Sensitivity of Ceres-Maize Yields to Statistical Structure of Daily Weather Series

To study impacts of climate variations on cropproduction, the growth models are used to simulateyields in present vs. changed climate conditions.Met&Roll is a four-variate (precipitation amount,solar radiation, minimum and maximum temperatures) stochasticweather generator used to supply synthetic dailyweather series for the crop growth model CERES-Maize.Three groups of experiments were conducted in thisstudy: (1) Validation of Met&Roll reveals some discrepanciesin the statistical structure of synthetic weatherseries, e.g., (i) the frequency of occurrence of longdry spells, extreme values of daily precipitationamount and variability of monthly means areunderestimated by the generator; (ii) correlations andlag-1 correlations among weather characteristicsexhibit a significant annual cycle not assumed by themodel. On the whole, the best fit of the observed andsynthetic weather series is experienced in summermonths. (2) The Wilcoxon test was employed to comparedistributions of maize yields simulated with use ofobserved vs. synthetic weather series. As nostatistically significant differences were detected,it is assumed that the generator imperfections inreproducing the statistical structure of weatherseries negligibly affect the model yields. (3) Thesensitivity of model yields to selectedcharacteristics of the daily weather series wasexamined. Emphasis was placed on the characteristicsnot addressed by typical GCM-based climate changescenarios: daily amplitude of temperature, persistenceof the weather series, shape of the distribution ofdaily precipitation amount, and frequency ofoccurrence of wet days. The results indicate that someof these characteristics may significantly affect cropyields and should therefore be considered in thedevelopment of climate change scenarios.

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