Sow Replacement Research Articles

Modelling the impact of sow replacement rate on farm performance

Modelling the impact of sow replacement rate on farm performance

Validation of a phenotyping method to identify PRRSV-resilient sows and its impact on sow stayability.

Endemic and epidemic outbreaks of porcine reproductive and respiratory syndrome virus (PRRSV) are causing large economic losses in commercial pig production worldwide. Given the complexity of controlling this disease with vaccines or other biosecurity measures, the selection for pigs with a natural resilience to this infection has been proposed as an alternative approach. In this context, we previously reported a vaccine-based protocol to classify 6-week-old female piglets from one farm into resilient and susceptible phenotypes. Subsequent analysis showed that resilient sows had fewer lost piglets during a PRRSV epidemic. In the present study, we validated the results in four additional farms by showing a robust effect on the percentage of piglets lost (P<0.05). We were able to associate the resilient phenotype with a 2-4% reduction in piglet losses on sow farms in both endemic and endemic/epidemic situations. Also consistent with previous results, susceptible sows delivered on average, almost 0.5 more piglets born per parity (P<0.05). However, we show here that resilient sows have a longer stayability in the farm (+57 d; P<0.05) and +0.3 more successful parities (P<0.05), which balances the total number of piglets born and born alive in the full productive life of the sow between the two groups. Resilient sows thus contribute towards to a more sustainable production system, reducing sow replacement and piglet mortality. The validation of this protocol on four independent production farms paves the way for the study of the genetic variation underlying the resilient/susceptible classification, with a view to incorporating this information into selection programs in the future.

Unlocking Sows’ Welfare: The Farm-Level Economic Impact of Phasing out Farrowing Crates for Sows in the European Union’s Pig Breeding Industry

The main purpose of this study is to assess the farm-level economic consequences of the planned introduction of a ban on farrowing crates for sows in the EU pig sector, being a response by the European Commission to the ‘End the Cage Age’ Initiative. The impact assessment was carried out in three stages: farm-level analyses based on 225 farm surveys carried out in EU member states and expert consultations, assessments for five alternative scenarios, and the aggregation (scaling-up) of the scenario analysis to the EU-27 sectoral impact. Our findings indicate that the ban on farrowing crates is expected to cause a decrease in the sow population by 21–23%, depending on the scenario, and piglet output in the EU owing to increased sow space requirements, a reduction in production efficiency, and a significant demand for investments. The transition to alternative farrowing systems will lead to increased variable costs related to the farrowing period, namely, veterinary expenses, labour costs, and sow feed costs, as well as expenses associated with higher piglet mortality and increased sow replacement rates. The lower density, resulting from the larger space allowance per sow in free-farrowing systems (approximately 5.5 m2 in free farrowing with temporary confinement and 7 m2 in systems without confinement) will also cause fixed costs per unit (per sow or piglet) to rise in proportion to the decline in the sow herd. Expressed as a percentage of the total production costs per piglet, we can expect a 6–10% increase in the total production costs. The implementation of free-farrowing systems on pig farms will necessitate investments in the modernisation of the existing buildings and new pens for sows. Depending on the scenario, the value of investments ranged from 3.8 to 6.7 billion EUR, at 2021 prices. It should be recognised that beyond the actual improvement in the welfare of sows, there are lingering concerns, including increased production costs, higher piglet mortality rates, and heightened risks to employees. It is essential to consider providing financial assistance to support farms in making a smooth transition to the new systems, as well as an extended transition period to ensure a painless shift.

Exploring Appropriate Strategies for Vaccination against Classical Swine Fever under a Dynamic Change in Antibody Titer in Sows after Starting Vaccination in a Japanese Farm Setting

