Blueprint for a software-based decision-support system for countering anthelmintic resistance at farm level

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The global demand for high quality food and fibre has been a major driving force for research and development of sustainable animal husbandry and management practices (Bath, Hansen, Krecek, Van Wyk & Vatta 2001; Kaplan, Burke, Terrill, Miller, Getz, Mobini, Valencia, Williams, Williamson, Larsen & Vatta 2004). Gastrointestinal (GIT) parasitism is an important disease of livestock, leading to production losses (Bishop & Stear 2003). Several GIT nematodes of the family Trichostrongylidae parasitise sheep (Fontenot, Miller, Peña, Larsen & Gillespie 2003) and of these, Haemonchus contortus (Rudolphi 1803) is the predominant and economically the most important nematode parasite of sheep and goats in tropical and subtropical regions of the world (Besier & Dunsmore 1993a; Achi, Zinsstag, Yao, Dorchies & Jacquiet 2003; Fontenot et al. 2003; Terrill, Larsen, Samples, Husted, Miller, Kaplan & Gelaye 2004). This parasite has been responsible for extensive production losses in sheep and McLeod (1995) estimated that in Australia, costs of treatment and loss of production due to nematode infections amounted to approximately AUS $222 million annually. In the southern United States, findings from a recent 7-year review of clinical cases at Auburn University indicated that H. contortus infection was the primary reason for examination of 70 % of sheep and 91 % of goats treated by hospital clinicians, and that abomasal or intestinal worm infection was the predominant disease condition on 74 % of sheep farms (Kaplan et al. 2004). In West Africa, Haemonchus is the dominant genus in small ruminants, and causes major economic losses in the Gambia, Mauritania, Nigeria, and Ivory Coast (Achi et al. 2003). In South Africa, with approximately 29 million sheep and 6 million goats (Vatta 2001), the effect of morbidity and resultant losses in production in small ruminants due to haemonchosis is considerable (Vatta 2001; Vatta, Letty, Van der Linde, Van Wijk, Hansen & Krecek 2001; Van Wyk & Bath 2002).

Ecology and pathology of Haemonchus contortus

Haemonchus contortus is commonly known as the “stomach worm” or “wireworm” of ruminants, and “barber’s pole worm” in Australia (Donald, Southcott & Dineen 1978). It is one of the most pathogenic parasites of small ruminants, because of its blood sucking habit (Veglia 1918; Soulsby 1982), and it is particularly pathogenic in young hosts (Whitfield 1994). Male worms are 10–20mm in length and more or less uniformly light brown in colour, while females are from 18–30mm long with whitish ovaries and uteri that are spirally wound around a red intestine, giving the appearance of a barber’s pole (Soulsby 1982). Sexually mature female worms in the abomasum produce large numbers of eggs that are voided with the faeces, followed by egg-hatch and development into free-living larval stages on pastures if suitable climatic conditions prevail. The rate and success of free-living larval development and survival is largely determined by climatic variables such as temperature and moisture (Donald et al. 1978). The ecology of free-living stages of the major trichostrongylid parasites has recently been reviewed by O’Connor et al. (2006). Under optimal environmental conditions, larvae on pasture develop to the infective third stage larvae (L3) in four to six days, while low temperatures below 9ºC result in little or no development (Soulsby 1982). The pre-patent period of H. contortus is about two weeks after ingested L3 have moulted into fourth-stage larvae (Dunn 1969; Hansen & Perry 1994). Within six hours of entering the host, the L3 enter the mucous membrane or glands in the wall of the abomasum, where they moult into fourth stage larvae (L4) within about four days.

Biological control

The development of anthelmintic resistance in parasite populations worldwide has brought about concerted interest in the development of biological agents that have the potential to control GIT nematodes of livestock. One of the main thrusts of research towards biological control has been directed at nematode-destroying microfungi such as Duddingtonia flagrans (Panchadcharam 2004). It is a nematode-trapping fungus that produces thick-walled chlamydospores that destroy larval nematodes in faecal matter by trapping them in a sticky hyphal network (Fontenot et al. 2003), as has been demonstrated in vitro (Faedo 2001; Peña, Miller, Fontenot, Gillespie & Larsen 2002). Spores of this fungus are able to survive passage through the ruminant gastrointestinal tract, and germinate in faecal material deposited on pastures (Faedo 2001; Panchadcharam 2004).

Scope of the study

Several issues of importance in the application of the FAMACHA© system of targeted selective treatment were investigated in this study. Although much of the work was concerned with the application of supplemental epidemiological techniques to the FAMACHA© system, further validation of the system in South Africa was also undertaken, as well as the application of a stochastic model to the data gathered during five years of FAMACHA© trials. The main aim of this work was to evaluate the applicability of these supplemental techniques, specifically Receiver Operating Characteristic curve analysis, stochastic estimation of worm burdens, and temporal availability of rainfall, which could be used in a computerised predictive system to treat flocks on a selective basis. It is envisaged that such a “black box” predictive system would eventually be specific enough to enable producers to make decisions based on inputs into the “black box” model, which would then allow the producer to decide when the flock should be evaluated, which class of animal is most at risk, which FAMACHA© categories of animal should be drenched, how many animals should be sampled to evaluate the anaemia status of the flock, etc.


