Novel approaches to an automated decision support system for on-farm management of internal parasites of small ruminants

Abstract

The global problem of anthelmintic resistance (AR) in small ruminants has prompted a paradigm shift in the approach to anthelmintic-based control of parasites, in order to delay the onset and propagation of AR. This shift is from the conventional approach of dosing the whole flock on a regular basis, either routinely or in response to observed effects of worm infection, to a sustainable integrated parasite management (sIPM) approach. This approach uses targeted selective treatment (TST), which consists of treating only those individuals within a flock or herd that are in need of treatment, and / or targeted treatment (TT) strategies, whereby the flock or herd is treated in response to indicators of high risk of disease or production loss. However, at present, the paradigm shift risks is still largely a theoretical concept due to limited practical adoption by farmers, attributed mainly to implementation complexity and labour demands associated with application of TST and TT, in the face of conflicting advocacy for their implementation. A set of tools involving epidemiological modelling and monitoring of animal activity were explored in this study, to facilitate the efficient application of TST and TT strategies, by supporting farmer treatment decisions. The outcomes are expected to contribute positively towards a global effort to increase practical adoption and sustenance of TST and TT strategies amongst livestock farmers. A novel predictive epidemiological model framework was developed for haemonchosis in sheep. The model was parameterised and tested using meta-data covering one transmission season on a commercial Merino farm in South Africa that applied a FAMACHA©-based TST strategy for Haemonchus contortus control. The model incorporated farm management practices and H. contortus transmission dynamics. After model parameterization from the literature and expert opinion, and model fitting to observed data, the model was applied to analyse the application of FAMACHA©-based TST strategy. Sensitivity analysis was used to identify potential areas for optimisation. It was deduced from the model that sustained application of FAMACHA© based TST strategy led to a decrease in H. contortus cases over time. Thereafter, observed cases did not reflect short-term changes in infection pressure well enough to optimise the model for within-season decision support. Also, the model suggested that to avoid significant production losses, all the animals in a flock needed to be evaluated competently at relatively short intervals of approximately seven days. The predicted risk of infection with H. contortus at each of the serial FAMACHA© evaluation events was correlated with weather elements (rainfall, temperature and entropy) to determine which elements were the most significant drivers of infection at each evaluation interval on this particular farm. From this it was deduced that the risk of infection was significantly associated with total rainfall and average temperature from five weeks up to one week before each FAMACHA© evaluation event. The model showed adequate flexibility to capture the practical complexities of the FAMACHA©-based TST strategy as manifest on different farms. Its wider application could therefore yield insights into optimisation of the system for local conditions. The second part of the thesis developed the concept of animal activity monitoring as an indicator of the need for anthelmintic treatment, as part of TST and TT strategies. This focused on developing and optimising a practical system for field use, including data management and interpretation. A prototype relatively unsophisticated remote activity level monitoring system was evaluated to establish key performance measures when placed on sheep, compared with baseline performance under controlled conditions. The system was based on accelerometers, intended to be attached to sheep, and a means of data transmission, storage and analysis to enable tracking of activity levels over time from a remote location, without the need to capture or handle the animals. Data transmission rate (DTR) was measured as a function of (i) Common obstacles to transmission found on farms; (ii) Distance between the telemeter tags and tag reader; (iii) Movement of telemeter tags and (iv) Disposition and number of tags within the reader?s read range. This was done initially through hand simulation movements of telemeter tags. The activity monitoring system was then deployed on sheep on study farms with the set-up optimised, based on established performance measures. Subsequently the system was explored, using data from the tagged animals, for its potential to detect changes in activity associated with health status. More specifically, the system was evaluated for the potential of relayed activity scores to act as a risk indicator for clinical infection with H. contortus. This was done by characterising changes in animal activity between observed changes in behavioural and health states.