After 26 years of absence in Japan, a classical swine fever (CSF) outbreak occurred at a domestic pig farm in 2018. Vaccination against the CSF virus with a live attenuated vaccine at pig farms was restarted in October 2019, which was 13 years after the 2006 ban on vaccination. An individual-based simulation model for CSF antibody dynamics was developed to determine an effective CSF vaccination strategy for pig populations. In creating a simulated pig herd, the optimal vaccination age of piglets and the effect of vaccinating piglets twice were evaluated. Additionally, the herd immunity was monitored every 6 months for 4 years after the start of vaccination, and the effects of intensive sow replacement policies were assessed. The simulation results indicated that the vaccination age should be delayed relative to the age used before the 2006 ban on vaccination and shifted earlier, from 8 weeks to 6 weeks, as time elapses. The simulations indicated a tradeoff in protection between the weaning period (i.e., maternally derived antibodies) and the fattening period (i.e., by vaccine-induced antibodies). Mixing sows with high and low antibody titers, particularly sows that received the first vaccination and those born after the start of vaccination, resulted in a high variation in antibody titer among pigs on the farm. This study also clarified the positive effect of intensive sow replacement strategies on shortening the period in which sows show diverse titers. Differences in sow replacement rates among farms and/or the time lag in starting vaccination in different prefectures result in heterogeneity in herd immunity in Japan; thus, herd immunity status should be examined at every farm using this simulation model.

From data to decision – Implementation of a sow replacement model

An implementation of a sow replacement model is presented. The model estimates the economic value and potential of sows based on litter size history and inseminations of the individual sow as well as herd specific information about production level, farrowing rates and mortality. Herd specific prices and feeding are also considered. The software, called SoLiv, is both a decision support application for culling strategies in pig farms and an evaluation tool for consultants and veterinarians. The application supports the management software AgroSoft and Cloudfarms from which historical data is extracted and analysed using dynamical models, in order to assess herd specific trends. Further, the software can display historical trends, determine the optimal age distribution, and ascertain historical losses by having a non-optimal culling strategy.

204 Genomics of age at puberty in gilts

Abstract Our previous research demonstrated that age at puberty is characterized by moderate heritability and is an early pre-breeding indicator of sow fertility. Early selection of gilts, based on genomic prediction of age at puberty and fertility in general, is considered a potential approach to reduce sow culling. Phenotypes, genotypes and tissues from UNL resource population (n &gt; 1,700) were used in initial genome-wide association to identify regions of the genome associated with age at puberty. The proportion of phenotypic variation for age at puberty explained by the Porcine SNP60 BeadArray was 27.3%, with top genomic regions responsible for relatively small effects. The top 1-Mb windows (1%) explained 6.7% of the genetic variation with one window located on SSC2 (12–12.9 Mb) displaying pleiotropic features, affecting both age at puberty and litter size. These top genomic regions were oversaturated with new genetic variants discovered by deep genomic and RNA (arcuate nucleus) sequencing, which were integrated in SowPro90, a custom Affymetrix SNP array. Using a subset of sows representing extremes of the distribution for age at puberty (n = 283) and genotyped with the SowPro90 led to identification of a SNP in the proximal promoter of the UBE2L6 (SSC2, 13.3Mb; F = 13.39; P = 0.0003), a gene known to modulate function of ISG15, a marker for pregnancy. This novel SNP, which exceeded the effect of the original top SNP in the region (DIAS0004771; F = 6.98), explained 6.8% of the proportion of phenotypic variance. Identification of functional polymorphisms that influence age at puberty could lead to improvements in the accuracy of genomic prediction while reducing sow replacement rates. This project was supported by AFRI Competitive Grant no. 2013-68004-20370 from the USDA-NIFA.

Modeling the Costs of Postpartum Dysgalactia Syndrome and Locomotory Disorders on Sow Productivity and Replacement.

Postpartum dysgalactia syndrome (PPDS) and locomotory disorders are common health problems in sows. Previous research suggests that they can cause substantial losses, reduce sow welfare, and result in premature removal of the sow from the herd. However, economic consequences of PPDS and locomotory disorders have not been investigated thoroughly. The goal of this study was to examine economic losses caused by PPDS and locomotory disorders and their impacts on sow longevity. A stochastic dynamic programming model, which maximizes return on sow space unit and assesses sow replacement under several scenarios, was developed. The state variables were litter size, parity number, and sow’s health status. The model describes changes in the production parameters such as the number of piglets born and piglet mortality. Herd data originating from commercial sow herds and from a research farm were used to parameterize the model. Sow longevity, health, and economic results are related to each other. Eliminating the risk of PPDS from the model increased the value of sow space unit by €279 when compared to the baseline scenario. Eliminating the risk of locomotory disorders increased value by €110. Results suggest that these estimates correspond to about €29.1 and €11.5 in economic costs per housed sow during her lifetime. The estimated magnitude of losses was €300–€470 per affected sow for PPDS and €290–€330 per affected sow for locomotory disorders. However, realistically speaking, not all of these costs are avoidable. Due to premature replacement associated with these two disorders, the average number of litters that the sow would deliver during her lifetime is decreased by about 0.1–0.4 litters depending on the scenario. We also observed that the optimal lifetime of a sow is not a fixed number, but it depends on her productivity level as well as health status. In general, a healthy sow could stay in the herd until she has produced 6–10 litters. Research is needed to understand the structures and interactions underlying health impairments, performance, replacement policies, and farm economics, and to provide pork producers with management recommendations.

