Estimating actual evapotranspiration of various agricultural water optimization practices using the soil moisture-based evapotranspiration model

Accurate estimation of actual evapotranspiration (ET a ) is important for optimizing agricultural water use. A recently developed soil moisture-based ET (SMET) model allows estimation of ET a in small plot trials. This study evaluated how four irrigation technologies [low-elevation Nelson Advantage (LENA ® ), low-elevation precision application (LEPA), low-elevation spray application (LESA), and mid-elevation spray applicators (MESA)], two irrigation levels (100% and 50% of ET c ), three crops [alfalfa ( Medicago sativa ), silage corn ( Zea mays L.), and small grain forage (SGF) ( Triticum aestivum , Hordeum vulgare , and Secale cereale × Triticum aestivum )], drought-tolerant (DT) genetics, no-till (NT), and cover cropping (CC) in some crops affect ET a , crop water productivity (WP c ), and irrigation water productivity (WP i ). The field trials were conducted at Vernal and Cedar City, Utah, during 2022-23. Soil moisture sensors were installed at 15, 45, and 75 cm depths at both sites in each treatment. Results from the study show that low-elevation sprinkler technologies (i.e., LEPA, LESA, LENA ® ) had higher WP c and WP i compared to MESA in alfalfa, small grain forage, and grain corn, while MESA was higher in silage corn when averaged across irrigation levels and management treatments. Deficit irrigation at 50% of ET c has mixed effects on WP c and WP i , while deep sensor installation is required for precise ET a estimations. The DT genetics of alfalfa improved WP c and WP i compared to the non-DT genetics; however, no-till in SGF and the combined effect of DT genetics, NT, and CC in silage or grain corn reduced WP c and WP i , likely due to lower crop stands under NT. These findings suggest that low-elevation irrigation technologies, moderate deficit irrigation, and DT genetics can improve WP c and WP i .