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Field of Research : Agricultural Engineering
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  • Funded Activity

    Linkage Projects - Grant ID: LP0348025

    Funder
    Australian Research Council
    Funding Amount
    $180,000.00
    Summary
    Forecasting locust outbreaks: evaluation of an Insect Monitoring Radar network. A recently developed technology that allows high-altitude migrations of insects to be detected automatically and at modest cost will be adapted to the specific task of forecasting Australian plague locusts. A network of two 'Insect Monitoring Radars' (IMRs) will be operated in a locust outbreak area, and observation summaries, customised to identify major locust migration events and to estimate their source and desti .... Forecasting locust outbreaks: evaluation of an Insect Monitoring Radar network. A recently developed technology that allows high-altitude migrations of insects to be detected automatically and at modest cost will be adapted to the specific task of forecasting Australian plague locusts. A network of two 'Insect Monitoring Radars' (IMRs) will be operated in a locust outbreak area, and observation summaries, customised to identify major locust migration events and to estimate their source and destination regions, will be presented daily to forecasters at the Australian Plague Locust Commission (APLC). The potential contribution of IMRs to APLC's strategic goal of reducing chemical insecticide usage while maintaining protection of valuable crops will be evaluated.
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    Funded Activity

    Linkage Projects - Grant ID: LP0667730

    Funder
    Australian Research Council
    Funding Amount
    $412,148.00
    Summary
    Sensor Integration for Low-Cost Robust Machine Automation. Machine automation (MA) radically improves efficiency of mining and construction operataions. When used for farming, it makes Australia more competitive with subsidised competitors in Europe and USA. In one case, a 50% reduction in tractor fleet resulted when night plowing was made possible using MA techniques. The systems developed in this project will make MA far more attractive to Australian agriculture, mining and construction indust .... Sensor Integration for Low-Cost Robust Machine Automation. Machine automation (MA) radically improves efficiency of mining and construction operataions. When used for farming, it makes Australia more competitive with subsidised competitors in Europe and USA. In one case, a 50% reduction in tractor fleet resulted when night plowing was made possible using MA techniques. The systems developed in this project will make MA far more attractive to Australian agriculture, mining and construction industries, by making it more robust and less susceptible to difficult conditions, such as under trees.
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    Funded Activity

    Linkage Projects - Grant ID: LP0883296

    Funder
    Australian Research Council
    Funding Amount
    $262,000.00
    Summary
    Integrated assessment of climate change, climate input errors and land-use change on soil-moisture and carbon-balance in a catchment simulation framework. Assessing soil moisture and carbon balance changes in a warmer climate is important for land-use and agricultural planning. A decision support tool is proposed that performs the assessment and allows us to develop plans that reduce adverse impacts. The tool consists of three parts. The first part models changes in rainfall accuracy to calibrat .... Integrated assessment of climate change, climate input errors and land-use change on soil-moisture and carbon-balance in a catchment simulation framework. Assessing soil moisture and carbon balance changes in a warmer climate is important for land-use and agricultural planning. A decision support tool is proposed that performs the assessment and allows us to develop plans that reduce adverse impacts. The tool consists of three parts. The first part models changes in rainfall accuracy to calibrate approaches for catchment simulation. The second part simulates rainfall under climate change conditions using stochastic downscaling. The third part simulates future soil moisture and carbon balance using downscaled climate inputs. The end result is a probabilistic simulation of the catchment hydrology under future climates.
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