About FARM-CARE

FARM-CARE is a project funded by the Joint Programming Initiative on Antimicrobial Resistance (JPIAMR). The project focuses on controlling spread of AMR in pig farms because pigs are the main contributors to antimicrobial use in animals and a recognized source of AMR to farm workers, the community, and the environment. The project builds upon the established notion that stress is a major driver for pig disease, which in turn contributes to antimicrobial use.

Our hypothesis is that AMR spread can be controlled by limiting the common practice of mixing, which is a recognized cause of stress and disease in pigs, and by applying biosecurity measures to prevent AMR transmission to farm workers and the community. We aim to understand the impact of these two interventions (WP1-2) on AMU and AMR reduction, and to develop two complementary interventions based on machine learning (WP3), assessing cost-effectiveness of each intervention from the perspective of the farmers, the environment, and the wider society. 

Objectives

FARM-CARE pursues 4 objectives with the overall goal to control AMR and reduce antimicrobial use in pig farming:

  1. Understand the impact of pig mixing control on AMR in pigs and its spread to agricultural fields via slurry
  2. Understand the impact of biosecurity measures on AMR spread to farm workers and their family households and propose effective biosecurity protocols for this purpose
  3. Develop innovative machine learning tools to identify predictors of stress and disease in new-born piglets
  4. Assess the relative merits of the different interventions from the perspective of the farmer and society using business case and cost-effectiveness analyses 

Interventions

FARM-CARE will evaluate efficacy and sustainability of 4 different interventions (A, B, C and D) for controlling AMR spread and reducing antimicrobial use in pig farming based on 3 distinct approaches:

 

Intervention A consists of avoiding mixing and re-mixing between litters from weaning to slaughter, including separation of healthy pigs from diseased pigs that undergo antimicrobial treatment. The period around weaning is the most stressful time in the pig’s life due to separation from the sow and its littermates, drastic changes in the diet, as well as environmental and social changes associated with relocation and mixing of 3-4 week-old piglets to new facilities together with ’foreign‘ piglets from other litters.

We hypothesize that reducing this practice to the bare minimum will result in a clear improvement of pig health and welfare with positive consequences on antimicrobial use and AMR.

 

 

Intervention B consists in the implementation of a defined package of biosecurity measures based on access to facilities for hand washing and disinfection, footwear disinfection, showering, and change of clothes and footwear. Preliminary data have shown that household members of conventional farm workers are at increased risk of being MRSA carriers when compared to household members of persons working on farms with high biosecurity standards.

Our hypothesis is that implementation of biosecurity measures is a powerful tool for reducing spillover of AMR into the general population.

 

 

Two distinct machine learning approaches will be used to identify high-risk piglets predisposed to post-weaning diarrhoea and other diseases with the intention of being able to separate them from healthy individuals and/or reduce their occurrence by breeding management.

The first approach is based on facial feature analysis of stress in sows (intervention C), whereas the second is based on integrated analysis of faecal metagenomics data and metadata collected longitudinally from individual piglets (intervention D). In both approaches, data will be analysed retrospectively to identify behavioural and metagenomics markers that can be used to predict disease.