Irrigation, essential for food production and landscape maintenance, affects soil water dynamics, water quality, and soil chemistry. However, the multidimensional role of irrigation has not been a focus in irrigation sciences. We evaluated the performance of different irrigation scheduling methods regarding water saving, water quality protection, and greenhouse gas emission for improved irrigation sustainability. Plot-scale experiments with snap beans and turfgrass were conducted with four irrigation scheduling methods: two conventional practices and two information-driven approaches based on soil water sensing and modeling. The results showed that information-driven scheduling significantly reduced irrigation water use while maintaining the yield and biomass of snap beans and the health of turfgrass. The information-driven methods also reduced nutrient leaching into deeper soil layers. Greenhouse gas emissions were either below the detection limit or did not change between irrigation treatments. This study demonstrated that soil water sensing and modeling could improve the sustainability of irrigation by reducing water use and nutrient losses to the aquifer while maintaining agricultural productivity.

灌溉对粮食生产和景观维护至关重要，它会影响土壤水动态、水质和土壤化学。然而，灌溉的多维作用并没有成为灌溉科学的重点。我们评估了不同灌溉调度方法在节水、水质保护和温室气体排放方面的表现，以提高灌溉的可持续性。使用四种灌溉调度方法对脆豆和草坪草进行了小区规模试验：两种常规做法和两种基于土壤水分传感和建模的信息驱动方法。结果表明，信息驱动的调度显着减少了灌溉用水，同时保持了脆豆的产量和生物量以及草坪草的健康。信息驱动的方法还减少了养分渗入更深的土壤层。温室气体排放要么低于检测限，要么在灌溉处理之间没有变化。这项研究表明，土壤水分传感和建模可以通过减少含水层的用水和养分损失来提高灌溉的可持续性，同时保持农业生产力。