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

Evolution of Sow Productivity and Evaluation Parameters: Spanish Farms as a Benchmark

This study examines the global evolution of sow productivity, with a particular focus on Spain. The analysis is based on key performance metrics such as piglets weaned per sow per year (PWSY), prolificacy, and pre-weaning mortality, utilizing data from literature reviews, the InterPIG, and BDporc® databases. Globally, significant advancements in genetic selection and management practices have led to productivity increases across major pig-producing countries, with notable improvements in prolificacy. However, higher prolificacy has been accompanied by rising piglet mortality rates during lactation, posing sustainability challenges. In Spain, the average productivity of commercial sows increased from 23.78 PWSY in 2009 to 29.45 PWSY in 2023, while Iberian sows reached an average of 17.44 PWSY. Despite these gains, Spain's figures remain slightly below the European Union average. The study highlights the need for new benchmarks, such as non-productive days, piglet survival, and sow longevity, to more accurately assess farm efficiency. These indicators, combined with considerations for animal welfare and environmental sustainability, are crucial for addressing current challenges such as piglet mortality, sow culling, and the carbon footprint. The findings emphasize the importance of adopting comprehensive management strategies that balance productivity with growing social and environmental demands on the swine industry.

The impact of herd age structure on the performance of commercial sow-breeding farms

BACKGROUND

The herd age structure, i.e., distribution of sows within a farm based on their parity number, and its management are essential to optimizing farm reproductive efficiency. The objective of this study is to define different types of herd age structure using data from 623 Spanish commercial sow farms. Additionally, this study aims to determine which type of herd age structure can enhance reproductive efficiency at the farm level.

RESULTS

Farms are classified into three groups according to the quadratic function fitted to the percentage of sows by parities. This classification unveils three types of herd structures: type 1 (HS1) exhibits a concave-downward trend, with a higher percentage of sows in intermediate parities (mean of 45.5% sows between the 3rd to 5th parity); type 2 (HS2) presents a trend curve that is close to a straight line, with a gradual decrease in the percentage of sows per parity (approximately 2% loss of sows census per parity); and type 3 (HS3) shows an upward concave trend curve, with an increase in the percentage of sows in later parities (19.0% of sows between 7th and ≥ 8th parity). Parametric tests assess productivity differences between the three types of herd structures (p < 0.01). HS1 farms have the best productive outcomes over a year, with 31.2 piglets weaned per sow and year (PWSY) and a farrowing rate of 87%, surpassing HS2 and HS3 farms (30.1 and 28.7 PWSY; 85.3% and 83.4% farrowing rates, respectively). HS1 also have the lowest percentage of sows returning to oestrus (11.8%) and the highest number of weaned piglets per litter (12.8), compared to HS2 (13.2% and 12.4 piglets weaned) and HS3 (15.1%, 11.9 piglets weaned). These differences show a medium effect size (η2 between 0.06 to < 0.14).

CONCLUSIONS

This study shows the importance of herd age structure on sow-breeding farms as a factor of reproductive efficiency. The results endorse the proposed classification based on the curvature of the trend parabola obtained with the quadratic function to categorize herd structures into three groups. Additionally, these findings highlight the importance of considering the herd age structure in farm decision-making.

Characterization of Removal Reasons for Nurse Sows and the Associated Removal Due to Their Extended Lactation Length in Hyperprolific Farrow-Wean Herds