GENOMICS SYMPOSIUM: Using genomic approaches to uncover sources of variation in age at puberty and reproductive longevity in sows1,2

Genetic variants associated with traits such as age at puberty and litter size could provide insight into the underlying genetic sources of variation impacting sow reproductive longevity and productivity. Genomewide characterization and gene expression profiling were used using gilts from the University of Nebraska-Lincoln swine resource population ( = 1,644) to identify genetic variants associated with age at puberty and litter size traits. From all reproductive traits studied, the largest fraction of phenotypic variation explained by the Porcine SNP60 BeadArray was for age at puberty (27.3%). In an evaluation data set, the predictive ability of all SNP from high-ranked 1-Mb windows (1 to 50%), based on genetic variance explained in training, was greater (12.3 to 36.8%) compared with the most informative SNP from these windows (6.5 to 23.7%). In the integrated data set ( = 1,644), the top 1% of the 1-Mb windows explained 6.7% of the genetic variation of age at puberty. One of the high-ranked windows detected (SSC2, 12-12.9 Mb) showed pleiotropic features, affecting both age at puberty and litter size traits. The RNA sequencing of the hypothalami arcuate nucleus uncovered 17 differentially expressed genes (adjusted 180 d of age). Twelve of the differentially expressed genes are upregulated in the late pubertal gilts. One of these genes is involved in energy homeostasis (), a function in which the arcuate nucleus plays an important contribution, linking nutrition with reproductive development. Energy restriction during the gilt development period delayed age at puberty by 7 d but increased the probability of a sow to produce up to 3 parities ( < 0.05). Identification of pleotropic functional polymorphisms may improve accuracy of genomic prediction while facilitating a reduction in sow replacement rates and addressing welfare concerns.

GENOMICS SYMPOSIUM: Using genomic approaches to uncover sources of variation in age at puberty and reproductive longevity in sows

Genetic variants associated with traits such as age at puberty and litter size could provide insight into the underlying genetic sources of variation impacting sow reproductive longevity and productivity. Genomewide characterization and gene expression profiling were used using gilts from the University of Nebraska-Lincoln swine resource population ( = 1,644) to identify genetic variants associated with age at puberty and litter size traits. From all reproductive traits studied, the largest fraction of phenotypic variation explained by the Porcine SNP60 BeadArray was for age at puberty (27.3%). In an evaluation data set, the predictive ability of all SNP from high-ranked 1-Mb windows (1 to 50%), based on genetic variance explained in training, was greater (12.3 to 36.8%) compared with the most informative SNP from these windows (6.5 to 23.7%). In the integrated data set ( = 1,644), the top 1% of the 1-Mb windows explained 6.7% of the genetic variation of age at puberty. One of the high-ranked windows detected (SSC2, 12-12.9 Mb) showed pleiotropic features, affecting both age at puberty and litter size traits. The RNA sequencing of the hypothalami arcuate nucleus uncovered 17 differentially expressed genes (adjusted < 0.05) between gilts that became pubertal early (<155 d of age) and late (>180 d of age). Twelve of the differentially expressed genes are upregulated in the late pubertal gilts. One of these genes is involved in energy homeostasis (), a function in which the arcuate nucleus plays an important contribution, linking nutrition with reproductive development. Energy restriction during the gilt development period delayed age at puberty by 7 d but increased the probability of a sow to produce up to 3 parities ( < 0.05). Identification of pleotropic functional polymorphisms may improve accuracy of genomic prediction while facilitating a reduction in sow replacement rates and addressing welfare concerns.

