Submitted15-10-2020|
Accepted06-04-2021|
First Online 22-04-2021|
ABSTRACT
Methods: The study was conducted in 28 commercial dairy farms based on two types of data. Primary data: generated from stock inventory and farm management information using a pretested survey questionnaire and analyzed data of average production performance and quality of milk traits indifferent crosses collected directly four times season wise (summer, monsoon and winter) from each selected farms of Chittagong, Bangladesh from 2014 to 2015.
Result: Overall management system of irrespective categories of farms was in moderate condition with some exception. Highest number of milking animal was 50% HF × 50% L cross under all farm categories. Crosses of 75% HF × 25% L were highest in number under farm category A and lowest C. In terms of milk quality and quantity, farms with higher number of animals were in better condition. Daily average milk yield was significantly higher in monsoon followed by summer and winter. Therefore, this current study would be helpful for the dairy farmers to find out an expected solution to overcome the problems in this sector.
INTRODUCTION
Annually about 18 million liter liquid milk is produced by the country of which only 7 percent is sold to milk processors and the rest amount is sold at the local market. Though commercial dairy farming is an emerging industry in Chittagong, but current milk production is inadequate to meet the demand. There are 400 farms operating in Chittagong area and produces approximately 60,000 liters of milk per day (Barua et al., 2018). Relationship between quality and quantity of milk with farming system is seriously neglected in this area (Choudhury et al., 2017). The dairy sector of Chittagong is facing a lot of problems in different issues, including milk quality and quantity, as there are no data on relationship of farming management with milk quality and quantity of commercial dairying in Chittagong Therefore, the study was conducted with the objective to (i) increase the profitability of dairy farming by focusing on their problems under existing farm management system and (ii) identify the correlated problems with quality and quantity of milk produced by different crosses of Holstein under commercial dairying in Chittagong.
MATERIALS AND METHODS
The primary data of farming condition were analyzed by Microsoft Excel and milk yield and composition were analyzed by the PROC GLM of SAS (SAS, 2009), following the model as:
Yijkl= µ + Fi + GJ + SK+ eijkl
Here, µ = Overall mean.
Fi = ith Farm effect [i- 1 to 3, (A, B and C type farm)].
Gj= jth Genotype effect (j = 1 to 5 Holstein grade).
Sk= kth Season effect [k = 1 to 3 (summer, monsoon and winter)].
eijk= Random error, which is distributed normally as N (0, σ2).
The studied parameter mean differences were compared using the least significant different (LSD) test at 5% level of significance (P<0.05).
RESULTS AND DISCUSSION
Different management conditions of the farm are presented in Table 1. It was observed, that the floor condition was good in 32.08%, medium in 25.63% and poor in 42.29% farms irrespective of category under commercial dairying of Chittagong. Appropriate cleaning of cattle housing and environment around the cows can increase both quantity and quality of milk produced.
Feeding and housing practices in the case of calf rearing have a significant effect on health status and production performance of a farm. Study showed that most of the farm belongs to fair (46.43), followed by good (39.29%), very good (10.71%) and poor (3.57%) condition for this characteristics. Matondi et al., (2014) stated that effect of improper housing and feeding of calf affects their productive life and total farm production. A very limited number, 21% (n=6) dairy farms had sufficient own fodder land for cultivation and most of the farmers (79%, n=22) were dependent on purchased green forage. Many researches work (for example, Basset et al., 2010; Faruk et al., 2015) have shown relation to the ownership of fodder land of dairy farmers. For routine management, regularity of feeding with other routine activities such as regularity in cleaning of barn and cows at least twice a day was considered for the farm. Most of the farms irrespective of categories were under moderate condition (67.86%) followed by good (21.43%) and poor (10.71%) for this activities. That might be related to hard size, adaptation of technologies in management and socioeconomic condition of the farmers. It was seen that farms with large herd size were strictly maintained the routine and management activities. These findings were supported by Mariammal et al., (2018).
Farm classification according to the overall management
Classification of farms based on management type is shown in Table 2. It was observed that the highest percentage (42.86%) of farms under category A were following good management practices whereas the lowest were under category C and vice versa.
These results suggested that farmers with long time experience likely had a better understanding and know how to appropriately manage their dairy herds under harsh climatic and economic conditions than less experienced farmers. These characteristics might be the cause of being difference in the management condition of the farms in Chittagong. Our finding is supported by Yeamkong et al., (2010).