This study aimed to characterize and quantify reasons for the removal of nurse sows and identify the removal associated with their extended lactation length (ELL). A total of 100,756 removed nurse sows within a period of 2016-2022 from 53 sow herds in the Midwest USA were analyzed. Reproductive failure was the most common removal reason (χ2 = 8748.421, p < 0.001) affecting P1, P2, and P3 nurse sows. Failure to conceive and absence of estrus were the main causes of reproductive failure (χ2 = 352.480, p < 0.001) affecting P1 and P2 nurse sows and P1 and P5 nurse sows, respectively. When P2 and P6 nurse sows had an ELL of 0-7 d, they faced a high chance (χ2 = 13.312, p = 0.021) of removal due to conception failure and failure to return to heat, respectively. When P2 and P5 nurse sows had an ELL of 8-14 d, they were highly vulnerable (χ2 = 59.847, p < 0.001) to removal due to failure to conceive and showing heat, respectively. Finally, when ELL was at 15-21 days, P4 and P5 nurse sows were more likely (χ2 = 41.751, p < 0.001) to be removed due to failure to express heat, whereas at the same time, P2 and P3 nurse sows experienced the same removal threat due to failing to conceive. These results could help producers manage nurse sow systems.

Effects of nursing a large litter and ovarian response to gonadotropins at weaning on subsequent fertility in first parity sows

Post-weaning fertility failures occur more often in parity 1 (P1) sows due to high metabolic demands for lactation and their inability to meet energy requirements for maintenance, growth, and reproduction. We hypothesized that body condition loss occurs more frequently in P1 sows nursing a large litter, resulting in impairment of ovarian follicle development during lactation and post-weaning, which can negatively impact estrus and subsequent fertility. At 24 h post-farrowing, P1 sows (n = 123) were assigned to treatment (TRT) based on sow weight and the number of functional teats to receive a high number (HN, 15 to 16) or low number (LN, 12) of nursing piglets. At weaning, sows in each TRT were assigned to receive PG600 or None (Control). During lactation, sow body measures were obtained and ovarian follicles were assessed in mid-lactation and post-weaning. Lactation data were analyzed for the effects of TRT, and fertility data after weaning were assessed for TRT x PG600, but there were no interactions (P > 0.10). During lactation, 22.2 % of HN sows lost ≥ 4 piglets due to death or removal, and so these sows were excluded from further analysis. The HN sows were lighter (-6.2 kg), had less backfat (-1.0 mm), had lower body condition score (-0.4), and lost more nursing piglets (-1.2) than LN sows (P < 0.05). However, HN sows weaned more pigs (14.0) than LN sows (11.0). There was no effect of TRT on wean to estrus interval (4.2 d), but the interval was 0.5 days shorter for PG600 (P = 0.004) than control. There were no effects of TRT or PG600 on estrus within seven days after weaning (87.3 %), but PG600 induced smaller (P = 0.002) follicles at estrus (6.7 mm) than control (7.3 mm). In the subsequent parity, there were no effects of TRT or PG600 on farrowing rate (93.9%) and total born (13.2). Overall, HN sows lost more piglets and body condition but still weaned more pigs without any detrimental effects on subsequent reproductive performance.

Effects of physical or fenceline boar exposure and exogenous gonadotropins on puberty induction and subsequent fertility in gilts

The present study was part of a larger experiment that evaluated litter of origin effects on gilt production. The objectives of this study were to determine the effect of physical or fenceline boar exposure and exogenous gonadotropins on puberty induction and subsequent fertility in a commercial farm environment. The experiment was performed in three replicates. Prepubertal gilts were assigned by pen (13/pen) to receive 15 min of daily Fenceline (FBE, n = 153) or Physical (PBE, n = 154) Boar Exposure (BE) for 3 weeks starting at 184 d of age in a purpose-designed Boar Exposure Area (BEAR). At the start of week 3, prepubertal gilts were randomly assigned to receive PG600 or none (Control). From weeks 4 to 6, estrus was checked using only FBE. During weeks 1 to 3, measures of reproductive status were obtained weekly or until expression of estrus. Upon detection of first estrus, gilts were relocated into stalls and inseminated at second estrus. PBE reduced age (P = 0.001) and days to puberty (P = 0.002), increased the proportion of gilts in estrus (P = 0.04) in week 1 (38.3 vs. 27.5%), and tended (P = 0.08) to improve estrus in week 2 (37.6 vs. 26.1%) compared to FBE, respectively. In week 3, more prepubertal gilts receiving PBE-PG600 exhibited estrus (P = 0.04; 81.8%) compared to PBE-Control (40.3%), FBE-PG600 (56.4%), and FBE-Control (47.8%). Overall, expression of estrus through week 6 tended (P = 0.08) to be greater for PBE than FBE (91.5 vs. 85.0%). PBE increased (P ≤ 0.05) or tended to increase (P > 0.05 and ≤0.10) service and farrowing rates in parities 1 through 4, but within parity, there were no effects (P > 0.10) on pig production or wean to service interval. Analyses also indicated that weeks from start of boar exposure to puberty, litter of origin traits, and follicle measures at puberty were related to the subsequent fertility. The results of this study confirm the advantages of using increased intensity of boar exposure, combined with PG600 treatment, for effective induction of pubertal estrus in a commercial setting.

