Assessing crop management options with crop simulation models based on generated weather data |
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| Authors: | Afshin Soltani Gerrit Hoogenboom |
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| Institution: | 1. Department of Agronomy and Plant Breeding, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan 49138-15739, Iran;2. Department of Biological and Agricultural Engineering, The University of Georgia, Griffin, Georgia 30223, USA |
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| Abstract: | The efficient use of crop simulation models is an effective complement to experimental research. Long-term weather data obtained at a specific site are normally required for the application of these crop simulation models to help determine alternate management practices and associated decisions. Stochastic weather generators sometimes are used to complement or substitute historical weather data. The objective of this study was to evaluate the suitability of weather data generated by the weather generators WGEN and SIMMETEO as input for crop simulation models in order to determine the best option(s) among a number of different crop management practices. Five locations across Iran representing different climates were selected. The wheat, maize, and soybean models of the Decision Support System for Agrotechnology Transfer (DSSAT) were applied in this study, using 30 years of observed weather data and 90 years of weather data generated by WGEN and SIMMETEO. Simulated grain yield using either observed weather data or weather data generated by WGEN and SIMMETEO in response to various ‘experimental’ factors, e.g., cultivar selection, planting date, planting density, irrigation threshold, and change in precipitation under irrigated and rainfed conditions were compared. The statistical evaluation was based on t, F, and Kolomogrov-Smirnov (K-S) tests. The average of the percentage rejected tests was 20% and the parameter estimation method had no impact on the number of rejected tests. Irrespective of some significant differences between simulated yield based on observed weather data and those based on weather data generated by WGEN and SIMMETEO, a similar conclusion could be drawn about the best cultivar, planting date, plant density and irrigation threshold and response to changes in the amount of precipitation. Based on the results of this study it can be concluded that for many crop model applications where only relative estimates or determination of the best management option(s) rather than absolute values are required, weather data generated by either WGEN and SIMMETEO are accurate and sufficient. |
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| Keywords: | Weather generation Stochastic model Simulation model Decision support systems DSSAT |
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