Using simulation techniques to investigate methods to determine resistance of helminths to anthelmintic treatment

The widespread and increasing resistance of internal parasites to anthelmintic control is a serious problem for the Australian sheep and wool industry. As part of control programmes, laboratories use the Faecal Egg Count Reduction Test (FECRT) to determine resistance to anthelmintics. It is important to have confidence in the measure of resistance, not only for the producer planning a drenching programme but also for companies investigating the efficacy of their products. The determination of resistance and corresponding confidence limits as given in anthelmintic efficacy guidelines of the Standing Committee on Agriculture (SCA) is based on a number of assumptions. This study evaluated the appropriateness of these assumptions for typical data and compared the effectiveness of the standard FECRT procedure with the effectiveness of alternative procedures. Several sets of historical experimental data from sheep and goats were analysed to determine that a negative binomial distribution was a more appropriate distribution to describe pre-treatment helminth egg counts in faeces than a normal distribution. Simulated egg counts for control animals were generated stochastically from negative binomial distributions and those for treated animals from negative binomial and binomial distributions. Three methods for determining resistance when percent reduction is based on arithmetic means were applied. The first was that advocated in the SCA guidelines, the second similar to the first but basing the variance estimates on negative binomial distributions, and the third using Wadley’s method with the distribution of the response variate assumed negative binomial and a logit link transformation. These were also compared with a fourth method recommended by the International Co-operation on Harmonisation of Technical Requirements for Registration of Veterinary Medicinal Products (VICH) programme, in which percent reduction is based on the geometric means. A wide selection of parameters was investigated and for each set 1000 simulations run. Percent reduction and confidence limits were then calculated for the methods, together with the number of times in each set of 1000 simulations the theoretical percent reduction fell within the estimated confidence limits and the number of times resistance would have been said to occur. These simulations provide the basis for setting conditions under which the methods could be recommended. The authors show that given the distribution of helminth egg counts found in Queensland flocks, the method based on arithmetic not geometric means should be used and suggest that resistance be redefined as occurring when the upper level of percent reduction is less than 95%. At least ten animals per group are required in most circumstances, though even 20 may be insufficient where effectiveness of the product is close to the cut off point for defining resistance.

Characterisation of humidity dependence of a metal oxide semiconductor (MOS) sensor array using Partial Least Squares (PLS)

Metal oxide semiconductor (MOS) sensors are a class of chemical sensor that have potential for being a practical core sensor module for an electronic nose system in various environmental monitoring applications. However, the responses of these sensors may be affected by changes in humidity and this must be taken into consideration when developing calibration models. This paper characterises the humidity dependence of a sensor array which consists of 12 MOS sensors. The results were used to develop calibration models using partial least squares. Effects of humidity on the response of the sensor array and predictive ability of partial least squares are discussed. It is shown that partial least squares can provide proper calibration models to compensate for effects caused by changes in humidity.

Characterisation of humidity dependence of a metal oxide semiconductor sensor array using partial least squares

Metal oxide semiconductor (MOS) sensors are a class of chemical sensors that have potential for being a practical core sensor module for an electronic nose system in various environmental monitoring applications. However, the responses of these sensors may be affected by changes in humidity and this must be taken into consideration when developing calibration models. This paper characterises the humidity dependence of a sensor array which consists of 12 MOS sensors. The results were used to develop calibration models using partial least squares (PLS). Effects of humidity on the response of the sensor array and predictive ability of partial least squares are discussed. It is shown that partial least squares can provide proper calibration models to compensate for effects caused by changes in humidity. Special Issue: Selected Paper from the 12th International Symposium on Olfaction and Electronic Noses - ISOEN 2007, International Symposium on Olfaction and Electronic Noses.

