Modeling the joint influence of multiple synoptic-scale, climate mode indices on Australian wheat yield using a vine copula-based approach

Nguyen-Huy, Thong and Deo, Ravinesh C. and Mushtaq, Shahbaz and An-Vo, Duc-Anh and Khan, Shahjahan (2018) Modeling the joint influence of multiple synoptic-scale, climate mode indices on Australian wheat yield using a vine copula-based approach. European Journal of Agronomy, 98. pp. 65-81. ISSN 1161-0301

Abstract

Twelve large-scale climate drivers are employed to investigate their spatio-temporal influence on the variability of seasonal wheat yield in five major wheat-producing states across Australia using data for the period 1983–2013. Generally, the fluctuations in the Indian Ocean appear to have a dominant effect on the Australian wheat crop in all states except Western Australia, while the impact of oceanic conditions in the Pacific region is much stronger in Queensland. The results show a statistically significant negative correlation between the Indian Ocean Dipole (IOD) and the anomalous wheat yield in the early growing stage of the crop in the eastern and southeastern wheat belt regions. This correlation suggests that the wheat yield can be skillfully forecast 3–6 months ahead, supporting early decision-making in regard to precision agriculture. In this study, we use vine copula models to capture climate-yield dependence structures, including the occurrence of extreme events (i.e., the tail dependences). The co-occurrence of extreme events is likely to enhance the impacts of climate mode and this can be quantified probabilistically through conditional copula-based models. Generally, the developed D-vine quantile regression model provide greater accuracy for the forecasting of wheat yield at given different confidence levels compared to the traditional linear quantile regression (LQR) method. A five-fold cross-validation approach is also used to estimate the out-of-sample accuracy of both copula-statistical forecasting models. These findings provide a comprehensive analysis of the spatio-temporal impacts of different climate mode indices on Australian wheat crops. Improved quantification of the impacts of large-scale climate drivers on the wheat yield can allow a development of suitable planning processes and crop production strategies designed to optimize the yield and agricultural profit.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to published version, in accordance with the copyright policy of the publisher. The project was financed by a University of Southern Queensland Post Graduate Research Scholarship (USQPRS 2015-2017), School of Agricultural, Computational and Environmental Sciences, Strategic Research Funding (SRF) Projects (Resilient Landscapes SRF and Computational Models SRF) and Climate Adaptation [DCAP] Projects (Producing Enhanced Crop Insurance Systems and Associated Financial Decision Support Tools).
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 03 Jun 2018 23:32
Last Modified: 03 Jun 2018 23:56
Uncontrolled Keywords: crop modeling; Australian wheat; crop yield forecasting; multiple climate indices; copula models; d-vine copulas; quantile regression; joint distribution; conditional probability; food security
Fields of Research : 07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070103 Agricultural Production Systems Simulation
07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070106 Farm Management, Rural Management and Agribusiness
07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070105 Agricultural Systems Analysis and Modelling
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
Socio-Economic Objective: D Environment > 96 Environment > 9603 Climate and Climate Change > 960303 Climate Change Models
E Expanding Knowledge > 97 Expanding Knowledge > 970107 Expanding Knowledge in the Agricultural and Veterinary Sciences
Funding Details:
Identification Number or DOI: 10.1016/j.eja.2018.05.006
URI: http://eprints.usq.edu.au/id/eprint/34219

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