Selection for litter size and litter birthweight in Large White pigs: Maximum, mean and variability of reproduction traits

Gradually increasing trend of litter size poses a challenge to pig farmers in terms of managing larger litters. Therefore, it seems that a balanced approach that optimises litter size, litter birthweight, and uniformity of those traits is needed in order to address animal welfare and farm management concerns. This study aimed to investigate this issue by defining several traits for total number born (TNB), number born alive (NBA) and litter birthweight (LW). First, the highest value from at least five records per sow was selected as maximum (max) value for each reproduction trait. Second, a mean (mean) for each reproduction trait was calculated per sow. Last, the variability of reproduction traits between parities of the sow was calculated as log-transformed variance of residuals of all observations per sow for each reproduction trait (LnVar). In total, 23 193 Large White sows from Topigs Norsvin with 152 282 litter records were used for analysis in ASReml 4.1. Also, a simulation of breeding schemes was performed with the use of SelAction 2.1 and estimates from genetic analysis. Maximum value of reproductive traits had a much higher heritability than repeated observations or mean of reproduction traits, e.g., 0.31 for maxTNB vs. 0.12 for TNB and 0.07 for meanTNB, which allows for a faster response under selection. The maximum value traits, however, were found to carry more risks, i.e. higher ratio of stillborn (not for maxNBA) and increased variability of traits. Thus, using them in breeding programme should be carefully considered. The genetic coefficient of variation on SD level estimated to indicate the genetic magnitude for variability phenotypes indicated a maximum change of 6-9% in genetic SD of TNB, NBA and LW. The genetic correlations between mean and corresponding variability traits varied from 0.66 to 0.74, whereas the correlation between other mean and variability traits ranged from 0.33 to 0.99. The simulation indicated that even with selection targeted against the variability of reproduction traits, a very limited change should be expected due to a complex genetic and phenotypic relationship between the traits. In the scenarios with selection against LnVarTNB and LnVarLW, this was a decrease of 0.1-0.6% per year, whereas in scenario with selection against LnVarNBA, the range was 0.6-1.1% per year. It is still possible to increase litter size and birthweight further, however, a balance between mean and variability of reproduction traits is required, which can be obtained only by a very well designed breeding programme.

A 10-year trend in piglet pre-weaning mortality in breeding herds associated with sow herd size and number of piglets born alive

BACKGROUND

Piglet pre-weaning mortality (PWM) is one of the biggest problems regarding sow performance and piglet welfare. Recently, PWM has increased in some countries, but it is not known if there are similar increases in other countries, nor whether increased PWM is related to either increased numbers of piglets born alive (PBA) or to sow herd size. So, the objectives of the present study were 1) to explore the trend in PWM in Spanish sow herds over a recent 10-year period, along with related measurements such as PBA, stillborn piglets, herd productivity and herd size; and 2) to examine the relationships between PWM and the related measurements.

