7 resultados para aphid
em eResearch Archive - Queensland Department of Agriculture
Resumo:
Aphids can cause substantial damage to cereals, oilseeds and legumes through direct feeding and through the transmission of plant pathogenic viruses. Aphid-resistant varieties are only available for a limited number of crops. In Australia, growers often use prophylactic sprays to control aphids, but this strategy can lead to non-target effects and the development of insecticide resistance. Insecticide resistance is a problem in one aphid pest of Australian grains in Australia, the green peach aphid (Myzus persicae). Molecular analyses of field-collected samples demonstrate that amplified E4 esterase resistance to organophosphate insecticides is widespread in Australian grains across Australia. Knockdown resistance to pyrethroids is less abundant, but has an increased frequency in areas with known frequent use of these insecticides. Modified acetylcholinesterase resistance to dimethyl carbamates, such as pirimicarb, has not been found in Australia, nor has resistance to imidacloprid. Australian grain growers should consider control options that are less likely to promote insecticide resistance, and have reduced impacts on natural enemies. Research is ongoing in Australia and overseas to provide new strategies for aphid management in the future.
Resumo:
The aims of the project are to 1) identify closely linked molecular markers to resistance genes and validate them in Australian wheat and barley backgrounds, and 2) introgress RWA resistance into Australian wheat and barley backgrounds.
Resumo:
The aphid parasitoid Lysiphlebus testaceipes is a potentially valuable biological control agent of Aphis gossypii a major worldwide pest of cotton. One means of increasing the abundance of a biological control agent is to provide an alternative host habitat adjacent to cropping, from which they can provide pest control services in the crop. Host selection and parasitism rate of an alternative host aphid, Aphis craccivora by L. testaceipes were studied in a series of experiments that tested its host suitability relative to A. gossypii on cotton, hibiscus and mungbean. Host acceptance, as measured by number of ovipositions was much greater in A. craccivora compared to A. gossypii, while more host aphids were accepted on mungbean than cotton. When given a choice L. testaceipes attacks more 4th instar and adult stages (63% and 70%, respectively) of both hosts than 2nd instar nymphs (47%). In a switching (host choice) experiment, L. testaceipes preferentially attacked A. craccivora on mungbean over A. gossypii on cotton. Observations of parasitoid contact with A. gossypii cornicle secretion suggest it provides a useful deterrent against parasitoid attack. From these experiments it appears L. testaceipes has a preference for A. craccivora and mungbean compared to A. gossypii and cotton, in this respect using A. craccivora and mungbean as alternative habitat may not work as the parasitoid is unlikely to switch away from its preferred host. © 2012.
Resumo:
Abacá mosaic virus (AbaMV) is related to members of the sugarcane mosaic virus subgroup of the genus Potyvirus. The ~2 kb 3′ terminal region of the viral genome was sequenced and, in all areas analysed, found to be most similar to Sugarcane mosaic virus (SCMV) and distinct from Johnsongrass mosaic virus (JGMV), Maize dwarf mosaic virus (MDMV) and Sorghum mosaic virus (SrMV). Cladograms of the 3′ terminal region of the NIb protein, the coat protein core and the 3′ untranslated region showed that AbaMV clustered with SCMV, which was a distinct clade and separate from JGMV, MDMV and SrMV. The N-terminal region of the AbaMV coat protein had a unique amino acid repeat motif different from those previously published for other strains of SCMV. The first experimental transmission of AbaMV from abacá (Musa textilis) to banana (Musa sp.), using the aphid vectors Rhopalosiphum maidis and Aphis gossypii, is reported. Polyclonal antisera for the detection of AbaMV in western blot assays and ELISA were prepared from recombinant coat protein expressed in E. coli. A reverse transcriptase PCR diagnostic assay, with microtitre plate colourimetric detection, was developed to discriminate between AbaMV and Banana bract mosaic virus, another Musa-infecting potyvirus. Sequence data, host reactions and serological relationships indicate that AbaMV should be considered a distinct strain of SCMV, and the strain designation SCMV-Ab is suggested.
Resumo:
Cotton bunchy top (CBT) disease has caused significant yield losses in Australia and is now managed by control of its vector, the cotton aphid (Aphis gossypii). Its mode of transmission and similarities in symptoms to cotton Blue Disease suggested it may also be caused by a luteovirus or related virus. Degenerate primers to conserved regions of the genomes of the family Luteoviridae were used to amplify viral cDNAs from CBT-affected cotton leaf tissue that were not present in healthy plants. Partial genome sequence of a new virus (Cotton bunchy top virus, CBTV) was obtained spanning part of the RNA-dependent-RNA-polymerase (RdRP), all of the coat protein and part of the aphid-transmission protein. CBTV sequences could be detected in viruliferous aphids able to transmit CBT, but not aphids from non-symptomatic plants, indicating that it is associated with the disease and may be the causal agent. All CBTV open-reading frames had their closest similarity to viruses of the genus Polerovirus. The partial RdRP had 90 % amino acid identity to the RdRP of Cotton leafroll dwarf virus (CLRDV) that causes cotton blue disease, while other parts of the genome were more similar to other poleroviruses. The sequence similarity and genome organization of CBTV suggest that it should be considered a new member of the genus Polerovirus. This partial genome sequence of CBTV opens up the possibility for developing diagnostic tests for detection of the virus in cotton plants, aphids and weeds as well as alternative strategies for engineering CBT resistance in cotton plants through biotechnology. © 2012 Australasian Plant Pathology Society Inc.
Resumo:
Parasitoid survival and fecundity is generally enhanced with access to carbohydrate food sources. In many agricultural ecosystems, there is often a scarcity of suitable carbohydrates for parasitoids. This study compared the suitability of aphid honeydew and buckwheat nectar as diet for the aphid parasitoid Lysiphlebus testaceipes. Wasp lifespan and egg load were both increased with access to carbohydrates, but no differences were detected between the various carbohydrates diets tested. As aphid honeydew is a sufficient source of nutrition and L.testaceipes is a short-lived species, adding additional sources of carbohydrates like floral nectar strips to the agricultural landscape is unlikely to significantly increase the biological control exerted by L.testaceipes. © 2012 Australian Entomological Society.
Resumo:
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.