Average herd size and herd composition under different category of farms
Average herd size and composition under different categories of farms are presented in Table 3.The average herd size of category A, B and C farms was 217.14, 52.88 and 28.38 number of animals, respectively, which was differed significantly (P<0.05).
In the present study the herd composition was found as, milking cows were 48%, dry cows with pregnant 14%, heifer 10%, yearling bull 11%, breeding bull 3% . These findings are supported by the report Heifer International. (2013).
Average percentage of identified crossbred milking cows under different categories of farms
The distribution of average percentage of milking cows in different crossbreds among farm category of farms is depleted in Table 4.
Among the five types of crosses, 50% HF × 50% L crossbred was found to be higher irrespective of farm category. The highest percentage of 75% HF × 25% L cross was found in category A and lowest in category C farms. The cross of 75 percentage Holstein performed significantly lower than the 50 percentage crosses in tropical wet and dry climatic zone (Gaulkande et al., 2013). This may be cause for higher number of 50% HF × 50% L crossbred present irrespective of farm category. According to Yeamkong et al., (2010) more experienced farmers are able to provide cows with better management and better nutrition; this may be cause of rearing highest percentage of 75% HF × 25% L crosses in larger size farm.
Milk quality and quantity in different category of farms
Quantity and quality of milk production in different category of farms are presented in Table 5. Results showed, that there were significant differences (p<0.05) in average milk yield, fat and protein content among different category of farms but no differences were found for other components of milk (Table 5). Daily average milk yield(cow/liter), fat and protein and SNF% were the highest for category A farm followed by B and C. Overall performance ware better in farms under category A followed by B and C. This might be due to different level of knowledge of dairy farmers in different farm category.
The farmers under category A had a better knowledge on overall management of dairy farming in terms of quality and quantity of milk production. This finding was concords with Yeamkong et al., (2010) who stated that different levels of knowledge, learning ability, adaptation of technological advices and accurate decission are important factors for of improving milk production quality and quantity.
Average milk quality and quantity of different crossbreds
The average milk production/cow/day of different crossbreds was differed significantly (p<0.05) (Table 6). The highest milk production was recorded for 75% HF × 25% L (16.60±2.613) and the lowest for 50%S × 50% L (10.75±5.232) crosses (Table 6). Our findings were supported by Khair et al., (2007). The milk fat% was differed significantly (p<0.05) among different crossbreds of cattle. Cow milk fat is influenced by numerous factors such as genetics of the cows, nutrition, milking time and interval. Similar finding was reported by Simoes et al., (2014) and Lee et al., (2014). The average milk protein, ash and lactose percentage didn’t differ significantly among crossbreds. This finding was agreed with the findings of Shibru et al., (2019), Wangdi et al., (2016) and Adesina (2012).
Average milk quality and quantity in different seasons
The seasonal difference of milk quality and quantity are given in the Table 7. Significant (p<0.05) seasonal variation was found in milk yield, fat, protein, lactose and ash % among different seasons, but no difference was observed for SNF%.
The daily average milk yield was significantly higher (p<0.05) in monsoon (13.96±3.403) followed by summer and winter. This might be due to availability of green grasses during monsoon compared to other seasons. The findings were similar to the findings of Harisha et al., (2015). Average fat and protein content was higher in winter but lactose, ash and SNF were higher during summer that might be due to seasonal effect that was similar with the findings of Diego and Hélio (2011).
This study also indicated that most of the dairy farmers with some exception, often supply excess amount of concentrate instead of green specially in the season when roughage is low, to obtain maximum amount of milk, this might be the major cause of affecting quality and quantity of milk in different season.
CONCLUSION
REFERENCES
- Adesina, K. (2012). Effect of breed on the composition of cow milk under traditional management practices in Ado-Ekiti, Nigeria. Journal of Applied Science and Environmental Management. 16(1): 55-59.
- Basset, M.A., Huque, K.S., Sarker N.R., Hossain M.M. and Islam M.N. (2010). Evaluation of milk urea nitrogen of dairy cows reared under different feed based in the different season. Journal Science Foundation. 8(1 and 2): 97-110.
- Chowdhury, S., Barua, S.R., Rakib, T.M., Rahman, M.M., Ferdushy, T., Hossain, M.A., Islam, M.S., Masuduzzaman M. (2017). Survey of calf management and hygiene practices adopted in commercial dairy farms in Chittagong, Bangladesh. Advances in Animal and Veterinary Sciences. 5(1): 14-22.
