Introduction

Dairy sector in India is contributing 4% of the GDP and 26% of the agricultural GDP (NAS, 2012) and playing a crucial role in livelihood activity for the farmers by contributing to the health, nutrition and earnings of the household. Intra-mammary infection is an imperative threat affecting the dairy sector (Bharti et al., 2015) and mastitis has been shown to be one of the diseases with the most significant adverse effect on economic dairying due to reduced milk production, premature culling of animals (Saravanan et al., 2015) and also lowering nutritive value of milk (Patil et al., 2015). Besides mastitis is also a major animal welfare concern in the dairy industry (Medrano-Galarza et al., 2012) which is characterized by physical, chemical and bacteriological changes in the milk and pathological changes in the glandular tissue of the udder (Sharma, 2007) and occurrence could be in clinical and subclinical forms. In developing countries mastitis losses may even be higher since standard mastitis control and prevention programmes recommended by National Mastitis Council (NMC) of USA are not being carried out in these countries (Nickerson, 1994). Besides there are no accurate control measures in order to cover the disease because of its multifactorial nature (Kavitha et al., 2009). In general, it is accepted that somatic cell count (SCC) is a gold standard at diagnostics of any form of mastitis in udder (Pyörärlä, 2003) and it can be used to monitor the level or occurrence of subclinical mastitis in herds or individual cows (Sharma et al., 2011).

In this concern, it is desired to know about the predisposing factors that govern intra-mammary infections (IMI) and possible ways that may be helpful for reducing the incidence of IMI. Among many factors, cow-specific factors like parity, production level, lactation stage and season of calving can be the major risk factors of clinical mastitis that has been shown in previous studies (Ghavi Hossein-Zadeh et al., 2011; Sabuncu et al., 2013; Saravanan et al., 2015). However, the literature on the effect of cow-specific factors on the milk somatic cell count under hot-humid environment is scanty and therefore, the present study was undertaken to investigate the influence of animal factors on the somatic cell count in milk of cows reared under hot-humid climatic condition.

Material and Methods

Experimental Animals and Location

This study was conducted on twenty four randomly selected lactating Jersey crossbred cows from the lactating herd of Eastern Regional Station, ICAR- National Dairy Research Institute located in Kalyani city of West Bengal.The altitude of Kalyani city is 9.75 meter above mean sea level, latitude and longitude position being 22°56′30″N and 88°32′04″E, respectively. The weather of Kalyani is hot and humid as the maximum ambient temperature in summer goes up to 39^oC and minimum temperature in winter comes down to about 8^oC and average annual rainfall is 1000-2000 mm (Prasad, 2013).

Collection of Milk Samples

Approximately 30 ml of representative quarter wise morning milk samples were collected aseptically from all the experimental lactating cows. Few teats were found as blind or non-functional at the time of sampling and so a total of 366 milk samples were collected at monthly interval for consecutive four months of experimental period to estimate somatic cell count. The collected samples were brought to the laboratory immediately to estimate somatic cell count (SCC) in milk by using the method described by Schalm et al. (1971).

Classification of Animal Factors

The data of SCC of all experimental animals were classified according to parity, stage of lactation, production level and season of calving. The lactation records were grouped in to 1st parity, 2nd parity, 3rd parity and 4th and above parity. The stage of lactation was classified into three stages- early stage of lactation (1 to 100 days), mid stage of lactation (101 to 200 days) and late stage of lactation (above 200 days). Level of milk production was divided into three categories as low yielders (<1800kg), medium yielders (1800-2200kg) and high yielders (>2200kg) considering the herd average milk production in a lactation period. Based on three major seasonal changes, the seasons of calving were classified into winter season (November- February), summer season (March- June) and rainy season (July- October) as described by Prasad, 2013.

Statistical Analysis

Firstly the data on SCC were transformed into log scale to minimize the heterogeneity of variance and then classified according to various categories of different animal factors. The data were analysed with the help of SAS software package, version 9.3 (SAS Institute Inc., 2011)

Results and Discussion

Effect of Parity on SCC

The results of the present study showed increasing trend for milk somatic cell count (SCC) with the parity, SCC being lowest in first parity cows and highest in 4th and above parity cows. The mean ± SE of Log10SCC for 1st, 2nd, 3rd and 4th and above parity were 4.914±0.045, 5.459±0.099, 5.496±0.064 and 5.973±0.071 respectively (Table 1) and the differences between them were found significant (P< 0.05) except between 2nd and 3rd parity cows. The analysis of variance also revealed a significant (P<0.01) effect of parity on the level of SCC, which has been shown in Table 2.

