Climate-aware Sustainable Crop Planning Approaches for Australia

  • Randall, Marcus (Chief Investigator)
  • mackey, brendan (Chief Investigator)
  • Montgomery, James (Chief Investigator)
  • Lewis, Andrew (Chief Investigator)

Project: Research

Project Details


The project aims to develop new planning models and techniques, incorporating climate change predictions and state-of-the-art agricultural practices, for determining suitable crop mixes for food producing regions in Australia in the coming decades. The models will balance food production with environmental sustainability by conceptually linking climate change projections with innovative modelling and multi-objective optimisation. The expected outcomes include new computational agriculture models and software tools for long term horizon planning for sustainable cropping. This should provide significant benefits for those in the field of computational agriculture as well as farmers, regional planners and governments.

Project Aims

- To identify a subset of climate models suitable for predicting temperature and rainfall in Australia, focusing on the Murrumbidgee Irrigation Area as the primary case study region and Tasmania as a second case study region, and to determine and describe how these may be integrated into crop planning problem definitions.
- To develop enhanced mathematical models and frameworks for crop planning incorporating ecological best practice and state-of-the-art agricultural practice, and to define how changes in practice may be incorporated into these models in the future.
- To identify the algorithmic features necessary to solve such crop planning problems robustly over time, accounting for uncertainties specified by climate models, and to implement a range of evolutionary computation approaches that incorporate those features.

New or advanced knowledge resulting from the research: The project will advance knowledge in both optimisation and agricultural planning. The immediate intellectual contributions will be a set of enhanced models for joint agricultural and environmental planning, integrating models of climate change, ecological response to river flows and crop behaviour under projected environmental conditions. These will be built on new, general-purpose robust techniques allowing for the seamless aggregation of a number of scenarios (in this case climate change models) to are presentative one with new ways of measuring variation; and a set of techniques that will allow for the interpretive analysis of solutions from high dimensional search spaces (problems having an extremely large number of decision variables).Environmental and economic benefits to Australia and international communities: Applying the newly acquired knowledge will result in economic, commercial and environmental benefits to farmers, regional planners and government agencies (such as the Department of Agriculture and Water Resources). The project outputs will include two practical models that farmers and regional planners can use for planning purposes. The first model is one that will predict the types of crops that can be economically and sustainably planted and harvested in this century given climate change prediction models. The second allows farmers to see what types of crops should be planted, and removed, at specific times, within a continuous time frame (such as a decade), to again determine the most sustainable and economical crop mixes. These tools will help to improve the profitability of farming operations while using environmentally appropriate amounts of water, according to various climate change prediction models. An outcome of the modelling will be that farmers will better understand the risk associated with climate change induced impacts on their production systems. This will enable them to adapt practices, including crop type, irrigation water source and quantities, to create more resilient farming systems. This will result in systemic changes that will lessen the impact of drought, frost and episodic storm events. In the long term, the use of these could mean potential savings of billions of dollars to the Australian economy (ABARES, 2019). The methods and models that will be output from this project are general enough to allow for their application to other countries. It would simply be a matter of obtaining the appropriate data (for that country/region) that the models require.Potential contribution to Australian government’s national science priorities: This research contributes to the ARC’s Science and Research Priorities of Environmental Change. The proposal uses models of projected environmental and climate change to determine the changing patterns of sustainable cropping in Australia over the next one hundred years.One of the largest considerations as part of this will be to determine fluctuating water availability to cropping regions in Australia. The insights gained in this project will help inform future policy and food producing practices, and on the-ground farming practice.
StatusNot started
Effective start/end date1/01/21 → …

Related Research Outputs

  • 4 Conference contribution
  • 2 Article

An Introduction to Temporal Optimisation using a Water Management Problem

Randall, M., Montgomery, J. & Lewis, A., Apr 2020, In : Journal of Computational Science. 42, 101108.

Research output: Contribution to journalArticleResearchpeer-review

  • 35 Downloads (Pure)

    Developing a Decision Support App for Computational Agriculture

    Lewis, A., Randall, M. & Stewart-Koster, B., 2020, Computational Science – ICCS 2020: 20th International Conference, Amsterdam, The Netherlands, June 3–5, 2020, Proceedings, Part II. Krzhizhanovskaya, V. V., Závodszky, G., Lees, M. H., Dongarra, J. J., Sloot, P. M. A., Brissos, S. & Teixeira, J. (eds.). Cham: Springer, p. 551-561 11 p. (Lecture Notes in Computer Science (LNCS); vol. 12138).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

  • Long Term Implications of Climate Change on Crop Planning

    Lewis, A., Randall, M., Elliott, S. & Montgomery, J., 8 Jun 2019, Computational Science – ICCS 2019. Rodrigues, J. M. F., Cardoso, P. J. S., Monteiro, J., Lam, R., Krzhizhanovskaya, V. V., Lees, M. H., Sloot, P. M. A. & Dongarra, J. J. (eds.). Cham: Springer, Vol. V. p. 369-382 14 p. (Lecture Notes in Computer Science; vol. 11540).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

    Open Access
  • 1 Citation (Scopus)
    18 Downloads (Pure)

    Related Activities

    • 1 Invited talk