Crop and Pasture Science Crop and Pasture Science Society
Plant sciences, sustainable farming systems and food quality

Re-inventing model-based decision support with Australian dryland farmers. 3. Relevance of APSIM to commercial crops

P. S. Carberry A F , Z. Hochman B , J. R. Hunt C , N. P. Dalgliesh A , R. L. McCown A , J. P. M. Whish A , M. J. Robertson D , M. A. Foale B , P. L. Poulton A and H. van Rees C E

A Agricultural Production Systems Research Unit (APSRU), CSIRO Sustainable Ecosystems, PO Box 102, Toowoomba, Qld 4350, Australia.

B Agricultural Production Systems Research Unit (APSRU), CSIRO Sustainable Ecosystems, 306 Carmody Road, St Lucia, Qld 4067, Australia.

C Birchip Cropping Group, PO Box 85, Birchip, Vic. 3483, Australia.

D CSIRO Sustainable Ecosystems, Private Bag 5, PO Wembley, WA 6913, Australia.

E Cropfacts P/L, 69 Rooney Rd, RSD Strathfieldsaye, Vic. 3551, Australia.

F Corresponding author. Email:

Crop and Pasture Science 60(11) 1044-1056
Submitted: 8 February 2009  Accepted: 20 July 2009   Published: 19 October 2009


Crop simulation models relevant to real-world agriculture have been a rationale for model development over many years. However, as crop models are generally developed and tested against experimental data and with large systematic gaps often reported between experimental and farmer yields, the relevance of simulated yields to the commercial yields of field crops may be questioned. This is the third paper in a series which describes a substantial effort to deliver model-based decision support to Australian farmers. First, the performance of the cropping systems simulator, APSIM, in simulating commercial crop yields is reported across a range of field crops and agricultural regions. Second, how APSIM is used in gaining farmer credibility for their planning and decision making is described using actual case studies.

Information was collated on APSIM performance in simulating the yields of over 700 commercial crops of barley, canola, chickpea, cotton, maize, mungbean, sorghum, sugarcane, and wheat monitored over the period 1992 to 2007 in all cropping regions of Australia. This evidence indicated that APSIM can predict the performance of commercial crops at a level close to that reported for its performance against experimental yields. Importantly, an essential requirement for simulating commercial yields across the Australian dryland cropping regions is to accurately describe the resources available to the crop being simulated, particularly soil water and nitrogen.

Five case studies of using APSIM with farmers are described in order to demonstrate how model credibility was gained in the context of each circumstance. The proposed process for creating mutual understanding and credibility involved dealing with immediate questions of the involved farmers, contextualising the simulations to the specific situation in question, providing simulation outputs in an iterative process, and together reviewing the ensuing seasonal results against provided simulations.

This paper is distinct from many other reports testing the performance and utility of cropping systems models. Here, the measured yields are from commercial crops not experimental plots and the described applications were from real-life situations identified by farmers. A key conclusion, from 17 years of effort, is the proven ability of APSIM to simulate yields from commercial crops provided soil properties are well characterised. Thus, the ambition of models being relevant to real-world agriculture is indeed attainable, at least in situations where biotic stresses are manageable.

Additional keywords: APSIM, crop simulation model, validation, decision support systems, commercial yield.


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