Northern Australian pasture and beef systems. 2. Validation and use of the Sustainable Grazing Systems (SGS) whole-farm biophysical model
Natalie A. Doran-Browne A D , Steven G. Bray B , Ian R. Johnson A , Peter J. O’Reagain C and Richard J. Eckard A
+ Author Affiliations
- Author Affiliations
A Melbourne School of Land and Environment, The University of Melbourne, Vic. 3010, Australia.
B Department of Agriculture, Fisheries and Forestry, PO Box 6014, Rockhampton, Qld 4702, Australia.
C Department of Agriculture, Fisheries and Forestry, PO Box 976, Charters Towers, Qld 4820, Australia.
D Corresponding author. Email: n.doran-browne@unimelb.edu.au
Animal Production Science 54(12) 1995-2002 https://doi.org/10.1071/AN14569
Submitted: 13 May 2014 Accepted: 1 August 2014 Published: 20 October 2014
Abstract
The Sustainable Grazing Systems (SGS) model is a biophysical, mechanistic whole-farm model that simulates pasture production based on climate and soil data. While the SGS model has been extensively used for southern temperate systems, the model has yet to be evaluated for use in the tropical rangeland systems of Australia. New pasture parameter sets were developed in SGS to represent groups of grasses with the following common characteristics: (1) 3P grasses represented tropical rangeland grasses that were perennial, palatable and productive, and (2) annual tropical grasses that include both productive and less productive grass species. Fifteen years of data from the long-term Wambiana grazing trial ~70 km south-west of Charters Towers, Queensland, were used to validate the model. The results showed that SGS is capable of representing northern Australian beef systems with modelled outputs for total standing dry matter and steer liveweight in agreement with the year-to-year variation in measured data over three different soil types and two stocking rates. Recommendations for further model development are made, such as incorporating fire, tree growth and the use of urea supplementation in the model. Further testing is required to verify that the new pasture parameter sets are suitable for other regions in northern Australia.
Additional keywords: cattle, modelling, rangelands.
References
Araújo MB, Guisan A (2006) Five (or so) challenges for species distribution modelling. Journal of Biogeography 33, 1677–1688.| Five (or so) challenges for species distribution modelling.Crossref | GoogleScholarGoogle Scholar |
Bell MJ, Eckard RJ, Harrison MT, Neal JS, Cullen BR (2013) Effect of warming on the productivity of perennial ryegrass and kikuyu pastures in south-eastern Australia. Crop and Pasture Science 64, 61–70.
| Effect of warming on the productivity of perennial ryegrass and kikuyu pastures in south-eastern Australia.Crossref | GoogleScholarGoogle Scholar |
Bray S, Doran-Browne N, O’Reagain P (2014) Northern Australian pasture and beef systems. 1. Calculation of greenhouse gas emissions. Animal Production Science
Clark SG, Donnelly JR, Moore AD (2000) The GrassGro decision support tool: its effectiveness in simulating pasture and animal production and value in determining research priorities. Australian Journal of Experimental Agriculture 40, 247–256.
| The GrassGro decision support tool: its effectiveness in simulating pasture and animal production and value in determining research priorities.Crossref | GoogleScholarGoogle Scholar |
Cullen BR, Eckard RJ, Callow MN, Johnson IR, Chapman DF, Rawnsley RP, Garcia SC, White T, Snow VO (2008) Simulating pasture growth rates in Australian and New Zealand grazing systems. Australian Journal of Agricultural Research 59, 761–768.
| Simulating pasture growth rates in Australian and New Zealand grazing systems.Crossref | GoogleScholarGoogle Scholar |
France J, Kebreab E (2008) ‘Mathematical modelling in animal nutrition.’ (CABI: Wallingford, UK)
FutureBeef (2014) Stocktake: balancing supply and demand. Available at http://futurebeef.com.au/resources/workshops/sustainable-grazing-workshops/stocktake-balancing-supply-and-demand/ [Verified 28 April 2014]
Graham JF, Cullen BR, Lodge GM, Andrew MH, Christy BP, Holst PJ, Wang X, Murphy SR, Thompsona AN (2003) SGS Animal Production Theme: effect of grazing system on animal productivity and sustainability across southern Australia. Australian Journal of Experimental Agriculture 43, 977–991.
