Sustainable Management of Animal Genetic Resources for Improving Livestock Production

A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Animal Genetics and Genomics".

Deadline for manuscript submissions: 30 April 2027 | Viewed by 584

Editor

College of Animal Science and Technology, Guangxi University, Nanning 530004, China
Interests: animal genetics; lactation physiology; multi-omics; food chemistry; genetic regulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Livestock systems are facing dual pressures of global food security demand and climate change challenges, while animal genetic resources serve as the core foundation for improving livestock production efficiency, enhancing environmental adaptability and realizing sustainable animal husbandry development. This Special Issue focuses on the sustainable management of animal genetic resources to advance high-quality livestock production, aiming to gather cutting-edge research on genetic resource characterization, diversity conservation, molecular innovation for genetic resource utilization and adaptive utilization strategies. It welcomes original research, reviews and technical reports covering genetic diversity evaluation, germplasm conservation technologies, genomic selection for genetic resources, climate-smart utilization of animal germplasm and sustainable production system optimization linked to genetic resources. This issue intends to bridge the gap between genetic resource protection and industrial application, provide theoretical and technical support for balancing genetic diversity maintenance and livestock productivity improvement and promote sustainable transformation of the global livestock industry in line with sustainable development goals.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  1. Evaluation of livestock animal genetic resource diversity, in situ/ ex situ conservation technologies and sustainable conservation strategies
  2. Development of high‑yield, stress‑tolerant and high‑efficiency livestock strains based on genomic selection and marker‑assisted breeding targeting animal genetic resources
  3. Climate‑smart utilization and improvement of animal genetic resources, and screening/utilization of livestock germplasm adapted to extreme environments
  4. Identification of key genes and dissection of epigenetic regulatory mechanisms related to the core traits of animal genetic resources and their utilization in germplasm innovation
  5. Innovative applications of digital breeding and artificial intelligence in animal genetic resource management and sustainable livestock production

I look forward to receiving your contributions.

Dr. Zhipeng Li
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Animals is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • animal genetic resources
  • sustainable management
  • livestock production
  • genetic conservation
  • germplasm innovation
  • genomic selection
  • climate-smart breeding
  • diversity conservation

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Published Papers (1 paper)

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Research

18 pages, 273 KB  
Article
Optimization of Ovum Pick-Up and Embryo Transfer Techniques for Enhancing Reproductive Efficiency in Nili-Ravi Buffaloes
by Yangyang Fan, Bo Wang, Leyi Wang, Cuijin Teng, Jinping Xiong, Jiaqi Jiang, Qingyou Liu, Kuiqing Cui and Zhipeng Li
Animals 2026, 16(13), 2086; https://doi.org/10.3390/ani16132086 - 6 Jul 2026
Viewed by 397
Abstract
Nili-Ravi buffalo is a core dairy breed for China’s genetic improvement, yet its assisted reproductive technologies are restricted by low ovum pick-up (OPU) efficiency and unstable embryo transfer (ET) pregnant rates. This study established statistically optimized key parameters for OPU procedures in Nili-Ravi [...] Read more.
Nili-Ravi buffalo is a core dairy breed for China’s genetic improvement, yet its assisted reproductive technologies are restricted by low ovum pick-up (OPU) efficiency and unstable embryo transfer (ET) pregnant rates. This study established statistically optimized key parameters for OPU procedures in Nili-Ravi buffalo, and evaluated the effects of multiple ET-related protocols. For OPU, 50–55 mmHg vacuum pressure balanced oocyte recovery and usable rate best (p < 0.05). Young and adult buffalo performed significantly better than prepubertal heifers (p < 0.05). Four consecutive weekly consecutive OPU sessions did not significantly reduce oocyte yield (p > 0.05). FSH treatment markedly enhanced oocyte quantity, quality and recovery efficiency (p < 0.05), with no significant difference between two FSH protocols (p > 0.05). For ET, three estrus synchronization protocols showed no significant effects on estrus, ovulation, transferable recipient and pregnant rates in breeding or non-breeding seasons (p > 0.05), though optimized protocols showed a numerical improvement. Combinations of CL grade and embryo quality, as well as post-transfer treatments with vitamin ADE, hCG and progesterone, did not significantly affect pregnancy or early abortion rates (p > 0.05). This study built a practical reference framework for OPU-ET procedures in Nili-Ravi buffalo, with key OPU parameters statistically optimized and ET protocols showing numerical improvements that require further validation with larger sample sizes. Full article
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