252 Irrigation control strategies may be used to improve the site-specific irrigation of 26 cotton via lateral move and centre pivot irrigation machines. A simulation framework 27 'VARIwise' has been created to aid the development, evaluation and management of 28 spatially and temporally varied site-specific irrigation control strategies. VARIwise 29 accommodates sub-field scale variations in all input parameters using a 1 m 2 cell size, 30 and permits application of differing control strategies within the field, as well as 31 differing irrigation amounts down to this scale. 32 33In this paper the motivation and objectives for the creation of VARIwise are discussed, 34 the structure of the software is outlined and an example of the use and utility of 35VARIwise is presented. Three irrigation control strategies have been simulated in 36VARIwise using a cotton model with a range of input parameters including spatially 37 variable soil properties, non-uniform irrigation application, three weather profiles and 38 two crop varieties. The simulated yield and water use efficiency were affected by the 39 combination of input parameters and the control strategy implemented. 40 41
26Model-based irrigation control strategies applied to irrigation make decisions (on 27 water application and/or timing) using a crop and/or soil production model. Decisions 28 are made with respect to an optimisation objective which, for irrigation, can be either 29 short-term (e.g. achieving/maintaining a set soil-water deficit) or predicted end-of-30season (e.g. maximising final yield) by predicting how the crop will respond at the 31 end of the season. In contrast, sensor-based irrigation strategies rely on achieving a 32 performance that is measurable during the crop season to provide the feedback 33 control, and may not necessarily optimise overall crop performance. Model-based 34 control potentially avoids this limitation. Research Highlights 55• Model Predictive Control was simulated for site-specific irrigation in 'VARIwise' 56• MPC accommodated both short-term (in-season) and long-term performance 57 objectives 58• MPC delivered the best performance when optimising crop yield 59• MPC resulted in higher (simulated) yield than sensor-based strategies 60• MPC required extensive data to accurately calibrate crop model 61 62 Keywords 63Variable-rate irrigation, centre pivot, lateral move, scheduling, irrigation automation, 64Model Predictive Control 65 66
27Feedback control systems offer opportunities to accommodate spatial and temporal 28 differences in crop water requirement and to improve the automated irrigation of field 29 crops via real-time data from in-field plant, soil-water and evaporation sensing. This 30 paper describes two sensor-based strategies applied to irrigation control, 'Iterative 31Learning Control' (ILC) and custom-designed 'Iterative Hill Climbing Control' 32 (IHCC), implemented in the control simulation and evaluation framework 33 'VARIwise'. Simulation of an irrigated cotton crop using soils and merged 2004 weather data of SE Queensland, Australia, and represented by the performance 35 of the well-validated cotton growth and production model OZCOT, permitted the 36 relative performance of differing sensor data types and availability to be evaluated 37 (both as alternatives and in combination) in meeting the requirement to optimise 38 either crop yield or water use efficiency. These simulations indicated that ILC would 39 perform better at maintaining soil-water deficit, whilst IHCC would be better at 40 maximising crop yield when plant and soil sensors were utilised in combination. This 41 work demonstrates that the optimal choice of field sensor(s) and control strategy will 42 be a function of the irrigation objective and the spatial and temporal availability and 43 type of field measurements. 44 45 Research highlights 46• Two site-specific sensor-based irrigation strategies were simulated in VARIwise 47• Iterative Learning Control (ILC) produced highest yield with soil-water data input 48• Iterative Hill Climbing Control (IHCC) performed best with soil-and-plant data 49 input 50 3• Both sensor-based strategies were superior to the industry-standard strategy 51 52
Spatial variability in crop production occurs as a result of spatial and temporal variations in soil structure and fertility; soil physical, chemical and hydraulic properties; irrigation applications; pests and diseases; and plant genetics. It is argued that this variability can be managed and the efficiency of irrigation water use increased by spatially variable application of irrigation water to meet the specific needs of individual management zones (areas of crop whose properties are relatively homogenous). The prospects for spatially varied irrigation applications and the need for adaptive control of irrigation application systems are identified. Current work at USQ directed toward adaptive control of furrow irrigation and centre pivot and lateral move machines is described. KEYWORDSReal-time control, furrow irrigation, centre pivot machines, lateral move machines INTRODUCTIONPractitioners of dry-land agriculture have embraced the concept and potential benefits of precision farming and substantial research has been undertaken on the yield mapping and variable rate technology that underlies the practice. Irrigation aspires to be a precision activity but one in which the traditional intention has been to deliver precisely the same quantity of water to each plant. The cost of any non-uniformity in irrigation applications is assumed to be reduced yield and lower efficiencies. However, this assumes that the requirements of each plant are exactly the same and ignores differences in crop water requirements due to spatial differences in soil hydraulic properties, fertility and other inputs. To counter the effects of this non-uniformity, irrigators are tempted apply larger water applications with a resultant reduction in volumetric and water use efficiencies.
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