Relationships Between Milk Somatic Cell Count and pH in Dairy Cows

Atasever, Savaş; Erdem, Hüseyin; Altop, Aydın

doi:10.3923/javaa.2010.1575.1577

Cited by 15 publications

(13 citation statements)

References 9 publications

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“…The findings of present study showed highly significant (P<0.01) positive correlation (r = 0.793) between SCC and milk pH and pH tended to increase with increase in SCC. The mean pH value for healthy group was lesser (6.634±0.009) as compared to subclinical infected group (6.837±0.020), which is in agreement with the results of previous reports (Batavani et al, 2007;Hassan, 2013), whereas, negative relationship between SCC and milk pH has been reported by Atasever et al (2010). These changes might be linked to the increase permeability of the mammary epithelium cell lead to the transfer of components from blood to milk such as citrates and bicarbonates that cause elevated pH levels (Harmon, 1994).…”

Section: Resultssupporting

confidence: 91%

Relationship Among Intramammary Infection and Raw Milk Parameters in Jersey Crossbred Cows under Hot-Humid Climate

Bhakat¹

2019

Preprint

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The raw milk quality has increasing importance for producer and consumer as it is directly related to processing, production and price. The aim of present study was to determine the relationship among intramammary infection (IMI) and raw milk parameters in Jersey crossbred cows. Total 24 lactating Jersey crossbred cows were randomly selected and representative animal wise morning milk samples were collected in monthly interval for four month. The level of somatic cell count (SCC) and milk parameters (fat (%), solid non-fat (%) and pH) was estimated from the collected sample. Test day milk yield for individual animals was also recorded and then the data obtained were statistically analyzed. A significant (P<0.01) negative correlation of Log10SCC with test day milk yield, fat and SNF percentage was found, while, milk pH was highly positive correlated with Log10SCC in milk. The mean±SE values of milk yield, fat and SNF were significantly (P<0.01) lower in subclinical infected group except for SCC and pH, which were higher in subclinical infected group. It can be concluded that IMI and higher SCC adversely affect the milk production and raw milk quality parameters and these parameters can be used as useful indicator as complimentary to SCC to monitor udder health and for early diagnosis of subclinical mastitis at dairy farm.

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Section: Resultssupporting

confidence: 91%

Relationship Among Intramammary Infection and Raw Milk Parameters in Jersey Crossbred Cows under Hot-Humid Climate

Bhakat¹

2019

Preprint

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“…Research in dairy cattle has showed that the pH of milk is not an effective predictor of MQT (Ikonen et al 2004; Atasever et al 2010). This is in accordance with our findings, where pH was not a significant predictor in most of the studied MQT with the exception of milk fat content.…”

Section: Resultsmentioning

confidence: 99%

“…For this reason, some of the selected physicochemical traits which can be routinely used are milk electrical conductivity (MEC), refractive index (RI) and pH. Studies on cows’ milk have showed that MEC and RI are correlated with milk's fat, protein and total solids content (Jääskeläinen et al 2001; Mabrook & Petty, 2003; Chigerwe & Hagey, 2014), whereas pH has not been found to be significantly correlated with MQT (Atasever et al 2010). Similar studies regarding the correlation between physicochemical traits and MQT in sheep milk are missing.…”

mentioning

confidence: 99%

Prediction of sheep milk chemical composition using milk yield, pH, electrical conductivity and refractive index

