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

Simulating pasture growth rates in Australian and New Zealand grazing systems

B. R. Cullen A H , R. J. Eckard A , M. N. Callow B , I. R. Johnson C , D. F. Chapman A , R. P. Rawnsley D , S. C. Garcia E , T. White F and V. O. Snow G
+ Author Affiliations
- Author Affiliations

A Faculty of Land and Food Resources, University of Melbourne, Vic. 3010, Australia.

B Department of Primary Industries and Fisheries, Mutdapilly Research Station, Peak Crossing, Qld 4306, Australia.

C IMJ Consultants, Armidale, NSW 2350, Australia.

D Tasmanian Institute of Agricultural Research, University of Tasmania, Burnie, Tas. 7320, Australia.

E University of Sydney, Camden, NSW 2570, Australia.

F AgResearch, Lincoln Research Centre, Christchurch, New Zealand.

G AgResearch, Grasslands Research Centre, Palmerston North, New Zealand.

H Corresponding author. Email: bcullen@unimelb.edu.au

Australian Journal of Agricultural Research 59(8) 761-768 https://doi.org/10.1071/AR07371
Submitted: 4 October 2007  Accepted: 28 April 2008   Published: 29 July 2008

Abstract

DairyMod, EcoMod, and the SGS Pasture Model are mechanistic biophysical models developed to explore scenarios in grazing systems. The aim of this manuscript was to test the ability of the models to simulate net herbage accumulation rates of ryegrass-based pastures across a range of environments and pasture management systems in Australia and New Zealand. Measured monthly net herbage accumulation rate and accumulated yield data were collated from ten grazing system experiments at eight sites ranging from cool temperate to subtropical environments. The local climate, soil, pasture species, and management (N fertiliser, irrigation, and grazing or cutting pattern) were described in the model for each site, and net herbage accumulation rates modelled. The model adequately simulated the monthly net herbage accumulation rates across the range of environments, based on the summary statistics and observed patterns of seasonal growth, particularly when the variability in measured herbage accumulation rates was taken into account. Agreement between modelled and observed growth rates was more accurate and precise in temperate than in subtropical environments, and in winter and summer than in autumn and spring. Similarly, agreement between predicted and observed accumulated yields was more accurate than monthly net herbage accumulation. Different temperature parameters were used to describe the growth of perennial ryegrass cultivars and annual ryegrass; these differences were in line with observed growth patterns and breeding objectives. Results are discussed in the context of the difficulties in measuring pasture growth rates and model limitations.

Additional keywords: DairyMod, EcoMod, SGS Pasture model, simulation models.


Acknowledgments

We acknowledge the contribution of Karen Christie (University of Tasmania) in developing the initial Elliott simulations, and thank Dr Jay Tharmaraj (University of Melbourne) and Kevin Lowe and Tom Bowdler (Department of Primary Industries and Fisheries) for the pasture growth rate data that they provided. Dr Jeremy Bryant (AgResearch) provided assistance with the statistical analyses. This work was funded by Dairy Australia, Meat & Livestock Australia, and AgResearch, New Zealand. The New Zealand sites were funded by the New Zealand Foundation for Science, Research and Technology (contract C10X0319).


References


Bibby J , Toutenburg H (1977) ‘Prediction and improved estimation in linear models.’ (Wiley: Berlin)

Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 327, 307–310.
Crossref | GoogleScholarGoogle Scholar | (accessed 28 July 2007)

Johnson IR, Chapman DF, Snow VO, Eckard RJ, Parsons AJ, Lambert MG, Cullen BR (2008) DairyMod and EcoMod: biophysical pasture-simulation models for Australia and New Zealand. Australian Journal of Experimental Agriculture 48, 621–631.
Crossref | GoogleScholarGoogle Scholar | open url image1

Johnson IR, Lodge GM, White RE (2003) The Sustainable Grazing Systems Pasture Model: description, philosophy and application to the SGS National Experiment. Australian Journal of Experimental Agriculture 43, 711–728.
Crossref | GoogleScholarGoogle Scholar | open url image1

Jouven M, Carrere P, Baumont R (2006) Model predicting dynamics of biomass, structure and digestibility of herbage in managed permanent pastures. 2. Model evaluation. Grass and Forage Science 61, 125–133.
Crossref | GoogleScholarGoogle Scholar | open url image1

Lowe KF, Bowdler TM, Casey ND, Lowe SA, White JA, Pepper PM (2007) Evaluating temperate species for the subtropics. 1. Annual ryegrasses. Tropical Grasslands 41, 9–25. open url image1

Lowe KF, Bowdler TM, Casey ND, Lowe SA, White JA, Pepper PM (2008) Evaluating temperate species in the subtropics. 2. Perennial grasses. Tropical Grasslands 42, 1–26. open url image1

Lowe KF, Bowdler TM, Casey ND, Moss RJ (1999) Performance of temperate perennial pastures in the Australian subtropics. 1. Yield, persistence and pasture quality. Australian Journal of Experimental Agriculture 39, 663–676.
Crossref | GoogleScholarGoogle Scholar | open url image1

McNamara RM (1992) Seasonal distribution of pasture production in New Zealand. North and East Otago downlands. New Zealand Journal of Agricultural Research 35, 163–169. open url image1

Mitchell KJ (1954) Influence of light and temperature on growth of ryegrass (Lolium spp.). 3. Pattern and rate of tissue formation. Physiologia Plantarum 7, 51–65.
Crossref | GoogleScholarGoogle Scholar | open url image1

Nie ZN, Chapman DF, Tharmaraj J, Clements R (2004) Effects of pasture species mixture, management, and environment on the productivity and persistence of dairy pastures in south-west Victoria. 1. Herbage accumulation and seasonal growth pattern. Australian Journal of Agricultural Research 55, 625–636.
Crossref | GoogleScholarGoogle Scholar | open url image1

NIWA (2004) ‘Climate database.’ (National Institute of Water and Atmospheric Research Ltd (NIWA): Wellington, New Zealand)

Robertson SM (2006) Predicting pasture and sheep production in the Victorian Mallee with the decision support tool, Grassgro. Australian Journal of Experimental Agriculture 46, 1005–1014.
Crossref | GoogleScholarGoogle Scholar | open url image1

Sheehy JE, Cobby JM, Ryle GJA (1979) The growth of perennial ryegrass: a model. Annals of Botany 43, 335–354. open url image1

Tedeschi LO (2006) Assessment of the adequacy of mathematical models. Agricultural Systems 89, 225–247.
Crossref | GoogleScholarGoogle Scholar | open url image1

Thamaraj J , Chapman DF , Hill J , Watson L , Grendon R , Fergusson A (2007) Pasture consumption rates in different dairy production systems in southwest Victoria, Australia. In ‘Meeting the challenges for pasture-based dairying. Proceedings of the 3rd Australasian Dairy Science Symposium’. (Eds DF Chapman, DA Clark, KL MacMillan, DP Nation) pp. 431–439. (National Dairy Alliance: Melbourne)