Wd. Rosenthal et al., PREDICTING REGIONAL GRAIN-SORGHUM PRODUCTION IN AUSTRALIA USING SPATIAL DATA AND CROP SIMULATION MODELING, Agricultural and forest meteorology, 91(3-4), 1998, pp. 263-274
Grain sorghum is the major dryland summer crop produced in the subtrop
ical region of Australia. Production variability is great and the cons
equent uncertainty about likely production restricts marketing options
and contributes to instability in grain price. Improved methods for p
redicting regional production would assist marketing decisions for far
mers and grain traders. The objective of this study was to determine w
hether reliable regional grain sorghum production predictions could be
generated by combining crop simulation and geographic information sys
tem technologies. We used historical shire production data to test the
approach using hindcasting. Geographical data bases of landscape and
soil attributes were used to define arable land boundaries and soil pr
operties. Geographical data bases of daily rainfall and climate were o
verlaid and used to drive a spatial simulation of sorghum production f
or all shires in Queensland for the period 1977-1988. The results of t
he simulation were compared with production statistics at the shire an
d aggregate state levels. The spatial integrity of the prediction syst
em was examined by comparing maps of predicted and reported shire prod
uction for specific years. There was a general tendency for the simula
ted yields per unit area to be greater and more Variable (from year to
year) than the historical shire production data. This probably reflec
ted the fact that the simulation assumed perfect management and pest-f
oe conditions. The relatively coarse spatial interpolation of rainfall
would likely also contribute to this outcome. Linear regression relat
ionships were developed between historical and simulated data at the s
hin scale to calibrate the simulated yields. Estimates of total produc
tion for each shire (in any year) were derived from the predicted yiel
d per unit area, which was derived from the regression correction of t
he simulated yield, and the reported area planted. Excellent agreement
between predicted and reported production occurred both at the indivi
dual shire and aggregate state (r = 0.96) scales. This approach was co
mpared with use of mean shire yield as the estimate of predicted yield
per unit area to examine the contribution of the yield simulation pro
cedure to production prediction. Significant improvement in production
prediction was attributed to the yield simulation. The spatial distri
bution of shire production estimates was examined by categorising and
mapping shire production predictions. Comparisons with reported produc
tion estimates showed the integrity of the spatial distribution was la
rgely retained. Hence, we conclude that reliable shire and state sorgh
um production estimates can be generated by combining crop simulation
and geographic information system technologies. The procedure is suita
ble for further development for use in real-time. By updating estimate
s as a season progresses, improved timeliness and accuracy of producti
on forecasts could be achieved. (C) 1998 Elsevier Science B.V. All rig
hts reserved.