Agronomic outcomes of precision irrigation management technologies with varying complexity

Authors: Thorp, Kelly; CALLEJA, SEBASTIAN - University Of Arizona; PAULI, DUKE - University Of Arizona; Thompson, Alison; ELSHIKHA, DIAA ELDIN - University Of Arizona

Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/23/2021
Publication Date: 2/10/2022
Citation: Thorp, K.R., Calleja, S., Pauli, D., Thompson, A.L., Elshikha, D. 2022. Agronomic outcomes of precision irrigation management technologies with varying complexity. Transactions of the ASABE. 65(1):135-150. https://doi.org/10.13031/ja.14950.
DOI: https://doi.org/10.13031/ja.14950

Interpretive Summary: A wide variety of information technologies have been developed to assist irrigation management decisions, particularly related to managing irrigation uniquely in spatial zones across a field. However, results are mixed on whether these technologies lead to meaningful improvements in agronomic outcomes like crop yield and water use. This study tested several methods for computing irrigation requirements, evaluating crop water stress, and applying irrigation to a cotton field trial in Maricopa, Arizona. The results demonstrated small but significant agronomic improvements by irrigating on a site-specific basis and identified advantages for thermal imaging from unmanned aircraft systems to identify crop water stress. In addition to producers, several commercial industries will benefit from this research, including industries supporting agricultural irrigation, U.S. cotton production, and the development of unmanned aircraft systems.

Technical 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 which evaluated 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 unmanned 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 treatments. 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.