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

Abstract

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 (R 2 < 0.4); however, they correlated better with modeled ET in 2018 (R 2 = 0. 69, VRI-Full; R 2 = 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.

Site-specific irrigation management in a sub-humid climate using a spatial evapotranspiration model with satellite and airborne imagery

“…Irrigation depths were split into two or three prescriptions when irrigation requirements exceeded the maximum irrigation depth that could be applied by irrigation system in a single irrigation pass. This methodology for irrigation management is described in detail by Barker et al (2018bBarker et al ( , 2019.…”

“…SETMI was used to compute irrigation requirements for VRI-L and VRIU plots. Refer to Barker et al (2018aBarker et al ( , 2019 for current information on SETMI in addition to the included water balance and TSEB models. Maize and soybean were considered as different fields in the model.…”

“…Real-time estimation and forecasting of spatial ET c helps in computing real-time dynamic VRI prescription maps (Barker et al, 2018b). Barker et al (2018bBarker et al ( , 2019 used a spatial ET model called Spatial EvapoTranspiration Modeling Interface (SETMI; Geli and Neale, 2012;Neale et al, 2012) and Landsat data to manage VRI on fields in Nebraska. The model included a water balance model based on reflectance-based crop coefficients (Neale et al, 1989) and the twosource energy balance model (TSEB; Norman et al, 1995).…”

A Decade of Unmanned Aerial Systems in Irrigated Agriculture in the Western U.S.

“…The software calculates ET a using a hybrid methodology combining a reflectance-based crop coefficient approach (Neale et al, 1989) using multispectral data and a two-source energy balance approach using thermal imagery, and was modified to maintain a spatial soil water balance (Barker et al, 2018a, b). For real time irrigation management, the model was initially used with Landsat satellite imagery (Barker et al, 2018b(Barker et al, , 2019, but was later used with imagery from unmanned aircraft systems (Bhatti et al, 2020). Soil water measurements at multiple locations in the field have been needed for ground truth for the soil water balance; however, ongoing improvements in SETMI are reducing the amount of drift in the soil water balance when unaided by soil water measurements (Barker et al, 2019).…”

“…For real time irrigation management, the model was initially used with Landsat satellite imagery (Barker et al, 2018b(Barker et al, , 2019, but was later used with imagery from unmanned aircraft systems (Bhatti et al, 2020). Soil water measurements at multiple locations in the field have been needed for ground truth for the soil water balance; however, ongoing improvements in SETMI are reducing the amount of drift in the soil water balance when unaided by soil water measurements (Barker et al, 2019). While many unmanned aircraft systems adequately capture spatial patterns in the crop canopy, a challenge for calculating ET a with unmanned aircraft imagery is the need for a high level of accuracy in the data (Barker et al, 2020), which has been a greater challenge for thermal imagery than multispectral imagery (Maguire, 2018).…”