Development of a model based on Bayesian networks to estimate the probability of sheep lice presence at shearing

This paper describes the development of a model, based on Bayesian networks, to estimate the likelihood that sheep flocks are infested with lice at shearing and to assist farm managers or advisers to assess whether or not to apply a lousicide treatment. The risk of lice comes from three main sources: (i) lice may have been present at the previous shearing and not eradicated; (ii) lice may have been introduced with purchased sheep; and (iii) lice may have entered with strays. A Bayesian network is used to assess the probability of each of these events independently and combine them for an overall assessment. Rubbing is a common indicator of lice but there are other causes too. If rubbing has been observed, an additional Bayesian network is used to assess the probability that lice are the cause. The presence or absence of rubbing and its possible cause are combined with these networks to improve the overall risk assessment.

North Queensland Food Industry Development

Provision of food industry development advice, guidance, and NATA microbiological testing service to the North Queensland food industry in 2008/2009.

The Potential for a New Value Adding Industry for Noni Tropical Fruit Producers

Morinda citrifolia (noni) grows widely throughout the Pacific and is native to Australia. It is a source of traditional medicine amongst Coastal Aboriginal Communities in Cape York, the Pacific Islands and South East Asia, and in recent years has experienced significant economic growth worldwide through a variety of health and cosmetic claims. The largest markets for noni are North America, Europe, Japan, Mexico, Asia and Australia with the worldwide market for these products estimated at US$400 million.

Quantitative methods to measure pigmentation variation in farmed Giant Tiger Prawns, Penaeus monodon, and the effects of different harvest methods on cooked colour

Cooked prawn colour is known to be a driver of market price and a visual indicator of product quality for the consumer. Although there is a general understanding that colour variation exists in farmed prawns, there has been no attempt to quantify this variation or identify where this variation is most prevalent. The objectives of this study were threefold: firstly to compare three different quantitative methods to measure prawn colour or pigmentation, two different colorimeters and colour quantification from digital images. Secondly, to quantify the amount of pigmentation variation that exists in farmed prawns within ponds, across ponds and across farms. Lastly, to assess the effects of ice storage or freeze-thawing of raw product prior to cooking. Each method was able to detect quantitative differences in prawn colour, although conversion of image based quantification of prawn colour from RGB to Lab was unreliable. Considerable colour variation was observed between prawns from different ponds and different farms, and this variation potentially affects product value. Different post-harvest methods prior to cooking were also shown to have a profound detrimental effect on prawn colour. Both long periods of ice storage and freeze thawing of raw product were detrimental to prawn colour. However, ice storage immediately after cooking was shown to be beneficial to prawn colour. Results demonstrated that darker prawn colour was preserved by holding harvested prawns alive in chilled seawater, limiting the time between harvesting and cooking, and avoiding long periods of ice storage or freeze thawing of uncooked product.

The utility of near infrared spectroscopy for age estimation of deepwater sharks

Reliable age information is vital for effective fisheries management, yet age determinations are absent for many deepwater sharks as they cannot be aged using traditional methods of growth bands counts. An alternative approach to ageing using near infrared spectroscopy (NIRS) was investigated using dorsal fin spines, vertebrae and fin clips of three species of deepwater sharks. Ages were successfully estimated for the two dogfish, Squalus megalops and Squalus montalbani, and NIRS spectra were correlated with body size in the catshark, Asymbolus pallidus. Correlations between estimated-ages of the dogfish dorsal fin spines and their NIRS spectra were good, with S. megalops R2=0.82 and S. montalbani R2=0.73. NIRS spectra from S. megalops vertebrae and fin clips that have no visible growth bands were correlated with estimated-ages, with R2=0.89 and 0.76, respectively. NIRS has the capacity to non-lethally estimate ages from fin spines and fin clips, and thus could significantly reduce the numbers of sharks that need to be lethally sampled for ageing studies. The detection of ageing materials by NIRS in poorly calcified deepwater shark vertebrae could potentially enable ageing of this group of sharks that are vulnerable to exploitation.

A novel use of near infrared spectroscopy: ageing deepwater sharks

To age sharks, the growth bands in the shark vertebrae (like the rings in a tree) or on the spines in front of each dorsal fin (which only some sharks have) are manually counted using a microscope. This is time-consuming and is only possible on dead animals. NIR spectroscopy is shown to be able to detect age in dorsal fin spines of sharks and hand-held NIR spectroscopy units could potentially be used for ageing of sharks in the field, at sea, using a hand-held unit to scan the fin spine on a live animal.

Estimation and evaluation of multi-decadal fire severity patterns using Landsat sensors

Retrospective identification of fire severity can improve our understanding of fire behaviour and ecological responses. However, burnt area records for many ecosystems are non-existent or incomplete, and those that are documented rarely include fire severity data. Retrospective analysis using satellite remote sensing data captured over extended periods can provide better estimates of fire history. This study aimed to assess the relationship between the Landsat differenced normalised burn ratio (dNBR) and field measured geometrically structured composite burn index (GeoCBI) for retrospective analysis of fire severity over a 23 year period in sclerophyll woodland and heath ecosystems. Further, we assessed for reduced dNBR fire severity classification accuracies associated with vegetation regrowth at increasing time between ignition and image capture. This was achieved by assessing four Landsat images captured at increasing time since ignition of the most recent burnt area. We found significant linear GeoCBI–dNBR relationships (R2 = 0.81 and 0.71) for data collected across ecosystems and for Eucalyptus racemosa ecosystems, respectively. Non-significant and weak linear relationships were observed for heath and Melaleuca quinquenervia ecosystems, suggesting that GeoCBI–dNBR was not appropriate for fire severity classification in specific ecosystems. Therefore, retrospective fire severity was classified across ecosystems. Landsat images captured within ~ 30 days after fire events were minimally affected by post burn vegetation regrowth.