McCarthy, Alison and Hancock, Nigel and Raine, Steven R. (2010) Simulation of site-specific irrigation control strategies with sparse input data. In: CIGR 2010: Sustainable Biosystems Through Engineering, 13-17 Jun 2010, Quebec City, Canada.
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Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions.
An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller.
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|Item Type:||Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)|
|Item Status:||Live Archive|
|Additional Information (displayed to public):||No evidence of copyright restrictions preventing deposit. Pub. no CSBE10051.|
|Depositing User:||Ms Alison McCarthy|
|Faculty / Department / School:||Historic - Faculty of Engineering and Surveying - Department of Agricultural, Civil and Environmental Engineering|
|Date Deposited:||07 Mar 2011 02:56|
|Last Modified:||11 Jun 2014 01:16|
|Uncontrolled Keywords:||adaptive control; automation; water use efficiency; spatial variability|
|Fields of Research (FoR):||07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070103 Agricultural Production Systems Simulation
09 Engineering > 0913 Mechanical Engineering > 091302 Automation and Control Engineering
07 Agricultural and Veterinary Sciences > 0799 Other Agricultural and Veterinary Sciences > 079901 Agricultural Hydrology (Drainage, Flooding, Irrigation, Quality, etc.)
|Socio-Economic Objective (SEO):||B Economic Development > 82 Plant Production and Plant Primary Products > 8298 Environmentally Sustainable Plant Production > 829805 Management of Water Consumption by Plant Production|
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