Characteristics and risk factors for severe repeat-breeder female pigs and their lifetime performance in commercial breeding herds

Artificial insemination and optimization of the use of seminal doses in swine

The optimization of processes associated with artificial insemination (AI) is of great importance for the success of the pig industry. Over the last two decades, great reproductive performance has been achieved, making further significant progress limited. Optimizing the AI program, however, is essential to the pig industry's sustainability. Thus, the aim is not only to reduce the number of sperm cells used per estrous sow but also to improve some practical management in sow farms and boar studs to transform the high reproductive performance to a more efficient program. As productivity is mainly influenced by the number of inseminated sows, guaranteeing a constant breeding group and with healthy animals is paramount. In the AI studs, all management must ensure conditions to the health of the boars. Some strategies have been proposed and discussed to achieve these targets. A constant flow of high-quality and well-managed breeding groups, quality control of semen doses produced, more reliable technology in the laboratory routine, removal of less fertile boars, the use of intrauterine AI, the use of a single AI with control of estrus and ovulation (fixed-time AI), estrus detection based on artificial intelligence technologies, and optimization regarding the use of semen doses from high genetic-indexed boars are some strategies in which improvement is sought. In addition to these new approaches, we must revisit the processes used in boar studs, semen delivery network, and sow farm management for a more efficient AI program. This review discusses the challenges and opportunities in adopting some technologies to achieve satisfactory reproductive performance and efficiency.

Metabolomics reveals early pregnancy biomarkers in sows: a non-invasive diagnostic approach

In an effort to enhance reproductive management and reduce non-productive periods in swine breeding, this study presents a novel, non-invasive metabolomics approach for the identification of early pregnancy biomarkers in sows. Utilizing an untargeted metabolomics approach with mass spectrometry analysis, we examined saliva samples from pregnant (n = 6) and non-pregnant control sows (n = 6, artificially inseminated with non-viable sperm). Our analysis revealed 286 differentially expressed metabolites, with 152 being up-regulated and 134 down-regulated in the pregnant group. Among these, three metabolites, namely Hyodeoxycholic acid, 2'-deoxyguanosine, and Thymidine, emerged as potential early pregnancy biomarkers. These biomarkers were further evaluated using targeted LC-MS/MS quantification and qualification, accompanied by ROC curve analysis. The study confirmed Hyodeoxycholic acid and 2'-deoxyguanosine as promising biomarkers for early pregnancy detection, offering potential for future implementation in swine production environments. This research establishes a robust theoretical foundation for the development of innovative molecular diagnostic techniques and explores new avenues for molecular genetic breeding and non-invasive diagnostics, ultimately enhancing fertility and productivity in sow herds.

Recurrence patterns and lifetime performance of parity 1 sows in breeding herds with different weaning-to-first-mating intervals

Background

Our objectives were 1) to compare reproductive performance across parities and lifetime performance of parity 1 sows in six weaning-to-first-mating interval groups (WMI 0-3, 4, 5, 6, 7-20 and 21 days or more), 2) to determine the recurrence patterns and repeatability of WMI, and 3) to quantify factors associated with the probability of parity 1 sows having WMI 4 days. Examined data comprised 691,276 parity and 144,052 lifetime records of sows in 155 Spanish herds, served between 2011 and 2016. Mixed-effects models were applied to the data. Variance components analysis determined WMI repeatability.

Results

Proportions of parity 1 sows with WMI 0-3, 4, 5, 6, 7-20 and 21 days or more were 4.1, 30.0, 38.4, 7.9, 12.7 and 6.9%, respectively. Of the parity 1 sows with WMI 0-4 days, 43.3-60.5% had WMI 4 days in later parities, whereas 33.9-48.9% of those with WMI ≥5 days had WMI 5 days; WMI repeatability was 0.11. Parity 1 sows with WMI 4 or 5 days had 0.3-2.1 days shorter WMI in later parities than those with WMI ≥7 days (P <  0.05). Parity 1 sows with WMI 4 or 5 days also had 0.6-2.1 more annualized lifetime piglets born alive than those with WMI ≥7 days (P <  0.05). Notably, parity 1 sows with WMI 4 days had 0.3 more annualized lifetime piglets born alive than those with WMI 5 days (P <  0.05).