Detecting the attributes of a wheat crop using digital imagery acquired from a low-altitude platform

A low-altitude platform utilising a 1.8-m diameter tethered helium balloon was used to position a multispectral sensor, consisting of two digital cameras, above a fertiliser trial plot where wheat (Triticum spp.) was being grown. Located in Cecil Plains, Queensland, Australia, the plot was a long-term fertiliser trial being conducted by a fertiliser company to monitor the response of crops to various levels of nutrition. The different levels of nutrition were achieved by varying nitrogen application rates between 0 and 120 units of N at 40 unit increments. Each plot had received the same application rate for 10 years. Colour and near-infrared images were acquired that captured the whole 2 ha plot. These images were examined and relationships sought between the captured digital information and the crop parameters imaged at anthesis and the at-harvest quality and quantity parameters. The statistical analysis techniques used were correlation analysis, discriminant analysis and partial least squares regression. A high correlation was found between the image and yield (R2 = 0.91) and a moderate correlation between the image and grain protein content (R2 = 0.66). The utility of the system could be extended by choosing a more mobile platform. This would increase the potential for the system to be used to diagnose the causes of the variability and allow remediation, and/or to segregate the crop at harvest to meet certain quality parameters.

Identifying major contributing sources to odour annoyance using a non-specific gas sensor array

Identification of major contributors to odour annoyance in areas with multiple emission sources is necessary to address and resolve odour disputes. In an effort to develop an appropriate tool for this task, odour samples were collected on-site at a piggery and an abattoir (the major odour sources in the area) and at surrounding off-site areas, then analysed using a commercial non-specific chemical sensor array to develop an odour fingerprint database. The developed odour fingerprint database was analysed using two pattern recognition algorithms including a partial least squares-discriminant analysis (PLS-DA) and a Kohonen self-organising map (KSOM). The KSOM model could identify odour samples sourced from the piggery shed 15, piggery pond 8, piggery pond 9, abattoir, motel and others with mean percentage values of 77.5, 65.0, 90.2, 75.7, 44.8 and 64.6%, respectively.

Leaf-clearing and staining techniques for the observation of conidiophores in the Phyllactinioideae (Erysiphaceae)

Some whole leaf-clearing and staining techniques are described for the microscopic observation of the origin of powdery mildew conidiophores, whether from external mycelium or internal mycelium, emerging through stomata. These techniques enable separation of the two genera, Oidiopsis and Streptopodium, in the Erysiphaceae.

Non-specific conducting polymer-based array capable of monitoring odour emissions from a biofiltration system in a piggery building

A commercial non-specific gas sensor array system was evaluated in terms of its capability to monitor the odour abatement performance of a biofiltration system developed for treating emissions from a commercial piggery building. The biofiltration system was a modular system comprising an inlet ducting system, humidifier and closed-bed biofilter. It also included a gravimetric moisture monitoring and water application system for precise control of moisture content of an organic woodchip medium. Principal component analysis (PCA) of the sensor array measurements indicated that the biofilter outlet air was significantly different to both inlet air of the system and post-humidifier air. Data pre-processing techniques including normalising and outlier handling were applied to improve the odour discrimination performance of the non-specific gas sensor array. To develop an odour quantification model using the sensor array responses of the non-specific sensor array, PCA regression, artificial neural network (ANN) and partial least squares (PLS) modelling techniques were applied. The correlation coefficient (r(2)) values of the PCA, ANN, and PLS models were 0.44, 0.62 and 0.79, respectively.

Control techniques and management strategies for the problematic Navua sedge (Cyperus aromaticus)