Refugia for sustainable worm management

Parasites which escape any given control measure, for example worms on pasture when their hosts are drenched, are said to be in refugia (Martin, Le Jambre & Claxton 1981; Martin 1989; Van Wyk 2001). One of the most effective ways of obtaining relatively large numbers of worms in refugia is to selectively treat only clinically affected animals, i.e. a system of targeted selective treatment. This may seem to defeat the purpose of anthelmintic treatment for worm control. However, worm burdens are markedly overdispersed within a given flock or herd (Barger 1985), to the extent that in most outbreaks of helminthosis only a minority of animals are unable to withstand the effect of worm infection without anthelmintic treatment. Previously, this phenomenon could not be utilised in practice, since individuals with high Haemonchus burdens could only be identified for treatment with the aid of laboratory faecal worm egg count testing which is not feasible, since every animal needs to be tested at relatively short intervals at the height of the worm season. For instance, with severe H. contortus challenge of sheep, Malan et al. (2001) recorded a drop of up to seven percentage points in haematocrit within seven days. In other words, a sheep which was apparently still coping well with mild anaemia could develop terminal anaemia within less than a fortnight.


  • CHAPTER 1 General introduction
    • 1 Preamble
    • 1.1 Haemonchus contortus: the parasite and its epidemiology
    • 1.1.1 Ecology and pathology of Haemonchus contortus
    • 1.2 Control of haemonchosis
    • 1.2.1 Chemical control
    • 1.2.2 Biological control
    • 1.2.3 Resistance management
    • Acquired immunity
    • Nutritional supplementation
    • Grazing management
    • Parasite community replacement
    • Selective breeding to withstand parasite challenge
    • 1.2.4 The FAMACHA©
    • system of selective treatment
    • 1.3. Scope of the study
  • CHAPTER 2 Blueprint for a software-based decision-support system for countering anthelmintic resistance at farm level
    • 2.1 Introduction
    • 2.2 Refugia for sustainable worm management
    • 2.3 Optimal application of targeted selected treatment is complex
    • 2.3.1 Worm infection not “all or nothing”; worm burdens are important
    • 2.3.2 Extension services progressively depleted
    • 2.4 Can software-based decision-support offer a solution?
    • 2.5 Factors to consider in an automated decision-support system
    • 2.5.1 Farm location
    • 2.5.2 Rainfall and other sources of moisture
    • 2.5.3 Temperature
    • 2.5.4 Animal hosts
    • Host species and breed
    • Host reproductive status
    • Host age
    • History of a given flock
    • Stud or commercial flock
    • 2.5.5 Pastures
    • Number of paddocks
    • Pasture type – current paddock and planned movement to the next paddock
    • Pasture herbage species
    • Pasture grazing history
    • 2.5.6 Worm species and anthelmintic resistance
    • 2.5.7 Diagnostic methods
    • 2.5.8 Anthelmintics to use or avoid
    • 2.5.9 Treatment in relation to movement of animals to other pastures
    • 2.6 Modelling approach
    • 2.6.1 Retrospective analysis of clinical evaluation data
    • 2.6.2 Model framework
    • 2.7 Effective technology transfer
    • 2.7.1 Technology transfer previously ineffective
    • 2.7.2 New approach required
    • 2.8 Discussion
    • 2.9 Conclusion
  • CHAPTER 3 Validation of the FAMACHA© eye colour chart on two South African sheep farms under commercial farming conditions
    • 3.1 Introduction
    • 3.2 Materials and methods
    • 3.2.1 Origin of data and FAMACHA©
    • test procedures
    • 3.2.2 Statistical analysis
    • 3.3 Results
    • 3.4 Discussion
    • 3.5 Conclusion
  • CHAPTER 4 Use of receiver operating curves for selection of treatment thresholds using the FAMACHA© diagnostic system for anaemia in sheep naturally infected with Haemonchus contortus
    • 4.1 Introduction
    • 4.2 Materials and methods
    • 4.2.1 Origin of data and FAMACHA©
    • test procedures
    • 4.2.2 Receiver operating characteristic analysis
    • 4.3 Results
    • 4.4 Discussion
    • 4.5 Conclusion
  • CHAPTER 5 A stochastic model to estimate worm burdens and associated risk factors in sheep naturally infected with Haemonchus contortus
    • 5.1 Introduction
    • 5.2 Materials and methods
    • 5.2.1 Origin of data and the model system
    • 5.2.2 Statistical analysis
    • 5.3 Results
    • 5.4 Discussion
    • 5.4.1 EWEREP class
    • 5.4.2 RAMREP class
    • 5.5 Conclusion
  • CHAPTER 6 Use of Shannon’s entropy to process rainfall data as a risk factor in sheep naturally infected with Haemonchus contortus
    • 6.1 Introduction
    • 6.1.1 Calculation of rainfall entropy
    • 6.1.2 Probabilistic interpretation of rainfall
    • 6.2 Materials and methods
    • 6.2.1 Rainfall data
    • 6.2.2 Sheep haemoglobin data
    • 6.3 Results
    • 6.4 Discussion
    • 6.5 Conclusion
  • CHAPTER 7 General results and conclusion

Software-based decision-support: a basis for the development of a predictive system for sustainable management of haemonchosis in small ruminants

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