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

Assessment of solar radiation models and temporal averaging schemes in predicting radiation and cotton production in the southern United States

A. G. Richardson, K. Raja Reddy*

Department of Plant and Soil Sciences, Box 955, 117 Dorman Hall, Mississippi State University, Mississippi State, Mississippi 39762, USA
*Corresponding author. Email:

ABSTRACT: Crop models require daily weather input data for solar radiation (Irad), minimum and maximum air temperatures, precipitation, and windspeed; but measured Irad may not be available at some locations, necessitating Irad estimates. A total of 28 scenarios (7 solar radiation models [SRMs] × 4 temporal-averaging schemes [TASs]) were examined to estimate Irad and cotton (Gossypium hirsutum L.) yield at 10 U.S. locations. The SRMs showed positive correlations of Irad with daylength and temperature range (Tmax - Tmin), and were relatively accurate in predicting Irad and yield. The Irad 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 Tmin, Tmax, precipitation and wind (TmRnWn) model performed best, and Irad estimation accuracy was highest in Shafter, California, and Maricopa, Arizona. Highest Irad estimation accuracy was obtained with the TmRnWn model, using a double TAS, in Maricopa (r2 = 0.99). Geographical variability in Irad was observed, showing effects of regional climate on measured Irad and on Irad estimation accuracy. Yield estimation accuracy depended on Irad estimation accuracy and yield response to Irad 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 Irad 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 Irad and crop yields, particularly in regions with unavailable measured Irad data.


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


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