Envirotyping of abiotic stresses: Large spatial and temporal variations across Australian barley production regions

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2026
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Abstract
Abstract Understanding and managing genotype × environment (G×E) interactions remain a major challenge in developing climate-resilient crops. Accurate characterization of the environments experienced by crops within the target population of environments (TPE) is essential to improve this task. This study applied advanced crop modelling and envirotyping approaches to simulate the growth and development of barley cultivars and to quantify seasonal patterns of water stress index, vapour pressure deficit, and maximum temperature, analysed both individually (univariate) and in combination (multivariate). Long-term APSIM simulations were conducted for standard cultivar, Gairdner, using 60 years of historical weather data (1965–2024) across 60 representative sites covering the Australian barley-growing regions. Simulations incorporated regionally representative management practices, and two additional cultivars differing in maturity type, Keel (quick), and Franklin (slow), were used to assess the effect of maturity on stress frequencies. Both univariate and multivariate environment types (ETs) were identified across the TPE, for water stress, vapour pressure deficit, and maximum temperature. The frequency of ETs varied greatly across locations and cultivars, with the slow-maturing cultivar consistently being exposed to greater stress. Comparison of simulations from recent (1995-2024) and older (1965-1994) climate indicated a 4-14% increase in the frequency of the most stressful environment types, reflecting the influence of realised climate change. These findings provide a framework to support climate-resilient breeding and agronomy in Australian barley production systems. Overall, this study enhances understanding of environmental diversity and stress dynamics, contributing to the long-term sustainability and yield stability of barley production under Australia’s evolving climate.
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openalex_W7171567018 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Bita Heidariask, Alan D. Severini, Samir Alahmad, Lee T. Hickey, Millicent Smith, Karine Chenu
Journal in silico Plants
Year 2026
DOI
10.1093/insilicoplants/diag020
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