The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Business As Usual Versus Climate-responsive, Optimised Crop Plans – A Predictive Model for Irrigated Agriculture in Australia in 2060
Open accessPublished version of a preprint
Abstract
Climate change is impacting people’s lives, with management of water resources and food security being major concerns for the future of many countries. In this paper, future water availability, crop water needs, yields, market costs and returns of current crops in a case study area in Australia are evaluated under future climatic conditions. The predictive methods on which the work is based have the advantage of being robust—they are able to simultaneously consider many climate change models—giving greater confidence in determining what the future will hold in this regard. The results indicate business as usual, in terms of the quantity and types of crops that can be grown presently, will not be sustainable in the medium and long term future. Instead, modelling indicates that changes in production and land use to maximise revenue per megalitre of water will be needed to adapt to future conditions and deliver climate-smart agriculture.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Scientists’ warning to humanity on the freshwater biodiversity crisis
Albert, J. S., Destouni, G., Duke-Sylvester, S. M., Magurran, A. E., Oberdorff, T., Reis, R. E., Winemiller, K. O., & Ripple, W. J. (2021) · Ambio · doi:10.1007/s13280-020-01318-8 · Cited by 943
Research works printed but not linked: 24
- Aspinall D, Beringer L, Momigliano A (2007) Optimisation validation. Electron Notes Theor Comput Sci 176(3):37–59 · DOI
- Connor J, Bryan B, Nolan M, Stock F, Gao L, Dunstall S, Graham P, Ernst A, Newth D, Grundy M et al (2015) Modelling Australian land use competition and ecosystem services with food price feedbacks at high spatial resolution. Environ Model Softw 69:141–154 · DOI
- Elnashar W, Elyamany A (2022) Managing risks of climate change on irrigation water in arid regions. Water Resour Manag. https://doi.org/10.1007/s11269-022-03267-1 · DOI
- Evans J, Ji F, Lee C, Smith P, Argüeso D, Fita L (2014) Design of a regional climate modelling projection ensemble experiment-NARCliM. Geosci Model Dev 7(2):621–629 · DOI
- Freund M, Henley B, Karoly D, Allen K, Baker P (2017) Multi-century cool- and warm-season rainfall reconstructions for Australia’s major climatic regions. Climate of the Past 13(12):1751–1770 · DOI
- Gorelick S, Zheng C (2015) Global change and the groundwater management challenge. Water Resour Res 51(5):3031–3051 · DOI
- Grose M, Narsey S, Delage F, Dowdy A, Bador M, Boschat G, Chung C, Kajtar J, Rauniyar S, Freund M et al (2020) Insights from CMIP6 for Australia’s future climate. Earth’s Future 8(5):e2019EF001469 · DOI
- Hochman Z, Carberry P, Robertson M, Gaydon D, Bell L, McIntosh P (2013) Prospects for ecological intensification of Australian agriculture. Eur J Agron 44:109–123 · DOI
- Inselberg A, Dimsdale B (1990) Parallel coordinates: a tool for visualizing multi-dimensional geometry. In: Proceedings of the First IEEE Conference on Visualization: Visualization 90 IEEE, pp 361–378 · DOI
- Ji F, Evans J, Teng J, Scorgie Y, Argüeso D, Di Luca A (2016) Evaluation of long-term precipitation and temperature weather research and forecasting simulations for southeast Australia. Climate Res 67(2):99–115 · DOI
- Khan S, Tariq R, Yuanlai C, Blackwell J (2006) Can irrigation be sustainable? Agric Water Manag 80(1–3):87–99 · DOI
- Kumar P (2013) Groundwater trading and management of local impacts-lower Murrumbidgee deep groundwater source-2013. NSW Office of Water, Sydney, Tech Rep
- Lewis A, Randall M (2017) Solving multi-objective water management problems using evolutionary computation. J Environ Manage 204:179–188 · DOI
- Lewis A, Randall M, Capon S, Jackwitz E (2017) Constrained optimisation of agricultural water management with parameter-sensitive objectives. In: Proceedings of the 15th International Conference on Computer Applications, pp 79–85
- Lewis A, Randall M, Montgomery J (2022) Computational methods for sustainable agriculture. https://doi.org/10.17605/OSF.IO/P9QKV’ · DOI
- Peel M, Finlayson B, McMahon T (2007) Updated world map of the Köppen-Geiger climate classification. Hydrol Earth Syst Sci 11(5):1633–1644 · DOI
- Randall M, Montgomery J, Lewis A (2020) An introduction to temporal optimisation using a water management problem. J Comput Sci p. 101108 · DOI
- Randall M, Montgomery J, Lewis A (2022) Robust temporal optimisation for a crop planning problem under climate change uncertainty. Oper Res Perspect 9:100219 · DOI
- Renton M, Lawes R (2009) Land-Use Sequence Optimiser (LUSO): A simulation model for analysing strategic and tactical decisions regarding ’break crops’ in agricultural rotations. In: Anderssen RS, Braddock RD, Newham LTH (eds) Proceedings of the 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation’, pp 581–587
- Settre C, Connor J, Wheeler S (2019) Emerging water and carbon market opportunities for environmental water and climate regulation ecosystem service provision. J Hydrol 578 · DOI
- Speer M, Leslie L, MacNamara S, Hartigan J (2021) From the 1990s climate change has decreased cool season catchment precipitation reducing river heights in Australia’s Southern Murray-Darling basin. Sci Rep 11(1):1–16 · DOI
- Tidwell V, Van Den Brink C (2008) Cooperative modeling: Linking science, communication, and ground water planning. Groundwater 46(2):174–182 · DOI
- Whish J, Bell L, DeVoil P, Zull A, Thornby D (2019) Simulating the efficiency and resilience of diverse crop sequences in Australia’s subtropical cropping zone. In: Proceedings of 19th Australian Agronomy Conference
- Xevi E, Khan S (2005) A multi-objective optimisation approach to water management. J Environ Manage 77(4):269–277 · DOI
Metadata
- Authors
- Lewis, A., Montgomery, J., Lewis, M., Randall, M., & Schiller, K.
- Affiliations
- Griffith University (AU)
- University of Tasmania (AU)
- Bond University (AU)
- Corresponding author
- Lewis, Andrew
- Venue
- Water Resources Management
- Published
- 2023-02-28
- Volume
- 37
- Issue
- 6-7
- Pages
- 2721-2735
- ISSN
- 0920-4741 (ISSN-L)
- 1573-1650
- OpenAlex
- W4322626813
- Cited by
- 9
- References
- 1
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 2 · 2023: 1
- FWCI
- 2.28
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Water Resources Management (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- Bond University Research Portal (Bond University) (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- Principles for Responsible Management Education (Grant number)
- Griffith University
- Topics
- Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Climate change impacts on agriculture
- Physical Sciences › Environmental Science › Water Science and Technology › Water resources management and optimization
- Life Sciences › Agricultural and Biological Sciences › Soil Science › Irrigation Practices and Water Management
- Keywords
- climate change adaptation, irrigated agriculture, crop planning, crop water requirements, crop yield, climate-smart agriculture, business as usual, land use change, climate change projections, Australia
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
