Effectiveness of dry cow therapy and/or internal teat sealant on existing infections in smallholder dairy farms in Kenya

Background and Aim: Dry cow therapy (DCT) can be an effective treatment of mastitis that has not responded to conventional treatment during lactation. The aim of this study was to establish the effectiveness of DCT options available in reducing intramammary infections in smallholder dairy farms in Kiambu County, Kenya. Materials and Methods: The study targeted smallholder dairy farms which were registered at the local dairy cooperatives and which had cows that were at the point of dry-off. A total of 32 cows with 121 quarters that were California Mastitis Test (CMT) positive were recruited, with the quarters randomly allocated to receive either DCT (DCT – neomycin sulfate, penethamate hydriodide, and procaine benzylpenicillin) and internal teat sealant (ITS) or ITS alone (bismuth nitrate) after aseptically collecting quarter milk samples for bacterial culture. Farm- and animal-level factors were captured through a questionnaire which was administered to the principal farmer or a person who was managing the animals. Post-calving, milk samples were also collected for bacterial culture to establish if the infection was cleared or if there was a new infection. Results: DCT with ITS significantly reduced the proportion of quarters infected with Staphylococcus aureus from 64.0% at dry-off to 44.0% post-calving (35% reduction). In the control group, ITS alone, there was a small reduction in proportions of S. aureus from 46.8% to 40.4%. Proportions of quarter infections by coagulase-negative Staphylococcus in the treatment group reduced from 16.0% at dry-off to 2.0% post-calving, with a significant reduction in the control group too from 19.1% to 4.3%, which could be due to self-cure. Actinomyces species, Escherichia coli, Streptococcus species, and Pseudomonas species proportions slightly increased in the treatment group, as did E. coli and Pseudomonas species proportions in the control group. Conclusion: In smallholder dairy farms with subclinical mastitis, DCT of CMT-positive cows leads to a significant decrease of S. aureus infections at calving.

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Prevalence of clinical and subclinical mastitis and quality of milk on smallholder dairy farms in Tanzania

Journal of the South African Veterinary Association ◽

10.4102/jsava.v80i3.195 ◽

2009 ◽

Vol 80 (3) ◽

Cited By ~ 14

Author(s):

R.H. Mdegela ◽

R. Ryoba ◽

E.D. Karimuribo ◽

E.J. Phiri ◽

T. Loken ◽

...

Keyword(s):

Antimicrobial Agents ◽

Dairy Farms ◽

Subclinical Mastitis ◽

Clinical Mastitis ◽

Milk Quality ◽

Herd Level ◽

Milk Samples ◽

Antimicrobial Residues ◽

Smallholder Dairy ◽

Smallholder Dairy Farms

A cross sectional study was conducted during October and November 2006 on 69 smallholder dairy farms with lactating cows in Mvomero and Njombe districts Tanzania, to determine the prevalence of mastitis and to assess the milk quality on the study farms. Clinical mastitis was investigated using clinical changes of udder and milk at animal level. Cow-side California Mastitis Test (CMT) and microbiological cultures were used to assess subclinical mastitis at quarter level. Milk quality was determined on bulk milk samples at herd level using alcohol and acidity tests, butter fat content, total solids, ash content as well as Delvotest® for antimicrobial residues. Overall prevalence of clinical mastitis at herd level in both districts was 21.7 % (n = 69). Based on CMT, prevalence of subclinical mastitis at animal level was 51.6 % (n = 91). Prevalence of bacterial isolates at animal level was 35.2 % (n = 91) while for fungal it was 16.7 % (n = 90). Based on CMT results, prevalence of subclinical mastitis at quarter level was 30 % (n = 353), while for bacteria and fungi it was 16 % and 6 % respectively. Contamination of milk with antimicrobial residues was 4.5 % (n =67). The milk quality parameters for most of the milk samples were within acceptable levels. Findings in this study have demonstrated high prevalence of subclinical mastitis that may contribute to low productivity of dairy cattle in both districts. About 20 % of CMT subclinical cases had no involvement of microbial pathogens that suggested the need for minimal interventions with antimicrobial agents. These findings call for use of udder disinfectants and improved milking hygiene as intervention strategies to control mastitis on the smallholder dairy farms in Tanzania.

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A longitudinal study of milk somatic cell counts and bacterial culture from cows on smallholder dairy farms in Kiambu District, Kenya

Preventive Veterinary Medicine ◽

10.1016/s0167-5877(96)01054-9 ◽

1996 ◽

Vol 29 (1) ◽

pp. 77-89 ◽

Cited By ~ 19

Author(s):

A.O. Omore ◽

J.J. McDermott ◽

S.M. Arimi ◽

M.N. Kyule ◽

D. Ouma

Keyword(s):

Longitudinal Study ◽

Somatic Cell ◽

Bacterial Culture ◽

Dairy Farms ◽

Somatic Cell Counts ◽

Cell Counts ◽

Smallholder Dairy ◽

Smallholder Dairy Farms

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Application of statistical process control for monitoring bulk tank milk somatic cell count of smallholder dairy farms

