Evaluating the accuracy of the Agricultural Production Systems Simulator (APSIM) simulating growth, development, and herbage nutritive characteristics of forage crops grown in the south-eastern dairy regions of Australia
K. G. Pembleton A E , R. P. Rawnsley A , J. L. Jacobs B , F. J. Mickan C , G. N. O’Brien C , B. R. Cullen D and T. Ramilan D
+ Author Affiliations
- Author Affiliations
A Tasmanian Institute of Agriculture, University of Tasmania, Private Bag 3523, Burnie, Tas. 7320, Australia.
B Victorian Department of Primary Industries, 78 Henna Street, Warrnambool, Vic. 3280, Australia.
C Victorian Department of Primary Industries, 1301 Hazeldean Road, Ellinbank, Vic. 3821, Australia.
D Melbourne School of Land and Environment, University of Melbourne, Melbourne, Vic. 3010, Australia.
E Corresponding author. Email: Keith.Pembleton@utas.edu.au
Crop and Pasture Science 64(2) 147-164 https://doi.org/10.1071/CP12372
Submitted: 24 April 2012 Accepted: 16 April 2013 Published: 5 June 2013
Abstract
Pasture-based dairy farms are a complex system involving interactions between soils, pastures, forage crops, and livestock as well as the economic and social aspects of the business. Consequently, biophysical and farm systems models are becoming important tools to study pasture-based dairy systems. However, there is currently a paucity of modelling tools available for the simulation of one key component of the system—forage crops. This study evaluated the accuracy of the Agricultural Production Systems Simulator (APSIM) in simulating dry matter (DM) yield, phenology, and herbage nutritive characteristics of forage crops grown in the dairy regions of south-eastern Australia. Simulation results were compared with data for forage wheat (Triticum aestivum L.), oats (Avena sativa L.), forage rape (Brassica napus L.), forage sorghum (Sorghum bicolor (L.) Moench), and maize (Zea mays L.) collated from previous field research and demonstration activities undertaken across the dairy regions of south-eastern Australia. This study showed that APSIM adequately predicted the DM yield of forage crops, as evidenced by the range of values for the coefficient of determination (0.58–0.95), correlation coefficient (0.76–0.94), and bias correction factor (0.97–1.00). Crop phenology for maize, forage wheat, and oats was predicted with similar accuracy to forage crop DM yield, whereas the phenology of forage rape and forage sorghum was poorly predicted (R2 values 0.38 and 0.80, correlation coefficient 0.62 and –0.90, and bias correction factors 0.67 and 0.28, respectively). Herbage nutritive characteristics for all crop species were poorly predicted. While the selection of a model to explore an aspect of agricultural production will depend on the specific problem being addressed, the performance of APSIM in simulating forage crop DM yield and, in many cases, crop phenology, coupled with its ease of use, open access, and science-based mechanistic methods of simulating agricultural and crop processes, makes it an ideal model for exploring the influence of management and environment on forage crops grown on dairy farms in south-eastern Australia. Potential future model developments and improvements are discussed in the context of the results of this validation analysis.
Additional keywords: brassicas, crop modelling, dry matter production, cereals, dairy, forage crops.
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