An irrigation schedule testing model for optimization of the Smartirrigation avocado app

A series of mobile irrigation apps have been developed on the basis that irrigation schedules can be estimated using an average of the previous five days of crop evapotranspiration (ETc). The application of this average ETc methodology for developing an irrigation schedule has not been fully evaluated for the suite of apps, including the avocado app. Thus, an irrigation testing model was developed and is presented here that simulates irrigation depths based on the Smartirrigation avocado app operation technique and uses a soil water balance to simulate drainage, soil water content, runoff and plant water stress. The objectives were to identify an optimum number of previous days average ETc needed for estimating of an app irrigation schedule and to evaluate seasonal influences (wet and dry seasons) on the irrigation schedule through development and application of an irrigation testing model. Four different methods (3, 4, 6 and 7 previous days ETc) were tested and compared to the current methodology (that uses the average of the previous 5 days ETc) for developing irrigation schedules for five years from 2010 to 2014. In general, no significant differences (p≤0.05) were observed for irrigation depths, drainage depths, runoff and soil water content simulated among the app scheduling methods. Sixty two to 67% water savings were predicted with the app irrigation scheduling methods compared to a time based irrigation method (38 mm week−1). Average irrigation and drainage depths simulated were 15 to 41% and 160 to 512% greater in wet seasons compared to dry seasons, respectively. Ninety seven to 100% of the drainage was simulated during rainfall events. The current app methodology (previous 5 days ETc) was found to capture weather variability for irrigation schedule development. Use of previous 6 and 7 days ETc was also sufficient. Addition of a wireless connection allowing users to modify the irrigation schedule using the smartphone app is recommended to reduce water losses during wet seasons and/or when rainfall events are predicted or occur.