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
Big data modelling to forecast crop yield to enable precision fertilisation. This project aims to lay a foundation for a generic data-driven approach to more precise management of our agricultural landscapes. A multitude of agriculture-related data streams are now available to growers to characterise their yield, management, soil and weather. However, currently there is no approach able to digest all these disparate data streams to enable a management decision. The project will develop an appro ....Big data modelling to forecast crop yield to enable precision fertilisation. This project aims to lay a foundation for a generic data-driven approach to more precise management of our agricultural landscapes. A multitude of agriculture-related data streams are now available to growers to characterise their yield, management, soil and weather. However, currently there is no approach able to digest all these disparate data streams to enable a management decision. The project will develop an approach to harness all of these data streams to guide spatially variable applications of nitrogen fertilisers with a focus on grains cropping. This should provide the opportunity to allocate fertiliser inputs as required at fine spatial scales according to local soil and weather conditions to maximise profit and minimise off-farm impacts of excessive fertilisation.Read moreRead less
Understanding the molecular basis of fungal rust diseases in plants. This project aims to utilise structural biology, biochemistry and molecular biology approaches to substantially deepen our understanding of rust fungi-plant interactions. Fungal rust pathogens cause disease and significant yield losses in our most important food crops. During colonisation, rust fungi utilise secreted effector proteins to cause plant disease. Effectors can also be recognised by plant immunity receptors, leading ....Understanding the molecular basis of fungal rust diseases in plants. This project aims to utilise structural biology, biochemistry and molecular biology approaches to substantially deepen our understanding of rust fungi-plant interactions. Fungal rust pathogens cause disease and significant yield losses in our most important food crops. During colonisation, rust fungi utilise secreted effector proteins to cause plant disease. Effectors can also be recognised by plant immunity receptors, leading to resistance. The intended outcome of this work is to generate knowledge that can be used for the development of disease management and engineering strategies to protect plants from rust fungi. This should provide significant benefits to agricultural productivity and global food security.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210100323
Funder
Australian Research Council
Funding Amount
$431,000.00
Summary
Synthetic biology to engineer novel disease resistance in cereal crops. This project aims to engineer disease resistance in crops to dangerous fungal pathogens. The strategy is to exploit our knowledge of the plant immune system using structural biology and directed evolution of natural resistance genes, improving their ability to recognise and respond to fungal attack. Fungal pathogens cause some of the most harmful crop diseases in Australia and worldwide. The rapid evolution of fungi overcome ....Synthetic biology to engineer novel disease resistance in cereal crops. This project aims to engineer disease resistance in crops to dangerous fungal pathogens. The strategy is to exploit our knowledge of the plant immune system using structural biology and directed evolution of natural resistance genes, improving their ability to recognise and respond to fungal attack. Fungal pathogens cause some of the most harmful crop diseases in Australia and worldwide. The rapid evolution of fungi overcomes natural plant resistance and management of these diseases is a major challenge to agriculture. Expected outcomes of the project include engineered wheat plants with more effective disease resistance, reducing fungicide usage. This project intends to accelerate crop breeding and contribute to world food security.Read moreRead less
FastStack - evolutionary computing to stack desirable alleles in wheat. This project aims to investigate rapid development of new, high-yielding wheat varieties with appropriate disease resistance. An emerging challenge in wheat breeding is how to stack desirable alleles for disease resistance, drought, and end-use quality into new varieties with high yielding backgrounds in the shortest time. As the number of known desirable alleles for these traits increases, the number of possible crossing c ....FastStack - evolutionary computing to stack desirable alleles in wheat. This project aims to investigate rapid development of new, high-yielding wheat varieties with appropriate disease resistance. An emerging challenge in wheat breeding is how to stack desirable alleles for disease resistance, drought, and end-use quality into new varieties with high yielding backgrounds in the shortest time. As the number of known desirable alleles for these traits increases, the number of possible crossing combinations that need to be considered increases. This project aims to use evolutionary computing with speed breeding and genomic selection, in the partners breeding program, to address this challenge. Potential outcomes will lead to more profitable wheat varieties for Australian growers, and expanded exports to high value markets that require quality grain.Read moreRead less
Digging deeper to improve yield stability. This project aims to provide innovative breeding solutions that harness the ‘hidden’ part of the plant, roots, to support the development of more productive crops in the face of climate variability. The project expects to generate new insights into the biology and genetics of root development in barley, a model cereal crop, by applying cutting-edge genome editing, phenotyping and genomics technologies. Anticipated outcomes include novel methodologies to ....Digging deeper to improve yield stability. This project aims to provide innovative breeding solutions that harness the ‘hidden’ part of the plant, roots, to support the development of more productive crops in the face of climate variability. The project expects to generate new insights into the biology and genetics of root development in barley, a model cereal crop, by applying cutting-edge genome editing, phenotyping and genomics technologies. Anticipated outcomes include novel methodologies to accelerate breeding for diverse production environments, with direct applications in barley, and other major cereals including wheat and oats. This should provide significant economic and social benefits to the Australian grains industry through yield stability amidst climate variability.Read moreRead less