Abstract
Sugarbeet is a deep-rooted crop in unrestricted soil profiles that can readily utilize stored soil water to reduce seasonal irrigation requirements. Soil water below 0.6 m is not commonly considered for irrigation scheduling due to the labor and expense of soil water monitoring at deeper depths and uncertainty in effective rooting depth and soil water holding capacity. Thermal-based crop water stress index (CWSI) irrigation scheduling for sugarbeet has the potential to overcome soil water monitoring limitations and facilitate utilization of stored soil water. In this study, canopy temperature of irrigated sugarbeet under full irrigation (FIT) and 25%FIT in 2014, 2015, 2017 and 2018 in southcentral Idaho and FIT and 60%FIT in 2018 in northwestern Wyoming USA was monitored from full cover through harvest along with meteorological conditions and soil water content. A neural network (NN) was used to predict well-watered canopy temperature based on 15-min average values for solar radiation, air temperature, relative humidity, and wind speed collected -1 to +2.5 hours of solar noon (13:00 – 16:00 MDT). A linear regression driven physical model for estimating the difference between a non-transpiring canopy and air temperature resulted in a value of 13.7 °C for the meteorological conditions of the study. A daily CWSI value calculated as the average 15-min CWSI calculated between 13:00 and 16:00 MDT was well correlated with irrigation amounts and timing. The daily CWSI value provided a more responsive indication of crop water stress than soil water monitoring in deficit irrigation treatments. The methodology used to calculate a daily CWSI could be used in irrigation scheduling to utilize soil water storage without knowledge of soil depth, crop rooting depth, or deep (> 0.6 m) soil water monitoring.
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