National Academy of Agricultural Sciences (NAAS)
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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706 Issues : 12 per year Publisher : Excellent Publishers Email : editorijcmas@gmail.com submit@ijcmas.com Editor-in-chief: Dr.M.Prakash Index Copernicus ICV 2018: 95.39 NAAS RATING 2020: 5.38 |
This study investigated factors affecting milk yield and lactation length in Sahiwal cattle under smallholder dairy production systems of Uttar Pradesh, India. Test-day milk yield data from 774 animals across 14 districts were analyzed using a Random Regression Model (RRM) within a linear mixed model framework. Fixed effects included lactation number, calving year, and district, while individual animal variation was modeled as a random effect. The fitted lactation curve captured the biological pattern of rapid rise to peak yield followed by gradual decline. Significant effects of parity, year, and district (p < 0.05) were observed, with milk yield improving steadily from 2018 to 2025, reflecting genetic progress and improved management under the Enhance Genetics Project. District-level differences highlighted opportunities for region-specific interventions. The study demonstrates the utility of RRM for indigenous breeds, providing a robust framework for longitudinal evaluation and precision breeding. Practical implications include guiding resource allocation, improving farmer-level management, and integrating genomic data for enhanced selection accuracy.
Milk Yield, Lactation, Calving Year, Random Regression Models
Banos, G., & Shook, G. E. (1990). Genotype by environment interaction and genetic correlations among parities for milk yield and fertility. J. Dairy Sci., 73(9), 2563–2573.
Bignardi, A. B., El Faro, L., Cardoso, V. L., et al., (2011). Random regression models to estimate test-day milk yield genetic parameters for Holstein cows in Brazil. Livest. Sci., 137, 176–183.
Guo, Z., & Swalve, H. H. (1995). Modeling of the lactation curve as a sub-model in the evaluation of test-day records. J. Dairy Sci., 78(8), 1903–1914.
Jamrozik, J., & Schaeffer, L. R. (1997). Estimation of genetic parameters for test-day models with random regressions for yield traits of first lactation Holsteins. J. Dairy Sci., 80(4), 762–770.
Macciotta, N. P. P., Vicario, D., & Cappio-Borlino, A. (2005). Detection of different shapes of lactation curve for milk yield in dairy cattle by empirical mathematical models. J. Dairy Sci., 88(3), 1178–1191.
Meyer, K. (1998). Estimating covariance functions for longitudinal data using a random regression model. Genet. Sel. Evol., 30, 221–240.
Misztal, I., Strabel, T., Jamrozik, J., Mäntysaari, E. A., & Meuwissen, T. H. E. (2000). Strategies for estimating parameters in test-day models. J. Dairy Sci., 83, 1125–1134.
Olori, V. E., Hill, W. G., McGuirk, B. J., & Brotherstone, S. (1999). Estimating variance components for test day milk records by random regression models. Livest. Prod. Sci., 61, 53–63.
Patel, M., Sharma, A., & Yadav, R. (2022). Influence of agroclimatic zones on milk production traits in crossbred cattle. Indian J. Anim. Res., 56(2), 243–248.
Schaeffer, L. R. (2004). Application of random regression models in animal breeding. Livest. Prod. Sci., 86, 35–45.
Singh, V. P., Patel, A., & Sharma, D. (2020). District-wise variation in milk yield and component traits among smallholder dairy systems in Uttar Pradesh. Indian J. Dairy Sci., 73(6), 601–609.
Swalve, H. H. (1995). The effect of test day models on the estimation of genetic parameters and breeding values for dairy yield traits. J. Dairy Sci., 78(4), 929–938.
Gupta, A., Singh, R., & Tomar, S. S. (2023). Impact of genetic improvement programs on milk yield trends in North Indian dairy herds. Indian Journal of Dairy Science, 76(2): 185–193.
Kakade, S., Bhagat, R., & Pandey, D. (2017). District-wise performance variation in crossbred cattle under smallholder conditions of Uttar Pradesh. Indian Journal of Animal Production, 34(1): 44–49.
Khan, M. S., Rehman, Z. U., & Ahmad, S. (2018). Lactation curve and parity effects in dairy cattle under field conditions. Journal of Animal Breeding and Genetics, 135(3): 207–215.
Kumar, R., Yadav, S. P., & Singh, M. (2022). Breed and management factors affecting milk yield of crossbred dairy cows in Northern India. Tropical Animal Health and Production, 54: 121.
Misztal, I., Legarra, A., & Aguilar, I. (2019). Random regression models for longitudinal data in dairy cattle breeding. Journal of Dairy Science, 102(6): 4995–5010.
Patil, R., Bhosale, P., & Koli, D. (2021). Temporal changes in milk yield and composition traits under organized breeding programs. Indian Journal of Animal Research, 55(9): 1041–1048.
Singh, P. K., Tiwari, R., & Dubey, R. (2020). Determinants of milk yield variation among smallholder dairy farmers in Eastern Uttar Pradesh. Journal of Animal Production Research, 60(4): 375–382.
Strabel, T., & Jamrozik, J. (2020). Use of mixed models and random regression for genetic evaluation of test-day milk yield. Livestock Science, 238: 104087.
Yadav, V. S., Patel, J. B., & Sharma, M. (2019). Effect of parity on milk yield and lactation persistency in crossbred cattle. Indian Journal of Dairy Science, 72(1): 34–40.
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