The availability of observed daily solar radiation (OSR) is restricted to recent years. Its estimation through different methods is necessary to develop long-term data sets for agricultural and environmental applications. The objective of this study was to analyze the impact of using generated daily solar radiation (GSR) on simulated growth and yield of cotton, maize, and peanut. Nine locations representing Georgia's major crop belt were selected. Daily weather data from the Georgia Automated Environmental Monitoring Network (AEMN), including solar radiation, maximum and minimum temperature, and precipitation, were duplicated. The OSR was removed from one set and then generated using a stochastic procedure. The Cropping System Models (CSM)-CROPGRO-Cotton, CERES-Maize, and CROPGRO-Peanut of the Decision Support System for Agrotechnology Transfer (DSSAT) v4 were used to simulate crop growth and yield at each location with both OSR and GSR and for rainfed and irrigated conditions. The statistical analysis included summary statistics, Pearson's coefficient of correlation, mean squared deviation (MSD) and its components, namely: squared bias (SB), squared difference between standard deviations (SDSD), lack of correlation weighted by the standard deviations (LCS), and regressions. Within locations, for the three crops under rainfed and irrigated conditions, GSR did not significantly affect simulated total evapotranspiration and aboveground biomass and yields. For the three crops, deviations of simulated water use and yields from GSR with respect to simulated water use and yields from OSR were lower for the rainfed than for the irrigated conditions. Yields from the CSM-CROPGRO-Cotton and -Peanut models had lower deviations than yields from the CSM-CERES-Maize model. LCS was the major component of the MSD suggesting that the extent of the difference between standard deviations of GSR and OSRG could affect the outputs of the crop models. Nevertheless, for most locations none of the MSD components of the GSR showed significant correlation with simulated yields and the overall performance of the models was not affected. It can be concluded based on the results of this study that GSR can be used as an input for crop model simulation models when OSR is not available.
|Original language||English (US)|
|Number of pages||15|
|State||Published - Jan 24 2008|
Bibliographical noteFunding Information:
This work was conducted under the auspices of the Southeast Climate Consortium (SECC; secc.coaps.fsu.edu) and supported by a partnership with the United States Department of Agriculture-Risk Management Agency (USDA-RMA), by grants from the US National Oceanic and Atmospheric Administration-Office of Global Programs (NOAA-OGP) and USDA Cooperative State Research, Education and Extension Services (USDA-CSREES) and by State and Federal funds allocated to Georgia Agricultural Experiment Stations Georgia Agricultural Experiment Stations Hatch project GEO01654.
- Cropping System Model (CSM)
- Decision Support System for Agrotechnology Transfer (DSSAT)
- Stochastic solar radiation
- Water use