Veterinary World ◽

10.14202/vetworld.2020.2429-2435 ◽

2020 ◽

Vol 13 (11) ◽

pp. 2429-2435

Author(s):

Veerasak Punyapornwithaya ◽

Chalutwan Sansamur ◽

Tawatchai Singhla ◽

Paramintra Vinitchaikul

Keyword(s):

Somatic Cell ◽

Dairy Farms ◽

Control Points ◽

Dairy Farmers ◽

Statistical Process ◽

Bulk Tank Milk ◽

Milk Samples ◽

Bulk Tank ◽

Smallholder Dairy ◽

Smallholder Dairy Farms

Background and Aim: Consistency in producing raw milk with less variation in bulk tank milk somatic cell count (BMSCC) is important for dairy farmers as their profit is highly affected by it in the long run. Statistical process control (SPC) is widely used for monitoring and detecting variations in an industrial process. Published reports on the application of the SPC method to smallholder farm data are very limited. Thus, the purpose of this study was to assess the capability of the SPC method for monitoring the variation of BMSCC levels in milk samples collected from smallholder dairy farms. Materials and Methods: Bulk tank milk samples (n=1302) from 31 farms were collected 3 times/month for 14 consecutive months. The samples were analyzed to determine the BMSCC levels. The SPC charts, including the individual chart (I-chart) and the moving range chart (MR-chart), were created to determine the BMSCC variations, out of control points, and process signals for each farm every month. The interpretation of the SPC charts was reported to dairy cooperative authorities and veterinarians. Results: Based on a set of BMSCC values as well as their variance from SPC charts, a series of BMSCC data could be classified into different scenarios, including farms with high BMSCC values but with low variations or farms with low BMSCC values and variations. Out of control points and signals or alarms corresponding to the SPC rules, such as trend and shift signals, were observed in some of the selected farms. The information from SPC charts was used by authorities and veterinarians to communicate with dairy farmers to monitor and control BMSCC for each farm. Conclusion: This study showed that the SPC method can be used to monitor the variation of BMSCC in milk sampled from smallholder farms. Moreover, information obtained from the SPC charts can serve as a guideline for dairy farmers, dairy cooperative boards, and veterinarians to manage somatic cell counts in bulk tanks from smallholder dairy farms.

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Understanding variability in greenhouse gas emission estimates of smallholder dairy farms in Indonesia

The International Journal of Life Cycle Assessment ◽

10.1007/s11367-021-01923-z ◽

2021 ◽

Author(s):

Titis Apdini ◽

Windi Al Zahra ◽

Simon J. Oosting ◽

Imke J. M. de Boer ◽

Marion de Vries ◽

...

Keyword(s):

Greenhouse Gas ◽

Rainy Season ◽

Greenhouse Gas Emission ◽

Dairy Farms ◽

Dry Season ◽

Enteric Fermentation ◽

Tropical Regions ◽

Population Mean ◽

Smallholder Dairy ◽

Smallholder Dairy Farms

Abstract Purpose Life cycle assessment studies on smallholder farms in tropical regions generally use data that is collected at one moment in time, which could hamper assessment of the exact situation. We assessed seasonal differences in greenhouse gas emissions (GHGEs) from Indonesian dairy farms by means of longitudinal observations and evaluated the implications of number of farm visits on the variance of the estimated GHGE per kg milk (GHGEI) for a single farm, and the population mean. Methods An LCA study was done on 32 smallholder dairy farms in the Lembang district area, West Java, Indonesia. Farm visits (FVs) were performed every 2 months throughout 1 year: FV1–FV3 (rainy season) and FV4–FV6 (dry season). GHGEs were assessed for all processes up to the farm-gate, including upstream processes (production and transportation of feed, fertiliser, fuel and electricity) and on-farm processes (keeping animals, manure management and forage cultivation). We compared means of GHGE per unit of fat-and-protein-corrected milk (FPCM) produced in the rainy and the dry season. We evaluated the implication of number of farm visits on the variance of the estimated GHGEI, and on the variance of GHGE from different processes. Results and discussion GHGEI was higher in the rainy (1.32 kg CO2-eq kg−1 FPCM) than in the dry (0.91 kg CO2-eq kg−1 FPCM) season (P < 0.05). The between farm variance was 0.025 kg CO2-eq kg−1 FPCM in both seasons. The within farm variance in the estimate for the single farm mean decreased from 0.69 (1 visit) to 0.027 (26 visits) kg CO2-eq kg−1 FPCM (rainy season), and from 0.32 to 0.012 kg CO2-eq kg−1 FPCM (dry season). The within farm variance in the estimate for the population mean was 0.02 (rainy) and 0.01 (dry) kg CO2-eq kg−1 FPCM (1 visit), and decreased with an increase in farm visits. Forage cultivation was the main source of between farm variance, enteric fermentation the main source of within farm variance. Conclusions The estimated GHGEI was significantly higher in the rainy than in the dry season. The main contribution to variability in GHGEI is due to variation between observations from visits to the same farm. This source of variability can be reduced by increasing the number of visits per farm. Estimates for variation within and between farms enable a more informed decision about the data collection procedure.

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