الفهرس 
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Abstract
This study aimed to:
• Estimate annual genetic and phenotypic trends of 305-day MY in Holstein Friesian cows raised in experimental herds in Egypt.
• Characterize lifetime performance traits (LT, LTDIM, TP and LTMY) for Holstein Friesian cows in these herds.
• Determine the strength of genetic and phenotypic relationships between lifetime (LT), lifetime days in milk (LTDIM) and total number of complete lactations (TP) and partial lifetime traits (DIM1, DIM2 and DIM3) that measured during the first three lactations, including productive (LTMY, MY1, MY2 and MY3) and reproductive performance (AFC), and estimate the expected correlated response of some lifetime traits (LTMY, LTDIM, TP and LT) due to phenotypic selection.
The dataset comprised 5,518 complete lactations involving 1,749 Friesian cows, daughters of 1,165 dams and 167 sires, raised in two experimental herds between 1992 and 2022. Data were meticulously curated from two experimental herds: Sakha and El-Karada, under the Animal Production Research Institute (APRI) of the Ministry of Agriculture and Land Reclamation.
Data analysis utilized five statistical models:
1. Non-Genetic Effects Model: This model was applied in two stages. First, a correction was made for non-genetic factors (parity, year of calving, and season of calving). Then, the adjusted data were analyzed using the second model to estimate the analysis of variance for the effects of herd, year of birth, and season of birth on lifetime and productive life traits (305-day MY, total and partial). Significant effects of herd and birth year were found on all longevity traits (LT, LTDIM, TP, LTMY, MY1, and AFC), but no significant effects were observed on DIM1, DIM2, DIM3, MY2, and MY3. The season of birth had no significant effect on any traits except for MY1, where a significant effect was found. There were varied effects from the interaction between these factors.
2. Phenotypic and Genetic Trends Model: This model included two sub-models. The first sub-model estimated the least squares means (LSMEANS) of 305-day MY for each year of calving, with values ranging from 1,847 kg to 3,537 kg and a non-significant, slightly negative annual decrease of -3.059 kg/year. The second sub-model used the Best Linear Unbiased Prediction (BLUP) method to predict breeding values for 305-day milk yield, with expected breeding values ranging from -163.5 to 1,143.34 kg. An initial non-significant annual decrease of -8.46 kg was observed, gradually reducing by 0.3 kg annually.
3. Genetic analysis model: This analyzed lifetime (LT, LTDIM, LTMY, TP), partial lifetime (MY1, MY2, MY3, DIM1, DIM2 and DIM3), and age at first calving (AFC) using a linear bivariate animal model. Heritability estimates were generally low for most traits, ranging from 0.09-0.13, but moderate for some others, ranging from 0.20-0.24, such as DIM2, DIM3, MY2, and MY3.Correlation coefficients among total lifetime performance traits were high, with genetic correlations ranging from 0.80 to 0.99 and phenotypic correlations from 0.88 to 0.97.
4. Correlated response model: This model assessed the expected correlated response to selection in longevity traits at a selection intensity of 0.97. The expected correlated response in LT, LTMY, TP, and LTDM showed varying degrees of improvement. For lifetime (LT), the expected correlated response is 19.7, 16.22, and 4.5 days per year, respectively. In terms of lifetime milk yield (LTMY), the expected responses are 176.84, 136.92, and 57.611 kg per year, respectively. Total number of lactations (TP) shows expected responses of 0.0434, 0.0315, and 0.011 lactations per year, respectively. For lifetime days in milk (LTDIM), the responses are 14.096, 9.9683, and 4.1495 days per year, respectively.
The study concluded:
1. The decreasing upward trend in 305-day MY over the years indicates a need for environmental improvement, as environmental factors may contribute to phenotypic differences. Adverse conditions such as foot-and-mouth disease, mastitis, nutritional deficiencies, and severe climatic changes can negatively affect these phenotypic traits in the course of the study.
2. The slight genetic improvement observed in the herds starting in 2009 may be attributed to the introduction of semen from bulls with high breeding values. However, the trends suggest that there has been no significant improvement in the herd’s genetics or performance over the years. This could be due to issues in herd care and management over time, as well as the lack of effective breeding plans.
3. Direct selection for improving total lifespan performance traits, such as lifetime days in milk (LTDIM) and total lifetime milk yield (LTMY), is not recommended due to their low heritabilities. Instead, early indirect selection focusing on correlated responses in early performance traits, particularly milk production during early lactations, is more practical.
4. This approach emphasizes the importance of considering long-term performance in dairy cattle breeding. By selecting traits that positively impact longevity, such as early lactation milk production, overall lifetime performance of Holstein cattle can be indirectly enhanced. This strategy aligns with the goal of improving both productive and reproductive aspects of Holstein cattle, ultimately contributing to farm profitability and sustainable dairy production.
5. The results support using primary traits (MY1, MY2, MY3, DIM1, DIM2 and DIM3) to reduce the generation interval and increase the annual return and profitability of dairy production projects.
The study suggested the following recommendation to the dairy producers to achieve a balanced approach to breeding and management, optimizing the lifetime performance of Holstein cattle and enhancing profitability and sustainability in the dairy industry:
1. Indirect selection: Focus on selecting early performance traits like milk production during the first few lactations, which have higher heritabilities and are positively correlated with longevity.
2. Breeding Programs: Incorporate traits related to productivity and longevity into breeding programs, balancing milk yield with overall lifespan improvement.
3. Early Management: Optimize management practices during the early stages of a cow’s life, particularly focusing on age at first calving to maximize lifetime productivity and reduce culling rates.
4. Herd-Specific Strategies: Develop herd-specific strategies recognizing that optimal age at first calving and other management practices may vary by herd.
5. Further Research: Continue research into genetic and environmental factors affecting longevity in dairy cattle to identify additional selection criteria and management practices.
6. Collaboration: Foster collaboration among dairy farmers, researchers, and breeding programs to exchange knowledge and best practices for enhancing productivity and longevity in dairy cattle.