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Spatial and temporal variability in wheat production in Australia is dominated by rainfall occurrence. The length of historical production records is inadequate, however, to analyse spatial and temporal patterns conclusively. In this study we used modelling and simulation to identify key spatial patterns in Australian wheat yield, identify groups of years in the historical record in which spatial patterns were similar, and examine association of those wheat yield year groups with indicators of the El Niño Southern Oscillation (ENSO). A simple stress index model was trained on 19 years of Australian Bureau of Statistics shire yield data (1975–93). The model was then used to simulate shire yield from 1901 to 1999 for all wheat-producing shires. Principal components analysis was used to determine the dominating spatial relationships in wheat yield among shires. Six major components of spatial variability were found. Five of these represented near spatially independent zones across the Australian wheatbelt that demonstrated coherent temporal (annual) variability in wheat yield. A second orthogonal component was required to explain the temporal variation in New South Wales. The principal component scores were used to identify high- and low-yielding years in each zone. Year type groupings identified in this way were tested for association with indicators of ENSO. Significant associations were found for all zones in the Australian wheatbelt. Associations were as strong or stronger when ENSO indicators preceding the wheat season (April–May phases of the Southern Oscillation Index) were used rather than indicators based on classification during the wheat season. Although this association suggests an obvious role for seasonal climate forecasting in national wheat crop forecasting, the discriminatory power of the ENSO indicators, although significant, was not strong. By examining the historical years forming the wheat yield analog sets within each zone, it may be possible to identify novel climate system or ocean–atmosphere features that may be causal and, hence, most useful in improving seasonal forecasting schemes.

The spatial population dynamics of an Old World screwworm fly, Chrysomya bezziana Villeneuve (OWS), outbreak in Australia have been modelled in two ways. The first model uses weekly growth indices derived from climatic data to predict the adult female population. The second is a detailed cohort life-cycle model. Due to technical and time constraints, the growth index model is preferred as the biological component of a much larger bioeconomic model because of its smaller program size and faster execution. In deciding whether adoption of the growth index model would be at the expense of scientific accuracy, the life-cycle model was developed as a yardstick. We showed that the growth index model was a practical and adequate substitution for the OWS life-cycle model and a novel spatial/temporal modelling approach with generic qualities. We elaborate on the previously reported growth index model, describe the life-cycle model and compare the results of both models. In the event of an OWS incursion in northern or eastern Australia, given average climatic conditions, both models predict that most of the suitable range (some 2.3M km2) will be colonized within 4-5 years if an eradication campaign is not attempted. Much of its permanent range would be in tropical and subtropical extensive grazing regions. Where computer or funding resources are restrictive, models incorporating growth indices may prove adequate for spatial population studies of some species.