An integrated framework for predicting the risk of experiencing temperature conditions that may trigger late-maturity alpha-amylase in wheat across Australia
Robert N. Armstrong
A E , Andries B. Potgieter A E , Daryl J. Mares B , Kolumbina Mrva B , Jason Brider C and Graeme L. Hammer D
A Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Gatton Campus, Gatton, Qld 4343, Australia.
B School of Agriculture Food and Wine, University of Adelaide, Waite Campus, Urrbrae, SA 5064, Australia.
C Department of Agriculture and Fisheries, 203 Tor St, Toowoomba, Qld 4350, Australia.
D Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Qld 4072, Australia.
E Corresponding authors. Email: r.armstrong1@uq.edu.au; a.potgieter@uq.edu.au
Crop and Pasture Science 71(1) 1-11 https://doi.org/10.1071/CP19005
Submitted: 4 January 2019 Accepted: 9 September 2019 Published: 17 December 2019
Journal Compilation © CSIRO 2020 Open Access CC BY-NC-ND
Abstract
Late-maturity alpha-amylase (LMA) is a key concern for Australia’s wheat industry because affected grain may not meet receival standards or market specifications, resulting in significant economic losses for producers and industry. The risk of LMA incidence across Australia’s wheatbelt is not well understood; therefore, a predictive model was developed to help to characterise likely LMA incidence. Preliminary development work is presented here based on diagnostic simulations for estimating the likelihood of experiencing environmental conditions similar to a potential triggering criterion currently used to phenotype wheat lines in a semi-controlled environment. Simulation inputs included crop phenology and long-term weather data (1901–2016) for >1750 stations across Australia’s wheatbelt. Frequency estimates for the likelihood of target conditions on a yearly basis were derived from scenarios using either: (i) weather-driven sowing dates each year and three reference maturity types, mimicking traditional cropping practices; or (ii) monthly fixed sowing dates for each year. Putative-risk ‘footprint’ maps were then generated at regional shire scale to highlight regions with a low (<33%), moderate (33–66%) or high (>66%) likelihood of experiencing temperatures similar to a cool-shock regime occurring in the field. Results suggested low risks for wheat regions across Queensland and relatively low risks for most regions across New South Wales, except for earlier planting with quick-maturing varieties. However, for fixed sowing dates of 1 May and 1 June and varying maturity types, the combined footprints for moderate-risk and high-risk categories ranged from 34% to 99% of the broad wheat region for South Australia, from 12% to 97% for Victoria, and from 9% to 59% for Western Australia. A further research component aims to conduct a field validation to improve quantification of the range of LMA triggering conditions; this would improve the predictive LMA framework and could assist industry with future decision-making based on a quantifiable LMA field risk.
Additional keywords: crop modelling, decision support, environmental modelling, Oz-Wheat, risk management, wheat quality.
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