WIT Press


Adaptive Scheduling In Deficit Irrigation – A Model-data Fusion Approach

Price

Free (open access)

Paper DOI

10.2495/SI080191

Volume

112

Pages

14

Page Range

187 - 200

Published

2008

Size

1202 kb

Author(s)

M. M. Holloway-Phillips, W. Peng, D. Smith & A. Terhorst

Abstract

The technological demands required to successfully practice either targeted irrigation control and/or deficit irrigation strategies are currently reliant on numerical models which are often underutilised due to their complexity and low operational focus. A simple and practical real-time control system is proposed using a model-data fusion approach, which integrates information from soil water representation models and heterogeneous sensor data sources. The system uses real-time soil moisture measurements provided by an in situ sensor network to generate site-specific soil water retention curves. This information is then used to predict the rate of soil drying. The decision to irrigate is made when soil water content drops below a pre-defined threshold and when the probability of rainfall is low. A deficit strategy can be incorporated by lowering the irrigation \“refill” point and setting the fill amount to a proportion of field capacity. Computer simulations show how significant water savings can be achieved through improved utilisation of rainfall water by plants, spatially targeted irrigation application, and precision timing through adaptive control Keywords: deficit irrigation, wireless sensor network, adaptive irrigation scheduling, model-data fusion, irrigation decision tree. 1 Introduction Australia is facing a severe water shortage due to below-average rainfall received over the past decade. The agricultural sector is the hardest hit by this as irrigation accounts for almost 65% of total water use nationally [1]. Long-term climate forecasts suggest that this situation is unlikely to improve [2]. Therefore

Keywords

deficit irrigation, wireless sensor network, adaptive irrigationscheduling, model-data fusion, irrigation decision tree.