Optimal replacement policies and economic value of clinical observations in sow herds

Even though several sow replacement models have been published, integrating information about the health status of sows has not yet been handled satisfactorily. This paper presents a framework for integrating a Weak Sow Index (WSI) into an existing sow replacement model. The WSI, which is developed as part of this study, quantifies various clinical signs into one numerical value representing the risk of a sow to be involuntarily culled. The objective of the study is to investigate the effect of observing clinical signs of sows on the optimal replacement policy. A second objective is to estimate the economic value of observing clinical signs of individual sows. Bayesian networks are used to develop the WSI models for lactating and pregnant sows, respectively. The optimization of the replacement policy is done in a multi-level hierarchical Markov decision process. To illustrate the behaviour of the model, the effect of the WSI on the replacement policy, and the economic benefit of observing clinical signs of individual sows are investigated in two fictitious herds with a high and a low risk of involuntary culling of sows. In general, the value of the WSI has a high influence on the optimal replacement policy, allowing for a better economic classification of sows when taking information about the health status into account. It is shown that the economic value of the WSI is higher in a high risk herd compared to a low risk herd. Among the individual clinical signs, “unwillingness to stand” made the lowest contribution to the economic value of the WSI. The highest contribution was made by the clinical sign “vulva bite” in pregnant sows.

Whole-genome association analyses for lifetime reproductive traits in the pig

Profits for commercial pork producers vary in part because of sow productivity or sow productive life (SPL) and replacement costs. During the last decade, culling rates of sows have increased to more than 50% in the United States. Both SPL and culling rates are influenced by genetic and nongenetic factors. A whole-genome association study was conducted for pig lifetime reproductive traits, including lifetime total number born (LTNB), lifetime number born alive (LNBA), removal parity, and the ratio between lifetime nonproductive days and herd life. The proportion of phenotypic variance explained by markers was 0.15 for LTNB and LNBA, 0.12 for removal parity, and 0.06 for the ratio between lifetime nonproductive days and herd life. Several informative QTL regions (e.g., 14 QTL regions for LTNB) and genes within the regions (e.g., SLC22A18 on SSC2 for LTNB) were associated with lifetime reproductive traits in this study. Genes associated with LTNB and LNBA were similar, reflecting the high genetic correlation (0.99 ± 0.003) between these traits. Functional annotation revealed that many genes at the associated regions are expressed in reproductive tissues. For instance, the SLC22A18 gene on SSC2 associated with LTNB has been shown to be expressed in the placenta of mice. Many of the QTL regions showing associations coincided with previously identified QTL for fat deposition. This reinforces the role of fat regulation for lifetime reproductive traits. Overall, this whole-genome association study provides a list of genomic locations and markers associated with pig lifetime reproductive traits that could be considered for SPL in future studies.

Reproductive performance of two sow lines under arid climatic conditions

Data (n = 4836) from a pig company in Namibia recorded over the period 2002 – 2007 were analysed to: (i) compare performance of two sow lines for the age at first farrowing (AFF), number born alive (NBA), life total born (LTB), weaning-to-conception interval (WCI), farrowing index (FI) and; (ii) estimate preweaning piglet mortality, non-productive sow days (NPSD) and replacement rates in two sow lines. The mating scheme involved a within herd grandparent programme where line A was the grandparent stock and line C the parent stock. NBA and WCI were analyzed as repeated measures traits. The least square means (d) for AFF were 363.3 ± 3.1 and 353.6 ± 2.4 for A and C, respectively. The NBA increased from first to fifth parity and then declined, but females served during the summer months of October – December (mean monthly temperature = 24.5 °C) had smaller litter sizes, compared to those in other seasons. Sows weaning piglets in the cold months of May – September (mean monthly temperature = 15.4 °C) had the shortest WCI. LTB increased by 10.9 ± .1 piglets per unit increase in parity at culling. The FI was 2.4 for both lines. The preweaning piglet mortality was 10.7% for A and 14% for C. The NPSD was 76 d in A and 52 d in C; replacement rate was 50.5% in A and 42.1% in C. Component traits of reproduction indicate depressed performance during the summer period. Management efforts should be directed at further mitigation of heat stress in breeding pigs and reduction of preweaning piglet mortality in line C.

