Precision Pollination: Data-driven enhancements to boost crop yield. The project aims to transform industrial crop pollination from an intuitive domain to one where decisions are based on sound data and best-practice principles. It proposes to achieve this modernisation of global pollination practice by developing novel technologies to operate a three-stage loop: honeybee pollination monitoring, simulation-based forecasting, and management. This is intended to ensure that the capability of honey ....Precision Pollination: Data-driven enhancements to boost crop yield. The project aims to transform industrial crop pollination from an intuitive domain to one where decisions are based on sound data and best-practice principles. It proposes to achieve this modernisation of global pollination practice by developing novel technologies to operate a three-stage loop: honeybee pollination monitoring, simulation-based forecasting, and management. This is intended to ensure that the capability of honeybees to provide essential ecosystem services is informed by transferable, standardised data acquisition and management techniques that maintain bee health and maximise pollination. The anticipated outcomes are higher fruit yields and quality, and a beneficial step-change in industry productivity and profitability.Read moreRead less
A World Without Bees: simulating important agricultural insect pollinators. The project plans to develop a software model to assess the viability of crops under changes in pollinator populations, and recommend which floral traits should be breeding targets to ensure sustainable crops. Insects are essential to agriculture, but their populations are changing in poorly understood ways that are likely to affect human food supplies. This project plans to construct evolutionary agent-based models of c ....A World Without Bees: simulating important agricultural insect pollinators. The project plans to develop a software model to assess the viability of crops under changes in pollinator populations, and recommend which floral traits should be breeding targets to ensure sustainable crops. Insects are essential to agriculture, but their populations are changing in poorly understood ways that are likely to affect human food supplies. This project plans to construct evolutionary agent-based models of change in crop-pollinating insects: honeybees, bumblebees, stingless bees and flies. It then plans to model how these population changes affect production, predicting floral traits to breed into crop plants for ongoing pollination success. Another expected outcome is a flexible plant–pollinator simulation of insect-specific visual perception, foraging behaviour, physiological factors and inter-species interactions.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE130100660
Funder
Australian Research Council
Funding Amount
$358,731.00
Summary
Simulating social networks to understand how neighbourhood factors influence health. Where you live and who you know has implications for your health. This study will use social network models to understand how social characteristics of neighbourhoods influence health. The new insights gained will help policy makers to develop better strategies for reducing health inequalities and improving health outcomes.
Pollination in a new climate: evolutionary simulation of bee and flower interactions for predicting impacts of climate change on pollination. This project uses computer simulation to understand the potential impact of temperature variation associated with climate change on insect pollinator behaviour. The result will be a model of bee and flower interactions under future Australian conditions to be used for agricultural and environmental resource management and planning.