Conclusion

The WMI in parity 1 could be a useful predictor for subsequent reproductive performance and lifetime performance of sows.

High-performing farms exploit reproductive potential of high and low prolific sows better than low-performing farms

Background

Our objective was to examine the impact of farm effects and sow potential on various aspects of sow performance. We examined the interaction between sow prolificacy groups categorized at parity 1 and farm productivity groups for reproductive performance across parities, and lifetime performance. Data included 419,290 service records of 85,096 sows, on 98 Spanish farms, from first-service as gilts to removal, that were served between 2008 and 2013. Farms were categorized into three productivity groups based on the upper and lower 25th percentiles of the farm means of annualized lifetime piglets weaned per sow over the 6 years: high-performing (HP), intermediate-performing (IP), and low-performing (LP) farms. Also, parity 1 sows were categorized into three groups based on the upper and lower 10th percentiles of piglets born alive (PBA) as follows: 15 piglets or more (H-prolific), 8 to 14 piglets, and 7 piglets or fewer (L-prolific). The farm groups represent farm effects, whereas the sow groups represent sow potential. Linear mixed effects models were performed with factorial arrangements and repeated measures.

Results

Mean parity at removal (4.8 ± 0.01) was not associated with three farm productivity groups (P = 0.43). However, HP farms had 7.7% higher farrowing rates than LP farms (P <  0.05). As a result, H-prolific and L-prolific sows on HP farms had 29.7 and 30.7 fewer non-productive days during lifetime than the respective sows on LP farms (P <  0.05). Furthermore, the H-prolific and L-prolific sows on HP farms had 4.9 and 6.2 more annualized piglets weaned than respective H-prolific and L-prolific sows on LP farms (P <  0.05), which was achieved by giving birth to 0.8–1.0 and 1.4–1.7 more PBA per litter, respectively, than on HP farms during parities 2–6 (P <  0.05). During the first parity, HP farms had 18.8% H-prolific sows compared to 6.2% on LP farms.

Conclusion

Farm effects substantially affected lifetime performance of sows. Higher lifetime productivity of sows on HP farms was achieved by higher farrowing rate, fewer non-productive days, more PBA and more piglets weaned per sow, regardless of prolific category of the sows.

Culling in served females and farrowed sows at consecutive parities in Spanish pig herds

Background

The objectives of our study were 1) to characterize culling and retention patterns in parities 0 to 6 in served females and farrowed sows in two herd groups, and 2) to quantify the factors associated with by-parity culling risks for both groups in commercial herds. Lifetime data from first-service to removal included 465,947 service records of 94,691 females served between 2008 and 2013 in 98 Spanish herds. Herds were categorized into two groups based on the upper 25th percentile of the herd means of annualized lifetime pigs weaned per sow: high-performing (> 24.7 pigs) and ordinary herds (≤ 24.7 pigs). Two-level log-binomial regression models were used to examine risk factors and relative risk ratios associated with by-parity culling risks.

Results

Mean by-parity culling risks (± SE) for served females and farrowed sows were 5.9 ± 0.03 and 12.4 ± 0.05%, respectively. Increased culling risks were associated with sows that farrowed 8 or fewer pigs born alive (PBA). Also, farrowed sows in high-performing herds in parities 2 to 6 had 1.5–5.6% higher culling risk than equivalent parity sows in ordinary herds (P < 0.05). Furthermore, sows in parities 1 to 6 that farrowed 3 or more stillborn piglets had 2.2–4.8% higher culling risk than for sows that did not farrow any stillborn piglets (P < 0.05). For served sows, culling risk in parity 1 to 6 sows with a weaning-to-first-service interval (WSI) of 7 days or more were 2.2–3.9% higher than equivalent parity sows with WSI 0–6 days (P < 0.05). With regard to relative risk ratios, served sows with WSI 7 days or more were 1.56–1.81 times more likely to be culled than those with WSI 0–6 days.

Conclusion

Producers should reduce non-productive days by culling sows after weaning, instead of after service or during pregnancy. Also, producers should pay special attention to sows farrowing stillborn piglets or having prolonged WSI, and reconsider culling policy for mid-parity sows when they farrow 8 or fewer PBA.

Electronic supplementary material

The online version of this article (10.1186/s40813-018-0080-y) contains supplementary material, which is available to authorized users.