An investigation of the success of production-based sow removal and replacement in the context of herd performance

Production-based removal and replacement has been used as a method to improve sow herd performance. Limited data are available as to the reliability of this approach. The purpose of this investigation was to use a retrospective case-control study to assess the success of replacement events when herd productivity was greater or less than the mean for removal events attributed to problems with fertility, fecundity, or old age. For each of 3 herds, 1,000 consecutive sows removed between parities 1 and 6 for reasons of fertility, fecundity, or old age were matched to sows with similar histories that were retained in the herd (controls) and to gilts that were first bred into the herd around the time of the case removal events. Controls and gilts were followed until their next parity or removal event, and the outcome was measured as a standardized calculation of born alive per mated female per year. Herd performance at the time of the case removal events was categorized according to greater or less than the mean for fertility or fecundity on monthly farrowing rates and average piglets born alive per litter. Success of removal/replacement events were evaluated according to removal reason and contemporary herd performance. A model was developed to estimate production and financial implications of changes to productivity-based culling, using a Monte Carlo simulation with a 1,000-iteration run. Born alive per mated female per year from gilts was greater (P = 0.0001) than from controls in 1 of 3 herds when herd fertility was greater than the mean, 1 of 3 herds when herd fertility was less than the mean (P = 0.0065), 3 of 3 herds when herd fecundity was greater than the mean (P < 0.030), and 2 of 3 herds when herd fecundity was less than the mean (P < 0.020). The financial model sensitivity analysis indicated greater likelihood of economic advantage for a scenario without production-based removals in parities 1 to 6.

Impact of biological and economic variables on optimal parity for replacement in swine breed-to-wean herds1

Voluntary and involuntary culling practices determine the average parity when sows are replaced in a herd. Underlying these practices is the economic effect of replacing a sow at different parities. A dynamic programming model was used to find the optimal parity and net present value in breed-to-wean swine herds. The model included income and costs per parity weighted by the discount rate and sow removal rate. Three scenarios that reflect a wide range of cases were considered: low removal rates per parity with no salvage value (LRNS), high removal rates per parity with no salvage value (HRNS), and high removal rates per parity with a percentage of the sows having a salvage value (HRYS). The optimal parity of replacement for the base biological and economic conditions was 4 and 5 parities in the high and low removal scenarios, respectively. Sensitivity analyses identified the variables influencing the optimal replacement parity. Optimal parity of replacement ranged from 3 to 7 parities in the low replacement scenario, compared with 1 to 5 parities in the high replacement scenarios. Sow replacement cost and salvage value had the greatest impact on optimal parity of replacement followed by revenues per piglet weaned. The discount rate and number of parities per year generally had little influence on optimal parity. For situations with high sow costs, low salvage values, and low revenues per piglet, the optimal parity at removal was as high as 6 to 10 parities, and for situations with low sow cost, high salvage values, and high revenues per piglet, the optimal parity at removal was as low as 1 to 2 parities depending on removal rates. The modified internal rate of return suggested that, for most LRNS and HRYS scenarios considered, investment in a swine breed-to-wean enterprise was favored over other investments involving a similar risk profile. Our results indicate that in US breeding herds, sows are culled on average near the optimal parity of 4. However, the optimization process should be a dynamic one that adapts to changes in replacement rates, salvage value, replacement cost, and revenues per piglet.

A sow replacement model using Bayesian updating in a three-level hierarchic Markov process: II. Optimization model

Recent methodological improvements in replacement models comprising multi-level hierarchical Markov processes and Bayesian updating have hardly been implemented in any replacement model and the aim of this study is to present a sow replacement model that really uses these methodological improvements. The biological model of the replacement model is described in a previous paper and in this paper the optimization model is described. The model is developed as a prototype for use under practical conditions. The application of the model is demonstrated using data from two commercial Danish sow herds. It is concluded that the Bayesian updating technique and the hierarchical structure decrease the size of the state space dramatically. Since parameter estimates vary considerably among herds it is concluded that decision support concerning sow replacement only makes sense with parameters estimated at herd level. It is argued that the multi-level formulation and the standard software comprise a flexible tool and a shortcut to working prototypes.

