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Comparing simulated crop yields with observed and synthetic weather data |
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| Authors: | Budong Qian Reinder De JongJingyi Yang Hong WangSam Gameda |
| Institution: | a Eastern Cereal and Oilseed Research Centre, Agriculture and Agri-Food Canada, Ottawa, Ontario, Canada b Greenhouse and Processing Crops Research Centre, Agriculture and Agri-Food Canada, Harrow, Ontario, Canada c Semiarid Prairie Agricultural Research Centre, Agriculture and Agri-Food Canada, Swift Current, Saskatchewan, Canada |
| Abstract: | Stochastic weather generators have been used in the development of climate scenarios which are input to agricultural simulation models that assess the climate impacts on crop growth and production. The synthetic data generated by a stochastic weather generator only mimic the observed weather data, thus discrepancies between the synthetic and the observed weather data often exist. For example, interannual variability in the synthetic data is often found to be weaker than in the observed data, i.e., the common problem of overdispersion. Here, we evaluate if the climate impact models are sensitive to such discrepancies. A stochastic weather generator (AAFC-WG) was used to generate 300 years long synthetic weather data for five Canadian locations, based on observed weather data for the baseline period of 1961−1990. The Decision Support System for Agrotechnology Transfer (DSSAT) v4.0 was employed to simulate crop growth and yield. Five major crops were simulated by the DSSAT model for three major soil types at each location, with 30-yr observed data and 300-yr synthetic data as weather input, respectively. Statistical tests were performed to investigate whether differences (both in mean and variance) of the simulated crop yields between the simulations with observed and synthetic weather data were statistically significant or not. Results showed that the differences in simulated crop yields were not statistically significant when synthetic weather data were used to substitute the observed data. Standard deviations of crop yield and biomass in simulations with synthetic weather data were, in 5 and 19% of all cases, respectively, found to be smaller by more than 20% to those simulated with observed weather. However, with only one exception, the differences in variances were not statistically significant. We conclude that reliable crop yield estimates can be obtained by combining the AAFC weather generator with the DSSAT crop growth models at the studied sites in Canada. |
| Keywords: | Climate impacts Crop growth models Stochastic weather generator DSSAT Statistical evaluation Canada |
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