Table 3_Preharvest antibiotic use influences antibiotic resistance in Salmonella species f

54 Salmonella isolates from 3,600 fecal samples show high rates of erythromycin (80–100%) and tetracycline (75–92.3%) resistance. The dataset, created by Timothy Obiebe Jason Odey and last updated in 2026, correlates farm management practices with antimicrobial resistance phenotypes and molecular determinants. It includes data on antibiotic use for growth promotion (53.8–85.7%) and prophylaxis (71.4–81.3%) from commercial farms.

Use Cases

• Modeling correlations between farm antibiotic use and resistance phenotypes based on reported correlation coefficients (r = 0.058–0.487).
• Analyzing the prevalence of specific resistance genes like tetA and sul1 across different animal species.
• Studying the co-occurrence of virulence factors (e.g., iroB, pipD) with antimicrobial resistance in Salmonella strains.
• Assessing the impact of management practices on multidrug resistance indices (MARI ranging 0.2 to 0.9).

Strengths

• Includes molecular analysis data for specific resistance determinants (e.g., tetA 70–100%) and virulence factors.
• Correlates phenotypic resistance data with farm management practices collected via structured questionnaires.
• Based on a substantial sample size of 3,600 fecal specimens from commercial farms.

Limitations

• Dataset is very small at 19.5 KB, indicating limited scope and likely summary statistics rather than raw sample-level data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Geographic coverage is limited to commercial farms in Lagos, Southwestern Nigeria.

Provenance

Source

figshare

Collection Method

Data collected via microbiological, biochemical, and molecular analysis of fecal samples, combined with farm management questionnaires.

Freshness

Last updated 2026-04-28 05:24:17; freshness should be verified.

Geography

Commercial poultry and swine farms in Lagos, Southwestern Nigeria.