Harnessing Digital Technologies for Smarter Water Management in Agriculture 2025

Data integration Once data is collected, it must be integrated into a unified and standardized system. Agricultural water management relies on data obtained from various sources including historical records, IoT sensors and external data such as weather forecasts. Creating a unified analysis of water resources depends on integrating these disparate sources into a single, accessible system. The main challenge in data integration is handling the vast, unstructured amounts of data generated by IoT.35 This calls for pre-processing and cleaning requirements before AI algorithms are used to extract meaningful insights. Some priorities include the following: –Data interoperability and portability: The agrifood ecosystem exhibits limited interoperability and portability among its various data-driven tools and platforms, requiring farmers to engage with multiple platforms daily. Fragmentation of data sources (e.g. from satellites, IoT sensors, private data providers) makes integration difficult. The absence of standardized, machine-readable and openly accessible data makes interoperability across platforms more challenging. –Data quality: In addition to relevance, data should meet a level of quality sufficient to support real-time decision-making. Ensuring data quality becomes harder when large-scale IoT deployments are required, as data must be rapidly processed to achieve real-time decision-making. Synchronized data must be cleaned and standardized to make sure its quality is at the desired level.As described in the World Economic Forum’s December 2024 publication, Food and Water Systems in the Intelligent Age, community engagement involving local farmers, researchers, extension workers, governments and water managers through mobile apps can play a significant role in providing manual data validation and feedback to achieve data efficacy.36 Even though farmers might possess fundamental data regarding on-farm water usage, they often lack insight into wider hydrological patterns, including regional groundwater depletion, recharge rates and long-term impacts from climate change. In the absence of standardized, integrated datasets, evaluating risks and optimizing large-scale water usage becomes challenging. AI-driven data integration tools can assist in addressing this challenge by consolidating, standardizing and assessing various datasets, such as satellite imagery, IoT sensor outputs and historical climate data. AI models, for instance, can enhance physics-based climate models by downscaling them, which improves long-term water forecasts and delivers insights tailored to specific locations. This functionality is especially valuable for anticipating extreme weather occurrences, monitoring variations in soil moisture and determining the best irrigation timings. AI-driven risk assessment models can identify water-scarce regions for investment, guaranteeing that areas experiencing significant depletion or regulatory challenges receive focused support. Enhancing data accessibility and interoperability through digital infrastructure can connect on-the- ground decision-making with broader resource planning efforts.Bridging the gap with data-sharing policies BOX 3 The performance of digital technologies in agriculture depends on precise and detailed datasets involving soil health, hydrological charts, weather trends and water supply figures. In numerous regions, these datasets are often disjointed or only accessible by certain government agencies. Clear data-sharing policies and frameworks can help agri-tech firms, research organizations and farmers gain access to these datasets and use them to maximize water efficiency. –Kenya’s Agricultural Data-Sharing Platform (KADP) serves as a prime example of a shared data ecosystem in an agricultural landscape. The platform allows both private entities and government to have access to shared agricultural data categories encompassing crops, livestock, weather, soil, pest and diseases.32 –India’s Agricultural Data Exchange (ADeX) is a collaborative initiative by the Government of Telangana, the Indian Institute of Science (IISc) and the World Economic Forum’s Centre for the Fourth Industrial Revolution (C4IR) India, serving as India’s first data exchange platform. This infrastructure enables secure data-sharing based on governance rules and a robust data management framework. The aggregated data is then used to develop new digital services for farmers, reducing the cost of data collection and service delivery.33 –Ethiopia’s Ministry of Agriculture has created a national soil and agronomy data-sharing policy to enable a structured data-sharing framework in agriculture. In collaboration with the International Centre for Tropical Agriculture (CIAT) and GIZ Ethiopia, stakeholders consolidated dispersed datasets that were scattered across multiple institutions to provide fertilizer recommendations and guide farmers to optimize inputs that boost productivity on degraded lands.34 Unified water analysis depends on integrating data from various sources, including historical records, IoT sensors and weather forecasts, into a single, accessible system. Harnessing Digital Technologies for Smarter Water Management in Agriculture 22