Biological Systems Engineering, Department of

 

First Advisor

Xin Qiao

Date of this Version

5-2020

Document Type

Article

Citation

Possignolo, I. P. (2020). Using Infrared Radriometry Thermometer For Irrigation Management Of Dry Edible Beans In Western Nebraska. Digital Commons. 1-103.

Comments

A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Mechanized Systems Management, Under the Supervision of Professor Xin Qiao. Lincoln, Nebraska: May, 2020

Copyright 2020 Isabella Presotto Possignolo

Abstract

Proper irrigation management requires farmers to determine the right timing and amount to irrigate. Soil water sensors are one of the most popular sensor-based approach used by farmers to decide when and how much to irrigate. However, installation and retrieval of soil water sensors require excavation of soil and can be challenging. Other than soil water sensors, there are plant-based water stress monitoring technologies that are less soil disturbing such as infrared radiometry thermometer (IRT). Using canopy temperature measured from IRT, researchers can calculate thermal-based indices such as crop water stress index (CWSI) for many crops around the world. Yet limited research has focused on detecting water stress using IRT and CWSI for dry edible beans (DEB), which is one of the most important crops in Western Nebraska. Therefore, in this research, we quantified parameters (baselines) that are crucial to the calculation of CWSI using canopy temperature measured from IRT; and evaluated the performance of calculated CWSI under four irrigation treatments that ranged from dryland to fully irrigated for DEB in Western Nebraska. The average lower baseline of DEB found was Tc – Ta = 2.78 - 1.59 VPD (n = 25, R2 = 0.81) and upper baseline was Tc – Ta = 3.76 (n = 11, SD = 0.42). Afternoon CWSI (12:00 PM to 3:00 PM) showed a significant difference among the irrigation treatments, with p-values of 0.0143 (2018) and 4.2 x 10-6 (2019).

Advisor: Xin Qiao

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