The previous studies had also reported a significant effect of parity on the level of milk somatic cell count and stated that as the parity advances, cows have greater likelihood of developing intra-mammary infection and increased SCC (Ruegg and Pantoja, 2013) and rise in incidence of mastitis (Kavitha et al., 2009).

Table 1: Mean ± SE of Log₁₀SCC under different animal factors in Jersey crossbred cows

Means within column under given factor having different superscript differ significantly (P<0.05)

The results in this study is consistent with some other studies where test day somatic cell count was lowest in first parity animals and increased with parity and was highest in 5th and above parity animals (McParland et al., 2013; Tančin, 2013). However, Tančin et al. (2007a) found only numerically higher SCC for multiparous cows as compared to primiparous cows. The rise in SCC level with parity may be attributed to increased prevalence of infection and permanent glandular damage from previous infections (Barlett et al., 1990).

Effect of Stage of Lactation on SCC

The results indicated that the incidence of intra-mammary infection and SCC was lowest in early stage of lactation and it was gradually increases with the advancement of lactation stages. The mean ± SE of Log10SCC in early, mid and late stages of lactation was 4.956±0.045, 5.096±0.055 and 5.864±0.059 respectively (Table 1) and the mean SCC of early stage of lactation differ significantly (P<0.05) from SCC of mid and late stage of lactation whereas, SCC of late lactation stage was only numerically higher than mid stage of lactation. The analysis of variance revealed that the stage of lactation had significant (P<0.01) effect on the Log10SCC (Table 2).

Table 2: Analysis of variance showing the effect of parity, stage of lactation, production level and season of calving on Log10SCC in Jersey crossbred cows

**Significant (p<0.01)

Similar to present study, the gradual increase in SCC level with the advancement of stage of lactation have been observed in previous studies (Fadlelmoula et al., 2008; Ruegg and Pantoja, 2013) and stage of lactation was found as an important risk factor as cows in late lactation (>200 days in milk) had higher risk to have more SCC than cows in early and mid-lactation (Sandrucci et al., 2014). Increased SCC values towards the end of lactation could be due to higher infection rate as the teat streak canals are dilated due to continuous milking and dilution effect of increased milk yield during early lactation and declines of milk yield during mid and late lactation. However, Saravanan et al. (2015) showed higher mean SCC values during early first lactation, decrease during mid-lactation and again increase during late stage of lactation in HF crossbreds. Non-significant changes in SCC during different stages of lactation were reported by some workers (De et al., 2011; Hamed et al., 2012).

Effect of Production level on SCC

In the present study, result indicated higher mean SCC values for low yielder as well as high yielder cows and lowest in medium yielder cows. The mean ± SE Log10SCC for different level of production is presented in the Table-2. The (mean ±SE) Log10SCC in case of low, medium and high yielder was 5.815 ± 0.062, 4.819 ± 0.038 and 5.487 ± 0.064 respectively and the mean difference among low, medium and high yielder was found to be significant (P<0.05). The level of production was also had significant (P<0.01) effect on SCC level (Table-2).

The results of this study are consistent with findings of previous investigations (Samanta et al., 2006; Ouedraogo et al., 2008) who reported higher SCC values for low yielding animals compared to high yielders and lowest in medium yielders. Some previous correlation studies have been shown that increased SCC is associated with reduction in milk yield (Kumaresan, 2013; Bharti et al., 2015), which supports results of this study. In contrast, Kamboz et.al (2007) reported higher SCC level for high yielder animals. The low yielder animal possibly get less attention in terms of feeding and cleaning and probably carrying the infection in sub-clinical form for longer period of time, thereby affecting the milk yield adversely whereas, more chances of wear and tear in the teat by milking machine and having voluminous udder could be the cause of higher SCC in high yielding animals.

Effect of Season of Calving on SCC

The result of effect of season of calving on SCC level showed higher incidence of intra-mammary infections and SCC for the cows calved in summer season as compared to rainy and winter season calved cows. The (mean ±SE) Log10SCC in case of summer, rainy and winter seasons of calving was 5.701 ± 0.065, 5.194 ± 0.058 and 5.152 ± 0.063 respectively that has been presented in the Table 1. The mean SCC value for summer calvers was significantly (P<0.05) greater than for rainy and winter calvers whereas, SCC level was only numerically higher for the cows calved in rainy season in comparison to winter calved cows. The analysis of variance indicated significant (P<0.01) effect of season of calving on SCC values (Table 2) which is in agreement with the results of previous studies which revealed that the parturition season influenced significantly changes in the somatic cell count (Wicks et al., 2006; Baul et al., 2011) and SCC are generally lowest during the winter and highest during the summer season (Khate and Yadav, 2010).

