The Impact of Training Intervention on Levels of Indicator Bacteria and Prevalence of Selected Pathogens in Raw Milk From Smallholder Women Dairy Farmers in Central Ethiopia

Contamination of milk is a serious public health risk, particularly in developing countries such as Ethiopia. Training is a tool for improving the quality and safety of milk. However, its effect on the microbial quality and safety of milk has not been well documented. This study assessed the impact of training interventions on the microbial quality and safety of milk, with a specific focus on smallholder dairy farms. The study was conducted from January to June 2022 in four locations in Central Ethiopia. Milk samples were collected from 120 dairy farmers one week before and four to six weeks after the training and analyzed for total coliforms, thermotolerant coliforms, Escherichia coli, Shiga toxin-producing E. coli (STEC), Salmonella enterica, and Campylobacter jejuni. Total and thermotolerant coliforms were determined by the Most Probable Number (MPN) method; categorized as high (≥1,001 MPN/ml), medium (101 10<sup>3</sup> MPN/ml), low (21-10<sup>2</sup> MPN/ml), and very low (≤20 MPN/ml); and analyzed using generalized linear mixed models. Prevalence of E. coli, STEC, S. enterica, and C. jejuni in raw milk samples were 67, 12, 3, and 4% pretraining and 45, 4, 3, and 2% posttraining, respectively. The prevalence of E. coli (p = 0.0389) and STEC (p = 0.0005) was significantly lower posttraining compared to pretraining. Most samples had total coliform counts exceeding 10<sup>3</sup> MPN/ml pretraining (71%); this proportion decreased to 62% posttraining. The estimated cumulative probability of being in the high category was marginally significantly higher (p = 0.0581) pretraining (76%) compared to posttraining (64%). In conclusion, reductions in the detection of some milk quality and safety parameters were observed. However, a considerable proportion of the milk samples were highly contaminated even after the training. Hence, comprehensive and continued risk mitigation strategies are needed to ensure milk safety for consumers.

Authors

Institutions

Publication Details

Journal
Journal of Food Protection
Published
2024-12-28
DOI
https://doi.org/10.1016/j.jfp.2024.100446
Citations
4
Primary Topic
Milk Quality and Mastitis in Dairy Cows
Type
article
Field-Weighted Citation Impact
2.27

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Impact of Training Intervention on Levels of Indicator Bacteria and Prevalence of Selected Pathogens in Raw Milk From Smallholder Women Dairy Farmers in Central Ethiopia

Seleshe Nigatu, Ahmed E. Yousef, Barbara Kowalcyk, Ahmed G. Abdelhamid et al.
4 citations
Journal of Food Protection
Milk Quality and Mastitis in Dairy Cows
2.27
article

The Impact of Training Intervention on Levels of Indicator Bacteria and Prevalence of Selected Pathogens in Raw Milk From Smallholder Women Dairy Farmers in Central Ethiopia

Seleshe Nigatu, Ahmed E. Yousef, Barbara Kowalcyk, Ahmed G. Abdelhamid, James A. Barkley, Kebede Amenu, Araya Mengistu, Achenef Melaku Beyene, Juan Carlos Archila-Godinez, Baye Gelaw, Mucheye Gizachew, Desalegne Degefaw, Binyam Mogess
article en
4 citations

Abstract

Contamination of milk is a serious public health risk, particularly in developing countries such as Ethiopia. Training is a tool for improving the quality and safety of milk. However, its effect on the microbial quality and safety of milk has not been well documented. This study assessed the impact of training interventions on the microbial quality and safety of milk, with a specific focus on smallholder dairy farms. The study was conducted from January to June 2022 in four locations in Central Ethiopia. Milk samples were collected from 120 dairy farmers one week before and four to six weeks after the training and analyzed for total coliforms, thermotolerant coliforms, Escherichia coli, Shiga toxin-producing E. coli (STEC), Salmonella enterica, and Campylobacter jejuni. Total and thermotolerant coliforms were determined by the Most Probable Number (MPN) method; categorized as high (≥1,001 MPN/ml), medium (101 10<sup>3</sup> MPN/ml), low (21-10<sup>2</sup> MPN/ml), and very low (≤20 MPN/ml); and analyzed using generalized linear mixed models. Prevalence of E. coli, STEC, S. enterica, and C. jejuni in raw milk samples were 67, 12, 3, and 4% pretraining and 45, 4, 3, and 2% posttraining, respectively. The prevalence of E. coli (p = 0.0389) and STEC (p = 0.0005) was significantly lower posttraining compared to pretraining. Most samples had total coliform counts exceeding 10<sup>3</sup> MPN/ml pretraining (71%); this proportion decreased to 62% posttraining. The estimated cumulative probability of being in the high category was marginally significantly higher (p = 0.0581) pretraining (76%) compared to posttraining (64%). In conclusion, reductions in the detection of some milk quality and safety parameters were observed. However, a considerable proportion of the milk samples were highly contaminated even after the training. Hence, comprehensive and continued risk mitigation strategies are needed to ensure milk safety for consumers.

Journal of Food ProtectionVol. 88(2)
International Trachoma Initiative (ET), Addis Ababa University (ET), The Ohio State University (US), International Livestock Research Institute (KE), Michigan State University (US), University of Gondar (ET)
Bill and Melinda Gates Foundation, Houston Advanced Research Center, King Saud University, Ethiopian Institute of Agricultural Research, University of Gondar, Addis Ababa University, Foreign, Commonwealth and Development Office
Zero hunger
Openalex Percentile: Top 12%
Milk Quality and Mastitis in Dairy Cows
2.27
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.