Navua sedge, a member of the Cyperaceae family, is an aggressive weed of pastures in Fiji, Sri Lanka, Malay Peninsula, Vanuatu, Samoa, Solomons, and Tahiti and is now a weed of pastures and roadsides in north Queensland, Australia. Primarily restricted to areas with an annual rainfall exceeding 2500 mm, Navua sedge is capable of forming dense stands smothering many tropical pasture species. Seventeen herbicides were field tested at three sites in north Queensland, with glyphosate, halosulfuron, hexazinone, imazapic, imazapyr, or MSMA the most effective for Navua sedge control. Environmental problems such as persistence in soil, lack of selectivity and movement off-site may occur using some herbicides at the predicted LC90 control level rates. A seasonality trial using halosulfuron (97.5 g ai/ha) gave better Navua sedge control (84%) spraying March to September than spraying at other times (50%). In a frequency trial, sequential glyphosate applications (2,160 g ae/ha) every two months was more effective for continued Navua sedge control (67%) than a single application of glyphosate (36%), though loss of ground cover would occur. In a management trial, single applications of glyphosate (2,160 to 3,570 g ae/ha) using either a rope wick, ground foliar spraying or a rotary rope wick gave 59 to 73% control, while other treatments (rotary hoe (3%), slashing (-13%) or crushing (-30%)) were less effective. In a second management trial, four monthly rotary wick applications were much more effective (98%) than four monthly crushing applications (42%). An effective management plan must include the application of regular herbicide treatments to eliminate Navua sedge seed being added to the soil seed bank. Treatments that result in seed burial, for example, discing are likely to prolong seed persistence and should be avoided. The sprouting activity of vegetative propagules and root fragmentation needs to also be considered when selecting control options.

Regional development, commercialisation and target adoption of new integrated management techniques for Sheep industry

This Sheep CRC2 project is directed towards regional development of, support for, commercialisation and target adoption of, new integrated management techniques, which will lift productivity and better match product with market specification. The outputs and benefits will help support and improve the productivity and profitability of producers remaining in the industry, and support industry in the current move towards one in which both meat and wool will be joint drivers of industry productivity.

Determining Optimum Irrigation Scheduling Techniques for Key Wildflower Crops

Determining Optimum Irrigation Scheduling Techniques for Key Wildflower Crops.

Laboratory-rearing Techniques for Tephritid Fruit Flies in the South Pacific

Laboratory colonies of 15 economically important species of multi-host fruit flies (Diptera:Tephritidae) have been established in eight South Pacific island countries for the purpose of undertaking biological studies, particularly host status testing and research on quarantine treatments. Laboratory rearing techniques are based on the development of artificial diets for larvae consisting predominately of the pulp of locally available fruits including pawpaw, breadfruit and banana. The pawpaw diet is the standard diet and is used in seven countries for rearing 11 species. Diet ingredients are standard proportions of fruit pulp, hydrolysed protein and a bacterial and fungal inhibitor. The diet is particularly suitable for post-harvest treatment studies when larvae of known age are required. Another major development in the laboratory rearing system is the use of pure strains of Enterobacteriaceae bacterial cultures as important adult-feeding supplements. These bacterial cultures are dissected out of the crop of wild females, isolated by sub-culturing, and identified before supply to adults on peptone yeast extract agar plates. Most species are egged using thin, plastic receptacles perforated with 1 mm oviposition holes, with fruit juice or larval diet smeared internally as an oviposition stimulant. Laboratory rearing techniques have been standardised for all of the Pacific countries. Quality control monitoring is based on acceptable ranges in per cent egg hatch, pupal weight and pupal mortality. Colonies are rejuvenated every 6 to 12 months by crossing wild males with laboratory-reared females and vice versa. The standard rearing techniques, equipment and ingredients used in collecting, establishment, maintenance and quality control of these fruit fly species are detailed in this paper.

Perennial pastures for marginal farming country in southern Queensland. 1. Grass establishment techniques

Efficient ways to re-establish pastures are needed on land that requires a rotation between pastures and crops. We conducted trials in southern inland Queensland with a range of tropical perennial grasses sown into wheat stubble that was modified in various ways. Differing seedbed preparations involved cultivation or herbicide sprays, with or without fertilizer at sowing. Seed was broadcast and sowing time ranged from spring through to autumn on 3 different soil types. Seed quality and post-sowing rainfall were major determinants of the density of sown grass plants in the first year. Light cultivation sometimes enhanced establishment compared with herbicide spraying of standing stubble, most often on harder-setting soils. A nitrogen + phosphorus mixed fertilizer rarely produced any improvement in sown grass establishment and sometimes increased weed competition. The effects were similar for all types of grass seed from hairy fascicles to large, smooth panicoid seeds and minute Eragrostis seeds. There was a strong inverse relationship between the initial density of sown grass established and the level of weed competition.

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.