Enhancing Genomic Prediction for Changing Environments in Wheat. Adverse weather is the primary risk faced by the Australian agriculture industry. This Project aims to develop the next generation of agriculture tools to unlock natural potential in wheat and improve yield stability across seasons and regions. Drawing on crop physiology, genetics and integrated modelling, this Project expects to generate new knowledge and technologies to untangle genetic and environmental interactions that affect ....Enhancing Genomic Prediction for Changing Environments in Wheat. Adverse weather is the primary risk faced by the Australian agriculture industry. This Project aims to develop the next generation of agriculture tools to unlock natural potential in wheat and improve yield stability across seasons and regions. Drawing on crop physiology, genetics and integrated modelling, this Project expects to generate new knowledge and technologies to untangle genetic and environmental interactions that affect productivity, enhance predictive capability, and initiate advanced breeding strategies to develop new crop varieties with superior resilience against changing climates. This should provide significant benefits, such as profit stability for wheat growers, elevated global market position and improved food security.Read moreRead less
CropVision: A next-generation system for predicting crop production. Accurate and timely production estimates are essential to Australia’s grain producers and industry to better deal with down side risk caused by climate extremes and market volatilities. However, current systems for predicting crop production are inaccurate and unreliable. This project aims to develop a next generation system for advance and high accuracy predictions for yield, crop type and area at field scale. This will be don ....CropVision: A next-generation system for predicting crop production. Accurate and timely production estimates are essential to Australia’s grain producers and industry to better deal with down side risk caused by climate extremes and market volatilities. However, current systems for predicting crop production are inaccurate and unreliable. This project aims to develop a next generation system for advance and high accuracy predictions for yield, crop type and area at field scale. This will be done by integrating the state of the art global climate models (GCM), biophysical crop modelling, and high-resolution earth observation technologies. This project will deliver a next generation crop prediction system to predict crop production at field scale for improved decision-making and enhancing resilience.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210100854
Funder
Australian Research Council
Funding Amount
$461,249.00
Summary
Model-directed bioengineering strategy for accelerating crop improvement. The aim is to use an advanced mechanistic crop model to investigate the interacting plant physiological processes that define yield consequences, using a sorghum model. This will involve unravelling the complex relationship between leaf gas exchange properties and crop field performance. Through a unique combination of model prediction and gene editing to target the photosynthetic pathway and stomata, the research is expec ....Model-directed bioengineering strategy for accelerating crop improvement. The aim is to use an advanced mechanistic crop model to investigate the interacting plant physiological processes that define yield consequences, using a sorghum model. This will involve unravelling the complex relationship between leaf gas exchange properties and crop field performance. Through a unique combination of model prediction and gene editing to target the photosynthetic pathway and stomata, the research is expected to gain a deep mechanistic understanding of the underpinning processes and drive the transfer of promising bioengineering targets into crops. The research is expected to discover new avenues for crop improvement, and significantly benefit crop breeding and food production capacity.Read moreRead less