Résumé
Surface irrigation is often described as low performing insofar as its practice is labour intensive and involves the use of large water flows that are difficult to quantify and manage. However, this method remains predominant worldwide, and modernisation towards localised irrigation systems is not always feasible nor advisable. To support border irrigation management, we previously developed a low-cost sensor for surface irrigation management, which remotely informs the farmer of water arrival downstream of his field and therefore of the moment to stop irrigation. The objectives of this article were: (i) to determine the optimal position of this sensor lengthwise in the field throughout the season, and (ii) to compare the influence of management scenarios (current farmer’s practices, sensor-based and time-based cutoff) on irrigation performance. To this end, an integrated agro-hydraulic model was developed to simulate surface water flow dynamics throughout the season including variations in infiltration and roughness. The model was run using monitoring data from the border irrigation of a hay field during a whole season in Southern France. The results showed that the optimal sensor position can change significantly over the course of the season, depending on inflow rates, initial soil moisture and Manning’s roughness. Sensor-based irrigation control was found to be more efficient than current farming practices, with an estimated water-saving potential of 33%, and more effective than an optimised fixed cutoff time in limiting water losses induced by variability or uncertainty in the initial conditions. For some irrigation events, water savings could reach 50%. The methods and findings should serve as a basis for larger-scale studies integrating the adoption of sensors and real-time data for surface irrigation management.