Poor Prediction Value of Sperm Head Morphometry for Fertility and Litter Size in Rabbit

This study was conducted to investigate the predictive capacity of fertility and litter size of sperm head morphometric measurements when the ejaculates fulfilled the minimum requirements commonly used in artificial insemination (AI). Semen samples from 11 rabbits (77 ejaculates) were evaluated for sperm motility, abnormal spermatozoa and sperm head morphometry using computer automated sperm analysis system. Morphometric dimensions for length, width, area and perimeter were analysed. Only ejaculates with more than 70% of motility rate and <15% of abnormal sperm were used for AI. A total of 1031 individual AI were performed in commercial rabbitries. Our results showed significant differences among animals for all sperm head measurements. The mean values for fertility and litter size obtained were 68.4 ± 0.01% and 9.3 ± 0.1% respectively. To assess the predictive value of morphometric dimensions in fertility, a logistic regression analysis was applied. Moreover, multiple linear regression analyses were used to examine the relationship between litter size and sperm head morphometric parameters. Logistic regression analysis rendered a significant model between fertility and area and perimeter, explaining the 0.65% variation. Multiple linear regression analysis rendered a significant model between litter size and width, area and perimeter that explained the 1.3% variation. By conclusion, the sperm head morphometric parameters assay showed low potential to predict fertility and litter size when the ejaculates fulfilled the minimum requirements commonly used in AI (motility and abnormal spermatozoa) in rabbit.     

Analysis of In vitro Fertilizing Capacity to Evaluate the Freezing Procedures of Boar Semen and to Predict the Subsequent Fertility

A porcine in vitro fertilization (IVF) system and seminal quality parameters of frozen–thawed boar semen were used to assess the effectiveness of two different thawing rates of frozen boar semen, and to address the question of whether differences between fertility of ejaculates could be predicted in a limited field trial. In the first experiment, two thawing procedures were analysed (37°C, 30 s; 50°C, 12 s) and no differences in sperm quality were found. However, when the procedure was 50°C, 12 s the IVF results showed a higher number of sperm per penetrated oocyte and a near 10 points higher rate of pronuclear formation. In the second experiment, the fertility results obtained in the limited field trial show to be efficient enough for application in a commercial use, especially for three of the employed boars (fertility ≥80%). In this limited study, the conventional seminal parameters are not accurate enough to discriminate good and bad boars in relation to fertility. On the contrary, parameters of in vitro penetrability are more precise to predict subsequent fertilities. As conclusion, the IVF fertilization system seems to be a good tool to evaluate the quality of frozen–thawed boar semen previous to its commercial way, to verify the bank semen storage quality and a good way to assay new sperm freezing procedures, as it is the more precise evaluating method in estimating the potential fertilizing ability.     

大尾寒羊产羔数对胎次的回归分析

该研究利用SPSS软件分析了大尾寒羊产羔数对胎次的回归,大尾寒羊产羔数对胎次的二次方回归和三次方回归的拟合度(R2)分别为0.9246和0.9269,可见两种模型下的回归方程的拟合度都比较高。产羔数对胎次的二次方回归方程中的所有回归参数均达到极显著水平(P<0.01),说明大尾寒羊产羔数对胎次用二次方回归方程具有一定的应用价值。     

Multivariate Analyses for Determining the Association of Field Porcine Fertility With Sperm Motion Traits Analysed by Computer‐Assisted Semen Analysis and With Sperm Morphology

This study investigates the association of semen traits with boar fertility. The fertility outcome (farrowing rate – FR and total piglets born – TB) of 14 boars was obtained from a field trial conducted during 10 week of breeding period on a commercial farm using multiparous sows (n = 948) through single‐sire mating with 2 × 10⁹ motile sperm cells per artificial insemination (AI) dose. Sperm motion parameters, evaluated with computer‐assisted semen analysis system in raw and stored semen at 17°C for 240 h, in addition to morphological sperm defects, measured on the collection day, were included in the analysis to determine which semen traits were important to discriminate the fertility potential of ejaculates from these boars. The data underwent multivariate cluster, canonical and discriminant analyses. Four clusters of boars were formed based on fertility outcome. One boar, with the lowest FR and TB values (89.7% and 11.98), and two boars, with the highest FR and TB values (97.8% and 14.16), were placed in different clusters. The other boars were separated in two distinct clusters (four and seven boars), including boars with intermediate TB (12.64 and 13.22) but divergent values for FR (95.9% vs 91.8%). Semen traits with higher discriminatory power included total motility, progressive motility, amplitude of lateral head displacement and cytoplasmatic droplets. Through multivariate discriminant analysis, more than 80% of the 140 ejaculates were correctly classified into their own group, showing that this analysis may be an efficient statistical tool to improve the discrimination of potential fertility of boars. Nevertheless, the validation of the relationship between fertility and semen traits using this statistical approach needs to be performed on a larger number of farms and with a greater number of boars.