| SGS Animal Production Theme: effect of grazing system on animal productivity and sustainability across southern Australia.Crossref | GoogleScholarGoogle Scholar |
Hirata M (2000) Quantifying spatial heterogeneity in herbage mass and consumption in pastures. Journal of Range Management 53, 315–321.
| Quantifying spatial heterogeneity in herbage mass and consumption in pastures.Crossref | GoogleScholarGoogle Scholar |
Hunt LP, McIvor JG, Grice AC, Bray SG (2014) Principles and guidelines for managing cattle grazing in the grazing lands of northern Australia: stocking rates, pasture resting, prescribed fire, paddock size and water points – a review. The Rangeland Journal 36, 105–119.
| Principles and guidelines for managing cattle grazing in the grazing lands of northern Australia: stocking rates, pasture resting, prescribed fire, paddock size and water points – a review.Crossref | GoogleScholarGoogle Scholar |
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.
| The Sustainable Grazing Systems Pasture Model: description, philosophy and application to the SGS national experiment.Crossref | GoogleScholarGoogle Scholar |
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.
| DairyMod and EcoMod: biophysical pasture-simulation models for Australia and New Zealand.Crossref | GoogleScholarGoogle Scholar |
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.
| Model predicting dynamics of biomass, structure and digestibility of herbage in managed permanent pastures. 2. Model evaluation.Crossref | GoogleScholarGoogle Scholar |
Lin LI (1989) A concordance correlation-coefficient to evaluate reproducibility. Biometrics 45, 255–268.
| A concordance correlation-coefficient to evaluate reproducibility.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DyaL1M3kslKrtg%3D%3D&md5=6fb00a32ae71a82eb32e1ddc029fe59aCAS | 2720055PubMed |
Lodge GM, Murphy SR, Harden S (2003) Effects of grazing and management on herbage mass, persistence, animal production and soil water content of native pastures. 1. A redgrass-wallaby grass pasture, Barraba, North West Slopes, New South Wales. Australian Journal of Experimental Agriculture 43, 875–890.
| Effects of grazing and management on herbage mass, persistence, animal production and soil water content of native pastures. 1. A redgrass-wallaby grass pasture, Barraba, North West Slopes, New South Wales.Crossref | GoogleScholarGoogle Scholar |
McLean I, Blakeley S (2014) Animal equivalent methodology: a methodology to accurately and consistently calculate cattle grazing loads in northern Australia B.NBP.0779. Meat and Livestock Australia, Sydney, NSW.
McLean I, Holmes P, Counsell D (2014) The Northern beef report 2013. Northern beef situation analysis. Meat and Livestock Australia, Sydney, NSW.
Murphy SR (2010) ‘Prime Facts: tropical perennial grasses – root depths, growth and water use efficiency.’ (Department of Primary Industries: Tamworth)
O’Reagain P, Bushell J (2011) ‘The Wambiana grazing trial: key learnings for sustainable and profitable management in a variable environment.’ (Department of Employment, Economic Development and Innovation: Brisbane)
O’Reagain P, Bushell J, Holloway C, Reid A (2009) Managing for rainfall variability: effect of grazing strategy on cattle production in a dry tropical savanna. Animal Production Science 49, 85–99.
| Managing for rainfall variability: effect of grazing strategy on cattle production in a dry tropical savanna.Crossref | GoogleScholarGoogle Scholar |
O’Reagain P, Bushell J, Holmes B (2011) Managing for rainfall variability: long-term profitability of different grazing strategies in a northern Australian tropical savanna. Animal Production Science 51, 210–224.
| Managing for rainfall variability: long-term profitability of different grazing strategies in a northern Australian tropical savanna.Crossref | GoogleScholarGoogle Scholar |
O’Reagain P, Scanlan J, Hunt L, Cowley R, Walsh D (2014) Sustainable grazing management for temporal and spatial variability in north Australian rangelands – a synthesis of the latest evidence and recommendations. The Rangeland Journal 36, 223–232.
