Use of a crop model and soil moisture sensors for estimating soil moisture and irrigation applications in a production soybean field.

There are multiple methods being used for scheduling irrigation applications that range from weather-based to ground-based methods. Growers must choose a method that is ideally both accurate and economical to use in a production agriculture system. One method that has been used is model-based scheduling. In this study, the CROPGRO-Soybean model in the Decision Support System for Agrotechnology Transfer (DSSAT) was used to simulate the growth and development of irrigated soybean for a production soybean field throughout the 2018 and 2020 growing seasons. Model predictions were compared to soil moisture data recorded from 44 sensor sets placed across the field on a 55- × 55-m grid at depths of 31 and 61 cm. Results showed that the model accurately predicted plant height and LAI in 2018 and 2020. In both seasons, the model underpredicted soil moisture and occasionally did not respond to irrigation or rainfall. Even though the model underpredicted soil moisture, it predicted fewer irrigation events and half the cumulative amount in 2018 than the farmer applied and similar irrigation events and quantity in 2020. DSSAT predicted yield well for both years as compared to measured yields, indicating the farmer may have over-irrigated in 2018. This study advances the application of DSSAT for estimating soybean vegetative characteristics and irrigation scheduling in a production environment. [ABSTRACT FROM AUTHOR]