Effectiveness of Using Solar Radiation Derived from Temperature for Corn Yield Simulation in US High Plains Region.

Real-time crop simulation and yield prediction has become feasible using advanced crop models such as Hybrid-Maize (www.hybridmaize.unl.edu). To do so, one must have (1) real-time daily weather data, including solar radiation, maximum (T_max) and minimum (T_min) temperature, precipitation and humidity, (2) historical weather records that represent the climatic pattern of the growing season, and (3) the weather data must be representative of the site of interest. The High Plains Regional Climate Center (HPRCC) at University of Nebraska archives COOP data for over 10,000 stations. Among them, about 1500 stations have records longer than 50 years. The COOP datasets are potentially very useful for real-time crop management and yield prediction using crop models. However, they have only data of T_max, T_min and precipitation, and do not have solar radiation, humidity or dew point temperature (T_dew) which are required by crop models. Our objective was to estimate solar radiation and T_dew from T_max and T_min using most suitable functions for each particular COOP station. The functions for estimating solar radiation and T_dew were derived from the datasets of the Automated Weather Data Network (AWDN), which have complete suites of weather variables required by crop models. The uncertainty associated with using estimated solar radiation and T_dew for crop simulation and yield prediction was quantified using the Hybrid-Maize model. Preliminary results suggest that the COOP datasets with solar radiation and T_dew estimated from T_max and T_min can be very useful, particularly in areas where no AWDN stations are available locally or AWDN records are too short to be representative of weather patterns. We plan to update the current HPRCC’s COOP datasets to include solar radiation and T_dew to facilitate applications for real-time crop growth assessment and yield prediction.
3, RESULTS FROM PRIOR RESEARCH