- Diego, B.N. and Hélio, L. (2011). Breed and season influence on milk quality parameters and in mastitis occurrence. Vetrinarian Brasilian dezembro. 31(12): 1045-1052.
- Faruk, M.S.A., Islam, S.K.M.A., Alam, M., Deb, A. and Chanda, G.C. (2015). Husbandry practices of dairy farming at Chittagong sub-urban area. International Journal of Natural Sciences. 5(2): 59-65.
- Galukande, E., Mulindwa, H., Wurzinger, M., Roschinsky, R., Mwai, A.O. and Sölkner, J. (2013). Cross breeding cattle for milk production in the tropics: achievements, challenges and opportunities. Animal Genetic Resources. 52: 111-125.
- Gebrehiwet, B.H. (2020). Dairy cattle cross-breeding in Ethiopia: Challenges and Opportunities: A Review. Asian Journal of Dairy and Food Research. 39(3): 180-186.
- Harisha, M., Satyanarayan, K., Jagadeeswary, V., Lalith Achoth, Rajeshwari, Y.B. and Nagaraj, C.S. (2015). Milk production trends in Kolar and Chikkaballapur districts of Karnataka, India, Asian Journal of Dairy and Food Research. 34(2): 113-115.
- Heifer International. (2013). Final Report on Dairy Value Chain Development in Bangladesh. pp 41.9116742http://heifer bangladesh.org.
- Hoque, M.J., Alam, N.D. and Nahid. A.K. (2018). Health consciousness and its effect on perceived knowledge and belief in the purchase intent of liquid milk: consumer insights from an emerging market. 2018. 7(9): 150
- Hossain, B. and Dev, S.R. (2013). Physiochemical characteristics of various raw milk samples in a selected dairy plant of Bangladesh. International Journal of Engineering and Applied Sciences. 1(3): 91-96.
- Kahir, M.A., Islam, M.A., Rahman, A.K.M.A., Nahar, A., Rahman, M.S. and Song, H.J. (2008). Prevalence and risk factors of subclinical bovine mastitis in some dairy farms of Sylhet district of Bangladesh. Korean Journal of Veterinary Services. 31(4): 497-504.
- Lee, J., Seo, J., Young, L.S., Ki, K.S. and Seo, S. (2014). Meta-analysis of factors affecting milk componentyields in dairy cattle. Journal of Animal Science and Technology. 56(5): 2-5.
- Mariammal, R., Seethalakshmi, M. and Narmatha, N. (2018). Knowledge and adoption of improved dairy management practices among women dairy farmers in Dindigul District of Tamil Nadu, India. Asian Journal Dairy and Food Research. 37(2): 105-108.
- Matondi, G.H.M., Nyamushamba, G.B., Motsi, T.T. and Masama, E. (2014). Evaluation of smallholder dairy calf rearing systems in Zimbabwe. Livestock Research for Rural Development. 26(3): 44.
- Nath, B.K., Das, B.C., Bari, M.S. and Rahman, M.A. (2014). Prevalence and risk factors of repeat breeding in commercial dairy farms of Chittagong district of Bangladesh. International Journal of Natural Sciences. 4(1): 21-27.
- Shibru, D., Tamir, B., Kasa, F. and Goshu, G. (2019). Effect of season, parity, exotic gene level and lactation stage on milk yield and composition of Holstein Friesian crosses in central highlands of Ethiopia. European Journal of Experimental Biology. 9: 4-15.
- Sikder, M.S.I., Akteruzzaman, M., Parveen S. and Shamsuddin, M. (2009). Economics of community dairy farming in Satkhira district. Bangladesh Journal of Animal Science. 38 (1-2): 164-169.
- Simões, M.G., Portal, R.E., Rabelo, J.G. and Ferreira, C.L.L.F. (2014). Seasonal variations affect the physicochemical composition of Buffalo milk and artisanal cheeses produced in Marajó Island (Pa, Brazil). Advance Journal of Food Science and Technology. 6(1): 81-91.
- Wangdi, J., Zangmo, T., Karma, Mindu. and Bhujel, P. (2016). Compositional quality of cow’s milk and its seasonal variations in Bhutan. Livestock Research for Rural Development 28(1):
- Yeamkong, S., Koonawootrittriron, S., Elzo, M.A. and Suwanasopee, T. (2010). Effect of experience, education, record keeping, labor and decision making on monthly milk yield and revenue of dairy farms supported by a private organization in Central Thailand. Asian-Australasian Journal of Animal Science. 23: 814-824.
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