Responding to the threat of climate change: identifying effective strategies for the wheat industry of south-east Australia. This project will first evaluate the probable impacts of climatic change and variability on wheat production in southern Australia and will then assess the effectiveness of actual and potential adaptive management strategies designed to mitigate these impacts. The expected outcomes will include quantified impacts of future climate change and variability on wheat productio ....Responding to the threat of climate change: identifying effective strategies for the wheat industry of south-east Australia. This project will first evaluate the probable impacts of climatic change and variability on wheat production in southern Australia and will then assess the effectiveness of actual and potential adaptive management strategies designed to mitigate these impacts. The expected outcomes will include quantified impacts of future climate change and variability on wheat production in southern Australia, identification of regions at greater risk in the future and least likely to be viable in the longer run, and identification of effective adaptive management strategies designed to cope with these risks.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