Abstract

This study concerns the investigation of how changes in climate variability could affect agricultural production. To date very few studies of this effect have been made, whereas changes in climate variability, in addition to changes in mean conditions could have serious effects on crops. Currently there is very little certainty regarding how climate variability will change in a greenhouse-gas-warmed world. In view of this uncertainty, it is desirable to perform sensitivity analyses of how crop-climate models (e.g. the CERES-Wheat model) respond to changes in climate variability. We apply the winter wheat model to two climate stations in the wheat growing region of Kansas. Goodland in the western region, and Topeka in the moister eastern region. Time series of temperature and precipitation, are simulated, systematically changing the interannual variance of the series (from one quarter to four times the actual variance), and determining how the crop climate model responds to these changes. In addition, these variability changes are combined with mean changes of both temperature and precipitation. We then also apply a GCM-derived climate change scenario. Results indicate that it is the relative magnitude of change of the mean and variance of the climate that determines their relative importance to changes in wheat yields. In general increases in variability of temperature and precipitation resulted in significant increases in yield variability and crop failures in the sensitivity experiments, but precipitation changes had a more pronounced effect. In the GCM-derived scenario, mean temperature effects dominate, resulting in increased yield variability and crop failures, because the magnitude of mean change is much greater than the magnitude of variance change. Over all experiments, the importance of considering not only mean but also variance changes of climate variables, when investigating the effect of climate change on wheat yields, is confirmed.

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