Cotton Irrigation Scheduling Using a Crop Growth Model and FAO-56 Methods: Field and Simulation Studies

• Main Authors: Thorp, Kelly R., Hunsaker, Douglas J., Bronson, Kevin F., Andrade-Sanchez, Pedro, Barnes, Edward M.
• Other Authors: Univ Arizona, Maricopa Agr Ctr
• Language: en
• Published: AMER SOC AGRICULTURAL & BIOLOGICAL ENGINEERS 2017
• Subjects: Cottonseed; Crop coefficient; Decision support; Depletion; Evapotranspiration; Fiber; Management; Simulation; Soil moisture; Yield
• Online Access: http://hdl.handle.net/10150/626603; http://arizona.openrepository.com/arizona/handle/10150/626603

Crop growth simulation models can address a variety of agricultural problems, but their use to directly assist in-season irrigation management decisions is less common. Confidence in model reliability can be increased if models are shown to provide improved in-season management recommendations, which are explicitly tested in the field. The objective of this study was to compare the CSM-CROPGRO-Cotton model (with recently updated ET routines) to a well-tested FAO-56 irrigation scheduling spreadsheet by (1) using both tools to schedule cotton irrigation during 2014 and 2015 in central Arizona and (2) conducting a post-hoc simulation study to further compare outputs from these tools. Two replications of each irrigation scheduling treatment and a water-stressed treatment were established on a 2.6 ha field. Irrigation schedules were developed on a weekly basis and administered via an overhead lateral-move sprinkler irrigation system. Neutron moisture meters were used weekly to estimate soil moisture status and crop water use, and destructive plant samples were routinely collected to estimate cotton leaf area index (LAI) and canopy weight. Cotton yield was estimated using two mechanical cotton pickers with differing capabilities: (1) a two-row picker that facilitated manual collection of yield samples from 32 m(2) areas and (2) a four-row picker equipped with a sensor-based cotton yield monitoring system. In addition to statistical testing of field data via mixed models, the data were used for post-hoc reparameterization and fine-tuning of the irrigation scheduling tools. Post-hoc simulations were conducted to compare measured and simulated evapotranspiration, crop coefficients, root zone soil moisture depletion, cotton growth metrics, and yield for each irrigation treatment. While total seasonal irrigation amounts were similar among the two scheduling tools, the crop model recommended more water during anthesis and less during the early season, which led to higher cotton fiber yield in both seasons (p < 0.05). The tools calculated cumulative evapotranspiration similarly, with root mean squared errors (RMSEs) less than 13%; however, FAO-56 crop coefficient (K-c) plots demonstrated subtle differences in daily evapotranspiration calculations. Root zone soil moisture depletion was better calculated by CSM-CROPGRO-Cotton, perhaps due to its more complex soil profile simulation; however, RMSEs for depletion always exceeded 20% for both tools and reached 149% for the FAO-56 spreadsheet in 2014. CSM-CROPGRO-Cotton simulated cotton LAI, canopy weight, canopy height, and yield with RMSEs less than 21%, while the FAO-56 spreadsheet had no capability for such outputs. Through field verification and thorough post-hoc data analysis, the results demonstrated that the CSM-CROPGRO-Cotton model with updated FAO-56 ET routines could match or exceed the accuracy and capability of an FAO-56 spreadsheet tool for cotton water use calculations and irrigation scheduling.