## 5 Conclusions  
Climate change and the growing pressure on water resources underscore the urgent need for forecasting systems that are more localized, accurate, and actionable to support decision-making in agriculture. In response to this challenge, the MAGDA project was designed to develop and assess an integrated framework that combines high-resolution numerical weather prediction, advanced data assimilation techniques, field-deployed observational infrastructure (including drone-based atmospheric profiling and in situ soil moisture monitoring), agro-hydrological modeling, and a user-centric digital platform. The overarching goal was not only to enhance the physical accuracy of meteorological forecasts, but also to ensure their direct applicability in operational contexts, such as irrigation scheduling and convective weather risk management, across both farm and regional scales. The integrated MAGDA system was tested at three pilot sites representing diverse climatic and agricultural conditions across Europe. The results demonstrate that data assimilation notably improves the representation of the atmospheric state, particularly for near-surface thermodynamic variables, leading to more accurate and stable forecasts. The French demonstrator clearly illustrated the added value of assimilation for convective storm prediction, improving both spatial precision and detection capability. In the irrigation-focused demonstrators in Romania and Italy, the assimilation of drone-based and ground-based observations led to improved soil moisture initialization and forecast realism, even under moderate rainfall scenarios where open-loop forecasts were already reasonably accurate. Significantly, the integration of meteorological forecasts with a soil moisture-sensitive crop irrigation model enabled more adaptive and efficient water use strategies than those based on conventional crop reference evapotranspiration alone. Local, high-resolution observational data proved particularly effective in increasing the field-scale relevance of forecasts, where satellite-derived products often fall short in spatial and temporal specificity. Furthermore, the analysis highlighted the operational advantage of frequent forecast updates (e.g., every two days), which resulted in reduced irrigation requirements without compromising forecast accuracy. **Key contributions of the MAGDA project can be summarized as follows:**  
* **Multi-source assimilation**: Integration of GNSS, drone-based profiling, radar, and in situ sensors into a high-resolution forecasting chain. * **Improved forecast accuracy**: Enhanced short-term and very-short-term forecasts for both convective events and agricultural variables, particularly near-surface temperature and moisture. * **Agro-hydrological coupling**: Operational linkage of weather forecasts with soil-moisture sensitive irrigation modeling (SPHY), enabling site-specific irrigation recommendations. * **Operational dashboards**: Development of a user-oriented interface and open APIs for integration with Farm Management Systems. * **Validated impact**: Demonstrated improvements in forecast skill, irrigation efficiency, and usability across three European pilot sites with diverse climatic and agricultural conditions. These findings support the conclusion that co-designed, integrated forecasting systems, combining assimilation, observation, modeling, and stakeholder-oriented interfaces, can effectively bridge the gap between scientific forecasting capabilities and real-world agricultural needs. While further refinement is still needed in background error characterization and observation network optimization, the framework developed through MAGDA lays a solid foundation for operational, climate-resilient agro-meteorological services, offering a cost-effective solution suitable for real-world deployment in diverse agricultural contexts. Conceptualization, M.L. and E.R. ; methodology, M.L., S.C. and E.R. ; software, A.G., A.F., L.H., F.U., Z.C. and J.O. ; validation, E.O., A.F.-R. and S.B. ; formal analysis, F.R. and I.O.