Combining soil water content data with computer simulation models for improved irrigation scheduling

Authors: Thorp, Kelly

Submitted to: Journal of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/30/2023
Publication Date: 9/15/2023
Citation: Thorp, K.R. 2023. Combining soil water content data with computer simulation models for improved irrigation scheduling. Journal of the ASABE. https://doi.org/10.13031/ja.15591.
DOI: https://doi.org/10.13031/ja.15591

Interpretive Summary: Irrigation scheduling models and soil water sensors have been separately developed as technologies to assist irrigation management decisions. However, the two technologies should ideally be integrated, because they are complimentary and can work together to provided improved recommendations. This study compared cotton yield and water use outcomes when managed by three irrigation scheduling models and when those models were assisted by field measurements of soil water status. The results showed that adding soil water measurements could reduce irrigation requirements by 9-21% while often maintaining cotton fiber yield. In addition to producers, several commercial industries will benefit from this research, including industries supporting agricultural irrigation, U.S. cotton production, and the development of soil water sensing equipment.

Technical Abstract: 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 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 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.