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Increasing climate variability and water scarcity are intensifying the need for adaptive, data-driven water management solutions in African agriculture. While digital and precision technologies are increasingly available, their effectiveness depends on how diverse data streams are integrated into usable decision support tools that respond to local conditions. This research focuses on the development of an open-source digital agriculture platform designed to support sustainable water management. The developed platform brings together multiple sources of real-time and near-real-time data, including IoT-based soil moisture and climate sensors, weather forecasts, crop information, and remote sensing products, and systematically compares these observations with inputted crop water and irrigation models. By continuously comparing field-level data with input models, the decision support system aims to enable more accurate irrigation scheduling, early detection of water stress, and adaptive responses to climate variability, supporting more informed, timely, and context-specific decision-making for farmers. Artificial intelligence and machine learning components can be integrated to further enhance the platform by identifying patterns, improving forecasts, and refining model performance over time. The presentation highlights the design and functionality of farmer-oriented decision support systems, outlining how open-source digital platforms can be tested, adapted, and refined in climate-vulnerable settings. By emphasizing interoperability, transparency, and community-driven innovation, the approach demonstrates how digital agriculture platforms can move beyond standalone technologies toward integrated decision support ecosystems for sustainable water management.