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In South Africa, as is done with routine Veterinary diagnostics worldwide, focus is on a fast turnaround time and relatively low cost. Staphylococci are identified using the coagulase test (Staphylase test, Oxoid). In more recent years, further diagnosis of coagulase positive staphylococci on maltose agar has been known to differentiate Staphylococcus aureus (S. aureus) from Staphylococcus intermedius group staphylococci (SIG), which comprises of S. pseudintermedius, S. intermedius, S. delphini and others. This became important due to the increase of coagulase positive maltose negative isolates (SIG) in dairy herds in South Africa in recent years, after first being identified in 2006. These organisms was responsible for low somatic cell count (SCC) and showed no antibiotic resistance, unlike the maltose positive S. aureus. The objective of this study was to confirm identification and investigate the seasonal, regional and SCC effects on antibiotic resistance of this emerging pathogen maltose negative S. aureus, initially phenotypically identified as potential SIG, of which little is known. In this study samples identified during routine diagnosis (phenotypic) as potential SIG (coagulase positive, maltose negative staphylococci), were sent for further diagnosis on Matrix Assisted Laser Desorption/Ionisation Time-of- Flight Mass Spectrometry (Maldi Tof, Bruker) and for genotypic identification using 16s RNA ribosomal sequencing and multi locus sequence typing (MLST) to confirm the diagnosis. This study analysed retrospective data of 272 of these isolates from milk samples (2009 to 2017). These isolates were tested for antibiotic resistance using the disc diffusion (Kirby Bauer) method for ampicillin, cloxacillin, penicillin G, clindamycin, oxy-tetracycline, cephalexin, cefuroxime, tylosin and cefoxitin, with clinical breakpoints established by CLSI. The samples were from 117 dairy herds, out of the estimated 2000 commercial dairy herds in South Africa. Further testing of 57 of these samples and 56 S. aureus (maltose positive) isolates was done using an automated minimum inhibitory concentration (MIC), (Microscan 40 Walkaway system, Beckman Coulter) with the PM32 panels. The susceptible breakpoints for the MIC used were 4 μg/ml and the resistance breakpoints were 32 μg/ml. The Maldi Tof, 16s sequencing and MLST done confirmed the isolates as a maltose negative strain of S. aureus and not part of the SIG group as originally suspected. Predictions of antibiotic resistance for cloxacillin, cefoxitin and cephalexin showed significant differences between SCC categories according to the general linear mixed model (GLMM) (p

The gross income generated by animal production industries in South Africa was estimated at R80.8 billion (US$6.86 billion) for 2012. The contribution from poultry meat was R30 billion with an addition of R8 million from eggs. The milk industry contributed for R11 billion. These industries represent more than 50% of the contribution of animal products to the gross income. Regular diseases outbreaks impact negatively on the growth and development of these livestock industries. The incidence of specific diseases within South Africa plays a decisive role in the selection of antimicrobials to be administered and judicious use of antimicrobials is paramount. Avian pathogenic Escherichia coli cause major losses in the poultry industry and became the predominant bacterial disease due to the increased intensive confinement housing. In addition E. coli infections are often secondary to viral disease. Standard practices currently employed in South Africa are to start prophylactic treatment to prevent secondary bacterial infection whenever a sanitary problem occurs in poultry. In commercial dairy herds, somatic cell count higher than 400 000 cells/ml leads also to the treatment of sub-clinical mastitis involving mainly Staphylococcus aureus. Time series analysis on E. coli and S. aureus resistance over a period of 10 years in relation with antibiotics use were performed. Seasonal and geographical patterns were also highlighted for some specific antibiotics family using generalized linear mixed models. The various temporal trends that have been highlighted according to the different antibiotics allowed us to better understand the farmers' practices leading to these resistances. Seasonal and regional patterns may vary according to the antibiotics that could be related to climatic variation. This better understanding of the trends and patterns of antimicrobial resistance in intensive farming in South Africa provided room for advising on farmers practices on the monitoring of antibiotics but also challenged research in terms of antimicrobial resistance drivers.

Objectives: The gross income generated by animal production industries in South Africa was estimated at R80.8 billion (US$6.86 billion) for 2012. The contribution from poultry meat was R30 billion with an addition of R8 million from eggs. The milk inductry contributed for R11 billion. These industries represent more than 50% of the contribution of animal products to the gross income. Regular diseases outbreaks impact negatively the growth and development of these livestock industries. The incidence of specific diseases within South Africa plays a decisive role in the selection of antimicrobials to be administered and judicious use of antimicrobials is paramount. Avian pathogenic Escherichia coli causes major losses in the poultry industry and became the predominant bacterial disease due to the increased intensive confinement housing. In addition E.coli infections are often secondary to viral disease. Standard practices currently employed in South Africa are to start prophylactic treatment to prevent secondary bacterial infection whenever a sanitary problem occurs in poultry. In commercial dairy herds, somatic cell count higher than 400 000 cells/ml leads also to the treatment of sub-clinical mastitis involving mainly Stapylococcus aureus. Material and methods: Time series analysis on E. coli and S. aureus resistance over a perid of 10 years in relation with antibiotics use were performed Seasonal and geographical patterns were also highlighted for some specific antibiotics family using general linear mixed models. Results: Various temporal trends have been highlighted according to the different antibiotics allowed us to better understand the farmers practices leading to these resistance. Seasonal and regional patterns may vary according to the antibiotics that could be related to climatic variation. Conclusion: This better understanding of the trends and patterns of antimicrobial resistance in intensive farming in South Africa provided room for advising farmers practices on the monitoring of antibiotics but chalenged also research in terms of antimicrobial resistance drivers.