TY - JOUR
T1 - AGRONOMIC OUTCOMES OF PRECISION IRRIGATION MANAGEMENT TECHNOLOGIES WITH VARYING COMPLEXITY
AU - Thorp, Kelly R.
AU - Calleja, Sebastian
AU - Pauli, Duke
AU - Thompson, Alison L.
AU - Elshikha, Diaa Eldin
N1 - Funding Information:
The authors acknowledge Cotton Incorporated (Project Nos. 17-642, 18-384, 20-720, and 21-830), Yuma Center of Excellence Small Grants Program Project (No. 2019-04), and University of Arizona startup funds for contributing partial funding for this research. In addition, the authors acknowledge the technicians, post-docs, and students at Maricopa who helped conduct field experiments, collect field data, and complete harvest and ginning activities: Matt Hagler, Suzette Maneely, Lianne Evans, Sharette Rockholt, Kathy Johnson, Melissa Stefanek, Patrick Ashe, Joe Griffin, Matthew Herritt, Bruno Rozzi, Ace Pugh, Emmanuel Gon-zalez, and Giovanni Melandri. Alanna Zubler is acknowledged for creating the Python script to relate GCP coordinates to sUAS image coordinates for georeferencing purposes. Karen Geldmacher and Americot are acknowledged for providing the cotton seed. Pedro Andrade-Sanchez and John Heun are acknowledged for providing spatial yield data from the cotton yield monitor. Finally, Andrew French and Jeffrey Demieville are acknowledged for conducting the calibration of the thermal imager.
Publisher Copyright:
© 2022 The authors.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Cotton
KW - Crop coefficient
KW - Drone
KW - FAO-56
KW - Irrigation scheduling
KW - Remote sensing
KW - Site-specific irrigation
KW - Soil mapping
KW - Unoccupied aircraft system
KW - Variable-rate irrigation
KW - Water stress
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U2 - 10.13031/ja.14950
DO - 10.13031/ja.14950
M3 - Article
AN - SCOPUS:85133840257
SN - 2769-3295
VL - 65
SP - 135
EP - 150
JO - Journal of the ASABE
JF - Journal of the ASABE
IS - 1
ER -