DAQ00116 Central Queensland Sustainable Farming Systems Phase 3

Improved economic and social performance of grain and mixed farming businesses in Central Queensland.

Control by glyphosate and its alternatives of glyphosate-susceptible and glyphosate-resistant Echinochloa colona in the fallow phase of crop rotations in subtropical Australia

Echinochloa colona is the most common grass weed of summer fallows in the grain-cropping systems of the subtropical region of Australia. Glyphosate is the most commonly used herbicide for summer grass control in fallows in this region. The world's first population of glyphosate-resistant E. colona was confirmed in Australia in 2007 and, since then, >70 populations have been confirmed to be resistant in the subtropical region. The efficacy of alternative herbicides on glyphosate-susceptible populations was evaluated in three field experiments and on both glyphosate-susceptible and glyphosate-resistant populations in two pot experiments. The treatments were knockdown and pre-emergence herbicides that were applied as a single application (alone or in a mixture) or as part of a sequential application to weeds at different growth stages. Glyphosate at 720 g ai ha−1 provided good control of small glyphosate-susceptible plants (pre- to early tillering), but was not always effective on larger susceptible plants. Paraquat was effective and the most reliable when applied at 500 g ai ha−1 on small plants, irrespective of the glyphosate resistance status. The sequential application of glyphosate followed by paraquat provided 96–100% control across all experiments, irrespective of the growth stage, and the addition of metolachlor and metolachlor + atrazine to glyphosate or paraquat significantly reduced subsequent emergence. Herbicide treatments have been identified that provide excellent control of small E. colona plants, irrespective of their glyphosate resistance status. These tactics of knockdown herbicides, sequential applications and pre-emergence herbicides should be incorporated into an integrated weed management strategy in order to greatly improve E. colona control, reduce seed production by the sprayed survivors and to minimize the risk of the further development of glyphosate resistance.

Controlling Pythium in Ginger: Phase 2

The fungal disease Pythium Soft Rot is regarded by the ginger industry as their most serious disease threat. This project was developed to further investigate the factors contributing to the persistence and spread of Pythium Soft Rot on ginger farms and to identify measures for their control. The study demonstrated that the pathogen capable of causing Pythium Soft Rot in ginger was spread in contaminated ‘seed’, soil and water and that it can be managed through a combination of strategies that rely on early detection, reducing pathogen levels in soils, preventing water logging and restricting movement of contaminated ‘seed’, soil and water. However, in order to have an effective level of control, all strategies need to be integrated in an effective manner.

Association between feline immunodeficiency virus (FIV) plasma viral RNA load, concentration of acute phase proteins and disease severity

Veterinarians have few tools to predict the rate of disease progression in FIV-infected cats. In contrast, in HIV infection, plasma viral RNA load and acute phase protein concentrations are commonly used as predictors of disease progression. This study evaluated these predictors in cats naturally infected with FIV. In older cats (>5 years), log10 FIV RNA load was higher in the terminal stages of disease compared to the asymptomatic stage. There was a significant association between log10 FIV RNA load and both log10 serum amyloid A concentration and age in unwell FIV-infected cats. This study suggests that viral RNA load and serum amyloid A warrant further investigation as predictors of disease status and prognosis in FIV-infected cats.

High-Throughput Prediction of Acacia and Eucalypt Lignin Syringyl/Guaiacyl Content Using FT-Raman Spectroscopy and Partial Least Squares Modeling

High-throughput techniques are necessary to efficiently screen potential lignocellulosic feedstocks for the production of renewable fuels, chemicals, and bio-based materials, thereby reducing experimental time and expense while supplanting tedious, destructive methods. The ratio of lignin syringyl (S) to guaiacyl (G) monomers has been routinely quantified as a way to probe biomass recalcitrance. Mid-infrared and Raman spectroscopy have been demonstrated to produce robust partial least squares models for the prediction of lignin S/G ratios in a diverse group of Acacia and eucalypt trees. The most accurate Raman model has now been used to predict the S/G ratio from 269 unknown Acacia and eucalypt feedstocks. This study demonstrates the application of a partial least squares model composed of Raman spectral data and lignin S/G ratios measured using pyrolysis/molecular beam mass spectrometry (pyMBMS) for the prediction of S/G ratios in an unknown data set. The predicted S/G ratios calculated by the model were averaged according to plant species, and the means were not found to differ from the pyMBMS ratios when evaluating the mean values of each method within the 95 % confidence interval. Pairwise comparisons within each data set were employed to assess statistical differences between each biomass species. While some pairwise appraisals failed to differentiate between species, Acacias, in both data sets, clearly display significant differences in their S/G composition which distinguish them from eucalypts. This research shows the power of using Raman spectroscopy to supplant tedious, destructive methods for the evaluation of the lignin S/G ratio of diverse plant biomass materials.