Re-inventing model-based decision support with Australian dryland farmers. 3. Relevance of APSIM to commercial crops
Abstract
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.
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The crop growth component of OZCOT is used as the cotton module of the APSIM modeling framework.
“The crop growth component of OZCOT is used as the cotton module of the Agricultural Production Systems sIMulator (APSIM) modeling framework (Keating et al., 2003), which is used to address farming systems issues (Carberry et al., 2009).”
From Development and Application of Process-based Simulation Models for Cotton Production: A Review of Past, Present, and Future Directions (2014) · cites this paper as Carberry et al., 2009
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- Authors
- Carberry, P. S., Hochman, Z., Hunt, J. R., Dalgliesh, N. P., McCown, R. L., Whish, J. P. M., Robertson, M. J., Foale, M. A., Poulton, P. L., & van Rees, H.
- Affiliations
- Commonwealth Scientific and Industrial Research Organisation (AU)
- CBirchip Cropping Group, PO Box 85, Birchip, Vic. 3483, Australia.
- Venue
- Crop & Pasture Science
- Publisher
- CSIRO Publishing
- Published
- 2009-10-19
- Volume
- 60
- Issue
- 11
- Pages
- 1044-1056
- ISSN
- 0004-9409 (ISSN-L)
- 1836-0947
- 1836-5795
- OpenAlex
- W2144038500
- Cited by
- 97
- References
- 50
- Citations by year
- 2026: 2 · 2024: 5 · 2023: 2 · 2022: 4 · 2021: 6 · 2020: 7 · 2019: 7 · 2018: 6 · 2017: 10 · 2016: 5 · 2015: 2 · 2014: 11 · 2013: 6 · 2012: 6
- FWCI
- 8.36
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Australian Journal of Agricultural Research (publishedVersion)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Climate change impacts on agriculture
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Crop Yield and Soil Fertility
- Keywords
- APSIM, crop simulation models, model-based decision support, rainfed agriculture, yield prediction, soil water, nitrogen availability, soil properties, barley, canola, chickpea, cotton, maize, mungbean, sorghum, sugarcane, wheat
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