Can gridded real-time weather data match direct ground observations for irrigation decision-support?

Agricultural decision-support systems are commonplace in extension and outreach. These systems typically rely on either historical or direct ground observations to make grower recommendations. Sensor data create many challenges for application developers, though, including managing device-level characteristics, ensuring observation data quality, and handling missing data. In many data flows for decision support, encapsulation is a best practice development approach where data collection and storage are isolated from application development by application programming interfaces (APIs). Here, we consider the data quality of gridded and non-gridded weather data types in agricultural modeling for predicting evapotranspiration (ET) and growing degree days (GDD). We compare API-accessible gridded datasets from GEMS Exchange to MESONET (mesoscale network of weather and climatological stations) data from the Minnesota Department of Agriculture (MDA). We evaluate the data sources directly for goodness-of-fit for solar radiation, temperature (min and max), dew point, and wind speed, as well as downstream predictions of reference ET (ETref) and GDD. Our findings show that gridded data, despite its tendency to overestimate solar radiation, does not significantly impact the accuracy of ET (R² = 0.92 for 2022 and 0.93 for 2023; root mean square error [RMSE] = 0.55 mm for 2023) or GDD predictions (R² = 0.99 for 2022 and 0.98 for 2023; RMSE = 0.53°C [2022], RMSE = 0.70°C [2023]). This suggests that application programming interface (API)-based gridded data, accessible for all locations, can be reliably used for ETref and GDD modeling for decision support and complements MESONET measures by providing developers with standard software interfaces for real-time weather information.