Tracking gastrointestinal nematode risk on cattle farms through pasture contamination mapping.

ABSTRACT

Gastrointestinal nematode (GIN) parasites in grazing cattle are a major cause of production loss and their control is increasingly difficult due to anthelmintic resistance and climate change. Rotational grazing can support control and decrease reliance on chemical intervention, but is often complex due to the need to track grazing periods and infection levels, and the effect of weather on larval availability. In this paper, a simulation model was developed to predict the availability of infective larvae of the bovine GIN, Ostertagia ostertagi, at the level of individual pastures. The model was applied within a complex rotational grazing system and successfully reproduced observed variation in larval density between fields and over time. Four groups of cattle in their second grazing season (n = 44) were followed throughout the temperate grazing season with regular assessment of GIN faecal egg counts, which were dominated by O. ostertagi, animal weight and recording of field rotations. Each group of cattle was rotationally grazed on six group-specific fields throughout the 2019 grazing season. Maps and calendars were produced to illustrate the change in pasture infectivity (density of L3 on herbage) across the 24 separate grazing fields. Simulations predicted differences in pasture contamination levels in relation to the timing of grazing and the return period. A proportion of L3 was predicted to persist on herbage over winter, declining to similar intensities across fields before the start of the following grazing season, irrespective of contamination levels in the previous year. Model predictions showed good agreement with pasture larval counts. The model also simulated differences in seasonal pasture infectivity under rotational grazing in systems that differed in temperature and rainfall profiles. Further application could support individual farm decisions on evasive grazing and refugia management, and improved regional evaluation of optimal grazing strategies for parasite control. The integration of weather and livestock movement is inherent to the model, and facilitates consideration of climate change adaptation through improved disease control.