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.

中文翻译：

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美国西部干旱地区甜菜的热力作物水分胁迫指数基线温度

摘要 甜菜是一种在不受限制的土壤剖面中的根深蒂固的作物，可以很容易地利用储存的土壤水分来减少季节性灌溉需求。由于在更深的深度进行土壤水分监测的劳动力和费用以及有效生根深度和土壤持水能力的不确定性，通常不考虑将低于 0.6 m 的土壤水分用于灌溉调度。基于热量的作物水分胁迫指数 (CWSI) 甜菜灌溉计划有可能克服土壤水分监测的局限性并促进储存土壤水分的利用。本研究中，2014、2015年全灌溉（FIT）和25%FIT下灌溉甜菜的冠层温度，2017 年和 2018 年爱达荷州中南部和美国怀俄明州西北部的 FIT 和 2018 年 60% FIT 从全覆盖到收获以及气象条件和土壤含水量进行了监测。使用神经网络 (NN) 根据收集到的太阳辐射、气温、相对湿度和风速的 15 分钟平均值来预测浇水充足的冠层温度 -1 到 +2.5 小时的太阳正午（13:00 – 16:00 MDT）。用于估计非蒸散冠层与气温之间差异的线性回归驱动的物理模型得出的研究气象条件值为 13.7 °C。每日 CWSI 值计算为 MDT 13:00 和 16:00 之间计算的平均 15 分钟 CWSI，与灌溉量和时间密切相关。与亏缺灌溉处理中的土壤水分监测相比，每日 CWSI 值提供了对作物水分胁迫更敏感的指示。用于计算每日 CWSI 的方法可用于灌溉调度以利用土壤储水量，而无需了解土壤深度、作物生根深度或深度 (> 0.6 m) 土壤水分监测。