Higher SCC in summer season calved cows can be better explained by the environmental condition of the particular season. Generally due to increased exposure to pathogens (due to favourable climatic conditions for microbial growth) more intra-mammary infections occur in summer season (Summer et al., 2007; Cicconi-Hogan et al., 2013) and heat stress can reduce the phagocytic ability of neutrophils, resulting in reduced capability of the cow to respond to IMI (do Amaral et al., 2011). Thus during hot-humid climatic conditions cows experience increased exposure to pathogens due to heat load and excess moisture and at the same time having diminished ability to clear pathogens, resulting in increased chances of intra-mammary infections and high SCC.

Conclusion

It can be concluded from the present study that cow level traits, such as parity, lactation stage, production level and calving season significantly influence the risk of high somatic cell count in milk. Systematic monitoring of individual cow SCC can be a key tool that can be considered to achieve the best strategy for the treatment and prevention of intra-mammary infections in dairy cows. Further, various animal factors should be taken into account during selection of animal in routine management of dairy herd.

Acknowledgments

The authors are highly obliged to the Director, ICAR- NDRI, Karnal, and Head, ICAR-NDRI (ERS), Kalyani for providing the facilities for conducting the research. The first author is extremely thankful to ICAR-NDRI for providing financial assistance in the form of institutional fellowship during entire study tenure.