| Sustainable grazing management for temporal and spatial variability in north Australian rangelands – a synthesis of the latest evidence and recommendations.Crossref | GoogleScholarGoogle Scholar |
Oreskes N, Shraderfrechette K, Belitz K (1994) Verification, validation, and confirmation of numerical-models in the earth-sciences. Science 263, 641–646.
| Verification, validation, and confirmation of numerical-models in the earth-sciences.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC3cvit1OrsQ%3D%3D&md5=2811e23f59f0d54a0a87abfaadf05dd7CAS | 17747657PubMed |
Pembleton KG, Rawnsley RP, Jacobs JL, Mickan FJ, O’Brien GN, Cullen BR, Ramilan T (2013) 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. Crop and Pasture Science 64, 147–164.
| 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.Crossref | GoogleScholarGoogle Scholar |
Pringle MJ, Allen DE, Dalal RC, Payne JE, Mayer DG, O’Reagain P, Marchant BP (2011) Soil carbon stock in the tropical rangelands of Australia: effects of soil type and grazing pressure, and determination of sampling requirement. Geoderma 167–168, 261–273.
| Soil carbon stock in the tropical rangelands of Australia: effects of soil type and grazing pressure, and determination of sampling requirement.Crossref | GoogleScholarGoogle Scholar |
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.
| Predicting pasture and sheep production in the Victorian Mallee with the decision support tool, GrassGro.Crossref | GoogleScholarGoogle Scholar |
Rykiel EJ (1996) Testing ecological models: the meaning of validation. Ecological Modelling 90, 229–244.
| Testing ecological models: the meaning of validation.Crossref | GoogleScholarGoogle Scholar |
Sanford P, Cullen BR, Dowling PM, Chapman DF, Garden DL, Lodge GM, Andrew MH, Quigley PE, Murphy SR, King WM, Johnston WH, Kemp DR (2003) SGS Pasture Theme: effect of climate, soil factors and management on pasture production and stability across the high rainfall zone of southern Australia. Australian Journal of Experimental Agriculture 43, 945–959.
| SGS Pasture Theme: effect of climate, soil factors and management on pasture production and stability across the high rainfall zone of southern Australia.Crossref | GoogleScholarGoogle Scholar |
Scanlan JC, MacLeod ND, O’Reagain PJ (2013) Scaling results up from a plot and paddock scale to a property – a case study from a long-term grazing experiment in northern Australia. The Rangeland Journal 35, 193–200.
| Scaling results up from a plot and paddock scale to a property – a case study from a long-term grazing experiment in northern Australia.Crossref | GoogleScholarGoogle Scholar |
Tedeschi LO (2006) Assessment of the adequacy of mathematical models. Agricultural Systems 89, 225–247.
| Assessment of the adequacy of mathematical models.Crossref | GoogleScholarGoogle Scholar |
Thornley JHM, Johnson IR (2000) ‘Plant and crop modelling.’ (Blackburn Press: Caldwell, NJ)
Tomkins NW, O’Reagain PJ, Swain D, Bishop-Hurley G, Charmley E (2009) Determining the effect of stocking rate on the spatial distribution of cattle for the subtropical savannas. The Rangeland Journal 31, 267–276.
| Determining the effect of stocking rate on the spatial distribution of cattle for the subtropical savannas.Crossref | GoogleScholarGoogle Scholar |
Tothill JC, Hargreaves JNG, Jones RM, McDonald CK (1992) BOTANAL: a comprehensive sampling procedure for estimating pasture yield and composition. 1. Field sampling. Tropical Agronomy Technical Memorandum No. 78. CSIRO Division of Tropical Crops and Pastures, Brisbane, Qld, Australia.
White TA, Johnson IR, Snow VO (2008) Comparison of outputs of a biophysical simulation model for pasture growth and composition with measured data under dryland and irrigated conditions in New Zealand. Grass and Forage Science 63, 339–349.
| Comparison of outputs of a biophysical simulation model for pasture growth and composition with measured data under dryland and irrigated conditions in New Zealand.Crossref | GoogleScholarGoogle Scholar |
