Familiarity with and uptake of alternative methods to control sheep gastro-intestinal parasites on farms in England

Abstract

A questionnaire was distributed electronically amongst sheep farmers in England; it aimed to provide a quantification of current anthelmintic practices, farmer awareness of the issue of anthelmintic resistance (AR) and the uptake, awareness and opinions surrounding conventional and alternative methods of nematode control. The majority of farmers relied on several anthelmintics and used faecal egg counts to identify worm problems. Although farmers were aware of the issue of AR amongst helminth parasites in the UK, there was a disconnection between such awareness and on farm problems and practice of nematode control. Grazing management was used by 52% of responders, while breeding for resistance and bioactive forages by 22 and 18% respectively. Farms with more than 500 ewes, and farmers who felt nematodes were a problem, had a higher probability of using selective breeding. Farmers who considered their wormer effective, had a qualification in agriculture and whose staff did not include any family members, were more likely to use bioactive forages; the opposite was the case if farmers dosed their lambs frequently. Amongst the alternatives, highest preference was for selective breeding and vaccination, if the latter was to become commercially available, with more respondents having a preference for breeding than actually using it. Several barriers to the uptake of an alternative were identified, the most influential factor being the cost to set it up and the length of time for which it would remain effective. The disconnection between awareness of AR and practice of nematode control on farm reinforces the need for emphasising the links between the causes of AR and the consequences of strategies to address its challenge.

Farmer Behavior and Gastrointestinal Nematodes in Ruminant Livestock—Uptake of Sustainable Control Approaches

“…Low awareness of both the risk of AR and concomitant information campaigns, and positive attitude toward their current use of anthelmintics were accordingly identified as barriers for the adoption of sustainable practices. However, later studies indicate a disconnection between the awareness of AR and on farm problems to nematode control ( 53 ). Treatment failure was not seen as a consequence of farmers' own behavior.…”

Moxidectin use in Scottish sheep flocks suggests a need for clearer product labelling and communication of updated SCOPS guidelines

“…44,46 Many UK sheep farmers develop their own worm control plans, without outside help, although they may gather advice from many external sources and choose what they deem relevant. 47,48 Melville et al 39 reported that just 12% of 149 UK farmers based lamb 'strongyle' GIN treatments on vet advice, and just 15% based ewe treatments on vet advice. Indeed, a survey of 325 UK farmers found that 24 purchased anthelmintics from their vet.…”

“…UK studies find that farmers value vet advice regarding appropriate worm control—one Scottish study found that greater than 73% of 400 farmers felt vet advice on worm control was reliable or cost effective, 43 but such advice is often sought elsewhere; including suitably qualified persons (SQPs), the farming press and other farmers 44,46 . Many UK sheep farmers develop their own worm control plans, without outside help, although they may gather advice from many external sources and choose what they deem relevant 47,48 . Melville et al 39 .…”

Determinants of Rulindo tea farmers’ perception towards organic farming: a MASS::stepAIC() approach

“…With 15 candidate variables in table1 for the multivariate logistic regression model, there are various selecting variable approaches like stepwise that combine both forward selection and backward elimination to be removed or added the predictor candidates for the model solely on statistical backgrounds without compromising the model prediction accuracy (MCCULLAGH, 2019;SHI et al, 2018;HOSMER & LEMESHOW, 2000). In this study, we used R's MASS::stepwiseAIC() which was developed by VENABLES & RIPLEY (2013) to automatically select a better subset of independent variables for our predictive modeling that had the lowest AIC at the same time decreasing the overfitting effect (GARCÍA et al, 2009;BARNES et al, 2008;ÇELIK & SENGER (2014), BUSKIRK & KOLENIKOV, 2015;ZHANG, 2016;MOORE et al, 2016;ABDULQADER, 2017). The final model was reached that had a higher significance level by excluding the 2 nd and 11 th observations, which did not go well with the model.…”