LAUSR

Abstract Twelve large-scale climate drivers are employed to investigate their spatio-temporal influence on the variability of seasonal wheat yield in five major wheat-producing states across Australia using data for the period 1983–2013. Generally, the fluctuations in the Indian Ocean appear to have a dominant effect on the Australian wheat crop in all states except Western Australia, while the impact of oceanic conditions in the Pacific region is much stronger in Queensland. The results show a statistically significant negative correlation between the Indian Ocean Dipole (IOD) and the anomalous wheat yield in the early growing stage of the crop in the eastern and southeastern wheat belt regions. This correlation suggests that the wheat yield can be skillfully forecast 3–6 months ahead, supporting early decision-making in regard to precision agriculture. In this study, we use vine copula models to capture climate-yield dependence structures, including the occurrence of extreme events (i.e., the tail dependences). The co-occurrence of extreme events is likely to enhance the impacts of climate mode and this can be quantified probabilistically through conditional copula-based models. Generally, the developed D-vine quantile regression model provide greater accuracy for the forecasting of wheat yield at given different confidence levels compared to the traditional linear quantile regression (LQR) method. A five-fold cross-validation approach is also used to estimate the out-of-sample accuracy of both copula-statistical forecasting models. These findings provide a comprehensive analysis of the spatio-temporal impacts of different climate mode indices on Australian wheat crops. Improved quantification of the impacts of large-scale climate drivers on the wheat yield can allow a development of suitable planning processes and crop production strategies designed to optimize the yield and agricultural profit.