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Modelling Dryland Agricultural Systems

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Innovations in Dryland Agriculture

Abstract

Irrespective of levels of endowment or investment, both large scale commercial and smallholder farmers face different though equally challenging and complex problems and opportunities, requiring new science and tools. We started modelling dryland agricultural systems in the early 1990s. Since then, value of the technology has been shown across multiple applications and disciplines, though particularly in (i) the synthesis and integration of knowledge about the functioning and dynamics of rainfed agricultural systems, where biotic processes interact with climatic, soil and biological drivers at a range of temporal and spatial scales; and (2) informing (and overcoming) the complexities in the management and improvement of dryland agricultural systems, both at the level of crop (G×M), cropping systems, farming systems, and farm business design. Here we provide a summary of our achievements in the use of modelling tools in dryland agricultural systems, and provide examples of the important opportunities for the development and application of integrative approaches in farming systems design to support the medium to long-term transformational changes required in our dryland agricultural systems. Particular emphasis is given to the role of modelling tools to quantify benefits and trade-offs in the management of crops and farm businesses in highly-variable climates; and the medium and longer term benefits from changes in strategies, farming systems designs and allocation of limited resource. We also propose that field crops research will increasingly require cross-links between disciplines integration and participatory approaches to allow for the sustainable intensification of agricultural production, and that the modelling agricultural systems will continue to be a crucial tool in making better informed decisions across a range relevant scales, the crop, the farm, the landscape and region.

There is nothing so practical as a good theory. Emanuel Kant (1724–1804)

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Notes

  1. 1.

    The yield ‘achieved’ from applying optimum agronomic management under rainfed conditions, also called water-limited yield. Water-limited yield can be calculated using crop simulation models assuming optimum or recommended sowing dates, planting densities and cultivars. An important limitation of this concept is that farmers tend to maximize profits from the entire farm business rather than from individual enterprises.

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Acknowledgements

The authors thank the Australian Centre for International Agriculture Research (ACIAR) and the Grains Research and Development Corporation for their support with the many studies reported within.

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Authors and Affiliations

  1. Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, 102, Toowoomba, QLD, 4350, Australia

    Daniel Rodriguez & Peter de Voil

  2. Commonwealth Scientific and Industrial Research Organisation, 102, Toowoomba, QLD, 4350, Australia

    B. Power

Authors
  1. Daniel Rodriguez
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  2. Peter de Voil
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  3. B. Power
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Corresponding author

Correspondence to Daniel Rodriguez .

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Editors and Affiliations

  1. Department of Agronomy, University of Agriculture, Faisalabad, Pakistan

    Muhammad Farooq

  2. The University of Western Australia Institute of Agriculture, The University of Western Australia, Perth, WA, Australia

    Kadambot H.M. Siddique

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Rodriguez, D., de Voil, P., Power, B. (2016). Modelling Dryland Agricultural Systems. In: Farooq, M., Siddique, K. (eds) Innovations in Dryland Agriculture. Springer, Cham. https://doi.org/10.1007/978-3-319-47928-6_9

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