Characteristics of frozen thawed semen in predicting the fertility of buffalo bulls

Information regarding the fertility index in relation to sperm attributes, which helps in the selection of future breeding bulls are meager in buffaloes. The present study was conducted to measure the differences in motility characteristics, head biometry, acrosome, plasma membrane and DNA of cryopr...

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Veröffentlicht in:Indian journal of animal sciences 2014-04, Vol.84 (4), p.389-392
Hauptverfasser: KUMAR, D, KUMAR, PRADEEP, SINGH, PAWAN, YADAV, S P, SARKAR, SUSHEEL KUMAR, BHARADWAJ, A, YADAV, P S
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Sprache:eng
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Zusammenfassung:Information regarding the fertility index in relation to sperm attributes, which helps in the selection of future breeding bulls are meager in buffaloes. The present study was conducted to measure the differences in motility characteristics, head biometry, acrosome, plasma membrane and DNA of cryopreserved semen of fertile and sub- fertile buffalo bulls. The fertility of bulls was classified on the basis of conception rates (CR), where bulls having CR 28–35% and >55% were considered as sub-fertile and fertile bulls respectively. Computer assisted semen analyzer was used for motility and viability studies. Total motility, average path velocity (VAP), straight linear velocity (VSL) and curvilinear velocity (VCL) of sperm for fertile bulls were significantly higher than sub-fertile bulls. Significant differences were found in the length and width of sperm head between the 2 groups. The percentage of intactness of sperm acrosome of fertile bulls was significantly higher than sub-fertile bulls. The percentage of apoptotic sperm differed significantly between fertile and sub-fertile bulls. The sperm DNA integrity of fertile and sub-fertile bulls was not significantly different. In conclusion, the total motility, VAP, VCL, VSL, length and width of sperm head, acrosome integrity and percentage of apoptotic sperm, are useful for evaluating bulls’ semen quality to reduce the risk of using semen of poor-fertility bulls in AI programme.
ISSN:0367-8318
2394-3327
DOI:10.56093/ijans.v84i4.39838