METHODS

Herd-level annual data from 2007 to 2016 for 91 herds in Spain were abstracted from a sow database compiled by a veterinary consultancy firm that asked client producers to mail data files on a regular basis. The database software automatically calculated herd-level PWM (%) as follows: the total number of piglets born alive to a sow completely weaned during a year (TPBA) minus the total number of piglets weaned by the completely weaned sow during the year divided by TPBA × 100. All the statistical analyses were performed by using SAS University Edition. A growth curve model was applied to incorporate correlations for all of the observations arising from the same farm.

RESULTS

Over the 10 years, herd means of PWM (standard deviation) increased from 11.9 (4.1) % to 14.4 (3.2) %, and mean PBA increased by 1.9 pigs. Mean age of piglet death during lactation increased by 3.8 days, and later years were significantly associated with herd size and the number of piglets weaned per sow per year (PSY; P <  0.05). Higher PWM was associated with more PBA, more stillborn piglets and small-to-mid herds (lower than the median size: < 570 sows; P <  0.05). Also, there was a significant interaction between the herd size groups and PBA for PWM (P <  0.05): as PBA increased from 9 to 14 pigs, PWM increased by 9.6% in small-to-mid herds, compared with an increase of only 6.6% in large herds (> 570 sows). Furthermore, as PWM decreased from 18 to 8%, herd productivity measured as PSY increased by 2.2 pigs in large herds, compared with only 0.6 pigs in small-to-mid herds.

CONCLUSION

Large herds were better than small-to-mid herds at alleviating the association between increased PBA and increased PWM. Also, the relationship between decreased PWM and increased herd productivity was improved more in large herds than in small-to-mid herds.

Farm data analysis for lifetime performance components of sows and their predictors in breeding herds

Our objectives in this review are 1) to define the four components of sow lifetime performance, 2) to organize the four components and other key measures in a lifetime performance tree, and 3) to compile information about sow and herd-level predictors for sow lifetime performance that can help producers or veterinarians improve their decision making. First, we defined the four components of sow lifetime performance: lifetime efficiency, sow longevity, fertility and prolificacy. We propose that lifetime efficiency should be measured as annualized piglets weaned or annualized piglets born alive which is an integrated measure for sow lifetime performance, whereas longevity should be measured as sow life days and herd-life days which are the number of days from birth to removal and the number of days from date of first-mating to removal, respectively. We also propose that fertility should be measured as lifetime non-productive days, whereas prolificacy should be measured as lifetime pigs born alive. Second, we propose two lifetime performance trees for annualized piglets weaned and annualized piglets born alive, respectively, and show inter-relationships between the four components of the lifetime performance in these trees. Third, we describe sow and herd-level predictors for high lifetime performance of sows. An example of a sow-level predictor is that gilts with lower age at first-mating are associated with higher lifetime performance in all four components. Other examples are that no re-service in parity 0 and shorter weaning-to-first-mating interval in parity 1 are associated with higher fertility, whereas more piglets born in parity 1 is associated with higher prolificacy. It appears that fertility and prolificacy are independent each other. Furthermore, sows with high prolificacy and high fertility are more likely to have high longevity and high efficiency. Also, an increased number of stillborn piglets indicates that sows have farrowing difficulty or a herd health problem. Regarding herd-level predictors, large herd size is associated with higher efficiency. Also, herd-level predictors can interact with sow level predictors for sow lifetime performance. For example, sow longevity decreases more in large herds than small-to-mid herds, whereas gilt age at first-mating increases. So, it appears that herd size alters the impact of delayed gilt age at first-mating on sow longevity. Increased knowledge of these four components of sow lifetime performance and their predictors should help producers and veterinarians maximize a sow's potential and optimize her lifetime productivity in breeding herds.

