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Irrigation scheduling models can be used to guide irrigation management decisions, but their simulations often deviate from reality. Combining in-season field data with models may improve the simulations, leading to better irrigation decisions and improved agronomic outcomes. The objective of this study was to evaluate cotton fiber yield and water productivity outcomes from a field trial that compared three computer simulation models for irrigation scheduling: (1) AquaCrop-OSPy (AQC), (2) the CROPGRO-Cotton module within the DSSAT Cropping System Model (CSM), and (3) the pyfao56 evapotranspiration-based, soil water balance model (FAO). Six irrigation scheduling treatments were established, including the three models used as stand-alone scheduling tools (AQC, CSM, and FAO) and the use of the three models in combination with weekly soil water content (SWC) data from neutron moisture meters (AQCSWC, CSMSWC, and FAOSWC). Two cotton varieties were also evaluated (NexGen 3195 and NexGen 4936). The field trial was conducted during the 2021 and 2022 cotton growing seasons at Maricopa, Arizona. Seasonal irrigation amounts were different among irrigation scheduling treatments (p < 0.05), with 9–21% less water recommended for the AQCSWC, CSMSWC, and FAOSWC treatments as compared to AQC, CSM, and FAO. In 2021, the differences in irrigation amount did not lead to any statistical differences in fiber yield among irrigation treatments, but water productivity for the stand-alone CSM model was significantly reduced compared to the other five irrigation treatments (p < 0.05). In 2022, treatments based on soil water content data reduced yield by 15% as compared to stand-alone model treatments, but the reduction was significant only for FAOSWC. Water productivity differences in 2022 were due to the choice of model rather than the inclusion of soil water content data. In both years, the shorter-season cotton variety (NexGen 3195) yielded greater than the longer-season variety (NexGen 4926), and the former achieved greater water productivity than the latter through the yield improvements (p < 0.05). Taken together, the results suggest that combining soil water content data with irrigation scheduling models was useful for reducing irrigation amounts while often maintaining cotton fiber yield and water productivity; however, issues with weather station aridity and other measurement uncertainties must be addressed to improve the methodology. [ABSTRACT FROM AUTHOR]

Effective irrigation systems that increase crop water productivity by minimizing evaporative losses are paramount for extending the longevity of finite groundwater resources in the semi-arid U.S. Southern High Plains (SHP). Although subsurface drip irrigation (SDI) acreage has increased in recent years, center-pivot sprinkler systems still account for greater than 85% of the irrigated area in the SHP. Modern sprinkler configurations are typically classified according to application height as either mid-elevation spray application (MESA) or low-elevation spray application (LESA). While application drift and evaporative losses are easily measured under fallow conditions, quantifying evaporative losses under cropped conditions is difficult. Lysimeter-derived daily evapotranspiration (ET) values for SDI-irrigated and sprinklerirrigated fields planted to corn in 2016 (MESA) and 2018 (LESA) near Bushland, TX, were compared for days when sprinkler irrigation events occurred and for subsequent days, when possible. Differences (extra ET) were attributed to evaporative losses associated with MESA and LESA irrigation. Average daily extra ET values for both sprinkler irrigation methods were similar on the day of irrigation, although MESA was slightly larger than LESA at 1.4 and 1.2 mm, respectively. The average daily extra ET values for incomplete canopy conditions were 2.2 mm for MESA and 1.9 mm for LESA, while values were identical for both methods at 0.6 mm for full canopy conditions. Average daily extra ET values were also expressed as a percentage of daily standardized grass reference ET (ETos) values. Average values for MESA and LESA were 20.1% and 13.5%, respectively, for the season, with similar findings of 29.3% and 19.4% for incomplete canopy conditions. Average extra ET/ETos values for incomplete canopy conditions were similar at 7.5% and 7.7% for MESA and LESA, respectively. Evaporative irrigation losses, calculated as the percentage of extra ET to irrigation depth, were slightly larger overall for the day of irrigation for MESA (5.4%) than LESA (5.2%). Losses of 7.9% and 7.0% were observed for incomplete canopy conditions for MESA and LESA, respectively. Average losses for LESA (3.5%) under full canopy conditions were greater than those for MESA (1.9%). A comparison of extra ET values for days following irrigation revealed that evaporative losses from irrigation events extended beyond the day of irrigation. MESA extra ET values for the day following irrigations increased by 57.1% (2.2 mm) overall, 13.6% (2.5 mm) for incomplete canopy conditions, and 150.0% (1.5 mm) for full canopy conditions. The same was true for LESA, with increases of 125.0% (2.7 mm) overall, 78.9% (3.4 mm) for incomplete, and 216.7% (1.9 mm) for full canopy conditions. Summing of extra ET values for the day of irrigation and the subsequent day yielded average values more than double those for the day of irrigation only, at 3.9 and 4.3 mm for MESA and LESA, respectively. Similarly, values for extra ET as a percentage of irrigation depth were also more than double those for the day of irrigation only, with the greatest loss values of 39.5% for MESA and 28.0% for LESA. These findings suggest that although LESA appears to mitigate evaporative losses marginally more in corn than MESA on the day of irrigation, considerably more evaporative losses occurred for both methods during the subsequent day, with slightly increased losses for LESA, resulting in little difference between overall losses over the two days. This may in part be explained by the temporary cooling effect of the irrigation inside the canopy on the day of irrigation, which is diminished by the second day. A greater discrepancy between evaporative losses for MESA and LESA is likely to be observed for crops having shorter stature or lower leaf density, such as cotton, although more study is needed to corroborate this claim. Knowledge of these findings provides useful information for both producers and water managers when considering irrigation management and water planning strategies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]