Background: In breeding programs for layers, selection of hens and cocks is based on recording phenotypic data from hens in different housing systems. Genomic information can provide additional information for selection and/or allow for a strong reduction in the generation interval. In this study, a typical conventional layer breeding program using a four-line cross was modeled and the expected genetic progress was derived deterministically with the software ZPLAN+. This non-genomic reference scenario was compared to two genomic breeding programs to determine the best strategy for implementing genomic information in layer breeding programs.
Results: In scenario I, genomic information was used in addition to all other information available in the conventional breeding program, so the generation interval was the same as in the reference scenario, i.e. 14.5 months. Here, we assumed that either only young cocks or young cocks and hens were genotyped as selection candidates. In scenario II, we assumed that breeders of both sexes were used at the biologically earliest possible age, so that at the time of selection only performance data of the parent generation and genomic information of the selection candidates were available. In this case, the generation interval was reduced to eight months. In both scenarios, the number of genotyped male selection candidates was varied between 800 and 4800 males and two sizes of the calibration set (500 or 2000 animals) were considered. All genomic scenarios increased the expected genetic gain and the economic profit of the breeding program. In scenario II, the increase was much more pronounced and even in the most conservative implementation led to a 60% improvement in genetic gain and economic profit. This increase was in all cases associated with higher breeding costs.
Conclusions: While genomic selection is shown to have the potential to improve genetic gain in layer breeding programs, its implementation remains a business decision of the breeding company; the possible extra profit for the breeding company depends on whether the customers of breeding stock are willing to pay more for improved genetic quality.
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http://dx.doi.org/10.1186/1297-9686-45-29 | DOI Listing |
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January 2025
Key Laboratory of Organ Regeneration and Reconstruction, State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China.
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January 2025
Hebei Agricultural University, Baoding, Hebei 071000, China; Key Laboratory of Intelligent Equipment and New Energy Utilization in Livestock and Poultry Farming of Hebei Province, Baoding, Hebei 071000, China.
At present, in the context of the highly intensive development of livestock and poultry breeding, digital management is becoming increasingly important, and digital twin systems are gradually being applied. To solve the contradiction between data acquisition and sensor network congestion, a virtual acquisition method based on historical data and real-time reference of point data is proposed when constructing a digital twin system. Firstly, computational fluid dynamics (CFD) simulation was used to analyze and determine the temperature distribution and environmental characteristics inside the layer house, and the collection area was preliminarily divided according to the CFD simulation results.
View Article and Find Full Text PDFVet Res Commun
January 2025
Department of Veterinary Medicine, University of Perugia, Via San Costanzo, 4, Perugia, 06126, Italy.
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View Article and Find Full Text PDFBiology (Basel)
December 2024
College of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.
The eggshell quality traits in avian species, including Yunnong chickens (YN), are crucial for commercial and breeding purposes. The use of advanced biotechnologies, such as gene sequencing, has become increasingly common for identifying genetic markers and comparing specific traits in livestock. However, genetic markers related to eggshell traits in YN chickens remain unexplored.
View Article and Find Full Text PDFAnimals (Basel)
December 2024
College of Information Engineering, Northwest A&F University, Yangling 712100, China.
Livestock behavior serves as a crucial indicator of physiological health. Leveraging deep learning techniques to automatically recognize dairy goat behaviors, particularly abnormal ones, enables early detection of potential health and environmental issues. To address the challenges of recognizing small-target behaviors in complex environments, a multi-scale and lightweight behavior recognition model for dairy goats called GSCW-YOLO was proposed.
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