An integrated framework for predicting the risk of experiencing temperature conditions that may trigger late-maturity alpha-amylase in wheat across Australia

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.