Effect of information-driven irrigation scheduling on water use efficiency, nutrient leaching, greenhouse gas emission, and plant growth in South Florida

Abstract

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.