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Biophysical Simulation of Sheep Grazing Systems Using the SGS Pasture Model

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  • Andrew P. Smith

    (Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3000, Australia
    Grains Research & Development Corporation, 187 Fullarton Road, Dulwich, SA 5065, Australia)

Abstract

The performance of farming systems models for grazed grasslands are seldom evaluated against comprehensive field data. The aim of this study was to evaluate the capacity of a daily time step, grazing systems simulation model—the SGS (Sustainable Grazing Systems) Pasture Model—to simulate production and aspects of sustainability. This was completed by evaluating temporal changes in soil water balance, some major nitrogen (N) fluxes, as well as plant and animal production using data from two large scale experimental sites with grazing sheep. The simulations were broadly in agreement with the measurements. In cases where divergence occurred the reasons were apparent and could be explained by reference to the model structure or aspects of the field data. In particular, the simulations showed good agreement with the observed soil water, but poorer agreement with the volumes of runoff. The simulated N in leachate and soil inorganic N were less in agreement with the measured data. The model outputs were sensitive to symbiotic biological fixation by subterranean (sub) clover and mineralisation of soil organic matter, which were not measured. Similarly, there were variable results for the simulation of animal growth and production. The complexities of simulating grazing systems and comparing field observations to simulated values are discussed.

Suggested Citation

  • Andrew P. Smith, 2022. "Biophysical Simulation of Sheep Grazing Systems Using the SGS Pasture Model," Agriculture, MDPI, vol. 12(12), pages 1-21, November.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:12:p:2032-:d:986668
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    References listed on IDEAS

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    1. Moore, A. D. & Donnelly, J. R. & Freer, M., 1997. "GRAZPLAN: Decision support systems for Australian grazing enterprises. III. Pasture growth and soil moisture submodels, and the GrassGro DSS," Agricultural Systems, Elsevier, vol. 55(4), pages 535-582, December.
    2. Ghahramani, Afshin & Moore, Andrew D., 2015. "Systemic adaptations to climate change in southern Australian grasslands and livestock: Production, profitability, methane emission and ecosystem function," Agricultural Systems, Elsevier, vol. 133(C), pages 158-166.
    3. Donnelly, J. R. & Moore, A. D. & Freer, M., 1997. "GRAZPLAN: Decision support systems for Australian grazing enterprises--I. Overview of the GRAZPLAN project, and a description of the MetAccess and LambAlive DSS," Agricultural Systems, Elsevier, vol. 54(1), pages 57-76, May.
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