PREDICTING REGIONAL GRAIN-SORGHUM PRODUCTION IN AUSTRALIA USING SPATIAL DATA AND CROP SIMULATION MODELING

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.