Introduction: The application of new technologies to the control and automation of irrigation processes is becoming very important in the last decade. Although automation of irrigation execution (irrigation programmers) is now widespread the automatic generation and execution of irrigation schedules is receiving growing attention due to the possibilities offered by the telemetry / remote control systems currently being installed in collective pressurized networks. In this paper, a prototype autonomous irrigation controller for solid set systems is presented. Material and Methods: The autonomous irrigation controller for solid set sprinkler irrigation systems is composed by software and hardware. The software simulates drop trajectories emitted by each sprinkler (Playán et al., 2006), water distribution in the soil and crop growth and yield (Dechmi et al., 2004, Zapata et al., 2009). Although the model simulates sprinkler water application, the control unit was the irrigated block of the solid set irrigated field. Variability inside an irrigated block was represented by decision variables (irrigation, soil and crop performance indexes) that can control the irrigation timing of the different blocks of the farm. The hardware was a research prototype translating the irrigation schedules into electrical orders to open or close solenoid valves and collecting and transmitting local sensor readings to the software. A field experiment was designed to test and validate the automatic programmer over a corn crop during two irrigation seasons (2009 and 2010). A randomized experimental design with four replicates was used to analyze three treatments: T0) a manual programmer acting as an advanced farmer; T1) automatic scheduling controlled by the estimated water content in the soil and simulated irrigation performance; and T2) advanced automatic scheduling controlled by crop and irrigation simulations. Results/Conclusions: Experimental results in 2009 and 2010 indicated that T0 was the treatment receiving most water, followed by T1 and T2. Yield parameters did not show significant differences between treatments. Automatic irrigation scheduling resulted in similar maize yield to manual irrigation. Differences were relevant in applied water (9% between T0 and T1, and 15% between T0 and T2).

This research was funded by the MCINN of the Government of Spain through grants AGL2007-66716-C03-01/02 and AGL2010-21681-C03-01.

6 Pags., 1 Tabl., 2 Figs.

Peer reviewed