Milking speed is an important trait influencing the udder health of dairy cows, as well as labor efficiency. However, it has received little attention in genomic association studies. The main objective of this study was to determine regions and genes on the genome with a potential effect on milking speed in Fleckvieh (dual-purpose Simmental) cattle. Genome-wide association studies were conducted using deregressed breeding values of bulls as phenotypes. We found 6 SNPs on 4 autosomes that were significantly associated with milking speed for additive effects. Significant regions on BTA4 and BTA19 correspond with findings for other dairy cattle breeds. Based on the observations of Fleckvieh breed managers, variation of milking speed in batches of daughters of some bulls is much higher than in daughter groups of other bulls. This difference in within-family variation may be caused by the transmission of alternative alleles from bulls being heterozygous for a gene affecting milking speed. To check on this, we considered the SD of yield deviations in milking speed of half-sib daughters as a new trait and performed GWAS for dominance effects. One signal on BTA5 passed the genome-wide Bonferroni threshold that corresponded to the significant signal from standard GWAS on deregressed breeding values. The key conclusion of this study is that several strong genomic signals were found for milking speed in Fleckvieh cattle, and that the strongest of them are supported by similar findings in Brown Swiss and Holstein Friesian cattle. Milking speed is a complex trait whose subprocesses have not yet been elucidated in detail. Hence, it remains a challenge to link the associated regions on the genome with causal genes and their functions.

Download full-text PDF

Source
http://dx.doi.org/10.3168/jds.2024-24854DOI Listing

Publication Analysis

Top Keywords

milking speed
36
speed fleckvieh
12
milking
9
speed
9
genome-wide association
8
fleckvieh cattle
8
cattle milking
8
association studies
8
deregressed breeding
8
breeding values
8

Similar Publications

As consumers increasingly prioritize food safety and nutritional value, the dairy industry faces a pressing need for rapid and accurate methods to detect essential nutritional components in milk, such as fat, protein, and lactose. Hyperspectral imaging (HSI) technology, known for its non-destructive, fast, and precise nature, shows great promise in food quality assessment. However, the high dimensionality of HSI data poses challenges for effective band selection and model optimization.

View Article and Find Full Text PDF

Background: Bovine mastitis significantly impacts the dairy industry, causing economic losses through reduced milk production, lower milk quality, and increased health risks, and early detection is critical for effective treatment. This study analyzed milk electrical conductivity data from 9,846 Chinese Holstein cows over a two-year period, collected during three daily milking sessions, alongside smart collar data and dairy herd improvement test results. The aim was to conduct a comprehensive genetic analysis and assess the potential of milk electrical conductivity as a biomarker for the early detection of bovine subclinical mastitis.

View Article and Find Full Text PDF

The objective of this randomized clinical trial was to assess whether early intervention with a nonsteroidal anti-inflammatory drug (NSAID) following a disease alert generated by automated milk feeders could reduce diarrhea severity and improve performance in dairy calves. Seventy-one Holstein calves were enrolled on an automated milk feeder (recorded milk intake and drinking speed) at 3 d of age and received up to 15 L/d (150 g/L) of milk replacer until 35 d of age. An alert that was previously validated as diagnostically accurate to identify calves at risk for diarrhea was used using automated milk feeder data (≤60% rolling dividends in milk intake and/or drinking speed over 2 d).

View Article and Find Full Text PDF

This study evaluates the energy efficiency of an urban dairy farm in Tlemcen, Algeria, by assessing the feasibility of a grid-connected photovoltaic (PV)/wind hybrid energy system. Using HOMER and MATLAB software, the study explores the potential for replacing the farm's existing energy systems with a hybrid system integrated into a low-voltage electrical grid. The HOMER software determined the configuration that resulted in the lowest net present cost, energy cost in kWh, greenhouse gas emission mitigation, and renewable fraction (RF).

View Article and Find Full Text PDF

Exploring how milk production, body weight and body condition dynamics affect reproductive success after artificial insemination in dairy goats.

Theriogenology

December 2024

Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 91120, Palaiseau, France.

In the context of agroecological transition, breeding females with robust reproductive performance, leading to prolonged lactation sequences, is valuable for farmers. This study aimed to explore the relationship between artificial insemination (AI) success and phenotypic lactation curves that serve as proxies for key biological functions in Alpine and Saanen goats. Using data from two French experimental farms (1996-2021), the study analyzed time series data on milk yield (MY), body weight (BW), and sternal body condition score (BCS_S).

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!