Increased age at first-mating interacting with herd size or herd productivity decreases longevity and lifetime reproductive efficiency of sows in breeding herds

Background

Our objectives were to characterize sow life and herd-life performance and examine two-way interactions between age at first-mating (AFM) and either herd size or herd productivity groups for the performance of sows. Data contained 146,140 sows in 143 Spanish herds. Sow life days is defined as the number of days from birth to removal, whereas the herd-life days is from AFM date to removal date. Herds were categorized into two herd size groups and two productivity groups based on the respective 75th percentiles of farm means of herd size and the number of piglets weaned per sows per year: large (> 1017 sows) or small-to-mid herds (< 1017 sows), and high productivity (> 26.5 piglets) or ordinary herds (< 26.5 piglets). A two-level liner mixed-effects model was applied to examine AFM, herd size groups, productivity groups and their interactions for sow life or herd-life performance.

Results

No differences were found between either herd size or herd productivity groups for AFM or the number of parity at removal. However, late AFM was associated with decreased removal parity, herd-life days, herd-life piglets born alive and herd-life annualized piglets weaned, as well as with increased sow life days and herd-life nonproductive days (P < 0.05). Also, significant two-way interactions between AFM and both herd size and productivity groups were found for longevity, prolificacy, fertility and reproductive efficiency of sows. For example, as AFM increased from 190 to 370 days, sows in large herds decreased herd-life days by 156 days, whereas for sows in small-to-mid herds the decrease was only 42 days. Also, for the same AFM increase, sows in large herds had 5 fewer sow life annualized piglets weaned, whereas for sows in small-to-mid herds this sow reproductive efficiency measure was only decreased by 3.5 piglets. Additionally, for ordinary herds, sows in large herds had more herd-life annualized piglets weaned than those in small-to-mid herds (P < 0.05), but no such association was found for high productivity herds (P > 0.10).

Conclusion

We recommend decreasing the number of late AFM sows in the herd and also recommend improving longevity and lifetime efficiency of individual sows.

An Analysis of Culling Patterns during the Breeding Cycle and Lifetime Production from the Aspect of Culling Reasons for Gilts and Sows in Southwest China

Simple Summary

Unplanned removal of gilts and sows shortens the longevity and decreases the production efficiency of commercial herds. A comparison between sow culling studies is problematic due to differences in genetics, housing conditions, and feeding and management among different countries. Analyzing and monitoring culling patterns in sows in a particular area could help improve domestic feeding and the managing level. Based on the large sample of production data, we found that disease, lameness, return to estrus, and anestrus beyond seven days sharply decreased lifetime production in sows. We suggest that producers take effective measures to reduce anestrus beyond nine months, reproductive system disease, return to estrus, and low or no milk production for gilts, weaned sows, gestating sows, and lactating sows, respectively. We also recommend producers pay more attention to disease, lameness, and return to estrus for sows at low parity.

Abstract

To investigate culling patterns during the breeding cycle and lifetime production associated with culling reasons, 19,471 culling records were collected in southwest China. Lifetime pigs born alive (LPBA) and parity for culling reasons, and reason distribution at different parities and breeding cycle were analyzed. Sows culled for stress and death (SD), lameness (LA), common disease (CD), not being pregnant, return to estrus, and abortion (NP) had fewer than 20 LPBA (p < 0.05). Gilts were mainly culled for anestrus beyond nine months (AB9), CD, and LA, while weaned sows were culled for reproductive system disease (RS), CD, and anestrus beyond seven days (p < 0.0033). Gestating sows were mainly culled for NP, CD, and SD, while lactating sows were mainly culled for low or no milk production (NM), poor litter size, and CD (p < 0.0033). Moreover, sows were mainly culled at parity 0, 1, and 2 (p < 0.0024). Besides CD and RS, LA and NP were the primary reasons for parity 1 and 2 culls, respectively. In conclusion, SD, LA, CD, and NP sharply decrease sow lifetime production. AB9, RS, NP, and NM mainly occurred in gilts, weaned, gestating, and lactating sows, respectively. Low parity sows had a higher risk of CD, RS, LA, and NP.