Pesticide residues in food - acute dietary exposure

Consumer risk assessment is a crucial step in the regulatory approval of pesticide use on food crops. Recently, an additional hurdle has been added to the formal consumer risk assessment process with the introduction of short-term intake or exposure assessment and a comparable short-term toxicity reference, the acute reference dose. Exposure to residues during one meal or over one day is important for short-term or acute intake. Exposure in the short term can be substantially higher than average because the consumption of a food on a single occasion can be very large compared with typical long-term or mean consumption and the food may have a much larger residue than average. Furthermore, the residue level in a single unit of a fruit or vegetable may be higher by a factor (defined as the variability factor, which we have shown to be typically ×3 for the 97.5th percentile unit) than the average residue in the lot. Available marketplace data and supervised residue trial data are examined in an investigation of the variability of residues in units of fruit and vegetables. A method is described for estimating the 97.5th percentile value from sets of unit residue data. Variability appears to be generally independent of the pesticide, the crop, crop unit size and the residue level. The deposition of pesticide on the individual unit during application is probably the most significant factor. The diets used in the calculations ideally come from individual and household surveys with enough consumers of each specific food to determine large portion sizes. The diets should distinguish the different forms of a food consumed, eg canned, frozen or fresh, because the residue levels associated with the different forms may be quite different. Dietary intakes may be calculated by a deterministic method or a probabilistic method. In the deterministic method the intake is estimated with the assumptions of large portion consumption of a ‘high residue’ food (high residue in the sense that the pesticide was used at the highest recommended label rate, the crop was harvested at the smallest interval after treatment and the residue in the edible portion was the highest found in any of the supervised trials in line with these use conditions). The deterministic calculation also includes a variability factor for those foods consumed as units (eg apples, carrots) to allow for the elevated residue in some single units which may not be seen in composited samples. In the probabilistic method the distribution of dietary consumption and the distribution of possible residues are combined in repeated probabilistic calculations to yield a distribution of possible residue intakes. Additional information such as percentage commodity treated and combination of residues from multiple commodities may be incorporated into probabilistic calculations. The IUPAC Advisory Committee on Crop Protection Chemistry has made 11 recommendations relating to acute dietary exposure.

Global trade in ornamental fish from an Australian perspective: The case for revised import risk analysis and management strategies

Over 1 billion ornamental fish comprising more than 4000 freshwater and 1400 marine species are traded internationally each year, with 8-10 million imported into Australia alone. Compared to other commodities, the pathogens and disease translocation risks associated with this pattern of trade have been poorly documented. The aim of this study was to conduct an appraisal of the effectiveness of risk analysis and quarantine controls as they are applied according to the Sanitary and Phytosanitary (SPS) agreement in Australia. Ornamental fish originate from about 100 countries and hazards are mostly unknown; since 2000 there have been 16-fold fewer scientific publications on ornamental fish disease compared to farmed fish disease, and 470 fewer compared to disease in terrestrial species (cattle). The import quarantine policies of a range of countries were reviewed and classified as stringent or non-stringent based on the levels of pre-border and border controls. Australia has a stringent policy which includes pre-border health certification and a mandatory quarantine period at border of 1-3 weeks in registered quarantine premises supervised by government quarantine staff. Despite these measures there have been many disease incursions as well as establishment of significant exotic viral, bacterial, fungal, protozoal and metazoan pathogens from ornamental fish in farmed native Australian fish and free-living introduced species. Recent examples include Megalocytivirus and Aeromonas salmonicida atypical strain. In 2006, there were 22 species of alien ornamental fish with established breeding populations in waterways in Australia and freshwater plants and molluscs have also been introduced, proving a direct transmission pathway for establishment of pathogens in native fish species. Australia's stringent quarantine policies for imported ornamental fish are based on import risk analysis under the SPS agreement but have not provided an acceptable level of protection (ALOP) consistent with government objectives to prevent introduction of pests and diseases, promote development of future aquaculture industries or maintain biodiversity. It is concluded that the risk analysis process described by the Office International des Epizooties under the SPS agreement cannot be used in a meaningful way for current patterns of ornamental fish trade. Transboundary disease incursions will continue and exotic pathogens will become established in new regions as a result of the ornamental fish trade, and this will be an international phenomenon. Ornamental fish represent a special case in live animal trade where OIE guidelines for risk analysis need to be revised. Alternatively, for countries such as Australia with implied very high ALOP, the number of species traded and the number of sources permitted need to be dramatically reduced to facilitate hazard identification, risk assessment and import quarantine controls. Lead papers of the eleventh symposium of the International Society for Veterinary Epidemiology and Economics (ISVEE), Cairns, Australia

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.