Rapid and efficient screening of a Representational Difference Analysis library using reverse Southern hybridisation: Identification of genetic differences between Haemophilus parasuis isolates

Representational Difference Analysis (RDA) is an established technique used for isolation of specific genetic differences between or within bacterial species. This method was used to investigate the genetic basis of serovar-specificity and the relationship between serovar and virulence in Haemophilus parasuis. An RDA clone library of 96 isolates was constructed using H. parasuis strains H425(P) (serovar 12) and HS1967 (serovar 4). To screen such a large clone library to determine which clones are strain-specific would typically involved separately labelling each clone for use in Southern hybridisation against genomic DNA from each of the strains. In this study, a novel application of reverse Southern hybridisation was used to screen the RDA library: genomic DNA from each strain was labelled and used to probe the library to identify strain-specific clones. This novel approach represents a significant improvement in methodology that is rapid and efficient.

Infection with Menangle virus in flying foxes (Pteropus spp.) in Australia

Objective: To examine flying foxes (Pteropus spp.) for evidence of infection with Menangle virus. Design: Clustered non-random sampling for serology, virus isolation and electron microscopy (EM). Procedure: Serum samples were collected from 306 Pteropus spp. in northern and eastern Australia and tested for antibodies against Menangle virus (MenV) using a virus neutralisation test (VNT). Virus isolation was attempted from tissues and faeces collected from 215 Pteropus spp. in New South Wales. Faecal samples from 68 individual Pteropus spp. and four pools of faeces were examined by transmission EM following routine negative staining and immunogold labelling. Results: Neutralising antibodies (VNT titres ≥ 8) against MenV were detected in 46% of black flying foxes (P. alecto), 41% of grey-headed flying foxes (P. poliocephalus), 25% of spectacled flying foxes (P. conspicillatus) and 1% of little red flying foxes (P. scapulatus) in Australia. Positive sera included samples collected from P. poliocephalus in a colony adjacent to a piggery that had experienced reproductive disease caused by MenV. Virus-like particles were observed by EM in faeces from Pteropus spp. and reactivity was detected in pooled faeces and urine by immunogold EM using sera from sows that had been exposed to MenV. Attempts to isolate the virus from the faeces and tissues from Pteropus spp. were unsuccessful. Conclusion: Serological evidence of infection with MenV was detected in Pteropus spp. in Australia. Although virus-like particles were detected in faeces, no viruses were isolated from faeces, urine or tissues of Pteropus spp.

Stakeholder expectations for environmental assurance in agriculture: Lessons from the pastoral industry

This paper outlines the expectations of a wide range of stakeholders for environmental assurance in the pastoral industries and agriculture generally. Stakeholders consulted were domestic consumers, rangeland graziers, members of environmental groups, companies within meat and wool supply chains, and agricultural industry, environmental and consumer groups. Most stakeholders were in favour of the application of environmental assurance to agriculture, although supply chains and consumers had less enthusiasm for this than environmental and consumer groups. General public good benefits were more important to environmental and consumer groups, while private benefits were more important to consumers and supply chains. The 'ideal' form of environmental assurance appears to be a management system that provides for continuous improvement in environmental, quality and food safety outcomes, combined with elements of ISO 14024 eco-labelling such as life-cycle assessment, environmental performance criteria, third-party certification, labelling and multi-stakeholder involvement. However, market failure prevents this from being implemented and will continue to do so for the foreseeable future. In the short term, members of supply chains (the people that must implement and fund environmental assurance) want this to be kept simple and low cost, to be built into their existing industry standards and to add value to their businesses. As a starting point, several agricultural industry organisations favour the use of a basic management system, combining continuous improvement, risk assessment and industry best management practice programs, which can be built on over time to meet regulator, market and community expectations.

Automating Crop Damage Assessments with Unmanned Aerial Systems and Machine Learning

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising methodology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of this approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labelling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means clustering. The results show the algorithm delivers consistent decision boundaries that classify the field into three clusters, one for each crop health level as shown in Figure 1. The methodology presented in this paper represents a venue for further esearch towards automated crop damage assessments and biosecurity surveillance.