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RESEARCH ARTICLE
Contents Vol 48(5)

DairyMod and EcoMod: biophysical pasture-simulation models for Australia and New Zealand

I. R. Johnson A D , D. F. Chapman B , V. O. Snow C , R. J. Eckard B , A. J. Parsons C , M. G. Lambert C and B. R. Cullen B
+ Author Affiliations
- Author Affiliations

A IMJ Consultants, PO Box 1590, Armidale, NSW 2350, Australia.

B The University of Melbourne, Vic. 3010, Australia.

C AgResearch, Private Bag 11008, Palmerston North, New Zealand.

D Corresponding author. Email: ian@imj.com.au

Australian Journal of Experimental Agriculture 48(5) 621-631 https://doi.org/10.1071/EA07133
Submitted: 9 May 2007  Accepted: 27 August 2007   Published: 7 April 2008

Abstract

DairyMod and EcoMod, which are biophysical pasture-simulation models for Australian and New Zealand grazing systems, are described. Each model has a common underlying biophysical structure, with the main differences being in their available management options. The third model in this group is the SGS Pasture Model, which has been previously described, and these models are referred to collectively as ‘the model’.

The model includes modules for pasture growth and utilisation by grazing animals, water and nutrient dynamics, animal physiology and production and a range of options for pasture management, irrigation and fertiliser application. Up to 100 independent paddocks can be defined to represent spatial variation within a notional farm. Paddocks can have different soil types, nutrient status, pasture species, fertiliser and irrigation management, but are subject to the same weather. Management options include commonly used rotational grazing management strategies and continuous grazing with fixed or variable stock numbers. A cutting regime simulates calculation of seasonal pasture growth rates.

The focus of the present paper is on recent developments to the management routines and nutrient dynamics, including organic matter, inorganic nutrients, leaching and gaseous nitrogen losses, and greenhouse gases. Some model applications are presented and the role of the model in research projects is discussed.

Additional keywords: beef, dairy, deer, grazing, model, pasture, sheep.


Acknowledgements

We appreciate the funding for this project from Dairy Australia, Meat and Livestock Australia, AgResearch New Zealand, The University of Melbourne, through the Whole Farm Systems Ability and Tools for the Grazing Industries (WFSAT) project (www.wfsat.org). We are grateful to Drs Greg Lodge, Jeremy Bryant and Todd White for helpful comments on an earlier draft. We thank colleagues within the National Dairy Farming Systems teams and AgResearch for input and collaboration. We also thank Mark Callow (Queensland Department of Primary Industries) for allowing us to use the Rhodes Grass pasture growth rate data and Jay Tharmaraj (The University of Melbourne) for assistance with the dairy cattle grazing system simulation.


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