## 4 Discussion
While improvements in temperature and humidity were consistent, results for wind speed were more variable, underscoring the need for more robust wind-related observational data. * **Convective storm prediction**: DA improved key forecast skill metrics for convective storms, such as Probability of Detection (PODY) and False Alarm Ratio (FAR), highlighting better storm localization and reduced false positives. * **Demonstrator-specific outcomes**: In the French demonstrator (nowcasting of convective storms), DA notably enhanced spatial accuracy and reduced false alarms. In contrast, in the Romanian and Italian demonstrators focused on irrigation, DA improved model initialization but had a subtler effect on precipitation forecasts due to already moderate rainfall events and longer forecasts losing the assimilation effect that typically influences the first 6-12 h.
* **Irrigation strategy refinement**: Even when rainfall forecasts were similar between DA and OL runs, DA contributed to more consistent and realistic system states. This led to better-informed irrigation strategies, with potential water savings compared to traditional approaches based only on reference evapotranspiration. A comparison with the ICITID method used operationally at the Romanian pilot site showed that MAGDA's long-term DA forecasts produced irrigation recommendations of the same order of magnitude. However, the ICITID method provided quotas approximately 35% lower than MAGDA. This discrepancy highlights the value of simulating soil moisture dynamics and climate forcings in a coupled atmosphere-crop-soil framework, offering a potential advantage over coarse, county-scale approaches. * **Sensor-driven initialization**: The hydrological models benefited from initialization with in situ soil moisture sensors. Although not formally compared to satellite-based initializations, these site-specific measurements appear more suitable for field-scale applications. * **Update frequency**: More frequent forecast updates (every 2 days vs. 5) led to better irrigation efficiency, reinforcing the value of short-term updates in operational advisory systems. * **User engagement and customization**: The MAGDA dashboard demonstrated a high degree of operational flexibility and user-centered design. Throughout the project,multiple stakeholder engagement activities--including field demonstrations, user interviews, and national workshops--allowed for iterative refinement of the system. Feedback from farmers, cooperatives, and technical advisors led to improvements such as the display of long time series, the creation of customized indicators (e.g., crop-specific water stress or disease risk), and the simplification of data visualization to enhance usability across different user profiles. * **Alignment with EU policy and strategic integration**: MAGDA's outcomes directly contribute to the broader objectives of the European Green Deal and the Common Agricultural Policy (CAP) by promoting sustainable water management, resilience to climate variability, and resource efficiency. The system's architecture, featuring an open API and modular services, facilitates interoperability with existing Farm Management Systems (FMS), enabling adoption by both public-sector platforms and commercial agri-tech providers. Furthermore, the project's clustering efforts and alignment with other EU-funded initiatives have strengthened the strategic visibility and replicability of MAGDA across European agricultural value chains. * **Economic sustainability and exploitation potential**: The MAGDA system, as implemented and tested, proved to be an economically sustainable solution for high-value crop contexts, thanks to the scalability of its sensor infrastructure and the modularity of its digital services. The project established the groundwork for both commercial and non-commercial uptake pathways, including precision agriculture solution providers, public authorities responsible for water and risk management, and environmental research institutions.