The use of plant protection products (PPPs) remains a major concern for biodiversity, ecosystem integrity, and human health, even under robust regulatory oversight. Current regulatory assessments are often fragmented: they typically examine single substances and single crops in isolation at local or simplified scales. As a result, they struggle to capture the cumulative and combined effects of multiple PPPs and the way exposure propagates and interacts across space and time (fields, crops, and seasons). Our previously proposed landscape-based Environmental Risk Assessment (ERA) framework offered an integrative solution. By jointly representing agricultural practices, environmental characteristics, species movement among habitats, and the combined impacts of multiple PPPs, the current framework delivers predictions that are more adapted to the field reality. These insights are valuable both for regulatory decision-making and for understanding how PPP risks contribute to the overall environmental stress. In this manuscript, we explore the needs, challenges, opportunities, and modelling tools for implementing a landscape-based ERA in both prospective (ex-ante) and retrospective (ex-post) contexts. Drawing on expert discussions and collaborative initiatives, we propose a conceptual framework with four pillars: (1) flexibility to meet diverse user and stakeholder needs, different decision contexts, and varying data availability; (2) ecological realism, the capacity to represent multiple stressors, cumulative effects, exposure pathways driven by species movement, and recovery dynamics; (3) data integration and transparency, combining monitoring and regulatory datasets for calibration, validation, uncertainty analysis, and reproducibility; and (4) regulatory uptake and interoperability, ensuring compatibility with existing ERA methodologies and producing outputs that can be interpreted and used at the landscape level across jurisdictions and tools. Beyond regulatory compliance, landscape-based ERA is a dynamic and adaptative system that provides a robust scientific basis for setting protection goals, designing targeted risk mitigation measures, shaping sustainable agricultural strategies, and communicating realistic, multi-stressor risk trade-offs to stakeholders and the public.