TY - JOUR
T1 - Variable Rate Irrigation of Maize and Soybean in West-Central Nebraska Under Full and Deficit Irrigation
AU - Barker, J. Burdette
AU - Bhatti, Sandeep
AU - Heeren, Derek M.
AU - Neale, Christopher M.U.
AU - Rudnick, Daran R.
N1 - Funding Information:
Funding for the project was partially provided by an award from the Irrigation Innovation Consortium funded through a Foundation for Food and Agriculture Research grant and a U.S. Geological Survey 104(b) grant from the Nebraska Water Center (Project No. 2017NE291B). Additional support was received from a U.S. Department of Agriculture, National Institute of Food and Agriculture (NIFA) Agriculture Food and Research Initiative grant (Award No. 2017-67021-26249), the Daugherty Water for Food Global Institute at the University of Nebraska, the Hatch Act (USDA NIFA, Accession No. 1009760), and the University of Nebraska-Lincoln Institute of Agriculture and Natural Resources Agricultural Research Division.
Funding Information:
We thank those who provided research support in the field, including: Alan Boldt, Turner Dorr, Julienne Irihose, Tsz Him Lo, Troy Nelson, Jacob Nickel, Isabella Possignolo, Jacob Rix, and Rene-Francis Simbi-Mvuyekure. We thank Toby Spiehs, who managed the research field. Mesonet and National Weather Service weather data were provided by the High Plains Regional Climate Center at the University of Nebraska-Lincoln. We thank two reviewers for their insightful reviews. Funding. Funding for the project was partially provided by an award from the Irrigation Innovation Consortium funded through a Foundation for Food and Agriculture Research grant and a U.S. Geological Survey 104(b) grant from the Nebraska Water Center (Project No. 2017NE291B). Additional support was received from a U.S. Department of Agriculture, National Institute of Food and Agriculture (NIFA) Agriculture Food and Research Initiative grant (Award No. 2017-67021-26249), the Daugherty Water for Food Global Institute at the University of Nebraska, the Hatch Act (USDA NIFA, Accession No. 1009760), and the University of Nebraska-Lincoln Institute of Agriculture and Natural Resources Agricultural Research Division.
Publisher Copyright:
Copyright © 2019 Barker, Bhatti, Heeren, Neale and Rudnick.
PY - 2019/9/24
Y1 - 2019/9/24
N2 - Variable rate irrigation (VRI) may improve center pivot irrigation management, including deficit irrigation. A remote-sensing-based evapotranspiration model was implemented with Landsat imagery to manage irrigations for a VRI equipped center pivot irrigated field located in West-Central Nebraska planted to maize in 2017 and soybean in 2018. In 2017, the study included VRI using the model, and uniform irrigation using neutron attenuation for full irrigation with no intended water stress (VRI-Full and Uniform-Full treatments, respectively). In 2018, two deficit irrigation treatments were added (VRI-Deficit and Uniform-Deficit, respectively) and the model was modified in an attempt to reduce water balance drift; model performance was promising, as it was executed unaided by measurements of soil water content throughout the season. VRI prescriptions did not correlate well with available water capacity (R2 < 0.4); however, they correlated better with modeled ET in 2018 (R2 = 0. 69, VRI-Full; R2 = 0.55, VRI-Deficit). No significant differences were observed in total intended gross irrigation depth in 2017 (VRI-Full = 351 mm, Uniform Full = 344). However, in 2018, VRI resulted in lower mean prescribed gross irrigation than the corresponding uniform treatments (VRI-Full = 265 mm, Uniform Full = 282 mm, VRI-Deficit = 234 mm, and Uniform Deficit = 267 mm). Notwithstanding the differences in prescribed irrigation (in 2018), VRI did not affect dry grain yield, with no statistically significant differences being found between any treatments in either year (F = 0.03, p = 0.87 in 2017; F = 0.00, p = 0.96 for VRI/Uniform and F = 0.01, p = 0.93 for Full/Deficit in 2018). Likewise, any reduction in irrigation application apparently did not result in detectable reductions in deep percolation potential or actual evapotranspiration. Additional research is needed to further vet the model as a deficit irrigation management tool. Suggested model improvements include a continuous function for water stress and an optimization routine in computing the basal crop coefficient.
AB - Variable rate irrigation (VRI) may improve center pivot irrigation management, including deficit irrigation. A remote-sensing-based evapotranspiration model was implemented with Landsat imagery to manage irrigations for a VRI equipped center pivot irrigated field located in West-Central Nebraska planted to maize in 2017 and soybean in 2018. In 2017, the study included VRI using the model, and uniform irrigation using neutron attenuation for full irrigation with no intended water stress (VRI-Full and Uniform-Full treatments, respectively). In 2018, two deficit irrigation treatments were added (VRI-Deficit and Uniform-Deficit, respectively) and the model was modified in an attempt to reduce water balance drift; model performance was promising, as it was executed unaided by measurements of soil water content throughout the season. VRI prescriptions did not correlate well with available water capacity (R2 < 0.4); however, they correlated better with modeled ET in 2018 (R2 = 0. 69, VRI-Full; R2 = 0.55, VRI-Deficit). No significant differences were observed in total intended gross irrigation depth in 2017 (VRI-Full = 351 mm, Uniform Full = 344). However, in 2018, VRI resulted in lower mean prescribed gross irrigation than the corresponding uniform treatments (VRI-Full = 265 mm, Uniform Full = 282 mm, VRI-Deficit = 234 mm, and Uniform Deficit = 267 mm). Notwithstanding the differences in prescribed irrigation (in 2018), VRI did not affect dry grain yield, with no statistically significant differences being found between any treatments in either year (F = 0.03, p = 0.87 in 2017; F = 0.00, p = 0.96 for VRI/Uniform and F = 0.01, p = 0.93 for Full/Deficit in 2018). Likewise, any reduction in irrigation application apparently did not result in detectable reductions in deep percolation potential or actual evapotranspiration. Additional research is needed to further vet the model as a deficit irrigation management tool. Suggested model improvements include a continuous function for water stress and an optimization routine in computing the basal crop coefficient.
KW - deficit irrigation
KW - evapotranspiration modeling
KW - irrigation management
KW - remote sensing
KW - site-specific irrigation
UR - http://www.scopus.com/inward/record.url?scp=85076346168&partnerID=8YFLogxK
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U2 - 10.3389/fdata.2019.00034
DO - 10.3389/fdata.2019.00034
M3 - Article
C2 - 33693357
AN - SCOPUS:85076346168
SN - 2624-909X
VL - 2
JO - Frontiers in Big Data
JF - Frontiers in Big Data
M1 - 34
ER -