References

1. Baul S, Cziszter LT, Acatincai S, Cismas T, Gavojdian D, Tripon I, Erina S and Raducan G. 2011. Researches regarding the season influence on somatic cell count in milk during lactation in Romanian black and white cows. Lucrari Stiintifice. 56(16): 370- 373.
2. Barlett PC, Miller GY, Anderson CR and Kirk JH. 1990. Milk production and somatic cell count in Michigan Dairy Herds. J. Dairy Sci., 73: 2794-2800.
3. Bharti P, Bhakat C, Ghosh MK, Dutta TK and Das R. 2015. Relationship among intramammary infection and raw milk parameters in Jersey crossbred cows under hot-humid climate. J. Anim. Res., 5 (2): 317-320.
4. Bharti P, Bhakat C, Pankaj PK, Bhat SA, Prakash MA, Thul MR and Japheth KP. 2015. Relationship of udder and teat conformation with intra-mammary infection in crossbred cows under hot-humid climate. Veterinary World., 8(7): 898-901.
5. Cicconi-Hogan KM, Gamroth M, Richert RM, Ruegg PL, Stiglbauer KE and Schukken YH. 2013. Associations of risk factors with somatic cell count in bulk tank milk on organic and conventional dairy farms in the United States. J. Dairy Sci., 96: 3689–3702.
6. do Amaral BC, Connor EE, Tao S, Hayen MJ, Bubolz JW and Dahl GE. 2011. Heat stress abatement during the dry period influences metabolic gene expression and improves immune status in the transition period of dairy cows. J. Dairy Sci., 94: 86–96.
7. De k, Mukherjee J, Prasad S and Dang AK. 2011b. Effect of parity and stage of lactation on milk SCC and DLC in native and crossbred cows. Indian J. Dairy Sci., 64(4): 326-328.
8. Fadlelmoula AA, Anacker G, Fahr RD and Swalve HH. 2008. Factors affecting test-day somatic Cell Counts and milk yield of dairy cows. Int. J. Dairy Sci., 3(2): 105-111.
9. Ghavi Hossein-Zadeh N and Ardalan M. 2011. Cow-specific risk factors for retained placenta, metritis and clinical mastitis in Holstein cows. Vet. Res. Commun., 35(6): 345-354.
10. Hamed H, Trujillo AJ, Juan B, Guamis B, ElFeki A and Gargouri A. 2012. Interrelationships between somatic cell counts, lactation stage and lactation number and their influence on plasmin activity and protein fraction distribution in dromedary (Camelus dromedaries) and cow milks. Small Ruminant Res., 105 (1-3): 300– 307.
11. Kavitha KL, Rajesh K, Suresh K, Satheesh K and SyamaSundar N. 2009. Buffalo mastitis – risk factors. Buffalo Bulletin., 28(3): 134- 137.
12. Khate K and Yadav BR. 2010. Incidence of mastitis in Sahiwal and Murrah buffaloes of a closed organized herd. Indian J. Anim. Sci., 80: 467- 469.
13. Kumaresan G. 2013. Somatic cell pattern and composition of milk of Holstein Friesian cross bred cattle. Int. J. Sci. Env. Tech., 2(6): 1421 – 1425.
14. McParland S, O’Brien B and McCarthy J. 2013. The association between herd- and cow level factors and somatic cell count of Irish dairy cows. Irish J. Agricultural and Food Res., 52: 151–158.
15. Medrano-Galarza C, Gibbons J, Wagner S, de Passille AM and Rushen J. 2012. Behavioral changes in dairy cows with mastitis. J. Dairy Sci., 95:6994–7002.
16. NAS. 2012. National Accounts Statistics, Central Statistical Organisation; Ministry of Statistics & Programme Implementation, GOI, New Delhi.
17. Nickerson SC. 1994. Progress in the development of mastitis vaccine. Proc. National Mastitis Council Inc., Arlington, USA, pp: 133-134.
18. Ouedraogo GA, Millogo V, Anago-Sidibe AG and Kanwe BA. 2008. Relationship between somatic cell counts, dairy cattle milk yield and composition in Burkina Faso. African J. Biochemistry Res. 2(2): 56-60.
19. Patil MP, Nagvekar AS, Ingole SD, Bharucha SV and Palve VT. 2015. Somatic cell count and alkaline phosphatase activity in milk for evaluation of mastitis in buffalo. Veterinary World, 8(3): 363-366.
20. Prasad, Y. 2013. Studies on somatic cell count in milk of jersey crossbred cows under the hot and humid condition of West Bengal, M.V.Sc. thesis. ICAR- National Dairy Research Institute, Karnal.
21. Pyörärlä S. 2003. Indicators of inflammation in the diagnosis of mastitis. Veterinary Res., 34: 565-578.
22. Ruegg PL and Pantoja JCF. 2013. Understanding and using somatic cell counts to improve milk quality. Irish J. Agr. Food Res., 52: 101–117.
23. Samanta A, Prasad S and Ghosh CP. 2006. Incidence of sub clinical mastitis in Karan swiss and Karan Fries crossbred cows. Indian J. Dairy Sci. 59(1): 13-18.
24. Sandrucci A, Bava L, Zucali M and Tamburini A. 2014. Management factors and cow traits influencing milk somatic cell counts and teat hyperkeratosis during different seasons. R. Bras. Zootec. 43(9): 505-511.
25. Saravanan R, Das DN, De S and Panneerselvam S. 2015. Effect of season and parity on somatic cell count across zebu and crossbred cattle population. Indian J. Anim. Res., 49 (3): 383-387.
26. Sabuncu A, Enginler SO and Dumen E. 2013. The effect of parity, age and season on somatic cell count of dairy cows with subclinical mastitis. J. Anim. Vet. Adv. 12 (4): 472- 477.
27. SAS Institute Inc. 2011. SAS® 9.3 System Options: Reference. 2nd ed. SAS Institute Inc., Cary, NC.
28. Sharma N. 2007. Alternative approach to control intramammary infection in dairy cows- A review. Asian J. Anim. Vet. Adv., 2(2): 50-62.
29. Sharma N, Singh NK and Bhadwal MS. 2011. Relationship of somatic cell count and mastitis: An overview. Asian-Aust. J. Anim. Sci., 24: 429-438.
30. Schalm OW, Carrol JE and Jain NC. 1971. Bovine Mastitis. 1st Ed., Lea and Febiger, Philadelphia, USA, pp: 132-153.
31. Summer A, Sandri S, Francheschi P., Malacarne M, Formaggioni P and Mariani P. 2007. Seasonal trend of some parameters of the milk quality payment for Parmigiano-Reggiano cheese. Italian J. Anim. Sci., 6: 475–477.
32. Tančin V. 2013. Somatic cell counts in milk of dairy cows under practical conditions. Slovak J. Anim. Sci., 46(1): 31-34.
33. Tančin V, Ipema AH and Hogewerf P. 2007a. Interaction of somatic cell count and quarter milk flow patterns. J. Dairy Sci., 90: 2223- 2228.
34. Wicks HCF and Leaver JD. 2006. Influence of genetic merit and environment on somatic cell counts of Holstein–Friesian cows. Vet. J. 172: 52–57.