#agriculture

Strategic silage use for productivity and sustainability in Northern Territory beef systems

This project will develop economically and sustainability optimised silage feeding strategies for northern beef producers. The expected outcome is to combine feeding trials on commercial properties in the NT with economic and sustainability modelling to develop improved silage feeding strategies. This project will allow data-driven decision making by northern cattle producers to optimise feeding strategies for better economic and sustainability outcomes. 

Valuing non-crop vegetation in horticultural landscapes of Australia

This project will explore the extent to which areas on farms such as semi-natural vegetation, tree plantings, woodlands, and shelterbelts benefit producers through ecological mechanisms, such as enhancing pollinators or pest predators along with broader aesthetic and Indigenous cultural benefits. The outcome of this project will provide an improved understanding of the scale and relative importance of these benefits in the agricultural production landscape of northern New South Wales. This project will directly support horticulturalists in the region and guide future policy.

AI-empowered visual recognition system for dairy cow identification, health and behaviour monitoring

This project develops an AI-driven system to identify individual cows, and monitor and detect their health and behaviour, through the use of facial recognition, posture analysis, and thermal imaging. The expected outcome is early detection of illness and abnormal activity of cows. This project will support productivity, reduce losses, and improve animal welfare in the dairy industry.

Ecological drivers of disease spread in horticultural tree crops

This project investigates ecological interactions that influence disease spread in tree crop horticultural systems. The expected outcomes are improved understanding of ecological drivers of the dynamics of diseases and ecological intervention/restoration strategies for disease management. The potential benefit is chemically limited sustainable disease management in horticulture, benefiting industry and the environment.

Improving cropping decisions with AI-enhanced weather forecasts

This project will investigate the use of artificial intelligence (AI) to improve weather forecasts and discover how AI forecasts can advance farming decisions by coupling with crop models and smart farming tools. This is an exciting opportunity to develop or integrate novel AI-enhanced weather forecasts into real world modelling applications, for example in the sugarcane industry. With research being undertaken alongside real farm advisors, your research can help industry to optimise resource use and enhance overall farm productivity while minimising environmental impact.

Crop disease management

This project aims to integrate novel automated disease surveillance technology into real-time management of crop diseases for improved yield and sustainability outcomes. The expected outcome is grower recommendations for crop diseases informed by real-time automated disease surveillance. The potential benefit is improved productivity and sustainability through reduced crop losses and chemical inputs.