Harnessing Digital Technologies for Smarter Water Management in Agriculture 2025

Implementing digital agricultural water management strategies demands a strong data ecosystem. These technologies cannot operate efficiently at scale without dependable data collection and integration processes. Data collection To achieve effective water management, data needs to be gathered from a variety of sources, including existing water infrastructure, weather stations, IoT sensors and satellites. Core data parameters include precipitation amounts, soil moisture levels, crop-specific data, groundwater levels and water consumption patterns. Relevant and up-to-date data gathering should be carried out in real time, enabling accurate monitoring of water conditions and overall operational performance. The challenges to be considered are data inconsistencies across various data sources, data gaps in remote areas and possible downtime of sensors and monitoring equipment. The key performance indicators (KPIs) highlighted in Figure 5 can address these challenges.2.1 Establishing data infrastructure for smart agriculture KPIs for agricultural data collection systems FIGURE 5 Latency of data availability Time delay between when data is collected and when it becomes available for analysis or action.Data transmission success rate Percentage of data that is successfully transmitted from the source to the central system without loss.Data accuracy Percentage of collected data that is validated and deemed reliable for use in decision-making.Data collection frequency Rate at which data is gathered from each source, such as weather stations, IoT sensors or infrastructure.Sensor uptime Percentage of time monitoring equipment remains functional and actively collecting data. As pointed out in the World Economic Forum’s March 2025 publication, Water Futures: Mobilizing Multi-Stakeholder Action for Resilience, developing shared metrics for defining and measuring success is significant for effective data governance.31 Participatory methods play a key role in this process by addressing data fragmentation and enhancing coordination across water actors. Data ecosystems can be further strengthened through the integration of citizen science, which enhances local data granularity while embedding data equity principles to ensure that the voices of smallholder farmers and marginalized regions are reflected from the outset. Farmers often assume they have enough local data to make informed decisions, but water systems don’t operate in isolation. Understanding regional trends such as what’s happening with groundwater or neighbouring farms requires integrating multiple data sources to get the full picture. Arik Tashie, Climate Ai Harnessing Digital Technologies for Smarter Water Management in Agriculture 21