supports and emulates the viewpoints of diverse SHs.