Estimating Soil Water Use in Australian Cotton Systems to Improve Irrigation Management

Date Issued:2019-06-30

Abstract

Irrigation is a key component of cotton production in Australian agriculture, where increasing pressures of water scarcity requires growers to improve their water use efficiency. Monitoring of soil water deficits is a key component of maintaining optimal irrigation management. A number of technologies have been provided over the years to allow irrigators to monitor and predict soil water to better time their irrigation applications. This study compared four different methodologies for measuring and predicting soil water status within an irrigated cotton system. A Neutron Moisture Meter (NMM) device was calibrated to gravimetric soil water measurements. The calibrated NMM readings were then compared to an EM38 device, crop-modelling software HydroLOGIC, and remote-sensing software IrriSAT throughout the 2017-18 growing season. Each methodology produced estimations of PAW Deficits (mm) on 15 separate dates, at 13 sites within a 4.25-hectare field. To enable a fair comparison of the two technologies HydroLOGIC the soil water was not corrected by inputing soil water measurements, with just the crop parameters and irrigation dates entered up until the run date. IrriSAT had slightly higher correlation with NMM readings compared to HydroLOGIC when average across the measurement period. However the accuracy varied significantly during different periods which could signicantly impact on irrigation timing. During early to peak flowering IrriSAT overestimated NMM deficits by 20 - 30mm, which if relied on would result in irrigating much earlier than required whereas HydroLOGIC run without any soil water inputs underestimated crop water use after cut-out. . The EM38 device did not correlate well with NMM readings but as other studies have found strong correlations further calibration is likely required to test this. Overall, this study demonstrates that collaborative use of proximal devices such as the NMM with specialised predictive software could provide accurate estimations of soil water deficits throughout the full season, whilst saving time and labour.

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