نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی کارشناسیارشد، بخش علوم دام، دانشکده کشاورزی، دانشگاه بیرجند
2 هیات علمی گروه علوم دامی، دانشکده کشاورزی، دانشگاه بیرجند
3 بخش علوم دام، دانشکده کشاورزی، دانشگاه بیرجند
4 بخش علوم دام، دانشکده کشاورزی، دانشگاه بیرجند ، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Introduction: The lactation curve represents a fundamental biological pattern describing the temporal changes in milk yield across mammalian species, governed by two interconnected physiological processes: cellular proliferation and apoptosis. These dynamic mechanisms modulate milk production primarily through alterations in both the number and the secretory activity of mammary epithelial cells. Given its substantial practical implications, the lactation curve has been extensively applied in genetic evaluations, test-day milk record analyses, health status monitoring, nutritional management, and strategic planning at the herd level, as it furnishes critical information for selection programs and assessments of production efficiency. In Iranian dairy cattle, the majority of previous investigations have relied upon empirical models—including the Wood gamma function, the Wilmink exponential equation, and the Ali–Schaeffer polynomial approach—to characterize the lactation curve; nevertheless, the application of the mechanistic MilkBot function has remained notably limited in this context. To bridge this knowledge gap and expand the methodological repertoire, the present study was therefore designed and conducted to perform a comprehensive genetic analysis of lactation curve traits in Iranian Holstein cows, utilizing the MilkBot function as a biologically meaningful alternative to conventional empirical formulations.
Materials and methods: The dataset employed in this study was obtained from the Animal Breeding Centre and Improvement of Livestock Products of Iran and comprised 2,794,876 test-day milk records collected from 984,690 dairy cows across 1,994 herds, with first calving dates spanning from 1983 to 2016. To characterize the shape of the lactation curve, the MilkBot nonlinear function was individually fitted to the test-day records of each cow, allowing estimation of its parameters per animal. The investigated traits encompassed the MilkBot function parameters, including Scale (overall production level), Ramp (rate of increase in early lactation), Offset (effective initiation time of lactation), and Decay (rate of decline after peak production). Additionally, lactation curve traits—including persistency, peak yield, time to peak yield, and 305-day milk production were computed. The statistical model incorporated herd–year–season of calving as a fixed contemporary group, while age at first calving (AFC) and Holstein gene percentage (HF) were treated as covariates. The additive genetic effect of each animal was included as a random effect. Genetic variance components were estimated via the derivative-free restricted maximum likelihood (DF-REML) method, applying a single-trait animal model implemented in the DMU software.
Results and Discussion: The heritability estimates for the studied traits ranged from low to moderately high. The highest heritability was observed for Decay (0.47), followed by 305-day milk yield (0.33) and peak milk yield (0.12), suggesting a favorable potential for genetic improvement in these traits through selection. In contrast, low heritabilities were obtained for ramp (0.008), offset (0.007), and time to peak yield (0.02), indicating that these traits are largely influenced by environmental factors. Genetic trends were significant only for 305-day milk yield and peak yield (P < 0.05), whereas phenotypic trends were significant for all traits except Offset (P < 0.05). The effects of AFC and HF were significant for all traits except offset and ramp (P < 0.05). Increasing AFC was associated with higher values of decay, scale, 305-day milk yield, and peak yield, suggesting that older cows at first calving tend to have higher production levels but faster post-peak decline. Moreover, a higher HF positively influenced scale, 305-day milk yield, peak yield, and persistency, while it reduced decay and time to peak yield, reflecting that animals with a greater HF reach peak production earlier, produce more milk, and exhibit greater lactation persistency.
Conclusions: The results of this study demonstrated that management factors including calving season and age at first calving, as well as the genetic factor of Holstein gene percentage, had significant effects on the parameters of the MilkBot function. Based on the findings, it is recommended that insemination planning be arranged so that calvings are concentrated in summer and autumn to enhance lactation persistency; moreover, age at first calving should be managed within the range of 22 to 24 months to establish an appropriate balance between peak yield and lactation persistency. Furthermore, prioritizing animals with a higher percentage of Holstein genes (more purebred) in breeding programs can improve both peak yield and lactation persistency. The relatively high heritability of the decay trait indicates its favorable potential for response to selection. Accordingly, selecting cows with lower decay (i.e., higher persistency) can lead to considerable genetic improvement in lactation persistency per generation and prevent a sharp decline in milk production during late lactation.
کلیدواژهها [English]
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