McCarthy, Alison C. 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
|
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.
|
Statistics for this ePrint Item |
| Item Type: | Article (Commonwealth Reporting Category C) |
|---|---|
| Refereed: | Yes |
| Item Status: | Live Archive |
| Additional Information: | © 2013 Elsevier B.V. Permanent restricted access to published version due to publisher copyright policy. |
| Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019) |
| Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019) |
| Date Deposited: | 16 Feb 2014 03:04 |
| Last Modified: | 01 Jul 2016 04:15 |
| Uncontrolled Keywords: | variable-rate irrigation; centre pivot; lateral move; scheduling; irrigation automation; Model Predictive Control |
| Fields of Research (2008): | 07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070103 Agricultural Production Systems Simulation 09 Engineering > 0906 Electrical and Electronic Engineering > 090602 Control Systems, Robotics and Automation 07 Agricultural and Veterinary Sciences > 0799 Other Agricultural and Veterinary Sciences > 079901 Agricultural Hydrology (Drainage, Flooding, Irrigation, Quality, etc.) |
| Socio-Economic Objectives (2008): | B Economic Development > 82 Plant Production and Plant Primary Products > 8203 Industrial Crops > 820301 Cotton |
| Identification Number or DOI: | https://doi.org/10.1016/j.compag.2013.12.004 |
| URI: | http://eprints.usq.edu.au/id/eprint/24556 |
Actions (login required)
 |
Archive Repository Staff Only |