No abstract

No abstract

This study aimed to detect the cecal microbiome, antimicrobial resistance (AMR) and heavy metal resistance genes (MRGs) in fattening pigs raised under antibiotic-free (ABF) conditions compared with ordinary industrial pigs (control, C) using whole-genome shotgun sequencing. ABF pigs showed the enrichment of Prevotella (33%) and Lactobacillus (13%), whereas Escherichia coli (40%), Fusobacterium and Bacteroides (each at 4%) were notably observed in the C group. Distinct clusters of cecal microbiota of ABF and C pigs were revealed; however, microbiota of some C pigs (C1) appeared in the same cluster as ABF and were totally separated from the remaining C pigs (C2). For AMR genes, the highest abundance tet(Q) (35.7%) and mef(A) (12.7%) were markedly observed in the ABF group whereas tet(Q) (26.2%) and tet(W) (10.4%) were shown in the C group. tet(Q) was positively correlated to Prevotella in ABF and C1 samples. In the C2 group, the prominent tet(W) was positively correlated to Fusobacterium and Bacteroides. Pigs have never received tetracycline but pregnant sows used chlortetracycline once 7 d before parturition. Chromosomal Cu and Zn resistance genes were also shown in both groups regardless the received Cu and Zn feed additives. A higher abundance of multi-metal resistance genes was observed in the C group (44%) compared with the ABF group (41%). In conclusion, the microbiome clusters in some C pigs were similar to that in ABF pigs. High abundant tetracycline resistance genes interrelated to major bacteria were observed in both ABF and C pigs. MRGs were also observed. IMPORTANCE: Owing to the increased problem of AMR in farm animals, raising farm animals without antibiotics is one method that could solve this problem. Our study showed that only some tetracycline and macrolide resistance genes, tet(Q), tet(W) and mef(A), were markedly abundant in ABF and C groups. The tet(Q) and tet(W) genes interrelated to different predominant bacteria in each group, showing the potential role of major bacteria as reservoirs of AMR genes. In addition, chromosomal Cu and Zn resistance genes were also observed in both pig groups, not depending on the use of Cu and Zn additives in both farms. The association of MRGs and AMR genotypes and phenotypes together with the method to re-sensitize bacteria to antibiotics should be studied further to unveil the cause of high resistance genes and solve the problems.

Following the implementation of artificial insemination (AI) services for smallholder pig farms, we investigated the reproductive performance after AI and its influencing factors. A small-scale boar station with an AI lab was established with two active boars having good genetics and free from reproductive diseases. Individual sow cards were used for reproductive data recording. A total of 171 sows on 92 farms situated within a radius of 50 km from the AI center were included in this study. Sows bred by AI (n = 121) were inseminated twice per estrus by two trained inseminators. A further 50 sows were mated by natural services using local rental boars. The impact of boar stimulation and distance from the AI center to the farm were also determined. Non-return (P = 0.02) and farrowing rates (P = 0.03) were higher for AI than for naturally bred sows (84.0% and 76.0% vs. 74.0% and 70.0% for AI and naturally bred, respectively). For sows bred by AI, boar stimulation increased non-return rate (84.1% vs. 70.0%; P = 0.09), farrowing rate (83.7% vs. 69.2%; P = 0.01) and litter size (11.2 +/- 2.3 vs. 9.7 +/- 1.7; P < 0.01). There was no effect on performance due to distance of semen transport. These results clearly indicate that sow performance on smallholder farms will improve if AI is utilized and boar stimulation is employed.

No abstract