Reliable precipitation data is crucial for agricultural decision-making, as it supports efficient irrigation scheduling, improved water-use efficiency, and risk management. However, conventional rainfall monitoring methods, including rain gauges and remote sensing systems, often fail to provide sufficiently accurate or representative rain estimates, due to both technical limitations and practical constraints.Rainfall causes attenuation of signals received by Commercial Microwave Links (CMLs), which serve as wireless infrastructure for transmitting information between cellular base stations. Over the past two decades, numerous studies have demonstrated that these systems can function as embedded, low-cost sensor networks for monitoring rainfall at ground level. However, most previous work in this field has concentrated on technical and accuracy-related aspects of the method and its potential for hydrometeorological applications. In this study, we focus on the potential of the proposed method for agricultural needs.We analyzed 68, 76 and 82 rainy days across two agricultural test sites in northern Israel, using hourly CML-based rainfall estimates. These estimates were evaluated against rain gauge observations and compared with ERA5, a reanalysis product widely used in agricultural applications. The results show that CMLs provide reliable rainfall estimates, while outperforming ERA5 in both agricultural domains. When compared to dedicated rain gauges at the test sites, CMLs achieved mean Pearson correlations of 0.304 and 0.997, with RMSEs of 0.143 and 18.355 mm at the first and second sites, respectively Conversely, ERA5 yielded correlations of −0.492 and 0.995, with RMSEs of 0.377 and 19.732 mm, based on the same ground truth at the same test sites. The results highlight the effectiveness of CML networks as a cost-efficient tool for rainfall monitoring in agriculture, with potential to enhance water management and risk assessment, particularly in data-scarce regions.