Speaker
Description
Wheat grain yield is strongly modulated by underlying genotype (G) by environment (E) and management (M) (GxExM) interactions. These interactions are difficult to interpret. Process-based crop modelling has the potential to disentangle the nature of GxExM interactions and assist in developing breeding and management synergies to increase crop performance for target environments. However, current crop models were developed using data collected from outdated cultivars and often assume uniformity of many important physiological traits (e.g. leaf size, tillering, resource use efficiency and partition of resources to different organs) across genotypes. Consequently, insights regarding the impact of elite traits of modern cultivars to optimise GxExM are limited.
Advances in trait genetics and digital field phenotyping have enabled large-scale data collection to quantify gene-trait linkages. Incorporating such data with established trait physiological models allows simulations of gene-trait-yield relationships across environments to develop value propositions for elite traits (or wheat lines) across a wide range of geographical regions in current and future climates. Further, the models have significant potential to assist breeding as they facilitate the evaluation of elite traits arising from, and knowledge underpinning targeted breeding efforts to enhance genetic yield potential.
We present recent model development for simulation of grain yield of novel wheat genotypes with the new genetic traits of early vigour and long coleoptiles. Our results revealed that these novel genotypes, coupled with deep sowing, could increase national wheat yields by 18–20% under historical climate (1901–2020) in Australia, with benefits also under future warming. We demonstrate how incorporation of genetic understanding and data into farming systems modelling can enable gene-trait-yield simulations across environments to assist in the design of ideotypes and management strategies to optimise GxExM for increased productivity and resilience of wheat under climate change.
| Keywords | GxExM, Gene-based modelling, trait modelling, wheat |
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