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.
Local habitat is a strong determinant of spatial and temporal patterns of macrophyte diversity and composition in boreal lakes
