## 4 Discussion  
A number of recent studies have investigated the integration of weather forecasts into irrigation scheduling models, highlighting both the benefits and limitations of such approaches. For example, [27] demonstrated that coupling real-time optimization with probabilistic forecasts, even when imperfect, can lead to significant gains in profitability and reductions in water. Likewise, [70] emphasized how forecast confidence, lead time, soil characteristics, and nitrogen availability interact to influence yield and environmental efficiency across different climatic zones. However, these studies are predominantly based on simulation frameworks that may face challenges when applied in operational settings, particularly under variable observational conditions or in areas with heterogeneous agricultural practices. The MAGDA approach builds upon and extends these efforts by developing and validating an integrated, operational framework that combines high-resolution meteorological forecasting, data assimilation from multi-source sensor networks (GNSS, Meteorrones, in situ, and satellite data), and agro-hydrological modeling tailored to site-specific conditions. In doing so, MAGDA not only addresses the physical accuracy of short- and medium-term forecasts but also enhances their usability through an open-access dashboard and API, enabling direct integration with Farm Management Systems (FMS). This shift toward user-oriented design is crucial for the operational uptake of decision-support tools in agriculture. Furthermore, while earlier studies such as [71] explored interactive systems where farmers could respond to model recommendations in real-time, MAGDA takes a complementary approach by embedding user feedback into the system design and validation process. This ensures that the forecasts and irrigation advisories are not only technically sound but also aligned with the real needs of diverse stakeholders. The integration of localized observational data and flexible assimilation cycles enables MAGDA to adapt dynamically to different agronomic contexts, offering both robustness and scalability. Figure 11: Simple indicators based on MAGDA data can be customized and displayed by the farmer, e.g., disease risk, crop maturity, or water stress. Thus, MAGDA's results offer a foundation for next-generation advisory systems that combine robust modeling with adaptive learning from farmer behavior. Expanding the observational network and further refining assimilation modules may enhance medium- and long-term forecast performance, potentially overcoming some of the limitations of short-term forecasting observed in prior work [27]. From the GNSS sensors' point of view, PPP-based integrated water-vapor (IWV) retrievals within MAGDA, using both geodetic-grade and low-cost GNSS receivers, confirm the robustness of this technique, in accordance with the work by [72; 73; 74]. In contrast, deriving soil moisture from GNSS reflectometry proved more challenging: in the MAGDA project, sensors relied solely on low-cost units, and agricultural deployment introduced uneven terrain, seasonal snow cover, and dense, rapidly changing vegetation that limited the usage of SNR-based reflections. Although controlled-environment studies have recently reported encouraging accuracy [75; 76; 77], MAGDA's field experience underscores the practical limitations that still constrain large-scale operational use. * **Data assimilation impact**: The DA framework showed consistent improvements in model performance, especially for 2-meter temperature (2 mT) forecasts. Radar reflectivity and Zenith Total Delay (ZTD) assimilation also improved forecasts, though their benefit was more constrained by observational noise. * **Vertical profiling via drones**: The assimilation of eight Meteorone-based vertical profiles significantly refined thermodynamic fields in the planetary boundary layer and lower troposphere.