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What crop will grow where?

J. Walcott and M. Kirschbaum

Bureau of Rural Resources, PO Box Ell, Queen Victoria Terrace, Parkes ACT 2600
CSIRO Division of Forestry, PO Box 4008, Queen Victoria Terrace, Parkes ACT 2600

Rapid and confident prediction of the potential distribution of plant species allows us to challenge preconceptions about the distribution of established crops, to better target research and development of novel crops, and to examine options in planning for Australia's rural resources. We want to explore options for sustainable agriculture by predicting and comparing the potential distribution of possible alternatives to the established crops.

The basis of our system is the PLANTGRO model (1) for predicting plant performance. Plant files are compiled, from published information and expert experience, using ratings of plant responses to 10 climate and 13 soil parameters. Performance at a locality is predicted from monthly calculations, using different growth phases if necessary, of the most limiting factor(s) for the given climate and soil data. Our project extends this prediction, from a single location to a continental basis, by using interpolated surfaces of mean climatic data (ESOCLIM, pers. comm. from M.F. Hutchinson, Centre for Resource and Environmental Studies, Australian National University) and soil information for the major soils (Atlas of Australian Soils recently digitised by the National Resource Information Centre). A map of the predicted performance is generated using a grid of 1/8th degree resolution. The program is written in QuickBASIC and runs on an IBM-compatible PC.

The relatively coarse scale in these predictions has several advantages over more precise methods. First, by providing rapid calculation, large areas can be assessed for national strategic overviews or comparisons between regions easily made to identify areas for more detailed investigations. Second, this technique, which can use less detailed measurements on many factors taken from exploratory trials, would assist in making such assessments when information is limited. Also, it is easy to refine or update the responses in the plant files as new experience is gained. Third, potential biological constraints are then easily identified or eliminated and non-biological constraints isolated.

On the other hand, there are a some imperfections with the system. It does not yet include assessments for year to year climatic variation, pests and diseases, responses to the minor elements or the chemical status of the lower soil horizons. The information on soils effectively limits the accuracy of the predictions in this system because of generalisations of soil properties and data necessary at this resolution. Precise validation of the model is difficult because of the paucity of data having sufficient range and detail.

The spring wheat (winter crop) and grain sorghum (summer crop) predictions produced here highlight the small portion of the Australian continent that is suitable for cropping. Both maps show only a limited capacity to extend cropping beyond their present boundaries, except into some of the higher rainfall areas of the south east and north east. The exercise has emphasised how little we know about relating soils information to plant growth.

Reference

Hackett, C. 1991. In: PLANTGRO; a software package for coarse prediction of plant growth. (CSIRO: Melbourne).

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