Bridging the gap between crop pollination services and pollinator health. Insect pollinators play an integral role in the quantity and quality of production for many food crops, yet there is growing concern that in agricultural landscapes, the limited availability of floral and non-floral resources might be contributing to global pollinator health declines. This project will synthesize global datasets, develop new methodological tools and conduct new, targeted empirical work to develop an integ ....Bridging the gap between crop pollination services and pollinator health. Insect pollinators play an integral role in the quantity and quality of production for many food crops, yet there is growing concern that in agricultural landscapes, the limited availability of floral and non-floral resources might be contributing to global pollinator health declines. This project will synthesize global datasets, develop new methodological tools and conduct new, targeted empirical work to develop an integrated approach to pollinator resource management with the explicit objectives of maintaining both wild pollinator health and to support crop pollination service delivery in modified systems.Read moreRead less
Improved seasonal rainfall prediction for grain growers using farm level data and novel modelling. Successful grain production, a key export commodity for Australia, depends heavily on reliable seasonal forecasts. However, the highly variable climate means that for Australia’s 25,000 grain growers current forecasts lack detail in space and time. Using a combination of fuzzy classification and artificial neural networks, this project will develop a locally detailed continuously updating data-driv ....Improved seasonal rainfall prediction for grain growers using farm level data and novel modelling. Successful grain production, a key export commodity for Australia, depends heavily on reliable seasonal forecasts. However, the highly variable climate means that for Australia’s 25,000 grain growers current forecasts lack detail in space and time. Using a combination of fuzzy classification and artificial neural networks, this project will develop a locally detailed continuously updating data-driven seasonal forecast system using high density climate data from the 17,000 Grain Growers Association members and climate drivers such as sea surface temperature from the Bureau of Meteorology. After validation against observed data, the forecasts will be delivered via a web-based portal to users.Read moreRead less