3 resultados para Lepidopteran pest

em eResearch Archive - Queensland Department of Agriculture


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Sulfuryl fluoride (SF), an effective structural fumigant, is registered recently as Profume™ for controlling insect pests of stored grains and processed commodities. Information on its effectiveness in disinfestation of bulk grain, however, is limited. The ongoing problem with the strong level of resistance to phosphine has been addressed recently through deployment of SF as a ‘resistance breaker’ in bulk storages in Australia. This paper discusses important results on the efficacy of SF against key phosphine- resistant insect pests, lesser grain borer, Rhyzopertha dominca, red flour beetle, Tribolium castaneum, rice weevil, Sitophilus oryzae and the rusty grain beetle, Cryptolestes ferrugineus. We have established CT (g-hm3) profiles for SF against these insect pests at two temperature regimes 25 and 30°C, that showed that both temperature and exposure period (t) has significant influence on the effectiveness of SF than the concentration. Over a seven days fumigation period, CTs of 800 and 400 g-hm3 achieved complete control of all the target pests, including the most strongly phosphine - resistant species, C. ferrugineus at 25 and 30°C, respectively. Results from four industry scale field trials involving currently registered rate of SF (1500 g-hm3) over 2–14 d exposure period, confirmed its effectiveness in achieving complete control of the target pest species. The assessment of postfumigation grain samples across all the test storages indicated that the reinfestation occurs after three months. Monitoring resistance to phosphine in C. ferrugineus over a six year period (2009–2015), showed a significant reduction in resistant populations after the introduction of SF into the fumigation strategy at problematic storage sites. Overall our research concludes that SF is a good candidate to be used as a ‘resistance breaker’ where phosphine resistance is prevalent.

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Efficient crop monitoring and pest damage assessments are key to protecting the Australian agricultural industry and ensuring its leading position internationally. An important element in pest detection is gathering reliable crop data frequently and integrating analysis tools for decision making. Unmanned aerial systems are emerging as a cost-effective solution to a number of precision agriculture challenges. An important advantage of this technology is it provides a non-invasive aerial sensor platform to accurately monitor broad acre crops. In this presentation, we will give an overview on how unmanned aerial systems and machine learning can be combined to address crop protection challenges. A recent 2015 study on insect damage in sorghum will illustrate the effectiveness of this methodology. A UAV platform equipped with a high-resolution camera was deployed to autonomously perform a flight pattern over the target area. We describe the image processing pipeline implemented to create a georeferenced orthoimage and visualize the spatial distribution of the damage. An image analysis tool has been developed to minimize human input requirements. The computer program is based on a machine learning algorithm that automatically creates a meaningful partition of the image into clusters. Results show the algorithm delivers decision boundaries that accurately classify the field into crop health levels. The methodology presented in this paper represents a venue for further research towards automated crop protection assessments in the cotton industry, with applications in detecting, quantifying and monitoring the presence of mealybugs, mites and aphid pests.

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Effective pest management relies on accurate delimitation of species and, beyond this, on accurate species identification. Mitochondrial COI sequences are useful for providing initial indications in delimiting species but, despite acknowledged limitations in the method, many studies involving COI sequences and species problems remain unresolved. Here we illustrate how such impasses can be resolved with microsatellite and nuclear sequence data, to assess more directly the amount of gene flow between divergent lineages. We use a population genetics approach to test for random mating between two 8 ± 2% divergent COI lineages of the rusty grain beetle, Cryptolestes ferrugineus (Stephens). This species has become strongly resistant to phosphine, a fumigant used worldwide for disinfesting grain. The possibility of cryptic species would have significant consequences for resistance management, especially if resistance was confined to one mitochondrial lineage. We find no evidence of restricted gene flow or nonrandom mating across the two COI lineages of these beetles, rather we hypothesize that historic population structure associated with early Pleistocene climate changes likely contributed to divergent lineages within this species.