CR 27:85-103 (2004)  -  doi:10.3354/cr027085

ABSTRACT: Crop models require daily weather input data for solar radiation (I_rad), minimum and maximum air temperatures, precipitation, and windspeed; but measured I_rad may not be available at some locations, necessitating I_rad estimates. A total of 28 scenarios (7 solar radiation models [SRMs] × 4 temporal-averaging schemes [TASs]) were examined to estimate I_rad and cotton (Gossypium hirsutum L.) yield at 10 U.S. locations. The SRMs showed positive correlations of I_rad with daylength and temperature range (T_max - T_min), and were relatively accurate in predicting I_rad and yield. The I_rad estimation accuracy depended on SRM, TAS, and location. Temporal averaging smoothed out short-term fluctuations, resulting in decreased temporal scatter in the weather parameters. The combination of T_min, T_max, precipitation and wind (TmRnWn) model performed best, and I_rad estimation accuracy was highest in Shafter, California, and Maricopa, Arizona. Highest I_rad estimation accuracy was obtained with the TmRnWn model, using a double TAS, in Maricopa (r² = 0.99). Geographical variability in I_rad was observed, showing effects of regional climate on measured I_rad and on I_rad estimation accuracy. Yield estimation accuracy depended on I_rad estimation accuracy and yield response to I_rad changes, and depended more strongly on location and management practice (rainfed [RF] versus irrigated [IRR]) than on SRM and TAS. All 7 SRMs performed comparably well in predicting RF and IRR yields. Estimation accuracies for I_rad and RF + IRR cotton yields among the 28 scenarios were highest for Shafter and Maricopa (e.g. r² > 0.99 for yield). Coupled with crop simulation models, SRMs are useful for predicting I_rad and crop yields, particularly in regions with unavailable measured I_rad data.

KEY WORDS: Temporal-averaging scheme · TAS · Solar radiation model · SRM · Crop simulation model · CSM · GOSSYM · Cotton