Seasonal predictability of weather and crop yield in regions of Central European continental climate

Abstract Currently, predictability of weather is uncertain, resulting from increasingly variable weather conditions. Thus, crop yield forecasting based on seasonal weather information remains a challenge for the agricultural sector. In this study, non-linear regression analysis was carried out to model long-term meteorological data. To assess the predictability of weather based on long-term meteorological data representing a continental climate with four seasons, principal component analysis was done. Predictability of crop yield based on previous long-term yield data was tested with the Wald-Wolfowitz Runs method. Finally, non-linear regression analysis was applied to investigate the predictability of maize yield based on winter wheat yield. The hypothesis that the weather of a season would be predictable based on long-term daily temperature and precipitation dataset was disproved, although results showed that for the investigated region, if winter is warm, spring can be expected to be warm, as well. It was also statistically disproved that crop yield would be predictable based on time series analysis of previous yield data. However, a cross effect between the yields of maize and wheat as model crops was proved; when the crop yield of winter wheat is low, that of maize is expected also to be low, while in case of high winter wheat yield, maize yield can be high in cases where there is no climate stress in the flowering and ripening periods. However, as an overall conclusion, even if relations are found between weather variabilities and responding crop yield variations, crop yield cannot be estimated, since weather represented by a chaotic model with scale-independent distribution cannot be predicted in advance.

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