Real-Time Data Requirements for Model-Based Adaptive Control of Irrigation Scheduling in Cotton

Abstract

Model-based adaptive control strategies can be used to determine site-specific irrigation volumes with the aim of maximising crop water use efficiencies and/or yield. These strategies require infield weather, soil and crop measurements to calibrate a crop model: the crop model is then used to determine the irrigation applications throughout the crop season which produce the desired simulated crop response or condition (eg. maximum yield). However, data collection spatially over a field and throughout the crop season will potentially lead to a large sensed data requirement which may be impractical in a field implementation. Not all the collected data may be required to sufficiently calibrate the crop model and determine irrigation applications for model-based adaptive control; rather, a smaller dataset consisting of only the most influential sensor variables may be sufficient for adaptive control purposes. This paper reports on afield study which examined the utility of five sensed variables – evaporative demand, soil moisture, plant height, square count and boll count – to calibrate the cotton model OZCOT within a model-based controller and evaluate the relative significance of each sensed variable (either individually or in combination) as a control input. For the field study conditions, OZCOT was most effectively calibrated (and therefore able to predict the soil and crop response to irrigation application) using full data input, while for situations where only two data inputs were available, the simulations suggested that either weather-and-plant or soil-and-plant inputs were preferable.