TY - JOUR
T1 - Evaluation of variable rate irrigation using a remote-sensing-based model
AU - Barker, J. Burdette
AU - Heeren, Derek M.
AU - Neale, Christopher M.U.
AU - Rudnick, Daran R.
N1 - Funding Information:
Funding for the project was provided through the Robert B. Daugherty Water for Food Global Institute at the University of Nebraska and the Agricultural Research Division, University of Nebraska-Lincoln . This project was also partially supported by the Nebraska Agricultural Experiment Station with funding from the Hatch Act (Accession Number 1009760 ), and an Agricultural and Food Research Initiative grant (Award Number 2017-67021-26249 ), both through the USDA National Institute of Food and Agriculture . Barker was supported, in part, by a University of Nebraska Presidential Fellowship . We thank Mr. Mark Schroeder, Facilities Director of the University of Nebraska Eastern Nebraska Research and Extension Center , and others at that facility for their support of this research including provision of yield data. We thank personnel at the University of Nebraska-Lincoln West Central Research and Extension Center for providing data and field support for the Brule site. We are grateful for those who helped with data collection, laboratory work, field operations, equipment, facilities, and/or advisory input, especially Tsz Him Lo. Others include: Raoni Bosquilla, Clayton Blagburn, Alan Boldt, Isidro Campos, Blaine Clowser, Roger Elmore, Ryan Freiberger, Tomie Galusha, Isaiah Krutak, Rodrigo Dal Sasso Lourenço, Keith Miller, Mumba Mwape, Christopher Proctor, Matthew Russell, Aaron Steckly, Keith Stewart, and Christian Uwineza. Dr. Joe Luck provided the electrical conductivity data and advise on EC a and yield processing for Mead. Dr. Trenton Franz provided the interpolated electrical conductivity data for Brule. The Nebraska Mesonet weather data were provided by the High Plains Regional Climate Center at the University of Nebraska-Lincoln. The Ogallala AWOS weather station data were provided by the Utah Climate Center. Landsat imagery including surface reflectance "data available from the U.S. Geological Survey" ( https://lta.cr.usgs.gov/citation ). We thank Dr. Kent Eskridge and the University of Nebraska-Lincoln Statistical Cross-disciplinary Collaboration and Consulting Lab, who provided statistical support. We thank Drs. Derrel Martin, William Kranz, Trenton Franz, and Kent Eskridge, and Ms. Ronica Stromberg, for their reviews of earlier versions of this manuscript. We also thank the annonymous reviewers for thier input.
Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2018/4/30
Y1 - 2018/4/30
N2 - Improvements in soil water balance modeling can be beneficial for optimizing irrigation management to account for spatial variability in soil properties and evapotranspiration (ET). A remote-sensing-based ET and water balance model was tested for irrigation management in an experiment at two University of Nebraska-Lincoln research sites located near Mead and Brule, Nebraska. Both fields included a center pivot equipped with variable rate irrigation (VRI). The study included maize in 2015 and 2016 and soybean in 2016 at Mead, and maize in 2016 at Brule, for a total of 210 plot-years. Four irrigation treatments were applied at Mead, including: VRI based on a remote sensing model (VRI-RS); VRI based on neutron probe soil water content measurement (VRI-NP); uniform irrigation based on neutron probe measurement; and rainfed. Only the VRI-RS and uniform treatments were applied at Brule. Landsat 7 and 8 imagery were used for model input. In 2015, the remote sensing model included reflectance-based crop coefficients for ET estimation in the water balance. In 2016, a hybrid component of the model was activated, which included energy-balance-modeled ET as an input. Both 2015 and 2016 had above-average precipitation at Mead; subsequently, irrigation amounts were relatively low. Seasonal irrigation was greatest for the VRI-RS treatment in all cases because of drift in the water balance model. This was likely caused by excessive soil evaporation estimates. Irrigation application for the VRI-NP at Mead was about 0 mm, 6 mm, and –12 mm less in separate analyses than for the uniform treatment. Irrigation for the VRI-RS was about 40 mm, 50 mm, and –98 mm greater in separate analyses than the uniform at Mead and about 18 mm greater at Brule. For maize at Mead, treatment effects were primarily limited to hydrologic responses (e.g., ET), with differences in yield generally attributed to random error. Rainfed soybean yields were greater than VRI-RS yields, which may have been related to yield loss from lodging, perhaps due to over-irrigation. Regarding the magnitude of spatial variability in the fields, soil available water capacity generally ranked above ET, precipitation, and yield. Future research should include increased cloud-free imagery frequency, incorporation of soil water content measurements into the model, and improved wet soil evaporation and drainage estimates.
AB - Improvements in soil water balance modeling can be beneficial for optimizing irrigation management to account for spatial variability in soil properties and evapotranspiration (ET). A remote-sensing-based ET and water balance model was tested for irrigation management in an experiment at two University of Nebraska-Lincoln research sites located near Mead and Brule, Nebraska. Both fields included a center pivot equipped with variable rate irrigation (VRI). The study included maize in 2015 and 2016 and soybean in 2016 at Mead, and maize in 2016 at Brule, for a total of 210 plot-years. Four irrigation treatments were applied at Mead, including: VRI based on a remote sensing model (VRI-RS); VRI based on neutron probe soil water content measurement (VRI-NP); uniform irrigation based on neutron probe measurement; and rainfed. Only the VRI-RS and uniform treatments were applied at Brule. Landsat 7 and 8 imagery were used for model input. In 2015, the remote sensing model included reflectance-based crop coefficients for ET estimation in the water balance. In 2016, a hybrid component of the model was activated, which included energy-balance-modeled ET as an input. Both 2015 and 2016 had above-average precipitation at Mead; subsequently, irrigation amounts were relatively low. Seasonal irrigation was greatest for the VRI-RS treatment in all cases because of drift in the water balance model. This was likely caused by excessive soil evaporation estimates. Irrigation application for the VRI-NP at Mead was about 0 mm, 6 mm, and –12 mm less in separate analyses than for the uniform treatment. Irrigation for the VRI-RS was about 40 mm, 50 mm, and –98 mm greater in separate analyses than the uniform at Mead and about 18 mm greater at Brule. For maize at Mead, treatment effects were primarily limited to hydrologic responses (e.g., ET), with differences in yield generally attributed to random error. Rainfed soybean yields were greater than VRI-RS yields, which may have been related to yield loss from lodging, perhaps due to over-irrigation. Regarding the magnitude of spatial variability in the fields, soil available water capacity generally ranked above ET, precipitation, and yield. Future research should include increased cloud-free imagery frequency, incorporation of soil water content measurements into the model, and improved wet soil evaporation and drainage estimates.
KW - Evapotranspiration
KW - Remote sensing
KW - Soil water balance
KW - Variable rate irrigation
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U2 - 10.1016/j.agwat.2018.02.022
DO - 10.1016/j.agwat.2018.02.022
M3 - Article
AN - SCOPUS:85043487748
VL - 203
SP - 63
EP - 74
JO - Agricultural Water Management
JF - Agricultural Water Management
SN - 0378-3774
ER -