A sow replacement model using Bayesian updating in a three-level hierarchic Markov process: I. Biological model

Several replacement models have been presented in literature. In other applicational areas like dairy cow replacement, various methodological improvements like hierarchical Markov processes and Bayesian updating have been implemented, but not in sow models. Furthermore, there are methodological improvements like multi-level hierarchical Markov processes with decisions on multiple time scales, efficient methods for parameter estimations at herd level and standard software that has been hardly implemented at all in any replacement model. The aim of this study is to present a sow replacement model that really uses all these methodological improvements. In this paper, the biological model describing the performance and feed intake of sows is presented. In particular, estimation of herd specific parameters is emphasized. The optimization model is described in a subsequent paper.

Estimation of farm specific parameters in a longitudinal model for litter size with variance components and random dropout

The estimation of farm specific parameters is often ignored in studies of decision support systems such as, e.g., sow replacement problems. This study concerns the specification and estimation of farm specific parameters in a model for litter size required by a replacement problem considering culling based on productive performance. This implies a joint distribution of the litter size at the parities under consideration. A non-linear parametric mean curve is used to model the expected litter size at different parities for sows within the same herd. The covariance matrix is modeled using variance components of random effects, serial correlation and measurement error. Data from a total of 43 herds are used to devise a likelihood based inference, using maximum likelihood to obtain initial estimates for maximum posterior estimation of farm specific parameters. The maximum posterior approach is required to obtain reasonable estimates in the cases where on-farm registrations are inadequate.

A multi-attribute utility model to optimise sow replacement decisions

An existing stochastic dynamic programming model is expanded so that its output can be used as input to a multi-attribute utility model to determine the optimal replacement policy in swine breeding herds. The models include six attributes: future profitability of the sow (from the dynamic programming model), piglet mortality rate, pigs crushed by the dam, savaged and stillborn pigs, undersized pigs at birth, and undersized pigs at weaning. The aim is to rank sows for replacement purposes. The model provides stable rankings that differ from those based only on the dynamic programming model. There was good correspondence between the model@ ranking of 30 sows and rankings provided by two experts. Copyright 1998 by Oxford University Press.

Integrated Decision Support and Expert Systems: Application in Farm Management

Recent advances in computer technology have increased the possibilities for effective management information systems (MIS) to support farm decisions. Two potential applications of MIS are described: decision support systems (DSS) and expert systems (ES). The current challenge in building MIS is to incorporate DSS and ES to create an effective tool for the farm manager. An integrated DSS-ES system to support sow replacement decisions is presented. The method of acquiring the knowledge for the ES and the validation of the integrated system are described. Formal validation results indicate a high correspondence between the system’s ranking of 30 blind test scenarios with the rankings provided by two experts.

Integrated systems analysis of sow replacement rates in a hierarchical swine breeding structure

Sow replacement rates in a three-tiered breeding structure were investigated for a 10-yr planning horizon using a stochastic life-cycle swine production model. Market hogs were produced in a three-breed static crossing program and marketed on a liveweight basis. Growth and reproductive traits of individual pigs were simulated using genetic, environmental, and economic parameters. Sows were culled after a maximum of 1, 5, or 10 parities. Systems were defined by maximum sow age at culling and included combinations of 1- and 5-parity nucleus and 1-, 5-, and 10-parity multiplier and commercial tiers. Economic response to index selection was considerable for all culling alternatives with yearly increases in system profits ranging from $1.06 to 1.44 for each commercial hog marketed. When sows were culled after one parity in nucleus, multiplier, and commercial tiers, respectively (1,1,1), annual changes in net returns and all cost measures were 40 to 50% larger than responses in systems with lower sow replacement rates. Based on 10-yr averages for net returns, systems with low multiplier- and commercial-level replacement rates were more profitable than systems with higher replacement rates. The most profitable system (5,10,10) differed from the least profitable system (1,1,1) by more than $10 per pig, but when the (1,1,1) system was excluded, the range was only $3 per pig. The system with lowest replacement rates supported 3,388 more multiplier and 34,151 more commercial sows from a 750-sow nucleus level than the (1,1,1) system. Output from the two extremes differed by > 664,000 commercial market hogs sold.(ABSTRACT TRUNCATED AT 250 WORDS)