McCarthy, Alison C. 
ORCID: https://orcid.org/0000-0003-4595-6447 and Hancock, Nigel H. and Raine, Steven R.
(2014)
Simulation of irrigation control strategies for cotton using model predictive control within the VARIwise simulation framework.
Computers and Electronics in Agriculture, 101.
pp. 135-147.
ISSN 0168-1699
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Text (Accepted Version)
McCarthy_Hancock_Raine_CEAa_2014_AV.pdf Download (2MB) | Preview |
Abstract
Model-based irrigation control strategies applied to irrigation make decisions (on water application and/or timing) using a crop and/or soil production model. Decisions are made with respect to an optimisation objective which, for irrigation, can be either short-term (e.g. achieving/maintaining a set soil-water deficit) or predicted end-of-season (e.g. maximising final yield) by predicting how the crop will respond at the end of the season. In contrast, sensor-based irrigation strategies rely on achieving a performance that is measurable during the crop season to provide the feedback control, and may not necessarily optimise overall crop performance. Model-based control potentially avoids this limitation.
This paper describes the application of Model Predictive Control (MPC) methodology to the feedback control of irrigation via a model-based irrigation strategy implemented in the irrigation control simulation framework 'VARIwise'. The requirement to also accommodate spatial and temporal differences in crop water requirement across a heterogeneous field is met by defining management 'zones' according to differing soil and crop properties across the field and separately applying the control algorithm for each of these zones.
Case studies were conducted to evaluate MPC for a centre pivot irrigation machine-irrigated cotton crop (under typical Australian growing conditions) with: (i) different in-season performance objectives (maintaining soil-water deficit; maximising square count); (ii) different predicted end-of-season performance objectives (maximising yield; maximising water use efficiency); and (iii) maximising yield with different field data inputs for model calibration. The model predictive control strategy produced significantly higher simulated yields and water use efficiency than an industry-standard irrigation management strategy; and (in most but not all situations) direct sensor-based adaptive control strategies.
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