Variable Rate Irrigation of Maize and Soybean in West-Central Nebraska Under Full and Deficit Irrigation

J. Burdette Barker, Sandeep Bhatti, Derek M. Heeren, Christopher M.U. Neale, Daran R. Rudnick

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish (US)
Article number34
JournalFrontiers in Big Data
StatePublished - Sep 24 2019


  • deficit irrigation
  • evapotranspiration modeling
  • irrigation management
  • remote sensing
  • site-specific irrigation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science (miscellaneous)
  • Information Systems


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