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Agronomic Outcomes of Precision Irrigation Management Technologies with Varying Complexity  Open Access

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Journal of the ASABE. 65(1): 135-150. (doi: 10.13031/ja.14950) @2022
Authors:   Kelly R. Thorp, Sebastian Calleja, Duke Pauli, Alison L. Thompson, Diaa Eldin Elshikha
Keywords:   Cotton, Crop coefficient, Drone, FAO-56, Irrigation scheduling, Remote sensing, Site-specific irrigation, Soil mapping, Unoccupied aircraft system, Variable-rate irrigation, Water stress.


Cotton yield and water productivity were measured for different precision irrigation management solutions.

Agronomic improvements from site-specific irrigation based on spatial FAO-56 crop coefficient data were minor.

Thermal remote sensing data from unoccupied aircraft systems were able to identify crop water limitations.

Integrated sensing and modeling tools that can achieve intended agronomic outcomes should be prioritized.

Abstract. Diverse technologies, methodologies, and data sources have been proposed to inform precision irrigation management decisions, and the technological complexity of different solutions is highly variable. Additional field studies are needed to identify solutions that achieve intended agronomic outcomes in simple and cost-effective ways. The objective of this study was to compare cotton yield and water productivity outcomes resulting from different solutions for scheduling and conducting precision irrigation management. A cotton field study was conducted at Maricopa, Arizona, in 2019 and 2020 that evaluated the outcomes of four management solutions with varying technological complexity: (1) a stand-alone evapotranspiration-based soil water balance model with field-average soil parameters (MDL), (2) using site-specific soil data to spatialize the modeling framework (SOL), (3) driving the model with spatial crop coefficients estimated from an unoccupied aircraft system (UAS), and (4) using commercial variable-rate irrigation technology for site-specific irrigation applications (VRI). Soil water content data and thermal UAS data were also collected but used only in post hoc data analysis. Applied irrigation, cotton fiber yield, and water productivity were statistically identical for MDL and SOL. As compared to MDL, the UAS crop coefficient approach significantly reduced applied irrigation by 7% and 14% but also reduced yield by 5% and 26% in 2019 and 2020, respectively (p = 0.05). In 2019 only, the VRI approach maintained yield while significantly reducing applied irrigation by 8% compared to MDL, and water productivity was significantly increased from 0.200 to 0.211 kg m-3 when one outlier datum was removed (p = 0.05). Post hoc data analysis showed that crop water stress information, particularly from UAS thermal imaging data, would likely benefit the irrigation scheduling protocol. Efforts to develop integrated sensing and modeling tools that can guide precision irrigation management to achieve intended agronomic outcomes should be prioritized and will be relevant whether irrigation applications are site-specific or uniform.

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