Gelasakis

Giannakou

Valergakis

et al. 2018

Journal of Dairy Research

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This Research Communication addresses the hypothesis that fat, protein, lactose and total solids content can be predicted using daily milk yield (DMY), pH, electrical conductivity (MEC) and refractive index (RI) of milk as predictors. It also addresses the possibility of these measurements being used for on-farm benchmarking activities towards selecting the highest yielding animals and flocks regarding milk quality traits (MQT). A total of 308 purebred Frizarta ewes were used for the study. From each individual ewe, a composite milk sample was collected. pH, MEC and RI of milk were measured and the samples were assayed for fat, protein, lactose and total solids content, using an automatic infrared milk analyser. The predictive value of DMY, pH, MEC and RI of milk on its MQT was assessed using multiple linear regression analysis. Significant regression equations were produced for all of the studied traits. RI and MEC were significant and reliable predictors for all studied MQT, whereas DMY was a significant predictor for most MQT with the exception of protein content. pH was a marginally significant predictor for some of the MQTs at the initial development of the equations but proved unreliable after bootstraping. Using these equations a number of ewes varying from 75 (for fat) to 97 (for protein) out of the 100 highest MQT yielders were correctly predicted, whereas, none of the ewes out of the 100 lowest MQT yielders was mispredicted as a high yielder for protein-, lactose- and total solids- content. Three out of 100 lowest fat-yielders were mispredicted as high fat-yielders. Similar equations can be used for benchmarking activities towards selecting the highest protein-, fat-, lactose- and total solids- yielding animals and flocks in cases where laboratories for MQT analyses are not readily available or the cost of chemical analyses is high. The method can be regarded as a handy tool for the dairy industry to readily assess milk quality at the farm level.

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“…This could easily be caused by environmental factors as previously mentioned. Many of the samples used for the study were shown to be outside the normal range of milk pH where external factors such as bacteria in the milk making the milk acidic or mastitis making the milk more alkaline might affect the tails of the distribution (Atasever et al 2010). We analyzed the tails of the trait within this experiment, and therefore, we might be analyzing the impact of the external factors instead of the intended trait.…”

Section: Identification Of Possible Candidate Genesmentioning

confidence: 99%

Detection of genetic variation affecting milk coagulation properties in Danish Holstein dairy cattle by analyses of pooled whole-genome sequences from phenotypically extreme samples (pool-seq)1

Bertelsen

Gregersen

Poulsen

et al. 2016

Journal of Animal Science

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Rennet-induced milk coagulation is an important trait for cheese production. Recent studies have reported an alarming frequency of cows producing poorly coagulating milk unsuitable for cheese production. Several genetic factors are known to affect milk coagulation, including variation in the major milk proteins; however, recent association studies indicate genetic effects from other genomic regions as well. The aim of this study was to detect genetic variation affecting milk coagulation properties, measured as curd-firming rate (CFR) and milk pH. This was achieved by examining allele frequency differences between pooled whole-genome sequences of phenotypically extreme samples (pool-seq).. Curd-firming rate and raw milk pH were measured for 415 Danish Holstein cows, and each animal was sequenced at low coverage. Pools were created containing whole genome sequence reads from samples with "extreme" values (high or low) for both phenotypic traits. A total of 6,992,186 and 5,295,501 SNP were assessed in relation to CFR and milk pH, respectively. Allele frequency differences were calculated between pools and 32 significantly different SNP were detected, 1 for milk pH and 31 for CFR, of which 19 are located on chromosome 6. A total of 9 significant SNP, which were selected based on the possible function of proximal candidate genes, were genotyped in the entire sample set ( = 415) to test for an association. The most significant SNP was located proximal to , explaining 33% of the phenotypic variance. , coding for κ-casein, is the most studied in relation to milk coagulation due to its position on the surface of the casein micelles and the direct involvement in milk coagulation. Three additional SNP located on chromosome 6 showed significant associations explaining 7, 3.6, and 1.3% of the phenotypic variance of CFR. The significant SNP on chromosome 6 were shown to be in linkage disequilibrium with the SNP peaking proximal to ; however, after accounting for the genotype of the peak SNP within this QTL, significant effects (-value < 0.1) could still be detected for 2 of the SNP accounting for 2 and 1% of the phenotypic variance. These 2 interesting SNP were located within introns or proximal to the candidate genes-solute carrier family 4 (sodium bicarbonate cotransporter), member 4 () and LIM and calponin homology domains 1 (), respectively-making them interesting targets for further analysis.

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Relationships Between Milk Somatic Cell Count and pH in Dairy Cows

Cited by 15 publications

References 9 publications

Relationship Among Intramammary Infection and Raw Milk Parameters in Jersey Crossbred Cows under Hot-Humid Climate

Relationship Among Intramammary Infection and Raw Milk Parameters in Jersey Crossbred Cows under Hot-Humid Climate

Prediction of sheep milk chemical composition using milk yield, pH, electrical conductivity and refractive index

Detection of genetic variation affecting milk coagulation properties in Danish Holstein dairy cattle by analyses of pooled whole-genome sequences from phenotypically extreme samples (pool-seq)1

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